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