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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 computationMon, 27 Dec 2010 23:09:03 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t12934913271vg79phyhb77w3s.htm/, Retrieved Sun, 05 May 2024 00:23:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116170, Retrieved Sun, 05 May 2024 00:23:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-27 23:09:03] [d7127d50f40450f0f3837a0965e389eb] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
ParentalCriticism[t] = + 16.0234048905969 -1.34666003590030Month[t] + 0.150828087380878DoubtsActions[t] + 0.441218810283992ParentalExpectations[t] + 0.0296860262939057Standards[t] -0.105384926748471`Organization `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ParentalCriticism[t] =  +  16.0234048905969 -1.34666003590030Month[t] +  0.150828087380878DoubtsActions[t] +  0.441218810283992ParentalExpectations[t] +  0.0296860262939057Standards[t] -0.105384926748471`Organization
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ParentalCriticism[t] =  +  16.0234048905969 -1.34666003590030Month[t] +  0.150828087380878DoubtsActions[t] +  0.441218810283992ParentalExpectations[t] +  0.0296860262939057Standards[t] -0.105384926748471`Organization
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&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
ParentalCriticism[t] = + 16.0234048905969 -1.34666003590030Month[t] + 0.150828087380878DoubtsActions[t] + 0.441218810283992ParentalExpectations[t] + 0.0296860262939057Standards[t] -0.105384926748471`Organization `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.02340489059699.0917591.76240.0799970.039998
Month-1.346660035900300.897792-1.50.1356830.067841
DoubtsActions0.1508280873808780.0611322.46730.0147180.007359
ParentalExpectations0.4412188102839920.0530058.324100
Standards0.02968602629390570.0457090.64950.5170160.258508
`Organization `-0.1053849267484710.048531-2.17150.0314350.015717

\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) & 16.0234048905969 & 9.091759 & 1.7624 & 0.079997 & 0.039998 \tabularnewline
Month & -1.34666003590030 & 0.897792 & -1.5 & 0.135683 & 0.067841 \tabularnewline
DoubtsActions & 0.150828087380878 & 0.061132 & 2.4673 & 0.014718 & 0.007359 \tabularnewline
ParentalExpectations & 0.441218810283992 & 0.053005 & 8.3241 & 0 & 0 \tabularnewline
Standards & 0.0296860262939057 & 0.045709 & 0.6495 & 0.517016 & 0.258508 \tabularnewline
`Organization
` & -0.105384926748471 & 0.048531 & -2.1715 & 0.031435 & 0.015717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&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]16.0234048905969[/C][C]9.091759[/C][C]1.7624[/C][C]0.079997[/C][C]0.039998[/C][/ROW]
[ROW][C]Month[/C][C]-1.34666003590030[/C][C]0.897792[/C][C]-1.5[/C][C]0.135683[/C][C]0.067841[/C][/ROW]
[ROW][C]DoubtsActions[/C][C]0.150828087380878[/C][C]0.061132[/C][C]2.4673[/C][C]0.014718[/C][C]0.007359[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.441218810283992[/C][C]0.053005[/C][C]8.3241[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Standards[/C][C]0.0296860262939057[/C][C]0.045709[/C][C]0.6495[/C][C]0.517016[/C][C]0.258508[/C][/ROW]
[ROW][C]`Organization
`[/C][C]-0.105384926748471[/C][C]0.048531[/C][C]-2.1715[/C][C]0.031435[/C][C]0.015717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&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)16.02340489059699.0917591.76240.0799970.039998
Month-1.346660035900300.897792-1.50.1356830.067841
DoubtsActions0.1508280873808780.0611322.46730.0147180.007359
ParentalExpectations0.4412188102839920.0530058.324100
Standards0.02968602629390570.0457090.64950.5170160.258508
`Organization `-0.1053849267484710.048531-2.17150.0314350.015717






Multiple Linear Regression - Regression Statistics
Multiple R0.630246087960682
R-squared0.397210131389744
Adjusted R-squared0.377511116075683
F-TEST (value)20.1639587084449
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value1.99840144432528e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.13581302072108
Sum Squared Residuals697.939680700698

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.630246087960682 \tabularnewline
R-squared & 0.397210131389744 \tabularnewline
Adjusted R-squared & 0.377511116075683 \tabularnewline
F-TEST (value) & 20.1639587084449 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 1.99840144432528e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.13581302072108 \tabularnewline
Sum Squared Residuals & 697.939680700698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.630246087960682[/C][/ROW]
[ROW][C]R-squared[/C][C]0.397210131389744[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.377511116075683[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.1639587084449[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]1.99840144432528e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.13581302072108[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]697.939680700698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&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 R0.630246087960682
R-squared0.397210131389744
Adjusted R-squared0.377511116075683
F-TEST (value)20.1639587084449
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value1.99840144432528e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.13581302072108
Sum Squared Residuals697.939680700698






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1128.84092123954383.15907876045619
286.969402542804531.03059745719547
3810.5651104867126-2.56511048671264
488.26577090327395-0.265770903273949
599.05549396019402-0.0554939601940226
678.30330086747107-1.30330086747107
747.02601980716237-3.02601980716237
8117.539015548949323.46098445105068
978.4508737116258-1.45087371162580
1077.94054140993561-0.940541409935612
11128.729429269142023.27057073085798
121010.0933483628200-0.0933483628199535
13106.743658149706973.25634185029303
1487.67313220247970.326867797520302
1587.433630622200850.566369377799147
1647.62914379635547-3.62914379635547
1796.430620654074152.56937934592585
1887.767821390897780.232178609102220
1979.38800334197132-2.38800334197132
201110.74419759954440.255802400455617
2198.142327898472840.857672101527163
221111.4277005320023-0.42770053200232
23138.442271699863924.55772830013608
2487.19108101584150.808918984158504
2586.712262982828311.28773701717169
2698.535251747697240.464748252302757
2768.74545188766593-2.74545188766593
2897.684898109334521.31510189066548
2999.26236959840449-0.262369598404491
3067.8793411149647-1.87934111496470
3165.72204247960050.277957520399497
321611.15459095647804.84540904352204
33511.1218181589461-6.12181815894612
3477.37338462674195-0.373384626741947
3599.94404428847933-0.944044288479331
3667.06190988930681-1.06190988930681
3766.6996231331024-0.699623133102397
3858.4508737116258-3.4508737116258
391210.82142051303921.17857948696080
4078.09840875088068-1.09840875088069
411010.2206252212254-0.220625221225379
4298.409618004130410.590381995869588
4389.30742817305315-1.30742817305315
4459.2920556246984-4.2920556246984
4586.825256876669231.17474312333077
4687.17924585045460.8207541495454
47108.8505713302291.14942866977101
4867.06008164692371-1.06008164692371
4987.67800847094330.321991529056693
50710.6981847253837-3.69818472538372
5145.32968834390435-1.32968834390435
5287.303051351638330.696948648361666
5387.002653705731420.997346294268578
5444.86821748401608-0.868217484016083
552013.13556293130446.8644370686956
5687.468497146301210.531502853698792
5787.286154790243330.713845209756673
5868.3344888172041-2.3344888172041
5944.40344744475664-0.403447444756638
6089.28451591613803-1.28451591613803
6197.286724503771581.71327549622842
6267.98415632818492-1.98415632818492
6378.30549160623679-1.30549160623679
6496.23054653890762.76945346109240
6557.05867350289588-2.05867350289588
6655.94560179799634-0.945601797996344
6786.740690480267371.25930951973263
6888.23854109993117-0.238541099931174
6966.85916910125758-0.85916910125758
7087.176847894543250.82315210545675
7177.3333070914605-0.333307091460493
7276.084779847534830.91522015246517
7398.59405408675680.405945913243198
741111.0200897169638-0.0200897169638392
7568.64006696091746-2.64006696091746
7687.740348192970720.259651807029279
7768.39302567207825-2.39302567207825
7898.667659260642860.332340739357141
7986.405545412058441.59445458794156
8068.37544029535665-2.37544029535665
81108.300999923756931.69900007624307
8286.079834320539141.92016567946086
8388.34815222497409-0.348152224974092
84109.035842610569050.964157389430953
8555.91678971457353-0.916789714573533
8679.33989674124215-2.33989674124215
8757.16109076020475-2.16109076020475
8886.26598277653621.73401722346379
89149.909296866177314.09070313382269
9077.651290114089-0.651290114089004
9188.82088530393509-0.820885303935087
9264.875491708391041.12450829160896
9356.26769191712096-1.26769191712096
9469.22876160315898-3.22876160315898
95106.545677761108933.45432223889107
961211.54062516731120.459374832688838
9799.135900657124-0.135900657123995
981210.86983134311121.13016865688879
9977.53014805300203-0.530148053002031
10088.33296480416384-0.332964804163844
101109.153062702704410.846937297295586
10267.25817790453418-1.25817790453418
1031011.2049764113201-1.20497641132014
104108.686649548606381.31335045139363
105107.059362318222472.94063768177753
10658.44569321381935-3.44569321381935
10776.918422052047050.0815779479529461
108108.633362450070761.36663754992924
109119.726689025934021.27331097406598
11068.1952304110247-2.19523041102471
11177.47134571394247-0.471345713942473
112128.82804042651173.17195957348829
113116.339164619281084.66083538071892
1141110.80200689393450.197993106065491
115115.428564985459455.57143501454055
11657.59344207454145-2.59344207454145
117810.229254513576-2.22925451357600
11866.77828970299657-0.778289702996566
11999.4105342456886-0.410534245688608
12046.76157826914606-2.76157826914606
12145.7578134599466-1.75781345994660
12278.32904283521224-1.32904283521224
123119.585298148028691.41470185197131
12464.369839449511131.63016055048887
12577.03252809235574-0.0325280923557444
12689.91618650456852-1.91618650456852
12746.29947166998338-2.29947166998338
12887.041776941500870.958223058499127
12998.125547206090250.874452793909746
13087.659018182979790.340981817020209
131118.767293976056632.23270602394337
13287.470776000414220.52922399958578
13356.71043474044521-1.71043474044521
13445.84489368127223-1.84489368127223
13587.410834234298160.589165765701845
1361011.7953141684003-1.79531416840026
13768.26853135556792-2.26853135556792
13899.35598861829815-0.355988618298145
13997.152223264257461.84777673574254
140138.8845140681924.11548593180800
14197.846756677763271.15324332223673
142109.527450108481130.472549891518867
1432014.82720648642925.17279351357077
14456.00192582450364-1.00192582450364
1451110.38259965866660.617400341333438
14668.09574531078392-2.09574531078392
147910.1548141419763-1.15481414197627
14877.28337224834823-0.283372248348227
14998.135739256638560.864260743361444
150108.426514565525421.57348543447458
15196.919376351559062.08062364844094
152810.2127812833222-2.21278128332217
153711.2862403955649-4.28624039556491
15469.39073604060016-3.39073604060016
1551311.64505579454761.35494420545237
15668.00105288957993-2.00105288957993
15787.705031057140450.294968942859549
158109.470476011804670.529523988195328
1591611.75017523711074.24982476288931

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 8.8409212395438 & 3.15907876045619 \tabularnewline
2 & 8 & 6.96940254280453 & 1.03059745719547 \tabularnewline
3 & 8 & 10.5651104867126 & -2.56511048671264 \tabularnewline
4 & 8 & 8.26577090327395 & -0.265770903273949 \tabularnewline
5 & 9 & 9.05549396019402 & -0.0554939601940226 \tabularnewline
6 & 7 & 8.30330086747107 & -1.30330086747107 \tabularnewline
7 & 4 & 7.02601980716237 & -3.02601980716237 \tabularnewline
8 & 11 & 7.53901554894932 & 3.46098445105068 \tabularnewline
9 & 7 & 8.4508737116258 & -1.45087371162580 \tabularnewline
10 & 7 & 7.94054140993561 & -0.940541409935612 \tabularnewline
11 & 12 & 8.72942926914202 & 3.27057073085798 \tabularnewline
12 & 10 & 10.0933483628200 & -0.0933483628199535 \tabularnewline
13 & 10 & 6.74365814970697 & 3.25634185029303 \tabularnewline
14 & 8 & 7.6731322024797 & 0.326867797520302 \tabularnewline
15 & 8 & 7.43363062220085 & 0.566369377799147 \tabularnewline
16 & 4 & 7.62914379635547 & -3.62914379635547 \tabularnewline
17 & 9 & 6.43062065407415 & 2.56937934592585 \tabularnewline
18 & 8 & 7.76782139089778 & 0.232178609102220 \tabularnewline
19 & 7 & 9.38800334197132 & -2.38800334197132 \tabularnewline
20 & 11 & 10.7441975995444 & 0.255802400455617 \tabularnewline
21 & 9 & 8.14232789847284 & 0.857672101527163 \tabularnewline
22 & 11 & 11.4277005320023 & -0.42770053200232 \tabularnewline
23 & 13 & 8.44227169986392 & 4.55772830013608 \tabularnewline
24 & 8 & 7.1910810158415 & 0.808918984158504 \tabularnewline
25 & 8 & 6.71226298282831 & 1.28773701717169 \tabularnewline
26 & 9 & 8.53525174769724 & 0.464748252302757 \tabularnewline
27 & 6 & 8.74545188766593 & -2.74545188766593 \tabularnewline
28 & 9 & 7.68489810933452 & 1.31510189066548 \tabularnewline
29 & 9 & 9.26236959840449 & -0.262369598404491 \tabularnewline
30 & 6 & 7.8793411149647 & -1.87934111496470 \tabularnewline
31 & 6 & 5.7220424796005 & 0.277957520399497 \tabularnewline
32 & 16 & 11.1545909564780 & 4.84540904352204 \tabularnewline
33 & 5 & 11.1218181589461 & -6.12181815894612 \tabularnewline
34 & 7 & 7.37338462674195 & -0.373384626741947 \tabularnewline
35 & 9 & 9.94404428847933 & -0.944044288479331 \tabularnewline
36 & 6 & 7.06190988930681 & -1.06190988930681 \tabularnewline
37 & 6 & 6.6996231331024 & -0.699623133102397 \tabularnewline
38 & 5 & 8.4508737116258 & -3.4508737116258 \tabularnewline
39 & 12 & 10.8214205130392 & 1.17857948696080 \tabularnewline
40 & 7 & 8.09840875088068 & -1.09840875088069 \tabularnewline
41 & 10 & 10.2206252212254 & -0.220625221225379 \tabularnewline
42 & 9 & 8.40961800413041 & 0.590381995869588 \tabularnewline
43 & 8 & 9.30742817305315 & -1.30742817305315 \tabularnewline
44 & 5 & 9.2920556246984 & -4.2920556246984 \tabularnewline
45 & 8 & 6.82525687666923 & 1.17474312333077 \tabularnewline
46 & 8 & 7.1792458504546 & 0.8207541495454 \tabularnewline
47 & 10 & 8.850571330229 & 1.14942866977101 \tabularnewline
48 & 6 & 7.06008164692371 & -1.06008164692371 \tabularnewline
49 & 8 & 7.6780084709433 & 0.321991529056693 \tabularnewline
50 & 7 & 10.6981847253837 & -3.69818472538372 \tabularnewline
51 & 4 & 5.32968834390435 & -1.32968834390435 \tabularnewline
52 & 8 & 7.30305135163833 & 0.696948648361666 \tabularnewline
53 & 8 & 7.00265370573142 & 0.997346294268578 \tabularnewline
54 & 4 & 4.86821748401608 & -0.868217484016083 \tabularnewline
55 & 20 & 13.1355629313044 & 6.8644370686956 \tabularnewline
56 & 8 & 7.46849714630121 & 0.531502853698792 \tabularnewline
57 & 8 & 7.28615479024333 & 0.713845209756673 \tabularnewline
58 & 6 & 8.3344888172041 & -2.3344888172041 \tabularnewline
59 & 4 & 4.40344744475664 & -0.403447444756638 \tabularnewline
60 & 8 & 9.28451591613803 & -1.28451591613803 \tabularnewline
61 & 9 & 7.28672450377158 & 1.71327549622842 \tabularnewline
62 & 6 & 7.98415632818492 & -1.98415632818492 \tabularnewline
63 & 7 & 8.30549160623679 & -1.30549160623679 \tabularnewline
64 & 9 & 6.2305465389076 & 2.76945346109240 \tabularnewline
65 & 5 & 7.05867350289588 & -2.05867350289588 \tabularnewline
66 & 5 & 5.94560179799634 & -0.945601797996344 \tabularnewline
67 & 8 & 6.74069048026737 & 1.25930951973263 \tabularnewline
68 & 8 & 8.23854109993117 & -0.238541099931174 \tabularnewline
69 & 6 & 6.85916910125758 & -0.85916910125758 \tabularnewline
70 & 8 & 7.17684789454325 & 0.82315210545675 \tabularnewline
71 & 7 & 7.3333070914605 & -0.333307091460493 \tabularnewline
72 & 7 & 6.08477984753483 & 0.91522015246517 \tabularnewline
73 & 9 & 8.5940540867568 & 0.405945913243198 \tabularnewline
74 & 11 & 11.0200897169638 & -0.0200897169638392 \tabularnewline
75 & 6 & 8.64006696091746 & -2.64006696091746 \tabularnewline
76 & 8 & 7.74034819297072 & 0.259651807029279 \tabularnewline
77 & 6 & 8.39302567207825 & -2.39302567207825 \tabularnewline
78 & 9 & 8.66765926064286 & 0.332340739357141 \tabularnewline
79 & 8 & 6.40554541205844 & 1.59445458794156 \tabularnewline
80 & 6 & 8.37544029535665 & -2.37544029535665 \tabularnewline
81 & 10 & 8.30099992375693 & 1.69900007624307 \tabularnewline
82 & 8 & 6.07983432053914 & 1.92016567946086 \tabularnewline
83 & 8 & 8.34815222497409 & -0.348152224974092 \tabularnewline
84 & 10 & 9.03584261056905 & 0.964157389430953 \tabularnewline
85 & 5 & 5.91678971457353 & -0.916789714573533 \tabularnewline
86 & 7 & 9.33989674124215 & -2.33989674124215 \tabularnewline
87 & 5 & 7.16109076020475 & -2.16109076020475 \tabularnewline
88 & 8 & 6.2659827765362 & 1.73401722346379 \tabularnewline
89 & 14 & 9.90929686617731 & 4.09070313382269 \tabularnewline
90 & 7 & 7.651290114089 & -0.651290114089004 \tabularnewline
91 & 8 & 8.82088530393509 & -0.820885303935087 \tabularnewline
92 & 6 & 4.87549170839104 & 1.12450829160896 \tabularnewline
93 & 5 & 6.26769191712096 & -1.26769191712096 \tabularnewline
94 & 6 & 9.22876160315898 & -3.22876160315898 \tabularnewline
95 & 10 & 6.54567776110893 & 3.45432223889107 \tabularnewline
96 & 12 & 11.5406251673112 & 0.459374832688838 \tabularnewline
97 & 9 & 9.135900657124 & -0.135900657123995 \tabularnewline
98 & 12 & 10.8698313431112 & 1.13016865688879 \tabularnewline
99 & 7 & 7.53014805300203 & -0.530148053002031 \tabularnewline
100 & 8 & 8.33296480416384 & -0.332964804163844 \tabularnewline
101 & 10 & 9.15306270270441 & 0.846937297295586 \tabularnewline
102 & 6 & 7.25817790453418 & -1.25817790453418 \tabularnewline
103 & 10 & 11.2049764113201 & -1.20497641132014 \tabularnewline
104 & 10 & 8.68664954860638 & 1.31335045139363 \tabularnewline
105 & 10 & 7.05936231822247 & 2.94063768177753 \tabularnewline
106 & 5 & 8.44569321381935 & -3.44569321381935 \tabularnewline
107 & 7 & 6.91842205204705 & 0.0815779479529461 \tabularnewline
108 & 10 & 8.63336245007076 & 1.36663754992924 \tabularnewline
109 & 11 & 9.72668902593402 & 1.27331097406598 \tabularnewline
110 & 6 & 8.1952304110247 & -2.19523041102471 \tabularnewline
111 & 7 & 7.47134571394247 & -0.471345713942473 \tabularnewline
112 & 12 & 8.8280404265117 & 3.17195957348829 \tabularnewline
113 & 11 & 6.33916461928108 & 4.66083538071892 \tabularnewline
114 & 11 & 10.8020068939345 & 0.197993106065491 \tabularnewline
115 & 11 & 5.42856498545945 & 5.57143501454055 \tabularnewline
116 & 5 & 7.59344207454145 & -2.59344207454145 \tabularnewline
117 & 8 & 10.229254513576 & -2.22925451357600 \tabularnewline
118 & 6 & 6.77828970299657 & -0.778289702996566 \tabularnewline
119 & 9 & 9.4105342456886 & -0.410534245688608 \tabularnewline
120 & 4 & 6.76157826914606 & -2.76157826914606 \tabularnewline
121 & 4 & 5.7578134599466 & -1.75781345994660 \tabularnewline
122 & 7 & 8.32904283521224 & -1.32904283521224 \tabularnewline
123 & 11 & 9.58529814802869 & 1.41470185197131 \tabularnewline
124 & 6 & 4.36983944951113 & 1.63016055048887 \tabularnewline
125 & 7 & 7.03252809235574 & -0.0325280923557444 \tabularnewline
126 & 8 & 9.91618650456852 & -1.91618650456852 \tabularnewline
127 & 4 & 6.29947166998338 & -2.29947166998338 \tabularnewline
128 & 8 & 7.04177694150087 & 0.958223058499127 \tabularnewline
129 & 9 & 8.12554720609025 & 0.874452793909746 \tabularnewline
130 & 8 & 7.65901818297979 & 0.340981817020209 \tabularnewline
131 & 11 & 8.76729397605663 & 2.23270602394337 \tabularnewline
132 & 8 & 7.47077600041422 & 0.52922399958578 \tabularnewline
133 & 5 & 6.71043474044521 & -1.71043474044521 \tabularnewline
134 & 4 & 5.84489368127223 & -1.84489368127223 \tabularnewline
135 & 8 & 7.41083423429816 & 0.589165765701845 \tabularnewline
136 & 10 & 11.7953141684003 & -1.79531416840026 \tabularnewline
137 & 6 & 8.26853135556792 & -2.26853135556792 \tabularnewline
138 & 9 & 9.35598861829815 & -0.355988618298145 \tabularnewline
139 & 9 & 7.15222326425746 & 1.84777673574254 \tabularnewline
140 & 13 & 8.884514068192 & 4.11548593180800 \tabularnewline
141 & 9 & 7.84675667776327 & 1.15324332223673 \tabularnewline
142 & 10 & 9.52745010848113 & 0.472549891518867 \tabularnewline
143 & 20 & 14.8272064864292 & 5.17279351357077 \tabularnewline
144 & 5 & 6.00192582450364 & -1.00192582450364 \tabularnewline
145 & 11 & 10.3825996586666 & 0.617400341333438 \tabularnewline
146 & 6 & 8.09574531078392 & -2.09574531078392 \tabularnewline
147 & 9 & 10.1548141419763 & -1.15481414197627 \tabularnewline
148 & 7 & 7.28337224834823 & -0.283372248348227 \tabularnewline
149 & 9 & 8.13573925663856 & 0.864260743361444 \tabularnewline
150 & 10 & 8.42651456552542 & 1.57348543447458 \tabularnewline
151 & 9 & 6.91937635155906 & 2.08062364844094 \tabularnewline
152 & 8 & 10.2127812833222 & -2.21278128332217 \tabularnewline
153 & 7 & 11.2862403955649 & -4.28624039556491 \tabularnewline
154 & 6 & 9.39073604060016 & -3.39073604060016 \tabularnewline
155 & 13 & 11.6450557945476 & 1.35494420545237 \tabularnewline
156 & 6 & 8.00105288957993 & -2.00105288957993 \tabularnewline
157 & 8 & 7.70503105714045 & 0.294968942859549 \tabularnewline
158 & 10 & 9.47047601180467 & 0.529523988195328 \tabularnewline
159 & 16 & 11.7501752371107 & 4.24982476288931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&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]12[/C][C]8.8409212395438[/C][C]3.15907876045619[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]6.96940254280453[/C][C]1.03059745719547[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]10.5651104867126[/C][C]-2.56511048671264[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]8.26577090327395[/C][C]-0.265770903273949[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.05549396019402[/C][C]-0.0554939601940226[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]8.30330086747107[/C][C]-1.30330086747107[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]7.02601980716237[/C][C]-3.02601980716237[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]7.53901554894932[/C][C]3.46098445105068[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]8.4508737116258[/C][C]-1.45087371162580[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]7.94054140993561[/C][C]-0.940541409935612[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]8.72942926914202[/C][C]3.27057073085798[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]10.0933483628200[/C][C]-0.0933483628199535[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]6.74365814970697[/C][C]3.25634185029303[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]7.6731322024797[/C][C]0.326867797520302[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.43363062220085[/C][C]0.566369377799147[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.62914379635547[/C][C]-3.62914379635547[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.43062065407415[/C][C]2.56937934592585[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]7.76782139089778[/C][C]0.232178609102220[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]9.38800334197132[/C][C]-2.38800334197132[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]10.7441975995444[/C][C]0.255802400455617[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.14232789847284[/C][C]0.857672101527163[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.4277005320023[/C][C]-0.42770053200232[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]8.44227169986392[/C][C]4.55772830013608[/C][/ROW]
[ROW][C]24[/C][C]8[/C][C]7.1910810158415[/C][C]0.808918984158504[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]6.71226298282831[/C][C]1.28773701717169[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]8.53525174769724[/C][C]0.464748252302757[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]8.74545188766593[/C][C]-2.74545188766593[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]7.68489810933452[/C][C]1.31510189066548[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]9.26236959840449[/C][C]-0.262369598404491[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]7.8793411149647[/C][C]-1.87934111496470[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]5.7220424796005[/C][C]0.277957520399497[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]11.1545909564780[/C][C]4.84540904352204[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]11.1218181589461[/C][C]-6.12181815894612[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]7.37338462674195[/C][C]-0.373384626741947[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.94404428847933[/C][C]-0.944044288479331[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]7.06190988930681[/C][C]-1.06190988930681[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]6.6996231331024[/C][C]-0.699623133102397[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]8.4508737116258[/C][C]-3.4508737116258[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]10.8214205130392[/C][C]1.17857948696080[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]8.09840875088068[/C][C]-1.09840875088069[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]10.2206252212254[/C][C]-0.220625221225379[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]8.40961800413041[/C][C]0.590381995869588[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]9.30742817305315[/C][C]-1.30742817305315[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]9.2920556246984[/C][C]-4.2920556246984[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]6.82525687666923[/C][C]1.17474312333077[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]7.1792458504546[/C][C]0.8207541495454[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]8.850571330229[/C][C]1.14942866977101[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]7.06008164692371[/C][C]-1.06008164692371[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.6780084709433[/C][C]0.321991529056693[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]10.6981847253837[/C][C]-3.69818472538372[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]5.32968834390435[/C][C]-1.32968834390435[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.30305135163833[/C][C]0.696948648361666[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]7.00265370573142[/C][C]0.997346294268578[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.86821748401608[/C][C]-0.868217484016083[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]13.1355629313044[/C][C]6.8644370686956[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]7.46849714630121[/C][C]0.531502853698792[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]7.28615479024333[/C][C]0.713845209756673[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]8.3344888172041[/C][C]-2.3344888172041[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.40344744475664[/C][C]-0.403447444756638[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]9.28451591613803[/C][C]-1.28451591613803[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]7.28672450377158[/C][C]1.71327549622842[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]7.98415632818492[/C][C]-1.98415632818492[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]8.30549160623679[/C][C]-1.30549160623679[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]6.2305465389076[/C][C]2.76945346109240[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]7.05867350289588[/C][C]-2.05867350289588[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.94560179799634[/C][C]-0.945601797996344[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]6.74069048026737[/C][C]1.25930951973263[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]8.23854109993117[/C][C]-0.238541099931174[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]6.85916910125758[/C][C]-0.85916910125758[/C][/ROW]
[ROW][C]70[/C][C]8[/C][C]7.17684789454325[/C][C]0.82315210545675[/C][/ROW]
[ROW][C]71[/C][C]7[/C][C]7.3333070914605[/C][C]-0.333307091460493[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]6.08477984753483[/C][C]0.91522015246517[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]8.5940540867568[/C][C]0.405945913243198[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]11.0200897169638[/C][C]-0.0200897169638392[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]8.64006696091746[/C][C]-2.64006696091746[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.74034819297072[/C][C]0.259651807029279[/C][/ROW]
[ROW][C]77[/C][C]6[/C][C]8.39302567207825[/C][C]-2.39302567207825[/C][/ROW]
[ROW][C]78[/C][C]9[/C][C]8.66765926064286[/C][C]0.332340739357141[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]6.40554541205844[/C][C]1.59445458794156[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.37544029535665[/C][C]-2.37544029535665[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]8.30099992375693[/C][C]1.69900007624307[/C][/ROW]
[ROW][C]82[/C][C]8[/C][C]6.07983432053914[/C][C]1.92016567946086[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]8.34815222497409[/C][C]-0.348152224974092[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]9.03584261056905[/C][C]0.964157389430953[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]5.91678971457353[/C][C]-0.916789714573533[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]9.33989674124215[/C][C]-2.33989674124215[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]7.16109076020475[/C][C]-2.16109076020475[/C][/ROW]
[ROW][C]88[/C][C]8[/C][C]6.2659827765362[/C][C]1.73401722346379[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]9.90929686617731[/C][C]4.09070313382269[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]7.651290114089[/C][C]-0.651290114089004[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]8.82088530393509[/C][C]-0.820885303935087[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]4.87549170839104[/C][C]1.12450829160896[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]6.26769191712096[/C][C]-1.26769191712096[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]9.22876160315898[/C][C]-3.22876160315898[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]6.54567776110893[/C][C]3.45432223889107[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.5406251673112[/C][C]0.459374832688838[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]9.135900657124[/C][C]-0.135900657123995[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]10.8698313431112[/C][C]1.13016865688879[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]7.53014805300203[/C][C]-0.530148053002031[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]8.33296480416384[/C][C]-0.332964804163844[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]9.15306270270441[/C][C]0.846937297295586[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]7.25817790453418[/C][C]-1.25817790453418[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]11.2049764113201[/C][C]-1.20497641132014[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]8.68664954860638[/C][C]1.31335045139363[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]7.05936231822247[/C][C]2.94063768177753[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]8.44569321381935[/C][C]-3.44569321381935[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]6.91842205204705[/C][C]0.0815779479529461[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]8.63336245007076[/C][C]1.36663754992924[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]9.72668902593402[/C][C]1.27331097406598[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]8.1952304110247[/C][C]-2.19523041102471[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]7.47134571394247[/C][C]-0.471345713942473[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]8.8280404265117[/C][C]3.17195957348829[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]6.33916461928108[/C][C]4.66083538071892[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.8020068939345[/C][C]0.197993106065491[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]5.42856498545945[/C][C]5.57143501454055[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]7.59344207454145[/C][C]-2.59344207454145[/C][/ROW]
[ROW][C]117[/C][C]8[/C][C]10.229254513576[/C][C]-2.22925451357600[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]6.77828970299657[/C][C]-0.778289702996566[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]9.4105342456886[/C][C]-0.410534245688608[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]6.76157826914606[/C][C]-2.76157826914606[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.7578134599466[/C][C]-1.75781345994660[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]8.32904283521224[/C][C]-1.32904283521224[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]9.58529814802869[/C][C]1.41470185197131[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]4.36983944951113[/C][C]1.63016055048887[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]7.03252809235574[/C][C]-0.0325280923557444[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]9.91618650456852[/C][C]-1.91618650456852[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]6.29947166998338[/C][C]-2.29947166998338[/C][/ROW]
[ROW][C]128[/C][C]8[/C][C]7.04177694150087[/C][C]0.958223058499127[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]8.12554720609025[/C][C]0.874452793909746[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]7.65901818297979[/C][C]0.340981817020209[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]8.76729397605663[/C][C]2.23270602394337[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]7.47077600041422[/C][C]0.52922399958578[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]6.71043474044521[/C][C]-1.71043474044521[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]5.84489368127223[/C][C]-1.84489368127223[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]7.41083423429816[/C][C]0.589165765701845[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]11.7953141684003[/C][C]-1.79531416840026[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]8.26853135556792[/C][C]-2.26853135556792[/C][/ROW]
[ROW][C]138[/C][C]9[/C][C]9.35598861829815[/C][C]-0.355988618298145[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]7.15222326425746[/C][C]1.84777673574254[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]8.884514068192[/C][C]4.11548593180800[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.84675667776327[/C][C]1.15324332223673[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]9.52745010848113[/C][C]0.472549891518867[/C][/ROW]
[ROW][C]143[/C][C]20[/C][C]14.8272064864292[/C][C]5.17279351357077[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]6.00192582450364[/C][C]-1.00192582450364[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]10.3825996586666[/C][C]0.617400341333438[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]8.09574531078392[/C][C]-2.09574531078392[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.1548141419763[/C][C]-1.15481414197627[/C][/ROW]
[ROW][C]148[/C][C]7[/C][C]7.28337224834823[/C][C]-0.283372248348227[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]8.13573925663856[/C][C]0.864260743361444[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]8.42651456552542[/C][C]1.57348543447458[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]6.91937635155906[/C][C]2.08062364844094[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]10.2127812833222[/C][C]-2.21278128332217[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]11.2862403955649[/C][C]-4.28624039556491[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]9.39073604060016[/C][C]-3.39073604060016[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]11.6450557945476[/C][C]1.35494420545237[/C][/ROW]
[ROW][C]156[/C][C]6[/C][C]8.00105288957993[/C][C]-2.00105288957993[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]7.70503105714045[/C][C]0.294968942859549[/C][/ROW]
[ROW][C]158[/C][C]10[/C][C]9.47047601180467[/C][C]0.529523988195328[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]11.7501752371107[/C][C]4.24982476288931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&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
1128.84092123954383.15907876045619
286.969402542804531.03059745719547
3810.5651104867126-2.56511048671264
488.26577090327395-0.265770903273949
599.05549396019402-0.0554939601940226
678.30330086747107-1.30330086747107
747.02601980716237-3.02601980716237
8117.539015548949323.46098445105068
978.4508737116258-1.45087371162580
1077.94054140993561-0.940541409935612
11128.729429269142023.27057073085798
121010.0933483628200-0.0933483628199535
13106.743658149706973.25634185029303
1487.67313220247970.326867797520302
1587.433630622200850.566369377799147
1647.62914379635547-3.62914379635547
1796.430620654074152.56937934592585
1887.767821390897780.232178609102220
1979.38800334197132-2.38800334197132
201110.74419759954440.255802400455617
2198.142327898472840.857672101527163
221111.4277005320023-0.42770053200232
23138.442271699863924.55772830013608
2487.19108101584150.808918984158504
2586.712262982828311.28773701717169
2698.535251747697240.464748252302757
2768.74545188766593-2.74545188766593
2897.684898109334521.31510189066548
2999.26236959840449-0.262369598404491
3067.8793411149647-1.87934111496470
3165.72204247960050.277957520399497
321611.15459095647804.84540904352204
33511.1218181589461-6.12181815894612
3477.37338462674195-0.373384626741947
3599.94404428847933-0.944044288479331
3667.06190988930681-1.06190988930681
3766.6996231331024-0.699623133102397
3858.4508737116258-3.4508737116258
391210.82142051303921.17857948696080
4078.09840875088068-1.09840875088069
411010.2206252212254-0.220625221225379
4298.409618004130410.590381995869588
4389.30742817305315-1.30742817305315
4459.2920556246984-4.2920556246984
4586.825256876669231.17474312333077
4687.17924585045460.8207541495454
47108.8505713302291.14942866977101
4867.06008164692371-1.06008164692371
4987.67800847094330.321991529056693
50710.6981847253837-3.69818472538372
5145.32968834390435-1.32968834390435
5287.303051351638330.696948648361666
5387.002653705731420.997346294268578
5444.86821748401608-0.868217484016083
552013.13556293130446.8644370686956
5687.468497146301210.531502853698792
5787.286154790243330.713845209756673
5868.3344888172041-2.3344888172041
5944.40344744475664-0.403447444756638
6089.28451591613803-1.28451591613803
6197.286724503771581.71327549622842
6267.98415632818492-1.98415632818492
6378.30549160623679-1.30549160623679
6496.23054653890762.76945346109240
6557.05867350289588-2.05867350289588
6655.94560179799634-0.945601797996344
6786.740690480267371.25930951973263
6888.23854109993117-0.238541099931174
6966.85916910125758-0.85916910125758
7087.176847894543250.82315210545675
7177.3333070914605-0.333307091460493
7276.084779847534830.91522015246517
7398.59405408675680.405945913243198
741111.0200897169638-0.0200897169638392
7568.64006696091746-2.64006696091746
7687.740348192970720.259651807029279
7768.39302567207825-2.39302567207825
7898.667659260642860.332340739357141
7986.405545412058441.59445458794156
8068.37544029535665-2.37544029535665
81108.300999923756931.69900007624307
8286.079834320539141.92016567946086
8388.34815222497409-0.348152224974092
84109.035842610569050.964157389430953
8555.91678971457353-0.916789714573533
8679.33989674124215-2.33989674124215
8757.16109076020475-2.16109076020475
8886.26598277653621.73401722346379
89149.909296866177314.09070313382269
9077.651290114089-0.651290114089004
9188.82088530393509-0.820885303935087
9264.875491708391041.12450829160896
9356.26769191712096-1.26769191712096
9469.22876160315898-3.22876160315898
95106.545677761108933.45432223889107
961211.54062516731120.459374832688838
9799.135900657124-0.135900657123995
981210.86983134311121.13016865688879
9977.53014805300203-0.530148053002031
10088.33296480416384-0.332964804163844
101109.153062702704410.846937297295586
10267.25817790453418-1.25817790453418
1031011.2049764113201-1.20497641132014
104108.686649548606381.31335045139363
105107.059362318222472.94063768177753
10658.44569321381935-3.44569321381935
10776.918422052047050.0815779479529461
108108.633362450070761.36663754992924
109119.726689025934021.27331097406598
11068.1952304110247-2.19523041102471
11177.47134571394247-0.471345713942473
112128.82804042651173.17195957348829
113116.339164619281084.66083538071892
1141110.80200689393450.197993106065491
115115.428564985459455.57143501454055
11657.59344207454145-2.59344207454145
117810.229254513576-2.22925451357600
11866.77828970299657-0.778289702996566
11999.4105342456886-0.410534245688608
12046.76157826914606-2.76157826914606
12145.7578134599466-1.75781345994660
12278.32904283521224-1.32904283521224
123119.585298148028691.41470185197131
12464.369839449511131.63016055048887
12577.03252809235574-0.0325280923557444
12689.91618650456852-1.91618650456852
12746.29947166998338-2.29947166998338
12887.041776941500870.958223058499127
12998.125547206090250.874452793909746
13087.659018182979790.340981817020209
131118.767293976056632.23270602394337
13287.470776000414220.52922399958578
13356.71043474044521-1.71043474044521
13445.84489368127223-1.84489368127223
13587.410834234298160.589165765701845
1361011.7953141684003-1.79531416840026
13768.26853135556792-2.26853135556792
13899.35598861829815-0.355988618298145
13997.152223264257461.84777673574254
140138.8845140681924.11548593180800
14197.846756677763271.15324332223673
142109.527450108481130.472549891518867
1432014.82720648642925.17279351357077
14456.00192582450364-1.00192582450364
1451110.38259965866660.617400341333438
14668.09574531078392-2.09574531078392
147910.1548141419763-1.15481414197627
14877.28337224834823-0.283372248348227
14998.135739256638560.864260743361444
150108.426514565525421.57348543447458
15196.919376351559062.08062364844094
152810.2127812833222-2.21278128332217
153711.2862403955649-4.28624039556491
15469.39073604060016-3.39073604060016
1551311.64505579454761.35494420545237
15668.00105288957993-2.00105288957993
15787.705031057140450.294968942859549
158109.470476011804670.529523988195328
1591611.75017523711074.24982476288931






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8386603366341670.3226793267316650.161339663365833
100.7554826392277830.4890347215444350.244517360772217
110.8728844559958110.2542310880083770.127115544004189
120.8094931749591210.3810136500817570.190506825040879
130.8605277290438070.2789445419123860.139472270956193
140.8269127789368050.3461744421263890.173087221063195
150.7578209852313370.4843580295373250.242179014768663
160.7320750554179610.5358498891640780.267924944582039
170.7075832428977830.5848335142044350.292416757102217
180.6529042172137410.6941915655725190.347095782786259
190.5896645172614010.8206709654771970.410335482738598
200.5167486007791820.9665027984416350.483251399220818
210.4428788368966950.885757673793390.557121163103305
220.3676220759076760.7352441518153520.632377924092324
230.6429889209614560.7140221580770890.357011079038544
240.5844686118501280.8310627762997440.415531388149872
250.5241927162778630.9516145674442740.475807283722137
260.4723858925244950.944771785048990.527614107475505
270.4673971223267940.9347942446535880.532602877673206
280.4159190161676610.8318380323353220.584080983832339
290.3588424503060760.7176849006121520.641157549693924
300.346202246693910.692404493387820.65379775330609
310.2888840072964930.5777680145929850.711115992703507
320.5659242060960750.8681515878078510.434075793903925
330.7928815271523580.4142369456952840.207118472847642
340.770208850972350.4595822980552990.229791149027649
350.7296458901373070.5407082197253850.270354109862693
360.7079778121945240.5840443756109510.292022187805476
370.6590788180520580.6818423638958840.340921181947942
380.8291195606248180.3417608787503640.170880439375182
390.8292365893871320.3415268212257360.170763410612868
400.7971036542908280.4057926914183440.202896345709172
410.7569786630400180.4860426739199650.243021336959982
420.7233742856820750.553251428635850.276625714317925
430.7052755884964760.5894488230070480.294724411503524
440.7914815815812730.4170368368374540.208518418418727
450.7657565186510340.4684869626979320.234243481348966
460.7266251230829340.5467497538341330.273374876917066
470.6892352832992340.6215294334015310.310764716700766
480.6524147789483310.6951704421033380.347585221051669
490.607145112450380.785709775099240.39285488754962
500.6722295100824080.6555409798351840.327770489917592
510.6789186797736280.6421626404527440.321081320226372
520.6360474653881160.7279050692237680.363952534611884
530.593403548000880.813192903998240.40659645199912
540.5655677179954430.8688645640091150.434432282004557
550.9400051937212480.1199896125575040.0599948062787521
560.9248896349757870.1502207300484260.0751103650242132
570.9088094975264390.1823810049471220.091190502473561
580.910060074791790.1798798504164210.0899399252082107
590.8895551726282840.2208896547434320.110444827371716
600.8773915821060270.2452168357879470.122608417893973
610.8681041564294240.2637916871411520.131895843570576
620.8631406744296740.2737186511406530.136859325570326
630.8466826967573650.3066346064852710.153317303242635
640.8635006181332830.2729987637334340.136499381866717
650.8612906212601150.277418757479770.138709378739885
660.8400955301093110.3198089397813770.159904469890689
670.8202823798164960.3594352403670080.179717620183504
680.7893110840078480.4213778319843040.210688915992152
690.7606976358859330.4786047282281340.239302364114067
700.7282303939352070.5435392121295860.271769606064793
710.6888013665024350.6223972669951310.311198633497565
720.6534599893251230.6930800213497530.346540010674877
730.6114271298061180.7771457403877640.388572870193882
740.5657669160328820.8684661679342360.434233083967118
750.5900200584121480.8199598831757030.409979941587852
760.5444638547181210.9110722905637570.455536145281879
770.5569347100685780.8861305798628430.443065289931422
780.5112499699368690.9775000601262610.488750030063131
790.4864761889907890.9729523779815770.513523811009211
800.4973845349877350.994769069975470.502615465012265
810.4764619684174840.9529239368349680.523538031582516
820.4637088260049220.9274176520098440.536291173995078
830.4188606495207330.8377212990414650.581139350479267
840.3825661754149230.7651323508298470.617433824585077
850.3484672740546490.6969345481092970.651532725945351
860.3565015850898140.7130031701796280.643498414910186
870.360858324983070.721716649966140.63914167501693
880.3409725313623850.681945062724770.659027468637615
890.4534179293166260.9068358586332510.546582070683374
900.4108396668220080.8216793336440150.589160333177992
910.3720068400753240.7440136801506480.627993159924676
920.3387138150286980.6774276300573960.661286184971302
930.3099932324630820.6199864649261640.690006767536918
940.3620919562500470.7241839125000950.637908043749953
950.429239445132040.858478890264080.57076055486796
960.3854802040646340.7709604081292680.614519795935366
970.3406583282622930.6813166565245860.659341671737707
980.3093142233454790.6186284466909580.690685776654521
990.2698817404130030.5397634808260070.730118259586997
1000.2319250276446540.4638500552893090.768074972355346
1010.2015910453467070.4031820906934130.798408954653293
1020.1789586522711750.3579173045423490.821041347728826
1030.1584828584358820.3169657168717640.841517141564118
1040.1395854635282940.2791709270565890.860414536471706
1050.1710414631222520.3420829262445040.828958536877748
1060.2189119120645280.4378238241290570.781088087935472
1070.1850107782741160.3700215565482310.814989221725884
1080.1652879305815540.3305758611631080.834712069418446
1090.1461075434962780.2922150869925570.853892456503722
1100.1417777807872930.2835555615745850.858222219212707
1110.1161124332984320.2322248665968640.883887566701568
1120.1559579014883220.3119158029766440.844042098511678
1130.2861308476465060.5722616952930130.713869152353494
1140.2481020173694150.496204034738830.751897982630585
1150.5938535764757260.8122928470485470.406146423524274
1160.5908319889881020.8183360220237950.409168011011898
1170.5998346649489130.8003306701021750.400165335051087
1180.5505937044396690.8988125911206620.449406295560331
1190.496421688302350.99284337660470.50357831169765
1200.5820279396947490.8359441206105030.417972060305251
1210.5502916341729070.8994167316541850.449708365827093
1220.5479662365741790.9040675268516430.452033763425821
1230.5080606877440510.9838786245118990.491939312255949
1240.527685599454170.944628801091660.47231440054583
1250.4684680192970940.9369360385941880.531531980702906
1260.4532201835361150.906440367072230.546779816463885
1270.4292285027841170.8584570055682340.570771497215883
1280.4113703831729730.8227407663459470.588629616827027
1290.3853226493884360.7706452987768720.614677350611564
1300.3430511486385620.6861022972771250.656948851361438
1310.3405170970413280.6810341940826570.659482902958672
1320.2863806516975150.5727613033950290.713619348302485
1330.2396191964275240.4792383928550470.760380803572476
1340.2046022108430680.4092044216861350.795397789156932
1350.1646175896505230.3292351793010460.835382410349477
1360.1520300537941040.3040601075882070.847969946205896
1370.1631001964496020.3262003928992050.836899803550398
1380.1473517569402280.2947035138804560.852648243059772
1390.1820739004252550.3641478008505110.817926099574745
1400.2318952790347190.4637905580694390.76810472096528
1410.1769922806012130.3539845612024260.823007719398787
1420.1407748956207760.2815497912415520.859225104379224
1430.2129721689071780.4259443378143560.787027831092822
1440.1617095290220660.3234190580441310.838290470977934
1450.1163985678807630.2327971357615260.883601432119237
1460.08503171245338070.1700634249067610.91496828754662
1470.05139799291057280.1027959858211460.948602007089427
1480.02797050120828390.05594100241656780.972029498791716
1490.01595898723351660.03191797446703320.984041012766483
1500.008440950934377350.01688190186875470.991559049065623

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.838660336634167 & 0.322679326731665 & 0.161339663365833 \tabularnewline
10 & 0.755482639227783 & 0.489034721544435 & 0.244517360772217 \tabularnewline
11 & 0.872884455995811 & 0.254231088008377 & 0.127115544004189 \tabularnewline
12 & 0.809493174959121 & 0.381013650081757 & 0.190506825040879 \tabularnewline
13 & 0.860527729043807 & 0.278944541912386 & 0.139472270956193 \tabularnewline
14 & 0.826912778936805 & 0.346174442126389 & 0.173087221063195 \tabularnewline
15 & 0.757820985231337 & 0.484358029537325 & 0.242179014768663 \tabularnewline
16 & 0.732075055417961 & 0.535849889164078 & 0.267924944582039 \tabularnewline
17 & 0.707583242897783 & 0.584833514204435 & 0.292416757102217 \tabularnewline
18 & 0.652904217213741 & 0.694191565572519 & 0.347095782786259 \tabularnewline
19 & 0.589664517261401 & 0.820670965477197 & 0.410335482738598 \tabularnewline
20 & 0.516748600779182 & 0.966502798441635 & 0.483251399220818 \tabularnewline
21 & 0.442878836896695 & 0.88575767379339 & 0.557121163103305 \tabularnewline
22 & 0.367622075907676 & 0.735244151815352 & 0.632377924092324 \tabularnewline
23 & 0.642988920961456 & 0.714022158077089 & 0.357011079038544 \tabularnewline
24 & 0.584468611850128 & 0.831062776299744 & 0.415531388149872 \tabularnewline
25 & 0.524192716277863 & 0.951614567444274 & 0.475807283722137 \tabularnewline
26 & 0.472385892524495 & 0.94477178504899 & 0.527614107475505 \tabularnewline
27 & 0.467397122326794 & 0.934794244653588 & 0.532602877673206 \tabularnewline
28 & 0.415919016167661 & 0.831838032335322 & 0.584080983832339 \tabularnewline
29 & 0.358842450306076 & 0.717684900612152 & 0.641157549693924 \tabularnewline
30 & 0.34620224669391 & 0.69240449338782 & 0.65379775330609 \tabularnewline
31 & 0.288884007296493 & 0.577768014592985 & 0.711115992703507 \tabularnewline
32 & 0.565924206096075 & 0.868151587807851 & 0.434075793903925 \tabularnewline
33 & 0.792881527152358 & 0.414236945695284 & 0.207118472847642 \tabularnewline
34 & 0.77020885097235 & 0.459582298055299 & 0.229791149027649 \tabularnewline
35 & 0.729645890137307 & 0.540708219725385 & 0.270354109862693 \tabularnewline
36 & 0.707977812194524 & 0.584044375610951 & 0.292022187805476 \tabularnewline
37 & 0.659078818052058 & 0.681842363895884 & 0.340921181947942 \tabularnewline
38 & 0.829119560624818 & 0.341760878750364 & 0.170880439375182 \tabularnewline
39 & 0.829236589387132 & 0.341526821225736 & 0.170763410612868 \tabularnewline
40 & 0.797103654290828 & 0.405792691418344 & 0.202896345709172 \tabularnewline
41 & 0.756978663040018 & 0.486042673919965 & 0.243021336959982 \tabularnewline
42 & 0.723374285682075 & 0.55325142863585 & 0.276625714317925 \tabularnewline
43 & 0.705275588496476 & 0.589448823007048 & 0.294724411503524 \tabularnewline
44 & 0.791481581581273 & 0.417036836837454 & 0.208518418418727 \tabularnewline
45 & 0.765756518651034 & 0.468486962697932 & 0.234243481348966 \tabularnewline
46 & 0.726625123082934 & 0.546749753834133 & 0.273374876917066 \tabularnewline
47 & 0.689235283299234 & 0.621529433401531 & 0.310764716700766 \tabularnewline
48 & 0.652414778948331 & 0.695170442103338 & 0.347585221051669 \tabularnewline
49 & 0.60714511245038 & 0.78570977509924 & 0.39285488754962 \tabularnewline
50 & 0.672229510082408 & 0.655540979835184 & 0.327770489917592 \tabularnewline
51 & 0.678918679773628 & 0.642162640452744 & 0.321081320226372 \tabularnewline
52 & 0.636047465388116 & 0.727905069223768 & 0.363952534611884 \tabularnewline
53 & 0.59340354800088 & 0.81319290399824 & 0.40659645199912 \tabularnewline
54 & 0.565567717995443 & 0.868864564009115 & 0.434432282004557 \tabularnewline
55 & 0.940005193721248 & 0.119989612557504 & 0.0599948062787521 \tabularnewline
56 & 0.924889634975787 & 0.150220730048426 & 0.0751103650242132 \tabularnewline
57 & 0.908809497526439 & 0.182381004947122 & 0.091190502473561 \tabularnewline
58 & 0.91006007479179 & 0.179879850416421 & 0.0899399252082107 \tabularnewline
59 & 0.889555172628284 & 0.220889654743432 & 0.110444827371716 \tabularnewline
60 & 0.877391582106027 & 0.245216835787947 & 0.122608417893973 \tabularnewline
61 & 0.868104156429424 & 0.263791687141152 & 0.131895843570576 \tabularnewline
62 & 0.863140674429674 & 0.273718651140653 & 0.136859325570326 \tabularnewline
63 & 0.846682696757365 & 0.306634606485271 & 0.153317303242635 \tabularnewline
64 & 0.863500618133283 & 0.272998763733434 & 0.136499381866717 \tabularnewline
65 & 0.861290621260115 & 0.27741875747977 & 0.138709378739885 \tabularnewline
66 & 0.840095530109311 & 0.319808939781377 & 0.159904469890689 \tabularnewline
67 & 0.820282379816496 & 0.359435240367008 & 0.179717620183504 \tabularnewline
68 & 0.789311084007848 & 0.421377831984304 & 0.210688915992152 \tabularnewline
69 & 0.760697635885933 & 0.478604728228134 & 0.239302364114067 \tabularnewline
70 & 0.728230393935207 & 0.543539212129586 & 0.271769606064793 \tabularnewline
71 & 0.688801366502435 & 0.622397266995131 & 0.311198633497565 \tabularnewline
72 & 0.653459989325123 & 0.693080021349753 & 0.346540010674877 \tabularnewline
73 & 0.611427129806118 & 0.777145740387764 & 0.388572870193882 \tabularnewline
74 & 0.565766916032882 & 0.868466167934236 & 0.434233083967118 \tabularnewline
75 & 0.590020058412148 & 0.819959883175703 & 0.409979941587852 \tabularnewline
76 & 0.544463854718121 & 0.911072290563757 & 0.455536145281879 \tabularnewline
77 & 0.556934710068578 & 0.886130579862843 & 0.443065289931422 \tabularnewline
78 & 0.511249969936869 & 0.977500060126261 & 0.488750030063131 \tabularnewline
79 & 0.486476188990789 & 0.972952377981577 & 0.513523811009211 \tabularnewline
80 & 0.497384534987735 & 0.99476906997547 & 0.502615465012265 \tabularnewline
81 & 0.476461968417484 & 0.952923936834968 & 0.523538031582516 \tabularnewline
82 & 0.463708826004922 & 0.927417652009844 & 0.536291173995078 \tabularnewline
83 & 0.418860649520733 & 0.837721299041465 & 0.581139350479267 \tabularnewline
84 & 0.382566175414923 & 0.765132350829847 & 0.617433824585077 \tabularnewline
85 & 0.348467274054649 & 0.696934548109297 & 0.651532725945351 \tabularnewline
86 & 0.356501585089814 & 0.713003170179628 & 0.643498414910186 \tabularnewline
87 & 0.36085832498307 & 0.72171664996614 & 0.63914167501693 \tabularnewline
88 & 0.340972531362385 & 0.68194506272477 & 0.659027468637615 \tabularnewline
89 & 0.453417929316626 & 0.906835858633251 & 0.546582070683374 \tabularnewline
90 & 0.410839666822008 & 0.821679333644015 & 0.589160333177992 \tabularnewline
91 & 0.372006840075324 & 0.744013680150648 & 0.627993159924676 \tabularnewline
92 & 0.338713815028698 & 0.677427630057396 & 0.661286184971302 \tabularnewline
93 & 0.309993232463082 & 0.619986464926164 & 0.690006767536918 \tabularnewline
94 & 0.362091956250047 & 0.724183912500095 & 0.637908043749953 \tabularnewline
95 & 0.42923944513204 & 0.85847889026408 & 0.57076055486796 \tabularnewline
96 & 0.385480204064634 & 0.770960408129268 & 0.614519795935366 \tabularnewline
97 & 0.340658328262293 & 0.681316656524586 & 0.659341671737707 \tabularnewline
98 & 0.309314223345479 & 0.618628446690958 & 0.690685776654521 \tabularnewline
99 & 0.269881740413003 & 0.539763480826007 & 0.730118259586997 \tabularnewline
100 & 0.231925027644654 & 0.463850055289309 & 0.768074972355346 \tabularnewline
101 & 0.201591045346707 & 0.403182090693413 & 0.798408954653293 \tabularnewline
102 & 0.178958652271175 & 0.357917304542349 & 0.821041347728826 \tabularnewline
103 & 0.158482858435882 & 0.316965716871764 & 0.841517141564118 \tabularnewline
104 & 0.139585463528294 & 0.279170927056589 & 0.860414536471706 \tabularnewline
105 & 0.171041463122252 & 0.342082926244504 & 0.828958536877748 \tabularnewline
106 & 0.218911912064528 & 0.437823824129057 & 0.781088087935472 \tabularnewline
107 & 0.185010778274116 & 0.370021556548231 & 0.814989221725884 \tabularnewline
108 & 0.165287930581554 & 0.330575861163108 & 0.834712069418446 \tabularnewline
109 & 0.146107543496278 & 0.292215086992557 & 0.853892456503722 \tabularnewline
110 & 0.141777780787293 & 0.283555561574585 & 0.858222219212707 \tabularnewline
111 & 0.116112433298432 & 0.232224866596864 & 0.883887566701568 \tabularnewline
112 & 0.155957901488322 & 0.311915802976644 & 0.844042098511678 \tabularnewline
113 & 0.286130847646506 & 0.572261695293013 & 0.713869152353494 \tabularnewline
114 & 0.248102017369415 & 0.49620403473883 & 0.751897982630585 \tabularnewline
115 & 0.593853576475726 & 0.812292847048547 & 0.406146423524274 \tabularnewline
116 & 0.590831988988102 & 0.818336022023795 & 0.409168011011898 \tabularnewline
117 & 0.599834664948913 & 0.800330670102175 & 0.400165335051087 \tabularnewline
118 & 0.550593704439669 & 0.898812591120662 & 0.449406295560331 \tabularnewline
119 & 0.49642168830235 & 0.9928433766047 & 0.50357831169765 \tabularnewline
120 & 0.582027939694749 & 0.835944120610503 & 0.417972060305251 \tabularnewline
121 & 0.550291634172907 & 0.899416731654185 & 0.449708365827093 \tabularnewline
122 & 0.547966236574179 & 0.904067526851643 & 0.452033763425821 \tabularnewline
123 & 0.508060687744051 & 0.983878624511899 & 0.491939312255949 \tabularnewline
124 & 0.52768559945417 & 0.94462880109166 & 0.47231440054583 \tabularnewline
125 & 0.468468019297094 & 0.936936038594188 & 0.531531980702906 \tabularnewline
126 & 0.453220183536115 & 0.90644036707223 & 0.546779816463885 \tabularnewline
127 & 0.429228502784117 & 0.858457005568234 & 0.570771497215883 \tabularnewline
128 & 0.411370383172973 & 0.822740766345947 & 0.588629616827027 \tabularnewline
129 & 0.385322649388436 & 0.770645298776872 & 0.614677350611564 \tabularnewline
130 & 0.343051148638562 & 0.686102297277125 & 0.656948851361438 \tabularnewline
131 & 0.340517097041328 & 0.681034194082657 & 0.659482902958672 \tabularnewline
132 & 0.286380651697515 & 0.572761303395029 & 0.713619348302485 \tabularnewline
133 & 0.239619196427524 & 0.479238392855047 & 0.760380803572476 \tabularnewline
134 & 0.204602210843068 & 0.409204421686135 & 0.795397789156932 \tabularnewline
135 & 0.164617589650523 & 0.329235179301046 & 0.835382410349477 \tabularnewline
136 & 0.152030053794104 & 0.304060107588207 & 0.847969946205896 \tabularnewline
137 & 0.163100196449602 & 0.326200392899205 & 0.836899803550398 \tabularnewline
138 & 0.147351756940228 & 0.294703513880456 & 0.852648243059772 \tabularnewline
139 & 0.182073900425255 & 0.364147800850511 & 0.817926099574745 \tabularnewline
140 & 0.231895279034719 & 0.463790558069439 & 0.76810472096528 \tabularnewline
141 & 0.176992280601213 & 0.353984561202426 & 0.823007719398787 \tabularnewline
142 & 0.140774895620776 & 0.281549791241552 & 0.859225104379224 \tabularnewline
143 & 0.212972168907178 & 0.425944337814356 & 0.787027831092822 \tabularnewline
144 & 0.161709529022066 & 0.323419058044131 & 0.838290470977934 \tabularnewline
145 & 0.116398567880763 & 0.232797135761526 & 0.883601432119237 \tabularnewline
146 & 0.0850317124533807 & 0.170063424906761 & 0.91496828754662 \tabularnewline
147 & 0.0513979929105728 & 0.102795985821146 & 0.948602007089427 \tabularnewline
148 & 0.0279705012082839 & 0.0559410024165678 & 0.972029498791716 \tabularnewline
149 & 0.0159589872335166 & 0.0319179744670332 & 0.984041012766483 \tabularnewline
150 & 0.00844095093437735 & 0.0168819018687547 & 0.991559049065623 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.838660336634167[/C][C]0.322679326731665[/C][C]0.161339663365833[/C][/ROW]
[ROW][C]10[/C][C]0.755482639227783[/C][C]0.489034721544435[/C][C]0.244517360772217[/C][/ROW]
[ROW][C]11[/C][C]0.872884455995811[/C][C]0.254231088008377[/C][C]0.127115544004189[/C][/ROW]
[ROW][C]12[/C][C]0.809493174959121[/C][C]0.381013650081757[/C][C]0.190506825040879[/C][/ROW]
[ROW][C]13[/C][C]0.860527729043807[/C][C]0.278944541912386[/C][C]0.139472270956193[/C][/ROW]
[ROW][C]14[/C][C]0.826912778936805[/C][C]0.346174442126389[/C][C]0.173087221063195[/C][/ROW]
[ROW][C]15[/C][C]0.757820985231337[/C][C]0.484358029537325[/C][C]0.242179014768663[/C][/ROW]
[ROW][C]16[/C][C]0.732075055417961[/C][C]0.535849889164078[/C][C]0.267924944582039[/C][/ROW]
[ROW][C]17[/C][C]0.707583242897783[/C][C]0.584833514204435[/C][C]0.292416757102217[/C][/ROW]
[ROW][C]18[/C][C]0.652904217213741[/C][C]0.694191565572519[/C][C]0.347095782786259[/C][/ROW]
[ROW][C]19[/C][C]0.589664517261401[/C][C]0.820670965477197[/C][C]0.410335482738598[/C][/ROW]
[ROW][C]20[/C][C]0.516748600779182[/C][C]0.966502798441635[/C][C]0.483251399220818[/C][/ROW]
[ROW][C]21[/C][C]0.442878836896695[/C][C]0.88575767379339[/C][C]0.557121163103305[/C][/ROW]
[ROW][C]22[/C][C]0.367622075907676[/C][C]0.735244151815352[/C][C]0.632377924092324[/C][/ROW]
[ROW][C]23[/C][C]0.642988920961456[/C][C]0.714022158077089[/C][C]0.357011079038544[/C][/ROW]
[ROW][C]24[/C][C]0.584468611850128[/C][C]0.831062776299744[/C][C]0.415531388149872[/C][/ROW]
[ROW][C]25[/C][C]0.524192716277863[/C][C]0.951614567444274[/C][C]0.475807283722137[/C][/ROW]
[ROW][C]26[/C][C]0.472385892524495[/C][C]0.94477178504899[/C][C]0.527614107475505[/C][/ROW]
[ROW][C]27[/C][C]0.467397122326794[/C][C]0.934794244653588[/C][C]0.532602877673206[/C][/ROW]
[ROW][C]28[/C][C]0.415919016167661[/C][C]0.831838032335322[/C][C]0.584080983832339[/C][/ROW]
[ROW][C]29[/C][C]0.358842450306076[/C][C]0.717684900612152[/C][C]0.641157549693924[/C][/ROW]
[ROW][C]30[/C][C]0.34620224669391[/C][C]0.69240449338782[/C][C]0.65379775330609[/C][/ROW]
[ROW][C]31[/C][C]0.288884007296493[/C][C]0.577768014592985[/C][C]0.711115992703507[/C][/ROW]
[ROW][C]32[/C][C]0.565924206096075[/C][C]0.868151587807851[/C][C]0.434075793903925[/C][/ROW]
[ROW][C]33[/C][C]0.792881527152358[/C][C]0.414236945695284[/C][C]0.207118472847642[/C][/ROW]
[ROW][C]34[/C][C]0.77020885097235[/C][C]0.459582298055299[/C][C]0.229791149027649[/C][/ROW]
[ROW][C]35[/C][C]0.729645890137307[/C][C]0.540708219725385[/C][C]0.270354109862693[/C][/ROW]
[ROW][C]36[/C][C]0.707977812194524[/C][C]0.584044375610951[/C][C]0.292022187805476[/C][/ROW]
[ROW][C]37[/C][C]0.659078818052058[/C][C]0.681842363895884[/C][C]0.340921181947942[/C][/ROW]
[ROW][C]38[/C][C]0.829119560624818[/C][C]0.341760878750364[/C][C]0.170880439375182[/C][/ROW]
[ROW][C]39[/C][C]0.829236589387132[/C][C]0.341526821225736[/C][C]0.170763410612868[/C][/ROW]
[ROW][C]40[/C][C]0.797103654290828[/C][C]0.405792691418344[/C][C]0.202896345709172[/C][/ROW]
[ROW][C]41[/C][C]0.756978663040018[/C][C]0.486042673919965[/C][C]0.243021336959982[/C][/ROW]
[ROW][C]42[/C][C]0.723374285682075[/C][C]0.55325142863585[/C][C]0.276625714317925[/C][/ROW]
[ROW][C]43[/C][C]0.705275588496476[/C][C]0.589448823007048[/C][C]0.294724411503524[/C][/ROW]
[ROW][C]44[/C][C]0.791481581581273[/C][C]0.417036836837454[/C][C]0.208518418418727[/C][/ROW]
[ROW][C]45[/C][C]0.765756518651034[/C][C]0.468486962697932[/C][C]0.234243481348966[/C][/ROW]
[ROW][C]46[/C][C]0.726625123082934[/C][C]0.546749753834133[/C][C]0.273374876917066[/C][/ROW]
[ROW][C]47[/C][C]0.689235283299234[/C][C]0.621529433401531[/C][C]0.310764716700766[/C][/ROW]
[ROW][C]48[/C][C]0.652414778948331[/C][C]0.695170442103338[/C][C]0.347585221051669[/C][/ROW]
[ROW][C]49[/C][C]0.60714511245038[/C][C]0.78570977509924[/C][C]0.39285488754962[/C][/ROW]
[ROW][C]50[/C][C]0.672229510082408[/C][C]0.655540979835184[/C][C]0.327770489917592[/C][/ROW]
[ROW][C]51[/C][C]0.678918679773628[/C][C]0.642162640452744[/C][C]0.321081320226372[/C][/ROW]
[ROW][C]52[/C][C]0.636047465388116[/C][C]0.727905069223768[/C][C]0.363952534611884[/C][/ROW]
[ROW][C]53[/C][C]0.59340354800088[/C][C]0.81319290399824[/C][C]0.40659645199912[/C][/ROW]
[ROW][C]54[/C][C]0.565567717995443[/C][C]0.868864564009115[/C][C]0.434432282004557[/C][/ROW]
[ROW][C]55[/C][C]0.940005193721248[/C][C]0.119989612557504[/C][C]0.0599948062787521[/C][/ROW]
[ROW][C]56[/C][C]0.924889634975787[/C][C]0.150220730048426[/C][C]0.0751103650242132[/C][/ROW]
[ROW][C]57[/C][C]0.908809497526439[/C][C]0.182381004947122[/C][C]0.091190502473561[/C][/ROW]
[ROW][C]58[/C][C]0.91006007479179[/C][C]0.179879850416421[/C][C]0.0899399252082107[/C][/ROW]
[ROW][C]59[/C][C]0.889555172628284[/C][C]0.220889654743432[/C][C]0.110444827371716[/C][/ROW]
[ROW][C]60[/C][C]0.877391582106027[/C][C]0.245216835787947[/C][C]0.122608417893973[/C][/ROW]
[ROW][C]61[/C][C]0.868104156429424[/C][C]0.263791687141152[/C][C]0.131895843570576[/C][/ROW]
[ROW][C]62[/C][C]0.863140674429674[/C][C]0.273718651140653[/C][C]0.136859325570326[/C][/ROW]
[ROW][C]63[/C][C]0.846682696757365[/C][C]0.306634606485271[/C][C]0.153317303242635[/C][/ROW]
[ROW][C]64[/C][C]0.863500618133283[/C][C]0.272998763733434[/C][C]0.136499381866717[/C][/ROW]
[ROW][C]65[/C][C]0.861290621260115[/C][C]0.27741875747977[/C][C]0.138709378739885[/C][/ROW]
[ROW][C]66[/C][C]0.840095530109311[/C][C]0.319808939781377[/C][C]0.159904469890689[/C][/ROW]
[ROW][C]67[/C][C]0.820282379816496[/C][C]0.359435240367008[/C][C]0.179717620183504[/C][/ROW]
[ROW][C]68[/C][C]0.789311084007848[/C][C]0.421377831984304[/C][C]0.210688915992152[/C][/ROW]
[ROW][C]69[/C][C]0.760697635885933[/C][C]0.478604728228134[/C][C]0.239302364114067[/C][/ROW]
[ROW][C]70[/C][C]0.728230393935207[/C][C]0.543539212129586[/C][C]0.271769606064793[/C][/ROW]
[ROW][C]71[/C][C]0.688801366502435[/C][C]0.622397266995131[/C][C]0.311198633497565[/C][/ROW]
[ROW][C]72[/C][C]0.653459989325123[/C][C]0.693080021349753[/C][C]0.346540010674877[/C][/ROW]
[ROW][C]73[/C][C]0.611427129806118[/C][C]0.777145740387764[/C][C]0.388572870193882[/C][/ROW]
[ROW][C]74[/C][C]0.565766916032882[/C][C]0.868466167934236[/C][C]0.434233083967118[/C][/ROW]
[ROW][C]75[/C][C]0.590020058412148[/C][C]0.819959883175703[/C][C]0.409979941587852[/C][/ROW]
[ROW][C]76[/C][C]0.544463854718121[/C][C]0.911072290563757[/C][C]0.455536145281879[/C][/ROW]
[ROW][C]77[/C][C]0.556934710068578[/C][C]0.886130579862843[/C][C]0.443065289931422[/C][/ROW]
[ROW][C]78[/C][C]0.511249969936869[/C][C]0.977500060126261[/C][C]0.488750030063131[/C][/ROW]
[ROW][C]79[/C][C]0.486476188990789[/C][C]0.972952377981577[/C][C]0.513523811009211[/C][/ROW]
[ROW][C]80[/C][C]0.497384534987735[/C][C]0.99476906997547[/C][C]0.502615465012265[/C][/ROW]
[ROW][C]81[/C][C]0.476461968417484[/C][C]0.952923936834968[/C][C]0.523538031582516[/C][/ROW]
[ROW][C]82[/C][C]0.463708826004922[/C][C]0.927417652009844[/C][C]0.536291173995078[/C][/ROW]
[ROW][C]83[/C][C]0.418860649520733[/C][C]0.837721299041465[/C][C]0.581139350479267[/C][/ROW]
[ROW][C]84[/C][C]0.382566175414923[/C][C]0.765132350829847[/C][C]0.617433824585077[/C][/ROW]
[ROW][C]85[/C][C]0.348467274054649[/C][C]0.696934548109297[/C][C]0.651532725945351[/C][/ROW]
[ROW][C]86[/C][C]0.356501585089814[/C][C]0.713003170179628[/C][C]0.643498414910186[/C][/ROW]
[ROW][C]87[/C][C]0.36085832498307[/C][C]0.72171664996614[/C][C]0.63914167501693[/C][/ROW]
[ROW][C]88[/C][C]0.340972531362385[/C][C]0.68194506272477[/C][C]0.659027468637615[/C][/ROW]
[ROW][C]89[/C][C]0.453417929316626[/C][C]0.906835858633251[/C][C]0.546582070683374[/C][/ROW]
[ROW][C]90[/C][C]0.410839666822008[/C][C]0.821679333644015[/C][C]0.589160333177992[/C][/ROW]
[ROW][C]91[/C][C]0.372006840075324[/C][C]0.744013680150648[/C][C]0.627993159924676[/C][/ROW]
[ROW][C]92[/C][C]0.338713815028698[/C][C]0.677427630057396[/C][C]0.661286184971302[/C][/ROW]
[ROW][C]93[/C][C]0.309993232463082[/C][C]0.619986464926164[/C][C]0.690006767536918[/C][/ROW]
[ROW][C]94[/C][C]0.362091956250047[/C][C]0.724183912500095[/C][C]0.637908043749953[/C][/ROW]
[ROW][C]95[/C][C]0.42923944513204[/C][C]0.85847889026408[/C][C]0.57076055486796[/C][/ROW]
[ROW][C]96[/C][C]0.385480204064634[/C][C]0.770960408129268[/C][C]0.614519795935366[/C][/ROW]
[ROW][C]97[/C][C]0.340658328262293[/C][C]0.681316656524586[/C][C]0.659341671737707[/C][/ROW]
[ROW][C]98[/C][C]0.309314223345479[/C][C]0.618628446690958[/C][C]0.690685776654521[/C][/ROW]
[ROW][C]99[/C][C]0.269881740413003[/C][C]0.539763480826007[/C][C]0.730118259586997[/C][/ROW]
[ROW][C]100[/C][C]0.231925027644654[/C][C]0.463850055289309[/C][C]0.768074972355346[/C][/ROW]
[ROW][C]101[/C][C]0.201591045346707[/C][C]0.403182090693413[/C][C]0.798408954653293[/C][/ROW]
[ROW][C]102[/C][C]0.178958652271175[/C][C]0.357917304542349[/C][C]0.821041347728826[/C][/ROW]
[ROW][C]103[/C][C]0.158482858435882[/C][C]0.316965716871764[/C][C]0.841517141564118[/C][/ROW]
[ROW][C]104[/C][C]0.139585463528294[/C][C]0.279170927056589[/C][C]0.860414536471706[/C][/ROW]
[ROW][C]105[/C][C]0.171041463122252[/C][C]0.342082926244504[/C][C]0.828958536877748[/C][/ROW]
[ROW][C]106[/C][C]0.218911912064528[/C][C]0.437823824129057[/C][C]0.781088087935472[/C][/ROW]
[ROW][C]107[/C][C]0.185010778274116[/C][C]0.370021556548231[/C][C]0.814989221725884[/C][/ROW]
[ROW][C]108[/C][C]0.165287930581554[/C][C]0.330575861163108[/C][C]0.834712069418446[/C][/ROW]
[ROW][C]109[/C][C]0.146107543496278[/C][C]0.292215086992557[/C][C]0.853892456503722[/C][/ROW]
[ROW][C]110[/C][C]0.141777780787293[/C][C]0.283555561574585[/C][C]0.858222219212707[/C][/ROW]
[ROW][C]111[/C][C]0.116112433298432[/C][C]0.232224866596864[/C][C]0.883887566701568[/C][/ROW]
[ROW][C]112[/C][C]0.155957901488322[/C][C]0.311915802976644[/C][C]0.844042098511678[/C][/ROW]
[ROW][C]113[/C][C]0.286130847646506[/C][C]0.572261695293013[/C][C]0.713869152353494[/C][/ROW]
[ROW][C]114[/C][C]0.248102017369415[/C][C]0.49620403473883[/C][C]0.751897982630585[/C][/ROW]
[ROW][C]115[/C][C]0.593853576475726[/C][C]0.812292847048547[/C][C]0.406146423524274[/C][/ROW]
[ROW][C]116[/C][C]0.590831988988102[/C][C]0.818336022023795[/C][C]0.409168011011898[/C][/ROW]
[ROW][C]117[/C][C]0.599834664948913[/C][C]0.800330670102175[/C][C]0.400165335051087[/C][/ROW]
[ROW][C]118[/C][C]0.550593704439669[/C][C]0.898812591120662[/C][C]0.449406295560331[/C][/ROW]
[ROW][C]119[/C][C]0.49642168830235[/C][C]0.9928433766047[/C][C]0.50357831169765[/C][/ROW]
[ROW][C]120[/C][C]0.582027939694749[/C][C]0.835944120610503[/C][C]0.417972060305251[/C][/ROW]
[ROW][C]121[/C][C]0.550291634172907[/C][C]0.899416731654185[/C][C]0.449708365827093[/C][/ROW]
[ROW][C]122[/C][C]0.547966236574179[/C][C]0.904067526851643[/C][C]0.452033763425821[/C][/ROW]
[ROW][C]123[/C][C]0.508060687744051[/C][C]0.983878624511899[/C][C]0.491939312255949[/C][/ROW]
[ROW][C]124[/C][C]0.52768559945417[/C][C]0.94462880109166[/C][C]0.47231440054583[/C][/ROW]
[ROW][C]125[/C][C]0.468468019297094[/C][C]0.936936038594188[/C][C]0.531531980702906[/C][/ROW]
[ROW][C]126[/C][C]0.453220183536115[/C][C]0.90644036707223[/C][C]0.546779816463885[/C][/ROW]
[ROW][C]127[/C][C]0.429228502784117[/C][C]0.858457005568234[/C][C]0.570771497215883[/C][/ROW]
[ROW][C]128[/C][C]0.411370383172973[/C][C]0.822740766345947[/C][C]0.588629616827027[/C][/ROW]
[ROW][C]129[/C][C]0.385322649388436[/C][C]0.770645298776872[/C][C]0.614677350611564[/C][/ROW]
[ROW][C]130[/C][C]0.343051148638562[/C][C]0.686102297277125[/C][C]0.656948851361438[/C][/ROW]
[ROW][C]131[/C][C]0.340517097041328[/C][C]0.681034194082657[/C][C]0.659482902958672[/C][/ROW]
[ROW][C]132[/C][C]0.286380651697515[/C][C]0.572761303395029[/C][C]0.713619348302485[/C][/ROW]
[ROW][C]133[/C][C]0.239619196427524[/C][C]0.479238392855047[/C][C]0.760380803572476[/C][/ROW]
[ROW][C]134[/C][C]0.204602210843068[/C][C]0.409204421686135[/C][C]0.795397789156932[/C][/ROW]
[ROW][C]135[/C][C]0.164617589650523[/C][C]0.329235179301046[/C][C]0.835382410349477[/C][/ROW]
[ROW][C]136[/C][C]0.152030053794104[/C][C]0.304060107588207[/C][C]0.847969946205896[/C][/ROW]
[ROW][C]137[/C][C]0.163100196449602[/C][C]0.326200392899205[/C][C]0.836899803550398[/C][/ROW]
[ROW][C]138[/C][C]0.147351756940228[/C][C]0.294703513880456[/C][C]0.852648243059772[/C][/ROW]
[ROW][C]139[/C][C]0.182073900425255[/C][C]0.364147800850511[/C][C]0.817926099574745[/C][/ROW]
[ROW][C]140[/C][C]0.231895279034719[/C][C]0.463790558069439[/C][C]0.76810472096528[/C][/ROW]
[ROW][C]141[/C][C]0.176992280601213[/C][C]0.353984561202426[/C][C]0.823007719398787[/C][/ROW]
[ROW][C]142[/C][C]0.140774895620776[/C][C]0.281549791241552[/C][C]0.859225104379224[/C][/ROW]
[ROW][C]143[/C][C]0.212972168907178[/C][C]0.425944337814356[/C][C]0.787027831092822[/C][/ROW]
[ROW][C]144[/C][C]0.161709529022066[/C][C]0.323419058044131[/C][C]0.838290470977934[/C][/ROW]
[ROW][C]145[/C][C]0.116398567880763[/C][C]0.232797135761526[/C][C]0.883601432119237[/C][/ROW]
[ROW][C]146[/C][C]0.0850317124533807[/C][C]0.170063424906761[/C][C]0.91496828754662[/C][/ROW]
[ROW][C]147[/C][C]0.0513979929105728[/C][C]0.102795985821146[/C][C]0.948602007089427[/C][/ROW]
[ROW][C]148[/C][C]0.0279705012082839[/C][C]0.0559410024165678[/C][C]0.972029498791716[/C][/ROW]
[ROW][C]149[/C][C]0.0159589872335166[/C][C]0.0319179744670332[/C][C]0.984041012766483[/C][/ROW]
[ROW][C]150[/C][C]0.00844095093437735[/C][C]0.0168819018687547[/C][C]0.991559049065623[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8386603366341670.3226793267316650.161339663365833
100.7554826392277830.4890347215444350.244517360772217
110.8728844559958110.2542310880083770.127115544004189
120.8094931749591210.3810136500817570.190506825040879
130.8605277290438070.2789445419123860.139472270956193
140.8269127789368050.3461744421263890.173087221063195
150.7578209852313370.4843580295373250.242179014768663
160.7320750554179610.5358498891640780.267924944582039
170.7075832428977830.5848335142044350.292416757102217
180.6529042172137410.6941915655725190.347095782786259
190.5896645172614010.8206709654771970.410335482738598
200.5167486007791820.9665027984416350.483251399220818
210.4428788368966950.885757673793390.557121163103305
220.3676220759076760.7352441518153520.632377924092324
230.6429889209614560.7140221580770890.357011079038544
240.5844686118501280.8310627762997440.415531388149872
250.5241927162778630.9516145674442740.475807283722137
260.4723858925244950.944771785048990.527614107475505
270.4673971223267940.9347942446535880.532602877673206
280.4159190161676610.8318380323353220.584080983832339
290.3588424503060760.7176849006121520.641157549693924
300.346202246693910.692404493387820.65379775330609
310.2888840072964930.5777680145929850.711115992703507
320.5659242060960750.8681515878078510.434075793903925
330.7928815271523580.4142369456952840.207118472847642
340.770208850972350.4595822980552990.229791149027649
350.7296458901373070.5407082197253850.270354109862693
360.7079778121945240.5840443756109510.292022187805476
370.6590788180520580.6818423638958840.340921181947942
380.8291195606248180.3417608787503640.170880439375182
390.8292365893871320.3415268212257360.170763410612868
400.7971036542908280.4057926914183440.202896345709172
410.7569786630400180.4860426739199650.243021336959982
420.7233742856820750.553251428635850.276625714317925
430.7052755884964760.5894488230070480.294724411503524
440.7914815815812730.4170368368374540.208518418418727
450.7657565186510340.4684869626979320.234243481348966
460.7266251230829340.5467497538341330.273374876917066
470.6892352832992340.6215294334015310.310764716700766
480.6524147789483310.6951704421033380.347585221051669
490.607145112450380.785709775099240.39285488754962
500.6722295100824080.6555409798351840.327770489917592
510.6789186797736280.6421626404527440.321081320226372
520.6360474653881160.7279050692237680.363952534611884
530.593403548000880.813192903998240.40659645199912
540.5655677179954430.8688645640091150.434432282004557
550.9400051937212480.1199896125575040.0599948062787521
560.9248896349757870.1502207300484260.0751103650242132
570.9088094975264390.1823810049471220.091190502473561
580.910060074791790.1798798504164210.0899399252082107
590.8895551726282840.2208896547434320.110444827371716
600.8773915821060270.2452168357879470.122608417893973
610.8681041564294240.2637916871411520.131895843570576
620.8631406744296740.2737186511406530.136859325570326
630.8466826967573650.3066346064852710.153317303242635
640.8635006181332830.2729987637334340.136499381866717
650.8612906212601150.277418757479770.138709378739885
660.8400955301093110.3198089397813770.159904469890689
670.8202823798164960.3594352403670080.179717620183504
680.7893110840078480.4213778319843040.210688915992152
690.7606976358859330.4786047282281340.239302364114067
700.7282303939352070.5435392121295860.271769606064793
710.6888013665024350.6223972669951310.311198633497565
720.6534599893251230.6930800213497530.346540010674877
730.6114271298061180.7771457403877640.388572870193882
740.5657669160328820.8684661679342360.434233083967118
750.5900200584121480.8199598831757030.409979941587852
760.5444638547181210.9110722905637570.455536145281879
770.5569347100685780.8861305798628430.443065289931422
780.5112499699368690.9775000601262610.488750030063131
790.4864761889907890.9729523779815770.513523811009211
800.4973845349877350.994769069975470.502615465012265
810.4764619684174840.9529239368349680.523538031582516
820.4637088260049220.9274176520098440.536291173995078
830.4188606495207330.8377212990414650.581139350479267
840.3825661754149230.7651323508298470.617433824585077
850.3484672740546490.6969345481092970.651532725945351
860.3565015850898140.7130031701796280.643498414910186
870.360858324983070.721716649966140.63914167501693
880.3409725313623850.681945062724770.659027468637615
890.4534179293166260.9068358586332510.546582070683374
900.4108396668220080.8216793336440150.589160333177992
910.3720068400753240.7440136801506480.627993159924676
920.3387138150286980.6774276300573960.661286184971302
930.3099932324630820.6199864649261640.690006767536918
940.3620919562500470.7241839125000950.637908043749953
950.429239445132040.858478890264080.57076055486796
960.3854802040646340.7709604081292680.614519795935366
970.3406583282622930.6813166565245860.659341671737707
980.3093142233454790.6186284466909580.690685776654521
990.2698817404130030.5397634808260070.730118259586997
1000.2319250276446540.4638500552893090.768074972355346
1010.2015910453467070.4031820906934130.798408954653293
1020.1789586522711750.3579173045423490.821041347728826
1030.1584828584358820.3169657168717640.841517141564118
1040.1395854635282940.2791709270565890.860414536471706
1050.1710414631222520.3420829262445040.828958536877748
1060.2189119120645280.4378238241290570.781088087935472
1070.1850107782741160.3700215565482310.814989221725884
1080.1652879305815540.3305758611631080.834712069418446
1090.1461075434962780.2922150869925570.853892456503722
1100.1417777807872930.2835555615745850.858222219212707
1110.1161124332984320.2322248665968640.883887566701568
1120.1559579014883220.3119158029766440.844042098511678
1130.2861308476465060.5722616952930130.713869152353494
1140.2481020173694150.496204034738830.751897982630585
1150.5938535764757260.8122928470485470.406146423524274
1160.5908319889881020.8183360220237950.409168011011898
1170.5998346649489130.8003306701021750.400165335051087
1180.5505937044396690.8988125911206620.449406295560331
1190.496421688302350.99284337660470.50357831169765
1200.5820279396947490.8359441206105030.417972060305251
1210.5502916341729070.8994167316541850.449708365827093
1220.5479662365741790.9040675268516430.452033763425821
1230.5080606877440510.9838786245118990.491939312255949
1240.527685599454170.944628801091660.47231440054583
1250.4684680192970940.9369360385941880.531531980702906
1260.4532201835361150.906440367072230.546779816463885
1270.4292285027841170.8584570055682340.570771497215883
1280.4113703831729730.8227407663459470.588629616827027
1290.3853226493884360.7706452987768720.614677350611564
1300.3430511486385620.6861022972771250.656948851361438
1310.3405170970413280.6810341940826570.659482902958672
1320.2863806516975150.5727613033950290.713619348302485
1330.2396191964275240.4792383928550470.760380803572476
1340.2046022108430680.4092044216861350.795397789156932
1350.1646175896505230.3292351793010460.835382410349477
1360.1520300537941040.3040601075882070.847969946205896
1370.1631001964496020.3262003928992050.836899803550398
1380.1473517569402280.2947035138804560.852648243059772
1390.1820739004252550.3641478008505110.817926099574745
1400.2318952790347190.4637905580694390.76810472096528
1410.1769922806012130.3539845612024260.823007719398787
1420.1407748956207760.2815497912415520.859225104379224
1430.2129721689071780.4259443378143560.787027831092822
1440.1617095290220660.3234190580441310.838290470977934
1450.1163985678807630.2327971357615260.883601432119237
1460.08503171245338070.1700634249067610.91496828754662
1470.05139799291057280.1027959858211460.948602007089427
1480.02797050120828390.05594100241656780.972029498791716
1490.01595898723351660.03191797446703320.984041012766483
1500.008440950934377350.01688190186875470.991559049065623






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0140845070422535OK
10% type I error level30.0211267605633803OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0140845070422535 & OK \tabularnewline
10% type I error level & 3 & 0.0211267605633803 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116170&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0140845070422535[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0211267605633803[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116170&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116170&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 level00OK
5% type I error level20.0140845070422535OK
10% type I error level30.0211267605633803OK


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 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}