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