Statistics for Business and Economics

1. Statistics, Data, and Statistical Thinking.


1.1 The Science of Statistics



1.2 Types of Statistical Applications



1.3 Fundamental Elements of Statistics



1.4 Processes (Optional)



1.5 Types of Data



1.6 Collecting Data



1.7 The Role of Statistics in Managerial Decision-Making



Statistics in Action: A "20/20" View of Survey Results - Fact or Fiction?



Using Technology: Creating and Listing Data in SPSS, MINITAB, and EXCEL



2. Methods for Describing Sets of Data.


2.1 Describing Qualitative Data



2.2 Graphical Methods for Describing Quantitative Data



2.3 Summation Notation



2.4 Numerical Measures of Central Tendency



2.5 Numerical Measures of Variability



2.6 Interpreting the Standard Deviation



2.7 Numerical Measures of Relative Standing



2.8 Methods for Detecting Outliers (Optional)



2.9 Graphing Bivariate Relationships (Optional)



2.10 The Time Series Plot (Optional)



2.11 Distorting the Truth with Descriptive Techniques



Statistics In Action: Characteristics of Physicians who Use or Refuse Ethics Consultation



Using Technology: Describing Data using SPSS, MINITAB, and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART I (A Case Covering Chapters 1 and 2)



3. Probability.


3.1 Events, Sample Spaces, and Probability



3.2 Unions and Intersections



3.3 Complementary Events



3.4 The Additive Rule and Mutually Exclusive Events.



3.5 Conditional Probability



3.6 The Multiplicative Rule and Independent Events



3.7 Random Sampling



3.8 Bayes' Rule (Optional)



Statistics In Action: Lottery Buster!



Using Technology: Generating a Random Sample Using SPSS, MINITAB, and EXCEL/PHStat2



4. Discrete Random Variables.


4.1 Two Types of Random Variables



4.2 Probability Distributions for Discrete Random Variables



4.3 Expected Values of Discrete Random Variables



4.4 The Binomial Random Variable



4.5 The Poisson Random Variable (Optional)



4.6 The Hypergeometric Random Variable (Optional)



Statistics in Action: Probability in a Reverse Cocaine Sting



Using Technology: Binomial, Poisson, and Hypergeometric Probabilities using SPSS, MINITAB, and EXCEL/PHStat2



5. Continuous Random Variables


5.1Continuous Probability Distributions



5.2The Uniform Distribution (Optional)



5.3The Normal Distribution



5.4Descriptive Methods for Assessing Normality



5.5Approximating a Binomial Distribution with a Normal Distribution



5.6The Exponential Distribution (Optional)



Statistics in Action: Super Weapons Development - Optimizing the Hit Ratio



Using Technology: Cumulative Probabilities and Normal Probability Plots using SPSS, MINITAB, and EXCEL/PHStat2



6. Sampling Distributions


6.1The Concept of Sampling Distributions



6.2Properties of Sampling Distributions: Unbiasedness and Minimum Variance (Optional)



6.3The Sampling Distribution of and the Central Limit Theorem



Statistics in Action: The Insomnia Pill



Using Technology: Simulating a Sampling Distribution using MINITAB and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: THE FURNITURE FIRE CASE (A Case Covering Chapters 3-6)



7. Inferences Based on a Single Sample: Estimation with Confidence Intervals


7.1Large-Sample Confidence Interval for a Population Mean



7.2Small-Sample Confidence Interval for a Population Mean



7.3Large-Sample Confidence Interval for a Population Proportion



7.4Determining the Sample Size



7.5Finite Population Correction for Simple Random Sampling (Optional)



7.6Sample survey Designs (Optional)



Statistics in Action: Scallops, Sampling, and the Law



Using Technology: Confidence Intervals using SPSS, MINITAB and EXCEL/PHStat2



8. Inferences Based on a Single Sample: Tests of Hypothesis


8.1The Elements of a Test of Hypothesis



8.2Large-Sample Test of Hypothesis About a Population Mean



8.3Observed Significance Levels: p-Values



8.4Small-Sample Test of Hypothesis About a Population Mean



8.5Large-Sample Test of Hypothesis About a Population Proportion



8.6Calculating Type II Error Probabilities: More About _ (Optional)



8.7Test of Hypothesis About a Population Variance (Optional)



Statistics in Action: Diary of a Kleenex User



Using Technology: Tests of Hypotheses using SPSS, MINITAB and EXCEL/PHStat2



9. Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses


9.1Comparing Two Population Means: Independent Sampling



9.2Comparing Two Population Means: Paired Difference Experiments



9.3Comparing Two Population Proportions: Independent Sampling



9.4Determining the Sample Size



9.5Comparing Two Population Variances: Independent Sampling



Statistics in Action: The Effect of Self-Managed Work Teams on Family Life



Using Technology: Two-Sample Inferences using SPSS, MINITAB and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART II (A Case Covering Chapters 7-9)



10. Design of Experiments and Analysis of Variance


10.1Elements of a Designed Experiment



10.2The Completely Randomized Design



10.3Multiple Comparisons of Means



10.4The Randomized Block Design (Optional)



10.5Factorial Experiments



Statistics in Action: The Ethics of Downsizing



Using Technology: Analysis of Variance using SPSS, MINITAB and EXCEL/PHStat2



11. The Chi-Square Test and the Analysis of Contingency Tables


11.1Categorical Data and the Multinomial Distribution



11.2Testing Category Probabilities: One-Way Table



11.3Testing Category Probabilities: Two-Way (Contingency) Table



11.4A Word of Caution About Chi-Square Tests



Statistics in Action: A Study of Coupon Users—Mail versus the Internet



Using Technology: Chi-Square Analyses using SPSS, MINITAB and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: DISCRIMINATION IN THE WORKPLACE (A Case Covering Chapters 10-11)



12. Simple Linear Regression


12.1Probabilistic Models



12.2Fitting the Model: The Least Squares Approach



12.3Model Assumptions



12.4An Estimator of _2



12.5Making Inferences About the Slope _1



12.6The Coefficient of Correlation



12.7The Coefficient of Determination



12.8Using the Model for Estimation and Prediction



12.9A Complete Example



Statistics in Action: Can "Dowsers" Really Detect Water?



Using Technology: Simple Linear Regression using SPSS, MINITAB and EXCEL/PHStat2



13. Multiple Regression and Model Building


13.1Multiple Regression Models



13.2The First-Order Model: Estimating and Interpreting the _-Parameters



13.3Model Assumptions



13.4Inferences About the Individual _ Parameters



13.5Checking the Overall Utility of a Model



13.6Using the Model for Estimation and Prediction



13.7Model Building: Interaction Models



13.8Model Building: Quadratic and other Higher-Order Models



13.9Model Building: Qualitative (Dummy) Variable Models



13.10Model Building: Models with both Quantitative and Qualitative Variables (Optional)



13.11Model Building: Comparing Nested Models (Optional)



13.12Model Building: Stepwise Regression (Optional)



13.13Residual Analysis: Checking the Regression Assumptions



13.14Some Pitfalls: Estimability, Multicollinearity, and Extrapolation



Statistics in Action: Bid-Rigging in the Highway construction Industry



Using Technology: Multiple Regression using SPSS, MINITAB and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: THE CONDO SALES CASE (A Case Covering Chapters 12-13)



14. Methods for Quality Improvement


14.1Quality, Processes, and Systems



14.2Statistical Control



14.3The Logic of Control Charts



14.4A Control Chart for Monitoring the Mean of a Process: The -Chart



14.5A Control Chart for Monitoring the Variation of a Process: The R-Chart



14.6A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart



14.7Diagnosing the Causes of Variation (Optional)



14.8Capability Analysis (Optional)



Statistics in Action: Testing Jet Fuel Additive for Safety



Using Technology: Control Charts using SPSS, MINITAB and EXCEL/PHStat2



15. Time Series: Descriptive Analyses, Models, and Forecasting


15.1Descriptive Analysis: Index Numbers



15.2Descriptive Analysis: Exponential Smoothing



15.3Time Series Components



15.4Forecasting: Exponential Smoothing



15.5Forecasting Trends: The Holt-Winters Model (Optional)



15.6Measuring Forecast Accuracy: MAD and RMSE



15.7Forecasting Trends: Simple Linear Regression



15.8Seasonal Regression Models



15.9Autocorrelation and the Durbin-Watson Test



Statistics In Action: Forecasting the Monthly Sales of a New Cold Medicine



Using Technology: Forecasting using SPSS, MINITAB and EXCEL/PHStat2



APPLYING STATISTICS TO THE REAL WORLD: THE GASKET MANUFACTURING CASE (A Case Covering Chapters 14-15)



16. Nonparametric Statistics


16.1Single Population Inferences: The Sign Test



16.2Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples



16.3Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment



16.4The Kruskal-Wallis H-Test for a Completely Randomized Design



16.5The Friedman Fr - Test for a Randomized Block Design (Optional) 16.6Spearman's Rank Correlation Coefficient



Statistics in Action: Deadly Exposure—Agent Orange and Vietnam Vets



Using Technology: Nonparametric Analyses using SPSS, MINITAB and EXCEL/PHStat2



Appendix ABasic Counting Rules


Appendix BTables


Table IRandom Numbers



Table IIBinomial Probabilities



Table IIIPoisson Probabilities



Table IVNormal Curve Areas



Table VExponentials



Table VICritical Values of t



Table VIICritical Values of _2



Table VIIIPercentage Points of the F Distribution, _=.10



Table IX Percentage Points of the F Distribution, _=.05



Table X Percentage Points of the F Distribution, _=.025



Table XI Percentage Points of the F Distribution, _=.01



Table XIICritical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples



Table XIIICritical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test



Table XIVCritical Values of Spearman's Rank Correlation Coefficient



Appendix CCalculation Formulas for Analysis of Variance Short Answers to Selected Odd-Numbered Exercises Index