Pearson chi-squared and unweighted residual sum of square tests of fit for a probit model.

Likewise the logistic model and the probit model is often used to consider the associations between a dichotomous dependent variable and some covariates. The probit model is a model that has a lot of important and engaging applications in practice especially in economics and finance. In the framework paper, we propose the Pearson chi-squared (PSC) and unweighted residual sum of square (URSS) tests of fit for a probit model, to enrich and diversify the topic of goodness of fit (GOF) test for regression models. This is a very interesting topic in theory and has enormous practical applications. Theoretically, we have proved that both the proposed approaches are asymptotic to the standard normal distribution. In terms of numerical research and practical applications, several simulations and a real-life data set a fishing data set are investigated in this work. The obtained results in these parts also help to illustrate numerical that the proposed formulas are very reliable. Besides, the findings in the empirical analysis are very consistent with reality. It will be very meaningful evidence to illustrate to everyone how to fish to get the most amount of fish while fishing. Finally, some discussions, conclusions, and future work are also included in this study. [ABSTRACT FROM AUTHOR]