Statistics: Further Study#
Though this statistics primer touches on most of the foundational statistical concepts topics you will need to know for causal inference, it is not fully comprehensive. There may information we glossed over or omitted entirely that you may want to know.
For further study, feel free to visit:
Statistics Resources: Videos#
CrashCourse Statistics#
Provides an engaging, easy-to-understand presentation of foundational concepts in statistics, linear regression, and even salient discussion topics like big data and replicability.
StatQuest#
Provides a comprehensive, topical breakdown of key statistical concepts spanning the introductory level through intermediate to advanced levels.
Statistics Resources: Websites#
Introduction to Econometrics with R: https://bookdown.org/machar1991/ITER/3-arosur.html#
Provides context and explanations for foundational elements of statistics with clear applications to R programming. Covers statistics, linear regression, and other techniques of causal inference.
Statistics How To: https://www.statisticshowto.com#
Provides easy-to-understand explanations of common statistical concepts, formulas, and tests. It’s suited for students needing quick clarification on specific topics. They also have a section on “Calculus Based Statistics” for students “who want to create statistics.”
The Book of Statistical Proofs: https://statproofbook.github.io#
Provides definitions and proofs for theorems and concepts used in statistics. This resource is primarily for the interest of the mathematically-inclined student.
Statistics Resources: Texts#
OpenIntro Statistics (Diez, Çetinkaya-Rundel, & Barr)#
Presents a comprehensive introduction to statistical ideas in a clear and accessible manner. This is a good text for any general audience, but especially for students with rudimentary understandings of statistics.
Introduction to Modern Statistics (Çetinkaya-Rundel, & Hardin)#
A derivative of OpenIntro Statistics, this text presents contemporary applications of statistics with connections to data science. This is a good text for data scientists or econometricians who want to understand statistics as it applies to their work with data.
Note: You can also download the above two PDFs from this book’s GitHub repository here.
An Introduction to Mathematical Statistics and Its Applications (Larsen & Marx)#
Presents the mathematics behind our concepts of statistical theory as well as their applications. This is a good text for the math-inclined reader who wants to reference the mathematical intuition of statistics.