Using R for introductory statistics
 Responsibility
 John Verzani.
 Language
 English.
 Imprint
 Boca Raton : Chapman & Hall/CRC, c2005.
 Physical description
 xvi, 414 p. : ill. ; 25 cm.
Access
Available online
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 Course
 STATS26601  Advanced Statistical Methods for Observational Studies
 Instructor(s)
 Baiocchi, Michael
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Creators/Contributors
 Author/Creator
 Verzani, John.
Contents/Summary
 Contents

 DATA What Is Data? Some R Essentials Accessing Data by Using Indices Reading in Other Sources of Data UNIVARIATE DATA Categorical Data Numeric Data Shape of a Distribution BIVARIATE DATA Pairs of Categorical Variables Comparing Independent Samples Relationships in Numeric Data Simple Linear Regression MULTIVARIATE DATA Viewing Multivariate Data R Basics: Data Frames and Lists Using Model Formula with Multivariate Data Lattice Graphics Types of Data in R DESCRIBING POPULATIONS Populations Families of Distributions The Central Limit Theorem SIMULATION The Normal Approximation for the Binomial for loops Simulations Related to the Central Limit Theorem Defining a Function Investigating Distributions Bootstrap Samples Alternates to for loops CONFIDENCE INTERVALS Confidence Interval Ideas Confidence Intervals for a Population Proportion, p Confidence Intervals for the Population Mean, A Other Confidence Intervals Confidence Intervals for Differences Confidence Intervals for the Median SIGNIFICANCE TESTS Significance Test for a Population Proportion Significance Test for the Mean (tTests) Significance Tests and Confidence Intervals Significance Tests for the Median TwoSample Tests of Proportion TwoSample Tests of Center GOODNESS OF FIT The ChiSquared GoodnessofFit Test The ChiSquared Test of Independence GoodnessofFit Tests for Continuous Distributions LINEAR REGRESSION The Simple Linear Regression Model Statistical Inference for Simple Linear Regression Multiple Linear Regression ANALYSIS OF VARIANCE OneWay ANOVA Using lm() for ANOVA ANCOVA TwoWay ANOVA TWO EXTENSIONS OF THE LINEAR MODEL Logistic Regression Nonlinear Models APPENDIX A: GETTING, INSTALLING, AND RUNNING R Installing and Starting R Extending R Using Additional Packages APPENDIX B: GRAPHICAL USER INTERFACES AND R The Windows GUI The Mac OS X GUI Rcdmr APPENDIX C: TEACHING WITH R APPENDIX D: MORE ON GRAPHICS WITH R Low and HighLevel Graphic Functions Creating New Graphics in R APPENDIX E: PROGRAMMING IN R Editing Functions Using Functions Using Files and a Better Editor ObjectOriented Programming with R INDEX.
 (source: Nielsen Book Data)
 Publisher's Summary
 The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful opensource software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption. "Using R for Introductory Statistics" fills this gap in the literature, making the software accessible to the introductory student.The author presents a selfcontained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.
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Bibliographic information
 Publication date
 2005
 Note
 Includes index.
 ISBN
 1584884509 (alk. paper)
 9781584884507 (alk. paper)