Generalized linear model

Thanks to this notes at link: http://bwlewis.github.io/GLM/, I was finally able to implements my own version of generalized linear model correctly in C# using the iteratively reweighted least square and its QR and SVD Netwon variant.

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: 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).