https://file.aiquickdraw.com/mathbot/file/1729435593008j7k8yqoh.jpg

The statement in the image asks:

**"If the correlation between two variables is positive, the slope of the linear model will be positive."**

This statement is **True**. Here's why:

- A positive correlation between two variables indicates that as one variable increases, the other also tends to increase.
- In the context of a linear regression, this would result in a **positive slope** for the line that models the relationship, since the line must rise from left to right when there is a positive correlation.

Let me know if you need more details or further explanation!

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**Follow-up questions:**
1. What is the difference between positive and negative correlation?
2. How is the correlation coefficient related to the strength of the relationship between two variables?
3. Can a correlation be positive even if the data points do not fall exactly on a line?
4. What does a slope of zero indicate about the relationship between two variables?
5. How is the linear regression equation calculated using real-world data?

**Tip:** Remember, correlation does not imply causation. Even if two variables have a strong correlation, one may not necessarily cause the other to change.

If the correlation between two variables is positive, the slope of the linear model will be positive.

Explore how a positive correlation between two variables impacts the slope of the linear model. Learn about the relationship between correlation and regression slope, including key concepts from statistics and linear regression. Suitable for high school students.

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