Introduction . 1 1 Making the Transition . 5 Adjusting Your Expectations .6 Analyzing Data: The Packages .7 Storing and Arranging Data: Data Frames .7 The User Interface .8 Special Characters .9 Using the Tilde .
9 Using the Assignment Operator < .11 Obtaining R .14 Contributed Packages .16 Running Scripts.18 Importing Data into R from Excel .19 Exporting Data from R to Excel .26 Exporting via a CSV File .27 Using the Direct Export .
28 2 Descriptive Statistics .31 Descriptive Statistics in Excel .32 Using the Descriptive Statistics Tool .33 Understanding the Results .34 Using the Excel Descriptive Statistics Tool on R''s Pizza File .38 Using R''s DescTools Package .41 Entering Some Useful Commands .42 Controlling the Type of Notation .
43 The Reported Statistics .46 Running the Desc Function on Nominal Variables .55 Running Bivariate Analyses with Desc .56 Two Numeric Variables .57 Breaking Down a Numeric Variable by a Factor.63 Analyzing One Factor by Another: The Contingency Table .72 The Pearson Chi-square .76 The Likelihood Ratio .
79 The Mantel-Haenszel Chi-square .80 Estimating the Strength of the Relationships .83 3 Regression Analysis in Excel and R .85 Worksheet Functions .85 The CORREL( ) Function .86 The COVARIANCE.P( ) Function .87 The SLOPE( ) Function .
88 The INTERCEPT( ) Function .91 The RSQ( ) Function .93 The LINEST( ) Function .95 The TREND( ) Function .99 Functions for Statistical Inference .100 The T.DIST Functions .100 The F.
DIST Functions.102 Other Sources of Regression Analysis in Excel .104 The Regression Tool .104 Chart Trendlines .108 Regression Analysis in R .110 Correlation and Simple Regression .110 Analyzing a Multiple Regression Model .114 Models Comparison in R .
116 4 Analysis of Variance and Covariance in Excel and R .121 Single-Factor Analysis of Variance .122 Using Excel''s Worksheet Functions .122 Using the ANOVA: Single Factor Tool .124 Using the Regression Approach to ANOVA .125 Single-Factor ANOVA Using R .127 Setting Up Your Data.127 Arranging for the ANOVA Table .
129 The Single-Factor ANOVA with Missing Values .131 The Factorial ANOVA .134 Balanced Two-Factor Designs in Excel .135 Balanced Two-Factor Designs and the ANOVA Tool .137 Using Regression with Two-Factor ANOVA Designs .139 Analyzing Balanced Factorial Designs with R .145 Analyzing Unbalanced Two-Factor Designs in Excel and R .148 Dealing with the Ambiguity .
152 Specifying the Effects .157 Multiple Comparison Procedures in Excel and R .158 Tukey''s HSD Method .159 The Newman-Keuls Method .163 Using Scheffé Procedure in Excel and R.166 Analysis of Covariance in Excel and R .170 ANCOVA Using Regression in Excel .170 ANCOVA in R .
173 5 Logistic Regression in Excel and R .179 Problems with Linear Regression and Nominal Variables .180 Problems with Probabilities .181 Using Odds Instead of Probabilities .184 Using the Logarithms of the Odds .185 From the Log Odds to the Probabilities .187 Recoding Text Variables .188 Defining Names .
188 Calculating the Logits .189 Calculating the Odds .189 Calculating the Probabilities .190 Getting the Log Likelihood .190 Deploying Solver .192 Installing Solver .192 Using Solver for Logistic Regression.193 Statistical Tests in Logistic Regression .
196 R2 and t in Logistic Regression .196 The Likelihood Ratio Test .198 Constraints and Degrees of Freedom .201 Logistic Regression with R''s mlogit Package .202 Running the mlogit Package .202 Comparing Models with mlogit .208 Using R''s glm Function .208 6 Principal Components Analysis .
211 Principal Components Using Excel .212 Navigating the Dialog Box .213 The Principal Components Worksheet: The R Matrix and Its Inverse.216 The Principal Components Worksheet: Eigenvalues and Eigenvectors.219 Variable Communalities .222 The Factor Scores .222 Rotated Factors in Excel .224 Rotated Factor Coefficients and Scores .
226 Principal Components Analysis Using R .227 Preparing the Data .227 Calling the Function .229 The Varimax Rotation in R .232 TOC, 9780789757852, 10/21/2016.