Consider the following regression results:
Call: lm(formula = log(y) ~ log(x), data = d) Residuals: Min 1Q Median 3Q Max -6.6119 -1.4477 0.1735 1.5365 4.8160 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1264 0.2520 0.501 0.618 log(x) 0.2870 0.2279 1.259 0.212 Residual standard error: 2.251 on 79 degrees of freedom Multiple R-squared: 0.01967, Adjusted R-squared: 0.007263 F-statistic: 1.585 on 1 and 79 DF, p-value: 0.2117
Describe how the response
y depends on the regressor
The presented results describe a log-log regression.
The mean of the response
y increases with increasing
x increases by 1 percent then a change of
y by about 0.29 percent can be expected.
However, the effect of
x is not significant at the 5 percent level.