NMath Stats User's Guide

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Contents

Chapter 1. Introduction

1.1 Product Features

1.2 Software Requirements

1.3 Namespaces

1.4 Building and Deploying NMath Stats Applications

1.5 Documentation

1.6 Visualization

1.7 Technical Support

Chapter 2. Data Frames

2.1 Column Types

2.2 Creating DataFrames

2.3 Adding and Removing Columns

2.4 Adding and Removing Rows

2.5 Properties of DataFrames

2.6 Accessing DataFrames

2.7 Subsets

2.8 Accessing Sub-Frames

2.9 Reordering DataFrames

2.10 Factors

2.11 Cross-Tabulation

2.12 Exporting Data from DataFrames

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. Probability Distributions

4.1 Distribution Classes

4.2 Correlated Random Inputs

4.3 Box-Cox Power Transformations

Chapter 5. Hypothesis Tests

5.1 Common Interface

5.2 One Sample Z-Test

5.3 One Sample T-Test

5.4 Two Sample Paired T-Test

5.5 Two Sample Unpaired T-Test

5.6 Two Sample F-Test

5.7 Pearson's Chi-Square Test

5.8 Fisher's Exact Test

Chapter 6. Linear Regression

6.1 Creating Linear Regressions

6.2 Regression Results

6.3 Predictions

6.4 Accessing and Modifying the Model

6.5 Significance of Parameters

6.6 Significance of the Overall Model

Chapter 7. Logistic Regression

7.1 Regression Calculators

7.2 Creating Logistic Regressions

7.3 Check for Convergence

7.4 Goodness of Fit

7.5 Parameter Estimates

7.6 Predicted Probabilities

Chapter 8. Analysis of Variance

8.1 One-Way ANOVA

8.2 One-Way Repeated Measures ANOVA

8.3 Two-Way ANOVA

8.4 Two-Way Repeated Measures ANOVA

Chapter 9. Non-Parametric Tests

9.1 One Sample Kolmogorov-Smirnov Test

9.2 Two Sample Kolmogorov-Smirnov Test

9.3 Shapiro-Wilk Test

9.4 One Sample Anderson-Darling Test

9.5 Kruskal-Wallis Test

Chapter 10. Multivariate Techniques

10.1 Principal Component Analysis

10.2 Factor Analysis

10.3 Hierarchical Cluster Analysis

10.4 K-Means Clustering

Chapter 11. Nonnegative Matrix Factorization

11.1 Nonnegative Matrix Factorization

11.2 Data Clustering Using NMF

Chapter 12. Partial Least Squares

12.1 Computing a PLS Regression

12.2 Error Checking

12.3 Predicted Values

12.4 Analysis of Variance

12.5 PLS Algorithms

12.6 Cross Validation

Chapter 13. Goodness of Fit

13.1 Significance of the Overall Model

13.2 Significance of Parameters

Chapter 14. Process Control

14.1 Process Capability

14.2 Process Performance

14.3 Z Bench

Index


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