Enable the student to apply and interpret statistical analysis procedures applicable to simple experimental models used in health research.
The course emphasizes the principles and limitations as well as the assumptions and limitations involved in the different models of statistical analysis rather than on calculation procedures involved.
We discuss the different levels of measurement in biomedical experiments and how they affect the model and interpretation of experimental results and the appropriate statistical model.
The current availability of computer programs and the difficulty of having any statistical advice has led many researchers to conduct their own analysis of the data not always in the most appropriate way.
Thus, the correct choice of model used and its proper interpretation are more important than the work of calculation involved, which can undoubtedly be better executed in a computer.
• Brief review of the concepts of mean, median, standard deviation, semi-quartile range and correlation
• Brief review of probability concepts
• Probability distributions: binomial, hypergeometric, Poisson, normal, chi-square, Student and F.
• Test one mean, two independent means, and two paired means
• Simple models of variance analysis: assumptions and requirements. Tests of more than two independent and paired means. Tests of more than two non-independent medium. Twoway Analysis of Variance: Interaction and additivity. Multiple comparisons. Contrasts. Correlation and regression.
• Non-parametric analysis: general idea and applicability; Wilcoxon test; Mann-Whitney test; Kruskal-Wallis test; Friedman test; Non-parametric correlation; Spearman and Kendall.
• Association tests: chi-square and Fisher
• Testing one or two proportions
• Odds ratio and relative risk
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