Statistics for Business: Decision Making and Analysis, 3rd edition

Published by Pearson (January 25, 2021) © 2018

  • Robert A. Stine Wharton School of the University of Pennsylvania
  • Dean Foster Wharton School of the University of Pennsylvania

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ISBN-13: 9780137399727 (2021 update)
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Statistics for Business will teach you how to work with data to make sound business decisions. The text uses an application-based approach, with hints in every chapter on using Excel, Minitab Express, and JMP to successfully analyze data. You'll learn how to apply a 4M Analytics decision making strategy -- motivation, method, mechanics and message -- to better understand how the context around a business decision motivates the statistical process and how the results inform a course of action.

The 3rd Edition includes 90 new and updated data sets and more than 100 enhanced exercises. Examples and illustrations have been added to help you make connections between business and data. You'll also get step-by-step instructions in every chapter to walk you through completing analytic exercises with the latest version of Excel.

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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