Probability and Statistics with R 2nd Edition by Maria Dolores Ugarte, Ana Militino, Alan Arnholt 1466504390 9781466504394

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ISBN 10: 1466504390

ISBN 13: 9781466504394

Author: Maria Dolores Ugarte; Ana F. Militino; Alan T. Arnholt

Cohesively Incorporates Statistical Theory with R ImplementationSince the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs.New to the Second EditionImprovements to existing examples, problems, concepts, data, and functionsNew examples and exercises that use the most modern functions Coverage probability of a confidence interval and model validationHighlighted R code for calculations and graph creationGets Students Up to Date on Practical Statistical TopicsKeeping pace with today’s statistical landscape, this textbook expands your students’ knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R.Web ResourcesA supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.

Probability and Statistics with R 2nd Table of contents:

1. Getting Started with R 1.1. What is R?
   1.2. Introduction to R
   1.3. Downloading and Installing R
   1.4. Working with Vectors
   1.5. Mode and Class of an Object
   1.6. Getting Help in R
   1.7. Using External Editors
   1.8. Introduction to RStudio
   1.9. Installing and Using Packages

2. Data Structures and Management 2.1. R Data Structures Overview
   2.2. Importing and Saving Data in R
   2.3. Working with Data Frames and Logical Operators
   2.4. Summarizing Data: Tables and Functions
   2.5. Probability Functions in R
   2.6. Flow Control Structures
   2.7. Creating Custom Functions
   2.8. Simple Imputation Techniques

3. Data Visualization 3.1. Using the plot() function
   3.2. Coordinate Systems in Graphs
   3.3. Traditional Graphics States in R

4. Exploring Data 4.1. What is Statistics?
   4.2. Working with Data: Qualitative and Quantitative
   4.3. Displaying Data Visually: Graphs and Charts
   4.4. Summary Measures: Location and Spread
   4.5. Analyzing Bivariate and Multivariate Data

5. Probability and Random Variables 5.1. Introduction to Probability
   5.2. Counting Techniques
   5.3. Axiomatic Probability Theory
   5.4. Understanding Random Variables
   5.5. Moment Generating Functions

6. Univariate Probability Distributions 6.1. Overview of Univariate Distributions
   6.2. Discrete and Continuous Distributions

7. Multivariate Probability Distributions 7.1. Joint Distribution of Two Random Variables
   7.2. Independence and Conditional Distributions
   7.3. Expected Values, Covariance, and Correlation
   7.4. Multinomial and Bivariate Normal Distributions

8. Sampling and Sampling Distributions 8.1. Understanding Sampling and Parameters
   8.2. Estimators and Sample Means
   8.3. Sampling Distribution for Infinite Populations
   8.4. Sampling Distributions and the Normal Distribution

9. Point Estimation 9.1. Properties of Point Estimators
   9.2. Techniques for Point Estimation

10. Confidence Intervals 10.1. Introduction to Confidence Intervals
    10.2. Confidence Intervals for Means and Variances
    10.3. Confidence Intervals for Large Samples

11. Hypothesis Testing 11.1. Introduction to Hypothesis Testing
    11.2. Type I and Type II Errors
    11.3. Power Function and Tests of Significance
    11.4. Hypothesis Tests for Population Parameters

12. Nonparametric Methods 12.1. Overview of Nonparametric Methods
    12.2. Common Nonparametric Tests: Sign Test, Wilcoxon, Kruskal-Wallis, etc.
    12.3. Categorical Data Analysis
    12.4. Nonparametric Bootstrapping and Permutation Tests

13. Experimental Design 13.1. Introduction to Experimental Design
    13.2. Fixed and Random Effects Models
    13.3. Analysis of Variance (ANOVA)
    13.4. Power Analysis and Multiple Comparisons

14. Regression Analysis 14.1. Introduction to Regression
    14.2. Simple and Multiple Linear Regression
    14.3. Ordinary Least Squares and Maximum Likelihood
    14.4. Model Building and Validation
    14.5. Interpreting Transformed Models
    14.6. Predicting and Estimating Responses

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