To do this I am comparing the changes in A: Body length (L), B: Girth (G) and C: Shoulder height (Sh) to Body mass (Mb). I have a sample of 42 animals, each measured once (after being culled). They range in body mass from foetuses of about 20kg to adult bulls of about 260kg.

I would like to see whether Sex and Season have an effect on the regression equations generated from my data. (I want to know if for example male and female girths change at the same rate during growth). In the end I should produce formulae that could be used to predict a wildebeests body mass from any of the other measurements or a combination of measurements, in that case I want to know if the same formula can be used for males and females.

I would also like to be able to use my data to test the predictive power of some equations already published on Wildebeest.

On visual inspection the data fit a power curve. (I plotted each of L,G and Sh against Body Mass).

Guided by what many students of allometry have done I plotted power curves by fitting straight lines to the data after I log transformed it. I then also plotted the 95% confidence intervals of these curves. I then compared the data in different groups (for example Male and Female) by plotting the male data with 95% Confidence intervals, then plotting the female data on the same set of axes. If most of the female data fell within the 95% Confidence intervals of the male data I concluded that the data sets were not different.

I used these simple methods because I didn't think that my data lent themselves to more formal analysis.

The more I read the less I like my methods. I now question the validity of my original regressions because:

1. I do not believe that any of the variables are normally distributed in the population. (In a natural population of animals of all ages there will not be more of average body mass, girth or length than animals of extreme values of those measurements)

2. I have measured both the dependant and independent variables so they all have some error

3. I did not select the individuals randomly from the population but rather tried (not very successfully) to have the same number of animals from each body mass class.

4. The residuals will be dependant on the value of X. (The foetuses weighing 20 kg will have a variation in girth of only a few cm, whereas the natural variation in girth of adults weighing 200kg will be far more).

5. The variables definitely affect one another. Mass, Girth, Length and Height are all related. Sex and Season are not.

I find that my data do not lend themselves to simple analysis. If anyone can recommend a new starting point for me. A better method of regression analysis to begin with I would be very grateful. Am I possibly mistake? Will I still be able to make useful predictions using the methods that I have already used, if so how can I improve them.

My supervisor suggests that I use the t-test to compare the measured body masses with those predicted by the East African equations, or the Male equation on the Female data to see whether the two sets of Data are significantly different. To my mind that is a totally inappropriate test to use on these data. Is that so?

I have looked at beginning with multiple linear regression but the same constraints as for simple linear regression will apply.

I looked at Generalized Linear Regression (mainly because it was recommended to me) but I can't even begin to understand how to choose which parameters to select for my data.

Thanks for reading my long explanation. I hope the detail may help you understand my predicament. I'd appreciate any comments or advice or even a friendly hello.

Have a Great Day

Gnu