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TSC Training

Statistical Tests and Sampling Plans

 

Objectives: 

Offer a overview of the main parametric and non-parametric statistical tests, to identify the optimal technique in relation to the type of data and the objectives of the analysis.

 

Techniques presented: 

Parametric and non-parametric statistical tests,  analysis of variance, calculation of test significance.

Tutorials:

​Practical exercises are planned for each of the topics covered.

 

Prerequisites: 

Attending the INTRO course or having an intermediate knowledge of Statistics for Data Analysis is preparatory.

Subjects:

Theory of estimation and inference

  • The estimate on a sample basis

    • The sample mean and sample variance estimators

    • The standard error

    • Confidence intervals

  • Testing a statistical hypothesis

  • Sample theory

    • Typologies of finite populations

    • Error profile

    • Probability and non-probability samples

    • Estimators for expansion (average and total) and alternatives

    • Drawing effect

    • Practical applications

  • Methods for calculating statistical significance

    • Asymptotic method

    • Exact method

  • Monte Carlo method

 

Analysis of two phenomena jointly considered

  • Association between two categorical variables

    • The contingency tables

    • Graphic representations

    • the 2 as a measure and statistical test of the significance of the association

    • Other measures of association

    • The relative risk and the odds ratio

  • Comparison of means of a quantitative variable to levels of a classifying variable (stratified means)

    • Graphic representations

    • The T-test statistic

    • Introduction to the Analysis of Variance model

    • Nonparametric tests (Mann-Whitney, Wilcoxon, Kruskall-Wallis)

    • Summary of the main nonparametric tests available in Statistics

  • Linear correlation between two continuous variables

    • Covariance and correlation

    • Graphic representation

    • The simple linear regression model

 

Introduction to the design of experiments

  • Univariate ANOVA and simple factorial

    • Univariate analysis of variance

    • Comparisons for the identification of homogeneous subgroups

    • Evaluation of interactions between factors

    • Introduction to Generalized Linear models

    • Examples of Generalized Linear Models

  • Introduction to the analysis of test power

    • Type i and ii error (alpha and beta)

    • Effect size

    • Significance threshold (alpha) Sample size

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