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.
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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
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The estimate on a sample basis
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The sample mean and sample variance estimators
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The standard error
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Confidence intervals
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Testing a statistical hypothesis
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Sample theory
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Typologies of finite populations
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Error profile
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Probability and non-probability samples
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Estimators for expansion (average and total) and alternatives
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Drawing effect
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Practical applications
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Methods for calculating statistical significance
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Asymptotic method
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Exact method
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Monte Carlo method
Analysis of two phenomena jointly considered
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Association between two categorical variables
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The contingency tables
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Graphic representations
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the 2 as a measure and statistical test of the significance of the association
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Other measures of association
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The relative risk and the odds ratio
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Comparison of means of a quantitative variable to levels of a classifying variable (stratified means)
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Graphic representations
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The T-test statistic
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Introduction to the Analysis of Variance model
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Nonparametric tests (Mann-Whitney, Wilcoxon, Kruskall-Wallis)
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Summary of the main nonparametric tests available in Statistics
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Linear correlation between two continuous variables
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Covariance and correlation
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Graphic representation
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The simple linear regression model
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Introduction to the design of experiments
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Univariate ANOVA and simple factorial
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Univariate analysis of variance
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Comparisons for the identification of homogeneous subgroups
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Evaluation of interactions between factors
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Introduction to Generalized Linear models
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Examples of Generalized Linear Models
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Introduction to the analysis of test power
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Type i and ii error (alpha and beta)
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Effect size
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Significance threshold (alpha) Sample size
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