# Statistics

1. GENERAL
 SCHOOL AGRICULTURAL AND FORESTRY SCIENCES DEPARTMENT AGRICULTURAL DEVELOPMENT STUDY LEVEL Undergraduate COURSE CODE B0019 SEMESTER 3d COURSE TITLE STATISTICS INDEPENDENT TEACHING ACTIVITIES TEACHING HOURS PER WEEK ECTS Lectures and exercises 4 5 COURSE TYPE Compulsory PREREQUISITE COURSE(S): – LANGUAGE (TEACHING AND EXAMS) Greek THE COURSE IS OFFERED TO ERASMUS STUDENTS No COURSE WEBSITE (URL)

1. TEACHING OUTCOMES
 Teaching outcomes Upon the completion of the course the students should be able to; ·         to advance their knowledge in Statistic Theory. ·         To be able to use in different scientific fields included in earth science. General capabilities Analytical and Synthetical Thinking

1. COURSE CONTENT
 The concept of Statistics (Definition of Statistics, types of statistics, The steps of a statistic survey, Statistics and Economic Management). Descriptive Statistics (The subject of descriptive statistics, Basic Concepts, statistic analysis). Methods of Survey Census and sampling Classification, ranking and presentation of statistic data (Distribution tables of frequencies-relative frequencies, Cummulative Frequency, Relative Cummulative Frequency, Grouping of observation, Diagrams Histograms Bar diagram, Cyclical diagrams) Measures of position and dispersion (Arithmetic Mean, Weighted Mean, Geometric Mean, Harmonic mean, median, mode, Relations between arithmetic mean, median, mode, Range, Quartile range, mean deviation, Variance (Standard Deviation) Dispersion coefficient (Pearson coefficient). Relevant position of two different samples. Likelihood theory ( Relative position of the values of two different samples) Basic Elements in Probability Theory, Definition of Probability, Basic references on the theory of probability (random experiment-possibly-sampling space, definition of probability, axioms of probability, Conditional probability, Independent contigencies, Law of Bayes). Data Combination (Basic multiplier principle of counting – trees, Probability in the sample). Random variables (Definition of a random variable, probability distribution of a random variable, Discrete probability distributions (for discrete variable x) probability function or probability distribution, cumulative distribution function, continuous probability distributions (for constant random variable X), Density Function, Cumulative density function, , Mean values and dispersion, variance of a random variable Χ (Mean or Expected Value of a random variable X, Dispersion – Variance of a random variable Χ – Standard Deviation), Basic Distributions (Bernoulli Distribution, Binomial Distribution, Poisson Distribution, Geometric Distribution, Negative Binomial Distribution, Normal Distribution, Typical Normal Distribution, Exponential Distribution, Uniform distribution)

1. TEACHING AND LEARNING ASSESSMENT METHODS
 DELIVERING METHOD In classroom IT USE §  Power point §  e-class TEACHING ORGANIZATION Activity Semester workload Lectures 39 Exercises 26 Individual study 60 Course total (25-hour workload per credit unit) 125 STUDENT ASSESSMENT Written exams at the end of the semester both on theory and exercises Two tests are taken within the semester and the average of the grade of those tests is multiplied by 0,3 and is added to the grade of the final test. The precondition for the validity of this bonus is the grade of the final test to be equal or over three. 2. Assignments are delegated to the students that are graded with ranking 0-2. The grade of this assignment is added to the final grade of the semester The precondition for the validity of this bonus is the grade of the final test to be equal or over three
1. PROPOSED LITERATURE
 §  Koutroumanidis Th., Zafeiriou E., Malesios Ch., Statistics I (in Greek)