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

A Multiple Regression Analysis of Personality’s Impact on Actuarial Exam Performance

Existing literature indicates that there is some connection between personality and both academic and work-related performance. The author\u27s intent for the research described herein is to explore this connection for students majoring in actuarial mathematics with regard to their performance on actuarial certification exams. Specifically, using the five-factor model of personality, the author seeks to predict the number of attempts required to pass the first two exams in the process (Exam 1/P - probability; Exam 2/FM - financial mathematics) using measures of the five dimensions of the five-factor model (openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability) through regression analysis. The author also examined the same variables’ effect on a binary passing indicator. The sample consists of 100 actuarial mathematics majors at three universities in southern New England. Although the results are not conclusive, it appears that conscientiousness correlates positively with performance and neuroticism correlates negatively with performance. In the future, the author suggests research with a larger sample size and an examination of non-linear relationships

Quantifying image distortion based on Gabor filter bank and multiple regression analysis

Image quality assessment is indispensable for image-based applications. The approaches towards image quality assessment fall into two main categories: subjective and objective methods. Subjective assessment has been widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would not only alleviate the difficulties described above but would also help to expand the application field. Therefore, several works have been developed for quantifying the distortion presented on a image achieving goodness of fit between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of image quality assessment should be adapted in order to identify and quantify the distortion of images at the same time. That combination can improve processes such as enhancement, restoration, compression, transmission, among others. We present an approach based on the power of the experimental design and the joint localization of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore, we achieve a correct identification and quantification of the distortion affecting images. This method provides accurate scores and differentiability between distortions

Pengaruh Kualitas Produk, Store Atmosphere, Dan Lokasi Terhadap Keputusan Pembelian Untuk Produk Fashion Pada Distro Di Kota Palembang

This study was conducted to determine the effect of product quality, store atmosphere, and the location of the purchasing decision for fashion products in the city of Palembang. This type of research is done by using a field study / survey. The population in this study are all consumers who never make purchases in the distribution of fashion products. Used as a sample of 100 respondents and using purposive sampling technique. Hypothesis testing using multiple regression method with SPSS ver. 17.0. Data analysis techniques used are validity, reliability test, classic assumption test, multiple regression analysis, t test and F test validity test results showed all the data is valid, reliability testing showed all the data is reliable. The results of multiple regression test showed the quality products, store atmosphere and the location has a positive and significant impact on purchasing decisions

Estimators of the multiple correlation coefficient: local robustness and confidence intervals.

Many robust regression estimators are defined by minimizing a measure of spread of the residuals. An accompanying R-2-measure, or multiple correlation coefficient, is then easily obtained. In this paper, local robustness properties of these robust R-2-coefficients axe investigated. It is also shown how confidence intervals for the population multiple correlation coefficient can be constructed in the case of multivariate normality.Cautionary note; High breakdown-point; Influence function; Intervals; Model; Multiple correlation coefficient; R-2-measure; Regression analysis; Residuals; Robustness; Squares regression;