scholarly journals Diversity Analysis in Rice Using GENSTAT and SPSS Programs

1970 ◽  
Vol 8 (2) ◽  
pp. 14-21
Author(s):  
MM Rahman ◽  
A Ansari ◽  
MM Rashid
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Multivariate Analyses ◽  
Breeding Strategy ◽  
Morphological Data ◽  
Physiological Data ◽  
Diversity Analysis ◽  
Rice Cultivars ◽  
Spss Software ◽  
Effective Development

A study was conducted to assess the morpho-physiological divergence among 21 T. Aman rice cultivars at BRRI regional station, Sonagazi, Feni, during July 2008 to December 2008. Data were collected on 13 morphological and 14 physiological traits. Cluster analysis were carried out separately by using two softwares viz. GENSTAT v 5.5 and SPSS v 12.0 where in, both the software divided the cultivars into five clusters in both cases. The resulted clusters seemed to be very similar to some swapping genotypes which indicate that the softwares showed little dissimilarity. When clusterings were carried out using 13 morphological data, multivariate analysis showed five clusters in both GENSTAT and SPSS software with 19.05% swapping genotypes. When multivariate analyses were done with GENSTAT and SPSS softwares based on 14 physiological data, five clusters were also found with 14.29 % swapping genotypes. Of the two programs, GENSTAT appeared to be more reliable than the SPSS program. Inclusion of BR3, BR5, BR11, BR23, BRRI dhan33, BRRI dhan38, BRRI dhan44 and BRRI dhan46 giving emphasis on BRRI dhan33, BRRI dhan38, BRRI dhan44 and BRRI dhan46 is recommended for effective development of a breeding strategy in diallel fashion. Keywords: Clustering; multivariate analysis; rice; transplanted aman. DOI: 10.3329/agric.v8i2.7572 The Agriculturists 8(2): 14-21 (2010)

scholarly journals Exploratory Analysis of Influencing Variables on Deforestation in Ghana

2018 ◽  
Author(s):  
Naramena McCray
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Spatial Association ◽  
Energy Access ◽  
National Scale ◽  
Cooking Fuel ◽  
Cropland Area ◽  
Spss Software ◽  
The Relationship ◽  
Exploratory Multivariate Analysis

The relationship between energy access and deforestation in Ghana is examined through this exploratory multivariate analysis. Variables of electrification rate, wood use as cooking fuel, built-up area, population density, cropland area, and education are used to understand the relative influence of energy access on deforestation. This research uses RStudio, GeoDa and SPSS software to perform statistical processes: enter and stepwise multivariate linear regression, cluster analysis, and Bivariate Local Indicators of Spatial Association (BiLISA). The methods conducted at the sub-national scale improve upon on recent research that found wood use as cooking fuel to be a significant driver of deforestation that is being minimized by increasing access to electricity. This research conversely reveals that energy access is in fact influencing an increase in deforestation in Ghana.

Multivariate consumption profiling (MCP) for intelligent meter systems: a methodology to define categories and levels

2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Reference Population ◽  
Statistical Analyses ◽  
Conceptual Schema ◽  
Data Variability ◽  
Water Companies ◽  
Analyse Data

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.

scholarly journals Prediction of Outcome for Transabdominal Cerclage in Women with Cervical Insufficiency

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Ji Eun Song ◽  
Keun Young Lee ◽  
Ga Hyun Son
Keyword(s):  
Risk Factors ◽  
Multivariate Analysis ◽  
Cohort Study ◽  
Pregnancy Outcomes ◽  
Retrospective Cohort ◽  
Multivariate Analyses ◽  
Univariate Analysis ◽  
Cervical Length ◽  
Cervical Insufficiency ◽  
Prediction Of Outcome

We investigated pregnancy outcome following transabdominal cerclage (TAC) in women with cervical insufficiency (CI) and explored parameters for predicting pregnancy outcomes following TAC. In this retrospective cohort study, we included 161 women with TAC. We considered demographic, obstetric, and gynecologic histories, pre- and postoperative cervical length (CL), and CL at 20–24 weeks as parameters for predicting outcomes following TAC. Univariate and multivariate analyses were used to identify risk factors for predicting delivery before 34 weeks after TAC. 182 pregnancies occurred after TAC, and 290 pregnancies prior to TAC were identified. The rate of delivery <34 weeks significantly decreased following TAC (5% versus 82%,P<0.001). Univariate analysis demonstrated that a short CL (<25 mm) at 20–24 weeks and adenomyosis were associated with delivery at <34 weeks’ gestation following TAC (P=0.015andP=0.005, resp.). However, multivariate analysis demonstrated that only a short CL (<25 mm) at 20–24 weeks was a significant predictor (P=0.005). TAC is an efficacious procedure that prolongs pregnancy in women with CI. A short CL at 20–24 weeks may predict the delivery at <34 weeks’ gestation following TAC.

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

scholarly journals Population structure and genetic diversity analysis in Sali (Oryza sativa) rice germplasm of Assam using microsatellite markers

2021 ◽  
Vol 58 (2) ◽  
pp. 279-286
Author(s):  
Sandhani Saikia ◽  
Pratap Jyoti Handique ◽  
Mahendra K Modi
Keyword(s):  
Genetic Diversity ◽  
Ssr Markers ◽  
Genetic Improvement ◽  
Crop Improvement ◽  
Mean Value ◽  
Diversity Analysis ◽  
Rice Cultivars ◽  
Improvement Program ◽  
Information Index ◽  
Improvement Programs

Genetic diversity is the source of novel allelic combinations that can be efficiently utilized in any crop improvement program. To facilitate future crop improvement programs in rice, a study was designed to identify the underlying genetic variations in the Sali rice germplasms of Assam using SSR markers. The 129 SSR markers that were used in the study amplified a total of 765 fragments with an average of 5.93 alleles per locus. The Shannon's Information Index was found to be in the range from 0.533 to 1.786. The Polymorphism Information Content (PIC) fell into the range from 0.304 to 0.691 with a mean value of 0.55. The overall FST value was found to be 0.519 that indicated the presence of genetic differentiation amongst the genotypes used in the study. The Sali population was divided into two clusters. The information obtained from the present study will facilitate the genetic improvement of Sali rice cultivars.

University Student Absenteeism

2014 ◽  
pp. 454-466 ◽  
Author(s):  
Xavier M. Triado ◽  
Pilar Aparicio-Chueca ◽  
Joan Guàrdia-Olmos ◽  
Natalia Jaría-Chacón ◽  
Maribel Peró Cebollero ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Student Performance ◽  
Multivariate Analyses ◽  
University Student ◽  
Faculty Members ◽  
Empirical Approach ◽  
Interesting Topic ◽  
Two Samples ◽  
Performance Profiles ◽  
Student Absenteeism

Work on university student absenteeism is an interesting topic that treats motivation problems and its important consequences, like dropout, but is not easy to measure. In this chapter, the authors make a revision of the concept and an empirical approach to the possible reasons of student absenteeism through multivariate analyses—which the students themselves believe to be justified—and those offered by the faculty members in the case of the authors’ big school (with nine studies and 12,000 students), of the authors’ university (with 70,000 students), in the authors’ country. The analysis was carried out on two samples (1,161 students and 181 professors), which indicates that the reasons offered by each population are not the same. Through a cluster analysis, it is possible to identify six student performance profiles, which sheds some light on understanding this fact and the opportunity to suggest some ways of action.

Cluster Analysis

2014 ◽  
pp. 123-159
Author(s):  
Pankaj Nagar
Keyword(s):  
Cluster Analysis ◽  
Policy Development ◽  
Good Governance ◽  
Unsupervised Classification ◽  
Business Organizations ◽  
Statistical Tools ◽  
Managerial Decisions ◽  
Wide Range ◽  
Spss Software ◽  
Societal Problems

The cluster analysis, also known as grouping, clumping, unsupervised classification, is one of the multivariate analysis techniques. The technique of cluster analysis is highly useful in a wide range of problems related to managerial decisions, psychological solutions, categorization of business organizations on the basis of their performance for constructing separate policies for each clusters, in health sectors, societal problems, etc. For good governance there is a need to apply the proper statistical tools with ICT. Even today, the statistical tools are rarely used in the region of e-governance for better policy development. This chapter discusses the use of cluster analysis in classifying a large amount of data into sub-groups (known as clusters), which are homogeneous in a certain sense, and analyzes each sub-group separately to find solutions for each of them. The method in explained with the help of an illustration, by using the SPSS software.

scholarly journals Genetic diversity in potato (Solanum tuberosum L.)

1970 ◽  
Vol 36 (2) ◽  
pp. 121-125 ◽  
Author(s):  
MAA Mondal ◽  
MM Hossain ◽  
MG Rasul ◽  
M Shalim Uddin
Keyword(s):  
Genetic Diversity ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Solanum Tuberosum ◽  
Key Words ◽  
Diversity Pattern ◽  
Wide Range ◽  
Cluster Distance ◽  
Range Of Variation

Genetic diversity in 31 potato genotypes (parents and their hybrid progenies) was determined using multivariate analysis. Cluster analysis revealed that the parents and their hybrid progenies could be grouped into five different clusters. The maximum number of genotypes were included in clusters II and V. Cluster V had maximum and cluster I had minimum intra-cluster distance. Cluster mean showed wide range of variation for several characters among single as well as multi-genotypic clusters. Considering diversity pattern, parents should be selected from clusters I, III and V for the improvement of potato.   Key words: Genetic diversity, Cluster analysis, Potato DOI = 10.3329/bjb.v36i2.1499 Bangladesh J. Bot. 36(2): 121-125, 2007 (December)

A Quantitative Study of Soil-Plant Relations in the Eastern Goldfields of Western Australia.

1998 ◽  
Vol 20 (1) ◽  
pp. 119 ◽  
Author(s):  
B Fergusson ◽  
AJ Graham
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Plant Community ◽  
Plant Communities ◽  
Western Australia ◽  
Environmental Variables ◽  
Function Analysis ◽  
Field Site ◽  
Disturbed Areas ◽  
Exchangeable Ca

The soil and plants at a 27.4 ha field site near Kalgoorlie, Western Australia, were surveyed and analysed with multivariate statistics. Cluster analysis identified four distinct plant communities at the study site. These were: Acacia acuminata shrubland Eucalyptus gvfithsii woodland Eucalyptus salrnonophloia woodland 'Ground Covers' - areas characterised by the presence of generalist herbs, low shrubs and weeds, and the absence of dominant upper storey species. Discriminant function analysis identified site elevation and soil exchangeable Ca as the primary environmental discriminants between the plant communities. Using these two variables, sample points were classified into one of the four plant communities. The two methods of classification matched well, with classification based on the two environmental variables providing an indication of which plant community would be most likely to establish in disturbed areas. This type of information can be important to revegetation programs in the region, guiding the use of appropriate plant species under different rehabilitation conditions. Key wcrds: environmental variables, plant communities, multivariate analysis, classification, revegetation

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