Explanation:

Regression analysis is a statistical process used to estimate the relationship between two variables. The purpose of regression analysis is to predict the value of one variable based on the value of another. The line of regression is the line that best fits the data points. It is used to estimate the relationship between two variables.

What are lines of regression?

Lines of regression are straight lines that are drawn through a scatter plot of data points. The lines of regression are used to determine the relationship between two variables. The slope of the line of regression represents the rate of change in one variable with respect to changes in the other variable. The intercept of the line of regression represents the value of the dependent variable when the independent variable is zero.

How to find the lines of regression?

To find the lines of regression, we use the least-squares method. This method minimizes the sum of the squared differences between the actual data points and the predicted values of the dependent variable. The line of regression is the line that best fits the data points. It is used to estimate the relationship between two variables.

Do lines of regression pass through the points?

Lines of regression do not necessarily pass through the points on the scatter plot. The purpose of the line of regression is to estimate the relationship between the two variables. The line of regression is the line that best fits the data points. The line of regression is used to predict the value of the dependent variable based on the value of the independent variable. The line of regression may pass through some of the data points, but it will not pass through all of the data points.

Conclusion

In conclusion, the lines of regression are used to estimate the relationship between two variables. The line of regression is the line that best fits the data points. The line of regression does not necessarily pass through the points on the scatter plot. The purpose of the line of regression is to predict the value of the dependent variable based on the value of the independent variable.