Analysis of neutrosophic multiple regression

The idea of Neutrosophic statistics is utilized for the analysis of the uncertainty observation data. Neutrosophic multiple regression is one of a vital roles in the analysis of the impact between the dependent and independent variables. The Neutrosophic regression equation is useful to predict the future value of the dependent variable. This paper to predict the students' performance in campus interviews is based on aptitude and personality tests, which measures conscientiousness, and predict the future trend. Neutrosophic multiple regression is to authenticate the claim and examine the null hypothesis using the F-test. This study exhibits that Neutrosophic multiple regression is the most efficient model for uncertainty rather than the classical regression model

Multiple Regression Analysis: SFA Professors\u27 Salaries

There is always a certain curiosity and controversy surrounding professor’s salaries and whether they are overpaid or not paid enough. We have decided to try and untangle this wonder by creating a regression model in which the average person could easily understand while solving this lingering question. It is the average student’s opinion that professors make way too much compared to their daily tasks. On the other hand, many professors are always complaining that they are not paid enough with their advanced degree and work load, not to mention the option always hovers over their head of pursuing a different career with greater return. Other studies have proven that professors at SFASU do earn less, on average holding all other variables constant, than other schools of its caliber in peer-group comparison. With this data, the public can better understand what all goes into determining each professor’s salary and in turn, prove that all the tuition funds spent on the salaries is justified

Modeling inequality and spread in multiple regression

We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u$^{th}$ quantile fall short of the egalitarian situation where everyone has the same income. Gini introduced a summary measure of inequality that is the average over u of the difference between the Lorenz curve and its values in the egalitarian case. More generally, measures of inequality are useful for other response variables in addition to income, e.g. wealth, sales, dividends, taxes, market share and test scores. In this paper we show that a generalized van Zwet type dispersion ordering for distributions of positive random variables induces an ordering on the Lorenz curve, the Gini coefficient and other measures of inequality. We use this result and distributional orderings based on transformations of distributions to motivate parametric and semiparametric models whose regression coefficients measure effects of covariates on inequality. In particular, we extend a parametric Pareto regression model to a flexible semiparametric regression model and give partial likelihood estimates of the regression coefficients and a baseline distribution that can be used to construct estimates of the various conditional measures of inequality.Comment: Published at http://dx.doi.org/10.1214/074921706000000428 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Multiple regression

In this Techniques article Peter Cahusac explains multiple regression, a much used statistical procedure, but one that is frequently misunderstood or misused. Multiple regression allows the effects of many explanatory (independent) variables on the measured (dependent) variable to be analysed simultaneously for situations when a single explanatory variable fails to account for most of the variation in the dependent variable – a common occurrence

Variable selection in multiple regression with random design

We propose a method for variable selection in multiple regression with random predictors. This method is based on a criterion that permits to reduce the variable selection problem to a problem of estimating suitable permutation and dimensionality. Then, estimators for these parameters are proposed and the resulting method for selecting variables is shown to be consistent. A simulation study that permits to gain understanding of the performances of the proposed approach and to compare it with an existing method is given

MULTIPLE REGRESSION TOOL FOR CREDIT RISK MANAGEMENT

In classical theory, the risk is limited to mathematical expectation of losses that can occur when choosing one of the possible variants. For banks, risk is represented as losses arising from the completion of one or another decision. Bank risk is a phenomenon that occurs during the activity of banking operations and that cause negative effects for those activities: deterioration of business or record bank losses affecting functionality. It can be caused by internal or external causes, generated by the competitive environment. The concept of risk can be defined as a commitment bearing the uncertainty due to the likelihood of gain or lossbanking system, credit risk, multiple regression.

Lasso Estimation of an Interval-Valued Multiple Regression Model

A multiple interval-valued linear regression model considering all the cross-relationships between the mids and spreads of the intervals has been introduced recently. A least-squares estimation of the regression parameters has been carried out by transforming a quadratic optimization problem with inequality constraints into a linear complementary problem and using Lemke's algorithm to solve it. Due to the irrelevance of certain cross-relationships, an alternative estimation process, the LASSO (Least Absolut Shrinkage and Selection Operator), is developed. A comparative study showing the differences between the proposed estimators is provided