This course explores advanced statistical modeling techniques essential for data science. Topics include multivariate statistical methods, advanced regression techniques, and time series analysis. Students will develop skills in dimension reduction, predictive modeling, and forecasting, applying methods such as PCA, GLMs, mixed-effects models, ARIMA, and GARCH to real-world datasets.
Athena Title
Advanced Stat Modeling
Non-Traditional Format
This course will be taught 95% or more online.
Prerequisite
STAT 6384E and STAT 6385E and STAT 6386E
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will apply multivariate statistical methods such as PCA, factor analysis, and discriminant analysis to uncover patterns and relationships in complex datasets.
Students will develop and interpret advanced regression models, including generalized linear models and mixed-effects models, to analyze structured and unstructured data.
Students will implement the Expectation-Maximization (EM) algorithm for handling missing data and optimizing model estimation in complex statistical problems.
Students will analyze and forecast time series data using ARMA, ARIMA, and seasonal models to identify trends, seasonality, and long-term patterns.
Students will evaluate and compare time series models by assessing their performance using diagnostics and statistical validation techniques.
Students will construct models for financial and economic time series data using ARCH and GARCH techniques to analyze volatility and risk.
Students will integrate statistical modeling techniques into real-world data science applications, effectively communicating insights through visualization and interpretation.
Topical Outline
1. Multivariate Statistical Methods
o Principal Component Analysis
o Factor Analysis
o Canonical Correlation Analysis
o Cluster Analysis
o Discriminant Analysis
2. Advanced Regression Techniques
o Generalized Linear Models
o Mixed-Effects Models
o EM Algorithm
3. Time Series Analysis
o Exploring Time Series Data
o Statistical Basics for Time Series Analysis
o ARMA Models
o ARMA Fitting and Forecasting
o Model Assessment
o ARIMA and Seasonal Models
o Intervention Models
o ARCH and GARCH Models
o Time Series Regression
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
