Provide the exact linear regression coefficient outputs and regression statistics.

The data set below includes information on the quantities consumed of good: American cheese (a proxy for the quantities demanded for this good), the price per unit of this good, the price per unit of at least one related good in consumption (Ham), and a proxy for consumer income.

You will use excel to run a linear regression using this data this is provided. We don't know what the real demand is for this good, but we can use linear regression analysis to obtain a linear approximation of it. In other words, if the unknown demand for this good is

QD = F (P; PR, M)

Where QD is the quantity demanded for this good, P is the price per unit, PR is the price of the related good(s) in consumption (substitute or complement) and M is consumer income.

A linear approximation of this demand equation looks like

QD = a + b P + c PR + d M

You will use the data provided and excel to estimate the unknown parameters a, b, c, and d.

Analyze the regression results and produce useful information for decision making and provide recommendations to the suppliers of this good.

Observation	Quantity Demanded of American Cheese	Price of American Cheese	Price of ham	Average Income
1985	2906.2	2.53	1.28	29066
1986	2917.2	2.6	1.47	30759
1987	3011.7	2.69	1.54	32410
1988	2817.6	2.78	1.6	34017
1989	2728.1	2.93	1.67	36520
1990	2783.2	3.43	1.61	37403
1991	2796.9	3.44	1.59	37922
1992	2892.6	3.32	1.64	38840
1993	2944.3	3.09	1.55	41428
1994	3009.4	3.07	1.87	43133
1995	3115.1	3.07	1.94	44938
1996	3184.3	3.34	2.11	47123
1997	3225.2	3.45	2.05	49692
1998	3306.5	3.59	2.28	51855
1999	3546.2	3.81	2.32	54737
2000	3582.4	3.7	1.93	57135
2001	3653.2	3.87	1.85	58208
2002	3696.9	3.88	1.91	57852
2003	3649.4	3.77	1.89	59067
2004	3776.9	3.98	2.12	60466
2005	3750.4	3.88	2.23	63344
2006	3906.6	3.72	2.16	66570
2007	3863.5	3.84	2.25	67609
2008	4002.2	4.04	0.91	68424
2009	4105.9	3.9	0.91	67976
2010	4121	3.91	0.87	67392
2011	4068.1	4.07	0.99	69677
2012	4165.1	4.19	0.86	71274
2013	4223.6	4.17	1.01	72641