Syllabus (5/1)
Lecture 1 (5/1) Descriptive Statistics and Data Visualization
Lecture 2 (5/3) Random variables and Probability Distributions
Lecture 3 (5/8) More on Probability Distributions
Lecture 4 (5/10) Parameter Estimation
Lecture 5 (5/15) Hypothesis Testing I – Basics on hypothesis testing, type I/II error, rejection regions
Lecture 6 (5/17) Hypothesis Testing II – t-test, confidence interval
Lecture 7 (5/22) Hypothesis Testing III – ANOVA, Chi-squre test, Binomial test
Lecture 8 (5/24) Linear regression
Lecture 9 (5/29) Multiple hypothesis testing
Lecture 10 (5/31) Cross validation and boostrapping
Exercises 1 (5/1) Introduction to R
Exercises 2 (5/3) Descriptive Statistics and Visualization
Dataset 1 RMA_Filtered.txt
Exercises 3 (5/8) Distributions
Exercises 5 (5/15) Maximum Likelihood Estimation (MLE)
Exercises 6 (5/17) t-Test
Exercise 8 (5/24) Linear regression
Exercise 9 (5/29) Multiple hypothesis testing
Problem Set 1 Due 5/10.
Problem Set 2 Due 5/17.
Problem Set 3 Due 5/24.
Problem Set 4 Due 5/31.