Comparing multiple Y continuous variable data sets

[#1]

I would like to compare the means of multiple (Y continuous variable) data sets, either by using t-tests and a one-way anova. I collect my data and enter it as I go into columns that are each named by the specific group. Thus, I am eliminating the column with the categorical/grouping variable, because each time I enter new data that would require shifting all the data around. Also, it is easier for me to gauge the ​n ​for each group across the columns and assess whether I am close to getting equal samples in each group, instead of having to scroll down through everything and count through each group. This is just a better visual organization for me. However, when I try to analyze the columns, JMP requires me to enter a nominal/ordinal grouping variable. Is there any way for me to get around having to enter the name of each group next to each data point, in my very large data set? I haven't had this problem with other statistical software programs. I'm new to JMP.

[#2 · Accepted solution]

The simple solution is to go to

     Tables==>Stack

and to stack the 2 or 3 or....... columns into one column.  It will automatically create a nominal column that you can use to discriminate between the groups.

Now if the data on a given row are actually matched(repeated measure) data, then you can use the

     Analyze==>Matched Pairs

analysis, which does not require any stacking of the data

[#3]

Thank you!