Beginning R: An Introduction to Statistical ProgrammingBeginning R: An Introduction to Statistical Programming is a handson book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful opensource implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commerciallyavailable SPlus language, and has become the de facto standard for doing, teaching, and learning computational statistics.
R is both an objectoriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.

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Contents
CHAPTER 1 Getting R and Getting Started  1 
CHAPTER 2 Programming in R  25 
CHAPTER 3 Writing Reusable Functions  47 
CHAPTER 4 Summary Statistics  65 
CHAPTER 5 Creating Tables and Graphs  77 
CHAPTER 6 Discrete Probability Distributions  93 
CHAPTER 7 Computing Normal Probabilities  103 
CHAPTER 8 Creating Confidence Intervals  113 
CHAPTER 12 Correlation and Regression  165 
CHAPTER 13 Multiple Regression  185 
CHAPTER 14 Logistic Regression  201 
CHAPTER 15 ChiSquare Tests  217 
CHAPTER 16 Nonparametric Tests  229 
CHAPTER 17 Using R for Simulation  247 
Resampling and Bootstrapping  257 
CHAPTER 19 Making an R Package  269 
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95 percent confidence alternative hypothesis analysis binomial distribution bootstrapping boxplot calculate Chapter chisquare distribution chisquare test coefficient columns Commander confidence interval correlation covariance create critical values data frame data set degrees of freedom discrete probability distributions discussed documentation equal examine example F distribution F value Pr(>F factor FALSE Figure formula Fratio function gender histogram HSGPA hypothesis testing install linear model logistic regression matrix mean(x median multiple regression nonleader nonleader nonleader nonparametric null hypothesis onesample t test output pvalue pairedsamples t test percent confidence interval permutation test plot population posttest prediction intervals predictors prime number produce ranks repeatedmeasures ANOVA resampling Residuals sample estimates Sample ttest data scores script significant simply simulation SPSS standard deviation standard error standard normal distribution statistics sum of squares summary(results TRUE twoway ANOVA Type I error variable variance vector workspace z score