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Exxon is developing a new faster pump design. They've narrowedthe development to two design options and are wondering how thepump design might affect daily gas sales (Sales). In a test in 31stations, they try out the new design A in some stations (code =2), the new design B in other stations (code = 3) and for controlthey have stations that have not had a change (code = 1). Fromprior research, Exxon knows that three other factors are crucial inpredicting gas sales for any particular station: advertising amountin the market (Ad), relative pricing (relprice), and the number ofcompeting stations and their density (compet). For any changes, 95%confidence

output: regression results tables

a) run a multiple regression analysis to assess the effect ofthe new pump designs & Interpret b) are the new designs betterthan the current design (ie leading to higher sales) c) Assume a 1%profit margin and an investment of $1M for 100 stations for thechange to a new design; will any change be profitable within thefirst year?

Data:

Store	Pump Design	Sales	ad	relprice	compet
1	2	29100	25410	1.18	9.4
2	3	25620	26400	1.14	9.4
3	1	23850	25950	1.18	9.7
4	1	25200	27010	1.20	11.9
5	2	21420	27850	1.24	13.4
6	3	21300	25090	1.46	9.6
7	1	21900	25700	1.54	9.2
8	1	23700	26670	1.48	13.6
9	2	22080	28780	1.48	14.4
10	3	21960	28350	1.48	15.3
11	1	17580	28970	1.48	15.1
12	1	19440	27440	1.66	11.8
13	2	20940	25820	1.76	12.8
14	3	19110	26130	1.88	12.4
15	1	20310	25290	2.00	9.3
16	1	20460	25440	2.14	7.9
17	2	25020	26330	2.08	7.8
18	3	22380	28780	2.02	8.4
19	1	23940	30510	1.88	9.1
20	1	25860	32740	1.70	8.8
21	2	28980	35940	1.58	9.2
22	3	24480	37740	1.50	9.8
23	1	24600	38610	1.50	10.3
24	1	26460	39190	1.44	8.8
25	2	29880	40400	1.48	8.2
26	3	29670	41330	1.46	7.5
27	1	24390	43030	1.42	7.1
28	1	25980	43930	1.40	7.2
29	2	30450	45600	1.42	8.9
30	3	32130	45870	1.42	7.7
31	1	26850	47160	1.38	7.4