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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 23:21:00 +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/t1293492266exmojc41t6wjk4u.htm/, Retrieved Sat, 04 May 2024 20:23:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116175, Retrieved Sat, 04 May 2024 20:23:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

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 23:21:00] [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 time15 seconds
R Server'George Udny Yule' @ 72.249.76.132
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 & 15 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \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=116175&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/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=116175&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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 time15 seconds
R Server'George Udny Yule' @ 72.249.76.132
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] = + 16.601138887406 -1.41670508965093Month[t] + 0.150238793812559DoubtsActions[t] + 0.440939298378178ParentalExpectations[t] + 0.0298388827345439Standards[t] -0.103111653402745`Organization `[t] + 0.000949138149842271t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ParentalCriticism[t] =  +  16.601138887406 -1.41670508965093Month[t] +  0.150238793812559DoubtsActions[t] +  0.440939298378178ParentalExpectations[t] +  0.0298388827345439Standards[t] -0.103111653402745`Organization
`[t] +  0.000949138149842271t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ParentalCriticism[t] =  +  16.601138887406 -1.41670508965093Month[t] +  0.150238793812559DoubtsActions[t] +  0.440939298378178ParentalExpectations[t] +  0.0298388827345439Standards[t] -0.103111653402745`Organization
`[t] +  0.000949138149842271t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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] = + 16.601138887406 -1.41670508965093Month[t] + 0.150238793812559DoubtsActions[t] + 0.440939298378178ParentalExpectations[t] + 0.0298388827345439Standards[t] -0.103111653402745`Organization `[t] + 0.000949138149842271t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.6011388874069.4432871.7580.0807630.040382
Month-1.416705089650930.948296-1.49390.1372620.068631
DoubtsActions0.1502387938125590.0613722.4480.0155040.007752
ParentalExpectations0.4409392983781780.0531828.291100
Standards0.02983888273454390.0458550.65070.516210.258105
`Organization `-0.1031116534027450.049627-2.07770.0394150.019708
t0.0009491381498422710.0040250.23580.8138930.406947

\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) & 16.601138887406 & 9.443287 & 1.758 & 0.080763 & 0.040382 \tabularnewline
Month & -1.41670508965093 & 0.948296 & -1.4939 & 0.137262 & 0.068631 \tabularnewline
DoubtsActions & 0.150238793812559 & 0.061372 & 2.448 & 0.015504 & 0.007752 \tabularnewline
ParentalExpectations & 0.440939298378178 & 0.053182 & 8.2911 & 0 & 0 \tabularnewline
Standards & 0.0298388827345439 & 0.045855 & 0.6507 & 0.51621 & 0.258105 \tabularnewline
`Organization
` & -0.103111653402745 & 0.049627 & -2.0777 & 0.039415 & 0.019708 \tabularnewline
t & 0.000949138149842271 & 0.004025 & 0.2358 & 0.813893 & 0.406947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&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]16.601138887406[/C][C]9.443287[/C][C]1.758[/C][C]0.080763[/C][C]0.040382[/C][/ROW]
[ROW][C]Month[/C][C]-1.41670508965093[/C][C]0.948296[/C][C]-1.4939[/C][C]0.137262[/C][C]0.068631[/C][/ROW]
[ROW][C]DoubtsActions[/C][C]0.150238793812559[/C][C]0.061372[/C][C]2.448[/C][C]0.015504[/C][C]0.007752[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.440939298378178[/C][C]0.053182[/C][C]8.2911[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Standards[/C][C]0.0298388827345439[/C][C]0.045855[/C][C]0.6507[/C][C]0.51621[/C][C]0.258105[/C][/ROW]
[ROW][C]`Organization
`[/C][C]-0.103111653402745[/C][C]0.049627[/C][C]-2.0777[/C][C]0.039415[/C][C]0.019708[/C][/ROW]
[ROW][C]t[/C][C]0.000949138149842271[/C][C]0.004025[/C][C]0.2358[/C][C]0.813893[/C][C]0.406947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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)16.6011388874069.4432871.7580.0807630.040382
Month-1.416705089650930.948296-1.49390.1372620.068631
DoubtsActions0.1502387938125590.0613722.4480.0155040.007752
ParentalExpectations0.4409392983781780.0531828.291100
Standards0.02983888273454390.0458550.65070.516210.258105
`Organization `-0.1031116534027450.049627-2.07770.0394150.019708
t0.0009491381498422710.0040250.23580.8138930.406947






Multiple Linear Regression - Regression Statistics
Multiple R0.630420955153921
R-squared0.397430580697182
Adjusted R-squared0.373644945724703
F-TEST (value)16.7088488979595
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value9.43689570931383e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.14243533599839
Sum Squared Residuals697.684433678052

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.630420955153921 \tabularnewline
R-squared & 0.397430580697182 \tabularnewline
Adjusted R-squared & 0.373644945724703 \tabularnewline
F-TEST (value) & 16.7088488979595 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 9.43689570931383e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.14243533599839 \tabularnewline
Sum Squared Residuals & 697.684433678052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.630420955153921[/C][/ROW]
[ROW][C]R-squared[/C][C]0.397430580697182[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.373644945724703[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.7088488979595[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]9.43689570931383e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.14243533599839[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]697.684433678052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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 R0.630420955153921
R-squared0.397430580697182
Adjusted R-squared0.373644945724703
F-TEST (value)16.7088488979595
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value9.43689570931383e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.14243533599839
Sum Squared Residuals697.684433678052






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1128.84064781139073.15935218860930
286.966297217533141.03370278246686
3810.5684164772692-2.5684164772692
488.26221888637267-0.262218886372668
599.04605470784323-0.046054707843233
678.31636489959075-1.31636489959075
746.96163291152608-2.96163291152608
8117.465589691630383.53441030836962
978.3765271474112-1.37652714741119
1077.87724218560636-0.877242185606358
11128.663042097693153.33695790230685
121010.0143009592033-0.0143009592032610
13106.677796285662823.32220371433718
1487.590381757238480.40961824276152
1587.369122005276530.630877994723465
1647.58284239077288-3.58284239077288
1796.36504852126572.63495147873431
1887.683819627704940.316180372295060
1979.33177577716726-2.33177577716726
201110.67644317453800.32355682546202
2198.078883662828570.921116337171431
221111.3600805713719-0.360080571371874
23138.382811540449264.61718845955074
2487.135135264609150.86486473539085
2586.670426818154861.32957318184514
2698.486380030194350.513619969805654
2768.69724572762584-2.69724572762584
2897.637282972612061.36271702738794
2999.2133560729719-0.213356072971898
3067.83180955051812-1.83180955051812
3165.690087016754970.309912983245029
321611.10631975293204.89368024706796
33511.0642614906392-6.06426149063919
3477.3365935314667-0.3365935314667
3599.89605238978916-0.896052389789164
3667.02495596152595-1.02495596152594
3766.65731886148943-0.657318861489429
3858.40405215375662-3.40405215375662
391210.77790961700531.22209038299473
4078.05995019421785-1.05995019421785
411010.1957144882390-0.195714488239030
4298.373655154898040.626344845101964
4389.2562245955751-1.25622459557510
4459.25743202795408-4.25743202795408
4586.792823052381951.20717694761805
4687.149932473322340.850067526677662
47108.817610982165491.18238901783451
4867.03160798670806-1.03160798670806
4987.648659319003130.351340680996865
50710.6614834310996-3.66148343109959
5145.3026258506591-1.30262585065909
5287.278800755299180.721199244700818
5386.987703190916061.01229680908394
5444.83020679070682-0.830206790706819
552013.10367751469626.89632248530384
5687.45751086433870.542489135661306
5787.273644693325440.726355306674562
5868.31187534215115-2.31187534215115
5944.39766291195563-0.397662911955634
6089.2447869698316-1.24478696983160
6197.273747993448651.72625200655135
6267.96898687719197-1.96898687719197
6378.29890628773413-1.29890628773413
6496.216905043038442.78309495696156
6557.04387293461517-2.04387293461517
6655.94946728881653-0.949467288816528
6786.731698618090621.26830138190938
6888.22329452069033-0.223294520690335
6966.85122326346855-0.851223263468548
7087.171667408778710.828332591221287
7177.32298001040457-0.322980010404573
7276.078077276564240.92192272343576
7398.594360541182180.405639458817822
741111.0095397666120-0.00953976661196645
7568.63969270541552-2.63969270541552
7687.731314942181650.268685057818353
7768.39155082308347-2.39155082308347
7898.665912208123660.334087791876342
7986.417384768481611.58261523151839
8068.37237234724173-2.37237234724173
81108.308479599815521.69152040018448
8286.090040382234931.90995961776507
8388.34642525909655-0.346425259096550
84109.042047106732460.957952893267536
8555.92854636411674-0.928546364116737
8679.3454673507971-2.34546735079711
8757.17051449965076-2.17051449965076
8886.285465301077131.71453469892287
89149.913649045006494.08635095499351
9077.65508622807581-0.655086228075807
9188.829534178024-0.829534178024006
9264.892376147060841.10762385293916
9356.27913123439787-1.27913123439787
9469.23400674288517-3.23400674288517
95106.569339437155993.43066056284401
961211.56125971638290.438740283617103
9799.15195047208361-0.151950472083608
981210.87838703591951.12161296408053
9977.54322856034637-0.543228560346373
10088.34157909749244-0.341579097492441
101109.167377939265870.83262206073413
10267.27543726990533-1.27543726990533
1031011.2088362340758-1.2088362340758
104108.706958347218451.29304165278155
105107.093962598177172.90603740182283
10658.44895823576181-3.44895823576181
10776.931150888587180.0688491114128244
108108.641218905224141.35878109477586
109119.74608733698031.2539126630197
11068.2153464008538-2.2153464008538
11177.49124720019924-0.491247200199236
112128.859750795786243.14024920421376
113116.365604755307084.63439524469292
1141110.82233145533620.177668544663818
115115.465945599067955.53405440093205
11657.63024610145471-2.63024610145471
117810.2449469104578-2.24494691045777
11866.80482294995854-0.804822949958538
11999.4462437582705-0.446243758270487
12046.7755796691547-2.77557966915471
12145.79549010385074-1.79549010385074
12278.35125570969704-1.35125570969704
123119.614175927709031.38582407229097
12464.418313581868911.58168641813109
12577.07416795979127-0.0741679597912713
12689.95732271130627-1.95732271130626
12746.34770760870298-2.34770760870298
12887.093766885332280.906233114667723
12998.177778476600460.822221523399537
13087.709170961941470.290829038058529
131118.803386123760122.19661387623988
13287.514872353822080.48512764617792
13356.76819610572183-1.76819610572183
13445.89716839983827-1.89716839983827
13587.461735255278680.538264744721324
1361011.8424156426398-1.84241564263980
13768.3132015214279-2.31320152142790
13899.38631050343167-0.386310503431667
13997.21113698052291.7888630194771
140138.921123851094794.07887614890521
14197.882444424059941.11755557594006
142109.582691834039040.417308165960961
1432014.85120102417425.14879897582581
14456.06285773606914-1.06285773606914
1451110.43862334265020.561376657349783
14668.15778529610135-2.15778529610135
147910.2085791693770-1.20857916937699
14877.34142533291684-0.341425332916843
14998.17686763318410.823132366815906
150108.486063827803961.51393617219604
15196.986766266608482.01323373339152
152810.2786599686877-2.27865996868767
153711.3667477763246-4.3667477763246
15469.4351535187033-3.4351535187033
1551311.70651722119811.29348277880191
15668.0681076160001-2.06810761600011
15787.778231579920870.221768420079131
158109.539244659107270.460755340892733
1591611.81169626534034.18830373465973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 8.8406478113907 & 3.15935218860930 \tabularnewline
2 & 8 & 6.96629721753314 & 1.03370278246686 \tabularnewline
3 & 8 & 10.5684164772692 & -2.5684164772692 \tabularnewline
4 & 8 & 8.26221888637267 & -0.262218886372668 \tabularnewline
5 & 9 & 9.04605470784323 & -0.046054707843233 \tabularnewline
6 & 7 & 8.31636489959075 & -1.31636489959075 \tabularnewline
7 & 4 & 6.96163291152608 & -2.96163291152608 \tabularnewline
8 & 11 & 7.46558969163038 & 3.53441030836962 \tabularnewline
9 & 7 & 8.3765271474112 & -1.37652714741119 \tabularnewline
10 & 7 & 7.87724218560636 & -0.877242185606358 \tabularnewline
11 & 12 & 8.66304209769315 & 3.33695790230685 \tabularnewline
12 & 10 & 10.0143009592033 & -0.0143009592032610 \tabularnewline
13 & 10 & 6.67779628566282 & 3.32220371433718 \tabularnewline
14 & 8 & 7.59038175723848 & 0.40961824276152 \tabularnewline
15 & 8 & 7.36912200527653 & 0.630877994723465 \tabularnewline
16 & 4 & 7.58284239077288 & -3.58284239077288 \tabularnewline
17 & 9 & 6.3650485212657 & 2.63495147873431 \tabularnewline
18 & 8 & 7.68381962770494 & 0.316180372295060 \tabularnewline
19 & 7 & 9.33177577716726 & -2.33177577716726 \tabularnewline
20 & 11 & 10.6764431745380 & 0.32355682546202 \tabularnewline
21 & 9 & 8.07888366282857 & 0.921116337171431 \tabularnewline
22 & 11 & 11.3600805713719 & -0.360080571371874 \tabularnewline
23 & 13 & 8.38281154044926 & 4.61718845955074 \tabularnewline
24 & 8 & 7.13513526460915 & 0.86486473539085 \tabularnewline
25 & 8 & 6.67042681815486 & 1.32957318184514 \tabularnewline
26 & 9 & 8.48638003019435 & 0.513619969805654 \tabularnewline
27 & 6 & 8.69724572762584 & -2.69724572762584 \tabularnewline
28 & 9 & 7.63728297261206 & 1.36271702738794 \tabularnewline
29 & 9 & 9.2133560729719 & -0.213356072971898 \tabularnewline
30 & 6 & 7.83180955051812 & -1.83180955051812 \tabularnewline
31 & 6 & 5.69008701675497 & 0.309912983245029 \tabularnewline
32 & 16 & 11.1063197529320 & 4.89368024706796 \tabularnewline
33 & 5 & 11.0642614906392 & -6.06426149063919 \tabularnewline
34 & 7 & 7.3365935314667 & -0.3365935314667 \tabularnewline
35 & 9 & 9.89605238978916 & -0.896052389789164 \tabularnewline
36 & 6 & 7.02495596152595 & -1.02495596152594 \tabularnewline
37 & 6 & 6.65731886148943 & -0.657318861489429 \tabularnewline
38 & 5 & 8.40405215375662 & -3.40405215375662 \tabularnewline
39 & 12 & 10.7779096170053 & 1.22209038299473 \tabularnewline
40 & 7 & 8.05995019421785 & -1.05995019421785 \tabularnewline
41 & 10 & 10.1957144882390 & -0.195714488239030 \tabularnewline
42 & 9 & 8.37365515489804 & 0.626344845101964 \tabularnewline
43 & 8 & 9.2562245955751 & -1.25622459557510 \tabularnewline
44 & 5 & 9.25743202795408 & -4.25743202795408 \tabularnewline
45 & 8 & 6.79282305238195 & 1.20717694761805 \tabularnewline
46 & 8 & 7.14993247332234 & 0.850067526677662 \tabularnewline
47 & 10 & 8.81761098216549 & 1.18238901783451 \tabularnewline
48 & 6 & 7.03160798670806 & -1.03160798670806 \tabularnewline
49 & 8 & 7.64865931900313 & 0.351340680996865 \tabularnewline
50 & 7 & 10.6614834310996 & -3.66148343109959 \tabularnewline
51 & 4 & 5.3026258506591 & -1.30262585065909 \tabularnewline
52 & 8 & 7.27880075529918 & 0.721199244700818 \tabularnewline
53 & 8 & 6.98770319091606 & 1.01229680908394 \tabularnewline
54 & 4 & 4.83020679070682 & -0.830206790706819 \tabularnewline
55 & 20 & 13.1036775146962 & 6.89632248530384 \tabularnewline
56 & 8 & 7.4575108643387 & 0.542489135661306 \tabularnewline
57 & 8 & 7.27364469332544 & 0.726355306674562 \tabularnewline
58 & 6 & 8.31187534215115 & -2.31187534215115 \tabularnewline
59 & 4 & 4.39766291195563 & -0.397662911955634 \tabularnewline
60 & 8 & 9.2447869698316 & -1.24478696983160 \tabularnewline
61 & 9 & 7.27374799344865 & 1.72625200655135 \tabularnewline
62 & 6 & 7.96898687719197 & -1.96898687719197 \tabularnewline
63 & 7 & 8.29890628773413 & -1.29890628773413 \tabularnewline
64 & 9 & 6.21690504303844 & 2.78309495696156 \tabularnewline
65 & 5 & 7.04387293461517 & -2.04387293461517 \tabularnewline
66 & 5 & 5.94946728881653 & -0.949467288816528 \tabularnewline
67 & 8 & 6.73169861809062 & 1.26830138190938 \tabularnewline
68 & 8 & 8.22329452069033 & -0.223294520690335 \tabularnewline
69 & 6 & 6.85122326346855 & -0.851223263468548 \tabularnewline
70 & 8 & 7.17166740877871 & 0.828332591221287 \tabularnewline
71 & 7 & 7.32298001040457 & -0.322980010404573 \tabularnewline
72 & 7 & 6.07807727656424 & 0.92192272343576 \tabularnewline
73 & 9 & 8.59436054118218 & 0.405639458817822 \tabularnewline
74 & 11 & 11.0095397666120 & -0.00953976661196645 \tabularnewline
75 & 6 & 8.63969270541552 & -2.63969270541552 \tabularnewline
76 & 8 & 7.73131494218165 & 0.268685057818353 \tabularnewline
77 & 6 & 8.39155082308347 & -2.39155082308347 \tabularnewline
78 & 9 & 8.66591220812366 & 0.334087791876342 \tabularnewline
79 & 8 & 6.41738476848161 & 1.58261523151839 \tabularnewline
80 & 6 & 8.37237234724173 & -2.37237234724173 \tabularnewline
81 & 10 & 8.30847959981552 & 1.69152040018448 \tabularnewline
82 & 8 & 6.09004038223493 & 1.90995961776507 \tabularnewline
83 & 8 & 8.34642525909655 & -0.346425259096550 \tabularnewline
84 & 10 & 9.04204710673246 & 0.957952893267536 \tabularnewline
85 & 5 & 5.92854636411674 & -0.928546364116737 \tabularnewline
86 & 7 & 9.3454673507971 & -2.34546735079711 \tabularnewline
87 & 5 & 7.17051449965076 & -2.17051449965076 \tabularnewline
88 & 8 & 6.28546530107713 & 1.71453469892287 \tabularnewline
89 & 14 & 9.91364904500649 & 4.08635095499351 \tabularnewline
90 & 7 & 7.65508622807581 & -0.655086228075807 \tabularnewline
91 & 8 & 8.829534178024 & -0.829534178024006 \tabularnewline
92 & 6 & 4.89237614706084 & 1.10762385293916 \tabularnewline
93 & 5 & 6.27913123439787 & -1.27913123439787 \tabularnewline
94 & 6 & 9.23400674288517 & -3.23400674288517 \tabularnewline
95 & 10 & 6.56933943715599 & 3.43066056284401 \tabularnewline
96 & 12 & 11.5612597163829 & 0.438740283617103 \tabularnewline
97 & 9 & 9.15195047208361 & -0.151950472083608 \tabularnewline
98 & 12 & 10.8783870359195 & 1.12161296408053 \tabularnewline
99 & 7 & 7.54322856034637 & -0.543228560346373 \tabularnewline
100 & 8 & 8.34157909749244 & -0.341579097492441 \tabularnewline
101 & 10 & 9.16737793926587 & 0.83262206073413 \tabularnewline
102 & 6 & 7.27543726990533 & -1.27543726990533 \tabularnewline
103 & 10 & 11.2088362340758 & -1.2088362340758 \tabularnewline
104 & 10 & 8.70695834721845 & 1.29304165278155 \tabularnewline
105 & 10 & 7.09396259817717 & 2.90603740182283 \tabularnewline
106 & 5 & 8.44895823576181 & -3.44895823576181 \tabularnewline
107 & 7 & 6.93115088858718 & 0.0688491114128244 \tabularnewline
108 & 10 & 8.64121890522414 & 1.35878109477586 \tabularnewline
109 & 11 & 9.7460873369803 & 1.2539126630197 \tabularnewline
110 & 6 & 8.2153464008538 & -2.2153464008538 \tabularnewline
111 & 7 & 7.49124720019924 & -0.491247200199236 \tabularnewline
112 & 12 & 8.85975079578624 & 3.14024920421376 \tabularnewline
113 & 11 & 6.36560475530708 & 4.63439524469292 \tabularnewline
114 & 11 & 10.8223314553362 & 0.177668544663818 \tabularnewline
115 & 11 & 5.46594559906795 & 5.53405440093205 \tabularnewline
116 & 5 & 7.63024610145471 & -2.63024610145471 \tabularnewline
117 & 8 & 10.2449469104578 & -2.24494691045777 \tabularnewline
118 & 6 & 6.80482294995854 & -0.804822949958538 \tabularnewline
119 & 9 & 9.4462437582705 & -0.446243758270487 \tabularnewline
120 & 4 & 6.7755796691547 & -2.77557966915471 \tabularnewline
121 & 4 & 5.79549010385074 & -1.79549010385074 \tabularnewline
122 & 7 & 8.35125570969704 & -1.35125570969704 \tabularnewline
123 & 11 & 9.61417592770903 & 1.38582407229097 \tabularnewline
124 & 6 & 4.41831358186891 & 1.58168641813109 \tabularnewline
125 & 7 & 7.07416795979127 & -0.0741679597912713 \tabularnewline
126 & 8 & 9.95732271130627 & -1.95732271130626 \tabularnewline
127 & 4 & 6.34770760870298 & -2.34770760870298 \tabularnewline
128 & 8 & 7.09376688533228 & 0.906233114667723 \tabularnewline
129 & 9 & 8.17777847660046 & 0.822221523399537 \tabularnewline
130 & 8 & 7.70917096194147 & 0.290829038058529 \tabularnewline
131 & 11 & 8.80338612376012 & 2.19661387623988 \tabularnewline
132 & 8 & 7.51487235382208 & 0.48512764617792 \tabularnewline
133 & 5 & 6.76819610572183 & -1.76819610572183 \tabularnewline
134 & 4 & 5.89716839983827 & -1.89716839983827 \tabularnewline
135 & 8 & 7.46173525527868 & 0.538264744721324 \tabularnewline
136 & 10 & 11.8424156426398 & -1.84241564263980 \tabularnewline
137 & 6 & 8.3132015214279 & -2.31320152142790 \tabularnewline
138 & 9 & 9.38631050343167 & -0.386310503431667 \tabularnewline
139 & 9 & 7.2111369805229 & 1.7888630194771 \tabularnewline
140 & 13 & 8.92112385109479 & 4.07887614890521 \tabularnewline
141 & 9 & 7.88244442405994 & 1.11755557594006 \tabularnewline
142 & 10 & 9.58269183403904 & 0.417308165960961 \tabularnewline
143 & 20 & 14.8512010241742 & 5.14879897582581 \tabularnewline
144 & 5 & 6.06285773606914 & -1.06285773606914 \tabularnewline
145 & 11 & 10.4386233426502 & 0.561376657349783 \tabularnewline
146 & 6 & 8.15778529610135 & -2.15778529610135 \tabularnewline
147 & 9 & 10.2085791693770 & -1.20857916937699 \tabularnewline
148 & 7 & 7.34142533291684 & -0.341425332916843 \tabularnewline
149 & 9 & 8.1768676331841 & 0.823132366815906 \tabularnewline
150 & 10 & 8.48606382780396 & 1.51393617219604 \tabularnewline
151 & 9 & 6.98676626660848 & 2.01323373339152 \tabularnewline
152 & 8 & 10.2786599686877 & -2.27865996868767 \tabularnewline
153 & 7 & 11.3667477763246 & -4.3667477763246 \tabularnewline
154 & 6 & 9.4351535187033 & -3.4351535187033 \tabularnewline
155 & 13 & 11.7065172211981 & 1.29348277880191 \tabularnewline
156 & 6 & 8.0681076160001 & -2.06810761600011 \tabularnewline
157 & 8 & 7.77823157992087 & 0.221768420079131 \tabularnewline
158 & 10 & 9.53924465910727 & 0.460755340892733 \tabularnewline
159 & 16 & 11.8116962653403 & 4.18830373465973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&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]8.8406478113907[/C][C]3.15935218860930[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]6.96629721753314[/C][C]1.03370278246686[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]10.5684164772692[/C][C]-2.5684164772692[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]8.26221888637267[/C][C]-0.262218886372668[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.04605470784323[/C][C]-0.046054707843233[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]8.31636489959075[/C][C]-1.31636489959075[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]6.96163291152608[/C][C]-2.96163291152608[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]7.46558969163038[/C][C]3.53441030836962[/C][/ROW]
[ROW][C]9[/C][C]7[/C][C]8.3765271474112[/C][C]-1.37652714741119[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]7.87724218560636[/C][C]-0.877242185606358[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]8.66304209769315[/C][C]3.33695790230685[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]10.0143009592033[/C][C]-0.0143009592032610[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]6.67779628566282[/C][C]3.32220371433718[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]7.59038175723848[/C][C]0.40961824276152[/C][/ROW]
[ROW][C]15[/C][C]8[/C][C]7.36912200527653[/C][C]0.630877994723465[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.58284239077288[/C][C]-3.58284239077288[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.3650485212657[/C][C]2.63495147873431[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]7.68381962770494[/C][C]0.316180372295060[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]9.33177577716726[/C][C]-2.33177577716726[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]10.6764431745380[/C][C]0.32355682546202[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.07888366282857[/C][C]0.921116337171431[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.3600805713719[/C][C]-0.360080571371874[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]8.38281154044926[/C][C]4.61718845955074[/C][/ROW]
[ROW][C]24[/C][C]8[/C][C]7.13513526460915[/C][C]0.86486473539085[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]6.67042681815486[/C][C]1.32957318184514[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]8.48638003019435[/C][C]0.513619969805654[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]8.69724572762584[/C][C]-2.69724572762584[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]7.63728297261206[/C][C]1.36271702738794[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]9.2133560729719[/C][C]-0.213356072971898[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]7.83180955051812[/C][C]-1.83180955051812[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]5.69008701675497[/C][C]0.309912983245029[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]11.1063197529320[/C][C]4.89368024706796[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]11.0642614906392[/C][C]-6.06426149063919[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]7.3365935314667[/C][C]-0.3365935314667[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.89605238978916[/C][C]-0.896052389789164[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]7.02495596152595[/C][C]-1.02495596152594[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]6.65731886148943[/C][C]-0.657318861489429[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]8.40405215375662[/C][C]-3.40405215375662[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]10.7779096170053[/C][C]1.22209038299473[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]8.05995019421785[/C][C]-1.05995019421785[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]10.1957144882390[/C][C]-0.195714488239030[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]8.37365515489804[/C][C]0.626344845101964[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]9.2562245955751[/C][C]-1.25622459557510[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]9.25743202795408[/C][C]-4.25743202795408[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]6.79282305238195[/C][C]1.20717694761805[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]7.14993247332234[/C][C]0.850067526677662[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]8.81761098216549[/C][C]1.18238901783451[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]7.03160798670806[/C][C]-1.03160798670806[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.64865931900313[/C][C]0.351340680996865[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]10.6614834310996[/C][C]-3.66148343109959[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]5.3026258506591[/C][C]-1.30262585065909[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.27880075529918[/C][C]0.721199244700818[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]6.98770319091606[/C][C]1.01229680908394[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]4.83020679070682[/C][C]-0.830206790706819[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]13.1036775146962[/C][C]6.89632248530384[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]7.4575108643387[/C][C]0.542489135661306[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]7.27364469332544[/C][C]0.726355306674562[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]8.31187534215115[/C][C]-2.31187534215115[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.39766291195563[/C][C]-0.397662911955634[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]9.2447869698316[/C][C]-1.24478696983160[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]7.27374799344865[/C][C]1.72625200655135[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]7.96898687719197[/C][C]-1.96898687719197[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]8.29890628773413[/C][C]-1.29890628773413[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]6.21690504303844[/C][C]2.78309495696156[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]7.04387293461517[/C][C]-2.04387293461517[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]5.94946728881653[/C][C]-0.949467288816528[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]6.73169861809062[/C][C]1.26830138190938[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]8.22329452069033[/C][C]-0.223294520690335[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]6.85122326346855[/C][C]-0.851223263468548[/C][/ROW]
[ROW][C]70[/C][C]8[/C][C]7.17166740877871[/C][C]0.828332591221287[/C][/ROW]
[ROW][C]71[/C][C]7[/C][C]7.32298001040457[/C][C]-0.322980010404573[/C][/ROW]
[ROW][C]72[/C][C]7[/C][C]6.07807727656424[/C][C]0.92192272343576[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]8.59436054118218[/C][C]0.405639458817822[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]11.0095397666120[/C][C]-0.00953976661196645[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]8.63969270541552[/C][C]-2.63969270541552[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.73131494218165[/C][C]0.268685057818353[/C][/ROW]
[ROW][C]77[/C][C]6[/C][C]8.39155082308347[/C][C]-2.39155082308347[/C][/ROW]
[ROW][C]78[/C][C]9[/C][C]8.66591220812366[/C][C]0.334087791876342[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]6.41738476848161[/C][C]1.58261523151839[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.37237234724173[/C][C]-2.37237234724173[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]8.30847959981552[/C][C]1.69152040018448[/C][/ROW]
[ROW][C]82[/C][C]8[/C][C]6.09004038223493[/C][C]1.90995961776507[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]8.34642525909655[/C][C]-0.346425259096550[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]9.04204710673246[/C][C]0.957952893267536[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]5.92854636411674[/C][C]-0.928546364116737[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]9.3454673507971[/C][C]-2.34546735079711[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]7.17051449965076[/C][C]-2.17051449965076[/C][/ROW]
[ROW][C]88[/C][C]8[/C][C]6.28546530107713[/C][C]1.71453469892287[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]9.91364904500649[/C][C]4.08635095499351[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]7.65508622807581[/C][C]-0.655086228075807[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]8.829534178024[/C][C]-0.829534178024006[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]4.89237614706084[/C][C]1.10762385293916[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]6.27913123439787[/C][C]-1.27913123439787[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]9.23400674288517[/C][C]-3.23400674288517[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]6.56933943715599[/C][C]3.43066056284401[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.5612597163829[/C][C]0.438740283617103[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]9.15195047208361[/C][C]-0.151950472083608[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]10.8783870359195[/C][C]1.12161296408053[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]7.54322856034637[/C][C]-0.543228560346373[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]8.34157909749244[/C][C]-0.341579097492441[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]9.16737793926587[/C][C]0.83262206073413[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]7.27543726990533[/C][C]-1.27543726990533[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]11.2088362340758[/C][C]-1.2088362340758[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]8.70695834721845[/C][C]1.29304165278155[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]7.09396259817717[/C][C]2.90603740182283[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]8.44895823576181[/C][C]-3.44895823576181[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]6.93115088858718[/C][C]0.0688491114128244[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]8.64121890522414[/C][C]1.35878109477586[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]9.7460873369803[/C][C]1.2539126630197[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]8.2153464008538[/C][C]-2.2153464008538[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]7.49124720019924[/C][C]-0.491247200199236[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]8.85975079578624[/C][C]3.14024920421376[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]6.36560475530708[/C][C]4.63439524469292[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.8223314553362[/C][C]0.177668544663818[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]5.46594559906795[/C][C]5.53405440093205[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]7.63024610145471[/C][C]-2.63024610145471[/C][/ROW]
[ROW][C]117[/C][C]8[/C][C]10.2449469104578[/C][C]-2.24494691045777[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]6.80482294995854[/C][C]-0.804822949958538[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]9.4462437582705[/C][C]-0.446243758270487[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]6.7755796691547[/C][C]-2.77557966915471[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.79549010385074[/C][C]-1.79549010385074[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]8.35125570969704[/C][C]-1.35125570969704[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]9.61417592770903[/C][C]1.38582407229097[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]4.41831358186891[/C][C]1.58168641813109[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]7.07416795979127[/C][C]-0.0741679597912713[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]9.95732271130627[/C][C]-1.95732271130626[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]6.34770760870298[/C][C]-2.34770760870298[/C][/ROW]
[ROW][C]128[/C][C]8[/C][C]7.09376688533228[/C][C]0.906233114667723[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]8.17777847660046[/C][C]0.822221523399537[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]7.70917096194147[/C][C]0.290829038058529[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]8.80338612376012[/C][C]2.19661387623988[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]7.51487235382208[/C][C]0.48512764617792[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]6.76819610572183[/C][C]-1.76819610572183[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]5.89716839983827[/C][C]-1.89716839983827[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]7.46173525527868[/C][C]0.538264744721324[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]11.8424156426398[/C][C]-1.84241564263980[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]8.3132015214279[/C][C]-2.31320152142790[/C][/ROW]
[ROW][C]138[/C][C]9[/C][C]9.38631050343167[/C][C]-0.386310503431667[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]7.2111369805229[/C][C]1.7888630194771[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]8.92112385109479[/C][C]4.07887614890521[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.88244442405994[/C][C]1.11755557594006[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]9.58269183403904[/C][C]0.417308165960961[/C][/ROW]
[ROW][C]143[/C][C]20[/C][C]14.8512010241742[/C][C]5.14879897582581[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]6.06285773606914[/C][C]-1.06285773606914[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]10.4386233426502[/C][C]0.561376657349783[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]8.15778529610135[/C][C]-2.15778529610135[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.2085791693770[/C][C]-1.20857916937699[/C][/ROW]
[ROW][C]148[/C][C]7[/C][C]7.34142533291684[/C][C]-0.341425332916843[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]8.1768676331841[/C][C]0.823132366815906[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]8.48606382780396[/C][C]1.51393617219604[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]6.98676626660848[/C][C]2.01323373339152[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]10.2786599686877[/C][C]-2.27865996868767[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]11.3667477763246[/C][C]-4.3667477763246[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]9.4351535187033[/C][C]-3.4351535187033[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]11.7065172211981[/C][C]1.29348277880191[/C][/ROW]
[ROW][C]156[/C][C]6[/C][C]8.0681076160001[/C][C]-2.06810761600011[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]7.77823157992087[/C][C]0.221768420079131[/C][/ROW]
[ROW][C]158[/C][C]10[/C][C]9.53924465910727[/C][C]0.460755340892733[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]11.8116962653403[/C][C]4.18830373465973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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
1128.84064781139073.15935218860930
286.966297217533141.03370278246686
3810.5684164772692-2.5684164772692
488.26221888637267-0.262218886372668
599.04605470784323-0.046054707843233
678.31636489959075-1.31636489959075
746.96163291152608-2.96163291152608
8117.465589691630383.53441030836962
978.3765271474112-1.37652714741119
1077.87724218560636-0.877242185606358
11128.663042097693153.33695790230685
121010.0143009592033-0.0143009592032610
13106.677796285662823.32220371433718
1487.590381757238480.40961824276152
1587.369122005276530.630877994723465
1647.58284239077288-3.58284239077288
1796.36504852126572.63495147873431
1887.683819627704940.316180372295060
1979.33177577716726-2.33177577716726
201110.67644317453800.32355682546202
2198.078883662828570.921116337171431
221111.3600805713719-0.360080571371874
23138.382811540449264.61718845955074
2487.135135264609150.86486473539085
2586.670426818154861.32957318184514
2698.486380030194350.513619969805654
2768.69724572762584-2.69724572762584
2897.637282972612061.36271702738794
2999.2133560729719-0.213356072971898
3067.83180955051812-1.83180955051812
3165.690087016754970.309912983245029
321611.10631975293204.89368024706796
33511.0642614906392-6.06426149063919
3477.3365935314667-0.3365935314667
3599.89605238978916-0.896052389789164
3667.02495596152595-1.02495596152594
3766.65731886148943-0.657318861489429
3858.40405215375662-3.40405215375662
391210.77790961700531.22209038299473
4078.05995019421785-1.05995019421785
411010.1957144882390-0.195714488239030
4298.373655154898040.626344845101964
4389.2562245955751-1.25622459557510
4459.25743202795408-4.25743202795408
4586.792823052381951.20717694761805
4687.149932473322340.850067526677662
47108.817610982165491.18238901783451
4867.03160798670806-1.03160798670806
4987.648659319003130.351340680996865
50710.6614834310996-3.66148343109959
5145.3026258506591-1.30262585065909
5287.278800755299180.721199244700818
5386.987703190916061.01229680908394
5444.83020679070682-0.830206790706819
552013.10367751469626.89632248530384
5687.45751086433870.542489135661306
5787.273644693325440.726355306674562
5868.31187534215115-2.31187534215115
5944.39766291195563-0.397662911955634
6089.2447869698316-1.24478696983160
6197.273747993448651.72625200655135
6267.96898687719197-1.96898687719197
6378.29890628773413-1.29890628773413
6496.216905043038442.78309495696156
6557.04387293461517-2.04387293461517
6655.94946728881653-0.949467288816528
6786.731698618090621.26830138190938
6888.22329452069033-0.223294520690335
6966.85122326346855-0.851223263468548
7087.171667408778710.828332591221287
7177.32298001040457-0.322980010404573
7276.078077276564240.92192272343576
7398.594360541182180.405639458817822
741111.0095397666120-0.00953976661196645
7568.63969270541552-2.63969270541552
7687.731314942181650.268685057818353
7768.39155082308347-2.39155082308347
7898.665912208123660.334087791876342
7986.417384768481611.58261523151839
8068.37237234724173-2.37237234724173
81108.308479599815521.69152040018448
8286.090040382234931.90995961776507
8388.34642525909655-0.346425259096550
84109.042047106732460.957952893267536
8555.92854636411674-0.928546364116737
8679.3454673507971-2.34546735079711
8757.17051449965076-2.17051449965076
8886.285465301077131.71453469892287
89149.913649045006494.08635095499351
9077.65508622807581-0.655086228075807
9188.829534178024-0.829534178024006
9264.892376147060841.10762385293916
9356.27913123439787-1.27913123439787
9469.23400674288517-3.23400674288517
95106.569339437155993.43066056284401
961211.56125971638290.438740283617103
9799.15195047208361-0.151950472083608
981210.87838703591951.12161296408053
9977.54322856034637-0.543228560346373
10088.34157909749244-0.341579097492441
101109.167377939265870.83262206073413
10267.27543726990533-1.27543726990533
1031011.2088362340758-1.2088362340758
104108.706958347218451.29304165278155
105107.093962598177172.90603740182283
10658.44895823576181-3.44895823576181
10776.931150888587180.0688491114128244
108108.641218905224141.35878109477586
109119.74608733698031.2539126630197
11068.2153464008538-2.2153464008538
11177.49124720019924-0.491247200199236
112128.859750795786243.14024920421376
113116.365604755307084.63439524469292
1141110.82233145533620.177668544663818
115115.465945599067955.53405440093205
11657.63024610145471-2.63024610145471
117810.2449469104578-2.24494691045777
11866.80482294995854-0.804822949958538
11999.4462437582705-0.446243758270487
12046.7755796691547-2.77557966915471
12145.79549010385074-1.79549010385074
12278.35125570969704-1.35125570969704
123119.614175927709031.38582407229097
12464.418313581868911.58168641813109
12577.07416795979127-0.0741679597912713
12689.95732271130627-1.95732271130626
12746.34770760870298-2.34770760870298
12887.093766885332280.906233114667723
12998.177778476600460.822221523399537
13087.709170961941470.290829038058529
131118.803386123760122.19661387623988
13287.514872353822080.48512764617792
13356.76819610572183-1.76819610572183
13445.89716839983827-1.89716839983827
13587.461735255278680.538264744721324
1361011.8424156426398-1.84241564263980
13768.3132015214279-2.31320152142790
13899.38631050343167-0.386310503431667
13997.21113698052291.7888630194771
140138.921123851094794.07887614890521
14197.882444424059941.11755557594006
142109.582691834039040.417308165960961
1432014.85120102417425.14879897582581
14456.06285773606914-1.06285773606914
1451110.43862334265020.561376657349783
14668.15778529610135-2.15778529610135
147910.2085791693770-1.20857916937699
14877.34142533291684-0.341425332916843
14998.17686763318410.823132366815906
150108.486063827803961.51393617219604
15196.986766266608482.01323373339152
152810.2786599686877-2.27865996868767
153711.3667477763246-4.3667477763246
15469.4351535187033-3.4351535187033
1551311.70651722119811.29348277880191
15668.0681076160001-2.06810761600011
15787.778231579920870.221768420079131
158109.539244659107270.460755340892733
1591611.81169626534034.18830373465973






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8319100672611350.3361798654777290.168089932738864
110.8313060909680180.3373878180639650.168693909031982
120.8321397934778430.3357204130443140.167860206522157
130.9059827983223040.1880344033553910.0940172016776957
140.874792259852050.25041548029590.12520774014795
150.8213360116364880.3573279767270240.178663988363512
160.7861555052614630.4276889894770730.213844494738537
170.7723071609937960.4553856780124090.227692839006204
180.7217280472921550.556543905415690.278271952707845
190.6574439305403860.6851121389192280.342556069459614
200.5867482047705370.8265035904589270.413251795229463
210.5126530258193940.9746939483612110.487346974180606
220.4330837570575290.8661675141150580.566916242942471
230.669122868612590.6617542627748190.330877131387409
240.63647296227790.7270540754442010.363527037722101
250.5721921696352470.8556156607295070.427807830364753
260.5073360965613270.9853278068773450.492663903438673
270.5221753794247990.9556492411504020.477824620575201
280.4648400733213860.9296801466427710.535159926678614
290.3996207972864010.7992415945728020.600379202713599
300.3993597299213990.7987194598427990.6006402700786
310.3382614342402520.6765228684805040.661738565759748
320.588974486582190.822051026835620.41102551341781
330.8236162692557290.3527674614885420.176383730744271
340.7973147382290940.4053705235418120.202685261770906
350.7552498357893730.4895003284212540.244750164210627
360.7404946437772610.5190107124454780.259505356222739
370.6930654386079150.613869122784170.306934561392085
380.8372783043349940.3254433913300120.162721695665006
390.8452694320073870.3094611359852250.154730567992613
400.8127144728327810.3745710543344370.187285527167219
410.7744903619272600.4510192761454810.225509638072740
420.7468402660118720.5063194679762550.253159733988128
430.7166112134155920.5667775731688170.283388786584408
440.780220806896130.4395583862077390.219779193103869
450.7656239685223750.468752062955250.234376031477625
460.7292090702933840.5415818594132310.270790929706616
470.6969218314217570.6061563371564860.303078168578243
480.6563663165726640.6872673668546730.343633683427336
490.6160036338660880.7679927322678240.383996366133912
500.6638918058496510.6722163883006970.336108194150349
510.6522873333149660.6954253333700680.347712666685034
520.618018353134450.76396329373110.38198164686555
530.580188696779340.839622606441320.41981130322066
540.5369875320238030.9260249359523940.463012467976197
550.9449513326092930.1100973347814140.0550486673907069
560.9310626784471820.1378746431056350.0689373215528176
570.9163046457833910.1673907084332180.0836953542166089
580.9149714513079350.1700570973841290.0850285486920645
590.894864332940340.2102713341193210.105135667059660
600.8793580645156120.2412838709687760.120641935484388
610.8731859401772560.2536281196454890.126814059822744
620.8647096944240780.2705806111518440.135290305575922
630.8454593773069580.3090812453860850.154540622693042
640.8673724408419570.2652551183160870.132627559158044
650.8608558405302920.2782883189394150.139144159469708
660.8384932795808110.3230134408383780.161506720419189
670.8214006735318260.3571986529363480.178599326468174
680.7904808485502780.4190383028994440.209519151449722
690.7594803338236190.4810393323527620.240519666176381
700.72880490288040.54239019423920.2711950971196
710.6886035908177110.6227928183645780.311396409182289
720.6551103114125550.689779377174890.344889688587445
730.6138556194060480.7722887611879040.386144380593952
740.5679460470245030.8641079059509940.432053952975497
750.5864297763459930.8271404473080150.413570223654007
760.5414267606405960.9171464787188080.458573239359404
770.5477063917609910.9045872164780180.452293608239009
780.5031357283597860.9937285432804270.496864271640214
790.4807282011163660.9614564022327330.519271798883634
800.488321224729920.976642449459840.51167877527008
810.4696414314768570.9392828629537150.530358568523142
820.458844661213060.917689322426120.54115533878694
830.4131132298285740.8262264596571470.586886770171426
840.3783297383534520.7566594767069040.621670261646548
850.3427969459691380.6855938919382760.657203054030862
860.3477975075778290.6955950151556580.652202492422171
870.3527505110873530.7055010221747060.647249488912647
880.3326373442387460.6652746884774920.667362655761254
890.4466132814621120.8932265629242250.553386718537888
900.4032767585743490.8065535171486980.596723241425651
910.3634613498912060.7269226997824130.636538650108794
920.3308940236033860.6617880472067720.669105976396614
930.3013306869384950.6026613738769910.698669313061505
940.352474204593940.704948409187880.64752579540606
950.4173952046295040.8347904092590080.582604795370496
960.3751620507243420.7503241014486840.624837949275658
970.3303707785220820.6607415570441640.669629221477918
980.2990195224423740.5980390448847470.700980477557626
990.2598976774958760.5197953549917510.740102322504125
1000.2226457251048750.4452914502097490.777354274895125
1010.1929506242959370.3859012485918740.807049375704063
1020.1709232624077250.3418465248154490.829076737592275
1030.1527913597432010.3055827194864030.847208640256799
1040.1333223572993930.2666447145987860.866677642700607
1050.1617638536582370.3235277073164740.838236146341763
1060.2077749276707880.4155498553415760.792225072329212
1070.1755455632283390.3510911264566780.82445443677166
1080.1560774295181010.3121548590362030.843922570481899
1090.1363875274819290.2727750549638590.86361247251807
1100.1338625326548050.267725065309610.866137467345195
1110.1097769086049950.2195538172099900.890223091395005
1120.1441995143425410.2883990286850830.855800485657459
1130.2710509590666050.5421019181332110.728949040933395
1140.2315775244645180.4631550489290360.768422475535482
1150.5852876141010310.8294247717979380.414712385898969
1160.574097852885310.851804294229380.42590214711469
1170.5706483700205710.8587032599588590.429351629979429
1180.519809933553040.960380132893920.48019006644696
1190.4647202899699020.9294405799398050.535279710030098
1200.5515038224933420.8969923550133160.448496177506658
1210.5225278002323450.9549443995353110.477472199767655
1220.5370470876616330.9259058246767330.462952912338367
1230.4888013198953050.977602639790610.511198680104695
1240.4973468144137190.9946936288274380.502653185586281
1250.4387770001743390.8775540003486770.561222999825661
1260.4439773973442260.8879547946884520.556022602655774
1270.4340714173520190.8681428347040380.565928582647981
1280.3977354412737980.7954708825475950.602264558726202
1290.356646308102530.713292616205060.64335369189747
1300.3083461821849520.6166923643699040.691653817815048
1310.2990218698924560.5980437397849120.700978130107544
1320.2471135445567710.4942270891135420.752886455443229
1330.2036768100539440.4073536201078880.796323189946056
1340.1719572874657220.3439145749314440.828042712534278
1350.1349425771846510.2698851543693020.865057422815349
1360.1276625290117250.2553250580234490.872337470988276
1370.1689740021300010.3379480042600030.831025997869999
1380.1754544231869290.3509088463738580.82454557681307
1390.1564498125704180.3128996251408370.843550187429582
1400.1881285591895930.3762571183791850.811871440810407
1410.1385965674204540.2771931348409080.861403432579546
1420.104487832738710.208975665477420.89551216726129
1430.1686169121708350.337233824341670.831383087829165
1440.1189959633532940.2379919267065880.881004036646706
1450.09140861326331480.1828172265266300.908591386736685
1460.05788526393063280.1157705278612660.942114736069367
1470.0337956032000560.0675912064001120.966204396799944
1480.01674489870674780.03348979741349570.983255101293252
1490.00737298239011370.01474596478022740.992627017609886

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.831910067261135 & 0.336179865477729 & 0.168089932738864 \tabularnewline
11 & 0.831306090968018 & 0.337387818063965 & 0.168693909031982 \tabularnewline
12 & 0.832139793477843 & 0.335720413044314 & 0.167860206522157 \tabularnewline
13 & 0.905982798322304 & 0.188034403355391 & 0.0940172016776957 \tabularnewline
14 & 0.87479225985205 & 0.2504154802959 & 0.12520774014795 \tabularnewline
15 & 0.821336011636488 & 0.357327976727024 & 0.178663988363512 \tabularnewline
16 & 0.786155505261463 & 0.427688989477073 & 0.213844494738537 \tabularnewline
17 & 0.772307160993796 & 0.455385678012409 & 0.227692839006204 \tabularnewline
18 & 0.721728047292155 & 0.55654390541569 & 0.278271952707845 \tabularnewline
19 & 0.657443930540386 & 0.685112138919228 & 0.342556069459614 \tabularnewline
20 & 0.586748204770537 & 0.826503590458927 & 0.413251795229463 \tabularnewline
21 & 0.512653025819394 & 0.974693948361211 & 0.487346974180606 \tabularnewline
22 & 0.433083757057529 & 0.866167514115058 & 0.566916242942471 \tabularnewline
23 & 0.66912286861259 & 0.661754262774819 & 0.330877131387409 \tabularnewline
24 & 0.6364729622779 & 0.727054075444201 & 0.363527037722101 \tabularnewline
25 & 0.572192169635247 & 0.855615660729507 & 0.427807830364753 \tabularnewline
26 & 0.507336096561327 & 0.985327806877345 & 0.492663903438673 \tabularnewline
27 & 0.522175379424799 & 0.955649241150402 & 0.477824620575201 \tabularnewline
28 & 0.464840073321386 & 0.929680146642771 & 0.535159926678614 \tabularnewline
29 & 0.399620797286401 & 0.799241594572802 & 0.600379202713599 \tabularnewline
30 & 0.399359729921399 & 0.798719459842799 & 0.6006402700786 \tabularnewline
31 & 0.338261434240252 & 0.676522868480504 & 0.661738565759748 \tabularnewline
32 & 0.58897448658219 & 0.82205102683562 & 0.41102551341781 \tabularnewline
33 & 0.823616269255729 & 0.352767461488542 & 0.176383730744271 \tabularnewline
34 & 0.797314738229094 & 0.405370523541812 & 0.202685261770906 \tabularnewline
35 & 0.755249835789373 & 0.489500328421254 & 0.244750164210627 \tabularnewline
36 & 0.740494643777261 & 0.519010712445478 & 0.259505356222739 \tabularnewline
37 & 0.693065438607915 & 0.61386912278417 & 0.306934561392085 \tabularnewline
38 & 0.837278304334994 & 0.325443391330012 & 0.162721695665006 \tabularnewline
39 & 0.845269432007387 & 0.309461135985225 & 0.154730567992613 \tabularnewline
40 & 0.812714472832781 & 0.374571054334437 & 0.187285527167219 \tabularnewline
41 & 0.774490361927260 & 0.451019276145481 & 0.225509638072740 \tabularnewline
42 & 0.746840266011872 & 0.506319467976255 & 0.253159733988128 \tabularnewline
43 & 0.716611213415592 & 0.566777573168817 & 0.283388786584408 \tabularnewline
44 & 0.78022080689613 & 0.439558386207739 & 0.219779193103869 \tabularnewline
45 & 0.765623968522375 & 0.46875206295525 & 0.234376031477625 \tabularnewline
46 & 0.729209070293384 & 0.541581859413231 & 0.270790929706616 \tabularnewline
47 & 0.696921831421757 & 0.606156337156486 & 0.303078168578243 \tabularnewline
48 & 0.656366316572664 & 0.687267366854673 & 0.343633683427336 \tabularnewline
49 & 0.616003633866088 & 0.767992732267824 & 0.383996366133912 \tabularnewline
50 & 0.663891805849651 & 0.672216388300697 & 0.336108194150349 \tabularnewline
51 & 0.652287333314966 & 0.695425333370068 & 0.347712666685034 \tabularnewline
52 & 0.61801835313445 & 0.7639632937311 & 0.38198164686555 \tabularnewline
53 & 0.58018869677934 & 0.83962260644132 & 0.41981130322066 \tabularnewline
54 & 0.536987532023803 & 0.926024935952394 & 0.463012467976197 \tabularnewline
55 & 0.944951332609293 & 0.110097334781414 & 0.0550486673907069 \tabularnewline
56 & 0.931062678447182 & 0.137874643105635 & 0.0689373215528176 \tabularnewline
57 & 0.916304645783391 & 0.167390708433218 & 0.0836953542166089 \tabularnewline
58 & 0.914971451307935 & 0.170057097384129 & 0.0850285486920645 \tabularnewline
59 & 0.89486433294034 & 0.210271334119321 & 0.105135667059660 \tabularnewline
60 & 0.879358064515612 & 0.241283870968776 & 0.120641935484388 \tabularnewline
61 & 0.873185940177256 & 0.253628119645489 & 0.126814059822744 \tabularnewline
62 & 0.864709694424078 & 0.270580611151844 & 0.135290305575922 \tabularnewline
63 & 0.845459377306958 & 0.309081245386085 & 0.154540622693042 \tabularnewline
64 & 0.867372440841957 & 0.265255118316087 & 0.132627559158044 \tabularnewline
65 & 0.860855840530292 & 0.278288318939415 & 0.139144159469708 \tabularnewline
66 & 0.838493279580811 & 0.323013440838378 & 0.161506720419189 \tabularnewline
67 & 0.821400673531826 & 0.357198652936348 & 0.178599326468174 \tabularnewline
68 & 0.790480848550278 & 0.419038302899444 & 0.209519151449722 \tabularnewline
69 & 0.759480333823619 & 0.481039332352762 & 0.240519666176381 \tabularnewline
70 & 0.7288049028804 & 0.5423901942392 & 0.2711950971196 \tabularnewline
71 & 0.688603590817711 & 0.622792818364578 & 0.311396409182289 \tabularnewline
72 & 0.655110311412555 & 0.68977937717489 & 0.344889688587445 \tabularnewline
73 & 0.613855619406048 & 0.772288761187904 & 0.386144380593952 \tabularnewline
74 & 0.567946047024503 & 0.864107905950994 & 0.432053952975497 \tabularnewline
75 & 0.586429776345993 & 0.827140447308015 & 0.413570223654007 \tabularnewline
76 & 0.541426760640596 & 0.917146478718808 & 0.458573239359404 \tabularnewline
77 & 0.547706391760991 & 0.904587216478018 & 0.452293608239009 \tabularnewline
78 & 0.503135728359786 & 0.993728543280427 & 0.496864271640214 \tabularnewline
79 & 0.480728201116366 & 0.961456402232733 & 0.519271798883634 \tabularnewline
80 & 0.48832122472992 & 0.97664244945984 & 0.51167877527008 \tabularnewline
81 & 0.469641431476857 & 0.939282862953715 & 0.530358568523142 \tabularnewline
82 & 0.45884466121306 & 0.91768932242612 & 0.54115533878694 \tabularnewline
83 & 0.413113229828574 & 0.826226459657147 & 0.586886770171426 \tabularnewline
84 & 0.378329738353452 & 0.756659476706904 & 0.621670261646548 \tabularnewline
85 & 0.342796945969138 & 0.685593891938276 & 0.657203054030862 \tabularnewline
86 & 0.347797507577829 & 0.695595015155658 & 0.652202492422171 \tabularnewline
87 & 0.352750511087353 & 0.705501022174706 & 0.647249488912647 \tabularnewline
88 & 0.332637344238746 & 0.665274688477492 & 0.667362655761254 \tabularnewline
89 & 0.446613281462112 & 0.893226562924225 & 0.553386718537888 \tabularnewline
90 & 0.403276758574349 & 0.806553517148698 & 0.596723241425651 \tabularnewline
91 & 0.363461349891206 & 0.726922699782413 & 0.636538650108794 \tabularnewline
92 & 0.330894023603386 & 0.661788047206772 & 0.669105976396614 \tabularnewline
93 & 0.301330686938495 & 0.602661373876991 & 0.698669313061505 \tabularnewline
94 & 0.35247420459394 & 0.70494840918788 & 0.64752579540606 \tabularnewline
95 & 0.417395204629504 & 0.834790409259008 & 0.582604795370496 \tabularnewline
96 & 0.375162050724342 & 0.750324101448684 & 0.624837949275658 \tabularnewline
97 & 0.330370778522082 & 0.660741557044164 & 0.669629221477918 \tabularnewline
98 & 0.299019522442374 & 0.598039044884747 & 0.700980477557626 \tabularnewline
99 & 0.259897677495876 & 0.519795354991751 & 0.740102322504125 \tabularnewline
100 & 0.222645725104875 & 0.445291450209749 & 0.777354274895125 \tabularnewline
101 & 0.192950624295937 & 0.385901248591874 & 0.807049375704063 \tabularnewline
102 & 0.170923262407725 & 0.341846524815449 & 0.829076737592275 \tabularnewline
103 & 0.152791359743201 & 0.305582719486403 & 0.847208640256799 \tabularnewline
104 & 0.133322357299393 & 0.266644714598786 & 0.866677642700607 \tabularnewline
105 & 0.161763853658237 & 0.323527707316474 & 0.838236146341763 \tabularnewline
106 & 0.207774927670788 & 0.415549855341576 & 0.792225072329212 \tabularnewline
107 & 0.175545563228339 & 0.351091126456678 & 0.82445443677166 \tabularnewline
108 & 0.156077429518101 & 0.312154859036203 & 0.843922570481899 \tabularnewline
109 & 0.136387527481929 & 0.272775054963859 & 0.86361247251807 \tabularnewline
110 & 0.133862532654805 & 0.26772506530961 & 0.866137467345195 \tabularnewline
111 & 0.109776908604995 & 0.219553817209990 & 0.890223091395005 \tabularnewline
112 & 0.144199514342541 & 0.288399028685083 & 0.855800485657459 \tabularnewline
113 & 0.271050959066605 & 0.542101918133211 & 0.728949040933395 \tabularnewline
114 & 0.231577524464518 & 0.463155048929036 & 0.768422475535482 \tabularnewline
115 & 0.585287614101031 & 0.829424771797938 & 0.414712385898969 \tabularnewline
116 & 0.57409785288531 & 0.85180429422938 & 0.42590214711469 \tabularnewline
117 & 0.570648370020571 & 0.858703259958859 & 0.429351629979429 \tabularnewline
118 & 0.51980993355304 & 0.96038013289392 & 0.48019006644696 \tabularnewline
119 & 0.464720289969902 & 0.929440579939805 & 0.535279710030098 \tabularnewline
120 & 0.551503822493342 & 0.896992355013316 & 0.448496177506658 \tabularnewline
121 & 0.522527800232345 & 0.954944399535311 & 0.477472199767655 \tabularnewline
122 & 0.537047087661633 & 0.925905824676733 & 0.462952912338367 \tabularnewline
123 & 0.488801319895305 & 0.97760263979061 & 0.511198680104695 \tabularnewline
124 & 0.497346814413719 & 0.994693628827438 & 0.502653185586281 \tabularnewline
125 & 0.438777000174339 & 0.877554000348677 & 0.561222999825661 \tabularnewline
126 & 0.443977397344226 & 0.887954794688452 & 0.556022602655774 \tabularnewline
127 & 0.434071417352019 & 0.868142834704038 & 0.565928582647981 \tabularnewline
128 & 0.397735441273798 & 0.795470882547595 & 0.602264558726202 \tabularnewline
129 & 0.35664630810253 & 0.71329261620506 & 0.64335369189747 \tabularnewline
130 & 0.308346182184952 & 0.616692364369904 & 0.691653817815048 \tabularnewline
131 & 0.299021869892456 & 0.598043739784912 & 0.700978130107544 \tabularnewline
132 & 0.247113544556771 & 0.494227089113542 & 0.752886455443229 \tabularnewline
133 & 0.203676810053944 & 0.407353620107888 & 0.796323189946056 \tabularnewline
134 & 0.171957287465722 & 0.343914574931444 & 0.828042712534278 \tabularnewline
135 & 0.134942577184651 & 0.269885154369302 & 0.865057422815349 \tabularnewline
136 & 0.127662529011725 & 0.255325058023449 & 0.872337470988276 \tabularnewline
137 & 0.168974002130001 & 0.337948004260003 & 0.831025997869999 \tabularnewline
138 & 0.175454423186929 & 0.350908846373858 & 0.82454557681307 \tabularnewline
139 & 0.156449812570418 & 0.312899625140837 & 0.843550187429582 \tabularnewline
140 & 0.188128559189593 & 0.376257118379185 & 0.811871440810407 \tabularnewline
141 & 0.138596567420454 & 0.277193134840908 & 0.861403432579546 \tabularnewline
142 & 0.10448783273871 & 0.20897566547742 & 0.89551216726129 \tabularnewline
143 & 0.168616912170835 & 0.33723382434167 & 0.831383087829165 \tabularnewline
144 & 0.118995963353294 & 0.237991926706588 & 0.881004036646706 \tabularnewline
145 & 0.0914086132633148 & 0.182817226526630 & 0.908591386736685 \tabularnewline
146 & 0.0578852639306328 & 0.115770527861266 & 0.942114736069367 \tabularnewline
147 & 0.033795603200056 & 0.067591206400112 & 0.966204396799944 \tabularnewline
148 & 0.0167448987067478 & 0.0334897974134957 & 0.983255101293252 \tabularnewline
149 & 0.0073729823901137 & 0.0147459647802274 & 0.992627017609886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.831910067261135[/C][C]0.336179865477729[/C][C]0.168089932738864[/C][/ROW]
[ROW][C]11[/C][C]0.831306090968018[/C][C]0.337387818063965[/C][C]0.168693909031982[/C][/ROW]
[ROW][C]12[/C][C]0.832139793477843[/C][C]0.335720413044314[/C][C]0.167860206522157[/C][/ROW]
[ROW][C]13[/C][C]0.905982798322304[/C][C]0.188034403355391[/C][C]0.0940172016776957[/C][/ROW]
[ROW][C]14[/C][C]0.87479225985205[/C][C]0.2504154802959[/C][C]0.12520774014795[/C][/ROW]
[ROW][C]15[/C][C]0.821336011636488[/C][C]0.357327976727024[/C][C]0.178663988363512[/C][/ROW]
[ROW][C]16[/C][C]0.786155505261463[/C][C]0.427688989477073[/C][C]0.213844494738537[/C][/ROW]
[ROW][C]17[/C][C]0.772307160993796[/C][C]0.455385678012409[/C][C]0.227692839006204[/C][/ROW]
[ROW][C]18[/C][C]0.721728047292155[/C][C]0.55654390541569[/C][C]0.278271952707845[/C][/ROW]
[ROW][C]19[/C][C]0.657443930540386[/C][C]0.685112138919228[/C][C]0.342556069459614[/C][/ROW]
[ROW][C]20[/C][C]0.586748204770537[/C][C]0.826503590458927[/C][C]0.413251795229463[/C][/ROW]
[ROW][C]21[/C][C]0.512653025819394[/C][C]0.974693948361211[/C][C]0.487346974180606[/C][/ROW]
[ROW][C]22[/C][C]0.433083757057529[/C][C]0.866167514115058[/C][C]0.566916242942471[/C][/ROW]
[ROW][C]23[/C][C]0.66912286861259[/C][C]0.661754262774819[/C][C]0.330877131387409[/C][/ROW]
[ROW][C]24[/C][C]0.6364729622779[/C][C]0.727054075444201[/C][C]0.363527037722101[/C][/ROW]
[ROW][C]25[/C][C]0.572192169635247[/C][C]0.855615660729507[/C][C]0.427807830364753[/C][/ROW]
[ROW][C]26[/C][C]0.507336096561327[/C][C]0.985327806877345[/C][C]0.492663903438673[/C][/ROW]
[ROW][C]27[/C][C]0.522175379424799[/C][C]0.955649241150402[/C][C]0.477824620575201[/C][/ROW]
[ROW][C]28[/C][C]0.464840073321386[/C][C]0.929680146642771[/C][C]0.535159926678614[/C][/ROW]
[ROW][C]29[/C][C]0.399620797286401[/C][C]0.799241594572802[/C][C]0.600379202713599[/C][/ROW]
[ROW][C]30[/C][C]0.399359729921399[/C][C]0.798719459842799[/C][C]0.6006402700786[/C][/ROW]
[ROW][C]31[/C][C]0.338261434240252[/C][C]0.676522868480504[/C][C]0.661738565759748[/C][/ROW]
[ROW][C]32[/C][C]0.58897448658219[/C][C]0.82205102683562[/C][C]0.41102551341781[/C][/ROW]
[ROW][C]33[/C][C]0.823616269255729[/C][C]0.352767461488542[/C][C]0.176383730744271[/C][/ROW]
[ROW][C]34[/C][C]0.797314738229094[/C][C]0.405370523541812[/C][C]0.202685261770906[/C][/ROW]
[ROW][C]35[/C][C]0.755249835789373[/C][C]0.489500328421254[/C][C]0.244750164210627[/C][/ROW]
[ROW][C]36[/C][C]0.740494643777261[/C][C]0.519010712445478[/C][C]0.259505356222739[/C][/ROW]
[ROW][C]37[/C][C]0.693065438607915[/C][C]0.61386912278417[/C][C]0.306934561392085[/C][/ROW]
[ROW][C]38[/C][C]0.837278304334994[/C][C]0.325443391330012[/C][C]0.162721695665006[/C][/ROW]
[ROW][C]39[/C][C]0.845269432007387[/C][C]0.309461135985225[/C][C]0.154730567992613[/C][/ROW]
[ROW][C]40[/C][C]0.812714472832781[/C][C]0.374571054334437[/C][C]0.187285527167219[/C][/ROW]
[ROW][C]41[/C][C]0.774490361927260[/C][C]0.451019276145481[/C][C]0.225509638072740[/C][/ROW]
[ROW][C]42[/C][C]0.746840266011872[/C][C]0.506319467976255[/C][C]0.253159733988128[/C][/ROW]
[ROW][C]43[/C][C]0.716611213415592[/C][C]0.566777573168817[/C][C]0.283388786584408[/C][/ROW]
[ROW][C]44[/C][C]0.78022080689613[/C][C]0.439558386207739[/C][C]0.219779193103869[/C][/ROW]
[ROW][C]45[/C][C]0.765623968522375[/C][C]0.46875206295525[/C][C]0.234376031477625[/C][/ROW]
[ROW][C]46[/C][C]0.729209070293384[/C][C]0.541581859413231[/C][C]0.270790929706616[/C][/ROW]
[ROW][C]47[/C][C]0.696921831421757[/C][C]0.606156337156486[/C][C]0.303078168578243[/C][/ROW]
[ROW][C]48[/C][C]0.656366316572664[/C][C]0.687267366854673[/C][C]0.343633683427336[/C][/ROW]
[ROW][C]49[/C][C]0.616003633866088[/C][C]0.767992732267824[/C][C]0.383996366133912[/C][/ROW]
[ROW][C]50[/C][C]0.663891805849651[/C][C]0.672216388300697[/C][C]0.336108194150349[/C][/ROW]
[ROW][C]51[/C][C]0.652287333314966[/C][C]0.695425333370068[/C][C]0.347712666685034[/C][/ROW]
[ROW][C]52[/C][C]0.61801835313445[/C][C]0.7639632937311[/C][C]0.38198164686555[/C][/ROW]
[ROW][C]53[/C][C]0.58018869677934[/C][C]0.83962260644132[/C][C]0.41981130322066[/C][/ROW]
[ROW][C]54[/C][C]0.536987532023803[/C][C]0.926024935952394[/C][C]0.463012467976197[/C][/ROW]
[ROW][C]55[/C][C]0.944951332609293[/C][C]0.110097334781414[/C][C]0.0550486673907069[/C][/ROW]
[ROW][C]56[/C][C]0.931062678447182[/C][C]0.137874643105635[/C][C]0.0689373215528176[/C][/ROW]
[ROW][C]57[/C][C]0.916304645783391[/C][C]0.167390708433218[/C][C]0.0836953542166089[/C][/ROW]
[ROW][C]58[/C][C]0.914971451307935[/C][C]0.170057097384129[/C][C]0.0850285486920645[/C][/ROW]
[ROW][C]59[/C][C]0.89486433294034[/C][C]0.210271334119321[/C][C]0.105135667059660[/C][/ROW]
[ROW][C]60[/C][C]0.879358064515612[/C][C]0.241283870968776[/C][C]0.120641935484388[/C][/ROW]
[ROW][C]61[/C][C]0.873185940177256[/C][C]0.253628119645489[/C][C]0.126814059822744[/C][/ROW]
[ROW][C]62[/C][C]0.864709694424078[/C][C]0.270580611151844[/C][C]0.135290305575922[/C][/ROW]
[ROW][C]63[/C][C]0.845459377306958[/C][C]0.309081245386085[/C][C]0.154540622693042[/C][/ROW]
[ROW][C]64[/C][C]0.867372440841957[/C][C]0.265255118316087[/C][C]0.132627559158044[/C][/ROW]
[ROW][C]65[/C][C]0.860855840530292[/C][C]0.278288318939415[/C][C]0.139144159469708[/C][/ROW]
[ROW][C]66[/C][C]0.838493279580811[/C][C]0.323013440838378[/C][C]0.161506720419189[/C][/ROW]
[ROW][C]67[/C][C]0.821400673531826[/C][C]0.357198652936348[/C][C]0.178599326468174[/C][/ROW]
[ROW][C]68[/C][C]0.790480848550278[/C][C]0.419038302899444[/C][C]0.209519151449722[/C][/ROW]
[ROW][C]69[/C][C]0.759480333823619[/C][C]0.481039332352762[/C][C]0.240519666176381[/C][/ROW]
[ROW][C]70[/C][C]0.7288049028804[/C][C]0.5423901942392[/C][C]0.2711950971196[/C][/ROW]
[ROW][C]71[/C][C]0.688603590817711[/C][C]0.622792818364578[/C][C]0.311396409182289[/C][/ROW]
[ROW][C]72[/C][C]0.655110311412555[/C][C]0.68977937717489[/C][C]0.344889688587445[/C][/ROW]
[ROW][C]73[/C][C]0.613855619406048[/C][C]0.772288761187904[/C][C]0.386144380593952[/C][/ROW]
[ROW][C]74[/C][C]0.567946047024503[/C][C]0.864107905950994[/C][C]0.432053952975497[/C][/ROW]
[ROW][C]75[/C][C]0.586429776345993[/C][C]0.827140447308015[/C][C]0.413570223654007[/C][/ROW]
[ROW][C]76[/C][C]0.541426760640596[/C][C]0.917146478718808[/C][C]0.458573239359404[/C][/ROW]
[ROW][C]77[/C][C]0.547706391760991[/C][C]0.904587216478018[/C][C]0.452293608239009[/C][/ROW]
[ROW][C]78[/C][C]0.503135728359786[/C][C]0.993728543280427[/C][C]0.496864271640214[/C][/ROW]
[ROW][C]79[/C][C]0.480728201116366[/C][C]0.961456402232733[/C][C]0.519271798883634[/C][/ROW]
[ROW][C]80[/C][C]0.48832122472992[/C][C]0.97664244945984[/C][C]0.51167877527008[/C][/ROW]
[ROW][C]81[/C][C]0.469641431476857[/C][C]0.939282862953715[/C][C]0.530358568523142[/C][/ROW]
[ROW][C]82[/C][C]0.45884466121306[/C][C]0.91768932242612[/C][C]0.54115533878694[/C][/ROW]
[ROW][C]83[/C][C]0.413113229828574[/C][C]0.826226459657147[/C][C]0.586886770171426[/C][/ROW]
[ROW][C]84[/C][C]0.378329738353452[/C][C]0.756659476706904[/C][C]0.621670261646548[/C][/ROW]
[ROW][C]85[/C][C]0.342796945969138[/C][C]0.685593891938276[/C][C]0.657203054030862[/C][/ROW]
[ROW][C]86[/C][C]0.347797507577829[/C][C]0.695595015155658[/C][C]0.652202492422171[/C][/ROW]
[ROW][C]87[/C][C]0.352750511087353[/C][C]0.705501022174706[/C][C]0.647249488912647[/C][/ROW]
[ROW][C]88[/C][C]0.332637344238746[/C][C]0.665274688477492[/C][C]0.667362655761254[/C][/ROW]
[ROW][C]89[/C][C]0.446613281462112[/C][C]0.893226562924225[/C][C]0.553386718537888[/C][/ROW]
[ROW][C]90[/C][C]0.403276758574349[/C][C]0.806553517148698[/C][C]0.596723241425651[/C][/ROW]
[ROW][C]91[/C][C]0.363461349891206[/C][C]0.726922699782413[/C][C]0.636538650108794[/C][/ROW]
[ROW][C]92[/C][C]0.330894023603386[/C][C]0.661788047206772[/C][C]0.669105976396614[/C][/ROW]
[ROW][C]93[/C][C]0.301330686938495[/C][C]0.602661373876991[/C][C]0.698669313061505[/C][/ROW]
[ROW][C]94[/C][C]0.35247420459394[/C][C]0.70494840918788[/C][C]0.64752579540606[/C][/ROW]
[ROW][C]95[/C][C]0.417395204629504[/C][C]0.834790409259008[/C][C]0.582604795370496[/C][/ROW]
[ROW][C]96[/C][C]0.375162050724342[/C][C]0.750324101448684[/C][C]0.624837949275658[/C][/ROW]
[ROW][C]97[/C][C]0.330370778522082[/C][C]0.660741557044164[/C][C]0.669629221477918[/C][/ROW]
[ROW][C]98[/C][C]0.299019522442374[/C][C]0.598039044884747[/C][C]0.700980477557626[/C][/ROW]
[ROW][C]99[/C][C]0.259897677495876[/C][C]0.519795354991751[/C][C]0.740102322504125[/C][/ROW]
[ROW][C]100[/C][C]0.222645725104875[/C][C]0.445291450209749[/C][C]0.777354274895125[/C][/ROW]
[ROW][C]101[/C][C]0.192950624295937[/C][C]0.385901248591874[/C][C]0.807049375704063[/C][/ROW]
[ROW][C]102[/C][C]0.170923262407725[/C][C]0.341846524815449[/C][C]0.829076737592275[/C][/ROW]
[ROW][C]103[/C][C]0.152791359743201[/C][C]0.305582719486403[/C][C]0.847208640256799[/C][/ROW]
[ROW][C]104[/C][C]0.133322357299393[/C][C]0.266644714598786[/C][C]0.866677642700607[/C][/ROW]
[ROW][C]105[/C][C]0.161763853658237[/C][C]0.323527707316474[/C][C]0.838236146341763[/C][/ROW]
[ROW][C]106[/C][C]0.207774927670788[/C][C]0.415549855341576[/C][C]0.792225072329212[/C][/ROW]
[ROW][C]107[/C][C]0.175545563228339[/C][C]0.351091126456678[/C][C]0.82445443677166[/C][/ROW]
[ROW][C]108[/C][C]0.156077429518101[/C][C]0.312154859036203[/C][C]0.843922570481899[/C][/ROW]
[ROW][C]109[/C][C]0.136387527481929[/C][C]0.272775054963859[/C][C]0.86361247251807[/C][/ROW]
[ROW][C]110[/C][C]0.133862532654805[/C][C]0.26772506530961[/C][C]0.866137467345195[/C][/ROW]
[ROW][C]111[/C][C]0.109776908604995[/C][C]0.219553817209990[/C][C]0.890223091395005[/C][/ROW]
[ROW][C]112[/C][C]0.144199514342541[/C][C]0.288399028685083[/C][C]0.855800485657459[/C][/ROW]
[ROW][C]113[/C][C]0.271050959066605[/C][C]0.542101918133211[/C][C]0.728949040933395[/C][/ROW]
[ROW][C]114[/C][C]0.231577524464518[/C][C]0.463155048929036[/C][C]0.768422475535482[/C][/ROW]
[ROW][C]115[/C][C]0.585287614101031[/C][C]0.829424771797938[/C][C]0.414712385898969[/C][/ROW]
[ROW][C]116[/C][C]0.57409785288531[/C][C]0.85180429422938[/C][C]0.42590214711469[/C][/ROW]
[ROW][C]117[/C][C]0.570648370020571[/C][C]0.858703259958859[/C][C]0.429351629979429[/C][/ROW]
[ROW][C]118[/C][C]0.51980993355304[/C][C]0.96038013289392[/C][C]0.48019006644696[/C][/ROW]
[ROW][C]119[/C][C]0.464720289969902[/C][C]0.929440579939805[/C][C]0.535279710030098[/C][/ROW]
[ROW][C]120[/C][C]0.551503822493342[/C][C]0.896992355013316[/C][C]0.448496177506658[/C][/ROW]
[ROW][C]121[/C][C]0.522527800232345[/C][C]0.954944399535311[/C][C]0.477472199767655[/C][/ROW]
[ROW][C]122[/C][C]0.537047087661633[/C][C]0.925905824676733[/C][C]0.462952912338367[/C][/ROW]
[ROW][C]123[/C][C]0.488801319895305[/C][C]0.97760263979061[/C][C]0.511198680104695[/C][/ROW]
[ROW][C]124[/C][C]0.497346814413719[/C][C]0.994693628827438[/C][C]0.502653185586281[/C][/ROW]
[ROW][C]125[/C][C]0.438777000174339[/C][C]0.877554000348677[/C][C]0.561222999825661[/C][/ROW]
[ROW][C]126[/C][C]0.443977397344226[/C][C]0.887954794688452[/C][C]0.556022602655774[/C][/ROW]
[ROW][C]127[/C][C]0.434071417352019[/C][C]0.868142834704038[/C][C]0.565928582647981[/C][/ROW]
[ROW][C]128[/C][C]0.397735441273798[/C][C]0.795470882547595[/C][C]0.602264558726202[/C][/ROW]
[ROW][C]129[/C][C]0.35664630810253[/C][C]0.71329261620506[/C][C]0.64335369189747[/C][/ROW]
[ROW][C]130[/C][C]0.308346182184952[/C][C]0.616692364369904[/C][C]0.691653817815048[/C][/ROW]
[ROW][C]131[/C][C]0.299021869892456[/C][C]0.598043739784912[/C][C]0.700978130107544[/C][/ROW]
[ROW][C]132[/C][C]0.247113544556771[/C][C]0.494227089113542[/C][C]0.752886455443229[/C][/ROW]
[ROW][C]133[/C][C]0.203676810053944[/C][C]0.407353620107888[/C][C]0.796323189946056[/C][/ROW]
[ROW][C]134[/C][C]0.171957287465722[/C][C]0.343914574931444[/C][C]0.828042712534278[/C][/ROW]
[ROW][C]135[/C][C]0.134942577184651[/C][C]0.269885154369302[/C][C]0.865057422815349[/C][/ROW]
[ROW][C]136[/C][C]0.127662529011725[/C][C]0.255325058023449[/C][C]0.872337470988276[/C][/ROW]
[ROW][C]137[/C][C]0.168974002130001[/C][C]0.337948004260003[/C][C]0.831025997869999[/C][/ROW]
[ROW][C]138[/C][C]0.175454423186929[/C][C]0.350908846373858[/C][C]0.82454557681307[/C][/ROW]
[ROW][C]139[/C][C]0.156449812570418[/C][C]0.312899625140837[/C][C]0.843550187429582[/C][/ROW]
[ROW][C]140[/C][C]0.188128559189593[/C][C]0.376257118379185[/C][C]0.811871440810407[/C][/ROW]
[ROW][C]141[/C][C]0.138596567420454[/C][C]0.277193134840908[/C][C]0.861403432579546[/C][/ROW]
[ROW][C]142[/C][C]0.10448783273871[/C][C]0.20897566547742[/C][C]0.89551216726129[/C][/ROW]
[ROW][C]143[/C][C]0.168616912170835[/C][C]0.33723382434167[/C][C]0.831383087829165[/C][/ROW]
[ROW][C]144[/C][C]0.118995963353294[/C][C]0.237991926706588[/C][C]0.881004036646706[/C][/ROW]
[ROW][C]145[/C][C]0.0914086132633148[/C][C]0.182817226526630[/C][C]0.908591386736685[/C][/ROW]
[ROW][C]146[/C][C]0.0578852639306328[/C][C]0.115770527861266[/C][C]0.942114736069367[/C][/ROW]
[ROW][C]147[/C][C]0.033795603200056[/C][C]0.067591206400112[/C][C]0.966204396799944[/C][/ROW]
[ROW][C]148[/C][C]0.0167448987067478[/C][C]0.0334897974134957[/C][C]0.983255101293252[/C][/ROW]
[ROW][C]149[/C][C]0.0073729823901137[/C][C]0.0147459647802274[/C][C]0.992627017609886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8319100672611350.3361798654777290.168089932738864
110.8313060909680180.3373878180639650.168693909031982
120.8321397934778430.3357204130443140.167860206522157
130.9059827983223040.1880344033553910.0940172016776957
140.874792259852050.25041548029590.12520774014795
150.8213360116364880.3573279767270240.178663988363512
160.7861555052614630.4276889894770730.213844494738537
170.7723071609937960.4553856780124090.227692839006204
180.7217280472921550.556543905415690.278271952707845
190.6574439305403860.6851121389192280.342556069459614
200.5867482047705370.8265035904589270.413251795229463
210.5126530258193940.9746939483612110.487346974180606
220.4330837570575290.8661675141150580.566916242942471
230.669122868612590.6617542627748190.330877131387409
240.63647296227790.7270540754442010.363527037722101
250.5721921696352470.8556156607295070.427807830364753
260.5073360965613270.9853278068773450.492663903438673
270.5221753794247990.9556492411504020.477824620575201
280.4648400733213860.9296801466427710.535159926678614
290.3996207972864010.7992415945728020.600379202713599
300.3993597299213990.7987194598427990.6006402700786
310.3382614342402520.6765228684805040.661738565759748
320.588974486582190.822051026835620.41102551341781
330.8236162692557290.3527674614885420.176383730744271
340.7973147382290940.4053705235418120.202685261770906
350.7552498357893730.4895003284212540.244750164210627
360.7404946437772610.5190107124454780.259505356222739
370.6930654386079150.613869122784170.306934561392085
380.8372783043349940.3254433913300120.162721695665006
390.8452694320073870.3094611359852250.154730567992613
400.8127144728327810.3745710543344370.187285527167219
410.7744903619272600.4510192761454810.225509638072740
420.7468402660118720.5063194679762550.253159733988128
430.7166112134155920.5667775731688170.283388786584408
440.780220806896130.4395583862077390.219779193103869
450.7656239685223750.468752062955250.234376031477625
460.7292090702933840.5415818594132310.270790929706616
470.6969218314217570.6061563371564860.303078168578243
480.6563663165726640.6872673668546730.343633683427336
490.6160036338660880.7679927322678240.383996366133912
500.6638918058496510.6722163883006970.336108194150349
510.6522873333149660.6954253333700680.347712666685034
520.618018353134450.76396329373110.38198164686555
530.580188696779340.839622606441320.41981130322066
540.5369875320238030.9260249359523940.463012467976197
550.9449513326092930.1100973347814140.0550486673907069
560.9310626784471820.1378746431056350.0689373215528176
570.9163046457833910.1673907084332180.0836953542166089
580.9149714513079350.1700570973841290.0850285486920645
590.894864332940340.2102713341193210.105135667059660
600.8793580645156120.2412838709687760.120641935484388
610.8731859401772560.2536281196454890.126814059822744
620.8647096944240780.2705806111518440.135290305575922
630.8454593773069580.3090812453860850.154540622693042
640.8673724408419570.2652551183160870.132627559158044
650.8608558405302920.2782883189394150.139144159469708
660.8384932795808110.3230134408383780.161506720419189
670.8214006735318260.3571986529363480.178599326468174
680.7904808485502780.4190383028994440.209519151449722
690.7594803338236190.4810393323527620.240519666176381
700.72880490288040.54239019423920.2711950971196
710.6886035908177110.6227928183645780.311396409182289
720.6551103114125550.689779377174890.344889688587445
730.6138556194060480.7722887611879040.386144380593952
740.5679460470245030.8641079059509940.432053952975497
750.5864297763459930.8271404473080150.413570223654007
760.5414267606405960.9171464787188080.458573239359404
770.5477063917609910.9045872164780180.452293608239009
780.5031357283597860.9937285432804270.496864271640214
790.4807282011163660.9614564022327330.519271798883634
800.488321224729920.976642449459840.51167877527008
810.4696414314768570.9392828629537150.530358568523142
820.458844661213060.917689322426120.54115533878694
830.4131132298285740.8262264596571470.586886770171426
840.3783297383534520.7566594767069040.621670261646548
850.3427969459691380.6855938919382760.657203054030862
860.3477975075778290.6955950151556580.652202492422171
870.3527505110873530.7055010221747060.647249488912647
880.3326373442387460.6652746884774920.667362655761254
890.4466132814621120.8932265629242250.553386718537888
900.4032767585743490.8065535171486980.596723241425651
910.3634613498912060.7269226997824130.636538650108794
920.3308940236033860.6617880472067720.669105976396614
930.3013306869384950.6026613738769910.698669313061505
940.352474204593940.704948409187880.64752579540606
950.4173952046295040.8347904092590080.582604795370496
960.3751620507243420.7503241014486840.624837949275658
970.3303707785220820.6607415570441640.669629221477918
980.2990195224423740.5980390448847470.700980477557626
990.2598976774958760.5197953549917510.740102322504125
1000.2226457251048750.4452914502097490.777354274895125
1010.1929506242959370.3859012485918740.807049375704063
1020.1709232624077250.3418465248154490.829076737592275
1030.1527913597432010.3055827194864030.847208640256799
1040.1333223572993930.2666447145987860.866677642700607
1050.1617638536582370.3235277073164740.838236146341763
1060.2077749276707880.4155498553415760.792225072329212
1070.1755455632283390.3510911264566780.82445443677166
1080.1560774295181010.3121548590362030.843922570481899
1090.1363875274819290.2727750549638590.86361247251807
1100.1338625326548050.267725065309610.866137467345195
1110.1097769086049950.2195538172099900.890223091395005
1120.1441995143425410.2883990286850830.855800485657459
1130.2710509590666050.5421019181332110.728949040933395
1140.2315775244645180.4631550489290360.768422475535482
1150.5852876141010310.8294247717979380.414712385898969
1160.574097852885310.851804294229380.42590214711469
1170.5706483700205710.8587032599588590.429351629979429
1180.519809933553040.960380132893920.48019006644696
1190.4647202899699020.9294405799398050.535279710030098
1200.5515038224933420.8969923550133160.448496177506658
1210.5225278002323450.9549443995353110.477472199767655
1220.5370470876616330.9259058246767330.462952912338367
1230.4888013198953050.977602639790610.511198680104695
1240.4973468144137190.9946936288274380.502653185586281
1250.4387770001743390.8775540003486770.561222999825661
1260.4439773973442260.8879547946884520.556022602655774
1270.4340714173520190.8681428347040380.565928582647981
1280.3977354412737980.7954708825475950.602264558726202
1290.356646308102530.713292616205060.64335369189747
1300.3083461821849520.6166923643699040.691653817815048
1310.2990218698924560.5980437397849120.700978130107544
1320.2471135445567710.4942270891135420.752886455443229
1330.2036768100539440.4073536201078880.796323189946056
1340.1719572874657220.3439145749314440.828042712534278
1350.1349425771846510.2698851543693020.865057422815349
1360.1276625290117250.2553250580234490.872337470988276
1370.1689740021300010.3379480042600030.831025997869999
1380.1754544231869290.3509088463738580.82454557681307
1390.1564498125704180.3128996251408370.843550187429582
1400.1881285591895930.3762571183791850.811871440810407
1410.1385965674204540.2771931348409080.861403432579546
1420.104487832738710.208975665477420.89551216726129
1430.1686169121708350.337233824341670.831383087829165
1440.1189959633532940.2379919267065880.881004036646706
1450.09140861326331480.1828172265266300.908591386736685
1460.05788526393063280.1157705278612660.942114736069367
1470.0337956032000560.0675912064001120.966204396799944
1480.01674489870674780.03348979741349570.983255101293252
1490.00737298239011370.01474596478022740.992627017609886






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0142857142857143OK
10% type I error level30.0214285714285714OK

\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 & 0.0142857142857143 & OK \tabularnewline
10% type I error level & 3 & 0.0214285714285714 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116175&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]0.0142857142857143[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0214285714285714[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116175&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116175&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 level20.0142857142857143OK
10% type I error level30.0214285714285714OK


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 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}