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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 14 Dec 2010 13:02:24 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t1292331660w0w02thaiwqhhpa.htm/, Retrieved Fri, 17 May 2024 09:50:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109546, Retrieved Fri, 17 May 2024 09:50:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Linear R...] [2010-12-14 13:02:24] [e192c8164fa91adb027f71579ac0a49a] [Current]
-    D    [Multiple Regression] [Multiple Linear R...] [2010-12-14 15:31:03] [1429a1a14191a86916b95357f6de790b]
-    D    [Multiple Regression] [Multiple Linear R...] [2010-12-14 15:37:51] [1429a1a14191a86916b95357f6de790b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6	2	1	1	3	73	62	66
4	1	1	1	1	58	54	54
5	1	1	1	3	68	41	82
4	1	1	1	3	62	49	61
4	1	1	2	3	65	49	65
6	1	1	1	3	81	72	77
6	1	1	1	1	73	78	66
4	2	1	4	3	64	58	66
4	1	1	1	3	68	58	66
6	1	1	1	1	51	23	48
4	1	1	1	1	68	39	57
6	1	1	1	3	61	63	80
5	1	1	1	1	69	46	60
4	1	1	3	3	73	58	70
6	2	1	1	3	61	39	85
3	2	1	1	1	62	44	59
5	1	1	1	1	63	49	72
6	1	1	6	1	69	57	70
4	2	1	1	3	47	76	74
6	2	1	1	1	66	63	70
2	1	1	1	3	58	18	51
7	2	1	1	3	63	40	70
5	1	1	1	1	69	59	71
2	2	1	1	3	59	62	72
4	1	1	1	1	59	70	50
4	2	1	1	4	63	65	69
6	2	1	1	3	65	56	73
6	1	1	1	3	65	45	66
5	2	1	1	3	71	57	73
6	1	1	4	3	60	50	58
6	2	1	1	1	81	40	78
4	1	1	1	3	67	58	83
6	2	1	1	3	66	49	76
6	1	1	1	3	62	49	77
6	1	1	1	3	63	27	79
2	2	1	1	1	73	51	71
4	2	1	1	3	55	75	79
5	1	1	1	1	59	65	60
3	1	1	1	2	64	47	73
7	2	1	1	3	63	49	70
5	1	1	1	1	64	65	42
3	1	1	1	1	73	61	74
8	1	1	1	3	54	46	68
8	1	1	1	3	76	69	83
5	2	1	2	1	74	55	62
6	2	1	1	3	63	78	79
3	2	1	1	3	73	58	61
5	2	1	1	3	67	34	86
4	2	1	2	3	68	67	64
5	1	1	4	3	66	45	75
5	2	1	1	1	62	68	59
6	2	1	4	3	71	49	82
5	1	1	1	2	63	19	61
6	1	1	1	1	75	72	69
6	1	1	2	2	77	59	60
4	2	1	3	3	62	46	59
8	1	1	1	3	74	56	81
6	2	1	2	1	67	45	65
4	2	1	1	3	56	53	60
6	2	1	1	1	60	67	60
5	2	1	1	3	58	73	45
5	1	1	1	3	65	46	75
6	2	1	1	3	49	70	84
6	1	1	1	3	61	38	77
6	2	1	1	3	66	54	64
6	2	1	1	3	64	46	54
6	2	1	1	1	65	46	72
6	1	1	1	3	46	45	56
7	2	1	1	3	65	47	67
4	2	1	1	3	81	25	81
4	1	1	1	1	72	63	73
3	2	1	1	1	65	46	67
6	2	1	1	3	74	69	72
5	1	1	1	3	59	43	69
5	1	1	1	1	69	49	71
3	2	1	2	3	58	39	77
5	1	1	1	1	71	65	63
4	2	1	1	3	79	54	49
3	2	1	1	3	68	50	74
7	1	1	1	3	66	42	76
4	2	1	1	3	62	45	65
4	1	1	1	3	69	50	65
5	2	1	2	7	63	55	69
6	1	1	1	1	62	38	71
2	1	1	1	3	61	40	68
2	2	1	1	1	65	51	49
6	1	1	1	3	64	49	86
4	2	1	1	1	56	39	63
5	2	1	1	3	56	57	77
6	1	1	1	3	48	30	52
7	1	1	1	1	74	51	73
8	1	1	1	1	69	48	63
6	1	1	4	3	62	56	54
6	1	1	1	2	73	66	56
3	1	1	1	1	64	72	54
7	1	1	1	1	57	28	61
3	1	1	1	2	57	52	70
6	2	1	1	2	60	53	68
4	2	1	1	1	61	70	63
4	1	1	1	2	72	63	76
6	1	1	1	3	57	46	69
6	1	1	2	3	51	45	71
6	1	2	1	2	63	68	39
4	1	1	1	3	54	54	54
7	1	2	1	1	72	60	64
5	1	1	1	3	62	50	70
7	1	1	1	2	68	66	76
4	1	1	1	3	62	56	71
6	2	1	1	2	63	54	73
6	1	1	1	3	77	72	81
6	1	1	1	1	57	34	50
5	1	1	1	1	57	39	42
5	1	1	1	3	61	66	66
6	1	1	1	3	65	27	77
7	1	1	1	3	63	63	62
4	2	1	1	1	66	65	66
4	1	1	1	3	68	63	69
8	1	1	1	3	72	49	72
6	1	1	1	1	68	42	67
3	1	1	1	1	59	51	59
4	1	1	4	3	56	50	66
5	1	1	1	1	62	64	68
5	2	1	1	3	72	68	72
6	2	1	1	3	68	66	73
8	1	1	1	3	67	59	69
2	1	1	2	1	54	32	57
4	2	1	1	1	69	62	55
7	1	1	2	3	61	52	72
5	1	1	1	3	55	34	68
6	2	1	1	3	75	63	83
6	1	1	1	3	55	48	74
4	1	1	1	3	49	53	72
5	2	1	1	3	54	39	66
6	1	1	1	3	66	51	61
6	1	1	1	3	73	60	86
6	2	1	1	2	63	70	81
6	2	1	4	3	61	40	79
5	1	1	1	3	74	61	73
5	2	1	5	3	81	35	59
6	1	1	1	1	62	39	64
4	1	1	1	2	64	31	75
6	1	1	1	3	62	36	68
3	1	1	1	1	85	51	84
6	1	1	1	1	74	55	68
8	1	1	1	3	51	67	68
4	1	1	1	3	66	40	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Celebrity[t] = + 1.43920785893858 -0.478161596531039Gender[t] + 1.68226050071978Age[t] + 0.00423606441239899Raised[t] + 0.132214711993717Marital[t] + 0.00306708618950928NV[t] + 0.00539714070616665ANX[t] + 0.0275617336070562GR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Celebrity[t] =  +  1.43920785893858 -0.478161596531039Gender[t] +  1.68226050071978Age[t] +  0.00423606441239899Raised[t] +  0.132214711993717Marital[t] +  0.00306708618950928NV[t] +  0.00539714070616665ANX[t] +  0.0275617336070562GR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Celebrity[t] =  +  1.43920785893858 -0.478161596531039Gender[t] +  1.68226050071978Age[t] +  0.00423606441239899Raised[t] +  0.132214711993717Marital[t] +  0.00306708618950928NV[t] +  0.00539714070616665ANX[t] +  0.0275617336070562GR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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
Celebrity[t] = + 1.43920785893858 -0.478161596531039Gender[t] + 1.68226050071978Age[t] + 0.00423606441239899Raised[t] + 0.132214711993717Marital[t] + 0.00306708618950928NV[t] + 0.00539714070616665ANX[t] + 0.0275617336070562GR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.439207858938581.6262750.8850.3777110.188855
Gender-0.4781615965310390.238367-2.0060.0468120.023406
Age1.682260500719781.0062191.67190.0968170.048409
Raised0.004236064412398990.1338590.03160.97480.4874
Marital0.1322147119937170.1244421.06250.2898840.144942
NV0.003067086189509280.0166030.18470.853710.426855
ANX0.005397140706166650.0092520.58340.5606060.280303
GR0.02756173360705620.0131872.090.0384490.019224

\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) & 1.43920785893858 & 1.626275 & 0.885 & 0.377711 & 0.188855 \tabularnewline
Gender & -0.478161596531039 & 0.238367 & -2.006 & 0.046812 & 0.023406 \tabularnewline
Age & 1.68226050071978 & 1.006219 & 1.6719 & 0.096817 & 0.048409 \tabularnewline
Raised & 0.00423606441239899 & 0.133859 & 0.0316 & 0.9748 & 0.4874 \tabularnewline
Marital & 0.132214711993717 & 0.124442 & 1.0625 & 0.289884 & 0.144942 \tabularnewline
NV & 0.00306708618950928 & 0.016603 & 0.1847 & 0.85371 & 0.426855 \tabularnewline
ANX & 0.00539714070616665 & 0.009252 & 0.5834 & 0.560606 & 0.280303 \tabularnewline
GR & 0.0275617336070562 & 0.013187 & 2.09 & 0.038449 & 0.019224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&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]1.43920785893858[/C][C]1.626275[/C][C]0.885[/C][C]0.377711[/C][C]0.188855[/C][/ROW]
[ROW][C]Gender[/C][C]-0.478161596531039[/C][C]0.238367[/C][C]-2.006[/C][C]0.046812[/C][C]0.023406[/C][/ROW]
[ROW][C]Age[/C][C]1.68226050071978[/C][C]1.006219[/C][C]1.6719[/C][C]0.096817[/C][C]0.048409[/C][/ROW]
[ROW][C]Raised[/C][C]0.00423606441239899[/C][C]0.133859[/C][C]0.0316[/C][C]0.9748[/C][C]0.4874[/C][/ROW]
[ROW][C]Marital[/C][C]0.132214711993717[/C][C]0.124442[/C][C]1.0625[/C][C]0.289884[/C][C]0.144942[/C][/ROW]
[ROW][C]NV[/C][C]0.00306708618950928[/C][C]0.016603[/C][C]0.1847[/C][C]0.85371[/C][C]0.426855[/C][/ROW]
[ROW][C]ANX[/C][C]0.00539714070616665[/C][C]0.009252[/C][C]0.5834[/C][C]0.560606[/C][C]0.280303[/C][/ROW]
[ROW][C]GR[/C][C]0.0275617336070562[/C][C]0.013187[/C][C]2.09[/C][C]0.038449[/C][C]0.019224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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)1.439207858938581.6262750.8850.3777110.188855
Gender-0.4781615965310390.238367-2.0060.0468120.023406
Age1.682260500719781.0062191.67190.0968170.048409
Raised0.004236064412398990.1338590.03160.97480.4874
Marital0.1322147119937170.1244421.06250.2898840.144942
NV0.003067086189509280.0166030.18470.853710.426855
ANX0.005397140706166650.0092520.58340.5606060.280303
GR0.02756173360705620.0131872.090.0384490.019224






Multiple Linear Regression - Regression Statistics
Multiple R0.300778597585664
R-squared0.0904677647655988
Adjusted R-squared0.0443320716739987
F-TEST (value)1.96090615970545
F-TEST (DF numerator)7
F-TEST (DF denominator)138
p-value0.0646904918120191
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.36325856776735
Sum Squared Residuals256.469401317569

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.300778597585664 \tabularnewline
R-squared & 0.0904677647655988 \tabularnewline
Adjusted R-squared & 0.0443320716739987 \tabularnewline
F-TEST (value) & 1.96090615970545 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0.0646904918120191 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.36325856776735 \tabularnewline
Sum Squared Residuals & 256.469401317569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.300778597585664[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0904677647655988[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0443320716739987[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.96090615970545[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0.0646904918120191[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.36325856776735[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]256.469401317569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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 R0.300778597585664
R-squared0.0904677647655988
Adjusted R-squared0.0443320716739987
F-TEST (value)1.96090615970545
F-TEST (DF numerator)7
F-TEST (DF denominator)138
p-value0.0646904918120191
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.36325856776735
Sum Squared Residuals256.469401317569






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
164.943619800672041.05638019932796
244.737427751439-0.737427751439003
355.73409374913894-0.734093749138936
445.18007195190303-1.18007195190303
545.30375620931218-1.30375620931218
665.803468563458440.196531436541558
765.243706224514320.756293775485685
844.90713565537899-0.90713565537899
945.38485740343087-1.38485740343087
1064.383276384578931.61672361542107
1144.76982670356276-0.769826703562764
1265.776237774133930.223762225866075
1354.893358975516610.106641024483392
1445.51891189763144-1.51891189763144
1565.306353468690170.693646531309833
1634.35537176063961-1.35537176063961
1755.22188868378273-0.221888683782726
1865.2495251814170.750474818583002
1945.15992939848759-1.15992939848759
2064.773364848492441.22663515150756
2124.72487490918327-2.72487490918327
2274.904458777669512.09554122233049
2355.26670087437439-0.266700874374392
2425.06605099566125-3.06605099566125
2544.71660215449895-0.716602154498953
2645.14404027371034-1.14404027371034
2765.079632402168360.920367597831637
2865.305493315682180.694506684317824
2955.10343206001159-0.103432060011586
3065.109357912646210.89064208735379
3164.915730773949691.08426922605031
3245.85033978856132-1.85033978856132
3365.127604704235870.872395295764125
3465.621059689615930.378940310384068
3565.560513147483890.439486852516112
3624.75763049695206-2.75763049695206
3745.31687761533277-1.31687761533277
3854.965233787038680.0347662129613183
3935.37393793416068-2.37393793416068
4074.953033044025012.04696695597499
4154.484458013059220.515541986940783
4235.37244870136593-2.37244870136593
4385.332275975517852.66772402448215
4485.937312112034732.06268788796527
4554.538466607915130.461533392084873
4665.357605726967350.642394273032652
4734.7842225698121-1.7842225698121
4855.32533201590345-0.325332015903446
4944.90438267045362-0.904382670453618
5055.56932419757239-0.569324197572388
5154.484903137587610.515096862412386
5265.321018730062950.678981269937045
5354.889010104913830.110989895086174
5465.30014275347750.699857246522498
5565.124509270618970.875490729381035
5644.63906759486418-0.63906759486418
5785.805891643261432.19410835673857
5864.545710798348061.45428920165194
5944.67753466745255-0.67753466745255
6064.500933558109491.49906644189051
6154.378185649849060.621814350150941
6255.55894605885185-0.558946058851848
6365.409298062700170.590701937299834
6465.558624055658590.44137594434141
6564.823849604482031.17615039551797
6664.498920970383121.50107902961688
6764.733669837512211.26633016248779
6864.971601342010941.02839865798906
6974.865687734170532.13431226582947
7045.1818882881658-1.1818882881658
7145.3526141629817-1.3526141629817
7234.59586116947693-1.59586116947693
7365.149837273447060.850162726552943
7455.35898171795395-0.358981717953955
7555.21272946731273-0.212729467312726
7635.08089440567759-2.08089440567759
7755.08472402213396-0.0847240221339615
7844.45029572083981-0.450295720839812
7935.08401255010695-2.08401255010695
8075.567986315823751.43201368417625
8144.79056872697555-0.790568726975553
8245.31718563036399-1.31718563036399
8355.49094906704222-0.490949067042221
8465.131891316218330.868108683781672
8525.32136273460742-3.32136273460742
8624.12673566808075-2.12673566808075
8765.875249464458460.124750535541543
8844.42023047439995-0.420230474399951
8955.16767270159717-0.167672701597171
9064.786531469369231.21346853063077
9175.293982646886721.70601735311328
9284.986838457750113.01316154224989
9365.0376279948340.962372005165996
9465.035537911963470.96446208803653
9534.85297880128706-1.85297880128706
9674.786967142138552.21303285786145
9735.29676883354378-2.29676883354378
9864.778082169073321.22191783092668
9944.60287726723866-0.602877267238663
10045.56751407579659-1.56751407579658
10165.369038967693440.630961032306563
10265.404598841476730.595401158523274
10366.22937236088053-0.229372360880531
10444.9895888306684-0.989588830668399
10576.770627639119470.229372360880531
10655.43352469507271-0.433524695072706
10775.571437153157051.42856284684295
10845.49346927291676-1.49346927291676
10964.93048923638331.06951076361671
11065.901447153128630.0985528468713707
11164.516170916697931.48382908330207
11254.322662751372320.677337248627681
11355.40656492575364-0.406564925753638
11465.511523852648790.488476147351206
11575.286260741585931.71373925841407
11644.67391219547654-0.673912195476545
11745.49452830778287-1.49452830778287
11885.513921883475742.48607811652426
11965.061635461751830.938364538248175
12034.86211208354529-1.86211208354529
12145.31758343674462-1.31758343674462
12255.18953177375749-0.189531773757492
12355.13830596036187-0.138305960361872
12465.142805067798560.857194932201442
12585.46987265876872.5301273412313
12624.69334357637887-2.69334357637887
12744.36374296224895-0.363742962248955
12875.500611421922041.49938857807796
12955.27057737323336-0.270577373233362
13065.423700585077180.576299414922816
13165.511507744762030.488492255237968
13245.4649674639417-1.4649674639417
13354.761210926829540.238789073170465
13465.20313457807340.796865421926596
13565.962221787931870.0377782120681267
13665.237337356538410.76266264346159
13765.159088400991190.840911599008806
13855.61238347793582-0.612383477935819
13954.646445813521820.353554186478179
14064.94435632167511.0556436783249
14145.34270715007612-1.34270715007612
14265.302841257972260.69715874202774
14335.63089966464894-2.63089966464894
14465.17776254167610.822237458323896
14585.436414671778822.56358532822118
14645.36425989916202-1.36425989916202

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6 & 4.94361980067204 & 1.05638019932796 \tabularnewline
2 & 4 & 4.737427751439 & -0.737427751439003 \tabularnewline
3 & 5 & 5.73409374913894 & -0.734093749138936 \tabularnewline
4 & 4 & 5.18007195190303 & -1.18007195190303 \tabularnewline
5 & 4 & 5.30375620931218 & -1.30375620931218 \tabularnewline
6 & 6 & 5.80346856345844 & 0.196531436541558 \tabularnewline
7 & 6 & 5.24370622451432 & 0.756293775485685 \tabularnewline
8 & 4 & 4.90713565537899 & -0.90713565537899 \tabularnewline
9 & 4 & 5.38485740343087 & -1.38485740343087 \tabularnewline
10 & 6 & 4.38327638457893 & 1.61672361542107 \tabularnewline
11 & 4 & 4.76982670356276 & -0.769826703562764 \tabularnewline
12 & 6 & 5.77623777413393 & 0.223762225866075 \tabularnewline
13 & 5 & 4.89335897551661 & 0.106641024483392 \tabularnewline
14 & 4 & 5.51891189763144 & -1.51891189763144 \tabularnewline
15 & 6 & 5.30635346869017 & 0.693646531309833 \tabularnewline
16 & 3 & 4.35537176063961 & -1.35537176063961 \tabularnewline
17 & 5 & 5.22188868378273 & -0.221888683782726 \tabularnewline
18 & 6 & 5.249525181417 & 0.750474818583002 \tabularnewline
19 & 4 & 5.15992939848759 & -1.15992939848759 \tabularnewline
20 & 6 & 4.77336484849244 & 1.22663515150756 \tabularnewline
21 & 2 & 4.72487490918327 & -2.72487490918327 \tabularnewline
22 & 7 & 4.90445877766951 & 2.09554122233049 \tabularnewline
23 & 5 & 5.26670087437439 & -0.266700874374392 \tabularnewline
24 & 2 & 5.06605099566125 & -3.06605099566125 \tabularnewline
25 & 4 & 4.71660215449895 & -0.716602154498953 \tabularnewline
26 & 4 & 5.14404027371034 & -1.14404027371034 \tabularnewline
27 & 6 & 5.07963240216836 & 0.920367597831637 \tabularnewline
28 & 6 & 5.30549331568218 & 0.694506684317824 \tabularnewline
29 & 5 & 5.10343206001159 & -0.103432060011586 \tabularnewline
30 & 6 & 5.10935791264621 & 0.89064208735379 \tabularnewline
31 & 6 & 4.91573077394969 & 1.08426922605031 \tabularnewline
32 & 4 & 5.85033978856132 & -1.85033978856132 \tabularnewline
33 & 6 & 5.12760470423587 & 0.872395295764125 \tabularnewline
34 & 6 & 5.62105968961593 & 0.378940310384068 \tabularnewline
35 & 6 & 5.56051314748389 & 0.439486852516112 \tabularnewline
36 & 2 & 4.75763049695206 & -2.75763049695206 \tabularnewline
37 & 4 & 5.31687761533277 & -1.31687761533277 \tabularnewline
38 & 5 & 4.96523378703868 & 0.0347662129613183 \tabularnewline
39 & 3 & 5.37393793416068 & -2.37393793416068 \tabularnewline
40 & 7 & 4.95303304402501 & 2.04696695597499 \tabularnewline
41 & 5 & 4.48445801305922 & 0.515541986940783 \tabularnewline
42 & 3 & 5.37244870136593 & -2.37244870136593 \tabularnewline
43 & 8 & 5.33227597551785 & 2.66772402448215 \tabularnewline
44 & 8 & 5.93731211203473 & 2.06268788796527 \tabularnewline
45 & 5 & 4.53846660791513 & 0.461533392084873 \tabularnewline
46 & 6 & 5.35760572696735 & 0.642394273032652 \tabularnewline
47 & 3 & 4.7842225698121 & -1.7842225698121 \tabularnewline
48 & 5 & 5.32533201590345 & -0.325332015903446 \tabularnewline
49 & 4 & 4.90438267045362 & -0.904382670453618 \tabularnewline
50 & 5 & 5.56932419757239 & -0.569324197572388 \tabularnewline
51 & 5 & 4.48490313758761 & 0.515096862412386 \tabularnewline
52 & 6 & 5.32101873006295 & 0.678981269937045 \tabularnewline
53 & 5 & 4.88901010491383 & 0.110989895086174 \tabularnewline
54 & 6 & 5.3001427534775 & 0.699857246522498 \tabularnewline
55 & 6 & 5.12450927061897 & 0.875490729381035 \tabularnewline
56 & 4 & 4.63906759486418 & -0.63906759486418 \tabularnewline
57 & 8 & 5.80589164326143 & 2.19410835673857 \tabularnewline
58 & 6 & 4.54571079834806 & 1.45428920165194 \tabularnewline
59 & 4 & 4.67753466745255 & -0.67753466745255 \tabularnewline
60 & 6 & 4.50093355810949 & 1.49906644189051 \tabularnewline
61 & 5 & 4.37818564984906 & 0.621814350150941 \tabularnewline
62 & 5 & 5.55894605885185 & -0.558946058851848 \tabularnewline
63 & 6 & 5.40929806270017 & 0.590701937299834 \tabularnewline
64 & 6 & 5.55862405565859 & 0.44137594434141 \tabularnewline
65 & 6 & 4.82384960448203 & 1.17615039551797 \tabularnewline
66 & 6 & 4.49892097038312 & 1.50107902961688 \tabularnewline
67 & 6 & 4.73366983751221 & 1.26633016248779 \tabularnewline
68 & 6 & 4.97160134201094 & 1.02839865798906 \tabularnewline
69 & 7 & 4.86568773417053 & 2.13431226582947 \tabularnewline
70 & 4 & 5.1818882881658 & -1.1818882881658 \tabularnewline
71 & 4 & 5.3526141629817 & -1.3526141629817 \tabularnewline
72 & 3 & 4.59586116947693 & -1.59586116947693 \tabularnewline
73 & 6 & 5.14983727344706 & 0.850162726552943 \tabularnewline
74 & 5 & 5.35898171795395 & -0.358981717953955 \tabularnewline
75 & 5 & 5.21272946731273 & -0.212729467312726 \tabularnewline
76 & 3 & 5.08089440567759 & -2.08089440567759 \tabularnewline
77 & 5 & 5.08472402213396 & -0.0847240221339615 \tabularnewline
78 & 4 & 4.45029572083981 & -0.450295720839812 \tabularnewline
79 & 3 & 5.08401255010695 & -2.08401255010695 \tabularnewline
80 & 7 & 5.56798631582375 & 1.43201368417625 \tabularnewline
81 & 4 & 4.79056872697555 & -0.790568726975553 \tabularnewline
82 & 4 & 5.31718563036399 & -1.31718563036399 \tabularnewline
83 & 5 & 5.49094906704222 & -0.490949067042221 \tabularnewline
84 & 6 & 5.13189131621833 & 0.868108683781672 \tabularnewline
85 & 2 & 5.32136273460742 & -3.32136273460742 \tabularnewline
86 & 2 & 4.12673566808075 & -2.12673566808075 \tabularnewline
87 & 6 & 5.87524946445846 & 0.124750535541543 \tabularnewline
88 & 4 & 4.42023047439995 & -0.420230474399951 \tabularnewline
89 & 5 & 5.16767270159717 & -0.167672701597171 \tabularnewline
90 & 6 & 4.78653146936923 & 1.21346853063077 \tabularnewline
91 & 7 & 5.29398264688672 & 1.70601735311328 \tabularnewline
92 & 8 & 4.98683845775011 & 3.01316154224989 \tabularnewline
93 & 6 & 5.037627994834 & 0.962372005165996 \tabularnewline
94 & 6 & 5.03553791196347 & 0.96446208803653 \tabularnewline
95 & 3 & 4.85297880128706 & -1.85297880128706 \tabularnewline
96 & 7 & 4.78696714213855 & 2.21303285786145 \tabularnewline
97 & 3 & 5.29676883354378 & -2.29676883354378 \tabularnewline
98 & 6 & 4.77808216907332 & 1.22191783092668 \tabularnewline
99 & 4 & 4.60287726723866 & -0.602877267238663 \tabularnewline
100 & 4 & 5.56751407579659 & -1.56751407579658 \tabularnewline
101 & 6 & 5.36903896769344 & 0.630961032306563 \tabularnewline
102 & 6 & 5.40459884147673 & 0.595401158523274 \tabularnewline
103 & 6 & 6.22937236088053 & -0.229372360880531 \tabularnewline
104 & 4 & 4.9895888306684 & -0.989588830668399 \tabularnewline
105 & 7 & 6.77062763911947 & 0.229372360880531 \tabularnewline
106 & 5 & 5.43352469507271 & -0.433524695072706 \tabularnewline
107 & 7 & 5.57143715315705 & 1.42856284684295 \tabularnewline
108 & 4 & 5.49346927291676 & -1.49346927291676 \tabularnewline
109 & 6 & 4.9304892363833 & 1.06951076361671 \tabularnewline
110 & 6 & 5.90144715312863 & 0.0985528468713707 \tabularnewline
111 & 6 & 4.51617091669793 & 1.48382908330207 \tabularnewline
112 & 5 & 4.32266275137232 & 0.677337248627681 \tabularnewline
113 & 5 & 5.40656492575364 & -0.406564925753638 \tabularnewline
114 & 6 & 5.51152385264879 & 0.488476147351206 \tabularnewline
115 & 7 & 5.28626074158593 & 1.71373925841407 \tabularnewline
116 & 4 & 4.67391219547654 & -0.673912195476545 \tabularnewline
117 & 4 & 5.49452830778287 & -1.49452830778287 \tabularnewline
118 & 8 & 5.51392188347574 & 2.48607811652426 \tabularnewline
119 & 6 & 5.06163546175183 & 0.938364538248175 \tabularnewline
120 & 3 & 4.86211208354529 & -1.86211208354529 \tabularnewline
121 & 4 & 5.31758343674462 & -1.31758343674462 \tabularnewline
122 & 5 & 5.18953177375749 & -0.189531773757492 \tabularnewline
123 & 5 & 5.13830596036187 & -0.138305960361872 \tabularnewline
124 & 6 & 5.14280506779856 & 0.857194932201442 \tabularnewline
125 & 8 & 5.4698726587687 & 2.5301273412313 \tabularnewline
126 & 2 & 4.69334357637887 & -2.69334357637887 \tabularnewline
127 & 4 & 4.36374296224895 & -0.363742962248955 \tabularnewline
128 & 7 & 5.50061142192204 & 1.49938857807796 \tabularnewline
129 & 5 & 5.27057737323336 & -0.270577373233362 \tabularnewline
130 & 6 & 5.42370058507718 & 0.576299414922816 \tabularnewline
131 & 6 & 5.51150774476203 & 0.488492255237968 \tabularnewline
132 & 4 & 5.4649674639417 & -1.4649674639417 \tabularnewline
133 & 5 & 4.76121092682954 & 0.238789073170465 \tabularnewline
134 & 6 & 5.2031345780734 & 0.796865421926596 \tabularnewline
135 & 6 & 5.96222178793187 & 0.0377782120681267 \tabularnewline
136 & 6 & 5.23733735653841 & 0.76266264346159 \tabularnewline
137 & 6 & 5.15908840099119 & 0.840911599008806 \tabularnewline
138 & 5 & 5.61238347793582 & -0.612383477935819 \tabularnewline
139 & 5 & 4.64644581352182 & 0.353554186478179 \tabularnewline
140 & 6 & 4.9443563216751 & 1.0556436783249 \tabularnewline
141 & 4 & 5.34270715007612 & -1.34270715007612 \tabularnewline
142 & 6 & 5.30284125797226 & 0.69715874202774 \tabularnewline
143 & 3 & 5.63089966464894 & -2.63089966464894 \tabularnewline
144 & 6 & 5.1777625416761 & 0.822237458323896 \tabularnewline
145 & 8 & 5.43641467177882 & 2.56358532822118 \tabularnewline
146 & 4 & 5.36425989916202 & -1.36425989916202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&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]6[/C][C]4.94361980067204[/C][C]1.05638019932796[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]4.737427751439[/C][C]-0.737427751439003[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]5.73409374913894[/C][C]-0.734093749138936[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]5.18007195190303[/C][C]-1.18007195190303[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]5.30375620931218[/C][C]-1.30375620931218[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.80346856345844[/C][C]0.196531436541558[/C][/ROW]
[ROW][C]7[/C][C]6[/C][C]5.24370622451432[/C][C]0.756293775485685[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.90713565537899[/C][C]-0.90713565537899[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.38485740343087[/C][C]-1.38485740343087[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]4.38327638457893[/C][C]1.61672361542107[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]4.76982670356276[/C][C]-0.769826703562764[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]5.77623777413393[/C][C]0.223762225866075[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]4.89335897551661[/C][C]0.106641024483392[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]5.51891189763144[/C][C]-1.51891189763144[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.30635346869017[/C][C]0.693646531309833[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]4.35537176063961[/C][C]-1.35537176063961[/C][/ROW]
[ROW][C]17[/C][C]5[/C][C]5.22188868378273[/C][C]-0.221888683782726[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]5.249525181417[/C][C]0.750474818583002[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]5.15992939848759[/C][C]-1.15992939848759[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]4.77336484849244[/C][C]1.22663515150756[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.72487490918327[/C][C]-2.72487490918327[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]4.90445877766951[/C][C]2.09554122233049[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]5.26670087437439[/C][C]-0.266700874374392[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]5.06605099566125[/C][C]-3.06605099566125[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.71660215449895[/C][C]-0.716602154498953[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]5.14404027371034[/C][C]-1.14404027371034[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]5.07963240216836[/C][C]0.920367597831637[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]5.30549331568218[/C][C]0.694506684317824[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]5.10343206001159[/C][C]-0.103432060011586[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]5.10935791264621[/C][C]0.89064208735379[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]4.91573077394969[/C][C]1.08426922605031[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]5.85033978856132[/C][C]-1.85033978856132[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]5.12760470423587[/C][C]0.872395295764125[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]5.62105968961593[/C][C]0.378940310384068[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]5.56051314748389[/C][C]0.439486852516112[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]4.75763049695206[/C][C]-2.75763049695206[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]5.31687761533277[/C][C]-1.31687761533277[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]4.96523378703868[/C][C]0.0347662129613183[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]5.37393793416068[/C][C]-2.37393793416068[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]4.95303304402501[/C][C]2.04696695597499[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.48445801305922[/C][C]0.515541986940783[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]5.37244870136593[/C][C]-2.37244870136593[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]5.33227597551785[/C][C]2.66772402448215[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]5.93731211203473[/C][C]2.06268788796527[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]4.53846660791513[/C][C]0.461533392084873[/C][/ROW]
[ROW][C]46[/C][C]6[/C][C]5.35760572696735[/C][C]0.642394273032652[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]4.7842225698121[/C][C]-1.7842225698121[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]5.32533201590345[/C][C]-0.325332015903446[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.90438267045362[/C][C]-0.904382670453618[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]5.56932419757239[/C][C]-0.569324197572388[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]4.48490313758761[/C][C]0.515096862412386[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.32101873006295[/C][C]0.678981269937045[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.88901010491383[/C][C]0.110989895086174[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]5.3001427534775[/C][C]0.699857246522498[/C][/ROW]
[ROW][C]55[/C][C]6[/C][C]5.12450927061897[/C][C]0.875490729381035[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]4.63906759486418[/C][C]-0.63906759486418[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]5.80589164326143[/C][C]2.19410835673857[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]4.54571079834806[/C][C]1.45428920165194[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.67753466745255[/C][C]-0.67753466745255[/C][/ROW]
[ROW][C]60[/C][C]6[/C][C]4.50093355810949[/C][C]1.49906644189051[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]4.37818564984906[/C][C]0.621814350150941[/C][/ROW]
[ROW][C]62[/C][C]5[/C][C]5.55894605885185[/C][C]-0.558946058851848[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]5.40929806270017[/C][C]0.590701937299834[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]5.55862405565859[/C][C]0.44137594434141[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]4.82384960448203[/C][C]1.17615039551797[/C][/ROW]
[ROW][C]66[/C][C]6[/C][C]4.49892097038312[/C][C]1.50107902961688[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]4.73366983751221[/C][C]1.26633016248779[/C][/ROW]
[ROW][C]68[/C][C]6[/C][C]4.97160134201094[/C][C]1.02839865798906[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]4.86568773417053[/C][C]2.13431226582947[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]5.1818882881658[/C][C]-1.1818882881658[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]5.3526141629817[/C][C]-1.3526141629817[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]4.59586116947693[/C][C]-1.59586116947693[/C][/ROW]
[ROW][C]73[/C][C]6[/C][C]5.14983727344706[/C][C]0.850162726552943[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]5.35898171795395[/C][C]-0.358981717953955[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]5.21272946731273[/C][C]-0.212729467312726[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]5.08089440567759[/C][C]-2.08089440567759[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.08472402213396[/C][C]-0.0847240221339615[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]4.45029572083981[/C][C]-0.450295720839812[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]5.08401255010695[/C][C]-2.08401255010695[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]5.56798631582375[/C][C]1.43201368417625[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.79056872697555[/C][C]-0.790568726975553[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]5.31718563036399[/C][C]-1.31718563036399[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]5.49094906704222[/C][C]-0.490949067042221[/C][/ROW]
[ROW][C]84[/C][C]6[/C][C]5.13189131621833[/C][C]0.868108683781672[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]5.32136273460742[/C][C]-3.32136273460742[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]4.12673566808075[/C][C]-2.12673566808075[/C][/ROW]
[ROW][C]87[/C][C]6[/C][C]5.87524946445846[/C][C]0.124750535541543[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.42023047439995[/C][C]-0.420230474399951[/C][/ROW]
[ROW][C]89[/C][C]5[/C][C]5.16767270159717[/C][C]-0.167672701597171[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]4.78653146936923[/C][C]1.21346853063077[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]5.29398264688672[/C][C]1.70601735311328[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]4.98683845775011[/C][C]3.01316154224989[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.037627994834[/C][C]0.962372005165996[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]5.03553791196347[/C][C]0.96446208803653[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]4.85297880128706[/C][C]-1.85297880128706[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]4.78696714213855[/C][C]2.21303285786145[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]5.29676883354378[/C][C]-2.29676883354378[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]4.77808216907332[/C][C]1.22191783092668[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]4.60287726723866[/C][C]-0.602877267238663[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]5.56751407579659[/C][C]-1.56751407579658[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]5.36903896769344[/C][C]0.630961032306563[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]5.40459884147673[/C][C]0.595401158523274[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]6.22937236088053[/C][C]-0.229372360880531[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]4.9895888306684[/C][C]-0.989588830668399[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]6.77062763911947[/C][C]0.229372360880531[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]5.43352469507271[/C][C]-0.433524695072706[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]5.57143715315705[/C][C]1.42856284684295[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]5.49346927291676[/C][C]-1.49346927291676[/C][/ROW]
[ROW][C]109[/C][C]6[/C][C]4.9304892363833[/C][C]1.06951076361671[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]5.90144715312863[/C][C]0.0985528468713707[/C][/ROW]
[ROW][C]111[/C][C]6[/C][C]4.51617091669793[/C][C]1.48382908330207[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]4.32266275137232[/C][C]0.677337248627681[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]5.40656492575364[/C][C]-0.406564925753638[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.51152385264879[/C][C]0.488476147351206[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]5.28626074158593[/C][C]1.71373925841407[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.67391219547654[/C][C]-0.673912195476545[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]5.49452830778287[/C][C]-1.49452830778287[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]5.51392188347574[/C][C]2.48607811652426[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.06163546175183[/C][C]0.938364538248175[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]4.86211208354529[/C][C]-1.86211208354529[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.31758343674462[/C][C]-1.31758343674462[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.18953177375749[/C][C]-0.189531773757492[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.13830596036187[/C][C]-0.138305960361872[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]5.14280506779856[/C][C]0.857194932201442[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]5.4698726587687[/C][C]2.5301273412313[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]4.69334357637887[/C][C]-2.69334357637887[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]4.36374296224895[/C][C]-0.363742962248955[/C][/ROW]
[ROW][C]128[/C][C]7[/C][C]5.50061142192204[/C][C]1.49938857807796[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.27057737323336[/C][C]-0.270577373233362[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]5.42370058507718[/C][C]0.576299414922816[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]5.51150774476203[/C][C]0.488492255237968[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]5.4649674639417[/C][C]-1.4649674639417[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]4.76121092682954[/C][C]0.238789073170465[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.2031345780734[/C][C]0.796865421926596[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]5.96222178793187[/C][C]0.0377782120681267[/C][/ROW]
[ROW][C]136[/C][C]6[/C][C]5.23733735653841[/C][C]0.76266264346159[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]5.15908840099119[/C][C]0.840911599008806[/C][/ROW]
[ROW][C]138[/C][C]5[/C][C]5.61238347793582[/C][C]-0.612383477935819[/C][/ROW]
[ROW][C]139[/C][C]5[/C][C]4.64644581352182[/C][C]0.353554186478179[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]4.9443563216751[/C][C]1.0556436783249[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.34270715007612[/C][C]-1.34270715007612[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.30284125797226[/C][C]0.69715874202774[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]5.63089966464894[/C][C]-2.63089966464894[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]5.1777625416761[/C][C]0.822237458323896[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]5.43641467177882[/C][C]2.56358532822118[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]5.36425989916202[/C][C]-1.36425989916202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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
164.943619800672041.05638019932796
244.737427751439-0.737427751439003
355.73409374913894-0.734093749138936
445.18007195190303-1.18007195190303
545.30375620931218-1.30375620931218
665.803468563458440.196531436541558
765.243706224514320.756293775485685
844.90713565537899-0.90713565537899
945.38485740343087-1.38485740343087
1064.383276384578931.61672361542107
1144.76982670356276-0.769826703562764
1265.776237774133930.223762225866075
1354.893358975516610.106641024483392
1445.51891189763144-1.51891189763144
1565.306353468690170.693646531309833
1634.35537176063961-1.35537176063961
1755.22188868378273-0.221888683782726
1865.2495251814170.750474818583002
1945.15992939848759-1.15992939848759
2064.773364848492441.22663515150756
2124.72487490918327-2.72487490918327
2274.904458777669512.09554122233049
2355.26670087437439-0.266700874374392
2425.06605099566125-3.06605099566125
2544.71660215449895-0.716602154498953
2645.14404027371034-1.14404027371034
2765.079632402168360.920367597831637
2865.305493315682180.694506684317824
2955.10343206001159-0.103432060011586
3065.109357912646210.89064208735379
3164.915730773949691.08426922605031
3245.85033978856132-1.85033978856132
3365.127604704235870.872395295764125
3465.621059689615930.378940310384068
3565.560513147483890.439486852516112
3624.75763049695206-2.75763049695206
3745.31687761533277-1.31687761533277
3854.965233787038680.0347662129613183
3935.37393793416068-2.37393793416068
4074.953033044025012.04696695597499
4154.484458013059220.515541986940783
4235.37244870136593-2.37244870136593
4385.332275975517852.66772402448215
4485.937312112034732.06268788796527
4554.538466607915130.461533392084873
4665.357605726967350.642394273032652
4734.7842225698121-1.7842225698121
4855.32533201590345-0.325332015903446
4944.90438267045362-0.904382670453618
5055.56932419757239-0.569324197572388
5154.484903137587610.515096862412386
5265.321018730062950.678981269937045
5354.889010104913830.110989895086174
5465.30014275347750.699857246522498
5565.124509270618970.875490729381035
5644.63906759486418-0.63906759486418
5785.805891643261432.19410835673857
5864.545710798348061.45428920165194
5944.67753466745255-0.67753466745255
6064.500933558109491.49906644189051
6154.378185649849060.621814350150941
6255.55894605885185-0.558946058851848
6365.409298062700170.590701937299834
6465.558624055658590.44137594434141
6564.823849604482031.17615039551797
6664.498920970383121.50107902961688
6764.733669837512211.26633016248779
6864.971601342010941.02839865798906
6974.865687734170532.13431226582947
7045.1818882881658-1.1818882881658
7145.3526141629817-1.3526141629817
7234.59586116947693-1.59586116947693
7365.149837273447060.850162726552943
7455.35898171795395-0.358981717953955
7555.21272946731273-0.212729467312726
7635.08089440567759-2.08089440567759
7755.08472402213396-0.0847240221339615
7844.45029572083981-0.450295720839812
7935.08401255010695-2.08401255010695
8075.567986315823751.43201368417625
8144.79056872697555-0.790568726975553
8245.31718563036399-1.31718563036399
8355.49094906704222-0.490949067042221
8465.131891316218330.868108683781672
8525.32136273460742-3.32136273460742
8624.12673566808075-2.12673566808075
8765.875249464458460.124750535541543
8844.42023047439995-0.420230474399951
8955.16767270159717-0.167672701597171
9064.786531469369231.21346853063077
9175.293982646886721.70601735311328
9284.986838457750113.01316154224989
9365.0376279948340.962372005165996
9465.035537911963470.96446208803653
9534.85297880128706-1.85297880128706
9674.786967142138552.21303285786145
9735.29676883354378-2.29676883354378
9864.778082169073321.22191783092668
9944.60287726723866-0.602877267238663
10045.56751407579659-1.56751407579658
10165.369038967693440.630961032306563
10265.404598841476730.595401158523274
10366.22937236088053-0.229372360880531
10444.9895888306684-0.989588830668399
10576.770627639119470.229372360880531
10655.43352469507271-0.433524695072706
10775.571437153157051.42856284684295
10845.49346927291676-1.49346927291676
10964.93048923638331.06951076361671
11065.901447153128630.0985528468713707
11164.516170916697931.48382908330207
11254.322662751372320.677337248627681
11355.40656492575364-0.406564925753638
11465.511523852648790.488476147351206
11575.286260741585931.71373925841407
11644.67391219547654-0.673912195476545
11745.49452830778287-1.49452830778287
11885.513921883475742.48607811652426
11965.061635461751830.938364538248175
12034.86211208354529-1.86211208354529
12145.31758343674462-1.31758343674462
12255.18953177375749-0.189531773757492
12355.13830596036187-0.138305960361872
12465.142805067798560.857194932201442
12585.46987265876872.5301273412313
12624.69334357637887-2.69334357637887
12744.36374296224895-0.363742962248955
12875.500611421922041.49938857807796
12955.27057737323336-0.270577373233362
13065.423700585077180.576299414922816
13165.511507744762030.488492255237968
13245.4649674639417-1.4649674639417
13354.761210926829540.238789073170465
13465.20313457807340.796865421926596
13565.962221787931870.0377782120681267
13665.237337356538410.76266264346159
13765.159088400991190.840911599008806
13855.61238347793582-0.612383477935819
13954.646445813521820.353554186478179
14064.94435632167511.0556436783249
14145.34270715007612-1.34270715007612
14265.302841257972260.69715874202774
14335.63089966464894-2.63089966464894
14465.17776254167610.822237458323896
14585.436414671778822.56358532822118
14645.36425989916202-1.36425989916202






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5794060302948930.8411879394102140.420593969705107
120.4235066372127980.8470132744255970.576493362787202
130.2813242383188350.562648476637670.718675761681165
140.1947141252438970.3894282504877950.805285874756103
150.1333692143918580.2667384287837160.866630785608142
160.3255404676941690.6510809353883370.674459532305831
170.2390142747339320.4780285494678640.760985725266068
180.248877996220280.497755992440560.75112200377972
190.2031709827558610.4063419655117210.79682901724414
200.1594489007120080.3188978014240160.840551099287992
210.1635525372220120.3271050744440250.836447462777988
220.3069313949770990.6138627899541990.6930686050229
230.2546007948480860.5092015896961730.745399205151914
240.4895823657273340.9791647314546670.510417634272666
250.420229411920080.8404588238401590.57977058807992
260.3570868770328240.7141737540656490.642913122967176
270.3334745655061210.6669491310122410.666525434493879
280.3482392820200110.6964785640400220.651760717979989
290.2869885070769110.5739770141538230.713011492923089
300.3520419844791220.7040839689582450.647958015520878
310.3030443593498930.6060887186997870.696955640650107
320.3203860148555990.6407720297111990.6796139851444
330.2882932112320510.5765864224641020.711706788767949
340.2591642704986070.5183285409972130.740835729501393
350.2170651027022450.4341302054044890.782934897297755
360.4703789801718350.940757960343670.529621019828165
370.438861776697130.877723553394260.56113822330287
380.3882275631722610.7764551263445230.611772436827739
390.4835710592543620.9671421185087230.516428940745638
400.5758729298749720.8482541402500550.424127070125028
410.5462018201985530.9075963596028940.453798179801447
420.6302396824112180.7395206351775640.369760317588782
430.7933806326382830.4132387347234330.206619367361717
440.852106448250240.2957871034995180.147893551749759
450.8216572746927730.3566854506144540.178342725307227
460.7957656531836640.4084686936326730.204234346816336
470.8100518969236720.3798962061526560.189948103076328
480.7750937891924180.4498124216151640.224906210807582
490.7465608995884650.5068782008230690.253439100411535
500.7099477022843380.5801045954313240.290052297715662
510.6727190444167780.6545619111664440.327280955583222
520.6329900180632120.7340199638735760.367009981936788
530.5836565117191990.8326869765616020.416343488280801
540.5489493810223140.9021012379553720.451050618977686
550.5230063483122410.9539873033755190.476993651687759
560.4793494098551910.9586988197103810.520650590144809
570.5537188852921680.8925622294156640.446281114707832
580.554842356946020.890315286107960.44515764305398
590.5130155515378080.9739688969243850.486984448462193
600.5231247571556170.9537504856887670.476875242844383
610.4942043943798520.9884087887597040.505795605620148
620.4500097265281150.900019453056230.549990273471885
630.4103756086534910.8207512173069820.589624391346509
640.3685081852575970.7370163705151940.631491814742403
650.3568720953791670.7137441907583340.643127904620833
660.3661444544742860.7322889089485720.633855545525714
670.3556346792283130.7112693584566270.644365320771687
680.3408181870014080.6816363740028170.659181812998592
690.4058010622900790.8116021245801580.594198937709921
700.3938132517946030.7876265035892060.606186748205397
710.3906365861972680.7812731723945370.609363413802732
720.4046419192454410.8092838384908830.595358080754559
730.3762380804063180.7524761608126350.623761919593682
740.3326236563392480.6652473126784970.667376343660752
750.2897059362316440.5794118724632870.710294063768356
760.3390972431723470.6781944863446950.660902756827653
770.2948118105486750.5896236210973510.705188189451325
780.2575202133966460.5150404267932920.742479786603354
790.3071504285842830.6143008571685660.692849571415717
800.308269995969250.61653999193850.69173000403075
810.2793483793384560.5586967586769120.720651620661544
820.2754926180213110.5509852360426230.724507381978689
830.2463340132384210.4926680264768410.75366598676158
840.2242481210237970.4484962420475940.775751878976203
850.4501622512772010.9003245025544020.549837748722799
860.5379834865667250.924033026866550.462016513433275
870.4877668063037130.9755336126074250.512233193696287
880.4452746401775030.8905492803550070.554725359822497
890.3983053244938310.7966106489876620.601694675506169
900.3763283950712610.7526567901425220.623671604928739
910.4083358800925890.8166717601851780.591664119907411
920.6147106633842640.7705786732314730.385289336615736
930.5957540551843430.8084918896313150.404245944815657
940.5721510861894370.8556978276211270.427848913810563
950.585622631260710.828754737478580.41437736873929
960.6825663270725510.6348673458548980.317433672927449
970.7522658074798750.4954683850402490.247734192520124
980.7317486467298180.5365027065403650.268251353270182
990.695348242456690.609303515086620.30465175754331
1000.7031573372240890.5936853255518220.296842662775911
1010.6603482618767030.6793034762465940.339651738123297
1020.6183902861862480.7632194276275040.381609713813752
1030.5783738290021410.8432523419957180.421626170997859
1040.586296864093440.827406271813120.41370313590656
1050.5298526429303830.9402947141392340.470147357069617
1060.4861788583181130.9723577166362260.513821141681887
1070.5040163117669440.9919673764661120.495983688233056
1080.5409349834109750.918130033178050.459065016589025
1090.5104317352060070.9791365295879850.489568264793993
1100.4513586557563010.9027173115126020.548641344243699
1110.4750363952372480.9500727904744960.524963604762752
1120.4386599697890920.8773199395781850.561340030210908
1130.4100937866171310.8201875732342630.589906213382869
1140.3608721612471050.7217443224942110.639127838752894
1150.3422209145400490.6844418290800990.657779085459951
1160.2929763624350160.5859527248700330.707023637564984
1170.3719744539162660.7439489078325320.628025546083734
1180.4701718555330860.9403437110661710.529828144466914
1190.5298163588208590.9403672823582820.470183641179141
1200.5394092097248290.9211815805503430.460590790275171
1210.6407892805159440.7184214389681110.359210719484056
1220.5731241807970750.853751638405850.426875819202925
1230.5475417767145140.9049164465709720.452458223285486
1240.4747651295798650.9495302591597310.525234870420135
1250.5392930972195620.9214138055608770.460706902780438
1260.7429756276834270.5140487446331470.257024372316573
1270.7859890843959470.4280218312081070.214010915604053
1280.7589604384243130.4820791231513750.241039561575687
1290.6720781582596810.6558436834806380.327921841740319
1300.619328850110380.761342299779240.38067114988962
1310.5195960027609210.9608079944781580.480403997239079
1320.9168024840293580.1663950319412850.0831975159706423
1330.8438355789601070.3123288420797860.156164421039893
1340.7287985526898140.5424028946203720.271201447310186
1350.7822872334246780.4354255331506450.217712766575322

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.579406030294893 & 0.841187939410214 & 0.420593969705107 \tabularnewline
12 & 0.423506637212798 & 0.847013274425597 & 0.576493362787202 \tabularnewline
13 & 0.281324238318835 & 0.56264847663767 & 0.718675761681165 \tabularnewline
14 & 0.194714125243897 & 0.389428250487795 & 0.805285874756103 \tabularnewline
15 & 0.133369214391858 & 0.266738428783716 & 0.866630785608142 \tabularnewline
16 & 0.325540467694169 & 0.651080935388337 & 0.674459532305831 \tabularnewline
17 & 0.239014274733932 & 0.478028549467864 & 0.760985725266068 \tabularnewline
18 & 0.24887799622028 & 0.49775599244056 & 0.75112200377972 \tabularnewline
19 & 0.203170982755861 & 0.406341965511721 & 0.79682901724414 \tabularnewline
20 & 0.159448900712008 & 0.318897801424016 & 0.840551099287992 \tabularnewline
21 & 0.163552537222012 & 0.327105074444025 & 0.836447462777988 \tabularnewline
22 & 0.306931394977099 & 0.613862789954199 & 0.6930686050229 \tabularnewline
23 & 0.254600794848086 & 0.509201589696173 & 0.745399205151914 \tabularnewline
24 & 0.489582365727334 & 0.979164731454667 & 0.510417634272666 \tabularnewline
25 & 0.42022941192008 & 0.840458823840159 & 0.57977058807992 \tabularnewline
26 & 0.357086877032824 & 0.714173754065649 & 0.642913122967176 \tabularnewline
27 & 0.333474565506121 & 0.666949131012241 & 0.666525434493879 \tabularnewline
28 & 0.348239282020011 & 0.696478564040022 & 0.651760717979989 \tabularnewline
29 & 0.286988507076911 & 0.573977014153823 & 0.713011492923089 \tabularnewline
30 & 0.352041984479122 & 0.704083968958245 & 0.647958015520878 \tabularnewline
31 & 0.303044359349893 & 0.606088718699787 & 0.696955640650107 \tabularnewline
32 & 0.320386014855599 & 0.640772029711199 & 0.6796139851444 \tabularnewline
33 & 0.288293211232051 & 0.576586422464102 & 0.711706788767949 \tabularnewline
34 & 0.259164270498607 & 0.518328540997213 & 0.740835729501393 \tabularnewline
35 & 0.217065102702245 & 0.434130205404489 & 0.782934897297755 \tabularnewline
36 & 0.470378980171835 & 0.94075796034367 & 0.529621019828165 \tabularnewline
37 & 0.43886177669713 & 0.87772355339426 & 0.56113822330287 \tabularnewline
38 & 0.388227563172261 & 0.776455126344523 & 0.611772436827739 \tabularnewline
39 & 0.483571059254362 & 0.967142118508723 & 0.516428940745638 \tabularnewline
40 & 0.575872929874972 & 0.848254140250055 & 0.424127070125028 \tabularnewline
41 & 0.546201820198553 & 0.907596359602894 & 0.453798179801447 \tabularnewline
42 & 0.630239682411218 & 0.739520635177564 & 0.369760317588782 \tabularnewline
43 & 0.793380632638283 & 0.413238734723433 & 0.206619367361717 \tabularnewline
44 & 0.85210644825024 & 0.295787103499518 & 0.147893551749759 \tabularnewline
45 & 0.821657274692773 & 0.356685450614454 & 0.178342725307227 \tabularnewline
46 & 0.795765653183664 & 0.408468693632673 & 0.204234346816336 \tabularnewline
47 & 0.810051896923672 & 0.379896206152656 & 0.189948103076328 \tabularnewline
48 & 0.775093789192418 & 0.449812421615164 & 0.224906210807582 \tabularnewline
49 & 0.746560899588465 & 0.506878200823069 & 0.253439100411535 \tabularnewline
50 & 0.709947702284338 & 0.580104595431324 & 0.290052297715662 \tabularnewline
51 & 0.672719044416778 & 0.654561911166444 & 0.327280955583222 \tabularnewline
52 & 0.632990018063212 & 0.734019963873576 & 0.367009981936788 \tabularnewline
53 & 0.583656511719199 & 0.832686976561602 & 0.416343488280801 \tabularnewline
54 & 0.548949381022314 & 0.902101237955372 & 0.451050618977686 \tabularnewline
55 & 0.523006348312241 & 0.953987303375519 & 0.476993651687759 \tabularnewline
56 & 0.479349409855191 & 0.958698819710381 & 0.520650590144809 \tabularnewline
57 & 0.553718885292168 & 0.892562229415664 & 0.446281114707832 \tabularnewline
58 & 0.55484235694602 & 0.89031528610796 & 0.44515764305398 \tabularnewline
59 & 0.513015551537808 & 0.973968896924385 & 0.486984448462193 \tabularnewline
60 & 0.523124757155617 & 0.953750485688767 & 0.476875242844383 \tabularnewline
61 & 0.494204394379852 & 0.988408788759704 & 0.505795605620148 \tabularnewline
62 & 0.450009726528115 & 0.90001945305623 & 0.549990273471885 \tabularnewline
63 & 0.410375608653491 & 0.820751217306982 & 0.589624391346509 \tabularnewline
64 & 0.368508185257597 & 0.737016370515194 & 0.631491814742403 \tabularnewline
65 & 0.356872095379167 & 0.713744190758334 & 0.643127904620833 \tabularnewline
66 & 0.366144454474286 & 0.732288908948572 & 0.633855545525714 \tabularnewline
67 & 0.355634679228313 & 0.711269358456627 & 0.644365320771687 \tabularnewline
68 & 0.340818187001408 & 0.681636374002817 & 0.659181812998592 \tabularnewline
69 & 0.405801062290079 & 0.811602124580158 & 0.594198937709921 \tabularnewline
70 & 0.393813251794603 & 0.787626503589206 & 0.606186748205397 \tabularnewline
71 & 0.390636586197268 & 0.781273172394537 & 0.609363413802732 \tabularnewline
72 & 0.404641919245441 & 0.809283838490883 & 0.595358080754559 \tabularnewline
73 & 0.376238080406318 & 0.752476160812635 & 0.623761919593682 \tabularnewline
74 & 0.332623656339248 & 0.665247312678497 & 0.667376343660752 \tabularnewline
75 & 0.289705936231644 & 0.579411872463287 & 0.710294063768356 \tabularnewline
76 & 0.339097243172347 & 0.678194486344695 & 0.660902756827653 \tabularnewline
77 & 0.294811810548675 & 0.589623621097351 & 0.705188189451325 \tabularnewline
78 & 0.257520213396646 & 0.515040426793292 & 0.742479786603354 \tabularnewline
79 & 0.307150428584283 & 0.614300857168566 & 0.692849571415717 \tabularnewline
80 & 0.30826999596925 & 0.6165399919385 & 0.69173000403075 \tabularnewline
81 & 0.279348379338456 & 0.558696758676912 & 0.720651620661544 \tabularnewline
82 & 0.275492618021311 & 0.550985236042623 & 0.724507381978689 \tabularnewline
83 & 0.246334013238421 & 0.492668026476841 & 0.75366598676158 \tabularnewline
84 & 0.224248121023797 & 0.448496242047594 & 0.775751878976203 \tabularnewline
85 & 0.450162251277201 & 0.900324502554402 & 0.549837748722799 \tabularnewline
86 & 0.537983486566725 & 0.92403302686655 & 0.462016513433275 \tabularnewline
87 & 0.487766806303713 & 0.975533612607425 & 0.512233193696287 \tabularnewline
88 & 0.445274640177503 & 0.890549280355007 & 0.554725359822497 \tabularnewline
89 & 0.398305324493831 & 0.796610648987662 & 0.601694675506169 \tabularnewline
90 & 0.376328395071261 & 0.752656790142522 & 0.623671604928739 \tabularnewline
91 & 0.408335880092589 & 0.816671760185178 & 0.591664119907411 \tabularnewline
92 & 0.614710663384264 & 0.770578673231473 & 0.385289336615736 \tabularnewline
93 & 0.595754055184343 & 0.808491889631315 & 0.404245944815657 \tabularnewline
94 & 0.572151086189437 & 0.855697827621127 & 0.427848913810563 \tabularnewline
95 & 0.58562263126071 & 0.82875473747858 & 0.41437736873929 \tabularnewline
96 & 0.682566327072551 & 0.634867345854898 & 0.317433672927449 \tabularnewline
97 & 0.752265807479875 & 0.495468385040249 & 0.247734192520124 \tabularnewline
98 & 0.731748646729818 & 0.536502706540365 & 0.268251353270182 \tabularnewline
99 & 0.69534824245669 & 0.60930351508662 & 0.30465175754331 \tabularnewline
100 & 0.703157337224089 & 0.593685325551822 & 0.296842662775911 \tabularnewline
101 & 0.660348261876703 & 0.679303476246594 & 0.339651738123297 \tabularnewline
102 & 0.618390286186248 & 0.763219427627504 & 0.381609713813752 \tabularnewline
103 & 0.578373829002141 & 0.843252341995718 & 0.421626170997859 \tabularnewline
104 & 0.58629686409344 & 0.82740627181312 & 0.41370313590656 \tabularnewline
105 & 0.529852642930383 & 0.940294714139234 & 0.470147357069617 \tabularnewline
106 & 0.486178858318113 & 0.972357716636226 & 0.513821141681887 \tabularnewline
107 & 0.504016311766944 & 0.991967376466112 & 0.495983688233056 \tabularnewline
108 & 0.540934983410975 & 0.91813003317805 & 0.459065016589025 \tabularnewline
109 & 0.510431735206007 & 0.979136529587985 & 0.489568264793993 \tabularnewline
110 & 0.451358655756301 & 0.902717311512602 & 0.548641344243699 \tabularnewline
111 & 0.475036395237248 & 0.950072790474496 & 0.524963604762752 \tabularnewline
112 & 0.438659969789092 & 0.877319939578185 & 0.561340030210908 \tabularnewline
113 & 0.410093786617131 & 0.820187573234263 & 0.589906213382869 \tabularnewline
114 & 0.360872161247105 & 0.721744322494211 & 0.639127838752894 \tabularnewline
115 & 0.342220914540049 & 0.684441829080099 & 0.657779085459951 \tabularnewline
116 & 0.292976362435016 & 0.585952724870033 & 0.707023637564984 \tabularnewline
117 & 0.371974453916266 & 0.743948907832532 & 0.628025546083734 \tabularnewline
118 & 0.470171855533086 & 0.940343711066171 & 0.529828144466914 \tabularnewline
119 & 0.529816358820859 & 0.940367282358282 & 0.470183641179141 \tabularnewline
120 & 0.539409209724829 & 0.921181580550343 & 0.460590790275171 \tabularnewline
121 & 0.640789280515944 & 0.718421438968111 & 0.359210719484056 \tabularnewline
122 & 0.573124180797075 & 0.85375163840585 & 0.426875819202925 \tabularnewline
123 & 0.547541776714514 & 0.904916446570972 & 0.452458223285486 \tabularnewline
124 & 0.474765129579865 & 0.949530259159731 & 0.525234870420135 \tabularnewline
125 & 0.539293097219562 & 0.921413805560877 & 0.460706902780438 \tabularnewline
126 & 0.742975627683427 & 0.514048744633147 & 0.257024372316573 \tabularnewline
127 & 0.785989084395947 & 0.428021831208107 & 0.214010915604053 \tabularnewline
128 & 0.758960438424313 & 0.482079123151375 & 0.241039561575687 \tabularnewline
129 & 0.672078158259681 & 0.655843683480638 & 0.327921841740319 \tabularnewline
130 & 0.61932885011038 & 0.76134229977924 & 0.38067114988962 \tabularnewline
131 & 0.519596002760921 & 0.960807994478158 & 0.480403997239079 \tabularnewline
132 & 0.916802484029358 & 0.166395031941285 & 0.0831975159706423 \tabularnewline
133 & 0.843835578960107 & 0.312328842079786 & 0.156164421039893 \tabularnewline
134 & 0.728798552689814 & 0.542402894620372 & 0.271201447310186 \tabularnewline
135 & 0.782287233424678 & 0.435425533150645 & 0.217712766575322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&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]11[/C][C]0.579406030294893[/C][C]0.841187939410214[/C][C]0.420593969705107[/C][/ROW]
[ROW][C]12[/C][C]0.423506637212798[/C][C]0.847013274425597[/C][C]0.576493362787202[/C][/ROW]
[ROW][C]13[/C][C]0.281324238318835[/C][C]0.56264847663767[/C][C]0.718675761681165[/C][/ROW]
[ROW][C]14[/C][C]0.194714125243897[/C][C]0.389428250487795[/C][C]0.805285874756103[/C][/ROW]
[ROW][C]15[/C][C]0.133369214391858[/C][C]0.266738428783716[/C][C]0.866630785608142[/C][/ROW]
[ROW][C]16[/C][C]0.325540467694169[/C][C]0.651080935388337[/C][C]0.674459532305831[/C][/ROW]
[ROW][C]17[/C][C]0.239014274733932[/C][C]0.478028549467864[/C][C]0.760985725266068[/C][/ROW]
[ROW][C]18[/C][C]0.24887799622028[/C][C]0.49775599244056[/C][C]0.75112200377972[/C][/ROW]
[ROW][C]19[/C][C]0.203170982755861[/C][C]0.406341965511721[/C][C]0.79682901724414[/C][/ROW]
[ROW][C]20[/C][C]0.159448900712008[/C][C]0.318897801424016[/C][C]0.840551099287992[/C][/ROW]
[ROW][C]21[/C][C]0.163552537222012[/C][C]0.327105074444025[/C][C]0.836447462777988[/C][/ROW]
[ROW][C]22[/C][C]0.306931394977099[/C][C]0.613862789954199[/C][C]0.6930686050229[/C][/ROW]
[ROW][C]23[/C][C]0.254600794848086[/C][C]0.509201589696173[/C][C]0.745399205151914[/C][/ROW]
[ROW][C]24[/C][C]0.489582365727334[/C][C]0.979164731454667[/C][C]0.510417634272666[/C][/ROW]
[ROW][C]25[/C][C]0.42022941192008[/C][C]0.840458823840159[/C][C]0.57977058807992[/C][/ROW]
[ROW][C]26[/C][C]0.357086877032824[/C][C]0.714173754065649[/C][C]0.642913122967176[/C][/ROW]
[ROW][C]27[/C][C]0.333474565506121[/C][C]0.666949131012241[/C][C]0.666525434493879[/C][/ROW]
[ROW][C]28[/C][C]0.348239282020011[/C][C]0.696478564040022[/C][C]0.651760717979989[/C][/ROW]
[ROW][C]29[/C][C]0.286988507076911[/C][C]0.573977014153823[/C][C]0.713011492923089[/C][/ROW]
[ROW][C]30[/C][C]0.352041984479122[/C][C]0.704083968958245[/C][C]0.647958015520878[/C][/ROW]
[ROW][C]31[/C][C]0.303044359349893[/C][C]0.606088718699787[/C][C]0.696955640650107[/C][/ROW]
[ROW][C]32[/C][C]0.320386014855599[/C][C]0.640772029711199[/C][C]0.6796139851444[/C][/ROW]
[ROW][C]33[/C][C]0.288293211232051[/C][C]0.576586422464102[/C][C]0.711706788767949[/C][/ROW]
[ROW][C]34[/C][C]0.259164270498607[/C][C]0.518328540997213[/C][C]0.740835729501393[/C][/ROW]
[ROW][C]35[/C][C]0.217065102702245[/C][C]0.434130205404489[/C][C]0.782934897297755[/C][/ROW]
[ROW][C]36[/C][C]0.470378980171835[/C][C]0.94075796034367[/C][C]0.529621019828165[/C][/ROW]
[ROW][C]37[/C][C]0.43886177669713[/C][C]0.87772355339426[/C][C]0.56113822330287[/C][/ROW]
[ROW][C]38[/C][C]0.388227563172261[/C][C]0.776455126344523[/C][C]0.611772436827739[/C][/ROW]
[ROW][C]39[/C][C]0.483571059254362[/C][C]0.967142118508723[/C][C]0.516428940745638[/C][/ROW]
[ROW][C]40[/C][C]0.575872929874972[/C][C]0.848254140250055[/C][C]0.424127070125028[/C][/ROW]
[ROW][C]41[/C][C]0.546201820198553[/C][C]0.907596359602894[/C][C]0.453798179801447[/C][/ROW]
[ROW][C]42[/C][C]0.630239682411218[/C][C]0.739520635177564[/C][C]0.369760317588782[/C][/ROW]
[ROW][C]43[/C][C]0.793380632638283[/C][C]0.413238734723433[/C][C]0.206619367361717[/C][/ROW]
[ROW][C]44[/C][C]0.85210644825024[/C][C]0.295787103499518[/C][C]0.147893551749759[/C][/ROW]
[ROW][C]45[/C][C]0.821657274692773[/C][C]0.356685450614454[/C][C]0.178342725307227[/C][/ROW]
[ROW][C]46[/C][C]0.795765653183664[/C][C]0.408468693632673[/C][C]0.204234346816336[/C][/ROW]
[ROW][C]47[/C][C]0.810051896923672[/C][C]0.379896206152656[/C][C]0.189948103076328[/C][/ROW]
[ROW][C]48[/C][C]0.775093789192418[/C][C]0.449812421615164[/C][C]0.224906210807582[/C][/ROW]
[ROW][C]49[/C][C]0.746560899588465[/C][C]0.506878200823069[/C][C]0.253439100411535[/C][/ROW]
[ROW][C]50[/C][C]0.709947702284338[/C][C]0.580104595431324[/C][C]0.290052297715662[/C][/ROW]
[ROW][C]51[/C][C]0.672719044416778[/C][C]0.654561911166444[/C][C]0.327280955583222[/C][/ROW]
[ROW][C]52[/C][C]0.632990018063212[/C][C]0.734019963873576[/C][C]0.367009981936788[/C][/ROW]
[ROW][C]53[/C][C]0.583656511719199[/C][C]0.832686976561602[/C][C]0.416343488280801[/C][/ROW]
[ROW][C]54[/C][C]0.548949381022314[/C][C]0.902101237955372[/C][C]0.451050618977686[/C][/ROW]
[ROW][C]55[/C][C]0.523006348312241[/C][C]0.953987303375519[/C][C]0.476993651687759[/C][/ROW]
[ROW][C]56[/C][C]0.479349409855191[/C][C]0.958698819710381[/C][C]0.520650590144809[/C][/ROW]
[ROW][C]57[/C][C]0.553718885292168[/C][C]0.892562229415664[/C][C]0.446281114707832[/C][/ROW]
[ROW][C]58[/C][C]0.55484235694602[/C][C]0.89031528610796[/C][C]0.44515764305398[/C][/ROW]
[ROW][C]59[/C][C]0.513015551537808[/C][C]0.973968896924385[/C][C]0.486984448462193[/C][/ROW]
[ROW][C]60[/C][C]0.523124757155617[/C][C]0.953750485688767[/C][C]0.476875242844383[/C][/ROW]
[ROW][C]61[/C][C]0.494204394379852[/C][C]0.988408788759704[/C][C]0.505795605620148[/C][/ROW]
[ROW][C]62[/C][C]0.450009726528115[/C][C]0.90001945305623[/C][C]0.549990273471885[/C][/ROW]
[ROW][C]63[/C][C]0.410375608653491[/C][C]0.820751217306982[/C][C]0.589624391346509[/C][/ROW]
[ROW][C]64[/C][C]0.368508185257597[/C][C]0.737016370515194[/C][C]0.631491814742403[/C][/ROW]
[ROW][C]65[/C][C]0.356872095379167[/C][C]0.713744190758334[/C][C]0.643127904620833[/C][/ROW]
[ROW][C]66[/C][C]0.366144454474286[/C][C]0.732288908948572[/C][C]0.633855545525714[/C][/ROW]
[ROW][C]67[/C][C]0.355634679228313[/C][C]0.711269358456627[/C][C]0.644365320771687[/C][/ROW]
[ROW][C]68[/C][C]0.340818187001408[/C][C]0.681636374002817[/C][C]0.659181812998592[/C][/ROW]
[ROW][C]69[/C][C]0.405801062290079[/C][C]0.811602124580158[/C][C]0.594198937709921[/C][/ROW]
[ROW][C]70[/C][C]0.393813251794603[/C][C]0.787626503589206[/C][C]0.606186748205397[/C][/ROW]
[ROW][C]71[/C][C]0.390636586197268[/C][C]0.781273172394537[/C][C]0.609363413802732[/C][/ROW]
[ROW][C]72[/C][C]0.404641919245441[/C][C]0.809283838490883[/C][C]0.595358080754559[/C][/ROW]
[ROW][C]73[/C][C]0.376238080406318[/C][C]0.752476160812635[/C][C]0.623761919593682[/C][/ROW]
[ROW][C]74[/C][C]0.332623656339248[/C][C]0.665247312678497[/C][C]0.667376343660752[/C][/ROW]
[ROW][C]75[/C][C]0.289705936231644[/C][C]0.579411872463287[/C][C]0.710294063768356[/C][/ROW]
[ROW][C]76[/C][C]0.339097243172347[/C][C]0.678194486344695[/C][C]0.660902756827653[/C][/ROW]
[ROW][C]77[/C][C]0.294811810548675[/C][C]0.589623621097351[/C][C]0.705188189451325[/C][/ROW]
[ROW][C]78[/C][C]0.257520213396646[/C][C]0.515040426793292[/C][C]0.742479786603354[/C][/ROW]
[ROW][C]79[/C][C]0.307150428584283[/C][C]0.614300857168566[/C][C]0.692849571415717[/C][/ROW]
[ROW][C]80[/C][C]0.30826999596925[/C][C]0.6165399919385[/C][C]0.69173000403075[/C][/ROW]
[ROW][C]81[/C][C]0.279348379338456[/C][C]0.558696758676912[/C][C]0.720651620661544[/C][/ROW]
[ROW][C]82[/C][C]0.275492618021311[/C][C]0.550985236042623[/C][C]0.724507381978689[/C][/ROW]
[ROW][C]83[/C][C]0.246334013238421[/C][C]0.492668026476841[/C][C]0.75366598676158[/C][/ROW]
[ROW][C]84[/C][C]0.224248121023797[/C][C]0.448496242047594[/C][C]0.775751878976203[/C][/ROW]
[ROW][C]85[/C][C]0.450162251277201[/C][C]0.900324502554402[/C][C]0.549837748722799[/C][/ROW]
[ROW][C]86[/C][C]0.537983486566725[/C][C]0.92403302686655[/C][C]0.462016513433275[/C][/ROW]
[ROW][C]87[/C][C]0.487766806303713[/C][C]0.975533612607425[/C][C]0.512233193696287[/C][/ROW]
[ROW][C]88[/C][C]0.445274640177503[/C][C]0.890549280355007[/C][C]0.554725359822497[/C][/ROW]
[ROW][C]89[/C][C]0.398305324493831[/C][C]0.796610648987662[/C][C]0.601694675506169[/C][/ROW]
[ROW][C]90[/C][C]0.376328395071261[/C][C]0.752656790142522[/C][C]0.623671604928739[/C][/ROW]
[ROW][C]91[/C][C]0.408335880092589[/C][C]0.816671760185178[/C][C]0.591664119907411[/C][/ROW]
[ROW][C]92[/C][C]0.614710663384264[/C][C]0.770578673231473[/C][C]0.385289336615736[/C][/ROW]
[ROW][C]93[/C][C]0.595754055184343[/C][C]0.808491889631315[/C][C]0.404245944815657[/C][/ROW]
[ROW][C]94[/C][C]0.572151086189437[/C][C]0.855697827621127[/C][C]0.427848913810563[/C][/ROW]
[ROW][C]95[/C][C]0.58562263126071[/C][C]0.82875473747858[/C][C]0.41437736873929[/C][/ROW]
[ROW][C]96[/C][C]0.682566327072551[/C][C]0.634867345854898[/C][C]0.317433672927449[/C][/ROW]
[ROW][C]97[/C][C]0.752265807479875[/C][C]0.495468385040249[/C][C]0.247734192520124[/C][/ROW]
[ROW][C]98[/C][C]0.731748646729818[/C][C]0.536502706540365[/C][C]0.268251353270182[/C][/ROW]
[ROW][C]99[/C][C]0.69534824245669[/C][C]0.60930351508662[/C][C]0.30465175754331[/C][/ROW]
[ROW][C]100[/C][C]0.703157337224089[/C][C]0.593685325551822[/C][C]0.296842662775911[/C][/ROW]
[ROW][C]101[/C][C]0.660348261876703[/C][C]0.679303476246594[/C][C]0.339651738123297[/C][/ROW]
[ROW][C]102[/C][C]0.618390286186248[/C][C]0.763219427627504[/C][C]0.381609713813752[/C][/ROW]
[ROW][C]103[/C][C]0.578373829002141[/C][C]0.843252341995718[/C][C]0.421626170997859[/C][/ROW]
[ROW][C]104[/C][C]0.58629686409344[/C][C]0.82740627181312[/C][C]0.41370313590656[/C][/ROW]
[ROW][C]105[/C][C]0.529852642930383[/C][C]0.940294714139234[/C][C]0.470147357069617[/C][/ROW]
[ROW][C]106[/C][C]0.486178858318113[/C][C]0.972357716636226[/C][C]0.513821141681887[/C][/ROW]
[ROW][C]107[/C][C]0.504016311766944[/C][C]0.991967376466112[/C][C]0.495983688233056[/C][/ROW]
[ROW][C]108[/C][C]0.540934983410975[/C][C]0.91813003317805[/C][C]0.459065016589025[/C][/ROW]
[ROW][C]109[/C][C]0.510431735206007[/C][C]0.979136529587985[/C][C]0.489568264793993[/C][/ROW]
[ROW][C]110[/C][C]0.451358655756301[/C][C]0.902717311512602[/C][C]0.548641344243699[/C][/ROW]
[ROW][C]111[/C][C]0.475036395237248[/C][C]0.950072790474496[/C][C]0.524963604762752[/C][/ROW]
[ROW][C]112[/C][C]0.438659969789092[/C][C]0.877319939578185[/C][C]0.561340030210908[/C][/ROW]
[ROW][C]113[/C][C]0.410093786617131[/C][C]0.820187573234263[/C][C]0.589906213382869[/C][/ROW]
[ROW][C]114[/C][C]0.360872161247105[/C][C]0.721744322494211[/C][C]0.639127838752894[/C][/ROW]
[ROW][C]115[/C][C]0.342220914540049[/C][C]0.684441829080099[/C][C]0.657779085459951[/C][/ROW]
[ROW][C]116[/C][C]0.292976362435016[/C][C]0.585952724870033[/C][C]0.707023637564984[/C][/ROW]
[ROW][C]117[/C][C]0.371974453916266[/C][C]0.743948907832532[/C][C]0.628025546083734[/C][/ROW]
[ROW][C]118[/C][C]0.470171855533086[/C][C]0.940343711066171[/C][C]0.529828144466914[/C][/ROW]
[ROW][C]119[/C][C]0.529816358820859[/C][C]0.940367282358282[/C][C]0.470183641179141[/C][/ROW]
[ROW][C]120[/C][C]0.539409209724829[/C][C]0.921181580550343[/C][C]0.460590790275171[/C][/ROW]
[ROW][C]121[/C][C]0.640789280515944[/C][C]0.718421438968111[/C][C]0.359210719484056[/C][/ROW]
[ROW][C]122[/C][C]0.573124180797075[/C][C]0.85375163840585[/C][C]0.426875819202925[/C][/ROW]
[ROW][C]123[/C][C]0.547541776714514[/C][C]0.904916446570972[/C][C]0.452458223285486[/C][/ROW]
[ROW][C]124[/C][C]0.474765129579865[/C][C]0.949530259159731[/C][C]0.525234870420135[/C][/ROW]
[ROW][C]125[/C][C]0.539293097219562[/C][C]0.921413805560877[/C][C]0.460706902780438[/C][/ROW]
[ROW][C]126[/C][C]0.742975627683427[/C][C]0.514048744633147[/C][C]0.257024372316573[/C][/ROW]
[ROW][C]127[/C][C]0.785989084395947[/C][C]0.428021831208107[/C][C]0.214010915604053[/C][/ROW]
[ROW][C]128[/C][C]0.758960438424313[/C][C]0.482079123151375[/C][C]0.241039561575687[/C][/ROW]
[ROW][C]129[/C][C]0.672078158259681[/C][C]0.655843683480638[/C][C]0.327921841740319[/C][/ROW]
[ROW][C]130[/C][C]0.61932885011038[/C][C]0.76134229977924[/C][C]0.38067114988962[/C][/ROW]
[ROW][C]131[/C][C]0.519596002760921[/C][C]0.960807994478158[/C][C]0.480403997239079[/C][/ROW]
[ROW][C]132[/C][C]0.916802484029358[/C][C]0.166395031941285[/C][C]0.0831975159706423[/C][/ROW]
[ROW][C]133[/C][C]0.843835578960107[/C][C]0.312328842079786[/C][C]0.156164421039893[/C][/ROW]
[ROW][C]134[/C][C]0.728798552689814[/C][C]0.542402894620372[/C][C]0.271201447310186[/C][/ROW]
[ROW][C]135[/C][C]0.782287233424678[/C][C]0.435425533150645[/C][C]0.217712766575322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&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
110.5794060302948930.8411879394102140.420593969705107
120.4235066372127980.8470132744255970.576493362787202
130.2813242383188350.562648476637670.718675761681165
140.1947141252438970.3894282504877950.805285874756103
150.1333692143918580.2667384287837160.866630785608142
160.3255404676941690.6510809353883370.674459532305831
170.2390142747339320.4780285494678640.760985725266068
180.248877996220280.497755992440560.75112200377972
190.2031709827558610.4063419655117210.79682901724414
200.1594489007120080.3188978014240160.840551099287992
210.1635525372220120.3271050744440250.836447462777988
220.3069313949770990.6138627899541990.6930686050229
230.2546007948480860.5092015896961730.745399205151914
240.4895823657273340.9791647314546670.510417634272666
250.420229411920080.8404588238401590.57977058807992
260.3570868770328240.7141737540656490.642913122967176
270.3334745655061210.6669491310122410.666525434493879
280.3482392820200110.6964785640400220.651760717979989
290.2869885070769110.5739770141538230.713011492923089
300.3520419844791220.7040839689582450.647958015520878
310.3030443593498930.6060887186997870.696955640650107
320.3203860148555990.6407720297111990.6796139851444
330.2882932112320510.5765864224641020.711706788767949
340.2591642704986070.5183285409972130.740835729501393
350.2170651027022450.4341302054044890.782934897297755
360.4703789801718350.940757960343670.529621019828165
370.438861776697130.877723553394260.56113822330287
380.3882275631722610.7764551263445230.611772436827739
390.4835710592543620.9671421185087230.516428940745638
400.5758729298749720.8482541402500550.424127070125028
410.5462018201985530.9075963596028940.453798179801447
420.6302396824112180.7395206351775640.369760317588782
430.7933806326382830.4132387347234330.206619367361717
440.852106448250240.2957871034995180.147893551749759
450.8216572746927730.3566854506144540.178342725307227
460.7957656531836640.4084686936326730.204234346816336
470.8100518969236720.3798962061526560.189948103076328
480.7750937891924180.4498124216151640.224906210807582
490.7465608995884650.5068782008230690.253439100411535
500.7099477022843380.5801045954313240.290052297715662
510.6727190444167780.6545619111664440.327280955583222
520.6329900180632120.7340199638735760.367009981936788
530.5836565117191990.8326869765616020.416343488280801
540.5489493810223140.9021012379553720.451050618977686
550.5230063483122410.9539873033755190.476993651687759
560.4793494098551910.9586988197103810.520650590144809
570.5537188852921680.8925622294156640.446281114707832
580.554842356946020.890315286107960.44515764305398
590.5130155515378080.9739688969243850.486984448462193
600.5231247571556170.9537504856887670.476875242844383
610.4942043943798520.9884087887597040.505795605620148
620.4500097265281150.900019453056230.549990273471885
630.4103756086534910.8207512173069820.589624391346509
640.3685081852575970.7370163705151940.631491814742403
650.3568720953791670.7137441907583340.643127904620833
660.3661444544742860.7322889089485720.633855545525714
670.3556346792283130.7112693584566270.644365320771687
680.3408181870014080.6816363740028170.659181812998592
690.4058010622900790.8116021245801580.594198937709921
700.3938132517946030.7876265035892060.606186748205397
710.3906365861972680.7812731723945370.609363413802732
720.4046419192454410.8092838384908830.595358080754559
730.3762380804063180.7524761608126350.623761919593682
740.3326236563392480.6652473126784970.667376343660752
750.2897059362316440.5794118724632870.710294063768356
760.3390972431723470.6781944863446950.660902756827653
770.2948118105486750.5896236210973510.705188189451325
780.2575202133966460.5150404267932920.742479786603354
790.3071504285842830.6143008571685660.692849571415717
800.308269995969250.61653999193850.69173000403075
810.2793483793384560.5586967586769120.720651620661544
820.2754926180213110.5509852360426230.724507381978689
830.2463340132384210.4926680264768410.75366598676158
840.2242481210237970.4484962420475940.775751878976203
850.4501622512772010.9003245025544020.549837748722799
860.5379834865667250.924033026866550.462016513433275
870.4877668063037130.9755336126074250.512233193696287
880.4452746401775030.8905492803550070.554725359822497
890.3983053244938310.7966106489876620.601694675506169
900.3763283950712610.7526567901425220.623671604928739
910.4083358800925890.8166717601851780.591664119907411
920.6147106633842640.7705786732314730.385289336615736
930.5957540551843430.8084918896313150.404245944815657
940.5721510861894370.8556978276211270.427848913810563
950.585622631260710.828754737478580.41437736873929
960.6825663270725510.6348673458548980.317433672927449
970.7522658074798750.4954683850402490.247734192520124
980.7317486467298180.5365027065403650.268251353270182
990.695348242456690.609303515086620.30465175754331
1000.7031573372240890.5936853255518220.296842662775911
1010.6603482618767030.6793034762465940.339651738123297
1020.6183902861862480.7632194276275040.381609713813752
1030.5783738290021410.8432523419957180.421626170997859
1040.586296864093440.827406271813120.41370313590656
1050.5298526429303830.9402947141392340.470147357069617
1060.4861788583181130.9723577166362260.513821141681887
1070.5040163117669440.9919673764661120.495983688233056
1080.5409349834109750.918130033178050.459065016589025
1090.5104317352060070.9791365295879850.489568264793993
1100.4513586557563010.9027173115126020.548641344243699
1110.4750363952372480.9500727904744960.524963604762752
1120.4386599697890920.8773199395781850.561340030210908
1130.4100937866171310.8201875732342630.589906213382869
1140.3608721612471050.7217443224942110.639127838752894
1150.3422209145400490.6844418290800990.657779085459951
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1200.5394092097248290.9211815805503430.460590790275171
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1260.7429756276834270.5140487446331470.257024372316573
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1280.7589604384243130.4820791231513750.241039561575687
1290.6720781582596810.6558436834806380.327921841740319
1300.619328850110380.761342299779240.38067114988962
1310.5195960027609210.9608079944781580.480403997239079
1320.9168024840293580.1663950319412850.0831975159706423
1330.8438355789601070.3123288420797860.156164421039893
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1350.7822872334246780.4354255331506450.217712766575322






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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109546&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109546&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109546&T=6

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

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


Figures (Output of Computation)

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Input Parameters & R Code

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