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.

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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.

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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