# 1 Contents

• Course details

## 1.1 Lecture 1

• Precision vs. Bias
• Random Sampling and Randomization
• Introduction to R

## 1.2 Lecture 2

• Types of variables
• Introduction to probability
• Definition
• Probability rules
• Probability trees
• Bayes Theorem
• Two common probability distributions
• Binomial distribution
• Normal distribution
• Other probability distributions for categorical and continuous variables

## 1.3 Lecture 3

• Mean and standard deviation
• Central Limit Theorem
• Confidence intervals
• Inference for a single mean or difference in means
• Sample size calculation for a single mean or difference in means

## 1.4 Lecture 4

• Hypothesis testing
• Inference for a single mean or difference in means
• Sample size calculation for a single mean or difference in means
• Confidence intervals vs. hypothesis testing: Quantifying uncertainty vs. making decisions
• Extra Problems

## 1.5 Lecture 5

• Sample Size Calculation

## 1.6 Lecture 6

• Bayesian inference for a single mean and for the difference between means
• Hypothesis testing and the risk of wrong conclusions

## 1.7 Lecture 7

• Confidence intervals for
• a single proportion
• difference between two proportions
• Hypothesis testing for
• a single proportion
• difference between two proportions
• Sample size calculations for studies of one or two proportions

## 1.8 Lecture 8

• Odds ratio
• Risk ratio (or relative risk)
• Number needed to treat
• Chi-squared test
• Fisher’s exact test

## 1.9 Lecture 9

• Hypothesis tests
• Sign test
• Signed rank test
• Rank Sum test
• Bootstrap confidence intervals

## 1.10 Lecture 10

• One-way ANOVA
• Estimation and checking of assumptions
• Multiple comparisons
• ANOVA in R

## 1.11 Lecture 11

• Extension of one-way ANOVA
• Randomized block design (or Repeated Measures)
• Two-way ANOVA
• Correlation
• Correlation vs. Causation
• Inference for the correlation coefficient

## 1.12 Lecture 12

• Simple and Multiple Linear Regression