This sheet provides some information from a random sample of 100 solar installation projects by SD Solar during 2020.
	The company would like to find a model to estimate Electricity Generation based on 1) number of panels installed,
	2) energy score (categorical variable), 3) house size, and 4) pre-installation consumption.
	a) Create a multiple regression model to estimate electricity generation based on the above mentioned factors.
	b) Check the necessary conditions for residuals and multicollinearity. Is there any non-significant factor.
	c) Now remove all the non-significant variables and create another model with only significant factors. Is this model a better model, why?
	A sample of solar installation projects by SD SOLAR in the past year.
	Number	House Size (SQFT)	Energy Score (1-10)	Pre-install Consumption (KW)	Number of Panels installed	Electricity Generation (KW)	Cost of Installation ($)
	1	1746	L8	441	16	544	17140
	2	1093	L8	517	19	494	16100
	3	1755	L9	349	14	420	12350
	4	1745	L10	313	13	338	12300
	5	1784	L10	400	15	465	15480
	6	1721	L7	406	15	435	15170
	7	1957	L10	481	19	570	18300
	8	1254	L10	235	11	286	12280
	9	1964	L7	481	17	425	15410
	10	1634	L9	647	23	621	26220
	11	1431	L7	515	18	576	14940
	12	1733	L7	284	11	341	10080
	13	1483	L8	279	10	300	9140
	14	1438	L8	497	19	608	18850
	15	1478	L8	457	18	450	18710
	16	2103	L9	481	18	594	17370
	17	1833	L8	781	28	700	28680
	18	1684	L9	509	19	570	17220
	19	1828	L8	334	12	372	10700
	20	1974	L10	552	22	682	21610
	21	1372	L8	410	17	527	18790
	22	2160	L10	851	29	812	33700
	23	1666	L8	317	12	360	10410
	24	1866	L9	383	13	390	10500
	25	1114	L7	392	14	434	16500
	26	1729	L9	763	26	806	29460
	27	1763	L7	601	22	638	23100
	28	1561	L9	633	23	621	20040
	29	1177	L9	428	17	425	15320
	30	2093	L7	671	24	672	25950
	31	1491	L9	306	12	420	11870
	32	1498	L9	449	16	528	13300
	33	1855	L8	558	21	672	17730
	34	1437	L10	446	16	480	14760
	35	1566	L10	407	15	465	16140
	36	2005	L8	383	16	464	19030
	37	1116	L9	357	13	338	11590
	38	2068	L7	483	17	561	19450
	39	1638	L8	202	9	279	8660
	40	1686	L8	493	18	576	17930
	41	1963	L7	521	21	714	18360
	42	1512	L7	421	15	495	15210
	43	1744	L10	500	19	475	21610
	44	1650	L7	193	9	297	9790
	45	1270	L10	375	13	338	15550
	46	1738	L7	330	12	396	13000
	47	1599	L7	650	25	825	23700
	48	1235	L10	448	16	544	18130
	49	1479	L10	261	11	352	11000
	50	2008	L10	431	18	630	16440
	51	1984	L9	430	16	528	16000
	52	1835	L7	721	27	864	27680
	53	1548	L10	227	10	260	8930
	54	2016	L8	571	22	660	25370
	55	1285	L8	416	17	544	17040
	56	1990	L9	775	29	754	26370
	57	2067	L7	625	23	644	20290
	58	2265	L7	727	26	728	24290
	59	1599	L8	461	16	480	16480
	60	1773	L8	487	17	578	15470
	61	1690	L8	587	23	575	18770
	62	2064	L10	484	18	540	15340
	63	1674	L7	582	22	638	17600
	64	1866	L8	704	25	675	29230
	65	2026	L10	400	16	528	14560
	66	2083	L10	642	25	700	21580
	67	1389	L8	206	8	272	7790
	68	1881	L10	484	17	442	19420
	69	1696	L10	659	23	598	21850
	70	2041	L9	323	13	338	13560
	71	1452	L10	191	9	297	8540
	72	1482	L9	385	15	390	17760
	73	1715	L9	656	25	750	23900
	74	2148	L7	379	14	476	12300
	75	2009	L10	394	16	512	13810
	76	1659	L9	404	17	595	15640
	77	1214	L10	564	22	638	23370
	78	1472	L10	501	19	532	20050
	79	1624	L10	649	23	713	27240
	80	1638	L7	514	19	532	15680
	81	1552	L9	505	20	540	19360
	82	1608	L9	254	9	315	8700
	83	2369	L7	744	27	729	23390
	84	1118	L9	423	16	544	15300
	85	1055	L9	556	20	520	18800
	86	1712	L7	386	16	544	18310
	87	1962	L10	325	13	377	12760
	88	1551	L8	254	11	319	12650
	89	1980	L10	383	16	432	18530
	90	2184	L9	425	15	465	16130
	91	2100	L9	510	19	646	17860
	92	2300	L7	842	29	899	28800
	93	1480	L10	430	15	495	14990
	94	1045	L8	318	12	420	11550
	95	1493	L7	449	17	578	14680
	96	2077	L10	523	20	680	19260
	97	1716	L8	378	16	464	14080
	98	1962	L10	430	16	544	15520
	99	1092	L7	580	20	520	16680
	100	1836	L10	362	15	405	12710