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Description of Statistical Computation

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, 22 Nov 2010 19:33:23 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/22/t1290454742cv9f4o6pnkdkfb0.htm/, Retrieved Sat, 04 May 2024 05:13:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98707, Retrieved Sat, 04 May 2024 05:13:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

Tree of Dependent Computations

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]
-   PD    [Multiple Regression] [WS7] [2010-11-22 19:33:23] [66b4703b90a9701067ac75b10c82aca9] [Current]
-    D      [Multiple Regression] [MR - Happiness] [2010-12-18 17:31:06] [87116ee6ef949037dfa02b8eb1a3bf97]
-    D      [Multiple Regression] [MR - Happiness] [2010-12-18 18:05:27] [87116ee6ef949037dfa02b8eb1a3bf97]
-    D        [Multiple Regression] [MR] [2010-12-18 23:03:08] [87116ee6ef949037dfa02b8eb1a3bf97]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	1	12	53	32	3	3
18	4	11	86	51	3	3
11	1	14	66	42	4	4
12	2	12	67	41	3	3
16	3	21	76	46	3	2
18	2	12	78	47	3	3
14	2	22	53	37	3	4
14	1	11	80	49	2	2
15	2	10	74	45	3	3
15	1	13	76	47	3	4
17	2	10	79	49	3	3
19	2	8	54	33	3	3
10	2	15	67	42	3	4
16	2	14	54	33	2	2
18	1	10	87	53	3	2
14	2	14	58	36	3	3
14	4	14	75	45	2	2
17	2	11	88	54	3	4
14	2	10	64	41	2	2
16	2	13	57	36	1	1
18	1	7	66	41	2	3
11	1	14	68	44	3	4
14	2	12	54	33	3	2
12	2	14	56	37	3	3
17	2	11	86	52	3	3
9	2	9	80	47	3	4
16	1	11	76	43	2	3
14	3	15	69	44	3	3
15	2	14	78	45	3	4
11	2	13	67	44	4	4
16	4	9	80	49	3	4
13	3	15	54	33	3	3
17	1	10	71	43	3	4
15	1	11	84	54	3	3
14	2	13	74	42	2	2
16	2	8	71	44	3	4
9	1	20	63	37	3	3
15	1	12	71	43	3	2
17	3	10	76	46	3	4
13	2	10	69	42	4	4
15	2	9	74	45	3	4
16	3	14	75	44	3	4
16	1	8	54	33	1	2
12	1	14	52	31	2	2
12	1	11	69	42	3	3
11	3	13	68	40	4	4
15	3	9	65	43	4	5
15	2	11	75	46	2	2
17	2	15	74	42	1	3
13	1	11	75	45	3	3
16	2	10	72	44	3	2
14	2	14	67	40	1	2
11	1	18	63	37	3	3
12	2	14	62	46	2	2
12	1	11	63	36	3	4
15	3	12	76	47	3	3
16	2	13	74	45	2	3
15	2	9	67	42	4	4
12	1	10	73	43	1	1
12	2	15	70	43	3	4
8	1	20	53	32	2	2
13	2	12	77	45	4	4
11	1	12	77	45	3	4
14	1	14	52	31	4	4
15	2	13	54	33	3	2
10	4	11	80	49	3	4
11	1	17	66	42	3	2
12	2	12	73	41	3	4
15	3	13	63	38	3	4
15	2	14	69	42	1	1
14	4	13	67	44	3	4
16	3	15	54	33	3	4
15	1	13	81	48	3	3
15	1	10	69	40	2	3
13	4	11	84	50	3	3
12	2	19	80	49	3	3
17	2	13	70	43	3	3
13	1	17	69	44	2	3
15	2	13	77	47	3	4
13	2	9	54	33	2	1
15	2	11	79	46	2	3
16	3	10	30	0	3	4
15	4	9	71	45	3	3
16	2	12	73	43	2	3
15	2	12	72	44	2	4
14	2	13	77	47	3	3
15	3	13	75	45	2	2
14	1	12	69	42	3	3
13	2	15	54	33	4	4
7	1	22	70	43	2	3
17	4	13	73	46	3	4
13	3	15	54	33	2	3
15	2	13	77	46	4	4
14	2	15	82	48	3	4
13	3	10	80	47	3	3
16	2	11	80	47	3	2
12	2	16	69	43	3	1
14	3	11	78	46	2	2
17	2	11	81	48	3	2
15	1	10	76	46	4	3
17	2	10	76	45	4	4
12	1	16	73	45	4	4
16	2	12	85	52	3	3
11	1	11	66	42	3	3
15	2	16	79	47	1	1
9	2	19	68	41	4	3
16	5	11	76	47	1	3
15	1	16	71	43	3	4
10	3	15	54	33	2	2
10	2	24	46	30	2	2
15	3	14	82	49	3	3
11	2	15	74	44	3	3
13	2	11	88	55	2	3
14	1	15	38	11	3	4
18	2	12	76	47	3	4
16	3	10	86	53	4	4
14	3	14	54	33	4	4
14	3	13	70	44	3	2
14	3	9	69	42	3	3
14	2	15	90	55	3	4
12	3	15	54	33	3	3
14	3	14	76	46	3	4
15	2	11	89	54	1	2
15	2	8	76	47	2	4
15	1	11	73	45	4	4
13	2	11	79	47	3	3
17	1	8	90	55	4	4
17	3	10	74	44	3	3
19	2	11	81	53	2	3
15	2	13	72	44	1	1
13	1	11	71	42	4	4
9	2	20	66	40	3	4
15	1	10	77	46	3	2
15	1	15	65	40	3	3
15	2	12	74	46	4	4
16	2	14	82	53	3	3
11	2	23	54	33	3	4
14	2	14	63	42	1	2
11	1	16	54	35	4	5
15	1	11	64	40	2	3
13	1	12	69	41	2	4
15	1	10	54	33	3	3
16	4	14	84	51	3	4
14	2	12	86	53	2	2
15	1	12	77	46	3	3
16	2	11	89	55	3	3
16	2	12	76	47	2	2
11	3	13	60	38	4	3
12	1	11	75	46	3	4
9	3	19	73	46	4	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 15 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=0

As an alternative you can also use a QR Code:  
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 time15 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
happiness[t] = + 16.8047526382411 + 0.312091001977892luck[t] -0.357308069520522depression[t] + 0.0686057180561021belonging[t] -0.0592332381375093belonging_final[t] -0.352905082725952popularity[t] -0.0280289123784063friends[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
happiness[t] =  +  16.8047526382411 +  0.312091001977892luck[t] -0.357308069520522depression[t] +  0.0686057180561021belonging[t] -0.0592332381375093belonging_final[t] -0.352905082725952popularity[t] -0.0280289123784063friends[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]happiness[t] =  +  16.8047526382411 +  0.312091001977892luck[t] -0.357308069520522depression[t] +  0.0686057180561021belonging[t] -0.0592332381375093belonging_final[t] -0.352905082725952popularity[t] -0.0280289123784063friends[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
happiness[t] = + 16.8047526382411 + 0.312091001977892luck[t] -0.357308069520522depression[t] + 0.0686057180561021belonging[t] -0.0592332381375093belonging_final[t] -0.352905082725952popularity[t] -0.0280289123784063friends[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.80475263824111.61689910.393200
luck0.3120910019778920.1820791.7140.0886880.044344
depression-0.3573080695205220.053825-6.638300
belonging0.06860571805610210.0476961.43840.1525090.076255
belonging_final-0.05923323813750930.068828-0.86060.3909030.195452
popularity-0.3529050827259520.263224-1.34070.1821430.091072
friends-0.02802891237840630.232349-0.12060.9041510.452076

\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) & 16.8047526382411 & 1.616899 & 10.3932 & 0 & 0 \tabularnewline
luck & 0.312091001977892 & 0.182079 & 1.714 & 0.088688 & 0.044344 \tabularnewline
depression & -0.357308069520522 & 0.053825 & -6.6383 & 0 & 0 \tabularnewline
belonging & 0.0686057180561021 & 0.047696 & 1.4384 & 0.152509 & 0.076255 \tabularnewline
belonging_final & -0.0592332381375093 & 0.068828 & -0.8606 & 0.390903 & 0.195452 \tabularnewline
popularity & -0.352905082725952 & 0.263224 & -1.3407 & 0.182143 & 0.091072 \tabularnewline
friends & -0.0280289123784063 & 0.232349 & -0.1206 & 0.904151 & 0.452076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&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]16.8047526382411[/C][C]1.616899[/C][C]10.3932[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]luck[/C][C]0.312091001977892[/C][C]0.182079[/C][C]1.714[/C][C]0.088688[/C][C]0.044344[/C][/ROW]
[ROW][C]depression[/C][C]-0.357308069520522[/C][C]0.053825[/C][C]-6.6383[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]belonging[/C][C]0.0686057180561021[/C][C]0.047696[/C][C]1.4384[/C][C]0.152509[/C][C]0.076255[/C][/ROW]
[ROW][C]belonging_final[/C][C]-0.0592332381375093[/C][C]0.068828[/C][C]-0.8606[/C][C]0.390903[/C][C]0.195452[/C][/ROW]
[ROW][C]popularity[/C][C]-0.352905082725952[/C][C]0.263224[/C][C]-1.3407[/C][C]0.182143[/C][C]0.091072[/C][/ROW]
[ROW][C]friends[/C][C]-0.0280289123784063[/C][C]0.232349[/C][C]-0.1206[/C][C]0.904151[/C][C]0.452076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=2

As an alternative you can also use a QR Code:  
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)16.80475263824111.61689910.393200
luck0.3120910019778920.1820791.7140.0886880.044344
depression-0.3573080695205220.053825-6.638300
belonging0.06860571805610210.0476961.43840.1525090.076255
belonging_final-0.05923323813750930.068828-0.86060.3909030.195452
popularity-0.3529050827259520.263224-1.34070.1821430.091072
friends-0.02802891237840630.232349-0.12060.9041510.452076






Multiple Linear Regression - Regression Statistics
Multiple R0.581195686546521
R-squared0.337788426060282
Adjusted R-squared0.310003325055818
F-TEST (value)12.1571782663671
F-TEST (DF numerator)6
F-TEST (DF denominator)143
p-value5.10060882419339e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93458580398152
Sum Squared Residuals535.194979314254

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.581195686546521 \tabularnewline
R-squared & 0.337788426060282 \tabularnewline
Adjusted R-squared & 0.310003325055818 \tabularnewline
F-TEST (value) & 12.1571782663671 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 5.10060882419339e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93458580398152 \tabularnewline
Sum Squared Residuals & 535.194979314254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.581195686546521[/C][/ROW]
[ROW][C]R-squared[/C][C]0.337788426060282[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.310003325055818[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.1571782663671[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]5.10060882419339e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.93458580398152[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]535.194979314254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.581195686546521
R-squared0.337788426060282
Adjusted R-squared0.310003325055818
F-TEST (value)12.1571782663671
F-TEST (DF numerator)6
F-TEST (DF denominator)143
p-value5.10060882419339e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93458580398152
Sum Squared Residuals535.194979314254






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.42698425723300.573015742767027
21815.85912250392572.14087749607433
31112.6309760764416-1.63097607644163
41214.1664561687585-2.16645616875852
51611.61208872924754.3879112707525
61814.56571963855063.43428036144941
7149.841799460939524.15820053906048
81415.0106156610348-1.01061566103476
91515.1243793816422-0.124379381642241
101513.73108021856161.26891978143843
111715.23047501937271.76952498062729
121915.17768001721143.82231998278865
131013.0072698096810-3.00726980968104
141613.41476559519262.58523440480742
151815.2583257216722.74167427832799
161413.13055475790010.869445242099893
171414.7688688206764-0.768868820676399
181715.16642330929121.83357669070884
191415.0561891487356-1.05618914873562
201614.18112509957321.81887490042676
211815.92520487905312.07479512094691
221113.0026261190048-2.00262611900476
231413.77647665150770.223523348492326
241212.9341100836504-0.934110083650394
251715.17570726183241.82429273816762
26915.7468263708459-6.74682637084595
271615.0635633052570.936436694742997
281413.36613468387450.633865316125474
291513.94154106340621.05845893659384
301113.2505143897211-2.25051438972111
311616.2525418985267-0.252541898526714
321312.98861453254560.0113854674544035
331714.69690878939272.30309121060734
341514.60793834746730.392061652532734
351414.6110888825976-0.611088882597563
361615.66438269227410.335617307725919
37910.9584106909421-1.95841069094209
381514.03835047510840.961649524891574
391715.48641966921641.51358033078358
401314.5781165106699-1.5781165106699
411515.4536585387844-0.453658538784356
421614.10704814935331.89295185064675
431615.59942809306380.400571906936228
441213.0839296333775-1.08392963337751
451214.2896514342758-2.28965143427585
461113.8681440623051-2.86814406230514
471514.8858305594280.114169440572010
481515.1573777871447-0.157377787144671
491714.22134891390412.77865108609593
501314.5235860281999-1.52358602819993
511615.07443009604600.925569903954048
521414.2449123456853-0.244912345685298
531111.6730268299831-0.673026829983132
541213.1935792438538-1.19357924385378
551214.2053876423859-2.20538764238588
561514.74059920441630.259400795583724
571614.40536025580661.59463974419337
581514.79821314407820.201786855921782
591215.6240171280920-3.62401712809198
601213.1538537257118-1.15385372571184
61810.9494536961730-2.94945369617297
621314.2346464016651-1.23464640166515
631114.2754604824132-3.27546048241321
641412.32206164316881.67793835683121
651513.41916858198721.58083141801285
661015.5379257594857-5.53792575948567
671111.9680147753628-0.96801477536282
681214.5500615647167-2.55006156471673
691513.99648703102561.00351296897439
701514.29168621790090.70831378209911
711414.2276014764028-0.227601476402849
721612.96058562016723.03941437983281
731514.04290448308300.957095516917027
741515.1183310627973-0.118331062797338
751315.7811443059510-2.78114430595098
761212.0833081117441-0.0833081117441266
771713.89649877713133.10350122286871
781312.38024162360370.619758376396347
791514.11177693859560.88822306140444
801315.2293348551736-2.22933485517360
811515.4037717469907-0.403771746990673
821615.05528559296110.94471440703885
831515.9000523009502-0.900052300950239
841614.81252908354611.18747091645393
851514.65666121497410.34333878502595
861414.1398058509740-0.139805850973965
871514.81408588821900.185914111780971
881413.93234336475530.067656635244675
891312.29558953546330.704410464536653
90710.7215402321947-3.72154023219466
911714.52076930846442.47923069153556
921313.3415196152715-0.341519615271548
931513.81810509400711.18189490599288
941413.68095615169750.319043848302482
951315.7296382156817-2.72963821568173
961615.08826805656170.91173194343828
971212.8120266752704-0.812026675270434
981415.6752859432909-1.67528594329087
991715.09764053648031.90235946351969
1001514.53736149491310.462638505086907
1011714.88065682265012.11934317734991
1021212.2189002493808-0.218900249380763
1031614.74979347425581.25020652574424
1041114.0838342801075-3.08383428010754
1051513.96696106873331.03303893126668
106911.3810003174450-2.38100031744502
1071616.4278994433445-0.427899443344484
1081512.55306037226952.44693962773047
1091013.3695485276500-3.36954852764995
110109.470538869951080.529461130048915
1111514.31915089743680.680849102563173
1121113.3970722721771-2.39707227217714
1131315.488124066258-2.48812406625801
1141412.54184336633901.45815663366102
1151814.40047929006003.59952070994002
1161615.40493910008890.595060899911075
1171412.96498860696181.03501139303824
1181414.1773854533501-0.177385453350076
1191415.6284495772727-1.62844957727267
1201413.81516922918380.18483077081623
1211212.9886145325456-0.988614532545597
1221414.0571873911343-0.0571873911343368
1231515.9968970175560-0.996897017555977
1241516.1826166508680-1.18261665086802
1251514.00544059698340.99455940301663
1261314.9916334261272-1.99163342612721
1271715.65132963112361.34867036887643
1281715.49570362175761.50429637824236
1291915.12635051614033.87364948385969
1301514.73634496531470.263655034685301
1311314.0459288752837-1.04592887528369
132911.2705902202974-2.27059022029735
1331514.98690120807360.0130987919264474
1341512.70446276024442.29553723975563
1351513.96959600935931.03040399064067
1361613.77012694290892.2298730570911
137119.790030062025131.20996993797487
1381413.85202299718590.147977002814128
1391111.4796950753115-0.479695075311511
1401514.41799440299630.582005597003694
1411314.3164527732404-1.31645277324038
1421514.15097287619240.84902712380758
1431614.62195794687351.37804205312650
1441415.1400999492787-1.14009994927871
1451514.24425615665410.755743843345896
1461615.20382470158820.796175298411842
1471614.80944219754271.19055780245726
1481113.4657937065098-2.46579370650975
1491214.4363238776840-2.43632387768402
150911.7119248066375-2.71192480663747

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.4269842572330 & 0.573015742767027 \tabularnewline
2 & 18 & 15.8591225039257 & 2.14087749607433 \tabularnewline
3 & 11 & 12.6309760764416 & -1.63097607644163 \tabularnewline
4 & 12 & 14.1664561687585 & -2.16645616875852 \tabularnewline
5 & 16 & 11.6120887292475 & 4.3879112707525 \tabularnewline
6 & 18 & 14.5657196385506 & 3.43428036144941 \tabularnewline
7 & 14 & 9.84179946093952 & 4.15820053906048 \tabularnewline
8 & 14 & 15.0106156610348 & -1.01061566103476 \tabularnewline
9 & 15 & 15.1243793816422 & -0.124379381642241 \tabularnewline
10 & 15 & 13.7310802185616 & 1.26891978143843 \tabularnewline
11 & 17 & 15.2304750193727 & 1.76952498062729 \tabularnewline
12 & 19 & 15.1776800172114 & 3.82231998278865 \tabularnewline
13 & 10 & 13.0072698096810 & -3.00726980968104 \tabularnewline
14 & 16 & 13.4147655951926 & 2.58523440480742 \tabularnewline
15 & 18 & 15.258325721672 & 2.74167427832799 \tabularnewline
16 & 14 & 13.1305547579001 & 0.869445242099893 \tabularnewline
17 & 14 & 14.7688688206764 & -0.768868820676399 \tabularnewline
18 & 17 & 15.1664233092912 & 1.83357669070884 \tabularnewline
19 & 14 & 15.0561891487356 & -1.05618914873562 \tabularnewline
20 & 16 & 14.1811250995732 & 1.81887490042676 \tabularnewline
21 & 18 & 15.9252048790531 & 2.07479512094691 \tabularnewline
22 & 11 & 13.0026261190048 & -2.00262611900476 \tabularnewline
23 & 14 & 13.7764766515077 & 0.223523348492326 \tabularnewline
24 & 12 & 12.9341100836504 & -0.934110083650394 \tabularnewline
25 & 17 & 15.1757072618324 & 1.82429273816762 \tabularnewline
26 & 9 & 15.7468263708459 & -6.74682637084595 \tabularnewline
27 & 16 & 15.063563305257 & 0.936436694742997 \tabularnewline
28 & 14 & 13.3661346838745 & 0.633865316125474 \tabularnewline
29 & 15 & 13.9415410634062 & 1.05845893659384 \tabularnewline
30 & 11 & 13.2505143897211 & -2.25051438972111 \tabularnewline
31 & 16 & 16.2525418985267 & -0.252541898526714 \tabularnewline
32 & 13 & 12.9886145325456 & 0.0113854674544035 \tabularnewline
33 & 17 & 14.6969087893927 & 2.30309121060734 \tabularnewline
34 & 15 & 14.6079383474673 & 0.392061652532734 \tabularnewline
35 & 14 & 14.6110888825976 & -0.611088882597563 \tabularnewline
36 & 16 & 15.6643826922741 & 0.335617307725919 \tabularnewline
37 & 9 & 10.9584106909421 & -1.95841069094209 \tabularnewline
38 & 15 & 14.0383504751084 & 0.961649524891574 \tabularnewline
39 & 17 & 15.4864196692164 & 1.51358033078358 \tabularnewline
40 & 13 & 14.5781165106699 & -1.5781165106699 \tabularnewline
41 & 15 & 15.4536585387844 & -0.453658538784356 \tabularnewline
42 & 16 & 14.1070481493533 & 1.89295185064675 \tabularnewline
43 & 16 & 15.5994280930638 & 0.400571906936228 \tabularnewline
44 & 12 & 13.0839296333775 & -1.08392963337751 \tabularnewline
45 & 12 & 14.2896514342758 & -2.28965143427585 \tabularnewline
46 & 11 & 13.8681440623051 & -2.86814406230514 \tabularnewline
47 & 15 & 14.885830559428 & 0.114169440572010 \tabularnewline
48 & 15 & 15.1573777871447 & -0.157377787144671 \tabularnewline
49 & 17 & 14.2213489139041 & 2.77865108609593 \tabularnewline
50 & 13 & 14.5235860281999 & -1.52358602819993 \tabularnewline
51 & 16 & 15.0744300960460 & 0.925569903954048 \tabularnewline
52 & 14 & 14.2449123456853 & -0.244912345685298 \tabularnewline
53 & 11 & 11.6730268299831 & -0.673026829983132 \tabularnewline
54 & 12 & 13.1935792438538 & -1.19357924385378 \tabularnewline
55 & 12 & 14.2053876423859 & -2.20538764238588 \tabularnewline
56 & 15 & 14.7405992044163 & 0.259400795583724 \tabularnewline
57 & 16 & 14.4053602558066 & 1.59463974419337 \tabularnewline
58 & 15 & 14.7982131440782 & 0.201786855921782 \tabularnewline
59 & 12 & 15.6240171280920 & -3.62401712809198 \tabularnewline
60 & 12 & 13.1538537257118 & -1.15385372571184 \tabularnewline
61 & 8 & 10.9494536961730 & -2.94945369617297 \tabularnewline
62 & 13 & 14.2346464016651 & -1.23464640166515 \tabularnewline
63 & 11 & 14.2754604824132 & -3.27546048241321 \tabularnewline
64 & 14 & 12.3220616431688 & 1.67793835683121 \tabularnewline
65 & 15 & 13.4191685819872 & 1.58083141801285 \tabularnewline
66 & 10 & 15.5379257594857 & -5.53792575948567 \tabularnewline
67 & 11 & 11.9680147753628 & -0.96801477536282 \tabularnewline
68 & 12 & 14.5500615647167 & -2.55006156471673 \tabularnewline
69 & 15 & 13.9964870310256 & 1.00351296897439 \tabularnewline
70 & 15 & 14.2916862179009 & 0.70831378209911 \tabularnewline
71 & 14 & 14.2276014764028 & -0.227601476402849 \tabularnewline
72 & 16 & 12.9605856201672 & 3.03941437983281 \tabularnewline
73 & 15 & 14.0429044830830 & 0.957095516917027 \tabularnewline
74 & 15 & 15.1183310627973 & -0.118331062797338 \tabularnewline
75 & 13 & 15.7811443059510 & -2.78114430595098 \tabularnewline
76 & 12 & 12.0833081117441 & -0.0833081117441266 \tabularnewline
77 & 17 & 13.8964987771313 & 3.10350122286871 \tabularnewline
78 & 13 & 12.3802416236037 & 0.619758376396347 \tabularnewline
79 & 15 & 14.1117769385956 & 0.88822306140444 \tabularnewline
80 & 13 & 15.2293348551736 & -2.22933485517360 \tabularnewline
81 & 15 & 15.4037717469907 & -0.403771746990673 \tabularnewline
82 & 16 & 15.0552855929611 & 0.94471440703885 \tabularnewline
83 & 15 & 15.9000523009502 & -0.900052300950239 \tabularnewline
84 & 16 & 14.8125290835461 & 1.18747091645393 \tabularnewline
85 & 15 & 14.6566612149741 & 0.34333878502595 \tabularnewline
86 & 14 & 14.1398058509740 & -0.139805850973965 \tabularnewline
87 & 15 & 14.8140858882190 & 0.185914111780971 \tabularnewline
88 & 14 & 13.9323433647553 & 0.067656635244675 \tabularnewline
89 & 13 & 12.2955895354633 & 0.704410464536653 \tabularnewline
90 & 7 & 10.7215402321947 & -3.72154023219466 \tabularnewline
91 & 17 & 14.5207693084644 & 2.47923069153556 \tabularnewline
92 & 13 & 13.3415196152715 & -0.341519615271548 \tabularnewline
93 & 15 & 13.8181050940071 & 1.18189490599288 \tabularnewline
94 & 14 & 13.6809561516975 & 0.319043848302482 \tabularnewline
95 & 13 & 15.7296382156817 & -2.72963821568173 \tabularnewline
96 & 16 & 15.0882680565617 & 0.91173194343828 \tabularnewline
97 & 12 & 12.8120266752704 & -0.812026675270434 \tabularnewline
98 & 14 & 15.6752859432909 & -1.67528594329087 \tabularnewline
99 & 17 & 15.0976405364803 & 1.90235946351969 \tabularnewline
100 & 15 & 14.5373614949131 & 0.462638505086907 \tabularnewline
101 & 17 & 14.8806568226501 & 2.11934317734991 \tabularnewline
102 & 12 & 12.2189002493808 & -0.218900249380763 \tabularnewline
103 & 16 & 14.7497934742558 & 1.25020652574424 \tabularnewline
104 & 11 & 14.0838342801075 & -3.08383428010754 \tabularnewline
105 & 15 & 13.9669610687333 & 1.03303893126668 \tabularnewline
106 & 9 & 11.3810003174450 & -2.38100031744502 \tabularnewline
107 & 16 & 16.4278994433445 & -0.427899443344484 \tabularnewline
108 & 15 & 12.5530603722695 & 2.44693962773047 \tabularnewline
109 & 10 & 13.3695485276500 & -3.36954852764995 \tabularnewline
110 & 10 & 9.47053886995108 & 0.529461130048915 \tabularnewline
111 & 15 & 14.3191508974368 & 0.680849102563173 \tabularnewline
112 & 11 & 13.3970722721771 & -2.39707227217714 \tabularnewline
113 & 13 & 15.488124066258 & -2.48812406625801 \tabularnewline
114 & 14 & 12.5418433663390 & 1.45815663366102 \tabularnewline
115 & 18 & 14.4004792900600 & 3.59952070994002 \tabularnewline
116 & 16 & 15.4049391000889 & 0.595060899911075 \tabularnewline
117 & 14 & 12.9649886069618 & 1.03501139303824 \tabularnewline
118 & 14 & 14.1773854533501 & -0.177385453350076 \tabularnewline
119 & 14 & 15.6284495772727 & -1.62844957727267 \tabularnewline
120 & 14 & 13.8151692291838 & 0.18483077081623 \tabularnewline
121 & 12 & 12.9886145325456 & -0.988614532545597 \tabularnewline
122 & 14 & 14.0571873911343 & -0.0571873911343368 \tabularnewline
123 & 15 & 15.9968970175560 & -0.996897017555977 \tabularnewline
124 & 15 & 16.1826166508680 & -1.18261665086802 \tabularnewline
125 & 15 & 14.0054405969834 & 0.99455940301663 \tabularnewline
126 & 13 & 14.9916334261272 & -1.99163342612721 \tabularnewline
127 & 17 & 15.6513296311236 & 1.34867036887643 \tabularnewline
128 & 17 & 15.4957036217576 & 1.50429637824236 \tabularnewline
129 & 19 & 15.1263505161403 & 3.87364948385969 \tabularnewline
130 & 15 & 14.7363449653147 & 0.263655034685301 \tabularnewline
131 & 13 & 14.0459288752837 & -1.04592887528369 \tabularnewline
132 & 9 & 11.2705902202974 & -2.27059022029735 \tabularnewline
133 & 15 & 14.9869012080736 & 0.0130987919264474 \tabularnewline
134 & 15 & 12.7044627602444 & 2.29553723975563 \tabularnewline
135 & 15 & 13.9695960093593 & 1.03040399064067 \tabularnewline
136 & 16 & 13.7701269429089 & 2.2298730570911 \tabularnewline
137 & 11 & 9.79003006202513 & 1.20996993797487 \tabularnewline
138 & 14 & 13.8520229971859 & 0.147977002814128 \tabularnewline
139 & 11 & 11.4796950753115 & -0.479695075311511 \tabularnewline
140 & 15 & 14.4179944029963 & 0.582005597003694 \tabularnewline
141 & 13 & 14.3164527732404 & -1.31645277324038 \tabularnewline
142 & 15 & 14.1509728761924 & 0.84902712380758 \tabularnewline
143 & 16 & 14.6219579468735 & 1.37804205312650 \tabularnewline
144 & 14 & 15.1400999492787 & -1.14009994927871 \tabularnewline
145 & 15 & 14.2442561566541 & 0.755743843345896 \tabularnewline
146 & 16 & 15.2038247015882 & 0.796175298411842 \tabularnewline
147 & 16 & 14.8094421975427 & 1.19055780245726 \tabularnewline
148 & 11 & 13.4657937065098 & -2.46579370650975 \tabularnewline
149 & 12 & 14.4363238776840 & -2.43632387768402 \tabularnewline
150 & 9 & 11.7119248066375 & -2.71192480663747 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&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]13.4269842572330[/C][C]0.573015742767027[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.8591225039257[/C][C]2.14087749607433[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]12.6309760764416[/C][C]-1.63097607644163[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.1664561687585[/C][C]-2.16645616875852[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.6120887292475[/C][C]4.3879112707525[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.5657196385506[/C][C]3.43428036144941[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]9.84179946093952[/C][C]4.15820053906048[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.0106156610348[/C][C]-1.01061566103476[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1243793816422[/C][C]-0.124379381642241[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.7310802185616[/C][C]1.26891978143843[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2304750193727[/C][C]1.76952498062729[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.1776800172114[/C][C]3.82231998278865[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.0072698096810[/C][C]-3.00726980968104[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.4147655951926[/C][C]2.58523440480742[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.258325721672[/C][C]2.74167427832799[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.1305547579001[/C][C]0.869445242099893[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.7688688206764[/C][C]-0.768868820676399[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.1664233092912[/C][C]1.83357669070884[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.0561891487356[/C][C]-1.05618914873562[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.1811250995732[/C][C]1.81887490042676[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.9252048790531[/C][C]2.07479512094691[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.0026261190048[/C][C]-2.00262611900476[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.7764766515077[/C][C]0.223523348492326[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]12.9341100836504[/C][C]-0.934110083650394[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.1757072618324[/C][C]1.82429273816762[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.7468263708459[/C][C]-6.74682637084595[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.063563305257[/C][C]0.936436694742997[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.3661346838745[/C][C]0.633865316125474[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.9415410634062[/C][C]1.05845893659384[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.2505143897211[/C][C]-2.25051438972111[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.2525418985267[/C][C]-0.252541898526714[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.9886145325456[/C][C]0.0113854674544035[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.6969087893927[/C][C]2.30309121060734[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.6079383474673[/C][C]0.392061652532734[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.6110888825976[/C][C]-0.611088882597563[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.6643826922741[/C][C]0.335617307725919[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.9584106909421[/C][C]-1.95841069094209[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.0383504751084[/C][C]0.961649524891574[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.4864196692164[/C][C]1.51358033078358[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.5781165106699[/C][C]-1.5781165106699[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.4536585387844[/C][C]-0.453658538784356[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.1070481493533[/C][C]1.89295185064675[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.5994280930638[/C][C]0.400571906936228[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.0839296333775[/C][C]-1.08392963337751[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.2896514342758[/C][C]-2.28965143427585[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.8681440623051[/C][C]-2.86814406230514[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.885830559428[/C][C]0.114169440572010[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1573777871447[/C][C]-0.157377787144671[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.2213489139041[/C][C]2.77865108609593[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.5235860281999[/C][C]-1.52358602819993[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.0744300960460[/C][C]0.925569903954048[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2449123456853[/C][C]-0.244912345685298[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.6730268299831[/C][C]-0.673026829983132[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.1935792438538[/C][C]-1.19357924385378[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.2053876423859[/C][C]-2.20538764238588[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.7405992044163[/C][C]0.259400795583724[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.4053602558066[/C][C]1.59463974419337[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.7982131440782[/C][C]0.201786855921782[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.6240171280920[/C][C]-3.62401712809198[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.1538537257118[/C][C]-1.15385372571184[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.9494536961730[/C][C]-2.94945369617297[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2346464016651[/C][C]-1.23464640166515[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.2754604824132[/C][C]-3.27546048241321[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.3220616431688[/C][C]1.67793835683121[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.4191685819872[/C][C]1.58083141801285[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.5379257594857[/C][C]-5.53792575948567[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]11.9680147753628[/C][C]-0.96801477536282[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.5500615647167[/C][C]-2.55006156471673[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.9964870310256[/C][C]1.00351296897439[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.2916862179009[/C][C]0.70831378209911[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]14.2276014764028[/C][C]-0.227601476402849[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.9605856201672[/C][C]3.03941437983281[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.0429044830830[/C][C]0.957095516917027[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.1183310627973[/C][C]-0.118331062797338[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.7811443059510[/C][C]-2.78114430595098[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.0833081117441[/C][C]-0.0833081117441266[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]13.8964987771313[/C][C]3.10350122286871[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.3802416236037[/C][C]0.619758376396347[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.1117769385956[/C][C]0.88822306140444[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]15.2293348551736[/C][C]-2.22933485517360[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.4037717469907[/C][C]-0.403771746990673[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.0552855929611[/C][C]0.94471440703885[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.9000523009502[/C][C]-0.900052300950239[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.8125290835461[/C][C]1.18747091645393[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.6566612149741[/C][C]0.34333878502595[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1398058509740[/C][C]-0.139805850973965[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.8140858882190[/C][C]0.185914111780971[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]13.9323433647553[/C][C]0.067656635244675[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.2955895354633[/C][C]0.704410464536653[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.7215402321947[/C][C]-3.72154023219466[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.5207693084644[/C][C]2.47923069153556[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.3415196152715[/C][C]-0.341519615271548[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]13.8181050940071[/C][C]1.18189490599288[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.6809561516975[/C][C]0.319043848302482[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.7296382156817[/C][C]-2.72963821568173[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0882680565617[/C][C]0.91173194343828[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.8120266752704[/C][C]-0.812026675270434[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]15.6752859432909[/C][C]-1.67528594329087[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.0976405364803[/C][C]1.90235946351969[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.5373614949131[/C][C]0.462638505086907[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.8806568226501[/C][C]2.11934317734991[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.2189002493808[/C][C]-0.218900249380763[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.7497934742558[/C][C]1.25020652574424[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0838342801075[/C][C]-3.08383428010754[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.9669610687333[/C][C]1.03303893126668[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.3810003174450[/C][C]-2.38100031744502[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]16.4278994433445[/C][C]-0.427899443344484[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.5530603722695[/C][C]2.44693962773047[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.3695485276500[/C][C]-3.36954852764995[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.47053886995108[/C][C]0.529461130048915[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.3191508974368[/C][C]0.680849102563173[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.3970722721771[/C][C]-2.39707227217714[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.488124066258[/C][C]-2.48812406625801[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.5418433663390[/C][C]1.45815663366102[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.4004792900600[/C][C]3.59952070994002[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.4049391000889[/C][C]0.595060899911075[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9649886069618[/C][C]1.03501139303824[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.1773854533501[/C][C]-0.177385453350076[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.6284495772727[/C][C]-1.62844957727267[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.8151692291838[/C][C]0.18483077081623[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.9886145325456[/C][C]-0.988614532545597[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]14.0571873911343[/C][C]-0.0571873911343368[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.9968970175560[/C][C]-0.996897017555977[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1826166508680[/C][C]-1.18261665086802[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.0054405969834[/C][C]0.99455940301663[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.9916334261272[/C][C]-1.99163342612721[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.6513296311236[/C][C]1.34867036887643[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.4957036217576[/C][C]1.50429637824236[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.1263505161403[/C][C]3.87364948385969[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.7363449653147[/C][C]0.263655034685301[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.0459288752837[/C][C]-1.04592887528369[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.2705902202974[/C][C]-2.27059022029735[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.9869012080736[/C][C]0.0130987919264474[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.7044627602444[/C][C]2.29553723975563[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.9695960093593[/C][C]1.03040399064067[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.7701269429089[/C][C]2.2298730570911[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]9.79003006202513[/C][C]1.20996993797487[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.8520229971859[/C][C]0.147977002814128[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]11.4796950753115[/C][C]-0.479695075311511[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.4179944029963[/C][C]0.582005597003694[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.3164527732404[/C][C]-1.31645277324038[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.1509728761924[/C][C]0.84902712380758[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.6219579468735[/C][C]1.37804205312650[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.1400999492787[/C][C]-1.14009994927871[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.2442561566541[/C][C]0.755743843345896[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.2038247015882[/C][C]0.796175298411842[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.8094421975427[/C][C]1.19055780245726[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.4657937065098[/C][C]-2.46579370650975[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.4363238776840[/C][C]-2.43632387768402[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.7119248066375[/C][C]-2.71192480663747[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.42698425723300.573015742767027
21815.85912250392572.14087749607433
31112.6309760764416-1.63097607644163
41214.1664561687585-2.16645616875852
51611.61208872924754.3879112707525
61814.56571963855063.43428036144941
7149.841799460939524.15820053906048
81415.0106156610348-1.01061566103476
91515.1243793816422-0.124379381642241
101513.73108021856161.26891978143843
111715.23047501937271.76952498062729
121915.17768001721143.82231998278865
131013.0072698096810-3.00726980968104
141613.41476559519262.58523440480742
151815.2583257216722.74167427832799
161413.13055475790010.869445242099893
171414.7688688206764-0.768868820676399
181715.16642330929121.83357669070884
191415.0561891487356-1.05618914873562
201614.18112509957321.81887490042676
211815.92520487905312.07479512094691
221113.0026261190048-2.00262611900476
231413.77647665150770.223523348492326
241212.9341100836504-0.934110083650394
251715.17570726183241.82429273816762
26915.7468263708459-6.74682637084595
271615.0635633052570.936436694742997
281413.36613468387450.633865316125474
291513.94154106340621.05845893659384
301113.2505143897211-2.25051438972111
311616.2525418985267-0.252541898526714
321312.98861453254560.0113854674544035
331714.69690878939272.30309121060734
341514.60793834746730.392061652532734
351414.6110888825976-0.611088882597563
361615.66438269227410.335617307725919
37910.9584106909421-1.95841069094209
381514.03835047510840.961649524891574
391715.48641966921641.51358033078358
401314.5781165106699-1.5781165106699
411515.4536585387844-0.453658538784356
421614.10704814935331.89295185064675
431615.59942809306380.400571906936228
441213.0839296333775-1.08392963337751
451214.2896514342758-2.28965143427585
461113.8681440623051-2.86814406230514
471514.8858305594280.114169440572010
481515.1573777871447-0.157377787144671
491714.22134891390412.77865108609593
501314.5235860281999-1.52358602819993
511615.07443009604600.925569903954048
521414.2449123456853-0.244912345685298
531111.6730268299831-0.673026829983132
541213.1935792438538-1.19357924385378
551214.2053876423859-2.20538764238588
561514.74059920441630.259400795583724
571614.40536025580661.59463974419337
581514.79821314407820.201786855921782
591215.6240171280920-3.62401712809198
601213.1538537257118-1.15385372571184
61810.9494536961730-2.94945369617297
621314.2346464016651-1.23464640166515
631114.2754604824132-3.27546048241321
641412.32206164316881.67793835683121
651513.41916858198721.58083141801285
661015.5379257594857-5.53792575948567
671111.9680147753628-0.96801477536282
681214.5500615647167-2.55006156471673
691513.99648703102561.00351296897439
701514.29168621790090.70831378209911
711414.2276014764028-0.227601476402849
721612.96058562016723.03941437983281
731514.04290448308300.957095516917027
741515.1183310627973-0.118331062797338
751315.7811443059510-2.78114430595098
761212.0833081117441-0.0833081117441266
771713.89649877713133.10350122286871
781312.38024162360370.619758376396347
791514.11177693859560.88822306140444
801315.2293348551736-2.22933485517360
811515.4037717469907-0.403771746990673
821615.05528559296110.94471440703885
831515.9000523009502-0.900052300950239
841614.81252908354611.18747091645393
851514.65666121497410.34333878502595
861414.1398058509740-0.139805850973965
871514.81408588821900.185914111780971
881413.93234336475530.067656635244675
891312.29558953546330.704410464536653
90710.7215402321947-3.72154023219466
911714.52076930846442.47923069153556
921313.3415196152715-0.341519615271548
931513.81810509400711.18189490599288
941413.68095615169750.319043848302482
951315.7296382156817-2.72963821568173
961615.08826805656170.91173194343828
971212.8120266752704-0.812026675270434
981415.6752859432909-1.67528594329087
991715.09764053648031.90235946351969
1001514.53736149491310.462638505086907
1011714.88065682265012.11934317734991
1021212.2189002493808-0.218900249380763
1031614.74979347425581.25020652574424
1041114.0838342801075-3.08383428010754
1051513.96696106873331.03303893126668
106911.3810003174450-2.38100031744502
1071616.4278994433445-0.427899443344484
1081512.55306037226952.44693962773047
1091013.3695485276500-3.36954852764995
110109.470538869951080.529461130048915
1111514.31915089743680.680849102563173
1121113.3970722721771-2.39707227217714
1131315.488124066258-2.48812406625801
1141412.54184336633901.45815663366102
1151814.40047929006003.59952070994002
1161615.40493910008890.595060899911075
1171412.96498860696181.03501139303824
1181414.1773854533501-0.177385453350076
1191415.6284495772727-1.62844957727267
1201413.81516922918380.18483077081623
1211212.9886145325456-0.988614532545597
1221414.0571873911343-0.0571873911343368
1231515.9968970175560-0.996897017555977
1241516.1826166508680-1.18261665086802
1251514.00544059698340.99455940301663
1261314.9916334261272-1.99163342612721
1271715.65132963112361.34867036887643
1281715.49570362175761.50429637824236
1291915.12635051614033.87364948385969
1301514.73634496531470.263655034685301
1311314.0459288752837-1.04592887528369
132911.2705902202974-2.27059022029735
1331514.98690120807360.0130987919264474
1341512.70446276024442.29553723975563
1351513.96959600935931.03040399064067
1361613.77012694290892.2298730570911
137119.790030062025131.20996993797487
1381413.85202299718590.147977002814128
1391111.4796950753115-0.479695075311511
1401514.41799440299630.582005597003694
1411314.3164527732404-1.31645277324038
1421514.15097287619240.84902712380758
1431614.62195794687351.37804205312650
1441415.1400999492787-1.14009994927871
1451514.24425615665410.755743843345896
1461615.20382470158820.796175298411842
1471614.80944219754271.19055780245726
1481113.4657937065098-2.46579370650975
1491214.4363238776840-2.43632387768402
150911.7119248066375-2.71192480663747






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7079265707037740.5841468585924530.292073429296226
110.7914220929547320.4171558140905350.208577907045268
120.904551812129910.1908963757401800.0954481878700898
130.9788219017914240.04235619641715210.0211780982085761
140.9652657025761310.06946859484773730.0347342974238686
150.9586890063280040.0826219873439910.0413109936719955
160.9390390956813760.1219218086372480.0609609043186238
170.9624555156837580.07508896863248360.0375444843162418
180.9646977113490970.07060457730180650.0353022886509033
190.9610330265207480.07793394695850350.0389669734792517
200.9490697353651280.1018605292697440.050930264634872
210.9544759547937580.0910480904124830.0455240452062415
220.9572771065643970.08544578687120650.0427228934356032
230.9464155505010730.1071688989978540.053584449498927
240.9343497433978460.1313005132043080.065650256602154
250.9167402408247110.1665195183505780.083259759175289
260.996625774718590.006748450562818470.00337422528140924
270.995707445511430.008585108977141740.00429255448857087
280.9935313219326170.01293735613476620.0064686780673831
290.9912647756243840.01747044875123150.00873522437561573
300.9910339850176160.01793202996476730.00896601498238367
310.987622997267320.02475400546536180.0123770027326809
320.982537537067720.03492492586456020.0174624629322801
330.9864902514725230.02701949705495360.0135097485274768
340.9806926882642380.03861462347152480.0193073117357624
350.9797729483333380.04045410333332450.0202270516666623
360.973734855058190.0525302898836220.026265144941811
370.97896950849540.04206098300920190.0210304915046010
380.9720870702347640.05582585953047280.0279129297652364
390.9686190342631540.06276193147369170.0313809657368459
400.9622423630338130.07551527393237490.0377576369661874
410.949943248191250.1001135036174990.0500567518087494
420.9479483090747290.1041033818505430.0520516909252713
430.9324492735082440.1351014529835120.067550726491756
440.923208287628420.1535834247431620.0767917123715808
450.9283160093118690.1433679813762630.0716839906881313
460.938336899853290.1233262002934220.061663100146711
470.922418582811940.1551628343761200.0775814171880598
480.908324311263850.1833513774723010.0916756887361503
490.9155551436346960.1688897127306080.084444856365304
500.9054373222867310.1891253554265370.0945626777132687
510.886622077038620.226755845922760.11337792296138
520.8753264339605140.2493471320789730.124673566039486
530.8494325651231340.3011348697537320.150567434876866
540.8568272372696730.2863455254606540.143172762730327
550.8561991769613890.2876016460772220.143800823038611
560.8272515069985280.3454969860029440.172748493001472
570.810067310198310.3798653796033810.189932689801691
580.780251151640220.4394976967195610.219748848359781
590.874216557388530.2515668852229410.125783442611470
600.8583540285115470.2832919429769060.141645971488453
610.889976243758960.2200475124820790.110023756241040
620.8732656154221210.2534687691557580.126734384577879
630.909617188731410.1807656225371810.0903828112685906
640.9140115450045250.1719769099909490.0859884549954746
650.9066619353506210.1866761292987580.093338064649379
660.9874952846370220.02500943072595610.0125047153629781
670.9843648683650970.03127026326980520.0156351316349026
680.9876570634279060.02468587314418690.0123429365720935
690.9844644065436150.03107118691277090.0155355934563854
700.980688472707150.03862305458569900.0193115272928495
710.9744961794625990.05100764107480290.0255038205374014
720.9836389763878310.03272204722433760.0163610236121688
730.9796093287973640.04078134240527310.0203906712026366
740.9731351304177120.05372973916457630.0268648695822882
750.9810154885088560.03796902298228740.0189845114911437
760.9746052669155660.05078946616886840.0253947330844342
770.9846467704956680.03070645900866440.0153532295043322
780.9800955706332060.03980885873358820.0199044293667941
790.9747550807045550.050489838590890.025244919295445
800.9756727857954740.0486544284090520.024327214204526
810.9683811561649950.06323768767000960.0316188438350048
820.961014324517710.07797135096457950.0389856754822897
830.9523212066592460.09535758668150870.0476787933407543
840.9438520626038420.1122958747923170.0561479373961585
850.9285742846505770.1428514306988460.0714257153494229
860.9100393143805720.1799213712388550.0899606856194277
870.8891105813736190.2217788372527620.110889418626381
880.8636875024235360.2726249951529270.136312497576464
890.8386247882462270.3227504235075450.161375211753773
900.9075056163938450.184988767212310.092494383606155
910.925038945587850.1499221088243010.0749610544121505
920.90686826335220.1862634732956000.0931317366477998
930.8921646578947820.2156706842104350.107835342105218
940.8669493544163580.2661012911672840.133050645583642
950.8915150828115120.2169698343769760.108484917188488
960.8712651707284410.2574696585431180.128734829271559
970.8452606804769150.3094786390461700.154739319523085
980.835907450268630.3281850994627390.164092549731370
990.8347777606680840.3304444786638320.165222239331916
1000.8016236540349060.3967526919301880.198376345965094
1010.8078738478932130.3842523042135730.192126152106786
1020.7703929579986260.4592140840027480.229607042001374
1030.7444842605900670.5110314788198650.255515739409933
1040.8122784875814780.3754430248370450.187721512418522
1050.7898123238379310.4203753523241370.210187676162069
1060.8029292797478150.394141440504370.197070720252185
1070.7662082035082120.4675835929835760.233791796491788
1080.7774992568911510.4450014862176970.222500743108849
1090.8426494693497470.3147010613005060.157350530650253
1100.810028614923740.379942770152520.18997138507626
1110.7773354551499410.4453290897001180.222664544850059
1120.798485498500530.4030290029989410.201514501499470
1130.836489106088060.3270217878238800.163510893911940
1140.8927270673029010.2145458653941970.107272932697099
1150.9498293849602950.1003412300794110.0501706150397054
1160.9320214343206040.1359571313587920.0679785656793958
1170.9332450943726290.1335098112547420.0667549056273708
1180.9100352953779940.1799294092440110.0899647046220055
1190.8942173291399940.2115653417200130.105782670860007
1200.8599529063025090.2800941873949820.140047093697491
1210.82058163444870.3588367311026010.179418365551300
1220.7767793728581750.4464412542836490.223220627141825
1230.7483709721362590.5032580557274820.251629027863741
1240.7304767726054160.5390464547891680.269523227394584
1250.6800878316264530.6398243367470930.319912168373547
1260.679162027378230.641675945243540.32083797262177
1270.6218882006212450.756223598757510.378111799378755
1280.6231016237701750.753796752459650.376898376229825
1290.7032656727361420.5934686545277170.296734327263858
1300.630204814432140.7395903711357210.369795185567861
1310.5541074851101720.8917850297796560.445892514889828
1320.5812361532743660.8375276934512680.418763846725634
1330.4974234957603650.9948469915207310.502576504239635
1340.4921218789379460.9842437578758930.507878121062054
1350.4590996763468350.918199352693670.540900323653165
1360.572108844738930.855782310522140.42789115526107
1370.5221199011694340.9557601976611330.477880098830566
1380.3933743547180990.7867487094361980.606625645281901
1390.4500204312739690.9000408625479380.549979568726031
1400.4370075200431810.8740150400863620.562992479956819

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.707926570703774 & 0.584146858592453 & 0.292073429296226 \tabularnewline
11 & 0.791422092954732 & 0.417155814090535 & 0.208577907045268 \tabularnewline
12 & 0.90455181212991 & 0.190896375740180 & 0.0954481878700898 \tabularnewline
13 & 0.978821901791424 & 0.0423561964171521 & 0.0211780982085761 \tabularnewline
14 & 0.965265702576131 & 0.0694685948477373 & 0.0347342974238686 \tabularnewline
15 & 0.958689006328004 & 0.082621987343991 & 0.0413109936719955 \tabularnewline
16 & 0.939039095681376 & 0.121921808637248 & 0.0609609043186238 \tabularnewline
17 & 0.962455515683758 & 0.0750889686324836 & 0.0375444843162418 \tabularnewline
18 & 0.964697711349097 & 0.0706045773018065 & 0.0353022886509033 \tabularnewline
19 & 0.961033026520748 & 0.0779339469585035 & 0.0389669734792517 \tabularnewline
20 & 0.949069735365128 & 0.101860529269744 & 0.050930264634872 \tabularnewline
21 & 0.954475954793758 & 0.091048090412483 & 0.0455240452062415 \tabularnewline
22 & 0.957277106564397 & 0.0854457868712065 & 0.0427228934356032 \tabularnewline
23 & 0.946415550501073 & 0.107168898997854 & 0.053584449498927 \tabularnewline
24 & 0.934349743397846 & 0.131300513204308 & 0.065650256602154 \tabularnewline
25 & 0.916740240824711 & 0.166519518350578 & 0.083259759175289 \tabularnewline
26 & 0.99662577471859 & 0.00674845056281847 & 0.00337422528140924 \tabularnewline
27 & 0.99570744551143 & 0.00858510897714174 & 0.00429255448857087 \tabularnewline
28 & 0.993531321932617 & 0.0129373561347662 & 0.0064686780673831 \tabularnewline
29 & 0.991264775624384 & 0.0174704487512315 & 0.00873522437561573 \tabularnewline
30 & 0.991033985017616 & 0.0179320299647673 & 0.00896601498238367 \tabularnewline
31 & 0.98762299726732 & 0.0247540054653618 & 0.0123770027326809 \tabularnewline
32 & 0.98253753706772 & 0.0349249258645602 & 0.0174624629322801 \tabularnewline
33 & 0.986490251472523 & 0.0270194970549536 & 0.0135097485274768 \tabularnewline
34 & 0.980692688264238 & 0.0386146234715248 & 0.0193073117357624 \tabularnewline
35 & 0.979772948333338 & 0.0404541033333245 & 0.0202270516666623 \tabularnewline
36 & 0.97373485505819 & 0.052530289883622 & 0.026265144941811 \tabularnewline
37 & 0.9789695084954 & 0.0420609830092019 & 0.0210304915046010 \tabularnewline
38 & 0.972087070234764 & 0.0558258595304728 & 0.0279129297652364 \tabularnewline
39 & 0.968619034263154 & 0.0627619314736917 & 0.0313809657368459 \tabularnewline
40 & 0.962242363033813 & 0.0755152739323749 & 0.0377576369661874 \tabularnewline
41 & 0.94994324819125 & 0.100113503617499 & 0.0500567518087494 \tabularnewline
42 & 0.947948309074729 & 0.104103381850543 & 0.0520516909252713 \tabularnewline
43 & 0.932449273508244 & 0.135101452983512 & 0.067550726491756 \tabularnewline
44 & 0.92320828762842 & 0.153583424743162 & 0.0767917123715808 \tabularnewline
45 & 0.928316009311869 & 0.143367981376263 & 0.0716839906881313 \tabularnewline
46 & 0.93833689985329 & 0.123326200293422 & 0.061663100146711 \tabularnewline
47 & 0.92241858281194 & 0.155162834376120 & 0.0775814171880598 \tabularnewline
48 & 0.90832431126385 & 0.183351377472301 & 0.0916756887361503 \tabularnewline
49 & 0.915555143634696 & 0.168889712730608 & 0.084444856365304 \tabularnewline
50 & 0.905437322286731 & 0.189125355426537 & 0.0945626777132687 \tabularnewline
51 & 0.88662207703862 & 0.22675584592276 & 0.11337792296138 \tabularnewline
52 & 0.875326433960514 & 0.249347132078973 & 0.124673566039486 \tabularnewline
53 & 0.849432565123134 & 0.301134869753732 & 0.150567434876866 \tabularnewline
54 & 0.856827237269673 & 0.286345525460654 & 0.143172762730327 \tabularnewline
55 & 0.856199176961389 & 0.287601646077222 & 0.143800823038611 \tabularnewline
56 & 0.827251506998528 & 0.345496986002944 & 0.172748493001472 \tabularnewline
57 & 0.81006731019831 & 0.379865379603381 & 0.189932689801691 \tabularnewline
58 & 0.78025115164022 & 0.439497696719561 & 0.219748848359781 \tabularnewline
59 & 0.87421655738853 & 0.251566885222941 & 0.125783442611470 \tabularnewline
60 & 0.858354028511547 & 0.283291942976906 & 0.141645971488453 \tabularnewline
61 & 0.88997624375896 & 0.220047512482079 & 0.110023756241040 \tabularnewline
62 & 0.873265615422121 & 0.253468769155758 & 0.126734384577879 \tabularnewline
63 & 0.90961718873141 & 0.180765622537181 & 0.0903828112685906 \tabularnewline
64 & 0.914011545004525 & 0.171976909990949 & 0.0859884549954746 \tabularnewline
65 & 0.906661935350621 & 0.186676129298758 & 0.093338064649379 \tabularnewline
66 & 0.987495284637022 & 0.0250094307259561 & 0.0125047153629781 \tabularnewline
67 & 0.984364868365097 & 0.0312702632698052 & 0.0156351316349026 \tabularnewline
68 & 0.987657063427906 & 0.0246858731441869 & 0.0123429365720935 \tabularnewline
69 & 0.984464406543615 & 0.0310711869127709 & 0.0155355934563854 \tabularnewline
70 & 0.98068847270715 & 0.0386230545856990 & 0.0193115272928495 \tabularnewline
71 & 0.974496179462599 & 0.0510076410748029 & 0.0255038205374014 \tabularnewline
72 & 0.983638976387831 & 0.0327220472243376 & 0.0163610236121688 \tabularnewline
73 & 0.979609328797364 & 0.0407813424052731 & 0.0203906712026366 \tabularnewline
74 & 0.973135130417712 & 0.0537297391645763 & 0.0268648695822882 \tabularnewline
75 & 0.981015488508856 & 0.0379690229822874 & 0.0189845114911437 \tabularnewline
76 & 0.974605266915566 & 0.0507894661688684 & 0.0253947330844342 \tabularnewline
77 & 0.984646770495668 & 0.0307064590086644 & 0.0153532295043322 \tabularnewline
78 & 0.980095570633206 & 0.0398088587335882 & 0.0199044293667941 \tabularnewline
79 & 0.974755080704555 & 0.05048983859089 & 0.025244919295445 \tabularnewline
80 & 0.975672785795474 & 0.048654428409052 & 0.024327214204526 \tabularnewline
81 & 0.968381156164995 & 0.0632376876700096 & 0.0316188438350048 \tabularnewline
82 & 0.96101432451771 & 0.0779713509645795 & 0.0389856754822897 \tabularnewline
83 & 0.952321206659246 & 0.0953575866815087 & 0.0476787933407543 \tabularnewline
84 & 0.943852062603842 & 0.112295874792317 & 0.0561479373961585 \tabularnewline
85 & 0.928574284650577 & 0.142851430698846 & 0.0714257153494229 \tabularnewline
86 & 0.910039314380572 & 0.179921371238855 & 0.0899606856194277 \tabularnewline
87 & 0.889110581373619 & 0.221778837252762 & 0.110889418626381 \tabularnewline
88 & 0.863687502423536 & 0.272624995152927 & 0.136312497576464 \tabularnewline
89 & 0.838624788246227 & 0.322750423507545 & 0.161375211753773 \tabularnewline
90 & 0.907505616393845 & 0.18498876721231 & 0.092494383606155 \tabularnewline
91 & 0.92503894558785 & 0.149922108824301 & 0.0749610544121505 \tabularnewline
92 & 0.9068682633522 & 0.186263473295600 & 0.0931317366477998 \tabularnewline
93 & 0.892164657894782 & 0.215670684210435 & 0.107835342105218 \tabularnewline
94 & 0.866949354416358 & 0.266101291167284 & 0.133050645583642 \tabularnewline
95 & 0.891515082811512 & 0.216969834376976 & 0.108484917188488 \tabularnewline
96 & 0.871265170728441 & 0.257469658543118 & 0.128734829271559 \tabularnewline
97 & 0.845260680476915 & 0.309478639046170 & 0.154739319523085 \tabularnewline
98 & 0.83590745026863 & 0.328185099462739 & 0.164092549731370 \tabularnewline
99 & 0.834777760668084 & 0.330444478663832 & 0.165222239331916 \tabularnewline
100 & 0.801623654034906 & 0.396752691930188 & 0.198376345965094 \tabularnewline
101 & 0.807873847893213 & 0.384252304213573 & 0.192126152106786 \tabularnewline
102 & 0.770392957998626 & 0.459214084002748 & 0.229607042001374 \tabularnewline
103 & 0.744484260590067 & 0.511031478819865 & 0.255515739409933 \tabularnewline
104 & 0.812278487581478 & 0.375443024837045 & 0.187721512418522 \tabularnewline
105 & 0.789812323837931 & 0.420375352324137 & 0.210187676162069 \tabularnewline
106 & 0.802929279747815 & 0.39414144050437 & 0.197070720252185 \tabularnewline
107 & 0.766208203508212 & 0.467583592983576 & 0.233791796491788 \tabularnewline
108 & 0.777499256891151 & 0.445001486217697 & 0.222500743108849 \tabularnewline
109 & 0.842649469349747 & 0.314701061300506 & 0.157350530650253 \tabularnewline
110 & 0.81002861492374 & 0.37994277015252 & 0.18997138507626 \tabularnewline
111 & 0.777335455149941 & 0.445329089700118 & 0.222664544850059 \tabularnewline
112 & 0.79848549850053 & 0.403029002998941 & 0.201514501499470 \tabularnewline
113 & 0.83648910608806 & 0.327021787823880 & 0.163510893911940 \tabularnewline
114 & 0.892727067302901 & 0.214545865394197 & 0.107272932697099 \tabularnewline
115 & 0.949829384960295 & 0.100341230079411 & 0.0501706150397054 \tabularnewline
116 & 0.932021434320604 & 0.135957131358792 & 0.0679785656793958 \tabularnewline
117 & 0.933245094372629 & 0.133509811254742 & 0.0667549056273708 \tabularnewline
118 & 0.910035295377994 & 0.179929409244011 & 0.0899647046220055 \tabularnewline
119 & 0.894217329139994 & 0.211565341720013 & 0.105782670860007 \tabularnewline
120 & 0.859952906302509 & 0.280094187394982 & 0.140047093697491 \tabularnewline
121 & 0.8205816344487 & 0.358836731102601 & 0.179418365551300 \tabularnewline
122 & 0.776779372858175 & 0.446441254283649 & 0.223220627141825 \tabularnewline
123 & 0.748370972136259 & 0.503258055727482 & 0.251629027863741 \tabularnewline
124 & 0.730476772605416 & 0.539046454789168 & 0.269523227394584 \tabularnewline
125 & 0.680087831626453 & 0.639824336747093 & 0.319912168373547 \tabularnewline
126 & 0.67916202737823 & 0.64167594524354 & 0.32083797262177 \tabularnewline
127 & 0.621888200621245 & 0.75622359875751 & 0.378111799378755 \tabularnewline
128 & 0.623101623770175 & 0.75379675245965 & 0.376898376229825 \tabularnewline
129 & 0.703265672736142 & 0.593468654527717 & 0.296734327263858 \tabularnewline
130 & 0.63020481443214 & 0.739590371135721 & 0.369795185567861 \tabularnewline
131 & 0.554107485110172 & 0.891785029779656 & 0.445892514889828 \tabularnewline
132 & 0.581236153274366 & 0.837527693451268 & 0.418763846725634 \tabularnewline
133 & 0.497423495760365 & 0.994846991520731 & 0.502576504239635 \tabularnewline
134 & 0.492121878937946 & 0.984243757875893 & 0.507878121062054 \tabularnewline
135 & 0.459099676346835 & 0.91819935269367 & 0.540900323653165 \tabularnewline
136 & 0.57210884473893 & 0.85578231052214 & 0.42789115526107 \tabularnewline
137 & 0.522119901169434 & 0.955760197661133 & 0.477880098830566 \tabularnewline
138 & 0.393374354718099 & 0.786748709436198 & 0.606625645281901 \tabularnewline
139 & 0.450020431273969 & 0.900040862547938 & 0.549979568726031 \tabularnewline
140 & 0.437007520043181 & 0.874015040086362 & 0.562992479956819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.707926570703774[/C][C]0.584146858592453[/C][C]0.292073429296226[/C][/ROW]
[ROW][C]11[/C][C]0.791422092954732[/C][C]0.417155814090535[/C][C]0.208577907045268[/C][/ROW]
[ROW][C]12[/C][C]0.90455181212991[/C][C]0.190896375740180[/C][C]0.0954481878700898[/C][/ROW]
[ROW][C]13[/C][C]0.978821901791424[/C][C]0.0423561964171521[/C][C]0.0211780982085761[/C][/ROW]
[ROW][C]14[/C][C]0.965265702576131[/C][C]0.0694685948477373[/C][C]0.0347342974238686[/C][/ROW]
[ROW][C]15[/C][C]0.958689006328004[/C][C]0.082621987343991[/C][C]0.0413109936719955[/C][/ROW]
[ROW][C]16[/C][C]0.939039095681376[/C][C]0.121921808637248[/C][C]0.0609609043186238[/C][/ROW]
[ROW][C]17[/C][C]0.962455515683758[/C][C]0.0750889686324836[/C][C]0.0375444843162418[/C][/ROW]
[ROW][C]18[/C][C]0.964697711349097[/C][C]0.0706045773018065[/C][C]0.0353022886509033[/C][/ROW]
[ROW][C]19[/C][C]0.961033026520748[/C][C]0.0779339469585035[/C][C]0.0389669734792517[/C][/ROW]
[ROW][C]20[/C][C]0.949069735365128[/C][C]0.101860529269744[/C][C]0.050930264634872[/C][/ROW]
[ROW][C]21[/C][C]0.954475954793758[/C][C]0.091048090412483[/C][C]0.0455240452062415[/C][/ROW]
[ROW][C]22[/C][C]0.957277106564397[/C][C]0.0854457868712065[/C][C]0.0427228934356032[/C][/ROW]
[ROW][C]23[/C][C]0.946415550501073[/C][C]0.107168898997854[/C][C]0.053584449498927[/C][/ROW]
[ROW][C]24[/C][C]0.934349743397846[/C][C]0.131300513204308[/C][C]0.065650256602154[/C][/ROW]
[ROW][C]25[/C][C]0.916740240824711[/C][C]0.166519518350578[/C][C]0.083259759175289[/C][/ROW]
[ROW][C]26[/C][C]0.99662577471859[/C][C]0.00674845056281847[/C][C]0.00337422528140924[/C][/ROW]
[ROW][C]27[/C][C]0.99570744551143[/C][C]0.00858510897714174[/C][C]0.00429255448857087[/C][/ROW]
[ROW][C]28[/C][C]0.993531321932617[/C][C]0.0129373561347662[/C][C]0.0064686780673831[/C][/ROW]
[ROW][C]29[/C][C]0.991264775624384[/C][C]0.0174704487512315[/C][C]0.00873522437561573[/C][/ROW]
[ROW][C]30[/C][C]0.991033985017616[/C][C]0.0179320299647673[/C][C]0.00896601498238367[/C][/ROW]
[ROW][C]31[/C][C]0.98762299726732[/C][C]0.0247540054653618[/C][C]0.0123770027326809[/C][/ROW]
[ROW][C]32[/C][C]0.98253753706772[/C][C]0.0349249258645602[/C][C]0.0174624629322801[/C][/ROW]
[ROW][C]33[/C][C]0.986490251472523[/C][C]0.0270194970549536[/C][C]0.0135097485274768[/C][/ROW]
[ROW][C]34[/C][C]0.980692688264238[/C][C]0.0386146234715248[/C][C]0.0193073117357624[/C][/ROW]
[ROW][C]35[/C][C]0.979772948333338[/C][C]0.0404541033333245[/C][C]0.0202270516666623[/C][/ROW]
[ROW][C]36[/C][C]0.97373485505819[/C][C]0.052530289883622[/C][C]0.026265144941811[/C][/ROW]
[ROW][C]37[/C][C]0.9789695084954[/C][C]0.0420609830092019[/C][C]0.0210304915046010[/C][/ROW]
[ROW][C]38[/C][C]0.972087070234764[/C][C]0.0558258595304728[/C][C]0.0279129297652364[/C][/ROW]
[ROW][C]39[/C][C]0.968619034263154[/C][C]0.0627619314736917[/C][C]0.0313809657368459[/C][/ROW]
[ROW][C]40[/C][C]0.962242363033813[/C][C]0.0755152739323749[/C][C]0.0377576369661874[/C][/ROW]
[ROW][C]41[/C][C]0.94994324819125[/C][C]0.100113503617499[/C][C]0.0500567518087494[/C][/ROW]
[ROW][C]42[/C][C]0.947948309074729[/C][C]0.104103381850543[/C][C]0.0520516909252713[/C][/ROW]
[ROW][C]43[/C][C]0.932449273508244[/C][C]0.135101452983512[/C][C]0.067550726491756[/C][/ROW]
[ROW][C]44[/C][C]0.92320828762842[/C][C]0.153583424743162[/C][C]0.0767917123715808[/C][/ROW]
[ROW][C]45[/C][C]0.928316009311869[/C][C]0.143367981376263[/C][C]0.0716839906881313[/C][/ROW]
[ROW][C]46[/C][C]0.93833689985329[/C][C]0.123326200293422[/C][C]0.061663100146711[/C][/ROW]
[ROW][C]47[/C][C]0.92241858281194[/C][C]0.155162834376120[/C][C]0.0775814171880598[/C][/ROW]
[ROW][C]48[/C][C]0.90832431126385[/C][C]0.183351377472301[/C][C]0.0916756887361503[/C][/ROW]
[ROW][C]49[/C][C]0.915555143634696[/C][C]0.168889712730608[/C][C]0.084444856365304[/C][/ROW]
[ROW][C]50[/C][C]0.905437322286731[/C][C]0.189125355426537[/C][C]0.0945626777132687[/C][/ROW]
[ROW][C]51[/C][C]0.88662207703862[/C][C]0.22675584592276[/C][C]0.11337792296138[/C][/ROW]
[ROW][C]52[/C][C]0.875326433960514[/C][C]0.249347132078973[/C][C]0.124673566039486[/C][/ROW]
[ROW][C]53[/C][C]0.849432565123134[/C][C]0.301134869753732[/C][C]0.150567434876866[/C][/ROW]
[ROW][C]54[/C][C]0.856827237269673[/C][C]0.286345525460654[/C][C]0.143172762730327[/C][/ROW]
[ROW][C]55[/C][C]0.856199176961389[/C][C]0.287601646077222[/C][C]0.143800823038611[/C][/ROW]
[ROW][C]56[/C][C]0.827251506998528[/C][C]0.345496986002944[/C][C]0.172748493001472[/C][/ROW]
[ROW][C]57[/C][C]0.81006731019831[/C][C]0.379865379603381[/C][C]0.189932689801691[/C][/ROW]
[ROW][C]58[/C][C]0.78025115164022[/C][C]0.439497696719561[/C][C]0.219748848359781[/C][/ROW]
[ROW][C]59[/C][C]0.87421655738853[/C][C]0.251566885222941[/C][C]0.125783442611470[/C][/ROW]
[ROW][C]60[/C][C]0.858354028511547[/C][C]0.283291942976906[/C][C]0.141645971488453[/C][/ROW]
[ROW][C]61[/C][C]0.88997624375896[/C][C]0.220047512482079[/C][C]0.110023756241040[/C][/ROW]
[ROW][C]62[/C][C]0.873265615422121[/C][C]0.253468769155758[/C][C]0.126734384577879[/C][/ROW]
[ROW][C]63[/C][C]0.90961718873141[/C][C]0.180765622537181[/C][C]0.0903828112685906[/C][/ROW]
[ROW][C]64[/C][C]0.914011545004525[/C][C]0.171976909990949[/C][C]0.0859884549954746[/C][/ROW]
[ROW][C]65[/C][C]0.906661935350621[/C][C]0.186676129298758[/C][C]0.093338064649379[/C][/ROW]
[ROW][C]66[/C][C]0.987495284637022[/C][C]0.0250094307259561[/C][C]0.0125047153629781[/C][/ROW]
[ROW][C]67[/C][C]0.984364868365097[/C][C]0.0312702632698052[/C][C]0.0156351316349026[/C][/ROW]
[ROW][C]68[/C][C]0.987657063427906[/C][C]0.0246858731441869[/C][C]0.0123429365720935[/C][/ROW]
[ROW][C]69[/C][C]0.984464406543615[/C][C]0.0310711869127709[/C][C]0.0155355934563854[/C][/ROW]
[ROW][C]70[/C][C]0.98068847270715[/C][C]0.0386230545856990[/C][C]0.0193115272928495[/C][/ROW]
[ROW][C]71[/C][C]0.974496179462599[/C][C]0.0510076410748029[/C][C]0.0255038205374014[/C][/ROW]
[ROW][C]72[/C][C]0.983638976387831[/C][C]0.0327220472243376[/C][C]0.0163610236121688[/C][/ROW]
[ROW][C]73[/C][C]0.979609328797364[/C][C]0.0407813424052731[/C][C]0.0203906712026366[/C][/ROW]
[ROW][C]74[/C][C]0.973135130417712[/C][C]0.0537297391645763[/C][C]0.0268648695822882[/C][/ROW]
[ROW][C]75[/C][C]0.981015488508856[/C][C]0.0379690229822874[/C][C]0.0189845114911437[/C][/ROW]
[ROW][C]76[/C][C]0.974605266915566[/C][C]0.0507894661688684[/C][C]0.0253947330844342[/C][/ROW]
[ROW][C]77[/C][C]0.984646770495668[/C][C]0.0307064590086644[/C][C]0.0153532295043322[/C][/ROW]
[ROW][C]78[/C][C]0.980095570633206[/C][C]0.0398088587335882[/C][C]0.0199044293667941[/C][/ROW]
[ROW][C]79[/C][C]0.974755080704555[/C][C]0.05048983859089[/C][C]0.025244919295445[/C][/ROW]
[ROW][C]80[/C][C]0.975672785795474[/C][C]0.048654428409052[/C][C]0.024327214204526[/C][/ROW]
[ROW][C]81[/C][C]0.968381156164995[/C][C]0.0632376876700096[/C][C]0.0316188438350048[/C][/ROW]
[ROW][C]82[/C][C]0.96101432451771[/C][C]0.0779713509645795[/C][C]0.0389856754822897[/C][/ROW]
[ROW][C]83[/C][C]0.952321206659246[/C][C]0.0953575866815087[/C][C]0.0476787933407543[/C][/ROW]
[ROW][C]84[/C][C]0.943852062603842[/C][C]0.112295874792317[/C][C]0.0561479373961585[/C][/ROW]
[ROW][C]85[/C][C]0.928574284650577[/C][C]0.142851430698846[/C][C]0.0714257153494229[/C][/ROW]
[ROW][C]86[/C][C]0.910039314380572[/C][C]0.179921371238855[/C][C]0.0899606856194277[/C][/ROW]
[ROW][C]87[/C][C]0.889110581373619[/C][C]0.221778837252762[/C][C]0.110889418626381[/C][/ROW]
[ROW][C]88[/C][C]0.863687502423536[/C][C]0.272624995152927[/C][C]0.136312497576464[/C][/ROW]
[ROW][C]89[/C][C]0.838624788246227[/C][C]0.322750423507545[/C][C]0.161375211753773[/C][/ROW]
[ROW][C]90[/C][C]0.907505616393845[/C][C]0.18498876721231[/C][C]0.092494383606155[/C][/ROW]
[ROW][C]91[/C][C]0.92503894558785[/C][C]0.149922108824301[/C][C]0.0749610544121505[/C][/ROW]
[ROW][C]92[/C][C]0.9068682633522[/C][C]0.186263473295600[/C][C]0.0931317366477998[/C][/ROW]
[ROW][C]93[/C][C]0.892164657894782[/C][C]0.215670684210435[/C][C]0.107835342105218[/C][/ROW]
[ROW][C]94[/C][C]0.866949354416358[/C][C]0.266101291167284[/C][C]0.133050645583642[/C][/ROW]
[ROW][C]95[/C][C]0.891515082811512[/C][C]0.216969834376976[/C][C]0.108484917188488[/C][/ROW]
[ROW][C]96[/C][C]0.871265170728441[/C][C]0.257469658543118[/C][C]0.128734829271559[/C][/ROW]
[ROW][C]97[/C][C]0.845260680476915[/C][C]0.309478639046170[/C][C]0.154739319523085[/C][/ROW]
[ROW][C]98[/C][C]0.83590745026863[/C][C]0.328185099462739[/C][C]0.164092549731370[/C][/ROW]
[ROW][C]99[/C][C]0.834777760668084[/C][C]0.330444478663832[/C][C]0.165222239331916[/C][/ROW]
[ROW][C]100[/C][C]0.801623654034906[/C][C]0.396752691930188[/C][C]0.198376345965094[/C][/ROW]
[ROW][C]101[/C][C]0.807873847893213[/C][C]0.384252304213573[/C][C]0.192126152106786[/C][/ROW]
[ROW][C]102[/C][C]0.770392957998626[/C][C]0.459214084002748[/C][C]0.229607042001374[/C][/ROW]
[ROW][C]103[/C][C]0.744484260590067[/C][C]0.511031478819865[/C][C]0.255515739409933[/C][/ROW]
[ROW][C]104[/C][C]0.812278487581478[/C][C]0.375443024837045[/C][C]0.187721512418522[/C][/ROW]
[ROW][C]105[/C][C]0.789812323837931[/C][C]0.420375352324137[/C][C]0.210187676162069[/C][/ROW]
[ROW][C]106[/C][C]0.802929279747815[/C][C]0.39414144050437[/C][C]0.197070720252185[/C][/ROW]
[ROW][C]107[/C][C]0.766208203508212[/C][C]0.467583592983576[/C][C]0.233791796491788[/C][/ROW]
[ROW][C]108[/C][C]0.777499256891151[/C][C]0.445001486217697[/C][C]0.222500743108849[/C][/ROW]
[ROW][C]109[/C][C]0.842649469349747[/C][C]0.314701061300506[/C][C]0.157350530650253[/C][/ROW]
[ROW][C]110[/C][C]0.81002861492374[/C][C]0.37994277015252[/C][C]0.18997138507626[/C][/ROW]
[ROW][C]111[/C][C]0.777335455149941[/C][C]0.445329089700118[/C][C]0.222664544850059[/C][/ROW]
[ROW][C]112[/C][C]0.79848549850053[/C][C]0.403029002998941[/C][C]0.201514501499470[/C][/ROW]
[ROW][C]113[/C][C]0.83648910608806[/C][C]0.327021787823880[/C][C]0.163510893911940[/C][/ROW]
[ROW][C]114[/C][C]0.892727067302901[/C][C]0.214545865394197[/C][C]0.107272932697099[/C][/ROW]
[ROW][C]115[/C][C]0.949829384960295[/C][C]0.100341230079411[/C][C]0.0501706150397054[/C][/ROW]
[ROW][C]116[/C][C]0.932021434320604[/C][C]0.135957131358792[/C][C]0.0679785656793958[/C][/ROW]
[ROW][C]117[/C][C]0.933245094372629[/C][C]0.133509811254742[/C][C]0.0667549056273708[/C][/ROW]
[ROW][C]118[/C][C]0.910035295377994[/C][C]0.179929409244011[/C][C]0.0899647046220055[/C][/ROW]
[ROW][C]119[/C][C]0.894217329139994[/C][C]0.211565341720013[/C][C]0.105782670860007[/C][/ROW]
[ROW][C]120[/C][C]0.859952906302509[/C][C]0.280094187394982[/C][C]0.140047093697491[/C][/ROW]
[ROW][C]121[/C][C]0.8205816344487[/C][C]0.358836731102601[/C][C]0.179418365551300[/C][/ROW]
[ROW][C]122[/C][C]0.776779372858175[/C][C]0.446441254283649[/C][C]0.223220627141825[/C][/ROW]
[ROW][C]123[/C][C]0.748370972136259[/C][C]0.503258055727482[/C][C]0.251629027863741[/C][/ROW]
[ROW][C]124[/C][C]0.730476772605416[/C][C]0.539046454789168[/C][C]0.269523227394584[/C][/ROW]
[ROW][C]125[/C][C]0.680087831626453[/C][C]0.639824336747093[/C][C]0.319912168373547[/C][/ROW]
[ROW][C]126[/C][C]0.67916202737823[/C][C]0.64167594524354[/C][C]0.32083797262177[/C][/ROW]
[ROW][C]127[/C][C]0.621888200621245[/C][C]0.75622359875751[/C][C]0.378111799378755[/C][/ROW]
[ROW][C]128[/C][C]0.623101623770175[/C][C]0.75379675245965[/C][C]0.376898376229825[/C][/ROW]
[ROW][C]129[/C][C]0.703265672736142[/C][C]0.593468654527717[/C][C]0.296734327263858[/C][/ROW]
[ROW][C]130[/C][C]0.63020481443214[/C][C]0.739590371135721[/C][C]0.369795185567861[/C][/ROW]
[ROW][C]131[/C][C]0.554107485110172[/C][C]0.891785029779656[/C][C]0.445892514889828[/C][/ROW]
[ROW][C]132[/C][C]0.581236153274366[/C][C]0.837527693451268[/C][C]0.418763846725634[/C][/ROW]
[ROW][C]133[/C][C]0.497423495760365[/C][C]0.994846991520731[/C][C]0.502576504239635[/C][/ROW]
[ROW][C]134[/C][C]0.492121878937946[/C][C]0.984243757875893[/C][C]0.507878121062054[/C][/ROW]
[ROW][C]135[/C][C]0.459099676346835[/C][C]0.91819935269367[/C][C]0.540900323653165[/C][/ROW]
[ROW][C]136[/C][C]0.57210884473893[/C][C]0.85578231052214[/C][C]0.42789115526107[/C][/ROW]
[ROW][C]137[/C][C]0.522119901169434[/C][C]0.955760197661133[/C][C]0.477880098830566[/C][/ROW]
[ROW][C]138[/C][C]0.393374354718099[/C][C]0.786748709436198[/C][C]0.606625645281901[/C][/ROW]
[ROW][C]139[/C][C]0.450020431273969[/C][C]0.900040862547938[/C][C]0.549979568726031[/C][/ROW]
[ROW][C]140[/C][C]0.437007520043181[/C][C]0.874015040086362[/C][C]0.562992479956819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7079265707037740.5841468585924530.292073429296226
110.7914220929547320.4171558140905350.208577907045268
120.904551812129910.1908963757401800.0954481878700898
130.9788219017914240.04235619641715210.0211780982085761
140.9652657025761310.06946859484773730.0347342974238686
150.9586890063280040.0826219873439910.0413109936719955
160.9390390956813760.1219218086372480.0609609043186238
170.9624555156837580.07508896863248360.0375444843162418
180.9646977113490970.07060457730180650.0353022886509033
190.9610330265207480.07793394695850350.0389669734792517
200.9490697353651280.1018605292697440.050930264634872
210.9544759547937580.0910480904124830.0455240452062415
220.9572771065643970.08544578687120650.0427228934356032
230.9464155505010730.1071688989978540.053584449498927
240.9343497433978460.1313005132043080.065650256602154
250.9167402408247110.1665195183505780.083259759175289
260.996625774718590.006748450562818470.00337422528140924
270.995707445511430.008585108977141740.00429255448857087
280.9935313219326170.01293735613476620.0064686780673831
290.9912647756243840.01747044875123150.00873522437561573
300.9910339850176160.01793202996476730.00896601498238367
310.987622997267320.02475400546536180.0123770027326809
320.982537537067720.03492492586456020.0174624629322801
330.9864902514725230.02701949705495360.0135097485274768
340.9806926882642380.03861462347152480.0193073117357624
350.9797729483333380.04045410333332450.0202270516666623
360.973734855058190.0525302898836220.026265144941811
370.97896950849540.04206098300920190.0210304915046010
380.9720870702347640.05582585953047280.0279129297652364
390.9686190342631540.06276193147369170.0313809657368459
400.9622423630338130.07551527393237490.0377576369661874
410.949943248191250.1001135036174990.0500567518087494
420.9479483090747290.1041033818505430.0520516909252713
430.9324492735082440.1351014529835120.067550726491756
440.923208287628420.1535834247431620.0767917123715808
450.9283160093118690.1433679813762630.0716839906881313
460.938336899853290.1233262002934220.061663100146711
470.922418582811940.1551628343761200.0775814171880598
480.908324311263850.1833513774723010.0916756887361503
490.9155551436346960.1688897127306080.084444856365304
500.9054373222867310.1891253554265370.0945626777132687
510.886622077038620.226755845922760.11337792296138
520.8753264339605140.2493471320789730.124673566039486
530.8494325651231340.3011348697537320.150567434876866
540.8568272372696730.2863455254606540.143172762730327
550.8561991769613890.2876016460772220.143800823038611
560.8272515069985280.3454969860029440.172748493001472
570.810067310198310.3798653796033810.189932689801691
580.780251151640220.4394976967195610.219748848359781
590.874216557388530.2515668852229410.125783442611470
600.8583540285115470.2832919429769060.141645971488453
610.889976243758960.2200475124820790.110023756241040
620.8732656154221210.2534687691557580.126734384577879
630.909617188731410.1807656225371810.0903828112685906
640.9140115450045250.1719769099909490.0859884549954746
650.9066619353506210.1866761292987580.093338064649379
660.9874952846370220.02500943072595610.0125047153629781
670.9843648683650970.03127026326980520.0156351316349026
680.9876570634279060.02468587314418690.0123429365720935
690.9844644065436150.03107118691277090.0155355934563854
700.980688472707150.03862305458569900.0193115272928495
710.9744961794625990.05100764107480290.0255038205374014
720.9836389763878310.03272204722433760.0163610236121688
730.9796093287973640.04078134240527310.0203906712026366
740.9731351304177120.05372973916457630.0268648695822882
750.9810154885088560.03796902298228740.0189845114911437
760.9746052669155660.05078946616886840.0253947330844342
770.9846467704956680.03070645900866440.0153532295043322
780.9800955706332060.03980885873358820.0199044293667941
790.9747550807045550.050489838590890.025244919295445
800.9756727857954740.0486544284090520.024327214204526
810.9683811561649950.06323768767000960.0316188438350048
820.961014324517710.07797135096457950.0389856754822897
830.9523212066592460.09535758668150870.0476787933407543
840.9438520626038420.1122958747923170.0561479373961585
850.9285742846505770.1428514306988460.0714257153494229
860.9100393143805720.1799213712388550.0899606856194277
870.8891105813736190.2217788372527620.110889418626381
880.8636875024235360.2726249951529270.136312497576464
890.8386247882462270.3227504235075450.161375211753773
900.9075056163938450.184988767212310.092494383606155
910.925038945587850.1499221088243010.0749610544121505
920.90686826335220.1862634732956000.0931317366477998
930.8921646578947820.2156706842104350.107835342105218
940.8669493544163580.2661012911672840.133050645583642
950.8915150828115120.2169698343769760.108484917188488
960.8712651707284410.2574696585431180.128734829271559
970.8452606804769150.3094786390461700.154739319523085
980.835907450268630.3281850994627390.164092549731370
990.8347777606680840.3304444786638320.165222239331916
1000.8016236540349060.3967526919301880.198376345965094
1010.8078738478932130.3842523042135730.192126152106786
1020.7703929579986260.4592140840027480.229607042001374
1030.7444842605900670.5110314788198650.255515739409933
1040.8122784875814780.3754430248370450.187721512418522
1050.7898123238379310.4203753523241370.210187676162069
1060.8029292797478150.394141440504370.197070720252185
1070.7662082035082120.4675835929835760.233791796491788
1080.7774992568911510.4450014862176970.222500743108849
1090.8426494693497470.3147010613005060.157350530650253
1100.810028614923740.379942770152520.18997138507626
1110.7773354551499410.4453290897001180.222664544850059
1120.798485498500530.4030290029989410.201514501499470
1130.836489106088060.3270217878238800.163510893911940
1140.8927270673029010.2145458653941970.107272932697099
1150.9498293849602950.1003412300794110.0501706150397054
1160.9320214343206040.1359571313587920.0679785656793958
1170.9332450943726290.1335098112547420.0667549056273708
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1200.8599529063025090.2800941873949820.140047093697491
1210.82058163444870.3588367311026010.179418365551300
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1230.7483709721362590.5032580557274820.251629027863741
1240.7304767726054160.5390464547891680.269523227394584
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1290.7032656727361420.5934686545277170.296734327263858
1300.630204814432140.7395903711357210.369795185567861
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1330.4974234957603650.9948469915207310.502576504239635
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1350.4590996763468350.918199352693670.540900323653165
1360.572108844738930.855782310522140.42789115526107
1370.5221199011694340.9557601976611330.477880098830566
1380.3933743547180990.7867487094361980.606625645281901
1390.4500204312739690.9000408625479380.549979568726031
1400.4370075200431810.8740150400863620.562992479956819






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0152671755725191NOK
5% type I error level230.175572519083969NOK
10% type I error level410.312977099236641NOK

\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 & 2 & 0.0152671755725191 & NOK \tabularnewline
5% type I error level & 23 & 0.175572519083969 & NOK \tabularnewline
10% type I error level & 41 & 0.312977099236641 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98707&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]2[/C][C]0.0152671755725191[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.175572519083969[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.312977099236641[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98707&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98707&T=6

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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 level20.0152671755725191NOK
5% type I error level230.175572519083969NOK
10% type I error level410.312977099236641NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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')
}