H3ABioNet Introduction to Biostatistics and Population Genetics Workshop, Tunisia 2015

Course: H3ABioNet Introduction to Biostatistics and Population Genetics Workshop, Tunisia 2015
Dates: 16th to 26th March 2015

Venue: Institute Pasteur of Tunis, Tunisia

Purpose: Results from this survey will be used by H3ABioNet to improve the quality of workshops delivered by H3ABioNet to consortium and H3Africa members and for reporting purposes.

Instructions:
All course participants should complete this form. Any information provided will be treated as confidential and anonymous and will not be used to discriminate or bias against any participant. The Personal Details section is only required to determine participants who complete this survey and therefore eligible to obtain a Certificate of Completion. The Personal Details will not be linked to survey responses or any subsequent analyses of survey data.

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    Section A: Feedback on Lecturers

    Jean-Baka Domelevo Entfellner
    Ahmed Rebai
    Please enter one response per row
    Jean-Baka Domelevo Entfellner
    Ahmed Rebai
    Please enter one response per row
    Jean-Baka Domelevo Entfellner
    Ahmed Rebai
    Please enter one response per row
    Jean-Baka Domelevo Entfellner
    Ahmed Rebai
    Please enter one response per row

    Section B: Feedback on the Delivery of Workshop

    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    This is a required question

    Section C: Relevance of the Workshop

    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    Concepts of basic probability theory: population, sample, random variables, discrete vs continuous
    Well-known probability distributions (binomial, uniform, normal, chi-squared, etc)
    Descriptive statistics and graphical representations (histograms, boxplots, etc)
    The framework for statistical hypothesis testing
    T-tests, chi-squared tests, anova
    Measuring correlation
    Linear and logistic regression
    Dimensionality reduction through PCA
    Likelihood, Bayes' rule and the Bayesian approach
    Basics of genetics, Mendelian and multi-factor inheritance
    Genetic drift, natural selection, population stratification and admixture concepts
    Organisation of genetic variation, haplotypes, linkage disequilibrium
    Measures of linkage disequilibrium, power/sample size in association testing and inference from population genotypes
    Study designs for association studies
    Structure plots and K-Means clustering for population stratification and relatedness
    Methods and Computer tools for SNP tagging and genotype imputation
    Bayesian testing of association
    GWAS and testing of multiple SNPs associations using Plink and R packages
    Unphased genotypes and combining single locus tests
    Calculating risk associated to SNPs
    Multiple hypothesis testing and correction of p-values
    Testing association in missing data
    Please enter one response per row
    This is a required question

    Section D - Workshop Logistics

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    Section E: Overall Workshop

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    Section F: Personal Details

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