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