Intermediate MicroeconomicsExcel Project 1: LP. Production Function Estimation. Cost Estimation....

Question

Intermediate MicroeconomicsExcel Project 1: LP. Production Function Estimation. Cost Estimation. Excel.Directions: Type your answers on this WORD file (as best you can). Use the Excel file provided to do your Excel work. Save your file. When you post your answers online, please use the following naming structure:Excel Project 1 “your name”.docx.Excel Project 1 “your name”.xlsx.    1. Use sheets “basecase”, “tax K”, in Excel Project 1 – LP Production Cost.xlsx as posted on Canvas to answer this question.  You have two inputs to production, robotic equipment, K and labor, L. You have 10 different processes you could employ to produce your product. The K and L requirements necessary to produce one unit of your product are given in cells A:3 through K:4. The wage rate is $15 per hour and the price of using K is $7.5 per hour. You good sells for $250. a. Use the “basecase” spreadsheet. Since you have 10 processes, you have 10 process rays.  State the algeaic formulas for each of your process rays below..    In cells B:10 through K:10, calculate the per unit cost of using each process (these will be your cost coefficients).c.    If production equaled 100 units, use SOLVER to determine how much K and L use to minimize costs. What production process or processes are you employing? How much K and L are you using? What are your costs? What are your profits?d.    In the “tax K” spreadsheet, add at 15 percent tax to the price of K (cell B:7).  Re-solve your minimization problem. What production process or processes are you employing now? How much K and L are you using? What has happened to costs? What has happened to profits? Explain the economic logic behind this result.2.    Use the Production data spreadsheet in Excel Project 1 – LP Production Cost.xlsx for this question.  This data measures output, capital inputs and labor inputs for 27 primary metal manufacturing establishments in the United States.  a. In this file, in columns F, G, and H, calculate the natural log value of each of the 27 observations using the Excel function “ln”.. Run the following regression in Excel (you may need to add in the “Data Analysis Pak” – see me and I’ll help you get started.ln(Q) = ln(a) + α ln(L) + β ln(K)    You can preserve the regression output in in a separate worksheet (Sheet2 for example). Note, ln(a) is the regression constant. You should also write the equation out (as above AND in its standard exponential form) with the numerical values for ln(a), α, and β.c. What is the marginal rate of technical substitution (ΔK/ΔL)? (Round figures to out to the 100th decimal point (for example, let 10.055 = 10.06).d. Does the production function exhibit increasing, constant, or decreasing returns to scale? Explain.e. Suppose a firm increased capital inputs by 10 percent. What would happen to output? Explain.f. Suppose the price of labor is  $15.80 per unit L  and the price of capital is  $10.00 per unit K. If a firm targets an output level of 322, then what should L and K be to maximize profits? (Round figures to out to the 100th decimal point). Explain the economic logic behind this result (you can use Solver here if you want, but honestly I would solve this just using basic mathematics – you’ll understand the solution better!)3.    Use the Cost data spreadsheet in Excel Project 1 – LP Production Cost.xlsx for this question.  This data comes from a 2017 study by Acik and Baser (Journal of Transportation and Logistics) who studies economies of scale in the seaborne coal shipping business focusing on the ISDEMIR Port in Turkey.  They graciously provided me with their data.  We are not going to replicate their results but rather use their data as applied to procedures we discussed in this class.    The data measure volume of coal shipped from source port to ISDEMIR as well as the distance traveled between ports over the period 2008 to XXXXXXXXXXWe are going to use volume shipped as our measure of Q (output) and distance traveled as a proxy for real shipping costs (the idea here is that we assume the cost of shipping one unit of coal one mile is constant).  We are also going to estimate Average Cost (AC) directly (see class notes) and then calculate Marginal Cost (MC) values (see class notes) and the cost elasticity of output.a.     in column F calculate distance/volume (this is our AC measure). IN column G copy volume values again (just copy column B here).  In column H calculate volume squared..    Use Excel to estimate using regression the following    Use variable labels. Put your output starting in J2.c.    Do your estimated coefficients’ signs turn out to be as expected for a U shaped AC curve (see notes)? Explain.d.    What do your t-statistics tell you about volume’s effect on AC? Do we have reason to believe the are dis-economies of scale for larger shipping volumes? Explain.e.    Column V contains volume values from 500 to XXXXXXXXXXand column W contains the squared values of volume. In Column X and Y, use your estimated coefficients to calculate values for AC and MC (use your class notes for help). Produce a plot in Excel of these two AC and MC curves measured against volume.f.    In column Z calculate the cost elasticity of output. For what level of shipping volume to we reach minimum efficient scale?

Komalavalli · Accepted Answer

basecase
	 
		Process 1	Process 2	Process 3	Process 4	Process 5	Process 6	Process 7	Process 8	Process 9	Process 10
	L	5	6	7	8.5	10.5	13.5	17	23	32	45
	K	30	22	17	12.5	9	6.5	4.5	2.5	1.5	1
	w	15	 
	r	7.5	 
	cost coeff	300	255	232.5	221.25	225	251.25	288.75	363.75	491.25	682.5
		q1	q2	q3	q4	q5	q6	q7	q8	q9	q10
	Production	436865560.40787	63161294.3963188	94741936.5944782	110532257.693558	105268817.327198	68424734.7626787	15790331.0990798	-89478476.2281181	-268435448.684355	-536870907.368709
	min cost	-291,745,887,094	 	 
	production target	100.0000001192	=	100
												Total inputs
	L	2,184,327,802.04	378,967,766.38	663,193,556.16	939,524,190.40	1,105,322,581.94	923,733,919.30	268,435,628.68	0	0	0	6,463,505,444.89
	K	13,105,966,812.24	1,389,548,476.72	1,610,612,922.11	1,381,653,221.17	947,419,355.94	444,760,775.96	71,056,489.95	0	0	0	18,951,018,054.08
	price of the good	250
	profit	-239,085,192,079
tax K
	 
		Process 1	Process 2	Process 3	Process 4	Process 5	Process 6	Process 7	Process 8	Process 9	Process 10
	L	5	6	7	8.5	10.5	13.5	17	23	32	45
	K	30	22	17	12.5	9	6.5	4.5	2.5	1.5	1
	w	15	 
	r	8.625	 
	cost coeff	333.75	279.75	251.625	235.3125	235.125	258.5625	293.8125	366.5625	492.9375	683.625
		q1	q2	q3	q4	q5	q6	q7	q8	q9	q10
	Production	143568385.009102	82861116.2176787	126017942.37272	151048901.542643	151336613.71701	115372591.921143	61282703.1401582	-50349620.5142143	-244267626.037532	-536870907.368709
	min cost	-284,114,353,262	 	 
	production target	100	=	100
												Total inputs
	L	717,841,925.05	497,166,697.31	882,125,596.61	1,283,915,663.11	1,589,034,444.03	1,557,529,990.94	1,041,805,953.38	0	0	0	7,569,420,270.42
	K	4,307,051,550.27	1,822,944,556.79	2,142,305,020.34	1,888,111,269.28	1,362,029,523.45	749,921,847.49	275,772,164.13	0	0	0	12,548,135,931.75
	price of the good	250
	profit	-221,768,951,468
 Q2 b
	SUMMARY OUTPUT
	Regression Statistics
	Multiple R	0.97
	R Square	0.94
	Adjusted R Square	0.94
	Standard Error	0.19
	Observations	27
	ANOVA
		df	SS	MS	F	Significance F
	Regression	2	14.21	7.11	200.25	0.00
	Residual	24	0.85	0.04
	Total	26	15.06
		Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
	Intercept	1.17	0.33	3.58	0.00	0.50	1.85
	LN(L)	0.60	0.13	4.79	0.00	0.34	0.86
	LN(K)	0.38	0.09	4.40	0.00	0.20	0.55
Q2 f
	 
		Process 1	Process 2	Process 3	Process 4	Process 5	Process 6	Process 7	Process 8	Process 9	Process 10
	L	5	6	7	8.5	10.5	13.5	17	23	32	45
	K	30	22	17	12.5	9	6.5	4.5	2.5	1.5	1
	w	15.8	 
	r	10	 
	cost coeff	379	314.8	280.6	259.3	255.9	278.3	313.6	388.4	520.6	721
		q1	q2	q3	q4	q5	q6	q7	q8	q9	q10
	Production	-15558518997.56	1728724368.84	1728724368.84	1728724368.84	1728724368.84	1728724368.84	1728724368.84	1728724368.84	1728724368.84	1728724368.84
	min cost	-135,704,740,916	 	 
	production target	321.9999995232	=	322
												Total inputs
	L	- 0	10,372,346,213.04	12,101,070,581.88	14,694,157,135.14	18,151,605,872.82	23,337,778,979.34	29,388,314,270.28	39760660483.32	55319179802.88	77792596597.8	280,917,709,936.50
	K	- 0	38,031,936,114.48	29,388,314,270.28	21,609,054,610.50	15,558,519,319.56	11,236,708,397.46	7,779,259,659.78	4321810922.1	2593086553.26	1728724368.84	132,247,414,216.26
	price of the good	250
	profit	-5,760,973,878,659
Production data
	Observation Number Obs	Value Added (Output) Q	Labor Input L	Capital Input K		LN(Q)	LN(L)	LN(K)
	1	657.29	162.31	279.99		6.4881253214	5.0895080869	5.6347538882
	2	935.93	214.43	542.5		6.8415406874	5.3679833448	6.2961880854
	3	1110.65	186.44	721.51		7.0127007085	5.2281094715	6.5813462381
	4	1200.89	245.83	1167.68		7.0908182275	5.5046402401	7.0627741532
	5	1052.68	211.4	811.77		6.9590945723	5.3537520734	6.6992170488
	6	3406.02	690.61	4558.02		8.1332997332	6.537575265	8.4246435977
	7	2427.89	452.79	3069.91		7.7947778464	6.1154284418	8.0294035242
	8	4257.46	714.2	5585.01		8.3564280173	6.5711630352	8.627841502
	9	1625.19	320.54	1618.75		7.393380011	5.7700070736	7.3894095254
	10	1272.05	253.17	1562.08		7.1483850513	5.5340611998	7.3537735455
	11	1004.45	236.44	662.04		6.912195407	5.465694476	6.4953259771
	12	598.87	140.73	875.37		6.3950445462	4.946843161	6.7746466541
	13	853.1	145.04	1696.98		6.7488767739	4.9770095664	7.4366054797
	14	1165.63	240.27	1078.79		7.0610169924	5.481763291	6.9835953217
	15	1917.55	536.73	2109.34		7.5588036085	6.2854951748	7.6541303813
	16	9849.17	1564.83	13989.55		9.1951424667	7.3555324709	9.5460659013
	17	1088.27	214.62	884.24		6.9923445584	5.3688690225	6.784728519
	18	8095.63	1083.1	9119.7		8.9990796889	6.9875825788	9.1181921878
	19	3175.39	521.74	5686.99		8.0631857384	6.2571693795	8.6459363889
	20	1653.38	304.85	1701.06		7.4105769565	5.7198198524	7.4390068651
	21	5159.31	835.69	5206.36		8.5485581286	6.7282577309	8.5576362341
	22	3378.4	284	3288.72		8.1251575036	5.6489742382	8.0982537103
	23	592.85	150.77	357.32		6.3849414159	5.0157554968	5.8786317388
	24	1601.98	259.91	2031.93		7.3789956432	5.5603354172	7.6167413593
	25	2065.85	497.6	2492.98		7.633297043	6.2097965414	7.821234061
	26	2293.87	275.2	1711.74		7.7379956265	5.6174981061	7.445265676
	27	745.67	137	768.59		6.6142831431	4.9199809258	6.6445576674
Cost data
	YEAR	VOLUME	DISTANCE	PORT		AC	VOLUME	Volume squared														V	V^2	AC	MC	Eco
	2008	2095	1016	ROMANIA-CONSTANTA		0.4849642005	2095	4389025		SUMMARY OUTPUT												500	250000	0.2449417979	0.2466307406	-0.0068808921
	2011	2439	1542	ITALY-SAVONA		0.6322263223	2439	5948721														1000	1000000	-0.003374207	0.0000107274	0.999000999
	2008	2902	1016	ROMANIA-CONSTANTA		0.350103377	2902	8421604		Regression Statistics												1500	2250000	-0.0050562542	0.0000317211	1.0000000028
	2010	2969	1468	RUSIA-ROSTOV ON DON		0.4944425733	2969	8814961		Multiple R	0.55											2000	4000000	-0.0067416723	0.000056393	1
	2009	3065	1429	RUSSIA-AZOV		0.4662316476	3065	9394225		R Square	0.30											2500	6250000	-0.0084270904	0.0000881141	1
	2009	3112	1429	RUSSIA-AZOV		0.4591902314	3112	9684544		Adjusted R Square	0.29											3000	9000000	-0.0101125084	0.0001268842	1
	2010	3117	1429	RUSSIA-AZOV		0.4584536413	3117	9715689		Standard Error	0.11											3500	12250000	-0.0117979265	0.0001727036	1
	2010	3200	1468	RUSIA-ROSTOV ON DON		0.45875	3200	10240000		Observations	313											4000	16000000	-0.0134833446	0.000225572	1
	2009	3206	1429	RUSSIA-AZOV		0.4457267623	3206	10278436														4500	20250000	-0.0151687627	0.0002854896	1
	2009	3250	1429	RUSSIA-AZOV		0.4396923077	3250	10562500		ANOVA												5000	25000000	-0.0168541807	0.0003524562	1
	2010	3450	1378	UKRAINE-MARIUPOL		0.3994202899	3450	11902500			df	SS	MS	F	Significance F							5500	30250000	-0.0185395988	0.0004264721	1
	2008	3690	1016	ROMANIA-CONSTANTA		0.2753387534	3690	13616100		Regression	2	1.49	0.74	65.62	0.00							6000	36000000	-0.0202250169	0.000507537	1
	2010	3874	1429	RUSSIA-AZOV		0.3688693856	3874	15007876		Residual	310	3.52	0.01									6500	42250000	-0.021910435	0.000595651	1
	2009	3914	1429	RUSSIA-AZOV		0.3650996423	3914	15319396		Total	312	5.00										7000	49000000	-0.023595853	0.0006908142	1
	2009	3954	1208	CROTIA-PLOCE		0.3055134041	3954	15634116														7500	56250000	-0.0252812711	0.0007930265	1
	2010	4044	1355	UKRAINE-BERDYANSK		0.3350642928	4044	16353936			Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%				8000	64000000	-0.0269666892	0.000902288	1
	2009	4166	1355	UKRAINE-BERDYANSK		0.3252520403	4166	17355556		Intercept	0.25	0.01	23.85	0.00	0.23	0.27	0.23	0.27				8500	72250000	-0.0286521073	0.0010185985	1
	2009	4382	1378	UKRAINE-MARIUPOL		0.3144682793	4382	19201924		VOLUME	-0.0000033708361	0.00	-8.56	0.00	-0.00	-0.00	-0.00	-0.00				9000	81000000	-0.0303375253	0.0011419582	1
	2009	4420	1378	UKRAINE-MARIUPOL		0.3117647059	4420	19536400		Volume squared	0.0000000000141	0.00	5.66	0.00	0.00	0.00	0.00	0.00				9500	90250000	-0.0320229434	0.001272367	1
	2008	4468	1378	UKRAINE-MARIUPOL		0.3084153984	4468	19963024														10000	100000000	-0.0337083615	0.001409825	1
	2008	4500	1378	UKRAINE-MARIUPOL		0.3062222222	4500	20250000														10500	110250000	-0.0353937795	0.001554332	1
	2010	4598	1355	UKRAINE-BERDYANSK		0.2946933449	4598	21141604														11000	121000000	-0.0370791976	0.0017058882	1
	2011	4606	1355	UKRAINE-BERDYANSK		0.2941815024	4606	21215236														11500	132250000	-0.0387646157	0.0018644935	1
	2010	4633	1429	RUSSIA-AZOV		0.3084394561	4633	21464689														12000	144000000	-0.0404500338	0.002030148	1
	2008	4688	1396	RUSIA-YEİSK		0.29778157	4688	21977344														12500	156250000	-0.0421354518	0.0022028515	1
	2010	4693	1429	RUSSIA-AZOV		0.304496058	4693	22024249														13000	169000000	-0.0438208699	0.0023826042	1
	2010	4733	1429	RUSSIA-AZOV		0.3019226706	4733	22401289														13500	182250000	-0.045506288	0.002569406	1
	2010	4932	1468	RUSIA-ROSTOV ON DON		0.297648013	4932	24324624														14000	196000000	-0.0471917061	0.0027632569	1
	2009	4966	3458	BELGIUM-ANTWERPEN		0.6963350785	4966	24661156														14500	210250000	-0.0488771241	0.002964157	1
	2010	4972	1429	RUSSIA-AZOV		0.2874094932	4972	24720784														15000	225000000	-0.0505625422	0.0031721062	1
	2008	5002	1396	RUSIA-YEİSK		0.2790883647	5002	25020004														15500	240250000	-0.0522479603	0.0033871045	1
	2011	5002	1239	UKRAINE-KHERSON		0.2477009196	5002	25020004														16000	256000000	-0.0539333784	0.0036091519	1
	2008	5004	1396	RUSIA-YEİSK		0.2789768185	5004	25040016														16500	272250000	-0.0556187964	0.0038382485	1
	2008	5021	1239	UKRAINE-KHERSON		0.2467635929	5021	25210441														17000	289000000	-0.0573042145	0.0040743942	1
	2010	5085	1429	RUSSIA-AZOV		0.2810226155	5085	25857225														17500	306250000	-0.0589896326	0.004317589	1
	2010	5157	1429	RUSSIA-AZOV		0.2770990886	5157	26594649														18000	324000000	-0.0606750507	0.0045678329	1
	2010	5178	1264	UKRAINE-KERCH		0.2441096949	5178	26811684														18500	342250000	-0.0623604687	0.004825126	1
	2009	5205	1355	UKRAINE-BERDYANSK		0.260326609	5205	27092025														19000	361000000	-0.0640458868	0.0050894681	1
	2011	5228	1378	UKRAINE-MARIUPOL		0.2635807192	5228	27331984														19500	380250000	-0.0657313049	0.0053608594	1
	2010	5240	1468	RUSIA-ROSTOV ON DON		0.2801526718	5240	27457600														20000	400000000	-0.0674167229	0.0056392999	1
	2010	5259	1239	UKRAINE-KHERSON		0.2355961209	5259	27657081														20500	420250000	-0.069102141	0.0059247894	1
	2011	5291	1239	UKRAINE-KHERSON		0.2341712342	5291	27994681														21000	441000000	-0.0707875591	0.0062173281	1
	2010	5348	1429	RUSSIA-AZOV		0.2672026926	5348	28601104														21500	462250000	-0.0724729772	0.0065169159	1
	2010	5408	1239	UKRAINE-KHERSON		0.2291050296	5408	29246464														22000	484000000	-0.0741583952	0.0068235528	1
	2009	5439	1429	RUSSIA-AZOV		0.2627321199	5439	29582721														22500	506250000	-0.0758438133	0.0071372389	1
	2010	5500	1542	ITALY-SAVONA		0.2803636364	5500	30250000														23000	529000000	-0.0775292314	0.0074579741	1
	2011	5628	1417	ITALY-PIOMBINO		0.2517768301	5628	31674384														23500	552250000	-0.0792146495	0.0077857584	1
	2010	5701	1016	ROMANIA-CONSTANTA		0.1782143484	5701	32501401														24000	576000000	-0.0809000675	0.0081205918	1
	2011	5729	1468	RUSIA-ROSTOV ON DON		0.2562401815	5729	32821441														24500	600250000	-0.0825854856	0.0084624744	1
	2008	5968	1231	UKRAINE-NIKOLAYEV		0.206266756	5968	35617024														25000	625000000	-0.0842709037	0.0088114061	1
	2008	6000	1355	UKRAINE-BERDYANSK		0.2258333333	6000	36000000														25500	650250000	-0.0859563218	0.0091673869	1
	2008	6114	1378	UKRAINE-MARIUPOL		0.2253843638	6114	37380996														26000	676000000	-0.0876417398	0.0095304168	1
	2008	6178	1378	UKRAINE-MARIUPOL		0.2230495306	6178	38167684														26500	702250000	-0.0893271579	0.0099004958	1
	2011	6196	6111	COLOMBIA-BARRANQUILL		0.9862814719	6196	38390416														27000	729000000	-0.091012576	0.010277624	1
	2009	6200	1208	CROTIA-PLOCE		0.1948387097	6200	38440000														27500	756250000	-0.0926979941	0.0106618013	1
	2011	6237	6111	COLOMBIA-BARRANQUILL		0.9797979798	6237	38900169														28000	784000000	-0.0943834121	0.0110530278	1
	2011	6312	1355	UKRAINE-BERDYANSK		0.2146704689	6312	39841344														28500	812250000	-0.0960688302	0.0114513033	1
	2008	6386	1016	ROMANIA-CONSTANTA		0.1590980269	6386	40780996														29000	841000000	-0.0977542483	0.011856628	1
	2010	6770	1378	UKRAINE-MARIUPOL		0.2035450517	6770	45832900														29500	870250000	-0.0994396663	0.0122690018	1
	2010	6823	1378	UKRAINE-MARIUPOL		0.2019639455	6823	46553329														30000	900000000	-0.1011250844	0.0126884247	1
	2009	7100	1264	UKRAINE-KERCH		0.178028169	7100	50410000														30500	930250000	-0.1028105025	0.0131148968	1
	2010	7101	1355	UKRAINE-BERDYANSK		0.1908181946	7101	50424201														31000	961000000	-0.1044959206	0.0135484179	1
	2009	7457	1208	CROTIA-PLOCE		0.1619954405	7457	55606849														31500	992250000	-0.1061813386	0.0139889883	1
	2008	7693	1355	UKRAINE-BERDYANSK		0.1761341479	7693	59182249														32000	1024000000	-0.1078667567	0.0144366077	1
	2011	7718	1355	UKRAINE-BERDYANSK		0.1755636175	7718	59567524														32500	1056250000	-0.1095521748	0.0148912762	1
	2010	7724	1378	UKRAINE-MARIUPOL		0.1784049715	7724	59660176														33000	1089000000	-0.1112375929	0.0153529939	1
	2008	7756	455	EGYPT-ALEXANDRIA		0.0586642599	7756	60155536														33500	1122250000	-0.1129230109	0.0158217607	1
	2010	7814	1355	UKRAINE-BERDYANSK		0.1734067059	7814	61058596														34000	1156000000	-0.114608429	0.0162975766	1
	2009	7883	1378	UKRAINE-MARIUPOL		0.1748065457	7883	62141689														34500	1190250000	-0.1162938471	0.0167804417	1
	2011	7937	1378	UKRAINE-MARIUPOL		0.1736172357	7937	62995969														35000	1225000000	-0.1179792652	0.0172703559	1
	2010	8000	5943	VENEZUELA-PUNTA CARD		0.742875	8000	64000000														35500	1260250000	-0.1196646832	0.0177673192	1
	2010	8001	1355	UKRAINE-BERDYANSK		0.1693538308	8001	64016001														36000	1296000000	-0.1213501013	0.0182713316	1
	2011	8054	1355	UKRAINE-BERDYANSK		0.1682393842	8054	64866916														36500	1332250000	-0.1230355194	0.0187823931	1
	2008	8310	455	EGYPT-ALEXANDRIA		0.0547533093	8310	69056100														37000	1369000000	-0.1247209375	0.0193005038	1
	2009	8433	1378	UKRAINE-MARIUPOL		0.1634056682	8433	71115489														37500	1406250000	-0.1264063555	0.0198256636	1
	2009	8531	1378	UKRAINE-MARIUPOL		0.161528543	8531	72777961														38000	1444000000	-0.1280917736	0.0203578725	1
	2011	8549	1378	UKRAINE-MARIUPOL		0.1611884431	8549	73085401														38500	1482250000	-0.1297771917	0.0208971306	1
	2011	8721	1355	UKRAINE-BERDYANSK		0.1553720904	8721	76055841														39000	1521000000	-0.1314626098	0.0214434378	1
	2010	9079	1378	UKRAINE-MARIUPOL		0.1517788303	9079	82428241														39500	1560250000	-0.1331480278	0.0219967941	1
	2008	9200	1378	UKRAINE-MARIUPOL		0.1497826087	9200	84640000														40000	1600000000	-0.1348334459	0.0225571995	1
	2011	9200	1378	UKRAINE-MARIUPOL		0.1497826087	9200	84640000														40500	1640250000	-0.136518864	0.0231246541	1
	2010	9212	1378	UKRAINE-MARIUPOL		0.1495874946	9212	84860944														41000	1681000000	-0.138204282	0.0236991577	1
	2008	9221	1378	UKRAINE-MARIUPOL		0.1494414922	9221	85026841														41500	1722250000	-0.1398897001	0.0242807105	1
	2009	9286	1378	UKRAINE-MARIUPOL		0.148395434	9286	86229796														42000	1764000000	-0.1415751182	0.0248693124	1
	2011	9290	1314	RUSIA-TUAPSE		0.1414424112	9290	86304100														42500	1806250000	-0.1432605363	0.0254649635	1
	2010	9343	1378	UKRAINE-MARIUPOL		0.1474900995	9343	87291649														43000	1849000000	-0.1449459543	0.0260676637	1
	2011	9400	1378	UKRAINE-MARIUPOL		0.1465957447	9400	88360000														43500	1892250000	-0.1466313724	0.026677413	1
	2010	9500	1378	UKRAINE-MARIUPOL		0.1450526316	9500	90250000														44000	1936000000	-0.1483167905	0.0272942114	1
	2011	9509	6111	COLOMBIA-BARRANQUILL		0.6426543275	9509	90421081														44500	1980250000	-0.1500022086	0.0279180589	1
	2008	9544	1314	RUSIA-TUAPSE		0.1376781224	9544	91087936														45000	2025000000	-0.1516876266	0.0285489556	1
	2010	9558	1378	UKRAINE-MARIUPOL		0.144172421	9558	91355364														45500	2070250000	-0.1533730447	0.0291869014	1
	2011	9568	1378	UKRAINE-MARIUPOL		0.1440217391	9568	91546624														46000	2116000000	-0.1550584628	0.0298318963	1
	2010	9645	1314	RUSIA-TUAPSE		0.1362363919	9645	93026025														46500	2162250000	-0.1567438809	0.0304839404	1
	2009	9647	1314	RUSIA-TUAPSE		0.1362081476	9647	93064609														47000	2209000000	-0.1584292989	0.0311430336	1
	2010	9649	1378	UKRAINE-MARIUPOL		0.1428127267	9649	93103201														47500	2256250000	-0.160114717	0.0318091759	1
	2011	9657	1179	UKRAINE-YUZHNYY		0.1220876048	9657	93257649														48000	2304000000	-0.1618001351	0.0324823673	1
	2009	9693	1179	UKRAINE-YUZHNYY		0.121634169	9693	93954249														48500	2352250000	-0.1634855532	0.0331626078	1
	2009	9700	1355	UKRAINE-BERDYANSK		0.1396907216	9700	94090000														49000	2401000000	-0.1651709712	0.0338498975	1
	2011	9702	1355	UKRAINE-BERDYANSK		0.1396619254	9702	94128804														49500	2450250000	-0.1668563893	0.0345442363	1
	2009	9705	1179	UKRAINE-YUZHNYY		0.1214837713	9705	94187025														50000	2500000000	-0.1685418074	0.0352456242	1
	2010	9719	1179	UKRAINE-YUZHNYY		0.1213087766	9719	94458961														50500	2550250000	-0.1702272254	0.0359540613	1
	2011	9800	1378	UKRAINE-MARIUPOL		0.1406122449	9800	96040000														51000	2601000000	-0.1719126435	0.0366695474	1
	2011	9842	1355	UKRAINE-BERDYANSK		0.1376752693	9842	96864964														51500	2652250000	-0.1735980616	0.0373920827	1
	2010	9877	1378	UKRAINE-MARIUPOL		0.1395160474	9877	97555129														52000	2704000000	-0.1752834797	0.0381216672	1
	2011	9895	1378	UKRAINE-MARIUPOL		0.1392622537	9895	97911025														52500	2756250000	-0.1769688977	0.0388583007	1
	2011	9899	1378	UKRAINE-MARIUPOL		0.1392059804	9899	97990201														53000	2809000000	-0.1786543158	0.0396019834	1
	2010	10000	1378	UKRAINE-MARIUPOL		0.1378	10000	100000000														53500	2862250000	-0.1803397339	0.0403527152	1
	2009	10231	1378	UKRAINE-MARIUPOL		0.1346886912	10231	104673361														54000	2916000000	-0.182025152	0.0411104961	1
	2008	10238	1264	UKRAINE-KERCH		0.1234616136	10238	104816644														54500	2970250000	-0.18371057	0.0418753261	1
	2009	10396	1314	RUSIA-TUAPSE		0.1263947672	10396	108076816														55000	3025000000	-0.1853959881	0.0426472053	1
	2010	10473	1355	UKRAINE-BERDYANSK		0.1293803113	10473	109683729														55500	3080250000	-0.1870814062	0.0434261336	1
	2010	10500	1378	UKRAINE-MARIUPOL		0.1312380952	10500	110250000														56000	3136000000	-0.1887668243	0.044212111	1
	2011	10546	1378	UKRAINE-MARIUPOL		0.1306656552	10546	111218116														56500	3192250000	-0.1904522423	0.0450051376	1
	2008	10548	1378	UKRAINE-MARIUPOL		0.1306408798	10548	111260304														57000	3249000000	-0.1921376604	0.0458052132	1
	2009	10600	1179	UKRAINE-YUZHNYY		0.1112264151	10600	112360000														57500	3306250000	-0.1938230785	0.046612338	1
	2010	10695	1378	UKRAINE-MARIUPOL		0.1288452548	10695	114383025														58000	3364000000	-0.1955084966	0.0474265119	1
	2011	10717	1378	UKRAINE-MARIUPOL		0.1285807595	10717	114854089														58500	3422250000	-0.1971939146	0.048247735	1
	2011	10814	1378	UKRAINE-MARIUPOL		0.1274274089	10814	116942596														59000	3481000000	-0.1988793327	0.0490760072	1
	2011	10855	1179	UKRAINE-YUZHNYY		0.1086135421	10855	117831025														59500	3540250000	-0.2005647508	0.0499113285	1
	2010	10877	1179	UKRAINE-YUZHNYY		0.1083938586	10877	118309129														60000	3600000000	-0.2022501688	0.0507536989	1
	2009	10910	1314	RUSIA-TUAPSE		0.1204399633	10910	119028100														60500	3660250000	-0.2039355869	0.

Intermediate Microeconomics Excel Project 1: LP. Production Function Estimation. Cost Estimation. Excel. Directions: Type your answers on this WORD file (as best you can). Use the Excel file provided...

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