Prediction using linear regression equation

Reference no: EM1322403

A CEO of a large pharmaceutical company would like to determine if he should be placing more money allotted in the budget next year for television advertising of a new drug marketed for controlling asthma. He wonders whether there is a strong relationship between the amount of money spent on television advertising for this new drug called XBC and the number of orders received. The manufacturing process of this drug is very difficult and requires stability so the CEO would prefer to generate a stable number of orders. The cost of advertising is always an important consideration in the phase I roll-out of a new drug. Data that have been collected over the past 20 months indicate the amount of money spent of television advertising and the number of orders received.

The use of linear regression is a critical tool for a manager's decision-making ability. Please carefully read the example below and try to answer the questions in terms of the problem context. The results are as follows:

Month	Advertising Cost (thousands of dollars)	Number of Orders
1	$68.93	4,902,000
2	72.62	3,893,000
3	79.58	5,299,000
4	58.67	4,130,000
5	69.18	4,367,000
6	70.14	5,111,000
7	83.37	3,923,000
8	68.88	4,935,000
9	82.99	5,276,000
10	75.23	4,654,000
11	81.38	4,598,000
12	52.90	2,967,000
13	61.27	3,999,000
14	79.19	4,345,000
15	80.03	4,934,000
16	78.21	4,653,000
17	83.77	5,625,000
18	62.53	3,978,000
19	88.76	4,999,000
20	72.64	5,834,000

Predict the monthly advertising cost when the number of orders is 5,100,000.

Reference no: EM1322403

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