2021-22 University Bulletin 
    
    Mar 28, 2024  
2021-22 University Bulletin [ARCHIVED CATALOG]

MTH (0144) 364 - Introduction to Regression Analysis and Big Data Analytics


Credits: 3.00

Students will utilize regression analysis as a statistical problem solving methodology. Students will learn polynomial regression, nonparametric regression, analysis of variance (ANOVA), variable selection, model validation, multicollinearity, ridge regression, principal-component regression, Poisson regression, classification and regression tree (CART), etc. Students will explore the area of the analysis of big data. 

Prerequisite 1: MTH 225 CSC 263  
Course Learning Goals: Students will:

• Explain, use, and evaluate simple linear regression models. This will be assessed by Quiz 1 and the Examination 1. 

● Explain, use, and evaluate multiple regression models. This will be assessed by Quiz 2 and the Examination 1.

● Apply categorical predictors into regression models and non-linear regression models. This will be assessed by Quiz 3 and Examination 1.

● Apply Analysis of Variance in the context of regression models. This will be assessed by Quizzes 4-6, Examination 1, and Examination 2.

● Explain and apply the Big Data flow. This will be assessed by Quizzes 7-10 and the Final Examination. 

● Utilize and apply the Data Analytics lifecycle to Big Data analytics problems. This will be assessed by Quizzes 7-10 and the Final Examination.