Dear all

I have observations done in 4 different classes and the between classes
*variance* is too high that I decided to run a model without pooling the
*variance*. I used the following code first :
                   model<-lm(y~x+factor(class))
and got the following output:
Coefficients:
                Estimate Std. Error t value Pr(>|t|)
(Intercept)     52.41405   17.38161   3.015  0.00658 **
x                0.27679    0.07387   3.747  0.00119 **
factor(class)2  92.68083   32.26645   2.872  0.00912 **
factor(class)3 197.82029   33.24916   5.950 6.63e-06 ***
factor(class)4 105.61266   55.18373   1.914  0.06937 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 43.07 on 21 degrees of freedom
Multiple R-squared:  0.9206,    Adjusted R-squared:  0.9055
F-statistic: 60.91 on 4 and 21 DF,  p-value: 2.976e-11

My understanding of this output is that class 1 is used as a baseline
(constant) and each other class's p values means for example the dependent
value in class 2 is significantly different from that of class 1.
Now I ran the model again, but without using a constant i.e
                    model<-lm(y~x+factor(class)-1)
and got the following output:
Coefficients:
                Estimate Std. Error t value Pr(>|t|)
x                0.27679    0.07387   3.747  0.00119 **
factor(class)1  52.41405   17.38161   3.015  0.00658 **
factor(class)2 145.09488   39.42651   3.680  0.00139 **
factor(class)3 250.23434   40.61189   6.162 4.11e-06 ***
factor(class)4 158.02672   64.09549   2.465  0.02238 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 43.07 on 21 degrees of freedom
Multiple R-squared:  0.9801,    Adjusted R-squared:  0.9754
F-statistic: 207.1 on 5 and 21 DF,  p-value: < 2.2e-16

Can somebody please tell me how to interpret this one now? what do the
classes' P values mean ? Do they merely show if they significantly
contribute to the model or whether they are significantly different from
the overall mean or not? Does it mean if one class had a p value > 0.05 it
would mean the observations from that class are not significantly
contributing to the model?

Thanks in advance

David

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