Applied Statistical Inference with MINITAB®

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.

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.