## Overview

Focusing on the analysis of data using modern statistical and spreadsheet software, Hildebrand and Ott emphasize making sense of data and discuss not only how a statistical method is applied, but why and why not. Throughout the book, the authors integrate computer use into the development of statistical concepts, emphasizing the value of looking at data to make sure the right questions are being asked.
The real-life applications and examples throughout challenge students to think like managers. The case that concludes every chapter asks students to deal with a relatively unstructured situation and to explain the statistical reasoning in nontechnical language. Modern statistical methods, including resampling and bootstrapping are included. In addition, the authors emphasize quality control and improvement throughout the book and include three full chapters on regression and correlation methods.

## Table of Contents

1. DATA

What Do We Mean by "Data?" / Data About What? / How Do You Gather Data? / What Should You Do with the Data? / How Can You Evaluate Other People's Data? / The Role of the Computer / Summary

2. SUMMARIZING DATA ABOUT ONE VARIABLE

The Distribution of Values of a Variable / On the Average: Typical Values / Measuring Variability / The Normal Distribution: A Preview / Calculators and Computer Software Systems / Statistical Methods and Quality Improvement / Summing Up

3. A FIRST LOOK AT PROBABILITY

Basic Principles of Probability / Statistical Independence / Probability Tables, Trees, and Simulations / Summing Up / Review Exercises for Chapters 2 and 3

4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS

Random Variable: Basic Ideas / Probability Distributions: Discrete Random Variables / Probability Distributions: Continuous Random Variables / Expected Value and Standard Deviation: Discrete Random Variables / Expected Value and Standard Deviation: Continuous Random Variables / Joint Probability Distributions and Independence / Covariance and Correlation of Random Variables / Summing Up

5. SOME SPECIAL PROBABILITY DISTRIBUTIONS

Counting Possible Outcomes / Bernoulli Trials and the Binomial Distribution / The Hypergeometric Distribution / Geometric and Negative Binomial Distributions / The Poisson Distribution / The Uniform Distribution / Exponential Distribution / The Normal Distribution / Summing Up

6. RANDOM SAMPLING AND SAMPLING DISTRIBUTIONS

Random Sampling / Sample Statistics and Sampling Distributions / Sampling Distributions for Means and Sums / Checking Normality / Summing Up / Appendix: Standard Error of a Mean / Review Exercises for Chapters 4 to 6

7. ESTIMATION

Point Estimators / Interval Estimation of a Mean, Known Standard Deviation / Confidence Intervals for a Proportion / How Large a Sample is Needed? / The t Distribution / Confidence Intervals with the t Distribution / Assumptions for Interval Estimation / Summing Up

8. HYPOTHESIS TESTING

A Test for a Mean, Known Standard Deviation / Type II Error, Beta Probability, and Power of a Test / The p-Value for a Hypothesis Test / Hypothesis Testing with the t Distribution / Assumptions for t Tests / Testing a Proportion: Normal Approximation / Hypothesis Tests and Confidence Intervals / Summing Up / Review Exercises for Chapters 7 and 8

9. COMPARING TWO SAMPLES

Comparing the Means of Two Populations / A Nonparametric Test: The Wilcoxon Rank Sum Test / Paired-Sample Methods / The Signed-Rank Method / Summing Up

10. METHODS FOR PROPORTIONS

Two-Sample Procedures for Proportions / Tests for Several Proportions / Chi-Squared Tests for Count Data / Measuring Strength of Relation / Odds / Summing Up

11. ANALYSIS OF VARIANCE AND DESIGNED EXPERIMENTS

Testing the Equality of Several Population Means / Comparing Several Distributions by Rank Test / Specific Comparisons Among Means / Two-Factor Experiments / Randomized Block Designs / More Complex Experiments / Summing Up / Review Exercises for Chapters 9-11

12. LINEAR REGRESSION AND CORRELATION METHODS

The Linear Regression Model / Estimating Model Parameters / Inferences About Regression Parameters / Predicting New Y Values Using Regression / Correlation / Summing Up

13. MULTIPLE REGRESSION MODELS

The Multiple Regression Model / Estimating Multiple Regression Coefficients / Inferences in Multiple Regression / Testing a Subset of the Regression Coefficients / Inferences in Multiple Regression / Testing a Subset of the Regression Coefficients / Forecasting Using Multiple Regression / Summing Up / Appendix: Some Multiple Regression Theory

14. CONSTRUCTING A MULTIPLE REGRESSION MODEL

Selecting Possible Independent Variables (Step 1) / Using Qualitative Predictors: Dummy Variables (Step 1) / Lagged Predictor Variables (Step 1) / Nonlinearity and Interaction (Step 2) / Choosing Among Regression Models (Step 3) / Residuals Analysis (Step 4) / Autoregression (Step 4) / Model Validation / Summing / Review Exercises for Chapters 12-14

15. TIME SERIES ANALYSIS

Index Numbers / The Classical, Cyclic, and Seasonal Approach / Smoothing Methods / The ARIMA Approach / Summing Up

16. SOME ALTERNATIVE SAMPLING METHODS

Taking a Simple Random Sample / Stratified Random Sampling / Cluster Sampling / Selecting the Sample Size / Other Sampling Techniques / Summing Up

17. DATA MANAGEMENT AND REPORT PREPARATION

Preparing Data for Statistical Analysis Guidelines for a Statistical Analysis and Report / Documentation and Storage of Results / SUMMARY