Assigning Column Filters to Variables and why am I getting an LS Mean of 0?

kand1231

Hi everyone! 

 

I was wondering if someone could be able to help me. I am trying to analyze some data that is looking at inactivation of microorganisms over a period of time with different quality characteristics and with different sanitizers and different concentrations. 

First, I am struggling to find the best column filter for my variables. I have two different sanitizers (A and , 3 levels of water characteristics (low, med, high) and time intervals at 0, 15, 30 and 60 seconds. Sanitizer A has 6 concentrations, while Sanitizer B has 4 concentrations. I have made the water characteristics into an ordinal numeric variables. Would the concentrations and time intervals be numeric continuous or ordinal?

 

Also, when I run a Standard Least Squares test some of my values are 0, and it tells me my model is missing an effect. Does anyone have any suggestions that may help me fix these issues?

 

Thank you so much! 

Thierry_S

Hi,
You are asking a set of very broad questions while providing limited information. The first thing you need to figure out before performing any analysis in JMP (or any other analysis software) is what is your question and what are your assumptions. This has more to do with the technical nature of your experiment and less with the statistical tool you will eventually use. One way to approach this first step is to explore graphically your data including individual relationships of experimental factors with your data. Have you looked at the plot of microorganism inactivation data by all your variables? The nice thing in JMP Graph Builder is that you can easily nest several variables to figure out if there are gaps in your data.
The second general consideration you are asking about is the modeling format of your variables (I don't think you actually meant "filtering"). Here again, it depends on the question(s) you need to address. In general, if you have a continuous variable (e.g. time, concentration, physical measures), it is best to keep those as numerical continuous which should provide the best power in your model. The ordinal modeling is usually reserved for categorical variables that have a clear ranking but for which the spacing of each category may differ or be unclear (e.g. user preference, qualitative assessment).
Finally, would it be possible for you to share your data if these are not confidential or proprietary? This would help me and other on the board to figure out the best approach to your issue.
Best,
TS