> install.packages("UsingR")

simpleR Using R for Introductory Statistics.
By John Verzani Version 0.4 (August 22, 2002).

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Preface

These notes are an introduction to using the statistical software package R for an introductory statistics course. They are meant to accompany an introductory statistics book such as Kitchens ``Exploring Statistics''. The goals are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course.

These notes were written to take advantage of R version 1.5.0 or later. For pedagogical reasons the equals sign, =, is used as an assignment operator and not the traditional arrow combination <-. This was added to R in version 1.4.0. If only an older version is available the reader will have to make the minor adjustment.

There are several references to data and functions in this text that need to be installed prior to their use. To install the data is easy, but the instructions vary depending on your system. For Windows users, you need to download the ``zip'' file , and then install from the ``packages'' menu. In UNIX, one uses the command R CMD INSTALL packagename.tar.gz. Some of the datasets are borrowed from other authors notably Kitchens. Credit is given in the help files for the datasets. This material is available as an R package from:

http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple_0.4.zip for Windows users.
http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple_0.4.tar.gz for UNIX users.

If necessary, the file can sent in an email. As well, the individual data sets can be found online in the directory

http://www.math.csi.cuny.edu/Statistics/R/simpleR/Simple.

This is version 0.4 of these notes and were last generated on August 22, 2002. Before printing these notes, you should check for the most recent version available from

the CSI Math department.

Copyright © John Verzani (verzani@math.csi.cuny.edu), 2001-2. All rights reserved.

• Table of Contents
• Introduction
  • What is R
  • A note on notation
• Data
  • Starting R
  • Entering data with c
  • Typing less
  • Applying a function
  • Data is a vector
  • Problems
• Univariate Data
  • Categorical data
  • Using tables
  • Factors
  • Bar charts
  • Pie charts
  • Numerical data
  • Numeric measures of center and spread
  • Resistant measures of center and spread
  • Stem-and-leaf Charts
  • Histograms
  • Boxplots
  • Frequency Polygons
  • Densities
  • Problems
• Bivariate Data
  • Handling bivariate categorical data
  • Plotting tabular data
  • Handling bivariate data: categorical vs. numerical
  • Bivariate data: numerical vs. numerical
  • Comparing two distributions with plots
  • Using scatterplots to compare relationships
  • Linear regression.
  • Residual plots
  • Correlation coefficients
  • Locating points
  • Resistant regression
  • Using rlm or lqs for resistant regression
  • Problems
• Multivariate Data
  • Storing multivariate data in data frames
  • Accessing data in data frames
  • Manipulating data frames: stack and unstack
  • Using R's model formula notation
  • Ways to view multivariate data
  • The lattice package
  • Problems
• Random Data
  • Random number generators in R-- the ``r'' functions.
  • Sampling with and without replacement using sample
  • A bootstrap sample
  • d, p and q functions
  • Standardizing, scale and z scores
  • Problems
• Simulations
  • The central limit theorem
  • For loops
  • Normal plots
  • Using simple.sim and functions
  • Problems
• Exploratory Data Analysis
  • Our toolbox
  • Examples
  • Problems
• Confidence Interval Estimation
  • Population proportion theory
  • Proportion test
  • The z-test
  • The t-test
  • Confidence interval for the median
  • Problems
• Hypothesis Testing
  • Testing a population parameter
  • Testing a mean
  • Tests for the median
  • Problems
• Two-sample tests
  • Two-sample tests of proportion
  • Two-sample t-tests
  • Equal variances
  • Unequal variances
  • Matched samples
  • Resistant two-sample tests
  • Problems
• Chi Square Tests
  • The chi-squared distribution
  • Chi-squared goodness of fit tests
  • Chi-squared tests of independence
  • Chi-squared tests for homogeneity
  • Problems
• Regression Analysis
  • Simple linear regression model
  • Testing the assumptions of the model
  • Statistical inference
  • About s
  • Inferences about b<sub>1</sub>
  • Inferences about b<sub>0</sub>
  • Confidence intervals
  • Problems
• Multiple Linear Regression
  • The model
  • Problems
• Analysis of Variance
  • one-way analysis of variance
  • Analysis of variance described
  • The Kruskal-Wallis test
  • Problems
• Installing R
• External Packages
• A sample R session
  • A sample session involving regression
  • t-tests
  • A simulation example
• What happens when R starts?
• Using Functions
  • The basic template
  • For loops
  • Conditional expressions
• Entering Data into R
  • Using c
  • using scan
  • Using scan with a file
  • Editing your data
  • Reading in tables of data
  • Fixed-width fields
  • Spreadsheet data
  • XML, urls
  • ``Foreign'' formats
• Teaching Tricks
• Sources of help, documentation
• Index

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This document was translated from L^AT_EX by H^EV^EA and H^AC^HA.