COSC 6323: Statistical Methods – SPRING 2026

By /
KEY INFORMATION
Instructors
  • Prof. Ioannis Pavlidis — ipavlidis[@]uh.edu — Office Hours: Fri 3–4 pm @ Health 1, Room 306 and @ TEAMS
Grading and Project
  • 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.

Day, Time, and Room
  • Friday, 4:00–7:00 pm @ Room 315 in Health 1 and @ TEAMS
Required Software
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. Modern Mathematical Statistics With Applications. Springer, 2012.
  3. Terence C. Mills. Applied Time Series Analysis. 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.

Project Milestone 1 due at 4 pm on 02/27/2026

Lesson 7: Linear Regression
03/06/2026

Topics to Cover: The regression model; estimation of parameters; inferences for regression; correlation; regression diagnostics.

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 03/27/2026

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: Time Series
04/24/2026

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

Lesson 14: Nonparametric Methods
05/01/2026

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

Scroll to Top