R for Starters, v0.57721

The eBook

The book, R for Starters, is divided into four parts. The goal of the book is to cover the theory, as well as the implementation of the theory. To that end, it entwines the equations with the code in an effort to allow people to do statistics, rather than just read about it.

The first two parts cover the typical topics in an introductory statistics course. These include the relationship of statistics to science, and the theory behind statistical testing. The first part includes an introduction to R and to statistics. It covers installing and using R, the scientific method, and simple descriptive statistics.

The second part explores inferential statistics, focusing on the assumptions needed to create the statistical tests. This includes tests with discrete independent variables, both discrete vs. continuous and discrete vs. discrete analysis. For those with a background in elementary statistics, use this as both a review of some statistical ideas and as an introduction to using R. It will allow an easy entry into scripting while not having to learn new statistics. Here, we will also learn how to produce some powerful graphics that can be easily saved and used in your research, both as graphical tests and as production-quality graphs.

Part Three covers the classical linear model, as well as the ordinary least squares method for estimating the parameters. Emphasis on assumptions continues here, as well as what to do when the assumptions are violated by the data and the model.

The fourth part deals with a systematized improvement to the Classical Linear Model: the Generalized Linear Models (GLMs). This type of model is quite prevalent in the research, as it allows one to easily handle dependent variables that are non-continuous or are bounded. The Classical Linear Model (CLM) makes several assumptions about the dependent variable being analyzed, the most limiting of which are the assumptions that the dependent variable is continuous and unbounded. GLMs relax those two assumptions, thus allowing us to model count data, binary data, proportion data, and survival data.

The Chapters

If you use this book as a reference, please reference and cite it properly. For the citation, see bottom.

Part 0: Frontmatter and Preface
	Frontmatter	15-Feb-15
	Preface	10-Jan-14

Part 1: Statistics and The Search
01	An Introduction to R	10-Aug-14
02	Statistics and the Scientific Method
03	Collecting Data
04	Descriptive Statistics	12-Apr-22

Part 2: Discrete Independent Variables
05	A Single Population	10-Aug-14
06	Comparing Two Groups	10-Aug-14
07	Comparing Three or More Means	10-Aug-14
08	Proportions Tests	17-Mar-15
09	Two-by-Two Tables
10	The Chi-squared Test
11	Estimators and Intervals	10-Aug-14

Part 3: The Classical Linear Model
12	Linear Regression	15-Feb-15
13	Assumptions of Linear Regression	15-Feb-15
14	Linear Regression and Transformations	12-Apr-13

Part 4: Generalized Linear Models
15	Generalized Linear Models	15-Feb-15
16	Binary Dependent Variables	2-Jul-11
17	Categorical Dependent Variables	19-Aug-11
18	Count Dependent Variables	16-Jul-11
19	Lifetime Models

Part 5: The Appendices
A	Discrete Probability Distributions	9-Sep-15
B	Continuous Probability Distributions	9-Sep-15
C	The Standard Normal Table	9-Sep-15

Reference Styles

BibTeX entry for this book with my usual citekey:

@unpublished{forsberg2015,
  title     = "R for Starters: Advanced Methods of Research Inquiry for the Social
Sciences using the {R} Statistical Environment",
  author    = "Ole J. Forsberg",
  year      = 2015,
  edition   = "v0.55721",
  howpublished = "\url{https://rfs.kvasaheim.com/chapters/rfs57721.pdf}",
  note         = "Accessed: November 21, 2024",
}