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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 computationMon, 05 Nov 2012 07:42:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352119402bxwppcd8o13vwkt.htm/, Retrieved Wed, 01 Feb 2023 15:38:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186018, Retrieved Wed, 01 Feb 2023 15:38:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 12:42:37] [d6d323dfc7646cd233dc8d53404fc370] [Current]
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Dataseries X:
14	10	-15	11	20
14	12	-14	11	19
15	13	-18	11	18
13	15	-13	11	13
8	3	-2	11	17
7	2	-1	9	17
3	-2	5	8	13
3	1	8	6	14
4	1	6	7	13
4	-1	7	8	17
0	-6	15	6	17
-4	-13	23	5	15
-14	-25	43	2	9
-18	-26	60	3	10
-8	-9	36	3	9
-1	1	28	7	14
1	3	23	8	18
2	6	23	7	18
0	2	22	7	12
1	5	22	6	16
0	5	24	6	12
-1	0	32	7	19
-3	-5	27	5	13
-3	-4	27	5	12
-3	-2	27	5	13
-4	-1	29	4	11
-8	-8	38	4	10
-9	-16	40	4	16
-13	-19	45	1	12
-18	-28	50	-1	6
-11	-11	43	3	8
-9	-4	44	4	6
-10	-9	44	3	8
-13	-12	49	2	8
-11	-10	42	1	9
-5	-2	36	4	13
-15	-13	57	3	8
-6	0	42	5	11
-6	0	39	6	8
-3	4	33	6	10
-1	7	32	6	15
-3	5	34	6	12
-4	2	37	6	13
-6	-2	38	5	12
0	6	28	6	15
-4	-3	31	5	13
-2	1	28	6	13
-2	0	30	5	16
-6	-7	39	7	14
-7	-6	38	4	12
-6	-4	39	5	15
-6	-4	38	6	14
-3	-2	37	6	19
-2	2	32	5	16
-5	-5	32	3	16
-11	-15	44	2	11
-11	-16	43	3	13
-11	-18	42	3	12
-10	-13	38	2	11
-14	-23	37	0	6
-8	-10	35	4	9
-9	-10	37	4	6
-5	-6	33	5	15
-1	-3	24	6	17
-2	-4	24	6	13
-5	-7	31	5	12
-4	-7	25	5	13
-6	-7	28	3	10
-2	-3	24	5	14
-2	0	25	5	13
-2	-5	16	5	10
-2	-3	17	3	11
2	3	11	6	12
1	2	12	6	7
-8	-7	39	4	11
-1	-1	19	6	9
1	0	14	5	13
-1	-3	15	4	12
2	4	7	5	5
2	2	12	5	13
1	3	12	4	11
-1	0	14	3	8
-2	-10	9	2	8
-2	-10	8	3	8
-1	-9	4	2	8
-8	-22	7	-1	0
-4	-16	3	0	3
-6	-18	5	-2	0
-3	-14	0	1	-1
-3	-12	-2	-2	-1
-7	-17	6	-2	-4
-9	-23	11	-2	1
-11	-28	9	-6	-1
-13	-31	17	-4	0
-11	-21	21	-2	-1
-9	-19	21	0	6
-17	-22	41	-5	0
-22	-22	57	-4	-3
-25	-25	65	-5	-3
-20	-16	68	-1	4
-24	-22	73	-2	1
-24	-21	71	-4	0
-22	-10	71	-1	-4
-19	-7	70	1	-2
-18	-5	69	1	3
-17	-4	65	-2	2
-11	7	57	1	5
-11	6	57	1	6
-12	3	57	3	6
-10	10	55	3	3
-15	0	65	1	4
-15	-2	65	1	7
-15	-1	64	0	5
-13	2	60	2	6
-8	8	43	2	1
-13	-6	47	-1	3
-9	-4	40	1	6
-7	4	31	0	0
-4	7	27	1	3
-4	3	24	1	4
-2	3	23	3	7
0	8	17	2	6
-2	3	16	0	6
-3	-3	15	0	6
1	4	8	3	6
-2	-5	5	-2	2
-1	-1	6	0	2
1	5	5	1	2
-3	0	12	-1	3
-4	-6	8	-2	-1
-9	-13	17	-1	-4
-9	-15	22	-1	4
-7	-8	24	1	5
-14	-20	36	-2	3
-12	-10	31	-5	-1
-16	-22	34	-5	-4
-20	-25	47	-6	0
-12	-10	33	-4	-1
-12	-8	35	-3	-1
-10	-9	31	-3	3
-10	-5	35	-1	2
-13	-7	39	-2	-4
-16	-11	46	-3	-3
-14	-11	40	-3	-1
-17	-16	50	-3	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
consumentenvertrouwen[t] = -0.0313446664931441 + 0.247632020567418situatie[t] -0.249944883685243werkloosheid[t] + 0.287919111634212`financi\303\253le`[t] + 0.2330272082951spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
consumentenvertrouwen[t] =  -0.0313446664931441 +  0.247632020567418situatie[t] -0.249944883685243werkloosheid[t] +  0.287919111634212`financi\303\253le`[t] +  0.2330272082951spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]consumentenvertrouwen[t] =  -0.0313446664931441 +  0.247632020567418situatie[t] -0.249944883685243werkloosheid[t] +  0.287919111634212`financi\303\253le`[t] +  0.2330272082951spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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
consumentenvertrouwen[t] = -0.0313446664931441 + 0.247632020567418situatie[t] -0.249944883685243werkloosheid[t] + 0.287919111634212`financi\303\253le`[t] + 0.2330272082951spaarvermogen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03134466649314410.071042-0.44120.6597410.32987
situatie0.2476320205674180.00385364.265600
werkloosheid-0.2499448836852430.001461-171.097800
`financi\303\253le`0.2879191116342120.01872915.373200
spaarvermogen0.23302720829510.00947524.592800

\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) & -0.0313446664931441 & 0.071042 & -0.4412 & 0.659741 & 0.32987 \tabularnewline
situatie & 0.247632020567418 & 0.003853 & 64.2656 & 0 & 0 \tabularnewline
werkloosheid & -0.249944883685243 & 0.001461 & -171.0978 & 0 & 0 \tabularnewline
`financi\303\253le` & 0.287919111634212 & 0.018729 & 15.3732 & 0 & 0 \tabularnewline
spaarvermogen & 0.2330272082951 & 0.009475 & 24.5928 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&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]-0.0313446664931441[/C][C]0.071042[/C][C]-0.4412[/C][C]0.659741[/C][C]0.32987[/C][/ROW]
[ROW][C]situatie[/C][C]0.247632020567418[/C][C]0.003853[/C][C]64.2656[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]werkloosheid[/C][C]-0.249944883685243[/C][C]0.001461[/C][C]-171.0978[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`financi\303\253le`[/C][C]0.287919111634212[/C][C]0.018729[/C][C]15.3732[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]spaarvermogen[/C][C]0.2330272082951[/C][C]0.009475[/C][C]24.5928[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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)-0.03134466649314410.071042-0.44120.6597410.32987
situatie0.2476320205674180.00385364.265600
werkloosheid-0.2499448836852430.001461-171.097800
`financi\303\253le`0.2879191116342120.01872915.373200
spaarvermogen0.23302720829510.00947524.592800







Multiple Linear Regression - Regression Statistics
Multiple R0.9991451716503
R-squared0.998291074032108
Adjusted R-squared0.998242247575883
F-TEST (value)20445.6999586873
F-TEST (DF numerator)4
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.315469938430729
Sum Squared Residuals13.9329794874883

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.9991451716503 \tabularnewline
R-squared & 0.998291074032108 \tabularnewline
Adjusted R-squared & 0.998242247575883 \tabularnewline
F-TEST (value) & 20445.6999586873 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.315469938430729 \tabularnewline
Sum Squared Residuals & 13.9329794874883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.9991451716503[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998291074032108[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.998242247575883[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20445.6999586873[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/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]0.315469938430729[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.9329794874883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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 R0.9991451716503
R-squared0.998291074032108
Adjusted R-squared0.998242247575883
F-TEST (value)20445.6999586873
F-TEST (DF numerator)4
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.315469938430729
Sum Squared Residuals13.9329794874883







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.021803188338-0.0218031883380003
21414.0340951374925-0.0340951374925006
31515.0484794845058-0.0484794845057929
41313.1288830657389-0.128883065738915
588.34001393157262-0.340013931572617
677.26659880405153-0.266598804051533
733.55637347485579-0.556373474855791
833.20662387052899-0.206623870528992
943.761405541238590.238594458761411
1044.23622456123312-0.236224561233121
1100.422667165645661-0.422667165645661
12-4-4.064289576032620.0642895760326238
13-14-14.29669208121970.296692081219741
14-18-18.2724408045070.272440804506982
15-8-8.297046454710130.297046454710134
16-1-1.504354691541660.504354691541661
1711.460661712834-0.460661712834002
1821.915638662902050.0843613370979543
190-0.2231077854529820.223107785452982
2011.16397799779546-0.16397799779546
210-0.2680206027554250.268020602755425
22-1-1.586630205374550.586630205374553
23-3-3.549067362824450.54906736282445
24-3-3.534462550552130.534462550552131
25-3-2.80617130112219-0.193828698877805
26-4-3.81240257614967-0.187597423850326
27-8-8.028357881583890.0283578815838902
28-9-9.111140563723130.111140563723125
29-13-12.8996272119346-0.10037278806537
30-18-18.35204128850660.352041288506632
31-11-10.7749518899368-0.225048110063228
32-9-9.469607934606070.469607934606075
33-10-10.52963273248720.529632732487179
34-13-12.8101723242499-0.189827675750138
35-11-10.6201860006574-0.379813999342566
36-5-5.34359436592360.343594365923595
37-15-14.769444302665-0.230555697334985
38-6-6.526134931856210.526134931856205
39-6-6.187462794051560.187462794051563
40-3-3.231210993080230.231210993080232
41-1-1.073234006217240.0732340062172355
42-3-2.76746943960786-0.232530560392143
43-4-4.027172944070740.0271729440707427
44-6-5.78859222995497-0.21140777004503
450-0.3210864920436810.321086492043681
46-4-4.053582856430590.0535828564305856
47-2-2.025301011470970.0253010114709717
48-2-2.361660286157790.361660286157789
49-6-6.234804576618680.234804576618682
50-7-7.067039423858850.067039423858855
51-6-5.83471952988975-0.165280470110249
52-6-5.5298827428654-0.470117257134605
53-3-3.619537776569820.619537776569818
54-2-2.366286012393440.366286012393439
55-5-4.67554837963379-0.32445162036621
56-11-11.60426234264060.604262342640601
57-11-10.8479759512984-0.152024048701635
58-11-11.32632231704310.326322317043058
59-10-9.60932899939431-0.390671000605695
60-14-13.5766785861272-0.423321413872833
61-8-8.00681447995810.00681447995809705
62-9-9.205785872213880.205785872213882
63-5-4.83031426891313-0.169685731086872
64-1-1.083940725819270.083940725819273
65-2-2.263681579567090.26368157956709
66-5-5.277138146995360.277138146995359
67-4-3.5444416365888-0.4555583634112
68-6-5.56919613579825-0.430803864201749
69-2-2.070941462338780.0709414623387837
70-2-1.81101749261687-0.188982507383128
71-2-1.49875526717207-0.501244732827927
72-2-1.5962471246958-0.403752875304196
7322.4859988440179-0.4859988440179
7410.823285898289740.17671410171026
75-8-7.79764353640662-0.202356463593384
76-1-1.203169932619020.203169932619018
7710.9383762279208030.0616237720791967
78-1-0.575411037396006-0.424588962603994
7921.814300829626380.185699170373618
8021.933530036426130.0664699635738737
8111.42718852876913-0.427188528769134
82-1-0.802598036823118-0.197401963176882
83-2-2.31711293570530.317112935705296
84-2-1.77924894038584-0.220751059614159
85-1-0.819756496711662-0.180243503288338
86-8-7.51678241640726-0.48321758359274
87-4-4.044210021742270.0442100217422666
88-6-6.314283678401310.314283678401313
89-3-3.443301051097890.443301051097888
90-3-3.31190457749520.311904577495202
91-7-7.248705374699540.248705374699535
92-9-8.81908587505476-0.180914124945236
93-11-11.17508707364840.175087073648415
94-13-13.10867677326910.108676773269093
95-11-11.28932508736260.289325087362558
96-9-8.5870323648936-0.412967635106399
97-17-17.16658490824240.166584908242378
98-22-21.5768655604574-0.423134439542644
99-25-24.6072398032758-0.392760196724233
100-20-20.34551936462220.345519364622189
101-24-24.06803664297240.0680366429724257
102-24-24.1293802865980.129380286598044
103-22-21.4737795586342-0.526220441365794
104-19-19.43904597338810.439045973388084
105-18-17.5287010070925-0.471298992907494
106-17-17.37807399498190.378073994981851
107-11-11.09172373947040.0917237394703691
108-11-11.10632855174270.106328551742689
109-12-11.2733863901765-0.726613609823479
110-10-9.7391541037194-0.260845896280597
111-15-15.05773416121930.0577341612193442
112-15-14.8539165774689-0.14608342253112
113-15-15.11031320144060.110313201440631
114-13-12.5587721734339-0.44122782656612
115-8-7.98905306885573-0.0109469311442665
116-13-12.853383809853-0.146616190147001
117-9-9.333585734767740.333585734767737
118-7-6.78910797846601-0.210892021533989
119-4-4.059431645503270.0594316455032724
120-4-4.067097868422120.0670978684221166
121-2-2.542233136583150.542233136583151
1220-0.3253500515639120.325350051563912
123-2-1.88940349398418-0.110596506015816
124-3-3.125250733703450.12525073370345
12511.22154493096782-0.221544930967815
126-2-2.629012994434680.629012994434675
127-1-1.312591572581820.312591572581823
12810.7110645461421420.288935453857858
129-3-2.61952075746498-0.380479242535024
130-4-4.325561290943120.325561290943123
131-9-8.71965190133333-0.280348098666673
132-9-8.60042269453358-0.399577305466419
133-7-6.55802288636862-0.441977113631382
134-14-13.8587574888934-0.141242511106609
135-12-11.928579032876-0.0714209671239757
136-16-16.34907955562610.349079555626074
137-20-19.6970693836903-0.302930616309697
138-12-12.14054968861230.140549688612299
139-12-11.8572563032137-0.142743696786263
140-10-10.17299995585980.172999955859784
141-10-9.83944039335776-0.16055960664224
142-13-13.02056633063840.0205663306383807
143-16-15.8156005020439-0.184399497956131
144-14-13.8498767833422-0.150123216657791
145-17-16.6553768898513-0.344623110148666

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.021803188338 & -0.0218031883380003 \tabularnewline
2 & 14 & 14.0340951374925 & -0.0340951374925006 \tabularnewline
3 & 15 & 15.0484794845058 & -0.0484794845057929 \tabularnewline
4 & 13 & 13.1288830657389 & -0.128883065738915 \tabularnewline
5 & 8 & 8.34001393157262 & -0.340013931572617 \tabularnewline
6 & 7 & 7.26659880405153 & -0.266598804051533 \tabularnewline
7 & 3 & 3.55637347485579 & -0.556373474855791 \tabularnewline
8 & 3 & 3.20662387052899 & -0.206623870528992 \tabularnewline
9 & 4 & 3.76140554123859 & 0.238594458761411 \tabularnewline
10 & 4 & 4.23622456123312 & -0.236224561233121 \tabularnewline
11 & 0 & 0.422667165645661 & -0.422667165645661 \tabularnewline
12 & -4 & -4.06428957603262 & 0.0642895760326238 \tabularnewline
13 & -14 & -14.2966920812197 & 0.296692081219741 \tabularnewline
14 & -18 & -18.272440804507 & 0.272440804506982 \tabularnewline
15 & -8 & -8.29704645471013 & 0.297046454710134 \tabularnewline
16 & -1 & -1.50435469154166 & 0.504354691541661 \tabularnewline
17 & 1 & 1.460661712834 & -0.460661712834002 \tabularnewline
18 & 2 & 1.91563866290205 & 0.0843613370979543 \tabularnewline
19 & 0 & -0.223107785452982 & 0.223107785452982 \tabularnewline
20 & 1 & 1.16397799779546 & -0.16397799779546 \tabularnewline
21 & 0 & -0.268020602755425 & 0.268020602755425 \tabularnewline
22 & -1 & -1.58663020537455 & 0.586630205374553 \tabularnewline
23 & -3 & -3.54906736282445 & 0.54906736282445 \tabularnewline
24 & -3 & -3.53446255055213 & 0.534462550552131 \tabularnewline
25 & -3 & -2.80617130112219 & -0.193828698877805 \tabularnewline
26 & -4 & -3.81240257614967 & -0.187597423850326 \tabularnewline
27 & -8 & -8.02835788158389 & 0.0283578815838902 \tabularnewline
28 & -9 & -9.11114056372313 & 0.111140563723125 \tabularnewline
29 & -13 & -12.8996272119346 & -0.10037278806537 \tabularnewline
30 & -18 & -18.3520412885066 & 0.352041288506632 \tabularnewline
31 & -11 & -10.7749518899368 & -0.225048110063228 \tabularnewline
32 & -9 & -9.46960793460607 & 0.469607934606075 \tabularnewline
33 & -10 & -10.5296327324872 & 0.529632732487179 \tabularnewline
34 & -13 & -12.8101723242499 & -0.189827675750138 \tabularnewline
35 & -11 & -10.6201860006574 & -0.379813999342566 \tabularnewline
36 & -5 & -5.3435943659236 & 0.343594365923595 \tabularnewline
37 & -15 & -14.769444302665 & -0.230555697334985 \tabularnewline
38 & -6 & -6.52613493185621 & 0.526134931856205 \tabularnewline
39 & -6 & -6.18746279405156 & 0.187462794051563 \tabularnewline
40 & -3 & -3.23121099308023 & 0.231210993080232 \tabularnewline
41 & -1 & -1.07323400621724 & 0.0732340062172355 \tabularnewline
42 & -3 & -2.76746943960786 & -0.232530560392143 \tabularnewline
43 & -4 & -4.02717294407074 & 0.0271729440707427 \tabularnewline
44 & -6 & -5.78859222995497 & -0.21140777004503 \tabularnewline
45 & 0 & -0.321086492043681 & 0.321086492043681 \tabularnewline
46 & -4 & -4.05358285643059 & 0.0535828564305856 \tabularnewline
47 & -2 & -2.02530101147097 & 0.0253010114709717 \tabularnewline
48 & -2 & -2.36166028615779 & 0.361660286157789 \tabularnewline
49 & -6 & -6.23480457661868 & 0.234804576618682 \tabularnewline
50 & -7 & -7.06703942385885 & 0.067039423858855 \tabularnewline
51 & -6 & -5.83471952988975 & -0.165280470110249 \tabularnewline
52 & -6 & -5.5298827428654 & -0.470117257134605 \tabularnewline
53 & -3 & -3.61953777656982 & 0.619537776569818 \tabularnewline
54 & -2 & -2.36628601239344 & 0.366286012393439 \tabularnewline
55 & -5 & -4.67554837963379 & -0.32445162036621 \tabularnewline
56 & -11 & -11.6042623426406 & 0.604262342640601 \tabularnewline
57 & -11 & -10.8479759512984 & -0.152024048701635 \tabularnewline
58 & -11 & -11.3263223170431 & 0.326322317043058 \tabularnewline
59 & -10 & -9.60932899939431 & -0.390671000605695 \tabularnewline
60 & -14 & -13.5766785861272 & -0.423321413872833 \tabularnewline
61 & -8 & -8.0068144799581 & 0.00681447995809705 \tabularnewline
62 & -9 & -9.20578587221388 & 0.205785872213882 \tabularnewline
63 & -5 & -4.83031426891313 & -0.169685731086872 \tabularnewline
64 & -1 & -1.08394072581927 & 0.083940725819273 \tabularnewline
65 & -2 & -2.26368157956709 & 0.26368157956709 \tabularnewline
66 & -5 & -5.27713814699536 & 0.277138146995359 \tabularnewline
67 & -4 & -3.5444416365888 & -0.4555583634112 \tabularnewline
68 & -6 & -5.56919613579825 & -0.430803864201749 \tabularnewline
69 & -2 & -2.07094146233878 & 0.0709414623387837 \tabularnewline
70 & -2 & -1.81101749261687 & -0.188982507383128 \tabularnewline
71 & -2 & -1.49875526717207 & -0.501244732827927 \tabularnewline
72 & -2 & -1.5962471246958 & -0.403752875304196 \tabularnewline
73 & 2 & 2.4859988440179 & -0.4859988440179 \tabularnewline
74 & 1 & 0.82328589828974 & 0.17671410171026 \tabularnewline
75 & -8 & -7.79764353640662 & -0.202356463593384 \tabularnewline
76 & -1 & -1.20316993261902 & 0.203169932619018 \tabularnewline
77 & 1 & 0.938376227920803 & 0.0616237720791967 \tabularnewline
78 & -1 & -0.575411037396006 & -0.424588962603994 \tabularnewline
79 & 2 & 1.81430082962638 & 0.185699170373618 \tabularnewline
80 & 2 & 1.93353003642613 & 0.0664699635738737 \tabularnewline
81 & 1 & 1.42718852876913 & -0.427188528769134 \tabularnewline
82 & -1 & -0.802598036823118 & -0.197401963176882 \tabularnewline
83 & -2 & -2.3171129357053 & 0.317112935705296 \tabularnewline
84 & -2 & -1.77924894038584 & -0.220751059614159 \tabularnewline
85 & -1 & -0.819756496711662 & -0.180243503288338 \tabularnewline
86 & -8 & -7.51678241640726 & -0.48321758359274 \tabularnewline
87 & -4 & -4.04421002174227 & 0.0442100217422666 \tabularnewline
88 & -6 & -6.31428367840131 & 0.314283678401313 \tabularnewline
89 & -3 & -3.44330105109789 & 0.443301051097888 \tabularnewline
90 & -3 & -3.3119045774952 & 0.311904577495202 \tabularnewline
91 & -7 & -7.24870537469954 & 0.248705374699535 \tabularnewline
92 & -9 & -8.81908587505476 & -0.180914124945236 \tabularnewline
93 & -11 & -11.1750870736484 & 0.175087073648415 \tabularnewline
94 & -13 & -13.1086767732691 & 0.108676773269093 \tabularnewline
95 & -11 & -11.2893250873626 & 0.289325087362558 \tabularnewline
96 & -9 & -8.5870323648936 & -0.412967635106399 \tabularnewline
97 & -17 & -17.1665849082424 & 0.166584908242378 \tabularnewline
98 & -22 & -21.5768655604574 & -0.423134439542644 \tabularnewline
99 & -25 & -24.6072398032758 & -0.392760196724233 \tabularnewline
100 & -20 & -20.3455193646222 & 0.345519364622189 \tabularnewline
101 & -24 & -24.0680366429724 & 0.0680366429724257 \tabularnewline
102 & -24 & -24.129380286598 & 0.129380286598044 \tabularnewline
103 & -22 & -21.4737795586342 & -0.526220441365794 \tabularnewline
104 & -19 & -19.4390459733881 & 0.439045973388084 \tabularnewline
105 & -18 & -17.5287010070925 & -0.471298992907494 \tabularnewline
106 & -17 & -17.3780739949819 & 0.378073994981851 \tabularnewline
107 & -11 & -11.0917237394704 & 0.0917237394703691 \tabularnewline
108 & -11 & -11.1063285517427 & 0.106328551742689 \tabularnewline
109 & -12 & -11.2733863901765 & -0.726613609823479 \tabularnewline
110 & -10 & -9.7391541037194 & -0.260845896280597 \tabularnewline
111 & -15 & -15.0577341612193 & 0.0577341612193442 \tabularnewline
112 & -15 & -14.8539165774689 & -0.14608342253112 \tabularnewline
113 & -15 & -15.1103132014406 & 0.110313201440631 \tabularnewline
114 & -13 & -12.5587721734339 & -0.44122782656612 \tabularnewline
115 & -8 & -7.98905306885573 & -0.0109469311442665 \tabularnewline
116 & -13 & -12.853383809853 & -0.146616190147001 \tabularnewline
117 & -9 & -9.33358573476774 & 0.333585734767737 \tabularnewline
118 & -7 & -6.78910797846601 & -0.210892021533989 \tabularnewline
119 & -4 & -4.05943164550327 & 0.0594316455032724 \tabularnewline
120 & -4 & -4.06709786842212 & 0.0670978684221166 \tabularnewline
121 & -2 & -2.54223313658315 & 0.542233136583151 \tabularnewline
122 & 0 & -0.325350051563912 & 0.325350051563912 \tabularnewline
123 & -2 & -1.88940349398418 & -0.110596506015816 \tabularnewline
124 & -3 & -3.12525073370345 & 0.12525073370345 \tabularnewline
125 & 1 & 1.22154493096782 & -0.221544930967815 \tabularnewline
126 & -2 & -2.62901299443468 & 0.629012994434675 \tabularnewline
127 & -1 & -1.31259157258182 & 0.312591572581823 \tabularnewline
128 & 1 & 0.711064546142142 & 0.288935453857858 \tabularnewline
129 & -3 & -2.61952075746498 & -0.380479242535024 \tabularnewline
130 & -4 & -4.32556129094312 & 0.325561290943123 \tabularnewline
131 & -9 & -8.71965190133333 & -0.280348098666673 \tabularnewline
132 & -9 & -8.60042269453358 & -0.399577305466419 \tabularnewline
133 & -7 & -6.55802288636862 & -0.441977113631382 \tabularnewline
134 & -14 & -13.8587574888934 & -0.141242511106609 \tabularnewline
135 & -12 & -11.928579032876 & -0.0714209671239757 \tabularnewline
136 & -16 & -16.3490795556261 & 0.349079555626074 \tabularnewline
137 & -20 & -19.6970693836903 & -0.302930616309697 \tabularnewline
138 & -12 & -12.1405496886123 & 0.140549688612299 \tabularnewline
139 & -12 & -11.8572563032137 & -0.142743696786263 \tabularnewline
140 & -10 & -10.1729999558598 & 0.172999955859784 \tabularnewline
141 & -10 & -9.83944039335776 & -0.16055960664224 \tabularnewline
142 & -13 & -13.0205663306384 & 0.0205663306383807 \tabularnewline
143 & -16 & -15.8156005020439 & -0.184399497956131 \tabularnewline
144 & -14 & -13.8498767833422 & -0.150123216657791 \tabularnewline
145 & -17 & -16.6553768898513 & -0.344623110148666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&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]14[/C][C]14.021803188338[/C][C]-0.0218031883380003[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]14.0340951374925[/C][C]-0.0340951374925006[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]15.0484794845058[/C][C]-0.0484794845057929[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]13.1288830657389[/C][C]-0.128883065738915[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]8.34001393157262[/C][C]-0.340013931572617[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]7.26659880405153[/C][C]-0.266598804051533[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.55637347485579[/C][C]-0.556373474855791[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]3.20662387052899[/C][C]-0.206623870528992[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]3.76140554123859[/C][C]0.238594458761411[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.23622456123312[/C][C]-0.236224561233121[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.422667165645661[/C][C]-0.422667165645661[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-4.06428957603262[/C][C]0.0642895760326238[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-14.2966920812197[/C][C]0.296692081219741[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-18.272440804507[/C][C]0.272440804506982[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-8.29704645471013[/C][C]0.297046454710134[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]-1.50435469154166[/C][C]0.504354691541661[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.460661712834[/C][C]-0.460661712834002[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.91563866290205[/C][C]0.0843613370979543[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.223107785452982[/C][C]0.223107785452982[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.16397799779546[/C][C]-0.16397799779546[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-0.268020602755425[/C][C]0.268020602755425[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]-1.58663020537455[/C][C]0.586630205374553[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-3.54906736282445[/C][C]0.54906736282445[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-3.53446255055213[/C][C]0.534462550552131[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-2.80617130112219[/C][C]-0.193828698877805[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-3.81240257614967[/C][C]-0.187597423850326[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-8.02835788158389[/C][C]0.0283578815838902[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-9.11114056372313[/C][C]0.111140563723125[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-12.8996272119346[/C][C]-0.10037278806537[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-18.3520412885066[/C][C]0.352041288506632[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-10.7749518899368[/C][C]-0.225048110063228[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-9.46960793460607[/C][C]0.469607934606075[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-10.5296327324872[/C][C]0.529632732487179[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-12.8101723242499[/C][C]-0.189827675750138[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-10.6201860006574[/C][C]-0.379813999342566[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-5.3435943659236[/C][C]0.343594365923595[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-14.769444302665[/C][C]-0.230555697334985[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-6.52613493185621[/C][C]0.526134931856205[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-6.18746279405156[/C][C]0.187462794051563[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-3.23121099308023[/C][C]0.231210993080232[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-1.07323400621724[/C][C]0.0732340062172355[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-2.76746943960786[/C][C]-0.232530560392143[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-4.02717294407074[/C][C]0.0271729440707427[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-5.78859222995497[/C][C]-0.21140777004503[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-0.321086492043681[/C][C]0.321086492043681[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-4.05358285643059[/C][C]0.0535828564305856[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-2.02530101147097[/C][C]0.0253010114709717[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-2.36166028615779[/C][C]0.361660286157789[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]-6.23480457661868[/C][C]0.234804576618682[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-7.06703942385885[/C][C]0.067039423858855[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-5.83471952988975[/C][C]-0.165280470110249[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-5.5298827428654[/C][C]-0.470117257134605[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-3.61953777656982[/C][C]0.619537776569818[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-2.36628601239344[/C][C]0.366286012393439[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-4.67554837963379[/C][C]-0.32445162036621[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-11.6042623426406[/C][C]0.604262342640601[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-10.8479759512984[/C][C]-0.152024048701635[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-11.3263223170431[/C][C]0.326322317043058[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-9.60932899939431[/C][C]-0.390671000605695[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-13.5766785861272[/C][C]-0.423321413872833[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-8.0068144799581[/C][C]0.00681447995809705[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-9.20578587221388[/C][C]0.205785872213882[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-4.83031426891313[/C][C]-0.169685731086872[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-1.08394072581927[/C][C]0.083940725819273[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-2.26368157956709[/C][C]0.26368157956709[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-5.27713814699536[/C][C]0.277138146995359[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-3.5444416365888[/C][C]-0.4555583634112[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-5.56919613579825[/C][C]-0.430803864201749[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-2.07094146233878[/C][C]0.0709414623387837[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-1.81101749261687[/C][C]-0.188982507383128[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-1.49875526717207[/C][C]-0.501244732827927[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-1.5962471246958[/C][C]-0.403752875304196[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]2.4859988440179[/C][C]-0.4859988440179[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.82328589828974[/C][C]0.17671410171026[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-7.79764353640662[/C][C]-0.202356463593384[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]-1.20316993261902[/C][C]0.203169932619018[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.938376227920803[/C][C]0.0616237720791967[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]-0.575411037396006[/C][C]-0.424588962603994[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.81430082962638[/C][C]0.185699170373618[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.93353003642613[/C][C]0.0664699635738737[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.42718852876913[/C][C]-0.427188528769134[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-0.802598036823118[/C][C]-0.197401963176882[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-2.3171129357053[/C][C]0.317112935705296[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-1.77924894038584[/C][C]-0.220751059614159[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-0.819756496711662[/C][C]-0.180243503288338[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-7.51678241640726[/C][C]-0.48321758359274[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-4.04421002174227[/C][C]0.0442100217422666[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-6.31428367840131[/C][C]0.314283678401313[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-3.44330105109789[/C][C]0.443301051097888[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-3.3119045774952[/C][C]0.311904577495202[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-7.24870537469954[/C][C]0.248705374699535[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-8.81908587505476[/C][C]-0.180914124945236[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-11.1750870736484[/C][C]0.175087073648415[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-13.1086767732691[/C][C]0.108676773269093[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-11.2893250873626[/C][C]0.289325087362558[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-8.5870323648936[/C][C]-0.412967635106399[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-17.1665849082424[/C][C]0.166584908242378[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-21.5768655604574[/C][C]-0.423134439542644[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-24.6072398032758[/C][C]-0.392760196724233[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-20.3455193646222[/C][C]0.345519364622189[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-24.0680366429724[/C][C]0.0680366429724257[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-24.129380286598[/C][C]0.129380286598044[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-21.4737795586342[/C][C]-0.526220441365794[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-19.4390459733881[/C][C]0.439045973388084[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-17.5287010070925[/C][C]-0.471298992907494[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-17.3780739949819[/C][C]0.378073994981851[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-11.0917237394704[/C][C]0.0917237394703691[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-11.1063285517427[/C][C]0.106328551742689[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-11.2733863901765[/C][C]-0.726613609823479[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-9.7391541037194[/C][C]-0.260845896280597[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-15.0577341612193[/C][C]0.0577341612193442[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-14.8539165774689[/C][C]-0.14608342253112[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-15.1103132014406[/C][C]0.110313201440631[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-12.5587721734339[/C][C]-0.44122782656612[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-7.98905306885573[/C][C]-0.0109469311442665[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-12.853383809853[/C][C]-0.146616190147001[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-9.33358573476774[/C][C]0.333585734767737[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-6.78910797846601[/C][C]-0.210892021533989[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-4.05943164550327[/C][C]0.0594316455032724[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-4.06709786842212[/C][C]0.0670978684221166[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-2.54223313658315[/C][C]0.542233136583151[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-0.325350051563912[/C][C]0.325350051563912[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-1.88940349398418[/C][C]-0.110596506015816[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-3.12525073370345[/C][C]0.12525073370345[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.22154493096782[/C][C]-0.221544930967815[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-2.62901299443468[/C][C]0.629012994434675[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-1.31259157258182[/C][C]0.312591572581823[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.711064546142142[/C][C]0.288935453857858[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-2.61952075746498[/C][C]-0.380479242535024[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-4.32556129094312[/C][C]0.325561290943123[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-8.71965190133333[/C][C]-0.280348098666673[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-8.60042269453358[/C][C]-0.399577305466419[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-6.55802288636862[/C][C]-0.441977113631382[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-13.8587574888934[/C][C]-0.141242511106609[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-11.928579032876[/C][C]-0.0714209671239757[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-16.3490795556261[/C][C]0.349079555626074[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-19.6970693836903[/C][C]-0.302930616309697[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-12.1405496886123[/C][C]0.140549688612299[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-11.8572563032137[/C][C]-0.142743696786263[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-10.1729999558598[/C][C]0.172999955859784[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-9.83944039335776[/C][C]-0.16055960664224[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-13.0205663306384[/C][C]0.0205663306383807[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-15.8156005020439[/C][C]-0.184399497956131[/C][/ROW]
[ROW][C]144[/C][C]-14[/C][C]-13.8498767833422[/C][C]-0.150123216657791[/C][/ROW]
[ROW][C]145[/C][C]-17[/C][C]-16.6553768898513[/C][C]-0.344623110148666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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
11414.021803188338-0.0218031883380003
21414.0340951374925-0.0340951374925006
31515.0484794845058-0.0484794845057929
41313.1288830657389-0.128883065738915
588.34001393157262-0.340013931572617
677.26659880405153-0.266598804051533
733.55637347485579-0.556373474855791
833.20662387052899-0.206623870528992
943.761405541238590.238594458761411
1044.23622456123312-0.236224561233121
1100.422667165645661-0.422667165645661
12-4-4.064289576032620.0642895760326238
13-14-14.29669208121970.296692081219741
14-18-18.2724408045070.272440804506982
15-8-8.297046454710130.297046454710134
16-1-1.504354691541660.504354691541661
1711.460661712834-0.460661712834002
1821.915638662902050.0843613370979543
190-0.2231077854529820.223107785452982
2011.16397799779546-0.16397799779546
210-0.2680206027554250.268020602755425
22-1-1.586630205374550.586630205374553
23-3-3.549067362824450.54906736282445
24-3-3.534462550552130.534462550552131
25-3-2.80617130112219-0.193828698877805
26-4-3.81240257614967-0.187597423850326
27-8-8.028357881583890.0283578815838902
28-9-9.111140563723130.111140563723125
29-13-12.8996272119346-0.10037278806537
30-18-18.35204128850660.352041288506632
31-11-10.7749518899368-0.225048110063228
32-9-9.469607934606070.469607934606075
33-10-10.52963273248720.529632732487179
34-13-12.8101723242499-0.189827675750138
35-11-10.6201860006574-0.379813999342566
36-5-5.34359436592360.343594365923595
37-15-14.769444302665-0.230555697334985
38-6-6.526134931856210.526134931856205
39-6-6.187462794051560.187462794051563
40-3-3.231210993080230.231210993080232
41-1-1.073234006217240.0732340062172355
42-3-2.76746943960786-0.232530560392143
43-4-4.027172944070740.0271729440707427
44-6-5.78859222995497-0.21140777004503
450-0.3210864920436810.321086492043681
46-4-4.053582856430590.0535828564305856
47-2-2.025301011470970.0253010114709717
48-2-2.361660286157790.361660286157789
49-6-6.234804576618680.234804576618682
50-7-7.067039423858850.067039423858855
51-6-5.83471952988975-0.165280470110249
52-6-5.5298827428654-0.470117257134605
53-3-3.619537776569820.619537776569818
54-2-2.366286012393440.366286012393439
55-5-4.67554837963379-0.32445162036621
56-11-11.60426234264060.604262342640601
57-11-10.8479759512984-0.152024048701635
58-11-11.32632231704310.326322317043058
59-10-9.60932899939431-0.390671000605695
60-14-13.5766785861272-0.423321413872833
61-8-8.00681447995810.00681447995809705
62-9-9.205785872213880.205785872213882
63-5-4.83031426891313-0.169685731086872
64-1-1.083940725819270.083940725819273
65-2-2.263681579567090.26368157956709
66-5-5.277138146995360.277138146995359
67-4-3.5444416365888-0.4555583634112
68-6-5.56919613579825-0.430803864201749
69-2-2.070941462338780.0709414623387837
70-2-1.81101749261687-0.188982507383128
71-2-1.49875526717207-0.501244732827927
72-2-1.5962471246958-0.403752875304196
7322.4859988440179-0.4859988440179
7410.823285898289740.17671410171026
75-8-7.79764353640662-0.202356463593384
76-1-1.203169932619020.203169932619018
7710.9383762279208030.0616237720791967
78-1-0.575411037396006-0.424588962603994
7921.814300829626380.185699170373618
8021.933530036426130.0664699635738737
8111.42718852876913-0.427188528769134
82-1-0.802598036823118-0.197401963176882
83-2-2.31711293570530.317112935705296
84-2-1.77924894038584-0.220751059614159
85-1-0.819756496711662-0.180243503288338
86-8-7.51678241640726-0.48321758359274
87-4-4.044210021742270.0442100217422666
88-6-6.314283678401310.314283678401313
89-3-3.443301051097890.443301051097888
90-3-3.31190457749520.311904577495202
91-7-7.248705374699540.248705374699535
92-9-8.81908587505476-0.180914124945236
93-11-11.17508707364840.175087073648415
94-13-13.10867677326910.108676773269093
95-11-11.28932508736260.289325087362558
96-9-8.5870323648936-0.412967635106399
97-17-17.16658490824240.166584908242378
98-22-21.5768655604574-0.423134439542644
99-25-24.6072398032758-0.392760196724233
100-20-20.34551936462220.345519364622189
101-24-24.06803664297240.0680366429724257
102-24-24.1293802865980.129380286598044
103-22-21.4737795586342-0.526220441365794
104-19-19.43904597338810.439045973388084
105-18-17.5287010070925-0.471298992907494
106-17-17.37807399498190.378073994981851
107-11-11.09172373947040.0917237394703691
108-11-11.10632855174270.106328551742689
109-12-11.2733863901765-0.726613609823479
110-10-9.7391541037194-0.260845896280597
111-15-15.05773416121930.0577341612193442
112-15-14.8539165774689-0.14608342253112
113-15-15.11031320144060.110313201440631
114-13-12.5587721734339-0.44122782656612
115-8-7.98905306885573-0.0109469311442665
116-13-12.853383809853-0.146616190147001
117-9-9.333585734767740.333585734767737
118-7-6.78910797846601-0.210892021533989
119-4-4.059431645503270.0594316455032724
120-4-4.067097868422120.0670978684221166
121-2-2.542233136583150.542233136583151
1220-0.3253500515639120.325350051563912
123-2-1.88940349398418-0.110596506015816
124-3-3.125250733703450.12525073370345
12511.22154493096782-0.221544930967815
126-2-2.629012994434680.629012994434675
127-1-1.312591572581820.312591572581823
12810.7110645461421420.288935453857858
129-3-2.61952075746498-0.380479242535024
130-4-4.325561290943120.325561290943123
131-9-8.71965190133333-0.280348098666673
132-9-8.60042269453358-0.399577305466419
133-7-6.55802288636862-0.441977113631382
134-14-13.8587574888934-0.141242511106609
135-12-11.928579032876-0.0714209671239757
136-16-16.34907955562610.349079555626074
137-20-19.6970693836903-0.302930616309697
138-12-12.14054968861230.140549688612299
139-12-11.8572563032137-0.142743696786263
140-10-10.17299995585980.172999955859784
141-10-9.83944039335776-0.16055960664224
142-13-13.02056633063840.0205663306383807
143-16-15.8156005020439-0.184399497956131
144-14-13.8498767833422-0.150123216657791
145-17-16.6553768898513-0.344623110148666







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001529998327360050.00305999665472010.99847000167264
90.3150787982884220.6301575965768430.684921201711578
100.1951861835597040.3903723671194080.804813816440296
110.1521745890187740.3043491780375490.847825410981226
120.2721646224697460.5443292449394920.727835377530254
130.3723847450287590.7447694900575190.627615254971241
140.3196354516371910.6392709032743820.680364548362809
150.2385543708630110.4771087417260220.761445629136989
160.19803379008760.39606758017520.8019662099124
170.4133297000816630.8266594001633260.586670299918337
180.3328005193302220.6656010386604440.667199480669778
190.2621430361750080.5242860723500160.737856963824992
200.2378664303247850.4757328606495710.762133569675215
210.1837569221266880.3675138442533760.816243077873312
220.3295867053042370.6591734106084730.670413294695763
230.3999543026280260.7999086052560520.600045697371974
240.4279284882691770.8558569765383540.572071511730823
250.4652770616584460.9305541233168920.534722938341554
260.5038902603058210.9922194793883590.49610973969418
270.4633142329279660.9266284658559310.536685767072034
280.4004459156122930.8008918312245860.599554084387707
290.3594801385941340.7189602771882690.640519861405866
300.342399529524030.6847990590480590.65760047047597
310.4064299610738290.8128599221476590.593570038926171
320.3828165264136190.7656330528272380.617183473586381
330.3950774830221570.7901549660443150.604922516977843
340.4496643838958790.8993287677917580.550335616104121
350.5147297258653140.9705405482693710.485270274134686
360.4983627627433260.9967255254866520.501637237256674
370.5825962268957320.8348075462085370.417403773104268
380.604339519673640.7913209606527190.39566048032636
390.5606914117995830.8786171764008340.439308588200417
400.5152275130080440.9695449739839110.484772486991956
410.4653510358654460.9307020717308910.534648964134554
420.4900477333174230.9800954666348450.509952266682577
430.444470035914730.8889400718294610.55552996408527
440.4432325552339870.8864651104679750.556767444766012
450.4314555932980590.8629111865961180.568544406701941
460.3808524176216250.761704835243250.619147582378375
470.3326940548611840.6653881097223680.667305945138816
480.3405590960218630.6811181920437270.659440903978137
490.3129544023203670.6259088046407350.687045597679633
500.271914299547610.543828599095220.72808570045239
510.2573714544739310.5147429089478620.742628545526069
520.3444337924538520.6888675849077050.655566207546147
530.4808996145811240.9617992291622490.519100385418876
540.4944687176837820.9889374353675640.505531282316218
550.5021207448368530.9957585103262930.497879255163147
560.6507076629371930.6985846741256140.349292337062807
570.6231442069294840.7537115861410320.376855793070516
580.6561988264529480.6876023470941030.343801173547052
590.6751029936884070.6497940126231860.324897006311593
600.6785868164521420.6428263670957150.321413183547858
610.6377738518583720.7244522962832560.362226148141628
620.6204287477186120.7591425045627750.379571252281388
630.5897479632191670.8205040735616660.410252036780833
640.5628956229187850.874208754162430.437104377081215
650.5825682438916210.8348635122167580.417431756108379
660.6169776939790370.7660446120419250.383022306020963
670.6335748955081190.7328502089837620.366425104491881
680.639873206977710.7202535860445790.36012679302229
690.6179331731634130.7641336536731740.382066826836587
700.579996776810060.8400064463798790.420003223189939
710.5990078713771880.8019842572456230.400992128622812
720.5823415621301920.8353168757396160.417658437869808
730.6068303581383220.7863392837233560.393169641861678
740.5970282222899630.8059435554200730.402971777710037
750.5705971273038290.8588057453923410.429402872696171
760.5673451820544970.8653096358910050.432654817945503
770.5432413168883150.913517366223370.456758683111685
780.5315636497264850.936872700547030.468436350273515
790.5277933637971090.9444132724057830.472206636202891
800.5013580416777030.9972839166445940.498641958322297
810.5062983101098860.9874033797802280.493701689890114
820.467556889849950.9351137796998990.53244311015005
830.5542023023445770.8915953953108460.445797697655423
840.5098050521346550.980389895730690.490194947865345
850.4719968344537790.9439936689075580.528003165546221
860.5094966511661880.9810066976676230.490503348833812
870.4938914826040680.9877829652081350.506108517395932
880.5337375183457240.9325249633085510.466262481654275
890.5991944800761540.8016110398476910.400805519923846
900.5974157142459840.8051685715080320.402584285754016
910.5758720458331010.8482559083337980.424127954166899
920.5351466789186570.9297066421626870.464853321081343
930.5008484369320290.9983031261359430.499151563067971
940.4636695607711430.9273391215422850.536330439228857
950.4792228027266320.9584456054532630.520777197273368
960.482715534198640.965431068397280.51728446580136
970.4455292619886770.8910585239773540.554470738011323
980.4986972986188410.9973945972376830.501302701381159
990.5273426433972030.9453147132055940.472657356602797
1000.5832927392358060.8334145215283880.416707260764194
1010.5809737060565230.8380525878869550.419026293943477
1020.5771572644335080.8456854711329830.422842735566492
1030.6421151572966350.7157696854067310.357884842703365
1040.8199206607680750.3601586784638490.180079339231925
1050.8182488661250540.3635022677498920.181751133874946
1060.8557209295595080.2885581408809840.144279070440492
1070.8265991157766790.3468017684466410.173400884223321
1080.7992069004144020.4015861991711970.200793099585598
1090.8895922720668740.2208154558662530.110407727933126
1100.87306572252060.25386855495880.1269342774794
1110.8571681568430360.2856636863139290.142831843156964
1120.8232846531220670.3534306937558650.176715346877932
1130.8249974636058270.3500050727883460.175002536394173
1140.8112572134864760.3774855730270480.188742786513524
1150.7650450907031820.4699098185936370.234954909296818
1160.7133437184319110.5733125631361780.286656281568089
1170.800871908575530.398256182848940.19912809142447
1180.7844822618274860.4310354763450280.215517738172514
1190.7320000355160630.5359999289678750.267999964483937
1200.6727680057533110.6544639884933780.327231994246689
1210.9094164765926920.1811670468146150.0905835234073075
1220.9451866403152490.1096267193695020.0548133596847512
1230.9234095509774960.1531808980450090.0765904490225045
1240.9070205666731620.1859588666536770.0929794333268385
1250.8694197814024060.2611604371951880.130580218597594
1260.9139768765447820.1720462469104370.0860231234552185
1270.9142012943717620.1715974112564750.0857987056282376
1280.9369247863995230.1261504272009530.0630752136004766
1290.9602196379919750.07956072401604920.0397803620080246
1300.950755757646050.09848848470789980.0492442423539499
1310.9568237109735180.08635257805296450.0431762890264823
1320.9597972609777140.08040547804457190.0402027390222859
1330.986827644777930.02634471044413940.0131723552220697
1340.9887675272133070.02246494557338510.0112324727866926
1350.9772497644097520.0455004711804960.022750235590248
1360.9583067564043870.08338648719122510.0416932435956125
1370.9314291377600690.1371417244798630.0685708622399314

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00152999832736005 & 0.0030599966547201 & 0.99847000167264 \tabularnewline
9 & 0.315078798288422 & 0.630157596576843 & 0.684921201711578 \tabularnewline
10 & 0.195186183559704 & 0.390372367119408 & 0.804813816440296 \tabularnewline
11 & 0.152174589018774 & 0.304349178037549 & 0.847825410981226 \tabularnewline
12 & 0.272164622469746 & 0.544329244939492 & 0.727835377530254 \tabularnewline
13 & 0.372384745028759 & 0.744769490057519 & 0.627615254971241 \tabularnewline
14 & 0.319635451637191 & 0.639270903274382 & 0.680364548362809 \tabularnewline
15 & 0.238554370863011 & 0.477108741726022 & 0.761445629136989 \tabularnewline
16 & 0.1980337900876 & 0.3960675801752 & 0.8019662099124 \tabularnewline
17 & 0.413329700081663 & 0.826659400163326 & 0.586670299918337 \tabularnewline
18 & 0.332800519330222 & 0.665601038660444 & 0.667199480669778 \tabularnewline
19 & 0.262143036175008 & 0.524286072350016 & 0.737856963824992 \tabularnewline
20 & 0.237866430324785 & 0.475732860649571 & 0.762133569675215 \tabularnewline
21 & 0.183756922126688 & 0.367513844253376 & 0.816243077873312 \tabularnewline
22 & 0.329586705304237 & 0.659173410608473 & 0.670413294695763 \tabularnewline
23 & 0.399954302628026 & 0.799908605256052 & 0.600045697371974 \tabularnewline
24 & 0.427928488269177 & 0.855856976538354 & 0.572071511730823 \tabularnewline
25 & 0.465277061658446 & 0.930554123316892 & 0.534722938341554 \tabularnewline
26 & 0.503890260305821 & 0.992219479388359 & 0.49610973969418 \tabularnewline
27 & 0.463314232927966 & 0.926628465855931 & 0.536685767072034 \tabularnewline
28 & 0.400445915612293 & 0.800891831224586 & 0.599554084387707 \tabularnewline
29 & 0.359480138594134 & 0.718960277188269 & 0.640519861405866 \tabularnewline
30 & 0.34239952952403 & 0.684799059048059 & 0.65760047047597 \tabularnewline
31 & 0.406429961073829 & 0.812859922147659 & 0.593570038926171 \tabularnewline
32 & 0.382816526413619 & 0.765633052827238 & 0.617183473586381 \tabularnewline
33 & 0.395077483022157 & 0.790154966044315 & 0.604922516977843 \tabularnewline
34 & 0.449664383895879 & 0.899328767791758 & 0.550335616104121 \tabularnewline
35 & 0.514729725865314 & 0.970540548269371 & 0.485270274134686 \tabularnewline
36 & 0.498362762743326 & 0.996725525486652 & 0.501637237256674 \tabularnewline
37 & 0.582596226895732 & 0.834807546208537 & 0.417403773104268 \tabularnewline
38 & 0.60433951967364 & 0.791320960652719 & 0.39566048032636 \tabularnewline
39 & 0.560691411799583 & 0.878617176400834 & 0.439308588200417 \tabularnewline
40 & 0.515227513008044 & 0.969544973983911 & 0.484772486991956 \tabularnewline
41 & 0.465351035865446 & 0.930702071730891 & 0.534648964134554 \tabularnewline
42 & 0.490047733317423 & 0.980095466634845 & 0.509952266682577 \tabularnewline
43 & 0.44447003591473 & 0.888940071829461 & 0.55552996408527 \tabularnewline
44 & 0.443232555233987 & 0.886465110467975 & 0.556767444766012 \tabularnewline
45 & 0.431455593298059 & 0.862911186596118 & 0.568544406701941 \tabularnewline
46 & 0.380852417621625 & 0.76170483524325 & 0.619147582378375 \tabularnewline
47 & 0.332694054861184 & 0.665388109722368 & 0.667305945138816 \tabularnewline
48 & 0.340559096021863 & 0.681118192043727 & 0.659440903978137 \tabularnewline
49 & 0.312954402320367 & 0.625908804640735 & 0.687045597679633 \tabularnewline
50 & 0.27191429954761 & 0.54382859909522 & 0.72808570045239 \tabularnewline
51 & 0.257371454473931 & 0.514742908947862 & 0.742628545526069 \tabularnewline
52 & 0.344433792453852 & 0.688867584907705 & 0.655566207546147 \tabularnewline
53 & 0.480899614581124 & 0.961799229162249 & 0.519100385418876 \tabularnewline
54 & 0.494468717683782 & 0.988937435367564 & 0.505531282316218 \tabularnewline
55 & 0.502120744836853 & 0.995758510326293 & 0.497879255163147 \tabularnewline
56 & 0.650707662937193 & 0.698584674125614 & 0.349292337062807 \tabularnewline
57 & 0.623144206929484 & 0.753711586141032 & 0.376855793070516 \tabularnewline
58 & 0.656198826452948 & 0.687602347094103 & 0.343801173547052 \tabularnewline
59 & 0.675102993688407 & 0.649794012623186 & 0.324897006311593 \tabularnewline
60 & 0.678586816452142 & 0.642826367095715 & 0.321413183547858 \tabularnewline
61 & 0.637773851858372 & 0.724452296283256 & 0.362226148141628 \tabularnewline
62 & 0.620428747718612 & 0.759142504562775 & 0.379571252281388 \tabularnewline
63 & 0.589747963219167 & 0.820504073561666 & 0.410252036780833 \tabularnewline
64 & 0.562895622918785 & 0.87420875416243 & 0.437104377081215 \tabularnewline
65 & 0.582568243891621 & 0.834863512216758 & 0.417431756108379 \tabularnewline
66 & 0.616977693979037 & 0.766044612041925 & 0.383022306020963 \tabularnewline
67 & 0.633574895508119 & 0.732850208983762 & 0.366425104491881 \tabularnewline
68 & 0.63987320697771 & 0.720253586044579 & 0.36012679302229 \tabularnewline
69 & 0.617933173163413 & 0.764133653673174 & 0.382066826836587 \tabularnewline
70 & 0.57999677681006 & 0.840006446379879 & 0.420003223189939 \tabularnewline
71 & 0.599007871377188 & 0.801984257245623 & 0.400992128622812 \tabularnewline
72 & 0.582341562130192 & 0.835316875739616 & 0.417658437869808 \tabularnewline
73 & 0.606830358138322 & 0.786339283723356 & 0.393169641861678 \tabularnewline
74 & 0.597028222289963 & 0.805943555420073 & 0.402971777710037 \tabularnewline
75 & 0.570597127303829 & 0.858805745392341 & 0.429402872696171 \tabularnewline
76 & 0.567345182054497 & 0.865309635891005 & 0.432654817945503 \tabularnewline
77 & 0.543241316888315 & 0.91351736622337 & 0.456758683111685 \tabularnewline
78 & 0.531563649726485 & 0.93687270054703 & 0.468436350273515 \tabularnewline
79 & 0.527793363797109 & 0.944413272405783 & 0.472206636202891 \tabularnewline
80 & 0.501358041677703 & 0.997283916644594 & 0.498641958322297 \tabularnewline
81 & 0.506298310109886 & 0.987403379780228 & 0.493701689890114 \tabularnewline
82 & 0.46755688984995 & 0.935113779699899 & 0.53244311015005 \tabularnewline
83 & 0.554202302344577 & 0.891595395310846 & 0.445797697655423 \tabularnewline
84 & 0.509805052134655 & 0.98038989573069 & 0.490194947865345 \tabularnewline
85 & 0.471996834453779 & 0.943993668907558 & 0.528003165546221 \tabularnewline
86 & 0.509496651166188 & 0.981006697667623 & 0.490503348833812 \tabularnewline
87 & 0.493891482604068 & 0.987782965208135 & 0.506108517395932 \tabularnewline
88 & 0.533737518345724 & 0.932524963308551 & 0.466262481654275 \tabularnewline
89 & 0.599194480076154 & 0.801611039847691 & 0.400805519923846 \tabularnewline
90 & 0.597415714245984 & 0.805168571508032 & 0.402584285754016 \tabularnewline
91 & 0.575872045833101 & 0.848255908333798 & 0.424127954166899 \tabularnewline
92 & 0.535146678918657 & 0.929706642162687 & 0.464853321081343 \tabularnewline
93 & 0.500848436932029 & 0.998303126135943 & 0.499151563067971 \tabularnewline
94 & 0.463669560771143 & 0.927339121542285 & 0.536330439228857 \tabularnewline
95 & 0.479222802726632 & 0.958445605453263 & 0.520777197273368 \tabularnewline
96 & 0.48271553419864 & 0.96543106839728 & 0.51728446580136 \tabularnewline
97 & 0.445529261988677 & 0.891058523977354 & 0.554470738011323 \tabularnewline
98 & 0.498697298618841 & 0.997394597237683 & 0.501302701381159 \tabularnewline
99 & 0.527342643397203 & 0.945314713205594 & 0.472657356602797 \tabularnewline
100 & 0.583292739235806 & 0.833414521528388 & 0.416707260764194 \tabularnewline
101 & 0.580973706056523 & 0.838052587886955 & 0.419026293943477 \tabularnewline
102 & 0.577157264433508 & 0.845685471132983 & 0.422842735566492 \tabularnewline
103 & 0.642115157296635 & 0.715769685406731 & 0.357884842703365 \tabularnewline
104 & 0.819920660768075 & 0.360158678463849 & 0.180079339231925 \tabularnewline
105 & 0.818248866125054 & 0.363502267749892 & 0.181751133874946 \tabularnewline
106 & 0.855720929559508 & 0.288558140880984 & 0.144279070440492 \tabularnewline
107 & 0.826599115776679 & 0.346801768446641 & 0.173400884223321 \tabularnewline
108 & 0.799206900414402 & 0.401586199171197 & 0.200793099585598 \tabularnewline
109 & 0.889592272066874 & 0.220815455866253 & 0.110407727933126 \tabularnewline
110 & 0.8730657225206 & 0.2538685549588 & 0.1269342774794 \tabularnewline
111 & 0.857168156843036 & 0.285663686313929 & 0.142831843156964 \tabularnewline
112 & 0.823284653122067 & 0.353430693755865 & 0.176715346877932 \tabularnewline
113 & 0.824997463605827 & 0.350005072788346 & 0.175002536394173 \tabularnewline
114 & 0.811257213486476 & 0.377485573027048 & 0.188742786513524 \tabularnewline
115 & 0.765045090703182 & 0.469909818593637 & 0.234954909296818 \tabularnewline
116 & 0.713343718431911 & 0.573312563136178 & 0.286656281568089 \tabularnewline
117 & 0.80087190857553 & 0.39825618284894 & 0.19912809142447 \tabularnewline
118 & 0.784482261827486 & 0.431035476345028 & 0.215517738172514 \tabularnewline
119 & 0.732000035516063 & 0.535999928967875 & 0.267999964483937 \tabularnewline
120 & 0.672768005753311 & 0.654463988493378 & 0.327231994246689 \tabularnewline
121 & 0.909416476592692 & 0.181167046814615 & 0.0905835234073075 \tabularnewline
122 & 0.945186640315249 & 0.109626719369502 & 0.0548133596847512 \tabularnewline
123 & 0.923409550977496 & 0.153180898045009 & 0.0765904490225045 \tabularnewline
124 & 0.907020566673162 & 0.185958866653677 & 0.0929794333268385 \tabularnewline
125 & 0.869419781402406 & 0.261160437195188 & 0.130580218597594 \tabularnewline
126 & 0.913976876544782 & 0.172046246910437 & 0.0860231234552185 \tabularnewline
127 & 0.914201294371762 & 0.171597411256475 & 0.0857987056282376 \tabularnewline
128 & 0.936924786399523 & 0.126150427200953 & 0.0630752136004766 \tabularnewline
129 & 0.960219637991975 & 0.0795607240160492 & 0.0397803620080246 \tabularnewline
130 & 0.95075575764605 & 0.0984884847078998 & 0.0492442423539499 \tabularnewline
131 & 0.956823710973518 & 0.0863525780529645 & 0.0431762890264823 \tabularnewline
132 & 0.959797260977714 & 0.0804054780445719 & 0.0402027390222859 \tabularnewline
133 & 0.98682764477793 & 0.0263447104441394 & 0.0131723552220697 \tabularnewline
134 & 0.988767527213307 & 0.0224649455733851 & 0.0112324727866926 \tabularnewline
135 & 0.977249764409752 & 0.045500471180496 & 0.022750235590248 \tabularnewline
136 & 0.958306756404387 & 0.0833864871912251 & 0.0416932435956125 \tabularnewline
137 & 0.931429137760069 & 0.137141724479863 & 0.0685708622399314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&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]8[/C][C]0.00152999832736005[/C][C]0.0030599966547201[/C][C]0.99847000167264[/C][/ROW]
[ROW][C]9[/C][C]0.315078798288422[/C][C]0.630157596576843[/C][C]0.684921201711578[/C][/ROW]
[ROW][C]10[/C][C]0.195186183559704[/C][C]0.390372367119408[/C][C]0.804813816440296[/C][/ROW]
[ROW][C]11[/C][C]0.152174589018774[/C][C]0.304349178037549[/C][C]0.847825410981226[/C][/ROW]
[ROW][C]12[/C][C]0.272164622469746[/C][C]0.544329244939492[/C][C]0.727835377530254[/C][/ROW]
[ROW][C]13[/C][C]0.372384745028759[/C][C]0.744769490057519[/C][C]0.627615254971241[/C][/ROW]
[ROW][C]14[/C][C]0.319635451637191[/C][C]0.639270903274382[/C][C]0.680364548362809[/C][/ROW]
[ROW][C]15[/C][C]0.238554370863011[/C][C]0.477108741726022[/C][C]0.761445629136989[/C][/ROW]
[ROW][C]16[/C][C]0.1980337900876[/C][C]0.3960675801752[/C][C]0.8019662099124[/C][/ROW]
[ROW][C]17[/C][C]0.413329700081663[/C][C]0.826659400163326[/C][C]0.586670299918337[/C][/ROW]
[ROW][C]18[/C][C]0.332800519330222[/C][C]0.665601038660444[/C][C]0.667199480669778[/C][/ROW]
[ROW][C]19[/C][C]0.262143036175008[/C][C]0.524286072350016[/C][C]0.737856963824992[/C][/ROW]
[ROW][C]20[/C][C]0.237866430324785[/C][C]0.475732860649571[/C][C]0.762133569675215[/C][/ROW]
[ROW][C]21[/C][C]0.183756922126688[/C][C]0.367513844253376[/C][C]0.816243077873312[/C][/ROW]
[ROW][C]22[/C][C]0.329586705304237[/C][C]0.659173410608473[/C][C]0.670413294695763[/C][/ROW]
[ROW][C]23[/C][C]0.399954302628026[/C][C]0.799908605256052[/C][C]0.600045697371974[/C][/ROW]
[ROW][C]24[/C][C]0.427928488269177[/C][C]0.855856976538354[/C][C]0.572071511730823[/C][/ROW]
[ROW][C]25[/C][C]0.465277061658446[/C][C]0.930554123316892[/C][C]0.534722938341554[/C][/ROW]
[ROW][C]26[/C][C]0.503890260305821[/C][C]0.992219479388359[/C][C]0.49610973969418[/C][/ROW]
[ROW][C]27[/C][C]0.463314232927966[/C][C]0.926628465855931[/C][C]0.536685767072034[/C][/ROW]
[ROW][C]28[/C][C]0.400445915612293[/C][C]0.800891831224586[/C][C]0.599554084387707[/C][/ROW]
[ROW][C]29[/C][C]0.359480138594134[/C][C]0.718960277188269[/C][C]0.640519861405866[/C][/ROW]
[ROW][C]30[/C][C]0.34239952952403[/C][C]0.684799059048059[/C][C]0.65760047047597[/C][/ROW]
[ROW][C]31[/C][C]0.406429961073829[/C][C]0.812859922147659[/C][C]0.593570038926171[/C][/ROW]
[ROW][C]32[/C][C]0.382816526413619[/C][C]0.765633052827238[/C][C]0.617183473586381[/C][/ROW]
[ROW][C]33[/C][C]0.395077483022157[/C][C]0.790154966044315[/C][C]0.604922516977843[/C][/ROW]
[ROW][C]34[/C][C]0.449664383895879[/C][C]0.899328767791758[/C][C]0.550335616104121[/C][/ROW]
[ROW][C]35[/C][C]0.514729725865314[/C][C]0.970540548269371[/C][C]0.485270274134686[/C][/ROW]
[ROW][C]36[/C][C]0.498362762743326[/C][C]0.996725525486652[/C][C]0.501637237256674[/C][/ROW]
[ROW][C]37[/C][C]0.582596226895732[/C][C]0.834807546208537[/C][C]0.417403773104268[/C][/ROW]
[ROW][C]38[/C][C]0.60433951967364[/C][C]0.791320960652719[/C][C]0.39566048032636[/C][/ROW]
[ROW][C]39[/C][C]0.560691411799583[/C][C]0.878617176400834[/C][C]0.439308588200417[/C][/ROW]
[ROW][C]40[/C][C]0.515227513008044[/C][C]0.969544973983911[/C][C]0.484772486991956[/C][/ROW]
[ROW][C]41[/C][C]0.465351035865446[/C][C]0.930702071730891[/C][C]0.534648964134554[/C][/ROW]
[ROW][C]42[/C][C]0.490047733317423[/C][C]0.980095466634845[/C][C]0.509952266682577[/C][/ROW]
[ROW][C]43[/C][C]0.44447003591473[/C][C]0.888940071829461[/C][C]0.55552996408527[/C][/ROW]
[ROW][C]44[/C][C]0.443232555233987[/C][C]0.886465110467975[/C][C]0.556767444766012[/C][/ROW]
[ROW][C]45[/C][C]0.431455593298059[/C][C]0.862911186596118[/C][C]0.568544406701941[/C][/ROW]
[ROW][C]46[/C][C]0.380852417621625[/C][C]0.76170483524325[/C][C]0.619147582378375[/C][/ROW]
[ROW][C]47[/C][C]0.332694054861184[/C][C]0.665388109722368[/C][C]0.667305945138816[/C][/ROW]
[ROW][C]48[/C][C]0.340559096021863[/C][C]0.681118192043727[/C][C]0.659440903978137[/C][/ROW]
[ROW][C]49[/C][C]0.312954402320367[/C][C]0.625908804640735[/C][C]0.687045597679633[/C][/ROW]
[ROW][C]50[/C][C]0.27191429954761[/C][C]0.54382859909522[/C][C]0.72808570045239[/C][/ROW]
[ROW][C]51[/C][C]0.257371454473931[/C][C]0.514742908947862[/C][C]0.742628545526069[/C][/ROW]
[ROW][C]52[/C][C]0.344433792453852[/C][C]0.688867584907705[/C][C]0.655566207546147[/C][/ROW]
[ROW][C]53[/C][C]0.480899614581124[/C][C]0.961799229162249[/C][C]0.519100385418876[/C][/ROW]
[ROW][C]54[/C][C]0.494468717683782[/C][C]0.988937435367564[/C][C]0.505531282316218[/C][/ROW]
[ROW][C]55[/C][C]0.502120744836853[/C][C]0.995758510326293[/C][C]0.497879255163147[/C][/ROW]
[ROW][C]56[/C][C]0.650707662937193[/C][C]0.698584674125614[/C][C]0.349292337062807[/C][/ROW]
[ROW][C]57[/C][C]0.623144206929484[/C][C]0.753711586141032[/C][C]0.376855793070516[/C][/ROW]
[ROW][C]58[/C][C]0.656198826452948[/C][C]0.687602347094103[/C][C]0.343801173547052[/C][/ROW]
[ROW][C]59[/C][C]0.675102993688407[/C][C]0.649794012623186[/C][C]0.324897006311593[/C][/ROW]
[ROW][C]60[/C][C]0.678586816452142[/C][C]0.642826367095715[/C][C]0.321413183547858[/C][/ROW]
[ROW][C]61[/C][C]0.637773851858372[/C][C]0.724452296283256[/C][C]0.362226148141628[/C][/ROW]
[ROW][C]62[/C][C]0.620428747718612[/C][C]0.759142504562775[/C][C]0.379571252281388[/C][/ROW]
[ROW][C]63[/C][C]0.589747963219167[/C][C]0.820504073561666[/C][C]0.410252036780833[/C][/ROW]
[ROW][C]64[/C][C]0.562895622918785[/C][C]0.87420875416243[/C][C]0.437104377081215[/C][/ROW]
[ROW][C]65[/C][C]0.582568243891621[/C][C]0.834863512216758[/C][C]0.417431756108379[/C][/ROW]
[ROW][C]66[/C][C]0.616977693979037[/C][C]0.766044612041925[/C][C]0.383022306020963[/C][/ROW]
[ROW][C]67[/C][C]0.633574895508119[/C][C]0.732850208983762[/C][C]0.366425104491881[/C][/ROW]
[ROW][C]68[/C][C]0.63987320697771[/C][C]0.720253586044579[/C][C]0.36012679302229[/C][/ROW]
[ROW][C]69[/C][C]0.617933173163413[/C][C]0.764133653673174[/C][C]0.382066826836587[/C][/ROW]
[ROW][C]70[/C][C]0.57999677681006[/C][C]0.840006446379879[/C][C]0.420003223189939[/C][/ROW]
[ROW][C]71[/C][C]0.599007871377188[/C][C]0.801984257245623[/C][C]0.400992128622812[/C][/ROW]
[ROW][C]72[/C][C]0.582341562130192[/C][C]0.835316875739616[/C][C]0.417658437869808[/C][/ROW]
[ROW][C]73[/C][C]0.606830358138322[/C][C]0.786339283723356[/C][C]0.393169641861678[/C][/ROW]
[ROW][C]74[/C][C]0.597028222289963[/C][C]0.805943555420073[/C][C]0.402971777710037[/C][/ROW]
[ROW][C]75[/C][C]0.570597127303829[/C][C]0.858805745392341[/C][C]0.429402872696171[/C][/ROW]
[ROW][C]76[/C][C]0.567345182054497[/C][C]0.865309635891005[/C][C]0.432654817945503[/C][/ROW]
[ROW][C]77[/C][C]0.543241316888315[/C][C]0.91351736622337[/C][C]0.456758683111685[/C][/ROW]
[ROW][C]78[/C][C]0.531563649726485[/C][C]0.93687270054703[/C][C]0.468436350273515[/C][/ROW]
[ROW][C]79[/C][C]0.527793363797109[/C][C]0.944413272405783[/C][C]0.472206636202891[/C][/ROW]
[ROW][C]80[/C][C]0.501358041677703[/C][C]0.997283916644594[/C][C]0.498641958322297[/C][/ROW]
[ROW][C]81[/C][C]0.506298310109886[/C][C]0.987403379780228[/C][C]0.493701689890114[/C][/ROW]
[ROW][C]82[/C][C]0.46755688984995[/C][C]0.935113779699899[/C][C]0.53244311015005[/C][/ROW]
[ROW][C]83[/C][C]0.554202302344577[/C][C]0.891595395310846[/C][C]0.445797697655423[/C][/ROW]
[ROW][C]84[/C][C]0.509805052134655[/C][C]0.98038989573069[/C][C]0.490194947865345[/C][/ROW]
[ROW][C]85[/C][C]0.471996834453779[/C][C]0.943993668907558[/C][C]0.528003165546221[/C][/ROW]
[ROW][C]86[/C][C]0.509496651166188[/C][C]0.981006697667623[/C][C]0.490503348833812[/C][/ROW]
[ROW][C]87[/C][C]0.493891482604068[/C][C]0.987782965208135[/C][C]0.506108517395932[/C][/ROW]
[ROW][C]88[/C][C]0.533737518345724[/C][C]0.932524963308551[/C][C]0.466262481654275[/C][/ROW]
[ROW][C]89[/C][C]0.599194480076154[/C][C]0.801611039847691[/C][C]0.400805519923846[/C][/ROW]
[ROW][C]90[/C][C]0.597415714245984[/C][C]0.805168571508032[/C][C]0.402584285754016[/C][/ROW]
[ROW][C]91[/C][C]0.575872045833101[/C][C]0.848255908333798[/C][C]0.424127954166899[/C][/ROW]
[ROW][C]92[/C][C]0.535146678918657[/C][C]0.929706642162687[/C][C]0.464853321081343[/C][/ROW]
[ROW][C]93[/C][C]0.500848436932029[/C][C]0.998303126135943[/C][C]0.499151563067971[/C][/ROW]
[ROW][C]94[/C][C]0.463669560771143[/C][C]0.927339121542285[/C][C]0.536330439228857[/C][/ROW]
[ROW][C]95[/C][C]0.479222802726632[/C][C]0.958445605453263[/C][C]0.520777197273368[/C][/ROW]
[ROW][C]96[/C][C]0.48271553419864[/C][C]0.96543106839728[/C][C]0.51728446580136[/C][/ROW]
[ROW][C]97[/C][C]0.445529261988677[/C][C]0.891058523977354[/C][C]0.554470738011323[/C][/ROW]
[ROW][C]98[/C][C]0.498697298618841[/C][C]0.997394597237683[/C][C]0.501302701381159[/C][/ROW]
[ROW][C]99[/C][C]0.527342643397203[/C][C]0.945314713205594[/C][C]0.472657356602797[/C][/ROW]
[ROW][C]100[/C][C]0.583292739235806[/C][C]0.833414521528388[/C][C]0.416707260764194[/C][/ROW]
[ROW][C]101[/C][C]0.580973706056523[/C][C]0.838052587886955[/C][C]0.419026293943477[/C][/ROW]
[ROW][C]102[/C][C]0.577157264433508[/C][C]0.845685471132983[/C][C]0.422842735566492[/C][/ROW]
[ROW][C]103[/C][C]0.642115157296635[/C][C]0.715769685406731[/C][C]0.357884842703365[/C][/ROW]
[ROW][C]104[/C][C]0.819920660768075[/C][C]0.360158678463849[/C][C]0.180079339231925[/C][/ROW]
[ROW][C]105[/C][C]0.818248866125054[/C][C]0.363502267749892[/C][C]0.181751133874946[/C][/ROW]
[ROW][C]106[/C][C]0.855720929559508[/C][C]0.288558140880984[/C][C]0.144279070440492[/C][/ROW]
[ROW][C]107[/C][C]0.826599115776679[/C][C]0.346801768446641[/C][C]0.173400884223321[/C][/ROW]
[ROW][C]108[/C][C]0.799206900414402[/C][C]0.401586199171197[/C][C]0.200793099585598[/C][/ROW]
[ROW][C]109[/C][C]0.889592272066874[/C][C]0.220815455866253[/C][C]0.110407727933126[/C][/ROW]
[ROW][C]110[/C][C]0.8730657225206[/C][C]0.2538685549588[/C][C]0.1269342774794[/C][/ROW]
[ROW][C]111[/C][C]0.857168156843036[/C][C]0.285663686313929[/C][C]0.142831843156964[/C][/ROW]
[ROW][C]112[/C][C]0.823284653122067[/C][C]0.353430693755865[/C][C]0.176715346877932[/C][/ROW]
[ROW][C]113[/C][C]0.824997463605827[/C][C]0.350005072788346[/C][C]0.175002536394173[/C][/ROW]
[ROW][C]114[/C][C]0.811257213486476[/C][C]0.377485573027048[/C][C]0.188742786513524[/C][/ROW]
[ROW][C]115[/C][C]0.765045090703182[/C][C]0.469909818593637[/C][C]0.234954909296818[/C][/ROW]
[ROW][C]116[/C][C]0.713343718431911[/C][C]0.573312563136178[/C][C]0.286656281568089[/C][/ROW]
[ROW][C]117[/C][C]0.80087190857553[/C][C]0.39825618284894[/C][C]0.19912809142447[/C][/ROW]
[ROW][C]118[/C][C]0.784482261827486[/C][C]0.431035476345028[/C][C]0.215517738172514[/C][/ROW]
[ROW][C]119[/C][C]0.732000035516063[/C][C]0.535999928967875[/C][C]0.267999964483937[/C][/ROW]
[ROW][C]120[/C][C]0.672768005753311[/C][C]0.654463988493378[/C][C]0.327231994246689[/C][/ROW]
[ROW][C]121[/C][C]0.909416476592692[/C][C]0.181167046814615[/C][C]0.0905835234073075[/C][/ROW]
[ROW][C]122[/C][C]0.945186640315249[/C][C]0.109626719369502[/C][C]0.0548133596847512[/C][/ROW]
[ROW][C]123[/C][C]0.923409550977496[/C][C]0.153180898045009[/C][C]0.0765904490225045[/C][/ROW]
[ROW][C]124[/C][C]0.907020566673162[/C][C]0.185958866653677[/C][C]0.0929794333268385[/C][/ROW]
[ROW][C]125[/C][C]0.869419781402406[/C][C]0.261160437195188[/C][C]0.130580218597594[/C][/ROW]
[ROW][C]126[/C][C]0.913976876544782[/C][C]0.172046246910437[/C][C]0.0860231234552185[/C][/ROW]
[ROW][C]127[/C][C]0.914201294371762[/C][C]0.171597411256475[/C][C]0.0857987056282376[/C][/ROW]
[ROW][C]128[/C][C]0.936924786399523[/C][C]0.126150427200953[/C][C]0.0630752136004766[/C][/ROW]
[ROW][C]129[/C][C]0.960219637991975[/C][C]0.0795607240160492[/C][C]0.0397803620080246[/C][/ROW]
[ROW][C]130[/C][C]0.95075575764605[/C][C]0.0984884847078998[/C][C]0.0492442423539499[/C][/ROW]
[ROW][C]131[/C][C]0.956823710973518[/C][C]0.0863525780529645[/C][C]0.0431762890264823[/C][/ROW]
[ROW][C]132[/C][C]0.959797260977714[/C][C]0.0804054780445719[/C][C]0.0402027390222859[/C][/ROW]
[ROW][C]133[/C][C]0.98682764477793[/C][C]0.0263447104441394[/C][C]0.0131723552220697[/C][/ROW]
[ROW][C]134[/C][C]0.988767527213307[/C][C]0.0224649455733851[/C][C]0.0112324727866926[/C][/ROW]
[ROW][C]135[/C][C]0.977249764409752[/C][C]0.045500471180496[/C][C]0.022750235590248[/C][/ROW]
[ROW][C]136[/C][C]0.958306756404387[/C][C]0.0833864871912251[/C][C]0.0416932435956125[/C][/ROW]
[ROW][C]137[/C][C]0.931429137760069[/C][C]0.137141724479863[/C][C]0.0685708622399314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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
80.001529998327360050.00305999665472010.99847000167264
90.3150787982884220.6301575965768430.684921201711578
100.1951861835597040.3903723671194080.804813816440296
110.1521745890187740.3043491780375490.847825410981226
120.2721646224697460.5443292449394920.727835377530254
130.3723847450287590.7447694900575190.627615254971241
140.3196354516371910.6392709032743820.680364548362809
150.2385543708630110.4771087417260220.761445629136989
160.19803379008760.39606758017520.8019662099124
170.4133297000816630.8266594001633260.586670299918337
180.3328005193302220.6656010386604440.667199480669778
190.2621430361750080.5242860723500160.737856963824992
200.2378664303247850.4757328606495710.762133569675215
210.1837569221266880.3675138442533760.816243077873312
220.3295867053042370.6591734106084730.670413294695763
230.3999543026280260.7999086052560520.600045697371974
240.4279284882691770.8558569765383540.572071511730823
250.4652770616584460.9305541233168920.534722938341554
260.5038902603058210.9922194793883590.49610973969418
270.4633142329279660.9266284658559310.536685767072034
280.4004459156122930.8008918312245860.599554084387707
290.3594801385941340.7189602771882690.640519861405866
300.342399529524030.6847990590480590.65760047047597
310.4064299610738290.8128599221476590.593570038926171
320.3828165264136190.7656330528272380.617183473586381
330.3950774830221570.7901549660443150.604922516977843
340.4496643838958790.8993287677917580.550335616104121
350.5147297258653140.9705405482693710.485270274134686
360.4983627627433260.9967255254866520.501637237256674
370.5825962268957320.8348075462085370.417403773104268
380.604339519673640.7913209606527190.39566048032636
390.5606914117995830.8786171764008340.439308588200417
400.5152275130080440.9695449739839110.484772486991956
410.4653510358654460.9307020717308910.534648964134554
420.4900477333174230.9800954666348450.509952266682577
430.444470035914730.8889400718294610.55552996408527
440.4432325552339870.8864651104679750.556767444766012
450.4314555932980590.8629111865961180.568544406701941
460.3808524176216250.761704835243250.619147582378375
470.3326940548611840.6653881097223680.667305945138816
480.3405590960218630.6811181920437270.659440903978137
490.3129544023203670.6259088046407350.687045597679633
500.271914299547610.543828599095220.72808570045239
510.2573714544739310.5147429089478620.742628545526069
520.3444337924538520.6888675849077050.655566207546147
530.4808996145811240.9617992291622490.519100385418876
540.4944687176837820.9889374353675640.505531282316218
550.5021207448368530.9957585103262930.497879255163147
560.6507076629371930.6985846741256140.349292337062807
570.6231442069294840.7537115861410320.376855793070516
580.6561988264529480.6876023470941030.343801173547052
590.6751029936884070.6497940126231860.324897006311593
600.6785868164521420.6428263670957150.321413183547858
610.6377738518583720.7244522962832560.362226148141628
620.6204287477186120.7591425045627750.379571252281388
630.5897479632191670.8205040735616660.410252036780833
640.5628956229187850.874208754162430.437104377081215
650.5825682438916210.8348635122167580.417431756108379
660.6169776939790370.7660446120419250.383022306020963
670.6335748955081190.7328502089837620.366425104491881
680.639873206977710.7202535860445790.36012679302229
690.6179331731634130.7641336536731740.382066826836587
700.579996776810060.8400064463798790.420003223189939
710.5990078713771880.8019842572456230.400992128622812
720.5823415621301920.8353168757396160.417658437869808
730.6068303581383220.7863392837233560.393169641861678
740.5970282222899630.8059435554200730.402971777710037
750.5705971273038290.8588057453923410.429402872696171
760.5673451820544970.8653096358910050.432654817945503
770.5432413168883150.913517366223370.456758683111685
780.5315636497264850.936872700547030.468436350273515
790.5277933637971090.9444132724057830.472206636202891
800.5013580416777030.9972839166445940.498641958322297
810.5062983101098860.9874033797802280.493701689890114
820.467556889849950.9351137796998990.53244311015005
830.5542023023445770.8915953953108460.445797697655423
840.5098050521346550.980389895730690.490194947865345
850.4719968344537790.9439936689075580.528003165546221
860.5094966511661880.9810066976676230.490503348833812
870.4938914826040680.9877829652081350.506108517395932
880.5337375183457240.9325249633085510.466262481654275
890.5991944800761540.8016110398476910.400805519923846
900.5974157142459840.8051685715080320.402584285754016
910.5758720458331010.8482559083337980.424127954166899
920.5351466789186570.9297066421626870.464853321081343
930.5008484369320290.9983031261359430.499151563067971
940.4636695607711430.9273391215422850.536330439228857
950.4792228027266320.9584456054532630.520777197273368
960.482715534198640.965431068397280.51728446580136
970.4455292619886770.8910585239773540.554470738011323
980.4986972986188410.9973945972376830.501302701381159
990.5273426433972030.9453147132055940.472657356602797
1000.5832927392358060.8334145215283880.416707260764194
1010.5809737060565230.8380525878869550.419026293943477
1020.5771572644335080.8456854711329830.422842735566492
1030.6421151572966350.7157696854067310.357884842703365
1040.8199206607680750.3601586784638490.180079339231925
1050.8182488661250540.3635022677498920.181751133874946
1060.8557209295595080.2885581408809840.144279070440492
1070.8265991157766790.3468017684466410.173400884223321
1080.7992069004144020.4015861991711970.200793099585598
1090.8895922720668740.2208154558662530.110407727933126
1100.87306572252060.25386855495880.1269342774794
1110.8571681568430360.2856636863139290.142831843156964
1120.8232846531220670.3534306937558650.176715346877932
1130.8249974636058270.3500050727883460.175002536394173
1140.8112572134864760.3774855730270480.188742786513524
1150.7650450907031820.4699098185936370.234954909296818
1160.7133437184319110.5733125631361780.286656281568089
1170.800871908575530.398256182848940.19912809142447
1180.7844822618274860.4310354763450280.215517738172514
1190.7320000355160630.5359999289678750.267999964483937
1200.6727680057533110.6544639884933780.327231994246689
1210.9094164765926920.1811670468146150.0905835234073075
1220.9451866403152490.1096267193695020.0548133596847512
1230.9234095509774960.1531808980450090.0765904490225045
1240.9070205666731620.1859588666536770.0929794333268385
1250.8694197814024060.2611604371951880.130580218597594
1260.9139768765447820.1720462469104370.0860231234552185
1270.9142012943717620.1715974112564750.0857987056282376
1280.9369247863995230.1261504272009530.0630752136004766
1290.9602196379919750.07956072401604920.0397803620080246
1300.950755757646050.09848848470789980.0492442423539499
1310.9568237109735180.08635257805296450.0431762890264823
1320.9597972609777140.08040547804457190.0402027390222859
1330.986827644777930.02634471044413940.0131723552220697
1340.9887675272133070.02246494557338510.0112324727866926
1350.9772497644097520.0455004711804960.022750235590248
1360.9583067564043870.08338648719122510.0416932435956125
1370.9314291377600690.1371417244798630.0685708622399314







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00769230769230769OK
5% type I error level40.0307692307692308OK
10% type I error level90.0692307692307692OK

\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 & 1 & 0.00769230769230769 & OK \tabularnewline
5% type I error level & 4 & 0.0307692307692308 & OK \tabularnewline
10% type I error level & 9 & 0.0692307692307692 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186018&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]1[/C][C]0.00769230769230769[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0307692307692308[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0692307692307692[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186018&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186018&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 level10.00769230769230769OK
5% type I error level40.0307692307692308OK
10% type I error level90.0692307692307692OK



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