Mathematical Statistics
Course code
MLM6509.DT
old course code
Course title in Estonian
Matemaatiline statistika
Course title in English
Mathematical Statistics
ECTS credits
4.0
Assessment form
Examination
lecturer of 2025/2026 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2026/2027 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
The course aims to provide students with essential skills and knowledge in collecting, describing and analyzing statistical data, and in applying probability distributions and inferential methods to solve scientific and practical problems.
Brief description of the course
Statistical data: population and sample; types of variables; data presentation; measures of central tendency; measures of dispersion and shape. Distribution laws of discrete and continuous random variables (discrete and continuous uniform, Bernoulli, binomial, Poisson, exponential and normal distributions). Correlation and covariance; Pearson’s, Spearman’s, Kendall’s and Fechner’s correlation coefficients. Linear and nonlinear regression. Point and interval estimation; confidence intervals for the mean in large and small samples. Statistical hypotheses and their testing: Student’s t-test, z-test and chi-square test.
Learning outcomes in the course
Upon completing the course the student:
- determines and describes statistical data (statistical population, sample, types of variables), presents and analyses data using descriptive statistics (averages, dispersion, shape of the distribution);
- understands and applies the main discrete and continuous laws of distributions (uniform, Bernoulli, binomial, Poisson, exponential, and normal) to calculate probabilities and distribution parameters;
- calculates and interprets correlation, covariance, and Pearson, Spearman, Kendall and Fechner correlation coefficients, and is able to perform linear and nonlinear regression analysis;
- constructs point estimation, interval estimation and calculates confidence intervals for the average value for samples of both large and small size;
- formulates and tests statistical hypothesis, applying Student’s t-test, z-test and chi-squared test.
- determines and describes statistical data (statistical population, sample, types of variables), presents and analyses data using descriptive statistics (averages, dispersion, shape of the distribution);
- understands and applies the main discrete and continuous laws of distributions (uniform, Bernoulli, binomial, Poisson, exponential, and normal) to calculate probabilities and distribution parameters;
- calculates and interprets correlation, covariance, and Pearson, Spearman, Kendall and Fechner correlation coefficients, and is able to perform linear and nonlinear regression analysis;
- constructs point estimation, interval estimation and calculates confidence intervals for the average value for samples of both large and small size;
- formulates and tests statistical hypothesis, applying Student’s t-test, z-test and chi-squared test.
Teacher
Tõnu Tõnso
Prerequisite course 1
Study programmes containing that course