Topics List

Statistical framework (parameter vs statistic)

Definitions (bold words from slides)

Quantitative vs Categorical variables

What is a distribution?

• What values?
• How frequently?

Data visualizations

• Explanatory/response variables
• Univariate plots
• Bivariate plots
• Which plots associated with which variable types
• Which bar chart appropriate for conditional proportions

Numerical summaries

• Measures of centrality
• Measures of spread
• Percentiles
• When is each useful?

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

Regression

• Correlation (pearson/spearman)
• Regression to the mean
• Simple linear regression, interpret slope and intercept
• Regression with categorical predictors/indicator variables
• \(R^2\) as summary statistic

Probability

• Marginal, joint, conditional probability
• Multiplication rule
• Addition rule
• Bayes Theorem

Do not need to know

Formulas for correlation or standard deviation

R programming