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)
- Introduction
- Parametric tests
- Pearson correlation coefficient
- Analysis of variance (one-way ANOVA)
- Real-life problem addressed by ANOVA
- Conclusion
- Literature
- Appendix
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The objective of this case study is to analyze the impact of vaccination status on the hospitalization rates of COVID-19 patients in Germany, using statistical tests. It explores the relationship between different vaccination statuses and hospitalization rates across age groups, specifically focusing on individuals aged 18-59 and 60 and over.
- Parametric statistical tests and their applications
- The impact of vaccination on COVID-19 hospitalization rates
- Analysis of variance (ANOVA) as a method for comparing groups
- Interpretation of statistical results and conclusions
- Application of Python programming for data analysis
Zusammenfassung der Kapitel (Chapter Summaries)
The introduction chapter provides context regarding the COVID-19 pandemic in Germany, highlighting the discrepancy between infection and death rates, and how vaccination might be influencing this trend. It introduces the research question: how does vaccination status impact the hospitalization rates of COVID-19 patients? The chapter also outlines the methodology, focusing on parametric tests, particularly the Pearson correlation coefficient and one-way ANOVA, and describes the data source from the Robert-Koch Institut.
Chapter 2 introduces the concept of parametric tests, emphasizing their reliance on normally distributed data. It discusses the Pearson correlation coefficient, explaining its calculation and interpretation, and its application in analyzing linear relationships between variables.
Chapter 2.2 delves into the analysis of variance (one-way ANOVA), its main characteristic, and the underlying principles behind this method. It outlines the role of F-statistics in comparing group means, highlighting the null and alternative hypotheses involved in this statistical test.
Schlüsselwörter (Keywords)
This case study focuses on parametric tests, specifically Pearson correlation coefficient and one-way ANOVA, applied to analyze the impact of vaccination status on COVID-19 hospitalization rates in Germany. Key terms include statistical hypothesis testing, normal distribution, linear association, group comparisons, F-statistics, and data analysis using Python.
- Quote paper
- Stonia Thorand (Author), 2022, Testing statistical hypotheses using parametric tests, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/1243694
