Introduction: descriptive statistics
Definitions of: character, individual, universe and statistical sample. Descriptive statistics and inferential statistics; sample characteristics (representativeness, numerosity); random selection (random number tables). Measurement scales (nominal, ordinal, numerical).
Frequency distributions; number of classes; module (constant or variable); frequency density; cumulative frequency; percentiles.
Graphical representation of a frequency distribution.
Summary indicators: central trend indexes (arithmetic mean, fashion and median), variability indices (range, deviance, variance, standard deviation, coefficient of variation), distribution shape indices.
Probability and statistical inference. Definitions. Additive law and multiplicative law of probabilities; conditional probability; Bayes theorem.
Diagnostic tests; sensitivity and specificity of a test; positive and negative predictive value.
Distributions of theoretical probabilities
Binomial distribution
Poisson distribution
Distribution of Gauss; parameters; standard deviated normal.
Distribution of sample averages. Sampling. Standard error of the average; confidence interval. Statistical test concept; hypothesis zero (Ho) and choice of the alternative hypothesis (H1) (one-tailed or two-tailed test); level of significance.
Statistical tests
Parametric. t of Student; Analysis of variance.
Non-parametric. Frequency analysis. Expected frequencies based on a hypothesis; Chi-square. Test of signs. Wilcoxon test
Study of the connection of two quantitative variables.
Linear correlation. Correlation tables. Correlation coefficient
Linear regression. Least squares principle; calculation and meaning of the coefficients of the regression line.
Survival analysis: Kaplan Meier method
Multivariate statistics: overview
Computer skills program for medical statistics
The spreadsheet
Use of statistical functions
Statistical applications
The main software for statistical data analysis:
Statistics
SPSS