Medical statistics
The course is aimed at providing students with knowledge and practical skills concerning basic statistical methods. After the course students should understand an be able to apply such tools as estimating parameters of probability distribution of a stochastic variable: quantiles, expectation value, standard deviation, asymmetry, kurtosis etc. from data for a statistical sample. They should be able as well to estimate degree of correlation between two stochastic variables, to calculate parameters of the linear regression and to test simplest statistical hypotheses.
Teachers:
dr hab. Przemysław Staszewski, prof. UMK
dr Anita Dąbrowska
Contact:
Syllabus
- Katedra Podstaw Teoretycznych Nauk Biomedycznych i Informatyki Medycznej
- Head of the unit: dr hab. Przemysław Staszewski, Prof. UMK
- Faculty of Medicine, Medical Program, 2nd year
- Course coordinator: dr hab. Przemysław Staszewski, Prof. UMK
- Form of classes: lectures, tutorials
- Form of crediting: Credit with grade, 2 ECTS points
- Number of hours: 15 (lectures), 20 (tutorials)
- Aim of the course:
Lectures:
Basic notions of probability theory: random events and their properties, probability and its properties, combinatorial formulae, conditional probability, independent events, Bernoulli experiment and Bernoulli scheme.
Random variables: One-dimensional random variables: discrete and continuous. Examples of important distribution functions of random variables: binomial distribution, Poisson distribution, normal distribution.
Parameters of distribution functions of random variables: mean (expectation) value, variance, moments and central moments of order n, median.
Statistical inference for probabilistic experiments of two possible outcomes:
de Moivre-Laplace theorem. A statistical hypothesis, significance level and significance test, statistical hypothesis testing, test functions. Verifying statistical hypotheses of and . The power of a statistical test. Estimation of the parameter p. A confidence level and confidence interval.
Two-dimensional random variables. Populations and samples of two-dimensional random variables. Examples of significance tests and their applications: A chi-square test for fit of a distribution, A chi-square test for independence, Student’s t-test.
Pearson’s linear correlation coefficient.
Computer laboratory classes:
Solving simple problems of probability theory using combinatorics.
Determination and analysis of the distribution parameters of a sample.
Verification of statistical hypotheses given in the lectures.
Determination of the coefficient of linear correlation to verify the dependence of random variables.
- Self-study topics:
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- Booklist:
Medical Statistics at a Glance, Aviva Petrie & Caroline Sabin, Blackwell, 2005.
Essentials of Statistics In Heath Information Technology, Carol E. Osborn, Jones & Bartlett, 2007.
- Detailed list of required practical skills and confirmation of completing
List of acquirements: Applcation of Bayes theorem, law of total probability and conditional probability in solving medical problems; Determining the shape and estimation of the parameters of distribution function of random variable; Standardizing the random variable and using the tables of distributions of random variables; Interval estimation of distribution parameters, confidence interval; Putting and verification of hypotheses, the choice of parametric and nonparametric tests of significance, checking assumptions of the tests; Application of computer programs to medical statistics calculations.
Teaching method: lecture and computer laboratory classes.
Crediting conditions: classes credit.