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I can help you with this problem involving linear regression. Let's break it down step by step.

The table provided shows the following dataI can help you with this problem involving linear regression. Let's break it down step by step.

The table provided shows the following 
- **Day (1 to 10)**
- **Number of reported cases of COVID-19**
- **Number of deaths**
- **Number of recovered patients**

You are asked to:
1. Develop a simple linear regression model for the number of deaths using the number of reported COVID-19 cases as the independent variable (part a).
2. Predict the number of deaths if the reported COVID-19 cases reach 1500 (part b).
3. Develop a simple linear regression model for the number of recovered patients using the number of reported COVID-19 cases as the independent variable (part c).
4. Predict the number of recovered patients if the reported COVID-19 cases reach 1500 (part d).

### Step 1: Organize the Data
Let me first summarize the data from the image:

| Day | Reported Cases | Deaths | Recovered Patients |
|-----|----------------|--------|--------------------|
| 1   | 101            | 7      | 2                  |
| 2   | 125            | 10     | 5                  |
| 3   | 141            | 15     | 9                  |
| 4   | 173            | 21     | 12                 |
| 5   | 205            | 27     | 16                 |
| 6   | 239            | 31     | 21                 |
| 7   | 254            | 38     | 25                 |
| 8   | 267            | 44     | 31                 |
| 9   | 284            | 51     | 36                 |
| 10  | 301            | 59     | 42                 |

### Step 2: Perform Linear Regression (part a and part c)
- For **part a**, the independent variable (X) is the number of reported COVID-19 cases, and the dependent variable (Y) is the number of deaths.
- For **part c**, the independent variable (X) is still the number of reported COVID-19 cases, but the dependent variable (Y) is now the number of recovered patients.

I will now calculate the linear regression equations and predictions for both parts using this data. Let's proceed!### Linear Regression Results:

#### Part A: Linear Regression Equation for Deaths
The equation for predicting the number of deaths based on the number of reported COVID-19 cases is:

\[
\text{Deaths} = 0.2435 \times (\text{Reported Cases}) - 20.588
\]

#### Part B: Predicted Number of Deaths for 1500 Reported Cases
For 1500 reported cases, the predicted number of deaths is approximately **344.64**.

#### Part C: Linear Regression Equation for Recovered Patients
The equation for predicting the number of recovered patients based on the number of reported COVID-19 cases is:

\[
\text{Recovered Patients} = 0.1859 \times (\text{Reported Cases}) - 18.959
\]

#### Part D: Predicted Number of Recovered Patients for 1500 Reported Cases
For 1500 reported cases, the predicted number of recovered patients is approximately **259.93**.

Would you like more details or any further explanation on how these results were derived?

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Here are 5 related questions you might find useful:
1. How is the slope of a linear regression equation interpreted?
2. What does the intercept in the linear regression equation represent?
3. How can you calculate the coefficient of determination (R²) for these models?
4. How can we assess the accuracy of these predictions?
5. Can we use polynomial regression for better predictions?

**Tip:** In linear regression, the slope indicates how much the dependent variable changes for every unit increase in the independent variable.

The data below shows numbers of reported cases of COVID-19, deaths, and recovered patients from Day 1 to Day 10. Find the following: a. Simple linear regression equation to predict the relationship between the number of reported cases and the number of deaths. b. Predicted number of deaths if reported cases reach 1500. c. Simple linear regression equation for predicting recovered patients based on reported cases. d. Predicted number of recovered patients if reported cases reach 1500.

Learn how to develop linear regression equations to predict COVID-19-related deaths and recoveries based on reported cases. Using real-world data over 10 days, this problem explores predictive modeling and algebraic solutions. Suitable for Grades 10-12.

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