The strength of the linear relationship between two numerical variables may be measured by the A. Coefficient of correlation B. Coefficient of determination C. Scatter diagram D. Slope

The strength of the linear relationship between two numerical variables can be measured by both the Coefficient of Correlation and the Coefficient of Determination.

A. Coefficient of Correlation: This is a statistical measure that calculates the strength of the relationship between the relative movements of two variables. The range of values for the coefficient of correlation is -1 to 1. A correlation of -1 shows a perfect negative correlation, while a correlation of 1 shows a perfect positive correlation.

B. Coefficient of Determination: This is a key output of regression analysis. It is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It is denoted as R^2 and pronounced as "R squared".

C. Scatter Diagram: This is a plot using Cartesian coordinates to display values for two variables for a set of data. It is a useful tool to visualize the relationship between two variables, but it does not measure the strength of the relationship.

D. Slope: This is the measure of the steepness of a line. In the context of linear regression, it represents the rate of change in the dependent variable as the independent variable changes. While it can give information about the direction and rate of the relationship between two variables, it does not measure the strength of the relationship.

So, the best answers are A. Coefficient of Correlation and B. Coefficient of Determination.