Linear Regression
Master linear regression techniques, from simple to multiple models. Learn to use R for data analysis, model fitting, and statistical inference.
Master linear regression techniques, from simple to multiple models. Learn to use R for data analysis, model fitting, and statistical inference.
This intermediate-level course focuses on linear regression, covering simple and multiple linear regression models, and regression with qualitative predictors. Designed for individuals with a technical background in mathematics, statistics, computer science, or engineering, it provides a comprehensive understanding of regression techniques. Students will learn to use R for data analysis, model fitting, and statistical inference. The course covers least squares estimation, properties of estimators, hypothesis testing, and prediction intervals. By the end, students will be able to apply regression models to real-world data and interpret results effectively.
4.6
(13 ratings)
Instructors:
English
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What you'll learn
Describe the assumptions of linear regression models
Use R to fit simple and multiple linear regression models
Interpret and draw conclusions from linear regression analyses
Perform statistical inference based on regression models
Apply least squares estimation and understand its properties
Work with qualitative predictors in regression models
Skills you'll gain
This course includes:
3 Hours PreRecorded video
13 quizzes, 4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to linear regression, covering simple linear regression, multiple linear regression, and regression models with qualitative predictors. Students will learn the theoretical foundations of regression analysis and gain practical skills using R for data analysis and model fitting. The curriculum is divided into four modules: Simple Linear Regression, Multiple Linear Regression, Regression Models with Qualitative Predictors, and a Summative Assessment. Throughout the course, students will learn to derive parameter estimations, perform statistical inferences, create prediction intervals, and interpret regression results. The course emphasizes both theoretical understanding and practical application, preparing students for data-driven roles in various industries.
Simple linear regression
Module 1 · 11 Hours to complete
Multiple Linear Regression
Module 2 · 5 Hours to complete
Regression Models with Qualitative Predictors
Module 3 · 6 Hours to complete
Summative Course Assessment
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Chair and Director of Undergraduate Studies in Applied Mathematics at Illinois Tech
Kiah Ong is the Associate Chair and Director of Undergraduate Studies in the Department of Applied Mathematics at Illinois Tech. He teaches several courses, including "Linear Regression," "Model Diagnostics and Remedial Measures," and "Variable Selection, Model Validation, Nonlinear Regression." His courses focus on statistical modeling techniques, providing students with the necessary skills to analyze data effectively and make informed decisions based on their findings. Through a combination of theoretical concepts and practical applications, Kiah Ong prepares students for advanced studies and careers in data analysis and applied mathematics.
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4.6 course rating
13 ratings
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