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Shalabh and S. S. Dhar
> summary(lm(rain~ windspeed+precipitation+relhum))
Call:
lm(formula = rain ~ windspeed + precipitation + relhum)
Residuals:
Min
1Q
Median
3Q
Max
-1.09906 -0.87756 -0.07462 0.70157 1.39258
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
22.2817
6.3460
3.511 0.012654 *
windspeed
1.5187
0.2169
7.001 0.000423 ***
precipitation
0.5504
0.1252
4.395 0.004592 **
relhum
1.0963
0.0598 18.334 1.7e-06 ***
--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.138 on 6 degrees of freedom
Multiple R-squared: 0.9865,
Adjusted R-squared: 0.9797
F-statistic: 145.8 on 3 and 6 DF, p-value: 5.395e-06
The screen shot of this outcome when executed over the R software is as follows:
Now we understand the interpretation of this outcome. We want to fit here a
model y = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + , where y denotes rain, X 1 denotes
windspeed, X 2 denotes precipitation, and X 3 denotes relhum for which
observations (y i , x 1i , x 2i , x 3i ), i = 1, 2, . . . , 10 are available.