Hi to all
I think this is more an general question to GLMs.
The result was better in all prior GLMs when I admitted the non
significant factors, but this is the first time that the result is worse
than before. What could be the reason for that?
glm(data1~data2+data3+data4+data5+data6,family="gaussian")
The result:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.3670852 0.8978306 3.750 0.000445 ***
data2 0.0002623 0.0001168 2.245 0.029024 *
data3 -0.9742336 0.5032712 -1.936 0.058337 .
data4 0.0628245 0.1503066 0.418 0.677686
data5 -0.0438871 0.0740210 -0.593 0.555818
data6$ -0.0012216 0.0187702 -0.065 0.948357
if I test only or lm() of course
glm(data1~data2,family="gaussian")
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.473e+00 2.787e-01 8.876 2.86e-12 ***
data2 7.289e-05 7.485e-05 0.974 0.334
Kind regards Knut
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