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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