scholarly journals Statistical Analysis of Crop Production Sustainability in India: A Micro Level Study

2017 ◽  
Vol 8 (3) ◽  
pp. 191-194
Author(s):  
Varinderpal Kaur ◽  
Keyword(s):  
Statistical Analysis ◽  
Crop Production ◽  
Micro Level

Meddling in the Ballot Box

2020 ◽  
Author(s):  
Dov H. Levin
Keyword(s):  
Statistical Analysis ◽  
Theoretical Model ◽  
Preliminary Analysis ◽  
Great Powers ◽  
Archival Research ◽  
Micro Level ◽  
Election Results ◽  
Level Analysis ◽  
The U.S ◽  
Scholarly Attention

This book examines why partisan electoral interventions occur as well as their effects on the election results in countries in which the great powers intervened. A new dataset shows that the U.S. and the USSR/Russia have intervened in one out of every nine elections between 1946 and 2000 in other countries in order to help or hinder one of the candidates or parties; the Russian intervention in the 2016 U.S. elections is just the latest example. Nevertheless, electoral interventions receive scant scholarly attention. This book develops a new theoretical model to answer both questions. It argues that electoral interventions are usually “inside jobs,” occurring only if a significant domestic actor within the target wants it. Likewise, electoral interventions won’t happen unless the intervening country fears its interests are endangered by another significant party or candidate with very different and inflexible preferences. As for the effects it argues that such meddling usually gives a significant boost to the preferred side, with overt interventions being more effective than covert ones in this regard. However, unlike in later elections, electoral interventions in founding elections usually harm the aided side. A multi-method framework is used in order to study these questions, including in-depth archival research into six cases in which the U.S. seriously considered intervening, the statistical analysis of the aforementioned dataset (PEIG), and a micro-level analysis of election surveys from three intervention cases. It also includes a preliminary analysis of the Russian intervention in the 2016 U.S. elections and the cyber-future of such meddling in general.

scholarly journals Using the Intermediate Data Structure (IDS) to Construct Files for Statistical Analysis

2015 ◽  
Vol 2 ◽  
pp. 86-107
Author(s):  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Statistical Analysis ◽  
Demographic Data ◽  
Intermediate Data ◽  
Micro Level ◽  
And Storage

The use of longitudinal historical micro-level demographic data for research presents many challenges. The Intermediate Data Structure (IDS) was developed to try to solve some of these challenges by facilitating the storing and sharing of such data. This article proposes an extension to the IDS, which allows the standardization and storage of constructed variables. It also describes how to produce a rectangular episodes file for statistical analysis from data stored in the IDS and presents programs developed for such purpose.

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.

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