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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Dec 2012 09:53:53 -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/10/t13551513675yp9ondzlcierkr.htm/, Retrieved Fri, 29 Mar 2024 09:17:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198173, Retrieved Fri, 29 Mar 2024 09:17:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 20:18:32] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS 10 Residuals] [2012-12-10 14:53:53] [885fe6c051c4f145d5c497ce1b2b5522] [Current]
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Dataseries X:
18897	22424	19364	19434	22831	23072	37471	14690
17518	22125	18586	18389	22727	22551	36160	13824
8632	7653	8225	8405	8344	8695	9197	9477
832	554	822	854	830	935	1051	1150
3351	3357	3270	3346	3235	3329	3480	3447
8	8	3	4	5	5	4	4
1	1	1	1	1	1	1	2
7	10	11	9	10	9	10	9
217	222	204	205	191	197	196	191
911	947	918	939	937	967	1007	962
1932	1901	1862	1921	1823	1879	1982	2003
274	267	270	267	269	271	281	276
131	109	87	66	68	64	76	81
1708	1668	1738	1715	1726	1771	1861	2079
2609	1965	2308	2424	2486	2594	2729	2720
133	32	119	89	93	107	102	23
2476	1933	2189	2335	2393	2487	2627	2697
10	37	23	21	22	27	21	18
1510	1616	1378	1605	1534	1654	1421	1650
6427	7719	8279	6133	11706	9235	24339	1324
3812	5127	5890	4487	7888	6772	9522	3656
724	99	154	157	367	153	171	-211
1560	1996	1917	1223	2860	1964	13508	-2076
156	113	166	116	435	166	975	-178
3	3	2	2	15	13	27	13
172	380	151	148	141	167	137	120
65	1700	163	568	80	2043	768	338
593	2931	292	1348	774	631	122	698
281	469	227	309	266	267	292	321
1191	145	747	874	38	275	423	218
72	57	2	32	32	57	16	21
113	-6	27	120	32	184	860	604
19	86	2	18	3	3	12	246
97	11	27	120	32	185	860	381
18897	22424	19364	19434	22831	23072	37471	14690
16770	18775	17704	16289	21687	20252	35933	13873
6132	5145	5705	5818	5817	6171	6504	6749
648	299	484	535	511	548	638	758
1739	1710	1776	1910	1843	1990	2141	2097
160	167	176	193	183	202	163	179
621	570	592	743	655	735	851	835
804	821	842	831	847	869	886	881
3	3	3	3	3	3	3	3
150	149	163	140	156	182	238	199
549	528	558	354	470	438	450	453
95	80	132	-46	88	49	57	62
354	353	339	323	308	314	325	322
100	95	87	77	73	75	68	70
342	343	357	354	339	343	343	305
2854	2265	2530	2664	2653	2852	2932	3135
167	99	168	132	137	147	132	35
2687	2166	2362	2533	2516	2705	2799	3100
645	770	634	680	581	675	814	677
6113	7729	8065	5931	11602	9279	24726	1473
3567	4697	5792	4959	8473	6753	9199	3926
472	241	87	262	330	64	172	-142
1665	2360	1934	584	2229	1972	13856	-2338
328	318	154	6	256	181	1301	-241
0	1	0	0	12	10	21	10
81	112	99	120	302	300	177	259
1322	1286	1317	1325	1314	1322	1308	1370
154	143	156	152	144	151	151	171
1277	1448	1340	1689	1529	1544	1264	1656
1127	2253	486	694	699	1110	1165	1776
456	1356	63	2861	89	82	1019	926
224	200	149	91	165	216	94	131
1444	1990	1445	176	888	2517	413	-264
3	8	4	7	40	38	-64	-10
1444	2084	1443	187	850	2483	489	-231






Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=198173&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=198173&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
2011-III[t] = -18.9779124693251 + 2.48119079055184`2010-I`[t] + 0.135764318755094`2010-II`[t] -1.20369075650056`2010-III`[t] + 0.450866640842221`2010-IV`[t] + 2.24822610044223`2011-I`[t] -0.846583834938722`2011-II`[t] -1.99524909389644`2011-IV`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
2011-III[t] =  -18.9779124693251 +  2.48119079055184`2010-I`[t] +  0.135764318755094`2010-II`[t] -1.20369075650056`2010-III`[t] +  0.450866640842221`2010-IV`[t] +  2.24822610044223`2011-I`[t] -0.846583834938722`2011-II`[t] -1.99524909389644`2011-IV`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]2011-III[t] =  -18.9779124693251 +  2.48119079055184`2010-I`[t] +  0.135764318755094`2010-II`[t] -1.20369075650056`2010-III`[t] +  0.450866640842221`2010-IV`[t] +  2.24822610044223`2011-I`[t] -0.846583834938722`2011-II`[t] -1.99524909389644`2011-IV`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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
2011-III[t] = -18.9779124693251 + 2.48119079055184`2010-I`[t] + 0.135764318755094`2010-II`[t] -1.20369075650056`2010-III`[t] + 0.450866640842221`2010-IV`[t] + 2.24822610044223`2011-I`[t] -0.846583834938722`2011-II`[t] -1.99524909389644`2011-IV`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-18.9779124693251141.289693-0.13430.8935920.446796
`2010-I`2.481190790551840.6031714.11360.0001195.9e-05
`2010-II`0.1357643187550940.3884490.34950.7279150.363958
`2010-III`-1.203690756500561.160068-1.03760.303550.151775
`2010-IV`0.4508666408422210.3956171.13970.2588850.129443
`2011-I`2.248226100442230.471354.76981.2e-056e-06
`2011-II`-0.8465838349387220.568112-1.49020.1413320.070666
`2011-IV`-1.995249093896440.190183-10.491200

\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) & -18.9779124693251 & 141.289693 & -0.1343 & 0.893592 & 0.446796 \tabularnewline
`2010-I` & 2.48119079055184 & 0.603171 & 4.1136 & 0.000119 & 5.9e-05 \tabularnewline
`2010-II` & 0.135764318755094 & 0.388449 & 0.3495 & 0.727915 & 0.363958 \tabularnewline
`2010-III` & -1.20369075650056 & 1.160068 & -1.0376 & 0.30355 & 0.151775 \tabularnewline
`2010-IV` & 0.450866640842221 & 0.395617 & 1.1397 & 0.258885 & 0.129443 \tabularnewline
`2011-I` & 2.24822610044223 & 0.47135 & 4.7698 & 1.2e-05 & 6e-06 \tabularnewline
`2011-II` & -0.846583834938722 & 0.568112 & -1.4902 & 0.141332 & 0.070666 \tabularnewline
`2011-IV` & -1.99524909389644 & 0.190183 & -10.4912 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&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]-18.9779124693251[/C][C]141.289693[/C][C]-0.1343[/C][C]0.893592[/C][C]0.446796[/C][/ROW]
[ROW][C]`2010-I`[/C][C]2.48119079055184[/C][C]0.603171[/C][C]4.1136[/C][C]0.000119[/C][C]5.9e-05[/C][/ROW]
[ROW][C]`2010-II`[/C][C]0.135764318755094[/C][C]0.388449[/C][C]0.3495[/C][C]0.727915[/C][C]0.363958[/C][/ROW]
[ROW][C]`2010-III`[/C][C]-1.20369075650056[/C][C]1.160068[/C][C]-1.0376[/C][C]0.30355[/C][C]0.151775[/C][/ROW]
[ROW][C]`2010-IV`[/C][C]0.450866640842221[/C][C]0.395617[/C][C]1.1397[/C][C]0.258885[/C][C]0.129443[/C][/ROW]
[ROW][C]`2011-I`[/C][C]2.24822610044223[/C][C]0.47135[/C][C]4.7698[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]`2011-II`[/C][C]-0.846583834938722[/C][C]0.568112[/C][C]-1.4902[/C][C]0.141332[/C][C]0.070666[/C][/ROW]
[ROW][C]`2011-IV`[/C][C]-1.99524909389644[/C][C]0.190183[/C][C]-10.4912[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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)-18.9779124693251141.289693-0.13430.8935920.446796
`2010-I`2.481190790551840.6031714.11360.0001195.9e-05
`2010-II`0.1357643187550940.3884490.34950.7279150.363958
`2010-III`-1.203690756500561.160068-1.03760.303550.151775
`2010-IV`0.4508666408422210.3956171.13970.2588850.129443
`2011-I`2.248226100442230.471354.76981.2e-056e-06
`2011-II`-0.8465838349387220.568112-1.49020.1413320.070666
`2011-IV`-1.995249093896440.190183-10.491200







Multiple Linear Regression - Regression Statistics
Multiple R0.994547797361699
R-squared0.989125321237006
Adjusted R-squared0.987877407280597
F-TEST (value)792.623013916146
F-TEST (DF numerator)7
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1039.2738924807
Sum Squared Residuals65885503.6391114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.994547797361699 \tabularnewline
R-squared & 0.989125321237006 \tabularnewline
Adjusted R-squared & 0.987877407280597 \tabularnewline
F-TEST (value) & 792.623013916146 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1039.2738924807 \tabularnewline
Sum Squared Residuals & 65885503.6391114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.994547797361699[/C][/ROW]
[ROW][C]R-squared[/C][C]0.989125321237006[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.987877407280597[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]792.623013916146[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/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]1039.2738924807[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]65885503.6391114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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 R0.994547797361699
R-squared0.989125321237006
Adjusted R-squared0.987877407280597
F-TEST (value)792.623013916146
F-TEST (DF numerator)7
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1039.2738924807
Sum Squared Residuals65885503.6391114







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13747137852.9966997555-381.996699755488
23616036791.2972160254-631.297216025446
391978816.01944150986380.980558490143
41051296.127887014367754.872112985633
534803900.89447299056-420.894472990558
64-0.8226623490696084.82266234906961
71-19.702637397966220.7026373979662
810-12.528967656931822.5289676569318
9196277.996507657913-81.9965076579127
1010071056.84302826805-49.843028268055
1119822168.91451346588-186.914513465882
12281317.162177934359-36.1621779343592
1376182.97532710569-106.97532710569
1418611359.58800719926501.411992800739
1527293001.98230030246-272.982300302456
16102284.862679728689-182.862679728689
1726272698.14170810444-71.1417081044412
1821-16.670685735446837.6706857354468
1914211768.33858662678-347.338586626784
202433925633.2326258938-1294.23262589383
2195229774.99616784028-252.99616784028
221712792.83178448692-2621.83178448692
231350811275.97313693932232.02686306071
249751428.5168594408-453.516859440798
2527-15.853131940945242.8531319409452
26137280.538791705606-143.538791705606
27768-1791.117390992432559.11739099243
281221919.83270991248-1797.83270991248
29292339.505455114736-47.5054551147363
304231868.36432902682-1445.36432902682
3116161.214467264217-145.214467264217
32860-1006.772235701971866.77223570197
3312-441.077688315756453.077688315756
34860-600.0693308279941460.06933082799
353747137852.9966997555-381.996699755497
363593334105.76522479121827.23477520878
3765046038.00403193238465.995968067619
38638460.571360426415177.428639573584
3921411526.11187952132614.888120478681
40163159.1187977053633.8812022946367
41851405.952200157381445.047799842619
42886859.27625318875426.7237468112455
433-15.166339973558518.1663399735585
4423839.9397700410283198.060229958972
45450684.841448546757-234.841448546757
465780.62515790203-23.62515790203
47325429.023296975716-104.023296975716
4868132.993693536023-64.9936935360228
49343469.265109047367-126.265109047367
5029322820.59874086583111.401259134167
51132379.841400346882-247.841400346882
5227992422.23029469047376.769705309533
53814613.369688234796200.630311765204
542472624453.6538031152272.34619688477
55919910232.0766219755-1033.07662197547
561722169.32792461938-1997.32792461938
571385610374.70177815773481.29822184229
5813011558.53178275811-257.531782758107
5921-20.281764233614441.2817642336144
6017740.3623738062138136.637626193786
6113081549.38080691794-241.380806917937
62151118.0185425832132.9814574167903
6312641320.93737023279-56.9373702327878
641165-100.6515436098851265.65154360988
651019793.710033570563225.289966429437
6694352.358196889134-258.358196889134
674132566.37099449568-2153.37099449568
68-6465.6044271412459-129.604427141246
694892464.00779349345-1975.00779349345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 37471 & 37852.9966997555 & -381.996699755488 \tabularnewline
2 & 36160 & 36791.2972160254 & -631.297216025446 \tabularnewline
3 & 9197 & 8816.01944150986 & 380.980558490143 \tabularnewline
4 & 1051 & 296.127887014367 & 754.872112985633 \tabularnewline
5 & 3480 & 3900.89447299056 & -420.894472990558 \tabularnewline
6 & 4 & -0.822662349069608 & 4.82266234906961 \tabularnewline
7 & 1 & -19.7026373979662 & 20.7026373979662 \tabularnewline
8 & 10 & -12.5289676569318 & 22.5289676569318 \tabularnewline
9 & 196 & 277.996507657913 & -81.9965076579127 \tabularnewline
10 & 1007 & 1056.84302826805 & -49.843028268055 \tabularnewline
11 & 1982 & 2168.91451346588 & -186.914513465882 \tabularnewline
12 & 281 & 317.162177934359 & -36.1621779343592 \tabularnewline
13 & 76 & 182.97532710569 & -106.97532710569 \tabularnewline
14 & 1861 & 1359.58800719926 & 501.411992800739 \tabularnewline
15 & 2729 & 3001.98230030246 & -272.982300302456 \tabularnewline
16 & 102 & 284.862679728689 & -182.862679728689 \tabularnewline
17 & 2627 & 2698.14170810444 & -71.1417081044412 \tabularnewline
18 & 21 & -16.6706857354468 & 37.6706857354468 \tabularnewline
19 & 1421 & 1768.33858662678 & -347.338586626784 \tabularnewline
20 & 24339 & 25633.2326258938 & -1294.23262589383 \tabularnewline
21 & 9522 & 9774.99616784028 & -252.99616784028 \tabularnewline
22 & 171 & 2792.83178448692 & -2621.83178448692 \tabularnewline
23 & 13508 & 11275.9731369393 & 2232.02686306071 \tabularnewline
24 & 975 & 1428.5168594408 & -453.516859440798 \tabularnewline
25 & 27 & -15.8531319409452 & 42.8531319409452 \tabularnewline
26 & 137 & 280.538791705606 & -143.538791705606 \tabularnewline
27 & 768 & -1791.11739099243 & 2559.11739099243 \tabularnewline
28 & 122 & 1919.83270991248 & -1797.83270991248 \tabularnewline
29 & 292 & 339.505455114736 & -47.5054551147363 \tabularnewline
30 & 423 & 1868.36432902682 & -1445.36432902682 \tabularnewline
31 & 16 & 161.214467264217 & -145.214467264217 \tabularnewline
32 & 860 & -1006.77223570197 & 1866.77223570197 \tabularnewline
33 & 12 & -441.077688315756 & 453.077688315756 \tabularnewline
34 & 860 & -600.069330827994 & 1460.06933082799 \tabularnewline
35 & 37471 & 37852.9966997555 & -381.996699755497 \tabularnewline
36 & 35933 & 34105.7652247912 & 1827.23477520878 \tabularnewline
37 & 6504 & 6038.00403193238 & 465.995968067619 \tabularnewline
38 & 638 & 460.571360426415 & 177.428639573584 \tabularnewline
39 & 2141 & 1526.11187952132 & 614.888120478681 \tabularnewline
40 & 163 & 159.118797705363 & 3.8812022946367 \tabularnewline
41 & 851 & 405.952200157381 & 445.047799842619 \tabularnewline
42 & 886 & 859.276253188754 & 26.7237468112455 \tabularnewline
43 & 3 & -15.1663399735585 & 18.1663399735585 \tabularnewline
44 & 238 & 39.9397700410283 & 198.060229958972 \tabularnewline
45 & 450 & 684.841448546757 & -234.841448546757 \tabularnewline
46 & 57 & 80.62515790203 & -23.62515790203 \tabularnewline
47 & 325 & 429.023296975716 & -104.023296975716 \tabularnewline
48 & 68 & 132.993693536023 & -64.9936935360228 \tabularnewline
49 & 343 & 469.265109047367 & -126.265109047367 \tabularnewline
50 & 2932 & 2820.59874086583 & 111.401259134167 \tabularnewline
51 & 132 & 379.841400346882 & -247.841400346882 \tabularnewline
52 & 2799 & 2422.23029469047 & 376.769705309533 \tabularnewline
53 & 814 & 613.369688234796 & 200.630311765204 \tabularnewline
54 & 24726 & 24453.6538031152 & 272.34619688477 \tabularnewline
55 & 9199 & 10232.0766219755 & -1033.07662197547 \tabularnewline
56 & 172 & 2169.32792461938 & -1997.32792461938 \tabularnewline
57 & 13856 & 10374.7017781577 & 3481.29822184229 \tabularnewline
58 & 1301 & 1558.53178275811 & -257.531782758107 \tabularnewline
59 & 21 & -20.2817642336144 & 41.2817642336144 \tabularnewline
60 & 177 & 40.3623738062138 & 136.637626193786 \tabularnewline
61 & 1308 & 1549.38080691794 & -241.380806917937 \tabularnewline
62 & 151 & 118.01854258321 & 32.9814574167903 \tabularnewline
63 & 1264 & 1320.93737023279 & -56.9373702327878 \tabularnewline
64 & 1165 & -100.651543609885 & 1265.65154360988 \tabularnewline
65 & 1019 & 793.710033570563 & 225.289966429437 \tabularnewline
66 & 94 & 352.358196889134 & -258.358196889134 \tabularnewline
67 & 413 & 2566.37099449568 & -2153.37099449568 \tabularnewline
68 & -64 & 65.6044271412459 & -129.604427141246 \tabularnewline
69 & 489 & 2464.00779349345 & -1975.00779349345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&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]37471[/C][C]37852.9966997555[/C][C]-381.996699755488[/C][/ROW]
[ROW][C]2[/C][C]36160[/C][C]36791.2972160254[/C][C]-631.297216025446[/C][/ROW]
[ROW][C]3[/C][C]9197[/C][C]8816.01944150986[/C][C]380.980558490143[/C][/ROW]
[ROW][C]4[/C][C]1051[/C][C]296.127887014367[/C][C]754.872112985633[/C][/ROW]
[ROW][C]5[/C][C]3480[/C][C]3900.89447299056[/C][C]-420.894472990558[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]-0.822662349069608[/C][C]4.82266234906961[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]-19.7026373979662[/C][C]20.7026373979662[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]-12.5289676569318[/C][C]22.5289676569318[/C][/ROW]
[ROW][C]9[/C][C]196[/C][C]277.996507657913[/C][C]-81.9965076579127[/C][/ROW]
[ROW][C]10[/C][C]1007[/C][C]1056.84302826805[/C][C]-49.843028268055[/C][/ROW]
[ROW][C]11[/C][C]1982[/C][C]2168.91451346588[/C][C]-186.914513465882[/C][/ROW]
[ROW][C]12[/C][C]281[/C][C]317.162177934359[/C][C]-36.1621779343592[/C][/ROW]
[ROW][C]13[/C][C]76[/C][C]182.97532710569[/C][C]-106.97532710569[/C][/ROW]
[ROW][C]14[/C][C]1861[/C][C]1359.58800719926[/C][C]501.411992800739[/C][/ROW]
[ROW][C]15[/C][C]2729[/C][C]3001.98230030246[/C][C]-272.982300302456[/C][/ROW]
[ROW][C]16[/C][C]102[/C][C]284.862679728689[/C][C]-182.862679728689[/C][/ROW]
[ROW][C]17[/C][C]2627[/C][C]2698.14170810444[/C][C]-71.1417081044412[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]-16.6706857354468[/C][C]37.6706857354468[/C][/ROW]
[ROW][C]19[/C][C]1421[/C][C]1768.33858662678[/C][C]-347.338586626784[/C][/ROW]
[ROW][C]20[/C][C]24339[/C][C]25633.2326258938[/C][C]-1294.23262589383[/C][/ROW]
[ROW][C]21[/C][C]9522[/C][C]9774.99616784028[/C][C]-252.99616784028[/C][/ROW]
[ROW][C]22[/C][C]171[/C][C]2792.83178448692[/C][C]-2621.83178448692[/C][/ROW]
[ROW][C]23[/C][C]13508[/C][C]11275.9731369393[/C][C]2232.02686306071[/C][/ROW]
[ROW][C]24[/C][C]975[/C][C]1428.5168594408[/C][C]-453.516859440798[/C][/ROW]
[ROW][C]25[/C][C]27[/C][C]-15.8531319409452[/C][C]42.8531319409452[/C][/ROW]
[ROW][C]26[/C][C]137[/C][C]280.538791705606[/C][C]-143.538791705606[/C][/ROW]
[ROW][C]27[/C][C]768[/C][C]-1791.11739099243[/C][C]2559.11739099243[/C][/ROW]
[ROW][C]28[/C][C]122[/C][C]1919.83270991248[/C][C]-1797.83270991248[/C][/ROW]
[ROW][C]29[/C][C]292[/C][C]339.505455114736[/C][C]-47.5054551147363[/C][/ROW]
[ROW][C]30[/C][C]423[/C][C]1868.36432902682[/C][C]-1445.36432902682[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]161.214467264217[/C][C]-145.214467264217[/C][/ROW]
[ROW][C]32[/C][C]860[/C][C]-1006.77223570197[/C][C]1866.77223570197[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]-441.077688315756[/C][C]453.077688315756[/C][/ROW]
[ROW][C]34[/C][C]860[/C][C]-600.069330827994[/C][C]1460.06933082799[/C][/ROW]
[ROW][C]35[/C][C]37471[/C][C]37852.9966997555[/C][C]-381.996699755497[/C][/ROW]
[ROW][C]36[/C][C]35933[/C][C]34105.7652247912[/C][C]1827.23477520878[/C][/ROW]
[ROW][C]37[/C][C]6504[/C][C]6038.00403193238[/C][C]465.995968067619[/C][/ROW]
[ROW][C]38[/C][C]638[/C][C]460.571360426415[/C][C]177.428639573584[/C][/ROW]
[ROW][C]39[/C][C]2141[/C][C]1526.11187952132[/C][C]614.888120478681[/C][/ROW]
[ROW][C]40[/C][C]163[/C][C]159.118797705363[/C][C]3.8812022946367[/C][/ROW]
[ROW][C]41[/C][C]851[/C][C]405.952200157381[/C][C]445.047799842619[/C][/ROW]
[ROW][C]42[/C][C]886[/C][C]859.276253188754[/C][C]26.7237468112455[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-15.1663399735585[/C][C]18.1663399735585[/C][/ROW]
[ROW][C]44[/C][C]238[/C][C]39.9397700410283[/C][C]198.060229958972[/C][/ROW]
[ROW][C]45[/C][C]450[/C][C]684.841448546757[/C][C]-234.841448546757[/C][/ROW]
[ROW][C]46[/C][C]57[/C][C]80.62515790203[/C][C]-23.62515790203[/C][/ROW]
[ROW][C]47[/C][C]325[/C][C]429.023296975716[/C][C]-104.023296975716[/C][/ROW]
[ROW][C]48[/C][C]68[/C][C]132.993693536023[/C][C]-64.9936935360228[/C][/ROW]
[ROW][C]49[/C][C]343[/C][C]469.265109047367[/C][C]-126.265109047367[/C][/ROW]
[ROW][C]50[/C][C]2932[/C][C]2820.59874086583[/C][C]111.401259134167[/C][/ROW]
[ROW][C]51[/C][C]132[/C][C]379.841400346882[/C][C]-247.841400346882[/C][/ROW]
[ROW][C]52[/C][C]2799[/C][C]2422.23029469047[/C][C]376.769705309533[/C][/ROW]
[ROW][C]53[/C][C]814[/C][C]613.369688234796[/C][C]200.630311765204[/C][/ROW]
[ROW][C]54[/C][C]24726[/C][C]24453.6538031152[/C][C]272.34619688477[/C][/ROW]
[ROW][C]55[/C][C]9199[/C][C]10232.0766219755[/C][C]-1033.07662197547[/C][/ROW]
[ROW][C]56[/C][C]172[/C][C]2169.32792461938[/C][C]-1997.32792461938[/C][/ROW]
[ROW][C]57[/C][C]13856[/C][C]10374.7017781577[/C][C]3481.29822184229[/C][/ROW]
[ROW][C]58[/C][C]1301[/C][C]1558.53178275811[/C][C]-257.531782758107[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]-20.2817642336144[/C][C]41.2817642336144[/C][/ROW]
[ROW][C]60[/C][C]177[/C][C]40.3623738062138[/C][C]136.637626193786[/C][/ROW]
[ROW][C]61[/C][C]1308[/C][C]1549.38080691794[/C][C]-241.380806917937[/C][/ROW]
[ROW][C]62[/C][C]151[/C][C]118.01854258321[/C][C]32.9814574167903[/C][/ROW]
[ROW][C]63[/C][C]1264[/C][C]1320.93737023279[/C][C]-56.9373702327878[/C][/ROW]
[ROW][C]64[/C][C]1165[/C][C]-100.651543609885[/C][C]1265.65154360988[/C][/ROW]
[ROW][C]65[/C][C]1019[/C][C]793.710033570563[/C][C]225.289966429437[/C][/ROW]
[ROW][C]66[/C][C]94[/C][C]352.358196889134[/C][C]-258.358196889134[/C][/ROW]
[ROW][C]67[/C][C]413[/C][C]2566.37099449568[/C][C]-2153.37099449568[/C][/ROW]
[ROW][C]68[/C][C]-64[/C][C]65.6044271412459[/C][C]-129.604427141246[/C][/ROW]
[ROW][C]69[/C][C]489[/C][C]2464.00779349345[/C][C]-1975.00779349345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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
13747137852.9966997555-381.996699755488
23616036791.2972160254-631.297216025446
391978816.01944150986380.980558490143
41051296.127887014367754.872112985633
534803900.89447299056-420.894472990558
64-0.8226623490696084.82266234906961
71-19.702637397966220.7026373979662
810-12.528967656931822.5289676569318
9196277.996507657913-81.9965076579127
1010071056.84302826805-49.843028268055
1119822168.91451346588-186.914513465882
12281317.162177934359-36.1621779343592
1376182.97532710569-106.97532710569
1418611359.58800719926501.411992800739
1527293001.98230030246-272.982300302456
16102284.862679728689-182.862679728689
1726272698.14170810444-71.1417081044412
1821-16.670685735446837.6706857354468
1914211768.33858662678-347.338586626784
202433925633.2326258938-1294.23262589383
2195229774.99616784028-252.99616784028
221712792.83178448692-2621.83178448692
231350811275.97313693932232.02686306071
249751428.5168594408-453.516859440798
2527-15.853131940945242.8531319409452
26137280.538791705606-143.538791705606
27768-1791.117390992432559.11739099243
281221919.83270991248-1797.83270991248
29292339.505455114736-47.5054551147363
304231868.36432902682-1445.36432902682
3116161.214467264217-145.214467264217
32860-1006.772235701971866.77223570197
3312-441.077688315756453.077688315756
34860-600.0693308279941460.06933082799
353747137852.9966997555-381.996699755497
363593334105.76522479121827.23477520878
3765046038.00403193238465.995968067619
38638460.571360426415177.428639573584
3921411526.11187952132614.888120478681
40163159.1187977053633.8812022946367
41851405.952200157381445.047799842619
42886859.27625318875426.7237468112455
433-15.166339973558518.1663399735585
4423839.9397700410283198.060229958972
45450684.841448546757-234.841448546757
465780.62515790203-23.62515790203
47325429.023296975716-104.023296975716
4868132.993693536023-64.9936935360228
49343469.265109047367-126.265109047367
5029322820.59874086583111.401259134167
51132379.841400346882-247.841400346882
5227992422.23029469047376.769705309533
53814613.369688234796200.630311765204
542472624453.6538031152272.34619688477
55919910232.0766219755-1033.07662197547
561722169.32792461938-1997.32792461938
571385610374.70177815773481.29822184229
5813011558.53178275811-257.531782758107
5921-20.281764233614441.2817642336144
6017740.3623738062138136.637626193786
6113081549.38080691794-241.380806917937
62151118.0185425832132.9814574167903
6312641320.93737023279-56.9373702327878
641165-100.6515436098851265.65154360988
651019793.710033570563225.289966429437
6694352.358196889134-258.358196889134
674132566.37099449568-2153.37099449568
68-6465.6044271412459-129.604427141246
694892464.00779349345-1975.00779349345







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
117.25796249277763e-050.0001451592498555530.999927420375072
122.84136399496661e-065.68272798993322e-060.999997158636005
136.06916581316241e-071.21383316263248e-060.999999393083419
144.63045924298048e-079.26091848596097e-070.999999536954076
155.44237869928072e-081.08847573985614e-070.999999945576213
164.00036219668597e-098.00072439337195e-090.999999995999638
172.55952362199607e-105.11904724399215e-100.999999999744048
182.25184051080713e-114.50368102161426e-110.999999999977482
192.65062613305189e-095.30125226610378e-090.999999997349374
202.35831302399914e-094.71662604799829e-090.999999997641687
213.87001707751555e-077.74003415503111e-070.999999612998292
220.0007432918018977320.001486583603795460.999256708198102
230.004738068920072570.009476137840145150.995261931079927
240.002562784783443750.005125569566887510.997437215216556
250.001211507111015140.002423014222030270.998788492888985
260.0006017079840965380.001203415968193080.999398292015903
270.001989666043697080.003979332087394160.998010333956303
280.005465485262848150.01093097052569630.994534514737152
290.003340300201021990.006680600402043990.996659699798978
300.366717067094340.7334341341886810.63328293290566
310.2953000979810570.5906001959621150.704699902018943
320.6965330329124610.6069339341750780.303466967087539
330.6421548328631050.7156903342737890.357845167136895
340.8587508114839960.2824983770320070.141249188516004
350.8315182093528850.3369635812942310.168481790647115
360.8933201646257050.2133596707485890.106679835374295
370.8652855999046070.2694288001907870.134714400095394
380.8462044400040220.3075911199919570.153795559995978
390.8250158124814160.3499683750371670.174984187518584
400.7715035102889060.4569929794221880.228496489711094
410.7916859717478460.4166280565043080.208314028252154
420.7338487028713850.5323025942572310.266151297128615
430.6701290194144720.6597419611710550.329870980585527
440.6099680924728360.7800638150543280.390031907527164
450.680532299167850.63893540166430.31946770083215
460.6205352491682170.7589295016635660.379464750831783
470.549309854970750.90138029005850.45069014502925
480.4590169444732240.9180338889464490.540983055526776
490.3792306823897970.7584613647795940.620769317610203
500.3190958798898270.6381917597796540.680904120110173
510.2418626656512270.4837253313024540.758137334348773
520.848655339385930.3026893212281410.15134466061407
530.7748393572411030.4503212855177940.225160642758897
540.7746896666074830.4506206667850350.225310333392517
550.9984943665807730.003011266838454950.00150563341922747
560.9993031649775610.001393670044878890.000696835022439447
570.9995480537779520.0009038924440955450.000451946222047773
580.9978951603146930.004209679370614840.00210483968530742

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 7.25796249277763e-05 & 0.000145159249855553 & 0.999927420375072 \tabularnewline
12 & 2.84136399496661e-06 & 5.68272798993322e-06 & 0.999997158636005 \tabularnewline
13 & 6.06916581316241e-07 & 1.21383316263248e-06 & 0.999999393083419 \tabularnewline
14 & 4.63045924298048e-07 & 9.26091848596097e-07 & 0.999999536954076 \tabularnewline
15 & 5.44237869928072e-08 & 1.08847573985614e-07 & 0.999999945576213 \tabularnewline
16 & 4.00036219668597e-09 & 8.00072439337195e-09 & 0.999999995999638 \tabularnewline
17 & 2.55952362199607e-10 & 5.11904724399215e-10 & 0.999999999744048 \tabularnewline
18 & 2.25184051080713e-11 & 4.50368102161426e-11 & 0.999999999977482 \tabularnewline
19 & 2.65062613305189e-09 & 5.30125226610378e-09 & 0.999999997349374 \tabularnewline
20 & 2.35831302399914e-09 & 4.71662604799829e-09 & 0.999999997641687 \tabularnewline
21 & 3.87001707751555e-07 & 7.74003415503111e-07 & 0.999999612998292 \tabularnewline
22 & 0.000743291801897732 & 0.00148658360379546 & 0.999256708198102 \tabularnewline
23 & 0.00473806892007257 & 0.00947613784014515 & 0.995261931079927 \tabularnewline
24 & 0.00256278478344375 & 0.00512556956688751 & 0.997437215216556 \tabularnewline
25 & 0.00121150711101514 & 0.00242301422203027 & 0.998788492888985 \tabularnewline
26 & 0.000601707984096538 & 0.00120341596819308 & 0.999398292015903 \tabularnewline
27 & 0.00198966604369708 & 0.00397933208739416 & 0.998010333956303 \tabularnewline
28 & 0.00546548526284815 & 0.0109309705256963 & 0.994534514737152 \tabularnewline
29 & 0.00334030020102199 & 0.00668060040204399 & 0.996659699798978 \tabularnewline
30 & 0.36671706709434 & 0.733434134188681 & 0.63328293290566 \tabularnewline
31 & 0.295300097981057 & 0.590600195962115 & 0.704699902018943 \tabularnewline
32 & 0.696533032912461 & 0.606933934175078 & 0.303466967087539 \tabularnewline
33 & 0.642154832863105 & 0.715690334273789 & 0.357845167136895 \tabularnewline
34 & 0.858750811483996 & 0.282498377032007 & 0.141249188516004 \tabularnewline
35 & 0.831518209352885 & 0.336963581294231 & 0.168481790647115 \tabularnewline
36 & 0.893320164625705 & 0.213359670748589 & 0.106679835374295 \tabularnewline
37 & 0.865285599904607 & 0.269428800190787 & 0.134714400095394 \tabularnewline
38 & 0.846204440004022 & 0.307591119991957 & 0.153795559995978 \tabularnewline
39 & 0.825015812481416 & 0.349968375037167 & 0.174984187518584 \tabularnewline
40 & 0.771503510288906 & 0.456992979422188 & 0.228496489711094 \tabularnewline
41 & 0.791685971747846 & 0.416628056504308 & 0.208314028252154 \tabularnewline
42 & 0.733848702871385 & 0.532302594257231 & 0.266151297128615 \tabularnewline
43 & 0.670129019414472 & 0.659741961171055 & 0.329870980585527 \tabularnewline
44 & 0.609968092472836 & 0.780063815054328 & 0.390031907527164 \tabularnewline
45 & 0.68053229916785 & 0.6389354016643 & 0.31946770083215 \tabularnewline
46 & 0.620535249168217 & 0.758929501663566 & 0.379464750831783 \tabularnewline
47 & 0.54930985497075 & 0.9013802900585 & 0.45069014502925 \tabularnewline
48 & 0.459016944473224 & 0.918033888946449 & 0.540983055526776 \tabularnewline
49 & 0.379230682389797 & 0.758461364779594 & 0.620769317610203 \tabularnewline
50 & 0.319095879889827 & 0.638191759779654 & 0.680904120110173 \tabularnewline
51 & 0.241862665651227 & 0.483725331302454 & 0.758137334348773 \tabularnewline
52 & 0.84865533938593 & 0.302689321228141 & 0.15134466061407 \tabularnewline
53 & 0.774839357241103 & 0.450321285517794 & 0.225160642758897 \tabularnewline
54 & 0.774689666607483 & 0.450620666785035 & 0.225310333392517 \tabularnewline
55 & 0.998494366580773 & 0.00301126683845495 & 0.00150563341922747 \tabularnewline
56 & 0.999303164977561 & 0.00139367004487889 & 0.000696835022439447 \tabularnewline
57 & 0.999548053777952 & 0.000903892444095545 & 0.000451946222047773 \tabularnewline
58 & 0.997895160314693 & 0.00420967937061484 & 0.00210483968530742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&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]11[/C][C]7.25796249277763e-05[/C][C]0.000145159249855553[/C][C]0.999927420375072[/C][/ROW]
[ROW][C]12[/C][C]2.84136399496661e-06[/C][C]5.68272798993322e-06[/C][C]0.999997158636005[/C][/ROW]
[ROW][C]13[/C][C]6.06916581316241e-07[/C][C]1.21383316263248e-06[/C][C]0.999999393083419[/C][/ROW]
[ROW][C]14[/C][C]4.63045924298048e-07[/C][C]9.26091848596097e-07[/C][C]0.999999536954076[/C][/ROW]
[ROW][C]15[/C][C]5.44237869928072e-08[/C][C]1.08847573985614e-07[/C][C]0.999999945576213[/C][/ROW]
[ROW][C]16[/C][C]4.00036219668597e-09[/C][C]8.00072439337195e-09[/C][C]0.999999995999638[/C][/ROW]
[ROW][C]17[/C][C]2.55952362199607e-10[/C][C]5.11904724399215e-10[/C][C]0.999999999744048[/C][/ROW]
[ROW][C]18[/C][C]2.25184051080713e-11[/C][C]4.50368102161426e-11[/C][C]0.999999999977482[/C][/ROW]
[ROW][C]19[/C][C]2.65062613305189e-09[/C][C]5.30125226610378e-09[/C][C]0.999999997349374[/C][/ROW]
[ROW][C]20[/C][C]2.35831302399914e-09[/C][C]4.71662604799829e-09[/C][C]0.999999997641687[/C][/ROW]
[ROW][C]21[/C][C]3.87001707751555e-07[/C][C]7.74003415503111e-07[/C][C]0.999999612998292[/C][/ROW]
[ROW][C]22[/C][C]0.000743291801897732[/C][C]0.00148658360379546[/C][C]0.999256708198102[/C][/ROW]
[ROW][C]23[/C][C]0.00473806892007257[/C][C]0.00947613784014515[/C][C]0.995261931079927[/C][/ROW]
[ROW][C]24[/C][C]0.00256278478344375[/C][C]0.00512556956688751[/C][C]0.997437215216556[/C][/ROW]
[ROW][C]25[/C][C]0.00121150711101514[/C][C]0.00242301422203027[/C][C]0.998788492888985[/C][/ROW]
[ROW][C]26[/C][C]0.000601707984096538[/C][C]0.00120341596819308[/C][C]0.999398292015903[/C][/ROW]
[ROW][C]27[/C][C]0.00198966604369708[/C][C]0.00397933208739416[/C][C]0.998010333956303[/C][/ROW]
[ROW][C]28[/C][C]0.00546548526284815[/C][C]0.0109309705256963[/C][C]0.994534514737152[/C][/ROW]
[ROW][C]29[/C][C]0.00334030020102199[/C][C]0.00668060040204399[/C][C]0.996659699798978[/C][/ROW]
[ROW][C]30[/C][C]0.36671706709434[/C][C]0.733434134188681[/C][C]0.63328293290566[/C][/ROW]
[ROW][C]31[/C][C]0.295300097981057[/C][C]0.590600195962115[/C][C]0.704699902018943[/C][/ROW]
[ROW][C]32[/C][C]0.696533032912461[/C][C]0.606933934175078[/C][C]0.303466967087539[/C][/ROW]
[ROW][C]33[/C][C]0.642154832863105[/C][C]0.715690334273789[/C][C]0.357845167136895[/C][/ROW]
[ROW][C]34[/C][C]0.858750811483996[/C][C]0.282498377032007[/C][C]0.141249188516004[/C][/ROW]
[ROW][C]35[/C][C]0.831518209352885[/C][C]0.336963581294231[/C][C]0.168481790647115[/C][/ROW]
[ROW][C]36[/C][C]0.893320164625705[/C][C]0.213359670748589[/C][C]0.106679835374295[/C][/ROW]
[ROW][C]37[/C][C]0.865285599904607[/C][C]0.269428800190787[/C][C]0.134714400095394[/C][/ROW]
[ROW][C]38[/C][C]0.846204440004022[/C][C]0.307591119991957[/C][C]0.153795559995978[/C][/ROW]
[ROW][C]39[/C][C]0.825015812481416[/C][C]0.349968375037167[/C][C]0.174984187518584[/C][/ROW]
[ROW][C]40[/C][C]0.771503510288906[/C][C]0.456992979422188[/C][C]0.228496489711094[/C][/ROW]
[ROW][C]41[/C][C]0.791685971747846[/C][C]0.416628056504308[/C][C]0.208314028252154[/C][/ROW]
[ROW][C]42[/C][C]0.733848702871385[/C][C]0.532302594257231[/C][C]0.266151297128615[/C][/ROW]
[ROW][C]43[/C][C]0.670129019414472[/C][C]0.659741961171055[/C][C]0.329870980585527[/C][/ROW]
[ROW][C]44[/C][C]0.609968092472836[/C][C]0.780063815054328[/C][C]0.390031907527164[/C][/ROW]
[ROW][C]45[/C][C]0.68053229916785[/C][C]0.6389354016643[/C][C]0.31946770083215[/C][/ROW]
[ROW][C]46[/C][C]0.620535249168217[/C][C]0.758929501663566[/C][C]0.379464750831783[/C][/ROW]
[ROW][C]47[/C][C]0.54930985497075[/C][C]0.9013802900585[/C][C]0.45069014502925[/C][/ROW]
[ROW][C]48[/C][C]0.459016944473224[/C][C]0.918033888946449[/C][C]0.540983055526776[/C][/ROW]
[ROW][C]49[/C][C]0.379230682389797[/C][C]0.758461364779594[/C][C]0.620769317610203[/C][/ROW]
[ROW][C]50[/C][C]0.319095879889827[/C][C]0.638191759779654[/C][C]0.680904120110173[/C][/ROW]
[ROW][C]51[/C][C]0.241862665651227[/C][C]0.483725331302454[/C][C]0.758137334348773[/C][/ROW]
[ROW][C]52[/C][C]0.84865533938593[/C][C]0.302689321228141[/C][C]0.15134466061407[/C][/ROW]
[ROW][C]53[/C][C]0.774839357241103[/C][C]0.450321285517794[/C][C]0.225160642758897[/C][/ROW]
[ROW][C]54[/C][C]0.774689666607483[/C][C]0.450620666785035[/C][C]0.225310333392517[/C][/ROW]
[ROW][C]55[/C][C]0.998494366580773[/C][C]0.00301126683845495[/C][C]0.00150563341922747[/C][/ROW]
[ROW][C]56[/C][C]0.999303164977561[/C][C]0.00139367004487889[/C][C]0.000696835022439447[/C][/ROW]
[ROW][C]57[/C][C]0.999548053777952[/C][C]0.000903892444095545[/C][C]0.000451946222047773[/C][/ROW]
[ROW][C]58[/C][C]0.997895160314693[/C][C]0.00420967937061484[/C][C]0.00210483968530742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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
117.25796249277763e-050.0001451592498555530.999927420375072
122.84136399496661e-065.68272798993322e-060.999997158636005
136.06916581316241e-071.21383316263248e-060.999999393083419
144.63045924298048e-079.26091848596097e-070.999999536954076
155.44237869928072e-081.08847573985614e-070.999999945576213
164.00036219668597e-098.00072439337195e-090.999999995999638
172.55952362199607e-105.11904724399215e-100.999999999744048
182.25184051080713e-114.50368102161426e-110.999999999977482
192.65062613305189e-095.30125226610378e-090.999999997349374
202.35831302399914e-094.71662604799829e-090.999999997641687
213.87001707751555e-077.74003415503111e-070.999999612998292
220.0007432918018977320.001486583603795460.999256708198102
230.004738068920072570.009476137840145150.995261931079927
240.002562784783443750.005125569566887510.997437215216556
250.001211507111015140.002423014222030270.998788492888985
260.0006017079840965380.001203415968193080.999398292015903
270.001989666043697080.003979332087394160.998010333956303
280.005465485262848150.01093097052569630.994534514737152
290.003340300201021990.006680600402043990.996659699798978
300.366717067094340.7334341341886810.63328293290566
310.2953000979810570.5906001959621150.704699902018943
320.6965330329124610.6069339341750780.303466967087539
330.6421548328631050.7156903342737890.357845167136895
340.8587508114839960.2824983770320070.141249188516004
350.8315182093528850.3369635812942310.168481790647115
360.8933201646257050.2133596707485890.106679835374295
370.8652855999046070.2694288001907870.134714400095394
380.8462044400040220.3075911199919570.153795559995978
390.8250158124814160.3499683750371670.174984187518584
400.7715035102889060.4569929794221880.228496489711094
410.7916859717478460.4166280565043080.208314028252154
420.7338487028713850.5323025942572310.266151297128615
430.6701290194144720.6597419611710550.329870980585527
440.6099680924728360.7800638150543280.390031907527164
450.680532299167850.63893540166430.31946770083215
460.6205352491682170.7589295016635660.379464750831783
470.549309854970750.90138029005850.45069014502925
480.4590169444732240.9180338889464490.540983055526776
490.3792306823897970.7584613647795940.620769317610203
500.3190958798898270.6381917597796540.680904120110173
510.2418626656512270.4837253313024540.758137334348773
520.848655339385930.3026893212281410.15134466061407
530.7748393572411030.4503212855177940.225160642758897
540.7746896666074830.4506206667850350.225310333392517
550.9984943665807730.003011266838454950.00150563341922747
560.9993031649775610.001393670044878890.000696835022439447
570.9995480537779520.0009038924440955450.000451946222047773
580.9978951603146930.004209679370614840.00210483968530742







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.458333333333333NOK
5% type I error level230.479166666666667NOK
10% type I error level230.479166666666667NOK

\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 & 22 & 0.458333333333333 & NOK \tabularnewline
5% type I error level & 23 & 0.479166666666667 & NOK \tabularnewline
10% type I error level & 23 & 0.479166666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198173&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]22[/C][C]0.458333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.479166666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.479166666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198173&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198173&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 level220.458333333333333NOK
5% type I error level230.479166666666667NOK
10% type I error level230.479166666666667NOK



Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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')
}