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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationMon, 17 Dec 2012 15:47:02 -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/17/t1355777280gduh7x0ngz0vxdo.htm/, Retrieved Thu, 28 Mar 2024 22:22:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201201, Retrieved Thu, 28 Mar 2024 22:22:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7 - Multiple Li...] [2012-11-18 15:41:09] [65803b142704cbeb62f79b9de3d73760]
- RM      [Multiple Regression] [Paper Multiple li...] [2012-12-17 20:47:02] [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 time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' 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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' 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=201201&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' 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=201201&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' 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
2010-I[t] = + 13.553980866236 + 0.232362179271225`2010-II`[t] + 1.35518866840613`2010-III`[t] + 0.0216853276726292`2010-IV`[t] -0.635946736880319`2011-I`[t] -0.211294046882823`2011-II`[t] + 0.0875230339979512`2011-III`[t] + 0.208325086554488`2011-IV\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
2010-I[t] =  +  13.553980866236 +  0.232362179271225`2010-II`[t] +  1.35518866840613`2010-III`[t] +  0.0216853276726292`2010-IV`[t] -0.635946736880319`2011-I`[t] -0.211294046882823`2011-II`[t] +  0.0875230339979512`2011-III`[t] +  0.208325086554488`2011-IV\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]2010-I[t] =  +  13.553980866236 +  0.232362179271225`2010-II`[t] +  1.35518866840613`2010-III`[t] +  0.0216853276726292`2010-IV`[t] -0.635946736880319`2011-I`[t] -0.211294046882823`2011-II`[t] +  0.0875230339979512`2011-III`[t] +  0.208325086554488`2011-IV\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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
2010-I[t] = + 13.553980866236 + 0.232362179271225`2010-II`[t] + 1.35518866840613`2010-III`[t] + 0.0216853276726292`2010-IV`[t] -0.635946736880319`2011-I`[t] -0.211294046882823`2011-II`[t] + 0.0875230339979512`2011-III`[t] + 0.208325086554488`2011-IV\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.55398086623626.48350.51180.6106460.305323
`2010-II`0.2323621792712250.0666953.4840.0009210.00046
`2010-III`1.355188668406130.13491410.044800
`2010-IV`0.02168532767262920.0750380.2890.7735690.386785
`2011-I`-0.6359467368803190.064264-9.895800
`2011-II`-0.2112940468828230.105202-2.00850.0490280.024514
`2011-III`0.08752303399795120.0212774.11360.0001195.9e-05
`2011-IV\r`0.2083250865544880.053543.8910.000250.000125

\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) & 13.553980866236 & 26.4835 & 0.5118 & 0.610646 & 0.305323 \tabularnewline
`2010-II` & 0.232362179271225 & 0.066695 & 3.484 & 0.000921 & 0.00046 \tabularnewline
`2010-III` & 1.35518866840613 & 0.134914 & 10.0448 & 0 & 0 \tabularnewline
`2010-IV` & 0.0216853276726292 & 0.075038 & 0.289 & 0.773569 & 0.386785 \tabularnewline
`2011-I` & -0.635946736880319 & 0.064264 & -9.8958 & 0 & 0 \tabularnewline
`2011-II` & -0.211294046882823 & 0.105202 & -2.0085 & 0.049028 & 0.024514 \tabularnewline
`2011-III` & 0.0875230339979512 & 0.021277 & 4.1136 & 0.000119 & 5.9e-05 \tabularnewline
`2011-IV\r` & 0.208325086554488 & 0.05354 & 3.891 & 0.00025 & 0.000125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&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]13.553980866236[/C][C]26.4835[/C][C]0.5118[/C][C]0.610646[/C][C]0.305323[/C][/ROW]
[ROW][C]`2010-II`[/C][C]0.232362179271225[/C][C]0.066695[/C][C]3.484[/C][C]0.000921[/C][C]0.00046[/C][/ROW]
[ROW][C]`2010-III`[/C][C]1.35518866840613[/C][C]0.134914[/C][C]10.0448[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`2010-IV`[/C][C]0.0216853276726292[/C][C]0.075038[/C][C]0.289[/C][C]0.773569[/C][C]0.386785[/C][/ROW]
[ROW][C]`2011-I`[/C][C]-0.635946736880319[/C][C]0.064264[/C][C]-9.8958[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`2011-II`[/C][C]-0.211294046882823[/C][C]0.105202[/C][C]-2.0085[/C][C]0.049028[/C][C]0.024514[/C][/ROW]
[ROW][C]`2011-III`[/C][C]0.0875230339979512[/C][C]0.021277[/C][C]4.1136[/C][C]0.000119[/C][C]5.9e-05[/C][/ROW]
[ROW][C]`2011-IV\r`[/C][C]0.208325086554488[/C][C]0.05354[/C][C]3.891[/C][C]0.00025[/C][C]0.000125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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)13.55398086623626.48350.51180.6106460.305323
`2010-II`0.2323621792712250.0666953.4840.0009210.00046
`2010-III`1.355188668406130.13491410.044800
`2010-IV`0.02168532767262920.0750380.2890.7735690.386785
`2011-I`-0.6359467368803190.064264-9.895800
`2011-II`-0.2112940468828230.105202-2.00850.0490280.024514
`2011-III`0.08752303399795120.0212774.11360.0001195.9e-05
`2011-IV\r`0.2083250865544880.053543.8910.000250.000125







Multiple Linear Regression - Regression Statistics
Multiple R0.999082961055013
R-squared0.998166763070453
Adjusted R-squared0.997956391619522
F-TEST (value)4744.7824248504
F-TEST (DF numerator)7
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation195.191597897025
Sum Squared Residuals2324085.35326522

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999082961055013 \tabularnewline
R-squared & 0.998166763070453 \tabularnewline
Adjusted R-squared & 0.997956391619522 \tabularnewline
F-TEST (value) & 4744.7824248504 \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 & 195.191597897025 \tabularnewline
Sum Squared Residuals & 2324085.35326522 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999082961055013[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998166763070453[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997956391619522[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4744.7824248504[/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]195.191597897025[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2324085.35326522[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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 R0.999082961055013
R-squared0.998166763070453
Adjusted R-squared0.997956391619522
F-TEST (value)4744.7824248504
F-TEST (DF numerator)7
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation195.191597897025
Sum Squared Residuals2324085.35326522







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11889718832.94445087864.055549121994
21751817567.540644372-49.5406443720448
386328756.21859433832-124.21859433832
4832880.931816268165-48.9318162681647
533513559.61102454585-208.611024545848
6816.5123741797091-8.5123741797091
7114.8201494649298-13.8201494649298
8725.4688882886912-18.4688882886912
9217239.896217407637-22.8962174076369
10911986.36767762632-75.3676776263201
1119322054.68668501911-122.686685019106
12274301.046943206313-27.0469432063133
13131124.9729896709236.02701032907749
1417081917.78473487111-209.784734871114
1526092326.9205938041282.0794061959
16133116.15433321832216.845666781678
1724762224.32024145201251.679758547986
181039.6681805484726-29.6681805484726
1915101434.3901747541575.6098252458489
2064276170.11325720182256.886742798179
2138124432.03789633828-620.03789633828
22724-46.0491080642611770.049108064261
2315601617.75581177142-57.7558117714155
241563.82917450514424152.170825494856
2539.79013977667915-6.79013977667915
26172221.729596728878-49.7295967288785
2765296.864797329382-231.864797329382
28593650.093843821845-57.0938438218448
29281303.712173106989-22.712173106989
3011911078.69068190161112.309318098394
31723.5840720161529668.415927983847
32113193.221902468829-80.2219024688291
331986.3943668675712-67.3943668675712
3497150.504271167906-53.5042711679063
351889718832.94445087864.055549121991
361677016685.801570829584.1984291705087
3761326038.6120734052393.3879265947727
38648523.535429343122124.464570656878
3917391890.84689129117-151.846891291173
40160187.553733400319-27.5537334003185
41621640.971625297798-19.9716252977985
42804902.231092639715-98.2310926397155
43316.7275114026543-13.7275114026543
44150196.731611231868-46.7316112318685
45549642.427965128011-93.4279651280105
4695161.618581526138-66.618581526138
47354395.298887816451-41.2988878164515
48100113.462729341329-13.4627293413285
49342390.232919150546-48.2329191505462
5028542646.19072827997207.809271720026
51167167.751486831863-0.751486831863091
5226872491.92738460802195.072615391977
53645766.579794990305-121.579794990305
5461135999.79944239228113.200557607715
5535673869.51272361349-302.512723613494
56472-44.7772065419315516.777206541931
5716652086.98580975667-421.985809756665
58328158.888855019616169.111144980384
5907.96327631361681-7.96327631361681
6081-9.651891729917390.6518917299173
6113221410.80903367528-88.809033675279
62154186.845211324054-32.8452113240537
6312771359.60863978481-82.6086397848139
6411271003.6238082236123.376191776403
65456684.225334905623-228.225334905623
66224148.86991895370775.1300810462927
6714441322.6203329702121.379667029801
683-20.166338030434623.1663380304346
6914441386.8669911248957.133008875109

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18897 & 18832.944450878 & 64.055549121994 \tabularnewline
2 & 17518 & 17567.540644372 & -49.5406443720448 \tabularnewline
3 & 8632 & 8756.21859433832 & -124.21859433832 \tabularnewline
4 & 832 & 880.931816268165 & -48.9318162681647 \tabularnewline
5 & 3351 & 3559.61102454585 & -208.611024545848 \tabularnewline
6 & 8 & 16.5123741797091 & -8.5123741797091 \tabularnewline
7 & 1 & 14.8201494649298 & -13.8201494649298 \tabularnewline
8 & 7 & 25.4688882886912 & -18.4688882886912 \tabularnewline
9 & 217 & 239.896217407637 & -22.8962174076369 \tabularnewline
10 & 911 & 986.36767762632 & -75.3676776263201 \tabularnewline
11 & 1932 & 2054.68668501911 & -122.686685019106 \tabularnewline
12 & 274 & 301.046943206313 & -27.0469432063133 \tabularnewline
13 & 131 & 124.972989670923 & 6.02701032907749 \tabularnewline
14 & 1708 & 1917.78473487111 & -209.784734871114 \tabularnewline
15 & 2609 & 2326.9205938041 & 282.0794061959 \tabularnewline
16 & 133 & 116.154333218322 & 16.845666781678 \tabularnewline
17 & 2476 & 2224.32024145201 & 251.679758547986 \tabularnewline
18 & 10 & 39.6681805484726 & -29.6681805484726 \tabularnewline
19 & 1510 & 1434.39017475415 & 75.6098252458489 \tabularnewline
20 & 6427 & 6170.11325720182 & 256.886742798179 \tabularnewline
21 & 3812 & 4432.03789633828 & -620.03789633828 \tabularnewline
22 & 724 & -46.0491080642611 & 770.049108064261 \tabularnewline
23 & 1560 & 1617.75581177142 & -57.7558117714155 \tabularnewline
24 & 156 & 3.82917450514424 & 152.170825494856 \tabularnewline
25 & 3 & 9.79013977667915 & -6.79013977667915 \tabularnewline
26 & 172 & 221.729596728878 & -49.7295967288785 \tabularnewline
27 & 65 & 296.864797329382 & -231.864797329382 \tabularnewline
28 & 593 & 650.093843821845 & -57.0938438218448 \tabularnewline
29 & 281 & 303.712173106989 & -22.712173106989 \tabularnewline
30 & 1191 & 1078.69068190161 & 112.309318098394 \tabularnewline
31 & 72 & 3.58407201615296 & 68.415927983847 \tabularnewline
32 & 113 & 193.221902468829 & -80.2219024688291 \tabularnewline
33 & 19 & 86.3943668675712 & -67.3943668675712 \tabularnewline
34 & 97 & 150.504271167906 & -53.5042711679063 \tabularnewline
35 & 18897 & 18832.944450878 & 64.055549121991 \tabularnewline
36 & 16770 & 16685.8015708295 & 84.1984291705087 \tabularnewline
37 & 6132 & 6038.61207340523 & 93.3879265947727 \tabularnewline
38 & 648 & 523.535429343122 & 124.464570656878 \tabularnewline
39 & 1739 & 1890.84689129117 & -151.846891291173 \tabularnewline
40 & 160 & 187.553733400319 & -27.5537334003185 \tabularnewline
41 & 621 & 640.971625297798 & -19.9716252977985 \tabularnewline
42 & 804 & 902.231092639715 & -98.2310926397155 \tabularnewline
43 & 3 & 16.7275114026543 & -13.7275114026543 \tabularnewline
44 & 150 & 196.731611231868 & -46.7316112318685 \tabularnewline
45 & 549 & 642.427965128011 & -93.4279651280105 \tabularnewline
46 & 95 & 161.618581526138 & -66.618581526138 \tabularnewline
47 & 354 & 395.298887816451 & -41.2988878164515 \tabularnewline
48 & 100 & 113.462729341329 & -13.4627293413285 \tabularnewline
49 & 342 & 390.232919150546 & -48.2329191505462 \tabularnewline
50 & 2854 & 2646.19072827997 & 207.809271720026 \tabularnewline
51 & 167 & 167.751486831863 & -0.751486831863091 \tabularnewline
52 & 2687 & 2491.92738460802 & 195.072615391977 \tabularnewline
53 & 645 & 766.579794990305 & -121.579794990305 \tabularnewline
54 & 6113 & 5999.79944239228 & 113.200557607715 \tabularnewline
55 & 3567 & 3869.51272361349 & -302.512723613494 \tabularnewline
56 & 472 & -44.7772065419315 & 516.777206541931 \tabularnewline
57 & 1665 & 2086.98580975667 & -421.985809756665 \tabularnewline
58 & 328 & 158.888855019616 & 169.111144980384 \tabularnewline
59 & 0 & 7.96327631361681 & -7.96327631361681 \tabularnewline
60 & 81 & -9.6518917299173 & 90.6518917299173 \tabularnewline
61 & 1322 & 1410.80903367528 & -88.809033675279 \tabularnewline
62 & 154 & 186.845211324054 & -32.8452113240537 \tabularnewline
63 & 1277 & 1359.60863978481 & -82.6086397848139 \tabularnewline
64 & 1127 & 1003.6238082236 & 123.376191776403 \tabularnewline
65 & 456 & 684.225334905623 & -228.225334905623 \tabularnewline
66 & 224 & 148.869918953707 & 75.1300810462927 \tabularnewline
67 & 1444 & 1322.6203329702 & 121.379667029801 \tabularnewline
68 & 3 & -20.1663380304346 & 23.1663380304346 \tabularnewline
69 & 1444 & 1386.86699112489 & 57.133008875109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&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]18897[/C][C]18832.944450878[/C][C]64.055549121994[/C][/ROW]
[ROW][C]2[/C][C]17518[/C][C]17567.540644372[/C][C]-49.5406443720448[/C][/ROW]
[ROW][C]3[/C][C]8632[/C][C]8756.21859433832[/C][C]-124.21859433832[/C][/ROW]
[ROW][C]4[/C][C]832[/C][C]880.931816268165[/C][C]-48.9318162681647[/C][/ROW]
[ROW][C]5[/C][C]3351[/C][C]3559.61102454585[/C][C]-208.611024545848[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]16.5123741797091[/C][C]-8.5123741797091[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]14.8201494649298[/C][C]-13.8201494649298[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]25.4688882886912[/C][C]-18.4688882886912[/C][/ROW]
[ROW][C]9[/C][C]217[/C][C]239.896217407637[/C][C]-22.8962174076369[/C][/ROW]
[ROW][C]10[/C][C]911[/C][C]986.36767762632[/C][C]-75.3676776263201[/C][/ROW]
[ROW][C]11[/C][C]1932[/C][C]2054.68668501911[/C][C]-122.686685019106[/C][/ROW]
[ROW][C]12[/C][C]274[/C][C]301.046943206313[/C][C]-27.0469432063133[/C][/ROW]
[ROW][C]13[/C][C]131[/C][C]124.972989670923[/C][C]6.02701032907749[/C][/ROW]
[ROW][C]14[/C][C]1708[/C][C]1917.78473487111[/C][C]-209.784734871114[/C][/ROW]
[ROW][C]15[/C][C]2609[/C][C]2326.9205938041[/C][C]282.0794061959[/C][/ROW]
[ROW][C]16[/C][C]133[/C][C]116.154333218322[/C][C]16.845666781678[/C][/ROW]
[ROW][C]17[/C][C]2476[/C][C]2224.32024145201[/C][C]251.679758547986[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]39.6681805484726[/C][C]-29.6681805484726[/C][/ROW]
[ROW][C]19[/C][C]1510[/C][C]1434.39017475415[/C][C]75.6098252458489[/C][/ROW]
[ROW][C]20[/C][C]6427[/C][C]6170.11325720182[/C][C]256.886742798179[/C][/ROW]
[ROW][C]21[/C][C]3812[/C][C]4432.03789633828[/C][C]-620.03789633828[/C][/ROW]
[ROW][C]22[/C][C]724[/C][C]-46.0491080642611[/C][C]770.049108064261[/C][/ROW]
[ROW][C]23[/C][C]1560[/C][C]1617.75581177142[/C][C]-57.7558117714155[/C][/ROW]
[ROW][C]24[/C][C]156[/C][C]3.82917450514424[/C][C]152.170825494856[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]9.79013977667915[/C][C]-6.79013977667915[/C][/ROW]
[ROW][C]26[/C][C]172[/C][C]221.729596728878[/C][C]-49.7295967288785[/C][/ROW]
[ROW][C]27[/C][C]65[/C][C]296.864797329382[/C][C]-231.864797329382[/C][/ROW]
[ROW][C]28[/C][C]593[/C][C]650.093843821845[/C][C]-57.0938438218448[/C][/ROW]
[ROW][C]29[/C][C]281[/C][C]303.712173106989[/C][C]-22.712173106989[/C][/ROW]
[ROW][C]30[/C][C]1191[/C][C]1078.69068190161[/C][C]112.309318098394[/C][/ROW]
[ROW][C]31[/C][C]72[/C][C]3.58407201615296[/C][C]68.415927983847[/C][/ROW]
[ROW][C]32[/C][C]113[/C][C]193.221902468829[/C][C]-80.2219024688291[/C][/ROW]
[ROW][C]33[/C][C]19[/C][C]86.3943668675712[/C][C]-67.3943668675712[/C][/ROW]
[ROW][C]34[/C][C]97[/C][C]150.504271167906[/C][C]-53.5042711679063[/C][/ROW]
[ROW][C]35[/C][C]18897[/C][C]18832.944450878[/C][C]64.055549121991[/C][/ROW]
[ROW][C]36[/C][C]16770[/C][C]16685.8015708295[/C][C]84.1984291705087[/C][/ROW]
[ROW][C]37[/C][C]6132[/C][C]6038.61207340523[/C][C]93.3879265947727[/C][/ROW]
[ROW][C]38[/C][C]648[/C][C]523.535429343122[/C][C]124.464570656878[/C][/ROW]
[ROW][C]39[/C][C]1739[/C][C]1890.84689129117[/C][C]-151.846891291173[/C][/ROW]
[ROW][C]40[/C][C]160[/C][C]187.553733400319[/C][C]-27.5537334003185[/C][/ROW]
[ROW][C]41[/C][C]621[/C][C]640.971625297798[/C][C]-19.9716252977985[/C][/ROW]
[ROW][C]42[/C][C]804[/C][C]902.231092639715[/C][C]-98.2310926397155[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]16.7275114026543[/C][C]-13.7275114026543[/C][/ROW]
[ROW][C]44[/C][C]150[/C][C]196.731611231868[/C][C]-46.7316112318685[/C][/ROW]
[ROW][C]45[/C][C]549[/C][C]642.427965128011[/C][C]-93.4279651280105[/C][/ROW]
[ROW][C]46[/C][C]95[/C][C]161.618581526138[/C][C]-66.618581526138[/C][/ROW]
[ROW][C]47[/C][C]354[/C][C]395.298887816451[/C][C]-41.2988878164515[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]113.462729341329[/C][C]-13.4627293413285[/C][/ROW]
[ROW][C]49[/C][C]342[/C][C]390.232919150546[/C][C]-48.2329191505462[/C][/ROW]
[ROW][C]50[/C][C]2854[/C][C]2646.19072827997[/C][C]207.809271720026[/C][/ROW]
[ROW][C]51[/C][C]167[/C][C]167.751486831863[/C][C]-0.751486831863091[/C][/ROW]
[ROW][C]52[/C][C]2687[/C][C]2491.92738460802[/C][C]195.072615391977[/C][/ROW]
[ROW][C]53[/C][C]645[/C][C]766.579794990305[/C][C]-121.579794990305[/C][/ROW]
[ROW][C]54[/C][C]6113[/C][C]5999.79944239228[/C][C]113.200557607715[/C][/ROW]
[ROW][C]55[/C][C]3567[/C][C]3869.51272361349[/C][C]-302.512723613494[/C][/ROW]
[ROW][C]56[/C][C]472[/C][C]-44.7772065419315[/C][C]516.777206541931[/C][/ROW]
[ROW][C]57[/C][C]1665[/C][C]2086.98580975667[/C][C]-421.985809756665[/C][/ROW]
[ROW][C]58[/C][C]328[/C][C]158.888855019616[/C][C]169.111144980384[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]7.96327631361681[/C][C]-7.96327631361681[/C][/ROW]
[ROW][C]60[/C][C]81[/C][C]-9.6518917299173[/C][C]90.6518917299173[/C][/ROW]
[ROW][C]61[/C][C]1322[/C][C]1410.80903367528[/C][C]-88.809033675279[/C][/ROW]
[ROW][C]62[/C][C]154[/C][C]186.845211324054[/C][C]-32.8452113240537[/C][/ROW]
[ROW][C]63[/C][C]1277[/C][C]1359.60863978481[/C][C]-82.6086397848139[/C][/ROW]
[ROW][C]64[/C][C]1127[/C][C]1003.6238082236[/C][C]123.376191776403[/C][/ROW]
[ROW][C]65[/C][C]456[/C][C]684.225334905623[/C][C]-228.225334905623[/C][/ROW]
[ROW][C]66[/C][C]224[/C][C]148.869918953707[/C][C]75.1300810462927[/C][/ROW]
[ROW][C]67[/C][C]1444[/C][C]1322.6203329702[/C][C]121.379667029801[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-20.1663380304346[/C][C]23.1663380304346[/C][/ROW]
[ROW][C]69[/C][C]1444[/C][C]1386.86699112489[/C][C]57.133008875109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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
11889718832.94445087864.055549121994
21751817567.540644372-49.5406443720448
386328756.21859433832-124.21859433832
4832880.931816268165-48.9318162681647
533513559.61102454585-208.611024545848
6816.5123741797091-8.5123741797091
7114.8201494649298-13.8201494649298
8725.4688882886912-18.4688882886912
9217239.896217407637-22.8962174076369
10911986.36767762632-75.3676776263201
1119322054.68668501911-122.686685019106
12274301.046943206313-27.0469432063133
13131124.9729896709236.02701032907749
1417081917.78473487111-209.784734871114
1526092326.9205938041282.0794061959
16133116.15433321832216.845666781678
1724762224.32024145201251.679758547986
181039.6681805484726-29.6681805484726
1915101434.3901747541575.6098252458489
2064276170.11325720182256.886742798179
2138124432.03789633828-620.03789633828
22724-46.0491080642611770.049108064261
2315601617.75581177142-57.7558117714155
241563.82917450514424152.170825494856
2539.79013977667915-6.79013977667915
26172221.729596728878-49.7295967288785
2765296.864797329382-231.864797329382
28593650.093843821845-57.0938438218448
29281303.712173106989-22.712173106989
3011911078.69068190161112.309318098394
31723.5840720161529668.415927983847
32113193.221902468829-80.2219024688291
331986.3943668675712-67.3943668675712
3497150.504271167906-53.5042711679063
351889718832.94445087864.055549121991
361677016685.801570829584.1984291705087
3761326038.6120734052393.3879265947727
38648523.535429343122124.464570656878
3917391890.84689129117-151.846891291173
40160187.553733400319-27.5537334003185
41621640.971625297798-19.9716252977985
42804902.231092639715-98.2310926397155
43316.7275114026543-13.7275114026543
44150196.731611231868-46.7316112318685
45549642.427965128011-93.4279651280105
4695161.618581526138-66.618581526138
47354395.298887816451-41.2988878164515
48100113.462729341329-13.4627293413285
49342390.232919150546-48.2329191505462
5028542646.19072827997207.809271720026
51167167.751486831863-0.751486831863091
5226872491.92738460802195.072615391977
53645766.579794990305-121.579794990305
5461135999.79944239228113.200557607715
5535673869.51272361349-302.512723613494
56472-44.7772065419315516.777206541931
5716652086.98580975667-421.985809756665
58328158.888855019616169.111144980384
5907.96327631361681-7.96327631361681
6081-9.651891729917390.6518917299173
6113221410.80903367528-88.809033675279
62154186.845211324054-32.8452113240537
6312771359.60863978481-82.6086397848139
6411271003.6238082236123.376191776403
65456684.225334905623-228.225334905623
66224148.86991895370775.1300810462927
6714441322.6203329702121.379667029801
683-20.166338030434623.1663380304346
6914441386.8669911248957.133008875109







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0002985443386494350.000597088677298870.999701455661351
120.0001040973581178960.0002081947162357910.999895902641882
130.0001441756356694630.0002883512713389260.999855824364331
145.38307061795721e-050.0001076614123591440.99994616929382
157.90656750898492e-061.58131350179698e-050.999992093432491
168.41340333149288e-061.68268066629858e-050.999991586596669
171.56113515531151e-063.12227031062303e-060.999998438864845
182.74560293067454e-075.49120586134908e-070.999999725439707
191.86943784411217e-073.73887568822434e-070.999999813056216
206.7344452713212e-071.34688905426424e-060.999999326555473
215.4032898265481e-061.08065796530962e-050.999994596710173
220.05369730454645520.107394609092910.946302695453545
230.0377911005455290.0755822010910580.962208899454471
240.08724513745347860.1744902749069570.912754862546521
250.05738498111221980.114769962224440.94261501888778
260.05911976191850620.1182395238370120.940880238081494
270.2856403667711090.5712807335422170.714359633228891
280.2847858671412750.5695717342825510.715214132858724
290.2295584823985290.4591169647970570.770441517601471
300.4313202897774370.8626405795548740.568679710222563
310.3632797059532430.7265594119064850.636720294046757
320.3146187136732780.6292374273465560.685381286326722
330.2608069713434980.5216139426869970.739193028656502
340.22631940522130.45263881044260.7736805947787
350.1756801828872560.3513603657745120.824319817112744
360.5824577183257160.8350845633485670.417542281674284
370.5363011837008540.9273976325982920.463698816299146
380.469013932709510.9380278654190190.53098606729049
390.4753611753116160.9507223506232310.524638824688384
400.3989637432584650.7979274865169310.601036256741535
410.3524190297830450.704838059566090.647580970216955
420.2873788789088970.5747577578177940.712621121091103
430.2233942503288820.4467885006577640.776605749671118
440.1721288323211740.3442576646423480.827871167678826
450.1422437123785460.2844874247570910.857756287621454
460.1090828439198990.2181656878397970.890917156080101
470.07551272473128210.1510254494625640.924487275268718
480.04997905452099650.09995810904199290.950020945479004
490.03217336787578410.06434673575156810.967826632124216
500.03433212299897630.06866424599795260.965667877001024
510.02036013156565060.04072026313130120.979639868434349
520.4731298533467990.9462597066935990.526870146653201
530.3777443927685410.7554887855370820.622255607231459
540.8048313911130130.3903372177739740.195168608886987
550.9993915184537750.001216963092449730.000608481546224867
560.9982241216213810.003551756757238940.00177587837861947
570.996368205770110.007263588459779140.00363179422988957
580.9857977951024120.02840440979517520.0142022048975876

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.000298544338649435 & 0.00059708867729887 & 0.999701455661351 \tabularnewline
12 & 0.000104097358117896 & 0.000208194716235791 & 0.999895902641882 \tabularnewline
13 & 0.000144175635669463 & 0.000288351271338926 & 0.999855824364331 \tabularnewline
14 & 5.38307061795721e-05 & 0.000107661412359144 & 0.99994616929382 \tabularnewline
15 & 7.90656750898492e-06 & 1.58131350179698e-05 & 0.999992093432491 \tabularnewline
16 & 8.41340333149288e-06 & 1.68268066629858e-05 & 0.999991586596669 \tabularnewline
17 & 1.56113515531151e-06 & 3.12227031062303e-06 & 0.999998438864845 \tabularnewline
18 & 2.74560293067454e-07 & 5.49120586134908e-07 & 0.999999725439707 \tabularnewline
19 & 1.86943784411217e-07 & 3.73887568822434e-07 & 0.999999813056216 \tabularnewline
20 & 6.7344452713212e-07 & 1.34688905426424e-06 & 0.999999326555473 \tabularnewline
21 & 5.4032898265481e-06 & 1.08065796530962e-05 & 0.999994596710173 \tabularnewline
22 & 0.0536973045464552 & 0.10739460909291 & 0.946302695453545 \tabularnewline
23 & 0.037791100545529 & 0.075582201091058 & 0.962208899454471 \tabularnewline
24 & 0.0872451374534786 & 0.174490274906957 & 0.912754862546521 \tabularnewline
25 & 0.0573849811122198 & 0.11476996222444 & 0.94261501888778 \tabularnewline
26 & 0.0591197619185062 & 0.118239523837012 & 0.940880238081494 \tabularnewline
27 & 0.285640366771109 & 0.571280733542217 & 0.714359633228891 \tabularnewline
28 & 0.284785867141275 & 0.569571734282551 & 0.715214132858724 \tabularnewline
29 & 0.229558482398529 & 0.459116964797057 & 0.770441517601471 \tabularnewline
30 & 0.431320289777437 & 0.862640579554874 & 0.568679710222563 \tabularnewline
31 & 0.363279705953243 & 0.726559411906485 & 0.636720294046757 \tabularnewline
32 & 0.314618713673278 & 0.629237427346556 & 0.685381286326722 \tabularnewline
33 & 0.260806971343498 & 0.521613942686997 & 0.739193028656502 \tabularnewline
34 & 0.2263194052213 & 0.4526388104426 & 0.7736805947787 \tabularnewline
35 & 0.175680182887256 & 0.351360365774512 & 0.824319817112744 \tabularnewline
36 & 0.582457718325716 & 0.835084563348567 & 0.417542281674284 \tabularnewline
37 & 0.536301183700854 & 0.927397632598292 & 0.463698816299146 \tabularnewline
38 & 0.46901393270951 & 0.938027865419019 & 0.53098606729049 \tabularnewline
39 & 0.475361175311616 & 0.950722350623231 & 0.524638824688384 \tabularnewline
40 & 0.398963743258465 & 0.797927486516931 & 0.601036256741535 \tabularnewline
41 & 0.352419029783045 & 0.70483805956609 & 0.647580970216955 \tabularnewline
42 & 0.287378878908897 & 0.574757757817794 & 0.712621121091103 \tabularnewline
43 & 0.223394250328882 & 0.446788500657764 & 0.776605749671118 \tabularnewline
44 & 0.172128832321174 & 0.344257664642348 & 0.827871167678826 \tabularnewline
45 & 0.142243712378546 & 0.284487424757091 & 0.857756287621454 \tabularnewline
46 & 0.109082843919899 & 0.218165687839797 & 0.890917156080101 \tabularnewline
47 & 0.0755127247312821 & 0.151025449462564 & 0.924487275268718 \tabularnewline
48 & 0.0499790545209965 & 0.0999581090419929 & 0.950020945479004 \tabularnewline
49 & 0.0321733678757841 & 0.0643467357515681 & 0.967826632124216 \tabularnewline
50 & 0.0343321229989763 & 0.0686642459979526 & 0.965667877001024 \tabularnewline
51 & 0.0203601315656506 & 0.0407202631313012 & 0.979639868434349 \tabularnewline
52 & 0.473129853346799 & 0.946259706693599 & 0.526870146653201 \tabularnewline
53 & 0.377744392768541 & 0.755488785537082 & 0.622255607231459 \tabularnewline
54 & 0.804831391113013 & 0.390337217773974 & 0.195168608886987 \tabularnewline
55 & 0.999391518453775 & 0.00121696309244973 & 0.000608481546224867 \tabularnewline
56 & 0.998224121621381 & 0.00355175675723894 & 0.00177587837861947 \tabularnewline
57 & 0.99636820577011 & 0.00726358845977914 & 0.00363179422988957 \tabularnewline
58 & 0.985797795102412 & 0.0284044097951752 & 0.0142022048975876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&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]0.000298544338649435[/C][C]0.00059708867729887[/C][C]0.999701455661351[/C][/ROW]
[ROW][C]12[/C][C]0.000104097358117896[/C][C]0.000208194716235791[/C][C]0.999895902641882[/C][/ROW]
[ROW][C]13[/C][C]0.000144175635669463[/C][C]0.000288351271338926[/C][C]0.999855824364331[/C][/ROW]
[ROW][C]14[/C][C]5.38307061795721e-05[/C][C]0.000107661412359144[/C][C]0.99994616929382[/C][/ROW]
[ROW][C]15[/C][C]7.90656750898492e-06[/C][C]1.58131350179698e-05[/C][C]0.999992093432491[/C][/ROW]
[ROW][C]16[/C][C]8.41340333149288e-06[/C][C]1.68268066629858e-05[/C][C]0.999991586596669[/C][/ROW]
[ROW][C]17[/C][C]1.56113515531151e-06[/C][C]3.12227031062303e-06[/C][C]0.999998438864845[/C][/ROW]
[ROW][C]18[/C][C]2.74560293067454e-07[/C][C]5.49120586134908e-07[/C][C]0.999999725439707[/C][/ROW]
[ROW][C]19[/C][C]1.86943784411217e-07[/C][C]3.73887568822434e-07[/C][C]0.999999813056216[/C][/ROW]
[ROW][C]20[/C][C]6.7344452713212e-07[/C][C]1.34688905426424e-06[/C][C]0.999999326555473[/C][/ROW]
[ROW][C]21[/C][C]5.4032898265481e-06[/C][C]1.08065796530962e-05[/C][C]0.999994596710173[/C][/ROW]
[ROW][C]22[/C][C]0.0536973045464552[/C][C]0.10739460909291[/C][C]0.946302695453545[/C][/ROW]
[ROW][C]23[/C][C]0.037791100545529[/C][C]0.075582201091058[/C][C]0.962208899454471[/C][/ROW]
[ROW][C]24[/C][C]0.0872451374534786[/C][C]0.174490274906957[/C][C]0.912754862546521[/C][/ROW]
[ROW][C]25[/C][C]0.0573849811122198[/C][C]0.11476996222444[/C][C]0.94261501888778[/C][/ROW]
[ROW][C]26[/C][C]0.0591197619185062[/C][C]0.118239523837012[/C][C]0.940880238081494[/C][/ROW]
[ROW][C]27[/C][C]0.285640366771109[/C][C]0.571280733542217[/C][C]0.714359633228891[/C][/ROW]
[ROW][C]28[/C][C]0.284785867141275[/C][C]0.569571734282551[/C][C]0.715214132858724[/C][/ROW]
[ROW][C]29[/C][C]0.229558482398529[/C][C]0.459116964797057[/C][C]0.770441517601471[/C][/ROW]
[ROW][C]30[/C][C]0.431320289777437[/C][C]0.862640579554874[/C][C]0.568679710222563[/C][/ROW]
[ROW][C]31[/C][C]0.363279705953243[/C][C]0.726559411906485[/C][C]0.636720294046757[/C][/ROW]
[ROW][C]32[/C][C]0.314618713673278[/C][C]0.629237427346556[/C][C]0.685381286326722[/C][/ROW]
[ROW][C]33[/C][C]0.260806971343498[/C][C]0.521613942686997[/C][C]0.739193028656502[/C][/ROW]
[ROW][C]34[/C][C]0.2263194052213[/C][C]0.4526388104426[/C][C]0.7736805947787[/C][/ROW]
[ROW][C]35[/C][C]0.175680182887256[/C][C]0.351360365774512[/C][C]0.824319817112744[/C][/ROW]
[ROW][C]36[/C][C]0.582457718325716[/C][C]0.835084563348567[/C][C]0.417542281674284[/C][/ROW]
[ROW][C]37[/C][C]0.536301183700854[/C][C]0.927397632598292[/C][C]0.463698816299146[/C][/ROW]
[ROW][C]38[/C][C]0.46901393270951[/C][C]0.938027865419019[/C][C]0.53098606729049[/C][/ROW]
[ROW][C]39[/C][C]0.475361175311616[/C][C]0.950722350623231[/C][C]0.524638824688384[/C][/ROW]
[ROW][C]40[/C][C]0.398963743258465[/C][C]0.797927486516931[/C][C]0.601036256741535[/C][/ROW]
[ROW][C]41[/C][C]0.352419029783045[/C][C]0.70483805956609[/C][C]0.647580970216955[/C][/ROW]
[ROW][C]42[/C][C]0.287378878908897[/C][C]0.574757757817794[/C][C]0.712621121091103[/C][/ROW]
[ROW][C]43[/C][C]0.223394250328882[/C][C]0.446788500657764[/C][C]0.776605749671118[/C][/ROW]
[ROW][C]44[/C][C]0.172128832321174[/C][C]0.344257664642348[/C][C]0.827871167678826[/C][/ROW]
[ROW][C]45[/C][C]0.142243712378546[/C][C]0.284487424757091[/C][C]0.857756287621454[/C][/ROW]
[ROW][C]46[/C][C]0.109082843919899[/C][C]0.218165687839797[/C][C]0.890917156080101[/C][/ROW]
[ROW][C]47[/C][C]0.0755127247312821[/C][C]0.151025449462564[/C][C]0.924487275268718[/C][/ROW]
[ROW][C]48[/C][C]0.0499790545209965[/C][C]0.0999581090419929[/C][C]0.950020945479004[/C][/ROW]
[ROW][C]49[/C][C]0.0321733678757841[/C][C]0.0643467357515681[/C][C]0.967826632124216[/C][/ROW]
[ROW][C]50[/C][C]0.0343321229989763[/C][C]0.0686642459979526[/C][C]0.965667877001024[/C][/ROW]
[ROW][C]51[/C][C]0.0203601315656506[/C][C]0.0407202631313012[/C][C]0.979639868434349[/C][/ROW]
[ROW][C]52[/C][C]0.473129853346799[/C][C]0.946259706693599[/C][C]0.526870146653201[/C][/ROW]
[ROW][C]53[/C][C]0.377744392768541[/C][C]0.755488785537082[/C][C]0.622255607231459[/C][/ROW]
[ROW][C]54[/C][C]0.804831391113013[/C][C]0.390337217773974[/C][C]0.195168608886987[/C][/ROW]
[ROW][C]55[/C][C]0.999391518453775[/C][C]0.00121696309244973[/C][C]0.000608481546224867[/C][/ROW]
[ROW][C]56[/C][C]0.998224121621381[/C][C]0.00355175675723894[/C][C]0.00177587837861947[/C][/ROW]
[ROW][C]57[/C][C]0.99636820577011[/C][C]0.00726358845977914[/C][C]0.00363179422988957[/C][/ROW]
[ROW][C]58[/C][C]0.985797795102412[/C][C]0.0284044097951752[/C][C]0.0142022048975876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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
110.0002985443386494350.000597088677298870.999701455661351
120.0001040973581178960.0002081947162357910.999895902641882
130.0001441756356694630.0002883512713389260.999855824364331
145.38307061795721e-050.0001076614123591440.99994616929382
157.90656750898492e-061.58131350179698e-050.999992093432491
168.41340333149288e-061.68268066629858e-050.999991586596669
171.56113515531151e-063.12227031062303e-060.999998438864845
182.74560293067454e-075.49120586134908e-070.999999725439707
191.86943784411217e-073.73887568822434e-070.999999813056216
206.7344452713212e-071.34688905426424e-060.999999326555473
215.4032898265481e-061.08065796530962e-050.999994596710173
220.05369730454645520.107394609092910.946302695453545
230.0377911005455290.0755822010910580.962208899454471
240.08724513745347860.1744902749069570.912754862546521
250.05738498111221980.114769962224440.94261501888778
260.05911976191850620.1182395238370120.940880238081494
270.2856403667711090.5712807335422170.714359633228891
280.2847858671412750.5695717342825510.715214132858724
290.2295584823985290.4591169647970570.770441517601471
300.4313202897774370.8626405795548740.568679710222563
310.3632797059532430.7265594119064850.636720294046757
320.3146187136732780.6292374273465560.685381286326722
330.2608069713434980.5216139426869970.739193028656502
340.22631940522130.45263881044260.7736805947787
350.1756801828872560.3513603657745120.824319817112744
360.5824577183257160.8350845633485670.417542281674284
370.5363011837008540.9273976325982920.463698816299146
380.469013932709510.9380278654190190.53098606729049
390.4753611753116160.9507223506232310.524638824688384
400.3989637432584650.7979274865169310.601036256741535
410.3524190297830450.704838059566090.647580970216955
420.2873788789088970.5747577578177940.712621121091103
430.2233942503288820.4467885006577640.776605749671118
440.1721288323211740.3442576646423480.827871167678826
450.1422437123785460.2844874247570910.857756287621454
460.1090828439198990.2181656878397970.890917156080101
470.07551272473128210.1510254494625640.924487275268718
480.04997905452099650.09995810904199290.950020945479004
490.03217336787578410.06434673575156810.967826632124216
500.03433212299897630.06866424599795260.965667877001024
510.02036013156565060.04072026313130120.979639868434349
520.4731298533467990.9462597066935990.526870146653201
530.3777443927685410.7554887855370820.622255607231459
540.8048313911130130.3903372177739740.195168608886987
550.9993915184537750.001216963092449730.000608481546224867
560.9982241216213810.003551756757238940.00177587837861947
570.996368205770110.007263588459779140.00363179422988957
580.9857977951024120.02840440979517520.0142022048975876







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.291666666666667NOK
5% type I error level160.333333333333333NOK
10% type I error level200.416666666666667NOK

\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 & 14 & 0.291666666666667 & NOK \tabularnewline
5% type I error level & 16 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 20 & 0.416666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201201&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]14[/C][C]0.291666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.416666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201201&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201201&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 level140.291666666666667NOK
5% type I error level160.333333333333333NOK
10% type I error level200.416666666666667NOK



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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}