Note that it is up to the user on how to use or interpret this information for his/her application, especially when comparing p-values that are well outside of 'rejecting' range.

In this example, we cannot reject the null hypothesis. These statistics are not computed for the full model (F), containing all predictors, since the test results in 0 degrees of freedom for the F distribution, which is not defined. Note: N/A values that the user observes for full models (F) simply indicate that the F-statistic and p-value were not computed. Rejecting the null hypothesis means that the full model (F) does provide a statistically significant fit compared to reduced model (R). Given a threshold say T=0.05, we reject the null hypothesis if the p-value is less than T otherwise, there is insufficient evidence to reject the null hypothesis.

the complement of the CDF of the F distribution. The 'Probability' metric corresponds exactly to the p-value for the computed F-Statistic, i.e. This variable has been derived from the MEDV variable by assigning a 1 for MEDV levels above 30 (>= 30) and a 0 for levels below 30 (= 30) and a 0 for levels below 30 ( The figure below displays a portion of the data observe the last column (CAT.

This dataset includes fourteen variables pertaining to housing prices from census tracts in the Boston area collected by the US Census Bureau. This example illustrates how to fit a model using Data Mining's Logistic Regression algorithm using the Boston_Housing dataset.Ĭlick Help - Example Models on the Data Mining ribbon, then Forecasting/Data Mining Examples and open the example file, Boston_Housing.xlsx.

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