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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 sau:

Khi tăng chi phí khuyến mãi lên 1 ngàn đồng thì doanh thu:

a.
Tăng 14092,441 ngàn đồng.

b.
Giảm 14092,441 ngàn đồng.

c.
Giảm 20,440 ngàn đồng.

d.
Tăng 20,440 ngàn đồng.

To determine the relationship between the **Chi phí khuyến mãi (Promotion Cost)** and **Doanh thu (Revenue)**, we can calculate the linear regression between these two variables. Linear regression helps us estimate the effect of a 1-unit increase in promotion costs on revenue.

Let me perform the linear regression to determine the correct result.

I'll calculate this now.The linear regression analysis shows that when the promotion cost increases by 1,000 đồng, the revenue increases by approximately **20,440 đồng**. Therefore, the correct answer is:

**d. Tăng 20,440 ngàn đồng.**

Would you like more details or have any questions? 

Here are five related questions for further exploration:
1. How is linear regression used to predict outcomes in business settings?
2. What other factors might affect the relationship between promotion costs and revenue?
3. Can we use this regression model to predict revenues for higher promotion costs?
4. How can we test if this relationship is statistically significant?
5. What limitations exist in using just promotion cost to predict revenue?

**Tip:** Always check the assumptions of linear regression (linearity, independence, etc.) before fully trusting the results.

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 sau: Khi tăng chi phí khuyến mãi lên 1 ngàn đồng thì doanh thu: a. Tăng 14092,441 ngàn đồng. b. Giảm 14092,441 ngàn đồng. c. Giảm 20,440 ngàn đồng. d. Tăng 20,440 ngàn đồng.

This problem explores the relationship between promotion cost and retail revenue using a linear regression model. When promotion costs increase by 1,000 VND, the revenue increases by 20,440 VND. This problem applies linear regression, a key concept in algebra and business analytics, suitable for students in Grades 10-12.

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