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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 computationFri, 24 Dec 2010 10:52:02 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t1293187830erjtqmqx7w62udr.htm/, Retrieved Tue, 30 Apr 2024 01:33:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114738, Retrieved Tue, 30 Apr 2024 01:33:27 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-24 10:52:02] [40b262140b988d7b8204c4955f8b7651] [Current]
-   PD    [Multiple Regression] [] [2010-12-24 20:20:00] [cc61d4f8286f3f36f43e751ed98b6d78]
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Dataset

Dataseries X:
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Histogram
Boxplots 
9,1	4,5	1,0	-1,0	1989,3
9,0	4,3	1,0	3,0	2097,8
9,0	4,3	1,3	2,0	2154,9
8,9	4,2	1,1	3,0	2152,2
8,8	4,0	0,8	5,0	2250,3
8,7	3,8	0,7	5,0	2346,9
8,5	4,1	0,7	3,0	2525,6
8,3	4,2	0,9	2,0	2409,4
8,1	4,0	1,3	1,0	2394,4
7,9	4,3	1,4	-4,0	2401,3
7,8	4,7	1,6	1,0	2354,3
7,6	5,0	2,1	1,0	2450,4
7,4	5,1	0,3	6,0	2504,7
7,2	5,4	2,1	3,0	2661,4
7,0	5,4	2,5	2,0	2880,4
7,0	5,4	2,3	2,0	3064,4
6,8	5,5	2,4	2,0	3141,1
6,8	5,8	3,0	-8,0	3327,7
6,7	5,7	1,7	0,0	3565,0
6,8	5,5	3,5	-2,0	3403,1
6,7	5,6	4,0	3,0	3149,9
6,7	5,6	3,7	5,0	3006,8
6,7	5,5	3,7	8,0	3230,7
6,5	5,5	3,0	8,0	3361,1
6,3	5,7	2,7	9,0	3484,7
6,3	5,6	2,5	11,0	3411,1
6,3	5,6	2,2	13,0	3288,2
6,5	5,4	2,9	12,0	3280,4
6,6	5,2	3,1	13,0	3174,0
6,5	5,1	3,0	15,0	3165,3
6,3	5,1	2,8	13,0	3092,7
6,3	5,0	2,5	16,0	3053,1
6,5	5,3	1,9	10,0	3182,0
7,0	5,4	1,9	14,0	2999,9
7,1	5,3	1,8	14,0	3249,6
7,3	5,1	2,0	15,0	3210,5
7,3	5,0	2,6	13,0	3030,3
7,4	5,0	2,5	8,0	2803,5
7,4	4,6	2,5	7,0	2767,6
7,3	4,8	1,6	3,0	2882,6
7,4	5,1	1,4	3,0	2863,4
7,5	5,1	0,8	4,0	2897,1
7,7	5,1	1,1	4,0	3012,6
7,7	5,4	1,3	0,0	3143,0
7,7	5,3	1,2	-4,0	3032,9
7,7	5,3	1,3	-14,0	3045,8
7,7	5,1	1,1	-18,0	3110,5
7,8	4,9	1,3	-8,0	3013,2
8,0	4,7	1,2	-1,0	2987,1
8,1	4,4	1,6	1,0	2995,6
8,1	4,6	1,7	2,0	2833,2
8,2	4,5	1,5	0,0	2849,0
8,2	4,2	0,9	1,0	2794,8
8,2	4,0	1,5	0,0	2845,3
8,1	3,9	1,4	-1,0	2915,0
8,1	4,1	1,6	-3,0	2892,6
8,2	4,1	1,7	-3,0	2604,4
8,3	3,7	1,4	-3,0	2641,7
8,3	3,8	1,8	-4,0	2659,8
8,4	4,1	1,7	-8,0	2638,5
8,5	4,1	1,4	-9,0	2720,3
8,5	4,0	1,2	-13,0	2745,9
8,4	4,3	1,0	-18,0	2735,7
8,0	4,4	1,7	-11,0	2811,7
7,9	4,2	2,4	-9,0	2799,4
8,1	4,2	2,0	-10,0	2555,3
8,5	4,0	2,1	-13,0	2305,0
8,8	4,0	2,0	-11,0	2215,0
8,8	4,3	1,8	-5,0	2065,8
8,6	4,4	2,7	-15,0	1940,5
8,3	4,4	2,3	-6,0	2042,0
8,3	4,3	1,9	-6,0	1995,4
8,3	4,1	2,0	-3,0	1946,8
8,4	4,1	2,3	-1,0	1765,9
8,4	3,9	2,8	-3,0	1635,3
8,5	3,8	2,4	-4,0	1833,4
8,6	3,7	2,3	-6,0	1910,4
8,6	3,5	2,7	0,0	1959,7
8,6	3,7	2,7	-4,0	1969,6
8,6	3,7	2,9	-2,0	2061,4
8,6	3,5	3,0	-2,0	2093,5
8,5	3,3	2,2	-6,0	2120,9
8,4	3,2	2,3	-7,0	2174,6
8,4	3,3	2,8	-6,0	2196,7
8,4	3,1	2,8	-6,0	2350,4
8,5	3,2	2,8	-3,0	2440,3
8,5	3,4	2,2	-2,0	2408,6
8,6	3,5	2,6	-5,0	2472,8
8,6	3,3	2,8	-11,0	2407,6
8,4	3,5	2,5	-11,0	2454,6
8,2	3,5	2,4	-11,0	2448,1
8,0	3,8	2,3	-10,0	2497,8
8,0	4,0	1,9	-14,0	2645,6
8,0	4,0	1,7	-8,0	2756,8
8,0	4,1	2,0	-9,0	2849,3
7,9	4,0	2,1	-5,0	2921,4
7,9	3,8	1,7	-1,0	2981,9
7,8	3,7	1,8	-2,0	3080,6
7,8	3,8	1,8	-5,0	3106,2
8,0	3,7	1,8	-4,0	3119,3
7,8	4,0	1,3	-6,0	3061,3
7,4	4,2	1,3	-2,0	3097,3
7,2	4,0	1,3	-2,0	3161,7
7,0	4,1	1,2	-2,0	3257,2
7,0	4,2	1,4	-2,0	3277,0
7,2	4,5	2,2	2,0	3295,3
7,2	4,6	2,9	1,0	3364,0
7,2	4,5	3,1	-8,0	3494,2
7,0	4,5	3,5	-1,0	3667,0
6,9	4,5	3,6	1,0	3813,1
6,8	4,4	4,4	-1,0	3918,0
6,8	4,3	4,1	2,0	3895,5
6,8	4,5	5,1	2,0	3801,1
6,9	4,1	5,8	1,0	3570,1
7,2	4,1	5,9	-1,0	3701,6
7,2	4,3	5,4	-2,0	3862,3
7,2	4,4	5,5	-2,0	3970,1
7,1	4,7	4,8	-1,0	4138,5
7,2	5,0	3,2	-8,0	4199,8
7,3	4,7	2,7	-4,0	4290,9
7,5	4,5	2,1	-6,0	4443,9
7,6	4,5	1,9	-3,0	4502,6
7,7	4,5	0,6	-3,0	4357,0
7,7	5,5	0,7	-7,0	4591,3
7,7	4,5	-0,2	-9,0	4697,0
7,8	4,4	-1,0	-11,0	4621,4
8,0	4,2	-1,7	-13,0	4562,8
8,1	3,9	-0,7	-11,0	4202,5
8,1	3,9	-1,0	-9,0	4296,5
8,0	4,2	-0,9	-17,0	4435,2
8,1	4,0	0,0	-22,0	4105,2
8,2	3,8	0,3	-25,0	4116,7
8,3	3,7	0,8	-20,0	3844,5
8,4	3,7	0,8	-24,0	3721,0
8,4	3,7	1,9	-24,0	3674,4
8,4	3,7	2,1	-22,0	3857,6
8,5	3,7	2,5	-19,0	3801,1
8,5	3,8	2,7	-18,0	3504,4
8,6	3,7	2,4	-17,0	3032,6
8,6	3,5	2,4	-11,0	3047,0
8,5	3,5	2,9	-11,0	2962,3
8,5	3,1	3,1	-12,0	2197,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 11.6359824396043 -0.559320335978252rente[t] -0.143899561563292inflatie[t] -0.0296059693049306consumer[t] -0.000333689284785689Bel20[t] -0.00268120125236932t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid[t] =  +  11.6359824396043 -0.559320335978252rente[t] -0.143899561563292inflatie[t] -0.0296059693049306consumer[t] -0.000333689284785689Bel20[t] -0.00268120125236932t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid[t] =  +  11.6359824396043 -0.559320335978252rente[t] -0.143899561563292inflatie[t] -0.0296059693049306consumer[t] -0.000333689284785689Bel20[t] -0.00268120125236932t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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
Werkloosheid[t] = + 11.6359824396043 -0.559320335978252rente[t] -0.143899561563292inflatie[t] -0.0296059693049306consumer[t] -0.000333689284785689Bel20[t] -0.00268120125236932t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.63598243960430.26736943.520400
rente-0.5593203359782520.075582-7.400200
inflatie-0.1438995615632920.024294-5.923200
consumer-0.02960596930493060.004442-6.664700
Bel20-0.0003336892847856897e-05-4.79384e-062e-06
t-0.002681201252369320.001503-1.78390.0766660.038333

\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) & 11.6359824396043 & 0.267369 & 43.5204 & 0 & 0 \tabularnewline
rente & -0.559320335978252 & 0.075582 & -7.4002 & 0 & 0 \tabularnewline
inflatie & -0.143899561563292 & 0.024294 & -5.9232 & 0 & 0 \tabularnewline
consumer & -0.0296059693049306 & 0.004442 & -6.6647 & 0 & 0 \tabularnewline
Bel20 & -0.000333689284785689 & 7e-05 & -4.7938 & 4e-06 & 2e-06 \tabularnewline
t & -0.00268120125236932 & 0.001503 & -1.7839 & 0.076666 & 0.038333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&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]11.6359824396043[/C][C]0.267369[/C][C]43.5204[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]rente[/C][C]-0.559320335978252[/C][C]0.075582[/C][C]-7.4002[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.143899561563292[/C][C]0.024294[/C][C]-5.9232[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]consumer[/C][C]-0.0296059693049306[/C][C]0.004442[/C][C]-6.6647[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bel20[/C][C]-0.000333689284785689[/C][C]7e-05[/C][C]-4.7938[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.00268120125236932[/C][C]0.001503[/C][C]-1.7839[/C][C]0.076666[/C][C]0.038333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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)11.63598243960430.26736943.520400
rente-0.5593203359782520.075582-7.400200
inflatie-0.1438995615632920.024294-5.923200
consumer-0.02960596930493060.004442-6.664700
Bel20-0.0003336892847856897e-05-4.79384e-062e-06
t-0.002681201252369320.001503-1.78390.0766660.038333






Multiple Linear Regression - Regression Statistics
Multiple R0.906296630636011
R-squared0.821373582702186
Adjusted R-squared0.814806435007414
F-TEST (value)125.073109495618
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.307203185474167
Sum Squared Residuals12.8348364145047

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.906296630636011 \tabularnewline
R-squared & 0.821373582702186 \tabularnewline
Adjusted R-squared & 0.814806435007414 \tabularnewline
F-TEST (value) & 125.073109495618 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.307203185474167 \tabularnewline
Sum Squared Residuals & 12.8348364145047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.906296630636011[/C][/ROW]
[ROW][C]R-squared[/C][C]0.821373582702186[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.814806435007414[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]125.073109495618[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.307203185474167[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12.8348364145047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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 R0.906296630636011
R-squared0.821373582702186
Adjusted R-squared0.814806435007414
F-TEST (value)125.073109495618
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.307203185474167
Sum Squared Residuals12.8348364145047






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.18.338258039967320.761741960032681
298.292811741291610.70718825870839
398.257512982713920.74248701728608
48.98.310838719136020.589161280863977
58.88.371244596100960.428755403899044
68.78.462583033290270.237416966709730
78.58.291687394663080.208312605336917
88.38.272674911697260.0273250883027432
98.18.35890926159194-0.258909261591938
107.98.3197693938494-0.419769393849395
117.87.93223369575334-0.132233695753342
127.67.65773907265795-0.0577390726579463
137.47.69199587393316-0.291995873933161
147.27.29902815806226-0.0990281580622647
1577.19531514812144-0.195315148121443
1677.16001503078117-0.160015030781166
176.87.06141787163158-0.26141787163158
186.87.03839410515606-0.238394105156057
196.76.9626821458147-0.262682145814703
206.86.92608203476072-0.126082034760723
216.76.73197929951197-0.0319792995119653
226.76.76100696477155-0.0610069647715545
236.76.65072685833870.0492731416612976
246.56.70526226744458-0.205262267444584
256.36.56303690256111-0.263036902561110
266.36.61041523996959-0.31041523996959
276.36.63270238167651-0.332702381676508
286.56.67336430025174-0.173364300251743
296.66.75966582447863-0.159665824478633
306.56.77099777114819-0.270997771148192
316.36.88053426289378-0.580534262893784
326.36.90135115147095-0.601351151470949
336.56.95183685338379-0.451836853383787
3476.835564560073340.164435439926656
357.16.819883134164140.280116865835857
367.36.883727369524960.416272630475044
377.36.969981212660680.330018787339322
387.47.205400543878690.194599456121315
397.47.46803289164635-0.0680328916463535
407.37.56304683807467-0.263046838074666
417.47.42775628260936-0.0277562826093639
427.57.470563520092760.0294364799072381
437.77.386171337978660.313828662021342
447.77.261824918103820.438175081896177
457.77.484628774080240.215371225919765
467.77.75931271794711-0.0593127179471072
477.77.99410967669714-0.294109676697135
487.87.8109209046881-0.0109209046880991
4987.73596123198610.264038768013899
508.17.781468009371350.318531990628649
518.17.677117955311270.422882044688732
528.27.813088347879630.38691165212037
538.28.053022974289160.146977025710835
548.28.088620763717720.111379236282276
558.18.16260937837488-0.0626093783748768
568.18.085970776203260.0140292237967404
578.28.16506887066980.0349311293302029
588.38.41683906195521-0.116839061955208
598.38.32423219573-0.0242321957300066
608.48.293676308826150.106323691173851
618.58.336475161852230.163524838147771
628.58.52838733803955-0.028387338039552
638.48.53812342553583-0.138123425535833
6488.1461783268131-0.146178326813107
657.98.09952393925509-0.199523939255086
668.18.26546208634915-0.165462086349151
678.58.53259533203275-0.032595332032753
688.88.515124183957560.284875816042437
698.88.245577419684820.554422580315182
708.68.395325539860610.204674460139386
718.38.149880977083440.150119022916562
728.38.276241554725220.0237584452747763
738.38.298433855837970.00156614416203172
748.48.253735239124480.146264760875518
758.48.393760083488990.00623991651101146
768.58.468072862448650.0319271375513528
778.68.56923151463180.0307684853682044
788.68.426767858380240.173232141619758
798.68.427342943232570.172657056767434
808.68.306037214714350.293962785285649
818.68.390118698459680.209881301540318
828.58.72370200447019-0.223702004470191
838.48.77424973537126-0.374249735371256
848.48.60670621724072-0.206706217240722
858.48.66460104011244-0.264601040112442
868.58.487171230645220.0128287693547775
878.58.439937680157950.060062319842046
888.68.3911596765140.208840323486006
898.68.67095498734223-0.070954987342227
908.48.58389619097827-0.183896190978267
918.28.59777392623333-0.397773926233335
9288.39549625358504-0.395496253585039
9388.40761541069073-0.407615410690734
9488.21897205745327-0.218972057453271
9588.11592866459634-0.115928664596343
967.98.0123066661327-0.112306666132699
977.98.04043727775204-0.140437277752040
987.88.07596899083775-0.275968990837750
997.88.09763121821183-0.297631218211834
10088.11690475162167-0.116904751621666
1017.88.0969431474849-0.296943147484899
1027.47.85196118756487-0.451961187564871
1037.27.93965446356795-0.739654463567954
10477.86356385817706-0.863563858177056
10577.76956366317545-0.769563663175446
1067.27.35943632074767-0.159436320747667
1077.27.20677490824332-0.00677490824332195
1087.27.4542532071414-0.254253207141398
10977.12910888771823-0.129108887718231
1106.97.00407378719248-0.104073787192481
1116.86.96641290292315-0.166412902923146
1126.86.98152370473048-0.181523704730476
1136.86.754579143202930.0454208567970666
1146.96.98158457733799-0.0815845773379848
1157.26.979845217589830.220154782410171
1167.26.913231831163330.286768168836674
1177.26.80425693525690.395743064743095
1187.16.648710081442520.451289918557476
1197.26.89525870987510.304741290124902
1207.36.983500419134150.316499580865848
1217.57.187180500053060.312819499946941
1227.67.104873742181640.495126257818364
1237.77.337847130826340.362152869173657
1247.76.801696115233830.898303884766172
1257.77.511785836574690.188214163425313
1267.87.764595166710440.0354048332895637
12788.05327385644632-0.0532738564463242
1288.18.13550550512256-0.0355055051225612
1298.18.085415440959460.0145845590405365
13088.09111323339696-0.0911132333969588
1318.18.3289338044372-0.228933804437208
1328.28.47992728305126-0.279927283051258
1338.38.40402871140908-0.104028711409078
1348.48.56098201404746-0.160982014047464
1358.48.41556121574649-0.0155612157464867
1368.48.263756286598860.136243713401140
1378.58.133550797396770.366449202603227
1388.58.11555729172490.384442708275096
1398.68.33980662779630.260193372203695
1408.68.266548552209090.333451447790911
1418.58.220181052596420.279818947403579
1428.58.69715950094628-0.197159500946283

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.1 & 8.33825803996732 & 0.761741960032681 \tabularnewline
2 & 9 & 8.29281174129161 & 0.70718825870839 \tabularnewline
3 & 9 & 8.25751298271392 & 0.74248701728608 \tabularnewline
4 & 8.9 & 8.31083871913602 & 0.589161280863977 \tabularnewline
5 & 8.8 & 8.37124459610096 & 0.428755403899044 \tabularnewline
6 & 8.7 & 8.46258303329027 & 0.237416966709730 \tabularnewline
7 & 8.5 & 8.29168739466308 & 0.208312605336917 \tabularnewline
8 & 8.3 & 8.27267491169726 & 0.0273250883027432 \tabularnewline
9 & 8.1 & 8.35890926159194 & -0.258909261591938 \tabularnewline
10 & 7.9 & 8.3197693938494 & -0.419769393849395 \tabularnewline
11 & 7.8 & 7.93223369575334 & -0.132233695753342 \tabularnewline
12 & 7.6 & 7.65773907265795 & -0.0577390726579463 \tabularnewline
13 & 7.4 & 7.69199587393316 & -0.291995873933161 \tabularnewline
14 & 7.2 & 7.29902815806226 & -0.0990281580622647 \tabularnewline
15 & 7 & 7.19531514812144 & -0.195315148121443 \tabularnewline
16 & 7 & 7.16001503078117 & -0.160015030781166 \tabularnewline
17 & 6.8 & 7.06141787163158 & -0.26141787163158 \tabularnewline
18 & 6.8 & 7.03839410515606 & -0.238394105156057 \tabularnewline
19 & 6.7 & 6.9626821458147 & -0.262682145814703 \tabularnewline
20 & 6.8 & 6.92608203476072 & -0.126082034760723 \tabularnewline
21 & 6.7 & 6.73197929951197 & -0.0319792995119653 \tabularnewline
22 & 6.7 & 6.76100696477155 & -0.0610069647715545 \tabularnewline
23 & 6.7 & 6.6507268583387 & 0.0492731416612976 \tabularnewline
24 & 6.5 & 6.70526226744458 & -0.205262267444584 \tabularnewline
25 & 6.3 & 6.56303690256111 & -0.263036902561110 \tabularnewline
26 & 6.3 & 6.61041523996959 & -0.31041523996959 \tabularnewline
27 & 6.3 & 6.63270238167651 & -0.332702381676508 \tabularnewline
28 & 6.5 & 6.67336430025174 & -0.173364300251743 \tabularnewline
29 & 6.6 & 6.75966582447863 & -0.159665824478633 \tabularnewline
30 & 6.5 & 6.77099777114819 & -0.270997771148192 \tabularnewline
31 & 6.3 & 6.88053426289378 & -0.580534262893784 \tabularnewline
32 & 6.3 & 6.90135115147095 & -0.601351151470949 \tabularnewline
33 & 6.5 & 6.95183685338379 & -0.451836853383787 \tabularnewline
34 & 7 & 6.83556456007334 & 0.164435439926656 \tabularnewline
35 & 7.1 & 6.81988313416414 & 0.280116865835857 \tabularnewline
36 & 7.3 & 6.88372736952496 & 0.416272630475044 \tabularnewline
37 & 7.3 & 6.96998121266068 & 0.330018787339322 \tabularnewline
38 & 7.4 & 7.20540054387869 & 0.194599456121315 \tabularnewline
39 & 7.4 & 7.46803289164635 & -0.0680328916463535 \tabularnewline
40 & 7.3 & 7.56304683807467 & -0.263046838074666 \tabularnewline
41 & 7.4 & 7.42775628260936 & -0.0277562826093639 \tabularnewline
42 & 7.5 & 7.47056352009276 & 0.0294364799072381 \tabularnewline
43 & 7.7 & 7.38617133797866 & 0.313828662021342 \tabularnewline
44 & 7.7 & 7.26182491810382 & 0.438175081896177 \tabularnewline
45 & 7.7 & 7.48462877408024 & 0.215371225919765 \tabularnewline
46 & 7.7 & 7.75931271794711 & -0.0593127179471072 \tabularnewline
47 & 7.7 & 7.99410967669714 & -0.294109676697135 \tabularnewline
48 & 7.8 & 7.8109209046881 & -0.0109209046880991 \tabularnewline
49 & 8 & 7.7359612319861 & 0.264038768013899 \tabularnewline
50 & 8.1 & 7.78146800937135 & 0.318531990628649 \tabularnewline
51 & 8.1 & 7.67711795531127 & 0.422882044688732 \tabularnewline
52 & 8.2 & 7.81308834787963 & 0.38691165212037 \tabularnewline
53 & 8.2 & 8.05302297428916 & 0.146977025710835 \tabularnewline
54 & 8.2 & 8.08862076371772 & 0.111379236282276 \tabularnewline
55 & 8.1 & 8.16260937837488 & -0.0626093783748768 \tabularnewline
56 & 8.1 & 8.08597077620326 & 0.0140292237967404 \tabularnewline
57 & 8.2 & 8.1650688706698 & 0.0349311293302029 \tabularnewline
58 & 8.3 & 8.41683906195521 & -0.116839061955208 \tabularnewline
59 & 8.3 & 8.32423219573 & -0.0242321957300066 \tabularnewline
60 & 8.4 & 8.29367630882615 & 0.106323691173851 \tabularnewline
61 & 8.5 & 8.33647516185223 & 0.163524838147771 \tabularnewline
62 & 8.5 & 8.52838733803955 & -0.028387338039552 \tabularnewline
63 & 8.4 & 8.53812342553583 & -0.138123425535833 \tabularnewline
64 & 8 & 8.1461783268131 & -0.146178326813107 \tabularnewline
65 & 7.9 & 8.09952393925509 & -0.199523939255086 \tabularnewline
66 & 8.1 & 8.26546208634915 & -0.165462086349151 \tabularnewline
67 & 8.5 & 8.53259533203275 & -0.032595332032753 \tabularnewline
68 & 8.8 & 8.51512418395756 & 0.284875816042437 \tabularnewline
69 & 8.8 & 8.24557741968482 & 0.554422580315182 \tabularnewline
70 & 8.6 & 8.39532553986061 & 0.204674460139386 \tabularnewline
71 & 8.3 & 8.14988097708344 & 0.150119022916562 \tabularnewline
72 & 8.3 & 8.27624155472522 & 0.0237584452747763 \tabularnewline
73 & 8.3 & 8.29843385583797 & 0.00156614416203172 \tabularnewline
74 & 8.4 & 8.25373523912448 & 0.146264760875518 \tabularnewline
75 & 8.4 & 8.39376008348899 & 0.00623991651101146 \tabularnewline
76 & 8.5 & 8.46807286244865 & 0.0319271375513528 \tabularnewline
77 & 8.6 & 8.5692315146318 & 0.0307684853682044 \tabularnewline
78 & 8.6 & 8.42676785838024 & 0.173232141619758 \tabularnewline
79 & 8.6 & 8.42734294323257 & 0.172657056767434 \tabularnewline
80 & 8.6 & 8.30603721471435 & 0.293962785285649 \tabularnewline
81 & 8.6 & 8.39011869845968 & 0.209881301540318 \tabularnewline
82 & 8.5 & 8.72370200447019 & -0.223702004470191 \tabularnewline
83 & 8.4 & 8.77424973537126 & -0.374249735371256 \tabularnewline
84 & 8.4 & 8.60670621724072 & -0.206706217240722 \tabularnewline
85 & 8.4 & 8.66460104011244 & -0.264601040112442 \tabularnewline
86 & 8.5 & 8.48717123064522 & 0.0128287693547775 \tabularnewline
87 & 8.5 & 8.43993768015795 & 0.060062319842046 \tabularnewline
88 & 8.6 & 8.391159676514 & 0.208840323486006 \tabularnewline
89 & 8.6 & 8.67095498734223 & -0.070954987342227 \tabularnewline
90 & 8.4 & 8.58389619097827 & -0.183896190978267 \tabularnewline
91 & 8.2 & 8.59777392623333 & -0.397773926233335 \tabularnewline
92 & 8 & 8.39549625358504 & -0.395496253585039 \tabularnewline
93 & 8 & 8.40761541069073 & -0.407615410690734 \tabularnewline
94 & 8 & 8.21897205745327 & -0.218972057453271 \tabularnewline
95 & 8 & 8.11592866459634 & -0.115928664596343 \tabularnewline
96 & 7.9 & 8.0123066661327 & -0.112306666132699 \tabularnewline
97 & 7.9 & 8.04043727775204 & -0.140437277752040 \tabularnewline
98 & 7.8 & 8.07596899083775 & -0.275968990837750 \tabularnewline
99 & 7.8 & 8.09763121821183 & -0.297631218211834 \tabularnewline
100 & 8 & 8.11690475162167 & -0.116904751621666 \tabularnewline
101 & 7.8 & 8.0969431474849 & -0.296943147484899 \tabularnewline
102 & 7.4 & 7.85196118756487 & -0.451961187564871 \tabularnewline
103 & 7.2 & 7.93965446356795 & -0.739654463567954 \tabularnewline
104 & 7 & 7.86356385817706 & -0.863563858177056 \tabularnewline
105 & 7 & 7.76956366317545 & -0.769563663175446 \tabularnewline
106 & 7.2 & 7.35943632074767 & -0.159436320747667 \tabularnewline
107 & 7.2 & 7.20677490824332 & -0.00677490824332195 \tabularnewline
108 & 7.2 & 7.4542532071414 & -0.254253207141398 \tabularnewline
109 & 7 & 7.12910888771823 & -0.129108887718231 \tabularnewline
110 & 6.9 & 7.00407378719248 & -0.104073787192481 \tabularnewline
111 & 6.8 & 6.96641290292315 & -0.166412902923146 \tabularnewline
112 & 6.8 & 6.98152370473048 & -0.181523704730476 \tabularnewline
113 & 6.8 & 6.75457914320293 & 0.0454208567970666 \tabularnewline
114 & 6.9 & 6.98158457733799 & -0.0815845773379848 \tabularnewline
115 & 7.2 & 6.97984521758983 & 0.220154782410171 \tabularnewline
116 & 7.2 & 6.91323183116333 & 0.286768168836674 \tabularnewline
117 & 7.2 & 6.8042569352569 & 0.395743064743095 \tabularnewline
118 & 7.1 & 6.64871008144252 & 0.451289918557476 \tabularnewline
119 & 7.2 & 6.8952587098751 & 0.304741290124902 \tabularnewline
120 & 7.3 & 6.98350041913415 & 0.316499580865848 \tabularnewline
121 & 7.5 & 7.18718050005306 & 0.312819499946941 \tabularnewline
122 & 7.6 & 7.10487374218164 & 0.495126257818364 \tabularnewline
123 & 7.7 & 7.33784713082634 & 0.362152869173657 \tabularnewline
124 & 7.7 & 6.80169611523383 & 0.898303884766172 \tabularnewline
125 & 7.7 & 7.51178583657469 & 0.188214163425313 \tabularnewline
126 & 7.8 & 7.76459516671044 & 0.0354048332895637 \tabularnewline
127 & 8 & 8.05327385644632 & -0.0532738564463242 \tabularnewline
128 & 8.1 & 8.13550550512256 & -0.0355055051225612 \tabularnewline
129 & 8.1 & 8.08541544095946 & 0.0145845590405365 \tabularnewline
130 & 8 & 8.09111323339696 & -0.0911132333969588 \tabularnewline
131 & 8.1 & 8.3289338044372 & -0.228933804437208 \tabularnewline
132 & 8.2 & 8.47992728305126 & -0.279927283051258 \tabularnewline
133 & 8.3 & 8.40402871140908 & -0.104028711409078 \tabularnewline
134 & 8.4 & 8.56098201404746 & -0.160982014047464 \tabularnewline
135 & 8.4 & 8.41556121574649 & -0.0155612157464867 \tabularnewline
136 & 8.4 & 8.26375628659886 & 0.136243713401140 \tabularnewline
137 & 8.5 & 8.13355079739677 & 0.366449202603227 \tabularnewline
138 & 8.5 & 8.1155572917249 & 0.384442708275096 \tabularnewline
139 & 8.6 & 8.3398066277963 & 0.260193372203695 \tabularnewline
140 & 8.6 & 8.26654855220909 & 0.333451447790911 \tabularnewline
141 & 8.5 & 8.22018105259642 & 0.279818947403579 \tabularnewline
142 & 8.5 & 8.69715950094628 & -0.197159500946283 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&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]9.1[/C][C]8.33825803996732[/C][C]0.761741960032681[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]8.29281174129161[/C][C]0.70718825870839[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]8.25751298271392[/C][C]0.74248701728608[/C][/ROW]
[ROW][C]4[/C][C]8.9[/C][C]8.31083871913602[/C][C]0.589161280863977[/C][/ROW]
[ROW][C]5[/C][C]8.8[/C][C]8.37124459610096[/C][C]0.428755403899044[/C][/ROW]
[ROW][C]6[/C][C]8.7[/C][C]8.46258303329027[/C][C]0.237416966709730[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]8.29168739466308[/C][C]0.208312605336917[/C][/ROW]
[ROW][C]8[/C][C]8.3[/C][C]8.27267491169726[/C][C]0.0273250883027432[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.35890926159194[/C][C]-0.258909261591938[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]8.3197693938494[/C][C]-0.419769393849395[/C][/ROW]
[ROW][C]11[/C][C]7.8[/C][C]7.93223369575334[/C][C]-0.132233695753342[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.65773907265795[/C][C]-0.0577390726579463[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]7.69199587393316[/C][C]-0.291995873933161[/C][/ROW]
[ROW][C]14[/C][C]7.2[/C][C]7.29902815806226[/C][C]-0.0990281580622647[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.19531514812144[/C][C]-0.195315148121443[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]7.16001503078117[/C][C]-0.160015030781166[/C][/ROW]
[ROW][C]17[/C][C]6.8[/C][C]7.06141787163158[/C][C]-0.26141787163158[/C][/ROW]
[ROW][C]18[/C][C]6.8[/C][C]7.03839410515606[/C][C]-0.238394105156057[/C][/ROW]
[ROW][C]19[/C][C]6.7[/C][C]6.9626821458147[/C][C]-0.262682145814703[/C][/ROW]
[ROW][C]20[/C][C]6.8[/C][C]6.92608203476072[/C][C]-0.126082034760723[/C][/ROW]
[ROW][C]21[/C][C]6.7[/C][C]6.73197929951197[/C][C]-0.0319792995119653[/C][/ROW]
[ROW][C]22[/C][C]6.7[/C][C]6.76100696477155[/C][C]-0.0610069647715545[/C][/ROW]
[ROW][C]23[/C][C]6.7[/C][C]6.6507268583387[/C][C]0.0492731416612976[/C][/ROW]
[ROW][C]24[/C][C]6.5[/C][C]6.70526226744458[/C][C]-0.205262267444584[/C][/ROW]
[ROW][C]25[/C][C]6.3[/C][C]6.56303690256111[/C][C]-0.263036902561110[/C][/ROW]
[ROW][C]26[/C][C]6.3[/C][C]6.61041523996959[/C][C]-0.31041523996959[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]6.63270238167651[/C][C]-0.332702381676508[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]6.67336430025174[/C][C]-0.173364300251743[/C][/ROW]
[ROW][C]29[/C][C]6.6[/C][C]6.75966582447863[/C][C]-0.159665824478633[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]6.77099777114819[/C][C]-0.270997771148192[/C][/ROW]
[ROW][C]31[/C][C]6.3[/C][C]6.88053426289378[/C][C]-0.580534262893784[/C][/ROW]
[ROW][C]32[/C][C]6.3[/C][C]6.90135115147095[/C][C]-0.601351151470949[/C][/ROW]
[ROW][C]33[/C][C]6.5[/C][C]6.95183685338379[/C][C]-0.451836853383787[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]6.83556456007334[/C][C]0.164435439926656[/C][/ROW]
[ROW][C]35[/C][C]7.1[/C][C]6.81988313416414[/C][C]0.280116865835857[/C][/ROW]
[ROW][C]36[/C][C]7.3[/C][C]6.88372736952496[/C][C]0.416272630475044[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]6.96998121266068[/C][C]0.330018787339322[/C][/ROW]
[ROW][C]38[/C][C]7.4[/C][C]7.20540054387869[/C][C]0.194599456121315[/C][/ROW]
[ROW][C]39[/C][C]7.4[/C][C]7.46803289164635[/C][C]-0.0680328916463535[/C][/ROW]
[ROW][C]40[/C][C]7.3[/C][C]7.56304683807467[/C][C]-0.263046838074666[/C][/ROW]
[ROW][C]41[/C][C]7.4[/C][C]7.42775628260936[/C][C]-0.0277562826093639[/C][/ROW]
[ROW][C]42[/C][C]7.5[/C][C]7.47056352009276[/C][C]0.0294364799072381[/C][/ROW]
[ROW][C]43[/C][C]7.7[/C][C]7.38617133797866[/C][C]0.313828662021342[/C][/ROW]
[ROW][C]44[/C][C]7.7[/C][C]7.26182491810382[/C][C]0.438175081896177[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]7.48462877408024[/C][C]0.215371225919765[/C][/ROW]
[ROW][C]46[/C][C]7.7[/C][C]7.75931271794711[/C][C]-0.0593127179471072[/C][/ROW]
[ROW][C]47[/C][C]7.7[/C][C]7.99410967669714[/C][C]-0.294109676697135[/C][/ROW]
[ROW][C]48[/C][C]7.8[/C][C]7.8109209046881[/C][C]-0.0109209046880991[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.7359612319861[/C][C]0.264038768013899[/C][/ROW]
[ROW][C]50[/C][C]8.1[/C][C]7.78146800937135[/C][C]0.318531990628649[/C][/ROW]
[ROW][C]51[/C][C]8.1[/C][C]7.67711795531127[/C][C]0.422882044688732[/C][/ROW]
[ROW][C]52[/C][C]8.2[/C][C]7.81308834787963[/C][C]0.38691165212037[/C][/ROW]
[ROW][C]53[/C][C]8.2[/C][C]8.05302297428916[/C][C]0.146977025710835[/C][/ROW]
[ROW][C]54[/C][C]8.2[/C][C]8.08862076371772[/C][C]0.111379236282276[/C][/ROW]
[ROW][C]55[/C][C]8.1[/C][C]8.16260937837488[/C][C]-0.0626093783748768[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]8.08597077620326[/C][C]0.0140292237967404[/C][/ROW]
[ROW][C]57[/C][C]8.2[/C][C]8.1650688706698[/C][C]0.0349311293302029[/C][/ROW]
[ROW][C]58[/C][C]8.3[/C][C]8.41683906195521[/C][C]-0.116839061955208[/C][/ROW]
[ROW][C]59[/C][C]8.3[/C][C]8.32423219573[/C][C]-0.0242321957300066[/C][/ROW]
[ROW][C]60[/C][C]8.4[/C][C]8.29367630882615[/C][C]0.106323691173851[/C][/ROW]
[ROW][C]61[/C][C]8.5[/C][C]8.33647516185223[/C][C]0.163524838147771[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]8.52838733803955[/C][C]-0.028387338039552[/C][/ROW]
[ROW][C]63[/C][C]8.4[/C][C]8.53812342553583[/C][C]-0.138123425535833[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]8.1461783268131[/C][C]-0.146178326813107[/C][/ROW]
[ROW][C]65[/C][C]7.9[/C][C]8.09952393925509[/C][C]-0.199523939255086[/C][/ROW]
[ROW][C]66[/C][C]8.1[/C][C]8.26546208634915[/C][C]-0.165462086349151[/C][/ROW]
[ROW][C]67[/C][C]8.5[/C][C]8.53259533203275[/C][C]-0.032595332032753[/C][/ROW]
[ROW][C]68[/C][C]8.8[/C][C]8.51512418395756[/C][C]0.284875816042437[/C][/ROW]
[ROW][C]69[/C][C]8.8[/C][C]8.24557741968482[/C][C]0.554422580315182[/C][/ROW]
[ROW][C]70[/C][C]8.6[/C][C]8.39532553986061[/C][C]0.204674460139386[/C][/ROW]
[ROW][C]71[/C][C]8.3[/C][C]8.14988097708344[/C][C]0.150119022916562[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]8.27624155472522[/C][C]0.0237584452747763[/C][/ROW]
[ROW][C]73[/C][C]8.3[/C][C]8.29843385583797[/C][C]0.00156614416203172[/C][/ROW]
[ROW][C]74[/C][C]8.4[/C][C]8.25373523912448[/C][C]0.146264760875518[/C][/ROW]
[ROW][C]75[/C][C]8.4[/C][C]8.39376008348899[/C][C]0.00623991651101146[/C][/ROW]
[ROW][C]76[/C][C]8.5[/C][C]8.46807286244865[/C][C]0.0319271375513528[/C][/ROW]
[ROW][C]77[/C][C]8.6[/C][C]8.5692315146318[/C][C]0.0307684853682044[/C][/ROW]
[ROW][C]78[/C][C]8.6[/C][C]8.42676785838024[/C][C]0.173232141619758[/C][/ROW]
[ROW][C]79[/C][C]8.6[/C][C]8.42734294323257[/C][C]0.172657056767434[/C][/ROW]
[ROW][C]80[/C][C]8.6[/C][C]8.30603721471435[/C][C]0.293962785285649[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.39011869845968[/C][C]0.209881301540318[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]8.72370200447019[/C][C]-0.223702004470191[/C][/ROW]
[ROW][C]83[/C][C]8.4[/C][C]8.77424973537126[/C][C]-0.374249735371256[/C][/ROW]
[ROW][C]84[/C][C]8.4[/C][C]8.60670621724072[/C][C]-0.206706217240722[/C][/ROW]
[ROW][C]85[/C][C]8.4[/C][C]8.66460104011244[/C][C]-0.264601040112442[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]8.48717123064522[/C][C]0.0128287693547775[/C][/ROW]
[ROW][C]87[/C][C]8.5[/C][C]8.43993768015795[/C][C]0.060062319842046[/C][/ROW]
[ROW][C]88[/C][C]8.6[/C][C]8.391159676514[/C][C]0.208840323486006[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]8.67095498734223[/C][C]-0.070954987342227[/C][/ROW]
[ROW][C]90[/C][C]8.4[/C][C]8.58389619097827[/C][C]-0.183896190978267[/C][/ROW]
[ROW][C]91[/C][C]8.2[/C][C]8.59777392623333[/C][C]-0.397773926233335[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]8.39549625358504[/C][C]-0.395496253585039[/C][/ROW]
[ROW][C]93[/C][C]8[/C][C]8.40761541069073[/C][C]-0.407615410690734[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]8.21897205745327[/C][C]-0.218972057453271[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]8.11592866459634[/C][C]-0.115928664596343[/C][/ROW]
[ROW][C]96[/C][C]7.9[/C][C]8.0123066661327[/C][C]-0.112306666132699[/C][/ROW]
[ROW][C]97[/C][C]7.9[/C][C]8.04043727775204[/C][C]-0.140437277752040[/C][/ROW]
[ROW][C]98[/C][C]7.8[/C][C]8.07596899083775[/C][C]-0.275968990837750[/C][/ROW]
[ROW][C]99[/C][C]7.8[/C][C]8.09763121821183[/C][C]-0.297631218211834[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]8.11690475162167[/C][C]-0.116904751621666[/C][/ROW]
[ROW][C]101[/C][C]7.8[/C][C]8.0969431474849[/C][C]-0.296943147484899[/C][/ROW]
[ROW][C]102[/C][C]7.4[/C][C]7.85196118756487[/C][C]-0.451961187564871[/C][/ROW]
[ROW][C]103[/C][C]7.2[/C][C]7.93965446356795[/C][C]-0.739654463567954[/C][/ROW]
[ROW][C]104[/C][C]7[/C][C]7.86356385817706[/C][C]-0.863563858177056[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]7.76956366317545[/C][C]-0.769563663175446[/C][/ROW]
[ROW][C]106[/C][C]7.2[/C][C]7.35943632074767[/C][C]-0.159436320747667[/C][/ROW]
[ROW][C]107[/C][C]7.2[/C][C]7.20677490824332[/C][C]-0.00677490824332195[/C][/ROW]
[ROW][C]108[/C][C]7.2[/C][C]7.4542532071414[/C][C]-0.254253207141398[/C][/ROW]
[ROW][C]109[/C][C]7[/C][C]7.12910888771823[/C][C]-0.129108887718231[/C][/ROW]
[ROW][C]110[/C][C]6.9[/C][C]7.00407378719248[/C][C]-0.104073787192481[/C][/ROW]
[ROW][C]111[/C][C]6.8[/C][C]6.96641290292315[/C][C]-0.166412902923146[/C][/ROW]
[ROW][C]112[/C][C]6.8[/C][C]6.98152370473048[/C][C]-0.181523704730476[/C][/ROW]
[ROW][C]113[/C][C]6.8[/C][C]6.75457914320293[/C][C]0.0454208567970666[/C][/ROW]
[ROW][C]114[/C][C]6.9[/C][C]6.98158457733799[/C][C]-0.0815845773379848[/C][/ROW]
[ROW][C]115[/C][C]7.2[/C][C]6.97984521758983[/C][C]0.220154782410171[/C][/ROW]
[ROW][C]116[/C][C]7.2[/C][C]6.91323183116333[/C][C]0.286768168836674[/C][/ROW]
[ROW][C]117[/C][C]7.2[/C][C]6.8042569352569[/C][C]0.395743064743095[/C][/ROW]
[ROW][C]118[/C][C]7.1[/C][C]6.64871008144252[/C][C]0.451289918557476[/C][/ROW]
[ROW][C]119[/C][C]7.2[/C][C]6.8952587098751[/C][C]0.304741290124902[/C][/ROW]
[ROW][C]120[/C][C]7.3[/C][C]6.98350041913415[/C][C]0.316499580865848[/C][/ROW]
[ROW][C]121[/C][C]7.5[/C][C]7.18718050005306[/C][C]0.312819499946941[/C][/ROW]
[ROW][C]122[/C][C]7.6[/C][C]7.10487374218164[/C][C]0.495126257818364[/C][/ROW]
[ROW][C]123[/C][C]7.7[/C][C]7.33784713082634[/C][C]0.362152869173657[/C][/ROW]
[ROW][C]124[/C][C]7.7[/C][C]6.80169611523383[/C][C]0.898303884766172[/C][/ROW]
[ROW][C]125[/C][C]7.7[/C][C]7.51178583657469[/C][C]0.188214163425313[/C][/ROW]
[ROW][C]126[/C][C]7.8[/C][C]7.76459516671044[/C][C]0.0354048332895637[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]8.05327385644632[/C][C]-0.0532738564463242[/C][/ROW]
[ROW][C]128[/C][C]8.1[/C][C]8.13550550512256[/C][C]-0.0355055051225612[/C][/ROW]
[ROW][C]129[/C][C]8.1[/C][C]8.08541544095946[/C][C]0.0145845590405365[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]8.09111323339696[/C][C]-0.0911132333969588[/C][/ROW]
[ROW][C]131[/C][C]8.1[/C][C]8.3289338044372[/C][C]-0.228933804437208[/C][/ROW]
[ROW][C]132[/C][C]8.2[/C][C]8.47992728305126[/C][C]-0.279927283051258[/C][/ROW]
[ROW][C]133[/C][C]8.3[/C][C]8.40402871140908[/C][C]-0.104028711409078[/C][/ROW]
[ROW][C]134[/C][C]8.4[/C][C]8.56098201404746[/C][C]-0.160982014047464[/C][/ROW]
[ROW][C]135[/C][C]8.4[/C][C]8.41556121574649[/C][C]-0.0155612157464867[/C][/ROW]
[ROW][C]136[/C][C]8.4[/C][C]8.26375628659886[/C][C]0.136243713401140[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]8.13355079739677[/C][C]0.366449202603227[/C][/ROW]
[ROW][C]138[/C][C]8.5[/C][C]8.1155572917249[/C][C]0.384442708275096[/C][/ROW]
[ROW][C]139[/C][C]8.6[/C][C]8.3398066277963[/C][C]0.260193372203695[/C][/ROW]
[ROW][C]140[/C][C]8.6[/C][C]8.26654855220909[/C][C]0.333451447790911[/C][/ROW]
[ROW][C]141[/C][C]8.5[/C][C]8.22018105259642[/C][C]0.279818947403579[/C][/ROW]
[ROW][C]142[/C][C]8.5[/C][C]8.69715950094628[/C][C]-0.197159500946283[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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
19.18.338258039967320.761741960032681
298.292811741291610.70718825870839
398.257512982713920.74248701728608
48.98.310838719136020.589161280863977
58.88.371244596100960.428755403899044
68.78.462583033290270.237416966709730
78.58.291687394663080.208312605336917
88.38.272674911697260.0273250883027432
98.18.35890926159194-0.258909261591938
107.98.3197693938494-0.419769393849395
117.87.93223369575334-0.132233695753342
127.67.65773907265795-0.0577390726579463
137.47.69199587393316-0.291995873933161
147.27.29902815806226-0.0990281580622647
1577.19531514812144-0.195315148121443
1677.16001503078117-0.160015030781166
176.87.06141787163158-0.26141787163158
186.87.03839410515606-0.238394105156057
196.76.9626821458147-0.262682145814703
206.86.92608203476072-0.126082034760723
216.76.73197929951197-0.0319792995119653
226.76.76100696477155-0.0610069647715545
236.76.65072685833870.0492731416612976
246.56.70526226744458-0.205262267444584
256.36.56303690256111-0.263036902561110
266.36.61041523996959-0.31041523996959
276.36.63270238167651-0.332702381676508
286.56.67336430025174-0.173364300251743
296.66.75966582447863-0.159665824478633
306.56.77099777114819-0.270997771148192
316.36.88053426289378-0.580534262893784
326.36.90135115147095-0.601351151470949
336.56.95183685338379-0.451836853383787
3476.835564560073340.164435439926656
357.16.819883134164140.280116865835857
367.36.883727369524960.416272630475044
377.36.969981212660680.330018787339322
387.47.205400543878690.194599456121315
397.47.46803289164635-0.0680328916463535
407.37.56304683807467-0.263046838074666
417.47.42775628260936-0.0277562826093639
427.57.470563520092760.0294364799072381
437.77.386171337978660.313828662021342
447.77.261824918103820.438175081896177
457.77.484628774080240.215371225919765
467.77.75931271794711-0.0593127179471072
477.77.99410967669714-0.294109676697135
487.87.8109209046881-0.0109209046880991
4987.73596123198610.264038768013899
508.17.781468009371350.318531990628649
518.17.677117955311270.422882044688732
528.27.813088347879630.38691165212037
538.28.053022974289160.146977025710835
548.28.088620763717720.111379236282276
558.18.16260937837488-0.0626093783748768
568.18.085970776203260.0140292237967404
578.28.16506887066980.0349311293302029
588.38.41683906195521-0.116839061955208
598.38.32423219573-0.0242321957300066
608.48.293676308826150.106323691173851
618.58.336475161852230.163524838147771
628.58.52838733803955-0.028387338039552
638.48.53812342553583-0.138123425535833
6488.1461783268131-0.146178326813107
657.98.09952393925509-0.199523939255086
668.18.26546208634915-0.165462086349151
678.58.53259533203275-0.032595332032753
688.88.515124183957560.284875816042437
698.88.245577419684820.554422580315182
708.68.395325539860610.204674460139386
718.38.149880977083440.150119022916562
728.38.276241554725220.0237584452747763
738.38.298433855837970.00156614416203172
748.48.253735239124480.146264760875518
758.48.393760083488990.00623991651101146
768.58.468072862448650.0319271375513528
778.68.56923151463180.0307684853682044
788.68.426767858380240.173232141619758
798.68.427342943232570.172657056767434
808.68.306037214714350.293962785285649
818.68.390118698459680.209881301540318
828.58.72370200447019-0.223702004470191
838.48.77424973537126-0.374249735371256
848.48.60670621724072-0.206706217240722
858.48.66460104011244-0.264601040112442
868.58.487171230645220.0128287693547775
878.58.439937680157950.060062319842046
888.68.3911596765140.208840323486006
898.68.67095498734223-0.070954987342227
908.48.58389619097827-0.183896190978267
918.28.59777392623333-0.397773926233335
9288.39549625358504-0.395496253585039
9388.40761541069073-0.407615410690734
9488.21897205745327-0.218972057453271
9588.11592866459634-0.115928664596343
967.98.0123066661327-0.112306666132699
977.98.04043727775204-0.140437277752040
987.88.07596899083775-0.275968990837750
997.88.09763121821183-0.297631218211834
10088.11690475162167-0.116904751621666
1017.88.0969431474849-0.296943147484899
1027.47.85196118756487-0.451961187564871
1037.27.93965446356795-0.739654463567954
10477.86356385817706-0.863563858177056
10577.76956366317545-0.769563663175446
1067.27.35943632074767-0.159436320747667
1077.27.20677490824332-0.00677490824332195
1087.27.4542532071414-0.254253207141398
10977.12910888771823-0.129108887718231
1106.97.00407378719248-0.104073787192481
1116.86.96641290292315-0.166412902923146
1126.86.98152370473048-0.181523704730476
1136.86.754579143202930.0454208567970666
1146.96.98158457733799-0.0815845773379848
1157.26.979845217589830.220154782410171
1167.26.913231831163330.286768168836674
1177.26.80425693525690.395743064743095
1187.16.648710081442520.451289918557476
1197.26.89525870987510.304741290124902
1207.36.983500419134150.316499580865848
1217.57.187180500053060.312819499946941
1227.67.104873742181640.495126257818364
1237.77.337847130826340.362152869173657
1247.76.801696115233830.898303884766172
1257.77.511785836574690.188214163425313
1267.87.764595166710440.0354048332895637
12788.05327385644632-0.0532738564463242
1288.18.13550550512256-0.0355055051225612
1298.18.085415440959460.0145845590405365
13088.09111323339696-0.0911132333969588
1318.18.3289338044372-0.228933804437208
1328.28.47992728305126-0.279927283051258
1338.38.40402871140908-0.104028711409078
1348.48.56098201404746-0.160982014047464
1358.48.41556121574649-0.0155612157464867
1368.48.263756286598860.136243713401140
1378.58.133550797396770.366449202603227
1388.58.11555729172490.384442708275096
1398.68.33980662779630.260193372203695
1408.68.266548552209090.333451447790911
1418.58.220181052596420.279818947403579
1428.58.69715950094628-0.197159500946283






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.03154002496774090.06308004993548180.96845997503226
100.008811648907539350.01762329781507870.99118835109246
110.001945980481690210.003891960963380430.99805401951831
120.0004380719929687150.000876143985937430.999561928007031
130.0001232592052267880.0002465184104535760.999876740794773
144.75379913333986e-059.50759826667972e-050.999952462008667
151.69271248550467e-053.38542497100934e-050.999983072875145
167.9031314218711e-061.58062628437422e-050.999992096868578
171.69267735581695e-063.38535471163391e-060.999998307322644
181.18167725473103e-052.36335450946206e-050.999988183227453
193.6173365798665e-067.234673159733e-060.99999638266342
204.04591394702583e-058.09182789405167e-050.99995954086053
219.91331545541987e-050.0001982663091083970.999900866845446
220.0003394398207998790.0006788796415997580.9996605601792
230.0003004866589644470.0006009733179288940.999699513341036
240.0002070559058520200.0004141118117040390.999792944094148
250.0001228633854902160.0002457267709804320.99987713661451
269.85592687716826e-050.0001971185375433650.999901440731228
270.0001422925536136910.0002845851072273820.999857707446386
280.0004625667282249320.0009251334564498640.999537433271775
290.0008955482711961130.001791096542392230.999104451728804
300.0006426894745196760.001285378949039350.99935731052548
310.000562017135538560.001124034271077120.999437982864462
320.0005247806774414090.001049561354882820.999475219322559
330.01467237822863120.02934475645726240.985327621771369
340.2168783792535010.4337567585070020.783121620746499
350.5708045752598940.8583908494802130.429195424740106
360.8114731022605940.3770537954788110.188526897739406
370.8653629530558650.2692740938882700.134637046944135
380.8502557308458160.2994885383083680.149744269154184
390.8181955382077160.3636089235845690.181804461792284
400.7999442045559290.4001115908881430.200055795444071
410.7702302040169250.459539591966150.229769795983075
420.7380655937553210.5238688124893580.261934406244679
430.7645643917384340.4708712165231310.235435608261566
440.8176975558693430.3646048882613150.182302444130657
450.7881644554934940.4236710890130110.211835544506506
460.7615699231650520.4768601536698950.238430076834948
470.7824079064768110.4351841870463770.217592093523188
480.7502223114612130.4995553770775740.249777688538787
490.7314583269890380.5370833460219240.268541673010962
500.7301495933245940.5397008133508120.269850406675406
510.7334871470595150.533025705880970.266512852940485
520.7302841512113440.5394316975773120.269715848788656
530.7049050755159710.5901898489680570.295094924484029
540.6770641665820680.6458716668358630.322935833417931
550.6481016962445380.7037966075109250.351898303755462
560.6084540780112280.7830918439775440.391545921988772
570.5835887673167070.8328224653665860.416411232683293
580.5729700444534310.8540599110931380.427029955546569
590.5396117057909190.9207765884181620.460388294209081
600.5011105658755970.9977788682488060.498889434124403
610.4750777160326160.9501554320652310.524922283967384
620.4352704551832380.8705409103664770.564729544816762
630.4072865439293640.8145730878587270.592713456070636
640.3757332717599780.7514665435199570.624266728240022
650.3465929078338990.6931858156677980.653407092166101
660.3300869600438860.6601739200877730.669913039956114
670.2938880801334880.5877761602669770.706111919866512
680.2776442312370660.5552884624741310.722355768762935
690.3280498643906620.6560997287813230.671950135609338
700.2903828978412110.5807657956824220.709617102158789
710.2648997858997240.5297995717994470.735100214100276
720.2543528809373990.5087057618747980.7456471190626
730.2438451662853090.4876903325706180.756154833714691
740.2299286764516270.4598573529032540.770071323548373
750.2124994103828300.4249988207656590.78750058961717
760.1953415059725030.3906830119450050.804658494027497
770.1804149699695730.3608299399391470.819585030030427
780.1912642637398970.3825285274797930.808735736260103
790.2048814891818160.4097629783636320.795118510818184
800.2770801742476690.5541603484953390.722919825752331
810.3420397991132150.684079598226430.657960200886785
820.3433766697867560.6867533395735120.656623330213244
830.3489066754275140.6978133508550280.651093324572486
840.3210959078070480.6421918156140960.678904092192952
850.2868243273572480.5736486547144960.713175672642752
860.3057684283790460.6115368567580910.694231571620954
870.3740788162807090.7481576325614190.62592118371929
880.5678820240361870.8642359519276250.432117975963813
890.6361632463864830.7276735072270340.363836753613517
900.6674532898502280.6650934202995450.332546710149772
910.6690645398223760.6618709203552480.330935460177624
920.6562187484522740.6875625030954520.343781251547726
930.6329679785745630.7340640428508730.367032021425437
940.6299721935386850.740055612922630.370027806461315
950.6630862049656540.6738275900686930.336913795034346
960.7228347524490570.5543304951018870.277165247550943
970.8039505431749340.3920989136501330.196049456825066
980.8535386291306380.2929227417387240.146461370869362
990.9061892365693530.1876215268612930.0938107634306467
1000.9961249445183730.007750110963254270.00387505548162713
1010.9999435260821750.0001129478356495885.64739178247939e-05
1020.999989787870942.04242581215696e-051.02121290607848e-05
1030.9999934432241751.31135516509342e-056.55677582546711e-06
1040.9999933052301071.33895397863576e-056.69476989317878e-06
1050.9999920367743781.59264512433291e-057.96322562166453e-06
1060.9999927936397451.44127205095094e-057.20636025475472e-06
1070.999998539160782.9216784410777e-061.46083922053885e-06
1080.9999998321548253.35690349142549e-071.67845174571275e-07
1090.999999976824964.63500801424569e-082.31750400712285e-08
1100.999999987779832.44403394219493e-081.22201697109747e-08
1110.999999969784786.04304412126695e-083.02152206063347e-08
1120.9999999387767481.22446504004248e-076.1223252002124e-08
1130.9999999176728571.64654285255342e-078.2327142627671e-08
1140.9999998741311522.51737695795819e-071.25868847897910e-07
1150.9999998805072522.38985495621698e-071.19492747810849e-07
1160.9999998769691132.46061774139566e-071.23030887069783e-07
1170.9999998583701982.83259604450322e-071.41629802225161e-07
1180.9999997416342745.16731451300734e-072.58365725650367e-07
1190.999999554960238.90079540227023e-074.45039770113512e-07
1200.9999995108741589.78251683741077e-074.89125841870538e-07
1210.999998877340032.24531994183964e-061.12265997091982e-06
1220.9999971870730185.62585396374125e-062.81292698187063e-06
1230.9999916632367511.66735264973201e-058.33676324866005e-06
1240.9999919581748691.60836502626534e-058.04182513132669e-06
1250.9999792620983944.14758032111096e-052.07379016055548e-05
1260.999976581112364.68377752782574e-052.34188876391287e-05
1270.9999082650979570.0001834698040851189.1734902042559e-05
1280.9996673683363060.0006652633273882170.000332631663694109
1290.999603565502230.000792868995539350.000396434497769675
1300.9987188886615920.002562222676815350.00128111133840768
1310.998218307705310.003563384589381570.00178169229469078
1320.9975956686394630.004808662721074470.00240433136053724
1330.9898617816643160.02027643667136840.0101382183356842

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0315400249677409 & 0.0630800499354818 & 0.96845997503226 \tabularnewline
10 & 0.00881164890753935 & 0.0176232978150787 & 0.99118835109246 \tabularnewline
11 & 0.00194598048169021 & 0.00389196096338043 & 0.99805401951831 \tabularnewline
12 & 0.000438071992968715 & 0.00087614398593743 & 0.999561928007031 \tabularnewline
13 & 0.000123259205226788 & 0.000246518410453576 & 0.999876740794773 \tabularnewline
14 & 4.75379913333986e-05 & 9.50759826667972e-05 & 0.999952462008667 \tabularnewline
15 & 1.69271248550467e-05 & 3.38542497100934e-05 & 0.999983072875145 \tabularnewline
16 & 7.9031314218711e-06 & 1.58062628437422e-05 & 0.999992096868578 \tabularnewline
17 & 1.69267735581695e-06 & 3.38535471163391e-06 & 0.999998307322644 \tabularnewline
18 & 1.18167725473103e-05 & 2.36335450946206e-05 & 0.999988183227453 \tabularnewline
19 & 3.6173365798665e-06 & 7.234673159733e-06 & 0.99999638266342 \tabularnewline
20 & 4.04591394702583e-05 & 8.09182789405167e-05 & 0.99995954086053 \tabularnewline
21 & 9.91331545541987e-05 & 0.000198266309108397 & 0.999900866845446 \tabularnewline
22 & 0.000339439820799879 & 0.000678879641599758 & 0.9996605601792 \tabularnewline
23 & 0.000300486658964447 & 0.000600973317928894 & 0.999699513341036 \tabularnewline
24 & 0.000207055905852020 & 0.000414111811704039 & 0.999792944094148 \tabularnewline
25 & 0.000122863385490216 & 0.000245726770980432 & 0.99987713661451 \tabularnewline
26 & 9.85592687716826e-05 & 0.000197118537543365 & 0.999901440731228 \tabularnewline
27 & 0.000142292553613691 & 0.000284585107227382 & 0.999857707446386 \tabularnewline
28 & 0.000462566728224932 & 0.000925133456449864 & 0.999537433271775 \tabularnewline
29 & 0.000895548271196113 & 0.00179109654239223 & 0.999104451728804 \tabularnewline
30 & 0.000642689474519676 & 0.00128537894903935 & 0.99935731052548 \tabularnewline
31 & 0.00056201713553856 & 0.00112403427107712 & 0.999437982864462 \tabularnewline
32 & 0.000524780677441409 & 0.00104956135488282 & 0.999475219322559 \tabularnewline
33 & 0.0146723782286312 & 0.0293447564572624 & 0.985327621771369 \tabularnewline
34 & 0.216878379253501 & 0.433756758507002 & 0.783121620746499 \tabularnewline
35 & 0.570804575259894 & 0.858390849480213 & 0.429195424740106 \tabularnewline
36 & 0.811473102260594 & 0.377053795478811 & 0.188526897739406 \tabularnewline
37 & 0.865362953055865 & 0.269274093888270 & 0.134637046944135 \tabularnewline
38 & 0.850255730845816 & 0.299488538308368 & 0.149744269154184 \tabularnewline
39 & 0.818195538207716 & 0.363608923584569 & 0.181804461792284 \tabularnewline
40 & 0.799944204555929 & 0.400111590888143 & 0.200055795444071 \tabularnewline
41 & 0.770230204016925 & 0.45953959196615 & 0.229769795983075 \tabularnewline
42 & 0.738065593755321 & 0.523868812489358 & 0.261934406244679 \tabularnewline
43 & 0.764564391738434 & 0.470871216523131 & 0.235435608261566 \tabularnewline
44 & 0.817697555869343 & 0.364604888261315 & 0.182302444130657 \tabularnewline
45 & 0.788164455493494 & 0.423671089013011 & 0.211835544506506 \tabularnewline
46 & 0.761569923165052 & 0.476860153669895 & 0.238430076834948 \tabularnewline
47 & 0.782407906476811 & 0.435184187046377 & 0.217592093523188 \tabularnewline
48 & 0.750222311461213 & 0.499555377077574 & 0.249777688538787 \tabularnewline
49 & 0.731458326989038 & 0.537083346021924 & 0.268541673010962 \tabularnewline
50 & 0.730149593324594 & 0.539700813350812 & 0.269850406675406 \tabularnewline
51 & 0.733487147059515 & 0.53302570588097 & 0.266512852940485 \tabularnewline
52 & 0.730284151211344 & 0.539431697577312 & 0.269715848788656 \tabularnewline
53 & 0.704905075515971 & 0.590189848968057 & 0.295094924484029 \tabularnewline
54 & 0.677064166582068 & 0.645871666835863 & 0.322935833417931 \tabularnewline
55 & 0.648101696244538 & 0.703796607510925 & 0.351898303755462 \tabularnewline
56 & 0.608454078011228 & 0.783091843977544 & 0.391545921988772 \tabularnewline
57 & 0.583588767316707 & 0.832822465366586 & 0.416411232683293 \tabularnewline
58 & 0.572970044453431 & 0.854059911093138 & 0.427029955546569 \tabularnewline
59 & 0.539611705790919 & 0.920776588418162 & 0.460388294209081 \tabularnewline
60 & 0.501110565875597 & 0.997778868248806 & 0.498889434124403 \tabularnewline
61 & 0.475077716032616 & 0.950155432065231 & 0.524922283967384 \tabularnewline
62 & 0.435270455183238 & 0.870540910366477 & 0.564729544816762 \tabularnewline
63 & 0.407286543929364 & 0.814573087858727 & 0.592713456070636 \tabularnewline
64 & 0.375733271759978 & 0.751466543519957 & 0.624266728240022 \tabularnewline
65 & 0.346592907833899 & 0.693185815667798 & 0.653407092166101 \tabularnewline
66 & 0.330086960043886 & 0.660173920087773 & 0.669913039956114 \tabularnewline
67 & 0.293888080133488 & 0.587776160266977 & 0.706111919866512 \tabularnewline
68 & 0.277644231237066 & 0.555288462474131 & 0.722355768762935 \tabularnewline
69 & 0.328049864390662 & 0.656099728781323 & 0.671950135609338 \tabularnewline
70 & 0.290382897841211 & 0.580765795682422 & 0.709617102158789 \tabularnewline
71 & 0.264899785899724 & 0.529799571799447 & 0.735100214100276 \tabularnewline
72 & 0.254352880937399 & 0.508705761874798 & 0.7456471190626 \tabularnewline
73 & 0.243845166285309 & 0.487690332570618 & 0.756154833714691 \tabularnewline
74 & 0.229928676451627 & 0.459857352903254 & 0.770071323548373 \tabularnewline
75 & 0.212499410382830 & 0.424998820765659 & 0.78750058961717 \tabularnewline
76 & 0.195341505972503 & 0.390683011945005 & 0.804658494027497 \tabularnewline
77 & 0.180414969969573 & 0.360829939939147 & 0.819585030030427 \tabularnewline
78 & 0.191264263739897 & 0.382528527479793 & 0.808735736260103 \tabularnewline
79 & 0.204881489181816 & 0.409762978363632 & 0.795118510818184 \tabularnewline
80 & 0.277080174247669 & 0.554160348495339 & 0.722919825752331 \tabularnewline
81 & 0.342039799113215 & 0.68407959822643 & 0.657960200886785 \tabularnewline
82 & 0.343376669786756 & 0.686753339573512 & 0.656623330213244 \tabularnewline
83 & 0.348906675427514 & 0.697813350855028 & 0.651093324572486 \tabularnewline
84 & 0.321095907807048 & 0.642191815614096 & 0.678904092192952 \tabularnewline
85 & 0.286824327357248 & 0.573648654714496 & 0.713175672642752 \tabularnewline
86 & 0.305768428379046 & 0.611536856758091 & 0.694231571620954 \tabularnewline
87 & 0.374078816280709 & 0.748157632561419 & 0.62592118371929 \tabularnewline
88 & 0.567882024036187 & 0.864235951927625 & 0.432117975963813 \tabularnewline
89 & 0.636163246386483 & 0.727673507227034 & 0.363836753613517 \tabularnewline
90 & 0.667453289850228 & 0.665093420299545 & 0.332546710149772 \tabularnewline
91 & 0.669064539822376 & 0.661870920355248 & 0.330935460177624 \tabularnewline
92 & 0.656218748452274 & 0.687562503095452 & 0.343781251547726 \tabularnewline
93 & 0.632967978574563 & 0.734064042850873 & 0.367032021425437 \tabularnewline
94 & 0.629972193538685 & 0.74005561292263 & 0.370027806461315 \tabularnewline
95 & 0.663086204965654 & 0.673827590068693 & 0.336913795034346 \tabularnewline
96 & 0.722834752449057 & 0.554330495101887 & 0.277165247550943 \tabularnewline
97 & 0.803950543174934 & 0.392098913650133 & 0.196049456825066 \tabularnewline
98 & 0.853538629130638 & 0.292922741738724 & 0.146461370869362 \tabularnewline
99 & 0.906189236569353 & 0.187621526861293 & 0.0938107634306467 \tabularnewline
100 & 0.996124944518373 & 0.00775011096325427 & 0.00387505548162713 \tabularnewline
101 & 0.999943526082175 & 0.000112947835649588 & 5.64739178247939e-05 \tabularnewline
102 & 0.99998978787094 & 2.04242581215696e-05 & 1.02121290607848e-05 \tabularnewline
103 & 0.999993443224175 & 1.31135516509342e-05 & 6.55677582546711e-06 \tabularnewline
104 & 0.999993305230107 & 1.33895397863576e-05 & 6.69476989317878e-06 \tabularnewline
105 & 0.999992036774378 & 1.59264512433291e-05 & 7.96322562166453e-06 \tabularnewline
106 & 0.999992793639745 & 1.44127205095094e-05 & 7.20636025475472e-06 \tabularnewline
107 & 0.99999853916078 & 2.9216784410777e-06 & 1.46083922053885e-06 \tabularnewline
108 & 0.999999832154825 & 3.35690349142549e-07 & 1.67845174571275e-07 \tabularnewline
109 & 0.99999997682496 & 4.63500801424569e-08 & 2.31750400712285e-08 \tabularnewline
110 & 0.99999998777983 & 2.44403394219493e-08 & 1.22201697109747e-08 \tabularnewline
111 & 0.99999996978478 & 6.04304412126695e-08 & 3.02152206063347e-08 \tabularnewline
112 & 0.999999938776748 & 1.22446504004248e-07 & 6.1223252002124e-08 \tabularnewline
113 & 0.999999917672857 & 1.64654285255342e-07 & 8.2327142627671e-08 \tabularnewline
114 & 0.999999874131152 & 2.51737695795819e-07 & 1.25868847897910e-07 \tabularnewline
115 & 0.999999880507252 & 2.38985495621698e-07 & 1.19492747810849e-07 \tabularnewline
116 & 0.999999876969113 & 2.46061774139566e-07 & 1.23030887069783e-07 \tabularnewline
117 & 0.999999858370198 & 2.83259604450322e-07 & 1.41629802225161e-07 \tabularnewline
118 & 0.999999741634274 & 5.16731451300734e-07 & 2.58365725650367e-07 \tabularnewline
119 & 0.99999955496023 & 8.90079540227023e-07 & 4.45039770113512e-07 \tabularnewline
120 & 0.999999510874158 & 9.78251683741077e-07 & 4.89125841870538e-07 \tabularnewline
121 & 0.99999887734003 & 2.24531994183964e-06 & 1.12265997091982e-06 \tabularnewline
122 & 0.999997187073018 & 5.62585396374125e-06 & 2.81292698187063e-06 \tabularnewline
123 & 0.999991663236751 & 1.66735264973201e-05 & 8.33676324866005e-06 \tabularnewline
124 & 0.999991958174869 & 1.60836502626534e-05 & 8.04182513132669e-06 \tabularnewline
125 & 0.999979262098394 & 4.14758032111096e-05 & 2.07379016055548e-05 \tabularnewline
126 & 0.99997658111236 & 4.68377752782574e-05 & 2.34188876391287e-05 \tabularnewline
127 & 0.999908265097957 & 0.000183469804085118 & 9.1734902042559e-05 \tabularnewline
128 & 0.999667368336306 & 0.000665263327388217 & 0.000332631663694109 \tabularnewline
129 & 0.99960356550223 & 0.00079286899553935 & 0.000396434497769675 \tabularnewline
130 & 0.998718888661592 & 0.00256222267681535 & 0.00128111133840768 \tabularnewline
131 & 0.99821830770531 & 0.00356338458938157 & 0.00178169229469078 \tabularnewline
132 & 0.997595668639463 & 0.00480866272107447 & 0.00240433136053724 \tabularnewline
133 & 0.989861781664316 & 0.0202764366713684 & 0.0101382183356842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&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]9[/C][C]0.0315400249677409[/C][C]0.0630800499354818[/C][C]0.96845997503226[/C][/ROW]
[ROW][C]10[/C][C]0.00881164890753935[/C][C]0.0176232978150787[/C][C]0.99118835109246[/C][/ROW]
[ROW][C]11[/C][C]0.00194598048169021[/C][C]0.00389196096338043[/C][C]0.99805401951831[/C][/ROW]
[ROW][C]12[/C][C]0.000438071992968715[/C][C]0.00087614398593743[/C][C]0.999561928007031[/C][/ROW]
[ROW][C]13[/C][C]0.000123259205226788[/C][C]0.000246518410453576[/C][C]0.999876740794773[/C][/ROW]
[ROW][C]14[/C][C]4.75379913333986e-05[/C][C]9.50759826667972e-05[/C][C]0.999952462008667[/C][/ROW]
[ROW][C]15[/C][C]1.69271248550467e-05[/C][C]3.38542497100934e-05[/C][C]0.999983072875145[/C][/ROW]
[ROW][C]16[/C][C]7.9031314218711e-06[/C][C]1.58062628437422e-05[/C][C]0.999992096868578[/C][/ROW]
[ROW][C]17[/C][C]1.69267735581695e-06[/C][C]3.38535471163391e-06[/C][C]0.999998307322644[/C][/ROW]
[ROW][C]18[/C][C]1.18167725473103e-05[/C][C]2.36335450946206e-05[/C][C]0.999988183227453[/C][/ROW]
[ROW][C]19[/C][C]3.6173365798665e-06[/C][C]7.234673159733e-06[/C][C]0.99999638266342[/C][/ROW]
[ROW][C]20[/C][C]4.04591394702583e-05[/C][C]8.09182789405167e-05[/C][C]0.99995954086053[/C][/ROW]
[ROW][C]21[/C][C]9.91331545541987e-05[/C][C]0.000198266309108397[/C][C]0.999900866845446[/C][/ROW]
[ROW][C]22[/C][C]0.000339439820799879[/C][C]0.000678879641599758[/C][C]0.9996605601792[/C][/ROW]
[ROW][C]23[/C][C]0.000300486658964447[/C][C]0.000600973317928894[/C][C]0.999699513341036[/C][/ROW]
[ROW][C]24[/C][C]0.000207055905852020[/C][C]0.000414111811704039[/C][C]0.999792944094148[/C][/ROW]
[ROW][C]25[/C][C]0.000122863385490216[/C][C]0.000245726770980432[/C][C]0.99987713661451[/C][/ROW]
[ROW][C]26[/C][C]9.85592687716826e-05[/C][C]0.000197118537543365[/C][C]0.999901440731228[/C][/ROW]
[ROW][C]27[/C][C]0.000142292553613691[/C][C]0.000284585107227382[/C][C]0.999857707446386[/C][/ROW]
[ROW][C]28[/C][C]0.000462566728224932[/C][C]0.000925133456449864[/C][C]0.999537433271775[/C][/ROW]
[ROW][C]29[/C][C]0.000895548271196113[/C][C]0.00179109654239223[/C][C]0.999104451728804[/C][/ROW]
[ROW][C]30[/C][C]0.000642689474519676[/C][C]0.00128537894903935[/C][C]0.99935731052548[/C][/ROW]
[ROW][C]31[/C][C]0.00056201713553856[/C][C]0.00112403427107712[/C][C]0.999437982864462[/C][/ROW]
[ROW][C]32[/C][C]0.000524780677441409[/C][C]0.00104956135488282[/C][C]0.999475219322559[/C][/ROW]
[ROW][C]33[/C][C]0.0146723782286312[/C][C]0.0293447564572624[/C][C]0.985327621771369[/C][/ROW]
[ROW][C]34[/C][C]0.216878379253501[/C][C]0.433756758507002[/C][C]0.783121620746499[/C][/ROW]
[ROW][C]35[/C][C]0.570804575259894[/C][C]0.858390849480213[/C][C]0.429195424740106[/C][/ROW]
[ROW][C]36[/C][C]0.811473102260594[/C][C]0.377053795478811[/C][C]0.188526897739406[/C][/ROW]
[ROW][C]37[/C][C]0.865362953055865[/C][C]0.269274093888270[/C][C]0.134637046944135[/C][/ROW]
[ROW][C]38[/C][C]0.850255730845816[/C][C]0.299488538308368[/C][C]0.149744269154184[/C][/ROW]
[ROW][C]39[/C][C]0.818195538207716[/C][C]0.363608923584569[/C][C]0.181804461792284[/C][/ROW]
[ROW][C]40[/C][C]0.799944204555929[/C][C]0.400111590888143[/C][C]0.200055795444071[/C][/ROW]
[ROW][C]41[/C][C]0.770230204016925[/C][C]0.45953959196615[/C][C]0.229769795983075[/C][/ROW]
[ROW][C]42[/C][C]0.738065593755321[/C][C]0.523868812489358[/C][C]0.261934406244679[/C][/ROW]
[ROW][C]43[/C][C]0.764564391738434[/C][C]0.470871216523131[/C][C]0.235435608261566[/C][/ROW]
[ROW][C]44[/C][C]0.817697555869343[/C][C]0.364604888261315[/C][C]0.182302444130657[/C][/ROW]
[ROW][C]45[/C][C]0.788164455493494[/C][C]0.423671089013011[/C][C]0.211835544506506[/C][/ROW]
[ROW][C]46[/C][C]0.761569923165052[/C][C]0.476860153669895[/C][C]0.238430076834948[/C][/ROW]
[ROW][C]47[/C][C]0.782407906476811[/C][C]0.435184187046377[/C][C]0.217592093523188[/C][/ROW]
[ROW][C]48[/C][C]0.750222311461213[/C][C]0.499555377077574[/C][C]0.249777688538787[/C][/ROW]
[ROW][C]49[/C][C]0.731458326989038[/C][C]0.537083346021924[/C][C]0.268541673010962[/C][/ROW]
[ROW][C]50[/C][C]0.730149593324594[/C][C]0.539700813350812[/C][C]0.269850406675406[/C][/ROW]
[ROW][C]51[/C][C]0.733487147059515[/C][C]0.53302570588097[/C][C]0.266512852940485[/C][/ROW]
[ROW][C]52[/C][C]0.730284151211344[/C][C]0.539431697577312[/C][C]0.269715848788656[/C][/ROW]
[ROW][C]53[/C][C]0.704905075515971[/C][C]0.590189848968057[/C][C]0.295094924484029[/C][/ROW]
[ROW][C]54[/C][C]0.677064166582068[/C][C]0.645871666835863[/C][C]0.322935833417931[/C][/ROW]
[ROW][C]55[/C][C]0.648101696244538[/C][C]0.703796607510925[/C][C]0.351898303755462[/C][/ROW]
[ROW][C]56[/C][C]0.608454078011228[/C][C]0.783091843977544[/C][C]0.391545921988772[/C][/ROW]
[ROW][C]57[/C][C]0.583588767316707[/C][C]0.832822465366586[/C][C]0.416411232683293[/C][/ROW]
[ROW][C]58[/C][C]0.572970044453431[/C][C]0.854059911093138[/C][C]0.427029955546569[/C][/ROW]
[ROW][C]59[/C][C]0.539611705790919[/C][C]0.920776588418162[/C][C]0.460388294209081[/C][/ROW]
[ROW][C]60[/C][C]0.501110565875597[/C][C]0.997778868248806[/C][C]0.498889434124403[/C][/ROW]
[ROW][C]61[/C][C]0.475077716032616[/C][C]0.950155432065231[/C][C]0.524922283967384[/C][/ROW]
[ROW][C]62[/C][C]0.435270455183238[/C][C]0.870540910366477[/C][C]0.564729544816762[/C][/ROW]
[ROW][C]63[/C][C]0.407286543929364[/C][C]0.814573087858727[/C][C]0.592713456070636[/C][/ROW]
[ROW][C]64[/C][C]0.375733271759978[/C][C]0.751466543519957[/C][C]0.624266728240022[/C][/ROW]
[ROW][C]65[/C][C]0.346592907833899[/C][C]0.693185815667798[/C][C]0.653407092166101[/C][/ROW]
[ROW][C]66[/C][C]0.330086960043886[/C][C]0.660173920087773[/C][C]0.669913039956114[/C][/ROW]
[ROW][C]67[/C][C]0.293888080133488[/C][C]0.587776160266977[/C][C]0.706111919866512[/C][/ROW]
[ROW][C]68[/C][C]0.277644231237066[/C][C]0.555288462474131[/C][C]0.722355768762935[/C][/ROW]
[ROW][C]69[/C][C]0.328049864390662[/C][C]0.656099728781323[/C][C]0.671950135609338[/C][/ROW]
[ROW][C]70[/C][C]0.290382897841211[/C][C]0.580765795682422[/C][C]0.709617102158789[/C][/ROW]
[ROW][C]71[/C][C]0.264899785899724[/C][C]0.529799571799447[/C][C]0.735100214100276[/C][/ROW]
[ROW][C]72[/C][C]0.254352880937399[/C][C]0.508705761874798[/C][C]0.7456471190626[/C][/ROW]
[ROW][C]73[/C][C]0.243845166285309[/C][C]0.487690332570618[/C][C]0.756154833714691[/C][/ROW]
[ROW][C]74[/C][C]0.229928676451627[/C][C]0.459857352903254[/C][C]0.770071323548373[/C][/ROW]
[ROW][C]75[/C][C]0.212499410382830[/C][C]0.424998820765659[/C][C]0.78750058961717[/C][/ROW]
[ROW][C]76[/C][C]0.195341505972503[/C][C]0.390683011945005[/C][C]0.804658494027497[/C][/ROW]
[ROW][C]77[/C][C]0.180414969969573[/C][C]0.360829939939147[/C][C]0.819585030030427[/C][/ROW]
[ROW][C]78[/C][C]0.191264263739897[/C][C]0.382528527479793[/C][C]0.808735736260103[/C][/ROW]
[ROW][C]79[/C][C]0.204881489181816[/C][C]0.409762978363632[/C][C]0.795118510818184[/C][/ROW]
[ROW][C]80[/C][C]0.277080174247669[/C][C]0.554160348495339[/C][C]0.722919825752331[/C][/ROW]
[ROW][C]81[/C][C]0.342039799113215[/C][C]0.68407959822643[/C][C]0.657960200886785[/C][/ROW]
[ROW][C]82[/C][C]0.343376669786756[/C][C]0.686753339573512[/C][C]0.656623330213244[/C][/ROW]
[ROW][C]83[/C][C]0.348906675427514[/C][C]0.697813350855028[/C][C]0.651093324572486[/C][/ROW]
[ROW][C]84[/C][C]0.321095907807048[/C][C]0.642191815614096[/C][C]0.678904092192952[/C][/ROW]
[ROW][C]85[/C][C]0.286824327357248[/C][C]0.573648654714496[/C][C]0.713175672642752[/C][/ROW]
[ROW][C]86[/C][C]0.305768428379046[/C][C]0.611536856758091[/C][C]0.694231571620954[/C][/ROW]
[ROW][C]87[/C][C]0.374078816280709[/C][C]0.748157632561419[/C][C]0.62592118371929[/C][/ROW]
[ROW][C]88[/C][C]0.567882024036187[/C][C]0.864235951927625[/C][C]0.432117975963813[/C][/ROW]
[ROW][C]89[/C][C]0.636163246386483[/C][C]0.727673507227034[/C][C]0.363836753613517[/C][/ROW]
[ROW][C]90[/C][C]0.667453289850228[/C][C]0.665093420299545[/C][C]0.332546710149772[/C][/ROW]
[ROW][C]91[/C][C]0.669064539822376[/C][C]0.661870920355248[/C][C]0.330935460177624[/C][/ROW]
[ROW][C]92[/C][C]0.656218748452274[/C][C]0.687562503095452[/C][C]0.343781251547726[/C][/ROW]
[ROW][C]93[/C][C]0.632967978574563[/C][C]0.734064042850873[/C][C]0.367032021425437[/C][/ROW]
[ROW][C]94[/C][C]0.629972193538685[/C][C]0.74005561292263[/C][C]0.370027806461315[/C][/ROW]
[ROW][C]95[/C][C]0.663086204965654[/C][C]0.673827590068693[/C][C]0.336913795034346[/C][/ROW]
[ROW][C]96[/C][C]0.722834752449057[/C][C]0.554330495101887[/C][C]0.277165247550943[/C][/ROW]
[ROW][C]97[/C][C]0.803950543174934[/C][C]0.392098913650133[/C][C]0.196049456825066[/C][/ROW]
[ROW][C]98[/C][C]0.853538629130638[/C][C]0.292922741738724[/C][C]0.146461370869362[/C][/ROW]
[ROW][C]99[/C][C]0.906189236569353[/C][C]0.187621526861293[/C][C]0.0938107634306467[/C][/ROW]
[ROW][C]100[/C][C]0.996124944518373[/C][C]0.00775011096325427[/C][C]0.00387505548162713[/C][/ROW]
[ROW][C]101[/C][C]0.999943526082175[/C][C]0.000112947835649588[/C][C]5.64739178247939e-05[/C][/ROW]
[ROW][C]102[/C][C]0.99998978787094[/C][C]2.04242581215696e-05[/C][C]1.02121290607848e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999993443224175[/C][C]1.31135516509342e-05[/C][C]6.55677582546711e-06[/C][/ROW]
[ROW][C]104[/C][C]0.999993305230107[/C][C]1.33895397863576e-05[/C][C]6.69476989317878e-06[/C][/ROW]
[ROW][C]105[/C][C]0.999992036774378[/C][C]1.59264512433291e-05[/C][C]7.96322562166453e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999992793639745[/C][C]1.44127205095094e-05[/C][C]7.20636025475472e-06[/C][/ROW]
[ROW][C]107[/C][C]0.99999853916078[/C][C]2.9216784410777e-06[/C][C]1.46083922053885e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999832154825[/C][C]3.35690349142549e-07[/C][C]1.67845174571275e-07[/C][/ROW]
[ROW][C]109[/C][C]0.99999997682496[/C][C]4.63500801424569e-08[/C][C]2.31750400712285e-08[/C][/ROW]
[ROW][C]110[/C][C]0.99999998777983[/C][C]2.44403394219493e-08[/C][C]1.22201697109747e-08[/C][/ROW]
[ROW][C]111[/C][C]0.99999996978478[/C][C]6.04304412126695e-08[/C][C]3.02152206063347e-08[/C][/ROW]
[ROW][C]112[/C][C]0.999999938776748[/C][C]1.22446504004248e-07[/C][C]6.1223252002124e-08[/C][/ROW]
[ROW][C]113[/C][C]0.999999917672857[/C][C]1.64654285255342e-07[/C][C]8.2327142627671e-08[/C][/ROW]
[ROW][C]114[/C][C]0.999999874131152[/C][C]2.51737695795819e-07[/C][C]1.25868847897910e-07[/C][/ROW]
[ROW][C]115[/C][C]0.999999880507252[/C][C]2.38985495621698e-07[/C][C]1.19492747810849e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999876969113[/C][C]2.46061774139566e-07[/C][C]1.23030887069783e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999999858370198[/C][C]2.83259604450322e-07[/C][C]1.41629802225161e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999999741634274[/C][C]5.16731451300734e-07[/C][C]2.58365725650367e-07[/C][/ROW]
[ROW][C]119[/C][C]0.99999955496023[/C][C]8.90079540227023e-07[/C][C]4.45039770113512e-07[/C][/ROW]
[ROW][C]120[/C][C]0.999999510874158[/C][C]9.78251683741077e-07[/C][C]4.89125841870538e-07[/C][/ROW]
[ROW][C]121[/C][C]0.99999887734003[/C][C]2.24531994183964e-06[/C][C]1.12265997091982e-06[/C][/ROW]
[ROW][C]122[/C][C]0.999997187073018[/C][C]5.62585396374125e-06[/C][C]2.81292698187063e-06[/C][/ROW]
[ROW][C]123[/C][C]0.999991663236751[/C][C]1.66735264973201e-05[/C][C]8.33676324866005e-06[/C][/ROW]
[ROW][C]124[/C][C]0.999991958174869[/C][C]1.60836502626534e-05[/C][C]8.04182513132669e-06[/C][/ROW]
[ROW][C]125[/C][C]0.999979262098394[/C][C]4.14758032111096e-05[/C][C]2.07379016055548e-05[/C][/ROW]
[ROW][C]126[/C][C]0.99997658111236[/C][C]4.68377752782574e-05[/C][C]2.34188876391287e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999908265097957[/C][C]0.000183469804085118[/C][C]9.1734902042559e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999667368336306[/C][C]0.000665263327388217[/C][C]0.000332631663694109[/C][/ROW]
[ROW][C]129[/C][C]0.99960356550223[/C][C]0.00079286899553935[/C][C]0.000396434497769675[/C][/ROW]
[ROW][C]130[/C][C]0.998718888661592[/C][C]0.00256222267681535[/C][C]0.00128111133840768[/C][/ROW]
[ROW][C]131[/C][C]0.99821830770531[/C][C]0.00356338458938157[/C][C]0.00178169229469078[/C][/ROW]
[ROW][C]132[/C][C]0.997595668639463[/C][C]0.00480866272107447[/C][C]0.00240433136053724[/C][/ROW]
[ROW][C]133[/C][C]0.989861781664316[/C][C]0.0202764366713684[/C][C]0.0101382183356842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&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
90.03154002496774090.06308004993548180.96845997503226
100.008811648907539350.01762329781507870.99118835109246
110.001945980481690210.003891960963380430.99805401951831
120.0004380719929687150.000876143985937430.999561928007031
130.0001232592052267880.0002465184104535760.999876740794773
144.75379913333986e-059.50759826667972e-050.999952462008667
151.69271248550467e-053.38542497100934e-050.999983072875145
167.9031314218711e-061.58062628437422e-050.999992096868578
171.69267735581695e-063.38535471163391e-060.999998307322644
181.18167725473103e-052.36335450946206e-050.999988183227453
193.6173365798665e-067.234673159733e-060.99999638266342
204.04591394702583e-058.09182789405167e-050.99995954086053
219.91331545541987e-050.0001982663091083970.999900866845446
220.0003394398207998790.0006788796415997580.9996605601792
230.0003004866589644470.0006009733179288940.999699513341036
240.0002070559058520200.0004141118117040390.999792944094148
250.0001228633854902160.0002457267709804320.99987713661451
269.85592687716826e-050.0001971185375433650.999901440731228
270.0001422925536136910.0002845851072273820.999857707446386
280.0004625667282249320.0009251334564498640.999537433271775
290.0008955482711961130.001791096542392230.999104451728804
300.0006426894745196760.001285378949039350.99935731052548
310.000562017135538560.001124034271077120.999437982864462
320.0005247806774414090.001049561354882820.999475219322559
330.01467237822863120.02934475645726240.985327621771369
340.2168783792535010.4337567585070020.783121620746499
350.5708045752598940.8583908494802130.429195424740106
360.8114731022605940.3770537954788110.188526897739406
370.8653629530558650.2692740938882700.134637046944135
380.8502557308458160.2994885383083680.149744269154184
390.8181955382077160.3636089235845690.181804461792284
400.7999442045559290.4001115908881430.200055795444071
410.7702302040169250.459539591966150.229769795983075
420.7380655937553210.5238688124893580.261934406244679
430.7645643917384340.4708712165231310.235435608261566
440.8176975558693430.3646048882613150.182302444130657
450.7881644554934940.4236710890130110.211835544506506
460.7615699231650520.4768601536698950.238430076834948
470.7824079064768110.4351841870463770.217592093523188
480.7502223114612130.4995553770775740.249777688538787
490.7314583269890380.5370833460219240.268541673010962
500.7301495933245940.5397008133508120.269850406675406
510.7334871470595150.533025705880970.266512852940485
520.7302841512113440.5394316975773120.269715848788656
530.7049050755159710.5901898489680570.295094924484029
540.6770641665820680.6458716668358630.322935833417931
550.6481016962445380.7037966075109250.351898303755462
560.6084540780112280.7830918439775440.391545921988772
570.5835887673167070.8328224653665860.416411232683293
580.5729700444534310.8540599110931380.427029955546569
590.5396117057909190.9207765884181620.460388294209081
600.5011105658755970.9977788682488060.498889434124403
610.4750777160326160.9501554320652310.524922283967384
620.4352704551832380.8705409103664770.564729544816762
630.4072865439293640.8145730878587270.592713456070636
640.3757332717599780.7514665435199570.624266728240022
650.3465929078338990.6931858156677980.653407092166101
660.3300869600438860.6601739200877730.669913039956114
670.2938880801334880.5877761602669770.706111919866512
680.2776442312370660.5552884624741310.722355768762935
690.3280498643906620.6560997287813230.671950135609338
700.2903828978412110.5807657956824220.709617102158789
710.2648997858997240.5297995717994470.735100214100276
720.2543528809373990.5087057618747980.7456471190626
730.2438451662853090.4876903325706180.756154833714691
740.2299286764516270.4598573529032540.770071323548373
750.2124994103828300.4249988207656590.78750058961717
760.1953415059725030.3906830119450050.804658494027497
770.1804149699695730.3608299399391470.819585030030427
780.1912642637398970.3825285274797930.808735736260103
790.2048814891818160.4097629783636320.795118510818184
800.2770801742476690.5541603484953390.722919825752331
810.3420397991132150.684079598226430.657960200886785
820.3433766697867560.6867533395735120.656623330213244
830.3489066754275140.6978133508550280.651093324572486
840.3210959078070480.6421918156140960.678904092192952
850.2868243273572480.5736486547144960.713175672642752
860.3057684283790460.6115368567580910.694231571620954
870.3740788162807090.7481576325614190.62592118371929
880.5678820240361870.8642359519276250.432117975963813
890.6361632463864830.7276735072270340.363836753613517
900.6674532898502280.6650934202995450.332546710149772
910.6690645398223760.6618709203552480.330935460177624
920.6562187484522740.6875625030954520.343781251547726
930.6329679785745630.7340640428508730.367032021425437
940.6299721935386850.740055612922630.370027806461315
950.6630862049656540.6738275900686930.336913795034346
960.7228347524490570.5543304951018870.277165247550943
970.8039505431749340.3920989136501330.196049456825066
980.8535386291306380.2929227417387240.146461370869362
990.9061892365693530.1876215268612930.0938107634306467
1000.9961249445183730.007750110963254270.00387505548162713
1010.9999435260821750.0001129478356495885.64739178247939e-05
1020.999989787870942.04242581215696e-051.02121290607848e-05
1030.9999934432241751.31135516509342e-056.55677582546711e-06
1040.9999933052301071.33895397863576e-056.69476989317878e-06
1050.9999920367743781.59264512433291e-057.96322562166453e-06
1060.9999927936397451.44127205095094e-057.20636025475472e-06
1070.999998539160782.9216784410777e-061.46083922053885e-06
1080.9999998321548253.35690349142549e-071.67845174571275e-07
1090.999999976824964.63500801424569e-082.31750400712285e-08
1100.999999987779832.44403394219493e-081.22201697109747e-08
1110.999999969784786.04304412126695e-083.02152206063347e-08
1120.9999999387767481.22446504004248e-076.1223252002124e-08
1130.9999999176728571.64654285255342e-078.2327142627671e-08
1140.9999998741311522.51737695795819e-071.25868847897910e-07
1150.9999998805072522.38985495621698e-071.19492747810849e-07
1160.9999998769691132.46061774139566e-071.23030887069783e-07
1170.9999998583701982.83259604450322e-071.41629802225161e-07
1180.9999997416342745.16731451300734e-072.58365725650367e-07
1190.999999554960238.90079540227023e-074.45039770113512e-07
1200.9999995108741589.78251683741077e-074.89125841870538e-07
1210.999998877340032.24531994183964e-061.12265997091982e-06
1220.9999971870730185.62585396374125e-062.81292698187063e-06
1230.9999916632367511.66735264973201e-058.33676324866005e-06
1240.9999919581748691.60836502626534e-058.04182513132669e-06
1250.9999792620983944.14758032111096e-052.07379016055548e-05
1260.999976581112364.68377752782574e-052.34188876391287e-05
1270.9999082650979570.0001834698040851189.1734902042559e-05
1280.9996673683363060.0006652633273882170.000332631663694109
1290.999603565502230.000792868995539350.000396434497769675
1300.9987188886615920.002562222676815350.00128111133840768
1310.998218307705310.003563384589381570.00178169229469078
1320.9975956686394630.004808662721074470.00240433136053724
1330.9898617816643160.02027643667136840.0101382183356842






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.44NOK
5% type I error level580.464NOK
10% type I error level590.472NOK

\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 & 55 & 0.44 & NOK \tabularnewline
5% type I error level & 58 & 0.464 & NOK \tabularnewline
10% type I error level & 59 & 0.472 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114738&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]55[/C][C]0.44[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]58[/C][C]0.464[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.472[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114738&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114738&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.44NOK
5% type I error level580.464NOK
10% type I error level590.472NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}