1) Statistical approach: a brief overview.
2) Useful counting rules (multiplication principle, permutations, k-permutations, combinations).
3) Practical notion of probability; basic probability tools.
4) Conditional probability (multiplication rule; law of the total probability; Bayes theorem); Independence.
5) Random variables (cumulative distribution function; discrete and continuous random variables; probability function; probability density function; mean and variance).
6) Useful discrete distributions (Bernoulli; Binomial; Poisson).
7) Useful continuous distributions (Normal; ; t and F).
8) Central limit theorem.
9) The role of probability in statistics.
10) Descriptive statistics (frequency table; numerical descriptive measures; barchart; piechart; box plot; histograms).
11) Sampling distributions.
12) Estimation; point estimation (properties of an estimator); interval estimation (confidence intervals for a (difference of) population mean (s) or proportion (s));
13) Testing hypotheses for a (difference of) population mean (s) or proportion (s));
14) Analysis of variance (single-factor ANOVA; two-factor ANOVA).
15) Goodness-of-fit test; Chi-Square test of independence.
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2. Κουνιάς, Σ., Κολυβά-Μαχαίρα, Φ., Μπαγιάτης, Κ. και Μπόρα-Σέντα, Ε., Εισαγωγή στη Στατιστική, Εκδόσεις Χριστοδουλίδη, Θεσσαλονίκη.
3. Κούτρας, Μ. Β., Εισαγωγή στις Πιθανότητες-Θεωρία και Εφαρμογές, Εκδόσεις Σταμούλη, 2002.
4. Larsen, R. J. and Marx, M. R., An Introduction to Mathematical Statistics and its Applications, Pearson Prentice Hall, Fourth Edition, 2006.
5. Zar, J.H., Biostatistical Analysis, Prentice Hall, Fifth Edition, 2010.
