Exploring Determinants of Air Pollution in Czech Republic: A Multi-variable Cross-sectional Aanalysis (2017-2021)

Maria Camila Monsalve Blanco

Master's thesis

Regression Analysis for Assessing Air Pollution

Anotácia:

The objective of this thesis is to investigate both the paired and partial relationships between pollution levels and the explanatory variables Precipitation, Temperature, Population Density, Percentage of Urban Municipalities, Environmental Protection expenditure, GDP per Capita, Passenger Cars, Autobuses, and Cattle in the 14 regions of the Czech Republic for each year from 2017 to 2021 using separate cross-sectional regression analyses. By examining paired relationships, this study will assess the lineal relationships between individual variables and pollution levels, while partial relationships will account for the presence of other explanatory variables. Data Scraping was used to collect the necessary air pollution data from the Czech Hydrometeorological Institute website. The author conducts an initial exploratory analysis of the pollution and the explanatory variables for each year to gain an understanding of the overall trends, distributions, and patterns across the 14 regions. This involves calculating summary statistics and visualizing the data using scatter plots. Next, separate cross-sectional regression analyses for each year from 2017 through 2021 are performed to examine the paired as well as the partial relationships between pollution levels and the explanatory variables.

Abstract:

The objective of this thesis is to investigate both the paired and partial relationships between pollution levels and the explanatory variables Precipitation, Temperature, Population Density, Percentage of Urban Municipalities, Environmental Protection expenditure, GDP per Capita, Passenger Cars, Autobuses, and Cattle in the 14 regions of the Czech Republic for each year from 2017 to 2021 using separate cross-sectional regression analyses. By examining paired relationships, this study will assess the lineal relationships between individual variables and pollution levels, while partial relationships will account for the presence of other explanatory variables. Data Scraping was used to collect the necessary air pollution data from the Czech Hydrometeorological Institute website. The author conducts an initial exploratory analysis of the pollution and the explanatory variables for each year to gain an understanding of the overall trends, distributions, and patterns across the 14 regions. This involves calculating summary statistics and visualizing the data using scatter plots. Next, separate cross-sectional regression analyses for each year from 2017 through 2021 are performed to examine the paired as well as the partial relationships between pollution levels and the explanatory variables.

Kľúčové slová

Air Pollution, Linear Relationship, Paired Relationships, Descriptive Regression, Partial Relationships, Exploratory Data Analysis, Regression Analysis

Keywords

Exploratory Data Analysis, Paired Relationships, Partial Relationships, Linear Relationship, Regression Analysis, Air Pollution, Descriptive Regression

Jazyk práce: English

Datum vytvoření / odevzdání či podání práce: 1. 5. 2023

Obhajoba závěrečné práce

• Obhajoba proběhla 5. 6. 2023
• Vedúci: Karel Helman
• Oponent: Adam Čabla

Vysoká škola ekonomická v Praze

Master programme / odbor:
Economic Data Analysis / Data Analysis and Modeling