Questions #1 - What is the R squared value of the regression if you only consider the data after they installed the new equipment? Round your answer to two decimal points.

Month	Units Produced	Total Cost
1	321	31884
2	306	29253
3	452	32731
4	407	31974
5	512	36700
6	450	28431
7	413	28654
8	473	35233
9	263	28553
10	440	29263
11	439	35629
12	391	31760
13	391	29953
14	549	32569
15	319	31229
16	378	29699
17	507	32107
18	488	33192
19	396	24917
20	518	27466
21	358	25253
22	404	24277
23	359	24847
24	347	23126
25	313	23161
26	396	23920
27	385	24496
28	480	25842
29	408	25688
30	451	24531
31	482	25040
32	460	25348
33	234	23009
34	369	24445
35	373	23709
36	456	25629
37	463	26751
38	556	26176
39	388	25076
40	290	23574
41	426	24733
42	516	27654
43	412	23588
44	356	22973
45	429	23586
46	465	24705
47	420	26268
48	340	24806

3. The file P13_03.xlsx contains monthly data on produc- tion levels and production costs during a four-year pe- riod for a company that produces a single product. Use simple regression on all of the data to see how Total Cost is related to Units Produced. Use the resulting equation to predict total cost in month 49, given that the proposed production level for that month is 450 units. Do you see anything wrong with the analysis? How should you modify your analysis if your main task is to find an equation useful for predicting future costs, and you know that the company installed new machinery at the end of month 18? Write a concise memo to management that describes your findings