03755ntm a22005777i 4500 000677126 CZ-PrVSE 20200829134027.0 m d cr n|||||||||| 200829s2020 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Dadamirzaev, Gayrat ISIS:136107 dis Regression analysis on tests results for diabetes diagnosis using R eng Regression analysis on tests results for diabetes diagnosis using R / Gayrat Dadamirzaev 2020 ?? stran : digital, PDF soubor Vedoucí práce: Karel Helman Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2020 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2020 In multivariate analysis, such as multiple linear regression, unusual points in dataset may influence on fitting of regression model, i.e. may affect on overall estimation results of model and statistical significance of coefficients. Especially, when dataset is full of outliers, it is a question on how to deal with such outlier points in order to avoid violations of regression assumptions and to keep model statistically significant. This master thesis is aimed to explore and answer to this question by using classical linear predictive approach. Theoretical part of this thesis starts with introduction, where it is described the main idea and tasks of research as well as steps of implementation of assigned tasks. Thesis theory will introduce the basic linear regression terms, such as determination coefficient, test of significance of coefficients, regression assumptions and issues like multicollinearity, outliers, etc. Methods of outlier detection and predictive analysis are some of the most main topics in this thesis. Practical part is realization of thesis targets based on terms and methods, which are mentioned in theory of the thesis. Using official R Studio software, four different scenarios or situations with data will be discovered individually and then will be compared between each other. As a conclusion, based on the outcomes of four identical regression models, the quality of these models will be determined. Způsob přístupu: Internet quantitative economic analysis [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas residual diagnostics prediction outlier detection prediction accuracy Classical linear regression outliers influential outliers Helman, Karel xx0260953 ths Bašta, Milan ISIS:51301 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/71159/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/71159 Hlavní práce https://insis.vse.cz/zp/71159/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/71159/posudek/oponent/65728 Oponentura https://insis.vse.cz/zp/71159/priloha/20793 Přiloha k práci https://insis.vse.cz/zp/71159/priloha/20795 Přiloha k práci https://insis.vse.cz/zp/71159/priloha/20796 Přiloha k práci https://insis.vse.cz/zp/71159/podrobnosti dc:identifier NEPOSILAT VSKP vse71159 200828 71159