2023-10-11
A good way to think about all statistical models is that the observed data comes from some true model with some random error.
DATA = MODEL + RESIDUAL
The MODEL
is a mathematical formula (like \(y = f(x)\)).
The formulation of the MODEL
will change depending on the number of, and data types of explanatory variables. One goal of inferential analysis is to explain the variation in our data, using information contained in other measures.
Moving from:
“What descriptive measures should be used to examine the data”
to
“What statistical analyses should be performed?
REF: PMA6 Chapter 6
Repeated measures is a topic typically taught in MATH 456 (but also covered in Chapter 18 of PMA6)
Table 6.2 in PMA6 shows which statistical analyses procedures are appropriate depending on the combination of explanatory and response variable.
Go to the HackMD collaborative notes on Choosing appropriate analysis and work in pairs to answer an assigned question.
PMA6 does not go over T-test, ANOVA or \(\chi^{2}\) tests. To see more examples (with R code) and more mathematical detail see Chapter 5 in the Applied Stats Course Notes