scholarly journals Statistical Analyses of Femur Parameters for Designing Anatomical Plates

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Wang ◽  
Kunjin He ◽  
Zhengming Chen
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Individual Differences ◽  
Discriminant Analysis ◽  
Statistical Methods ◽  
Proper Class ◽  
The Mean ◽  
Parameter Values ◽  
Bayes Discriminant Analysis

Femur parameters are key prerequisites for scientifically designing anatomical plates. Meanwhile, individual differences in femurs present a challenge to design well-fitting anatomical plates. Therefore, to design anatomical plates more scientifically, analyses of femur parameters with statistical methods were performed in this study. The specific steps were as follows. First, taking eight anatomical femur parameters as variables, 100 femur samples were classified into three classes with factor analysis and Q-type cluster analysis. Second, based on the mean parameter values of the three classes of femurs, three sizes of average anatomical plates corresponding to the three classes of femurs were designed. Finally, based on Bayes discriminant analysis, a new femur could be assigned to the proper class. Thereafter, the average anatomical plate suitable for that new femur was selected from the three available sizes of plates. Experimental results showed that the classification of femurs was quite reasonable based on the anatomical aspects of the femurs. For instance, three sizes of condylar buttress plates were designed. Meanwhile, 20 new femurs are judged to which classes the femurs belong. Thereafter, suitable condylar buttress plates were determined and selected.

Multivariate consumption profiling (MCP) for intelligent meter systems: a methodology to define categories and levels

2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Reference Population ◽  
Statistical Analyses ◽  
Conceptual Schema ◽  
Data Variability ◽  
Water Companies ◽  
Analyse Data

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.

Classification of Personality Disorder

1979 ◽  
Vol 135 (2) ◽  
pp. 163-167 ◽  
Author(s):  
Peter Tyrer ◽  
John Alexander
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Personality Disorder ◽  
Personality Disorders ◽  
Psychiatric Patients ◽  
Interview Schedule ◽  
Personality Variables ◽  
Current Classification ◽  
Personality Disordered

SummaryAn interview schedule was used to record the personality traits of 130 psychiatric patients, 65 with a primary clinical diagnosis of personality disorder and 65 with other diagnoses. The results were analysed by factor analysis and three types of cluster analysis. Factor analysis showed a similar structure of personality variables in both groups of patients, supporting the notion that personality disorders differ only in degree from the personalities of other psychiatric patients. Cluster analysis revealed five discrete categories; sociopathic, passive-dependent, anankastic, schizoid and a non-personality-disordered group. Of all the personality-disordered patients 63 per cent fell into the passive-dependent or sociopathic category. The results suggest that the current classification of personality disorder could be simplified.

Assessment of DSM–III–R Personality Disorders by Self-Report Questionnaire: the Role of Informants and a Screening Test for Co-morbid Personality Disorders (STCPD)

1992 ◽  
Vol 161 (3) ◽  
pp. 344-352 ◽  
Author(s):  
J. H. Dowson
Keyword(s):  
Cluster Analysis ◽  
Personality Disorders ◽  
Screening Test ◽  
Psychiatric Patients ◽  
Self Report ◽  
The Mean

A modified version of the revised Personality Diagnostic Questionnaire (PDQ–R), based on DSM–III–R personality disorders (PDs), was completed by 60 psychiatric patients. An informant's version was also completed by 60 relatives or friends nominated by each subject. Discrete DSM–III–R PDs were rare; the mean number of PDs per subject was 4.5. Cluster analysis showed that only antisocial PD was a basis for classification of patients, while most patients formed two groups which were mainly distinguished by quantitative differences related to the total scores of positive PD criteria. A shorter version of the questionnaire can be used as a screening test for co-morbid PDs (STCPD) which can predict the number of co-morbid DSM–III–R PDs. The total scores of positive PD criteria from the STCPD were usually (and significantly) higher than the corresponding scores from informants' questionnaires but when an informant's total score exceeded that of the patient, this indicated a subject's under-reporting.

Does Mutual Fund Management in India Correspond to its Investment Objective Classification?

2005 ◽  
Vol 08 (04) ◽  
pp. 659-685 ◽  
Author(s):  
Luis Ferruz Agudo ◽  
Cristina Ortiz Lázaro
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Mutual Fund ◽  
The Other ◽  
Fund Management ◽  
Institutional Control ◽  
Other Hand ◽  
Objective Classification ◽  
And Cluster Analysis

The aim of this article is to investigate the mutual fund market in India and verify whether or not the fund classification obtained from the name given to identify them corresponds to that which would be obtained were prior management to be taken into account. This industry has undergone spectacular growth in recent years, making this study extremely interesting, not least because institutional control could be less in times of expansion. The methodologies employed in the study are factor analysis and cluster analysis. The former determines that risk would clearly identify two groups of funds in the same manner as public classification of the funds. Cluster analysis, on the other hand, identifies funds that are, in fact, very close to one another, when for the bulk of investors they are not.

Classification of Three Green Tea Varieties by Micellar Electrokinetic Electrophoresis-Laser Induced Fluorescence and Pattern Recognition Methods

2012 ◽  
Vol 554-556 ◽  
pp. 1289-1292
Author(s):  
Neng Sheng Ye ◽  
Ya Li Xie ◽  
Chang Liu ◽  
Jian Li
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Laser Induced Fluorescence ◽  
Partial Least Square ◽  
Least Square ◽  
Sodium Borate ◽  
Laser Induced Fluorescence Detection ◽  
Pattern Recognition Methods

A micellar electrokinetic electrophoresis-laser induced fluorescence detection (MEKC-LIF) method was applied for the profiling of native fluorescence in three different kinds of Chinese green teas. The running buffer was 100 mM sodium borate (pH9.8) containing 20 mM SDS, and tea infusion was injected at the pressure of 0.5 psi for 5 seconds. The applied voltage was set at 25 kV, and the excited wavelength was 488 nm, and the detected wavelength was 520 nm. Ten tea samples were collected and analyzed by the developed method. Eighteen common peaks in the MEKC-LIF profiling of green teas were selected and these samples were classified into 3 groups by cluster analysis and partial least square discriminant analysis (PLS-DA). Our primary results indicated that the developed method could be applied for the classification of three different Chinese green teas

scholarly journals Application of multivariate statistical techniques for investigating climate change and anthropogenic effects on surface water quality assessment: case study of Zohreh river, Hendijan, Iran

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Jalal Valiallahi ◽  
Saideh Khaffaf Roudy
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Monitoring Program ◽  
T Test ◽  
Total Variance ◽  
Quality Data ◽  
Water Quality Data ◽  
Significant Difference ◽  
The Mean

AbstractIn the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

scholarly journals Methods of adaptability and stability applied to eucalyptus breeding

2018 ◽  
Vol 53 (1) ◽  
pp. 53-62
Author(s):  
Osmarino Pires dos Santos ◽  
Ivan Ricardo Carvalho ◽  
Maicon Nardino ◽  
Tiago Olivoto ◽  
Alan Junior de Pelegrin ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Field Experiments ◽  
Block Design ◽  
Genetic Breeding ◽  
Breast Height ◽  
Randomized Complete Block Design ◽  
Productive Potential ◽  
The Mean ◽  
Highly Correlated

Abstract: The objective of this work was to evaluate the consistency of the methods of Annicchiarico, Lin & Binns, Wricke, and factor analysis in identifying eucalyptus clones with stability, adaptability, and high productive potential. Eight-four clones, with three years of age, from the genetic breeding program of the company CMPC Celulose Riograndense were used. Three field experiments were carried out in a randomized complete block design, in an 84x3 factorial arragement, with 20 replicates of one plant per plot. The clones were evaluated as to diameter at breast height, plant height, and volume of wood. The methods of Annicchiarico and Lin & Binns are highly correlated with each other, and their use together with the method of Wricke is a sound strategy for the evaluation of eucalyptus clones. The factor analysis identified broadly adaptable clones, and some of them were the same ones identified by the methods of Annicchiarico and Lin & Binns. The use of the mean classification of the clones, along with the factor analysis, is efficient to identify the most adapted, stable, and productive ones among a high number of genotypes.

GIS-Based Quantitative Classification of Neighborhood Types for Zhongshan City, China

2011 ◽  
Vol 368-373 ◽  
pp. 1794-1797
Author(s):  
Yuan Li ◽  
Yi Zhang
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Quantitative Classification ◽  
And Cluster Analysis ◽  
Major Factors ◽  
Neighborhood Type ◽  
Type Classification

In this paper, we propose a GIS-based method that combines factor analysis and cluster analysis to quantitatively classify neighborhoods type for Zhangshan City, China. First, the possible measures for neighborhood type classification are reviewed. Then, ten measures are chosen for the case study. Factor analysis is adopted to generate five major factors and cluster analysis is used to identify six neighborhood types.

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