Statistics for Business: Decision Making and Analysis [With CDROM and Access Code]

Statistics for Business: Decision Making and Analysis [With CDROM and Access Code]

by Robert A. Stine, Alan Dean Foster
     
 

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ISBN-10: 0321286200

ISBN-13: 9780321286208

Pub. Date: 04/28/2010

Publisher: Pearson

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

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.

Product Details

ISBN-13:
9780321286208
Publisher:
Pearson
Publication date:
04/28/2010
Edition description:
Older Edition
Pages:
742
Product dimensions:
8.80(w) x 11.20(h) x 1.30(d)

Related Subjects

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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