KEY INFORMATION
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 π2 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