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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 12:08:14 -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/t1353431316hw2ngzoy0vi68rd.htm/, Retrieved Mon, 29 Apr 2024 18:32:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191193, Retrieved Mon, 29 Apr 2024 18:32:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94

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] [WS7 Multiple Regr...] [2012-11-17 13:02:00] [f8ee2fa4f3a14474001c30fec05fcd2b]
- R PD    [Multiple Regression] [WS7 Tabel 1] [2012-11-20 16:31:02] [f8ee2fa4f3a14474001c30fec05fcd2b]
-           [Multiple Regression] [WS 7 Tabel 3] [2012-11-20 16:52:47] [f8ee2fa4f3a14474001c30fec05fcd2b]
-   P           [Multiple Regression] [WS 7 Trend.1] [2012-11-20 17:08:14] [4c93b3a0c48c946a3a36627369b78a37] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26	21	21	23	17	23	4	14	12	127
20	16	15	24	17	20	4	18	11	108
19	19	18	22	18	20	6	11	14	110
19	18	11	20	21	21	8	12	12	102
20	16	8	24	20	24	8	16	21	104
25	23	19	27	28	22	4	18	12	140
25	17	4	28	19	23	4	14	22	112
22	12	20	27	22	20	8	14	11	115
26	19	16	24	16	25	5	15	10	121
22	16	14	23	18	23	4	15	13	112
17	19	10	24	25	27	4	17	10	118
22	20	13	27	17	27	4	19	8	122
19	13	14	27	14	22	4	10	15	105
24	20	8	28	11	24	4	16	14	111
26	27	23	27	27	25	4	18	10	151
21	17	11	23	20	22	8	14	14	106
13	8	9	24	22	28	4	14	14	100
26	25	24	28	22	28	4	17	11	149
20	26	5	27	21	27	4	14	10	122
22	13	15	25	23	25	8	16	13	115
14	19	5	19	17	16	4	18	7	86
21	15	19	24	24	28	7	11	14	124
7	5	6	20	14	21	4	14	12	69
23	16	13	28	17	24	4	12	14	117
17	14	11	26	23	27	5	17	11	113
25	24	17	23	24	14	4	9	9	123
25	24	17	23	24	14	4	16	11	123
19	9	5	20	8	27	4	14	15	84
20	19	9	11	22	20	4	15	14	97
23	19	15	24	23	21	4	11	13	121
22	25	17	25	25	22	4	16	9	132
22	19	17	23	21	21	4	13	15	119
21	18	20	18	24	12	15	17	10	98
15	15	12	20	15	20	10	15	11	87
20	12	7	20	22	24	4	14	13	101
22	21	16	24	21	19	8	16	8	115
18	12	7	23	25	28	4	9	20	109
20	15	14	25	16	23	4	15	12	109
28	28	24	28	28	27	4	17	10	159
22	25	15	26	23	22	4	13	10	129
18	19	15	26	21	27	7	15	9	119
23	20	10	23	21	26	4	16	14	119
20	24	14	22	26	22	6	16	8	122
25	26	18	24	22	21	5	12	14	131
26	25	12	21	21	19	4	12	11	120
15	12	9	20	18	24	16	11	13	82
17	12	9	22	12	19	5	15	9	86
23	15	8	20	25	26	12	15	11	105
21	17	18	25	17	22	6	17	15	114
13	14	10	20	24	28	9	13	11	100
18	16	17	22	15	21	9	16	10	100
19	11	14	23	13	23	4	14	14	99
22	20	16	25	26	28	5	11	18	132
16	11	10	23	16	10	4	12	14	82
24	22	19	23	24	24	4	12	11	132
18	20	10	22	21	21	5	15	12	107
20	19	14	24	20	21	4	16	13	114
24	17	10	25	14	24	4	15	9	110
14	21	4	21	25	24	4	12	10	105
22	23	19	12	25	25	5	12	15	121
24	18	9	17	20	25	4	8	20	109
18	17	12	20	22	23	6	13	12	106
21	27	16	23	20	21	4	11	12	124
23	25	11	23	26	16	4	14	14	120
17	19	18	20	18	17	18	15	13	91
22	22	11	28	22	25	4	10	11	126
24	24	24	24	24	24	6	11	17	138
21	20	17	24	17	23	4	12	12	118
22	19	18	24	24	25	4	15	13	128
16	11	9	24	20	23	5	15	14	98
21	22	19	28	19	28	4	14	13	133
23	22	18	25	20	26	4	16	15	130
22	16	12	21	15	22	5	15	13	103
24	20	23	25	23	19	10	15	10	124
24	24	22	25	26	26	5	13	11	142
16	16	14	18	22	18	8	12	19	96
16	16	14	17	20	18	8	17	13	93
21	22	16	26	24	25	5	13	17	129
26	24	23	28	26	27	4	15	13	150
15	16	7	21	21	12	4	13	9	88
25	27	10	27	25	15	4	15	11	125
18	11	12	22	13	21	5	16	10	92
23	21	12	21	20	23	4	15	9	0
20	20	12	25	22	22	4	16	12	117
17	20	17	22	23	21	8	15	12	112
25	27	21	23	28	24	4	14	13	144
24	20	16	26	22	27	5	15	13	130
17	12	11	19	20	22	14	14	12	87
19	8	14	25	6	28	8	13	15	92
20	21	13	21	21	26	8	7	22	114
15	18	9	13	20	10	4	17	13	81
27	24	19	24	18	19	4	13	15	127
22	16	13	25	23	22	6	15	13	115
23	18	19	26	20	21	4	14	15	123
16	20	13	25	24	24	7	13	10	115
19	20	13	25	22	25	7	16	11	117
25	19	13	22	21	21	4	12	16	117
19	17	14	21	18	20	6	14	11	103
19	16	12	23	21	21	4	17	11	108
26	26	22	25	23	24	7	15	10	139
21	15	11	24	23	23	4	17	10	113
20	22	5	21	15	18	4	12	16	97
24	17	18	21	21	24	8	16	12	117
22	23	19	25	24	24	4	11	11	133
20	21	14	22	23	19	4	15	16	115
18	19	15	20	21	20	10	9	19	103
18	14	12	20	21	18	8	16	11	95
24	17	19	23	20	20	6	15	16	117
24	12	15	28	11	27	4	10	15	113
22	24	17	23	22	23	4	10	24	127
23	18	8	28	27	26	4	15	14	126
22	20	10	24	25	23	5	11	15	119
20	16	12	18	18	17	4	13	11	97
18	20	12	20	20	21	6	14	15	105
25	22	20	28	24	25	4	18	12	140
18	12	12	21	10	23	5	16	10	91
16	16	12	21	27	27	7	14	14	112
20	17	14	25	21	24	8	14	13	113
19	22	6	19	21	20	5	14	9	102
15	12	10	18	18	27	8	14	15	92
19	14	18	21	15	21	10	12	15	98
19	23	18	22	24	24	8	14	14	122
16	15	7	24	22	21	5	15	11	100
17	17	18	15	14	15	12	15	8	84
28	28	9	28	28	25	4	15	11	142
23	20	17	26	18	25	5	13	11	124
25	23	22	23	26	22	4	17	8	137
20	13	11	26	17	24	6	17	10	105
17	18	15	20	19	21	4	19	11	106
23	23	17	22	22	22	4	15	13	125
16	19	15	20	18	23	7	13	11	104
23	23	22	23	24	22	7	9	20	130
11	12	9	22	15	20	10	15	10	79
18	16	13	24	18	23	4	15	15	108
24	23	20	23	26	25	5	15	12	136
23	13	14	22	11	23	8	16	14	98
21	22	14	26	26	22	11	11	23	120
16	18	12	23	21	25	7	14	14	108
24	23	20	27	23	26	4	11	16	139
23	20	20	23	23	22	8	15	11	123
18	10	8	21	15	24	6	13	12	90
20	17	17	26	22	24	7	15	10	119
9	18	9	23	26	25	5	16	14	105
24	15	18	21	16	20	4	14	12	110
25	23	22	27	20	26	8	15	12	135
20	17	10	19	18	21	4	16	11	101
21	17	13	23	22	26	8	16	12	114
25	22	15	25	16	21	6	11	13	118
22	20	18	23	19	22	4	12	11	120
21	20	18	22	20	16	9	9	19	108
21	19	12	22	19	26	5	16	12	114
22	18	12	25	23	28	6	13	17	122
27	22	20	25	24	18	4	16	9	132
24	20	12	28	25	25	4	12	12	130
24	22	16	28	21	23	4	9	19	130
21	18	16	20	21	21	5	13	18	112
18	16	18	25	23	20	6	13	15	114
16	16	16	19	27	25	16	14	14	103
22	16	13	25	23	22	6	19	11	115
20	16	17	22	18	21	6	13	9	108
18	17	13	18	16	16	4	12	18	94
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 time18 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 & 18 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=191193&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]18 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=191193&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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 time18 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] = -7.23245812461319 + 0.69284878264744I1[t] + 0.902423664222079I2[t] + 1.17903408262516I3[t] + 1.37766564736893E1[t] + 1.06956252199474E2[t] + 0.818834879664929E3[t] -0.894605266788159A[t] + 0.0789442773936765Happiness[t] + 0.367179961372851Depression[t] -0.00506485399414405t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Motivatie
[t] =  -7.23245812461319 +  0.69284878264744I1[t] +  0.902423664222079I2[t] +  1.17903408262516I3[t] +  1.37766564736893E1[t] +  1.06956252199474E2[t] +  0.818834879664929E3[t] -0.894605266788159A[t] +  0.0789442773936765Happiness[t] +  0.367179961372851Depression[t] -0.00506485399414405t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191193&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Motivatie
[t] =  -7.23245812461319 +  0.69284878264744I1[t] +  0.902423664222079I2[t] +  1.17903408262516I3[t] +  1.37766564736893E1[t] +  1.06956252199474E2[t] +  0.818834879664929E3[t] -0.894605266788159A[t] +  0.0789442773936765Happiness[t] +  0.367179961372851Depression[t] -0.00506485399414405t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191193&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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] = -7.23245812461319 + 0.69284878264744I1[t] + 0.902423664222079I2[t] + 1.17903408262516I3[t] + 1.37766564736893E1[t] + 1.06956252199474E2[t] + 0.818834879664929E3[t] -0.894605266788159A[t] + 0.0789442773936765Happiness[t] + 0.367179961372851Depression[t] -0.00506485399414405t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-7.2324581246131910.797282-0.66980.5039820.251991
I10.692848782647440.3001882.30810.0223540.011177
I20.9024236642220790.2596793.47520.0006670.000333
I31.179034082625160.2025015.822400
E11.377665647368930.2896464.75645e-062e-06
E21.069562521994740.2214564.82973e-062e-06
E30.8188348796649290.2284013.58510.0004540.000227
A-0.8946052667881590.317894-2.81420.0055420.002771
Happiness0.07894427739367650.3766720.20960.8342750.417138
Depression0.3671799613728510.2821241.30150.1950750.097537
t-0.005064853994144050.015988-0.31680.7518470.375923

\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) & -7.23245812461319 & 10.797282 & -0.6698 & 0.503982 & 0.251991 \tabularnewline
I1 & 0.69284878264744 & 0.300188 & 2.3081 & 0.022354 & 0.011177 \tabularnewline
I2 & 0.902423664222079 & 0.259679 & 3.4752 & 0.000667 & 0.000333 \tabularnewline
I3 & 1.17903408262516 & 0.202501 & 5.8224 & 0 & 0 \tabularnewline
E1 & 1.37766564736893 & 0.289646 & 4.7564 & 5e-06 & 2e-06 \tabularnewline
E2 & 1.06956252199474 & 0.221456 & 4.8297 & 3e-06 & 2e-06 \tabularnewline
E3 & 0.818834879664929 & 0.228401 & 3.5851 & 0.000454 & 0.000227 \tabularnewline
A & -0.894605266788159 & 0.317894 & -2.8142 & 0.005542 & 0.002771 \tabularnewline
Happiness & 0.0789442773936765 & 0.376672 & 0.2096 & 0.834275 & 0.417138 \tabularnewline
Depression & 0.367179961372851 & 0.282124 & 1.3015 & 0.195075 & 0.097537 \tabularnewline
t & -0.00506485399414405 & 0.015988 & -0.3168 & 0.751847 & 0.375923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191193&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]-7.23245812461319[/C][C]10.797282[/C][C]-0.6698[/C][C]0.503982[/C][C]0.251991[/C][/ROW]
[ROW][C]I1[/C][C]0.69284878264744[/C][C]0.300188[/C][C]2.3081[/C][C]0.022354[/C][C]0.011177[/C][/ROW]
[ROW][C]I2[/C][C]0.902423664222079[/C][C]0.259679[/C][C]3.4752[/C][C]0.000667[/C][C]0.000333[/C][/ROW]
[ROW][C]I3[/C][C]1.17903408262516[/C][C]0.202501[/C][C]5.8224[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]1.37766564736893[/C][C]0.289646[/C][C]4.7564[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E2[/C][C]1.06956252199474[/C][C]0.221456[/C][C]4.8297[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E3[/C][C]0.818834879664929[/C][C]0.228401[/C][C]3.5851[/C][C]0.000454[/C][C]0.000227[/C][/ROW]
[ROW][C]A[/C][C]-0.894605266788159[/C][C]0.317894[/C][C]-2.8142[/C][C]0.005542[/C][C]0.002771[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0789442773936765[/C][C]0.376672[/C][C]0.2096[/C][C]0.834275[/C][C]0.417138[/C][/ROW]
[ROW][C]Depression[/C][C]0.367179961372851[/C][C]0.282124[/C][C]1.3015[/C][C]0.195075[/C][C]0.097537[/C][/ROW]
[ROW][C]t[/C][C]-0.00506485399414405[/C][C]0.015988[/C][C]-0.3168[/C][C]0.751847[/C][C]0.375923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191193&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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)-7.2324581246131910.797282-0.66980.5039820.251991
I10.692848782647440.3001882.30810.0223540.011177
I20.9024236642220790.2596793.47520.0006670.000333
I31.179034082625160.2025015.822400
E11.377665647368930.2896464.75645e-062e-06
E21.069562521994740.2214564.82973e-062e-06
E30.8188348796649290.2284013.58510.0004540.000227
A-0.8946052667881590.317894-2.81420.0055420.002771
Happiness0.07894427739367650.3766720.20960.8342750.417138
Depression0.3671799613728510.2821241.30150.1950750.097537
t-0.005064853994144050.015988-0.31680.7518470.375923






Multiple Linear Regression - Regression Statistics
Multiple R0.873452570349703
R-squared0.762919392650502
Adjusted R-squared0.747218690177026
F-TEST (value)48.5914177368375
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.25286516927742
Sum Squared Residuals12927.9425899649

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.873452570349703 \tabularnewline
R-squared & 0.762919392650502 \tabularnewline
Adjusted R-squared & 0.747218690177026 \tabularnewline
F-TEST (value) & 48.5914177368375 \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 & 9.25286516927742 \tabularnewline
Sum Squared Residuals & 12927.9425899649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191193&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.873452570349703[/C][/ROW]
[ROW][C]R-squared[/C][C]0.762919392650502[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.747218690177026[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]48.5914177368375[/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]9.25286516927742[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12927.9425899649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191193&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.873452570349703
R-squared0.762919392650502
Adjusted R-squared0.747218690177026
F-TEST (value)48.5914177368375
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.25286516927742
Sum Squared Residuals12927.9425899649






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127125.122191402541.87780859745952
2108108.243469192376-0.243469192376404
3110110.86387943232-0.863879432319897
4102100.5307173077111.46928269228949
5104106.39455312845-2.39455312845033
6140140.623590074444-0.623590074443876
7112113.444932328975-1.44493232897488
8115117.470864765297-2.47086476529706
9121117.7774068015813.22259319841871
10112111.0555424472780.944457552721759
11118116.7736598328731.22634016712686
12122119.6723902024042.32760979759573
13105107.007746702296-2.00774670229641
14111109.6228204590471.37717954095316
15151150.2492683527870.750731647212707
16106105.7277307381640.272269261836368
17100101.708215516242-1.70821551624158
18149148.3828539053550.617146094644949
19122118.8513966072383.14860339276214
20115115.717993871578-0.71799387157819
218683.27468683602192.7253131639781
22124124.5514643947-0.551464394700474
236970.7429936100165-1.74299361001651
24117117.266396915089-0.266396915089178
25113113.458448583803-0.458448583802962
26123120.9150183980972.08498160190302
27123122.1969234086040.803076591395727
288481.05968933621792.94031066378211
299792.04265087756494.9573491224351
30121120.3054306137720.694569386227736
31132130.6417542402141.35824575978621
32119119.335978074561-0.335978074561494
339898.8628078017692-0.862807801769214
348786.92337197285390.0766280271461479
3510198.56543404500882.43456595499124
36115113.7696798278611.23032017213857
37109109.962169141118-0.962169141117901
38109108.8746315953010.125368404699217
39159157.600820820371.3991791796302
40129127.6069901215871.39300987841255
41119118.4779302990560.522069700943734
42119116.5911906722462.40880932775372
43122119.535927599832.4640724001696
44131129.9562445705631.04375542943723
45120115.6202364984814.37976350151874
468282.1531987814684-0.153198781468392
478684.4653284559671.53467154403304
48105101.4985419119753.50145808802462
49114115.753696190617-1.75369619061683
5010099.10960336145320.890396638546826
5110099.89394563745540.106054362544633
529999.192576984477-0.192576984476951
53132132.83703984719-0.837039847190496
548284.7937101735985-2.79371017359851
55132129.7880512380022.21194876199846
56107105.876289290471.12371070953004
57114114.097132946347-0.0971329463470607
58110106.2116105955083.78838940449192
59105102.1984203578352.80157964216486
60121116.5876429262634.41235707373734
61109105.621060081153.3789399188499
62106104.39610450411.6038954958996
63124122.1974830883461.80251691165359
64120119.1724915466480.827508453352268
659193.1656589323994-2.1656589323994
66126124.3245171355031.67548286449743
67138139.139881336278-1.1398813362778
68118116.9198193695551.08018063044546
69128127.6128339234580.387166076542157
709899.1766351634519-1.17663516345189
71133133.336570849217-0.336570849216645
72130129.7293137759830.27068622401664
73103100.7009294732222.29907052677833
74124124.696723658667-0.696723658666983
75142140.7451688431011.25483115689899
769698.2477427094932-2.24774270949319
779392.91752878287190.0824712171280908
78129130.395161600933-1.39516160093278
79150150.028326641137-0.0283266411374103
808887.41738680543340.582613194566637
81125123.6975694209521.30243057904766
829290.76894245256391.23105754743612
830111.447780947973-111.447780947973
84117116.4731829971270.526817002872573
85112112.745107563993-0.745107563993456
86144142.3645745987031.63542540129676
87130128.810990359121.18900964088003
888786.46689368593470.533106314065274
899292.3702890499532-0.370289049953246
90114114.602246077232-0.602246077231821
918179.58052839998231.4194716000177
92127125.8978255565661.10217444343439
93115114.9512239746090.0487760253905504
94123124.312829108106-1.31282910810598
95115114.9468948032550.053105196744628
96117116.3040989264330.695901073567129
97117115.2797426189941.72025738100594
98103101.6193619446251.38063805537502
99108107.1627023673030.837297632697197
100139136.9642331215112.03576687848945
101113111.2440929670781.75590703292242
1029794.81362734911892.18637265088105
103117114.994302985622.00569701438009
104133131.732986508561.26701349144013
105115115.497149401714-0.497149401713677
106103104.665194896571-1.66519489657077
1079594.37762049861020.622379501389842
108117117.737428154032-0.737428154031801
109113112.5255275302080.474472469791785
110127129.228056885628-2.22805688562788
111126125.3055593443750.694440655624691
112119117.8210665139781.17893348602182
1139794.09651076296892.90348923703108
114105104.2436924472640.756307552735969
115140139.904051605870.0959483941300038
1169188.55047762696182.44952237303824
117112111.7489330142590.251066985740911
118113113.150752710679-0.150752710678588
11910297.20644728614594.79355271385411
1209290.78664190201581.21335809798419
1219898.8542931778262-0.854293177826181
122122122.011193413138-0.0111934131379454
12310099.55973979394130.440260206058678
1248481.7894689280122.21053107198805
125142138.0513537847523.94864621524832
126124122.2914780284841.7085219715158
127137134.3503932326782.64960676732239
128105103.9772263070021.02277369299788
129106104.8527753271941.14722467280569
130125123.0764262090041.92357379099648
131104102.4628462733891.53715372661077
132130131.891035870383-1.89103587038285
1337979.7943343896671-0.794334389667141
134108108.589096908904-0.589096908904097
135136134.1316881157371.86831188426262
1369896.40604850427911.59395149572094
137120124.098447067743-4.09844706774346
138108108.147704812266-0.147704812265783
139139139.279786605622-0.279786605622098
140123121.9900561037371.00994389626335
1419087.67244193157852.32755806842149
142119118.8855362921220.114463707878438
143105107.030248514225-2.03024851422489
144110107.7791770271482.22082297285229
145135134.3602630605010.639736939498564
14610197.36356407834363.6364359216564
147114112.2602962248721.73970377512808
148118115.9022638602542.09773613974619
149120117.9568936168842.04310638311576
150108110.26544810174-2.26544810173988
151114110.9633126417263.03668735827433
152122121.5020514034680.497948596532432
153132129.973015180722.0269848192798
154130128.3724503856381.6275496143618
155130131.305876240709-1.30587624070895
156112112.007567325017-0.00756732501655002
157114116.689650210149-2.68965021014875
158103103.812961875839-0.812961875838661
159115114.1983607978250.801639202175054
160108106.0160646901831.98393530981664
1619494.0825134813642-0.0825134813642288
162105104.8192915961030.180708403896626

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 125.12219140254 & 1.87780859745952 \tabularnewline
2 & 108 & 108.243469192376 & -0.243469192376404 \tabularnewline
3 & 110 & 110.86387943232 & -0.863879432319897 \tabularnewline
4 & 102 & 100.530717307711 & 1.46928269228949 \tabularnewline
5 & 104 & 106.39455312845 & -2.39455312845033 \tabularnewline
6 & 140 & 140.623590074444 & -0.623590074443876 \tabularnewline
7 & 112 & 113.444932328975 & -1.44493232897488 \tabularnewline
8 & 115 & 117.470864765297 & -2.47086476529706 \tabularnewline
9 & 121 & 117.777406801581 & 3.22259319841871 \tabularnewline
10 & 112 & 111.055542447278 & 0.944457552721759 \tabularnewline
11 & 118 & 116.773659832873 & 1.22634016712686 \tabularnewline
12 & 122 & 119.672390202404 & 2.32760979759573 \tabularnewline
13 & 105 & 107.007746702296 & -2.00774670229641 \tabularnewline
14 & 111 & 109.622820459047 & 1.37717954095316 \tabularnewline
15 & 151 & 150.249268352787 & 0.750731647212707 \tabularnewline
16 & 106 & 105.727730738164 & 0.272269261836368 \tabularnewline
17 & 100 & 101.708215516242 & -1.70821551624158 \tabularnewline
18 & 149 & 148.382853905355 & 0.617146094644949 \tabularnewline
19 & 122 & 118.851396607238 & 3.14860339276214 \tabularnewline
20 & 115 & 115.717993871578 & -0.71799387157819 \tabularnewline
21 & 86 & 83.2746868360219 & 2.7253131639781 \tabularnewline
22 & 124 & 124.5514643947 & -0.551464394700474 \tabularnewline
23 & 69 & 70.7429936100165 & -1.74299361001651 \tabularnewline
24 & 117 & 117.266396915089 & -0.266396915089178 \tabularnewline
25 & 113 & 113.458448583803 & -0.458448583802962 \tabularnewline
26 & 123 & 120.915018398097 & 2.08498160190302 \tabularnewline
27 & 123 & 122.196923408604 & 0.803076591395727 \tabularnewline
28 & 84 & 81.0596893362179 & 2.94031066378211 \tabularnewline
29 & 97 & 92.0426508775649 & 4.9573491224351 \tabularnewline
30 & 121 & 120.305430613772 & 0.694569386227736 \tabularnewline
31 & 132 & 130.641754240214 & 1.35824575978621 \tabularnewline
32 & 119 & 119.335978074561 & -0.335978074561494 \tabularnewline
33 & 98 & 98.8628078017692 & -0.862807801769214 \tabularnewline
34 & 87 & 86.9233719728539 & 0.0766280271461479 \tabularnewline
35 & 101 & 98.5654340450088 & 2.43456595499124 \tabularnewline
36 & 115 & 113.769679827861 & 1.23032017213857 \tabularnewline
37 & 109 & 109.962169141118 & -0.962169141117901 \tabularnewline
38 & 109 & 108.874631595301 & 0.125368404699217 \tabularnewline
39 & 159 & 157.60082082037 & 1.3991791796302 \tabularnewline
40 & 129 & 127.606990121587 & 1.39300987841255 \tabularnewline
41 & 119 & 118.477930299056 & 0.522069700943734 \tabularnewline
42 & 119 & 116.591190672246 & 2.40880932775372 \tabularnewline
43 & 122 & 119.53592759983 & 2.4640724001696 \tabularnewline
44 & 131 & 129.956244570563 & 1.04375542943723 \tabularnewline
45 & 120 & 115.620236498481 & 4.37976350151874 \tabularnewline
46 & 82 & 82.1531987814684 & -0.153198781468392 \tabularnewline
47 & 86 & 84.465328455967 & 1.53467154403304 \tabularnewline
48 & 105 & 101.498541911975 & 3.50145808802462 \tabularnewline
49 & 114 & 115.753696190617 & -1.75369619061683 \tabularnewline
50 & 100 & 99.1096033614532 & 0.890396638546826 \tabularnewline
51 & 100 & 99.8939456374554 & 0.106054362544633 \tabularnewline
52 & 99 & 99.192576984477 & -0.192576984476951 \tabularnewline
53 & 132 & 132.83703984719 & -0.837039847190496 \tabularnewline
54 & 82 & 84.7937101735985 & -2.79371017359851 \tabularnewline
55 & 132 & 129.788051238002 & 2.21194876199846 \tabularnewline
56 & 107 & 105.87628929047 & 1.12371070953004 \tabularnewline
57 & 114 & 114.097132946347 & -0.0971329463470607 \tabularnewline
58 & 110 & 106.211610595508 & 3.78838940449192 \tabularnewline
59 & 105 & 102.198420357835 & 2.80157964216486 \tabularnewline
60 & 121 & 116.587642926263 & 4.41235707373734 \tabularnewline
61 & 109 & 105.62106008115 & 3.3789399188499 \tabularnewline
62 & 106 & 104.3961045041 & 1.6038954958996 \tabularnewline
63 & 124 & 122.197483088346 & 1.80251691165359 \tabularnewline
64 & 120 & 119.172491546648 & 0.827508453352268 \tabularnewline
65 & 91 & 93.1656589323994 & -2.1656589323994 \tabularnewline
66 & 126 & 124.324517135503 & 1.67548286449743 \tabularnewline
67 & 138 & 139.139881336278 & -1.1398813362778 \tabularnewline
68 & 118 & 116.919819369555 & 1.08018063044546 \tabularnewline
69 & 128 & 127.612833923458 & 0.387166076542157 \tabularnewline
70 & 98 & 99.1766351634519 & -1.17663516345189 \tabularnewline
71 & 133 & 133.336570849217 & -0.336570849216645 \tabularnewline
72 & 130 & 129.729313775983 & 0.27068622401664 \tabularnewline
73 & 103 & 100.700929473222 & 2.29907052677833 \tabularnewline
74 & 124 & 124.696723658667 & -0.696723658666983 \tabularnewline
75 & 142 & 140.745168843101 & 1.25483115689899 \tabularnewline
76 & 96 & 98.2477427094932 & -2.24774270949319 \tabularnewline
77 & 93 & 92.9175287828719 & 0.0824712171280908 \tabularnewline
78 & 129 & 130.395161600933 & -1.39516160093278 \tabularnewline
79 & 150 & 150.028326641137 & -0.0283266411374103 \tabularnewline
80 & 88 & 87.4173868054334 & 0.582613194566637 \tabularnewline
81 & 125 & 123.697569420952 & 1.30243057904766 \tabularnewline
82 & 92 & 90.7689424525639 & 1.23105754743612 \tabularnewline
83 & 0 & 111.447780947973 & -111.447780947973 \tabularnewline
84 & 117 & 116.473182997127 & 0.526817002872573 \tabularnewline
85 & 112 & 112.745107563993 & -0.745107563993456 \tabularnewline
86 & 144 & 142.364574598703 & 1.63542540129676 \tabularnewline
87 & 130 & 128.81099035912 & 1.18900964088003 \tabularnewline
88 & 87 & 86.4668936859347 & 0.533106314065274 \tabularnewline
89 & 92 & 92.3702890499532 & -0.370289049953246 \tabularnewline
90 & 114 & 114.602246077232 & -0.602246077231821 \tabularnewline
91 & 81 & 79.5805283999823 & 1.4194716000177 \tabularnewline
92 & 127 & 125.897825556566 & 1.10217444343439 \tabularnewline
93 & 115 & 114.951223974609 & 0.0487760253905504 \tabularnewline
94 & 123 & 124.312829108106 & -1.31282910810598 \tabularnewline
95 & 115 & 114.946894803255 & 0.053105196744628 \tabularnewline
96 & 117 & 116.304098926433 & 0.695901073567129 \tabularnewline
97 & 117 & 115.279742618994 & 1.72025738100594 \tabularnewline
98 & 103 & 101.619361944625 & 1.38063805537502 \tabularnewline
99 & 108 & 107.162702367303 & 0.837297632697197 \tabularnewline
100 & 139 & 136.964233121511 & 2.03576687848945 \tabularnewline
101 & 113 & 111.244092967078 & 1.75590703292242 \tabularnewline
102 & 97 & 94.8136273491189 & 2.18637265088105 \tabularnewline
103 & 117 & 114.99430298562 & 2.00569701438009 \tabularnewline
104 & 133 & 131.73298650856 & 1.26701349144013 \tabularnewline
105 & 115 & 115.497149401714 & -0.497149401713677 \tabularnewline
106 & 103 & 104.665194896571 & -1.66519489657077 \tabularnewline
107 & 95 & 94.3776204986102 & 0.622379501389842 \tabularnewline
108 & 117 & 117.737428154032 & -0.737428154031801 \tabularnewline
109 & 113 & 112.525527530208 & 0.474472469791785 \tabularnewline
110 & 127 & 129.228056885628 & -2.22805688562788 \tabularnewline
111 & 126 & 125.305559344375 & 0.694440655624691 \tabularnewline
112 & 119 & 117.821066513978 & 1.17893348602182 \tabularnewline
113 & 97 & 94.0965107629689 & 2.90348923703108 \tabularnewline
114 & 105 & 104.243692447264 & 0.756307552735969 \tabularnewline
115 & 140 & 139.90405160587 & 0.0959483941300038 \tabularnewline
116 & 91 & 88.5504776269618 & 2.44952237303824 \tabularnewline
117 & 112 & 111.748933014259 & 0.251066985740911 \tabularnewline
118 & 113 & 113.150752710679 & -0.150752710678588 \tabularnewline
119 & 102 & 97.2064472861459 & 4.79355271385411 \tabularnewline
120 & 92 & 90.7866419020158 & 1.21335809798419 \tabularnewline
121 & 98 & 98.8542931778262 & -0.854293177826181 \tabularnewline
122 & 122 & 122.011193413138 & -0.0111934131379454 \tabularnewline
123 & 100 & 99.5597397939413 & 0.440260206058678 \tabularnewline
124 & 84 & 81.789468928012 & 2.21053107198805 \tabularnewline
125 & 142 & 138.051353784752 & 3.94864621524832 \tabularnewline
126 & 124 & 122.291478028484 & 1.7085219715158 \tabularnewline
127 & 137 & 134.350393232678 & 2.64960676732239 \tabularnewline
128 & 105 & 103.977226307002 & 1.02277369299788 \tabularnewline
129 & 106 & 104.852775327194 & 1.14722467280569 \tabularnewline
130 & 125 & 123.076426209004 & 1.92357379099648 \tabularnewline
131 & 104 & 102.462846273389 & 1.53715372661077 \tabularnewline
132 & 130 & 131.891035870383 & -1.89103587038285 \tabularnewline
133 & 79 & 79.7943343896671 & -0.794334389667141 \tabularnewline
134 & 108 & 108.589096908904 & -0.589096908904097 \tabularnewline
135 & 136 & 134.131688115737 & 1.86831188426262 \tabularnewline
136 & 98 & 96.4060485042791 & 1.59395149572094 \tabularnewline
137 & 120 & 124.098447067743 & -4.09844706774346 \tabularnewline
138 & 108 & 108.147704812266 & -0.147704812265783 \tabularnewline
139 & 139 & 139.279786605622 & -0.279786605622098 \tabularnewline
140 & 123 & 121.990056103737 & 1.00994389626335 \tabularnewline
141 & 90 & 87.6724419315785 & 2.32755806842149 \tabularnewline
142 & 119 & 118.885536292122 & 0.114463707878438 \tabularnewline
143 & 105 & 107.030248514225 & -2.03024851422489 \tabularnewline
144 & 110 & 107.779177027148 & 2.22082297285229 \tabularnewline
145 & 135 & 134.360263060501 & 0.639736939498564 \tabularnewline
146 & 101 & 97.3635640783436 & 3.6364359216564 \tabularnewline
147 & 114 & 112.260296224872 & 1.73970377512808 \tabularnewline
148 & 118 & 115.902263860254 & 2.09773613974619 \tabularnewline
149 & 120 & 117.956893616884 & 2.04310638311576 \tabularnewline
150 & 108 & 110.26544810174 & -2.26544810173988 \tabularnewline
151 & 114 & 110.963312641726 & 3.03668735827433 \tabularnewline
152 & 122 & 121.502051403468 & 0.497948596532432 \tabularnewline
153 & 132 & 129.97301518072 & 2.0269848192798 \tabularnewline
154 & 130 & 128.372450385638 & 1.6275496143618 \tabularnewline
155 & 130 & 131.305876240709 & -1.30587624070895 \tabularnewline
156 & 112 & 112.007567325017 & -0.00756732501655002 \tabularnewline
157 & 114 & 116.689650210149 & -2.68965021014875 \tabularnewline
158 & 103 & 103.812961875839 & -0.812961875838661 \tabularnewline
159 & 115 & 114.198360797825 & 0.801639202175054 \tabularnewline
160 & 108 & 106.016064690183 & 1.98393530981664 \tabularnewline
161 & 94 & 94.0825134813642 & -0.0825134813642288 \tabularnewline
162 & 105 & 104.819291596103 & 0.180708403896626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191193&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]125.12219140254[/C][C]1.87780859745952[/C][/ROW]
[ROW][C]2[/C][C]108[/C][C]108.243469192376[/C][C]-0.243469192376404[/C][/ROW]
[ROW][C]3[/C][C]110[/C][C]110.86387943232[/C][C]-0.863879432319897[/C][/ROW]
[ROW][C]4[/C][C]102[/C][C]100.530717307711[/C][C]1.46928269228949[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]106.39455312845[/C][C]-2.39455312845033[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]140.623590074444[/C][C]-0.623590074443876[/C][/ROW]
[ROW][C]7[/C][C]112[/C][C]113.444932328975[/C][C]-1.44493232897488[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]117.470864765297[/C][C]-2.47086476529706[/C][/ROW]
[ROW][C]9[/C][C]121[/C][C]117.777406801581[/C][C]3.22259319841871[/C][/ROW]
[ROW][C]10[/C][C]112[/C][C]111.055542447278[/C][C]0.944457552721759[/C][/ROW]
[ROW][C]11[/C][C]118[/C][C]116.773659832873[/C][C]1.22634016712686[/C][/ROW]
[ROW][C]12[/C][C]122[/C][C]119.672390202404[/C][C]2.32760979759573[/C][/ROW]
[ROW][C]13[/C][C]105[/C][C]107.007746702296[/C][C]-2.00774670229641[/C][/ROW]
[ROW][C]14[/C][C]111[/C][C]109.622820459047[/C][C]1.37717954095316[/C][/ROW]
[ROW][C]15[/C][C]151[/C][C]150.249268352787[/C][C]0.750731647212707[/C][/ROW]
[ROW][C]16[/C][C]106[/C][C]105.727730738164[/C][C]0.272269261836368[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]101.708215516242[/C][C]-1.70821551624158[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]148.382853905355[/C][C]0.617146094644949[/C][/ROW]
[ROW][C]19[/C][C]122[/C][C]118.851396607238[/C][C]3.14860339276214[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]115.717993871578[/C][C]-0.71799387157819[/C][/ROW]
[ROW][C]21[/C][C]86[/C][C]83.2746868360219[/C][C]2.7253131639781[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]124.5514643947[/C][C]-0.551464394700474[/C][/ROW]
[ROW][C]23[/C][C]69[/C][C]70.7429936100165[/C][C]-1.74299361001651[/C][/ROW]
[ROW][C]24[/C][C]117[/C][C]117.266396915089[/C][C]-0.266396915089178[/C][/ROW]
[ROW][C]25[/C][C]113[/C][C]113.458448583803[/C][C]-0.458448583802962[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]120.915018398097[/C][C]2.08498160190302[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]122.196923408604[/C][C]0.803076591395727[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]81.0596893362179[/C][C]2.94031066378211[/C][/ROW]
[ROW][C]29[/C][C]97[/C][C]92.0426508775649[/C][C]4.9573491224351[/C][/ROW]
[ROW][C]30[/C][C]121[/C][C]120.305430613772[/C][C]0.694569386227736[/C][/ROW]
[ROW][C]31[/C][C]132[/C][C]130.641754240214[/C][C]1.35824575978621[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]119.335978074561[/C][C]-0.335978074561494[/C][/ROW]
[ROW][C]33[/C][C]98[/C][C]98.8628078017692[/C][C]-0.862807801769214[/C][/ROW]
[ROW][C]34[/C][C]87[/C][C]86.9233719728539[/C][C]0.0766280271461479[/C][/ROW]
[ROW][C]35[/C][C]101[/C][C]98.5654340450088[/C][C]2.43456595499124[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]113.769679827861[/C][C]1.23032017213857[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]109.962169141118[/C][C]-0.962169141117901[/C][/ROW]
[ROW][C]38[/C][C]109[/C][C]108.874631595301[/C][C]0.125368404699217[/C][/ROW]
[ROW][C]39[/C][C]159[/C][C]157.60082082037[/C][C]1.3991791796302[/C][/ROW]
[ROW][C]40[/C][C]129[/C][C]127.606990121587[/C][C]1.39300987841255[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]118.477930299056[/C][C]0.522069700943734[/C][/ROW]
[ROW][C]42[/C][C]119[/C][C]116.591190672246[/C][C]2.40880932775372[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]119.53592759983[/C][C]2.4640724001696[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]129.956244570563[/C][C]1.04375542943723[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]115.620236498481[/C][C]4.37976350151874[/C][/ROW]
[ROW][C]46[/C][C]82[/C][C]82.1531987814684[/C][C]-0.153198781468392[/C][/ROW]
[ROW][C]47[/C][C]86[/C][C]84.465328455967[/C][C]1.53467154403304[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]101.498541911975[/C][C]3.50145808802462[/C][/ROW]
[ROW][C]49[/C][C]114[/C][C]115.753696190617[/C][C]-1.75369619061683[/C][/ROW]
[ROW][C]50[/C][C]100[/C][C]99.1096033614532[/C][C]0.890396638546826[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]99.8939456374554[/C][C]0.106054362544633[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]99.192576984477[/C][C]-0.192576984476951[/C][/ROW]
[ROW][C]53[/C][C]132[/C][C]132.83703984719[/C][C]-0.837039847190496[/C][/ROW]
[ROW][C]54[/C][C]82[/C][C]84.7937101735985[/C][C]-2.79371017359851[/C][/ROW]
[ROW][C]55[/C][C]132[/C][C]129.788051238002[/C][C]2.21194876199846[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]105.87628929047[/C][C]1.12371070953004[/C][/ROW]
[ROW][C]57[/C][C]114[/C][C]114.097132946347[/C][C]-0.0971329463470607[/C][/ROW]
[ROW][C]58[/C][C]110[/C][C]106.211610595508[/C][C]3.78838940449192[/C][/ROW]
[ROW][C]59[/C][C]105[/C][C]102.198420357835[/C][C]2.80157964216486[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]116.587642926263[/C][C]4.41235707373734[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]105.62106008115[/C][C]3.3789399188499[/C][/ROW]
[ROW][C]62[/C][C]106[/C][C]104.3961045041[/C][C]1.6038954958996[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]122.197483088346[/C][C]1.80251691165359[/C][/ROW]
[ROW][C]64[/C][C]120[/C][C]119.172491546648[/C][C]0.827508453352268[/C][/ROW]
[ROW][C]65[/C][C]91[/C][C]93.1656589323994[/C][C]-2.1656589323994[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]124.324517135503[/C][C]1.67548286449743[/C][/ROW]
[ROW][C]67[/C][C]138[/C][C]139.139881336278[/C][C]-1.1398813362778[/C][/ROW]
[ROW][C]68[/C][C]118[/C][C]116.919819369555[/C][C]1.08018063044546[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]127.612833923458[/C][C]0.387166076542157[/C][/ROW]
[ROW][C]70[/C][C]98[/C][C]99.1766351634519[/C][C]-1.17663516345189[/C][/ROW]
[ROW][C]71[/C][C]133[/C][C]133.336570849217[/C][C]-0.336570849216645[/C][/ROW]
[ROW][C]72[/C][C]130[/C][C]129.729313775983[/C][C]0.27068622401664[/C][/ROW]
[ROW][C]73[/C][C]103[/C][C]100.700929473222[/C][C]2.29907052677833[/C][/ROW]
[ROW][C]74[/C][C]124[/C][C]124.696723658667[/C][C]-0.696723658666983[/C][/ROW]
[ROW][C]75[/C][C]142[/C][C]140.745168843101[/C][C]1.25483115689899[/C][/ROW]
[ROW][C]76[/C][C]96[/C][C]98.2477427094932[/C][C]-2.24774270949319[/C][/ROW]
[ROW][C]77[/C][C]93[/C][C]92.9175287828719[/C][C]0.0824712171280908[/C][/ROW]
[ROW][C]78[/C][C]129[/C][C]130.395161600933[/C][C]-1.39516160093278[/C][/ROW]
[ROW][C]79[/C][C]150[/C][C]150.028326641137[/C][C]-0.0283266411374103[/C][/ROW]
[ROW][C]80[/C][C]88[/C][C]87.4173868054334[/C][C]0.582613194566637[/C][/ROW]
[ROW][C]81[/C][C]125[/C][C]123.697569420952[/C][C]1.30243057904766[/C][/ROW]
[ROW][C]82[/C][C]92[/C][C]90.7689424525639[/C][C]1.23105754743612[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]111.447780947973[/C][C]-111.447780947973[/C][/ROW]
[ROW][C]84[/C][C]117[/C][C]116.473182997127[/C][C]0.526817002872573[/C][/ROW]
[ROW][C]85[/C][C]112[/C][C]112.745107563993[/C][C]-0.745107563993456[/C][/ROW]
[ROW][C]86[/C][C]144[/C][C]142.364574598703[/C][C]1.63542540129676[/C][/ROW]
[ROW][C]87[/C][C]130[/C][C]128.81099035912[/C][C]1.18900964088003[/C][/ROW]
[ROW][C]88[/C][C]87[/C][C]86.4668936859347[/C][C]0.533106314065274[/C][/ROW]
[ROW][C]89[/C][C]92[/C][C]92.3702890499532[/C][C]-0.370289049953246[/C][/ROW]
[ROW][C]90[/C][C]114[/C][C]114.602246077232[/C][C]-0.602246077231821[/C][/ROW]
[ROW][C]91[/C][C]81[/C][C]79.5805283999823[/C][C]1.4194716000177[/C][/ROW]
[ROW][C]92[/C][C]127[/C][C]125.897825556566[/C][C]1.10217444343439[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]114.951223974609[/C][C]0.0487760253905504[/C][/ROW]
[ROW][C]94[/C][C]123[/C][C]124.312829108106[/C][C]-1.31282910810598[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]114.946894803255[/C][C]0.053105196744628[/C][/ROW]
[ROW][C]96[/C][C]117[/C][C]116.304098926433[/C][C]0.695901073567129[/C][/ROW]
[ROW][C]97[/C][C]117[/C][C]115.279742618994[/C][C]1.72025738100594[/C][/ROW]
[ROW][C]98[/C][C]103[/C][C]101.619361944625[/C][C]1.38063805537502[/C][/ROW]
[ROW][C]99[/C][C]108[/C][C]107.162702367303[/C][C]0.837297632697197[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]136.964233121511[/C][C]2.03576687848945[/C][/ROW]
[ROW][C]101[/C][C]113[/C][C]111.244092967078[/C][C]1.75590703292242[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]94.8136273491189[/C][C]2.18637265088105[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]114.99430298562[/C][C]2.00569701438009[/C][/ROW]
[ROW][C]104[/C][C]133[/C][C]131.73298650856[/C][C]1.26701349144013[/C][/ROW]
[ROW][C]105[/C][C]115[/C][C]115.497149401714[/C][C]-0.497149401713677[/C][/ROW]
[ROW][C]106[/C][C]103[/C][C]104.665194896571[/C][C]-1.66519489657077[/C][/ROW]
[ROW][C]107[/C][C]95[/C][C]94.3776204986102[/C][C]0.622379501389842[/C][/ROW]
[ROW][C]108[/C][C]117[/C][C]117.737428154032[/C][C]-0.737428154031801[/C][/ROW]
[ROW][C]109[/C][C]113[/C][C]112.525527530208[/C][C]0.474472469791785[/C][/ROW]
[ROW][C]110[/C][C]127[/C][C]129.228056885628[/C][C]-2.22805688562788[/C][/ROW]
[ROW][C]111[/C][C]126[/C][C]125.305559344375[/C][C]0.694440655624691[/C][/ROW]
[ROW][C]112[/C][C]119[/C][C]117.821066513978[/C][C]1.17893348602182[/C][/ROW]
[ROW][C]113[/C][C]97[/C][C]94.0965107629689[/C][C]2.90348923703108[/C][/ROW]
[ROW][C]114[/C][C]105[/C][C]104.243692447264[/C][C]0.756307552735969[/C][/ROW]
[ROW][C]115[/C][C]140[/C][C]139.90405160587[/C][C]0.0959483941300038[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]88.5504776269618[/C][C]2.44952237303824[/C][/ROW]
[ROW][C]117[/C][C]112[/C][C]111.748933014259[/C][C]0.251066985740911[/C][/ROW]
[ROW][C]118[/C][C]113[/C][C]113.150752710679[/C][C]-0.150752710678588[/C][/ROW]
[ROW][C]119[/C][C]102[/C][C]97.2064472861459[/C][C]4.79355271385411[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]90.7866419020158[/C][C]1.21335809798419[/C][/ROW]
[ROW][C]121[/C][C]98[/C][C]98.8542931778262[/C][C]-0.854293177826181[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]122.011193413138[/C][C]-0.0111934131379454[/C][/ROW]
[ROW][C]123[/C][C]100[/C][C]99.5597397939413[/C][C]0.440260206058678[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]81.789468928012[/C][C]2.21053107198805[/C][/ROW]
[ROW][C]125[/C][C]142[/C][C]138.051353784752[/C][C]3.94864621524832[/C][/ROW]
[ROW][C]126[/C][C]124[/C][C]122.291478028484[/C][C]1.7085219715158[/C][/ROW]
[ROW][C]127[/C][C]137[/C][C]134.350393232678[/C][C]2.64960676732239[/C][/ROW]
[ROW][C]128[/C][C]105[/C][C]103.977226307002[/C][C]1.02277369299788[/C][/ROW]
[ROW][C]129[/C][C]106[/C][C]104.852775327194[/C][C]1.14722467280569[/C][/ROW]
[ROW][C]130[/C][C]125[/C][C]123.076426209004[/C][C]1.92357379099648[/C][/ROW]
[ROW][C]131[/C][C]104[/C][C]102.462846273389[/C][C]1.53715372661077[/C][/ROW]
[ROW][C]132[/C][C]130[/C][C]131.891035870383[/C][C]-1.89103587038285[/C][/ROW]
[ROW][C]133[/C][C]79[/C][C]79.7943343896671[/C][C]-0.794334389667141[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]108.589096908904[/C][C]-0.589096908904097[/C][/ROW]
[ROW][C]135[/C][C]136[/C][C]134.131688115737[/C][C]1.86831188426262[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]96.4060485042791[/C][C]1.59395149572094[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]124.098447067743[/C][C]-4.09844706774346[/C][/ROW]
[ROW][C]138[/C][C]108[/C][C]108.147704812266[/C][C]-0.147704812265783[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]139.279786605622[/C][C]-0.279786605622098[/C][/ROW]
[ROW][C]140[/C][C]123[/C][C]121.990056103737[/C][C]1.00994389626335[/C][/ROW]
[ROW][C]141[/C][C]90[/C][C]87.6724419315785[/C][C]2.32755806842149[/C][/ROW]
[ROW][C]142[/C][C]119[/C][C]118.885536292122[/C][C]0.114463707878438[/C][/ROW]
[ROW][C]143[/C][C]105[/C][C]107.030248514225[/C][C]-2.03024851422489[/C][/ROW]
[ROW][C]144[/C][C]110[/C][C]107.779177027148[/C][C]2.22082297285229[/C][/ROW]
[ROW][C]145[/C][C]135[/C][C]134.360263060501[/C][C]0.639736939498564[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]97.3635640783436[/C][C]3.6364359216564[/C][/ROW]
[ROW][C]147[/C][C]114[/C][C]112.260296224872[/C][C]1.73970377512808[/C][/ROW]
[ROW][C]148[/C][C]118[/C][C]115.902263860254[/C][C]2.09773613974619[/C][/ROW]
[ROW][C]149[/C][C]120[/C][C]117.956893616884[/C][C]2.04310638311576[/C][/ROW]
[ROW][C]150[/C][C]108[/C][C]110.26544810174[/C][C]-2.26544810173988[/C][/ROW]
[ROW][C]151[/C][C]114[/C][C]110.963312641726[/C][C]3.03668735827433[/C][/ROW]
[ROW][C]152[/C][C]122[/C][C]121.502051403468[/C][C]0.497948596532432[/C][/ROW]
[ROW][C]153[/C][C]132[/C][C]129.97301518072[/C][C]2.0269848192798[/C][/ROW]
[ROW][C]154[/C][C]130[/C][C]128.372450385638[/C][C]1.6275496143618[/C][/ROW]
[ROW][C]155[/C][C]130[/C][C]131.305876240709[/C][C]-1.30587624070895[/C][/ROW]
[ROW][C]156[/C][C]112[/C][C]112.007567325017[/C][C]-0.00756732501655002[/C][/ROW]
[ROW][C]157[/C][C]114[/C][C]116.689650210149[/C][C]-2.68965021014875[/C][/ROW]
[ROW][C]158[/C][C]103[/C][C]103.812961875839[/C][C]-0.812961875838661[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]114.198360797825[/C][C]0.801639202175054[/C][/ROW]
[ROW][C]160[/C][C]108[/C][C]106.016064690183[/C][C]1.98393530981664[/C][/ROW]
[ROW][C]161[/C][C]94[/C][C]94.0825134813642[/C][C]-0.0825134813642288[/C][/ROW]
[ROW][C]162[/C][C]105[/C][C]104.819291596103[/C][C]0.180708403896626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191193&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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
1127125.122191402541.87780859745952
2108108.243469192376-0.243469192376404
3110110.86387943232-0.863879432319897
4102100.5307173077111.46928269228949
5104106.39455312845-2.39455312845033
6140140.623590074444-0.623590074443876
7112113.444932328975-1.44493232897488
8115117.470864765297-2.47086476529706
9121117.7774068015813.22259319841871
10112111.0555424472780.944457552721759
11118116.7736598328731.22634016712686
12122119.6723902024042.32760979759573
13105107.007746702296-2.00774670229641
14111109.6228204590471.37717954095316
15151150.2492683527870.750731647212707
16106105.7277307381640.272269261836368
17100101.708215516242-1.70821551624158
18149148.3828539053550.617146094644949
19122118.8513966072383.14860339276214
20115115.717993871578-0.71799387157819
218683.27468683602192.7253131639781
22124124.5514643947-0.551464394700474
236970.7429936100165-1.74299361001651
24117117.266396915089-0.266396915089178
25113113.458448583803-0.458448583802962
26123120.9150183980972.08498160190302
27123122.1969234086040.803076591395727
288481.05968933621792.94031066378211
299792.04265087756494.9573491224351
30121120.3054306137720.694569386227736
31132130.6417542402141.35824575978621
32119119.335978074561-0.335978074561494
339898.8628078017692-0.862807801769214
348786.92337197285390.0766280271461479
3510198.56543404500882.43456595499124
36115113.7696798278611.23032017213857
37109109.962169141118-0.962169141117901
38109108.8746315953010.125368404699217
39159157.600820820371.3991791796302
40129127.6069901215871.39300987841255
41119118.4779302990560.522069700943734
42119116.5911906722462.40880932775372
43122119.535927599832.4640724001696
44131129.9562445705631.04375542943723
45120115.6202364984814.37976350151874
468282.1531987814684-0.153198781468392
478684.4653284559671.53467154403304
48105101.4985419119753.50145808802462
49114115.753696190617-1.75369619061683
5010099.10960336145320.890396638546826
5110099.89394563745540.106054362544633
529999.192576984477-0.192576984476951
53132132.83703984719-0.837039847190496
548284.7937101735985-2.79371017359851
55132129.7880512380022.21194876199846
56107105.876289290471.12371070953004
57114114.097132946347-0.0971329463470607
58110106.2116105955083.78838940449192
59105102.1984203578352.80157964216486
60121116.5876429262634.41235707373734
61109105.621060081153.3789399188499
62106104.39610450411.6038954958996
63124122.1974830883461.80251691165359
64120119.1724915466480.827508453352268
659193.1656589323994-2.1656589323994
66126124.3245171355031.67548286449743
67138139.139881336278-1.1398813362778
68118116.9198193695551.08018063044546
69128127.6128339234580.387166076542157
709899.1766351634519-1.17663516345189
71133133.336570849217-0.336570849216645
72130129.7293137759830.27068622401664
73103100.7009294732222.29907052677833
74124124.696723658667-0.696723658666983
75142140.7451688431011.25483115689899
769698.2477427094932-2.24774270949319
779392.91752878287190.0824712171280908
78129130.395161600933-1.39516160093278
79150150.028326641137-0.0283266411374103
808887.41738680543340.582613194566637
81125123.6975694209521.30243057904766
829290.76894245256391.23105754743612
830111.447780947973-111.447780947973
84117116.4731829971270.526817002872573
85112112.745107563993-0.745107563993456
86144142.3645745987031.63542540129676
87130128.810990359121.18900964088003
888786.46689368593470.533106314065274
899292.3702890499532-0.370289049953246
90114114.602246077232-0.602246077231821
918179.58052839998231.4194716000177
92127125.8978255565661.10217444343439
93115114.9512239746090.0487760253905504
94123124.312829108106-1.31282910810598
95115114.9468948032550.053105196744628
96117116.3040989264330.695901073567129
97117115.2797426189941.72025738100594
98103101.6193619446251.38063805537502
99108107.1627023673030.837297632697197
100139136.9642331215112.03576687848945
101113111.2440929670781.75590703292242
1029794.81362734911892.18637265088105
103117114.994302985622.00569701438009
104133131.732986508561.26701349144013
105115115.497149401714-0.497149401713677
106103104.665194896571-1.66519489657077
1079594.37762049861020.622379501389842
108117117.737428154032-0.737428154031801
109113112.5255275302080.474472469791785
110127129.228056885628-2.22805688562788
111126125.3055593443750.694440655624691
112119117.8210665139781.17893348602182
1139794.09651076296892.90348923703108
114105104.2436924472640.756307552735969
115140139.904051605870.0959483941300038
1169188.55047762696182.44952237303824
117112111.7489330142590.251066985740911
118113113.150752710679-0.150752710678588
11910297.20644728614594.79355271385411
1209290.78664190201581.21335809798419
1219898.8542931778262-0.854293177826181
122122122.011193413138-0.0111934131379454
12310099.55973979394130.440260206058678
1248481.7894689280122.21053107198805
125142138.0513537847523.94864621524832
126124122.2914780284841.7085219715158
127137134.3503932326782.64960676732239
128105103.9772263070021.02277369299788
129106104.8527753271941.14722467280569
130125123.0764262090041.92357379099648
131104102.4628462733891.53715372661077
132130131.891035870383-1.89103587038285
1337979.7943343896671-0.794334389667141
134108108.589096908904-0.589096908904097
135136134.1316881157371.86831188426262
1369896.40604850427911.59395149572094
137120124.098447067743-4.09844706774346
138108108.147704812266-0.147704812265783
139139139.279786605622-0.279786605622098
140123121.9900561037371.00994389626335
1419087.67244193157852.32755806842149
142119118.8855362921220.114463707878438
143105107.030248514225-2.03024851422489
144110107.7791770271482.22082297285229
145135134.3602630605010.639736939498564
14610197.36356407834363.6364359216564
147114112.2602962248721.73970377512808
148118115.9022638602542.09773613974619
149120117.9568936168842.04310638311576
150108110.26544810174-2.26544810173988
151114110.9633126417263.03668735827433
152122121.5020514034680.497948596532432
153132129.973015180722.0269848192798
154130128.3724503856381.6275496143618
155130131.305876240709-1.30587624070895
156112112.007567325017-0.00756732501655002
157114116.689650210149-2.68965021014875
158103103.812961875839-0.812961875838661
159115114.1983607978250.801639202175054
160108106.0160646901831.98393530981664
1619494.0825134813642-0.0825134813642288
162105104.8192915961030.180708403896626






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
144.37971572817444e-468.75943145634887e-461
152.4018015158017e-614.8036030316034e-611
168.09310634289542e-761.61862126857908e-751
179.3369547915368e-951.86739095830736e-941
186.97723867835097e-1111.39544773567019e-1101
191.43777234274829e-1192.87554468549659e-1191
202.39466529359407e-1344.78933058718815e-1341
219.07425360021945e-1531.81485072004389e-1521
222.01933333201772e-1714.03866666403545e-1711
234.5926502320118e-1789.1853004640236e-1781
243.15035235338756e-1946.30070470677512e-1941
256.55607865702668e-2171.31121573140534e-2161
265.72329904172579e-2301.14465980834516e-2291
272.97398305932052e-2445.94796611864103e-2441
281.47234330088488e-2612.94468660176976e-2611
291.63990098977863e-2663.27980197955726e-2661
301.1581347793097e-2942.3162695586194e-2941
311.63326723045141e-3083.26653446090283e-3081
324.94065645841247e-3249.88131291682493e-3241
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
13011.13155497657559e-3145.65777488287794e-315
13111.04184118023505e-3035.20920590117525e-304
13216.87638801704082e-2863.43819400852041e-286
13313.33724860107896e-2651.66862430053948e-265
13415.75283114650095e-2672.87641557325048e-267
13516.91065368555762e-2513.45532684277881e-251
13617.0247944791878e-2263.5123972395939e-226
13711.99748699018818e-2089.9874349509409e-209
13813.2391127024446e-2001.6195563512223e-200
13915.98901268530282e-1832.99450634265141e-183
14013.64869074461437e-1641.82434537230718e-164
14116.18608964716642e-1493.09304482358321e-149
14213.00859288870691e-1391.50429644435346e-139
14311.97773325643044e-1269.88866628215219e-127
14411.3452669708665e-1096.72633485433248e-110
14513.47321955883809e-961.73660977941905e-96
14614.9022090314935e-762.45110451574675e-76
14712.40267274352893e-601.20133637176447e-60
14816.6064068824931e-493.30320344124655e-49

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 4.37971572817444e-46 & 8.75943145634887e-46 & 1 \tabularnewline
15 & 2.4018015158017e-61 & 4.8036030316034e-61 & 1 \tabularnewline
16 & 8.09310634289542e-76 & 1.61862126857908e-75 & 1 \tabularnewline
17 & 9.3369547915368e-95 & 1.86739095830736e-94 & 1 \tabularnewline
18 & 6.97723867835097e-111 & 1.39544773567019e-110 & 1 \tabularnewline
19 & 1.43777234274829e-119 & 2.87554468549659e-119 & 1 \tabularnewline
20 & 2.39466529359407e-134 & 4.78933058718815e-134 & 1 \tabularnewline
21 & 9.07425360021945e-153 & 1.81485072004389e-152 & 1 \tabularnewline
22 & 2.01933333201772e-171 & 4.03866666403545e-171 & 1 \tabularnewline
23 & 4.5926502320118e-178 & 9.1853004640236e-178 & 1 \tabularnewline
24 & 3.15035235338756e-194 & 6.30070470677512e-194 & 1 \tabularnewline
25 & 6.55607865702668e-217 & 1.31121573140534e-216 & 1 \tabularnewline
26 & 5.72329904172579e-230 & 1.14465980834516e-229 & 1 \tabularnewline
27 & 2.97398305932052e-244 & 5.94796611864103e-244 & 1 \tabularnewline
28 & 1.47234330088488e-261 & 2.94468660176976e-261 & 1 \tabularnewline
29 & 1.63990098977863e-266 & 3.27980197955726e-266 & 1 \tabularnewline
30 & 1.1581347793097e-294 & 2.3162695586194e-294 & 1 \tabularnewline
31 & 1.63326723045141e-308 & 3.26653446090283e-308 & 1 \tabularnewline
32 & 4.94065645841247e-324 & 9.88131291682493e-324 & 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 & 1.13155497657559e-314 & 5.65777488287794e-315 \tabularnewline
131 & 1 & 1.04184118023505e-303 & 5.20920590117525e-304 \tabularnewline
132 & 1 & 6.87638801704082e-286 & 3.43819400852041e-286 \tabularnewline
133 & 1 & 3.33724860107896e-265 & 1.66862430053948e-265 \tabularnewline
134 & 1 & 5.75283114650095e-267 & 2.87641557325048e-267 \tabularnewline
135 & 1 & 6.91065368555762e-251 & 3.45532684277881e-251 \tabularnewline
136 & 1 & 7.0247944791878e-226 & 3.5123972395939e-226 \tabularnewline
137 & 1 & 1.99748699018818e-208 & 9.9874349509409e-209 \tabularnewline
138 & 1 & 3.2391127024446e-200 & 1.6195563512223e-200 \tabularnewline
139 & 1 & 5.98901268530282e-183 & 2.99450634265141e-183 \tabularnewline
140 & 1 & 3.64869074461437e-164 & 1.82434537230718e-164 \tabularnewline
141 & 1 & 6.18608964716642e-149 & 3.09304482358321e-149 \tabularnewline
142 & 1 & 3.00859288870691e-139 & 1.50429644435346e-139 \tabularnewline
143 & 1 & 1.97773325643044e-126 & 9.88866628215219e-127 \tabularnewline
144 & 1 & 1.3452669708665e-109 & 6.72633485433248e-110 \tabularnewline
145 & 1 & 3.47321955883809e-96 & 1.73660977941905e-96 \tabularnewline
146 & 1 & 4.9022090314935e-76 & 2.45110451574675e-76 \tabularnewline
147 & 1 & 2.40267274352893e-60 & 1.20133637176447e-60 \tabularnewline
148 & 1 & 6.6064068824931e-49 & 3.30320344124655e-49 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191193&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]4.37971572817444e-46[/C][C]8.75943145634887e-46[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]2.4018015158017e-61[/C][C]4.8036030316034e-61[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]8.09310634289542e-76[/C][C]1.61862126857908e-75[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]9.3369547915368e-95[/C][C]1.86739095830736e-94[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]6.97723867835097e-111[/C][C]1.39544773567019e-110[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]1.43777234274829e-119[/C][C]2.87554468549659e-119[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]2.39466529359407e-134[/C][C]4.78933058718815e-134[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]9.07425360021945e-153[/C][C]1.81485072004389e-152[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]2.01933333201772e-171[/C][C]4.03866666403545e-171[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]4.5926502320118e-178[/C][C]9.1853004640236e-178[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.15035235338756e-194[/C][C]6.30070470677512e-194[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]6.55607865702668e-217[/C][C]1.31121573140534e-216[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]5.72329904172579e-230[/C][C]1.14465980834516e-229[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]2.97398305932052e-244[/C][C]5.94796611864103e-244[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.47234330088488e-261[/C][C]2.94468660176976e-261[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.63990098977863e-266[/C][C]3.27980197955726e-266[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.1581347793097e-294[/C][C]2.3162695586194e-294[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.63326723045141e-308[/C][C]3.26653446090283e-308[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]4.94065645841247e-324[/C][C]9.88131291682493e-324[/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]1.13155497657559e-314[/C][C]5.65777488287794e-315[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.04184118023505e-303[/C][C]5.20920590117525e-304[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]6.87638801704082e-286[/C][C]3.43819400852041e-286[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]3.33724860107896e-265[/C][C]1.66862430053948e-265[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]5.75283114650095e-267[/C][C]2.87641557325048e-267[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]6.91065368555762e-251[/C][C]3.45532684277881e-251[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]7.0247944791878e-226[/C][C]3.5123972395939e-226[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.99748699018818e-208[/C][C]9.9874349509409e-209[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]3.2391127024446e-200[/C][C]1.6195563512223e-200[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]5.98901268530282e-183[/C][C]2.99450634265141e-183[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]3.64869074461437e-164[/C][C]1.82434537230718e-164[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]6.18608964716642e-149[/C][C]3.09304482358321e-149[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]3.00859288870691e-139[/C][C]1.50429644435346e-139[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.97773325643044e-126[/C][C]9.88866628215219e-127[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.3452669708665e-109[/C][C]6.72633485433248e-110[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]3.47321955883809e-96[/C][C]1.73660977941905e-96[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]4.9022090314935e-76[/C][C]2.45110451574675e-76[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]2.40267274352893e-60[/C][C]1.20133637176447e-60[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]6.6064068824931e-49[/C][C]3.30320344124655e-49[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191193&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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
144.37971572817444e-468.75943145634887e-461
152.4018015158017e-614.8036030316034e-611
168.09310634289542e-761.61862126857908e-751
179.3369547915368e-951.86739095830736e-941
186.97723867835097e-1111.39544773567019e-1101
191.43777234274829e-1192.87554468549659e-1191
202.39466529359407e-1344.78933058718815e-1341
219.07425360021945e-1531.81485072004389e-1521
222.01933333201772e-1714.03866666403545e-1711
234.5926502320118e-1789.1853004640236e-1781
243.15035235338756e-1946.30070470677512e-1941
256.55607865702668e-2171.31121573140534e-2161
265.72329904172579e-2301.14465980834516e-2291
272.97398305932052e-2445.94796611864103e-2441
281.47234330088488e-2612.94468660176976e-2611
291.63990098977863e-2663.27980197955726e-2661
301.1581347793097e-2942.3162695586194e-2941
311.63326723045141e-3083.26653446090283e-3081
324.94065645841247e-3249.88131291682493e-3241
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
13011.13155497657559e-3145.65777488287794e-315
13111.04184118023505e-3035.20920590117525e-304
13216.87638801704082e-2863.43819400852041e-286
13313.33724860107896e-2651.66862430053948e-265
13415.75283114650095e-2672.87641557325048e-267
13516.91065368555762e-2513.45532684277881e-251
13617.0247944791878e-2263.5123972395939e-226
13711.99748699018818e-2089.9874349509409e-209
13813.2391127024446e-2001.6195563512223e-200
13915.98901268530282e-1832.99450634265141e-183
14013.64869074461437e-1641.82434537230718e-164
14116.18608964716642e-1493.09304482358321e-149
14213.00859288870691e-1391.50429644435346e-139
14311.97773325643044e-1269.88866628215219e-127
14411.3452669708665e-1096.72633485433248e-110
14513.47321955883809e-961.73660977941905e-96
14614.9022090314935e-762.45110451574675e-76
14712.40267274352893e-601.20133637176447e-60
14816.6064068824931e-493.30320344124655e-49






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=191193&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=191193&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191193&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 6
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Figure 7
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}