Data considerations for Orthogonal Regression

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data should include only one continuous predictor, which contains measurement error

If you have one continuous predictor but it does not contain measurement error, use Fitted Line Plot.

The response variable should be continuous

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 you are not assessing the comparability of measurements, you can consider the following alternative analyses.

• If your response variable has two categories, such as pass and fail, use Fit Binary Logistic Model.
• If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
• If your response variable contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.
• If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.

You must specify the ratio of the measurement error variances in the response and predictor variables

One way to obtain estimates of the error variances is to perform a separate Gage R&R study for each variable.

Select units to measure that represent the actual or expected range of measurements

To verify that two instruments or methods provide comparable measurements, select units to measure that represent all the values where the measurements need to be comparable. Then, measure the units with both instruments or methods.

Collect data using best practices

To ensure that your results are valid, consider the following guidelines:

• Make certain that the data represent the population of interest.
• Collect enough data to provide the necessary precision.
• Measure variables as accurately and precisely as possible.
• Record the data in the order it is collected.

The model should provide a good fit to the data

If the model does not fit the data, the results can be misleading. In the output, use residual plots and the fitted line plot to determine how well the model fits the data.