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Data Regression

402432 - Data Regression
Faculty

Faculty of Information Technology

Department

Data Science and AI

Credit Hours

3

Pre-requisite

402331

Co-requisite

-

Distribution

3 + 0
This course introduces students to data regression. Data regression is the most widely used statistical technique. It estimates relationships between independent variables (predictors) and a dependent variable (outcome). Regression models can be used to help understand and explain relationships among variables; they can also be used to predict outcomes. In addition, students will learn how to derive multiple linear regression models, how to use software to implement them, and what assumptions underlie the models. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.