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Using R for introductory statistics

Author: John Verzani
Publisher: Boca Raton, Fla. [u.a.] : Chapman & Hall/CRC, 2005.
Edition/Format:   Print book : EnglishView all editions and formats

Offers a treatment of statistical topics and the intricacies of the R software. This book helps students master data manipulation and exploration. It treats exploratory data analysis with attention,  Read more...


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Document Type: Book
All Authors / Contributors: John Verzani
ISBN: 1584884509 9781584884507
OCLC Number: 249196328
Description: XVI, 414 S. : graph. Darst
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, u 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 (t-Tests) Significance Tests and Confidence Intervals Significance Tests for the Median Two-Sample Tests of Proportion Two-Sample Tests of Center GOODNESS OF FIT The Chi-Squared Goodness-of-Fit Test The Chi-Squared Test of Independence Goodness-of-Fit Tests for Continuous Distributions LINEAR REGRESSION The Simple Linear Regression Model Statistical Inference for Simple Linear Regression Multiple Linear Regression ANALYSIS OF VARIANCE One-Way ANOVA Using lm() for ANOVA ANCOVA Two-Way 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 High-Level Graphic Functions Creating New Graphics in R APPENDIX E: PROGRAMMING IN R Editing Functions Using Functions Using Files and a Better Editor Object-Oriented Programming with R INDEX
Responsibility: John Verzani.


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The author has made a very serious effort to introduce entry-level students of statistics to the open-source software package R. One mistake most authors of similar texts make is to assume some basic Read more...

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