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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 07:06:12 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586396208saal9qsm2o0u71.htm/, Retrieved Fri, 29 Mar 2024 07:34:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57743, Retrieved Fri, 29 Mar 2024 07:34:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7_4] [2009-11-18 19:13:17] [8b1aef4e7013bd33fbc2a5833375c5f5]
-             [Multiple Regression] [] [2009-11-19 14:06:12] [2a6f24d4847085573f343c759dfbabef] [Current]
-    D          [Multiple Regression] [] [2009-11-20 17:36:35] [4d62210f0915d3a20cbf115865da7cd4]
-    D            [Multiple Regression] [verbetering] [2009-11-26 18:08:09] [42ad1186d39724f834063794eac7cea3]
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Dataseries X:
3.22	157	2.88	3.29	3.98	3.88
3.62	157.4	3.22	2.88	3.29	3.98
3.82	157.2	3.62	3.22	2.88	3.29
3.54	157.5	3.82	3.62	3.22	2.88
2.53	158	3.54	3.82	3.62	3.22
2.22	158.5	2.53	3.54	3.82	3.62
2.85	159	2.22	2.53	3.54	3.82
2.78	159.3	2.85	2.22	2.53	3.54
2.28	160	2.78	2.85	2.22	2.53
2.26	160.8	2.28	2.78	2.85	2.22
2.71	161.9	2.26	2.28	2.78	2.85
2.77	162.5	2.71	2.26	2.28	2.78
2.77	162.7	2.77	2.71	2.26	2.28
2.64	162.8	2.77	2.77	2.71	2.26
2.56	162.9	2.64	2.77	2.77	2.71
2.07	163	2.56	2.64	2.77	2.77
2.32	164	2.07	2.56	2.64	2.77
2.16	164.7	2.32	2.07	2.56	2.64
2.23	164.8	2.16	2.32	2.07	2.56
2.4	164.9	2.23	2.16	2.32	2.07
2.84	165	2.4	2.23	2.16	2.32
2.77	165.8	2.84	2.4	2.23	2.16
2.93	166.1	2.77	2.84	2.4	2.23
2.91	167.2	2.93	2.77	2.84	2.4
2.69	167.7	2.91	2.93	2.77	2.84
2.38	168.3	2.69	2.91	2.93	2.77
2.58	168.6	2.38	2.69	2.91	2.93
3.19	168.9	2.58	2.38	2.69	2.91
2.82	169.1	3.19	2.58	2.38	2.69
2.72	169.5	2.82	3.19	2.58	2.38
2.53	169.6	2.72	2.82	3.19	2.58
2.7	169.7	2.53	2.72	2.82	3.19
2.42	169.8	2.7	2.53	2.72	2.82
2.5	170.4	2.42	2.7	2.53	2.72
2.31	170.9	2.5	2.42	2.7	2.53
2.41	171.9	2.31	2.5	2.42	2.7
2.56	171.9	2.41	2.31	2.5	2.42
2.76	172	2.56	2.41	2.31	2.5
2.71	172	2.76	2.56	2.41	2.31
2.44	172.4	2.71	2.76	2.56	2.41
2.46	173	2.44	2.71	2.76	2.56
2.12	173.7	2.46	2.44	2.71	2.76
1.99	173.8	2.12	2.46	2.44	2.71
1.86	173.8	1.99	2.12	2.46	2.44
1.88	173.9	1.86	1.99	2.12	2.46
1.82	174.6	1.88	1.86	1.99	2.12
1.74	175	1.82	1.88	1.86	1.99
1.71	175.9	1.74	1.82	1.88	1.86
1.38	176	1.71	1.74	1.82	1.88
1.27	175.1	1.38	1.71	1.74	1.82
1.19	175.6	1.27	1.38	1.71	1.74
1.28	175.9	1.19	1.27	1.38	1.71
1.19	176.7	1.28	1.19	1.27	1.38
1.22	176.1	1.19	1.28	1.19	1.27
1.47	176.1	1.22	1.19	1.28	1.19
1.46	176.2	1.47	1.22	1.19	1.28
1.96	176.3	1.46	1.47	1.22	1.19
1.88	177.8	1.96	1.46	1.47	1.22
2.03	178.5	1.88	1.96	1.46	1.47
2.04	179.4	2.03	1.88	1.96	1.46
1.9	179.5	2.04	2.03	1.88	1.96
1.8	179.6	1.9	2.04	2.03	1.88
1.92	179.7	1.8	1.9	2.04	2.03
1.92	179.7	1.92	1.8	1.9	2.04
1.97	179.8	1.92	1.92	1.8	1.9
2.46	179.9	1.97	1.92	1.92	1.8
2.36	180.2	2.46	1.97	1.92	1.92
2.53	180.4	2.36	2.46	1.97	1.92
2.31	180.4	2.53	2.36	2.46	1.97
1.98	181.3	2.31	2.53	2.36	2.46
1.46	181.9	1.98	2.31	2.53	2.36
1.26	182.5	1.46	1.98	2.31	2.53
1.58	182.7	1.26	1.46	1.98	2.31
1.74	183.1	1.58	1.26	1.46	1.98
1.89	183.6	1.74	1.58	1.26	1.46
1.85	183.7	1.89	1.74	1.58	1.26
1.62	183.8	1.85	1.89	1.74	1.58
1.3	183.9	1.62	1.85	1.89	1.74
1.42	184.1	1.3	1.62	1.85	1.89
1.15	184.4	1.42	1.3	1.62	1.85
0.42	184.5	1.15	1.42	1.3	1.62
0.74	185.9	0.42	1.15	1.42	1.3
1.02	186.6	0.74	0.42	1.15	1.42
1.51	187.6	1.02	0.74	0.42	1.15
1.86	187.8	1.51	1.02	0.74	0.42
1.59	187.9	1.86	1.51	1.02	0.74
1.03	188	1.59	1.86	1.51	1.02
0.44	188.3	1.03	1.59	1.86	1.51
0.82	188.4	0.44	1.03	1.59	1.86
0.86	188.5	0.82	0.44	1.03	1.59
0.58	188.5	0.86	0.82	0.44	1.03
0.59	188.6	0.58	0.86	0.82	0.44
0.95	188.6	0.59	0.58	0.86	0.82
0.98	189.4	0.95	0.59	0.58	0.86
1.23	190	0.98	0.95	0.59	0.58
1.17	191.9	1.23	0.98	0.95	0.59
0.84	192.5	1.17	1.23	0.98	0.95
0.74	193	0.84	1.17	1.23	0.98
0.65	193.5	0.74	0.84	1.17	1.23
0.91	193.9	0.65	0.74	0.84	1.17
1.19	194.2	0.91	0.65	0.74	0.84
1.3	194.9	1.19	0.91	0.65	0.74
1.53	194.9	1.3	1.19	0.91	0.65
1.94	194.9	1.53	1.3	1.19	0.91
1.79	194.9	1.94	1.53	1.3	1.19
1.95	195.5	1.79	1.94	1.53	1.3
2.26	196	1.95	1.79	1.94	1.53
2.04	196.2	2.26	1.95	1.79	1.94
2.16	196.2	2.04	2.26	1.95	1.79
2.75	196.2	2.16	2.04	2.26	1.95
2.79	196.2	2.75	2.16	2.04	2.26
2.88	197	2.79	2.75	2.16	2.04
3.36	197.7	2.88	2.79	2.75	2.16
2.97	198	3.36	2.88	2.79	2.75
3.1	198.2	2.97	3.36	2.88	2.79
2.49	198.5	3.1	2.97	3.36	2.88
2.2	198.6	2.49	3.1	2.97	3.36
2.25	199.5	2.2	2.49	3.1	2.97
2.09	200	2.25	2.2	2.49	3.1
2.79	201.3	2.09	2.25	2.2	2.49
3.14	202.2	2.79	2.09	2.25	2.2
2.93	202.9	3.14	2.79	2.09	2.25
2.65	203.5	2.93	3.14	2.79	2.09
2.67	203.5	2.65	2.93	3.14	2.79
2.26	204	2.67	2.65	2.93	3.14
2.35	204.1	2.26	2.67	2.65	2.93
2.13	204.3	2.35	2.26	2.67	2.65
2.18	204.5	2.13	2.35	2.26	2.67
2.9	204.8	2.18	2.13	2.35	2.26
2.63	205.1	2.9	2.18	2.13	2.35
2.67	205.7	2.63	2.9	2.18	2.13
1.81	206.5	2.67	2.63	2.9	2.18
1.33	206.9	1.81	2.67	2.63	2.9
0.88	207.1	1.33	1.81	2.67	2.63
1.28	207.8	0.88	1.33	1.81	2.67
1.26	208	1.28	0.88	1.33	1.81
1.26	208.5	1.26	1.28	0.88	1.33
1.29	208.6	1.26	1.26	1.28	0.88
1.1	209	1.29	1.26	1.26	1.28
1.37	209.1	1.1	1.29	1.26	1.26
1.21	209.7	1.37	1.1	1.29	1.26
1.74	209.8	1.21	1.37	1.1	1.29
1.76	209.9	1.74	1.21	1.37	1.1
1.48	210	1.76	1.74	1.21	1.37
1.04	210.8	1.48	1.76	1.74	1.21
1.62	211.4	1.04	1.48	1.76	1.74
1.49	211.7	1.62	1.04	1.48	1.76
1.79	212	1.49	1.62	1.04	1.48
1.8	212.2	1.79	1.49	1.62	1.04
1.58	212.4	1.8	1.79	1.49	1.62
1.86	212.9	1.58	1.8	1.79	1.49
1.74	213.4	1.86	1.58	1.8	1.79
1.59	213.7	1.74	1.86	1.58	1.8
1.26	214	1.59	1.74	1.86	1.58
1.13	214.3	1.26	1.59	1.74	1.86
1.92	214.8	1.13	1.26	1.59	1.74
2.61	215	1.92	1.13	1.26	1.59
2.26	215.9	2.61	1.92	1.13	1.26
2.41	216.4	2.26	2.61	1.92	1.13
2.26	216.9	2.41	2.26	2.61	1.92
2.03	217.2	2.26	2.41	2.26	2.61
2.86	217.5	2.03	2.26	2.41	2.26
2.55	217.9	2.86	2.03	2.26	2.41
2.27	218.1	2.55	2.86	2.03	2.26
2.26	218.6	2.27	2.55	2.86	2.03
2.57	218.9	2.26	2.27	2.55	2.86
3.07	219.3	2.57	2.26	2.27	2.55
2.76	220.4	3.07	2.57	2.26	2.27
2.51	220.9	2.76	3.07	2.57	2.26
2.87	221	2.51	2.76	3.07	2.57
3.14	221.8	2.87	2.51	2.76	3.07
3.11	222	3.14	2.87	2.51	2.76
3.16	222.2	3.11	3.14	2.87	2.51
2.47	222.5	3.16	3.11	3.14	2.87
2.57	222.9	2.47	3.16	3.11	3.14
2.89	223.1	2.57	2.47	3.16	3.11
2.63	223.4	2.89	2.57	2.47	3.16
2.38	224	2.63	2.89	2.57	2.47
1.69	225.1	2.38	2.63	2.89	2.57
1.96	225.5	1.69	2.38	2.63	2.89
2.19	225.9	1.96	1.69	2.38	2.63
1.87	226.3	2.19	1.96	1.69	2.38
1.6	226.5	1.87	2.19	1.96	1.69
1.63	227	1.6	1.87	2.19	1.96
1.22	227.3	1.63	1.6	1.87	2.19
1.21	227.8	1.22	1.63	1.6	1.87
1.49	228.1	1.21	1.22	1.63	1.6
1.64	228.4	1.49	1.21	1.22	1.63
1.66	228.5	1.64	1.49	1.21	1.22
1.77	228.8	1.66	1.64	1.49	1.21
1.82	229	1.77	1.66	1.64	1.49
1.78	229.1	1.82	1.77	1.66	1.64
1.28	229.3	1.78	1.82	1.77	1.66
1.29	229.6	1.28	1.78	1.82	1.77
1.37	229.9	1.29	1.28	1.78	1.82
1.12	230	1.37	1.29	1.28	1.78
1.51	230.2	1.12	1.37	1.29	1.28
2.24	230.8	1.51	1.12	1.37	1.29
2.94	231	2.24	1.51	1.12	1.37
3.09	231.7	2.94	2.24	1.51	1.12
3.46	231.9	3.09	2.94	2.24	1.51
3.64	233	3.46	3.09	2.94	2.24
4.39	235.1	3.64	3.46	3.09	2.94
4.15	236	4.39	3.64	3.46	3.09
5.21	236.9	4.15	4.39	3.64	3.46
5.8	237.1	5.21	4.15	4.39	3.64
5.91	237.5	5.8	5.21	4.15	4.39
5.39	238.2	5.91	5.8	5.21	4.15
5.46	238.9	5.39	5.91	5.8	5.21
4.72	239.1	5.46	5.39	5.91	5.8
3.14	240	4.72	5.46	5.39	5.91
2.63	240.2	3.14	4.72	5.46	5.39
2.32	240.5	2.63	3.14	4.72	5.46
1.93	240.7	2.32	2.63	3.14	4.72
0.62	241.1	1.93	2.32	2.63	3.14
0.6	241.4	0.62	1.93	2.32	2.63
-0.37	242.2	0.6	0.62	1.93	2.32
-1.1	242.9	-0.37	0.6	0.62	1.93
-1.68	243.2	-1.1	-0.37	0.6	0.62
-0.78	243.9	-1.68	-1.1	-0.37	0.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.97800660075982 -0.0178884775131215X[t] + 1.13770027938383Y1[t] -0.189411036310105Y2[t] + 0.0135050512884166Y3[t] -0.0350137447939528Y4[t] -0.00610584212118887M1[t] -0.0165912030097572M2[t] -0.0290877279644277M3[t] -0.0262203834456709M4[t] -0.0363526671664465M5[t] -0.0528874969185614M6[t] -0.0144954812421030M7[t] -0.0228599371456861M8[t] -0.0554478823797208M9[t] -0.0148607415103697M10[t] -0.0222110183577529M11[t] + 0.00667066163419205t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2.97800660075982 -0.0178884775131215X[t] +  1.13770027938383Y1[t] -0.189411036310105Y2[t] +  0.0135050512884166Y3[t] -0.0350137447939528Y4[t] -0.00610584212118887M1[t] -0.0165912030097572M2[t] -0.0290877279644277M3[t] -0.0262203834456709M4[t] -0.0363526671664465M5[t] -0.0528874969185614M6[t] -0.0144954812421030M7[t] -0.0228599371456861M8[t] -0.0554478823797208M9[t] -0.0148607415103697M10[t] -0.0222110183577529M11[t] +  0.00667066163419205t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2.97800660075982 -0.0178884775131215X[t] +  1.13770027938383Y1[t] -0.189411036310105Y2[t] +  0.0135050512884166Y3[t] -0.0350137447939528Y4[t] -0.00610584212118887M1[t] -0.0165912030097572M2[t] -0.0290877279644277M3[t] -0.0262203834456709M4[t] -0.0363526671664465M5[t] -0.0528874969185614M6[t] -0.0144954812421030M7[t] -0.0228599371456861M8[t] -0.0554478823797208M9[t] -0.0148607415103697M10[t] -0.0222110183577529M11[t] +  0.00667066163419205t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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
Y[t] = + 2.97800660075982 -0.0178884775131215X[t] + 1.13770027938383Y1[t] -0.189411036310105Y2[t] + 0.0135050512884166Y3[t] -0.0350137447939528Y4[t] -0.00610584212118887M1[t] -0.0165912030097572M2[t] -0.0290877279644277M3[t] -0.0262203834456709M4[t] -0.0363526671664465M5[t] -0.0528874969185614M6[t] -0.0144954812421030M7[t] -0.0228599371456861M8[t] -0.0554478823797208M9[t] -0.0148607415103697M10[t] -0.0222110183577529M11[t] + 0.00667066163419205t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.978006600759823.5397720.84130.4011750.200588
X-0.01788847751312150.02285-0.78290.4346310.217316
Y11.137700279383830.07140615.932900
Y2-0.1894110363101050.109253-1.73370.0844990.042249
Y30.01350505128841660.1086970.12420.9012450.450622
Y4-0.03501374479395280.075492-0.46380.6432840.321642
M1-0.006105842121188870.12356-0.04940.9606370.480318
M2-0.01659120300975720.123516-0.13430.893280.44664
M3-0.02908772796442770.123525-0.23550.8140750.407037
M4-0.02622038344567090.123576-0.21220.832180.41609
M5-0.03635266716644650.125872-0.28880.7730250.386513
M6-0.05288749691856140.126127-0.41930.6754290.337715
M7-0.01449548124210300.126396-0.11470.908810.454405
M8-0.02285993714568610.126652-0.18050.8569450.428473
M9-0.05544788237972080.127066-0.43640.6630330.331516
M10-0.01486074151036970.125978-0.1180.9062140.453107
M11-0.02221101835775290.125466-0.1770.8596640.429832
t0.006670661634192050.008750.76240.4467350.223367

\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) & 2.97800660075982 & 3.539772 & 0.8413 & 0.401175 & 0.200588 \tabularnewline
X & -0.0178884775131215 & 0.02285 & -0.7829 & 0.434631 & 0.217316 \tabularnewline
Y1 & 1.13770027938383 & 0.071406 & 15.9329 & 0 & 0 \tabularnewline
Y2 & -0.189411036310105 & 0.109253 & -1.7337 & 0.084499 & 0.042249 \tabularnewline
Y3 & 0.0135050512884166 & 0.108697 & 0.1242 & 0.901245 & 0.450622 \tabularnewline
Y4 & -0.0350137447939528 & 0.075492 & -0.4638 & 0.643284 & 0.321642 \tabularnewline
M1 & -0.00610584212118887 & 0.12356 & -0.0494 & 0.960637 & 0.480318 \tabularnewline
M2 & -0.0165912030097572 & 0.123516 & -0.1343 & 0.89328 & 0.44664 \tabularnewline
M3 & -0.0290877279644277 & 0.123525 & -0.2355 & 0.814075 & 0.407037 \tabularnewline
M4 & -0.0262203834456709 & 0.123576 & -0.2122 & 0.83218 & 0.41609 \tabularnewline
M5 & -0.0363526671664465 & 0.125872 & -0.2888 & 0.773025 & 0.386513 \tabularnewline
M6 & -0.0528874969185614 & 0.126127 & -0.4193 & 0.675429 & 0.337715 \tabularnewline
M7 & -0.0144954812421030 & 0.126396 & -0.1147 & 0.90881 & 0.454405 \tabularnewline
M8 & -0.0228599371456861 & 0.126652 & -0.1805 & 0.856945 & 0.428473 \tabularnewline
M9 & -0.0554478823797208 & 0.127066 & -0.4364 & 0.663033 & 0.331516 \tabularnewline
M10 & -0.0148607415103697 & 0.125978 & -0.118 & 0.906214 & 0.453107 \tabularnewline
M11 & -0.0222110183577529 & 0.125466 & -0.177 & 0.859664 & 0.429832 \tabularnewline
t & 0.00667066163419205 & 0.00875 & 0.7624 & 0.446735 & 0.223367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&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]2.97800660075982[/C][C]3.539772[/C][C]0.8413[/C][C]0.401175[/C][C]0.200588[/C][/ROW]
[ROW][C]X[/C][C]-0.0178884775131215[/C][C]0.02285[/C][C]-0.7829[/C][C]0.434631[/C][C]0.217316[/C][/ROW]
[ROW][C]Y1[/C][C]1.13770027938383[/C][C]0.071406[/C][C]15.9329[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.189411036310105[/C][C]0.109253[/C][C]-1.7337[/C][C]0.084499[/C][C]0.042249[/C][/ROW]
[ROW][C]Y3[/C][C]0.0135050512884166[/C][C]0.108697[/C][C]0.1242[/C][C]0.901245[/C][C]0.450622[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0350137447939528[/C][C]0.075492[/C][C]-0.4638[/C][C]0.643284[/C][C]0.321642[/C][/ROW]
[ROW][C]M1[/C][C]-0.00610584212118887[/C][C]0.12356[/C][C]-0.0494[/C][C]0.960637[/C][C]0.480318[/C][/ROW]
[ROW][C]M2[/C][C]-0.0165912030097572[/C][C]0.123516[/C][C]-0.1343[/C][C]0.89328[/C][C]0.44664[/C][/ROW]
[ROW][C]M3[/C][C]-0.0290877279644277[/C][C]0.123525[/C][C]-0.2355[/C][C]0.814075[/C][C]0.407037[/C][/ROW]
[ROW][C]M4[/C][C]-0.0262203834456709[/C][C]0.123576[/C][C]-0.2122[/C][C]0.83218[/C][C]0.41609[/C][/ROW]
[ROW][C]M5[/C][C]-0.0363526671664465[/C][C]0.125872[/C][C]-0.2888[/C][C]0.773025[/C][C]0.386513[/C][/ROW]
[ROW][C]M6[/C][C]-0.0528874969185614[/C][C]0.126127[/C][C]-0.4193[/C][C]0.675429[/C][C]0.337715[/C][/ROW]
[ROW][C]M7[/C][C]-0.0144954812421030[/C][C]0.126396[/C][C]-0.1147[/C][C]0.90881[/C][C]0.454405[/C][/ROW]
[ROW][C]M8[/C][C]-0.0228599371456861[/C][C]0.126652[/C][C]-0.1805[/C][C]0.856945[/C][C]0.428473[/C][/ROW]
[ROW][C]M9[/C][C]-0.0554478823797208[/C][C]0.127066[/C][C]-0.4364[/C][C]0.663033[/C][C]0.331516[/C][/ROW]
[ROW][C]M10[/C][C]-0.0148607415103697[/C][C]0.125978[/C][C]-0.118[/C][C]0.906214[/C][C]0.453107[/C][/ROW]
[ROW][C]M11[/C][C]-0.0222110183577529[/C][C]0.125466[/C][C]-0.177[/C][C]0.859664[/C][C]0.429832[/C][/ROW]
[ROW][C]t[/C][C]0.00667066163419205[/C][C]0.00875[/C][C]0.7624[/C][C]0.446735[/C][C]0.223367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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)2.978006600759823.5397720.84130.4011750.200588
X-0.01788847751312150.02285-0.78290.4346310.217316
Y11.137700279383830.07140615.932900
Y2-0.1894110363101050.109253-1.73370.0844990.042249
Y30.01350505128841660.1086970.12420.9012450.450622
Y4-0.03501374479395280.075492-0.46380.6432840.321642
M1-0.006105842121188870.12356-0.04940.9606370.480318
M2-0.01659120300975720.123516-0.13430.893280.44664
M3-0.02908772796442770.123525-0.23550.8140750.407037
M4-0.02622038344567090.123576-0.21220.832180.41609
M5-0.03635266716644650.125872-0.28880.7730250.386513
M6-0.05288749691856140.126127-0.41930.6754290.337715
M7-0.01449548124210300.126396-0.11470.908810.454405
M8-0.02285993714568610.126652-0.18050.8569450.428473
M9-0.05544788237972080.127066-0.43640.6630330.331516
M10-0.01486074151036970.125978-0.1180.9062140.453107
M11-0.02221101835775290.125466-0.1770.8596640.429832
t0.006670661634192050.008750.76240.4467350.223367







Multiple Linear Regression - Regression Statistics
Multiple R0.934225822739988
R-squared0.872777887874208
Adjusted R-squared0.862071076457681
F-TEST (value)81.5161352825347
F-TEST (DF numerator)17
F-TEST (DF denominator)202
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.375339855393874
Sum Squared Residuals28.4577614235130

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.934225822739988 \tabularnewline
R-squared & 0.872777887874208 \tabularnewline
Adjusted R-squared & 0.862071076457681 \tabularnewline
F-TEST (value) & 81.5161352825347 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 202 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.375339855393874 \tabularnewline
Sum Squared Residuals & 28.4577614235130 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.934225822739988[/C][/ROW]
[ROW][C]R-squared[/C][C]0.872777887874208[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.862071076457681[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81.5161352825347[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]202[/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]0.375339855393874[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.4577614235130[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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 R0.934225822739988
R-squared0.872777887874208
Adjusted R-squared0.862071076457681
F-TEST (value)81.5161352825347
F-TEST (DF numerator)17
F-TEST (DF denominator)202
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.375339855393874
Sum Squared Residuals28.4577614235130







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13.222.741391720205310.478608279794694
23.623.18207838995490.437921610045099
33.823.589132994424730.230867005575270
43.543.76402745148004-0.224027451480041
52.533.38868065243283-0.858680652432829
62.222.26252556588761-0.0425255658876074
72.852.126477901186370.72352209881363
82.782.89104990767199-0.111049907671993
92.282.68482003372321-0.404820033723214
102.262.181538130263880.0784618697361195
112.712.210128689543250.499871310456749
122.772.739729065967620.0302709340323820
132.772.736980011772690.0330199882273122
142.642.72678935056406-0.0867893505640593
152.562.55632772109240.00367227890760273
162.072.49558346717600-0.425583467176004
172.321.930157456915540.389842543084463
182.162.28847921489648-0.128479214896478
192.232.098551865129070.131448134870930
202.42.225547006245990.174452993754008
212.842.367076905443750.472923094556254
222.772.87495972545018-0.104959725450181
232.932.705778588033190.224221411966807
242.912.91026364595576-0.000263645955755940
252.692.83247305401538-0.142473054015377
262.382.57603119785661-0.19603119785661
272.582.247949832468630.332050167531370
283.192.536107936112970.653892063887032
292.823.18870003964114-0.368700039641137
302.722.648745916140590.0712540838594077
312.532.64956713352343-0.119567133523431
322.72.422507288749780.277492711250216
332.422.63580288223772-0.215802882237722
342.52.322507058567800.177492941432202
352.312.46588278734546-0.155882787345463
362.412.235826302860820.174173697139178
372.562.397033499856510.162966500143491
382.762.537776831799070.222223168200931
392.712.73908248554853-0.0290824855485330
402.442.64522226267889-0.205222262678888
412.462.330767979004890.129232020995106
422.122.37459886049598-0.254598860495979
431.992.02537069773044-0.0353706977304384
441.861.94989943160672-0.0898994316067203
451.881.793623706322040.0863762936779588
461.821.88588603143684-0.065886031436839
471.741.80879691788489-0.0687969178848873
481.711.74674949579190-0.0367494957919047
491.381.72503676410371-0.345036764103705
501.271.36858335408834-0.0985833540883417
511.191.29356781130628-0.103567811306280
521.281.224152211267340.0558477887326618
531.191.33399469535989-0.14399469535989
541.221.218194703161650.00180529683835325
551.471.318451936321200.151548063678805
561.461.58934534140973-0.129345341409733
571.961.506465836757320.453534163242678
581.882.09996102352448-0.219961023524484
592.031.892149446834980.137850553165023
602.042.09784208496964-0.0578420849696427
611.92.06099612757861-0.160996127578607
621.81.89904728837286-0.0990472883728599
631.921.799063083239890.120936916760107
641.921.96182538192159-0.0418253819215934
651.971.937397006868800.0326029931312032
662.461.987750985602760.472249014397242
672.362.57124805536677-0.211248055366770
682.532.360070382428840.169929617571160
692.312.55137003784689-0.241370037846885
701.982.28152703287358-0.301527032873581
711.461.94214090014250-0.482140900142502
721.261.42226754243089-0.162267542430894
731.581.293454706375250.286545293624747
741.741.688962821892500.0510371781074968
751.891.811119369932820.0788806300671848
761.851.97054216980349-0.120542169803493
771.621.88232844321581-0.262328443215810
781.31.61300436306693-0.313004363066929
791.421.328197530052820.0918024699471758
801.151.51656714567021-0.366567145670212
810.421.16268415941853-0.742684159418527
820.740.4183428737459070.321657126254093
831.020.8996274569595860.120372543040414
841.511.168160229700470.341839770299528
851.861.699467050553980.160532949446024
861.591.99182420956737-0.401824209567368
871.031.60755018694242-0.577550186942424
880.441.01332050619213-0.573320506192129
890.820.4269958773256780.393004122674322
900.860.961312361418113-0.101312361418114
910.580.991546572930725-0.411546572930725
920.590.687721440148176-0.0977214401481764
930.950.7134512285388360.236548771461164
940.981.14889427509444-0.168894275094444
951.231.113363507738450.116636492261552
961.171.39151150022801-0.221511500228007
970.841.25352866080541-0.413528660805411
980.740.879019143254701-0.139019143254701
990.650.803420915945821-0.153420915945821
1000.910.7199957673424460.190004232657554
1011.191.034220698562800.155779301437205
1021.31.283429724835970.0165702751640306
1031.531.407266893078460.122733106921544
1041.941.641086789787560.298913210212437
1051.792.02974378948319-0.239743789483185
1061.951.817209588553140.132790411446861
1072.262.015513344456960.244486655543043
1082.042.34681725668687-0.306817256686865
1092.162.045783463404530.114216536595467
1102.752.218747592396820.531252407603184
1112.792.84761019738601-0.0576101973860074
1122.882.785916551290130.0940834487098701
1133.362.868515909521390.491484090478607
1142.973.36221643160897-0.392216431608967
1153.12.868895911852650.231104088147351
1162.493.08693810239714-0.596938102397144
1172.22.31853779839796-0.11853779839796
1182.252.150714639404680.0992853605953172
1192.092.24011513182490-0.150115131824905
1202.792.071681113983540.718318886016456
1213.142.89367150366770.246328496332297
1222.933.13903074707556-0.209030747075564
1232.652.83231661093700-0.182316610936996
1242.672.543292003082770.126707996917227
1252.262.59158436654568-0.331584366545684
1262.352.113257487448850.236742512551150
1272.132.34486796885664-0.214867968856639
1282.182.066018078428140.113981921571859
1292.92.148860783513260.751139216486745
1302.632.99430334378881-0.364303343788807
1312.672.547713896909920.122286103090076
1321.812.66690673555841-0.856906735558415
1331.331.64546122224415-0.315461222244153
1340.881.26486009775551-0.384860097755509
1351.280.8124495779821760.467550422017824
1361.261.38235436262994-0.122354362629939
1371.261.28215940609639-0.0221594060963866
1381.291.295452816626-0.00545281662599885
1391.11.35321551236957-0.253215512369566
1401.371.128587761072510.241412238927489
1411.211.43550971483600-0.225509714836002
1421.741.244189272994470.495810727005526
1431.761.88530669927174-0.125306699271741
1441.481.82315116855518-0.343151168555181
1451.041.49982078345391-0.459820783453905
1461.621.019432781214570.600567218785426
1471.491.74696570340259-0.256965703402591
1481.791.497239354897250.292760645102752
1491.81.87937253330012-0.079372533300124
1501.581.79842073293240-0.218420732932396
1511.861.590954301868680.269045698131319
1521.741.93017370213312-0.190173702133123
1531.591.70600950285506-0.116009502855064
1541.261.61145948276974-0.351459482769735
1551.131.24695943285554-0.116959432855543
1561.921.183677371435380.736322628564624
1572.612.104866545673210.505133454326791
1582.262.73012956986139-0.470129569861393
1592.412.201691532287100.208308467712897
1602.262.42089183130139-0.160891831301388
1612.032.18411071674800-0.154110716748005
1622.861.949901164935530.910098835064474
1632.552.96838640206848-0.418386402068475
1642.272.45536256547284-0.185362565472843
1652.262.179924740017090.0800752599829052
1662.572.23022611256130.339773887438698
1673.072.584045149840320.485954850159681
1682.763.11305102103304-0.353051021033036
1692.512.66181570037279-0.151815700372787
1702.872.426402769535360.443597230464644
1713.142.84149754556370.2985024544363
1723.113.093926956640040.0160730433599578
1733.163.015230905527910.144769094472091
1742.473.05360895493659-0.583608954936594
1752.572.287173634018630.282826365981371
1762.892.528091452147210.361908547852793
1772.632.83086143843654-0.200861438436539
1782.382.53648253920985-0.156482539209849
1791.692.28176764025979-0.591767640259792
1801.961.551117783880120.408882216119881
1812.191.988127213699800.201872786300197
1821.872.18712215870421-0.317122158704212
1831.61.79789581988266-0.197895819882660
1841.631.545574494166620.0844255058333843
1851.221.60964353929644-0.389643539296437
1861.211.126253721271470.0837462787285282
1871.491.242090240054510.247909759945489
1881.641.548892607749690.091107392250313
1891.661.653027012991910.00697298700808624
1901.771.693392174191380.0766078258086229
1911.821.802715582632540.0172844173674633
1921.781.86087625415493-0.080876254154925
1931.281.80367009592029-0.523670095920292
1941.291.230038895809550.0599611041904451
1951.371.322638120892800.0473618791072038
1961.121.41415721542959-0.294157215429588
1971.511.125181867999480.384818132000524
1982.241.596370748066030.643629251933967
1992.942.388329267257690.551670732742306
2003.093.046254083992390.0437459160076078
2013.463.051029748351330.408970251648672
2023.643.455041175718240.184958824281755
2034.393.529015860919340.860984139080655
2044.154.36072394140915-0.210723941409154
2055.213.91955861053381.29044138946620
2065.85.167413475041490.632586524958512
2075.915.595398166158820.314601833841176
2085.395.62852741047812-0.238527410478118
2095.464.970957905637220.489042094362779
2104.725.11647624666809-0.396476246668091
2113.144.27940817633388-1.13940817633388
2122.632.63588691288794-0.00588691288793656
2132.322.311200680829360.00879931917063523
2141.932.10336551985128-0.173365519851277
2150.621.75897896654663-1.13897896654663
2160.60.3796474853982680.220352514601732
217-0.370.596863265762979-0.966863265762979
218-1.1-0.52329067474488-0.57670932525512
219-1.68-1.13567767539438-0.544322324605621
220-0.78-1.672657333890740.892657333890744

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3.22 & 2.74139172020531 & 0.478608279794694 \tabularnewline
2 & 3.62 & 3.1820783899549 & 0.437921610045099 \tabularnewline
3 & 3.82 & 3.58913299442473 & 0.230867005575270 \tabularnewline
4 & 3.54 & 3.76402745148004 & -0.224027451480041 \tabularnewline
5 & 2.53 & 3.38868065243283 & -0.858680652432829 \tabularnewline
6 & 2.22 & 2.26252556588761 & -0.0425255658876074 \tabularnewline
7 & 2.85 & 2.12647790118637 & 0.72352209881363 \tabularnewline
8 & 2.78 & 2.89104990767199 & -0.111049907671993 \tabularnewline
9 & 2.28 & 2.68482003372321 & -0.404820033723214 \tabularnewline
10 & 2.26 & 2.18153813026388 & 0.0784618697361195 \tabularnewline
11 & 2.71 & 2.21012868954325 & 0.499871310456749 \tabularnewline
12 & 2.77 & 2.73972906596762 & 0.0302709340323820 \tabularnewline
13 & 2.77 & 2.73698001177269 & 0.0330199882273122 \tabularnewline
14 & 2.64 & 2.72678935056406 & -0.0867893505640593 \tabularnewline
15 & 2.56 & 2.5563277210924 & 0.00367227890760273 \tabularnewline
16 & 2.07 & 2.49558346717600 & -0.425583467176004 \tabularnewline
17 & 2.32 & 1.93015745691554 & 0.389842543084463 \tabularnewline
18 & 2.16 & 2.28847921489648 & -0.128479214896478 \tabularnewline
19 & 2.23 & 2.09855186512907 & 0.131448134870930 \tabularnewline
20 & 2.4 & 2.22554700624599 & 0.174452993754008 \tabularnewline
21 & 2.84 & 2.36707690544375 & 0.472923094556254 \tabularnewline
22 & 2.77 & 2.87495972545018 & -0.104959725450181 \tabularnewline
23 & 2.93 & 2.70577858803319 & 0.224221411966807 \tabularnewline
24 & 2.91 & 2.91026364595576 & -0.000263645955755940 \tabularnewline
25 & 2.69 & 2.83247305401538 & -0.142473054015377 \tabularnewline
26 & 2.38 & 2.57603119785661 & -0.19603119785661 \tabularnewline
27 & 2.58 & 2.24794983246863 & 0.332050167531370 \tabularnewline
28 & 3.19 & 2.53610793611297 & 0.653892063887032 \tabularnewline
29 & 2.82 & 3.18870003964114 & -0.368700039641137 \tabularnewline
30 & 2.72 & 2.64874591614059 & 0.0712540838594077 \tabularnewline
31 & 2.53 & 2.64956713352343 & -0.119567133523431 \tabularnewline
32 & 2.7 & 2.42250728874978 & 0.277492711250216 \tabularnewline
33 & 2.42 & 2.63580288223772 & -0.215802882237722 \tabularnewline
34 & 2.5 & 2.32250705856780 & 0.177492941432202 \tabularnewline
35 & 2.31 & 2.46588278734546 & -0.155882787345463 \tabularnewline
36 & 2.41 & 2.23582630286082 & 0.174173697139178 \tabularnewline
37 & 2.56 & 2.39703349985651 & 0.162966500143491 \tabularnewline
38 & 2.76 & 2.53777683179907 & 0.222223168200931 \tabularnewline
39 & 2.71 & 2.73908248554853 & -0.0290824855485330 \tabularnewline
40 & 2.44 & 2.64522226267889 & -0.205222262678888 \tabularnewline
41 & 2.46 & 2.33076797900489 & 0.129232020995106 \tabularnewline
42 & 2.12 & 2.37459886049598 & -0.254598860495979 \tabularnewline
43 & 1.99 & 2.02537069773044 & -0.0353706977304384 \tabularnewline
44 & 1.86 & 1.94989943160672 & -0.0898994316067203 \tabularnewline
45 & 1.88 & 1.79362370632204 & 0.0863762936779588 \tabularnewline
46 & 1.82 & 1.88588603143684 & -0.065886031436839 \tabularnewline
47 & 1.74 & 1.80879691788489 & -0.0687969178848873 \tabularnewline
48 & 1.71 & 1.74674949579190 & -0.0367494957919047 \tabularnewline
49 & 1.38 & 1.72503676410371 & -0.345036764103705 \tabularnewline
50 & 1.27 & 1.36858335408834 & -0.0985833540883417 \tabularnewline
51 & 1.19 & 1.29356781130628 & -0.103567811306280 \tabularnewline
52 & 1.28 & 1.22415221126734 & 0.0558477887326618 \tabularnewline
53 & 1.19 & 1.33399469535989 & -0.14399469535989 \tabularnewline
54 & 1.22 & 1.21819470316165 & 0.00180529683835325 \tabularnewline
55 & 1.47 & 1.31845193632120 & 0.151548063678805 \tabularnewline
56 & 1.46 & 1.58934534140973 & -0.129345341409733 \tabularnewline
57 & 1.96 & 1.50646583675732 & 0.453534163242678 \tabularnewline
58 & 1.88 & 2.09996102352448 & -0.219961023524484 \tabularnewline
59 & 2.03 & 1.89214944683498 & 0.137850553165023 \tabularnewline
60 & 2.04 & 2.09784208496964 & -0.0578420849696427 \tabularnewline
61 & 1.9 & 2.06099612757861 & -0.160996127578607 \tabularnewline
62 & 1.8 & 1.89904728837286 & -0.0990472883728599 \tabularnewline
63 & 1.92 & 1.79906308323989 & 0.120936916760107 \tabularnewline
64 & 1.92 & 1.96182538192159 & -0.0418253819215934 \tabularnewline
65 & 1.97 & 1.93739700686880 & 0.0326029931312032 \tabularnewline
66 & 2.46 & 1.98775098560276 & 0.472249014397242 \tabularnewline
67 & 2.36 & 2.57124805536677 & -0.211248055366770 \tabularnewline
68 & 2.53 & 2.36007038242884 & 0.169929617571160 \tabularnewline
69 & 2.31 & 2.55137003784689 & -0.241370037846885 \tabularnewline
70 & 1.98 & 2.28152703287358 & -0.301527032873581 \tabularnewline
71 & 1.46 & 1.94214090014250 & -0.482140900142502 \tabularnewline
72 & 1.26 & 1.42226754243089 & -0.162267542430894 \tabularnewline
73 & 1.58 & 1.29345470637525 & 0.286545293624747 \tabularnewline
74 & 1.74 & 1.68896282189250 & 0.0510371781074968 \tabularnewline
75 & 1.89 & 1.81111936993282 & 0.0788806300671848 \tabularnewline
76 & 1.85 & 1.97054216980349 & -0.120542169803493 \tabularnewline
77 & 1.62 & 1.88232844321581 & -0.262328443215810 \tabularnewline
78 & 1.3 & 1.61300436306693 & -0.313004363066929 \tabularnewline
79 & 1.42 & 1.32819753005282 & 0.0918024699471758 \tabularnewline
80 & 1.15 & 1.51656714567021 & -0.366567145670212 \tabularnewline
81 & 0.42 & 1.16268415941853 & -0.742684159418527 \tabularnewline
82 & 0.74 & 0.418342873745907 & 0.321657126254093 \tabularnewline
83 & 1.02 & 0.899627456959586 & 0.120372543040414 \tabularnewline
84 & 1.51 & 1.16816022970047 & 0.341839770299528 \tabularnewline
85 & 1.86 & 1.69946705055398 & 0.160532949446024 \tabularnewline
86 & 1.59 & 1.99182420956737 & -0.401824209567368 \tabularnewline
87 & 1.03 & 1.60755018694242 & -0.577550186942424 \tabularnewline
88 & 0.44 & 1.01332050619213 & -0.573320506192129 \tabularnewline
89 & 0.82 & 0.426995877325678 & 0.393004122674322 \tabularnewline
90 & 0.86 & 0.961312361418113 & -0.101312361418114 \tabularnewline
91 & 0.58 & 0.991546572930725 & -0.411546572930725 \tabularnewline
92 & 0.59 & 0.687721440148176 & -0.0977214401481764 \tabularnewline
93 & 0.95 & 0.713451228538836 & 0.236548771461164 \tabularnewline
94 & 0.98 & 1.14889427509444 & -0.168894275094444 \tabularnewline
95 & 1.23 & 1.11336350773845 & 0.116636492261552 \tabularnewline
96 & 1.17 & 1.39151150022801 & -0.221511500228007 \tabularnewline
97 & 0.84 & 1.25352866080541 & -0.413528660805411 \tabularnewline
98 & 0.74 & 0.879019143254701 & -0.139019143254701 \tabularnewline
99 & 0.65 & 0.803420915945821 & -0.153420915945821 \tabularnewline
100 & 0.91 & 0.719995767342446 & 0.190004232657554 \tabularnewline
101 & 1.19 & 1.03422069856280 & 0.155779301437205 \tabularnewline
102 & 1.3 & 1.28342972483597 & 0.0165702751640306 \tabularnewline
103 & 1.53 & 1.40726689307846 & 0.122733106921544 \tabularnewline
104 & 1.94 & 1.64108678978756 & 0.298913210212437 \tabularnewline
105 & 1.79 & 2.02974378948319 & -0.239743789483185 \tabularnewline
106 & 1.95 & 1.81720958855314 & 0.132790411446861 \tabularnewline
107 & 2.26 & 2.01551334445696 & 0.244486655543043 \tabularnewline
108 & 2.04 & 2.34681725668687 & -0.306817256686865 \tabularnewline
109 & 2.16 & 2.04578346340453 & 0.114216536595467 \tabularnewline
110 & 2.75 & 2.21874759239682 & 0.531252407603184 \tabularnewline
111 & 2.79 & 2.84761019738601 & -0.0576101973860074 \tabularnewline
112 & 2.88 & 2.78591655129013 & 0.0940834487098701 \tabularnewline
113 & 3.36 & 2.86851590952139 & 0.491484090478607 \tabularnewline
114 & 2.97 & 3.36221643160897 & -0.392216431608967 \tabularnewline
115 & 3.1 & 2.86889591185265 & 0.231104088147351 \tabularnewline
116 & 2.49 & 3.08693810239714 & -0.596938102397144 \tabularnewline
117 & 2.2 & 2.31853779839796 & -0.11853779839796 \tabularnewline
118 & 2.25 & 2.15071463940468 & 0.0992853605953172 \tabularnewline
119 & 2.09 & 2.24011513182490 & -0.150115131824905 \tabularnewline
120 & 2.79 & 2.07168111398354 & 0.718318886016456 \tabularnewline
121 & 3.14 & 2.8936715036677 & 0.246328496332297 \tabularnewline
122 & 2.93 & 3.13903074707556 & -0.209030747075564 \tabularnewline
123 & 2.65 & 2.83231661093700 & -0.182316610936996 \tabularnewline
124 & 2.67 & 2.54329200308277 & 0.126707996917227 \tabularnewline
125 & 2.26 & 2.59158436654568 & -0.331584366545684 \tabularnewline
126 & 2.35 & 2.11325748744885 & 0.236742512551150 \tabularnewline
127 & 2.13 & 2.34486796885664 & -0.214867968856639 \tabularnewline
128 & 2.18 & 2.06601807842814 & 0.113981921571859 \tabularnewline
129 & 2.9 & 2.14886078351326 & 0.751139216486745 \tabularnewline
130 & 2.63 & 2.99430334378881 & -0.364303343788807 \tabularnewline
131 & 2.67 & 2.54771389690992 & 0.122286103090076 \tabularnewline
132 & 1.81 & 2.66690673555841 & -0.856906735558415 \tabularnewline
133 & 1.33 & 1.64546122224415 & -0.315461222244153 \tabularnewline
134 & 0.88 & 1.26486009775551 & -0.384860097755509 \tabularnewline
135 & 1.28 & 0.812449577982176 & 0.467550422017824 \tabularnewline
136 & 1.26 & 1.38235436262994 & -0.122354362629939 \tabularnewline
137 & 1.26 & 1.28215940609639 & -0.0221594060963866 \tabularnewline
138 & 1.29 & 1.295452816626 & -0.00545281662599885 \tabularnewline
139 & 1.1 & 1.35321551236957 & -0.253215512369566 \tabularnewline
140 & 1.37 & 1.12858776107251 & 0.241412238927489 \tabularnewline
141 & 1.21 & 1.43550971483600 & -0.225509714836002 \tabularnewline
142 & 1.74 & 1.24418927299447 & 0.495810727005526 \tabularnewline
143 & 1.76 & 1.88530669927174 & -0.125306699271741 \tabularnewline
144 & 1.48 & 1.82315116855518 & -0.343151168555181 \tabularnewline
145 & 1.04 & 1.49982078345391 & -0.459820783453905 \tabularnewline
146 & 1.62 & 1.01943278121457 & 0.600567218785426 \tabularnewline
147 & 1.49 & 1.74696570340259 & -0.256965703402591 \tabularnewline
148 & 1.79 & 1.49723935489725 & 0.292760645102752 \tabularnewline
149 & 1.8 & 1.87937253330012 & -0.079372533300124 \tabularnewline
150 & 1.58 & 1.79842073293240 & -0.218420732932396 \tabularnewline
151 & 1.86 & 1.59095430186868 & 0.269045698131319 \tabularnewline
152 & 1.74 & 1.93017370213312 & -0.190173702133123 \tabularnewline
153 & 1.59 & 1.70600950285506 & -0.116009502855064 \tabularnewline
154 & 1.26 & 1.61145948276974 & -0.351459482769735 \tabularnewline
155 & 1.13 & 1.24695943285554 & -0.116959432855543 \tabularnewline
156 & 1.92 & 1.18367737143538 & 0.736322628564624 \tabularnewline
157 & 2.61 & 2.10486654567321 & 0.505133454326791 \tabularnewline
158 & 2.26 & 2.73012956986139 & -0.470129569861393 \tabularnewline
159 & 2.41 & 2.20169153228710 & 0.208308467712897 \tabularnewline
160 & 2.26 & 2.42089183130139 & -0.160891831301388 \tabularnewline
161 & 2.03 & 2.18411071674800 & -0.154110716748005 \tabularnewline
162 & 2.86 & 1.94990116493553 & 0.910098835064474 \tabularnewline
163 & 2.55 & 2.96838640206848 & -0.418386402068475 \tabularnewline
164 & 2.27 & 2.45536256547284 & -0.185362565472843 \tabularnewline
165 & 2.26 & 2.17992474001709 & 0.0800752599829052 \tabularnewline
166 & 2.57 & 2.2302261125613 & 0.339773887438698 \tabularnewline
167 & 3.07 & 2.58404514984032 & 0.485954850159681 \tabularnewline
168 & 2.76 & 3.11305102103304 & -0.353051021033036 \tabularnewline
169 & 2.51 & 2.66181570037279 & -0.151815700372787 \tabularnewline
170 & 2.87 & 2.42640276953536 & 0.443597230464644 \tabularnewline
171 & 3.14 & 2.8414975455637 & 0.2985024544363 \tabularnewline
172 & 3.11 & 3.09392695664004 & 0.0160730433599578 \tabularnewline
173 & 3.16 & 3.01523090552791 & 0.144769094472091 \tabularnewline
174 & 2.47 & 3.05360895493659 & -0.583608954936594 \tabularnewline
175 & 2.57 & 2.28717363401863 & 0.282826365981371 \tabularnewline
176 & 2.89 & 2.52809145214721 & 0.361908547852793 \tabularnewline
177 & 2.63 & 2.83086143843654 & -0.200861438436539 \tabularnewline
178 & 2.38 & 2.53648253920985 & -0.156482539209849 \tabularnewline
179 & 1.69 & 2.28176764025979 & -0.591767640259792 \tabularnewline
180 & 1.96 & 1.55111778388012 & 0.408882216119881 \tabularnewline
181 & 2.19 & 1.98812721369980 & 0.201872786300197 \tabularnewline
182 & 1.87 & 2.18712215870421 & -0.317122158704212 \tabularnewline
183 & 1.6 & 1.79789581988266 & -0.197895819882660 \tabularnewline
184 & 1.63 & 1.54557449416662 & 0.0844255058333843 \tabularnewline
185 & 1.22 & 1.60964353929644 & -0.389643539296437 \tabularnewline
186 & 1.21 & 1.12625372127147 & 0.0837462787285282 \tabularnewline
187 & 1.49 & 1.24209024005451 & 0.247909759945489 \tabularnewline
188 & 1.64 & 1.54889260774969 & 0.091107392250313 \tabularnewline
189 & 1.66 & 1.65302701299191 & 0.00697298700808624 \tabularnewline
190 & 1.77 & 1.69339217419138 & 0.0766078258086229 \tabularnewline
191 & 1.82 & 1.80271558263254 & 0.0172844173674633 \tabularnewline
192 & 1.78 & 1.86087625415493 & -0.080876254154925 \tabularnewline
193 & 1.28 & 1.80367009592029 & -0.523670095920292 \tabularnewline
194 & 1.29 & 1.23003889580955 & 0.0599611041904451 \tabularnewline
195 & 1.37 & 1.32263812089280 & 0.0473618791072038 \tabularnewline
196 & 1.12 & 1.41415721542959 & -0.294157215429588 \tabularnewline
197 & 1.51 & 1.12518186799948 & 0.384818132000524 \tabularnewline
198 & 2.24 & 1.59637074806603 & 0.643629251933967 \tabularnewline
199 & 2.94 & 2.38832926725769 & 0.551670732742306 \tabularnewline
200 & 3.09 & 3.04625408399239 & 0.0437459160076078 \tabularnewline
201 & 3.46 & 3.05102974835133 & 0.408970251648672 \tabularnewline
202 & 3.64 & 3.45504117571824 & 0.184958824281755 \tabularnewline
203 & 4.39 & 3.52901586091934 & 0.860984139080655 \tabularnewline
204 & 4.15 & 4.36072394140915 & -0.210723941409154 \tabularnewline
205 & 5.21 & 3.9195586105338 & 1.29044138946620 \tabularnewline
206 & 5.8 & 5.16741347504149 & 0.632586524958512 \tabularnewline
207 & 5.91 & 5.59539816615882 & 0.314601833841176 \tabularnewline
208 & 5.39 & 5.62852741047812 & -0.238527410478118 \tabularnewline
209 & 5.46 & 4.97095790563722 & 0.489042094362779 \tabularnewline
210 & 4.72 & 5.11647624666809 & -0.396476246668091 \tabularnewline
211 & 3.14 & 4.27940817633388 & -1.13940817633388 \tabularnewline
212 & 2.63 & 2.63588691288794 & -0.00588691288793656 \tabularnewline
213 & 2.32 & 2.31120068082936 & 0.00879931917063523 \tabularnewline
214 & 1.93 & 2.10336551985128 & -0.173365519851277 \tabularnewline
215 & 0.62 & 1.75897896654663 & -1.13897896654663 \tabularnewline
216 & 0.6 & 0.379647485398268 & 0.220352514601732 \tabularnewline
217 & -0.37 & 0.596863265762979 & -0.966863265762979 \tabularnewline
218 & -1.1 & -0.52329067474488 & -0.57670932525512 \tabularnewline
219 & -1.68 & -1.13567767539438 & -0.544322324605621 \tabularnewline
220 & -0.78 & -1.67265733389074 & 0.892657333890744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&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]3.22[/C][C]2.74139172020531[/C][C]0.478608279794694[/C][/ROW]
[ROW][C]2[/C][C]3.62[/C][C]3.1820783899549[/C][C]0.437921610045099[/C][/ROW]
[ROW][C]3[/C][C]3.82[/C][C]3.58913299442473[/C][C]0.230867005575270[/C][/ROW]
[ROW][C]4[/C][C]3.54[/C][C]3.76402745148004[/C][C]-0.224027451480041[/C][/ROW]
[ROW][C]5[/C][C]2.53[/C][C]3.38868065243283[/C][C]-0.858680652432829[/C][/ROW]
[ROW][C]6[/C][C]2.22[/C][C]2.26252556588761[/C][C]-0.0425255658876074[/C][/ROW]
[ROW][C]7[/C][C]2.85[/C][C]2.12647790118637[/C][C]0.72352209881363[/C][/ROW]
[ROW][C]8[/C][C]2.78[/C][C]2.89104990767199[/C][C]-0.111049907671993[/C][/ROW]
[ROW][C]9[/C][C]2.28[/C][C]2.68482003372321[/C][C]-0.404820033723214[/C][/ROW]
[ROW][C]10[/C][C]2.26[/C][C]2.18153813026388[/C][C]0.0784618697361195[/C][/ROW]
[ROW][C]11[/C][C]2.71[/C][C]2.21012868954325[/C][C]0.499871310456749[/C][/ROW]
[ROW][C]12[/C][C]2.77[/C][C]2.73972906596762[/C][C]0.0302709340323820[/C][/ROW]
[ROW][C]13[/C][C]2.77[/C][C]2.73698001177269[/C][C]0.0330199882273122[/C][/ROW]
[ROW][C]14[/C][C]2.64[/C][C]2.72678935056406[/C][C]-0.0867893505640593[/C][/ROW]
[ROW][C]15[/C][C]2.56[/C][C]2.5563277210924[/C][C]0.00367227890760273[/C][/ROW]
[ROW][C]16[/C][C]2.07[/C][C]2.49558346717600[/C][C]-0.425583467176004[/C][/ROW]
[ROW][C]17[/C][C]2.32[/C][C]1.93015745691554[/C][C]0.389842543084463[/C][/ROW]
[ROW][C]18[/C][C]2.16[/C][C]2.28847921489648[/C][C]-0.128479214896478[/C][/ROW]
[ROW][C]19[/C][C]2.23[/C][C]2.09855186512907[/C][C]0.131448134870930[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.22554700624599[/C][C]0.174452993754008[/C][/ROW]
[ROW][C]21[/C][C]2.84[/C][C]2.36707690544375[/C][C]0.472923094556254[/C][/ROW]
[ROW][C]22[/C][C]2.77[/C][C]2.87495972545018[/C][C]-0.104959725450181[/C][/ROW]
[ROW][C]23[/C][C]2.93[/C][C]2.70577858803319[/C][C]0.224221411966807[/C][/ROW]
[ROW][C]24[/C][C]2.91[/C][C]2.91026364595576[/C][C]-0.000263645955755940[/C][/ROW]
[ROW][C]25[/C][C]2.69[/C][C]2.83247305401538[/C][C]-0.142473054015377[/C][/ROW]
[ROW][C]26[/C][C]2.38[/C][C]2.57603119785661[/C][C]-0.19603119785661[/C][/ROW]
[ROW][C]27[/C][C]2.58[/C][C]2.24794983246863[/C][C]0.332050167531370[/C][/ROW]
[ROW][C]28[/C][C]3.19[/C][C]2.53610793611297[/C][C]0.653892063887032[/C][/ROW]
[ROW][C]29[/C][C]2.82[/C][C]3.18870003964114[/C][C]-0.368700039641137[/C][/ROW]
[ROW][C]30[/C][C]2.72[/C][C]2.64874591614059[/C][C]0.0712540838594077[/C][/ROW]
[ROW][C]31[/C][C]2.53[/C][C]2.64956713352343[/C][C]-0.119567133523431[/C][/ROW]
[ROW][C]32[/C][C]2.7[/C][C]2.42250728874978[/C][C]0.277492711250216[/C][/ROW]
[ROW][C]33[/C][C]2.42[/C][C]2.63580288223772[/C][C]-0.215802882237722[/C][/ROW]
[ROW][C]34[/C][C]2.5[/C][C]2.32250705856780[/C][C]0.177492941432202[/C][/ROW]
[ROW][C]35[/C][C]2.31[/C][C]2.46588278734546[/C][C]-0.155882787345463[/C][/ROW]
[ROW][C]36[/C][C]2.41[/C][C]2.23582630286082[/C][C]0.174173697139178[/C][/ROW]
[ROW][C]37[/C][C]2.56[/C][C]2.39703349985651[/C][C]0.162966500143491[/C][/ROW]
[ROW][C]38[/C][C]2.76[/C][C]2.53777683179907[/C][C]0.222223168200931[/C][/ROW]
[ROW][C]39[/C][C]2.71[/C][C]2.73908248554853[/C][C]-0.0290824855485330[/C][/ROW]
[ROW][C]40[/C][C]2.44[/C][C]2.64522226267889[/C][C]-0.205222262678888[/C][/ROW]
[ROW][C]41[/C][C]2.46[/C][C]2.33076797900489[/C][C]0.129232020995106[/C][/ROW]
[ROW][C]42[/C][C]2.12[/C][C]2.37459886049598[/C][C]-0.254598860495979[/C][/ROW]
[ROW][C]43[/C][C]1.99[/C][C]2.02537069773044[/C][C]-0.0353706977304384[/C][/ROW]
[ROW][C]44[/C][C]1.86[/C][C]1.94989943160672[/C][C]-0.0898994316067203[/C][/ROW]
[ROW][C]45[/C][C]1.88[/C][C]1.79362370632204[/C][C]0.0863762936779588[/C][/ROW]
[ROW][C]46[/C][C]1.82[/C][C]1.88588603143684[/C][C]-0.065886031436839[/C][/ROW]
[ROW][C]47[/C][C]1.74[/C][C]1.80879691788489[/C][C]-0.0687969178848873[/C][/ROW]
[ROW][C]48[/C][C]1.71[/C][C]1.74674949579190[/C][C]-0.0367494957919047[/C][/ROW]
[ROW][C]49[/C][C]1.38[/C][C]1.72503676410371[/C][C]-0.345036764103705[/C][/ROW]
[ROW][C]50[/C][C]1.27[/C][C]1.36858335408834[/C][C]-0.0985833540883417[/C][/ROW]
[ROW][C]51[/C][C]1.19[/C][C]1.29356781130628[/C][C]-0.103567811306280[/C][/ROW]
[ROW][C]52[/C][C]1.28[/C][C]1.22415221126734[/C][C]0.0558477887326618[/C][/ROW]
[ROW][C]53[/C][C]1.19[/C][C]1.33399469535989[/C][C]-0.14399469535989[/C][/ROW]
[ROW][C]54[/C][C]1.22[/C][C]1.21819470316165[/C][C]0.00180529683835325[/C][/ROW]
[ROW][C]55[/C][C]1.47[/C][C]1.31845193632120[/C][C]0.151548063678805[/C][/ROW]
[ROW][C]56[/C][C]1.46[/C][C]1.58934534140973[/C][C]-0.129345341409733[/C][/ROW]
[ROW][C]57[/C][C]1.96[/C][C]1.50646583675732[/C][C]0.453534163242678[/C][/ROW]
[ROW][C]58[/C][C]1.88[/C][C]2.09996102352448[/C][C]-0.219961023524484[/C][/ROW]
[ROW][C]59[/C][C]2.03[/C][C]1.89214944683498[/C][C]0.137850553165023[/C][/ROW]
[ROW][C]60[/C][C]2.04[/C][C]2.09784208496964[/C][C]-0.0578420849696427[/C][/ROW]
[ROW][C]61[/C][C]1.9[/C][C]2.06099612757861[/C][C]-0.160996127578607[/C][/ROW]
[ROW][C]62[/C][C]1.8[/C][C]1.89904728837286[/C][C]-0.0990472883728599[/C][/ROW]
[ROW][C]63[/C][C]1.92[/C][C]1.79906308323989[/C][C]0.120936916760107[/C][/ROW]
[ROW][C]64[/C][C]1.92[/C][C]1.96182538192159[/C][C]-0.0418253819215934[/C][/ROW]
[ROW][C]65[/C][C]1.97[/C][C]1.93739700686880[/C][C]0.0326029931312032[/C][/ROW]
[ROW][C]66[/C][C]2.46[/C][C]1.98775098560276[/C][C]0.472249014397242[/C][/ROW]
[ROW][C]67[/C][C]2.36[/C][C]2.57124805536677[/C][C]-0.211248055366770[/C][/ROW]
[ROW][C]68[/C][C]2.53[/C][C]2.36007038242884[/C][C]0.169929617571160[/C][/ROW]
[ROW][C]69[/C][C]2.31[/C][C]2.55137003784689[/C][C]-0.241370037846885[/C][/ROW]
[ROW][C]70[/C][C]1.98[/C][C]2.28152703287358[/C][C]-0.301527032873581[/C][/ROW]
[ROW][C]71[/C][C]1.46[/C][C]1.94214090014250[/C][C]-0.482140900142502[/C][/ROW]
[ROW][C]72[/C][C]1.26[/C][C]1.42226754243089[/C][C]-0.162267542430894[/C][/ROW]
[ROW][C]73[/C][C]1.58[/C][C]1.29345470637525[/C][C]0.286545293624747[/C][/ROW]
[ROW][C]74[/C][C]1.74[/C][C]1.68896282189250[/C][C]0.0510371781074968[/C][/ROW]
[ROW][C]75[/C][C]1.89[/C][C]1.81111936993282[/C][C]0.0788806300671848[/C][/ROW]
[ROW][C]76[/C][C]1.85[/C][C]1.97054216980349[/C][C]-0.120542169803493[/C][/ROW]
[ROW][C]77[/C][C]1.62[/C][C]1.88232844321581[/C][C]-0.262328443215810[/C][/ROW]
[ROW][C]78[/C][C]1.3[/C][C]1.61300436306693[/C][C]-0.313004363066929[/C][/ROW]
[ROW][C]79[/C][C]1.42[/C][C]1.32819753005282[/C][C]0.0918024699471758[/C][/ROW]
[ROW][C]80[/C][C]1.15[/C][C]1.51656714567021[/C][C]-0.366567145670212[/C][/ROW]
[ROW][C]81[/C][C]0.42[/C][C]1.16268415941853[/C][C]-0.742684159418527[/C][/ROW]
[ROW][C]82[/C][C]0.74[/C][C]0.418342873745907[/C][C]0.321657126254093[/C][/ROW]
[ROW][C]83[/C][C]1.02[/C][C]0.899627456959586[/C][C]0.120372543040414[/C][/ROW]
[ROW][C]84[/C][C]1.51[/C][C]1.16816022970047[/C][C]0.341839770299528[/C][/ROW]
[ROW][C]85[/C][C]1.86[/C][C]1.69946705055398[/C][C]0.160532949446024[/C][/ROW]
[ROW][C]86[/C][C]1.59[/C][C]1.99182420956737[/C][C]-0.401824209567368[/C][/ROW]
[ROW][C]87[/C][C]1.03[/C][C]1.60755018694242[/C][C]-0.577550186942424[/C][/ROW]
[ROW][C]88[/C][C]0.44[/C][C]1.01332050619213[/C][C]-0.573320506192129[/C][/ROW]
[ROW][C]89[/C][C]0.82[/C][C]0.426995877325678[/C][C]0.393004122674322[/C][/ROW]
[ROW][C]90[/C][C]0.86[/C][C]0.961312361418113[/C][C]-0.101312361418114[/C][/ROW]
[ROW][C]91[/C][C]0.58[/C][C]0.991546572930725[/C][C]-0.411546572930725[/C][/ROW]
[ROW][C]92[/C][C]0.59[/C][C]0.687721440148176[/C][C]-0.0977214401481764[/C][/ROW]
[ROW][C]93[/C][C]0.95[/C][C]0.713451228538836[/C][C]0.236548771461164[/C][/ROW]
[ROW][C]94[/C][C]0.98[/C][C]1.14889427509444[/C][C]-0.168894275094444[/C][/ROW]
[ROW][C]95[/C][C]1.23[/C][C]1.11336350773845[/C][C]0.116636492261552[/C][/ROW]
[ROW][C]96[/C][C]1.17[/C][C]1.39151150022801[/C][C]-0.221511500228007[/C][/ROW]
[ROW][C]97[/C][C]0.84[/C][C]1.25352866080541[/C][C]-0.413528660805411[/C][/ROW]
[ROW][C]98[/C][C]0.74[/C][C]0.879019143254701[/C][C]-0.139019143254701[/C][/ROW]
[ROW][C]99[/C][C]0.65[/C][C]0.803420915945821[/C][C]-0.153420915945821[/C][/ROW]
[ROW][C]100[/C][C]0.91[/C][C]0.719995767342446[/C][C]0.190004232657554[/C][/ROW]
[ROW][C]101[/C][C]1.19[/C][C]1.03422069856280[/C][C]0.155779301437205[/C][/ROW]
[ROW][C]102[/C][C]1.3[/C][C]1.28342972483597[/C][C]0.0165702751640306[/C][/ROW]
[ROW][C]103[/C][C]1.53[/C][C]1.40726689307846[/C][C]0.122733106921544[/C][/ROW]
[ROW][C]104[/C][C]1.94[/C][C]1.64108678978756[/C][C]0.298913210212437[/C][/ROW]
[ROW][C]105[/C][C]1.79[/C][C]2.02974378948319[/C][C]-0.239743789483185[/C][/ROW]
[ROW][C]106[/C][C]1.95[/C][C]1.81720958855314[/C][C]0.132790411446861[/C][/ROW]
[ROW][C]107[/C][C]2.26[/C][C]2.01551334445696[/C][C]0.244486655543043[/C][/ROW]
[ROW][C]108[/C][C]2.04[/C][C]2.34681725668687[/C][C]-0.306817256686865[/C][/ROW]
[ROW][C]109[/C][C]2.16[/C][C]2.04578346340453[/C][C]0.114216536595467[/C][/ROW]
[ROW][C]110[/C][C]2.75[/C][C]2.21874759239682[/C][C]0.531252407603184[/C][/ROW]
[ROW][C]111[/C][C]2.79[/C][C]2.84761019738601[/C][C]-0.0576101973860074[/C][/ROW]
[ROW][C]112[/C][C]2.88[/C][C]2.78591655129013[/C][C]0.0940834487098701[/C][/ROW]
[ROW][C]113[/C][C]3.36[/C][C]2.86851590952139[/C][C]0.491484090478607[/C][/ROW]
[ROW][C]114[/C][C]2.97[/C][C]3.36221643160897[/C][C]-0.392216431608967[/C][/ROW]
[ROW][C]115[/C][C]3.1[/C][C]2.86889591185265[/C][C]0.231104088147351[/C][/ROW]
[ROW][C]116[/C][C]2.49[/C][C]3.08693810239714[/C][C]-0.596938102397144[/C][/ROW]
[ROW][C]117[/C][C]2.2[/C][C]2.31853779839796[/C][C]-0.11853779839796[/C][/ROW]
[ROW][C]118[/C][C]2.25[/C][C]2.15071463940468[/C][C]0.0992853605953172[/C][/ROW]
[ROW][C]119[/C][C]2.09[/C][C]2.24011513182490[/C][C]-0.150115131824905[/C][/ROW]
[ROW][C]120[/C][C]2.79[/C][C]2.07168111398354[/C][C]0.718318886016456[/C][/ROW]
[ROW][C]121[/C][C]3.14[/C][C]2.8936715036677[/C][C]0.246328496332297[/C][/ROW]
[ROW][C]122[/C][C]2.93[/C][C]3.13903074707556[/C][C]-0.209030747075564[/C][/ROW]
[ROW][C]123[/C][C]2.65[/C][C]2.83231661093700[/C][C]-0.182316610936996[/C][/ROW]
[ROW][C]124[/C][C]2.67[/C][C]2.54329200308277[/C][C]0.126707996917227[/C][/ROW]
[ROW][C]125[/C][C]2.26[/C][C]2.59158436654568[/C][C]-0.331584366545684[/C][/ROW]
[ROW][C]126[/C][C]2.35[/C][C]2.11325748744885[/C][C]0.236742512551150[/C][/ROW]
[ROW][C]127[/C][C]2.13[/C][C]2.34486796885664[/C][C]-0.214867968856639[/C][/ROW]
[ROW][C]128[/C][C]2.18[/C][C]2.06601807842814[/C][C]0.113981921571859[/C][/ROW]
[ROW][C]129[/C][C]2.9[/C][C]2.14886078351326[/C][C]0.751139216486745[/C][/ROW]
[ROW][C]130[/C][C]2.63[/C][C]2.99430334378881[/C][C]-0.364303343788807[/C][/ROW]
[ROW][C]131[/C][C]2.67[/C][C]2.54771389690992[/C][C]0.122286103090076[/C][/ROW]
[ROW][C]132[/C][C]1.81[/C][C]2.66690673555841[/C][C]-0.856906735558415[/C][/ROW]
[ROW][C]133[/C][C]1.33[/C][C]1.64546122224415[/C][C]-0.315461222244153[/C][/ROW]
[ROW][C]134[/C][C]0.88[/C][C]1.26486009775551[/C][C]-0.384860097755509[/C][/ROW]
[ROW][C]135[/C][C]1.28[/C][C]0.812449577982176[/C][C]0.467550422017824[/C][/ROW]
[ROW][C]136[/C][C]1.26[/C][C]1.38235436262994[/C][C]-0.122354362629939[/C][/ROW]
[ROW][C]137[/C][C]1.26[/C][C]1.28215940609639[/C][C]-0.0221594060963866[/C][/ROW]
[ROW][C]138[/C][C]1.29[/C][C]1.295452816626[/C][C]-0.00545281662599885[/C][/ROW]
[ROW][C]139[/C][C]1.1[/C][C]1.35321551236957[/C][C]-0.253215512369566[/C][/ROW]
[ROW][C]140[/C][C]1.37[/C][C]1.12858776107251[/C][C]0.241412238927489[/C][/ROW]
[ROW][C]141[/C][C]1.21[/C][C]1.43550971483600[/C][C]-0.225509714836002[/C][/ROW]
[ROW][C]142[/C][C]1.74[/C][C]1.24418927299447[/C][C]0.495810727005526[/C][/ROW]
[ROW][C]143[/C][C]1.76[/C][C]1.88530669927174[/C][C]-0.125306699271741[/C][/ROW]
[ROW][C]144[/C][C]1.48[/C][C]1.82315116855518[/C][C]-0.343151168555181[/C][/ROW]
[ROW][C]145[/C][C]1.04[/C][C]1.49982078345391[/C][C]-0.459820783453905[/C][/ROW]
[ROW][C]146[/C][C]1.62[/C][C]1.01943278121457[/C][C]0.600567218785426[/C][/ROW]
[ROW][C]147[/C][C]1.49[/C][C]1.74696570340259[/C][C]-0.256965703402591[/C][/ROW]
[ROW][C]148[/C][C]1.79[/C][C]1.49723935489725[/C][C]0.292760645102752[/C][/ROW]
[ROW][C]149[/C][C]1.8[/C][C]1.87937253330012[/C][C]-0.079372533300124[/C][/ROW]
[ROW][C]150[/C][C]1.58[/C][C]1.79842073293240[/C][C]-0.218420732932396[/C][/ROW]
[ROW][C]151[/C][C]1.86[/C][C]1.59095430186868[/C][C]0.269045698131319[/C][/ROW]
[ROW][C]152[/C][C]1.74[/C][C]1.93017370213312[/C][C]-0.190173702133123[/C][/ROW]
[ROW][C]153[/C][C]1.59[/C][C]1.70600950285506[/C][C]-0.116009502855064[/C][/ROW]
[ROW][C]154[/C][C]1.26[/C][C]1.61145948276974[/C][C]-0.351459482769735[/C][/ROW]
[ROW][C]155[/C][C]1.13[/C][C]1.24695943285554[/C][C]-0.116959432855543[/C][/ROW]
[ROW][C]156[/C][C]1.92[/C][C]1.18367737143538[/C][C]0.736322628564624[/C][/ROW]
[ROW][C]157[/C][C]2.61[/C][C]2.10486654567321[/C][C]0.505133454326791[/C][/ROW]
[ROW][C]158[/C][C]2.26[/C][C]2.73012956986139[/C][C]-0.470129569861393[/C][/ROW]
[ROW][C]159[/C][C]2.41[/C][C]2.20169153228710[/C][C]0.208308467712897[/C][/ROW]
[ROW][C]160[/C][C]2.26[/C][C]2.42089183130139[/C][C]-0.160891831301388[/C][/ROW]
[ROW][C]161[/C][C]2.03[/C][C]2.18411071674800[/C][C]-0.154110716748005[/C][/ROW]
[ROW][C]162[/C][C]2.86[/C][C]1.94990116493553[/C][C]0.910098835064474[/C][/ROW]
[ROW][C]163[/C][C]2.55[/C][C]2.96838640206848[/C][C]-0.418386402068475[/C][/ROW]
[ROW][C]164[/C][C]2.27[/C][C]2.45536256547284[/C][C]-0.185362565472843[/C][/ROW]
[ROW][C]165[/C][C]2.26[/C][C]2.17992474001709[/C][C]0.0800752599829052[/C][/ROW]
[ROW][C]166[/C][C]2.57[/C][C]2.2302261125613[/C][C]0.339773887438698[/C][/ROW]
[ROW][C]167[/C][C]3.07[/C][C]2.58404514984032[/C][C]0.485954850159681[/C][/ROW]
[ROW][C]168[/C][C]2.76[/C][C]3.11305102103304[/C][C]-0.353051021033036[/C][/ROW]
[ROW][C]169[/C][C]2.51[/C][C]2.66181570037279[/C][C]-0.151815700372787[/C][/ROW]
[ROW][C]170[/C][C]2.87[/C][C]2.42640276953536[/C][C]0.443597230464644[/C][/ROW]
[ROW][C]171[/C][C]3.14[/C][C]2.8414975455637[/C][C]0.2985024544363[/C][/ROW]
[ROW][C]172[/C][C]3.11[/C][C]3.09392695664004[/C][C]0.0160730433599578[/C][/ROW]
[ROW][C]173[/C][C]3.16[/C][C]3.01523090552791[/C][C]0.144769094472091[/C][/ROW]
[ROW][C]174[/C][C]2.47[/C][C]3.05360895493659[/C][C]-0.583608954936594[/C][/ROW]
[ROW][C]175[/C][C]2.57[/C][C]2.28717363401863[/C][C]0.282826365981371[/C][/ROW]
[ROW][C]176[/C][C]2.89[/C][C]2.52809145214721[/C][C]0.361908547852793[/C][/ROW]
[ROW][C]177[/C][C]2.63[/C][C]2.83086143843654[/C][C]-0.200861438436539[/C][/ROW]
[ROW][C]178[/C][C]2.38[/C][C]2.53648253920985[/C][C]-0.156482539209849[/C][/ROW]
[ROW][C]179[/C][C]1.69[/C][C]2.28176764025979[/C][C]-0.591767640259792[/C][/ROW]
[ROW][C]180[/C][C]1.96[/C][C]1.55111778388012[/C][C]0.408882216119881[/C][/ROW]
[ROW][C]181[/C][C]2.19[/C][C]1.98812721369980[/C][C]0.201872786300197[/C][/ROW]
[ROW][C]182[/C][C]1.87[/C][C]2.18712215870421[/C][C]-0.317122158704212[/C][/ROW]
[ROW][C]183[/C][C]1.6[/C][C]1.79789581988266[/C][C]-0.197895819882660[/C][/ROW]
[ROW][C]184[/C][C]1.63[/C][C]1.54557449416662[/C][C]0.0844255058333843[/C][/ROW]
[ROW][C]185[/C][C]1.22[/C][C]1.60964353929644[/C][C]-0.389643539296437[/C][/ROW]
[ROW][C]186[/C][C]1.21[/C][C]1.12625372127147[/C][C]0.0837462787285282[/C][/ROW]
[ROW][C]187[/C][C]1.49[/C][C]1.24209024005451[/C][C]0.247909759945489[/C][/ROW]
[ROW][C]188[/C][C]1.64[/C][C]1.54889260774969[/C][C]0.091107392250313[/C][/ROW]
[ROW][C]189[/C][C]1.66[/C][C]1.65302701299191[/C][C]0.00697298700808624[/C][/ROW]
[ROW][C]190[/C][C]1.77[/C][C]1.69339217419138[/C][C]0.0766078258086229[/C][/ROW]
[ROW][C]191[/C][C]1.82[/C][C]1.80271558263254[/C][C]0.0172844173674633[/C][/ROW]
[ROW][C]192[/C][C]1.78[/C][C]1.86087625415493[/C][C]-0.080876254154925[/C][/ROW]
[ROW][C]193[/C][C]1.28[/C][C]1.80367009592029[/C][C]-0.523670095920292[/C][/ROW]
[ROW][C]194[/C][C]1.29[/C][C]1.23003889580955[/C][C]0.0599611041904451[/C][/ROW]
[ROW][C]195[/C][C]1.37[/C][C]1.32263812089280[/C][C]0.0473618791072038[/C][/ROW]
[ROW][C]196[/C][C]1.12[/C][C]1.41415721542959[/C][C]-0.294157215429588[/C][/ROW]
[ROW][C]197[/C][C]1.51[/C][C]1.12518186799948[/C][C]0.384818132000524[/C][/ROW]
[ROW][C]198[/C][C]2.24[/C][C]1.59637074806603[/C][C]0.643629251933967[/C][/ROW]
[ROW][C]199[/C][C]2.94[/C][C]2.38832926725769[/C][C]0.551670732742306[/C][/ROW]
[ROW][C]200[/C][C]3.09[/C][C]3.04625408399239[/C][C]0.0437459160076078[/C][/ROW]
[ROW][C]201[/C][C]3.46[/C][C]3.05102974835133[/C][C]0.408970251648672[/C][/ROW]
[ROW][C]202[/C][C]3.64[/C][C]3.45504117571824[/C][C]0.184958824281755[/C][/ROW]
[ROW][C]203[/C][C]4.39[/C][C]3.52901586091934[/C][C]0.860984139080655[/C][/ROW]
[ROW][C]204[/C][C]4.15[/C][C]4.36072394140915[/C][C]-0.210723941409154[/C][/ROW]
[ROW][C]205[/C][C]5.21[/C][C]3.9195586105338[/C][C]1.29044138946620[/C][/ROW]
[ROW][C]206[/C][C]5.8[/C][C]5.16741347504149[/C][C]0.632586524958512[/C][/ROW]
[ROW][C]207[/C][C]5.91[/C][C]5.59539816615882[/C][C]0.314601833841176[/C][/ROW]
[ROW][C]208[/C][C]5.39[/C][C]5.62852741047812[/C][C]-0.238527410478118[/C][/ROW]
[ROW][C]209[/C][C]5.46[/C][C]4.97095790563722[/C][C]0.489042094362779[/C][/ROW]
[ROW][C]210[/C][C]4.72[/C][C]5.11647624666809[/C][C]-0.396476246668091[/C][/ROW]
[ROW][C]211[/C][C]3.14[/C][C]4.27940817633388[/C][C]-1.13940817633388[/C][/ROW]
[ROW][C]212[/C][C]2.63[/C][C]2.63588691288794[/C][C]-0.00588691288793656[/C][/ROW]
[ROW][C]213[/C][C]2.32[/C][C]2.31120068082936[/C][C]0.00879931917063523[/C][/ROW]
[ROW][C]214[/C][C]1.93[/C][C]2.10336551985128[/C][C]-0.173365519851277[/C][/ROW]
[ROW][C]215[/C][C]0.62[/C][C]1.75897896654663[/C][C]-1.13897896654663[/C][/ROW]
[ROW][C]216[/C][C]0.6[/C][C]0.379647485398268[/C][C]0.220352514601732[/C][/ROW]
[ROW][C]217[/C][C]-0.37[/C][C]0.596863265762979[/C][C]-0.966863265762979[/C][/ROW]
[ROW][C]218[/C][C]-1.1[/C][C]-0.52329067474488[/C][C]-0.57670932525512[/C][/ROW]
[ROW][C]219[/C][C]-1.68[/C][C]-1.13567767539438[/C][C]-0.544322324605621[/C][/ROW]
[ROW][C]220[/C][C]-0.78[/C][C]-1.67265733389074[/C][C]0.892657333890744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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
13.222.741391720205310.478608279794694
23.623.18207838995490.437921610045099
33.823.589132994424730.230867005575270
43.543.76402745148004-0.224027451480041
52.533.38868065243283-0.858680652432829
62.222.26252556588761-0.0425255658876074
72.852.126477901186370.72352209881363
82.782.89104990767199-0.111049907671993
92.282.68482003372321-0.404820033723214
102.262.181538130263880.0784618697361195
112.712.210128689543250.499871310456749
122.772.739729065967620.0302709340323820
132.772.736980011772690.0330199882273122
142.642.72678935056406-0.0867893505640593
152.562.55632772109240.00367227890760273
162.072.49558346717600-0.425583467176004
172.321.930157456915540.389842543084463
182.162.28847921489648-0.128479214896478
192.232.098551865129070.131448134870930
202.42.225547006245990.174452993754008
212.842.367076905443750.472923094556254
222.772.87495972545018-0.104959725450181
232.932.705778588033190.224221411966807
242.912.91026364595576-0.000263645955755940
252.692.83247305401538-0.142473054015377
262.382.57603119785661-0.19603119785661
272.582.247949832468630.332050167531370
283.192.536107936112970.653892063887032
292.823.18870003964114-0.368700039641137
302.722.648745916140590.0712540838594077
312.532.64956713352343-0.119567133523431
322.72.422507288749780.277492711250216
332.422.63580288223772-0.215802882237722
342.52.322507058567800.177492941432202
352.312.46588278734546-0.155882787345463
362.412.235826302860820.174173697139178
372.562.397033499856510.162966500143491
382.762.537776831799070.222223168200931
392.712.73908248554853-0.0290824855485330
402.442.64522226267889-0.205222262678888
412.462.330767979004890.129232020995106
422.122.37459886049598-0.254598860495979
431.992.02537069773044-0.0353706977304384
441.861.94989943160672-0.0898994316067203
451.881.793623706322040.0863762936779588
461.821.88588603143684-0.065886031436839
471.741.80879691788489-0.0687969178848873
481.711.74674949579190-0.0367494957919047
491.381.72503676410371-0.345036764103705
501.271.36858335408834-0.0985833540883417
511.191.29356781130628-0.103567811306280
521.281.224152211267340.0558477887326618
531.191.33399469535989-0.14399469535989
541.221.218194703161650.00180529683835325
551.471.318451936321200.151548063678805
561.461.58934534140973-0.129345341409733
571.961.506465836757320.453534163242678
581.882.09996102352448-0.219961023524484
592.031.892149446834980.137850553165023
602.042.09784208496964-0.0578420849696427
611.92.06099612757861-0.160996127578607
621.81.89904728837286-0.0990472883728599
631.921.799063083239890.120936916760107
641.921.96182538192159-0.0418253819215934
651.971.937397006868800.0326029931312032
662.461.987750985602760.472249014397242
672.362.57124805536677-0.211248055366770
682.532.360070382428840.169929617571160
692.312.55137003784689-0.241370037846885
701.982.28152703287358-0.301527032873581
711.461.94214090014250-0.482140900142502
721.261.42226754243089-0.162267542430894
731.581.293454706375250.286545293624747
741.741.688962821892500.0510371781074968
751.891.811119369932820.0788806300671848
761.851.97054216980349-0.120542169803493
771.621.88232844321581-0.262328443215810
781.31.61300436306693-0.313004363066929
791.421.328197530052820.0918024699471758
801.151.51656714567021-0.366567145670212
810.421.16268415941853-0.742684159418527
820.740.4183428737459070.321657126254093
831.020.8996274569595860.120372543040414
841.511.168160229700470.341839770299528
851.861.699467050553980.160532949446024
861.591.99182420956737-0.401824209567368
871.031.60755018694242-0.577550186942424
880.441.01332050619213-0.573320506192129
890.820.4269958773256780.393004122674322
900.860.961312361418113-0.101312361418114
910.580.991546572930725-0.411546572930725
920.590.687721440148176-0.0977214401481764
930.950.7134512285388360.236548771461164
940.981.14889427509444-0.168894275094444
951.231.113363507738450.116636492261552
961.171.39151150022801-0.221511500228007
970.841.25352866080541-0.413528660805411
980.740.879019143254701-0.139019143254701
990.650.803420915945821-0.153420915945821
1000.910.7199957673424460.190004232657554
1011.191.034220698562800.155779301437205
1021.31.283429724835970.0165702751640306
1031.531.407266893078460.122733106921544
1041.941.641086789787560.298913210212437
1051.792.02974378948319-0.239743789483185
1061.951.817209588553140.132790411446861
1072.262.015513344456960.244486655543043
1082.042.34681725668687-0.306817256686865
1092.162.045783463404530.114216536595467
1102.752.218747592396820.531252407603184
1112.792.84761019738601-0.0576101973860074
1122.882.785916551290130.0940834487098701
1133.362.868515909521390.491484090478607
1142.973.36221643160897-0.392216431608967
1153.12.868895911852650.231104088147351
1162.493.08693810239714-0.596938102397144
1172.22.31853779839796-0.11853779839796
1182.252.150714639404680.0992853605953172
1192.092.24011513182490-0.150115131824905
1202.792.071681113983540.718318886016456
1213.142.89367150366770.246328496332297
1222.933.13903074707556-0.209030747075564
1232.652.83231661093700-0.182316610936996
1242.672.543292003082770.126707996917227
1252.262.59158436654568-0.331584366545684
1262.352.113257487448850.236742512551150
1272.132.34486796885664-0.214867968856639
1282.182.066018078428140.113981921571859
1292.92.148860783513260.751139216486745
1302.632.99430334378881-0.364303343788807
1312.672.547713896909920.122286103090076
1321.812.66690673555841-0.856906735558415
1331.331.64546122224415-0.315461222244153
1340.881.26486009775551-0.384860097755509
1351.280.8124495779821760.467550422017824
1361.261.38235436262994-0.122354362629939
1371.261.28215940609639-0.0221594060963866
1381.291.295452816626-0.00545281662599885
1391.11.35321551236957-0.253215512369566
1401.371.128587761072510.241412238927489
1411.211.43550971483600-0.225509714836002
1421.741.244189272994470.495810727005526
1431.761.88530669927174-0.125306699271741
1441.481.82315116855518-0.343151168555181
1451.041.49982078345391-0.459820783453905
1461.621.019432781214570.600567218785426
1471.491.74696570340259-0.256965703402591
1481.791.497239354897250.292760645102752
1491.81.87937253330012-0.079372533300124
1501.581.79842073293240-0.218420732932396
1511.861.590954301868680.269045698131319
1521.741.93017370213312-0.190173702133123
1531.591.70600950285506-0.116009502855064
1541.261.61145948276974-0.351459482769735
1551.131.24695943285554-0.116959432855543
1561.921.183677371435380.736322628564624
1572.612.104866545673210.505133454326791
1582.262.73012956986139-0.470129569861393
1592.412.201691532287100.208308467712897
1602.262.42089183130139-0.160891831301388
1612.032.18411071674800-0.154110716748005
1622.861.949901164935530.910098835064474
1632.552.96838640206848-0.418386402068475
1642.272.45536256547284-0.185362565472843
1652.262.179924740017090.0800752599829052
1662.572.23022611256130.339773887438698
1673.072.584045149840320.485954850159681
1682.763.11305102103304-0.353051021033036
1692.512.66181570037279-0.151815700372787
1702.872.426402769535360.443597230464644
1713.142.84149754556370.2985024544363
1723.113.093926956640040.0160730433599578
1733.163.015230905527910.144769094472091
1742.473.05360895493659-0.583608954936594
1752.572.287173634018630.282826365981371
1762.892.528091452147210.361908547852793
1772.632.83086143843654-0.200861438436539
1782.382.53648253920985-0.156482539209849
1791.692.28176764025979-0.591767640259792
1801.961.551117783880120.408882216119881
1812.191.988127213699800.201872786300197
1821.872.18712215870421-0.317122158704212
1831.61.79789581988266-0.197895819882660
1841.631.545574494166620.0844255058333843
1851.221.60964353929644-0.389643539296437
1861.211.126253721271470.0837462787285282
1871.491.242090240054510.247909759945489
1881.641.548892607749690.091107392250313
1891.661.653027012991910.00697298700808624
1901.771.693392174191380.0766078258086229
1911.821.802715582632540.0172844173674633
1921.781.86087625415493-0.080876254154925
1931.281.80367009592029-0.523670095920292
1941.291.230038895809550.0599611041904451
1951.371.322638120892800.0473618791072038
1961.121.41415721542959-0.294157215429588
1971.511.125181867999480.384818132000524
1982.241.596370748066030.643629251933967
1992.942.388329267257690.551670732742306
2003.093.046254083992390.0437459160076078
2013.463.051029748351330.408970251648672
2023.643.455041175718240.184958824281755
2034.393.529015860919340.860984139080655
2044.154.36072394140915-0.210723941409154
2055.213.91955861053381.29044138946620
2065.85.167413475041490.632586524958512
2075.915.595398166158820.314601833841176
2085.395.62852741047812-0.238527410478118
2095.464.970957905637220.489042094362779
2104.725.11647624666809-0.396476246668091
2113.144.27940817633388-1.13940817633388
2122.632.63588691288794-0.00588691288793656
2132.322.311200680829360.00879931917063523
2141.932.10336551985128-0.173365519851277
2150.621.75897896654663-1.13897896654663
2160.60.3796474853982680.220352514601732
217-0.370.596863265762979-0.966863265762979
218-1.1-0.52329067474488-0.57670932525512
219-1.68-1.13567767539438-0.544322324605621
220-0.78-1.672657333890740.892657333890744







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.9215164394356970.1569671211286050.0784835605643025
220.8524445963839780.2951108072320430.147555403616022
230.7638188883912390.4723622232175220.236181111608761
240.6648382273128050.670323545374390.335161772687195
250.5560161020406320.8879677959187360.443983897959368
260.4469332710316820.8938665420633650.553066728968318
270.3830521469156840.7661042938313670.616947853084316
280.5410721264693440.9178557470613110.458927873530656
290.454650503246280.909301006492560.54534949675372
300.5351370933468490.9297258133063020.464862906653151
310.4764348973778740.952869794755750.523565102622126
320.4068943837417420.8137887674834840.593105616258258
330.3895929825980070.7791859651960140.610407017401993
340.3193338479888010.6386676959776020.680666152011199
350.3112050211029670.6224100422059340.688794978897033
360.2525263134188900.5050526268377810.74747368658111
370.2071384890905540.4142769781811080.792861510909446
380.1777349412384190.3554698824768370.822265058761581
390.1360317710903260.2720635421806520.863968228909674
400.1045421968906380.2090843937812760.895457803109362
410.0982374996148130.1964749992296260.901762500385187
420.07966160120298950.1593232024059790.92033839879701
430.06559194330381670.1311838866076330.934408056696183
440.0504320915403270.1008641830806540.949567908459673
450.03641588675994240.07283177351988490.963584113240058
460.02772027608789040.05544055217578080.97227972391211
470.02106573450725210.04213146901450430.978934265492748
480.01448362932579900.02896725865159790.9855163706742
490.01371033376896620.02742066753793240.986289666231034
500.00929482015599450.0185896403119890.990705179844006
510.006384549174631780.01276909834926360.993615450825368
520.004501712030560420.009003424061120840.99549828796944
530.003251412454109150.006502824908218290.99674858754589
540.003790614383885020.007581228767770040.996209385616115
550.003829198814357620.007658397628715240.996170801185642
560.002688948443032670.005377896886065330.997311051556967
570.007644800084005110.01528960016801020.992355199915995
580.005218457693881090.01043691538776220.994781542306119
590.004196014121629660.008392028243259320.99580398587837
600.002950333258727950.00590066651745590.997049666741272
610.001951986575899990.003903973151799980.9980480134241
620.001269187543751140.002538375087502270.99873081245625
630.0008754210287566890.001750842057513380.999124578971243
640.0005609568883481780.001121913776696360.999439043111652
650.0004748955408007090.0009497910816014170.9995251044592
660.001370917266492960.002741834532985910.998629082733507
670.0009517306196793320.001903461239358660.99904826938032
680.0009047335951720340.001809467190344070.999095266404828
690.0006398618679944560.001279723735988910.999360138132006
700.0005440855720990230.001088171144198050.9994559144279
710.0009329090541040840.001865818108208170.999067090945896
720.0007303130436532420.001460626087306480.999269686956347
730.0005827096671608930.001165419334321790.99941729033284
740.0003859841844469860.0007719683688939720.999614015815553
750.0002630201630366270.0005260403260732540.999736979836963
760.0001720546061894360.0003441092123788730.99982794539381
770.0001144310628478160.0002288621256956320.999885568937152
788.6933330786413e-050.0001738666615728260.999913066669214
795.56724362407067e-050.0001113448724814130.99994432756376
805.57077159013673e-050.0001114154318027350.999944292284099
810.0002534557006760300.0005069114013520590.999746544299324
820.0002339372226486940.0004678744452973890.999766062777351
830.0001590904494991790.0003181808989983580.9998409095505
840.0001654727705698340.0003309455411396670.99983452722943
850.0001506157036016030.0003012314072032050.999849384296398
860.0001139466116501220.0002278932233002440.99988605338835
870.0001462523806559340.0002925047613118680.999853747619344
880.0002100233165174810.0004200466330349620.999789976683483
890.0002322433039133730.0004644866078267460.999767756696087
900.0001685832119620290.0003371664239240590.999831416788038
910.0001979737238078610.0003959474476157220.999802026276192
920.0001346477506221710.0002692955012443420.999865352249378
930.0001159184656094420.0002318369312188850.99988408153439
947.90556784341398e-050.0001581113568682800.999920944321566
956.08835880740868e-050.0001217671761481740.999939116411926
964.0065436854982e-058.0130873709964e-050.999959934563145
973.60494659249742e-057.20989318499484e-050.999963950534075
982.3206797962799e-054.6413595925598e-050.999976793202037
991.57026138666035e-053.14052277332070e-050.999984297386133
1001.23292948607548e-052.46585897215097e-050.99998767070514
1019.99619765774281e-061.99923953154856e-050.999990003802342
1026.9739809762315e-061.3947961952463e-050.999993026019024
1036.03072971240190e-061.20614594248038e-050.999993969270288
1049.40780404759356e-061.88156080951871e-050.999990592195952
1056.1599594211869e-061.23199188423738e-050.999993840040579
1066.1447604175878e-061.22895208351756e-050.999993855239582
1076.35291396091382e-061.27058279218276e-050.99999364708604
1084.31425941105944e-068.62851882211887e-060.99999568574059
1093.69388416991716e-067.38776833983431e-060.99999630611583
1101.05306275721846e-052.10612551443693e-050.999989469372428
1116.67654774756987e-061.33530954951397e-050.999993323452252
1125.79838336603088e-061.15967667320618e-050.999994201616634
1131.16644214710618e-052.33288429421235e-050.99998833557853
1141.17507956507553e-052.35015913015106e-050.99998824920435
1159.03579320194948e-061.80715864038990e-050.999990964206798
1161.99354542363491e-053.98709084726982e-050.999980064545764
1171.41085383065957e-052.82170766131914e-050.999985891461693
1188.89770255283898e-061.77954051056780e-050.999991102297447
1196.79902135635606e-061.35980427127121e-050.999993200978644
1202.53573775597269e-055.07147551194538e-050.99997464262244
1212.14161226822689e-054.28322453645379e-050.999978583877318
1221.47575478458564e-052.95150956917128e-050.999985242452154
1239.7257458622417e-061.94514917244834e-050.999990274254138
1246.31681012694012e-061.26336202538802e-050.999993683189873
1256.35210793996486e-061.27042158799297e-050.99999364789206
1264.70360975161386e-069.40721950322772e-060.999995296390248
1273.74876761022917e-067.49753522045833e-060.99999625123239
1282.37796336719768e-064.75592673439535e-060.999997622036633
1291.09213489944080e-052.18426979888159e-050.999989078651006
1309.45731079337781e-061.89146215867556e-050.999990542689207
1316.61200869114593e-061.32240173822919e-050.999993387991309
1323.17653797937762e-056.35307595875524e-050.999968234620206
1333.18902509196824e-056.37805018393649e-050.99996810974908
1343.74970222274626e-057.49940444549252e-050.999962502977773
1354.27915914321057e-058.55831828642114e-050.999957208408568
1362.95373427798412e-055.90746855596825e-050.99997046265722
1371.89417402507514e-053.78834805015028e-050.99998105825975
1381.20421868055271e-052.40843736110542e-050.999987957813194
1398.81429359582e-061.762858719164e-050.999991185706404
1406.82366976053194e-061.36473395210639e-050.99999317633024
1414.70914384724795e-069.4182876944959e-060.999995290856153
1427.10039255040312e-061.42007851008062e-050.99999289960745
1434.37773045012308e-068.75546090024615e-060.99999562226955
1443.85816465018212e-067.71632930036423e-060.99999614183535
1454.64627205320642e-069.29254410641285e-060.999995353727947
1468.56662386570902e-061.71332477314180e-050.999991433376134
1476.19387655397951e-061.23877531079590e-050.999993806123446
1484.8698349098634e-069.7396698197268e-060.99999513016509
1493.13902079812645e-066.27804159625291e-060.999996860979202
1502.23619777186206e-064.47239554372412e-060.999997763802228
1511.78890118512571e-063.57780237025142e-060.999998211098815
1521.16291213823851e-062.32582427647702e-060.999998837087862
1537.07457187098803e-071.41491437419761e-060.999999292542813
1546.64672149065831e-071.32934429813166e-060.999999335327851
1554.10415738636009e-078.20831477272017e-070.999999589584261
1561.29063218720680e-062.58126437441359e-060.999998709367813
1571.91095198171495e-063.8219039634299e-060.999998089048018
1582.20795022584366e-064.41590045168731e-060.999997792049774
1591.65417922893378e-063.30835845786756e-060.999998345820771
1601.14778542970548e-062.29557085941096e-060.99999885221457
1617.40916722146151e-071.48183344429230e-060.999999259083278
1626.31644521155898e-061.26328904231180e-050.999993683554788
1635.92721258359694e-061.18544251671939e-050.999994072787416
1644.16259948584106e-068.32519897168211e-060.999995837400514
1652.48203726264974e-064.96407452529947e-060.999997517962737
1662.00490227793163e-064.00980455586327e-060.999997995097722
1673.51331782180361e-067.02663564360722e-060.999996486682178
1683.22295424983807e-066.44590849967613e-060.99999677704575
1692.06332843977796e-064.12665687955591e-060.99999793667156
1702.08198229800661e-064.16396459601322e-060.999997918017702
1711.99498241834682e-063.98996483669364e-060.999998005017582
1721.08591247580098e-062.17182495160196e-060.999998914087524
1736.2704754067323e-071.25409508134646e-060.99999937295246
1741.26936847799313e-062.53873695598626e-060.999998730631522
1759.74576598902521e-071.94915319780504e-060.9999990254234
1768.84448116827963e-071.76889623365593e-060.999999115551883
1775.52677853914556e-071.10535570782911e-060.999999447322146
1783.20086505137867e-076.40173010275734e-070.999999679913495
1794.47799896486332e-078.95599792972664e-070.999999552200104
1804.80371783067018e-079.60743566134036e-070.999999519628217
1814.00615889135638e-078.01231778271276e-070.999999599384111
1822.68256079395785e-075.3651215879157e-070.99999973174392
1831.45140203042927e-072.90280406085853e-070.999999854859797
1846.7136633768207e-081.34273267536414e-070.999999932863366
1851.07626588205392e-072.15253176410783e-070.999999892373412
1865.19802465906873e-081.03960493181375e-070.999999948019753
1873.71027812409579e-087.42055624819157e-080.999999962897219
1881.53876251153675e-083.07752502307351e-080.999999984612375
1891.64704506729820e-083.29409013459641e-080.99999998352955
1901.87805428263500e-083.75610856527000e-080.999999981219457
1911.21013772843429e-082.42027545686857e-080.999999987898623
1921.60445964719372e-083.20891929438745e-080.999999983955403
1932.46749181323483e-074.93498362646966e-070.999999753250819
1941.59037006745192e-073.18074013490383e-070.999999840962993
1955.77384715309238e-081.15476943061848e-070.999999942261528
1962.56294680177683e-075.12589360355366e-070.99999974370532
1979.43311907783696e-061.88662381556739e-050.999990566880922
1980.0004126375363595430.0008252750727190860.99958736246364
1990.0002472761709700420.0004945523419400840.99975272382903

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.921516439435697 & 0.156967121128605 & 0.0784835605643025 \tabularnewline
22 & 0.852444596383978 & 0.295110807232043 & 0.147555403616022 \tabularnewline
23 & 0.763818888391239 & 0.472362223217522 & 0.236181111608761 \tabularnewline
24 & 0.664838227312805 & 0.67032354537439 & 0.335161772687195 \tabularnewline
25 & 0.556016102040632 & 0.887967795918736 & 0.443983897959368 \tabularnewline
26 & 0.446933271031682 & 0.893866542063365 & 0.553066728968318 \tabularnewline
27 & 0.383052146915684 & 0.766104293831367 & 0.616947853084316 \tabularnewline
28 & 0.541072126469344 & 0.917855747061311 & 0.458927873530656 \tabularnewline
29 & 0.45465050324628 & 0.90930100649256 & 0.54534949675372 \tabularnewline
30 & 0.535137093346849 & 0.929725813306302 & 0.464862906653151 \tabularnewline
31 & 0.476434897377874 & 0.95286979475575 & 0.523565102622126 \tabularnewline
32 & 0.406894383741742 & 0.813788767483484 & 0.593105616258258 \tabularnewline
33 & 0.389592982598007 & 0.779185965196014 & 0.610407017401993 \tabularnewline
34 & 0.319333847988801 & 0.638667695977602 & 0.680666152011199 \tabularnewline
35 & 0.311205021102967 & 0.622410042205934 & 0.688794978897033 \tabularnewline
36 & 0.252526313418890 & 0.505052626837781 & 0.74747368658111 \tabularnewline
37 & 0.207138489090554 & 0.414276978181108 & 0.792861510909446 \tabularnewline
38 & 0.177734941238419 & 0.355469882476837 & 0.822265058761581 \tabularnewline
39 & 0.136031771090326 & 0.272063542180652 & 0.863968228909674 \tabularnewline
40 & 0.104542196890638 & 0.209084393781276 & 0.895457803109362 \tabularnewline
41 & 0.098237499614813 & 0.196474999229626 & 0.901762500385187 \tabularnewline
42 & 0.0796616012029895 & 0.159323202405979 & 0.92033839879701 \tabularnewline
43 & 0.0655919433038167 & 0.131183886607633 & 0.934408056696183 \tabularnewline
44 & 0.050432091540327 & 0.100864183080654 & 0.949567908459673 \tabularnewline
45 & 0.0364158867599424 & 0.0728317735198849 & 0.963584113240058 \tabularnewline
46 & 0.0277202760878904 & 0.0554405521757808 & 0.97227972391211 \tabularnewline
47 & 0.0210657345072521 & 0.0421314690145043 & 0.978934265492748 \tabularnewline
48 & 0.0144836293257990 & 0.0289672586515979 & 0.9855163706742 \tabularnewline
49 & 0.0137103337689662 & 0.0274206675379324 & 0.986289666231034 \tabularnewline
50 & 0.0092948201559945 & 0.018589640311989 & 0.990705179844006 \tabularnewline
51 & 0.00638454917463178 & 0.0127690983492636 & 0.993615450825368 \tabularnewline
52 & 0.00450171203056042 & 0.00900342406112084 & 0.99549828796944 \tabularnewline
53 & 0.00325141245410915 & 0.00650282490821829 & 0.99674858754589 \tabularnewline
54 & 0.00379061438388502 & 0.00758122876777004 & 0.996209385616115 \tabularnewline
55 & 0.00382919881435762 & 0.00765839762871524 & 0.996170801185642 \tabularnewline
56 & 0.00268894844303267 & 0.00537789688606533 & 0.997311051556967 \tabularnewline
57 & 0.00764480008400511 & 0.0152896001680102 & 0.992355199915995 \tabularnewline
58 & 0.00521845769388109 & 0.0104369153877622 & 0.994781542306119 \tabularnewline
59 & 0.00419601412162966 & 0.00839202824325932 & 0.99580398587837 \tabularnewline
60 & 0.00295033325872795 & 0.0059006665174559 & 0.997049666741272 \tabularnewline
61 & 0.00195198657589999 & 0.00390397315179998 & 0.9980480134241 \tabularnewline
62 & 0.00126918754375114 & 0.00253837508750227 & 0.99873081245625 \tabularnewline
63 & 0.000875421028756689 & 0.00175084205751338 & 0.999124578971243 \tabularnewline
64 & 0.000560956888348178 & 0.00112191377669636 & 0.999439043111652 \tabularnewline
65 & 0.000474895540800709 & 0.000949791081601417 & 0.9995251044592 \tabularnewline
66 & 0.00137091726649296 & 0.00274183453298591 & 0.998629082733507 \tabularnewline
67 & 0.000951730619679332 & 0.00190346123935866 & 0.99904826938032 \tabularnewline
68 & 0.000904733595172034 & 0.00180946719034407 & 0.999095266404828 \tabularnewline
69 & 0.000639861867994456 & 0.00127972373598891 & 0.999360138132006 \tabularnewline
70 & 0.000544085572099023 & 0.00108817114419805 & 0.9994559144279 \tabularnewline
71 & 0.000932909054104084 & 0.00186581810820817 & 0.999067090945896 \tabularnewline
72 & 0.000730313043653242 & 0.00146062608730648 & 0.999269686956347 \tabularnewline
73 & 0.000582709667160893 & 0.00116541933432179 & 0.99941729033284 \tabularnewline
74 & 0.000385984184446986 & 0.000771968368893972 & 0.999614015815553 \tabularnewline
75 & 0.000263020163036627 & 0.000526040326073254 & 0.999736979836963 \tabularnewline
76 & 0.000172054606189436 & 0.000344109212378873 & 0.99982794539381 \tabularnewline
77 & 0.000114431062847816 & 0.000228862125695632 & 0.999885568937152 \tabularnewline
78 & 8.6933330786413e-05 & 0.000173866661572826 & 0.999913066669214 \tabularnewline
79 & 5.56724362407067e-05 & 0.000111344872481413 & 0.99994432756376 \tabularnewline
80 & 5.57077159013673e-05 & 0.000111415431802735 & 0.999944292284099 \tabularnewline
81 & 0.000253455700676030 & 0.000506911401352059 & 0.999746544299324 \tabularnewline
82 & 0.000233937222648694 & 0.000467874445297389 & 0.999766062777351 \tabularnewline
83 & 0.000159090449499179 & 0.000318180898998358 & 0.9998409095505 \tabularnewline
84 & 0.000165472770569834 & 0.000330945541139667 & 0.99983452722943 \tabularnewline
85 & 0.000150615703601603 & 0.000301231407203205 & 0.999849384296398 \tabularnewline
86 & 0.000113946611650122 & 0.000227893223300244 & 0.99988605338835 \tabularnewline
87 & 0.000146252380655934 & 0.000292504761311868 & 0.999853747619344 \tabularnewline
88 & 0.000210023316517481 & 0.000420046633034962 & 0.999789976683483 \tabularnewline
89 & 0.000232243303913373 & 0.000464486607826746 & 0.999767756696087 \tabularnewline
90 & 0.000168583211962029 & 0.000337166423924059 & 0.999831416788038 \tabularnewline
91 & 0.000197973723807861 & 0.000395947447615722 & 0.999802026276192 \tabularnewline
92 & 0.000134647750622171 & 0.000269295501244342 & 0.999865352249378 \tabularnewline
93 & 0.000115918465609442 & 0.000231836931218885 & 0.99988408153439 \tabularnewline
94 & 7.90556784341398e-05 & 0.000158111356868280 & 0.999920944321566 \tabularnewline
95 & 6.08835880740868e-05 & 0.000121767176148174 & 0.999939116411926 \tabularnewline
96 & 4.0065436854982e-05 & 8.0130873709964e-05 & 0.999959934563145 \tabularnewline
97 & 3.60494659249742e-05 & 7.20989318499484e-05 & 0.999963950534075 \tabularnewline
98 & 2.3206797962799e-05 & 4.6413595925598e-05 & 0.999976793202037 \tabularnewline
99 & 1.57026138666035e-05 & 3.14052277332070e-05 & 0.999984297386133 \tabularnewline
100 & 1.23292948607548e-05 & 2.46585897215097e-05 & 0.99998767070514 \tabularnewline
101 & 9.99619765774281e-06 & 1.99923953154856e-05 & 0.999990003802342 \tabularnewline
102 & 6.9739809762315e-06 & 1.3947961952463e-05 & 0.999993026019024 \tabularnewline
103 & 6.03072971240190e-06 & 1.20614594248038e-05 & 0.999993969270288 \tabularnewline
104 & 9.40780404759356e-06 & 1.88156080951871e-05 & 0.999990592195952 \tabularnewline
105 & 6.1599594211869e-06 & 1.23199188423738e-05 & 0.999993840040579 \tabularnewline
106 & 6.1447604175878e-06 & 1.22895208351756e-05 & 0.999993855239582 \tabularnewline
107 & 6.35291396091382e-06 & 1.27058279218276e-05 & 0.99999364708604 \tabularnewline
108 & 4.31425941105944e-06 & 8.62851882211887e-06 & 0.99999568574059 \tabularnewline
109 & 3.69388416991716e-06 & 7.38776833983431e-06 & 0.99999630611583 \tabularnewline
110 & 1.05306275721846e-05 & 2.10612551443693e-05 & 0.999989469372428 \tabularnewline
111 & 6.67654774756987e-06 & 1.33530954951397e-05 & 0.999993323452252 \tabularnewline
112 & 5.79838336603088e-06 & 1.15967667320618e-05 & 0.999994201616634 \tabularnewline
113 & 1.16644214710618e-05 & 2.33288429421235e-05 & 0.99998833557853 \tabularnewline
114 & 1.17507956507553e-05 & 2.35015913015106e-05 & 0.99998824920435 \tabularnewline
115 & 9.03579320194948e-06 & 1.80715864038990e-05 & 0.999990964206798 \tabularnewline
116 & 1.99354542363491e-05 & 3.98709084726982e-05 & 0.999980064545764 \tabularnewline
117 & 1.41085383065957e-05 & 2.82170766131914e-05 & 0.999985891461693 \tabularnewline
118 & 8.89770255283898e-06 & 1.77954051056780e-05 & 0.999991102297447 \tabularnewline
119 & 6.79902135635606e-06 & 1.35980427127121e-05 & 0.999993200978644 \tabularnewline
120 & 2.53573775597269e-05 & 5.07147551194538e-05 & 0.99997464262244 \tabularnewline
121 & 2.14161226822689e-05 & 4.28322453645379e-05 & 0.999978583877318 \tabularnewline
122 & 1.47575478458564e-05 & 2.95150956917128e-05 & 0.999985242452154 \tabularnewline
123 & 9.7257458622417e-06 & 1.94514917244834e-05 & 0.999990274254138 \tabularnewline
124 & 6.31681012694012e-06 & 1.26336202538802e-05 & 0.999993683189873 \tabularnewline
125 & 6.35210793996486e-06 & 1.27042158799297e-05 & 0.99999364789206 \tabularnewline
126 & 4.70360975161386e-06 & 9.40721950322772e-06 & 0.999995296390248 \tabularnewline
127 & 3.74876761022917e-06 & 7.49753522045833e-06 & 0.99999625123239 \tabularnewline
128 & 2.37796336719768e-06 & 4.75592673439535e-06 & 0.999997622036633 \tabularnewline
129 & 1.09213489944080e-05 & 2.18426979888159e-05 & 0.999989078651006 \tabularnewline
130 & 9.45731079337781e-06 & 1.89146215867556e-05 & 0.999990542689207 \tabularnewline
131 & 6.61200869114593e-06 & 1.32240173822919e-05 & 0.999993387991309 \tabularnewline
132 & 3.17653797937762e-05 & 6.35307595875524e-05 & 0.999968234620206 \tabularnewline
133 & 3.18902509196824e-05 & 6.37805018393649e-05 & 0.99996810974908 \tabularnewline
134 & 3.74970222274626e-05 & 7.49940444549252e-05 & 0.999962502977773 \tabularnewline
135 & 4.27915914321057e-05 & 8.55831828642114e-05 & 0.999957208408568 \tabularnewline
136 & 2.95373427798412e-05 & 5.90746855596825e-05 & 0.99997046265722 \tabularnewline
137 & 1.89417402507514e-05 & 3.78834805015028e-05 & 0.99998105825975 \tabularnewline
138 & 1.20421868055271e-05 & 2.40843736110542e-05 & 0.999987957813194 \tabularnewline
139 & 8.81429359582e-06 & 1.762858719164e-05 & 0.999991185706404 \tabularnewline
140 & 6.82366976053194e-06 & 1.36473395210639e-05 & 0.99999317633024 \tabularnewline
141 & 4.70914384724795e-06 & 9.4182876944959e-06 & 0.999995290856153 \tabularnewline
142 & 7.10039255040312e-06 & 1.42007851008062e-05 & 0.99999289960745 \tabularnewline
143 & 4.37773045012308e-06 & 8.75546090024615e-06 & 0.99999562226955 \tabularnewline
144 & 3.85816465018212e-06 & 7.71632930036423e-06 & 0.99999614183535 \tabularnewline
145 & 4.64627205320642e-06 & 9.29254410641285e-06 & 0.999995353727947 \tabularnewline
146 & 8.56662386570902e-06 & 1.71332477314180e-05 & 0.999991433376134 \tabularnewline
147 & 6.19387655397951e-06 & 1.23877531079590e-05 & 0.999993806123446 \tabularnewline
148 & 4.8698349098634e-06 & 9.7396698197268e-06 & 0.99999513016509 \tabularnewline
149 & 3.13902079812645e-06 & 6.27804159625291e-06 & 0.999996860979202 \tabularnewline
150 & 2.23619777186206e-06 & 4.47239554372412e-06 & 0.999997763802228 \tabularnewline
151 & 1.78890118512571e-06 & 3.57780237025142e-06 & 0.999998211098815 \tabularnewline
152 & 1.16291213823851e-06 & 2.32582427647702e-06 & 0.999998837087862 \tabularnewline
153 & 7.07457187098803e-07 & 1.41491437419761e-06 & 0.999999292542813 \tabularnewline
154 & 6.64672149065831e-07 & 1.32934429813166e-06 & 0.999999335327851 \tabularnewline
155 & 4.10415738636009e-07 & 8.20831477272017e-07 & 0.999999589584261 \tabularnewline
156 & 1.29063218720680e-06 & 2.58126437441359e-06 & 0.999998709367813 \tabularnewline
157 & 1.91095198171495e-06 & 3.8219039634299e-06 & 0.999998089048018 \tabularnewline
158 & 2.20795022584366e-06 & 4.41590045168731e-06 & 0.999997792049774 \tabularnewline
159 & 1.65417922893378e-06 & 3.30835845786756e-06 & 0.999998345820771 \tabularnewline
160 & 1.14778542970548e-06 & 2.29557085941096e-06 & 0.99999885221457 \tabularnewline
161 & 7.40916722146151e-07 & 1.48183344429230e-06 & 0.999999259083278 \tabularnewline
162 & 6.31644521155898e-06 & 1.26328904231180e-05 & 0.999993683554788 \tabularnewline
163 & 5.92721258359694e-06 & 1.18544251671939e-05 & 0.999994072787416 \tabularnewline
164 & 4.16259948584106e-06 & 8.32519897168211e-06 & 0.999995837400514 \tabularnewline
165 & 2.48203726264974e-06 & 4.96407452529947e-06 & 0.999997517962737 \tabularnewline
166 & 2.00490227793163e-06 & 4.00980455586327e-06 & 0.999997995097722 \tabularnewline
167 & 3.51331782180361e-06 & 7.02663564360722e-06 & 0.999996486682178 \tabularnewline
168 & 3.22295424983807e-06 & 6.44590849967613e-06 & 0.99999677704575 \tabularnewline
169 & 2.06332843977796e-06 & 4.12665687955591e-06 & 0.99999793667156 \tabularnewline
170 & 2.08198229800661e-06 & 4.16396459601322e-06 & 0.999997918017702 \tabularnewline
171 & 1.99498241834682e-06 & 3.98996483669364e-06 & 0.999998005017582 \tabularnewline
172 & 1.08591247580098e-06 & 2.17182495160196e-06 & 0.999998914087524 \tabularnewline
173 & 6.2704754067323e-07 & 1.25409508134646e-06 & 0.99999937295246 \tabularnewline
174 & 1.26936847799313e-06 & 2.53873695598626e-06 & 0.999998730631522 \tabularnewline
175 & 9.74576598902521e-07 & 1.94915319780504e-06 & 0.9999990254234 \tabularnewline
176 & 8.84448116827963e-07 & 1.76889623365593e-06 & 0.999999115551883 \tabularnewline
177 & 5.52677853914556e-07 & 1.10535570782911e-06 & 0.999999447322146 \tabularnewline
178 & 3.20086505137867e-07 & 6.40173010275734e-07 & 0.999999679913495 \tabularnewline
179 & 4.47799896486332e-07 & 8.95599792972664e-07 & 0.999999552200104 \tabularnewline
180 & 4.80371783067018e-07 & 9.60743566134036e-07 & 0.999999519628217 \tabularnewline
181 & 4.00615889135638e-07 & 8.01231778271276e-07 & 0.999999599384111 \tabularnewline
182 & 2.68256079395785e-07 & 5.3651215879157e-07 & 0.99999973174392 \tabularnewline
183 & 1.45140203042927e-07 & 2.90280406085853e-07 & 0.999999854859797 \tabularnewline
184 & 6.7136633768207e-08 & 1.34273267536414e-07 & 0.999999932863366 \tabularnewline
185 & 1.07626588205392e-07 & 2.15253176410783e-07 & 0.999999892373412 \tabularnewline
186 & 5.19802465906873e-08 & 1.03960493181375e-07 & 0.999999948019753 \tabularnewline
187 & 3.71027812409579e-08 & 7.42055624819157e-08 & 0.999999962897219 \tabularnewline
188 & 1.53876251153675e-08 & 3.07752502307351e-08 & 0.999999984612375 \tabularnewline
189 & 1.64704506729820e-08 & 3.29409013459641e-08 & 0.99999998352955 \tabularnewline
190 & 1.87805428263500e-08 & 3.75610856527000e-08 & 0.999999981219457 \tabularnewline
191 & 1.21013772843429e-08 & 2.42027545686857e-08 & 0.999999987898623 \tabularnewline
192 & 1.60445964719372e-08 & 3.20891929438745e-08 & 0.999999983955403 \tabularnewline
193 & 2.46749181323483e-07 & 4.93498362646966e-07 & 0.999999753250819 \tabularnewline
194 & 1.59037006745192e-07 & 3.18074013490383e-07 & 0.999999840962993 \tabularnewline
195 & 5.77384715309238e-08 & 1.15476943061848e-07 & 0.999999942261528 \tabularnewline
196 & 2.56294680177683e-07 & 5.12589360355366e-07 & 0.99999974370532 \tabularnewline
197 & 9.43311907783696e-06 & 1.88662381556739e-05 & 0.999990566880922 \tabularnewline
198 & 0.000412637536359543 & 0.000825275072719086 & 0.99958736246364 \tabularnewline
199 & 0.000247276170970042 & 0.000494552341940084 & 0.99975272382903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&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]21[/C][C]0.921516439435697[/C][C]0.156967121128605[/C][C]0.0784835605643025[/C][/ROW]
[ROW][C]22[/C][C]0.852444596383978[/C][C]0.295110807232043[/C][C]0.147555403616022[/C][/ROW]
[ROW][C]23[/C][C]0.763818888391239[/C][C]0.472362223217522[/C][C]0.236181111608761[/C][/ROW]
[ROW][C]24[/C][C]0.664838227312805[/C][C]0.67032354537439[/C][C]0.335161772687195[/C][/ROW]
[ROW][C]25[/C][C]0.556016102040632[/C][C]0.887967795918736[/C][C]0.443983897959368[/C][/ROW]
[ROW][C]26[/C][C]0.446933271031682[/C][C]0.893866542063365[/C][C]0.553066728968318[/C][/ROW]
[ROW][C]27[/C][C]0.383052146915684[/C][C]0.766104293831367[/C][C]0.616947853084316[/C][/ROW]
[ROW][C]28[/C][C]0.541072126469344[/C][C]0.917855747061311[/C][C]0.458927873530656[/C][/ROW]
[ROW][C]29[/C][C]0.45465050324628[/C][C]0.90930100649256[/C][C]0.54534949675372[/C][/ROW]
[ROW][C]30[/C][C]0.535137093346849[/C][C]0.929725813306302[/C][C]0.464862906653151[/C][/ROW]
[ROW][C]31[/C][C]0.476434897377874[/C][C]0.95286979475575[/C][C]0.523565102622126[/C][/ROW]
[ROW][C]32[/C][C]0.406894383741742[/C][C]0.813788767483484[/C][C]0.593105616258258[/C][/ROW]
[ROW][C]33[/C][C]0.389592982598007[/C][C]0.779185965196014[/C][C]0.610407017401993[/C][/ROW]
[ROW][C]34[/C][C]0.319333847988801[/C][C]0.638667695977602[/C][C]0.680666152011199[/C][/ROW]
[ROW][C]35[/C][C]0.311205021102967[/C][C]0.622410042205934[/C][C]0.688794978897033[/C][/ROW]
[ROW][C]36[/C][C]0.252526313418890[/C][C]0.505052626837781[/C][C]0.74747368658111[/C][/ROW]
[ROW][C]37[/C][C]0.207138489090554[/C][C]0.414276978181108[/C][C]0.792861510909446[/C][/ROW]
[ROW][C]38[/C][C]0.177734941238419[/C][C]0.355469882476837[/C][C]0.822265058761581[/C][/ROW]
[ROW][C]39[/C][C]0.136031771090326[/C][C]0.272063542180652[/C][C]0.863968228909674[/C][/ROW]
[ROW][C]40[/C][C]0.104542196890638[/C][C]0.209084393781276[/C][C]0.895457803109362[/C][/ROW]
[ROW][C]41[/C][C]0.098237499614813[/C][C]0.196474999229626[/C][C]0.901762500385187[/C][/ROW]
[ROW][C]42[/C][C]0.0796616012029895[/C][C]0.159323202405979[/C][C]0.92033839879701[/C][/ROW]
[ROW][C]43[/C][C]0.0655919433038167[/C][C]0.131183886607633[/C][C]0.934408056696183[/C][/ROW]
[ROW][C]44[/C][C]0.050432091540327[/C][C]0.100864183080654[/C][C]0.949567908459673[/C][/ROW]
[ROW][C]45[/C][C]0.0364158867599424[/C][C]0.0728317735198849[/C][C]0.963584113240058[/C][/ROW]
[ROW][C]46[/C][C]0.0277202760878904[/C][C]0.0554405521757808[/C][C]0.97227972391211[/C][/ROW]
[ROW][C]47[/C][C]0.0210657345072521[/C][C]0.0421314690145043[/C][C]0.978934265492748[/C][/ROW]
[ROW][C]48[/C][C]0.0144836293257990[/C][C]0.0289672586515979[/C][C]0.9855163706742[/C][/ROW]
[ROW][C]49[/C][C]0.0137103337689662[/C][C]0.0274206675379324[/C][C]0.986289666231034[/C][/ROW]
[ROW][C]50[/C][C]0.0092948201559945[/C][C]0.018589640311989[/C][C]0.990705179844006[/C][/ROW]
[ROW][C]51[/C][C]0.00638454917463178[/C][C]0.0127690983492636[/C][C]0.993615450825368[/C][/ROW]
[ROW][C]52[/C][C]0.00450171203056042[/C][C]0.00900342406112084[/C][C]0.99549828796944[/C][/ROW]
[ROW][C]53[/C][C]0.00325141245410915[/C][C]0.00650282490821829[/C][C]0.99674858754589[/C][/ROW]
[ROW][C]54[/C][C]0.00379061438388502[/C][C]0.00758122876777004[/C][C]0.996209385616115[/C][/ROW]
[ROW][C]55[/C][C]0.00382919881435762[/C][C]0.00765839762871524[/C][C]0.996170801185642[/C][/ROW]
[ROW][C]56[/C][C]0.00268894844303267[/C][C]0.00537789688606533[/C][C]0.997311051556967[/C][/ROW]
[ROW][C]57[/C][C]0.00764480008400511[/C][C]0.0152896001680102[/C][C]0.992355199915995[/C][/ROW]
[ROW][C]58[/C][C]0.00521845769388109[/C][C]0.0104369153877622[/C][C]0.994781542306119[/C][/ROW]
[ROW][C]59[/C][C]0.00419601412162966[/C][C]0.00839202824325932[/C][C]0.99580398587837[/C][/ROW]
[ROW][C]60[/C][C]0.00295033325872795[/C][C]0.0059006665174559[/C][C]0.997049666741272[/C][/ROW]
[ROW][C]61[/C][C]0.00195198657589999[/C][C]0.00390397315179998[/C][C]0.9980480134241[/C][/ROW]
[ROW][C]62[/C][C]0.00126918754375114[/C][C]0.00253837508750227[/C][C]0.99873081245625[/C][/ROW]
[ROW][C]63[/C][C]0.000875421028756689[/C][C]0.00175084205751338[/C][C]0.999124578971243[/C][/ROW]
[ROW][C]64[/C][C]0.000560956888348178[/C][C]0.00112191377669636[/C][C]0.999439043111652[/C][/ROW]
[ROW][C]65[/C][C]0.000474895540800709[/C][C]0.000949791081601417[/C][C]0.9995251044592[/C][/ROW]
[ROW][C]66[/C][C]0.00137091726649296[/C][C]0.00274183453298591[/C][C]0.998629082733507[/C][/ROW]
[ROW][C]67[/C][C]0.000951730619679332[/C][C]0.00190346123935866[/C][C]0.99904826938032[/C][/ROW]
[ROW][C]68[/C][C]0.000904733595172034[/C][C]0.00180946719034407[/C][C]0.999095266404828[/C][/ROW]
[ROW][C]69[/C][C]0.000639861867994456[/C][C]0.00127972373598891[/C][C]0.999360138132006[/C][/ROW]
[ROW][C]70[/C][C]0.000544085572099023[/C][C]0.00108817114419805[/C][C]0.9994559144279[/C][/ROW]
[ROW][C]71[/C][C]0.000932909054104084[/C][C]0.00186581810820817[/C][C]0.999067090945896[/C][/ROW]
[ROW][C]72[/C][C]0.000730313043653242[/C][C]0.00146062608730648[/C][C]0.999269686956347[/C][/ROW]
[ROW][C]73[/C][C]0.000582709667160893[/C][C]0.00116541933432179[/C][C]0.99941729033284[/C][/ROW]
[ROW][C]74[/C][C]0.000385984184446986[/C][C]0.000771968368893972[/C][C]0.999614015815553[/C][/ROW]
[ROW][C]75[/C][C]0.000263020163036627[/C][C]0.000526040326073254[/C][C]0.999736979836963[/C][/ROW]
[ROW][C]76[/C][C]0.000172054606189436[/C][C]0.000344109212378873[/C][C]0.99982794539381[/C][/ROW]
[ROW][C]77[/C][C]0.000114431062847816[/C][C]0.000228862125695632[/C][C]0.999885568937152[/C][/ROW]
[ROW][C]78[/C][C]8.6933330786413e-05[/C][C]0.000173866661572826[/C][C]0.999913066669214[/C][/ROW]
[ROW][C]79[/C][C]5.56724362407067e-05[/C][C]0.000111344872481413[/C][C]0.99994432756376[/C][/ROW]
[ROW][C]80[/C][C]5.57077159013673e-05[/C][C]0.000111415431802735[/C][C]0.999944292284099[/C][/ROW]
[ROW][C]81[/C][C]0.000253455700676030[/C][C]0.000506911401352059[/C][C]0.999746544299324[/C][/ROW]
[ROW][C]82[/C][C]0.000233937222648694[/C][C]0.000467874445297389[/C][C]0.999766062777351[/C][/ROW]
[ROW][C]83[/C][C]0.000159090449499179[/C][C]0.000318180898998358[/C][C]0.9998409095505[/C][/ROW]
[ROW][C]84[/C][C]0.000165472770569834[/C][C]0.000330945541139667[/C][C]0.99983452722943[/C][/ROW]
[ROW][C]85[/C][C]0.000150615703601603[/C][C]0.000301231407203205[/C][C]0.999849384296398[/C][/ROW]
[ROW][C]86[/C][C]0.000113946611650122[/C][C]0.000227893223300244[/C][C]0.99988605338835[/C][/ROW]
[ROW][C]87[/C][C]0.000146252380655934[/C][C]0.000292504761311868[/C][C]0.999853747619344[/C][/ROW]
[ROW][C]88[/C][C]0.000210023316517481[/C][C]0.000420046633034962[/C][C]0.999789976683483[/C][/ROW]
[ROW][C]89[/C][C]0.000232243303913373[/C][C]0.000464486607826746[/C][C]0.999767756696087[/C][/ROW]
[ROW][C]90[/C][C]0.000168583211962029[/C][C]0.000337166423924059[/C][C]0.999831416788038[/C][/ROW]
[ROW][C]91[/C][C]0.000197973723807861[/C][C]0.000395947447615722[/C][C]0.999802026276192[/C][/ROW]
[ROW][C]92[/C][C]0.000134647750622171[/C][C]0.000269295501244342[/C][C]0.999865352249378[/C][/ROW]
[ROW][C]93[/C][C]0.000115918465609442[/C][C]0.000231836931218885[/C][C]0.99988408153439[/C][/ROW]
[ROW][C]94[/C][C]7.90556784341398e-05[/C][C]0.000158111356868280[/C][C]0.999920944321566[/C][/ROW]
[ROW][C]95[/C][C]6.08835880740868e-05[/C][C]0.000121767176148174[/C][C]0.999939116411926[/C][/ROW]
[ROW][C]96[/C][C]4.0065436854982e-05[/C][C]8.0130873709964e-05[/C][C]0.999959934563145[/C][/ROW]
[ROW][C]97[/C][C]3.60494659249742e-05[/C][C]7.20989318499484e-05[/C][C]0.999963950534075[/C][/ROW]
[ROW][C]98[/C][C]2.3206797962799e-05[/C][C]4.6413595925598e-05[/C][C]0.999976793202037[/C][/ROW]
[ROW][C]99[/C][C]1.57026138666035e-05[/C][C]3.14052277332070e-05[/C][C]0.999984297386133[/C][/ROW]
[ROW][C]100[/C][C]1.23292948607548e-05[/C][C]2.46585897215097e-05[/C][C]0.99998767070514[/C][/ROW]
[ROW][C]101[/C][C]9.99619765774281e-06[/C][C]1.99923953154856e-05[/C][C]0.999990003802342[/C][/ROW]
[ROW][C]102[/C][C]6.9739809762315e-06[/C][C]1.3947961952463e-05[/C][C]0.999993026019024[/C][/ROW]
[ROW][C]103[/C][C]6.03072971240190e-06[/C][C]1.20614594248038e-05[/C][C]0.999993969270288[/C][/ROW]
[ROW][C]104[/C][C]9.40780404759356e-06[/C][C]1.88156080951871e-05[/C][C]0.999990592195952[/C][/ROW]
[ROW][C]105[/C][C]6.1599594211869e-06[/C][C]1.23199188423738e-05[/C][C]0.999993840040579[/C][/ROW]
[ROW][C]106[/C][C]6.1447604175878e-06[/C][C]1.22895208351756e-05[/C][C]0.999993855239582[/C][/ROW]
[ROW][C]107[/C][C]6.35291396091382e-06[/C][C]1.27058279218276e-05[/C][C]0.99999364708604[/C][/ROW]
[ROW][C]108[/C][C]4.31425941105944e-06[/C][C]8.62851882211887e-06[/C][C]0.99999568574059[/C][/ROW]
[ROW][C]109[/C][C]3.69388416991716e-06[/C][C]7.38776833983431e-06[/C][C]0.99999630611583[/C][/ROW]
[ROW][C]110[/C][C]1.05306275721846e-05[/C][C]2.10612551443693e-05[/C][C]0.999989469372428[/C][/ROW]
[ROW][C]111[/C][C]6.67654774756987e-06[/C][C]1.33530954951397e-05[/C][C]0.999993323452252[/C][/ROW]
[ROW][C]112[/C][C]5.79838336603088e-06[/C][C]1.15967667320618e-05[/C][C]0.999994201616634[/C][/ROW]
[ROW][C]113[/C][C]1.16644214710618e-05[/C][C]2.33288429421235e-05[/C][C]0.99998833557853[/C][/ROW]
[ROW][C]114[/C][C]1.17507956507553e-05[/C][C]2.35015913015106e-05[/C][C]0.99998824920435[/C][/ROW]
[ROW][C]115[/C][C]9.03579320194948e-06[/C][C]1.80715864038990e-05[/C][C]0.999990964206798[/C][/ROW]
[ROW][C]116[/C][C]1.99354542363491e-05[/C][C]3.98709084726982e-05[/C][C]0.999980064545764[/C][/ROW]
[ROW][C]117[/C][C]1.41085383065957e-05[/C][C]2.82170766131914e-05[/C][C]0.999985891461693[/C][/ROW]
[ROW][C]118[/C][C]8.89770255283898e-06[/C][C]1.77954051056780e-05[/C][C]0.999991102297447[/C][/ROW]
[ROW][C]119[/C][C]6.79902135635606e-06[/C][C]1.35980427127121e-05[/C][C]0.999993200978644[/C][/ROW]
[ROW][C]120[/C][C]2.53573775597269e-05[/C][C]5.07147551194538e-05[/C][C]0.99997464262244[/C][/ROW]
[ROW][C]121[/C][C]2.14161226822689e-05[/C][C]4.28322453645379e-05[/C][C]0.999978583877318[/C][/ROW]
[ROW][C]122[/C][C]1.47575478458564e-05[/C][C]2.95150956917128e-05[/C][C]0.999985242452154[/C][/ROW]
[ROW][C]123[/C][C]9.7257458622417e-06[/C][C]1.94514917244834e-05[/C][C]0.999990274254138[/C][/ROW]
[ROW][C]124[/C][C]6.31681012694012e-06[/C][C]1.26336202538802e-05[/C][C]0.999993683189873[/C][/ROW]
[ROW][C]125[/C][C]6.35210793996486e-06[/C][C]1.27042158799297e-05[/C][C]0.99999364789206[/C][/ROW]
[ROW][C]126[/C][C]4.70360975161386e-06[/C][C]9.40721950322772e-06[/C][C]0.999995296390248[/C][/ROW]
[ROW][C]127[/C][C]3.74876761022917e-06[/C][C]7.49753522045833e-06[/C][C]0.99999625123239[/C][/ROW]
[ROW][C]128[/C][C]2.37796336719768e-06[/C][C]4.75592673439535e-06[/C][C]0.999997622036633[/C][/ROW]
[ROW][C]129[/C][C]1.09213489944080e-05[/C][C]2.18426979888159e-05[/C][C]0.999989078651006[/C][/ROW]
[ROW][C]130[/C][C]9.45731079337781e-06[/C][C]1.89146215867556e-05[/C][C]0.999990542689207[/C][/ROW]
[ROW][C]131[/C][C]6.61200869114593e-06[/C][C]1.32240173822919e-05[/C][C]0.999993387991309[/C][/ROW]
[ROW][C]132[/C][C]3.17653797937762e-05[/C][C]6.35307595875524e-05[/C][C]0.999968234620206[/C][/ROW]
[ROW][C]133[/C][C]3.18902509196824e-05[/C][C]6.37805018393649e-05[/C][C]0.99996810974908[/C][/ROW]
[ROW][C]134[/C][C]3.74970222274626e-05[/C][C]7.49940444549252e-05[/C][C]0.999962502977773[/C][/ROW]
[ROW][C]135[/C][C]4.27915914321057e-05[/C][C]8.55831828642114e-05[/C][C]0.999957208408568[/C][/ROW]
[ROW][C]136[/C][C]2.95373427798412e-05[/C][C]5.90746855596825e-05[/C][C]0.99997046265722[/C][/ROW]
[ROW][C]137[/C][C]1.89417402507514e-05[/C][C]3.78834805015028e-05[/C][C]0.99998105825975[/C][/ROW]
[ROW][C]138[/C][C]1.20421868055271e-05[/C][C]2.40843736110542e-05[/C][C]0.999987957813194[/C][/ROW]
[ROW][C]139[/C][C]8.81429359582e-06[/C][C]1.762858719164e-05[/C][C]0.999991185706404[/C][/ROW]
[ROW][C]140[/C][C]6.82366976053194e-06[/C][C]1.36473395210639e-05[/C][C]0.99999317633024[/C][/ROW]
[ROW][C]141[/C][C]4.70914384724795e-06[/C][C]9.4182876944959e-06[/C][C]0.999995290856153[/C][/ROW]
[ROW][C]142[/C][C]7.10039255040312e-06[/C][C]1.42007851008062e-05[/C][C]0.99999289960745[/C][/ROW]
[ROW][C]143[/C][C]4.37773045012308e-06[/C][C]8.75546090024615e-06[/C][C]0.99999562226955[/C][/ROW]
[ROW][C]144[/C][C]3.85816465018212e-06[/C][C]7.71632930036423e-06[/C][C]0.99999614183535[/C][/ROW]
[ROW][C]145[/C][C]4.64627205320642e-06[/C][C]9.29254410641285e-06[/C][C]0.999995353727947[/C][/ROW]
[ROW][C]146[/C][C]8.56662386570902e-06[/C][C]1.71332477314180e-05[/C][C]0.999991433376134[/C][/ROW]
[ROW][C]147[/C][C]6.19387655397951e-06[/C][C]1.23877531079590e-05[/C][C]0.999993806123446[/C][/ROW]
[ROW][C]148[/C][C]4.8698349098634e-06[/C][C]9.7396698197268e-06[/C][C]0.99999513016509[/C][/ROW]
[ROW][C]149[/C][C]3.13902079812645e-06[/C][C]6.27804159625291e-06[/C][C]0.999996860979202[/C][/ROW]
[ROW][C]150[/C][C]2.23619777186206e-06[/C][C]4.47239554372412e-06[/C][C]0.999997763802228[/C][/ROW]
[ROW][C]151[/C][C]1.78890118512571e-06[/C][C]3.57780237025142e-06[/C][C]0.999998211098815[/C][/ROW]
[ROW][C]152[/C][C]1.16291213823851e-06[/C][C]2.32582427647702e-06[/C][C]0.999998837087862[/C][/ROW]
[ROW][C]153[/C][C]7.07457187098803e-07[/C][C]1.41491437419761e-06[/C][C]0.999999292542813[/C][/ROW]
[ROW][C]154[/C][C]6.64672149065831e-07[/C][C]1.32934429813166e-06[/C][C]0.999999335327851[/C][/ROW]
[ROW][C]155[/C][C]4.10415738636009e-07[/C][C]8.20831477272017e-07[/C][C]0.999999589584261[/C][/ROW]
[ROW][C]156[/C][C]1.29063218720680e-06[/C][C]2.58126437441359e-06[/C][C]0.999998709367813[/C][/ROW]
[ROW][C]157[/C][C]1.91095198171495e-06[/C][C]3.8219039634299e-06[/C][C]0.999998089048018[/C][/ROW]
[ROW][C]158[/C][C]2.20795022584366e-06[/C][C]4.41590045168731e-06[/C][C]0.999997792049774[/C][/ROW]
[ROW][C]159[/C][C]1.65417922893378e-06[/C][C]3.30835845786756e-06[/C][C]0.999998345820771[/C][/ROW]
[ROW][C]160[/C][C]1.14778542970548e-06[/C][C]2.29557085941096e-06[/C][C]0.99999885221457[/C][/ROW]
[ROW][C]161[/C][C]7.40916722146151e-07[/C][C]1.48183344429230e-06[/C][C]0.999999259083278[/C][/ROW]
[ROW][C]162[/C][C]6.31644521155898e-06[/C][C]1.26328904231180e-05[/C][C]0.999993683554788[/C][/ROW]
[ROW][C]163[/C][C]5.92721258359694e-06[/C][C]1.18544251671939e-05[/C][C]0.999994072787416[/C][/ROW]
[ROW][C]164[/C][C]4.16259948584106e-06[/C][C]8.32519897168211e-06[/C][C]0.999995837400514[/C][/ROW]
[ROW][C]165[/C][C]2.48203726264974e-06[/C][C]4.96407452529947e-06[/C][C]0.999997517962737[/C][/ROW]
[ROW][C]166[/C][C]2.00490227793163e-06[/C][C]4.00980455586327e-06[/C][C]0.999997995097722[/C][/ROW]
[ROW][C]167[/C][C]3.51331782180361e-06[/C][C]7.02663564360722e-06[/C][C]0.999996486682178[/C][/ROW]
[ROW][C]168[/C][C]3.22295424983807e-06[/C][C]6.44590849967613e-06[/C][C]0.99999677704575[/C][/ROW]
[ROW][C]169[/C][C]2.06332843977796e-06[/C][C]4.12665687955591e-06[/C][C]0.99999793667156[/C][/ROW]
[ROW][C]170[/C][C]2.08198229800661e-06[/C][C]4.16396459601322e-06[/C][C]0.999997918017702[/C][/ROW]
[ROW][C]171[/C][C]1.99498241834682e-06[/C][C]3.98996483669364e-06[/C][C]0.999998005017582[/C][/ROW]
[ROW][C]172[/C][C]1.08591247580098e-06[/C][C]2.17182495160196e-06[/C][C]0.999998914087524[/C][/ROW]
[ROW][C]173[/C][C]6.2704754067323e-07[/C][C]1.25409508134646e-06[/C][C]0.99999937295246[/C][/ROW]
[ROW][C]174[/C][C]1.26936847799313e-06[/C][C]2.53873695598626e-06[/C][C]0.999998730631522[/C][/ROW]
[ROW][C]175[/C][C]9.74576598902521e-07[/C][C]1.94915319780504e-06[/C][C]0.9999990254234[/C][/ROW]
[ROW][C]176[/C][C]8.84448116827963e-07[/C][C]1.76889623365593e-06[/C][C]0.999999115551883[/C][/ROW]
[ROW][C]177[/C][C]5.52677853914556e-07[/C][C]1.10535570782911e-06[/C][C]0.999999447322146[/C][/ROW]
[ROW][C]178[/C][C]3.20086505137867e-07[/C][C]6.40173010275734e-07[/C][C]0.999999679913495[/C][/ROW]
[ROW][C]179[/C][C]4.47799896486332e-07[/C][C]8.95599792972664e-07[/C][C]0.999999552200104[/C][/ROW]
[ROW][C]180[/C][C]4.80371783067018e-07[/C][C]9.60743566134036e-07[/C][C]0.999999519628217[/C][/ROW]
[ROW][C]181[/C][C]4.00615889135638e-07[/C][C]8.01231778271276e-07[/C][C]0.999999599384111[/C][/ROW]
[ROW][C]182[/C][C]2.68256079395785e-07[/C][C]5.3651215879157e-07[/C][C]0.99999973174392[/C][/ROW]
[ROW][C]183[/C][C]1.45140203042927e-07[/C][C]2.90280406085853e-07[/C][C]0.999999854859797[/C][/ROW]
[ROW][C]184[/C][C]6.7136633768207e-08[/C][C]1.34273267536414e-07[/C][C]0.999999932863366[/C][/ROW]
[ROW][C]185[/C][C]1.07626588205392e-07[/C][C]2.15253176410783e-07[/C][C]0.999999892373412[/C][/ROW]
[ROW][C]186[/C][C]5.19802465906873e-08[/C][C]1.03960493181375e-07[/C][C]0.999999948019753[/C][/ROW]
[ROW][C]187[/C][C]3.71027812409579e-08[/C][C]7.42055624819157e-08[/C][C]0.999999962897219[/C][/ROW]
[ROW][C]188[/C][C]1.53876251153675e-08[/C][C]3.07752502307351e-08[/C][C]0.999999984612375[/C][/ROW]
[ROW][C]189[/C][C]1.64704506729820e-08[/C][C]3.29409013459641e-08[/C][C]0.99999998352955[/C][/ROW]
[ROW][C]190[/C][C]1.87805428263500e-08[/C][C]3.75610856527000e-08[/C][C]0.999999981219457[/C][/ROW]
[ROW][C]191[/C][C]1.21013772843429e-08[/C][C]2.42027545686857e-08[/C][C]0.999999987898623[/C][/ROW]
[ROW][C]192[/C][C]1.60445964719372e-08[/C][C]3.20891929438745e-08[/C][C]0.999999983955403[/C][/ROW]
[ROW][C]193[/C][C]2.46749181323483e-07[/C][C]4.93498362646966e-07[/C][C]0.999999753250819[/C][/ROW]
[ROW][C]194[/C][C]1.59037006745192e-07[/C][C]3.18074013490383e-07[/C][C]0.999999840962993[/C][/ROW]
[ROW][C]195[/C][C]5.77384715309238e-08[/C][C]1.15476943061848e-07[/C][C]0.999999942261528[/C][/ROW]
[ROW][C]196[/C][C]2.56294680177683e-07[/C][C]5.12589360355366e-07[/C][C]0.99999974370532[/C][/ROW]
[ROW][C]197[/C][C]9.43311907783696e-06[/C][C]1.88662381556739e-05[/C][C]0.999990566880922[/C][/ROW]
[ROW][C]198[/C][C]0.000412637536359543[/C][C]0.000825275072719086[/C][C]0.99958736246364[/C][/ROW]
[ROW][C]199[/C][C]0.000247276170970042[/C][C]0.000494552341940084[/C][C]0.99975272382903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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
210.9215164394356970.1569671211286050.0784835605643025
220.8524445963839780.2951108072320430.147555403616022
230.7638188883912390.4723622232175220.236181111608761
240.6648382273128050.670323545374390.335161772687195
250.5560161020406320.8879677959187360.443983897959368
260.4469332710316820.8938665420633650.553066728968318
270.3830521469156840.7661042938313670.616947853084316
280.5410721264693440.9178557470613110.458927873530656
290.454650503246280.909301006492560.54534949675372
300.5351370933468490.9297258133063020.464862906653151
310.4764348973778740.952869794755750.523565102622126
320.4068943837417420.8137887674834840.593105616258258
330.3895929825980070.7791859651960140.610407017401993
340.3193338479888010.6386676959776020.680666152011199
350.3112050211029670.6224100422059340.688794978897033
360.2525263134188900.5050526268377810.74747368658111
370.2071384890905540.4142769781811080.792861510909446
380.1777349412384190.3554698824768370.822265058761581
390.1360317710903260.2720635421806520.863968228909674
400.1045421968906380.2090843937812760.895457803109362
410.0982374996148130.1964749992296260.901762500385187
420.07966160120298950.1593232024059790.92033839879701
430.06559194330381670.1311838866076330.934408056696183
440.0504320915403270.1008641830806540.949567908459673
450.03641588675994240.07283177351988490.963584113240058
460.02772027608789040.05544055217578080.97227972391211
470.02106573450725210.04213146901450430.978934265492748
480.01448362932579900.02896725865159790.9855163706742
490.01371033376896620.02742066753793240.986289666231034
500.00929482015599450.0185896403119890.990705179844006
510.006384549174631780.01276909834926360.993615450825368
520.004501712030560420.009003424061120840.99549828796944
530.003251412454109150.006502824908218290.99674858754589
540.003790614383885020.007581228767770040.996209385616115
550.003829198814357620.007658397628715240.996170801185642
560.002688948443032670.005377896886065330.997311051556967
570.007644800084005110.01528960016801020.992355199915995
580.005218457693881090.01043691538776220.994781542306119
590.004196014121629660.008392028243259320.99580398587837
600.002950333258727950.00590066651745590.997049666741272
610.001951986575899990.003903973151799980.9980480134241
620.001269187543751140.002538375087502270.99873081245625
630.0008754210287566890.001750842057513380.999124578971243
640.0005609568883481780.001121913776696360.999439043111652
650.0004748955408007090.0009497910816014170.9995251044592
660.001370917266492960.002741834532985910.998629082733507
670.0009517306196793320.001903461239358660.99904826938032
680.0009047335951720340.001809467190344070.999095266404828
690.0006398618679944560.001279723735988910.999360138132006
700.0005440855720990230.001088171144198050.9994559144279
710.0009329090541040840.001865818108208170.999067090945896
720.0007303130436532420.001460626087306480.999269686956347
730.0005827096671608930.001165419334321790.99941729033284
740.0003859841844469860.0007719683688939720.999614015815553
750.0002630201630366270.0005260403260732540.999736979836963
760.0001720546061894360.0003441092123788730.99982794539381
770.0001144310628478160.0002288621256956320.999885568937152
788.6933330786413e-050.0001738666615728260.999913066669214
795.56724362407067e-050.0001113448724814130.99994432756376
805.57077159013673e-050.0001114154318027350.999944292284099
810.0002534557006760300.0005069114013520590.999746544299324
820.0002339372226486940.0004678744452973890.999766062777351
830.0001590904494991790.0003181808989983580.9998409095505
840.0001654727705698340.0003309455411396670.99983452722943
850.0001506157036016030.0003012314072032050.999849384296398
860.0001139466116501220.0002278932233002440.99988605338835
870.0001462523806559340.0002925047613118680.999853747619344
880.0002100233165174810.0004200466330349620.999789976683483
890.0002322433039133730.0004644866078267460.999767756696087
900.0001685832119620290.0003371664239240590.999831416788038
910.0001979737238078610.0003959474476157220.999802026276192
920.0001346477506221710.0002692955012443420.999865352249378
930.0001159184656094420.0002318369312188850.99988408153439
947.90556784341398e-050.0001581113568682800.999920944321566
956.08835880740868e-050.0001217671761481740.999939116411926
964.0065436854982e-058.0130873709964e-050.999959934563145
973.60494659249742e-057.20989318499484e-050.999963950534075
982.3206797962799e-054.6413595925598e-050.999976793202037
991.57026138666035e-053.14052277332070e-050.999984297386133
1001.23292948607548e-052.46585897215097e-050.99998767070514
1019.99619765774281e-061.99923953154856e-050.999990003802342
1026.9739809762315e-061.3947961952463e-050.999993026019024
1036.03072971240190e-061.20614594248038e-050.999993969270288
1049.40780404759356e-061.88156080951871e-050.999990592195952
1056.1599594211869e-061.23199188423738e-050.999993840040579
1066.1447604175878e-061.22895208351756e-050.999993855239582
1076.35291396091382e-061.27058279218276e-050.99999364708604
1084.31425941105944e-068.62851882211887e-060.99999568574059
1093.69388416991716e-067.38776833983431e-060.99999630611583
1101.05306275721846e-052.10612551443693e-050.999989469372428
1116.67654774756987e-061.33530954951397e-050.999993323452252
1125.79838336603088e-061.15967667320618e-050.999994201616634
1131.16644214710618e-052.33288429421235e-050.99998833557853
1141.17507956507553e-052.35015913015106e-050.99998824920435
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1911.21013772843429e-082.42027545686857e-080.999999987898623
1921.60445964719372e-083.20891929438745e-080.999999983955403
1932.46749181323483e-074.93498362646966e-070.999999753250819
1941.59037006745192e-073.18074013490383e-070.999999840962993
1955.77384715309238e-081.15476943061848e-070.999999942261528
1962.56294680177683e-075.12589360355366e-070.99999974370532
1979.43311907783696e-061.88662381556739e-050.999990566880922
1980.0004126375363595430.0008252750727190860.99958736246364
1990.0002472761709700420.0004945523419400840.99975272382903







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1460.815642458100559NOK
5% type I error level1530.854748603351955NOK
10% type I error level1550.865921787709497NOK

\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 & 146 & 0.815642458100559 & NOK \tabularnewline
5% type I error level & 153 & 0.854748603351955 & NOK \tabularnewline
10% type I error level & 155 & 0.865921787709497 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57743&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]146[/C][C]0.815642458100559[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]153[/C][C]0.854748603351955[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]155[/C][C]0.865921787709497[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57743&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57743&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 level1460.815642458100559NOK
5% type I error level1530.854748603351955NOK
10% type I error level1550.865921787709497NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}