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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 22 Dec 2010 18:46:07 +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/t1293043547hl7or06sge19bc6.htm/, Retrieved Mon, 06 May 2024 05:41:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114484, Retrieved Mon, 06 May 2024 05:41:16 +0000

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116

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-     [Multiple Regression] [] [2010-12-20 17:52:04] [8a12eeeb546060995f18439d3f99cdb1]
-         [Multiple Regression] [] [2010-12-22 18:46:07] [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	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 101.747169187514 + 0.85898554456651PS[t] + 0.0483563309262616L[t] + 0.00897769792082362WB[t] -0.00229591302762992WBR[t] -0.0465855631325104TG[t] -24.6233778463787P[t] -39.2386116719056S[t] + 11.5295140043652D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  101.747169187514 +  0.85898554456651PS[t] +  0.0483563309262616L[t] +  0.00897769792082362WB[t] -0.00229591302762992WBR[t] -0.0465855631325104TG[t] -24.6233778463787P[t] -39.2386116719056S[t] +  11.5295140043652D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  101.747169187514 +  0.85898554456651PS[t] +  0.0483563309262616L[t] +  0.00897769792082362WB[t] -0.00229591302762992WBR[t] -0.0465855631325104TG[t] -24.6233778463787P[t] -39.2386116719056S[t] +  11.5295140043652D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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
SWS[t] = + 101.747169187514 + 0.85898554456651PS[t] + 0.0483563309262616L[t] + 0.00897769792082362WB[t] -0.00229591302762992WBR[t] -0.0465855631325104TG[t] -24.6233778463787P[t] -39.2386116719056S[t] + 11.5295140043652D[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)	101.747169187514	69.11223	1.4722	0.14688	0.07344
PS	0.85898554456651	0.075204	11.4221	0	0
L	0.0483563309262616	0.118259	0.4089	0.684258	0.342129
WB	0.00897769792082362	0.300033	0.0299	0.976241	0.488121
WBR	-0.00229591302762992	0.163977	-0.014	0.988881	0.494441
TG	-0.0465855631325104	0.104604	-0.4454	0.65788	0.32894
P	-24.6233778463787	51.141729	-0.4815	0.632162	0.316081
S	-39.2386116719056	33.633703	-1.1666	0.248576	0.124288
D	11.5295140043652	67.319369	0.1713	0.864667	0.432333

\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) & 101.747169187514 & 69.11223 & 1.4722 & 0.14688 & 0.07344 \tabularnewline
PS & 0.85898554456651 & 0.075204 & 11.4221 & 0 & 0 \tabularnewline
L & 0.0483563309262616 & 0.118259 & 0.4089 & 0.684258 & 0.342129 \tabularnewline
WB & 0.00897769792082362 & 0.300033 & 0.0299 & 0.976241 & 0.488121 \tabularnewline
WBR & -0.00229591302762992 & 0.163977 & -0.014 & 0.988881 & 0.494441 \tabularnewline
TG & -0.0465855631325104 & 0.104604 & -0.4454 & 0.65788 & 0.32894 \tabularnewline
P & -24.6233778463787 & 51.141729 & -0.4815 & 0.632162 & 0.316081 \tabularnewline
S & -39.2386116719056 & 33.633703 & -1.1666 & 0.248576 & 0.124288 \tabularnewline
D & 11.5295140043652 & 67.319369 & 0.1713 & 0.864667 & 0.432333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&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]101.747169187514[/C][C]69.11223[/C][C]1.4722[/C][C]0.14688[/C][C]0.07344[/C][/ROW]
[ROW][C]PS[/C][C]0.85898554456651[/C][C]0.075204[/C][C]11.4221[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0483563309262616[/C][C]0.118259[/C][C]0.4089[/C][C]0.684258[/C][C]0.342129[/C][/ROW]
[ROW][C]WB[/C][C]0.00897769792082362[/C][C]0.300033[/C][C]0.0299[/C][C]0.976241[/C][C]0.488121[/C][/ROW]
[ROW][C]WBR[/C][C]-0.00229591302762992[/C][C]0.163977[/C][C]-0.014[/C][C]0.988881[/C][C]0.494441[/C][/ROW]
[ROW][C]TG[/C][C]-0.0465855631325104[/C][C]0.104604[/C][C]-0.4454[/C][C]0.65788[/C][C]0.32894[/C][/ROW]
[ROW][C]P[/C][C]-24.6233778463787[/C][C]51.141729[/C][C]-0.4815[/C][C]0.632162[/C][C]0.316081[/C][/ROW]
[ROW][C]S[/C][C]-39.2386116719056[/C][C]33.633703[/C][C]-1.1666[/C][C]0.248576[/C][C]0.124288[/C][/ROW]
[ROW][C]D[/C][C]11.5295140043652[/C][C]67.319369[/C][C]0.1713[/C][C]0.864667[/C][C]0.432333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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)	101.747169187514	69.11223	1.4722	0.14688	0.07344
PS	0.85898554456651	0.075204	11.4221	0	0
L	0.0483563309262616	0.118259	0.4089	0.684258	0.342129
WB	0.00897769792082362	0.300033	0.0299	0.976241	0.488121
WBR	-0.00229591302762992	0.163977	-0.014	0.988881	0.494441
TG	-0.0465855631325104	0.104604	-0.4454	0.65788	0.32894
P	-24.6233778463787	51.141729	-0.4815	0.632162	0.316081
S	-39.2386116719056	33.633703	-1.1666	0.248576	0.124288
D	11.5295140043652	67.319369	0.1713	0.864667	0.432333

Multiple Linear Regression - Regression Statistics
Multiple R	0.87368168438707
R-squared	0.763319685633427
Adjusted R-squared	0.727594355163
F-TEST (value)	21.3663435882087
F-TEST (DF numerator)	8
F-TEST (DF denominator)	53
p-value	4.61852778244065e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	215.781932434378
Sum Squared Residuals	2467777.64535107

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.87368168438707 \tabularnewline
R-squared & 0.763319685633427 \tabularnewline
Adjusted R-squared & 0.727594355163 \tabularnewline
F-TEST (value) & 21.3663435882087 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 4.61852778244065e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 215.781932434378 \tabularnewline
Sum Squared Residuals & 2467777.64535107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.87368168438707[/C][/ROW]
[ROW][C]R-squared[/C][C]0.763319685633427[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.727594355163[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.3663435882087[/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]4.61852778244065e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]215.781932434378[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2467777.64535107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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	0.87368168438707
R-squared	0.763319685633427
Adjusted R-squared	0.727594355163
F-TEST (value)	21.3663435882087
F-TEST (DF numerator)	8
F-TEST (DF denominator)	53
p-value	4.61852778244065e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	215.781932434378
Sum Squared Residuals	2467777.64535107

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-999	-1019.98855021996	20.9885502199617
2	6.3	23.1997715882405	-16.8997715882405
3	-999	-810.9017891256	-188.0982108744
4	-999	-972.862401292858	-26.1375987071421
5	2.1	-134.086488336087	136.186488336087
6	0.1	-113.967656659258	114.067656659258
7	15.8	52.0525305885091	-36.2525305885091
8	5.2	-161.705444917625	166.905444917625
9	10.9	11.6583677803470	-0.758367780347039
10	8.3	41.7784854532164	-33.4784854532164
11	11	-135.807599473810	146.807599473810
12	3.2	-167.750313490837	170.950313490837
13	7.6	36.8705213185434	-29.2705213185434
14	-999	-1032.39350498793	33.3935049879323
15	6.3	49.4291350555717	-43.1291350555717
16	8.6	-1.83482579365001	10.43482579365
17	6.6	-1.06338497633856	7.66338497633856
18	9.5	-6.98404494666681	16.4840449466668
19	4.8	59.448336106581	-54.6483361065811
20	12	101.884670301323	-89.8846703013234
21	-999	-173.749779364453	-825.250220635547
22	3.3	-165.428959208572	168.728959208572
23	11	14.0600049279436	-3.0600049279436
24	-999	-935.340615687664	-63.6593843123363
25	4.7	-40.216033767178	44.916033767178
26	-999	-810.81158510603	-188.18841489397
27	10.4	-23.9767045456582	34.3767045456582
28	7.4	-95.083911500768	102.483911500768
29	2.1	-169.482820352693	171.582820352693
30	2.1	-811.749928402108	813.849928402108
31	-999	-980.887992807227	-18.1120071927734
32	7.7	-53.4024472946121	61.1024472946121
33	17.9	49.9634543470547	-32.0634543470547
34	6.1	40.9700660191365	-34.8700660191365
35	8.2	-22.8584528150715	31.0584528150715
36	8.4	-24.7786764252750	33.1786764252750
37	11.9	-1.00896958411760	12.9089695841176
38	10.8	-0.979621721599862	11.7796217215999
39	13.8	29.2851876082209	-15.4851876082209
40	14.3	22.1848456725160	-7.88484567251605
41	-999	-177.293234109293	-821.706765890707
42	15.2	-7.34458808446734	22.5445880844673
43	10	-113.926646744838	123.926646744838
44	11.9	37.6920520027063	-25.7920520027063
45	6.5	-108.691925502891	115.191925502891
46	7.5	-159.721196195239	167.221196195239
47	-999	-863.368253027925	-135.631746972075
48	10.6	24.7074578556765	-14.1074578556765
49	7.4	49.4500082748432	-42.0500082748432
50	8.4	-47.7132297017045	56.1132297017045
51	5.7	-12.3094586953449	18.0094586953449
52	4.9	-22.2957438554390	27.1957438554390
53	-999	-1024.34644921044	25.3464492104357
54	3.2	-165.324047451923	168.524047451923
55	-999	-864.639046277187	-134.360953722813
56	8.1	60.293687659716	-52.193687659716
57	11	35.7210768395585	-24.7210768395585
58	4.9	14.6917342828975	-9.79173428289748
59	13.2	-27.3443197157807	40.5443197157807
60	9.7	-76.5466925864284	86.2466925864284
61	12.8	66.1288512629375	-53.3288512629375
62	-999	-859.10291098299	-139.897089017009

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -1019.98855021996 & 20.9885502199617 \tabularnewline
2 & 6.3 & 23.1997715882405 & -16.8997715882405 \tabularnewline
3 & -999 & -810.9017891256 & -188.0982108744 \tabularnewline
4 & -999 & -972.862401292858 & -26.1375987071421 \tabularnewline
5 & 2.1 & -134.086488336087 & 136.186488336087 \tabularnewline
6 & 0.1 & -113.967656659258 & 114.067656659258 \tabularnewline
7 & 15.8 & 52.0525305885091 & -36.2525305885091 \tabularnewline
8 & 5.2 & -161.705444917625 & 166.905444917625 \tabularnewline
9 & 10.9 & 11.6583677803470 & -0.758367780347039 \tabularnewline
10 & 8.3 & 41.7784854532164 & -33.4784854532164 \tabularnewline
11 & 11 & -135.807599473810 & 146.807599473810 \tabularnewline
12 & 3.2 & -167.750313490837 & 170.950313490837 \tabularnewline
13 & 7.6 & 36.8705213185434 & -29.2705213185434 \tabularnewline
14 & -999 & -1032.39350498793 & 33.3935049879323 \tabularnewline
15 & 6.3 & 49.4291350555717 & -43.1291350555717 \tabularnewline
16 & 8.6 & -1.83482579365001 & 10.43482579365 \tabularnewline
17 & 6.6 & -1.06338497633856 & 7.66338497633856 \tabularnewline
18 & 9.5 & -6.98404494666681 & 16.4840449466668 \tabularnewline
19 & 4.8 & 59.448336106581 & -54.6483361065811 \tabularnewline
20 & 12 & 101.884670301323 & -89.8846703013234 \tabularnewline
21 & -999 & -173.749779364453 & -825.250220635547 \tabularnewline
22 & 3.3 & -165.428959208572 & 168.728959208572 \tabularnewline
23 & 11 & 14.0600049279436 & -3.0600049279436 \tabularnewline
24 & -999 & -935.340615687664 & -63.6593843123363 \tabularnewline
25 & 4.7 & -40.216033767178 & 44.916033767178 \tabularnewline
26 & -999 & -810.81158510603 & -188.18841489397 \tabularnewline
27 & 10.4 & -23.9767045456582 & 34.3767045456582 \tabularnewline
28 & 7.4 & -95.083911500768 & 102.483911500768 \tabularnewline
29 & 2.1 & -169.482820352693 & 171.582820352693 \tabularnewline
30 & 2.1 & -811.749928402108 & 813.849928402108 \tabularnewline
31 & -999 & -980.887992807227 & -18.1120071927734 \tabularnewline
32 & 7.7 & -53.4024472946121 & 61.1024472946121 \tabularnewline
33 & 17.9 & 49.9634543470547 & -32.0634543470547 \tabularnewline
34 & 6.1 & 40.9700660191365 & -34.8700660191365 \tabularnewline
35 & 8.2 & -22.8584528150715 & 31.0584528150715 \tabularnewline
36 & 8.4 & -24.7786764252750 & 33.1786764252750 \tabularnewline
37 & 11.9 & -1.00896958411760 & 12.9089695841176 \tabularnewline
38 & 10.8 & -0.979621721599862 & 11.7796217215999 \tabularnewline
39 & 13.8 & 29.2851876082209 & -15.4851876082209 \tabularnewline
40 & 14.3 & 22.1848456725160 & -7.88484567251605 \tabularnewline
41 & -999 & -177.293234109293 & -821.706765890707 \tabularnewline
42 & 15.2 & -7.34458808446734 & 22.5445880844673 \tabularnewline
43 & 10 & -113.926646744838 & 123.926646744838 \tabularnewline
44 & 11.9 & 37.6920520027063 & -25.7920520027063 \tabularnewline
45 & 6.5 & -108.691925502891 & 115.191925502891 \tabularnewline
46 & 7.5 & -159.721196195239 & 167.221196195239 \tabularnewline
47 & -999 & -863.368253027925 & -135.631746972075 \tabularnewline
48 & 10.6 & 24.7074578556765 & -14.1074578556765 \tabularnewline
49 & 7.4 & 49.4500082748432 & -42.0500082748432 \tabularnewline
50 & 8.4 & -47.7132297017045 & 56.1132297017045 \tabularnewline
51 & 5.7 & -12.3094586953449 & 18.0094586953449 \tabularnewline
52 & 4.9 & -22.2957438554390 & 27.1957438554390 \tabularnewline
53 & -999 & -1024.34644921044 & 25.3464492104357 \tabularnewline
54 & 3.2 & -165.324047451923 & 168.524047451923 \tabularnewline
55 & -999 & -864.639046277187 & -134.360953722813 \tabularnewline
56 & 8.1 & 60.293687659716 & -52.193687659716 \tabularnewline
57 & 11 & 35.7210768395585 & -24.7210768395585 \tabularnewline
58 & 4.9 & 14.6917342828975 & -9.79173428289748 \tabularnewline
59 & 13.2 & -27.3443197157807 & 40.5443197157807 \tabularnewline
60 & 9.7 & -76.5466925864284 & 86.2466925864284 \tabularnewline
61 & 12.8 & 66.1288512629375 & -53.3288512629375 \tabularnewline
62 & -999 & -859.10291098299 & -139.897089017009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&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]-1019.98855021996[/C][C]20.9885502199617[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]23.1997715882405[/C][C]-16.8997715882405[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-810.9017891256[/C][C]-188.0982108744[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-972.862401292858[/C][C]-26.1375987071421[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]-134.086488336087[/C][C]136.186488336087[/C][/ROW]
[ROW][C]6[/C][C]0.1[/C][C]-113.967656659258[/C][C]114.067656659258[/C][/ROW]
[ROW][C]7[/C][C]15.8[/C][C]52.0525305885091[/C][C]-36.2525305885091[/C][/ROW]
[ROW][C]8[/C][C]5.2[/C][C]-161.705444917625[/C][C]166.905444917625[/C][/ROW]
[ROW][C]9[/C][C]10.9[/C][C]11.6583677803470[/C][C]-0.758367780347039[/C][/ROW]
[ROW][C]10[/C][C]8.3[/C][C]41.7784854532164[/C][C]-33.4784854532164[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]-135.807599473810[/C][C]146.807599473810[/C][/ROW]
[ROW][C]12[/C][C]3.2[/C][C]-167.750313490837[/C][C]170.950313490837[/C][/ROW]
[ROW][C]13[/C][C]7.6[/C][C]36.8705213185434[/C][C]-29.2705213185434[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-1032.39350498793[/C][C]33.3935049879323[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]49.4291350555717[/C][C]-43.1291350555717[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]-1.83482579365001[/C][C]10.43482579365[/C][/ROW]
[ROW][C]17[/C][C]6.6[/C][C]-1.06338497633856[/C][C]7.66338497633856[/C][/ROW]
[ROW][C]18[/C][C]9.5[/C][C]-6.98404494666681[/C][C]16.4840449466668[/C][/ROW]
[ROW][C]19[/C][C]4.8[/C][C]59.448336106581[/C][C]-54.6483361065811[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]101.884670301323[/C][C]-89.8846703013234[/C][/ROW]
[ROW][C]21[/C][C]-999[/C][C]-173.749779364453[/C][C]-825.250220635547[/C][/ROW]
[ROW][C]22[/C][C]3.3[/C][C]-165.428959208572[/C][C]168.728959208572[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]14.0600049279436[/C][C]-3.0600049279436[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-935.340615687664[/C][C]-63.6593843123363[/C][/ROW]
[ROW][C]25[/C][C]4.7[/C][C]-40.216033767178[/C][C]44.916033767178[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-810.81158510603[/C][C]-188.18841489397[/C][/ROW]
[ROW][C]27[/C][C]10.4[/C][C]-23.9767045456582[/C][C]34.3767045456582[/C][/ROW]
[ROW][C]28[/C][C]7.4[/C][C]-95.083911500768[/C][C]102.483911500768[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]-169.482820352693[/C][C]171.582820352693[/C][/ROW]
[ROW][C]30[/C][C]2.1[/C][C]-811.749928402108[/C][C]813.849928402108[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-980.887992807227[/C][C]-18.1120071927734[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]-53.4024472946121[/C][C]61.1024472946121[/C][/ROW]
[ROW][C]33[/C][C]17.9[/C][C]49.9634543470547[/C][C]-32.0634543470547[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]40.9700660191365[/C][C]-34.8700660191365[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]-22.8584528150715[/C][C]31.0584528150715[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]-24.7786764252750[/C][C]33.1786764252750[/C][/ROW]
[ROW][C]37[/C][C]11.9[/C][C]-1.00896958411760[/C][C]12.9089695841176[/C][/ROW]
[ROW][C]38[/C][C]10.8[/C][C]-0.979621721599862[/C][C]11.7796217215999[/C][/ROW]
[ROW][C]39[/C][C]13.8[/C][C]29.2851876082209[/C][C]-15.4851876082209[/C][/ROW]
[ROW][C]40[/C][C]14.3[/C][C]22.1848456725160[/C][C]-7.88484567251605[/C][/ROW]
[ROW][C]41[/C][C]-999[/C][C]-177.293234109293[/C][C]-821.706765890707[/C][/ROW]
[ROW][C]42[/C][C]15.2[/C][C]-7.34458808446734[/C][C]22.5445880844673[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]-113.926646744838[/C][C]123.926646744838[/C][/ROW]
[ROW][C]44[/C][C]11.9[/C][C]37.6920520027063[/C][C]-25.7920520027063[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]-108.691925502891[/C][C]115.191925502891[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]-159.721196195239[/C][C]167.221196195239[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-863.368253027925[/C][C]-135.631746972075[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]24.7074578556765[/C][C]-14.1074578556765[/C][/ROW]
[ROW][C]49[/C][C]7.4[/C][C]49.4500082748432[/C][C]-42.0500082748432[/C][/ROW]
[ROW][C]50[/C][C]8.4[/C][C]-47.7132297017045[/C][C]56.1132297017045[/C][/ROW]
[ROW][C]51[/C][C]5.7[/C][C]-12.3094586953449[/C][C]18.0094586953449[/C][/ROW]
[ROW][C]52[/C][C]4.9[/C][C]-22.2957438554390[/C][C]27.1957438554390[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-1024.34644921044[/C][C]25.3464492104357[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]-165.324047451923[/C][C]168.524047451923[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-864.639046277187[/C][C]-134.360953722813[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]60.293687659716[/C][C]-52.193687659716[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]35.7210768395585[/C][C]-24.7210768395585[/C][/ROW]
[ROW][C]58[/C][C]4.9[/C][C]14.6917342828975[/C][C]-9.79173428289748[/C][/ROW]
[ROW][C]59[/C][C]13.2[/C][C]-27.3443197157807[/C][C]40.5443197157807[/C][/ROW]
[ROW][C]60[/C][C]9.7[/C][C]-76.5466925864284[/C][C]86.2466925864284[/C][/ROW]
[ROW][C]61[/C][C]12.8[/C][C]66.1288512629375[/C][C]-53.3288512629375[/C][/ROW]
[ROW][C]62[/C][C]-999[/C][C]-859.10291098299[/C][C]-139.897089017009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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	-1019.98855021996	20.9885502199617
2	6.3	23.1997715882405	-16.8997715882405
3	-999	-810.9017891256	-188.0982108744
4	-999	-972.862401292858	-26.1375987071421
5	2.1	-134.086488336087	136.186488336087
6	0.1	-113.967656659258	114.067656659258
7	15.8	52.0525305885091	-36.2525305885091
8	5.2	-161.705444917625	166.905444917625
9	10.9	11.6583677803470	-0.758367780347039
10	8.3	41.7784854532164	-33.4784854532164
11	11	-135.807599473810	146.807599473810
12	3.2	-167.750313490837	170.950313490837
13	7.6	36.8705213185434	-29.2705213185434
14	-999	-1032.39350498793	33.3935049879323
15	6.3	49.4291350555717	-43.1291350555717
16	8.6	-1.83482579365001	10.43482579365
17	6.6	-1.06338497633856	7.66338497633856
18	9.5	-6.98404494666681	16.4840449466668
19	4.8	59.448336106581	-54.6483361065811
20	12	101.884670301323	-89.8846703013234
21	-999	-173.749779364453	-825.250220635547
22	3.3	-165.428959208572	168.728959208572
23	11	14.0600049279436	-3.0600049279436
24	-999	-935.340615687664	-63.6593843123363
25	4.7	-40.216033767178	44.916033767178
26	-999	-810.81158510603	-188.18841489397
27	10.4	-23.9767045456582	34.3767045456582
28	7.4	-95.083911500768	102.483911500768
29	2.1	-169.482820352693	171.582820352693
30	2.1	-811.749928402108	813.849928402108
31	-999	-980.887992807227	-18.1120071927734
32	7.7	-53.4024472946121	61.1024472946121
33	17.9	49.9634543470547	-32.0634543470547
34	6.1	40.9700660191365	-34.8700660191365
35	8.2	-22.8584528150715	31.0584528150715
36	8.4	-24.7786764252750	33.1786764252750
37	11.9	-1.00896958411760	12.9089695841176
38	10.8	-0.979621721599862	11.7796217215999
39	13.8	29.2851876082209	-15.4851876082209
40	14.3	22.1848456725160	-7.88484567251605
41	-999	-177.293234109293	-821.706765890707
42	15.2	-7.34458808446734	22.5445880844673
43	10	-113.926646744838	123.926646744838
44	11.9	37.6920520027063	-25.7920520027063
45	6.5	-108.691925502891	115.191925502891
46	7.5	-159.721196195239	167.221196195239
47	-999	-863.368253027925	-135.631746972075
48	10.6	24.7074578556765	-14.1074578556765
49	7.4	49.4500082748432	-42.0500082748432
50	8.4	-47.7132297017045	56.1132297017045
51	5.7	-12.3094586953449	18.0094586953449
52	4.9	-22.2957438554390	27.1957438554390
53	-999	-1024.34644921044	25.3464492104357
54	3.2	-165.324047451923	168.524047451923
55	-999	-864.639046277187	-134.360953722813
56	8.1	60.293687659716	-52.193687659716
57	11	35.7210768395585	-24.7210768395585
58	4.9	14.6917342828975	-9.79173428289748
59	13.2	-27.3443197157807	40.5443197157807
60	9.7	-76.5466925864284	86.2466925864284
61	12.8	66.1288512629375	-53.3288512629375
62	-999	-859.10291098299	-139.897089017009

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	9.96973862224977e-07	1.99394772444995e-06	0.999999003026138
13	8.49260436245877e-08	1.69852087249175e-07	0.999999915073956
14	2.46545252722155e-09	4.9309050544431e-09	0.999999997534547
15	4.77975522074018e-11	9.55951044148035e-11	0.999999999952202
16	7.21835601031414e-13	1.44367120206283e-12	0.999999999999278
17	1.79564723558843e-14	3.59129447117687e-14	0.999999999999982
18	3.20455910313872e-16	6.40911820627745e-16	1
19	9.98058767162516e-18	1.99611753432503e-17	1
20	1.31542306454434e-19	2.63084612908868e-19	1
21	0.705842943502995	0.588314112994009	0.294157056497005
22	0.637633725615691	0.724732548768618	0.362366274384309
23	0.54895564434901	0.90208871130198	0.45104435565099
24	0.462912500910653	0.925825001821306	0.537087499089347
25	0.379949463749086	0.759898927498172	0.620050536250914
26	0.333257744374903	0.666515488749805	0.666742255625097
27	0.258305163028266	0.516610326056531	0.741694836971734
28	0.202685680245681	0.405371360491362	0.797314319754319
29	0.228803130144019	0.457606260288039	0.77119686985598
30	0.999576128750102	0.000847742499796067	0.000423871249898033
31	0.99909151284502	0.00181697430996150	0.000908487154980748
32	0.998094974771878	0.00381005045624387	0.00190502522812194
33	0.996209025993802	0.00758194801239593	0.00379097400619796
34	0.9998839372825	0.000232125435000408	0.000116062717500204
35	0.99973095016094	0.00053809967811941	0.000269049839059705
36	0.99995348929557	9.30214088604778e-05	4.65107044302389e-05
37	0.999881195113487	0.000237609773025733	0.000118804886512867
38	0.999755074258572	0.000489851482855775	0.000244925741427888
39	0.999362298760348	0.00127540247930338	0.000637701239651692
40	0.99841836536591	0.00316326926818024	0.00158163463409012
41	1	3.93149082272239e-22	1.96574541136119e-22
42	1	5.67553500437773e-21	2.83776750218887e-21
43	1	3.07398365230395e-19	1.53699182615197e-19
44	1	2.01560109383107e-17	1.00780054691553e-17
45	1	9.70110436599107e-16	4.85055218299554e-16
46	0.999999999999963	7.46333488779698e-14	3.73166744389849e-14
47	0.999999999996005	7.99054504135233e-12	3.99527252067617e-12
48	0.999999999686606	6.26788520370011e-10	3.13394260185005e-10
49	0.999999968497706	6.30045888298112e-08	3.15022944149056e-08
50	0.999996975078445	6.04984311026282e-06	3.02492155513141e-06

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 9.96973862224977e-07 & 1.99394772444995e-06 & 0.999999003026138 \tabularnewline
13 & 8.49260436245877e-08 & 1.69852087249175e-07 & 0.999999915073956 \tabularnewline
14 & 2.46545252722155e-09 & 4.9309050544431e-09 & 0.999999997534547 \tabularnewline
15 & 4.77975522074018e-11 & 9.55951044148035e-11 & 0.999999999952202 \tabularnewline
16 & 7.21835601031414e-13 & 1.44367120206283e-12 & 0.999999999999278 \tabularnewline
17 & 1.79564723558843e-14 & 3.59129447117687e-14 & 0.999999999999982 \tabularnewline
18 & 3.20455910313872e-16 & 6.40911820627745e-16 & 1 \tabularnewline
19 & 9.98058767162516e-18 & 1.99611753432503e-17 & 1 \tabularnewline
20 & 1.31542306454434e-19 & 2.63084612908868e-19 & 1 \tabularnewline
21 & 0.705842943502995 & 0.588314112994009 & 0.294157056497005 \tabularnewline
22 & 0.637633725615691 & 0.724732548768618 & 0.362366274384309 \tabularnewline
23 & 0.54895564434901 & 0.90208871130198 & 0.45104435565099 \tabularnewline
24 & 0.462912500910653 & 0.925825001821306 & 0.537087499089347 \tabularnewline
25 & 0.379949463749086 & 0.759898927498172 & 0.620050536250914 \tabularnewline
26 & 0.333257744374903 & 0.666515488749805 & 0.666742255625097 \tabularnewline
27 & 0.258305163028266 & 0.516610326056531 & 0.741694836971734 \tabularnewline
28 & 0.202685680245681 & 0.405371360491362 & 0.797314319754319 \tabularnewline
29 & 0.228803130144019 & 0.457606260288039 & 0.77119686985598 \tabularnewline
30 & 0.999576128750102 & 0.000847742499796067 & 0.000423871249898033 \tabularnewline
31 & 0.99909151284502 & 0.00181697430996150 & 0.000908487154980748 \tabularnewline
32 & 0.998094974771878 & 0.00381005045624387 & 0.00190502522812194 \tabularnewline
33 & 0.996209025993802 & 0.00758194801239593 & 0.00379097400619796 \tabularnewline
34 & 0.9998839372825 & 0.000232125435000408 & 0.000116062717500204 \tabularnewline
35 & 0.99973095016094 & 0.00053809967811941 & 0.000269049839059705 \tabularnewline
36 & 0.99995348929557 & 9.30214088604778e-05 & 4.65107044302389e-05 \tabularnewline
37 & 0.999881195113487 & 0.000237609773025733 & 0.000118804886512867 \tabularnewline
38 & 0.999755074258572 & 0.000489851482855775 & 0.000244925741427888 \tabularnewline
39 & 0.999362298760348 & 0.00127540247930338 & 0.000637701239651692 \tabularnewline
40 & 0.99841836536591 & 0.00316326926818024 & 0.00158163463409012 \tabularnewline
41 & 1 & 3.93149082272239e-22 & 1.96574541136119e-22 \tabularnewline
42 & 1 & 5.67553500437773e-21 & 2.83776750218887e-21 \tabularnewline
43 & 1 & 3.07398365230395e-19 & 1.53699182615197e-19 \tabularnewline
44 & 1 & 2.01560109383107e-17 & 1.00780054691553e-17 \tabularnewline
45 & 1 & 9.70110436599107e-16 & 4.85055218299554e-16 \tabularnewline
46 & 0.999999999999963 & 7.46333488779698e-14 & 3.73166744389849e-14 \tabularnewline
47 & 0.999999999996005 & 7.99054504135233e-12 & 3.99527252067617e-12 \tabularnewline
48 & 0.999999999686606 & 6.26788520370011e-10 & 3.13394260185005e-10 \tabularnewline
49 & 0.999999968497706 & 6.30045888298112e-08 & 3.15022944149056e-08 \tabularnewline
50 & 0.999996975078445 & 6.04984311026282e-06 & 3.02492155513141e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114484&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]9.96973862224977e-07[/C][C]1.99394772444995e-06[/C][C]0.999999003026138[/C][/ROW]
[ROW][C]13[/C][C]8.49260436245877e-08[/C][C]1.69852087249175e-07[/C][C]0.999999915073956[/C][/ROW]
[ROW][C]14[/C][C]2.46545252722155e-09[/C][C]4.9309050544431e-09[/C][C]0.999999997534547[/C][/ROW]
[ROW][C]15[/C][C]4.77975522074018e-11[/C][C]9.55951044148035e-11[/C][C]0.999999999952202[/C][/ROW]
[ROW][C]16[/C][C]7.21835601031414e-13[/C][C]1.44367120206283e-12[/C][C]0.999999999999278[/C][/ROW]
[ROW][C]17[/C][C]1.79564723558843e-14[/C][C]3.59129447117687e-14[/C][C]0.999999999999982[/C][/ROW]
[ROW][C]18[/C][C]3.20455910313872e-16[/C][C]6.40911820627745e-16[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]9.98058767162516e-18[/C][C]1.99611753432503e-17[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.31542306454434e-19[/C][C]2.63084612908868e-19[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.705842943502995[/C][C]0.588314112994009[/C][C]0.294157056497005[/C][/ROW]
[ROW][C]22[/C][C]0.637633725615691[/C][C]0.724732548768618[/C][C]0.362366274384309[/C][/ROW]
[ROW][C]23[/C][C]0.54895564434901[/C][C]0.90208871130198[/C][C]0.45104435565099[/C][/ROW]
[ROW][C]24[/C][C]0.462912500910653[/C][C]0.925825001821306[/C][C]0.537087499089347[/C][/ROW]
[ROW][C]25[/C][C]0.379949463749086[/C][C]0.759898927498172[/C][C]0.620050536250914[/C][/ROW]
[ROW][C]26[/C][C]0.333257744374903[/C][C]0.666515488749805[/C][C]0.666742255625097[/C][/ROW]
[ROW][C]27[/C][C]0.258305163028266[/C][C]0.516610326056531[/C][C]0.741694836971734[/C][/ROW]
[ROW][C]28[/C][C]0.202685680245681[/C][C]0.405371360491362[/C][C]0.797314319754319[/C][/ROW]
[ROW][C]29[/C][C]0.228803130144019[/C][C]0.457606260288039[/C][C]0.77119686985598[/C][/ROW]
[ROW][C]30[/C][C]0.999576128750102[/C][C]0.000847742499796067[/C][C]0.000423871249898033[/C][/ROW]
[ROW][C]31[/C][C]0.99909151284502[/C][C]0.00181697430996150[/C][C]0.000908487154980748[/C][/ROW]
[ROW][C]32[/C][C]0.998094974771878[/C][C]0.00381005045624387[/C][C]0.00190502522812194[/C][/ROW]
[ROW][C]33[/C][C]0.996209025993802[/C][C]0.00758194801239593[/C][C]0.00379097400619796[/C][/ROW]
[ROW][C]34[/C][C]0.9998839372825[/C][C]0.000232125435000408[/C][C]0.000116062717500204[/C][/ROW]
[ROW][C]35[/C][C]0.99973095016094[/C][C]0.00053809967811941[/C][C]0.000269049839059705[/C][/ROW]
[ROW][C]36[/C][C]0.99995348929557[/C][C]9.30214088604778e-05[/C][C]4.65107044302389e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999881195113487[/C][C]0.000237609773025733[/C][C]0.000118804886512867[/C][/ROW]
[ROW][C]38[/C][C]0.999755074258572[/C][C]0.000489851482855775[/C][C]0.000244925741427888[/C][/ROW]
[ROW][C]39[/C][C]0.999362298760348[/C][C]0.00127540247930338[/C][C]0.000637701239651692[/C][/ROW]
[ROW][C]40[/C][C]0.99841836536591[/C][C]0.00316326926818024[/C][C]0.00158163463409012[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]3.93149082272239e-22[/C][C]1.96574541136119e-22[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]5.67553500437773e-21[/C][C]2.83776750218887e-21[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]3.07398365230395e-19[/C][C]1.53699182615197e-19[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]2.01560109383107e-17[/C][C]1.00780054691553e-17[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]9.70110436599107e-16[/C][C]4.85055218299554e-16[/C][/ROW]
[ROW][C]46[/C][C]0.999999999999963[/C][C]7.46333488779698e-14[/C][C]3.73166744389849e-14[/C][/ROW]
[ROW][C]47[/C][C]0.999999999996005[/C][C]7.99054504135233e-12[/C][C]3.99527252067617e-12[/C][/ROW]
[ROW][C]48[/C][C]0.999999999686606[/C][C]6.26788520370011e-10[/C][C]3.13394260185005e-10[/C][/ROW]
[ROW][C]49[/C][C]0.999999968497706[/C][C]6.30045888298112e-08[/C][C]3.15022944149056e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999996975078445[/C][C]6.04984311026282e-06[/C][C]3.02492155513141e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114484&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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	9.96973862224977e-07	1.99394772444995e-06	0.999999003026138
13	8.49260436245877e-08	1.69852087249175e-07	0.999999915073956
14	2.46545252722155e-09	4.9309050544431e-09	0.999999997534547
15	4.77975522074018e-11	9.55951044148035e-11	0.999999999952202
16	7.21835601031414e-13	1.44367120206283e-12	0.999999999999278
17	1.79564723558843e-14	3.59129447117687e-14	0.999999999999982
18	3.20455910313872e-16	6.40911820627745e-16	1
19	9.98058767162516e-18	1.99611753432503e-17	1
20	1.31542306454434e-19	2.63084612908868e-19	1
21	0.705842943502995	0.588314112994009	0.294157056497005
22	0.637633725615691	0.724732548768618	0.362366274384309
23	0.54895564434901	0.90208871130198	0.45104435565099
24	0.462912500910653	0.925825001821306	0.537087499089347
25	0.379949463749086	0.759898927498172	0.620050536250914
26	0.333257744374903	0.666515488749805	0.666742255625097
27	0.258305163028266	0.516610326056531	0.741694836971734
28	0.202685680245681	0.405371360491362	0.797314319754319
29	0.228803130144019	0.457606260288039	0.77119686985598
30	0.999576128750102	0.000847742499796067	0.000423871249898033
31	0.99909151284502	0.00181697430996150	0.000908487154980748
32	0.998094974771878	0.00381005045624387	0.00190502522812194
33	0.996209025993802	0.00758194801239593	0.00379097400619796
34	0.9998839372825	0.000232125435000408	0.000116062717500204
35	0.99973095016094	0.00053809967811941	0.000269049839059705
36	0.99995348929557	9.30214088604778e-05	4.65107044302389e-05
37	0.999881195113487	0.000237609773025733	0.000118804886512867
38	0.999755074258572	0.000489851482855775	0.000244925741427888
39	0.999362298760348	0.00127540247930338	0.000637701239651692
40	0.99841836536591	0.00316326926818024	0.00158163463409012
41	1	3.93149082272239e-22	1.96574541136119e-22
42	1	5.67553500437773e-21	2.83776750218887e-21
43	1	3.07398365230395e-19	1.53699182615197e-19
44	1	2.01560109383107e-17	1.00780054691553e-17
45	1	9.70110436599107e-16	4.85055218299554e-16
46	0.999999999999963	7.46333488779698e-14	3.73166744389849e-14
47	0.999999999996005	7.99054504135233e-12	3.99527252067617e-12
48	0.999999999686606	6.26788520370011e-10	3.13394260185005e-10
49	0.999999968497706	6.30045888298112e-08	3.15022944149056e-08
50	0.999996975078445	6.04984311026282e-06	3.02492155513141e-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=114484&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=114484&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114484&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 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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code