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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, 20 Nov 2012 13:49:36 -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/t13534374029x0cu7gaz35c6jz.htm/, Retrieved Mon, 29 Apr 2024 20:08:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191233, Retrieved Mon, 29 Apr 2024 20:08:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66

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]
-   PD  [Multiple Regression] [WS 7: Multiple Re...] [2012-11-20 17:39:44] [3175d908ed4615b229d48bd9d09ab12a]
-   PD      [Multiple Regression] [WS 7: Multiple Re...] [2012-11-20 18:49:36] [4d6e08f41f6a03e9e6e7c5dd2227ef0b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	21	21	23	17	23	4	14	12	127
9	20	16	15	24	17	20	4	18	11	108
9	19	19	18	22	18	20	6	11	14	110
9	19	18	11	20	21	21	8	12	12	102
9	20	16	8	24	20	24	8	16	21	104
9	25	23	19	27	28	22	4	18	12	140
9	25	17	4	28	19	23	4	14	22	112
9	22	12	20	27	22	20	8	14	11	115
9	26	19	16	24	16	25	5	15	10	121
9	22	16	14	23	18	23	4	15	13	112
9	17	19	10	24	25	27	4	17	10	118
9	22	20	13	27	17	27	4	19	8	122
9	19	13	14	27	14	22	4	10	15	105
9	24	20	8	28	11	24	4	16	14	111
9	26	27	23	27	27	25	4	18	10	151
9	21	17	11	23	20	22	8	14	14	106
9	13	8	9	24	22	28	4	14	14	100
9	26	25	24	28	22	28	4	17	11	149
9	20	26	5	27	21	27	4	14	10	122
9	22	13	15	25	23	25	8	16	13	115
9	14	19	5	19	17	16	4	18	7	86
9	21	15	19	24	24	28	7	11	14	124
9	7	5	6	20	14	21	4	14	12	69
9	23	16	13	28	17	24	4	12	14	117
9	17	14	11	26	23	27	5	17	11	113
9	25	24	17	23	24	14	4	9	9	123
9	25	24	17	23	24	14	4	16	11	123
9	19	9	5	20	8	27	4	14	15	84
9	20	19	9	11	22	20	4	15	14	97
9	23	19	15	24	23	21	4	11	13	121
9	22	25	17	25	25	22	4	16	9	132
9	22	19	17	23	21	21	4	13	15	119
9	21	18	20	18	24	12	15	17	10	98
9	15	15	12	20	15	20	10	15	11	87
9	20	12	7	20	22	24	4	14	13	101
9	22	21	16	24	21	19	8	16	8	115
9	18	12	7	23	25	28	4	9	20	109
9	20	15	14	25	16	23	4	15	12	109
9	28	28	24	28	28	27	4	17	10	159
9	22	25	15	26	23	22	4	13	10	129
9	18	19	15	26	21	27	7	15	9	119
9	23	20	10	23	21	26	4	16	14	119
9	20	24	14	22	26	22	6	16	8	122
9	25	26	18	24	22	21	5	12	14	131
9	26	25	12	21	21	19	4	12	11	120
9	15	12	9	20	18	24	16	11	13	82
9	17	12	9	22	12	19	5	15	9	86
9	23	15	8	20	25	26	12	15	11	105
9	21	17	18	25	17	22	6	17	15	114
9	13	14	10	20	24	28	9	13	11	100
9	18	16	17	22	15	21	9	16	10	100
9	19	11	14	23	13	23	4	14	14	99
9	22	20	16	25	26	28	5	11	18	132
9	16	11	10	23	16	10	4	12	14	82
9	24	22	19	23	24	24	4	12	11	132
9	18	20	10	22	21	21	5	15	12	107
9	20	19	14	24	20	21	4	16	13	114
9	24	17	10	25	14	24	4	15	9	110
9	14	21	4	21	25	24	4	12	10	105
9	22	23	19	12	25	25	5	12	15	121
9	24	18	9	17	20	25	4	8	20	109
9	18	17	12	20	22	23	6	13	12	106
9	21	27	16	23	20	21	4	11	12	124
9	23	25	11	23	26	16	4	14	14	120
9	17	19	18	20	18	17	18	15	13	91
10	22	22	11	28	22	25	4	10	11	126
10	24	24	24	24	24	24	6	11	17	138
10	21	20	17	24	17	23	4	12	12	118
10	22	19	18	24	24	25	4	15	13	128
10	16	11	9	24	20	23	5	15	14	98
10	21	22	19	28	19	28	4	14	13	133
10	23	22	18	25	20	26	4	16	15	130
10	22	16	12	21	15	22	5	15	13	103
10	24	20	23	25	23	19	10	15	10	124
10	24	24	22	25	26	26	5	13	11	142
10	16	16	14	18	22	18	8	12	19	96
10	16	16	14	17	20	18	8	17	13	93
10	21	22	16	26	24	25	5	13	17	129
10	26	24	23	28	26	27	4	15	13	150
10	15	16	7	21	21	12	4	13	9	88
10	25	27	10	27	25	15	4	15	11	125
10	18	11	12	22	13	21	5	16	10	92
10	23	21	12	21	20	23	4	15	9	0
10	20	20	12	25	22	22	4	16	12	117
10	17	20	17	22	23	21	8	15	12	112
10	25	27	21	23	28	24	4	14	13	144
10	24	20	16	26	22	27	5	15	13	130
10	17	12	11	19	20	22	14	14	12	87
10	19	8	14	25	6	28	8	13	15	92
10	20	21	13	21	21	26	8	7	22	114
10	15	18	9	13	20	10	4	17	13	81
10	27	24	19	24	18	19	4	13	15	127
10	22	16	13	25	23	22	6	15	13	115
10	23	18	19	26	20	21	4	14	15	123
10	16	20	13	25	24	24	7	13	10	115
10	19	20	13	25	22	25	7	16	11	117
10	25	19	13	22	21	21	4	12	16	117
10	19	17	14	21	18	20	6	14	11	103
10	19	16	12	23	21	21	4	17	11	108
10	26	26	22	25	23	24	7	15	10	139
10	21	15	11	24	23	23	4	17	10	113
10	20	22	5	21	15	18	4	12	16	97
10	24	17	18	21	21	24	8	16	12	117
10	22	23	19	25	24	24	4	11	11	133
10	20	21	14	22	23	19	4	15	16	115
10	18	19	15	20	21	20	10	9	19	103
10	18	14	12	20	21	18	8	16	11	95
10	24	17	19	23	20	20	6	15	16	117
10	24	12	15	28	11	27	4	10	15	113
10	22	24	17	23	22	23	4	10	24	127
10	23	18	8	28	27	26	4	15	14	126
10	22	20	10	24	25	23	5	11	15	119
10	20	16	12	18	18	17	4	13	11	97
10	18	20	12	20	20	21	6	14	15	105
10	25	22	20	28	24	25	4	18	12	140
10	18	12	12	21	10	23	5	16	10	91
10	16	16	12	21	27	27	7	14	14	112
10	20	17	14	25	21	24	8	14	13	113
10	19	22	6	19	21	20	5	14	9	102
10	15	12	10	18	18	27	8	14	15	92
10	19	14	18	21	15	21	10	12	15	98
10	19	23	18	22	24	24	8	14	14	122
10	16	15	7	24	22	21	5	15	11	100
10	17	17	18	15	14	15	12	15	8	84
10	28	28	9	28	28	25	4	15	11	142
10	23	20	17	26	18	25	5	13	11	124
10	25	23	22	23	26	22	4	17	8	137
10	20	13	11	26	17	24	6	17	10	105
10	17	18	15	20	19	21	4	19	11	106
10	23	23	17	22	22	22	4	15	13	125
10	16	19	15	20	18	23	7	13	11	104
10	23	23	22	23	24	22	7	9	20	130
10	11	12	9	22	15	20	10	15	10	79
10	18	16	13	24	18	23	4	15	15	108
10	24	23	20	23	26	25	5	15	12	136
10	23	13	14	22	11	23	8	16	14	98
10	21	22	14	26	26	22	11	11	23	120
10	16	18	12	23	21	25	7	14	14	108
10	24	23	20	27	23	26	4	11	16	139
10	23	20	20	23	23	22	8	15	11	123
10	18	10	8	21	15	24	6	13	12	90
10	20	17	17	26	22	24	7	15	10	119
10	9	18	9	23	26	25	5	16	14	105
10	24	15	18	21	16	20	4	14	12	110
10	25	23	22	27	20	26	8	15	12	135
10	20	17	10	19	18	21	4	16	11	101
10	21	17	13	23	22	26	8	16	12	114
10	25	22	15	25	16	21	6	11	13	118
10	22	20	18	23	19	22	4	12	11	120
10	21	20	18	22	20	16	9	9	19	108
10	21	19	12	22	19	26	5	16	12	114
10	22	18	12	25	23	28	6	13	17	122
10	27	22	20	25	24	18	4	16	9	132
9	24	20	12	28	25	25	4	12	12	130
10	24	22	16	28	21	23	4	9	19	130
10	21	18	16	20	21	21	5	13	18	112
10	18	16	18	25	23	20	6	13	15	114
10	16	16	16	19	27	25	16	14	14	103
10	22	16	13	25	23	22	6	19	11	115
10	20	16	17	22	18	21	6	13	9	108
10	18	17	13	18	16	16	4	12	18	94
11	20	18	17	20	16	18	4	13	16	105

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 12 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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 time12 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Motivatie [t] = + 9.14633507867205 -1.81476642609309Month[t] + 0.660426923209475I1[t] + 0.913320682573446I2[t] + 1.21857176661374I3[t] + 1.37907750059826E1[t] + 1.05887786115399E2[t] + 0.808400555186031E3[t] -0.904306609396609A[t] + 0.106491027159751Happiness[t] + 0.415498095627485Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Motivatie
[t] =  +  9.14633507867205 -1.81476642609309Month[t] +  0.660426923209475I1[t] +  0.913320682573446I2[t] +  1.21857176661374I3[t] +  1.37907750059826E1[t] +  1.05887786115399E2[t] +  0.808400555186031E3[t] -0.904306609396609A[t] +  0.106491027159751Happiness[t] +  0.415498095627485Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Motivatie
[t] =  +  9.14633507867205 -1.81476642609309Month[t] +  0.660426923209475I1[t] +  0.913320682573446I2[t] +  1.21857176661374I3[t] +  1.37907750059826E1[t] +  1.05887786115399E2[t] +  0.808400555186031E3[t] -0.904306609396609A[t] +  0.106491027159751Happiness[t] +  0.415498095627485Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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
Motivatie [t] = + 9.14633507867205 -1.81476642609309Month[t] + 0.660426923209475I1[t] + 0.913320682573446I2[t] + 1.21857176661374I3[t] + 1.37907750059826E1[t] + 1.05887786115399E2[t] + 0.808400555186031E3[t] -0.904306609396609A[t] + 0.106491027159751Happiness[t] + 0.415498095627485Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.1463350786720517.461240.52380.601180.30059
Month-1.814766426093091.493619-1.2150.2262580.113129
I10.6604269232094750.2998682.20240.0291540.014577
I20.9133206825734460.2586253.53150.0005480.000274
I31.218571766613740.2036725.98300
E11.379077500598260.287964.78914e-062e-06
E21.058877861153990.2206364.79924e-062e-06
E30.8084005551860310.2275343.55290.0005090.000254
A-0.9043066093966090.316114-2.86070.0048270.002413
Happiness0.1064910271597510.37540.28370.7770490.388525
Depression0.4154980956274850.2833331.46650.1446010.0723

\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) & 9.14633507867205 & 17.46124 & 0.5238 & 0.60118 & 0.30059 \tabularnewline
Month & -1.81476642609309 & 1.493619 & -1.215 & 0.226258 & 0.113129 \tabularnewline
I1 & 0.660426923209475 & 0.299868 & 2.2024 & 0.029154 & 0.014577 \tabularnewline
I2 & 0.913320682573446 & 0.258625 & 3.5315 & 0.000548 & 0.000274 \tabularnewline
I3 & 1.21857176661374 & 0.203672 & 5.983 & 0 & 0 \tabularnewline
E1 & 1.37907750059826 & 0.28796 & 4.7891 & 4e-06 & 2e-06 \tabularnewline
E2 & 1.05887786115399 & 0.220636 & 4.7992 & 4e-06 & 2e-06 \tabularnewline
E3 & 0.808400555186031 & 0.227534 & 3.5529 & 0.000509 & 0.000254 \tabularnewline
A & -0.904306609396609 & 0.316114 & -2.8607 & 0.004827 & 0.002413 \tabularnewline
Happiness & 0.106491027159751 & 0.3754 & 0.2837 & 0.777049 & 0.388525 \tabularnewline
Depression & 0.415498095627485 & 0.283333 & 1.4665 & 0.144601 & 0.0723 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&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]9.14633507867205[/C][C]17.46124[/C][C]0.5238[/C][C]0.60118[/C][C]0.30059[/C][/ROW]
[ROW][C]Month[/C][C]-1.81476642609309[/C][C]1.493619[/C][C]-1.215[/C][C]0.226258[/C][C]0.113129[/C][/ROW]
[ROW][C]I1[/C][C]0.660426923209475[/C][C]0.299868[/C][C]2.2024[/C][C]0.029154[/C][C]0.014577[/C][/ROW]
[ROW][C]I2[/C][C]0.913320682573446[/C][C]0.258625[/C][C]3.5315[/C][C]0.000548[/C][C]0.000274[/C][/ROW]
[ROW][C]I3[/C][C]1.21857176661374[/C][C]0.203672[/C][C]5.983[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]1.37907750059826[/C][C]0.28796[/C][C]4.7891[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E2[/C][C]1.05887786115399[/C][C]0.220636[/C][C]4.7992[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E3[/C][C]0.808400555186031[/C][C]0.227534[/C][C]3.5529[/C][C]0.000509[/C][C]0.000254[/C][/ROW]
[ROW][C]A[/C][C]-0.904306609396609[/C][C]0.316114[/C][C]-2.8607[/C][C]0.004827[/C][C]0.002413[/C][/ROW]
[ROW][C]Happiness[/C][C]0.106491027159751[/C][C]0.3754[/C][C]0.2837[/C][C]0.777049[/C][C]0.388525[/C][/ROW]
[ROW][C]Depression[/C][C]0.415498095627485[/C][C]0.283333[/C][C]1.4665[/C][C]0.144601[/C][C]0.0723[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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)9.1463350786720517.461240.52380.601180.30059
Month-1.814766426093091.493619-1.2150.2262580.113129
I10.6604269232094750.2998682.20240.0291540.014577
I20.9133206825734460.2586253.53150.0005480.000274
I31.218571766613740.2036725.98300
E11.379077500598260.287964.78914e-062e-06
E21.058877861153990.2206364.79924e-062e-06
E30.8084005551860310.2275343.55290.0005090.000254
A-0.9043066093966090.316114-2.86070.0048270.002413
Happiness0.1064910271597510.37540.28370.7770490.388525
Depression0.4154980956274850.2833331.46650.1446010.0723






Multiple Linear Regression - Regression Statistics
Multiple R0.874676366947763
R-squared0.765058746896938
Adjusted R-squared0.74949972351263
F-TEST (value)49.1713861468002
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.21102282747211
Sum Squared Residuals12811.2841707601

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.874676366947763 \tabularnewline
R-squared & 0.765058746896938 \tabularnewline
Adjusted R-squared & 0.74949972351263 \tabularnewline
F-TEST (value) & 49.1713861468002 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.21102282747211 \tabularnewline
Sum Squared Residuals & 12811.2841707601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.874676366947763[/C][/ROW]
[ROW][C]R-squared[/C][C]0.765058746896938[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.74949972351263[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]49.1713861468002[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/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]9.21102282747211[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12811.2841707601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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 R0.874676366947763
R-squared0.765058746896938
Adjusted R-squared0.74949972351263
F-TEST (value)49.1713861468002
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.21102282747211
Sum Squared Residuals12811.2841707601






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127125.9268226930481.07317730695217
2108109.050568989293-1.05056898929301
3110111.778986151574-1.77898615157352
4102101.0294238572670.97057614273303
5104107.255574891572-3.25557489157187
6140141.437423630298-1.43742363029765
7112114.065517188685-2.0655171886852
8115118.199430199826-3.19943019982613
9121118.3155114711572.68448852884325
10112111.7693762049880.23062379501224
11118117.3242290870630.675770912936877
12122120.2435951611531.75640483884743
13105107.819072445686-2.81907244568628
14111110.2457143349050.754285665095247
15151151.160747963405-0.160747963404976
16106106.367690462514-0.367690462513767
17100102.391708392058-2.39170839205834
18149149.371575293726-0.371575293725976
19122119.1881437773372.81185622266342
20115116.406595930793-1.40659593079299
218683.85126972537382.14873027462623
22124125.339449034525-1.3394490345245
236971.5563365360578-2.55633653605781
24117117.952166572755-0.952166572755087
25113114.127788205929-1.12778820592905
26123121.4896613598261.51033864017385
27123123.066094741199-0.0660947411993835
288481.65980102932632.34019897067372
299792.77250344072674.22749655927334
30121121.019038529888-0.0190385298884379
31132131.4513757667280.548624233271807
32119120.342900162575-1.34290016257467
339899.8316098675682-1.83160986756821
348787.8000199282711-0.800019928271051
3510199.06542573282631.93457426717368
36115114.5070061258340.49299387416555
37109110.668071726002-1.66807172600188
38109109.760102859256-0.760102859256429
39159158.5617597039760.438240296024335
40129128.3715790259390.628420974060756
41119119.258758421785-0.258758421785039
42119117.3326231638431.66737683615663
43122120.2590199828331.74098001716691
44131130.9476571063990.0523428936006416
45120116.2282335965463.77176640345371
468282.7937708111519-0.793770811151905
478685.18885414540510.811145854594933
48105101.8397169713553.16028302864525
49114117.022798642444-3.02279864244444
5010099.81713161300240.182868386997577
51100100.949335310853-0.949335310853034
5299100.23610978519-1.2361097851899
53132133.878202894969-1.8782028949694
548285.8349862608306-3.83498626083059
55132130.6742114292921.32578857070821
56107106.7676144434560.232385556543756
57114115.175007545983-1.17500754598307
58110106.7983153967823.20168460321812
59105102.7632697797482.23673022025246
60121117.7217899483193.27821005168125
61109106.447153891852.55284610815048
62106105.2650312355770.73496876442321
63124123.2521127309830.747887269016917
64120120.115200042914-0.115200042914454
659194.4335623386522-3.43356233865217
66126123.163067305422.83693269458026
67138138.735907029824-0.735907029824474
68118116.1884093481581.81159065184228
69128126.9180046709641.0819953290358
709898.340610702839-0.340610702839021
71133132.756742897110.24325710288975
72130129.2078474714770.792152528522924
7310399.86997049642613.13002950357393
74124124.042500398091-0.0425003980912473
75142140.0366979198271.9633020801735
769697.8464584600315-1.84645846003152
779392.38909179915910.610908200840869
78129129.863254982238-0.863254982237941
79150149.4700414448110.529958555188918
808886.45271698852181.54728301147815
81125122.7383853881042.2616146118962
829289.97457735410252.0254226458975
830110.442104920345-110.442104920345
84117115.7261539517011.27384604829926
85112112.227259354568-0.227259354567974
86144141.8031085626932.1968914373067
87130128.0679294465931.93207055340688
888785.57146708068791.42853291931213
899291.76117469130660.238825308693456
90114114.095796032336-0.0957960323356651
918179.0961594017331.90384059826699
92127125.4196581053881.5803418946121
93115114.1715685452680.828431454731858
94123123.897229178146-0.897229178145628
95115114.1741857572330.825814242767441
96117115.5810825368461.41891746315422
97117114.5650570075252.43494299247546
98103100.9571925878772.04280741212287
99108106.4780038121931.5219961878068
100139136.3796291774572.62037082254288
101113110.3651014470762.63489855292421
1029794.09676397320732.90323602679266
103117114.3637170057392.6362829942607
104133131.103575813391.89642418661003
105115115.128563216653-0.128563216652951
106103104.313838070927-1.31383807092667
1079593.70478389173781.29521610826223
108117117.412078265795-0.41207826579486
109113111.8561384127471.14386158725269
110127129.190425900043-2.19042590004297
111126124.3962354821311.60376451786861
112119117.0255534446291.97444655537096
1139793.40684331956633.59315668043372
114105103.8086553385661.19134466143379
115140139.4966760097850.503323990214753
1169187.94898806298783.05101193701222
117112111.1563399166220.843660083378263
118113112.5665482901470.433451709852506
11910296.26701086824165.73298913175845
1209290.24954496394121.75045503605879
1219898.5550684689398-0.555068468939809
122122121.7152317061280.284768293871702
12310098.81121022495881.18878977504121
1248481.39280366767512.60719633232491
125142137.0541317096124.94586829038753
126124121.7297834894352.2702165105651
127137133.8758233696753.12417663032496
128105103.2827083293851.71729167061516
129106104.5975004609791.40249953902091
130125122.7120301687912.28796983120887
131104102.0564514784131.94354852158719
132130131.85834290301-1.8583429030101
1337979.2905253673476-0.290525367347554
134108108.304404011296-0.304404011296267
135136133.748158297592.25184170240998
1369895.98861481090842.01138518909156
137120123.972832368826-3.97283236882611
138108107.7710680056690.228931994330829
139139139.036570154975-0.0365701549746791
140123121.6175161538231.38248384617716
1419086.9580659927213.04193400727898
142119118.4245223014150.575477698585144
143105106.808348822244-1.80834882224429
144110107.4133284973452.58667150265472
145135134.1042523164920.895747683507705
14610196.81517327173394.18482672826611
147114111.7234113757652.27658862423522
148118115.4234072516622.57659274833823
149120117.5821876088772.41781239112287
150108110.234136351665-2.23413635166477
151114110.4886897184283.51131028157247
152122121.0790518031080.920948196892075
153132129.5620171269052.43798287309477
154130129.4957317128080.504268287192434
155130131.118594751241-1.11859475124144
156112111.9407695397760.059230460224019
157114116.623932712062-2.62393271206233
158103103.516911386939-0.516911386938614
159115113.7665364626521.23346353734782
160108105.6100049657242.38999503427606
1619494.0937212870732-0.0937212870731477
162105103.0378674039011.96213259609924

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 125.926822693048 & 1.07317730695217 \tabularnewline
2 & 108 & 109.050568989293 & -1.05056898929301 \tabularnewline
3 & 110 & 111.778986151574 & -1.77898615157352 \tabularnewline
4 & 102 & 101.029423857267 & 0.97057614273303 \tabularnewline
5 & 104 & 107.255574891572 & -3.25557489157187 \tabularnewline
6 & 140 & 141.437423630298 & -1.43742363029765 \tabularnewline
7 & 112 & 114.065517188685 & -2.0655171886852 \tabularnewline
8 & 115 & 118.199430199826 & -3.19943019982613 \tabularnewline
9 & 121 & 118.315511471157 & 2.68448852884325 \tabularnewline
10 & 112 & 111.769376204988 & 0.23062379501224 \tabularnewline
11 & 118 & 117.324229087063 & 0.675770912936877 \tabularnewline
12 & 122 & 120.243595161153 & 1.75640483884743 \tabularnewline
13 & 105 & 107.819072445686 & -2.81907244568628 \tabularnewline
14 & 111 & 110.245714334905 & 0.754285665095247 \tabularnewline
15 & 151 & 151.160747963405 & -0.160747963404976 \tabularnewline
16 & 106 & 106.367690462514 & -0.367690462513767 \tabularnewline
17 & 100 & 102.391708392058 & -2.39170839205834 \tabularnewline
18 & 149 & 149.371575293726 & -0.371575293725976 \tabularnewline
19 & 122 & 119.188143777337 & 2.81185622266342 \tabularnewline
20 & 115 & 116.406595930793 & -1.40659593079299 \tabularnewline
21 & 86 & 83.8512697253738 & 2.14873027462623 \tabularnewline
22 & 124 & 125.339449034525 & -1.3394490345245 \tabularnewline
23 & 69 & 71.5563365360578 & -2.55633653605781 \tabularnewline
24 & 117 & 117.952166572755 & -0.952166572755087 \tabularnewline
25 & 113 & 114.127788205929 & -1.12778820592905 \tabularnewline
26 & 123 & 121.489661359826 & 1.51033864017385 \tabularnewline
27 & 123 & 123.066094741199 & -0.0660947411993835 \tabularnewline
28 & 84 & 81.6598010293263 & 2.34019897067372 \tabularnewline
29 & 97 & 92.7725034407267 & 4.22749655927334 \tabularnewline
30 & 121 & 121.019038529888 & -0.0190385298884379 \tabularnewline
31 & 132 & 131.451375766728 & 0.548624233271807 \tabularnewline
32 & 119 & 120.342900162575 & -1.34290016257467 \tabularnewline
33 & 98 & 99.8316098675682 & -1.83160986756821 \tabularnewline
34 & 87 & 87.8000199282711 & -0.800019928271051 \tabularnewline
35 & 101 & 99.0654257328263 & 1.93457426717368 \tabularnewline
36 & 115 & 114.507006125834 & 0.49299387416555 \tabularnewline
37 & 109 & 110.668071726002 & -1.66807172600188 \tabularnewline
38 & 109 & 109.760102859256 & -0.760102859256429 \tabularnewline
39 & 159 & 158.561759703976 & 0.438240296024335 \tabularnewline
40 & 129 & 128.371579025939 & 0.628420974060756 \tabularnewline
41 & 119 & 119.258758421785 & -0.258758421785039 \tabularnewline
42 & 119 & 117.332623163843 & 1.66737683615663 \tabularnewline
43 & 122 & 120.259019982833 & 1.74098001716691 \tabularnewline
44 & 131 & 130.947657106399 & 0.0523428936006416 \tabularnewline
45 & 120 & 116.228233596546 & 3.77176640345371 \tabularnewline
46 & 82 & 82.7937708111519 & -0.793770811151905 \tabularnewline
47 & 86 & 85.1888541454051 & 0.811145854594933 \tabularnewline
48 & 105 & 101.839716971355 & 3.16028302864525 \tabularnewline
49 & 114 & 117.022798642444 & -3.02279864244444 \tabularnewline
50 & 100 & 99.8171316130024 & 0.182868386997577 \tabularnewline
51 & 100 & 100.949335310853 & -0.949335310853034 \tabularnewline
52 & 99 & 100.23610978519 & -1.2361097851899 \tabularnewline
53 & 132 & 133.878202894969 & -1.8782028949694 \tabularnewline
54 & 82 & 85.8349862608306 & -3.83498626083059 \tabularnewline
55 & 132 & 130.674211429292 & 1.32578857070821 \tabularnewline
56 & 107 & 106.767614443456 & 0.232385556543756 \tabularnewline
57 & 114 & 115.175007545983 & -1.17500754598307 \tabularnewline
58 & 110 & 106.798315396782 & 3.20168460321812 \tabularnewline
59 & 105 & 102.763269779748 & 2.23673022025246 \tabularnewline
60 & 121 & 117.721789948319 & 3.27821005168125 \tabularnewline
61 & 109 & 106.44715389185 & 2.55284610815048 \tabularnewline
62 & 106 & 105.265031235577 & 0.73496876442321 \tabularnewline
63 & 124 & 123.252112730983 & 0.747887269016917 \tabularnewline
64 & 120 & 120.115200042914 & -0.115200042914454 \tabularnewline
65 & 91 & 94.4335623386522 & -3.43356233865217 \tabularnewline
66 & 126 & 123.16306730542 & 2.83693269458026 \tabularnewline
67 & 138 & 138.735907029824 & -0.735907029824474 \tabularnewline
68 & 118 & 116.188409348158 & 1.81159065184228 \tabularnewline
69 & 128 & 126.918004670964 & 1.0819953290358 \tabularnewline
70 & 98 & 98.340610702839 & -0.340610702839021 \tabularnewline
71 & 133 & 132.75674289711 & 0.24325710288975 \tabularnewline
72 & 130 & 129.207847471477 & 0.792152528522924 \tabularnewline
73 & 103 & 99.8699704964261 & 3.13002950357393 \tabularnewline
74 & 124 & 124.042500398091 & -0.0425003980912473 \tabularnewline
75 & 142 & 140.036697919827 & 1.9633020801735 \tabularnewline
76 & 96 & 97.8464584600315 & -1.84645846003152 \tabularnewline
77 & 93 & 92.3890917991591 & 0.610908200840869 \tabularnewline
78 & 129 & 129.863254982238 & -0.863254982237941 \tabularnewline
79 & 150 & 149.470041444811 & 0.529958555188918 \tabularnewline
80 & 88 & 86.4527169885218 & 1.54728301147815 \tabularnewline
81 & 125 & 122.738385388104 & 2.2616146118962 \tabularnewline
82 & 92 & 89.9745773541025 & 2.0254226458975 \tabularnewline
83 & 0 & 110.442104920345 & -110.442104920345 \tabularnewline
84 & 117 & 115.726153951701 & 1.27384604829926 \tabularnewline
85 & 112 & 112.227259354568 & -0.227259354567974 \tabularnewline
86 & 144 & 141.803108562693 & 2.1968914373067 \tabularnewline
87 & 130 & 128.067929446593 & 1.93207055340688 \tabularnewline
88 & 87 & 85.5714670806879 & 1.42853291931213 \tabularnewline
89 & 92 & 91.7611746913066 & 0.238825308693456 \tabularnewline
90 & 114 & 114.095796032336 & -0.0957960323356651 \tabularnewline
91 & 81 & 79.096159401733 & 1.90384059826699 \tabularnewline
92 & 127 & 125.419658105388 & 1.5803418946121 \tabularnewline
93 & 115 & 114.171568545268 & 0.828431454731858 \tabularnewline
94 & 123 & 123.897229178146 & -0.897229178145628 \tabularnewline
95 & 115 & 114.174185757233 & 0.825814242767441 \tabularnewline
96 & 117 & 115.581082536846 & 1.41891746315422 \tabularnewline
97 & 117 & 114.565057007525 & 2.43494299247546 \tabularnewline
98 & 103 & 100.957192587877 & 2.04280741212287 \tabularnewline
99 & 108 & 106.478003812193 & 1.5219961878068 \tabularnewline
100 & 139 & 136.379629177457 & 2.62037082254288 \tabularnewline
101 & 113 & 110.365101447076 & 2.63489855292421 \tabularnewline
102 & 97 & 94.0967639732073 & 2.90323602679266 \tabularnewline
103 & 117 & 114.363717005739 & 2.6362829942607 \tabularnewline
104 & 133 & 131.10357581339 & 1.89642418661003 \tabularnewline
105 & 115 & 115.128563216653 & -0.128563216652951 \tabularnewline
106 & 103 & 104.313838070927 & -1.31383807092667 \tabularnewline
107 & 95 & 93.7047838917378 & 1.29521610826223 \tabularnewline
108 & 117 & 117.412078265795 & -0.41207826579486 \tabularnewline
109 & 113 & 111.856138412747 & 1.14386158725269 \tabularnewline
110 & 127 & 129.190425900043 & -2.19042590004297 \tabularnewline
111 & 126 & 124.396235482131 & 1.60376451786861 \tabularnewline
112 & 119 & 117.025553444629 & 1.97444655537096 \tabularnewline
113 & 97 & 93.4068433195663 & 3.59315668043372 \tabularnewline
114 & 105 & 103.808655338566 & 1.19134466143379 \tabularnewline
115 & 140 & 139.496676009785 & 0.503323990214753 \tabularnewline
116 & 91 & 87.9489880629878 & 3.05101193701222 \tabularnewline
117 & 112 & 111.156339916622 & 0.843660083378263 \tabularnewline
118 & 113 & 112.566548290147 & 0.433451709852506 \tabularnewline
119 & 102 & 96.2670108682416 & 5.73298913175845 \tabularnewline
120 & 92 & 90.2495449639412 & 1.75045503605879 \tabularnewline
121 & 98 & 98.5550684689398 & -0.555068468939809 \tabularnewline
122 & 122 & 121.715231706128 & 0.284768293871702 \tabularnewline
123 & 100 & 98.8112102249588 & 1.18878977504121 \tabularnewline
124 & 84 & 81.3928036676751 & 2.60719633232491 \tabularnewline
125 & 142 & 137.054131709612 & 4.94586829038753 \tabularnewline
126 & 124 & 121.729783489435 & 2.2702165105651 \tabularnewline
127 & 137 & 133.875823369675 & 3.12417663032496 \tabularnewline
128 & 105 & 103.282708329385 & 1.71729167061516 \tabularnewline
129 & 106 & 104.597500460979 & 1.40249953902091 \tabularnewline
130 & 125 & 122.712030168791 & 2.28796983120887 \tabularnewline
131 & 104 & 102.056451478413 & 1.94354852158719 \tabularnewline
132 & 130 & 131.85834290301 & -1.8583429030101 \tabularnewline
133 & 79 & 79.2905253673476 & -0.290525367347554 \tabularnewline
134 & 108 & 108.304404011296 & -0.304404011296267 \tabularnewline
135 & 136 & 133.74815829759 & 2.25184170240998 \tabularnewline
136 & 98 & 95.9886148109084 & 2.01138518909156 \tabularnewline
137 & 120 & 123.972832368826 & -3.97283236882611 \tabularnewline
138 & 108 & 107.771068005669 & 0.228931994330829 \tabularnewline
139 & 139 & 139.036570154975 & -0.0365701549746791 \tabularnewline
140 & 123 & 121.617516153823 & 1.38248384617716 \tabularnewline
141 & 90 & 86.958065992721 & 3.04193400727898 \tabularnewline
142 & 119 & 118.424522301415 & 0.575477698585144 \tabularnewline
143 & 105 & 106.808348822244 & -1.80834882224429 \tabularnewline
144 & 110 & 107.413328497345 & 2.58667150265472 \tabularnewline
145 & 135 & 134.104252316492 & 0.895747683507705 \tabularnewline
146 & 101 & 96.8151732717339 & 4.18482672826611 \tabularnewline
147 & 114 & 111.723411375765 & 2.27658862423522 \tabularnewline
148 & 118 & 115.423407251662 & 2.57659274833823 \tabularnewline
149 & 120 & 117.582187608877 & 2.41781239112287 \tabularnewline
150 & 108 & 110.234136351665 & -2.23413635166477 \tabularnewline
151 & 114 & 110.488689718428 & 3.51131028157247 \tabularnewline
152 & 122 & 121.079051803108 & 0.920948196892075 \tabularnewline
153 & 132 & 129.562017126905 & 2.43798287309477 \tabularnewline
154 & 130 & 129.495731712808 & 0.504268287192434 \tabularnewline
155 & 130 & 131.118594751241 & -1.11859475124144 \tabularnewline
156 & 112 & 111.940769539776 & 0.059230460224019 \tabularnewline
157 & 114 & 116.623932712062 & -2.62393271206233 \tabularnewline
158 & 103 & 103.516911386939 & -0.516911386938614 \tabularnewline
159 & 115 & 113.766536462652 & 1.23346353734782 \tabularnewline
160 & 108 & 105.610004965724 & 2.38999503427606 \tabularnewline
161 & 94 & 94.0937212870732 & -0.0937212870731477 \tabularnewline
162 & 105 & 103.037867403901 & 1.96213259609924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]127[/C][C]125.926822693048[/C][C]1.07317730695217[/C][/ROW]
[ROW][C]2[/C][C]108[/C][C]109.050568989293[/C][C]-1.05056898929301[/C][/ROW]
[ROW][C]3[/C][C]110[/C][C]111.778986151574[/C][C]-1.77898615157352[/C][/ROW]
[ROW][C]4[/C][C]102[/C][C]101.029423857267[/C][C]0.97057614273303[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]107.255574891572[/C][C]-3.25557489157187[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]141.437423630298[/C][C]-1.43742363029765[/C][/ROW]
[ROW][C]7[/C][C]112[/C][C]114.065517188685[/C][C]-2.0655171886852[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]118.199430199826[/C][C]-3.19943019982613[/C][/ROW]
[ROW][C]9[/C][C]121[/C][C]118.315511471157[/C][C]2.68448852884325[/C][/ROW]
[ROW][C]10[/C][C]112[/C][C]111.769376204988[/C][C]0.23062379501224[/C][/ROW]
[ROW][C]11[/C][C]118[/C][C]117.324229087063[/C][C]0.675770912936877[/C][/ROW]
[ROW][C]12[/C][C]122[/C][C]120.243595161153[/C][C]1.75640483884743[/C][/ROW]
[ROW][C]13[/C][C]105[/C][C]107.819072445686[/C][C]-2.81907244568628[/C][/ROW]
[ROW][C]14[/C][C]111[/C][C]110.245714334905[/C][C]0.754285665095247[/C][/ROW]
[ROW][C]15[/C][C]151[/C][C]151.160747963405[/C][C]-0.160747963404976[/C][/ROW]
[ROW][C]16[/C][C]106[/C][C]106.367690462514[/C][C]-0.367690462513767[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]102.391708392058[/C][C]-2.39170839205834[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]149.371575293726[/C][C]-0.371575293725976[/C][/ROW]
[ROW][C]19[/C][C]122[/C][C]119.188143777337[/C][C]2.81185622266342[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]116.406595930793[/C][C]-1.40659593079299[/C][/ROW]
[ROW][C]21[/C][C]86[/C][C]83.8512697253738[/C][C]2.14873027462623[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]125.339449034525[/C][C]-1.3394490345245[/C][/ROW]
[ROW][C]23[/C][C]69[/C][C]71.5563365360578[/C][C]-2.55633653605781[/C][/ROW]
[ROW][C]24[/C][C]117[/C][C]117.952166572755[/C][C]-0.952166572755087[/C][/ROW]
[ROW][C]25[/C][C]113[/C][C]114.127788205929[/C][C]-1.12778820592905[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]121.489661359826[/C][C]1.51033864017385[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]123.066094741199[/C][C]-0.0660947411993835[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]81.6598010293263[/C][C]2.34019897067372[/C][/ROW]
[ROW][C]29[/C][C]97[/C][C]92.7725034407267[/C][C]4.22749655927334[/C][/ROW]
[ROW][C]30[/C][C]121[/C][C]121.019038529888[/C][C]-0.0190385298884379[/C][/ROW]
[ROW][C]31[/C][C]132[/C][C]131.451375766728[/C][C]0.548624233271807[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]120.342900162575[/C][C]-1.34290016257467[/C][/ROW]
[ROW][C]33[/C][C]98[/C][C]99.8316098675682[/C][C]-1.83160986756821[/C][/ROW]
[ROW][C]34[/C][C]87[/C][C]87.8000199282711[/C][C]-0.800019928271051[/C][/ROW]
[ROW][C]35[/C][C]101[/C][C]99.0654257328263[/C][C]1.93457426717368[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]114.507006125834[/C][C]0.49299387416555[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]110.668071726002[/C][C]-1.66807172600188[/C][/ROW]
[ROW][C]38[/C][C]109[/C][C]109.760102859256[/C][C]-0.760102859256429[/C][/ROW]
[ROW][C]39[/C][C]159[/C][C]158.561759703976[/C][C]0.438240296024335[/C][/ROW]
[ROW][C]40[/C][C]129[/C][C]128.371579025939[/C][C]0.628420974060756[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]119.258758421785[/C][C]-0.258758421785039[/C][/ROW]
[ROW][C]42[/C][C]119[/C][C]117.332623163843[/C][C]1.66737683615663[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]120.259019982833[/C][C]1.74098001716691[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]130.947657106399[/C][C]0.0523428936006416[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]116.228233596546[/C][C]3.77176640345371[/C][/ROW]
[ROW][C]46[/C][C]82[/C][C]82.7937708111519[/C][C]-0.793770811151905[/C][/ROW]
[ROW][C]47[/C][C]86[/C][C]85.1888541454051[/C][C]0.811145854594933[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]101.839716971355[/C][C]3.16028302864525[/C][/ROW]
[ROW][C]49[/C][C]114[/C][C]117.022798642444[/C][C]-3.02279864244444[/C][/ROW]
[ROW][C]50[/C][C]100[/C][C]99.8171316130024[/C][C]0.182868386997577[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]100.949335310853[/C][C]-0.949335310853034[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]100.23610978519[/C][C]-1.2361097851899[/C][/ROW]
[ROW][C]53[/C][C]132[/C][C]133.878202894969[/C][C]-1.8782028949694[/C][/ROW]
[ROW][C]54[/C][C]82[/C][C]85.8349862608306[/C][C]-3.83498626083059[/C][/ROW]
[ROW][C]55[/C][C]132[/C][C]130.674211429292[/C][C]1.32578857070821[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]106.767614443456[/C][C]0.232385556543756[/C][/ROW]
[ROW][C]57[/C][C]114[/C][C]115.175007545983[/C][C]-1.17500754598307[/C][/ROW]
[ROW][C]58[/C][C]110[/C][C]106.798315396782[/C][C]3.20168460321812[/C][/ROW]
[ROW][C]59[/C][C]105[/C][C]102.763269779748[/C][C]2.23673022025246[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]117.721789948319[/C][C]3.27821005168125[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]106.44715389185[/C][C]2.55284610815048[/C][/ROW]
[ROW][C]62[/C][C]106[/C][C]105.265031235577[/C][C]0.73496876442321[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]123.252112730983[/C][C]0.747887269016917[/C][/ROW]
[ROW][C]64[/C][C]120[/C][C]120.115200042914[/C][C]-0.115200042914454[/C][/ROW]
[ROW][C]65[/C][C]91[/C][C]94.4335623386522[/C][C]-3.43356233865217[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]123.16306730542[/C][C]2.83693269458026[/C][/ROW]
[ROW][C]67[/C][C]138[/C][C]138.735907029824[/C][C]-0.735907029824474[/C][/ROW]
[ROW][C]68[/C][C]118[/C][C]116.188409348158[/C][C]1.81159065184228[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]126.918004670964[/C][C]1.0819953290358[/C][/ROW]
[ROW][C]70[/C][C]98[/C][C]98.340610702839[/C][C]-0.340610702839021[/C][/ROW]
[ROW][C]71[/C][C]133[/C][C]132.75674289711[/C][C]0.24325710288975[/C][/ROW]
[ROW][C]72[/C][C]130[/C][C]129.207847471477[/C][C]0.792152528522924[/C][/ROW]
[ROW][C]73[/C][C]103[/C][C]99.8699704964261[/C][C]3.13002950357393[/C][/ROW]
[ROW][C]74[/C][C]124[/C][C]124.042500398091[/C][C]-0.0425003980912473[/C][/ROW]
[ROW][C]75[/C][C]142[/C][C]140.036697919827[/C][C]1.9633020801735[/C][/ROW]
[ROW][C]76[/C][C]96[/C][C]97.8464584600315[/C][C]-1.84645846003152[/C][/ROW]
[ROW][C]77[/C][C]93[/C][C]92.3890917991591[/C][C]0.610908200840869[/C][/ROW]
[ROW][C]78[/C][C]129[/C][C]129.863254982238[/C][C]-0.863254982237941[/C][/ROW]
[ROW][C]79[/C][C]150[/C][C]149.470041444811[/C][C]0.529958555188918[/C][/ROW]
[ROW][C]80[/C][C]88[/C][C]86.4527169885218[/C][C]1.54728301147815[/C][/ROW]
[ROW][C]81[/C][C]125[/C][C]122.738385388104[/C][C]2.2616146118962[/C][/ROW]
[ROW][C]82[/C][C]92[/C][C]89.9745773541025[/C][C]2.0254226458975[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]110.442104920345[/C][C]-110.442104920345[/C][/ROW]
[ROW][C]84[/C][C]117[/C][C]115.726153951701[/C][C]1.27384604829926[/C][/ROW]
[ROW][C]85[/C][C]112[/C][C]112.227259354568[/C][C]-0.227259354567974[/C][/ROW]
[ROW][C]86[/C][C]144[/C][C]141.803108562693[/C][C]2.1968914373067[/C][/ROW]
[ROW][C]87[/C][C]130[/C][C]128.067929446593[/C][C]1.93207055340688[/C][/ROW]
[ROW][C]88[/C][C]87[/C][C]85.5714670806879[/C][C]1.42853291931213[/C][/ROW]
[ROW][C]89[/C][C]92[/C][C]91.7611746913066[/C][C]0.238825308693456[/C][/ROW]
[ROW][C]90[/C][C]114[/C][C]114.095796032336[/C][C]-0.0957960323356651[/C][/ROW]
[ROW][C]91[/C][C]81[/C][C]79.096159401733[/C][C]1.90384059826699[/C][/ROW]
[ROW][C]92[/C][C]127[/C][C]125.419658105388[/C][C]1.5803418946121[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]114.171568545268[/C][C]0.828431454731858[/C][/ROW]
[ROW][C]94[/C][C]123[/C][C]123.897229178146[/C][C]-0.897229178145628[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]114.174185757233[/C][C]0.825814242767441[/C][/ROW]
[ROW][C]96[/C][C]117[/C][C]115.581082536846[/C][C]1.41891746315422[/C][/ROW]
[ROW][C]97[/C][C]117[/C][C]114.565057007525[/C][C]2.43494299247546[/C][/ROW]
[ROW][C]98[/C][C]103[/C][C]100.957192587877[/C][C]2.04280741212287[/C][/ROW]
[ROW][C]99[/C][C]108[/C][C]106.478003812193[/C][C]1.5219961878068[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]136.379629177457[/C][C]2.62037082254288[/C][/ROW]
[ROW][C]101[/C][C]113[/C][C]110.365101447076[/C][C]2.63489855292421[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]94.0967639732073[/C][C]2.90323602679266[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]114.363717005739[/C][C]2.6362829942607[/C][/ROW]
[ROW][C]104[/C][C]133[/C][C]131.10357581339[/C][C]1.89642418661003[/C][/ROW]
[ROW][C]105[/C][C]115[/C][C]115.128563216653[/C][C]-0.128563216652951[/C][/ROW]
[ROW][C]106[/C][C]103[/C][C]104.313838070927[/C][C]-1.31383807092667[/C][/ROW]
[ROW][C]107[/C][C]95[/C][C]93.7047838917378[/C][C]1.29521610826223[/C][/ROW]
[ROW][C]108[/C][C]117[/C][C]117.412078265795[/C][C]-0.41207826579486[/C][/ROW]
[ROW][C]109[/C][C]113[/C][C]111.856138412747[/C][C]1.14386158725269[/C][/ROW]
[ROW][C]110[/C][C]127[/C][C]129.190425900043[/C][C]-2.19042590004297[/C][/ROW]
[ROW][C]111[/C][C]126[/C][C]124.396235482131[/C][C]1.60376451786861[/C][/ROW]
[ROW][C]112[/C][C]119[/C][C]117.025553444629[/C][C]1.97444655537096[/C][/ROW]
[ROW][C]113[/C][C]97[/C][C]93.4068433195663[/C][C]3.59315668043372[/C][/ROW]
[ROW][C]114[/C][C]105[/C][C]103.808655338566[/C][C]1.19134466143379[/C][/ROW]
[ROW][C]115[/C][C]140[/C][C]139.496676009785[/C][C]0.503323990214753[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]87.9489880629878[/C][C]3.05101193701222[/C][/ROW]
[ROW][C]117[/C][C]112[/C][C]111.156339916622[/C][C]0.843660083378263[/C][/ROW]
[ROW][C]118[/C][C]113[/C][C]112.566548290147[/C][C]0.433451709852506[/C][/ROW]
[ROW][C]119[/C][C]102[/C][C]96.2670108682416[/C][C]5.73298913175845[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]90.2495449639412[/C][C]1.75045503605879[/C][/ROW]
[ROW][C]121[/C][C]98[/C][C]98.5550684689398[/C][C]-0.555068468939809[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]121.715231706128[/C][C]0.284768293871702[/C][/ROW]
[ROW][C]123[/C][C]100[/C][C]98.8112102249588[/C][C]1.18878977504121[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]81.3928036676751[/C][C]2.60719633232491[/C][/ROW]
[ROW][C]125[/C][C]142[/C][C]137.054131709612[/C][C]4.94586829038753[/C][/ROW]
[ROW][C]126[/C][C]124[/C][C]121.729783489435[/C][C]2.2702165105651[/C][/ROW]
[ROW][C]127[/C][C]137[/C][C]133.875823369675[/C][C]3.12417663032496[/C][/ROW]
[ROW][C]128[/C][C]105[/C][C]103.282708329385[/C][C]1.71729167061516[/C][/ROW]
[ROW][C]129[/C][C]106[/C][C]104.597500460979[/C][C]1.40249953902091[/C][/ROW]
[ROW][C]130[/C][C]125[/C][C]122.712030168791[/C][C]2.28796983120887[/C][/ROW]
[ROW][C]131[/C][C]104[/C][C]102.056451478413[/C][C]1.94354852158719[/C][/ROW]
[ROW][C]132[/C][C]130[/C][C]131.85834290301[/C][C]-1.8583429030101[/C][/ROW]
[ROW][C]133[/C][C]79[/C][C]79.2905253673476[/C][C]-0.290525367347554[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]108.304404011296[/C][C]-0.304404011296267[/C][/ROW]
[ROW][C]135[/C][C]136[/C][C]133.74815829759[/C][C]2.25184170240998[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]95.9886148109084[/C][C]2.01138518909156[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]123.972832368826[/C][C]-3.97283236882611[/C][/ROW]
[ROW][C]138[/C][C]108[/C][C]107.771068005669[/C][C]0.228931994330829[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]139.036570154975[/C][C]-0.0365701549746791[/C][/ROW]
[ROW][C]140[/C][C]123[/C][C]121.617516153823[/C][C]1.38248384617716[/C][/ROW]
[ROW][C]141[/C][C]90[/C][C]86.958065992721[/C][C]3.04193400727898[/C][/ROW]
[ROW][C]142[/C][C]119[/C][C]118.424522301415[/C][C]0.575477698585144[/C][/ROW]
[ROW][C]143[/C][C]105[/C][C]106.808348822244[/C][C]-1.80834882224429[/C][/ROW]
[ROW][C]144[/C][C]110[/C][C]107.413328497345[/C][C]2.58667150265472[/C][/ROW]
[ROW][C]145[/C][C]135[/C][C]134.104252316492[/C][C]0.895747683507705[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]96.8151732717339[/C][C]4.18482672826611[/C][/ROW]
[ROW][C]147[/C][C]114[/C][C]111.723411375765[/C][C]2.27658862423522[/C][/ROW]
[ROW][C]148[/C][C]118[/C][C]115.423407251662[/C][C]2.57659274833823[/C][/ROW]
[ROW][C]149[/C][C]120[/C][C]117.582187608877[/C][C]2.41781239112287[/C][/ROW]
[ROW][C]150[/C][C]108[/C][C]110.234136351665[/C][C]-2.23413635166477[/C][/ROW]
[ROW][C]151[/C][C]114[/C][C]110.488689718428[/C][C]3.51131028157247[/C][/ROW]
[ROW][C]152[/C][C]122[/C][C]121.079051803108[/C][C]0.920948196892075[/C][/ROW]
[ROW][C]153[/C][C]132[/C][C]129.562017126905[/C][C]2.43798287309477[/C][/ROW]
[ROW][C]154[/C][C]130[/C][C]129.495731712808[/C][C]0.504268287192434[/C][/ROW]
[ROW][C]155[/C][C]130[/C][C]131.118594751241[/C][C]-1.11859475124144[/C][/ROW]
[ROW][C]156[/C][C]112[/C][C]111.940769539776[/C][C]0.059230460224019[/C][/ROW]
[ROW][C]157[/C][C]114[/C][C]116.623932712062[/C][C]-2.62393271206233[/C][/ROW]
[ROW][C]158[/C][C]103[/C][C]103.516911386939[/C][C]-0.516911386938614[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]113.766536462652[/C][C]1.23346353734782[/C][/ROW]
[ROW][C]160[/C][C]108[/C][C]105.610004965724[/C][C]2.38999503427606[/C][/ROW]
[ROW][C]161[/C][C]94[/C][C]94.0937212870732[/C][C]-0.0937212870731477[/C][/ROW]
[ROW][C]162[/C][C]105[/C][C]103.037867403901[/C][C]1.96213259609924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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
1127125.9268226930481.07317730695217
2108109.050568989293-1.05056898929301
3110111.778986151574-1.77898615157352
4102101.0294238572670.97057614273303
5104107.255574891572-3.25557489157187
6140141.437423630298-1.43742363029765
7112114.065517188685-2.0655171886852
8115118.199430199826-3.19943019982613
9121118.3155114711572.68448852884325
10112111.7693762049880.23062379501224
11118117.3242290870630.675770912936877
12122120.2435951611531.75640483884743
13105107.819072445686-2.81907244568628
14111110.2457143349050.754285665095247
15151151.160747963405-0.160747963404976
16106106.367690462514-0.367690462513767
17100102.391708392058-2.39170839205834
18149149.371575293726-0.371575293725976
19122119.1881437773372.81185622266342
20115116.406595930793-1.40659593079299
218683.85126972537382.14873027462623
22124125.339449034525-1.3394490345245
236971.5563365360578-2.55633653605781
24117117.952166572755-0.952166572755087
25113114.127788205929-1.12778820592905
26123121.4896613598261.51033864017385
27123123.066094741199-0.0660947411993835
288481.65980102932632.34019897067372
299792.77250344072674.22749655927334
30121121.019038529888-0.0190385298884379
31132131.4513757667280.548624233271807
32119120.342900162575-1.34290016257467
339899.8316098675682-1.83160986756821
348787.8000199282711-0.800019928271051
3510199.06542573282631.93457426717368
36115114.5070061258340.49299387416555
37109110.668071726002-1.66807172600188
38109109.760102859256-0.760102859256429
39159158.5617597039760.438240296024335
40129128.3715790259390.628420974060756
41119119.258758421785-0.258758421785039
42119117.3326231638431.66737683615663
43122120.2590199828331.74098001716691
44131130.9476571063990.0523428936006416
45120116.2282335965463.77176640345371
468282.7937708111519-0.793770811151905
478685.18885414540510.811145854594933
48105101.8397169713553.16028302864525
49114117.022798642444-3.02279864244444
5010099.81713161300240.182868386997577
51100100.949335310853-0.949335310853034
5299100.23610978519-1.2361097851899
53132133.878202894969-1.8782028949694
548285.8349862608306-3.83498626083059
55132130.6742114292921.32578857070821
56107106.7676144434560.232385556543756
57114115.175007545983-1.17500754598307
58110106.7983153967823.20168460321812
59105102.7632697797482.23673022025246
60121117.7217899483193.27821005168125
61109106.447153891852.55284610815048
62106105.2650312355770.73496876442321
63124123.2521127309830.747887269016917
64120120.115200042914-0.115200042914454
659194.4335623386522-3.43356233865217
66126123.163067305422.83693269458026
67138138.735907029824-0.735907029824474
68118116.1884093481581.81159065184228
69128126.9180046709641.0819953290358
709898.340610702839-0.340610702839021
71133132.756742897110.24325710288975
72130129.2078474714770.792152528522924
7310399.86997049642613.13002950357393
74124124.042500398091-0.0425003980912473
75142140.0366979198271.9633020801735
769697.8464584600315-1.84645846003152
779392.38909179915910.610908200840869
78129129.863254982238-0.863254982237941
79150149.4700414448110.529958555188918
808886.45271698852181.54728301147815
81125122.7383853881042.2616146118962
829289.97457735410252.0254226458975
830110.442104920345-110.442104920345
84117115.7261539517011.27384604829926
85112112.227259354568-0.227259354567974
86144141.8031085626932.1968914373067
87130128.0679294465931.93207055340688
888785.57146708068791.42853291931213
899291.76117469130660.238825308693456
90114114.095796032336-0.0957960323356651
918179.0961594017331.90384059826699
92127125.4196581053881.5803418946121
93115114.1715685452680.828431454731858
94123123.897229178146-0.897229178145628
95115114.1741857572330.825814242767441
96117115.5810825368461.41891746315422
97117114.5650570075252.43494299247546
98103100.9571925878772.04280741212287
99108106.4780038121931.5219961878068
100139136.3796291774572.62037082254288
101113110.3651014470762.63489855292421
1029794.09676397320732.90323602679266
103117114.3637170057392.6362829942607
104133131.103575813391.89642418661003
105115115.128563216653-0.128563216652951
106103104.313838070927-1.31383807092667
1079593.70478389173781.29521610826223
108117117.412078265795-0.41207826579486
109113111.8561384127471.14386158725269
110127129.190425900043-2.19042590004297
111126124.3962354821311.60376451786861
112119117.0255534446291.97444655537096
1139793.40684331956633.59315668043372
114105103.8086553385661.19134466143379
115140139.4966760097850.503323990214753
1169187.94898806298783.05101193701222
117112111.1563399166220.843660083378263
118113112.5665482901470.433451709852506
11910296.26701086824165.73298913175845
1209290.24954496394121.75045503605879
1219898.5550684689398-0.555068468939809
122122121.7152317061280.284768293871702
12310098.81121022495881.18878977504121
1248481.39280366767512.60719633232491
125142137.0541317096124.94586829038753
126124121.7297834894352.2702165105651
127137133.8758233696753.12417663032496
128105103.2827083293851.71729167061516
129106104.5975004609791.40249953902091
130125122.7120301687912.28796983120887
131104102.0564514784131.94354852158719
132130131.85834290301-1.8583429030101
1337979.2905253673476-0.290525367347554
134108108.304404011296-0.304404011296267
135136133.748158297592.25184170240998
1369895.98861481090842.01138518909156
137120123.972832368826-3.97283236882611
138108107.7710680056690.228931994330829
139139139.036570154975-0.0365701549746791
140123121.6175161538231.38248384617716
1419086.9580659927213.04193400727898
142119118.4245223014150.575477698585144
143105106.808348822244-1.80834882224429
144110107.4133284973452.58667150265472
145135134.1042523164920.895747683507705
14610196.81517327173394.18482672826611
147114111.7234113757652.27658862423522
148118115.4234072516622.57659274833823
149120117.5821876088772.41781239112287
150108110.234136351665-2.23413635166477
151114110.4886897184283.51131028157247
152122121.0790518031080.920948196892075
153132129.5620171269052.43798287309477
154130129.4957317128080.504268287192434
155130131.118594751241-1.11859475124144
156112111.9407695397760.059230460224019
157114116.623932712062-2.62393271206233
158103103.516911386939-0.516911386938614
159115113.7665364626521.23346353734782
160108105.6100049657242.38999503427606
1619494.0937212870732-0.0937212870731477
162105103.0378674039011.96213259609924






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
147.68291080295446e-461.53658216059089e-451
157.73965437886944e-611.54793087577389e-601
161.52333314881676e-753.04666629763352e-751
171.49313803942259e-942.98627607884518e-941
181.25213227074104e-1102.50426454148208e-1101
193.52442996569439e-1197.04885993138877e-1191
204.43509000136717e-1348.87018000273433e-1341
211.07165667821454e-1522.14331335642907e-1521
223.0796422061008e-1716.1592844122016e-1711
236.27931113307557e-1781.25586222661511e-1771
244.16196188825657e-1948.32392377651313e-1941
251.42265092009399e-2162.84530184018799e-2161
269.03720918077032e-2301.80744183615406e-2291
274.81081231635664e-2449.62162463271329e-2441
281.68009770297189e-2613.36019540594379e-2611
295.78538448490591e-2661.15707689698118e-2651
301.3274005045497e-2942.6548010090994e-2941
312.52193167362801e-3085.04386334725603e-3081
329.38724727098368e-3231.87744945419674e-3221
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
13011.57071418339055e-3137.85357091695276e-314
13112.3837353283947e-3021.19186766419735e-302
13211.21522894106968e-2856.0761447053484e-286
13312.80091371024345e-2651.40045685512173e-265
13411.57334488136451e-2667.86672440682257e-267
13511.47794578461202e-2487.38972892306008e-249
13611.9311020724604e-2269.65551036230199e-227
13714.36394933782826e-2092.18197466891413e-209
13813.96777884414631e-1981.98388942207315e-198
13915.03657785685886e-1832.51828892842943e-183
14013.99099911382509e-1641.99549955691254e-164
14115.5221432953879e-1492.76107164769395e-149
14213.91201689624834e-1401.95600844812417e-140
14311.92113176105958e-1249.60565880529791e-125
14417.18902572284261e-1103.59451286142131e-110
14513.54186771575893e-941.77093385787946e-94
14611.14322312378847e-755.71611561894237e-76
14714.22199361424355e-602.11099680712177e-60
14816.99066492989151e-483.49533246494576e-48

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 7.68291080295446e-46 & 1.53658216059089e-45 & 1 \tabularnewline
15 & 7.73965437886944e-61 & 1.54793087577389e-60 & 1 \tabularnewline
16 & 1.52333314881676e-75 & 3.04666629763352e-75 & 1 \tabularnewline
17 & 1.49313803942259e-94 & 2.98627607884518e-94 & 1 \tabularnewline
18 & 1.25213227074104e-110 & 2.50426454148208e-110 & 1 \tabularnewline
19 & 3.52442996569439e-119 & 7.04885993138877e-119 & 1 \tabularnewline
20 & 4.43509000136717e-134 & 8.87018000273433e-134 & 1 \tabularnewline
21 & 1.07165667821454e-152 & 2.14331335642907e-152 & 1 \tabularnewline
22 & 3.0796422061008e-171 & 6.1592844122016e-171 & 1 \tabularnewline
23 & 6.27931113307557e-178 & 1.25586222661511e-177 & 1 \tabularnewline
24 & 4.16196188825657e-194 & 8.32392377651313e-194 & 1 \tabularnewline
25 & 1.42265092009399e-216 & 2.84530184018799e-216 & 1 \tabularnewline
26 & 9.03720918077032e-230 & 1.80744183615406e-229 & 1 \tabularnewline
27 & 4.81081231635664e-244 & 9.62162463271329e-244 & 1 \tabularnewline
28 & 1.68009770297189e-261 & 3.36019540594379e-261 & 1 \tabularnewline
29 & 5.78538448490591e-266 & 1.15707689698118e-265 & 1 \tabularnewline
30 & 1.3274005045497e-294 & 2.6548010090994e-294 & 1 \tabularnewline
31 & 2.52193167362801e-308 & 5.04386334725603e-308 & 1 \tabularnewline
32 & 9.38724727098368e-323 & 1.87744945419674e-322 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0 & 0 & 1 \tabularnewline
83 & 1 & 0 & 0 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 1.57071418339055e-313 & 7.85357091695276e-314 \tabularnewline
131 & 1 & 2.3837353283947e-302 & 1.19186766419735e-302 \tabularnewline
132 & 1 & 1.21522894106968e-285 & 6.0761447053484e-286 \tabularnewline
133 & 1 & 2.80091371024345e-265 & 1.40045685512173e-265 \tabularnewline
134 & 1 & 1.57334488136451e-266 & 7.86672440682257e-267 \tabularnewline
135 & 1 & 1.47794578461202e-248 & 7.38972892306008e-249 \tabularnewline
136 & 1 & 1.9311020724604e-226 & 9.65551036230199e-227 \tabularnewline
137 & 1 & 4.36394933782826e-209 & 2.18197466891413e-209 \tabularnewline
138 & 1 & 3.96777884414631e-198 & 1.98388942207315e-198 \tabularnewline
139 & 1 & 5.03657785685886e-183 & 2.51828892842943e-183 \tabularnewline
140 & 1 & 3.99099911382509e-164 & 1.99549955691254e-164 \tabularnewline
141 & 1 & 5.5221432953879e-149 & 2.76107164769395e-149 \tabularnewline
142 & 1 & 3.91201689624834e-140 & 1.95600844812417e-140 \tabularnewline
143 & 1 & 1.92113176105958e-124 & 9.60565880529791e-125 \tabularnewline
144 & 1 & 7.18902572284261e-110 & 3.59451286142131e-110 \tabularnewline
145 & 1 & 3.54186771575893e-94 & 1.77093385787946e-94 \tabularnewline
146 & 1 & 1.14322312378847e-75 & 5.71611561894237e-76 \tabularnewline
147 & 1 & 4.22199361424355e-60 & 2.11099680712177e-60 \tabularnewline
148 & 1 & 6.99066492989151e-48 & 3.49533246494576e-48 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&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]14[/C][C]7.68291080295446e-46[/C][C]1.53658216059089e-45[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]7.73965437886944e-61[/C][C]1.54793087577389e-60[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]1.52333314881676e-75[/C][C]3.04666629763352e-75[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.49313803942259e-94[/C][C]2.98627607884518e-94[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.25213227074104e-110[/C][C]2.50426454148208e-110[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]3.52442996569439e-119[/C][C]7.04885993138877e-119[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]4.43509000136717e-134[/C][C]8.87018000273433e-134[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.07165667821454e-152[/C][C]2.14331335642907e-152[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]3.0796422061008e-171[/C][C]6.1592844122016e-171[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]6.27931113307557e-178[/C][C]1.25586222661511e-177[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]4.16196188825657e-194[/C][C]8.32392377651313e-194[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.42265092009399e-216[/C][C]2.84530184018799e-216[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]9.03720918077032e-230[/C][C]1.80744183615406e-229[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]4.81081231635664e-244[/C][C]9.62162463271329e-244[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.68009770297189e-261[/C][C]3.36019540594379e-261[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]5.78538448490591e-266[/C][C]1.15707689698118e-265[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.3274005045497e-294[/C][C]2.6548010090994e-294[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]2.52193167362801e-308[/C][C]5.04386334725603e-308[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]9.38724727098368e-323[/C][C]1.87744945419674e-322[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.57071418339055e-313[/C][C]7.85357091695276e-314[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]2.3837353283947e-302[/C][C]1.19186766419735e-302[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.21522894106968e-285[/C][C]6.0761447053484e-286[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]2.80091371024345e-265[/C][C]1.40045685512173e-265[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.57334488136451e-266[/C][C]7.86672440682257e-267[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.47794578461202e-248[/C][C]7.38972892306008e-249[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.9311020724604e-226[/C][C]9.65551036230199e-227[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]4.36394933782826e-209[/C][C]2.18197466891413e-209[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]3.96777884414631e-198[/C][C]1.98388942207315e-198[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]5.03657785685886e-183[/C][C]2.51828892842943e-183[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]3.99099911382509e-164[/C][C]1.99549955691254e-164[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]5.5221432953879e-149[/C][C]2.76107164769395e-149[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]3.91201689624834e-140[/C][C]1.95600844812417e-140[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.92113176105958e-124[/C][C]9.60565880529791e-125[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]7.18902572284261e-110[/C][C]3.59451286142131e-110[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]3.54186771575893e-94[/C][C]1.77093385787946e-94[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.14322312378847e-75[/C][C]5.71611561894237e-76[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]4.22199361424355e-60[/C][C]2.11099680712177e-60[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]6.99066492989151e-48[/C][C]3.49533246494576e-48[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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
147.68291080295446e-461.53658216059089e-451
157.73965437886944e-611.54793087577389e-601
161.52333314881676e-753.04666629763352e-751
171.49313803942259e-942.98627607884518e-941
181.25213227074104e-1102.50426454148208e-1101
193.52442996569439e-1197.04885993138877e-1191
204.43509000136717e-1348.87018000273433e-1341
211.07165667821454e-1522.14331335642907e-1521
223.0796422061008e-1716.1592844122016e-1711
236.27931113307557e-1781.25586222661511e-1771
244.16196188825657e-1948.32392377651313e-1941
251.42265092009399e-2162.84530184018799e-2161
269.03720918077032e-2301.80744183615406e-2291
274.81081231635664e-2449.62162463271329e-2441
281.68009770297189e-2613.36019540594379e-2611
295.78538448490591e-2661.15707689698118e-2651
301.3274005045497e-2942.6548010090994e-2941
312.52193167362801e-3085.04386334725603e-3081
329.38724727098368e-3231.87744945419674e-3221
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
13011.57071418339055e-3137.85357091695276e-314
13112.3837353283947e-3021.19186766419735e-302
13211.21522894106968e-2856.0761447053484e-286
13312.80091371024345e-2651.40045685512173e-265
13411.57334488136451e-2667.86672440682257e-267
13511.47794578461202e-2487.38972892306008e-249
13611.9311020724604e-2269.65551036230199e-227
13714.36394933782826e-2092.18197466891413e-209
13813.96777884414631e-1981.98388942207315e-198
13915.03657785685886e-1832.51828892842943e-183
14013.99099911382509e-1641.99549955691254e-164
14115.5221432953879e-1492.76107164769395e-149
14213.91201689624834e-1401.95600844812417e-140
14311.92113176105958e-1249.60565880529791e-125
14417.18902572284261e-1103.59451286142131e-110
14513.54186771575893e-941.77093385787946e-94
14611.14322312378847e-755.71611561894237e-76
14714.22199361424355e-602.11099680712177e-60
14816.99066492989151e-483.49533246494576e-48






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1351NOK
5% type I error level1351NOK
10% type I error level1351NOK

\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 & 135 & 1 & NOK \tabularnewline
5% type I error level & 135 & 1 & NOK \tabularnewline
10% type I error level & 135 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191233&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]135[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]135[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]135[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191233&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191233&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 level1351NOK
5% type I error level1351NOK
10% type I error level1351NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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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 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; 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')
}