 top of page  ### 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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