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!

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.