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 computationTue, 18 Dec 2012 15:14:20 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/18/t13558616780d9kzxwpflsihpx.htm/, Retrieved Thu, 28 Mar 2024 21:53:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201625, Retrieved Thu, 28 Mar 2024 21:53:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Bouwvergunningen ...] [2008-10-20 21:37:46] [1376d48f59a7212e8dd85a587491a69b]
F   PD  [Univariate Data Series] [Bouwvergunningen ...] [2008-10-24 14:19:20] [1376d48f59a7212e8dd85a587491a69b]
-   PD    [Univariate Data Series] [] [2008-11-21 13:47:50] [1376d48f59a7212e8dd85a587491a69b]
- RMPD        [Multiple Regression] [Multiple regression] [2012-12-18 20:14:20] [fb932d62b365c77ae1a4ae64527e1257] [Current]
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Dataseries X:
46	26	95556	47.38555556
48	20	54565	24.06138889
37	24	63016	31.4825
75	25	79774	42.36388889
31	15	31258	23.94611111
18	16	52491	10.34916667
79	20	91256	85.01527778
16	18	22807	9.097222222
38	19	77411	32.36166667
24	20	48821	36.26083333
65	30	52295	44.96555556
74	37	63262	35.63166667
43	23	50466	28.43055556
42	36	62932	53.61777778
55	29	38439	39.32611111
121	35	70817	70.43305556
42	24	105965	50.30833333
102	22	73795	55.12
36	19	82043	31.62583333
50	30	74349	44.42777778
48	27	82204	46.33944444
56	26	55709	79.63194444
19	15	37137	25.46027778
32	30	70780	30.07722222
77	28	55027	40.65055556
90	24	56699	40.31722222
81	21	65911	44.92777778
55	27	56316	44.69583333
34	21	26982	29.69111111
38	30	54628	52.26388889
53	30	96750	52.61138889
48	33	53009	35.96777778
63	30	64664	56.675
25	20	36990	17.42527778
56	27	85224	67.67361111
37	25	37048	46.45972222
83	30	59635	73.48
50	20	42051	33.89555556
26	8	26998	22.49
108	24	63717	58.27638889
55	25	55071	62.27916667
41	25	40001	32.21416667
49	21	54506	38.38638889
31	21	35838	22.52944444
49	21	50838	25.86805556
96	26	86997	84.93222222
42	26	33032	21.88888889
55	30	61704	44.12083333
70	34	117986	61.59583333
39	30	56733	36.41888889
53	18	55064	35.75944444
24	4	5950	6.718888889
209	31	84607	71.57277778
17	18	32551	18.06361111
58	14	31701	27.24055556
27	20	71170	48.21861111
58	36	101773	50.01166667
114	24	101653	54.79611111
75	26	81493	58.90555556
51	22	55901	39.32833333
86	31	109104	68.08527778
77	21	114425	57.46638889
62	31	36311	40.47111111
60	26	70027	47.39861111
39	24	73713	39.46222222
35	15	40671	31.89444444
86	19	89041	31.51694444
102	28	57231	40.35694444
49	24	68608	41.94416667
35	18	59155	25.50333333
33	25	55827	33.00194444
28	20	22618	19.2975
44	25	58425	35.175
37	24	65724	40.53
33	23	56979	27.33138889
45	25	72369	53.035
57	20	79194	55.22138889
58	23	202316	29.49805556
36	22	44970	24.81055556
42	25	49319	33.43388889
30	18	36252	27.44194444
67	30	75741	76.37583333
53	22	38417	36.88833333
59	25	64102	37.56972222
25	8	56622	22.48694444
39	21	15430	30.34361111
36	22	72571	26.84277778
114	24	67271	62.83083333
54	30	43460	47.57944444
70	27	99501	32.72638889
51	24	28340	37.10027778
49	25	76013	42.27583333
42	21	37361	31.11222222
51	24	48204	47.11472222
51	24	76168	52.07861111
27	20	85168	36.25916667
29	20	125410	39.53861111
54	24	123328	52.71222222
92	40	83038	56.00083333
72	22	120087	68.565
63	31	91939	43.31861111
41	26	103646	50.71694444
111	20	29467	29.54194444
14	19	43750	12.02416667
45	15	34497	35.41472222
91	21	66477	35.53611111
29	22	71181	41.39055556
64	24	74482	52.12583333
32	19	174949	20.58666667
65	24	46765	26.11277778
42	23	90257	49.0625
55	27	51370	39.42583333
10	1	1168	6.371666667
53	24	51360	34.97972222
25	11	25162	17.1825
33	27	21067	25.35833333
66	22	58233	70.86111111
16	0	855	5.848333333
35	17	85903	46.97027778
19	8	14116	8.726111111
76	24	57637	52.41694444
35	31	94137	38.20666667
46	24	62147	21.435
29	20	62832	20.71305556
34	8	8773	10.615
25	22	63785	25.26694444
48	33	65196	53.95111111
38	33	73087	37.5725
50	31	72631	67.85333333
65	33	86281	56.04111111
72	35	162365	71.22277778
23	21	56530	38.65111111
29	20	35606	21.24166667
194	24	70111	52.63944444
114	29	92046	77.87055556
15	20	63989	14.16638889
86	27	104911	70.35388889
50	24	43448	28.6775
33	26	60029	46.68305556
50	26	38650	35.76888889
72	12	47261	21.04055556
81	21	73586	69.23111111
54	24	83042	42.32388889
63	21	37238	48.12777778
69	30	63958	54.77694444
39	32	78956	18.75194444
49	24	99518	38.72472222
67	29	111436	51.49055556
0	0	0	0
10	0	6023	4.08




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
D[t] = -0.434016427826643 + 0.244237988900703A[t] + 0.821117675389936B[t] + 0.000133857509532733C[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
D[t] =  -0.434016427826643 +  0.244237988900703A[t] +  0.821117675389936B[t] +  0.000133857509532733C[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]D[t] =  -0.434016427826643 +  0.244237988900703A[t] +  0.821117675389936B[t] +  0.000133857509532733C[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
D[t] = -0.434016427826643 + 0.244237988900703A[t] + 0.821117675389936B[t] + 0.000133857509532733C[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4340164278266433.289025-0.1320.8951980.447599
A0.2442379889007030.0359676.790700
B0.8211176753899360.1600985.12881e-060
C0.0001338575095327333.5e-053.83250.0001889.4e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.434016427826643 & 3.289025 & -0.132 & 0.895198 & 0.447599 \tabularnewline
A & 0.244237988900703 & 0.035967 & 6.7907 & 0 & 0 \tabularnewline
B & 0.821117675389936 & 0.160098 & 5.1288 & 1e-06 & 0 \tabularnewline
C & 0.000133857509532733 & 3.5e-05 & 3.8325 & 0.000188 & 9.4e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.434016427826643[/C][C]3.289025[/C][C]-0.132[/C][C]0.895198[/C][C]0.447599[/C][/ROW]
[ROW][C]A[/C][C]0.244237988900703[/C][C]0.035967[/C][C]6.7907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]B[/C][C]0.821117675389936[/C][C]0.160098[/C][C]5.1288[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]C[/C][C]0.000133857509532733[/C][C]3.5e-05[/C][C]3.8325[/C][C]0.000188[/C][C]9.4e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.4340164278266433.289025-0.1320.8951980.447599
A0.2442379889007030.0359676.790700
B0.8211176753899360.1600985.12881e-060
C0.0001338575095327333.5e-053.83250.0001889.4e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.763264080768363
R-squared0.582572056991174
Adjusted R-squared0.573994770490992
F-TEST (value)67.9203215351213
F-TEST (DF numerator)3
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.5246010031384
Sum Squared Residuals19391.1985291048

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.763264080768363 \tabularnewline
R-squared & 0.582572056991174 \tabularnewline
Adjusted R-squared & 0.573994770490992 \tabularnewline
F-TEST (value) & 67.9203215351213 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.5246010031384 \tabularnewline
Sum Squared Residuals & 19391.1985291048 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.763264080768363[/C][/ROW]
[ROW][C]R-squared[/C][C]0.582572056991174[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.573994770490992[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]67.9203215351213[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.5246010031384[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19391.1985291048[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.763264080768363
R-squared0.582572056991174
Adjusted R-squared0.573994770490992
F-TEST (value)67.9203215351213
F-TEST (DF numerator)3
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.5246010031384
Sum Squared Residuals19391.1985291048







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.3855555644.94087880265382.44467675734615
224.0613888935.0156955548594-10.9543066648594
331.482536.7447781915725-5.26227819157252
442.3638888949.0901235899387-6.72623469993871
523.9461111123.63824439191840.307866718081648
610.3491666724.1264647115077-13.7772980415077
785.0152777847.498439093046737.5168386869533
89.09722222221.3067977715165-12.2095755495165
932.3616666734.8103066532472-2.44863998324723
1036.2608333328.38510628648657.8757270435135
1144.9655555647.0750615734314-2.1095060134314
1235.6316666756.4890425083127-20.8573758383128
1328.4305555635.709176704951-7.27862114495101
1453.6177777847.80813620995455.80964157004548
1539.3261111141.9568343569489-2.63072324694889
1670.4330555667.33728612038573.09576943961428
1750.3083333343.71501431299746.59331901700264
1855.1252.42086221459172.69913778540832
1931.6258333334.9418586596014-3.31602532960144
2044.4277777846.3635852551557-1.93580747515573
2146.3394444444.46320698856411.87623745143587
2279.6319444442.049438509310137.5825059306899
2325.4602777821.49433682365293.96594095634715
2430.0772222241.4895640034208-11.4123417834208
2540.6505555648.7293808055034-8.07882524550338
2640.3172222248.8438137155915-8.52659149559151
2744.9277777845.4154141671309-0.487636387130909
2844.6958333342.70756970408571.98826362591433
2929.6911111128.72528970019810.965821409801886
3052.2638888940.792925442852311.4709634471477
3152.6113888950.09484129290062.51654759709941
3235.9677777845.4819430500956-9.51416527009562
3356.67548.24226913104048.43273086895964
3417.4252777827.0456760801054-9.62039830010544
3567.6736111146.821360578558620.8522505314414
3646.4597222234.089884059416512.3698381605835
3773.4852.453859493614321.0261405063857
3833.8955555633.82907865836820.066476901631823
3922.4916.09899772907586.39100227092415
4058.2763888954.17950951770494.09687937229512
4162.2791666740.898681753937621.3804849160624
4232.2141666735.4621172406694-3.24795057066943
4338.3863888936.07315362608762.31323526391241
4422.5294444429.1780178379179-6.64857339791789
4525.8680555635.5821642811215-9.71410872112153
4684.9322222256.007091823598328.9251303964017
4721.8888888935.5946199210264-13.7057310310265
4844.1208333345.8921469916178-1.77131366161784
4961.5958333360.37395587820941.22187745179061
5036.4188888941.3189334893194-4.90004459931939
5135.7594444434.66144504583991.09799939416014
526.7188888899.50861818906974-2.78972930006974
5371.5727777887.3916534985442-15.8188757185442
5418.0636111122.8553433333041-4.79173222330414
5527.2405555629.4708512935704-2.2302957335704
5648.2186111132.109401733735616.1092093762644
5750.0116666756.9151035601266-6.90343689012664
5854.7961111160.7229559327428-5.92684482274285
5958.9055555650.14134232421548.7642132357846
6039.3283333337.56947850507711.7588548249229
6168.0852777860.62948827478117.4557895052189
6257.4663888950.93242542899916.53396346100091
6340.4711111145.023886849748-4.55277573974802
6447.3986111144.94296228640252.45564882359745
6539.4622222238.66512794884560.797094271154436
6631.8944444425.87519708475286.01924735524722
6731.5169444448.0904929563467-16.5735485163467
6840.3569444455.1303524790311-14.7734080390311
6941.9441666740.4241652516881.520001418312
7025.5033333330.8127723171256-5.30943898712562
7133.0019444435.6266422753288-2.62469783532882
7219.297525.8545899198031-6.55708991980311
7335.17538.6610219630026-3.4860219630026
7440.5337.10726432738723.42273567261284
7527.3313888934.1386107755307-6.80722188553066
7653.03540.771769064827712.2632309351723
7755.2213888940.510614057247414.7107748327526
7829.4980555659.699009361007-30.200953801007
7924.8105555632.4427122348643-7.63215667486425
8033.4338888936.9536395033961-3.51975061339613
8127.4419444426.52584383179390.916100608206074
8276.3758333350.701960719737325.6738726102628
8336.8883333335.71758978620821.17074354379179
8437.5697222243.0845008781305-5.51477865813047
8522.4869444419.82015460257282.66678983742719
8630.3436111128.40015769457951.9434534154205
8726.8427777836.1373133554772-9.29453557547721
8862.8308333356.12066703998846.71016629001156
8947.5794444443.2058125988024.37363184119803
9032.7263888952.1517760867673-19.4253871967673
9137.1002777835.52246703562531.57781074437467
9242.2758333342.23649778516780.03933554483218
9331.1122222232.068500702844-0.956278482843972
9447.1147222238.18141260498358.93330961501647
9552.0786111141.924604001556910.1540071084431
9636.2591666733.98313915217482.27602751782516
9739.5386111139.8583090285925-0.319697918592469
9852.7122222248.97003811782263.74218410217736
9956.0008333365.9958454432145-9.99501211321454
10068.56551.290254378859817.2747456211402
10143.3186111152.7143503789356-9.39573926893558
10250.7169444444.80259611027015.91434832972987
10329.5419444447.0431330813512-17.5011886413512
10412.0241666724.442817291249-12.418650621249
10535.4147222227.49114070990477.92358151009528
10635.5361111147.9335574065335-12.3974462965335
10741.3905555634.24158549492187.14897006507822
10852.1258333344.87401409619387.25181923380618
10920.5866666746.4010724846467-25.8144058146467
11026.1127777841.4081234933758-15.2953457133758
11149.062540.79126287786738.27123712213273
11239.4258333342.0455104619368-2.61967713193677
1136.3716666672.985826707704563.38583995929544
11434.9797222239.0923428828702-4.11262066287024
11517.182518.0723503788428-0.889850378842849
11625.3583333332.6159905947509-7.25765726475091
11770.8611111141.54520405081829.315907059182
1185.8483333333.58823956523512.2600937677649
11946.9702777833.572075306717213.3982024732828
1208.72611111112.6649793689703-3.93886825797026
12152.4169444445.55004021492346.86690422507663
12238.2066666746.1699054956688-7.96323882566884
12321.43538.8265979158949-17.3915979158949
12420.7130555631.4817737970531-10.7687182370531
12510.61515.6133485290474-4.99834852904742
12625.2669444432.2746233988149-7.00767895881488
12753.9511111147.1132645187716.83784659122896
12837.572545.7271542374868-8.1546542374868
12967.8533333346.954735729168420.8985976008316
13056.0411111154.08769591858071.95341519141935
13171.2227777867.62401194695393.59876583304613
13238.6511111129.99389351396368.65721759603644
13321.2416666727.8373692425149-6.59570257251495
13452.6394444476.0398614791176-23.4004170391176
13577.8705555663.542575215611614.3279803443884
13614.1663888928.2173150909727-14.0509262009727
13770.3538888956.783753035750613.5701358542494
13828.677537.3005483007451-8.62304830074515
13946.6830555637.01022920577539.67282635422469
14035.7688888938.300535320787-2.53164643078697
14121.0405555633.3307706357297-12.2902150757297
14269.2311111146.442770552794622.7883405572054
14342.3238888943.577454488787-1.25356559878698
14448.1277777837.181033996086210.9467437839138
14554.7769444449.61319366271455.16375077728553
14618.7519444445.9358842744452-27.1839398344452
14738.7247222244.5617008713448-5.83697865134477
14851.4905555654.6588868471182-3.16833128711821
1490-0.4340164278266390.434016427826639
1504.082.814587241096041.26541275890396

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 47.38555556 & 44.9408788026538 & 2.44467675734615 \tabularnewline
2 & 24.06138889 & 35.0156955548594 & -10.9543066648594 \tabularnewline
3 & 31.4825 & 36.7447781915725 & -5.26227819157252 \tabularnewline
4 & 42.36388889 & 49.0901235899387 & -6.72623469993871 \tabularnewline
5 & 23.94611111 & 23.6382443919184 & 0.307866718081648 \tabularnewline
6 & 10.34916667 & 24.1264647115077 & -13.7772980415077 \tabularnewline
7 & 85.01527778 & 47.4984390930467 & 37.5168386869533 \tabularnewline
8 & 9.097222222 & 21.3067977715165 & -12.2095755495165 \tabularnewline
9 & 32.36166667 & 34.8103066532472 & -2.44863998324723 \tabularnewline
10 & 36.26083333 & 28.3851062864865 & 7.8757270435135 \tabularnewline
11 & 44.96555556 & 47.0750615734314 & -2.1095060134314 \tabularnewline
12 & 35.63166667 & 56.4890425083127 & -20.8573758383128 \tabularnewline
13 & 28.43055556 & 35.709176704951 & -7.27862114495101 \tabularnewline
14 & 53.61777778 & 47.8081362099545 & 5.80964157004548 \tabularnewline
15 & 39.32611111 & 41.9568343569489 & -2.63072324694889 \tabularnewline
16 & 70.43305556 & 67.3372861203857 & 3.09576943961428 \tabularnewline
17 & 50.30833333 & 43.7150143129974 & 6.59331901700264 \tabularnewline
18 & 55.12 & 52.4208622145917 & 2.69913778540832 \tabularnewline
19 & 31.62583333 & 34.9418586596014 & -3.31602532960144 \tabularnewline
20 & 44.42777778 & 46.3635852551557 & -1.93580747515573 \tabularnewline
21 & 46.33944444 & 44.4632069885641 & 1.87623745143587 \tabularnewline
22 & 79.63194444 & 42.0494385093101 & 37.5825059306899 \tabularnewline
23 & 25.46027778 & 21.4943368236529 & 3.96594095634715 \tabularnewline
24 & 30.07722222 & 41.4895640034208 & -11.4123417834208 \tabularnewline
25 & 40.65055556 & 48.7293808055034 & -8.07882524550338 \tabularnewline
26 & 40.31722222 & 48.8438137155915 & -8.52659149559151 \tabularnewline
27 & 44.92777778 & 45.4154141671309 & -0.487636387130909 \tabularnewline
28 & 44.69583333 & 42.7075697040857 & 1.98826362591433 \tabularnewline
29 & 29.69111111 & 28.7252897001981 & 0.965821409801886 \tabularnewline
30 & 52.26388889 & 40.7929254428523 & 11.4709634471477 \tabularnewline
31 & 52.61138889 & 50.0948412929006 & 2.51654759709941 \tabularnewline
32 & 35.96777778 & 45.4819430500956 & -9.51416527009562 \tabularnewline
33 & 56.675 & 48.2422691310404 & 8.43273086895964 \tabularnewline
34 & 17.42527778 & 27.0456760801054 & -9.62039830010544 \tabularnewline
35 & 67.67361111 & 46.8213605785586 & 20.8522505314414 \tabularnewline
36 & 46.45972222 & 34.0898840594165 & 12.3698381605835 \tabularnewline
37 & 73.48 & 52.4538594936143 & 21.0261405063857 \tabularnewline
38 & 33.89555556 & 33.8290786583682 & 0.066476901631823 \tabularnewline
39 & 22.49 & 16.0989977290758 & 6.39100227092415 \tabularnewline
40 & 58.27638889 & 54.1795095177049 & 4.09687937229512 \tabularnewline
41 & 62.27916667 & 40.8986817539376 & 21.3804849160624 \tabularnewline
42 & 32.21416667 & 35.4621172406694 & -3.24795057066943 \tabularnewline
43 & 38.38638889 & 36.0731536260876 & 2.31323526391241 \tabularnewline
44 & 22.52944444 & 29.1780178379179 & -6.64857339791789 \tabularnewline
45 & 25.86805556 & 35.5821642811215 & -9.71410872112153 \tabularnewline
46 & 84.93222222 & 56.0070918235983 & 28.9251303964017 \tabularnewline
47 & 21.88888889 & 35.5946199210264 & -13.7057310310265 \tabularnewline
48 & 44.12083333 & 45.8921469916178 & -1.77131366161784 \tabularnewline
49 & 61.59583333 & 60.3739558782094 & 1.22187745179061 \tabularnewline
50 & 36.41888889 & 41.3189334893194 & -4.90004459931939 \tabularnewline
51 & 35.75944444 & 34.6614450458399 & 1.09799939416014 \tabularnewline
52 & 6.718888889 & 9.50861818906974 & -2.78972930006974 \tabularnewline
53 & 71.57277778 & 87.3916534985442 & -15.8188757185442 \tabularnewline
54 & 18.06361111 & 22.8553433333041 & -4.79173222330414 \tabularnewline
55 & 27.24055556 & 29.4708512935704 & -2.2302957335704 \tabularnewline
56 & 48.21861111 & 32.1094017337356 & 16.1092093762644 \tabularnewline
57 & 50.01166667 & 56.9151035601266 & -6.90343689012664 \tabularnewline
58 & 54.79611111 & 60.7229559327428 & -5.92684482274285 \tabularnewline
59 & 58.90555556 & 50.1413423242154 & 8.7642132357846 \tabularnewline
60 & 39.32833333 & 37.5694785050771 & 1.7588548249229 \tabularnewline
61 & 68.08527778 & 60.6294882747811 & 7.4557895052189 \tabularnewline
62 & 57.46638889 & 50.9324254289991 & 6.53396346100091 \tabularnewline
63 & 40.47111111 & 45.023886849748 & -4.55277573974802 \tabularnewline
64 & 47.39861111 & 44.9429622864025 & 2.45564882359745 \tabularnewline
65 & 39.46222222 & 38.6651279488456 & 0.797094271154436 \tabularnewline
66 & 31.89444444 & 25.8751970847528 & 6.01924735524722 \tabularnewline
67 & 31.51694444 & 48.0904929563467 & -16.5735485163467 \tabularnewline
68 & 40.35694444 & 55.1303524790311 & -14.7734080390311 \tabularnewline
69 & 41.94416667 & 40.424165251688 & 1.520001418312 \tabularnewline
70 & 25.50333333 & 30.8127723171256 & -5.30943898712562 \tabularnewline
71 & 33.00194444 & 35.6266422753288 & -2.62469783532882 \tabularnewline
72 & 19.2975 & 25.8545899198031 & -6.55708991980311 \tabularnewline
73 & 35.175 & 38.6610219630026 & -3.4860219630026 \tabularnewline
74 & 40.53 & 37.1072643273872 & 3.42273567261284 \tabularnewline
75 & 27.33138889 & 34.1386107755307 & -6.80722188553066 \tabularnewline
76 & 53.035 & 40.7717690648277 & 12.2632309351723 \tabularnewline
77 & 55.22138889 & 40.5106140572474 & 14.7107748327526 \tabularnewline
78 & 29.49805556 & 59.699009361007 & -30.200953801007 \tabularnewline
79 & 24.81055556 & 32.4427122348643 & -7.63215667486425 \tabularnewline
80 & 33.43388889 & 36.9536395033961 & -3.51975061339613 \tabularnewline
81 & 27.44194444 & 26.5258438317939 & 0.916100608206074 \tabularnewline
82 & 76.37583333 & 50.7019607197373 & 25.6738726102628 \tabularnewline
83 & 36.88833333 & 35.7175897862082 & 1.17074354379179 \tabularnewline
84 & 37.56972222 & 43.0845008781305 & -5.51477865813047 \tabularnewline
85 & 22.48694444 & 19.8201546025728 & 2.66678983742719 \tabularnewline
86 & 30.34361111 & 28.4001576945795 & 1.9434534154205 \tabularnewline
87 & 26.84277778 & 36.1373133554772 & -9.29453557547721 \tabularnewline
88 & 62.83083333 & 56.1206670399884 & 6.71016629001156 \tabularnewline
89 & 47.57944444 & 43.205812598802 & 4.37363184119803 \tabularnewline
90 & 32.72638889 & 52.1517760867673 & -19.4253871967673 \tabularnewline
91 & 37.10027778 & 35.5224670356253 & 1.57781074437467 \tabularnewline
92 & 42.27583333 & 42.2364977851678 & 0.03933554483218 \tabularnewline
93 & 31.11222222 & 32.068500702844 & -0.956278482843972 \tabularnewline
94 & 47.11472222 & 38.1814126049835 & 8.93330961501647 \tabularnewline
95 & 52.07861111 & 41.9246040015569 & 10.1540071084431 \tabularnewline
96 & 36.25916667 & 33.9831391521748 & 2.27602751782516 \tabularnewline
97 & 39.53861111 & 39.8583090285925 & -0.319697918592469 \tabularnewline
98 & 52.71222222 & 48.9700381178226 & 3.74218410217736 \tabularnewline
99 & 56.00083333 & 65.9958454432145 & -9.99501211321454 \tabularnewline
100 & 68.565 & 51.2902543788598 & 17.2747456211402 \tabularnewline
101 & 43.31861111 & 52.7143503789356 & -9.39573926893558 \tabularnewline
102 & 50.71694444 & 44.8025961102701 & 5.91434832972987 \tabularnewline
103 & 29.54194444 & 47.0431330813512 & -17.5011886413512 \tabularnewline
104 & 12.02416667 & 24.442817291249 & -12.418650621249 \tabularnewline
105 & 35.41472222 & 27.4911407099047 & 7.92358151009528 \tabularnewline
106 & 35.53611111 & 47.9335574065335 & -12.3974462965335 \tabularnewline
107 & 41.39055556 & 34.2415854949218 & 7.14897006507822 \tabularnewline
108 & 52.12583333 & 44.8740140961938 & 7.25181923380618 \tabularnewline
109 & 20.58666667 & 46.4010724846467 & -25.8144058146467 \tabularnewline
110 & 26.11277778 & 41.4081234933758 & -15.2953457133758 \tabularnewline
111 & 49.0625 & 40.7912628778673 & 8.27123712213273 \tabularnewline
112 & 39.42583333 & 42.0455104619368 & -2.61967713193677 \tabularnewline
113 & 6.371666667 & 2.98582670770456 & 3.38583995929544 \tabularnewline
114 & 34.97972222 & 39.0923428828702 & -4.11262066287024 \tabularnewline
115 & 17.1825 & 18.0723503788428 & -0.889850378842849 \tabularnewline
116 & 25.35833333 & 32.6159905947509 & -7.25765726475091 \tabularnewline
117 & 70.86111111 & 41.545204050818 & 29.315907059182 \tabularnewline
118 & 5.848333333 & 3.5882395652351 & 2.2600937677649 \tabularnewline
119 & 46.97027778 & 33.5720753067172 & 13.3982024732828 \tabularnewline
120 & 8.726111111 & 12.6649793689703 & -3.93886825797026 \tabularnewline
121 & 52.41694444 & 45.5500402149234 & 6.86690422507663 \tabularnewline
122 & 38.20666667 & 46.1699054956688 & -7.96323882566884 \tabularnewline
123 & 21.435 & 38.8265979158949 & -17.3915979158949 \tabularnewline
124 & 20.71305556 & 31.4817737970531 & -10.7687182370531 \tabularnewline
125 & 10.615 & 15.6133485290474 & -4.99834852904742 \tabularnewline
126 & 25.26694444 & 32.2746233988149 & -7.00767895881488 \tabularnewline
127 & 53.95111111 & 47.113264518771 & 6.83784659122896 \tabularnewline
128 & 37.5725 & 45.7271542374868 & -8.1546542374868 \tabularnewline
129 & 67.85333333 & 46.9547357291684 & 20.8985976008316 \tabularnewline
130 & 56.04111111 & 54.0876959185807 & 1.95341519141935 \tabularnewline
131 & 71.22277778 & 67.6240119469539 & 3.59876583304613 \tabularnewline
132 & 38.65111111 & 29.9938935139636 & 8.65721759603644 \tabularnewline
133 & 21.24166667 & 27.8373692425149 & -6.59570257251495 \tabularnewline
134 & 52.63944444 & 76.0398614791176 & -23.4004170391176 \tabularnewline
135 & 77.87055556 & 63.5425752156116 & 14.3279803443884 \tabularnewline
136 & 14.16638889 & 28.2173150909727 & -14.0509262009727 \tabularnewline
137 & 70.35388889 & 56.7837530357506 & 13.5701358542494 \tabularnewline
138 & 28.6775 & 37.3005483007451 & -8.62304830074515 \tabularnewline
139 & 46.68305556 & 37.0102292057753 & 9.67282635422469 \tabularnewline
140 & 35.76888889 & 38.300535320787 & -2.53164643078697 \tabularnewline
141 & 21.04055556 & 33.3307706357297 & -12.2902150757297 \tabularnewline
142 & 69.23111111 & 46.4427705527946 & 22.7883405572054 \tabularnewline
143 & 42.32388889 & 43.577454488787 & -1.25356559878698 \tabularnewline
144 & 48.12777778 & 37.1810339960862 & 10.9467437839138 \tabularnewline
145 & 54.77694444 & 49.6131936627145 & 5.16375077728553 \tabularnewline
146 & 18.75194444 & 45.9358842744452 & -27.1839398344452 \tabularnewline
147 & 38.72472222 & 44.5617008713448 & -5.83697865134477 \tabularnewline
148 & 51.49055556 & 54.6588868471182 & -3.16833128711821 \tabularnewline
149 & 0 & -0.434016427826639 & 0.434016427826639 \tabularnewline
150 & 4.08 & 2.81458724109604 & 1.26541275890396 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]47.38555556[/C][C]44.9408788026538[/C][C]2.44467675734615[/C][/ROW]
[ROW][C]2[/C][C]24.06138889[/C][C]35.0156955548594[/C][C]-10.9543066648594[/C][/ROW]
[ROW][C]3[/C][C]31.4825[/C][C]36.7447781915725[/C][C]-5.26227819157252[/C][/ROW]
[ROW][C]4[/C][C]42.36388889[/C][C]49.0901235899387[/C][C]-6.72623469993871[/C][/ROW]
[ROW][C]5[/C][C]23.94611111[/C][C]23.6382443919184[/C][C]0.307866718081648[/C][/ROW]
[ROW][C]6[/C][C]10.34916667[/C][C]24.1264647115077[/C][C]-13.7772980415077[/C][/ROW]
[ROW][C]7[/C][C]85.01527778[/C][C]47.4984390930467[/C][C]37.5168386869533[/C][/ROW]
[ROW][C]8[/C][C]9.097222222[/C][C]21.3067977715165[/C][C]-12.2095755495165[/C][/ROW]
[ROW][C]9[/C][C]32.36166667[/C][C]34.8103066532472[/C][C]-2.44863998324723[/C][/ROW]
[ROW][C]10[/C][C]36.26083333[/C][C]28.3851062864865[/C][C]7.8757270435135[/C][/ROW]
[ROW][C]11[/C][C]44.96555556[/C][C]47.0750615734314[/C][C]-2.1095060134314[/C][/ROW]
[ROW][C]12[/C][C]35.63166667[/C][C]56.4890425083127[/C][C]-20.8573758383128[/C][/ROW]
[ROW][C]13[/C][C]28.43055556[/C][C]35.709176704951[/C][C]-7.27862114495101[/C][/ROW]
[ROW][C]14[/C][C]53.61777778[/C][C]47.8081362099545[/C][C]5.80964157004548[/C][/ROW]
[ROW][C]15[/C][C]39.32611111[/C][C]41.9568343569489[/C][C]-2.63072324694889[/C][/ROW]
[ROW][C]16[/C][C]70.43305556[/C][C]67.3372861203857[/C][C]3.09576943961428[/C][/ROW]
[ROW][C]17[/C][C]50.30833333[/C][C]43.7150143129974[/C][C]6.59331901700264[/C][/ROW]
[ROW][C]18[/C][C]55.12[/C][C]52.4208622145917[/C][C]2.69913778540832[/C][/ROW]
[ROW][C]19[/C][C]31.62583333[/C][C]34.9418586596014[/C][C]-3.31602532960144[/C][/ROW]
[ROW][C]20[/C][C]44.42777778[/C][C]46.3635852551557[/C][C]-1.93580747515573[/C][/ROW]
[ROW][C]21[/C][C]46.33944444[/C][C]44.4632069885641[/C][C]1.87623745143587[/C][/ROW]
[ROW][C]22[/C][C]79.63194444[/C][C]42.0494385093101[/C][C]37.5825059306899[/C][/ROW]
[ROW][C]23[/C][C]25.46027778[/C][C]21.4943368236529[/C][C]3.96594095634715[/C][/ROW]
[ROW][C]24[/C][C]30.07722222[/C][C]41.4895640034208[/C][C]-11.4123417834208[/C][/ROW]
[ROW][C]25[/C][C]40.65055556[/C][C]48.7293808055034[/C][C]-8.07882524550338[/C][/ROW]
[ROW][C]26[/C][C]40.31722222[/C][C]48.8438137155915[/C][C]-8.52659149559151[/C][/ROW]
[ROW][C]27[/C][C]44.92777778[/C][C]45.4154141671309[/C][C]-0.487636387130909[/C][/ROW]
[ROW][C]28[/C][C]44.69583333[/C][C]42.7075697040857[/C][C]1.98826362591433[/C][/ROW]
[ROW][C]29[/C][C]29.69111111[/C][C]28.7252897001981[/C][C]0.965821409801886[/C][/ROW]
[ROW][C]30[/C][C]52.26388889[/C][C]40.7929254428523[/C][C]11.4709634471477[/C][/ROW]
[ROW][C]31[/C][C]52.61138889[/C][C]50.0948412929006[/C][C]2.51654759709941[/C][/ROW]
[ROW][C]32[/C][C]35.96777778[/C][C]45.4819430500956[/C][C]-9.51416527009562[/C][/ROW]
[ROW][C]33[/C][C]56.675[/C][C]48.2422691310404[/C][C]8.43273086895964[/C][/ROW]
[ROW][C]34[/C][C]17.42527778[/C][C]27.0456760801054[/C][C]-9.62039830010544[/C][/ROW]
[ROW][C]35[/C][C]67.67361111[/C][C]46.8213605785586[/C][C]20.8522505314414[/C][/ROW]
[ROW][C]36[/C][C]46.45972222[/C][C]34.0898840594165[/C][C]12.3698381605835[/C][/ROW]
[ROW][C]37[/C][C]73.48[/C][C]52.4538594936143[/C][C]21.0261405063857[/C][/ROW]
[ROW][C]38[/C][C]33.89555556[/C][C]33.8290786583682[/C][C]0.066476901631823[/C][/ROW]
[ROW][C]39[/C][C]22.49[/C][C]16.0989977290758[/C][C]6.39100227092415[/C][/ROW]
[ROW][C]40[/C][C]58.27638889[/C][C]54.1795095177049[/C][C]4.09687937229512[/C][/ROW]
[ROW][C]41[/C][C]62.27916667[/C][C]40.8986817539376[/C][C]21.3804849160624[/C][/ROW]
[ROW][C]42[/C][C]32.21416667[/C][C]35.4621172406694[/C][C]-3.24795057066943[/C][/ROW]
[ROW][C]43[/C][C]38.38638889[/C][C]36.0731536260876[/C][C]2.31323526391241[/C][/ROW]
[ROW][C]44[/C][C]22.52944444[/C][C]29.1780178379179[/C][C]-6.64857339791789[/C][/ROW]
[ROW][C]45[/C][C]25.86805556[/C][C]35.5821642811215[/C][C]-9.71410872112153[/C][/ROW]
[ROW][C]46[/C][C]84.93222222[/C][C]56.0070918235983[/C][C]28.9251303964017[/C][/ROW]
[ROW][C]47[/C][C]21.88888889[/C][C]35.5946199210264[/C][C]-13.7057310310265[/C][/ROW]
[ROW][C]48[/C][C]44.12083333[/C][C]45.8921469916178[/C][C]-1.77131366161784[/C][/ROW]
[ROW][C]49[/C][C]61.59583333[/C][C]60.3739558782094[/C][C]1.22187745179061[/C][/ROW]
[ROW][C]50[/C][C]36.41888889[/C][C]41.3189334893194[/C][C]-4.90004459931939[/C][/ROW]
[ROW][C]51[/C][C]35.75944444[/C][C]34.6614450458399[/C][C]1.09799939416014[/C][/ROW]
[ROW][C]52[/C][C]6.718888889[/C][C]9.50861818906974[/C][C]-2.78972930006974[/C][/ROW]
[ROW][C]53[/C][C]71.57277778[/C][C]87.3916534985442[/C][C]-15.8188757185442[/C][/ROW]
[ROW][C]54[/C][C]18.06361111[/C][C]22.8553433333041[/C][C]-4.79173222330414[/C][/ROW]
[ROW][C]55[/C][C]27.24055556[/C][C]29.4708512935704[/C][C]-2.2302957335704[/C][/ROW]
[ROW][C]56[/C][C]48.21861111[/C][C]32.1094017337356[/C][C]16.1092093762644[/C][/ROW]
[ROW][C]57[/C][C]50.01166667[/C][C]56.9151035601266[/C][C]-6.90343689012664[/C][/ROW]
[ROW][C]58[/C][C]54.79611111[/C][C]60.7229559327428[/C][C]-5.92684482274285[/C][/ROW]
[ROW][C]59[/C][C]58.90555556[/C][C]50.1413423242154[/C][C]8.7642132357846[/C][/ROW]
[ROW][C]60[/C][C]39.32833333[/C][C]37.5694785050771[/C][C]1.7588548249229[/C][/ROW]
[ROW][C]61[/C][C]68.08527778[/C][C]60.6294882747811[/C][C]7.4557895052189[/C][/ROW]
[ROW][C]62[/C][C]57.46638889[/C][C]50.9324254289991[/C][C]6.53396346100091[/C][/ROW]
[ROW][C]63[/C][C]40.47111111[/C][C]45.023886849748[/C][C]-4.55277573974802[/C][/ROW]
[ROW][C]64[/C][C]47.39861111[/C][C]44.9429622864025[/C][C]2.45564882359745[/C][/ROW]
[ROW][C]65[/C][C]39.46222222[/C][C]38.6651279488456[/C][C]0.797094271154436[/C][/ROW]
[ROW][C]66[/C][C]31.89444444[/C][C]25.8751970847528[/C][C]6.01924735524722[/C][/ROW]
[ROW][C]67[/C][C]31.51694444[/C][C]48.0904929563467[/C][C]-16.5735485163467[/C][/ROW]
[ROW][C]68[/C][C]40.35694444[/C][C]55.1303524790311[/C][C]-14.7734080390311[/C][/ROW]
[ROW][C]69[/C][C]41.94416667[/C][C]40.424165251688[/C][C]1.520001418312[/C][/ROW]
[ROW][C]70[/C][C]25.50333333[/C][C]30.8127723171256[/C][C]-5.30943898712562[/C][/ROW]
[ROW][C]71[/C][C]33.00194444[/C][C]35.6266422753288[/C][C]-2.62469783532882[/C][/ROW]
[ROW][C]72[/C][C]19.2975[/C][C]25.8545899198031[/C][C]-6.55708991980311[/C][/ROW]
[ROW][C]73[/C][C]35.175[/C][C]38.6610219630026[/C][C]-3.4860219630026[/C][/ROW]
[ROW][C]74[/C][C]40.53[/C][C]37.1072643273872[/C][C]3.42273567261284[/C][/ROW]
[ROW][C]75[/C][C]27.33138889[/C][C]34.1386107755307[/C][C]-6.80722188553066[/C][/ROW]
[ROW][C]76[/C][C]53.035[/C][C]40.7717690648277[/C][C]12.2632309351723[/C][/ROW]
[ROW][C]77[/C][C]55.22138889[/C][C]40.5106140572474[/C][C]14.7107748327526[/C][/ROW]
[ROW][C]78[/C][C]29.49805556[/C][C]59.699009361007[/C][C]-30.200953801007[/C][/ROW]
[ROW][C]79[/C][C]24.81055556[/C][C]32.4427122348643[/C][C]-7.63215667486425[/C][/ROW]
[ROW][C]80[/C][C]33.43388889[/C][C]36.9536395033961[/C][C]-3.51975061339613[/C][/ROW]
[ROW][C]81[/C][C]27.44194444[/C][C]26.5258438317939[/C][C]0.916100608206074[/C][/ROW]
[ROW][C]82[/C][C]76.37583333[/C][C]50.7019607197373[/C][C]25.6738726102628[/C][/ROW]
[ROW][C]83[/C][C]36.88833333[/C][C]35.7175897862082[/C][C]1.17074354379179[/C][/ROW]
[ROW][C]84[/C][C]37.56972222[/C][C]43.0845008781305[/C][C]-5.51477865813047[/C][/ROW]
[ROW][C]85[/C][C]22.48694444[/C][C]19.8201546025728[/C][C]2.66678983742719[/C][/ROW]
[ROW][C]86[/C][C]30.34361111[/C][C]28.4001576945795[/C][C]1.9434534154205[/C][/ROW]
[ROW][C]87[/C][C]26.84277778[/C][C]36.1373133554772[/C][C]-9.29453557547721[/C][/ROW]
[ROW][C]88[/C][C]62.83083333[/C][C]56.1206670399884[/C][C]6.71016629001156[/C][/ROW]
[ROW][C]89[/C][C]47.57944444[/C][C]43.205812598802[/C][C]4.37363184119803[/C][/ROW]
[ROW][C]90[/C][C]32.72638889[/C][C]52.1517760867673[/C][C]-19.4253871967673[/C][/ROW]
[ROW][C]91[/C][C]37.10027778[/C][C]35.5224670356253[/C][C]1.57781074437467[/C][/ROW]
[ROW][C]92[/C][C]42.27583333[/C][C]42.2364977851678[/C][C]0.03933554483218[/C][/ROW]
[ROW][C]93[/C][C]31.11222222[/C][C]32.068500702844[/C][C]-0.956278482843972[/C][/ROW]
[ROW][C]94[/C][C]47.11472222[/C][C]38.1814126049835[/C][C]8.93330961501647[/C][/ROW]
[ROW][C]95[/C][C]52.07861111[/C][C]41.9246040015569[/C][C]10.1540071084431[/C][/ROW]
[ROW][C]96[/C][C]36.25916667[/C][C]33.9831391521748[/C][C]2.27602751782516[/C][/ROW]
[ROW][C]97[/C][C]39.53861111[/C][C]39.8583090285925[/C][C]-0.319697918592469[/C][/ROW]
[ROW][C]98[/C][C]52.71222222[/C][C]48.9700381178226[/C][C]3.74218410217736[/C][/ROW]
[ROW][C]99[/C][C]56.00083333[/C][C]65.9958454432145[/C][C]-9.99501211321454[/C][/ROW]
[ROW][C]100[/C][C]68.565[/C][C]51.2902543788598[/C][C]17.2747456211402[/C][/ROW]
[ROW][C]101[/C][C]43.31861111[/C][C]52.7143503789356[/C][C]-9.39573926893558[/C][/ROW]
[ROW][C]102[/C][C]50.71694444[/C][C]44.8025961102701[/C][C]5.91434832972987[/C][/ROW]
[ROW][C]103[/C][C]29.54194444[/C][C]47.0431330813512[/C][C]-17.5011886413512[/C][/ROW]
[ROW][C]104[/C][C]12.02416667[/C][C]24.442817291249[/C][C]-12.418650621249[/C][/ROW]
[ROW][C]105[/C][C]35.41472222[/C][C]27.4911407099047[/C][C]7.92358151009528[/C][/ROW]
[ROW][C]106[/C][C]35.53611111[/C][C]47.9335574065335[/C][C]-12.3974462965335[/C][/ROW]
[ROW][C]107[/C][C]41.39055556[/C][C]34.2415854949218[/C][C]7.14897006507822[/C][/ROW]
[ROW][C]108[/C][C]52.12583333[/C][C]44.8740140961938[/C][C]7.25181923380618[/C][/ROW]
[ROW][C]109[/C][C]20.58666667[/C][C]46.4010724846467[/C][C]-25.8144058146467[/C][/ROW]
[ROW][C]110[/C][C]26.11277778[/C][C]41.4081234933758[/C][C]-15.2953457133758[/C][/ROW]
[ROW][C]111[/C][C]49.0625[/C][C]40.7912628778673[/C][C]8.27123712213273[/C][/ROW]
[ROW][C]112[/C][C]39.42583333[/C][C]42.0455104619368[/C][C]-2.61967713193677[/C][/ROW]
[ROW][C]113[/C][C]6.371666667[/C][C]2.98582670770456[/C][C]3.38583995929544[/C][/ROW]
[ROW][C]114[/C][C]34.97972222[/C][C]39.0923428828702[/C][C]-4.11262066287024[/C][/ROW]
[ROW][C]115[/C][C]17.1825[/C][C]18.0723503788428[/C][C]-0.889850378842849[/C][/ROW]
[ROW][C]116[/C][C]25.35833333[/C][C]32.6159905947509[/C][C]-7.25765726475091[/C][/ROW]
[ROW][C]117[/C][C]70.86111111[/C][C]41.545204050818[/C][C]29.315907059182[/C][/ROW]
[ROW][C]118[/C][C]5.848333333[/C][C]3.5882395652351[/C][C]2.2600937677649[/C][/ROW]
[ROW][C]119[/C][C]46.97027778[/C][C]33.5720753067172[/C][C]13.3982024732828[/C][/ROW]
[ROW][C]120[/C][C]8.726111111[/C][C]12.6649793689703[/C][C]-3.93886825797026[/C][/ROW]
[ROW][C]121[/C][C]52.41694444[/C][C]45.5500402149234[/C][C]6.86690422507663[/C][/ROW]
[ROW][C]122[/C][C]38.20666667[/C][C]46.1699054956688[/C][C]-7.96323882566884[/C][/ROW]
[ROW][C]123[/C][C]21.435[/C][C]38.8265979158949[/C][C]-17.3915979158949[/C][/ROW]
[ROW][C]124[/C][C]20.71305556[/C][C]31.4817737970531[/C][C]-10.7687182370531[/C][/ROW]
[ROW][C]125[/C][C]10.615[/C][C]15.6133485290474[/C][C]-4.99834852904742[/C][/ROW]
[ROW][C]126[/C][C]25.26694444[/C][C]32.2746233988149[/C][C]-7.00767895881488[/C][/ROW]
[ROW][C]127[/C][C]53.95111111[/C][C]47.113264518771[/C][C]6.83784659122896[/C][/ROW]
[ROW][C]128[/C][C]37.5725[/C][C]45.7271542374868[/C][C]-8.1546542374868[/C][/ROW]
[ROW][C]129[/C][C]67.85333333[/C][C]46.9547357291684[/C][C]20.8985976008316[/C][/ROW]
[ROW][C]130[/C][C]56.04111111[/C][C]54.0876959185807[/C][C]1.95341519141935[/C][/ROW]
[ROW][C]131[/C][C]71.22277778[/C][C]67.6240119469539[/C][C]3.59876583304613[/C][/ROW]
[ROW][C]132[/C][C]38.65111111[/C][C]29.9938935139636[/C][C]8.65721759603644[/C][/ROW]
[ROW][C]133[/C][C]21.24166667[/C][C]27.8373692425149[/C][C]-6.59570257251495[/C][/ROW]
[ROW][C]134[/C][C]52.63944444[/C][C]76.0398614791176[/C][C]-23.4004170391176[/C][/ROW]
[ROW][C]135[/C][C]77.87055556[/C][C]63.5425752156116[/C][C]14.3279803443884[/C][/ROW]
[ROW][C]136[/C][C]14.16638889[/C][C]28.2173150909727[/C][C]-14.0509262009727[/C][/ROW]
[ROW][C]137[/C][C]70.35388889[/C][C]56.7837530357506[/C][C]13.5701358542494[/C][/ROW]
[ROW][C]138[/C][C]28.6775[/C][C]37.3005483007451[/C][C]-8.62304830074515[/C][/ROW]
[ROW][C]139[/C][C]46.68305556[/C][C]37.0102292057753[/C][C]9.67282635422469[/C][/ROW]
[ROW][C]140[/C][C]35.76888889[/C][C]38.300535320787[/C][C]-2.53164643078697[/C][/ROW]
[ROW][C]141[/C][C]21.04055556[/C][C]33.3307706357297[/C][C]-12.2902150757297[/C][/ROW]
[ROW][C]142[/C][C]69.23111111[/C][C]46.4427705527946[/C][C]22.7883405572054[/C][/ROW]
[ROW][C]143[/C][C]42.32388889[/C][C]43.577454488787[/C][C]-1.25356559878698[/C][/ROW]
[ROW][C]144[/C][C]48.12777778[/C][C]37.1810339960862[/C][C]10.9467437839138[/C][/ROW]
[ROW][C]145[/C][C]54.77694444[/C][C]49.6131936627145[/C][C]5.16375077728553[/C][/ROW]
[ROW][C]146[/C][C]18.75194444[/C][C]45.9358842744452[/C][C]-27.1839398344452[/C][/ROW]
[ROW][C]147[/C][C]38.72472222[/C][C]44.5617008713448[/C][C]-5.83697865134477[/C][/ROW]
[ROW][C]148[/C][C]51.49055556[/C][C]54.6588868471182[/C][C]-3.16833128711821[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.434016427826639[/C][C]0.434016427826639[/C][/ROW]
[ROW][C]150[/C][C]4.08[/C][C]2.81458724109604[/C][C]1.26541275890396[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.3855555644.94087880265382.44467675734615
224.0613888935.0156955548594-10.9543066648594
331.482536.7447781915725-5.26227819157252
442.3638888949.0901235899387-6.72623469993871
523.9461111123.63824439191840.307866718081648
610.3491666724.1264647115077-13.7772980415077
785.0152777847.498439093046737.5168386869533
89.09722222221.3067977715165-12.2095755495165
932.3616666734.8103066532472-2.44863998324723
1036.2608333328.38510628648657.8757270435135
1144.9655555647.0750615734314-2.1095060134314
1235.6316666756.4890425083127-20.8573758383128
1328.4305555635.709176704951-7.27862114495101
1453.6177777847.80813620995455.80964157004548
1539.3261111141.9568343569489-2.63072324694889
1670.4330555667.33728612038573.09576943961428
1750.3083333343.71501431299746.59331901700264
1855.1252.42086221459172.69913778540832
1931.6258333334.9418586596014-3.31602532960144
2044.4277777846.3635852551557-1.93580747515573
2146.3394444444.46320698856411.87623745143587
2279.6319444442.049438509310137.5825059306899
2325.4602777821.49433682365293.96594095634715
2430.0772222241.4895640034208-11.4123417834208
2540.6505555648.7293808055034-8.07882524550338
2640.3172222248.8438137155915-8.52659149559151
2744.9277777845.4154141671309-0.487636387130909
2844.6958333342.70756970408571.98826362591433
2929.6911111128.72528970019810.965821409801886
3052.2638888940.792925442852311.4709634471477
3152.6113888950.09484129290062.51654759709941
3235.9677777845.4819430500956-9.51416527009562
3356.67548.24226913104048.43273086895964
3417.4252777827.0456760801054-9.62039830010544
3567.6736111146.821360578558620.8522505314414
3646.4597222234.089884059416512.3698381605835
3773.4852.453859493614321.0261405063857
3833.8955555633.82907865836820.066476901631823
3922.4916.09899772907586.39100227092415
4058.2763888954.17950951770494.09687937229512
4162.2791666740.898681753937621.3804849160624
4232.2141666735.4621172406694-3.24795057066943
4338.3863888936.07315362608762.31323526391241
4422.5294444429.1780178379179-6.64857339791789
4525.8680555635.5821642811215-9.71410872112153
4684.9322222256.007091823598328.9251303964017
4721.8888888935.5946199210264-13.7057310310265
4844.1208333345.8921469916178-1.77131366161784
4961.5958333360.37395587820941.22187745179061
5036.4188888941.3189334893194-4.90004459931939
5135.7594444434.66144504583991.09799939416014
526.7188888899.50861818906974-2.78972930006974
5371.5727777887.3916534985442-15.8188757185442
5418.0636111122.8553433333041-4.79173222330414
5527.2405555629.4708512935704-2.2302957335704
5648.2186111132.109401733735616.1092093762644
5750.0116666756.9151035601266-6.90343689012664
5854.7961111160.7229559327428-5.92684482274285
5958.9055555650.14134232421548.7642132357846
6039.3283333337.56947850507711.7588548249229
6168.0852777860.62948827478117.4557895052189
6257.4663888950.93242542899916.53396346100091
6340.4711111145.023886849748-4.55277573974802
6447.3986111144.94296228640252.45564882359745
6539.4622222238.66512794884560.797094271154436
6631.8944444425.87519708475286.01924735524722
6731.5169444448.0904929563467-16.5735485163467
6840.3569444455.1303524790311-14.7734080390311
6941.9441666740.4241652516881.520001418312
7025.5033333330.8127723171256-5.30943898712562
7133.0019444435.6266422753288-2.62469783532882
7219.297525.8545899198031-6.55708991980311
7335.17538.6610219630026-3.4860219630026
7440.5337.10726432738723.42273567261284
7527.3313888934.1386107755307-6.80722188553066
7653.03540.771769064827712.2632309351723
7755.2213888940.510614057247414.7107748327526
7829.4980555659.699009361007-30.200953801007
7924.8105555632.4427122348643-7.63215667486425
8033.4338888936.9536395033961-3.51975061339613
8127.4419444426.52584383179390.916100608206074
8276.3758333350.701960719737325.6738726102628
8336.8883333335.71758978620821.17074354379179
8437.5697222243.0845008781305-5.51477865813047
8522.4869444419.82015460257282.66678983742719
8630.3436111128.40015769457951.9434534154205
8726.8427777836.1373133554772-9.29453557547721
8862.8308333356.12066703998846.71016629001156
8947.5794444443.2058125988024.37363184119803
9032.7263888952.1517760867673-19.4253871967673
9137.1002777835.52246703562531.57781074437467
9242.2758333342.23649778516780.03933554483218
9331.1122222232.068500702844-0.956278482843972
9447.1147222238.18141260498358.93330961501647
9552.0786111141.924604001556910.1540071084431
9636.2591666733.98313915217482.27602751782516
9739.5386111139.8583090285925-0.319697918592469
9852.7122222248.97003811782263.74218410217736
9956.0008333365.9958454432145-9.99501211321454
10068.56551.290254378859817.2747456211402
10143.3186111152.7143503789356-9.39573926893558
10250.7169444444.80259611027015.91434832972987
10329.5419444447.0431330813512-17.5011886413512
10412.0241666724.442817291249-12.418650621249
10535.4147222227.49114070990477.92358151009528
10635.5361111147.9335574065335-12.3974462965335
10741.3905555634.24158549492187.14897006507822
10852.1258333344.87401409619387.25181923380618
10920.5866666746.4010724846467-25.8144058146467
11026.1127777841.4081234933758-15.2953457133758
11149.062540.79126287786738.27123712213273
11239.4258333342.0455104619368-2.61967713193677
1136.3716666672.985826707704563.38583995929544
11434.9797222239.0923428828702-4.11262066287024
11517.182518.0723503788428-0.889850378842849
11625.3583333332.6159905947509-7.25765726475091
11770.8611111141.54520405081829.315907059182
1185.8483333333.58823956523512.2600937677649
11946.9702777833.572075306717213.3982024732828
1208.72611111112.6649793689703-3.93886825797026
12152.4169444445.55004021492346.86690422507663
12238.2066666746.1699054956688-7.96323882566884
12321.43538.8265979158949-17.3915979158949
12420.7130555631.4817737970531-10.7687182370531
12510.61515.6133485290474-4.99834852904742
12625.2669444432.2746233988149-7.00767895881488
12753.9511111147.1132645187716.83784659122896
12837.572545.7271542374868-8.1546542374868
12967.8533333346.954735729168420.8985976008316
13056.0411111154.08769591858071.95341519141935
13171.2227777867.62401194695393.59876583304613
13238.6511111129.99389351396368.65721759603644
13321.2416666727.8373692425149-6.59570257251495
13452.6394444476.0398614791176-23.4004170391176
13577.8705555663.542575215611614.3279803443884
13614.1663888928.2173150909727-14.0509262009727
13770.3538888956.783753035750613.5701358542494
13828.677537.3005483007451-8.62304830074515
13946.6830555637.01022920577539.67282635422469
14035.7688888938.300535320787-2.53164643078697
14121.0405555633.3307706357297-12.2902150757297
14269.2311111146.442770552794622.7883405572054
14342.3238888943.577454488787-1.25356559878698
14448.1277777837.181033996086210.9467437839138
14554.7769444449.61319366271455.16375077728553
14618.7519444445.9358842744452-27.1839398344452
14738.7247222244.5617008713448-5.83697865134477
14851.4905555654.6588868471182-3.16833128711821
1490-0.4340164278266390.434016427826639
1504.082.814587241096041.26541275890396







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7653202264814430.4693595470371130.234679773518557
80.7451821896403170.5096356207193660.254817810359683
90.6771705051783860.6456589896432280.322829494821614
100.7957674430146190.4084651139707610.204232556985381
110.7328146959564190.5343706080871610.267185304043581
120.6871012796375380.6257974407249250.312898720362462
130.5954961754945950.809007649010810.404503824505405
140.8035753104997570.3928493790004860.196424689500243
150.7583329178770440.4833341642459120.241667082122956
160.6881173099030280.6237653801939440.311882690096972
170.6140183419525770.7719633160948460.385981658047423
180.5600585173910890.8798829652178220.439941482608911
190.509795941724850.9804081165503010.49020405827515
200.4326211430364350.865242286072870.567378856963565
210.3596689400860250.719337880172050.640331059913975
220.9223527529499010.1552944941001990.0776472470500994
230.9029123331612550.1941753336774890.0970876668387447
240.886429838394570.227140323210860.11357016160543
250.8696570443298960.2606859113402080.130342955670104
260.8645604308663180.2708791382673650.135439569133682
270.8318838710948510.3362322578102980.168116128905149
280.7946785292196960.4106429415606080.205321470780304
290.7600251518834420.4799496962331160.239974848116558
300.7832832092470340.4334335815059320.216716790752966
310.7370746478060290.5258507043879420.262925352193971
320.702892494555150.59421501088970.29710750544485
330.6803497925204210.6393004149591580.319650207479579
340.6469915403581710.7060169192836580.353008459641829
350.7179487030143820.5641025939712370.282051296985618
360.7514296840203080.4971406319593850.248570315979692
370.8241137263397750.351772547320450.175886273660225
380.786069732236450.4278605355271010.21393026776355
390.7575690004101440.4848619991797120.242430999589856
400.7175104733986520.5649790532026950.282489526601348
410.8039733783940830.3920532432118350.196026621605917
420.7664559971843250.4670880056313490.233544002815675
430.724708267495930.5505834650081410.27529173250407
440.6893066462753190.6213867074493610.310693353724681
450.6804232164021520.6391535671956960.319576783597848
460.8130354865664860.3739290268670270.186964513433514
470.8121241061964380.3757517876071240.187875893803562
480.7767643867929340.4464712264141310.223235613207066
490.7499124430738080.5001751138523840.250087556926192
500.7124328878914990.5751342242170030.287567112108501
510.6698219360141360.6603561279717280.330178063985864
520.6244520595196920.7510958809606160.375547940480308
530.7433115667192850.5133768665614310.256688433280715
540.7072256517976250.5855486964047510.292774348202375
550.6640761399392680.6718477201214650.335923860060732
560.6818242918676860.6363514162646280.318175708132314
570.6705352600675870.6589294798648260.329464739932413
580.6629737337426070.6740525325147860.337026266257393
590.6365790174868630.7268419650262750.363420982513137
600.5906731128986830.8186537742026350.409326887101317
610.556217519529260.887564960941480.44378248047074
620.5252563326282340.9494873347435330.474743667371766
630.4817146772164990.9634293544329980.518285322783501
640.4353879201172690.8707758402345380.564612079882731
650.3901219064507160.7802438129014320.609878093549284
660.3557289301538770.7114578603077540.644271069846123
670.4373571683694250.8747143367388490.562642831630575
680.4570514189021550.914102837804310.542948581097845
690.4106765994547730.8213531989095460.589323400545227
700.3789441239579230.7578882479158460.621055876042077
710.3371264426741160.6742528853482330.662873557325884
720.3050836176235830.6101672352471660.694916382376417
730.2686916466721170.5373832933442350.731308353327883
740.232913212474740.465826424949480.76708678752526
750.2116360610794370.4232721221588730.788363938920563
760.2111237861135070.4222475722270130.788876213886494
770.2257485903311910.4514971806623810.774251409668809
780.5539224026122110.8921551947755780.446077597387789
790.5265258142764780.9469483714470440.473474185723522
800.4830726658743850.966145331748770.516927334125615
810.4358773949016440.8717547898032870.564122605098356
820.6182803986560060.7634392026879880.381719601343994
830.572527228295610.8549455434087790.42747277170439
840.5348703563276630.9302592873446730.465129643672337
850.4893023444368220.9786046888736430.510697655563178
860.4431149602407680.8862299204815350.556885039759232
870.4246501400526990.8493002801053990.575349859947301
880.3956775065202740.7913550130405480.604322493479726
890.3574760894248320.7149521788496630.642523910575169
900.4351257229464920.8702514458929830.564874277053508
910.3893404698150810.7786809396301620.610659530184919
920.3433421238726970.6866842477453940.656657876127303
930.2996801528670480.5993603057340960.700319847132952
940.2843095793077140.5686191586154280.715690420692286
950.2756156098530230.5512312197060460.724384390146977
960.2373385627868680.4746771255737360.762661437213132
970.2009112820027570.4018225640055140.799088717997243
980.1708443809722210.3416887619444430.829155619027779
990.1580772222440390.3161544444880780.841922777755961
1000.1990835756022780.3981671512045550.800916424397722
1010.1836653557329030.3673307114658050.816334644267097
1020.161252840003990.3225056800079810.83874715999601
1030.1991283513643920.3982567027287840.800871648635608
1040.2008356578671520.4016713157343050.799164342132848
1050.1817019226324880.3634038452649760.818298077367512
1060.1827280247253160.3654560494506320.817271975274684
1070.1634718049378420.3269436098756830.836528195062158
1080.1449485408822380.2898970817644760.855051459117762
1090.3040347105371210.6080694210742420.695965289462879
1100.3241964328319540.6483928656639080.675803567168046
1110.2925738693930040.5851477387860080.707426130606996
1120.248023088050650.49604617610130.75197691194935
1130.2099521688205510.4199043376411020.790047831179449
1140.1749642047174850.349928409434970.825035795282515
1150.1412052064265430.2824104128530860.858794793573457
1160.117260065626320.234520131252640.88273993437368
1170.3227919148463070.6455838296926130.677208085153693
1180.2767429575062270.5534859150124540.723257042493773
1190.2868312474260890.5736624948521780.713168752573911
1200.2396930670721680.4793861341443350.760306932927832
1210.2144819458751570.4289638917503150.785518054124843
1220.1902994765433810.3805989530867610.809700523456619
1230.2312997243637030.4625994487274050.768700275636297
1240.2210404312998940.4420808625997870.778959568700106
1250.1790946777110470.3581893554220940.820905322288953
1260.1543181648991150.308636329798230.845681835100885
1270.1277655223151110.2555310446302230.872234477684889
1280.1118493032937690.2236986065875390.888150696706231
1290.1831906477212210.3663812954424420.816809352278779
1300.1414961564029150.282992312805830.858503843597085
1310.1048215834470720.2096431668941440.895178416552928
1320.08909056563873850.1781811312774770.910909434361261
1330.0649606499850380.1299212999700760.935039350014962
1340.4477057389990140.8954114779980280.552294261000986
1350.3741873165997790.7483746331995580.625812683400221
1360.3062677288456550.6125354576913110.693732271154345
1370.2754247457273550.550849491454710.724575254272645
1380.2366994851276940.4733989702553880.763300514872306
1390.3164557524996380.6329115049992760.683544247500362
1400.2230045865842140.4460091731684280.776995413415786
1410.9969694205298730.006061158940253150.00303057947012658
1420.9884302027967160.02313959440656840.0115697972032842
1430.9574851116870910.08502977662581780.0425148883129089

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.765320226481443 & 0.469359547037113 & 0.234679773518557 \tabularnewline
8 & 0.745182189640317 & 0.509635620719366 & 0.254817810359683 \tabularnewline
9 & 0.677170505178386 & 0.645658989643228 & 0.322829494821614 \tabularnewline
10 & 0.795767443014619 & 0.408465113970761 & 0.204232556985381 \tabularnewline
11 & 0.732814695956419 & 0.534370608087161 & 0.267185304043581 \tabularnewline
12 & 0.687101279637538 & 0.625797440724925 & 0.312898720362462 \tabularnewline
13 & 0.595496175494595 & 0.80900764901081 & 0.404503824505405 \tabularnewline
14 & 0.803575310499757 & 0.392849379000486 & 0.196424689500243 \tabularnewline
15 & 0.758332917877044 & 0.483334164245912 & 0.241667082122956 \tabularnewline
16 & 0.688117309903028 & 0.623765380193944 & 0.311882690096972 \tabularnewline
17 & 0.614018341952577 & 0.771963316094846 & 0.385981658047423 \tabularnewline
18 & 0.560058517391089 & 0.879882965217822 & 0.439941482608911 \tabularnewline
19 & 0.50979594172485 & 0.980408116550301 & 0.49020405827515 \tabularnewline
20 & 0.432621143036435 & 0.86524228607287 & 0.567378856963565 \tabularnewline
21 & 0.359668940086025 & 0.71933788017205 & 0.640331059913975 \tabularnewline
22 & 0.922352752949901 & 0.155294494100199 & 0.0776472470500994 \tabularnewline
23 & 0.902912333161255 & 0.194175333677489 & 0.0970876668387447 \tabularnewline
24 & 0.88642983839457 & 0.22714032321086 & 0.11357016160543 \tabularnewline
25 & 0.869657044329896 & 0.260685911340208 & 0.130342955670104 \tabularnewline
26 & 0.864560430866318 & 0.270879138267365 & 0.135439569133682 \tabularnewline
27 & 0.831883871094851 & 0.336232257810298 & 0.168116128905149 \tabularnewline
28 & 0.794678529219696 & 0.410642941560608 & 0.205321470780304 \tabularnewline
29 & 0.760025151883442 & 0.479949696233116 & 0.239974848116558 \tabularnewline
30 & 0.783283209247034 & 0.433433581505932 & 0.216716790752966 \tabularnewline
31 & 0.737074647806029 & 0.525850704387942 & 0.262925352193971 \tabularnewline
32 & 0.70289249455515 & 0.5942150108897 & 0.29710750544485 \tabularnewline
33 & 0.680349792520421 & 0.639300414959158 & 0.319650207479579 \tabularnewline
34 & 0.646991540358171 & 0.706016919283658 & 0.353008459641829 \tabularnewline
35 & 0.717948703014382 & 0.564102593971237 & 0.282051296985618 \tabularnewline
36 & 0.751429684020308 & 0.497140631959385 & 0.248570315979692 \tabularnewline
37 & 0.824113726339775 & 0.35177254732045 & 0.175886273660225 \tabularnewline
38 & 0.78606973223645 & 0.427860535527101 & 0.21393026776355 \tabularnewline
39 & 0.757569000410144 & 0.484861999179712 & 0.242430999589856 \tabularnewline
40 & 0.717510473398652 & 0.564979053202695 & 0.282489526601348 \tabularnewline
41 & 0.803973378394083 & 0.392053243211835 & 0.196026621605917 \tabularnewline
42 & 0.766455997184325 & 0.467088005631349 & 0.233544002815675 \tabularnewline
43 & 0.72470826749593 & 0.550583465008141 & 0.27529173250407 \tabularnewline
44 & 0.689306646275319 & 0.621386707449361 & 0.310693353724681 \tabularnewline
45 & 0.680423216402152 & 0.639153567195696 & 0.319576783597848 \tabularnewline
46 & 0.813035486566486 & 0.373929026867027 & 0.186964513433514 \tabularnewline
47 & 0.812124106196438 & 0.375751787607124 & 0.187875893803562 \tabularnewline
48 & 0.776764386792934 & 0.446471226414131 & 0.223235613207066 \tabularnewline
49 & 0.749912443073808 & 0.500175113852384 & 0.250087556926192 \tabularnewline
50 & 0.712432887891499 & 0.575134224217003 & 0.287567112108501 \tabularnewline
51 & 0.669821936014136 & 0.660356127971728 & 0.330178063985864 \tabularnewline
52 & 0.624452059519692 & 0.751095880960616 & 0.375547940480308 \tabularnewline
53 & 0.743311566719285 & 0.513376866561431 & 0.256688433280715 \tabularnewline
54 & 0.707225651797625 & 0.585548696404751 & 0.292774348202375 \tabularnewline
55 & 0.664076139939268 & 0.671847720121465 & 0.335923860060732 \tabularnewline
56 & 0.681824291867686 & 0.636351416264628 & 0.318175708132314 \tabularnewline
57 & 0.670535260067587 & 0.658929479864826 & 0.329464739932413 \tabularnewline
58 & 0.662973733742607 & 0.674052532514786 & 0.337026266257393 \tabularnewline
59 & 0.636579017486863 & 0.726841965026275 & 0.363420982513137 \tabularnewline
60 & 0.590673112898683 & 0.818653774202635 & 0.409326887101317 \tabularnewline
61 & 0.55621751952926 & 0.88756496094148 & 0.44378248047074 \tabularnewline
62 & 0.525256332628234 & 0.949487334743533 & 0.474743667371766 \tabularnewline
63 & 0.481714677216499 & 0.963429354432998 & 0.518285322783501 \tabularnewline
64 & 0.435387920117269 & 0.870775840234538 & 0.564612079882731 \tabularnewline
65 & 0.390121906450716 & 0.780243812901432 & 0.609878093549284 \tabularnewline
66 & 0.355728930153877 & 0.711457860307754 & 0.644271069846123 \tabularnewline
67 & 0.437357168369425 & 0.874714336738849 & 0.562642831630575 \tabularnewline
68 & 0.457051418902155 & 0.91410283780431 & 0.542948581097845 \tabularnewline
69 & 0.410676599454773 & 0.821353198909546 & 0.589323400545227 \tabularnewline
70 & 0.378944123957923 & 0.757888247915846 & 0.621055876042077 \tabularnewline
71 & 0.337126442674116 & 0.674252885348233 & 0.662873557325884 \tabularnewline
72 & 0.305083617623583 & 0.610167235247166 & 0.694916382376417 \tabularnewline
73 & 0.268691646672117 & 0.537383293344235 & 0.731308353327883 \tabularnewline
74 & 0.23291321247474 & 0.46582642494948 & 0.76708678752526 \tabularnewline
75 & 0.211636061079437 & 0.423272122158873 & 0.788363938920563 \tabularnewline
76 & 0.211123786113507 & 0.422247572227013 & 0.788876213886494 \tabularnewline
77 & 0.225748590331191 & 0.451497180662381 & 0.774251409668809 \tabularnewline
78 & 0.553922402612211 & 0.892155194775578 & 0.446077597387789 \tabularnewline
79 & 0.526525814276478 & 0.946948371447044 & 0.473474185723522 \tabularnewline
80 & 0.483072665874385 & 0.96614533174877 & 0.516927334125615 \tabularnewline
81 & 0.435877394901644 & 0.871754789803287 & 0.564122605098356 \tabularnewline
82 & 0.618280398656006 & 0.763439202687988 & 0.381719601343994 \tabularnewline
83 & 0.57252722829561 & 0.854945543408779 & 0.42747277170439 \tabularnewline
84 & 0.534870356327663 & 0.930259287344673 & 0.465129643672337 \tabularnewline
85 & 0.489302344436822 & 0.978604688873643 & 0.510697655563178 \tabularnewline
86 & 0.443114960240768 & 0.886229920481535 & 0.556885039759232 \tabularnewline
87 & 0.424650140052699 & 0.849300280105399 & 0.575349859947301 \tabularnewline
88 & 0.395677506520274 & 0.791355013040548 & 0.604322493479726 \tabularnewline
89 & 0.357476089424832 & 0.714952178849663 & 0.642523910575169 \tabularnewline
90 & 0.435125722946492 & 0.870251445892983 & 0.564874277053508 \tabularnewline
91 & 0.389340469815081 & 0.778680939630162 & 0.610659530184919 \tabularnewline
92 & 0.343342123872697 & 0.686684247745394 & 0.656657876127303 \tabularnewline
93 & 0.299680152867048 & 0.599360305734096 & 0.700319847132952 \tabularnewline
94 & 0.284309579307714 & 0.568619158615428 & 0.715690420692286 \tabularnewline
95 & 0.275615609853023 & 0.551231219706046 & 0.724384390146977 \tabularnewline
96 & 0.237338562786868 & 0.474677125573736 & 0.762661437213132 \tabularnewline
97 & 0.200911282002757 & 0.401822564005514 & 0.799088717997243 \tabularnewline
98 & 0.170844380972221 & 0.341688761944443 & 0.829155619027779 \tabularnewline
99 & 0.158077222244039 & 0.316154444488078 & 0.841922777755961 \tabularnewline
100 & 0.199083575602278 & 0.398167151204555 & 0.800916424397722 \tabularnewline
101 & 0.183665355732903 & 0.367330711465805 & 0.816334644267097 \tabularnewline
102 & 0.16125284000399 & 0.322505680007981 & 0.83874715999601 \tabularnewline
103 & 0.199128351364392 & 0.398256702728784 & 0.800871648635608 \tabularnewline
104 & 0.200835657867152 & 0.401671315734305 & 0.799164342132848 \tabularnewline
105 & 0.181701922632488 & 0.363403845264976 & 0.818298077367512 \tabularnewline
106 & 0.182728024725316 & 0.365456049450632 & 0.817271975274684 \tabularnewline
107 & 0.163471804937842 & 0.326943609875683 & 0.836528195062158 \tabularnewline
108 & 0.144948540882238 & 0.289897081764476 & 0.855051459117762 \tabularnewline
109 & 0.304034710537121 & 0.608069421074242 & 0.695965289462879 \tabularnewline
110 & 0.324196432831954 & 0.648392865663908 & 0.675803567168046 \tabularnewline
111 & 0.292573869393004 & 0.585147738786008 & 0.707426130606996 \tabularnewline
112 & 0.24802308805065 & 0.4960461761013 & 0.75197691194935 \tabularnewline
113 & 0.209952168820551 & 0.419904337641102 & 0.790047831179449 \tabularnewline
114 & 0.174964204717485 & 0.34992840943497 & 0.825035795282515 \tabularnewline
115 & 0.141205206426543 & 0.282410412853086 & 0.858794793573457 \tabularnewline
116 & 0.11726006562632 & 0.23452013125264 & 0.88273993437368 \tabularnewline
117 & 0.322791914846307 & 0.645583829692613 & 0.677208085153693 \tabularnewline
118 & 0.276742957506227 & 0.553485915012454 & 0.723257042493773 \tabularnewline
119 & 0.286831247426089 & 0.573662494852178 & 0.713168752573911 \tabularnewline
120 & 0.239693067072168 & 0.479386134144335 & 0.760306932927832 \tabularnewline
121 & 0.214481945875157 & 0.428963891750315 & 0.785518054124843 \tabularnewline
122 & 0.190299476543381 & 0.380598953086761 & 0.809700523456619 \tabularnewline
123 & 0.231299724363703 & 0.462599448727405 & 0.768700275636297 \tabularnewline
124 & 0.221040431299894 & 0.442080862599787 & 0.778959568700106 \tabularnewline
125 & 0.179094677711047 & 0.358189355422094 & 0.820905322288953 \tabularnewline
126 & 0.154318164899115 & 0.30863632979823 & 0.845681835100885 \tabularnewline
127 & 0.127765522315111 & 0.255531044630223 & 0.872234477684889 \tabularnewline
128 & 0.111849303293769 & 0.223698606587539 & 0.888150696706231 \tabularnewline
129 & 0.183190647721221 & 0.366381295442442 & 0.816809352278779 \tabularnewline
130 & 0.141496156402915 & 0.28299231280583 & 0.858503843597085 \tabularnewline
131 & 0.104821583447072 & 0.209643166894144 & 0.895178416552928 \tabularnewline
132 & 0.0890905656387385 & 0.178181131277477 & 0.910909434361261 \tabularnewline
133 & 0.064960649985038 & 0.129921299970076 & 0.935039350014962 \tabularnewline
134 & 0.447705738999014 & 0.895411477998028 & 0.552294261000986 \tabularnewline
135 & 0.374187316599779 & 0.748374633199558 & 0.625812683400221 \tabularnewline
136 & 0.306267728845655 & 0.612535457691311 & 0.693732271154345 \tabularnewline
137 & 0.275424745727355 & 0.55084949145471 & 0.724575254272645 \tabularnewline
138 & 0.236699485127694 & 0.473398970255388 & 0.763300514872306 \tabularnewline
139 & 0.316455752499638 & 0.632911504999276 & 0.683544247500362 \tabularnewline
140 & 0.223004586584214 & 0.446009173168428 & 0.776995413415786 \tabularnewline
141 & 0.996969420529873 & 0.00606115894025315 & 0.00303057947012658 \tabularnewline
142 & 0.988430202796716 & 0.0231395944065684 & 0.0115697972032842 \tabularnewline
143 & 0.957485111687091 & 0.0850297766258178 & 0.0425148883129089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.765320226481443[/C][C]0.469359547037113[/C][C]0.234679773518557[/C][/ROW]
[ROW][C]8[/C][C]0.745182189640317[/C][C]0.509635620719366[/C][C]0.254817810359683[/C][/ROW]
[ROW][C]9[/C][C]0.677170505178386[/C][C]0.645658989643228[/C][C]0.322829494821614[/C][/ROW]
[ROW][C]10[/C][C]0.795767443014619[/C][C]0.408465113970761[/C][C]0.204232556985381[/C][/ROW]
[ROW][C]11[/C][C]0.732814695956419[/C][C]0.534370608087161[/C][C]0.267185304043581[/C][/ROW]
[ROW][C]12[/C][C]0.687101279637538[/C][C]0.625797440724925[/C][C]0.312898720362462[/C][/ROW]
[ROW][C]13[/C][C]0.595496175494595[/C][C]0.80900764901081[/C][C]0.404503824505405[/C][/ROW]
[ROW][C]14[/C][C]0.803575310499757[/C][C]0.392849379000486[/C][C]0.196424689500243[/C][/ROW]
[ROW][C]15[/C][C]0.758332917877044[/C][C]0.483334164245912[/C][C]0.241667082122956[/C][/ROW]
[ROW][C]16[/C][C]0.688117309903028[/C][C]0.623765380193944[/C][C]0.311882690096972[/C][/ROW]
[ROW][C]17[/C][C]0.614018341952577[/C][C]0.771963316094846[/C][C]0.385981658047423[/C][/ROW]
[ROW][C]18[/C][C]0.560058517391089[/C][C]0.879882965217822[/C][C]0.439941482608911[/C][/ROW]
[ROW][C]19[/C][C]0.50979594172485[/C][C]0.980408116550301[/C][C]0.49020405827515[/C][/ROW]
[ROW][C]20[/C][C]0.432621143036435[/C][C]0.86524228607287[/C][C]0.567378856963565[/C][/ROW]
[ROW][C]21[/C][C]0.359668940086025[/C][C]0.71933788017205[/C][C]0.640331059913975[/C][/ROW]
[ROW][C]22[/C][C]0.922352752949901[/C][C]0.155294494100199[/C][C]0.0776472470500994[/C][/ROW]
[ROW][C]23[/C][C]0.902912333161255[/C][C]0.194175333677489[/C][C]0.0970876668387447[/C][/ROW]
[ROW][C]24[/C][C]0.88642983839457[/C][C]0.22714032321086[/C][C]0.11357016160543[/C][/ROW]
[ROW][C]25[/C][C]0.869657044329896[/C][C]0.260685911340208[/C][C]0.130342955670104[/C][/ROW]
[ROW][C]26[/C][C]0.864560430866318[/C][C]0.270879138267365[/C][C]0.135439569133682[/C][/ROW]
[ROW][C]27[/C][C]0.831883871094851[/C][C]0.336232257810298[/C][C]0.168116128905149[/C][/ROW]
[ROW][C]28[/C][C]0.794678529219696[/C][C]0.410642941560608[/C][C]0.205321470780304[/C][/ROW]
[ROW][C]29[/C][C]0.760025151883442[/C][C]0.479949696233116[/C][C]0.239974848116558[/C][/ROW]
[ROW][C]30[/C][C]0.783283209247034[/C][C]0.433433581505932[/C][C]0.216716790752966[/C][/ROW]
[ROW][C]31[/C][C]0.737074647806029[/C][C]0.525850704387942[/C][C]0.262925352193971[/C][/ROW]
[ROW][C]32[/C][C]0.70289249455515[/C][C]0.5942150108897[/C][C]0.29710750544485[/C][/ROW]
[ROW][C]33[/C][C]0.680349792520421[/C][C]0.639300414959158[/C][C]0.319650207479579[/C][/ROW]
[ROW][C]34[/C][C]0.646991540358171[/C][C]0.706016919283658[/C][C]0.353008459641829[/C][/ROW]
[ROW][C]35[/C][C]0.717948703014382[/C][C]0.564102593971237[/C][C]0.282051296985618[/C][/ROW]
[ROW][C]36[/C][C]0.751429684020308[/C][C]0.497140631959385[/C][C]0.248570315979692[/C][/ROW]
[ROW][C]37[/C][C]0.824113726339775[/C][C]0.35177254732045[/C][C]0.175886273660225[/C][/ROW]
[ROW][C]38[/C][C]0.78606973223645[/C][C]0.427860535527101[/C][C]0.21393026776355[/C][/ROW]
[ROW][C]39[/C][C]0.757569000410144[/C][C]0.484861999179712[/C][C]0.242430999589856[/C][/ROW]
[ROW][C]40[/C][C]0.717510473398652[/C][C]0.564979053202695[/C][C]0.282489526601348[/C][/ROW]
[ROW][C]41[/C][C]0.803973378394083[/C][C]0.392053243211835[/C][C]0.196026621605917[/C][/ROW]
[ROW][C]42[/C][C]0.766455997184325[/C][C]0.467088005631349[/C][C]0.233544002815675[/C][/ROW]
[ROW][C]43[/C][C]0.72470826749593[/C][C]0.550583465008141[/C][C]0.27529173250407[/C][/ROW]
[ROW][C]44[/C][C]0.689306646275319[/C][C]0.621386707449361[/C][C]0.310693353724681[/C][/ROW]
[ROW][C]45[/C][C]0.680423216402152[/C][C]0.639153567195696[/C][C]0.319576783597848[/C][/ROW]
[ROW][C]46[/C][C]0.813035486566486[/C][C]0.373929026867027[/C][C]0.186964513433514[/C][/ROW]
[ROW][C]47[/C][C]0.812124106196438[/C][C]0.375751787607124[/C][C]0.187875893803562[/C][/ROW]
[ROW][C]48[/C][C]0.776764386792934[/C][C]0.446471226414131[/C][C]0.223235613207066[/C][/ROW]
[ROW][C]49[/C][C]0.749912443073808[/C][C]0.500175113852384[/C][C]0.250087556926192[/C][/ROW]
[ROW][C]50[/C][C]0.712432887891499[/C][C]0.575134224217003[/C][C]0.287567112108501[/C][/ROW]
[ROW][C]51[/C][C]0.669821936014136[/C][C]0.660356127971728[/C][C]0.330178063985864[/C][/ROW]
[ROW][C]52[/C][C]0.624452059519692[/C][C]0.751095880960616[/C][C]0.375547940480308[/C][/ROW]
[ROW][C]53[/C][C]0.743311566719285[/C][C]0.513376866561431[/C][C]0.256688433280715[/C][/ROW]
[ROW][C]54[/C][C]0.707225651797625[/C][C]0.585548696404751[/C][C]0.292774348202375[/C][/ROW]
[ROW][C]55[/C][C]0.664076139939268[/C][C]0.671847720121465[/C][C]0.335923860060732[/C][/ROW]
[ROW][C]56[/C][C]0.681824291867686[/C][C]0.636351416264628[/C][C]0.318175708132314[/C][/ROW]
[ROW][C]57[/C][C]0.670535260067587[/C][C]0.658929479864826[/C][C]0.329464739932413[/C][/ROW]
[ROW][C]58[/C][C]0.662973733742607[/C][C]0.674052532514786[/C][C]0.337026266257393[/C][/ROW]
[ROW][C]59[/C][C]0.636579017486863[/C][C]0.726841965026275[/C][C]0.363420982513137[/C][/ROW]
[ROW][C]60[/C][C]0.590673112898683[/C][C]0.818653774202635[/C][C]0.409326887101317[/C][/ROW]
[ROW][C]61[/C][C]0.55621751952926[/C][C]0.88756496094148[/C][C]0.44378248047074[/C][/ROW]
[ROW][C]62[/C][C]0.525256332628234[/C][C]0.949487334743533[/C][C]0.474743667371766[/C][/ROW]
[ROW][C]63[/C][C]0.481714677216499[/C][C]0.963429354432998[/C][C]0.518285322783501[/C][/ROW]
[ROW][C]64[/C][C]0.435387920117269[/C][C]0.870775840234538[/C][C]0.564612079882731[/C][/ROW]
[ROW][C]65[/C][C]0.390121906450716[/C][C]0.780243812901432[/C][C]0.609878093549284[/C][/ROW]
[ROW][C]66[/C][C]0.355728930153877[/C][C]0.711457860307754[/C][C]0.644271069846123[/C][/ROW]
[ROW][C]67[/C][C]0.437357168369425[/C][C]0.874714336738849[/C][C]0.562642831630575[/C][/ROW]
[ROW][C]68[/C][C]0.457051418902155[/C][C]0.91410283780431[/C][C]0.542948581097845[/C][/ROW]
[ROW][C]69[/C][C]0.410676599454773[/C][C]0.821353198909546[/C][C]0.589323400545227[/C][/ROW]
[ROW][C]70[/C][C]0.378944123957923[/C][C]0.757888247915846[/C][C]0.621055876042077[/C][/ROW]
[ROW][C]71[/C][C]0.337126442674116[/C][C]0.674252885348233[/C][C]0.662873557325884[/C][/ROW]
[ROW][C]72[/C][C]0.305083617623583[/C][C]0.610167235247166[/C][C]0.694916382376417[/C][/ROW]
[ROW][C]73[/C][C]0.268691646672117[/C][C]0.537383293344235[/C][C]0.731308353327883[/C][/ROW]
[ROW][C]74[/C][C]0.23291321247474[/C][C]0.46582642494948[/C][C]0.76708678752526[/C][/ROW]
[ROW][C]75[/C][C]0.211636061079437[/C][C]0.423272122158873[/C][C]0.788363938920563[/C][/ROW]
[ROW][C]76[/C][C]0.211123786113507[/C][C]0.422247572227013[/C][C]0.788876213886494[/C][/ROW]
[ROW][C]77[/C][C]0.225748590331191[/C][C]0.451497180662381[/C][C]0.774251409668809[/C][/ROW]
[ROW][C]78[/C][C]0.553922402612211[/C][C]0.892155194775578[/C][C]0.446077597387789[/C][/ROW]
[ROW][C]79[/C][C]0.526525814276478[/C][C]0.946948371447044[/C][C]0.473474185723522[/C][/ROW]
[ROW][C]80[/C][C]0.483072665874385[/C][C]0.96614533174877[/C][C]0.516927334125615[/C][/ROW]
[ROW][C]81[/C][C]0.435877394901644[/C][C]0.871754789803287[/C][C]0.564122605098356[/C][/ROW]
[ROW][C]82[/C][C]0.618280398656006[/C][C]0.763439202687988[/C][C]0.381719601343994[/C][/ROW]
[ROW][C]83[/C][C]0.57252722829561[/C][C]0.854945543408779[/C][C]0.42747277170439[/C][/ROW]
[ROW][C]84[/C][C]0.534870356327663[/C][C]0.930259287344673[/C][C]0.465129643672337[/C][/ROW]
[ROW][C]85[/C][C]0.489302344436822[/C][C]0.978604688873643[/C][C]0.510697655563178[/C][/ROW]
[ROW][C]86[/C][C]0.443114960240768[/C][C]0.886229920481535[/C][C]0.556885039759232[/C][/ROW]
[ROW][C]87[/C][C]0.424650140052699[/C][C]0.849300280105399[/C][C]0.575349859947301[/C][/ROW]
[ROW][C]88[/C][C]0.395677506520274[/C][C]0.791355013040548[/C][C]0.604322493479726[/C][/ROW]
[ROW][C]89[/C][C]0.357476089424832[/C][C]0.714952178849663[/C][C]0.642523910575169[/C][/ROW]
[ROW][C]90[/C][C]0.435125722946492[/C][C]0.870251445892983[/C][C]0.564874277053508[/C][/ROW]
[ROW][C]91[/C][C]0.389340469815081[/C][C]0.778680939630162[/C][C]0.610659530184919[/C][/ROW]
[ROW][C]92[/C][C]0.343342123872697[/C][C]0.686684247745394[/C][C]0.656657876127303[/C][/ROW]
[ROW][C]93[/C][C]0.299680152867048[/C][C]0.599360305734096[/C][C]0.700319847132952[/C][/ROW]
[ROW][C]94[/C][C]0.284309579307714[/C][C]0.568619158615428[/C][C]0.715690420692286[/C][/ROW]
[ROW][C]95[/C][C]0.275615609853023[/C][C]0.551231219706046[/C][C]0.724384390146977[/C][/ROW]
[ROW][C]96[/C][C]0.237338562786868[/C][C]0.474677125573736[/C][C]0.762661437213132[/C][/ROW]
[ROW][C]97[/C][C]0.200911282002757[/C][C]0.401822564005514[/C][C]0.799088717997243[/C][/ROW]
[ROW][C]98[/C][C]0.170844380972221[/C][C]0.341688761944443[/C][C]0.829155619027779[/C][/ROW]
[ROW][C]99[/C][C]0.158077222244039[/C][C]0.316154444488078[/C][C]0.841922777755961[/C][/ROW]
[ROW][C]100[/C][C]0.199083575602278[/C][C]0.398167151204555[/C][C]0.800916424397722[/C][/ROW]
[ROW][C]101[/C][C]0.183665355732903[/C][C]0.367330711465805[/C][C]0.816334644267097[/C][/ROW]
[ROW][C]102[/C][C]0.16125284000399[/C][C]0.322505680007981[/C][C]0.83874715999601[/C][/ROW]
[ROW][C]103[/C][C]0.199128351364392[/C][C]0.398256702728784[/C][C]0.800871648635608[/C][/ROW]
[ROW][C]104[/C][C]0.200835657867152[/C][C]0.401671315734305[/C][C]0.799164342132848[/C][/ROW]
[ROW][C]105[/C][C]0.181701922632488[/C][C]0.363403845264976[/C][C]0.818298077367512[/C][/ROW]
[ROW][C]106[/C][C]0.182728024725316[/C][C]0.365456049450632[/C][C]0.817271975274684[/C][/ROW]
[ROW][C]107[/C][C]0.163471804937842[/C][C]0.326943609875683[/C][C]0.836528195062158[/C][/ROW]
[ROW][C]108[/C][C]0.144948540882238[/C][C]0.289897081764476[/C][C]0.855051459117762[/C][/ROW]
[ROW][C]109[/C][C]0.304034710537121[/C][C]0.608069421074242[/C][C]0.695965289462879[/C][/ROW]
[ROW][C]110[/C][C]0.324196432831954[/C][C]0.648392865663908[/C][C]0.675803567168046[/C][/ROW]
[ROW][C]111[/C][C]0.292573869393004[/C][C]0.585147738786008[/C][C]0.707426130606996[/C][/ROW]
[ROW][C]112[/C][C]0.24802308805065[/C][C]0.4960461761013[/C][C]0.75197691194935[/C][/ROW]
[ROW][C]113[/C][C]0.209952168820551[/C][C]0.419904337641102[/C][C]0.790047831179449[/C][/ROW]
[ROW][C]114[/C][C]0.174964204717485[/C][C]0.34992840943497[/C][C]0.825035795282515[/C][/ROW]
[ROW][C]115[/C][C]0.141205206426543[/C][C]0.282410412853086[/C][C]0.858794793573457[/C][/ROW]
[ROW][C]116[/C][C]0.11726006562632[/C][C]0.23452013125264[/C][C]0.88273993437368[/C][/ROW]
[ROW][C]117[/C][C]0.322791914846307[/C][C]0.645583829692613[/C][C]0.677208085153693[/C][/ROW]
[ROW][C]118[/C][C]0.276742957506227[/C][C]0.553485915012454[/C][C]0.723257042493773[/C][/ROW]
[ROW][C]119[/C][C]0.286831247426089[/C][C]0.573662494852178[/C][C]0.713168752573911[/C][/ROW]
[ROW][C]120[/C][C]0.239693067072168[/C][C]0.479386134144335[/C][C]0.760306932927832[/C][/ROW]
[ROW][C]121[/C][C]0.214481945875157[/C][C]0.428963891750315[/C][C]0.785518054124843[/C][/ROW]
[ROW][C]122[/C][C]0.190299476543381[/C][C]0.380598953086761[/C][C]0.809700523456619[/C][/ROW]
[ROW][C]123[/C][C]0.231299724363703[/C][C]0.462599448727405[/C][C]0.768700275636297[/C][/ROW]
[ROW][C]124[/C][C]0.221040431299894[/C][C]0.442080862599787[/C][C]0.778959568700106[/C][/ROW]
[ROW][C]125[/C][C]0.179094677711047[/C][C]0.358189355422094[/C][C]0.820905322288953[/C][/ROW]
[ROW][C]126[/C][C]0.154318164899115[/C][C]0.30863632979823[/C][C]0.845681835100885[/C][/ROW]
[ROW][C]127[/C][C]0.127765522315111[/C][C]0.255531044630223[/C][C]0.872234477684889[/C][/ROW]
[ROW][C]128[/C][C]0.111849303293769[/C][C]0.223698606587539[/C][C]0.888150696706231[/C][/ROW]
[ROW][C]129[/C][C]0.183190647721221[/C][C]0.366381295442442[/C][C]0.816809352278779[/C][/ROW]
[ROW][C]130[/C][C]0.141496156402915[/C][C]0.28299231280583[/C][C]0.858503843597085[/C][/ROW]
[ROW][C]131[/C][C]0.104821583447072[/C][C]0.209643166894144[/C][C]0.895178416552928[/C][/ROW]
[ROW][C]132[/C][C]0.0890905656387385[/C][C]0.178181131277477[/C][C]0.910909434361261[/C][/ROW]
[ROW][C]133[/C][C]0.064960649985038[/C][C]0.129921299970076[/C][C]0.935039350014962[/C][/ROW]
[ROW][C]134[/C][C]0.447705738999014[/C][C]0.895411477998028[/C][C]0.552294261000986[/C][/ROW]
[ROW][C]135[/C][C]0.374187316599779[/C][C]0.748374633199558[/C][C]0.625812683400221[/C][/ROW]
[ROW][C]136[/C][C]0.306267728845655[/C][C]0.612535457691311[/C][C]0.693732271154345[/C][/ROW]
[ROW][C]137[/C][C]0.275424745727355[/C][C]0.55084949145471[/C][C]0.724575254272645[/C][/ROW]
[ROW][C]138[/C][C]0.236699485127694[/C][C]0.473398970255388[/C][C]0.763300514872306[/C][/ROW]
[ROW][C]139[/C][C]0.316455752499638[/C][C]0.632911504999276[/C][C]0.683544247500362[/C][/ROW]
[ROW][C]140[/C][C]0.223004586584214[/C][C]0.446009173168428[/C][C]0.776995413415786[/C][/ROW]
[ROW][C]141[/C][C]0.996969420529873[/C][C]0.00606115894025315[/C][C]0.00303057947012658[/C][/ROW]
[ROW][C]142[/C][C]0.988430202796716[/C][C]0.0231395944065684[/C][C]0.0115697972032842[/C][/ROW]
[ROW][C]143[/C][C]0.957485111687091[/C][C]0.0850297766258178[/C][C]0.0425148883129089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7653202264814430.4693595470371130.234679773518557
80.7451821896403170.5096356207193660.254817810359683
90.6771705051783860.6456589896432280.322829494821614
100.7957674430146190.4084651139707610.204232556985381
110.7328146959564190.5343706080871610.267185304043581
120.6871012796375380.6257974407249250.312898720362462
130.5954961754945950.809007649010810.404503824505405
140.8035753104997570.3928493790004860.196424689500243
150.7583329178770440.4833341642459120.241667082122956
160.6881173099030280.6237653801939440.311882690096972
170.6140183419525770.7719633160948460.385981658047423
180.5600585173910890.8798829652178220.439941482608911
190.509795941724850.9804081165503010.49020405827515
200.4326211430364350.865242286072870.567378856963565
210.3596689400860250.719337880172050.640331059913975
220.9223527529499010.1552944941001990.0776472470500994
230.9029123331612550.1941753336774890.0970876668387447
240.886429838394570.227140323210860.11357016160543
250.8696570443298960.2606859113402080.130342955670104
260.8645604308663180.2708791382673650.135439569133682
270.8318838710948510.3362322578102980.168116128905149
280.7946785292196960.4106429415606080.205321470780304
290.7600251518834420.4799496962331160.239974848116558
300.7832832092470340.4334335815059320.216716790752966
310.7370746478060290.5258507043879420.262925352193971
320.702892494555150.59421501088970.29710750544485
330.6803497925204210.6393004149591580.319650207479579
340.6469915403581710.7060169192836580.353008459641829
350.7179487030143820.5641025939712370.282051296985618
360.7514296840203080.4971406319593850.248570315979692
370.8241137263397750.351772547320450.175886273660225
380.786069732236450.4278605355271010.21393026776355
390.7575690004101440.4848619991797120.242430999589856
400.7175104733986520.5649790532026950.282489526601348
410.8039733783940830.3920532432118350.196026621605917
420.7664559971843250.4670880056313490.233544002815675
430.724708267495930.5505834650081410.27529173250407
440.6893066462753190.6213867074493610.310693353724681
450.6804232164021520.6391535671956960.319576783597848
460.8130354865664860.3739290268670270.186964513433514
470.8121241061964380.3757517876071240.187875893803562
480.7767643867929340.4464712264141310.223235613207066
490.7499124430738080.5001751138523840.250087556926192
500.7124328878914990.5751342242170030.287567112108501
510.6698219360141360.6603561279717280.330178063985864
520.6244520595196920.7510958809606160.375547940480308
530.7433115667192850.5133768665614310.256688433280715
540.7072256517976250.5855486964047510.292774348202375
550.6640761399392680.6718477201214650.335923860060732
560.6818242918676860.6363514162646280.318175708132314
570.6705352600675870.6589294798648260.329464739932413
580.6629737337426070.6740525325147860.337026266257393
590.6365790174868630.7268419650262750.363420982513137
600.5906731128986830.8186537742026350.409326887101317
610.556217519529260.887564960941480.44378248047074
620.5252563326282340.9494873347435330.474743667371766
630.4817146772164990.9634293544329980.518285322783501
640.4353879201172690.8707758402345380.564612079882731
650.3901219064507160.7802438129014320.609878093549284
660.3557289301538770.7114578603077540.644271069846123
670.4373571683694250.8747143367388490.562642831630575
680.4570514189021550.914102837804310.542948581097845
690.4106765994547730.8213531989095460.589323400545227
700.3789441239579230.7578882479158460.621055876042077
710.3371264426741160.6742528853482330.662873557325884
720.3050836176235830.6101672352471660.694916382376417
730.2686916466721170.5373832933442350.731308353327883
740.232913212474740.465826424949480.76708678752526
750.2116360610794370.4232721221588730.788363938920563
760.2111237861135070.4222475722270130.788876213886494
770.2257485903311910.4514971806623810.774251409668809
780.5539224026122110.8921551947755780.446077597387789
790.5265258142764780.9469483714470440.473474185723522
800.4830726658743850.966145331748770.516927334125615
810.4358773949016440.8717547898032870.564122605098356
820.6182803986560060.7634392026879880.381719601343994
830.572527228295610.8549455434087790.42747277170439
840.5348703563276630.9302592873446730.465129643672337
850.4893023444368220.9786046888736430.510697655563178
860.4431149602407680.8862299204815350.556885039759232
870.4246501400526990.8493002801053990.575349859947301
880.3956775065202740.7913550130405480.604322493479726
890.3574760894248320.7149521788496630.642523910575169
900.4351257229464920.8702514458929830.564874277053508
910.3893404698150810.7786809396301620.610659530184919
920.3433421238726970.6866842477453940.656657876127303
930.2996801528670480.5993603057340960.700319847132952
940.2843095793077140.5686191586154280.715690420692286
950.2756156098530230.5512312197060460.724384390146977
960.2373385627868680.4746771255737360.762661437213132
970.2009112820027570.4018225640055140.799088717997243
980.1708443809722210.3416887619444430.829155619027779
990.1580772222440390.3161544444880780.841922777755961
1000.1990835756022780.3981671512045550.800916424397722
1010.1836653557329030.3673307114658050.816334644267097
1020.161252840003990.3225056800079810.83874715999601
1030.1991283513643920.3982567027287840.800871648635608
1040.2008356578671520.4016713157343050.799164342132848
1050.1817019226324880.3634038452649760.818298077367512
1060.1827280247253160.3654560494506320.817271975274684
1070.1634718049378420.3269436098756830.836528195062158
1080.1449485408822380.2898970817644760.855051459117762
1090.3040347105371210.6080694210742420.695965289462879
1100.3241964328319540.6483928656639080.675803567168046
1110.2925738693930040.5851477387860080.707426130606996
1120.248023088050650.49604617610130.75197691194935
1130.2099521688205510.4199043376411020.790047831179449
1140.1749642047174850.349928409434970.825035795282515
1150.1412052064265430.2824104128530860.858794793573457
1160.117260065626320.234520131252640.88273993437368
1170.3227919148463070.6455838296926130.677208085153693
1180.2767429575062270.5534859150124540.723257042493773
1190.2868312474260890.5736624948521780.713168752573911
1200.2396930670721680.4793861341443350.760306932927832
1210.2144819458751570.4289638917503150.785518054124843
1220.1902994765433810.3805989530867610.809700523456619
1230.2312997243637030.4625994487274050.768700275636297
1240.2210404312998940.4420808625997870.778959568700106
1250.1790946777110470.3581893554220940.820905322288953
1260.1543181648991150.308636329798230.845681835100885
1270.1277655223151110.2555310446302230.872234477684889
1280.1118493032937690.2236986065875390.888150696706231
1290.1831906477212210.3663812954424420.816809352278779
1300.1414961564029150.282992312805830.858503843597085
1310.1048215834470720.2096431668941440.895178416552928
1320.08909056563873850.1781811312774770.910909434361261
1330.0649606499850380.1299212999700760.935039350014962
1340.4477057389990140.8954114779980280.552294261000986
1350.3741873165997790.7483746331995580.625812683400221
1360.3062677288456550.6125354576913110.693732271154345
1370.2754247457273550.550849491454710.724575254272645
1380.2366994851276940.4733989702553880.763300514872306
1390.3164557524996380.6329115049992760.683544247500362
1400.2230045865842140.4460091731684280.776995413415786
1410.9969694205298730.006061158940253150.00303057947012658
1420.9884302027967160.02313959440656840.0115697972032842
1430.9574851116870910.08502977662581780.0425148883129089







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0072992700729927OK
5% type I error level20.0145985401459854OK
10% type I error level30.0218978102189781OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.0072992700729927 & OK \tabularnewline
5% type I error level & 2 & 0.0145985401459854 & OK \tabularnewline
10% type I error level & 3 & 0.0218978102189781 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201625&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]1[/C][C]0.0072992700729927[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0145985401459854[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0218978102189781[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201625&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201625&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0072992700729927OK
5% type I error level20.0145985401459854OK
10% type I error level30.0218978102189781OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}