Statistics - R

The goal of the course is to familiarize students with the basics of probability, the basics and procedures of statistics in engineering and to master the statistical programming language R.

Upon completion of the course, the student is able to: a) apply and monitor the basic methods of statistics with programming. b) identifies the basic behaviors and characteristics of the data in order to determine methods for their further analysis b) understand the problem that needs to be solved, c) solves the problem based on appropriate statistical procedures and g) makes appropriate conclusions that have practical application.

Basics of probability (data collection, probability, conditional probability, Central Limit Theorem, interpretation of probability. Descriptive statistics and basic concepts (population, types and characteristics of samples and how they are obtained, graphical representations of data, measures of location, measures of variation). Basic discrete and continuous distributions and derivative functions. Parametric confidence intervals and parametric hypothesis testing (for arithmetic mean, difference of arithmetic means, proportion, difference of proportions, variance and difference of variances), Non-parametric testing (Comparisons with theoretical distributions - Kolmogorov and Chi-square tests, distribution-independent tests - U-test Mann-Whitney, Kolmogorov-Smirnov, Andersonov, Medians and Difference of Medians), One-factor and two-factor parametric analysis of variance, Simple and multiple linear regression and correlation , Non-parametric regression and correlation (Spearman and Kendall test, Cherbyshev polynomials). Structural Equation Modelling -SEM.

Practical teaching follows lectures and includes laboratory exercises where problems and tasks are solved by programming in R.

Enrolled in the semester and completion of the courses Programming (position 1,.1.1) and Analysis (position 2.2.1.).

1. V. Simonović: Introduction to probability theory and mathematical statistics, fifth edition, Admiral, Belgrade, 2008; 2. Slobodan Radojević, Zorica Veljković, Quantitative methods - Theoretical foundations, tasks, CD, MF Belgrade 2003; 3. handouts 4. Douglas C. Montgomery, George C, Runger, Applied statistics and probability for Engineers, 6th edition, Weley, USA, 2014 5. W. John Braun, Duncan J. Murdoch, A first course in statistical programming with R. CambridgeUP, UK 2007.

Total assigned hours: 75

New material: 25
Elaboration and examples (recapitulation): 5

Auditory exercises: 20
Laboratory exercises: 10
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 3
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 0
Test: 6
Final exam: 6

Activity during lectures: 0
Test/test: 0
Laboratory practice: 40
Calculation tasks: 0
Seminar paper: 0
Project: 0
Final exam: 60
Requirement for taking the exam (required number of points): 30

handouts from lectures and exercises; W. John Braun, Duncan J. Murdoch,A first course in statistical programming with R. CambridgeUP, UK 2007; Handouts; Douglas C. Montgomery, George C, Runger, Applied statistics and probability for Engineers, 6edition, Weley, USA, 2014;