Use the dataset RuralLand.xls to test for Multicollinearity. Price is observed land price per acre excluding...

Use the dataset RuralLand.xls to test for Multicollinearity. Price is observed land price per acre excluding improvements; WL is the proportion of acreage that is wooded; DA is the distance from parcel to Sarasota airport; D75 is the distance form parcel to I-75; A is acreage of parcel; and MO is month in which the parcel was sold. Price is assumed to be dependent on these other factors.

Please provide a thorough walk through on analysis and any testing done as opposed to just posting the answers.

N	Price	WL	DA	D75	A	MO
1	5556.0	1.0	12.1	4.9	36.0	33.0
2	5236.0	1.0	12.1	4.9	38.2	30.0
3	5952.0	1.0	12.0	4.9	21.0	15.0
4	7000.0	0.0	16.0	1.2	40.0	44.0
5	3750.0	0.0	15.5	3.2	40.0	43.0
6	7000.0	0.0	13.7	3.2	20.0	25.0
7	5952.0	0.0	14.5	2.5	21.0	24.0
8	2009.0	0.0	16.1	0.1	656.0	19.0
9	2583.0	1.0	15.2	3.0	60.0	18.0
10	2449.0	0.0	15.5	1.0	156.0	18.0
11	2500.0	0.5	15.2	2.0	40.0	3.0
12	3000.0	0.0	15.5	3.2	13.0	3.0
13	3704.0	0.0	13.5	2.5	27.0	3.0
14	3500.0	0.0	15.5	1.0	10.0	3.0
15	3500.0	0.0	17.5	5.4	20.0	38.0
16	4537.0	1.0	18.0	5.9	38.0	24.0
17	3700.0	0.0	17.2	5.1	5.0	3.0
18	2020.0	1.0	34.2	22.0	5.0	27.0
19	5000.0	0.0	11.1	5.1	3.5	13.0
20	4764.0	0.0	14.2	2.0	237.6	40.0
21	871.0	1.0	14.2	2.0	237.6	7.0
22	3500.0	1.0	11.1	3.1	20.0	41.0
23	15200.0	1.0	14.7	2.4	5.0	36.0
24	4767.0	0.0	12.1	4.1	30.0	22.0
25	16316.0	1.0	14.8	2.5	3.8	21.0

Expert Solution

Given the dataset, the following R-script can be used to run the regression and derive VIF using the R-package 'car' as shown below.

The results can be found below. As observed, all the VIF are less than 5. Hence, in this particular case, collinearity is not serious.

Rahul Sunny answered 2 years ago

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