Study Design Info (from Exam 1)
Normal distribution
- Basic probability concepts using the Normal distribution
- mostly relevant for sampling dist. and CI stuff
Confidence Intervals
Practice: Look over the CI labs and understand the
solutions. Questions will mostly be extremely similar to
labs
Practice: Go through the CI quick-guides and know
how to use formulas, use correct t or z values, and check conditions
- Sampling Distributions
- Standard Error
- Margin of Error
- 68-95-99.7% Rule
- Central Limit Theorem (CLT)
- Goal for CIs
- HUGE: define/describe parameters and statistics in
context
- ‘confidence’ meaning
- Make CI’s for the 4 scenarios (formulas will be given)
- Check conditions
- Know which z* or t* value to use in CI
- Interpret CIs
- Check if certain values are plausible
- When and why do we use t-distribution instead of Normal
- Bootstrapping logic (won’t perform one)
Hypothesis Testing
- Means, diff in Means, proportions, diff in proportions
- Setting up H0 and HA corresponding to the context
- Checking conditions
- How to calculate test-stats for each of these cases
- Understanding parts of the test-statistic
- measuring difference between data and null value, measured by
standard error
- P-values from R output
- P-value interpretations / definitions in context
- Answering research questions / conclusions using p-value
- Strength of Evidence Approach (conclusions)
- Decision Making approach
- What do we do to see if p-values are significant (less than
alpha?)
- Type-I and Type-II error definitions and describing these in
context
- issues with this approach
Short Answer Questions