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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 12:12:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t13560235776t5h3qzuxp7rwgy.htm/, Retrieved Thu, 28 Mar 2024 19:00:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202920, Retrieved Thu, 28 Mar 2024 19:00:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multiple Regressi...] [2009-12-15 19:14:24] [1eab65e90adf64584b8e6f0da23ff414]
- R           [Multiple Regression] [multiple regressi...] [2012-12-20 17:12:25] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
103.34	98.60	96.33
102.60	96.90	96.33
100.69	95.10	95.05
105.67	97.00	96.84
123.61	112.70	96.92
113.08	102.90	97.44
106.46	97.40	97.78
123.38	111.40	97.69
109.87	87.40	96.67
95.74	96.80	98.29
123.06	114.10	98.20
123.39	110.30	98.71
120.28	103.90	98.54
115.33	101.60	98.20
110.40	94.60	100.80
114.49	95.90	101.33
132.03	104.70	101.88
123.16	102.80	101.85
118.82	98.10	102.04
128.32	113.90	102.22
112.24	80.90	102.63
104.53	95.70	102.65
132.57	113.20	102.54
122.52	105.90	102.37
131.80	108.80	102.68
124.55	102.30	102.76
120.96	99.00	102.82
122.60	100.70	103.31
145.52	115.50	103.23
118.57	100.70	103.60
134.25	109.90	103.95
136.70	114.60	103.93
121.37	85.40	104.25
111.63	100.50	104.38
134.42	114.80	104.36
137.65	116.50	104.32
137.86	112.90	104.58
119.77	102.00	104.68
130.69	106.00	104.92
128.28	105.30	105.46
147.45	118.80	105.23
128.42	106.10	105.58
136.90	109.30	105.34
143.95	117.20	105.28
135.64	92.50	105.70
122.48	104.20	105.67
136.83	112.50	105.71
153.04	122.40	106.19
142.71	113.30	106.93
123.46	100.00	107.44
144.37	110.70	107.85
146.15	112.80	108.71
147.61	109.80	109.32
158.51	117.30	109.49
147.40	109.10	110.20
165.05	115.90	110.62
154.64	96.00	111.22
126.20	99.80	110.88
157.36	116.80	111.15
154.15	115.70	111.29
123.21	99.40	111.09
113.07	94.30	111.24
110.45	91.00	111.45
113.57	93.20	111.75
122.44	103.10	111.07
114.93	94.10	111.17
111.85	91.80	110.96
126.04	102.70	110.50
121.34	82.60	110.48
124.36	89.10	110.66




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&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]8 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=202920&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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 time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Uitvoer[t] = -153.26923086961 + 1.12814174357858TIP[t] + 1.55877189719339cons[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Uitvoer[t] =  -153.26923086961 +  1.12814174357858TIP[t] +  1.55877189719339cons[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Uitvoer[t] =  -153.26923086961 +  1.12814174357858TIP[t] +  1.55877189719339cons[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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
Uitvoer[t] = -153.26923086961 + 1.12814174357858TIP[t] + 1.55877189719339cons[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-153.2692308696123.230546-6.597700
TIP1.128141743578580.10180511.081400
cons1.558771897193390.2012597.745100

\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) & -153.26923086961 & 23.230546 & -6.5977 & 0 & 0 \tabularnewline
TIP & 1.12814174357858 & 0.101805 & 11.0814 & 0 & 0 \tabularnewline
cons & 1.55877189719339 & 0.201259 & 7.7451 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&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]-153.26923086961[/C][C]23.230546[/C][C]-6.5977[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]TIP[/C][C]1.12814174357858[/C][C]0.101805[/C][C]11.0814[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]cons[/C][C]1.55877189719339[/C][C]0.201259[/C][C]7.7451[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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)-153.2692308696123.230546-6.597700
TIP1.128141743578580.10180511.081400
cons1.558771897193390.2012597.745100







Multiple Linear Regression - Regression Statistics
Multiple R0.859237237376913
R-squared0.73828863009511
Adjusted R-squared0.730476350396457
F-TEST (value)94.5036095190456
F-TEST (DF numerator)2
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.00601229043713
Sum Squared Residuals4294.44759724024

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.859237237376913 \tabularnewline
R-squared & 0.73828863009511 \tabularnewline
Adjusted R-squared & 0.730476350396457 \tabularnewline
F-TEST (value) & 94.5036095190456 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.00601229043713 \tabularnewline
Sum Squared Residuals & 4294.44759724024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.859237237376913[/C][/ROW]
[ROW][C]R-squared[/C][C]0.73828863009511[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.730476350396457[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]94.5036095190456[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/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]8.00601229043713[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4294.44759724024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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 R0.859237237376913
R-squared0.73828863009511
Adjusted R-squared0.730476350396457
F-TEST (value)94.5036095190456
F-TEST (DF numerator)2
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.00601229043713
Sum Squared Residuals4294.44759724024







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.34108.122041903877-4.78204190387708
2102.6106.204200939793-3.6042009397935
3100.69102.178317772944-1.48831777294448
4105.67107.11198878172-1.44198878171997
5123.61124.948515907679-1.33851590767921
6113.08114.70328820715-1.62328820714965
7106.46109.028491062513-2.5684910625132
8123.38124.682186001866-1.30218600186596
9109.8796.016836820842713.8531631791573
1095.74109.146579683935-13.4065796839347
11123.06128.523142377097-5.46314237709675
12123.39125.031177419067-1.64117741906675
13120.28117.5460790376412.73392096235903
14115.33114.4213705823640.908629417635538
15110.4110.577185310017-0.177185310017169
16114.49112.8699186821821.62008131781815
17132.03123.654890569138.37510943087027
18123.16121.4646580994151.69534190058538
19118.82116.4585585650622.36144143493796
20128.32134.563777055098-6.24377705509847
21112.2497.974195994854514.2658040051455
22104.53114.701869237761-10.1718692377614
23132.57134.272884841695-1.70288484169536
24122.52125.772458891049-3.25245889104881
25131.8129.5272892355572.27271076444336
26124.55122.3190696540712.23093034592867
27120.96118.6897282140942.27027178590641
28122.6121.3713674078021.22863259219804
29145.52137.943163460997.57683653901049
30118.57121.823411257988-3.25341125798803
31134.25132.7478854629291.5021145370713
32136.7138.018976219804-1.31897621980418
33121.37105.57604431441115.7939556855886
34111.63122.813624989083-11.1836249890832
35134.42138.914876484313-4.49487648431305
36137.65140.770366572509-3.12036657250888
37137.86137.1143369888960.745663011103738
38119.77124.973469173609-5.20346917360906
39130.69129.860141403250.8298585967502
40128.28129.912179007229-1.6321790072292
41147.45144.7835750091862.66642499081437
42128.42131.001745029755-2.58174502975529
43136.9134.237693353882.66230664611966
44143.95143.056486814320.89351318568043
45135.64115.8460699447519.7939300552502
46122.48128.998565187703-6.51856518770339
47136.83138.424492535293-1.59449253529335
48153.04150.3413063073742.69869369262582
49142.71141.2287076447321.48129235526784
50123.46127.019396122706-3.55939612270563
51144.37139.7296092568464.64039074315425
52146.15143.4392507499472.71074925005291
53147.61141.0056763764996.60432362350071
54158.51149.7317306758628.77826932413842
55147.4141.5876964255245.81230357447552
56165.05149.9137444786815.1362555213199
57154.64128.39898691978226.2410130802177
58126.2132.155943100335-5.95594310033516
59157.36151.7552211534135.6047788465867
60154.15150.7324933010843.41750669891605
61123.21132.032028501314-8.82202850131437
62113.07126.512321393643-13.4423213936426
63110.45123.116795738244-12.6667957382439
64113.57126.066339143275-12.4963391432748
65122.44136.174977514611-13.7349775146112
66114.93126.177579012123-11.2475790121233
67111.85123.255510903482-11.405510903482
68126.04134.83522083578-8.79522083577957
69121.34112.1283963519069.21160364809383
70124.36119.7418966266624.61810337333823

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.34 & 108.122041903877 & -4.78204190387708 \tabularnewline
2 & 102.6 & 106.204200939793 & -3.6042009397935 \tabularnewline
3 & 100.69 & 102.178317772944 & -1.48831777294448 \tabularnewline
4 & 105.67 & 107.11198878172 & -1.44198878171997 \tabularnewline
5 & 123.61 & 124.948515907679 & -1.33851590767921 \tabularnewline
6 & 113.08 & 114.70328820715 & -1.62328820714965 \tabularnewline
7 & 106.46 & 109.028491062513 & -2.5684910625132 \tabularnewline
8 & 123.38 & 124.682186001866 & -1.30218600186596 \tabularnewline
9 & 109.87 & 96.0168368208427 & 13.8531631791573 \tabularnewline
10 & 95.74 & 109.146579683935 & -13.4065796839347 \tabularnewline
11 & 123.06 & 128.523142377097 & -5.46314237709675 \tabularnewline
12 & 123.39 & 125.031177419067 & -1.64117741906675 \tabularnewline
13 & 120.28 & 117.546079037641 & 2.73392096235903 \tabularnewline
14 & 115.33 & 114.421370582364 & 0.908629417635538 \tabularnewline
15 & 110.4 & 110.577185310017 & -0.177185310017169 \tabularnewline
16 & 114.49 & 112.869918682182 & 1.62008131781815 \tabularnewline
17 & 132.03 & 123.65489056913 & 8.37510943087027 \tabularnewline
18 & 123.16 & 121.464658099415 & 1.69534190058538 \tabularnewline
19 & 118.82 & 116.458558565062 & 2.36144143493796 \tabularnewline
20 & 128.32 & 134.563777055098 & -6.24377705509847 \tabularnewline
21 & 112.24 & 97.9741959948545 & 14.2658040051455 \tabularnewline
22 & 104.53 & 114.701869237761 & -10.1718692377614 \tabularnewline
23 & 132.57 & 134.272884841695 & -1.70288484169536 \tabularnewline
24 & 122.52 & 125.772458891049 & -3.25245889104881 \tabularnewline
25 & 131.8 & 129.527289235557 & 2.27271076444336 \tabularnewline
26 & 124.55 & 122.319069654071 & 2.23093034592867 \tabularnewline
27 & 120.96 & 118.689728214094 & 2.27027178590641 \tabularnewline
28 & 122.6 & 121.371367407802 & 1.22863259219804 \tabularnewline
29 & 145.52 & 137.94316346099 & 7.57683653901049 \tabularnewline
30 & 118.57 & 121.823411257988 & -3.25341125798803 \tabularnewline
31 & 134.25 & 132.747885462929 & 1.5021145370713 \tabularnewline
32 & 136.7 & 138.018976219804 & -1.31897621980418 \tabularnewline
33 & 121.37 & 105.576044314411 & 15.7939556855886 \tabularnewline
34 & 111.63 & 122.813624989083 & -11.1836249890832 \tabularnewline
35 & 134.42 & 138.914876484313 & -4.49487648431305 \tabularnewline
36 & 137.65 & 140.770366572509 & -3.12036657250888 \tabularnewline
37 & 137.86 & 137.114336988896 & 0.745663011103738 \tabularnewline
38 & 119.77 & 124.973469173609 & -5.20346917360906 \tabularnewline
39 & 130.69 & 129.86014140325 & 0.8298585967502 \tabularnewline
40 & 128.28 & 129.912179007229 & -1.6321790072292 \tabularnewline
41 & 147.45 & 144.783575009186 & 2.66642499081437 \tabularnewline
42 & 128.42 & 131.001745029755 & -2.58174502975529 \tabularnewline
43 & 136.9 & 134.23769335388 & 2.66230664611966 \tabularnewline
44 & 143.95 & 143.05648681432 & 0.89351318568043 \tabularnewline
45 & 135.64 & 115.84606994475 & 19.7939300552502 \tabularnewline
46 & 122.48 & 128.998565187703 & -6.51856518770339 \tabularnewline
47 & 136.83 & 138.424492535293 & -1.59449253529335 \tabularnewline
48 & 153.04 & 150.341306307374 & 2.69869369262582 \tabularnewline
49 & 142.71 & 141.228707644732 & 1.48129235526784 \tabularnewline
50 & 123.46 & 127.019396122706 & -3.55939612270563 \tabularnewline
51 & 144.37 & 139.729609256846 & 4.64039074315425 \tabularnewline
52 & 146.15 & 143.439250749947 & 2.71074925005291 \tabularnewline
53 & 147.61 & 141.005676376499 & 6.60432362350071 \tabularnewline
54 & 158.51 & 149.731730675862 & 8.77826932413842 \tabularnewline
55 & 147.4 & 141.587696425524 & 5.81230357447552 \tabularnewline
56 & 165.05 & 149.91374447868 & 15.1362555213199 \tabularnewline
57 & 154.64 & 128.398986919782 & 26.2410130802177 \tabularnewline
58 & 126.2 & 132.155943100335 & -5.95594310033516 \tabularnewline
59 & 157.36 & 151.755221153413 & 5.6047788465867 \tabularnewline
60 & 154.15 & 150.732493301084 & 3.41750669891605 \tabularnewline
61 & 123.21 & 132.032028501314 & -8.82202850131437 \tabularnewline
62 & 113.07 & 126.512321393643 & -13.4423213936426 \tabularnewline
63 & 110.45 & 123.116795738244 & -12.6667957382439 \tabularnewline
64 & 113.57 & 126.066339143275 & -12.4963391432748 \tabularnewline
65 & 122.44 & 136.174977514611 & -13.7349775146112 \tabularnewline
66 & 114.93 & 126.177579012123 & -11.2475790121233 \tabularnewline
67 & 111.85 & 123.255510903482 & -11.405510903482 \tabularnewline
68 & 126.04 & 134.83522083578 & -8.79522083577957 \tabularnewline
69 & 121.34 & 112.128396351906 & 9.21160364809383 \tabularnewline
70 & 124.36 & 119.741896626662 & 4.61810337333823 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&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]103.34[/C][C]108.122041903877[/C][C]-4.78204190387708[/C][/ROW]
[ROW][C]2[/C][C]102.6[/C][C]106.204200939793[/C][C]-3.6042009397935[/C][/ROW]
[ROW][C]3[/C][C]100.69[/C][C]102.178317772944[/C][C]-1.48831777294448[/C][/ROW]
[ROW][C]4[/C][C]105.67[/C][C]107.11198878172[/C][C]-1.44198878171997[/C][/ROW]
[ROW][C]5[/C][C]123.61[/C][C]124.948515907679[/C][C]-1.33851590767921[/C][/ROW]
[ROW][C]6[/C][C]113.08[/C][C]114.70328820715[/C][C]-1.62328820714965[/C][/ROW]
[ROW][C]7[/C][C]106.46[/C][C]109.028491062513[/C][C]-2.5684910625132[/C][/ROW]
[ROW][C]8[/C][C]123.38[/C][C]124.682186001866[/C][C]-1.30218600186596[/C][/ROW]
[ROW][C]9[/C][C]109.87[/C][C]96.0168368208427[/C][C]13.8531631791573[/C][/ROW]
[ROW][C]10[/C][C]95.74[/C][C]109.146579683935[/C][C]-13.4065796839347[/C][/ROW]
[ROW][C]11[/C][C]123.06[/C][C]128.523142377097[/C][C]-5.46314237709675[/C][/ROW]
[ROW][C]12[/C][C]123.39[/C][C]125.031177419067[/C][C]-1.64117741906675[/C][/ROW]
[ROW][C]13[/C][C]120.28[/C][C]117.546079037641[/C][C]2.73392096235903[/C][/ROW]
[ROW][C]14[/C][C]115.33[/C][C]114.421370582364[/C][C]0.908629417635538[/C][/ROW]
[ROW][C]15[/C][C]110.4[/C][C]110.577185310017[/C][C]-0.177185310017169[/C][/ROW]
[ROW][C]16[/C][C]114.49[/C][C]112.869918682182[/C][C]1.62008131781815[/C][/ROW]
[ROW][C]17[/C][C]132.03[/C][C]123.65489056913[/C][C]8.37510943087027[/C][/ROW]
[ROW][C]18[/C][C]123.16[/C][C]121.464658099415[/C][C]1.69534190058538[/C][/ROW]
[ROW][C]19[/C][C]118.82[/C][C]116.458558565062[/C][C]2.36144143493796[/C][/ROW]
[ROW][C]20[/C][C]128.32[/C][C]134.563777055098[/C][C]-6.24377705509847[/C][/ROW]
[ROW][C]21[/C][C]112.24[/C][C]97.9741959948545[/C][C]14.2658040051455[/C][/ROW]
[ROW][C]22[/C][C]104.53[/C][C]114.701869237761[/C][C]-10.1718692377614[/C][/ROW]
[ROW][C]23[/C][C]132.57[/C][C]134.272884841695[/C][C]-1.70288484169536[/C][/ROW]
[ROW][C]24[/C][C]122.52[/C][C]125.772458891049[/C][C]-3.25245889104881[/C][/ROW]
[ROW][C]25[/C][C]131.8[/C][C]129.527289235557[/C][C]2.27271076444336[/C][/ROW]
[ROW][C]26[/C][C]124.55[/C][C]122.319069654071[/C][C]2.23093034592867[/C][/ROW]
[ROW][C]27[/C][C]120.96[/C][C]118.689728214094[/C][C]2.27027178590641[/C][/ROW]
[ROW][C]28[/C][C]122.6[/C][C]121.371367407802[/C][C]1.22863259219804[/C][/ROW]
[ROW][C]29[/C][C]145.52[/C][C]137.94316346099[/C][C]7.57683653901049[/C][/ROW]
[ROW][C]30[/C][C]118.57[/C][C]121.823411257988[/C][C]-3.25341125798803[/C][/ROW]
[ROW][C]31[/C][C]134.25[/C][C]132.747885462929[/C][C]1.5021145370713[/C][/ROW]
[ROW][C]32[/C][C]136.7[/C][C]138.018976219804[/C][C]-1.31897621980418[/C][/ROW]
[ROW][C]33[/C][C]121.37[/C][C]105.576044314411[/C][C]15.7939556855886[/C][/ROW]
[ROW][C]34[/C][C]111.63[/C][C]122.813624989083[/C][C]-11.1836249890832[/C][/ROW]
[ROW][C]35[/C][C]134.42[/C][C]138.914876484313[/C][C]-4.49487648431305[/C][/ROW]
[ROW][C]36[/C][C]137.65[/C][C]140.770366572509[/C][C]-3.12036657250888[/C][/ROW]
[ROW][C]37[/C][C]137.86[/C][C]137.114336988896[/C][C]0.745663011103738[/C][/ROW]
[ROW][C]38[/C][C]119.77[/C][C]124.973469173609[/C][C]-5.20346917360906[/C][/ROW]
[ROW][C]39[/C][C]130.69[/C][C]129.86014140325[/C][C]0.8298585967502[/C][/ROW]
[ROW][C]40[/C][C]128.28[/C][C]129.912179007229[/C][C]-1.6321790072292[/C][/ROW]
[ROW][C]41[/C][C]147.45[/C][C]144.783575009186[/C][C]2.66642499081437[/C][/ROW]
[ROW][C]42[/C][C]128.42[/C][C]131.001745029755[/C][C]-2.58174502975529[/C][/ROW]
[ROW][C]43[/C][C]136.9[/C][C]134.23769335388[/C][C]2.66230664611966[/C][/ROW]
[ROW][C]44[/C][C]143.95[/C][C]143.05648681432[/C][C]0.89351318568043[/C][/ROW]
[ROW][C]45[/C][C]135.64[/C][C]115.84606994475[/C][C]19.7939300552502[/C][/ROW]
[ROW][C]46[/C][C]122.48[/C][C]128.998565187703[/C][C]-6.51856518770339[/C][/ROW]
[ROW][C]47[/C][C]136.83[/C][C]138.424492535293[/C][C]-1.59449253529335[/C][/ROW]
[ROW][C]48[/C][C]153.04[/C][C]150.341306307374[/C][C]2.69869369262582[/C][/ROW]
[ROW][C]49[/C][C]142.71[/C][C]141.228707644732[/C][C]1.48129235526784[/C][/ROW]
[ROW][C]50[/C][C]123.46[/C][C]127.019396122706[/C][C]-3.55939612270563[/C][/ROW]
[ROW][C]51[/C][C]144.37[/C][C]139.729609256846[/C][C]4.64039074315425[/C][/ROW]
[ROW][C]52[/C][C]146.15[/C][C]143.439250749947[/C][C]2.71074925005291[/C][/ROW]
[ROW][C]53[/C][C]147.61[/C][C]141.005676376499[/C][C]6.60432362350071[/C][/ROW]
[ROW][C]54[/C][C]158.51[/C][C]149.731730675862[/C][C]8.77826932413842[/C][/ROW]
[ROW][C]55[/C][C]147.4[/C][C]141.587696425524[/C][C]5.81230357447552[/C][/ROW]
[ROW][C]56[/C][C]165.05[/C][C]149.91374447868[/C][C]15.1362555213199[/C][/ROW]
[ROW][C]57[/C][C]154.64[/C][C]128.398986919782[/C][C]26.2410130802177[/C][/ROW]
[ROW][C]58[/C][C]126.2[/C][C]132.155943100335[/C][C]-5.95594310033516[/C][/ROW]
[ROW][C]59[/C][C]157.36[/C][C]151.755221153413[/C][C]5.6047788465867[/C][/ROW]
[ROW][C]60[/C][C]154.15[/C][C]150.732493301084[/C][C]3.41750669891605[/C][/ROW]
[ROW][C]61[/C][C]123.21[/C][C]132.032028501314[/C][C]-8.82202850131437[/C][/ROW]
[ROW][C]62[/C][C]113.07[/C][C]126.512321393643[/C][C]-13.4423213936426[/C][/ROW]
[ROW][C]63[/C][C]110.45[/C][C]123.116795738244[/C][C]-12.6667957382439[/C][/ROW]
[ROW][C]64[/C][C]113.57[/C][C]126.066339143275[/C][C]-12.4963391432748[/C][/ROW]
[ROW][C]65[/C][C]122.44[/C][C]136.174977514611[/C][C]-13.7349775146112[/C][/ROW]
[ROW][C]66[/C][C]114.93[/C][C]126.177579012123[/C][C]-11.2475790121233[/C][/ROW]
[ROW][C]67[/C][C]111.85[/C][C]123.255510903482[/C][C]-11.405510903482[/C][/ROW]
[ROW][C]68[/C][C]126.04[/C][C]134.83522083578[/C][C]-8.79522083577957[/C][/ROW]
[ROW][C]69[/C][C]121.34[/C][C]112.128396351906[/C][C]9.21160364809383[/C][/ROW]
[ROW][C]70[/C][C]124.36[/C][C]119.741896626662[/C][C]4.61810337333823[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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
1103.34108.122041903877-4.78204190387708
2102.6106.204200939793-3.6042009397935
3100.69102.178317772944-1.48831777294448
4105.67107.11198878172-1.44198878171997
5123.61124.948515907679-1.33851590767921
6113.08114.70328820715-1.62328820714965
7106.46109.028491062513-2.5684910625132
8123.38124.682186001866-1.30218600186596
9109.8796.016836820842713.8531631791573
1095.74109.146579683935-13.4065796839347
11123.06128.523142377097-5.46314237709675
12123.39125.031177419067-1.64117741906675
13120.28117.5460790376412.73392096235903
14115.33114.4213705823640.908629417635538
15110.4110.577185310017-0.177185310017169
16114.49112.8699186821821.62008131781815
17132.03123.654890569138.37510943087027
18123.16121.4646580994151.69534190058538
19118.82116.4585585650622.36144143493796
20128.32134.563777055098-6.24377705509847
21112.2497.974195994854514.2658040051455
22104.53114.701869237761-10.1718692377614
23132.57134.272884841695-1.70288484169536
24122.52125.772458891049-3.25245889104881
25131.8129.5272892355572.27271076444336
26124.55122.3190696540712.23093034592867
27120.96118.6897282140942.27027178590641
28122.6121.3713674078021.22863259219804
29145.52137.943163460997.57683653901049
30118.57121.823411257988-3.25341125798803
31134.25132.7478854629291.5021145370713
32136.7138.018976219804-1.31897621980418
33121.37105.57604431441115.7939556855886
34111.63122.813624989083-11.1836249890832
35134.42138.914876484313-4.49487648431305
36137.65140.770366572509-3.12036657250888
37137.86137.1143369888960.745663011103738
38119.77124.973469173609-5.20346917360906
39130.69129.860141403250.8298585967502
40128.28129.912179007229-1.6321790072292
41147.45144.7835750091862.66642499081437
42128.42131.001745029755-2.58174502975529
43136.9134.237693353882.66230664611966
44143.95143.056486814320.89351318568043
45135.64115.8460699447519.7939300552502
46122.48128.998565187703-6.51856518770339
47136.83138.424492535293-1.59449253529335
48153.04150.3413063073742.69869369262582
49142.71141.2287076447321.48129235526784
50123.46127.019396122706-3.55939612270563
51144.37139.7296092568464.64039074315425
52146.15143.4392507499472.71074925005291
53147.61141.0056763764996.60432362350071
54158.51149.7317306758628.77826932413842
55147.4141.5876964255245.81230357447552
56165.05149.9137444786815.1362555213199
57154.64128.39898691978226.2410130802177
58126.2132.155943100335-5.95594310033516
59157.36151.7552211534135.6047788465867
60154.15150.7324933010843.41750669891605
61123.21132.032028501314-8.82202850131437
62113.07126.512321393643-13.4423213936426
63110.45123.116795738244-12.6667957382439
64113.57126.066339143275-12.4963391432748
65122.44136.174977514611-13.7349775146112
66114.93126.177579012123-11.2475790121233
67111.85123.255510903482-11.405510903482
68126.04134.83522083578-8.79522083577957
69121.34112.1283963519069.21160364809383
70124.36119.7418966266624.61810337333823







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.01296586996457310.02593173992914620.987034130035427
70.002236845566165580.004473691132331150.997763154433834
80.0003502713370764310.0007005426741528620.999649728662924
90.127669122578230.2553382451564610.87233087742177
100.3328373366042960.6656746732085920.667162663395704
110.240760894092340.481521788184680.75923910590766
120.1841849776305790.3683699552611580.815815022369421
130.1568178567862840.3136357135725690.843182143213716
140.1079549869775020.2159099739550030.892045013022498
150.06790343663832550.1358068732766510.932096563361675
160.04205931080012730.08411862160025460.957940689199873
170.04980739239839820.09961478479679630.950192607601602
180.03002440443626620.06004880887253240.969975595563734
190.01753968530888990.03507937061777990.98246031469111
200.0143851698281680.02877033965633590.985614830171832
210.01567387904643750.0313477580928750.984326120953563
220.05365552305702060.1073110461140410.946344476942979
230.03577530236091760.07155060472183520.964224697639082
240.02441778392527190.04883556785054370.975582216074728
250.01681766407286420.03363532814572840.983182335927136
260.01028032795809220.02056065591618450.989719672041908
270.006019114561846090.01203822912369220.993980885438154
280.003409270416993060.006818540833986120.996590729583007
290.004806464893613610.009612929787227210.995193535106386
300.003683825201404290.007367650402808580.996316174798596
310.002093925373518960.004187850747037920.997906074626481
320.001168273370298790.002336546740597580.998831726629701
330.003226722888083890.006453445776167780.996773277111916
340.01038842935047680.02077685870095370.989611570649523
350.007380074424763870.01476014884952770.992619925575236
360.004888893144299520.009777786288599050.9951111068557
370.002968998930407760.005937997860815520.997031001069592
380.002678845287258510.005357690574517010.997321154712742
390.001520936404790580.003041872809581170.998479063595209
400.0009105421148267010.00182108422965340.999089457885173
410.0006290382241467040.001258076448293410.999370961775853
420.0004021070885649680.0008042141771299360.999597892911435
430.0002218758928292670.0004437517856585330.999778124107171
440.0001315258842468570.0002630517684937150.999868474115753
450.002111631378452440.004223262756904870.997888368621548
460.00209358215029980.004187164300599590.9979064178497
470.001283560336590080.002567120673180150.99871643966341
480.0009498652203000970.001899730440600190.9990501347797
490.000565442742989280.001130885485978560.999434557257011
500.000567352507194710.001134705014389420.999432647492805
510.0003663411979725510.0007326823959451020.999633658802027
520.0002872023582916550.000574404716583310.999712797641708
530.0001719885485621680.0003439770971243350.999828011451438
540.00014106206168210.0002821241233642010.999858937938318
556.87510380037203e-050.0001375020760074410.999931248961996
560.0001257135507203840.0002514271014407670.99987428644928
570.1495690158813540.2991380317627090.850430984118646
580.1532666115960160.3065332231920320.846733388403984
590.1925281258535670.3850562517071350.807471874146433
600.925347009293020.1493059814139590.0746529907069797
610.9270683894440740.1458632211118520.0729316105559259
620.9083483595624690.1833032808750630.0916516404375313
630.8627449371624240.2745101256751520.137255062837576
640.8233610523753090.3532778952493820.176638947624691

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0129658699645731 & 0.0259317399291462 & 0.987034130035427 \tabularnewline
7 & 0.00223684556616558 & 0.00447369113233115 & 0.997763154433834 \tabularnewline
8 & 0.000350271337076431 & 0.000700542674152862 & 0.999649728662924 \tabularnewline
9 & 0.12766912257823 & 0.255338245156461 & 0.87233087742177 \tabularnewline
10 & 0.332837336604296 & 0.665674673208592 & 0.667162663395704 \tabularnewline
11 & 0.24076089409234 & 0.48152178818468 & 0.75923910590766 \tabularnewline
12 & 0.184184977630579 & 0.368369955261158 & 0.815815022369421 \tabularnewline
13 & 0.156817856786284 & 0.313635713572569 & 0.843182143213716 \tabularnewline
14 & 0.107954986977502 & 0.215909973955003 & 0.892045013022498 \tabularnewline
15 & 0.0679034366383255 & 0.135806873276651 & 0.932096563361675 \tabularnewline
16 & 0.0420593108001273 & 0.0841186216002546 & 0.957940689199873 \tabularnewline
17 & 0.0498073923983982 & 0.0996147847967963 & 0.950192607601602 \tabularnewline
18 & 0.0300244044362662 & 0.0600488088725324 & 0.969975595563734 \tabularnewline
19 & 0.0175396853088899 & 0.0350793706177799 & 0.98246031469111 \tabularnewline
20 & 0.014385169828168 & 0.0287703396563359 & 0.985614830171832 \tabularnewline
21 & 0.0156738790464375 & 0.031347758092875 & 0.984326120953563 \tabularnewline
22 & 0.0536555230570206 & 0.107311046114041 & 0.946344476942979 \tabularnewline
23 & 0.0357753023609176 & 0.0715506047218352 & 0.964224697639082 \tabularnewline
24 & 0.0244177839252719 & 0.0488355678505437 & 0.975582216074728 \tabularnewline
25 & 0.0168176640728642 & 0.0336353281457284 & 0.983182335927136 \tabularnewline
26 & 0.0102803279580922 & 0.0205606559161845 & 0.989719672041908 \tabularnewline
27 & 0.00601911456184609 & 0.0120382291236922 & 0.993980885438154 \tabularnewline
28 & 0.00340927041699306 & 0.00681854083398612 & 0.996590729583007 \tabularnewline
29 & 0.00480646489361361 & 0.00961292978722721 & 0.995193535106386 \tabularnewline
30 & 0.00368382520140429 & 0.00736765040280858 & 0.996316174798596 \tabularnewline
31 & 0.00209392537351896 & 0.00418785074703792 & 0.997906074626481 \tabularnewline
32 & 0.00116827337029879 & 0.00233654674059758 & 0.998831726629701 \tabularnewline
33 & 0.00322672288808389 & 0.00645344577616778 & 0.996773277111916 \tabularnewline
34 & 0.0103884293504768 & 0.0207768587009537 & 0.989611570649523 \tabularnewline
35 & 0.00738007442476387 & 0.0147601488495277 & 0.992619925575236 \tabularnewline
36 & 0.00488889314429952 & 0.00977778628859905 & 0.9951111068557 \tabularnewline
37 & 0.00296899893040776 & 0.00593799786081552 & 0.997031001069592 \tabularnewline
38 & 0.00267884528725851 & 0.00535769057451701 & 0.997321154712742 \tabularnewline
39 & 0.00152093640479058 & 0.00304187280958117 & 0.998479063595209 \tabularnewline
40 & 0.000910542114826701 & 0.0018210842296534 & 0.999089457885173 \tabularnewline
41 & 0.000629038224146704 & 0.00125807644829341 & 0.999370961775853 \tabularnewline
42 & 0.000402107088564968 & 0.000804214177129936 & 0.999597892911435 \tabularnewline
43 & 0.000221875892829267 & 0.000443751785658533 & 0.999778124107171 \tabularnewline
44 & 0.000131525884246857 & 0.000263051768493715 & 0.999868474115753 \tabularnewline
45 & 0.00211163137845244 & 0.00422326275690487 & 0.997888368621548 \tabularnewline
46 & 0.0020935821502998 & 0.00418716430059959 & 0.9979064178497 \tabularnewline
47 & 0.00128356033659008 & 0.00256712067318015 & 0.99871643966341 \tabularnewline
48 & 0.000949865220300097 & 0.00189973044060019 & 0.9990501347797 \tabularnewline
49 & 0.00056544274298928 & 0.00113088548597856 & 0.999434557257011 \tabularnewline
50 & 0.00056735250719471 & 0.00113470501438942 & 0.999432647492805 \tabularnewline
51 & 0.000366341197972551 & 0.000732682395945102 & 0.999633658802027 \tabularnewline
52 & 0.000287202358291655 & 0.00057440471658331 & 0.999712797641708 \tabularnewline
53 & 0.000171988548562168 & 0.000343977097124335 & 0.999828011451438 \tabularnewline
54 & 0.0001410620616821 & 0.000282124123364201 & 0.999858937938318 \tabularnewline
55 & 6.87510380037203e-05 & 0.000137502076007441 & 0.999931248961996 \tabularnewline
56 & 0.000125713550720384 & 0.000251427101440767 & 0.99987428644928 \tabularnewline
57 & 0.149569015881354 & 0.299138031762709 & 0.850430984118646 \tabularnewline
58 & 0.153266611596016 & 0.306533223192032 & 0.846733388403984 \tabularnewline
59 & 0.192528125853567 & 0.385056251707135 & 0.807471874146433 \tabularnewline
60 & 0.92534700929302 & 0.149305981413959 & 0.0746529907069797 \tabularnewline
61 & 0.927068389444074 & 0.145863221111852 & 0.0729316105559259 \tabularnewline
62 & 0.908348359562469 & 0.183303280875063 & 0.0916516404375313 \tabularnewline
63 & 0.862744937162424 & 0.274510125675152 & 0.137255062837576 \tabularnewline
64 & 0.823361052375309 & 0.353277895249382 & 0.176638947624691 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&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]0.0129658699645731[/C][C]0.0259317399291462[/C][C]0.987034130035427[/C][/ROW]
[ROW][C]7[/C][C]0.00223684556616558[/C][C]0.00447369113233115[/C][C]0.997763154433834[/C][/ROW]
[ROW][C]8[/C][C]0.000350271337076431[/C][C]0.000700542674152862[/C][C]0.999649728662924[/C][/ROW]
[ROW][C]9[/C][C]0.12766912257823[/C][C]0.255338245156461[/C][C]0.87233087742177[/C][/ROW]
[ROW][C]10[/C][C]0.332837336604296[/C][C]0.665674673208592[/C][C]0.667162663395704[/C][/ROW]
[ROW][C]11[/C][C]0.24076089409234[/C][C]0.48152178818468[/C][C]0.75923910590766[/C][/ROW]
[ROW][C]12[/C][C]0.184184977630579[/C][C]0.368369955261158[/C][C]0.815815022369421[/C][/ROW]
[ROW][C]13[/C][C]0.156817856786284[/C][C]0.313635713572569[/C][C]0.843182143213716[/C][/ROW]
[ROW][C]14[/C][C]0.107954986977502[/C][C]0.215909973955003[/C][C]0.892045013022498[/C][/ROW]
[ROW][C]15[/C][C]0.0679034366383255[/C][C]0.135806873276651[/C][C]0.932096563361675[/C][/ROW]
[ROW][C]16[/C][C]0.0420593108001273[/C][C]0.0841186216002546[/C][C]0.957940689199873[/C][/ROW]
[ROW][C]17[/C][C]0.0498073923983982[/C][C]0.0996147847967963[/C][C]0.950192607601602[/C][/ROW]
[ROW][C]18[/C][C]0.0300244044362662[/C][C]0.0600488088725324[/C][C]0.969975595563734[/C][/ROW]
[ROW][C]19[/C][C]0.0175396853088899[/C][C]0.0350793706177799[/C][C]0.98246031469111[/C][/ROW]
[ROW][C]20[/C][C]0.014385169828168[/C][C]0.0287703396563359[/C][C]0.985614830171832[/C][/ROW]
[ROW][C]21[/C][C]0.0156738790464375[/C][C]0.031347758092875[/C][C]0.984326120953563[/C][/ROW]
[ROW][C]22[/C][C]0.0536555230570206[/C][C]0.107311046114041[/C][C]0.946344476942979[/C][/ROW]
[ROW][C]23[/C][C]0.0357753023609176[/C][C]0.0715506047218352[/C][C]0.964224697639082[/C][/ROW]
[ROW][C]24[/C][C]0.0244177839252719[/C][C]0.0488355678505437[/C][C]0.975582216074728[/C][/ROW]
[ROW][C]25[/C][C]0.0168176640728642[/C][C]0.0336353281457284[/C][C]0.983182335927136[/C][/ROW]
[ROW][C]26[/C][C]0.0102803279580922[/C][C]0.0205606559161845[/C][C]0.989719672041908[/C][/ROW]
[ROW][C]27[/C][C]0.00601911456184609[/C][C]0.0120382291236922[/C][C]0.993980885438154[/C][/ROW]
[ROW][C]28[/C][C]0.00340927041699306[/C][C]0.00681854083398612[/C][C]0.996590729583007[/C][/ROW]
[ROW][C]29[/C][C]0.00480646489361361[/C][C]0.00961292978722721[/C][C]0.995193535106386[/C][/ROW]
[ROW][C]30[/C][C]0.00368382520140429[/C][C]0.00736765040280858[/C][C]0.996316174798596[/C][/ROW]
[ROW][C]31[/C][C]0.00209392537351896[/C][C]0.00418785074703792[/C][C]0.997906074626481[/C][/ROW]
[ROW][C]32[/C][C]0.00116827337029879[/C][C]0.00233654674059758[/C][C]0.998831726629701[/C][/ROW]
[ROW][C]33[/C][C]0.00322672288808389[/C][C]0.00645344577616778[/C][C]0.996773277111916[/C][/ROW]
[ROW][C]34[/C][C]0.0103884293504768[/C][C]0.0207768587009537[/C][C]0.989611570649523[/C][/ROW]
[ROW][C]35[/C][C]0.00738007442476387[/C][C]0.0147601488495277[/C][C]0.992619925575236[/C][/ROW]
[ROW][C]36[/C][C]0.00488889314429952[/C][C]0.00977778628859905[/C][C]0.9951111068557[/C][/ROW]
[ROW][C]37[/C][C]0.00296899893040776[/C][C]0.00593799786081552[/C][C]0.997031001069592[/C][/ROW]
[ROW][C]38[/C][C]0.00267884528725851[/C][C]0.00535769057451701[/C][C]0.997321154712742[/C][/ROW]
[ROW][C]39[/C][C]0.00152093640479058[/C][C]0.00304187280958117[/C][C]0.998479063595209[/C][/ROW]
[ROW][C]40[/C][C]0.000910542114826701[/C][C]0.0018210842296534[/C][C]0.999089457885173[/C][/ROW]
[ROW][C]41[/C][C]0.000629038224146704[/C][C]0.00125807644829341[/C][C]0.999370961775853[/C][/ROW]
[ROW][C]42[/C][C]0.000402107088564968[/C][C]0.000804214177129936[/C][C]0.999597892911435[/C][/ROW]
[ROW][C]43[/C][C]0.000221875892829267[/C][C]0.000443751785658533[/C][C]0.999778124107171[/C][/ROW]
[ROW][C]44[/C][C]0.000131525884246857[/C][C]0.000263051768493715[/C][C]0.999868474115753[/C][/ROW]
[ROW][C]45[/C][C]0.00211163137845244[/C][C]0.00422326275690487[/C][C]0.997888368621548[/C][/ROW]
[ROW][C]46[/C][C]0.0020935821502998[/C][C]0.00418716430059959[/C][C]0.9979064178497[/C][/ROW]
[ROW][C]47[/C][C]0.00128356033659008[/C][C]0.00256712067318015[/C][C]0.99871643966341[/C][/ROW]
[ROW][C]48[/C][C]0.000949865220300097[/C][C]0.00189973044060019[/C][C]0.9990501347797[/C][/ROW]
[ROW][C]49[/C][C]0.00056544274298928[/C][C]0.00113088548597856[/C][C]0.999434557257011[/C][/ROW]
[ROW][C]50[/C][C]0.00056735250719471[/C][C]0.00113470501438942[/C][C]0.999432647492805[/C][/ROW]
[ROW][C]51[/C][C]0.000366341197972551[/C][C]0.000732682395945102[/C][C]0.999633658802027[/C][/ROW]
[ROW][C]52[/C][C]0.000287202358291655[/C][C]0.00057440471658331[/C][C]0.999712797641708[/C][/ROW]
[ROW][C]53[/C][C]0.000171988548562168[/C][C]0.000343977097124335[/C][C]0.999828011451438[/C][/ROW]
[ROW][C]54[/C][C]0.0001410620616821[/C][C]0.000282124123364201[/C][C]0.999858937938318[/C][/ROW]
[ROW][C]55[/C][C]6.87510380037203e-05[/C][C]0.000137502076007441[/C][C]0.999931248961996[/C][/ROW]
[ROW][C]56[/C][C]0.000125713550720384[/C][C]0.000251427101440767[/C][C]0.99987428644928[/C][/ROW]
[ROW][C]57[/C][C]0.149569015881354[/C][C]0.299138031762709[/C][C]0.850430984118646[/C][/ROW]
[ROW][C]58[/C][C]0.153266611596016[/C][C]0.306533223192032[/C][C]0.846733388403984[/C][/ROW]
[ROW][C]59[/C][C]0.192528125853567[/C][C]0.385056251707135[/C][C]0.807471874146433[/C][/ROW]
[ROW][C]60[/C][C]0.92534700929302[/C][C]0.149305981413959[/C][C]0.0746529907069797[/C][/ROW]
[ROW][C]61[/C][C]0.927068389444074[/C][C]0.145863221111852[/C][C]0.0729316105559259[/C][/ROW]
[ROW][C]62[/C][C]0.908348359562469[/C][C]0.183303280875063[/C][C]0.0916516404375313[/C][/ROW]
[ROW][C]63[/C][C]0.862744937162424[/C][C]0.274510125675152[/C][C]0.137255062837576[/C][/ROW]
[ROW][C]64[/C][C]0.823361052375309[/C][C]0.353277895249382[/C][C]0.176638947624691[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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
60.01296586996457310.02593173992914620.987034130035427
70.002236845566165580.004473691132331150.997763154433834
80.0003502713370764310.0007005426741528620.999649728662924
90.127669122578230.2553382451564610.87233087742177
100.3328373366042960.6656746732085920.667162663395704
110.240760894092340.481521788184680.75923910590766
120.1841849776305790.3683699552611580.815815022369421
130.1568178567862840.3136357135725690.843182143213716
140.1079549869775020.2159099739550030.892045013022498
150.06790343663832550.1358068732766510.932096563361675
160.04205931080012730.08411862160025460.957940689199873
170.04980739239839820.09961478479679630.950192607601602
180.03002440443626620.06004880887253240.969975595563734
190.01753968530888990.03507937061777990.98246031469111
200.0143851698281680.02877033965633590.985614830171832
210.01567387904643750.0313477580928750.984326120953563
220.05365552305702060.1073110461140410.946344476942979
230.03577530236091760.07155060472183520.964224697639082
240.02441778392527190.04883556785054370.975582216074728
250.01681766407286420.03363532814572840.983182335927136
260.01028032795809220.02056065591618450.989719672041908
270.006019114561846090.01203822912369220.993980885438154
280.003409270416993060.006818540833986120.996590729583007
290.004806464893613610.009612929787227210.995193535106386
300.003683825201404290.007367650402808580.996316174798596
310.002093925373518960.004187850747037920.997906074626481
320.001168273370298790.002336546740597580.998831726629701
330.003226722888083890.006453445776167780.996773277111916
340.01038842935047680.02077685870095370.989611570649523
350.007380074424763870.01476014884952770.992619925575236
360.004888893144299520.009777786288599050.9951111068557
370.002968998930407760.005937997860815520.997031001069592
380.002678845287258510.005357690574517010.997321154712742
390.001520936404790580.003041872809581170.998479063595209
400.0009105421148267010.00182108422965340.999089457885173
410.0006290382241467040.001258076448293410.999370961775853
420.0004021070885649680.0008042141771299360.999597892911435
430.0002218758928292670.0004437517856585330.999778124107171
440.0001315258842468570.0002630517684937150.999868474115753
450.002111631378452440.004223262756904870.997888368621548
460.00209358215029980.004187164300599590.9979064178497
470.001283560336590080.002567120673180150.99871643966341
480.0009498652203000970.001899730440600190.9990501347797
490.000565442742989280.001130885485978560.999434557257011
500.000567352507194710.001134705014389420.999432647492805
510.0003663411979725510.0007326823959451020.999633658802027
520.0002872023582916550.000574404716583310.999712797641708
530.0001719885485621680.0003439770971243350.999828011451438
540.00014106206168210.0002821241233642010.999858937938318
556.87510380037203e-050.0001375020760074410.999931248961996
560.0001257135507203840.0002514271014407670.99987428644928
570.1495690158813540.2991380317627090.850430984118646
580.1532666115960160.3065332231920320.846733388403984
590.1925281258535670.3850562517071350.807471874146433
600.925347009293020.1493059814139590.0746529907069797
610.9270683894440740.1458632211118520.0729316105559259
620.9083483595624690.1833032808750630.0916516404375313
630.8627449371624240.2745101256751520.137255062837576
640.8233610523753090.3532778952493820.176638947624691







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level290.491525423728814NOK
5% type I error level390.661016949152542NOK
10% type I error level430.728813559322034NOK

\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 & 29 & 0.491525423728814 & NOK \tabularnewline
5% type I error level & 39 & 0.661016949152542 & NOK \tabularnewline
10% type I error level & 43 & 0.728813559322034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202920&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]29[/C][C]0.491525423728814[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]39[/C][C]0.661016949152542[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.728813559322034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202920&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202920&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 level290.491525423728814NOK
5% type I error level390.661016949152542NOK
10% type I error level430.728813559322034NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}