# Statistics for Business: Decision Making and Analysis (Pearson New International Edition)

By: Robert A. Stine (author), Dean Foster (author)Mixed Media

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### Contents

Preface Index of Application PART ONE: VARIATION 1. Introduction 1.1 What Is Statistics? 1.2 Previews 2. Data 2.1 Data Tables 2.2 Categorical and Numerical Data 2.3 Recoding and Aggregation 2.4 Time Series 2.5 Further Attributes of Data Chapter Summary 3. Describing Categorical Data 3.1 Looking at Data 3.2 Charts of Categorical Data 3.3 The Area Principle 3.4 Mode and Median Chapter Summary 4. Describing Numerical Data 4.1 Summaries of Numerical Variables 4.2 Histograms 4.3 Boxplot 4.4 Shape of a Distribution 4.5 Epilog Chapter Summary 5. Association between Categorical Variables 5.1 Contingency Tables 5.2 Lurking Variables and Simpson's Paradox 5.3 Strength of Association Chapter Summary 6. Association between Quantitative Variables 6.1 Scatterplots 6.2 Association in Scatterplots 6.3 Measuring Association 6.4 Summarizing Association with a Line 6.5 Spurious Correlation Chapter Summary Statistics in Action: Financial Time Series Statistics in Action: Executive Compensation PART TWO: PROBABILITY 7. Probability 7.1 From Data to Probability 7.2 Rules for Probability 7.3 Independent Events Chapter Summary 8. Conditional Probability 8.1 From Tables to Probabilities 8.2 Dependent Events 8.3 O rganizing Probabilities 8.4 O rder in Conditional Probabilities Chapter Summary 9. Random Variables 9.1 Random Variables 9.2 Properties of Random Variables 9.3 Properties of Expected Values 9.4 Comparing Random Variables Chapter Summary 10. Association between Random Variables 10.1 Portfolios and Random Variables 10.2 Joint Probability Distribution 10.3 Sums of Random Variables 10.4 Dependence between Random Variables 10.5 IID Random Variables 10.6 Weighted Sums Chapter Summary 11. Probability Models for Counts 11.1 Random Variables for Counts 11.2 Binomial Model 11.3 Properties of Binomial Random Variables 11.4 Poisson Model Chapter Summary 12. The Normal Probability Model 12.1 Normal Random Variable 12.2 The Normal Model 12.3 Percentiles 12.4 Departures from Normality Chapter Summary Statistics in Action: Managing Financial Risk Statistics in Action: Modeling Sampling Variation PART THREE: INFERENCE 13. Samples and Surveys 13.1 Two Surprising Properties of Samples 13.2 Variation 13.3 Alternative Sampling Methods 13.4 Questions to Ask Chapter Summary 14. Sampling Variation and Quality 14.1 Sampling Distribution of the Mean 14.2 Control Limits 14.3 Using a Control Chart 14.4 Control Charts for Variation Chapter Summary 15. Confidence Intervals 15.1 Ranges for Parameters 15.2 Confidence Interval for the Mean 15.3 Interpreting Confidence Intervals 15.4 Manipulating Confidence Intervals 15.5 Margin of Error Chapter Summary 16. Statistical Tests 16.1 Concepts of Statistical Tests 16.2 Testing the Proportion 16.3 Testing the Mean 16.4 Significance versus Importance 16.5 Confidence Interval or Test? Chapter Summary 17. Comparison 17.1 Data for Comparisons 17.2 Two-Sample z-test for Proportions 17.3 Two-Sample Confidence Interval for Proportions 17.4 Two-Sample T-test 17.5 Confidence Interval for the Difference between Means 17.6 Paired Comparisons Chapter Summary 18. Inference for Counts 18.1 Chi-Squared Tests 18.2 Test of Independence 18.3 General versus Specific Hypotheses 18.4 Tests of Goodness of Fit Chapter Summary Statistics in Action: Rare Events Statistics in Action: Data Mining Using Chi-Squared PART FOUR: REGRESSION MODELS 19. Linear Patterns 19.1 Fitting a Line to Data 19.2 Interpreting the Fitted Line 19.3 Properties of Residuals 19.4 Explaining Variation 19.5 Conditions for Simple Regression Chapter Summary 20. Curved Patterns 20.1 Detecting Nonlinear Patterns 20.2 Transformations 20.3 Reciprocal Transformation 20.4 Logarithm Transformation Chapter Summary 21. The Simple Regression Model 21.1 The Simple Regression Model 21.2 Conditions for the SRM 21.3 Inference in Regression 21.4 Prediction Intervals Chapter Summary 22. Regression Diagnostics 22.1 Changing Variation 22.2 Outliers 22.3 Dependent Errors and Time Series Chapter Summary 23. Multiple Regression 23.1 The Multiple Regression Model 23.2 Interpreting Multiple Regression 23.3 Checking Conditions 23.4 Inference in Multiple Regression 23.5 Steps in Fitting a Multiple Regression Chapter Summary 24. Building Regression Models 24.1 Identifying Explanatory Variables 24.2 Collinearity 24.3 Removing Explanatory Variables Chapter Summary 25. Categorical Explanatory Variables 25.1 Two-Sample Comparisons 25.2 Analysis of Covariance 25.3 Checking Conditions 25.4 Interactions and Inference 25.5 Regression with Several Groups Chapter Summary 26. Analysis of Variance 26.1 Comparing Several Groups 26.2 Inference in ANOVA Regression Models 26.3 Multiple Comparisons 26.4 Groups of Different Size Chapter Summary 27. Time Series 27.1 Decomposing a Time Series 27.2 Regression Models 27.3 Checking the Model Chapter Summary Statistics in Action: Analyzing Experiments Statistics in Action: Automated Modeling Appendix: Tables Answers Photo Acknowledgments Index Supplementary Material (online-only) Alternative Approaches to Inference More Regression 2-Way ANOVA

### Product Details

• publication date: 15/08/2013
• ISBN13: 9781292023397
• Format: Mixed Media
• Number Of Pages: 944
• ID: 9781292023397
• weight: 1956
• ISBN10: 1292023392
• edition: Pearson New International Edition

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