A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x a
-
- Site Admin
- Posts: 899603
- Joined: Mon Aug 02, 2021 8:13 am
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x a
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x = 3.8333, -2.1370, y 2.9000, s, 1.6125, and r=-0.9287, determine the least squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). 1 2 3 566 5.0 5.8 5.2 2.9 21 24 (a) Choose the correct graph below. QA There appears to be (b)y=x+ D (Round to the deck (c) Choose the corne OA NU no relationship a Inear, negative linear positive a constant a nonlinear OB. OB. Q a G OC Oc ರ ರ ಬ OD.
What does it mean to say that the linear correlation coefficient between two variables equals 1? What would the scatter diagram look like? Choose the correct answer below. A. When the linear correlation coefficient is 1, there is a perfect horizontal linear relation between the two variables. The scatter diagram would contain points that all lie on a horizontal line. B. When the linear correlation coefficient is 1, there is a perfect positive linear relation between the two variables. The scatter diagram would contain points that all lie on a line with a positive slope. C. When the linear correlation coefficient is 1, there is a perfect negative linear relation between the two variables. The scatter diagram would contain points that all lie on a line with a negative slope. D. When the linear correlation coefficient is 1, there is no linear relation between the variables. The scatter diagram would contain points that show no discernable relationship.
Fill in the blank below. If r= If r= then a perfect negative linear relation exists between the two quantitative variables. 1, -1, 2, 0, then a perfect negative linear relation exists between the two quantitative variables. -2,