The facility manager at a sports textile company wants to build a regression model to forecast monthly electricity costs. Two main variables are thought to determine electricity cost (in $): average outdoor temperature (in F) and tons of product produced. A portion of the past year's monthly data is shown in the following table.

Electricity cost ($)	Average outdoor temperature (F)	Tons of product
24100	26	80
23700	32	73
26800	46	90
27400	61	69
30200	66	75
31500	73	80
30100	81	82
29500	85	80
26800	76	78
27500	61	79
28900	51	75
26000	39	69
26600	72	87
26600	41	74
24000	84	83
27300	56	84
29300	64	84
28500	85	69
29000	66	72
30200	61	80
30800	51	88
29000	57	70
25100	54	69
30800	74	86
27800	68	82
31800	50	87
28600	46	75
29600	29	90
27600	43	81
27800	45	69
24600	37	72
24200	48	71
27800	38	78
25100	26	84
24400	60	90
29900	36	74
24500	69	83
26900	42	87
27900	77	74
30600	71	86

a. State the simple linear regression model where electricity cost is treated as the dependent variable while: i. the average outdoor temperature (in F) is the sole independent variable. ii. the tons of product produced is the sole independent variable.

b. Based on the ordinary least square estimate, form the simple linear regression equation by calculating the estimated slope (b1) and the intercept (b0) when: i. the average outdoor temperature (in F) is the sole independent variable. ii. the tons of product produced is the sole independent variable.