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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 Wed, 01 Feb 2023 16:35:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186261, Retrieved Wed, 01 Feb 2023 16:35:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact48
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]
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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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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] -0.0766895543898539`Belgi\303\253`[t] -0.471005882032356Euroraad[t] + 0.39574811492089`EU-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] -0.0766895543898539`Belgi\303\253`[t] -0.471005882032356Euroraad[t] +  0.39574811492089`EU-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] -0.0766895543898539`Belgi\303\253`[t] -0.471005882032356Euroraad[t] +  0.39574811492089`EU-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] -0.0766895543898539`Belgi\303\253`[t] -0.471005882032356Euroraad[t] + 0.39574811492089`EU-27`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.374902930784530.6589512.08650.0387730.019387
t0.004626755877375210.003751.23370.2194010.109701
Jonger_dan_250.995381941063480.003406292.280300
Vanaf_251.000426821970740.002953338.746200
`Belgi\303\253`-0.07668955438985390.113077-0.67820.4987770.249389
Euroraad-0.4710058820323560.369384-1.27510.2044120.102206
`EU-27`0.395748114920890.3473381.13940.256520.12826

\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]Euroraad[/C][C]-0.471005882032356[/C][C]0.369384[/C][C]-1.2751[/C][C]0.204412[/C][C]0.102206[/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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.374902930784530.6589512.08650.0387730.019387
t0.004626755877375210.003751.23370.2194010.109701
Jonger_dan_250.995381941063480.003406292.280300
Vanaf_251.000426821970740.002953338.746200
`Belgi\303\253`-0.07668955438985390.113077-0.67820.4987770.249389
Euroraad-0.4710058820323560.369384-1.27510.2044120.102206
`EU-27`0.395748114920890.3473381.13940.256520.12826







Multiple Linear Regression - Regression Statistics
Multiple R0.999941073250629
R-squared0.99988214997362
Adjusted R-squared0.99987702605943
F-TEST (value)195140.299547789
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.502471465439334
Sum Squared Residuals34.8419051541437

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999941073250629 \tabularnewline
R-squared & 0.99988214997362 \tabularnewline
Adjusted R-squared & 0.99987702605943 \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]Adjusted R-squared[/C][C]0.99987702605943[/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 R0.999941073250629
R-squared0.99988214997362
Adjusted R-squared0.99987702605943
F-TEST (value)195140.299547789
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.502471465439334
Sum Squared Residuals34.8419051541437







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.843418862524-0.843418862524401
2485484.8910952752440.108904724755721
3464463.9499214151550.050078584844949
4460459.9882151389290.0117848610714721
5467467.058268147682-0.0582681476823249
6460460.082710747925-0.0827107479252916
7448448.154540632893-0.154540632893337
8443443.156830010761-0.156830010760827
9436436.183693417379-0.183693417379325
10431431.203944780657-0.203944780656514
11484484.090139703736-0.0901397037359613
12510509.0592488449510.940751155049516
13513513.037531141487-0.0375311414866017
14503502.9928033595160.00719664048350291
15471471.083681926553-0.0836819265532486
16471471.04867378151-0.0486737815101796
17476476.099911160913-0.0999111609127638
18475474.1112931388590.88870686114071
19470470.129347249576-0.129347249575489
20461461.150455310073-0.150455310073087
21455455.12297599502-0.122975995020112
22456455.1276027508970.872397249102516
23517516.9390784259880.0609215740115813
24525525.892052889623-0.892052889622588
25523522.8456745168530.154325483147211
26519518.8889530321050.111046967894713
27509508.8799459527980.120054047201607
28512511.8959429364020.1040570635975
29519518.8857386393930.114261360606811
30517516.8549217313160.145078268684095
31510509.8817851379340.118214862065593
32509509.898922462002-0.898922462001835
33501499.9372192390541.06278076094564
34507506.8825985178020.117401482198104
35569569.712319821545-0.712319821545423
36580579.7279563380750.272043661924652
37578577.6820047872760.317995212723717
38565565.779059076781-0.779059076780624
39547547.771682302615-0.771682302615076
40555554.8162475492480.18375245075201
41562561.8060432522390.193956747761323
42561560.8253778288670.174622171132908
43555555.87852255212-0.878522552119566
44544543.8993977400560.100602259944517
45537536.938974983020.061025016979771
46543542.8736344098260.126365590173689
47594593.6826926708870.317307329113197
48611610.6261646057840.373835394215546
49613612.6366898865110.363310113489438
50611610.724008435650.275991564349463
51594593.7400653497730.259934650227314
52595595.765725273221-0.765725273220583
53591590.8405429119840.159457088016217
54589589.852208533173-0.85220853317322
55584583.8718298063230.128170193676519
56573572.8969859250590.103014074940984
57567566.924132974920.0758670250795623
58569568.9322374492710.0677625507289914
59621620.7301994680340.269800531965621
60629628.7054075291310.294592470869271
61628627.7096074630370.290392536962629
62612611.8050976433870.194902356613413
63595595.821438200752-0.821438200751641
64597596.8592419598470.140758040152803
65593592.8973324154650.102667584535412
66590589.9182942439550.0817057560445901
67580579.956447842280.0435521577198931
68574574.013079941819-0.0130799418190837
69573572.9928397069550.00716029304459668
70573573.015285269515-0.0152852695146464
71620619.9014001565740.0985998434264505
72626625.893656469710.106343530290236
73620619.9082929513810.091707048618876
74588586.9820335725441.01796642745585
75566565.9936159455240.00638405447621257
76557558.092437612339-1.09243761233943
77561561.061250918441-0.0612509184408246
78549549.098550872824-0.0985508728240327
79532532.133716717354-0.133716717353552
80526526.1507740054-0.150774005400432
81511511.191981553507-0.191981553506531
82499499.200346173252-0.200346173252393
83555555.087821562244-0.0878215622440683
84565564.0813583412920.918641658707494
85542542.122059031407-0.122059031407316
86527527.110774663519-0.110774663518885
87510510.192981006823-0.192981006823285
88514514.164785119999-0.164785119998787
89517517.178361297227-0.178361297227366
90508507.2217630446150.778236955384492
91493493.258352533785-0.258352533785086
92490490.224687997362-0.224687997361531
93469469.258229643308-0.258229643307583
94478477.2359416000790.764058399921055
95528529.101123641246-1.10112364124624
96534533.0720834292270.927916570772599
97518518.125017309758-0.125017309757566
98506506.117923839769-0.117923839768749
99502502.12597127797-0.12597127796992
100516516.056662682559-0.0566626825586083
101528528.050312155312-0.0503121553120145
102533533.019157780032-0.0191577800316743
103536535.9602887856640.0397112143359877
104537537.010105234639-0.0101052346393137
105524523.0415897534730.958410246526724
106536536.055965750683-0.0559657506831727
107587586.8711394858730.128860514127398
108597595.8812442100961.11875578990383
109581580.8820326215160.117967378484162
110564564.895892283077-0.895892283077003
111558556.9592796046851.04072039531487
112575574.9016849696720.0983150303281993
113580580.97268436948-0.972684369479816
114575574.9822158808160.0177841191844497
115563564.007371999551-1.0073719995511
116552550.9896187670491.01038123295081
117537537.023584181687-0.0235841816870423
118545545.029061528227-0.0290615282269552
119601600.9240026045010.0759973954985876
120604604.907736318257-0.907736318257168
121586586.927483876822-0.927483876822016
122564563.9955461346060.00445386539424042
123549548.0490406970870.950959302912638
124551551.093089427916-0.0930894279156862
125556556.125338055768-0.1253380557676
126548548.209168258589-0.209168258589311
127540540.198654106277-0.198654106276708
128531531.211950032607-0.211950032607489
129521520.2817258437420.718274156257616
130519518.2383983674750.761601632524957
131572572.10195315678-0.101953156779925
132581582.068008189303-1.06800818930272
133563563.072134193747-0.0721341937466972
134548548.07385001966-0.0738500196601954
135539539.097378886533-0.0973788865333247
136541541.073517415402-0.0735174154024493
137562561.0539304294470.946069570552844
138559559.055222645579-0.0552226455791648
139546546.06937526913-0.0693752691303587
140536536.988470019055-0.988470019054668
141528526.9938133467041.00618665329644
142530530.982531851938-0.982531851938224
143582581.8330798429070.166920157093175
144599598.8316677683310.168332231669284
145584583.7878364873510.21216351264897

\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 IndexActualsInterpolationForecastResidualsPrediction Error
1501501.843418862524-0.843418862524401
2485484.8910952752440.108904724755721
3464463.9499214151550.050078584844949
4460459.9882151389290.0117848610714721
5467467.058268147682-0.0582681476823249
6460460.082710747925-0.0827107479252916
7448448.154540632893-0.154540632893337
8443443.156830010761-0.156830010760827
9436436.183693417379-0.183693417379325
10431431.203944780657-0.203944780656514
11484484.090139703736-0.0901397037359613
12510509.0592488449510.940751155049516
13513513.037531141487-0.0375311414866017
14503502.9928033595160.00719664048350291
15471471.083681926553-0.0836819265532486
16471471.04867378151-0.0486737815101796
17476476.099911160913-0.0999111609127638
18475474.1112931388590.88870686114071
19470470.129347249576-0.129347249575489
20461461.150455310073-0.150455310073087
21455455.12297599502-0.122975995020112
22456455.1276027508970.872397249102516
23517516.9390784259880.0609215740115813
24525525.892052889623-0.892052889622588
25523522.8456745168530.154325483147211
26519518.8889530321050.111046967894713
27509508.8799459527980.120054047201607
28512511.8959429364020.1040570635975
29519518.8857386393930.114261360606811
30517516.8549217313160.145078268684095
31510509.8817851379340.118214862065593
32509509.898922462002-0.898922462001835
33501499.9372192390541.06278076094564
34507506.8825985178020.117401482198104
35569569.712319821545-0.712319821545423
36580579.7279563380750.272043661924652
37578577.6820047872760.317995212723717
38565565.779059076781-0.779059076780624
39547547.771682302615-0.771682302615076
40555554.8162475492480.18375245075201
41562561.8060432522390.193956747761323
42561560.8253778288670.174622171132908
43555555.87852255212-0.878522552119566
44544543.8993977400560.100602259944517
45537536.938974983020.061025016979771
46543542.8736344098260.126365590173689
47594593.6826926708870.317307329113197
48611610.6261646057840.373835394215546
49613612.6366898865110.363310113489438
50611610.724008435650.275991564349463
51594593.7400653497730.259934650227314
52595595.765725273221-0.765725273220583
53591590.8405429119840.159457088016217
54589589.852208533173-0.85220853317322
55584583.8718298063230.128170193676519
56573572.8969859250590.103014074940984
57567566.924132974920.0758670250795623
58569568.9322374492710.0677625507289914
59621620.7301994680340.269800531965621
60629628.7054075291310.294592470869271
61628627.7096074630370.290392536962629
62612611.8050976433870.194902356613413
63595595.821438200752-0.821438200751641
64597596.8592419598470.140758040152803
65593592.8973324154650.102667584535412
66590589.9182942439550.0817057560445901
67580579.956447842280.0435521577198931
68574574.013079941819-0.0130799418190837
69573572.9928397069550.00716029304459668
70573573.015285269515-0.0152852695146464
71620619.9014001565740.0985998434264505
72626625.893656469710.106343530290236
73620619.9082929513810.091707048618876
74588586.9820335725441.01796642745585
75566565.9936159455240.00638405447621257
76557558.092437612339-1.09243761233943
77561561.061250918441-0.0612509184408246
78549549.098550872824-0.0985508728240327
79532532.133716717354-0.133716717353552
80526526.1507740054-0.150774005400432
81511511.191981553507-0.191981553506531
82499499.200346173252-0.200346173252393
83555555.087821562244-0.0878215622440683
84565564.0813583412920.918641658707494
85542542.122059031407-0.122059031407316
86527527.110774663519-0.110774663518885
87510510.192981006823-0.192981006823285
88514514.164785119999-0.164785119998787
89517517.178361297227-0.178361297227366
90508507.2217630446150.778236955384492
91493493.258352533785-0.258352533785086
92490490.224687997362-0.224687997361531
93469469.258229643308-0.258229643307583
94478477.2359416000790.764058399921055
95528529.101123641246-1.10112364124624
96534533.0720834292270.927916570772599
97518518.125017309758-0.125017309757566
98506506.117923839769-0.117923839768749
99502502.12597127797-0.12597127796992
100516516.056662682559-0.0566626825586083
101528528.050312155312-0.0503121553120145
102533533.019157780032-0.0191577800316743
103536535.9602887856640.0397112143359877
104537537.010105234639-0.0101052346393137
105524523.0415897534730.958410246526724
106536536.055965750683-0.0559657506831727
107587586.8711394858730.128860514127398
108597595.8812442100961.11875578990383
109581580.8820326215160.117967378484162
110564564.895892283077-0.895892283077003
111558556.9592796046851.04072039531487
112575574.9016849696720.0983150303281993
113580580.97268436948-0.972684369479816
114575574.9822158808160.0177841191844497
115563564.007371999551-1.0073719995511
116552550.9896187670491.01038123295081
117537537.023584181687-0.0235841816870423
118545545.029061528227-0.0290615282269552
119601600.9240026045010.0759973954985876
120604604.907736318257-0.907736318257168
121586586.927483876822-0.927483876822016
122564563.9955461346060.00445386539424042
123549548.0490406970870.950959302912638
124551551.093089427916-0.0930894279156862
125556556.125338055768-0.1253380557676
126548548.209168258589-0.209168258589311
127540540.198654106277-0.198654106276708
128531531.211950032607-0.211950032607489
129521520.2817258437420.718274156257616
130519518.2383983674750.761601632524957
131572572.10195315678-0.101953156779925
132581582.068008189303-1.06800818930272
133563563.072134193747-0.0721341937466972
134548548.07385001966-0.0738500196601954
135539539.097378886533-0.0973788865333247
136541541.073517415402-0.0735174154024493
137562561.0539304294470.946069570552844
138559559.055222645579-0.0552226455791648
139546546.06937526913-0.0693752691303587
140536536.988470019055-0.988470019054668
141528526.9938133467041.00618665329644
142530530.982531851938-0.982531851938224
143582581.8330798429070.166920157093175
144599598.8316677683310.168332231669284
145584583.7878364873510.21216351264897







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09966754686905150.1993350937381030.900332453130949
110.05330923015109690.1066184603021940.946690769848903
120.265178132653740.530356265307480.73482186734626
130.2174735285959020.4349470571918040.782526471404098
140.163701679662910.327403359325820.83629832033709
150.1057099597245140.2114199194490290.894290040275486
160.08308549250741930.1661709850148390.916914507492581
170.06519423399391340.1303884679878270.934805766006087
180.1853044539343330.3706089078686660.814695546065667
190.1840464040626030.3680928081252050.815953595937397
200.1536119652379780.3072239304759560.846388034762022
210.108322988198770.2166459763975410.89167701180123
220.175591994662430.351183989324860.82440800533757
230.1488286433502340.2976572867004670.851171356649766
240.2670189687795410.5340379375590810.732981031220459
250.2476200559042040.4952401118084070.752379944095796
260.1927245384384850.385449076876970.807275461561515
270.1473726297150260.2947452594300520.852627370284974
280.113753497012580.2275069940251590.88624650298742
290.08635557509262520.172711150185250.913644424907375
300.06297737954886910.1259547590977380.937022620451131
310.0452933641513260.09058672830265210.954706635848674
320.1267045974513710.2534091949027420.873295402548629
330.2581366543526860.5162733087053720.741863345647314
340.2120749613926290.4241499227852580.787925038607371
350.2573143257988330.5146286515976660.742685674201167
360.2317274365022330.4634548730044670.768272563497767
370.2087396716385540.4174793432771090.791260328361446
380.2841036088269170.5682072176538330.715896391173083
390.3362242844085150.672448568817030.663775715591485
400.2908506530447560.5817013060895120.709149346955244
410.2454717573776310.4909435147552610.754528242622369
420.2032539464669330.4065078929338660.796746053533067
430.3346987355409930.6693974710819860.665301264459007
440.2868148980328790.5736297960657580.713185101967121
450.2418764777726020.4837529555452030.758123522227398
460.2029180136545050.405836027309010.797081986345495
470.1903111912781530.3806223825563070.809688808721847
480.1799979845015820.3599959690031650.820002015498418
490.1593769181836140.3187538363672290.840623081816386
500.1328147054473820.2656294108947640.867185294552618
510.1082988386280650.216597677256130.891701161371935
520.1623578410094370.3247156820188740.837642158990563
530.1328788193059640.2657576386119290.867121180694036
540.2036320179382860.4072640358765730.796367982061714
550.172197200023730.344394400047460.82780279997627
560.1420602306121570.2841204612243140.857939769387843
570.1152738691418290.2305477382836580.884726130858171
580.09268226057952580.1853645211590520.907317739420474
590.07400950750570030.1480190150114010.9259904924943
600.0580182003677020.1160364007354040.941981799632298
610.04506281619695410.09012563239390810.954937183803046
620.03451099736737970.06902199473475940.96548900263262
630.08348926861316390.1669785372263280.916510731386836
640.06670315079464750.1334063015892950.933296849205352
650.05222242881309390.1044448576261880.947777571186906
660.04041017940280210.08082035880560410.959589820597198
670.03107414157471130.06214828314942260.968925858425289
680.02409749190600890.04819498381201790.975902508093991
690.01799508193050680.03599016386101360.982004918069493
700.01335647785356880.02671295570713760.986643522146431
710.009609272231322770.01921854446264550.990390727768677
720.006821429145798390.01364285829159680.993178570854202
730.004777645186988450.00955529037397690.995222354813012
740.009164198274187980.0183283965483760.990835801725812
750.007129623827443280.01425924765488660.992870376172557
760.03213932330005510.06427864660011020.967860676699945
770.02432585344730670.04865170689461350.975674146552693
780.01823881430186570.03647762860373140.981761185698134
790.01361139338838140.02722278677676290.986388606611619
800.009935489405547860.01987097881109570.990064510594452
810.007342152021525880.01468430404305180.992657847978474
820.005833722951787050.01166744590357410.994166277048213
830.004205882003513520.008411764007027040.995794117996486
840.00872422184420660.01744844368841320.991275778155793
850.006506854162109780.01301370832421960.99349314583789
860.004622354588568730.009244709177137450.995377645411431
870.003386763889479750.006773527778959510.99661323611052
880.002341411018541640.004682822037083270.997658588981458
890.001603291921407990.003206583842815980.998396708078592
900.003012927960600180.006025855921200350.9969870720394
910.002246310313402260.004492620626804510.997753689686598
920.001596769736105520.003193539472211050.998403230263894
930.0012862386108780.002572477221756010.998713761389122
940.001863918765313220.003727837530626440.998136081234687
950.006182585418404170.01236517083680830.993817414581596
960.01579552897166250.03159105794332510.984204471028337
970.01178621588731540.02357243177463070.988213784112685
980.008481658547306840.01696331709461370.991518341452693
990.006127534742470690.01225506948494140.993872465257529
1000.004256457524599570.008512915049199140.9957435424754
1010.002971748030425050.005943496060850090.997028251969575
1020.002096315256019330.004192630512038660.997903684743981
1030.001542673845754430.003085347691508870.998457326154246
1040.001303950853901950.002607901707803910.998696049146098
1050.001437618406502980.002875236813005970.998562381593497
1060.001192073830975490.002384147661950990.998807926169024
1070.000762611572280050.00152522314456010.99923738842772
1080.002901605773747710.005803211547495420.997098394226252
1090.002169465577939880.004338931155879770.99783053442206
1100.009069360597660060.01813872119532010.99093063940234
1110.01505221047718710.03010442095437410.984947789522813
1120.01180655643554270.02361311287108530.988193443564457
1130.0200911318076980.04018226361539590.979908868192302
1140.0148093083520560.02961861670411190.985190691647944
1150.03658066618405890.07316133236811770.963419333815941
1160.06927977585243330.1385595517048670.930720224147567
1170.05059899591192940.1011979918238590.949401004088071
1180.03627895907197590.07255791814395180.963721040928024
1190.03195959203208670.06391918406417340.968040407967913
1200.03378962017509690.06757924035019370.966210379824903
1210.05382280985366140.1076456197073230.946177190146339
1220.04069684328966750.08139368657933490.959303156710333
1230.07218532352951960.1443706470590390.92781467647048
1240.05970055232073990.119401104641480.94029944767926
1250.05780903554306460.1156180710861290.942190964456935
1260.06069581208106090.1213916241621220.939304187918939
1270.04736153350006920.09472306700013850.952638466499931
1280.03008490970688960.06016981941377930.96991509029311
1290.02566485353442470.05132970706884950.974335146465575
1300.02249243419077990.04498486838155980.97750756580922
1310.01615351452238010.03230702904476020.98384648547762
1320.03671416019151070.07342832038302130.963285839808489
1330.1311877779169120.2623755558338240.868812222083088
1340.07439580945972510.148791618919450.925604190540275
1350.04999678897953140.09999357795906290.950003211020469

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09966754686905150.1993350937381030.900332453130949
110.05330923015109690.1066184603021940.946690769848903
120.265178132653740.530356265307480.73482186734626
130.2174735285959020.4349470571918040.782526471404098
140.163701679662910.327403359325820.83629832033709
150.1057099597245140.2114199194490290.894290040275486
160.08308549250741930.1661709850148390.916914507492581
170.06519423399391340.1303884679878270.934805766006087
180.1853044539343330.3706089078686660.814695546065667
190.1840464040626030.3680928081252050.815953595937397
200.1536119652379780.3072239304759560.846388034762022
210.108322988198770.2166459763975410.89167701180123
220.175591994662430.351183989324860.82440800533757
230.1488286433502340.2976572867004670.851171356649766
240.2670189687795410.5340379375590810.732981031220459
250.2476200559042040.4952401118084070.752379944095796
260.1927245384384850.385449076876970.807275461561515
270.1473726297150260.2947452594300520.852627370284974
280.113753497012580.2275069940251590.88624650298742
290.08635557509262520.172711150185250.913644424907375
300.06297737954886910.1259547590977380.937022620451131
310.0452933641513260.09058672830265210.954706635848674
320.1267045974513710.2534091949027420.873295402548629
330.2581366543526860.5162733087053720.741863345647314
340.2120749613926290.4241499227852580.787925038607371
350.2573143257988330.5146286515976660.742685674201167
360.2317274365022330.4634548730044670.768272563497767
370.2087396716385540.4174793432771090.791260328361446
380.2841036088269170.5682072176538330.715896391173083
390.3362242844085150.672448568817030.663775715591485
400.2908506530447560.5817013060895120.709149346955244
410.2454717573776310.4909435147552610.754528242622369
420.2032539464669330.4065078929338660.796746053533067
430.3346987355409930.6693974710819860.665301264459007
440.2868148980328790.5736297960657580.713185101967121
450.2418764777726020.4837529555452030.758123522227398
460.2029180136545050.405836027309010.797081986345495
470.1903111912781530.3806223825563070.809688808721847
480.1799979845015820.3599959690031650.820002015498418
490.1593769181836140.3187538363672290.840623081816386
500.1328147054473820.2656294108947640.867185294552618
510.1082988386280650.216597677256130.891701161371935
520.1623578410094370.3247156820188740.837642158990563
530.1328788193059640.2657576386119290.867121180694036
540.2036320179382860.4072640358765730.796367982061714
550.172197200023730.344394400047460.82780279997627
560.1420602306121570.2841204612243140.857939769387843
570.1152738691418290.2305477382836580.884726130858171
580.09268226057952580.1853645211590520.907317739420474
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600.0580182003677020.1160364007354040.941981799632298
610.04506281619695410.09012563239390810.954937183803046
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650.05222242881309390.1044448576261880.947777571186906
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680.02409749190600890.04819498381201790.975902508093991
690.01799508193050680.03599016386101360.982004918069493
700.01335647785356880.02671295570713760.986643522146431
710.009609272231322770.01921854446264550.990390727768677
720.006821429145798390.01364285829159680.993178570854202
730.004777645186988450.00955529037397690.995222354813012
740.009164198274187980.0183283965483760.990835801725812
750.007129623827443280.01425924765488660.992870376172557
760.03213932330005510.06427864660011020.967860676699945
770.02432585344730670.04865170689461350.975674146552693
780.01823881430186570.03647762860373140.981761185698134
790.01361139338838140.02722278677676290.986388606611619
800.009935489405547860.01987097881109570.990064510594452
810.007342152021525880.01468430404305180.992657847978474
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830.004205882003513520.008411764007027040.995794117996486
840.00872422184420660.01744844368841320.991275778155793
850.006506854162109780.01301370832421960.99349314583789
860.004622354588568730.009244709177137450.995377645411431
870.003386763889479750.006773527778959510.99661323611052
880.002341411018541640.004682822037083270.997658588981458
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930.0012862386108780.002572477221756010.998713761389122
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980.008481658547306840.01696331709461370.991518341452693
990.006127534742470690.01225506948494140.993872465257529
1000.004256457524599570.008512915049199140.9957435424754
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1200.03378962017509690.06757924035019370.966210379824903
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1340.07439580945972510.148791618919450.925604190540275
1350.04999678897953140.09999357795906290.950003211020469







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level480.380952380952381NOK
10% type I error level640.507936507936508NOK

\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 testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level480.380952380952381NOK
10% type I error level640.507936507936508NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}