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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, 17 Dec 2011 09:27:26 -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/17/t1324132126rrmnsjewp7jzrid.htm/, Retrieved Fri, 29 Mar 2024 00:14:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156336, Retrieved Fri, 29 Mar 2024 00:14:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MR 1 gewogen som] [2011-12-17 14:27:26] [10a6f28c51bb1cb94db47cee32729d66] [Current]
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Dataseries X:
79	30	115
108	30	116
43	26	100
78	38	140
86	44	166
44	30	99
104	40	139
158	47	181
102	30	116
77	31	116
80	30	108
123	34	129
73	31	118
105	33	125
107	33	127
84	36	136
33	14	46
42	17	54
96	32	124
106	30	115
56	35	128
59	28	97
76	34	125
91	39	149
115	39	149
76	29	108
101	44	166
94	21	80
92	28	107
75	28	107
128	38	146
56	32	123
41	29	111
67	27	105
77	40	155
66	40	155
69	28	104
105	34	132
116	33	127
62	33	122
100	35	87
67	29	109
46	20	78
135	37	141
124	33	124
58	29	112
68	28	108
37	21	78
93	41	158
56	20	78
83	30	119
59	22	88
133	42	155
106	32	123
71	36	136
116	31	117
98	33	124
64	40	151
32	38	145
25	24	87
46	43	165
63	31	120
95	40	150
113	37	136
111	31	116
120	39	150
87	32	118
25	18	71
131	39	144
47	30	110
109	37	147
37	32	111
15	17	68
54	12	48
16	13	51
22	17	68
37	17	64
29	20	76
55	17	66
5	17	68
0	17	66
27	22	83
37	15	55
29	12	41
17	17	66




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
fdb120[t] = + 0.715095705119632 + 0.0100774363911148blog[t] + 3.71089560381921reviews[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
fdb120[t] =  +  0.715095705119632 +  0.0100774363911148blog[t] +  3.71089560381921reviews[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]fdb120[t] =  +  0.715095705119632 +  0.0100774363911148blog[t] +  3.71089560381921reviews[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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
fdb120[t] = + 0.715095705119632 + 0.0100774363911148blog[t] + 3.71089560381921reviews[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7150957051196322.5201260.28380.7773140.388657
blog0.01007743639111480.0278210.36220.7181180.359059
reviews3.710895603819210.11354632.681800

\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) & 0.715095705119632 & 2.520126 & 0.2838 & 0.777314 & 0.388657 \tabularnewline
blog & 0.0100774363911148 & 0.027821 & 0.3622 & 0.718118 & 0.359059 \tabularnewline
reviews & 3.71089560381921 & 0.113546 & 32.6818 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&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]0.715095705119632[/C][C]2.520126[/C][C]0.2838[/C][C]0.777314[/C][C]0.388657[/C][/ROW]
[ROW][C]blog[/C][C]0.0100774363911148[/C][C]0.027821[/C][C]0.3622[/C][C]0.718118[/C][C]0.359059[/C][/ROW]
[ROW][C]reviews[/C][C]3.71089560381921[/C][C]0.113546[/C][C]32.6818[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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)0.7150957051196322.5201260.28380.7773140.388657
blog0.01007743639111480.0278210.36220.7181180.359059
reviews3.710895603819210.11354632.681800







Multiple Linear Regression - Regression Statistics
Multiple R0.981571240955586
R-squared0.963482101071089
Adjusted R-squared0.962591420609409
F-TEST (value)1081.7371016009
F-TEST (DF numerator)2
F-TEST (DF denominator)82
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.2874555929212
Sum Squared Residuals3241.6320223024

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.981571240955586 \tabularnewline
R-squared & 0.963482101071089 \tabularnewline
Adjusted R-squared & 0.962591420609409 \tabularnewline
F-TEST (value) & 1081.7371016009 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 82 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.2874555929212 \tabularnewline
Sum Squared Residuals & 3241.6320223024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.981571240955586[/C][/ROW]
[ROW][C]R-squared[/C][C]0.963482101071089[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.962591420609409[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1081.7371016009[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]82[/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]6.2874555929212[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3241.6320223024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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 R0.981571240955586
R-squared0.963482101071089
Adjusted R-squared0.962591420609409
F-TEST (value)1081.7371016009
F-TEST (DF numerator)2
F-TEST (DF denominator)82
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.2874555929212
Sum Squared Residuals3241.6320223024







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1115112.8380812945942.16191870540591
2116113.1303269499362.86967305006376
310097.63171116923692.36828883076308
4140142.515168688756-2.51516868875642
5166164.8611618028011.13883819719943
699112.485371020905-13.4853710209049
7139150.198973242564-11.1989732425638
8181176.7194240344184.28057596558153
9116113.069862331592.93013766841046
10116116.528822025631-0.52882202563087
11108112.848158730985-4.84815873098501
12129128.125070911080.874929088920228
13118116.4885122800661.51148771993359
14125124.232781452220.767218547779501
15127124.2529363250032.74706367499727
16136135.1538420994650.846157900535299
174653.0001895594953-7.00018955949533
185464.223573298473-10.223573298473
19124120.4311889208813.56881107911874
20115113.1101720771541.889827922846
21128131.160778276694-3.16077827669428
2297105.214741359133-8.21474135913319
23125127.651431400697-2.65143140069737
24149146.357070965662.64292903433988
25149146.5989294390472.40107056095312
26108109.096953381601-1.09695338160135
27166165.0123233486670.98767665133271
288079.59118240608780.408817593912229
29107105.547296760041.45270323996002
30107105.3759803413911.62401965860898
31146143.0190405083122.98095949168784
32123120.0280914652372.97190853476334
33111108.7442431079122.25575689208768
34105101.5844652464433.4155347535571
35155149.9268824600045.07311753999629
36155149.8160306597015.18396934029855
37104105.315515723044-1.31551572304434
38132127.943677056044.0563229439603
39127124.3436332525232.65636674747724
40122123.799451687403-1.79945168740256
4187131.604185477903-44.6041854779033
42109109.006256454081-0.00625645408131051
437875.3965698554952.60343014450495
44141139.3786869592311.62131304076923
45124124.424252743652-0.424252743651684
46112108.9155595265613.08444047343872
47108105.3054382866532.69456171334678
487879.0167685317942-1.01676853179422
49158153.7990170460814.20098295391924
507875.49734421940622.5026557805938
51119112.8783910401586.12160895984165
528882.9493677362185.05063226378205
53155157.913010105545-2.91301010554456
54123120.5319632847922.46803671520759
55136135.022835426380.977164573619793
56117116.9218420448840.0781579551156475
57124124.162239397483-0.162239397482694
58151149.7958757869191.20412421308078
59145142.0516066147652.94839338523487
608790.0285261065585-3.02852610655846
61165160.7471687433374.25283125666323
62120116.3877379161553.61226208384474
63150150.108276315044-0.108276315043783
64136139.156983358626-3.15698335862624
65116116.871454862929-0.871454862928778
66150146.6493166210023.35068337899755
67118120.340491993361-2.34049199336122
687167.76315248364323.23684751635678
69144146.760168421305-2.76016842130472
70110112.515603330078-2.51560333007822
71147139.1166736130627.88332638693822
72111119.836620173805-8.83662017380548
736863.95148251591294.04851748408713
744845.79002451607032.20997548392967
755149.11797753702721.88202246297284
766864.02202457065073.97797542934933
776464.1731861165174-0.173186116517394
787675.22525343684610.774746563153908
796664.35457997155751.64542002844253
806863.85070815200174.14929184799828
816663.80032097004612.19967902995386
828382.62688977170230.373110228297724
835556.751394908879-1.75139490887899
844145.5380886062925-4.53808860629246
856663.97163738869512.0283626113049

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 115 & 112.838081294594 & 2.16191870540591 \tabularnewline
2 & 116 & 113.130326949936 & 2.86967305006376 \tabularnewline
3 & 100 & 97.6317111692369 & 2.36828883076308 \tabularnewline
4 & 140 & 142.515168688756 & -2.51516868875642 \tabularnewline
5 & 166 & 164.861161802801 & 1.13883819719943 \tabularnewline
6 & 99 & 112.485371020905 & -13.4853710209049 \tabularnewline
7 & 139 & 150.198973242564 & -11.1989732425638 \tabularnewline
8 & 181 & 176.719424034418 & 4.28057596558153 \tabularnewline
9 & 116 & 113.06986233159 & 2.93013766841046 \tabularnewline
10 & 116 & 116.528822025631 & -0.52882202563087 \tabularnewline
11 & 108 & 112.848158730985 & -4.84815873098501 \tabularnewline
12 & 129 & 128.12507091108 & 0.874929088920228 \tabularnewline
13 & 118 & 116.488512280066 & 1.51148771993359 \tabularnewline
14 & 125 & 124.23278145222 & 0.767218547779501 \tabularnewline
15 & 127 & 124.252936325003 & 2.74706367499727 \tabularnewline
16 & 136 & 135.153842099465 & 0.846157900535299 \tabularnewline
17 & 46 & 53.0001895594953 & -7.00018955949533 \tabularnewline
18 & 54 & 64.223573298473 & -10.223573298473 \tabularnewline
19 & 124 & 120.431188920881 & 3.56881107911874 \tabularnewline
20 & 115 & 113.110172077154 & 1.889827922846 \tabularnewline
21 & 128 & 131.160778276694 & -3.16077827669428 \tabularnewline
22 & 97 & 105.214741359133 & -8.21474135913319 \tabularnewline
23 & 125 & 127.651431400697 & -2.65143140069737 \tabularnewline
24 & 149 & 146.35707096566 & 2.64292903433988 \tabularnewline
25 & 149 & 146.598929439047 & 2.40107056095312 \tabularnewline
26 & 108 & 109.096953381601 & -1.09695338160135 \tabularnewline
27 & 166 & 165.012323348667 & 0.98767665133271 \tabularnewline
28 & 80 & 79.5911824060878 & 0.408817593912229 \tabularnewline
29 & 107 & 105.54729676004 & 1.45270323996002 \tabularnewline
30 & 107 & 105.375980341391 & 1.62401965860898 \tabularnewline
31 & 146 & 143.019040508312 & 2.98095949168784 \tabularnewline
32 & 123 & 120.028091465237 & 2.97190853476334 \tabularnewline
33 & 111 & 108.744243107912 & 2.25575689208768 \tabularnewline
34 & 105 & 101.584465246443 & 3.4155347535571 \tabularnewline
35 & 155 & 149.926882460004 & 5.07311753999629 \tabularnewline
36 & 155 & 149.816030659701 & 5.18396934029855 \tabularnewline
37 & 104 & 105.315515723044 & -1.31551572304434 \tabularnewline
38 & 132 & 127.94367705604 & 4.0563229439603 \tabularnewline
39 & 127 & 124.343633252523 & 2.65636674747724 \tabularnewline
40 & 122 & 123.799451687403 & -1.79945168740256 \tabularnewline
41 & 87 & 131.604185477903 & -44.6041854779033 \tabularnewline
42 & 109 & 109.006256454081 & -0.00625645408131051 \tabularnewline
43 & 78 & 75.396569855495 & 2.60343014450495 \tabularnewline
44 & 141 & 139.378686959231 & 1.62131304076923 \tabularnewline
45 & 124 & 124.424252743652 & -0.424252743651684 \tabularnewline
46 & 112 & 108.915559526561 & 3.08444047343872 \tabularnewline
47 & 108 & 105.305438286653 & 2.69456171334678 \tabularnewline
48 & 78 & 79.0167685317942 & -1.01676853179422 \tabularnewline
49 & 158 & 153.799017046081 & 4.20098295391924 \tabularnewline
50 & 78 & 75.4973442194062 & 2.5026557805938 \tabularnewline
51 & 119 & 112.878391040158 & 6.12160895984165 \tabularnewline
52 & 88 & 82.949367736218 & 5.05063226378205 \tabularnewline
53 & 155 & 157.913010105545 & -2.91301010554456 \tabularnewline
54 & 123 & 120.531963284792 & 2.46803671520759 \tabularnewline
55 & 136 & 135.02283542638 & 0.977164573619793 \tabularnewline
56 & 117 & 116.921842044884 & 0.0781579551156475 \tabularnewline
57 & 124 & 124.162239397483 & -0.162239397482694 \tabularnewline
58 & 151 & 149.795875786919 & 1.20412421308078 \tabularnewline
59 & 145 & 142.051606614765 & 2.94839338523487 \tabularnewline
60 & 87 & 90.0285261065585 & -3.02852610655846 \tabularnewline
61 & 165 & 160.747168743337 & 4.25283125666323 \tabularnewline
62 & 120 & 116.387737916155 & 3.61226208384474 \tabularnewline
63 & 150 & 150.108276315044 & -0.108276315043783 \tabularnewline
64 & 136 & 139.156983358626 & -3.15698335862624 \tabularnewline
65 & 116 & 116.871454862929 & -0.871454862928778 \tabularnewline
66 & 150 & 146.649316621002 & 3.35068337899755 \tabularnewline
67 & 118 & 120.340491993361 & -2.34049199336122 \tabularnewline
68 & 71 & 67.7631524836432 & 3.23684751635678 \tabularnewline
69 & 144 & 146.760168421305 & -2.76016842130472 \tabularnewline
70 & 110 & 112.515603330078 & -2.51560333007822 \tabularnewline
71 & 147 & 139.116673613062 & 7.88332638693822 \tabularnewline
72 & 111 & 119.836620173805 & -8.83662017380548 \tabularnewline
73 & 68 & 63.9514825159129 & 4.04851748408713 \tabularnewline
74 & 48 & 45.7900245160703 & 2.20997548392967 \tabularnewline
75 & 51 & 49.1179775370272 & 1.88202246297284 \tabularnewline
76 & 68 & 64.0220245706507 & 3.97797542934933 \tabularnewline
77 & 64 & 64.1731861165174 & -0.173186116517394 \tabularnewline
78 & 76 & 75.2252534368461 & 0.774746563153908 \tabularnewline
79 & 66 & 64.3545799715575 & 1.64542002844253 \tabularnewline
80 & 68 & 63.8507081520017 & 4.14929184799828 \tabularnewline
81 & 66 & 63.8003209700461 & 2.19967902995386 \tabularnewline
82 & 83 & 82.6268897717023 & 0.373110228297724 \tabularnewline
83 & 55 & 56.751394908879 & -1.75139490887899 \tabularnewline
84 & 41 & 45.5380886062925 & -4.53808860629246 \tabularnewline
85 & 66 & 63.9716373886951 & 2.0283626113049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&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]115[/C][C]112.838081294594[/C][C]2.16191870540591[/C][/ROW]
[ROW][C]2[/C][C]116[/C][C]113.130326949936[/C][C]2.86967305006376[/C][/ROW]
[ROW][C]3[/C][C]100[/C][C]97.6317111692369[/C][C]2.36828883076308[/C][/ROW]
[ROW][C]4[/C][C]140[/C][C]142.515168688756[/C][C]-2.51516868875642[/C][/ROW]
[ROW][C]5[/C][C]166[/C][C]164.861161802801[/C][C]1.13883819719943[/C][/ROW]
[ROW][C]6[/C][C]99[/C][C]112.485371020905[/C][C]-13.4853710209049[/C][/ROW]
[ROW][C]7[/C][C]139[/C][C]150.198973242564[/C][C]-11.1989732425638[/C][/ROW]
[ROW][C]8[/C][C]181[/C][C]176.719424034418[/C][C]4.28057596558153[/C][/ROW]
[ROW][C]9[/C][C]116[/C][C]113.06986233159[/C][C]2.93013766841046[/C][/ROW]
[ROW][C]10[/C][C]116[/C][C]116.528822025631[/C][C]-0.52882202563087[/C][/ROW]
[ROW][C]11[/C][C]108[/C][C]112.848158730985[/C][C]-4.84815873098501[/C][/ROW]
[ROW][C]12[/C][C]129[/C][C]128.12507091108[/C][C]0.874929088920228[/C][/ROW]
[ROW][C]13[/C][C]118[/C][C]116.488512280066[/C][C]1.51148771993359[/C][/ROW]
[ROW][C]14[/C][C]125[/C][C]124.23278145222[/C][C]0.767218547779501[/C][/ROW]
[ROW][C]15[/C][C]127[/C][C]124.252936325003[/C][C]2.74706367499727[/C][/ROW]
[ROW][C]16[/C][C]136[/C][C]135.153842099465[/C][C]0.846157900535299[/C][/ROW]
[ROW][C]17[/C][C]46[/C][C]53.0001895594953[/C][C]-7.00018955949533[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]64.223573298473[/C][C]-10.223573298473[/C][/ROW]
[ROW][C]19[/C][C]124[/C][C]120.431188920881[/C][C]3.56881107911874[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]113.110172077154[/C][C]1.889827922846[/C][/ROW]
[ROW][C]21[/C][C]128[/C][C]131.160778276694[/C][C]-3.16077827669428[/C][/ROW]
[ROW][C]22[/C][C]97[/C][C]105.214741359133[/C][C]-8.21474135913319[/C][/ROW]
[ROW][C]23[/C][C]125[/C][C]127.651431400697[/C][C]-2.65143140069737[/C][/ROW]
[ROW][C]24[/C][C]149[/C][C]146.35707096566[/C][C]2.64292903433988[/C][/ROW]
[ROW][C]25[/C][C]149[/C][C]146.598929439047[/C][C]2.40107056095312[/C][/ROW]
[ROW][C]26[/C][C]108[/C][C]109.096953381601[/C][C]-1.09695338160135[/C][/ROW]
[ROW][C]27[/C][C]166[/C][C]165.012323348667[/C][C]0.98767665133271[/C][/ROW]
[ROW][C]28[/C][C]80[/C][C]79.5911824060878[/C][C]0.408817593912229[/C][/ROW]
[ROW][C]29[/C][C]107[/C][C]105.54729676004[/C][C]1.45270323996002[/C][/ROW]
[ROW][C]30[/C][C]107[/C][C]105.375980341391[/C][C]1.62401965860898[/C][/ROW]
[ROW][C]31[/C][C]146[/C][C]143.019040508312[/C][C]2.98095949168784[/C][/ROW]
[ROW][C]32[/C][C]123[/C][C]120.028091465237[/C][C]2.97190853476334[/C][/ROW]
[ROW][C]33[/C][C]111[/C][C]108.744243107912[/C][C]2.25575689208768[/C][/ROW]
[ROW][C]34[/C][C]105[/C][C]101.584465246443[/C][C]3.4155347535571[/C][/ROW]
[ROW][C]35[/C][C]155[/C][C]149.926882460004[/C][C]5.07311753999629[/C][/ROW]
[ROW][C]36[/C][C]155[/C][C]149.816030659701[/C][C]5.18396934029855[/C][/ROW]
[ROW][C]37[/C][C]104[/C][C]105.315515723044[/C][C]-1.31551572304434[/C][/ROW]
[ROW][C]38[/C][C]132[/C][C]127.94367705604[/C][C]4.0563229439603[/C][/ROW]
[ROW][C]39[/C][C]127[/C][C]124.343633252523[/C][C]2.65636674747724[/C][/ROW]
[ROW][C]40[/C][C]122[/C][C]123.799451687403[/C][C]-1.79945168740256[/C][/ROW]
[ROW][C]41[/C][C]87[/C][C]131.604185477903[/C][C]-44.6041854779033[/C][/ROW]
[ROW][C]42[/C][C]109[/C][C]109.006256454081[/C][C]-0.00625645408131051[/C][/ROW]
[ROW][C]43[/C][C]78[/C][C]75.396569855495[/C][C]2.60343014450495[/C][/ROW]
[ROW][C]44[/C][C]141[/C][C]139.378686959231[/C][C]1.62131304076923[/C][/ROW]
[ROW][C]45[/C][C]124[/C][C]124.424252743652[/C][C]-0.424252743651684[/C][/ROW]
[ROW][C]46[/C][C]112[/C][C]108.915559526561[/C][C]3.08444047343872[/C][/ROW]
[ROW][C]47[/C][C]108[/C][C]105.305438286653[/C][C]2.69456171334678[/C][/ROW]
[ROW][C]48[/C][C]78[/C][C]79.0167685317942[/C][C]-1.01676853179422[/C][/ROW]
[ROW][C]49[/C][C]158[/C][C]153.799017046081[/C][C]4.20098295391924[/C][/ROW]
[ROW][C]50[/C][C]78[/C][C]75.4973442194062[/C][C]2.5026557805938[/C][/ROW]
[ROW][C]51[/C][C]119[/C][C]112.878391040158[/C][C]6.12160895984165[/C][/ROW]
[ROW][C]52[/C][C]88[/C][C]82.949367736218[/C][C]5.05063226378205[/C][/ROW]
[ROW][C]53[/C][C]155[/C][C]157.913010105545[/C][C]-2.91301010554456[/C][/ROW]
[ROW][C]54[/C][C]123[/C][C]120.531963284792[/C][C]2.46803671520759[/C][/ROW]
[ROW][C]55[/C][C]136[/C][C]135.02283542638[/C][C]0.977164573619793[/C][/ROW]
[ROW][C]56[/C][C]117[/C][C]116.921842044884[/C][C]0.0781579551156475[/C][/ROW]
[ROW][C]57[/C][C]124[/C][C]124.162239397483[/C][C]-0.162239397482694[/C][/ROW]
[ROW][C]58[/C][C]151[/C][C]149.795875786919[/C][C]1.20412421308078[/C][/ROW]
[ROW][C]59[/C][C]145[/C][C]142.051606614765[/C][C]2.94839338523487[/C][/ROW]
[ROW][C]60[/C][C]87[/C][C]90.0285261065585[/C][C]-3.02852610655846[/C][/ROW]
[ROW][C]61[/C][C]165[/C][C]160.747168743337[/C][C]4.25283125666323[/C][/ROW]
[ROW][C]62[/C][C]120[/C][C]116.387737916155[/C][C]3.61226208384474[/C][/ROW]
[ROW][C]63[/C][C]150[/C][C]150.108276315044[/C][C]-0.108276315043783[/C][/ROW]
[ROW][C]64[/C][C]136[/C][C]139.156983358626[/C][C]-3.15698335862624[/C][/ROW]
[ROW][C]65[/C][C]116[/C][C]116.871454862929[/C][C]-0.871454862928778[/C][/ROW]
[ROW][C]66[/C][C]150[/C][C]146.649316621002[/C][C]3.35068337899755[/C][/ROW]
[ROW][C]67[/C][C]118[/C][C]120.340491993361[/C][C]-2.34049199336122[/C][/ROW]
[ROW][C]68[/C][C]71[/C][C]67.7631524836432[/C][C]3.23684751635678[/C][/ROW]
[ROW][C]69[/C][C]144[/C][C]146.760168421305[/C][C]-2.76016842130472[/C][/ROW]
[ROW][C]70[/C][C]110[/C][C]112.515603330078[/C][C]-2.51560333007822[/C][/ROW]
[ROW][C]71[/C][C]147[/C][C]139.116673613062[/C][C]7.88332638693822[/C][/ROW]
[ROW][C]72[/C][C]111[/C][C]119.836620173805[/C][C]-8.83662017380548[/C][/ROW]
[ROW][C]73[/C][C]68[/C][C]63.9514825159129[/C][C]4.04851748408713[/C][/ROW]
[ROW][C]74[/C][C]48[/C][C]45.7900245160703[/C][C]2.20997548392967[/C][/ROW]
[ROW][C]75[/C][C]51[/C][C]49.1179775370272[/C][C]1.88202246297284[/C][/ROW]
[ROW][C]76[/C][C]68[/C][C]64.0220245706507[/C][C]3.97797542934933[/C][/ROW]
[ROW][C]77[/C][C]64[/C][C]64.1731861165174[/C][C]-0.173186116517394[/C][/ROW]
[ROW][C]78[/C][C]76[/C][C]75.2252534368461[/C][C]0.774746563153908[/C][/ROW]
[ROW][C]79[/C][C]66[/C][C]64.3545799715575[/C][C]1.64542002844253[/C][/ROW]
[ROW][C]80[/C][C]68[/C][C]63.8507081520017[/C][C]4.14929184799828[/C][/ROW]
[ROW][C]81[/C][C]66[/C][C]63.8003209700461[/C][C]2.19967902995386[/C][/ROW]
[ROW][C]82[/C][C]83[/C][C]82.6268897717023[/C][C]0.373110228297724[/C][/ROW]
[ROW][C]83[/C][C]55[/C][C]56.751394908879[/C][C]-1.75139490887899[/C][/ROW]
[ROW][C]84[/C][C]41[/C][C]45.5380886062925[/C][C]-4.53808860629246[/C][/ROW]
[ROW][C]85[/C][C]66[/C][C]63.9716373886951[/C][C]2.0283626113049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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
1115112.8380812945942.16191870540591
2116113.1303269499362.86967305006376
310097.63171116923692.36828883076308
4140142.515168688756-2.51516868875642
5166164.8611618028011.13883819719943
699112.485371020905-13.4853710209049
7139150.198973242564-11.1989732425638
8181176.7194240344184.28057596558153
9116113.069862331592.93013766841046
10116116.528822025631-0.52882202563087
11108112.848158730985-4.84815873098501
12129128.125070911080.874929088920228
13118116.4885122800661.51148771993359
14125124.232781452220.767218547779501
15127124.2529363250032.74706367499727
16136135.1538420994650.846157900535299
174653.0001895594953-7.00018955949533
185464.223573298473-10.223573298473
19124120.4311889208813.56881107911874
20115113.1101720771541.889827922846
21128131.160778276694-3.16077827669428
2297105.214741359133-8.21474135913319
23125127.651431400697-2.65143140069737
24149146.357070965662.64292903433988
25149146.5989294390472.40107056095312
26108109.096953381601-1.09695338160135
27166165.0123233486670.98767665133271
288079.59118240608780.408817593912229
29107105.547296760041.45270323996002
30107105.3759803413911.62401965860898
31146143.0190405083122.98095949168784
32123120.0280914652372.97190853476334
33111108.7442431079122.25575689208768
34105101.5844652464433.4155347535571
35155149.9268824600045.07311753999629
36155149.8160306597015.18396934029855
37104105.315515723044-1.31551572304434
38132127.943677056044.0563229439603
39127124.3436332525232.65636674747724
40122123.799451687403-1.79945168740256
4187131.604185477903-44.6041854779033
42109109.006256454081-0.00625645408131051
437875.3965698554952.60343014450495
44141139.3786869592311.62131304076923
45124124.424252743652-0.424252743651684
46112108.9155595265613.08444047343872
47108105.3054382866532.69456171334678
487879.0167685317942-1.01676853179422
49158153.7990170460814.20098295391924
507875.49734421940622.5026557805938
51119112.8783910401586.12160895984165
528882.9493677362185.05063226378205
53155157.913010105545-2.91301010554456
54123120.5319632847922.46803671520759
55136135.022835426380.977164573619793
56117116.9218420448840.0781579551156475
57124124.162239397483-0.162239397482694
58151149.7958757869191.20412421308078
59145142.0516066147652.94839338523487
608790.0285261065585-3.02852610655846
61165160.7471687433374.25283125666323
62120116.3877379161553.61226208384474
63150150.108276315044-0.108276315043783
64136139.156983358626-3.15698335862624
65116116.871454862929-0.871454862928778
66150146.6493166210023.35068337899755
67118120.340491993361-2.34049199336122
687167.76315248364323.23684751635678
69144146.760168421305-2.76016842130472
70110112.515603330078-2.51560333007822
71147139.1166736130627.88332638693822
72111119.836620173805-8.83662017380548
736863.95148251591294.04851748408713
744845.79002451607032.20997548392967
755149.11797753702721.88202246297284
766864.02202457065073.97797542934933
776464.1731861165174-0.173186116517394
787675.22525343684610.774746563153908
796664.35457997155751.64542002844253
806863.85070815200174.14929184799828
816663.80032097004612.19967902995386
828382.62688977170230.373110228297724
835556.751394908879-1.75139490887899
844145.5380886062925-4.53808860629246
856663.97163738869512.0283626113049







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.6459300941639650.7081398116720690.354069905836035
70.8194579661055780.3610840677888440.180542033894422
80.7328587384193380.5342825231613240.267141261580662
90.621828866319360.756342267361280.37817113368064
100.5044449141924770.9911101716150460.495555085807523
110.4417533753357820.8835067506715650.558246624664218
120.3451382191386740.6902764382773490.654861780861326
130.2827918422701770.5655836845403540.717208157729823
140.2041036256989690.4082072513979390.795896374301031
150.1465906717284570.2931813434569140.853409328271543
160.1090870476206540.2181740952413080.890912952379346
170.09854096297503420.1970819259500680.901459037024966
180.1159479740936110.2318959481872230.884052025906389
190.09405713654112090.1881142730822420.905942863458879
200.06400375058592040.1280075011718410.93599624941408
210.04412305525061020.08824611050122030.95587694474939
220.04212089063104990.08424178126209970.95787910936895
230.02756427384126150.05512854768252290.972435726158739
240.02111494094251680.04222988188503370.978885059057483
250.01331792325239790.02663584650479570.986682076747602
260.008160015592666470.01632003118533290.991839984407334
270.004837524862428560.009675049724857130.995162475137571
280.002762294017860360.005524588035720730.99723770598214
290.00162766988979940.003255339779598790.998372330110201
300.001146034018457070.002292068036914130.998853965981543
310.0006527069733349480.00130541394666990.999347293026665
320.0008812540985073950.001762508197014790.999118745901493
330.001083246871936740.002166493743873480.998916753128063
340.0009975282692094450.001995056538418890.999002471730791
350.001039420030270090.002078840060540180.99896057996973
360.001102918985586610.002205837971173220.998897081014413
370.0006201089553246330.001240217910649270.999379891044675
380.0004241762443636140.0008483524887272290.999575823755636
390.0002468482384952950.000493696476990590.999753151761505
400.0001324535082282670.0002649070164565350.999867546491772
410.9999999940387571.1922485795825e-085.9612428979125e-09
420.9999999850072822.99854357423523e-081.49927178711762e-08
430.9999999695911816.08176373573039e-083.04088186786519e-08
440.9999999249951811.50009637291386e-077.50048186456929e-08
450.9999998176945123.64610975927265e-071.82305487963632e-07
460.9999996508093076.98381386878386e-073.49190693439193e-07
470.999999292476471.41504705906159e-067.07523529530795e-07
480.9999985580775652.88384487056893e-061.44192243528446e-06
490.99999785004064.29991880019283e-062.14995940009641e-06
500.9999957131987528.57360249638809e-064.28680124819405e-06
510.9999968265045186.3469909643171e-063.17349548215855e-06
520.9999966176884946.76462301196843e-063.38231150598422e-06
530.9999942826882951.14346234098056e-055.7173117049028e-06
540.9999889558715092.20882569818111e-051.10441284909056e-05
550.9999751187510744.97624978510148e-052.48812489255074e-05
560.9999447590706570.0001104818586850345.52409293425172e-05
570.9998809941887930.0002380116224143170.000119005811207158
580.999753079275690.0004938414486208630.000246920724310431
590.9995636661605360.0008726676789286220.000436333839464311
600.9994103926709950.001179214658010370.000589607329005187
610.9992923802652960.001415239469407220.000707619734703609
620.9990487073344150.001902585331170010.000951292665585003
630.99814321071230.003713578575399250.00185678928769963
640.997123234667380.005753530665239990.00287676533261999
650.9949011993676050.01019760126479090.00509880063239545
660.9929587819961290.01408243600774250.00704121800387127
670.9883322760760940.02333544784781250.0116677239239062
680.9819427357151030.03611452856979450.0180572642848973
690.9764700740050240.04705985198995260.0235299259949763
700.9648654151269780.0702691697460440.035134584873022
710.9968861045593910.006227790881218530.00311389544060927
720.9992014910288650.001597017942268990.000798508971134496
730.9984562008591630.003087598281673580.00154379914083679
740.9984792245368760.003041550926247240.00152077546312362
750.9964525136157770.007094972768446480.00354748638422324
760.9951700507951320.009659898409735750.00482994920486788
770.984480793022320.03103841395536040.0155192069776802
780.9572969817461650.08540603650767040.0427030182538352
790.9710857635160040.05782847296799130.0289142364839956

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.645930094163965 & 0.708139811672069 & 0.354069905836035 \tabularnewline
7 & 0.819457966105578 & 0.361084067788844 & 0.180542033894422 \tabularnewline
8 & 0.732858738419338 & 0.534282523161324 & 0.267141261580662 \tabularnewline
9 & 0.62182886631936 & 0.75634226736128 & 0.37817113368064 \tabularnewline
10 & 0.504444914192477 & 0.991110171615046 & 0.495555085807523 \tabularnewline
11 & 0.441753375335782 & 0.883506750671565 & 0.558246624664218 \tabularnewline
12 & 0.345138219138674 & 0.690276438277349 & 0.654861780861326 \tabularnewline
13 & 0.282791842270177 & 0.565583684540354 & 0.717208157729823 \tabularnewline
14 & 0.204103625698969 & 0.408207251397939 & 0.795896374301031 \tabularnewline
15 & 0.146590671728457 & 0.293181343456914 & 0.853409328271543 \tabularnewline
16 & 0.109087047620654 & 0.218174095241308 & 0.890912952379346 \tabularnewline
17 & 0.0985409629750342 & 0.197081925950068 & 0.901459037024966 \tabularnewline
18 & 0.115947974093611 & 0.231895948187223 & 0.884052025906389 \tabularnewline
19 & 0.0940571365411209 & 0.188114273082242 & 0.905942863458879 \tabularnewline
20 & 0.0640037505859204 & 0.128007501171841 & 0.93599624941408 \tabularnewline
21 & 0.0441230552506102 & 0.0882461105012203 & 0.95587694474939 \tabularnewline
22 & 0.0421208906310499 & 0.0842417812620997 & 0.95787910936895 \tabularnewline
23 & 0.0275642738412615 & 0.0551285476825229 & 0.972435726158739 \tabularnewline
24 & 0.0211149409425168 & 0.0422298818850337 & 0.978885059057483 \tabularnewline
25 & 0.0133179232523979 & 0.0266358465047957 & 0.986682076747602 \tabularnewline
26 & 0.00816001559266647 & 0.0163200311853329 & 0.991839984407334 \tabularnewline
27 & 0.00483752486242856 & 0.00967504972485713 & 0.995162475137571 \tabularnewline
28 & 0.00276229401786036 & 0.00552458803572073 & 0.99723770598214 \tabularnewline
29 & 0.0016276698897994 & 0.00325533977959879 & 0.998372330110201 \tabularnewline
30 & 0.00114603401845707 & 0.00229206803691413 & 0.998853965981543 \tabularnewline
31 & 0.000652706973334948 & 0.0013054139466699 & 0.999347293026665 \tabularnewline
32 & 0.000881254098507395 & 0.00176250819701479 & 0.999118745901493 \tabularnewline
33 & 0.00108324687193674 & 0.00216649374387348 & 0.998916753128063 \tabularnewline
34 & 0.000997528269209445 & 0.00199505653841889 & 0.999002471730791 \tabularnewline
35 & 0.00103942003027009 & 0.00207884006054018 & 0.99896057996973 \tabularnewline
36 & 0.00110291898558661 & 0.00220583797117322 & 0.998897081014413 \tabularnewline
37 & 0.000620108955324633 & 0.00124021791064927 & 0.999379891044675 \tabularnewline
38 & 0.000424176244363614 & 0.000848352488727229 & 0.999575823755636 \tabularnewline
39 & 0.000246848238495295 & 0.00049369647699059 & 0.999753151761505 \tabularnewline
40 & 0.000132453508228267 & 0.000264907016456535 & 0.999867546491772 \tabularnewline
41 & 0.999999994038757 & 1.1922485795825e-08 & 5.9612428979125e-09 \tabularnewline
42 & 0.999999985007282 & 2.99854357423523e-08 & 1.49927178711762e-08 \tabularnewline
43 & 0.999999969591181 & 6.08176373573039e-08 & 3.04088186786519e-08 \tabularnewline
44 & 0.999999924995181 & 1.50009637291386e-07 & 7.50048186456929e-08 \tabularnewline
45 & 0.999999817694512 & 3.64610975927265e-07 & 1.82305487963632e-07 \tabularnewline
46 & 0.999999650809307 & 6.98381386878386e-07 & 3.49190693439193e-07 \tabularnewline
47 & 0.99999929247647 & 1.41504705906159e-06 & 7.07523529530795e-07 \tabularnewline
48 & 0.999998558077565 & 2.88384487056893e-06 & 1.44192243528446e-06 \tabularnewline
49 & 0.9999978500406 & 4.29991880019283e-06 & 2.14995940009641e-06 \tabularnewline
50 & 0.999995713198752 & 8.57360249638809e-06 & 4.28680124819405e-06 \tabularnewline
51 & 0.999996826504518 & 6.3469909643171e-06 & 3.17349548215855e-06 \tabularnewline
52 & 0.999996617688494 & 6.76462301196843e-06 & 3.38231150598422e-06 \tabularnewline
53 & 0.999994282688295 & 1.14346234098056e-05 & 5.7173117049028e-06 \tabularnewline
54 & 0.999988955871509 & 2.20882569818111e-05 & 1.10441284909056e-05 \tabularnewline
55 & 0.999975118751074 & 4.97624978510148e-05 & 2.48812489255074e-05 \tabularnewline
56 & 0.999944759070657 & 0.000110481858685034 & 5.52409293425172e-05 \tabularnewline
57 & 0.999880994188793 & 0.000238011622414317 & 0.000119005811207158 \tabularnewline
58 & 0.99975307927569 & 0.000493841448620863 & 0.000246920724310431 \tabularnewline
59 & 0.999563666160536 & 0.000872667678928622 & 0.000436333839464311 \tabularnewline
60 & 0.999410392670995 & 0.00117921465801037 & 0.000589607329005187 \tabularnewline
61 & 0.999292380265296 & 0.00141523946940722 & 0.000707619734703609 \tabularnewline
62 & 0.999048707334415 & 0.00190258533117001 & 0.000951292665585003 \tabularnewline
63 & 0.9981432107123 & 0.00371357857539925 & 0.00185678928769963 \tabularnewline
64 & 0.99712323466738 & 0.00575353066523999 & 0.00287676533261999 \tabularnewline
65 & 0.994901199367605 & 0.0101976012647909 & 0.00509880063239545 \tabularnewline
66 & 0.992958781996129 & 0.0140824360077425 & 0.00704121800387127 \tabularnewline
67 & 0.988332276076094 & 0.0233354478478125 & 0.0116677239239062 \tabularnewline
68 & 0.981942735715103 & 0.0361145285697945 & 0.0180572642848973 \tabularnewline
69 & 0.976470074005024 & 0.0470598519899526 & 0.0235299259949763 \tabularnewline
70 & 0.964865415126978 & 0.070269169746044 & 0.035134584873022 \tabularnewline
71 & 0.996886104559391 & 0.00622779088121853 & 0.00311389544060927 \tabularnewline
72 & 0.999201491028865 & 0.00159701794226899 & 0.000798508971134496 \tabularnewline
73 & 0.998456200859163 & 0.00308759828167358 & 0.00154379914083679 \tabularnewline
74 & 0.998479224536876 & 0.00304155092624724 & 0.00152077546312362 \tabularnewline
75 & 0.996452513615777 & 0.00709497276844648 & 0.00354748638422324 \tabularnewline
76 & 0.995170050795132 & 0.00965989840973575 & 0.00482994920486788 \tabularnewline
77 & 0.98448079302232 & 0.0310384139553604 & 0.0155192069776802 \tabularnewline
78 & 0.957296981746165 & 0.0854060365076704 & 0.0427030182538352 \tabularnewline
79 & 0.971085763516004 & 0.0578284729679913 & 0.0289142364839956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&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]6[/C][C]0.645930094163965[/C][C]0.708139811672069[/C][C]0.354069905836035[/C][/ROW]
[ROW][C]7[/C][C]0.819457966105578[/C][C]0.361084067788844[/C][C]0.180542033894422[/C][/ROW]
[ROW][C]8[/C][C]0.732858738419338[/C][C]0.534282523161324[/C][C]0.267141261580662[/C][/ROW]
[ROW][C]9[/C][C]0.62182886631936[/C][C]0.75634226736128[/C][C]0.37817113368064[/C][/ROW]
[ROW][C]10[/C][C]0.504444914192477[/C][C]0.991110171615046[/C][C]0.495555085807523[/C][/ROW]
[ROW][C]11[/C][C]0.441753375335782[/C][C]0.883506750671565[/C][C]0.558246624664218[/C][/ROW]
[ROW][C]12[/C][C]0.345138219138674[/C][C]0.690276438277349[/C][C]0.654861780861326[/C][/ROW]
[ROW][C]13[/C][C]0.282791842270177[/C][C]0.565583684540354[/C][C]0.717208157729823[/C][/ROW]
[ROW][C]14[/C][C]0.204103625698969[/C][C]0.408207251397939[/C][C]0.795896374301031[/C][/ROW]
[ROW][C]15[/C][C]0.146590671728457[/C][C]0.293181343456914[/C][C]0.853409328271543[/C][/ROW]
[ROW][C]16[/C][C]0.109087047620654[/C][C]0.218174095241308[/C][C]0.890912952379346[/C][/ROW]
[ROW][C]17[/C][C]0.0985409629750342[/C][C]0.197081925950068[/C][C]0.901459037024966[/C][/ROW]
[ROW][C]18[/C][C]0.115947974093611[/C][C]0.231895948187223[/C][C]0.884052025906389[/C][/ROW]
[ROW][C]19[/C][C]0.0940571365411209[/C][C]0.188114273082242[/C][C]0.905942863458879[/C][/ROW]
[ROW][C]20[/C][C]0.0640037505859204[/C][C]0.128007501171841[/C][C]0.93599624941408[/C][/ROW]
[ROW][C]21[/C][C]0.0441230552506102[/C][C]0.0882461105012203[/C][C]0.95587694474939[/C][/ROW]
[ROW][C]22[/C][C]0.0421208906310499[/C][C]0.0842417812620997[/C][C]0.95787910936895[/C][/ROW]
[ROW][C]23[/C][C]0.0275642738412615[/C][C]0.0551285476825229[/C][C]0.972435726158739[/C][/ROW]
[ROW][C]24[/C][C]0.0211149409425168[/C][C]0.0422298818850337[/C][C]0.978885059057483[/C][/ROW]
[ROW][C]25[/C][C]0.0133179232523979[/C][C]0.0266358465047957[/C][C]0.986682076747602[/C][/ROW]
[ROW][C]26[/C][C]0.00816001559266647[/C][C]0.0163200311853329[/C][C]0.991839984407334[/C][/ROW]
[ROW][C]27[/C][C]0.00483752486242856[/C][C]0.00967504972485713[/C][C]0.995162475137571[/C][/ROW]
[ROW][C]28[/C][C]0.00276229401786036[/C][C]0.00552458803572073[/C][C]0.99723770598214[/C][/ROW]
[ROW][C]29[/C][C]0.0016276698897994[/C][C]0.00325533977959879[/C][C]0.998372330110201[/C][/ROW]
[ROW][C]30[/C][C]0.00114603401845707[/C][C]0.00229206803691413[/C][C]0.998853965981543[/C][/ROW]
[ROW][C]31[/C][C]0.000652706973334948[/C][C]0.0013054139466699[/C][C]0.999347293026665[/C][/ROW]
[ROW][C]32[/C][C]0.000881254098507395[/C][C]0.00176250819701479[/C][C]0.999118745901493[/C][/ROW]
[ROW][C]33[/C][C]0.00108324687193674[/C][C]0.00216649374387348[/C][C]0.998916753128063[/C][/ROW]
[ROW][C]34[/C][C]0.000997528269209445[/C][C]0.00199505653841889[/C][C]0.999002471730791[/C][/ROW]
[ROW][C]35[/C][C]0.00103942003027009[/C][C]0.00207884006054018[/C][C]0.99896057996973[/C][/ROW]
[ROW][C]36[/C][C]0.00110291898558661[/C][C]0.00220583797117322[/C][C]0.998897081014413[/C][/ROW]
[ROW][C]37[/C][C]0.000620108955324633[/C][C]0.00124021791064927[/C][C]0.999379891044675[/C][/ROW]
[ROW][C]38[/C][C]0.000424176244363614[/C][C]0.000848352488727229[/C][C]0.999575823755636[/C][/ROW]
[ROW][C]39[/C][C]0.000246848238495295[/C][C]0.00049369647699059[/C][C]0.999753151761505[/C][/ROW]
[ROW][C]40[/C][C]0.000132453508228267[/C][C]0.000264907016456535[/C][C]0.999867546491772[/C][/ROW]
[ROW][C]41[/C][C]0.999999994038757[/C][C]1.1922485795825e-08[/C][C]5.9612428979125e-09[/C][/ROW]
[ROW][C]42[/C][C]0.999999985007282[/C][C]2.99854357423523e-08[/C][C]1.49927178711762e-08[/C][/ROW]
[ROW][C]43[/C][C]0.999999969591181[/C][C]6.08176373573039e-08[/C][C]3.04088186786519e-08[/C][/ROW]
[ROW][C]44[/C][C]0.999999924995181[/C][C]1.50009637291386e-07[/C][C]7.50048186456929e-08[/C][/ROW]
[ROW][C]45[/C][C]0.999999817694512[/C][C]3.64610975927265e-07[/C][C]1.82305487963632e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999999650809307[/C][C]6.98381386878386e-07[/C][C]3.49190693439193e-07[/C][/ROW]
[ROW][C]47[/C][C]0.99999929247647[/C][C]1.41504705906159e-06[/C][C]7.07523529530795e-07[/C][/ROW]
[ROW][C]48[/C][C]0.999998558077565[/C][C]2.88384487056893e-06[/C][C]1.44192243528446e-06[/C][/ROW]
[ROW][C]49[/C][C]0.9999978500406[/C][C]4.29991880019283e-06[/C][C]2.14995940009641e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999995713198752[/C][C]8.57360249638809e-06[/C][C]4.28680124819405e-06[/C][/ROW]
[ROW][C]51[/C][C]0.999996826504518[/C][C]6.3469909643171e-06[/C][C]3.17349548215855e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999996617688494[/C][C]6.76462301196843e-06[/C][C]3.38231150598422e-06[/C][/ROW]
[ROW][C]53[/C][C]0.999994282688295[/C][C]1.14346234098056e-05[/C][C]5.7173117049028e-06[/C][/ROW]
[ROW][C]54[/C][C]0.999988955871509[/C][C]2.20882569818111e-05[/C][C]1.10441284909056e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999975118751074[/C][C]4.97624978510148e-05[/C][C]2.48812489255074e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999944759070657[/C][C]0.000110481858685034[/C][C]5.52409293425172e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999880994188793[/C][C]0.000238011622414317[/C][C]0.000119005811207158[/C][/ROW]
[ROW][C]58[/C][C]0.99975307927569[/C][C]0.000493841448620863[/C][C]0.000246920724310431[/C][/ROW]
[ROW][C]59[/C][C]0.999563666160536[/C][C]0.000872667678928622[/C][C]0.000436333839464311[/C][/ROW]
[ROW][C]60[/C][C]0.999410392670995[/C][C]0.00117921465801037[/C][C]0.000589607329005187[/C][/ROW]
[ROW][C]61[/C][C]0.999292380265296[/C][C]0.00141523946940722[/C][C]0.000707619734703609[/C][/ROW]
[ROW][C]62[/C][C]0.999048707334415[/C][C]0.00190258533117001[/C][C]0.000951292665585003[/C][/ROW]
[ROW][C]63[/C][C]0.9981432107123[/C][C]0.00371357857539925[/C][C]0.00185678928769963[/C][/ROW]
[ROW][C]64[/C][C]0.99712323466738[/C][C]0.00575353066523999[/C][C]0.00287676533261999[/C][/ROW]
[ROW][C]65[/C][C]0.994901199367605[/C][C]0.0101976012647909[/C][C]0.00509880063239545[/C][/ROW]
[ROW][C]66[/C][C]0.992958781996129[/C][C]0.0140824360077425[/C][C]0.00704121800387127[/C][/ROW]
[ROW][C]67[/C][C]0.988332276076094[/C][C]0.0233354478478125[/C][C]0.0116677239239062[/C][/ROW]
[ROW][C]68[/C][C]0.981942735715103[/C][C]0.0361145285697945[/C][C]0.0180572642848973[/C][/ROW]
[ROW][C]69[/C][C]0.976470074005024[/C][C]0.0470598519899526[/C][C]0.0235299259949763[/C][/ROW]
[ROW][C]70[/C][C]0.964865415126978[/C][C]0.070269169746044[/C][C]0.035134584873022[/C][/ROW]
[ROW][C]71[/C][C]0.996886104559391[/C][C]0.00622779088121853[/C][C]0.00311389544060927[/C][/ROW]
[ROW][C]72[/C][C]0.999201491028865[/C][C]0.00159701794226899[/C][C]0.000798508971134496[/C][/ROW]
[ROW][C]73[/C][C]0.998456200859163[/C][C]0.00308759828167358[/C][C]0.00154379914083679[/C][/ROW]
[ROW][C]74[/C][C]0.998479224536876[/C][C]0.00304155092624724[/C][C]0.00152077546312362[/C][/ROW]
[ROW][C]75[/C][C]0.996452513615777[/C][C]0.00709497276844648[/C][C]0.00354748638422324[/C][/ROW]
[ROW][C]76[/C][C]0.995170050795132[/C][C]0.00965989840973575[/C][C]0.00482994920486788[/C][/ROW]
[ROW][C]77[/C][C]0.98448079302232[/C][C]0.0310384139553604[/C][C]0.0155192069776802[/C][/ROW]
[ROW][C]78[/C][C]0.957296981746165[/C][C]0.0854060365076704[/C][C]0.0427030182538352[/C][/ROW]
[ROW][C]79[/C][C]0.971085763516004[/C][C]0.0578284729679913[/C][C]0.0289142364839956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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
60.6459300941639650.7081398116720690.354069905836035
70.8194579661055780.3610840677888440.180542033894422
80.7328587384193380.5342825231613240.267141261580662
90.621828866319360.756342267361280.37817113368064
100.5044449141924770.9911101716150460.495555085807523
110.4417533753357820.8835067506715650.558246624664218
120.3451382191386740.6902764382773490.654861780861326
130.2827918422701770.5655836845403540.717208157729823
140.2041036256989690.4082072513979390.795896374301031
150.1465906717284570.2931813434569140.853409328271543
160.1090870476206540.2181740952413080.890912952379346
170.09854096297503420.1970819259500680.901459037024966
180.1159479740936110.2318959481872230.884052025906389
190.09405713654112090.1881142730822420.905942863458879
200.06400375058592040.1280075011718410.93599624941408
210.04412305525061020.08824611050122030.95587694474939
220.04212089063104990.08424178126209970.95787910936895
230.02756427384126150.05512854768252290.972435726158739
240.02111494094251680.04222988188503370.978885059057483
250.01331792325239790.02663584650479570.986682076747602
260.008160015592666470.01632003118533290.991839984407334
270.004837524862428560.009675049724857130.995162475137571
280.002762294017860360.005524588035720730.99723770598214
290.00162766988979940.003255339779598790.998372330110201
300.001146034018457070.002292068036914130.998853965981543
310.0006527069733349480.00130541394666990.999347293026665
320.0008812540985073950.001762508197014790.999118745901493
330.001083246871936740.002166493743873480.998916753128063
340.0009975282692094450.001995056538418890.999002471730791
350.001039420030270090.002078840060540180.99896057996973
360.001102918985586610.002205837971173220.998897081014413
370.0006201089553246330.001240217910649270.999379891044675
380.0004241762443636140.0008483524887272290.999575823755636
390.0002468482384952950.000493696476990590.999753151761505
400.0001324535082282670.0002649070164565350.999867546491772
410.9999999940387571.1922485795825e-085.9612428979125e-09
420.9999999850072822.99854357423523e-081.49927178711762e-08
430.9999999695911816.08176373573039e-083.04088186786519e-08
440.9999999249951811.50009637291386e-077.50048186456929e-08
450.9999998176945123.64610975927265e-071.82305487963632e-07
460.9999996508093076.98381386878386e-073.49190693439193e-07
470.999999292476471.41504705906159e-067.07523529530795e-07
480.9999985580775652.88384487056893e-061.44192243528446e-06
490.99999785004064.29991880019283e-062.14995940009641e-06
500.9999957131987528.57360249638809e-064.28680124819405e-06
510.9999968265045186.3469909643171e-063.17349548215855e-06
520.9999966176884946.76462301196843e-063.38231150598422e-06
530.9999942826882951.14346234098056e-055.7173117049028e-06
540.9999889558715092.20882569818111e-051.10441284909056e-05
550.9999751187510744.97624978510148e-052.48812489255074e-05
560.9999447590706570.0001104818586850345.52409293425172e-05
570.9998809941887930.0002380116224143170.000119005811207158
580.999753079275690.0004938414486208630.000246920724310431
590.9995636661605360.0008726676789286220.000436333839464311
600.9994103926709950.001179214658010370.000589607329005187
610.9992923802652960.001415239469407220.000707619734703609
620.9990487073344150.001902585331170010.000951292665585003
630.99814321071230.003713578575399250.00185678928769963
640.997123234667380.005753530665239990.00287676533261999
650.9949011993676050.01019760126479090.00509880063239545
660.9929587819961290.01408243600774250.00704121800387127
670.9883322760760940.02333544784781250.0116677239239062
680.9819427357151030.03611452856979450.0180572642848973
690.9764700740050240.04705985198995260.0235299259949763
700.9648654151269780.0702691697460440.035134584873022
710.9968861045593910.006227790881218530.00311389544060927
720.9992014910288650.001597017942268990.000798508971134496
730.9984562008591630.003087598281673580.00154379914083679
740.9984792245368760.003041550926247240.00152077546312362
750.9964525136157770.007094972768446480.00354748638422324
760.9951700507951320.009659898409735750.00482994920486788
770.984480793022320.03103841395536040.0155192069776802
780.9572969817461650.08540603650767040.0427030182538352
790.9710857635160040.05782847296799130.0289142364839956







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level440.594594594594595NOK
5% type I error level530.716216216216216NOK
10% type I error level590.797297297297297NOK

\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 & 44 & 0.594594594594595 & NOK \tabularnewline
5% type I error level & 53 & 0.716216216216216 & NOK \tabularnewline
10% type I error level & 59 & 0.797297297297297 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156336&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]44[/C][C]0.594594594594595[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]53[/C][C]0.716216216216216[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.797297297297297[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156336&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156336&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 level440.594594594594595NOK
5% type I error level530.716216216216216NOK
10% type I error level590.797297297297297NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}