This paper presents a Logistic Maturity Model. The goal is to provide companies with a system that allows both to assess their logistic processes current status and to outline an action plan for improvement, considering four key elements: Modeling Framework, Maturity Framework, Performance Framework and Improvement Systems.

1. The Effect of Response Level Ordering on Parameter Estimate Interpretation 2. Odds Ratios 2.1 Binary Explanatory Variable Modeling the Event 2.2 Binary Explanatory Variable Modeling the Nonevent 2.3 Continuous Explanatory Variable 3. Predicted Probabilities 4. Predicted by Observed Classification Tables 4.1 Classification Using Predicted Probabilities 4.2 Classification Using Bias-adjusted P...

Logistic regression may be useful when we are trying to model a categorical dependent variable (DV) as a function of one or more independent variables. This paper reviews the case when the DV has more than two levels, either ordered or not, gives and explains SAS R © code for these methods, and illustrates them with examples.

In this study, we consider two different inequivalent formulations of the logistic difference equation $x_{n+1}= beta x_n(1- x_n), n=0,1,..., $ where $x_n$ is a sequence of fuzzy numbers and $beta$ is a positive fuzzy number. The major contribution of this paper is to study the existence, uniqueness and global behavior of the solutions for two corresponding equations, using the concept of Huku...