Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 27 May 2020 15:27:42 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/May/27/t1590586141qw22t8cxtx4s4x6.htm/, Retrieved Wed, 21 Apr 2021 09:14:27 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Wed, 21 Apr 2021 09:14:27 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
0.00	3752420.00	101409.00	9858.00	47743.00	72052.00	6765061.35	959.62	547049.00	426360.00	44940096.20	242205620.32	35165438.08	322311154.60	9.00	39.00	54.00
2868911.37	433987.00	56939.00	750.00	28802.00	35103.00	1485070.50	1009.70	300749.00	218069.00	30238489.60	178583901.31	18950813.65	227773204.56	4.00	8.00	13.00
2777480.34	850131.00	21311.00	2845.00	10249.00	12802.00	604714.74	796.32	89042.00	60798.00	10066534.67	32911898.96	5265173.78	48243607.41	5.00	10.00	21.00
3084697.79	174107.00	19492.00	168.00	10001.00	8396.00	647090.46	912.12	77594.00	66217.00	8178090.83	48075670.31	4396094.59	60649855.73	1.00	5.00	6.00
1627453.48	124710.00	20915.00	108.00	10742.00	7987.00	228143.17	906.02	83325.00	71168.00	7582356.26	39429756.68	3269447.67	50281560.61	0.00	6.00	8.00
2041753.59	135372.00	7609.00	19.00	4097.00	2434.00	855592.30	966.70	30002.00	21791.00	1915468.04	12865573.11	1451560.74	16232601.89	0.00	4.00	5.00
1381396.91	87039.00	13194.00	29.00	6745.00	4904.00	736970.78	1018.39	73973.00	69578.00	4884727.64	25143507.42	2600510.50	32628745.56	0.00	4.00	4.00
1704235.70	155846.00	10413.00	29.00	5346.00	4215.00	258332.27	973.86	49256.00	35666.00	4665525.64	17881826.89	2151032.55	24698385.08	1.00	3.00	6.00
2742801.72	133504.00	9056.00	24.00	4645.00	4397.00	550011.22	986.00	50071.00	43010.00	2628361.94	13663961.02	1471367.16	17763690.12	0.00	3.00	3.00
0.00	109911.00	15316.00	132.00	7795.00	6660.00	364300.18	861.08	51716.00	43243.00	6091831.88	36254407.50	3245225.10	45591464.48	2.00	4.00	6.00
1335948.27	93173.00	25912.00	602.00	13030.00	11679.00	798013.21	964.90	96251.00	86248.00	10621555.13	63259467.47	6208365.04	80089387.64	0.00	0.00	2.00
2062873.99	1258025.00	50237.00	2419.00	24280.00	33127.00	1766691.22	980.85	237848.00	178372.00	21561966.00	107391552.03	14624713.00	143578231.03	5.00	24.00	32.00
1526437.47	41530.00	6205.00	3.00	3195.00	2105.00	814797.18	897.21	23504.00	18253.00	2740835.95	15133586.21	1146659.95	19021082.11	0.00	0.00	1.00
783414.09	373488.00	30356.00	1231.00	16036.00	11770.00	890272.98	1049.26	165529.00	121785.00	17540769.12	60452823.66	7283554.18	85277146.96	0.00	8.00	10.00
1939633.32	94962.00	15535.00	28.00	7960.00	5995.00	241471.63	871.91	43118.00	37321.00	5779817.76	30478816.22	3066129.67	39324763.65	2.00	4.00	7.00
1874737.91	121417.00	12537.00	93.00	6590.00	4981.00	594254.03	962.43	43467.00	33212.00	5013336.67	19503131.45	2325065.71	26841533.83	0.00	4.00	5.00
0.00	152831.00	28525.00	542.00	14453.00	12840.00	445522.48	862.66	80458.00	65402.00	12682498.79	43883742.87	5561321.91	62127563.57	1.00	7.00	11.00
1190823.79	207638.00	19635.00	186.00	10087.00	9142.00	731546.85	1039.85	124556.00	97774.00	8154249.85	41937180.25	4554553.58	54645983.68	0.00	7.00	7.00
1732300.40	327427.00	63795.00	6518.00	32724.00	30489.00	1965416.25	1027.84	310639.00	271231.00	23468485.32	130130775.77	15705036.25	169304297.34	1.00	10.00	13.00
766026.62	225152.00	32657.00	309.00	16483.00	16092.00	713451.57	1069.23	214727.00	173940.00	14326868.26	75149977.87	8527510.88	98004357.01	1.00	6.00	8.00
2186330.10	140114.00	22377.00	322.00	11662.00	7884.00	1001526.86	1003.27	118218.00	104416.00	7837535.97	40330306.46	3941292.55	52109134.98	1.00	5.00	7.00
2162424.62	344374.00	26363.00	1074.00	13369.00	12180.00	939656.64	921.02	77768.00	62436.00	9659945.22	47941983.89	4414705.62	62016634.73	2.00	9.00	12.00
1158521.14	76298.00	7443.00	25.00	4011.00	2427.00	186929.03	891.17	21756.00	16694.00	3351668.13	18087221.89	1385054.52	22823944.54	1.00	1.00	4.00
1308524.73	555382.00	72694.00	872.00	36928.00	44040.00	1955652.30	1039.16	394787.00	290521.00	38152838.08	226196388.55	23789202.65	288138429.28	5.00	12.00	19.00
3724300.59	45266.00	13312.00	40.00	6786.00	6284.00	1221956.76	1014.65	55683.00	53704.00	4277504.77	30882759.22	4069506.60	39229770.59	0.00	2.00	2.00
1110802.80	213523.00	19078.00	102.00	10053.00	8096.00	559947.23	982.25	74794.00	56302.00	8604193.98	46111259.81	3955625.33	58671079.12	0.00	9.00	9.00
3226671.02	112231.00	9228.00	16.00	4958.00	2497.00	741378.48	800.68	25517.00	19028.00	3479586.11	13911554.50	1291805.23	18682945.84	0.00	2.00	3.00
1612684.81	377346.00	58029.00	2181.00	29166.00	24872.00	1643630.30	827.76	164027.00	157589.00	18754494.45	104124108.21	12460231.38	135338834.04	2.00	23.00	25.00
2285325.92	345172.00	52294.00	1321.00	27233.00	32567.00	1284241.39	1044.16	261130.00	203015.00	23707344.94	140978187.00	15160518.89	179846050.83	2.00	10.00	12.00
2294231.34	57412.00	2626.00	0.00	1323.00	791.00	130335.21	746.00	6693.00	5296.00	944807.49	3835098.89	376223.68	5156130.06	0.00	2.00	2.00
2903221.09	121046.00	9501.00	39.00	4863.00	4036.00	433243.51	929.54	32663.00	25041.00	3761200.84	14846621.57	1829985.16	20437807.57	0.00	6.00	7.00
2718452.55	327848.00	16666.00	241.00	8662.00	7111.00	351501.10	1019.88	87210.00	64428.00	8532025.30	45868709.45	3908643.19	58309377.94	4.00	7.00	12.00
1954143.38	89132.00	20311.00	34.00	10736.00	6905.00	708513.67	906.00	76748.00	63588.00	8025803.73	32013553.36	3440167.43	43479524.52	0.00	7.00	8.00
1041392.45	50167.00	4320.00	6.00	2249.00	1427.00	172609.70	838.33	10247.00	7936.00	1521094.47	7924900.83	591157.36	10037152.66	0.00	1.00	2.00
2208825.11	91568.00	15925.00	50.00	8015.00	5957.00	1524395.16	1062.88	98113.00	91288.00	6833614.18	36127333.98	3662821.25	46623769.41	0.00	5.00	5.00
1178431.88	113212.00	15509.00	15.00	7982.00	5683.00	481232.97	833.85	47132.00	42222.00	4809331.47	24383686.92	2499635.56	31692653.95	1.00	7.00	8.00
4998743.54	140112.00	17458.00	1436.00	8930.00	9548.00	545338.51	895.70	81323.00	69461.00	2980306.66	24692493.63	2590635.55	30263435.84	0.00	6.00	8.00
1128951.96	176890.00	24892.00	732.00	12218.00	12292.00	487751.50	907.81	87383.00	76348.00	9569274.17	49732007.32	5371659.48	64672940.97	1.00	0.00	5.00
576728.74	35177.00	6014.00	10.00	3172.00	1809.00	203361.62	968.42	18420.00	14285.00	2513848.70	13367436.54	991704.06	16872989.30	0.00	2.00	3.00
921261.35	211206.00	15687.00	61.00	8195.00	6590.00	143799.81	1074.21	66290.00	48327.00	7133022.80	38833668.16	3295864.50	49262555.46	1.00	7.00	8.00
1792001.85	262611.00	44325.00	2426.00	22938.00	17855.00	1288322.83	1053.11	228679.00	187428.00	20205379.08	120558410.26	10730676.07	151494465.41	2.00	10.00	14.00
1201875.48	303900.00	40981.00	610.00	21520.00	18589.00	1106895.45	1035.29	305846.00	242635.00	22376339.88	124340914.55	14677361.91	161394616.34	2.00	8.00	11.00
1393102.53	207471.00	31737.00	3141.00	16434.00	12800.00	1929751.60	927.92	132882.00	108359.00	14073887.21	81871054.52	7359329.37	103304271.10	1.00	1.00	4.00
3622411.55	256961.00	38787.00	1119.00	19187.00	18114.00	971933.91	1083.09	129145.00	119988.00	13334979.17	73738655.59	8948926.83	96022561.59	1.00	11.00	14.00
3660747.87	783677.00	74385.00	882.00	39023.00	37728.00	1777601.97	883.38	551200.00	447046.00	37320912.58	210317981.74	25420486.31	273059380.63	3.00	19.00	25.00
2080740.09	790116.00	74639.00	906.00	39234.00	37749.00	1780312.83	1062.80	555194.00	450423.00	37364612.75	210387441.94	25398709.54	273150764.23	3.00	18.00	24.00
2677082.97	166366.00	38109.00	445.00	19453.00	15281.00	2267679.16	960.64	122012.00	126550.00	11203641.30	62292350.93	7392173.02	80888165.25	0.00	6.00	6.00
5682692.91	220194.00	22460.00	479.00	11595.00	8370.00	732998.39	953.16	117287.00	95944.00	9740804.99	51247858.67	5069148.40	66057812.06	0.00	8.00	8.00
14470815.30	2131167.00	100841.00	18564.00	51554.00	74767.00	2848562.00	951.45	303809.00	224509.00	45654474.38	269200358.81	34609952.11	349464785.30	12.00	34.00	51.00
2086512.95	2189393.00	99148.00	18605.00	50671.00	73349.00	2892061.95	987.88	298857.00	221746.00	45210345.62	266570496.19	34270196.16	346051037.97	12.00	33.00	50.00
2315043.69	2127946.00	97912.00	17706.00	50053.00	72547.00	2835524.07	1035.26	299672.00	221573.00	44361013.20	261655948.37	33669620.23	339686581.80	12.00	34.00	51.00
3541493.80	85566.00	6175.00	19.00	3403.00	1498.00	690051.24	994.58	26236.00	20324.00	2780784.36	10882570.69	1003162.20	14666517.25	0.00	2.00	4.00
1495743.63	275017.00	36909.00	6213.00	19090.00	17131.00	1086314.58	1010.74	158680.00	127739.00	16179231.04	95029847.49	9084833.51	120293912.04	2.00	6.00	9.00
1796075.10	152443.00	23439.00	2874.00	11901.00	9921.00	946452.90	983.55	95861.00	84846.00	10607861.07	61338976.69	5644571.28	77591409.04	0.00	2.00	3.00
814360.46	228263.00	30461.00	2217.00	15598.00	13129.00	963448.75	906.91	132826.00	108924.00	13516054.33	79310698.15	7160044.96	99986797.44	0.00	3.00	6.00
0.00	90598.00	23875.00	68.00	12185.00	10277.00	449005.62	644.00	83641.00	76900.00	8180522.36	42518933.45	4668195.05	55367650.86	1.00	5.00	6.00
2607759.09	53716.00	9904.00	7.00	5265.00	2079.00	628710.15	762.42	21687.00	17743.00	3194524.72	16363861.55	925250.94	20483637.21	0.00	0.00	0.00
838812.66	30388.00	4844.00	3.00	2548.00	1789.00	354998.49	944.23	12056.00	10298.00	1921887.59	9646451.23	957754.12	12526092.94	0.00	0.00	0.00
2142257.86	117135.00	21999.00	130.00	10845.00	9693.00	354385.86	874.44	50450.00	45935.00	7510335.47	43772999.46	4109697.45	55393032.38	1.00	5.00	6.00
4374046.05	2668951.00	125682.00	13533.00	62122.00	86933.00	6008295.50	1028.28	568455.00	449848.00	50600146.99	274246992.20	40050748.43	364897887.62	10.00	38.00	50.00
1066570.41	84560.00	8116.00	3.00	4155.00	2771.00	453107.80	904.37	31649.00	24629.00	2525387.00	10451378.35	1083541.32	14060306.67	0.00	3.00	3.00
1171192.33	152772.00	25730.00	154.00	13378.00	12366.00	322039.63	1002.64	121619.00	100402.00	9605950.87	49378411.31	5464280.96	64448643.14	0.00	1.00	3.00
2365988.04	122635.00	17072.00	266.00	8533.00	7450.00	447409.73	844.73	61096.00	58770.00	5879487.69	30351893.62	3403035.98	39634417.29	0.00	6.00	7.00
1959140.52	145311.00	6699.00	16.00	3453.00	2513.00	182613.51	981.16	25874.00	19764.00	1801024.54	12394743.11	1513863.19	15709630.84	0.00	4.00	6.00
1750883.41	185397.00	18998.00	42.00	9830.00	6898.00	503010.76	1112.00	64311.00	50971.00	6033581.76	31548238.00	2421253.88	40003073.64	1.00	12.00	14.00
1394790.92	108421.00	23972.00	2306.00	11995.00	10723.00	1010550.93	986.94	94008.00	83536.00	10279619.00	60144995.22	5719229.02	76143843.24	2.00	2.00	4.00
787392.36	21235.00	3320.00	3.00	1636.00	1386.00	608255.48	1011.03	22193.00	21255.00	2084130.61	8313723.79	974795.89	11372650.29	0.00	0.00	0.00
1539196.30	200894.00	20153.00	46.00	10537.00	7808.00	584269.14	972.25	83167.00	71475.00	7830296.03	40294033.95	3168524.98	51292854.96	1.00	3.00	4.00
803162.96	49231.00	11909.00	9.00	6120.00	4783.00	524245.53	872.25	49609.00	47305.00	4105312.36	21462248.26	2238667.64	27806228.26	0.00	0.00	0.00
4279847.57	479203.00	62834.00	10406.00	32129.00	32702.00	2067257.03	1026.97	264611.00	248369.00	22349399.56	123051164.07	15640790.64	161041354.27	3.00	12.00	15.00
1606008.71	111150.00	5528.00	3.00	2914.00	1650.00	166505.25	956.33	19940.00	16223.00	1372142.59	9258384.81	997726.43	11628253.83	0.00	2.00	3.00
833546.19	58457.00	18473.00	35.00	9444.00	9142.00	524273.47	842.38	57494.00	54225.00	8132948.85	44848155.81	4806661.40	57787766.06	0.00	1.00	1.00
1158397.26	33120.00	8110.00	11.00	4091.00	3769.00	463759.69	875.05	26721.00	24478.00	3819167.16	20313303.55	2178513.46	26310984.17	0.00	2.00	2.00
1509865.07	73014.00	12146.00	72.00	6192.00	4972.00	600653.33	923.26	24520.00	21744.00	4105562.64	21132464.63	2241702.24	27479729.51	1.00	4.00	5.00
1166034.95	116638.00	26794.00	441.00	13326.00	12236.00	1087374.62	1025.80	74605.00	73798.00	9617054.62	57185482.88	5590615.93	72393153.43	1.00	6.00	10.00
3379670.02	207209.00	10870.00	148.00	5177.00	5974.00	126778.15	903.41	33889.00	27061.00	3939565.49	19815977.15	1955762.41	25711305.05	1.00	8.00	10.00
1013967.52	161487.00	29782.00	4149.00	15243.00	13854.00	2047769.52	1101.14	104261.00	102510.00	10836338.57	54601499.50	6276946.19	71714784.26	0.00	4.00	4.00
1411718.15	114603.00	12611.00	12.00	6671.00	3886.00	453056.88	932.73	41329.00	32845.00	4341900.60	22163388.19	1594205.19	28099493.98	0.00	4.00	6.00
3150961.81	379665.00	41467.00	2350.00	21732.00	18741.00	980896.65	862.63	264299.00	206163.00	22082462.68	130088768.11	12462497.14	164633727.93	1.00	8.00	10.00
1301232.52	65706.00	5774.00	8.00	3129.00	1838.00	301540.97	882.06	26429.00	20869.00	2480840.93	10090024.92	1025090.87	13595956.72	0.00	2.00	2.00
2475909.24	175017.00	20755.00	187.00	10580.00	8861.00	694143.66	1026.54	66990.00	61400.00	7371813.97	40952986.11	4900563.06	53225363.14	2.00	9.00	12.00
4951169.77	277039.00	21141.00	1858.00	10447.00	13421.00	560689.48	976.69	117535.00	87086.00	3623989.65	30822730.20	3385932.63	37832652.48	0.00	5.00	5.00
2754103.97	94028.00	28698.00	1400.00	14375.00	13074.00	1414723.68	957.40	129499.00	127770.00	12923980.12	68319325.43	7330123.59	88573429.14	0.00	2.00	2.00
676856.45	303718.00	60853.00	6468.00	31035.00	28033.00	1826107.73	964.59	242384.00	218100.00	20798992.54	115739064.05	13720601.48	150258658.07	3.00	15.00	18.00
1525088.29	149432.00	11384.00	40.00	5855.00	4659.00	187989.49	998.55	46560.00	40550.00	3958506.91	20408161.82	2189047.00	26555715.73	2.00	5.00	7.00
1806979.03	912680.00	28826.00	1036.00	13610.00	19803.00	821029.07	986.83	136762.00	109105.00	13918033.97	71036148.25	9001357.86	93955540.08	3.00	18.00	22.00
3116409.44	176423.00	23299.00	445.00	12269.00	10190.00	1159301.71	1053.42	113183.00	99309.00	8369875.49	43092289.41	4649973.30	56112138.20	0.00	4.00	5.00
4270223.89	179047.00	23866.00	446.00	12524.00	10471.00	1150115.30	1077.30	118457.00	103512.00	8664015.63	44635144.62	4823512.25	58122672.50	0.00	4.00	5.00
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2250919.78	75339.00	18503.00	120.00	9367.00	7051.00	504525.93	963.47	94922.00	88499.00	7387393.19	44462872.72	4135218.91	55985484.82	0.00	1.00	2.00
4642570.68	98331.00	21828.00	359.00	11201.00	8482.00	776983.38	936.29	83104.00	70397.00	10862543.70	58234195.03	4706111.67	73802850.40	0.00	6.00	6.00
1898648.08	114130.00	23340.00	1078.00	11827.00	9349.00	839361.55	1002.98	87308.00	79675.00	9715597.32	56614314.82	5226793.44	71556705.58	0.00	3.00	3.00
3002102.08	96102.00	12964.00	82.00	6841.00	4442.00	342362.51	969.57	40648.00	32142.00	5593362.44	29676743.35	2265502.24	37535608.03	0.00	4.00	4.00
1264707.73	235479.00	41418.00	8339.00	20408.00	26890.00	2159165.60	939.86	134750.00	120994.00	17491939.88	96321274.79	13092515.88	126905730.55	0.00	4.00	4.00
3417029.29	140928.00	34777.00	417.00	17554.00	14698.00	652692.29	906.89	122099.00	114665.00	13751479.83	82482331.38	7817034.52	104050845.73	2.00	5.00	7.00
852044.89	283998.00	46193.00	1509.00	23822.00	27713.00	1124322.66	1036.60	220754.00	169412.00	20006533.51	118605211.77	12565558.90	151177304.18	3.00	8.00	11.00
3365117.62	96944.00	15350.00	115.00	7886.00	5144.00	570025.21	1071.04	55920.00	44327.00	7642537.97	26586387.83	2967085.66	37196011.46	0.00	2.00	4.00
2245969.05	109988.00	22659.00	214.00	12005.00	8900.00	1268604.26	964.43	66047.00	55127.00	8572067.14	44080563.52	3616584.24	56269214.90	0.00	5.00	6.00
5020727.62	246613.00	25714.00	204.00	13062.00	9823.00	1558030.82	1028.37	92002.00	92893.00	8072719.05	44478352.22	5203099.74	57754171.01	0.00	5.00	5.00
2218835.41	155129.00	22902.00	87.00	11564.00	8900.00	541202.64	764.67	119332.00	105934.00	9827264.42	51597449.35	5305164.40	66729878.17	1.00	4.00	6.00
1719379.25	131278.00	23343.00	1149.00	11860.00	8739.00	950302.66	920.44	109872.00	100032.00	9428216.13	49195578.38	5044068.65	63667863.16	0.00	4.00	4.00
3028729.55	2188190.00	94635.00	18743.00	48034.00	71592.00	2884699.13	850.97	271717.00	200347.00	43583785.60	257175278.89	33332923.69	334091988.18	11.00	31.00	48.00
3046916.36	112371.00	17950.00	73.00	9125.00	6739.00	1855021.97	943.57	108597.00	88541.00	8745929.60	53599949.07	4753314.16	67099192.83	0.00	2.00	3.00
760742.84	72019.00	3974.00	27.00	2041.00	1568.00	88074.42	889.73	20547.00	16770.00	1618514.22	6303625.05	736975.06	8659114.33	0.00	4.00	4.00
4103321.42	184289.00	18528.00	351.00	9532.00	7989.00	549528.05	1000.02	109011.00	86431.00	4054221.72	33318117.22	2933611.68	40305950.62	1.00	3.00	6.00
1411329.45	115351.00	11400.00	17.00	5819.00	4286.00	301524.40	963.41	34302.00	26001.00	4980604.43	26570983.73	2169763.25	33721351.41	3.00	1.00	5.00
5091179.03	304496.00	27473.00	1648.00	13402.00	14264.00	1042268.34	1017.91	92639.00	70156.00	11013800.99	41630898.26	5895900.45	58540599.70	2.00	7.00	9.00
1637087.58	68584.00	16905.00	15.00	8798.00	6312.00	1244226.66	947.00	82577.00	67524.00	7528204.77	30099018.86	3364598.27	40991821.90	0.00	1.00	2.00
13890908.64	3685928.00	119773.00	11574.00	57214.00	83974.00	7068035.34	995.46	626774.00	485957.00	52029950.91	280990921.85	40832321.90	373853194.66	10.00	46.00	63.00
737851.91	57158.00	10264.00	122.00	5446.00	3986.00	460567.30	957.04	34913.00	32523.00	4106619.35	21231969.75	1729571.45	27068160.55	0.00	0.00	0.00
2658583.68	73782.00	13441.00	142.00	7019.00	4426.00	899539.22	1023.18	44451.00	34404.00	5079803.21	19926281.67	2029683.40	27035768.28	0.00	1.00	2.00
256739.99	138189.00	23458.00	75.00	12072.00	9181.00	433923.26	964.20	104794.00	85590.00	8245401.69	42826226.83	4285771.03	55357399.55	1.00	7.00	10.00
5453032.81	3461979.00	83024.00	6706.00	38454.00	58233.00	5727546.55	1005.09	470202.00	368727.00	38661053.85	207910424.08	29705063.72	276276541.65	9.00	37.00	52.00
1758795.53	23356.00	5352.00	6.00	2738.00	2338.00	541497.38	0.00	20119.00	19119.00	2029224.92	10477415.62	1137235.12	13643875.66	0.00	0.00	0.00
2053431.27	20856.00	4581.00	19.00	2377.00	1045.00	719648.92	0.00	10403.00	8679.00	1605133.69	6542510.71	571462.03	8719106.43	0.00	0.00	0.00
1082052.84	36332.00	5094.00	0.00	2673.00	1771.00	441757.01	0.00	19593.00	15878.00	2227515.41	9208663.08	998912.91	12435091.40	0.00	2.00	2.00




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in mysum$coefficients[i, 1] : subscript out of bounds
Execution halted

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & 
Error in mysum$coefficients[i, 1] : subscript out of bounds
Execution halted
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Engine error message[/C][C]
Error in mysum$coefficients[i, 1] : subscript out of bounds
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in mysum$coefficients[i, 1] : subscript out of bounds
Execution halted



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')