In this format, each column represents a different variable, while each row represents a different subject (measurements of each variable for each subject get placed into their appropriate column on that subject's row). In order to facilitate this increased density of data information, Prism offers our Multiple variables data table to house data in a standard data structure that is used almost universally by other statistics software and packages out there (such as R, SPSS, and MATLAB). Using these sorts of "multiple variables" analyses means you can explore the outcome of interest without wasting any potentially useful information. Numerous statistical techniques are designed to analyze this sort of "multiple variables" data, such as multiple linear regression and multiple logistic regression. It's likely that in addition to the recorded blood pressure measurements, you also recorded a wealth of information on each subject's age, height, weight, gender, race, and any number of other potential variables. As a simple example, imagine measuring the blood pressure of individuals after giving them either an experimental drug intended to reduce blood pressure or a placebo. Often times in research we find ourselves with an abundance of information on different variables from our experiments. Prism will automatically encode categorical text variables into numeric "dummy" variables Automatic variable encoding - Enter your data and let Prism take care of the rest.
Instead of coding a variable like "0" and "1", simply enter "Male" and "Female" directly in the data table
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