## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 14:43:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352144630qrxqcolu2sk4sby.htm/, Retrieved Fri, 14 Jun 2024 19:45:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186261, Retrieved Fri, 14 Jun 2024 19:45:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7] [2012-11-05 19:43:30] [9fce0523ac0e7dfdcafaec3da59cfa0a] [Current]
Feedback Forum

Post a new message
Dataseries X:
1	501	134	368	6.70	8.50	8.70
2	485	124	361	6.80	8.40	8.60
3	464	113	351	6.70	8.40	8.60
4	460	109	351	6.60	8.30	8.50
5	467	109	358	6.40	8.20	8.50
6	460	106	354	6.30	8.20	8.50
7	448	101	347	6.30	8.10	8.50
8	443	98	345	6.50	8.10	8.50
9	436	93	343	6.50	8.10	8.50
10	431	91	340	6.40	8.10	8.50
11	484	122	362	6.20	8.10	8.50
12	510	139	370	6.20	8.10	8.60
13	513	140	373	6.50	8.10	8.60
14	503	132	371	7.00	8.20	8.60
15	471	117	354	7.20	8.20	8.70
16	471	114	357	7.30	8.30	8.70
17	476	113	363	7.40	8.20	8.70
18	475	110	364	7.40	8.30	8.80
19	470	107	363	7.40	8.30	8.80
20	461	103	358	7.30	8.40	8.90
21	455	98	357	7.40	8.50	8.90
22	456	98	357	7.40	8.50	8.90
23	517	137	380	7.60	8.60	9.00
24	525	148	378	7.60	8.60	9.00
25	523	147	376	7.70	8.70	9.00
26	519	139	380	7.70	8.70	9.00
27	509	130	379	7.80	8.80	9.00
28	512	128	384	7.80	8.80	9.00
29	519	127	392	8.00	8.90	9.10
30	517	123	394	8.10	9.00	9.10
31	510	118	392	8.10	9.00	9.10
32	509	114	396	8.20	9.00	9.10
33	501	108	392	8.10	9.00	9.10
34	507	111	396	8.10	9.10	9.10
35	569	151	419	8.10	9.10	9.10
36	580	159	421	8.10	9.00	9.10
37	578	158	420	8.20	9.10	9.10
38	565	148	418	8.20	9.00	9.10
39	547	138	410	8.30	9.10	9.10
40	555	137	418	8.40	9.10	9.20
41	562	136	426	8.60	9.20	9.30
42	561	133	428	8.60	9.20	9.30
43	555	126	430	8.40	9.20	9.30
44	544	120	424	8.00	9.20	9.20
45	537	114	423	7.90	9.20	9.20
46	543	116	427	8.10	9.30	9.20
47	594	153	441	8.50	9.30	9.20
48	611	162	449	8.80	9.30	9.20
49	613	161	452	8.80	9.30	9.20
50	611	149	462	8.50	9.30	9.20
51	594	139	455	8.30	9.40	9.20
52	595	135	461	8.30	9.40	9.20
53	591	130	461	8.30	9.30	9.20
54	589	127	463	8.40	9.30	9.20
55	584	122	462	8.50	9.30	9.20
56	573	117	456	8.50	9.30	9.20
57	567	112	455	8.60	9.20	9.10
58	569	113	456	8.50	9.20	9.10
59	621	149	472	8.60	9.20	9.00
60	629	157	472	8.60	9.10	8.90
61	628	157	471	8.60	9.10	8.90
62	612	147	465	8.50	9.10	9.00
63	595	137	459	8.40	9.10	8.90
64	597	132	465	8.40	9.00	8.80
65	593	125	468	8.50	8.90	8.70
66	590	123	467	8.50	8.80	8.60
67	580	117	463	8.50	8.70	8.50
68	574	114	460	8.60	8.60	8.50
69	573	111	462	8.60	8.60	8.40
70	573	112	461	8.40	8.50	8.30
71	620	144	476	8.20	8.40	8.20
72	626	150	476	8.00	8.40	8.20
73	620	149	471	8.00	8.30	8.10
74	588	134	453	8.00	8.20	8.00
75	566	123	443	8.00	8.20	7.90
76	557	116	442	7.90	8.00	7.80
77	561	117	444	7.90	7.90	7.60
78	549	111	438	7.90	7.80	7.50
79	532	105	427	7.90	7.70	7.40
80	526	102	424	8.00	7.60	7.30
81	511	95	416	7.90	7.60	7.30
82	499	93	406	7.40	7.60	7.20
83	555	124	431	7.20	7.60	7.20
84	565	130	434	7.00	7.60	7.20
85	542	124	418	6.90	7.50	7.10
86	527	115	412	7.10	7.50	7.00
87	510	106	404	7.20	7.40	7.00
88	514	105	409	7.20	7.40	6.90
89	517	105	412	7.10	7.40	6.90
90	508	101	406	6.90	7.30	6.80
91	493	95	398	6.80	7.30	6.80
92	490	93	397	6.80	7.40	6.80
93	469	84	385	6.80	7.50	6.90
94	478	87	390	6.90	7.60	7.00
95	528	116	413	7.10	7.60	7.00
96	534	120	413	7.20	7.70	7.10
97	518	117	401	7.20	7.70	7.20
98	506	109	397	7.10	7.90	7.30
99	502	105	397	7.10	8.10	7.50
100	516	107	409	7.20	8.40	7.70
101	528	109	419	7.50	8.70	8.10
102	533	109	424	7.70	9.00	8.40
103	536	108	428	7.80	9.30	8.60
104	537	107	430	7.70	9.40	8.80
105	524	99	424	7.70	9.50	8.90
106	536	103	433	7.80	9.60	9.10
107	587	131	456	8.00	9.80	9.20
108	597	137	459	8.10	9.80	9.30
109	581	135	446	8.10	9.90	9.40
110	564	124	441	8.00	10.00	9.40
111	558	118	439	8.10	10.00	9.50
112	575	121	454	8.20	10.10	9.50
113	580	121	460	8.40	10.10	9.70
114	575	118	457	8.50	10.10	9.70
115	563	113	451	8.50	10.10	9.70
116	552	107	444	8.50	10.20	9.70
117	537	100	437	8.50	10.20	9.70
118	545	102	443	8.50	10.10	9.60
119	601	130	471	8.40	10.10	9.60
120	604	136	469	8.30	10.10	9.60
121	586	133	454	8.20	10.10	9.60
122	564	120	444	8.10	10.10	9.60
123	549	112	436	7.90	10.10	9.60
124	551	109	442	7.60	10.10	9.60
125	556	110	446	7.30	10.00	9.50
126	548	106	442	7.10	9.90	9.50
127	540	102	438	7.00	9.90	9.40
128	531	98	433	7.10	9.90	9.40
129	521	92	428	7.10	9.90	9.50
130	519	92	426	7.10	10.00	9.50
131	572	120	452	7.30	10.10	9.60
132	581	127	455	7.30	10.20	9.70
133	563	124	439	7.30	10.30	9.80
134	548	114	434	7.20	10.50	9.90
135	539	108	431	7.20	10.60	10.00
136	541	106	435	7.10	10.70	10.00
137	562	111	450	7.10	10.80	10.10
138	559	110	449	7.10	10.90	10.20
139	546	104	442	7.20	11.00	10.30
140	536	100	437	7.30	11.20	10.30
141	528	96	431	7.40	11.30	10.40
142	530	98	433	7.40	11.40	10.50
143	582	122	460	7.50	11.50	10.50
144	599	134	465	7.40	11.50	10.60
145	584	133	451	7.40	11.60	10.60

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 10 seconds R Server 'Sir Maurice George Kendall' @ kendall.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Totale_werkloosheid[t] = + 1.37490293078453 + 0.00462675587737521t + 0.99538194106348Jonger_dan_25[t] + 1.00042682197074Vanaf_25[t] -0.0766895543898539Belgi\303\253[t] -0.471005882032356Euroraad[t] + 0.39574811492089EU-27[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_werkloosheid[t] =  +  1.37490293078453 +  0.00462675587737521t +  0.99538194106348Jonger_dan_25[t] +  1.00042682197074Vanaf_25[t] -0.0766895543898539Belgi\303\253[t] -0.471005882032356Euroraad[t] +  0.39574811492089EU-27[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_werkloosheid[t] =  +  1.37490293078453 +  0.00462675587737521t +  0.99538194106348Jonger_dan_25[t] +  1.00042682197074Vanaf_25[t] -0.0766895543898539Belgi\303\253[t] -0.471005882032356Euroraad[t] +  0.39574811492089EU-27[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 Totale_werkloosheid[t] = + 1.37490293078453 + 0.00462675587737521t + 0.99538194106348Jonger_dan_25[t] + 1.00042682197074Vanaf_25[t] -0.0766895543898539Belgi\303\253[t] -0.471005882032356Euroraad[t] + 0.39574811492089EU-27[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) 1.37490293078453 0.658951 2.0865 0.038773 0.019387 t 0.00462675587737521 0.00375 1.2337 0.219401 0.109701 Jonger_dan_25 0.99538194106348 0.003406 292.2803 0 0 Vanaf_25 1.00042682197074 0.002953 338.7462 0 0 Belgi\303\253 -0.0766895543898539 0.113077 -0.6782 0.498777 0.249389 Euroraad -0.471005882032356 0.369384 -1.2751 0.204412 0.102206 EU-27 0.39574811492089 0.347338 1.1394 0.25652 0.12826

\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) & 1.37490293078453 & 0.658951 & 2.0865 & 0.038773 & 0.019387 \tabularnewline
t & 0.00462675587737521 & 0.00375 & 1.2337 & 0.219401 & 0.109701 \tabularnewline
Jonger_dan_25 & 0.99538194106348 & 0.003406 & 292.2803 & 0 & 0 \tabularnewline
Vanaf_25 & 1.00042682197074 & 0.002953 & 338.7462 & 0 & 0 \tabularnewline
Belgi\303\253 & -0.0766895543898539 & 0.113077 & -0.6782 & 0.498777 & 0.249389 \tabularnewline
Euroraad & -0.471005882032356 & 0.369384 & -1.2751 & 0.204412 & 0.102206 \tabularnewline
EU-27 & 0.39574811492089 & 0.347338 & 1.1394 & 0.25652 & 0.12826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&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]1.37490293078453[/C][C]0.658951[/C][C]2.0865[/C][C]0.038773[/C][C]0.019387[/C][/ROW]
[ROW][C]t[/C][C]0.00462675587737521[/C][C]0.00375[/C][C]1.2337[/C][C]0.219401[/C][C]0.109701[/C][/ROW]
[ROW][C]Jonger_dan_25[/C][C]0.99538194106348[/C][C]0.003406[/C][C]292.2803[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25[/C][C]1.00042682197074[/C][C]0.002953[/C][C]338.7462[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belgi\303\253[/C][C]-0.0766895543898539[/C][C]0.113077[/C][C]-0.6782[/C][C]0.498777[/C][C]0.249389[/C][/ROW]
[ROW][C]EU-27[/C][C]0.39574811492089[/C][C]0.347338[/C][C]1.1394[/C][C]0.25652[/C][C]0.12826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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) 1.37490293078453 0.658951 2.0865 0.038773 0.019387 t 0.00462675587737521 0.00375 1.2337 0.219401 0.109701 Jonger_dan_25 0.99538194106348 0.003406 292.2803 0 0 Vanaf_25 1.00042682197074 0.002953 338.7462 0 0 Belgi\303\253 -0.0766895543898539 0.113077 -0.6782 0.498777 0.249389 Euroraad -0.471005882032356 0.369384 -1.2751 0.204412 0.102206 EU-27 0.39574811492089 0.347338 1.1394 0.25652 0.12826

 Multiple Linear Regression - Regression Statistics Multiple R 0.999941073250629 R-squared 0.99988214997362 Adjusted R-squared 0.99987702605943 F-TEST (value) 195140.299547789 F-TEST (DF numerator) 6 F-TEST (DF denominator) 138 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.502471465439334 Sum Squared Residuals 34.8419051541437

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999941073250629 \tabularnewline
R-squared & 0.99988214997362 \tabularnewline
F-TEST (value) & 195140.299547789 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.502471465439334 \tabularnewline
Sum Squared Residuals & 34.8419051541437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999941073250629[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99988214997362[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]195140.299547789[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.502471465439334[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.8419051541437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 0.999941073250629 R-squared 0.99988214997362 Adjusted R-squared 0.99987702605943 F-TEST (value) 195140.299547789 F-TEST (DF numerator) 6 F-TEST (DF denominator) 138 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.502471465439334 Sum Squared Residuals 34.8419051541437

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 501 501.843418862524 -0.843418862524401 2 485 484.891095275244 0.108904724755721 3 464 463.949921415155 0.050078584844949 4 460 459.988215138929 0.0117848610714721 5 467 467.058268147682 -0.0582681476823249 6 460 460.082710747925 -0.0827107479252916 7 448 448.154540632893 -0.154540632893337 8 443 443.156830010761 -0.156830010760827 9 436 436.183693417379 -0.183693417379325 10 431 431.203944780657 -0.203944780656514 11 484 484.090139703736 -0.0901397037359613 12 510 509.059248844951 0.940751155049516 13 513 513.037531141487 -0.0375311414866017 14 503 502.992803359516 0.00719664048350291 15 471 471.083681926553 -0.0836819265532486 16 471 471.04867378151 -0.0486737815101796 17 476 476.099911160913 -0.0999111609127638 18 475 474.111293138859 0.88870686114071 19 470 470.129347249576 -0.129347249575489 20 461 461.150455310073 -0.150455310073087 21 455 455.12297599502 -0.122975995020112 22 456 455.127602750897 0.872397249102516 23 517 516.939078425988 0.0609215740115813 24 525 525.892052889623 -0.892052889622588 25 523 522.845674516853 0.154325483147211 26 519 518.888953032105 0.111046967894713 27 509 508.879945952798 0.120054047201607 28 512 511.895942936402 0.1040570635975 29 519 518.885738639393 0.114261360606811 30 517 516.854921731316 0.145078268684095 31 510 509.881785137934 0.118214862065593 32 509 509.898922462002 -0.898922462001835 33 501 499.937219239054 1.06278076094564 34 507 506.882598517802 0.117401482198104 35 569 569.712319821545 -0.712319821545423 36 580 579.727956338075 0.272043661924652 37 578 577.682004787276 0.317995212723717 38 565 565.779059076781 -0.779059076780624 39 547 547.771682302615 -0.771682302615076 40 555 554.816247549248 0.18375245075201 41 562 561.806043252239 0.193956747761323 42 561 560.825377828867 0.174622171132908 43 555 555.87852255212 -0.878522552119566 44 544 543.899397740056 0.100602259944517 45 537 536.93897498302 0.061025016979771 46 543 542.873634409826 0.126365590173689 47 594 593.682692670887 0.317307329113197 48 611 610.626164605784 0.373835394215546 49 613 612.636689886511 0.363310113489438 50 611 610.72400843565 0.275991564349463 51 594 593.740065349773 0.259934650227314 52 595 595.765725273221 -0.765725273220583 53 591 590.840542911984 0.159457088016217 54 589 589.852208533173 -0.85220853317322 55 584 583.871829806323 0.128170193676519 56 573 572.896985925059 0.103014074940984 57 567 566.92413297492 0.0758670250795623 58 569 568.932237449271 0.0677625507289914 59 621 620.730199468034 0.269800531965621 60 629 628.705407529131 0.294592470869271 61 628 627.709607463037 0.290392536962629 62 612 611.805097643387 0.194902356613413 63 595 595.821438200752 -0.821438200751641 64 597 596.859241959847 0.140758040152803 65 593 592.897332415465 0.102667584535412 66 590 589.918294243955 0.0817057560445901 67 580 579.95644784228 0.0435521577198931 68 574 574.013079941819 -0.0130799418190837 69 573 572.992839706955 0.00716029304459668 70 573 573.015285269515 -0.0152852695146464 71 620 619.901400156574 0.0985998434264505 72 626 625.89365646971 0.106343530290236 73 620 619.908292951381 0.091707048618876 74 588 586.982033572544 1.01796642745585 75 566 565.993615945524 0.00638405447621257 76 557 558.092437612339 -1.09243761233943 77 561 561.061250918441 -0.0612509184408246 78 549 549.098550872824 -0.0985508728240327 79 532 532.133716717354 -0.133716717353552 80 526 526.1507740054 -0.150774005400432 81 511 511.191981553507 -0.191981553506531 82 499 499.200346173252 -0.200346173252393 83 555 555.087821562244 -0.0878215622440683 84 565 564.081358341292 0.918641658707494 85 542 542.122059031407 -0.122059031407316 86 527 527.110774663519 -0.110774663518885 87 510 510.192981006823 -0.192981006823285 88 514 514.164785119999 -0.164785119998787 89 517 517.178361297227 -0.178361297227366 90 508 507.221763044615 0.778236955384492 91 493 493.258352533785 -0.258352533785086 92 490 490.224687997362 -0.224687997361531 93 469 469.258229643308 -0.258229643307583 94 478 477.235941600079 0.764058399921055 95 528 529.101123641246 -1.10112364124624 96 534 533.072083429227 0.927916570772599 97 518 518.125017309758 -0.125017309757566 98 506 506.117923839769 -0.117923839768749 99 502 502.12597127797 -0.12597127796992 100 516 516.056662682559 -0.0566626825586083 101 528 528.050312155312 -0.0503121553120145 102 533 533.019157780032 -0.0191577800316743 103 536 535.960288785664 0.0397112143359877 104 537 537.010105234639 -0.0101052346393137 105 524 523.041589753473 0.958410246526724 106 536 536.055965750683 -0.0559657506831727 107 587 586.871139485873 0.128860514127398 108 597 595.881244210096 1.11875578990383 109 581 580.882032621516 0.117967378484162 110 564 564.895892283077 -0.895892283077003 111 558 556.959279604685 1.04072039531487 112 575 574.901684969672 0.0983150303281993 113 580 580.97268436948 -0.972684369479816 114 575 574.982215880816 0.0177841191844497 115 563 564.007371999551 -1.0073719995511 116 552 550.989618767049 1.01038123295081 117 537 537.023584181687 -0.0235841816870423 118 545 545.029061528227 -0.0290615282269552 119 601 600.924002604501 0.0759973954985876 120 604 604.907736318257 -0.907736318257168 121 586 586.927483876822 -0.927483876822016 122 564 563.995546134606 0.00445386539424042 123 549 548.049040697087 0.950959302912638 124 551 551.093089427916 -0.0930894279156862 125 556 556.125338055768 -0.1253380557676 126 548 548.209168258589 -0.209168258589311 127 540 540.198654106277 -0.198654106276708 128 531 531.211950032607 -0.211950032607489 129 521 520.281725843742 0.718274156257616 130 519 518.238398367475 0.761601632524957 131 572 572.10195315678 -0.101953156779925 132 581 582.068008189303 -1.06800818930272 133 563 563.072134193747 -0.0721341937466972 134 548 548.07385001966 -0.0738500196601954 135 539 539.097378886533 -0.0973788865333247 136 541 541.073517415402 -0.0735174154024493 137 562 561.053930429447 0.946069570552844 138 559 559.055222645579 -0.0552226455791648 139 546 546.06937526913 -0.0693752691303587 140 536 536.988470019055 -0.988470019054668 141 528 526.993813346704 1.00618665329644 142 530 530.982531851938 -0.982531851938224 143 582 581.833079842907 0.166920157093175 144 599 598.831667768331 0.168332231669284 145 584 583.787836487351 0.21216351264897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.843418862524 & -0.843418862524401 \tabularnewline
2 & 485 & 484.891095275244 & 0.108904724755721 \tabularnewline
3 & 464 & 463.949921415155 & 0.050078584844949 \tabularnewline
4 & 460 & 459.988215138929 & 0.0117848610714721 \tabularnewline
5 & 467 & 467.058268147682 & -0.0582681476823249 \tabularnewline
6 & 460 & 460.082710747925 & -0.0827107479252916 \tabularnewline
7 & 448 & 448.154540632893 & -0.154540632893337 \tabularnewline
8 & 443 & 443.156830010761 & -0.156830010760827 \tabularnewline
9 & 436 & 436.183693417379 & -0.183693417379325 \tabularnewline
10 & 431 & 431.203944780657 & -0.203944780656514 \tabularnewline
11 & 484 & 484.090139703736 & -0.0901397037359613 \tabularnewline
12 & 510 & 509.059248844951 & 0.940751155049516 \tabularnewline
13 & 513 & 513.037531141487 & -0.0375311414866017 \tabularnewline
14 & 503 & 502.992803359516 & 0.00719664048350291 \tabularnewline
15 & 471 & 471.083681926553 & -0.0836819265532486 \tabularnewline
16 & 471 & 471.04867378151 & -0.0486737815101796 \tabularnewline
17 & 476 & 476.099911160913 & -0.0999111609127638 \tabularnewline
18 & 475 & 474.111293138859 & 0.88870686114071 \tabularnewline
19 & 470 & 470.129347249576 & -0.129347249575489 \tabularnewline
20 & 461 & 461.150455310073 & -0.150455310073087 \tabularnewline
21 & 455 & 455.12297599502 & -0.122975995020112 \tabularnewline
22 & 456 & 455.127602750897 & 0.872397249102516 \tabularnewline
23 & 517 & 516.939078425988 & 0.0609215740115813 \tabularnewline
24 & 525 & 525.892052889623 & -0.892052889622588 \tabularnewline
25 & 523 & 522.845674516853 & 0.154325483147211 \tabularnewline
26 & 519 & 518.888953032105 & 0.111046967894713 \tabularnewline
27 & 509 & 508.879945952798 & 0.120054047201607 \tabularnewline
28 & 512 & 511.895942936402 & 0.1040570635975 \tabularnewline
29 & 519 & 518.885738639393 & 0.114261360606811 \tabularnewline
30 & 517 & 516.854921731316 & 0.145078268684095 \tabularnewline
31 & 510 & 509.881785137934 & 0.118214862065593 \tabularnewline
32 & 509 & 509.898922462002 & -0.898922462001835 \tabularnewline
33 & 501 & 499.937219239054 & 1.06278076094564 \tabularnewline
34 & 507 & 506.882598517802 & 0.117401482198104 \tabularnewline
35 & 569 & 569.712319821545 & -0.712319821545423 \tabularnewline
36 & 580 & 579.727956338075 & 0.272043661924652 \tabularnewline
37 & 578 & 577.682004787276 & 0.317995212723717 \tabularnewline
38 & 565 & 565.779059076781 & -0.779059076780624 \tabularnewline
39 & 547 & 547.771682302615 & -0.771682302615076 \tabularnewline
40 & 555 & 554.816247549248 & 0.18375245075201 \tabularnewline
41 & 562 & 561.806043252239 & 0.193956747761323 \tabularnewline
42 & 561 & 560.825377828867 & 0.174622171132908 \tabularnewline
43 & 555 & 555.87852255212 & -0.878522552119566 \tabularnewline
44 & 544 & 543.899397740056 & 0.100602259944517 \tabularnewline
45 & 537 & 536.93897498302 & 0.061025016979771 \tabularnewline
46 & 543 & 542.873634409826 & 0.126365590173689 \tabularnewline
47 & 594 & 593.682692670887 & 0.317307329113197 \tabularnewline
48 & 611 & 610.626164605784 & 0.373835394215546 \tabularnewline
49 & 613 & 612.636689886511 & 0.363310113489438 \tabularnewline
50 & 611 & 610.72400843565 & 0.275991564349463 \tabularnewline
51 & 594 & 593.740065349773 & 0.259934650227314 \tabularnewline
52 & 595 & 595.765725273221 & -0.765725273220583 \tabularnewline
53 & 591 & 590.840542911984 & 0.159457088016217 \tabularnewline
54 & 589 & 589.852208533173 & -0.85220853317322 \tabularnewline
55 & 584 & 583.871829806323 & 0.128170193676519 \tabularnewline
56 & 573 & 572.896985925059 & 0.103014074940984 \tabularnewline
57 & 567 & 566.92413297492 & 0.0758670250795623 \tabularnewline
58 & 569 & 568.932237449271 & 0.0677625507289914 \tabularnewline
59 & 621 & 620.730199468034 & 0.269800531965621 \tabularnewline
60 & 629 & 628.705407529131 & 0.294592470869271 \tabularnewline
61 & 628 & 627.709607463037 & 0.290392536962629 \tabularnewline
62 & 612 & 611.805097643387 & 0.194902356613413 \tabularnewline
63 & 595 & 595.821438200752 & -0.821438200751641 \tabularnewline
64 & 597 & 596.859241959847 & 0.140758040152803 \tabularnewline
65 & 593 & 592.897332415465 & 0.102667584535412 \tabularnewline
66 & 590 & 589.918294243955 & 0.0817057560445901 \tabularnewline
67 & 580 & 579.95644784228 & 0.0435521577198931 \tabularnewline
68 & 574 & 574.013079941819 & -0.0130799418190837 \tabularnewline
69 & 573 & 572.992839706955 & 0.00716029304459668 \tabularnewline
70 & 573 & 573.015285269515 & -0.0152852695146464 \tabularnewline
71 & 620 & 619.901400156574 & 0.0985998434264505 \tabularnewline
72 & 626 & 625.89365646971 & 0.106343530290236 \tabularnewline
73 & 620 & 619.908292951381 & 0.091707048618876 \tabularnewline
74 & 588 & 586.982033572544 & 1.01796642745585 \tabularnewline
75 & 566 & 565.993615945524 & 0.00638405447621257 \tabularnewline
76 & 557 & 558.092437612339 & -1.09243761233943 \tabularnewline
77 & 561 & 561.061250918441 & -0.0612509184408246 \tabularnewline
78 & 549 & 549.098550872824 & -0.0985508728240327 \tabularnewline
79 & 532 & 532.133716717354 & -0.133716717353552 \tabularnewline
80 & 526 & 526.1507740054 & -0.150774005400432 \tabularnewline
81 & 511 & 511.191981553507 & -0.191981553506531 \tabularnewline
82 & 499 & 499.200346173252 & -0.200346173252393 \tabularnewline
83 & 555 & 555.087821562244 & -0.0878215622440683 \tabularnewline
84 & 565 & 564.081358341292 & 0.918641658707494 \tabularnewline
85 & 542 & 542.122059031407 & -0.122059031407316 \tabularnewline
86 & 527 & 527.110774663519 & -0.110774663518885 \tabularnewline
87 & 510 & 510.192981006823 & -0.192981006823285 \tabularnewline
88 & 514 & 514.164785119999 & -0.164785119998787 \tabularnewline
89 & 517 & 517.178361297227 & -0.178361297227366 \tabularnewline
90 & 508 & 507.221763044615 & 0.778236955384492 \tabularnewline
91 & 493 & 493.258352533785 & -0.258352533785086 \tabularnewline
92 & 490 & 490.224687997362 & -0.224687997361531 \tabularnewline
93 & 469 & 469.258229643308 & -0.258229643307583 \tabularnewline
94 & 478 & 477.235941600079 & 0.764058399921055 \tabularnewline
95 & 528 & 529.101123641246 & -1.10112364124624 \tabularnewline
96 & 534 & 533.072083429227 & 0.927916570772599 \tabularnewline
97 & 518 & 518.125017309758 & -0.125017309757566 \tabularnewline
98 & 506 & 506.117923839769 & -0.117923839768749 \tabularnewline
99 & 502 & 502.12597127797 & -0.12597127796992 \tabularnewline
100 & 516 & 516.056662682559 & -0.0566626825586083 \tabularnewline
101 & 528 & 528.050312155312 & -0.0503121553120145 \tabularnewline
102 & 533 & 533.019157780032 & -0.0191577800316743 \tabularnewline
103 & 536 & 535.960288785664 & 0.0397112143359877 \tabularnewline
104 & 537 & 537.010105234639 & -0.0101052346393137 \tabularnewline
105 & 524 & 523.041589753473 & 0.958410246526724 \tabularnewline
106 & 536 & 536.055965750683 & -0.0559657506831727 \tabularnewline
107 & 587 & 586.871139485873 & 0.128860514127398 \tabularnewline
108 & 597 & 595.881244210096 & 1.11875578990383 \tabularnewline
109 & 581 & 580.882032621516 & 0.117967378484162 \tabularnewline
110 & 564 & 564.895892283077 & -0.895892283077003 \tabularnewline
111 & 558 & 556.959279604685 & 1.04072039531487 \tabularnewline
112 & 575 & 574.901684969672 & 0.0983150303281993 \tabularnewline
113 & 580 & 580.97268436948 & -0.972684369479816 \tabularnewline
114 & 575 & 574.982215880816 & 0.0177841191844497 \tabularnewline
115 & 563 & 564.007371999551 & -1.0073719995511 \tabularnewline
116 & 552 & 550.989618767049 & 1.01038123295081 \tabularnewline
117 & 537 & 537.023584181687 & -0.0235841816870423 \tabularnewline
118 & 545 & 545.029061528227 & -0.0290615282269552 \tabularnewline
119 & 601 & 600.924002604501 & 0.0759973954985876 \tabularnewline
120 & 604 & 604.907736318257 & -0.907736318257168 \tabularnewline
121 & 586 & 586.927483876822 & -0.927483876822016 \tabularnewline
122 & 564 & 563.995546134606 & 0.00445386539424042 \tabularnewline
123 & 549 & 548.049040697087 & 0.950959302912638 \tabularnewline
124 & 551 & 551.093089427916 & -0.0930894279156862 \tabularnewline
125 & 556 & 556.125338055768 & -0.1253380557676 \tabularnewline
126 & 548 & 548.209168258589 & -0.209168258589311 \tabularnewline
127 & 540 & 540.198654106277 & -0.198654106276708 \tabularnewline
128 & 531 & 531.211950032607 & -0.211950032607489 \tabularnewline
129 & 521 & 520.281725843742 & 0.718274156257616 \tabularnewline
130 & 519 & 518.238398367475 & 0.761601632524957 \tabularnewline
131 & 572 & 572.10195315678 & -0.101953156779925 \tabularnewline
132 & 581 & 582.068008189303 & -1.06800818930272 \tabularnewline
133 & 563 & 563.072134193747 & -0.0721341937466972 \tabularnewline
134 & 548 & 548.07385001966 & -0.0738500196601954 \tabularnewline
135 & 539 & 539.097378886533 & -0.0973788865333247 \tabularnewline
136 & 541 & 541.073517415402 & -0.0735174154024493 \tabularnewline
137 & 562 & 561.053930429447 & 0.946069570552844 \tabularnewline
138 & 559 & 559.055222645579 & -0.0552226455791648 \tabularnewline
139 & 546 & 546.06937526913 & -0.0693752691303587 \tabularnewline
140 & 536 & 536.988470019055 & -0.988470019054668 \tabularnewline
141 & 528 & 526.993813346704 & 1.00618665329644 \tabularnewline
142 & 530 & 530.982531851938 & -0.982531851938224 \tabularnewline
143 & 582 & 581.833079842907 & 0.166920157093175 \tabularnewline
144 & 599 & 598.831667768331 & 0.168332231669284 \tabularnewline
145 & 584 & 583.787836487351 & 0.21216351264897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&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]501[/C][C]501.843418862524[/C][C]-0.843418862524401[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.891095275244[/C][C]0.108904724755721[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.949921415155[/C][C]0.050078584844949[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.988215138929[/C][C]0.0117848610714721[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.058268147682[/C][C]-0.0582681476823249[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.082710747925[/C][C]-0.0827107479252916[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.154540632893[/C][C]-0.154540632893337[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.156830010761[/C][C]-0.156830010760827[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.183693417379[/C][C]-0.183693417379325[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.203944780657[/C][C]-0.203944780656514[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.090139703736[/C][C]-0.0901397037359613[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.059248844951[/C][C]0.940751155049516[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.037531141487[/C][C]-0.0375311414866017[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.992803359516[/C][C]0.00719664048350291[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.083681926553[/C][C]-0.0836819265532486[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.04867378151[/C][C]-0.0486737815101796[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.099911160913[/C][C]-0.0999111609127638[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.111293138859[/C][C]0.88870686114071[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.129347249576[/C][C]-0.129347249575489[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.150455310073[/C][C]-0.150455310073087[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.12297599502[/C][C]-0.122975995020112[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.127602750897[/C][C]0.872397249102516[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.939078425988[/C][C]0.0609215740115813[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.892052889623[/C][C]-0.892052889622588[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.845674516853[/C][C]0.154325483147211[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.888953032105[/C][C]0.111046967894713[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.879945952798[/C][C]0.120054047201607[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.895942936402[/C][C]0.1040570635975[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.885738639393[/C][C]0.114261360606811[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.854921731316[/C][C]0.145078268684095[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.881785137934[/C][C]0.118214862065593[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.898922462002[/C][C]-0.898922462001835[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.937219239054[/C][C]1.06278076094564[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.882598517802[/C][C]0.117401482198104[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.712319821545[/C][C]-0.712319821545423[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.727956338075[/C][C]0.272043661924652[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.682004787276[/C][C]0.317995212723717[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.779059076781[/C][C]-0.779059076780624[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.771682302615[/C][C]-0.771682302615076[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.816247549248[/C][C]0.18375245075201[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.806043252239[/C][C]0.193956747761323[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.825377828867[/C][C]0.174622171132908[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.87852255212[/C][C]-0.878522552119566[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.899397740056[/C][C]0.100602259944517[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]536.93897498302[/C][C]0.061025016979771[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.873634409826[/C][C]0.126365590173689[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.682692670887[/C][C]0.317307329113197[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.626164605784[/C][C]0.373835394215546[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.636689886511[/C][C]0.363310113489438[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.72400843565[/C][C]0.275991564349463[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.740065349773[/C][C]0.259934650227314[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.765725273221[/C][C]-0.765725273220583[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.840542911984[/C][C]0.159457088016217[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.852208533173[/C][C]-0.85220853317322[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.871829806323[/C][C]0.128170193676519[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.896985925059[/C][C]0.103014074940984[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.92413297492[/C][C]0.0758670250795623[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.932237449271[/C][C]0.0677625507289914[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.730199468034[/C][C]0.269800531965621[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.705407529131[/C][C]0.294592470869271[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.709607463037[/C][C]0.290392536962629[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.805097643387[/C][C]0.194902356613413[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.821438200752[/C][C]-0.821438200751641[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.859241959847[/C][C]0.140758040152803[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.897332415465[/C][C]0.102667584535412[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.918294243955[/C][C]0.0817057560445901[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.95644784228[/C][C]0.0435521577198931[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.013079941819[/C][C]-0.0130799418190837[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]572.992839706955[/C][C]0.00716029304459668[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.015285269515[/C][C]-0.0152852695146464[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.901400156574[/C][C]0.0985998434264505[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.89365646971[/C][C]0.106343530290236[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.908292951381[/C][C]0.091707048618876[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.982033572544[/C][C]1.01796642745585[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.993615945524[/C][C]0.00638405447621257[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.092437612339[/C][C]-1.09243761233943[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.061250918441[/C][C]-0.0612509184408246[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.098550872824[/C][C]-0.0985508728240327[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.133716717354[/C][C]-0.133716717353552[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.1507740054[/C][C]-0.150774005400432[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.191981553507[/C][C]-0.191981553506531[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.200346173252[/C][C]-0.200346173252393[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.087821562244[/C][C]-0.0878215622440683[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.081358341292[/C][C]0.918641658707494[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.122059031407[/C][C]-0.122059031407316[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.110774663519[/C][C]-0.110774663518885[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.192981006823[/C][C]-0.192981006823285[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.164785119999[/C][C]-0.164785119998787[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.178361297227[/C][C]-0.178361297227366[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.221763044615[/C][C]0.778236955384492[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.258352533785[/C][C]-0.258352533785086[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.224687997362[/C][C]-0.224687997361531[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.258229643308[/C][C]-0.258229643307583[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.235941600079[/C][C]0.764058399921055[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.101123641246[/C][C]-1.10112364124624[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.072083429227[/C][C]0.927916570772599[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.125017309758[/C][C]-0.125017309757566[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.117923839769[/C][C]-0.117923839768749[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.12597127797[/C][C]-0.12597127796992[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.056662682559[/C][C]-0.0566626825586083[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.050312155312[/C][C]-0.0503121553120145[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.019157780032[/C][C]-0.0191577800316743[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.960288785664[/C][C]0.0397112143359877[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.010105234639[/C][C]-0.0101052346393137[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.041589753473[/C][C]0.958410246526724[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.055965750683[/C][C]-0.0559657506831727[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.871139485873[/C][C]0.128860514127398[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.881244210096[/C][C]1.11875578990383[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.882032621516[/C][C]0.117967378484162[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.895892283077[/C][C]-0.895892283077003[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.959279604685[/C][C]1.04072039531487[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.901684969672[/C][C]0.0983150303281993[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.97268436948[/C][C]-0.972684369479816[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.982215880816[/C][C]0.0177841191844497[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]564.007371999551[/C][C]-1.0073719995511[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.989618767049[/C][C]1.01038123295081[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.023584181687[/C][C]-0.0235841816870423[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.029061528227[/C][C]-0.0290615282269552[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.924002604501[/C][C]0.0759973954985876[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.907736318257[/C][C]-0.907736318257168[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.927483876822[/C][C]-0.927483876822016[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.995546134606[/C][C]0.00445386539424042[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.049040697087[/C][C]0.950959302912638[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.093089427916[/C][C]-0.0930894279156862[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.125338055768[/C][C]-0.1253380557676[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.209168258589[/C][C]-0.209168258589311[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.198654106277[/C][C]-0.198654106276708[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.211950032607[/C][C]-0.211950032607489[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.281725843742[/C][C]0.718274156257616[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.238398367475[/C][C]0.761601632524957[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.10195315678[/C][C]-0.101953156779925[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.068008189303[/C][C]-1.06800818930272[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.072134193747[/C][C]-0.0721341937466972[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.07385001966[/C][C]-0.0738500196601954[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.097378886533[/C][C]-0.0973788865333247[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.073517415402[/C][C]-0.0735174154024493[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.053930429447[/C][C]0.946069570552844[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.055222645579[/C][C]-0.0552226455791648[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.06937526913[/C][C]-0.0693752691303587[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.988470019055[/C][C]-0.988470019054668[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]526.993813346704[/C][C]1.00618665329644[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]530.982531851938[/C][C]-0.982531851938224[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.833079842907[/C][C]0.166920157093175[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.831667768331[/C][C]0.168332231669284[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.787836487351[/C][C]0.21216351264897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 1 501 501.843418862524 -0.843418862524401 2 485 484.891095275244 0.108904724755721 3 464 463.949921415155 0.050078584844949 4 460 459.988215138929 0.0117848610714721 5 467 467.058268147682 -0.0582681476823249 6 460 460.082710747925 -0.0827107479252916 7 448 448.154540632893 -0.154540632893337 8 443 443.156830010761 -0.156830010760827 9 436 436.183693417379 -0.183693417379325 10 431 431.203944780657 -0.203944780656514 11 484 484.090139703736 -0.0901397037359613 12 510 509.059248844951 0.940751155049516 13 513 513.037531141487 -0.0375311414866017 14 503 502.992803359516 0.00719664048350291 15 471 471.083681926553 -0.0836819265532486 16 471 471.04867378151 -0.0486737815101796 17 476 476.099911160913 -0.0999111609127638 18 475 474.111293138859 0.88870686114071 19 470 470.129347249576 -0.129347249575489 20 461 461.150455310073 -0.150455310073087 21 455 455.12297599502 -0.122975995020112 22 456 455.127602750897 0.872397249102516 23 517 516.939078425988 0.0609215740115813 24 525 525.892052889623 -0.892052889622588 25 523 522.845674516853 0.154325483147211 26 519 518.888953032105 0.111046967894713 27 509 508.879945952798 0.120054047201607 28 512 511.895942936402 0.1040570635975 29 519 518.885738639393 0.114261360606811 30 517 516.854921731316 0.145078268684095 31 510 509.881785137934 0.118214862065593 32 509 509.898922462002 -0.898922462001835 33 501 499.937219239054 1.06278076094564 34 507 506.882598517802 0.117401482198104 35 569 569.712319821545 -0.712319821545423 36 580 579.727956338075 0.272043661924652 37 578 577.682004787276 0.317995212723717 38 565 565.779059076781 -0.779059076780624 39 547 547.771682302615 -0.771682302615076 40 555 554.816247549248 0.18375245075201 41 562 561.806043252239 0.193956747761323 42 561 560.825377828867 0.174622171132908 43 555 555.87852255212 -0.878522552119566 44 544 543.899397740056 0.100602259944517 45 537 536.93897498302 0.061025016979771 46 543 542.873634409826 0.126365590173689 47 594 593.682692670887 0.317307329113197 48 611 610.626164605784 0.373835394215546 49 613 612.636689886511 0.363310113489438 50 611 610.72400843565 0.275991564349463 51 594 593.740065349773 0.259934650227314 52 595 595.765725273221 -0.765725273220583 53 591 590.840542911984 0.159457088016217 54 589 589.852208533173 -0.85220853317322 55 584 583.871829806323 0.128170193676519 56 573 572.896985925059 0.103014074940984 57 567 566.92413297492 0.0758670250795623 58 569 568.932237449271 0.0677625507289914 59 621 620.730199468034 0.269800531965621 60 629 628.705407529131 0.294592470869271 61 628 627.709607463037 0.290392536962629 62 612 611.805097643387 0.194902356613413 63 595 595.821438200752 -0.821438200751641 64 597 596.859241959847 0.140758040152803 65 593 592.897332415465 0.102667584535412 66 590 589.918294243955 0.0817057560445901 67 580 579.95644784228 0.0435521577198931 68 574 574.013079941819 -0.0130799418190837 69 573 572.992839706955 0.00716029304459668 70 573 573.015285269515 -0.0152852695146464 71 620 619.901400156574 0.0985998434264505 72 626 625.89365646971 0.106343530290236 73 620 619.908292951381 0.091707048618876 74 588 586.982033572544 1.01796642745585 75 566 565.993615945524 0.00638405447621257 76 557 558.092437612339 -1.09243761233943 77 561 561.061250918441 -0.0612509184408246 78 549 549.098550872824 -0.0985508728240327 79 532 532.133716717354 -0.133716717353552 80 526 526.1507740054 -0.150774005400432 81 511 511.191981553507 -0.191981553506531 82 499 499.200346173252 -0.200346173252393 83 555 555.087821562244 -0.0878215622440683 84 565 564.081358341292 0.918641658707494 85 542 542.122059031407 -0.122059031407316 86 527 527.110774663519 -0.110774663518885 87 510 510.192981006823 -0.192981006823285 88 514 514.164785119999 -0.164785119998787 89 517 517.178361297227 -0.178361297227366 90 508 507.221763044615 0.778236955384492 91 493 493.258352533785 -0.258352533785086 92 490 490.224687997362 -0.224687997361531 93 469 469.258229643308 -0.258229643307583 94 478 477.235941600079 0.764058399921055 95 528 529.101123641246 -1.10112364124624 96 534 533.072083429227 0.927916570772599 97 518 518.125017309758 -0.125017309757566 98 506 506.117923839769 -0.117923839768749 99 502 502.12597127797 -0.12597127796992 100 516 516.056662682559 -0.0566626825586083 101 528 528.050312155312 -0.0503121553120145 102 533 533.019157780032 -0.0191577800316743 103 536 535.960288785664 0.0397112143359877 104 537 537.010105234639 -0.0101052346393137 105 524 523.041589753473 0.958410246526724 106 536 536.055965750683 -0.0559657506831727 107 587 586.871139485873 0.128860514127398 108 597 595.881244210096 1.11875578990383 109 581 580.882032621516 0.117967378484162 110 564 564.895892283077 -0.895892283077003 111 558 556.959279604685 1.04072039531487 112 575 574.901684969672 0.0983150303281993 113 580 580.97268436948 -0.972684369479816 114 575 574.982215880816 0.0177841191844497 115 563 564.007371999551 -1.0073719995511 116 552 550.989618767049 1.01038123295081 117 537 537.023584181687 -0.0235841816870423 118 545 545.029061528227 -0.0290615282269552 119 601 600.924002604501 0.0759973954985876 120 604 604.907736318257 -0.907736318257168 121 586 586.927483876822 -0.927483876822016 122 564 563.995546134606 0.00445386539424042 123 549 548.049040697087 0.950959302912638 124 551 551.093089427916 -0.0930894279156862 125 556 556.125338055768 -0.1253380557676 126 548 548.209168258589 -0.209168258589311 127 540 540.198654106277 -0.198654106276708 128 531 531.211950032607 -0.211950032607489 129 521 520.281725843742 0.718274156257616 130 519 518.238398367475 0.761601632524957 131 572 572.10195315678 -0.101953156779925 132 581 582.068008189303 -1.06800818930272 133 563 563.072134193747 -0.0721341937466972 134 548 548.07385001966 -0.0738500196601954 135 539 539.097378886533 -0.0973788865333247 136 541 541.073517415402 -0.0735174154024493 137 562 561.053930429447 0.946069570552844 138 559 559.055222645579 -0.0552226455791648 139 546 546.06937526913 -0.0693752691303587 140 536 536.988470019055 -0.988470019054668 141 528 526.993813346704 1.00618665329644 142 530 530.982531851938 -0.982531851938224 143 582 581.833079842907 0.166920157093175 144 599 598.831667768331 0.168332231669284 145 584 583.787836487351 0.21216351264897

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0996675468690515 0.199335093738103 0.900332453130949 11 0.0533092301510969 0.106618460302194 0.946690769848903 12 0.26517813265374 0.53035626530748 0.73482186734626 13 0.217473528595902 0.434947057191804 0.782526471404098 14 0.16370167966291 0.32740335932582 0.83629832033709 15 0.105709959724514 0.211419919449029 0.894290040275486 16 0.0830854925074193 0.166170985014839 0.916914507492581 17 0.0651942339939134 0.130388467987827 0.934805766006087 18 0.185304453934333 0.370608907868666 0.814695546065667 19 0.184046404062603 0.368092808125205 0.815953595937397 20 0.153611965237978 0.307223930475956 0.846388034762022 21 0.10832298819877 0.216645976397541 0.89167701180123 22 0.17559199466243 0.35118398932486 0.82440800533757 23 0.148828643350234 0.297657286700467 0.851171356649766 24 0.267018968779541 0.534037937559081 0.732981031220459 25 0.247620055904204 0.495240111808407 0.752379944095796 26 0.192724538438485 0.38544907687697 0.807275461561515 27 0.147372629715026 0.294745259430052 0.852627370284974 28 0.11375349701258 0.227506994025159 0.88624650298742 29 0.0863555750926252 0.17271115018525 0.913644424907375 30 0.0629773795488691 0.125954759097738 0.937022620451131 31 0.045293364151326 0.0905867283026521 0.954706635848674 32 0.126704597451371 0.253409194902742 0.873295402548629 33 0.258136654352686 0.516273308705372 0.741863345647314 34 0.212074961392629 0.424149922785258 0.787925038607371 35 0.257314325798833 0.514628651597666 0.742685674201167 36 0.231727436502233 0.463454873004467 0.768272563497767 37 0.208739671638554 0.417479343277109 0.791260328361446 38 0.284103608826917 0.568207217653833 0.715896391173083 39 0.336224284408515 0.67244856881703 0.663775715591485 40 0.290850653044756 0.581701306089512 0.709149346955244 41 0.245471757377631 0.490943514755261 0.754528242622369 42 0.203253946466933 0.406507892933866 0.796746053533067 43 0.334698735540993 0.669397471081986 0.665301264459007 44 0.286814898032879 0.573629796065758 0.713185101967121 45 0.241876477772602 0.483752955545203 0.758123522227398 46 0.202918013654505 0.40583602730901 0.797081986345495 47 0.190311191278153 0.380622382556307 0.809688808721847 48 0.179997984501582 0.359995969003165 0.820002015498418 49 0.159376918183614 0.318753836367229 0.840623081816386 50 0.132814705447382 0.265629410894764 0.867185294552618 51 0.108298838628065 0.21659767725613 0.891701161371935 52 0.162357841009437 0.324715682018874 0.837642158990563 53 0.132878819305964 0.265757638611929 0.867121180694036 54 0.203632017938286 0.407264035876573 0.796367982061714 55 0.17219720002373 0.34439440004746 0.82780279997627 56 0.142060230612157 0.284120461224314 0.857939769387843 57 0.115273869141829 0.230547738283658 0.884726130858171 58 0.0926822605795258 0.185364521159052 0.907317739420474 59 0.0740095075057003 0.148019015011401 0.9259904924943 60 0.058018200367702 0.116036400735404 0.941981799632298 61 0.0450628161969541 0.0901256323939081 0.954937183803046 62 0.0345109973673797 0.0690219947347594 0.96548900263262 63 0.0834892686131639 0.166978537226328 0.916510731386836 64 0.0667031507946475 0.133406301589295 0.933296849205352 65 0.0522224288130939 0.104444857626188 0.947777571186906 66 0.0404101794028021 0.0808203588056041 0.959589820597198 67 0.0310741415747113 0.0621482831494226 0.968925858425289 68 0.0240974919060089 0.0481949838120179 0.975902508093991 69 0.0179950819305068 0.0359901638610136 0.982004918069493 70 0.0133564778535688 0.0267129557071376 0.986643522146431 71 0.00960927223132277 0.0192185444626455 0.990390727768677 72 0.00682142914579839 0.0136428582915968 0.993178570854202 73 0.00477764518698845 0.0095552903739769 0.995222354813012 74 0.00916419827418798 0.018328396548376 0.990835801725812 75 0.00712962382744328 0.0142592476548866 0.992870376172557 76 0.0321393233000551 0.0642786466001102 0.967860676699945 77 0.0243258534473067 0.0486517068946135 0.975674146552693 78 0.0182388143018657 0.0364776286037314 0.981761185698134 79 0.0136113933883814 0.0272227867767629 0.986388606611619 80 0.00993548940554786 0.0198709788110957 0.990064510594452 81 0.00734215202152588 0.0146843040430518 0.992657847978474 82 0.00583372295178705 0.0116674459035741 0.994166277048213 83 0.00420588200351352 0.00841176400702704 0.995794117996486 84 0.0087242218442066 0.0174484436884132 0.991275778155793 85 0.00650685416210978 0.0130137083242196 0.99349314583789 86 0.00462235458856873 0.00924470917713745 0.995377645411431 87 0.00338676388947975 0.00677352777895951 0.99661323611052 88 0.00234141101854164 0.00468282203708327 0.997658588981458 89 0.00160329192140799 0.00320658384281598 0.998396708078592 90 0.00301292796060018 0.00602585592120035 0.9969870720394 91 0.00224631031340226 0.00449262062680451 0.997753689686598 92 0.00159676973610552 0.00319353947221105 0.998403230263894 93 0.001286238610878 0.00257247722175601 0.998713761389122 94 0.00186391876531322 0.00372783753062644 0.998136081234687 95 0.00618258541840417 0.0123651708368083 0.993817414581596 96 0.0157955289716625 0.0315910579433251 0.984204471028337 97 0.0117862158873154 0.0235724317746307 0.988213784112685 98 0.00848165854730684 0.0169633170946137 0.991518341452693 99 0.00612753474247069 0.0122550694849414 0.993872465257529 100 0.00425645752459957 0.00851291504919914 0.9957435424754 101 0.00297174803042505 0.00594349606085009 0.997028251969575 102 0.00209631525601933 0.00419263051203866 0.997903684743981 103 0.00154267384575443 0.00308534769150887 0.998457326154246 104 0.00130395085390195 0.00260790170780391 0.998696049146098 105 0.00143761840650298 0.00287523681300597 0.998562381593497 106 0.00119207383097549 0.00238414766195099 0.998807926169024 107 0.00076261157228005 0.0015252231445601 0.99923738842772 108 0.00290160577374771 0.00580321154749542 0.997098394226252 109 0.00216946557793988 0.00433893115587977 0.99783053442206 110 0.00906936059766006 0.0181387211953201 0.99093063940234 111 0.0150522104771871 0.0301044209543741 0.984947789522813 112 0.0118065564355427 0.0236131128710853 0.988193443564457 113 0.020091131807698 0.0401822636153959 0.979908868192302 114 0.014809308352056 0.0296186167041119 0.985190691647944 115 0.0365806661840589 0.0731613323681177 0.963419333815941 116 0.0692797758524333 0.138559551704867 0.930720224147567 117 0.0505989959119294 0.101197991823859 0.949401004088071 118 0.0362789590719759 0.0725579181439518 0.963721040928024 119 0.0319595920320867 0.0639191840641734 0.968040407967913 120 0.0337896201750969 0.0675792403501937 0.966210379824903 121 0.0538228098536614 0.107645619707323 0.946177190146339 122 0.0406968432896675 0.0813936865793349 0.959303156710333 123 0.0721853235295196 0.144370647059039 0.92781467647048 124 0.0597005523207399 0.11940110464148 0.94029944767926 125 0.0578090355430646 0.115618071086129 0.942190964456935 126 0.0606958120810609 0.121391624162122 0.939304187918939 127 0.0473615335000692 0.0947230670001385 0.952638466499931 128 0.0300849097068896 0.0601698194137793 0.96991509029311 129 0.0256648535344247 0.0513297070688495 0.974335146465575 130 0.0224924341907799 0.0449848683815598 0.97750756580922 131 0.0161535145223801 0.0323070290447602 0.98384648547762 132 0.0367141601915107 0.0734283203830213 0.963285839808489 133 0.131187777916912 0.262375555833824 0.868812222083088 134 0.0743958094597251 0.14879161891945 0.925604190540275 135 0.0499967889795314 0.0999935779590629 0.950003211020469

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0996675468690515 & 0.199335093738103 & 0.900332453130949 \tabularnewline
11 & 0.0533092301510969 & 0.106618460302194 & 0.946690769848903 \tabularnewline
12 & 0.26517813265374 & 0.53035626530748 & 0.73482186734626 \tabularnewline
13 & 0.217473528595902 & 0.434947057191804 & 0.782526471404098 \tabularnewline
14 & 0.16370167966291 & 0.32740335932582 & 0.83629832033709 \tabularnewline
15 & 0.105709959724514 & 0.211419919449029 & 0.894290040275486 \tabularnewline
16 & 0.0830854925074193 & 0.166170985014839 & 0.916914507492581 \tabularnewline
17 & 0.0651942339939134 & 0.130388467987827 & 0.934805766006087 \tabularnewline
18 & 0.185304453934333 & 0.370608907868666 & 0.814695546065667 \tabularnewline
19 & 0.184046404062603 & 0.368092808125205 & 0.815953595937397 \tabularnewline
20 & 0.153611965237978 & 0.307223930475956 & 0.846388034762022 \tabularnewline
21 & 0.10832298819877 & 0.216645976397541 & 0.89167701180123 \tabularnewline
22 & 0.17559199466243 & 0.35118398932486 & 0.82440800533757 \tabularnewline
23 & 0.148828643350234 & 0.297657286700467 & 0.851171356649766 \tabularnewline
24 & 0.267018968779541 & 0.534037937559081 & 0.732981031220459 \tabularnewline
25 & 0.247620055904204 & 0.495240111808407 & 0.752379944095796 \tabularnewline
26 & 0.192724538438485 & 0.38544907687697 & 0.807275461561515 \tabularnewline
27 & 0.147372629715026 & 0.294745259430052 & 0.852627370284974 \tabularnewline
28 & 0.11375349701258 & 0.227506994025159 & 0.88624650298742 \tabularnewline
29 & 0.0863555750926252 & 0.17271115018525 & 0.913644424907375 \tabularnewline
30 & 0.0629773795488691 & 0.125954759097738 & 0.937022620451131 \tabularnewline
31 & 0.045293364151326 & 0.0905867283026521 & 0.954706635848674 \tabularnewline
32 & 0.126704597451371 & 0.253409194902742 & 0.873295402548629 \tabularnewline
33 & 0.258136654352686 & 0.516273308705372 & 0.741863345647314 \tabularnewline
34 & 0.212074961392629 & 0.424149922785258 & 0.787925038607371 \tabularnewline
35 & 0.257314325798833 & 0.514628651597666 & 0.742685674201167 \tabularnewline
36 & 0.231727436502233 & 0.463454873004467 & 0.768272563497767 \tabularnewline
37 & 0.208739671638554 & 0.417479343277109 & 0.791260328361446 \tabularnewline
38 & 0.284103608826917 & 0.568207217653833 & 0.715896391173083 \tabularnewline
39 & 0.336224284408515 & 0.67244856881703 & 0.663775715591485 \tabularnewline
40 & 0.290850653044756 & 0.581701306089512 & 0.709149346955244 \tabularnewline
41 & 0.245471757377631 & 0.490943514755261 & 0.754528242622369 \tabularnewline
42 & 0.203253946466933 & 0.406507892933866 & 0.796746053533067 \tabularnewline
43 & 0.334698735540993 & 0.669397471081986 & 0.665301264459007 \tabularnewline
44 & 0.286814898032879 & 0.573629796065758 & 0.713185101967121 \tabularnewline
45 & 0.241876477772602 & 0.483752955545203 & 0.758123522227398 \tabularnewline
46 & 0.202918013654505 & 0.40583602730901 & 0.797081986345495 \tabularnewline
47 & 0.190311191278153 & 0.380622382556307 & 0.809688808721847 \tabularnewline
48 & 0.179997984501582 & 0.359995969003165 & 0.820002015498418 \tabularnewline
49 & 0.159376918183614 & 0.318753836367229 & 0.840623081816386 \tabularnewline
50 & 0.132814705447382 & 0.265629410894764 & 0.867185294552618 \tabularnewline
51 & 0.108298838628065 & 0.21659767725613 & 0.891701161371935 \tabularnewline
52 & 0.162357841009437 & 0.324715682018874 & 0.837642158990563 \tabularnewline
53 & 0.132878819305964 & 0.265757638611929 & 0.867121180694036 \tabularnewline
54 & 0.203632017938286 & 0.407264035876573 & 0.796367982061714 \tabularnewline
55 & 0.17219720002373 & 0.34439440004746 & 0.82780279997627 \tabularnewline
56 & 0.142060230612157 & 0.284120461224314 & 0.857939769387843 \tabularnewline
57 & 0.115273869141829 & 0.230547738283658 & 0.884726130858171 \tabularnewline
58 & 0.0926822605795258 & 0.185364521159052 & 0.907317739420474 \tabularnewline
59 & 0.0740095075057003 & 0.148019015011401 & 0.9259904924943 \tabularnewline
60 & 0.058018200367702 & 0.116036400735404 & 0.941981799632298 \tabularnewline
61 & 0.0450628161969541 & 0.0901256323939081 & 0.954937183803046 \tabularnewline
62 & 0.0345109973673797 & 0.0690219947347594 & 0.96548900263262 \tabularnewline
63 & 0.0834892686131639 & 0.166978537226328 & 0.916510731386836 \tabularnewline
64 & 0.0667031507946475 & 0.133406301589295 & 0.933296849205352 \tabularnewline
65 & 0.0522224288130939 & 0.104444857626188 & 0.947777571186906 \tabularnewline
66 & 0.0404101794028021 & 0.0808203588056041 & 0.959589820597198 \tabularnewline
67 & 0.0310741415747113 & 0.0621482831494226 & 0.968925858425289 \tabularnewline
68 & 0.0240974919060089 & 0.0481949838120179 & 0.975902508093991 \tabularnewline
69 & 0.0179950819305068 & 0.0359901638610136 & 0.982004918069493 \tabularnewline
70 & 0.0133564778535688 & 0.0267129557071376 & 0.986643522146431 \tabularnewline
71 & 0.00960927223132277 & 0.0192185444626455 & 0.990390727768677 \tabularnewline
72 & 0.00682142914579839 & 0.0136428582915968 & 0.993178570854202 \tabularnewline
73 & 0.00477764518698845 & 0.0095552903739769 & 0.995222354813012 \tabularnewline
74 & 0.00916419827418798 & 0.018328396548376 & 0.990835801725812 \tabularnewline
75 & 0.00712962382744328 & 0.0142592476548866 & 0.992870376172557 \tabularnewline
76 & 0.0321393233000551 & 0.0642786466001102 & 0.967860676699945 \tabularnewline
77 & 0.0243258534473067 & 0.0486517068946135 & 0.975674146552693 \tabularnewline
78 & 0.0182388143018657 & 0.0364776286037314 & 0.981761185698134 \tabularnewline
79 & 0.0136113933883814 & 0.0272227867767629 & 0.986388606611619 \tabularnewline
80 & 0.00993548940554786 & 0.0198709788110957 & 0.990064510594452 \tabularnewline
81 & 0.00734215202152588 & 0.0146843040430518 & 0.992657847978474 \tabularnewline
82 & 0.00583372295178705 & 0.0116674459035741 & 0.994166277048213 \tabularnewline
83 & 0.00420588200351352 & 0.00841176400702704 & 0.995794117996486 \tabularnewline
84 & 0.0087242218442066 & 0.0174484436884132 & 0.991275778155793 \tabularnewline
85 & 0.00650685416210978 & 0.0130137083242196 & 0.99349314583789 \tabularnewline
86 & 0.00462235458856873 & 0.00924470917713745 & 0.995377645411431 \tabularnewline
87 & 0.00338676388947975 & 0.00677352777895951 & 0.99661323611052 \tabularnewline
88 & 0.00234141101854164 & 0.00468282203708327 & 0.997658588981458 \tabularnewline
89 & 0.00160329192140799 & 0.00320658384281598 & 0.998396708078592 \tabularnewline
90 & 0.00301292796060018 & 0.00602585592120035 & 0.9969870720394 \tabularnewline
91 & 0.00224631031340226 & 0.00449262062680451 & 0.997753689686598 \tabularnewline
92 & 0.00159676973610552 & 0.00319353947221105 & 0.998403230263894 \tabularnewline
93 & 0.001286238610878 & 0.00257247722175601 & 0.998713761389122 \tabularnewline
94 & 0.00186391876531322 & 0.00372783753062644 & 0.998136081234687 \tabularnewline
95 & 0.00618258541840417 & 0.0123651708368083 & 0.993817414581596 \tabularnewline
96 & 0.0157955289716625 & 0.0315910579433251 & 0.984204471028337 \tabularnewline
97 & 0.0117862158873154 & 0.0235724317746307 & 0.988213784112685 \tabularnewline
98 & 0.00848165854730684 & 0.0169633170946137 & 0.991518341452693 \tabularnewline
99 & 0.00612753474247069 & 0.0122550694849414 & 0.993872465257529 \tabularnewline
100 & 0.00425645752459957 & 0.00851291504919914 & 0.9957435424754 \tabularnewline
101 & 0.00297174803042505 & 0.00594349606085009 & 0.997028251969575 \tabularnewline
102 & 0.00209631525601933 & 0.00419263051203866 & 0.997903684743981 \tabularnewline
103 & 0.00154267384575443 & 0.00308534769150887 & 0.998457326154246 \tabularnewline
104 & 0.00130395085390195 & 0.00260790170780391 & 0.998696049146098 \tabularnewline
105 & 0.00143761840650298 & 0.00287523681300597 & 0.998562381593497 \tabularnewline
106 & 0.00119207383097549 & 0.00238414766195099 & 0.998807926169024 \tabularnewline
107 & 0.00076261157228005 & 0.0015252231445601 & 0.99923738842772 \tabularnewline
108 & 0.00290160577374771 & 0.00580321154749542 & 0.997098394226252 \tabularnewline
109 & 0.00216946557793988 & 0.00433893115587977 & 0.99783053442206 \tabularnewline
110 & 0.00906936059766006 & 0.0181387211953201 & 0.99093063940234 \tabularnewline
111 & 0.0150522104771871 & 0.0301044209543741 & 0.984947789522813 \tabularnewline
112 & 0.0118065564355427 & 0.0236131128710853 & 0.988193443564457 \tabularnewline
113 & 0.020091131807698 & 0.0401822636153959 & 0.979908868192302 \tabularnewline
114 & 0.014809308352056 & 0.0296186167041119 & 0.985190691647944 \tabularnewline
115 & 0.0365806661840589 & 0.0731613323681177 & 0.963419333815941 \tabularnewline
116 & 0.0692797758524333 & 0.138559551704867 & 0.930720224147567 \tabularnewline
117 & 0.0505989959119294 & 0.101197991823859 & 0.949401004088071 \tabularnewline
118 & 0.0362789590719759 & 0.0725579181439518 & 0.963721040928024 \tabularnewline
119 & 0.0319595920320867 & 0.0639191840641734 & 0.968040407967913 \tabularnewline
120 & 0.0337896201750969 & 0.0675792403501937 & 0.966210379824903 \tabularnewline
121 & 0.0538228098536614 & 0.107645619707323 & 0.946177190146339 \tabularnewline
122 & 0.0406968432896675 & 0.0813936865793349 & 0.959303156710333 \tabularnewline
123 & 0.0721853235295196 & 0.144370647059039 & 0.92781467647048 \tabularnewline
124 & 0.0597005523207399 & 0.11940110464148 & 0.94029944767926 \tabularnewline
125 & 0.0578090355430646 & 0.115618071086129 & 0.942190964456935 \tabularnewline
126 & 0.0606958120810609 & 0.121391624162122 & 0.939304187918939 \tabularnewline
127 & 0.0473615335000692 & 0.0947230670001385 & 0.952638466499931 \tabularnewline
128 & 0.0300849097068896 & 0.0601698194137793 & 0.96991509029311 \tabularnewline
129 & 0.0256648535344247 & 0.0513297070688495 & 0.974335146465575 \tabularnewline
130 & 0.0224924341907799 & 0.0449848683815598 & 0.97750756580922 \tabularnewline
131 & 0.0161535145223801 & 0.0323070290447602 & 0.98384648547762 \tabularnewline
132 & 0.0367141601915107 & 0.0734283203830213 & 0.963285839808489 \tabularnewline
133 & 0.131187777916912 & 0.262375555833824 & 0.868812222083088 \tabularnewline
134 & 0.0743958094597251 & 0.14879161891945 & 0.925604190540275 \tabularnewline
135 & 0.0499967889795314 & 0.0999935779590629 & 0.950003211020469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&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]10[/C][C]0.0996675468690515[/C][C]0.199335093738103[/C][C]0.900332453130949[/C][/ROW]
[ROW][C]11[/C][C]0.0533092301510969[/C][C]0.106618460302194[/C][C]0.946690769848903[/C][/ROW]
[ROW][C]12[/C][C]0.26517813265374[/C][C]0.53035626530748[/C][C]0.73482186734626[/C][/ROW]
[ROW][C]13[/C][C]0.217473528595902[/C][C]0.434947057191804[/C][C]0.782526471404098[/C][/ROW]
[ROW][C]14[/C][C]0.16370167966291[/C][C]0.32740335932582[/C][C]0.83629832033709[/C][/ROW]
[ROW][C]15[/C][C]0.105709959724514[/C][C]0.211419919449029[/C][C]0.894290040275486[/C][/ROW]
[ROW][C]16[/C][C]0.0830854925074193[/C][C]0.166170985014839[/C][C]0.916914507492581[/C][/ROW]
[ROW][C]17[/C][C]0.0651942339939134[/C][C]0.130388467987827[/C][C]0.934805766006087[/C][/ROW]
[ROW][C]18[/C][C]0.185304453934333[/C][C]0.370608907868666[/C][C]0.814695546065667[/C][/ROW]
[ROW][C]19[/C][C]0.184046404062603[/C][C]0.368092808125205[/C][C]0.815953595937397[/C][/ROW]
[ROW][C]20[/C][C]0.153611965237978[/C][C]0.307223930475956[/C][C]0.846388034762022[/C][/ROW]
[ROW][C]21[/C][C]0.10832298819877[/C][C]0.216645976397541[/C][C]0.89167701180123[/C][/ROW]
[ROW][C]22[/C][C]0.17559199466243[/C][C]0.35118398932486[/C][C]0.82440800533757[/C][/ROW]
[ROW][C]23[/C][C]0.148828643350234[/C][C]0.297657286700467[/C][C]0.851171356649766[/C][/ROW]
[ROW][C]24[/C][C]0.267018968779541[/C][C]0.534037937559081[/C][C]0.732981031220459[/C][/ROW]
[ROW][C]25[/C][C]0.247620055904204[/C][C]0.495240111808407[/C][C]0.752379944095796[/C][/ROW]
[ROW][C]26[/C][C]0.192724538438485[/C][C]0.38544907687697[/C][C]0.807275461561515[/C][/ROW]
[ROW][C]27[/C][C]0.147372629715026[/C][C]0.294745259430052[/C][C]0.852627370284974[/C][/ROW]
[ROW][C]28[/C][C]0.11375349701258[/C][C]0.227506994025159[/C][C]0.88624650298742[/C][/ROW]
[ROW][C]29[/C][C]0.0863555750926252[/C][C]0.17271115018525[/C][C]0.913644424907375[/C][/ROW]
[ROW][C]30[/C][C]0.0629773795488691[/C][C]0.125954759097738[/C][C]0.937022620451131[/C][/ROW]
[ROW][C]31[/C][C]0.045293364151326[/C][C]0.0905867283026521[/C][C]0.954706635848674[/C][/ROW]
[ROW][C]32[/C][C]0.126704597451371[/C][C]0.253409194902742[/C][C]0.873295402548629[/C][/ROW]
[ROW][C]33[/C][C]0.258136654352686[/C][C]0.516273308705372[/C][C]0.741863345647314[/C][/ROW]
[ROW][C]34[/C][C]0.212074961392629[/C][C]0.424149922785258[/C][C]0.787925038607371[/C][/ROW]
[ROW][C]35[/C][C]0.257314325798833[/C][C]0.514628651597666[/C][C]0.742685674201167[/C][/ROW]
[ROW][C]36[/C][C]0.231727436502233[/C][C]0.463454873004467[/C][C]0.768272563497767[/C][/ROW]
[ROW][C]37[/C][C]0.208739671638554[/C][C]0.417479343277109[/C][C]0.791260328361446[/C][/ROW]
[ROW][C]38[/C][C]0.284103608826917[/C][C]0.568207217653833[/C][C]0.715896391173083[/C][/ROW]
[ROW][C]39[/C][C]0.336224284408515[/C][C]0.67244856881703[/C][C]0.663775715591485[/C][/ROW]
[ROW][C]40[/C][C]0.290850653044756[/C][C]0.581701306089512[/C][C]0.709149346955244[/C][/ROW]
[ROW][C]41[/C][C]0.245471757377631[/C][C]0.490943514755261[/C][C]0.754528242622369[/C][/ROW]
[ROW][C]42[/C][C]0.203253946466933[/C][C]0.406507892933866[/C][C]0.796746053533067[/C][/ROW]
[ROW][C]43[/C][C]0.334698735540993[/C][C]0.669397471081986[/C][C]0.665301264459007[/C][/ROW]
[ROW][C]44[/C][C]0.286814898032879[/C][C]0.573629796065758[/C][C]0.713185101967121[/C][/ROW]
[ROW][C]45[/C][C]0.241876477772602[/C][C]0.483752955545203[/C][C]0.758123522227398[/C][/ROW]
[ROW][C]46[/C][C]0.202918013654505[/C][C]0.40583602730901[/C][C]0.797081986345495[/C][/ROW]
[ROW][C]47[/C][C]0.190311191278153[/C][C]0.380622382556307[/C][C]0.809688808721847[/C][/ROW]
[ROW][C]48[/C][C]0.179997984501582[/C][C]0.359995969003165[/C][C]0.820002015498418[/C][/ROW]
[ROW][C]49[/C][C]0.159376918183614[/C][C]0.318753836367229[/C][C]0.840623081816386[/C][/ROW]
[ROW][C]50[/C][C]0.132814705447382[/C][C]0.265629410894764[/C][C]0.867185294552618[/C][/ROW]
[ROW][C]51[/C][C]0.108298838628065[/C][C]0.21659767725613[/C][C]0.891701161371935[/C][/ROW]
[ROW][C]52[/C][C]0.162357841009437[/C][C]0.324715682018874[/C][C]0.837642158990563[/C][/ROW]
[ROW][C]53[/C][C]0.132878819305964[/C][C]0.265757638611929[/C][C]0.867121180694036[/C][/ROW]
[ROW][C]54[/C][C]0.203632017938286[/C][C]0.407264035876573[/C][C]0.796367982061714[/C][/ROW]
[ROW][C]55[/C][C]0.17219720002373[/C][C]0.34439440004746[/C][C]0.82780279997627[/C][/ROW]
[ROW][C]56[/C][C]0.142060230612157[/C][C]0.284120461224314[/C][C]0.857939769387843[/C][/ROW]
[ROW][C]57[/C][C]0.115273869141829[/C][C]0.230547738283658[/C][C]0.884726130858171[/C][/ROW]
[ROW][C]58[/C][C]0.0926822605795258[/C][C]0.185364521159052[/C][C]0.907317739420474[/C][/ROW]
[ROW][C]59[/C][C]0.0740095075057003[/C][C]0.148019015011401[/C][C]0.9259904924943[/C][/ROW]
[ROW][C]60[/C][C]0.058018200367702[/C][C]0.116036400735404[/C][C]0.941981799632298[/C][/ROW]
[ROW][C]61[/C][C]0.0450628161969541[/C][C]0.0901256323939081[/C][C]0.954937183803046[/C][/ROW]
[ROW][C]62[/C][C]0.0345109973673797[/C][C]0.0690219947347594[/C][C]0.96548900263262[/C][/ROW]
[ROW][C]63[/C][C]0.0834892686131639[/C][C]0.166978537226328[/C][C]0.916510731386836[/C][/ROW]
[ROW][C]64[/C][C]0.0667031507946475[/C][C]0.133406301589295[/C][C]0.933296849205352[/C][/ROW]
[ROW][C]65[/C][C]0.0522224288130939[/C][C]0.104444857626188[/C][C]0.947777571186906[/C][/ROW]
[ROW][C]66[/C][C]0.0404101794028021[/C][C]0.0808203588056041[/C][C]0.959589820597198[/C][/ROW]
[ROW][C]67[/C][C]0.0310741415747113[/C][C]0.0621482831494226[/C][C]0.968925858425289[/C][/ROW]
[ROW][C]68[/C][C]0.0240974919060089[/C][C]0.0481949838120179[/C][C]0.975902508093991[/C][/ROW]
[ROW][C]69[/C][C]0.0179950819305068[/C][C]0.0359901638610136[/C][C]0.982004918069493[/C][/ROW]
[ROW][C]70[/C][C]0.0133564778535688[/C][C]0.0267129557071376[/C][C]0.986643522146431[/C][/ROW]
[ROW][C]71[/C][C]0.00960927223132277[/C][C]0.0192185444626455[/C][C]0.990390727768677[/C][/ROW]
[ROW][C]72[/C][C]0.00682142914579839[/C][C]0.0136428582915968[/C][C]0.993178570854202[/C][/ROW]
[ROW][C]73[/C][C]0.00477764518698845[/C][C]0.0095552903739769[/C][C]0.995222354813012[/C][/ROW]
[ROW][C]74[/C][C]0.00916419827418798[/C][C]0.018328396548376[/C][C]0.990835801725812[/C][/ROW]
[ROW][C]75[/C][C]0.00712962382744328[/C][C]0.0142592476548866[/C][C]0.992870376172557[/C][/ROW]
[ROW][C]76[/C][C]0.0321393233000551[/C][C]0.0642786466001102[/C][C]0.967860676699945[/C][/ROW]
[ROW][C]77[/C][C]0.0243258534473067[/C][C]0.0486517068946135[/C][C]0.975674146552693[/C][/ROW]
[ROW][C]78[/C][C]0.0182388143018657[/C][C]0.0364776286037314[/C][C]0.981761185698134[/C][/ROW]
[ROW][C]79[/C][C]0.0136113933883814[/C][C]0.0272227867767629[/C][C]0.986388606611619[/C][/ROW]
[ROW][C]80[/C][C]0.00993548940554786[/C][C]0.0198709788110957[/C][C]0.990064510594452[/C][/ROW]
[ROW][C]81[/C][C]0.00734215202152588[/C][C]0.0146843040430518[/C][C]0.992657847978474[/C][/ROW]
[ROW][C]82[/C][C]0.00583372295178705[/C][C]0.0116674459035741[/C][C]0.994166277048213[/C][/ROW]
[ROW][C]83[/C][C]0.00420588200351352[/C][C]0.00841176400702704[/C][C]0.995794117996486[/C][/ROW]
[ROW][C]84[/C][C]0.0087242218442066[/C][C]0.0174484436884132[/C][C]0.991275778155793[/C][/ROW]
[ROW][C]85[/C][C]0.00650685416210978[/C][C]0.0130137083242196[/C][C]0.99349314583789[/C][/ROW]
[ROW][C]86[/C][C]0.00462235458856873[/C][C]0.00924470917713745[/C][C]0.995377645411431[/C][/ROW]
[ROW][C]87[/C][C]0.00338676388947975[/C][C]0.00677352777895951[/C][C]0.99661323611052[/C][/ROW]
[ROW][C]88[/C][C]0.00234141101854164[/C][C]0.00468282203708327[/C][C]0.997658588981458[/C][/ROW]
[ROW][C]89[/C][C]0.00160329192140799[/C][C]0.00320658384281598[/C][C]0.998396708078592[/C][/ROW]
[ROW][C]90[/C][C]0.00301292796060018[/C][C]0.00602585592120035[/C][C]0.9969870720394[/C][/ROW]
[ROW][C]91[/C][C]0.00224631031340226[/C][C]0.00449262062680451[/C][C]0.997753689686598[/C][/ROW]
[ROW][C]92[/C][C]0.00159676973610552[/C][C]0.00319353947221105[/C][C]0.998403230263894[/C][/ROW]
[ROW][C]93[/C][C]0.001286238610878[/C][C]0.00257247722175601[/C][C]0.998713761389122[/C][/ROW]
[ROW][C]94[/C][C]0.00186391876531322[/C][C]0.00372783753062644[/C][C]0.998136081234687[/C][/ROW]
[ROW][C]95[/C][C]0.00618258541840417[/C][C]0.0123651708368083[/C][C]0.993817414581596[/C][/ROW]
[ROW][C]96[/C][C]0.0157955289716625[/C][C]0.0315910579433251[/C][C]0.984204471028337[/C][/ROW]
[ROW][C]97[/C][C]0.0117862158873154[/C][C]0.0235724317746307[/C][C]0.988213784112685[/C][/ROW]
[ROW][C]98[/C][C]0.00848165854730684[/C][C]0.0169633170946137[/C][C]0.991518341452693[/C][/ROW]
[ROW][C]99[/C][C]0.00612753474247069[/C][C]0.0122550694849414[/C][C]0.993872465257529[/C][/ROW]
[ROW][C]100[/C][C]0.00425645752459957[/C][C]0.00851291504919914[/C][C]0.9957435424754[/C][/ROW]
[ROW][C]101[/C][C]0.00297174803042505[/C][C]0.00594349606085009[/C][C]0.997028251969575[/C][/ROW]
[ROW][C]102[/C][C]0.00209631525601933[/C][C]0.00419263051203866[/C][C]0.997903684743981[/C][/ROW]
[ROW][C]103[/C][C]0.00154267384575443[/C][C]0.00308534769150887[/C][C]0.998457326154246[/C][/ROW]
[ROW][C]104[/C][C]0.00130395085390195[/C][C]0.00260790170780391[/C][C]0.998696049146098[/C][/ROW]
[ROW][C]105[/C][C]0.00143761840650298[/C][C]0.00287523681300597[/C][C]0.998562381593497[/C][/ROW]
[ROW][C]106[/C][C]0.00119207383097549[/C][C]0.00238414766195099[/C][C]0.998807926169024[/C][/ROW]
[ROW][C]107[/C][C]0.00076261157228005[/C][C]0.0015252231445601[/C][C]0.99923738842772[/C][/ROW]
[ROW][C]108[/C][C]0.00290160577374771[/C][C]0.00580321154749542[/C][C]0.997098394226252[/C][/ROW]
[ROW][C]109[/C][C]0.00216946557793988[/C][C]0.00433893115587977[/C][C]0.99783053442206[/C][/ROW]
[ROW][C]110[/C][C]0.00906936059766006[/C][C]0.0181387211953201[/C][C]0.99093063940234[/C][/ROW]
[ROW][C]111[/C][C]0.0150522104771871[/C][C]0.0301044209543741[/C][C]0.984947789522813[/C][/ROW]
[ROW][C]112[/C][C]0.0118065564355427[/C][C]0.0236131128710853[/C][C]0.988193443564457[/C][/ROW]
[ROW][C]113[/C][C]0.020091131807698[/C][C]0.0401822636153959[/C][C]0.979908868192302[/C][/ROW]
[ROW][C]114[/C][C]0.014809308352056[/C][C]0.0296186167041119[/C][C]0.985190691647944[/C][/ROW]
[ROW][C]115[/C][C]0.0365806661840589[/C][C]0.0731613323681177[/C][C]0.963419333815941[/C][/ROW]
[ROW][C]116[/C][C]0.0692797758524333[/C][C]0.138559551704867[/C][C]0.930720224147567[/C][/ROW]
[ROW][C]117[/C][C]0.0505989959119294[/C][C]0.101197991823859[/C][C]0.949401004088071[/C][/ROW]
[ROW][C]118[/C][C]0.0362789590719759[/C][C]0.0725579181439518[/C][C]0.963721040928024[/C][/ROW]
[ROW][C]119[/C][C]0.0319595920320867[/C][C]0.0639191840641734[/C][C]0.968040407967913[/C][/ROW]
[ROW][C]120[/C][C]0.0337896201750969[/C][C]0.0675792403501937[/C][C]0.966210379824903[/C][/ROW]
[ROW][C]121[/C][C]0.0538228098536614[/C][C]0.107645619707323[/C][C]0.946177190146339[/C][/ROW]
[ROW][C]122[/C][C]0.0406968432896675[/C][C]0.0813936865793349[/C][C]0.959303156710333[/C][/ROW]
[ROW][C]123[/C][C]0.0721853235295196[/C][C]0.144370647059039[/C][C]0.92781467647048[/C][/ROW]
[ROW][C]124[/C][C]0.0597005523207399[/C][C]0.11940110464148[/C][C]0.94029944767926[/C][/ROW]
[ROW][C]125[/C][C]0.0578090355430646[/C][C]0.115618071086129[/C][C]0.942190964456935[/C][/ROW]
[ROW][C]126[/C][C]0.0606958120810609[/C][C]0.121391624162122[/C][C]0.939304187918939[/C][/ROW]
[ROW][C]127[/C][C]0.0473615335000692[/C][C]0.0947230670001385[/C][C]0.952638466499931[/C][/ROW]
[ROW][C]128[/C][C]0.0300849097068896[/C][C]0.0601698194137793[/C][C]0.96991509029311[/C][/ROW]
[ROW][C]129[/C][C]0.0256648535344247[/C][C]0.0513297070688495[/C][C]0.974335146465575[/C][/ROW]
[ROW][C]130[/C][C]0.0224924341907799[/C][C]0.0449848683815598[/C][C]0.97750756580922[/C][/ROW]
[ROW][C]131[/C][C]0.0161535145223801[/C][C]0.0323070290447602[/C][C]0.98384648547762[/C][/ROW]
[ROW][C]132[/C][C]0.0367141601915107[/C][C]0.0734283203830213[/C][C]0.963285839808489[/C][/ROW]
[ROW][C]133[/C][C]0.131187777916912[/C][C]0.262375555833824[/C][C]0.868812222083088[/C][/ROW]
[ROW][C]134[/C][C]0.0743958094597251[/C][C]0.14879161891945[/C][C]0.925604190540275[/C][/ROW]
[ROW][C]135[/C][C]0.0499967889795314[/C][C]0.0999935779590629[/C][C]0.950003211020469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 10 0.0996675468690515 0.199335093738103 0.900332453130949 11 0.0533092301510969 0.106618460302194 0.946690769848903 12 0.26517813265374 0.53035626530748 0.73482186734626 13 0.217473528595902 0.434947057191804 0.782526471404098 14 0.16370167966291 0.32740335932582 0.83629832033709 15 0.105709959724514 0.211419919449029 0.894290040275486 16 0.0830854925074193 0.166170985014839 0.916914507492581 17 0.0651942339939134 0.130388467987827 0.934805766006087 18 0.185304453934333 0.370608907868666 0.814695546065667 19 0.184046404062603 0.368092808125205 0.815953595937397 20 0.153611965237978 0.307223930475956 0.846388034762022 21 0.10832298819877 0.216645976397541 0.89167701180123 22 0.17559199466243 0.35118398932486 0.82440800533757 23 0.148828643350234 0.297657286700467 0.851171356649766 24 0.267018968779541 0.534037937559081 0.732981031220459 25 0.247620055904204 0.495240111808407 0.752379944095796 26 0.192724538438485 0.38544907687697 0.807275461561515 27 0.147372629715026 0.294745259430052 0.852627370284974 28 0.11375349701258 0.227506994025159 0.88624650298742 29 0.0863555750926252 0.17271115018525 0.913644424907375 30 0.0629773795488691 0.125954759097738 0.937022620451131 31 0.045293364151326 0.0905867283026521 0.954706635848674 32 0.126704597451371 0.253409194902742 0.873295402548629 33 0.258136654352686 0.516273308705372 0.741863345647314 34 0.212074961392629 0.424149922785258 0.787925038607371 35 0.257314325798833 0.514628651597666 0.742685674201167 36 0.231727436502233 0.463454873004467 0.768272563497767 37 0.208739671638554 0.417479343277109 0.791260328361446 38 0.284103608826917 0.568207217653833 0.715896391173083 39 0.336224284408515 0.67244856881703 0.663775715591485 40 0.290850653044756 0.581701306089512 0.709149346955244 41 0.245471757377631 0.490943514755261 0.754528242622369 42 0.203253946466933 0.406507892933866 0.796746053533067 43 0.334698735540993 0.669397471081986 0.665301264459007 44 0.286814898032879 0.573629796065758 0.713185101967121 45 0.241876477772602 0.483752955545203 0.758123522227398 46 0.202918013654505 0.40583602730901 0.797081986345495 47 0.190311191278153 0.380622382556307 0.809688808721847 48 0.179997984501582 0.359995969003165 0.820002015498418 49 0.159376918183614 0.318753836367229 0.840623081816386 50 0.132814705447382 0.265629410894764 0.867185294552618 51 0.108298838628065 0.21659767725613 0.891701161371935 52 0.162357841009437 0.324715682018874 0.837642158990563 53 0.132878819305964 0.265757638611929 0.867121180694036 54 0.203632017938286 0.407264035876573 0.796367982061714 55 0.17219720002373 0.34439440004746 0.82780279997627 56 0.142060230612157 0.284120461224314 0.857939769387843 57 0.115273869141829 0.230547738283658 0.884726130858171 58 0.0926822605795258 0.185364521159052 0.907317739420474 59 0.0740095075057003 0.148019015011401 0.9259904924943 60 0.058018200367702 0.116036400735404 0.941981799632298 61 0.0450628161969541 0.0901256323939081 0.954937183803046 62 0.0345109973673797 0.0690219947347594 0.96548900263262 63 0.0834892686131639 0.166978537226328 0.916510731386836 64 0.0667031507946475 0.133406301589295 0.933296849205352 65 0.0522224288130939 0.104444857626188 0.947777571186906 66 0.0404101794028021 0.0808203588056041 0.959589820597198 67 0.0310741415747113 0.0621482831494226 0.968925858425289 68 0.0240974919060089 0.0481949838120179 0.975902508093991 69 0.0179950819305068 0.0359901638610136 0.982004918069493 70 0.0133564778535688 0.0267129557071376 0.986643522146431 71 0.00960927223132277 0.0192185444626455 0.990390727768677 72 0.00682142914579839 0.0136428582915968 0.993178570854202 73 0.00477764518698845 0.0095552903739769 0.995222354813012 74 0.00916419827418798 0.018328396548376 0.990835801725812 75 0.00712962382744328 0.0142592476548866 0.992870376172557 76 0.0321393233000551 0.0642786466001102 0.967860676699945 77 0.0243258534473067 0.0486517068946135 0.975674146552693 78 0.0182388143018657 0.0364776286037314 0.981761185698134 79 0.0136113933883814 0.0272227867767629 0.986388606611619 80 0.00993548940554786 0.0198709788110957 0.990064510594452 81 0.00734215202152588 0.0146843040430518 0.992657847978474 82 0.00583372295178705 0.0116674459035741 0.994166277048213 83 0.00420588200351352 0.00841176400702704 0.995794117996486 84 0.0087242218442066 0.0174484436884132 0.991275778155793 85 0.00650685416210978 0.0130137083242196 0.99349314583789 86 0.00462235458856873 0.00924470917713745 0.995377645411431 87 0.00338676388947975 0.00677352777895951 0.99661323611052 88 0.00234141101854164 0.00468282203708327 0.997658588981458 89 0.00160329192140799 0.00320658384281598 0.998396708078592 90 0.00301292796060018 0.00602585592120035 0.9969870720394 91 0.00224631031340226 0.00449262062680451 0.997753689686598 92 0.00159676973610552 0.00319353947221105 0.998403230263894 93 0.001286238610878 0.00257247722175601 0.998713761389122 94 0.00186391876531322 0.00372783753062644 0.998136081234687 95 0.00618258541840417 0.0123651708368083 0.993817414581596 96 0.0157955289716625 0.0315910579433251 0.984204471028337 97 0.0117862158873154 0.0235724317746307 0.988213784112685 98 0.00848165854730684 0.0169633170946137 0.991518341452693 99 0.00612753474247069 0.0122550694849414 0.993872465257529 100 0.00425645752459957 0.00851291504919914 0.9957435424754 101 0.00297174803042505 0.00594349606085009 0.997028251969575 102 0.00209631525601933 0.00419263051203866 0.997903684743981 103 0.00154267384575443 0.00308534769150887 0.998457326154246 104 0.00130395085390195 0.00260790170780391 0.998696049146098 105 0.00143761840650298 0.00287523681300597 0.998562381593497 106 0.00119207383097549 0.00238414766195099 0.998807926169024 107 0.00076261157228005 0.0015252231445601 0.99923738842772 108 0.00290160577374771 0.00580321154749542 0.997098394226252 109 0.00216946557793988 0.00433893115587977 0.99783053442206 110 0.00906936059766006 0.0181387211953201 0.99093063940234 111 0.0150522104771871 0.0301044209543741 0.984947789522813 112 0.0118065564355427 0.0236131128710853 0.988193443564457 113 0.020091131807698 0.0401822636153959 0.979908868192302 114 0.014809308352056 0.0296186167041119 0.985190691647944 115 0.0365806661840589 0.0731613323681177 0.963419333815941 116 0.0692797758524333 0.138559551704867 0.930720224147567 117 0.0505989959119294 0.101197991823859 0.949401004088071 118 0.0362789590719759 0.0725579181439518 0.963721040928024 119 0.0319595920320867 0.0639191840641734 0.968040407967913 120 0.0337896201750969 0.0675792403501937 0.966210379824903 121 0.0538228098536614 0.107645619707323 0.946177190146339 122 0.0406968432896675 0.0813936865793349 0.959303156710333 123 0.0721853235295196 0.144370647059039 0.92781467647048 124 0.0597005523207399 0.11940110464148 0.94029944767926 125 0.0578090355430646 0.115618071086129 0.942190964456935 126 0.0606958120810609 0.121391624162122 0.939304187918939 127 0.0473615335000692 0.0947230670001385 0.952638466499931 128 0.0300849097068896 0.0601698194137793 0.96991509029311 129 0.0256648535344247 0.0513297070688495 0.974335146465575 130 0.0224924341907799 0.0449848683815598 0.97750756580922 131 0.0161535145223801 0.0323070290447602 0.98384648547762 132 0.0367141601915107 0.0734283203830213 0.963285839808489 133 0.131187777916912 0.262375555833824 0.868812222083088 134 0.0743958094597251 0.14879161891945 0.925604190540275 135 0.0499967889795314 0.0999935779590629 0.950003211020469

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.166666666666667 NOK 5% type I error level 48 0.380952380952381 NOK 10% type I error level 64 0.507936507936508 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 & 21 & 0.166666666666667 & NOK \tabularnewline
5% type I error level & 48 & 0.380952380952381 & NOK \tabularnewline
10% type I error level & 64 & 0.507936507936508 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186261&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]21[/C][C]0.166666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.380952380952381[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]64[/C][C]0.507936507936508[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186261&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186261&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 21 0.166666666666667 NOK 5% type I error level 48 0.380952380952381 NOK 10% type I error level 64 0.507936507936508 NOK

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()
}
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.row.end(a)
a<-table.row.start(a)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
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.row.end(a)
a<-table.row.start(a)
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,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,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')
}