Applied Statistical Inference with MINITAB®

About the Book

Applied Statistical Inference presents a step-by-step approach to working out mathematical problems, illustrating the techniques and methods of applied inferences using the statistical software package Minitab(R). Requiring only basic knowledge of statistics and intermediate knowledge of algebra, the text provides a fundamental understanding of applied statistics, which serves as a foundation for studying more advanced topics. Intended for a second course in applied statistics, this comprehensive undergraduate textbook contains a solutions manual as well as reference datasets on an accompanying CD-ROM and for download on the web.

Table of Contents:

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 R²

^{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.}

About the Author :
Sally A. Lesik is professor of mathematics at Central Connecticut State University. Dr. Lesik has taught many mathematics, statistics, engineering, and physics courses. Her primary research is in applied statistical inference. .

Review :
! 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