Using RStudio and the following data:

Price($000)	Assessed Value	Type	Time
194.1	178.17	0	10
201.9	180.24	0	10
188.65	174.03	1	11
215.5	186.31	1	2
187.5	175.22	1	5
172	165.54	1	4
191.5	172.43	1	17
213.9	185.61	1	13
169.34	160.8	1	6
196.9	181.88	0	5
196	179.11	1	7
161.9	159.93	1	4
193	175.27	1	11
209.5	185.88	0	10
193.75	176.64	1	17
206.7	184.36	1	12
181.5	172.94	1	5
194.5	176.5	0	14
169	166.28	1	1
196.9	179.74	0	3
186.5	172.78	1	14
197.9	177.9	0	12
183	174.31	1	11
197.3	179.85	0	12
200.8	184.78	0	2
197.9	181.61	0	6
190.5	174.92	1	12
197	179.98	0	4
192	177.96	1	9
195.9	179.07	0	12

1. Boston Housing data Random sample a training data set that contains 90% of the original data points. (You may stay with the same data set from Case 2.) (i) Repeat linear regression analysis as in Case 2 Fit a regression tree (CART) on the same data; repeat all the analysis as in Case 2. What do you find comparing CART to the linear regression model fits from case 2? Now repeat previous steps for another random sample (that is, to draw another training data set with 90% of original data, and the rest 10% as testing; or you can try 80% training vs. 20% left as testing). Do you get similar results? What's your conclusion? (iv) Write a brief report including all labeled figures and tables. 1. Boston Housing data Random sample a training data set that contains 90% of the original data points. (You may stay with the same data set from Case 2.) (i) Repeat linear regression analysis as in Case 2 Fit a regression tree (CART) on the same data; repeat all the analysis as in Case 2. What do you find comparing CART to the linear regression model fits from case 2? Now repeat previous steps for another random sample (that is, to draw another training data set with 90% of original data, and the rest 10% as testing; or you can try 80% training vs. 20% left as testing). Do you get similar results? What's your conclusion? (iv) Write a brief report including all labeled figures and tables