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The task in the image involves calculating percentages from a contingency table to explore the relationship between COVID-19 cases and federal states ("Bundesländer") in Germany, specifically between Berlin and Mecklenburg-Vorpommern. Let's go step-by-step to determine how we can calculate meaningful percentages for interpretation.

### 1. Structure of the Contingency Table:
The table contains the following categories:
- **Genesene (Recovered)**
- **Aktive Fälle (Active Cases)**
- **Todesfälle (Deaths)**
- **Nicht infiziert (Not infected)**
  
Each category lists counts for **Berlin** and **Mecklenburg-Vorpommern**, with the **total** for both columns at the end.

---

### 2. What Percentages Can We Calculate?
There are a few meaningful ways to calculate percentages for comparison:
1. **Row percentages**:  
   - Show the proportion of each category (e.g., recovered) within Berlin or Mecklenburg-Vorpommern, relative to the total population in that region.
   - Formula:  
     \[
     \text{Percentage} = \frac{\text{Category Value (e.g., Genesene)}}{\text{Total Cases for Region}} \times 100
     \]

2. **Column percentages**:  
   - Indicate how each state contributes to the total of a particular category (e.g., deaths).
   - Formula:  
     \[
     \text{Percentage} = \frac{\text{Category Value for Region}}{\text{Total for Category}} \times 100
     \]

---

### 3. Example Calculations:

**Berlin**  
- **Recovered (Genesene)**:  
  \[
  \frac{17000}{3669491} \times 100 \approx 0.463\%
  \]
  
- **Active Cases (Aktive Fälle)**:  
  \[
  \frac{5484}{3669491} \times 100 \approx 0.15\%
  \]
  
- **Deaths (Todesfälle)**:  
  \[
  \frac{243}{3669491} \times 100 \approx 0.007\%
  \]

---

**Mecklenburg-Vorpommern**  
- **Recovered (Genesene)**:  
  \[
  \frac{1400}{1608138} \times 100 \approx 0.087\%
  \]

- **Active Cases (Aktive Fälle)**:  
  \[
  \frac{369}{1608138} \times 100 \approx 0.023\%
  \]

- **Deaths (Todesfälle)**:  
  \[
  \frac{21}{1608138} \times 100 \approx 0.0013\%
  \]

---

### 4. Interpretation:
- Berlin has a higher percentage of recovered individuals (0.463%) compared to Mecklenburg-Vorpommern (0.087%).  
- Active cases also show a similar pattern, with Berlin having more cases proportionally.  
- The death rate in Berlin is higher than in Mecklenburg-Vorpommern, though in both cases, the percentage is quite small.  
- The majority of the population in both states remains "Nicht infiziert" (not infected), suggesting low overall infection penetration.

---

Would you like to see more calculations or a specific breakdown of percentages?  

Here are some related questions to explore:
1. How do the infection rates differ between the two regions?
2. What is the proportion of active vs. recovered cases within each region?
3. Can the mortality rate be derived from these numbers?
4. What is the overall percentage of infected individuals in the total population?
5. How can we compare the two states better using visualization tools?

**Tip:** A bar chart or pie chart can make such comparisons more intuitive.

Berechnen Sie für die Kontingenztabelle eine Prozentuierung, die es Ihrer Ansicht nach erlaubt, den Zusammenhang zwischen Covid-19-Fällen und Bundesland adäquat zu interpretieren.

Learn how to calculate percentages in a contingency table to analyze the distribution of Covid-19 cases, recoveries, deaths, and uninfected individuals between Berlin and Mecklenburg-Vorpommern. This problem covers percentage calculations within and across categories, using real data from German federal states.

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