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# R Statistical Application Development by Example Beginner's Guide

Learn Learn the nature of data through software which takes the preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization which respects the nature of variables and with rich alternative options Do exploratory data analysis for useful first understanding which builds up the right attitude towards effective inference Learn statistical inference through simulation combining the classical inference and modern computational power Delve deep into regression models such as linear and logistic for continuous and discrete regressands which form the fundamentals of modern statistics Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity "R Statistical Application Development by Example Beginner’s Guide" explores statistical concepts and the R software, which are well integrated from the word go. This demarcates the separate learning of theory and applications and hence the title begins with “R Statistical …”. Almost every concept has an R code going with it which exemplifies the strength of R and applications. Thus, the reader first understands the data characteristics, descriptive statistics, and the exploratory attitude which gives the first firm footing of data analysis. Statistical inference and the use of simulation which makes use of the computational power complete the technical footing of statistical methods. Regression modeling, linear, logistic, and CART, builds the essential toolkit which helps the reader complete complex problems in the real world.The reader will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code.The data analysis journey begins with exploratory analysis, which is more than simple descriptive data summaries, and then takes the traditional path up to linear regression modeling, and ends with logistic regression, CART, and spatial statistics.True to the title R Statistical Application Development by Example Beginner’s Guide, the reader will enjoy the examples and R software. A self-learning guide for the user who needs statistical tools for understanding uncertainty in computer science data Essential descriptive statistics, effective data visualization, and efficient model building Every method explained through real data sets enables clarity and confidence for unforeseen scenarios 344 10 hours 19 minutes 9781849519441 23 Jul 2013
 Questionnaire and its components Experiments with uncertainty in computer science R installation Continuous distribution Summary
 data.frame and other formats 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 Summary
 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 A brief peek at ggplot2 Time for action – qplot Time for action – ggplot Summary
 Essential summary statistics 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
 Maximum likelihood estimator Time for action – visualizing the likelihood function Time for action – finding the MLE using mle and fitdistr functions Confidence intervals Time for action – confidence intervals Hypotheses testing 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 Summary
 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 Regression diagnostics The multicollinearity problem Time for action – addressing the multicollinearity problem for the Gasoline data Model selection Time for action – model selection using the backward, forward, and AIC criteria Summary
 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 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 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
 The overfitting problem Time for action – understanding overfitting Regression spline 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 Summary
 Recursive partitions 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 Summary
 Improving CART Time for action – cross-validation predictions Bagging Time for action – understanding the bootstrap technique 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