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Daily Data Science Tip #8
What is a Linear Regression model, and what is its purpose?
Linear regression model are governed by a linear function expression (y = f(x) = a + bx). Linear models have a commonplace in predictive analysis. Predictive analysis involves the modelling of the correlation between two or more component.
A basic stock prediction algorithm could leverage a linear regression model based on previous stock prices and a time component. Another example is the prediction of a person height based on their weight.
To conduct either of the examples of the prediction mentioned, we need to have two correlated components: a predictor and a dependent variable. The two components must be correlated somehow.
A positive correlation will imply that if there is an increase in one component, the other increases. A negative correlation means that if there is an increase in one component, the other decreases, or vice versa.
