Overview

This package consists of the textbook plus an access kit for MyMathLab/MyStatLab.   In the competitive world of business, effective decision making is crucial. To help you stand out from the crowd, Robert Stine and Dean Foster of the Wharton School of the University of Pennsylvania have written an exciting new book for business statistics. This book teaches you how to use data to make informed decisions; every chapter highlights issues in the modern business world. The authors provide strong connections between the statistical concepts in the text and the problems you will face in your future careers, showing you how to find patterns, create statistical models from the data, and deliver your findings to an audience.   MyMathLab provides a wide range of homework, tutorial, and assessment tools that make it easy to manage your course online.

Full Product Details

Author:   Robert A. Stine ,  Dean Foster
Publisher:   Pearson Education (US)
Imprint:   Pearson
Dimensions:   Width: 21.60cm , Height: 3.30cm , Length: 28.20cm
Weight:   1.792kg
ISBN:  

9780321286208

ISBN 10:   0321286200
Pages:   742
Publication Date:   28 March 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Replaced By:   9781408259009
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

PART 1: VARIATION IN DATA 1. Introduction 1.1 What is Statistics? 1.2 Previews 1.3 How to Use This Book 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 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 4. Describing numerical data 4.1 Summaries of Numerical Variables 4.2 Histograms and the Distribution of Numerical Data 4.3 Boxplot 4.4 Shape of a Distribution 5. Association in categorical data 5.1 Contingency Tables 5.2 Lurking Variables and Simpson's Paradox 5.3 Strength of Association 6. Association in numerical data 6.1 Scatterplots 6.2 Association in Scatterplots 6.3 Measuring Association 6.4 Summarizing Association with a Line 6.5 Spurious Correlation Statistics in Action: Financial time series Statistics in Action: Executive compensation PART 2: PROBABILITY 7. Probability 7.1 From Data to Probability 7.2 Rules for Probability 7.3 Independent Events 7.4 Boole's Inequality 8. Conditional Probability 8.1 From Tables to Probabilities 8.2 Dependent Events 8.3 Organizing Probabilities 8.4 Order in Conditional Probabilities 9. Random Variables 9.1 Properties of Random Variables 9.2 Expected Values 9.3 Comparing Random Variables 10. Association between Random Variables 10.1 Portfolios and Random Variables 10.2 Probability Distribution 10.3 Sums of Random Variables 10.4 Measure Dependence between Random Variables 10.5 IID Random Variables 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 12. Normality 12.1 Normal Random Variable 12.2 The Normal Model 12.3 Percentiles of the Normal Distribution 12.4 Departures from Normality Statistics in Action: Managing Financial Risk Statistics in Action: Modeling Sampling Variation PART 3: INFERENCE 13. Samples and Surveys 13.1 Two Surprises in Sampling 13.2 Variation 13.3 Alternative Sampling Methods 13.4 Checklist for Surveys 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 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 16. Hypothesis Tests 16.1 Concepts of Statistical Tests 16.2 Testing the Proportion 16.3 Testing the Mean 16.4 Effects of Sample Size 17. Alternative Approaches to Inference 17.1 A Confidence Interval for the Median 17.2 Transformations and Intervals 17.3 Prediction Intervals 17.4 Proportions Based on Small Samples 18. Comparison 18.1 Data for Comparisons 18.2 Two-sample T-test 18.3 Confidence Interval for the Difference 18.4 Other Comparisons Statistics in Action: Rare Events Statistics in Action: Testing Association PART 4: 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 a Simple Regression 20. Curved Patterns 20.1 Detecting Nonlinear Patterns 20.2 Reciprocal Transformation 20.3 Comparing a Linear and Nonlinear Equation 20.4 Logarithm Transformation 20.5 Comparing Equations 21. Simple Regression 21.1 The Simple Regression Model 21.2 Conditions for the Simple Regression Model 21.3 Inference in Regression 21.4 Prediction Intervals 22. Regression Diagnostics 22.1 Changing Variation 22.2 Leveraged Outliers 22.3 Dependent Errors and Time Series 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 Building a Multiple Regression 24. Building Regression Models 24.1 Identifying Explanatory Variables 24.2 Collinearity 24.3 Removing Explanatory Variables 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 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 27. Time Series 27.1 Decomposing a Time Series 27.2 Regression Models 27.3 Checking Conditions Statistics in Action: Analyzing Experiments Statistics in Action: Automated Regression Modeling

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