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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 18:43:19 +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/Nov/23/t1290537694hte75qf3rpz1kcb.htm/, Retrieved Wed, 05 Nov 2025 10:00:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99566, Retrieved Wed, 05 Nov 2025 10:00:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact474

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 12:52:07] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D    [Multiple Regression] [WS7 comp 3] [2010-11-23 09:24:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-             [Multiple Regression] [ws 7] [2010-11-23 18:43:19] [7131fefee4115a2a717140ef0bdd6369] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	24	14	11	12	24
9	23	25	11	7	8	25
9	25	17	6	17	8	30
9	23	18	12	10	8	19
9	19	18	8	12	9	22
9	29	16	10	12	7	22
10	25	20	10	11	4	25
10	21	16	11	11	11	23
10	22	18	16	12	7	17
10	25	17	11	13	7	21
10	24	23	13	14	12	19
10	18	30	12	16	10	19
10	22	23	8	11	10	15
10	15	18	12	10	8	16
10	22	15	11	11	7.9	23
10	28	12	4	15	4	27
10	20	21	9	9	9	22
10	12	15	8	11	8	14
10	24	20	8	17	7	22
10	20	31	14	17	11	23
10	21	27	15	11	9	23
10	20	34	16	18	11	21
10	21	21	9	14	13	19
10	23	31	14	10	8	18
10	28	19	11	11	8	20
10	24	16	8	15	9	23
10	24	20	9	15	6	25
10	24	21	9	13	9	19
10	23	22	9	16	9	24
10	23	17	9	13	6	22
10	29	24	10	9	6	25
10	24	25	16	18	16	26
10	18	26	11	18	5	29
10	25	25	8	12	7	32
10	21	17	9	17	9	25
10	26	32	16	9	6	29
10	22	33	11	9	6	28
10	22	13	16	12	5	17
10	22	32	12	18	12	28
10	23	25	12	12	7	29
10	30	29	14	18	10	26
10	23	22	9	14	9	25
10	17	18	10	15	8	14
10	23	17	9	16	5	25
10	23	20	10	10	8	26
10	25	15	12	11	8	20
10	24	20	14	14	10	18
10	24	33	14	9	6	32
10	23	29	10	12	8	25
10	21	23	14	17	7	25
10	24	26	16	5	4	23
10	24	18	9	12	8	21
10	28	20	10	12	8	20
10	16	11	6	6	4	15
10	20	28	8	24	20	30
10	29	26	13	12	8	24
10	27	22	10	12	8	26
10	22	17	8	14	6	24
10	28	12	7	7	4	22
10	16	14	15	13	8	14
10	25	17	9	12	9	24
10	24	21	10	13	6	24
10	28	19	12	14	7	24
10	24	18	13	8	9	24
10	23	10	10	11	5	19
10	30	29	11	9	5	31
10	24	31	8	11	8	22
10	21	19	9	13	8	27
10	25	9	13	10	6	19
10	25	20	11	11	8	25
10	22	28	8	12	7	20
10	23	19	9	9	7	21
10	26	30	9	15	9	27
10	23	29	15	18	11	23
10	25	26	9	15	6	25
10	21	23	10	12	8	20
10	25	13	14	13	6	21
10	24	21	12	14	9	22
10	29	19	12	10	8	23
10	22	28	11	13	6	25
10	27	23	14	13	10	25
10	26	18	6	11	8	17
10	22	21	12	13	8	19
10	24	20	8	16	10	25
10	27	23	14	8	5	19
10	24	21	11	16	7	20
10	24	21	10	11	5	26
10	29	15	14	9	8	23
10	22	28	12	16	14	27
10	21	19	10	12	7	17
10	24	26	14	14	8	17
10	24	10	5	8	6	19
10	23	16	11	9	5	17
10	20	22	10	15	6	22
10	27	19	9	11	10	21
10	26	31	10	21	12	32
10	25	31	16	14	9	21
10	21	29	13	18	12	21
10	21	19	9	12	7	18
10	19	22	10	13	8	18
10	21	23	10	15	10	23
10	21	15	7	12	6	19
10	16	20	9	19	10	20
10	22	18	8	15	10	21
10	29	23	14	11	10	20
10	15	25	14	11	5	17
10	17	21	8	10	7	18
10	15	24	9	13	10	19
10	21	25	14	15	11	22
10	21	17	14	12	6	15
10	19	13	8	12	7	14
10	24	28	8	16	12	18
10	20	21	8	9	11	24
10	17	25	7	18	11	35
10	23	9	6	8	11	29
10	24	16	8	13	5	21
10	14	19	6	17	8	25
10	19	17	11	9	6	20
10	24	25	14	15	9	22
10	13	20	11	8	4	13
10	22	29	11	7	4	26
10	16	14	11	12	7	17
10	19	22	14	14	11	25
10	25	15	8	6	6	20
10	25	19	20	8	7	19
10	23	20	11	17	8	21
10	24	15	8	10	4	22
10	26	20	11	11	8	24
10	26	18	10	14	9	21
10	25	33	14	11	8	26
10	18	22	11	13	11	24
10	21	16	9	12	8	16
10	26	17	9	11	5	23
10	23	16	8	9	4	18
10	23	21	10	12	8	16
10	22	26	13	20	10	26
10	20	18	13	12	6	19
10	13	18	12	13	9	21
10	24	17	8	12	9	21
10	15	22	13	12	13	22
10	14	30	14	9	9	23
10	22	30	12	15	10	29
10	10	24	14	24	20	21
10	24	21	15	7	5	21
10	22	21	13	17	11	23
10	24	29	16	11	6	27
10	19	31	9	17	9	25
10	20	20	9	11	7	21
10	13	16	9	12	9	10
10	20	22	8	14	10	20
10	22	20	7	11	9	26
10	24	28	16	16	8	24
10	29	38	11	21	7	29
10	12	22	9	14	6	19
10	20	20	11	20	13	24
10	21	17	9	13	6	19
10	24	28	14	11	8	24
10	22	22	13	15	10	22
10	20	31	16	19	16	17

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=99566&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=99566&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&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
Parameter[t] = + 28.2113145628771 -1.21076082808972Variable[t] -0.0662441512071265S.D.[t] + 0.219982378476394`T-STAT`[t] -0.138042493202293`2-tail`[t] -0.267683206546856`1-tail`[t] + 0.415179865256042MultipleLinearRegression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Parameter[t] =  +  28.2113145628771 -1.21076082808972Variable[t] -0.0662441512071265S.D.[t] +  0.219982378476394`T-STAT`[t] -0.138042493202293`2-tail`[t] -0.267683206546856`1-tail`[t] +  0.415179865256042MultipleLinearRegression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Parameter[t] =  +  28.2113145628771 -1.21076082808972Variable[t] -0.0662441512071265S.D.[t] +  0.219982378476394`T-STAT`[t] -0.138042493202293`2-tail`[t] -0.267683206546856`1-tail`[t] +  0.415179865256042MultipleLinearRegression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&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
Parameter[t] = + 28.2113145628771 -1.21076082808972Variable[t] -0.0662441512071265S.D.[t] + 0.219982378476394`T-STAT`[t] -0.138042493202293`2-tail`[t] -0.267683206546856`1-tail`[t] + 0.415179865256042MultipleLinearRegression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.211314562877114.9457561.88760.0609880.030494
Variable-1.210760828089721.484819-0.81540.4161040.208052
S.D.-0.06624415120712650.063225-1.04780.2964150.148207
`T-STAT`0.2199823784763940.1127691.95070.0529270.026464
`2-tail`-0.1380424932022930.105255-1.31150.1916670.095833
`1-tail`-0.2676832065468560.1315-2.03560.0435260.021763
MultipleLinearRegression0.4151798652560420.0762655.443900

\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) & 28.2113145628771 & 14.945756 & 1.8876 & 0.060988 & 0.030494 \tabularnewline
Variable & -1.21076082808972 & 1.484819 & -0.8154 & 0.416104 & 0.208052 \tabularnewline
S.D. & -0.0662441512071265 & 0.063225 & -1.0478 & 0.296415 & 0.148207 \tabularnewline
`T-STAT` & 0.219982378476394 & 0.112769 & 1.9507 & 0.052927 & 0.026464 \tabularnewline
`2-tail` & -0.138042493202293 & 0.105255 & -1.3115 & 0.191667 & 0.095833 \tabularnewline
`1-tail` & -0.267683206546856 & 0.1315 & -2.0356 & 0.043526 & 0.021763 \tabularnewline
MultipleLinearRegression & 0.415179865256042 & 0.076265 & 5.4439 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&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]28.2113145628771[/C][C]14.945756[/C][C]1.8876[/C][C]0.060988[/C][C]0.030494[/C][/ROW]
[ROW][C]Variable[/C][C]-1.21076082808972[/C][C]1.484819[/C][C]-0.8154[/C][C]0.416104[/C][C]0.208052[/C][/ROW]
[ROW][C]S.D.[/C][C]-0.0662441512071265[/C][C]0.063225[/C][C]-1.0478[/C][C]0.296415[/C][C]0.148207[/C][/ROW]
[ROW][C]`T-STAT`[/C][C]0.219982378476394[/C][C]0.112769[/C][C]1.9507[/C][C]0.052927[/C][C]0.026464[/C][/ROW]
[ROW][C]`2-tail`[/C][C]-0.138042493202293[/C][C]0.105255[/C][C]-1.3115[/C][C]0.191667[/C][C]0.095833[/C][/ROW]
[ROW][C]`1-tail`[/C][C]-0.267683206546856[/C][C]0.1315[/C][C]-2.0356[/C][C]0.043526[/C][C]0.021763[/C][/ROW]
[ROW][C]MultipleLinearRegression[/C][C]0.415179865256042[/C][C]0.076265[/C][C]5.4439[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&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)28.211314562877114.9457561.88760.0609880.030494
Variable-1.210760828089721.484819-0.81540.4161040.208052
S.D.-0.06624415120712650.063225-1.04780.2964150.148207
`T-STAT`0.2199823784763940.1127691.95070.0529270.026464
`2-tail`-0.1380424932022930.105255-1.31150.1916670.095833
`1-tail`-0.2676832065468560.1315-2.03560.0435260.021763
MultipleLinearRegression0.4151798652560420.0762655.443900






Multiple Linear Regression - Regression Statistics
Multiple R0.475107146524304
R-squared0.225726800678466
Adjusted R-squared0.195163384915774
F-TEST (value)7.38552269259133
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value6.01600129268576e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50327124217684
Sum Squared Residuals1865.48222823202

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.475107146524304 \tabularnewline
R-squared & 0.225726800678466 \tabularnewline
Adjusted R-squared & 0.195163384915774 \tabularnewline
F-TEST (value) & 7.38552269259133 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 6.01600129268576e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.50327124217684 \tabularnewline
Sum Squared Residuals & 1865.48222823202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.475107146524304[/C][/ROW]
[ROW][C]R-squared[/C][C]0.225726800678466[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.195163384915774[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.38552269259133[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]6.01600129268576e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.50327124217684[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1865.48222823202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&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 R0.475107146524304
R-squared0.225726800678466
Adjusted R-squared0.195163384915774
F-TEST (value)7.38552269259133
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value6.01600129268576e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50327124217684
Sum Squared Residuals1865.48222823202






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.03801164212541.96198835787455
22325.3499030197419-2.34990301974192
32525.4754187312743-0.475418731274257
42323.1283877855251-0.128387785525072
51922.9502296744362-3.95022967443618
62924.05804914689694.94195085310306
72524.76894342258970.231056577410302
82122.5497602295545-1.54976022955452
92221.95879496097110.0412050390288867
102522.44780418761812.55219581238185
112420.18348578087953.81651421912048
121819.7590757706424-1.75907577064236
132218.37234832017403.62765167982603
141520.6720873616672-5.67208736166723
152223.4458223210569-1.4458223210569
162824.25719211909133.74280788090874
172022.1748462508084-2.17484625080836
181219.0224880776687-7.02248807766865
192421.45213449101452.54786550898554
202021.3877901376630-1.38779013766305
212123.2363704932754-2.23637049327542
222020.6606202172801-0.660620217280076
232119.16836136284131.83163863715866
242321.08123788343951.91876211656055
252821.90853779980566.09146220019442
262421.87300953440992.12699046559012
272423.46142465821040.538575341789579
282420.37713668223113.62286331776894
292321.97266437769731.02733562230274
302322.69070250246830.30929749753174
312924.24468539107214.75531460892793
322421.99430087169012.00569912830986
331825.0181996958846-7.0181996958846
342525.9629248535507-0.962924853550714
352122.5810225057866-1.58102250578665
362626.6953459132976-0.695345913297585
372225.1140100044524-3.11401000445245
382222.8253821301005-0.825382130100459
392222.5517548560342-0.551754856034195
402325.5973147716882-2.59731477168816
413022.895458749197.10454125081001
422322.66392922935790.336070770642107
431718.7115504081909-1.71155040819089
442323.7897978251764-0.789797825176364
452324.2514329548606-1.25143295486061
462522.39349678311052.60650321688952
472420.72238716081503.27761283918504
482427.4346766009058-3.4346766009058
492322.96397074233580.0360292576641574
502123.8188359040196-2.81883590401957
512425.689268014907-1.68926801490697
522421.81195456611372.18804543388633
532821.48426877691986.51573122308023
541621.0236250829993-5.0236250829993
552019.79744106588060.202558934119395
562923.40747046613045.59252953386964
572723.84285966604183.15714033395823
582223.1630373613017-1.16303736130166
592823.94557987385864.05442012614142
601620.3525238918059-4.35252389180595
612522.85605510654212.14394489345793
622423.47606800662820.523931993371768
632823.64279536624614.35720463375388
642424.2219104420497-0.221910442049688
652322.67262253657790.327377463422146
663026.89220941159593.10779058840406
672421.28402058040302.71597941959703
682124.0987471132405-3.09874711324051
692523.26917310986961.73082689013040
702523.91819297487871.08180702512133
712220.78203401685681.21796598314317
722322.42752110106030.57247889893972
732622.82629325701073.17370674298933
742321.60221832535141.3977816746486
752523.06395975096771.93604024903234
762121.2855363232984-0.285536323298390
772523.64041113442271.35958886557731
782422.14458092022611.85541907977393
792923.51210226725245.48789773274761
802223.6475211919108-1.64752119191078
812723.56795625718823.43204374281183
822619.62933046286266.37066953713738
832221.30476702420710.695232975792905
842422.03266696034431.96733303965569
852723.10550556439773.89449443560234
862421.35352023792672.64647976207328
872424.8501959300918-0.850195930091759
882924.3550861222364.64491387776402
892222.1422701689175-0.142270168917533
902120.57265653890560.427343461094374
912420.44510880140993.55489119859013
922421.71915491725592.28084508274409
932321.9408652637041.05913473629601
942022.3033791385044-2.30337913850444
952721.34838649501515.65161350498487
962623.42462623170582.57537376829417
972521.94688905680433.05311094319566
982120.06421063133970.935789368660327
992120.76785402568530.232145974314726
1001920.3833782507911-1.38337825079114
1012121.5815820263659-0.581582026365927
1022121.2757289453639-0.275728945363892
1031619.7626225329336-3.76262253293362
1042220.64247829493671.35752170506331
1052921.76814191731257.23185808268746
1061521.7285300518644-6.72853005186444
1071720.6914683311992-3.69146833119921
1081519.9107210220628-4.91072102206282
1092121.6461601660544-0.646160166054349
1102121.0223978312602-0.0223978312602254
1111919.2846170934275-0.284617093427470
1122418.06108828080135.93891171919871
1132022.2498571897503-2.24985718975033
1141725.0894942854413-8.08949428544127
1152324.8187640667656-1.81876406676557
1162422.38946761648981.61053238351019
1171422.0562702744901-8.05627027449007
1181922.8524775017181-3.85247750171813
1192422.18152657914811.81847342085194
1201320.4208948976005-7.42089489760046
1212225.3600782782672-3.36007827826717
1221621.1238596734177-5.12385967341765
1231923.2284747086461-4.22847470864615
1242522.73914614831012.26085385168991
1252524.15501002699080.844989973009183
1262321.42921855464071.57078144535926
1272423.55270231910670.44729768089329
1282623.50301310962262.49698689037737
1292621.48816875163864.51183124836138
1302524.13214600987120.867853990128754
1311822.2913902011632-4.29139020116322
1322119.86854354224771.13145645775229
1332623.64965056067572.35034943932426
1342321.96378120007771.03621879992229
1352319.75730516468853.24269483531153
1362222.5981238379304-0.59812383793039
1372022.3968907626009-2.39689076260087
1381322.0661760017937-9.0661760017937
1392421.39053313229752.60946686770245
1401521.5036713077125-6.5036713077125
1411423.0937406475822-9.09374064758223
1422224.0489169164051-2.04891691640508
1431017.6456931542631-7.64569315426309
1442424.4263784690027-0.426378469002686
1452221.83024927125790.169750728742082
1462425.7876336500023-1.78763365000229
1471921.6536043888869-2.65360438888688
1482022.0851919634486-2.08519196344857
1491317.1097811441646-4.1097811441646
1502020.1003643180544-0.100364318054432
1512223.1857601196823-1.18576011968228
1522423.38275932633610.617240673663883
1532923.27377598869855.72622401130151
1541220.9758996574622-8.97589965746221
1552020.9222146380677-0.922214638067708
1562121.4451629067001-0.445162906700133
1572423.63300703539480.366992964605206
1582221.89259344774620.107406552253809
1592017.72217468394072.27782531605929

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.0380116421254 & 1.96198835787455 \tabularnewline
2 & 23 & 25.3499030197419 & -2.34990301974192 \tabularnewline
3 & 25 & 25.4754187312743 & -0.475418731274257 \tabularnewline
4 & 23 & 23.1283877855251 & -0.128387785525072 \tabularnewline
5 & 19 & 22.9502296744362 & -3.95022967443618 \tabularnewline
6 & 29 & 24.0580491468969 & 4.94195085310306 \tabularnewline
7 & 25 & 24.7689434225897 & 0.231056577410302 \tabularnewline
8 & 21 & 22.5497602295545 & -1.54976022955452 \tabularnewline
9 & 22 & 21.9587949609711 & 0.0412050390288867 \tabularnewline
10 & 25 & 22.4478041876181 & 2.55219581238185 \tabularnewline
11 & 24 & 20.1834857808795 & 3.81651421912048 \tabularnewline
12 & 18 & 19.7590757706424 & -1.75907577064236 \tabularnewline
13 & 22 & 18.3723483201740 & 3.62765167982603 \tabularnewline
14 & 15 & 20.6720873616672 & -5.67208736166723 \tabularnewline
15 & 22 & 23.4458223210569 & -1.4458223210569 \tabularnewline
16 & 28 & 24.2571921190913 & 3.74280788090874 \tabularnewline
17 & 20 & 22.1748462508084 & -2.17484625080836 \tabularnewline
18 & 12 & 19.0224880776687 & -7.02248807766865 \tabularnewline
19 & 24 & 21.4521344910145 & 2.54786550898554 \tabularnewline
20 & 20 & 21.3877901376630 & -1.38779013766305 \tabularnewline
21 & 21 & 23.2363704932754 & -2.23637049327542 \tabularnewline
22 & 20 & 20.6606202172801 & -0.660620217280076 \tabularnewline
23 & 21 & 19.1683613628413 & 1.83163863715866 \tabularnewline
24 & 23 & 21.0812378834395 & 1.91876211656055 \tabularnewline
25 & 28 & 21.9085377998056 & 6.09146220019442 \tabularnewline
26 & 24 & 21.8730095344099 & 2.12699046559012 \tabularnewline
27 & 24 & 23.4614246582104 & 0.538575341789579 \tabularnewline
28 & 24 & 20.3771366822311 & 3.62286331776894 \tabularnewline
29 & 23 & 21.9726643776973 & 1.02733562230274 \tabularnewline
30 & 23 & 22.6907025024683 & 0.30929749753174 \tabularnewline
31 & 29 & 24.2446853910721 & 4.75531460892793 \tabularnewline
32 & 24 & 21.9943008716901 & 2.00569912830986 \tabularnewline
33 & 18 & 25.0181996958846 & -7.0181996958846 \tabularnewline
34 & 25 & 25.9629248535507 & -0.962924853550714 \tabularnewline
35 & 21 & 22.5810225057866 & -1.58102250578665 \tabularnewline
36 & 26 & 26.6953459132976 & -0.695345913297585 \tabularnewline
37 & 22 & 25.1140100044524 & -3.11401000445245 \tabularnewline
38 & 22 & 22.8253821301005 & -0.825382130100459 \tabularnewline
39 & 22 & 22.5517548560342 & -0.551754856034195 \tabularnewline
40 & 23 & 25.5973147716882 & -2.59731477168816 \tabularnewline
41 & 30 & 22.89545874919 & 7.10454125081001 \tabularnewline
42 & 23 & 22.6639292293579 & 0.336070770642107 \tabularnewline
43 & 17 & 18.7115504081909 & -1.71155040819089 \tabularnewline
44 & 23 & 23.7897978251764 & -0.789797825176364 \tabularnewline
45 & 23 & 24.2514329548606 & -1.25143295486061 \tabularnewline
46 & 25 & 22.3934967831105 & 2.60650321688952 \tabularnewline
47 & 24 & 20.7223871608150 & 3.27761283918504 \tabularnewline
48 & 24 & 27.4346766009058 & -3.4346766009058 \tabularnewline
49 & 23 & 22.9639707423358 & 0.0360292576641574 \tabularnewline
50 & 21 & 23.8188359040196 & -2.81883590401957 \tabularnewline
51 & 24 & 25.689268014907 & -1.68926801490697 \tabularnewline
52 & 24 & 21.8119545661137 & 2.18804543388633 \tabularnewline
53 & 28 & 21.4842687769198 & 6.51573122308023 \tabularnewline
54 & 16 & 21.0236250829993 & -5.0236250829993 \tabularnewline
55 & 20 & 19.7974410658806 & 0.202558934119395 \tabularnewline
56 & 29 & 23.4074704661304 & 5.59252953386964 \tabularnewline
57 & 27 & 23.8428596660418 & 3.15714033395823 \tabularnewline
58 & 22 & 23.1630373613017 & -1.16303736130166 \tabularnewline
59 & 28 & 23.9455798738586 & 4.05442012614142 \tabularnewline
60 & 16 & 20.3525238918059 & -4.35252389180595 \tabularnewline
61 & 25 & 22.8560551065421 & 2.14394489345793 \tabularnewline
62 & 24 & 23.4760680066282 & 0.523931993371768 \tabularnewline
63 & 28 & 23.6427953662461 & 4.35720463375388 \tabularnewline
64 & 24 & 24.2219104420497 & -0.221910442049688 \tabularnewline
65 & 23 & 22.6726225365779 & 0.327377463422146 \tabularnewline
66 & 30 & 26.8922094115959 & 3.10779058840406 \tabularnewline
67 & 24 & 21.2840205804030 & 2.71597941959703 \tabularnewline
68 & 21 & 24.0987471132405 & -3.09874711324051 \tabularnewline
69 & 25 & 23.2691731098696 & 1.73082689013040 \tabularnewline
70 & 25 & 23.9181929748787 & 1.08180702512133 \tabularnewline
71 & 22 & 20.7820340168568 & 1.21796598314317 \tabularnewline
72 & 23 & 22.4275211010603 & 0.57247889893972 \tabularnewline
73 & 26 & 22.8262932570107 & 3.17370674298933 \tabularnewline
74 & 23 & 21.6022183253514 & 1.3977816746486 \tabularnewline
75 & 25 & 23.0639597509677 & 1.93604024903234 \tabularnewline
76 & 21 & 21.2855363232984 & -0.285536323298390 \tabularnewline
77 & 25 & 23.6404111344227 & 1.35958886557731 \tabularnewline
78 & 24 & 22.1445809202261 & 1.85541907977393 \tabularnewline
79 & 29 & 23.5121022672524 & 5.48789773274761 \tabularnewline
80 & 22 & 23.6475211919108 & -1.64752119191078 \tabularnewline
81 & 27 & 23.5679562571882 & 3.43204374281183 \tabularnewline
82 & 26 & 19.6293304628626 & 6.37066953713738 \tabularnewline
83 & 22 & 21.3047670242071 & 0.695232975792905 \tabularnewline
84 & 24 & 22.0326669603443 & 1.96733303965569 \tabularnewline
85 & 27 & 23.1055055643977 & 3.89449443560234 \tabularnewline
86 & 24 & 21.3535202379267 & 2.64647976207328 \tabularnewline
87 & 24 & 24.8501959300918 & -0.850195930091759 \tabularnewline
88 & 29 & 24.355086122236 & 4.64491387776402 \tabularnewline
89 & 22 & 22.1422701689175 & -0.142270168917533 \tabularnewline
90 & 21 & 20.5726565389056 & 0.427343461094374 \tabularnewline
91 & 24 & 20.4451088014099 & 3.55489119859013 \tabularnewline
92 & 24 & 21.7191549172559 & 2.28084508274409 \tabularnewline
93 & 23 & 21.940865263704 & 1.05913473629601 \tabularnewline
94 & 20 & 22.3033791385044 & -2.30337913850444 \tabularnewline
95 & 27 & 21.3483864950151 & 5.65161350498487 \tabularnewline
96 & 26 & 23.4246262317058 & 2.57537376829417 \tabularnewline
97 & 25 & 21.9468890568043 & 3.05311094319566 \tabularnewline
98 & 21 & 20.0642106313397 & 0.935789368660327 \tabularnewline
99 & 21 & 20.7678540256853 & 0.232145974314726 \tabularnewline
100 & 19 & 20.3833782507911 & -1.38337825079114 \tabularnewline
101 & 21 & 21.5815820263659 & -0.581582026365927 \tabularnewline
102 & 21 & 21.2757289453639 & -0.275728945363892 \tabularnewline
103 & 16 & 19.7626225329336 & -3.76262253293362 \tabularnewline
104 & 22 & 20.6424782949367 & 1.35752170506331 \tabularnewline
105 & 29 & 21.7681419173125 & 7.23185808268746 \tabularnewline
106 & 15 & 21.7285300518644 & -6.72853005186444 \tabularnewline
107 & 17 & 20.6914683311992 & -3.69146833119921 \tabularnewline
108 & 15 & 19.9107210220628 & -4.91072102206282 \tabularnewline
109 & 21 & 21.6461601660544 & -0.646160166054349 \tabularnewline
110 & 21 & 21.0223978312602 & -0.0223978312602254 \tabularnewline
111 & 19 & 19.2846170934275 & -0.284617093427470 \tabularnewline
112 & 24 & 18.0610882808013 & 5.93891171919871 \tabularnewline
113 & 20 & 22.2498571897503 & -2.24985718975033 \tabularnewline
114 & 17 & 25.0894942854413 & -8.08949428544127 \tabularnewline
115 & 23 & 24.8187640667656 & -1.81876406676557 \tabularnewline
116 & 24 & 22.3894676164898 & 1.61053238351019 \tabularnewline
117 & 14 & 22.0562702744901 & -8.05627027449007 \tabularnewline
118 & 19 & 22.8524775017181 & -3.85247750171813 \tabularnewline
119 & 24 & 22.1815265791481 & 1.81847342085194 \tabularnewline
120 & 13 & 20.4208948976005 & -7.42089489760046 \tabularnewline
121 & 22 & 25.3600782782672 & -3.36007827826717 \tabularnewline
122 & 16 & 21.1238596734177 & -5.12385967341765 \tabularnewline
123 & 19 & 23.2284747086461 & -4.22847470864615 \tabularnewline
124 & 25 & 22.7391461483101 & 2.26085385168991 \tabularnewline
125 & 25 & 24.1550100269908 & 0.844989973009183 \tabularnewline
126 & 23 & 21.4292185546407 & 1.57078144535926 \tabularnewline
127 & 24 & 23.5527023191067 & 0.44729768089329 \tabularnewline
128 & 26 & 23.5030131096226 & 2.49698689037737 \tabularnewline
129 & 26 & 21.4881687516386 & 4.51183124836138 \tabularnewline
130 & 25 & 24.1321460098712 & 0.867853990128754 \tabularnewline
131 & 18 & 22.2913902011632 & -4.29139020116322 \tabularnewline
132 & 21 & 19.8685435422477 & 1.13145645775229 \tabularnewline
133 & 26 & 23.6496505606757 & 2.35034943932426 \tabularnewline
134 & 23 & 21.9637812000777 & 1.03621879992229 \tabularnewline
135 & 23 & 19.7573051646885 & 3.24269483531153 \tabularnewline
136 & 22 & 22.5981238379304 & -0.59812383793039 \tabularnewline
137 & 20 & 22.3968907626009 & -2.39689076260087 \tabularnewline
138 & 13 & 22.0661760017937 & -9.0661760017937 \tabularnewline
139 & 24 & 21.3905331322975 & 2.60946686770245 \tabularnewline
140 & 15 & 21.5036713077125 & -6.5036713077125 \tabularnewline
141 & 14 & 23.0937406475822 & -9.09374064758223 \tabularnewline
142 & 22 & 24.0489169164051 & -2.04891691640508 \tabularnewline
143 & 10 & 17.6456931542631 & -7.64569315426309 \tabularnewline
144 & 24 & 24.4263784690027 & -0.426378469002686 \tabularnewline
145 & 22 & 21.8302492712579 & 0.169750728742082 \tabularnewline
146 & 24 & 25.7876336500023 & -1.78763365000229 \tabularnewline
147 & 19 & 21.6536043888869 & -2.65360438888688 \tabularnewline
148 & 20 & 22.0851919634486 & -2.08519196344857 \tabularnewline
149 & 13 & 17.1097811441646 & -4.1097811441646 \tabularnewline
150 & 20 & 20.1003643180544 & -0.100364318054432 \tabularnewline
151 & 22 & 23.1857601196823 & -1.18576011968228 \tabularnewline
152 & 24 & 23.3827593263361 & 0.617240673663883 \tabularnewline
153 & 29 & 23.2737759886985 & 5.72622401130151 \tabularnewline
154 & 12 & 20.9758996574622 & -8.97589965746221 \tabularnewline
155 & 20 & 20.9222146380677 & -0.922214638067708 \tabularnewline
156 & 21 & 21.4451629067001 & -0.445162906700133 \tabularnewline
157 & 24 & 23.6330070353948 & 0.366992964605206 \tabularnewline
158 & 22 & 21.8925934477462 & 0.107406552253809 \tabularnewline
159 & 20 & 17.7221746839407 & 2.27782531605929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&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]26[/C][C]24.0380116421254[/C][C]1.96198835787455[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.3499030197419[/C][C]-2.34990301974192[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]25.4754187312743[/C][C]-0.475418731274257[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]23.1283877855251[/C][C]-0.128387785525072[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]22.9502296744362[/C][C]-3.95022967443618[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.0580491468969[/C][C]4.94195085310306[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]24.7689434225897[/C][C]0.231056577410302[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.5497602295545[/C][C]-1.54976022955452[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]21.9587949609711[/C][C]0.0412050390288867[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]22.4478041876181[/C][C]2.55219581238185[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]20.1834857808795[/C][C]3.81651421912048[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]19.7590757706424[/C][C]-1.75907577064236[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]18.3723483201740[/C][C]3.62765167982603[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]20.6720873616672[/C][C]-5.67208736166723[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]23.4458223210569[/C][C]-1.4458223210569[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]24.2571921190913[/C][C]3.74280788090874[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]22.1748462508084[/C][C]-2.17484625080836[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]19.0224880776687[/C][C]-7.02248807766865[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]21.4521344910145[/C][C]2.54786550898554[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]21.3877901376630[/C][C]-1.38779013766305[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]23.2363704932754[/C][C]-2.23637049327542[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]20.6606202172801[/C][C]-0.660620217280076[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.1683613628413[/C][C]1.83163863715866[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.0812378834395[/C][C]1.91876211656055[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]21.9085377998056[/C][C]6.09146220019442[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]21.8730095344099[/C][C]2.12699046559012[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]23.4614246582104[/C][C]0.538575341789579[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]20.3771366822311[/C][C]3.62286331776894[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]21.9726643776973[/C][C]1.02733562230274[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]22.6907025024683[/C][C]0.30929749753174[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.2446853910721[/C][C]4.75531460892793[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]21.9943008716901[/C][C]2.00569912830986[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.0181996958846[/C][C]-7.0181996958846[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]25.9629248535507[/C][C]-0.962924853550714[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]22.5810225057866[/C][C]-1.58102250578665[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]26.6953459132976[/C][C]-0.695345913297585[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.1140100044524[/C][C]-3.11401000445245[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]22.8253821301005[/C][C]-0.825382130100459[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]22.5517548560342[/C][C]-0.551754856034195[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]25.5973147716882[/C][C]-2.59731477168816[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]22.89545874919[/C][C]7.10454125081001[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]22.6639292293579[/C][C]0.336070770642107[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.7115504081909[/C][C]-1.71155040819089[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]23.7897978251764[/C][C]-0.789797825176364[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.2514329548606[/C][C]-1.25143295486061[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.3934967831105[/C][C]2.60650321688952[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]20.7223871608150[/C][C]3.27761283918504[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.4346766009058[/C][C]-3.4346766009058[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]22.9639707423358[/C][C]0.0360292576641574[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]23.8188359040196[/C][C]-2.81883590401957[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]25.689268014907[/C][C]-1.68926801490697[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]21.8119545661137[/C][C]2.18804543388633[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.4842687769198[/C][C]6.51573122308023[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.0236250829993[/C][C]-5.0236250829993[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]19.7974410658806[/C][C]0.202558934119395[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.4074704661304[/C][C]5.59252953386964[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]23.8428596660418[/C][C]3.15714033395823[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.1630373613017[/C][C]-1.16303736130166[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]23.9455798738586[/C][C]4.05442012614142[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.3525238918059[/C][C]-4.35252389180595[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]22.8560551065421[/C][C]2.14394489345793[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.4760680066282[/C][C]0.523931993371768[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.6427953662461[/C][C]4.35720463375388[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.2219104420497[/C][C]-0.221910442049688[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.6726225365779[/C][C]0.327377463422146[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.8922094115959[/C][C]3.10779058840406[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.2840205804030[/C][C]2.71597941959703[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.0987471132405[/C][C]-3.09874711324051[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.2691731098696[/C][C]1.73082689013040[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]23.9181929748787[/C][C]1.08180702512133[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]20.7820340168568[/C][C]1.21796598314317[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.4275211010603[/C][C]0.57247889893972[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]22.8262932570107[/C][C]3.17370674298933[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.6022183253514[/C][C]1.3977816746486[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.0639597509677[/C][C]1.93604024903234[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.2855363232984[/C][C]-0.285536323298390[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6404111344227[/C][C]1.35958886557731[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.1445809202261[/C][C]1.85541907977393[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5121022672524[/C][C]5.48789773274761[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.6475211919108[/C][C]-1.64752119191078[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.5679562571882[/C][C]3.43204374281183[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.6293304628626[/C][C]6.37066953713738[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3047670242071[/C][C]0.695232975792905[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.0326669603443[/C][C]1.96733303965569[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.1055055643977[/C][C]3.89449443560234[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.3535202379267[/C][C]2.64647976207328[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.8501959300918[/C][C]-0.850195930091759[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.355086122236[/C][C]4.64491387776402[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.1422701689175[/C][C]-0.142270168917533[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.5726565389056[/C][C]0.427343461094374[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4451088014099[/C][C]3.55489119859013[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.7191549172559[/C][C]2.28084508274409[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.940865263704[/C][C]1.05913473629601[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.3033791385044[/C][C]-2.30337913850444[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.3483864950151[/C][C]5.65161350498487[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.4246262317058[/C][C]2.57537376829417[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.9468890568043[/C][C]3.05311094319566[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]20.0642106313397[/C][C]0.935789368660327[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.7678540256853[/C][C]0.232145974314726[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.3833782507911[/C][C]-1.38337825079114[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.5815820263659[/C][C]-0.581582026365927[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]21.2757289453639[/C][C]-0.275728945363892[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.7626225329336[/C][C]-3.76262253293362[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.6424782949367[/C][C]1.35752170506331[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.7681419173125[/C][C]7.23185808268746[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.7285300518644[/C][C]-6.72853005186444[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.6914683311992[/C][C]-3.69146833119921[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.9107210220628[/C][C]-4.91072102206282[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.6461601660544[/C][C]-0.646160166054349[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]21.0223978312602[/C][C]-0.0223978312602254[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]19.2846170934275[/C][C]-0.284617093427470[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]18.0610882808013[/C][C]5.93891171919871[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]22.2498571897503[/C][C]-2.24985718975033[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]25.0894942854413[/C][C]-8.08949428544127[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]24.8187640667656[/C][C]-1.81876406676557[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]22.3894676164898[/C][C]1.61053238351019[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]22.0562702744901[/C][C]-8.05627027449007[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.8524775017181[/C][C]-3.85247750171813[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]22.1815265791481[/C][C]1.81847342085194[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]20.4208948976005[/C][C]-7.42089489760046[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]25.3600782782672[/C][C]-3.36007827826717[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]21.1238596734177[/C][C]-5.12385967341765[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]23.2284747086461[/C][C]-4.22847470864615[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.7391461483101[/C][C]2.26085385168991[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]24.1550100269908[/C][C]0.844989973009183[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]21.4292185546407[/C][C]1.57078144535926[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]23.5527023191067[/C][C]0.44729768089329[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]23.5030131096226[/C][C]2.49698689037737[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]21.4881687516386[/C][C]4.51183124836138[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]24.1321460098712[/C][C]0.867853990128754[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]22.2913902011632[/C][C]-4.29139020116322[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.8685435422477[/C][C]1.13145645775229[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]23.6496505606757[/C][C]2.35034943932426[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.9637812000777[/C][C]1.03621879992229[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.7573051646885[/C][C]3.24269483531153[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]22.5981238379304[/C][C]-0.59812383793039[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.3968907626009[/C][C]-2.39689076260087[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]22.0661760017937[/C][C]-9.0661760017937[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]21.3905331322975[/C][C]2.60946686770245[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]21.5036713077125[/C][C]-6.5036713077125[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]23.0937406475822[/C][C]-9.09374064758223[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]24.0489169164051[/C][C]-2.04891691640508[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]17.6456931542631[/C][C]-7.64569315426309[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]24.4263784690027[/C][C]-0.426378469002686[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]21.8302492712579[/C][C]0.169750728742082[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]25.7876336500023[/C][C]-1.78763365000229[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]21.6536043888869[/C][C]-2.65360438888688[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]22.0851919634486[/C][C]-2.08519196344857[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]17.1097811441646[/C][C]-4.1097811441646[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]20.1003643180544[/C][C]-0.100364318054432[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]23.1857601196823[/C][C]-1.18576011968228[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.3827593263361[/C][C]0.617240673663883[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]23.2737759886985[/C][C]5.72622401130151[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]20.9758996574622[/C][C]-8.97589965746221[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.9222146380677[/C][C]-0.922214638067708[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]21.4451629067001[/C][C]-0.445162906700133[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.6330070353948[/C][C]0.366992964605206[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.8925934477462[/C][C]0.107406552253809[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]17.7221746839407[/C][C]2.27782531605929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&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
12624.03801164212541.96198835787455
22325.3499030197419-2.34990301974192
32525.4754187312743-0.475418731274257
42323.1283877855251-0.128387785525072
51922.9502296744362-3.95022967443618
62924.05804914689694.94195085310306
72524.76894342258970.231056577410302
82122.5497602295545-1.54976022955452
92221.95879496097110.0412050390288867
102522.44780418761812.55219581238185
112420.18348578087953.81651421912048
121819.7590757706424-1.75907577064236
132218.37234832017403.62765167982603
141520.6720873616672-5.67208736166723
152223.4458223210569-1.4458223210569
162824.25719211909133.74280788090874
172022.1748462508084-2.17484625080836
181219.0224880776687-7.02248807766865
192421.45213449101452.54786550898554
202021.3877901376630-1.38779013766305
212123.2363704932754-2.23637049327542
222020.6606202172801-0.660620217280076
232119.16836136284131.83163863715866
242321.08123788343951.91876211656055
252821.90853779980566.09146220019442
262421.87300953440992.12699046559012
272423.46142465821040.538575341789579
282420.37713668223113.62286331776894
292321.97266437769731.02733562230274
302322.69070250246830.30929749753174
312924.24468539107214.75531460892793
322421.99430087169012.00569912830986
331825.0181996958846-7.0181996958846
342525.9629248535507-0.962924853550714
352122.5810225057866-1.58102250578665
362626.6953459132976-0.695345913297585
372225.1140100044524-3.11401000445245
382222.8253821301005-0.825382130100459
392222.5517548560342-0.551754856034195
402325.5973147716882-2.59731477168816
413022.895458749197.10454125081001
422322.66392922935790.336070770642107
431718.7115504081909-1.71155040819089
442323.7897978251764-0.789797825176364
452324.2514329548606-1.25143295486061
462522.39349678311052.60650321688952
472420.72238716081503.27761283918504
482427.4346766009058-3.4346766009058
492322.96397074233580.0360292576641574
502123.8188359040196-2.81883590401957
512425.689268014907-1.68926801490697
522421.81195456611372.18804543388633
532821.48426877691986.51573122308023
541621.0236250829993-5.0236250829993
552019.79744106588060.202558934119395
562923.40747046613045.59252953386964
572723.84285966604183.15714033395823
582223.1630373613017-1.16303736130166
592823.94557987385864.05442012614142
601620.3525238918059-4.35252389180595
612522.85605510654212.14394489345793
622423.47606800662820.523931993371768
632823.64279536624614.35720463375388
642424.2219104420497-0.221910442049688
652322.67262253657790.327377463422146
663026.89220941159593.10779058840406
672421.28402058040302.71597941959703
682124.0987471132405-3.09874711324051
692523.26917310986961.73082689013040
702523.91819297487871.08180702512133
712220.78203401685681.21796598314317
722322.42752110106030.57247889893972
732622.82629325701073.17370674298933
742321.60221832535141.3977816746486
752523.06395975096771.93604024903234
762121.2855363232984-0.285536323298390
772523.64041113442271.35958886557731
782422.14458092022611.85541907977393
792923.51210226725245.48789773274761
802223.6475211919108-1.64752119191078
812723.56795625718823.43204374281183
822619.62933046286266.37066953713738
832221.30476702420710.695232975792905
842422.03266696034431.96733303965569
852723.10550556439773.89449443560234
862421.35352023792672.64647976207328
872424.8501959300918-0.850195930091759
882924.3550861222364.64491387776402
892222.1422701689175-0.142270168917533
902120.57265653890560.427343461094374
912420.44510880140993.55489119859013
922421.71915491725592.28084508274409
932321.9408652637041.05913473629601
942022.3033791385044-2.30337913850444
952721.34838649501515.65161350498487
962623.42462623170582.57537376829417
972521.94688905680433.05311094319566
982120.06421063133970.935789368660327
992120.76785402568530.232145974314726
1001920.3833782507911-1.38337825079114
1012121.5815820263659-0.581582026365927
1022121.2757289453639-0.275728945363892
1031619.7626225329336-3.76262253293362
1042220.64247829493671.35752170506331
1052921.76814191731257.23185808268746
1061521.7285300518644-6.72853005186444
1071720.6914683311992-3.69146833119921
1081519.9107210220628-4.91072102206282
1092121.6461601660544-0.646160166054349
1102121.0223978312602-0.0223978312602254
1111919.2846170934275-0.284617093427470
1122418.06108828080135.93891171919871
1132022.2498571897503-2.24985718975033
1141725.0894942854413-8.08949428544127
1152324.8187640667656-1.81876406676557
1162422.38946761648981.61053238351019
1171422.0562702744901-8.05627027449007
1181922.8524775017181-3.85247750171813
1192422.18152657914811.81847342085194
1201320.4208948976005-7.42089489760046
1212225.3600782782672-3.36007827826717
1221621.1238596734177-5.12385967341765
1231923.2284747086461-4.22847470864615
1242522.73914614831012.26085385168991
1252524.15501002699080.844989973009183
1262321.42921855464071.57078144535926
1272423.55270231910670.44729768089329
1282623.50301310962262.49698689037737
1292621.48816875163864.51183124836138
1302524.13214600987120.867853990128754
1311822.2913902011632-4.29139020116322
1322119.86854354224771.13145645775229
1332623.64965056067572.35034943932426
1342321.96378120007771.03621879992229
1352319.75730516468853.24269483531153
1362222.5981238379304-0.59812383793039
1372022.3968907626009-2.39689076260087
1381322.0661760017937-9.0661760017937
1392421.39053313229752.60946686770245
1401521.5036713077125-6.5036713077125
1411423.0937406475822-9.09374064758223
1422224.0489169164051-2.04891691640508
1431017.6456931542631-7.64569315426309
1442424.4263784690027-0.426378469002686
1452221.83024927125790.169750728742082
1462425.7876336500023-1.78763365000229
1471921.6536043888869-2.65360438888688
1482022.0851919634486-2.08519196344857
1491317.1097811441646-4.1097811441646
1502020.1003643180544-0.100364318054432
1512223.1857601196823-1.18576011968228
1522423.38275932633610.617240673663883
1532923.27377598869855.72622401130151
1541220.9758996574622-8.97589965746221
1552020.9222146380677-0.922214638067708
1562121.4451629067001-0.445162906700133
1572423.63300703539480.366992964605206
1582221.89259344774620.107406552253809
1592017.72217468394072.27782531605929






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5270113394415070.9459773211169860.472988660558493
110.5620507582690620.8758984834618750.437949241730938
120.4887970480408750.977594096081750.511202951959125
130.5403126089106880.9193747821786240.459687391089312
140.7416063012671040.5167873974657910.258393698732896
150.6549753164256380.6900493671487250.345024683574363
160.6243498140962790.7513003718074410.375650185903721
170.5386871065367840.9226257869264320.461312893463216
180.6765238499578480.6469523000843040.323476150042152
190.60178771425670.7964245714865990.398212285743300
200.589009299802560.821981400394880.41099070019744
210.5250276261210640.9499447477578720.474972373878936
220.4556446102758780.9112892205517550.544355389724122
230.4048025144985250.809605028997050.595197485501475
240.4092926959496120.8185853918992230.590707304050388
250.5718371575252940.8563256849494120.428162842474706
260.5105762121293710.9788475757412580.489423787870629
270.4435195626866040.8870391253732090.556480437313396
280.4379320352942180.8758640705884350.562067964705782
290.3737718316251930.7475436632503860.626228168374807
300.3133360257589180.6266720515178360.686663974241082
310.3472632973775540.6945265947551070.652736702622446
320.2961631536981900.5923263073963790.70383684630181
330.5219273653580150.956145269283970.478072634641985
340.470812921787910.941625843575820.52918707821209
350.4328628993345430.8657257986690870.567137100665457
360.3755462363391690.7510924726783380.624453763660831
370.3489102873234240.6978205746468480.651089712676576
380.2972724968701430.5945449937402860.702727503129857
390.2499040285422920.4998080570845830.750095971457708
400.222560825336940.445121650673880.77743917466306
410.3752039313679040.7504078627358090.624796068632096
420.3234194853661940.6468389707323880.676580514633806
430.2913721848914370.5827443697828730.708627815108563
440.2477868890165760.4955737780331510.752213110983424
450.2113939048447770.4227878096895540.788606095155223
460.1919870179778910.3839740359557820.80801298202211
470.1795478822850890.3590957645701780.820452117714911
480.1644598925383510.3289197850767020.83554010746165
490.1343692400060660.2687384800121310.865630759993934
500.1247284562930190.2494569125860390.875271543706981
510.1029544401586440.2059088803172880.897045559841356
520.08783297010282040.1756659402056410.91216702989718
530.1473927475819360.2947854951638720.852607252418064
540.1767598662292670.3535197324585340.823240133770733
550.1653140667463530.3306281334927050.834685933253647
560.2227463280511850.4454926561023690.777253671948815
570.215184324890160.430368649780320.78481567510984
580.1841954106317120.3683908212634250.815804589368287
590.1973960359864410.3947920719728820.80260396401356
600.2251276343909580.4502552687819160.774872365609042
610.1989056339765730.3978112679531460.801094366023427
620.1671440635645950.334288127129190.832855936435405
630.1823840140699600.3647680281399190.81761598593004
640.1532974835765050.3065949671530110.846702516423495
650.1264764446759090.2529528893518190.87352355532409
660.1229283101839720.2458566203679450.877071689816028
670.1122909241959020.2245818483918040.887709075804098
680.1113897087490070.2227794174980140.888610291250993
690.0947978505473060.1895957010946120.905202149452694
700.07739540345627430.1547908069125490.922604596543726
710.06289922008769070.1257984401753810.93710077991231
720.04968201329844750.0993640265968950.950317986701552
730.04664004627491790.09328009254983590.953359953725082
740.03736217520759850.0747243504151970.962637824792401
750.0310517330200510.0621034660401020.968948266979949
760.02381720173285790.04763440346571580.976182798267142
770.01870023051216360.03740046102432720.981299769487836
780.01496894036217070.02993788072434130.98503105963783
790.02249240070487020.04498480140974040.97750759929513
800.01793142703084700.03586285406169390.982068572969153
810.01753389421050300.03506778842100590.982466105789497
820.03136148030692810.06272296061385630.968638519693072
830.0241862122156630.0483724244313260.975813787784337
840.02017989864544880.04035979729089760.97982010135455
850.02210208039269870.04420416078539740.977897919607301
860.01958969636059250.03917939272118510.980410303639407
870.01489208240969640.02978416481939280.985107917590304
880.01944328733149340.03888657466298690.980556712668507
890.01529083099031760.03058166198063520.984709169009682
900.01144189070491450.02288378140982910.988558109295085
910.01150151838410460.02300303676820910.988498481615895
920.01005566057448910.02011132114897830.98994433942551
930.007711865229676230.01542373045935250.992288134770324
940.006374596726677220.01274919345335440.993625403273323
950.01167998736334040.02335997472668070.98832001263666
960.01057812008116410.02115624016232830.989421879918836
970.01006125627655050.02012251255310110.98993874372345
980.007699113041842850.01539822608368570.992300886958157
990.005680824815820940.01136164963164190.99431917518418
1000.00434897856817230.00869795713634460.995651021431828
1010.003213483084356860.006426966168713730.996786516915643
1020.002286472255777890.004572944511555790.997713527744222
1030.002444720233493720.004889440466987440.997555279766506
1040.001915245177990580.003830490355981160.99808475482201
1050.00811448135931830.01622896271863660.991885518640682
1060.01816187192056150.03632374384112300.981838128079439
1070.01802450373780930.03604900747561860.98197549626219
1080.02271794819215930.04543589638431860.97728205180784
1090.01752772183889990.03505544367779970.9824722781611
1100.01280204888416140.02560409776832280.987197951115839
1110.00926861401690130.01853722803380260.990731385983099
1120.02272837335947710.04545674671895420.977271626640523
1130.02059718112020940.04119436224041890.97940281887979
1140.05720029384146890.1144005876829380.942799706158531
1150.04846270489123690.09692540978247380.951537295108763
1160.03927643678381860.07855287356763730.960723563216181
1170.1157314667644110.2314629335288230.884268533235589
1180.1108871133418220.2217742266836440.889112886658178
1190.09831279105594820.1966255821118960.901687208944052
1200.1718339306144410.3436678612288820.82816606938556
1210.1637124934368240.3274249868736480.836287506563176
1220.1950669671064050.390133934212810.804933032893595
1230.1880540532034080.3761081064068150.811945946796592
1240.1872354249148740.3744708498297470.812764575085126
1250.1700024472393310.3400048944786630.829997552760669
1260.1383413988774320.2766827977548650.861658601122568
1270.1081785680436790.2163571360873590.89182143195632
1280.1116304649352940.2232609298705890.888369535064706
1290.1601335009212970.3202670018425940.839866499078703
1300.1379518520969660.2759037041939310.862048147903034
1310.1204231043296330.2408462086592670.879576895670366
1320.1042990079808010.2085980159616020.8957009920192
1330.09567417585490770.1913483517098150.904325824145092
1340.07810059259818720.1562011851963740.921899407401813
1350.1065804352457800.2131608704915590.89341956475422
1360.07902873211884730.1580574642376950.920971267881153
1370.0576357982509480.1152715965018960.942364201749052
1380.1468708337537470.2937416675074940.853129166246253
1390.208134149949640.416268299899280.79186585005036
1400.1885344897264470.3770689794528940.811465510273553
1410.4060558457058960.8121116914117920.593944154294104
1420.3564043402773840.7128086805547680.643595659722616
1430.666036975633690.6679260487326190.333963024366309
1440.6048309234602920.7903381530794170.395169076539708
1450.4942065231289880.9884130462579760.505793476871012
1460.4233169578654720.8466339157309430.576683042134528
1470.4336110339575320.8672220679150640.566388966042468
1480.3033072181422690.6066144362845380.69669278185773
1490.2114213023067070.4228426046134140.788578697693293

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.527011339441507 & 0.945977321116986 & 0.472988660558493 \tabularnewline
11 & 0.562050758269062 & 0.875898483461875 & 0.437949241730938 \tabularnewline
12 & 0.488797048040875 & 0.97759409608175 & 0.511202951959125 \tabularnewline
13 & 0.540312608910688 & 0.919374782178624 & 0.459687391089312 \tabularnewline
14 & 0.741606301267104 & 0.516787397465791 & 0.258393698732896 \tabularnewline
15 & 0.654975316425638 & 0.690049367148725 & 0.345024683574363 \tabularnewline
16 & 0.624349814096279 & 0.751300371807441 & 0.375650185903721 \tabularnewline
17 & 0.538687106536784 & 0.922625786926432 & 0.461312893463216 \tabularnewline
18 & 0.676523849957848 & 0.646952300084304 & 0.323476150042152 \tabularnewline
19 & 0.6017877142567 & 0.796424571486599 & 0.398212285743300 \tabularnewline
20 & 0.58900929980256 & 0.82198140039488 & 0.41099070019744 \tabularnewline
21 & 0.525027626121064 & 0.949944747757872 & 0.474972373878936 \tabularnewline
22 & 0.455644610275878 & 0.911289220551755 & 0.544355389724122 \tabularnewline
23 & 0.404802514498525 & 0.80960502899705 & 0.595197485501475 \tabularnewline
24 & 0.409292695949612 & 0.818585391899223 & 0.590707304050388 \tabularnewline
25 & 0.571837157525294 & 0.856325684949412 & 0.428162842474706 \tabularnewline
26 & 0.510576212129371 & 0.978847575741258 & 0.489423787870629 \tabularnewline
27 & 0.443519562686604 & 0.887039125373209 & 0.556480437313396 \tabularnewline
28 & 0.437932035294218 & 0.875864070588435 & 0.562067964705782 \tabularnewline
29 & 0.373771831625193 & 0.747543663250386 & 0.626228168374807 \tabularnewline
30 & 0.313336025758918 & 0.626672051517836 & 0.686663974241082 \tabularnewline
31 & 0.347263297377554 & 0.694526594755107 & 0.652736702622446 \tabularnewline
32 & 0.296163153698190 & 0.592326307396379 & 0.70383684630181 \tabularnewline
33 & 0.521927365358015 & 0.95614526928397 & 0.478072634641985 \tabularnewline
34 & 0.47081292178791 & 0.94162584357582 & 0.52918707821209 \tabularnewline
35 & 0.432862899334543 & 0.865725798669087 & 0.567137100665457 \tabularnewline
36 & 0.375546236339169 & 0.751092472678338 & 0.624453763660831 \tabularnewline
37 & 0.348910287323424 & 0.697820574646848 & 0.651089712676576 \tabularnewline
38 & 0.297272496870143 & 0.594544993740286 & 0.702727503129857 \tabularnewline
39 & 0.249904028542292 & 0.499808057084583 & 0.750095971457708 \tabularnewline
40 & 0.22256082533694 & 0.44512165067388 & 0.77743917466306 \tabularnewline
41 & 0.375203931367904 & 0.750407862735809 & 0.624796068632096 \tabularnewline
42 & 0.323419485366194 & 0.646838970732388 & 0.676580514633806 \tabularnewline
43 & 0.291372184891437 & 0.582744369782873 & 0.708627815108563 \tabularnewline
44 & 0.247786889016576 & 0.495573778033151 & 0.752213110983424 \tabularnewline
45 & 0.211393904844777 & 0.422787809689554 & 0.788606095155223 \tabularnewline
46 & 0.191987017977891 & 0.383974035955782 & 0.80801298202211 \tabularnewline
47 & 0.179547882285089 & 0.359095764570178 & 0.820452117714911 \tabularnewline
48 & 0.164459892538351 & 0.328919785076702 & 0.83554010746165 \tabularnewline
49 & 0.134369240006066 & 0.268738480012131 & 0.865630759993934 \tabularnewline
50 & 0.124728456293019 & 0.249456912586039 & 0.875271543706981 \tabularnewline
51 & 0.102954440158644 & 0.205908880317288 & 0.897045559841356 \tabularnewline
52 & 0.0878329701028204 & 0.175665940205641 & 0.91216702989718 \tabularnewline
53 & 0.147392747581936 & 0.294785495163872 & 0.852607252418064 \tabularnewline
54 & 0.176759866229267 & 0.353519732458534 & 0.823240133770733 \tabularnewline
55 & 0.165314066746353 & 0.330628133492705 & 0.834685933253647 \tabularnewline
56 & 0.222746328051185 & 0.445492656102369 & 0.777253671948815 \tabularnewline
57 & 0.21518432489016 & 0.43036864978032 & 0.78481567510984 \tabularnewline
58 & 0.184195410631712 & 0.368390821263425 & 0.815804589368287 \tabularnewline
59 & 0.197396035986441 & 0.394792071972882 & 0.80260396401356 \tabularnewline
60 & 0.225127634390958 & 0.450255268781916 & 0.774872365609042 \tabularnewline
61 & 0.198905633976573 & 0.397811267953146 & 0.801094366023427 \tabularnewline
62 & 0.167144063564595 & 0.33428812712919 & 0.832855936435405 \tabularnewline
63 & 0.182384014069960 & 0.364768028139919 & 0.81761598593004 \tabularnewline
64 & 0.153297483576505 & 0.306594967153011 & 0.846702516423495 \tabularnewline
65 & 0.126476444675909 & 0.252952889351819 & 0.87352355532409 \tabularnewline
66 & 0.122928310183972 & 0.245856620367945 & 0.877071689816028 \tabularnewline
67 & 0.112290924195902 & 0.224581848391804 & 0.887709075804098 \tabularnewline
68 & 0.111389708749007 & 0.222779417498014 & 0.888610291250993 \tabularnewline
69 & 0.094797850547306 & 0.189595701094612 & 0.905202149452694 \tabularnewline
70 & 0.0773954034562743 & 0.154790806912549 & 0.922604596543726 \tabularnewline
71 & 0.0628992200876907 & 0.125798440175381 & 0.93710077991231 \tabularnewline
72 & 0.0496820132984475 & 0.099364026596895 & 0.950317986701552 \tabularnewline
73 & 0.0466400462749179 & 0.0932800925498359 & 0.953359953725082 \tabularnewline
74 & 0.0373621752075985 & 0.074724350415197 & 0.962637824792401 \tabularnewline
75 & 0.031051733020051 & 0.062103466040102 & 0.968948266979949 \tabularnewline
76 & 0.0238172017328579 & 0.0476344034657158 & 0.976182798267142 \tabularnewline
77 & 0.0187002305121636 & 0.0374004610243272 & 0.981299769487836 \tabularnewline
78 & 0.0149689403621707 & 0.0299378807243413 & 0.98503105963783 \tabularnewline
79 & 0.0224924007048702 & 0.0449848014097404 & 0.97750759929513 \tabularnewline
80 & 0.0179314270308470 & 0.0358628540616939 & 0.982068572969153 \tabularnewline
81 & 0.0175338942105030 & 0.0350677884210059 & 0.982466105789497 \tabularnewline
82 & 0.0313614803069281 & 0.0627229606138563 & 0.968638519693072 \tabularnewline
83 & 0.024186212215663 & 0.048372424431326 & 0.975813787784337 \tabularnewline
84 & 0.0201798986454488 & 0.0403597972908976 & 0.97982010135455 \tabularnewline
85 & 0.0221020803926987 & 0.0442041607853974 & 0.977897919607301 \tabularnewline
86 & 0.0195896963605925 & 0.0391793927211851 & 0.980410303639407 \tabularnewline
87 & 0.0148920824096964 & 0.0297841648193928 & 0.985107917590304 \tabularnewline
88 & 0.0194432873314934 & 0.0388865746629869 & 0.980556712668507 \tabularnewline
89 & 0.0152908309903176 & 0.0305816619806352 & 0.984709169009682 \tabularnewline
90 & 0.0114418907049145 & 0.0228837814098291 & 0.988558109295085 \tabularnewline
91 & 0.0115015183841046 & 0.0230030367682091 & 0.988498481615895 \tabularnewline
92 & 0.0100556605744891 & 0.0201113211489783 & 0.98994433942551 \tabularnewline
93 & 0.00771186522967623 & 0.0154237304593525 & 0.992288134770324 \tabularnewline
94 & 0.00637459672667722 & 0.0127491934533544 & 0.993625403273323 \tabularnewline
95 & 0.0116799873633404 & 0.0233599747266807 & 0.98832001263666 \tabularnewline
96 & 0.0105781200811641 & 0.0211562401623283 & 0.989421879918836 \tabularnewline
97 & 0.0100612562765505 & 0.0201225125531011 & 0.98993874372345 \tabularnewline
98 & 0.00769911304184285 & 0.0153982260836857 & 0.992300886958157 \tabularnewline
99 & 0.00568082481582094 & 0.0113616496316419 & 0.99431917518418 \tabularnewline
100 & 0.0043489785681723 & 0.0086979571363446 & 0.995651021431828 \tabularnewline
101 & 0.00321348308435686 & 0.00642696616871373 & 0.996786516915643 \tabularnewline
102 & 0.00228647225577789 & 0.00457294451155579 & 0.997713527744222 \tabularnewline
103 & 0.00244472023349372 & 0.00488944046698744 & 0.997555279766506 \tabularnewline
104 & 0.00191524517799058 & 0.00383049035598116 & 0.99808475482201 \tabularnewline
105 & 0.0081144813593183 & 0.0162289627186366 & 0.991885518640682 \tabularnewline
106 & 0.0181618719205615 & 0.0363237438411230 & 0.981838128079439 \tabularnewline
107 & 0.0180245037378093 & 0.0360490074756186 & 0.98197549626219 \tabularnewline
108 & 0.0227179481921593 & 0.0454358963843186 & 0.97728205180784 \tabularnewline
109 & 0.0175277218388999 & 0.0350554436777997 & 0.9824722781611 \tabularnewline
110 & 0.0128020488841614 & 0.0256040977683228 & 0.987197951115839 \tabularnewline
111 & 0.0092686140169013 & 0.0185372280338026 & 0.990731385983099 \tabularnewline
112 & 0.0227283733594771 & 0.0454567467189542 & 0.977271626640523 \tabularnewline
113 & 0.0205971811202094 & 0.0411943622404189 & 0.97940281887979 \tabularnewline
114 & 0.0572002938414689 & 0.114400587682938 & 0.942799706158531 \tabularnewline
115 & 0.0484627048912369 & 0.0969254097824738 & 0.951537295108763 \tabularnewline
116 & 0.0392764367838186 & 0.0785528735676373 & 0.960723563216181 \tabularnewline
117 & 0.115731466764411 & 0.231462933528823 & 0.884268533235589 \tabularnewline
118 & 0.110887113341822 & 0.221774226683644 & 0.889112886658178 \tabularnewline
119 & 0.0983127910559482 & 0.196625582111896 & 0.901687208944052 \tabularnewline
120 & 0.171833930614441 & 0.343667861228882 & 0.82816606938556 \tabularnewline
121 & 0.163712493436824 & 0.327424986873648 & 0.836287506563176 \tabularnewline
122 & 0.195066967106405 & 0.39013393421281 & 0.804933032893595 \tabularnewline
123 & 0.188054053203408 & 0.376108106406815 & 0.811945946796592 \tabularnewline
124 & 0.187235424914874 & 0.374470849829747 & 0.812764575085126 \tabularnewline
125 & 0.170002447239331 & 0.340004894478663 & 0.829997552760669 \tabularnewline
126 & 0.138341398877432 & 0.276682797754865 & 0.861658601122568 \tabularnewline
127 & 0.108178568043679 & 0.216357136087359 & 0.89182143195632 \tabularnewline
128 & 0.111630464935294 & 0.223260929870589 & 0.888369535064706 \tabularnewline
129 & 0.160133500921297 & 0.320267001842594 & 0.839866499078703 \tabularnewline
130 & 0.137951852096966 & 0.275903704193931 & 0.862048147903034 \tabularnewline
131 & 0.120423104329633 & 0.240846208659267 & 0.879576895670366 \tabularnewline
132 & 0.104299007980801 & 0.208598015961602 & 0.8957009920192 \tabularnewline
133 & 0.0956741758549077 & 0.191348351709815 & 0.904325824145092 \tabularnewline
134 & 0.0781005925981872 & 0.156201185196374 & 0.921899407401813 \tabularnewline
135 & 0.106580435245780 & 0.213160870491559 & 0.89341956475422 \tabularnewline
136 & 0.0790287321188473 & 0.158057464237695 & 0.920971267881153 \tabularnewline
137 & 0.057635798250948 & 0.115271596501896 & 0.942364201749052 \tabularnewline
138 & 0.146870833753747 & 0.293741667507494 & 0.853129166246253 \tabularnewline
139 & 0.20813414994964 & 0.41626829989928 & 0.79186585005036 \tabularnewline
140 & 0.188534489726447 & 0.377068979452894 & 0.811465510273553 \tabularnewline
141 & 0.406055845705896 & 0.812111691411792 & 0.593944154294104 \tabularnewline
142 & 0.356404340277384 & 0.712808680554768 & 0.643595659722616 \tabularnewline
143 & 0.66603697563369 & 0.667926048732619 & 0.333963024366309 \tabularnewline
144 & 0.604830923460292 & 0.790338153079417 & 0.395169076539708 \tabularnewline
145 & 0.494206523128988 & 0.988413046257976 & 0.505793476871012 \tabularnewline
146 & 0.423316957865472 & 0.846633915730943 & 0.576683042134528 \tabularnewline
147 & 0.433611033957532 & 0.867222067915064 & 0.566388966042468 \tabularnewline
148 & 0.303307218142269 & 0.606614436284538 & 0.69669278185773 \tabularnewline
149 & 0.211421302306707 & 0.422842604613414 & 0.788578697693293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.527011339441507[/C][C]0.945977321116986[/C][C]0.472988660558493[/C][/ROW]
[ROW][C]11[/C][C]0.562050758269062[/C][C]0.875898483461875[/C][C]0.437949241730938[/C][/ROW]
[ROW][C]12[/C][C]0.488797048040875[/C][C]0.97759409608175[/C][C]0.511202951959125[/C][/ROW]
[ROW][C]13[/C][C]0.540312608910688[/C][C]0.919374782178624[/C][C]0.459687391089312[/C][/ROW]
[ROW][C]14[/C][C]0.741606301267104[/C][C]0.516787397465791[/C][C]0.258393698732896[/C][/ROW]
[ROW][C]15[/C][C]0.654975316425638[/C][C]0.690049367148725[/C][C]0.345024683574363[/C][/ROW]
[ROW][C]16[/C][C]0.624349814096279[/C][C]0.751300371807441[/C][C]0.375650185903721[/C][/ROW]
[ROW][C]17[/C][C]0.538687106536784[/C][C]0.922625786926432[/C][C]0.461312893463216[/C][/ROW]
[ROW][C]18[/C][C]0.676523849957848[/C][C]0.646952300084304[/C][C]0.323476150042152[/C][/ROW]
[ROW][C]19[/C][C]0.6017877142567[/C][C]0.796424571486599[/C][C]0.398212285743300[/C][/ROW]
[ROW][C]20[/C][C]0.58900929980256[/C][C]0.82198140039488[/C][C]0.41099070019744[/C][/ROW]
[ROW][C]21[/C][C]0.525027626121064[/C][C]0.949944747757872[/C][C]0.474972373878936[/C][/ROW]
[ROW][C]22[/C][C]0.455644610275878[/C][C]0.911289220551755[/C][C]0.544355389724122[/C][/ROW]
[ROW][C]23[/C][C]0.404802514498525[/C][C]0.80960502899705[/C][C]0.595197485501475[/C][/ROW]
[ROW][C]24[/C][C]0.409292695949612[/C][C]0.818585391899223[/C][C]0.590707304050388[/C][/ROW]
[ROW][C]25[/C][C]0.571837157525294[/C][C]0.856325684949412[/C][C]0.428162842474706[/C][/ROW]
[ROW][C]26[/C][C]0.510576212129371[/C][C]0.978847575741258[/C][C]0.489423787870629[/C][/ROW]
[ROW][C]27[/C][C]0.443519562686604[/C][C]0.887039125373209[/C][C]0.556480437313396[/C][/ROW]
[ROW][C]28[/C][C]0.437932035294218[/C][C]0.875864070588435[/C][C]0.562067964705782[/C][/ROW]
[ROW][C]29[/C][C]0.373771831625193[/C][C]0.747543663250386[/C][C]0.626228168374807[/C][/ROW]
[ROW][C]30[/C][C]0.313336025758918[/C][C]0.626672051517836[/C][C]0.686663974241082[/C][/ROW]
[ROW][C]31[/C][C]0.347263297377554[/C][C]0.694526594755107[/C][C]0.652736702622446[/C][/ROW]
[ROW][C]32[/C][C]0.296163153698190[/C][C]0.592326307396379[/C][C]0.70383684630181[/C][/ROW]
[ROW][C]33[/C][C]0.521927365358015[/C][C]0.95614526928397[/C][C]0.478072634641985[/C][/ROW]
[ROW][C]34[/C][C]0.47081292178791[/C][C]0.94162584357582[/C][C]0.52918707821209[/C][/ROW]
[ROW][C]35[/C][C]0.432862899334543[/C][C]0.865725798669087[/C][C]0.567137100665457[/C][/ROW]
[ROW][C]36[/C][C]0.375546236339169[/C][C]0.751092472678338[/C][C]0.624453763660831[/C][/ROW]
[ROW][C]37[/C][C]0.348910287323424[/C][C]0.697820574646848[/C][C]0.651089712676576[/C][/ROW]
[ROW][C]38[/C][C]0.297272496870143[/C][C]0.594544993740286[/C][C]0.702727503129857[/C][/ROW]
[ROW][C]39[/C][C]0.249904028542292[/C][C]0.499808057084583[/C][C]0.750095971457708[/C][/ROW]
[ROW][C]40[/C][C]0.22256082533694[/C][C]0.44512165067388[/C][C]0.77743917466306[/C][/ROW]
[ROW][C]41[/C][C]0.375203931367904[/C][C]0.750407862735809[/C][C]0.624796068632096[/C][/ROW]
[ROW][C]42[/C][C]0.323419485366194[/C][C]0.646838970732388[/C][C]0.676580514633806[/C][/ROW]
[ROW][C]43[/C][C]0.291372184891437[/C][C]0.582744369782873[/C][C]0.708627815108563[/C][/ROW]
[ROW][C]44[/C][C]0.247786889016576[/C][C]0.495573778033151[/C][C]0.752213110983424[/C][/ROW]
[ROW][C]45[/C][C]0.211393904844777[/C][C]0.422787809689554[/C][C]0.788606095155223[/C][/ROW]
[ROW][C]46[/C][C]0.191987017977891[/C][C]0.383974035955782[/C][C]0.80801298202211[/C][/ROW]
[ROW][C]47[/C][C]0.179547882285089[/C][C]0.359095764570178[/C][C]0.820452117714911[/C][/ROW]
[ROW][C]48[/C][C]0.164459892538351[/C][C]0.328919785076702[/C][C]0.83554010746165[/C][/ROW]
[ROW][C]49[/C][C]0.134369240006066[/C][C]0.268738480012131[/C][C]0.865630759993934[/C][/ROW]
[ROW][C]50[/C][C]0.124728456293019[/C][C]0.249456912586039[/C][C]0.875271543706981[/C][/ROW]
[ROW][C]51[/C][C]0.102954440158644[/C][C]0.205908880317288[/C][C]0.897045559841356[/C][/ROW]
[ROW][C]52[/C][C]0.0878329701028204[/C][C]0.175665940205641[/C][C]0.91216702989718[/C][/ROW]
[ROW][C]53[/C][C]0.147392747581936[/C][C]0.294785495163872[/C][C]0.852607252418064[/C][/ROW]
[ROW][C]54[/C][C]0.176759866229267[/C][C]0.353519732458534[/C][C]0.823240133770733[/C][/ROW]
[ROW][C]55[/C][C]0.165314066746353[/C][C]0.330628133492705[/C][C]0.834685933253647[/C][/ROW]
[ROW][C]56[/C][C]0.222746328051185[/C][C]0.445492656102369[/C][C]0.777253671948815[/C][/ROW]
[ROW][C]57[/C][C]0.21518432489016[/C][C]0.43036864978032[/C][C]0.78481567510984[/C][/ROW]
[ROW][C]58[/C][C]0.184195410631712[/C][C]0.368390821263425[/C][C]0.815804589368287[/C][/ROW]
[ROW][C]59[/C][C]0.197396035986441[/C][C]0.394792071972882[/C][C]0.80260396401356[/C][/ROW]
[ROW][C]60[/C][C]0.225127634390958[/C][C]0.450255268781916[/C][C]0.774872365609042[/C][/ROW]
[ROW][C]61[/C][C]0.198905633976573[/C][C]0.397811267953146[/C][C]0.801094366023427[/C][/ROW]
[ROW][C]62[/C][C]0.167144063564595[/C][C]0.33428812712919[/C][C]0.832855936435405[/C][/ROW]
[ROW][C]63[/C][C]0.182384014069960[/C][C]0.364768028139919[/C][C]0.81761598593004[/C][/ROW]
[ROW][C]64[/C][C]0.153297483576505[/C][C]0.306594967153011[/C][C]0.846702516423495[/C][/ROW]
[ROW][C]65[/C][C]0.126476444675909[/C][C]0.252952889351819[/C][C]0.87352355532409[/C][/ROW]
[ROW][C]66[/C][C]0.122928310183972[/C][C]0.245856620367945[/C][C]0.877071689816028[/C][/ROW]
[ROW][C]67[/C][C]0.112290924195902[/C][C]0.224581848391804[/C][C]0.887709075804098[/C][/ROW]
[ROW][C]68[/C][C]0.111389708749007[/C][C]0.222779417498014[/C][C]0.888610291250993[/C][/ROW]
[ROW][C]69[/C][C]0.094797850547306[/C][C]0.189595701094612[/C][C]0.905202149452694[/C][/ROW]
[ROW][C]70[/C][C]0.0773954034562743[/C][C]0.154790806912549[/C][C]0.922604596543726[/C][/ROW]
[ROW][C]71[/C][C]0.0628992200876907[/C][C]0.125798440175381[/C][C]0.93710077991231[/C][/ROW]
[ROW][C]72[/C][C]0.0496820132984475[/C][C]0.099364026596895[/C][C]0.950317986701552[/C][/ROW]
[ROW][C]73[/C][C]0.0466400462749179[/C][C]0.0932800925498359[/C][C]0.953359953725082[/C][/ROW]
[ROW][C]74[/C][C]0.0373621752075985[/C][C]0.074724350415197[/C][C]0.962637824792401[/C][/ROW]
[ROW][C]75[/C][C]0.031051733020051[/C][C]0.062103466040102[/C][C]0.968948266979949[/C][/ROW]
[ROW][C]76[/C][C]0.0238172017328579[/C][C]0.0476344034657158[/C][C]0.976182798267142[/C][/ROW]
[ROW][C]77[/C][C]0.0187002305121636[/C][C]0.0374004610243272[/C][C]0.981299769487836[/C][/ROW]
[ROW][C]78[/C][C]0.0149689403621707[/C][C]0.0299378807243413[/C][C]0.98503105963783[/C][/ROW]
[ROW][C]79[/C][C]0.0224924007048702[/C][C]0.0449848014097404[/C][C]0.97750759929513[/C][/ROW]
[ROW][C]80[/C][C]0.0179314270308470[/C][C]0.0358628540616939[/C][C]0.982068572969153[/C][/ROW]
[ROW][C]81[/C][C]0.0175338942105030[/C][C]0.0350677884210059[/C][C]0.982466105789497[/C][/ROW]
[ROW][C]82[/C][C]0.0313614803069281[/C][C]0.0627229606138563[/C][C]0.968638519693072[/C][/ROW]
[ROW][C]83[/C][C]0.024186212215663[/C][C]0.048372424431326[/C][C]0.975813787784337[/C][/ROW]
[ROW][C]84[/C][C]0.0201798986454488[/C][C]0.0403597972908976[/C][C]0.97982010135455[/C][/ROW]
[ROW][C]85[/C][C]0.0221020803926987[/C][C]0.0442041607853974[/C][C]0.977897919607301[/C][/ROW]
[ROW][C]86[/C][C]0.0195896963605925[/C][C]0.0391793927211851[/C][C]0.980410303639407[/C][/ROW]
[ROW][C]87[/C][C]0.0148920824096964[/C][C]0.0297841648193928[/C][C]0.985107917590304[/C][/ROW]
[ROW][C]88[/C][C]0.0194432873314934[/C][C]0.0388865746629869[/C][C]0.980556712668507[/C][/ROW]
[ROW][C]89[/C][C]0.0152908309903176[/C][C]0.0305816619806352[/C][C]0.984709169009682[/C][/ROW]
[ROW][C]90[/C][C]0.0114418907049145[/C][C]0.0228837814098291[/C][C]0.988558109295085[/C][/ROW]
[ROW][C]91[/C][C]0.0115015183841046[/C][C]0.0230030367682091[/C][C]0.988498481615895[/C][/ROW]
[ROW][C]92[/C][C]0.0100556605744891[/C][C]0.0201113211489783[/C][C]0.98994433942551[/C][/ROW]
[ROW][C]93[/C][C]0.00771186522967623[/C][C]0.0154237304593525[/C][C]0.992288134770324[/C][/ROW]
[ROW][C]94[/C][C]0.00637459672667722[/C][C]0.0127491934533544[/C][C]0.993625403273323[/C][/ROW]
[ROW][C]95[/C][C]0.0116799873633404[/C][C]0.0233599747266807[/C][C]0.98832001263666[/C][/ROW]
[ROW][C]96[/C][C]0.0105781200811641[/C][C]0.0211562401623283[/C][C]0.989421879918836[/C][/ROW]
[ROW][C]97[/C][C]0.0100612562765505[/C][C]0.0201225125531011[/C][C]0.98993874372345[/C][/ROW]
[ROW][C]98[/C][C]0.00769911304184285[/C][C]0.0153982260836857[/C][C]0.992300886958157[/C][/ROW]
[ROW][C]99[/C][C]0.00568082481582094[/C][C]0.0113616496316419[/C][C]0.99431917518418[/C][/ROW]
[ROW][C]100[/C][C]0.0043489785681723[/C][C]0.0086979571363446[/C][C]0.995651021431828[/C][/ROW]
[ROW][C]101[/C][C]0.00321348308435686[/C][C]0.00642696616871373[/C][C]0.996786516915643[/C][/ROW]
[ROW][C]102[/C][C]0.00228647225577789[/C][C]0.00457294451155579[/C][C]0.997713527744222[/C][/ROW]
[ROW][C]103[/C][C]0.00244472023349372[/C][C]0.00488944046698744[/C][C]0.997555279766506[/C][/ROW]
[ROW][C]104[/C][C]0.00191524517799058[/C][C]0.00383049035598116[/C][C]0.99808475482201[/C][/ROW]
[ROW][C]105[/C][C]0.0081144813593183[/C][C]0.0162289627186366[/C][C]0.991885518640682[/C][/ROW]
[ROW][C]106[/C][C]0.0181618719205615[/C][C]0.0363237438411230[/C][C]0.981838128079439[/C][/ROW]
[ROW][C]107[/C][C]0.0180245037378093[/C][C]0.0360490074756186[/C][C]0.98197549626219[/C][/ROW]
[ROW][C]108[/C][C]0.0227179481921593[/C][C]0.0454358963843186[/C][C]0.97728205180784[/C][/ROW]
[ROW][C]109[/C][C]0.0175277218388999[/C][C]0.0350554436777997[/C][C]0.9824722781611[/C][/ROW]
[ROW][C]110[/C][C]0.0128020488841614[/C][C]0.0256040977683228[/C][C]0.987197951115839[/C][/ROW]
[ROW][C]111[/C][C]0.0092686140169013[/C][C]0.0185372280338026[/C][C]0.990731385983099[/C][/ROW]
[ROW][C]112[/C][C]0.0227283733594771[/C][C]0.0454567467189542[/C][C]0.977271626640523[/C][/ROW]
[ROW][C]113[/C][C]0.0205971811202094[/C][C]0.0411943622404189[/C][C]0.97940281887979[/C][/ROW]
[ROW][C]114[/C][C]0.0572002938414689[/C][C]0.114400587682938[/C][C]0.942799706158531[/C][/ROW]
[ROW][C]115[/C][C]0.0484627048912369[/C][C]0.0969254097824738[/C][C]0.951537295108763[/C][/ROW]
[ROW][C]116[/C][C]0.0392764367838186[/C][C]0.0785528735676373[/C][C]0.960723563216181[/C][/ROW]
[ROW][C]117[/C][C]0.115731466764411[/C][C]0.231462933528823[/C][C]0.884268533235589[/C][/ROW]
[ROW][C]118[/C][C]0.110887113341822[/C][C]0.221774226683644[/C][C]0.889112886658178[/C][/ROW]
[ROW][C]119[/C][C]0.0983127910559482[/C][C]0.196625582111896[/C][C]0.901687208944052[/C][/ROW]
[ROW][C]120[/C][C]0.171833930614441[/C][C]0.343667861228882[/C][C]0.82816606938556[/C][/ROW]
[ROW][C]121[/C][C]0.163712493436824[/C][C]0.327424986873648[/C][C]0.836287506563176[/C][/ROW]
[ROW][C]122[/C][C]0.195066967106405[/C][C]0.39013393421281[/C][C]0.804933032893595[/C][/ROW]
[ROW][C]123[/C][C]0.188054053203408[/C][C]0.376108106406815[/C][C]0.811945946796592[/C][/ROW]
[ROW][C]124[/C][C]0.187235424914874[/C][C]0.374470849829747[/C][C]0.812764575085126[/C][/ROW]
[ROW][C]125[/C][C]0.170002447239331[/C][C]0.340004894478663[/C][C]0.829997552760669[/C][/ROW]
[ROW][C]126[/C][C]0.138341398877432[/C][C]0.276682797754865[/C][C]0.861658601122568[/C][/ROW]
[ROW][C]127[/C][C]0.108178568043679[/C][C]0.216357136087359[/C][C]0.89182143195632[/C][/ROW]
[ROW][C]128[/C][C]0.111630464935294[/C][C]0.223260929870589[/C][C]0.888369535064706[/C][/ROW]
[ROW][C]129[/C][C]0.160133500921297[/C][C]0.320267001842594[/C][C]0.839866499078703[/C][/ROW]
[ROW][C]130[/C][C]0.137951852096966[/C][C]0.275903704193931[/C][C]0.862048147903034[/C][/ROW]
[ROW][C]131[/C][C]0.120423104329633[/C][C]0.240846208659267[/C][C]0.879576895670366[/C][/ROW]
[ROW][C]132[/C][C]0.104299007980801[/C][C]0.208598015961602[/C][C]0.8957009920192[/C][/ROW]
[ROW][C]133[/C][C]0.0956741758549077[/C][C]0.191348351709815[/C][C]0.904325824145092[/C][/ROW]
[ROW][C]134[/C][C]0.0781005925981872[/C][C]0.156201185196374[/C][C]0.921899407401813[/C][/ROW]
[ROW][C]135[/C][C]0.106580435245780[/C][C]0.213160870491559[/C][C]0.89341956475422[/C][/ROW]
[ROW][C]136[/C][C]0.0790287321188473[/C][C]0.158057464237695[/C][C]0.920971267881153[/C][/ROW]
[ROW][C]137[/C][C]0.057635798250948[/C][C]0.115271596501896[/C][C]0.942364201749052[/C][/ROW]
[ROW][C]138[/C][C]0.146870833753747[/C][C]0.293741667507494[/C][C]0.853129166246253[/C][/ROW]
[ROW][C]139[/C][C]0.20813414994964[/C][C]0.41626829989928[/C][C]0.79186585005036[/C][/ROW]
[ROW][C]140[/C][C]0.188534489726447[/C][C]0.377068979452894[/C][C]0.811465510273553[/C][/ROW]
[ROW][C]141[/C][C]0.406055845705896[/C][C]0.812111691411792[/C][C]0.593944154294104[/C][/ROW]
[ROW][C]142[/C][C]0.356404340277384[/C][C]0.712808680554768[/C][C]0.643595659722616[/C][/ROW]
[ROW][C]143[/C][C]0.66603697563369[/C][C]0.667926048732619[/C][C]0.333963024366309[/C][/ROW]
[ROW][C]144[/C][C]0.604830923460292[/C][C]0.790338153079417[/C][C]0.395169076539708[/C][/ROW]
[ROW][C]145[/C][C]0.494206523128988[/C][C]0.988413046257976[/C][C]0.505793476871012[/C][/ROW]
[ROW][C]146[/C][C]0.423316957865472[/C][C]0.846633915730943[/C][C]0.576683042134528[/C][/ROW]
[ROW][C]147[/C][C]0.433611033957532[/C][C]0.867222067915064[/C][C]0.566388966042468[/C][/ROW]
[ROW][C]148[/C][C]0.303307218142269[/C][C]0.606614436284538[/C][C]0.69669278185773[/C][/ROW]
[ROW][C]149[/C][C]0.211421302306707[/C][C]0.422842604613414[/C][C]0.788578697693293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5270113394415070.9459773211169860.472988660558493
110.5620507582690620.8758984834618750.437949241730938
120.4887970480408750.977594096081750.511202951959125
130.5403126089106880.9193747821786240.459687391089312
140.7416063012671040.5167873974657910.258393698732896
150.6549753164256380.6900493671487250.345024683574363
160.6243498140962790.7513003718074410.375650185903721
170.5386871065367840.9226257869264320.461312893463216
180.6765238499578480.6469523000843040.323476150042152
190.60178771425670.7964245714865990.398212285743300
200.589009299802560.821981400394880.41099070019744
210.5250276261210640.9499447477578720.474972373878936
220.4556446102758780.9112892205517550.544355389724122
230.4048025144985250.809605028997050.595197485501475
240.4092926959496120.8185853918992230.590707304050388
250.5718371575252940.8563256849494120.428162842474706
260.5105762121293710.9788475757412580.489423787870629
270.4435195626866040.8870391253732090.556480437313396
280.4379320352942180.8758640705884350.562067964705782
290.3737718316251930.7475436632503860.626228168374807
300.3133360257589180.6266720515178360.686663974241082
310.3472632973775540.6945265947551070.652736702622446
320.2961631536981900.5923263073963790.70383684630181
330.5219273653580150.956145269283970.478072634641985
340.470812921787910.941625843575820.52918707821209
350.4328628993345430.8657257986690870.567137100665457
360.3755462363391690.7510924726783380.624453763660831
370.3489102873234240.6978205746468480.651089712676576
380.2972724968701430.5945449937402860.702727503129857
390.2499040285422920.4998080570845830.750095971457708
400.222560825336940.445121650673880.77743917466306
410.3752039313679040.7504078627358090.624796068632096
420.3234194853661940.6468389707323880.676580514633806
430.2913721848914370.5827443697828730.708627815108563
440.2477868890165760.4955737780331510.752213110983424
450.2113939048447770.4227878096895540.788606095155223
460.1919870179778910.3839740359557820.80801298202211
470.1795478822850890.3590957645701780.820452117714911
480.1644598925383510.3289197850767020.83554010746165
490.1343692400060660.2687384800121310.865630759993934
500.1247284562930190.2494569125860390.875271543706981
510.1029544401586440.2059088803172880.897045559841356
520.08783297010282040.1756659402056410.91216702989718
530.1473927475819360.2947854951638720.852607252418064
540.1767598662292670.3535197324585340.823240133770733
550.1653140667463530.3306281334927050.834685933253647
560.2227463280511850.4454926561023690.777253671948815
570.215184324890160.430368649780320.78481567510984
580.1841954106317120.3683908212634250.815804589368287
590.1973960359864410.3947920719728820.80260396401356
600.2251276343909580.4502552687819160.774872365609042
610.1989056339765730.3978112679531460.801094366023427
620.1671440635645950.334288127129190.832855936435405
630.1823840140699600.3647680281399190.81761598593004
640.1532974835765050.3065949671530110.846702516423495
650.1264764446759090.2529528893518190.87352355532409
660.1229283101839720.2458566203679450.877071689816028
670.1122909241959020.2245818483918040.887709075804098
680.1113897087490070.2227794174980140.888610291250993
690.0947978505473060.1895957010946120.905202149452694
700.07739540345627430.1547908069125490.922604596543726
710.06289922008769070.1257984401753810.93710077991231
720.04968201329844750.0993640265968950.950317986701552
730.04664004627491790.09328009254983590.953359953725082
740.03736217520759850.0747243504151970.962637824792401
750.0310517330200510.0621034660401020.968948266979949
760.02381720173285790.04763440346571580.976182798267142
770.01870023051216360.03740046102432720.981299769487836
780.01496894036217070.02993788072434130.98503105963783
790.02249240070487020.04498480140974040.97750759929513
800.01793142703084700.03586285406169390.982068572969153
810.01753389421050300.03506778842100590.982466105789497
820.03136148030692810.06272296061385630.968638519693072
830.0241862122156630.0483724244313260.975813787784337
840.02017989864544880.04035979729089760.97982010135455
850.02210208039269870.04420416078539740.977897919607301
860.01958969636059250.03917939272118510.980410303639407
870.01489208240969640.02978416481939280.985107917590304
880.01944328733149340.03888657466298690.980556712668507
890.01529083099031760.03058166198063520.984709169009682
900.01144189070491450.02288378140982910.988558109295085
910.01150151838410460.02300303676820910.988498481615895
920.01005566057448910.02011132114897830.98994433942551
930.007711865229676230.01542373045935250.992288134770324
940.006374596726677220.01274919345335440.993625403273323
950.01167998736334040.02335997472668070.98832001263666
960.01057812008116410.02115624016232830.989421879918836
970.01006125627655050.02012251255310110.98993874372345
980.007699113041842850.01539822608368570.992300886958157
990.005680824815820940.01136164963164190.99431917518418
1000.00434897856817230.00869795713634460.995651021431828
1010.003213483084356860.006426966168713730.996786516915643
1020.002286472255777890.004572944511555790.997713527744222
1030.002444720233493720.004889440466987440.997555279766506
1040.001915245177990580.003830490355981160.99808475482201
1050.00811448135931830.01622896271863660.991885518640682
1060.01816187192056150.03632374384112300.981838128079439
1070.01802450373780930.03604900747561860.98197549626219
1080.02271794819215930.04543589638431860.97728205180784
1090.01752772183889990.03505544367779970.9824722781611
1100.01280204888416140.02560409776832280.987197951115839
1110.00926861401690130.01853722803380260.990731385983099
1120.02272837335947710.04545674671895420.977271626640523
1130.02059718112020940.04119436224041890.97940281887979
1140.05720029384146890.1144005876829380.942799706158531
1150.04846270489123690.09692540978247380.951537295108763
1160.03927643678381860.07855287356763730.960723563216181
1170.1157314667644110.2314629335288230.884268533235589
1180.1108871133418220.2217742266836440.889112886658178
1190.09831279105594820.1966255821118960.901687208944052
1200.1718339306144410.3436678612288820.82816606938556
1210.1637124934368240.3274249868736480.836287506563176
1220.1950669671064050.390133934212810.804933032893595
1230.1880540532034080.3761081064068150.811945946796592
1240.1872354249148740.3744708498297470.812764575085126
1250.1700024472393310.3400048944786630.829997552760669
1260.1383413988774320.2766827977548650.861658601122568
1270.1081785680436790.2163571360873590.89182143195632
1280.1116304649352940.2232609298705890.888369535064706
1290.1601335009212970.3202670018425940.839866499078703
1300.1379518520969660.2759037041939310.862048147903034
1310.1204231043296330.2408462086592670.879576895670366
1320.1042990079808010.2085980159616020.8957009920192
1330.09567417585490770.1913483517098150.904325824145092
1340.07810059259818720.1562011851963740.921899407401813
1350.1065804352457800.2131608704915590.89341956475422
1360.07902873211884730.1580574642376950.920971267881153
1370.0576357982509480.1152715965018960.942364201749052
1380.1468708337537470.2937416675074940.853129166246253
1390.208134149949640.416268299899280.79186585005036
1400.1885344897264470.3770689794528940.811465510273553
1410.4060558457058960.8121116914117920.593944154294104
1420.3564043402773840.7128086805547680.643595659722616
1430.666036975633690.6679260487326190.333963024366309
1440.6048309234602920.7903381530794170.395169076539708
1450.4942065231289880.9884130462579760.505793476871012
1460.4233169578654720.8466339157309430.576683042134528
1470.4336110339575320.8672220679150640.566388966042468
1480.3033072181422690.6066144362845380.69669278185773
1490.2114213023067070.4228426046134140.788578697693293






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0357142857142857NOK
5% type I error level370.264285714285714NOK
10% type I error level440.314285714285714NOK

\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 & 5 & 0.0357142857142857 & NOK \tabularnewline
5% type I error level & 37 & 0.264285714285714 & NOK \tabularnewline
10% type I error level & 44 & 0.314285714285714 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99566&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]5[/C][C]0.0357142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.264285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.314285714285714[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99566&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99566&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0357142857142857NOK
5% type I error level370.264285714285714NOK
10% type I error level440.314285714285714NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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