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 analysis : probability of simple and compound events, Bayes theorem.
Odds.Odds ratios. Likelihood ratios. Applications.
Probability distributions : binomial distribution, Poisson distribution, normal and standard normal distribution. Tables and their use.
How to summarise the data. Units of measure. Measurements of position, order and variation. Indices of central tendency, mean median, mode. Indices of variabilità, variance, standard deviation, CV. Percentile and their use.
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.
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. Logistic regression.
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 and exercises
Assessment methods and criteria
written examination
Other information
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