Hello
I have a dataset from a 2-level factorial design with 2 factors.
The dataset contains purchase amount per each user.
Each row is a unique user. Purchase with 0 value means user didn't purchase. A and B are treatments.
I want to calculate the effect of treatments A and B on average purchase per user (sum of purchase in USD divided by number of users).
However, I assume we need to account for two types of variance.
First – the variance of the purchase rate, that is, did the user order or not. Second – the variance in average purchase value.
Can you suggest the appropriate method to do such an analysis in JMP?
I have attached the dataset.
You only have 265 purchases out of 15,771 observations. OLS regression is inappropriate in this case. It fails miserably and the model assumptions are grossly violated. I transformed the purchase amount to a binary response Purchased = { no, yes } and then modeled it with logistic regression. The diagnostics look bad. The model never predicted Purchased = yes. I tried Poisson log-linear model and it worked, but demonstrated very little evidence for an effect of A or B.
I attached your data table with my additions.
