We deal with a credit scoring problem. We try to determine by using logistic regression the factors underlying the agreement or refusal of a credit to customers. We perform the following steps:

- Estimating the parameters of the classifier;

- Retrieving the covariance matrix of coefficients;

- Assessment using the Hosmer and Lemeshow goodness of fit test;

- Assessment using the reliability diagram;

- Assessment using the ROC curve;

- Analysis of residuals, detection of outliers and influential points.

On the one hand, we use Tanagra 1.4.33. Then, on the other hand, we perform the same analysis using the R 2.9.2 software [glm(.) procedure].

**Keywords**: logistic regression, residual analysis, outliers, influential points, pearson residual, deviance residual, leverage, cook's distance, dfbeta, dfbetas, hosmer-lemeshow goodness of fit test, reliability diagram, calibration plot, glm()

**Components**: BINARY LOGISTIC REGRESSION, HOSMER LEMESHOW TEST, RELIABILITY DIAGRAM, LOGISTIC REGRESSION RESIDUALS

**Tutorial**: en_Tanagra_Logistic_Regression_Diagnostics.pdf

**Dataset**: logistic_regression_diagnostics.zip

**References**:

D. Garson, "Logistic Regression"

D. Hosmer, S. Lemeshow, « Applied Logistic Regression », John Wiley &Sons, Inc, Second Edition, 2000.