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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 22 Dec 2010 18:39:39 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t1293043157fsd787p7h50ydb4.htm/, Retrieved Mon, 06 May 2024 02:19:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114475, Retrieved Mon, 06 May 2024 02:19:48 +0000

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Estimated Impact

115

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-     [Multiple Regression] [] [2010-12-20 17:54:33] [8a12eeeb546060995f18439d3f99cdb1]
-         [Multiple Regression] [] [2010-12-22 18:39:39] [a5ae4a79649e10f10ac7ff219d0ba7a7] [Current]

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-999	-999	38.6	6.654	5.712	645	3	5	3
6.3	2	4.5	1	 6.6	 42	3	1	3
-999	-999	14	3.385	44.5	 60	1	1	1
-999	-999	-999	0.92	 5.7	 25	5	2	3
2.1	1.8	69	2547	 4603	 624	3	5	4
0.1	0.7	27	10.55	0.5	 180	4	4	4
15.8	3.9	19	0.023	0.3	 35	1	1	1
5.2	1	30.4	160	 169	 392	4	5	4
10.9	3.6	28	3.3	 25.6	 63	1	2	1
8.3	1.4	50	52.16	440	 230	1	1	1
11	1.5	7	0.425	6.4	 112	5	4	4
3.2	0.7	30	465	 423	 281	5	5	5
7.6	2.7	-999	0.55	 2.4	 -999	2	1	2
-999	-999	40	187.1	419	 365	5	5	5
6.3	2.1	3.5	0.075	1.2	 42	1	1	1
8.6	0	50	3	 25	 28	2	2	2
6.6	4.1	6	0.785	3.5	 42	2	2	2
9.5	1.2	10.4	0.2	 5	 120	2	2	2
4.8	1.3	34	1.41	 17.5	 -999	1	2	1
12	6.1	7	60	 81	 -999	1	1	1
-999	0.3	28	529	 680	 400	5	5	5
3.3	0.5	20	27.66	115	 148	5	5	5
11	3.4	3.9	0.12	 1	 16	3	1	2
-999	-999	39.3	207	 406	 252	1	4	1
4.7	1.5	41	85	 325	 310	1	3	1
-999	-999	16.2	36.33	119.5	63	1	1	1
10.4	3.4	9	0.101	4	 28	5	1	3
7.4	0.8	7.6	1.04	 5.5	 68	5	3	4
2.1	0.8	46	521	 655	 336	5	5	5
2.1	-999	22.4	100	 157	 100	1	1	1
-999	-999	16.3	35	 56	 33	3	5	4
7.7	1.4	2.6	0.005	0.14	 21.5	5	2	4
17.9	2	24	0.01	 0.25	 50	1	1	1
6.1	1.9	100	62	 1320	 267	1	1	1
8.2	2.4	-999	0.122	3	 30	2	1	1
8.4	2.8	-999	1.35	 8.1	 45	3	1	3
11.9	1.3	3.2	0.23	 0.4	 19	4	1	3
10.8	2	2	0.048	0.33 	30	4	1	3
13.8	5.6	5	1.7 	6.3 	12 	2	1	1
14.3	3.1	6.5	3.5	 10.8 	120	2	1	1
-999	1	23.6	250	 490 	440	5	5	5
15.2	1.8	12	0.48	 15.5 	140	2	2	2
10	0.9	20.2	10 	115 	170	4	4	4
11.9	1.8	13	1.62	 11.4 	17	2	1	2
6.5	1.9	27	192 	180 	 115	4	4	4
7.5	0.9	18	2.5 	12.1 	31	5	5	5
-999	-999	13.7	4.288	39.2 	63	2	2	2
10.6	2.6	4.7	0.28 	1.9 	21	3	1	3
7.4	2.4	9.8	4.235	50.4	 52	1	1	1
8.4	1.2	29	6.8 	179	 164	2	3	2
5.7	0.9	7	0.75 	12.3 	225	2	2	2
4.9	0.5	6	3.6	 21 	150	3	2	3
-999	-999	17	14.83	98.2 	151	5	5	5
3.2	0.6	20	55.5 	175 	150	5	5	5
-999	-999	12.7	1.4 	12.5 	90	2	2	2
8.1	2.2	3.5	0.06 	1 	-999	3	1	2
11	2.3	4.5	0.9 	2.6 	60	2	1	2
4.9	0.5	7.5	2 	12.3 	200	3	1	3
13.2	2.6	2.3	0.104	2.5 	46	3	2	2
9.7	0.6	24	4.19	 58 	210	4	3	4
12.8	6.6	3	3.5 	3.9 	14	1	1	2
-999	-999	13	4.05 	17 	38	3	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
PS[t] = -147.448186216999 + 0.827854459100156SWS[t] + 0.0153005707500430L[t] -0.000146716870009194WB[t] + 0.0270059287704017WBR[t] -0.0209700304582949TG[t] + 3.15985232691063P[t] -9.02103450957327S[t] + 50.7708110913449D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  -147.448186216999 +  0.827854459100156SWS[t] +  0.0153005707500430L[t] -0.000146716870009194WB[t] +  0.0270059287704017WBR[t] -0.0209700304582949TG[t] +  3.15985232691063P[t] -9.02103450957327S[t] +  50.7708110913449D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  -147.448186216999 +  0.827854459100156SWS[t] +  0.0153005707500430L[t] -0.000146716870009194WB[t] +  0.0270059287704017WBR[t] -0.0209700304582949TG[t] +  3.15985232691063P[t] -9.02103450957327S[t] +  50.7708110913449D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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
PS[t] = -147.448186216999 + 0.827854459100156SWS[t] + 0.0153005707500430L[t] -0.000146716870009194WB[t] + 0.0270059287704017WBR[t] -0.0209700304582949TG[t] + 3.15985232691063P[t] -9.02103450957327S[t] + 50.7708110913449D[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-147.448186216999	66.192415	-2.2276	0.030178	0.015089
SWS	0.827854459100156	0.072478	11.4221	0	0
L	0.0153005707500430	0.11626	0.1316	0.895794	0.447897
WB	-0.000146716870009194	0.294549	-5e-04	0.999604	0.499802
WBR	0.0270059287704017	0.160936	0.1678	0.867375	0.433688
TG	-0.0209700304582949	0.102843	-0.2039	0.83921	0.419605
P	3.15985232691063	50.314252	0.0628	0.95016	0.47508
S	-9.02103450957327	33.416919	-0.27	0.788244	0.394122
D	50.7708110913449	65.737628	0.7723	0.443352	0.221676

\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) & -147.448186216999 & 66.192415 & -2.2276 & 0.030178 & 0.015089 \tabularnewline
SWS & 0.827854459100156 & 0.072478 & 11.4221 & 0 & 0 \tabularnewline
L & 0.0153005707500430 & 0.11626 & 0.1316 & 0.895794 & 0.447897 \tabularnewline
WB & -0.000146716870009194 & 0.294549 & -5e-04 & 0.999604 & 0.499802 \tabularnewline
WBR & 0.0270059287704017 & 0.160936 & 0.1678 & 0.867375 & 0.433688 \tabularnewline
TG & -0.0209700304582949 & 0.102843 & -0.2039 & 0.83921 & 0.419605 \tabularnewline
P & 3.15985232691063 & 50.314252 & 0.0628 & 0.95016 & 0.47508 \tabularnewline
S & -9.02103450957327 & 33.416919 & -0.27 & 0.788244 & 0.394122 \tabularnewline
D & 50.7708110913449 & 65.737628 & 0.7723 & 0.443352 & 0.221676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&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]-147.448186216999[/C][C]66.192415[/C][C]-2.2276[/C][C]0.030178[/C][C]0.015089[/C][/ROW]
[ROW][C]SWS[/C][C]0.827854459100156[/C][C]0.072478[/C][C]11.4221[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0153005707500430[/C][C]0.11626[/C][C]0.1316[/C][C]0.895794[/C][C]0.447897[/C][/ROW]
[ROW][C]WB[/C][C]-0.000146716870009194[/C][C]0.294549[/C][C]-5e-04[/C][C]0.999604[/C][C]0.499802[/C][/ROW]
[ROW][C]WBR[/C][C]0.0270059287704017[/C][C]0.160936[/C][C]0.1678[/C][C]0.867375[/C][C]0.433688[/C][/ROW]
[ROW][C]TG[/C][C]-0.0209700304582949[/C][C]0.102843[/C][C]-0.2039[/C][C]0.83921[/C][C]0.419605[/C][/ROW]
[ROW][C]P[/C][C]3.15985232691063[/C][C]50.314252[/C][C]0.0628[/C][C]0.95016[/C][C]0.47508[/C][/ROW]
[ROW][C]S[/C][C]-9.02103450957327[/C][C]33.416919[/C][C]-0.27[/C][C]0.788244[/C][C]0.394122[/C][/ROW]
[ROW][C]D[/C][C]50.7708110913449[/C][C]65.737628[/C][C]0.7723[/C][C]0.443352[/C][C]0.221676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-147.448186216999	66.192415	-2.2276	0.030178	0.015089
SWS	0.827854459100156	0.072478	11.4221	0	0
L	0.0153005707500430	0.11626	0.1316	0.895794	0.447897
WB	-0.000146716870009194	0.294549	-5e-04	0.999604	0.499802
WBR	0.0270059287704017	0.160936	0.1678	0.867375	0.433688
TG	-0.0209700304582949	0.102843	-0.2039	0.83921	0.419605
P	3.15985232691063	50.314252	0.0628	0.95016	0.47508
S	-9.02103450957327	33.416919	-0.27	0.788244	0.394122
D	50.7708110913449	65.737628	0.7723	0.443352	0.221676

Multiple Linear Regression - Regression Statistics
Multiple R	0.86874491034203
R-squared	0.754717719245183
Adjusted R-squared	0.71769397875389
F-TEST (value)	20.3846966630147
F-TEST (DF numerator)	8
F-TEST (DF denominator)	53
p-value	1.15019105351166e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	211.835697265371
Sum Squared Residuals	2378341.21970301

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.86874491034203 \tabularnewline
R-squared & 0.754717719245183 \tabularnewline
Adjusted R-squared & 0.71769397875389 \tabularnewline
F-TEST (value) & 20.3846966630147 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 1.15019105351166e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 211.835697265371 \tabularnewline
Sum Squared Residuals & 2378341.21970301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.86874491034203[/C][/ROW]
[ROW][C]R-squared[/C][C]0.754717719245183[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.71769397875389[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.3846966630147[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]1.15019105351166e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]211.835697265371[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2378341.21970301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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 R	0.86874491034203
R-squared	0.754717719245183
Adjusted R-squared	0.71769397875389
F-TEST (value)	20.3846966630147
F-TEST (DF numerator)	8
F-TEST (DF denominator)	53
p-value	1.15019105351166e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	211.835697265371
Sum Squared Residuals	2378341.21970301

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-999	-870.56975915472	-128.430240845279
2	2	9.90445632266566	-7.90445632266566
3	-999	-929.407888592692	-69.5921114073079
4	-999	-840.060887094893	-158.939112905107
5	1.8	133.652979583378	-131.852979583378
6	0.7	28.9235798928042	-28.2235798928042
7	3.9	-89.8935986721811	93.793598672181
8	1	24.2595507696231	-23.2595507696231
9	3.6	-102.737806540719	106.337806540719
10	1.4	-87.8504878586546	89.2504878586546
11	1.5	42.3878169681239	-40.8878169681239
12	0.7	85.6708156845783	-84.9708156845783
13	2.7	-36.5876762175865	39.2876762175865
14	-999	-745.6806511735	-253.3193488265
15	2.1	-98.1178673868504	100.217867386850
16	0	-49.6568042980538	49.6568042980538
17	4.1	-52.8596212463687	56.9596212463687
18	1.2	-51.9865884569005	53.1865884569005
19	1.3	-85.6442136981765	86.9442136981765
20	6.1	-69.3694621578266	75.4694621578266
21	0.3	-739.599824177322	739.899824177322
22	0.5	80.1359485685872	-79.6359485685872
23	3.4	-36.590502451711	39.990502451711
24	-999	-950.377364034415	-48.6226359655849
25	1.5	-113.798640494583	115.298640494583
26	-999	-927.416526357919	-71.5834736420808
27	3.4	19.9107137372525	-16.5107137372525
28	0.8	49.3360415407831	-48.5360415407831
29	0.8	90.1087925663753	-89.3087925663753
30	-999	-98.3290740752834	-900.670925924717
31	-999	-805.952576841016	-193.047423158984
32	1.4	59.3594360243981	-57.9594360243981
33	2	-88.3945003003143	90.3945003003143
34	1.9	-65.9188566341772	67.8188566341772
35	2.4	-108.423669622974	110.823669622974
36	2.8	-3.73362461001548	6.53362461001548
37	1.3	17.9953897927168	-16.6953897927168
38	2	16.8338551552218	-14.8338551552218
39	5.6	-87.9595630249994	93.5595630249993
40	3.1	-89.6661856397193	92.7661856397193
41	1	-745.596140366598	746.596140366598
42	1.8	-47.3792165646299	49.1792165646299
43	0.9	40.3172549998679	-39.4172549998679
44	1.8	-38.6063790181572	40.4063790181572
45	1.9	40.4058428490583	-38.5058428490583
46	0.9	83.2606110449034	-82.3606110449034
47	-999	-884.709023854426	-114.290976145574
48	2.6	13.7808390214966	-11.1808390214966
49	2.4	-95.9924528373733	98.3924528373733
50	1.2	-57.8582903209903	59.0582903209903
51	0.9	-57.1892479544068	58.0892479544068
52	0.5	-2.12888295258638	2.62888295258638
53	-999	-750.183204817024	-248.816795182976
54	0.6	81.6274941903236	-81.0274941903236
55	-999	-886.0111498274	-112.988850172600
56	2.2	-17.7128108932199	19.9128108932199
57	2.3	-40.6207607294625	42.9207607294625
58	0.5	5.63188405688672	-5.13188405688672
59	2.6	-44.4033277375874	47.0033277375874
60	0.6	46.7707886073313	-46.1707886073313
61	6.6	-41.3140782414969	47.9140782414969
62	-999	-923.384904447443	-75.615095552557

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -870.56975915472 & -128.430240845279 \tabularnewline
2 & 2 & 9.90445632266566 & -7.90445632266566 \tabularnewline
3 & -999 & -929.407888592692 & -69.5921114073079 \tabularnewline
4 & -999 & -840.060887094893 & -158.939112905107 \tabularnewline
5 & 1.8 & 133.652979583378 & -131.852979583378 \tabularnewline
6 & 0.7 & 28.9235798928042 & -28.2235798928042 \tabularnewline
7 & 3.9 & -89.8935986721811 & 93.793598672181 \tabularnewline
8 & 1 & 24.2595507696231 & -23.2595507696231 \tabularnewline
9 & 3.6 & -102.737806540719 & 106.337806540719 \tabularnewline
10 & 1.4 & -87.8504878586546 & 89.2504878586546 \tabularnewline
11 & 1.5 & 42.3878169681239 & -40.8878169681239 \tabularnewline
12 & 0.7 & 85.6708156845783 & -84.9708156845783 \tabularnewline
13 & 2.7 & -36.5876762175865 & 39.2876762175865 \tabularnewline
14 & -999 & -745.6806511735 & -253.3193488265 \tabularnewline
15 & 2.1 & -98.1178673868504 & 100.217867386850 \tabularnewline
16 & 0 & -49.6568042980538 & 49.6568042980538 \tabularnewline
17 & 4.1 & -52.8596212463687 & 56.9596212463687 \tabularnewline
18 & 1.2 & -51.9865884569005 & 53.1865884569005 \tabularnewline
19 & 1.3 & -85.6442136981765 & 86.9442136981765 \tabularnewline
20 & 6.1 & -69.3694621578266 & 75.4694621578266 \tabularnewline
21 & 0.3 & -739.599824177322 & 739.899824177322 \tabularnewline
22 & 0.5 & 80.1359485685872 & -79.6359485685872 \tabularnewline
23 & 3.4 & -36.590502451711 & 39.990502451711 \tabularnewline
24 & -999 & -950.377364034415 & -48.6226359655849 \tabularnewline
25 & 1.5 & -113.798640494583 & 115.298640494583 \tabularnewline
26 & -999 & -927.416526357919 & -71.5834736420808 \tabularnewline
27 & 3.4 & 19.9107137372525 & -16.5107137372525 \tabularnewline
28 & 0.8 & 49.3360415407831 & -48.5360415407831 \tabularnewline
29 & 0.8 & 90.1087925663753 & -89.3087925663753 \tabularnewline
30 & -999 & -98.3290740752834 & -900.670925924717 \tabularnewline
31 & -999 & -805.952576841016 & -193.047423158984 \tabularnewline
32 & 1.4 & 59.3594360243981 & -57.9594360243981 \tabularnewline
33 & 2 & -88.3945003003143 & 90.3945003003143 \tabularnewline
34 & 1.9 & -65.9188566341772 & 67.8188566341772 \tabularnewline
35 & 2.4 & -108.423669622974 & 110.823669622974 \tabularnewline
36 & 2.8 & -3.73362461001548 & 6.53362461001548 \tabularnewline
37 & 1.3 & 17.9953897927168 & -16.6953897927168 \tabularnewline
38 & 2 & 16.8338551552218 & -14.8338551552218 \tabularnewline
39 & 5.6 & -87.9595630249994 & 93.5595630249993 \tabularnewline
40 & 3.1 & -89.6661856397193 & 92.7661856397193 \tabularnewline
41 & 1 & -745.596140366598 & 746.596140366598 \tabularnewline
42 & 1.8 & -47.3792165646299 & 49.1792165646299 \tabularnewline
43 & 0.9 & 40.3172549998679 & -39.4172549998679 \tabularnewline
44 & 1.8 & -38.6063790181572 & 40.4063790181572 \tabularnewline
45 & 1.9 & 40.4058428490583 & -38.5058428490583 \tabularnewline
46 & 0.9 & 83.2606110449034 & -82.3606110449034 \tabularnewline
47 & -999 & -884.709023854426 & -114.290976145574 \tabularnewline
48 & 2.6 & 13.7808390214966 & -11.1808390214966 \tabularnewline
49 & 2.4 & -95.9924528373733 & 98.3924528373733 \tabularnewline
50 & 1.2 & -57.8582903209903 & 59.0582903209903 \tabularnewline
51 & 0.9 & -57.1892479544068 & 58.0892479544068 \tabularnewline
52 & 0.5 & -2.12888295258638 & 2.62888295258638 \tabularnewline
53 & -999 & -750.183204817024 & -248.816795182976 \tabularnewline
54 & 0.6 & 81.6274941903236 & -81.0274941903236 \tabularnewline
55 & -999 & -886.0111498274 & -112.988850172600 \tabularnewline
56 & 2.2 & -17.7128108932199 & 19.9128108932199 \tabularnewline
57 & 2.3 & -40.6207607294625 & 42.9207607294625 \tabularnewline
58 & 0.5 & 5.63188405688672 & -5.13188405688672 \tabularnewline
59 & 2.6 & -44.4033277375874 & 47.0033277375874 \tabularnewline
60 & 0.6 & 46.7707886073313 & -46.1707886073313 \tabularnewline
61 & 6.6 & -41.3140782414969 & 47.9140782414969 \tabularnewline
62 & -999 & -923.384904447443 & -75.615095552557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&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]-999[/C][C]-870.56975915472[/C][C]-128.430240845279[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]9.90445632266566[/C][C]-7.90445632266566[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-929.407888592692[/C][C]-69.5921114073079[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-840.060887094893[/C][C]-158.939112905107[/C][/ROW]
[ROW][C]5[/C][C]1.8[/C][C]133.652979583378[/C][C]-131.852979583378[/C][/ROW]
[ROW][C]6[/C][C]0.7[/C][C]28.9235798928042[/C][C]-28.2235798928042[/C][/ROW]
[ROW][C]7[/C][C]3.9[/C][C]-89.8935986721811[/C][C]93.793598672181[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]24.2595507696231[/C][C]-23.2595507696231[/C][/ROW]
[ROW][C]9[/C][C]3.6[/C][C]-102.737806540719[/C][C]106.337806540719[/C][/ROW]
[ROW][C]10[/C][C]1.4[/C][C]-87.8504878586546[/C][C]89.2504878586546[/C][/ROW]
[ROW][C]11[/C][C]1.5[/C][C]42.3878169681239[/C][C]-40.8878169681239[/C][/ROW]
[ROW][C]12[/C][C]0.7[/C][C]85.6708156845783[/C][C]-84.9708156845783[/C][/ROW]
[ROW][C]13[/C][C]2.7[/C][C]-36.5876762175865[/C][C]39.2876762175865[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-745.6806511735[/C][C]-253.3193488265[/C][/ROW]
[ROW][C]15[/C][C]2.1[/C][C]-98.1178673868504[/C][C]100.217867386850[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]-49.6568042980538[/C][C]49.6568042980538[/C][/ROW]
[ROW][C]17[/C][C]4.1[/C][C]-52.8596212463687[/C][C]56.9596212463687[/C][/ROW]
[ROW][C]18[/C][C]1.2[/C][C]-51.9865884569005[/C][C]53.1865884569005[/C][/ROW]
[ROW][C]19[/C][C]1.3[/C][C]-85.6442136981765[/C][C]86.9442136981765[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]-69.3694621578266[/C][C]75.4694621578266[/C][/ROW]
[ROW][C]21[/C][C]0.3[/C][C]-739.599824177322[/C][C]739.899824177322[/C][/ROW]
[ROW][C]22[/C][C]0.5[/C][C]80.1359485685872[/C][C]-79.6359485685872[/C][/ROW]
[ROW][C]23[/C][C]3.4[/C][C]-36.590502451711[/C][C]39.990502451711[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-950.377364034415[/C][C]-48.6226359655849[/C][/ROW]
[ROW][C]25[/C][C]1.5[/C][C]-113.798640494583[/C][C]115.298640494583[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-927.416526357919[/C][C]-71.5834736420808[/C][/ROW]
[ROW][C]27[/C][C]3.4[/C][C]19.9107137372525[/C][C]-16.5107137372525[/C][/ROW]
[ROW][C]28[/C][C]0.8[/C][C]49.3360415407831[/C][C]-48.5360415407831[/C][/ROW]
[ROW][C]29[/C][C]0.8[/C][C]90.1087925663753[/C][C]-89.3087925663753[/C][/ROW]
[ROW][C]30[/C][C]-999[/C][C]-98.3290740752834[/C][C]-900.670925924717[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-805.952576841016[/C][C]-193.047423158984[/C][/ROW]
[ROW][C]32[/C][C]1.4[/C][C]59.3594360243981[/C][C]-57.9594360243981[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]-88.3945003003143[/C][C]90.3945003003143[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]-65.9188566341772[/C][C]67.8188566341772[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]-108.423669622974[/C][C]110.823669622974[/C][/ROW]
[ROW][C]36[/C][C]2.8[/C][C]-3.73362461001548[/C][C]6.53362461001548[/C][/ROW]
[ROW][C]37[/C][C]1.3[/C][C]17.9953897927168[/C][C]-16.6953897927168[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]16.8338551552218[/C][C]-14.8338551552218[/C][/ROW]
[ROW][C]39[/C][C]5.6[/C][C]-87.9595630249994[/C][C]93.5595630249993[/C][/ROW]
[ROW][C]40[/C][C]3.1[/C][C]-89.6661856397193[/C][C]92.7661856397193[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]-745.596140366598[/C][C]746.596140366598[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]-47.3792165646299[/C][C]49.1792165646299[/C][/ROW]
[ROW][C]43[/C][C]0.9[/C][C]40.3172549998679[/C][C]-39.4172549998679[/C][/ROW]
[ROW][C]44[/C][C]1.8[/C][C]-38.6063790181572[/C][C]40.4063790181572[/C][/ROW]
[ROW][C]45[/C][C]1.9[/C][C]40.4058428490583[/C][C]-38.5058428490583[/C][/ROW]
[ROW][C]46[/C][C]0.9[/C][C]83.2606110449034[/C][C]-82.3606110449034[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-884.709023854426[/C][C]-114.290976145574[/C][/ROW]
[ROW][C]48[/C][C]2.6[/C][C]13.7808390214966[/C][C]-11.1808390214966[/C][/ROW]
[ROW][C]49[/C][C]2.4[/C][C]-95.9924528373733[/C][C]98.3924528373733[/C][/ROW]
[ROW][C]50[/C][C]1.2[/C][C]-57.8582903209903[/C][C]59.0582903209903[/C][/ROW]
[ROW][C]51[/C][C]0.9[/C][C]-57.1892479544068[/C][C]58.0892479544068[/C][/ROW]
[ROW][C]52[/C][C]0.5[/C][C]-2.12888295258638[/C][C]2.62888295258638[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-750.183204817024[/C][C]-248.816795182976[/C][/ROW]
[ROW][C]54[/C][C]0.6[/C][C]81.6274941903236[/C][C]-81.0274941903236[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-886.0111498274[/C][C]-112.988850172600[/C][/ROW]
[ROW][C]56[/C][C]2.2[/C][C]-17.7128108932199[/C][C]19.9128108932199[/C][/ROW]
[ROW][C]57[/C][C]2.3[/C][C]-40.6207607294625[/C][C]42.9207607294625[/C][/ROW]
[ROW][C]58[/C][C]0.5[/C][C]5.63188405688672[/C][C]-5.13188405688672[/C][/ROW]
[ROW][C]59[/C][C]2.6[/C][C]-44.4033277375874[/C][C]47.0033277375874[/C][/ROW]
[ROW][C]60[/C][C]0.6[/C][C]46.7707886073313[/C][C]-46.1707886073313[/C][/ROW]
[ROW][C]61[/C][C]6.6[/C][C]-41.3140782414969[/C][C]47.9140782414969[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-923.384904447443[/C][C]-75.615095552557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-999	-870.56975915472	-128.430240845279
2	2	9.90445632266566	-7.90445632266566
3	-999	-929.407888592692	-69.5921114073079
4	-999	-840.060887094893	-158.939112905107
5	1.8	133.652979583378	-131.852979583378
6	0.7	28.9235798928042	-28.2235798928042
7	3.9	-89.8935986721811	93.793598672181
8	1	24.2595507696231	-23.2595507696231
9	3.6	-102.737806540719	106.337806540719
10	1.4	-87.8504878586546	89.2504878586546
11	1.5	42.3878169681239	-40.8878169681239
12	0.7	85.6708156845783	-84.9708156845783
13	2.7	-36.5876762175865	39.2876762175865
14	-999	-745.6806511735	-253.3193488265
15	2.1	-98.1178673868504	100.217867386850
16	0	-49.6568042980538	49.6568042980538
17	4.1	-52.8596212463687	56.9596212463687
18	1.2	-51.9865884569005	53.1865884569005
19	1.3	-85.6442136981765	86.9442136981765
20	6.1	-69.3694621578266	75.4694621578266
21	0.3	-739.599824177322	739.899824177322
22	0.5	80.1359485685872	-79.6359485685872
23	3.4	-36.590502451711	39.990502451711
24	-999	-950.377364034415	-48.6226359655849
25	1.5	-113.798640494583	115.298640494583
26	-999	-927.416526357919	-71.5834736420808
27	3.4	19.9107137372525	-16.5107137372525
28	0.8	49.3360415407831	-48.5360415407831
29	0.8	90.1087925663753	-89.3087925663753
30	-999	-98.3290740752834	-900.670925924717
31	-999	-805.952576841016	-193.047423158984
32	1.4	59.3594360243981	-57.9594360243981
33	2	-88.3945003003143	90.3945003003143
34	1.9	-65.9188566341772	67.8188566341772
35	2.4	-108.423669622974	110.823669622974
36	2.8	-3.73362461001548	6.53362461001548
37	1.3	17.9953897927168	-16.6953897927168
38	2	16.8338551552218	-14.8338551552218
39	5.6	-87.9595630249994	93.5595630249993
40	3.1	-89.6661856397193	92.7661856397193
41	1	-745.596140366598	746.596140366598
42	1.8	-47.3792165646299	49.1792165646299
43	0.9	40.3172549998679	-39.4172549998679
44	1.8	-38.6063790181572	40.4063790181572
45	1.9	40.4058428490583	-38.5058428490583
46	0.9	83.2606110449034	-82.3606110449034
47	-999	-884.709023854426	-114.290976145574
48	2.6	13.7808390214966	-11.1808390214966
49	2.4	-95.9924528373733	98.3924528373733
50	1.2	-57.8582903209903	59.0582903209903
51	0.9	-57.1892479544068	58.0892479544068
52	0.5	-2.12888295258638	2.62888295258638
53	-999	-750.183204817024	-248.816795182976
54	0.6	81.6274941903236	-81.0274941903236
55	-999	-886.0111498274	-112.988850172600
56	2.2	-17.7128108932199	19.9128108932199
57	2.3	-40.6207607294625	42.9207607294625
58	0.5	5.63188405688672	-5.13188405688672
59	2.6	-44.4033277375874	47.0033277375874
60	0.6	46.7707886073313	-46.1707886073313
61	6.6	-41.3140782414969	47.9140782414969
62	-999	-923.384904447443	-75.615095552557

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	9.46804519936682e-07	1.89360903987336e-06	0.99999905319548
13	7.9819937869379e-08	1.59639875738758e-07	0.999999920180062
14	2.65934814392256e-09	5.31869628784513e-09	0.999999997340652
15	5.38217896052781e-11	1.07643579210556e-10	0.999999999946178
16	8.44315293097584e-13	1.68863058619517e-12	0.999999999999156
17	2.20933975009410e-14	4.41867950018821e-14	0.999999999999978
18	4.1729430221813e-16	8.3458860443626e-16	1
19	1.40035374310464e-17	2.80070748620928e-17	1
20	2.05281127880055e-19	4.10562255760111e-19	1
21	0.538827153600218	0.922345692799564	0.461172846399782
22	0.446730314617958	0.893460629235916	0.553269685382042
23	0.356018735440757	0.712037470881513	0.643981264559243
24	0.275168166151238	0.550336332302476	0.724831833848762
25	0.254180723705874	0.508361447411748	0.745819276294126
26	0.208346242738713	0.416692485477427	0.791653757261287
27	0.151331510044566	0.302663020089132	0.848668489955434
28	0.10660475292595	0.2132095058519	0.89339524707405
29	0.100271256837565	0.200542513675131	0.899728743162435
30	0.999775816598942	0.000448366802116225	0.000224183401058112
31	0.99966234246299	0.000675315074018896	0.000337657537009448
32	0.999248968733381	0.00150206253323725	0.000751031266618623
33	0.998527947483167	0.00294410503366530	0.00147205251683265
34	0.999957973609397	8.40527812056543e-05	4.20263906028271e-05
35	0.999912509274482	0.000174981451036571	8.74907255182856e-05
36	0.99998594446268	2.81110746418541e-05	1.40555373209270e-05
37	0.999960444196716	7.91116065675302e-05	3.95558032837651e-05
38	0.99990903391786	0.000181932164278221	9.09660821391105e-05
39	0.999752138385124	0.000495723229752425	0.000247861614876212
40	0.99935069728785	0.00129860542430221	0.000649302712151103
41	1	3.77052106115249e-22	1.88526053057625e-22
42	1	5.46264601692828e-21	2.73132300846414e-21
43	1	3.21724030047991e-19	1.60862015023995e-19
44	1	2.09716872798975e-17	1.04858436399487e-17
45	1	1.04698409116610e-15	5.23492045583048e-16
46	0.999999999999958	8.4130959256597e-14	4.20654796282985e-14
47	0.999999999995544	8.91270535751416e-12	4.45635267875708e-12
48	0.999999999657233	6.85534503086415e-10	3.42767251543207e-10
49	0.999999966549504	6.69009930125927e-08	3.34504965062963e-08
50	0.999996832623228	6.33475354315818e-06	3.16737677157909e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 9.46804519936682e-07 & 1.89360903987336e-06 & 0.99999905319548 \tabularnewline
13 & 7.9819937869379e-08 & 1.59639875738758e-07 & 0.999999920180062 \tabularnewline
14 & 2.65934814392256e-09 & 5.31869628784513e-09 & 0.999999997340652 \tabularnewline
15 & 5.38217896052781e-11 & 1.07643579210556e-10 & 0.999999999946178 \tabularnewline
16 & 8.44315293097584e-13 & 1.68863058619517e-12 & 0.999999999999156 \tabularnewline
17 & 2.20933975009410e-14 & 4.41867950018821e-14 & 0.999999999999978 \tabularnewline
18 & 4.1729430221813e-16 & 8.3458860443626e-16 & 1 \tabularnewline
19 & 1.40035374310464e-17 & 2.80070748620928e-17 & 1 \tabularnewline
20 & 2.05281127880055e-19 & 4.10562255760111e-19 & 1 \tabularnewline
21 & 0.538827153600218 & 0.922345692799564 & 0.461172846399782 \tabularnewline
22 & 0.446730314617958 & 0.893460629235916 & 0.553269685382042 \tabularnewline
23 & 0.356018735440757 & 0.712037470881513 & 0.643981264559243 \tabularnewline
24 & 0.275168166151238 & 0.550336332302476 & 0.724831833848762 \tabularnewline
25 & 0.254180723705874 & 0.508361447411748 & 0.745819276294126 \tabularnewline
26 & 0.208346242738713 & 0.416692485477427 & 0.791653757261287 \tabularnewline
27 & 0.151331510044566 & 0.302663020089132 & 0.848668489955434 \tabularnewline
28 & 0.10660475292595 & 0.2132095058519 & 0.89339524707405 \tabularnewline
29 & 0.100271256837565 & 0.200542513675131 & 0.899728743162435 \tabularnewline
30 & 0.999775816598942 & 0.000448366802116225 & 0.000224183401058112 \tabularnewline
31 & 0.99966234246299 & 0.000675315074018896 & 0.000337657537009448 \tabularnewline
32 & 0.999248968733381 & 0.00150206253323725 & 0.000751031266618623 \tabularnewline
33 & 0.998527947483167 & 0.00294410503366530 & 0.00147205251683265 \tabularnewline
34 & 0.999957973609397 & 8.40527812056543e-05 & 4.20263906028271e-05 \tabularnewline
35 & 0.999912509274482 & 0.000174981451036571 & 8.74907255182856e-05 \tabularnewline
36 & 0.99998594446268 & 2.81110746418541e-05 & 1.40555373209270e-05 \tabularnewline
37 & 0.999960444196716 & 7.91116065675302e-05 & 3.95558032837651e-05 \tabularnewline
38 & 0.99990903391786 & 0.000181932164278221 & 9.09660821391105e-05 \tabularnewline
39 & 0.999752138385124 & 0.000495723229752425 & 0.000247861614876212 \tabularnewline
40 & 0.99935069728785 & 0.00129860542430221 & 0.000649302712151103 \tabularnewline
41 & 1 & 3.77052106115249e-22 & 1.88526053057625e-22 \tabularnewline
42 & 1 & 5.46264601692828e-21 & 2.73132300846414e-21 \tabularnewline
43 & 1 & 3.21724030047991e-19 & 1.60862015023995e-19 \tabularnewline
44 & 1 & 2.09716872798975e-17 & 1.04858436399487e-17 \tabularnewline
45 & 1 & 1.04698409116610e-15 & 5.23492045583048e-16 \tabularnewline
46 & 0.999999999999958 & 8.4130959256597e-14 & 4.20654796282985e-14 \tabularnewline
47 & 0.999999999995544 & 8.91270535751416e-12 & 4.45635267875708e-12 \tabularnewline
48 & 0.999999999657233 & 6.85534503086415e-10 & 3.42767251543207e-10 \tabularnewline
49 & 0.999999966549504 & 6.69009930125927e-08 & 3.34504965062963e-08 \tabularnewline
50 & 0.999996832623228 & 6.33475354315818e-06 & 3.16737677157909e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&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]12[/C][C]9.46804519936682e-07[/C][C]1.89360903987336e-06[/C][C]0.99999905319548[/C][/ROW]
[ROW][C]13[/C][C]7.9819937869379e-08[/C][C]1.59639875738758e-07[/C][C]0.999999920180062[/C][/ROW]
[ROW][C]14[/C][C]2.65934814392256e-09[/C][C]5.31869628784513e-09[/C][C]0.999999997340652[/C][/ROW]
[ROW][C]15[/C][C]5.38217896052781e-11[/C][C]1.07643579210556e-10[/C][C]0.999999999946178[/C][/ROW]
[ROW][C]16[/C][C]8.44315293097584e-13[/C][C]1.68863058619517e-12[/C][C]0.999999999999156[/C][/ROW]
[ROW][C]17[/C][C]2.20933975009410e-14[/C][C]4.41867950018821e-14[/C][C]0.999999999999978[/C][/ROW]
[ROW][C]18[/C][C]4.1729430221813e-16[/C][C]8.3458860443626e-16[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]1.40035374310464e-17[/C][C]2.80070748620928e-17[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]2.05281127880055e-19[/C][C]4.10562255760111e-19[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.538827153600218[/C][C]0.922345692799564[/C][C]0.461172846399782[/C][/ROW]
[ROW][C]22[/C][C]0.446730314617958[/C][C]0.893460629235916[/C][C]0.553269685382042[/C][/ROW]
[ROW][C]23[/C][C]0.356018735440757[/C][C]0.712037470881513[/C][C]0.643981264559243[/C][/ROW]
[ROW][C]24[/C][C]0.275168166151238[/C][C]0.550336332302476[/C][C]0.724831833848762[/C][/ROW]
[ROW][C]25[/C][C]0.254180723705874[/C][C]0.508361447411748[/C][C]0.745819276294126[/C][/ROW]
[ROW][C]26[/C][C]0.208346242738713[/C][C]0.416692485477427[/C][C]0.791653757261287[/C][/ROW]
[ROW][C]27[/C][C]0.151331510044566[/C][C]0.302663020089132[/C][C]0.848668489955434[/C][/ROW]
[ROW][C]28[/C][C]0.10660475292595[/C][C]0.2132095058519[/C][C]0.89339524707405[/C][/ROW]
[ROW][C]29[/C][C]0.100271256837565[/C][C]0.200542513675131[/C][C]0.899728743162435[/C][/ROW]
[ROW][C]30[/C][C]0.999775816598942[/C][C]0.000448366802116225[/C][C]0.000224183401058112[/C][/ROW]
[ROW][C]31[/C][C]0.99966234246299[/C][C]0.000675315074018896[/C][C]0.000337657537009448[/C][/ROW]
[ROW][C]32[/C][C]0.999248968733381[/C][C]0.00150206253323725[/C][C]0.000751031266618623[/C][/ROW]
[ROW][C]33[/C][C]0.998527947483167[/C][C]0.00294410503366530[/C][C]0.00147205251683265[/C][/ROW]
[ROW][C]34[/C][C]0.999957973609397[/C][C]8.40527812056543e-05[/C][C]4.20263906028271e-05[/C][/ROW]
[ROW][C]35[/C][C]0.999912509274482[/C][C]0.000174981451036571[/C][C]8.74907255182856e-05[/C][/ROW]
[ROW][C]36[/C][C]0.99998594446268[/C][C]2.81110746418541e-05[/C][C]1.40555373209270e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999960444196716[/C][C]7.91116065675302e-05[/C][C]3.95558032837651e-05[/C][/ROW]
[ROW][C]38[/C][C]0.99990903391786[/C][C]0.000181932164278221[/C][C]9.09660821391105e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999752138385124[/C][C]0.000495723229752425[/C][C]0.000247861614876212[/C][/ROW]
[ROW][C]40[/C][C]0.99935069728785[/C][C]0.00129860542430221[/C][C]0.000649302712151103[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]3.77052106115249e-22[/C][C]1.88526053057625e-22[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]5.46264601692828e-21[/C][C]2.73132300846414e-21[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]3.21724030047991e-19[/C][C]1.60862015023995e-19[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]2.09716872798975e-17[/C][C]1.04858436399487e-17[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.04698409116610e-15[/C][C]5.23492045583048e-16[/C][/ROW]
[ROW][C]46[/C][C]0.999999999999958[/C][C]8.4130959256597e-14[/C][C]4.20654796282985e-14[/C][/ROW]
[ROW][C]47[/C][C]0.999999999995544[/C][C]8.91270535751416e-12[/C][C]4.45635267875708e-12[/C][/ROW]
[ROW][C]48[/C][C]0.999999999657233[/C][C]6.85534503086415e-10[/C][C]3.42767251543207e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999966549504[/C][C]6.69009930125927e-08[/C][C]3.34504965062963e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999996832623228[/C][C]6.33475354315818e-06[/C][C]3.16737677157909e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	9.46804519936682e-07	1.89360903987336e-06	0.99999905319548
13	7.9819937869379e-08	1.59639875738758e-07	0.999999920180062
14	2.65934814392256e-09	5.31869628784513e-09	0.999999997340652
15	5.38217896052781e-11	1.07643579210556e-10	0.999999999946178
16	8.44315293097584e-13	1.68863058619517e-12	0.999999999999156
17	2.20933975009410e-14	4.41867950018821e-14	0.999999999999978
18	4.1729430221813e-16	8.3458860443626e-16	1
19	1.40035374310464e-17	2.80070748620928e-17	1
20	2.05281127880055e-19	4.10562255760111e-19	1
21	0.538827153600218	0.922345692799564	0.461172846399782
22	0.446730314617958	0.893460629235916	0.553269685382042
23	0.356018735440757	0.712037470881513	0.643981264559243
24	0.275168166151238	0.550336332302476	0.724831833848762
25	0.254180723705874	0.508361447411748	0.745819276294126
26	0.208346242738713	0.416692485477427	0.791653757261287
27	0.151331510044566	0.302663020089132	0.848668489955434
28	0.10660475292595	0.2132095058519	0.89339524707405
29	0.100271256837565	0.200542513675131	0.899728743162435
30	0.999775816598942	0.000448366802116225	0.000224183401058112
31	0.99966234246299	0.000675315074018896	0.000337657537009448
32	0.999248968733381	0.00150206253323725	0.000751031266618623
33	0.998527947483167	0.00294410503366530	0.00147205251683265
34	0.999957973609397	8.40527812056543e-05	4.20263906028271e-05
35	0.999912509274482	0.000174981451036571	8.74907255182856e-05
36	0.99998594446268	2.81110746418541e-05	1.40555373209270e-05
37	0.999960444196716	7.91116065675302e-05	3.95558032837651e-05
38	0.99990903391786	0.000181932164278221	9.09660821391105e-05
39	0.999752138385124	0.000495723229752425	0.000247861614876212
40	0.99935069728785	0.00129860542430221	0.000649302712151103
41	1	3.77052106115249e-22	1.88526053057625e-22
42	1	5.46264601692828e-21	2.73132300846414e-21
43	1	3.21724030047991e-19	1.60862015023995e-19
44	1	2.09716872798975e-17	1.04858436399487e-17
45	1	1.04698409116610e-15	5.23492045583048e-16
46	0.999999999999958	8.4130959256597e-14	4.20654796282985e-14
47	0.999999999995544	8.91270535751416e-12	4.45635267875708e-12
48	0.999999999657233	6.85534503086415e-10	3.42767251543207e-10
49	0.999999966549504	6.69009930125927e-08	3.34504965062963e-08
50	0.999996832623228	6.33475354315818e-06	3.16737677157909e-06

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	30	0.769230769230769	NOK
5% type I error level	30	0.769230769230769	NOK
10% type I error level	30	0.769230769230769	NOK

\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 & 30 & 0.769230769230769 & NOK \tabularnewline
5% type I error level & 30 & 0.769230769230769 & NOK \tabularnewline
10% type I error level & 30 & 0.769230769230769 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114475&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]30[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.769230769230769[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114475&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114475&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 tests	OK/NOK
1% type I error level	30	0.769230769230769	NOK
5% type I error level	30	0.769230769230769	NOK
10% type I error level	30	0.769230769230769	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code