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:

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?

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.