Introduction 1 What''s in the Book 1 Why Use Excel? 3 1 Components of Decision Analytics 5 Classifying According to Existing Categories 5 Using a Two-Step Approach 6 Multiple Regression and Decision Analytics 6 Access to a Reference Sample 8 Multivariate Analysis of Variance 9 Discriminant Function Analysis 10 Logistic Regression 12 Classifying According to Naturally Occurring Clusters 13 Principal Components Analysis 13 Cluster Analysis 14 Some Terminology Problems 16 The Design Sets the Terms 17 Causation Versus Prediction 18 Why the Terms Matter 18 2 Logistic Regression 21 The Rationale for Logistic Regression 22 The Scaling Problem 24 About Underlying Assumptions 25 Equal Spread 25 Equal Variances with Dichotomies 27 Equal Spread and the Range 28 The Distribution of the Residuals 29 Calculating the Residuals 30 The Residuals of a Dichotomy 30 Using Logistic Regression 31 Using Odds Rather Than Probabilities 32 Using Log Odds 33 Using Maximum Likelihood Instead of Least Squares 34 Maximizing the Log Likelihood 35 Setting Up the Data 35 Setting Up the Logistic Regression Equation 36 Getting the Odds 38 Getting the Probabilities 39 Calculating the Log Likelihood 40 Finding and Installing Solver 41 Running Solver 41 The Rationale for Log Likelihood 43 The Probability of a Correct Classification 44 Using the Log Likelihood 45 The Statistical Significance of the Log Likelihood 48 Setting Up the Reduced Model 50 Setting Up the Full Model 51 3 Univariate Analysis of Variance (ANOVA) 53 The Logic of ANOVA 54 Using Variance 54 Partitioning Variance 55 Expected Values of Variances (Within Groups) 56 Expected Values of Variances (Between Groups) 58 The F-Ratio 61 The Noncentral F Distribution 64 Single Factor ANOVA 66 Adopting an Error Rate 66 Computing the Statistics 67 Deriving the Standard Error of the Mean 70 Using the Data Analysis Add-In 72 Installing the Data Analysis Add-In 73 Using the ANOVA: Single Factor Tool 73 Understanding the ANOVA Output 75 Using the Descriptive Statistics 75 Using the Inferential Statistics 76 The Regression Approach 79 Using Effect Coding 80 The LINEST() Formula 82 The LINEST() Results 83 LINEST() Inferential Statistics 85 4 Multivariate Analysis of Variance (MANOVA) 89 The Rationale for MANOVA 89 Correlated Variables 90 Correlated Variables in ANOVA 91 Visualizing Multivariate ANOVA 92 Univariate ANOVA Results 93 Multivariate ANOVA Results 93 Means and Centroids 95 From ANOVA to MANOVA 96 Using SSCP Instead of SS 98 Getting the Among and the Within SSCP Matrices 102 Sums of Squares and SSCP Matrices 104 Getting to a Multivariate F-Ratio 105 Wilks'' Lambda and the F-Ratio 107 Converting Wilks'' Lambda to an F Value 108 Running a MANOVA in Excel 110 Laying Out the Data 110 Running the MANOVA Code 111 Descriptive Statistics 112 Equality of the Dispersion Matrices 113 The Univariate and Multivariate F-Tests 115 After the Multivariate Test 116 5 Discriminant Function Analysis: The Basics 119 Treating a Category as a Number 120 The Rationale for Discriminant Analysis 122 Multiple Regression and Discriminant Analysis 122 Adjusting Your Viewpoint 123 Discriminant Analysis and Multiple Regression 125 Regression, Discriminant Analysis, and Canonical Correlation 125 Coding and Multiple Regression 127 The Discriminant Function and the Regression Equation 129 From Discriminant Weights to Regression Coefficients 130 Eigenstructures from Regression and Discriminant Analysis 133 Structure Coefficients Can Mislead 136 Wrapping It Up 137 6 Discriminant Function Analysis: Further Issues 139 Using the Discriminant Workbook 139 Opening the Discriminant Workbook 140 Using.