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  • e-book 
    Learning Statistics with R: A tutorial for psychology students and other beginners

    Learning Statistics with R: A tutorial for psychology students and other beginners

    Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

    Subjects:
    Psychology and Mathematics and Statistics
    Keywords:
    Statistics -- Computer programs R (Computer program language) Textbooks Statistics Social sciences -- Statistical methods
    Resource Type:
    e-book
     
  • e-book 
    Think Bayes: Bayesian Statistics Made Simple

    Think Bayes: Bayesian Statistics Made Simple

    Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.

    Subjects:
    Computing, Data Science and Artificial Intelligence and Mathematics and Statistics
    Keywords:
    Python (Computer program language) Textbooks Bayesian statistical decision theory
    Resource Type:
    e-book