Subject

(The course is intended for graduate students with backgrounds varying from limited to satisfactory knowledge of Statistics; therefore, the materials in parts i and ii below are covered quickly to bridge the gap. Each lecture starts with a sufficient overview of the associated theory and continues with the implementation using real data; the lectures are given in the Laboratory of Agribusiness Management and the students learn the mechanics using statistical packages.)

(i) Descriptive Statistics with One or Two Samples: tabulation and graphical representation of quantitative and qualitative data, measures of central tendency, variability, skewness and kurtosis, box-plots, trimmed means and robust measures.

(ii) Univariate and Bivariate (independent or related) populations: point and interval estimations of parameters, hypotheses testing and the meaning of p-value the Pearson’s correlation coefficient and its interpretation.

(iii) Basic Experimental Designs, Analysis of Variance and the Linear Models’ approach: principles of experimentation, experiments involving one or two factors (with or without interaction), point and interval estimation of effects, multiple comparisons and diagnostic checking for departures from the assumptions.

(iv) Non Parametric Statistics: the tests of Kolmogorov-Smirnov (with Lilliefor’s correction), Shapiro-Wilk, Mann-Whitney, Wilcoxon, Kruskal-Wallis, Friedman and the Spearman’s correlation coefficient.

(v) Multiple Regression: point estimation using the method of Ordinary Least Squares, standard errors of the estimators, interval estimation of the parameters, hypotheses testing for the significance of each regression coefficient, overall significance test with the Analysis of Variance, the coefficient of multiple determination and the adjusted coefficient of multiple determination, indicator variables and predictions. Model selection methods; Backward Elimination, Forward Selection and Stepwise Regression. Violations of the classical assumptions: model specification errors (omitted and irrelevant variables, incorrect functional form etc.), heteroscedasticity, collinearity (or multicollinearity), autocorrelation, lack of normality of the error term, use of random regressors. The possible causes and consequences of each violation, and methods for detection; residual analysis, graphical techniques, statistical tests, Variance Inflation Factors and Condition Indices. Various remedies including data transformations, and the methods of Generalized Least Squares, ARIMA and Weighted Least Squares for the estimation of parameters, in the regression models with AR(1) and heteroscedastic errors.

(vi) Multivariate Methods: Factor Analysis with various methods of extraction and rotation, Cluster Analysis. 

Educational Aims

Student Evaluation

Written exams at the end of the semester.

Bibliography

1. Γ. Κ. Παπαδόπουλος, Εισαγωγή στις Πιθανότητες και τη Στατιστική, Εκδόσεις Γ. Δαρδάνος – Κ Δαρδάνος Ο.Ε., Έκδοση 1^η, 2015 (in Greek).
2. Χ. Ζαχαροπούλου, Στατιστική Τόμος Α’, Εκδόσεις «Σοφία» Ανώνυμη Εκδοτική & Εμπορική Εταιρεία, Έκδοση 6^η, 2014 (in Greek).
3. Aczel, A. και Souderpandian, J., Στατιστική στον Κόσμο των Επιχειρήσεων, Π. Χ. Πασχαλίδης, Αθήνα 2013 (in Greek).
4. Keller, G., Στατιστική για Οικονομικά & Διοίκηση Επιχειρήσεων, 1^η Έκδοση, Εκδόσεις Επίκεντρο Α.Ε., Θεσσαλονίκη 2010 (inGreek).
5. Ζαφειρόπουλος Κ., Μυλωνάς Ν., Στατιστική με SPSS, Εκδόσεις Τζιόλα, 2017.Χαλικιάς, Ι., Στατιστική (μέθοδοι ανάλυσης για επιχειρηματικές αποφάσεις), Rosili, Αθήνα 2010 (in Greek).
6. Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, Howard M. Reid, Paperback: 632 pages, Publisher: SAGE Publications, Inc; 1 edition (August 28, 2013), Language: English, ISBN-10: 1452271968.
7. Introduction to Statistics and Data Analysis,  Heumann, Christian, Schomaker, Michael, Shalabh, Publisher: Springer International Publishing, 1st Edition, ISBN: 978-3-319-46160-1.
8. Introduction to Statistics, Carmine DeSanto,‎ Richard Moscatelli,‎ Rachel Rojas,‎ Mike Totoro, Paperback: 872 pages, Publisher: Pearson Learning Solutions; 10 edition (January 25, 2015), Language: English, ISBN-10: 1323056300