Statistical Analysis - Part 1
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What this part is about
This section is a first course in Statistical inference which is the process of inferring the characteristics of populations from samples using data analysis. In this first course we take what is called a frequentist - or classical - approach to statistical inference. This is the approach that is most commonly taught in introductory statistics courses. The first chapter in the section explains the logic of hypothesis testing in making statistical inferences. We then cover confidence intervals and explain what is a statistical model, and specifically, what is a linear model.
This remaining chapters cover regression, t-tests and ANOVA which are special cases of a much more widely applicable statistical model known as the “general linear model.” It is common for t-tests and ANOVA to be taught using the t.test() and aov() functions in R respectively. Here we teach them using the lm() function. This is to emphasise that regression, t-tests and ANOVA are fundamentally the same model known as the General Linear Model. This approach allows you to became familiar with the language and terminology of statistics and to more easily build on your knowledge. The output of lm() is typical of statistical modelling functions in R general and you will find it easier to build on your understanding if you are familiar with the output of lm()