Index
A
- adabag package
- AdaBoost.M1 algorithm
- advanced exploratory data analysis
- about / Introduction
- agglomerative hierarchical clustering
- about / How it works...
- aggregate function
- about / There's more...
- Akaike Information Criterion (AIC)
- about / How it works...
- alternative hypothesis (H1)
- about / How it works...
- Amazon EMR
- RHadoop, configuring / Getting ready, How to do it..., How it works...
- reference link / How to do it...
- ANOVA
- about / Conducting a one-way ANOVA
- one-way ANOVA, conducting / Conducting a one-way ANOVA, How to do it..., How it works...
- reference link / There's more...
- two-way ANOVA, performing / Performing a two-way ANOVA, How to do it..., How it works...
- Apriori rule
- associations, mining / Mining associations with the Apriori rule, How to do it..., How it works..., See also
- area under curve (AUC)
- association analysis
- about / Introduction
- associations
- displaying / Displaying transactions and associations, How to do it..., How it works...
- mining, with Apriori rule / Mining associations with the Apriori rule, How to do it..., How it works..., See also
- associations rules
- visualizing / Visualizing association rules, How to do it..., How it works..., See also
- AWS
- reference link / Getting ready
B
- bagging method
- about / Introduction
- used, for classifying data / Classifying data with the bagging method, How to do it..., How it works...
- used, for performing cross-validation / Performing cross-validation with the bagging method, How it works...
- bam package
- using / See also
- Bartlett Test
- about / There's more...
- basic exploratory data analysis
- about / Introduction
- basic statistics
- applying / Applying basic statistics, How to do it..., How it works...
- Bayes theorem
- reference link / See also
- Binary Tree Class
- about / See also
- Binomial model
- applying, for GLM / Applying the Binomial model for generalized linear regression, How it works...
- binomial test
- conducting / Conducting an exact binomial test, Getting ready, How it works...
- null hypothesis (H0) / How it works...
- alternative hypothesis (H1) / How it works...
- biplot
- used, for visualizing multivariate data / Visualizing multivariate data using biplot, How to do it..., How it works...
- bivariate cluster plot
- boosting method
- about / Introduction
- used, for classifying data / Classifying data with the boosting method, How to do it..., How it works..., There's more...
- used, for performing cross-validation / Performing cross-validation with the boosting method, How to do it...
- Breusch-Pagan test
- about / How it works...
C
- C50
- caret package
- about / There's more..., Predicting labels based on a model trained by nnet
- k-fold cross-validation, performing / Performing cross-validation with the caret package, How it works..., See also
- variable importance, ranking / Ranking the variable importance with the caret package, How to do it..., There's more...
- highly correlated features, searching / Finding highly correlated features with the caret package, How it works...
- features, selecting / Selecting features using the caret package, How to do it..., How it works...
- used, for comparing ROC curve / Comparing an ROC curve using the caret package, How to do it..., How it works...
- used, for measuring performance differences between models / Measuring performance differences between models with the caret package, How to do it..., How it works...
- character variables
- classification
- about / Introduction
- versus regression / Introduction
- classification model
- training dataset, preparing / Preparing the training and testing datasets, How it works...
- testing dataset, preparing / Preparing the training and testing datasets, How it works...
- building, with recursive partitioning tree / Building a classification model with recursive partitioning trees, How to do it..., How it works...
- building, with conditional inference tree / Building a classification model with a conditional inference tree, How it works...
- classifier
- margin, calculating / Calculating the margins of a classifier, How to do it..., How it works...
- Cloudera QuickStart VM
- about / Preparing the RHadoop environment
- used, for preparing RHadoop environment / How to do it..., How it works...
- URL / How to do it...
- URL, for downloading VMW / How to do it...
- URL, for downloading KVM / How to do it...
- URL, for downloading VirtualBox / How to do it...
- clustering
- about / Introduction
- hierarchical clustering / Introduction
- k-means clustering / Introduction
- model-based clustering / Introduction
- density-based clustering / Introduction
- methods, comparing / Comparing clustering methods, How to do it..., How it works...
- silhouette information, extracting / Extracting silhouette information from clustering, Getting ready, How it works...
- clusters
- validating, externally / Validating clusters externally, How to do it..., How it works...
- competitions, Kaggle
- URL / There's more...
- conditional inference tree
- classification model, building / Building a classification model with a conditional inference tree, How it works...
- visualizing / Visualizing a conditional inference tree, How to do it..., How it works...
- prediction performance, measuring / Measuring the prediction performance of a conditional inference tree, How to do it..., How it works...
- advantages / How it works...
- disadvantages / How it works...
- confidence intervals
- reference link / See also
- confusion matrix
- used, for validating survival prediction / Validating the power of prediction with a confusion matrix, How it works..., There's more...
- used, for measuring prediction performance / Measuring prediction performance with a confusion matrix, Getting ready, How it works...
- Confusion_matrix
- reference link / See also
- correlations
- CP (cost complexity parameter)
- about / How it works...
- CRAN
- URL / How to do it..., How it works
- about / Installing and loading packages
- Crantastic
- about / See also
- cross-validation
- performing, with bagging method / Performing cross-validation with the bagging method, How it works...
- performing, with boosting method / Performing cross-validation with the boosting method, How to do it...
- cSPADE algorithm
- used, for mining frequent sequential patterns / Mining frequent sequential patterns with cSPADE, How to do it..., How it works...
- cutree function
- used, for separating data into clusters / Cutting trees into clusters, How it works..., There's more...
D
- data
- writing / Reading and writing data, How to do it..., See also
- reading / Reading and writing data, How to do it..., See also
- manipulating / Getting ready, How to do it...
- manipulating, subset function used / How to do it...
- merging / How to do it...
- ordering, with order function / How to do it...
- visualizing / Visualizing data, Getting ready, How to do it..., How it works..., Exploring and visualizing data, How to do it..., How it works..., There's more...
- exploring / Exploring and visualizing data, How to do it..., How it works..., There's more...
- classifying, with K-nearest neighbor (knn) classifier / Classifying data with the k-nearest neighbor classifier, How to do it..., How it works...
- classifying, with logistic regression / Classifying data with logistic regression, How to do it..., How it works...
- classifying, with Naïve Bayes classifier / Classifying data with the Naïve Bayes classifier, How to do it..., How it works..., See also
- transforming, into transactions / Transforming data into transactions, How to do it..., How it works...
- data collection
- about / Introduction
- data exploration
- about / Introduction
- right questions, asking / Introduction
- data collection / Introduction
- data munging / Introduction
- basic exploratory data analysis / Introduction
- advanced exploratory data analysis / Introduction
- model assessment / Introduction
- data exploration, with RMS Titanic
- dataset, reading from CSV file / Reading a Titanic dataset from a CSV file, How to do it..., There's more...
- character variables, converting / Converting types on character variables, How to do it..., How it works...
- missing values, detecting / Detecting missing values, How to do it..., How it works..., There's more...
- missing values, imputing / Imputing missing values, How to do it..., How it works...
- data, exploring / Exploring and visualizing data, How to do it..., How it works..., There's more...
- data, visualizing / Exploring and visualizing data, How to do it..., How it works..., There's more...
- passenger survival, predicting with decision tree / Predicting passenger survival with a decision tree, How to do it..., How it works..., There's more...
- survival prediction, validating with confusion matrix / Validating the power of prediction with a confusion matrix, How it works..., There's more...
- survival prediction, assessing with ROC curve / Assessing performance with the ROC curve, How to do it..., How it works...
- data munging
- about / Introduction
- data sampling
- dataset
- obtaining, for machine learning / Getting a dataset for machine learning, How to do it..., How it works...
- DBSCAN
- about / Clustering data with the density-based method
- used, for performing density-based clustering / Clustering data with the density-based method, How to do it..., How it works..., See also
- decision tree
- used, for predicting passenger survival / Predicting passenger survival with a decision tree, How to do it..., How it works..., There's more...
- density-based clustering
- about / Introduction
- used, for clustering data / Clustering data with the density-based method, How to do it..., How it works..., See also
- performing, with DBSCAN / Clustering data with the density-based method, How to do it..., How it works...
- descriptive statistics
- about / Introduction
- univariate descriptive statistics / Working with univariate descriptive statistics in R, How to do it..., How it works...
- diagnostic plot
- generating, of regression model / Generating a diagnostic plot of a fitted model, How it works..., There's more...
- dimension reduction
- about / Introduction
- feature extraction / Introduction
- feature selection / Introduction
- performing, PCA used / Performing dimension reduction with PCA, How it works..., There's more...
- performing, MDS used / Performing dimension reduction with MDS, How to do it..., How it works..., There's more...
- performing, SVD used / Reducing dimensions with SVD, How to do it..., How it works...
- dissimilarity matrix
- distance functions
- single linkage / How it works...
- complete linkage / How it works...
- average linkage / How it works...
- ward method / How it works...
- divisive hierarchical clustering
- about / How it works...
E
- e1071 package
- k-fold cross-validation, performing / Performing cross-validation with the e1071 package, How to do it..., How it works...
- Eclat algorithm
- used, for mining frequent itemsets / Mining frequent itemsets with Eclat, Getting ready, How to do it..., How it works...
- ensemble learning
- about / Introduction
- bagging method / Introduction
- boosting method / Introduction
- random forest / Introduction
- ensemble method
- error evolution, calculating / Calculating the error evolution of the ensemble method, How to do it..., How it works...
- erroreset function
- error evolution
- calculating, of ensemble method / Calculating the error evolution of the ensemble method, How to do it..., How it works...
F
- feature extraction
- about / Introduction
- feature selection
- about / Introduction
- performing, FSelector package used / Performing feature selection with FSelector, How to do it..., How it works..., See also
- FP-Growth
- FSelector package
- used, for performing feature selection / Performing feature selection with FSelector, How to do it..., How it works..., See also
G
- GAM
- about / Fitting a generalized additive model to data
- fitting, to data / Fitting a generalized additive model to data, Getting ready, How it works
- visualizing / Visualizing a generalized additive model, How to do it..., How it works...
- diagnosing / Diagnosing a generalized additive model, How to do it..., How it works..., There's more...
- Generalized Cross Validation (GCV)
- about / How it works...
- ggplot2
- GLM
- about / Applying the Gaussian model for generalized linear regression
- fitting, with Gaussian model / Applying the Gaussian model for generalized linear regression, Getting ready, How to do it..., How it works...
- fitting, with Poisson model / Applying the Poisson model for generalized linear regression, How it works...
- fitting, with Binomial model / Applying the Binomial model for generalized linear regression, How it works...
- glm function
- Google compute engine
- URL / See also
- gradient boosting
- about / Classifying data with gradient boosting
- used, for classifying data / Classifying data with gradient boosting, How to do it..., How it works..., There's more...
- gsub function
- about / There's more...
H
- HDFS
- operating, rhdfs package used / Operating HDFS with rhdfs, How to do it..., How it works...
- heteroscedasticity
- about / How it works...
- hierarchical clustering
- about / Introduction
- used, for clustering data / Clustering data with hierarchical clustering, How to do it..., How it works..., There's more...
- agglomerative hierarchical clustering / How it works...
- divisive hierarchical clustering / How it works...
- honorific entry
- reference link / There's more...
- Hontonworks Sandbox
- URL / See also
- hypothesis methods
- Proportional test / There's more...
- Z-test / There's more...
- Bartlett Test / There's more...
- Kruskal-Wallis Rank Sum Test / There's more...
- Shapiro-Wilk test / There's more...
I
- images
- compressing, SVD used / Compressing images with SVD, How to do it..., How it works...
- inferential statistics
- about / Introduction
- installation, packages
- installation, plyrmr package
- about / Installing plyrmr, See also
- installation, R
- about / Getting ready, How to do it..., How it works...
- on Windows / How to do it...
- on Mac OS X / How to do it...
- on Ubuntu / How to do it...
- on CentOS 5 / How to do it...
- on CentOS 6 / How to do it...
- installation, rhdfs package
- about / Installing rhdfs, How it works...
- installation, rmr2 package
- about / Installing rmr2, How to do it..., See also
- installation, RStudio
- integrated development environment (IDE)
- Interquartile Range (IQR)
- about / How to do it...
- interval variables
- about / How it works...
- ipred package
- ISOMAP
- about / Introduction
- nonlinear dimension reduction, performing / Performing nonlinear dimension reduction with ISOMAP, How to do it..., How it works..., There's more...
- itemsets
- about / Introduction
K
- k-fold cross-validation
- used, for estimating model performance / Estimating model performance with k-fold cross-validation, How it works...
- performing, with e1071 package / Performing cross-validation with the e1071 package, How to do it..., How it works...
- performing, with caret package / Performing cross-validation with the caret package, How it works..., See also
- k-means clustering
- about / Introduction
- used, for clustering data / Clustering data with the k-means method, How to do it..., How it works..., See also
- optimum number of clusters, obtaining / Obtaining the optimum number of clusters for k-means, How to do it..., See also
- reference link / See also
- K-nearest neighbor (knn) classifier
- about / Classifying data with the k-nearest neighbor classifier, How it works...
- data, classifying / Classifying data with the k-nearest neighbor classifier, How to do it..., How it works...
- advantages / How it works..., How it works...
- disadvantages / How it works..., How it works...
- URL / See also
- Kaggle
- Kaiser method
- used, for determining number of principal components / Determining the number of principal components using the Kaiser method, How to do it..., How it works...
- KDnuggets
- Kolmogorov-Smirnov test (K-S test)
- performing / Performing the Kolmogorov-Smirnov test, How to do it..., How it works..., See also
- about / How it works...
- Kruskal-Wallis Rank Sum Test
- about / There's more...
L
- labels
- predicting, of trained neural network by SVM / Predicting labels based on a model trained by a support vector machine, How to do it..., How it works..., There's more...
- predicting, of trained neural network by neuralnet / Predicting labels based on a model trained by neuralnet, How to do it..., How it works...
- predicting, of trained neural network by nnet package / Predicting labels based on a model trained by nnet, How to do it..., How it works...
- libsvm
- linear methods
- PCA / Introduction
- MDS / Introduction
- SVD / Introduction
- linear regression
- conducting, for multivariate analysis / Operating linear regression and multivariate analysis, Getting ready, How to do it..., How it works...
- linear regression model
- fitting, with lm function / Fitting a linear regression model with lm, How to do it..., How it works...
- information obtaining, summary function used / Summarizing linear model fits, How it works..., See also
- used, for predicting unknown values / Using linear regression to predict unknown values, How to do it..., See also
- case study / Studying a case of linear regression on SLID data, How to do it..., How it works...
- LLE
- about / Introduction
- nonlinear dimension reduction, performing / Performing nonlinear dimension reduction with Local Linear Embedding, How to do it..., How it works...
- lm function
- used, for fitting linear regression model / Fitting a linear regression model with lm, How to do it..., How it works...
- used, for fitting polynomial regression model / Fitting a polynomial regression model with lm, How to do it..., How it works
- logistic regression
- used, for classifying data / Classifying data with logistic regression, How to do it..., How it works...
- advantages / How it works...
- disadvantages / How it works...
M
- machine learning
- about / Introduction
- with R / Introduction
- dataset, obtaining / Getting a dataset for machine learning, How to do it..., How it works...
- with RHadoop / Conducting machine learning with RHadoop, How to do it..., How it works...
- reference link, for algorithms / See also
- mapR Sandbox
- URL / See also
- margin
- about / Calculating the margins of a classifier
- calculating, of classifier / Calculating the margins of a classifier, How to do it..., How it works...
- mboost package
- about / There's more...
- MDS
- about / Introduction
- used, for performing dimension reduction / Performing dimension reduction with MDS, How to do it..., How it works..., There's more...
- minimum support (minsup)
- about / How it works...
- missing values
- detecting / Detecting missing values, How to do it..., There's more...
- imputing / Imputing missing values, How to do it..., How it works...
- model-based clustering
- about / Introduction
- used, for clustering data / Clustering data with the model-based method, How to do it..., How it works...
- model assessment
- about / Introduction
- model evaluation
- about / Introduction
- multivariate analysis
- performing / Performing correlations and multivariate analysis, How to do it..., How it works...
- linear regression, conducting / Operating linear regression and multivariate analysis, How to do it..., How it works...
- multivariate data
- visualizing, biplot used / Visualizing multivariate data using biplot, How to do it..., How it works...
N
- NA (not available)
- about / Getting ready
- NaN (not a number)
- about / Getting ready
- Naïve Bayes classifier
- data, classifying / Classifying data with the Naïve Bayes classifier, How to do it..., How it works..., See also
- advantages / How it works...
- disadvantages / How it works...
- neuralnet
- neural networks (NN), training / Training a neural network with neuralnet, How to do it..., How it works...
- neural networks (NN), visualizing / Visualizing a neural network trained by neuralnet, How to do it..., How it works...
- labels, predicting of trained neural networks / Predicting labels based on a model trained by neuralnet, How to do it..., How it works...
- neural networks (NN)
- about / Introduction
- versus SVM / Introduction
- training, with neuralnet / Training a neural network with neuralnet, How to do it..., How it works...
- advantages / How it works...
- visualizing, by neuralnet / Visualizing a neural network trained by neuralnet, How to do it..., How it works...
- training, with nnet package / Training a neural network with nnet, How to do it..., How it works...
- nnet package
- used, for training neural networks (NN) / Training a neural network with nnet, How to do it..., How it works...
- about / Training a neural network with nnet
- labels, predicting of trained neural network / Predicting labels based on a model trained by nnet, How to do it..., How it works...
- nominal variables
- about / How it works...
- nonlinear dimension reduction
- performing, with ISOMAP / Performing nonlinear dimension reduction with ISOMAP, How to do it..., How it works..., There's more...
- performing, with LLE / Performing nonlinear dimension reduction with Local Linear Embedding, How to do it..., How it works...
- nonlinear methods
- ISOMAP / Introduction
- LLE / Introduction
- null hypothesis (H0)
- about / How it works...
O
- one-way ANOVA
- conducting / Conducting a one-way ANOVA, How to do it..., How it works...
- order function
- using / How to do it...
- ordinal variables
- about / How it works...
P
- packages
- loading / Installing and loading packages, How to do it..., How it works
- installing / Installing and loading packages, How to do it..., How it works
- party package
- about / There's more..., There's more...
- PCA
- about / Introduction
- used, for performing dimension reduction / Performing dimension reduction with PCA, How it works..., There's more...
- Pearson's Chi-squared test
- plyrmr package
- about / Introduction
- installing / Installing plyrmr, How it works...
- used, for manipulating data / Manipulating data with plyrmr, How to do it..., See also
- Poisson model
- applying, for GLM / Applying the Poisson model for generalized linear regression, How it works...
- poly function
- polynomial regression model
- fitting, with lm function / Fitting a polynomial regression model with lm, How to do it..., How it works
- Port of Embarkation / How to do it...
- prediction error
- estimating, of different classifiers / Estimating the prediction errors of different classifiers, How to do it...
- probability distribution
- Proportional test
- about / There's more...
- Pruning (decision_trees)
- reference link / See also
Q
- quantile-comparison plot
- about / How it works...
R
- R
- about / Introduction
- using, for machine learning / Introduction
- downloading / Downloading and installing R, How to do it..., How it works...
- installing / Downloading and installing R, How to do it..., How it works...
- URL / Getting ready
- installing, on Windows / How to do it...
- installing, on Mac OS X / How to do it...
- installing, on Ubuntu / How to do it...
- installing, on CentOS 5 / How to do it...
- installing, on CentOS 6 / How to do it...
- data, manipulating / Using R to manipulate data, How to do it..., How it works
- R-Forge
- about / See also
- random forest
- about / Introduction
- used, for classifying data / Classifying data with random forest, Getting ready, How to do it..., How it works..., There's more...
- ntree parameter / How it works...
- mtry parameter / How it works...
- advantages / How it works...
- ratio variables
- about / How it works...
- raw data
- about / Introduction
- receiver operating characteristic (ROC)
- about / Measuring prediction performance using ROCR
- reference link / See also
- recursive partitioning tree
- used, for building classification model / Building a classification model with recursive partitioning trees, How to do it..., How it works...
- visualizing / Visualizing a recursive partitioning tree, How to do it..., How it works..., See also
- prediction performance, measuring / Measuring the prediction performance of a recursive partitioning tree, How it works..., See also
- pruning / Pruning a recursive partitioning tree, How to do it..., How it works..., See also
- advantages / How it works...
- disadvantages / How it works...
- redundant rules
- pruning / Pruning redundant rules, How it works..., See also
- regression
- about / Introduction
- types / Introduction
- versus classification / Introduction
- regression model
- performance, measuring / Measuring the performance of the regression model, Getting ready, How to do it..., How it works...
- relative square error (RSE)
- reshape function
- about / There's more...
- residual degrees of freedom (Res.DF)
- about / How it works...
- residual sum of squares (RSS Df)
- about / How it works...
- RHadoop
- about / Introduction
- rmr package / Introduction
- rhdfs package / Introduction
- rhbase package / Introduction
- plyrmr package / Introduction
- ravro package / Introduction
- integrated environment, preparing / Preparing the RHadoop environment , How to do it..., How it works...
- word count problem, implementing / Implementing a word count problem with RHadoop, How to do it..., How it works..., See also
- input file, URL / Getting ready
- Java MapReduce program, URL / See also
- machine learning / Conducting machine learning with RHadoop, How to do it..., How it works...
- configuring, on Amazon EMR / Getting ready, How to do it..., How it works...
- rhbase package
- about / Introduction
- rhdfs package
- about / Introduction
- installing / Installing rhdfs, How it works...
- used, for operating HDFS / Operating HDFS with rhdfs, How to do it..., How it works...
- rlm function
- used, for fitting robust linear regression model / Fitting a robust linear regression model with rlm, How it works, There's more...
- R MapReduce program
- comparing, to standard R program / Comparing the performance between an R MapReduce program and a standard R program, How it works...
- testing / Testing and debugging the rmr2 program, How to do it..., How it works...
- debugging / Testing and debugging the rmr2 program, How to do it..., How it works...
- rminer package
- variable importance, ranking / Ranking the variable importance with the rminer package, How to do it..., See also
- rmr2 package
- installing / Installing rmr2, How to do it..., See also
- rmr package
- about / Introduction
- RnavGraph package
- about / There's more...
- URL / There's more...
- robust linear regression model
- fitting, with rlm function / Fitting a robust linear regression model with rlm, How it works, There's more...
- ROC curve
- used, for assessing survival prediction / Assessing performance with the ROC curve, How to do it..., How it works...
- ROCR package
- used, for measuring prediction performance / Measuring prediction performance using ROCR, Getting ready, How it works...
- installing / How to do it...
- root mean square error (RMSE)
- root square mean error (RMSE)
- about / How it works...
- rpart package
- about / There's more...
- RStudio
- downloading / Downloading and installing RStudio, How to do it..., How it works
- installing / Downloading and installing RStudio, How to do it..., How it works
- URL / How to do it..., See also
S
- SAMME algorithm
- Scale-Location plot
- about / How it works...
- scree test
- used, for determining number of principal components / Determining the number of principal components using the scree test, How to do it..., How it works...
- Sequential PAttern Discovery using Equivalence classes (SPADE)
- Shapiro-Wilk test
- about / There's more...
- silhouette information
- about / Extracting silhouette information from clustering
- extracting, from clustering / Extracting silhouette information from clustering, Getting ready, How it works...
- Silhouette Value
- reference link / See also
- standard R program
- comparing, to R MapReduce program / Comparing the performance between an R MapReduce program and a standard R program, How it works...
- statistical methods
- descriptive statistics / Introduction
- inferential statistics / Introduction
- student's t-test
- performing / Performing student's t-test, Getting ready, How to do it..., How it works...
- about / How it works...
- sub function
- about / There's more...
- subset function
- using / How to do it...
- summary function
- used, for obtaining information of linear regression model / Summarizing linear model fits, How it works..., See also
- Survey of Labor and Income Dynamics (SLID) dataset
- SVD
- about / Introduction, Reducing dimensions with SVD
- used, for performing dimension reduction / Reducing dimensions with SVD, How to do it..., How it works...
- used, for compressing images / Compressing images with SVD, How to do it..., How it works...
- SVM
- about / Introduction
- versus neural networks (NN) / Introduction
- data, classifying / Classifying data with a support vector machine, How to do it..., How it works...
- advantages / How it works...
- cost, selecting / Choosing the cost of a support vector machine, Getting ready, How to do it..., How it works...
- visualizing / Visualizing an SVM fit, How it works...
- labels, predicting of testing dataset / Predicting labels based on a model trained by a support vector machine, How to do it..., How it works..., There's more...
- tuning / Tuning a support vector machine, How to do it..., How it works...
- SVMLight
- SVMLite
T
- training data, Kaggle
- URL / How to do it...
- transactions
- data, transforming / Transforming data into transactions, How to do it..., How it works...
- displaying / Displaying transactions and associations, How to do it..., How it works...
- creating, with temporal information / Creating transactions with temporal information, How to do it..., How it works...
- two-way ANOVA
- performing / Performing a two-way ANOVA, How to do it..., How it works...
U
- UCI machine learning repository
- URL / How to do it...
- Unbiased Risk Estimator (UBRE)
- about / How it works...
- univariate descriptive statistics
- about / Working with univariate descriptive statistics in R
- working with / Getting ready, How to do it..., How it works...
V
- visualization, data
W
- Wilcoxon Rank Sum Test
- Wilcoxon Signed Rank Test
- within-cluster sum of squares (WCSS)
- about / How it works...
X
- XQuartz-2.X.X.dmg
- URL, for downloading / How to do it...
Z
- Z-test
- about / There's more...