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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 28 Apr 2008 06:45:27 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Apr/28/t12093870516xv0lmyd41yi6ku.htm/, Retrieved Thu, 31 Oct 2024 23:48:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=10906, Retrieved Thu, 31 Oct 2024 23:48:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordstest sales
Estimated Impact316
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD    [Multiple Regression] [regressie] [2008-04-28 12:45:27] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
56421
53152
53536
52408
41454
38271
35306
26414
31917
38030
27534
18387
50556
43901
48572
43899
37532
40357
35489
29027
34485
42598
30306
26451
47460
50104
61465
53726
39477
43895
31481
29896
33842
39120
33702
25094
51442
45594
52518
48564
41745
49585
32747
33379
35645
37034
35681
20972
58552
54955
65540
51570
51145
46641
35704
33253
35193
41668
34865
21210
56126
49231
59723
48103
47472
50497
40059
34149
36860
46356
36577




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
sales[t] = + 18935.8222222222 + 31487.6691358025M1[t] + 27454.1419753086M2[t] + 34760.1148148148M3[t] + 27482.587654321M4[t] + 20811.5604938272M5[t] + 22451.5333333334M6[t] + 12611.3395061728M7[t] + 8403.14567901236M8[t] + 11943.6185185185M9[t] + 17990.7580246914M10[t] + 10203.7308641976M11[t] + 96.8604938271604t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
sales[t] =  +  18935.8222222222 +  31487.6691358025M1[t] +  27454.1419753086M2[t] +  34760.1148148148M3[t] +  27482.587654321M4[t] +  20811.5604938272M5[t] +  22451.5333333334M6[t] +  12611.3395061728M7[t] +  8403.14567901236M8[t] +  11943.6185185185M9[t] +  17990.7580246914M10[t] +  10203.7308641976M11[t] +  96.8604938271604t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]sales[t] =  +  18935.8222222222 +  31487.6691358025M1[t] +  27454.1419753086M2[t] +  34760.1148148148M3[t] +  27482.587654321M4[t] +  20811.5604938272M5[t] +  22451.5333333334M6[t] +  12611.3395061728M7[t] +  8403.14567901236M8[t] +  11943.6185185185M9[t] +  17990.7580246914M10[t] +  10203.7308641976M11[t] +  96.8604938271604t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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
sales[t] = + 18935.8222222222 + 31487.6691358025M1[t] + 27454.1419753086M2[t] + 34760.1148148148M3[t] + 27482.587654321M4[t] + 20811.5604938272M5[t] + 22451.5333333334M6[t] + 12611.3395061728M7[t] + 8403.14567901236M8[t] + 11943.6185185185M9[t] + 17990.7580246914M10[t] + 10203.7308641976M11[t] + 96.8604938271604t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18935.82222222221714.88938311.04200
M131487.66913580252102.75600314.974500
M227454.14197530862101.87288413.061800
M334760.11481481482101.18575716.543100
M427482.5876543212100.69481413.082600
M520811.56049382722100.4001939.908400
M622451.53333333342100.30197710.689700
M712611.33950617282100.4001936.004300
M88403.145679012362100.6948144.00020.0001829.1e-05
M911943.61851851852101.1857575.684200
M1017990.75802469142101.8728848.559400
M1110203.73086419762102.7560034.85261e-055e-06
t96.860493827160420.311984.76861.3e-056e-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) & 18935.8222222222 & 1714.889383 & 11.042 & 0 & 0 \tabularnewline
M1 & 31487.6691358025 & 2102.756003 & 14.9745 & 0 & 0 \tabularnewline
M2 & 27454.1419753086 & 2101.872884 & 13.0618 & 0 & 0 \tabularnewline
M3 & 34760.1148148148 & 2101.185757 & 16.5431 & 0 & 0 \tabularnewline
M4 & 27482.587654321 & 2100.694814 & 13.0826 & 0 & 0 \tabularnewline
M5 & 20811.5604938272 & 2100.400193 & 9.9084 & 0 & 0 \tabularnewline
M6 & 22451.5333333334 & 2100.301977 & 10.6897 & 0 & 0 \tabularnewline
M7 & 12611.3395061728 & 2100.400193 & 6.0043 & 0 & 0 \tabularnewline
M8 & 8403.14567901236 & 2100.694814 & 4.0002 & 0.000182 & 9.1e-05 \tabularnewline
M9 & 11943.6185185185 & 2101.185757 & 5.6842 & 0 & 0 \tabularnewline
M10 & 17990.7580246914 & 2101.872884 & 8.5594 & 0 & 0 \tabularnewline
M11 & 10203.7308641976 & 2102.756003 & 4.8526 & 1e-05 & 5e-06 \tabularnewline
t & 96.8604938271604 & 20.31198 & 4.7686 & 1.3e-05 & 6e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&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]18935.8222222222[/C][C]1714.889383[/C][C]11.042[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]31487.6691358025[/C][C]2102.756003[/C][C]14.9745[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]27454.1419753086[/C][C]2101.872884[/C][C]13.0618[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]34760.1148148148[/C][C]2101.185757[/C][C]16.5431[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]27482.587654321[/C][C]2100.694814[/C][C]13.0826[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]20811.5604938272[/C][C]2100.400193[/C][C]9.9084[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]22451.5333333334[/C][C]2100.301977[/C][C]10.6897[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]12611.3395061728[/C][C]2100.400193[/C][C]6.0043[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]8403.14567901236[/C][C]2100.694814[/C][C]4.0002[/C][C]0.000182[/C][C]9.1e-05[/C][/ROW]
[ROW][C]M9[/C][C]11943.6185185185[/C][C]2101.185757[/C][C]5.6842[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]17990.7580246914[/C][C]2101.872884[/C][C]8.5594[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]10203.7308641976[/C][C]2102.756003[/C][C]4.8526[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]t[/C][C]96.8604938271604[/C][C]20.31198[/C][C]4.7686[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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)18935.82222222221714.88938311.04200
M131487.66913580252102.75600314.974500
M227454.14197530862101.87288413.061800
M334760.11481481482101.18575716.543100
M427482.5876543212100.69481413.082600
M520811.56049382722100.4001939.908400
M622451.53333333342100.30197710.689700
M712611.33950617282100.4001936.004300
M88403.145679012362100.6948144.00020.0001829.1e-05
M911943.61851851852101.1857575.684200
M1017990.75802469142101.8728848.559400
M1110203.73086419762102.7560034.85261e-055e-06
t96.860493827160420.311984.76861.3e-056e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.952646944818744
R-squared0.907536201472488
Adjusted R-squared0.88840576039783
F-TEST (value)47.4393767467658
F-TEST (DF numerator)12
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3468.53455910986
Sum Squared Residuals697782455.288888

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.952646944818744 \tabularnewline
R-squared & 0.907536201472488 \tabularnewline
Adjusted R-squared & 0.88840576039783 \tabularnewline
F-TEST (value) & 47.4393767467658 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3468.53455910986 \tabularnewline
Sum Squared Residuals & 697782455.288888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.952646944818744[/C][/ROW]
[ROW][C]R-squared[/C][C]0.907536201472488[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.88840576039783[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]47.4393767467658[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3468.53455910986[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]697782455.288888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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 R0.952646944818744
R-squared0.907536201472488
Adjusted R-squared0.88840576039783
F-TEST (value)47.4393767467658
F-TEST (DF numerator)12
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3468.53455910986
Sum Squared Residuals697782455.288888







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15642150520.35185185185900.6481481482
25315246583.68518518536568.3148148147
35353653986.5185185185-450.518518518497
45240846805.85185185185602.14814814817
54145440231.68518518521222.31481481484
63827141968.5185185185-3697.51851851849
73530632225.18518518523080.81481481477
82641428113.8518518518-1699.85185185184
93191731751.1851851852165.814814814821
103803037895.1851851852134.814814814837
112753430205.0185185185-2671.01851851852
121838720098.1481481481-1711.14814814815
135055651682.6777777778-1126.67777777779
144390147746.0111111111-3845.01111111109
154857255148.8444444444-6576.84444444445
164389947968.1777777778-4069.17777777778
173753241394.0111111111-3862.01111111112
184035743130.8444444445-2773.84444444445
193548933387.51111111112101.48888888889
202902729276.1777777778-249.177777777783
213448532913.51111111111571.48888888889
224259839057.51111111113540.48888888888
233030631367.3444444444-1061.34444444445
242645121260.47407407415190.52592592594
254746052845.0037037037-5385.00370370372
265010448908.3370370371195.66296296298
276146556311.17037037045153.82962962962
285372649130.50370370374595.49629629629
293947742556.337037037-3079.33703703704
304389544293.1703703704-398.170370370378
313148134549.8370370370-3068.83703703703
322989630438.5037037037-542.503703703708
333384234075.8370370370-233.837037037040
343912040219.837037037-1099.83703703704
353370232529.67037037041172.32962962963
362509422422.82671.20000000001
375144254007.3296296296-2565.32962962964
384559450070.662962963-4476.66296296294
395251857473.4962962963-4955.4962962963
404856450292.8296296296-1728.82962962963
414174543718.662962963-1973.66296296297
424958545455.49629629634129.5037037037
433274735712.1629629630-2965.16296296296
443337931600.82962962961778.17037037037
453564535238.1629629630406.837037037037
463703441382.162962963-4348.16296296297
473568133691.99629629631989.00370370371
482097223585.1259259259-2613.12592592592
495855255169.65555555563382.34444444443
505495551232.98888888893722.01111111113
516554058635.82222222226904.17777777777
525157051455.1555555556114.844444444442
535114544880.98888888896264.01111111111
544664146617.822222222223.1777777777726
553570436874.4888888889-1170.48888888888
563325332763.1555555556489.844444444443
573519336400.4888888889-1207.48888888889
584166842544.4888888889-876.488888888891
593486534854.322222222210.6777777777801
602121024747.4518518518-3537.45185185184
615612656331.9814814815-205.981481481489
624923152395.3148148148-3164.31481481479
635972359798.1481481482-75.148148148152
644810352617.4814814815-4514.48148148148
654747246043.31481481481428.68518518518
665049747780.14814814822716.85185185185
674005938036.81481481482022.18518518519
683414933925.4814814815223.518518518516
693686037562.8148148148-702.814814814815
704635643706.81481481482649.18518518518
713657736016.6481481481560.351851851855

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 56421 & 50520.3518518518 & 5900.6481481482 \tabularnewline
2 & 53152 & 46583.6851851853 & 6568.3148148147 \tabularnewline
3 & 53536 & 53986.5185185185 & -450.518518518497 \tabularnewline
4 & 52408 & 46805.8518518518 & 5602.14814814817 \tabularnewline
5 & 41454 & 40231.6851851852 & 1222.31481481484 \tabularnewline
6 & 38271 & 41968.5185185185 & -3697.51851851849 \tabularnewline
7 & 35306 & 32225.1851851852 & 3080.81481481477 \tabularnewline
8 & 26414 & 28113.8518518518 & -1699.85185185184 \tabularnewline
9 & 31917 & 31751.1851851852 & 165.814814814821 \tabularnewline
10 & 38030 & 37895.1851851852 & 134.814814814837 \tabularnewline
11 & 27534 & 30205.0185185185 & -2671.01851851852 \tabularnewline
12 & 18387 & 20098.1481481481 & -1711.14814814815 \tabularnewline
13 & 50556 & 51682.6777777778 & -1126.67777777779 \tabularnewline
14 & 43901 & 47746.0111111111 & -3845.01111111109 \tabularnewline
15 & 48572 & 55148.8444444444 & -6576.84444444445 \tabularnewline
16 & 43899 & 47968.1777777778 & -4069.17777777778 \tabularnewline
17 & 37532 & 41394.0111111111 & -3862.01111111112 \tabularnewline
18 & 40357 & 43130.8444444445 & -2773.84444444445 \tabularnewline
19 & 35489 & 33387.5111111111 & 2101.48888888889 \tabularnewline
20 & 29027 & 29276.1777777778 & -249.177777777783 \tabularnewline
21 & 34485 & 32913.5111111111 & 1571.48888888889 \tabularnewline
22 & 42598 & 39057.5111111111 & 3540.48888888888 \tabularnewline
23 & 30306 & 31367.3444444444 & -1061.34444444445 \tabularnewline
24 & 26451 & 21260.4740740741 & 5190.52592592594 \tabularnewline
25 & 47460 & 52845.0037037037 & -5385.00370370372 \tabularnewline
26 & 50104 & 48908.337037037 & 1195.66296296298 \tabularnewline
27 & 61465 & 56311.1703703704 & 5153.82962962962 \tabularnewline
28 & 53726 & 49130.5037037037 & 4595.49629629629 \tabularnewline
29 & 39477 & 42556.337037037 & -3079.33703703704 \tabularnewline
30 & 43895 & 44293.1703703704 & -398.170370370378 \tabularnewline
31 & 31481 & 34549.8370370370 & -3068.83703703703 \tabularnewline
32 & 29896 & 30438.5037037037 & -542.503703703708 \tabularnewline
33 & 33842 & 34075.8370370370 & -233.837037037040 \tabularnewline
34 & 39120 & 40219.837037037 & -1099.83703703704 \tabularnewline
35 & 33702 & 32529.6703703704 & 1172.32962962963 \tabularnewline
36 & 25094 & 22422.8 & 2671.20000000001 \tabularnewline
37 & 51442 & 54007.3296296296 & -2565.32962962964 \tabularnewline
38 & 45594 & 50070.662962963 & -4476.66296296294 \tabularnewline
39 & 52518 & 57473.4962962963 & -4955.4962962963 \tabularnewline
40 & 48564 & 50292.8296296296 & -1728.82962962963 \tabularnewline
41 & 41745 & 43718.662962963 & -1973.66296296297 \tabularnewline
42 & 49585 & 45455.4962962963 & 4129.5037037037 \tabularnewline
43 & 32747 & 35712.1629629630 & -2965.16296296296 \tabularnewline
44 & 33379 & 31600.8296296296 & 1778.17037037037 \tabularnewline
45 & 35645 & 35238.1629629630 & 406.837037037037 \tabularnewline
46 & 37034 & 41382.162962963 & -4348.16296296297 \tabularnewline
47 & 35681 & 33691.9962962963 & 1989.00370370371 \tabularnewline
48 & 20972 & 23585.1259259259 & -2613.12592592592 \tabularnewline
49 & 58552 & 55169.6555555556 & 3382.34444444443 \tabularnewline
50 & 54955 & 51232.9888888889 & 3722.01111111113 \tabularnewline
51 & 65540 & 58635.8222222222 & 6904.17777777777 \tabularnewline
52 & 51570 & 51455.1555555556 & 114.844444444442 \tabularnewline
53 & 51145 & 44880.9888888889 & 6264.01111111111 \tabularnewline
54 & 46641 & 46617.8222222222 & 23.1777777777726 \tabularnewline
55 & 35704 & 36874.4888888889 & -1170.48888888888 \tabularnewline
56 & 33253 & 32763.1555555556 & 489.844444444443 \tabularnewline
57 & 35193 & 36400.4888888889 & -1207.48888888889 \tabularnewline
58 & 41668 & 42544.4888888889 & -876.488888888891 \tabularnewline
59 & 34865 & 34854.3222222222 & 10.6777777777801 \tabularnewline
60 & 21210 & 24747.4518518518 & -3537.45185185184 \tabularnewline
61 & 56126 & 56331.9814814815 & -205.981481481489 \tabularnewline
62 & 49231 & 52395.3148148148 & -3164.31481481479 \tabularnewline
63 & 59723 & 59798.1481481482 & -75.148148148152 \tabularnewline
64 & 48103 & 52617.4814814815 & -4514.48148148148 \tabularnewline
65 & 47472 & 46043.3148148148 & 1428.68518518518 \tabularnewline
66 & 50497 & 47780.1481481482 & 2716.85185185185 \tabularnewline
67 & 40059 & 38036.8148148148 & 2022.18518518519 \tabularnewline
68 & 34149 & 33925.4814814815 & 223.518518518516 \tabularnewline
69 & 36860 & 37562.8148148148 & -702.814814814815 \tabularnewline
70 & 46356 & 43706.8148148148 & 2649.18518518518 \tabularnewline
71 & 36577 & 36016.6481481481 & 560.351851851855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&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]56421[/C][C]50520.3518518518[/C][C]5900.6481481482[/C][/ROW]
[ROW][C]2[/C][C]53152[/C][C]46583.6851851853[/C][C]6568.3148148147[/C][/ROW]
[ROW][C]3[/C][C]53536[/C][C]53986.5185185185[/C][C]-450.518518518497[/C][/ROW]
[ROW][C]4[/C][C]52408[/C][C]46805.8518518518[/C][C]5602.14814814817[/C][/ROW]
[ROW][C]5[/C][C]41454[/C][C]40231.6851851852[/C][C]1222.31481481484[/C][/ROW]
[ROW][C]6[/C][C]38271[/C][C]41968.5185185185[/C][C]-3697.51851851849[/C][/ROW]
[ROW][C]7[/C][C]35306[/C][C]32225.1851851852[/C][C]3080.81481481477[/C][/ROW]
[ROW][C]8[/C][C]26414[/C][C]28113.8518518518[/C][C]-1699.85185185184[/C][/ROW]
[ROW][C]9[/C][C]31917[/C][C]31751.1851851852[/C][C]165.814814814821[/C][/ROW]
[ROW][C]10[/C][C]38030[/C][C]37895.1851851852[/C][C]134.814814814837[/C][/ROW]
[ROW][C]11[/C][C]27534[/C][C]30205.0185185185[/C][C]-2671.01851851852[/C][/ROW]
[ROW][C]12[/C][C]18387[/C][C]20098.1481481481[/C][C]-1711.14814814815[/C][/ROW]
[ROW][C]13[/C][C]50556[/C][C]51682.6777777778[/C][C]-1126.67777777779[/C][/ROW]
[ROW][C]14[/C][C]43901[/C][C]47746.0111111111[/C][C]-3845.01111111109[/C][/ROW]
[ROW][C]15[/C][C]48572[/C][C]55148.8444444444[/C][C]-6576.84444444445[/C][/ROW]
[ROW][C]16[/C][C]43899[/C][C]47968.1777777778[/C][C]-4069.17777777778[/C][/ROW]
[ROW][C]17[/C][C]37532[/C][C]41394.0111111111[/C][C]-3862.01111111112[/C][/ROW]
[ROW][C]18[/C][C]40357[/C][C]43130.8444444445[/C][C]-2773.84444444445[/C][/ROW]
[ROW][C]19[/C][C]35489[/C][C]33387.5111111111[/C][C]2101.48888888889[/C][/ROW]
[ROW][C]20[/C][C]29027[/C][C]29276.1777777778[/C][C]-249.177777777783[/C][/ROW]
[ROW][C]21[/C][C]34485[/C][C]32913.5111111111[/C][C]1571.48888888889[/C][/ROW]
[ROW][C]22[/C][C]42598[/C][C]39057.5111111111[/C][C]3540.48888888888[/C][/ROW]
[ROW][C]23[/C][C]30306[/C][C]31367.3444444444[/C][C]-1061.34444444445[/C][/ROW]
[ROW][C]24[/C][C]26451[/C][C]21260.4740740741[/C][C]5190.52592592594[/C][/ROW]
[ROW][C]25[/C][C]47460[/C][C]52845.0037037037[/C][C]-5385.00370370372[/C][/ROW]
[ROW][C]26[/C][C]50104[/C][C]48908.337037037[/C][C]1195.66296296298[/C][/ROW]
[ROW][C]27[/C][C]61465[/C][C]56311.1703703704[/C][C]5153.82962962962[/C][/ROW]
[ROW][C]28[/C][C]53726[/C][C]49130.5037037037[/C][C]4595.49629629629[/C][/ROW]
[ROW][C]29[/C][C]39477[/C][C]42556.337037037[/C][C]-3079.33703703704[/C][/ROW]
[ROW][C]30[/C][C]43895[/C][C]44293.1703703704[/C][C]-398.170370370378[/C][/ROW]
[ROW][C]31[/C][C]31481[/C][C]34549.8370370370[/C][C]-3068.83703703703[/C][/ROW]
[ROW][C]32[/C][C]29896[/C][C]30438.5037037037[/C][C]-542.503703703708[/C][/ROW]
[ROW][C]33[/C][C]33842[/C][C]34075.8370370370[/C][C]-233.837037037040[/C][/ROW]
[ROW][C]34[/C][C]39120[/C][C]40219.837037037[/C][C]-1099.83703703704[/C][/ROW]
[ROW][C]35[/C][C]33702[/C][C]32529.6703703704[/C][C]1172.32962962963[/C][/ROW]
[ROW][C]36[/C][C]25094[/C][C]22422.8[/C][C]2671.20000000001[/C][/ROW]
[ROW][C]37[/C][C]51442[/C][C]54007.3296296296[/C][C]-2565.32962962964[/C][/ROW]
[ROW][C]38[/C][C]45594[/C][C]50070.662962963[/C][C]-4476.66296296294[/C][/ROW]
[ROW][C]39[/C][C]52518[/C][C]57473.4962962963[/C][C]-4955.4962962963[/C][/ROW]
[ROW][C]40[/C][C]48564[/C][C]50292.8296296296[/C][C]-1728.82962962963[/C][/ROW]
[ROW][C]41[/C][C]41745[/C][C]43718.662962963[/C][C]-1973.66296296297[/C][/ROW]
[ROW][C]42[/C][C]49585[/C][C]45455.4962962963[/C][C]4129.5037037037[/C][/ROW]
[ROW][C]43[/C][C]32747[/C][C]35712.1629629630[/C][C]-2965.16296296296[/C][/ROW]
[ROW][C]44[/C][C]33379[/C][C]31600.8296296296[/C][C]1778.17037037037[/C][/ROW]
[ROW][C]45[/C][C]35645[/C][C]35238.1629629630[/C][C]406.837037037037[/C][/ROW]
[ROW][C]46[/C][C]37034[/C][C]41382.162962963[/C][C]-4348.16296296297[/C][/ROW]
[ROW][C]47[/C][C]35681[/C][C]33691.9962962963[/C][C]1989.00370370371[/C][/ROW]
[ROW][C]48[/C][C]20972[/C][C]23585.1259259259[/C][C]-2613.12592592592[/C][/ROW]
[ROW][C]49[/C][C]58552[/C][C]55169.6555555556[/C][C]3382.34444444443[/C][/ROW]
[ROW][C]50[/C][C]54955[/C][C]51232.9888888889[/C][C]3722.01111111113[/C][/ROW]
[ROW][C]51[/C][C]65540[/C][C]58635.8222222222[/C][C]6904.17777777777[/C][/ROW]
[ROW][C]52[/C][C]51570[/C][C]51455.1555555556[/C][C]114.844444444442[/C][/ROW]
[ROW][C]53[/C][C]51145[/C][C]44880.9888888889[/C][C]6264.01111111111[/C][/ROW]
[ROW][C]54[/C][C]46641[/C][C]46617.8222222222[/C][C]23.1777777777726[/C][/ROW]
[ROW][C]55[/C][C]35704[/C][C]36874.4888888889[/C][C]-1170.48888888888[/C][/ROW]
[ROW][C]56[/C][C]33253[/C][C]32763.1555555556[/C][C]489.844444444443[/C][/ROW]
[ROW][C]57[/C][C]35193[/C][C]36400.4888888889[/C][C]-1207.48888888889[/C][/ROW]
[ROW][C]58[/C][C]41668[/C][C]42544.4888888889[/C][C]-876.488888888891[/C][/ROW]
[ROW][C]59[/C][C]34865[/C][C]34854.3222222222[/C][C]10.6777777777801[/C][/ROW]
[ROW][C]60[/C][C]21210[/C][C]24747.4518518518[/C][C]-3537.45185185184[/C][/ROW]
[ROW][C]61[/C][C]56126[/C][C]56331.9814814815[/C][C]-205.981481481489[/C][/ROW]
[ROW][C]62[/C][C]49231[/C][C]52395.3148148148[/C][C]-3164.31481481479[/C][/ROW]
[ROW][C]63[/C][C]59723[/C][C]59798.1481481482[/C][C]-75.148148148152[/C][/ROW]
[ROW][C]64[/C][C]48103[/C][C]52617.4814814815[/C][C]-4514.48148148148[/C][/ROW]
[ROW][C]65[/C][C]47472[/C][C]46043.3148148148[/C][C]1428.68518518518[/C][/ROW]
[ROW][C]66[/C][C]50497[/C][C]47780.1481481482[/C][C]2716.85185185185[/C][/ROW]
[ROW][C]67[/C][C]40059[/C][C]38036.8148148148[/C][C]2022.18518518519[/C][/ROW]
[ROW][C]68[/C][C]34149[/C][C]33925.4814814815[/C][C]223.518518518516[/C][/ROW]
[ROW][C]69[/C][C]36860[/C][C]37562.8148148148[/C][C]-702.814814814815[/C][/ROW]
[ROW][C]70[/C][C]46356[/C][C]43706.8148148148[/C][C]2649.18518518518[/C][/ROW]
[ROW][C]71[/C][C]36577[/C][C]36016.6481481481[/C][C]560.351851851855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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
15642150520.35185185185900.6481481482
25315246583.68518518536568.3148148147
35353653986.5185185185-450.518518518497
45240846805.85185185185602.14814814817
54145440231.68518518521222.31481481484
63827141968.5185185185-3697.51851851849
73530632225.18518518523080.81481481477
82641428113.8518518518-1699.85185185184
93191731751.1851851852165.814814814821
103803037895.1851851852134.814814814837
112753430205.0185185185-2671.01851851852
121838720098.1481481481-1711.14814814815
135055651682.6777777778-1126.67777777779
144390147746.0111111111-3845.01111111109
154857255148.8444444444-6576.84444444445
164389947968.1777777778-4069.17777777778
173753241394.0111111111-3862.01111111112
184035743130.8444444445-2773.84444444445
193548933387.51111111112101.48888888889
202902729276.1777777778-249.177777777783
213448532913.51111111111571.48888888889
224259839057.51111111113540.48888888888
233030631367.3444444444-1061.34444444445
242645121260.47407407415190.52592592594
254746052845.0037037037-5385.00370370372
265010448908.3370370371195.66296296298
276146556311.17037037045153.82962962962
285372649130.50370370374595.49629629629
293947742556.337037037-3079.33703703704
304389544293.1703703704-398.170370370378
313148134549.8370370370-3068.83703703703
322989630438.5037037037-542.503703703708
333384234075.8370370370-233.837037037040
343912040219.837037037-1099.83703703704
353370232529.67037037041172.32962962963
362509422422.82671.20000000001
375144254007.3296296296-2565.32962962964
384559450070.662962963-4476.66296296294
395251857473.4962962963-4955.4962962963
404856450292.8296296296-1728.82962962963
414174543718.662962963-1973.66296296297
424958545455.49629629634129.5037037037
433274735712.1629629630-2965.16296296296
443337931600.82962962961778.17037037037
453564535238.1629629630406.837037037037
463703441382.162962963-4348.16296296297
473568133691.99629629631989.00370370371
482097223585.1259259259-2613.12592592592
495855255169.65555555563382.34444444443
505495551232.98888888893722.01111111113
516554058635.82222222226904.17777777777
525157051455.1555555556114.844444444442
535114544880.98888888896264.01111111111
544664146617.822222222223.1777777777726
553570436874.4888888889-1170.48888888888
563325332763.1555555556489.844444444443
573519336400.4888888889-1207.48888888889
584166842544.4888888889-876.488888888891
593486534854.322222222210.6777777777801
602121024747.4518518518-3537.45185185184
615612656331.9814814815-205.981481481489
624923152395.3148148148-3164.31481481479
635972359798.1481481482-75.148148148152
644810352617.4814814815-4514.48148148148
654747246043.31481481481428.68518518518
665049747780.14814814822716.85185185185
674005938036.81481481482022.18518518519
683414933925.4814814815223.518518518516
693686037562.8148148148-702.814814814815
704635643706.81481481482649.18518518518
713657736016.6481481481560.351851851855







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1036778206025560.2073556412051120.896322179397444
170.09219828344044020.1843965668808800.90780171655956
180.4420584423109980.8841168846219960.557941557689002
190.4609251785653300.9218503571306590.53907482143467
200.5493513952148220.9012972095703560.450648604785178
210.5740991666296470.8518016667407050.425900833370353
220.6693162288680490.6613675422639030.330683771131951
230.6410205867149810.7179588265700380.358979413285019
240.8182900234819460.3634199530361090.181709976518054
250.8299608691354430.3400782617291140.170039130864557
260.8028773781066390.3942452437867220.197122621893361
270.9427896177802220.1144207644395570.0572103822197784
280.9677829714379180.06443405712416330.0322170285620817
290.9590835832657480.08183283346850470.0409164167342523
300.9470854011090730.1058291977818550.0529145988909275
310.9381028845536350.1237942308927290.0618971154463646
320.9095327593284140.1809344813431720.0904672406715862
330.8702713144346970.2594573711306060.129728685565303
340.8242563330415980.3514873339168030.175743666958402
350.7887908303207340.4224183393585320.211209169679266
360.8138291249876390.3723417500247230.186170875012361
370.7771845056317360.4456309887365270.222815494368264
380.7808329698075310.4383340603849380.219167030192469
390.880473847765660.2390523044686790.119526152234340
400.8344943243555820.3310113512888360.165505675644418
410.8803117753912130.2393764492175750.119688224608787
420.8968242670909810.2063514658180380.103175732909019
430.8949554745227440.2100890509545130.105044525477256
440.8580377349963960.2839245300072080.141962265003604
450.7977615425629430.4044769148741140.202238457437057
460.8932203114606250.2135593770787510.106779688539376
470.8499120238684110.3001759522631770.150087976131589
480.7898516209035450.4202967581929090.210148379096455
490.7491008752890160.5017982494219670.250899124710984
500.8039403469373440.3921193061253120.196059653062656
510.9174675319107880.1650649361784240.0825324680892118
520.9391264048075280.1217471903849440.060873595192472
530.9941050338091230.01178993238175390.00589496619087696
540.9819059216178860.03618815676422880.0180940783821144
550.9621220034449330.07575599311013330.0378779965550666

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.103677820602556 & 0.207355641205112 & 0.896322179397444 \tabularnewline
17 & 0.0921982834404402 & 0.184396566880880 & 0.90780171655956 \tabularnewline
18 & 0.442058442310998 & 0.884116884621996 & 0.557941557689002 \tabularnewline
19 & 0.460925178565330 & 0.921850357130659 & 0.53907482143467 \tabularnewline
20 & 0.549351395214822 & 0.901297209570356 & 0.450648604785178 \tabularnewline
21 & 0.574099166629647 & 0.851801666740705 & 0.425900833370353 \tabularnewline
22 & 0.669316228868049 & 0.661367542263903 & 0.330683771131951 \tabularnewline
23 & 0.641020586714981 & 0.717958826570038 & 0.358979413285019 \tabularnewline
24 & 0.818290023481946 & 0.363419953036109 & 0.181709976518054 \tabularnewline
25 & 0.829960869135443 & 0.340078261729114 & 0.170039130864557 \tabularnewline
26 & 0.802877378106639 & 0.394245243786722 & 0.197122621893361 \tabularnewline
27 & 0.942789617780222 & 0.114420764439557 & 0.0572103822197784 \tabularnewline
28 & 0.967782971437918 & 0.0644340571241633 & 0.0322170285620817 \tabularnewline
29 & 0.959083583265748 & 0.0818328334685047 & 0.0409164167342523 \tabularnewline
30 & 0.947085401109073 & 0.105829197781855 & 0.0529145988909275 \tabularnewline
31 & 0.938102884553635 & 0.123794230892729 & 0.0618971154463646 \tabularnewline
32 & 0.909532759328414 & 0.180934481343172 & 0.0904672406715862 \tabularnewline
33 & 0.870271314434697 & 0.259457371130606 & 0.129728685565303 \tabularnewline
34 & 0.824256333041598 & 0.351487333916803 & 0.175743666958402 \tabularnewline
35 & 0.788790830320734 & 0.422418339358532 & 0.211209169679266 \tabularnewline
36 & 0.813829124987639 & 0.372341750024723 & 0.186170875012361 \tabularnewline
37 & 0.777184505631736 & 0.445630988736527 & 0.222815494368264 \tabularnewline
38 & 0.780832969807531 & 0.438334060384938 & 0.219167030192469 \tabularnewline
39 & 0.88047384776566 & 0.239052304468679 & 0.119526152234340 \tabularnewline
40 & 0.834494324355582 & 0.331011351288836 & 0.165505675644418 \tabularnewline
41 & 0.880311775391213 & 0.239376449217575 & 0.119688224608787 \tabularnewline
42 & 0.896824267090981 & 0.206351465818038 & 0.103175732909019 \tabularnewline
43 & 0.894955474522744 & 0.210089050954513 & 0.105044525477256 \tabularnewline
44 & 0.858037734996396 & 0.283924530007208 & 0.141962265003604 \tabularnewline
45 & 0.797761542562943 & 0.404476914874114 & 0.202238457437057 \tabularnewline
46 & 0.893220311460625 & 0.213559377078751 & 0.106779688539376 \tabularnewline
47 & 0.849912023868411 & 0.300175952263177 & 0.150087976131589 \tabularnewline
48 & 0.789851620903545 & 0.420296758192909 & 0.210148379096455 \tabularnewline
49 & 0.749100875289016 & 0.501798249421967 & 0.250899124710984 \tabularnewline
50 & 0.803940346937344 & 0.392119306125312 & 0.196059653062656 \tabularnewline
51 & 0.917467531910788 & 0.165064936178424 & 0.0825324680892118 \tabularnewline
52 & 0.939126404807528 & 0.121747190384944 & 0.060873595192472 \tabularnewline
53 & 0.994105033809123 & 0.0117899323817539 & 0.00589496619087696 \tabularnewline
54 & 0.981905921617886 & 0.0361881567642288 & 0.0180940783821144 \tabularnewline
55 & 0.962122003444933 & 0.0757559931101333 & 0.0378779965550666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&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]16[/C][C]0.103677820602556[/C][C]0.207355641205112[/C][C]0.896322179397444[/C][/ROW]
[ROW][C]17[/C][C]0.0921982834404402[/C][C]0.184396566880880[/C][C]0.90780171655956[/C][/ROW]
[ROW][C]18[/C][C]0.442058442310998[/C][C]0.884116884621996[/C][C]0.557941557689002[/C][/ROW]
[ROW][C]19[/C][C]0.460925178565330[/C][C]0.921850357130659[/C][C]0.53907482143467[/C][/ROW]
[ROW][C]20[/C][C]0.549351395214822[/C][C]0.901297209570356[/C][C]0.450648604785178[/C][/ROW]
[ROW][C]21[/C][C]0.574099166629647[/C][C]0.851801666740705[/C][C]0.425900833370353[/C][/ROW]
[ROW][C]22[/C][C]0.669316228868049[/C][C]0.661367542263903[/C][C]0.330683771131951[/C][/ROW]
[ROW][C]23[/C][C]0.641020586714981[/C][C]0.717958826570038[/C][C]0.358979413285019[/C][/ROW]
[ROW][C]24[/C][C]0.818290023481946[/C][C]0.363419953036109[/C][C]0.181709976518054[/C][/ROW]
[ROW][C]25[/C][C]0.829960869135443[/C][C]0.340078261729114[/C][C]0.170039130864557[/C][/ROW]
[ROW][C]26[/C][C]0.802877378106639[/C][C]0.394245243786722[/C][C]0.197122621893361[/C][/ROW]
[ROW][C]27[/C][C]0.942789617780222[/C][C]0.114420764439557[/C][C]0.0572103822197784[/C][/ROW]
[ROW][C]28[/C][C]0.967782971437918[/C][C]0.0644340571241633[/C][C]0.0322170285620817[/C][/ROW]
[ROW][C]29[/C][C]0.959083583265748[/C][C]0.0818328334685047[/C][C]0.0409164167342523[/C][/ROW]
[ROW][C]30[/C][C]0.947085401109073[/C][C]0.105829197781855[/C][C]0.0529145988909275[/C][/ROW]
[ROW][C]31[/C][C]0.938102884553635[/C][C]0.123794230892729[/C][C]0.0618971154463646[/C][/ROW]
[ROW][C]32[/C][C]0.909532759328414[/C][C]0.180934481343172[/C][C]0.0904672406715862[/C][/ROW]
[ROW][C]33[/C][C]0.870271314434697[/C][C]0.259457371130606[/C][C]0.129728685565303[/C][/ROW]
[ROW][C]34[/C][C]0.824256333041598[/C][C]0.351487333916803[/C][C]0.175743666958402[/C][/ROW]
[ROW][C]35[/C][C]0.788790830320734[/C][C]0.422418339358532[/C][C]0.211209169679266[/C][/ROW]
[ROW][C]36[/C][C]0.813829124987639[/C][C]0.372341750024723[/C][C]0.186170875012361[/C][/ROW]
[ROW][C]37[/C][C]0.777184505631736[/C][C]0.445630988736527[/C][C]0.222815494368264[/C][/ROW]
[ROW][C]38[/C][C]0.780832969807531[/C][C]0.438334060384938[/C][C]0.219167030192469[/C][/ROW]
[ROW][C]39[/C][C]0.88047384776566[/C][C]0.239052304468679[/C][C]0.119526152234340[/C][/ROW]
[ROW][C]40[/C][C]0.834494324355582[/C][C]0.331011351288836[/C][C]0.165505675644418[/C][/ROW]
[ROW][C]41[/C][C]0.880311775391213[/C][C]0.239376449217575[/C][C]0.119688224608787[/C][/ROW]
[ROW][C]42[/C][C]0.896824267090981[/C][C]0.206351465818038[/C][C]0.103175732909019[/C][/ROW]
[ROW][C]43[/C][C]0.894955474522744[/C][C]0.210089050954513[/C][C]0.105044525477256[/C][/ROW]
[ROW][C]44[/C][C]0.858037734996396[/C][C]0.283924530007208[/C][C]0.141962265003604[/C][/ROW]
[ROW][C]45[/C][C]0.797761542562943[/C][C]0.404476914874114[/C][C]0.202238457437057[/C][/ROW]
[ROW][C]46[/C][C]0.893220311460625[/C][C]0.213559377078751[/C][C]0.106779688539376[/C][/ROW]
[ROW][C]47[/C][C]0.849912023868411[/C][C]0.300175952263177[/C][C]0.150087976131589[/C][/ROW]
[ROW][C]48[/C][C]0.789851620903545[/C][C]0.420296758192909[/C][C]0.210148379096455[/C][/ROW]
[ROW][C]49[/C][C]0.749100875289016[/C][C]0.501798249421967[/C][C]0.250899124710984[/C][/ROW]
[ROW][C]50[/C][C]0.803940346937344[/C][C]0.392119306125312[/C][C]0.196059653062656[/C][/ROW]
[ROW][C]51[/C][C]0.917467531910788[/C][C]0.165064936178424[/C][C]0.0825324680892118[/C][/ROW]
[ROW][C]52[/C][C]0.939126404807528[/C][C]0.121747190384944[/C][C]0.060873595192472[/C][/ROW]
[ROW][C]53[/C][C]0.994105033809123[/C][C]0.0117899323817539[/C][C]0.00589496619087696[/C][/ROW]
[ROW][C]54[/C][C]0.981905921617886[/C][C]0.0361881567642288[/C][C]0.0180940783821144[/C][/ROW]
[ROW][C]55[/C][C]0.962122003444933[/C][C]0.0757559931101333[/C][C]0.0378779965550666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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
160.1036778206025560.2073556412051120.896322179397444
170.09219828344044020.1843965668808800.90780171655956
180.4420584423109980.8841168846219960.557941557689002
190.4609251785653300.9218503571306590.53907482143467
200.5493513952148220.9012972095703560.450648604785178
210.5740991666296470.8518016667407050.425900833370353
220.6693162288680490.6613675422639030.330683771131951
230.6410205867149810.7179588265700380.358979413285019
240.8182900234819460.3634199530361090.181709976518054
250.8299608691354430.3400782617291140.170039130864557
260.8028773781066390.3942452437867220.197122621893361
270.9427896177802220.1144207644395570.0572103822197784
280.9677829714379180.06443405712416330.0322170285620817
290.9590835832657480.08183283346850470.0409164167342523
300.9470854011090730.1058291977818550.0529145988909275
310.9381028845536350.1237942308927290.0618971154463646
320.9095327593284140.1809344813431720.0904672406715862
330.8702713144346970.2594573711306060.129728685565303
340.8242563330415980.3514873339168030.175743666958402
350.7887908303207340.4224183393585320.211209169679266
360.8138291249876390.3723417500247230.186170875012361
370.7771845056317360.4456309887365270.222815494368264
380.7808329698075310.4383340603849380.219167030192469
390.880473847765660.2390523044686790.119526152234340
400.8344943243555820.3310113512888360.165505675644418
410.8803117753912130.2393764492175750.119688224608787
420.8968242670909810.2063514658180380.103175732909019
430.8949554745227440.2100890509545130.105044525477256
440.8580377349963960.2839245300072080.141962265003604
450.7977615425629430.4044769148741140.202238457437057
460.8932203114606250.2135593770787510.106779688539376
470.8499120238684110.3001759522631770.150087976131589
480.7898516209035450.4202967581929090.210148379096455
490.7491008752890160.5017982494219670.250899124710984
500.8039403469373440.3921193061253120.196059653062656
510.9174675319107880.1650649361784240.0825324680892118
520.9391264048075280.1217471903849440.060873595192472
530.9941050338091230.01178993238175390.00589496619087696
540.9819059216178860.03618815676422880.0180940783821144
550.9621220034449330.07575599311013330.0378779965550666







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.05NOK
10% type I error level50.125NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.05 & NOK \tabularnewline
10% type I error level & 5 & 0.125 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=10906&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.05[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.125[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=10906&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=10906&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 level20.05NOK
10% type I error level50.125NOK



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