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Using R for introductory statistics

By: Verzani, John.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC the R series.Publisher: Boca Raton CRC Press 2014Edition: 2nd ed.Description: xvii, 502 p.ISBN: 9781466590731.Subject(s): Statistics - Data processing | R - Computer program languageDDC classification: 519.502855133 Summary: The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: • Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. • Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. • Use of knitr package makes code easier to read and therefore easier to reason about. • Additional information on computer-intensive approaches motivates the traditional approach. • Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing. (https://www.crcpress.com/Using-R-for-Introductory-Statistics-Second-Edition/Verzani/9781466590731)
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Table of Contents:

1. GETTING STARTED

1.1. What Is Data?
1.2. Some R Essentials
1.3. Accessing Data by Using Indices
1.4. Reading in Other Sources of Data

2. UNIVARIATE DATA

2.1. Categorical Data
2.2. Numeric Data
2.3. Shape of a Distribution

3. BIVARIATE DATA

3.1. Pairs of Categorical Variables
3.2. Comparing Independent Samples
3.3. Relationships in Numeric Data
3.4. Simple Linear Regression

4. MULTIVARIATE DATA

4.1. Viewing Multivariate Data
4.2. R Basics: Data Frames and Lists
4.3. Using Model Formula with Multivariate Data
4.4. Lattice Graphics
4.5. Types of Data in R

5. DESCRIBING POPULATIONS

5.1. Populations
5.2. Families of Distributions
5.3. The Central Limit Theorem

6. SIMULATION

6.1. The Normal Approximation for the Binomial
6.2. for loops
6.3. Simulations Related to the Central Limit Theorem
6.4. Defining a Function
6.5. Investigating Distributions
6.6. Bootstrap Samples
6.7. Alternates to for loops

7. CONFIDENCE INTERVALS

7.1. Confidence Interval Ideas
7.2. Confidence Intervals for a Population Proportion, p
7.3. Confidence Intervals for the Population Mean, µ
7.4. Other Confidence Intervals
7.5. Confidence Intervals for Differences
7.6. Confidence Intervals for the Median

8. SIGNIFICANCE TESTS

8.1. Significance Test for a Population Proportion
8.2. Significance Test for the Mean (t-Tests)
8.3. Significance Tests and Confidence Intervals
8.4. Significance Tests for the Median
8.5. Two-Sample Tests of Proportion
8.6. Two-Sample Tests of Center

9. GOODNESS OF FIT

9.1. The Chi-Squared Goodness-of-Fit Test
9.2. The Chi-Squared Test of Independence
9.3. Goodness-of-Fit Tests for Continuous Distributions

10. LINEAR REGRESSION

10.1. The Simple Linear Regression Model
10.2. Statistical Inference for Simple Linear Regression
10.3. Multiple Linear Regression

11. ANALYSIS OF VARIANCE

11.1. One-Way ANOVA
11.2. Using lm() for ANOVA
11.3. ANCOVA
11.4. Two-Way ANOVA

12. TWO EXTENSIONS OF THE LINEAR MODEL

12.1. Logistic Regression
12.2. Nonlinear Models

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.

See What’s New in the Second Edition:

• Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.
• Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible.
• Use of knitr package makes code easier to read and therefore easier to reason about.
• Additional information on computer-intensive approaches motivates the traditional approach.
• Updated examples and data make the information current and topical.

The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text.

The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.


(https://www.crcpress.com/Using-R-for-Introductory-Statistics-Second-Edition/Verzani/9781466590731)

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