Introductory

Business Statistics

Introductory

Business Statistics

Thomas K. Tiemann

Copyright © 2010 by Tomas K. Tiemann

For any questions about this text, please email: [email protected]

Editor-In-Chief: Thomas K. Tiemann

Associate Editor: Marisa Drexel

Editorial Assistants: Jaclyn Sharman, LaKwanzaa Walton

Proofreader: Kristyna Mauch

The Global Text Project is funded by the Jacobs Foundation, Zurich, Switzerland.

This book is licensed under a Creative Commons Attribution 3.0 License

This book is licensed under a Creative Commons Attribution 3.0 License

Table of Contents

What is statistics? ............................................................................................................................................ 5

1. Descriptive statistics and frequency distributions................................................................10

Descriptive statistics....................................................................................................................................... 12

2. The normal and t-distributions.............................................................................................18

Normal things................................................................................................................................................. 18

The t-distribution........................................................................................................................................... 22

3. Making estimates...................................................................................................................26

Estimating the population mean.................................................................................................................... 26

Estimating the population proportion........................................................................................................... 27

Estimating population variance..................................................................................................................... 29

4. Hypothesis testing ................................................................................................................ 32

The strategy of hypothesis testing.................................................................................................................. 33

5. The t-test.................................................................................................................................41

The t-distribution............................................................................................................................................ 41

6. F-test and one-way anova......................................................................................................52

Analysis of variance (ANOVA)........................................................................................................................ 55

7. Some non-parametric tests....................................................................................................59

Do these populations have the same location? The Mann-Whitney U test.................................................. 60

Testing with matched pairs: the Wilcoxon signed ranks test........................................................................ 63

Are these two variables related? Spearman's rank correlation..................................................................... 66

8. Regression basics...................................................................................................................70

What is regression? ........................................................................................................................................ 70

Correlation and covariance............................................................................................................................. 79

Covariance, correlation, and regression......................................................................................................... 81

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About the author

Author, Thomas K. Tiemann

Thomas K. Tiemann is Jefferson Pilot Professor of Economics at Elon University in North Carolina, USA. He

earned an AB in Economics at Dartmouth College and a PhD at Vanderbilt University. He has been teaching basic

business and economics statistics for over 30 years, and tries to take an intuitive approach, rather than a

mathematical approach, when teaching statistics. He started working on this book 15 years ago, but got sidetracked

by administrative duties. He hopes that this intuitive approach helps students around the world better understand

the mysteries of statistics.

A note from the author: Why did I write this text?

I have been teaching introductory statistics to undergraduate economics and business students for almost 30

years. When I took the course as an undergraduate, before computers were widely available to students, we had lots

of homework, and learned how to do the arithmetic needed to get the mathematical answer. When I got to graduate

school, I found out that I did not have any idea of how statistics worked, or what test to use in what situation. The

first few times I taught the course, I stressed learning what test to use in what situation and what the arithmetic

answer meant.

As computers became more and more available, students would do statistical studies that would have taken

months to perform before, and it became even more important that students understand some of the basic ideas

behind statistics, especially the sampling distribution, so I shifted my courses toward an intuitive understanding of

sampling distributions and their place in hypothesis testing. That is what is presented here—my attempt to help

students understand how statistics works, not just how to “get the right number”.

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What is statistics?

There are two common definitions of statistics. The first is "turning data into information", the second is

"making inferences about populations from samples". These two definitions are quite different, but between them

they capture most of what you will learn in most introductory statistics courses. The first, "turning data into

information," is a good definition of descriptive statistics—the topic of the first part of this, and most, introductory

texts. The second, "making inferences about populations from samples", is a good definition of inferential statistics

—the topic of the latter part of this, and most, introductory texts.

To reach an understanding of the second definition an understanding of the first definition is needed; that is

why we will study descriptive statistics before inferential statistics. To reach an understanding of how to turn data

into information, an understanding of some terms and concepts is needed. This first chapter provides an

explanation of the terms and concepts you will need before you can do anything statistical.

Before starting in on statistics, I want to introduce you to the two young managers who will be using statistics to

solve problems throughout this book. Ann Howard and Kevin Schmidt just graduated from college last year, and

were hired as "Assistants to the General Manager" at Foothill Mills, a small manufacturer of socks, stockings, and

pantyhose. Since Foothill is a small firm, Ann and Kevin get a wide variety of assignments. Their boss, John

McGrath, knows a lot about knitting hosiery, but is from the old school of management, and doesn't know much

about using statistics to solve business problems. We will see Ann or Kevin, or both, in every chapter. By the end of

the book, they may solve enough problems, and use enough statistics, to earn promotions.

Data and information; samples and populations

Though we tend to use data and information interchangeably in normal conversation, we need to think of them

as different things when we are thinking about statistics. Data is the raw numbers before we do anything with them.

Information is the product of arranging and summarizing those numbers. A listing of the score everyone earned on

the first statistics test I gave last semester is data. If you summarize that data by computing the mean (the average

score), or by producing a table that shows how many students earned A's, how many B's, etc. you have turned the

data into information.

Imagine that one of Foothill Mill's high profile, but small sales, products is "Easy Bounce", a cushioned sock that

helps keep basketball players from bruising their feet as they come down from jumping. John McGrath gave Ann

and Kevin the task of finding new markets for Easy Bounce socks. Ann and Kevin have decided that a good

extension of this market is college volleyball players. Before they start, they want to learn about what size socks

college volleyball players wear. First they need to gather some data, maybe by calling some equipment managers

from nearby colleges to ask how many of what size volleyball socks were used last season. Then they will want to

turn that data into information by arranging and summarizing their data, possibly even comparing the sizes of

volleyball socks used at nearby colleges to the sizes of socks sold to basketball players.

Some definitions and important concepts

It may seem obvious, but a population is all of the members of a certain group. A sample is some of the members

of the population. The same group of individuals may be a population in one context and a sample in another. The

women in your stat class are the population of "women enrolled in this statistics class", and they are also a sample

of "all students enrolled in this statistics class". It is important to be aware of what sample you are using to make an

inference about what population.

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What is statistics?

How exact is statistics? Upon close inspection, you will find that statistics is not all that exact; sometimes I have

told my classes that statistics is "knowing when its close enough to call it equal". When making estimations, you will

find that you are almost never exactly right. If you make the estimations using the correct method however, you will

seldom be far from wrong. The same idea goes for hypothesis testing. You can never be sure that you've made the

correct judgement, but if you conduct the hypothesis test with the correct method, you can be sure that the chance

you've made the wrong judgement is small.

A term that needs to be defined is probability. Probability is a measure of the chance that something will

occur. In statistics, when an inference is made, it is made with some probability that it is wrong (or some confidence

that it is right). Think about repeating some action, like using a certain procedure to infer the mean of a population,

over and over and over. Inevitably, sometimes the procedure will give a faulty estimate, sometimes you will be

wrong. The probability that the procedure gives the wrong answer is simply the proportion of the times that the

estimate is wrong. The confidence is simply the proportion of times that the answer is right. The probability of

something happening is expressed as the proportion of the time that it can be expected to happen. Proportions are

written as decimal fractions, and so are probabilities. If the probability that Foothill Hosiery's best salesperson will

make the sale is .75, three-quarters of the time the sale is made.

Why bother with stat?

Reflect on what you have just read. What you are going to learn to do by learning statistics is to learn the right

way to make educated guesses. For most students, statistics is not a favorite course. Its viewed as hard, or cosmic,

or just plain confusing. By now, you should be thinking: "I could just skip stat, and avoid making inferences about

what populations are like by always collecting data on the whole population and knowing for sure what the

population is like." Well, many things come back to money, and its money that makes you take stat. Collecting data

on a whole population is usually very expensive, and often almost impossible. If you can make a good, educated

inference about a population from data collected from a small portion of that population, you will be able to save

yourself, and your employer, a lot of time and money. You will also be able to make inferences about populations

for which collecting data on the whole population is virtually impossible. Learning statistics now will allow you to

save resources later and if the resources saved later are greater than the cost of learning statistics now, it will be

worthwhile to learn statistics. It is my hope that the approach followed in this text will reduce the initial cost of

learning statistics. If you have already had finance, you'll understand it this way—this approach to learning

statistics will increase the net present value of investing in learning statistics by decreasing the initial cost.

Imagine how long it would take and how expensive it would be if Ann and Kevin decided that they had to find

out what size sock every college volleyball player wore in order to see if volleyball players wore the same size socks

as basketball players. By knowing how samples are related to populations, Ann and Kevin can quickly and

inexpensively get a good idea of what size socks volleyball players wear, saving Foothill a lot of money and keeping

John McGrath happy.

There are two basic types of inferences that can be made. The first is to estimate something about the

population, usually its mean. The second is to see if the population has certain characteristics, for example you

might want to infer if a population has a mean greater than 5.6. This second type of inference, hypothesis testing, is

what we will concentrate on. If you understand hypothesis testing, estimation is easy. There are many applications,

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especially in more advanced statistics, in which the difference between estimation and hypothesis testing seems

blurred.

Estimation

Estimation is one of the basic inferential statistics techniques. The idea is simple; collect data from a sample and

process it in some way that yields a good inference of something about the population. There are two types of

estimates: point estimates and interval estimates. To make a point estimate, you simply find the single number that

you think is your best guess of the characteristic of the population. As you can imagine, you will seldom be exactly

correct, but if you make your estimate correctly, you will seldom be very far wrong. How to correctly make these

estimates is an important part of statistics.

To make an interval estimate, you define an interval within which you believe the population characteristic lies.

Generally, the wider the interval, the more confident you are that it contains the population characteristic. At one

extreme, you have complete confidence that the mean of a population lies between - ∞ and + ∞ but that information

has little value. At the other extreme, though you can feel comfortable that the population mean has a value close to

that guessed by a correctly conducted point estimate, you have almost no confidence ("zero plus" to statisticians)

that the population mean is exactly equal to the estimate. There is a trade-off between width of the interval, and

confidence that it contains the population mean. How to find a narrow range with an acceptable level of confidence

is another skill learned when learning statistics.

Hypothesis testing

The other type of inference is hypothesis testing. Though hypothesis testing and interval estimation use similar

mathematics, they make quite different inferences about the population. Estimation makes no prior statement

about the population; it is designed to make an educated guess about a population that you know nothing about.

Hypothesis testing tests to see if the population has a certain characteristic—say a certain mean. This works by

using statisticians' knowledge of how samples taken from populations with certain characteristics are likely to look

to see if the sample you have is likely to have come from such a population.

A simple example is probably the best way to get to this. Statisticians know that if the means of a large number

of samples of the same size taken from the same population are averaged together, the mean of those sample means

equals the mean of the original population, and that most of those sample means will be fairly close to the

population mean. If you have a sample that you suspect comes from a certain population, you can test the

hypothesis that the population mean equals some number, m, by seeing if your sample has a mean close to m or

not. If your sample has a mean close to m, you can comfortably say that your sample is likely to be one of the

samples from a population with a mean of m.

Sampling

It is important to recognize that there is another cost to using statistics, even after you have learned statistics. As

we said before, you are never sure that your inferences are correct. The more precise you want your inference to be,

either the larger the sample you will have to collect (and the more time and money you'll have to spend on

collecting it), or the greater the chance you must take that you'll make a mistake. Basically, if your sample is a good

representation of the whole population—if it contains members from across the range of the population in

proportions similar to that in the population—the inferences made will be good. If you manage to pick a sample that

is not a good representation of the population, your inferences are likely to be wrong. By choosing samples

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What is statistics?

carefully, you can increase the chance of a sample which is representative of the population, and increase the

chance of an accurate inference.

The intuition behind this is easy. Imagine that you want to infer the mean of a population. The way to do this is

to choose a sample, find the mean of that sample, and use that sample mean as your inference of the population

mean. If your sample happened to include all, or almost all, observations with values that are at the high end of

those in the population, your sample mean will overestimate the population mean. If your sample includes roughly

equal numbers of observations with "high" and "low" and "middle" values, the mean of the sample will be close to

the population mean, and the sample mean will provide a good inference of the population mean. If your sample

includes mostly observations from the middle of the population, you will also get a good inference. Note that the

sample mean will seldom be exactly equal to the population mean, however, because most samples will have a

rough balance between high and low and middle values, the sample mean will usually be close to the true

population mean. The key to good sampling is to avoid choosing the members of your sample in a manner that

tends to choose too many "high" or too many "low" observations.

There are three basic ways to accomplish this goal. You can choose your sample randomly, you can choose a

stratified sample, or you can choose a cluster sample. While there is no way to insure that a single sample will be

representative, following the discipline of random, stratified, or cluster sampling greatly reduces the probability of

choosing an unrepresentative sample.

The sampling distribution

The thing that makes statistics work is that statisticians have discovered how samples are related to populations.

This means that statisticians (and, by the end of the course, you) know that if all of the possible samples from a

population are taken and something (generically called a “statistic”) is computed for each sample, something is

known about how the new population of statistics computed from each sample is related to the original population.

For example, if all of the samples of a given size are taken from a population, the mean of each sample is computed,

and then the mean of those sample means is found, statisticians know that the mean of the sample means is equal

to the mean of the original population.

There are many possible sampling distributions. Many different statistics can be computed from the samples,

and each different original population will generate a different set of samples. The amazing thing, and the thing that

makes it possible to make inferences about populations from samples, is that there are a few statistics which all

have about the same sampling distribution when computed from the samples from many different populations.

You are probably still a little confused about what a sampling distribution is. It will be discussed more in the

chapter on the Normal and t-distributions. An example here will help. Imagine that you have a population—the

sock sizes of all of the volleyball players in the South Atlantic Conference. You take a sample of a certain size, say

six, and find the mean of that sample. Then take another sample of six sock sizes, and find the mean of that sample.

Keep taking different samples until you've found the mean of all of the possible samples of six. You will have

generated a new population, the population of sample means. This population is the sampling distribution. Because

statisticians often can find what proportion of members of this new population will take on certain values if they

know certain things about the original population, we will be able to make certain inferences about the original

population from a single sample.

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Univariate and multivariate statistics statistics and the idea of an observation.

A population may include just one thing about every member of a group, or it may include two or more things

about every member. In either case there will be one observation for each group member. Univariate statistics are

concerned with making inferences about one variable populations, like "what is the mean shoe size of business

students?" Multivariate statistics is concerned with making inferences about the way that two or more variables are

connected in the population like, "do students with high grade point averages usually have big feet?" What's

important about multivariate statistics is that it allows you to make better predictions. If you had to predict the shoe

size of a business student and you had found out that students with high grade point averages usually have big feet,

knowing the student's grade point average might help. Multivariate statistics are powerful and find applications in

economics, finance, and cost accounting.

Ann Howard and Kevin Schmidt might use multivariate statistics if Mr McGrath asked them to study the effects

of radio advertising on sock sales. They could collect a multivariate sample by collecting two variables from each of

a number of cities—recent changes in sales and the amount spent on radio ads. By using multivariate techniques

you will learn in later chapters, Ann and Kevin can see if more radio advertising means more sock sales.

Conclusion

As you can see, there is a lot of ground to cover by the end of this course. There are a few ideas that tie most of

what you learn together: populations and samples, the difference between data and information, and most

important, sampling distributions. We'll start out with the easiest part, descriptive statistics, turning data into

information. Your professor will probably skip some chapters, or do a chapter toward the end of the book before

one that's earlier in the book. As long as you cover the chapters “Descriptive Statistics and frequency distributions”,

“The normal and the t-distributions”, “Making estimates” and that is alright.

You should learn more than just statistics by the time the semester is over. Statistics is fairly difficult, largely

because understanding what is going on requires that you learn to stand back and think about things; you cannot

memorize it all, you have to figure out much of it. This will help you learn to use statistics, not just learn statistics

for its own sake.

You will do much better if you attend class regularly and if you read each chapter at least three times. First, the

day before you are going to discuss a topic in class, read the chapter carefully, but do not worry if you understand

everything. Second, soon after a topic has been covered in class, read the chapter again, this time going slowly,

making sure you can see what is going on. Finally, read it again before the exam. Though this is a great statistics

book, the stuff is hard, and no one understands statistics the first time.

Introductory Business Statistics 9 A Global Text

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1. Descriptive statistics and

frequency distributions

This chapter is about describing populations and samples, a subject known as descriptive statistics. This will all

make more sense if you keep in mind that the information you want to produce is a description of the population or

sample as a whole, not a description of one member of the population. The first topic in this chapter is a discussion

of "distributions", essentially pictures of populations (or samples). Second will be the discussion of descriptive

statistics. The topics are arranged in this order because the descriptive statistics can be thought of as ways to

describe the picture of a population, the distribution.

Distributions

The first step in turning data into information is to create a distribution. The most primitive way to present a

distribution is to simply list, in one column, each value that occurs in the population and, in the next column, the

number of times it occurs. It is customary to list the values from lowest to highest. This is simple listing is called a

"frequency distribution". A more elegant way to turn data into information is to draw a graph of the distribution.

Customarily, the values that occur are put along the horizontal axis and the frequency of the value is on the vertical

axis.

Ann Howard called the equipment manager at two nearby colleges and found out the following data on sock

sizes used by volleyball players. At Piedmont State last year, 14 pairs of size 7 socks, 18 pairs of size 8, 15 pairs of

size 9, and 6 pairs of size 10 socks were used. At Graham College, the volleyball team used 3 pairs of size 6, 10 pairs

of size 7, 15 pairs of size 8, 5 pairs of size 9, and 11 pairs of size 10. Ann arranged her data into a distribution and

then drew a graph called a Histogram:

Exhibit 1: Frequency graph of sock sizes

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1. Descriptive statistics and frequency distributions

Ann could have created a relative frequency distribution as well as a frequency distribution. The difference is

that instead of listing how many times each value occurred, Ann would list what proportion of her sample was made

up of socks of each size:

Exhibit 2: Relative frequency graph of sock sizes

Notice that Ann has drawn the graphs differently. In the first graph, she has used bars for each value, while on

the second, she has drawn a point for the relative frequency of each size, and the "connected the dots". While both

methods are correct, when you have a values that are continuous, you will want to do something more like the

"connect the dots" graph. Sock sizes are discrete, they only take on a limited number of values. Other things have

continuous values, they can take on an infinite number of values, though we are often in the habit of rounding

them off. An example is how much students weigh. While we usually give our weight in whole pounds in the US ("I

weigh 156 pounds."), few have a weight that is exactly so many pounds. When you say "I weigh 156", you actually

mean that you weigh between 155 1/2 and 156 1/2 pounds. We are heading toward a graph of a distribution of a

continuous variable where the relative frequency of any exact value is very small, but the relative frequency of

observations between two values is measurable. What we want to do is to get used to the idea that the total area

under a "connect the dots" relative frequency graph, from the lowest to the highest possible value is one. Then the

part of the area under the graph between two values is the relative frequency of observations with values within that

range. The height of the line above any particular value has lost any direct meaning, because it is now the area

under the line between two values that is the relative frequency of an observation between those two values

occurring.

You can get some idea of how this works if you go back to the bar graph of the distribution of sock sizes, but

draw it with relative frequency on the vertical axis. If you arbitrarily decide that each bar has a width of one, then

the area "under the curve" between 7.5 and 8.5 is simply the height times the width of the bar for sock size 8: 0.3510

x 1. If you wanted to find the relative frequency of sock sizes between 6.5 and 8.5, you could simply add together the

area of the bar for size 7 (that's between 6.5 and 7.5) and the bar for size 8 (between 7.5 and 8.5).

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Descriptive statistics

Now that you see how a distribution is created, you are ready to learn how to describe one. There are two main

things that need to be described about a distribution: its location and its shape. Generally, it is best to give a single

measure as the description of the location and a single measure as the description of the shape.

Mean

To describe the location of a distribution, statisticians use a "typical" value from the distribution. There are a

number of different ways to find the typical value, but by far the most used is the "arithmetic mean", usually simply

called the "mean". You already know how to find the arithmetic mean, you are just used to calling it the "average".

Statisticians use average more generally—the arithmetic mean is one of a number of different averages. Look at the

formula for the arithmetic mean:

All you do is add up all of the members of the population, ∑x, and divide by how many members there are, N.

The only trick is to remember that if there is more than one member of the population with a certain value, to add

that value once for every member that has it. To reflect this, the equation for the mean sometimes is written :

where

f

i

is the frequency of members of the population with the value

x

i

.

This is really the same formula as above. If there are seven members with a value of ten, the first formula would

have you add seven ten times. The second formula simply has you multiply seven by ten—the same thing as adding

together ten sevens.

Other measures of location are the median and the mode. The median is the value of the member of the

population that is in the middle when the members are sorted from smallest to largest. Half of the members of the

population have values higher than the median, and half have values lower. The median is a better measure of

location if there are one or two members of the population that are a lot larger (or a lot smaller) than all the rest.

Such extreme values can make the mean a poor measure of location, while they have little effect on the median. If

there are an odd number of members of the population, there is no problem finding which member has the median

value. If there are an even number of members of the population, then there is no single member in the middle. In

that case, just average together the values of the two members that share the middle.

The third common measure of location is the mode. If you have arranged the population into a frequency or

relative frequency distribution, the mode is easy to find because it is the value that occurs most often. While in

some sense, the mode is really the most typical member of the population, it is often not very near the middle of the

population. You can also have multiple modes. I am sure you have heard someone say that "it was a bimodal

distribution". That simply means that there were two modes, two values that occurred equally most often.

If you think about it, you should not be surprised to learn that for bell-shaped distributions, the mean, median,

and mode will be equal. Most of what statisticians do with the describing or inferring the location of a population is

done with the mean. Another thing to think about is using a spreadsheet program, like Microsoft Excel when

arranging data into a frequency distribution or when finding the median or mode. By using the sort and

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1. Descriptive statistics and frequency distributions

distribution commands in 1-2-3, or similar commands in Excel, data can quickly be arranged in order or placed into

value classes and the number in each class found. Excel also has a function, =AVERAGE(...), for finding the

arithmetic mean. You can also have the spreadsheet program draw your frequency or relative frequency

distribution.

One of the reasons that the arithmetic mean is the most used measure of location is because the mean of a

sample is an "unbiased estimator" of the population mean. Because the sample mean is an unbiased estimator of

the population mean, the sample mean is a good way to make an inference about the population mean. If you have

a sample from a population, and you want to guess what the mean of that population is, you can legitimately guess

that the population mean is equal to the mean of your sample. This is a legitimate way to make this inference

because the mean of all the sample means equals the mean of the population, so, if you used this method many

times to infer the population mean, on average you'd be correct.

All of these measures of location can be found for samples as well as populations, using the same formulas.

Generally,µ is used for a population mean, and x is is used for sample means. Upper-case N, really a Greek "nu", is

used for the size of a population, while lower case n is used for sample size. Though it is not universal, statisticians

tend to use the Greek alphabet for population characteristics and the Roman alphabet for sample characteristics.

Measuring population shape

Measuring the shape of a distribution is more difficult. Location has only one dimension ("where?"), but shape

has a lot of dimensions. We will talk about two,and you will find that most of the time, only one dimension of shape

is measured. The two dimensions of shape discussed here are the width and symmetry of the distribution. The

simplest way to measure the width is to do just that—the range in the distance between the lowest and highest

members of the population. The range is obviously affected by one or two population members which are much

higher or lower than all the rest.

The most common measures of distribution width are the standard deviation and the variance. The standard

deviation is simply the square root of the variance, so if you know one (and have a calculator that does squares and

square roots) you know the other. The standard deviation is just a strange measure of the mean distance between

the members of a population and the mean of the population. This is easiest to see if you start out by looking at the

formula for the variance:

Look at the numerator. To find the variance, the first step (after you have the mean, µ) is to take each member of

the population, and find the difference between its value and the mean; you should have N differences. Square each

of those, and add them together, dividing the sum by N, the number of members of the population. Since you find

the mean of a group of things by adding them together and then dividing by the number in the group, the variance

is simply the "mean of the squared distances between members of the population and the population mean".

Notice that this is the formula for a population characteristic, so we use the Greek σ and that we write the

variance as σ

2

, or "sigma square" because the standard deviation is simply the square root of the variance, its

symbol is simply "sigma", σ.

One of the things statisticians have discovered is that 75 per cent of the members of any population are with two

standard deviations of the mean of the population. This is known as Chebyshev's Theorem. If the mean of a

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population of shoe sizes is 9.6 and the standard deviation is 1.1, then 75 per cent of the shoe sizes are between 7.4

(two standard deviations below the mean) and 11.8 (two standard deviations above the mean). This same theorem

can be stated in probability terms: the probability that anything is within two standard deviations of the mean of its

population is .75.

It is important to be careful when dealing with variances and standard deviations. In later chapters, there are

formulas using the variance, and formulas using the standard deviation. Be sure you know which one you are

supposed to be using. Here again, spreadsheet programs will figure out the standard deviation for you. In Excel,

there is a function, =STDEVP(...), that does all of the arithmetic. Most calculators will also compute the standard

deviation. Read the little instruction booklet, and find out how to have your calculator do the numbers before you

do any homework or have a test.

The other measure of shape we will discuss here is the measure of "skewness". Skewness is simply a measure of

whether or not the distribution is symmetric or if it has a long tail on one side, but not the other. There are a

number of ways to measure skewness, with many of the measures based on a formula much like the variance. The

formula looks a lot like that for the variance, except the distances between the members and the population mean

are cubed, rather than squared, before they are added together:

At first it might not seem that cubing rather than squaring those distances would make much difference.

Remember, however, that when you square either a positive or negative number you get a positive number, but that

when you cube a positive, you get a positive and when you cube a negative you get a negative. Also remember that

when you square a number, it gets larger, but that when you cube a number, it gets a whole lot larger. Think about a

distribution with a long tail out to the left. There are a few members of that population much smaller than the

mean, members for which

x−

is large and negative. When these are cubed, you end up with some really big

negative numbers. Because there are not any members with such large, positive

x−

, there are not any

corresponding really big positive numbers to add in when you sum up the

x−

3

, and the sum will be

negative. A negative measure of skewness means that there is a tail out to the left, a positive measure means a tail to

the right. Take a minute and convince yourself that if the distribution is symmetric, with equal tails on the left and

right, the measure of skew is zero.

To be really complete, there is one more thing to measure, "kurtosis" or "peakedness". As you might expect by

now, it is measured by taking the distances between the members and the mean and raising them to the fourth

power before averaging them together.

Measuring sample shape

Measuring the location of a sample is done in exactly the way that the location of a population is done.

Measuring the shape of a sample is done a little differently than measuring the shape of a population, however. The

reason behind the difference is the desire to have the sample measurement serve as an unbiased estimator of the

population measurement. If we took all of the possible samples of a certain size, n, from a population and found the

variance of each one, and then found the mean of those sample variances, that mean would be a little smaller than

the variance of the population.

Introductory Business Statistics 14 A Global Text

1. Descriptive statistics and frequency distributions

You can see why this is so if you think it through. If you knew the population mean, you could find

∑

x−

2

/n

for each sample, and have an unbiased estimate for σ

2

. However, you do not know the population

mean, so you will have to infer it. The best way to infer the population mean is to use the sample mean

x

. The

variance of a sample will then be found by averaging together all of the

∑

x−

x

2

/n

.

The mean of a sample is obviously determined by where the members of that sample lie. If you have a sample

that is mostly from the high (or right) side of a population's distribution, then the sample mean will almost for sure

be greater than the population mean. For such a sample,

∑

x−

x

2

/n

would underestimate σ

2

. The same is

true for samples that are mostly from the low (or left) side of the population. If you think about what kind of

samples will have

∑

x−

x

2

/n

that is greater than the population σ

2

, you will come to the realization that it is

only those samples with a few very high members and a few very low members—and there are not very many

samples like that. By now you should have convinced yourself that

∑

x−

x

2

/n

will result in a biased estimate

of σ

2

. You can see that, on average, it is too small.

How can an unbiased estimate of the population variance, σ

2

, be found? If is

∑

x−

x

2

/n

on average too

small, we need to do something to make it a little bigger. We want to keep the ∑(x-x)

2

, but if we divide it by

something a little smaller, the result will be a little larger. Statisticians have found out that the following way to

compute the sample variance results in an unbiased estimator of the population variance:

If we took all of the possible samples of some size, n, from a population, and found the sample variance for each

of those samples, using this formula, the mean of those sample variances would equal the population variance, σ

2

.

Note that we use s

2

instead of σ

2

, and n instead of N (really "nu", not "en") since this is for a sample and we want

to use the Roman letters rather than the Greek letters, which are used for populations.

There is another way to see why you divide by n-1. We also have to address something called "degrees of

freedom" before too long, and it is the degrees of freedom that is the key of the other explanation. As we go through

this explanation, you should be able to see that the two explanations are related.

Imagine that you have a sample with 10 members (n=10), and you want to use it to estimate the variance of the

population form which it was drawn. You write each of the 10 values on a separate scrap of paper. If you know the

population mean, you could start by computing all 10

x−

2

. In the usual case, you do not know μ , however,

and you must start by finding x from the values on the 10 scraps to use as an estimate of m. Once you have found x ,

you could lose any one of the 10 scraps and still be able to find the value that was on the lost scrap from and the

other 9 scraps. If you are going to use x in the formula for sample variance, only 9 (or n-1), of the x's are free to take

on any value. Because only n-1 of the x's can vary freely, you should divide

∑

x−

x

2

by n-1, the number of

(x’s) that are really free. Once you use x in the formula for sample variance, you use up one "degree of freedom",

leaving only n-1. Generally, whenever you use something you have previously computed from a sample within a

formula, you use up a degree of freedom.

15

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A little thought will link the two explanations. The first explanation is based on the idea that x , the estimator of

μ, varies with the sample. It is because x varies with the sample that a degree of freedom is used up in the second

explanation.

The sample standard deviation is found simply by taking the square root of the sample variance:

s=√ [∑x – x

2

/n− 1]

While the sample variance is an unbiased estimator of population variance, the sample standard deviation is not

an unbiased estimator of the population standard deviation—the square root of the average is not the same as the

average of the square roots. This causes statisticians to use variance where it seems as though they are trying to get

at standard deviation. In general, statisticians tend to use variance more than standard deviation. Be careful with

formulas using sample variance and standard deviation in the following chapters. Make sure you are using the right

one. Also note that many calculators will find standard deviation using both the population and sample formulas.

Some use σ and s to show the difference between population and sample formulas, some use s

n

and s

n-1

to show the

difference.

If Ann Howard wanted to infer what the population distribution of volleyball players' sock sizes looked like she

could do so from her sample. If she is going to send volleyball coaches packages of socks for the players to try, she

will want to have the packages contain an assortment of sizes that will allow each player to have a pair that fits. Ann

wants to infer what the distribution of volleyball players sock sizes looks like. She wants to know the mean and

variance of that distribution. Her data, again, is:

size frequency

6 3

7 24

8 33

9 20

10 17

The mean sock size can be found:

=[(3x6)+(24x7)+(33x8)+(20x9)+(17x10)]/97 = 8.25.

To find the sample standard deviation, Ann decides to use Excel. She lists the sock sizes that were in the sample

in column A, and the frequency of each of those sizes in column B. For column C, she has the computer findfor each

of

∑

x−

x

2

the sock sizes, using the formula = (A1-8.25)^2 in the first row, and then copying it down to the

other four rows. In D1, she multiplies C1, by the frequency using the formula =B1*C1, and copying it down into the

other rows. Finally, she finds the sample standard deviation by adding up the five numbers in column D and

dividing by n-1 = 96 using the Excel formula =sum(D1:D5)/96. The spreadsheet appears like this when she is done:

A B C D E

1 6 3 5.06 15.19

Introductory Business Statistics 16 A Global Text

1. Descriptive statistics and frequency distributions

2 7 24 1.56 37.5

3 8 33 0.06 2.06

4 9 20 0.56 11.25

5 10 17 3.06 52.06

6 n= 97 Var = 1.217139

7 Std.dev = 1.103.24

8

Ann now has an estimate of the variance of the sizes of socks worn by college volleyball players, 1.22. She has

inferred that the population of college volleyball players' sock sizes has a mean of 8.25 and a variance of 1.22.

Summary

To describe a population you need to describe the picture or graph of its distribution. The two things that need

to be described about the distribution are its location and its shape. Location is measured by an average, most often

the arithmetic mean. The most important measure of shape is a measure of dispersion, roughly width, most often

the variance or its square root the standard deviation.

Samples need to be described, too. If all we wanted to do with sample descriptions was describe the sample, we

could use exactly the same measures for sample location and dispersion that are used for populations. We want to

use the sample describers for dual purposes, however: (a) to describe the sample, and (b) to make inferences about

the description of the population that sample came from. Because we want to use them to make inferences, we want

our sample descriptions to be "unbiased estimators". Our desire to measure sample dispersion with an unbiased

estimator of population dispersion means that the formula we use for computing sample variance is a little

difference than the used for computing population variance.

17

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2. The normal and t-

distributions

The normal distribution is simply a distribution with a certain shape. It is "normal" because many things have

this same shape. The normal distribution is the “bell-shaped distribution” that describes how so many natural,

machine-made, or human performance outcomes are distributed. If you ever took a class when you were "graded on

a bell curve", the instructor was fitting the class' grades into a normal distribution—not a bad practice if the class is

large and the tests are objective, since human performance in such situations is normally distributed. This chapter

will discuss the normal distribution and then move onto a common sampling distribution, the t-distribution. The t-

distribution can be formed by taking many samples (strictly, all possible samples) of the same size from a normal

population. For each sample, the same statistic, called the t-statistic, which we will learn more about later, is

calculated. The relative frequency distribution of these t-statistics is the t-distribution. It turns out that t-statistics

can be computed a number of different ways on samples drawn in a number of different situations and still have the

same relative frequency distribution. This makes the t-distribution useful for making many different inferences, so

it is one of the most important links between samples and populations used by statisticians. In between discussing

the normal and t-distributions, we will discuss the central limit theorem. The t-distribution and the central limit

theorem give us knowledge about the relationship between sample means and population means that allows us to

make inferences about the population mean.

The way the t-distribution is used to make inferences about populations from samples is the model for many of

the inferences that statisticians make. Since you will be learning to make inferences like a statistician, try to

understand the general model of inference making as well as the specific cases presented. Briefly, the general model

of inference-making is to use statisticians' knowledge of a sampling distribution like the t-distribution as a guide to

the probable limits of where the sample lies relative to the population. Remember that the sample you are using to

make an inference about the population is only one of many possible samples from the population. The samples will

vary, some being highly representative of the population, most being fairly representative, and a few not being very

representative at all. By assuming that the sample is at least fairly representative of the population, the sampling

distribution can be used as a link between the sample and the population so you can make an inference about some

characteristic of the population.

These ideas will be developed more later on. The immediate goal of this chapter is to introduce you to the

normal distribution, the central limit theorem, and the t-distribution.

Normal things

Normal distributions are bell-shaped and symmetric. The mean, median, and mode are equal. Most of the

members of a normally distributed population have values close to the mean—in a normal population 96 per cent of

the members (much better than Chebyshev's 75 per cent), are within 2 σ of the mean.

Introductory Business Statistics 18 A Global Text

2. The normal and t-distributions

Statisticians have found that many things are normally distributed. In nature, the weights, lengths, and

thicknesses of all sorts of plants and animals are normally distributed. In manufacturing, the diameter, weight,

strength, and many other characteristics of man- or machine-made items are normally distributed. In human

performance, scores on objective tests, the outcomes of many athletic exercises, and college student grade point

averages are normally distributed. The normal distribution really is a normal occurrence.

If you are a skeptic, you are wondering how can GPAs and the exact diameter of holes drilled by some machine

have the same distribution—they are not even measured with the same units. In order to see that so many things

have the same normal shape, all must be measured in the same units (or have the units eliminated)—they must all

be "standardized." Statisticians standardize many measures by using the STANDARD deviation. All normal

distributions have the same shape because they all have the same relative frequency distribution when the values

for their members are measured in standard deviations above or below the mean.

Using the United States customary system of measurement, if the weight of pet cats is normally distributed with

a mean of 10.8 pounds and a standard deviation of 2.3 pounds and the daily sales at The First Brew Expresso Cafe

are normally distributed with μ=$341.46 and σ=$53.21, then the same proportion of pet cats weigh between 8.5

pounds (μ-1σ) and 10.8 pounds (μ) as the proportion of daily First Brew sales which lie between μ – 1σ ($288.25)

and μ ($341.46). Any normally distributed population will have the same proportion of its members between the

mean and one standard deviation below the mean. Converting the values of the members of a normal population so

that each is now expressed in terms of standard deviations from the mean makes the populations all the same. This

process is known as "standardization" and it makes all normal populations have the same location and shape.

This standardization process is accomplished by computing a "z-score" for every member of the normal

population. The z-score is found by:

z = (x - μ)/σ

This converts the original value, in its original units, into a standardized value in units of "standard deviations

from the mean." Look at the formula. The numerator is simply the difference between the value of this member of

the population, x, and the mean of the population

. It can be measured in centimeters, or points, or whatever.

The denominator is the standard deviation of the population,

, and it is also measured in centimeters, or

points, or whatever. If the numerator is 15cm and the standard deviation is 10cm, then the z will be 1.5. This

particular member of the population, one with a diameter 15cm greater than the mean diameter of the population,

has a z-value of 1.5 because its value is 1.5 standard deviations greater than the mean. Because the mean of the x's is

, the mean of the z-scores is zero.

We could convert the value of every member of any normal population into a z-score. If we did that for any

normal population and arranged those z-scores into a relative frequency distribution, they would all be the same.

Each and every one of those standardized normal distributions would have a mean of zero and the same shape.

There are many tables which show what proportion of any normal population will have a z-score less than a certain

value. Because the standard normal distribution is symmetric with a mean of zero, the same proportion of the

population that is less than some positive z is also greater than the same negative z. Some values from a "standard

normal" table appear below:

Proportion below .75 .90 .95 .975 .99 .995

19

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z-score 0.674 1.282 1.645 1.960 2.326 2.576

John McGrath has asked Kevin Schmidt "How much does a pair of size 11 mens dress socks usually weigh?"

Kevin asks the people in quality control what they know about the weight of these socks and is told that the mean

weight is 4.25 ounces with a standard deviation of .021 ounces. Kevin decides that Mr. McGrath probably wants

more than the mean weight, and decides to give his boss the range of weights within which 95% of size 11 men's

dress socks falls. Kevin sees that leaving 2.5% (.025 ) in the left tail and 2.5% (.025) in the right tail will leave 95%

(.95) in the middle. He assumes that sock weights are normally distributed, a reasonable assumption for a machine-

made product, and consulting a standard normal table, sees that .975 of the members of any normal population

have a z-score less than 1.96 and that .975 have a z-score greater than -1.96, so .95 have a z-score between ±1.96..

Now that he knows that 95% of the socks will have a weight with a z-score between ±1.96, Kevin can translate

those z's into ounces. By solving the equation for both +1.96 and -1.96, he will find the boundaries of the interval

within which 95% of the weights of the socks fall:

1.96 = (x - 4.25)/.021

solving for x, Kevin finds that the upper limit is 4.29 ounces. He then solves for z=-1.96:

- 1.96 = (x -4.25)/ .021

and finds that the lower limit is 4.21 ounces. He can now go to John McGrath and tell him: "95% of size 11 mens'

dress socks weigh between 4.21 and 4.29 ounces."

The central limit theorem

If this was a statistics course for math majors, you would probably have to prove this theorem. Because this text

is designed for business and other non-math students, you will only have to learn to understand what the theorem

says and why it is important. To understand what it says, it helps to understand why it works. Here is an

explanation of why it works.

The theorem is about sampling distributions and the relationship between the location and shape of a

population and the location and shape of a sampling distribution generated from that population. Specifically, the

central limit theorem explains the relationship between a population and the distribution of sample means found

by taking all of the possible samples of a certain size from the original population, finding the mean of each sample,

and arranging them into a distribution.

The sampling distribution of means is an easy concept. Assume that you have a population of x's. You take a

sample of n of those x's and find the mean of that sample, giving you one

x

. Then take another sample of the

same size, n, and find its

x

...Do this over and over until you have chosen all possible samples of size n. You will

have generated a new population, a population of

x

's. Arrange this population into a distribution, and you have

the sampling distribution of means. You could find the sampling distribution of medians, or variances, or some

other sample statistic by collecting all of the possible samples of some size, n, finding the median, variance, or other

statistic about each sample, and arranging them into a distribution.

The central limit theorem is about the sampling distribution of means. It links the sampling distribution of x ’s

with the original distribution of x's. It tells us that:

Introductory Business Statistics 20 A Global Text

2. The normal and t-distributions

(1) The mean of the sample means equals the mean of the original population, μ

x

= μ. This is what makes x an

unbiased estimator of μ.

(2) The distribution of

x

’s will be bell-shaped, no matter what the shape of the original distribution of x's.

This makes sense when you stop and think about it. It means that only a small portion of the samples have

means that are far from the population mean. For a sample to have a mean that is far from

x

, almost all of its

members have to be from the right tail of the distribution of x's, or almost all have to be from the left tail. There are

many more samples with most of their members from the middle of the distribution, or with some members from

the right tail and some from the left tail, and all of those samples will have an x close to

x

.

(3a) The larger the samples, the closer the sampling distribution will be to normal, and

(3b) if the distribution of x's is normal, so is the distribution of

x

’ s.

These come from the same basic reasoning as 2), but would require a formal proof since "normal distribution" is

a mathematical concept. It is not too hard to see that larger samples will generate a "more-bell-shaped" distribution

of sample means than smaller samples, and that is what makes 3a) work.

(4) The variance of the

x

’s is equal to the variance of the x's divided by the sample size, or:

σ

2

x

= σ

2

/ n

therefore the standard deviation of the sampling distribution is:

σ

x

= σ / √n

While it is a difficult to see why this exact formula holds without going through a formal proof, the basic idea

that larger samples yield sampling distributions with smaller standard deviations can be understood intuitively. If

x

=

x

/

n

then

x

A

. Furthermore, when the sample size, n, rises, σ

2

x

gets smaller. This is because it

becomes more unusual to get a sample with an

x

that is far from

as n gets larger. The standard deviation

of the sampling distribution includes an

x−

for each, but remember that there are not many x 's that are as

far from μ as there are x's that are far from μ, and as n grows there are fewer and fewer samples with an x far from

μ. This means that there are not many

x−

that are as large as quite a few (x -μ) are. By the time you square

everything, the average

x−

2

is going to be much smaller that the average (x – μ)

2

, so,

x

is going to be

smaller than

x

. If the mean volume of soft drink in a population of 12 ounce cans is 12.05 ounces with a

variance of .04 (and a standard deviation of .2), then the sampling distribution of means of samples of 9 cans will

have a mean of 12.05 ounces and a variance of .04/9=.0044 (and a standard deviation of .2/3=.0667).

You can follow this same line of reasoning once again, and see that as the sample size gets larger, the variance

and standard deviation of the sampling distribution will get smaller. Just remember that as sample size grows,

samples with an

x

that is far from μ get rarer and rarer, so that the average

x−

2

will get smaller. The

average

x−

2

is the variance. If larger samples of soft drink bottles are taken, say samples of 16, even fewer of

the samples will have means that are very far from the mean of 12.05 ounces. The variance of the sampling

distribution when n=16 will therefore be smaller. According to what you have just learned, the variance will be

only .04/16=.0025 (and the standard deviation will be .2/4=.05). The formula matches what logically is happening;

as the samples get bigger, the probability of getting a sample with a mean that is far away from the population mean

21

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gets smaller, so the sampling distribution of means gets narrower and the variance (and standard deviation) get

smaller. In the formula, you divide the population variance by the sample size to get the sampling distribution

variance. Since bigger samples means dividing by a bigger number, the variance falls as sample size rises. If you are

using the sample mean as to infer the population mean, using a bigger sample will increase the probability that your

inference is very close to correct because more of the sample means are very close to the population mean.. There is

obviously a trade-off here. The reason you wanted to use statistics in the first place was to avoid having to go to the

bother and expense of collecting lots of data, but if you collect more data, your statistics will probably be more

accurate.

The t-distribution

The central limit theorem tells us about the relationship between the sampling distribution of means and the

original population. Notice that if we want to know the variance of the sampling distribution we need to know the

variance of the original population. You do not need to know the variance of the sampling distribution to make a

point estimate of the mean, but other, more elaborate, estimation techniques require that you either know or

estimate the variance of the population. If you reflect for a moment, you will realize that it would be strange to

know the variance of the population when you do not know the mean. Since you need to know the population mean

to calculate the population variance and standard deviation, the only time when you would know the population

variance without the population mean are examples and problems in textbooks. The usual case occurs when you

have to estimate both the population variance and mean. Statisticians have figured out how to handle these cases by

using the sample variance as an estimate of the population variance (and being able to use that to estimate the

variance of the sampling distribution). Remember that

s

2

is an unbiased estimator of

2

. Remember, too,

that the variance of the sampling distribution of means is related to the variance of the original population

according to the equation:

σ

2

x

= σ

2

/ n

so, the estimated standard deviation of a sampling distribution of means is:

estimated σ

x

= s / √n

Following this thought, statisticians found that if they took samples of a constant size from a normal population,

computed a statistic called a "t-score" for each sample, and put those into a relative frequency distribution, the

distribution would be the same for samples of the same size drawn from any normal population. The shape of this

sampling distribution of t's varies somewhat as sample size varies, but for any n it's always the same. For example,

for samples of 5, 90% of the samples have t-scores between -1.943 and +1.943, while for samples of 15, 90% have t-

scores between ± 1.761. The bigger the samples, the narrower the range of scores that covers any particular

proportion of the samples. That t-score is computed by the formula:

t = (x - μ) / (s/√n)

By comparing the formula for the t-score with the formula for the z-score, you will be able to see that the t is just

an estimated z. Since there is one t-score for each sample, the t is just another sampling distribution. It turns out

that there are other things that can be computed from a sample that have the same distribution as this t. Notice that

we've used the sample standard deviation, s, in computing each t-score. Since we've used s, we've used up one

degree of freedom. Because there are other useful sampling distributions that have this same shape, but use up

various numbers of degrees of freedom, it is the usual practice to refer to the t-distribution not as the distribution

Introductory Business Statistics 22 A Global Text

2. The normal and t-distributions

for a particular sample size, but as the distribution for a particular number of degrees of freedom. There are

published tables showing the shapes of the t-distributions, and they are arranged by degrees of freedom so that they

can be used in all situations.

Looking at the formula, you can see that the mean t-score will be zero since the mean

x

equals

. Each

t-distribution is symmetric, with half of the t-scores being positive and half negative because we know from the

central limit theorem that the sampling distribution of means is normal, and therefore symmetric, when the

original population is normal.

An excerpt from a typical t-table is printed below. Note that there is one line each for various degrees of

freedom. Across the top are the proportions of the distributions that will be left out in the tail--the amount shaded

in the picture. The body of the table shows which t-score divides the bulk of the distribution of t's for that df from

the area shaded in the tail, which t-score leaves that proportion of t's to its right. For example, if you chose all of the

possible samples with 9 df, and found the t-score for each, .025 (2 1/2 %) of those samples would have t-scores

greater than 2.262, and .975 would have t-scores less than 2.262.

df prob = .10 prob. = .05 prob. = .025 prob. = .01 prob. = .005

1 3.078 6.314 12.70 13.81 63.65

5 1.476 2.015 2.571 3.365 4.032

6 1.440 1.943 2.447 3.143 3.707

7 1.415 1.895 2.365 2.998 3.499

8 1.397 1.860 2.306 2.896 3.355

9 1.383 1.833 2.262 2.821 3.250

10 1.372 1.812 2.228 2.764 3.169

20 1.325 1.725 2.086 2.528 2.845

30 1.310 1.697 2.046 2.457 2.750

40 1.303 1.684 2.021 2.423 2.704

Infinity 1.282 1.645 1.960 2.326 2.58

Exhibit 3: A sampling of a student's t-table. The table shows the probability of exceeding the value in the body.

With 5 df, there is a .05 probability that a sample will have a t-score > 2.015.

Since the t-distributions are symmetric, if 2 1/2% (.025) of the t's with 9df are greater than 2.262, then 2 1/2%

are less than -2.262. The middle 95% (.95) of the t's, when there are 9df, are between -2.262 and +2.262. The

23

This book is licensed under a Creative Commons Attribution 3.0 License

middle .90 of t=scores when there are 14df are between ±1.761, because -1.761 leaves .05 in the left tail and +1.761

leaves .05 in the right tail. The t-distribution gets closer and closer to the normal distribution as the number of

degrees of freedom rises. As a result, the last line in the t-table, for infinity df, can also be used to find the z-scores

that leave different proportions of the sample in the tail.

What could Kevin have done if he had been asked "about how much does a pair of size 11 socks weigh?" and he

could not easily find good data on the population? Since he knows statistics, he could take a sample and make an

inference about the population mean. Because the distribution of weights of socks is the result of a manufacturing

process, it is almost certainly normal. The characteristics of almost every manufactured product are normally

distributed. In a manufacturing process, even one that is precise and well-controlled, each individual piece varies

slightly as the temperature varies some, the strength of the power varies as other machines are turned on and off,

the consistency of the raw material varies slightly, and dozens of other forces that affect the final outcome vary

slightly. Most of the socks, or bolts, or whatever is being manufactured, will be very close to the mean weight,or

size, with just as many a little heavier or larger as there are that are a little lighter or smaller. Even though the

process is supposed to be producing a population of "identical" items, there will be some variation among them.

This is what causes so many populations to be normally distributed. Because the distribution of weights is normal,

he can use the t-table to find the shape of the distribution of sample t-scores. Because he can use the t-table to tell

him about the shape of the distribution of sample t-scores, he can make a good inference about the mean weight of

a pair of socks. This is how he could make that inference:

STEP 1. Take a sample of n, say 15, pairs size 11 socks and carefully weigh each pair.

STEP 2. Find

x

and s for his sample.

STEP 3 (where the tricky part starts). Look at the t-table, and find the t-scores that leave some proportion,

say .95, of sample t's with n-1df in the middle.

STEP 4 (the heart of the tricky part). Assume that his sample has a t-score that is in the middle part of the

distribution of t-scores.

STEP 5 (the arithmetic). Take his

x

, s, n, and t's from the t-table, and set up two equations, one for

each of his two table t-values. When he solves each of these equations for m, he will find a interval that he is

95% sure (a statistician would say "with .95 confidence") contains the population mean.

Kevin decides this is the way he will go to answer the question. His sample contains pairs of socks with weights

of :

4.36, 4.32, 4.29, 4.41, 4.45, 4.50, 4.36, 4.35, 4.33, 4.30, 4.39, 4.41, 4.43, 4.28, 4.46 oz.

He finds his sample mean,

x

= 4.376 ounces, and his sample standard deviation (remembering to use the

sample formula), s = .067 ounces. The t-table tells him that .95 of sample t's with 14df are between ±2.145. He

solves these two equations for μ:

+2.145 = (4.376 – μ)/(.067/√14) and -2.145 = (4.376 – μ)/(.067/√14)

finding μ= 4.366 ounces and μ= 4.386. With these results, Kevin can report that he is "95 per cent sure that the

mean weight of a pair of size 11 socks is between 4.366 and 4.386 ounces". Notice that this is different from when he

knew more about the population in the previous example.

Introductory Business Statistics 24 A Global Text

2. The normal and t-distributions

Summary

A lot of material has been covered in this chapter, and not much of it has been easy. We are getting into real

statistics now, and it will require care on your part if you are going to keep making sense of statistics.

The chapter outline is simple:

• Many things are distributed the same way, at least once we've standardized the members' values into z-scores.

• The central limit theorem gives users of statistics a lot of useful information about how the sampling

distribution of is related to the original population of x's.

• The t-distribution lets us do many of the things the central limit theorem permits, even when the variance of

the population,

s

x

, is not known.

We will soon see that statisticians have learned about other sampling distributions and how they can be used to

make inferences about populations from samples. It is through these known sampling distributions that most

statistics is done. It is these known sampling distributions that give us the link between the sample we have and the

population that we want to make an inference about.

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3. Making estimates

The most basic kind of inference about a population is an estimate of the location (or shape) of a distribution.

The central limit theorem says that the sample mean is an unbiased estimator of the population mean and can be

used to make a single point inference of the population mean. While making this kind of inference will give you the

correct estimate on average, it seldom gives you exactly the correct estimate. As an alternative, statisticians have

found out how to estimate an interval that almost certainly contains the population mean. In the next few pages,

you will learn how to make three different inferences about a population from a sample. You will learn how to make

interval estimates of the mean, the proportion of members with a certain characteristic, and the variance. Each of

these procedures follows the same outline, yet each uses a different sampling distribution to link the sample you

have chosen with the population you are trying to learn about.

Estimating the population mean

Though the sample mean is an unbiased estimator of the population mean, very few samples have a mean

exactly equal to the population mean. Though few samples have a mean, exactly equal to the population mean, m,

the central limit theorem tells us that most samples have a mean that is close to the population mean. As a result, if

you use the central limit theorem to estimate μ, you will seldom be exactly right, but you will seldom be far wrong.

Statisticians have learned how often a point estimate will be how wrong. Using this knowledge you can find an

interval, a range of values, which probably contains the population mean. You even get to choose how great a

probability you want to have, though to raise the probability, the interval must be wider.

Most of the time, estimates are interval estimates. When you make an interval estimate, you can say "I am z per

cent sure that the mean of this population is between x and y". Quite often, you will hear someone say that they

have estimated that the mean is some number "± so much". What they have done is quoted the midpoint of the

interval for the "some number", so that the interval between x and y can then be split in half with + "so much"

above the midpoint and - "so much" below. They usually do not tell you that they are only "z per cent sure". Making

such an estimate is not hard— it is what Kevin Schmidt did at the end of the last chapter. It is worth your while to

go through the steps carefully now, because the same basic steps are followed for making any interval estimate.

In making any interval estimate, you need to use a sampling distribution. In making an interval estimate of the

population mean, the sampling distribution you use is the t-distribution.

The basic method is to pick a sample and then find the range of population means that would put your sample's

t-score in the central part of the t-distribution. To make this a little clearer, look at the formula for t:

n is your sample's size and

x

and s are computed from your sample. μ is what you are trying to estimate. From

the t-table, you can find the range of t-scores that include the middle 80 per cent, or 90 per cent, or whatever per

Introductory Business Statistics 26 A Global Text

3. Making estimates

cent, for n-1 degrees of freedom. Choose the percentage you want and use the table. You now have the lowest and

highest t-scores,

x

, s and n. You can then substitute the lowest t-score into the equation and solve for μ to find

one of the limits for μ if your sample's t-score is in the middle of the distribution. Then substitute the highest t-

score into the equation, and find the other limit. Remember that you want two μ's because you want to be able to

say that the population mean is between two numbers.

The two t-scores are almost always ± the same number. The only heroic thing you have done is to assume that

your sample has a t-score that is "in the middle" of the distribution. As long as your sample meets that assumption,

the population mean will be within the limits of your interval. The probability part of your interval estimate, "I am z

per cent sure that the mean is between...", or "with z confidence, the mean is between...", comes from how much of

the t-distribution you want to include as "in the middle". If you have a sample of 25 (so there are 24df), looking at

the table you will see that .95 of all samples of 25 will have a t-score between ±2.064; that also means that for any

sample of 25, the probability that its t is between ±2.064 is .95.

As the probability goes up, the range of t-scores necessary to cover the larger proportion of the sample gets

larger. This makes sense. If you want to improve the chance that your interval contains the population mean, you

could simply choose a wider interval. For example, if your sample mean was 15, sample standard deviation was 10,

and sample size was 25, to be .95 sure you were correct, you would need to base your mean on t-scores of ±2.064.

Working through the arithmetic gives you an interval from 10.872 to 19.128. To have .99 confidence, you would

need to base your interval on t-scores of ±2.797. Using these larger t-scores gives you a wider interval, one from

9.416 to 20.584. This trade-off between precision (a narrower interval is more precise) and confidence (probability

of being correct), occurs in any interval estimation situation. There is also a trade-off with sample size. Looking at

the t-table, note that the t-scores for any level of confidence are smaller when there are more degrees of freedom.

Because sample size determines degrees of freedom, you can make an interval estimate for any level of confidence

more precise if you have a larger sample. Larger samples are more expensive to collect, however, and one of the

main reasons we want to learn statistics is to save money. There is a three-way trade-off in interval estimation

between precision, confidence, and cost.

At Foothill Hosiery, John McGrath has become concerned that the hiring practices discriminate against older

workers. He asks Kevin to look into the age at which new workers are hired, and Kevin decides to find the average

age at hiring. He goes to the personnel office, and finds out that over 2,500 different people have worked at Foothill

in the past fifteen years. In order to save time and money, Kevin decides to make an interval estimate of the mean

age at date of hire. He decides that he wants to make this estimate with .95 confidence. Going into the personnel

files, Kevin chooses 30 folders, and records the birth date and date of hiring from each. He finds the age at hiring

for each person, and computes the sample mean and standard deviation, finding

x

= 24.71 years and s = 2.13

years. Going to the t-table, he finds that .95 of t-scores with 29df are between ±2.045. He solves two equations:

± 2.045 = (24.71 – μ)/ (2.13/√30)

and finds that the limits to his interval are 23.91 and 25.51. Kevin tells Mr McGrath: "With .95 confidence, the mean

age at date of hire is between 23.91 years and 25.51 years."

Estimating the population proportion

There are many times when you, or your boss, will want to estimate the proportion of a population that has a

certain characteristic. The best known examples are political polls when the proportion of voters who would vote

27

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for a certain candidate is estimated. This is a little trickier than estimating a population mean. It should only be

done with large samples and there are adjustments that should be made under various conditions. We will cover the

simplest case here, assuming that the population is very large, the sample is large, and that once a member of the

population is chosen to be in the sample, it is replaced so that it might be chosen again. Statisticians have found

that, when all of the assumptions are met, there is a sample statistic that follows the standard normal distribution.

If all of the possible samples of a certain size are chosen, and for each sample, p, the proportion of the sample with a

certain characteristic, is found, and for each sample a z-statistic is computed with the formula:

where π = proportion of population with the characteristic these will be distributed normally. Looking at the

bottom line of the t-table, .90 of these z's will be between ±1.645, .99 will be between ±2.326, etc.

Because statisticians know that the z-scores found from sample will be distributed normally, you can make an

interval estimate of the proportion of the population with the characteristic. This is simple to do, and the method is

parallel to that used to make an interval estimate of the population mean: (1) choose the sample, (2) find the sample

p, (3) assume that your sample has a z-score that is not in the tails of the sampling distribution, (4) using the

sample p as an estimate of the population π in the denominator and the table z-values for the desired level of

confidence, solve twice to find the limits of the interval that you believe contains the population proportion, p.

At Foothill Hosiery, Ann Howard is also asked by John McGrath to look into the age at hiring at the plant. Ann

takes a different approach than Kevin, and decides to investigate what proportion of new hires were at least 35. She

looks at the personnel records and, like Kevin, decides to make an inference from a sample after finding that over

2,500 different people have worked at Foothill at some time in the last fifteen years. She chooses 100 personnel

files, replacing each file after she has recorded the age of the person at hiring. She finds 17 who were 35 or older

when they first worked at Foothill. She decides to make her inference with .95 confidence, and from the last line of

the t-table finds that .95 of z-scores lie between ±1.96. She finds her upper and lower bounds:

π = .17 -(.038)(1.96) = .095

and, she finds the other boundary:

-1.96 =

.17 - p

(.17)(1-.17)

100

π = .17 - (.038)(1.96) = .245

and concludes, that with .95 confidence, the proportion of people who have worked at Foothills Hosiery who were

over 35 when hired is between .095 and .245. This is a fairly wide interval. Looking at the equation for constructing

the interval, you should be able to see that a larger sample size will result in a narrower interval, just as it did when

estimating the population mean.

Introductory Business Statistics 28 A Global Text

3. Making estimates

Estimating population variance

Another common interval estimation task is to estimate the variance of a population. High quality products not

only need to have the proper mean dimension, the variance should be small. The estimation of population variance

follows the same strategy as the other estimations. By choosing a sample and assuming that it is from the middle of

the population, you can use a known sampling distribution to find a range of values that you are confident contains

the population variance. Once again, we will use a sampling distribution that statisticians have discovered forms a

link between samples and populations.

Take a sample of size n from a normal population with known variance, and compute a statistic called "

2

"

(pronounced "chi square") for that sample using the following formula:

You can see that

2

will always be positive, because both the numerator and denominator will always be

positive. Thinking it through a little, you can also see that as n gets larger,

2

,will generally be larger since the

numerator will tend to be larger as more and more

x−

x

2

are summed together. It should not be too

surprising by now to find out that if all of the possible samples of a size n are taken from any normal population,

that when

2

is computed for each sample and those

2

are arranged into a relative frequency distribution,

the distribution is always the same.

Because the size of the sample obviously affects

2

, there is a different distribution for each different sample

size. There are other sample statistics that are distributed like

2

, so, like the t-distribution, tables of the

2

distribution are arranged by degrees of freedom so that they can be used in any procedure where appropriate. As

you might expect, in this procedure, df = n-1. A portion of a

2

table is reproduced below.

29

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The

2

distribution

p .95 .90 .10 .05

n df

2 1 0.004 0.02 2.706 3.841

10 9 3.33 4.17 14.68 19.92

15 14 6.57 7.79 21.1 23.7

20 19 10.12 11.65 27.2 30.1

30 19 17.71 19.77 39.1 42.6

Exhibit 4: The

2

distribution

Variance is important in quality control because you want your product to be consistently the same. John

McGrath has just returned from a seminar called "Quality Socks, Quality Profits". He learned something about

variance, and has asked Kevin to measure the variance of the weight of Foothill's socks. Kevin decides that he can

fulfill this request by using the data he collected when Mr McGrath asked about the average weight of size 11 men's

dress socks. Kevin knows that the sample variance is an unbiased estimator of the population variance, but he

decides to produce an interval estimate of the variance of the weight of pairs of size 11 men's socks. He also decides

that .90 confidence will be good until he finds out more about what Mr McGrath wants.

Kevin goes and finds the data for the size 11 socks, and gets ready to use the

2

distribution to make a .90

confidence interval estimate of the variance of the weights of socks. His sample has 15 pairs in it, so he will have 14

df. From the

2

table he sees that .95 of

2

are greater than 6.57 and only .05 are greater than 23.7 when

there are 14df. This means that .90 are between 6.57 and 23.7. Assuming that his sample has a

2

that is in the

middle .90, Kevin gets ready to compute the limits of his interval. He notices that he will have to find

∑

x−

x

2

and decides to use his spreadsheet program rather than find

x−

x

2

fifteen times. He puts the

original sample values in the first column, and has the program compute the mean. Then he has the program find

x−

x

2

fifteen times. Finally, he has the spreadsheet sum up the squared differences and finds 0.062.

Introductory Business Statistics 30 A Global Text

3. Making estimates

Kevin then takes the

2

formula, and solves it twice, once by setting

2

equal to 6.57:

χ2 = 6.57 = .062/σ2

Solving for σ

2

, he finds one limit for his interval is .0094. He solves the second time by setting

x

2

=23.6

:

23.6 = .062/σ2 a

and find that the other limit is .0026. Armed with his data, Kevin reports to Mr McGrath that "with .90 confidence,

the variance of weights of size 11 men's socks is between .0026 and .0094."

What is this confidence stuff mean anyway?

In the example we just did, Ann found "that with .95 confidence..." What exactly does "with .95 confidence"

mean? The easiest way to understand this is to think about the assumption that Ann had made that she had a

sample with a z-score that was not in the tails of the sampling distribution. More specifically, she assumed that her

sample had a z-score between ±1.96; that it was in the middle 95 per cent of z-scores. Her assumption is true 95% of

the time because 95% of z-scores are between ±1.96. If Ann did this same estimate, including drawing a new

sample, over and over, in .95 of those repetitions, the population proportion would be within the interval because

in .95 of the samples the z-score would be between ±1.96. In .95 of the repetitions, her estimate would be right.

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4. Hypothesis testing

Hypothesis testing is the other widely used form of inferential statistics. It is different from estimation because

you start a hypothesis test with some idea of what the population is like and then test to see if the sample supports

your idea. Though the mathematics of hypothesis testing is very much like the mathematics used in interval

estimation, the inference being made is quite different. In estimation, you are answering the question "what is the

population like?" While in hypothesis testing you are answering the question "is the population like this or not?"

A hypothesis is essentially an idea about the population that you think might be true, but which you cannot

prove to be true. While you usually have good reasons to think it is true, and you often hope that it is true, you need

to show that the sample data supports your idea. Hypothesis testing allows you to find out, in a formal manner, if

the sample supports your idea about the population. Because the samples drawn from any population vary, you can

never be positive of your finding, but by following generally accepted hypothesis testing procedures, you can limit

the uncertainty of your results.

As you will learn in this chapter, you need to choose between two statements about the population. These two

statements are the hypotheses. The first, known as the "null hypothesis", is basically "the population is like this". It

states, in formal terms, that the population is no different than usual. The second, known as the "alternative

hypothesis", is "the population is like something else". It states that the population is different than the usual, that

something has happened to this population, and as a result it has a different mean, or different shape than the usual

case. Between the two hypotheses, all possibilities must be covered. Remember that you are making an inference

about a population from a sample. Keeping this inference in mind, you can informally translate the two hypotheses

into "I am almost positive that the sample came from a population like this" and "I really doubt that the sample

came from a population like this, so it probably came from a population that is like something else". Notice that you

are never entirely sure, even after you have chosen the hypothesis which is best. Though the formal hypotheses are

written as though you will choose with certainty between the one that is true and the one that is false, the informal

translations of the hypotheses, with "almost positive" or "probably came", is a better reflection of what you actually

find.

Hypothesis testing has many applications in business, though few managers are aware that that is what they are

doing. As you will see, hypothesis testing, though disguised, is used in quality control, marketing, and other

business applications. Many decisions are made by thinking as though a hypothesis is being tested, even though the

manager is not aware of it. Learning the formal details of hypothesis testing will help you make better decisions and

better understand the decisions made by others.

The next section will give an overview of the hypothesis testing method by following along with a young

decision-maker as he uses hypothesis testing. The rest of the chapter will present some specific applications of

hypothesis tests as examples of the general method.

Introductory Business Statistics 32 A Global Text

4. Hypothesis testing

The strategy of hypothesis testing

Usually, when you use hypothesis testing, you have an idea that the world is a little bit surprising, that it is not

exactly as conventional wisdom says it is. Occasionally, when you use hypothesis testing, you are hoping to confirm

that the world is not surprising, that it is like conventional wisdom predicts. Keep in mind that in either case you

are asking "is the world different from the usual, is it surprising?" Because the world is usually not surprising and

because in statistics you are never 100 per cent sure about what a sample tells you about a population, you cannot

say that your sample implies that the world is surprising unless you are almost positive that it does. The dull,

unsurprising, usual case not only wins if there is a tie, it gets a big lead at the start. You cannot say that the world is

surprising, that the population is unusual, unless the evidence is very strong. This means that when you arrange

your tests, you have to do it in a manner that makes it difficult for the unusual, surprising world to win support.

The first step in the basic method of hypothesis testing is to decide what value some measure of the population

would take if the world was unsurprising. Second, decide what the sampling distribution of some sample statistic

would look like if the population measure had that unsurprising value. Third, compute that statistic from your

sample and see if it could easily have come from the sampling distribution of that statistic if the population was

unsurprising. Fourth, decide if the population your sample came from is surprising because your sample statistic

could not easily have come from the sampling distribution generated from the unsurprising population.

That all sounds complicated, but it is really pretty simple. You have a sample and the mean, or some other

statistic, from that sample. With conventional wisdom, the null hypothesis that the world is dull and not surprising,

tells you that your sample comes from a certain population. Combining the null hypothesis with what statisticians

know tells you what sampling distribution your sample statistic comes from if the null hypothesis is true. If you are

"almost positive" that the sample statistic came from that sampling distribution, the sample supports the null. If the

sample statistic "probably came" from a sampling distribution generated by some other population, the sample

supports the alternative hypothesis that the population is "like something else".

Imagine that Thad Stoykov works in the marketing department of Pedal Pushers, a company that makes clothes

for bicycle riders. Pedal Pushers has just completed a big advertising campaign in various bicycle and outdoor

magazines, and Thad wants to know if the campaign has raised the recognition of the Pedal Pushers brand so that

more than 30 per cent of the potential customers recognize it. One way to do this would be to take a sample of

prospective customers and see if at least 30 per cent of those in the sample recognize the Pedal Pushers brand.

However, what if the sample is small and just barely 30 per cent of the sample recognizes Pedal Pushers? Because

there is variance among samples, such a sample could easily have come from a population in which less than 30

percent recognize the brand—if the population actually had slightly less than 30 per cent recognition, the sampling

distribution would include quite a few samples with sample proportions a little above 30 per cent, especially if the

samples are small. In order to be comfortable that more than 30 per cent of the population recognizes Pedal

Pushers, Thad will want to find that a bit more than 30 per cent of the sample does. How much more depends on

the size of the sample, the variance within the sample, and how much chance he wants to take that he'll conclude

that the campaign did not work when it actually did.

Let us follow the formal hypothesis testing strategy along with Thad. First, he must explicitly describe the

population his sample could come from in two different cases. The first case is the unsurprising case, the case where

there is no difference between the population his sample came from and most other populations. This is the case

where the ad campaign did not really make a difference, and it generates the null hypothesis. The second case is the

33

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surprising case when his sample comes from a population that is different from most others. This is where the ad

campaign worked, and it generates the alternative hypothesis. The descriptions of these cases are written in a

formal manner. The null hypothesis is usually called "

H

o

" The alternative hypothesis is called either "

H

1

:"

or "

H

a

:". For Thad and the Pedal Pushers marketing department, the null will be :

H

o

: proportion of the population recognizing Pedal Pushers brand < .30 and the alternative will be:

H

a

: proportion of the population recognizing Pedal Pushers brand >.30.

Notice that Thad has stacked the deck against the campaign having worked by putting the value of the

population proportion that means that the campaign was successful in the alternative hypothesis. Also notice that

between

H

o

: and

H

a

: all possible values of the population proportion—>,=, and < .30 — have been

covered.

Second, Thad must create a rule for deciding between the two hypotheses. He must decide what statistic to

compute from his sample and what sampling distribution that statistic would come from if the null hypothesis,

H

o

:, is true. He also needs to divide the possible values of that statistic into "usual" and "unusual" ranges if the

null is true. Thad's decision rule will be that if his sample statistic has a "usual" value, one that could easily occur if

H

o

: is true, then his sample could easily have come from a population like that described in

H

o

:. If his

sample's statistic has a value that would be "unusual" if

H

o

: is true, then the sample probably comes from a

population like that described in

H

a

:. Notice that the hypotheses and the inference are about the original

population while the decision rule is about a sample statistic. The link between the population and the sample is the

sampling distribution. Knowing the relative frequency of a sample statistic when the original population has a

proportion with a known value is what allows Thad to decide what are "usual" and "unusual" values for the sample

statistic.

The basic idea behind the decision rule is to decide, with the help of what statisticians know about sampling

distributions, how far from the null hypothesis' value for the population the sample value can be before you are

uncomfortable deciding that the sample comes from a population like that hypothesized in the null. Though the

hypotheses are written in terms of descriptive statistics about the population—means, proportions, or even a

distribution of values—the decision rule is usually written in terms of one of the standardized sampling

distributions—the t, the normal z, or another of the statistics whose distributions are in the tables at the back of

statistics books. It is the sampling distributions in these tables that are the link between the sample statistic and the

population in the null hypothesis. If you learn to look at how the sample statistic is computed you will see that all of

the different hypothesis tests are simply variations on a theme. If you insist on simply trying to memorize how each

of the many different statistics is computed, you will not see that all of the hypothesis tests are conducted in a

similar manner, and you will have to learn many different things rather than learn the variations of one thing.

Thad has taken enough statistics to know that the sampling distribution of sample proportions is normally

distributed with a mean equal to the population proportion and a standard deviation that depends on the

population proportion and the sample size. Because the distribution of sample proportions is normally distributed,

he can look at the bottom line of a t-table and find out that only .05 of all samples will have a proportion more than

1.645 standard deviations above .30 if the null hypothesis is true. Thad decides that he is willing to take a 5 per cent

chance that he will conclude that the campaign did not work when it actually did, and therefore decides that he will

Introductory Business Statistics 34 A Global Text

4. Hypothesis testing

conclude that the sample comes from a population with a proportion that has heard of Pedal Pushers that is greater

than .30 if the sample's proportion is more than 1.645 standard deviations above .30. After doing a little arithmetic

(which you'll learn how to do later in the chapter), Thad finds that his decision rule is to decide that the campaign

was effective if the sample has a proportion which has heard of Pedal Pushers that is greater than .375. Otherwise

the sample could too easily have come from a population with a proportion equal to or less than .30.

alpha 0.1 0.05 0.03 0.01

df infinity 1.28 1.65 1.96 2.33

Exhibit 5: The bottom line of a t-table, showing the normal distribution

The final step is to compute the sample statistic and apply the decision rule. If the sample statistic falls in the

usual range, the data supports

H

o

:, and the world is probably unsurprising and the campaign did not make any

difference. If the sample statistic is outside the usual range, the data supports

H

a

:, and the world is a little

surprising, the campaign affected how many people have heard of Pedal Pushers. When Thad finally looks at the

sample data, he finds that .39 of the sample had heard of Pedal Pushers. The ad campaign was successful!

A straight-forward example: testing for "goodness-of-fit"

There are many different types of hypothesis tests, including many that are used more often than the "goodness-

of-fit" test. This test will be used to help introduce hypothesis testing because it gives a clear illustration of how the

strategy of hypothesis testing is put to use, not because it is used frequently. Follow this example carefully,

concentrating on matching the steps described in previous sections with the steps described in this section; the

arithmetic is not that important right now.

We will go back to Ann Howard's problem with marketing "Easy Bounce" socks to volleyball teams. Remember

that Ann works for Foothills Hosiery, and she is trying to market these sports socks to volleyball teams. She wants

to send out some samples to convince volleyball players that wearing "Easy Bounce" socks will be more comfortable

than wearing other socks. Her idea is to send out a package of socks to volleyball coaches in the area, so the players

can try them out. She needs to include an assortment of sizes in those packages and is trying to find out what sizes

to include. The Production Department knows what mix of sizes they currently produce, and Ann has collected a

sample of 97 volleyball players' sock sizes from nearby teams. She needs to test to see if her sample supports the

hypothesis that volleyball players have the same distribution of sock sizes as Foothills is currently producing—is the

distribution of volleyball players' sock sizes a "good fit" to the distribution of sizes now being produced?

Ann's sample, a sample of the sock sizes worn by volleyball players, as a frequency distribution of sizes:

size frequency

6 3

7 24

8 33

9 20

10 17

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From the Production Department, Ann finds that the current relative frequency distribution of production of

"Easy Bounce" socks is like this:

size re.

frequency

6 0.06

7 0.13

8 0.22

9 0.3

10 0.26

11 0.03

If the world in "unsurprising", volleyball players will wear the socks sized in the same proportions as other

athletes, so Ann writes her hypotheses:

H

o

: Volleyball players' sock sizes are distributed just like current production.

H

a

: Volleyball players' sock sizes are distributed differently.

Ann's sample has n=97. By applying the relative frequencies in the current production mix, she can find out how

many players would be "expected" to wear each size if her sample was perfectly representative of the distribution of

sizes in current production. This would give her a description of what a sample from the population in the null

hypothesis would be like. It would show what a sample that had a "very good fit" with the distribution of sizes in the

population currently being produced would look like.

Statisticians know the sampling distribution of a statistic which compares the "expected" frequency of a sample

with the actual, or "observed" frequency. For a sample with c different classes (the sizes here), this statistic is

distributed like

2

with c-1 df. The

2

is computed by the formula:

where:

O = observed frequency in the sample in this class

E = expected frequency in the sample in this class.

The expected frequency, E, is found by multiplying the relative frequency of this class in the H

o

:hypothesized

population by the sample size. This gives you the number in that class in the sample if the relative frequency

distribution across the classes in the sample exactly matches the distribution in the population.

Notice that

2

is always > 0 and equals 0 only if the observed is equal to the expected in each class. Look at

the equation and make sure that you see that a larger value of goes with samples with large differences between the

observed and expected frequencies.

Ann now needs to come up with a rule to decide if the data supports

H

o

: or

H

a

:. She looks at the

tableand sees that for 5 df (there are 6 classes—there is an expected frequency for size 11 socks), only .05 of samples

drawn from a given population will have a

2

> 11.07 and only .10 will have a

2

> 9.24. She decides that it

Introductory Business Statistics 36 A Global Text

4. Hypothesis testing

would not be all that surprising if volleyball players had a different distribution of sock sizes than the athletes who

are currently buying "Easy Bounce", since all of the volleyball players are women and many of the current

customers are men. As a result, she uses the smaller .10 value of 9.24 for her decision rule. Now she must compute

her sample

2

. Ann starts by finding the expected frequency of size 6 socks by multiplying the relative

frequency of size 6 in the population being produced by 97, the sample size. She gets E = .06*97=5.82. She then

finds O-E = 3-5.82 = -2.82, squares that and divides by 5.82, eventually getting 1.37. She then realizes that she will

have to do the same computation for the other five sizes, and quickly decides that a spreadsheet will make this

much easier. Her spreadsheet looks like this:

sock size

frequency in

sample

population relative

frequency

expected frequency =

97*C

(O-E)^2/E

6 3 0.06 5.82 1.3663918

7 24 0.13 12.61 10.288033

8 33 0.22 21.34 6.3709278

9 20 0.3 29.1 2.8457045

10 17 0.26 25.22 2.6791594

11 0 0.03 2.91 2.91

97

Χ

2

=

26.460217

Exhibit 6: Ann's Excel sheet

Ann performs her third step, computing her sample statistic, using the spreadsheet. As you can see, her sample

2

= 26.46, which is well into the "unusual" range which starts at 9.24 according to her decision rule. Ann has

found that her sample data supports the hypothesis that the distribution of sock sizes of volleyball players is

different from the distribution of sock sizes that are currently being manufactured. If Ann's employer, Foothill

Hosiery, is going to market "Easy Bounce" socks to volleyball players, they are going to have to send out packages of

samples that contain a different mix of sizes than they are currently making. If "Easy Bounce" are successfully

marketed to volleyball players, the mix of sizes manufactured will have to be altered.

Now, review what Ann has done to test to see if the data in her sample supports the hypothesis that the world is

"unsurprising" and that volleyball players have the same distribution of sock sizes as Foothill Hosiery is currently

producing for other athletes. The essence of Ann's test was to see if her sample

2

could easily have come from

the sampling distribution of

2

's generated by taking samples from the population of socks currently being

produced. Since her sample

2

would be way out in the tail of that sampling distribution, she judged that her

sample data supported the other hypothesis, that there is a difference between volleyball players and the athletes

who are currently buying "Easy Bounce" socks.

Formally, Ann first wrote null and alternative hypotheses, describing the population her sample comes from in

two different cases. The first case is the null hypothesis; this occurs if volleyball players wear socks of the same sizes

in the same proportions as Foothill is currently producing. The second case is the alternative hypothesis; this occurs

if volleyball players wear different sizes. After she wrote her hypotheses, she found that there was a sampling

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distribution that statisticians knew about that would help her choose between them. This is the

2

distribution.

Looking at the formula for computing

2

and consulting the tables, Ann decided that a sample

2

value

greater than 9.24 would be unusual if her null hypothesis was true. Finally, she computed her sample statistic, and

found that her

2

, at 26.46, was well above her cut-off value. Ann had found that the data in her sample

supported the alternative,

H

a

:, that the distribution of volleyball players' sock sizes is different from the

distribution that Foothill is currently manufacturing. Acting on this finding, Ann will send a different mix of sizes in

the sample packages she sends volleyball coaches.

Testing population proportions

As you learned in the chapter “Making estimates”, sample proportions can be used to compute a statistic that

has a known sampling distribution. Reviewing, the z-statistic is:

where: p = the proportion of the sample with a certain characteristic

= the proportion of the population with that characteristic.

These sample z-statistics are distributed normally, so that by using the bottom line of the t table, you can find

what portion of all samples from a population with a given population proportion, π, have z-statistics within

different ranges. If you look at the table, you can see that .95 of all samples from any population have a z-statistics

between ±1.96, for instance.

If you have a sample that you think is from a population containing a certain proportion, π, of members with

some characteristic, you can test to see if the data in your sample supports what you think. The basic strategy is the

same as that explained earlier in this chapter and followed in the "goodness-of-fit" example: (a) write two

hypotheses, (b) find a sample statistic and sampling distribution that will let you develop a decision rule for

choosing between the two hypotheses, and (c) compute your sample statistic and choose the hypothesis supported

by the data.

Foothill Hosiery recently received an order for children's socks decorated with embroidered patches of cartoon

characters. Foothill did not have the right machinery to sew on the embroidered patches and contracted out the

sewing. While the order was filled and Foothill made a profit on it, the sewing contractor's price seemed high, and

Foothill had to keep pressure on the contractor to deliver the socks by the date agreed upon. Foothill's CEO, John

McGrath has explored buying the machinery necessary to allow Foothill to sew patches on socks themselves. He has

discovered that if more than a quarter of the children's socks they make are ordered with patches, the machinery

will be a sound investment. Mr McGrath asks Kevin Schmidt to find out if more than 25 per cent of children's socks

are being sold with patches.

Kevin calls the major trade organizations for the hosiery, embroidery, and children's clothes industries, and no

one can answer his question. Kevin decides it must be time to take a sample and to test to see if more than 25 per

cent of children's socks are decorated with patches. He calls the sales manager at Foothill and she agrees to ask her

salespeople to look at store displays of children's socks, counting how many pairs are displayed and how many of

Introductory Business Statistics 38 A Global Text

4. Hypothesis testing

those are decorated with patches. Two weeks later, Kevin gets a memo from the sales manager telling him that of

the 2,483 pairs of children's socks on display at stores where the salespeople counted, 716 pairs had embroidered

patches.

Kevin writes his hypotheses, remembering that Foothill will be making a decision about spending a fair amount

of money based on what he finds. To be more certain that he is right if he recommends that the money be spent,

Kevin writes his hypotheses so that the "unusual" world would be the one where more than 25 per cent of children's

socks are decorated:

H

o

: π

decorated socks

< .25

H

a

:π

decorated socks

> .25

When writing his hypotheses, Kevin knows that if his sample has a proportion of decorated socks well below .25,

he will want to recommend against buying the machinery. He only wants to say the data supports the alternative if

the sample proportion is well above .25. To include the low values in the null hypothesis and only the high values in

the alternative, he uses a "one-tail" test, judging that the data supports the alternative only if his z-score is in the

upper tail. He will conclude that the machinery should be bought only if his z-statistic is too large to have easily

have come from the sampling distribution drawn from a population with a proportion of .25. Kevin will accept

H

a

: only if his z is large and positive.

Checking the bottom line of the t-table, Kevin sees that .95 of all z-scores are less than 1.645. His rule is

therefore to conclude that his sample data supports the null hypothesis that 25 per cent or less of children's socks

are decorated if his sample z is less than 1.645. If his sample z is greater than 1.645, he will conclude that more than

25 per cent of children's socks are decorated and that Foothill Hosiery should invest in the machinery needed to

sew embroidered patches on socks.

Using the data the salespeople collected, Kevin finds the proportion of the sample that is decorated:

Using this value, he computes his sample z-statistic:

Because his sample z-score is larger than 1.645, it is unlikely that his sample z came from the sampling

distribution of z's drawn from a population where π < .25, so it is unlikely that his sample comes from a population

with π < .25. Kevin can tell John McGrath that the sample the sales people collected supports the conclusion that

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more than 25 per cent of children's socks are decorated with embroidered patches. John can feel comfortable

making the decision to buy the embroidery and sewing machinery.

Summary

This chapter has been an introduction to hypothesis testing. You should be able to see the relationship between

the mathematics and strategies of hypothesis testing and the mathematics and strategies of interval estimation.

When making an interval estimate, you construct an interval around your sample statistic based on a known

sampling distribution. When testing a hypothesis, you construct an interval around a hypothesized population

parameter, using a known sampling distribution to determine the width of that interval. You then see if your sample

statistic falls within that interval to decide if your sample probably came from a population with that hypothesized

population parameter.

Hypothesis testing is a very widely used statistical technique. It forces you to think ahead about what you might

find. By forcing you to think ahead, it often helps with decision-making by forcing you to think about what goes into

your decision. All of statistics requires clear thinking, and clear thinking generally makes better decisions.

Hypothesis testing requires very clear thinking and often leads to better decision-making.

Introductory Business Statistics 40 A Global Text

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5. The t-test

In Chapter 3 a sampling distribution, the t-distribution, was introduced. In Chapter 4 you learned how to use the

t-distribution to make an important inference, an interval estimate of the population mean. Here you will learn how

to use that same t-distribution to make more inferences, this time in the form of hypothesis tests. Before we start to

learn about those tests, a quick review of the t-distribution is in order.

The t-distribution

The t-distribution is a sampling distribution. You could generate your own t-distribution with n-1 degrees of

freedom by starting with a normal population, choosing all possible samples of one size, n, computing a t-score for

each sample:

where:

x

= the sample mean

μ = the population mean

s = the sample standard deviation

n = the size of the sample.

When you have all of the samples' t-scores, form a relative frequency distribution and you will have your t-

distribution. Luckily, you do not have to generate your own t-distributions because any statistics book has a table

that shows the shape of the t-distribution for many different degrees of freedom. Exhibit 1 reproduces a portion of

a typical t-table. See below.

Introductory Business Statistics 41 A Global Text

5. The t-test

Exhibit 7: A portion of a typical t-table

When you look at the formula for the t-score, you should be able to see that the mean t-score is zero because the

mean of the

x

's is equal to μ. Because most samples have

x

's that are close to μ, most will have t-scores that

are close to zero. The t-distribution is symmetric, because half of the samples will have

x

's greater than μ, and

half less. As you can see from the table, if there are 10 df, only .005 of the samples taken from a normal population

will have a t-score greater than +3.17. Because the distribution is symmetric, .005 also have a t-score less than -3.17.

Ninety-nine per cent of samples will have a t-score between ±3.17. Like the example in Exhibit 1, most t-tables have

a picture showing what is in the body of the table. In Exhibit 1, the shaded area is in the right tail, the body of the

table shows the t-score that leaves the α in the right tail. This t-table also lists the two-tail α above the one-tail

where is has p = .xx. For 5 df, there is a .05 probability that a sample will have a t-score greater than 2.02, and a .10

probability that a sample will have a t score either > +2.02 or < -2.02.

There are other sample statistics which follow this same shape and which can be used as the basis for different

hypothesis tests. You will see the t-distribution used to test three different types of hypotheses in this chapter and

that the t-distribution can be used to test other hypotheses in later chapters.

Though t-tables show how the sampling distribution of t-scores is shaped if the original population is normal, it

turns out that the sampling distribution of t-scores is very close to the one in the table even if the original

population is not quite normal, and most researchers do not worry too much about the normality of the original

population. An even more important fact is that the sampling distribution of t-scores is very close to the one in the

table even if the original population is not very close to being normal as long as the samples are large. This means

that you can safely use the t-distribution to make inferences when you are not sure that the population is normal as

long as you are sure that it is bell-shaped. You can also make inferences based on samples of about 30 or more

using the t-distribution when you are not sure if the population is normal. Not only does the t-distribution describe

42

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the shape of the distributions of a number of sample statistics, it does a good job of describing those shapes when

the samples are drawn from a wide range of populations, normal or not.

A simple test: does this sample come from a population with that mean?

Imagine that you have taken all of the samples with n=10 from a population that you knew the mean of, found

the t-distribution for 9 df by computing a t-score for each sample and generated a relative frequency distribution of

the t's. When you were finished, someone brought you another sample (n=10) wondering if that new sample came

from the original population. You could use your sampling distribution of t's to test if the new sample comes from

the original population or not. To conduct the test, first hypothesize that the new sample comes from the original

population. With this hypothesis, you have hypothesized a value for μ, the mean of the original population, to use to

compute a t-score for the new sample. If the t for the new sample is close to zero—if the t-score for the new sample

could easily have come from the middle of the t-distribution you generated—your hypothesis that the new sample

comes from a population with the hypothesized mean seems reasonable and you can conclude that the data

supports the new sample coming from the original population. If the t-score from the new sample was far above or

far below zero, your hypothesis that this new sample comes from the original population seems unlikely to be true,

for few samples from the original population would have t-scores far from zero. In that case, conclude that the data

gives support to the idea that the new sample comes from some other population.

This is the basic method of using this t-test. Hypothesize the mean of the population you think a sample might

come from. Using that mean, compute the t-score for the sample. If the t-score is close to zero, conclude that your

hypothesis was probably correct and that you know the mean of the population from which the sample came. If the

t-score is far from zero, conclude that your hypothesis is incorrect, and the sample comes from a population with a

different mean.

Once you understand the basics, the details can be filled in. The details of conducting a "hypothesis test of the

population mean", testing to see if a sample comes from a population with a certain mean—are of two types. The

first type concerns how to do all of this in the formal language of statisticians. The