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Contents Preface Chapter 1 Introduction and Data Collection 1.1 Why Learn Statistics 1.2 Statistics for Managers USING STATISTICS @ Good Tunes 1.3 Basic Vocabulary of Statistics 1.4 Data Collection 1.5 Types of Variables Levels of Measurement and Measurement Scales 1.6 Microsoft Excel Worksheets Worksheet Cells Designing Effective Worksheets Summary Key Terms Chapter Review Problems Introduction to the Web Cases Excel Companion to Chapter 1 E1.1 Preliminaries: Basic Computing Skills E1.2 Basic Workbook Operations E1.3 Worksheet Entries E1.4 Worksheet Formatting E1.5 Copy-and-Paste Operations E1.6 Add-ins: Making Things Easier for You Chapter 2 Presenting Data in Tables and Charts USING STATISTICS@ CHOICE IS YOURS 2.1 Tables and Charts for Categorical Data The Summary Table The Bar Chart The Pie Chart The Pareto Diagram 2.2 Organizing Numerical Data The Ordered Array The Stem-and-Leaf Display 2.3 Tables and Charts for Numerical Data The Frequency Distribution The Relative Frequency Distribution and the Percentage Distribution The Cumulative Distribution The Histogram The Polygon The Cumulative Percentage Polygon (Ogive) 2.4 Cross Tabulations The Contingency Table The Side-by-Side Bar Chart 2.5 Scatter Plots and Time-Series Plots The Scatter Plot The Time-Series Plot 2.6 Misusing Graphs and Ethical Issues Summary Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 2 E2.1 Creating Summary Tables E2.2 Creating Charts E2.3 Creating Bar and Pie Charts from Summary Tables E2.4 Creating Pareto Diagrams from Summary Tables E2.5 Creating an Ordered Array E2.6 Creating Stem-and Leaf Displays E2.7 Creating Frequency Distributions and Histograms E2.8 Creating a Histogram from Summarized Data E2.9 Creating Polygons E2.10 Creating Contingency Tables E2.11 Creating Side-by-Side Charts E2.12 Creating Scatter Plots E2.13 Creating Time Series Plots 3 Numerical Descriptive Measures Using Statistics@ Choice Is Yours 3.1 Measures of Central Tendency The Mean The Median The Mode Quartiles The Geometric Mean 3.2 Variation and Shape The Range The Interquartile Range The Variance and the Standard Deviation The Coefficient of Variation Z Scores Shape Visual Explorations: Exploring Descriptive Statistics Microsoft Excel Descriptive Statistics Output 3.3 Numerical Descriptive Measures for a Population The Population Mean The Population Variance and Standard Deviation The Empirical Rule The Chebychev Rule 3.4 Exploratory Data Analysis The Five-Number Summary The Box-and-Whisker Plot 3.5 The Covariance and the Coefficient of Correlation The Covariance The Coefficient of Correlation 3.6 Pitfalls in Numerical Descriptive Measures and Ethical Issues Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 3 E3.1 Computing Measures of Central Tendency, Variation, and Shape E3.2 Creating Dot Scale Diagrams E3.3 Computing Measures for a Population E3.4 Creating Box-and-Whisker Plots E3.5 Computing the Covariance E3.6 Computing the Correlation Coefficient 4 Basic Probability Using Statistics@The Consumer Electronics Company 4.1 Basic Probability Concepts Events and Sample Spaces Contingency Tables Simple (Marginal) Probability Joint Probability General Addition Rule 4.2 Conditional Probability Computing Conditional Probabilities Decision Trees Statistical Independence Multiplication Rules Marginal Probability Using the General Multiplication Rule 4.3 Bayes' Theorem 4.4 Ethical Issues and Probability CD-ROM Topic 4.5 Counting Rules Summary Key Equations Key Terms Chapter Review Problems Web Case Excel Companion to Chapter 4 E4.1 Computing Basic Probabilities E4.2 Using Bayes' Theorem 5 Some Important Discrete Probability Distributions Using Statistics@ Saxon Home Improvement 5.1 The Probability Distribution for a Discrete Random Variable Expected Value of a Discrete Random Variable Variance and Standard Deviation of a Discrete Random Variable 5.2 Covariance and Its Application in Finance The Covariance The Expected Value, Variance, and Standard Deviation of the Sum of Two Random Variables Portfolio Expected Return and Portfolio Risk 5.3 Binomial Distribution 5.4 Poisson Distribution 5.5 Hypergeometric Distribution 5.6 (CD ROM Topic) Using the Poisson Distribution to Approximate the Binomial Distribution Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 5 E5.1 Computing the Expected Value of a Discrete Random Variable E5.2 Computing Portfolio Expected Return & Portfolio Risk E5.3 Computing Binomial Probabilities E5.4 Computing Poisson Probabilities E5.5 Computing Hypergeometric Probabilities E5.6 Creating Histograms for Discrete Probability Distributions 6 The Normal Distribution and Other Continuous Distributions Using Statistics@OurCampus! 6.1 Continuous Probability Distributions 6.2 The Normal Distribution 6.3 Evaluating Normality Comparing Data Characteristics to Theoretical Properties Constructing the Normal Probability Plot 6.4 The Uniform Distribution 6.5 The Exponential Distribution CD-ROM Topic 6.6 The Normal Approximation to the Binomial Distribution Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 6 E6.1 Computing Normal Probabilities E6.2 Creating Normal Probability Plots E6.3 Computing Exponential Probabilities 7 Sampling and Sampling Distributions Using Statistics@Oxford Cereals 7.1 Types of Survey Sampling Methods Simple Random Sample Systematic Sample Stratified Sample Cluster Sample 7.2 Evaluating Survey Worthiness Survey Errors Ethical Issues 7.3 Sampling Distributions 7.4 Sampling Distribution of the Mean The Unbiased Property of the Sample Mean Standard Error of the Mean Sampling from Normally Distributed Populations Sampling from Nonnormally Distributed Populations-The Central Limit Theorem 7.5 Sampling Distribution of the Proportion 7.6 (CD-ROM Topic) Sampling from Finite Populations Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 7 E7.1 Creating Simple Random Samples (without replacement) E7.2 Creating Simulated Sampling Distributions 8 Confidence Interval Estimation Using Statistics@ SAXON HOME IMPROVEMENT 8.1 Confidence Interval Estimation for the Mean (? Known) 8.2 Confidence Interval Estimation for the Mean (? Unknown) Student's t Distribution Properties of the t Distribution The Concept of Degrees of Freedom The Confidence Interval Statement 8.3 Confidence Interval Estimation for the Proportion 8.4 Determining Sample Size Sample Size Determination for the Mean Sample Size Determination for the Proportion 8.5 Applications of Confidence Interval Estimation in Auditing Estimating the Population Total Amount Difference Estimation One-Sided Confidence Interval Estimation of the Rate of Noncompliance with Internal Controls 8.6 Confidence Interval Estimation and Ethical Issues 8.7 CD-ROM Topic: Estimation and Sample Size Determination for Finite Populations Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case EXCEL COMPANION to Chapter 8 E8.1 Computing the Confidence Interval Estimate for the Mean (? known) E8.2 Computing the Confidence Interval Estimate for the Mean (? unknown) E8.3 Computing the Confidence Interval Estimate for the Proportion E8.4 Computing the Sample Size Needed for Estimating the Mean E8.5 Computing the Sample Size Needed for Estimating the Proportion E8.6 Computing the Confidence Interval Estimate for the Population Total E8.7 Computing the Confidence Interval Estimate for the Total Difference E8.8 Computing Finite Population Correction Factors 9 Fundamentals of Hypothesis Testing: One-Sample Tests Using Statistics@ Oxford Cereals, Part II 9.1 Hypothesis-Testing Methodology The Null and Alternative Hypotheses The Critical Value of the Test Statistic Regions of Rejection and Nonrejection Risks in Decision Making Using Hypothesis-Testing Methodology 9.2 Z Test of Hypothesis for the Mean ??? Known) The Critical Value Approach to Hypothesis Testing The p-Value Approach to Hypothesis Testing A Connection between Confidence Interval Estimation and Hypothesis Testing 9.3 One-Tail Tests The Critical Value Approach The p-Value Approach 9.4 t Test of Hypothesis for the Mean (? Unknown) The Critical Value Approach The p-Value Approach Checking Assumptions 9.5 Z Test of Hypothesis for the Proportion The Critical Value Approach The p-Value Approach 9.6 Potential Hypothesis-Testing Pitfalls and Ethical Issues 9.7 CD-ROM Topic The Power of a Test Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 9 E9.1 Using the Z Test for the Mean (? known) E9.2 Using the t Test for the Mean (? unknown) E9.3 Using the Z Test for the Proportion 10 Two-Sample Tests Using Statistics@BLK Foods 10.1 Comparing the Means of Two Independent Populations Z Test for the Difference Between Two Means Pooled-Variance t Test for the Difference Between Two Means Confidence Interval Estimate for the Difference Between Two Means Separate-Variance t Test for the Difference Between Two Means 10.2 Comparing the Means of Two Related Populations Paired t Test Confidence Interval Estimate for the Mean Difference 10.3 Comparing Two Population Proportions Z Test for the Difference Between Two Proportions Confidence Interval Estimate for the Difference Between Two Proportions 10.4 F Test for the Difference Between Two Variances Finding Lower-Tail Critical Values Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 10 Two-Sample Hypothesis Testing in Microsoft Excel E10.1 Using the Z Test for the Difference Between Two Means (Unsummarized Data) E10.2 Using the Z Test for the Difference Between Two Means (Summarized Data) E10.3 Using the Pooled-Variance t Test (Unsummarized Data) E10.4 Using the Pooled-Variance t Test (Summarized Data) E10.5 Using the Separate-Variance t Test for the Difference Between Two Means (Unsummarized Data) E10.6 Using the Paired t Test for the Difference Between Two Means (Unsummarized Data) E10.7 Using the Z Test for the Difference Between Two Proportions (Summarized Data) E10.8 Using the F Test for the Difference Between Two Variances (Unsummarized Data) E10.9 Using the F Test for the Difference Between Two Variances (Summarized Data) 11 Analysis of Variance Using Statistics @ Perfect Parachutes 11.1 The Completely Randomized Design: One-Way Analysis of Variance F Test for Differences Among More Than Two Means Multiple Comparisons: The Tukey-Kramer Procedure ANOVA Assumptions Levene's Test for Homogeneity of Variance 11.2 The Factorial Design: Two-Way Analysis of Variance Testing for Factor and Interaction Effects Interpreting Interaction Effects Multiple Comparisons: The Tukey Procedure 11.3 CD-ROM Topic The Randomized Block Design Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 11 E11.1 Using the F Test for Differences Among More Than Two Means E11.2 Using the Tukey-Kramer Procedure E11.3 Using the Levene Test for Homogeneity of Variance E11.4 Using The Two-Way ANOVA 12 Chi-Square Tests and Nonparametric Tests Using Statistics@ T.C. Resort Properties 12.1 Chi-Square Test for the Difference Between Two Proportions (Independent Samples) 12.2 Chi-Square Test for Differences Among More than Two Proportions The Marascuilo Procedure 12.3 Chi-Square Test of Independence 12.4 McNemar Test for the Difference Between Two Proportions (Related Samples) 12.5 Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations 12.6 Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way ANOVA 12.7 CD-ROM Topic Chi-Square Test for a Variance or Standard Deviation Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 12 E12.1 Using the Chi-Square Test for the Difference Between Two Proportions E12.2 Using the Chi-Square Test for the Differences in More Than Two Proportions E12.3 Using the Chi-Square Test of Independence E12.4 Using the McNemar Test E12.5 Using the Wilcoxon Rank Sum Test E12.6 Using the Kruskal-Wallis Rank Test 13 Simple Linear Regression Using Statistics@ Sunflowers Apparel 13.1 Types of Regression Models 13.2 Determining the Simple Linear Regression Equation The Least-Squares Method Visual Explorations: Exploring Simple Linear Regression Coefficients Predictions in Regression Analysis: Interpolation versus Extrapolation Computing the Y Intercept b0 and the Slope b1 13.3 Measures of Variation Computing the Sum of Squares The Coefficient of Determination Standard Error of the Estimate 13.4 Assumptions 13.5 Residual Analysis Evaluating the Assumptions 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic Residual Plots to Detect Autocorrelation The Durbin-Watson Statistic 13.7 Inferences About the Slope and Correlation Coefficient t Test for the Slope F Test for the Slope Confidence Interval Estimate for the Slope (?1) t Test for the Correlation Coefficient 13.8 Estimation of Mean Values and Prediction of Individual Values The Confidence Interval Estimate The Prediction Interval 13.9 Pitfalls in Regression and Ethical Issues Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 13 E13.1 Performing Simple Linear Regression Analysis E13.2 Creating Scatter Diagrams and Adding a Prediction Line E13.3 Performing Residual Analyses E13.4 Computing the Durbin-Watson Statistic E13.5 Estimating the Mean of Y and Predicting Y Values E13.6 Example: Sunflowers Site Selection Data 14 Introduction to Multiple Regression Using Statistics@ OMNIFOODS 14.1 Developing the Multiple Regression Model Interpreting the Regression Coefficients Predicting the Dependent Variable Y 14.2 R2, Adjusted R2, and the Overall F Test Coefficient of Multiple Determination Test for the Significance of the Overall Multiple Regression Model 14.3 Residual Analysis for the Multiple Regression Model 14.4 Inferences Concerning the Population Regression Coefficients Tests of Hypothesis Confidence Interval Estimation 14.5 Testing Portions of the Multiple Regression Model Coefficients of Partial Determination 14.6 Using Dummy Variables and Interaction Terms in Regression Models Interactions Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 14 E14.1 Creating Multiple Regression Models E14.2 Creating Multiple Regression Residual Plots E14.3 Computing the Confidence Interval Estimate of the Mean and Prediction Interval E14.4 Computing the Coefficients of Partial Determination E14.5 Creating Dummy Variables E14.6 Creating Interaction Terms 15 Multiple Regression Model Building USING STATISTICS@WTT-TV 15.1 The Quadratic Regression Model Finding the Regression Coefficients and Predicting Y Testing for the Significance of the Quadratic Model Testing the Quadratic Effect The Coefficient of Multiple Determination 15.2 Using Transformations in Regression Models The Square-Root Transformation The Log Transformation 15.3 Collinearity 15.4 Model Building The Stepwise Regression Approach to Model Building The Best-Subsets Approach to Model Building Model Validation 15.5 Pitfalls in Multiple Regression and Ethical Issues Pitfalls in Multiple Regression Ethical Issues Summary Key Equations Key Terms Chapter Review Problems The Mountain States Potato Company Case Web Case Excel Companion to Chapter 15 E15.1 Creating a Quadratic Term E15.2 Creating Transformations E15.3 Computing Variance Inflationary Factors E15.4 Using Stepwise Regression E15.5 Using Best-Subsets Regression 16 Time-Series Forecasting and Index Numbers Using Statistics@ THE PRINCIPLED 16.1 The Importance of Business Forecasting 16.2 Component Factors of the Classical Multiplicative Time-Series Model 16.3 Smoothing the Annual Time Series Moving Averages Exponential Smoothing 16.4 Least-Squares Trend-Fitting and Forecasting The Linear Trend Model The Quadratic Trend Model The Exponential Trend Model Model Selection Using First, Second, and Percentage Differences 16.5 Autoregressive Modeling for Trend-Fitting and Forecasting 16.6 Choosing an Appropriate Forecasting Model Performing a Residual Analysis Measuring the Magnitude of the Residual Error through Squared or Absolute Differences Principle of Parsimony A Comparison of Four Forecasting Methods 16.7 Time-Series Forecasting of Seasonal Data Least-Squares Forecasting with Monthly or Quarterly Data 16.8 Index Numbers The Price Index Aggregate Price Indexes Weighted Aggregate Price Indexes Some Common Price Indexes 16.9 Pitfalls Concerning Time-Series Forecasting Summary Key Equations Key Terms Chapter Review Problems Managing the Springville Herald Web Case Excel Companion to Chapter 16 E16.1 Computing Moving Averages E16.2 Creating Time-Series Plots E16.3 Creating Exponentially Smoothed Values E16.4 Creating Coded X Variables E16.5 Creating Quadratic and Exponential Terms E16.6 Using Least-Squares Linear Trend Fitting E16.7 Using Least-Squares Quadratic Trend Fitting E16.8 Using Least-Squares Exponential Trend Fitting E16.9 Creating Lagged Independent Variables E16.10 Creating First-Order Autoregressive Models E16.11 For Second-Order or Third-Order Autoregressive Models E16.12 Computing the Mean Absolute Deviation (MAD) E16.13 Creating Dummy Variables for Quarterly or Monthly Data E16.14 Calculating Index Numbers 17 Decision Making Using Statistics@Reliable Fund 17.1 Payoff Tables and Decision Trees 17.2 Criteria for Decision Making Expected Monetary Value Expected Opportunity Loss Return-to-Risk Ratio 17.3 Decision Making with Sample Information 17.4 Utility Summary Key Equations Key Terms Chapter Review Problems Web Case Excel Companion to Chapter 17 E17.1 Computing Opportunity Loss E17.2 Computing Expected Monetary Value 18 Statistical Applications in Quality and Productivity Management Using Statistics@ BEACHCOMBER HOTEL 18.1 Total Quality Management 18.2 Six Sigma Management 18.3 The Theory of Control Charts 18.4 Control Chart for the Proportion-The p Chart 18.5 The Red Bead Experiment: Understanding Process Variability 18.6 Control Charts for the Range and the Mean The R Chart The Chart 18.7 Process Capability Customer Satisfaction and Specification Limits Capability Indexes CPL, CPU, and Cpk Summary Key Equations Key Terms Chapter Review Problems The Harnswell Sewing Machine Company Case Managing the Springville Herald Excel Companion to Chapter 18 E18.1 Creating p Charts E18.2 Creating R and Charts Appendices A. Review of Arithmetic, Algebra, and Logarithms B. Summation Notation C. Statistical Symbols and Greek Alphabet D. CD-ROM Contents E. Tables F. Configuring Microsoft Excel and Installing PHStat Self-Test Solutions and Answers to Selected Even-Numbered Problems Index CD-ROM Topics (available as Adobe Reader .PDF files on the text CD) 4.5 Counting Rules 5.6 Using the Poisson Distribution to Approximate the Binomial Distribution 6.6 The Normal Approximation to the Binomial Distribution 7.6 Sampling from Finite Populations 8.7 Estimation and Sample Size Determination for Finite Populations 9.7 The Power of a Test 11.3 The Randomized Block Design 12.7 Chi-Square Test for a Variance or Standard Deviation

Library of Congress Subject Headings for this publication:

Microsoft Excel (Computer file).

Management -- Statistical methods.

Commercial statistics.

Electronic spreadsheets.

Management -- Statistical methods -- Computer programs.

Commercial statistics -- Computer programs.