Anumerical measure of linear association between two variables is the a. variance b. coefficient of variation c. correlation coefficient d. standard deviation

A numerical measure of linear association between two variables is the covariance

Step-by-step explanation:

A numerical measure of linear association between two variables is the covariance

Covariance is a measure of how changes in one variable are associated with changes in a second variable. Specifically, covariance measures the degree to which two variables are linearly associated. However, it is also often used informally as a general measure of how monotonically related two variables are.  It is similar to variance, but where variance tells you how a single variable varies, covariance tells you how two variables vary together

a. the residual plot for model 1 has a random pattern and is a good fit for the data.

- "runner b is moving at a slower rate."

- "the graphs shown are graphs of distance(km) against time(min).

the slopes of this graphs are are given by:

slope = (change in y)/(change in x)

= (change in distance)/(change in time)

this is the formula for finding the speed. so, the slope of these graphs represent the speed of the runner.

taking each division in the graph to represent 1 on both axis:

runner a ⇒ speed = (2-0)/(1-0)

= 2/1

= 2 km/min

runner b ⇒ speed = (3-0)/(3-0)

= 3/3

= 1 km/min

this shows that   runner b is moving at a slower rate."