The correct answer is Regression.

Regression is a statistical technique that models the relationship between two or more variables and is primarily used for prediction. It helps estimate the value of a dependent variable based on one or more independent variables.

Here’s a brief breakdown of the other options:

• Correlation measures the strength and direction of the linear relationship between two variables but does not involve prediction.
• Variability refers to how spread out or dispersed the data points are in a distribution (e.g., range, variance, standard deviation).
• The standard error of the estimate quantifies the accuracy of predictions made using regression, but it's not a method for prediction itself.

Would you like more details on regression or an example?

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Here are 5 related questions you might find interesting:

1. How does linear regression differ from multiple regression?
2. What is the significance of the correlation coefficient in regression analysis?
3. How do you interpret the coefficients in a regression model?
4. What is the standard error of the estimate, and how is it used?
5. What are the assumptions of linear regression?

Tip: Regression is like drawing a line through data points that best predicts the dependent variable based on the independent variables. It's the foundation for many predictive models!