ABSTRACT
Regression models depend on some foundational concepts of inferential statistics and it is challenging to design, run or interpret models without a good knowledge of these concepts. This chapter reviews some common measures in descriptive statistics such as mean, variance, standard deviation, covariance and correlation, with example data sets used as practical examples. The chapter then discusses the statistical properties of random variables, the central limit theorem and the idea of statistical confidence. The theory of hypothesis testing is introduced and then illustrated through three specific types of hypothesis tests: Welch's t-test of difference in means, correlation testing and Chi-square tests for different distributions between categories in a data set. The chapter uses example data sets to illustrate the steps involved in hypothesis testing and how to interpret results before illustrating the convenient functions in R and Python which perform these tests.
