. Develop a linear-regression-based forecasting model to predict costs. b. Predict costs when patient days are 520, 530, 540, and 550.

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a. Develop a linear-regression-based forecasting model to predict costs.

b. Predict costs when patient days are 520, 530, 540, and 550.

TABLE EX 2.6
Period
Cost
Patient Days
October: Year 1
32,996
516
November: Year 1
34,242
530
December: Year 1
27,825
528
January: Year 2
February: Year 2
29,807
517
28,692
500
March: Year 2
34,449
514
April: Year 2
33,335
515
May: Year 2
38,217
509
June: Year 2
36,690
524
July: Year 2
35,303
524
539
August: Year 2
September: Year 2
33,780
32,843
551
October: Year 2
37,781
543
November: Year 2
27,716
528
December: Year 2
31,876
531
542
January: Year 3
February: Year 3
31,463
29,829
558
Transcribed Image Text:TABLE EX 2.6 Period Cost Patient Days October: Year 1 32,996 516 November: Year 1 34,242 530 December: Year 1 27,825 528 January: Year 2 February: Year 2 29,807 517 28,692 500 March: Year 2 34,449 514 April: Year 2 33,335 515 May: Year 2 38,217 509 June: Year 2 36,690 524 July: Year 2 35,303 524 539 August: Year 2 September: Year 2 33,780 32,843 551 October: Year 2 37,781 543 November: Year 2 27,716 528 December: Year 2 31,876 531 542 January: Year 3 February: Year 3 31,463 29,829 558
2.6 A hospital pharmacy would like to develop a budget for allergy medications that is based
on patient days. Cost and patient days data were collected over a 17-month period as
shown in Table EX 2.6.
Transcribed Image Text:2.6 A hospital pharmacy would like to develop a budget for allergy medications that is based on patient days. Cost and patient days data were collected over a 17-month period as shown in Table EX 2.6.
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