Applied statistics

Course Information
TitleΕφαρμοσμένη Στατιστική / Applied statistics
Code13Υ
FacultyAgriculture, Forestry and Natural Environment
SchoolForestry and Natural Environment
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CommonNo
StatusActive
Course ID600019402

Programme of Study: PPS Tmīmatos Dasologías kai Fysikoý Perivállontos (2020-sīmera)

Registered students: 99
OrientationAttendance TypeSemesterYearECTS
Compulsory CoursesCompulsory Course114

Class Information
Academic Year2023 – 2024
Class PeriodWinter
Faculty Instructors
Weekly Hours4
Class ID
600231942
Course Type 2021
General Foundation
Course Type 2016-2020
  • Background
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
Language of Instruction
  • Greek (Instruction, Examination)
  • ONLY IN GREEK
Learning Outcomes
After successfully completing the course, students will be able: to design a simple statistical survey, to describe a set of data, to statistically examine a set of data , to conclude for the population from random samples and to construct simple models.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Advance free, creative and causative thinking
Course Content (Syllabus)
INTRODUCTION: Statistics as a concept and science throughout time. Forest Biometrics as a branch of applied Statistics today. Introduction to the statistical programming language R. Introduction to the statistical package IBM® SPSS® Statistics (SPSS). Introductory statistical definitions. STATISTICAL DATA PRE-PROCESSING: Methods and means of collecting primary data, pre-processing process,Exploratory Data Analysis (EDA), Figures and Tables EMPIRICAL FREQUENCY DISTRIBUTIONS LOCATION MEASURES-CENTRAL TENDENCY MEASURES: arithmetic mean or average, weighted arithmetic mean, trimmed mean, geometric mean, harmonic mean, quadratic mean, Μ-estimators, median, mode, quantiles, range, inter-quartile range, mean absolute deviation, variance, coefficient of variation, coefficient of quartile variation, Lorenz curve MEASURES OF SKEWNESS AND KURTOSIS: skewness, kurtosis PROBABILIES, PROBABILITY DISTRIBUTIONS: Basic definitions and concepts of probability theory – basic notation, statistically independent events, conditional probability, Bayes' theorem or Bayes' law or Bayes' rule, Combinatorial analysis,random or stochastic variable, binomial distribution, Poisson distribution, Normal and standard normal distribution, chi-squared distribution, t-distribution or Student–distribution, F-distribution STATISTICAL INFERENCE - HYPOTHESIS TESTING: Basic theory, ANOVA, one-way ANOVA, two-way ANOVA,factorial ANOVA CORRELATION - REGRESSION:covariance, correlation, partial correlation, simple linear regression, multiple linear regression, nonlinear regression, Ridge regression analysis, Lasso regression CATEGORICAL DATA HANDLING: goodness of fit test, Chi-square test for independence/of association, Test of Homogeneity , binomial logistic regression, Cluster Analysis, Κ-modes, Hierarchical clustering, Factorial Analysis of Mixed Data, Validity and Reliability of a questionaire. NON-PARAMETRIC TESTS:Wilcoxon Signed-Rank test, Wilcoxon Rank-Sum test, one sample Kolmogorov-Smirnov test,Kruskal-Wallis H test, Friedman test
Keywords
Statistical Analysis, Description of Statistical Data, Modeling
Educational Material Types
  • Slide presentations
  • Interactive excersises
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Description
During the teaching and laboratory training, a slideshow featuring relevant content, photos, and other necessary materials is utilized to provide trainees with a comprehensive understanding of the lecture topics. Additionally, there is instruction on using software such as SPSS and Excel, as well as the programming statistical language R, to enable students to solve statistical problems related to the primary forest data they are required to analyze.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures260.9
Laboratory Work220.7
Reading Assigment551.8
Written assigments140.5
Exams30.1
Total1204
Student Assessment
Description
Theory and laboratory evaluation. Theory assessment: Through written exams with multiple choice questions, short development and solving exercises. Laboratory evaluation: Through the delivery of written work using statistical and computer packages and the evaluation of students through questions on it. Grading: Laboratory evaluation with excellent performance of 20/100 units. Theory evaluation with excellent performance of 80/100 units. Leading grade 50/100, cumulatively.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Assignment (Formative, Summative)
  • Written Exam with Problem Solving (Formative, Summative)
  • Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
ΔΙΑΜΑΝΤΟΠΟΥΛΟΥ ΜΑΡΙΑ. ΔΑΣΙΚΗ ΣΤΑΤΙΣΤΙΚΗ - Θεωρία και Δασοβιομετρικές Εφαρμογές με χρήση IBM-SPSS και R (ΚΩΔΙΚΟΣ ΕΥΔΟΞΟΥ: 133036205)
Additional bibliography for study
Avery, T.E. and Burkhart, H.E. 2001. Forest Measurements. 5th edition: McGraw-Hill Publishing Company Διαμαντοπούλου Μ.Ι., 1997. Σημειώσεις Βιομετρίας-Βιοστατιστικής. Θεσσαλονίκη. 156 σελ. Διαμαντοπούλου Μ.Ι., 1999. Σημειώσεις εργαστηρίου Σχεδιασμού Πειραμάτων. Επίλυση ασκήσεων του μαθήματος του Σχεδιασμού Πειραμάτων με τη χρήση του στατιστικού πακέτου SPSS. Θεσσαλονίκη, 101 σελ. Διαμαντοπούλου Μ.Ι., 2002. Σημειώσεις Δασικής Βιομετρίας – Δενδρομετρίας Ι. Θεωρία – Εργαστήριο. Θεσσαλονίκη. 186 σελ. Fransis, A., 1990. Advanced Level Statistics. An Integrated Course. Second Edition.The Bath Press. Avon. Great Britain. Κιόχος, Π.Α., 1993. Στατιστική. Εκδόσεις Ιnterbooks. Αθήνα. 761 σελ. Κολυβά-Μαχαίρα Φ. και Μπόρα-Σέντα Ε., 2013. Στατιστική: Θεωρία-Εφαρμογές. Ζήτη. Θεσσαλονίκη. Κουνιάς, Σ.Γ., Κολυβά - Μαχαίρα, Φ., Μπαγιάτης, Κ. και Μπόρα-Σέντα, Ε., 1985. Εισαγωγή στη Στατιστική. Θεσσαλονίκη. 417 σελ. Μάτης Κ., 2003. Δασική Βιομετρία Ι. Στατιστική, Εκδόσεις Πήγασος, 598 σελ. Neter, J., Wasserman, W. and Kunter, M.H., 1990 Applied Linear Statistical Models. 3rd Edition. Richard D. Irwin, Inc. Homewood, Illinois 60430. 1181p. Παπαδημητρίου, Ι., 1990. Στατιστική. Τεύχος Ι. Περιγραφική Στατιστική. Εκδόσεις Παρατηρητής. Θεσσαλονίκη. 470 σελ. Ρούσσας, Γ. Γ., 1992. Θεωρία των πιθανοτήτων. Επιμέλεια - Μετάφραση Δημήτριος Ιωαννίδης. Εκδόσεις Ζήτη. Θεσσαλονίκη. 313 σελ. Σταματέλος, Γ., 1997. Σημειώσεις Πιθανοτήτων Στατιστικής Ι με Εφαρμογές στην Τεχνολογία. Θεσσαλονίκη. 149 σελ. The Empirical Rule and Chebyshev’s Theorem. (2021, January 11). Retrieved June 9, 2021, from https://stats.libretexts.org/@go/page/559
Last Update
30-07-2024