Author: rrasireddy

Linear regression is a statistical technique for finding the relationship between a variable (Y) and (X)

It aims to find the best-fitting line.

The general form of a simple linear regression equation:
Y=b0+b1X+ε

Where:

• Y is a variable (the one we want to predict).
• X is a  variable (the one we use to make predictions).
• b0​ is the intercept, which represents the predicted value of $Y$ when $X$ is zero.
• b1​ is the slope, which represents the change in $Y$ for a one-unit change in $X$.
• $\epsilon$ represents the error term.

Observation from the Dataset

• The Dataset provides information on Diabetes,  Obesity, and Inactivity of every state in the USA for the year 2018
• Diabetes has 3142 samples.
• Obesity has 363 samples.
• Inactivity has 1370 samples.

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