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Credit Scoring

It is to be noted by the researcher that the category which is most frequent for all “Good” cases produces the same and correct percentage of 70%. Te purpose of chi-square goodness of fit test is to investigate whether the step of judging null hypothesis is justified or not. I this case, te step has been taken from constant-only model to independent model. Te step of adding variables or variable in this scenario can be justified if the values are less than 0. I the step would be variables from equations of this model, tan it would be justified by taking the cutoff point as greater than 0.

Snce the sig. vlues are less 0.05, terefore null hypothesis can be rejected and the model is statistically significant. I Ordinary Least Square regression, te Nagelkerke R2 and Cox & Snell R square provide logistic analogy to R square. Lke R square in OLS, Ngelkerke adjusts the Cox-Snell measures which has range between 0-1. O the basis of Nagelkerke R2, i can be concluded that the Credit Risk is 36.

8 by its predictor variables. Te purpose of Hosmer Lemeshow goodness of fit test splits the observations into deciles which are based upon predicted probabilities. Ater this, ci square is computed from expected and observed frequencies. Te p-value of 0.540 has been computed with 8 degree of freedom by the distribution of chi square. I shows that the mentioned logistic model is a good fit. I Hosmer and Lemeshow goodness of fit test shows a value of 0.05 or lesser, ten difference can be found between the and values of dependent variable and therefore we reject the null hypothesis.

O the other hand, i the value is larger than 0.05, ten the null hypothesis can be accepted having no difference meaning that the model estimations fit at an acceptable level to the data. I this case, te model doesn’t explain the variances of dependent variable except the fact when it does to a significant level. Bth correct and incorrect estimates regarding the constant as well as the independents are highlighted in the above table. I be clearly that the rows reflect the observed values of Credit Risk as “Bad” and “Good” whereas, te columns exhibit the predicted values of Credit Risk as “Bad” and “Good”.

Snce the model applied is binary logistic regression, terefore the percentage correct values of rows “Bad” and “Good” are. ..

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