Question

1. Using any data sets, run two multiple regression equations. state the dependent and independent variable ( you need to start with at least three and end with at least two) and how you believe they will be related. Run the regression equation until you get to the final model. Then test for the assumptions and interpret the necessary statistics. (use excel Megastat).

Please select from any of the data sets.

Real Estate Data

Price	Bedrooms	Size	Pool	Distance	Twnship	Garage	Baths
263.1	4	2300	0	17	5	1	2
182.4	4	2100	1	19	4	0	2
242.1	3	2300	1	12	3	0	2
213.6	2	2200	1	16	2	0	2.5
139.9	2	2100	1	28	1	0	1.5
245.4	2	2100	0	12	1	1	2
327.2	6	2500	1	15	3	1	2
271.8	2	2100	1	9	2	1	2.5
221.1	3	2300	0	18	1	0	1.5
266.6	4	2400	1	13	4	1	2
292.4	4	2100	1	14	3	1	2
209	2	1700	1	8	4	1	1.5
270.8	6	2500	1	7	4	1	2
246.1	4	2100	1	18	3	1	2
194.4	2	2300	1	11	3	0	2
281.3	3	2100	1	16	2	1	2
172.7	4	2200	0	16	3	0	2
207.5	5	2300	0	21	4	0	2.5
198.9	3	2200	0	10	4	1	2
209.3	6	1900	0	15	4	1	2
252.3	4	2600	1	8	4	1	2
192.9	4	1900	0	14	2	1	2.5
209.3	5	2100	1	20	5	0	1.5
345.3	8	2600	1	9	4	1	2
326.3	6	2100	1	11	5	1	3
173.1	2	2200	0	21	5	1	1.5
187	2	1900	1	26	4	0	2
257.2	2	2100	1	9	4	1	2
233	3	2200	1	14	3	1	1.5
180.4	2	2000	1	11	5	0	2
234	2	1700	1	19	3	1	2
207.1	2	2000	1	11	5	1	2
247.7	5	2400	1	16	2	1	2
166.2	3	2000	0	16	2	1	2
177.1	2	1900	1	10	5	1	2
182.7	4	2000	0	14	4	0	2.5
216	4	2300	1	19	2	0	2
312.1	6	2600	1	7	5	1	2.5
199.8	3	2100	1	19	3	1	2
273.2	5	2200	1	16	2	1	3
206	3	2100	0	9	3	0	1.5
232.2	3	1900	0	16	1	1	1.5
198.3	4	2100	0	19	1	1	1.5
205.1	3	2000	0	20	4	0	2
175.6	4	2300	0	24	4	1	2
307.8	3	2400	0	21	2	1	3
269.2	5	2200	1	8	5	1	3
224.8	3	2200	1	17	1	1	2.5
171.6	3	2000	0	16	4	0	2
216.8	3	2200	1	15	1	1	2
192.6	6	2200	0	14	1	0	2
236.4	5	2200	1	20	3	1	2
172.4	3	2200	1	23	3	0	2
251.4	3	1900	1	12	2	1	2
246	6	2300	1	7	3	1	3
147.4	6	1700	0	12	1	0	2
176	4	2200	1	15	1	1	2
228.4	3	2300	1	17	5	1	1.5
166.5	3	1600	0	19	3	0	2.5
189.4	4	2200	1	24	1	1	2
312.1	7	2400	1	13	3	1	3
289.8	6	2000	1	21	3	1	3
269.9	5	2200	0	11	4	1	2.5
154.3	2	2000	1	13	2	0	2
222.1	2	2100	1	9	5	1	2
209.7	5	2200	0	13	2	1	2
190.9	3	2200	0	18	3	1	2
254.3	4	2500	0	15	3	1	2
207.5	3	2100	0	10	2	0	2
209.7	4	2200	0	19	2	1	2
294	2	2100	1	13	2	1	2.5
176.3	2	2000	0	17	3	0	2
294.3	7	2400	1	8	4	1	2
224	3	1900	0	6	1	1	2
125	2	1900	1	18	4	0	1.5
236.8	4	2600	0	17	5	1	2
164.1	4	2300	1	19	4	0	2
217.8	3	2500	1	12	3	0	2
192.2	2	2400	1	16	2	0	2.5
125.9	2	2400	1	28	1	0	1.5
220.9	2	2300	0	12	1	1	2
294.5	6	2700	1	15	3	1	2
244.6	2	2300	1	9	2	1	2.5
199	3	2500	0	18	1	0	1.5
240	4	2600	1	13	4	1	2
263.2	4	2300	1	14	3	1	2
188.1	2	1900	1	8	4	1	1.5
243.7	6	2700	1	7	4	1	2
221.5	4	2300	1	18	3	1	2
175	2	2500	1	11	3	0	2
253.2	3	2300	1	16	2	1	2
155.4	4	2400	0	16	3	0	2
186.7	5	2500	0	21	4	0	2.5
179	3	2400	0	10	4	1	2
188.3	6	2100	0	15	4	1	2
227.1	4	2900	1	8	4	1	2
173.6	4	2100	0	14	2	1	2.5
188.3	5	2300	1	20	5	0	1.5
310.8	8	2900	1	9	4	1	2
293.7	6	2400	1	11	5	1	3
179	3	2400	1	8	4	1	2
188.3	6	2100	0	14	2	1	2.5
227.1	4	2900	1	20	5	0	1.5
173.6	4	2100	1	9	4	1	2
188.3	5	2300	1	11	5	1	3

Baseball2012 Data

Team	League	Opened	Age	Seating Capacity	Salary 2012	Wins	Attendance	BA	ERA	HR	Errors	SB
San Diego Padres	0	2004	10	42691	55.2	76	2.12	0.247	4.01	121	121	155
Houston Astros	0	2000	14	40981	60.7	55	1.61	0.236	4.56	146	118	105
Pittsburgh Pirates	0	2001	13	38362	63.4	79	2.09	0.243	3.86	170	112	73
Arizona Diamondbacks	0	1998	16	48633	74.3	81	2.18	0.259	3.93	165	90	93
Colorado Rockies	0	1995	19	50398	78.1	64	2.63	0.274	5.22	166	122	100
Washington Nationals	0	2008	6	41487	81.3	98	2.37	0.261	3.33	194	94	105
Cincinnati Reds	0	2003	11	42319	82.2	97	2.35	0.251	3.34	172	89	87
Atlanta Braves	0	1996	18	49586	83.3	94	2.42	0.247	3.42	149	86	101
Chicago Cubs	0	1914	100	41009	88.2	61	2.88	0.24	4.51	137	105	94
New York Mets	0	2009	5	41922	93.4	95	2.24	0.249	4.09	139	101	79
Los Angeles Dodgers	0	1962	52	56000	95.1	86	3.32	0.252	3.34	116	98	104
Milwaukee Brewers	0	2001	13	41900	97.7	83	2.83	0.259	4.22	202	99	158
St. Louis Cardinals	0	2006	8	43975	110.3	88	3.26	0.271	3.71	159	107	91
San Francisco Giants	0	2000	14	41915	117.6	94	3.38	0.269	3.68	103	115	118
Miami Marlins	0	2012	2	36742	118.1	69	2.22	0.244	4.09	137	103	149
Philadelphia Phillies	0	2004	10	43651	174.5	81	3.57	0.255	3.83	158	101	116
Oakland Athletics	1	1966	48	35067	55.4	94	1.68	0.238	3.48	195	111	122
Kansas City Royals	1	1973	41	37903	60.9	72	1.74	0.265	4.3	131	113	132
Tampa Bay Rays	1	1990	24	34078	64.2	90	1.56	0.24	3.19	175	114	134
Toronto Blue Jays	1	1989	25	49260	75.5	73	2.1	0.245	4.64	198	101	123
Cleveland Indians	1	1994	20	43429	78.4	68	1.6	0.251	4.78	136	96	110
Baltimore Orioles	1	1992	22	45971	81.4	93	2.1	0.247	3.9	214	106	58
Seattle Mariners	1	1999	15	47860	82	75	1.72	0.234	3.76	149	72	104
Minnesota Twins	1	2010	4	39504	94.1	66	2.78	0.26	4.77	131	107	135
Chicago White Sox	1	1991	23	40615	96.9	85	1.97	0.255	4.02	211	70	109
Texas Rangers	1	1994	20	48194	120.5	93	3.46	0.273	3.99	200	85	91
Detroit Tigers	1	2000	14	41255	132.3	88	3.03	0.268	3.75	163	99	59
Los Angeles Angels	1	1966	48	45957	154.5	89	3.06	0.274	4.02	187	98	134
Boston Red Sox	1	1912	102	37495	173.2	69	3.04	0.26	4.7	165	101	97
New York Yankees	1	2009	5	50287	198	74	3.54	0.265	3.85	245	74	93

Data Set 3 --Buena School District Bus Data
Bus Number	Maintenance	Maint	Age	Age med	Miles	Type	Type-Dum	Bus-Mfg	Passenger
982	441	0	1	0	823	Diesel	0	Bluebird	55 Passenger
279	390	0	2	0	792	Diesel	0	Bluebird	55 Passenger
695	477	1	2	0	802	Diesel	0	Bluebird	55 Passenger
686	329	0	3	0	741	Diesel	0	Bluebird	55 Passenger
101	424	0	4	0	827	Diesel	0	Bluebird	55 Passenger
814	426	0	4	0	757	Diesel	0	Bluebird	55 Passenger
554	458	1	4	0	817	Diesel	0	Bluebird	14 Passenger
918	390	0	5	0	799	Diesel	0	Bluebird	55 Passenger
725	392	0	5	0	774	Diesel	0	Bluebird	55 Passenger
731	432	0	6	0	819	Diesel	0	Bluebird	42 Passenger
321	450	0	6	0	856	Diesel	0	Bluebird	6 Passenger
358	461	1	6	0	849	Diesel	0	Bluebird	55 Passenger
75	478	1	6	0	821	Diesel	0	Bluebird	55 Passenger
135	329	0	7	0	853	Diesel	0	Bluebird	55 Passenger
507	410	0	7	0	866	Diesel	0	Bluebird	55 Passenger
714	433	0	7	0	817	Diesel	0	Bluebird	42 Passenger
57	455	0	7	0	828	Diesel	0	Bluebird	55 Passenger
768	494	1	7	1	815	Diesel	0	Bluebird	42 Passenger
977	501	1	7	1	874	Diesel	0	Bluebird	55 Passenger
887	357	0	8	1	760	Diesel	0	Bluebird	6 Passenger
984	392	0	8	1	851	Diesel	0	Bluebird	55 Passenger
692	469	1	8	1	812	Diesel	0	Bluebird	55 Passenger
704	503	1	8	1	857	Diesel	0	Bluebird	55 Passenger
884	381	0	9	1	882	Diesel	0	Bluebird	55 Passenger
326	433	0	9	1	848	Diesel	0	Bluebird	55 Passenger
875	489	1	9	1	858	Diesel	0	Bluebird	55 Passenger
418	504	1	9	1	842	Diesel	0	Bluebird	55 Passenger
953	423	0	10	1	835	Diesel	0	Bluebird	55 Passenger
954	476	1	10	1	827	Diesel	0	Bluebird	42 Passenger
520	492	1	10	1	836	Diesel	0	Bluebird	55 Passenger
600	493	1	10	1	1008	Diesel	0	Bluebird	55 Passenger
200	505	1	10	1	822	Diesel	0	Bluebird	55 Passenger
883	436	0	2	0	785	Gasoline	1	Bluebird	55 Passenger
464	355	0	3	0	806	Gasoline	1	Bluebird	55 Passenger
540	529	1	4	0	846	Gasoline	1	Bluebird	55 Passenger
500	369	0	5	0	842	Gasoline	1	Bluebird	55 Passenger
660	337	0	6	0	819	Gasoline	1	Bluebird	55 Passenger
29	396	0	6	0	784	Gasoline	1	Bluebird	55 Passenger
39	411	0	6	0	804	Gasoline	1	Bluebird	55 Passenger
387	422	0	8	1	869	Gasoline	1	Bluebird	55 Passenger
43	439	0	9	1	832	Gasoline	1	Bluebird	55 Passenger
699	475	1	9	1	816	Gasoline	1	Bluebird	55 Passenger
40	466	1	10	1	865	Gasoline	1	Bluebird	55 Passenger
861	474	1	10	1	845	Gasoline	1	Bluebird	55 Passenger
490	497	1	10	1	859	Gasoline	1	Bluebird	55 Passenger
122	558	1	10	1	885	Gasoline	1	Bluebird	55 Passenger
482	514	1	11	1	980	Gasoline	1	Bluebird	55 Passenger
751	444	0	2	0	757	Diesel	0	Keiser	14 Passenger
705	403	0	4	0	806	Diesel	0	Keiser	42 Passenger
603	468	1	4	0	800	Diesel	0	Keiser	14 Passenger
365	462	1	6	0	799	Diesel	0	Keiser	55 Passenger
45	478	1	6	0	830	Diesel	0	Keiser	55 Passenger
767	493	1	6	0	816	Diesel	0	Keiser	55 Passenger
678	428	0	7	0	842	Diesel	0	Keiser	55 Passenger
724	448	0	8	1	790	Diesel	0	Keiser	42 Passenger
759	546	1	8	1	870	Diesel	0	Keiser	55 Passenger
989	380	0	9	1	803	Diesel	0	Keiser	55 Passenger
61	442	0	9	1	809	Diesel	0	Keiser	55 Passenger
948	452	0	9	1	831	Diesel	0	Keiser	42 Passenger
732	471	1	9	1	815	Diesel	0	Keiser	42 Passenger
120	503	1	10	1	883	Diesel	0	Keiser	42 Passenger
754	515	1	14	1	895	Diesel	0	Keiser	14 Passenger
481	382	0	3	0	818	Gasoline	1	Keiser	6 Passenger
162	406	0	3	0	798	Gasoline	1	Keiser	55 Passenger
9	414	0	4	0	864	Gasoline	1	Keiser	55 Passenger
353	449	0	4	0	817	Gasoline	1	Keiser	55 Passenger
10	427	0	5	0	780	Gasoline	1	Keiser	14 Passenger
38	432	0	6	0	837	Gasoline	1	Keiser	14 Passenger
427	359	0	7	0	751	Gasoline	1	Keiser	55 Passenger
370	459	1	8	1	826	Gasoline	1	Keiser	55 Passenger
693	469	1	9	1	775	Gasoline	1	Keiser	55 Passenger
880	474	1	9	1	857	Gasoline	1	Keiser	55 Passenger
396	457	1	2	0	815	Diesel	0	Thompson	55 Passenger
833	496	1	8	1	839	Diesel	0	Thompson	55 Passenger
398	570	1	9	1	844	Diesel	0	Thompson	14 Passenger
314	459	1	11	1	859	Diesel	0	Thompson	6 Passenger
193	540	1	11	1	847	Diesel	0	Thompson	55 Passenger
156	561	1	12	1	838	Diesel	0	Thompson	55 Passenger
168	467	1	7	0	827	Gasoline	1	Thompson	55 Passenger
671	504	1	8	1	866	Gasoline	1	Thompson	55 Passenger

Banking Chicago Data

Balance	ATM	Services	Debit	Interest	City
748	9	2	1	0	1
1501	10	1	0	0	1
740	6	3	0	0	3
1593	10	8	1	0	1
1169	6	4	0	0	4
2125	18	6	0	0	2
1554	12	6	1	0	3
1474	12	7	1	0	1
1913	6	5	0	0	1
1218	10	3	1	0	1
1006	12	4	0	0	1
2215	20	3	1	0	4
137	7	2	0	0	3
167	5	4	0	0	4
343	7	2	0	0	1
2557	20	7	1	0	4
2276	15	4	1	0	3
2144	17	3	0	0	3
1995	10	7	0	0	2
1053	8	4	1	0	3
1120	8	6	1	0	3
1746	11	2	0	0	2
1958	6	2	1	0	2
634	2	7	1	0	4
580	4	1	0	0	1
1320	4	5	1	0	1
1675	6	7	1	0	2
789	8	4	0	0	4
1784	11	5	0	0	1
1326	16	8	0	0	3
2051	14	4	1	0	4
1044	7	5	1	0	1
765	4	3	0	0	4
32	2	0	0	0	3
1266	11	7	0	0	4
2204	14	5	0	0	2
2409	16	8	0	0	2
1338	14	4	1	0	2
2076	12	5	1	0	2
1708	13	3	1	0	1
2375	12	4	0	0	2
1487	8	4	1	0	4
1125	6	4	1	0	2
2156	14	5	1	0	2
1756	13	4	0	1	2
1831	10	4	0	1	3
1622	14	6	0	1	4
1886	17	3	0	1	1
1494	11	2	0	1	1
1526	8	4	0	1	2
1838	7	5	1	1	3
1616	10	4	1	1	2
1735	12	7	0	1	3
1885	10	6	1	1	2
1790	11	4	0	1	3
1645	6	9	0	1	4
890	7	1	0	1	1
2138	18	5	0	1	4
1455	9	5	1	1	3
1989	12	3	0	1	2

International Data

x1	x2	x3	x4	x5	x6	x7	X8	X9	X10	X11	X12	X13	X14
Country	Area (KM)	G-20	Petroleum	Pop (1000's)	65 & over	Life Expectancy	Literacy %	GDP/cap	Labor force	Unemployment	Exports	Imports	Cell phones
Algeria	2,381,740	0	2	31,736	4.07	69.95	61.6	5.5	9.1	30	19.6	9.2	0.034
Argentina	2,766,890	1	1	37,385	10.42	75.26	96.2	12.9	15	15	26.5	25.2	3
Australia	7,686,850	1	1	19,357	12.5	79.87	100	23.2	9.5	6.4	69	77	6.4
Austria	83,858	0	0	8,150	15.38	77.84	98	25	3.7	5.4	63.2	65.6	4.5
Belgium	30,510	0	0	10,259	16.95	77.96	98	25.3	4.34	8.4	181.4	166	1
Brazil	8,511,965	1	1	174,469	5.45	63.24	83.3	6.5	79	7.1	55.1	55.8	4.4
Canada	9,976,140	1	1	31,592	12.77	79.56	97	24.8	16.1	6.8	272.3	238.2	4.2
China	9,596,960	1	1	1,273,111	7.11	71.62	81.5	3.6	700	10	232	197	65
Czech Republic	79	0	0	10,264	13.92	74.73	99.9	12.9	5.2	8.7	28.3	31.4	4.3
Denmark	43,094	0	1	5,352	14.85	76.72	100	25.5	2.9	5.3	50.8	43.6	1.4
Finland	337,030	0	0	5,175	15.03	77.58	100	22.9	2.6	9.8	44.4	32.7	2.2
France	547,030	1	0	59,551	16.13	78.9	99	24.4	25	9.7	325	320	11.1
Germany	357,021	1	0	83,029	16.61	77.61	99	23.4	40.5	9.9	578	505	15.3
Greece	131,940	0	1	10,623	17.72	78.59	95	17.2	4.32	11.3	15.8	33.9	0.937
Hungary	93,030	0	0	10,106	14.71	71.63	99	11.2	4.2	9.4	25.2	27.6	1.3
Iceland	103,000	0	0	278	11.81	79.52	100	24.8	0.16	2.7	2	2.2	0.066
India	3,287,590	1	1	1,029,991	4.68	62.68	52	2.2	*	*	43.1	60.8	2.93
Indonesia	1,919,440	1	2	228,437	4.63	68.27	83.8	2.9	99	17.5	64.7	40.4	1
Iran	1,648,000	0	2	66,129	4.65	69.95	72.1	6.3	17.3	14	25	15	0.265
Iraq	437,072	0	2	23,332	3.08	66.95	58	2.5	4.4	*	21.8	13.8	0
Ireland	70,280	0	0	3,840	11.35	76.99	98	21.6	1.82	4.1	73.5	45.7	2
Italy	301,230	1	0	57,680	18.35	79.14	98	22.1	23.4	10.4	241.1	231.4	20.5
Japan	377,835	1	0	126,771	17.35	80.8	99	24.9	67.7	4.7	450	355	63.9
Kuwait	17,820	0	2	2,041	2.42	76.27	78.6	15	1.3	1.8	23.2	7.6	0.21
Libya	1,759,540	0	2	5,240	3.95	75.65	76.2	8.9	1.5	30	13.9	7.6	0
Luxembourg	2,586	0	0	443	14.06	77.3	100	36.4	0.248	2.7	7.6	10	0.215
Mexico	1,972,550	1	1	101,879	4.4	71.76	89.6	9.1	39.8	2.2	168	176	2
Netherlands	41,526	0	1	15,981	13.72	78.43	99	24.4	7.2	2.6	210.3	201.2	4.1
New Zealand	286,680	0	0	3,864	11.53	77.99	99	17.7	1.88	6.3	14.6	14.3	0.6
Nigeria	923,768	0	2	126,635	2.82	51.07	57.1	0.95	66	28	22.2	10.7	0.027
Norway	324,220	0	1	4,503	15.1	78.79	100	27.7	2.4	3	59.2	35.2	2
Poland	312,685	0	0	38,634	12.44	73.42	99	8.5	17.2	12	28.4	42.7	1.8
Portugal	92,391	0	0	10,066	15.62	75.94	87.4	15.8	5	4.3	26.1	41	3
Qatar	11,437	0	2	769	2.48	72.62	79	20.3	0.233	*	9.8	3.8	0.043
Russia	17,075,200	1	1	145,470	12.81	67.34	98	7.7	66	10.5	105.1	44.2	2.5
Saudi Arabia	1,960,582	1	2	22,757	2.68	68.09	62.8	10.5	7	*	81.2	30.1	1
South Africa	1,219,912	1	0	43,586	4.88	48.09	81.1	8.5	17	30	30.8	27.6	2
South Korea	98,480	1	0	47,904	7.27	74.65	98	16.1	22	4.1	172.6	160.5	27
Spain	504,782	0	0	40,038	17.18	78.93	97	18	17	14	120.5	153.9	8.4
Sweden	449,964	0	0	8,875	17.28	79.71	99	22.2	4.4	6	95.5	80	3.8
Switzerland	41,290	0	0	7,283	15.3	79.73	99	28.6	3.9	1.9	91.3	91.6	2
Turkey	780,580	1	0	66,494	6.13	71.24	85	6.8	23	5.6	26.9	55.7	12.1
United Arab Emirates	82,880	0	2	2,407	2.4	74.29	79.2	22.8	1.4	*	46	34	1
United Kingdom	244,820	1	1	59,648	15.7	77.82	99	22.8	29.2	5.5	282	324	13
United States	9,629,091	1	1	278,059	12.61	77.26	97	36.2	140.9	4	776	1223	69
Venezuela	912,050	0	2	23,917	4.72	73.31	91.1	6.2	9.9	14	32.8	14.7	2

Variable descriptions

Real Estate Sales data

Variables

X1 = selling price in $000

X2= Number of bedrooms

X3= Size of the home in square feet

X4= Pool (1=yes, 0= no)

X5= Distance from the center of the city in miles

X6= Township

X7= Garage attached (1=yes, 0= no)

X8= Number of bathrooms

105 homes sold

Baseball Data

Variables

X1 = Team

X2= Language (American =1, National =0)

X3= Built (year stadium was built)

X4= Size (stadium capacity)

X5= Salary (total 2012 team salary, $ million)

X6= Wins

X7= Attendance (total for team in millions)

X8= BA (team batting average)

X9= ERA (Team earned run average)

X10= HR (Team home runs)

X11 = Errors (team errors)

X12= SB (team stolen bases)

X13= year

X14= Average player salary ($)

Buena School District Bus Data

Variables

X1 = Bus number

X2= Maintenance cost ($)

X3= (Age)

X4= Miles

X5= Bus type (diesel or gasoline)

X6= Bus Manufacturer (Bluebird, Keiser, Thompson)

X7= Passengers

2. Using any dataset, run an ANOVA test, and interpret the statistically significant Tukey output.

I will be glad if this two questions are answered. My previous question was not answered. Please remember to use MegastatThank you.

Answer 1

1. Using any data sets, run two multiple regression equations. state the dependent and independent variable ( you need to start with at least three and end with at least two) and how you believe they will be related. Run the regression equation until you get to the final model. Then test for the assumptions and interpret the necessary statistics. (use excel Megastat).

Real Estate Data used.

Dependent : Price

Independent: bedrooms, size, pool, distance, township, garage, baths

Model 1

Regression Analysis
	R²	0.534
	Adjusted R²	0.500	n	105
	R	0.730	k	7
	Std. Error	33.311	Dep. Var.	Price
ANOVA table
Source	SS	df	MS	F	p-value
Regression	123,136.4801	7	17,590.9257	15.85	1.01E-13
Residual	107,631.1090	97	1,109.5991
Total	230,767.5891	104
Regression output					confidence interval
variables	coefficients	std. error	t (df=97)	p-value	95% lower	95% upper
Intercept	43.1372	39.7393	1.086	.2804	-35.7342	122.0087
Bedrooms	7.3755	2.5900	2.848	.0054	2.2350	12.5160
Size	0.0386	0.0148	2.618	.0103	0.0093	0.0679
Pool	19.1114	7.1266	2.682	.0086	4.9672	33.2557
Distance	-1.0127	0.7414	-1.366	.1751	-2.4841	0.4588
Twnship	-1.7390	2.6994	-0.644	.5210	-7.0966	3.6186
Garage	35.4980	7.6758	4.625	1.16E-05	20.2636	50.7324
Baths	23.0925	9.0583	2.549	.0124	5.1143	41.0708

In this model bedrooms, size, pool, garage, baths are significant.

distance, township are not significant. For the next model this two are removed.

Model 2

Dependent : Price

Independent: bedrooms, size, pool, garage, baths

Regression Analysis
	R²	0.524
	Adjusted R²	0.500	n	105
	R	0.724	k	5
	Std. Error	33.317	Dep. Var.	Price
ANOVA table
Source	SS	df	MS	F	p-value
Regression	120,877.3239	5	24,175.4648	21.78	1.19E-14
Residual	109,890.2652	99	1,110.0027
Total	230,767.5891	104
Regression output					confidence interval
variables	coefficients	std. error	t (df=99)	p-value	95% lower	95% upper
Intercept	17.0125	35.2418	0.483	.6303	-52.9150	86.9400
Bedrooms	7.1689	2.5591	2.801	.0061	2.0910	12.2468
Size	0.0392	0.0147	2.666	.0090	0.0100	0.0683
Pool	19.1105	6.9941	2.732	.0074	5.2326	32.9883
Garage	38.8472	7.2809	5.335	6.05E-07	24.4002	53.2942
Baths	24.6236	8.9948	2.738	.0073	6.7759	42.4712

All the 5 independent variables are significant.

This is the final model. 52.4% in price is explained by the model.

The residuals plots show that there is no violation of assumptions.

2. Using any dataset, run an ANOVA test, and interpret the statistically significant Tukey output.

I will be glad if this two questions are answered. My previous question was not answered. Please remember to use MegastatThank you.

Banking Chicago Data used

Variable used = Balance and city

One factor ANOVA
	Mean	n	Std. Dev
	1,281.4	16	474.32	city1
	1,879.6	17	350.88	city2
	1,359.4	14	683.48	city3
	1,423.5	13	709.22	city4
	1,499.9	60	596.90	Total
ANOVA table
Source	SS	df	MS	F	p-value
Treatment	3,568,072.86	3	1,189,357.619	3.82	.0147
Error	17,453,360.08	56	311,667.144
Total	21,021,432.93	59
Post hoc analysis
p-values for pairwise t-tests
		city1	city3	city4	city2
		1,281.4	1,359.4	1,423.5	1,879.6
city1	1,281.4
city3	1,359.4	.7041
city4	1,423.5	.4983	.7667
city2	1,879.6	.0032	.0125	.0306
Tukey simultaneous comparison t-values (d.f. = 56)
		city1	city3	city4	city2
		1,281.4	1,359.4	1,423.5	1,879.6
city1	1,281.4
city3	1,359.4	0.38
city4	1,423.5	0.68	0.30
city2	1,879.6	3.08	2.58	2.22
critical values for experimentwise error rate:
		0.05	2.65
		0.01	3.26

Calculated F=3.82, P=0.0147 which is < 0.05 level of significance. We conclude there is significant difference in that average balances of the 4 citys.

Tukey test shows that city 1 and city 2 are significant. Other pairs of city are not significant.