Learning objectives
Introduce the student to the logic of statistical thinking and its application to practical problems.
Prerequisites
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Course unit content
Introduction: medical statistics and related disciplines. Logic and statistical planning. Overview of combinatorial analysis: permutations, arrangements, combinations. Applications. Overview of probability calculations: simple and compound probability, Bayes theorem. Odds. Odds ratios. Likelihood ratios. applications. Probability distributions : binomial distribution, Poisson distribution, normal and standard normal distribution. Tables and their use.
Summarising data. Units of measure. Measurements of position, order and variation. Indices of central tendency, mean median, mode. Indices of variability, variance, standard deviation, CV. Percentiles and their use.
General principles of 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, Variance analysis with 1 and 2 classification criteria. Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, mean test, Chi-square test, Fisher exact test.
Overview of linear regression and correlation.
Full programme
Introduction: medical statistics and related disciplines. Logic and statistical planning. Overview of combinatorial analysis: permutations, arrangements, combinations. Applications. Overview of probability calculations: simple and compound probability, Bayes theorem.
Odds. Odds ratios. Likelihood ratios. applications.
Probability distributions : binomial distribution, Poisson distribution, normal and standard normal distribution. Tables and their use.
Summarising data. Units of measure. Measurements of position, order and variation. Indices of central tendency, mean median, mode.
Indices of variability, variance, standard deviation, CV. Percentiles and their use.
General principles of 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 with 1 and 2 classification criteria.
Non-parametric test: Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Friedman test, median test, Chi-square test, Fisher exact test.
Overview of linear regression and correlation.
Bibliography
1) Lecture notes
2) Stanton A. Glantz : Statistica per discipline Bio-mediche, ed. McGraw-Hill
3) Sidney Siegel, N. John Castellan Jr. : Statistica non parametrica, ed. McGraw-Hill
4) Internet resources and links
Teaching methods
classroom lectures
Assessment methods and criteria
written examination
Other information
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