Please complete the assignment in the following excel doc and Microsoft doc for additional notes

Excel

• https://docs.google.com/spreadsheets/d/1TITLBI2_RD0FHdnPXN2oaexxSjKvL-Ehv5Ot1s0B_5Q/edit?usp=sharing

Word Doc

• https://docs.google.com/document/d/1-UH9SE2sOWJ9R38vEeHIaT58P49lephwqtBtbSFjCKg/edit

Instructions

You will estimate the learning curve from production and cost data.

Please answer the questions concisely in your report, and include figures and tables of regression results where called for. These can be copied and pasted from Excel or R. Make sure they are sized and formatted to be legible, number them, and refer to them by number in the text (e.g., "... as can be seen in Table 2..."). Always discuss statistical significance and goodness of fit for every regression.

Using the data set on this page, answer the following questions.

The table below is the Notes to understand what the variables are.

Monthly production and cost data for a machine
Variable	Definition
month	sequence of months since beginning of production
q	quantity produced in that month
cost	total cost of all units produced in that month, in dollars

Cost Dataset - The data consist of 60 monthly observations on production and cost of a piece of equipment over the first five years of its production run. Download the data and read the . In particular note that the cost variable is the total cost (not average or unit cost) for all units produced in that month.

month	q	cost
1	21	176600.40
2	51	214194.69
3	51	318549.28
4	9	114226.17
5	55	255729.91
6	22	117111.54
7	13	100576.08
8	40	99332.66
9	14	115385.40
10	10	109270.98
11	16	87049.94
12	15	92916.56
13	24	105648.97
14	20	135078.20
15	41	207268.98
16	21	96932.95
17	35	156226.20
18	58	285327.80
19	51	190745.54
20	46	277621.62
21	54	267857.89
22	50	172489.57
23	37	137954.70
24	28	101219.65
25	37	103051.14
26	61	217314.69
27	45	193982.53
28	29	164755.11
29	49	127478.29
30	60	179832.83
31	36	157612.78
32	67	335115.82
33	46	156943.16
34	35	132685.54
35	68	248609.23
36	48	254989.39
37	56	185735.95
38	40	222496.25
39	59	299309.87
40	34	111186.61
41	50	134704.78
42	51	227197.97
43	28	80646.25
44	41	132528.15
45	41	205941.86
46	63	232683.02
47	53	184840.65
48	61	205434.09
49	60	301110.81
50	33	152571.11
51	61	304542.80
52	47	194225.46
53	39	203678.34
54	51	125463.82
55	60	220550.07
56	32	161332.06
57	41	172417.25
58	64	158393.52
59	42	85403.26
60	63	223046.64

1. Estimating the simple learning curve
   • In preparation, please review the lecture from Module 5 on "Innovation and the Learning Curve."
   • Using the Required Data Set linked on this page, build any new variables needed to estimate the learning curve as a simple regression. What is the dependent (Y) variable? What is the regressor (X)?
   • Plot the relationship between your X and Y variables and discuss.
   • Run the learning curve regression and interpret the results, referring to the table of output. What does the regression tell you about any learning effect? Use words that a layperson could understand.
2. Taking another look
   • The production manager believes that cost efficiency has improved due to experience in production (learning). But she notes that unit costs are also directly a function of current production, and that because production ramped up over time, there may be a confounding effect on the learning curve estimate. Explain briefly what she might have in mind. Which direction would the bias go- that is, is the simple regression's learning effect overestimated or underestimated? Might the bias go in either direction? If so, what would determine the direction of the bias?
   • Address the manager's concern using multiple regression to control for the confounding effect. Explain what variable or variables you add to the simple learning curve and why.
   • Interpret the results and compare the estimated learning effect with what was obtained in #1 (simple regression). Include and refer to regression tables. Was the manager correct? Explain what is going on.
3. Yet another look
   • An engineer suggests that learning occurs simply with the passage of time, not with output experience. Add a regressor to your regression from #2 to test his conjecture. Discuss the table of results. Is the engineer correct? Explain carefully.
   • The engineer also notes that in month 18 of this production run, the former production manager quit and was immediately replaced by "a more competent individual," who remained in the position for the remainder of the time. Is this claim reflected in the data? Explain, using another regression.

If you could separate each by number that would be helpful when answering the questions. Thank you.