Dear colleagues,
I have performed the same analysis using the GLM
module of three statistical softwares: SYSTAT 10, JMP
4.0.2 and R 1.6.2 (see below for more details).
Although SYSTAT and R give roughly the same level of
significance for all variables, JMP yield a 20 percent
difference in probability for a categorical variable.
In fact, this difference is so important that I can
call this variable significant. Incidentally, Tukey's
test is in accordance with this result. Which
statistical software should I believe?
Thank you in advance for your insight.
Yves Claveau
DETAILS ON PERFORMED STATISTICAL ANALYSES
The categorical variable I am writing about is ESP
The model used is:
ptro=CONSTANT+classl+ht+esp+classl*ht+classl*esp+ht*esp+classl*ht*esp
Where:
- ptro is the dependent variable
- CONSTANT the constant in the model (defaut
procedure)
- classl a categorical variable with two classes
- ht a continuous variable
- esp a categorical variable with two classes
The results for each package are:
R 1.6.2
Call:
glm(formula = PTRO ~ ESP. * HT * CLASSL., family =
gaussian,
data = dataa)
Deviance Residuals:
Min 1Q Median 3Q Max
-20.21973 -4.41060 -0.03971 4.77046 14.29097
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
(Intercept) 35.54604 4.65265 7.640 3.41e-09
***
ESP -13.12051 12.32455 -1.065 0.294
HT 0.08005 0.04374 1.830 0.075 .
CLASSL 1.09480 5.54809 0.197 0.845
ESP:HT 0.01694 0.12375 0.137 0.892
ESP:CLASSL 5.89693 15.41378 0.383 0.704
HT:CLASSL -0.01952 0.04682 -0.417 0.679
ESP:HT:CLASSL -0.05547 0.13217 -0.420 0.677
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.'
0.1 ` ' 1
(Dispersion parameter for gaussian family taken to be
59.17901)
Null deviance: 4567.3 on 45 degrees of freedom
Residual deviance: 2248.8 on 38 degrees of freedom
AIC: 327.46
Number of Fisher Scoring iterations: 2
SYSTAT 10
Dep Var: PTRO N: 49 Multiple R: 0.7241 Squared
multiple R: 0.5244
Analysis of Variance
Source Sum-of-Squares df Mean-Square F-ratio P
ESP 113.6878 1 113.6878 1.6551
0.2055
CLASSL 20.6118 1 20.6118 0.3001
0.5868
HT 239.7713 1 239.7713 3.4908
0.0689
CLASSL*HT 26.3909 1 26.3909 0.3842
0.5388
CLASSL*ESP 5.9755 1 5.9755 0.0870 0.7695
ESP*HT 2.6415 1 2.6415 0.0385 0.8455
CLASSL*ESP*HT 12.9459 1 12.9459 0.1885
0.6665
Error 2816.1893 41 68.6875
JMP 4
RSquare 0.52438
RSquare Adj 0.443177
Root Mean Square Error 8.287795
Mean of Response 42.78898
Observations (or Sum Wgts) 49
Analysis of Variance
Source DF Sum of Squares Mean Square F Ratio
Model 7 3104.9018 443.557 6.4576
Error 41 2816.1893 68.688 Prob > F
C. Total 48 5921.0910 <.0001
Effect Tests
Source Nparm DF Sum of Squares F Ratio Prob > F
ESP 1 1 636.09249 9.2607 0.0041
CLASSL 1 1 8.26185 0.1203 0.7305
HT 1 1 239.77125 3.4908 0.0689
HT*CLASSL 1 1 26.39087 0.3842 0.5388
ESP*CLASSL 1 1 12.18491 0.1774 0.6758
ESP*HT 1 1 2.64154 0.0385 0.8455
ESP*HT*CLASSL 1 1 12.94593 0.1885 0.6665
__________________________________________________________
Lèche-vitrine ou lèche-écran ?
magasinage.yahoo.ca
______________________________________________
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
