Statistics for Business: Decision Making and Analysis / Edition 3

Statistics for Business: Decision Making and Analysis / Edition 3

ISBN-10:
0134497163
ISBN-13:
9780134497167
Pub. Date:
01/05/2017
Publisher:
Pearson Education
ISBN-10:
0134497163
ISBN-13:
9780134497167
Pub. Date:
01/05/2017
Publisher:
Pearson Education
Statistics for Business: Decision Making and Analysis / Edition 3

Statistics for Business: Decision Making and Analysis / Edition 3

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Overview

For one- and two-semester courses in introductory business statistics.

The 3rd Edition of Statistics for Business: Decision Making and Analysis emphasises an application-based approach, in which students learn how to work with data to make decisions. In this contemporary presentation of business statistics, students learn how to approach business decisions through a 4M Analytics decision making strategy–motivation, method, mechanics and message–to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the student in the right direction to get started with analysis of data.


Product Details

ISBN-13: 9780134497167
Publisher: Pearson Education
Publication date: 01/05/2017
Edition description: New Edition
Pages: 912
Product dimensions: 8.70(w) x 11.00(h) x 1.40(d)

About the Author

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.

Table of Contents

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
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