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Accessing Individual Students Academic Performance Using Random Effect Analysis(Multilevel Analysis)

Communications in Mathematics and Statistics 2016年第3期4卷 341-357页

作者： Ekow Ewusi Amissah Nana Kena Frempong emmanuel degraft johnson owusu-ansah Institute of Statistical Social and Economic Research(ISSER) University of GhanaLegonGhana Department of Mathematics Kwame Nkrumah University of Science and Technology(KNUST)KumasiGhana

Sometimes,people with interest in measuring quality of education take intoaccountlevel in academic performance and various associated ***,an aver-age academic performance is an accustomed way of assessment;however,this studyexamines on individual basis different factors that might have an impact on the acad-emic performance of undergraduate *** on the semester weighted averageof class of 2012 mathematics students were acquired from the Quality Assurance andPlanning Unit and the Examination Office of the Department of Mathematics,KwameNkrumah University of Science and *** main factors considered for thisresearch were entry age,gender,entry aggregate,Ghana education service gradedlevel of senior high school attended and geographical *** statistical methodconsidered was random *** the interaction or variation around the slope washighly insignificant,the random intercept model was the better alternative ahead ofthe random intercept and slope ***,not all the parameter estimatesare significant at a=0.05 level of *** was observed that the differencein geographical location was not significant in the main effect *** where astudent comes from has no influence on their academic ***,entryaggregate,entry age and gender were all ***,the geographical location with regard to the Northern Belt was significant in the linear trend with astandard deviation of approximately 0.712.

关键词： Random effect Random intercept model Random intercept and slopemodel Standard deviation SWA Estimate

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Probabilistic modeling for an integrated temporary acquired immunity with norovirus epidemiological data

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Infectious Disease Modelling 2019年第1期4卷 99-114页

作者： emmanuel de-Graft johnson owusu-ansah Benedict Barnes Robert Abaidoo Hald Tine Anders Dalsgaard Anders Permin Torben Wilde Schou Department of Mathematics Faculty of Physical and Computational ScienceCollege of ScienceKwame Nkrumah University of Science and TechnologyKumasiGhana Laboratory of Integrated Statistical Applications(LISA) Faculty of Physical and Computational SciencesCollege of ScienceKwame Nkrumah University of Science and TechnologyKumasiGhana Regional Water and Environmental Sanitation Center-Kumasi College of EngineeringKwame Nkrumah University of Science and TechnologyKumasiGhana Department of Theoretical and Applied Biology Faculty of Bio ScienceCollege of ScienceKwame Nkrumah University of Science and TechnologyKumasiGhana Research Group of Genomic Epidemiology National Food InstituteTechnical University of DenmarkKgs LyngbyDenmark Food Safety and Zoonoses Department of Veterinary and Animal SciencesFaculty of Health and Medical SciencesUniversity of CopenhagenStigb€ojlen 41870 Frederiksberg CDenmark Office for Innovation and Sector Services AdministrationTechnical University of DenmarkAnker Engelunds Vej2800 Kgs LyngbyDenmark Environment and Toxicology DHIHorsholmDenmark

Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to *** this study,a probabilistic model was set up as dose response for infection and a mathematical derivation was carried out by integrating immunity to obtain probability of illness *** acquire immunity from epidemiology studies which includes six different Norovirus transmission scenarios such as symptomatic individuals infectious,pre-and post-symptomatic infectiousness(low and high),innate genetic resistance,genogroup 2 type 4 and those with no immune boosting by asymptomatic infection were *** results on illness inflation factor as a function of dose and exposure indicated that high frequency exposures had immense immunity build up even at high dose levels;hence minimized the probability of *** Norovirus transmission dynamics data,results showed,and immunity included models had a reduction of 2e6 logs of magnitude difference in disease burden for both population and individual probable illness ***,the magnitude order of illness for each dose response remained largely the same for all transmission scenarios;symptomatic infectiousness and no immune boosting after asymptomatic infectiousness also remained the same *** integration of epidemiological data on acquired immunity into the risk assessment,more realistic results were achieved signifying an overestimation of probable risk of illness when epidemiological immunity data are not *** finding supported the call for rigorous integration of temporary acquired immunity in dose-response in all microbial risk assessments.

关键词： Quantitative risk assessment Probabilistic modeling Immunity integrated modeling

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Sensitivity and Specificity Analysis Relation to Statistical Hypothesis Testing and Its Errors: Application to Cryptosporidium Detection Techniques

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Open Journal of Applied Sciences 2016年第4期6卷 209-216页

作者： emmanuel de-Graft johnson owusu-ansah Angelina Sampson Amponsah K. Samuel Abaidoo Robert Department of Mathematics and Statistics University of Energy and Natural Resources Sunyani Ghana Department of Chemistry and Biochemistry South Dakota State University Brookings USA Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Theoretical and Applied Biology Kwame Nkrumah University of Science and Technology Kumasi Ghana

The use of Statistical Hypothesis Testing procedure to determine type I and type II errors was linked to the measurement of sensitivity and specificity in clinical trial test and experimental pathogen detection techniques. A theoretical analysis of establishing these types of errors was made and compared to determination of False Positive, False Negative, True Positive and True Negative. Experimental laboratory detection methods used to detect Cryptosporidium spp. were used to highlight the relationship between hypothesis testing, sensitivity, specificity and predicted values. The study finds that, sensitivity and specificity for the two laboratory methods used for Cryptosporidium detection were low hence lowering the probability of detecting a “false null hypothesis” for the presence of cryptosporidium in the water samples using either Microscopic or PCR. Nevertheless, both procedures for cryptosporidium detection had higher “true negatives” increasing its probability of failing to reject a “true null hypothesis” with specificity of 1.00 for both Microscopic and PCR laboratory detection methods.

关键词： Sensitivity Specificity Type I and II Errors Detection Methods Hypothesis Testing

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