"Using Python" Finding a regression model.

A cost estimation for a construction company has collected the data found in the file Construction.xlsx describing the total cost (Y) of 97 different projects and the following five independent variables thought to exert relative influence on the total cost: regular or premium wages paid (X1), total units of work required (X2), constructed units of work per day (X3), level of equipment required (X4), city/location of word (X5). The cost estimator would like to develop a regression model to predicts the total cos of a project as a function of these five independent variables.

a) Prepare five scatter plots showing the relation of the independent variables and dependent variables.

b) Which independent variables would you consider in your regression model? Build a regression model.

c) Build a regression model based on X2 and X5. Then compare this regression model withthe one you built in the section b.

Construction.xlsx

Wage - Regular (0) or Premium (1)	Units of Work	Contracted Units per Day	Level of Equipment Required	City	Total Cost
0	50	5	2	1	83680
1	25	2	3	1	73604
0	55	1	2	1	101562
0	68	3	2	1	91055
1	35	3	2	1	41790
0	24	2	2	1	75770
1	12	2	4	1	37420
0	20	1	2	1	58000
1	48	2	2	1	97800
0	36	2	3	1	73960
0	40	1	2	1	98720
1	39	4	2	1	54190
0	26	1	1	1	67800
1	25	1	4	1	66760
0	70	3	3	2	88055
0	36	2	2	2	68045
0	68	5	2	2	104580
0	68	2	1	2	93780
0	12	2	2	2	30000
0	12	1	2	2	53900
0	50	2	2	2	90800
0	180	10	2	2	259420
0	70	5	2	2	107385
0	212	6	2	2	274755
1	150	5	2	2	212800
1	30	2	2	2	74700
0	2	0.2	2	2	16564
1	56	4	1	2	92952
0	10	0.5	2	3	25634
1	15	2	2	3	67280
0	160	5	2	3	182055
0	64	5	2	3	68168
0	35	1	2	3	48240
0	33	1	2	3	45487
1	18	4	2	3	45140
0	150	8	2	3	104000
0	6	2	2	3	4240
0	30	1	2	3	45860
0	65	3	3	3	48920
1	15	2	2	3	57060
0	80	2	2	3	89150
0	75	4	2	3	86250
0	12	1	3	3	65210
0	68	3	3	4	91450
0	10	2	2	4	31160
0	48	3	2	4	90140
0	75	4	3	4	72000
1	10	1	2	4	28668
1	50	5	2	4	83600
0	40	1	3	4	107742
0	36	3	2	4	42400
0	60	5	2	4	57350
1	36	2	2	4	58460
0	102	3	3	4	105680
0	75	3	2	4	93000
1	24	0.5	3	4	55495
0	40	2	3	5	56875
0	75	3	2	5	92460
0	40	1	2	5	45800
0	43	5	2	5	57650
0	40	2	2	5	68500
0	12	0.5	2	5	23000
0	18	2	2	5	48770
0	65	2	5	5	68925
0	100	5	2	5	105000
1	28	4	2	5	68530
0	53	2	3	5	79800
0	100	5	2	5	68160
0	150	5	2	5	185060
0	35	3	4	5	59800
0	80	1	3	5	79128
0	48	2	2	5	50670
0	120	3	2	5	154628
0	210	6	4	5	186500
1	48	2	2	5	59760
0	10	5	2	5	35600
0	20	1	3	5	46425
0	25	1	4	5	45200
1	140	3	2	6	90000
0	25	2	1	6	49560
0	45	2	2	6	56870
0	75	3	2	6	96200
0	42	4	4	6	56550
0	60	5	2	6	95420
0	80	2	2	6	101545
0	12	1	2	6	28153
0	35	1	2	6	68520
0	75	3	3	6	97822
0	68	5	4	6	86250
0	60	3	3	6	87230
0	44	1	2	6	45920
0	62	4	3	6	75910
0	60	3	5	6	86280
1	250	2	2	6	255455
0	50	2	2	6	86480
0	78	1	2	6	85240
1	36	3	2	6	45256