Applied Statistics

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Department of Applied Mathematics

Synopsis:

-Basic data processing: continuous and discrete data. Characteristics (mean, quantiles, covariance, correlation coefficient). Data visualization (histograms, bar and time plots, xy-charts)

-Properties: Relations between variables, independence, correlation.

-Linear and non-linear regression analysis and prediction: Prediction of future or missing values in measured data.

-Distributional assumptions: Verification of the theoretical distribution of measured values.

-Tests of hypotheses: Evaluation of a statistically significant difference in the results of scientific experiments when the assumptions of the data distribution are verified.

-Hypothesis Tests: Evaluating a statistically significant difference in the results of scientific experiments without the assumption of data distribution

-Hypothesis tests: Suitability of measured data for use in regression analysis

-Hypothesis tests: Verification of the results of regression analysis.

-Hypothesis Tests: Qualitative data processing.

-Factor analysis: Reduction of the number of selected variables

-Clustering: Data processing of multimodal nature. Selection of variables for clustering.

-Clustering: basic clustering methods.

-Clustering: Evaluating the difference of clusters.

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No time-table has been prepared for this course

The course is a part of the following study plans: