INMBASTA Statistics

School of Business Administration in Karvina
Summer 2022
Extent and Intensity
2/1/0. 6 credit(s). Type of Completion: zk (examination).
doc. RNDr. David Bartl, Ph.D. (lecturer)
Mgr. Jiří Mazurek, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Bartl, Ph.D.
Contact Person: Mgr. Radmila Krkošková, Ph.D.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
  • 1. Introduction: statistics and the significance of statistics
    When the statistics are not reliable. Statistical methods is marketing, business and entrepreneurship. Applications of statistical methods: descriptive statistics, statistical induction, statistical decision making and inference.
    2. Descriptive statistics: categorical and numerical data
    Categorical (qualitative) data. Frequency distribution. Numerical (qualitative) data. Frequency distribution, statistical location (arithmetic mean, median, mode), statistical variability or dispersion (variance and standard deviation), shape of the distribution (skewness, kurtosis).
    3. Probability and random variables
    Intuitive definition of the probability and fundamental concepts. Combinatorics. Bernoulli trials. Probability as the relative frequency. Probability properties. Discrete and continuous random variable. The probability distribution of a random variable and its characteristics (expected value, variance and standard deviation, mode).
    4. Probability models (discrete and continuous)
    Discrete probability models, discrete random variables, their characteristics and charts: uniform distribution, binomial distribution, Poisson distribution. Continuous probability models, continuous random variables, their characteristics and charts: uniform distribution, Gauss normal distribution, exponential distribution, Student's t-distribution. Probability cumulative distribution function and the quantile function. Probability density function. Central limit theorem.
    5. Point and interval estimation
    Sample data, point estimates, properties of point estimates. Interval estimates, confidence interval for the mean value.
    6. Statistical hypothesis testing and analysis of variance.
    Parametric statistical tests. Statistical hypothesis, null hypothesis (H0), alternative hypothesis (H1). Hypothesis testing for a mean. Single-sided tests. Two-sided tests. Non-parametric statistical tests. The chi-squared distribution. Pearson's chi-squared test. Statistical tests of independence in 2x2 contingency table. Analysis of variance (ANOVA), single factor or one-way ANOVA, coefficient of determination and correlation ratio.
    7. Linear regression and regression analysis
    Stochastic dependence. Simple linear regression. Multiple linear regression. Other linear models. The choice of the regression function, regression parameters estimation, coefficient of determination and correlation ratio, linearized regression functions.
Language of instruction
Further Comments
The course is taught annually.
The course is also listed under the following terms Summer 2015, Summer 2016, Summer 2017, Summer 2018, Summer 2019, Winter 2021.
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