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Giả sử ta có số liệu về chi phí khuyến mãi và doanh thu của 10 cửa hàng bán lẻ của một doanh nghiệp như hình sau:

Hệ số xác định của hồi quy tuyến tính là:

a.
R2 = 0,61

b.
R2 = 0,56

c.
R2 = 0,82

d.
R2 = 0,78

To solve the problem, we need to calculate the coefficient of determination \( R^2 \) of a linear regression model. The table contains data on promotion costs ("Chi phí khuyến mãi") and revenues ("Doanh thu") for 10 stores.

### Steps:
1. **Extract Data**:
   - Independent variable (X): Promotion costs (Chi phí khuyến mãi).
   - Dependent variable (Y): Revenues (Doanh thu).

2. **Perform Linear Regression**:
   - The relationship between the independent variable (promotion costs) and the dependent variable (revenues) can be modeled using linear regression.
   - The coefficient of determination \( R^2 \) shows how well the independent variable explains the variance in the dependent variable.

I will extract the data from the table, perform a linear regression, and calculate the \( R^2 \). Let me compute that.The coefficient of determination \( R^2 \) from the linear regression is approximately 0.605, or 60.5%. Based on the provided options, the closest value is:

**a. \( R^2 = 0.61 \)**

Would you like more details on the calculation process or further clarifications? 

Here are five follow-up questions:
1. How do we interpret the \( R^2 \) value in the context of this data?
2. What does an \( R^2 \) value of 0.61 tell us about the relationship between promotion costs and revenues?
3. Can the regression line equation be derived from the data?
4. How can we improve the accuracy of this linear model?
5. What are some potential limitations of using linear regression for this data?

**Tip**: A higher \( R^2 \) value indicates a better fit of the regression model to the data, but it doesn't always imply a cause-effect relationship between variables.

Giả sử ta có số liệu về chi phí khuyến mãi và doanh thu của 10 cửa hàng bán lẻ của một doanh nghiệp như hình sau: Hệ số xác định của hồi quy tuyến tính là: a. R2 = 0,61 b. R2 = 0,56 c. R2 = 0,82 d. R2 = 0,78

This problem involves calculating the coefficient of determination (R^2) for a linear regression model based on data of promotion costs and revenues for 10 retail stores. Learn how to perform linear regression and interpret the R^2 value. Suitable for students in Grades 10-12.

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