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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 10 Dec 2011 07:36:38 -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/2011/Dec/10/t1323520694z4h34ph1cm06xin.htm/, Retrieved Sun, 05 May 2024 00:54:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153499, Retrieved Sun, 05 May 2024 00:54:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Regression] [2011-12-10 11:44:02] [4fa66579543b578a51dd12826fe0c6d2]
-    D    [Multiple Regression] [Multiple Regression] [2011-12-10 12:36:38] [03e2d80b807f43987d5267414f2021cf] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	26	41,41	43	100	93
-1	30	64,16	102	116	115
1	14	29,56	33	46	37
-4	35	41,42	56	128	95
2	28	42,46	59	97	90
-4	39	50,88	91	149	138
2	27	39,79	67	105	104
-4	29	36,27	67	109	107
-3	31	88,17	116	117	82
2	38	35,67	32	145	120
2	37	90,17	113	136	133
-2	31	54,40	111	116	98
0	39	70,69	120	150	117
-2	12	32,95	54	48	43
-3	17	27,25	55	66	47
0	17	23,36	17	66	63
0	47	90,22	158	181	168
0	34	48,53	123	129	120
-1	33	62,12	105	125	120
3	36	90,31	84	136	126
-2	32	73,82	96	124	120
2	29	60,82	76	108	96
0	21	94,88	94	80	78
-3	29	54,60	41	111	99
2	35	53,71	100	87	71
3	37	41,24	135	141	129
-1	29	67,52	58	112	104
-3	28	45,21	68	108	107
-3	20	37,04	56	78	56
0	22	28,20	59	88	87
0	33	67,99	98	124	115
2	31	63,68	63	120	119
-2	18	25,69	25	71	55
-3	37	75,52	109	147	86
3	32	26,45	37	111	48
4	30	49,81	108	116	103
-4	44	48,15	86	166	148
4	40	71,91	104	139	124
-3	30	74,90	106	115	93
-2	28	57,27	75	107	99
0	38	86,52	128	146	129
-2	32	57,55	56	123	114
2	40	54,16	66	155	151
4	33	76,54	116	127	115
-4	40	51,25	64	151	140
0	15	15,04	37	55	30
2	30	58,59	79	115	94
4	34	39,93	105	132	120
2	33	50,58	124	124	118
-1	24	27,18	25	87	66
0	17	30,12	22	68	65
1	12	16,50	29	41	24
0	31	65,77	77	116	71
2	44	67,79	101	166	164
0	21	23,77	37	78	36
3	30	27,99	83	119	93
0	32	42,35	106	123	83
1	13	18,19	16	51	46
2	20	21,20	29	76	68
-2	17	8,61	5	68	41
-4	22	41,83	27	83	71
4	33	81,78	107	127	124
0	17	26,82	42	54	38
0	28	46,52	69	104	72
1	41	62,52	93	158	139
0	39	62,31	131	144	132
0	17	27,26	15	68	48
2	17	33,85	37	64	52
1	17	8,83	0	66	47
0	38	65,89	78	140	123
4	30	36,98	44	99	90
1	30	95,64	80	108	108
3	31	48,45	73	118	114
2	34	100,64	76	125	110
2	33	42,31	62	122	98
3	20	31,28	46	78	73
0	42	67,64	133	155	110
3	36	36,80	71	136	98
6	30	50,45	47	110	73
3	39	77,21	115	149	133
5	28	64,81	92	107	102
2	40	50,61	77	155	138
3	43	47,92	46	165	139
5	40	97,67	95	150	141
3	32	55,41	87	118	105

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=0

As an alternative you can also use a QR Code:  
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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
totaal[t] = -0.604492496288522 + 0.310686161719778compendiumsreviewed[t] -0.000221739955477566timeinhours[t] -0.000631587197465898bloggedcomputations[t] -0.0883012318859086feedbackmessagesp1[t] + 0.0189471828420857feedbackmessagesp120[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal[t] =  -0.604492496288522 +  0.310686161719778compendiumsreviewed[t] -0.000221739955477566timeinhours[t] -0.000631587197465898bloggedcomputations[t] -0.0883012318859086feedbackmessagesp1[t] +  0.0189471828420857feedbackmessagesp120[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal[t] =  -0.604492496288522 +  0.310686161719778compendiumsreviewed[t] -0.000221739955477566timeinhours[t] -0.000631587197465898bloggedcomputations[t] -0.0883012318859086feedbackmessagesp1[t] +  0.0189471828420857feedbackmessagesp120[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
totaal[t] = -0.604492496288522 + 0.310686161719778compendiumsreviewed[t] -0.000221739955477566timeinhours[t] -0.000631587197465898bloggedcomputations[t] -0.0883012318859086feedbackmessagesp1[t] + 0.0189471828420857feedbackmessagesp120[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6044924962885221.013818-0.59630.552710.276355
compendiumsreviewed0.3106861617197780.1677291.85230.0677170.033859
timeinhours-0.0002217399554775660.018543-0.0120.9904890.495245
bloggedcomputations-0.0006315871974658980.012583-0.05020.9600950.480047
feedbackmessagesp1-0.08830123188590860.049209-1.79440.0765710.038286
feedbackmessagesp1200.01894718284208570.0212550.89140.37540.1877

\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) & -0.604492496288522 & 1.013818 & -0.5963 & 0.55271 & 0.276355 \tabularnewline
compendiumsreviewed & 0.310686161719778 & 0.167729 & 1.8523 & 0.067717 & 0.033859 \tabularnewline
timeinhours & -0.000221739955477566 & 0.018543 & -0.012 & 0.990489 & 0.495245 \tabularnewline
bloggedcomputations & -0.000631587197465898 & 0.012583 & -0.0502 & 0.960095 & 0.480047 \tabularnewline
feedbackmessagesp1 & -0.0883012318859086 & 0.049209 & -1.7944 & 0.076571 & 0.038286 \tabularnewline
feedbackmessagesp120 & 0.0189471828420857 & 0.021255 & 0.8914 & 0.3754 & 0.1877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&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]-0.604492496288522[/C][C]1.013818[/C][C]-0.5963[/C][C]0.55271[/C][C]0.276355[/C][/ROW]
[ROW][C]compendiumsreviewed[/C][C]0.310686161719778[/C][C]0.167729[/C][C]1.8523[/C][C]0.067717[/C][C]0.033859[/C][/ROW]
[ROW][C]timeinhours[/C][C]-0.000221739955477566[/C][C]0.018543[/C][C]-0.012[/C][C]0.990489[/C][C]0.495245[/C][/ROW]
[ROW][C]bloggedcomputations[/C][C]-0.000631587197465898[/C][C]0.012583[/C][C]-0.0502[/C][C]0.960095[/C][C]0.480047[/C][/ROW]
[ROW][C]feedbackmessagesp1[/C][C]-0.0883012318859086[/C][C]0.049209[/C][C]-1.7944[/C][C]0.076571[/C][C]0.038286[/C][/ROW]
[ROW][C]feedbackmessagesp120[/C][C]0.0189471828420857[/C][C]0.021255[/C][C]0.8914[/C][C]0.3754[/C][C]0.1877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=2

As an alternative you can also use a QR Code:  
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)-0.6044924962885221.013818-0.59630.552710.276355
compendiumsreviewed0.3106861617197780.1677291.85230.0677170.033859
timeinhours-0.0002217399554775660.018543-0.0120.9904890.495245
bloggedcomputations-0.0006315871974658980.012583-0.05020.9600950.480047
feedbackmessagesp1-0.08830123188590860.049209-1.79440.0765710.038286
feedbackmessagesp1200.01894718284208570.0212550.89140.37540.1877






Multiple Linear Regression - Regression Statistics
Multiple R0.250700233530257
R-squared0.0628506070921256
Adjusted R-squared0.00353735437643743
F-TEST (value)1.05963851609004
F-TEST (DF numerator)5
F-TEST (DF denominator)79
p-value0.389132396853084
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.457678281504
Sum Squared Residuals477.174420294739

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.250700233530257 \tabularnewline
R-squared & 0.0628506070921256 \tabularnewline
Adjusted R-squared & 0.00353735437643743 \tabularnewline
F-TEST (value) & 1.05963851609004 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 79 \tabularnewline
p-value & 0.389132396853084 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.457678281504 \tabularnewline
Sum Squared Residuals & 477.174420294739 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.250700233530257[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0628506070921256[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00353735437643743[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.05963851609004[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]79[/C][/ROW]
[ROW][C]p-value[/C][C]0.389132396853084[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.457678281504[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]477.174420294739[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.250700233530257
R-squared0.0628506070921256
Adjusted R-squared0.00353735437643743
F-TEST (value)1.05963851609004
F-TEST (DF numerator)5
F-TEST (DF denominator)79
p-value0.389132396853084
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.457678281504
Sum Squared Residuals477.174420294739






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.368972023101444-0.368972023101444
2-10.573426753694297-1.5734267536943
310.3569058555934460.643094144406554
4-40.722394500491559-4.72239450049156
521.188068271559760.81193172844024
6-40.901338928086147-4.90133892808615
720.4317721626433111.56822783735669
8-40.75756163170877-4.75756163170877
9-30.1563884526438-3.1563884526438
1020.6434447121257651.35655528787423
1121.310539623758010.689460376241986
12-20.558490704286886-2.55849070428689
1300.392438159271919-0.392438159271919
14-2-0.341400864161262-1.65859913583874
15-3-0.300971167591633-2.69902883240837
1600.0270466398122502-0.0270466398122502
1701.07856469467916-1.07856469467916
1800.753193764624354-0.753193764624354
19-10.804067654007657-1.80406765400766
2030.885508167276382.11449183272362
21-20.584772651471893-2.58477265147189
2220.6263158516475691.37368414835243
2300.253290727099295-0.253290727099295
24-30.441738478950477-3.44173847895048
2523.85749759886244-1.85749759886244
2630.7901995506373952.2098004493626
27-10.434571298693306-1.43457129869331
28-30.532562759475466-3.53256275947547
29-3-0.260805240846024-2.73919475915398
3000.0649828514531253-0.0649828514531253
3100.800752468526745-0.800752468526745
3220.6314350551185821.36856494488142
33-2-0.260920172310191-1.73907982768981
34-3-0.545516681427398-2.4544833185726
3530.4162589676999972.5837410323
3640.3454530047655763.65454699523442
37-41.14688390911123-5.14688390911123
3841.816903024045062.18309697595494
39-30.239982127142628-3.23998212714263
40-20.462191234379369-2.46219123437937
4100.653760278125867-0.653760278125867
42-20.588261983279544-2.58826198327954
4320.9435934483202691.05640655167973
4440.52258432669533.4774156733047
45-41.09028780226633-5.09028780226633
460-0.2590560341908830.259056034190883
4720.2795987429901321.72040125700987
4840.5015656021381213.49843439786188
4920.8450332425437531.15496675745625
50-10.398465706563218-1.39846570656322
510-0.1163183563617540.116318356361754
521-0.06385141275527871.06385141275528
5300.0658695489706336-0.0658695489706336
5421.436210053897030.563789946102967
550-0.314120090006920.31412009000692
563-0.08829447354783863.08829447354784
570-0.02730959737514740.0273095973751474
581-0.2115036543264031.2115036543264
5920.1637286322571651.83627136774283
60-2-0.555544135772566-1.44445586422743
61-40.220522560135046-4.22052256013505
6240.6976313396845623.30236866031544
6300.596424951208412-0.596424951208412
6400.22169422100696-0.22169422100696
6510.7431031199179210.256896880082079
6601.20136401487343-1.20136401487343
670-0.4333651780222830.433365178022283
682-0.0197277037611532.01972770376115
691-0.2621494217511111.26214942175111
7001.1060384275436-1.1060384275436
7141.64352707414552.3564729258545
7211.15412087343278-0.154120873432782
7330.7103628322272362.28963716777276
7420.9350565929480931.06494340705191
7520.6836842451485421.31631575485146
7630.06888996158764242.93111003841236
7700.74282587840426-0.74282587840426
7830.3750629862826432.62493701371736
7960.3452298162923725.65477018370763
8030.7856065081088142.21439349189119
8150.5066238812844044.4933761187156
8220.6911197890429761.30888021095702
8330.7792888217869862.22071117821301
8451.167663845139613.83233615486039
8530.8401388175135952.1598611824864

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.368972023101444 & -0.368972023101444 \tabularnewline
2 & -1 & 0.573426753694297 & -1.5734267536943 \tabularnewline
3 & 1 & 0.356905855593446 & 0.643094144406554 \tabularnewline
4 & -4 & 0.722394500491559 & -4.72239450049156 \tabularnewline
5 & 2 & 1.18806827155976 & 0.81193172844024 \tabularnewline
6 & -4 & 0.901338928086147 & -4.90133892808615 \tabularnewline
7 & 2 & 0.431772162643311 & 1.56822783735669 \tabularnewline
8 & -4 & 0.75756163170877 & -4.75756163170877 \tabularnewline
9 & -3 & 0.1563884526438 & -3.1563884526438 \tabularnewline
10 & 2 & 0.643444712125765 & 1.35655528787423 \tabularnewline
11 & 2 & 1.31053962375801 & 0.689460376241986 \tabularnewline
12 & -2 & 0.558490704286886 & -2.55849070428689 \tabularnewline
13 & 0 & 0.392438159271919 & -0.392438159271919 \tabularnewline
14 & -2 & -0.341400864161262 & -1.65859913583874 \tabularnewline
15 & -3 & -0.300971167591633 & -2.69902883240837 \tabularnewline
16 & 0 & 0.0270466398122502 & -0.0270466398122502 \tabularnewline
17 & 0 & 1.07856469467916 & -1.07856469467916 \tabularnewline
18 & 0 & 0.753193764624354 & -0.753193764624354 \tabularnewline
19 & -1 & 0.804067654007657 & -1.80406765400766 \tabularnewline
20 & 3 & 0.88550816727638 & 2.11449183272362 \tabularnewline
21 & -2 & 0.584772651471893 & -2.58477265147189 \tabularnewline
22 & 2 & 0.626315851647569 & 1.37368414835243 \tabularnewline
23 & 0 & 0.253290727099295 & -0.253290727099295 \tabularnewline
24 & -3 & 0.441738478950477 & -3.44173847895048 \tabularnewline
25 & 2 & 3.85749759886244 & -1.85749759886244 \tabularnewline
26 & 3 & 0.790199550637395 & 2.2098004493626 \tabularnewline
27 & -1 & 0.434571298693306 & -1.43457129869331 \tabularnewline
28 & -3 & 0.532562759475466 & -3.53256275947547 \tabularnewline
29 & -3 & -0.260805240846024 & -2.73919475915398 \tabularnewline
30 & 0 & 0.0649828514531253 & -0.0649828514531253 \tabularnewline
31 & 0 & 0.800752468526745 & -0.800752468526745 \tabularnewline
32 & 2 & 0.631435055118582 & 1.36856494488142 \tabularnewline
33 & -2 & -0.260920172310191 & -1.73907982768981 \tabularnewline
34 & -3 & -0.545516681427398 & -2.4544833185726 \tabularnewline
35 & 3 & 0.416258967699997 & 2.5837410323 \tabularnewline
36 & 4 & 0.345453004765576 & 3.65454699523442 \tabularnewline
37 & -4 & 1.14688390911123 & -5.14688390911123 \tabularnewline
38 & 4 & 1.81690302404506 & 2.18309697595494 \tabularnewline
39 & -3 & 0.239982127142628 & -3.23998212714263 \tabularnewline
40 & -2 & 0.462191234379369 & -2.46219123437937 \tabularnewline
41 & 0 & 0.653760278125867 & -0.653760278125867 \tabularnewline
42 & -2 & 0.588261983279544 & -2.58826198327954 \tabularnewline
43 & 2 & 0.943593448320269 & 1.05640655167973 \tabularnewline
44 & 4 & 0.5225843266953 & 3.4774156733047 \tabularnewline
45 & -4 & 1.09028780226633 & -5.09028780226633 \tabularnewline
46 & 0 & -0.259056034190883 & 0.259056034190883 \tabularnewline
47 & 2 & 0.279598742990132 & 1.72040125700987 \tabularnewline
48 & 4 & 0.501565602138121 & 3.49843439786188 \tabularnewline
49 & 2 & 0.845033242543753 & 1.15496675745625 \tabularnewline
50 & -1 & 0.398465706563218 & -1.39846570656322 \tabularnewline
51 & 0 & -0.116318356361754 & 0.116318356361754 \tabularnewline
52 & 1 & -0.0638514127552787 & 1.06385141275528 \tabularnewline
53 & 0 & 0.0658695489706336 & -0.0658695489706336 \tabularnewline
54 & 2 & 1.43621005389703 & 0.563789946102967 \tabularnewline
55 & 0 & -0.31412009000692 & 0.31412009000692 \tabularnewline
56 & 3 & -0.0882944735478386 & 3.08829447354784 \tabularnewline
57 & 0 & -0.0273095973751474 & 0.0273095973751474 \tabularnewline
58 & 1 & -0.211503654326403 & 1.2115036543264 \tabularnewline
59 & 2 & 0.163728632257165 & 1.83627136774283 \tabularnewline
60 & -2 & -0.555544135772566 & -1.44445586422743 \tabularnewline
61 & -4 & 0.220522560135046 & -4.22052256013505 \tabularnewline
62 & 4 & 0.697631339684562 & 3.30236866031544 \tabularnewline
63 & 0 & 0.596424951208412 & -0.596424951208412 \tabularnewline
64 & 0 & 0.22169422100696 & -0.22169422100696 \tabularnewline
65 & 1 & 0.743103119917921 & 0.256896880082079 \tabularnewline
66 & 0 & 1.20136401487343 & -1.20136401487343 \tabularnewline
67 & 0 & -0.433365178022283 & 0.433365178022283 \tabularnewline
68 & 2 & -0.019727703761153 & 2.01972770376115 \tabularnewline
69 & 1 & -0.262149421751111 & 1.26214942175111 \tabularnewline
70 & 0 & 1.1060384275436 & -1.1060384275436 \tabularnewline
71 & 4 & 1.6435270741455 & 2.3564729258545 \tabularnewline
72 & 1 & 1.15412087343278 & -0.154120873432782 \tabularnewline
73 & 3 & 0.710362832227236 & 2.28963716777276 \tabularnewline
74 & 2 & 0.935056592948093 & 1.06494340705191 \tabularnewline
75 & 2 & 0.683684245148542 & 1.31631575485146 \tabularnewline
76 & 3 & 0.0688899615876424 & 2.93111003841236 \tabularnewline
77 & 0 & 0.74282587840426 & -0.74282587840426 \tabularnewline
78 & 3 & 0.375062986282643 & 2.62493701371736 \tabularnewline
79 & 6 & 0.345229816292372 & 5.65477018370763 \tabularnewline
80 & 3 & 0.785606508108814 & 2.21439349189119 \tabularnewline
81 & 5 & 0.506623881284404 & 4.4933761187156 \tabularnewline
82 & 2 & 0.691119789042976 & 1.30888021095702 \tabularnewline
83 & 3 & 0.779288821786986 & 2.22071117821301 \tabularnewline
84 & 5 & 1.16766384513961 & 3.83233615486039 \tabularnewline
85 & 3 & 0.840138817513595 & 2.1598611824864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&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]0[/C][C]0.368972023101444[/C][C]-0.368972023101444[/C][/ROW]
[ROW][C]2[/C][C]-1[/C][C]0.573426753694297[/C][C]-1.5734267536943[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.356905855593446[/C][C]0.643094144406554[/C][/ROW]
[ROW][C]4[/C][C]-4[/C][C]0.722394500491559[/C][C]-4.72239450049156[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.18806827155976[/C][C]0.81193172844024[/C][/ROW]
[ROW][C]6[/C][C]-4[/C][C]0.901338928086147[/C][C]-4.90133892808615[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]0.431772162643311[/C][C]1.56822783735669[/C][/ROW]
[ROW][C]8[/C][C]-4[/C][C]0.75756163170877[/C][C]-4.75756163170877[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]0.1563884526438[/C][C]-3.1563884526438[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]0.643444712125765[/C][C]1.35655528787423[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]1.31053962375801[/C][C]0.689460376241986[/C][/ROW]
[ROW][C]12[/C][C]-2[/C][C]0.558490704286886[/C][C]-2.55849070428689[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.392438159271919[/C][C]-0.392438159271919[/C][/ROW]
[ROW][C]14[/C][C]-2[/C][C]-0.341400864161262[/C][C]-1.65859913583874[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-0.300971167591633[/C][C]-2.69902883240837[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.0270466398122502[/C][C]-0.0270466398122502[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]1.07856469467916[/C][C]-1.07856469467916[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.753193764624354[/C][C]-0.753193764624354[/C][/ROW]
[ROW][C]19[/C][C]-1[/C][C]0.804067654007657[/C][C]-1.80406765400766[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]0.88550816727638[/C][C]2.11449183272362[/C][/ROW]
[ROW][C]21[/C][C]-2[/C][C]0.584772651471893[/C][C]-2.58477265147189[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.626315851647569[/C][C]1.37368414835243[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.253290727099295[/C][C]-0.253290727099295[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]0.441738478950477[/C][C]-3.44173847895048[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]3.85749759886244[/C][C]-1.85749759886244[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]0.790199550637395[/C][C]2.2098004493626[/C][/ROW]
[ROW][C]27[/C][C]-1[/C][C]0.434571298693306[/C][C]-1.43457129869331[/C][/ROW]
[ROW][C]28[/C][C]-3[/C][C]0.532562759475466[/C][C]-3.53256275947547[/C][/ROW]
[ROW][C]29[/C][C]-3[/C][C]-0.260805240846024[/C][C]-2.73919475915398[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0649828514531253[/C][C]-0.0649828514531253[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.800752468526745[/C][C]-0.800752468526745[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]0.631435055118582[/C][C]1.36856494488142[/C][/ROW]
[ROW][C]33[/C][C]-2[/C][C]-0.260920172310191[/C][C]-1.73907982768981[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]-0.545516681427398[/C][C]-2.4544833185726[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]0.416258967699997[/C][C]2.5837410323[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]0.345453004765576[/C][C]3.65454699523442[/C][/ROW]
[ROW][C]37[/C][C]-4[/C][C]1.14688390911123[/C][C]-5.14688390911123[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]1.81690302404506[/C][C]2.18309697595494[/C][/ROW]
[ROW][C]39[/C][C]-3[/C][C]0.239982127142628[/C][C]-3.23998212714263[/C][/ROW]
[ROW][C]40[/C][C]-2[/C][C]0.462191234379369[/C][C]-2.46219123437937[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.653760278125867[/C][C]-0.653760278125867[/C][/ROW]
[ROW][C]42[/C][C]-2[/C][C]0.588261983279544[/C][C]-2.58826198327954[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]0.943593448320269[/C][C]1.05640655167973[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]0.5225843266953[/C][C]3.4774156733047[/C][/ROW]
[ROW][C]45[/C][C]-4[/C][C]1.09028780226633[/C][C]-5.09028780226633[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]-0.259056034190883[/C][C]0.259056034190883[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]0.279598742990132[/C][C]1.72040125700987[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]0.501565602138121[/C][C]3.49843439786188[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]0.845033242543753[/C][C]1.15496675745625[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]0.398465706563218[/C][C]-1.39846570656322[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]-0.116318356361754[/C][C]0.116318356361754[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]-0.0638514127552787[/C][C]1.06385141275528[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0658695489706336[/C][C]-0.0658695489706336[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.43621005389703[/C][C]0.563789946102967[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.31412009000692[/C][C]0.31412009000692[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]-0.0882944735478386[/C][C]3.08829447354784[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]-0.0273095973751474[/C][C]0.0273095973751474[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]-0.211503654326403[/C][C]1.2115036543264[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]0.163728632257165[/C][C]1.83627136774283[/C][/ROW]
[ROW][C]60[/C][C]-2[/C][C]-0.555544135772566[/C][C]-1.44445586422743[/C][/ROW]
[ROW][C]61[/C][C]-4[/C][C]0.220522560135046[/C][C]-4.22052256013505[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]0.697631339684562[/C][C]3.30236866031544[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.596424951208412[/C][C]-0.596424951208412[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.22169422100696[/C][C]-0.22169422100696[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.743103119917921[/C][C]0.256896880082079[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]1.20136401487343[/C][C]-1.20136401487343[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]-0.433365178022283[/C][C]0.433365178022283[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]-0.019727703761153[/C][C]2.01972770376115[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]-0.262149421751111[/C][C]1.26214942175111[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]1.1060384275436[/C][C]-1.1060384275436[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]1.6435270741455[/C][C]2.3564729258545[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.15412087343278[/C][C]-0.154120873432782[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]0.710362832227236[/C][C]2.28963716777276[/C][/ROW]
[ROW][C]74[/C][C]2[/C][C]0.935056592948093[/C][C]1.06494340705191[/C][/ROW]
[ROW][C]75[/C][C]2[/C][C]0.683684245148542[/C][C]1.31631575485146[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]0.0688899615876424[/C][C]2.93111003841236[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.74282587840426[/C][C]-0.74282587840426[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]0.375062986282643[/C][C]2.62493701371736[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]0.345229816292372[/C][C]5.65477018370763[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]0.785606508108814[/C][C]2.21439349189119[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]0.506623881284404[/C][C]4.4933761187156[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]0.691119789042976[/C][C]1.30888021095702[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]0.779288821786986[/C][C]2.22071117821301[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]1.16766384513961[/C][C]3.83233615486039[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]0.840138817513595[/C][C]2.1598611824864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.368972023101444-0.368972023101444
2-10.573426753694297-1.5734267536943
310.3569058555934460.643094144406554
4-40.722394500491559-4.72239450049156
521.188068271559760.81193172844024
6-40.901338928086147-4.90133892808615
720.4317721626433111.56822783735669
8-40.75756163170877-4.75756163170877
9-30.1563884526438-3.1563884526438
1020.6434447121257651.35655528787423
1121.310539623758010.689460376241986
12-20.558490704286886-2.55849070428689
1300.392438159271919-0.392438159271919
14-2-0.341400864161262-1.65859913583874
15-3-0.300971167591633-2.69902883240837
1600.0270466398122502-0.0270466398122502
1701.07856469467916-1.07856469467916
1800.753193764624354-0.753193764624354
19-10.804067654007657-1.80406765400766
2030.885508167276382.11449183272362
21-20.584772651471893-2.58477265147189
2220.6263158516475691.37368414835243
2300.253290727099295-0.253290727099295
24-30.441738478950477-3.44173847895048
2523.85749759886244-1.85749759886244
2630.7901995506373952.2098004493626
27-10.434571298693306-1.43457129869331
28-30.532562759475466-3.53256275947547
29-3-0.260805240846024-2.73919475915398
3000.0649828514531253-0.0649828514531253
3100.800752468526745-0.800752468526745
3220.6314350551185821.36856494488142
33-2-0.260920172310191-1.73907982768981
34-3-0.545516681427398-2.4544833185726
3530.4162589676999972.5837410323
3640.3454530047655763.65454699523442
37-41.14688390911123-5.14688390911123
3841.816903024045062.18309697595494
39-30.239982127142628-3.23998212714263
40-20.462191234379369-2.46219123437937
4100.653760278125867-0.653760278125867
42-20.588261983279544-2.58826198327954
4320.9435934483202691.05640655167973
4440.52258432669533.4774156733047
45-41.09028780226633-5.09028780226633
460-0.2590560341908830.259056034190883
4720.2795987429901321.72040125700987
4840.5015656021381213.49843439786188
4920.8450332425437531.15496675745625
50-10.398465706563218-1.39846570656322
510-0.1163183563617540.116318356361754
521-0.06385141275527871.06385141275528
5300.0658695489706336-0.0658695489706336
5421.436210053897030.563789946102967
550-0.314120090006920.31412009000692
563-0.08829447354783863.08829447354784
570-0.02730959737514740.0273095973751474
581-0.2115036543264031.2115036543264
5920.1637286322571651.83627136774283
60-2-0.555544135772566-1.44445586422743
61-40.220522560135046-4.22052256013505
6240.6976313396845623.30236866031544
6300.596424951208412-0.596424951208412
6400.22169422100696-0.22169422100696
6510.7431031199179210.256896880082079
6601.20136401487343-1.20136401487343
670-0.4333651780222830.433365178022283
682-0.0197277037611532.01972770376115
691-0.2621494217511111.26214942175111
7001.1060384275436-1.1060384275436
7141.64352707414552.3564729258545
7211.15412087343278-0.154120873432782
7330.7103628322272362.28963716777276
7420.9350565929480931.06494340705191
7520.6836842451485421.31631575485146
7630.06888996158764242.93111003841236
7700.74282587840426-0.74282587840426
7830.3750629862826432.62493701371736
7960.3452298162923725.65477018370763
8030.7856065081088142.21439349189119
8150.5066238812844044.4933761187156
8220.6911197890429761.30888021095702
8330.7792888217869862.22071117821301
8451.167663845139613.83233615486039
8530.8401388175135952.1598611824864






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.655150259252450.6896994814950990.344849740747549
100.6407444543081720.7185110913836550.359255545691828
110.516006835991520.967986328016960.48399316400848
120.6195469372511940.7609061254976130.380453062748806
130.7290449120635270.5419101758729460.270955087936473
140.6516090634470510.6967818731058980.348390936552949
150.5715458948388380.8569082103223240.428454105161162
160.4733059230362930.9466118460725860.526694076963707
170.4153401628950590.8306803257901170.584659837104941
180.4160092587713050.832018517542610.583990741228695
190.3493853590733540.6987707181467080.650614640926646
200.2902172300253770.5804344600507540.709782769974623
210.3096320554310030.6192641108620070.690367944568997
220.2804699694545560.5609399389091120.719530030545444
230.2289080845095890.4578161690191770.771091915490411
240.301665117960860.603330235921720.69833488203914
250.2800275112384540.5600550224769070.719972488761546
260.4047494032295390.8094988064590790.59525059677046
270.3463185832716520.6926371665433040.653681416728348
280.4054335686248420.8108671372496840.594566431375158
290.3822280112914510.7644560225829020.617771988708549
300.3269437640413810.6538875280827630.673056235958619
310.2757831122013710.5515662244027410.72421688779863
320.2453680283097190.4907360566194380.754631971690281
330.2077326957615560.4154653915231120.792267304238444
340.18826364265480.37652728530960.8117363573452
350.3069309040945550.6138618081891110.693069095905445
360.4351615261910810.8703230523821620.564838473808919
370.6372728475465010.7254543049069980.362727152453499
380.6405290027602630.7189419944794740.359470997239737
390.7135044061616150.572991187676770.286495593838385
400.7395752182925610.5208495634148780.260424781707439
410.7235048985878460.5529902028243070.276495101412154
420.7532037739902190.4935924520195620.246796226009781
430.7205006191248580.5589987617502830.279499380875142
440.763198935539260.4736021289214790.236801064460739
450.9361238890790820.1277522218418360.063876110920918
460.9153125532082610.1693748935834770.0846874467917387
470.9021089246104510.1957821507790980.097891075389549
480.9194919051315150.1610161897369690.0805080948684846
490.8945605598429470.2108788803141070.105439440157053
500.8813833386391050.2372333227217890.118616661360895
510.8557532585714920.2884934828570160.144246741428508
520.8202678052071940.3594643895856130.179732194792806
530.7774610360924020.4450779278151960.222538963907598
540.7475879611650070.5048240776699860.252412038834993
550.6910197373827840.6179605252344310.308980262617216
560.696207727448150.60758454510370.30379227255185
570.632079117331970.735841765336060.36792088266803
580.5708427076375730.8583145847248540.429157292362427
590.5210344916241260.9579310167517470.478965508375874
600.5060828652821680.9878342694356640.493917134717832
610.8454033570712880.3091932858574230.154596642928712
620.846242473866760.3075150522664790.153757526133239
630.8188088665980950.362382266803810.181191133401905
640.7932447015392510.4135105969214980.206755298460749
650.7489181372342770.5021637255314460.251081862765723
660.7498771867239640.5002456265520720.250122813276036
670.748627760942290.502744478115420.25137223905771
680.6892778484067760.6214443031864490.310722151593224
690.7613197376843470.4773605246313050.238680262315653
700.7958962552096110.4082074895807780.204103744790389
710.7984580177327980.4030839645344050.201541982267202
720.8004197263650880.3991605472698240.199580273634912
730.7076085401454060.5847829197091870.292391459854594
740.954530902514410.09093819497117790.0454690974855889
750.9102850855191740.1794298289616520.089714914480826
760.9913353834274520.01732923314509530.00866461657254763

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.65515025925245 & 0.689699481495099 & 0.344849740747549 \tabularnewline
10 & 0.640744454308172 & 0.718511091383655 & 0.359255545691828 \tabularnewline
11 & 0.51600683599152 & 0.96798632801696 & 0.48399316400848 \tabularnewline
12 & 0.619546937251194 & 0.760906125497613 & 0.380453062748806 \tabularnewline
13 & 0.729044912063527 & 0.541910175872946 & 0.270955087936473 \tabularnewline
14 & 0.651609063447051 & 0.696781873105898 & 0.348390936552949 \tabularnewline
15 & 0.571545894838838 & 0.856908210322324 & 0.428454105161162 \tabularnewline
16 & 0.473305923036293 & 0.946611846072586 & 0.526694076963707 \tabularnewline
17 & 0.415340162895059 & 0.830680325790117 & 0.584659837104941 \tabularnewline
18 & 0.416009258771305 & 0.83201851754261 & 0.583990741228695 \tabularnewline
19 & 0.349385359073354 & 0.698770718146708 & 0.650614640926646 \tabularnewline
20 & 0.290217230025377 & 0.580434460050754 & 0.709782769974623 \tabularnewline
21 & 0.309632055431003 & 0.619264110862007 & 0.690367944568997 \tabularnewline
22 & 0.280469969454556 & 0.560939938909112 & 0.719530030545444 \tabularnewline
23 & 0.228908084509589 & 0.457816169019177 & 0.771091915490411 \tabularnewline
24 & 0.30166511796086 & 0.60333023592172 & 0.69833488203914 \tabularnewline
25 & 0.280027511238454 & 0.560055022476907 & 0.719972488761546 \tabularnewline
26 & 0.404749403229539 & 0.809498806459079 & 0.59525059677046 \tabularnewline
27 & 0.346318583271652 & 0.692637166543304 & 0.653681416728348 \tabularnewline
28 & 0.405433568624842 & 0.810867137249684 & 0.594566431375158 \tabularnewline
29 & 0.382228011291451 & 0.764456022582902 & 0.617771988708549 \tabularnewline
30 & 0.326943764041381 & 0.653887528082763 & 0.673056235958619 \tabularnewline
31 & 0.275783112201371 & 0.551566224402741 & 0.72421688779863 \tabularnewline
32 & 0.245368028309719 & 0.490736056619438 & 0.754631971690281 \tabularnewline
33 & 0.207732695761556 & 0.415465391523112 & 0.792267304238444 \tabularnewline
34 & 0.1882636426548 & 0.3765272853096 & 0.8117363573452 \tabularnewline
35 & 0.306930904094555 & 0.613861808189111 & 0.693069095905445 \tabularnewline
36 & 0.435161526191081 & 0.870323052382162 & 0.564838473808919 \tabularnewline
37 & 0.637272847546501 & 0.725454304906998 & 0.362727152453499 \tabularnewline
38 & 0.640529002760263 & 0.718941994479474 & 0.359470997239737 \tabularnewline
39 & 0.713504406161615 & 0.57299118767677 & 0.286495593838385 \tabularnewline
40 & 0.739575218292561 & 0.520849563414878 & 0.260424781707439 \tabularnewline
41 & 0.723504898587846 & 0.552990202824307 & 0.276495101412154 \tabularnewline
42 & 0.753203773990219 & 0.493592452019562 & 0.246796226009781 \tabularnewline
43 & 0.720500619124858 & 0.558998761750283 & 0.279499380875142 \tabularnewline
44 & 0.76319893553926 & 0.473602128921479 & 0.236801064460739 \tabularnewline
45 & 0.936123889079082 & 0.127752221841836 & 0.063876110920918 \tabularnewline
46 & 0.915312553208261 & 0.169374893583477 & 0.0846874467917387 \tabularnewline
47 & 0.902108924610451 & 0.195782150779098 & 0.097891075389549 \tabularnewline
48 & 0.919491905131515 & 0.161016189736969 & 0.0805080948684846 \tabularnewline
49 & 0.894560559842947 & 0.210878880314107 & 0.105439440157053 \tabularnewline
50 & 0.881383338639105 & 0.237233322721789 & 0.118616661360895 \tabularnewline
51 & 0.855753258571492 & 0.288493482857016 & 0.144246741428508 \tabularnewline
52 & 0.820267805207194 & 0.359464389585613 & 0.179732194792806 \tabularnewline
53 & 0.777461036092402 & 0.445077927815196 & 0.222538963907598 \tabularnewline
54 & 0.747587961165007 & 0.504824077669986 & 0.252412038834993 \tabularnewline
55 & 0.691019737382784 & 0.617960525234431 & 0.308980262617216 \tabularnewline
56 & 0.69620772744815 & 0.6075845451037 & 0.30379227255185 \tabularnewline
57 & 0.63207911733197 & 0.73584176533606 & 0.36792088266803 \tabularnewline
58 & 0.570842707637573 & 0.858314584724854 & 0.429157292362427 \tabularnewline
59 & 0.521034491624126 & 0.957931016751747 & 0.478965508375874 \tabularnewline
60 & 0.506082865282168 & 0.987834269435664 & 0.493917134717832 \tabularnewline
61 & 0.845403357071288 & 0.309193285857423 & 0.154596642928712 \tabularnewline
62 & 0.84624247386676 & 0.307515052266479 & 0.153757526133239 \tabularnewline
63 & 0.818808866598095 & 0.36238226680381 & 0.181191133401905 \tabularnewline
64 & 0.793244701539251 & 0.413510596921498 & 0.206755298460749 \tabularnewline
65 & 0.748918137234277 & 0.502163725531446 & 0.251081862765723 \tabularnewline
66 & 0.749877186723964 & 0.500245626552072 & 0.250122813276036 \tabularnewline
67 & 0.74862776094229 & 0.50274447811542 & 0.25137223905771 \tabularnewline
68 & 0.689277848406776 & 0.621444303186449 & 0.310722151593224 \tabularnewline
69 & 0.761319737684347 & 0.477360524631305 & 0.238680262315653 \tabularnewline
70 & 0.795896255209611 & 0.408207489580778 & 0.204103744790389 \tabularnewline
71 & 0.798458017732798 & 0.403083964534405 & 0.201541982267202 \tabularnewline
72 & 0.800419726365088 & 0.399160547269824 & 0.199580273634912 \tabularnewline
73 & 0.707608540145406 & 0.584782919709187 & 0.292391459854594 \tabularnewline
74 & 0.95453090251441 & 0.0909381949711779 & 0.0454690974855889 \tabularnewline
75 & 0.910285085519174 & 0.179429828961652 & 0.089714914480826 \tabularnewline
76 & 0.991335383427452 & 0.0173292331450953 & 0.00866461657254763 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&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]9[/C][C]0.65515025925245[/C][C]0.689699481495099[/C][C]0.344849740747549[/C][/ROW]
[ROW][C]10[/C][C]0.640744454308172[/C][C]0.718511091383655[/C][C]0.359255545691828[/C][/ROW]
[ROW][C]11[/C][C]0.51600683599152[/C][C]0.96798632801696[/C][C]0.48399316400848[/C][/ROW]
[ROW][C]12[/C][C]0.619546937251194[/C][C]0.760906125497613[/C][C]0.380453062748806[/C][/ROW]
[ROW][C]13[/C][C]0.729044912063527[/C][C]0.541910175872946[/C][C]0.270955087936473[/C][/ROW]
[ROW][C]14[/C][C]0.651609063447051[/C][C]0.696781873105898[/C][C]0.348390936552949[/C][/ROW]
[ROW][C]15[/C][C]0.571545894838838[/C][C]0.856908210322324[/C][C]0.428454105161162[/C][/ROW]
[ROW][C]16[/C][C]0.473305923036293[/C][C]0.946611846072586[/C][C]0.526694076963707[/C][/ROW]
[ROW][C]17[/C][C]0.415340162895059[/C][C]0.830680325790117[/C][C]0.584659837104941[/C][/ROW]
[ROW][C]18[/C][C]0.416009258771305[/C][C]0.83201851754261[/C][C]0.583990741228695[/C][/ROW]
[ROW][C]19[/C][C]0.349385359073354[/C][C]0.698770718146708[/C][C]0.650614640926646[/C][/ROW]
[ROW][C]20[/C][C]0.290217230025377[/C][C]0.580434460050754[/C][C]0.709782769974623[/C][/ROW]
[ROW][C]21[/C][C]0.309632055431003[/C][C]0.619264110862007[/C][C]0.690367944568997[/C][/ROW]
[ROW][C]22[/C][C]0.280469969454556[/C][C]0.560939938909112[/C][C]0.719530030545444[/C][/ROW]
[ROW][C]23[/C][C]0.228908084509589[/C][C]0.457816169019177[/C][C]0.771091915490411[/C][/ROW]
[ROW][C]24[/C][C]0.30166511796086[/C][C]0.60333023592172[/C][C]0.69833488203914[/C][/ROW]
[ROW][C]25[/C][C]0.280027511238454[/C][C]0.560055022476907[/C][C]0.719972488761546[/C][/ROW]
[ROW][C]26[/C][C]0.404749403229539[/C][C]0.809498806459079[/C][C]0.59525059677046[/C][/ROW]
[ROW][C]27[/C][C]0.346318583271652[/C][C]0.692637166543304[/C][C]0.653681416728348[/C][/ROW]
[ROW][C]28[/C][C]0.405433568624842[/C][C]0.810867137249684[/C][C]0.594566431375158[/C][/ROW]
[ROW][C]29[/C][C]0.382228011291451[/C][C]0.764456022582902[/C][C]0.617771988708549[/C][/ROW]
[ROW][C]30[/C][C]0.326943764041381[/C][C]0.653887528082763[/C][C]0.673056235958619[/C][/ROW]
[ROW][C]31[/C][C]0.275783112201371[/C][C]0.551566224402741[/C][C]0.72421688779863[/C][/ROW]
[ROW][C]32[/C][C]0.245368028309719[/C][C]0.490736056619438[/C][C]0.754631971690281[/C][/ROW]
[ROW][C]33[/C][C]0.207732695761556[/C][C]0.415465391523112[/C][C]0.792267304238444[/C][/ROW]
[ROW][C]34[/C][C]0.1882636426548[/C][C]0.3765272853096[/C][C]0.8117363573452[/C][/ROW]
[ROW][C]35[/C][C]0.306930904094555[/C][C]0.613861808189111[/C][C]0.693069095905445[/C][/ROW]
[ROW][C]36[/C][C]0.435161526191081[/C][C]0.870323052382162[/C][C]0.564838473808919[/C][/ROW]
[ROW][C]37[/C][C]0.637272847546501[/C][C]0.725454304906998[/C][C]0.362727152453499[/C][/ROW]
[ROW][C]38[/C][C]0.640529002760263[/C][C]0.718941994479474[/C][C]0.359470997239737[/C][/ROW]
[ROW][C]39[/C][C]0.713504406161615[/C][C]0.57299118767677[/C][C]0.286495593838385[/C][/ROW]
[ROW][C]40[/C][C]0.739575218292561[/C][C]0.520849563414878[/C][C]0.260424781707439[/C][/ROW]
[ROW][C]41[/C][C]0.723504898587846[/C][C]0.552990202824307[/C][C]0.276495101412154[/C][/ROW]
[ROW][C]42[/C][C]0.753203773990219[/C][C]0.493592452019562[/C][C]0.246796226009781[/C][/ROW]
[ROW][C]43[/C][C]0.720500619124858[/C][C]0.558998761750283[/C][C]0.279499380875142[/C][/ROW]
[ROW][C]44[/C][C]0.76319893553926[/C][C]0.473602128921479[/C][C]0.236801064460739[/C][/ROW]
[ROW][C]45[/C][C]0.936123889079082[/C][C]0.127752221841836[/C][C]0.063876110920918[/C][/ROW]
[ROW][C]46[/C][C]0.915312553208261[/C][C]0.169374893583477[/C][C]0.0846874467917387[/C][/ROW]
[ROW][C]47[/C][C]0.902108924610451[/C][C]0.195782150779098[/C][C]0.097891075389549[/C][/ROW]
[ROW][C]48[/C][C]0.919491905131515[/C][C]0.161016189736969[/C][C]0.0805080948684846[/C][/ROW]
[ROW][C]49[/C][C]0.894560559842947[/C][C]0.210878880314107[/C][C]0.105439440157053[/C][/ROW]
[ROW][C]50[/C][C]0.881383338639105[/C][C]0.237233322721789[/C][C]0.118616661360895[/C][/ROW]
[ROW][C]51[/C][C]0.855753258571492[/C][C]0.288493482857016[/C][C]0.144246741428508[/C][/ROW]
[ROW][C]52[/C][C]0.820267805207194[/C][C]0.359464389585613[/C][C]0.179732194792806[/C][/ROW]
[ROW][C]53[/C][C]0.777461036092402[/C][C]0.445077927815196[/C][C]0.222538963907598[/C][/ROW]
[ROW][C]54[/C][C]0.747587961165007[/C][C]0.504824077669986[/C][C]0.252412038834993[/C][/ROW]
[ROW][C]55[/C][C]0.691019737382784[/C][C]0.617960525234431[/C][C]0.308980262617216[/C][/ROW]
[ROW][C]56[/C][C]0.69620772744815[/C][C]0.6075845451037[/C][C]0.30379227255185[/C][/ROW]
[ROW][C]57[/C][C]0.63207911733197[/C][C]0.73584176533606[/C][C]0.36792088266803[/C][/ROW]
[ROW][C]58[/C][C]0.570842707637573[/C][C]0.858314584724854[/C][C]0.429157292362427[/C][/ROW]
[ROW][C]59[/C][C]0.521034491624126[/C][C]0.957931016751747[/C][C]0.478965508375874[/C][/ROW]
[ROW][C]60[/C][C]0.506082865282168[/C][C]0.987834269435664[/C][C]0.493917134717832[/C][/ROW]
[ROW][C]61[/C][C]0.845403357071288[/C][C]0.309193285857423[/C][C]0.154596642928712[/C][/ROW]
[ROW][C]62[/C][C]0.84624247386676[/C][C]0.307515052266479[/C][C]0.153757526133239[/C][/ROW]
[ROW][C]63[/C][C]0.818808866598095[/C][C]0.36238226680381[/C][C]0.181191133401905[/C][/ROW]
[ROW][C]64[/C][C]0.793244701539251[/C][C]0.413510596921498[/C][C]0.206755298460749[/C][/ROW]
[ROW][C]65[/C][C]0.748918137234277[/C][C]0.502163725531446[/C][C]0.251081862765723[/C][/ROW]
[ROW][C]66[/C][C]0.749877186723964[/C][C]0.500245626552072[/C][C]0.250122813276036[/C][/ROW]
[ROW][C]67[/C][C]0.74862776094229[/C][C]0.50274447811542[/C][C]0.25137223905771[/C][/ROW]
[ROW][C]68[/C][C]0.689277848406776[/C][C]0.621444303186449[/C][C]0.310722151593224[/C][/ROW]
[ROW][C]69[/C][C]0.761319737684347[/C][C]0.477360524631305[/C][C]0.238680262315653[/C][/ROW]
[ROW][C]70[/C][C]0.795896255209611[/C][C]0.408207489580778[/C][C]0.204103744790389[/C][/ROW]
[ROW][C]71[/C][C]0.798458017732798[/C][C]0.403083964534405[/C][C]0.201541982267202[/C][/ROW]
[ROW][C]72[/C][C]0.800419726365088[/C][C]0.399160547269824[/C][C]0.199580273634912[/C][/ROW]
[ROW][C]73[/C][C]0.707608540145406[/C][C]0.584782919709187[/C][C]0.292391459854594[/C][/ROW]
[ROW][C]74[/C][C]0.95453090251441[/C][C]0.0909381949711779[/C][C]0.0454690974855889[/C][/ROW]
[ROW][C]75[/C][C]0.910285085519174[/C][C]0.179429828961652[/C][C]0.089714914480826[/C][/ROW]
[ROW][C]76[/C][C]0.991335383427452[/C][C]0.0173292331450953[/C][C]0.00866461657254763[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.655150259252450.6896994814950990.344849740747549
100.6407444543081720.7185110913836550.359255545691828
110.516006835991520.967986328016960.48399316400848
120.6195469372511940.7609061254976130.380453062748806
130.7290449120635270.5419101758729460.270955087936473
140.6516090634470510.6967818731058980.348390936552949
150.5715458948388380.8569082103223240.428454105161162
160.4733059230362930.9466118460725860.526694076963707
170.4153401628950590.8306803257901170.584659837104941
180.4160092587713050.832018517542610.583990741228695
190.3493853590733540.6987707181467080.650614640926646
200.2902172300253770.5804344600507540.709782769974623
210.3096320554310030.6192641108620070.690367944568997
220.2804699694545560.5609399389091120.719530030545444
230.2289080845095890.4578161690191770.771091915490411
240.301665117960860.603330235921720.69833488203914
250.2800275112384540.5600550224769070.719972488761546
260.4047494032295390.8094988064590790.59525059677046
270.3463185832716520.6926371665433040.653681416728348
280.4054335686248420.8108671372496840.594566431375158
290.3822280112914510.7644560225829020.617771988708549
300.3269437640413810.6538875280827630.673056235958619
310.2757831122013710.5515662244027410.72421688779863
320.2453680283097190.4907360566194380.754631971690281
330.2077326957615560.4154653915231120.792267304238444
340.18826364265480.37652728530960.8117363573452
350.3069309040945550.6138618081891110.693069095905445
360.4351615261910810.8703230523821620.564838473808919
370.6372728475465010.7254543049069980.362727152453499
380.6405290027602630.7189419944794740.359470997239737
390.7135044061616150.572991187676770.286495593838385
400.7395752182925610.5208495634148780.260424781707439
410.7235048985878460.5529902028243070.276495101412154
420.7532037739902190.4935924520195620.246796226009781
430.7205006191248580.5589987617502830.279499380875142
440.763198935539260.4736021289214790.236801064460739
450.9361238890790820.1277522218418360.063876110920918
460.9153125532082610.1693748935834770.0846874467917387
470.9021089246104510.1957821507790980.097891075389549
480.9194919051315150.1610161897369690.0805080948684846
490.8945605598429470.2108788803141070.105439440157053
500.8813833386391050.2372333227217890.118616661360895
510.8557532585714920.2884934828570160.144246741428508
520.8202678052071940.3594643895856130.179732194792806
530.7774610360924020.4450779278151960.222538963907598
540.7475879611650070.5048240776699860.252412038834993
550.6910197373827840.6179605252344310.308980262617216
560.696207727448150.60758454510370.30379227255185
570.632079117331970.735841765336060.36792088266803
580.5708427076375730.8583145847248540.429157292362427
590.5210344916241260.9579310167517470.478965508375874
600.5060828652821680.9878342694356640.493917134717832
610.8454033570712880.3091932858574230.154596642928712
620.846242473866760.3075150522664790.153757526133239
630.8188088665980950.362382266803810.181191133401905
640.7932447015392510.4135105969214980.206755298460749
650.7489181372342770.5021637255314460.251081862765723
660.7498771867239640.5002456265520720.250122813276036
670.748627760942290.502744478115420.25137223905771
680.6892778484067760.6214443031864490.310722151593224
690.7613197376843470.4773605246313050.238680262315653
700.7958962552096110.4082074895807780.204103744790389
710.7984580177327980.4030839645344050.201541982267202
720.8004197263650880.3991605472698240.199580273634912
730.7076085401454060.5847829197091870.292391459854594
740.954530902514410.09093819497117790.0454690974855889
750.9102850855191740.1794298289616520.089714914480826
760.9913353834274520.01732923314509530.00866461657254763






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0147058823529412OK
10% type I error level20.0294117647058824OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0147058823529412 & OK \tabularnewline
10% type I error level & 2 & 0.0294117647058824 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153499&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0147058823529412[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0294117647058824[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153499&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153499&T=6

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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 level00OK
5% type I error level10.0147058823529412OK
10% type I error level20.0294117647058824OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}