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Author's title

Multiple lineair regression aantal werklozen(onder 25jaar) - Rente op basis...

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 02:57:39 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258711271sa9syydth8qhpct.htm/, Retrieved Thu, 28 Mar 2024 19:28:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58000, Retrieved Thu, 28 Mar 2024 19:28:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Rente op basis-he...] [2009-11-03 09:33:19] [1ff3eeaee490dfcff07aa4917fec66b8]
- RMPD    [Multiple Regression] [Multiple lineair ...] [2009-11-20 09:57:39] [6df9bd2792d60592b4a24994398a86db] [Current]
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Dataseries X:
127	2.75
123	2.75
118	2.55
114	2.5
108	2.5
111	2.1
151	2
159	2
158	2
148	2
138	2
137	2
136	2
133	2
126	2
120	2
114	2
116	2
153	2
162	2
161	2
149	2
139	2
135	2
130	2
127	2
122	2
117	2
112	2
113	2
149	2
157	2
157	2
147	2
137	2
132	2.21
125	2.25
123	2.25
117	2.45
114	2.5
111	2.5
112	2.64
144	2.75
150	2.93
149	3
134	3.17
123	3.25
116	3.39
117	3.5
111	3.5
105	3.65
102	3.75
95	3.75
93	3.9
124	4
130	4
124	4
115	4
106	4
105	4
105	4
101	4
95	4
93	4
84	4
87	4
116	4.18
120	4.25
117	4.25
109	3.97
105	3.42
107	2.75




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 162.436002337051 -14.8388999402021Rente[t] + 1.70430583183854M1[t] -1.96236083482829M2[t] -7.42472166965657M3[t] -11.0107400039865M4[t] -17.0107400039865M5[t] -15.9494531695569M6[t] + 18.9344269942195M7[t] + 26.3860478250613M8[t] + 24.5591683243636M9[t] + 13.6204551587933M10[t] + 3.45807466347745M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid[t] =  +  162.436002337051 -14.8388999402021Rente[t] +  1.70430583183854M1[t] -1.96236083482829M2[t] -7.42472166965657M3[t] -11.0107400039865M4[t] -17.0107400039865M5[t] -15.9494531695569M6[t] +  18.9344269942195M7[t] +  26.3860478250613M8[t] +  24.5591683243636M9[t] +  13.6204551587933M10[t] +  3.45807466347745M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid[t] =  +  162.436002337051 -14.8388999402021Rente[t] +  1.70430583183854M1[t] -1.96236083482829M2[t] -7.42472166965657M3[t] -11.0107400039865M4[t] -17.0107400039865M5[t] -15.9494531695569M6[t] +  18.9344269942195M7[t] +  26.3860478250613M8[t] +  24.5591683243636M9[t] +  13.6204551587933M10[t] +  3.45807466347745M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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
Werkloosheid[t] = + 162.436002337051 -14.8388999402021Rente[t] + 1.70430583183854M1[t] -1.96236083482829M2[t] -7.42472166965657M3[t] -11.0107400039865M4[t] -17.0107400039865M5[t] -15.9494531695569M6[t] + 18.9344269942195M7[t] + 26.3860478250613M8[t] + 24.5591683243636M9[t] + 13.6204551587933M10[t] + 3.45807466347745M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)162.4360023370512.41750367.191600
Rente-14.83889994020210.605681-24.499500
M11.704305831838542.4981390.68220.4977630.248882
M2-1.962360834828292.498139-0.78550.4352870.217643
M3-7.424721669656572.498277-2.97190.0042780.002139
M4-11.01074000398652.49842-4.40714.5e-052.2e-05
M5-17.01074000398652.49842-6.808600
M6-15.94945316955692.498265-6.384200
M718.93442699421952.498787.577500
M826.38604782506132.49949810.556500
M924.55916832436362.4997459.824700
M1013.62045515879332.4993665.44961e-061e-06
M113.458074663477452.4983021.38420.171520.08576

\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) & 162.436002337051 & 2.417503 & 67.1916 & 0 & 0 \tabularnewline
Rente & -14.8388999402021 & 0.605681 & -24.4995 & 0 & 0 \tabularnewline
M1 & 1.70430583183854 & 2.498139 & 0.6822 & 0.497763 & 0.248882 \tabularnewline
M2 & -1.96236083482829 & 2.498139 & -0.7855 & 0.435287 & 0.217643 \tabularnewline
M3 & -7.42472166965657 & 2.498277 & -2.9719 & 0.004278 & 0.002139 \tabularnewline
M4 & -11.0107400039865 & 2.49842 & -4.4071 & 4.5e-05 & 2.2e-05 \tabularnewline
M5 & -17.0107400039865 & 2.49842 & -6.8086 & 0 & 0 \tabularnewline
M6 & -15.9494531695569 & 2.498265 & -6.3842 & 0 & 0 \tabularnewline
M7 & 18.9344269942195 & 2.49878 & 7.5775 & 0 & 0 \tabularnewline
M8 & 26.3860478250613 & 2.499498 & 10.5565 & 0 & 0 \tabularnewline
M9 & 24.5591683243636 & 2.499745 & 9.8247 & 0 & 0 \tabularnewline
M10 & 13.6204551587933 & 2.499366 & 5.4496 & 1e-06 & 1e-06 \tabularnewline
M11 & 3.45807466347745 & 2.498302 & 1.3842 & 0.17152 & 0.08576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&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]162.436002337051[/C][C]2.417503[/C][C]67.1916[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Rente[/C][C]-14.8388999402021[/C][C]0.605681[/C][C]-24.4995[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.70430583183854[/C][C]2.498139[/C][C]0.6822[/C][C]0.497763[/C][C]0.248882[/C][/ROW]
[ROW][C]M2[/C][C]-1.96236083482829[/C][C]2.498139[/C][C]-0.7855[/C][C]0.435287[/C][C]0.217643[/C][/ROW]
[ROW][C]M3[/C][C]-7.42472166965657[/C][C]2.498277[/C][C]-2.9719[/C][C]0.004278[/C][C]0.002139[/C][/ROW]
[ROW][C]M4[/C][C]-11.0107400039865[/C][C]2.49842[/C][C]-4.4071[/C][C]4.5e-05[/C][C]2.2e-05[/C][/ROW]
[ROW][C]M5[/C][C]-17.0107400039865[/C][C]2.49842[/C][C]-6.8086[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-15.9494531695569[/C][C]2.498265[/C][C]-6.3842[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]18.9344269942195[/C][C]2.49878[/C][C]7.5775[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]26.3860478250613[/C][C]2.499498[/C][C]10.5565[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]24.5591683243636[/C][C]2.499745[/C][C]9.8247[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]13.6204551587933[/C][C]2.499366[/C][C]5.4496[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M11[/C][C]3.45807466347745[/C][C]2.498302[/C][C]1.3842[/C][C]0.17152[/C][C]0.08576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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)162.4360023370512.41750367.191600
Rente-14.83889994020210.605681-24.499500
M11.704305831838542.4981390.68220.4977630.248882
M2-1.962360834828292.498139-0.78550.4352870.217643
M3-7.424721669656572.498277-2.97190.0042780.002139
M4-11.01074000398652.49842-4.40714.5e-052.2e-05
M5-17.01074000398652.49842-6.808600
M6-15.94945316955692.498265-6.384200
M718.93442699421952.498787.577500
M826.38604782506132.49949810.556500
M924.55916832436362.4997459.824700
M1013.62045515879332.4993665.44961e-061e-06
M113.458074663477452.4983021.38420.171520.08576







Multiple Linear Regression - Regression Statistics
Multiple R0.978730583058003
R-squared0.957913554213058
Adjusted R-squared0.949353599137748
F-TEST (value)111.906376351904
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.32682497197846
Sum Squared Residuals1104.56344595005

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.978730583058003 \tabularnewline
R-squared & 0.957913554213058 \tabularnewline
Adjusted R-squared & 0.949353599137748 \tabularnewline
F-TEST (value) & 111.906376351904 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.32682497197846 \tabularnewline
Sum Squared Residuals & 1104.56344595005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.978730583058003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.957913554213058[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.949353599137748[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]111.906376351904[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/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]4.32682497197846[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1104.56344595005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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 R0.978730583058003
R-squared0.957913554213058
Adjusted R-squared0.949353599137748
F-TEST (value)111.906376351904
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.32682497197846
Sum Squared Residuals1104.56344595005







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127123.3333333333333.66666666666746
2123119.6666666666673.33333333333329
3118117.1720858198790.827914180121201
4114114.328012482559-0.328012482558943
5108108.328012482559-0.328012482558939
6111115.324859293069-4.32485929306941
7151151.692629450866-0.69262945086605
8159159.144250281708-0.144250281707726
9158157.317370781010.682629218989846
10148146.3786576154401.62134238456025
11138136.2162771201241.78372287987606
12137132.7582024566474.2417975433535
13136134.4625082884851.53749171151496
14133130.7958416218182.20415837818179
15126125.333480786990.666519213010061
16120121.74746245266-1.74746245265997
17114115.74746245266-1.74746245265997
18116116.808749287090-0.8087492870896
19153151.6926294508661.30737054913397
20162159.1442502817082.8557497182922
21161157.317370781013.68262921898985
22149146.3786576154402.62134238456022
23139136.2162771201242.78372287987606
24135132.7582024566472.2417975433535
25130134.462508288485-4.46250828848505
26127130.795841621818-3.79584162181821
27122125.33348078699-3.33348078698994
28117121.74746245266-4.74746245265997
29112115.74746245266-3.74746245265997
30113116.808749287090-3.8087492870896
31149151.692629450866-2.69262945086603
32157159.144250281708-2.1442502817078
33157157.31737078101-0.317370781010147
34147146.3786576154400.621342384560223
35137136.2162771201240.783722879876056
36132129.6420334692042.35796653079593
37125130.752783303435-5.75278330343453
38123127.086116636768-4.08611663676769
39117118.655975813899-1.65597581389901
40114114.328012482559-0.328012482558939
41111108.3280124825592.67198751744106
42112107.3118533253604.68814667463973
43144140.5634544957143.43654550428552
44150145.344073337324.65592666268013
45149142.4784708408086.52152915919193
46134129.0171446854034.98285531459664
47123117.6676521948715.33234780512864
48116112.1321315397663.86786846023438
49117112.2041583781824.79584162181806
50111108.5374917115152.46250828848490
51105100.8492958856574.15070411434348
5210295.77938755730646.22061244269365
539589.77938755730645.22061244269365
549388.61483940070574.38516059929434
55124122.0148295704621.98517042953811
56130129.4664504013040.533549598696344
57124127.639570900606-3.639570900606
58115116.700857735036-1.70085773503563
59106106.538477239720-0.538477239719803
60105103.0804025762421.91959742375764
61105104.7847084080810.215291591919096
62101101.118041741414-0.118041741414070
639595.6556809065858-0.655680906585797
649392.06966257225580.93033742774417
658486.0696625722558-2.06966257225583
668787.1309494066855-0.130949406685456
67116119.343827581226-3.34382758122552
68120125.756725416253-5.75672541625314
69117123.929845915555-6.92984591555548
70109117.146024733242-8.1460247332417
71105115.145039205037-10.145039205037
72107121.629027501495-14.6290275014949

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 123.333333333333 & 3.66666666666746 \tabularnewline
2 & 123 & 119.666666666667 & 3.33333333333329 \tabularnewline
3 & 118 & 117.172085819879 & 0.827914180121201 \tabularnewline
4 & 114 & 114.328012482559 & -0.328012482558943 \tabularnewline
5 & 108 & 108.328012482559 & -0.328012482558939 \tabularnewline
6 & 111 & 115.324859293069 & -4.32485929306941 \tabularnewline
7 & 151 & 151.692629450866 & -0.69262945086605 \tabularnewline
8 & 159 & 159.144250281708 & -0.144250281707726 \tabularnewline
9 & 158 & 157.31737078101 & 0.682629218989846 \tabularnewline
10 & 148 & 146.378657615440 & 1.62134238456025 \tabularnewline
11 & 138 & 136.216277120124 & 1.78372287987606 \tabularnewline
12 & 137 & 132.758202456647 & 4.2417975433535 \tabularnewline
13 & 136 & 134.462508288485 & 1.53749171151496 \tabularnewline
14 & 133 & 130.795841621818 & 2.20415837818179 \tabularnewline
15 & 126 & 125.33348078699 & 0.666519213010061 \tabularnewline
16 & 120 & 121.74746245266 & -1.74746245265997 \tabularnewline
17 & 114 & 115.74746245266 & -1.74746245265997 \tabularnewline
18 & 116 & 116.808749287090 & -0.8087492870896 \tabularnewline
19 & 153 & 151.692629450866 & 1.30737054913397 \tabularnewline
20 & 162 & 159.144250281708 & 2.8557497182922 \tabularnewline
21 & 161 & 157.31737078101 & 3.68262921898985 \tabularnewline
22 & 149 & 146.378657615440 & 2.62134238456022 \tabularnewline
23 & 139 & 136.216277120124 & 2.78372287987606 \tabularnewline
24 & 135 & 132.758202456647 & 2.2417975433535 \tabularnewline
25 & 130 & 134.462508288485 & -4.46250828848505 \tabularnewline
26 & 127 & 130.795841621818 & -3.79584162181821 \tabularnewline
27 & 122 & 125.33348078699 & -3.33348078698994 \tabularnewline
28 & 117 & 121.74746245266 & -4.74746245265997 \tabularnewline
29 & 112 & 115.74746245266 & -3.74746245265997 \tabularnewline
30 & 113 & 116.808749287090 & -3.8087492870896 \tabularnewline
31 & 149 & 151.692629450866 & -2.69262945086603 \tabularnewline
32 & 157 & 159.144250281708 & -2.1442502817078 \tabularnewline
33 & 157 & 157.31737078101 & -0.317370781010147 \tabularnewline
34 & 147 & 146.378657615440 & 0.621342384560223 \tabularnewline
35 & 137 & 136.216277120124 & 0.783722879876056 \tabularnewline
36 & 132 & 129.642033469204 & 2.35796653079593 \tabularnewline
37 & 125 & 130.752783303435 & -5.75278330343453 \tabularnewline
38 & 123 & 127.086116636768 & -4.08611663676769 \tabularnewline
39 & 117 & 118.655975813899 & -1.65597581389901 \tabularnewline
40 & 114 & 114.328012482559 & -0.328012482558939 \tabularnewline
41 & 111 & 108.328012482559 & 2.67198751744106 \tabularnewline
42 & 112 & 107.311853325360 & 4.68814667463973 \tabularnewline
43 & 144 & 140.563454495714 & 3.43654550428552 \tabularnewline
44 & 150 & 145.34407333732 & 4.65592666268013 \tabularnewline
45 & 149 & 142.478470840808 & 6.52152915919193 \tabularnewline
46 & 134 & 129.017144685403 & 4.98285531459664 \tabularnewline
47 & 123 & 117.667652194871 & 5.33234780512864 \tabularnewline
48 & 116 & 112.132131539766 & 3.86786846023438 \tabularnewline
49 & 117 & 112.204158378182 & 4.79584162181806 \tabularnewline
50 & 111 & 108.537491711515 & 2.46250828848490 \tabularnewline
51 & 105 & 100.849295885657 & 4.15070411434348 \tabularnewline
52 & 102 & 95.7793875573064 & 6.22061244269365 \tabularnewline
53 & 95 & 89.7793875573064 & 5.22061244269365 \tabularnewline
54 & 93 & 88.6148394007057 & 4.38516059929434 \tabularnewline
55 & 124 & 122.014829570462 & 1.98517042953811 \tabularnewline
56 & 130 & 129.466450401304 & 0.533549598696344 \tabularnewline
57 & 124 & 127.639570900606 & -3.639570900606 \tabularnewline
58 & 115 & 116.700857735036 & -1.70085773503563 \tabularnewline
59 & 106 & 106.538477239720 & -0.538477239719803 \tabularnewline
60 & 105 & 103.080402576242 & 1.91959742375764 \tabularnewline
61 & 105 & 104.784708408081 & 0.215291591919096 \tabularnewline
62 & 101 & 101.118041741414 & -0.118041741414070 \tabularnewline
63 & 95 & 95.6556809065858 & -0.655680906585797 \tabularnewline
64 & 93 & 92.0696625722558 & 0.93033742774417 \tabularnewline
65 & 84 & 86.0696625722558 & -2.06966257225583 \tabularnewline
66 & 87 & 87.1309494066855 & -0.130949406685456 \tabularnewline
67 & 116 & 119.343827581226 & -3.34382758122552 \tabularnewline
68 & 120 & 125.756725416253 & -5.75672541625314 \tabularnewline
69 & 117 & 123.929845915555 & -6.92984591555548 \tabularnewline
70 & 109 & 117.146024733242 & -8.1460247332417 \tabularnewline
71 & 105 & 115.145039205037 & -10.145039205037 \tabularnewline
72 & 107 & 121.629027501495 & -14.6290275014949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&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]127[/C][C]123.333333333333[/C][C]3.66666666666746[/C][/ROW]
[ROW][C]2[/C][C]123[/C][C]119.666666666667[/C][C]3.33333333333329[/C][/ROW]
[ROW][C]3[/C][C]118[/C][C]117.172085819879[/C][C]0.827914180121201[/C][/ROW]
[ROW][C]4[/C][C]114[/C][C]114.328012482559[/C][C]-0.328012482558943[/C][/ROW]
[ROW][C]5[/C][C]108[/C][C]108.328012482559[/C][C]-0.328012482558939[/C][/ROW]
[ROW][C]6[/C][C]111[/C][C]115.324859293069[/C][C]-4.32485929306941[/C][/ROW]
[ROW][C]7[/C][C]151[/C][C]151.692629450866[/C][C]-0.69262945086605[/C][/ROW]
[ROW][C]8[/C][C]159[/C][C]159.144250281708[/C][C]-0.144250281707726[/C][/ROW]
[ROW][C]9[/C][C]158[/C][C]157.31737078101[/C][C]0.682629218989846[/C][/ROW]
[ROW][C]10[/C][C]148[/C][C]146.378657615440[/C][C]1.62134238456025[/C][/ROW]
[ROW][C]11[/C][C]138[/C][C]136.216277120124[/C][C]1.78372287987606[/C][/ROW]
[ROW][C]12[/C][C]137[/C][C]132.758202456647[/C][C]4.2417975433535[/C][/ROW]
[ROW][C]13[/C][C]136[/C][C]134.462508288485[/C][C]1.53749171151496[/C][/ROW]
[ROW][C]14[/C][C]133[/C][C]130.795841621818[/C][C]2.20415837818179[/C][/ROW]
[ROW][C]15[/C][C]126[/C][C]125.33348078699[/C][C]0.666519213010061[/C][/ROW]
[ROW][C]16[/C][C]120[/C][C]121.74746245266[/C][C]-1.74746245265997[/C][/ROW]
[ROW][C]17[/C][C]114[/C][C]115.74746245266[/C][C]-1.74746245265997[/C][/ROW]
[ROW][C]18[/C][C]116[/C][C]116.808749287090[/C][C]-0.8087492870896[/C][/ROW]
[ROW][C]19[/C][C]153[/C][C]151.692629450866[/C][C]1.30737054913397[/C][/ROW]
[ROW][C]20[/C][C]162[/C][C]159.144250281708[/C][C]2.8557497182922[/C][/ROW]
[ROW][C]21[/C][C]161[/C][C]157.31737078101[/C][C]3.68262921898985[/C][/ROW]
[ROW][C]22[/C][C]149[/C][C]146.378657615440[/C][C]2.62134238456022[/C][/ROW]
[ROW][C]23[/C][C]139[/C][C]136.216277120124[/C][C]2.78372287987606[/C][/ROW]
[ROW][C]24[/C][C]135[/C][C]132.758202456647[/C][C]2.2417975433535[/C][/ROW]
[ROW][C]25[/C][C]130[/C][C]134.462508288485[/C][C]-4.46250828848505[/C][/ROW]
[ROW][C]26[/C][C]127[/C][C]130.795841621818[/C][C]-3.79584162181821[/C][/ROW]
[ROW][C]27[/C][C]122[/C][C]125.33348078699[/C][C]-3.33348078698994[/C][/ROW]
[ROW][C]28[/C][C]117[/C][C]121.74746245266[/C][C]-4.74746245265997[/C][/ROW]
[ROW][C]29[/C][C]112[/C][C]115.74746245266[/C][C]-3.74746245265997[/C][/ROW]
[ROW][C]30[/C][C]113[/C][C]116.808749287090[/C][C]-3.8087492870896[/C][/ROW]
[ROW][C]31[/C][C]149[/C][C]151.692629450866[/C][C]-2.69262945086603[/C][/ROW]
[ROW][C]32[/C][C]157[/C][C]159.144250281708[/C][C]-2.1442502817078[/C][/ROW]
[ROW][C]33[/C][C]157[/C][C]157.31737078101[/C][C]-0.317370781010147[/C][/ROW]
[ROW][C]34[/C][C]147[/C][C]146.378657615440[/C][C]0.621342384560223[/C][/ROW]
[ROW][C]35[/C][C]137[/C][C]136.216277120124[/C][C]0.783722879876056[/C][/ROW]
[ROW][C]36[/C][C]132[/C][C]129.642033469204[/C][C]2.35796653079593[/C][/ROW]
[ROW][C]37[/C][C]125[/C][C]130.752783303435[/C][C]-5.75278330343453[/C][/ROW]
[ROW][C]38[/C][C]123[/C][C]127.086116636768[/C][C]-4.08611663676769[/C][/ROW]
[ROW][C]39[/C][C]117[/C][C]118.655975813899[/C][C]-1.65597581389901[/C][/ROW]
[ROW][C]40[/C][C]114[/C][C]114.328012482559[/C][C]-0.328012482558939[/C][/ROW]
[ROW][C]41[/C][C]111[/C][C]108.328012482559[/C][C]2.67198751744106[/C][/ROW]
[ROW][C]42[/C][C]112[/C][C]107.311853325360[/C][C]4.68814667463973[/C][/ROW]
[ROW][C]43[/C][C]144[/C][C]140.563454495714[/C][C]3.43654550428552[/C][/ROW]
[ROW][C]44[/C][C]150[/C][C]145.34407333732[/C][C]4.65592666268013[/C][/ROW]
[ROW][C]45[/C][C]149[/C][C]142.478470840808[/C][C]6.52152915919193[/C][/ROW]
[ROW][C]46[/C][C]134[/C][C]129.017144685403[/C][C]4.98285531459664[/C][/ROW]
[ROW][C]47[/C][C]123[/C][C]117.667652194871[/C][C]5.33234780512864[/C][/ROW]
[ROW][C]48[/C][C]116[/C][C]112.132131539766[/C][C]3.86786846023438[/C][/ROW]
[ROW][C]49[/C][C]117[/C][C]112.204158378182[/C][C]4.79584162181806[/C][/ROW]
[ROW][C]50[/C][C]111[/C][C]108.537491711515[/C][C]2.46250828848490[/C][/ROW]
[ROW][C]51[/C][C]105[/C][C]100.849295885657[/C][C]4.15070411434348[/C][/ROW]
[ROW][C]52[/C][C]102[/C][C]95.7793875573064[/C][C]6.22061244269365[/C][/ROW]
[ROW][C]53[/C][C]95[/C][C]89.7793875573064[/C][C]5.22061244269365[/C][/ROW]
[ROW][C]54[/C][C]93[/C][C]88.6148394007057[/C][C]4.38516059929434[/C][/ROW]
[ROW][C]55[/C][C]124[/C][C]122.014829570462[/C][C]1.98517042953811[/C][/ROW]
[ROW][C]56[/C][C]130[/C][C]129.466450401304[/C][C]0.533549598696344[/C][/ROW]
[ROW][C]57[/C][C]124[/C][C]127.639570900606[/C][C]-3.639570900606[/C][/ROW]
[ROW][C]58[/C][C]115[/C][C]116.700857735036[/C][C]-1.70085773503563[/C][/ROW]
[ROW][C]59[/C][C]106[/C][C]106.538477239720[/C][C]-0.538477239719803[/C][/ROW]
[ROW][C]60[/C][C]105[/C][C]103.080402576242[/C][C]1.91959742375764[/C][/ROW]
[ROW][C]61[/C][C]105[/C][C]104.784708408081[/C][C]0.215291591919096[/C][/ROW]
[ROW][C]62[/C][C]101[/C][C]101.118041741414[/C][C]-0.118041741414070[/C][/ROW]
[ROW][C]63[/C][C]95[/C][C]95.6556809065858[/C][C]-0.655680906585797[/C][/ROW]
[ROW][C]64[/C][C]93[/C][C]92.0696625722558[/C][C]0.93033742774417[/C][/ROW]
[ROW][C]65[/C][C]84[/C][C]86.0696625722558[/C][C]-2.06966257225583[/C][/ROW]
[ROW][C]66[/C][C]87[/C][C]87.1309494066855[/C][C]-0.130949406685456[/C][/ROW]
[ROW][C]67[/C][C]116[/C][C]119.343827581226[/C][C]-3.34382758122552[/C][/ROW]
[ROW][C]68[/C][C]120[/C][C]125.756725416253[/C][C]-5.75672541625314[/C][/ROW]
[ROW][C]69[/C][C]117[/C][C]123.929845915555[/C][C]-6.92984591555548[/C][/ROW]
[ROW][C]70[/C][C]109[/C][C]117.146024733242[/C][C]-8.1460247332417[/C][/ROW]
[ROW][C]71[/C][C]105[/C][C]115.145039205037[/C][C]-10.145039205037[/C][/ROW]
[ROW][C]72[/C][C]107[/C][C]121.629027501495[/C][C]-14.6290275014949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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
1127123.3333333333333.66666666666746
2123119.6666666666673.33333333333329
3118117.1720858198790.827914180121201
4114114.328012482559-0.328012482558943
5108108.328012482559-0.328012482558939
6111115.324859293069-4.32485929306941
7151151.692629450866-0.69262945086605
8159159.144250281708-0.144250281707726
9158157.317370781010.682629218989846
10148146.3786576154401.62134238456025
11138136.2162771201241.78372287987606
12137132.7582024566474.2417975433535
13136134.4625082884851.53749171151496
14133130.7958416218182.20415837818179
15126125.333480786990.666519213010061
16120121.74746245266-1.74746245265997
17114115.74746245266-1.74746245265997
18116116.808749287090-0.8087492870896
19153151.6926294508661.30737054913397
20162159.1442502817082.8557497182922
21161157.317370781013.68262921898985
22149146.3786576154402.62134238456022
23139136.2162771201242.78372287987606
24135132.7582024566472.2417975433535
25130134.462508288485-4.46250828848505
26127130.795841621818-3.79584162181821
27122125.33348078699-3.33348078698994
28117121.74746245266-4.74746245265997
29112115.74746245266-3.74746245265997
30113116.808749287090-3.8087492870896
31149151.692629450866-2.69262945086603
32157159.144250281708-2.1442502817078
33157157.31737078101-0.317370781010147
34147146.3786576154400.621342384560223
35137136.2162771201240.783722879876056
36132129.6420334692042.35796653079593
37125130.752783303435-5.75278330343453
38123127.086116636768-4.08611663676769
39117118.655975813899-1.65597581389901
40114114.328012482559-0.328012482558939
41111108.3280124825592.67198751744106
42112107.3118533253604.68814667463973
43144140.5634544957143.43654550428552
44150145.344073337324.65592666268013
45149142.4784708408086.52152915919193
46134129.0171446854034.98285531459664
47123117.6676521948715.33234780512864
48116112.1321315397663.86786846023438
49117112.2041583781824.79584162181806
50111108.5374917115152.46250828848490
51105100.8492958856574.15070411434348
5210295.77938755730646.22061244269365
539589.77938755730645.22061244269365
549388.61483940070574.38516059929434
55124122.0148295704621.98517042953811
56130129.4664504013040.533549598696344
57124127.639570900606-3.639570900606
58115116.700857735036-1.70085773503563
59106106.538477239720-0.538477239719803
60105103.0804025762421.91959742375764
61105104.7847084080810.215291591919096
62101101.118041741414-0.118041741414070
639595.6556809065858-0.655680906585797
649392.06966257225580.93033742774417
658486.0696625722558-2.06966257225583
668787.1309494066855-0.130949406685456
67116119.343827581226-3.34382758122552
68120125.756725416253-5.75672541625314
69117123.929845915555-6.92984591555548
70109117.146024733242-8.1460247332417
71105115.145039205037-10.145039205037
72107121.629027501495-14.6290275014949







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.001682698278718030.003365396557436060.998317301721282
170.0001784031481106270.0003568062962212540.99982159685189
180.002986826234564480.005973652469128960.997013173765436
190.001249092523658010.002498185047316020.998750907476342
200.001007246628172630.002014493256345270.998992753371827
210.0007568043985228820.001513608797045760.999243195601477
220.0002442006043539320.0004884012087078640.999755799395646
237.78177029534565e-050.0001556354059069130.999922182297047
243.50021285233935e-057.00042570467871e-050.999964997871477
250.0005213604091468560.001042720818293710.999478639590853
260.001018963260184270.002037926520368540.998981036739816
270.0006428700839877550.001285740167975510.999357129916012
280.0004314029843012850.0008628059686025710.9995685970157
290.0002201047575879520.0004402095151759040.999779895242412
300.0001193095904134150.0002386191808268290.999880690409587
318.58238534206005e-050.0001716477068412010.99991417614658
326.97669924176976e-050.0001395339848353950.999930233007582
333.72318104866142e-057.44636209732284e-050.999962768189513
341.56430849891774e-053.12861699783548e-050.99998435691501
356.47329539389757e-061.29465907877951e-050.999993526704606
363.51307333232687e-067.02614666465374e-060.999996486926668
372.19872489131853e-054.39744978263707e-050.999978012751087
383.93654353720136e-057.87308707440273e-050.999960634564628
392.85334442375619e-055.70668884751237e-050.999971466555762
401.97656204238692e-053.95312408477384e-050.999980234379576
411.28908414385940e-052.57816828771881e-050.999987109158561
421.10407334047671e-052.20814668095343e-050.999988959266595
434.28243105963752e-068.56486211927504e-060.99999571756894
441.77212350519045e-063.54424701038089e-060.999998227876495
451.89638032290184e-063.79276064580369e-060.999998103619677
465.28133276168864e-061.05626655233773e-050.999994718667238
473.61474127178305e-057.2294825435661e-050.999963852587282
480.0002547260316305810.0005094520632611630.99974527396837
490.0002730805762727910.0005461611525455820.999726919423727
500.000248395183402560.000496790366805120.999751604816597
510.0003165297994038560.0006330595988077130.999683470200596
520.0005014185657309740.001002837131461950.999498581434269
530.002137219262318750.004274438524637490.997862780737681
540.002171990986551010.004343981973102030.99782800901345
550.00764385213972980.01528770427945960.99235614786027
560.09018269013610030.1803653802722010.9098173098639

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00168269827871803 & 0.00336539655743606 & 0.998317301721282 \tabularnewline
17 & 0.000178403148110627 & 0.000356806296221254 & 0.99982159685189 \tabularnewline
18 & 0.00298682623456448 & 0.00597365246912896 & 0.997013173765436 \tabularnewline
19 & 0.00124909252365801 & 0.00249818504731602 & 0.998750907476342 \tabularnewline
20 & 0.00100724662817263 & 0.00201449325634527 & 0.998992753371827 \tabularnewline
21 & 0.000756804398522882 & 0.00151360879704576 & 0.999243195601477 \tabularnewline
22 & 0.000244200604353932 & 0.000488401208707864 & 0.999755799395646 \tabularnewline
23 & 7.78177029534565e-05 & 0.000155635405906913 & 0.999922182297047 \tabularnewline
24 & 3.50021285233935e-05 & 7.00042570467871e-05 & 0.999964997871477 \tabularnewline
25 & 0.000521360409146856 & 0.00104272081829371 & 0.999478639590853 \tabularnewline
26 & 0.00101896326018427 & 0.00203792652036854 & 0.998981036739816 \tabularnewline
27 & 0.000642870083987755 & 0.00128574016797551 & 0.999357129916012 \tabularnewline
28 & 0.000431402984301285 & 0.000862805968602571 & 0.9995685970157 \tabularnewline
29 & 0.000220104757587952 & 0.000440209515175904 & 0.999779895242412 \tabularnewline
30 & 0.000119309590413415 & 0.000238619180826829 & 0.999880690409587 \tabularnewline
31 & 8.58238534206005e-05 & 0.000171647706841201 & 0.99991417614658 \tabularnewline
32 & 6.97669924176976e-05 & 0.000139533984835395 & 0.999930233007582 \tabularnewline
33 & 3.72318104866142e-05 & 7.44636209732284e-05 & 0.999962768189513 \tabularnewline
34 & 1.56430849891774e-05 & 3.12861699783548e-05 & 0.99998435691501 \tabularnewline
35 & 6.47329539389757e-06 & 1.29465907877951e-05 & 0.999993526704606 \tabularnewline
36 & 3.51307333232687e-06 & 7.02614666465374e-06 & 0.999996486926668 \tabularnewline
37 & 2.19872489131853e-05 & 4.39744978263707e-05 & 0.999978012751087 \tabularnewline
38 & 3.93654353720136e-05 & 7.87308707440273e-05 & 0.999960634564628 \tabularnewline
39 & 2.85334442375619e-05 & 5.70668884751237e-05 & 0.999971466555762 \tabularnewline
40 & 1.97656204238692e-05 & 3.95312408477384e-05 & 0.999980234379576 \tabularnewline
41 & 1.28908414385940e-05 & 2.57816828771881e-05 & 0.999987109158561 \tabularnewline
42 & 1.10407334047671e-05 & 2.20814668095343e-05 & 0.999988959266595 \tabularnewline
43 & 4.28243105963752e-06 & 8.56486211927504e-06 & 0.99999571756894 \tabularnewline
44 & 1.77212350519045e-06 & 3.54424701038089e-06 & 0.999998227876495 \tabularnewline
45 & 1.89638032290184e-06 & 3.79276064580369e-06 & 0.999998103619677 \tabularnewline
46 & 5.28133276168864e-06 & 1.05626655233773e-05 & 0.999994718667238 \tabularnewline
47 & 3.61474127178305e-05 & 7.2294825435661e-05 & 0.999963852587282 \tabularnewline
48 & 0.000254726031630581 & 0.000509452063261163 & 0.99974527396837 \tabularnewline
49 & 0.000273080576272791 & 0.000546161152545582 & 0.999726919423727 \tabularnewline
50 & 0.00024839518340256 & 0.00049679036680512 & 0.999751604816597 \tabularnewline
51 & 0.000316529799403856 & 0.000633059598807713 & 0.999683470200596 \tabularnewline
52 & 0.000501418565730974 & 0.00100283713146195 & 0.999498581434269 \tabularnewline
53 & 0.00213721926231875 & 0.00427443852463749 & 0.997862780737681 \tabularnewline
54 & 0.00217199098655101 & 0.00434398197310203 & 0.99782800901345 \tabularnewline
55 & 0.0076438521397298 & 0.0152877042794596 & 0.99235614786027 \tabularnewline
56 & 0.0901826901361003 & 0.180365380272201 & 0.9098173098639 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&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]16[/C][C]0.00168269827871803[/C][C]0.00336539655743606[/C][C]0.998317301721282[/C][/ROW]
[ROW][C]17[/C][C]0.000178403148110627[/C][C]0.000356806296221254[/C][C]0.99982159685189[/C][/ROW]
[ROW][C]18[/C][C]0.00298682623456448[/C][C]0.00597365246912896[/C][C]0.997013173765436[/C][/ROW]
[ROW][C]19[/C][C]0.00124909252365801[/C][C]0.00249818504731602[/C][C]0.998750907476342[/C][/ROW]
[ROW][C]20[/C][C]0.00100724662817263[/C][C]0.00201449325634527[/C][C]0.998992753371827[/C][/ROW]
[ROW][C]21[/C][C]0.000756804398522882[/C][C]0.00151360879704576[/C][C]0.999243195601477[/C][/ROW]
[ROW][C]22[/C][C]0.000244200604353932[/C][C]0.000488401208707864[/C][C]0.999755799395646[/C][/ROW]
[ROW][C]23[/C][C]7.78177029534565e-05[/C][C]0.000155635405906913[/C][C]0.999922182297047[/C][/ROW]
[ROW][C]24[/C][C]3.50021285233935e-05[/C][C]7.00042570467871e-05[/C][C]0.999964997871477[/C][/ROW]
[ROW][C]25[/C][C]0.000521360409146856[/C][C]0.00104272081829371[/C][C]0.999478639590853[/C][/ROW]
[ROW][C]26[/C][C]0.00101896326018427[/C][C]0.00203792652036854[/C][C]0.998981036739816[/C][/ROW]
[ROW][C]27[/C][C]0.000642870083987755[/C][C]0.00128574016797551[/C][C]0.999357129916012[/C][/ROW]
[ROW][C]28[/C][C]0.000431402984301285[/C][C]0.000862805968602571[/C][C]0.9995685970157[/C][/ROW]
[ROW][C]29[/C][C]0.000220104757587952[/C][C]0.000440209515175904[/C][C]0.999779895242412[/C][/ROW]
[ROW][C]30[/C][C]0.000119309590413415[/C][C]0.000238619180826829[/C][C]0.999880690409587[/C][/ROW]
[ROW][C]31[/C][C]8.58238534206005e-05[/C][C]0.000171647706841201[/C][C]0.99991417614658[/C][/ROW]
[ROW][C]32[/C][C]6.97669924176976e-05[/C][C]0.000139533984835395[/C][C]0.999930233007582[/C][/ROW]
[ROW][C]33[/C][C]3.72318104866142e-05[/C][C]7.44636209732284e-05[/C][C]0.999962768189513[/C][/ROW]
[ROW][C]34[/C][C]1.56430849891774e-05[/C][C]3.12861699783548e-05[/C][C]0.99998435691501[/C][/ROW]
[ROW][C]35[/C][C]6.47329539389757e-06[/C][C]1.29465907877951e-05[/C][C]0.999993526704606[/C][/ROW]
[ROW][C]36[/C][C]3.51307333232687e-06[/C][C]7.02614666465374e-06[/C][C]0.999996486926668[/C][/ROW]
[ROW][C]37[/C][C]2.19872489131853e-05[/C][C]4.39744978263707e-05[/C][C]0.999978012751087[/C][/ROW]
[ROW][C]38[/C][C]3.93654353720136e-05[/C][C]7.87308707440273e-05[/C][C]0.999960634564628[/C][/ROW]
[ROW][C]39[/C][C]2.85334442375619e-05[/C][C]5.70668884751237e-05[/C][C]0.999971466555762[/C][/ROW]
[ROW][C]40[/C][C]1.97656204238692e-05[/C][C]3.95312408477384e-05[/C][C]0.999980234379576[/C][/ROW]
[ROW][C]41[/C][C]1.28908414385940e-05[/C][C]2.57816828771881e-05[/C][C]0.999987109158561[/C][/ROW]
[ROW][C]42[/C][C]1.10407334047671e-05[/C][C]2.20814668095343e-05[/C][C]0.999988959266595[/C][/ROW]
[ROW][C]43[/C][C]4.28243105963752e-06[/C][C]8.56486211927504e-06[/C][C]0.99999571756894[/C][/ROW]
[ROW][C]44[/C][C]1.77212350519045e-06[/C][C]3.54424701038089e-06[/C][C]0.999998227876495[/C][/ROW]
[ROW][C]45[/C][C]1.89638032290184e-06[/C][C]3.79276064580369e-06[/C][C]0.999998103619677[/C][/ROW]
[ROW][C]46[/C][C]5.28133276168864e-06[/C][C]1.05626655233773e-05[/C][C]0.999994718667238[/C][/ROW]
[ROW][C]47[/C][C]3.61474127178305e-05[/C][C]7.2294825435661e-05[/C][C]0.999963852587282[/C][/ROW]
[ROW][C]48[/C][C]0.000254726031630581[/C][C]0.000509452063261163[/C][C]0.99974527396837[/C][/ROW]
[ROW][C]49[/C][C]0.000273080576272791[/C][C]0.000546161152545582[/C][C]0.999726919423727[/C][/ROW]
[ROW][C]50[/C][C]0.00024839518340256[/C][C]0.00049679036680512[/C][C]0.999751604816597[/C][/ROW]
[ROW][C]51[/C][C]0.000316529799403856[/C][C]0.000633059598807713[/C][C]0.999683470200596[/C][/ROW]
[ROW][C]52[/C][C]0.000501418565730974[/C][C]0.00100283713146195[/C][C]0.999498581434269[/C][/ROW]
[ROW][C]53[/C][C]0.00213721926231875[/C][C]0.00427443852463749[/C][C]0.997862780737681[/C][/ROW]
[ROW][C]54[/C][C]0.00217199098655101[/C][C]0.00434398197310203[/C][C]0.99782800901345[/C][/ROW]
[ROW][C]55[/C][C]0.0076438521397298[/C][C]0.0152877042794596[/C][C]0.99235614786027[/C][/ROW]
[ROW][C]56[/C][C]0.0901826901361003[/C][C]0.180365380272201[/C][C]0.9098173098639[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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
160.001682698278718030.003365396557436060.998317301721282
170.0001784031481106270.0003568062962212540.99982159685189
180.002986826234564480.005973652469128960.997013173765436
190.001249092523658010.002498185047316020.998750907476342
200.001007246628172630.002014493256345270.998992753371827
210.0007568043985228820.001513608797045760.999243195601477
220.0002442006043539320.0004884012087078640.999755799395646
237.78177029534565e-050.0001556354059069130.999922182297047
243.50021285233935e-057.00042570467871e-050.999964997871477
250.0005213604091468560.001042720818293710.999478639590853
260.001018963260184270.002037926520368540.998981036739816
270.0006428700839877550.001285740167975510.999357129916012
280.0004314029843012850.0008628059686025710.9995685970157
290.0002201047575879520.0004402095151759040.999779895242412
300.0001193095904134150.0002386191808268290.999880690409587
318.58238534206005e-050.0001716477068412010.99991417614658
326.97669924176976e-050.0001395339848353950.999930233007582
333.72318104866142e-057.44636209732284e-050.999962768189513
341.56430849891774e-053.12861699783548e-050.99998435691501
356.47329539389757e-061.29465907877951e-050.999993526704606
363.51307333232687e-067.02614666465374e-060.999996486926668
372.19872489131853e-054.39744978263707e-050.999978012751087
383.93654353720136e-057.87308707440273e-050.999960634564628
392.85334442375619e-055.70668884751237e-050.999971466555762
401.97656204238692e-053.95312408477384e-050.999980234379576
411.28908414385940e-052.57816828771881e-050.999987109158561
421.10407334047671e-052.20814668095343e-050.999988959266595
434.28243105963752e-068.56486211927504e-060.99999571756894
441.77212350519045e-063.54424701038089e-060.999998227876495
451.89638032290184e-063.79276064580369e-060.999998103619677
465.28133276168864e-061.05626655233773e-050.999994718667238
473.61474127178305e-057.2294825435661e-050.999963852587282
480.0002547260316305810.0005094520632611630.99974527396837
490.0002730805762727910.0005461611525455820.999726919423727
500.000248395183402560.000496790366805120.999751604816597
510.0003165297994038560.0006330595988077130.999683470200596
520.0005014185657309740.001002837131461950.999498581434269
530.002137219262318750.004274438524637490.997862780737681
540.002171990986551010.004343981973102030.99782800901345
550.00764385213972980.01528770427945960.99235614786027
560.09018269013610030.1803653802722010.9098173098639







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.951219512195122NOK
5% type I error level400.97560975609756NOK
10% type I error level400.97560975609756NOK

\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 & 39 & 0.951219512195122 & NOK \tabularnewline
5% type I error level & 40 & 0.97560975609756 & NOK \tabularnewline
10% type I error level & 40 & 0.97560975609756 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58000&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]39[/C][C]0.951219512195122[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.97560975609756[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.97560975609756[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58000&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58000&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 level390.951219512195122NOK
5% type I error level400.97560975609756NOK
10% type I error level400.97560975609756NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}