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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 13:05:41 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322157993eicd57qyur0fohp.htm/, Retrieved Fri, 29 Mar 2024 10:18:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147125, Retrieved Fri, 29 Mar 2024 10:18:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [data ws 6, connec...] [2011-11-24 18:05:41] [5c55e7d277583a4a66c326a86fdb470e] [Current]
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Dataseries X:
41	38
39	32
30	35
31	33
34	37
35	29
39	31
34	36
36	35
37	38
38	31
36	34
38	35
39	38
33	37
32	33
36	32
38	38
39	38
32	32
32	33
31	31
39	38
37	39
39	32
41	32
36	35
33	37
33	33
34	33
31	28
27	32
37	31
34	37
34	30
32	33
29	31
36	33
29	31
35	33
37	32
34	33
38	32
35	33
38	28
37	35
38	39
33	34
36	38
38	32
32	38
32	30
32	33
34	38
32	32
37	32
39	34
29	34
37	36
35	34
30	28
38	34
34	35
31	35
34	31
35	37
36	35
30	27
39	40
35	37
38	36
31	38
34	39
38	41
34	27
39	30
37	37
34	31
28	31
37	27
33	36
37	38
35	37
37	33
32	34
33	31
38	39
33	34
29	32
33	33
31	36
36	32
35	41
32	28
29	30
39	36
37	35
35	31
37	34
32	36
38	36
37	35
36	37
32	28
33	39
40	32
38	35
41	39
36	35
43	42
30	34
31	33
32	41
32	33
37	34
37	32
33	40
34	40
33	35
38	36
33	37
31	27
38	39
37	38
33	31
31	33
39	32
44	39
33	36
35	33
32	33
28	32
40	37
27	30
37	38
32	29
28	22
34	35
30	35
35	34
31	35
32	34
30	34
30	35
31	23
40	31
32	27
36	36
32	31
35	32
38	39
42	37
34	38
35	39
35	34
33	31
36	32
32	37
33	36
34	32
32	35
34	36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=0

As an alternative you can also use a 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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Separate[t] = + 20.676735787394 + 0.387478838017858Connected[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Separate[t] =  +  20.676735787394 +  0.387478838017858Connected[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Separate[t] =  +  20.676735787394 +  0.387478838017858Connected[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Separate[t] = + 20.676735787394 + 0.387478838017858Connected[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)20.6767357873942.6911717.683200
Connected0.3874788380178580.0773625.00861e-061e-06

\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) & 20.676735787394 & 2.691171 & 7.6832 & 0 & 0 \tabularnewline
Connected & 0.387478838017858 & 0.077362 & 5.0086 & 1e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&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]20.676735787394[/C][C]2.691171[/C][C]7.6832[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.387478838017858[/C][C]0.077362[/C][C]5.0086[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=2

As an alternative you can also use a 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)20.6767357873942.6911717.683200
Connected0.3874788380178580.0773625.00861e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.368155267777861
R-squared0.135538301192589
Adjusted R-squared0.130135415575042
F-TEST (value)25.0862799598083
F-TEST (DF numerator)1
F-TEST (DF denominator)160
p-value1.43595671986407e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31308956303169
Sum Squared Residuals1756.24999242712

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.368155267777861 \tabularnewline
R-squared & 0.135538301192589 \tabularnewline
Adjusted R-squared & 0.130135415575042 \tabularnewline
F-TEST (value) & 25.0862799598083 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 1.43595671986407e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.31308956303169 \tabularnewline
Sum Squared Residuals & 1756.24999242712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.368155267777861[/C][/ROW]
[ROW][C]R-squared[/C][C]0.135538301192589[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.130135415575042[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.0862799598083[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]1.43595671986407e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.31308956303169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1756.24999242712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.368155267777861
R-squared0.135538301192589
Adjusted R-squared0.130135415575042
F-TEST (value)25.0862799598083
F-TEST (DF numerator)1
F-TEST (DF denominator)160
p-value1.43595671986407e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.31308956303169
Sum Squared Residuals1756.24999242712







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13836.56336814612631.43663185387373
23235.7884104700905-3.78841047009051
33532.30110092792982.69889907207022
43332.68857976594760.311420234052363
53733.85101628000123.14898371999879
62934.2384951180191-5.23849511801907
73135.7884104700905-4.7884104700905
83633.85101628000122.14898371999879
93534.62597395603690.374026043963072
103835.01345279405482.98654720594521
113135.4009316320726-4.40093163207264
123434.6259739560369-0.625973956036928
133535.4009316320726-0.400931632072644
143835.78841047009052.2115895299095
153733.46353744198343.53646255801665
163333.0760586039655-0.0760586039654949
173234.6259739560369-2.62597395603693
183835.40093163207262.59906836792736
193835.78841047009052.2115895299095
203233.0760586039655-1.0760586039655
213333.0760586039655-0.0760586039654949
223132.6885797659476-1.68857976594764
233835.78841047009052.2115895299095
243935.01345279405483.98654720594521
253235.7884104700905-3.7884104700905
263236.5633681461262-4.56336814612622
273534.62597395603690.374026043963072
283733.46353744198343.53646255801665
293333.4635374419834-0.463537441983353
303333.8510162800012-0.851016280001211
312832.6885797659476-4.68857976594764
323231.13866441387620.861335586123796
333135.0134527940548-4.01345279405479
343733.85101628000123.14898371999879
353033.8510162800012-3.85101628000121
363333.0760586039655-0.0760586039654949
373131.9136220899119-0.913622089911921
383334.6259739560369-1.62597395603693
393131.9136220899119-0.913622089911921
403334.2384951180191-1.23849511801907
413235.0134527940548-3.01345279405479
423333.8510162800012-0.851016280001211
433235.4009316320726-3.40093163207264
443334.2384951180191-1.23849511801907
452835.4009316320726-7.40093163207264
463535.0134527940548-0.0134527940547862
473935.40093163207263.59906836792736
483433.46353744198340.536462558016647
493834.62597395603693.37402604396307
503235.4009316320726-3.40093163207264
513833.07605860396554.92394139603451
523033.0760586039655-3.07605860396549
533333.0760586039655-0.0760586039654949
543833.85101628000124.14898371999879
553233.0760586039655-1.0760586039655
563235.0134527940548-3.01345279405479
573435.7884104700905-1.7884104700905
583431.91362208991192.08637791008808
593635.01345279405480.986547205945214
603434.2384951180191-0.23849511801907
612832.3011009279298-4.30110092792978
623435.4009316320726-1.40093163207264
633533.85101628000121.14898371999879
643532.68857976594762.31142023405236
653133.8510162800012-2.85101628000121
663734.23849511801912.76150488198093
673534.62597395603690.374026043963072
682732.3011009279298-5.30110092792978
694035.78841047009054.2115895299095
703734.23849511801912.76150488198093
713635.40093163207260.599068367927356
723832.68857976594765.31142023405236
733933.85101628000125.14898371999879
744135.40093163207265.59906836792736
752733.8510162800012-6.85101628000121
763035.7884104700905-5.7884104700905
773735.01345279405481.98654720594521
783133.8510162800012-2.85101628000121
793131.5261432518941-0.526143251894063
802735.0134527940548-8.01345279405479
813633.46353744198342.53646255801665
823835.01345279405482.98654720594521
833734.23849511801912.76150488198093
843335.0134527940548-2.01345279405479
853433.07605860396550.923941396034505
863133.4635374419834-2.46353744198335
873935.40093163207263.59906836792736
883433.46353744198340.536462558016647
893231.91362208991190.0863779100880792
903333.4635374419834-0.463537441983353
913632.68857976594763.31142023405236
923234.6259739560369-2.62597395603693
934134.23849511801916.76150488198093
942833.0760586039655-5.07605860396549
953031.9136220899119-1.91362208991192
963635.78841047009050.211589529909498
973535.0134527940548-0.0134527940547862
983134.2384951180191-3.23849511801907
993435.0134527940548-1.01345279405479
1003633.07605860396552.9239413960345
1013635.40093163207260.599068367927356
1023535.0134527940548-0.0134527940547862
1033734.62597395603692.37402604396307
1042833.0760586039655-5.07605860396549
1053933.46353744198345.53646255801665
1063236.1758893081084-4.17588930810836
1073535.4009316320726-0.400931632072644
1083936.56336814612622.43663185387378
1093534.62597395603690.374026043963072
1104237.33832582216194.66167417783806
1113432.30110092792981.69889907207022
1123332.68857976594760.311420234052363
1134133.07605860396557.92394139603451
1143333.0760586039655-0.0760586039654949
1153435.0134527940548-1.01345279405479
1163235.0134527940548-3.01345279405479
1174033.46353744198346.53646255801665
1184033.85101628000126.14898371999879
1193533.46353744198341.53646255801665
1203635.40093163207260.599068367927356
1213733.46353744198343.53646255801665
1222732.6885797659476-5.68857976594764
1233935.40093163207263.59906836792736
1243835.01345279405482.98654720594521
1253133.4635374419834-2.46353744198335
1263332.68857976594760.311420234052363
1273235.7884104700905-3.7884104700905
1283937.72580466017981.27419533982021
1293633.46353744198342.53646255801665
1303334.2384951180191-1.23849511801907
1313333.0760586039655-0.0760586039654949
1323231.52614325189410.473856748105937
1333736.17588930810840.824110691891639
1343031.1386644138762-1.1386644138762
1353835.01345279405482.98654720594521
1362933.0760586039655-4.07605860396549
1372231.5261432518941-9.52614325189406
1383533.85101628000121.14898371999879
1393532.30110092792982.69889907207022
1403434.2384951180191-0.23849511801907
1413532.68857976594762.31142023405236
1423433.07605860396550.923941396034505
1433432.30110092792981.69889907207022
1443532.30110092792982.69889907207022
1452332.6885797659476-9.68857976594764
1463136.1758893081084-5.17588930810836
1472733.0760586039655-6.07605860396549
1483634.62597395603691.37402604396307
1493133.0760586039655-2.0760586039655
1503234.2384951180191-2.23849511801907
1513935.40093163207263.59906836792736
1523736.95084698414410.0491530158559226
1533833.85101628000124.14898371999879
1543934.23849511801914.76150488198093
1553434.2384951180191-0.23849511801907
1563133.4635374419834-2.46353744198335
1573234.6259739560369-2.62597395603693
1583733.07605860396553.9239413960345
1593633.46353744198342.53646255801665
1603233.8510162800012-1.85101628000121
1613533.07605860396551.92394139603451
1623633.85101628000122.14898371999879

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 38 & 36.5633681461263 & 1.43663185387373 \tabularnewline
2 & 32 & 35.7884104700905 & -3.78841047009051 \tabularnewline
3 & 35 & 32.3011009279298 & 2.69889907207022 \tabularnewline
4 & 33 & 32.6885797659476 & 0.311420234052363 \tabularnewline
5 & 37 & 33.8510162800012 & 3.14898371999879 \tabularnewline
6 & 29 & 34.2384951180191 & -5.23849511801907 \tabularnewline
7 & 31 & 35.7884104700905 & -4.7884104700905 \tabularnewline
8 & 36 & 33.8510162800012 & 2.14898371999879 \tabularnewline
9 & 35 & 34.6259739560369 & 0.374026043963072 \tabularnewline
10 & 38 & 35.0134527940548 & 2.98654720594521 \tabularnewline
11 & 31 & 35.4009316320726 & -4.40093163207264 \tabularnewline
12 & 34 & 34.6259739560369 & -0.625973956036928 \tabularnewline
13 & 35 & 35.4009316320726 & -0.400931632072644 \tabularnewline
14 & 38 & 35.7884104700905 & 2.2115895299095 \tabularnewline
15 & 37 & 33.4635374419834 & 3.53646255801665 \tabularnewline
16 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
17 & 32 & 34.6259739560369 & -2.62597395603693 \tabularnewline
18 & 38 & 35.4009316320726 & 2.59906836792736 \tabularnewline
19 & 38 & 35.7884104700905 & 2.2115895299095 \tabularnewline
20 & 32 & 33.0760586039655 & -1.0760586039655 \tabularnewline
21 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
22 & 31 & 32.6885797659476 & -1.68857976594764 \tabularnewline
23 & 38 & 35.7884104700905 & 2.2115895299095 \tabularnewline
24 & 39 & 35.0134527940548 & 3.98654720594521 \tabularnewline
25 & 32 & 35.7884104700905 & -3.7884104700905 \tabularnewline
26 & 32 & 36.5633681461262 & -4.56336814612622 \tabularnewline
27 & 35 & 34.6259739560369 & 0.374026043963072 \tabularnewline
28 & 37 & 33.4635374419834 & 3.53646255801665 \tabularnewline
29 & 33 & 33.4635374419834 & -0.463537441983353 \tabularnewline
30 & 33 & 33.8510162800012 & -0.851016280001211 \tabularnewline
31 & 28 & 32.6885797659476 & -4.68857976594764 \tabularnewline
32 & 32 & 31.1386644138762 & 0.861335586123796 \tabularnewline
33 & 31 & 35.0134527940548 & -4.01345279405479 \tabularnewline
34 & 37 & 33.8510162800012 & 3.14898371999879 \tabularnewline
35 & 30 & 33.8510162800012 & -3.85101628000121 \tabularnewline
36 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
37 & 31 & 31.9136220899119 & -0.913622089911921 \tabularnewline
38 & 33 & 34.6259739560369 & -1.62597395603693 \tabularnewline
39 & 31 & 31.9136220899119 & -0.913622089911921 \tabularnewline
40 & 33 & 34.2384951180191 & -1.23849511801907 \tabularnewline
41 & 32 & 35.0134527940548 & -3.01345279405479 \tabularnewline
42 & 33 & 33.8510162800012 & -0.851016280001211 \tabularnewline
43 & 32 & 35.4009316320726 & -3.40093163207264 \tabularnewline
44 & 33 & 34.2384951180191 & -1.23849511801907 \tabularnewline
45 & 28 & 35.4009316320726 & -7.40093163207264 \tabularnewline
46 & 35 & 35.0134527940548 & -0.0134527940547862 \tabularnewline
47 & 39 & 35.4009316320726 & 3.59906836792736 \tabularnewline
48 & 34 & 33.4635374419834 & 0.536462558016647 \tabularnewline
49 & 38 & 34.6259739560369 & 3.37402604396307 \tabularnewline
50 & 32 & 35.4009316320726 & -3.40093163207264 \tabularnewline
51 & 38 & 33.0760586039655 & 4.92394139603451 \tabularnewline
52 & 30 & 33.0760586039655 & -3.07605860396549 \tabularnewline
53 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
54 & 38 & 33.8510162800012 & 4.14898371999879 \tabularnewline
55 & 32 & 33.0760586039655 & -1.0760586039655 \tabularnewline
56 & 32 & 35.0134527940548 & -3.01345279405479 \tabularnewline
57 & 34 & 35.7884104700905 & -1.7884104700905 \tabularnewline
58 & 34 & 31.9136220899119 & 2.08637791008808 \tabularnewline
59 & 36 & 35.0134527940548 & 0.986547205945214 \tabularnewline
60 & 34 & 34.2384951180191 & -0.23849511801907 \tabularnewline
61 & 28 & 32.3011009279298 & -4.30110092792978 \tabularnewline
62 & 34 & 35.4009316320726 & -1.40093163207264 \tabularnewline
63 & 35 & 33.8510162800012 & 1.14898371999879 \tabularnewline
64 & 35 & 32.6885797659476 & 2.31142023405236 \tabularnewline
65 & 31 & 33.8510162800012 & -2.85101628000121 \tabularnewline
66 & 37 & 34.2384951180191 & 2.76150488198093 \tabularnewline
67 & 35 & 34.6259739560369 & 0.374026043963072 \tabularnewline
68 & 27 & 32.3011009279298 & -5.30110092792978 \tabularnewline
69 & 40 & 35.7884104700905 & 4.2115895299095 \tabularnewline
70 & 37 & 34.2384951180191 & 2.76150488198093 \tabularnewline
71 & 36 & 35.4009316320726 & 0.599068367927356 \tabularnewline
72 & 38 & 32.6885797659476 & 5.31142023405236 \tabularnewline
73 & 39 & 33.8510162800012 & 5.14898371999879 \tabularnewline
74 & 41 & 35.4009316320726 & 5.59906836792736 \tabularnewline
75 & 27 & 33.8510162800012 & -6.85101628000121 \tabularnewline
76 & 30 & 35.7884104700905 & -5.7884104700905 \tabularnewline
77 & 37 & 35.0134527940548 & 1.98654720594521 \tabularnewline
78 & 31 & 33.8510162800012 & -2.85101628000121 \tabularnewline
79 & 31 & 31.5261432518941 & -0.526143251894063 \tabularnewline
80 & 27 & 35.0134527940548 & -8.01345279405479 \tabularnewline
81 & 36 & 33.4635374419834 & 2.53646255801665 \tabularnewline
82 & 38 & 35.0134527940548 & 2.98654720594521 \tabularnewline
83 & 37 & 34.2384951180191 & 2.76150488198093 \tabularnewline
84 & 33 & 35.0134527940548 & -2.01345279405479 \tabularnewline
85 & 34 & 33.0760586039655 & 0.923941396034505 \tabularnewline
86 & 31 & 33.4635374419834 & -2.46353744198335 \tabularnewline
87 & 39 & 35.4009316320726 & 3.59906836792736 \tabularnewline
88 & 34 & 33.4635374419834 & 0.536462558016647 \tabularnewline
89 & 32 & 31.9136220899119 & 0.0863779100880792 \tabularnewline
90 & 33 & 33.4635374419834 & -0.463537441983353 \tabularnewline
91 & 36 & 32.6885797659476 & 3.31142023405236 \tabularnewline
92 & 32 & 34.6259739560369 & -2.62597395603693 \tabularnewline
93 & 41 & 34.2384951180191 & 6.76150488198093 \tabularnewline
94 & 28 & 33.0760586039655 & -5.07605860396549 \tabularnewline
95 & 30 & 31.9136220899119 & -1.91362208991192 \tabularnewline
96 & 36 & 35.7884104700905 & 0.211589529909498 \tabularnewline
97 & 35 & 35.0134527940548 & -0.0134527940547862 \tabularnewline
98 & 31 & 34.2384951180191 & -3.23849511801907 \tabularnewline
99 & 34 & 35.0134527940548 & -1.01345279405479 \tabularnewline
100 & 36 & 33.0760586039655 & 2.9239413960345 \tabularnewline
101 & 36 & 35.4009316320726 & 0.599068367927356 \tabularnewline
102 & 35 & 35.0134527940548 & -0.0134527940547862 \tabularnewline
103 & 37 & 34.6259739560369 & 2.37402604396307 \tabularnewline
104 & 28 & 33.0760586039655 & -5.07605860396549 \tabularnewline
105 & 39 & 33.4635374419834 & 5.53646255801665 \tabularnewline
106 & 32 & 36.1758893081084 & -4.17588930810836 \tabularnewline
107 & 35 & 35.4009316320726 & -0.400931632072644 \tabularnewline
108 & 39 & 36.5633681461262 & 2.43663185387378 \tabularnewline
109 & 35 & 34.6259739560369 & 0.374026043963072 \tabularnewline
110 & 42 & 37.3383258221619 & 4.66167417783806 \tabularnewline
111 & 34 & 32.3011009279298 & 1.69889907207022 \tabularnewline
112 & 33 & 32.6885797659476 & 0.311420234052363 \tabularnewline
113 & 41 & 33.0760586039655 & 7.92394139603451 \tabularnewline
114 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
115 & 34 & 35.0134527940548 & -1.01345279405479 \tabularnewline
116 & 32 & 35.0134527940548 & -3.01345279405479 \tabularnewline
117 & 40 & 33.4635374419834 & 6.53646255801665 \tabularnewline
118 & 40 & 33.8510162800012 & 6.14898371999879 \tabularnewline
119 & 35 & 33.4635374419834 & 1.53646255801665 \tabularnewline
120 & 36 & 35.4009316320726 & 0.599068367927356 \tabularnewline
121 & 37 & 33.4635374419834 & 3.53646255801665 \tabularnewline
122 & 27 & 32.6885797659476 & -5.68857976594764 \tabularnewline
123 & 39 & 35.4009316320726 & 3.59906836792736 \tabularnewline
124 & 38 & 35.0134527940548 & 2.98654720594521 \tabularnewline
125 & 31 & 33.4635374419834 & -2.46353744198335 \tabularnewline
126 & 33 & 32.6885797659476 & 0.311420234052363 \tabularnewline
127 & 32 & 35.7884104700905 & -3.7884104700905 \tabularnewline
128 & 39 & 37.7258046601798 & 1.27419533982021 \tabularnewline
129 & 36 & 33.4635374419834 & 2.53646255801665 \tabularnewline
130 & 33 & 34.2384951180191 & -1.23849511801907 \tabularnewline
131 & 33 & 33.0760586039655 & -0.0760586039654949 \tabularnewline
132 & 32 & 31.5261432518941 & 0.473856748105937 \tabularnewline
133 & 37 & 36.1758893081084 & 0.824110691891639 \tabularnewline
134 & 30 & 31.1386644138762 & -1.1386644138762 \tabularnewline
135 & 38 & 35.0134527940548 & 2.98654720594521 \tabularnewline
136 & 29 & 33.0760586039655 & -4.07605860396549 \tabularnewline
137 & 22 & 31.5261432518941 & -9.52614325189406 \tabularnewline
138 & 35 & 33.8510162800012 & 1.14898371999879 \tabularnewline
139 & 35 & 32.3011009279298 & 2.69889907207022 \tabularnewline
140 & 34 & 34.2384951180191 & -0.23849511801907 \tabularnewline
141 & 35 & 32.6885797659476 & 2.31142023405236 \tabularnewline
142 & 34 & 33.0760586039655 & 0.923941396034505 \tabularnewline
143 & 34 & 32.3011009279298 & 1.69889907207022 \tabularnewline
144 & 35 & 32.3011009279298 & 2.69889907207022 \tabularnewline
145 & 23 & 32.6885797659476 & -9.68857976594764 \tabularnewline
146 & 31 & 36.1758893081084 & -5.17588930810836 \tabularnewline
147 & 27 & 33.0760586039655 & -6.07605860396549 \tabularnewline
148 & 36 & 34.6259739560369 & 1.37402604396307 \tabularnewline
149 & 31 & 33.0760586039655 & -2.0760586039655 \tabularnewline
150 & 32 & 34.2384951180191 & -2.23849511801907 \tabularnewline
151 & 39 & 35.4009316320726 & 3.59906836792736 \tabularnewline
152 & 37 & 36.9508469841441 & 0.0491530158559226 \tabularnewline
153 & 38 & 33.8510162800012 & 4.14898371999879 \tabularnewline
154 & 39 & 34.2384951180191 & 4.76150488198093 \tabularnewline
155 & 34 & 34.2384951180191 & -0.23849511801907 \tabularnewline
156 & 31 & 33.4635374419834 & -2.46353744198335 \tabularnewline
157 & 32 & 34.6259739560369 & -2.62597395603693 \tabularnewline
158 & 37 & 33.0760586039655 & 3.9239413960345 \tabularnewline
159 & 36 & 33.4635374419834 & 2.53646255801665 \tabularnewline
160 & 32 & 33.8510162800012 & -1.85101628000121 \tabularnewline
161 & 35 & 33.0760586039655 & 1.92394139603451 \tabularnewline
162 & 36 & 33.8510162800012 & 2.14898371999879 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&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]38[/C][C]36.5633681461263[/C][C]1.43663185387373[/C][/ROW]
[ROW][C]2[/C][C]32[/C][C]35.7884104700905[/C][C]-3.78841047009051[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]32.3011009279298[/C][C]2.69889907207022[/C][/ROW]
[ROW][C]4[/C][C]33[/C][C]32.6885797659476[/C][C]0.311420234052363[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]33.8510162800012[/C][C]3.14898371999879[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]34.2384951180191[/C][C]-5.23849511801907[/C][/ROW]
[ROW][C]7[/C][C]31[/C][C]35.7884104700905[/C][C]-4.7884104700905[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]33.8510162800012[/C][C]2.14898371999879[/C][/ROW]
[ROW][C]9[/C][C]35[/C][C]34.6259739560369[/C][C]0.374026043963072[/C][/ROW]
[ROW][C]10[/C][C]38[/C][C]35.0134527940548[/C][C]2.98654720594521[/C][/ROW]
[ROW][C]11[/C][C]31[/C][C]35.4009316320726[/C][C]-4.40093163207264[/C][/ROW]
[ROW][C]12[/C][C]34[/C][C]34.6259739560369[/C][C]-0.625973956036928[/C][/ROW]
[ROW][C]13[/C][C]35[/C][C]35.4009316320726[/C][C]-0.400931632072644[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]35.7884104700905[/C][C]2.2115895299095[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]33.4635374419834[/C][C]3.53646255801665[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]17[/C][C]32[/C][C]34.6259739560369[/C][C]-2.62597395603693[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]35.4009316320726[/C][C]2.59906836792736[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]35.7884104700905[/C][C]2.2115895299095[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.0760586039655[/C][C]-1.0760586039655[/C][/ROW]
[ROW][C]21[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]32.6885797659476[/C][C]-1.68857976594764[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]35.7884104700905[/C][C]2.2115895299095[/C][/ROW]
[ROW][C]24[/C][C]39[/C][C]35.0134527940548[/C][C]3.98654720594521[/C][/ROW]
[ROW][C]25[/C][C]32[/C][C]35.7884104700905[/C][C]-3.7884104700905[/C][/ROW]
[ROW][C]26[/C][C]32[/C][C]36.5633681461262[/C][C]-4.56336814612622[/C][/ROW]
[ROW][C]27[/C][C]35[/C][C]34.6259739560369[/C][C]0.374026043963072[/C][/ROW]
[ROW][C]28[/C][C]37[/C][C]33.4635374419834[/C][C]3.53646255801665[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.4635374419834[/C][C]-0.463537441983353[/C][/ROW]
[ROW][C]30[/C][C]33[/C][C]33.8510162800012[/C][C]-0.851016280001211[/C][/ROW]
[ROW][C]31[/C][C]28[/C][C]32.6885797659476[/C][C]-4.68857976594764[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]31.1386644138762[/C][C]0.861335586123796[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]35.0134527940548[/C][C]-4.01345279405479[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]33.8510162800012[/C][C]3.14898371999879[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]33.8510162800012[/C][C]-3.85101628000121[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]31.9136220899119[/C][C]-0.913622089911921[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]34.6259739560369[/C][C]-1.62597395603693[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]31.9136220899119[/C][C]-0.913622089911921[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]34.2384951180191[/C][C]-1.23849511801907[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]35.0134527940548[/C][C]-3.01345279405479[/C][/ROW]
[ROW][C]42[/C][C]33[/C][C]33.8510162800012[/C][C]-0.851016280001211[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]35.4009316320726[/C][C]-3.40093163207264[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]34.2384951180191[/C][C]-1.23849511801907[/C][/ROW]
[ROW][C]45[/C][C]28[/C][C]35.4009316320726[/C][C]-7.40093163207264[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]35.0134527940548[/C][C]-0.0134527940547862[/C][/ROW]
[ROW][C]47[/C][C]39[/C][C]35.4009316320726[/C][C]3.59906836792736[/C][/ROW]
[ROW][C]48[/C][C]34[/C][C]33.4635374419834[/C][C]0.536462558016647[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]34.6259739560369[/C][C]3.37402604396307[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]35.4009316320726[/C][C]-3.40093163207264[/C][/ROW]
[ROW][C]51[/C][C]38[/C][C]33.0760586039655[/C][C]4.92394139603451[/C][/ROW]
[ROW][C]52[/C][C]30[/C][C]33.0760586039655[/C][C]-3.07605860396549[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]33.8510162800012[/C][C]4.14898371999879[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.0760586039655[/C][C]-1.0760586039655[/C][/ROW]
[ROW][C]56[/C][C]32[/C][C]35.0134527940548[/C][C]-3.01345279405479[/C][/ROW]
[ROW][C]57[/C][C]34[/C][C]35.7884104700905[/C][C]-1.7884104700905[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]31.9136220899119[/C][C]2.08637791008808[/C][/ROW]
[ROW][C]59[/C][C]36[/C][C]35.0134527940548[/C][C]0.986547205945214[/C][/ROW]
[ROW][C]60[/C][C]34[/C][C]34.2384951180191[/C][C]-0.23849511801907[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]32.3011009279298[/C][C]-4.30110092792978[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]35.4009316320726[/C][C]-1.40093163207264[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]33.8510162800012[/C][C]1.14898371999879[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]32.6885797659476[/C][C]2.31142023405236[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]33.8510162800012[/C][C]-2.85101628000121[/C][/ROW]
[ROW][C]66[/C][C]37[/C][C]34.2384951180191[/C][C]2.76150488198093[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]34.6259739560369[/C][C]0.374026043963072[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]32.3011009279298[/C][C]-5.30110092792978[/C][/ROW]
[ROW][C]69[/C][C]40[/C][C]35.7884104700905[/C][C]4.2115895299095[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]34.2384951180191[/C][C]2.76150488198093[/C][/ROW]
[ROW][C]71[/C][C]36[/C][C]35.4009316320726[/C][C]0.599068367927356[/C][/ROW]
[ROW][C]72[/C][C]38[/C][C]32.6885797659476[/C][C]5.31142023405236[/C][/ROW]
[ROW][C]73[/C][C]39[/C][C]33.8510162800012[/C][C]5.14898371999879[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]35.4009316320726[/C][C]5.59906836792736[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]33.8510162800012[/C][C]-6.85101628000121[/C][/ROW]
[ROW][C]76[/C][C]30[/C][C]35.7884104700905[/C][C]-5.7884104700905[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.0134527940548[/C][C]1.98654720594521[/C][/ROW]
[ROW][C]78[/C][C]31[/C][C]33.8510162800012[/C][C]-2.85101628000121[/C][/ROW]
[ROW][C]79[/C][C]31[/C][C]31.5261432518941[/C][C]-0.526143251894063[/C][/ROW]
[ROW][C]80[/C][C]27[/C][C]35.0134527940548[/C][C]-8.01345279405479[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]33.4635374419834[/C][C]2.53646255801665[/C][/ROW]
[ROW][C]82[/C][C]38[/C][C]35.0134527940548[/C][C]2.98654720594521[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]34.2384951180191[/C][C]2.76150488198093[/C][/ROW]
[ROW][C]84[/C][C]33[/C][C]35.0134527940548[/C][C]-2.01345279405479[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]33.0760586039655[/C][C]0.923941396034505[/C][/ROW]
[ROW][C]86[/C][C]31[/C][C]33.4635374419834[/C][C]-2.46353744198335[/C][/ROW]
[ROW][C]87[/C][C]39[/C][C]35.4009316320726[/C][C]3.59906836792736[/C][/ROW]
[ROW][C]88[/C][C]34[/C][C]33.4635374419834[/C][C]0.536462558016647[/C][/ROW]
[ROW][C]89[/C][C]32[/C][C]31.9136220899119[/C][C]0.0863779100880792[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.4635374419834[/C][C]-0.463537441983353[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]32.6885797659476[/C][C]3.31142023405236[/C][/ROW]
[ROW][C]92[/C][C]32[/C][C]34.6259739560369[/C][C]-2.62597395603693[/C][/ROW]
[ROW][C]93[/C][C]41[/C][C]34.2384951180191[/C][C]6.76150488198093[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]33.0760586039655[/C][C]-5.07605860396549[/C][/ROW]
[ROW][C]95[/C][C]30[/C][C]31.9136220899119[/C][C]-1.91362208991192[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]35.7884104700905[/C][C]0.211589529909498[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]35.0134527940548[/C][C]-0.0134527940547862[/C][/ROW]
[ROW][C]98[/C][C]31[/C][C]34.2384951180191[/C][C]-3.23849511801907[/C][/ROW]
[ROW][C]99[/C][C]34[/C][C]35.0134527940548[/C][C]-1.01345279405479[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]33.0760586039655[/C][C]2.9239413960345[/C][/ROW]
[ROW][C]101[/C][C]36[/C][C]35.4009316320726[/C][C]0.599068367927356[/C][/ROW]
[ROW][C]102[/C][C]35[/C][C]35.0134527940548[/C][C]-0.0134527940547862[/C][/ROW]
[ROW][C]103[/C][C]37[/C][C]34.6259739560369[/C][C]2.37402604396307[/C][/ROW]
[ROW][C]104[/C][C]28[/C][C]33.0760586039655[/C][C]-5.07605860396549[/C][/ROW]
[ROW][C]105[/C][C]39[/C][C]33.4635374419834[/C][C]5.53646255801665[/C][/ROW]
[ROW][C]106[/C][C]32[/C][C]36.1758893081084[/C][C]-4.17588930810836[/C][/ROW]
[ROW][C]107[/C][C]35[/C][C]35.4009316320726[/C][C]-0.400931632072644[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]36.5633681461262[/C][C]2.43663185387378[/C][/ROW]
[ROW][C]109[/C][C]35[/C][C]34.6259739560369[/C][C]0.374026043963072[/C][/ROW]
[ROW][C]110[/C][C]42[/C][C]37.3383258221619[/C][C]4.66167417783806[/C][/ROW]
[ROW][C]111[/C][C]34[/C][C]32.3011009279298[/C][C]1.69889907207022[/C][/ROW]
[ROW][C]112[/C][C]33[/C][C]32.6885797659476[/C][C]0.311420234052363[/C][/ROW]
[ROW][C]113[/C][C]41[/C][C]33.0760586039655[/C][C]7.92394139603451[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]115[/C][C]34[/C][C]35.0134527940548[/C][C]-1.01345279405479[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]35.0134527940548[/C][C]-3.01345279405479[/C][/ROW]
[ROW][C]117[/C][C]40[/C][C]33.4635374419834[/C][C]6.53646255801665[/C][/ROW]
[ROW][C]118[/C][C]40[/C][C]33.8510162800012[/C][C]6.14898371999879[/C][/ROW]
[ROW][C]119[/C][C]35[/C][C]33.4635374419834[/C][C]1.53646255801665[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]35.4009316320726[/C][C]0.599068367927356[/C][/ROW]
[ROW][C]121[/C][C]37[/C][C]33.4635374419834[/C][C]3.53646255801665[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]32.6885797659476[/C][C]-5.68857976594764[/C][/ROW]
[ROW][C]123[/C][C]39[/C][C]35.4009316320726[/C][C]3.59906836792736[/C][/ROW]
[ROW][C]124[/C][C]38[/C][C]35.0134527940548[/C][C]2.98654720594521[/C][/ROW]
[ROW][C]125[/C][C]31[/C][C]33.4635374419834[/C][C]-2.46353744198335[/C][/ROW]
[ROW][C]126[/C][C]33[/C][C]32.6885797659476[/C][C]0.311420234052363[/C][/ROW]
[ROW][C]127[/C][C]32[/C][C]35.7884104700905[/C][C]-3.7884104700905[/C][/ROW]
[ROW][C]128[/C][C]39[/C][C]37.7258046601798[/C][C]1.27419533982021[/C][/ROW]
[ROW][C]129[/C][C]36[/C][C]33.4635374419834[/C][C]2.53646255801665[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]34.2384951180191[/C][C]-1.23849511801907[/C][/ROW]
[ROW][C]131[/C][C]33[/C][C]33.0760586039655[/C][C]-0.0760586039654949[/C][/ROW]
[ROW][C]132[/C][C]32[/C][C]31.5261432518941[/C][C]0.473856748105937[/C][/ROW]
[ROW][C]133[/C][C]37[/C][C]36.1758893081084[/C][C]0.824110691891639[/C][/ROW]
[ROW][C]134[/C][C]30[/C][C]31.1386644138762[/C][C]-1.1386644138762[/C][/ROW]
[ROW][C]135[/C][C]38[/C][C]35.0134527940548[/C][C]2.98654720594521[/C][/ROW]
[ROW][C]136[/C][C]29[/C][C]33.0760586039655[/C][C]-4.07605860396549[/C][/ROW]
[ROW][C]137[/C][C]22[/C][C]31.5261432518941[/C][C]-9.52614325189406[/C][/ROW]
[ROW][C]138[/C][C]35[/C][C]33.8510162800012[/C][C]1.14898371999879[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]32.3011009279298[/C][C]2.69889907207022[/C][/ROW]
[ROW][C]140[/C][C]34[/C][C]34.2384951180191[/C][C]-0.23849511801907[/C][/ROW]
[ROW][C]141[/C][C]35[/C][C]32.6885797659476[/C][C]2.31142023405236[/C][/ROW]
[ROW][C]142[/C][C]34[/C][C]33.0760586039655[/C][C]0.923941396034505[/C][/ROW]
[ROW][C]143[/C][C]34[/C][C]32.3011009279298[/C][C]1.69889907207022[/C][/ROW]
[ROW][C]144[/C][C]35[/C][C]32.3011009279298[/C][C]2.69889907207022[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]32.6885797659476[/C][C]-9.68857976594764[/C][/ROW]
[ROW][C]146[/C][C]31[/C][C]36.1758893081084[/C][C]-5.17588930810836[/C][/ROW]
[ROW][C]147[/C][C]27[/C][C]33.0760586039655[/C][C]-6.07605860396549[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.6259739560369[/C][C]1.37402604396307[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]33.0760586039655[/C][C]-2.0760586039655[/C][/ROW]
[ROW][C]150[/C][C]32[/C][C]34.2384951180191[/C][C]-2.23849511801907[/C][/ROW]
[ROW][C]151[/C][C]39[/C][C]35.4009316320726[/C][C]3.59906836792736[/C][/ROW]
[ROW][C]152[/C][C]37[/C][C]36.9508469841441[/C][C]0.0491530158559226[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]33.8510162800012[/C][C]4.14898371999879[/C][/ROW]
[ROW][C]154[/C][C]39[/C][C]34.2384951180191[/C][C]4.76150488198093[/C][/ROW]
[ROW][C]155[/C][C]34[/C][C]34.2384951180191[/C][C]-0.23849511801907[/C][/ROW]
[ROW][C]156[/C][C]31[/C][C]33.4635374419834[/C][C]-2.46353744198335[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]34.6259739560369[/C][C]-2.62597395603693[/C][/ROW]
[ROW][C]158[/C][C]37[/C][C]33.0760586039655[/C][C]3.9239413960345[/C][/ROW]
[ROW][C]159[/C][C]36[/C][C]33.4635374419834[/C][C]2.53646255801665[/C][/ROW]
[ROW][C]160[/C][C]32[/C][C]33.8510162800012[/C][C]-1.85101628000121[/C][/ROW]
[ROW][C]161[/C][C]35[/C][C]33.0760586039655[/C][C]1.92394139603451[/C][/ROW]
[ROW][C]162[/C][C]36[/C][C]33.8510162800012[/C][C]2.14898371999879[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13836.56336814612631.43663185387373
23235.7884104700905-3.78841047009051
33532.30110092792982.69889907207022
43332.68857976594760.311420234052363
53733.85101628000123.14898371999879
62934.2384951180191-5.23849511801907
73135.7884104700905-4.7884104700905
83633.85101628000122.14898371999879
93534.62597395603690.374026043963072
103835.01345279405482.98654720594521
113135.4009316320726-4.40093163207264
123434.6259739560369-0.625973956036928
133535.4009316320726-0.400931632072644
143835.78841047009052.2115895299095
153733.46353744198343.53646255801665
163333.0760586039655-0.0760586039654949
173234.6259739560369-2.62597395603693
183835.40093163207262.59906836792736
193835.78841047009052.2115895299095
203233.0760586039655-1.0760586039655
213333.0760586039655-0.0760586039654949
223132.6885797659476-1.68857976594764
233835.78841047009052.2115895299095
243935.01345279405483.98654720594521
253235.7884104700905-3.7884104700905
263236.5633681461262-4.56336814612622
273534.62597395603690.374026043963072
283733.46353744198343.53646255801665
293333.4635374419834-0.463537441983353
303333.8510162800012-0.851016280001211
312832.6885797659476-4.68857976594764
323231.13866441387620.861335586123796
333135.0134527940548-4.01345279405479
343733.85101628000123.14898371999879
353033.8510162800012-3.85101628000121
363333.0760586039655-0.0760586039654949
373131.9136220899119-0.913622089911921
383334.6259739560369-1.62597395603693
393131.9136220899119-0.913622089911921
403334.2384951180191-1.23849511801907
413235.0134527940548-3.01345279405479
423333.8510162800012-0.851016280001211
433235.4009316320726-3.40093163207264
443334.2384951180191-1.23849511801907
452835.4009316320726-7.40093163207264
463535.0134527940548-0.0134527940547862
473935.40093163207263.59906836792736
483433.46353744198340.536462558016647
493834.62597395603693.37402604396307
503235.4009316320726-3.40093163207264
513833.07605860396554.92394139603451
523033.0760586039655-3.07605860396549
533333.0760586039655-0.0760586039654949
543833.85101628000124.14898371999879
553233.0760586039655-1.0760586039655
563235.0134527940548-3.01345279405479
573435.7884104700905-1.7884104700905
583431.91362208991192.08637791008808
593635.01345279405480.986547205945214
603434.2384951180191-0.23849511801907
612832.3011009279298-4.30110092792978
623435.4009316320726-1.40093163207264
633533.85101628000121.14898371999879
643532.68857976594762.31142023405236
653133.8510162800012-2.85101628000121
663734.23849511801912.76150488198093
673534.62597395603690.374026043963072
682732.3011009279298-5.30110092792978
694035.78841047009054.2115895299095
703734.23849511801912.76150488198093
713635.40093163207260.599068367927356
723832.68857976594765.31142023405236
733933.85101628000125.14898371999879
744135.40093163207265.59906836792736
752733.8510162800012-6.85101628000121
763035.7884104700905-5.7884104700905
773735.01345279405481.98654720594521
783133.8510162800012-2.85101628000121
793131.5261432518941-0.526143251894063
802735.0134527940548-8.01345279405479
813633.46353744198342.53646255801665
823835.01345279405482.98654720594521
833734.23849511801912.76150488198093
843335.0134527940548-2.01345279405479
853433.07605860396550.923941396034505
863133.4635374419834-2.46353744198335
873935.40093163207263.59906836792736
883433.46353744198340.536462558016647
893231.91362208991190.0863779100880792
903333.4635374419834-0.463537441983353
913632.68857976594763.31142023405236
923234.6259739560369-2.62597395603693
934134.23849511801916.76150488198093
942833.0760586039655-5.07605860396549
953031.9136220899119-1.91362208991192
963635.78841047009050.211589529909498
973535.0134527940548-0.0134527940547862
983134.2384951180191-3.23849511801907
993435.0134527940548-1.01345279405479
1003633.07605860396552.9239413960345
1013635.40093163207260.599068367927356
1023535.0134527940548-0.0134527940547862
1033734.62597395603692.37402604396307
1042833.0760586039655-5.07605860396549
1053933.46353744198345.53646255801665
1063236.1758893081084-4.17588930810836
1073535.4009316320726-0.400931632072644
1083936.56336814612622.43663185387378
1093534.62597395603690.374026043963072
1104237.33832582216194.66167417783806
1113432.30110092792981.69889907207022
1123332.68857976594760.311420234052363
1134133.07605860396557.92394139603451
1143333.0760586039655-0.0760586039654949
1153435.0134527940548-1.01345279405479
1163235.0134527940548-3.01345279405479
1174033.46353744198346.53646255801665
1184033.85101628000126.14898371999879
1193533.46353744198341.53646255801665
1203635.40093163207260.599068367927356
1213733.46353744198343.53646255801665
1222732.6885797659476-5.68857976594764
1233935.40093163207263.59906836792736
1243835.01345279405482.98654720594521
1253133.4635374419834-2.46353744198335
1263332.68857976594760.311420234052363
1273235.7884104700905-3.7884104700905
1283937.72580466017981.27419533982021
1293633.46353744198342.53646255801665
1303334.2384951180191-1.23849511801907
1313333.0760586039655-0.0760586039654949
1323231.52614325189410.473856748105937
1333736.17588930810840.824110691891639
1343031.1386644138762-1.1386644138762
1353835.01345279405482.98654720594521
1362933.0760586039655-4.07605860396549
1372231.5261432518941-9.52614325189406
1383533.85101628000121.14898371999879
1393532.30110092792982.69889907207022
1403434.2384951180191-0.23849511801907
1413532.68857976594762.31142023405236
1423433.07605860396550.923941396034505
1433432.30110092792981.69889907207022
1443532.30110092792982.69889907207022
1452332.6885797659476-9.68857976594764
1463136.1758893081084-5.17588930810836
1472733.0760586039655-6.07605860396549
1483634.62597395603691.37402604396307
1493133.0760586039655-2.0760586039655
1503234.2384951180191-2.23849511801907
1513935.40093163207263.59906836792736
1523736.95084698414410.0491530158559226
1533833.85101628000124.14898371999879
1543934.23849511801914.76150488198093
1553434.2384951180191-0.23849511801907
1563133.4635374419834-2.46353744198335
1573234.6259739560369-2.62597395603693
1583733.07605860396553.9239413960345
1593633.46353744198342.53646255801665
1603233.8510162800012-1.85101628000121
1613533.07605860396551.92394139603451
1623633.85101628000122.14898371999879







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.4668213409218220.9336426818436440.533178659078178
60.7026152915889540.5947694168220930.297384708411046
70.6717559750882760.6564880498234470.328244024911724
80.6026419775238450.794716044952310.397358022476155
90.4966227072672670.9932454145345340.503377292732733
100.5245340261382970.9509319477234060.475465973861703
110.5236503000390940.9526993999218130.476349699960906
120.4257325215715250.851465043143050.574267478428475
130.3418302799296880.6836605598593750.658169720070312
140.3605068505195880.7210137010391760.639493149480412
150.3364717270566550.672943454113310.663528272943345
160.2757962096726880.5515924193453750.724203790327312
170.2494813108973220.4989626217946450.750518689102678
180.2571962611270840.5143925222541680.742803738872916
190.2482049685507080.4964099371014170.751795031449292
200.2121458850327260.4242917700654530.787854114967274
210.1650914475264670.3301828950529350.834908552473533
220.1458122822273690.2916245644547380.854187717772631
230.1330410923143890.2660821846287770.866958907685611
240.1579272659427970.3158545318855930.842072734057203
250.1729940956181010.3459881912362030.827005904381899
260.1956825077089290.3913650154178580.804317492291071
270.1546190534930050.3092381069860110.845380946506995
280.1527346766223550.305469353244710.847265323377645
290.1217550889162270.2435101778324530.878244911083773
300.09663242387030770.1932648477406150.903367576129692
310.1531298196244650.306259639248930.846870180375535
320.1205927195828820.2411854391657630.879407280417118
330.1322071747418020.2644143494836030.867792825258198
340.1323220063443910.2646440126887820.867677993655609
350.1473281856529820.2946563713059630.852671814347018
360.116871481340630.233742962681260.88312851865937
370.095309799820760.190619599641520.90469020017924
380.07714924847984720.1542984969596940.922850751520153
390.06115956195436750.1223191239087350.938840438045632
400.04744257045854720.09488514091709430.952557429541453
410.04337919035516840.08675838071033680.956620809644832
420.0326007099968120.06520141999362410.967399290003188
430.03104844804466550.06209689608933110.968951551955334
440.0233687575012350.04673751500247010.976631242498765
450.06671958636575140.1334391727315030.933280413634249
460.0524520916999650.104904183399930.947547908300035
470.06678202801914590.1335640560382920.933217971980854
480.05237732617857710.1047546523571540.947622673821423
490.05905926649917620.1181185329983520.940940733500824
500.05665561727449040.1133112345489810.94334438272551
510.07963003560468620.1592600712093720.920369964395314
520.0791145924903530.1582291849807060.920885407509647
530.06234701797823640.1246940359564730.937652982021764
540.07597995723111350.1519599144622270.924020042768886
550.06182431548008510.123648630960170.938175684519915
560.05724996294128750.1144999258825750.942750037058713
570.04674617177941080.09349234355882160.953253828220589
580.03908688990336920.07817377980673850.960913110096631
590.0318552884486910.0637105768973820.968144711551309
600.02420804770485320.04841609540970650.975791952295147
610.03238958390144390.06477916780288770.967610416098556
620.02554313912057330.05108627824114660.974456860879427
630.02024717061403050.04049434122806090.97975282938597
640.01745610909132270.03491221818264540.982543890908677
650.01620365465770240.03240730931540480.983796345342298
660.01571636137831760.03143272275663520.984283638621682
670.01184981121011750.0236996224202350.988150188789882
680.02088807589935040.04177615179870070.97911192410065
690.02811305650007870.05622611300015730.971886943499921
700.02684433662107420.05368867324214830.973155663378926
710.02093955492460840.04187910984921680.979060445075392
720.03265240738096360.06530481476192710.967347592619036
730.04784184529421550.09568369058843090.952158154705785
740.07690566923511630.1538113384702330.923094330764884
750.1474681373527290.2949362747054590.852531862647271
760.2048950582664410.4097901165328820.795104941733559
770.1862530992646340.3725061985292690.813746900735366
780.1786040407589930.3572080815179850.821395959241007
790.1517399134444050.3034798268888090.848260086555595
800.3151718171934630.6303436343869270.684828182806537
810.2985704189140210.5971408378280430.701429581085979
820.2925571517186190.5851143034372370.707442848281381
830.2807266749791230.5614533499582470.719273325020877
840.2582274768626950.5164549537253910.741772523137305
850.225411538060050.45082307612010.77458846193995
860.2111453506234480.4222907012468960.788854649376552
870.2172623946151420.4345247892302840.782737605384858
880.1861524569353640.3723049138707280.813847543064636
890.1575174914465460.3150349828930910.842482508553454
900.1323292429029890.2646584858059790.867670757097011
910.1315788596002440.2631577192004880.868421140399756
920.1234972267505420.2469944535010850.876502773249458
930.2088038467034880.4176076934069760.791196153296512
940.2553877135452160.5107754270904320.744612286454784
950.2315326574327250.4630653148654510.768467342567275
960.1991560492562960.3983120985125930.800843950743704
970.1692357822711490.3384715645422970.830764217728851
980.1693030428278140.3386060856556280.830696957172186
990.1456159838809030.2912319677618060.854384016119097
1000.1386004828666790.2772009657333580.861399517133321
1010.115621611941530.231243223883060.88437838805847
1020.09505536878328910.1901107375665780.904944631216711
1030.08461218940146150.1692243788029230.915387810598538
1040.1111993915143590.2223987830287180.888800608485641
1050.1507925475531280.3015850951062560.849207452446872
1060.1760526929369860.3521053858739720.823947307063014
1070.1496694942664360.2993389885328730.850330505733564
1080.1337155256772970.2674310513545930.866284474322704
1090.1100854979672390.2201709959344790.889914502032761
1100.1220064681981410.2440129363962810.877993531801859
1110.1049273038106670.2098546076213340.895072696189333
1120.08480917968238360.1696183593647670.915190820317616
1130.2014942986603310.4029885973206610.798505701339669
1140.1688649550159770.3377299100319540.831135044984023
1150.1437867541544840.2875735083089680.856213245845516
1160.1428299193993290.2856598387986580.857170080600671
1170.2321362559851920.4642725119703840.767863744014808
1180.3297133659646830.6594267319293660.670286634035317
1190.2958839444776370.5917678889552750.704116055522363
1200.2538133168884920.5076266337769840.746186683111508
1210.2627476463971630.5254952927943250.737252353602837
1220.3297152686959160.6594305373918320.670284731304084
1230.329043934050530.6580878681010610.67095606594947
1240.3166011937007910.6332023874015810.683398806299209
1250.2904934970738540.5809869941477080.709506502926146
1260.247843737901480.495687475802960.75215626209852
1270.2674764415856670.5349528831713340.732523558414333
1280.2265418463063750.4530836926127510.773458153693625
1290.2113570105924990.4227140211849980.788642989407501
1300.1777627490891620.3555254981783230.822237250910838
1310.1436028920383260.2872057840766520.856397107961674
1320.1186191528112190.2372383056224390.881380847188781
1330.09281051687308640.1856210337461730.907189483126914
1340.07188275191823170.1437655038364630.928117248081768
1350.06427963183053140.1285592636610630.935720368169469
1360.06557843126001920.1311568625200380.934421568739981
1370.2847793698634470.5695587397268950.715220630136553
1380.2379494396027040.4758988792054090.762050560397296
1390.2133551389413520.4267102778827030.786644861058649
1400.1688256812777350.337651362555470.831174318722265
1410.1455093558761440.2910187117522890.854490644123856
1420.1128690332918460.2257380665836920.887130966708154
1430.09136688218095310.1827337643619060.908633117819047
1440.08765308019520590.1753061603904120.912346919804794
1450.4168427620129320.8336855240258630.583157237987068
1460.5490733927509170.9018532144981660.450926607249083
1470.7860123166966340.4279753666067320.213987683303366
1480.7185377672402580.5629244655194830.281462232759742
1490.7208839157463830.5582321685072340.279116084253617
1500.7341885932940620.5316228134118760.265811406705938
1510.7300802425437260.5398395149125470.269919757456274
1520.6647130424617020.6705739150765970.335286957538298
1530.6733189680274920.6533620639450160.326681031972508
1540.8805566984366580.2388866031266840.119443301563342
1550.8071304882464180.3857390235071650.192869511753582
1560.9137250289557920.1725499420884150.0862749710442076
1570.8079729207820550.3840541584358890.192027079217945

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.466821340921822 & 0.933642681843644 & 0.533178659078178 \tabularnewline
6 & 0.702615291588954 & 0.594769416822093 & 0.297384708411046 \tabularnewline
7 & 0.671755975088276 & 0.656488049823447 & 0.328244024911724 \tabularnewline
8 & 0.602641977523845 & 0.79471604495231 & 0.397358022476155 \tabularnewline
9 & 0.496622707267267 & 0.993245414534534 & 0.503377292732733 \tabularnewline
10 & 0.524534026138297 & 0.950931947723406 & 0.475465973861703 \tabularnewline
11 & 0.523650300039094 & 0.952699399921813 & 0.476349699960906 \tabularnewline
12 & 0.425732521571525 & 0.85146504314305 & 0.574267478428475 \tabularnewline
13 & 0.341830279929688 & 0.683660559859375 & 0.658169720070312 \tabularnewline
14 & 0.360506850519588 & 0.721013701039176 & 0.639493149480412 \tabularnewline
15 & 0.336471727056655 & 0.67294345411331 & 0.663528272943345 \tabularnewline
16 & 0.275796209672688 & 0.551592419345375 & 0.724203790327312 \tabularnewline
17 & 0.249481310897322 & 0.498962621794645 & 0.750518689102678 \tabularnewline
18 & 0.257196261127084 & 0.514392522254168 & 0.742803738872916 \tabularnewline
19 & 0.248204968550708 & 0.496409937101417 & 0.751795031449292 \tabularnewline
20 & 0.212145885032726 & 0.424291770065453 & 0.787854114967274 \tabularnewline
21 & 0.165091447526467 & 0.330182895052935 & 0.834908552473533 \tabularnewline
22 & 0.145812282227369 & 0.291624564454738 & 0.854187717772631 \tabularnewline
23 & 0.133041092314389 & 0.266082184628777 & 0.866958907685611 \tabularnewline
24 & 0.157927265942797 & 0.315854531885593 & 0.842072734057203 \tabularnewline
25 & 0.172994095618101 & 0.345988191236203 & 0.827005904381899 \tabularnewline
26 & 0.195682507708929 & 0.391365015417858 & 0.804317492291071 \tabularnewline
27 & 0.154619053493005 & 0.309238106986011 & 0.845380946506995 \tabularnewline
28 & 0.152734676622355 & 0.30546935324471 & 0.847265323377645 \tabularnewline
29 & 0.121755088916227 & 0.243510177832453 & 0.878244911083773 \tabularnewline
30 & 0.0966324238703077 & 0.193264847740615 & 0.903367576129692 \tabularnewline
31 & 0.153129819624465 & 0.30625963924893 & 0.846870180375535 \tabularnewline
32 & 0.120592719582882 & 0.241185439165763 & 0.879407280417118 \tabularnewline
33 & 0.132207174741802 & 0.264414349483603 & 0.867792825258198 \tabularnewline
34 & 0.132322006344391 & 0.264644012688782 & 0.867677993655609 \tabularnewline
35 & 0.147328185652982 & 0.294656371305963 & 0.852671814347018 \tabularnewline
36 & 0.11687148134063 & 0.23374296268126 & 0.88312851865937 \tabularnewline
37 & 0.09530979982076 & 0.19061959964152 & 0.90469020017924 \tabularnewline
38 & 0.0771492484798472 & 0.154298496959694 & 0.922850751520153 \tabularnewline
39 & 0.0611595619543675 & 0.122319123908735 & 0.938840438045632 \tabularnewline
40 & 0.0474425704585472 & 0.0948851409170943 & 0.952557429541453 \tabularnewline
41 & 0.0433791903551684 & 0.0867583807103368 & 0.956620809644832 \tabularnewline
42 & 0.032600709996812 & 0.0652014199936241 & 0.967399290003188 \tabularnewline
43 & 0.0310484480446655 & 0.0620968960893311 & 0.968951551955334 \tabularnewline
44 & 0.023368757501235 & 0.0467375150024701 & 0.976631242498765 \tabularnewline
45 & 0.0667195863657514 & 0.133439172731503 & 0.933280413634249 \tabularnewline
46 & 0.052452091699965 & 0.10490418339993 & 0.947547908300035 \tabularnewline
47 & 0.0667820280191459 & 0.133564056038292 & 0.933217971980854 \tabularnewline
48 & 0.0523773261785771 & 0.104754652357154 & 0.947622673821423 \tabularnewline
49 & 0.0590592664991762 & 0.118118532998352 & 0.940940733500824 \tabularnewline
50 & 0.0566556172744904 & 0.113311234548981 & 0.94334438272551 \tabularnewline
51 & 0.0796300356046862 & 0.159260071209372 & 0.920369964395314 \tabularnewline
52 & 0.079114592490353 & 0.158229184980706 & 0.920885407509647 \tabularnewline
53 & 0.0623470179782364 & 0.124694035956473 & 0.937652982021764 \tabularnewline
54 & 0.0759799572311135 & 0.151959914462227 & 0.924020042768886 \tabularnewline
55 & 0.0618243154800851 & 0.12364863096017 & 0.938175684519915 \tabularnewline
56 & 0.0572499629412875 & 0.114499925882575 & 0.942750037058713 \tabularnewline
57 & 0.0467461717794108 & 0.0934923435588216 & 0.953253828220589 \tabularnewline
58 & 0.0390868899033692 & 0.0781737798067385 & 0.960913110096631 \tabularnewline
59 & 0.031855288448691 & 0.063710576897382 & 0.968144711551309 \tabularnewline
60 & 0.0242080477048532 & 0.0484160954097065 & 0.975791952295147 \tabularnewline
61 & 0.0323895839014439 & 0.0647791678028877 & 0.967610416098556 \tabularnewline
62 & 0.0255431391205733 & 0.0510862782411466 & 0.974456860879427 \tabularnewline
63 & 0.0202471706140305 & 0.0404943412280609 & 0.97975282938597 \tabularnewline
64 & 0.0174561090913227 & 0.0349122181826454 & 0.982543890908677 \tabularnewline
65 & 0.0162036546577024 & 0.0324073093154048 & 0.983796345342298 \tabularnewline
66 & 0.0157163613783176 & 0.0314327227566352 & 0.984283638621682 \tabularnewline
67 & 0.0118498112101175 & 0.023699622420235 & 0.988150188789882 \tabularnewline
68 & 0.0208880758993504 & 0.0417761517987007 & 0.97911192410065 \tabularnewline
69 & 0.0281130565000787 & 0.0562261130001573 & 0.971886943499921 \tabularnewline
70 & 0.0268443366210742 & 0.0536886732421483 & 0.973155663378926 \tabularnewline
71 & 0.0209395549246084 & 0.0418791098492168 & 0.979060445075392 \tabularnewline
72 & 0.0326524073809636 & 0.0653048147619271 & 0.967347592619036 \tabularnewline
73 & 0.0478418452942155 & 0.0956836905884309 & 0.952158154705785 \tabularnewline
74 & 0.0769056692351163 & 0.153811338470233 & 0.923094330764884 \tabularnewline
75 & 0.147468137352729 & 0.294936274705459 & 0.852531862647271 \tabularnewline
76 & 0.204895058266441 & 0.409790116532882 & 0.795104941733559 \tabularnewline
77 & 0.186253099264634 & 0.372506198529269 & 0.813746900735366 \tabularnewline
78 & 0.178604040758993 & 0.357208081517985 & 0.821395959241007 \tabularnewline
79 & 0.151739913444405 & 0.303479826888809 & 0.848260086555595 \tabularnewline
80 & 0.315171817193463 & 0.630343634386927 & 0.684828182806537 \tabularnewline
81 & 0.298570418914021 & 0.597140837828043 & 0.701429581085979 \tabularnewline
82 & 0.292557151718619 & 0.585114303437237 & 0.707442848281381 \tabularnewline
83 & 0.280726674979123 & 0.561453349958247 & 0.719273325020877 \tabularnewline
84 & 0.258227476862695 & 0.516454953725391 & 0.741772523137305 \tabularnewline
85 & 0.22541153806005 & 0.4508230761201 & 0.77458846193995 \tabularnewline
86 & 0.211145350623448 & 0.422290701246896 & 0.788854649376552 \tabularnewline
87 & 0.217262394615142 & 0.434524789230284 & 0.782737605384858 \tabularnewline
88 & 0.186152456935364 & 0.372304913870728 & 0.813847543064636 \tabularnewline
89 & 0.157517491446546 & 0.315034982893091 & 0.842482508553454 \tabularnewline
90 & 0.132329242902989 & 0.264658485805979 & 0.867670757097011 \tabularnewline
91 & 0.131578859600244 & 0.263157719200488 & 0.868421140399756 \tabularnewline
92 & 0.123497226750542 & 0.246994453501085 & 0.876502773249458 \tabularnewline
93 & 0.208803846703488 & 0.417607693406976 & 0.791196153296512 \tabularnewline
94 & 0.255387713545216 & 0.510775427090432 & 0.744612286454784 \tabularnewline
95 & 0.231532657432725 & 0.463065314865451 & 0.768467342567275 \tabularnewline
96 & 0.199156049256296 & 0.398312098512593 & 0.800843950743704 \tabularnewline
97 & 0.169235782271149 & 0.338471564542297 & 0.830764217728851 \tabularnewline
98 & 0.169303042827814 & 0.338606085655628 & 0.830696957172186 \tabularnewline
99 & 0.145615983880903 & 0.291231967761806 & 0.854384016119097 \tabularnewline
100 & 0.138600482866679 & 0.277200965733358 & 0.861399517133321 \tabularnewline
101 & 0.11562161194153 & 0.23124322388306 & 0.88437838805847 \tabularnewline
102 & 0.0950553687832891 & 0.190110737566578 & 0.904944631216711 \tabularnewline
103 & 0.0846121894014615 & 0.169224378802923 & 0.915387810598538 \tabularnewline
104 & 0.111199391514359 & 0.222398783028718 & 0.888800608485641 \tabularnewline
105 & 0.150792547553128 & 0.301585095106256 & 0.849207452446872 \tabularnewline
106 & 0.176052692936986 & 0.352105385873972 & 0.823947307063014 \tabularnewline
107 & 0.149669494266436 & 0.299338988532873 & 0.850330505733564 \tabularnewline
108 & 0.133715525677297 & 0.267431051354593 & 0.866284474322704 \tabularnewline
109 & 0.110085497967239 & 0.220170995934479 & 0.889914502032761 \tabularnewline
110 & 0.122006468198141 & 0.244012936396281 & 0.877993531801859 \tabularnewline
111 & 0.104927303810667 & 0.209854607621334 & 0.895072696189333 \tabularnewline
112 & 0.0848091796823836 & 0.169618359364767 & 0.915190820317616 \tabularnewline
113 & 0.201494298660331 & 0.402988597320661 & 0.798505701339669 \tabularnewline
114 & 0.168864955015977 & 0.337729910031954 & 0.831135044984023 \tabularnewline
115 & 0.143786754154484 & 0.287573508308968 & 0.856213245845516 \tabularnewline
116 & 0.142829919399329 & 0.285659838798658 & 0.857170080600671 \tabularnewline
117 & 0.232136255985192 & 0.464272511970384 & 0.767863744014808 \tabularnewline
118 & 0.329713365964683 & 0.659426731929366 & 0.670286634035317 \tabularnewline
119 & 0.295883944477637 & 0.591767888955275 & 0.704116055522363 \tabularnewline
120 & 0.253813316888492 & 0.507626633776984 & 0.746186683111508 \tabularnewline
121 & 0.262747646397163 & 0.525495292794325 & 0.737252353602837 \tabularnewline
122 & 0.329715268695916 & 0.659430537391832 & 0.670284731304084 \tabularnewline
123 & 0.32904393405053 & 0.658087868101061 & 0.67095606594947 \tabularnewline
124 & 0.316601193700791 & 0.633202387401581 & 0.683398806299209 \tabularnewline
125 & 0.290493497073854 & 0.580986994147708 & 0.709506502926146 \tabularnewline
126 & 0.24784373790148 & 0.49568747580296 & 0.75215626209852 \tabularnewline
127 & 0.267476441585667 & 0.534952883171334 & 0.732523558414333 \tabularnewline
128 & 0.226541846306375 & 0.453083692612751 & 0.773458153693625 \tabularnewline
129 & 0.211357010592499 & 0.422714021184998 & 0.788642989407501 \tabularnewline
130 & 0.177762749089162 & 0.355525498178323 & 0.822237250910838 \tabularnewline
131 & 0.143602892038326 & 0.287205784076652 & 0.856397107961674 \tabularnewline
132 & 0.118619152811219 & 0.237238305622439 & 0.881380847188781 \tabularnewline
133 & 0.0928105168730864 & 0.185621033746173 & 0.907189483126914 \tabularnewline
134 & 0.0718827519182317 & 0.143765503836463 & 0.928117248081768 \tabularnewline
135 & 0.0642796318305314 & 0.128559263661063 & 0.935720368169469 \tabularnewline
136 & 0.0655784312600192 & 0.131156862520038 & 0.934421568739981 \tabularnewline
137 & 0.284779369863447 & 0.569558739726895 & 0.715220630136553 \tabularnewline
138 & 0.237949439602704 & 0.475898879205409 & 0.762050560397296 \tabularnewline
139 & 0.213355138941352 & 0.426710277882703 & 0.786644861058649 \tabularnewline
140 & 0.168825681277735 & 0.33765136255547 & 0.831174318722265 \tabularnewline
141 & 0.145509355876144 & 0.291018711752289 & 0.854490644123856 \tabularnewline
142 & 0.112869033291846 & 0.225738066583692 & 0.887130966708154 \tabularnewline
143 & 0.0913668821809531 & 0.182733764361906 & 0.908633117819047 \tabularnewline
144 & 0.0876530801952059 & 0.175306160390412 & 0.912346919804794 \tabularnewline
145 & 0.416842762012932 & 0.833685524025863 & 0.583157237987068 \tabularnewline
146 & 0.549073392750917 & 0.901853214498166 & 0.450926607249083 \tabularnewline
147 & 0.786012316696634 & 0.427975366606732 & 0.213987683303366 \tabularnewline
148 & 0.718537767240258 & 0.562924465519483 & 0.281462232759742 \tabularnewline
149 & 0.720883915746383 & 0.558232168507234 & 0.279116084253617 \tabularnewline
150 & 0.734188593294062 & 0.531622813411876 & 0.265811406705938 \tabularnewline
151 & 0.730080242543726 & 0.539839514912547 & 0.269919757456274 \tabularnewline
152 & 0.664713042461702 & 0.670573915076597 & 0.335286957538298 \tabularnewline
153 & 0.673318968027492 & 0.653362063945016 & 0.326681031972508 \tabularnewline
154 & 0.880556698436658 & 0.238886603126684 & 0.119443301563342 \tabularnewline
155 & 0.807130488246418 & 0.385739023507165 & 0.192869511753582 \tabularnewline
156 & 0.913725028955792 & 0.172549942088415 & 0.0862749710442076 \tabularnewline
157 & 0.807972920782055 & 0.384054158435889 & 0.192027079217945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&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]5[/C][C]0.466821340921822[/C][C]0.933642681843644[/C][C]0.533178659078178[/C][/ROW]
[ROW][C]6[/C][C]0.702615291588954[/C][C]0.594769416822093[/C][C]0.297384708411046[/C][/ROW]
[ROW][C]7[/C][C]0.671755975088276[/C][C]0.656488049823447[/C][C]0.328244024911724[/C][/ROW]
[ROW][C]8[/C][C]0.602641977523845[/C][C]0.79471604495231[/C][C]0.397358022476155[/C][/ROW]
[ROW][C]9[/C][C]0.496622707267267[/C][C]0.993245414534534[/C][C]0.503377292732733[/C][/ROW]
[ROW][C]10[/C][C]0.524534026138297[/C][C]0.950931947723406[/C][C]0.475465973861703[/C][/ROW]
[ROW][C]11[/C][C]0.523650300039094[/C][C]0.952699399921813[/C][C]0.476349699960906[/C][/ROW]
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[ROW][C]14[/C][C]0.360506850519588[/C][C]0.721013701039176[/C][C]0.639493149480412[/C][/ROW]
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[ROW][C]16[/C][C]0.275796209672688[/C][C]0.551592419345375[/C][C]0.724203790327312[/C][/ROW]
[ROW][C]17[/C][C]0.249481310897322[/C][C]0.498962621794645[/C][C]0.750518689102678[/C][/ROW]
[ROW][C]18[/C][C]0.257196261127084[/C][C]0.514392522254168[/C][C]0.742803738872916[/C][/ROW]
[ROW][C]19[/C][C]0.248204968550708[/C][C]0.496409937101417[/C][C]0.751795031449292[/C][/ROW]
[ROW][C]20[/C][C]0.212145885032726[/C][C]0.424291770065453[/C][C]0.787854114967274[/C][/ROW]
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[ROW][C]24[/C][C]0.157927265942797[/C][C]0.315854531885593[/C][C]0.842072734057203[/C][/ROW]
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[ROW][C]26[/C][C]0.195682507708929[/C][C]0.391365015417858[/C][C]0.804317492291071[/C][/ROW]
[ROW][C]27[/C][C]0.154619053493005[/C][C]0.309238106986011[/C][C]0.845380946506995[/C][/ROW]
[ROW][C]28[/C][C]0.152734676622355[/C][C]0.30546935324471[/C][C]0.847265323377645[/C][/ROW]
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[ROW][C]41[/C][C]0.0433791903551684[/C][C]0.0867583807103368[/C][C]0.956620809644832[/C][/ROW]
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[ROW][C]46[/C][C]0.052452091699965[/C][C]0.10490418339993[/C][C]0.947547908300035[/C][/ROW]
[ROW][C]47[/C][C]0.0667820280191459[/C][C]0.133564056038292[/C][C]0.933217971980854[/C][/ROW]
[ROW][C]48[/C][C]0.0523773261785771[/C][C]0.104754652357154[/C][C]0.947622673821423[/C][/ROW]
[ROW][C]49[/C][C]0.0590592664991762[/C][C]0.118118532998352[/C][C]0.940940733500824[/C][/ROW]
[ROW][C]50[/C][C]0.0566556172744904[/C][C]0.113311234548981[/C][C]0.94334438272551[/C][/ROW]
[ROW][C]51[/C][C]0.0796300356046862[/C][C]0.159260071209372[/C][C]0.920369964395314[/C][/ROW]
[ROW][C]52[/C][C]0.079114592490353[/C][C]0.158229184980706[/C][C]0.920885407509647[/C][/ROW]
[ROW][C]53[/C][C]0.0623470179782364[/C][C]0.124694035956473[/C][C]0.937652982021764[/C][/ROW]
[ROW][C]54[/C][C]0.0759799572311135[/C][C]0.151959914462227[/C][C]0.924020042768886[/C][/ROW]
[ROW][C]55[/C][C]0.0618243154800851[/C][C]0.12364863096017[/C][C]0.938175684519915[/C][/ROW]
[ROW][C]56[/C][C]0.0572499629412875[/C][C]0.114499925882575[/C][C]0.942750037058713[/C][/ROW]
[ROW][C]57[/C][C]0.0467461717794108[/C][C]0.0934923435588216[/C][C]0.953253828220589[/C][/ROW]
[ROW][C]58[/C][C]0.0390868899033692[/C][C]0.0781737798067385[/C][C]0.960913110096631[/C][/ROW]
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[ROW][C]67[/C][C]0.0118498112101175[/C][C]0.023699622420235[/C][C]0.988150188789882[/C][/ROW]
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[ROW][C]72[/C][C]0.0326524073809636[/C][C]0.0653048147619271[/C][C]0.967347592619036[/C][/ROW]
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[ROW][C]98[/C][C]0.169303042827814[/C][C]0.338606085655628[/C][C]0.830696957172186[/C][/ROW]
[ROW][C]99[/C][C]0.145615983880903[/C][C]0.291231967761806[/C][C]0.854384016119097[/C][/ROW]
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[ROW][C]103[/C][C]0.0846121894014615[/C][C]0.169224378802923[/C][C]0.915387810598538[/C][/ROW]
[ROW][C]104[/C][C]0.111199391514359[/C][C]0.222398783028718[/C][C]0.888800608485641[/C][/ROW]
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[ROW][C]106[/C][C]0.176052692936986[/C][C]0.352105385873972[/C][C]0.823947307063014[/C][/ROW]
[ROW][C]107[/C][C]0.149669494266436[/C][C]0.299338988532873[/C][C]0.850330505733564[/C][/ROW]
[ROW][C]108[/C][C]0.133715525677297[/C][C]0.267431051354593[/C][C]0.866284474322704[/C][/ROW]
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[ROW][C]110[/C][C]0.122006468198141[/C][C]0.244012936396281[/C][C]0.877993531801859[/C][/ROW]
[ROW][C]111[/C][C]0.104927303810667[/C][C]0.209854607621334[/C][C]0.895072696189333[/C][/ROW]
[ROW][C]112[/C][C]0.0848091796823836[/C][C]0.169618359364767[/C][C]0.915190820317616[/C][/ROW]
[ROW][C]113[/C][C]0.201494298660331[/C][C]0.402988597320661[/C][C]0.798505701339669[/C][/ROW]
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[ROW][C]116[/C][C]0.142829919399329[/C][C]0.285659838798658[/C][C]0.857170080600671[/C][/ROW]
[ROW][C]117[/C][C]0.232136255985192[/C][C]0.464272511970384[/C][C]0.767863744014808[/C][/ROW]
[ROW][C]118[/C][C]0.329713365964683[/C][C]0.659426731929366[/C][C]0.670286634035317[/C][/ROW]
[ROW][C]119[/C][C]0.295883944477637[/C][C]0.591767888955275[/C][C]0.704116055522363[/C][/ROW]
[ROW][C]120[/C][C]0.253813316888492[/C][C]0.507626633776984[/C][C]0.746186683111508[/C][/ROW]
[ROW][C]121[/C][C]0.262747646397163[/C][C]0.525495292794325[/C][C]0.737252353602837[/C][/ROW]
[ROW][C]122[/C][C]0.329715268695916[/C][C]0.659430537391832[/C][C]0.670284731304084[/C][/ROW]
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[ROW][C]124[/C][C]0.316601193700791[/C][C]0.633202387401581[/C][C]0.683398806299209[/C][/ROW]
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[ROW][C]127[/C][C]0.267476441585667[/C][C]0.534952883171334[/C][C]0.732523558414333[/C][/ROW]
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[ROW][C]135[/C][C]0.0642796318305314[/C][C]0.128559263661063[/C][C]0.935720368169469[/C][/ROW]
[ROW][C]136[/C][C]0.0655784312600192[/C][C]0.131156862520038[/C][C]0.934421568739981[/C][/ROW]
[ROW][C]137[/C][C]0.284779369863447[/C][C]0.569558739726895[/C][C]0.715220630136553[/C][/ROW]
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[ROW][C]144[/C][C]0.0876530801952059[/C][C]0.175306160390412[/C][C]0.912346919804794[/C][/ROW]
[ROW][C]145[/C][C]0.416842762012932[/C][C]0.833685524025863[/C][C]0.583157237987068[/C][/ROW]
[ROW][C]146[/C][C]0.549073392750917[/C][C]0.901853214498166[/C][C]0.450926607249083[/C][/ROW]
[ROW][C]147[/C][C]0.786012316696634[/C][C]0.427975366606732[/C][C]0.213987683303366[/C][/ROW]
[ROW][C]148[/C][C]0.718537767240258[/C][C]0.562924465519483[/C][C]0.281462232759742[/C][/ROW]
[ROW][C]149[/C][C]0.720883915746383[/C][C]0.558232168507234[/C][C]0.279116084253617[/C][/ROW]
[ROW][C]150[/C][C]0.734188593294062[/C][C]0.531622813411876[/C][C]0.265811406705938[/C][/ROW]
[ROW][C]151[/C][C]0.730080242543726[/C][C]0.539839514912547[/C][C]0.269919757456274[/C][/ROW]
[ROW][C]152[/C][C]0.664713042461702[/C][C]0.670573915076597[/C][C]0.335286957538298[/C][/ROW]
[ROW][C]153[/C][C]0.673318968027492[/C][C]0.653362063945016[/C][C]0.326681031972508[/C][/ROW]
[ROW][C]154[/C][C]0.880556698436658[/C][C]0.238886603126684[/C][C]0.119443301563342[/C][/ROW]
[ROW][C]155[/C][C]0.807130488246418[/C][C]0.385739023507165[/C][C]0.192869511753582[/C][/ROW]
[ROW][C]156[/C][C]0.913725028955792[/C][C]0.172549942088415[/C][C]0.0862749710442076[/C][/ROW]
[ROW][C]157[/C][C]0.807972920782055[/C][C]0.384054158435889[/C][C]0.192027079217945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.4668213409218220.9336426818436440.533178659078178
60.7026152915889540.5947694168220930.297384708411046
70.6717559750882760.6564880498234470.328244024911724
80.6026419775238450.794716044952310.397358022476155
90.4966227072672670.9932454145345340.503377292732733
100.5245340261382970.9509319477234060.475465973861703
110.5236503000390940.9526993999218130.476349699960906
120.4257325215715250.851465043143050.574267478428475
130.3418302799296880.6836605598593750.658169720070312
140.3605068505195880.7210137010391760.639493149480412
150.3364717270566550.672943454113310.663528272943345
160.2757962096726880.5515924193453750.724203790327312
170.2494813108973220.4989626217946450.750518689102678
180.2571962611270840.5143925222541680.742803738872916
190.2482049685507080.4964099371014170.751795031449292
200.2121458850327260.4242917700654530.787854114967274
210.1650914475264670.3301828950529350.834908552473533
220.1458122822273690.2916245644547380.854187717772631
230.1330410923143890.2660821846287770.866958907685611
240.1579272659427970.3158545318855930.842072734057203
250.1729940956181010.3459881912362030.827005904381899
260.1956825077089290.3913650154178580.804317492291071
270.1546190534930050.3092381069860110.845380946506995
280.1527346766223550.305469353244710.847265323377645
290.1217550889162270.2435101778324530.878244911083773
300.09663242387030770.1932648477406150.903367576129692
310.1531298196244650.306259639248930.846870180375535
320.1205927195828820.2411854391657630.879407280417118
330.1322071747418020.2644143494836030.867792825258198
340.1323220063443910.2646440126887820.867677993655609
350.1473281856529820.2946563713059630.852671814347018
360.116871481340630.233742962681260.88312851865937
370.095309799820760.190619599641520.90469020017924
380.07714924847984720.1542984969596940.922850751520153
390.06115956195436750.1223191239087350.938840438045632
400.04744257045854720.09488514091709430.952557429541453
410.04337919035516840.08675838071033680.956620809644832
420.0326007099968120.06520141999362410.967399290003188
430.03104844804466550.06209689608933110.968951551955334
440.0233687575012350.04673751500247010.976631242498765
450.06671958636575140.1334391727315030.933280413634249
460.0524520916999650.104904183399930.947547908300035
470.06678202801914590.1335640560382920.933217971980854
480.05237732617857710.1047546523571540.947622673821423
490.05905926649917620.1181185329983520.940940733500824
500.05665561727449040.1133112345489810.94334438272551
510.07963003560468620.1592600712093720.920369964395314
520.0791145924903530.1582291849807060.920885407509647
530.06234701797823640.1246940359564730.937652982021764
540.07597995723111350.1519599144622270.924020042768886
550.06182431548008510.123648630960170.938175684519915
560.05724996294128750.1144999258825750.942750037058713
570.04674617177941080.09349234355882160.953253828220589
580.03908688990336920.07817377980673850.960913110096631
590.0318552884486910.0637105768973820.968144711551309
600.02420804770485320.04841609540970650.975791952295147
610.03238958390144390.06477916780288770.967610416098556
620.02554313912057330.05108627824114660.974456860879427
630.02024717061403050.04049434122806090.97975282938597
640.01745610909132270.03491221818264540.982543890908677
650.01620365465770240.03240730931540480.983796345342298
660.01571636137831760.03143272275663520.984283638621682
670.01184981121011750.0236996224202350.988150188789882
680.02088807589935040.04177615179870070.97911192410065
690.02811305650007870.05622611300015730.971886943499921
700.02684433662107420.05368867324214830.973155663378926
710.02093955492460840.04187910984921680.979060445075392
720.03265240738096360.06530481476192710.967347592619036
730.04784184529421550.09568369058843090.952158154705785
740.07690566923511630.1538113384702330.923094330764884
750.1474681373527290.2949362747054590.852531862647271
760.2048950582664410.4097901165328820.795104941733559
770.1862530992646340.3725061985292690.813746900735366
780.1786040407589930.3572080815179850.821395959241007
790.1517399134444050.3034798268888090.848260086555595
800.3151718171934630.6303436343869270.684828182806537
810.2985704189140210.5971408378280430.701429581085979
820.2925571517186190.5851143034372370.707442848281381
830.2807266749791230.5614533499582470.719273325020877
840.2582274768626950.5164549537253910.741772523137305
850.225411538060050.45082307612010.77458846193995
860.2111453506234480.4222907012468960.788854649376552
870.2172623946151420.4345247892302840.782737605384858
880.1861524569353640.3723049138707280.813847543064636
890.1575174914465460.3150349828930910.842482508553454
900.1323292429029890.2646584858059790.867670757097011
910.1315788596002440.2631577192004880.868421140399756
920.1234972267505420.2469944535010850.876502773249458
930.2088038467034880.4176076934069760.791196153296512
940.2553877135452160.5107754270904320.744612286454784
950.2315326574327250.4630653148654510.768467342567275
960.1991560492562960.3983120985125930.800843950743704
970.1692357822711490.3384715645422970.830764217728851
980.1693030428278140.3386060856556280.830696957172186
990.1456159838809030.2912319677618060.854384016119097
1000.1386004828666790.2772009657333580.861399517133321
1010.115621611941530.231243223883060.88437838805847
1020.09505536878328910.1901107375665780.904944631216711
1030.08461218940146150.1692243788029230.915387810598538
1040.1111993915143590.2223987830287180.888800608485641
1050.1507925475531280.3015850951062560.849207452446872
1060.1760526929369860.3521053858739720.823947307063014
1070.1496694942664360.2993389885328730.850330505733564
1080.1337155256772970.2674310513545930.866284474322704
1090.1100854979672390.2201709959344790.889914502032761
1100.1220064681981410.2440129363962810.877993531801859
1110.1049273038106670.2098546076213340.895072696189333
1120.08480917968238360.1696183593647670.915190820317616
1130.2014942986603310.4029885973206610.798505701339669
1140.1688649550159770.3377299100319540.831135044984023
1150.1437867541544840.2875735083089680.856213245845516
1160.1428299193993290.2856598387986580.857170080600671
1170.2321362559851920.4642725119703840.767863744014808
1180.3297133659646830.6594267319293660.670286634035317
1190.2958839444776370.5917678889552750.704116055522363
1200.2538133168884920.5076266337769840.746186683111508
1210.2627476463971630.5254952927943250.737252353602837
1220.3297152686959160.6594305373918320.670284731304084
1230.329043934050530.6580878681010610.67095606594947
1240.3166011937007910.6332023874015810.683398806299209
1250.2904934970738540.5809869941477080.709506502926146
1260.247843737901480.495687475802960.75215626209852
1270.2674764415856670.5349528831713340.732523558414333
1280.2265418463063750.4530836926127510.773458153693625
1290.2113570105924990.4227140211849980.788642989407501
1300.1777627490891620.3555254981783230.822237250910838
1310.1436028920383260.2872057840766520.856397107961674
1320.1186191528112190.2372383056224390.881380847188781
1330.09281051687308640.1856210337461730.907189483126914
1340.07188275191823170.1437655038364630.928117248081768
1350.06427963183053140.1285592636610630.935720368169469
1360.06557843126001920.1311568625200380.934421568739981
1370.2847793698634470.5695587397268950.715220630136553
1380.2379494396027040.4758988792054090.762050560397296
1390.2133551389413520.4267102778827030.786644861058649
1400.1688256812777350.337651362555470.831174318722265
1410.1455093558761440.2910187117522890.854490644123856
1420.1128690332918460.2257380665836920.887130966708154
1430.09136688218095310.1827337643619060.908633117819047
1440.08765308019520590.1753061603904120.912346919804794
1450.4168427620129320.8336855240258630.583157237987068
1460.5490733927509170.9018532144981660.450926607249083
1470.7860123166966340.4279753666067320.213987683303366
1480.7185377672402580.5629244655194830.281462232759742
1490.7208839157463830.5582321685072340.279116084253617
1500.7341885932940620.5316228134118760.265811406705938
1510.7300802425437260.5398395149125470.269919757456274
1520.6647130424617020.6705739150765970.335286957538298
1530.6733189680274920.6533620639450160.326681031972508
1540.8805566984366580.2388866031266840.119443301563342
1550.8071304882464180.3857390235071650.192869511753582
1560.9137250289557920.1725499420884150.0862749710442076
1570.8079729207820550.3840541584358890.192027079217945







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0588235294117647NOK
10% type I error level220.143790849673203NOK

\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 & 9 & 0.0588235294117647 & NOK \tabularnewline
10% type I error level & 22 & 0.143790849673203 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147125&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]9[/C][C]0.0588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.143790849673203[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147125&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147125&T=6

As an alternative you can also use a 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 level90.0588235294117647NOK
10% type I error level220.143790849673203NOK



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