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regression course online
#1
The linear regression course online is a popular technique in quantitative fields. Linear regression, also called OLS or linear fit, predicts response variables from explanatory variables and provides a means of interpreting the effects of the explanatory variables. Linear regression falls under a special class of statistical techniques called generalized linear models (GLMs). In this blog, we are going to discuss free online resources that can help the Northwestern community get started and familiarize themselves with linear regression in R.

For a Y response, linear regression predicts Y using the formula

Y = b1 * (explanatory variable 1) + b2 * (explanatory variable 2) +…. + bm * (explanatory variable m)

where b1, b2, .. bm are coefficients of the linear model. The coefficients are obtained from data by fitting the model.

This tutorial provides a useful introduction to linear regression, especially for those with no significant mathematical background. It briefly describes what a linear model is for a single explanatory variable - the theory of a straight line. Then we will explain how to fit the straight line to data with R (i.e. how to do linear regression) and use that straight line to predict invisible data.
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