COSC 6323: Statistical Methods

Teaching

To fulfill its mission, AC-DC carries out challenging human studies, which accrue big data, requiring sophisticated analysis. The skills required for such studies is a superset of the skills required for any other data-driven project.

END OF SEMESTER COMMENTS

★ ★ ★ ★ ★
The lectures were easy to understand and carefully explained by the professor including the doubts. The presentations that were saved in the blackboard are used as additional references. The TA explained the concepts and code very well. The marks or assignment evaluations were done very strictly, ie, it is 1 mark less if in one line says 16 and another has 14(by mistake), -1 for report quality. I just hope it would have been much better if it was -0.5 rather than -1. Thanks for all the help.

★ ★ ★ ★ ★
Learnt a lot. Thank to professor and TAs.

★ ★ ★ ★ ★
could have been a little linient on the grading.

★ ★ ★ ★ ★
Just an awesome class, but I felt that course load(hw) was a bit much

★ ★ ★ ★ ★
The class was very informative and challenging. I learned a fair good deal from this course. Excited to be taking Ubiquitous Computer next fall under Dr. Pavlidis!

★ ★ ★ ★ ★
Professor Pavlidis and the TAs Vitalii and Shaila instructed the course in an organzied manner, providing a learning enviornment encouraging participation and discussion. Weekly homeworks allowed for a steady practice of the information gained from lecture. Labs were extremly helpful in completing homework assignments. Professor Pavlidis provided interesting datasets from the medical field and educational research. Although I do wish I particpated in the in-person course provided due to the unique personalities of the professor and TAs the online form of the course was extremely convinient and useful.

★ ★ ★ ★ ★
One of the best classes I have ever taken. Professor: Dr. Ioannis T Pavlidis is the best professor as per me. He is always ready to clear your doubts. Getting responses from him is the easiest task even on weekends which makes him unique among all. If you ask him any questions at any moment, he is very happy and eager to answer everything. His knowledge in the field is beyond the limits. Always listen to the feedback provided by the students. Make necessary changes in class delivery also if students needed (until they are reasonable for sure). Definitely, A LOT to learn from him. TAs: Vitalii and Shaila, are the best at their level. For the practical session, you can always reach out to Vitalii, he is always ready to guide you in the right direction. For any grading, project related queries, Shaila is always ready to give you logically correct feedback. With the help of them, you will definitely learn how to write and organize your code and report. Class quality and workload: Definitely, you will have a huge workload as nothing comes easily. When you have time and want to invest it instead of waste it, surely go for this class. You will enjoy it a lot and will learn a lot. After finishing this class, you will 100% feel that it was useful and all the hard work (ofc smart work is needed 😇😇) that you have done gave you fruitful results. Kudos 🙌🏻🙌🏻🙌🏻 to one of the best teams I have ever worked with.

KEY INFORMATION

Instructors

Grading

Prof. Ioannis Pavlidis (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 3-4 pm on Fridays @ TEAMS
Vitalii Zhukov (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 3-4 pm on Mondays @ TEAMS
Shaila Zaman (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 2-3 pm on Fridays @ TEAMS

13 x 3% Homework
61% Project

Day, Time and Room

Course Project

Friday, 4:00-7:00 pm @ TEAMS & HBS 315

Requirements

R - 4.1.2 and higher
R Studio - 1.4.1103 and higher

Class Video Resources

The project can be done either individually or in pairs. Pairs need to be declared by the end of the second week of classes.

COURSE OUTLINE

Lesson 1: Statistics, Machine Learning, and Data Science 1/21/2022

Topics to Cover: Situating Statistics, Machine Learning, and Data Science; observations and variables; types of measurements for variables; distributions; numerical descriptive statistics; exploratory data analysis; bivariate data; data collection

Lesson 2: Probabilities and Sampling Distributions 1/28/2022

Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions
Homework #1 due at 7 pm on 01/31/2022

Lesson 3: Principles of Inference 2/11/2022

Topics to Cover: Hypothesis testing; estimation; sample size; assumptions
Homework #2 due at 7 pm on 02/15/2022
Assignment of Projects

Lesson 4: Inferences on a Single Population 2/18/2022

Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions
Homework #3 due at 11:59pm on 02/22/2022

Lesson 5: Inferences for Two Populations 2/25/2022

Topics to Cover: Inferences on the difference between means using independent samples; inferences on variances; inferences on means for dependent samples; inferences on proportions; assumptions and remedial methods
Homework #4 due at 11:59pm on 03/01/2022

Lesson 6: Inferences for Two or More Populations 3/04/2022

Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means
Homework #5 due at 11:59pm on 03/08/2022

Lesson 7: Linear Regression 3/11/2022

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics
Homework #6 due at 11:59pm on 03/22/2022

Lesson 8: Multiple Regression 3/25/2022

Topics to Cover: The multiple regression model; estimation of coefficients; inferential procedures; correlations; special models; multicollinearity; variable selection; detection of outliers
Homework #7 due at 11:59pm on 03/29/2022

Lesson 9: Dummy/Interval Variable Models 04/01/2022

Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors
Homework #8 due at 11:59pm on 04/05/2022

Lesson 10: Logistic and Multinomial Regression 4/08/2022

Topics to Cover: Logistic and Multinomial Regression
Homework #9 due at 11:59pm on 04/12/2022

Lesson 11: Experimental Designs 4/15/2022

Topics to Cover: Two-factor factorial experiment; randomized block design; randomized blocks with sampling; repeated measures designs
Homework #10 due at 11:59pm on 04/19/2022

Lesson 12: Categorical Data 4/22/2022

Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the 𝜒2 test; contingency tables; loglinear model
Homework #11 due at 11:59pm on 04/26/2022

Lesson 13: Nonparametric Methods 4/29/2022

Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap
Homework #12 due at 11:59pm on 05/03/2022

Lesson 14: Time Series 5/06/2022

Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA)
Homework #13 due at 11:59pm on 05/10/2022

Lesson 15: Project Presentations 5/13/2022

Project Reports due at 7 pm on 05/10/2022

WEEKLY GRADES AND STUDENT COMMENTS

Week 13 - April 29, 2022

Comments from students

★ ★ ★ ★ ★
Interesting assignment.

★ ★ ★ ★ ★
The session was really helpful in terms of explaining the rank variables. The grading consideration by the professor (i.e., backlog issue) was thoughtful and nice.

★ ★ ★ ★ ★
The class was very nice and organized.

Week 12 - April 22, 2022

Comments from students

★ ★ ★ ★ ★
The class was nice and informative as always.

★ ★ ★ ★ ★
Class was good enough and thanks for considering the request about the level of the homework.

★ ★ ★ ★ ★
Good lecture. Homework makes us think a lot about why we're doing what has been taught in the lecture. Great help from Vitalii for homework.

Week 11 - April 15, 2022

Comments from students

★ ★ ★ ★ ★
The class was very organized and helpful. I have a suggestion for which I might be wrong but I think it will be helpful to all the students: As the end semester is coming and we also have to spend more time on the project milestone 3 if the homework series will become a little bit easier than we can give more time to work on the project milestone. Hope you will consider this.

★ ★ ★ ★ ★
The class was right paced and all the materials were well explained.

Week 10 - April 8, 2022

Comments from students

★ ★ ★ ★ ★
Professor's lecture was so good and clear.

★ ★ ★ ★ ★
It was very disappointing after listening to the feedback from other students. I emailed the professor and TAs during the weekend, and I got all of my doubts resolved. There is no question that can be made about the response, delivery of the class, and the behavior of the TAs if one is attending the lecture carefully and regularly. It is one of the best classes I have ever taken, so no per me the professor can neglect those negative reviews, and I hope he will not think anything wrong with the students who are attending lectures sincerely and doing their job on time. Kudos to the professor and both TAs, they are doing their jobs at the best level.

Week 9 - April 1, 2022

Comments from students

★ ★ ★ ★ ★
Please provide some more detail description in homework.

★ ★ ★ ★ ★
The class was interesting. But I think today is the deadline for the project milestone. So, that might affect the overall interaction with the class.

★ ★ ★ ★ ★
A few more hints to assignments would be good.

★ ★ ★ ★ ★
Please be more descriptive in assignments and projects. What's taught in class is not enough to do the assignments. We have to wait till Monday to get clarification on many unclear instructions during the TA session. Vitalii helps us with our doubts but often times what he explains is not what is expected by Shaila. We confirmed with Vitalii that IQR is a good outlier removal method for HW 6 but that wasn't accepted by Shaila.

★ ★ ★ ★ ★
The session was very insightful. The knowledge which I got from the theory and practical session related to the general linear model helped me in working on the assignment.

★ ★ ★ ★ ★
In 6th assignment we followed the inputs per Dr. Pavlidis and Vitali. In the case of removing outliers we removed it after arrivign at a strategy after plotting the data. We removed the data after binding which abs difference is greater 16 as they are outliers. But, we were told this is not a good strategy but were never told which strategy to remove outliers is best suitable for the problem. I hope only way to make it clear would be to announce which strategy would be best suitable for current problem. Please correct me if I went wrong. Thanks.

Week 8 - March 25, 2022

Comments from students

★ ★ ★ ★ ★
It is really notable and good to get a response over the weekend via mail from the professor.

★ ★ ★ ★ ★
Thanks for adding more descriptions to assignments.

★ ★ ★ ★ ★
The materials covered are pretty intense. However, professor went through the topics at a much reasonable speed. Maybe the methods like BE, FS, etc could be explained in a bit more detail in a more interpretable manner. Other topics were greatly explained.

Week 7 - March 18, 2022

Comments from students

★ ★ ★ ★ ★
Excellent!

Week 6 - March 03, 2022

Comments from students

★ ★ ★ ★ ★
Need more directions on the assignments. HW 4 was very difficult

★ ★ ★ ★ ★
Unavailability of TA(Ms. Shaila) for doubts regarding Assignments before the deadline causing submissions with uncleared doubts.

Week 5 - February 25, 2022

Comments from students

★ ★ ★ ★ ★
Need more time for homeworks

★ ★ ★ ★ ★
The overall lecture is good. TA hours of Vitali is excellent. even he is answering on Sunday also. While this homework series is handling Shaila so Vitali is not going to certain answers as compared to the first homework series. so we asked Shaila for doubts but because of weekends, she might be busy so she didn't give answers to doubts. So it would be very helpful if homework is given by taking into consideration Vitali so students can ask doubts and submit the assignment by the deadline. Assignment complexity is difficult but because of not getting answers from the appropriate TA it becomes very difficult.

★ ★ ★ ★ ★
Very informative reagrding lecture content and clear on expectation of upcoming HW/Projects

★ ★ ★ ★ ★
need more time for assignments if possible.

★ ★ ★ ★ ★
The class is indeed helpful as always. However, I missed some visual plots today.

★ ★ ★ ★ ★
I understood the entire class and I have revised the slides also after the class. But what is to be expected from the homework part is quite confusing for me. I am literally struggling a lot with the interpretation of the tests' results.

Week 4 - February 18, 2022

Comments from students

★ ★ ★ ★ ★
Topics are very interesting.

★ ★ ★ ★ ★
Expecting a clear picture in the assignments.

★ ★ ★ ★ ★
Had a better understanding of hypothesis testing. how and what based the data should be tested ( one sided, two sided (alpha)) to determine whether to reject or accept the null and their interpretation.

Week 3 - February 11, 2022

Comments from students

★ ★ ★ ★ ★
I think Professor is at the right pace but the R programming part is going a little too fast. The TA is doing a great job though, just slightly slow should be perfect. If there is something in the R like Jupyter Notebook, where the code explanations can be put in English, that might be easier too.

★ ★ ★ ★ ★
Class is very long.

★ ★ ★ ★ ★
Knowledgeable and interesting.

★ ★ ★ ★ ★
it is very difficult to get into home work

★ ★ ★ ★ ★
It was an interesting class. Good to know that you have extended the deadline and given us some more time. Quite excellent and ready to help TA. Great work by Vitalii!!

Week 2 - January 28, 2022

Comments from students

★ ★ ★ ★ ★
It is a lot of information in 3 hours. I think a frequent 5 minute break should be given accordingly so we can process all the information and maybe try a thing or two on our own to completely grasp the concept. The homework is really meticulous and a great practice.

★ ★ ★ ★ ★
Please extend the deadlines for submission of the assignments

★ ★ ★ ★ ★
The topics are pretty interesting. I really like the idea of diving into complex topics slowly.

★ ★ ★ ★ ★
I think it would be great if the assignments are also allowed to do in teams with 2 members in each team .

★ ★ ★ ★ ★
Hello, I am content with the way the class is conducted. I like the theory part followed by the practical learning. The assignment was also good, I learned lot new things related to R language and how to do some analysis in it. Thanks, Nilesh

★ ★ ★ ★ ★
The class was good and Lab was very good. This is a new subject for us, the professor should give students some time for assignments and learn R and then submit assignments. else everything is good.

★ ★ ★ ★ ★
The HW is not difficult but a little bit confusing even with the explanations. Could you please make it clearer and easier to understand next time?

★ ★ ★ ★ ★
Hello Prof and TA, I have no complaints in teaching aspects but it's just if we have doubts in assignment during weekends, we have to wait for Monday (the day of deadline) for TA hours. Is there anything we can do about it? Thank you.

★ ★ ★ ★ ★
The content and the code explanation helped a lot for understanding and completing the assignment.

★ ★ ★ ★ ★
The session was quite excellent and useful while doing homework. I have one suggestion, it might be wrong but this is what I felt. Homework is assigned in the class, at that moment we are not prepared to make questions. Once we actually implement the script, that moment we encountered more queries. TA hour is on Monday, so we can clear maximum doubts in that session. The best thing is Vitalii was replying on Teams on Sunday also, but it might be possible he is not available. At that moment we might not get enough time to solve our doubts and fix the error in the script. So, I think the management can adjust this thing either by changing TA hour or by giving some more time after TA hour.

Week 1 - January 21, 2022

Comments from students

★ ★ ★ ★ ★
well organized class structure

★ ★ ★ ★ ★
course structure is nice

★ ★ ★ ★ ★
The intro session was good and I liked the immediate responses given by the TAs to the questions raised. But the break in between could be a bit longer in my opinion.

★ ★ ★ ★ ★
The way sessions are conducted that is first starting with some theory and then doing some practical is a great way of understanding the concepts.

★ ★ ★ ★ ★
I think the class is good and informative but I feel it is somewhat fast when it comes to R programming according to me.

★ ★ ★ ★ ★
The first session was very informative and haste. I would request you to explain the topics slowly so i can cope up.

★ ★ ★ ★ ★
Class was good and easy to understand, bit fast paced but was well organized.

★ ★ ★ ★ ★
The lecture was very informative.

★ ★ ★ ★ ★
It was a recall of the previous topics and the introduction to R was great! Looking forward to learning more about it.

★ ★ ★ ★ ★
From the first class, had a good understanding of the fundamentals, syllabus and requirements of this course.

★ ★ ★ ★ ★
The class is very informative and well-organised. The pace of the class seemed a little fast for me especially the session on R maybe because, I am completely new to R!

★ ★ ★ ★ ★
I felt that the assignment deadlines could be a little extended, and since the class is on Friday, and 3 days for submission will make us work on the weekends, which is okay sometimes but we might me occupied by other things or so.

★ ★ ★ ★ ★
The first class was good, I felt the first class was fast-paced, so maybe in the future session, it would be great if the pace was slowed down a little.