Discovering Statistics Using R

Synopsis

Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world.

The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect.

Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

Read more

Summary

Chapter 1: Introduction

* Introduces the field of statistics and its importance.
* Emphasizes the need for data analysis and interpretation.
* Presents the R programming language as a powerful tool for statistical analysis.

Example: Analyzing survey data to understand student preferences for online learning.

Chapter 2: Descriptive Statistics

* Summarizes the basics of descriptive statistics.
* Covers measures of central tendency (mean, median, mode) and dispersion (range, standard deviation).
* Demonstrates how to calculate and interpret these measures using R.

Example: Calculating the average age of a group of participants in a research study.

Chapter 3: Probability Distributions

* Introduces the concept of probability distributions.
* Explains the key characteristics of common distributions (e.g., binomial, normal).
* Shows how to model and visualize probability distributions using R.

Example: Modeling the probability of a coin flip landing on heads.

Chapter 4: Hypothesis Testing

* Explains the fundamental principles of hypothesis testing.
* Covers the steps involved in conducting a hypothesis test, including forming hypotheses, collecting data, and drawing conclusions.
* Demonstrates how to perform hypothesis tests using R for various types of data.

Example: Testing the hypothesis that a new drug is effective in reducing blood pressure.

Chapter 5: Regression Analysis

* Introduces the basics of regression analysis.
* Explains how to fit and interpret linear regression models.
* Covers the concepts of correlation, slope, and intercept.

Example: Modeling the relationship between study time and exam performance.

Chapter 6: Analysis of Variance (ANOVA)

* Explains the principles of ANOVA.
* Covers different types of ANOVA tests and their applications.
* Demonstrates how to perform ANOVA tests using R to compare means between groups.

Example: Testing the effect of different teaching methods on student learning.

Chapter 7: Non-parametric Tests

* Introduces non-parametric tests for data that do not meet the assumptions of parametric tests.
* Covers the Chi-square test, Kruskal-Wallis test, and Mann-Whitney U test.
* Explains how to interpret the results of these tests using R.

Example: Testing the hypothesis that two groups have the same distribution of scores.

Chapter 8: Statistical Modeling

* Discusses advanced statistical modeling techniques.
* Covers logistic regression, generalized linear models, and Bayesian statistics.
* Introduces the concepts of prediction, model selection, and cross-validation.

Example: Building a model to predict the probability of a customer purchasing a product based on their demographics and browsing history.