| Week | Topic | Lecture Note |
|---|---|---|
| 1 | Course overview | Week1-1 |
| Exploratory data analysis (EDA) | Week1-2 | |
| 2 | Statistical inference | Week2-1 |
| Common distributions: normal, binomial, poisson | Week2-2 | |
| 3 | Estimate of population proportion | Week3-1 |
| Hypothesis testing for a population proportion | Week3-2 | |
| 4 | Bootstrapping | Week4-1 |
| Type I & II error and Power | Week4-2 | |
| 5 | Bootstrap hypothesis testing and Normal approximation | Week5-1 |
| Maximum Likelihood Estimator (MLE) for Normal, Asymptotics, and Central Limit Theorem (CLT) | Week5-2 | |
| 6 | Comparison of two means, nonparametric | |
| Type I error and power | ||
| The t-test and its nonparametric alternatives | ||
| A case study: data cleaning, EDA, testing | ||
| 8 | Relationships between two continuous variables | |
| Measures of association for categorical variables | ||
| Association versus causation – the simpson’s paradox | ||
| 9 | Measure of association between continuous variables | |
| Simple linear regression | ||
| Type I error and power: exploring the performance of the t-test on normal and non-normal data | ||
| 10 | Simple linear regression model | |
| Hypotheses testing in simple linear regression | ||
| The lm() function and linear models | ||
| 11 | Residuals, degrees of freedom, and goodness-of-fit | |
| No class - Thanksgiving | ||
| A case study: data cleaning, EDA, modeling |