To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
A continuous variable can be measured and ordered, and has an infinite number of values between any two values. For example, the diameters of a sample of tires is a continuous variable.
Categorical variables contain a finite, countable number of categories or distinct groups. Categorical data might not have a logical order. For example, categorical predictors include gender, material type, and payment method.
If you have a discrete variable, you can decide whether to treat it as a continuous or categorical predictor. A discrete variable can be measured and ordered but it has a countable number of values. For example, the number of people that live in a household is a discrete variable. The decision to treat a discrete variable as continuous or categorical depends on the number of levels, as well as the purpose of the analysis. For more information, go to What are categorical, discrete, and continuous variables?.
If you perform the analysis with correlated response variables, PLS can detect multivariate response patterns and weaker relationships than are possible with a separate analysis for each response.
If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.
If your predictors are not highly correlated and you do not have more predictors than observations, you can consider the following alternative analyses.
If the model does not fit the data, the results can be misleading. In the output, use residual plots, model selection and validation statistics, and the response plot to determine how well the model fits the data.