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
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 01/17/2025
- 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 03/28/2025
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: One sample; two independent samples; more than two samples; rank correlation; the bootstrap
Lesson 14: Time Series 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
Project Presentations 05/02/2025
WEEKLY GRADES AND STUDENT COMMENTS
Week 7 - February 28, 2025
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Week 6 - February 23, 2025
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Comments from students |
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I missed the class last week, catching up with recording. |
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I found this class really interesting. |
Week 5 - February 16, 2025
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I was away at the International Neuropsychological Society conference and missed the lecture, but I plan to watch the recording. |
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A list of tasks for Assignment 1 would be helpful. |
Week 4 - February 09, 2025
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Comments from students |
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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
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Comments from students |
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I like that the class is divided by theorical and practical part |
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I would appreciate a more structured way of teaching, with proper notes and/or slides. |
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This week's class was really informative and enjoyable. Thank you! |
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The class is good and interesting |
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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. |
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.