Big Data Analytics with SAS: Get actionable insights from your Big Data using the power of SAS by David Pope

Author:David Pope [Pope, David]
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2017-11-23T00:00:00+00:00

Figure 4.9: PROC REG output

Notice that there are 59 observations as specified in the first output table with at least one of the input variables with missing values; as such those are not used in the development of the regression model. The Root Mean Squared Error (RMSE) and R-square are statistics that inform the analyst how good the model is in predicting the target. These range from 0 to 1.0 with higher values typically indicating a better model. The higher the R-squared values typically indicate a better performing model but conditions or the data used to train the model over-fit and don't represent the true value of the prediction power of that particular model.

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