Use least-squares regression to fit a straight line to: x = 0, 2, 4, 6, 9, 11, 12, 15, 17, 19 y = 5, 6, 7, 6, 9, 8, 8, 10, 12, 12 along with the slope and intercept of the straight line, compute the sum of the squares of the residuals, the standard error of the estimate, and the correlation coefficient. plot the data and the regression line. then, for a given value of x (which may not be among the samples), predict the value of y using the linear regression.

i think it will be zero as slope has a formula of difference between initial and final distance divided by itz time interval

c.

step-by-step explanation:

she should of added 9^2 and 12^2. that is because it is a^2+b^2=c^2. c^2 is the hypotenuse and should always be bigger than the other two numbers. which is why she should of added 9 squared and 12 squared together instead of 15 squared.

it’s the first one your welcome

both graphs increase on their domain

neither graph has a y-intercept

step-by-step explanation:

the following points can be visualized easily from the graph