Exam 3 Topics List
Decision Error
- Bonferonni/FWER/Multiple comparisons
- Type I vs Type II error
- What is statistical power?
- Identifying relationship between power and other characteristics
(effect size, sample size, variance, etc.,)
- Creating table with false positives/negatives
Types of tests and associated variables
- t-test/paired t-test (degrees of freedom)
- Chi-squared goodness of fit/independence
- ANOVA
- Regression
\(\chi^2\) Tests
- Null hypothesis
- Expected values
- Compute statistic
- Use critical value sheet
ANOVA
- Degrees of freedom
- What impacts F statistic (\(k\)
groups, \(n\))
- How does size of \(k\) relate to
power?
- Within vs between variability
- MSE and MSG
- Use for prediction
- Tukey HSD
Regression
- Interpret and write models
- Categorical (reference and indicators (testing difference))
- Inference for slope parameter
- Correlated predictors
- \(R^2\), \(F\) statistic and model comparison
- Residuals
Comprehensive Topics List
Exam 1
Statistical framework (parameter vs statistic)
Quantitative vs Categorical variables
What is a distribution?
- What values?
- How frequently?
Tables and Odds
- Conditional statistics (row/column/total)
- Associate plots with tables
- Use quantitative variable as categorical (i.e., enrollment as large
or small)
- Odds vs probability (go from one to the other)
- Exposure/non-exposure and event/non-event
- Odds ratios (OR < 1, OR = 1, OR > 1)
Z-scores
- What do the tell us about observations?
- Be able to construct given mean and sd
- Interpret
Exam 2
Sampling Distribution
- Distributional parameters
- Standard error vs standard deviation
- Sampling distribution definition
- Central Limit Theorem conditions
- When does it apply? When does it not?
- When is approximation likely correct?
- Normal distribution
- Standard normal distribution
Confidence Intervals
- Critical values and quantiles
- t-distribution
- How does each term impact location and size of CI
- Coverage probability (what does this mean?)
Hypothesis Testing
- What is a t-statistic? Can you write it?
- What is a t-test?
- Null Hypothesis and null distribution (sampling distribution when
null is true)
- Critical value and hypothesis testing
- Evaluate strength of evidence
Do not need to know
How to compute p-values directly
Types of study design
Computing SSE or SSG directly
R programming