Statistical Modelling for Cotton Yield Estimation Using Agricultural Climate Indices (A Case Study of Gharakhil District in Mazandaran Province, Iran)

2014 ◽  
Vol 8 (2) ◽  
pp. 109-116 ◽  
Author(s):  
S. Bazgeer ◽  
Gh. Fadavi ◽  
S.M. Hossainy
Keyword(s):  
Statistical Modelling ◽  
Climate Indices ◽  
Cotton Yield ◽  
Yield Estimation ◽  
Mazandaran Province

Effecting Factors on Development of the Handicraft in the Targeting Market (Case Study: Mazandaran Province Cooperatives)

2012 ◽  
Vol 3 (5) ◽  
pp. 219-221
Author(s):  
Ali sorayaei ◽  
◽  
Naser ali Yadollahzadeh Tabari ◽  
Sepideh Sadat Soleimanian
Keyword(s):  
Mazandaran Province

Correlates of academic misconduct and CSR proclivity of students

2014 ◽  
Vol 6 (1) ◽  
pp. 128-148 ◽  
Author(s):  
Stephen K. Nkundabanyanga ◽  
Charles Omagor ◽  
Irene Nalukenge
Keyword(s):  
Structural Equation ◽  
Statistical Modelling ◽  
Error Variance ◽  
Academic Misconduct ◽  
Fraud Triangle ◽  
Cross Sectional ◽  
Content Type ◽  
Quantitative Results ◽  
Corporate Social

Purpose – The purpose of this paper is to examine the effect of the fraud triangle, Machiavellianism, academic misconduct and corporate social responsibility (CSR) proclivity of students. Design/methodology/approach – The present study surveyed 471 university students. The study was cross-sectional and employed structural equation modelling in statistical modelling. Findings – The study provides evidence that perceived opportunity to cheat in examinations is the single most important factor accounting for significant variations in rationalization and academic misconduct. Similarly, low Machiavellians significantly get inclined to CSR ideals. The fraud triangle alone accounts for 36 per cent of the variations in academic misconduct, hence the error variance is 64 per cent of academic misconduct itself. This error variance increases to 78 per cent when a combination of perceived opportunity, rationalization, Machiavellianism is considered. Moreover, both Machiavellianism and academic misconduct account for 17 per cent of variations in students’ proclivity to CSR ideals. Research limitations/implications – Results imply that creating a setting that significantly increases a student's anticipated negative affect from academic misconduct, or effectively impedes rationalization ex ante, might prevent some students from academic misconduct in the first place and then they will become good African corporate citizens. Nevertheless, although the unit of analysis was students, these were from a single university – something akin to a case study. The quantitative results should therefore be interpreted with this shortcoming in mind. Originality/value – This paper contributes to the search for predictors of academic misconduct in the African setting and as a corollary, for a theory explaining academic misconduct. Those students perceiving opportunity to cheat in examinations are also able to rationalize and hence engage in academic misconduct. This rationalization is enhanced or reduced through Machiavellianism.

scholarly journals A Sociological Study on Adherence to Social Norms and Its Relevant Factors: (Case Study of Inhabitants and the Tourists of Mazandaran Province)

2017 ◽  
Vol 7 (2) ◽  
pp. 5-29
Author(s):  
Ghorbanali Ebrahimi ◽  
◽  
Akbar Aliverdinia ◽  
Vahid Janmohammadi largani ◽  
Seyede Fatemeh Andarvaj ◽  
...  
Keyword(s):  
Social Norms ◽  
Sociological Study ◽  
Mazandaran Province ◽  
Relevant Factors

scholarly journals Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

2013 ◽  
Vol 1 (2) ◽  
pp. 957-1000 ◽  
Author(s):  
M. Fressard ◽  
Y. Thiery ◽  
O. Maquaire
Keyword(s):  
Landslide Susceptibility ◽  
Roc Curves ◽  
Statistical Modelling ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Accuracy ◽  
Multivariate Statistical ◽  
The Impact ◽  
Modelling Methods

Abstract. The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

Forest fuel assessment by LiDAR data. A case study in NE Italy

2021 ◽  
Author(s):  
Flavio Taccaliti ◽  
Lorenzo Venturini ◽  
Niccolò Marchi ◽  
Emanuele Lingua
Keyword(s):  
Laser Scanner ◽  
Remote Sensing Data ◽  
Statistical Modelling ◽  
Pinus Nigra ◽  
Simulation Software ◽  
Lidar Data ◽  
Special Focus ◽  
Forest Fuels ◽  
Behaviour Simulation

<p>Fuel management is a crucial action to maintain wildland fires under the threshold of manageability; hence, in order to allocate resources in the best way, wildland fuel mapping is regarded as a necessary tool by land managers. Several studies have used Aerial Laser Scanner (ALS) data to estimate forest fuels characteristics at plot level, but few have extended such estimates at a zonal level.</p><p>In the context of the EU Interreg Project CROSSIT SAFER, a test of the possibilities of ALS data to predict fuels attributes has been performed in three different areas: an alpine basin, a coastal wildland-urban interface and a karstic highland. Eighteen sampling plots have been laid out over 6 forest categories, with a special focus on <em>Pinus nigra</em> J. F. Arnold artificial forests. Low density (average 4 points/m<sup>2</sup>) discrete return LiDAR data has been analysed with FUSION, a free point cloud analysis software tailored to forestry purposes; field and remote sensing data have been connected with simple statistical modelling and results have been spatialised over the case study areas to provide wall-to-wall inputs for FLAMMAP fire behaviour simulation software.</p><p>Resulting maps can be of relevance for land managers to better highlight the most vulnerable or fire prone areas at a mesoscale administrative level. Limitations and room for improvement are pointed out, in the view that land management should keep updated with the latest technology available.</p>

Use of Statistical Modelling Techniques for Simulation

1993 ◽  
Author(s):  
Ebrahim Mazharsolook ◽  
David C. Robinson ◽  
Jonathan D. Casey
Keyword(s):  
Quality Control ◽  
Statistical Modelling ◽  
Test Results ◽  
Final Test ◽  
Product Model ◽  
Design Changes ◽  
Discrete Manufacturing ◽  
Similar Product ◽  
Modelling Techniques

Abstract Statistical methods are explored for the use in modelling of discrete manufacturing. The developed methodologies based on Design of Experiments (DOE) and stepwise regression to obtain the product model are described. This model is then embedded within a software system which is used for simulation of design changes, process changes and disturbances. The software is used to predict final test results in respect of up-stream parameter changes. A case study is presented o show the implementation of this method of modelling in Quality Control of manufacture. This case study has successfully been implemented. The system is currently assisting the company in design of similar product. Feasibility of applying Artificial Intelligen (AI) techniques to Model-Based Quality Control (MBQC) is investigated. An outline of the future development of Hybrid MBQC is then presented.

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