STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH
Learning outcomes of the course unit
Introduce the student to the logic of statistical thinking and its application to practical problems.
Course contents summary
Review of general principles of statistical inference. Sampling distribution. Hypotheses and hypothesis tests. I and II type errors. Power of a test and operating curve. Parametric test : Student t-test, an overview of analysis of variance. Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, median test, Chi-square test, Fisher’s exact test.
Linear regression and correlation.
Overview of multivariate statistics: Main components. Discriminant analysis.Cluster analysis
Practical sessions with the statistical software R and SPSS on medical data.
Design and analysis of experiments.
Power analysis, effect size and sample size calculations.
Probability calculus, combinatorial analysis and special probability distributions.
Review of univariate statistics and elementary descriptive statistics.
Review of general principles of univariate statistical inference. Sampling distribution. Hypothesis and hypothesis testing. Type 1 and type 2 error. Power of a test and operating curve.
Parametric test : Student t-test, ANOVA and repeated measures ANOVA.
Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, median test, Chi-square test, Fisher exact test.
Overview of multiple regression, logistic regression, principal component analysis, classification and cluster analysis.
Practical session with the professional statistical systems "R" and "SPSS" on medical data.
1) Lecture notes
3) Sidney Siegel, N. John Castellan Jr. : Statistica non parametrica, ed. McGraw-Hill
4) 4) Internet resources and links
Assessment methods and criteria