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TSC Training

Statistical Tests and Sampling Plans



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.


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



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


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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