Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 17:47:28 -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/t1352155672qkht7w3ybmufxei.htm/, Retrieved Wed, 01 Feb 2023 15:36:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186351, Retrieved Wed, 01 Feb 2023 15:36:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-11-05 14:18:00] [d2c1a12335a0e7c18f8727e39be21dbc]
-    D    [Multiple Regression] [ws7] [2012-11-05 22:47:28] [9fce0523ac0e7dfdcafaec3da59cfa0a] [Current]
Feedback Forum

Post a new message
Dataseries X:
0	0	1	501	134	0	368	0	6.70	0.00	8.50	0.00	8.70	0
2	1	2	485	124	124	361	361	6.80	6.80	8.40	8.40	8.60	8.6
3	1	3	464	113	113	351	351	6.70	6.70	8.40	8.40	8.60	8.6
4	1	4	460	109	109	351	351	6.60	6.60	8.30	8.30	8.50	8.5
5	1	5	467	109	109	358	358	6.40	6.40	8.20	8.20	8.50	8.5
6	1	6	460	106	106	354	354	6.30	6.30	8.20	8.20	8.50	8.5
7	1	7	448	101	101	347	347	6.30	6.30	8.10	8.10	8.50	8.5
8	1	8	443	98	98	345	345	6.50	6.50	8.10	8.10	8.50	8.5
0	0	9	436	93	0	343	0	6.50	0.00	8.10	0.00	8.50	0
0	0	10	431	91	0	340	0	6.40	0.00	8.10	0.00	8.50	0
0	0	11	484	122	0	362	0	6.20	0.00	8.10	0.00	8.50	0
0	0	12	510	139	0	370	0	6.20	0.00	8.10	0.00	8.60	0
0	0	13	513	140	0	373	0	6.50	0.00	8.10	0.00	8.60	0
14	1	14	503	132	132	371	371	7.00	7.00	8.20	8.20	8.60	8.6
15	1	15	471	117	117	354	354	7.20	7.20	8.20	8.20	8.70	8.7
16	1	16	471	114	114	357	357	7.30	7.30	8.30	8.30	8.70	8.7
17	1	17	476	113	113	363	363	7.40	7.40	8.20	8.20	8.70	8.7
18	1	18	475	110	110	364	364	7.40	7.40	8.30	8.30	8.80	8.8
19	1	19	470	107	107	363	363	7.40	7.40	8.30	8.30	8.80	8.8
20	1	20	461	103	103	358	358	7.30	7.30	8.40	8.40	8.90	8.9
0	0	21	455	98	0	357	0	7.40	0.00	8.50	0.00	8.90	0
0	0	22	456	98	0	357	0	7.40	0.00	8.50	0.00	8.90	0
0	0	23	517	137	0	380	0	7.60	0.00	8.60	0.00	9.00	0
0	0	24	525	148	0	378	0	7.60	0.00	8.60	0.00	9.00	0
0	0	25	523	147	0	376	0	7.70	0.00	8.70	0.00	9.00	0
26	1	26	519	139	139	380	380	7.70	7.70	8.70	8.70	9.00	9
27	1	27	509	130	130	379	379	7.80	7.80	8.80	8.80	9.00	9
28	1	28	512	128	128	384	384	7.80	7.80	8.80	8.80	9.00	9
29	1	29	519	127	127	392	392	8.00	8.00	8.90	8.90	9.10	9.1
30	1	30	517	123	123	394	394	8.10	8.10	9.00	9.00	9.10	9.1
31	1	31	510	118	118	392	392	8.10	8.10	9.00	9.00	9.10	9.1
32	1	32	509	114	114	396	396	8.20	8.20	9.00	9.00	9.10	9.1
0	0	33	501	108	0	392	0	8.10	0.00	9.00	0.00	9.10	0
0	0	34	507	111	0	396	0	8.10	0.00	9.10	0.00	9.10	0
0	0	35	569	151	0	419	0	8.10	0.00	9.10	0.00	9.10	0
0	0	36	580	159	0	421	0	8.10	0.00	9.00	0.00	9.10	0
0	0	37	578	158	0	420	0	8.20	0.00	9.10	0.00	9.10	0
38	1	38	565	148	148	418	418	8.20	8.20	9.00	9.00	9.10	9.1
39	1	39	547	138	138	410	410	8.30	8.30	9.10	9.10	9.10	9.1
40	1	40	555	137	137	418	418	8.40	8.40	9.10	9.10	9.20	9.2
41	1	41	562	136	136	426	426	8.60	8.60	9.20	9.20	9.30	9.3
42	1	42	561	133	133	428	428	8.60	8.60	9.20	9.20	9.30	9.3
43	1	43	555	126	126	430	430	8.40	8.40	9.20	9.20	9.30	9.3
44	1	44	544	120	120	424	424	8.00	8.00	9.20	9.20	9.20	9.2
0	0	45	537	114	0	423	0	7.90	0.00	9.20	0.00	9.20	0
0	0	46	543	116	0	427	0	8.10	0.00	9.30	0.00	9.20	0
0	0	47	594	153	0	441	0	8.50	0.00	9.30	0.00	9.20	0
0	0	48	611	162	0	449	0	8.80	0.00	9.30	0.00	9.20	0
0	0	49	613	161	0	452	0	8.80	0.00	9.30	0.00	9.20	0
50	1	50	611	149	149	462	462	8.50	8.50	9.30	9.30	9.20	9.2
51	1	51	594	139	139	455	455	8.30	8.30	9.40	9.40	9.20	9.2
52	1	52	595	135	135	461	461	8.30	8.30	9.40	9.40	9.20	9.2
53	1	53	591	130	130	461	461	8.30	8.30	9.30	9.30	9.20	9.2
54	1	54	589	127	127	463	463	8.40	8.40	9.30	9.30	9.20	9.2
55	1	55	584	122	122	462	462	8.50	8.50	9.30	9.30	9.20	9.2
56	1	56	573	117	117	456	456	8.50	8.50	9.30	9.30	9.20	9.2
0	0	57	567	112	0	455	0	8.60	0.00	9.20	0.00	9.10	0
0	0	58	569	113	0	456	0	8.50	0.00	9.20	0.00	9.10	0
0	0	59	621	149	0	472	0	8.60	0.00	9.20	0.00	9.00	0
0	0	60	629	157	0	472	0	8.60	0.00	9.10	0.00	8.90	0
0	0	61	628	157	0	471	0	8.60	0.00	9.10	0.00	8.90	0
62	1	62	612	147	147	465	465	8.50	8.50	9.10	9.10	9.00	9
63	1	63	595	137	137	459	459	8.40	8.40	9.10	9.10	8.90	8.9
64	1	64	597	132	132	465	465	8.40	8.40	9.00	9.00	8.80	8.8
65	1	65	593	125	125	468	468	8.50	8.50	8.90	8.90	8.70	8.7
66	1	66	590	123	123	467	467	8.50	8.50	8.80	8.80	8.60	8.6
67	1	67	580	117	117	463	463	8.50	8.50	8.70	8.70	8.50	8.5
68	1	68	574	114	114	460	460	8.60	8.60	8.60	8.60	8.50	8.5
0	0	69	573	111	0	462	0	8.60	0.00	8.60	0.00	8.40	0
0	0	70	573	112	0	461	0	8.40	0.00	8.50	0.00	8.30	0
0	0	71	620	144	0	476	0	8.20	0.00	8.40	0.00	8.20	0
0	0	72	626	150	0	476	0	8.00	0.00	8.40	0.00	8.20	0
0	0	73	620	149	0	471	0	8.00	0.00	8.30	0.00	8.10	0
74	1	74	588	134	134	453	453	8.00	8.00	8.20	8.20	8.00	8
75	1	75	566	123	123	443	443	8.00	8.00	8.20	8.20	7.90	7.9
76	1	76	557	116	116	442	442	7.90	7.90	8.00	8.00	7.80	7.8
77	1	77	561	117	117	444	444	7.90	7.90	7.90	7.90	7.60	7.6
78	1	78	549	111	111	438	438	7.90	7.90	7.80	7.80	7.50	7.5
79	1	79	532	105	105	427	427	7.90	7.90	7.70	7.70	7.40	7.4
80	1	80	526	102	102	424	424	8.00	8.00	7.60	7.60	7.30	7.3
0	0	81	511	95	0	416	0	7.90	0.00	7.60	0.00	7.30	0
0	0	82	499	93	0	406	0	7.40	0.00	7.60	0.00	7.20	0
0	0	83	555	124	0	431	0	7.20	0.00	7.60	0.00	7.20	0
0	0	84	565	130	0	434	0	7.00	0.00	7.60	0.00	7.20	0
0	0	85	542	124	0	418	0	6.90	0.00	7.50	0.00	7.10	0
86	1	86	527	115	115	412	412	7.10	7.10	7.50	7.50	7.00	7
87	1	87	510	106	106	404	404	7.20	7.20	7.40	7.40	7.00	7
88	1	88	514	105	105	409	409	7.20	7.20	7.40	7.40	6.90	6.9
89	1	89	517	105	105	412	412	7.10	7.10	7.40	7.40	6.90	6.9
90	1	90	508	101	101	406	406	6.90	6.90	7.30	7.30	6.80	6.8
91	1	91	493	95	95	398	398	6.80	6.80	7.30	7.30	6.80	6.8
92	1	92	490	93	93	397	397	6.80	6.80	7.40	7.40	6.80	6.8
0	0	93	469	84	0	385	0	6.80	0.00	7.50	0.00	6.90	0
0	0	94	478	87	0	390	0	6.90	0.00	7.60	0.00	7.00	0
0	0	95	528	116	0	413	0	7.10	0.00	7.60	0.00	7.00	0
0	0	96	534	120	0	413	0	7.20	0.00	7.70	0.00	7.10	0
0	0	97	518	117	0	401	0	7.20	0.00	7.70	0.00	7.20	0
98	1	98	506	109	109	397	397	7.10	7.10	7.90	7.90	7.30	7.3
99	1	99	502	105	105	397	397	7.10	7.10	8.10	8.10	7.50	7.5
100	1	100	516	107	107	409	409	7.20	7.20	8.40	8.40	7.70	7.7
101	1	101	528	109	109	419	419	7.50	7.50	8.70	8.70	8.10	8.1
102	1	102	533	109	109	424	424	7.70	7.70	9.00	9.00	8.40	8.4
103	1	103	536	108	108	428	428	7.80	7.80	9.30	9.30	8.60	8.6
104	1	104	537	107	107	430	430	7.70	7.70	9.40	9.40	8.80	8.8
0	0	105	524	99	0	424	0	7.70	0.00	9.50	0.00	8.90	0
0	0	106	536	103	0	433	0	7.80	0.00	9.60	0.00	9.10	0
0	0	107	587	131	0	456	0	8.00	0.00	9.80	0.00	9.20	0
0	0	108	597	137	0	459	0	8.10	0.00	9.80	0.00	9.30	0
0	0	109	581	135	0	446	0	8.10	0.00	9.90	0.00	9.40	0
110	1	110	564	124	124	441	441	8.00	8.00	10.00	10.00	9.40	9.4
111	1	111	558	118	118	439	439	8.10	8.10	10.00	10.00	9.50	9.5
112	1	112	575	121	121	454	454	8.20	8.20	10.10	10.10	9.50	9.5
113	1	113	580	121	121	460	460	8.40	8.40	10.10	10.10	9.70	9.7
114	1	114	575	118	118	457	457	8.50	8.50	10.10	10.10	9.70	9.7
115	1	115	563	113	113	451	451	8.50	8.50	10.10	10.10	9.70	9.7
116	1	116	552	107	107	444	444	8.50	8.50	10.20	10.20	9.70	9.7
0	0	117	537	100	0	437	0	8.50	0.00	10.20	0.00	9.70	0
0	0	118	545	102	0	443	0	8.50	0.00	10.10	0.00	9.60	0
0	0	119	601	130	0	471	0	8.40	0.00	10.10	0.00	9.60	0
0	0	120	604	136	0	469	0	8.30	0.00	10.10	0.00	9.60	0
0	0	121	586	133	0	454	0	8.20	0.00	10.10	0.00	9.60	0
122	1	122	564	120	120	444	444	8.10	8.10	10.10	10.10	9.60	9.6
123	1	123	549	112	112	436	436	7.90	7.90	10.10	10.10	9.60	9.6
124	1	124	551	109	109	442	442	7.60	7.60	10.10	10.10	9.60	9.6
125	1	125	556	110	110	446	446	7.30	7.30	10.00	10.00	9.50	9.5
126	1	126	548	106	106	442	442	7.10	7.10	9.90	9.90	9.50	9.5
127	1	127	540	102	102	438	438	7.00	7.00	9.90	9.90	9.40	9.4
128	1	128	531	98	98	433	433	7.10	7.10	9.90	9.90	9.40	9.4
0	0	129	521	92	0	428	0	7.10	0.00	9.90	0.00	9.50	0
0	0	130	519	92	0	426	0	7.10	0.00	10.00	0.00	9.50	0
0	0	131	572	120	0	452	0	7.30	0.00	10.10	0.00	9.60	0
0	0	132	581	127	0	455	0	7.30	0.00	10.20	0.00	9.70	0
0	0	133	563	124	0	439	0	7.30	0.00	10.30	0.00	9.80	0
134	1	134	548	114	114	434	434	7.20	7.20	10.50	10.50	9.90	9.9
135	1	135	539	108	108	431	431	7.20	7.20	10.60	10.60	10.00	10
136	1	136	541	106	106	435	435	7.10	7.10	10.70	10.70	10.00	10
137	1	137	562	111	111	450	450	7.10	7.10	10.80	10.80	10.10	10.1
138	1	138	559	110	110	449	449	7.10	7.10	10.90	10.90	10.20	10.2
139	1	139	546	104	104	442	442	7.20	7.20	11.00	11.00	10.30	10.3
140	1	140	536	100	100	437	437	7.30	7.30	11.20	11.20	10.30	10.3
0	0	141	528	96	0	431	0	7.40	0.00	11.30	0.00	10.40	0
0	0	142	530	98	0	433	0	7.40	0.00	11.40	0.00	10.50	0
0	0	143	582	122	0	460	0	7.50	0.00	11.50	0.00	10.50	0
0	0	144	599	134	0	465	0	7.40	0.00	11.50	0.00	10.60	0
0	0	145	584	133	0	451	0	7.40	0.00	11.60	0.00	10.60	0




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=186351&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=186351&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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] = + 1.0631373518042 + 0.00873358040788804S_t[t] + 0.559429956874879s[t] -9.26671245417641e-05t + 0.991591664899729Jonger_dan_25[t] + 0.00772196663939016Jonger_dan_25_s[t] + 1.00287188593283Vanaf_25[t] -0.00338824381708286Vanaf_25_s[t] -0.0936569913157868Belgie[t] -0.0172056942539791Belgie_s[t] -0.169613905663898Euroraad[t] -0.651252503842849Euroraad_s[t] + 0.121091973648387`EU-27`[t] + 0.587652217252215`EU-27_s`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_werkloosheid[t] =  +  1.0631373518042 +  0.00873358040788804S_t[t] +  0.559429956874879s[t] -9.26671245417641e-05t +  0.991591664899729Jonger_dan_25[t] +  0.00772196663939016Jonger_dan_25_s[t] +  1.00287188593283Vanaf_25[t] -0.00338824381708286Vanaf_25_s[t] -0.0936569913157868Belgie[t] -0.0172056942539791Belgie_s[t] -0.169613905663898Euroraad[t] -0.651252503842849Euroraad_s[t] +  0.121091973648387`EU-27`[t] +  0.587652217252215`EU-27_s`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_werkloosheid[t] =  +  1.0631373518042 +  0.00873358040788804S_t[t] +  0.559429956874879s[t] -9.26671245417641e-05t +  0.991591664899729Jonger_dan_25[t] +  0.00772196663939016Jonger_dan_25_s[t] +  1.00287188593283Vanaf_25[t] -0.00338824381708286Vanaf_25_s[t] -0.0936569913157868Belgie[t] -0.0172056942539791Belgie_s[t] -0.169613905663898Euroraad[t] -0.651252503842849Euroraad_s[t] +  0.121091973648387`EU-27`[t] +  0.587652217252215`EU-27_s`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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] = + 1.0631373518042 + 0.00873358040788804S_t[t] + 0.559429956874879s[t] -9.26671245417641e-05t + 0.991591664899729Jonger_dan_25[t] + 0.00772196663939016Jonger_dan_25_s[t] + 1.00287188593283Vanaf_25[t] -0.00338824381708286Vanaf_25_s[t] -0.0936569913157868Belgie[t] -0.0172056942539791Belgie_s[t] -0.169613905663898Euroraad[t] -0.651252503842849Euroraad_s[t] + 0.121091973648387`EU-27`[t] + 0.587652217252215`EU-27_s`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.06313735180421.055111.00760.31550.15775
S_t0.008733580407888040.0078671.11010.268980.13449
s0.5594299568748791.3622080.41070.6819790.34099
t-9.26671245417641e-050.005869-0.01580.9874260.493713
Jonger_dan_250.9915916648997290.004367227.045100
Jonger_dan_25_s0.007721966639390160.0085360.90460.3673410.18367
Vanaf_251.002871885932830.004621217.039200
Vanaf_25_s-0.003388243817082860.006093-0.55610.5790860.289543
Belgie-0.09365699131578680.169963-0.5510.5825410.291271
Belgie_s-0.01720569425397910.24038-0.07160.9430480.471524
Euroraad-0.1696139056638980.546775-0.31020.7568960.378448
Euroraad_s-0.6512525038428490.767361-0.84870.3976010.198801
`EU-27`0.1210919736483870.5209920.23240.816570.408285
`EU-27_s`0.5876522172522150.7201630.8160.4159820.207991

\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) & 1.0631373518042 & 1.05511 & 1.0076 & 0.3155 & 0.15775 \tabularnewline
S_t & 0.00873358040788804 & 0.007867 & 1.1101 & 0.26898 & 0.13449 \tabularnewline
s & 0.559429956874879 & 1.362208 & 0.4107 & 0.681979 & 0.34099 \tabularnewline
t & -9.26671245417641e-05 & 0.005869 & -0.0158 & 0.987426 & 0.493713 \tabularnewline
Jonger_dan_25 & 0.991591664899729 & 0.004367 & 227.0451 & 0 & 0 \tabularnewline
Jonger_dan_25_s & 0.00772196663939016 & 0.008536 & 0.9046 & 0.367341 & 0.18367 \tabularnewline
Vanaf_25 & 1.00287188593283 & 0.004621 & 217.0392 & 0 & 0 \tabularnewline
Vanaf_25_s & -0.00338824381708286 & 0.006093 & -0.5561 & 0.579086 & 0.289543 \tabularnewline
Belgie & -0.0936569913157868 & 0.169963 & -0.551 & 0.582541 & 0.291271 \tabularnewline
Belgie_s & -0.0172056942539791 & 0.24038 & -0.0716 & 0.943048 & 0.471524 \tabularnewline
Euroraad & -0.169613905663898 & 0.546775 & -0.3102 & 0.756896 & 0.378448 \tabularnewline
Euroraad_s & -0.651252503842849 & 0.767361 & -0.8487 & 0.397601 & 0.198801 \tabularnewline
`EU-27` & 0.121091973648387 & 0.520992 & 0.2324 & 0.81657 & 0.408285 \tabularnewline
`EU-27_s` & 0.587652217252215 & 0.720163 & 0.816 & 0.415982 & 0.207991 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&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]1.0631373518042[/C][C]1.05511[/C][C]1.0076[/C][C]0.3155[/C][C]0.15775[/C][/ROW]
[ROW][C]S_t[/C][C]0.00873358040788804[/C][C]0.007867[/C][C]1.1101[/C][C]0.26898[/C][C]0.13449[/C][/ROW]
[ROW][C]s[/C][C]0.559429956874879[/C][C]1.362208[/C][C]0.4107[/C][C]0.681979[/C][C]0.34099[/C][/ROW]
[ROW][C]t[/C][C]-9.26671245417641e-05[/C][C]0.005869[/C][C]-0.0158[/C][C]0.987426[/C][C]0.493713[/C][/ROW]
[ROW][C]Jonger_dan_25[/C][C]0.991591664899729[/C][C]0.004367[/C][C]227.0451[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Jonger_dan_25_s[/C][C]0.00772196663939016[/C][C]0.008536[/C][C]0.9046[/C][C]0.367341[/C][C]0.18367[/C][/ROW]
[ROW][C]Vanaf_25[/C][C]1.00287188593283[/C][C]0.004621[/C][C]217.0392[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25_s[/C][C]-0.00338824381708286[/C][C]0.006093[/C][C]-0.5561[/C][C]0.579086[/C][C]0.289543[/C][/ROW]
[ROW][C]Belgie[/C][C]-0.0936569913157868[/C][C]0.169963[/C][C]-0.551[/C][C]0.582541[/C][C]0.291271[/C][/ROW]
[ROW][C]Belgie_s[/C][C]-0.0172056942539791[/C][C]0.24038[/C][C]-0.0716[/C][C]0.943048[/C][C]0.471524[/C][/ROW]
[ROW][C]Euroraad[/C][C]-0.169613905663898[/C][C]0.546775[/C][C]-0.3102[/C][C]0.756896[/C][C]0.378448[/C][/ROW]
[ROW][C]Euroraad_s[/C][C]-0.651252503842849[/C][C]0.767361[/C][C]-0.8487[/C][C]0.397601[/C][C]0.198801[/C][/ROW]
[ROW][C]`EU-27`[/C][C]0.121091973648387[/C][C]0.520992[/C][C]0.2324[/C][C]0.81657[/C][C]0.408285[/C][/ROW]
[ROW][C]`EU-27_s`[/C][C]0.587652217252215[/C][C]0.720163[/C][C]0.816[/C][C]0.415982[/C][C]0.207991[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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)1.06313735180421.055111.00760.31550.15775
S_t0.008733580407888040.0078671.11010.268980.13449
s0.5594299568748791.3622080.41070.6819790.34099
t-9.26671245417641e-050.005869-0.01580.9874260.493713
Jonger_dan_250.9915916648997290.004367227.045100
Jonger_dan_25_s0.007721966639390160.0085360.90460.3673410.18367
Vanaf_251.002871885932830.004621217.039200
Vanaf_25_s-0.003388243817082860.006093-0.55610.5790860.289543
Belgie-0.09365699131578680.169963-0.5510.5825410.291271
Belgie_s-0.01720569425397910.24038-0.07160.9430480.471524
Euroraad-0.1696139056638980.546775-0.31020.7568960.378448
Euroraad_s-0.6512525038428490.767361-0.84870.3976010.198801
`EU-27`0.1210919736483870.5209920.23240.816570.408285
`EU-27_s`0.5876522172522150.7201630.8160.4159820.207991







Multiple Linear Regression - Regression Statistics
Multiple R0.999943132217885
R-squared0.999886267669715
Adjusted R-squared0.99987498125526
F-TEST (value)88592.0211056611
F-TEST (DF numerator)13
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.506631776838051
Sum Squared Residuals33.6245242065726

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999943132217885 \tabularnewline
R-squared & 0.999886267669715 \tabularnewline
Adjusted R-squared & 0.99987498125526 \tabularnewline
F-TEST (value) & 88592.0211056611 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.506631776838051 \tabularnewline
Sum Squared Residuals & 33.6245242065726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999943132217885[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999886267669715[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99987498125526[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]88592.0211056611[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/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]0.506631776838051[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33.6245242065726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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 R0.999943132217885
R-squared0.999886267669715
Adjusted R-squared0.99987498125526
F-TEST (value)88592.0211056611
F-TEST (DF numerator)13
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.506631776838051
Sum Squared Residuals33.6245242065726







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.977461935308-0.977461935308176
2485484.8143901898970.185609810103049
3464463.8468310036490.153168996350524
4460459.8805158811940.119484118806095
5467466.9898014673520.010198532647855
6460460.013653186112-0.0136531861121218
7448448.11142708784-0.1114270878403
8443443.100987285161-0.100987285160818
9436436.312023754901-0.312023754901457
10431431.329497799311-0.329497799310527
11484484.150659632863-0.1506596328631
12510509.0427095538610.957290446138538
13513513.01472711204-0.0147271120404201
14503503.049427367555-0.0494273675549814
15471471.12584377376-0.125843773759875
16471471.041821809265-0.0418218092654609
17476476.119051316098-0.119051316097909
18475474.1180227550190.881977244980972
19470470.129239131569-0.129239131569265
20461461.143081354814-0.143081354813747
21455455.205376411975-0.205376411974878
22456455.205283744850.794716255149667
23517516.9197357938060.0802642061942993
24525525.821407668712-0.821407668712504
25523522.7976524751250.202347524875408
26519518.9391271184790.0608728815212293
27509508.8612887962870.138711203713358
28512511.8687206570710.131279342929486
29519518.8405323167660.159467683233809
30517516.7577130786170.242286921383079
31510509.7708185499730.229181450026834
32509509.769053237006-0.769053237006072
33501500.0945486111040.905451388896453
34507507.063757091843-0.0637570918431566
35569569.793384397163-0.793384397162957
36580579.7487302116680.251269788331698
37578577.7278469040130.272153095986776
38565565.786202315583-0.78620231558272
39547547.712664867041-0.712664867041217
40555554.7776494362450.222350563755449
41562561.749461095940.250538904059776
42561560.7591283988380.240871601162281
43555555.793713712693-0.793713712692692
44544543.7830416391850.216958360815336
45537537.128932873421-0.128932873420526
46543543.087818290997-0.0878182909972307
47594593.7793608316960.220639168303972
48611610.6984711387370.301528861263011
49613612.7154024645110.284597535488783
50611610.6578428501820.342157149817799
51594593.6170478494270.382952150572624
52595595.625336089249-0.625336089248751
53591590.7194954857870.280504514212803
54589589.718076520128-0.718076520127709
55584583.7195793650430.280420634957275
56573572.7347502679360.26524973206402
57567567.158868796691-0.158868796691404
58569569.162605379531-0.162605379531005
59621620.8842879272260.115712072774368
60629628.82178077250.178219227499522
61628627.8188162194430.181183780556905
62612611.7837819165730.216218083427389
63595595.742596511237-0.742596511237159
64597596.762783341380.237216658619962
65593592.774805713540.225194286459535
66590589.796547943490.203452056509584
67580579.8225847209370.17741527906332
68574573.9058341856490.0941658143508994
69573573.203272289672-0.203272289671478
70573573.215482992979-0.215482992978537
71620620.012985483103-0.012985483102544
72626625.981174203640.0188257963604697
73620619.9799826351530.0200173648473557
74588586.9880588810161.01194111898445
75566565.9385390071210.0614609928789889
76557558.056885988883-1.05688598888303
77561561.004145620708-0.00414562070755639
78549549.031215113922-0.0312151139222915
79532532.060866396558-0.0608663965582643
80526526.073241442181-0.073241442180643
81511511.306559521443-0.306559521442632
82499499.329283963483-0.32928396348333
83555555.159061454834-0.159061454834414
84565564.135865833170.864134166830087
85542542.154490894055-0.154490894054761
86527527.091600227194-0.0916002271935157
87510510.181549692092-0.181549692092478
88514514.117420765325-0.117420765325399
89517517.135598873513-0.135598873512985
90508507.183468166920.816531833080087
91493493.211444422599-0.211444422599496
92490490.139887789738-0.139887789738179
93469469.380458029688-0.380458029687633
94478477.3552818945930.644718105406683
95528529.158669487753-1.15866948775296
96534533.1107255878940.889274412105801
97518518.113504492241-0.113504492241294
98506506.09143145909-0.0914314590901474
99502502.080393402496-0.0803934024957967
100516515.9658679310180.0341320689824674
101528527.9719514763740.0280485236260784
102533532.922201397540.07779860245977
103536535.8138658945190.186134105481417
104537536.8929089262810.107091073719262
105524523.1838893928540.816110607145613
106536536.173901663756-0.173901663755984
107587586.9638840082480.0361159917520472
108597595.9247004865541.07529951344645
109581580.8992377793010.100762220698857
110564564.826874068585-0.826874068584499
111558556.9004540589351.09954594106529
112575574.8061175890640.193882410935963
113580580.931236656108-0.931236656108059
114575574.932399479870.0676005201301813
115563563.947570382763-0.947570382763066
116552550.8818573710511.11814262894924
117537537.114921821288-0.114921821287827
118545545.120095992761-0.120095992761304
119601600.974348448080.025651551919881
120604604.92742769762-0.927427697619857
121586586.918847445935-0.918847445935181
122564563.9803373568480.019662643152099
123549548.0207726180060.979227381993769
124551551.061633295038-0.0616332950376637
125556556.111993435855-0.111993435854683
126548548.229704432583-0.22970443258317
127540540.183368100714-0.183368100713952
128531531.186250008705-0.186250008705085
129521520.3130150880120.686984911988427
130519518.2902172584550.709782741545032
131572572.105776651312-0.105776651311831
132581582.050589103082-1.05058910308234
133563563.024919073132-0.0249190731317081
134548548.077363216336-0.0773632163360862
135539539.080459192177-0.0804591921768508
136541541.017407038451-0.0174070384512693
137562561.0036585193060.996341480694122
138559559.002289937074-0.00228993707374305
139546545.9963650758950.00363492410548168
140536536.835073701984-0.835073701984302
141528527.1303116108740.869688389126159
142530531.114293852213-1.11429385221287
143582581.963614973170.0363850268296009
144599598.8984566110030.101543388996809
145584583.8496044853530.150395514647158

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.977461935308 & -0.977461935308176 \tabularnewline
2 & 485 & 484.814390189897 & 0.185609810103049 \tabularnewline
3 & 464 & 463.846831003649 & 0.153168996350524 \tabularnewline
4 & 460 & 459.880515881194 & 0.119484118806095 \tabularnewline
5 & 467 & 466.989801467352 & 0.010198532647855 \tabularnewline
6 & 460 & 460.013653186112 & -0.0136531861121218 \tabularnewline
7 & 448 & 448.11142708784 & -0.1114270878403 \tabularnewline
8 & 443 & 443.100987285161 & -0.100987285160818 \tabularnewline
9 & 436 & 436.312023754901 & -0.312023754901457 \tabularnewline
10 & 431 & 431.329497799311 & -0.329497799310527 \tabularnewline
11 & 484 & 484.150659632863 & -0.1506596328631 \tabularnewline
12 & 510 & 509.042709553861 & 0.957290446138538 \tabularnewline
13 & 513 & 513.01472711204 & -0.0147271120404201 \tabularnewline
14 & 503 & 503.049427367555 & -0.0494273675549814 \tabularnewline
15 & 471 & 471.12584377376 & -0.125843773759875 \tabularnewline
16 & 471 & 471.041821809265 & -0.0418218092654609 \tabularnewline
17 & 476 & 476.119051316098 & -0.119051316097909 \tabularnewline
18 & 475 & 474.118022755019 & 0.881977244980972 \tabularnewline
19 & 470 & 470.129239131569 & -0.129239131569265 \tabularnewline
20 & 461 & 461.143081354814 & -0.143081354813747 \tabularnewline
21 & 455 & 455.205376411975 & -0.205376411974878 \tabularnewline
22 & 456 & 455.20528374485 & 0.794716255149667 \tabularnewline
23 & 517 & 516.919735793806 & 0.0802642061942993 \tabularnewline
24 & 525 & 525.821407668712 & -0.821407668712504 \tabularnewline
25 & 523 & 522.797652475125 & 0.202347524875408 \tabularnewline
26 & 519 & 518.939127118479 & 0.0608728815212293 \tabularnewline
27 & 509 & 508.861288796287 & 0.138711203713358 \tabularnewline
28 & 512 & 511.868720657071 & 0.131279342929486 \tabularnewline
29 & 519 & 518.840532316766 & 0.159467683233809 \tabularnewline
30 & 517 & 516.757713078617 & 0.242286921383079 \tabularnewline
31 & 510 & 509.770818549973 & 0.229181450026834 \tabularnewline
32 & 509 & 509.769053237006 & -0.769053237006072 \tabularnewline
33 & 501 & 500.094548611104 & 0.905451388896453 \tabularnewline
34 & 507 & 507.063757091843 & -0.0637570918431566 \tabularnewline
35 & 569 & 569.793384397163 & -0.793384397162957 \tabularnewline
36 & 580 & 579.748730211668 & 0.251269788331698 \tabularnewline
37 & 578 & 577.727846904013 & 0.272153095986776 \tabularnewline
38 & 565 & 565.786202315583 & -0.78620231558272 \tabularnewline
39 & 547 & 547.712664867041 & -0.712664867041217 \tabularnewline
40 & 555 & 554.777649436245 & 0.222350563755449 \tabularnewline
41 & 562 & 561.74946109594 & 0.250538904059776 \tabularnewline
42 & 561 & 560.759128398838 & 0.240871601162281 \tabularnewline
43 & 555 & 555.793713712693 & -0.793713712692692 \tabularnewline
44 & 544 & 543.783041639185 & 0.216958360815336 \tabularnewline
45 & 537 & 537.128932873421 & -0.128932873420526 \tabularnewline
46 & 543 & 543.087818290997 & -0.0878182909972307 \tabularnewline
47 & 594 & 593.779360831696 & 0.220639168303972 \tabularnewline
48 & 611 & 610.698471138737 & 0.301528861263011 \tabularnewline
49 & 613 & 612.715402464511 & 0.284597535488783 \tabularnewline
50 & 611 & 610.657842850182 & 0.342157149817799 \tabularnewline
51 & 594 & 593.617047849427 & 0.382952150572624 \tabularnewline
52 & 595 & 595.625336089249 & -0.625336089248751 \tabularnewline
53 & 591 & 590.719495485787 & 0.280504514212803 \tabularnewline
54 & 589 & 589.718076520128 & -0.718076520127709 \tabularnewline
55 & 584 & 583.719579365043 & 0.280420634957275 \tabularnewline
56 & 573 & 572.734750267936 & 0.26524973206402 \tabularnewline
57 & 567 & 567.158868796691 & -0.158868796691404 \tabularnewline
58 & 569 & 569.162605379531 & -0.162605379531005 \tabularnewline
59 & 621 & 620.884287927226 & 0.115712072774368 \tabularnewline
60 & 629 & 628.8217807725 & 0.178219227499522 \tabularnewline
61 & 628 & 627.818816219443 & 0.181183780556905 \tabularnewline
62 & 612 & 611.783781916573 & 0.216218083427389 \tabularnewline
63 & 595 & 595.742596511237 & -0.742596511237159 \tabularnewline
64 & 597 & 596.76278334138 & 0.237216658619962 \tabularnewline
65 & 593 & 592.77480571354 & 0.225194286459535 \tabularnewline
66 & 590 & 589.79654794349 & 0.203452056509584 \tabularnewline
67 & 580 & 579.822584720937 & 0.17741527906332 \tabularnewline
68 & 574 & 573.905834185649 & 0.0941658143508994 \tabularnewline
69 & 573 & 573.203272289672 & -0.203272289671478 \tabularnewline
70 & 573 & 573.215482992979 & -0.215482992978537 \tabularnewline
71 & 620 & 620.012985483103 & -0.012985483102544 \tabularnewline
72 & 626 & 625.98117420364 & 0.0188257963604697 \tabularnewline
73 & 620 & 619.979982635153 & 0.0200173648473557 \tabularnewline
74 & 588 & 586.988058881016 & 1.01194111898445 \tabularnewline
75 & 566 & 565.938539007121 & 0.0614609928789889 \tabularnewline
76 & 557 & 558.056885988883 & -1.05688598888303 \tabularnewline
77 & 561 & 561.004145620708 & -0.00414562070755639 \tabularnewline
78 & 549 & 549.031215113922 & -0.0312151139222915 \tabularnewline
79 & 532 & 532.060866396558 & -0.0608663965582643 \tabularnewline
80 & 526 & 526.073241442181 & -0.073241442180643 \tabularnewline
81 & 511 & 511.306559521443 & -0.306559521442632 \tabularnewline
82 & 499 & 499.329283963483 & -0.32928396348333 \tabularnewline
83 & 555 & 555.159061454834 & -0.159061454834414 \tabularnewline
84 & 565 & 564.13586583317 & 0.864134166830087 \tabularnewline
85 & 542 & 542.154490894055 & -0.154490894054761 \tabularnewline
86 & 527 & 527.091600227194 & -0.0916002271935157 \tabularnewline
87 & 510 & 510.181549692092 & -0.181549692092478 \tabularnewline
88 & 514 & 514.117420765325 & -0.117420765325399 \tabularnewline
89 & 517 & 517.135598873513 & -0.135598873512985 \tabularnewline
90 & 508 & 507.18346816692 & 0.816531833080087 \tabularnewline
91 & 493 & 493.211444422599 & -0.211444422599496 \tabularnewline
92 & 490 & 490.139887789738 & -0.139887789738179 \tabularnewline
93 & 469 & 469.380458029688 & -0.380458029687633 \tabularnewline
94 & 478 & 477.355281894593 & 0.644718105406683 \tabularnewline
95 & 528 & 529.158669487753 & -1.15866948775296 \tabularnewline
96 & 534 & 533.110725587894 & 0.889274412105801 \tabularnewline
97 & 518 & 518.113504492241 & -0.113504492241294 \tabularnewline
98 & 506 & 506.09143145909 & -0.0914314590901474 \tabularnewline
99 & 502 & 502.080393402496 & -0.0803934024957967 \tabularnewline
100 & 516 & 515.965867931018 & 0.0341320689824674 \tabularnewline
101 & 528 & 527.971951476374 & 0.0280485236260784 \tabularnewline
102 & 533 & 532.92220139754 & 0.07779860245977 \tabularnewline
103 & 536 & 535.813865894519 & 0.186134105481417 \tabularnewline
104 & 537 & 536.892908926281 & 0.107091073719262 \tabularnewline
105 & 524 & 523.183889392854 & 0.816110607145613 \tabularnewline
106 & 536 & 536.173901663756 & -0.173901663755984 \tabularnewline
107 & 587 & 586.963884008248 & 0.0361159917520472 \tabularnewline
108 & 597 & 595.924700486554 & 1.07529951344645 \tabularnewline
109 & 581 & 580.899237779301 & 0.100762220698857 \tabularnewline
110 & 564 & 564.826874068585 & -0.826874068584499 \tabularnewline
111 & 558 & 556.900454058935 & 1.09954594106529 \tabularnewline
112 & 575 & 574.806117589064 & 0.193882410935963 \tabularnewline
113 & 580 & 580.931236656108 & -0.931236656108059 \tabularnewline
114 & 575 & 574.93239947987 & 0.0676005201301813 \tabularnewline
115 & 563 & 563.947570382763 & -0.947570382763066 \tabularnewline
116 & 552 & 550.881857371051 & 1.11814262894924 \tabularnewline
117 & 537 & 537.114921821288 & -0.114921821287827 \tabularnewline
118 & 545 & 545.120095992761 & -0.120095992761304 \tabularnewline
119 & 601 & 600.97434844808 & 0.025651551919881 \tabularnewline
120 & 604 & 604.92742769762 & -0.927427697619857 \tabularnewline
121 & 586 & 586.918847445935 & -0.918847445935181 \tabularnewline
122 & 564 & 563.980337356848 & 0.019662643152099 \tabularnewline
123 & 549 & 548.020772618006 & 0.979227381993769 \tabularnewline
124 & 551 & 551.061633295038 & -0.0616332950376637 \tabularnewline
125 & 556 & 556.111993435855 & -0.111993435854683 \tabularnewline
126 & 548 & 548.229704432583 & -0.22970443258317 \tabularnewline
127 & 540 & 540.183368100714 & -0.183368100713952 \tabularnewline
128 & 531 & 531.186250008705 & -0.186250008705085 \tabularnewline
129 & 521 & 520.313015088012 & 0.686984911988427 \tabularnewline
130 & 519 & 518.290217258455 & 0.709782741545032 \tabularnewline
131 & 572 & 572.105776651312 & -0.105776651311831 \tabularnewline
132 & 581 & 582.050589103082 & -1.05058910308234 \tabularnewline
133 & 563 & 563.024919073132 & -0.0249190731317081 \tabularnewline
134 & 548 & 548.077363216336 & -0.0773632163360862 \tabularnewline
135 & 539 & 539.080459192177 & -0.0804591921768508 \tabularnewline
136 & 541 & 541.017407038451 & -0.0174070384512693 \tabularnewline
137 & 562 & 561.003658519306 & 0.996341480694122 \tabularnewline
138 & 559 & 559.002289937074 & -0.00228993707374305 \tabularnewline
139 & 546 & 545.996365075895 & 0.00363492410548168 \tabularnewline
140 & 536 & 536.835073701984 & -0.835073701984302 \tabularnewline
141 & 528 & 527.130311610874 & 0.869688389126159 \tabularnewline
142 & 530 & 531.114293852213 & -1.11429385221287 \tabularnewline
143 & 582 & 581.96361497317 & 0.0363850268296009 \tabularnewline
144 & 599 & 598.898456611003 & 0.101543388996809 \tabularnewline
145 & 584 & 583.849604485353 & 0.150395514647158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&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]501.977461935308[/C][C]-0.977461935308176[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.814390189897[/C][C]0.185609810103049[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.846831003649[/C][C]0.153168996350524[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.880515881194[/C][C]0.119484118806095[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]466.989801467352[/C][C]0.010198532647855[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.013653186112[/C][C]-0.0136531861121218[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.11142708784[/C][C]-0.1114270878403[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.100987285161[/C][C]-0.100987285160818[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.312023754901[/C][C]-0.312023754901457[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.329497799311[/C][C]-0.329497799310527[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.150659632863[/C][C]-0.1506596328631[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.042709553861[/C][C]0.957290446138538[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.01472711204[/C][C]-0.0147271120404201[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]503.049427367555[/C][C]-0.0494273675549814[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.12584377376[/C][C]-0.125843773759875[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.041821809265[/C][C]-0.0418218092654609[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.119051316098[/C][C]-0.119051316097909[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.118022755019[/C][C]0.881977244980972[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.129239131569[/C][C]-0.129239131569265[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.143081354814[/C][C]-0.143081354813747[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.205376411975[/C][C]-0.205376411974878[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.20528374485[/C][C]0.794716255149667[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.919735793806[/C][C]0.0802642061942993[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.821407668712[/C][C]-0.821407668712504[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.797652475125[/C][C]0.202347524875408[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.939127118479[/C][C]0.0608728815212293[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.861288796287[/C][C]0.138711203713358[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.868720657071[/C][C]0.131279342929486[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.840532316766[/C][C]0.159467683233809[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.757713078617[/C][C]0.242286921383079[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.770818549973[/C][C]0.229181450026834[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.769053237006[/C][C]-0.769053237006072[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]500.094548611104[/C][C]0.905451388896453[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]507.063757091843[/C][C]-0.0637570918431566[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.793384397163[/C][C]-0.793384397162957[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.748730211668[/C][C]0.251269788331698[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.727846904013[/C][C]0.272153095986776[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.786202315583[/C][C]-0.78620231558272[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.712664867041[/C][C]-0.712664867041217[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.777649436245[/C][C]0.222350563755449[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.74946109594[/C][C]0.250538904059776[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.759128398838[/C][C]0.240871601162281[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.793713712693[/C][C]-0.793713712692692[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.783041639185[/C][C]0.216958360815336[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]537.128932873421[/C][C]-0.128932873420526[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]543.087818290997[/C][C]-0.0878182909972307[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.779360831696[/C][C]0.220639168303972[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.698471138737[/C][C]0.301528861263011[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.715402464511[/C][C]0.284597535488783[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.657842850182[/C][C]0.342157149817799[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.617047849427[/C][C]0.382952150572624[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.625336089249[/C][C]-0.625336089248751[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.719495485787[/C][C]0.280504514212803[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.718076520128[/C][C]-0.718076520127709[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.719579365043[/C][C]0.280420634957275[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.734750267936[/C][C]0.26524973206402[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]567.158868796691[/C][C]-0.158868796691404[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]569.162605379531[/C][C]-0.162605379531005[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.884287927226[/C][C]0.115712072774368[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.8217807725[/C][C]0.178219227499522[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.818816219443[/C][C]0.181183780556905[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.783781916573[/C][C]0.216218083427389[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.742596511237[/C][C]-0.742596511237159[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.76278334138[/C][C]0.237216658619962[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.77480571354[/C][C]0.225194286459535[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.79654794349[/C][C]0.203452056509584[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.822584720937[/C][C]0.17741527906332[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]573.905834185649[/C][C]0.0941658143508994[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]573.203272289672[/C][C]-0.203272289671478[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.215482992979[/C][C]-0.215482992978537[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]620.012985483103[/C][C]-0.012985483102544[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.98117420364[/C][C]0.0188257963604697[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.979982635153[/C][C]0.0200173648473557[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.988058881016[/C][C]1.01194111898445[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.938539007121[/C][C]0.0614609928789889[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.056885988883[/C][C]-1.05688598888303[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.004145620708[/C][C]-0.00414562070755639[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.031215113922[/C][C]-0.0312151139222915[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.060866396558[/C][C]-0.0608663965582643[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.073241442181[/C][C]-0.073241442180643[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.306559521443[/C][C]-0.306559521442632[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.329283963483[/C][C]-0.32928396348333[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.159061454834[/C][C]-0.159061454834414[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.13586583317[/C][C]0.864134166830087[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.154490894055[/C][C]-0.154490894054761[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.091600227194[/C][C]-0.0916002271935157[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.181549692092[/C][C]-0.181549692092478[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.117420765325[/C][C]-0.117420765325399[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.135598873513[/C][C]-0.135598873512985[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.18346816692[/C][C]0.816531833080087[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.211444422599[/C][C]-0.211444422599496[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.139887789738[/C][C]-0.139887789738179[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.380458029688[/C][C]-0.380458029687633[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.355281894593[/C][C]0.644718105406683[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.158669487753[/C][C]-1.15866948775296[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.110725587894[/C][C]0.889274412105801[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.113504492241[/C][C]-0.113504492241294[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.09143145909[/C][C]-0.0914314590901474[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.080393402496[/C][C]-0.0803934024957967[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]515.965867931018[/C][C]0.0341320689824674[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]527.971951476374[/C][C]0.0280485236260784[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]532.92220139754[/C][C]0.07779860245977[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.813865894519[/C][C]0.186134105481417[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]536.892908926281[/C][C]0.107091073719262[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.183889392854[/C][C]0.816110607145613[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.173901663756[/C][C]-0.173901663755984[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.963884008248[/C][C]0.0361159917520472[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.924700486554[/C][C]1.07529951344645[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.899237779301[/C][C]0.100762220698857[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.826874068585[/C][C]-0.826874068584499[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.900454058935[/C][C]1.09954594106529[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.806117589064[/C][C]0.193882410935963[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.931236656108[/C][C]-0.931236656108059[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.93239947987[/C][C]0.0676005201301813[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]563.947570382763[/C][C]-0.947570382763066[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.881857371051[/C][C]1.11814262894924[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.114921821288[/C][C]-0.114921821287827[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.120095992761[/C][C]-0.120095992761304[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.97434844808[/C][C]0.025651551919881[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.92742769762[/C][C]-0.927427697619857[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.918847445935[/C][C]-0.918847445935181[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.980337356848[/C][C]0.019662643152099[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.020772618006[/C][C]0.979227381993769[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.061633295038[/C][C]-0.0616332950376637[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.111993435855[/C][C]-0.111993435854683[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.229704432583[/C][C]-0.22970443258317[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.183368100714[/C][C]-0.183368100713952[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.186250008705[/C][C]-0.186250008705085[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.313015088012[/C][C]0.686984911988427[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.290217258455[/C][C]0.709782741545032[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.105776651312[/C][C]-0.105776651311831[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.050589103082[/C][C]-1.05058910308234[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.024919073132[/C][C]-0.0249190731317081[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.077363216336[/C][C]-0.0773632163360862[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.080459192177[/C][C]-0.0804591921768508[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.017407038451[/C][C]-0.0174070384512693[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.003658519306[/C][C]0.996341480694122[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.002289937074[/C][C]-0.00228993707374305[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]545.996365075895[/C][C]0.00363492410548168[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.835073701984[/C][C]-0.835073701984302[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]527.130311610874[/C][C]0.869688389126159[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]531.114293852213[/C][C]-1.11429385221287[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.96361497317[/C][C]0.0363850268296009[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.898456611003[/C][C]0.101543388996809[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.849604485353[/C][C]0.150395514647158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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
1501501.977461935308-0.977461935308176
2485484.8143901898970.185609810103049
3464463.8468310036490.153168996350524
4460459.8805158811940.119484118806095
5467466.9898014673520.010198532647855
6460460.013653186112-0.0136531861121218
7448448.11142708784-0.1114270878403
8443443.100987285161-0.100987285160818
9436436.312023754901-0.312023754901457
10431431.329497799311-0.329497799310527
11484484.150659632863-0.1506596328631
12510509.0427095538610.957290446138538
13513513.01472711204-0.0147271120404201
14503503.049427367555-0.0494273675549814
15471471.12584377376-0.125843773759875
16471471.041821809265-0.0418218092654609
17476476.119051316098-0.119051316097909
18475474.1180227550190.881977244980972
19470470.129239131569-0.129239131569265
20461461.143081354814-0.143081354813747
21455455.205376411975-0.205376411974878
22456455.205283744850.794716255149667
23517516.9197357938060.0802642061942993
24525525.821407668712-0.821407668712504
25523522.7976524751250.202347524875408
26519518.9391271184790.0608728815212293
27509508.8612887962870.138711203713358
28512511.8687206570710.131279342929486
29519518.8405323167660.159467683233809
30517516.7577130786170.242286921383079
31510509.7708185499730.229181450026834
32509509.769053237006-0.769053237006072
33501500.0945486111040.905451388896453
34507507.063757091843-0.0637570918431566
35569569.793384397163-0.793384397162957
36580579.7487302116680.251269788331698
37578577.7278469040130.272153095986776
38565565.786202315583-0.78620231558272
39547547.712664867041-0.712664867041217
40555554.7776494362450.222350563755449
41562561.749461095940.250538904059776
42561560.7591283988380.240871601162281
43555555.793713712693-0.793713712692692
44544543.7830416391850.216958360815336
45537537.128932873421-0.128932873420526
46543543.087818290997-0.0878182909972307
47594593.7793608316960.220639168303972
48611610.6984711387370.301528861263011
49613612.7154024645110.284597535488783
50611610.6578428501820.342157149817799
51594593.6170478494270.382952150572624
52595595.625336089249-0.625336089248751
53591590.7194954857870.280504514212803
54589589.718076520128-0.718076520127709
55584583.7195793650430.280420634957275
56573572.7347502679360.26524973206402
57567567.158868796691-0.158868796691404
58569569.162605379531-0.162605379531005
59621620.8842879272260.115712072774368
60629628.82178077250.178219227499522
61628627.8188162194430.181183780556905
62612611.7837819165730.216218083427389
63595595.742596511237-0.742596511237159
64597596.762783341380.237216658619962
65593592.774805713540.225194286459535
66590589.796547943490.203452056509584
67580579.8225847209370.17741527906332
68574573.9058341856490.0941658143508994
69573573.203272289672-0.203272289671478
70573573.215482992979-0.215482992978537
71620620.012985483103-0.012985483102544
72626625.981174203640.0188257963604697
73620619.9799826351530.0200173648473557
74588586.9880588810161.01194111898445
75566565.9385390071210.0614609928789889
76557558.056885988883-1.05688598888303
77561561.004145620708-0.00414562070755639
78549549.031215113922-0.0312151139222915
79532532.060866396558-0.0608663965582643
80526526.073241442181-0.073241442180643
81511511.306559521443-0.306559521442632
82499499.329283963483-0.32928396348333
83555555.159061454834-0.159061454834414
84565564.135865833170.864134166830087
85542542.154490894055-0.154490894054761
86527527.091600227194-0.0916002271935157
87510510.181549692092-0.181549692092478
88514514.117420765325-0.117420765325399
89517517.135598873513-0.135598873512985
90508507.183468166920.816531833080087
91493493.211444422599-0.211444422599496
92490490.139887789738-0.139887789738179
93469469.380458029688-0.380458029687633
94478477.3552818945930.644718105406683
95528529.158669487753-1.15866948775296
96534533.1107255878940.889274412105801
97518518.113504492241-0.113504492241294
98506506.09143145909-0.0914314590901474
99502502.080393402496-0.0803934024957967
100516515.9658679310180.0341320689824674
101528527.9719514763740.0280485236260784
102533532.922201397540.07779860245977
103536535.8138658945190.186134105481417
104537536.8929089262810.107091073719262
105524523.1838893928540.816110607145613
106536536.173901663756-0.173901663755984
107587586.9638840082480.0361159917520472
108597595.9247004865541.07529951344645
109581580.8992377793010.100762220698857
110564564.826874068585-0.826874068584499
111558556.9004540589351.09954594106529
112575574.8061175890640.193882410935963
113580580.931236656108-0.931236656108059
114575574.932399479870.0676005201301813
115563563.947570382763-0.947570382763066
116552550.8818573710511.11814262894924
117537537.114921821288-0.114921821287827
118545545.120095992761-0.120095992761304
119601600.974348448080.025651551919881
120604604.92742769762-0.927427697619857
121586586.918847445935-0.918847445935181
122564563.9803373568480.019662643152099
123549548.0207726180060.979227381993769
124551551.061633295038-0.0616332950376637
125556556.111993435855-0.111993435854683
126548548.229704432583-0.22970443258317
127540540.183368100714-0.183368100713952
128531531.186250008705-0.186250008705085
129521520.3130150880120.686984911988427
130519518.2902172584550.709782741545032
131572572.105776651312-0.105776651311831
132581582.050589103082-1.05058910308234
133563563.024919073132-0.0249190731317081
134548548.077363216336-0.0773632163360862
135539539.080459192177-0.0804591921768508
136541541.017407038451-0.0174070384512693
137562561.0036585193060.996341480694122
138559559.002289937074-0.00228993707374305
139546545.9963650758950.00363492410548168
140536536.835073701984-0.835073701984302
141528527.1303116108740.869688389126159
142530531.114293852213-1.11429385221287
143582581.963614973170.0363850268296009
144599598.8984566110030.101543388996809
145584583.8496044853530.150395514647158







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
173.51612460305068e-407.03224920610135e-401
180.06812808439816440.1362561687963290.931871915601836
190.1725092662323460.3450185324646920.827490733767654
200.1379084943916740.2758169887833470.862091505608326
210.07727829674785640.1545565934957130.922721703252144
220.2031052321829820.4062104643659630.796894767817018
230.1330114234757290.2660228469514580.866988576524271
240.1073574043365850.214714808673170.892642595663415
250.2907085967593790.5814171935187590.709291403240621
260.2189906266626570.4379812533253140.781009373337343
270.158603806632550.3172076132651010.84139619336745
280.1125828728259870.2251657456519730.887417127174013
290.0830928346078790.1661856692157580.916907165392121
300.05753367771317360.1150673554263470.942466322286826
310.03955200226090240.07910400452180470.960447997739098
320.09294046068137470.1858809213627490.907059539318625
330.07233142292323930.1446628458464790.927668577076761
340.1076678616038270.2153357232076530.892332138396173
350.1325513505251890.2651027010503780.867448649474811
360.1345101135482620.2690202270965250.865489886451738
370.1457295607781380.2914591215562770.854270439221862
380.1454834265537550.290966853107510.854516573446245
390.1235556733383280.2471113466766570.876444326661672
400.1290566585636480.2581133171272950.870943341436352
410.1037141150672430.2074282301344870.896285884932757
420.08192012887966390.1638402577593280.918079871120336
430.1137645272151520.2275290544303040.886235472784848
440.1408760782062270.2817521564124540.859123921793773
450.2389078117100270.4778156234200530.761092188289973
460.2094605606768830.4189211213537660.790539439323117
470.1708093225113740.3416186450227480.829190677488626
480.1355661046955240.2711322093910480.864433895304476
490.1062575335651140.2125150671302270.893742466434886
500.1206305567345830.2412611134691660.879369443265417
510.1216682369989040.2433364739978080.878331763001096
520.1266372142609070.2532744285218140.873362785739093
530.1137394845450950.2274789690901890.886260515454905
540.1275601750828640.2551203501657290.872439824917136
550.1218463564886870.2436927129773740.878153643511313
560.1118255429712160.2236510859424330.888174457028784
570.1232381475856780.2464762951713560.876761852414322
580.1138625291240230.2277250582480460.886137470875977
590.09044818586349720.1808963717269940.909551814136503
600.07046415967565330.1409283193513070.929535840324347
610.05483813074179940.1096762614835990.945161869258201
620.04703648132628180.09407296265256370.952963518673718
630.05477161924637240.1095432384927450.945228380753628
640.05004530491431190.1000906098286240.949954695085688
650.03923731887440060.07847463774880120.960762681125599
660.02943295936542310.05886591873084620.970567040634577
670.02176419533611020.04352839067222050.97823580466389
680.01607051997748970.03214103995497930.98392948002251
690.01274197595603580.02548395191207160.987258024043964
700.009759813932678070.01951962786535610.990240186067322
710.006840306917426910.01368061383485380.993159693082573
720.00483856332024040.009677126640480810.99516143667976
730.003457989466305850.00691597893261170.996542010533694
740.008062719346409390.01612543869281880.991937280653591
750.006939738544337330.01387947708867470.993060261455663
760.02157113435281120.04314226870562240.978428865647189
770.01576534018808550.0315306803761710.984234659811914
780.011312734817040.02262546963408010.98868726518296
790.007950666327315350.01590133265463070.992049333672685
800.005501917761497830.01100383552299570.994498082238502
810.003972869485998640.007945738971997280.996027130514001
820.003574489927134540.007148979854269070.996425510072866
830.002833976523328540.005667953046657080.997166023476671
840.003863151185260740.007726302370521480.996136848814739
850.003557353997187820.007114707994375640.996442646002812
860.002501896414200440.005003792828400880.9974981035858
870.001697859097496580.003395718194993150.998302140902503
880.001121603825009930.002243207650019860.99887839617499
890.000727974741892710.001455949483785420.999272025258107
900.001878766411636090.003757532823272180.998121233588364
910.001245817369154860.002491634738309720.998754182630845
920.0007923338401749760.001584667680349950.999207666159825
930.0009213603004132470.001842720600826490.999078639699587
940.0008131388980496720.001626277796099340.99918686110195
950.007179017038704070.01435803407740810.992820982961296
960.009826813239755370.01965362647951070.990173186760245
970.007716251895122620.01543250379024520.992283748104877
980.005353072719034330.01070614543806870.994646927280966
990.003831910988884450.00766382197776890.996168089011116
1000.002589552719448850.005179105438897690.997410447280551
1010.001713622869270810.003427245738541630.998286377130729
1020.001112033340412810.002224066680825620.998887966659587
1030.0007076896667211420.001415379333442280.999292310333279
1040.0004265106171331810.0008530212342663620.999573489382867
1050.0002683850862173480.0005367701724346960.999731614913783
1060.000723442042682490.001446884085364980.999276557957318
1070.001051512471573840.002103024943147680.998948487528426
1080.0009889007507053050.001977801501410610.999011099249295
1090.0007343289104805830.001468657820961170.999265671089519
1100.002950233254315710.005900466508631410.997049766745684
1110.006506549836883980.0130130996737680.993493450163116
1120.005193112231417690.01038622446283540.994806887768582
1130.008288424762301350.01657684952460270.991711575237699
1140.005246211680349590.01049242336069920.99475378831965
1150.05006336949676730.1001267389935350.949936630503233
1160.06836697752464030.1367339550492810.93163302247536
1170.05104016066779270.1020803213355850.948959839332207
1180.04449914961955430.08899829923910860.955500850380446
1190.07840973720190720.1568194744038140.921590262798093
1200.07139819920707350.1427963984141470.928601800792926
1210.05717709318108770.1143541863621750.942822906818912
1220.1052132739507360.2104265479014710.894786726049264
1230.1153332756680440.2306665513360880.884666724331956
1240.08873674260831730.1774734852166350.911263257391683
1250.05881099182286590.1176219836457320.941189008177134
1260.03320330495818290.06640660991636570.966796695041817
1270.01851896506019050.03703793012038090.98148103493981
1280.01485062789146230.02970125578292470.985149372108538

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 3.51612460305068e-40 & 7.03224920610135e-40 & 1 \tabularnewline
18 & 0.0681280843981644 & 0.136256168796329 & 0.931871915601836 \tabularnewline
19 & 0.172509266232346 & 0.345018532464692 & 0.827490733767654 \tabularnewline
20 & 0.137908494391674 & 0.275816988783347 & 0.862091505608326 \tabularnewline
21 & 0.0772782967478564 & 0.154556593495713 & 0.922721703252144 \tabularnewline
22 & 0.203105232182982 & 0.406210464365963 & 0.796894767817018 \tabularnewline
23 & 0.133011423475729 & 0.266022846951458 & 0.866988576524271 \tabularnewline
24 & 0.107357404336585 & 0.21471480867317 & 0.892642595663415 \tabularnewline
25 & 0.290708596759379 & 0.581417193518759 & 0.709291403240621 \tabularnewline
26 & 0.218990626662657 & 0.437981253325314 & 0.781009373337343 \tabularnewline
27 & 0.15860380663255 & 0.317207613265101 & 0.84139619336745 \tabularnewline
28 & 0.112582872825987 & 0.225165745651973 & 0.887417127174013 \tabularnewline
29 & 0.083092834607879 & 0.166185669215758 & 0.916907165392121 \tabularnewline
30 & 0.0575336777131736 & 0.115067355426347 & 0.942466322286826 \tabularnewline
31 & 0.0395520022609024 & 0.0791040045218047 & 0.960447997739098 \tabularnewline
32 & 0.0929404606813747 & 0.185880921362749 & 0.907059539318625 \tabularnewline
33 & 0.0723314229232393 & 0.144662845846479 & 0.927668577076761 \tabularnewline
34 & 0.107667861603827 & 0.215335723207653 & 0.892332138396173 \tabularnewline
35 & 0.132551350525189 & 0.265102701050378 & 0.867448649474811 \tabularnewline
36 & 0.134510113548262 & 0.269020227096525 & 0.865489886451738 \tabularnewline
37 & 0.145729560778138 & 0.291459121556277 & 0.854270439221862 \tabularnewline
38 & 0.145483426553755 & 0.29096685310751 & 0.854516573446245 \tabularnewline
39 & 0.123555673338328 & 0.247111346676657 & 0.876444326661672 \tabularnewline
40 & 0.129056658563648 & 0.258113317127295 & 0.870943341436352 \tabularnewline
41 & 0.103714115067243 & 0.207428230134487 & 0.896285884932757 \tabularnewline
42 & 0.0819201288796639 & 0.163840257759328 & 0.918079871120336 \tabularnewline
43 & 0.113764527215152 & 0.227529054430304 & 0.886235472784848 \tabularnewline
44 & 0.140876078206227 & 0.281752156412454 & 0.859123921793773 \tabularnewline
45 & 0.238907811710027 & 0.477815623420053 & 0.761092188289973 \tabularnewline
46 & 0.209460560676883 & 0.418921121353766 & 0.790539439323117 \tabularnewline
47 & 0.170809322511374 & 0.341618645022748 & 0.829190677488626 \tabularnewline
48 & 0.135566104695524 & 0.271132209391048 & 0.864433895304476 \tabularnewline
49 & 0.106257533565114 & 0.212515067130227 & 0.893742466434886 \tabularnewline
50 & 0.120630556734583 & 0.241261113469166 & 0.879369443265417 \tabularnewline
51 & 0.121668236998904 & 0.243336473997808 & 0.878331763001096 \tabularnewline
52 & 0.126637214260907 & 0.253274428521814 & 0.873362785739093 \tabularnewline
53 & 0.113739484545095 & 0.227478969090189 & 0.886260515454905 \tabularnewline
54 & 0.127560175082864 & 0.255120350165729 & 0.872439824917136 \tabularnewline
55 & 0.121846356488687 & 0.243692712977374 & 0.878153643511313 \tabularnewline
56 & 0.111825542971216 & 0.223651085942433 & 0.888174457028784 \tabularnewline
57 & 0.123238147585678 & 0.246476295171356 & 0.876761852414322 \tabularnewline
58 & 0.113862529124023 & 0.227725058248046 & 0.886137470875977 \tabularnewline
59 & 0.0904481858634972 & 0.180896371726994 & 0.909551814136503 \tabularnewline
60 & 0.0704641596756533 & 0.140928319351307 & 0.929535840324347 \tabularnewline
61 & 0.0548381307417994 & 0.109676261483599 & 0.945161869258201 \tabularnewline
62 & 0.0470364813262818 & 0.0940729626525637 & 0.952963518673718 \tabularnewline
63 & 0.0547716192463724 & 0.109543238492745 & 0.945228380753628 \tabularnewline
64 & 0.0500453049143119 & 0.100090609828624 & 0.949954695085688 \tabularnewline
65 & 0.0392373188744006 & 0.0784746377488012 & 0.960762681125599 \tabularnewline
66 & 0.0294329593654231 & 0.0588659187308462 & 0.970567040634577 \tabularnewline
67 & 0.0217641953361102 & 0.0435283906722205 & 0.97823580466389 \tabularnewline
68 & 0.0160705199774897 & 0.0321410399549793 & 0.98392948002251 \tabularnewline
69 & 0.0127419759560358 & 0.0254839519120716 & 0.987258024043964 \tabularnewline
70 & 0.00975981393267807 & 0.0195196278653561 & 0.990240186067322 \tabularnewline
71 & 0.00684030691742691 & 0.0136806138348538 & 0.993159693082573 \tabularnewline
72 & 0.0048385633202404 & 0.00967712664048081 & 0.99516143667976 \tabularnewline
73 & 0.00345798946630585 & 0.0069159789326117 & 0.996542010533694 \tabularnewline
74 & 0.00806271934640939 & 0.0161254386928188 & 0.991937280653591 \tabularnewline
75 & 0.00693973854433733 & 0.0138794770886747 & 0.993060261455663 \tabularnewline
76 & 0.0215711343528112 & 0.0431422687056224 & 0.978428865647189 \tabularnewline
77 & 0.0157653401880855 & 0.031530680376171 & 0.984234659811914 \tabularnewline
78 & 0.01131273481704 & 0.0226254696340801 & 0.98868726518296 \tabularnewline
79 & 0.00795066632731535 & 0.0159013326546307 & 0.992049333672685 \tabularnewline
80 & 0.00550191776149783 & 0.0110038355229957 & 0.994498082238502 \tabularnewline
81 & 0.00397286948599864 & 0.00794573897199728 & 0.996027130514001 \tabularnewline
82 & 0.00357448992713454 & 0.00714897985426907 & 0.996425510072866 \tabularnewline
83 & 0.00283397652332854 & 0.00566795304665708 & 0.997166023476671 \tabularnewline
84 & 0.00386315118526074 & 0.00772630237052148 & 0.996136848814739 \tabularnewline
85 & 0.00355735399718782 & 0.00711470799437564 & 0.996442646002812 \tabularnewline
86 & 0.00250189641420044 & 0.00500379282840088 & 0.9974981035858 \tabularnewline
87 & 0.00169785909749658 & 0.00339571819499315 & 0.998302140902503 \tabularnewline
88 & 0.00112160382500993 & 0.00224320765001986 & 0.99887839617499 \tabularnewline
89 & 0.00072797474189271 & 0.00145594948378542 & 0.999272025258107 \tabularnewline
90 & 0.00187876641163609 & 0.00375753282327218 & 0.998121233588364 \tabularnewline
91 & 0.00124581736915486 & 0.00249163473830972 & 0.998754182630845 \tabularnewline
92 & 0.000792333840174976 & 0.00158466768034995 & 0.999207666159825 \tabularnewline
93 & 0.000921360300413247 & 0.00184272060082649 & 0.999078639699587 \tabularnewline
94 & 0.000813138898049672 & 0.00162627779609934 & 0.99918686110195 \tabularnewline
95 & 0.00717901703870407 & 0.0143580340774081 & 0.992820982961296 \tabularnewline
96 & 0.00982681323975537 & 0.0196536264795107 & 0.990173186760245 \tabularnewline
97 & 0.00771625189512262 & 0.0154325037902452 & 0.992283748104877 \tabularnewline
98 & 0.00535307271903433 & 0.0107061454380687 & 0.994646927280966 \tabularnewline
99 & 0.00383191098888445 & 0.0076638219777689 & 0.996168089011116 \tabularnewline
100 & 0.00258955271944885 & 0.00517910543889769 & 0.997410447280551 \tabularnewline
101 & 0.00171362286927081 & 0.00342724573854163 & 0.998286377130729 \tabularnewline
102 & 0.00111203334041281 & 0.00222406668082562 & 0.998887966659587 \tabularnewline
103 & 0.000707689666721142 & 0.00141537933344228 & 0.999292310333279 \tabularnewline
104 & 0.000426510617133181 & 0.000853021234266362 & 0.999573489382867 \tabularnewline
105 & 0.000268385086217348 & 0.000536770172434696 & 0.999731614913783 \tabularnewline
106 & 0.00072344204268249 & 0.00144688408536498 & 0.999276557957318 \tabularnewline
107 & 0.00105151247157384 & 0.00210302494314768 & 0.998948487528426 \tabularnewline
108 & 0.000988900750705305 & 0.00197780150141061 & 0.999011099249295 \tabularnewline
109 & 0.000734328910480583 & 0.00146865782096117 & 0.999265671089519 \tabularnewline
110 & 0.00295023325431571 & 0.00590046650863141 & 0.997049766745684 \tabularnewline
111 & 0.00650654983688398 & 0.013013099673768 & 0.993493450163116 \tabularnewline
112 & 0.00519311223141769 & 0.0103862244628354 & 0.994806887768582 \tabularnewline
113 & 0.00828842476230135 & 0.0165768495246027 & 0.991711575237699 \tabularnewline
114 & 0.00524621168034959 & 0.0104924233606992 & 0.99475378831965 \tabularnewline
115 & 0.0500633694967673 & 0.100126738993535 & 0.949936630503233 \tabularnewline
116 & 0.0683669775246403 & 0.136733955049281 & 0.93163302247536 \tabularnewline
117 & 0.0510401606677927 & 0.102080321335585 & 0.948959839332207 \tabularnewline
118 & 0.0444991496195543 & 0.0889982992391086 & 0.955500850380446 \tabularnewline
119 & 0.0784097372019072 & 0.156819474403814 & 0.921590262798093 \tabularnewline
120 & 0.0713981992070735 & 0.142796398414147 & 0.928601800792926 \tabularnewline
121 & 0.0571770931810877 & 0.114354186362175 & 0.942822906818912 \tabularnewline
122 & 0.105213273950736 & 0.210426547901471 & 0.894786726049264 \tabularnewline
123 & 0.115333275668044 & 0.230666551336088 & 0.884666724331956 \tabularnewline
124 & 0.0887367426083173 & 0.177473485216635 & 0.911263257391683 \tabularnewline
125 & 0.0588109918228659 & 0.117621983645732 & 0.941189008177134 \tabularnewline
126 & 0.0332033049581829 & 0.0664066099163657 & 0.966796695041817 \tabularnewline
127 & 0.0185189650601905 & 0.0370379301203809 & 0.98148103493981 \tabularnewline
128 & 0.0148506278914623 & 0.0297012557829247 & 0.985149372108538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&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]17[/C][C]3.51612460305068e-40[/C][C]7.03224920610135e-40[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]0.0681280843981644[/C][C]0.136256168796329[/C][C]0.931871915601836[/C][/ROW]
[ROW][C]19[/C][C]0.172509266232346[/C][C]0.345018532464692[/C][C]0.827490733767654[/C][/ROW]
[ROW][C]20[/C][C]0.137908494391674[/C][C]0.275816988783347[/C][C]0.862091505608326[/C][/ROW]
[ROW][C]21[/C][C]0.0772782967478564[/C][C]0.154556593495713[/C][C]0.922721703252144[/C][/ROW]
[ROW][C]22[/C][C]0.203105232182982[/C][C]0.406210464365963[/C][C]0.796894767817018[/C][/ROW]
[ROW][C]23[/C][C]0.133011423475729[/C][C]0.266022846951458[/C][C]0.866988576524271[/C][/ROW]
[ROW][C]24[/C][C]0.107357404336585[/C][C]0.21471480867317[/C][C]0.892642595663415[/C][/ROW]
[ROW][C]25[/C][C]0.290708596759379[/C][C]0.581417193518759[/C][C]0.709291403240621[/C][/ROW]
[ROW][C]26[/C][C]0.218990626662657[/C][C]0.437981253325314[/C][C]0.781009373337343[/C][/ROW]
[ROW][C]27[/C][C]0.15860380663255[/C][C]0.317207613265101[/C][C]0.84139619336745[/C][/ROW]
[ROW][C]28[/C][C]0.112582872825987[/C][C]0.225165745651973[/C][C]0.887417127174013[/C][/ROW]
[ROW][C]29[/C][C]0.083092834607879[/C][C]0.166185669215758[/C][C]0.916907165392121[/C][/ROW]
[ROW][C]30[/C][C]0.0575336777131736[/C][C]0.115067355426347[/C][C]0.942466322286826[/C][/ROW]
[ROW][C]31[/C][C]0.0395520022609024[/C][C]0.0791040045218047[/C][C]0.960447997739098[/C][/ROW]
[ROW][C]32[/C][C]0.0929404606813747[/C][C]0.185880921362749[/C][C]0.907059539318625[/C][/ROW]
[ROW][C]33[/C][C]0.0723314229232393[/C][C]0.144662845846479[/C][C]0.927668577076761[/C][/ROW]
[ROW][C]34[/C][C]0.107667861603827[/C][C]0.215335723207653[/C][C]0.892332138396173[/C][/ROW]
[ROW][C]35[/C][C]0.132551350525189[/C][C]0.265102701050378[/C][C]0.867448649474811[/C][/ROW]
[ROW][C]36[/C][C]0.134510113548262[/C][C]0.269020227096525[/C][C]0.865489886451738[/C][/ROW]
[ROW][C]37[/C][C]0.145729560778138[/C][C]0.291459121556277[/C][C]0.854270439221862[/C][/ROW]
[ROW][C]38[/C][C]0.145483426553755[/C][C]0.29096685310751[/C][C]0.854516573446245[/C][/ROW]
[ROW][C]39[/C][C]0.123555673338328[/C][C]0.247111346676657[/C][C]0.876444326661672[/C][/ROW]
[ROW][C]40[/C][C]0.129056658563648[/C][C]0.258113317127295[/C][C]0.870943341436352[/C][/ROW]
[ROW][C]41[/C][C]0.103714115067243[/C][C]0.207428230134487[/C][C]0.896285884932757[/C][/ROW]
[ROW][C]42[/C][C]0.0819201288796639[/C][C]0.163840257759328[/C][C]0.918079871120336[/C][/ROW]
[ROW][C]43[/C][C]0.113764527215152[/C][C]0.227529054430304[/C][C]0.886235472784848[/C][/ROW]
[ROW][C]44[/C][C]0.140876078206227[/C][C]0.281752156412454[/C][C]0.859123921793773[/C][/ROW]
[ROW][C]45[/C][C]0.238907811710027[/C][C]0.477815623420053[/C][C]0.761092188289973[/C][/ROW]
[ROW][C]46[/C][C]0.209460560676883[/C][C]0.418921121353766[/C][C]0.790539439323117[/C][/ROW]
[ROW][C]47[/C][C]0.170809322511374[/C][C]0.341618645022748[/C][C]0.829190677488626[/C][/ROW]
[ROW][C]48[/C][C]0.135566104695524[/C][C]0.271132209391048[/C][C]0.864433895304476[/C][/ROW]
[ROW][C]49[/C][C]0.106257533565114[/C][C]0.212515067130227[/C][C]0.893742466434886[/C][/ROW]
[ROW][C]50[/C][C]0.120630556734583[/C][C]0.241261113469166[/C][C]0.879369443265417[/C][/ROW]
[ROW][C]51[/C][C]0.121668236998904[/C][C]0.243336473997808[/C][C]0.878331763001096[/C][/ROW]
[ROW][C]52[/C][C]0.126637214260907[/C][C]0.253274428521814[/C][C]0.873362785739093[/C][/ROW]
[ROW][C]53[/C][C]0.113739484545095[/C][C]0.227478969090189[/C][C]0.886260515454905[/C][/ROW]
[ROW][C]54[/C][C]0.127560175082864[/C][C]0.255120350165729[/C][C]0.872439824917136[/C][/ROW]
[ROW][C]55[/C][C]0.121846356488687[/C][C]0.243692712977374[/C][C]0.878153643511313[/C][/ROW]
[ROW][C]56[/C][C]0.111825542971216[/C][C]0.223651085942433[/C][C]0.888174457028784[/C][/ROW]
[ROW][C]57[/C][C]0.123238147585678[/C][C]0.246476295171356[/C][C]0.876761852414322[/C][/ROW]
[ROW][C]58[/C][C]0.113862529124023[/C][C]0.227725058248046[/C][C]0.886137470875977[/C][/ROW]
[ROW][C]59[/C][C]0.0904481858634972[/C][C]0.180896371726994[/C][C]0.909551814136503[/C][/ROW]
[ROW][C]60[/C][C]0.0704641596756533[/C][C]0.140928319351307[/C][C]0.929535840324347[/C][/ROW]
[ROW][C]61[/C][C]0.0548381307417994[/C][C]0.109676261483599[/C][C]0.945161869258201[/C][/ROW]
[ROW][C]62[/C][C]0.0470364813262818[/C][C]0.0940729626525637[/C][C]0.952963518673718[/C][/ROW]
[ROW][C]63[/C][C]0.0547716192463724[/C][C]0.109543238492745[/C][C]0.945228380753628[/C][/ROW]
[ROW][C]64[/C][C]0.0500453049143119[/C][C]0.100090609828624[/C][C]0.949954695085688[/C][/ROW]
[ROW][C]65[/C][C]0.0392373188744006[/C][C]0.0784746377488012[/C][C]0.960762681125599[/C][/ROW]
[ROW][C]66[/C][C]0.0294329593654231[/C][C]0.0588659187308462[/C][C]0.970567040634577[/C][/ROW]
[ROW][C]67[/C][C]0.0217641953361102[/C][C]0.0435283906722205[/C][C]0.97823580466389[/C][/ROW]
[ROW][C]68[/C][C]0.0160705199774897[/C][C]0.0321410399549793[/C][C]0.98392948002251[/C][/ROW]
[ROW][C]69[/C][C]0.0127419759560358[/C][C]0.0254839519120716[/C][C]0.987258024043964[/C][/ROW]
[ROW][C]70[/C][C]0.00975981393267807[/C][C]0.0195196278653561[/C][C]0.990240186067322[/C][/ROW]
[ROW][C]71[/C][C]0.00684030691742691[/C][C]0.0136806138348538[/C][C]0.993159693082573[/C][/ROW]
[ROW][C]72[/C][C]0.0048385633202404[/C][C]0.00967712664048081[/C][C]0.99516143667976[/C][/ROW]
[ROW][C]73[/C][C]0.00345798946630585[/C][C]0.0069159789326117[/C][C]0.996542010533694[/C][/ROW]
[ROW][C]74[/C][C]0.00806271934640939[/C][C]0.0161254386928188[/C][C]0.991937280653591[/C][/ROW]
[ROW][C]75[/C][C]0.00693973854433733[/C][C]0.0138794770886747[/C][C]0.993060261455663[/C][/ROW]
[ROW][C]76[/C][C]0.0215711343528112[/C][C]0.0431422687056224[/C][C]0.978428865647189[/C][/ROW]
[ROW][C]77[/C][C]0.0157653401880855[/C][C]0.031530680376171[/C][C]0.984234659811914[/C][/ROW]
[ROW][C]78[/C][C]0.01131273481704[/C][C]0.0226254696340801[/C][C]0.98868726518296[/C][/ROW]
[ROW][C]79[/C][C]0.00795066632731535[/C][C]0.0159013326546307[/C][C]0.992049333672685[/C][/ROW]
[ROW][C]80[/C][C]0.00550191776149783[/C][C]0.0110038355229957[/C][C]0.994498082238502[/C][/ROW]
[ROW][C]81[/C][C]0.00397286948599864[/C][C]0.00794573897199728[/C][C]0.996027130514001[/C][/ROW]
[ROW][C]82[/C][C]0.00357448992713454[/C][C]0.00714897985426907[/C][C]0.996425510072866[/C][/ROW]
[ROW][C]83[/C][C]0.00283397652332854[/C][C]0.00566795304665708[/C][C]0.997166023476671[/C][/ROW]
[ROW][C]84[/C][C]0.00386315118526074[/C][C]0.00772630237052148[/C][C]0.996136848814739[/C][/ROW]
[ROW][C]85[/C][C]0.00355735399718782[/C][C]0.00711470799437564[/C][C]0.996442646002812[/C][/ROW]
[ROW][C]86[/C][C]0.00250189641420044[/C][C]0.00500379282840088[/C][C]0.9974981035858[/C][/ROW]
[ROW][C]87[/C][C]0.00169785909749658[/C][C]0.00339571819499315[/C][C]0.998302140902503[/C][/ROW]
[ROW][C]88[/C][C]0.00112160382500993[/C][C]0.00224320765001986[/C][C]0.99887839617499[/C][/ROW]
[ROW][C]89[/C][C]0.00072797474189271[/C][C]0.00145594948378542[/C][C]0.999272025258107[/C][/ROW]
[ROW][C]90[/C][C]0.00187876641163609[/C][C]0.00375753282327218[/C][C]0.998121233588364[/C][/ROW]
[ROW][C]91[/C][C]0.00124581736915486[/C][C]0.00249163473830972[/C][C]0.998754182630845[/C][/ROW]
[ROW][C]92[/C][C]0.000792333840174976[/C][C]0.00158466768034995[/C][C]0.999207666159825[/C][/ROW]
[ROW][C]93[/C][C]0.000921360300413247[/C][C]0.00184272060082649[/C][C]0.999078639699587[/C][/ROW]
[ROW][C]94[/C][C]0.000813138898049672[/C][C]0.00162627779609934[/C][C]0.99918686110195[/C][/ROW]
[ROW][C]95[/C][C]0.00717901703870407[/C][C]0.0143580340774081[/C][C]0.992820982961296[/C][/ROW]
[ROW][C]96[/C][C]0.00982681323975537[/C][C]0.0196536264795107[/C][C]0.990173186760245[/C][/ROW]
[ROW][C]97[/C][C]0.00771625189512262[/C][C]0.0154325037902452[/C][C]0.992283748104877[/C][/ROW]
[ROW][C]98[/C][C]0.00535307271903433[/C][C]0.0107061454380687[/C][C]0.994646927280966[/C][/ROW]
[ROW][C]99[/C][C]0.00383191098888445[/C][C]0.0076638219777689[/C][C]0.996168089011116[/C][/ROW]
[ROW][C]100[/C][C]0.00258955271944885[/C][C]0.00517910543889769[/C][C]0.997410447280551[/C][/ROW]
[ROW][C]101[/C][C]0.00171362286927081[/C][C]0.00342724573854163[/C][C]0.998286377130729[/C][/ROW]
[ROW][C]102[/C][C]0.00111203334041281[/C][C]0.00222406668082562[/C][C]0.998887966659587[/C][/ROW]
[ROW][C]103[/C][C]0.000707689666721142[/C][C]0.00141537933344228[/C][C]0.999292310333279[/C][/ROW]
[ROW][C]104[/C][C]0.000426510617133181[/C][C]0.000853021234266362[/C][C]0.999573489382867[/C][/ROW]
[ROW][C]105[/C][C]0.000268385086217348[/C][C]0.000536770172434696[/C][C]0.999731614913783[/C][/ROW]
[ROW][C]106[/C][C]0.00072344204268249[/C][C]0.00144688408536498[/C][C]0.999276557957318[/C][/ROW]
[ROW][C]107[/C][C]0.00105151247157384[/C][C]0.00210302494314768[/C][C]0.998948487528426[/C][/ROW]
[ROW][C]108[/C][C]0.000988900750705305[/C][C]0.00197780150141061[/C][C]0.999011099249295[/C][/ROW]
[ROW][C]109[/C][C]0.000734328910480583[/C][C]0.00146865782096117[/C][C]0.999265671089519[/C][/ROW]
[ROW][C]110[/C][C]0.00295023325431571[/C][C]0.00590046650863141[/C][C]0.997049766745684[/C][/ROW]
[ROW][C]111[/C][C]0.00650654983688398[/C][C]0.013013099673768[/C][C]0.993493450163116[/C][/ROW]
[ROW][C]112[/C][C]0.00519311223141769[/C][C]0.0103862244628354[/C][C]0.994806887768582[/C][/ROW]
[ROW][C]113[/C][C]0.00828842476230135[/C][C]0.0165768495246027[/C][C]0.991711575237699[/C][/ROW]
[ROW][C]114[/C][C]0.00524621168034959[/C][C]0.0104924233606992[/C][C]0.99475378831965[/C][/ROW]
[ROW][C]115[/C][C]0.0500633694967673[/C][C]0.100126738993535[/C][C]0.949936630503233[/C][/ROW]
[ROW][C]116[/C][C]0.0683669775246403[/C][C]0.136733955049281[/C][C]0.93163302247536[/C][/ROW]
[ROW][C]117[/C][C]0.0510401606677927[/C][C]0.102080321335585[/C][C]0.948959839332207[/C][/ROW]
[ROW][C]118[/C][C]0.0444991496195543[/C][C]0.0889982992391086[/C][C]0.955500850380446[/C][/ROW]
[ROW][C]119[/C][C]0.0784097372019072[/C][C]0.156819474403814[/C][C]0.921590262798093[/C][/ROW]
[ROW][C]120[/C][C]0.0713981992070735[/C][C]0.142796398414147[/C][C]0.928601800792926[/C][/ROW]
[ROW][C]121[/C][C]0.0571770931810877[/C][C]0.114354186362175[/C][C]0.942822906818912[/C][/ROW]
[ROW][C]122[/C][C]0.105213273950736[/C][C]0.210426547901471[/C][C]0.894786726049264[/C][/ROW]
[ROW][C]123[/C][C]0.115333275668044[/C][C]0.230666551336088[/C][C]0.884666724331956[/C][/ROW]
[ROW][C]124[/C][C]0.0887367426083173[/C][C]0.177473485216635[/C][C]0.911263257391683[/C][/ROW]
[ROW][C]125[/C][C]0.0588109918228659[/C][C]0.117621983645732[/C][C]0.941189008177134[/C][/ROW]
[ROW][C]126[/C][C]0.0332033049581829[/C][C]0.0664066099163657[/C][C]0.966796695041817[/C][/ROW]
[ROW][C]127[/C][C]0.0185189650601905[/C][C]0.0370379301203809[/C][C]0.98148103493981[/C][/ROW]
[ROW][C]128[/C][C]0.0148506278914623[/C][C]0.0297012557829247[/C][C]0.985149372108538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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
173.51612460305068e-407.03224920610135e-401
180.06812808439816440.1362561687963290.931871915601836
190.1725092662323460.3450185324646920.827490733767654
200.1379084943916740.2758169887833470.862091505608326
210.07727829674785640.1545565934957130.922721703252144
220.2031052321829820.4062104643659630.796894767817018
230.1330114234757290.2660228469514580.866988576524271
240.1073574043365850.214714808673170.892642595663415
250.2907085967593790.5814171935187590.709291403240621
260.2189906266626570.4379812533253140.781009373337343
270.158603806632550.3172076132651010.84139619336745
280.1125828728259870.2251657456519730.887417127174013
290.0830928346078790.1661856692157580.916907165392121
300.05753367771317360.1150673554263470.942466322286826
310.03955200226090240.07910400452180470.960447997739098
320.09294046068137470.1858809213627490.907059539318625
330.07233142292323930.1446628458464790.927668577076761
340.1076678616038270.2153357232076530.892332138396173
350.1325513505251890.2651027010503780.867448649474811
360.1345101135482620.2690202270965250.865489886451738
370.1457295607781380.2914591215562770.854270439221862
380.1454834265537550.290966853107510.854516573446245
390.1235556733383280.2471113466766570.876444326661672
400.1290566585636480.2581133171272950.870943341436352
410.1037141150672430.2074282301344870.896285884932757
420.08192012887966390.1638402577593280.918079871120336
430.1137645272151520.2275290544303040.886235472784848
440.1408760782062270.2817521564124540.859123921793773
450.2389078117100270.4778156234200530.761092188289973
460.2094605606768830.4189211213537660.790539439323117
470.1708093225113740.3416186450227480.829190677488626
480.1355661046955240.2711322093910480.864433895304476
490.1062575335651140.2125150671302270.893742466434886
500.1206305567345830.2412611134691660.879369443265417
510.1216682369989040.2433364739978080.878331763001096
520.1266372142609070.2532744285218140.873362785739093
530.1137394845450950.2274789690901890.886260515454905
540.1275601750828640.2551203501657290.872439824917136
550.1218463564886870.2436927129773740.878153643511313
560.1118255429712160.2236510859424330.888174457028784
570.1232381475856780.2464762951713560.876761852414322
580.1138625291240230.2277250582480460.886137470875977
590.09044818586349720.1808963717269940.909551814136503
600.07046415967565330.1409283193513070.929535840324347
610.05483813074179940.1096762614835990.945161869258201
620.04703648132628180.09407296265256370.952963518673718
630.05477161924637240.1095432384927450.945228380753628
640.05004530491431190.1000906098286240.949954695085688
650.03923731887440060.07847463774880120.960762681125599
660.02943295936542310.05886591873084620.970567040634577
670.02176419533611020.04352839067222050.97823580466389
680.01607051997748970.03214103995497930.98392948002251
690.01274197595603580.02548395191207160.987258024043964
700.009759813932678070.01951962786535610.990240186067322
710.006840306917426910.01368061383485380.993159693082573
720.00483856332024040.009677126640480810.99516143667976
730.003457989466305850.00691597893261170.996542010533694
740.008062719346409390.01612543869281880.991937280653591
750.006939738544337330.01387947708867470.993060261455663
760.02157113435281120.04314226870562240.978428865647189
770.01576534018808550.0315306803761710.984234659811914
780.011312734817040.02262546963408010.98868726518296
790.007950666327315350.01590133265463070.992049333672685
800.005501917761497830.01100383552299570.994498082238502
810.003972869485998640.007945738971997280.996027130514001
820.003574489927134540.007148979854269070.996425510072866
830.002833976523328540.005667953046657080.997166023476671
840.003863151185260740.007726302370521480.996136848814739
850.003557353997187820.007114707994375640.996442646002812
860.002501896414200440.005003792828400880.9974981035858
870.001697859097496580.003395718194993150.998302140902503
880.001121603825009930.002243207650019860.99887839617499
890.000727974741892710.001455949483785420.999272025258107
900.001878766411636090.003757532823272180.998121233588364
910.001245817369154860.002491634738309720.998754182630845
920.0007923338401749760.001584667680349950.999207666159825
930.0009213603004132470.001842720600826490.999078639699587
940.0008131388980496720.001626277796099340.99918686110195
950.007179017038704070.01435803407740810.992820982961296
960.009826813239755370.01965362647951070.990173186760245
970.007716251895122620.01543250379024520.992283748104877
980.005353072719034330.01070614543806870.994646927280966
990.003831910988884450.00766382197776890.996168089011116
1000.002589552719448850.005179105438897690.997410447280551
1010.001713622869270810.003427245738541630.998286377130729
1020.001112033340412810.002224066680825620.998887966659587
1030.0007076896667211420.001415379333442280.999292310333279
1040.0004265106171331810.0008530212342663620.999573489382867
1050.0002683850862173480.0005367701724346960.999731614913783
1060.000723442042682490.001446884085364980.999276557957318
1070.001051512471573840.002103024943147680.998948487528426
1080.0009889007507053050.001977801501410610.999011099249295
1090.0007343289104805830.001468657820961170.999265671089519
1100.002950233254315710.005900466508631410.997049766745684
1110.006506549836883980.0130130996737680.993493450163116
1120.005193112231417690.01038622446283540.994806887768582
1130.008288424762301350.01657684952460270.991711575237699
1140.005246211680349590.01049242336069920.99475378831965
1150.05006336949676730.1001267389935350.949936630503233
1160.06836697752464030.1367339550492810.93163302247536
1170.05104016066779270.1020803213355850.948959839332207
1180.04449914961955430.08899829923910860.955500850380446
1190.07840973720190720.1568194744038140.921590262798093
1200.07139819920707350.1427963984141470.928601800792926
1210.05717709318108770.1143541863621750.942822906818912
1220.1052132739507360.2104265479014710.894786726049264
1230.1153332756680440.2306665513360880.884666724331956
1240.08873674260831730.1774734852166350.911263257391683
1250.05881099182286590.1176219836457320.941189008177134
1260.03320330495818290.06640660991636570.966796695041817
1270.01851896506019050.03703793012038090.98148103493981
1280.01485062789146230.02970125578292470.985149372108538







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level290.258928571428571NOK
5% type I error level510.455357142857143NOK
10% type I error level570.508928571428571NOK

\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 & 29 & 0.258928571428571 & NOK \tabularnewline
5% type I error level & 51 & 0.455357142857143 & NOK \tabularnewline
10% type I error level & 57 & 0.508928571428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186351&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]29[/C][C]0.258928571428571[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]51[/C][C]0.455357142857143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.508928571428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186351&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186351&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 level290.258928571428571NOK
5% type I error level510.455357142857143NOK
10% type I error level570.508928571428571NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}