glm() is another choice. Using glm(), you response variable can be a discrete
random bariable, however, you need to specify the distribution in the
argument: family = " distriubtion name"
Use Teds simulated data and glm(), you get the same result as that produced
in lm():
> summary(glm(Y ~ X + F, family="gaussian"))
Call:
glm(formula = Y ~ X + F, family = "gaussian")
Deviance Residuals:
Min 1Q Median 3Q Max
-0.53796 -0.16201 -0.08087 0.15080 0.47363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03723 0.08457 0.440 0.662267
X 0.51009 0.13036 3.913 0.000365 ***
FB 1.82578 0.15429 11.833 2.6e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.06096497)
Null deviance: 59.7558 on 40 degrees of freedom
Residual deviance: 2.3167 on 38 degrees of freedom
AIC: 6.5418
Number of Fisher Scoring iterations: 2
--
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