book

Business Statistics: For Contemporary Decision Making, 8th Edition

by Ken Black

October 2013

Intermediate to advanced

608 pages

34h 35m

English

Wiley

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CHANGES FOR THE SEVENTH EDITIONVIDEOTAPE TUTORIALS BY KEN BLACKFEATURES AND BENEFITSWILEYPLUSANCILLARY TEACHING AND LEARNING MATERIALS www.wiley.com/college/blackACKNOWLEDGMENTS
1.1 STATISTICS IN BUSINESS1.2 BASIC STATISTICAL CONCEPTS1.3 VARIABLES AND DATA1.4 DATA MEASUREMENTDIGIORNO PIZZA: INTRODUCING A FROZEN PIZZA TO COMPETE WITH CARRY-OUT

2.1 FREQUENCY DISTRIBUTIONS2.2 QUANTITATIVE DATA GRAPHS2.3 QUALITATIVE DATA GRAPHS2.4 CHARTS AND GRAPHS FOR TWO VARIABLESSOAP COMPANIES DO BATTLE
3.1 MEASURES OF CENTRAL TENDENCY: UNGROUPED DATA3.2 MEASURES OF VARIABILITY: UNGROUPED DATA3.3 MEASURES OF CENTRAL TENDENCY AND VARIABILITY: GROUPED DATA3.4 MEASURES OF SHAPE3.5 DESCRIPTIVE STATISTICS ON THE COMPUTERCOCA-COLA DEVELOPS THE AFRICAN MARKET
4.1 INTRODUCTION TO PROBABILITY4.2 METHODS OF ASSIGNING PROBABILITIES4.3 STRUCTURE OF PROBABILITY4.4 MARGINAL, UNION, JOINT, AND CONDITIONAL PROBABILITIES4.5 ADDITION LAWS4.6 MULTIPLICATION LAWS4.7 CONDITIONAL PROBABILITY4.8 REVISION OF PROBABILITIES: BAYES' RULECOLGATE-PALMOLIVE MAKES A “TOTAL” EFFORT
5.1 DISCRETE VERSUS CONTINUOUS DISTRIBUTIONS5.2 DESCRIBING A DISCRETE DISTRIBUTION5.3 BINOMIAL DISTRIBUTION5.4 POISSION DISTRIBUTION5.5 HYPERGEOMETRIC DISTRIBUTIONWHOLE FOODS MARKET GROWS THROUGH MERGERS AND ACQUISITIONS
6.1 THE UNIFORM DISTRIBUTION6.2 NORMAL DISTRIBUTION6.3 USING THE NORMAL CURVE TO APPROXIMATE BINOMIAL DISTRIBUTION PROBLEMS6.4 EXPONENTIAL DISTRIBUTIONMERCEDES GOES AFTER YOUNGER BUYERS
7.1 SAMPLING7.2 SAMPLING DISTRIBUTION OF7.3 SAMPLING DISTRIBUTION OFSHELL ATTEMPTS TO RETURN TO PREMIERE STATUS
8.1 ESTIMATING THE POPULATION MEAN USING THE z STATISTIC ( σ KNOWN)8.2 ESTIMATING THE POPULATION MEAN USING THE t STATISTIC ( σ UNKNOWN)8.3 ESTIMATING THE POPULATION PROPORTION8.4 ESTIMATING THE POPULATION VARIANCE8.5 ESTIMATING SAMPLE SIZETHE CONTAINER STORE
9.1 INTRODUCTION TO HYPOTHESIS TESTING9.2 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE z STATISTIC ( σ KNOWN)9.3 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN)9.4 TESTING HYPOTHESES ABOUT A PROPORTION9.5 TESTING HYPOTHESES ABOUT A VARIANCE9.6 SOLVING FOR TYPE II ERRORSFRITO-LAY TARGETS THE HISPANIC MARKET
10.1 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS USING THE z STATISTIC (POPULATION VARIANCES KNOWN)10.2 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS: INDEPENDENT SAMPLES AND POPULATION VARIANCES UNKNOWN10.3 STATISTICAL INFERENCES FOR TWO RELATED POPULATIONS10.4 STATISTICAL INFERENCES ABOUT TWO POPULATION PROPORTIONS, p 1 − p 210.5 TESTING HYPOTHESES ABOUT TWO POPULATION VARIANCESSEITZ CORPORATION: PRODUCING QUALITY GEAR-DRIVEN AND LINEAR-MOTION PRODUCTS
11.1 INTRODUCTION TO DESIGN OF EXPERIMENTS11.2 THE COMPLETELY RANDOMIZED DESIGN (ONE-WAY ANOVA)11.3 MULTIPLE COMPARISON TESTS11.4 THE RANDOMIZED BLOCK DESIGN11.5 A FACTORIAL DESIGN (TWO-WAY ANOVA)THE CLARKSON COMPANY: A DIVISION OF TYCO INTERNATIONAL
12.1 CORRELATION12.2 INTRODUCTION TO SIMPLE REGRESSION ANALYSIS12.3 DETERMINING THE EQUATION OF THE REGRESSION LINE12.4 RESIDUAL ANALYSIS12.5 STANDARD ERROR OF THE ESTIMATE12.6 COEFFICIENT OF DETERMINATION12.7 HYPOTHESIS TESTS FOR THE SLOPE OF THE REGRESSION MODEL AND TESTING THE OVERALL MODEL12.8 ESTIMATION12.9 USING REGRESSION TO DEVELOP A FORECASTING TREND LINE12.10 INTERPRETING THE OUTPUTDELTA WIRE USES TRAINING AS A WEAPON
13.1 THE MULTIPLE REGRESSION MODEL13.2 SIGNIFICANCE TESTS OF THE REGRESSION MODEL AND ITS COEFFICIENTS13.3 RESIDUALS, STANDARD ERROR OF THE ESTIMATE, AND R 213.4 INTERPRETING MULTIPLE REGRESSION COMPUTER OUTPUTSTARBUCKS INTRODUCES DEBIT CARD
14.1 NONLINEAR MODELS: MATHEMATICAL TRANSFORMATION14.2 INDICATOR (DUMMY) VARIABLES14.3 MODEL-BUILDING: SEARCH PROCEDURES14.4 MULTICOLLINEARITY14.5 LOGISTIC REGRESSIONVIRGINIA SEMICONDUCTOR
15.1 INTRODUCTION TO FORECASTING15.2 SMOOTHING TECHNIQUES15.3 TREND ANALYSIS15.4 SEASONAL EFFECTS15.5 AUTOCORRELATION AND AUTOREGRESSION15.6 INDEX NUMBERSDEBOURGH MANUFACTURING COMPANY
16.1 CHI-SQUARE GOODNESS-OF-FIT TEST16.2 CONTINGENCY ANALYSIS: CHI-SQUARE TEST OF INDEPENDENCEFOOT LOCKER IN THE SHOE MIX
17.1 RUNS TEST17.2 MANN-WHITNEY U TEST17.3 WILCOXON MATCHED-PAIRS SIGNED RANK TEST17.4 KRUSKAL-WALLIS TEST17.5 FRIEDMAN TEST17.6 SPEARMAN'S RANK CORRELATIONSCHWINN
18.1 INTRODUCTION TO QUALITY CONTROL18.2 PROCESS ANALYSIS18.3 CONTROL CHARTSROBOTRON-ELOTHERM

Content preview from Business Statistics: For Contemporary Decision Making, 8th Edition

CHAPTER 14 Building Multiple Regression Models

LEARNING OBJECTIVES

This chapter presents several advanced topics in multiple regression analysis, enabling you to:

1. Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.

2. Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.

3. Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the greatest variation in the dependent variable and are parsimonious.

4. Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it.

5. Explain when to use logistic regression, and interpret its results.

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Determining Compensation for CEOs

Chief executive officers for large companies receive widely varying amounts of compensation for their work. Why is the range so wide? What are some of the variables that seem to contribute to the diversity of CEO compensation packages?

As a starting place, one might examine the role of company size as measured by sales volume, number of employees, number ...