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Simple regression model

The linear regression model is a learning algorithm that is concerned with predicting a quantitative (also known as numerical) response using a combination of explanatory features (or inputs or predictors).

A simple linear regression model with only one feature takes the following form:

y = beta0 + beta1x

Here:

  • y is the predicted numerical value (response) → sales
  • x is the the feature
  • beta0 is called the intercept
  • beta1 is the coefficient of the feature x → TV ad

Both beta0 and beta1 are considered as model coefficients. In order to create a model that can predict the value of sales in the advertising example, we need to learn these coefficients because beta1 will be the learned effect of the feature x on the response y. For example, if beta1 = 0.04, it means that an additional $100 spent on TV ads is associated with an increase in sales by four widgets. So, we need to go ahead and see how can we learn these coefficients.

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