the data science process (target variablex (predictor variablefigure linear regression tries to fit line while minimizing the distance to each point okaywe cheated herequite heavily so we created predictor values that are meant to predict how the target variables behave for linear regressiona "linear relationbetween each (predictorand the (targetvariable is assumedas shown in figure wehowevercreated the target variablebased on the predictor by adding bit of randomness it shouldn' come as surprise that this gives us well-fitting model the results summary(outputs the table in figure mind youthe exact outcome depends on the random variables you got model fithigher is better but too high is suspicious -value to show whether predictor variable has significant influence on the target lower is better and < is often considered "significant linear equation coefficients xl figure linear regression model information output