Month X=Advertising cost (in thousands) Y=# of orders (in millions) 1 $68.93 4.9

Question

Month X=Advertising cost
(in thousands) Y=# of orders
(in millions) 1 $68.93 4.902 2 72.62 3.893 3 79.58 5.299 4 58.67 4.130 5 69.18 4.367 6 70.14 5.111 7 83.37 3.923 8 68.88 4.935 9 82.99 5.276 10 75.23 4.654 11 81.38 4.598 12 52.9 2.967 13 61.27 3.999 14 79.19 4.345 15 80.03 4.934 16 78.21 4.653 17 83.77 5.625 18 62.53 3.978 19 88.76 4.999 20 72.64 5.834 Question 10 (Mandatory) (5 points) A CEO of a large pharmaceutical company is evaluating whether to place more money in next year's budget for television advertising for XBC, a new drug for controlling asthma. The cost of advertising is always an important consideration in the phase roll-out of a new drug. He asks whether there is a strong relationship between the number of orders received and the amount of money spent on television advertising for drugs. Perform Data Analysis Regression on Orders vs Advertising. Do your work in this Excel file and keep it open for the questions following this part. The regression equation is A) Advertising 8.072 36.214 Orders O B) orders 1.516 0.042xAdvertising CO Orders 0.042 1.516xAdvertising O D) orders 1.401 0.043xAdvertising

Explanation / Answer

The statistical software output for regression is:

Simple linear regression results:
Dependent Variable: Y=# of orders
Independent Variable: X=Advertising cost
Y=# of orders = 1.5158571 + 0.042240445 X=Advertising cost
Sample size: 20
R (correlation coefficient) = 0.58390987
R-sq = 0.34095073
Estimate of error standard deviation: 0.5729013

Parameter estimates:

Analysis of variance table for regression model:

Predicted values:

Hence,

10. Option B

11. Option C

12. Option A

13. Option C

14. Option D

Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 1.5158571 1.025621 0 18 1.4779895 0.1567 Slope 0.042240445 0.013842207 0 18 3.0515685 0.0069

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