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Mitglied seit 31. März 2017
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A good statistician is a statistician who does not just understand the problem and proposes a solution but it is above all a statistician who knows how to program the solution and analyze it. I am here to accompany you in the reflection necessary to your project and help you to leave the sphere of questions to the world of discoveries and answers. I am Masters of Statistics , with expertise in Econometrics: Panel Data, Pooled Data, cross-section Data, Probit, Logit, Multinomial Logit, scoring, Economics modeling,.... ---Data Mining: Factor analysis, Principal Component Analysis, Regression (Simple, Multiple, logisitic, hierarchical, Poisson), Data Quality, Data Reliability, ANOVA, , ANCOVA, Clustering, Hierarchical Clustering... --Biostatistics: survival analysis, Cox models, Markov Chains... --- SPSS, SAS, STATA, R, Matlab, MS excel Also i will provide Output and well explained interpretations with a comprehensive report in the best quality within minimum time .
$7 USD/Std
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    “Great work again. Prompt and on time as expected. Thanks a lot Kamel”

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    “Kamel did a great job in delivering the required content on time and at the expected quality. Particularly I was impressed about the short time frame. Despite originally agreeing on a later time frame (evening), I got the required SPSS output already in the morning! This greatly helped me for my meeting. Also I am happy for the quick updates and questions, this always helps to set expectations rights. Thus great job Kamel!”

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Statistics Engineer

Mar 2016

Statistical analysis on nutritional health problems

Survey Statistician

Oct 2014 - Nov 2015 (1 year)

Development of a European survey quality report "European Health Interview Survey" EHIS for Eurostat. - Assessment of needs. - Bibliography research. - Implementation of methodology. - Monitoring the survey (setting up monitoring indicators, changes...). - Analysis of field and survey statistical data (SAS Programming). - Ensuring compliance with European protocol. - Developing a quality report in collaboration with Eurostat.

Statistician/SAS Programmer

Mar 2012 - Dec 2013 (1 year)

- Biostatistician advisor for organ removal: - Production of a performance indicator comparing teams on a national level. - Participation in working groups with external experts and professionals. - Handling of statistical analysis requests received by different transplant centers. - Implementation and completion of various statistical studies. - Automation of SAS programmes and creation of macros. - SAS Advisor for the French Agency for Biomedicine: Coordination of work with SAS users.

Statistician/SAS Programmer

Oct 2011 - Mar 2012 (5 months)

- Completion of an annual AMP Vigiliance (medically assisted reproduction compliance) report - Definition of needs, analyses and statistical indicators. - Automation of SAS programmes. - Production of a performance indicator for all liver transplant teams in France. - Preparation of data (data management, recoding of variables...). - Survival analysis, automation of SAS programmes. - Production of a performance indicator comparing teams on a national level.

Biostatistics Engineer

Oct 2009 - May 2010 (7 months)

Topic: Epidemiological study on depression amongst the elderly. - Definition of an analysis plan. - Data Management (Import of datasets, ‘merging’, creation of variables…). - Descriptive statistics and reports (Proc freq, Proc means, Proc phreg, Proc report …). - Development of a multi-state semi-Markov model. - Creation of SAS programme macros. - Analysis and interpretation of results. - Software: SAS, R.


Masters In Biostatistics

2008 - 2010 (2 years)

Professional Masters in Statistics and Computing

2006 - 2008 (2 years)


SAS Advancded Programmer (2011)

SAS Institute

using advanced DATA step programming statements and efficiency techniques to solve complex problems writing and interpreting SAS SQL code creating and using the SAS MACRO facility...


A semi-Markov model to assess reliably survival patterns in free-ranging populations

1. Semi-Markov models explicitly define the distribution of waiting time duration and have been used as a convenient framework for modelling the time spent in one physiological state in previous biological studies. 2. Here, we focus on the modelling of the time spent within a life-cycle stage (e.g. juvenile, adult and old) by individuals over their lifetime from Capture–Mark–Recapture data, which are commonly used to estimate demographic parameters in free-ranging populations. ... Wiley site


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  • Statistics 1


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