Statistical literacy is essential in today's world of big data, where we are constantly inundated with statistical arguments by those trying to influence us. On the flip side, we can use statistics in our own lives to analyze data such as financial returns, medical treatments, and housing prices, and discover the truth about anything that interests us. Crucial to this power is the statistical programming language R, a free, open-source computer language with millions of users worldwide. Designed for people comfortable with algebra, this course surveys the concepts and methods of college-level statistics through dozens of exercises conducted in RStudio, a free programming editor for the free programming language R.
Award-winning Professor Talithia Williams of Harvey Mudd College begins with descriptive statistics: you learn to draw conclusions from sample data using histograms, scatterplots, and other visual aids, and employ ideas such as the normal distribution, central limit theorem, and correlation. Next you explore the remarkable power of statistics to make inferences beyond the population being sampled: you study linear regression, multiple regression, ANOVA, and other classic techniques. In the final part of the course, you zero in on advanced topics such as experimental design, spatial statistics, time series analysis, and Bayesian inference. You conclude with a lecture that teaches you how to customize R for your own unique needs.