Understanding and Applying Linear Regression

Linear regression is a powerful tool, applicable in many common situations in business and data analysis. This course will cover both the theory and implementation of linear regression in Excel, Python, and R.
Course info
Rating
(46)
Level
Beginner
Updated
Feb 10, 2017
Duration
4h 12m
Table of contents
Course Overview
Modeling Relationships Between Variables Using Regression
Understanding Simple Regression Models
Implementing Simple Regression Models in Excel
Implementing Simple Regression Models in R
Implementing Simple Regression Models in Python
Understanding Multiple Regression Models
Implementing Multiple Regression Models in Excel
Implementing Multiple Regression Models in R
Implementing Multiple Regression Models in Python
Description
Course info
Rating
(46)
Level
Beginner
Updated
Feb 10, 2017
Duration
4h 12m
Description

Linear regression is a key technique used in forecasting and in quantifying cause-effect relationships. In this course, Understanding and Applying Linear Regression, you will learn how to identify patterns in data and test those relationships for statistical soundness. You will also learn simple regression and multiple regression. Finally, you'll explore the use of categorical variables. When you're finished with this course, you will have a strong applied knowledge of regression in Excel, R, and Python that will help with factor analysis, logistic regression, and other powerful techniques.

About the author
About the author

An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and studied at Stanford and INSEAD. He has worn many hats, each of which has involved writing code and building models. He is passionately devoted to his hobby of laughing at his own jokes.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Vitthal Srinisvasan. Welcome to my course, Understanding and Applying Linear Regression. I am co-founder of the start-up named Loonycorn, and before this I worked at Google and studied at Stanford. Linear regression is really the first crossover hit from machine learning. It became a mainstream technique taught widely in business schools and in courses on psychology, finance, and many other disciplines far before machine learning had achieved the popularity that it has today. See how linear regression is widely used in forecasting and quantifying cause/effect relationships. Some of the major topics that we will cover include building regression models in Excel, Python, and R, explaining variance and forecasting using regression, the use of categorical variables, and the interpretation of a regression statistic such as the R-squared, standard errors, E-statistics, and F-statistics. By the end of this course, you'll know how to build the robust well-designed regression models in any one of three powerful tools, Excel, R, and Python. You will apply this knowledge to explaining the performance of financial stocks and see how flaws in regression models can be identified and fixed. You will also know how to incorporate categorical variables into these models the right way. Before beginning the course, you should be familiar with at least one out of Excel, Python, and R. From here, you should feel comfortable diving into courses on topics such as factor analysis, principal component analysis, logistic regression, and machine learning. I hope you'll join me on this journey to learn how to connect the dots with the course, Understanding and Applying Linear Regression at Pluralsight.