KEY INFORMATION
- Prof. Ioannis Pavlidis — ipavlidis[@]uh.edu — Office Hours: Fri 3–4 pm @ TEAMS
- Fettah Kiran — fkiran[@]uh.edu — Office Hours: Tue 12–1 pm @ TEAMS
- 3 × 33.33% — Project milestones
Grade thresholds: A ≥ 93, A− ≥ 90, B+ ≥ 85, B ≥ 80, B− ≥ 75, C+ ≥ 70, C ≥ 65, C− ≥ 60, D+ ≥ 55, D ≥ 50, F < 50.
- Friday, 4:00–7:00 pm @ Room 315 in Health 1 and @ TEAMS
- Donna L. Mohr, William J. Wilson, Rudolf J. Freund. Statistical Methods. 4th Edition. Academic Press, 2021.
- Devore, J.L., Berk, K.N. and Carlton, M.A. Modern Mathematical Statistics With Applications. Springer, 2012.
- Terence C. Mills. Applied Time Series Analysis. Academic Press, 2019.
COURSE OUTLINE
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.
01/24/2025
Topics to Cover: Probability; discrete probability distributions; continuous probability distributions; sampling distributions.
01/31/2025
Topics to Cover: Hypothesis testing; estimation; sample size; assumptions.
02/07/2025
Topics to Cover: Inferences on the population mean; inferences on a proportion; inferences on the variance; assumptions.
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.
02/21/2025
Topics to Cover: Analysis of variance; linear model; assumptions; specific comparisons; random models; unequal sample sizes; analysis of means.
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
03/07/2025
Topics to Cover: The multiple regression model; estimation of coefficients; inferential procedures; correlations; special models; multicollinearity; variable selection; detection of outliers.
03/21/2025
Topics to Cover: The dummy variable model; unbalanced data; models with dummy and interval variables; weighted least squares; correlated errors.
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
04/04/2025
Topics to Cover: Hypothesis test for a multinomial population; goodness of fit using the χ² test; contingency tables; loglinear model.
04/11/2025
Topics to Cover: Logistic regression; multinomial regression.
04/18/2025
Topics to Cover: One sample; two independent samples; more than two samples; rank correlation; the bootstrap.
04/25/2025
Topics to Cover: Time series and their features; stationary processes (ARMA); nonstationary processes (ARIMA).
Project Milestone 3 due at 4 pm on 04/25/2025
05/02/2025
Topics to Cover: Final project presentations.
GRADES AND STUDENT COMMENTS
Weekly Grades and Student Comments
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!
Comments from students
★★★★★
I really find this clase interesting and I liked that it is relevant for our project.
Comments from students
★★★★★
I think that the topics that we saw in this class are highly important for statistical research.
Comments from students
★★★★★
Yes, I really liked learning about the Loglinear Model
Comments from students
★★★★★
I really like the topic and find it interesting
Comments from students
★★★★☆
It was a useful class, and very interesting
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.
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.
Comments from students
★★★★☆
I missed the class last week, catching up with recording.
★★★★★
I found this class really interesting.
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.
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.
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.
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.
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.