Math 40 is an introduction to continuous probability and statistical inference for data analysis. Topics include the theory of estimation and the theory of hypothesis testing using normal theory t-tests and nonparametric tests for means and medians, tests for variances, chi-square tests, and an introduction to the theory of the analysis of variance and regression analysis. Students need to know one of the following programming languages: R, Python, or Matlab.

## Lecture 25

Homework 7 due date is extended; it is now due at 11:59 pm March 10.

## Lecture 24

Your final is in a week. The Final will cover topics up to 7.7 (confidence interval). Please focus on the following four big topics

## Lecture 23

In lecture 23, we discussed t-tests (one-sample, paired two-sample, unpaired two-sample). We also discussed variance tests (one-sample using chi-square, t...

## Lecture 22

Homework 7 is due at 11:59 pm on  March 8.

## Lecture 21

Today we finished MLE and started hypothesis testing.

## Lecture 20

After discussing R^2 in the linear regression model (explained variance/total variance), we started looking at the maximum likelihood estimator (MLE; 6.10...

## Lecture 19

In lecture 19, we spent the whole time for the linear regression and ‘lm’ command in R. Do not forget that the regression is the conditional expectation t...

## Lecture 18

In lecture 18, we finished 6.4.3 (multidimensional MSE) and 6.4.4 (consistency) and started sections 6.5 and 6.6. As calculations are getting heavier, we ...

## Lecture 17

We are going to use X-hours (Feb 18) from 12:30 to 1:20 pm to learn visualization in R (qqplot etc). One of your classmates, Anoop (senior, Econ) will lea...

## Lecture 16

Homework 5 is on Canvas, which is due at 11:59 pm Feb 22.

## Lecture 15

This course is not open to the public anymore.

## Review materials for midterm test

Today we did review materials for the midterm test.

## Lecture 13

In HW4, you are okay to skip the second part of Q7. Today I also gave some hints on Q8 and Q9. Please check the recorded video for your reference.

## Lecture 12

In lecture 12, we discussed bivariate normal distributions (3.6) and optimal portfolio allocations (3.8). Please note that your textbook has several typos...

## Lecture 11

I am going to hold X-hour Feb 4 12:30 - 1:20 pm tomorrow (Feb 4, Thursday)

## Lecture 10

First of all, homework 3 due date is extended. It is now due at Feb 2 11:59 pm.

## Lecture 09

Due to an emergent event, I have office hours x 5:30-6:00 pm today (Jan 29, 2021; this change is only for today).

## Lecture 08

First of all, the midterm test is scheduled for Feb 10. You will have a 24-hour time window to finish the test. I have not decided how many problems there...

## Lecture 07

I extended your homework 2 due date. Homework 2 is now due at Jan 26 (Tuesday) 11:59 pm.

## Lecture 06

In lecture 06, we discussed normal and lognormal distributions. In addition to examples 2.34 and 2.32, we solved exercises 2.7.4 and 2.7.6.

## Homework 02

Your second homework is due at Jan 25 (Monday), 11:59 pm EDT.

## Lecture 05

In lecture 05, we discussed exponential and gamma distributions, and their connections with Poisson distributions. At the end of the lecture, we briefly d...

## Lecture 04

In lecture 04, we solved some of your homework problems (3 and 9).

## Office hours

Office hours MW 9:30 - 10:15 am F 1:00 - 1:30 pm

## Lecture 03

We discussed Poisson distributions and solved exercise 1.6.9, one of your homework problems.

## Lecture 02

Today, we talked about discrete random variables (section 1.2, 1.3, 1.4, and 1.6). Also, in the lecture, we solved exercise 1.6.4. Sometime later, I will ...

## Homework 01

Your first homework is due at Jan 19, 11:59 pm EDT.