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Learning Objectives: - Comprehension of statistical concepts and methods widely used in biomedical research - Ability to interpret the results of statistical analyses as well as the documented critical evaluation of the statistical methodology of biomedical publications.
Course content: – Random variables (continuous and discrete). – The concept of probability, joint probability, conditional probability, Bayes’ theorem. – Descriptive statistics (frequency distributions, measures of central tendency). – Key distributions: Poisson, Bernoulli, Binomial, Uniform, Normal, Student, Chi-square. – Normal distribution: properties and calculations – Sampling, central limit theorem, confidence region. – Statistical Hypotheses Tests: significance level, p-value, type I & II errors. – Parametric hypothesis tests (z-test, t-test, paired t-test), Analysis of Variance (ANOVA). – Applications: noise, selecting and applying statistical tests, fitting models to data, estimating variable correlation, dimension reduction, data classification, signal to noise ratio. |
– Intro to statistics using Python.
- Διδάσκων/Διδάσκουσα: Δημήτριος Τζεράνης