Statistical Software and Regression Diagnostic Reporting with Fuzzy-AHP Intelligent Zax (FAIZ)

Anjum, Shahid

doi:10.7763/lnse.2014.v2.92

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…The study has shown that for any prediction problem, it is always a wise strategy to use attribute selection algorithms before using the dataset in prediction, classification or forecasting algorithms or in regressions because of its demonstrated potential in saving time and efforts spent which may have otherwise been wasted either on plotting hundreds of variables against each other or on doing unnecessary juggling with running regressions in a traditional trial and error fashion in order to arrive at the best selection of variables and/or results. All this in turn reduces the excuses for regressioneering (Anjum 2014a) or regressgineering 2014c) and thus makes the completely analytical or experimental exercise more scientific as well as legitimate one.…”

Section: Discussionmentioning

confidence: 99%

Big Data Algorithms and Prediction: Bingos and Risky Zones in Sharia Stock Market Index

Anjum

Qaseem

2019

JIMF

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Each country with a stock exchange normally calculates various indexes. So is the casefor Malaysia’s Kuala Lumpur Stock exchange (KLSE). FTSE BURSA Malaysia EMASSharia price index (FTBMEMA) is one of its Sharia indexes. In an effort to find whichother indices may forecast this Sharia index, we selected 23 relevant indexes and twoexchange rates. Momentum indicators for short, medium and long term have beencalculated for the variables. The objective of this study is to find predictive indicatorsfor FTBMEMA out of the population of 188 original and derived variables. Difficultyarises in reducing the number of variables for regression or other predictive modelslike neural networks. In this preliminary study, data mining attribute selectionalgorithms along with cross validation criteria have been used, through the use of Javaclass library Weka (JCLW), for reducing the number to statistically relevant variablesfor our regression estimation in an effort to forecast various performance parametersfor FTBMEMA like performing either in a mean performance range, having jackpotsand bingos or falling into danger zones. Provided the extent of the required predictiveaccuracy, the results may bring additional insights for diversifying and hedgingvarious types of investment portfolios as well as for maximizing returns by portfoliomanagers.

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Section: Discussionmentioning

confidence: 99%

Big Data Algorithms and Prediction: Bingos and Risky Zones in Sharia Stock Market Index

Anjum

Qaseem

2019

JIMF

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“…Whereas, for the econometric analysis, multiple linear regression models has been used to analyse the data. The selection of these techniques have been made after reviewing vast variety of techniques from literature including stepwise linear function technique [5], logistic regression [8][9], variables ranking methodologies [10][11][12][13], mathematical rationale for regulatory variables [14], multi-criteria decision making methodologies [15], advanced econometric analysis [6,16] data mining based logic [17][18] and fuzzy based multi-criteria decision making methodologies [15,19] Cronbach's alpha is used to measure reliability. A reliability coefficient of .70 or higher is considered acceptable and a high level for alpha means that items in the test are highly correlated [20].…”

Section: Methods Of Data Analysismentioning

confidence: 99%

Aquaculture Employment and Economic Diversification

Shahid¹,

Abidin²

2022

Advances in Biological Sciences Research

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Facing the question of economic diversification for income and employment enhancement, the answer to the question of whether incorporation of modern digital technology and sectoral training facilities may replace imported labour for local youth, the study taking up the case study of the aquaculture sector in fisheries industry. Aquaculture is of high importance in Brunei because of its pristine tropical water and good current flows. The study is focused on the manpower training and development facilities as an effort to give ways to the local youth for employment in the sector which is dominated by imported labour at present. Primary data has been collected from 154 respondents through Google survey forms which has been analysed using SPSS and have applied descriptive analysis, Kruskal Wallis test, Cronbach Alpha, Spearman Correlation, ANOVA, logistic regression and multiple linear regression related econometric analysis. Findings show that education, manpower training and development, through the increase in work productivity, is vital in the aquaculture sector and is a valid option for employment for local youth. The interesting results demonstrate that e-training and instructor-led classroom training are significant when tested for whether and what form of training is needed, for interest to join the training program and for willingness to accept an internship employment while on-site and audio video training are not significant. Familiarity with the fishing industry and age has not changed the results while education has come up as significant for all except for the need of and forms of trainings' choice. In short, application of digital technology may have the potential to enhance the employment attractiveness for educated youth because of the efforts of diversification of job space and activities by the sector.

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Employee monitoring, productivity, privacy and harassment: Auditing technology training and adoption in public administration

Anjum

Ismail

2022

The 2nd International Conference of Science and Information Technology in Smart Administration (Icsintesa 2021)

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Statistical Software and Regression Diagnostic Reporting with Fuzzy-AHP Intelligent Zax (FAIZ)

Cited by 6 publications

References 10 publications

Big Data Algorithms and Prediction: Bingos and Risky Zones in Sharia Stock Market Index

Big Data Algorithms and Prediction: Bingos and Risky Zones in Sharia Stock Market Index

Aquaculture Employment and Economic Diversification

Employee monitoring, productivity, privacy and harassment: Auditing technology training and adoption in public administration

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