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Course ID: 
Course Description: 

The first part of the course includes a revision of basic concepts of probability theory and a short presentation of Descriptive Statistics. The second part, presents the fundamental concepts of statistics used to inference effectively on the characteristics of a population based on samples. A random sample is taken from a population and based on this, estimators for the population’s parameters are computed and their accuracy is examined. Additionally, the procedures used to test hypotheses about a population are presented. To this direction, the course focuses on estimation theory, confidence intervals, hypothesis testing, non-parametric test. Finally, the basic concepts of linear regression are also presented.


W 1. Introduction-Probability Theory, Distributions and moments- Exercises

W 2. Sample distributions, Student distribution, χ2 distribution, F distribution-   Exercises

W 3. Sampling, Central Limit Theorem-Exercises

W 4. Descriptive statistics

W 5. Estimation, Unbiased Estimators (bias, consistency, adequacy,     completeness)-Exercises

W 6. Estimator of minimum variance-Exercises

W 7. Maximum likelihood estimators-Exercises

W 8. Confidence intervals-Exercises

W 9. Hypothesis testing for the mean

W 10. Exercises in hypothesis testing for the mean

W 11. Hypothesis testing for binomial p, difference of means, variance- Exercises

W 12.  Goodness of fit, Kolmogorov-Smirnov test-Exercises

W 13. Correlation, Regression Analysis

A successful student should be able to:

  • understand and use basic statistical concepts underlying the characteristics of a population based on a random sample compute and interpret confidence intervals for estimations
  • conduct hypothesis testing for the mean of a population, the binomial p, the difference between the means of two population, the variance of a population
  • comprehend "non-parametric statistic" and conduct the appropriate tests
  • use linear regression to examine the relation between an independent and a dependent variable, along with interpreting the results of regression


Class schedule: 
ΠΕΜΠΤΗ 12.00-15.00, Μεγάλη Αίθουσα Α΄ ΤΝΕΥ (Κτίριο Κοραή)
Assessment methods: 

Final Exams = 20% + Assignment is SPSS = 20%

Recommended Reading:
[Option 1] Εισαγωγή στη Στατιστική, Τ. Παπαϊωάννου, Σ .Β.  Λουκάς,  Εκδόσεις Σταμούλη    Α.Ε., Κωδικός Βιβλίου στον Εύδοξο: 22745 (in greek)
[Option 2] Πιθανότητες και Στατιστική για Μηχανικούς, Γ. Ζιούτας,,  Εκδόσεις "σοφία"  Ανώνυμη Εκδοτική & Εμπορική Εταιρεία, Κωδικός Βιβλίου στον Εύδοξο:  12656654(in greek)

Additional Bibliography:
1. Εισαγωγή στις πιθανότητες και τη στατιστική, Δαμιανού Χ., Χαραλαμπίδης  Χ., Παπαδάκης Ν.,  Εκδόσεις Συμμετρία, 2010(in greek)
2. Πιθανότητες και Στατιστική, (Schaum's Outline of PROBABILITY AND  STATISTICS), Murray R. Spiegel, Μετάφραση: Σωτήριος Κ. Περσίδης(in greek)
3. Στατιστική,  Κολυβά-Μαχαίρα, Ε. Μπόρα-Σέντα, Ζήτη(in greek)
4. Ανάλυση Δεδομένων με τη Βοήθεια Στατιστικών Πακέτων SPSS,   Excel,   S-Plus, Ν. Δ. Σσάντας, Φρ. Θ. Μωϋσιάδης, Ντ. Μπαγιάτης, Θ. Φατζηπαντελής,  Εκδόσεις Ζήτη, Θεσσαλονίκη 1999.(in greek)
5. Introductory Statistics, S M. Ross, Second Edition,, Academic Press; 2 edition, 2005
6. Theoretical statistics, D. R. Cox, D. V. Hinkley, London:Chapman and Hall, New York, 1979.
7. Statistics: An Introduction using R, M. J. Crawley, Wiley; 1 edition, 2005.
8. Introduction to probability and statistics: principles and applications for engineering and the computing sciences, J. S. Milton, Jesse C. Arnold, 3rd ed. New York :McGraw-Hill, 1995.
9. Introduction to statistical theory, Paul G. Hoel, Sidney C. Port, Charles J. Stone, Boston :Houghton-Mifflin, 1971.
10. An Introduction to Statistics, G. Woodbury, Duxbury Press; 1 edition, 2001)