In this section we introduce expectation, an operation which takes a random variable and produces a deterministic quantity. The expectation of a random variable can be approximated with sample averages, and from them we can infer properties of the model (like parameters). Much of statistics relies on the fact that sample averages approximate expectations, and understanding how well these approximations work is a central goal of statistics. This will motivate us to study the probability distribution of sums of random variables, which leads to the CLT and the Normal distribution. By defining joint distribution where the conditionals are Normal, we will arrive at our first example of a linear regression model. We will study a linear regression model with a binary predictor and introduce the idea of a sample distribution in this context.
Material:
Expectation, conditional expectation, empirical averages, CLT, Normal random variables and their properties, coefficient of variation, sample distribution, linear regression model