ÖZLÜCE BARAJ GÖLÜ (ELAZIĞ-BİNGÖL) SAZAN POPULASYONUNUN (Cyprinus carpio) MORFOMETRİK VE MERİSTİK ÖZELLİKLERİ

2021 ◽  
Vol 16 (3) ◽  
pp. 110-119
Author(s):  
Mustafa Düşükcan ◽  
Mücahit Eroğlu ◽  
Mehmet Zülfü Çoban
Keyword(s):  
Standard Deviation ◽  
Lateral Line ◽  
Cyprinus Carpio ◽  
Standard Error ◽  
Minimum Standard ◽  
Mirror Carp ◽  
Coefficients Of Variation ◽  
Average Maximum ◽  
Pharyngeal Teeth ◽  
Maximum Minimum

This study was carried out to determine some metric and meristic characteristics of the Cyprinus carpio population captured from Özlüce Dam Lake at different times. For this purpose, the average, maximum, minimum, standard deviation, standard error and the coefficients of variation of 47 metric measurements and their ratio to each other belong to 60 scale and mirror carp samples captured from the dam lake are calculated. At the same time, 8 meristic features (the number of scales in the lateral line, the number of transversal scales, the number of spines in the first gill arch, the row and number of pharyngeal teeth and the number of simple and soft rays in the dorsal, ventral, pectoral and anal fins) were examined.

IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER

2017 ◽  
Vol 13 (9) ◽  
pp. 6489-6502
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Chettiyar Vani Vivekanand
Keyword(s):  
Standard Deviation ◽  
Visual Features ◽  
Minimum Standard ◽  
Fuzzy Classifier ◽  
Precise Result ◽  
Drowsiness Detection ◽  
Maximum Minimum ◽  
Improved Pso

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series .  

Mapping and genetic effect analysis of quantitative trait loci related to body length in mirror carp (Cyprinus carpio L.)

2011 ◽  
Vol 33 (11) ◽  
pp. 1245-1250 ◽  
Author(s):  
Tian-Qi ZHANG ◽  
Xiao-Feng ZHANG ◽  
Zhao-Jun TAN ◽  
Zhu CAO ◽  
Xuan-Peng WANG ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Body Length ◽  
Cyprinus Carpio ◽  
Quantitative Trait ◽  
Genetic Effect ◽  
Effect Analysis ◽  
Mirror Carp ◽  
Trait Loci

scholarly journals An alternative to the rejection of observations

1932 ◽  
Vol 137 (831) ◽  
pp. 78-87 ◽  
Keyword(s):  
Standard Deviation ◽  
Continuous Function ◽  
Large Deviations ◽  
Opposite Direction ◽  
Standard Error ◽  
The Other ◽  
Cause Of Error ◽  
The Mean ◽  
Normal Law ◽  
Usual Rule

1. It is widely felt that any method of rejecting observations with large deviations from the mean is open to some suspicion. Suppose that by some criterion, such as Peirce’s and Chauvenet’s, we decide to reject observations with deviations greater than 4 σ, where σ is the standard error, computed from the standard deviation by the usual rule; then we reject an observation deviating by 4·5 σ, and thereby alter the mean by about 4·5 σ/ n , where n is the number of observations, and at the same time we reduce the computed standard error. This may lead to the rejection of another observation deviating from the original mean by less than 4 σ, and if the process is repeated the mean may be shifted so much as to lead to doubt as to whether it is really sufficiently representative of the observations. In many cases, where we suspect that some abnormal cause has affected a fraction of the observations, there is a legitimate doubt as to whether it has affected a particular observation. Suppose that we have 50 observations. Then there is an even chance, according to the normal law, of a deviation exceeding 2·33 σ. But a deviation of 3 σ or more is not impossible, and if we make a mistake in rejecting it the mean of the remainder is not the most probable value. On the other hand, an observation deviating by only 2 σ may be affected by an abnormal cause of error, and then we should err in retaining it, even though no existing rule will instruct us to reject such an observation. It seems clear that the probability that a given observation has been affected by an abnormal cause of error is a continuous function of the deviation; it is never certain or impossible that it has been so affected, and a process that completely rejects certain observations, while retaining with full weight others with comparable deviations, possibly in the opposite direction, is unsatisfactory in principle.

Interpretation of the Standard Error of Measurement when True Scores and Error Scores on Mental Tests are Not Independent

1966 ◽  
Vol 19 (2) ◽  
pp. 611-617 ◽  
Author(s):  
Donald W. Zimmerman ◽  
Richard H. Williams
Keyword(s):  
Standard Deviation ◽  
Standard Error ◽  
Test Score ◽  
Test Reliability ◽  
True Score ◽  
Observed Score ◽  
Standard Error Of Measurement ◽  
True Scores ◽  
Modified Equation ◽  
Error Of Measurement

It is shown that for the case of non-independence of true scores and error scores interpretation of the standard error of measurement is modified in two ways. First, the standard deviation of the distribution of error scores is given by a modified equation. Second, the confidence interval for true score varies with the individual's observed score. It is shown that the equation, so=√[(N−O/a]+[so2(roō−roo)/roō]̄, where N is the number of items, O is the individual's observed score, a is the number of choices per item, so2 is observed variance, roo is test reliability as empirically determined, and roō is reliability for the case where only non-independent error is present, provides a more accurate interpretation of the test score of an individual.

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