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 |