The pandemic has spread during the last two years dramatically. In Germany alone roughly 25,66 million confirmed cases and 137 348 confirmed Corona deaths were documented. Interestingly the highest number of confirmed cases was around 1,6 million on the 21st March of 2022 while the confirmed death counts were at 1 520 in Germany at the same day. Conversely, this means that the counted deaths are roughly 1 000 times lower than the infection levels, while the highest death count was detected at the 14th of December in 2020 with a value of 6 410 with roughly 170 00 confirmed cases. Meaning that around 27 times less deaths were confirmed in comparison to infection cases.
The decrease of Corona deaths may be caused by the introduction of the Corona vaccinations. With the help of the accumulated data during the pandemic, the effect of the vaccination can be tested against the hospitalization values of Corona patients with statistical tests (Robert-Koch Institut, 2022). The test does indirectly adress the issue towards the decreasing Corona deaths with increasing infection cases. The increasing value of confirmed cases clearly shows, that the vaccination does not inhibit the infection, but somehow may have an weakening effect on the course of the disease. The approach mainly focus on different vaccination status and age groups of hospitalized Corona patients in Germany. Not included are factors like the Corona variation and the specific vaccines. Statistical tests to address such questions are parametric tests, which are powerful tools to evaluate data following a normal distribution. Within the assignment, two parametric tests are presented (pearson correlation coefficient and one-way ANOVA), while the one-way ANOVA was chosen to adress the question of interest desribed above. The results of the analysis indicate significant differences between hopsitalized Corona patients with different vaccination status, which is discussed in detail in the conclusion.
Inhaltsverzeichnis (Table of Contents)
- 1. Introduction
- 2. Parametric tests
- 2.1. Pearson correlation coefficient
- 2.2. Analysis of variance (one-way ANOVA)
- 3. Real-life problem addressed by ANOVA
- 4. Conclusion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The objective of this work is to demonstrate the application of parametric statistical tests, specifically the Pearson correlation coefficient and one-way ANOVA, to analyze the relationship between COVID-19 vaccination status and hospitalization rates in Germany. The analysis uses data from the Robert-Koch Institut to investigate whether vaccination status significantly impacts hospitalization outcomes.
- Application of parametric statistical tests
- Analysis of COVID-19 vaccination effects on hospitalization
- Interpretation of Pearson correlation coefficient
- Understanding and application of one-way ANOVA
- Statistical significance testing and hypothesis evaluation
Zusammenfassung der Kapitel (Chapter Summaries)
1. Introduction: This chapter introduces the context of the study by presenting the dramatic spread of the COVID-19 pandemic in Germany, citing confirmed cases and deaths. It highlights the significant discrepancy between infection and death rates, suggesting a potential mitigating effect of vaccination. The chapter establishes the research question: Does vaccination status significantly impact COVID-19 hospitalization rates? It outlines the methodology, focusing on the use of parametric tests, specifically one-way ANOVA, to analyze data from the Robert-Koch Institut. The introduction underscores the limitations of the study, such as excluding factors like virus variants and vaccine types.
2. Parametric tests: This chapter provides an overview of parametric statistical tests, emphasizing their assumption of normally distributed data. It introduces two key parametric tests: ANOVA, used to compare means across groups, and Pearson's correlation coefficient, used to measure the linear relationship between two variables. The chapter lays the theoretical groundwork for the subsequent analysis by defining these tests and their underlying principles.
2.1. Pearson correlation coefficient: This section delves into the Pearson correlation coefficient, explaining its formula and the conditions required for its application (normally distributed data and linear relationship between variables). It explains how to interpret the results, including the strength (small, medium, large) and direction (positive or negative) of the correlation. The section highlights the limitations of solely relying on correlation, setting the stage for the use of ANOVA in subsequent sections.
2.2. Analysis of variance (one-way ANOVA): This section details the one-way ANOVA test, a method used to compare the means of three or more groups. The chapter explains the underlying principles of ANOVA, including the calculation of F-statistics, mean squares (MSbetween and MSwithin), and the interpretation of the results in the context of hypothesis testing (null vs. alternative hypotheses). It lays out the mathematical foundation for the ANOVA analysis used later in the study.
Schlüsselwörter (Keywords)
Parametric tests, Pearson correlation coefficient, one-way ANOVA, COVID-19, vaccination, hospitalization, statistical significance, hypothesis testing, Robert-Koch Institut, Germany.
Frequently Asked Questions: Comprehensive Language Preview of Parametric Statistical Tests Applied to COVID-19 Vaccination Data
What is the main objective of this work?
The primary objective is to demonstrate the application of parametric statistical tests (specifically the Pearson correlation coefficient and one-way ANOVA) to analyze the relationship between COVID-19 vaccination status and hospitalization rates in Germany, using data from the Robert Koch Institut.
What key themes are explored in this preview?
Key themes include the application of parametric statistical tests, the analysis of COVID-19 vaccination effects on hospitalization, interpretation of the Pearson correlation coefficient, understanding and applying one-way ANOVA, and statistical significance testing and hypothesis evaluation.
What parametric tests are discussed?
The preview focuses on two key parametric tests: the Pearson correlation coefficient, used to measure the linear relationship between two variables, and one-way ANOVA, used to compare the means of three or more groups.
How is the Pearson correlation coefficient used in this analysis?
The Pearson correlation coefficient helps assess the strength and direction of the linear relationship between vaccination status and hospitalization rates. The preview explains its formula, interpretation (strength and direction of correlation), and limitations.
What is the role of one-way ANOVA in this analysis?
One-way ANOVA is employed to compare the average hospitalization rates across different vaccination status groups (e.g., vaccinated vs. unvaccinated). The preview details the principles of ANOVA, including F-statistics, mean squares, and hypothesis testing.
What data source is used in this analysis?
The analysis utilizes data from the Robert Koch Institut, a prominent German institution for disease control and prevention.
What is the research question addressed in this study?
The central research question is: Does vaccination status significantly impact COVID-19 hospitalization rates in Germany?
What are the limitations of this study?
The preview acknowledges limitations, such as the exclusion of factors like virus variants and vaccine types, which could influence the results.
What are the key chapters covered in this preview?
The preview includes summaries of an introduction, a chapter on parametric tests (further subdivided into sections on the Pearson correlation coefficient and one-way ANOVA), a chapter on a real-life problem addressed by ANOVA, and a conclusion.
What are the keywords associated with this work?
Keywords include: Parametric tests, Pearson correlation coefficient, one-way ANOVA, COVID-19, vaccination, hospitalization, statistical significance, hypothesis testing, Robert-Koch Institut, Germany.
- Quote paper
- Stonia Thorand (Author), 2022, Testing statistical hypotheses using parametric tests, Munich, GRIN Verlag, https://www.grin.com/document/1243694
