This course constitutes an introduction to Descriptive Statistics and Probability Theory. The course targets to present the fundamental tools of these two branches of Statistics, which are applicable to the construction of numerous scientific models. The knowledge of these Statistical Analysis techniques constitutes a fundamental skill that allows us to quantify real-life economic and financial problems, explore them by analyzing their available numerical data, and finally reach reasonable conclusions and future decisions.
Upon successful completion of the course the student will be able to:
Has an understanding of the basic concepts of Descriptive Statistics and Probability Theory.
Has knowledge of the basic tools and techniques of Descriptive Statistics and Probability Theory.
He is able to use Descriptive Statistics and Probability Theory methodologies in the Financial Science course.
General Competences
Decision making
Group work
Promote free, creative and inductive thinking
3. Syllabus
The basic sections that will be presented are:
Empirical Univariate Frequency Distributions: Discrete and Continuous Distributions of Frequencies and Cumulative Frequencies – Graphic Presentation Methods of Qualitative and Quantitative Statistical Data – Frequencies and Cumulative Frequencies Histogram
Univariate Populations Parameters: Central Tendency Parameters – Central Position Parameters – Dispersion Measures – Data Trasformation with Encoding – Skewness Parameters – Frequency Distribution Moments – Kurtosis Parameters
Fundamental Notions of Events: Random Experiment – Sample Space – Events – Calculus with Events
Combinatorics: Permutations of ν Objects – Arrangements With or Without Repetition of ν Objects Taken μ – Combinations With or Without Repetition of ν Objects Taken μ – Newton’s Binomial
The Notion of Probability: Classic, Statistical and Axiomatic Definition of Probability – Properties of Probabilities – Conditional Probability – Independent Events – Total Probability Theorem – Bayes’ Rule
Univariate Random Variables: Discrete and Continuous Random Variable – Probability Function and Density Function – Expectation – Cumulative Probability Distribution Function – Variance – Moments of Various Orders – Median and Quantiles – Skewness and Kurtosis – Moment Generator, Generator and Characteristic Function