Table 6 The cross tabulations predictions results.

Actual	Row number	y = 3	y = 4	y = 5
1	1	10	0	0
2	4	32	42	20
3	46	52	46	23
4	86	26	74	44
5	8	41	34	65
Column sum	509	161	196	152

Model Accuracy: The model made accurate predictions for 417 out of 509 total observations. This means that, for 417 instances, the model's prediction matched the actual outcome. Success Rate/Hit Rate: Dividing 417 (correct predictions) by 509 (total observations) gives a success rate of approximately 82%. This percentage, sometimes referred to as the "hit rate," indicates the model's overall prediction accuracy.
Implication of Success Rate: An 82% success rate suggests that the model is fairly reliable, correctly classifying or predicting the outcome for the majority of cases. This rate also implies that the probit model—commonly used in binary or categorical outcome modeling—correctly predicted the outcome for a significant portion of the dataset, exceeding mere chance.
Interpretation in Context: Achieving correct predictions in over half of the observations confirms that the model is effectively capturing relationships within the data, although there remains a 18% rate of incorrect predictions. This measure of success could be useful in contexts where an 82% hit rate is considered robust, particularly in fields like economics, finance, or social sciences where such rates often indicate strong predictive capability.

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