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A Course in Statistics with R 1st Edition by Prabhanjan Narayanachar Tattar, Suresh Ramaiah, Manjunath 1119152720 9781119152729

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ISBN 10: 1119152720

ISBN 13: 9781119152729

Author: Prabhanjan N. Tattar, Suresh Ramaiah, B. G. Manjunath

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.

Key features:

  • Integrates R basics with statistical concepts
  • Provides graphical presentations inclusive of mathematical expressions
  • Aids understanding of limit theorems of probability with and without the simulation approach
  • Presents detailed algorithmic development of statistical models from scratch
  • Includes practical applications with over 50 data sets

A Course in Statistics with R 1st Table of contents:

Part I. THE PRELIMINARIES

  1. WhyR?
    1.1 Why R?
    1.2 R Installation
    1.3 There is Nothing such as PRACTICALS
    1.4 Datasets in R and Internet
    1.4.1 List of Web-sites containing DATASETS
    1.4.2 Antique Datasets
    1.5 http://cran.r-project.org
    1.6 R and its Interface with other Software
    1.7 help and/or?
    1.8 R Books
    1.9 A Road Map

  2. The R Basics
    2.1 Introduction
    2.2 Simple Arithmetics and a Little Beyond
    2.2.1 Absolute Values, Remainders, etc.
    2.2.2 round, floor, etc.
    2.2.3 Summary Functions
    2.2.4 Trigonometric Functions
    2.2.5 Complex Numbers
    2.2.6 Special Mathematical Functions
    2.3 Some Basic R Functions
    2.3.1 Summary Statistics
    2.3.2 is, as, is.na, etc.
    2.3.3 factors, levels, etc.
    2.3.4 Control Programming
    2.3.5 Other Useful Functions
    2.3.6 Calculus*
    2.4 Vectors and Matrices in R
    2.4.1 Vectors
    2.4.2 Matrices
    2.5 Data Entering and Reading from Files
    2.5.1 Data Entering
    2.5.2 Reading Data from External Files
    2.6 Working with Packages
    2.7 R Session Management
    2.8 Further Reading
    2.9 Complements, Problems, and Programs

  3. Data Preparation and Other Tricks
    3.1 Introduction
    3.2 Manipulation with Complex Format Files
    3.3 Reading Datasets of Foreign Formats
    3.4 Displaying R Objects
    3.5 Manipulation Using R Functions
    3.6 Working with Time and Date
    3.7 Text Manipulations
    3.8 Scripts and Text Editors for R
    3.8.1 Text Editors for Linuxians
    3.9 Further Reading
    3.10 Complements, Problems, and Programs

  4. Exploratory Data Analysis
    4.1 Introduction: The Tukey’s School of Statistics
    4.2 Essential Summaries of EDA
    4.3 Graphical Techniques in EDA
    4.3.1 Boxplot
    4.3.2 Histogram
    4.3.3 Histogram Extensions and the Rootogram
    4.3.4 Pareto Chart
    4.3.5 Stem-and-Leaf Plot
    4.3.6 Run Chart
    4.3.7 Scatter Plot
    4.4 Quantitative Techniques in EDA
    4.4.1 Trimean
    4.4.2 Letter Values
    4.5 Exploratory Regression Models
    4.5.1 Resistant Line
    4.5.2 Median Polish
    4.6 Further Reading
    4.7 Complements, Problems, and Programs

Part II. PROBABILITY AND INFERENCE

  1. Probability Theory
  2. Probability and Sampling Distributions
  3. Parametric Inference
  4. Nonparametric Inference
  5. Bayesian Inference

Part III. STOCHASTIC PROCESSES AND MONTE CARLO

  1. Stochastic Processes
  2. Monte Carlo Computations

Part IV. LINEAR MODELS

  1. Linear Regression Models
  2. Experimental Designs
  3. Multivariate Statistical Analysis – I
  4. Multivariate Statistical Analysis – II
  5. Categorical Data Analysis
  6. Generalized Linear Models

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