Hypo- and Hyperglycemia in Relation to the Mean, Standard Deviation, Coefficient of Variation, and Nature of the Glucose Distribution

2012 ◽  
Vol 14 (10) ◽  
pp. 868-876 ◽  
Author(s):  
David Rodbard
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
The Mean ◽  
Deviation Coefficient

Mechanoreceptor and Photoreceptor Tonic Integration in the Crayfish

1973 ◽  
Vol 51 (12) ◽  
pp. 949-958 ◽  
Author(s):  
C. Galeano ◽  
S. Beliveau
Keyword(s):  
Statistical Analysis ◽  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Average Frequency ◽  
Ipsilateral Side ◽  
Average Interval ◽  
Caudal Photoreceptor ◽  
Deviation Coefficient ◽  
Variation Interval ◽  
Density Results

The activity of the caudal photoreceptor of crayfish was studied in: (1) intact tail ganglion, (2) partially isolated, and (3) totally isolated ganglion preparations. Statistical analysis of the photoreceptor activity included average frequency, average interval, variance, standard deviation, coefficient of variation, interval histograms, auto-expectation density, and cross-expectation density. Results showed that the average influence of the mechanoreceptor synapses on the photoreceptors during a period of several seconds was inhibitory, strong on the contralateral and weak on the ipsilateral side.

A Proof for Rhiel's Range Estimator of the Coefficient of Variation for Skewed Distributions

2007 ◽  
Vol 100 (1) ◽  
pp. 208-210 ◽  
Author(s):  
G. Steven Rhiel
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Research Study ◽  
Skewed Distributions ◽  
The Mean ◽  
Standard Deviations

In this research study is proof that the coefficient of variation ( CVhigh-low) calculated from the highest and lowest values in a set of data is applicable to specific skewed distributions with varying means and standard deviations. Earlier Rhiel provided values for dn, the standardized mean range, and an, an adjustment for bias in the range estimator of μ. These values are used in estimating the coefficient of variation from the range for skewed distributions. The dn and an values were specified for specific skewed distributions with a fixed mean and standard deviation. In this proof it is shown that the dn and an values are applicable for the specific skewed distributions when the mean and standard deviation can take on differing values. This will give the researcher confidence in using this statistic for skewed distributions regardless of the mean and standard deviation.

scholarly journals Participation of Community through Parents Teacher Councils regarding Curricular and Co-curricular Activities in Public Sector Schools of Khyber Pakhtunkhwa

2018 ◽  
Vol III (IV) ◽  
pp. 515-533
Author(s):  
Abdul Hafeez ◽  
Malik Amer Atta ◽  
Muhammad Ayaz
Keyword(s):  
Standard Deviation ◽  
Public Sector ◽  
Coefficient Of Variation ◽  
Point Of View ◽  
P Value ◽  
Statistical Tools ◽  
Khyber Pakhtunkhwa ◽  
Deviation Coefficient ◽  
T Distribution ◽  
Moderate Response

The study focuses on the participation of community through PTC in the arrangement of different curricular and co-curricular activities in schools for betterment of students and enhancement of their qualities. A valid and reliable questionnaire was served to the two categories of stakeholders i.e. PTC members (Chairman and secretary) and Non-PTC members (Parents and Teachers). A sample of one hundred schools randomly selected from two districts of Khyber Pakhtunkhwa. Mean, Standard Deviation, Coefficient of variation, t-distribution and p-value were applied as statistical tools. Responses of both groups show moderate response and show that participation of community is found for the first variable i.e. arranging curricular activities but there is opposite opinion found in the point of view of both group regarding the arrangement of co-curricular activities in schools; response of PTC members group is positive but non PTC members group did not support it.

scholarly journals Properties of the Standard Deviation that are Rarely Mentioned in Classrooms

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
Mohammad Fraiwan Al-Saleh ◽  
Adil Eltayeb Yousif
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Sample Variance ◽  
Mean Absolute Deviation ◽  
Hidden Information ◽  
Absolute Deviation ◽  
Sample Mean ◽  
The Mean ◽  
Vague Concept ◽  
Chebyshev's Inequality

Unlike the mean, the standard deviation ¾ is a vague concept. In this paper, several properties of ¾ are highlighted. These properties include the minimum and the maximum of ¾, its relationship to the mean absolute deviation and the range of the data, its role in Chebyshev’s inequality and the coefficient of variation. The hidden information in the formula itself is extracted. The confusion about the denominator of the sample variance being n ¡ 1 is also addressed. Some properties of the sample mean and varianceof normal data are carefully explained. Pointing out these and other properties in classrooms may have significant effects on the understanding and the retention of the concept.

scholarly journals Analisis Risiko Produksi Sayuran Daun Indigenous di Kecamatan Kadudampit, Kabupaten Sukabumi, Jawa Barat

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Syaima Lailatul Mubarokah ◽  
Wini Nahraeni ◽  
Arti Yusdiarti ◽  
Arifah Rahayu
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Seed Quality ◽  
Mitigation Strategies ◽  
Risk Level ◽  
Cropping Pattern ◽  
Expected Return ◽  
Production Risk ◽  
Process Risk ◽  
Deviation Coefficient

This study aims to identify 1) the source of production risk in some indigenous leaf vegetables, especially basil 2) the level of risks encountered, and 3) formulate strategies tocontrol the risk of production of indigenous leaf vegetable. The research was conducted in Kecamatan Kadudampit Sukabumi, the method of sampling used simple random sampling.The number of farmers were 42 farmers. In specialization activities, the method to process risk data used are analysis of variance, standard deviation and coefficient of variation. Theresults showed that the source of production risk faced by farmers were weather / climate, pest and disease diseases and seed quality. The expected return value of basil commodityRp.1.801.204 with the risk level of losses was 0.382 or 38%. Strategies that could be done to reduced the risk was the diversification activity with . timing and appropriate commodities of diversification activities, plan intercropping cropping pattern between two combination basil with lettuce were 60% and 40% and basil with gourd were 60% and 40%, risk managementin production through preventive strategies with improvement of physical facilities and mitigation strategies with pest and disease controls encountered.Keywords: basil, variance analysis, standard deviation, coefficient of variation, risk management.

Correction for Rhiel's Theory for the Range Estimator of the Coefficient of Variation for Skewed Distributions

2010 ◽  
Vol 106 (1) ◽  
pp. 93-94
Author(s):  
G. Steven Rhiel
Keyword(s):  
Standard Deviation ◽  
Correction Factor ◽  
Coefficient Of Variation ◽  
Unbiased Estimate ◽  
Correction Factors ◽  
Skewed Distributions ◽  
The Mean

In 2007, Rhiel presented a technique to estimate the coefficient of variation from the range when sampling from skewed distributions. To provide an unbiased estimate, a correction factor ( an) for the mean was included. Numerical correction factors for a number of skewed distributions were provided. In a follow-up paper, he provided a proof he claimed showed the correction factor was independent of the mean and standard deviation, making the factors useful as these parameters vary; however, that proof did not establish independence. Herein is a proof which establishes the independence.

scholarly journals The precision of temporal judgement: milliseconds, many minutes, and beyond

2009 ◽  
Vol 364 (1525) ◽  
pp. 1897-1905 ◽  
Author(s):  
P.A. Lewis ◽  
R.C. Miall
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Human Subjects ◽  
Short Interval ◽  
Time Measurement ◽  
Scalar Property ◽  
Timing Mechanisms ◽  
Timing Data ◽  
The Mean ◽  
Different Levels

The principle that the standard deviation of estimates scales with the mean estimate, commonly known as the scalar property, is one of the most broadly accepted fundamentals of interval timing. This property is measured using the coefficient of variation (CV) calculated as the ratio between the standard deviation and the mean. In 1997, John Gibbon suggested that different time measurement mechanisms may have different levels of absolute precision, and would therefore be associated with different CVs. Here, we test this proposal by examining the CVs produced by human subjects timing a broad range of intervals (68 ms to 16.7 min). Our data reveal no evidence for multiple mechanisms, but instead show a continuous logarithmic decrease in CV as timed intervals increase. This finding joins other recent reports in demonstrating a systematic violation of the scalar property in timing data. Interestingly, the estimated CV of circadian judgements fits onto the regression of decreasing CV, suggesting a link between short interval and circadian timing mechanisms.

Firing Variability Is Higher than Deduced from the Empirical Coefficient of Variation

2011 ◽  
Vol 23 (8) ◽  
pp. 1944-1966 ◽  
Author(s):  
Susanne Ditlevsen ◽  
Petr Lansky
Keyword(s):  
Experimental Data ◽  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Estimation Methods ◽  
Interspike Intervals ◽  
Estimation Errors ◽  
Empirical Standard Deviation ◽  
Receptor Neurons ◽  
The Mean ◽  
Empirical Standard

A convenient and often used summary measure to quantify the firing variability in neurons is the coefficient of variation (CV), defined as the standard deviation divided by the mean. It is therefore important to find an estimator that gives reliable results from experimental data, that is, the estimator should be unbiased and have low estimation variance. When the CV is evaluated in the standard way (empirical standard deviation of interspike intervals divided by their average), then the estimator is biased, underestimating the true CV, especially if the distribution of the interspike intervals is positively skewed. Moreover, the estimator has a large variance for commonly used distributions. The aim of this letter is to quantify the bias and propose alternative estimation methods. If the distribution is assumed known or can be determined from data, parametric estimators are proposed, which not only remove the bias but also decrease the estimation errors. If no distribution is assumed and the data are very positively skewed, we propose to correct the standard estimator. When defining the corrected estimator, we simply use that it is more stable to work on the log scale for positively skewed distributions. The estimators are evaluated through simulations and applied to experimental data from olfactory receptor neurons in rats.

scholarly journals Climate resources analysis for use of planning in crop production and rainfall water management in the central highlands of Ethiopia, the case of Bishoftu district, Oromia region

2010 ◽  
Vol 7 (3) ◽  
pp. 3733-3763 ◽  
Author(s):  
F. Yemenu ◽  
D. Chemeda
Keyword(s):  
Markov Chain ◽  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Rainy Season ◽  
Growing Season ◽  
Entire Study ◽  
Dry Spell ◽  
The Mean ◽  
Central Highlands ◽  
Main Rainy Season

Abstract. Agricultural practices and water resources management in the central highlands of Ethiopia is highly dependant and associated with climatic resources and their pattern and hence wise use of those resources is a priority for the region. Accordingly, a study was conducted to asses and critically quantity the climate resources of the central high lands of Ethiop, Bishoftu district. Thirty three years of weather record data has been used for the work. The onset, duration and end of the growing seasons were defined and quantified based on FAO and Reddy models while the dry and wet spell distributions and the drought events were calculated using the Markov chain models and the standardized precipitation index (SPI) respectively. The results revealed that the mean onset of the main (Kiremt) growing season was found to occur during the second meteorological decade and ended during the end of September. Similarly, though unreliable and only few occurred during the entire study period, the mean onset of the shorter (Belg) season was found to occur during the beginning of the first decade of April. The length of the growing season during the main rainy season, (Kiremt,) ranged from 112 to 144 days with a standard deviation of 9.6 days and coefficient of variation of 7.5%. However, the mean growing length during the Belg season was found to be 22.4 days with a standard deviation of 27 days and coefficient of variation of 122%. The results of analysis obtained both from the Markov Chain and Reddy models indicated higher probabilities of dry spell occurrences during the shorter season (Belg) but the occurrences of the same in the main rainy season (Kiremt) was very minimal. Like wise, the SPI model detected some drought events ranging from mild to severe classes in both seasons based on one a month time scale analysis. A considerable attention of maximizing crop harvest during the main rainy season is practically important.

scholarly journals Modelling Feedback Effects on the Production of Short Time Intervals

2021 ◽  
Vol 10 (1) ◽  
pp. 56-74
Author(s):  
John H. Wearden ◽  
Jordan Wehrman
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Time Intervals ◽  
Average Deviation ◽  
Feedback Effects ◽  
Absolute Change ◽  
Target Time ◽  
The Mean ◽  
Short Time ◽  
Relative Variability

Abstract People produced time intervals of 500 to 1250 ms, with accurate feedback in ms provided after each production. The mean times produced tracked the target times closely, and the coefficient of variation (standard deviation/mean) declined with increasing target time. The mean absolute change from one trial to another, and its standard deviation, measures of trial-by-trial change, also increased with target time. A model of feedback was fitted to all four measures. It assumed that the time produced resulted from a combination of a scalar timing process and a non-timing process. Although the non-timing process was on average invariant with target time, the timing process was assumed to be sensitive to feedback, in two different ways. If the previous production was close to the target the model repeated it (a repeat process), but if it was further away the next production was adjusted by an amount related to the discrepancy between the previous production and the target (an adjust process). The balance between the two was governed by a threshold, which was on average constant, and it was further assumed that the relative variability of the repeat process was lower than that of the adjust process. The model produced output which fitted three of the four measures well (average deviation of 3 or 4%) but fitted the standard deviation of change less well. Reducing the magnitude of the non-timing process produced output which conformed approximately to scalar timing, and the model could also mimic data resulting from the provision of inaccurate feedback.

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