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
Repeatable: No Grade Type: Regular
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.