I am sorry,
I have a problem. When I use the "predict" function I am always obtaining the
same result and I don't know why. In adittion, the intercept and the residual
values I get are wrong too.
std:
[1] 0.068 0.117 0.167 0.269 0.470 0.722
Concentration:
[1] 3.90625 7.81250 15.62500 31.25000 62.50000 125.00000
replica1:
Replica.1
1 0.080
2 1.325
3 1.309
4 1.072
5 1.595
6 1.384
replica2:
Replica.2
1 0.098
2 1.335
3 1.271
4 1.187
5 1.569
6 1.268
regresion <- lm(std ~ Concentration, mydata):
Call:
lm(formula = std ~ Concentration, data = mydata)
Residuals:
1 2 3 4 5 6
-0.035531 -0.007440 0.000742 0.019106 0.052834 -0.029711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0826219 0.0208118 3.97 0.016539 *
Concentration 0.0053527 0.0003532 15.15 0.000111 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0366 on 4 degrees of freedom
Multiple R-squared: 0.9829, Adjusted R-squared: 0.9786
F-statistic: 229.6 on 1 and 4 DF, p-value: 0.0001106
> predict(regresion, replica1, int = "p")
fit lwr upr
1 0.1035309 -0.012098349 0.2191602
2 0.1244399 0.009958707 0.2389212
3 0.1662580 0.053716164 0.2787998
4 0.2498941 0.139724612 0.3600636
5 0.4171663 0.305409665 0.5289230
6 0.7517107 0.614489312 0.8889322
> predict(regresion, replica2, int = "p")
fit lwr upr
1 0.1035309 -0.012098349 0.2191602
2 0.1244399 0.009958707 0.2389212
3 0.1662580 0.053716164 0.2787998
4 0.2498941 0.139724612 0.3600636
5 0.4171663 0.305409665 0.5289230
6 0.7517107 0.614489312 0.8889322
So, anyone knows what is happening to me?
Thank you very much!
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