scholarly journals A statistical study of variability in catch obtained by short repeated trawls taken over an inshore ground

1951 ◽  
Vol 29 (3) ◽  
pp. 649-660 ◽  
Author(s):  
H. Barnes ◽  
T. B. Bagenal
Keyword(s):  
Standard Deviation ◽  
Working Conditions ◽  
Coefficient Of Variation ◽  
Statistical Study ◽  
Sampling Period ◽  
Single Species ◽  
The Other ◽  
Population Changes ◽  
Flat Fish ◽  
The Mean

The variability in catch of a series of hauls with a V.D. trawl taken under, as far as possible, controlled working conditions has been determined.For analysis the species were divided into ‘round’ fish and ‘flat’ fish together with Nephrops; for both groups the coefficient of variation varied from 40 to 85 %. Taking 75 % as a representative value, the 2σ fiducial limits are 30–300 % for a single species from a single haul of half an hour duration.It has been found that the standard deviation is roughly proportional to the mean, and that the variance is greater than the mean and increases with the mean. This suggests aggregation of the population.The species are considered over the whole sampling period (4 weeks). Except for haddocks and dogfish the ‘round’ fish constituted a constant population. Changes were found in the other groups and their origin is discussed.

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.

scholarly journals Nature versus Nurture in Shallow Convection

2010 ◽  
Vol 67 (5) ◽  
pp. 1655-1666 ◽  
Author(s):  
David M. Romps ◽  
Zhiming Kuang
Keyword(s):  
Standard Deviation ◽  
The Other ◽  
Cloud Base ◽  
Shallow Convection ◽  
Eddy Simulation ◽  
Cloud Properties ◽  
Nature Versus Nurture ◽  
Large Eddy ◽  
The Mean ◽  
Single Set

Abstract Tracers are used in a large-eddy simulation of shallow convection to show that stochastic entrainment (and not cloud-base properties) determines the fate of convecting parcels. The tracers are used to diagnose the correlations between a parcel’s state above the cloud base and both the parcel’s state at the cloud base and its entrainment history. The correlation with the cloud-base state goes to zero a few hundred meters above the cloud base. On the other hand, correlations between a parcel’s state and its net entrainment are large. Evidence is found that the entrainment events may be described as a stochastic Poisson process. A parcel model is constructed with stochastic entrainment that is able to replicate the mean and standard deviation of cloud properties. Turning off cloud-base variability has little effect on the results, which suggests that stochastic mass-flux models may be initialized with a single set of properties. The success of the stochastic parcel model suggests that it holds promise as the framework for a convective parameterization.

Statistical Study to Reduce the Number of Slides Microscopically Examined in the Geotrichum Mold Count Methods

1978 ◽  
Vol 61 (4) ◽  
pp. 993-994
Author(s):  
Stanley M Cichowicz ◽  
John C Atkinson
Keyword(s):  
Standard Deviation ◽  
Statistical Study ◽  
Green Beans ◽  
Counting Time ◽  
The Mean ◽  
Mold Count

Abstract Variation in the data obtained from the 4 slides counted in the Geotrichum mold count methods was statistically examined to determine if the number of slides could be reduced. The mean and standard deviation for 4 replicate slides for each can of carrots, green beans, and pineapple demonstrated that mold count variation is a function of the mean, that significant variations exist among lots and among cans within a lot, and that quadruplicate slide counts are unnecessary. Duplicate counts would save 50% of the analyst’s counting time.

scholarly journals Catalogue of Clusters that are Members of Superclusters

1983 ◽  
Vol 104 ◽  
pp. 185-186
Author(s):  
M. Kalinkov ◽  
K. Stavrev ◽  
I. Kuneva
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
The Other ◽  
Clusters Of Galaxies ◽  
Number Of Clusters ◽  
Abell Clusters ◽  
The Mean ◽  
Abell Cluster ◽  
Image Position

An attempt is made to establish the membership of Abell clusters in superclusters of galaxies. The relation is used to calibrate the distances to the clusters of galaxies with two redshift estimates. One is m10, the magnitude of the ten-ranked galaxy, and the other is the “mean population,” P, defined by: where p = 40, 65, 105 … galaxies for richness groups 0, 1, 2 …, and r is the apparent radius in degrees given by: The first iteration for redshift, z1, is obtained from m10 alone: The standard deviation for Eq. (1) is 0.105, the number of clusters with known velocities is 342 and the correlation coefficient between observed and fitted values is 0.921. With zi from Eq. (1), we define Cartesian galactic coordinates Xi = Rih−1 cosBi cosLi, Yi = Rih−1 cosBi sinLi, Zi = Rih−1 sinBi for each Abell cluster, i = 1, …, 2712, where Ri is the distance to the cluster (Mpc), and Ho = 100 h km s−1 Mpc−1.

Rapid Rhino versus Merocel nasal packs in septal surgery

2011 ◽  
Vol 125 (12) ◽  
pp. 1244-1246 ◽  
Author(s):  
A Hesham ◽  
A Ghali
Keyword(s):  
Standard Deviation ◽  
Pain Score ◽  
Visual Analogue Score ◽  
Nasal Packing ◽  
The Other ◽  
Septal Surgery ◽  
Patient Tolerance ◽  
Operative Complications ◽  
The Mean

AbstractObjective:To compare Rapid Rhino and Merocel packs for nasal packing after septoplasty, in terms of patient tolerance (both with the pack in place and during removal) and post-operative complications.Materials and methods:Thirty patients (aged 18–40 years) scheduled for septoplasty were included. Following surgery, one nasal cavity was packed with Rapid Rhino and the other one with Merocel. Patients were asked to record pain levels on a visual analogue score, on both sides, with the packs in situ and during their removal the next day. After pack removal, bleeding was compared on both sides.Results:The mean ± standard deviation pain score for the Rapid Rhino pack in situ (4.17 ± 1.78) was less than that for the Merocel pack (4.73 ± 2.05), but not significantly so (p = 0.314). The mean pain score for Rapid Rhino pack removal (4.13 ± 1.76) was significantly less that that for Merocel (6.90 ± 1.67; p = 0.001). Bleeding after pack removal was significantly less for the Rapid Rhino sides compared with the Merocel sides (p <0.05).Conclusion:Rapid Rhino nasal packs are less painful and cause less bleeding, compared with Merocel packs, with no side effects. Thus, their use for nasal packing after septal surgery is recommended.

scholarly journals Nadir Correction of AIRS Radiances

2010 ◽  
Vol 27 (3) ◽  
pp. 470-480 ◽  
Author(s):  
Chee-Kiat Teo ◽  
Tieh-Yong Koh
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
The Other ◽  
Mean Square ◽  
Atmospheric Infrared Sounder ◽  
Limb Effect ◽  
Atmospheric Window ◽  
The Mean ◽  
Optimal Set ◽  
Atmospheric Windows

Abstract A statistical method to correct for the limb effect in off-nadir Atmospheric Infrared Sounder (AIRS) channel radiances is described, using the channel radiance itself and principal components (PCs) of the other channel radiances to account for the multicollinearity. A method of selecting an optimal set of predictors is proposed and demonstrated for one- and two-PC predictors. Validation results with a subset of AIRS channels in the spectral region 649–2664 cm−1 show that the mean nadir-corrected brightness temperature (BT) is largely independent of scan angle. More than 66% of the channels have a root-mean-square (rms) bias less than 0.10 K after nadir correction. Limb effect on the standard deviation (SD) of BT is discernible at larger scan angles, mainly for the atmospheric windows and the water vapor channels around 6.7 μm. After nadir correction, nearly all atmospheric window channels unaffected by solar glint and more than 76% of water vapor channels examined have BT SDs brought closer to nadir values. For the window channels affected by solar glint (wavenumber &gt; 2490 cm−1), BT SDs at the scan angles with the strongest impact from solar reflection were improved on average by more than 0.6 K after nadir correction.

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 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 Prevalencia de síndrome de burnout en los estudiantes de la Carrera de Enfermería de la Universidad Central del Ecuador

2020 ◽  
Vol 4 (8) ◽  
pp. 38-54
Author(s):  
Luzmila De Jesús Carvajal Andrade ◽  
◽  
Belén del Rocío Logacho Villacís ◽  
Ramiro Rogelio Rojas Jaramillo ◽  
◽  
...  
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Key Words ◽  
Nursing School ◽  
The Other ◽  
Burnout Syndrome ◽  
Information Analysis ◽  
Academic Burnout ◽  
Upper Limit ◽  
The Mean

This research has been conducted in order to determine the Prevalence of the burnout syndrome among students from third to eighth semester who are attending the Nursing School. It was a prevalence study; the data were collected using the survey’s technique, in a questionnaire divided in five sections applied to 172 students. The information analysis was calculated using the Mean method and the Standard Deviation for the Academic burnout, while for the Labor burnout it was utilized the punctuation of: high, medium and low scales. The outcome results showed that the prevalence of the burnout Syndrome in both academic and labor was low. The 2.3 % of students had the Academic burnout, (Confidence interval: 95%, lower limit: 0.44% and upper limit: 4.21%) with a probability of 3.52%, on the other hand the Labor burnout was of 4% among students in the shifting internship, with a probability of 1.22%. Key words: Syndrome burnout, Stress, nursing.

scholarly journals On The Estimation of Means and Variances In The Case Of Unequal Components

1975 ◽  
Vol 8 (3) ◽  
pp. 323-335 ◽  
Author(s):  
Esa Hovinen
Keyword(s):  
Standard Deviation ◽  
The Other ◽  
Insurance Companies ◽  
Loss Ratio ◽  
Weighted Mean ◽  
The Mean ◽  
Image Position ◽  
Statistical Work ◽  
Better Than

In practical statistical work one frequently meets certain problems. For instance, we may have the following data about loss ratios in certain insurance companies and corresponding, numbers of insurance in force:I assume further that we have no reason to believe that the companies, their loss ratios and their structure of insurances in force differ in any other way than by the size of companies. The problem is how to get quick estimates of mean losses and their variances in different companies?A straitforward way to estimate the mean loss ratio would be to compute the usual mean of numbers pi, (Σpi/6) = 14; its standard deviation is 6,5. As this procedure of the “first statistician” seems to be too simple and naive, a “more cautious” statistician would compute the weighted mean loss ratioThe “more cautious” statistician would arque that his result is much better than the other result 14. But what would be the variance of the estimate 7,1, and what is the variance in the different companies?

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