Advanced Machine Learning with R

Linear regression

Let's get started with the spurious regression then, which I have seen implemented in the real world far too often. Here we simply build a linear model and examine the results:

    > fit.lm <- lm(Temp ~ CO2, data = climate)    > summary(fit.lm)    Call:    lm(formula = Temp ~ CO2, data = climate)    Residuals:         Min       1Q  Median      3Q     Max     -0.36411 -0.08986 0.00011 0.09475 0.28763     Coefficients:                  Estimate  Std. Error  t value     Pr(>|t|)     (Intercept) -2.430e-01   2.357e-02   -10.31   <2e-16 ***    CO2          7.548e-05   5.047e-06    14.96   <2e-16 ***    ---    Signif. codes:     0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1    Residual standard error: 0.1299 on 93 degrees of freedom    Multiple R-squared: 0.7063, Adjusted R-squared: 0.7032  F-statistic: 223.7 on 1 and 93 DF, p-value: ...