Data Science: Linear Regression in R

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Product is rated as #32 in category Data Science
Learning Experience9.2

Learn how to implement linear regression in R, one of the most common statistical modeling approaches in data science. Also, learn to adjust confounding.

Last updated on October 14, 2021 11:13 pm

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

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Verified Certificate at

$99.00

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  • 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
Learning Experience
9.2
PROS: Course forums are full of people trying to figure out how to get the code to work. Videos are concise, to the point & recaps with Key Points listing. Videos examples codes are extremely useful. Assessments are a clear application of the lesson concepts, without bugs, and at the right complexity level.
CONS: Professors need to make concrete communication as well as improve knowledge. Need to focus more on execution of practical examples. Improvement in basic programming concepts as well as syntax. The lack of depth, and low level of difficulty.

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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Data Science: Linear Regression in R
Data Science: Linear Regression in R

$99.00

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