Statistics An Introduction Using R 2nd Edition by Michael J Crawley ISBN 1118941098 9781118941096

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ISBN 10: 1118941098 
ISBN 13: 9781118941096
Author: Michael J Crawley

A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R

This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology.  The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t–tests and chi–squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.

Includes numerous worked examples and exercises within each chapter.

Statistics An Introduction Using R 2nd Table of contents:

• Chapter 1: Introduction to R and RStudio

  • What is R? The Basics of R Syntax

  • Installing and Setting Up R and RStudio

  • Getting Started with RStudio Interface

  • Basic Operations in R

  • Using R for Data Entry and Data Frames

  • Introduction to R Packages and Libraries

• Chapter 2: Descriptive Statistics

  • Types of Data: Quantitative vs. Categorical

  • Measures of Central Tendency: Mean, Median, Mode

  • Measures of Dispersion: Range, Variance, Standard Deviation

  • Visualizing Data: Histograms, Box Plots, and Bar Charts

  • Summarizing Data with R: Functions like summary(), mean(), sd(), etc.

  • Data Transformation and Cleaning Techniques

• Chapter 3: Probability

  • The Basics of Probability

  • Random Variables and Probability Distributions

  • The Binomial Distribution

  • The Normal Distribution

  • Working with Probability Functions in R

  • Simulating Data Using R

• Chapter 4: Inferential Statistics

  • Introduction to Hypothesis Testing

  • p-values and Statistical Significance

  • Confidence Intervals

  • One-Sample and Two-Sample t-tests

  • Chi-Square Tests

  • The Role of Sampling Distributions

• Chapter 5: Regression and Correlation

  • Introduction to Correlation

  • Pearson’s Correlation Coefficient

  • Simple Linear Regression

  • Multiple Linear Regression

  • Evaluating the Fit of a Model: R-Squared, Residuals

  • Regression Diagnostics and Model Validation

  • Regression Analysis in R

• Chapter 6: Analysis of Variance (ANOVA)

  • One-Way ANOVA: Testing for Group Differences

  • Two-Way ANOVA: Interaction Effects

  • Post-hoc Tests: Tukey’s Test and Pairwise Comparisons

  • Assumptions of ANOVA and Dealing with Violations

  • ANOVA with R: Functions and Application

• Chapter 7: Non-Parametric Tests

  • Introduction to Non-Parametric Methods

  • The Wilcoxon Rank-Sum Test

  • The Kruskal-Wallis Test

  • The Friedman Test

  • Using Non-Parametric Tests in R

• Chapter 8: Advanced Regression Techniques

  • Logistic Regression

  • Generalized Linear Models (GLM)

  • Model Selection: Forward and Backward Selection

  • Multicollinearity and Regularization (Lasso, Ridge Regression)

  • Model Diagnostics and Interpretation

  • Regression Techniques in R

• Chapter 9: Time Series Analysis

  • Introduction to Time Series Data

  • Decomposition of Time Series: Trend, Seasonality, and Residuals

  • Forecasting with Time Series Models

  • Autoregressive Integrated Moving Average (ARIMA) Models

  • Time Series Analysis in R

• Chapter 10: Multivariate Analysis

  • Principal Component Analysis (PCA)

  • Factor Analysis

  • Cluster Analysis

  • Multivariate Analysis of Variance (MANOVA)

  • Multivariate Regression Techniques

  • Applications in R

• Chapter 11: Statistical Modeling and Machine Learning

  • Introduction to Machine Learning and Statistical Models

  • Decision Trees and Random Forests

  • Support Vector Machines (SVM)

  • k-Nearest Neighbors (k-NN)

  • Model Evaluation: Cross-Validation, Accuracy, Precision

  • Machine Learning in R

• Chapter 12: Reporting and Communicating Results

  • Creating Clear and Concise Statistical Reports

  • Presenting Statistical Findings Visually

  • Interpreting and Communicating Results to Non-Statisticians

  • R Markdown for Reproducible Reports

  • Writing R Scripts and Functions for Reports

• Chapter 13: Further Topics and Applications

  • Statistical Genetics and Genomics

  • Survey Analysis and Sampling

  • Experimental Design: Randomized Trials and Blocking

  • Advanced Statistical Methods in R

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Tags: Michael J Crawley, Statistics, R