Bivariate binary logistic regression with fisher scoring and BHHH iteration Available to Purchase

Regression analysis one of the methods to determine the cause and effect relationship between one varibale and another variable. In the relationship model, the variables that used are grouped into two, namely response variables and predictor variables. Logistic regression is a regression model that is often used for modeling the relationship between the qualitative (categorical) dependent variable and one or more independent variables. The model of logistic regression that has a dependent variable of two categories is called a dichotomous (binary) logistic regression model. Binary logistic regression using one response variable can be developed into a binary logistic regression model with two response variables namely bivariate logistic regression (BLR). This research is focused on developing a second-order bivariate binary logistic regression model for the independent variables which is the second order of the model have a polynomial with two degrees. For parameter estimation using Maximum Likelihood Estimator (MLE) method. The problem that arises in the parameter estimation of his model is MLE cannot find an implicit analytical solution, so it is necessary to apply iteration methods in the form of Fisher Scoring with the iteration $β^fs(r+1)=β^fs(r)+I(β^fs(r))−1g(β^fs(r))$⁠, for r = 0,1, 2, … and Berndt Hall-Hall-Hausmann (BHHH) using iteration $β^bhh(r+1)=β^bhh(r)−H(β^bhh(r))−1g(β^bhh(r))$⁠, for r 0,1, 2, …. The hypothesis testing for bivariate logistic regression model is carried out simultaneously dan partially by the Maximum Likelihood Ratio Test (MLRT) method.