This book is not intended to be a substitute for an introductory course or text in statistics. The introductory chapters will briefly cover certain key concepts mostly through readings and videos from external sources. Other concepts that are important to having an appreciation for the theory and use of statistical tests are not addressed. For this understanding, readers are encouraged to pursue an introductory undergraduate course, appropriate MOOC, or careful reading of an introductory textbook.
Luckily there are free resources available that cover the key concepts in an introductory statistics course. Some of these will be suggested as recommended reading in this book.
Diez, D.M., C.D. Barr, and M. Çetinkaya-Rundel. 2012. OpenIntro Statistics, 2nd ed. www.openintro.org/.
Types of data, plots, experimental design, sampling, probability, hypothesis testing, confidence limits, t-test, analysis of variance, chi-square test, linear regression, multiple regression, logistic regression.
Openstax College. 2013. Introductory Statistics. Rice University. openstaxcollege.org/textbooks/introductory-statistics.
Sampling, descriptive statistics, probability, distributions, confidence intervals, hypothesis testing, Type I and Type II errors, t-test, chi-square, linear regression, analysis of variance.
Lane, D.M. (ed.). No date. Introduction to Statistics, v. 2.0. onlinestatbook.com/.
Descriptive statistics, plots, correlation, probability, experimental design, confidence intervals, type I and type II errors, t-test, regression, analysis of variance, chi-square test, nonparametric tests, power.
Stockburger, D.W. 2013. Introductory Statistics: Concepts, Models, and Applications, 3rd web edition. www.psychstat.missouristate.edu/IntroBook3/sbk.htm.
Distributions, regression, correlation, hypothesis testing, t-test, chi-square test, analysis of variance.
Data analysis steps, kinds of variables, probability, hypothesis testing, confounding variables, descriptive statistics, chi-square test, t-test, analysis of variance, nonparametric tests, linear regression, logistic regression. Analyses in SAS programming language.
Mangiafico, S.S. 2015. An R Companion for the Handbook of Biological Statistics.
Using R, descriptive statistics, chi-square test, t-test, analysis of variance, nonparametric tests, linear regression, logistic regression. Analyses in R programming language.
Kabacoff, R. 2014. Quick-R: Accessing the Power of R. www.statmethods.net/
Data input, data management, basic statistics, advanced statistics, basic graphs, advanced graphs.
Venables, W.N., D.M. Smith, and R Core Team. 2015. An Introduction to R. cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
Crawley, M.J. 2012. The R Book. ISBN: 978-0-470-97392-9.
Peng, R.D. 2015. R Programming for Data Science. leanpub.com/rprogramming.
Statistics Learning Centre (Videos with Dr. Nic). YouTube channel.
Learn and Teach Statistics and Operations Research (Articles by Dr. Nic).
Online learning modules and online courses offer another alternative to learning basic statistical concepts, programming in R, or advanced statistical techniques.
Swirl has free, brief lessons for beginners to R programming and analysis. Topics include R programming, data analysis, data visualization, data manipulation, statistical inference.
DataCamp has short online modules covering topics such as an introduction to R, data manipulation, data visualization, and statistics with R. Limited parts of several courses are free. For the complete courses, there is a subscription charge. There is a discount for students.
Coursera hosts MOOC’s from various universities on a range of academic topics, including introductory statistics using R. Courses are free, though there is an option to pay for a certificate in a data science specialization.
edX also hosts free MOOC’s from universities on a variety of topics, including a course on introduction to R programming.
Udemy hosts relatively inexpensive online courses on a variety of subjects, including programming and statistics with R. Some are free.