R Statistical Application Development by Example Beginner's Guide

Questionnaire and its components

Experiments with uncertainty in computer science

Time for action – understanding constants, vectors, and basic arithmetic

Time for action – matrix computations

Time for action – creating a list object

Time for action – creating a data.frame object

Visualization techniques for categorical data

Time for action – bar charts in R

Time for action – dot charts in R

Time for action – the spine plot for the shift and operator data

Time for action – the mosaic plot for the Titanic dataset

Visualization techniques for continuous variable data

Time for action – using the boxplot

Time for action – understanding the effectiveness of histograms

Time for action – plot and pairs R functions

Time for action – the essential summary statistics for "The Wall" dataset

Time for action – the stem function in play

Time for action – the bagplot display for a multivariate dataset

Time for action – the resistant line as a first regression model

Time for action – smoothening the cow temperature data

Time for action – the median polish algorithm

Time for action – visualizing the likelihood function

Time for action – finding the MLE using mle and fitdistr functions

Time for action – confidence intervals

Time for action – testing the probability of success

Time for action – testing proportions

Time for action – testing one-sample hypotheses

Time for action – testing two-sample hypotheses

The simple linear regression model

Time for action – the arbitrary choice of parameters

Time for action – building a simple linear regression model

Time for action – ANOVA and the confidence intervals

Time for action – residual plots for model validation

Multiple linear regression model

Time for action – averaging k simple linear regression models

Time for action – building a multiple linear regression model

Time for action – the ANOVA and confidence intervals for the multiple linear regression model

Time for action – residual plots for the multiple linear regression model

Time for action – addressing the multicollinearity problem for the Gasoline data

Time for action – model selection using the backward, forward, and AIC criteria

Time for action – limitations of linear regression models

Time for action – understanding the constants

Time for action – fitting the logistic regression model

Time for action – The Hosmer-Lemeshow goodness-of-fit statistic

Model validation and diagnostics

Time for action – residual plots for the logistic regression model

Time for action – diagnostics for the logistic regression

Time for action – ROC construction

Logistic regression for the German credit screening dataset

Time for action – logistic regression for the German credit dataset

Regression Models with Regularization

Time for action – understanding overfitting

Time for action – fitting piecewise linear regression models

Time for action – fitting the spline regression models

Ridge regression for linear models

Time for action – ridge regression for the linear regression model

Ridge regression for logistic regression models

Time for action – ridge regression for the logistic regression model

Another look at model assessment

Time for action – selecting lambda iteratively and other topics

Classification and Regression Trees

Time for action – partitioning the display plot

Time for action – building our first tree

The construction of a regression tree

Time for action – the construction of a regression tree

The construction of a classification tree

Time for action – the construction of a classification tree

Classification tree for the German credit data

Time for action – the construction of a classification tree

Pruning and other finer aspects of a tree

Time for action – pruning a classification tree

Time for action – cross-validation predictions

Time for action – understanding the bootstrap technique

Time for action – the bagging algorithm

Time for action – random forests for the German credit data

Time for action – random forests for the low birth weight data