Sorry, result is not the same, since our datasets are different. I also run
lm() based on the dataset that used in glm(). THe results are exactly the
same:
> summary(lm(Y ~ X + F))
Call:
lm(formula = Y ~ X + F)
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
Residual standard error: 0.2469 on 38 degrees of freedom
Multiple R-squared: 0.9612, Adjusted R-squared: 0.9592
F-statistic: 471.1 on 2 and 38 DF, p-value: < 2.2e-16
===============
The dataset is given below:
> cbind(Y,X,F)
Y X F
[1,] -0.28473266 -1.00 1
[2,] -0.59041310 -0.95 1
[3,] -0.50431754 -0.90 1
[4,] -0.60095969 -0.85 1
[5,] -0.45849905 -0.80 1
[6,] -0.48287208 -0.75 1
[7,] -0.49598666 -0.70 1
[8,] -0.08746758 -0.65 1
[9,] -0.18665177 -0.60 1
[10,] -0.01007210 -0.55 1
[11,] -0.45765308 -0.50 1
[12,] -0.27318684 -0.45 1
[13,] 0.07638855 -0.40 1
[14,] 0.27043727 -0.35 1
[15,] 0.26926216 -0.30 1
[16,] -0.43047783 -0.25 1
[17,] 0.40884468 -0.20 1
[18,] -0.14638563 -0.15 1
[19,] -0.31374179 -0.10 1
[20,] -0.15028159 -0.05 1
[21,] -0.12540519 0.00 1
[22,] 1.58015611 0.05 2
[23,] 1.68200774 0.10 2
[24,] 2.02821901 0.15 2
[25,] 2.02359285 0.20 2
[26,] 2.14133171 0.25 2
[27,] 2.06931685 0.30 2
[28,] 2.05561726 0.35 2
[29,] 2.35720999 0.40 2
[30,] 1.96134404 0.45 2
[31,] 2.26144356 0.50 2
[32,] 2.24454620 0.55 2
[33,] 2.55707426 0.60 2
[34,] 2.18732022 0.65 2
[35,] 1.90950697 0.70 2
[36,] 2.10371010 0.75 2
[37,] 2.18266009 0.80 2
[38,] 2.18490441 0.85 2
[39,] 2.45248295 0.90 2
[40,] 2.79851838 0.95 2
[41,] 1.83514341 1.00 2
--
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