Statistics for Business with MathXL Access Card

Robert A. Stine, Dean Foster, . . Pearson Education (Autoren)

Buch | Softcover

2010
Pearson Education Limited (Verlag)
978-1-4082-5900-9 (ISBN)

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In the competitive world of business, effective decision making is crucial. To help your students 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 students 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 students will face in their future careers, showing students how to find patterns, create statistical models from the data, and deliver their findings to an audience.

Suitable for students at the undergraduate, graduate, or MBA level, Statistics for Business: Decision Making and Analysis equips students with the most important skill they'll need in the business world using statistics to make better business decisions.

To see a sample chapter or learn how to class test, please visit www.pearsonhighered.com/stine1etour.

Robert Stine holds a PhD from Princeton University. He has taught at the Wharton School since 1983, during which time he has regularly taught business statistics. During his tenure, Bob has received a variety of teaching awards. Bob also actively consults for industry. His clients include the pharmaceutical firms Merck and Pfizer, and he regularly works with the Federal Reserve Bank of Philadelphia on models for retail credit risk. This collaboration has produced three well-received conferences held at Wharton. His areas of research include computer software, time series analysis and forecasting, and general problems related to model identification and selection. Bob has published numerous articles in research journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, and The Annals of Statistics. Dean Foster holds a PhD from the University of Maryland. He has taught at the Wharton School since 1992 and previously taught at the University of Chicago. Dean teaches courses in introductory business statistics, probability and Markov chains, statistical computing, and advanced statistics for managers. Dean's research areas are statistical inference for stochastic processes, game theory, machine learning, and variable selection. He is published in a wide variety of journals, including The Annals of Statistics, Operations Research, Games and Economic Behaviour, Journal of Theoretical Population Biology, andEconometrica. Bob Stine and Dean Foster have co-authored two casebooks: Basic Business Statistics (Springer-Verlag) and Business Analysis Using Regression (Springer-Verlag). These casebooks offer a collection of data analysis examples that motivate and illustrate key ideas of statistics, ranging from standard error to regression diagnostics and time series analysis. They also have collaborated on a number of research articles.

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. Statistical Tests

16.1 Concepts of Statistical Tests
16.2 Testing the Proportion
16.3 Testing the Mean
16.4 Other Properties of Tests

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

Erscheint lt. Verlag	20.5.2010
Verlagsort	Harlow
Sprache	englisch
Maße	216 x 276 mm
Gewicht	1614 g
Themenwelt	Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
Themenwelt	Mathematik / Informatik ► Mathematik ► Statistik
ISBN-10	1-4082-5900-1 / 1408259001
ISBN-13	978-1-4082-5900-9 / 9781408259009
Zustand	Neuware