STATISTICAL METHODS IN RESEARCH

COURSES

Instructors

Prof. Ioannis Pavlidis (ipavlidis[@]uh.edu) Office Hours: 3-4 pm on Fridays @ Health 1 - Room 306 & @TEAMS

Grading & Project

3 x 33.33% Project

Grade-Thresholds: A >= 93, A- >= 90, B+ >= 85, B >= 80, B- >= 75, C+ >= 70, C >= 65, C- >=60, D+ >=55, D >= 50, F < 50

Day, Time and Room

Friday, 4:00-7:00 pm @ TEAMS & @ Room 315 in Health 1

Required Software

Class Repository

References

[1] Donna L. Mohr, William J. Wilson, Rudolf J. Freund. Statistical Methods. 4th Edition. Academic Press, 2021.

[2] Devore, J.L., Berk, K.N. and Carlton, M.A., 2012. Modern Mathematical Statistics With Applications (Vol. 285). New York: Springer.

[3] Terence C. Mills. Applied Time Series Analysis. 1st Edition. Academic Press, 2019.

COURSE OUTLINE

Lesson 1: Statistics, Machine/Deep Learning, and Data Science 01/23/2026

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

Lesson 2: Probabilities and Sampling Distributions 01/30/2026

Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions

Lesson 3: Principles of Inference 02/06/2026

Topics to Cover: Hypothesis testing; estimation; sample size; assumptions

Lesson 4: Inferences on a Single Population 02/13/2026

Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions

Lesson 5: Inferences for Two Populations 02/20/2026

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

Lesson 6: Inferences for Two or More Populations 02/27/2026

Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means

Lesson 7: Linear Regression 03/06/2026

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics
Project Milestone 1 due at 4 pm on 02/27/2025

Lesson 8: Multiple Regression 03/13/2026

Topics to Cover: The multiple regression model; estimation of coefficients; inferential procedures; correlations; special models; multicollinearity; variable selection; detection of outliers

Lesson 9: Dummy/Interval Variable Models 03/27/2026

Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors
Project Milestone 2 due at 4 pm on 03/27/2025

Lesson 10: Experimental Designs 04/03/2026

Topics to Cover: Two-factor factorial experiment; randomized block design; randomized blocks with sampling; repeated measures designs

Lesson 11: Categorical Data 04/10/2026

Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the 𝜒2 test; contingency tables; loglinear model

Lesson 12: Logistic and Multinomial Regression 04/17/2026

Topics to Cover: Logistic regression; multinomial regression

Lesson 13: Nonparametric Methods 04/24/2026

Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap

Lesson 14: Time Series 04/24/2025

Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA)

Project Milestone 3 due at 4 pm on 04/24/2026

Project Presentations 05/01/2025

WEEKLY GRADES AND STUDENT COMMENTS

KEY INFORMATION

COURSE OUTLINE

Lesson 1: Statistics, Machine/Deep Learning, and Data Science 01/17/2025

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

Lesson 2: Probabilities and Sampling Distributions 01/24/2025

Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions

Lesson 3: Principles of Inference 01/31/2025

Topics to Cover: Hypothesis testing; estimation; sample size; assumptions

Lesson 4: Inferences on a Single Population 02/07/2025

Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions

Lesson 5: Inferences for Two Populations 02/14/2025

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

Lesson 6: Inferences for Two or More Populations 02/21/2025

Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means

Lesson 7: Linear Regression 02/28/2025

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics
Project Milestone 1 due at 4 pm on 02/28/2025

Lesson 8: Multiple Regression 03/07/2025

Topics to Cover: The multiple regression model; estimation of coefficients; inferential procedures; correlations; special models; multicollinearity; variable selection; detection of outliers

Lesson 9: Dummy/Interval Variable Models 03/21/2025

Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors

Lesson 10: Experimental Designs 03/28/2025

Topics to Cover: Two-factor factorial experiment; randomized block design; randomized blocks with sampling; repeated measures designs
Project Milestone 2 due at 4 pm on 03/31/2025

Lesson 11: Categorical Data 04/04/2025

Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the 𝜒2 test; contingency tables; loglinear model

Lesson 12: Logistic and Multinomial Regression 04/11/2025

Topics to Cover: Logistic regression; multinomial regression

Lesson 13: Nonparametric Methods 04/18/2025

Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA)

Lesson 14: Time Series 04/25/2025

Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap

Project Milestone 3 due at 4 pm on 04/25/2025

Project Presentations 05/02/2025

WEEKLY GRADES AND STUDENT COMMENTS

Week 14 - April 25, 2025

Comments from students

★ ★ ★ ★ ★
Thanks for a great class!

★ ★ ★ ★ ★
Thank you for the class and for the semester! It was really enjoyable.

★ ★ ★ ★ ★
I’ve really enjoyed this course and learned so much. It was such a relief to hear there’s no presentation, especially since MS3 was a lot more time-consuming than MS1 and MS2. I really appreciate the Professor’s effort and enthusiasm for the subject.

★ ★ ★ ★ ★
I really liked this course!

Week 13 - April 18, 2025

Comments from students

★ ★ ★ ★ ★
I really find this clase interesting and I liked that it is relevant for our project.

Week 12 - April 11, 2025

Comments from students

★ ★ ★ ★ ★
I think that the topics that we saw in this class are highly important for statistical research.

Week 11 - April 04, 2025

Comments from students

★ ★ ★ ★ ★
Yes, I really liked learning about the Loglinear Model

Week 10 - March 28, 2025

Comments from students

★ ★ ★ ★ ★
I really like the topic and find it interesting

Week 9 - March 21, 2025

Comments from students

★ ★ ★ ★ ★
It was a useful class, and very interesting

Week 8 - March 07, 2025

Comments from students

★ ★ ★ ★ ★
This was an interesting topic

★ ★ ★ ★ ★
Multi and partial regression is a very interesting process however, I am particular excited at the use of data from the Mayo Clinic. Medical informatics work is very intriguing to me in this space.

Week 7 - February 28, 2025

Comments from students

★ ★ ★ ★ ★
I'm interested in this topic

★ ★ ★ ★ ★
A friend of mine who is a data scientist said to me "you can solve 90% of your problems with linear regression" and I thought that was funny.

★ ★ ★ ★ ★
The class was interesting and engaging, and the professor presented the material clearly and effectively.

★ ★ ★ ★ ★
The second part of the class is exciting. But if you could share on how we could get extra points by giving details through a text/message. it would be better for us to remember and solve.

Week 6 - February 23, 2025

Comments from students

★ ★ ★ ★ ★
I missed the class last week, catching up with recording.

★ ★ ★ ★ ★
I found this class really interesting.

Week 5 - February 16, 2025

Comments from students

★ ★ ★ ★ ★
I was away at the International Neuropsychological Society conference and missed the lecture, but I plan to watch the recording.

★ ★ ★ ★ ★
A list of tasks for Assignment 1 would be helpful.

Week 4 - February 09, 2025

Comments from students

★ ★ ★ ★ ★
I really liked it when we directly analyzed the real research data that is going on at the moment. It is helpful for us to better understand on what we are plotting when we analyze in class directly.

Week 3 - Jan 31 , 2025

Comments from students

★ ★ ★ ★ ★
I like that the class is divided by theorical and practical part

★ ★ ★ ★ ★
I would appreciate a more structured way of teaching, with proper notes and/or slides.

★ ★ ★ ★ ★
This week's class was really informative and enjoyable. Thank you!

★ ★ ★ ★ ★
The class is good and interesting

★ ★ ★ ★ ★
I really enjoyed this week's lecture. The focus on inference gave a real-world feel to how we should be applying the techniques we are learning. I particularly enjoyed the coding portion as well and was excited to receive the data for our project.

END OF SEMESTER COMMENTS

★ ★ ★ ★ ★
Positive: 1. Useful topics with practical applications 2. Realistic homework and project 3. TAs and Professor were very helpful. Negative: 1. Instructions for some homework were not clear 2. Grading was rough for some homework, especially for homework that requires visual elements. Overall, I learned a lot from this course. I would like the "coding" session to focus more on the visualization part. Specifically, I want it to show the different types of plots (for that topic, data, and assignments), how to "code" them properly, and when/how we should use them.

★ ★ ★ ★ ★
Too many assignments. It would be better to have less number of assignments, more complex if required. And more time as headsup

★ ★ ★ ★ ★
I got to implement so many things through the assignments and projects in R, though it had been tough initially slowly got to understand everything in detail, overall it was great!!

★ ★ ★ ★ ★
I have learned a lot through this course. Thanks to Professor used the codes to explain the statistic theory and TAs gave us so many help on homeworks. The course workload was heavy.

★ ★ ★ ★ ★
Nthg much Thank you

★ ★ ★ ★ ★
I feel that if exams were included with projects it might have been a good practice for the course.

★ ★ ★ ★ ★
Homeworks were hard, limited extra credit opportunities, no curve in the class.

★ ★ ★ ★ ★
The class is excellently structured, offering engaging and intellectually stimulating content that is thoroughly organized to enhance comprehension. Interactive teaching methods and a supportive environment encourage active participation and open communication. The teaching assistant is incredibly helpful, providing essential support that significantly enriches the learning experience. Thoughtfully designed assignments and projects deeply enhance subject knowledge, allowing us to apply theoretical concepts practically. Overall, the class is skillfully facilitated by a professor who demonstrates deep expertise and a genuine passion for the subject matter, greatly inspiring students.

★ ★ ★ ★ ★
positive - a lot to learn

★ ★ ★ ★ ★
The course is quite fun and has much more practical learning compared to theoretical learning, allowing us to gain hands-on knowledge.

★ ★ ★ ★ ★
At first, I thought it was very hard to survive in this course because of the weekly assignments and stuff but later on it made me flexible enough to accommodate the weekly assignments and concepts easily .

★ ★ ★ ★ ★
I recommend this course to data analysis enthusiasts. The best part about this class is the content it offers and the analytical approach our professor teaches. However, if someone is not willing to work weekly, as this course requires weekly assignments, I would suggest they reconsider, as it honestly requires consistent effort. You will be rewarded based on your work. The first two assignments made me feel lost, but other than those, we should be able to manage as we get all the help we need from the professor and the teaching assistants.

★ ★ ★ ★ ★
Positive thing is I learned a lot while doing this course and it's quite challenging as well.In my perspective I don't have any negatives.

★ ★ ★ ★ ★
The process of learning and the way assignments designed was too good. But, I think the number of assignments are more. Overall great experience.

★ ★ ★ ★ ★
I wanted to take a moment to express my gratitude for your exceptional lectures in our statistics course. Your dedication to teaching and your ability to explain complex concepts have truly enhanced my understanding of the subject. Additionally, I want to extend my appreciation to the teaching assistants for their invaluable support. Thank you once again for your commitment to our education. It has been a pleasure learning from you, and I am grateful for the opportunity to have been a part of your class this semester.

★ ★ ★ ★ ★
Excellent course. Learnt a lot from the course in many areas like data analysis, model building etc. Professor's grip on these topics is way beyond our imagination but still his teaching style with practical examples makes it easy for us to understand these topics. Also big kudos to both the TAs for their presence and attention to us every time we needed them. Even now I remember, I was not sure about taking the course coming into this semester. But, now I am glad I didn't drop this course. So big thanks to all 3 of the faculty!!

★ ★ ★ ★ ★
The TA’s are so helpful and I learnt a lot in this semester. Thanks to the professor and TA’s

KEY INFORMATION

Instructors

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
Fettah Kiran (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 12-1 pm on Tuesdays @ TEAMS
Ummey Tanin (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 12-1 pm on Thursday @ TEAMS

Grading & Project

13 x 3% Homework
61% Project

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

Grade-Thresholds: A >= 93, A- >= 90, B+ >= 85, B >= 80, B- >= 75, C+ >= 70, C >= 65, C- >=60, D+ >=55, D >= 50, F < 50

Day, Time and Room

Friday, 4:00-7:00 pm @ TEAMS & @ Room 315 in Health 1

Required Software

Class Repository

GitHub

References

[1] Donna L. Mohr, William J. Wilson, Rudolf J. Freund. Statistical Methods. 4th Edition. Academic Press, 2021.

[2] Devore, J.L., Berk, K.N. and Carlton, M.A., 2012. Modern Mathematical Statistics With Applications (Vol. 285). New York: Springer.

[3] Terence C. Mills. Applied Time Series Analysis. 1st Edition. Academic Press, 2019.

COURSE OUTLINE

Lesson 1: Statistics, Machine/Deep Learning, and Data Science 01/19/2024

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

Lesson 2: Probabilities and Sampling Distributions 01/26/2024

Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions

Lesson 3: Principles of Inference 02/02/2024

Topics to Cover: Hypothesis testing; estimation; sample size; assumptions
Homework #1 due at 8 pm on 02/01/2024
Assignment of Projects

Lesson 4: Inferences on a Single Population 02/09/2024

Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions
Homework #2 due at 4 pm on 02/08/2024

Lesson 5: Inferences for Two Populations 02/16/2024

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 #3 due at 11 pm on 02/15/2024

Lesson 6: Inferences for Two or More Populations 02/23/2024

Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means
Homework #4 due at 11 pm on 02/22/2024

Lesson 7: Linear Regression 03/01/2024

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics
Homework #5 due at 11 pm on 02/29/2024

Lesson 8: Multiple Regression 03/08/2024

Topics to Cover: The multiple regression model; estimation of coefficients; inferential procedures; correlations; special models; multicollinearity; variable selection; detection of outliers
Homework #6 due at 11 pm on 03/07/2024

Lesson 9: Dummy/Interval Variable Models 03/22/2024

Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors
Milestone 1 due at 4 pm on 03/18/2024
Homework #7 due at 11 pm on 03/21/2024

Lesson 10: Experimental Designs 03/29/2024

Topics to Cover: Two-factor factorial experiment; randomized block design; randomized blocks with sampling; repeated measures designs
Homework #8 due at 11 pm on 03/28/2024

Lesson 11: Categorical Data 04/05/2024

Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the 𝜒2 test; contingency tables; loglinear model
Homework #9 due at 11 pm on 04/04/2024

Lesson 12: Logistic and Multinomial Regression 04/12/2024

Topics to Cover: Logistic regression; multinomial regression
Homework #10 due at 11 pm on 04/11/2024

Lesson 13: Nonparametric Methods 04/19/2024

Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap
Homework #11 due at 11 pm on 04/18/2024

Lesson 14: Time Series 04/26/2024

Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA)
Homework #12 due at 11 pm on 04/25/2024

Project Presentations 05/03/2024

Homework #13 due at 11 pm on 05/02/2024
Project Reports due at 4 pm on 05/02/2024

WEEKLY GRADES AND STUDENT COMMENTS

Week 14 - April 28, 2024

Comments from students

★ ★ ★ ★ ★
I am learning more and more.

Week 13 - April 21, 2024

Comments from students

★ ★ ★ ★ ★
In the upcoming week, we hope to get hints on how output looks on our project(milestone 2) as we are trying our best to get the correct result and this would ensure us that we are in correct path. Also if possible please make our final assignment (13) easier or give hints like assignments 7,8,9 as it is the last week for every subject and every subject has deadline/exams this upcoming week. Thank you for taking our feedback into account.

★ ★ ★ ★ ★
Starter code would help in the hw.

★ ★ ★ ★ ★
I am able to solve homework problems better than before. Thank you!

Week 12 - April 14, 2024

Comments from students

★ ★ ★ ★ ★
The class was well organized and helpful.

Week 11 - April 07, 2024

Comments from students

★ ★ ★ ★ ★
Learning more.

Week 10 - March 31, 2024

Comments from students

★ ★ ★ ★ ★
Please give some hints about Bonus Homework in class, Thank you!

★ ★ ★ ★ ★
The classes are useful .

★ ★ ★ ★ ★
It will be good if considered marks for both plots and observations...Though observations are written, more marks are getting reduced if it is not as expected or incomplete.

Week 9 - March 24, 2024

Comments from students

★ ★ ★ ★ ★
Thank you for the homework. They are helping a lot.

★ ★ ★ ★ ★
Please shift the Tuesday's office hours to 2pm

★ ★ ★ ★ ★
Everything is good.

Week 8 - March 08, 2024

Comments from students

★ ★ ★ ★ ★
I think your are providing good help with homework. Kepp going

★ ★ ★ ★ ★
Great job so far, especially our dear TA's Fettah and Ummey they work so hard to help the class succeed, as well as professor he is always open to clarify the assignment requirements which helps immensely in completing homework on time.

★ ★ ★ ★ ★
Thanks for the project's hints.

Week 7 - March 01, 2024

Comments from students

★ ★ ★ ★ ★
Give more hints for project

★ ★ ★ ★ ★
Every week, I invest time in this course to learn and earn a good grade. However, I face challenges with every homework assignment due to some distracting comments on the table. I believe it's not an effective way to motivate students. I am truly disappointed because my hard work for this course seems to be in vain.

★ ★ ★ ★ ★
I'm concerned the project work can not be done well.

★ ★ ★ ★ ★
The guidance provided is helpful to do the homework. It has reduced the anxiety.

Week 6 - February 23, 2024

Comments from students

★ ★ ★ ★ ★
Keep Going.

★ ★ ★ ★ ★
Expect the next class.

★ ★ ★ ★ ★
Can you shift Tuesdays' office hours to 2pm or 3pm. Most of the students have classes on Tuesdays from 10 to 1.

Week 5 - February 16, 2024

Comments from students

★ ★ ★ ★ ★
Please reduce the difficulty levels of the homework.

★ ★ ★ ★ ★
Can you move the Tuesdays' office hours to afternoon time. Most of the Students have classes on Tuesday Morning.

★ ★ ★ ★ ★
Thank you for considering our concerns about TA hours and Assignment deadline extension. Also, thanks for providing hints in R as now we are able to focus more on statistics part of the assignment.

★ ★ ★ ★ ★
The class is good.

Week 4 - February 09, 2024

Comments from students

★ ★ ★ ★ ★
Kindly keep the assignment deadlines at end of every thursday i.e. 11:59 pm , instead of 4:00 pm , this would be very helpful if the change is made , kindly consider it.

★ ★ ★ ★ ★
Please do change the TA hours timings in Tuesday. We have other classes in that time.

★ ★ ★ ★ ★
Please reduce assignment difficulty level.

★ ★ ★ ★ ★
I have a class every Tuesday during the TA hours. so can you please shift the TA hours on tuesday to a different day. Thanks

★ ★ ★ ★ ★
Please reduce assignment difficulty level.

★ ★ ★ ★ ★
Descriptions of questions are unclear.

★ ★ ★ ★ ★
I feel the TA office hours of Ms. Ummey could be on Wednesday or Thursday instead of Friday, so that we could use those hours for the assignment. Also, most of the students in this class are also enrolled in Advanced Numerical Analysis by Prof. Nikolaos and unfortunately the TA hours of Mr. Fettah (11:00 AM - 12:00 PM on Tuesday) clash with the class hours of that class (10:00 AM - 11:30 AM). So we would appreciate a change in office hours of both the TAs. Thanks for the consideration.

★ ★ ★ ★ ★
Thanks the homework hints! It would be better if there were project hints.

★ ★ ★ ★ ★
The instruction on hw2 was not clear enough and was too difficult for me.

★ ★ ★ ★ ★
Please include more details in the questions for the homework. The number of assignments is kind-of overwhelming.

★ ★ ★ ★ ★
The number of assignments is kind-of overwhelming.

Week 3 - February 02 , 2024

Comments from students

★ ★ ★ ★ ★
Happy learning

★ ★ ★ ★ ★
I was a bit confusing at the beginning of the r session what the basic idea or problem we try to address in HW2. But questions from students helped to understand it better.

★ ★ ★ ★ ★
I feel that more R Programming practice needed so that the homework can be done easily.

★ ★ ★ ★ ★
Homework hints reduced my stress.

Week 2 - January 26, 2024

Comments from students

★ ★ ★ ★ ★
No comments so far everything is good.

★ ★ ★ ★ ★
In todays R session we covered content for two weeks. Hope next week we will have more time to explain the content.

★ ★ ★ ★ ★
Looking forward to this

★ ★ ★ ★ ★
EVERYTHING IS GOOD.

★ ★ ★ ★ ★
Everything was clear!

★ ★ ★ ★ ★
The homework is challenging but useful.

★ ★ ★ ★ ★
I am of the opinion that some more time has to be given to explain and understand R programming so that it would be easy to work on the assignments.

Week 1 - January 19, 2024

Comments from students

★ ★ ★ ★ ★
Hope there was more time to explain the examples where form R studio.

★ ★ ★ ★ ★
Class looks good but the lecture was bit hurry

★ ★ ★ ★ ★
Nice class

★ ★ ★ ★ ★
I would like to thank both Professor and TA for their performance and care.

★ ★ ★ ★ ★
A bit loud and clear voice will make the class more interesting.

★ ★ ★ ★ ★
Everything is good.

★ ★ ★ ★ ★
I learned a lot of basic knowledge of R. Thank you!

★ ★ ★ ★ ★
Great first week with much useful information! However, the R file that the TA showed was not available for the students, which made it a bit harder to follow. Please make it available since it has many useful tips for R & RStudio!

★ ★ ★ ★ ★
No comments so far the class is good.

★ ★ ★ ★ ★
More Hands-on learning would be benificial.

★ ★ ★ ★ ★
Looking forward to it.

★ ★ ★ ★ ★
I think we need to know more details about the project.

END OF SEMESTER COMMENTS

★ ★ ★ ★ ★
Everything is super good Professor has really good grip on what he is delivering and main thing i want mention is about the teaching assistant for this course Kiran Fetah is superb in helping students in all the way they need .
Thanks

★ ★ ★ ★ ★
Professor and TA's gave continuous support. I am gonna really miss this class.

★ ★ ★ ★ ★
The most positive thing is conducting TA office hours which is very useful to clarify doubts for solving assignments There is nothing like negative but the deadline time for assignments and project is bit hectic when it comes to the end of the semester though professor and TA worked a lot to give us more time

★ ★ ★ ★ ★
Both Professor and TA's are very supportive and helpful.

★ ★ ★ ★ ★
The topics were explained very clearly and thanks to professor and TA's help, we were able to grasp and solve problems statistically. They are no negative attributes of this class.

★ ★ ★ ★ ★
Too many assignments made me feel a little stress

★ ★ ★ ★ ★
lot of pratical knowledge learnt. overall good subject and good teaching

★ ★ ★ ★ ★
The positive is Professor way of teaching is at top level but coming to the assignments there was a ambiguity between the professor and TA's explanation

★ ★ ★ ★ ★
I really liked the weekly assignment that allow us to apply the concepts we have learnt in class, I like how professor deliver the entire concepts in class using sceneriors and thing we could very much relate to in everyday life. I very much appreciate the effort of the T.A's in ensuring we get the concept and apply them rightly in solving real word problems through their numerous office hours and rapid responses to personal messages on teams. But sometime the assignments get overwhelming.

★ ★ ★ ★ ★
The good thing is that this gave a good exposure to the applications on real world problems. There is nothing much negative but the only thing is the assignments sometimes are challenging. Overall, I feel very glad that I took this subject.

★ ★ ★ ★ ★
I found it awesome about lectures and TA hours
It may be some what fair if you can decrease the workload and reduce the difficulty of assignments
Also,if you can consider 10 marks as one grade, i would be satisfied in terms of Results.

★ ★ ★ ★ ★
Overall a good learning experience Thanks

★ ★ ★ ★ ★
Positive: Everything goes on in a sequential pattern. Lecture followed by TAs practical session, assignment, Office hours. The grading was on point and punctual. The TAs were very supportive. Negative: The course load was more because of assignments every week. But, that has helped us in learning alot.

★ ★ ★ ★ ★
Positive Attributes: Helps in the way to make sense of the complex data and to make decision. Learned about the assumptions that underline statistical models and the importance of understanding the limitations of these models. Negative Attributes: Difficulty in communicating the results.

★ ★ ★ ★ ★
I thoroughly enjoyed the course and found it to be an enriching learning experience. The course content was well-structured, and the topics covered provided a comprehensive understanding of the subject matter. Throughout the course, I gained valuable insights into statistical methods and their application in research. The practical examples and case studies helped in reinforcing the concepts and made the learning process engaging and enjoyable. I particularly appreciated the emphasis on real-world applications, which helped me relate the material to my own research interests.

★ ★ ★ ★ ★
Classes were informative and we had weekly assignments which was a bit stressful but managed to finish them on time. Each one was a new task and helped me learn new things. Our TA's were very helpful.

★ ★ ★ ★ ★
Negatives are more number of assignments.

★ ★ ★ ★ ★
I learned so much from the continuos weekly assessment and projects from real world data... This course will certainly help me a lot in my career !

★ ★ ★ ★ ★
Positive:

Very focused on real datasets
All assignments gave me tools and libraries I could directly use in my research.
The focus on practical details and statistical "tricks" over dry theory was very appreciated.
The labs were great, very in-depth.

Negative:

Homeworks/projects, while well-designed, required a lot of extra support from the TAs to be doable.
Solving HWs without attending office hours resulted in poor scores, which is annoying since all the requirements should be in the questions.

★ ★ ★ ★ ★
The course exceeded my expectations in terms of both content and instruction. The professor's, Vitalii's and Fettah's expertise, enthusiasm, and commitment to student success made this an exceptional learning experience, and I am grateful for the opportunity to have taken this course.

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: 11 am-12 pm on Mondays @ TEAMS
Fettah Kiran (This email address is being protected from spambots. You need JavaScript enabled to view it.) Office Hours: 10-11 am 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

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.

References

[1] Donna L. Mohr, William J. Wilson, Rudolf J. Freund. Statistical Methods. 4th Edition. Academic Press, 2021.

[2] Terence C. Mills. Applied Time Series Analysis. 1st Edition. Academic Press, 2019.

COURSE OUTLINE

Lesson 1: Statistics, Machine/Deep Learning, and Data Science 01/20/2023

Topics to Cover: Situating Statistics, Machine/Deep 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 01/27/2023

Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions
Homework #1 due at 4 pm on 02/03/2023

Lesson 3: Principles of Inference 02/03/2023

Topics to Cover: Hypothesis testing; estimation; sample size; assumptions
Homework #2 due at 4 pm on 02/09/2023
Assignment of Projects

Lesson 4: Inferences on a Single Population 02/10/2023

Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions
Homework #3 due at 4 pm on 02/16/2023

Lesson 5: Inferences for Two Populations 02/17/2023

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 4 pm on 02/23/2023

Lesson 6: Inferences for Two or More Populations 02/24/2023

Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means
Homework #5 due at 4 pm on 03/02/2023

Lesson 7: Linear Regression 03/03/2023

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics
Homework #6 due at 4 pm on 03/09/2023

Lesson 8: Multiple Regression 03/10/2023

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 4 pm on 03/23/2023

Lesson 9: Dummy/Interval Variable Models 03/24/2023

Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors
Homework #8 due at 4 pm on 03/30/2023

Lesson 10: Experimental Designs 03/31/2023

Topics to Cover: Two-factor factorial experiment; randomized block design; randomized blocks with sampling; repeated measures designs
Homework #9 due at 4 pm on 04/06/2023

Lesson 11: Categorical Data 04/07/2023

Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the 𝜒2 test; contingency tables; loglinear model
Homework #10 due at 4 pm on 04/13/2023

Lesson 12: Logistic and Multinomial Regression 04/14/2023

Topics to Cover: Logistic regression; multinomial regression
Homework #11 due at 4 pm on 04/20/2023

Lesson 13: Nonparametric Methods 04/21/2023

Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap
Homework #12 due at 4 pm on 04/27/2023

Lesson 14: Time Series 04/28/2023

Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA)
Homework #13 due at 4 pm on 05/04/2023

Lesson 15: Project Presentations 05/05/2023

Project Reports due at 4 pm on 05/04/2023

WEEKLY GRADES AND STUDENT COMMENTS

Week 15 - May 05, 2023

Comments from students

Week 14 - April 28, 2023

Comments from students

★ ★ ★ ★ ★
Everything is good

Week 13 - April 21, 2023

Comments from students

Week 12 - April 14, 2023

Comments from students

Week 11 - April 07, 2023

Comments from students

Week 10 - March 31, 2023

Comments from students

Week 9 - March 24, 2023

Comments from students

Week 8 - March 10, 2023

Comments from students

Week 7 - March 03, 2023

Comments from students

★ ★ ★ ★ ★
All good

Week 6 - February 24, 2023

Comments from students

★ ★ ★ ★ ★
Everything is good. Thank you.

★ ★ ★ ★ ★
This HW04 was not good with explanation and was hard to implement. It took a lot of my time to finish that. I hope from next week more description will provide by you and make it a little easier. Moreover, we also have a project to do.

Week 5 - February 17, 2023

Comments from students

★ ★ ★ ★ ★
Thanks for the lecture

★ ★ ★ ★ ★
I have some comments on weekly homework as follow: 1- we need rubrics for every homeworks since I missed the same grades for hw1(3 feedback) and hw2 (one feedback)!! 2- we need more details of requirements weekly homework, for example (For the curated PP, HR-E4 and HR-AW signals construct Q-Q plots. The plots should be done for each participant and laid out in the two-page format you are familiar with from Q1 in HW 1. The aim is to provide a comparative and insightful account of signal normality to the analyst. State your observations and thoughts) The solution showed to us in class was in four pages before and After and in Q2 mentioned in two-page! 3- we need the final solution of weekly homework.
Thank you!

Week 4 - February 10, 2023

Comments from students

★ ★ ★ ★ ★
No techincal difficulties this week, which is appreciated.

★ ★ ★ ★ ★
Today's class has lot of useful content. Thanks to professor for all the information provided in today's class about assignments and one more chance to makeup for Homework1

★ ★ ★ ★ ★
Very difficult and time consuming homeworks which require strong knowledge of R programming. Takes an entire week to complete leaving no time for other classes.

Week 3 - February 03 , 2023

Comments from students

★ ★ ★ ★ ★
Unfortunately the online portion had lots of technical problems today, especially towards the end of the first session and continuing throughout Vitalii's demo. The audio would cut out for minutes at a time. I have no complaints about the content, just the stream itself.

★ ★ ★ ★ ★
The calss is intersenting, however I need time to figure out the homework, I sugget to have one week for each homework since I am taking two classes besides this class. Please.. I have one comment to Vitalii please explain slowly since I am beginner in R language. Professor and Vitali and Fettah are doing greet job.

★ ★ ★ ★ ★
I have zero knowledge in R programming and for someone like me I felt the homeworks are very difficult.

★ ★ ★ ★ ★
Dear professor, I had no experience in R language. The pace of class as I felt is fast but I'm covering it up by listening again the recorded lectures. The assignment, I had felt difficult to solve and moving forward since the deadline is fixed at Wednesday, I'm concerned about the course work. Kindly requesting you to be considerate while giving assignments.

★ ★ ★ ★ ★
Good

★ ★ ★ ★ ★
I felt Assignment was too tough. Firstly , I am new to R and as the fisrt assignment was like more than beginners level. So I have a request that if there is any chance of reducing the assignment level so that I can learn from foundations.

★ ★ ★ ★ ★
I felt Homework 1 was too difficult to complete even the deadline was extended because I am not very much aware of R. I kindly request you to decline the level of difficulty if you can so that I could learn from basics.

★ ★ ★ ★ ★
I feel each assignment should have a duration of 1 week to complete them as it is my first time working with R and it consumes a lot of time. And we also have other subjects to work on.

★ ★ ★ ★ ★
The assignment is so difficult it is like i am understanding the topic but when it comes to assignment it is not helping that much. And the assignment is taking a lot of time which is causing problem for my other courses.

★ ★ ★ ★ ★
Please give one week for every assignment

★ ★ ★ ★ ★
Found the assignment somewhat interesting but di...

★ ★ ★ ★ ★
I feel HW01 is very difficult. I have followed the class and office hours but it was so hard for me to get my head around the assignment. If this is the difficulty level for HW01, I wonder how the level of upcoming homeworks is going to be.

★ ★ ★ ★ ★
Hope to extend the work time.

★ ★ ★ ★ ★
The homework 1 was very very difficult and it is taking a lot of time to solve the questions and even the questions are not 100% understandable to do the task. It is even affecting my other courses as i need to spend a lot of time on this course.

★ ★ ★ ★ ★
I felt that Homework-1 was so difficult. As we are new to R we cannot code to that level at the begining itself.I request to reduce the difficulty as the deadline was also a mere 3 days.

Week 2 - January 28, 2023

Comments from students

★ ★ ★ ★ ★
Professor and TA explanation is pretty good with examples.

Week 1 - January 20, 2023

Comments from students

★ ★ ★ ★ ★
Hope there was more time to explain the examples where form R studio.

★ ★ ★ ★ ★
The professor and TA has taught the class really well. The hands on session is very informative, I'd like to name it 'Crash Course on R'

★ ★ ★ ★ ★
Very good and informative session

★ ★ ★ ★ ★
If time permits, I'd recommend a very short introduction to tidyverse, and all the verbs that come with it. Especially the %>% (pipe operator) and dplyr seem very in-use these days.

★ ★ ★ ★ ★
Good session overall

★ ★ ★ ★ ★
The class pace can be bit slowed down for better understanding.

★ ★ ★ ★ ★
As someone who has never used R, I found the intro a bit rushed. However for most it would likely be fine. As we are given the recording, it allows me to rewatch with pause to absorbe it. \nAlso, I like that you are doing this - obtaining feedback early in the class. My first impression is that this will be unlike any university course I have ever taken, in a good way. I'm excited to see all of how it unfolds but from the materials perspective but also from an academic implementation perspective. \nThank you.

★ ★ ★ ★ ★
The class was interesting

★ ★ ★ ★ ★
Classes are interactive.

★ ★ ★ ★ ★
looks good, but if pace can be a bit slow , i think i can catch up easily.

★ ★ ★ ★ ★
It is good and i am looking forward to learn R much more.

★ ★ ★ ★ ★
The class was good as I learned the basics of R language

★ ★ ★ ★ ★
Looking forward to have a great semester.

★ ★ ★ ★ ★
No, comments everything was good.

★ ★ ★ ★ ★
The explanation is quite good and it would be more interesting if it includes much more kinds of examples.

★ ★ ★ ★ ★
Class was very Useful. Thank you.

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COURSES

COSC 6323: Statistical Methods - SPRING 2026

KEY INFORMATION

COURSE OUTLINE

WEEKLY GRADES AND STUDENT COMMENTS

COSC 6323: Statistical Methods - SPRING 2025

KEY INFORMATION

COURSE OUTLINE

WEEKLY GRADES AND STUDENT COMMENTS

COSC 6323: Statistical Methods - SPRING 2024

END OF SEMESTER COMMENTS

KEY INFORMATION

COURSE OUTLINE

WEEKLY GRADES AND STUDENT COMMENTS

COSC 6323: Statistical Methods - SPRING 2023

END OF SEMESTER COMMENTS

KEY INFORMATION

COURSE OUTLINE

WEEKLY GRADES AND STUDENT COMMENTS