scholarly journals Uncertainty-Based Trimmed Coefficient of Variation with Application

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Ali Hussein Al-Marshadi ◽  
Muhammad Aslam ◽  
Alharbey Abdullah
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Measurement Data ◽  
Descriptive Statistics

In this paper, the neutrosophic trimmed average, neutrosophic trimmed standard deviation, and neutrosophic trimmed coefficient of variation (NTCV) are introduced. The application of the proposed neutrosophic trimmed descriptive statistics is given with the help of measurement data. The comparisons of the proposed NTCV are compared with the existing neutrosophic coefficient of variation (NCV). From the comparisons, it is concluded that the proposed NTCV is more efficient than NCV in terms of consistency and measures of indeterminacy. Based on the study, it is recommended to apply the proposed NTCV in the industry when there is a need to make decisions on the basis of measurement data.

Inhomogeneity of Bone Mineral Distribution in 2D Projection Images of Trabecular Bone is Associated With Its Micro-Architecture and Biomechanical Properties

2011 ◽  
Author(s):  
X. Neil Dong ◽  
Ning Huang ◽  
Mukul V. Shirvaikar ◽  
Xiaodu Wang
Keyword(s):  
Standard Deviation ◽  
Stochastic Model ◽  
Spatial Variation ◽  
Bone Quality ◽  
Coefficient Of Variation ◽  
Biomechanical Properties ◽  
Descriptive Statistics ◽  
Inhomogeneous Distribution ◽  
Bone Properties ◽  
Projection Images

Bone quality is significantly correlated with the inhomogeneous distribution of material and ultrastructural properties (e.g., modulus and mineralization) of the tissue. Current techniques for quantifying inhomogeneity consist of descriptive statistics such as mean, standard deviation and coefficient of variation. However, these parameters do not describe the spatial variations of bone properties. The objective of this study was to apply a novel stochastic model [1] to characterize and quantitatively describe the spatial variation of bone properties such as mineralization.

scholarly journals Methods for classifying coefficients of variation in experimentation with poultrys

2019 ◽  
Vol 9 (4) ◽  
pp. 565-574 ◽  
Author(s):  
Ana Alice Salmito Noleto de Campos Ferreira ◽  
Leilane Rocha Barros Dourado ◽  
Daniel Biagiotti ◽  
Natanael Pereira da Silva Santos ◽  
Daphinne Cardoso Nagib Nascimento ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Laying Hens ◽  
Interquartile Range ◽  
Descriptive Statistics ◽  
Microsoft Excel ◽  
Maximum Value ◽  
Coefficients Of Variation ◽  
Value Range ◽  
Different Response

The aim of this work was to evaluate the coefficient of variation (CV) and to establish classification ranges for the main variables evaluated in experimentation with poultrys through different methods. The CV data of different response variables observed in poultry articles (broilers, laying hens, roosters and quails) published in five national journals were tabulated in a Microsoft Excel 2010® worksheet. Afterwards, they were subjected to normality evaluation through the Shapiro-Wilk method, and later the following data of the descriptive statistics were utilized: maximum value, minimum value, range, mean, median, standard deviation, midquartile, first quartile, third quartile, interquartile range, and pseudo sigma. According to the values obtained in the descriptive statistics of the CVs of each variable, classification ranges were elaborated for these values, considering six different forms of classification, adapted from the consulted literature. The results indicated that the classification ranges established for CVs in experiments with poultrys are different from those recommended in statistical textbooks. It is possible to utilize midquartile, pseudo sigma, mean and standard deviation through different methods in order to classify the CVs, but the use of the standard deviation promotes incoherent classifications in certain variables, and promotes increase in the amplitude between the lowest and the highest CV value obtained for the classification ranges.

Detection and Analysis of Rats’ Bioelectromagnetic Signal

2014 ◽  
Vol 536-537 ◽  
pp. 13-17
Author(s):  
Hong Long Cao ◽  
Fen Ju Qin ◽  
Xue Guan Liu ◽  
He Ming Zhao
Keyword(s):  
Standard Deviation ◽  
Automatic System ◽  
Measurement Method ◽  
Measurement Accuracy ◽  
Biological Activities ◽  
Measurement Data ◽  
Measured Data ◽  
The Mean ◽  
Frequency Window ◽  
Window Effects

In this paper, we designed an automatic system and automatic test software, and they can carry out Kunming rats bioelectromagnetic measurement in standard status and anesthesia automatically in anechoic chamber where the electromagnetic field outside is shielded, the reflection wave is absorbed, and the measurement accuracy will be improved. We get a great number of measurement data with frequency-sweep measurement method. The mean and standard deviation of amplitudes vs. frequencies is calculated and analyzed. The results show the measurement method is feasible. We have plotted the means of measured data as multiple sets of Y values in a series of bars with standard deviations bars included and distributed in the frequency axis of X. It is found that the fluctuation of the mean and standard deviation in some frequencies is not evident which may explain frequency window effects, while in other frequencies, such a fluctuation can be obviously observed, which may suggest that bioelectromagnetic signal is influenced by biological activities (standard and anaesthesia status) in these frequency points.

scholarly journals Classification of the coefficient of variation to variables in beef cattle experiments

2017 ◽  
Vol 47 (11) ◽  
Author(s):  
Marcos André Braz Vaz ◽  
Paulo Santana Pacheco ◽  
Enio Júnior Seidel ◽  
Angela Pellegrin Ansuj
Keyword(s):  
Standard Deviation ◽  
Beef Cattle ◽  
Coefficient Of Variation ◽  
Theses And Dissertations

ABSTRACT: This research was conducted to propose a classification of the coefficient of variation (CV%) in many categories of variables of production and carcass of beef cattle experiments. The data was collected from theses and dissertations. We used the methods of classification considering mean and standard deviation, and considering median and pseudo-sigma. The two methods showed similar results so both can be used to classify CV%. We propose only three categories to rank CV%: low, medium and high.

scholarly journals Examination of the Risk Assessment Levels of Athletes in Different Branches According to Some Variables

2018 ◽  
Vol 6 (10) ◽  
pp. 193
Author(s):  
Abdurrahman Kirtepe
Keyword(s):  
Risk Assessment ◽  
Data Analysis ◽  
Standard Deviation ◽  
Personal Information ◽  
Educational Status ◽  
Analysis Data ◽  
Descriptive Statistics ◽  
Income Status ◽  
Package Program ◽  
Sample Method

In this study, the risk assessment levels of athletes in different branches were examined in terms of various variables. Descriptive scanning model was used in the study. In the research, the survey was completed with a sample method of 105 people. The questionnaire was used as a data collection tool in the research. The questionnaire consists of questions about personal information and the Risk Assessment scale for athletes and coaches. Data analysis was performed in SPSS 21 package program. Descriptive statistics such as frequency, percent, and mean, standard deviation, minimum and maximum were used in data analysis. Data analysis was performed in SPSS 21 package program. Descriptive statistics such as frequency, percent, and mean, standard deviation, minimum and maximum were used in data analysis. As a result of the research, it was determined that the risk assessment perceptions of athletes according to their age, branches, educational status and income status did not differ. As a result of the research, it was determined that the risk assessment perceptions of athletes according to their age, branches, educational status and income status did not differ.

scholarly journals Application of descriptive statistics in analysis of experimental data

2008 ◽  
Vol 62 (1-2) ◽  
pp. 85-95
Author(s):  
Milorad Mirilovic ◽  
Ivana Pejin
Keyword(s):  
Experimental Data ◽  
Statistical Analysis ◽  
Standard Deviation ◽  
Arithmetic Mean ◽  
Descriptive Statistics ◽  
Qualitative Investigation ◽  
Scientific Methods ◽  
The Subject ◽  
Variation Interval ◽  
Descriptive Statistical Analysis

Statistics today represent a group of scientific methods for the quantitative and qualitative investigation of variations in mass appearances. In fact, statistics present a group of methods that are used for the accumulation, analysis, presentation and interpretation of data necessary for reaching certain conclusions. Statistical analysis is divided into descriptive statistical analysis and inferential statistics. The values which represent the results of an experiment, and which are the subject of observation of a certain occurrence, are called parameters and they are divided into descriptive and numerical. All numerical parameters are divided into non-continuous and continuous. The graphic presentation of the distribution of frequencies can be by poligon or histogram. The most frequently applied descriptive statistical methods are: arithmetic mean, standard deviation, standard error of arithmetic mean, variation coefficient, and variation interval.

scholarly journals Do aggression and impulsivity dominate dark triad across genders?

2020 ◽  
Author(s):  
Anupriya Singh ◽  
Aisha Naeem ◽  
Komal Chandiramani
Keyword(s):  
Gender Differences ◽  
Standard Deviation ◽  
Pearson Correlation ◽  
Correlation Method ◽  
Dark Triad ◽  
T Test ◽  
Descriptive Statistics ◽  
Regression Analyses ◽  
Future Directions ◽  
Significant Relationships

The study investigated gender differences in Overall Dark Triad (Machiavellianism, Narcissism, and Psychopathy), Aggression, Impulsivity, and impact of Dark triad on Aggression and Impulsivity. Data was collected from 214 young adults. Analyses was done using descriptive statistics (mean and standard deviation). T-test was employed to analyze gender differences. Pearson Correlation Method and Multiple regression were used to assess the relation and prediction amongst the variables, respectively. T-test suggested significant gender differences in Machiavellianism, Psychopathy, and Overall Dark Triad, with females scoring high than males. No gender difference was observed in Aggression and Impulsivity. Correlation and regression analyses revealed several significant relationships between the above constructs. The research was concluded by briefly foregrounding some of the study’s implications and future directions.

Impacts of Leadership Styles on Motivations of Employees

2019 ◽  
pp. 205-217
Author(s):  
Irfan Ullah ◽  
Alina Kiran ◽  
Bin Liu
Keyword(s):  
Standard Deviation ◽  
Data Collection ◽  
Leadership Styles ◽  
Multiple Regression ◽  
Employee Motivation ◽  
Descriptive Statistics ◽  
Cronbach Alpha ◽  
The Impact ◽  
The Relationship ◽  
Survey Approach

The aim of this study to investigate the different leadership styles and impact on employee motivation. Leadership is the most important phenomena. Therefore, leadership plays a very important role, and this research study emphasizes the impact of leadership styles on motivation of employees. In this chapter, a survey approach using structured questionnaire was used for data collection. Questionnaire consists of four styles of leadership with employees' motivation. All constructs were measured using five-point Likert scale. Descriptive statistics and inferential were used. Mean and standard deviation and Cronbach alpha were used in descriptive statistics and correlation, and multiple regression was used for inferential regression. The aim of this chapter is to identify the relationship between leadership styles and motivation of employees.

15. Quantitative Analysis: Description and Inference

2020 ◽  
pp. 393-421
Author(s):  
Sandra Halperin ◽  
Oliver Heath
Keyword(s):  
Quantitative Analysis ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quantitative Research ◽  
Building Blocks ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Margin Of Error ◽  
Univariate Statistics ◽  
The Mean

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

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