\( \def\e{\operatorname{E}} \def\p{\operatorname{P}} \def\var{\operatorname{Var}} \def\sd{\operatorname{SD}} \def\bin{\operatorname{Bin}} \def\n{\operatorname{N}} \)

Inference Types

In this next section, we will synthesize everything we’ve learned thus far about data and probability and see some real world examples of inference in each specific type of data/question that is covered in 240:

• Proportions inference: samples modeled after a binomial distribution, where we use the sample proportion to infer about the underlying probability parameter of the population.

• Means inference: samples modeled after a normal distribution, where we use the sample mean to infer about the underlying mean parameter of the population. This is also where we introduce the t-distribution.

• Regression inference: where we introduce linear regression to model the relationship between two numeric variables.