AGRICULTURAL
UNIVERSITY OF ATHENS
Department of Rural
Agricultural Economy

Statistics

Content

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) Goodness-of-fit test; Chi-Square test of independence.
15) Analysis of variance (single-factor ANOVA; two-factor ANOVA).

Learning results

After this course, the student is expected to be able to:

  • distinguish stochastic and deterministic phenomena and experiments
  • using enumeration methods and basic probability tools
  • apply simple probability calculus
  • recognize the practical value and importance of probabilities in the understanding of stochastic phenomena and experiments
  • describe and summarize data
  •  translate a research question into a statistical hypothesis  when given a data group and the type of experimental design or sampling procedure
  • apply estimation and testing methods  in order to make data-based decisions
  • identify the selected method’s assumptions  and keep in mind that it is required to apply checks for them
  • comprehend and interpret correctly the statistical significance
  • interpret results correctly, effectively, and in context without relying on statistical jargon
  • comprehend the notion of uncertainty which is always contained in statistical inference
  • critique data-based claims and evaluate data-based decisions

complete a research project that employs simple statistical inference comply to ethical issues.


Bibliography

1. Παπαδόπουλος, Γ. Κ., Εισαγωγή στις Πιθανότητες και τη Στατιστική, Πανεπιστημιακές Σημειώσεις, Έκδοση Γ.Π.Α., 2013.
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.

Faculty

2105294888
stefanos@aua.gr
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NEWSLETTER

It is the only Greek University Department with the objective of training agroeconomists able to meet the demands of this new period in Greek agriculture which was inaugurated with the entry of Greece into the E.U.
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