Intro to Statistics

Week Topic Lecture Note
1 Introduction and course overview Week1-1
Exploratory Data Analysis (EDA) Week1-2
2 Statistical inference Week2-1
Distributions: Normal, Binomial, Poisson
Data wrangling and tidy data
3 Estimators and their distribution
The bootstrap and confidence intervals
Simulating data from parametric distributions
4 The central limit theorem
Tests of significance / hypothesis testing
Sampling, resampling, and the bootstrap
5 Understanding the p-value
Comparison of two means, the t-test
How to compute p-values using distributions, permutations, bootstrap
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