Statistical analysis of data (wykład) - 2019/2020

Course description
General information
Lecturer:dr Małgorzata Nowak-Kępczyk
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
C1. The main aim of the course is to teach the students about the methods and the procedures of descriptive statistics and mathematical statistics.
C2. The students learn about the basic methods and the goals of descriptive statistics like using statistical measures, graphs, and about the methods of statistical inference, like estimation and principles of statistical tests.
Prerequisites
W1. Introduction to differential and integral calculus
W2. Foundations of probabilistic methods
Learning outcomes
KNOWLEDGE
W1. Students know fundamental probabilistic distributions (K_W02).
W2. Students know fundamental measures and graphs of descriptive statistics (K_W02).
W3. Students are able to compare various statistical tests and choose the adequate one for the considered problem (K_W02).
W4. Students know fundamental ideas of statistics, like estimation, statistical error, statistical hypothesis, significance level, prediction (K_W02).
W5. Students have knowledge about chosen statistical software (K_W025).
W6. Students know fundamental elements of regression analysis (K_W02).

SKILLS
U1. Students have ability to apply statistical measures for population and sample (K_U22, K_U28).
U2. Students have ability to conduct computer aided data analysis in case of descriptive statistics problems (K_U03, K_U22, K_U28).
U3. Students have ability to conduct simple statistical inference(K_U22, K_U28).
U4. Students have ability to conduct computer aided data analysis in case of simple statistical inference (K_U03, K_U22, K_U28).
U5. Students have ability to conduct statistical tests in the case of regression analysis (K_U22, K_U28).
U6. Students have ability to conduct simple forecasting in the case of regression analysis (K_U22, K_U28).
SOCIAL COMPETENCE
K1. Students are able to enter into discussion about the statistical inference and the methods of statistics (K_K07).
Teaching method
Lecture (with elements of discussion), individual work, exercises, using software applications devoted to statistical inference.
Course content description
1. The main aims and drawbacks of statistics – examples of statistical problems, basic definitions (population, sample, random variable), measurements scales.
2. Basic statistical concepts – empirical distribution, data series, time series, types of data, quantity, cumulated quantity.
3. Measures of descriptive statistics – average, median, quartiles, quantiles, mode, standard deviation, variance, range. Other measures of descriptive statistics.
4. Statistical graphs – histogram, box-and-whisker plot, pie plot, line plot, other plots.
5. Review of some random variable distributions – discrete distributions and continuous distribution (binomial distribution, Poisson distribution, normal distribution, exponential distribution, t-Student distribution).
6. Estimation – point estimation, estimator features, method of moments, maximum likelihood estimation, methods and examples of the interval estimation.
7. Statistical tests – the concept of null hypothesis, alternative hypothesis, significance level, types of errors, critical value. Example of the tonstruction of the statistical test.
8. Selected examples of statistical tests (chi-square tests, tests of means, Kolmogorow-Smirnov test etc.).
9. Introduction to multivariate analysis, concept of the dependence of variables (covariance and correlation factor). Foundations of regression analysis (linear and nonlinear).
10. Time series – time series smoothing, indices of dynamics. Discussion about basis of the time series forecasting.
11. Introduction to simulation methods – the Monte Carlo method and its applications.
Forms of assessment
PDuring classes: 2 tests, theirs terms are announced to students during the course.
Written exam (only for students, who completed classes), which checks knowledge from lectures and classes.
Below 50% - fail.
W1 - exam, tests, preparation for the course.
W2 - exam, tests, preparation for the course.
W3 - exam, tests, preparation for the course.
W4 - exam, tests, preparation for the course.
W5 - tests, preparation for the course.
W6 - exam, tests, preparation for the course.
U1 - exam, tests, preparation for the course.
U2 - tests, preparation for the course.
U3 - exam, tests, preparation for the course.
U4 - tests, preparation for the course.
U5 - exam, tests, preparation for the course.
U6 - exam, tests, preparation for the course.
K1 - preparation for the course, work during the course.
Lecture 30 hours.
Classes 30 hours.
Number of hours with a lecturer: 30.
Number of ECTS points with lecturer: 2.
Preparation for the course: 30.
Studying literature: 20.
Preparation for the exam and tests: 30
Sum of hours: 80.
Sum of ECTS points for the module: 5
Required reading list
REQUIRED READING
David Freedman, Robert Pisani, Roger Pruves “Statistics”
Amir D. Aczel “Complete business statistics”
RECOMMENDED READING
Roxy Peck, Chris Olsen, Jay Devore “Introduction to Statistics and Data Analysis”
William Mendenhall, Robert J. Beaver, Barbara M. Beaver “Introduction to Probability and Statistics”
Field of study: Informatics
Course listing in the Schedule of Courses:
Year/semester:Year II - Semester 4
Number of ECTS credits: 5
Form of assessment: Examination
Field of study: Mathematics
Course listing in the Schedule of Courses:
Year/semester:Year I - Semester 2
Number of ECTS credits: 5
Form of assessment: Examination