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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 08:20:42 -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/t1352121662zy422o4x4nf3enx.htm/, Retrieved Wed, 01 Feb 2023 14:59:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186042, Retrieved Wed, 01 Feb 2023 14:59:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2012-11-05 13:20:42] [5ea595149c423d240797ea96f874e024] [Current]
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Dataseries X:
9	5	-1	6	24
11	5	-4	6	29
13	9	-6	8	29
12	10	-9	4	25
13	14	-13	8	16
15	19	-13	10	18
13	18	-10	9	13
16	16	-12	12	22
10	8	-9	9	15
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Consumentenvertrouwen[t] = -0.000396097972544224 + 0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] + 0.270599259156973FinancieleSituatie[t] + 0.241069909023616Spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumentenvertrouwen[t] =  -0.000396097972544224 +  0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] +  0.270599259156973FinancieleSituatie[t] +  0.241069909023616Spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumentenvertrouwen[t] =  -0.000396097972544224 +  0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] +  0.270599259156973FinancieleSituatie[t] +  0.241069909023616Spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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] = -0.000396097972544224 + 0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] + 0.270599259156973FinancieleSituatie[t] + 0.241069909023616Spaarvermogen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.0003960979725442240.072453-0.00550.9956460.497823
EcoSituatie0.2502627109917520.00369767.686400
Werkloosheid-0.2506043241514460.001396-179.564600
FinancieleSituatie0.2705992591569730.01589317.02600
Spaarvermogen0.2410699090236160.00731932.936900

\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) & -0.000396097972544224 & 0.072453 & -0.0055 & 0.995646 & 0.497823 \tabularnewline
EcoSituatie & 0.250262710991752 & 0.003697 & 67.6864 & 0 & 0 \tabularnewline
Werkloosheid & -0.250604324151446 & 0.001396 & -179.5646 & 0 & 0 \tabularnewline
FinancieleSituatie & 0.270599259156973 & 0.015893 & 17.026 & 0 & 0 \tabularnewline
Spaarvermogen & 0.241069909023616 & 0.007319 & 32.9369 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]-0.000396097972544224[/C][C]0.072453[/C][C]-0.0055[/C][C]0.995646[/C][C]0.497823[/C][/ROW]
[ROW][C]EcoSituatie[/C][C]0.250262710991752[/C][C]0.003697[/C][C]67.6864[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Werkloosheid[/C][C]-0.250604324151446[/C][C]0.001396[/C][C]-179.5646[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FinancieleSituatie[/C][C]0.270599259156973[/C][C]0.015893[/C][C]17.026[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Spaarvermogen[/C][C]0.241069909023616[/C][C]0.007319[/C][C]32.9369[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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)-0.0003960979725442240.072453-0.00550.9956460.497823
EcoSituatie0.2502627109917520.00369767.686400
Werkloosheid-0.2506043241514460.001396-179.564600
FinancieleSituatie0.2705992591569730.01589317.02600
Spaarvermogen0.2410699090236160.00731932.936900







Multiple Linear Regression - Regression Statistics
Multiple R0.99931572168865
R-squared0.998631911614107
Adjusted R-squared0.99859282337451
F-TEST (value)25548.1424057924
F-TEST (DF numerator)4
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.318839147791067
Sum Squared Residuals14.2321763029787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99931572168865 \tabularnewline
R-squared & 0.998631911614107 \tabularnewline
Adjusted R-squared & 0.99859282337451 \tabularnewline
F-TEST (value) & 25548.1424057924 \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 & 0.318839147791067 \tabularnewline
Sum Squared Residuals & 14.2321763029787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99931572168865[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998631911614107[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99859282337451[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25548.1424057924[/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]0.318839147791067[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.2321763029787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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 R0.99931572168865
R-squared0.998631911614107
Adjusted R-squared0.99859282337451
F-TEST (value)25548.1424057924
F-TEST (DF numerator)4
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.318839147791067
Sum Squared Residuals14.2321763029787







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.91079515264630.0892048473537031
21110.86795767021870.132042329781276
31312.91141568080260.088584319197443
41211.86681469152630.133185308473711
51312.78305068751440.216949312485576
61515.0577025788344-0.0577025788343662
71312.57967809111320.420321908886778
81615.56178827611610.438211723883926
91010.3085864750915-0.308586475091487
101414.0592859054157-0.0592859054156907
111414.0681370942241-0.0681370942241344
121515.0797471927981-0.0797471927980519
131313.1219014489062-0.121901448906249
1488.32638098743378-0.326380987433785
1577.28431543397664-0.28431543397664
1633.54475974984952-0.54475974984952
1733.24360630108011-0.24360630108011
1843.774344299516360.225655700483642
1944.25809344863285-0.258093448632846
2000.460746782148573-0.460746782148573
21-4-4.048665865209460.048665865209463
22-14-14.3221221117520.322122111752021
23-18-18.32098916513780.320989165137757
24-8-8.293089207666890.293089207666889
25-1-1.497880922791830.497880922791828
2611.49054501520034-0.490545015200343
2721.970733889018630.0292661109813726
280-0.2261320849386350.226132084938635
2911.21833642497412-0.218336424974115
300-0.2471518594232410.247151859423241
31-1-1.545211385271280.545211385271281
32-3-3.531121291928460.531121291928459
33-3-3.521928489960320.521928489960323
34-3-2.7803331589532-0.219666841046798
35-4-3.78401817346855-0.215981826531454
36-8-8.032365976797440.0323659767974385
37-9-9.089256858892650.0892568588926511
38-13-12.8691440261905-0.130855973809479
39-18-18.36214801832920.362148018329165
40-11-10.7889148077341-0.211085192265871
41-9-9.499220713833570.499220713833567
42-10-10.53899370990210.53899370990207
43-13-12.8134027227915-0.186597277208473
44-11-10.5881763818813-0.411823618118739
45-5-5.306371335473180.306371335473184
46-15-14.7979007678379-0.202099232162129
47-6-6.521012417288610.521012417288612
48-6-6.221809912748150.221809912748152
49-3-3.234993305825240.234993305825237
50-1-1.028251303580450.0282513035804523
51-3-2.7531951009377-0.246804899062303
52-4-4.014726297343670.0147262973436749
53-6-5.77805063364272-0.221949366357281
540-0.2760967179664230.276096717966423
55-4-4.033013166550740.0330131665507364
56-2-2.009550090972420.00955009097241663
57-2-2.308410982353180.308410982353185
58-6-6.256630176391750.256630176391748
59-7-7.04970073676670.0497007367667014
60-6-5.80597065270682-0.194029347293179
61-6-5.52583697842202-0.474163021577981
62-3-3.569357687168990.569357687168987
63-2-2.309094208672570.309094208672571
64-5-4.60213170392878-0.397868296071216
65-11-11.58795950793870.587959507938708
66-11-10.8348788175748-0.16512118242519
67-11-11.32586982443050.325869824430486
68-10-9.58380814104653-0.41619185895347
69-14-13.5823789902446-0.417621009755363
70-8-8.022148335350220.0221483353502229
71-9-9.246566710723960.246566710723963
72-5-4.80287012978165-0.197129870218348
73-1-1.043904002239180.0439040022391795
74-2-2.25844634932540.258446349325397
75-5-5.275133919541360.275133919541362
76-4-3.53043806560907-0.469561934390927
77-6-5.5466592834482-0.453340716551796
78-2-2.0377129884670.0377129884670011
79-2-1.77859908866681-0.221400911333194
80-2-1.49768345333341-0.502316546666593
81-2-1.54789096479168-0.452109035208323
8222.51017893256205-0.510178932562046
8310.8039623523007670.196037647699233
84-8-7.79163768093352-0.208362319066485
85-1-1.218916231687380.218916231687377
8610.9780484769990960.0219515230009044
87-1-0.535013148308197-0.464986851691803
8821.804770317837290.195229682162706
8921.979782547285490.0202174527145082
9011.47730618107304-0.477306181073038
91-1-0.768499586432932-0.231500413567068
92-2-2.28870433475020.288704334750201
93-2-1.76750075144178-0.232499248558216
94-1-0.785420003001222-0.214579996998778
95-8-7.53100526800819-0.468994731991813
96-4-4.033202719224070.0332027192240696
97-6-6.299345034895260.299345034895261
98-3-3.474544701723720.474544701723719
99-3-3.284608408908240.284608408908244
100-7-7.263966284149420.263966284149419
101-9-8.81321462573908-0.186785374260919
102-11-11.12785638707010.127856387070076
103-13-13.10121068591930.101210685919336
104-11-11.30087226331730.300872263317263
105-9-8.5716589598545-0.428341040145502
106-17-17.13394932578520.133949325785228
107-22-21.5962289801222-0.403771019877766
108-25-24.622450965466-0.377549034533971
109-20-20.35201313920140.352013139201387
110-24-24.1004200121370.100420012136951
111-24-24.13121708017990.131217080179871
112-22-21.5308091178941-0.46919088210586
113-19-19.50607832440630.506078324406257
114-18-17.5495990331532-0.450400966846773
115-17-17.34978671205020.349786712050226
116-11-11.05705479338760.0570547933876185
117-11-11.06624759535580.0662475953557519
118-12-11.2758372100171-0.724162789982935
119-10-9.74599931184276-0.254000688157244
120-15-15.05479827256510.0547982725650647
121-15-14.8321139674777-0.167886032522279
122-15-15.08398600953870.0839860095387284
123-13-12.5485121526201-0.451487847379875
124-8-7.99201192121312-0.00798807878688052
125-13-12.8277651311271-0.172234868872877
126-9-9.30860119469870.308601194698703
127-7-6.76807930270034-0.231920697299655
128-4-4.021064886891480.021064886891484
129-4-4.029232849380540.029232849380542
130-2-2.51422027984430.514220279844301
1310-0.2709499481574550.270949948157455
132-2-1.81285769727872-0.187142302721283
133-3-3.063829639077790.0638296390777859
13411.25403738439552-0.254037384395519
135-2-2.563789973955250.563789973955247
136-1-1.272144935825740.272144935825736
13710.7506349134331970.249365086566803
138-3-2.55503751987601-0.444962480123987
139-4-4.289075384472180.289075384472184
140-9-8.74896374669134-0.251036253308663
141-9-8.57395151724314-0.426048482756861
142-7-6.5410527612662-0.458947238733799
143-14-13.8453947785027-0.154605221497271
144-12-11.8658234613934-0.13417653860664
145-16-16.34399869281960.343998692819574

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.9107951526463 & 0.0892048473537031 \tabularnewline
2 & 11 & 10.8679576702187 & 0.132042329781276 \tabularnewline
3 & 13 & 12.9114156808026 & 0.088584319197443 \tabularnewline
4 & 12 & 11.8668146915263 & 0.133185308473711 \tabularnewline
5 & 13 & 12.7830506875144 & 0.216949312485576 \tabularnewline
6 & 15 & 15.0577025788344 & -0.0577025788343662 \tabularnewline
7 & 13 & 12.5796780911132 & 0.420321908886778 \tabularnewline
8 & 16 & 15.5617882761161 & 0.438211723883926 \tabularnewline
9 & 10 & 10.3085864750915 & -0.308586475091487 \tabularnewline
10 & 14 & 14.0592859054157 & -0.0592859054156907 \tabularnewline
11 & 14 & 14.0681370942241 & -0.0681370942241344 \tabularnewline
12 & 15 & 15.0797471927981 & -0.0797471927980519 \tabularnewline
13 & 13 & 13.1219014489062 & -0.121901448906249 \tabularnewline
14 & 8 & 8.32638098743378 & -0.326380987433785 \tabularnewline
15 & 7 & 7.28431543397664 & -0.28431543397664 \tabularnewline
16 & 3 & 3.54475974984952 & -0.54475974984952 \tabularnewline
17 & 3 & 3.24360630108011 & -0.24360630108011 \tabularnewline
18 & 4 & 3.77434429951636 & 0.225655700483642 \tabularnewline
19 & 4 & 4.25809344863285 & -0.258093448632846 \tabularnewline
20 & 0 & 0.460746782148573 & -0.460746782148573 \tabularnewline
21 & -4 & -4.04866586520946 & 0.048665865209463 \tabularnewline
22 & -14 & -14.322122111752 & 0.322122111752021 \tabularnewline
23 & -18 & -18.3209891651378 & 0.320989165137757 \tabularnewline
24 & -8 & -8.29308920766689 & 0.293089207666889 \tabularnewline
25 & -1 & -1.49788092279183 & 0.497880922791828 \tabularnewline
26 & 1 & 1.49054501520034 & -0.490545015200343 \tabularnewline
27 & 2 & 1.97073388901863 & 0.0292661109813726 \tabularnewline
28 & 0 & -0.226132084938635 & 0.226132084938635 \tabularnewline
29 & 1 & 1.21833642497412 & -0.218336424974115 \tabularnewline
30 & 0 & -0.247151859423241 & 0.247151859423241 \tabularnewline
31 & -1 & -1.54521138527128 & 0.545211385271281 \tabularnewline
32 & -3 & -3.53112129192846 & 0.531121291928459 \tabularnewline
33 & -3 & -3.52192848996032 & 0.521928489960323 \tabularnewline
34 & -3 & -2.7803331589532 & -0.219666841046798 \tabularnewline
35 & -4 & -3.78401817346855 & -0.215981826531454 \tabularnewline
36 & -8 & -8.03236597679744 & 0.0323659767974385 \tabularnewline
37 & -9 & -9.08925685889265 & 0.0892568588926511 \tabularnewline
38 & -13 & -12.8691440261905 & -0.130855973809479 \tabularnewline
39 & -18 & -18.3621480183292 & 0.362148018329165 \tabularnewline
40 & -11 & -10.7889148077341 & -0.211085192265871 \tabularnewline
41 & -9 & -9.49922071383357 & 0.499220713833567 \tabularnewline
42 & -10 & -10.5389937099021 & 0.53899370990207 \tabularnewline
43 & -13 & -12.8134027227915 & -0.186597277208473 \tabularnewline
44 & -11 & -10.5881763818813 & -0.411823618118739 \tabularnewline
45 & -5 & -5.30637133547318 & 0.306371335473184 \tabularnewline
46 & -15 & -14.7979007678379 & -0.202099232162129 \tabularnewline
47 & -6 & -6.52101241728861 & 0.521012417288612 \tabularnewline
48 & -6 & -6.22180991274815 & 0.221809912748152 \tabularnewline
49 & -3 & -3.23499330582524 & 0.234993305825237 \tabularnewline
50 & -1 & -1.02825130358045 & 0.0282513035804523 \tabularnewline
51 & -3 & -2.7531951009377 & -0.246804899062303 \tabularnewline
52 & -4 & -4.01472629734367 & 0.0147262973436749 \tabularnewline
53 & -6 & -5.77805063364272 & -0.221949366357281 \tabularnewline
54 & 0 & -0.276096717966423 & 0.276096717966423 \tabularnewline
55 & -4 & -4.03301316655074 & 0.0330131665507364 \tabularnewline
56 & -2 & -2.00955009097242 & 0.00955009097241663 \tabularnewline
57 & -2 & -2.30841098235318 & 0.308410982353185 \tabularnewline
58 & -6 & -6.25663017639175 & 0.256630176391748 \tabularnewline
59 & -7 & -7.0497007367667 & 0.0497007367667014 \tabularnewline
60 & -6 & -5.80597065270682 & -0.194029347293179 \tabularnewline
61 & -6 & -5.52583697842202 & -0.474163021577981 \tabularnewline
62 & -3 & -3.56935768716899 & 0.569357687168987 \tabularnewline
63 & -2 & -2.30909420867257 & 0.309094208672571 \tabularnewline
64 & -5 & -4.60213170392878 & -0.397868296071216 \tabularnewline
65 & -11 & -11.5879595079387 & 0.587959507938708 \tabularnewline
66 & -11 & -10.8348788175748 & -0.16512118242519 \tabularnewline
67 & -11 & -11.3258698244305 & 0.325869824430486 \tabularnewline
68 & -10 & -9.58380814104653 & -0.41619185895347 \tabularnewline
69 & -14 & -13.5823789902446 & -0.417621009755363 \tabularnewline
70 & -8 & -8.02214833535022 & 0.0221483353502229 \tabularnewline
71 & -9 & -9.24656671072396 & 0.246566710723963 \tabularnewline
72 & -5 & -4.80287012978165 & -0.197129870218348 \tabularnewline
73 & -1 & -1.04390400223918 & 0.0439040022391795 \tabularnewline
74 & -2 & -2.2584463493254 & 0.258446349325397 \tabularnewline
75 & -5 & -5.27513391954136 & 0.275133919541362 \tabularnewline
76 & -4 & -3.53043806560907 & -0.469561934390927 \tabularnewline
77 & -6 & -5.5466592834482 & -0.453340716551796 \tabularnewline
78 & -2 & -2.037712988467 & 0.0377129884670011 \tabularnewline
79 & -2 & -1.77859908866681 & -0.221400911333194 \tabularnewline
80 & -2 & -1.49768345333341 & -0.502316546666593 \tabularnewline
81 & -2 & -1.54789096479168 & -0.452109035208323 \tabularnewline
82 & 2 & 2.51017893256205 & -0.510178932562046 \tabularnewline
83 & 1 & 0.803962352300767 & 0.196037647699233 \tabularnewline
84 & -8 & -7.79163768093352 & -0.208362319066485 \tabularnewline
85 & -1 & -1.21891623168738 & 0.218916231687377 \tabularnewline
86 & 1 & 0.978048476999096 & 0.0219515230009044 \tabularnewline
87 & -1 & -0.535013148308197 & -0.464986851691803 \tabularnewline
88 & 2 & 1.80477031783729 & 0.195229682162706 \tabularnewline
89 & 2 & 1.97978254728549 & 0.0202174527145082 \tabularnewline
90 & 1 & 1.47730618107304 & -0.477306181073038 \tabularnewline
91 & -1 & -0.768499586432932 & -0.231500413567068 \tabularnewline
92 & -2 & -2.2887043347502 & 0.288704334750201 \tabularnewline
93 & -2 & -1.76750075144178 & -0.232499248558216 \tabularnewline
94 & -1 & -0.785420003001222 & -0.214579996998778 \tabularnewline
95 & -8 & -7.53100526800819 & -0.468994731991813 \tabularnewline
96 & -4 & -4.03320271922407 & 0.0332027192240696 \tabularnewline
97 & -6 & -6.29934503489526 & 0.299345034895261 \tabularnewline
98 & -3 & -3.47454470172372 & 0.474544701723719 \tabularnewline
99 & -3 & -3.28460840890824 & 0.284608408908244 \tabularnewline
100 & -7 & -7.26396628414942 & 0.263966284149419 \tabularnewline
101 & -9 & -8.81321462573908 & -0.186785374260919 \tabularnewline
102 & -11 & -11.1278563870701 & 0.127856387070076 \tabularnewline
103 & -13 & -13.1012106859193 & 0.101210685919336 \tabularnewline
104 & -11 & -11.3008722633173 & 0.300872263317263 \tabularnewline
105 & -9 & -8.5716589598545 & -0.428341040145502 \tabularnewline
106 & -17 & -17.1339493257852 & 0.133949325785228 \tabularnewline
107 & -22 & -21.5962289801222 & -0.403771019877766 \tabularnewline
108 & -25 & -24.622450965466 & -0.377549034533971 \tabularnewline
109 & -20 & -20.3520131392014 & 0.352013139201387 \tabularnewline
110 & -24 & -24.100420012137 & 0.100420012136951 \tabularnewline
111 & -24 & -24.1312170801799 & 0.131217080179871 \tabularnewline
112 & -22 & -21.5308091178941 & -0.46919088210586 \tabularnewline
113 & -19 & -19.5060783244063 & 0.506078324406257 \tabularnewline
114 & -18 & -17.5495990331532 & -0.450400966846773 \tabularnewline
115 & -17 & -17.3497867120502 & 0.349786712050226 \tabularnewline
116 & -11 & -11.0570547933876 & 0.0570547933876185 \tabularnewline
117 & -11 & -11.0662475953558 & 0.0662475953557519 \tabularnewline
118 & -12 & -11.2758372100171 & -0.724162789982935 \tabularnewline
119 & -10 & -9.74599931184276 & -0.254000688157244 \tabularnewline
120 & -15 & -15.0547982725651 & 0.0547982725650647 \tabularnewline
121 & -15 & -14.8321139674777 & -0.167886032522279 \tabularnewline
122 & -15 & -15.0839860095387 & 0.0839860095387284 \tabularnewline
123 & -13 & -12.5485121526201 & -0.451487847379875 \tabularnewline
124 & -8 & -7.99201192121312 & -0.00798807878688052 \tabularnewline
125 & -13 & -12.8277651311271 & -0.172234868872877 \tabularnewline
126 & -9 & -9.3086011946987 & 0.308601194698703 \tabularnewline
127 & -7 & -6.76807930270034 & -0.231920697299655 \tabularnewline
128 & -4 & -4.02106488689148 & 0.021064886891484 \tabularnewline
129 & -4 & -4.02923284938054 & 0.029232849380542 \tabularnewline
130 & -2 & -2.5142202798443 & 0.514220279844301 \tabularnewline
131 & 0 & -0.270949948157455 & 0.270949948157455 \tabularnewline
132 & -2 & -1.81285769727872 & -0.187142302721283 \tabularnewline
133 & -3 & -3.06382963907779 & 0.0638296390777859 \tabularnewline
134 & 1 & 1.25403738439552 & -0.254037384395519 \tabularnewline
135 & -2 & -2.56378997395525 & 0.563789973955247 \tabularnewline
136 & -1 & -1.27214493582574 & 0.272144935825736 \tabularnewline
137 & 1 & 0.750634913433197 & 0.249365086566803 \tabularnewline
138 & -3 & -2.55503751987601 & -0.444962480123987 \tabularnewline
139 & -4 & -4.28907538447218 & 0.289075384472184 \tabularnewline
140 & -9 & -8.74896374669134 & -0.251036253308663 \tabularnewline
141 & -9 & -8.57395151724314 & -0.426048482756861 \tabularnewline
142 & -7 & -6.5410527612662 & -0.458947238733799 \tabularnewline
143 & -14 & -13.8453947785027 & -0.154605221497271 \tabularnewline
144 & -12 & -11.8658234613934 & -0.13417653860664 \tabularnewline
145 & -16 & -16.3439986928196 & 0.343998692819574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]9[/C][C]8.9107951526463[/C][C]0.0892048473537031[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.8679576702187[/C][C]0.132042329781276[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]12.9114156808026[/C][C]0.088584319197443[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.8668146915263[/C][C]0.133185308473711[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]12.7830506875144[/C][C]0.216949312485576[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]15.0577025788344[/C][C]-0.0577025788343662[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]12.5796780911132[/C][C]0.420321908886778[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]15.5617882761161[/C][C]0.438211723883926[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.3085864750915[/C][C]-0.308586475091487[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0592859054157[/C][C]-0.0592859054156907[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0681370942241[/C][C]-0.0681370942241344[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.0797471927981[/C][C]-0.0797471927980519[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.1219014489062[/C][C]-0.121901448906249[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]8.32638098743378[/C][C]-0.326380987433785[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.28431543397664[/C][C]-0.28431543397664[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.54475974984952[/C][C]-0.54475974984952[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.24360630108011[/C][C]-0.24360630108011[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.77434429951636[/C][C]0.225655700483642[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.25809344863285[/C][C]-0.258093448632846[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.460746782148573[/C][C]-0.460746782148573[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]-4.04866586520946[/C][C]0.048665865209463[/C][/ROW]
[ROW][C]22[/C][C]-14[/C][C]-14.322122111752[/C][C]0.322122111752021[/C][/ROW]
[ROW][C]23[/C][C]-18[/C][C]-18.3209891651378[/C][C]0.320989165137757[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-8.29308920766689[/C][C]0.293089207666889[/C][/ROW]
[ROW][C]25[/C][C]-1[/C][C]-1.49788092279183[/C][C]0.497880922791828[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.49054501520034[/C][C]-0.490545015200343[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.97073388901863[/C][C]0.0292661109813726[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]-0.226132084938635[/C][C]0.226132084938635[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.21833642497412[/C][C]-0.218336424974115[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]-0.247151859423241[/C][C]0.247151859423241[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-1.54521138527128[/C][C]0.545211385271281[/C][/ROW]
[ROW][C]32[/C][C]-3[/C][C]-3.53112129192846[/C][C]0.531121291928459[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-3.52192848996032[/C][C]0.521928489960323[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]-2.7803331589532[/C][C]-0.219666841046798[/C][/ROW]
[ROW][C]35[/C][C]-4[/C][C]-3.78401817346855[/C][C]-0.215981826531454[/C][/ROW]
[ROW][C]36[/C][C]-8[/C][C]-8.03236597679744[/C][C]0.0323659767974385[/C][/ROW]
[ROW][C]37[/C][C]-9[/C][C]-9.08925685889265[/C][C]0.0892568588926511[/C][/ROW]
[ROW][C]38[/C][C]-13[/C][C]-12.8691440261905[/C][C]-0.130855973809479[/C][/ROW]
[ROW][C]39[/C][C]-18[/C][C]-18.3621480183292[/C][C]0.362148018329165[/C][/ROW]
[ROW][C]40[/C][C]-11[/C][C]-10.7889148077341[/C][C]-0.211085192265871[/C][/ROW]
[ROW][C]41[/C][C]-9[/C][C]-9.49922071383357[/C][C]0.499220713833567[/C][/ROW]
[ROW][C]42[/C][C]-10[/C][C]-10.5389937099021[/C][C]0.53899370990207[/C][/ROW]
[ROW][C]43[/C][C]-13[/C][C]-12.8134027227915[/C][C]-0.186597277208473[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-10.5881763818813[/C][C]-0.411823618118739[/C][/ROW]
[ROW][C]45[/C][C]-5[/C][C]-5.30637133547318[/C][C]0.306371335473184[/C][/ROW]
[ROW][C]46[/C][C]-15[/C][C]-14.7979007678379[/C][C]-0.202099232162129[/C][/ROW]
[ROW][C]47[/C][C]-6[/C][C]-6.52101241728861[/C][C]0.521012417288612[/C][/ROW]
[ROW][C]48[/C][C]-6[/C][C]-6.22180991274815[/C][C]0.221809912748152[/C][/ROW]
[ROW][C]49[/C][C]-3[/C][C]-3.23499330582524[/C][C]0.234993305825237[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]-1.02825130358045[/C][C]0.0282513035804523[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]-2.7531951009377[/C][C]-0.246804899062303[/C][/ROW]
[ROW][C]52[/C][C]-4[/C][C]-4.01472629734367[/C][C]0.0147262973436749[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-5.77805063364272[/C][C]-0.221949366357281[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.276096717966423[/C][C]0.276096717966423[/C][/ROW]
[ROW][C]55[/C][C]-4[/C][C]-4.03301316655074[/C][C]0.0330131665507364[/C][/ROW]
[ROW][C]56[/C][C]-2[/C][C]-2.00955009097242[/C][C]0.00955009097241663[/C][/ROW]
[ROW][C]57[/C][C]-2[/C][C]-2.30841098235318[/C][C]0.308410982353185[/C][/ROW]
[ROW][C]58[/C][C]-6[/C][C]-6.25663017639175[/C][C]0.256630176391748[/C][/ROW]
[ROW][C]59[/C][C]-7[/C][C]-7.0497007367667[/C][C]0.0497007367667014[/C][/ROW]
[ROW][C]60[/C][C]-6[/C][C]-5.80597065270682[/C][C]-0.194029347293179[/C][/ROW]
[ROW][C]61[/C][C]-6[/C][C]-5.52583697842202[/C][C]-0.474163021577981[/C][/ROW]
[ROW][C]62[/C][C]-3[/C][C]-3.56935768716899[/C][C]0.569357687168987[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-2.30909420867257[/C][C]0.309094208672571[/C][/ROW]
[ROW][C]64[/C][C]-5[/C][C]-4.60213170392878[/C][C]-0.397868296071216[/C][/ROW]
[ROW][C]65[/C][C]-11[/C][C]-11.5879595079387[/C][C]0.587959507938708[/C][/ROW]
[ROW][C]66[/C][C]-11[/C][C]-10.8348788175748[/C][C]-0.16512118242519[/C][/ROW]
[ROW][C]67[/C][C]-11[/C][C]-11.3258698244305[/C][C]0.325869824430486[/C][/ROW]
[ROW][C]68[/C][C]-10[/C][C]-9.58380814104653[/C][C]-0.41619185895347[/C][/ROW]
[ROW][C]69[/C][C]-14[/C][C]-13.5823789902446[/C][C]-0.417621009755363[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-8.02214833535022[/C][C]0.0221483353502229[/C][/ROW]
[ROW][C]71[/C][C]-9[/C][C]-9.24656671072396[/C][C]0.246566710723963[/C][/ROW]
[ROW][C]72[/C][C]-5[/C][C]-4.80287012978165[/C][C]-0.197129870218348[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-1.04390400223918[/C][C]0.0439040022391795[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-2.2584463493254[/C][C]0.258446349325397[/C][/ROW]
[ROW][C]75[/C][C]-5[/C][C]-5.27513391954136[/C][C]0.275133919541362[/C][/ROW]
[ROW][C]76[/C][C]-4[/C][C]-3.53043806560907[/C][C]-0.469561934390927[/C][/ROW]
[ROW][C]77[/C][C]-6[/C][C]-5.5466592834482[/C][C]-0.453340716551796[/C][/ROW]
[ROW][C]78[/C][C]-2[/C][C]-2.037712988467[/C][C]0.0377129884670011[/C][/ROW]
[ROW][C]79[/C][C]-2[/C][C]-1.77859908866681[/C][C]-0.221400911333194[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-1.49768345333341[/C][C]-0.502316546666593[/C][/ROW]
[ROW][C]81[/C][C]-2[/C][C]-1.54789096479168[/C][C]-0.452109035208323[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]2.51017893256205[/C][C]-0.510178932562046[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.803962352300767[/C][C]0.196037647699233[/C][/ROW]
[ROW][C]84[/C][C]-8[/C][C]-7.79163768093352[/C][C]-0.208362319066485[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.21891623168738[/C][C]0.218916231687377[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.978048476999096[/C][C]0.0219515230009044[/C][/ROW]
[ROW][C]87[/C][C]-1[/C][C]-0.535013148308197[/C][C]-0.464986851691803[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.80477031783729[/C][C]0.195229682162706[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.97978254728549[/C][C]0.0202174527145082[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.47730618107304[/C][C]-0.477306181073038[/C][/ROW]
[ROW][C]91[/C][C]-1[/C][C]-0.768499586432932[/C][C]-0.231500413567068[/C][/ROW]
[ROW][C]92[/C][C]-2[/C][C]-2.2887043347502[/C][C]0.288704334750201[/C][/ROW]
[ROW][C]93[/C][C]-2[/C][C]-1.76750075144178[/C][C]-0.232499248558216[/C][/ROW]
[ROW][C]94[/C][C]-1[/C][C]-0.785420003001222[/C][C]-0.214579996998778[/C][/ROW]
[ROW][C]95[/C][C]-8[/C][C]-7.53100526800819[/C][C]-0.468994731991813[/C][/ROW]
[ROW][C]96[/C][C]-4[/C][C]-4.03320271922407[/C][C]0.0332027192240696[/C][/ROW]
[ROW][C]97[/C][C]-6[/C][C]-6.29934503489526[/C][C]0.299345034895261[/C][/ROW]
[ROW][C]98[/C][C]-3[/C][C]-3.47454470172372[/C][C]0.474544701723719[/C][/ROW]
[ROW][C]99[/C][C]-3[/C][C]-3.28460840890824[/C][C]0.284608408908244[/C][/ROW]
[ROW][C]100[/C][C]-7[/C][C]-7.26396628414942[/C][C]0.263966284149419[/C][/ROW]
[ROW][C]101[/C][C]-9[/C][C]-8.81321462573908[/C][C]-0.186785374260919[/C][/ROW]
[ROW][C]102[/C][C]-11[/C][C]-11.1278563870701[/C][C]0.127856387070076[/C][/ROW]
[ROW][C]103[/C][C]-13[/C][C]-13.1012106859193[/C][C]0.101210685919336[/C][/ROW]
[ROW][C]104[/C][C]-11[/C][C]-11.3008722633173[/C][C]0.300872263317263[/C][/ROW]
[ROW][C]105[/C][C]-9[/C][C]-8.5716589598545[/C][C]-0.428341040145502[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-17.1339493257852[/C][C]0.133949325785228[/C][/ROW]
[ROW][C]107[/C][C]-22[/C][C]-21.5962289801222[/C][C]-0.403771019877766[/C][/ROW]
[ROW][C]108[/C][C]-25[/C][C]-24.622450965466[/C][C]-0.377549034533971[/C][/ROW]
[ROW][C]109[/C][C]-20[/C][C]-20.3520131392014[/C][C]0.352013139201387[/C][/ROW]
[ROW][C]110[/C][C]-24[/C][C]-24.100420012137[/C][C]0.100420012136951[/C][/ROW]
[ROW][C]111[/C][C]-24[/C][C]-24.1312170801799[/C][C]0.131217080179871[/C][/ROW]
[ROW][C]112[/C][C]-22[/C][C]-21.5308091178941[/C][C]-0.46919088210586[/C][/ROW]
[ROW][C]113[/C][C]-19[/C][C]-19.5060783244063[/C][C]0.506078324406257[/C][/ROW]
[ROW][C]114[/C][C]-18[/C][C]-17.5495990331532[/C][C]-0.450400966846773[/C][/ROW]
[ROW][C]115[/C][C]-17[/C][C]-17.3497867120502[/C][C]0.349786712050226[/C][/ROW]
[ROW][C]116[/C][C]-11[/C][C]-11.0570547933876[/C][C]0.0570547933876185[/C][/ROW]
[ROW][C]117[/C][C]-11[/C][C]-11.0662475953558[/C][C]0.0662475953557519[/C][/ROW]
[ROW][C]118[/C][C]-12[/C][C]-11.2758372100171[/C][C]-0.724162789982935[/C][/ROW]
[ROW][C]119[/C][C]-10[/C][C]-9.74599931184276[/C][C]-0.254000688157244[/C][/ROW]
[ROW][C]120[/C][C]-15[/C][C]-15.0547982725651[/C][C]0.0547982725650647[/C][/ROW]
[ROW][C]121[/C][C]-15[/C][C]-14.8321139674777[/C][C]-0.167886032522279[/C][/ROW]
[ROW][C]122[/C][C]-15[/C][C]-15.0839860095387[/C][C]0.0839860095387284[/C][/ROW]
[ROW][C]123[/C][C]-13[/C][C]-12.5485121526201[/C][C]-0.451487847379875[/C][/ROW]
[ROW][C]124[/C][C]-8[/C][C]-7.99201192121312[/C][C]-0.00798807878688052[/C][/ROW]
[ROW][C]125[/C][C]-13[/C][C]-12.8277651311271[/C][C]-0.172234868872877[/C][/ROW]
[ROW][C]126[/C][C]-9[/C][C]-9.3086011946987[/C][C]0.308601194698703[/C][/ROW]
[ROW][C]127[/C][C]-7[/C][C]-6.76807930270034[/C][C]-0.231920697299655[/C][/ROW]
[ROW][C]128[/C][C]-4[/C][C]-4.02106488689148[/C][C]0.021064886891484[/C][/ROW]
[ROW][C]129[/C][C]-4[/C][C]-4.02923284938054[/C][C]0.029232849380542[/C][/ROW]
[ROW][C]130[/C][C]-2[/C][C]-2.5142202798443[/C][C]0.514220279844301[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.270949948157455[/C][C]0.270949948157455[/C][/ROW]
[ROW][C]132[/C][C]-2[/C][C]-1.81285769727872[/C][C]-0.187142302721283[/C][/ROW]
[ROW][C]133[/C][C]-3[/C][C]-3.06382963907779[/C][C]0.0638296390777859[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.25403738439552[/C][C]-0.254037384395519[/C][/ROW]
[ROW][C]135[/C][C]-2[/C][C]-2.56378997395525[/C][C]0.563789973955247[/C][/ROW]
[ROW][C]136[/C][C]-1[/C][C]-1.27214493582574[/C][C]0.272144935825736[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.750634913433197[/C][C]0.249365086566803[/C][/ROW]
[ROW][C]138[/C][C]-3[/C][C]-2.55503751987601[/C][C]-0.444962480123987[/C][/ROW]
[ROW][C]139[/C][C]-4[/C][C]-4.28907538447218[/C][C]0.289075384472184[/C][/ROW]
[ROW][C]140[/C][C]-9[/C][C]-8.74896374669134[/C][C]-0.251036253308663[/C][/ROW]
[ROW][C]141[/C][C]-9[/C][C]-8.57395151724314[/C][C]-0.426048482756861[/C][/ROW]
[ROW][C]142[/C][C]-7[/C][C]-6.5410527612662[/C][C]-0.458947238733799[/C][/ROW]
[ROW][C]143[/C][C]-14[/C][C]-13.8453947785027[/C][C]-0.154605221497271[/C][/ROW]
[ROW][C]144[/C][C]-12[/C][C]-11.8658234613934[/C][C]-0.13417653860664[/C][/ROW]
[ROW][C]145[/C][C]-16[/C][C]-16.3439986928196[/C][C]0.343998692819574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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
198.91079515264630.0892048473537031
21110.86795767021870.132042329781276
31312.91141568080260.088584319197443
41211.86681469152630.133185308473711
51312.78305068751440.216949312485576
61515.0577025788344-0.0577025788343662
71312.57967809111320.420321908886778
81615.56178827611610.438211723883926
91010.3085864750915-0.308586475091487
101414.0592859054157-0.0592859054156907
111414.0681370942241-0.0681370942241344
121515.0797471927981-0.0797471927980519
131313.1219014489062-0.121901448906249
1488.32638098743378-0.326380987433785
1577.28431543397664-0.28431543397664
1633.54475974984952-0.54475974984952
1733.24360630108011-0.24360630108011
1843.774344299516360.225655700483642
1944.25809344863285-0.258093448632846
2000.460746782148573-0.460746782148573
21-4-4.048665865209460.048665865209463
22-14-14.3221221117520.322122111752021
23-18-18.32098916513780.320989165137757
24-8-8.293089207666890.293089207666889
25-1-1.497880922791830.497880922791828
2611.49054501520034-0.490545015200343
2721.970733889018630.0292661109813726
280-0.2261320849386350.226132084938635
2911.21833642497412-0.218336424974115
300-0.2471518594232410.247151859423241
31-1-1.545211385271280.545211385271281
32-3-3.531121291928460.531121291928459
33-3-3.521928489960320.521928489960323
34-3-2.7803331589532-0.219666841046798
35-4-3.78401817346855-0.215981826531454
36-8-8.032365976797440.0323659767974385
37-9-9.089256858892650.0892568588926511
38-13-12.8691440261905-0.130855973809479
39-18-18.36214801832920.362148018329165
40-11-10.7889148077341-0.211085192265871
41-9-9.499220713833570.499220713833567
42-10-10.53899370990210.53899370990207
43-13-12.8134027227915-0.186597277208473
44-11-10.5881763818813-0.411823618118739
45-5-5.306371335473180.306371335473184
46-15-14.7979007678379-0.202099232162129
47-6-6.521012417288610.521012417288612
48-6-6.221809912748150.221809912748152
49-3-3.234993305825240.234993305825237
50-1-1.028251303580450.0282513035804523
51-3-2.7531951009377-0.246804899062303
52-4-4.014726297343670.0147262973436749
53-6-5.77805063364272-0.221949366357281
540-0.2760967179664230.276096717966423
55-4-4.033013166550740.0330131665507364
56-2-2.009550090972420.00955009097241663
57-2-2.308410982353180.308410982353185
58-6-6.256630176391750.256630176391748
59-7-7.04970073676670.0497007367667014
60-6-5.80597065270682-0.194029347293179
61-6-5.52583697842202-0.474163021577981
62-3-3.569357687168990.569357687168987
63-2-2.309094208672570.309094208672571
64-5-4.60213170392878-0.397868296071216
65-11-11.58795950793870.587959507938708
66-11-10.8348788175748-0.16512118242519
67-11-11.32586982443050.325869824430486
68-10-9.58380814104653-0.41619185895347
69-14-13.5823789902446-0.417621009755363
70-8-8.022148335350220.0221483353502229
71-9-9.246566710723960.246566710723963
72-5-4.80287012978165-0.197129870218348
73-1-1.043904002239180.0439040022391795
74-2-2.25844634932540.258446349325397
75-5-5.275133919541360.275133919541362
76-4-3.53043806560907-0.469561934390927
77-6-5.5466592834482-0.453340716551796
78-2-2.0377129884670.0377129884670011
79-2-1.77859908866681-0.221400911333194
80-2-1.49768345333341-0.502316546666593
81-2-1.54789096479168-0.452109035208323
8222.51017893256205-0.510178932562046
8310.8039623523007670.196037647699233
84-8-7.79163768093352-0.208362319066485
85-1-1.218916231687380.218916231687377
8610.9780484769990960.0219515230009044
87-1-0.535013148308197-0.464986851691803
8821.804770317837290.195229682162706
8921.979782547285490.0202174527145082
9011.47730618107304-0.477306181073038
91-1-0.768499586432932-0.231500413567068
92-2-2.28870433475020.288704334750201
93-2-1.76750075144178-0.232499248558216
94-1-0.785420003001222-0.214579996998778
95-8-7.53100526800819-0.468994731991813
96-4-4.033202719224070.0332027192240696
97-6-6.299345034895260.299345034895261
98-3-3.474544701723720.474544701723719
99-3-3.284608408908240.284608408908244
100-7-7.263966284149420.263966284149419
101-9-8.81321462573908-0.186785374260919
102-11-11.12785638707010.127856387070076
103-13-13.10121068591930.101210685919336
104-11-11.30087226331730.300872263317263
105-9-8.5716589598545-0.428341040145502
106-17-17.13394932578520.133949325785228
107-22-21.5962289801222-0.403771019877766
108-25-24.622450965466-0.377549034533971
109-20-20.35201313920140.352013139201387
110-24-24.1004200121370.100420012136951
111-24-24.13121708017990.131217080179871
112-22-21.5308091178941-0.46919088210586
113-19-19.50607832440630.506078324406257
114-18-17.5495990331532-0.450400966846773
115-17-17.34978671205020.349786712050226
116-11-11.05705479338760.0570547933876185
117-11-11.06624759535580.0662475953557519
118-12-11.2758372100171-0.724162789982935
119-10-9.74599931184276-0.254000688157244
120-15-15.05479827256510.0547982725650647
121-15-14.8321139674777-0.167886032522279
122-15-15.08398600953870.0839860095387284
123-13-12.5485121526201-0.451487847379875
124-8-7.99201192121312-0.00798807878688052
125-13-12.8277651311271-0.172234868872877
126-9-9.30860119469870.308601194698703
127-7-6.76807930270034-0.231920697299655
128-4-4.021064886891480.021064886891484
129-4-4.029232849380540.029232849380542
130-2-2.51422027984430.514220279844301
1310-0.2709499481574550.270949948157455
132-2-1.81285769727872-0.187142302721283
133-3-3.063829639077790.0638296390777859
13411.25403738439552-0.254037384395519
135-2-2.563789973955250.563789973955247
136-1-1.272144935825740.272144935825736
13710.7506349134331970.249365086566803
138-3-2.55503751987601-0.444962480123987
139-4-4.289075384472180.289075384472184
140-9-8.74896374669134-0.251036253308663
141-9-8.57395151724314-0.426048482756861
142-7-6.5410527612662-0.458947238733799
143-14-13.8453947785027-0.154605221497271
144-12-11.8658234613934-0.13417653860664
145-16-16.34399869281960.343998692819574







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3001153636175720.6002307272351430.699884636382428
90.3620401761964890.7240803523929780.637959823803511
100.2348961548181790.4697923096363570.765103845181821
110.1416857206802660.2833714413605310.858314279319734
120.07904691662294430.1580938332458890.920953083377056
130.05183718273901620.1036743654780320.948162817260984
140.02866718982576380.05733437965152770.971332810174236
150.01458861894780530.02917723789561060.985411381052195
160.008614998651010930.01722999730202190.991385001348989
170.004119571894577030.008239143789154060.995880428105423
180.02538455826707390.05076911653414790.974615441732926
190.01504865979449180.03009731958898370.984951340205508
200.01226260252121580.02452520504243150.987737397478784
210.03424939732105290.06849879464210590.965750602678947
220.09708755819107670.1941751163821530.902912441808923
230.07000235636318780.1400047127263760.929997643636812
240.05213088883068040.1042617776613610.94786911116932
250.03863750096002510.07727500192005010.961362499039975
260.2712848320560460.5425696641120930.728715167943954
270.2447206215144430.4894412430288860.755279378485557
280.1985692204998730.3971384409997450.801430779500127
290.2434303665023880.4868607330047760.756569633497612
300.1988781620208180.3977563240416370.801121837979182
310.241566454465650.4831329089313010.75843354553435
320.2831560409745070.5663120819490140.716843959025493
330.3081378505872030.6162757011744060.691862149412797
340.3513445243012180.7026890486024360.648655475698782
350.4012446489608930.8024892979217850.598755351039107
360.3547381222050340.7094762444100690.645261877794966
370.3040054154331160.6080108308662320.695994584566884
380.2834838121289920.5669676242579840.716516187871008
390.2828666620205510.5657333240411030.717133337979449
400.295668650795450.59133730159090.70433134920455
410.3010112308731380.6020224617462770.698988769126861
420.3280934198436790.6561868396873570.671906580156321
430.3544364098106830.7088728196213660.645563590189317
440.4633085745138990.9266171490277980.536691425486101
450.4333869017130450.866773803426090.566613098286955
460.4433047409528010.8866094819056020.556695259047199
470.4712943641409770.9425887282819540.528705635859023
480.4289142052971060.8578284105942120.571085794702894
490.3899792496001550.779958499200310.610020750399845
500.3596426002412470.7192852004824940.640357399758753
510.3840093238507060.7680186477014130.615990676149294
520.3436854269393520.6873708538787030.656314573060649
530.3404402706749830.6808805413499650.659559729325017
540.3173762901881050.6347525803762110.682623709811895
550.2756065639950090.5512131279900170.724393436004991
560.237262713895670.474525427791340.76273728610433
570.2293382531916880.4586765063833760.770661746808312
580.2175667008519650.435133401703930.782433299148035
590.1860313402210090.3720626804420180.813968659778991
600.1767395909312630.3534791818625270.823260409068737
610.2303101657166380.4606203314332750.769689834283362
620.3309118434009640.6618236868019280.669088156599036
630.3364196910844460.6728393821688920.663580308915554
640.3707076939918240.7414153879836470.629292306008176
650.51407580420460.97184839159080.4859241957954
660.480629601203140.961259202406280.51937039879686
670.5219731964369210.9560536071261580.478026803563079
680.547597512005350.90480497598930.45240248799465
690.5529406432513340.8941187134973330.447059356748666
700.5093091353211390.9813817293577210.490690864678861
710.501451127953540.9970977440929210.49854887204646
720.4718376084412770.9436752168825550.528162391558723
730.4462804946896720.8925609893793430.553719505310328
740.4704496363962370.9408992727924750.529550363603763
750.5132262801028350.9735474397943310.486773719897165
760.5294679495317580.9410641009364830.470532050468242
770.547758718997010.9044825620059810.45224128100299
780.5258224050811780.9483551898376440.474177594918822
790.4944296163278060.9888592326556110.505570383672194
800.5145917370852890.9708165258294230.485408262914711
810.5182771376428520.9634457247142950.481722862357148
820.5529232132367660.8941535735264680.447076786763234
830.5416352987486130.9167294025027740.458364701251387
840.5076367739657140.9847264520685710.492363226034286
850.513948911020620.9721021779587610.48605108897938
860.4823256069638350.9646512139276710.517674393036165
870.4814692666680070.9629385333360140.518530733331993
880.4738168707139170.9476337414278350.526183129286083
890.4411831341714550.8823662683429090.558816865828545
900.4570789819365660.9141579638731330.542921018063434
910.4164798756344120.8329597512688240.583520124365588
920.4721559381904240.9443118763808480.527844061809576
930.4234105429992430.8468210859984860.576589457000757
940.378860939910070.7577218798201390.62113906008993
950.4120872201053710.8241744402107420.587912779894629
960.3855559108109010.7711118216218020.614444089189099
970.4053025216606420.8106050433212840.594697478339358
980.4912849865789990.9825699731579980.508715013421001
990.4728879898368580.9457759796737170.527112010163142
1000.4528905755075320.9057811510150640.547109424492468
1010.4102751180365040.8205502360730080.589724881963496
1020.363569772133470.7271395442669390.63643022786653
1030.3219391193502230.6438782387004460.678060880649777
1040.3324208810219710.6648417620439430.667579118978029
1050.3336430559037970.6672861118075940.666356944096203
1060.2881421286794110.5762842573588210.711857871320589
1070.3186390753374830.6372781506749650.681360924662517
1080.3560731673852350.7121463347704710.643926832614765
1090.3888130179683440.7776260359366870.611186982031656
1100.3599447598750980.7198895197501960.640055240124902
1110.3228844637881080.6457689275762170.677115536211892
1120.3788539549512530.7577079099025050.621146045048747
1130.5757958290561940.8484083418876130.424204170943806
1140.5647402324478460.8705195351043070.435259767552154
1150.5782390688018940.8435218623962120.421760931198106
1160.5222762659627010.9554474680745970.477723734037299
1170.4705356368264090.9410712736528180.529464363173591
1180.6078594110810140.7842811778379720.392140588918986
1190.5649064644585810.8701870710828390.435093535541419
1200.5184605729455350.963078854108930.481539427054465
1210.4517583186671520.9035166373343050.548241681332847
1220.423916582240170.8478331644803390.57608341775983
1230.4065544880426590.8131089760853180.593445511957341
1240.3360727884892130.6721455769784260.663927211510787
1250.2765793789073320.5531587578146630.723420621092668
1260.3240911499199920.6481822998399830.675908850080008
1270.3025971956828180.6051943913656360.697402804317182
1280.2403523037308570.4807046074617130.759647696269143
1290.1801023443103210.3602046886206420.819897655689679
1300.4198790475234090.8397580950468170.580120952476591
1310.6194564261249140.7610871477501720.380543573875086
1320.5173648225062640.9652703549874710.482635177493736
1330.4464805656842640.8929611313685270.553519434315736
1340.3464632894247430.6929265788494850.653536710575257
1350.3152677289349220.6305354578698440.684732271065078
1360.2850822727419840.5701645454839690.714917727258016
1370.5232932115871610.9534135768256780.476706788412839

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.300115363617572 & 0.600230727235143 & 0.699884636382428 \tabularnewline
9 & 0.362040176196489 & 0.724080352392978 & 0.637959823803511 \tabularnewline
10 & 0.234896154818179 & 0.469792309636357 & 0.765103845181821 \tabularnewline
11 & 0.141685720680266 & 0.283371441360531 & 0.858314279319734 \tabularnewline
12 & 0.0790469166229443 & 0.158093833245889 & 0.920953083377056 \tabularnewline
13 & 0.0518371827390162 & 0.103674365478032 & 0.948162817260984 \tabularnewline
14 & 0.0286671898257638 & 0.0573343796515277 & 0.971332810174236 \tabularnewline
15 & 0.0145886189478053 & 0.0291772378956106 & 0.985411381052195 \tabularnewline
16 & 0.00861499865101093 & 0.0172299973020219 & 0.991385001348989 \tabularnewline
17 & 0.00411957189457703 & 0.00823914378915406 & 0.995880428105423 \tabularnewline
18 & 0.0253845582670739 & 0.0507691165341479 & 0.974615441732926 \tabularnewline
19 & 0.0150486597944918 & 0.0300973195889837 & 0.984951340205508 \tabularnewline
20 & 0.0122626025212158 & 0.0245252050424315 & 0.987737397478784 \tabularnewline
21 & 0.0342493973210529 & 0.0684987946421059 & 0.965750602678947 \tabularnewline
22 & 0.0970875581910767 & 0.194175116382153 & 0.902912441808923 \tabularnewline
23 & 0.0700023563631878 & 0.140004712726376 & 0.929997643636812 \tabularnewline
24 & 0.0521308888306804 & 0.104261777661361 & 0.94786911116932 \tabularnewline
25 & 0.0386375009600251 & 0.0772750019200501 & 0.961362499039975 \tabularnewline
26 & 0.271284832056046 & 0.542569664112093 & 0.728715167943954 \tabularnewline
27 & 0.244720621514443 & 0.489441243028886 & 0.755279378485557 \tabularnewline
28 & 0.198569220499873 & 0.397138440999745 & 0.801430779500127 \tabularnewline
29 & 0.243430366502388 & 0.486860733004776 & 0.756569633497612 \tabularnewline
30 & 0.198878162020818 & 0.397756324041637 & 0.801121837979182 \tabularnewline
31 & 0.24156645446565 & 0.483132908931301 & 0.75843354553435 \tabularnewline
32 & 0.283156040974507 & 0.566312081949014 & 0.716843959025493 \tabularnewline
33 & 0.308137850587203 & 0.616275701174406 & 0.691862149412797 \tabularnewline
34 & 0.351344524301218 & 0.702689048602436 & 0.648655475698782 \tabularnewline
35 & 0.401244648960893 & 0.802489297921785 & 0.598755351039107 \tabularnewline
36 & 0.354738122205034 & 0.709476244410069 & 0.645261877794966 \tabularnewline
37 & 0.304005415433116 & 0.608010830866232 & 0.695994584566884 \tabularnewline
38 & 0.283483812128992 & 0.566967624257984 & 0.716516187871008 \tabularnewline
39 & 0.282866662020551 & 0.565733324041103 & 0.717133337979449 \tabularnewline
40 & 0.29566865079545 & 0.5913373015909 & 0.70433134920455 \tabularnewline
41 & 0.301011230873138 & 0.602022461746277 & 0.698988769126861 \tabularnewline
42 & 0.328093419843679 & 0.656186839687357 & 0.671906580156321 \tabularnewline
43 & 0.354436409810683 & 0.708872819621366 & 0.645563590189317 \tabularnewline
44 & 0.463308574513899 & 0.926617149027798 & 0.536691425486101 \tabularnewline
45 & 0.433386901713045 & 0.86677380342609 & 0.566613098286955 \tabularnewline
46 & 0.443304740952801 & 0.886609481905602 & 0.556695259047199 \tabularnewline
47 & 0.471294364140977 & 0.942588728281954 & 0.528705635859023 \tabularnewline
48 & 0.428914205297106 & 0.857828410594212 & 0.571085794702894 \tabularnewline
49 & 0.389979249600155 & 0.77995849920031 & 0.610020750399845 \tabularnewline
50 & 0.359642600241247 & 0.719285200482494 & 0.640357399758753 \tabularnewline
51 & 0.384009323850706 & 0.768018647701413 & 0.615990676149294 \tabularnewline
52 & 0.343685426939352 & 0.687370853878703 & 0.656314573060649 \tabularnewline
53 & 0.340440270674983 & 0.680880541349965 & 0.659559729325017 \tabularnewline
54 & 0.317376290188105 & 0.634752580376211 & 0.682623709811895 \tabularnewline
55 & 0.275606563995009 & 0.551213127990017 & 0.724393436004991 \tabularnewline
56 & 0.23726271389567 & 0.47452542779134 & 0.76273728610433 \tabularnewline
57 & 0.229338253191688 & 0.458676506383376 & 0.770661746808312 \tabularnewline
58 & 0.217566700851965 & 0.43513340170393 & 0.782433299148035 \tabularnewline
59 & 0.186031340221009 & 0.372062680442018 & 0.813968659778991 \tabularnewline
60 & 0.176739590931263 & 0.353479181862527 & 0.823260409068737 \tabularnewline
61 & 0.230310165716638 & 0.460620331433275 & 0.769689834283362 \tabularnewline
62 & 0.330911843400964 & 0.661823686801928 & 0.669088156599036 \tabularnewline
63 & 0.336419691084446 & 0.672839382168892 & 0.663580308915554 \tabularnewline
64 & 0.370707693991824 & 0.741415387983647 & 0.629292306008176 \tabularnewline
65 & 0.5140758042046 & 0.9718483915908 & 0.4859241957954 \tabularnewline
66 & 0.48062960120314 & 0.96125920240628 & 0.51937039879686 \tabularnewline
67 & 0.521973196436921 & 0.956053607126158 & 0.478026803563079 \tabularnewline
68 & 0.54759751200535 & 0.9048049759893 & 0.45240248799465 \tabularnewline
69 & 0.552940643251334 & 0.894118713497333 & 0.447059356748666 \tabularnewline
70 & 0.509309135321139 & 0.981381729357721 & 0.490690864678861 \tabularnewline
71 & 0.50145112795354 & 0.997097744092921 & 0.49854887204646 \tabularnewline
72 & 0.471837608441277 & 0.943675216882555 & 0.528162391558723 \tabularnewline
73 & 0.446280494689672 & 0.892560989379343 & 0.553719505310328 \tabularnewline
74 & 0.470449636396237 & 0.940899272792475 & 0.529550363603763 \tabularnewline
75 & 0.513226280102835 & 0.973547439794331 & 0.486773719897165 \tabularnewline
76 & 0.529467949531758 & 0.941064100936483 & 0.470532050468242 \tabularnewline
77 & 0.54775871899701 & 0.904482562005981 & 0.45224128100299 \tabularnewline
78 & 0.525822405081178 & 0.948355189837644 & 0.474177594918822 \tabularnewline
79 & 0.494429616327806 & 0.988859232655611 & 0.505570383672194 \tabularnewline
80 & 0.514591737085289 & 0.970816525829423 & 0.485408262914711 \tabularnewline
81 & 0.518277137642852 & 0.963445724714295 & 0.481722862357148 \tabularnewline
82 & 0.552923213236766 & 0.894153573526468 & 0.447076786763234 \tabularnewline
83 & 0.541635298748613 & 0.916729402502774 & 0.458364701251387 \tabularnewline
84 & 0.507636773965714 & 0.984726452068571 & 0.492363226034286 \tabularnewline
85 & 0.51394891102062 & 0.972102177958761 & 0.48605108897938 \tabularnewline
86 & 0.482325606963835 & 0.964651213927671 & 0.517674393036165 \tabularnewline
87 & 0.481469266668007 & 0.962938533336014 & 0.518530733331993 \tabularnewline
88 & 0.473816870713917 & 0.947633741427835 & 0.526183129286083 \tabularnewline
89 & 0.441183134171455 & 0.882366268342909 & 0.558816865828545 \tabularnewline
90 & 0.457078981936566 & 0.914157963873133 & 0.542921018063434 \tabularnewline
91 & 0.416479875634412 & 0.832959751268824 & 0.583520124365588 \tabularnewline
92 & 0.472155938190424 & 0.944311876380848 & 0.527844061809576 \tabularnewline
93 & 0.423410542999243 & 0.846821085998486 & 0.576589457000757 \tabularnewline
94 & 0.37886093991007 & 0.757721879820139 & 0.62113906008993 \tabularnewline
95 & 0.412087220105371 & 0.824174440210742 & 0.587912779894629 \tabularnewline
96 & 0.385555910810901 & 0.771111821621802 & 0.614444089189099 \tabularnewline
97 & 0.405302521660642 & 0.810605043321284 & 0.594697478339358 \tabularnewline
98 & 0.491284986578999 & 0.982569973157998 & 0.508715013421001 \tabularnewline
99 & 0.472887989836858 & 0.945775979673717 & 0.527112010163142 \tabularnewline
100 & 0.452890575507532 & 0.905781151015064 & 0.547109424492468 \tabularnewline
101 & 0.410275118036504 & 0.820550236073008 & 0.589724881963496 \tabularnewline
102 & 0.36356977213347 & 0.727139544266939 & 0.63643022786653 \tabularnewline
103 & 0.321939119350223 & 0.643878238700446 & 0.678060880649777 \tabularnewline
104 & 0.332420881021971 & 0.664841762043943 & 0.667579118978029 \tabularnewline
105 & 0.333643055903797 & 0.667286111807594 & 0.666356944096203 \tabularnewline
106 & 0.288142128679411 & 0.576284257358821 & 0.711857871320589 \tabularnewline
107 & 0.318639075337483 & 0.637278150674965 & 0.681360924662517 \tabularnewline
108 & 0.356073167385235 & 0.712146334770471 & 0.643926832614765 \tabularnewline
109 & 0.388813017968344 & 0.777626035936687 & 0.611186982031656 \tabularnewline
110 & 0.359944759875098 & 0.719889519750196 & 0.640055240124902 \tabularnewline
111 & 0.322884463788108 & 0.645768927576217 & 0.677115536211892 \tabularnewline
112 & 0.378853954951253 & 0.757707909902505 & 0.621146045048747 \tabularnewline
113 & 0.575795829056194 & 0.848408341887613 & 0.424204170943806 \tabularnewline
114 & 0.564740232447846 & 0.870519535104307 & 0.435259767552154 \tabularnewline
115 & 0.578239068801894 & 0.843521862396212 & 0.421760931198106 \tabularnewline
116 & 0.522276265962701 & 0.955447468074597 & 0.477723734037299 \tabularnewline
117 & 0.470535636826409 & 0.941071273652818 & 0.529464363173591 \tabularnewline
118 & 0.607859411081014 & 0.784281177837972 & 0.392140588918986 \tabularnewline
119 & 0.564906464458581 & 0.870187071082839 & 0.435093535541419 \tabularnewline
120 & 0.518460572945535 & 0.96307885410893 & 0.481539427054465 \tabularnewline
121 & 0.451758318667152 & 0.903516637334305 & 0.548241681332847 \tabularnewline
122 & 0.42391658224017 & 0.847833164480339 & 0.57608341775983 \tabularnewline
123 & 0.406554488042659 & 0.813108976085318 & 0.593445511957341 \tabularnewline
124 & 0.336072788489213 & 0.672145576978426 & 0.663927211510787 \tabularnewline
125 & 0.276579378907332 & 0.553158757814663 & 0.723420621092668 \tabularnewline
126 & 0.324091149919992 & 0.648182299839983 & 0.675908850080008 \tabularnewline
127 & 0.302597195682818 & 0.605194391365636 & 0.697402804317182 \tabularnewline
128 & 0.240352303730857 & 0.480704607461713 & 0.759647696269143 \tabularnewline
129 & 0.180102344310321 & 0.360204688620642 & 0.819897655689679 \tabularnewline
130 & 0.419879047523409 & 0.839758095046817 & 0.580120952476591 \tabularnewline
131 & 0.619456426124914 & 0.761087147750172 & 0.380543573875086 \tabularnewline
132 & 0.517364822506264 & 0.965270354987471 & 0.482635177493736 \tabularnewline
133 & 0.446480565684264 & 0.892961131368527 & 0.553519434315736 \tabularnewline
134 & 0.346463289424743 & 0.692926578849485 & 0.653536710575257 \tabularnewline
135 & 0.315267728934922 & 0.630535457869844 & 0.684732271065078 \tabularnewline
136 & 0.285082272741984 & 0.570164545483969 & 0.714917727258016 \tabularnewline
137 & 0.523293211587161 & 0.953413576825678 & 0.476706788412839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]0.300115363617572[/C][C]0.600230727235143[/C][C]0.699884636382428[/C][/ROW]
[ROW][C]9[/C][C]0.362040176196489[/C][C]0.724080352392978[/C][C]0.637959823803511[/C][/ROW]
[ROW][C]10[/C][C]0.234896154818179[/C][C]0.469792309636357[/C][C]0.765103845181821[/C][/ROW]
[ROW][C]11[/C][C]0.141685720680266[/C][C]0.283371441360531[/C][C]0.858314279319734[/C][/ROW]
[ROW][C]12[/C][C]0.0790469166229443[/C][C]0.158093833245889[/C][C]0.920953083377056[/C][/ROW]
[ROW][C]13[/C][C]0.0518371827390162[/C][C]0.103674365478032[/C][C]0.948162817260984[/C][/ROW]
[ROW][C]14[/C][C]0.0286671898257638[/C][C]0.0573343796515277[/C][C]0.971332810174236[/C][/ROW]
[ROW][C]15[/C][C]0.0145886189478053[/C][C]0.0291772378956106[/C][C]0.985411381052195[/C][/ROW]
[ROW][C]16[/C][C]0.00861499865101093[/C][C]0.0172299973020219[/C][C]0.991385001348989[/C][/ROW]
[ROW][C]17[/C][C]0.00411957189457703[/C][C]0.00823914378915406[/C][C]0.995880428105423[/C][/ROW]
[ROW][C]18[/C][C]0.0253845582670739[/C][C]0.0507691165341479[/C][C]0.974615441732926[/C][/ROW]
[ROW][C]19[/C][C]0.0150486597944918[/C][C]0.0300973195889837[/C][C]0.984951340205508[/C][/ROW]
[ROW][C]20[/C][C]0.0122626025212158[/C][C]0.0245252050424315[/C][C]0.987737397478784[/C][/ROW]
[ROW][C]21[/C][C]0.0342493973210529[/C][C]0.0684987946421059[/C][C]0.965750602678947[/C][/ROW]
[ROW][C]22[/C][C]0.0970875581910767[/C][C]0.194175116382153[/C][C]0.902912441808923[/C][/ROW]
[ROW][C]23[/C][C]0.0700023563631878[/C][C]0.140004712726376[/C][C]0.929997643636812[/C][/ROW]
[ROW][C]24[/C][C]0.0521308888306804[/C][C]0.104261777661361[/C][C]0.94786911116932[/C][/ROW]
[ROW][C]25[/C][C]0.0386375009600251[/C][C]0.0772750019200501[/C][C]0.961362499039975[/C][/ROW]
[ROW][C]26[/C][C]0.271284832056046[/C][C]0.542569664112093[/C][C]0.728715167943954[/C][/ROW]
[ROW][C]27[/C][C]0.244720621514443[/C][C]0.489441243028886[/C][C]0.755279378485557[/C][/ROW]
[ROW][C]28[/C][C]0.198569220499873[/C][C]0.397138440999745[/C][C]0.801430779500127[/C][/ROW]
[ROW][C]29[/C][C]0.243430366502388[/C][C]0.486860733004776[/C][C]0.756569633497612[/C][/ROW]
[ROW][C]30[/C][C]0.198878162020818[/C][C]0.397756324041637[/C][C]0.801121837979182[/C][/ROW]
[ROW][C]31[/C][C]0.24156645446565[/C][C]0.483132908931301[/C][C]0.75843354553435[/C][/ROW]
[ROW][C]32[/C][C]0.283156040974507[/C][C]0.566312081949014[/C][C]0.716843959025493[/C][/ROW]
[ROW][C]33[/C][C]0.308137850587203[/C][C]0.616275701174406[/C][C]0.691862149412797[/C][/ROW]
[ROW][C]34[/C][C]0.351344524301218[/C][C]0.702689048602436[/C][C]0.648655475698782[/C][/ROW]
[ROW][C]35[/C][C]0.401244648960893[/C][C]0.802489297921785[/C][C]0.598755351039107[/C][/ROW]
[ROW][C]36[/C][C]0.354738122205034[/C][C]0.709476244410069[/C][C]0.645261877794966[/C][/ROW]
[ROW][C]37[/C][C]0.304005415433116[/C][C]0.608010830866232[/C][C]0.695994584566884[/C][/ROW]
[ROW][C]38[/C][C]0.283483812128992[/C][C]0.566967624257984[/C][C]0.716516187871008[/C][/ROW]
[ROW][C]39[/C][C]0.282866662020551[/C][C]0.565733324041103[/C][C]0.717133337979449[/C][/ROW]
[ROW][C]40[/C][C]0.29566865079545[/C][C]0.5913373015909[/C][C]0.70433134920455[/C][/ROW]
[ROW][C]41[/C][C]0.301011230873138[/C][C]0.602022461746277[/C][C]0.698988769126861[/C][/ROW]
[ROW][C]42[/C][C]0.328093419843679[/C][C]0.656186839687357[/C][C]0.671906580156321[/C][/ROW]
[ROW][C]43[/C][C]0.354436409810683[/C][C]0.708872819621366[/C][C]0.645563590189317[/C][/ROW]
[ROW][C]44[/C][C]0.463308574513899[/C][C]0.926617149027798[/C][C]0.536691425486101[/C][/ROW]
[ROW][C]45[/C][C]0.433386901713045[/C][C]0.86677380342609[/C][C]0.566613098286955[/C][/ROW]
[ROW][C]46[/C][C]0.443304740952801[/C][C]0.886609481905602[/C][C]0.556695259047199[/C][/ROW]
[ROW][C]47[/C][C]0.471294364140977[/C][C]0.942588728281954[/C][C]0.528705635859023[/C][/ROW]
[ROW][C]48[/C][C]0.428914205297106[/C][C]0.857828410594212[/C][C]0.571085794702894[/C][/ROW]
[ROW][C]49[/C][C]0.389979249600155[/C][C]0.77995849920031[/C][C]0.610020750399845[/C][/ROW]
[ROW][C]50[/C][C]0.359642600241247[/C][C]0.719285200482494[/C][C]0.640357399758753[/C][/ROW]
[ROW][C]51[/C][C]0.384009323850706[/C][C]0.768018647701413[/C][C]0.615990676149294[/C][/ROW]
[ROW][C]52[/C][C]0.343685426939352[/C][C]0.687370853878703[/C][C]0.656314573060649[/C][/ROW]
[ROW][C]53[/C][C]0.340440270674983[/C][C]0.680880541349965[/C][C]0.659559729325017[/C][/ROW]
[ROW][C]54[/C][C]0.317376290188105[/C][C]0.634752580376211[/C][C]0.682623709811895[/C][/ROW]
[ROW][C]55[/C][C]0.275606563995009[/C][C]0.551213127990017[/C][C]0.724393436004991[/C][/ROW]
[ROW][C]56[/C][C]0.23726271389567[/C][C]0.47452542779134[/C][C]0.76273728610433[/C][/ROW]
[ROW][C]57[/C][C]0.229338253191688[/C][C]0.458676506383376[/C][C]0.770661746808312[/C][/ROW]
[ROW][C]58[/C][C]0.217566700851965[/C][C]0.43513340170393[/C][C]0.782433299148035[/C][/ROW]
[ROW][C]59[/C][C]0.186031340221009[/C][C]0.372062680442018[/C][C]0.813968659778991[/C][/ROW]
[ROW][C]60[/C][C]0.176739590931263[/C][C]0.353479181862527[/C][C]0.823260409068737[/C][/ROW]
[ROW][C]61[/C][C]0.230310165716638[/C][C]0.460620331433275[/C][C]0.769689834283362[/C][/ROW]
[ROW][C]62[/C][C]0.330911843400964[/C][C]0.661823686801928[/C][C]0.669088156599036[/C][/ROW]
[ROW][C]63[/C][C]0.336419691084446[/C][C]0.672839382168892[/C][C]0.663580308915554[/C][/ROW]
[ROW][C]64[/C][C]0.370707693991824[/C][C]0.741415387983647[/C][C]0.629292306008176[/C][/ROW]
[ROW][C]65[/C][C]0.5140758042046[/C][C]0.9718483915908[/C][C]0.4859241957954[/C][/ROW]
[ROW][C]66[/C][C]0.48062960120314[/C][C]0.96125920240628[/C][C]0.51937039879686[/C][/ROW]
[ROW][C]67[/C][C]0.521973196436921[/C][C]0.956053607126158[/C][C]0.478026803563079[/C][/ROW]
[ROW][C]68[/C][C]0.54759751200535[/C][C]0.9048049759893[/C][C]0.45240248799465[/C][/ROW]
[ROW][C]69[/C][C]0.552940643251334[/C][C]0.894118713497333[/C][C]0.447059356748666[/C][/ROW]
[ROW][C]70[/C][C]0.509309135321139[/C][C]0.981381729357721[/C][C]0.490690864678861[/C][/ROW]
[ROW][C]71[/C][C]0.50145112795354[/C][C]0.997097744092921[/C][C]0.49854887204646[/C][/ROW]
[ROW][C]72[/C][C]0.471837608441277[/C][C]0.943675216882555[/C][C]0.528162391558723[/C][/ROW]
[ROW][C]73[/C][C]0.446280494689672[/C][C]0.892560989379343[/C][C]0.553719505310328[/C][/ROW]
[ROW][C]74[/C][C]0.470449636396237[/C][C]0.940899272792475[/C][C]0.529550363603763[/C][/ROW]
[ROW][C]75[/C][C]0.513226280102835[/C][C]0.973547439794331[/C][C]0.486773719897165[/C][/ROW]
[ROW][C]76[/C][C]0.529467949531758[/C][C]0.941064100936483[/C][C]0.470532050468242[/C][/ROW]
[ROW][C]77[/C][C]0.54775871899701[/C][C]0.904482562005981[/C][C]0.45224128100299[/C][/ROW]
[ROW][C]78[/C][C]0.525822405081178[/C][C]0.948355189837644[/C][C]0.474177594918822[/C][/ROW]
[ROW][C]79[/C][C]0.494429616327806[/C][C]0.988859232655611[/C][C]0.505570383672194[/C][/ROW]
[ROW][C]80[/C][C]0.514591737085289[/C][C]0.970816525829423[/C][C]0.485408262914711[/C][/ROW]
[ROW][C]81[/C][C]0.518277137642852[/C][C]0.963445724714295[/C][C]0.481722862357148[/C][/ROW]
[ROW][C]82[/C][C]0.552923213236766[/C][C]0.894153573526468[/C][C]0.447076786763234[/C][/ROW]
[ROW][C]83[/C][C]0.541635298748613[/C][C]0.916729402502774[/C][C]0.458364701251387[/C][/ROW]
[ROW][C]84[/C][C]0.507636773965714[/C][C]0.984726452068571[/C][C]0.492363226034286[/C][/ROW]
[ROW][C]85[/C][C]0.51394891102062[/C][C]0.972102177958761[/C][C]0.48605108897938[/C][/ROW]
[ROW][C]86[/C][C]0.482325606963835[/C][C]0.964651213927671[/C][C]0.517674393036165[/C][/ROW]
[ROW][C]87[/C][C]0.481469266668007[/C][C]0.962938533336014[/C][C]0.518530733331993[/C][/ROW]
[ROW][C]88[/C][C]0.473816870713917[/C][C]0.947633741427835[/C][C]0.526183129286083[/C][/ROW]
[ROW][C]89[/C][C]0.441183134171455[/C][C]0.882366268342909[/C][C]0.558816865828545[/C][/ROW]
[ROW][C]90[/C][C]0.457078981936566[/C][C]0.914157963873133[/C][C]0.542921018063434[/C][/ROW]
[ROW][C]91[/C][C]0.416479875634412[/C][C]0.832959751268824[/C][C]0.583520124365588[/C][/ROW]
[ROW][C]92[/C][C]0.472155938190424[/C][C]0.944311876380848[/C][C]0.527844061809576[/C][/ROW]
[ROW][C]93[/C][C]0.423410542999243[/C][C]0.846821085998486[/C][C]0.576589457000757[/C][/ROW]
[ROW][C]94[/C][C]0.37886093991007[/C][C]0.757721879820139[/C][C]0.62113906008993[/C][/ROW]
[ROW][C]95[/C][C]0.412087220105371[/C][C]0.824174440210742[/C][C]0.587912779894629[/C][/ROW]
[ROW][C]96[/C][C]0.385555910810901[/C][C]0.771111821621802[/C][C]0.614444089189099[/C][/ROW]
[ROW][C]97[/C][C]0.405302521660642[/C][C]0.810605043321284[/C][C]0.594697478339358[/C][/ROW]
[ROW][C]98[/C][C]0.491284986578999[/C][C]0.982569973157998[/C][C]0.508715013421001[/C][/ROW]
[ROW][C]99[/C][C]0.472887989836858[/C][C]0.945775979673717[/C][C]0.527112010163142[/C][/ROW]
[ROW][C]100[/C][C]0.452890575507532[/C][C]0.905781151015064[/C][C]0.547109424492468[/C][/ROW]
[ROW][C]101[/C][C]0.410275118036504[/C][C]0.820550236073008[/C][C]0.589724881963496[/C][/ROW]
[ROW][C]102[/C][C]0.36356977213347[/C][C]0.727139544266939[/C][C]0.63643022786653[/C][/ROW]
[ROW][C]103[/C][C]0.321939119350223[/C][C]0.643878238700446[/C][C]0.678060880649777[/C][/ROW]
[ROW][C]104[/C][C]0.332420881021971[/C][C]0.664841762043943[/C][C]0.667579118978029[/C][/ROW]
[ROW][C]105[/C][C]0.333643055903797[/C][C]0.667286111807594[/C][C]0.666356944096203[/C][/ROW]
[ROW][C]106[/C][C]0.288142128679411[/C][C]0.576284257358821[/C][C]0.711857871320589[/C][/ROW]
[ROW][C]107[/C][C]0.318639075337483[/C][C]0.637278150674965[/C][C]0.681360924662517[/C][/ROW]
[ROW][C]108[/C][C]0.356073167385235[/C][C]0.712146334770471[/C][C]0.643926832614765[/C][/ROW]
[ROW][C]109[/C][C]0.388813017968344[/C][C]0.777626035936687[/C][C]0.611186982031656[/C][/ROW]
[ROW][C]110[/C][C]0.359944759875098[/C][C]0.719889519750196[/C][C]0.640055240124902[/C][/ROW]
[ROW][C]111[/C][C]0.322884463788108[/C][C]0.645768927576217[/C][C]0.677115536211892[/C][/ROW]
[ROW][C]112[/C][C]0.378853954951253[/C][C]0.757707909902505[/C][C]0.621146045048747[/C][/ROW]
[ROW][C]113[/C][C]0.575795829056194[/C][C]0.848408341887613[/C][C]0.424204170943806[/C][/ROW]
[ROW][C]114[/C][C]0.564740232447846[/C][C]0.870519535104307[/C][C]0.435259767552154[/C][/ROW]
[ROW][C]115[/C][C]0.578239068801894[/C][C]0.843521862396212[/C][C]0.421760931198106[/C][/ROW]
[ROW][C]116[/C][C]0.522276265962701[/C][C]0.955447468074597[/C][C]0.477723734037299[/C][/ROW]
[ROW][C]117[/C][C]0.470535636826409[/C][C]0.941071273652818[/C][C]0.529464363173591[/C][/ROW]
[ROW][C]118[/C][C]0.607859411081014[/C][C]0.784281177837972[/C][C]0.392140588918986[/C][/ROW]
[ROW][C]119[/C][C]0.564906464458581[/C][C]0.870187071082839[/C][C]0.435093535541419[/C][/ROW]
[ROW][C]120[/C][C]0.518460572945535[/C][C]0.96307885410893[/C][C]0.481539427054465[/C][/ROW]
[ROW][C]121[/C][C]0.451758318667152[/C][C]0.903516637334305[/C][C]0.548241681332847[/C][/ROW]
[ROW][C]122[/C][C]0.42391658224017[/C][C]0.847833164480339[/C][C]0.57608341775983[/C][/ROW]
[ROW][C]123[/C][C]0.406554488042659[/C][C]0.813108976085318[/C][C]0.593445511957341[/C][/ROW]
[ROW][C]124[/C][C]0.336072788489213[/C][C]0.672145576978426[/C][C]0.663927211510787[/C][/ROW]
[ROW][C]125[/C][C]0.276579378907332[/C][C]0.553158757814663[/C][C]0.723420621092668[/C][/ROW]
[ROW][C]126[/C][C]0.324091149919992[/C][C]0.648182299839983[/C][C]0.675908850080008[/C][/ROW]
[ROW][C]127[/C][C]0.302597195682818[/C][C]0.605194391365636[/C][C]0.697402804317182[/C][/ROW]
[ROW][C]128[/C][C]0.240352303730857[/C][C]0.480704607461713[/C][C]0.759647696269143[/C][/ROW]
[ROW][C]129[/C][C]0.180102344310321[/C][C]0.360204688620642[/C][C]0.819897655689679[/C][/ROW]
[ROW][C]130[/C][C]0.419879047523409[/C][C]0.839758095046817[/C][C]0.580120952476591[/C][/ROW]
[ROW][C]131[/C][C]0.619456426124914[/C][C]0.761087147750172[/C][C]0.380543573875086[/C][/ROW]
[ROW][C]132[/C][C]0.517364822506264[/C][C]0.965270354987471[/C][C]0.482635177493736[/C][/ROW]
[ROW][C]133[/C][C]0.446480565684264[/C][C]0.892961131368527[/C][C]0.553519434315736[/C][/ROW]
[ROW][C]134[/C][C]0.346463289424743[/C][C]0.692926578849485[/C][C]0.653536710575257[/C][/ROW]
[ROW][C]135[/C][C]0.315267728934922[/C][C]0.630535457869844[/C][C]0.684732271065078[/C][/ROW]
[ROW][C]136[/C][C]0.285082272741984[/C][C]0.570164545483969[/C][C]0.714917727258016[/C][/ROW]
[ROW][C]137[/C][C]0.523293211587161[/C][C]0.953413576825678[/C][C]0.476706788412839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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
80.3001153636175720.6002307272351430.699884636382428
90.3620401761964890.7240803523929780.637959823803511
100.2348961548181790.4697923096363570.765103845181821
110.1416857206802660.2833714413605310.858314279319734
120.07904691662294430.1580938332458890.920953083377056
130.05183718273901620.1036743654780320.948162817260984
140.02866718982576380.05733437965152770.971332810174236
150.01458861894780530.02917723789561060.985411381052195
160.008614998651010930.01722999730202190.991385001348989
170.004119571894577030.008239143789154060.995880428105423
180.02538455826707390.05076911653414790.974615441732926
190.01504865979449180.03009731958898370.984951340205508
200.01226260252121580.02452520504243150.987737397478784
210.03424939732105290.06849879464210590.965750602678947
220.09708755819107670.1941751163821530.902912441808923
230.07000235636318780.1400047127263760.929997643636812
240.05213088883068040.1042617776613610.94786911116932
250.03863750096002510.07727500192005010.961362499039975
260.2712848320560460.5425696641120930.728715167943954
270.2447206215144430.4894412430288860.755279378485557
280.1985692204998730.3971384409997450.801430779500127
290.2434303665023880.4868607330047760.756569633497612
300.1988781620208180.3977563240416370.801121837979182
310.241566454465650.4831329089313010.75843354553435
320.2831560409745070.5663120819490140.716843959025493
330.3081378505872030.6162757011744060.691862149412797
340.3513445243012180.7026890486024360.648655475698782
350.4012446489608930.8024892979217850.598755351039107
360.3547381222050340.7094762444100690.645261877794966
370.3040054154331160.6080108308662320.695994584566884
380.2834838121289920.5669676242579840.716516187871008
390.2828666620205510.5657333240411030.717133337979449
400.295668650795450.59133730159090.70433134920455
410.3010112308731380.6020224617462770.698988769126861
420.3280934198436790.6561868396873570.671906580156321
430.3544364098106830.7088728196213660.645563590189317
440.4633085745138990.9266171490277980.536691425486101
450.4333869017130450.866773803426090.566613098286955
460.4433047409528010.8866094819056020.556695259047199
470.4712943641409770.9425887282819540.528705635859023
480.4289142052971060.8578284105942120.571085794702894
490.3899792496001550.779958499200310.610020750399845
500.3596426002412470.7192852004824940.640357399758753
510.3840093238507060.7680186477014130.615990676149294
520.3436854269393520.6873708538787030.656314573060649
530.3404402706749830.6808805413499650.659559729325017
540.3173762901881050.6347525803762110.682623709811895
550.2756065639950090.5512131279900170.724393436004991
560.237262713895670.474525427791340.76273728610433
570.2293382531916880.4586765063833760.770661746808312
580.2175667008519650.435133401703930.782433299148035
590.1860313402210090.3720626804420180.813968659778991
600.1767395909312630.3534791818625270.823260409068737
610.2303101657166380.4606203314332750.769689834283362
620.3309118434009640.6618236868019280.669088156599036
630.3364196910844460.6728393821688920.663580308915554
640.3707076939918240.7414153879836470.629292306008176
650.51407580420460.97184839159080.4859241957954
660.480629601203140.961259202406280.51937039879686
670.5219731964369210.9560536071261580.478026803563079
680.547597512005350.90480497598930.45240248799465
690.5529406432513340.8941187134973330.447059356748666
700.5093091353211390.9813817293577210.490690864678861
710.501451127953540.9970977440929210.49854887204646
720.4718376084412770.9436752168825550.528162391558723
730.4462804946896720.8925609893793430.553719505310328
740.4704496363962370.9408992727924750.529550363603763
750.5132262801028350.9735474397943310.486773719897165
760.5294679495317580.9410641009364830.470532050468242
770.547758718997010.9044825620059810.45224128100299
780.5258224050811780.9483551898376440.474177594918822
790.4944296163278060.9888592326556110.505570383672194
800.5145917370852890.9708165258294230.485408262914711
810.5182771376428520.9634457247142950.481722862357148
820.5529232132367660.8941535735264680.447076786763234
830.5416352987486130.9167294025027740.458364701251387
840.5076367739657140.9847264520685710.492363226034286
850.513948911020620.9721021779587610.48605108897938
860.4823256069638350.9646512139276710.517674393036165
870.4814692666680070.9629385333360140.518530733331993
880.4738168707139170.9476337414278350.526183129286083
890.4411831341714550.8823662683429090.558816865828545
900.4570789819365660.9141579638731330.542921018063434
910.4164798756344120.8329597512688240.583520124365588
920.4721559381904240.9443118763808480.527844061809576
930.4234105429992430.8468210859984860.576589457000757
940.378860939910070.7577218798201390.62113906008993
950.4120872201053710.8241744402107420.587912779894629
960.3855559108109010.7711118216218020.614444089189099
970.4053025216606420.8106050433212840.594697478339358
980.4912849865789990.9825699731579980.508715013421001
990.4728879898368580.9457759796737170.527112010163142
1000.4528905755075320.9057811510150640.547109424492468
1010.4102751180365040.8205502360730080.589724881963496
1020.363569772133470.7271395442669390.63643022786653
1030.3219391193502230.6438782387004460.678060880649777
1040.3324208810219710.6648417620439430.667579118978029
1050.3336430559037970.6672861118075940.666356944096203
1060.2881421286794110.5762842573588210.711857871320589
1070.3186390753374830.6372781506749650.681360924662517
1080.3560731673852350.7121463347704710.643926832614765
1090.3888130179683440.7776260359366870.611186982031656
1100.3599447598750980.7198895197501960.640055240124902
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1120.3788539549512530.7577079099025050.621146045048747
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1200.5184605729455350.963078854108930.481539427054465
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1340.3464632894247430.6929265788494850.653536710575257
1350.3152677289349220.6305354578698440.684732271065078
1360.2850822727419840.5701645454839690.714917727258016
1370.5232932115871610.9534135768256780.476706788412839







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00769230769230769OK
5% type I error level50.0384615384615385OK
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 & 5 & 0.0384615384615385 & OK \tabularnewline
10% type I error level & 9 & 0.0692307692307692 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]5[/C][C]0.0384615384615385[/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=186042&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&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 level50.0384615384615385OK
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')
}