Corrosion of Coated Pipe Samples: An Overview and Statistical Analysis of NBS-API Data

2016 ◽  
Author(s):  
Olivier Daigle ◽  
Mahesh D. Pandey
Keyword(s):  
Statistical Analysis ◽  
American Petroleum Institute ◽  
National Bureau ◽  
Data Set ◽  
Soil Corrosion ◽  
Steel Pipes ◽  
Wrought Iron ◽  
Depth Data ◽  
Pit Depth ◽  
Corrosion Of Iron

The National Bureau of Standards (NBS) had undertaken a comprehensive study of underground soil corrosion of iron pipes and plates. The maximum pit depth data for different types of wrought iron and carbon steel pipes have been widely analyzed and utilized in the corrosion literature. There is another important but relatively obscure data set about the testing of pipes with bituminous coating that NBS carried out in collaboration with the American Petroleum Institute (API). This program tested dozens of coatings on operating line pipes as well as short sections of pipes at 15 soil sites over a 10 year period (1930–1940). This paper presents an overview of this data and presents statistical analysis of protection offered by coatings.

scholarly journals A Statistical Analysis of Daily Snow Depth Trends in North America

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 820
Author(s):  
Jonathan Woody ◽  
Yang Xu ◽  
Jamie Dyer ◽  
Robert Lund ◽  
Anuradha P. Hewaarachchi
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
North America ◽  
Statistical Methods ◽  
Snow Depth ◽  
Statistical Methodology ◽  
Data Set ◽  
Depth Data ◽  
Snow Water ◽  
Climate Time Series

Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

scholarly journals A New Global Storage‐Area‐Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

2018 ◽  
Vol 54 (12) ◽  
Author(s):  
Wondmagegn Yigzaw ◽  
Hong‐Yi Li ◽  
Yonas Demissie ◽  
Mohamad I. Hejazi ◽  
L. Ruby Leung ◽  
...  
Keyword(s):  
Land Surface ◽  
Earth System ◽  
Data Set ◽  
System Models ◽  
Depth Data ◽  
Storage Area ◽  
Earth System Models

scholarly journals Statistical analysis of cytogenetic data

2021 ◽  
Vol 28 ◽  
pp. 146-150
Author(s):  
L. A. Atramentova
Keyword(s):  
Data Structure ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Statistical Method ◽  
Confidence Intervals ◽  
Study Design ◽  
Cytogenetic Study ◽  
Data Set ◽  
Statistical Errors ◽  
Cytogenetic Data

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described. Keywords: data structure, numerically unbalanced complex, confidence interval.

Women in cabinets

2017 ◽  
Vol 24 (6) ◽  
pp. 663-673 ◽  
Author(s):  
Daniel Stockemer ◽  
Aksel Sundström
Keyword(s):  
Statistical Analysis ◽  
Set Covering ◽  
Industrialized Countries ◽  
Left Wing ◽  
Data Set ◽  
Party Ideology ◽  
Liberal Government ◽  
Government Benefits ◽  
Heads Of Government

There is still relatively little research on what factors explain the share of women in cabinets across countries and time. Focusing on party ideology, we advance this budding research. First, we examine if heads of government from left-leaning and/or liberal parties tend to select a larger proportion female cabinet members than those from conservative parties. Second, we evaluate whether a switch toward a left-leaning or liberal government benefits women’s cabinet presence. We test both propositions empirically with a data set covering mainly Western and industrialized countries after 1968. Our statistical analysis only find lukewarm support for the first proposition, that is, left-wing parties are no longer more likely to nominate women to cabinet posts than other party families, particularly liberal parties. Rather, what we do find is that a change in government, regardless of whether the new formateur is left-wing, liberal, or conservative, benefits the nomination of women to cabinet posts.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

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