This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless. This course is part of the Performance Based Admission courses for the Data Science program. In this course, we will learn what happens to our regression model when these assumptions have not been met. How can we detect these discrepancies in model assumptions and how do we remediate the problems will be addressed in this course. Upon successful completion of this course, you will be able to: -describe the assumptions of the linear regression models. -use diagnostic plots to detect violations of the assumptions of a linear regression model. -perform a transformation of variables in building regression models. -use suitable tools to detect and remove heteroscedastic errors. -use suitable tools to remediate autocorrelation. -use suitable tools to remediate collinear data. -perform variable selections and model validations.
Module 2 Introduction Video
Skills You'll Learn
Data Validation, Time Series Analysis and Forecasting, Regression Analysis, Statistical Hypothesis Testing, Correlation Analysis, Exploratory Data Analysis, Plot (Graphics), Feature Engineering, Probability Distribution, Statistical Modeling, Statistical Inference, Data Transformation, Statistical Analysis, Linear Algebra, Statistical Methods, R Programming
From the lesson
Module 2: Model Diagnostics and Remediation Part II
Welcome to Module 2 鈥� This module will cover four topics including: Weighted Least Squares, Autocorrelation, Multicollinearity, and Variable Selection and Model Validation. There is a lot to read, watch, and consume in this module so, let鈥檚 get started!
Taught By
Kiah Ong
Associate Chair and Director of Undergraduate Studies of the Department of Applied Mathematics