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

• Robert A. Stine,
• Dean Foster

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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 emphasizes 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.

Also available with MyLab Statistics

MyLab™ Statistics from Pearson is the world’s leading online resource for teaching and learning statistics; it integrates interactive homework, assessment, and media in a flexible, easy-to-use format. MyLab Statistics is a course management system that helps individual students succeed. It provides engaging experiences that personalize, stimulate, and measure learning for each student. Tools are embedded to make it easy to integrate statistical software into the course.

ISBN-13: 9780137399727

## Overview

### 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

#### Regression with Big Data

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