You use a line of best fit for a set of data to make a prediction about an unknown value of the correlation coefficient for your data set is -0.015. how confident can you be that you're predicted value will be reasonably close to the actual value?

The r value is r = -0.015 which is much closer to 0 than it is to -1, so this means that the linear regression line is a bad model to estimate the points. You're better off using some other model (eg: quadratic model), or it's possible that the data isn't connected at all and it's random noise.

If r were closer to -1, then we'd be more confident that the lines all fall around the same straight line that slopes downward.

solution:

→c= a b , that is product of a and b is possible only when when a is m × n matrix and b is n × p matrix.

that is, number of columns of a= number of rows of b.

so, size of matrix , c= a b is , if a = (m × n)matrix

b= (n × p)matrix

then a b = [ (m × n)matrix] ×[   (n × p)matrix ]

→→c= a b= [m × p ] matrix

→→size of product a b will be equal to = number of rows of a × number of columns of b