Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Data Characteristics -- Questionnaire and its components -- Understanding the data characteristics in an R environment -- Experiments with uncertainty in computer science -- R installation -- Using R packages -- RSADBE - the book's R package -- Discrete distribution -- Discrete uniform distribution -- Binomial distribution -- Hypergeometric distribution -- Negative binomial distribution -- Poisson distribution -- Continuous distribution -- Uniform distribution -- Exponential distribution -- Normal distribution -- Summary -- Chapter 2: Import/Export Data -- data.frame and other formats -- Constants, vectors, and matrices -- Time for action - understanding constants, vectors, and basic arithmetic -- Time for action - matrix computations -- The list object -- Time for action - creating a list object -- The data.frame object -- Time for action - creating a data.frame object -- The table object -- Time for action - creating the Titanic dataset as a table object -- read.csv, read.xls, and the foreign package -- Time for action - importing data from external files -- Importing data from MySQL -- Exporting data/graphs -- Exporting R objects -- Exporting graphs -- Time for action - exporting a graph -- Managing an R session -- Time for action - session management -- Summary -- Chapter 3: Data Visualization -- Visualization techniques for categorical data -- Bar charts -- Going through the built-in examples of R -- Time for action - bar charts in R -- Dot charts -- Time for action - dot charts in R -- Spine and mosaic plots -- Time for action - the spine plot for the shift and operator data -- Time for action - the mosaic plot for the Titanic dataset -- Pie charts and the fourfold plot.

Visualization techniques for continuous variable data -- Boxplot -- Time for action - using the boxplot -- Histograms -- Time for action - understanding the effectiveness of histograms -- Scatter plots -- Time for action - plot and pairs R functions -- Pareto charts -- A brief peek at ggplot2 -- Time for action - qplot -- Time for action - ggplot -- Summary -- Chapter 4: Exploratory Analysis -- Essential summary statistics -- Percentiles, quantiles, and median -- Hinges -- The interquartile range -- Time for action - the essential summary statistics for "The Wall" dataset -- The stem-and-leaf plot -- Time for action - the stem function in play -- Letter values -- Data re-expression -- Bagplot - a bivariate boxplot -- Time for action - the bagplot display for a multivariate dataset -- The resistant line -- Time for action - the resistant line as a first regression model -- Smoothing data -- Time for action - smoothening the cow temperature data -- Median polish -- Time for action - the median polish algorithm -- Summary -- Chapter 5: Statistical Inference -- Maximum likelihood estimator -- Visualizing the likelihood function -- Time for action - visualizing the likelihood function -- Finding the maximum likelihood estimator -- Using the fitdistr function -- Time for action - finding the MLE using mle and fitdistr functions -- Confidence intervals -- Time for action - confidence intervals -- Hypotheses testing -- Binomial test -- Time for action - testing the probability of success -- Tests of proportions and the chi-square test -- Time for action - testing proportions -- Tests based on normal distribution - one sample -- Time for action - testing one-sample hypotheses -- Tests based on normal distribution - two sample -- Time for action - testing two-sample hypotheses -- Summary -- Chapter 6: Linear Regression Analysis.

The simple linear regression model -- What happens to the arbitrary choice of parameters? -- Time for action - the arbitrary choice of parameters -- Building a simple linear regression model -- Time for action - building a simple linear regression model -- ANOVA and the confidence intervals -- Time for action - ANOVA and the confidence intervals -- Model validation -- Time for action - residual plots for model validation -- Multiple linear regression model -- Averaging k simple linear regression models or a multiple linear regression model -- Time for action - averaging k simple linear regression models -- Building a multiple linear regression model -- Time for action - building a multiple linear regression model -- The ANOVA and confidence intervals for the multiple linear regression model -- Time for action - the ANOVA and confidence intervals for the multiple linear regression model -- Useful residual plots -- Time for action - residual plots for the multiple linear regression model -- Regression diagnostics -- Leverage points -- Influential points -- DFFITS and DFBETAS -- The multicollinearity problem -- Time for action - addressing the multicollinearity problem for the Gasoline data -- Model selection -- Stepwise procedures -- The backward elimination -- The forward selection -- Criterion-based procedures -- Time for action - model selection using the backward, forward, and AIC criteria -- Summary -- Chapter 7: The Logistic Regression Model -- The binary regression problem -- Time for action - limitations of linear regression models -- Probit regression model -- Time for action - understanding the constants -- Logistic regression model -- Time for action - fitting the logistic regression model -- Hosmer-Lemeshow goodness-of-fit test statistic -- Time for action - The Hosmer-Lemeshow goodness-of-fit statistic -- Model validation and diagnostics.

Residual plots for the GLM -- Time for action - residual plots for the logistic regression model -- Influence and leverage for the GLM -- Time for action - diagnostics for the logistic regression -- Receiving operator curves -- Time for action - ROC construction -- Logistic regression for the German credit screening dataset -- Time for action - logistic regression for the German credit dataset -- Summary -- Chapter 8: Regression Models with Regularization -- The overfitting problem -- Time for action - understanding overfitting -- Regression spline -- Basis functions -- Piecewise linear regression model -- Time for action - fitting piecewise linear regression models -- Natural cubic splines and the general B-splines -- 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 -- Summary -- Chapter 9: Classification and Regression Trees -- Recursive partitions -- Time for action - partitioning the display plot -- Splitting the data -- The first tree -- 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 -- Summary -- Chapter 10: CART and Beyond -- Improving CART -- Time for action - cross-validation predictions -- Bagging.

The bootstrap -- Time for action - understanding the bootstrap technique -- The bagging algorithm -- Time for action - the bagging algorithm -- Random forests -- Time for action - random forests for the German credit data -- The consolidation -- Time for action - random forests for the low birth weight data -- Summary -- Appendix: References -- Index.