Selecting a test to determine when to end an experiment?

[#1]

We have an experiment testing a population's response to a chemical with three sampling points in time. The measurements at these time points are the dosage with a significant effect on the test population. 

I want to know if the measurements at the first and last time points (middle time point too) are normally equal or different. It would save money to end the experiment at sampling point 1 if the results are typically representative of the later time points (2 or 3). I have a large data set of test of 100 chemicals I would like to do this analysis on

I am at a loss for structuring this and I have spend hours playing around with different approaches. Because the dosage values with an effect are not random, they depend on the range of dosage values selected in the experiment, and the effect magnitude is a function of the the organism's response to that specific chemical, I would think that a repeated measures anova is not correct. Also the data is non-normal even after log transformation. I just  care if the value is the same or different during the duration of the experiment

Maybe a chi square would be the correct here with discrete value "is the same or not"? Maybe an agreement statistic, but that would only work with two time cases. I just care if the value is the same or different across the three time points

This is the current structure

                                  Dosage with an effect

                        Day 10           Day 20         Day 30

Chem x            .5                   .5                  .3

Chem y             2                    1                  .2

Chem z             1                   .15               .15

[#2]

There is no direct way to save the parameter estimates to the original data table. Instead, save them to a new data table and then select Tables > Join to combine them by matching columns.

I do not know the benefit of determining the marginal distribution of the response. Is that evaluation for an assumption of yours?

Linear regression assumes that the residuals (estimated error) are normally distributed. The normal distribution is the model for the errors. The normal mean error is zero and the normal error variance is constant.