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A sales manager wants to know if the number of sales calls is related to the number of sales for a given month. He takes a random sample of 10 associates and notes the number of sales calls and the number of sales for each associate. The data is given in the table. Determine the correlation coefficient for this set of sample observations for the independent variable, number of sales calls, and dependent variable, number of sales. Associate 1 Calls 8 19 10 Sales 62 30 68 72 75 75 73 74 80 62 82 65 63 38 r= n(Exy) - (Ex) (Zy) in(E x2) - (Ex)? ] x (n(Ly=) - (Zy)?) Answer 10 Points Tables Keypad O 0.972 O 0.974 You are given the following set of observations for the predictor variable x and the response variable y, as shown in the image. The least-squares estimate for the y intercept of the regression line will be_ Answer 10 Points Tables Keypad 0 0.6498 O 3.4407 O-0.8061 O-1.2373 A local lighting company is doing a study to determine the association between the number of rooms in a private residence and the number of Kilowatt-hours (In 1000s) used in the home per month. The results of a random sample of 10 homes are presented in the table. The R-squared value for the model is Number 14 Rooms Kilowatt Hours 10 (1000) r = n(Exy) - (Ex)(Ey) Answer 10 Points Tables Keypad D. 80.7%, which means that 80.7% of the variation in the dependent variable, Kilowatt-hours, may be attributed to variation of the independent variable, number of rooms. 82.7%, which means that 82.7% of the variation in the dependent variable, Kilowatt-hours, may be attributed to variation of the independent variable, number of rooms. 81.7%, which means that 81.7% of the variation in the dependent variable, Kilowatt-hours, may be attributed to variation of the independent variable, number of rooms. 79.7%, which means that 79.7% of the variation in the dependent variable, Kilowatt-hours, may be attributed to variation of the independent variable, number of rooms. A sales manager wants to know if the number of sales calls is related to the number of sales for a given month. He takes a random sample of 10 associates and notes the number of sales calls and the number of sales for each associate. The data is given in the table. Use the least squares linear regression equation to predict the number of sales for 70 sales calls. Associate Calls 62 65 19 35 35 36 16 80 82 61 10 Sales b , - n(Exy) - (x)(Zy) n(Ex?) - (x)2 ExEx') -(x)(xy) n(Ex?) -(x) Answer 10 Points in Tables Keypad 0 40 O 35