Learning outcomes of the course unit
To provide the basic concept of the statistical data analysis
Course contents summary
Descriptive statistics: measures of location, measures of spread, properties of the deviance, variance, standard deviation and standard error, coefficient of variation. Grouped data, symmetry and kurtosis.
Probability, permutations and combinations, binomial distribution, poisson distribution, ipergeometric distribution, normal distribution, normal approximation.
Statistical hypothesis testing. Chi squares goodness of fit for more than two categories, bias and continuity correction. Contingency tables, Fisher exact test, comparing two proportions.
One-sample and two-sample hypotheses, confidence limits for the population means and for the populations mean difference; testing for difference between variances.
Power and sample size in test concerning the means and difference between two means.
Single factor analysis of variance. Two-factor and Three-factor analysis of variance. Two-factor analysis of variance with equal replication. Nested or hierarchical analysis of variance. Multiple comparisons: a priori e post-hoc. Data transformations.
Simple linear regression, interpretations of regression functions, testing the significance, confidence intervals in regression. Inverse prediction and calibration.
Simple linear correlation, hypotheses about the correlation coefficient. Regression vs. Correlation.
- Sokal, R. R. and F. J. Rohlf (1995). Biometry, 3rd Edition. W. H. Freeman & Co., New York.
- Zar, J. (1999). Biostatistical analysis, 4th Edition. Prentice Hall, Upper Saddle River, NJ. 663 p. plus appendices and index.
Teaching methodology: Frontal lectures and exercises
Examination method: Oral examination with exercises
Teaching langague: Italian