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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 15:15:24 -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/Nov/20/t1353443284sxe2pmsliqvpz2o.htm/, Retrieved Mon, 29 Apr 2024 19:33:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191275, Retrieved Mon, 29 Apr 2024 19:33:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [] [2012-11-20 20:15:24] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,3	97,2	95	109,3	108	97,4	98,5
110	100,9	101,4	115,7	113,8	100,9	98
129,2	109,4	112,8	118,5	122,1	109,2	99,9
99,9	101,3	76,4	100,9	100,6	103,1	98,5
100,6	101,2	93,5	99,9	100,2	101,8	104,7
117,2	104,8	117,6	109,9	112,3	103,9	100,4
136	111,4	118,6	120,7	125,7	110,9	108,6
126	106,9	110,1	113,3	117,5	106,6	109,9
125	103,9	113,1	96,4	105,8	103,2	109,5
112,5	107,6	91,2	116,2	115,1	108,7	109,2
119,3	104,7	106,2	119,2	119,3	104,6	100,9
121,1	109,1	115,5	117,9	119	108,7	98,4
127,1	109,3	106,2	126,5	126,8	109,5	94,2
116	102,2	95,9	116,3	116,3	102,6	94,7
131,9	109,8	113,2	124,7	127,1	109,5	95,2
101	106,2	78,3	108,8	106,5	108,1	100,3
92,6	95,1	79,8	96,7	95,5	96,1	100,9
127,2	118,7	121,2	118,4	121,3	118,5	97,9
124,3	116,9	125,6	115,4	118,3	116,3	106,9
103,8	105,3	97,2	91,7	95,6	105,9	100,8
106,4	119,5	102,8	82,5	90,1	120,7	106,6
84,2	96,5	88,8	81,7	82,6	97	108,2
91,9	99,3	95,3	84,5	86,9	99,6	98,4
103,4	113,8	107,6	92,4	95,9	114,2	102
93	102,7	95	86,8	88,8	103,3	95,7
110,6	98,8	87,5	85,1	93,2	99,6	100,8
107,9	109,9	106,7	94,7	98,9	110,1	98,8
72,9	103,6	75,8	82,5	79,6	105,7	99,6
80,9	96,6	80	80,5	80,7	97,8	106,1
103,8	111,6	117,2	102,3	102,9	111,1	106,3
100,4	111,6	106,6	102,2	101,7	112	105,7
101,2	107	104,7	93,3	95,9	107,2	103,7
100,3	111,5	95,2	81	87,1	112,7	111,2
84,5	102	94	88,6	87,5	102,5	114,8
97,2	113,5	95,7	91,8	93,6	114,9	103,6
111,8	125,5	112,6	108,4	109,6	126,4	107
105,2	106,7	99,1	102,1	103,2	107,3	104,8
97,9	102,9	91,6	99,1	98,9	103,8	104,7
117,7	123,6	111,5	111,7	113,7	124,5	102
79,1	107,7	76,6	91,6	87,9	110,1	103,4
89,4	105,5	83,4	89,5	89,6	107,1	107
128,1	117,1	113,5	108,1	114,4	117,3	104
117,7	113,3	106,4	104,7	108,8	113,8	105,4
113,3	118	104,1	100,1	104,3	119	107,9
119,4	118,4	108,4	86,9	97	119,1	110,1
103,7	105,8	91	98,7	100,4	106,9	111
116	114,6	108,3	100,3	105,3	115	98,5
137,3	140,3	121	115,4	122,3	141,7	101,9
113,8	113,8	95,4	101,8	105,6	115,2	103,4
129,8	117,4	109,9	110,9	116,9	117,9	102,9
121,5	115,4	101,4	105,3	110,5	116,5	101
87	105,9	86	89,2	88,6	107,4	103,4
93,3	120,4	96,5	94,8	94,5	122,2	107,2
131,3	126,9	124,6	108,5	115,7	126,9	104,5
123,7	117,1	109,3	100,5	107,8	117,7	104,7
121,8	113,8	104,5	99,6	106,6	114,4	107
124,5	112,8	101,8	89,9	100,7	113,6	110,3
103,9	106,7	101,5	98,4	100,4	107	107,9
110,6	107,3	103,4	97,5	101,7	107,5	97,1
133,7	121,8	125,9	107	115,2	121,4	98,6
108	101,1	96,8	97,5	100,9	101,3	95,3
116,1	103,1	104,4	100,4	105,3	102,9	101,7
122,9	110,4	121,1	103,9	109,8	109,5	96,3
84,3	108,3	83,7	94,8	92,1	110,2	99
98,3	116,3	91,5	89,6	92,5	118,2	104

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Duurzame-consumptiegoederen[t] = + 0.125719048260087 -0.484978620979913Investeringsgoederen[t] + 14.4196257715794Consumptiegoederen[t] -1.01895375373591`Intermediaire-goederen`[t] + 1.49938476771017`Intermediaire-en-investeringsgoederen`[t] -13.4090060352006`Niet-duurzame-consumptiegoederen`[t] -0.0114596547763776Energie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Duurzame-consumptiegoederen[t] =  +  0.125719048260087 -0.484978620979913Investeringsgoederen[t] +  14.4196257715794Consumptiegoederen[t] -1.01895375373591`Intermediaire-goederen`[t] +  1.49938476771017`Intermediaire-en-investeringsgoederen`[t] -13.4090060352006`Niet-duurzame-consumptiegoederen`[t] -0.0114596547763776Energie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Duurzame-consumptiegoederen[t] =  +  0.125719048260087 -0.484978620979913Investeringsgoederen[t] +  14.4196257715794Consumptiegoederen[t] -1.01895375373591`Intermediaire-goederen`[t] +  1.49938476771017`Intermediaire-en-investeringsgoederen`[t] -13.4090060352006`Niet-duurzame-consumptiegoederen`[t] -0.0114596547763776Energie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Duurzame-consumptiegoederen[t] = + 0.125719048260087 -0.484978620979913Investeringsgoederen[t] + 14.4196257715794Consumptiegoederen[t] -1.01895375373591`Intermediaire-goederen`[t] + 1.49938476771017`Intermediaire-en-investeringsgoederen`[t] -13.4090060352006`Niet-duurzame-consumptiegoederen`[t] -0.0114596547763776Energie[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1257190482600873.0117230.04170.9668470.483423
Investeringsgoederen-0.4849786209799130.319478-1.5180.1344380.067219
Consumptiegoederen14.41962577157940.216466.634200
`Intermediaire-goederen`-1.018953753735910.710152-1.43480.1567040.078352
`Intermediaire-en-investeringsgoederen`1.499384767710171.0290551.45710.1504950.075248
`Niet-duurzame-consumptiegoederen`-13.40900603520060.205874-65.13200
Energie-0.01145965477637760.024841-0.46130.6462980.323149

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.125719048260087 & 3.011723 & 0.0417 & 0.966847 & 0.483423 \tabularnewline
Investeringsgoederen & -0.484978620979913 & 0.319478 & -1.518 & 0.134438 & 0.067219 \tabularnewline
Consumptiegoederen & 14.4196257715794 & 0.2164 & 66.6342 & 0 & 0 \tabularnewline
`Intermediaire-goederen` & -1.01895375373591 & 0.710152 & -1.4348 & 0.156704 & 0.078352 \tabularnewline
`Intermediaire-en-investeringsgoederen` & 1.49938476771017 & 1.029055 & 1.4571 & 0.150495 & 0.075248 \tabularnewline
`Niet-duurzame-consumptiegoederen` & -13.4090060352006 & 0.205874 & -65.132 & 0 & 0 \tabularnewline
Energie & -0.0114596547763776 & 0.024841 & -0.4613 & 0.646298 & 0.323149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.125719048260087[/C][C]3.011723[/C][C]0.0417[/C][C]0.966847[/C][C]0.483423[/C][/ROW]
[ROW][C]Investeringsgoederen[/C][C]-0.484978620979913[/C][C]0.319478[/C][C]-1.518[/C][C]0.134438[/C][C]0.067219[/C][/ROW]
[ROW][C]Consumptiegoederen[/C][C]14.4196257715794[/C][C]0.2164[/C][C]66.6342[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Intermediaire-goederen`[/C][C]-1.01895375373591[/C][C]0.710152[/C][C]-1.4348[/C][C]0.156704[/C][C]0.078352[/C][/ROW]
[ROW][C]`Intermediaire-en-investeringsgoederen`[/C][C]1.49938476771017[/C][C]1.029055[/C][C]1.4571[/C][C]0.150495[/C][C]0.075248[/C][/ROW]
[ROW][C]`Niet-duurzame-consumptiegoederen`[/C][C]-13.4090060352006[/C][C]0.205874[/C][C]-65.132[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Energie[/C][C]-0.0114596547763776[/C][C]0.024841[/C][C]-0.4613[/C][C]0.646298[/C][C]0.323149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1257190482600873.0117230.04170.9668470.483423
Investeringsgoederen-0.4849786209799130.319478-1.5180.1344380.067219
Consumptiegoederen14.41962577157940.216466.634200
`Intermediaire-goederen`-1.018953753735910.710152-1.43480.1567040.078352
`Intermediaire-en-investeringsgoederen`1.499384767710171.0290551.45710.1504950.075248
`Niet-duurzame-consumptiegoederen`-13.40900603520060.205874-65.13200
Energie-0.01145965477637760.024841-0.46130.6462980.323149






Multiple Linear Regression - Regression Statistics
Multiple R0.998059516748956
R-squared0.99612279897316
Adjusted R-squared0.995721709211763
F-TEST (value)2483.54083012027
F-TEST (DF numerator)6
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.842315702019236
Sum Squared Residuals41.1507530283532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998059516748956 \tabularnewline
R-squared & 0.99612279897316 \tabularnewline
Adjusted R-squared & 0.995721709211763 \tabularnewline
F-TEST (value) & 2483.54083012027 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.842315702019236 \tabularnewline
Sum Squared Residuals & 41.1507530283532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998059516748956[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99612279897316[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.995721709211763[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2483.54083012027[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]0.842315702019236[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41.1507530283532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.998059516748956
R-squared0.99612279897316
Adjusted R-squared0.995721709211763
F-TEST (value)2483.54083012027
F-TEST (DF numerator)6
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.842315702019236
Sum Squared Residuals41.1507530283532






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19594.04104106195120.958958938048764
2101.4100.3635932311851.03640676881539
3112.8111.894122392090.905877607910376
476.476.8128211284014-0.412821128401386
593.592.81123134935660.688768650643386
6117.6116.5146210123271.085378987673
7118.6117.6965969617270.90340303827285
8110.1110.547198281994-0.447198281994492
9113.1113.0460206257560.0539793742436232
1091.292.4837674614124-1.28376746141236
11106.2105.681592743310.518407256689926
12115.5114.1815334626581.31846653734224
13106.2106.408711519005-0.20871151900504
1495.996.5788312763135-0.678831276313486
15113.2113.563099556352-0.36309955635248
1678.380.6664888464388-2.36648884643881
1779.881.4197678030719-1.61976780307188
18121.2121.1881500531310.0118499468688879
19125.6124.5946450076611.00535499233896
2097.296.90578418416960.294215815830384
21102.8103.011528719343-0.211528719343021
2288.889.4715461905438-0.671546190543805
2395.394.95533588540110.344664114598859
24107.6108.094640815788-0.494640815788393
259594.37144318840420.628556811595818
2687.587.48367140015620.0163285998437637
27106.7106.843852821364-0.143852821363867
2875.875.45803080454340.341969195456633
298080.1847121096392-0.184712109639155
30117.2118.104166075491-0.90416607549075
31106.6105.9944974021290.60550259787092
32104.7104.0346409901260.665359009874096
3395.294.86250233179150.337497668208465
349495.1250318938116-1.12503189381156
3595.794.53376814861561.16623185138445
36112.6113.321581281562-0.721581281562491
3799.198.39404832236670.705951677633328
3891.690.68198817225180.918011827748142
39111.5111.3822583528260.117741647173804
4076.675.70087018790870.899129812091256
4183.483.8569340307002-0.456934030700225
42113.5113.850640174107-0.350640174106527
43106.4106.0832055703360.316794429664034
44104.1104.173827921577-0.0738279215765318
45108.4108.1218775432330.278122456767321
469190.70257102699510.29742897300485
47108.3108.876996934224-0.576996934223771
48121121.175250040509-0.175250040509293
4995.494.59168056738080.808319432619168
50109.9110.214657657863-0.31465765786308
51101.4100.3051889697691.09481103023053
528685.61358733472910.386412665270878
5396.596.28618880990780.21381119009218
54124.6126.420472079665-1.82047207966452
55109.3108.46103099550.838969004499834
56104.5105.138884696436-0.638884696436174
57101.8101.1366858973580.663314102642127
58101.5102.583548949628-1.08354894962831
59103.4104.271487482381-0.871487482380601
60125.9126.31231535779-0.412315357790015
6196.898.0877090958087-1.28770909580874
62104.4105.113209454231-0.713209454231033
63121.1121.821958584187-0.721958584186537
6483.783.57704271168440.122957288315592
6591.590.71331506162550.786684938374489

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95 & 94.0410410619512 & 0.958958938048764 \tabularnewline
2 & 101.4 & 100.363593231185 & 1.03640676881539 \tabularnewline
3 & 112.8 & 111.89412239209 & 0.905877607910376 \tabularnewline
4 & 76.4 & 76.8128211284014 & -0.412821128401386 \tabularnewline
5 & 93.5 & 92.8112313493566 & 0.688768650643386 \tabularnewline
6 & 117.6 & 116.514621012327 & 1.085378987673 \tabularnewline
7 & 118.6 & 117.696596961727 & 0.90340303827285 \tabularnewline
8 & 110.1 & 110.547198281994 & -0.447198281994492 \tabularnewline
9 & 113.1 & 113.046020625756 & 0.0539793742436232 \tabularnewline
10 & 91.2 & 92.4837674614124 & -1.28376746141236 \tabularnewline
11 & 106.2 & 105.68159274331 & 0.518407256689926 \tabularnewline
12 & 115.5 & 114.181533462658 & 1.31846653734224 \tabularnewline
13 & 106.2 & 106.408711519005 & -0.20871151900504 \tabularnewline
14 & 95.9 & 96.5788312763135 & -0.678831276313486 \tabularnewline
15 & 113.2 & 113.563099556352 & -0.36309955635248 \tabularnewline
16 & 78.3 & 80.6664888464388 & -2.36648884643881 \tabularnewline
17 & 79.8 & 81.4197678030719 & -1.61976780307188 \tabularnewline
18 & 121.2 & 121.188150053131 & 0.0118499468688879 \tabularnewline
19 & 125.6 & 124.594645007661 & 1.00535499233896 \tabularnewline
20 & 97.2 & 96.9057841841696 & 0.294215815830384 \tabularnewline
21 & 102.8 & 103.011528719343 & -0.211528719343021 \tabularnewline
22 & 88.8 & 89.4715461905438 & -0.671546190543805 \tabularnewline
23 & 95.3 & 94.9553358854011 & 0.344664114598859 \tabularnewline
24 & 107.6 & 108.094640815788 & -0.494640815788393 \tabularnewline
25 & 95 & 94.3714431884042 & 0.628556811595818 \tabularnewline
26 & 87.5 & 87.4836714001562 & 0.0163285998437637 \tabularnewline
27 & 106.7 & 106.843852821364 & -0.143852821363867 \tabularnewline
28 & 75.8 & 75.4580308045434 & 0.341969195456633 \tabularnewline
29 & 80 & 80.1847121096392 & -0.184712109639155 \tabularnewline
30 & 117.2 & 118.104166075491 & -0.90416607549075 \tabularnewline
31 & 106.6 & 105.994497402129 & 0.60550259787092 \tabularnewline
32 & 104.7 & 104.034640990126 & 0.665359009874096 \tabularnewline
33 & 95.2 & 94.8625023317915 & 0.337497668208465 \tabularnewline
34 & 94 & 95.1250318938116 & -1.12503189381156 \tabularnewline
35 & 95.7 & 94.5337681486156 & 1.16623185138445 \tabularnewline
36 & 112.6 & 113.321581281562 & -0.721581281562491 \tabularnewline
37 & 99.1 & 98.3940483223667 & 0.705951677633328 \tabularnewline
38 & 91.6 & 90.6819881722518 & 0.918011827748142 \tabularnewline
39 & 111.5 & 111.382258352826 & 0.117741647173804 \tabularnewline
40 & 76.6 & 75.7008701879087 & 0.899129812091256 \tabularnewline
41 & 83.4 & 83.8569340307002 & -0.456934030700225 \tabularnewline
42 & 113.5 & 113.850640174107 & -0.350640174106527 \tabularnewline
43 & 106.4 & 106.083205570336 & 0.316794429664034 \tabularnewline
44 & 104.1 & 104.173827921577 & -0.0738279215765318 \tabularnewline
45 & 108.4 & 108.121877543233 & 0.278122456767321 \tabularnewline
46 & 91 & 90.7025710269951 & 0.29742897300485 \tabularnewline
47 & 108.3 & 108.876996934224 & -0.576996934223771 \tabularnewline
48 & 121 & 121.175250040509 & -0.175250040509293 \tabularnewline
49 & 95.4 & 94.5916805673808 & 0.808319432619168 \tabularnewline
50 & 109.9 & 110.214657657863 & -0.31465765786308 \tabularnewline
51 & 101.4 & 100.305188969769 & 1.09481103023053 \tabularnewline
52 & 86 & 85.6135873347291 & 0.386412665270878 \tabularnewline
53 & 96.5 & 96.2861888099078 & 0.21381119009218 \tabularnewline
54 & 124.6 & 126.420472079665 & -1.82047207966452 \tabularnewline
55 & 109.3 & 108.4610309955 & 0.838969004499834 \tabularnewline
56 & 104.5 & 105.138884696436 & -0.638884696436174 \tabularnewline
57 & 101.8 & 101.136685897358 & 0.663314102642127 \tabularnewline
58 & 101.5 & 102.583548949628 & -1.08354894962831 \tabularnewline
59 & 103.4 & 104.271487482381 & -0.871487482380601 \tabularnewline
60 & 125.9 & 126.31231535779 & -0.412315357790015 \tabularnewline
61 & 96.8 & 98.0877090958087 & -1.28770909580874 \tabularnewline
62 & 104.4 & 105.113209454231 & -0.713209454231033 \tabularnewline
63 & 121.1 & 121.821958584187 & -0.721958584186537 \tabularnewline
64 & 83.7 & 83.5770427116844 & 0.122957288315592 \tabularnewline
65 & 91.5 & 90.7133150616255 & 0.786684938374489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&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]95[/C][C]94.0410410619512[/C][C]0.958958938048764[/C][/ROW]
[ROW][C]2[/C][C]101.4[/C][C]100.363593231185[/C][C]1.03640676881539[/C][/ROW]
[ROW][C]3[/C][C]112.8[/C][C]111.89412239209[/C][C]0.905877607910376[/C][/ROW]
[ROW][C]4[/C][C]76.4[/C][C]76.8128211284014[/C][C]-0.412821128401386[/C][/ROW]
[ROW][C]5[/C][C]93.5[/C][C]92.8112313493566[/C][C]0.688768650643386[/C][/ROW]
[ROW][C]6[/C][C]117.6[/C][C]116.514621012327[/C][C]1.085378987673[/C][/ROW]
[ROW][C]7[/C][C]118.6[/C][C]117.696596961727[/C][C]0.90340303827285[/C][/ROW]
[ROW][C]8[/C][C]110.1[/C][C]110.547198281994[/C][C]-0.447198281994492[/C][/ROW]
[ROW][C]9[/C][C]113.1[/C][C]113.046020625756[/C][C]0.0539793742436232[/C][/ROW]
[ROW][C]10[/C][C]91.2[/C][C]92.4837674614124[/C][C]-1.28376746141236[/C][/ROW]
[ROW][C]11[/C][C]106.2[/C][C]105.68159274331[/C][C]0.518407256689926[/C][/ROW]
[ROW][C]12[/C][C]115.5[/C][C]114.181533462658[/C][C]1.31846653734224[/C][/ROW]
[ROW][C]13[/C][C]106.2[/C][C]106.408711519005[/C][C]-0.20871151900504[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]96.5788312763135[/C][C]-0.678831276313486[/C][/ROW]
[ROW][C]15[/C][C]113.2[/C][C]113.563099556352[/C][C]-0.36309955635248[/C][/ROW]
[ROW][C]16[/C][C]78.3[/C][C]80.6664888464388[/C][C]-2.36648884643881[/C][/ROW]
[ROW][C]17[/C][C]79.8[/C][C]81.4197678030719[/C][C]-1.61976780307188[/C][/ROW]
[ROW][C]18[/C][C]121.2[/C][C]121.188150053131[/C][C]0.0118499468688879[/C][/ROW]
[ROW][C]19[/C][C]125.6[/C][C]124.594645007661[/C][C]1.00535499233896[/C][/ROW]
[ROW][C]20[/C][C]97.2[/C][C]96.9057841841696[/C][C]0.294215815830384[/C][/ROW]
[ROW][C]21[/C][C]102.8[/C][C]103.011528719343[/C][C]-0.211528719343021[/C][/ROW]
[ROW][C]22[/C][C]88.8[/C][C]89.4715461905438[/C][C]-0.671546190543805[/C][/ROW]
[ROW][C]23[/C][C]95.3[/C][C]94.9553358854011[/C][C]0.344664114598859[/C][/ROW]
[ROW][C]24[/C][C]107.6[/C][C]108.094640815788[/C][C]-0.494640815788393[/C][/ROW]
[ROW][C]25[/C][C]95[/C][C]94.3714431884042[/C][C]0.628556811595818[/C][/ROW]
[ROW][C]26[/C][C]87.5[/C][C]87.4836714001562[/C][C]0.0163285998437637[/C][/ROW]
[ROW][C]27[/C][C]106.7[/C][C]106.843852821364[/C][C]-0.143852821363867[/C][/ROW]
[ROW][C]28[/C][C]75.8[/C][C]75.4580308045434[/C][C]0.341969195456633[/C][/ROW]
[ROW][C]29[/C][C]80[/C][C]80.1847121096392[/C][C]-0.184712109639155[/C][/ROW]
[ROW][C]30[/C][C]117.2[/C][C]118.104166075491[/C][C]-0.90416607549075[/C][/ROW]
[ROW][C]31[/C][C]106.6[/C][C]105.994497402129[/C][C]0.60550259787092[/C][/ROW]
[ROW][C]32[/C][C]104.7[/C][C]104.034640990126[/C][C]0.665359009874096[/C][/ROW]
[ROW][C]33[/C][C]95.2[/C][C]94.8625023317915[/C][C]0.337497668208465[/C][/ROW]
[ROW][C]34[/C][C]94[/C][C]95.1250318938116[/C][C]-1.12503189381156[/C][/ROW]
[ROW][C]35[/C][C]95.7[/C][C]94.5337681486156[/C][C]1.16623185138445[/C][/ROW]
[ROW][C]36[/C][C]112.6[/C][C]113.321581281562[/C][C]-0.721581281562491[/C][/ROW]
[ROW][C]37[/C][C]99.1[/C][C]98.3940483223667[/C][C]0.705951677633328[/C][/ROW]
[ROW][C]38[/C][C]91.6[/C][C]90.6819881722518[/C][C]0.918011827748142[/C][/ROW]
[ROW][C]39[/C][C]111.5[/C][C]111.382258352826[/C][C]0.117741647173804[/C][/ROW]
[ROW][C]40[/C][C]76.6[/C][C]75.7008701879087[/C][C]0.899129812091256[/C][/ROW]
[ROW][C]41[/C][C]83.4[/C][C]83.8569340307002[/C][C]-0.456934030700225[/C][/ROW]
[ROW][C]42[/C][C]113.5[/C][C]113.850640174107[/C][C]-0.350640174106527[/C][/ROW]
[ROW][C]43[/C][C]106.4[/C][C]106.083205570336[/C][C]0.316794429664034[/C][/ROW]
[ROW][C]44[/C][C]104.1[/C][C]104.173827921577[/C][C]-0.0738279215765318[/C][/ROW]
[ROW][C]45[/C][C]108.4[/C][C]108.121877543233[/C][C]0.278122456767321[/C][/ROW]
[ROW][C]46[/C][C]91[/C][C]90.7025710269951[/C][C]0.29742897300485[/C][/ROW]
[ROW][C]47[/C][C]108.3[/C][C]108.876996934224[/C][C]-0.576996934223771[/C][/ROW]
[ROW][C]48[/C][C]121[/C][C]121.175250040509[/C][C]-0.175250040509293[/C][/ROW]
[ROW][C]49[/C][C]95.4[/C][C]94.5916805673808[/C][C]0.808319432619168[/C][/ROW]
[ROW][C]50[/C][C]109.9[/C][C]110.214657657863[/C][C]-0.31465765786308[/C][/ROW]
[ROW][C]51[/C][C]101.4[/C][C]100.305188969769[/C][C]1.09481103023053[/C][/ROW]
[ROW][C]52[/C][C]86[/C][C]85.6135873347291[/C][C]0.386412665270878[/C][/ROW]
[ROW][C]53[/C][C]96.5[/C][C]96.2861888099078[/C][C]0.21381119009218[/C][/ROW]
[ROW][C]54[/C][C]124.6[/C][C]126.420472079665[/C][C]-1.82047207966452[/C][/ROW]
[ROW][C]55[/C][C]109.3[/C][C]108.4610309955[/C][C]0.838969004499834[/C][/ROW]
[ROW][C]56[/C][C]104.5[/C][C]105.138884696436[/C][C]-0.638884696436174[/C][/ROW]
[ROW][C]57[/C][C]101.8[/C][C]101.136685897358[/C][C]0.663314102642127[/C][/ROW]
[ROW][C]58[/C][C]101.5[/C][C]102.583548949628[/C][C]-1.08354894962831[/C][/ROW]
[ROW][C]59[/C][C]103.4[/C][C]104.271487482381[/C][C]-0.871487482380601[/C][/ROW]
[ROW][C]60[/C][C]125.9[/C][C]126.31231535779[/C][C]-0.412315357790015[/C][/ROW]
[ROW][C]61[/C][C]96.8[/C][C]98.0877090958087[/C][C]-1.28770909580874[/C][/ROW]
[ROW][C]62[/C][C]104.4[/C][C]105.113209454231[/C][C]-0.713209454231033[/C][/ROW]
[ROW][C]63[/C][C]121.1[/C][C]121.821958584187[/C][C]-0.721958584186537[/C][/ROW]
[ROW][C]64[/C][C]83.7[/C][C]83.5770427116844[/C][C]0.122957288315592[/C][/ROW]
[ROW][C]65[/C][C]91.5[/C][C]90.7133150616255[/C][C]0.786684938374489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19594.04104106195120.958958938048764
2101.4100.3635932311851.03640676881539
3112.8111.894122392090.905877607910376
476.476.8128211284014-0.412821128401386
593.592.81123134935660.688768650643386
6117.6116.5146210123271.085378987673
7118.6117.6965969617270.90340303827285
8110.1110.547198281994-0.447198281994492
9113.1113.0460206257560.0539793742436232
1091.292.4837674614124-1.28376746141236
11106.2105.681592743310.518407256689926
12115.5114.1815334626581.31846653734224
13106.2106.408711519005-0.20871151900504
1495.996.5788312763135-0.678831276313486
15113.2113.563099556352-0.36309955635248
1678.380.6664888464388-2.36648884643881
1779.881.4197678030719-1.61976780307188
18121.2121.1881500531310.0118499468688879
19125.6124.5946450076611.00535499233896
2097.296.90578418416960.294215815830384
21102.8103.011528719343-0.211528719343021
2288.889.4715461905438-0.671546190543805
2395.394.95533588540110.344664114598859
24107.6108.094640815788-0.494640815788393
259594.37144318840420.628556811595818
2687.587.48367140015620.0163285998437637
27106.7106.843852821364-0.143852821363867
2875.875.45803080454340.341969195456633
298080.1847121096392-0.184712109639155
30117.2118.104166075491-0.90416607549075
31106.6105.9944974021290.60550259787092
32104.7104.0346409901260.665359009874096
3395.294.86250233179150.337497668208465
349495.1250318938116-1.12503189381156
3595.794.53376814861561.16623185138445
36112.6113.321581281562-0.721581281562491
3799.198.39404832236670.705951677633328
3891.690.68198817225180.918011827748142
39111.5111.3822583528260.117741647173804
4076.675.70087018790870.899129812091256
4183.483.8569340307002-0.456934030700225
42113.5113.850640174107-0.350640174106527
43106.4106.0832055703360.316794429664034
44104.1104.173827921577-0.0738279215765318
45108.4108.1218775432330.278122456767321
469190.70257102699510.29742897300485
47108.3108.876996934224-0.576996934223771
48121121.175250040509-0.175250040509293
4995.494.59168056738080.808319432619168
50109.9110.214657657863-0.31465765786308
51101.4100.3051889697691.09481103023053
528685.61358733472910.386412665270878
5396.596.28618880990780.21381119009218
54124.6126.420472079665-1.82047207966452
55109.3108.46103099550.838969004499834
56104.5105.138884696436-0.638884696436174
57101.8101.1366858973580.663314102642127
58101.5102.583548949628-1.08354894962831
59103.4104.271487482381-0.871487482380601
60125.9126.31231535779-0.412315357790015
6196.898.0877090958087-1.28770909580874
62104.4105.113209454231-0.713209454231033
63121.1121.821958584187-0.721958584186537
6483.783.57704271168440.122957288315592
6591.590.71331506162550.786684938374489






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4453991168920960.8907982337841930.554600883107903
110.2876469907692910.5752939815385820.712353009230709
120.201656893099080.403313786198160.79834310690092
130.222810542864020.445621085728040.77718945713598
140.1893084702933310.3786169405866610.810691529706669
150.2350440580176510.4700881160353030.764955941982349
160.333583237895780.6671664757915610.66641676210422
170.4525355568311080.9050711136622160.547464443168892
180.585383337982930.829233324034140.41461666201707
190.6767380038973560.6465239922052870.323261996102644
200.6067746480465490.7864507039069020.393225351953451
210.5615432727718260.8769134544563480.438456727228174
220.5122644505891860.9754710988216270.487735549410814
230.4467599549627990.8935199099255970.553240045037201
240.4738842842315850.9477685684631710.526115715768415
250.47194726533270.9438945306654010.5280527346673
260.4379842084833010.8759684169666030.562015791516699
270.4336640988519010.8673281977038010.566335901148099
280.757376582218840.485246835562320.24262341778116
290.7287177159700830.5425645680598340.271282284029917
300.8082275539281090.3835448921437810.191772446071891
310.8406822211220820.3186355577558370.159317778877918
320.9130279940646570.1739440118706870.0869720059353433
330.8849537579727840.2300924840544320.115046242027216
340.8568093247285420.2863813505429160.143190675271458
350.9391791965260240.1216416069479530.0608208034739763
360.9163521892075090.1672956215849820.0836478107924909
370.9381261482560480.1237477034879050.0618738517439524
380.9741853286625050.05162934267498940.0258146713374947
390.9654360244424850.06912795111503020.0345639755575151
400.9738514703392090.05229705932158090.0261485296607905
410.9822182796745710.03556344065085770.0177817203254288
420.9715912491455880.05681750170882450.0284087508544122
430.9651795361392530.06964092772149340.0348204638607467
440.9425279386576560.1149441226846880.0574720613423439
450.9086911834983030.1826176330033930.0913088165016966
460.8707631048731020.2584737902537950.129236895126898
470.8269526049604450.3460947900791090.173047395039555
480.8041391343092430.3917217313815130.195860865690757
490.7412227269131070.5175545461737870.258777273086893
500.6595888003949120.6808223992101770.340411199605089
510.6681463657513340.6637072684973320.331853634248666
520.5506878381415240.8986243237169520.449312161858476
530.4255531536345520.8511063072691030.574446846365448
540.7212293981016660.5575412037966680.278770601898334
550.7690658783602190.4618682432795620.230934121639781

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.445399116892096 & 0.890798233784193 & 0.554600883107903 \tabularnewline
11 & 0.287646990769291 & 0.575293981538582 & 0.712353009230709 \tabularnewline
12 & 0.20165689309908 & 0.40331378619816 & 0.79834310690092 \tabularnewline
13 & 0.22281054286402 & 0.44562108572804 & 0.77718945713598 \tabularnewline
14 & 0.189308470293331 & 0.378616940586661 & 0.810691529706669 \tabularnewline
15 & 0.235044058017651 & 0.470088116035303 & 0.764955941982349 \tabularnewline
16 & 0.33358323789578 & 0.667166475791561 & 0.66641676210422 \tabularnewline
17 & 0.452535556831108 & 0.905071113662216 & 0.547464443168892 \tabularnewline
18 & 0.58538333798293 & 0.82923332403414 & 0.41461666201707 \tabularnewline
19 & 0.676738003897356 & 0.646523992205287 & 0.323261996102644 \tabularnewline
20 & 0.606774648046549 & 0.786450703906902 & 0.393225351953451 \tabularnewline
21 & 0.561543272771826 & 0.876913454456348 & 0.438456727228174 \tabularnewline
22 & 0.512264450589186 & 0.975471098821627 & 0.487735549410814 \tabularnewline
23 & 0.446759954962799 & 0.893519909925597 & 0.553240045037201 \tabularnewline
24 & 0.473884284231585 & 0.947768568463171 & 0.526115715768415 \tabularnewline
25 & 0.4719472653327 & 0.943894530665401 & 0.5280527346673 \tabularnewline
26 & 0.437984208483301 & 0.875968416966603 & 0.562015791516699 \tabularnewline
27 & 0.433664098851901 & 0.867328197703801 & 0.566335901148099 \tabularnewline
28 & 0.75737658221884 & 0.48524683556232 & 0.24262341778116 \tabularnewline
29 & 0.728717715970083 & 0.542564568059834 & 0.271282284029917 \tabularnewline
30 & 0.808227553928109 & 0.383544892143781 & 0.191772446071891 \tabularnewline
31 & 0.840682221122082 & 0.318635557755837 & 0.159317778877918 \tabularnewline
32 & 0.913027994064657 & 0.173944011870687 & 0.0869720059353433 \tabularnewline
33 & 0.884953757972784 & 0.230092484054432 & 0.115046242027216 \tabularnewline
34 & 0.856809324728542 & 0.286381350542916 & 0.143190675271458 \tabularnewline
35 & 0.939179196526024 & 0.121641606947953 & 0.0608208034739763 \tabularnewline
36 & 0.916352189207509 & 0.167295621584982 & 0.0836478107924909 \tabularnewline
37 & 0.938126148256048 & 0.123747703487905 & 0.0618738517439524 \tabularnewline
38 & 0.974185328662505 & 0.0516293426749894 & 0.0258146713374947 \tabularnewline
39 & 0.965436024442485 & 0.0691279511150302 & 0.0345639755575151 \tabularnewline
40 & 0.973851470339209 & 0.0522970593215809 & 0.0261485296607905 \tabularnewline
41 & 0.982218279674571 & 0.0355634406508577 & 0.0177817203254288 \tabularnewline
42 & 0.971591249145588 & 0.0568175017088245 & 0.0284087508544122 \tabularnewline
43 & 0.965179536139253 & 0.0696409277214934 & 0.0348204638607467 \tabularnewline
44 & 0.942527938657656 & 0.114944122684688 & 0.0574720613423439 \tabularnewline
45 & 0.908691183498303 & 0.182617633003393 & 0.0913088165016966 \tabularnewline
46 & 0.870763104873102 & 0.258473790253795 & 0.129236895126898 \tabularnewline
47 & 0.826952604960445 & 0.346094790079109 & 0.173047395039555 \tabularnewline
48 & 0.804139134309243 & 0.391721731381513 & 0.195860865690757 \tabularnewline
49 & 0.741222726913107 & 0.517554546173787 & 0.258777273086893 \tabularnewline
50 & 0.659588800394912 & 0.680822399210177 & 0.340411199605089 \tabularnewline
51 & 0.668146365751334 & 0.663707268497332 & 0.331853634248666 \tabularnewline
52 & 0.550687838141524 & 0.898624323716952 & 0.449312161858476 \tabularnewline
53 & 0.425553153634552 & 0.851106307269103 & 0.574446846365448 \tabularnewline
54 & 0.721229398101666 & 0.557541203796668 & 0.278770601898334 \tabularnewline
55 & 0.769065878360219 & 0.461868243279562 & 0.230934121639781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&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]10[/C][C]0.445399116892096[/C][C]0.890798233784193[/C][C]0.554600883107903[/C][/ROW]
[ROW][C]11[/C][C]0.287646990769291[/C][C]0.575293981538582[/C][C]0.712353009230709[/C][/ROW]
[ROW][C]12[/C][C]0.20165689309908[/C][C]0.40331378619816[/C][C]0.79834310690092[/C][/ROW]
[ROW][C]13[/C][C]0.22281054286402[/C][C]0.44562108572804[/C][C]0.77718945713598[/C][/ROW]
[ROW][C]14[/C][C]0.189308470293331[/C][C]0.378616940586661[/C][C]0.810691529706669[/C][/ROW]
[ROW][C]15[/C][C]0.235044058017651[/C][C]0.470088116035303[/C][C]0.764955941982349[/C][/ROW]
[ROW][C]16[/C][C]0.33358323789578[/C][C]0.667166475791561[/C][C]0.66641676210422[/C][/ROW]
[ROW][C]17[/C][C]0.452535556831108[/C][C]0.905071113662216[/C][C]0.547464443168892[/C][/ROW]
[ROW][C]18[/C][C]0.58538333798293[/C][C]0.82923332403414[/C][C]0.41461666201707[/C][/ROW]
[ROW][C]19[/C][C]0.676738003897356[/C][C]0.646523992205287[/C][C]0.323261996102644[/C][/ROW]
[ROW][C]20[/C][C]0.606774648046549[/C][C]0.786450703906902[/C][C]0.393225351953451[/C][/ROW]
[ROW][C]21[/C][C]0.561543272771826[/C][C]0.876913454456348[/C][C]0.438456727228174[/C][/ROW]
[ROW][C]22[/C][C]0.512264450589186[/C][C]0.975471098821627[/C][C]0.487735549410814[/C][/ROW]
[ROW][C]23[/C][C]0.446759954962799[/C][C]0.893519909925597[/C][C]0.553240045037201[/C][/ROW]
[ROW][C]24[/C][C]0.473884284231585[/C][C]0.947768568463171[/C][C]0.526115715768415[/C][/ROW]
[ROW][C]25[/C][C]0.4719472653327[/C][C]0.943894530665401[/C][C]0.5280527346673[/C][/ROW]
[ROW][C]26[/C][C]0.437984208483301[/C][C]0.875968416966603[/C][C]0.562015791516699[/C][/ROW]
[ROW][C]27[/C][C]0.433664098851901[/C][C]0.867328197703801[/C][C]0.566335901148099[/C][/ROW]
[ROW][C]28[/C][C]0.75737658221884[/C][C]0.48524683556232[/C][C]0.24262341778116[/C][/ROW]
[ROW][C]29[/C][C]0.728717715970083[/C][C]0.542564568059834[/C][C]0.271282284029917[/C][/ROW]
[ROW][C]30[/C][C]0.808227553928109[/C][C]0.383544892143781[/C][C]0.191772446071891[/C][/ROW]
[ROW][C]31[/C][C]0.840682221122082[/C][C]0.318635557755837[/C][C]0.159317778877918[/C][/ROW]
[ROW][C]32[/C][C]0.913027994064657[/C][C]0.173944011870687[/C][C]0.0869720059353433[/C][/ROW]
[ROW][C]33[/C][C]0.884953757972784[/C][C]0.230092484054432[/C][C]0.115046242027216[/C][/ROW]
[ROW][C]34[/C][C]0.856809324728542[/C][C]0.286381350542916[/C][C]0.143190675271458[/C][/ROW]
[ROW][C]35[/C][C]0.939179196526024[/C][C]0.121641606947953[/C][C]0.0608208034739763[/C][/ROW]
[ROW][C]36[/C][C]0.916352189207509[/C][C]0.167295621584982[/C][C]0.0836478107924909[/C][/ROW]
[ROW][C]37[/C][C]0.938126148256048[/C][C]0.123747703487905[/C][C]0.0618738517439524[/C][/ROW]
[ROW][C]38[/C][C]0.974185328662505[/C][C]0.0516293426749894[/C][C]0.0258146713374947[/C][/ROW]
[ROW][C]39[/C][C]0.965436024442485[/C][C]0.0691279511150302[/C][C]0.0345639755575151[/C][/ROW]
[ROW][C]40[/C][C]0.973851470339209[/C][C]0.0522970593215809[/C][C]0.0261485296607905[/C][/ROW]
[ROW][C]41[/C][C]0.982218279674571[/C][C]0.0355634406508577[/C][C]0.0177817203254288[/C][/ROW]
[ROW][C]42[/C][C]0.971591249145588[/C][C]0.0568175017088245[/C][C]0.0284087508544122[/C][/ROW]
[ROW][C]43[/C][C]0.965179536139253[/C][C]0.0696409277214934[/C][C]0.0348204638607467[/C][/ROW]
[ROW][C]44[/C][C]0.942527938657656[/C][C]0.114944122684688[/C][C]0.0574720613423439[/C][/ROW]
[ROW][C]45[/C][C]0.908691183498303[/C][C]0.182617633003393[/C][C]0.0913088165016966[/C][/ROW]
[ROW][C]46[/C][C]0.870763104873102[/C][C]0.258473790253795[/C][C]0.129236895126898[/C][/ROW]
[ROW][C]47[/C][C]0.826952604960445[/C][C]0.346094790079109[/C][C]0.173047395039555[/C][/ROW]
[ROW][C]48[/C][C]0.804139134309243[/C][C]0.391721731381513[/C][C]0.195860865690757[/C][/ROW]
[ROW][C]49[/C][C]0.741222726913107[/C][C]0.517554546173787[/C][C]0.258777273086893[/C][/ROW]
[ROW][C]50[/C][C]0.659588800394912[/C][C]0.680822399210177[/C][C]0.340411199605089[/C][/ROW]
[ROW][C]51[/C][C]0.668146365751334[/C][C]0.663707268497332[/C][C]0.331853634248666[/C][/ROW]
[ROW][C]52[/C][C]0.550687838141524[/C][C]0.898624323716952[/C][C]0.449312161858476[/C][/ROW]
[ROW][C]53[/C][C]0.425553153634552[/C][C]0.851106307269103[/C][C]0.574446846365448[/C][/ROW]
[ROW][C]54[/C][C]0.721229398101666[/C][C]0.557541203796668[/C][C]0.278770601898334[/C][/ROW]
[ROW][C]55[/C][C]0.769065878360219[/C][C]0.461868243279562[/C][C]0.230934121639781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4453991168920960.8907982337841930.554600883107903
110.2876469907692910.5752939815385820.712353009230709
120.201656893099080.403313786198160.79834310690092
130.222810542864020.445621085728040.77718945713598
140.1893084702933310.3786169405866610.810691529706669
150.2350440580176510.4700881160353030.764955941982349
160.333583237895780.6671664757915610.66641676210422
170.4525355568311080.9050711136622160.547464443168892
180.585383337982930.829233324034140.41461666201707
190.6767380038973560.6465239922052870.323261996102644
200.6067746480465490.7864507039069020.393225351953451
210.5615432727718260.8769134544563480.438456727228174
220.5122644505891860.9754710988216270.487735549410814
230.4467599549627990.8935199099255970.553240045037201
240.4738842842315850.9477685684631710.526115715768415
250.47194726533270.9438945306654010.5280527346673
260.4379842084833010.8759684169666030.562015791516699
270.4336640988519010.8673281977038010.566335901148099
280.757376582218840.485246835562320.24262341778116
290.7287177159700830.5425645680598340.271282284029917
300.8082275539281090.3835448921437810.191772446071891
310.8406822211220820.3186355577558370.159317778877918
320.9130279940646570.1739440118706870.0869720059353433
330.8849537579727840.2300924840544320.115046242027216
340.8568093247285420.2863813505429160.143190675271458
350.9391791965260240.1216416069479530.0608208034739763
360.9163521892075090.1672956215849820.0836478107924909
370.9381261482560480.1237477034879050.0618738517439524
380.9741853286625050.05162934267498940.0258146713374947
390.9654360244424850.06912795111503020.0345639755575151
400.9738514703392090.05229705932158090.0261485296607905
410.9822182796745710.03556344065085770.0177817203254288
420.9715912491455880.05681750170882450.0284087508544122
430.9651795361392530.06964092772149340.0348204638607467
440.9425279386576560.1149441226846880.0574720613423439
450.9086911834983030.1826176330033930.0913088165016966
460.8707631048731020.2584737902537950.129236895126898
470.8269526049604450.3460947900791090.173047395039555
480.8041391343092430.3917217313815130.195860865690757
490.7412227269131070.5175545461737870.258777273086893
500.6595888003949120.6808223992101770.340411199605089
510.6681463657513340.6637072684973320.331853634248666
520.5506878381415240.8986243237169520.449312161858476
530.4255531536345520.8511063072691030.574446846365448
540.7212293981016660.5575412037966680.278770601898334
550.7690658783602190.4618682432795620.230934121639781






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0217391304347826OK
10% type I error level60.130434782608696NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0217391304347826 & OK \tabularnewline
10% type I error level & 6 & 0.130434782608696 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191275&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0217391304347826[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191275&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191275&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level10.0217391304347826OK
10% type I error level60.130434782608696NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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