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In order to interpret a goodness-of-fit test, its important for statisticians to establish an alpha level, such as the p-value for the chi-square test. The p-value refers to the probability of getting results close to extremes of the observed results. This assumes that the null hypothesis is correct.
The HosmerLemeshow test is useful to determine if the poor predictions (lack of fit) are docHub, indicating that there are problems with the model. There are many possible reasons that a model may give poor predictions.
Application and Interpretation: A large value of Chi-squared (with small p-value 0.05) indicates poor fit and small Chi-squared values (with larger p-value closer to 1) indicate a good logistic regression model fit.

People also ask

Hosmer and Lemeshow goodness of fit test is used to indicate a good fitting model when its value is greater than 0.05 since it fails to reject the null hypothesis (there is no difference between the observed and model-predicted values), implying that the models estimates fit the data at an acceptable level.
estat gof computes goodness-of-fit tests: either the Pearson 𝜒2 test or the HosmerLemeshow test. By default, estat gof computes statistics for the estimation sample by using the last model fit by. logistic, logit, or probit.
- A commonly used goodness of fit measure in logistic regression is the Hosmer-Lemeshow test. The test groups the n observations into groups (according to their estimated probability of event) and calculates the corresponding generalized Pearson 2 statistic. Usually deciles (10 groups) are used.

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