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## Details

Genre/Form: | Electronic books |
---|---|

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Prabhanjan Narayanachar Tattar |

ISBN: | 9781849519458 1849519455 |

OCLC Number: | 857061699 |

Notes: | Includes index. |

Description: | 1 online resource (vi, 324 pages :) : illustrations |

Contents: | Preface Chapter 1: Data Characteristics Chapter 2: Import/Export Data Chapter 3: Data Visualization Chapter 4: Exploratory Analysis Chapter 5: Statistical Inference Chapter 6: Linear Regression Analysis Chapter 7: The Logistic Regression Model Chapter 8: Regression Models with Regularization Chapter 9: Classification and Regression Trees Chapter 10: CART and Beyond Appendix: References Index Preface Up 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 Up 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 Up 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 Up 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 Up 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 Up 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 Up 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 Up 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 Up 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 Up 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 |

Responsibility: | Prabhanjan Narayanachar Tattar. |

### Abstract:

In Detail "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. Approach Full of screenshots and examples, this Beginner's Guide by Example will teach you practically everything you need to know about R statistical application development from scratch. Who this book is for You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.

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