Statistical modeling

This chapter presents different statistical modeling methods to describe and analyze the relationships among attributes. First, the chapter deals with the linear regression method, the least squares method, linear regression estimates, regression and correlation, and the F-test in linear regression. It next focuses on the interpretation of regression analysis results, multiple regression, and regression diagnostics. Further, the chapter discusses the selection of predictor variables, regression, t-test, anova, nonlinear regression, and logistic regression. It presents the method of maximum likelihood, estimation of the logistic regression model, and the likelihood ratio test. The chapter deals with the interpretation of the results of logistic regression, regression coefficients and odds ratios. Finally, the chapter explains the applications of logistic regression, the ROC curve, model validation, the Cox proportional hazards model, assumptions of the Cox model and interpretation of Cox regression.