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Bowerman-O’Connell: Business Statistics in Practice, Third Edition 11. Simple Linear Regression Analysis Text © The McGraw-Hill Companies, 2003 Chapter Outline 11.1 The Simple Linear Regression Model 11.2 The Least Squares Estimates, and Point Estimation and Prediction 11.3 Model Assumptions and the Standard Error 11.4 Testing the Significance of the Slope and y Intercept 11.5 Confidence and Prediction Intervals 11.6 Simple Coefficients of Determination and Correlation 11.7 An F Test for the Model *11.8 Residual Analysis *11.9 Some Shortcut Formulas Chapter 11 Simple Linear Regression Analysis *Optional section

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BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

Text

The McGrawHill Companies, 2003

Chapter 11Simple Linear Regression Analysis

Chapter Outline 11.1 The Simple Linear Regression Model 11.2 The Least Squares Estimates, and Point Estimation and Prediction 11.3 Model Assumptions and the Standard Error 11.4 Testing the Signicance of the Slope and y Intercept*Optional section

11.5 Condence and Prediction Intervals 11.6 Simple Coefcients of Determination and Correlation 11.7 An F Test for the Model *11.8 Residual Analysis *11.9 Some Shortcut Formulas

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

Text

The McGrawHill Companies, 2003

M

anagers often make decisions by studying the relationships between variables, and process improvements can often be made by understanding how changes in one or more variables affect the process output. Regression analysis is a statistical technique in which we use observed data to relate a variable of interest, which is called the dependent (or response) variable, to one or more independent (or predictor) variables. The objective is to build a regression model, or prediction equation, that can be used to describe, predict, and control the dependent variable on the basis of the independent variables. For example, a company might wish to improve its marketing process. After collecting data concerning the demand for a product, the products price, and the advertising expenditures made to promote the product, the company might use regression analysis to develop an equation to predict demand on the basis of price and advertising

expenditure. Predictions of demand for various priceadvertising expenditure combinations can then be used to evaluate potential changes in the companys marketing strategies. As another example, a manufacturer might use regression analysis to describe the relationship between several input variables and an important output variable. Understanding the relationships between these variables would allow the manufacturer to identify control variables that can be used to improve the process performance. In the next two chapters we give a thorough presentation of regression analysis. We begin in this chapter by presenting simple linear regression analysis. Using this technique is appropriate when we are relating a dependent variable to a single independent variable and when a straight-line model describes the relationship between these two variables. We explain many of the methods of this chapter in the context of two new cases:

CThe Fuel Consumption Case: A management consulting rm uses simple linear regression analysis to predict the weekly amount of fuel (in millions of cubic feet of natural gas) that will be required to heat the homes and businesses in a small city on the basis of the weeks average hourly temperature. A natural gas company uses these predictions to improve its gas ordering process. One of the gas companys objectives is to reduce the nes imposed by its pipeline transmission system when the company places inaccurate natural gas orders. The QHIC Case: The marketing department at Quality Home Improvement Center (QHIC) uses simple linear regression analysis to predict home upkeep expenditure on the basis of home value. Predictions of home upkeep expenditures are used to help determine which homes should be sent advertising brochures promoting QHICs products and services.

11.1 The Simple Linear Regression ModelThe simple linear regression model assumes that the relationship between the dependent variable, which is denoted y, and the independent variable, denoted x, can be approximated by a straight line. We can tentatively decide whether there is an approximate straight-line relationship between y and x by making a scatter diagram, or scatter plot, of y versus x. First, data concerning the two variables are observed in pairs. To construct the scatter plot, each value of y is plotted against its corresponding value of x. If the y values tend to increase or decrease in a straight-line fashion as the x values increase, and if there is a scattering of the (x, y) points around the straight line, then it is reasonable to describe the relationship between y and x by using the simple linear regression model. We illustrate this in the following case study, which shows how regression analysis can help a natural gas company improve its gas ordering process.

CHAPTER 14

EXAMPLE 11.1 The Fuel Consumption Case: Reducing Natural GasTransmission FinesWhen the natural gas industry was deregulated in 1993, natural gas companies became responsible for acquiring the natural gas needed to heat the homes and businesses in the cities they serve. To do this, natural gas companies purchase natural gas from marketers (usually through longterm contracts) and periodically (daily, weekly, monthly, or the like) place orders for natural gas to be transmitted by pipeline transmission systems to their cities. There are hundreds of pipeline transmission systems in the United States, and many of these systems supply a large number of

C

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

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Chapter 11

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cities. For instance, the map on pages 448 and 449 illustrates the pipelines of and the cities served by the Columbia Gas System. To place an order (called a nomination) for an amount of natural gas to be transmitted to its city over a period of time (day, week, month), a natural gas company makes its best prediction of the citys natural gas needs for that period. The natural gas company then instructs its marketer(s) to deliver this amount of gas to its pipeline transmission system. If most of the natural gas companies being supplied by the transmission system can predict their cities natural gas needs with reasonable accuracy, then the overnominations of some companies will tend to cancel the undernominations of other companies. As a result, the transmission system will probably have enough natural gas to efciently meet the needs of the cities it supplies. In order to encourage natural gas companies to make accurate transmission nominations and to help control costs, pipeline transmission systems charge, in addition to their usual fees, transmission nes. A natural gas company is charged a transmission ne if it substantially undernominates natural gas, which can lead to an excessive number of unplanned transmissions, or if it substantially overnominates natural gas, which can lead to excessive storage of unused gas. Typically, pipeline transmission systems allow a certain percentage nomination error before they impose a ne. For example, some systems do not impose a ne unless the actual amount of natural gas used by a city differs from the nomination by more than 10 percent. Beyond the allowed percentage nomination error, nes are charged on a sliding scalethe larger the nomination error, the larger the transmission ne. Furthermore, some transmission systems evaluate nomination errors and assess nes more often than others. For instance, some transmission systems do this as frequently as daily, while others do this weekly or monthly (this frequency depends on the number of storage elds to which the transmission system has access, the systems accounting practices, and other factors). In any case, each natural gas company needs a way to accurately predict its citys natural gas needs so it can make accurate transmission nominations. Suppose we are analysts in a management consulting rm. The natural gas company serving a small city has hired the consulting rm to develop an accurate way to predict the amount of fuel (in millions of cubic feetMMcfof natural gas) that will be required to heat the city. Because the pipeline transmission system supplying the city evaluates nomination errors and assesses nes weekly, the natural gas company wants predictions of future weekly fuel consumptions.1 Moreover, since the pipeline transmission system allows a 10 percent nomination error before assessing a ne, the natural gas company would like the actual and predicted weekly fuel consumptions to differ by no more than 10 percent. Our experience suggests that weekly fuel consumption substantially depends on the average hourly temperature (in degrees Fahrenheit) measured in the city during the week. Therefore, we will try to predict the dependent (response) variable weekly fuel consumption (y) on the basis of the independent (predictor) variable average hourly temperature (x) during the week. To this end, we observe values of y and x for eight weeks. The data are given in Table 11.1. In Figure 11.1 we give an Excel output of a scatter plot of y versus x. This plot shows1 2

A tendency for the fuel consumption to decrease in a straight-line fashion as the temperatures increase. A scattering of points around the straight line.

A regression model describing the relationship between y and x must represent these two characteristics. We now develop such a model.2 We begin by considering a specic average hourly temperature x. For example, consider the average hourly temperature 28F, which was observed in week 1, or consider the average hourly temperature 45.9F, which was observed in week 5 (there is nothing special about these two average hourly temperatures, but we will use them throughout this example to help explain the idea of a regression model). For the specic average hourly temperature x that we consider, there are, in theory, many weeks that could have this temperature. However, although these weeks

1

For whatever period of time a transmission system evaluates nomination errors and charges nes, a natural gas company is free to actually make nominations more frequently. Sometimes this is a good strategy, but we will not further discuss it.

2

Generally, the larger the sample size isthat is, the more combinations of values of y and x that we have observedthe more accurately we can describe the relationship between y and x. Therefore, as the natural gas company observes values of y and x in future weeks, the new data should be added to the data in Table 11.1.

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

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11.1 TA B L E 1 1 . 1 The Fuel Consumption Data FuelCon1Weekly Fuel Consumption, y (MMcf)12.4 11.7 12.4 10.8 9.4 9.5 8.0 7.5

The Simple Linear Regression Model FIGURE 11.1 Excel Output of a Scatter Plot of y versus xC 15 13 11 9 7 5 20 D E F G

447

1 2 3 4 5 6 7 8

28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5

FUEL

Week

Average Hourly Temperature, x (F)

A B 1 TEMP FUELCONS 12.4 2 28 11.7 3 28 12.4 4 32.5 10.8 5 39 9.4 6 45.9 9.5 7 57.8 8 8 58.1 7.5 9 62.5 10 11 12 13 14

H

30

40 50 TEMP

60

70

each have the same average hourly temperature, other factors that affect fuel consumption could vary from week to week. For example, these weeks might have different average hourly wind velocities, different thermostat settings, and so forth. Therefore, the weeks could have different fuel consumptions. It follows that there is a population of weekly fuel consumptions that could be observed when the average hourly temperature is x. Furthermore, this population has a mean, which we denote as y | x (pronounced mu of y given x). We can represent the straight-line tendency we observe in Figure 11.1 by assuming that my x is related to x by the equation my x b0 b1x

This equation is the equation of a straight line with y-intercept B0 (pronounced beta zero) and slope B1 (pronounced beta one). To better understand the straight line and the meanings of b0 and b1, we must rst realize that the values of b0 and b1 determine the precise value of the mean weekly fuel consumption my x that corresponds to a given value of the average hourly temperature x. We cannot know the true values of b0 and b1, and in the next section we learn how to estimate these values. However, for illustrative purposes, let us suppose that the true value of b0 is 15.77 and the true value of b1 is .1281. It would then follow, for example, that the mean of the population of all weekly fuel consumptions that could be observed when the average hourly temperature is 28F is my 28 b0 b1(28) 15.77 .1281(28) 12.18 MMcf of natural gas

As another example, it would also follow that the mean of the population of all weekly fuel consumptions that could be observed when the average hourly temperature is 45.9F is my 45.9 b0 b1(45.9) 15.77 .1281(45.9) 9.89 MMcf of natural gas

Note that, as the average hourly temperature increases from 28F to 45.9F, mean weekly fuel consumption decreases from 12.18 MMcf to 9.89 MMcf of natural gas. This makes sense because we would expect to use less fuel if the average hourly temperature increases. Of course, because we do not know the true values of b0 and b1, we cannot actually calculate these mean weekly fuel consumptions. However, when we learn in the next section how to estimate b0 and b1, we will then be able to estimate the mean weekly fuel consumptions. For now, when we say that the equation my x b0 b1x is the equation of a straight line, we mean that the different mean weekly fuel consumptions that correspond to different average hourly temperatures lie exactly on a straight line. For example, consider the eight mean weekly fuel consumptions that correspond to the eight average hourly temperatures in Table 11.1. In Figure 11.2(a) we depict these mean weekly fuel consumptions as triangles that lie exactly on the straight line dened by

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

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Chapter 11

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Columbia Gas System

Michigan

Middleburg Heights Sandusky Lorain Toledo Elyria Parma Ohio Gulf of MexicoColumbia Gas Transmission Columbia Gulf Transmission Cove Point LNG Corporate Headquarters Cove Point Terminal Storage Fields Distribution Service Territory Independent Power Projects Communities Served by Columbia Companies Communities Served by Companies Supplied by Columbia

Mansfield Marion

Springfield Columbus IndianaDayton

Athens

Cincinnati

Ashland Frankfort Huntington Lexington

Kentucky

Tennessee

Source: Columbia Gas System 1995 Annual Report.

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

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11.1

The Simple Linear Regression Model

449

New York

Binghamton

Wilkes-Barre Elizabeth Allentown New Brighton Pittsburgh Uniontown Wheeling Cumberland Hagerstown Maryland Baltimore West Virginia Manassas Delaware Washington, D.C. Cove Point Terminal Charleston Staunton Fredericksburg Richmond Virginia Williamsburg Newport News Norfolk Chesapeake York Wilmington Atlantic City Harrisburg Bethlehem New Jersey

Pennsylvania

Lynchburg Roanoke Petersburg

Portsmouth

North Carolina

Reprinted courtesy of Columbia Gas System.

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450F I G U R E 11.2

Chapter 11

Simple Linear Regression Analysis

The Simple Linear Regression Model Relating Weekly Fuel Consumption (y) to Average Hourly Temperature (x)y 28

y 13 12 11 10 9 8 7

Mean weekly fuel consumption when x 28 The error term for the first week (a positive error term) 12.4 The observed fuel consumption for the first weeky 45.9

Mean weekly fuel consumption when x 45.9 The error term for the fifth week (a negative error term) 9.4 The observed fuel consumption for the fifth week The straight line defined by the equation y x 0 1x x

28.0

45.9

62.5

(a) The line of means and the error terms y1 0 0 1(c 1c 1

The change in mean weekly fuel consumption that is associated with a one-degree increase in average hourly temperature

1)

x c y 15 14 13 12 11 10 9 8 70

c

1

(b) The slope of the line of means

Mean weekly fuel consumption when the average hourly temperature is 0F

x 0 28 62.5 (c) The y-intercept of the line of means

the equation my x b0 b1x. Furthermore, in this gure we draw arrows pointing to the triangles that represent the previously discussed means my 28 and my 45.9. Sometimes we refer to the straight line dened by the equation my x b0 b1x as the line of means. In order to interpret the slope b1 of the line of means, consider two different weeks. Suppose that for the rst week the average hourly temperature is c. The mean weekly fuel consumption for all such weeks is b0 b1(c) 1). The mean weekly

For the second week, suppose that the average hourly temperature is (c fuel consumption for all such weeks is b0 b1(c 1)

It is easy to see that the difference between these mean weekly fuel consumptions is b1. Thus, as illustrated in Figure 11.2(b), the slope b1 is the change in mean weekly fuel consumption that is associated with a one-degree increase in average hourly temperature. To interpret the meaning of

BowermanOConnell: Business Statistics in Practice, Third Edition

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11.1

The Simple Linear Regression Model

451

the y-intercept b0, consider a week having an average hourly temperature of 0F. The mean weekly fuel consumption for all such weeks is b0 b1(0) b0 Therefore, as illustrated in Figure 11.2(c), the y-intercept b0 is the mean weekly fuel consumption when the average hourly temperature is 0F. However, because we have not observed any weeks with temperatures near 0, we have no data to tell us what the relationship between mean weekly fuel consumption and average hourly temperature looks like for temperatures near 0. Therefore, the interpretation of b0 is of dubious practical value. More will be said about this later. Now recall that the observed weekly fuel consumptions are not exactly on a straight line. Rather, they are scattered around a straight line. To represent this phenomenon, we use the simple linear regression model y my x e b0 b1x e This model says that the weekly fuel consumption y observed when the average hourly temperature is x differs from the mean weekly fuel consumption my x by an amount equal to e (pronounced epsilon). Here is called an error term. The error term describes the effect on y of all factors other than the average hourly temperature. Such factors would include the average hourly wind velocity and the average hourly thermostat setting in the city. For example, Figure 11.2(a) shows that the error term for the rst week is positive. Therefore, the observed fuel consumption y 12.4 in the rst week was above the corresponding mean weekly fuel consumption for all weeks when x 28. As another example, Figure 11.2(a) also shows that the error term for the fth week was negative. Therefore, the observed fuel consumption y 9.4 in the fth week was below the corresponding mean weekly fuel consumption for all weeks when x 45.9. More generally, Figure 11.2(a) illustrates that the simple linear regression model says that the eight observed fuel consumptions (the dots in the gure) deviate from the eight mean fuel consumptions (the triangles in the gure) by amounts equal to the error terms (the line segments in the gure). Of course, since we do not know the true values of b0 and b1, the relative positions of the quantities pictured in the gure are only hypothetical. With the fuel consumption example as background, we are ready to dene the simple linear regression model relating the dependent variable y to the independent variable x. We suppose that we have gathered n observationseach observation consists of an observed value of x and its corresponding value of y. Then:

The Simple Linear Regression Model

T1 2 3

he simple linear (or straight line) regression model is: y Here my x b0 b1x is the mean value of the dependent variable y when the value of the independent variable is x. b0 is the y-intercept. b0 is the mean value of y when x equals 0.3 b1 is the slope. b1 is the change (amount of increase or decrease) in the mean value of y

my x

e

b0

b1x

e

associated with a one-unit increase in x. If b1 is positive, the mean value of y increases as x increases. If b1 is negative, the mean value of y decreases as x increases.

4

e is an error term that describes the effects on y of all factors other than the value of the independent variable x.

This model is illustrated in Figure 11.3 (note that x0 in this gure denotes a specic value of the independent variable x). The y-intercept b0 and the slope b1 are called regression parameters. Because we do not know the true values of these parameters, we must use the sample data to3

As implied by the discussion of Example 11.1, if we have not observed any values of x near 0, this interpretation is of dubious practical value.

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452F I G U R E 11.3

Chapter 11 The Simple Linear Regression Model (Here B1y

Simple Linear Regression Analysis 0)

Error term

An observed value of y when x equals x0

Straight line defined by the equation y x 0 1x

Slope0

1

Mean value of y when x equals x0

One-unit change in x y-intercept

x 0 x0 A specific value of the independent variable x

estimate these values. We see how this is done in the next section. In later sections we show how to use these estimates to predict y. The fuel consumption data in Table 11.1 were observed sequentially over time (in eight consecutive weeks). When data are observed in time sequence, the data are called time series data. Many applications of regression utilize such data. Another frequently used type of data is called cross-sectional data. This kind of data is observed at a single point in time.

EXAMPLE 11.2 The QHIC Case

C

Quality Home Improvement Center (QHIC) operates ve stores in a large metropolitan area. The marketing department at QHIC wishes to study the relationship between x, home value (in thousands of dollars), and y, yearly expenditure on home upkeep (in dollars). A random sample of 40 homeowners is taken and asked to estimate their expenditures during the previous year on the types of home upkeep products and services offered by QHIC. Public records of the county auditor are used to obtain the previous years assessed values of the homeowners homes. The resulting x and y values are given in Table 11.2. Because the 40 observations are for the same year (for different homes), these data are cross-sectional. The MINITAB output of a scatter plot of y versus x is given in Figure 11.4. We see that the observed values of y tend to increase in a straight-line (or slightly curved) fashion as x increases. Assuming that my x and x have a straight-line relationship, it is reasonable to relate y to x by using the simple linear regression model having a positive slope (b1 0) y b0 b1x e

The slope b1 is the change (increase) in mean dollar yearly upkeep expenditure that is associated with each $1,000 increase in home value. In later examples the marketing department at QHIC will use predictions given by this simple linear regression model to help determine which homes should be sent advertising brochures promoting QHICs products and services. We have interpreted the slope b1 of the simple linear regression model to be the change in the mean value of y associated with a one-unit increase in x. We sometimes refer to this change as the effect of the independent variable x on the dependent variable y. However, we cannot prove that

BowermanOConnell: Business Statistics in Practice, Third Edition

11. Simple Linear Regression Analysis

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11.1 T A B L E 11.2Home1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

The Simple Linear Regression Model The QHIC Upkeep Expenditure DataUpkeep Expenditure, y (Dollars)1,412.08 797.20 872.48 1,003.42 852.90 288.48 1,288.46 423.08 1,351.74 378.04 918.08 1,627.24 1,204.78 857.04 775.00 869.26 1,396.00 711.50 1,475.18 1,413.32

453

QHICHome21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Value of Home, x (Thousands of Dollars)237.00 153.08 184.86 222.06 160.68 99.68 229.04 101.78 257.86 96.28 171.00 231.02 228.32 205.90 185.72 168.78 247.06 155.54 224.20 202.04

Value of Home, x (Thousands of Dollars)153.04 232.18 125.44 169.82 177.28 162.82 120.44 191.10 158.78 178.50 272.20 48.90 104.56 286.18 83.72 86.20 133.58 212.86 122.02 198.02

Upkeep Expenditure, y (Dollars)849.14 1,313.84 602.06 642.14 1,038.80 697.00 324.34 965.10 920.14 950.90 1,670.32 125.40 479.78 2,010.64 368.36 425.60 626.90 1,316.94 390.16 1,090.84

F I G U R E 11.4

MINITAB Plot of Upkeep Expenditure versus Value of Home for the QHIC Data

2000

UPKEEP

1000

0 100 200 VALUE 300

a change in an independent variable causes a change in the dependent variable. Rather, regression can be used only to establish that the two variables move together and that the independent variable contributes information for predicting the dependent variable. For instance, regression analysis might be used to establish that as liquor sales have increased over the years, college professors salaries have also increased. However, this does not prove that increases in liquor sales cause increases in college professors salaries. Rather, both variables are inuenced by a third variablelong-run growth in the national economy.

Exercises for Section 11.1CONCEPTS 11.1 When does the scatter plot of the values of a dependent variable y versus the values of an independent variable x suggest that the simple linear regression model y my x e b0 might appropriately relate y to x? b1x e

11.5, 11.6

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11. Simple Linear Regression Analysis

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Chapter 11

Simple Linear Regression Analysis

11.2 In the simple linear regression model, what are y, my x, and e? 11.3 In the simple linear regression model, dene the meanings of the slope b1 and the y-intercept b0. 11.4 What is the difference between time series data and cross-sectional data? METHODS AND APPLICATIONS 11.5 THE STARTING SALARY CASE StartSal The chairman of the marketing department at a large state university undertakes a study to relate starting salary (y) after graduation for marketing majors to grade point average (GPA) in major courses. To do this, records of seven recent marketing graduates are randomly selected.

1 2 3 4 5 6 7

3.26 2.60 3.35 2.86 3.82 2.21 3.47

33.8 29.8 33.5 30.4 36.4 27.6 35.3

StartSal

Marketing Graduate

GPA, x

Starting Salary, y (Thousands of Dollars)

37 36 35 34 33 32 31 30 29 28 27 2 3 GPA 4

Using the scatter plot (from MINITAB) of y versus x, explain why the simple linear regression model y my x b0 might appropriately relate y to x. 11.6 THE STARTING SALARY CASE StartSal Consider the simple linear regression model describing the starting salary data of Exercise 11.5. a Explain the meaning of my x 4.00 b0 b1(4.00). b Explain the meaning of my x 2.50 b0 b1(2.50). c Interpret the meaning of the slope parameter b1. d Interpret the meaning of the y-intercept b0. Why does this interpretation fail to make practical sense? e The error term e describes the effects of many factors on starting salary y. What are these factors? Give two specic examples. 11.7 THE SERVICE TIME CASE SrvcTime Accu-Copiers, Inc., sells and services the Accu-500 copying machine. As part of its standard service contract, the company agrees to perform routine service on this copier. To obtain information about the time it takes to perform routine service, Accu-Copiers has collected data for 11 service calls. The data are as follows: e b1x e

Service Call1 2 3 4 5 6 7 8 9 10 11

Number of Copiers Serviced, x4 2 5 7 1 3 4 5 2 4 6

Number of Minutes Required, y109 58 138 189 37 82 103 134 68 112 154Minutes

200 150 100 50 0 0 2 4 Copiers 6 8

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11.1

The Simple Linear Regression Model Using the scatter plot (from Excel) of y versus x, discuss why the simple linear regression model might appropriately relate y to x.

455

11.8 THE SERVICE TIME CASE

SrvcTime

Consider the simple linear regression model describing the service time data in Exercise 11.7. a Explain the meaning of my x 4 b0 b1(4). b Explain the meaning of my x 6 b0 b1(6). c Interpret the meaning of the slope parameter b1. d Interpret the meaning of the y-intercept b 0 . Does this interpretation make practical sense? e The error term e describes the effects of many factors on service time. What are these factors? Give two specic examples. 11.9 THE FRESH DETERGENT CASE Fresh Enterprise Industries produces Fresh, a brand of liquid laundry detergent. In order to study the relationship between price and demand for the large bottle of Fresh, the company has gathered data concerning demand for Fresh over the last 30 sales periods (each sales period is four weeks). Here, for each sales period, y x1 x2 x4 demand for the large bottle of Fresh (in hundreds of thousands of bottles) in the sales period the price (in dollars) of Fresh as offered by Enterprise Industries in the sales period the average industry price (in dollars) of competitors similar detergents in the sales period x2 x1 the price difference in the sales period

Note: We denote the price difference as x4 (rather than, for example, x3) to be consistent with other notation to be introduced in the Fresh detergent case in Chapter 12.Fresh Detergent Demand Data Sales x1 y Period7.38 8.51 9.52 7.50 9.33 8.28 8.75 7.87 7.10 8.00 7.89 8.15 9.10 8.86 8.90 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Sales Period1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

x13.85 3.75 3.70 3.70 3.60 3.60 3.60 3.80 3.80 3.85 3.90 3.90 3.70 3.75 3.75

x23.80 4.00 4.30 3.70 3.85 3.80 3.75 3.85 3.65 4.00 4.10 4.00 4.10 4.20 4.10

x4.05 .25 .60 0 .25 .20 .15 .05 .15 .15 .20 .10 .40 .45 .35

x2

x13.80 3.70 3.80 3.70 3.80 3.80 3.75 3.70 3.55 3.60 3.65 3.70 3.75 3.80 3.70

x24.10 4.20 4.30 4.10 3.75 3.75 3.65 3.90 3.65 4.10 4.25 3.65 3.75 3.85 4.25

x4.30 .50 .50 .40 .05 .05 .10 .20 .10 .50 .60 .05 0 .05 .55

x2

x1

y8.87 9.26 9.00 8.75 7.95 7.65 7.27 8.00 8.50 8.75 9.21 8.27 7.67 7.93 9.26

Using the scatter plot (from MINITAB) of y versus x4 shown below, discuss why the simple linear regression model might appropriately relate y to x4.9.5 Demand, y 9.0 8.5 8.0 7.5 7.0 -0.2-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 PriceDif, x4

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456Direct Labor Cost DirLab Data Direct Labor Cost, y ($100s)71 663 381 138 861 145 493 548 251 1024 435 772

Chapter 11 11.10 THE FRESH DETERGENT CASE Fresh

Simple Linear Regression Analysis

Batch Size, x5 62 35 12 83 14 46 52 23 100 41 75

Consider the simple linear regression model relating demand, y, to the price difference, x4, and the Fresh demand data of Exercise 11.9. a Explain the meaning of my x4 .10 b0 b1(.10). b Explain the meaning of my x4 .05 b0 b1( .05). c Explain the meaning of the slope parameter b1. d Explain the meaning of the intercept b0. Does this explanation make practical sense? e What factors are represented by the error term in this model? Give two specic examples. 11.11 THE DIRECT LABOR COST CASE DirLab An accountant wishes to predict direct labor cost (y) on the basis of the batch size (x) of a product produced in a job shop. Data for 12 production runs are given in the table in the margin. a Construct a scatter plot of y versus x. b Discuss whether the scatter plot suggests that a simple linear regression model might appropriately relate y to x. 11.12 THE DIRECT LABOR COST CASE DirLab Consider the simple linear regression model describing the direct labor cost data of Exercise 11.11. a Explain the meaning of my x 60 b0 b1(60). b Explain the meaning of my x 30 b0 b1(30). c Explain the meaning of the slope parameter b1. d Explain the meaning of the intercept b0. Does this explanation make practical sense? e What factors are represented by the error term in this model? Give two specic examples of these factors. 11.13 THE REAL ESTATE SALES PRICE CASE RealEst A real estate agency collects data concerning y the sales price of a house (in thousands of dollars), and x the home size (in hundreds of square feet). The data are given in the margin. a Construct a scatter plot of y versus x. b Discuss whether the scatter plot suggests that a simple linear regression model might appropriately relate y to x. 11.14 THE REAL ESTATE SALES PRICE CASE RealEst Consider the simple linear regression model describing the sales price data of Exercise 11.13. a Explain the meaning of my x 20 b0 b1(20). b Explain the meaning of my x 18 b0 b1(18). c Explain the meaning of the slope parameter b1. d Explain the meaning of the intercept b0. Does this explanation make practical sense? e What factors are represented by the error term in this model? Give two specic examples.

Real Estate Sales Price Data

RealEstSales Price (y)180 98.1 173.1 136.5 141 165.9 193.5 127.8 163.5 172.5

Home Size (x)23 11 20 17 15 21 24 13 19 25

Source: Reprinted with permission from The Real Estate Appraiser and Analyst Spring 1986 issue. Copyright 1986 by the Appraisal Institute, Chicago, Illinois.

11.2 The Least Squares Estimates, and Point Estimation and PredictionCHAPTER 15

The true values of the y-intercept (b0) and slope (b1) in the simple linear regression model are unknown. Therefore, it is necessary to use observed data to compute estimates of these regression parameters. To see how this is done, we begin with a simple example.

EXAMPLE 11.3 The Fuel Consumption Case

C

Consider the fuel consumption problem of Example 11.1. The scatter plot of y (fuel consumption) versus x (average hourly temperature) in Figure 11.1 suggests that the simple linear regression model appropriately relates y to x. We now wish to use the data in Table 11.1 to estimate the intercept b0 and the slope b1 of the line of means. To do this, it might be reasonable to estimate the line of means by tting the best straight line to the plotted data in Figure 11.1. But how do we t the best straight line? One approach would be to simply eyeball a line through the points. Then we could read the y-intercept and slope off the visually tted line and use these values as the estimates of b0 and b1. For example, Figure 11.5 shows a line that has been visually tted to the plot of the

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F I G U R E 11.5 Visually Fitting a Line to the Fuel Consumption Datay 16 15 14 13.8 12.8 13 12 11 10 9 8 7 x 0 10 20 30 40 50 60 70 Change in y Change in x y-intercept Slopechange in y change in x 12.8 20 13.8 10

.1

12.4

12.2

.2

F I G U R E 11.6

Using the Visually Fitted Line to Predict When xy 16 15 14 12.2 13 12 11 10 9 8 7 x 28

28

y

^

x 0 10 20 30 40 50 60 70

fuel consumption data. We see that this line intersects the y axis at y 15. Therefore, the y-intercept of the line is 15. In addition, the gure shows that the slope of the line is 12.8 13.8 1 change in y .1 change in x 20 10 10 Therefore, based on the visually tted line, we estimate that b0 is 15 and that b1 is .1. In order to evaluate how good our point estimates of b0 and b1 are, consider using the visually tted line to predict weekly fuel consumption. Denoting such a prediction as y (pronounced y hat), a prediction of weekly fuel consumption when average hourly temperature is x is y y 15 15 .1x 15 2.8 12.2 For instance, when temperature is 28F, predicted fuel consumption is .1(28) Here y is simply the point on the visually tted line corresponding to x 28 (see Figure 11.6). We can evaluate how well the visually determined line ts the points on the scatter plot by

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Calculation of SSE for a Line Visually Fitted to the Fuel Consumption Datay12.4 11.7 12.4 10.8 9.4 9.5 8.0 7.5

x28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5 SSE 15 15 15 15 15 15 15 15

y

15.1(28.0) .1(28.0) .1(32.5) .1(39.0) .1(45.9) .1(57.8) .1(58.1) .1(62.5)

.1x12.2 12.2 11.75 11.1 10.41 9.22 9.19 8.75 .04 .25

y

y.2 .5 .65 .3 1.01 .28 1.19 1.25 4.8796

(y(.2)2 ( .5)2 (.65)2 ( .3)2 ( 1.01)2 (.28)2 ( 1.19)2 ( 1.25)2

y)2.04 .25 .4225 .09 1.0201 .0784 1.4161 1.5625

12.4 12.2 11.7 12.2 12.4 11.75 10.8 11.1 9.4 10.41 9.5 9.22 8.0 9.19 7.5 8.75 1.5625

a(y

y )2

comparing each observed value of y with the corresponding predicted value of y given by the tted line. We do this by computing the deviation y y. For instance, looking at the rst obser vation in Table 11.1 (page 447), we observed y 12.4 and x 28.0. Since the predicted fuel consumption when x equals 28 is y 12.2, the deviation y y equals 12.4 12.2 .2. This deviation is illustrated in Figure 11.5. Table 11.3 gives the values of y, x, y, and y y for each observation in Table 11.1. The deviations (or prediction errors) are the vertical distances between the observed y values and the predictions obtained using the tted linethat is, they are the line segments depicted in Figure 11.5. If the visually determined line ts the data well, the deviations (errors) will be small. To obtain an overall measure of the quality of the t, we compute the sum of squared deviations or sum of squared errors, denoted SSE. Table 11.3 also gives the squared deviations and the SSE for our visually tted line. We nd that SSE 4.8796. Clearly, the line shown in Figure 11.5 is not the only line that could be tted to the observed fuel consumption data. Different people would obtain somewhat different visually tted lines. However, it can be shown that there is exactly one line that gives a value of SSE that is smaller than the value of SSE that would be given by any other line that could be tted to the data. This line is called the least squares regression line or the least squares prediction equation. To show how to nd the least squares line, we rst write the general form of a straight-line prediction equation as y b0 b1x

Here b0 (pronounced b zero) is the y-intercept and b1 (pronounced b one) is the slope of the line. In addition, y denotes the predicted value of the dependent variable when the value of the inde pendent variable is x. Now suppose we have collected n observations (x1, y1), (x2, y2), . . . , (xn, yn). If we consider a particular observation (xi, yi), the predicted value of yi is yi b0 b1xi

Furthermore, the prediction error (also called the residual) for this observation is ei yi yi yi (b0 b1xi)

Then the least squares line is the line that minimizes the sum of the squared prediction errors (that is, the sum of squared residuals):n

SSE

i

a (yi1

(b0

b1xi))2

To nd this line, we nd the values of the y-intercept b0 and the slope b1 that minimize SSE. These values of b0 and b1 are called the least squares point estimates of b0 and b1. Using calculus, it

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can be shown that these estimates are calculated as follows:4

The Least Squares Point Estimates

F1 2

or the simple linear regression model: The least squares point estimate of the slope B1 is b1 a xi SSxy a ( xi x )( yi y) a xi yi n a yi and y a xi n SSxx a (xi x )2 a xi2

SSxy where SSxx a xi n2

The least squares point estimate of the y-intercept B0 is b0 y a yi n and x

b1x where

Here n is the number of observations (an observation is an observed value of x and its corresponding value of y).

The following example illustrates how to calculate these point estimates and how to use these point estimates to estimate mean values and predict individual values of the dependent variable. Note that the quantities SSxy and SSxx used to calculate the least squares point estimates are also used throughout this chapter to perform other important calculations.

EXAMPLE 11.4 The Fuel Consumption CasePart 1: Calculating the least squares point estimates Again consider the fuel consumption problem. To compute the least squares point estimates of the regression parameters b0 and b1 we rst calculate the following preliminary summations:yi12.4 11.7 12.4 10.8 9.4 9.5 8.0 7.5 a yi 81.7 a xi

Cxi yi

xi28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5 351.8 (28.0) (28.0)2 (32.5)2 (39.0)2 (45.9)2 (57.8)2 (58.1)2 (62.5)2 a xi22

x2 i784 784 1,056.25 1,521 2,106.81 3,340.84 3,375.61 3,906.25 16,874.76

(28.0)(12.4) (28.0)(11.7) (32.5)(12.4) (39.0)(10.8) (45.9)(9.4) (57.8)(9.5) (58.1)(8.0) (62.5)(7.5) a xiyi

347.2 327.6 403 421.2 431.46 549.1 464.8 468.75

3,413.11

Using these summations, we calculate SSxy and SSxx as follows. a xi SSxy a xiyi 3,413.11 a yi

n (351.8)(81.7) 8 a xi2

179.6475

SSxx

a xi

2

n (351.8)2 8 1,404.355

16,874.764

In order to simplify notation, we will often drop the limits on summations in this and subsequent chapters. That is, instead ofn

using the summation a we will simply write a .i 1

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460T A B L E 11.4yi12.4 11.7 12.4 10.8 9.4 9.5 8.0 7.5

Chapter 11

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Calculation of SSE Obtained by Using the Least Squares Point Estimates yi15.84 15.84 15.84 15.84 15.84 15.84 15.84 15.84 SSE

xi28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5

15.84

.1279xi12.2588 12.2588 11.68325 10.8519 9.96939 8.44738 8.40901 7.84625 .0199374 .3122574

yi

yi

residual.1412 .5588 .71675 .0519 .56939 1.05262 .40901 .34625 2.5680112

(yi(.1412)2 ( .5588)2 (.71675)2 ( .0519)2 ( .56939)2 (1.05262)2 ( .40901)2 ( .34625)2

yi)2.0199374 .3122574 .5137306 .0026936 .324205 1.1080089 .1672892 .1198891

.1279(28.0) .1279(28.0) .1279(32.5) .1279(39.0) .1279(45.9) .1279(57.8) .1279(58.1) .1279(62.5) yi)2

12.4 12.2588 11.7 12.2588 12.4 11.68325 10.8 10.8519 9.4 9.96939 9.5 8.44738 8.0 8.40901 7.5 7.84625 .1198891

a (yi

It follows that the least squares point estimate of the slope b1 is b1 Furthermore, because 81.7 a yi 10.2125 and x 8 8 the least squares point estimate of the y-intercept b0 is y b0 y b1x 10.2125 a xi 8 351.8 8 15.84 43.98 SSxy SSxx 179.6475 1,404.355 .1279

( .1279)(43.98)

Since b1 .1279, we estimate that mean weekly fuel consumption decreases (since b1 is negative) by .1279 MMcf of natural gas when average hourly temperature increases by 1 degree. Since b0 15.84, we estimate that mean weekly fuel consumption is 15.84 MMcf of natural gas when average hourly temperature is 0F. However, we have not observed any weeks with temperatures near 0, so making this interpretation of b0 might be dangerous. We discuss this point more fully after this example. Table 11.4 gives predictions of fuel consumption for each observed week obtained by using the least squares line (or prediction equation) y b0 b1x 15.84 .1279x The table also gives each of the residuals and squared residuals and the sum of squared residuals (SSE 2.5680112) obtained by using this prediction equation. Notice that the SSE here, which was obtained using the least squares point estimates, is smaller than the SSE of Table 11.3, which was obtained using the visually tted line y 15 .1x. In general, it can be shown that the SSE obtained by using the least squares point estimates is smaller than the value of SSE that would be obtained by using any other estimates of b0 and b1. Figure 11.7(a) illustrates the eight observed fuel consumptions (the dots in the gure) and the eight predicted fuel consumptions (the squares in the gure) given by the least squares line. The distances between the observed and predicted fuel consumptions are the residuals. Therefore, when we say that the least squares point estimates minimize SSE, we are saying that these estimates position the least squares line so as to minimize the sum of the squared distances between the observed and predicted fuel consumptions. In this sense, the least squares line is the best straight line that can be tted to the eight observed fuel consumptions. Figure 11.7(b) gives the MINITAB output of this best t line. Note that this output gives the least squares estimates b0 15.8379 and b1 .127922. In general, we will rely on MINITAB, Excel, and MegaStat to compute the least squares estimates (and to perform many other regression calculations). Part 2: Estimating a mean fuel consumption and predicting an individual fuel consumption We dene the experimental region to be the range of the previously observed values of the average hourly temperature x. Because we have observed average hourly temperatures between 28F and 62.5F (see Table 11.4), the experimental region consists of the range of average hourly temperatures from 28F to 62.5F. The simple linear regression model relates

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11.2 F I G U R E 11.7

The Least Squares Estimates, and Point Estimation and Prediction The Least Squares Line for the Fuel Consumption Data

461

(a) The observed and predicted fuel consumptions

(b) The MINITAB output of the least squares lineBEST FIT LINE FOR FUEL CONSUMPTION DATA

y 16 15 14 13 12 11 10 9 8 7 The least squares line ^ y 15.84 .1279x FUELCONS

Y = 15.8379 0.127922X R-Squared = 0.899 14 13 Predicted fuel consumption when x 45.9 12 11 10 9 8 7 x 6 30 40 TEMP 50 60

Residual Observed fuel consumption when x 45.9

0

10

20

30

40

50

60

70

weekly fuel consumption y to average hourly temperature x for values of x that are in the experimental region. For such values of x, the least squares line is the estimate of the line of means. This implies that the point on the least squares line that corresponds to the average hourly temperature x y b0 b1x 15.84 .1279x

is the point estimate of the mean of all the weekly fuel consumptions that could be observed when the average hourly temperature is x: my x b0 b1x Note that y is an intuitively logical point estimate of my x. This is because the expression b0 b1x used to calculate y has been obtained from the expression b0 b1x for my x by replacing the unknown values of b0 and b1 by their least squares point estimates b0 and b1. The quantity y is also the point prediction of the individual value y b0 b1x e

which is the amount of fuel consumed in a single week when average hourly temperature equals x. To understand why y is the point prediction of y, note that y is the sum of the mean b0 b1x and the error term e. We have already seen that y b0 b1x is the point estimate of b0 b1x. We will now reason that we should predict the error term E to be 0, which implies that y is also the point prediction of y. To see why we should predict the error term to be 0, note that in the next section we discuss several assumptions concerning the simple linear regression model. One implication of these assumptions is that the error term has a 50 percent chance of being positive and a 50 percent chance of being negative. Therefore, it is reasonable to predict the error term to be 0 and to use y as the point prediction of a single value of y when the average hourly temperature equals x. Now suppose a weather forecasting service predicts that the average hourly temperature in the next week will be 40F. Because 40F is in the experimental region y 15.84 .1279(40) 10.72 MMcf of natural gas

is (1) the point estimate of the mean weekly fuel consumption when the average hourly temperature is 40F and (2) the point prediction of an individual weekly fuel consumption when the

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Point Estimation and Point Prediction in the Fuel Consumption Problemy 16 15 14 13 12^ y

The least squares line ^ y 15.84 .1279x An individual value of fuel consumption when x 40 The true mean fuel consumption when x 40 The point estimate of mean fuel consumption when x 40 The true line of means yx 0 1x

11 10.72 10 9 8 7

x 10 20 30 40 50 60 70

F I G U R E 11.9

The Danger of Extrapolation Outside the Experimental Regiony

22 21 True mean fuel consumption when x 10 Estimated mean fuel consumption when x 10 obtained by extrapolating the least squares line 20 19 18 17 16 15 14 13 12 11 10 9 8 7

The relationship between mean fuel consumption and x might become curved at low temperatures

The least squares line ^ y 15.84 .1279x

x 10 0 10 20 30 40 50 60 70 28 62.5 Experimental region

average hourly temperature is 40F. This says that (1) we estimate that the average of all possible weekly fuel consumptions that could potentially be observed when the average hourly temperature is 40F equals 10.72 MMcf of natural gas, and (2) we predict that the fuel consumption in a single week when the average hourly temperature is 40F will be 10.72 MMcf of natural gas. Figure 11.8 illustrates (1) the point estimate of mean fuel consumption when x is 40F (the square on the least squares line), (2) the true mean fuel consumption when x is 40F (the

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triangle on the true line of means), and (3) an individual value of fuel consumption when x is 40F (the dot in the gure). Of course this gure is only hypothetical. However, it illustrates that the point estimate of the mean value of y (which is also the point prediction of the individual value of y) will (unless we are extremely fortunate) differ from both the true mean value of y and the individual value of y. Therefore, it is very likely that the point prediction y 10.72, which is the natural gas companys transmission nomination for next week, will differ from next weeks actual fuel consumption, y. It follows that we might wish to predict the largest and smallest that y might reasonably be. We will see how to do this in Section 11.5. To conclude this example, note that Figure 11.9 illustrates the potential danger of using the least squares line to predict outside the experimental region. In the gure, we extrapolate the least squares line far beyond the experimental region to obtain a prediction for a temperature of 10F. As shown in Figure 11.1, for values of x in the experimental region the observed values of y tend to decrease in a straight-line fashion as the values of x increase. However, for temperatures lower than 28F the relationship between y and x might become curved. If it does, extrapolating the straight-line prediction equation to obtain a prediction for x 10 might badly underestimate mean weekly fuel consumption (see Figure 11.9). The previous example illustrates that when we are using a least squares regression line, we should not estimate a mean value or predict an individual value unless the corresponding value of x is in the experimental regionthe range of the previously observed values of x. Often the value x 0 is not in the experimental region. For example, consider the fuel consumption problem. Figure 11.9 illustrates that the average hourly temperature 0F is not in the experimental region. In such a situation, it would not be appropriate to interpret the y-intercept b0 as the estimate of the mean value of y when x equals 0. For example, in the fuel consumption problem it would not be appropriate to use b0 15.84 as the point estimate of the mean weekly fuel consumption when average hourly temperature is 0. Therefore, because it is not meaningful to interpret the y-intercept in many regression situations, we often omit such interpretations. We now present a general procedure for estimating a mean value and predicting an individual value:

Point Estimation and Point Prediction in Simple Linear Regression

L

et b0 and b1 be the least squares point estimates of the y-intercept b0 and the slope b1 in the simple linear regression model, and suppose that x0, a specied value of the independent variable x, is inside the experimental region. Then y b0 b1x0

is the point estimate of the mean value of the dependent variable when the value of the independent variable is x0. In addition, y is the point predic tion of an individual value of the dependent variable when the value of the independent variable is x0. Here we predict the error term to be 0.

EXAMPLE 11.5 The QHIC CaseConsider the simple linear regression model relating yearly home upkeep expenditure, y, to home value, x. Using the data in Table 11.2 (page 453), we can calculate the least squares point estimates of the y-intercept b0 and the slope b1 to be b0 348.3921 and b1 7.2583. Since b1 7.2583, we estimate that mean yearly upkeep expenditure increases by $7.26 for each additional $1,000 increase in home value. Consider a home worth $220,000, and note that x0 220 is in the range of previously observed values of x: 48.9 to 286.18 (see Table 11.2). It follows that y b1x0 348.3921 7.2583(220) 1,248.43 (or $1,248.43) b0

C

is the point estimate of the mean yearly upkeep expenditure for all homes worth $220,000 and is the point prediction of a yearly upkeep expenditure for an individual home worth $220,000.

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The marketing department at QHIC wishes to determine which homes should be sent advertising brochures promoting QHICs products and services. The prediction equation y b0 b1x implies that the home value x corresponding to a predicted upkeep expenditure of y is x y b1 b0 y ( 348.3921) 7.2583 y 348.3921 7.2583

Therefore, for example, if QHIC wishes to send an advertising brochure to any home that has a predicted upkeep expenditure of at least $500, then QHIC should send this brochure to any home that has a value of at least x y 348.3921 7.2583 500 348.3921 7.2583 116.886 ($116,886)

Exercises for Section 11.2CONCEPTS 11.15 What does SSE measure? 11.16 What is the least squares regression line, and what are the least squares point estimates? 11.19, 11.23 11.17 How do we obtain a point estimate of the mean value of the dependent variable and a point prediction of an individual value of the dependent variable? 11.18 Why is it dangerous to extrapolate outside the experimental region? METHODS AND APPLICATIONS Exercises 11.19, 11.20, and 11.21 are based on the following MINITAB and Excel output. At the left is the output obtained when MINITAB is used to t a least squares line to the starting salary data given in Exercise 11.5 (page 454). In the middle is the output obtained when Excel is used to t a least squares line to the service time data given in Exercise 11.7 (page 454). The rightmost output is obtained when MINITAB is used to t a least squares line to the Fresh detergent demand data given in Exercise 11.9 (page 455).

Regression Plot Y 37 36 35 34 33 32 31 30 29 28 27 2 3 GPA 4 14.8156 5.70657X R-Sq 0.977 200 180 160 140 120 100 80 60 40 20 0 0 2 Y

Copiers Line Fit Plot 11.4641 24.6022X 9.5 9.0 Demand 8.5 8.0 7.5 7.0 4 Copiers 6 8 Y

Regression Plot 7.81409 2.66522X R-Sq 0.792

StartSal

Minutes

0.2 0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 PriceDif

11.19 THE STARTING SALARY CASE

StartSal

Using the leftmost output a Identify and interpret the least squares point estimates b0 and b1. Does the interpretation of b0 make practical sense? b Use the least squares line to obtain a point estimate of the mean starting salary for all marketing graduates having a grade point average of 3.25 and a point prediction of the starting salary for an individual marketing graduate having a grade point average of 3.25.

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Model Assumptions and the Standard Error

465

Using the middle output a Identify and interpret the least squares point estimates b0 and b1. Does the interpretation of b0 make practical sense? b Use the least squares line to obtain a point estimate of the mean time to service four copiers and a point prediction of the time to service four copiers on a single call. 11.21 THE FRESH DETERGENT CASE Fresh

Using the rightmost output a Identify and interpret the least squares point estimates b0 and b1. Does the interpretation of b0 make practical sense? b Use the least squares line to obtain a point estimate of the mean demand in all sales periods when the price difference is .10 and a point prediction of the actual demand in an individual sales period when the price difference is .10. c If Enterprise Industries wishes to maintain a price difference that corresponds to a predicted demand of 850,000 bottles (that is, y 8.5), what should this price difference be? 11.22 THE DIRECT LABOR COST CASE DirLab

Consider the direct labor cost data given in Exercise 11.11 (page 456), and suppose that a simple linear regression model is appropriate. a Verify that b0 18.4880 and b1 10.1463 by using the formulas illustrated in Example 11.4 (pages 459460). b Interpret the meanings of b0 and b1. Does the interpretation of b0 make practical sense? c Write the least squares prediction equation. d Use the least squares line to obtain a point estimate of the mean direct labor cost for all batches of size 60 and a point prediction of the direct labor cost for an individual batch of size 60. 11.23 THE REAL ESTATE SALES PRICE CASE RealEst

Consider the sales price data given in Exercise 11.13 (page 456), and suppose that a simple linear regression model is appropriate. a Verify that b0 48.02 and b1 5.7003 by using the formulas illustrated in Example 11.4 (pages 459460). b Interpret the meanings of b0 and b1. Does the interpretation of b0 make practical sense? c Write the least squares prediction equation. d Use the least squares line to obtain a point estimate of the mean sales price of all houses having 2,000 square feet and a point prediction of the sales price of an individual house having 2,000 square feet.

11.3 Model Assumptions and the Standard ErrorModel assumptions In order to perform hypothesis tests and set up various types of intervals when using the simple linear regression model y my x b0 e b1x e

we need to make certain assumptions about the error term e. At any given value of x, there is a population of error term values that could potentially occur. These error term values describe the different potential effects on y of all factors other than the value of x. Therefore, these error term values explain the variation in the y values that could be observed when the independent variable is x. Our statement of the simple linear regression model assumes that my x, the mean of the population of all y values that could be observed when the independent variable is x, is b0 b1x. This model also implies that e y (b0 b1x), so this is equivalent to assuming that the mean of the corresponding population of potential error term values is 0. In total, we make four assumptionscalled the regression assumptionsabout the simple linear regression model.

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These assumptions can be stated in terms of potential y values or, equivalently, in terms of potential error term values. Following tradition, we begin by stating these assumptions in terms of potential error term values:

The Regression Assumptions 1 At any given value of x, the population of poten- 3tial error term values has a mean equal to 0.

2

Constant Variance Assumption At any given value of x, the population of potential error term values has a variance that does not depend on the value of x. That is, the different populations of potential error term values corresponding to different values of x have equal variances. We denote the constant variance as 2.

Normality Assumption At any given value of x, the population of potential error term values has a normal distribution. Independence Assumption Any one value of the error term E is statistically independent of any other value of E. That is, the value of the error term E corresponding to an observed value of y is statistically independent of the value of the error term corresponding to any other observed value of y.

4

Taken together, the rst three assumptions say that, at any given value of x, the population of potential error term values is normally distributed with mean zero and a variance S2 that does not depend on the value of x. Because the potential error term values cause the variation in the potential y values, these assumptions imply that the population of all y values that could be observed when the independent variable is x is normally distributed with mean B0 B1x and a variance S2 that does not depend on x. These three assumptions are illustrated in Figure 11.10 in the context of the fuel consumption problem. Specically, this gure depicts the populations of weekly fuel consumptions corresponding to two values of average hourly temperature32.5 and 45.9. Note that these populations are shown to be normally distributed with different means (each of which is on the line of means) and with the same variance (or spread). The independence assumption is most likely to be violated when time series data are being utilized in a regression study. Intuitively, this assumption says that there is no pattern of positive error terms being followed (in time) by other positive error terms, and there is no pattern of positive error terms being followed by negative error terms. That is, there is no pattern of higher-thanaverage y values being followed by other higher-than-average y values, and there is no pattern of higher-than-average y values being followed by lower-than-average y values. It is important to point out that the regression assumptions very seldom, if ever, hold exactly in any practical regression problem. However, it has been found that regression results are not extremely sensitive to mild departures from these assumptions. In practice, only pronounced

F I G U R E 11.10

An Illustration of the Model Assumptionsy 12.4 Observed value of y when x 32.5 32.5

The mean fuel consumption when x The mean fuel consumption when x 45.9 Population of y values when x 32.5 Population of y values when x 45.9 32.5 45.9 9.4

Observed value of y when x 45.9 The straight line defined by the equation y |x 0 1x (the line of means) x

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departures from these assumptions require attention. In optional Section 11.8 we show how to check the regression assumptions. Prior to doing this, we will suppose that the assumptions are valid in our examples. In Section 11.2 we stated that, when we predict an individual value of the dependent variable, we predict the error term to be 0. To see why we do this, note that the regression assumptions state that, at any given value of the independent variable, the population of all error term values that can potentially occur is normally distributed with a mean equal to 0. Since we also assume that successive error terms (observed over time) are statistically independent, each error term has a 50 percent chance of being positive and a 50 percent chance of being negative. Therefore, it is reasonable to predict any particular error term value to be 0. The mean square error and the standard error To present statistical inference formulas in later sections, we need to be able to compute point estimates of s2 and s, the constant variance and standard deviation of the error term populations. The point estimate of s2 is called the mean square error and the point estimate of s is called the standard error. In the following box, we show how to compute these estimates:

The Mean Square Error and the Standard Error

I

f the regression assumptions are satised and SSE is the sum of squared residuals: The point estimate of s2 is the mean square error s2 SSE n 2

1

2

The point estimate of s is the standard error s

SSE 2 Bn

In order to understand these point estimates, recall that s2 is the variance of the population of y values (for a given value of x) around the mean value my x. Because y is the point estimate of this mean, it seems natural to use SSE a (yi yi)2

to help construct a point estimate of s2. We divide SSE by n 2 because it can be proven that doing so makes the resulting s2 an unbiased point estimate of s2. Here we call n 2 the number of degrees of freedom associated with SSE.

EXAMPLE 11.6 The Fuel Consumption CaseConsider the fuel consumption situation, and recall that in Table 11.4 (page 460) we have calculated the sum of squared residuals to be SSE 2.568. It follows, because we have observed n 8 fuel consumptions, that the point estimate of s2 is the mean square error s2 2s2 SSE n 2 2.428 2.568 8 2 .428

C

This implies that the point estimate of s is the standard error s .6542

As another example, it can be veried that the standard error for the simple linear regression model describing the QHIC data is s 146.8970. To conclude this section, note that in optional Section 11.9 we present a shortcut formula for calculating SSE. The reader may study Section 11.9 now or at any later point.

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Exercises for Section 11.3CONCEPTS 11.24 What four assumptions do we make about the simple linear regression model? 11.25 What is estimated by the mean square error, and what is estimated by the standard error? 11.26, 11.31 METHODS AND APPLICATIONS 11.26 THE STARTING SALARY CASE StartSal 1.438, calculate s2 Refer to the starting salary data of Exercise 11.5 (page 454). Given that SSE and s. 11.27 THE SERVICE TIME CASE SrvcTime 191.70166, Refer to the service time data in Exercise 11.7 (page 454). Given that SSE calculate s2 and s. 11.28 THE FRESH DETERGENT CASE Fresh 2.8059, calculate

Refer to the Fresh detergent data of Exercise 11.9 (page 455). Given that SSE s2 and s. 11.29 THE DIRECT LABOR COST CASE DirLab Refer to the direct labor cost data of Exercise 11.11 (page 456). Given that SSE s2 and s. 11.30 THE REAL ESTATE SALES PRICE CASE RealEst Refer to the sales price data of Exercise 11.13 (page 456). Given that SSE and s.

747, calculate

896.8, calculate s2

11.31 Ten sales regions of equal sales potential for a company were randomly selected. The advertising expenditures (in units of $10,000) in these 10 sales regions were purposely set during July of last year at, respectively, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14. The sales volumes (in units of $10,000) were then recorded for the 10 sales regions and found to be, respectively, 89, 87, 98, 110, 103, 114, 116, 110, 126, and 130. Assuming that the simple linear regression model is appropriate, it can be shown that b0 66.2121, b1 4.4303, and SSE 222.8242. SalesVol Calculate s2 and s.

11.4 Testing the Signicance of the Slope and y InterceptTesting the signicance of the slope A simple linear regression model is not likely to be useful unless there is a signicant relationship between y and x. In order to judge the signicance of the relationship between y and x, we test the null hypothesis H0: b1 0

which says that there is no change in the mean value of y associated with an increase in x, versus the alternative hypothesis Ha: b1 0

which says that there is a (positive or negative) change in the mean value of y associated with an increase in x. It would be reasonable to conclude that x is signicantly related to y if we can be quite certain that we should reject H0 in favor of Ha. In order to test these hypotheses, recall that we compute the least squares point estimate b1 of the true slope b1 by using a sample of n observed values of the dependent variable y. A different sample of n observed y values would yield a different least squares point estimate b1. For example, consider the fuel consumption problem, and recall that we have observed eight average hourly temperatures. Corresponding to each temperature there is a (theoretically) innite population of fuel consumptions that could potentially be observed at that temperature [see Table 11.5(a)]. Sample 1 in Table 11.5(b) is the sample of eight fuel consumptions that we have actually observed from these populations (these are the same fuel consumptions originally given

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T A B L E 11.5 Three Samples in the Fuel Consumption Case (a) The Eight Populations of Fuel ConsumptionsWeek1 2 3 4 5 6 7 8

Average Hourly Temperature x28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5

Population of Potential Weekly Fuel ConsumptionsPopulation of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x Population of fuel consumptions when x 28.0 28.0 32.5 39.0 45.9 57.8 58.1 62.5

(b) Three Possible SamplesSample 1y1 y2 y3 y4 y5 y6 y7 y8 12.4 11.7 12.4 10.8 9.4 9.5 8.0 7.5

Sample 2y1 y2 y3 y4 y5 y6 y7 y8 12.0 11.8 12.3 11.5 9.1 9.2 8.5 7.2

Sample 3y1 y2 y3 y4 y5 y6 y7 y8 10.7 10.2 10.5 9.8 9.5 8.9 8.5 8.0

(c) Three Possible Values of b1b1 .1279 b1 .1285 b1 .0666

(d) Three Possible Values of b0b0 15.84 b0 15.852

b0

12.44

(e) Three Possible Values of ss2 .428 s2 .47

s2

.0667

(f) Three Possible Values of ss .6542 s .686 s .2582

in Table 11.1). Samples 2 and 3 in Table 11.5(b) are two other samples that we could have observed. In general, an innite number of such samples could be observed. Because each sample yields its own unique values of b1, b0, s2, and s [see Table 11.5(c)(f)], there is an innite population of potential values of each of these estimates. If the regression assumptions hold, then the population of all possible values of b1 is normally distributed with a mean of b1 and with a standard deviation of sb1 s 1SSxx s 1SSxx

The standard error s is the point estimate of s, so it follows that a point estimate of sb1 is sb1

which is called the standard error of the estimate b1. Furthermore, if the regression assumptions hold, then the population of all values of b1 sb1 has a t distribution with n 2 degrees of freedom. It follows that, if the null hypothesis H0: b1 0 is true, then the population of all possible values of the test statistic t b1 sb1 b1

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has a t distribution with n 2 degrees of freedom. Therefore, we can test the signicance of the regression relationship as follows:

Testing the Signicance of the Regression Relationship: Testing the Signicance of the Slope

D

ene the test statistic t b1 sb1 where sb1

s 1SSxx

and suppose that the regression assumptions hold. Then we can reject H0: b1 0 in favor of a particular alternative hypothesis at signicance level a (that is, by setting the probability of a Type I error equal to a) if and only if the appropriate rejection point condition holds, or, equivalently, the corresponding p-value is less than a.Alternative HypothesisHa: b1 Ha: b1 Ha: b1 0 0 0 t t

Rejection Point Condition: Reject H0 ift ta ta ta2

p-ValueTwice the area under the t curve to the right of t The area under the t curve to the right of t The area under the t curve to the left of t

Here tA/2, tA, and all p-values are based on n

2 degrees of freedom.

We usually use the two-sided alternative Ha: b1 0 for this test of signicance. However, sometimes a one-sided alternative is appropriate. For example, in the fuel consumption problem we can say that if the slope b1 is not 0, then it must be negative. A negative b1 would say that mean fuel consumption decreases as temperature x increases. Because of this, it would be appropriate to decide that x is signicantly related to y if we can reject H0: b1 0 in favor of the onesided alternative Ha: b1 0. Although this test would be slightly more effective than the usual two-sided test, there is little practical difference between using the one-sided or two-sided alternative. Furthermore, computer packages (such as MINITAB and Excel) present results for testing a two-sided alternative hypothesis. For these reasons we will emphasize the two-sided test. It should also be noted that1

If we can decide that the slope is signicant at the .05 signicance level, then we have concluded that x is signicantly related to y by using a test that allows only a .05 probability of concluding that x is signicantly related to y when it is not. This is usually regarded as strong evidence that the regression relationship is signicant. If we can decide that the slope is signicant at the .01 signicance level, this is usually regarded as very strong evidence that the regression relationship is signicant. The smaller the signicance level a at which H0 can be rejected, the stronger is the evidence that the regression relationship is signicant.

2 3

EXAMPLE 11.7 The Fuel Consumption CaseAgain consider the fuel consumption model For this model SSxx 1,404.355, b1 and 11.6 (page 467)]. Therefore sb1 and t b1 sb1 .1279 .01746 7.33 s 1SSxx y b0 .1279, and s .6542 11,404.355 b1x e

C.6542 [see Examples 11.4 (pages 459460) .01746

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F I G U R E 11.11 MINITAB and Excel Output of a Simple Linear Regression Analysis of the Fuel Consumption Data(a) The MINITAB outputRegression Analysis The regression equation is FUELCONS = 15.8 - 0.128 TEMP Predictor Constant TEMP S = 0.6542h Coef 15.8379a -0.12792b Stdev 0.8018c 0.01746d T 1 9 . 7 5e - 7 . 3 3f p 0 . 0 0 0g 0.000

R-Sq = 89.9%i SS 22.981j 2 . 5 6 8k 25.549l

R-Sq(adj) = 88.3% MS 22.981 0.428 F 53.69m p 0 . 0 0 0n

Analysis of Variance Source DF Regression Error Total F i to 10.721a j

1 6 7 Stdev Fitp 0.241

9 5 . 0 % C Iq (10.130, 11.312)

95.0% PIr (9.014, 12.428)

b0 bb1 csb0 dsb1 et for testing H0: b0 0 ft for testing H0: b1 0 gp-values for t statistics hs standard error ir2 Explained variation kSSE Unexplained variation lTotal variation mF(model) statistic np-value for F(model) oy psy q 95% condence interval when x 40 r95% prediction interval when x 40

(b) The Excel output Regression StatisticsMultiple R R Square Adjusted R Square Standard Error Observations 0.948413871 0.899488871i 0.882737016 0.654208646h 8

ANOVA dfRegression Residual Total 1 6 7

SS22.98081629j 2.567933713k 25.54875l

MS22.98082 0.427989

F53.69488m

Signicance F0.000330052n

CoefcientsIntercept Tempa j

Standard Error0.801773385c 0.01745733dg

t Stat19.75353e 7.32768f

P-Valueg1.09E-06 0.00033

Lower 95%13.87598718 0.170638294o

Upper 95%17.79972765 0.08520514o

15.83785741a 0.127921715b

b0 bb1 csb0 dsb1 et for testing H0: b0 0 ft for testing H0: b1 0 Explained variation kSSE Unexplained variation lTotal variation

m

p-values for t statistics hs standard error ir2 F(model) statistic np-value for F(model) o95% condence interval for b1

To test the signicance of the slope we compare t with ta degrees of freedom. Because t 7.33 t.025 2.447

2

based on n

2

8

2

6

we can reject H0: b1 0 in favor of Ha: b1 0 at level of signicance .05. The p-value for testing H0 versus Ha is twice the area to the right of t 7.33 under the curve of the t distribution having n 2 6 degrees of freedom. Since this p-value can be shown to be .00033, we can reject H0 in favor of Ha at level of signicance .05, .01, or .001. We therefore have extremely strong evidence that x is signicantly related to y and that the regression relationship is signicant. Figure 11.11 presents the MINITAB and Excel outputs of a simple linear regression analysis of the fuel consumption data. Note that b0 15.84, b1 .1279, s .6542, sb1 .01746, and t 7.33 (each of which has been previously calculated) are given on these outputs. Also note that Excel gives the p-value of .00033, and MINITAB has rounded this p-value to .000 (which means less than .001). Other quantities on the MINITAB and Excel outputs will be discussed later.

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In addition to testing the signicance of the slope, it is often useful to calculate a condence interval for b1. We show how this is done in the following box:

A Condence Interval for the Slope

I

f the regression assumptions hold, a 100(1 A) percent condence interval for the true slope B1 is [b1 ta 2 sb1]. Here ta 2 is based on n 2 degrees of freedom.

EXAMPLE 11.8 The Fuel Consumption Case

C

.01746. The MINITAB and Excel outputs in Figure 11.11 tell us that b1 .1279 and sb1 Thus, for instance, because t.025 based on n 2 8 2 6 degrees of freedom equals 2.447, a 95 percent condence interval for b1 is [b1 t.025 sb1] [ .1279 [ .1706, 2.447(.01746)] .0852]

This interval says we are 95 percent condent that, if average hourly temperature increases by one degree, then mean weekly fuel consumption will decrease (because both the lower bound and the upper bound of the interval are negative) by at least .0852 MMcf of natural gas and by at most .1706 MMcf of natural gas. Also, because the 95 percent condence interval for b1 does not contain 0, we can reject H0: b1 0 in favor of Ha: b1 0 at level of signicance .05. Note that the 95 percent condence interval for b1 is given on the Excel output but not on the MINITAB output.

EXAMPLE 11.9 The QHIC Case

C

Figure 11.12 presents the MegaStat output of a simple linear regression analysis of the QHIC data. Below we summarize some important quantities from the output (we discuss the other quantities later): b0 sb1 348.3931 .4156 t b1 sb1 b1 7.2583 s 146.897 .001

17.466

p-value for t

Since the p-value for testing the signicance of the slope is less than .001, we can reject H0: b1 0 in favor of Ha: b1 0 at the .001 level of signicance. It follows that we have extremely strong evidence that the regression relationship is signicant. The MegaStat output also tells us that a 95 percent condence interval for the true slope b1 is [6.4170, 8.0995]. This interval says we are 95 percent condent that mean yearly upkeep expenditure increases by between $6.42 and $8.10 for each additional $1,000 increase in home value.

Testing the signicance of the y-intercept We can also test the signicance of the y-intercept b0. We do this by testing the null hypothesis H0: b0 0 versus the alternative hypothesis Ha: b0 0. To carry out this test we use the test statistic t b0 sb0 where sb0 s 1 Bn x2 SSxx

Here the rejection point and p-value conditions for rejecting H0 are the same as those given previously for testing the signicance of the slope, except that t is calculated as b0 sb0. For example, if we consider the fuel consumption problem and the MINITAB output in Figure 11.11, we see that b0 15.8379, sb0 .8018, t 19.75, and p-value .000. Because t 19.75 t.025 2.447 and p-value .05, we can reject H0: b0 0 in favor of Ha: b0 0 at the .05 level of signicance.

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F I G U R E 11.12 MegaStat Output of a Simple Linear Regression Analysis of the QHIC DataRegression Analysisr2 0.889i r 0.943 Std. Error 146.897h n 40 k 1 Dep. Var. Upkeep

ANOVA table SourceRegression Residual Total

SS6,582,759.6972j 819,995.5427k 7,402,755.2399l

df1 38 39

MS6,582,759.6972 21,578.8301

F305.06m

p-value9.49E-20n

Regression output variablesIntercept Value

coefcients348.3921a 7.2583b

std. error76.1410c 0.4156d

t (df

38)4.576e 17.466f

p-valueg4.95E-05 9.49E-20

condence interval 95% lower 95% upper502.5315 6.4170o 194.2527 8.0995o

Predicted values for: Upkeep Value220.00a j

Predicted1,248.42597

P

95% Condence Intervalq lower upper1,187.78943 1,309.06251

95% Prediction Intervalr lower upper944.92878 1,551.92317

Leverages0.042

b0 bb1 csb0 dsb1 et for testing H0: b0 0 ft for testing H0: b1 0 gp-values for t statistics hs standard error ir2 Explained variation kSSE Unexplained variation lTotal variation mF(model) statistic np-value for F(model) o95% condence interval for b1 p y q95% condence interval when x 220 r95% prediction interval when x 220 sdistance value

In fact, since the p-value .001, we can also reject H0 at the .001 level of signicance. This provides extremely strong evidence that the y-intercept b0 does not equal 0 and that we should include b0 in the fuel consumption model. In general, if we fail to conclude that the intercept is significant at a level of significance of .05, it might be reasonable to drop the y-intercept from the model. However, remember that b0 equals the mean value of y when x equals 0. If, logically speaking, the mean value of y would not equal 0 when x equals 0 (for example, in the fuel consumption problem, mean fuel consumption would not equal 0 when the average hourly temperature is 0), it is common practice to include the y-intercept whether or not H0: b0 0 is rejected. In fact, experience suggests that it is definitely safest, when in doubt, to include the intercept b0.

Exercises for Section 11.4CONCEPTS 11.32 What do we conclude if we can reject H0: b1 0 in favor of Ha: b1 a a equal to .05? b a equal to .01? METHODS AND APPLICATIONS In Exercises 11.34 through 11.38, we refer to MINITAB, MegaStat, and Excel output of simple linear regression analyses of the data sets related to the ve case studies introduced in the exercises for Section 11.1. Using the appropriate output for each case study, a Identify the least squares point estimates b0 and b1 of b0 and b1. b Identify SSE, s2, and s. c Identify sb1 and the t statistic for testing the signicance of the slope. Show how t has been calculated by using b1 and sb1. d Using the t statistic and appropriate rejection points, test H0: b1 0 versus Ha: b1 0 by setting a equal to .05. What do you conclude about the relationship between y and x? e Using the t statistic and appropriate rejection points, test H0: b1 0 versus Ha: b1 0 by setting a equal to .01. What do you conclude about the relationship between y and x? f Identify the p-value for testing H0: b1 0 versus Ha: b1 0. Using the p-value, determine whether we can reject H0 by setting a equal to .10, .05, .01, and .001. What do you conclude about the relationship between y and x? g Calculate the 95 percent condence interval for b1. Discuss one practical application of this interval. 0 by setting 11.34, 11.38, 11.40

11.33 Give an example of a practical application of the condence interval for b1.

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h Calculate the 99 percent condence interval for b1. i Identify sb0 and the t statistic for testing the signicance of the y intercept. Show how t has been calculated by using b0 and sb0. j Identify the p-value for testing H0: b0 0 versus Ha: b0 0. Using the p-value, determine whether we can reject H0 by setting a equal to .10, .05, .01, and .001. What do you conclude? k Using the appropriate data set, show how sb0 and sb1 have been calculated. Hint: Calculate SSxx. 11.34 THE STARTING SALARY CASE StartSal

The MINITAB output of a simple linear regression analysis of the data set for this case (see Exercise 11.5 on page 454) is given in Figure 11.13. Recall that a labeled MINITAB regression output is on page 471. 11.35 THE SERVICE TIME CASE SrvcTime The MegaStat output of a simple linear regression analysis of the data set for this case (see Exercise 11.7 on page 454) is given in Figure 11.14. Recall that a labeled MegaStat regression output is on page 473.

F I G U R E 11.13

MINITAB Output of a Simple Linear Regression Analysis of the Starting Salary DataThe regression equation is SALARY = 14.8 + 5.71 GPA Predictor Constant GPA S = 0.5363 Coef 14.816 5.7066 StDev 1.235 0.3953 T 12.00 14.44 P 0.000 0.000

R-Sq = 97.7% SS 59.942 1.438 61.380

R-Sq(adj) = 97.2% MS 59.942 0.288 F 208.39 P 0.000

Analysis of Variance Source DF Regression 1 Error 5 Total 6 Fit 33.362 StDev Fit 0.213

95.0% CI ( 32.813, 33.911)

95.0% PI (31.878, 34.846)

F I G U R E 11.14Regression Analysis

MegaStat Output of a Simple Linear Regression Analysis of the Service Time Datar2 0.990 r 0.995 Std. Error 4.615 n k Dep. Var. 11 1 Minutes

ANOVA table SourceRegression Residual Total

SS19,918.8438 191.7017 20,110.5455

df1 9 10

MS19,918.8438 21.3002

F935.15

p-value2.09E-10

Regression output variablesIntercept Copiers

Coefcients11.4641 24.6022

std. error3.4390 0.8045

t (df

9)

p-value.0087 2.09E-10

condence interval 95% lower 95% upper3.6845 22.7823 19.2437 26.4221

3.334 30.580

Predicted values for: Minutes Copiers1 2 3 4 5 6 7

Predicted36.066 60.669 85.271 109