Using Excel, run a simple regression. where the number of cigarettes smoked is explained ONLY by the person's annual income (column D). You don't need the residuals or the plots at all so if they're checked, uncheck them. Have it put the results in N2. Interpret the coefficient on price (coefficient and p-value). b. Now run new variable in column E that is annual income squared. Run a second regression where Cigarettes smoked is explained by income and income2 . Put the results in N22. Interpret the results (and don't worry about the p-value here, the squared term won't be statistically significant). c. Now run a new variable in column J that is the inverse of Miles from nearest store (so 1/Miles from Nearest Store). Run a regression where cigarettes smoked is explained by Miles from Store and the inverse of that. Put the results in N43. Interpret those results (and don't worry about the p-value here, the inverse term won't be statistically significant. d. One last regression! Run a regression where the cigarettes smoked is explained by the information campaign. What kind of variable is the information campaign? Put the results in N64. Interpret the coefficient

Cigarettes Smoked This Year	Price of cigarette	Age of Person	Annual Income (in 10,000 $s)	Annual Income (in 10,000 $s) Squared	Under 18 Law	Under 21 Law	Information Campaigns	Miles from nearest Store	1/Miles from nearest store
2050	2.65	28	1639		1	1	0	7
2080	2.6	15	1088		1	1	0	4
2150	2.664	21	1193		1	1	0	5
2150	2.921	18	1635		1	1	0	6
1999	2.58	19	1732		1	1	0	4
1900	2.58	19	1534		1	0	0	4
1800	2.774	17	1765		1	0	0	4
1560	1.92	16	1161		1	1	0	5
1449	1.71	16	1010		1	1	0	3
1375	1.837	19	1191		1	0	0	5
1270	1.88	23	930		1	0	0	6
1250	2.15	30	984		1	0	0	3
1235	1.894	29	1112		1	1	0	5
1170	1.928	33	600		1	1	0	8
1180	1.83	15	733		1	0	0	3
1155	1.767	31	794		1	0	0	4
1110	1.63	30	867		1	0	1	3
1139	1.68	32	750		1	0	1	4
995	1.725	15	923		1	0	0	3
995	1.5	30	743		1	0	0	4
975	1.43	15	752		1	0	0	3
975	1.36	15	696		1	0	0	4
900	1.4	31	731		1	0	1	2
960	1.573	32	768		1	0	0	6
860	1.385	15	653		1	0	0	2
1695	2.931	43	1142		1	0	1	3
1553	2.2	43	1035		1	0	0	4
1300	2	15	1076		1	1	0	3
1020	1.478	68	626		1	0	1	3
1020	1.713	45	600		1	0	1	4
922	1.326	15	668		1	0	0	4
925	1.05	15	553		1	0	1	2
899	1.464	15	566		1	1	0	2
850	1.19	56	600		1	0	0	1
890	1.746	15	591		1	0	0	2
870	1.28	15	599		1	0	0	1
700	1.215	15	477		1	0	0	3
720	1.121	61	398		1	0	0	4
749	1.733	58	656		1	0	0	6
731	1.299	15	585		1	0	0	6
725	1.14	15	490		1	0	1	3
670	1.181	15	440		1	0	0	4
2150	2.848	19	1487		1	1	0	6
1599	2.44	15	1265		1	1	0	5
1350	2.253	38	939		1	1	0	4
1299	2.743	40	1232		1	1	1	5
1250	2.18	32	1141		1	0	1	4
1239	1.706	29	810		1	0	0	4
1200	1.948	15	899		1	0	0	4
1125	1.71	31	800		1	1	0	4
1100	1.657	15	865		1	0	0	4
1080	2.2	41	1076		1	0	0	4
1050	1.68	28	875		1	0	0	4
1049	1.9	49	690		1	0	0	3
955	1.565	15	648		1	1	0	3
934	1.543	35	820		1	0	0	3
875	1.173	21	456		1	0	0	4
889	1.549	15	723		1	0	0	4
855	1.9	15	780		1	0	0	3
835	1.56	15	638		1	0	1	5
810	1.365	15	673		1	0	0	2
805	1.258	22	821		1	0	1	4
799	1.314	15	671		1	0	0	2
750	1.338	15	649		1	0	1	3
759	0.997	19	461		1	0	0	4
750	1.03	15	486		1	0	0	1
730	1.027	15	427		1	0	0	3
729	1.007	34	513		1	0	0	6
710	1.083	37	504		1	0	0	4
670	1.35	15	622		1	0	0	2
619	0.837	15	342		1	0	0	2
1295	3.75	15	1200		0	1	1	4
975	1.5	22	700		0	1	1	3
939	1.428	55	701		0	0	0	2
820	1.375	15	585		0	0	0	1
780	1.08	15	600		0	1	0	3
770	0.9	15	391		0	0	0	3
700	1.505	15	591		0	0	1	2
540	1.142	15	223		0	0	0	1
1070	1.464	15	376		0	0	0	2
2100	2.116	40	1209		0	1	0	3
725	1.28	15	447		0	0	0	3
660	1.159	15	225		0	0	0	1
580	1.051	30	426		0	0	0	2
1844	2.25	55	915		0	1	0	6
1580	2.563	15	1189		0	1	0	2
699	1.4	60	481		0	1	1	1
1160	1.72	20	867		0	0	0	4
1109	1.74	19	816		0	0	0	3
1129	1.7	21	725		0	0	0	4
1050	1.62	21	800		0	0	0	4
1045	1.63	21	750		0	0	0	4
1050	1.92	23	944		0	0	0	4
1020	1.606	20	811		0	0	0	4
1000	1.535	22	668		0	0	1	5
1030	1.54	21	826		0	0	1	2
975	1.739	28	880		0	0	0	3
950	1.715	15	900		0	0	0	3
940	1.305	20	647		0	0	0	3
920	1.415	22	866		0	0	0	4
945	1.58	24	810		0	0	0	3
874	1.236	18	707		0	0	0	4
872	1.229	21	721		0	0	0	3
870	1.273	19	638		0	0	0	4
869	1.165	22	694		0	0	0	4
766	1.2	22	634		0	0	1	4
739	0.97	19	541		0	0	1	4