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

★ ★ ★ ★ ★
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.

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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

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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.

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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.

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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.

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Looking forward to it.

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