Student Solutions Manual for Statistics for Business: Decision Making and Analysis

Auteur

Robert Stine holds a Ph.D. 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, including regularly winning the MBA Core Teaching Award, which is presented to faculty for outstanding teaching of the required curriculum at Wharton. He also received the David W. Hauck Award for Outstanding Teaching, awarded to the most highly rated faculty member teaching in the Wharton undergraduate program. Bob 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 Ph.D. 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, and Econometrica.

Contenu

I. Variation

1. Introduction
2. Data
3. Describing Categorical Data
4. Describing Numerical Data
5. Association Between Categorical Variables
6. Association Between Quantitative Variables

II. Probability

1. Probability
2. Conditional Probability
3. Random Variables
4. Association Between Random Variables
5. Probability Models for Counts
6. The Normal Probability Model

III. Inference

1. Samples and Surveys
2. Sampling Variation and Quality
3. Confidence Intervals
4. Statistical Tests
5. Comparison
6. Inference for Counts

IV. Regression Models

1. Linear Patterns
2. Curved Patterns
3. The Simple Regression Model
4. Regression Diagnostics
5. Multiple Regression
6. Building Regression Models
7. Categorical Explanatory Variables
8. Analysis of Variance
9. Time Series

Supplementary Material (Online-Only)

Alternative Approaches to Inference

Two-Way Analysis of Variance

Regression with Big Data