Data Science: Linear Regression in R
| Learning Experience | 9.2 |
|---|
Learn how to implement linear regression in R, one of the most common statistical modeling approaches in data science. Also, learn to adjust confounding.
Introduction
Learn how to implement linear regression in R, one of the most common statistical modeling approaches in data science.
About this course
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.
We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.
What you will learn from this course on Linear Regression in R?
- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression using R
Syllabus on Linear Regression in R:
1. Introduction and Welcome
2. Section 1: Introduction to Regression
3. Section 2: Linear Models
4. Section 3: Confounding
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Description
Introduction
Learn how to implement linear regression in R, one of the most common statistical modeling approaches in data science.
About this course
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.
We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.
What you will learn from this course on Linear Regression in R?
- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression using R
Syllabus on Linear Regression in R:
1. Introduction and Welcome
2. Section 1: Introduction to Regression
3. Section 2: Linear Models
4. Section 3: Confounding
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
- EDX
- Harvard University
- Online Course
- Self-paced
- Beginner
- 1-3 Months
- Free Course (Affordable Certificate)
- English
- R
- None Pre-requisite
- Data Analysis Data Science Data Science with 'R' Practical Statistics
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