scholarly journals Variance Analysis and Autocorrelation Function for 2D Fiber Lap Statistical Analysis

10.5772/61795 ◽  
2016 ◽  
Author(s):  
Jean-Yves Drean ◽  
Omar Harzallah
Keyword(s):  
Statistical Analysis ◽  
Autocorrelation Function ◽  
Variance Analysis

scholarly journals Statistical analysis of water vapour and ozone in the UT/LS observed during SPURT and MOZAIC

2008 ◽  
Vol 8 (22) ◽  
pp. 6603-6615 ◽  
Author(s):  
A. Kunz ◽  
C. Schiller ◽  
F. Rohrer ◽  
H. G. J. Smit ◽  
P. Nedelec ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Vapour ◽  
Trace Gases ◽  
Distribution Functions ◽  
Variance Analysis ◽  
Trace Gas ◽  
Data Sets ◽  
Data Set ◽  
Atmospheric Processes ◽  
Passenger Aircraft

Abstract. A statistical analysis for the comparability of water (H2O) and ozone (O3) data sets sampled during the SPURT aircraft campaigns and the MOZAIC passenger aircraft flights is presented. The Kolmogoroff-Smirnoff test reveals that the distribution functions from SPURT and MOZAIC trace gases differ from each other with a confidence of 95%. A variance analysis shows a different variability character in both trace gas data sets. While the SPURT H2O data only contain atmospheric processes variable on a diurnal or synoptical timescale, MOZAIC H2O data also reveal processes, which vary on inter-seasonal and seasonal timescales. The SPURT H2O data set does not represent the full MOZAIC H2O variance in the UT/LS for climatological investigations, whereas the variance of O3 is much better represented. SPURT H2O data are better suited in the stratosphere, where the MOZAIC RH sensor looses its sensitivity.

scholarly journals The Effects of Some Drugs Used to Treat Honeybee (Apis mellifera L.) Diseases and Pests on Lifespan of Honeybees

2017 ◽  
Vol 5 (7) ◽  
pp. 720
Author(s):  
Duran Özkök ◽  
Ethem Akyol
Keyword(s):  
Statistical Analysis ◽  
Apis Mellifera ◽  
Variance Analysis ◽  
Control Group ◽  
Analysis Method ◽  
Control Groups ◽  
Average Lifespan ◽  
Group A ◽  
Observation Hive ◽  
And Control

This study was conducted to determine the effects of Bayvarol®, Fumidil-B®, Neo-Terramycin® on adult honeybee lifespan. Total twenty honeybee colonies were used and randomly divided into four groups (each group consisted of five colonies). Experimental groups: Bayvarol ®, Fumidil-B® and Neo-Terramycin® were treated to first, second and third groups, respectively. No treatment was done to forth group taken as control group. A hundred one day old worker bees were taken from each group and marked with different colors and numbered on the thorax. After the marked, all worker bees were given into the observation hive. Marked worker bees were controlled and counted daily. Statistical analysis of data was done by variance analysis method and between groups comparisons were done with Duncan's multiple range tests. Average lifespans of the first, second, third and control groups were 44.97±4.90, 46.86±6.56, 45.38±6.12 and 47.72±6.06 days, respectively. There were found statistically significant differences among average lifespan of first, second, third and control groups (P

Application of Variance Analysis Method in Agriculture Information in Statistics

2014 ◽  
Vol 651-653 ◽  
pp. 2130-2133
Author(s):  
Li Ying Cao ◽  
He Long Yu ◽  
Li Ma ◽  
Gui Fen Chen ◽  
Ting Ting Yang
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Agricultural Research ◽  
Variance Analysis ◽  
Analysis Tool ◽  
Analysis Method ◽  
Correct Conclusion ◽  
The Social ◽  
Data Analysis Tool ◽  
Scientific Treatment

Variance analysis is a method commonly used in biological statistics. How to use the statistical analysis software for the analysis of variance to achieve the results of the rapid and scientific treatment, to obtain the correct conclusion, is an important part of agricultural research. This paper introduces how to use SPSS (Statistical Package for the Social Science or Statistic Products andService Solution) data analysis tool for the variance analysis method; the data analysis and processing of fast, accurate and intuitive; compared with Excel, the statistical analysis function of SPSS is more powerful, can help to improve the efficiency of data processing, but also reduce the experimental cost.

scholarly journals The evolving interaction of low-frequency earthquakes during transient slip

2016 ◽  
Vol 2 (4) ◽  
pp. e1501616 ◽  
Author(s):  
William B. Frank ◽  
Nikolaï M. Shapiro ◽  
Allen L. Husker ◽  
Vladimir Kostoglodov ◽  
Alexander A. Gusev ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Poisson Process ◽  
Point Process ◽  
Autocorrelation Function ◽  
Collective Behavior ◽  
Low Frequency ◽  
Slow Slip ◽  
Plate Interface ◽  
Single Asperity ◽  
Seismogenic Faults

Observed along the roots of seismogenic faults where the locked interface transitions to a stably sliding one, low-frequency earthquakes (LFEs) primarily occur as event bursts during slow slip. Using an event catalog from Guerrero, Mexico, we employ a statistical analysis to consider the sequence of LFEs at a single asperity as a point process, and deduce the level of time clustering from the shape of its autocorrelation function. We show that while the plate interface remains locked, LFEs behave as a simple Poisson process, whereas they become strongly clustered in time during even the smallest slow slip, consistent with interaction between different LFE sources. Our results demonstrate that bursts of LFEs can result from the collective behavior of asperities whose interaction depends on the state of the fault interface.

scholarly journals Statistical analysis of water vapour and ozone in the UT/LS observed during SPURT and MOZAIC

2008 ◽  
Vol 8 (4) ◽  
pp. 12561-12594
Author(s):  
A. Kunz ◽  
C. Schiller ◽  
F. Rohrer ◽  
H. G. J. Smit ◽  
P. Nedelec ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Vapour ◽  
Trace Gases ◽  
Distribution Functions ◽  
Variance Analysis ◽  
Trace Gas ◽  
Data Sets ◽  
Data Set ◽  
Atmospheric Processes ◽  
Passenger Aircraft

Abstract. A statistical analysis for the comparability of water (H2O) and ozone (O3) data sets sampled during the SPURT aircraft campaigns and the MOZAIC passenger aircraft flights is presented. The Kolmogoroff–Smirnoff test reveals that the distribution functions from SPURT and MOZAIC trace gases differ from each other with a confidence of 95%. A variance analysis shows a different variability character in both trace gas data sets. While the SPURT data only contain atmospheric processes variable on a diurnal or synoptical timescale, MOZAIC data also reveal processes, which vary on inter-seasonal and seasonal timescales. The SPURT data set does not represent the full MOZAIC H2O variance in the UT/LS for climatological investigations, whereas the variance of O3 is much better represented. SPURT H2O data are better suited in the stratosphere, where the MOZAIC RH sensor looses its sensitivity.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Log-log scale roughness spectroscopy of surfaces

1993 ◽  
Vol 51 ◽  
pp. 224-225
Author(s):  
P. Fraundorf ◽  
B. Armbruster
Keyword(s):  
Power Spectrum ◽  
Autocorrelation Function ◽  
Power Spectra ◽  
Scanning Force Microscopy ◽  
Scanning Tunneling ◽  
Tunneling Microscopy ◽  
Force Microscopy ◽  
Scanning Force ◽  
Lateral Structure ◽  
Scale Roughness

Optical interferometry, confocal light microscopy, stereopair scanning electron microscopy, scanning tunneling microscopy, and scanning force microscopy, can produce topographic images of surfaces on size scales reaching from centimeters to Angstroms. Second moment (height variance) statistics of surface topography can be very helpful in quantifying “visually suggested” differences from one surface to the next. The two most common methods for displaying this information are the Fourier power spectrum and its direct space transform, the autocorrelation function or interferogram. Unfortunately, for a surface exhibiting lateral structure over several orders of magnitude in size, both the power spectrum and the autocorrelation function will find most of the information they contain pressed into the plot’s origin. This suggests that we plot power in units of LOG(frequency)≡-LOG(period), but rather than add this logarithmic constraint as another element of abstraction to the analysis of power spectra, we further recommend a shift in paradigm.

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