Concepts & Applications of Inferential Statistics

Very nice text book on inferential statistics, teach with easy-to-understand examples. I was able to implement the ANCOVA and two-way ANOVA in C# based on the explanation given there.

http://vassarstats.net/textbook/

Online Course: R Programming

Very easy-to-follow and concise course on R Programming, take me around a day to view all the lecture videos and practice in R while following the videos (I liked Dr. Roger Peng's pace as well as the selective inclusions of R features in the course materials, most of the book on R out there try to cover too much and only ends up making the books more like a desktop reference or instruction manual for R). I particularly likes the parts covering the explanation on the usage of lapply, sapply, apply, mapply, sapply and split, as well as the parts covering the subsetting of vector, list, matrix, and data frame, which in the past it is a bit confusing for me

https://class.coursera.org/rprog-013

Online Course: Data Analytics and Inference Statistics

Very nice course for statistics introduction, Dr. Mine Centinkaya gives very easy-to-understand and concise explanations for many basic concepts such as probability tree, bayes rule, CLT, confidence interval, hypothesis testing, chi square independence and GOF testing, distributions such as t distribution (used for when CLT does not hold for small samples), f statistics and ANOVA. The course is still continuing but i could not wait for the availability of the videos for the last two weeks and ends up reading the companion book "OpenIntro Statistics" (the last two chapters more on linear/logistic regression as well as related statistics such as predictor correlation, predictor coefficient confidence interval, R^2, residuals as useful tools such as backward elimination and forward model selection using p-value and R^2)

https://class.coursera.org/statistics-003

OpenIntro Statistics

Very nice book to start learning inferential statistics, very concise and contains a lot of examples. The book is freely downloadable from:

https://www.openintro.org/stat/textbook.php?stat_book=os

You will be able to get a clear explanations of concepts such as central limit theorem, confidence interval, standard error, hypothesis testing (for continuous, categorical variables), Chi square GOF and independence test, normal/t/f statistics, bootstrapping, ANOVA, multiple comparisons, regression model selection (forward, backward model selection).