Consider the following regression results:
Call:
lm(formula = y ~ x, data = d)
Residuals:
Min 1Q Median 3Q Max
-2.14867 -0.82868 -0.07472 0.66596 2.54119
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0001676 0.1254992 0.001 0.999
x 1.2492437 0.1241613 10.061 2.04e-14
Residual standard error: 0.9786 on 59 degrees of freedom
Multiple R-squared: 0.6318, Adjusted R-squared: 0.6255
F-statistic: 101.2 on 1 and 59 DF, p-value: 2.043e-14
Describe how the response y
depends on the regressor x
.
The presented results describe a linear regression.
The mean of the response y
increases with increasing x
.
If x
increases by 1 unit then a change of y
by about 1.25 units can be expected.
Also, the effect of x
is significant at the 5 percent level.