Theory of Discriminant Analysis of the Increasing Number of Independent Variables

2000 ◽  
pp. 187-226
Author(s):  
V. Serdobolskii
Keyword(s):  
Discriminant Analysis ◽  
Independent Variables

Euthanasia: A National Survey of Attitudes toward Voluntary Termination of Life

1981 ◽  
Vol 11 (4) ◽  
pp. 281-291 ◽  
Author(s):  
David E. Jorgenson ◽  
Ron C. Neubecker
Keyword(s):  
Discriminant Analysis ◽  
Social Class ◽  
Data Base ◽  
National Survey ◽  
National Sample ◽  
General Survey ◽  
Independent Variables ◽  
The Social ◽  
Attitudes Toward Suicide ◽  
Voluntary Termination

This study focused on the attitudes of a national sample of adults related to the voluntary termination of life. The data-base for this research were 1525 adults surveyed in the 1977 NORC General Survey. Two items in the survey delineated the pro-euthanasia and the anti-euthanasia groups. Several independent variables including structural, behavioral, and attitudinal variables were correlated with euthanasia attitudes. Several statistically significant correlations were found. These findings plus the results of a discriminant analysis showed that those persons with favorable attitudes toward suicide were also favorable toward euthanasia. Religiosity and other religious indicators were negatively associated with pro-euthanasia attitudes. Whites and males were more favorable toward euthanasia than Blacks and females. Finally, the social class variables were positively associated with pro-euthanasia attitudes.

Belief in an Afterlife: A National Survey

1990 ◽  
Vol 20 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Daniel J. Klenow ◽  
Robert C. Bolin
Keyword(s):  
Discriminant Analysis ◽  
Marital Status ◽  
National Survey ◽  
Church Attendance ◽  
General Social Survey ◽  
Social Survey ◽  
Factors Affecting ◽  
National Opinion Research ◽  
Independent Variables ◽  
Life After Death

This article presents an exploratory analysis of factors affecting belief in an afterlife. Data are taken from the 1978 subfile on the National Opinion Research Center's General Social Survey. With belief in life after death serving as the dependent variable, a number of variables are introduced in a tabular analysis. Among factors found to be statistically significant are sex, race, age, marital status, and several religious and residential variables. Controlling on frequency of church attendance and religious intensity, it is shown that Protestants have the highest incidence of belief in life after death, followed closely by Catholics, with Jews exhibiting the lowest level. A discriminant analysis was run in order to select a group of independent variables that were good predictors of belief in an afterlife. Race, religion, and church attendance were found to be significant discriminating variables of such belief.

scholarly journals An Empirical Evaluation Of Franchise Contracting Provisions In Spanish Chains

2011 ◽  
Vol 4 (1) ◽  
Author(s):  
Rosa M Mariz Perez ◽  
M Teresa Garcia Alvarez
Keyword(s):  
Discriminant Analysis ◽  
Descriptive Analysis ◽  
Empirical Evaluation ◽  
Correct Classification ◽  
Independent Variables ◽  
Group Service

In this paper, we pretend to identify the existing differences between contractual conditions fixed by franchisors of Spanish chains. With this objective, we analyze the two basic types of existing franchised chains those that commercialize services and those others that distribute products- and if contractual stipulations and other characteristics differ in a systematic manner between them. In this sense, we have considered a series of independent variables such as size, age, initial fee, royalties or contractual initial duration. After undergoing the descriptive analysis of these variables for our sample -440 Spanish franchised chains- we have divided the latter into two groups based on the type of chain- in order to detect significant differences between them. For this aim, we have conducted a discriminant analysis to discover which of the independent variables taken into account contribute, in a significant manner, to a correct classification of chains to their corresponding group service or product chains.

Factorizations of the residual likelihood criterion in discriminant analysis

1966 ◽  
Vol 62 (4) ◽  
pp. 743-752 ◽  
Author(s):  
J. Radcliffe
Keyword(s):  
Discriminant Analysis ◽  
Confidence Intervals ◽  
Goodness Of Fit ◽  
Discriminant Functions ◽  
Approximate Factorization ◽  
Analytic Proof ◽  
Independent Variables ◽  
Canonical Variables ◽  
Residual Likelihood ◽  
The Difference

Certain exact tests were developed by Williams (1952) to deal with the goodness of fit of a single hypothetical discriminant function. Bartlett (1951) generalized these results by the use of the geometric method to any number of dependent and independent variables. Bartlett's paper is divided into two parts. The first deals with an approximate factorization of the residual likelihood criterion into an effect due to the difference between the hypothetical and sample functions, and an effect due to non-collinearity. A method is given for constructing confidence intervals from the first factor. The second part of the paper gives two possible exact factorizations of the likelihood criterion, expressing the results in terms of the sample canonical variables. Kshirsagar (1964a) has expressed these results in terms of the original variables and given an analytic proof of the distribution of the factors. Williams (1955, 1961) has outlined a generalization of these results to several discriminant functions and given the result for one of the possible factorizations.

scholarly journals The use of discriminant analysis methods for diagnosis of the causes of differences in the properties of resin mortar containing various fillers

2018 ◽  
Vol 49 ◽  
pp. 00017 ◽  
Author(s):  
Bernardeta Dębska
Keyword(s):  
Discriminant Analysis ◽  
Model Building ◽  
Analysis Method ◽  
Learning Stage ◽  
Independent Variables ◽  
Prognostic Accuracy ◽  
Wide Range ◽  
Exploratory Data ◽  
Prediction Problems

Resin mortars belong to the group of concrete-like construction composites. They are obtained by mixing a synthetic resin with a hardener and an appropriately selected aggregate. The latter component is usually as much as 90% of the composite mass and can largely shape the characteristics of the finished product. The fact that the type of filler used can significantly differentiate the values of physical and mechanical parameters of epoxy mortars is confirmed by the results of the exploratory data analysis method used in this article, which is discriminant analysis. This allows us to examine differences between groups of objects based on a set of selected independent variables (predictors). It is used to solve a wide range of classification and prediction problems. The core of discriminant analysis is a model presented in the form of a linear combination of independent variables, which allows classification of observations (e.g. test mortars) into one of the groups that are of interest to the researcher. In discriminant analysis one can distinguish the learning stage (model building), in which classification rules are created based on research results (training set) and the classification stage, i.e. the use of the model, e.g. for testing its prognostic accuracy.

Potential prediction bias in regression and discriminant analysis

1986 ◽  
Vol 16 (6) ◽  
pp. 1255-1257 ◽  
Author(s):  
David Verbyla
Keyword(s):  
Discriminant Analysis ◽  
Model Prediction ◽  
Random Numbers ◽  
Prediction Errors ◽  
Bootstrap Procedure ◽  
Independent Variables ◽  
Prediction Bias ◽  
Resampling Procedure ◽  
Stepwise Procedure ◽  
The Difference

Prediction bias is the difference between a model's apparent and actual prediction errors. Prediction bias is likely to occur when a model contains many independent variables relative to sample size or when many different sets of independent variables are tested by a stepwise procedure. Examples of potential prediction bias are illustrated by comparing published models with models developed using random numbers. Model prediction bias can be estimated by using a resampling procedure called the bootstrap. The bootstrap procedure is illustrated with a simple example.

scholarly journals Study of a Vocal Feature Selection Method and Vocal Properties for Discriminating Four Constitution Types

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Keun Ho Kim ◽  
Boncho Ku ◽  
Namsik Kang ◽  
Young-Su Kim ◽  
Jun-Su Jang ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Feature Selection Method ◽  
Health Condition ◽  
Explanatory Variables ◽  
Independent Variables ◽  
Gender And Age ◽  
Constitution Type ◽  
Voice Classification ◽  
The Voice ◽  
Physical Quantities

The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.

scholarly journals KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)

2020 ◽  
Vol 8 (2) ◽  
pp. 88
Author(s):  
Amanah Saeroni ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Customer Data ◽  
Independent Variables ◽  
Premium Payment ◽  
Multivariate Technique ◽  
Fisher Discriminant

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.

scholarly journals THE INFLUENCE OF USING SHOPEEPAY AND SHOPEE PAYLATER FEATURES ON SHOPEE USER PURCHASING DECISIONS DURING THE COVID- 19

2021 ◽  
Vol 16 (4) ◽  
pp. 242-248
Author(s):  
Sri Lestari ◽  
Naniek Utami Handayani ◽  
Manik Mahacandra
Keyword(s):  
Consumer Behavior ◽  
Discriminant Analysis ◽  
Data Collection ◽  
Customer Satisfaction ◽  
Research Method ◽  
Computer Network ◽  
Purchasing Decisions ◽  
Independent Variables ◽  
Z Value ◽  
Data Collection Technique

A process of buying and selling products, information and services that is carried out electronically by utilizing a computer network is often known as E-commerce and providing ease of payment, namely by using the ShopeePay and Shopee PayLater features. This study aims to find out which independent variables are variables that can influence purchasing decisions for Shopee users and find out which factors are more dominant in influencing purchasing decisions for Shopee users on the use of Shopeepay and Shopee PayLater features. Moreover, the trend of consumer behavior in the future also can be explored from the Zscore. The research method used is the method of Discriminant Analysis. The data collection technique was carried out by surveying 55 respondents through questionnaires. From the equation formed, consumers' tendency to buy or not to make purchases through Shopee E-Commerce is determined by the Customer Satisfaction variable in the transaction. Furthermore, from the Z value, the group that does not make purchases (0) has a Z value = 0.214, while the group that often makes purchases (1) has a Z = -0.207 value. From the equation formed, consumers' tendency to buy or not to make purchases through Shopee E-Commerce is determined by the Customer Satisfaction variable in the transaction. Moreover, from the Z value, the group that does not make purchases (0) has a Z value = 0.214, while the group that often makes purchases (1) has a Z = -0.207 value. From the equation formed, consumers' tendency to buy or not to make purchases through Shopee E-Commerce is determined by the Customer Satisfaction variable in the transaction. Furthermore, from the Z value, the group that does not make purchases (0) has a Z value = 0.214, while the group that often makes purchases (1) has a Z = -0.207 value.

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