Chapter 1. Introduction 1.1 Product Features 1.2 Software Requirements 1.3 Namespaces 1.4 Documentation This Manual 1.5 Technical Support Chapter 2. Data Frames 2.1 Column Types Creating Columns Adding and Removing Data Accessing Column Data Column Properties Reordering Column Data Missing Values Transforming Column Data Exporting Column Data 2.2 Creating DataFrames Creating Empty DataFrames Creating DataFrames from Arrays of Columns Creating DataFrames from Matrices Creating DataFrames from ADO.NET Objects Creating DataFrames from Strings 2.3 Adding and Removing Columns 2.4 Adding and Removing Rows Modifying Row Keys 2.5 Properties of DataFrames 2.6 Accessing DataFrames Accessing Elements Accessing Columns Accessing Rows 2.7 Subsets Creating Subsets Properties of Subsets Accessing Elements Logical Operations on Subsets Arithmetic Operations on Subsets Manipulating Subsets Groupings Random Samples 2.8 Accessing Sub-Frames 2.9 Reordering DataFrames Sorting Rows Permuting Rows and Columns 2.10 Factors Creating Factors Properties of Factors Accessing Factors Creating Groupings with Factors 2.11 Cross-Tabulation Column Delegates Applying Column Delegates to Tabulated Data 2.12 Exporting Data from DataFrames Exporting to a Matrix Exporting to a String Exporting to an ADO.NET DataTable Binary and SOAP Serialization Chapter 3. Descriptive Statistics 3.1 Column Types 3.2 Missing Values 3.3 Counts and Sums 3.4 Min/Max Functions 3.5 Ranks, Percentiles, Deciles, and Quartiles 3.6 Central Tendency 3.7 Spread 3.8 Shape 3.9 Covariance, Correlation, and Autocorrelation 3.10 Sorting 3.11 Logical Functions Chapter 4. Special Functions 4.1 Combinatorial Functions 4.2 Gamma Function 4.3 Beta Function Chapter 5. Probability Distributions 5.1 Distribution Classes 5.2 Normal Distribution 5.3 Logistic Distribution 5.4 Poisson Distribution 5.5 Gamma Distribution 5.6 Chi-Square Distribution 5.7 Beta Distribution 5.8 Student's t Distribution 5.9 F Distribution 5.10 Binomial Distribution 5.11 Negative Binomial Distribution Chapter 6. Hypothesis Tests 6.1 Common Interface Static Properties Creating Hypothesis Test Objects Properties of Hypothesis Test Objects Modifying Hypothesis Test Objects Printing Results 6.2 One Sample Z-Test 6.3 One Sample T-Test 6.4 Two Sample Paired T-Test 6.5 Two Sample Unpaired T-Test 6.6 Two Sample F-Test 6.7 One Sample Kolmogorov-Smirnov Test 6.8 Two Sample Kolmogorov-Smirnov Test Chapter 7. Linear Regression 7.1 Creating Linear Regressions Parameter Calculation by Least Squares Minimization Intercept Parameters 7.2 Regression Results 7.3 Predictions 7.4 Accessing and Modifying the Model Accessing and Modifying Predictors Accessing and Modifying Observations Accessing and Modifying the Intercept Option Updating the Entire Model 7.5 Significance of Parameters Creating Linear Regression Parameter Objects Properties Linear Regression Parameters Hypothesis Tests Updating Linear Regression Parameters 7.6 Significance of the Overall Model Chapter 8. Analysis of Variance 8.1 One-Way ANOVA Creating One-Way ANOVA Objects The One-Way ANOVA Table Grand Mean, Group Means, and Group Sizes Critical Value of the F Statistic Updating One-Way ANOVA Objects 8.2 One-Way Repeated Measures ANOVA Creating One-Way RANOVA Objects The One-Way RANOVA Table Grand Mean, Subject Means, and Treatment Means Critical Value of the F Statistic Updating One-Way RANOVA Objects 8.3 Two-Way ANOVA Creating Two-Way ANOVA Objects The Two-Way ANOVA Table Cell Data Grand Mean, Cell Means, and Group Means ANOVA Regression Parameters 8.4 Two-Way Repeated Measures ANOVA Creating Two-Way RANOVA Objects Two-Way RANOVA Tables Chapter 9. Multivariate Techniques 9.1 Principal Component Analysis Creating Principal Component Analyses Principal Component Analysis Results 9.2 Hierarchical Cluster Analysis Distance Functions Linkage Functions Creating Cluster Analyses Cluster Analysis Results Reusing Cluster Analysis Objects Chapter 10. Nonnegative Matrix Factorization (NNMF) 10.1 NNMFact Class 10.2 Update Algorithms and Classes NNMFMultiplicativeUpdate NNMFGdClsUpdate NNMFAlsUpdate 10.3 Data with Missing Values Basic Algorithm The ApproximateValueDelagate Chapter 11. Partial Least Squares 11.1 Introduction 11.2 PLS1 and PLS2 11.3 Calculations Errors Algorithms 11.4 Analysis of Variance 11.5 Cross Validation The Holdout Method K-fold Cross Validation Cross Validation Classes Index
