Contents

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 Beta Distribution

5.3 Binomial Distribution

5.4 Chi-Square Distribution

5.5 Exponential Distribution

5.6 F Distribution

5.7 Gamma Distribution

5.8 Geometric Distribution

5.9 Logistic Distribution

5.10 Log-Normal Distribution

5.11 Negative Binomial Distribution

5.12 Normal Distribution

5.13 Poisson Distribution

5.14 Student's t Distribution

5.15 Triangular Distribution

5.16 Uniform Distribution

5.17 Weibull 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

10.1 Nonnegative Matrix Factorization

Update Algorithms

10.2 Data Clustering Using NMF

Creating NMFClustering Instances

Performing the Factorization

Cluster Results

Computing a Consensus Matrix

Chapter 11. Partial Least Squares

11.1 Computing a PLS Regression

11.2 Error Checking

11.3 Predicted Values

11.4 Analysis of Variance

11.5 PLS Algorithms

11.6 Cross Validation

Index

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