Use the preparation materials to prepare hand-written answers for the following questions. Please ask any questions on the "Questions -- Preparation Guide" on the course Team (see link on the homepage).
- Show how an observed value of the response variable can be expressed as a model prediction and error.
- Models are always used to predict which type of variable?
- How is a residual calculated?
- Visually or geometrically what is a residual? [Hint: What does it look like on a plot.]
- What does the sign of a residual tell you?
- What does the absolute value of a residual tell you?
- What is RSS (both in words and as a formula)?
- What does RSS tell you about a model?
- How are residual degrees-of-freedom calculated?
- Are small or large degrees-of-freedom “better”?
- How is a mean-square calculated?
- What is a variance relative to a standard deviation?
- What measures the “noise” around a model?
- [Optional] What questions do you have from this reading that you would like me to address? [Please be as specific as possible. Don't just say "everything" or "I don't understand anything." Of course, you can ask me questions about the reading before class on MS Teams.]