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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Dec 2012 10:49:53 -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/2012/Dec/19/t1355932209v7p0ku8tcbina99.htm/, Retrieved Thu, 31 Oct 2024 23:53:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202098, Retrieved Thu, 31 Oct 2024 23:53:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-10-31 14:44:12] [83c7ccdb194e46f99f0902896e3c3ab1]
- R       [Multiple Regression] [Paper deel 3] [2012-12-19 15:49:53] [413ae3079a2d7d57ac33b6b2184bdf0e] [Current]
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Dataseries X:
18.2	2687	1870	1890	145.7	352.2
143.8	13271	9115	8190	-279.0	83.0
23.4	13621	4848	4572	485.0	898.9
1.1	3614	367	90	14.1	24.6
49.5	6425	6131	2448	345.8	682.5
4.8	1022	1754	1370	72.0	119.5
20.8	1093	1679	1070	100.9	164.5
19.4	1529	1295	444	25.6	137.0
2.1	2788	271	304	23.5	28.9
79.4	19788	9084	10636	1092.9	2576.8
2.8	327	542	959	54.1	72.5
3.8	1117	1038	478	59.7	91.7
4.1	5401	550	376	25.6	37.5
13.2	1128	1516	430	-47.0	26.7
2.8	1633	701	679	74.3	135.9
48.5	44736	16197	4653	-732.5	-651.9
6.2	5651	1254	2002	310.7	407.9
10.8	5835	4053	1601	-93.8	173.8
3.8	278	205	853	44.8	50.5
21.9	5074	2557	1892	239.9	578.3
12.6	866	1487	944	71.7	115.4
128.0	4418	8793	4459	283.6	456.5
87.3	6914	7029	7957	400.6	754.7
16.0	862	1601	1093	66.9	106.8
0.7	401	176	1084	55.6	57.0
22.5	430	1155	1045	55.7	70.8
15.4	799	1140	683	57.6	89.2
3.0	4789	453	367	40.2	51.4
2.1	2548	264	181	22.2	26.2
4.1	5249	527	346	37.8	56.2
6.4	3494	1653	1442	160.9	320.3
26.6	1804	2564	483	70.5	164.9
304.0	26432	28285	33172	2336.0	3562.0
18.6	623	2247	797	57.0	93.8
65.0	1608	6615	829	56.1	134.0
66.2	4662	4781	2988	28.7	371.5
83.0	5769	6571	9462	482.0	792.0
62.0	6259	4152	3090	283.7	524.5
1.6	1654	451	779	84.8	130.4
400.2	52634	50056	95697	6555.0	9874.0
23.3	999	1878	393	-173.5	-108.1
4.6	1679	1354	687	93.8	154.6
164.6	4178	17124	2091	180.8	390.4
1.9	223	557	1040	60.6	63.7
57.5	6307	8199	598	-771.5	-524.3
2.4	3720	356	211	26.6	34.8
77.3	3442	5080	2673	235.4	361.5
15.8	33406	3222	1413	201.7	246.7
0.6	1257	355	181	167.5	304.0
3.5	1743	597	717	121.6	172.4
9.0	12505	1302	702	108.4	131.4
62.0	3940	4317	3940	315.2	566.3
7.4	8998	882	988	93.0	119.0
15.6	21419	2516	930	107.6	164.7
25.2	2366	3305	1117	131.2	256.5
25.4	2448	3484	1036	48.8	257.1
3.5	1440	1617	639	81.7	126.4
27.3	14045	15636	2754	418.0	1462.0
37.5	4084	4346	3023	302.7	521.7
3.4	3010	749	1120	146.3	209.2
14.3	1286	1734	361	69.2	145.7
6.1	707	706	275	61.4	77.8
4.9	3086	1739	1507	202.7	335.2
3.3	252	312	883	41.7	60.6
7.0	11052	1097	606	64.9	97.6
8.2	9672	1037	829	92.6	118.2
43.5	1112	3689	542	30.3	96.9
48.5	1104	5123	910	63.7	133.3
5.4	478	672	866	67.1	101.6
49.5	10348	5721	1915	223.6	322.5
29.1	2769	3725	663	-208.4	12.4
2.6	752	2149	101	11.1	15.2
0.8	4989	518	53	-3.1	-0.3
184.8	10528	14992	5377	312.7	710.7
2.3	1995	2662	341	34.7	100.7
8.0	2286	2235	2306	195.3	219.0
10.3	952	1307	309	35.4	92.8
50.0	2957	2806	457	40.6	93.5
118.1	2535	5958	1921	177.0	288.0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Aantal_werknemers[t] = + 6.45466517236696 -0.00148584042151191Activa[t] + 0.0104249780876803Omzet[t] + 0.000673311632243301Marktwaarde[t] + 0.0445309265089534Winst[t] -0.0377493453559151Cashflow[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Aantal_werknemers[t] =  +  6.45466517236696 -0.00148584042151191Activa[t] +  0.0104249780876803Omzet[t] +  0.000673311632243301Marktwaarde[t] +  0.0445309265089534Winst[t] -0.0377493453559151Cashflow[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Aantal_werknemers[t] =  +  6.45466517236696 -0.00148584042151191Activa[t] +  0.0104249780876803Omzet[t] +  0.000673311632243301Marktwaarde[t] +  0.0445309265089534Winst[t] -0.0377493453559151Cashflow[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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
Aantal_werknemers[t] = + 6.45466517236696 -0.00148584042151191Activa[t] + 0.0104249780876803Omzet[t] + 0.000673311632243301Marktwaarde[t] + 0.0445309265089534Winst[t] -0.0377493453559151Cashflow[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.454665172366963.4430351.87470.0648340.032417
Activa-0.001485840421511910.000439-3.3840.0011520.000576
Omzet0.01042497808768030.00097310.714700
Marktwaarde0.0006733116322433010.0012080.55750.5788610.28943
Winst0.04453092650895340.0276871.60840.1120670.056034
Cashflow-0.03774934535591510.017717-2.13070.0364820.018241

\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) & 6.45466517236696 & 3.443035 & 1.8747 & 0.064834 & 0.032417 \tabularnewline
Activa & -0.00148584042151191 & 0.000439 & -3.384 & 0.001152 & 0.000576 \tabularnewline
Omzet & 0.0104249780876803 & 0.000973 & 10.7147 & 0 & 0 \tabularnewline
Marktwaarde & 0.000673311632243301 & 0.001208 & 0.5575 & 0.578861 & 0.28943 \tabularnewline
Winst & 0.0445309265089534 & 0.027687 & 1.6084 & 0.112067 & 0.056034 \tabularnewline
Cashflow & -0.0377493453559151 & 0.017717 & -2.1307 & 0.036482 & 0.018241 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&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]6.45466517236696[/C][C]3.443035[/C][C]1.8747[/C][C]0.064834[/C][C]0.032417[/C][/ROW]
[ROW][C]Activa[/C][C]-0.00148584042151191[/C][C]0.000439[/C][C]-3.384[/C][C]0.001152[/C][C]0.000576[/C][/ROW]
[ROW][C]Omzet[/C][C]0.0104249780876803[/C][C]0.000973[/C][C]10.7147[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Marktwaarde[/C][C]0.000673311632243301[/C][C]0.001208[/C][C]0.5575[/C][C]0.578861[/C][C]0.28943[/C][/ROW]
[ROW][C]Winst[/C][C]0.0445309265089534[/C][C]0.027687[/C][C]1.6084[/C][C]0.112067[/C][C]0.056034[/C][/ROW]
[ROW][C]Cashflow[/C][C]-0.0377493453559151[/C][C]0.017717[/C][C]-2.1307[/C][C]0.036482[/C][C]0.018241[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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)6.454665172366963.4430351.87470.0648340.032417
Activa-0.001485840421511910.000439-3.3840.0011520.000576
Omzet0.01042497808768030.00097310.714700
Marktwaarde0.0006733116322433010.0012080.55750.5788610.28943
Winst0.04453092650895340.0276871.60840.1120670.056034
Cashflow-0.03774934535591510.017717-2.13070.0364820.018241







Multiple Linear Regression - Regression Statistics
Multiple R0.939495070764463
R-squared0.882650987990724
Adjusted R-squared0.874613384428444
F-TEST (value)109.815193191792
F-TEST (DF numerator)5
F-TEST (DF denominator)73
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.8409201345601
Sum Squared Residuals38084.6571793147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939495070764463 \tabularnewline
R-squared & 0.882650987990724 \tabularnewline
Adjusted R-squared & 0.874613384428444 \tabularnewline
F-TEST (value) & 109.815193191792 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 73 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22.8409201345601 \tabularnewline
Sum Squared Residuals & 38084.6571793147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939495070764463[/C][/ROW]
[ROW][C]R-squared[/C][C]0.882650987990724[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.874613384428444[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]109.815193191792[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]73[/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]22.8409201345601[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]38084.6571793147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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 R0.939495070764463
R-squared0.882650987990724
Adjusted R-squared0.874613384428444
F-TEST (value)109.815193191792
F-TEST (DF numerator)5
F-TEST (DF denominator)73
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.8409201345601
Sum Squared Residuals38084.6571793147







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
118.216.42231652666761.77768347333239
2143.871.716850315221672.0831496847784
323.427.4993201590538-4.09932015905381
41.14.67065506212421-3.57065506212421
549.552.1068141768362-2.60681417683619
64.822.8391647021591-18.0391647021591
720.821.3380264210753-0.538026421075281
819.413.9504435610065.44955643899396
92.15.29751856732953-3.19751856732953
1079.430.310034849029549.0899651509705
112.811.9371349192071-9.13713491920712
123.815.1348329802097-11.3348329802097
134.14.14093544551115-0.0409354455111481
1413.217.7715668927657-4.57156689276567
152.89.97188780754144-7.17188780754144
1648.5103.960291756308-55.4602917563081
176.210.9168742557593-4.71687425575932
1810.830.3773572960367-19.5773572960367
193.88.84170043259203-5.04170043259204
2021.915.69860830219056.20139169780954
2112.620.1420689411752-7.54206894117522
2212889.955745686236638.0442543137634
2387.364.165934353475623.1340656465243
241621.547679160879-5.54767916087896
250.78.74771594473479-8.04771594473479
2622.518.36793309343174.1320669065683
2715.416.8095653015247-1.4095653015247
2834.15842273086476-1.15842273086477
292.15.54236111911203-3.44236111911203
304.13.944173889850630.155826110149371
316.414.5404536500256-8.14045365002563
3226.627.743625656836-1.14362565683597
33304253.9476059929250.0523940070797
3418.628.487915940306-9.88791594030599
356571.0246118671701-6.02461186717006
3666.238.635508312705227.5644916872948
378364.322682514528618.6773174854714
386235.353724149216726.6462758507833
391.68.0769679277955-6.4769679277955
40400.2433.678734076102-33.4787340761024
4123.321.16761939508272.13238060491728
424.616.878876641234-12.278876641234
43164.6173.485890373617-8.88589037361714
441.912.9242204980115-11.0242204980115
4557.568.3968772993121-10.896877299312
462.44.65154518471247-2.25154518471247
4777.352.935244873969324.3647551260307
4815.8-8.9715268362427224.7715268362427
490.64.39282959114051-3.79282959114051
503.59.47829520046423-5.97829520046423
5191.787105391158317.21289460884169
526240.916625907731721.0833740922683
537.42.594339693571934.80566030642807
5415.60.059084382837312415.5409156171627
5525.234.3055588822512-9.10555888225125
5625.432.303254851619-6.90325485161902
573.520.4691501089661-16.9691501089661
5827.3113.869678436793-86.5696784367933
5937.541.5145467063215-4.01454670632147
603.49.16241461920355-5.76241461920355
6114.320.4453123896428-6.14531238964278
626.112.7466710420867-6.64667104208675
634.917.3859173959099-12.4859173959099
643.39.49669002762793-6.19669002762793
6571.083105668835785.91689433116422
668.23.11408540921785.0859145907822
6743.541.31646520200722.18353479799283
6848.556.638805958222-8.13880595822201
695.412.4857982799179-7.08579827991789
7049.549.7928311960457-0.29283119604573
7129.131.8714850671075-2.77148506710752
722.627.7290987955109-25.1290987955109
730.84.35091040680033-3.55091040680033
74184.8137.81976632666546.980233673335
752.329.2151685399705-26.9151685399705
76828.3402999329623-20.3402999329623
7710.316.9469002954369-6.64690029543692
785029.899618801407720.1003811985923
79118.163.103673325353854.9963266746462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18.2 & 16.4223165266676 & 1.77768347333239 \tabularnewline
2 & 143.8 & 71.7168503152216 & 72.0831496847784 \tabularnewline
3 & 23.4 & 27.4993201590538 & -4.09932015905381 \tabularnewline
4 & 1.1 & 4.67065506212421 & -3.57065506212421 \tabularnewline
5 & 49.5 & 52.1068141768362 & -2.60681417683619 \tabularnewline
6 & 4.8 & 22.8391647021591 & -18.0391647021591 \tabularnewline
7 & 20.8 & 21.3380264210753 & -0.538026421075281 \tabularnewline
8 & 19.4 & 13.950443561006 & 5.44955643899396 \tabularnewline
9 & 2.1 & 5.29751856732953 & -3.19751856732953 \tabularnewline
10 & 79.4 & 30.3100348490295 & 49.0899651509705 \tabularnewline
11 & 2.8 & 11.9371349192071 & -9.13713491920712 \tabularnewline
12 & 3.8 & 15.1348329802097 & -11.3348329802097 \tabularnewline
13 & 4.1 & 4.14093544551115 & -0.0409354455111481 \tabularnewline
14 & 13.2 & 17.7715668927657 & -4.57156689276567 \tabularnewline
15 & 2.8 & 9.97188780754144 & -7.17188780754144 \tabularnewline
16 & 48.5 & 103.960291756308 & -55.4602917563081 \tabularnewline
17 & 6.2 & 10.9168742557593 & -4.71687425575932 \tabularnewline
18 & 10.8 & 30.3773572960367 & -19.5773572960367 \tabularnewline
19 & 3.8 & 8.84170043259203 & -5.04170043259204 \tabularnewline
20 & 21.9 & 15.6986083021905 & 6.20139169780954 \tabularnewline
21 & 12.6 & 20.1420689411752 & -7.54206894117522 \tabularnewline
22 & 128 & 89.9557456862366 & 38.0442543137634 \tabularnewline
23 & 87.3 & 64.1659343534756 & 23.1340656465243 \tabularnewline
24 & 16 & 21.547679160879 & -5.54767916087896 \tabularnewline
25 & 0.7 & 8.74771594473479 & -8.04771594473479 \tabularnewline
26 & 22.5 & 18.3679330934317 & 4.1320669065683 \tabularnewline
27 & 15.4 & 16.8095653015247 & -1.4095653015247 \tabularnewline
28 & 3 & 4.15842273086476 & -1.15842273086477 \tabularnewline
29 & 2.1 & 5.54236111911203 & -3.44236111911203 \tabularnewline
30 & 4.1 & 3.94417388985063 & 0.155826110149371 \tabularnewline
31 & 6.4 & 14.5404536500256 & -8.14045365002563 \tabularnewline
32 & 26.6 & 27.743625656836 & -1.14362565683597 \tabularnewline
33 & 304 & 253.94760599292 & 50.0523940070797 \tabularnewline
34 & 18.6 & 28.487915940306 & -9.88791594030599 \tabularnewline
35 & 65 & 71.0246118671701 & -6.02461186717006 \tabularnewline
36 & 66.2 & 38.6355083127052 & 27.5644916872948 \tabularnewline
37 & 83 & 64.3226825145286 & 18.6773174854714 \tabularnewline
38 & 62 & 35.3537241492167 & 26.6462758507833 \tabularnewline
39 & 1.6 & 8.0769679277955 & -6.4769679277955 \tabularnewline
40 & 400.2 & 433.678734076102 & -33.4787340761024 \tabularnewline
41 & 23.3 & 21.1676193950827 & 2.13238060491728 \tabularnewline
42 & 4.6 & 16.878876641234 & -12.278876641234 \tabularnewline
43 & 164.6 & 173.485890373617 & -8.88589037361714 \tabularnewline
44 & 1.9 & 12.9242204980115 & -11.0242204980115 \tabularnewline
45 & 57.5 & 68.3968772993121 & -10.896877299312 \tabularnewline
46 & 2.4 & 4.65154518471247 & -2.25154518471247 \tabularnewline
47 & 77.3 & 52.9352448739693 & 24.3647551260307 \tabularnewline
48 & 15.8 & -8.97152683624272 & 24.7715268362427 \tabularnewline
49 & 0.6 & 4.39282959114051 & -3.79282959114051 \tabularnewline
50 & 3.5 & 9.47829520046423 & -5.97829520046423 \tabularnewline
51 & 9 & 1.78710539115831 & 7.21289460884169 \tabularnewline
52 & 62 & 40.9166259077317 & 21.0833740922683 \tabularnewline
53 & 7.4 & 2.59433969357193 & 4.80566030642807 \tabularnewline
54 & 15.6 & 0.0590843828373124 & 15.5409156171627 \tabularnewline
55 & 25.2 & 34.3055588822512 & -9.10555888225125 \tabularnewline
56 & 25.4 & 32.303254851619 & -6.90325485161902 \tabularnewline
57 & 3.5 & 20.4691501089661 & -16.9691501089661 \tabularnewline
58 & 27.3 & 113.869678436793 & -86.5696784367933 \tabularnewline
59 & 37.5 & 41.5145467063215 & -4.01454670632147 \tabularnewline
60 & 3.4 & 9.16241461920355 & -5.76241461920355 \tabularnewline
61 & 14.3 & 20.4453123896428 & -6.14531238964278 \tabularnewline
62 & 6.1 & 12.7466710420867 & -6.64667104208675 \tabularnewline
63 & 4.9 & 17.3859173959099 & -12.4859173959099 \tabularnewline
64 & 3.3 & 9.49669002762793 & -6.19669002762793 \tabularnewline
65 & 7 & 1.08310566883578 & 5.91689433116422 \tabularnewline
66 & 8.2 & 3.1140854092178 & 5.0859145907822 \tabularnewline
67 & 43.5 & 41.3164652020072 & 2.18353479799283 \tabularnewline
68 & 48.5 & 56.638805958222 & -8.13880595822201 \tabularnewline
69 & 5.4 & 12.4857982799179 & -7.08579827991789 \tabularnewline
70 & 49.5 & 49.7928311960457 & -0.29283119604573 \tabularnewline
71 & 29.1 & 31.8714850671075 & -2.77148506710752 \tabularnewline
72 & 2.6 & 27.7290987955109 & -25.1290987955109 \tabularnewline
73 & 0.8 & 4.35091040680033 & -3.55091040680033 \tabularnewline
74 & 184.8 & 137.819766326665 & 46.980233673335 \tabularnewline
75 & 2.3 & 29.2151685399705 & -26.9151685399705 \tabularnewline
76 & 8 & 28.3402999329623 & -20.3402999329623 \tabularnewline
77 & 10.3 & 16.9469002954369 & -6.64690029543692 \tabularnewline
78 & 50 & 29.8996188014077 & 20.1003811985923 \tabularnewline
79 & 118.1 & 63.1036733253538 & 54.9963266746462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&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]18.2[/C][C]16.4223165266676[/C][C]1.77768347333239[/C][/ROW]
[ROW][C]2[/C][C]143.8[/C][C]71.7168503152216[/C][C]72.0831496847784[/C][/ROW]
[ROW][C]3[/C][C]23.4[/C][C]27.4993201590538[/C][C]-4.09932015905381[/C][/ROW]
[ROW][C]4[/C][C]1.1[/C][C]4.67065506212421[/C][C]-3.57065506212421[/C][/ROW]
[ROW][C]5[/C][C]49.5[/C][C]52.1068141768362[/C][C]-2.60681417683619[/C][/ROW]
[ROW][C]6[/C][C]4.8[/C][C]22.8391647021591[/C][C]-18.0391647021591[/C][/ROW]
[ROW][C]7[/C][C]20.8[/C][C]21.3380264210753[/C][C]-0.538026421075281[/C][/ROW]
[ROW][C]8[/C][C]19.4[/C][C]13.950443561006[/C][C]5.44955643899396[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]5.29751856732953[/C][C]-3.19751856732953[/C][/ROW]
[ROW][C]10[/C][C]79.4[/C][C]30.3100348490295[/C][C]49.0899651509705[/C][/ROW]
[ROW][C]11[/C][C]2.8[/C][C]11.9371349192071[/C][C]-9.13713491920712[/C][/ROW]
[ROW][C]12[/C][C]3.8[/C][C]15.1348329802097[/C][C]-11.3348329802097[/C][/ROW]
[ROW][C]13[/C][C]4.1[/C][C]4.14093544551115[/C][C]-0.0409354455111481[/C][/ROW]
[ROW][C]14[/C][C]13.2[/C][C]17.7715668927657[/C][C]-4.57156689276567[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]9.97188780754144[/C][C]-7.17188780754144[/C][/ROW]
[ROW][C]16[/C][C]48.5[/C][C]103.960291756308[/C][C]-55.4602917563081[/C][/ROW]
[ROW][C]17[/C][C]6.2[/C][C]10.9168742557593[/C][C]-4.71687425575932[/C][/ROW]
[ROW][C]18[/C][C]10.8[/C][C]30.3773572960367[/C][C]-19.5773572960367[/C][/ROW]
[ROW][C]19[/C][C]3.8[/C][C]8.84170043259203[/C][C]-5.04170043259204[/C][/ROW]
[ROW][C]20[/C][C]21.9[/C][C]15.6986083021905[/C][C]6.20139169780954[/C][/ROW]
[ROW][C]21[/C][C]12.6[/C][C]20.1420689411752[/C][C]-7.54206894117522[/C][/ROW]
[ROW][C]22[/C][C]128[/C][C]89.9557456862366[/C][C]38.0442543137634[/C][/ROW]
[ROW][C]23[/C][C]87.3[/C][C]64.1659343534756[/C][C]23.1340656465243[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]21.547679160879[/C][C]-5.54767916087896[/C][/ROW]
[ROW][C]25[/C][C]0.7[/C][C]8.74771594473479[/C][C]-8.04771594473479[/C][/ROW]
[ROW][C]26[/C][C]22.5[/C][C]18.3679330934317[/C][C]4.1320669065683[/C][/ROW]
[ROW][C]27[/C][C]15.4[/C][C]16.8095653015247[/C][C]-1.4095653015247[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]4.15842273086476[/C][C]-1.15842273086477[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]5.54236111911203[/C][C]-3.44236111911203[/C][/ROW]
[ROW][C]30[/C][C]4.1[/C][C]3.94417388985063[/C][C]0.155826110149371[/C][/ROW]
[ROW][C]31[/C][C]6.4[/C][C]14.5404536500256[/C][C]-8.14045365002563[/C][/ROW]
[ROW][C]32[/C][C]26.6[/C][C]27.743625656836[/C][C]-1.14362565683597[/C][/ROW]
[ROW][C]33[/C][C]304[/C][C]253.94760599292[/C][C]50.0523940070797[/C][/ROW]
[ROW][C]34[/C][C]18.6[/C][C]28.487915940306[/C][C]-9.88791594030599[/C][/ROW]
[ROW][C]35[/C][C]65[/C][C]71.0246118671701[/C][C]-6.02461186717006[/C][/ROW]
[ROW][C]36[/C][C]66.2[/C][C]38.6355083127052[/C][C]27.5644916872948[/C][/ROW]
[ROW][C]37[/C][C]83[/C][C]64.3226825145286[/C][C]18.6773174854714[/C][/ROW]
[ROW][C]38[/C][C]62[/C][C]35.3537241492167[/C][C]26.6462758507833[/C][/ROW]
[ROW][C]39[/C][C]1.6[/C][C]8.0769679277955[/C][C]-6.4769679277955[/C][/ROW]
[ROW][C]40[/C][C]400.2[/C][C]433.678734076102[/C][C]-33.4787340761024[/C][/ROW]
[ROW][C]41[/C][C]23.3[/C][C]21.1676193950827[/C][C]2.13238060491728[/C][/ROW]
[ROW][C]42[/C][C]4.6[/C][C]16.878876641234[/C][C]-12.278876641234[/C][/ROW]
[ROW][C]43[/C][C]164.6[/C][C]173.485890373617[/C][C]-8.88589037361714[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]12.9242204980115[/C][C]-11.0242204980115[/C][/ROW]
[ROW][C]45[/C][C]57.5[/C][C]68.3968772993121[/C][C]-10.896877299312[/C][/ROW]
[ROW][C]46[/C][C]2.4[/C][C]4.65154518471247[/C][C]-2.25154518471247[/C][/ROW]
[ROW][C]47[/C][C]77.3[/C][C]52.9352448739693[/C][C]24.3647551260307[/C][/ROW]
[ROW][C]48[/C][C]15.8[/C][C]-8.97152683624272[/C][C]24.7715268362427[/C][/ROW]
[ROW][C]49[/C][C]0.6[/C][C]4.39282959114051[/C][C]-3.79282959114051[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]9.47829520046423[/C][C]-5.97829520046423[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]1.78710539115831[/C][C]7.21289460884169[/C][/ROW]
[ROW][C]52[/C][C]62[/C][C]40.9166259077317[/C][C]21.0833740922683[/C][/ROW]
[ROW][C]53[/C][C]7.4[/C][C]2.59433969357193[/C][C]4.80566030642807[/C][/ROW]
[ROW][C]54[/C][C]15.6[/C][C]0.0590843828373124[/C][C]15.5409156171627[/C][/ROW]
[ROW][C]55[/C][C]25.2[/C][C]34.3055588822512[/C][C]-9.10555888225125[/C][/ROW]
[ROW][C]56[/C][C]25.4[/C][C]32.303254851619[/C][C]-6.90325485161902[/C][/ROW]
[ROW][C]57[/C][C]3.5[/C][C]20.4691501089661[/C][C]-16.9691501089661[/C][/ROW]
[ROW][C]58[/C][C]27.3[/C][C]113.869678436793[/C][C]-86.5696784367933[/C][/ROW]
[ROW][C]59[/C][C]37.5[/C][C]41.5145467063215[/C][C]-4.01454670632147[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]9.16241461920355[/C][C]-5.76241461920355[/C][/ROW]
[ROW][C]61[/C][C]14.3[/C][C]20.4453123896428[/C][C]-6.14531238964278[/C][/ROW]
[ROW][C]62[/C][C]6.1[/C][C]12.7466710420867[/C][C]-6.64667104208675[/C][/ROW]
[ROW][C]63[/C][C]4.9[/C][C]17.3859173959099[/C][C]-12.4859173959099[/C][/ROW]
[ROW][C]64[/C][C]3.3[/C][C]9.49669002762793[/C][C]-6.19669002762793[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]1.08310566883578[/C][C]5.91689433116422[/C][/ROW]
[ROW][C]66[/C][C]8.2[/C][C]3.1140854092178[/C][C]5.0859145907822[/C][/ROW]
[ROW][C]67[/C][C]43.5[/C][C]41.3164652020072[/C][C]2.18353479799283[/C][/ROW]
[ROW][C]68[/C][C]48.5[/C][C]56.638805958222[/C][C]-8.13880595822201[/C][/ROW]
[ROW][C]69[/C][C]5.4[/C][C]12.4857982799179[/C][C]-7.08579827991789[/C][/ROW]
[ROW][C]70[/C][C]49.5[/C][C]49.7928311960457[/C][C]-0.29283119604573[/C][/ROW]
[ROW][C]71[/C][C]29.1[/C][C]31.8714850671075[/C][C]-2.77148506710752[/C][/ROW]
[ROW][C]72[/C][C]2.6[/C][C]27.7290987955109[/C][C]-25.1290987955109[/C][/ROW]
[ROW][C]73[/C][C]0.8[/C][C]4.35091040680033[/C][C]-3.55091040680033[/C][/ROW]
[ROW][C]74[/C][C]184.8[/C][C]137.819766326665[/C][C]46.980233673335[/C][/ROW]
[ROW][C]75[/C][C]2.3[/C][C]29.2151685399705[/C][C]-26.9151685399705[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]28.3402999329623[/C][C]-20.3402999329623[/C][/ROW]
[ROW][C]77[/C][C]10.3[/C][C]16.9469002954369[/C][C]-6.64690029543692[/C][/ROW]
[ROW][C]78[/C][C]50[/C][C]29.8996188014077[/C][C]20.1003811985923[/C][/ROW]
[ROW][C]79[/C][C]118.1[/C][C]63.1036733253538[/C][C]54.9963266746462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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
118.216.42231652666761.77768347333239
2143.871.716850315221672.0831496847784
323.427.4993201590538-4.09932015905381
41.14.67065506212421-3.57065506212421
549.552.1068141768362-2.60681417683619
64.822.8391647021591-18.0391647021591
720.821.3380264210753-0.538026421075281
819.413.9504435610065.44955643899396
92.15.29751856732953-3.19751856732953
1079.430.310034849029549.0899651509705
112.811.9371349192071-9.13713491920712
123.815.1348329802097-11.3348329802097
134.14.14093544551115-0.0409354455111481
1413.217.7715668927657-4.57156689276567
152.89.97188780754144-7.17188780754144
1648.5103.960291756308-55.4602917563081
176.210.9168742557593-4.71687425575932
1810.830.3773572960367-19.5773572960367
193.88.84170043259203-5.04170043259204
2021.915.69860830219056.20139169780954
2112.620.1420689411752-7.54206894117522
2212889.955745686236638.0442543137634
2387.364.165934353475623.1340656465243
241621.547679160879-5.54767916087896
250.78.74771594473479-8.04771594473479
2622.518.36793309343174.1320669065683
2715.416.8095653015247-1.4095653015247
2834.15842273086476-1.15842273086477
292.15.54236111911203-3.44236111911203
304.13.944173889850630.155826110149371
316.414.5404536500256-8.14045365002563
3226.627.743625656836-1.14362565683597
33304253.9476059929250.0523940070797
3418.628.487915940306-9.88791594030599
356571.0246118671701-6.02461186717006
3666.238.635508312705227.5644916872948
378364.322682514528618.6773174854714
386235.353724149216726.6462758507833
391.68.0769679277955-6.4769679277955
40400.2433.678734076102-33.4787340761024
4123.321.16761939508272.13238060491728
424.616.878876641234-12.278876641234
43164.6173.485890373617-8.88589037361714
441.912.9242204980115-11.0242204980115
4557.568.3968772993121-10.896877299312
462.44.65154518471247-2.25154518471247
4777.352.935244873969324.3647551260307
4815.8-8.9715268362427224.7715268362427
490.64.39282959114051-3.79282959114051
503.59.47829520046423-5.97829520046423
5191.787105391158317.21289460884169
526240.916625907731721.0833740922683
537.42.594339693571934.80566030642807
5415.60.059084382837312415.5409156171627
5525.234.3055588822512-9.10555888225125
5625.432.303254851619-6.90325485161902
573.520.4691501089661-16.9691501089661
5827.3113.869678436793-86.5696784367933
5937.541.5145467063215-4.01454670632147
603.49.16241461920355-5.76241461920355
6114.320.4453123896428-6.14531238964278
626.112.7466710420867-6.64667104208675
634.917.3859173959099-12.4859173959099
643.39.49669002762793-6.19669002762793
6571.083105668835785.91689433116422
668.23.11408540921785.0859145907822
6743.541.31646520200722.18353479799283
6848.556.638805958222-8.13880595822201
695.412.4857982799179-7.08579827991789
7049.549.7928311960457-0.29283119604573
7129.131.8714850671075-2.77148506710752
722.627.7290987955109-25.1290987955109
730.84.35091040680033-3.55091040680033
74184.8137.81976632666546.980233673335
752.329.2151685399705-26.9151685399705
76828.3402999329623-20.3402999329623
7710.316.9469002954369-6.64690029543692
785029.899618801407720.1003811985923
79118.163.103673325353854.9963266746462







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.05346229033744090.1069245806748820.946537709662559
100.02223998312069580.04447996624139160.977760016879304
110.006441527081604790.01288305416320960.993558472918395
120.001988726162556850.00397745232511370.998011273837443
130.0005012808024021020.00100256160480420.999498719197598
140.0008407662757464630.001681532551492930.999159233724254
150.0002378132789025550.000475626557805110.999762186721097
160.04267281680010010.08534563360020010.9573271831999
170.02529639180674850.05059278361349690.974703608193252
180.04344486338715750.0868897267743150.956555136612843
190.0283401264551510.05668025291030210.971659873544849
200.02602296316375690.05204592632751370.973977036836243
210.01567917670732640.03135835341465290.984320823292674
220.02325792526803320.04651585053606640.976742074731967
230.0949308453866060.1898616907732120.905069154613394
240.06684865931218850.1336973186243770.933151340687812
250.04856562694713540.09713125389427070.951434373052865
260.03254272522593610.06508545045187230.967457274774064
270.02063308229403870.04126616458807730.979366917705961
280.01900632857462720.03801265714925450.980993671425373
290.01300126379827160.02600252759654320.986998736201728
300.0119134773155420.02382695463108390.988086522684458
310.007668301052487020.0153366021049740.992331698947513
320.004735133576012660.009470267152025320.995264866423987
330.02836510440578670.05673020881157340.971634895594213
340.02150125770848980.04300251541697960.97849874229151
350.01474146858675330.02948293717350660.985258531413247
360.02545048430091170.05090096860182350.974549515699088
370.0212637351240640.04252747024812810.978736264875936
380.06790452278527020.135809045570540.93209547721473
390.04823358566416020.09646717132832050.95176641433584
400.7421006560133110.5157986879733780.257899343986689
410.7052505623015590.5894988753968830.294749437698441
420.6572903591016440.6854192817967110.342709640898356
430.8455016317380850.308996736523830.154498368261915
440.8217612201279030.3564775597441950.178238779872097
450.9061766640873280.1876466718253430.0938233359126716
460.8735460390610070.2529079218779850.126453960938993
470.8652314539754470.2695370920491070.134768546024553
480.904520102708530.190959794582940.0954798972914698
490.9346422391268580.1307155217462840.065357760873142
500.9111179670837340.1777640658325320.0888820329162662
510.8815050528349640.2369898943300720.118494947165036
520.8754049473292090.2491901053415820.124595052670791
530.8334233589304380.3331532821391240.166576641069562
540.7916476083984120.4167047832031760.208352391601588
550.7364783955639840.5270432088720330.263521604436016
560.6874394304716560.6251211390566880.312560569528344
570.6379348361542870.7241303276914260.362065163845713
580.909002170809350.1819956583812990.0909978291906496
590.8727660877256150.254467824548770.127233912274385
600.8195601676494390.3608796647011220.180439832350561
610.7515068487804310.4969863024391370.248493151219569
620.6730167382502440.6539665234995120.326983261749756
630.6299782095441590.7400435809116810.370021790455841
640.5398092354993060.9203815290013880.460190764500694
650.4450492571919650.8900985143839290.554950742808035
660.37557969191850.7511593838370010.6244203080815
670.2700496622465740.5400993244931480.729950337753426
680.2087258819156670.4174517638313340.791274118084333
690.1275977336176240.2551954672352470.872402266382376
700.07683157597927860.1536631519585570.923168424020721

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0534622903374409 & 0.106924580674882 & 0.946537709662559 \tabularnewline
10 & 0.0222399831206958 & 0.0444799662413916 & 0.977760016879304 \tabularnewline
11 & 0.00644152708160479 & 0.0128830541632096 & 0.993558472918395 \tabularnewline
12 & 0.00198872616255685 & 0.0039774523251137 & 0.998011273837443 \tabularnewline
13 & 0.000501280802402102 & 0.0010025616048042 & 0.999498719197598 \tabularnewline
14 & 0.000840766275746463 & 0.00168153255149293 & 0.999159233724254 \tabularnewline
15 & 0.000237813278902555 & 0.00047562655780511 & 0.999762186721097 \tabularnewline
16 & 0.0426728168001001 & 0.0853456336002001 & 0.9573271831999 \tabularnewline
17 & 0.0252963918067485 & 0.0505927836134969 & 0.974703608193252 \tabularnewline
18 & 0.0434448633871575 & 0.086889726774315 & 0.956555136612843 \tabularnewline
19 & 0.028340126455151 & 0.0566802529103021 & 0.971659873544849 \tabularnewline
20 & 0.0260229631637569 & 0.0520459263275137 & 0.973977036836243 \tabularnewline
21 & 0.0156791767073264 & 0.0313583534146529 & 0.984320823292674 \tabularnewline
22 & 0.0232579252680332 & 0.0465158505360664 & 0.976742074731967 \tabularnewline
23 & 0.094930845386606 & 0.189861690773212 & 0.905069154613394 \tabularnewline
24 & 0.0668486593121885 & 0.133697318624377 & 0.933151340687812 \tabularnewline
25 & 0.0485656269471354 & 0.0971312538942707 & 0.951434373052865 \tabularnewline
26 & 0.0325427252259361 & 0.0650854504518723 & 0.967457274774064 \tabularnewline
27 & 0.0206330822940387 & 0.0412661645880773 & 0.979366917705961 \tabularnewline
28 & 0.0190063285746272 & 0.0380126571492545 & 0.980993671425373 \tabularnewline
29 & 0.0130012637982716 & 0.0260025275965432 & 0.986998736201728 \tabularnewline
30 & 0.011913477315542 & 0.0238269546310839 & 0.988086522684458 \tabularnewline
31 & 0.00766830105248702 & 0.015336602104974 & 0.992331698947513 \tabularnewline
32 & 0.00473513357601266 & 0.00947026715202532 & 0.995264866423987 \tabularnewline
33 & 0.0283651044057867 & 0.0567302088115734 & 0.971634895594213 \tabularnewline
34 & 0.0215012577084898 & 0.0430025154169796 & 0.97849874229151 \tabularnewline
35 & 0.0147414685867533 & 0.0294829371735066 & 0.985258531413247 \tabularnewline
36 & 0.0254504843009117 & 0.0509009686018235 & 0.974549515699088 \tabularnewline
37 & 0.021263735124064 & 0.0425274702481281 & 0.978736264875936 \tabularnewline
38 & 0.0679045227852702 & 0.13580904557054 & 0.93209547721473 \tabularnewline
39 & 0.0482335856641602 & 0.0964671713283205 & 0.95176641433584 \tabularnewline
40 & 0.742100656013311 & 0.515798687973378 & 0.257899343986689 \tabularnewline
41 & 0.705250562301559 & 0.589498875396883 & 0.294749437698441 \tabularnewline
42 & 0.657290359101644 & 0.685419281796711 & 0.342709640898356 \tabularnewline
43 & 0.845501631738085 & 0.30899673652383 & 0.154498368261915 \tabularnewline
44 & 0.821761220127903 & 0.356477559744195 & 0.178238779872097 \tabularnewline
45 & 0.906176664087328 & 0.187646671825343 & 0.0938233359126716 \tabularnewline
46 & 0.873546039061007 & 0.252907921877985 & 0.126453960938993 \tabularnewline
47 & 0.865231453975447 & 0.269537092049107 & 0.134768546024553 \tabularnewline
48 & 0.90452010270853 & 0.19095979458294 & 0.0954798972914698 \tabularnewline
49 & 0.934642239126858 & 0.130715521746284 & 0.065357760873142 \tabularnewline
50 & 0.911117967083734 & 0.177764065832532 & 0.0888820329162662 \tabularnewline
51 & 0.881505052834964 & 0.236989894330072 & 0.118494947165036 \tabularnewline
52 & 0.875404947329209 & 0.249190105341582 & 0.124595052670791 \tabularnewline
53 & 0.833423358930438 & 0.333153282139124 & 0.166576641069562 \tabularnewline
54 & 0.791647608398412 & 0.416704783203176 & 0.208352391601588 \tabularnewline
55 & 0.736478395563984 & 0.527043208872033 & 0.263521604436016 \tabularnewline
56 & 0.687439430471656 & 0.625121139056688 & 0.312560569528344 \tabularnewline
57 & 0.637934836154287 & 0.724130327691426 & 0.362065163845713 \tabularnewline
58 & 0.90900217080935 & 0.181995658381299 & 0.0909978291906496 \tabularnewline
59 & 0.872766087725615 & 0.25446782454877 & 0.127233912274385 \tabularnewline
60 & 0.819560167649439 & 0.360879664701122 & 0.180439832350561 \tabularnewline
61 & 0.751506848780431 & 0.496986302439137 & 0.248493151219569 \tabularnewline
62 & 0.673016738250244 & 0.653966523499512 & 0.326983261749756 \tabularnewline
63 & 0.629978209544159 & 0.740043580911681 & 0.370021790455841 \tabularnewline
64 & 0.539809235499306 & 0.920381529001388 & 0.460190764500694 \tabularnewline
65 & 0.445049257191965 & 0.890098514383929 & 0.554950742808035 \tabularnewline
66 & 0.3755796919185 & 0.751159383837001 & 0.6244203080815 \tabularnewline
67 & 0.270049662246574 & 0.540099324493148 & 0.729950337753426 \tabularnewline
68 & 0.208725881915667 & 0.417451763831334 & 0.791274118084333 \tabularnewline
69 & 0.127597733617624 & 0.255195467235247 & 0.872402266382376 \tabularnewline
70 & 0.0768315759792786 & 0.153663151958557 & 0.923168424020721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0534622903374409[/C][C]0.106924580674882[/C][C]0.946537709662559[/C][/ROW]
[ROW][C]10[/C][C]0.0222399831206958[/C][C]0.0444799662413916[/C][C]0.977760016879304[/C][/ROW]
[ROW][C]11[/C][C]0.00644152708160479[/C][C]0.0128830541632096[/C][C]0.993558472918395[/C][/ROW]
[ROW][C]12[/C][C]0.00198872616255685[/C][C]0.0039774523251137[/C][C]0.998011273837443[/C][/ROW]
[ROW][C]13[/C][C]0.000501280802402102[/C][C]0.0010025616048042[/C][C]0.999498719197598[/C][/ROW]
[ROW][C]14[/C][C]0.000840766275746463[/C][C]0.00168153255149293[/C][C]0.999159233724254[/C][/ROW]
[ROW][C]15[/C][C]0.000237813278902555[/C][C]0.00047562655780511[/C][C]0.999762186721097[/C][/ROW]
[ROW][C]16[/C][C]0.0426728168001001[/C][C]0.0853456336002001[/C][C]0.9573271831999[/C][/ROW]
[ROW][C]17[/C][C]0.0252963918067485[/C][C]0.0505927836134969[/C][C]0.974703608193252[/C][/ROW]
[ROW][C]18[/C][C]0.0434448633871575[/C][C]0.086889726774315[/C][C]0.956555136612843[/C][/ROW]
[ROW][C]19[/C][C]0.028340126455151[/C][C]0.0566802529103021[/C][C]0.971659873544849[/C][/ROW]
[ROW][C]20[/C][C]0.0260229631637569[/C][C]0.0520459263275137[/C][C]0.973977036836243[/C][/ROW]
[ROW][C]21[/C][C]0.0156791767073264[/C][C]0.0313583534146529[/C][C]0.984320823292674[/C][/ROW]
[ROW][C]22[/C][C]0.0232579252680332[/C][C]0.0465158505360664[/C][C]0.976742074731967[/C][/ROW]
[ROW][C]23[/C][C]0.094930845386606[/C][C]0.189861690773212[/C][C]0.905069154613394[/C][/ROW]
[ROW][C]24[/C][C]0.0668486593121885[/C][C]0.133697318624377[/C][C]0.933151340687812[/C][/ROW]
[ROW][C]25[/C][C]0.0485656269471354[/C][C]0.0971312538942707[/C][C]0.951434373052865[/C][/ROW]
[ROW][C]26[/C][C]0.0325427252259361[/C][C]0.0650854504518723[/C][C]0.967457274774064[/C][/ROW]
[ROW][C]27[/C][C]0.0206330822940387[/C][C]0.0412661645880773[/C][C]0.979366917705961[/C][/ROW]
[ROW][C]28[/C][C]0.0190063285746272[/C][C]0.0380126571492545[/C][C]0.980993671425373[/C][/ROW]
[ROW][C]29[/C][C]0.0130012637982716[/C][C]0.0260025275965432[/C][C]0.986998736201728[/C][/ROW]
[ROW][C]30[/C][C]0.011913477315542[/C][C]0.0238269546310839[/C][C]0.988086522684458[/C][/ROW]
[ROW][C]31[/C][C]0.00766830105248702[/C][C]0.015336602104974[/C][C]0.992331698947513[/C][/ROW]
[ROW][C]32[/C][C]0.00473513357601266[/C][C]0.00947026715202532[/C][C]0.995264866423987[/C][/ROW]
[ROW][C]33[/C][C]0.0283651044057867[/C][C]0.0567302088115734[/C][C]0.971634895594213[/C][/ROW]
[ROW][C]34[/C][C]0.0215012577084898[/C][C]0.0430025154169796[/C][C]0.97849874229151[/C][/ROW]
[ROW][C]35[/C][C]0.0147414685867533[/C][C]0.0294829371735066[/C][C]0.985258531413247[/C][/ROW]
[ROW][C]36[/C][C]0.0254504843009117[/C][C]0.0509009686018235[/C][C]0.974549515699088[/C][/ROW]
[ROW][C]37[/C][C]0.021263735124064[/C][C]0.0425274702481281[/C][C]0.978736264875936[/C][/ROW]
[ROW][C]38[/C][C]0.0679045227852702[/C][C]0.13580904557054[/C][C]0.93209547721473[/C][/ROW]
[ROW][C]39[/C][C]0.0482335856641602[/C][C]0.0964671713283205[/C][C]0.95176641433584[/C][/ROW]
[ROW][C]40[/C][C]0.742100656013311[/C][C]0.515798687973378[/C][C]0.257899343986689[/C][/ROW]
[ROW][C]41[/C][C]0.705250562301559[/C][C]0.589498875396883[/C][C]0.294749437698441[/C][/ROW]
[ROW][C]42[/C][C]0.657290359101644[/C][C]0.685419281796711[/C][C]0.342709640898356[/C][/ROW]
[ROW][C]43[/C][C]0.845501631738085[/C][C]0.30899673652383[/C][C]0.154498368261915[/C][/ROW]
[ROW][C]44[/C][C]0.821761220127903[/C][C]0.356477559744195[/C][C]0.178238779872097[/C][/ROW]
[ROW][C]45[/C][C]0.906176664087328[/C][C]0.187646671825343[/C][C]0.0938233359126716[/C][/ROW]
[ROW][C]46[/C][C]0.873546039061007[/C][C]0.252907921877985[/C][C]0.126453960938993[/C][/ROW]
[ROW][C]47[/C][C]0.865231453975447[/C][C]0.269537092049107[/C][C]0.134768546024553[/C][/ROW]
[ROW][C]48[/C][C]0.90452010270853[/C][C]0.19095979458294[/C][C]0.0954798972914698[/C][/ROW]
[ROW][C]49[/C][C]0.934642239126858[/C][C]0.130715521746284[/C][C]0.065357760873142[/C][/ROW]
[ROW][C]50[/C][C]0.911117967083734[/C][C]0.177764065832532[/C][C]0.0888820329162662[/C][/ROW]
[ROW][C]51[/C][C]0.881505052834964[/C][C]0.236989894330072[/C][C]0.118494947165036[/C][/ROW]
[ROW][C]52[/C][C]0.875404947329209[/C][C]0.249190105341582[/C][C]0.124595052670791[/C][/ROW]
[ROW][C]53[/C][C]0.833423358930438[/C][C]0.333153282139124[/C][C]0.166576641069562[/C][/ROW]
[ROW][C]54[/C][C]0.791647608398412[/C][C]0.416704783203176[/C][C]0.208352391601588[/C][/ROW]
[ROW][C]55[/C][C]0.736478395563984[/C][C]0.527043208872033[/C][C]0.263521604436016[/C][/ROW]
[ROW][C]56[/C][C]0.687439430471656[/C][C]0.625121139056688[/C][C]0.312560569528344[/C][/ROW]
[ROW][C]57[/C][C]0.637934836154287[/C][C]0.724130327691426[/C][C]0.362065163845713[/C][/ROW]
[ROW][C]58[/C][C]0.90900217080935[/C][C]0.181995658381299[/C][C]0.0909978291906496[/C][/ROW]
[ROW][C]59[/C][C]0.872766087725615[/C][C]0.25446782454877[/C][C]0.127233912274385[/C][/ROW]
[ROW][C]60[/C][C]0.819560167649439[/C][C]0.360879664701122[/C][C]0.180439832350561[/C][/ROW]
[ROW][C]61[/C][C]0.751506848780431[/C][C]0.496986302439137[/C][C]0.248493151219569[/C][/ROW]
[ROW][C]62[/C][C]0.673016738250244[/C][C]0.653966523499512[/C][C]0.326983261749756[/C][/ROW]
[ROW][C]63[/C][C]0.629978209544159[/C][C]0.740043580911681[/C][C]0.370021790455841[/C][/ROW]
[ROW][C]64[/C][C]0.539809235499306[/C][C]0.920381529001388[/C][C]0.460190764500694[/C][/ROW]
[ROW][C]65[/C][C]0.445049257191965[/C][C]0.890098514383929[/C][C]0.554950742808035[/C][/ROW]
[ROW][C]66[/C][C]0.3755796919185[/C][C]0.751159383837001[/C][C]0.6244203080815[/C][/ROW]
[ROW][C]67[/C][C]0.270049662246574[/C][C]0.540099324493148[/C][C]0.729950337753426[/C][/ROW]
[ROW][C]68[/C][C]0.208725881915667[/C][C]0.417451763831334[/C][C]0.791274118084333[/C][/ROW]
[ROW][C]69[/C][C]0.127597733617624[/C][C]0.255195467235247[/C][C]0.872402266382376[/C][/ROW]
[ROW][C]70[/C][C]0.0768315759792786[/C][C]0.153663151958557[/C][C]0.923168424020721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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
90.05346229033744090.1069245806748820.946537709662559
100.02223998312069580.04447996624139160.977760016879304
110.006441527081604790.01288305416320960.993558472918395
120.001988726162556850.00397745232511370.998011273837443
130.0005012808024021020.00100256160480420.999498719197598
140.0008407662757464630.001681532551492930.999159233724254
150.0002378132789025550.000475626557805110.999762186721097
160.04267281680010010.08534563360020010.9573271831999
170.02529639180674850.05059278361349690.974703608193252
180.04344486338715750.0868897267743150.956555136612843
190.0283401264551510.05668025291030210.971659873544849
200.02602296316375690.05204592632751370.973977036836243
210.01567917670732640.03135835341465290.984320823292674
220.02325792526803320.04651585053606640.976742074731967
230.0949308453866060.1898616907732120.905069154613394
240.06684865931218850.1336973186243770.933151340687812
250.04856562694713540.09713125389427070.951434373052865
260.03254272522593610.06508545045187230.967457274774064
270.02063308229403870.04126616458807730.979366917705961
280.01900632857462720.03801265714925450.980993671425373
290.01300126379827160.02600252759654320.986998736201728
300.0119134773155420.02382695463108390.988086522684458
310.007668301052487020.0153366021049740.992331698947513
320.004735133576012660.009470267152025320.995264866423987
330.02836510440578670.05673020881157340.971634895594213
340.02150125770848980.04300251541697960.97849874229151
350.01474146858675330.02948293717350660.985258531413247
360.02545048430091170.05090096860182350.974549515699088
370.0212637351240640.04252747024812810.978736264875936
380.06790452278527020.135809045570540.93209547721473
390.04823358566416020.09646717132832050.95176641433584
400.7421006560133110.5157986879733780.257899343986689
410.7052505623015590.5894988753968830.294749437698441
420.6572903591016440.6854192817967110.342709640898356
430.8455016317380850.308996736523830.154498368261915
440.8217612201279030.3564775597441950.178238779872097
450.9061766640873280.1876466718253430.0938233359126716
460.8735460390610070.2529079218779850.126453960938993
470.8652314539754470.2695370920491070.134768546024553
480.904520102708530.190959794582940.0954798972914698
490.9346422391268580.1307155217462840.065357760873142
500.9111179670837340.1777640658325320.0888820329162662
510.8815050528349640.2369898943300720.118494947165036
520.8754049473292090.2491901053415820.124595052670791
530.8334233589304380.3331532821391240.166576641069562
540.7916476083984120.4167047832031760.208352391601588
550.7364783955639840.5270432088720330.263521604436016
560.6874394304716560.6251211390566880.312560569528344
570.6379348361542870.7241303276914260.362065163845713
580.909002170809350.1819956583812990.0909978291906496
590.8727660877256150.254467824548770.127233912274385
600.8195601676494390.3608796647011220.180439832350561
610.7515068487804310.4969863024391370.248493151219569
620.6730167382502440.6539665234995120.326983261749756
630.6299782095441590.7400435809116810.370021790455841
640.5398092354993060.9203815290013880.460190764500694
650.4450492571919650.8900985143839290.554950742808035
660.37557969191850.7511593838370010.6244203080815
670.2700496622465740.5400993244931480.729950337753426
680.2087258819156670.4174517638313340.791274118084333
690.1275977336176240.2551954672352470.872402266382376
700.07683157597927860.1536631519585570.923168424020721







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0806451612903226NOK
5% type I error level170.274193548387097NOK
10% type I error level270.435483870967742NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 & 0.0806451612903226 & NOK \tabularnewline
5% type I error level & 17 & 0.274193548387097 & NOK \tabularnewline
10% type I error level & 27 & 0.435483870967742 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202098&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]5[/C][C]0.0806451612903226[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.274193548387097[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.435483870967742[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202098&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202098&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 level50.0806451612903226NOK
5% type I error level170.274193548387097NOK
10% type I error level270.435483870967742NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}