Hello, Guys

I have designed, performed, and analysised a 3x3x2 full factorial experiment. For the analysis I have done an analysis with repeated ANOVA. All main factors and all interactions were stat. significat. Now, for a second purpose I need a different form of analysis. I need a function that can predict future dependent variables based on the choice of factorial levels. The dataset of the previously mentioned experiment was rich (over 2300 data points) and should have a decent power. I basically want a function that allows me to estimate Y based on my three factors (X1, X2, and X3). My factor levels are either continous or descrete (which will be transformed into contineous). I think regression analysis is the right tool for my purpose. However, I have not worked with this analytical tool before. I have done some reading already but not much of it in SPSS. Therefore, I have some questions: 1) Based on the experimental design, is the multiple linear regression the right statistical method to use for this purpose (assuming that the relations are linear)? Or are there other approaches in SPSS that could do equally well in order to obtain a function that can best explain and predict future values? 2) According to my ANOVA results, confirmed by my contrasts, I have interaction effects in all my two-way and also in my three-way interaction. The bottomline is, this dataset is quite interactive. However the interaction effects do not appear to be very strong. Is this a problem for regression analysis? If yes, how can it be tackled with? Are ther any aids that could be used to account for that? 3) Since I have three IVs, based on my DOE, could overfitting occur. How could I compensate that witout taking out IVs? Can overfitting occur by putting in too many results? 4) Does somebody know a good tutorial for doing this kind of multiple linear regression (based on the 3 IV and the 1 DV) with SPSS? Are there good books or articles or other online resources that especially cover this?

[此贴子已经被作者于2005-9-18 6:36:25编辑过]