© 2009 – Chapman and Hall/CRC

464 pages | 376 B/W Illus.

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Through clear, step-by-step mathematical calculations, **Applied Statistical Inference with MINITAB** enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis.

*Illustrates the techniques and methods using MINITAB*

After introducing some common terminology, the author explains how to create simple graphs using MINITAB and how to calculate descriptive statistics using both traditional hand computations and MINITAB. She then delves into statistical inference topics, such as confidence intervals and hypothesis testing, as well as linear regression, including the Ryan–Joiner test. Moving on to multiple regression analysis, the text addresses ANOVA, the issue of multicollinearity, assessing outliers, and more. It also provides a conceptual introduction to basic experimental design and one-way ANOVA. The final chapter discusses two-way ANOVA, nonparametric analyses, and time series analysis.

*Establishes a foundation for studying more complex topics*

Ideal for students in the social sciences, this text shows how to implement basic inferential techniques in practice using MINITAB. It establishes the foundation for students to build on work in more advanced inferential statistics.

… I was pleased to see this recently published textbook by Sally A. Lesik. Although its focus is on applications, formulas are not forgotten. The ample problem section at the end of the chapter requires the student to hand calculate values, but then verify the answers with the software. In fact, the book comes with a disc of MS Excel data worksheets. … a good choice for general students … This book is also a great choice for someone who wishes to teach themselves Minitab.

—*Significance*, May 2011

The book is a great reference for anyone who is new to Minitab or is an infrequent user who just wants a quick reference. It is well organized by topic… . **Applied Statistical Inference with MINITAB** (ASIM) could also be used as a text for students who have already had an introductory statistics class; I envision it for use by students in a research methods type of course. … One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff—inferential statistics. The author manages to do this very quickly. … (I will be taking some lessons on how to speed through the material.) … it is excellent as a quick review for those students who have already had some previous exposure. … Every set of clearly written Minitab instructions includes appropriate screen shots, all of which should be accessible to those having little software experience.… All in all, I recommend ASIM. … if one were looking for a book that efficiently covers basic statistical methodology and also introduces statistical software, ASIM fits the bill. …

—*The American Statistician*, February 2011, Vol. 65, No. 1

This book/CD-ROM package is intended for a first course on applied inference for undergraduates and graduates in any field that uses statistics. The text is written to be beginner-friendly and oriented toward practical use of statistics, with less emphasis on theory.

—*Book News*, June 2010

**Introduction**

What This Book Is About

Types of Studies

What Is Statistics?

Types of Variables

Classification of Variables

Entering Data into MINITAB

**Graphing Variables**

Introduction

Histograms

Using MINITAB to Create Histograms

Stem-and-Leaf Plots

Using MINITAB to Create a Stem-and-Leaf Plot

Bar Charts

Using MINITAB to Create a Bar Chart

Box Plots

Using MINITAB to Create Box Plots

Scatter Plots

Using MINITAB to Create Scatter Plots

Marginal Plots

Using MINITAB to Create Marginal Plots

**Descriptive Representations of Data and Random Variables**

Introduction

Descriptive Statistics

Measures of Center

Measures of Spread

Using MINITAB to Calculate Descriptive Statistics

Random Variables and Their Distributions

Sampling Distributions

**Basic Statistical Inference**

Introduction

Confidence Intervals

Using MINITAB to Calculate Confidence Intervals for a Population Mean

Hypothesis Testing: A One-Sample *t*-Test for a Population Mean

Using MINITAB for a One-Sample *t*-Test

Power Analysis for a One-Sample *t*-Test

Using MINITAB for a Power Analysis for a One-Sample *t*-Test

Confidence Interval for the Difference between Two Means

Using MINITAB to Calculate a Confidence Interval for the Difference between Two Means

Testing the Difference between Two Means

Using MINITAB to Test the Difference between Two Means

Using MINITAB to Create an Interval Plot

Using MINITAB for a Power Analysis for a Two-Sample *t*-Test

Confidence Intervals and Hypothesis Tests for Proportions

Using MINITAB for a One-Sample Proportion

Power Analysis for a One-Sample Proportion

Differences between Two Proportions

Using MINITAB for Two-Sample Proportion Confidence Intervals and Hypothesis Tests

Power Analysis for a Two-Sample Proportion

**Simple Linear Regression**

Introduction

The Simple Linear Regression Model

Model Assumptions

Finding the Equation of the Line of Best Fit

Using MINITAB for Simple Linear Regression

Regression Inference

Inferences about the Population Regression Parameters

Using MINITAB to Test the Population Slope Parameter

Confidence Intervals for the Mean Response for a Specific Value of the Predictor Variable

Prediction Intervals for a Response for a Specific Value of the Predictor Variable

Using MINITAB to Find Confidence and Prediction Intervals

**More on Simple Linear Regression**

Introduction

The Coefficient of Determination

Using MINITAB to Find the Coefficient of Determination

The Sample Coefficient of Correlation

Correlation Inference

Using MINITAB for Correlation Analysis

Assessing Linear Regression Model Assumptions

Using MINITAB to Create Exploratory Plots of Residuals

A Formal Test of the Normality Assumption

Using MINITAB for the Ryan–Joiner Test

Assessing Outliers

Assessing Outliers: Leverage Values

Using MINITAB to Calculate Leverage Values

Assessing Outliers: Internally Studentized Residuals

Assessing Outliers: Cook’s Distances

Using MINITAB to Find Cook’s Distances

How to Deal with Outliers

**Multiple Regression Analysis**

Introduction

Basics of Multiple Regression Analysis

Using MINITAB to Create a Matrix Plot

Using MINITAB for Multiple Regression

The Coefficient of Determination for Multiple Regression

The Analysis of Variance Table

Testing Individual Population Regression Parameters

Using MINITAB to Test Individual Regression Parameters

Multicollinearity

Variance Inflation Factors

Using MINITAB to Calculate Variance Inflation Factors

Multiple Regression Model Assumptions

Using MINITAB to Check Multiple Regression Model Assumptions

Quadratic and Higher-Order Predictor Variables

Using MINITAB to Create a Quadratic Variable

**More on Multiple Regression**

Introduction

Using Categorical Predictor Variables

Using MINITAB for Categorical Predictor Variables

The Adjusted *R*^{2}

Best Subsets Regression

Using MINITAB for Best Subsets Regression

Confidence and Prediction Intervals for Multiple Regression

Using MINITAB to Calculate Confidence and Prediction Intervals for a Multiple Regression Analysis

Assessing Outliers

**Analysis of Variance (ANOVA)**

Introduction

Basic Experimental Design

One-Way ANOVA

Model Assumptions

The Assumption of Constant Variance

The Normality Assumption

Using MINITAB for One-Way ANOVAs

Multiple Comparison Techniques

Using MINITAB for Multiple Comparisons

Power Analysis and One-Way ANOVA

**Other Topics**

Introduction

Two-Way Analysis of Variance

Using MINITAB for a Two-Way ANOVA

Nonparametric Statistics

Wilcoxon Signed-Rank Test

Using MINITAB for the Wilcoxon Signed-Rank Test

Kruskal–Wallis Test

Using MINITAB for the Kruskal–Wallis Test

Basic Time Series Analysis

**Index**

*Exercises appear at the end of each chapter.*

- MAT029000
- MATHEMATICS / Probability & Statistics / General