Adam B Kashlak: Teaching
Stat 378: Applied Regression Analysis
- Topics: simple & multiple regression, hypothesis testing, confidence & prediction intervals, residuals, tranformations, polynomial regression, splines, measures of influence & leverage, multicollinearity, variable selection, penalized regression, logistic & Poisson regression.
- Current Course Notes
- Years: Fall 2017, 2018, 2019, 2022
Stat 413: Computing for Data Science
- Topics: Survey of contemporary languages/environments suitable for algorithms of Statistics and Data Science. Introduction to Monte Carlo methods, random number generation and numerical integration in statistical context and optimization for both smooth and constrained alternatives, tailored to specific applications in statistics and machine learning.
- Current Course Notes
- Years: Winter 2023
Stat 479: Time Series Analysis
- Topics: Stationary series, spectral analysis, models in time series: autoregressive, moving average, ARMA and ARIMA. Smoothing series, computational techniques and computer packages for time series.
- Current Course Notes
- Years: Winter 2020, 2021
Stat 568: Design and Analysis of Experiments
- Topics: one-way ANOVA, multiple comparisons, Cochran's theorem, blocking, Latin squares, split plot, covariates, 2-level factorial designs, fractional factorial designs, 3-level designs, response surfaces, mixed level designs, Plackett-Burman designs.
- Current Course Notes
- Years: Winter 2018, 2019, 2020, 2021
Stat 571 - Probability and Measure
- Topics: Measure and integration, Laws of Large Numbers, convergence of probability measures. Conditional expectation.
- Current Course Notes
- Years: Winter 2022, 2023
Last Updated: 1 May 2023