Statistical analysis of data (ćwiczenia) - 2019/2020

Course description
General information
Lecturer:mgr Bruno Sadok
Organising unit:Faculty of Natural Sciences and Health - Instytut Matematyki, Informatyki i Architektury Krajobrazu
Number of hours (week/semester): 2/30
Language of instruction:Język polski
Course objective
Prerequisites
INTRODUCTORY COURSES AND PREREGUISITES:
Foundations of probabilistic methods
OBJECTIVES OF THE COURSE:
The main aim of the course is to teach the students about the methods and procedures of descriptive statistics and mathematical statistics. The students learn about basic methods and goals of descriptive statistics like statistical measures, graphs and about methods of statistical inference, like estimation and principles of statistical tests.
EXPECTED LEARNING OUTCOMES:
KNOWLEDGE:
Student knows fundamental probabilistic distributions.
Student knows fundamental measurement scales and graphs of descriptive statistics.
Student is able to compare various statistical tests and choose the adequate one for the given problem.
Student knows fundamental ideas of statistics, like estimator, statistical error, statistical hypothesis, significance level, prediction.
Student has knowledge about chosen statistical software.
Student knows fundamental elements of regression analysis.
SKILS:
Student is able to apply statistical measurement scales for population and sample.
Student is able to conduct computer aided data analysis in case of descriptive statistics problems.
Student is able to conduct simple statistical inference.
Student is able to conduct computer aided data analysis in case of simple statistical inference.
Student is able to conduct simple inference in case of regression analysis.
Student is able to conduct simple forecasting in case of regression analysis.
Learning outcomes
Teaching method
Course content description
[Mgr Adam Kiersztyn - 2011/12]
COURSE CONTENTS:
The main aims of statistics - examples of statistical problems, basic definitions (population, sample, random variable), measurements scales. Basic statistical concepts - empirical distribution, Gliwenko theorem, data series, time series, types of data, quantity, cumulated quantity. Measures of descriptive statistics - average, median, quartiles, quantiles, mode, standard deviation, variance, range. Other measures of descriptive statistics. Graphs - histogram, box-and-whisker plot, pie plot, line plot, other plots. Review of some random variable distributions - discrete distributions and continuous distribution (binomial distribution, Poisson distribution, normal distribution, exponential distribution, t-Student distribution). Estimation - point estimation, estimator features, method of moments, maximum likelihood estimation. Interval estimation. Statistical tests - null hypothesis, alternative hypothesis, significance level, types of errors, critical value. Examples of statistical tests. Tests of normality. Elements of multivariate analysis - dependence of variables (covariance and correlation factor), regression analysis (linear and nonlinear). Time series - time series smoothing, indices of dynamics, basis of time series forecasting. Introduction to simulation methods - Monte Carlo methods and their applications.
Forms of assessment
PLANNED LEARNING ACTIVITIES:
Laboratory
TEACHING AIDS REQUIREMENTS:
Ms Excel, SPSS, Statistica
ASSESSMENT METHODS AND CRITERIA:
2 tests
Required reading list
REQUIRED READING:
W. Niemiro, Rachunek prawdopodobieństwa i statystyka matematyczna, Biblioteka Szkoły Nauk Ścisłych, 1999
D. Aczel, Statystyka w zarządzaniu, PWN, 2000
A. Łomnicki, Wprowadzenie do statystyki dla przyrodników, PWN 2006
A. Plucińska, E. Pluciński, Probabilistyka, WNT 2000
RECOMMENDED READING:
H. Kassyk-Rokicka, Statystyka nie jest trudna. Mierniki statystyczne, PWE 2001
J. R. Thompson, Simulation - A Modeler\\\'s Approach, Wiley-Interscience, 2000
O. Lange, A. Banasiński, Teoria Statystyki, PWE, 1968
OTHER LEARNING RESOURCES
Ms Excel, SPSS, Statistica
Field of study: Informatics
Course listing in the Schedule of Courses:
Year/semester:Year II - Semester 4
Number of ECTS credits: 0
Form of assessment: Grade
Field of study: Mathematics
Course listing in the Schedule of Courses:
Year/semester:Year I - Semester 2
Number of ECTS credits: 0
Form of assessment: Grade