You built a linear regression model by simply using the
lm() function on your data. You also used the LINE approach to make sure that your model satisfied the assumptions of linear regression. Building SLRs is often straightforward, but it is very important that you know how to interpret the output.
There is a lot of information available within a linear regression model. You can see an expanded output by using the
The following is the output:
Call: lm(formula = revenues ~ marketing_total, data = adverts) Residuals: Min 1Q Median 3Q Max -8.6197 -1.8963 -0.0006 2.1705 9.3689 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.006696 0.635590 50.36 <2e-16 *** marketing_total 0.051929 0.002437 21.31 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.054 on 170 degrees...