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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 computationTue, 28 Dec 2010 11:47:43 +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/Dec/28/t1293536724g4w3es2qehr5590.htm/, Retrieved Sun, 05 May 2024 04:47:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116295, Retrieved Sun, 05 May 2024 04:47:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-26 15:59:17] [dcd1a35a8985187cb1e9de87792355b2]
-         [Multiple Regression] [] [2010-12-28 11:47:43] [82d760768aff8bf374d9817688c406af] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27	5	40	26	49	35
36	4	45	25	45	34
25	4	38	17	54	13
27	3	28	37	36	35
25	3		35	36	28
44	3	38	15	53	32
50	4	39	27	46	35
41	4	37	36	42	36
48	5	30	25	41	27
43	4	30	30	45	29
47	2	30	27	47	27
41	3	26	33	42	28
44	2	29	29	45	29
47	5	31	30	40	28
40	3	27	25	45	30
46	3	25	23	40	25
28	3	39	26	42	15
56	3	35	24	45	33
49	4	27	35	47	31
25	4	40	39	31	37
41	4	34	23	46	37
26	3	32	32	34	34
50	5	34	29	43	32
47	4	38	26	45	21
52	2	21	21	42	25
37	5	33	35	51	32
41	3	27	23	44	28
45	4	35	21	47	22
26	4	33	28	47	25
	3	36	30	41	26
52	4		21	44	34
46	2	37	29	51	34
58	3	37	28	46	36
54	5	37	19	47	36
29	3	32	26	46	26
50	3	25	33	38	26
43	2	31	34	50	34
30	3	33	33	48	33
47	2	18	40	36	31
45	3	42	24	51	33
48	1	26	35	35	22
48	3	26	35	49	29
26	4	32	32	38	24
46	5	31	20	47	37
	3	29	35	36	32
50	3	35	35	47	23
25	4	44	21	46	29
47	2	35	33	43	35
47	2	30	40	53	20
41	3	32	22	55	28
45	2	24	35	39	26
41	4	34	20	55	36
45	5	27	28	41	26
40	3	31	46	33	33
29	4	38	18	52	25
34	5	41	22	42	29
45	5	40	20	56	32
52	3	25	25	46	35
41	4	19	31	33	24
48	3	33	21	51	31
45	3	27	23	46	29
54	2	45	26	46	27
25	3	27	34	50	29
26	4	30	31	46	29
28	4	42	23	51	27
50	4	21	31	48	34
48	4	32	26	44	32
51	3	31	36	38	31
53	3	36	28	42	31
37	3	34	34	39	31
56	2	11	25	45	16
43	3	35	33	31	25
34	3	39	46	29	27
42	3	32	24	48	32
32	3	28	32	38	28
31	5	45	33	55	25
46	3	18	42	32	25
30	5	35	17	51	36
47	4	35	36	53	36
33	4	36	40	47	36
25	4	34	30	45	27
25	5	34	19	33	29
21	4	38	33	49	32
36	5	28	35	46	29
50	3	23	23	42	31
48	3	37	15	56	34
48	2	29	38	35	27
25	3	28	37	40	28
48	4	30	23	44	32
49	5	24	41	46	33
27	5	36	34	46	29
28	3	40	38	39	32
43	2	37	45	35	35
48	3	27	27	48	33
48	4	25	46	42	27
25	1	22	26	39	16
49	4	21	44	39	32
26	3	28	36	41	26
51	3	34	20	52	32
25	4	32	44	45	38
29	3	23	27	42	24
29	4	29	27	44	26
43	2	35	41	33	19
46	3	31	30	42	37
44	3	36	33	46	25
25	3	32	37	45	24
51	2	35	30	40	23
42	5	45	20	48	28
53	5	29	44	32	38
25	4	41	20	53	28
49	2	36	33	39	28
51	3	37	31	45	26
20	3	25	23	36	21
44	3	36	33	38	35
38	4	34	33	49	31
46	5	33	32	46	34
42	4	32	25	43	30
29		40	22	37	30
46	4	27	16	48	24
49	2	24	36	45	27
51	3	26	35	32	26
38	3		25	46	30
41	1	13	27	20	15
47	3	22	32	42	28
44	3	29	36	45	34
47	3	30	51	29	29
46	3	24	30	51	26
44	4		20	55	31
28	3	26	29	50	28
47	4	37	26	44	33
28	4	36	20	41	32
41	5	38	40	40	33
45	4	34	29	47	31
46	4	35	32	42	37
46	4	32	33	40	27
22	3	44	32	51	19
33	3	40	34	43	27
41	4	24	24	45	31
47	5	36	25	41	38
25	3	20	41	41	22
42	3	28	39	37	35
47	3	18	21	46	35
50	3	23	38	38	30
55	5	28	28	39	41
21	3	30	37	45	25
	3	30	26	46	28
52	3	43	30	39	45
49	4	20	25	21	21
46	4	37	38	31	33
	4	24	31	35	25
45	3	33	31	49	29
52	3	43	27	40	31
	3	27	21	45	29
40	4	22	26	46	31
49	4	28	37	45	31
38	5	18	28	34	25
32	5	38	29	41	27
46	4	23	33	43	26
32	3	38	41	45	26
41	3	21	19	48	23
43	3	25	37	43	27
44	4		36	45	24
47	5	30	27	45	35
28	3	25	33	34	24
52	1	17	29	40	32
27	2	26	42	40	24
45	5	39	27	55	24
27	4	27	47	44	38
25	4	33	17	44	36
28	4	47	34	48	24
25	3	37	32	51	18
52	4	34	25	49	34
44	3	24	27	33	23
43	3	25	37	43	35
47	4	20	34	44	22
52	4	34	27	44	34
40	2	22	37	41	28
42	3	39	32	45	34
45	5	33	26	44	32
45	2	35	29	44	24
50	5	26	28	40	34
49	3	32	19	48	33
52	2	22	46	49	33
48	3	39	31	46	29
51	3	35	42	49	38
49	4	21	33	55	24
31	4	27	39	51	25
43	3	31	27	46	37
31	3	20	35	37	33
28	4	28	23	43	30
43	4	26	32	41	22
31	3	36	22	45	28
51	3	16	17	39	24
58	4	34	35	38	33
25	5	30	34	41	37

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Intrinsieke_waarden[t] = + 92.3771983580845 -0.304005715886726leeftijd[t] -0.516637029897194opleiding[t] -0.541055956742284Neuroticisme[t] -0.47626425329013Extraversie[t] -0.332630416187557`Openheid `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intrinsieke_waarden[t] =  +  92.3771983580845 -0.304005715886726leeftijd[t] -0.516637029897194opleiding[t] -0.541055956742284Neuroticisme[t] -0.47626425329013Extraversie[t] -0.332630416187557`Openheid

`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intrinsieke_waarden[t] =  +  92.3771983580845 -0.304005715886726leeftijd[t] -0.516637029897194opleiding[t] -0.541055956742284Neuroticisme[t] -0.47626425329013Extraversie[t] -0.332630416187557`Openheid

`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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
Intrinsieke_waarden[t] = + 92.3771983580845 -0.304005715886726leeftijd[t] -0.516637029897194opleiding[t] -0.541055956742284Neuroticisme[t] -0.47626425329013Extraversie[t] -0.332630416187557`Openheid `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)92.37719835808457.260912.722600
leeftijd-0.3040057158867260.081221-3.74290.0002410.000121
opleiding-0.5166370298971940.087621-5.896300
Neuroticisme-0.5410559567422840.073745-7.336900
Extraversie-0.476264253290130.091552-5.20211e-060
`Openheid `-0.3326304161875570.082601-4.0278.2e-054.1e-05

\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) & 92.3771983580845 & 7.2609 & 12.7226 & 0 & 0 \tabularnewline
leeftijd & -0.304005715886726 & 0.081221 & -3.7429 & 0.000241 & 0.000121 \tabularnewline
opleiding & -0.516637029897194 & 0.087621 & -5.8963 & 0 & 0 \tabularnewline
Neuroticisme & -0.541055956742284 & 0.073745 & -7.3369 & 0 & 0 \tabularnewline
Extraversie & -0.47626425329013 & 0.091552 & -5.2021 & 1e-06 & 0 \tabularnewline
`Openheid

` & -0.332630416187557 & 0.082601 & -4.027 & 8.2e-05 & 4.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&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]92.3771983580845[/C][C]7.2609[/C][C]12.7226[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.304005715886726[/C][C]0.081221[/C][C]-3.7429[/C][C]0.000241[/C][C]0.000121[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.516637029897194[/C][C]0.087621[/C][C]-5.8963[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.541055956742284[/C][C]0.073745[/C][C]-7.3369[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.47626425329013[/C][C]0.091552[/C][C]-5.2021[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]`Openheid

`[/C][C]-0.332630416187557[/C][C]0.082601[/C][C]-4.027[/C][C]8.2e-05[/C][C]4.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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)92.37719835808457.260912.722600
leeftijd-0.3040057158867260.081221-3.74290.0002410.000121
opleiding-0.5166370298971940.087621-5.896300
Neuroticisme-0.5410559567422840.073745-7.336900
Extraversie-0.476264253290130.091552-5.20211e-060
`Openheid `-0.3326304161875570.082601-4.0278.2e-054.1e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.647112133930724
R-squared0.418754113880375
Adjusted R-squared0.403377238586205
F-TEST (value)27.2327183429225
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8843861455204
Sum Squared Residuals26694.1018365531

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.647112133930724 \tabularnewline
R-squared & 0.418754113880375 \tabularnewline
Adjusted R-squared & 0.403377238586205 \tabularnewline
F-TEST (value) & 27.2327183429225 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.8843861455204 \tabularnewline
Sum Squared Residuals & 26694.1018365531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.647112133930724[/C][/ROW]
[ROW][C]R-squared[/C][C]0.418754113880375[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.403377238586205[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.2327183429225[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.8843861455204[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]26694.1018365531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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 R0.647112133930724
R-squared0.418754113880375
Adjusted R-squared0.403377238586205
F-TEST (value)27.2327183429225
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8843861455204
Sum Squared Residuals26694.1018365531






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14032.53939102657647.46060897342359
24533.098719999583711.9012800004163
33843.4700909886035-5.47009098860349
42833.8124848549776-5.81248485497759
53535.7779925241264-0.777992524126385
61511.28503145232793.71496854767208
72715.816661389557611.1833386104424
83616.209482109718119.7905178902819
92526.012521920403-1.01252192040295
103021.86925363799018.13074636200994
112724.34346415998452.65653584001546
123327.33713084554525.66286915445479
132922.99390209966076.0060979003393
143026.55856784252063.44143215747942
152523.57953660646581.42046339353421
162335.6867592077983-12.6867592077983
172622.82071975540273.17928024459731
182417.01975635885526.98024364114482
193528.70239346374316.29760653625693
203922.463335204214416.5366647957856
212322.43677427527670.563225724723339
223223.71238830323428.2876116967658
232919.15201895612879.84798104387127
242619.88311934419586.11688065580424
252133.9815273841012-12.9815273841012
263517.33599082921317.664009170787
272325.4057514859759-2.40575148597589
282128.5230450879891-7.52304508798912
292835.7780259602269-7.77802596022694
304125.770163978110215.2298360218898
313425.11262109332188.88737890667822
323412.095180422750121.9048195772499
333616.194066608511719.8059333914883
343634.55556052767011.44443947232986
352623.91039536808522.08960463191481
362628.1833250494164-2.18332504941643
373427.57187732674786.42812267325217
383325.17692633391367.8230736660864
393121.87124835316599.12875164683409
403323.63723165424999.36276834575006
412227.6068327512708-5.6068327512708
422929.8050986306249-0.805098630624858
432425.4353701352059-1.43537013520592
443734.93824785297682.06175214702324
455034.966126327506315.0338736724937
462536.1011883120994-11.1011883120994
474734.68229704744812.317702952552
484732.547400368862814.4525996311372
494141.7506616865069-0.750661686506878
504537.0365285881817.96347141181904
514142.0785958985905-1.07859589859045
524532.179884632219112.8201153677809
534034.79204236255325.20795763744681
542939.046374803845-10.046374803845
553434.1029920598119-0.102992059811904
564536.21802618475568.78197381524437
575236.974958704548615.0250412954514
584138.78585183959742.21414816040256
594845.62059404755012.37940595244995
604538.72435663967436.27564336032571
615432.248067427859621.7519325721404
622538.6519987606336-13.6519987606336
632635.4307443692423-9.43074436924228
642833.5926395228081-5.59263952280808
655040.44639099276119.5536090072389
664834.166194225887313.8338057741127
675134.106507197385416.8934928026146
685336.726900585652816.2730994143472
693734.46762373156082.53237626843921
705649.86844840732436.13155159267575
714341.16152819525591.83847180474405
723434.5386225249966-0.538622524996606
734244.7650788261328-2.76507882613283
743235.6497987584591-3.64979875845913
753131.2451694013108-0.245169401310842
764638.57455632755817.42544367244187
773044.703843978225-14.703843978225
784727.465806096686819.5341939033132
793325.0510087468737.94899125312705
802531.1538757106491-6.15387571064914
812539.5294990328182-14.5294990328182
822136.1234666806205-15.1234666806205
833633.26578988052522.73421011947483
845043.18271629206286.81728370793717
854839.3588088792548.64119112074601
864830.253488177897817.7465118221022
872540.5219061335348-15.5219061335348
884841.64848188750726.35151811249281
894934.69509297599414.304907024006
902730.1836863881547-3.18368638815467
912830.0967677433173-2.09676774331727
924330.316332468132512.6836675318675
934840.19137830952297.8086216904771
944831.364072705064216.6359272949358
952545.9924981336161-20.9924981336161
964945.45327150183273.5467284981673
972637.0553285366894-11.0553285366894
985142.01164042355938.98835957644072
992527.9960979307587-2.99609793075874
1002937.5064670741246-8.50646707412458
1012942.2527611638624-13.2527611638624
1024334.17917623941268.82082376058744
1034646.1626339579283-0.162633957928265
1044430.747903461784913.2520965382151
1052536.3060603054146-11.3060603054146
1065138.567379161383412.4326208386166
1074237.54453852946164.4554614705384
1085332.166405717022520.8335942829775
1092534.6731417780003-9.6731417780003
1104932.59462981284816.405370187152
1115136.498650457604814.5013495423952
1122044.0841045910609-24.0841045910609
1134440.83813023546043.16186976453962
1143833.40867293496424.5913270650358
1154632.398996892534413.6010031074656
1164235.10683607384836.8931639261517
1172919.37846443702569.62153556297436
118421.2967246445253-17.2967246445253
119214.666621903141-12.666621903141
120316.4396784459303-13.4396784459303
121319.4273755534065-16.4273755534065
1221350.2730748361739-37.2730748361739
1232229.9084777145267-7.90847771452665
1242925.2316957782223.76830422177804
1253025.48721941300744.51278058699261
1262427.6734778966619-3.67347789666189
1272023.0984776134085-3.09847761340853
1282922.01079194304436.98920805695573
1292623.2087712798552.79122872014495
1302021.500645022831-1.50064502283098
1314018.897635285851821.1023647141482
1322918.10069551452410.899304485476
1333217.431752748597514.5682472514025
1343332.80954827317630.190451726823726
1353221.113473554389510.8865264456105
1363421.03731197209512.962688027905
1372424.016547310793-0.016547310792958
1382523.66114044620441.33885955379561
1394134.50085533378656.49914466621346
1403924.677395547868714.3226044521313
1412123.9763709875974-2.97637098759737
1423826.439802677562511.5601973224375
1432828.7780775037463-0.778077503746348
1443738.7140546792898-1.71405467928981
1454628.911264895722317.0887351042777
1463914.790853548670124.2091464513299
1472138.780305885236-17.780305885236
1483133.753746160025-2.75374616002501
1492528.1176105717317-3.11761057173165
1502913.770996294099415.2290037059006
1513142.0360213842561-11.0360213842561
1524042.4240390559347-2.42403905593473
1534934.570239182449714.4297608175503
1543842.0895052201835-4.08950522018346
1553238.6754747923305-6.67547479233046
1564641.8686588126734.13134118732701
1573232.5133332386866-0.513333238686616
1584147.3196832689716-6.31968326897162
1594340.06517270646652.93482729353352
1604431.077647093877212.9223529061228
161516.7399385020929-11.7399385020929
162326.7186080374193-23.7186080374193
163121.9011043646141-20.9011043646141
164221.3240454814979-19.3240454814979
16559.57740478933114-4.57740478933114
166419.5031923206486-15.5031923206486
167427.321978216279-23.321978216279
16849.37828119328644-5.37828119328644
169319.9414576990592-16.9414576990592
170413.4385706627882-9.43857066278816
171322.4876931676216-19.4876931676216
172317.7183904207536-14.7183904207536
173415.8052158379184-11.8052158379184
174412.9331713735988-8.93317137359878
175218.2126673382932-16.2126673382932
176310.8562759109007-7.85627591090074
177513.9188822022708-8.91888220227084
178212.0159889606893-10.0159889606893
179518.5411390670181-13.5411390670181
180314.396718147705-11.396718147705
18125.36960708630196-3.36960708630196
18236.38005898918652-3.38005898918652
18331.973596555256891.02640344474311
184410.1361983489485-6.13619834894854
185418.9183453140679-14.9183453140679
186317.6287945328651-14.6287945328651
187325.3155455445228-22.3155455445228
188427.4724203861792-23.4724203861792
189418.09587655163-14.09587655163
190324.7491279382184-21.7491279382184
191327.7905759433758-24.7905759433758
19247.41100623324648-3.41100623324648
193523.3665736085699-18.3665736085699
194516.8557878146264-11.8557878146264
195411.915572111654-7.91557211165402

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 40 & 32.5393910265764 & 7.46060897342359 \tabularnewline
2 & 45 & 33.0987199995837 & 11.9012800004163 \tabularnewline
3 & 38 & 43.4700909886035 & -5.47009098860349 \tabularnewline
4 & 28 & 33.8124848549776 & -5.81248485497759 \tabularnewline
5 & 35 & 35.7779925241264 & -0.777992524126385 \tabularnewline
6 & 15 & 11.2850314523279 & 3.71496854767208 \tabularnewline
7 & 27 & 15.8166613895576 & 11.1833386104424 \tabularnewline
8 & 36 & 16.2094821097181 & 19.7905178902819 \tabularnewline
9 & 25 & 26.012521920403 & -1.01252192040295 \tabularnewline
10 & 30 & 21.8692536379901 & 8.13074636200994 \tabularnewline
11 & 27 & 24.3434641599845 & 2.65653584001546 \tabularnewline
12 & 33 & 27.3371308455452 & 5.66286915445479 \tabularnewline
13 & 29 & 22.9939020996607 & 6.0060979003393 \tabularnewline
14 & 30 & 26.5585678425206 & 3.44143215747942 \tabularnewline
15 & 25 & 23.5795366064658 & 1.42046339353421 \tabularnewline
16 & 23 & 35.6867592077983 & -12.6867592077983 \tabularnewline
17 & 26 & 22.8207197554027 & 3.17928024459731 \tabularnewline
18 & 24 & 17.0197563588552 & 6.98024364114482 \tabularnewline
19 & 35 & 28.7023934637431 & 6.29760653625693 \tabularnewline
20 & 39 & 22.4633352042144 & 16.5366647957856 \tabularnewline
21 & 23 & 22.4367742752767 & 0.563225724723339 \tabularnewline
22 & 32 & 23.7123883032342 & 8.2876116967658 \tabularnewline
23 & 29 & 19.1520189561287 & 9.84798104387127 \tabularnewline
24 & 26 & 19.8831193441958 & 6.11688065580424 \tabularnewline
25 & 21 & 33.9815273841012 & -12.9815273841012 \tabularnewline
26 & 35 & 17.335990829213 & 17.664009170787 \tabularnewline
27 & 23 & 25.4057514859759 & -2.40575148597589 \tabularnewline
28 & 21 & 28.5230450879891 & -7.52304508798912 \tabularnewline
29 & 28 & 35.7780259602269 & -7.77802596022694 \tabularnewline
30 & 41 & 25.7701639781102 & 15.2298360218898 \tabularnewline
31 & 34 & 25.1126210933218 & 8.88737890667822 \tabularnewline
32 & 34 & 12.0951804227501 & 21.9048195772499 \tabularnewline
33 & 36 & 16.1940666085117 & 19.8059333914883 \tabularnewline
34 & 36 & 34.5555605276701 & 1.44443947232986 \tabularnewline
35 & 26 & 23.9103953680852 & 2.08960463191481 \tabularnewline
36 & 26 & 28.1833250494164 & -2.18332504941643 \tabularnewline
37 & 34 & 27.5718773267478 & 6.42812267325217 \tabularnewline
38 & 33 & 25.1769263339136 & 7.8230736660864 \tabularnewline
39 & 31 & 21.8712483531659 & 9.12875164683409 \tabularnewline
40 & 33 & 23.6372316542499 & 9.36276834575006 \tabularnewline
41 & 22 & 27.6068327512708 & -5.6068327512708 \tabularnewline
42 & 29 & 29.8050986306249 & -0.805098630624858 \tabularnewline
43 & 24 & 25.4353701352059 & -1.43537013520592 \tabularnewline
44 & 37 & 34.9382478529768 & 2.06175214702324 \tabularnewline
45 & 50 & 34.9661263275063 & 15.0338736724937 \tabularnewline
46 & 25 & 36.1011883120994 & -11.1011883120994 \tabularnewline
47 & 47 & 34.682297047448 & 12.317702952552 \tabularnewline
48 & 47 & 32.5474003688628 & 14.4525996311372 \tabularnewline
49 & 41 & 41.7506616865069 & -0.750661686506878 \tabularnewline
50 & 45 & 37.036528588181 & 7.96347141181904 \tabularnewline
51 & 41 & 42.0785958985905 & -1.07859589859045 \tabularnewline
52 & 45 & 32.1798846322191 & 12.8201153677809 \tabularnewline
53 & 40 & 34.7920423625532 & 5.20795763744681 \tabularnewline
54 & 29 & 39.046374803845 & -10.046374803845 \tabularnewline
55 & 34 & 34.1029920598119 & -0.102992059811904 \tabularnewline
56 & 45 & 36.2180261847556 & 8.78197381524437 \tabularnewline
57 & 52 & 36.9749587045486 & 15.0250412954514 \tabularnewline
58 & 41 & 38.7858518395974 & 2.21414816040256 \tabularnewline
59 & 48 & 45.6205940475501 & 2.37940595244995 \tabularnewline
60 & 45 & 38.7243566396743 & 6.27564336032571 \tabularnewline
61 & 54 & 32.2480674278596 & 21.7519325721404 \tabularnewline
62 & 25 & 38.6519987606336 & -13.6519987606336 \tabularnewline
63 & 26 & 35.4307443692423 & -9.43074436924228 \tabularnewline
64 & 28 & 33.5926395228081 & -5.59263952280808 \tabularnewline
65 & 50 & 40.4463909927611 & 9.5536090072389 \tabularnewline
66 & 48 & 34.1661942258873 & 13.8338057741127 \tabularnewline
67 & 51 & 34.1065071973854 & 16.8934928026146 \tabularnewline
68 & 53 & 36.7269005856528 & 16.2730994143472 \tabularnewline
69 & 37 & 34.4676237315608 & 2.53237626843921 \tabularnewline
70 & 56 & 49.8684484073243 & 6.13155159267575 \tabularnewline
71 & 43 & 41.1615281952559 & 1.83847180474405 \tabularnewline
72 & 34 & 34.5386225249966 & -0.538622524996606 \tabularnewline
73 & 42 & 44.7650788261328 & -2.76507882613283 \tabularnewline
74 & 32 & 35.6497987584591 & -3.64979875845913 \tabularnewline
75 & 31 & 31.2451694013108 & -0.245169401310842 \tabularnewline
76 & 46 & 38.5745563275581 & 7.42544367244187 \tabularnewline
77 & 30 & 44.703843978225 & -14.703843978225 \tabularnewline
78 & 47 & 27.4658060966868 & 19.5341939033132 \tabularnewline
79 & 33 & 25.051008746873 & 7.94899125312705 \tabularnewline
80 & 25 & 31.1538757106491 & -6.15387571064914 \tabularnewline
81 & 25 & 39.5294990328182 & -14.5294990328182 \tabularnewline
82 & 21 & 36.1234666806205 & -15.1234666806205 \tabularnewline
83 & 36 & 33.2657898805252 & 2.73421011947483 \tabularnewline
84 & 50 & 43.1827162920628 & 6.81728370793717 \tabularnewline
85 & 48 & 39.358808879254 & 8.64119112074601 \tabularnewline
86 & 48 & 30.2534881778978 & 17.7465118221022 \tabularnewline
87 & 25 & 40.5219061335348 & -15.5219061335348 \tabularnewline
88 & 48 & 41.6484818875072 & 6.35151811249281 \tabularnewline
89 & 49 & 34.695092975994 & 14.304907024006 \tabularnewline
90 & 27 & 30.1836863881547 & -3.18368638815467 \tabularnewline
91 & 28 & 30.0967677433173 & -2.09676774331727 \tabularnewline
92 & 43 & 30.3163324681325 & 12.6836675318675 \tabularnewline
93 & 48 & 40.1913783095229 & 7.8086216904771 \tabularnewline
94 & 48 & 31.3640727050642 & 16.6359272949358 \tabularnewline
95 & 25 & 45.9924981336161 & -20.9924981336161 \tabularnewline
96 & 49 & 45.4532715018327 & 3.5467284981673 \tabularnewline
97 & 26 & 37.0553285366894 & -11.0553285366894 \tabularnewline
98 & 51 & 42.0116404235593 & 8.98835957644072 \tabularnewline
99 & 25 & 27.9960979307587 & -2.99609793075874 \tabularnewline
100 & 29 & 37.5064670741246 & -8.50646707412458 \tabularnewline
101 & 29 & 42.2527611638624 & -13.2527611638624 \tabularnewline
102 & 43 & 34.1791762394126 & 8.82082376058744 \tabularnewline
103 & 46 & 46.1626339579283 & -0.162633957928265 \tabularnewline
104 & 44 & 30.7479034617849 & 13.2520965382151 \tabularnewline
105 & 25 & 36.3060603054146 & -11.3060603054146 \tabularnewline
106 & 51 & 38.5673791613834 & 12.4326208386166 \tabularnewline
107 & 42 & 37.5445385294616 & 4.4554614705384 \tabularnewline
108 & 53 & 32.1664057170225 & 20.8335942829775 \tabularnewline
109 & 25 & 34.6731417780003 & -9.6731417780003 \tabularnewline
110 & 49 & 32.594629812848 & 16.405370187152 \tabularnewline
111 & 51 & 36.4986504576048 & 14.5013495423952 \tabularnewline
112 & 20 & 44.0841045910609 & -24.0841045910609 \tabularnewline
113 & 44 & 40.8381302354604 & 3.16186976453962 \tabularnewline
114 & 38 & 33.4086729349642 & 4.5913270650358 \tabularnewline
115 & 46 & 32.3989968925344 & 13.6010031074656 \tabularnewline
116 & 42 & 35.1068360738483 & 6.8931639261517 \tabularnewline
117 & 29 & 19.3784644370256 & 9.62153556297436 \tabularnewline
118 & 4 & 21.2967246445253 & -17.2967246445253 \tabularnewline
119 & 2 & 14.666621903141 & -12.666621903141 \tabularnewline
120 & 3 & 16.4396784459303 & -13.4396784459303 \tabularnewline
121 & 3 & 19.4273755534065 & -16.4273755534065 \tabularnewline
122 & 13 & 50.2730748361739 & -37.2730748361739 \tabularnewline
123 & 22 & 29.9084777145267 & -7.90847771452665 \tabularnewline
124 & 29 & 25.231695778222 & 3.76830422177804 \tabularnewline
125 & 30 & 25.4872194130074 & 4.51278058699261 \tabularnewline
126 & 24 & 27.6734778966619 & -3.67347789666189 \tabularnewline
127 & 20 & 23.0984776134085 & -3.09847761340853 \tabularnewline
128 & 29 & 22.0107919430443 & 6.98920805695573 \tabularnewline
129 & 26 & 23.208771279855 & 2.79122872014495 \tabularnewline
130 & 20 & 21.500645022831 & -1.50064502283098 \tabularnewline
131 & 40 & 18.8976352858518 & 21.1023647141482 \tabularnewline
132 & 29 & 18.100695514524 & 10.899304485476 \tabularnewline
133 & 32 & 17.4317527485975 & 14.5682472514025 \tabularnewline
134 & 33 & 32.8095482731763 & 0.190451726823726 \tabularnewline
135 & 32 & 21.1134735543895 & 10.8865264456105 \tabularnewline
136 & 34 & 21.037311972095 & 12.962688027905 \tabularnewline
137 & 24 & 24.016547310793 & -0.016547310792958 \tabularnewline
138 & 25 & 23.6611404462044 & 1.33885955379561 \tabularnewline
139 & 41 & 34.5008553337865 & 6.49914466621346 \tabularnewline
140 & 39 & 24.6773955478687 & 14.3226044521313 \tabularnewline
141 & 21 & 23.9763709875974 & -2.97637098759737 \tabularnewline
142 & 38 & 26.4398026775625 & 11.5601973224375 \tabularnewline
143 & 28 & 28.7780775037463 & -0.778077503746348 \tabularnewline
144 & 37 & 38.7140546792898 & -1.71405467928981 \tabularnewline
145 & 46 & 28.9112648957223 & 17.0887351042777 \tabularnewline
146 & 39 & 14.7908535486701 & 24.2091464513299 \tabularnewline
147 & 21 & 38.780305885236 & -17.780305885236 \tabularnewline
148 & 31 & 33.753746160025 & -2.75374616002501 \tabularnewline
149 & 25 & 28.1176105717317 & -3.11761057173165 \tabularnewline
150 & 29 & 13.7709962940994 & 15.2290037059006 \tabularnewline
151 & 31 & 42.0360213842561 & -11.0360213842561 \tabularnewline
152 & 40 & 42.4240390559347 & -2.42403905593473 \tabularnewline
153 & 49 & 34.5702391824497 & 14.4297608175503 \tabularnewline
154 & 38 & 42.0895052201835 & -4.08950522018346 \tabularnewline
155 & 32 & 38.6754747923305 & -6.67547479233046 \tabularnewline
156 & 46 & 41.868658812673 & 4.13134118732701 \tabularnewline
157 & 32 & 32.5133332386866 & -0.513333238686616 \tabularnewline
158 & 41 & 47.3196832689716 & -6.31968326897162 \tabularnewline
159 & 43 & 40.0651727064665 & 2.93482729353352 \tabularnewline
160 & 44 & 31.0776470938772 & 12.9223529061228 \tabularnewline
161 & 5 & 16.7399385020929 & -11.7399385020929 \tabularnewline
162 & 3 & 26.7186080374193 & -23.7186080374193 \tabularnewline
163 & 1 & 21.9011043646141 & -20.9011043646141 \tabularnewline
164 & 2 & 21.3240454814979 & -19.3240454814979 \tabularnewline
165 & 5 & 9.57740478933114 & -4.57740478933114 \tabularnewline
166 & 4 & 19.5031923206486 & -15.5031923206486 \tabularnewline
167 & 4 & 27.321978216279 & -23.321978216279 \tabularnewline
168 & 4 & 9.37828119328644 & -5.37828119328644 \tabularnewline
169 & 3 & 19.9414576990592 & -16.9414576990592 \tabularnewline
170 & 4 & 13.4385706627882 & -9.43857066278816 \tabularnewline
171 & 3 & 22.4876931676216 & -19.4876931676216 \tabularnewline
172 & 3 & 17.7183904207536 & -14.7183904207536 \tabularnewline
173 & 4 & 15.8052158379184 & -11.8052158379184 \tabularnewline
174 & 4 & 12.9331713735988 & -8.93317137359878 \tabularnewline
175 & 2 & 18.2126673382932 & -16.2126673382932 \tabularnewline
176 & 3 & 10.8562759109007 & -7.85627591090074 \tabularnewline
177 & 5 & 13.9188822022708 & -8.91888220227084 \tabularnewline
178 & 2 & 12.0159889606893 & -10.0159889606893 \tabularnewline
179 & 5 & 18.5411390670181 & -13.5411390670181 \tabularnewline
180 & 3 & 14.396718147705 & -11.396718147705 \tabularnewline
181 & 2 & 5.36960708630196 & -3.36960708630196 \tabularnewline
182 & 3 & 6.38005898918652 & -3.38005898918652 \tabularnewline
183 & 3 & 1.97359655525689 & 1.02640344474311 \tabularnewline
184 & 4 & 10.1361983489485 & -6.13619834894854 \tabularnewline
185 & 4 & 18.9183453140679 & -14.9183453140679 \tabularnewline
186 & 3 & 17.6287945328651 & -14.6287945328651 \tabularnewline
187 & 3 & 25.3155455445228 & -22.3155455445228 \tabularnewline
188 & 4 & 27.4724203861792 & -23.4724203861792 \tabularnewline
189 & 4 & 18.09587655163 & -14.09587655163 \tabularnewline
190 & 3 & 24.7491279382184 & -21.7491279382184 \tabularnewline
191 & 3 & 27.7905759433758 & -24.7905759433758 \tabularnewline
192 & 4 & 7.41100623324648 & -3.41100623324648 \tabularnewline
193 & 5 & 23.3665736085699 & -18.3665736085699 \tabularnewline
194 & 5 & 16.8557878146264 & -11.8557878146264 \tabularnewline
195 & 4 & 11.915572111654 & -7.91557211165402 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&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]40[/C][C]32.5393910265764[/C][C]7.46060897342359[/C][/ROW]
[ROW][C]2[/C][C]45[/C][C]33.0987199995837[/C][C]11.9012800004163[/C][/ROW]
[ROW][C]3[/C][C]38[/C][C]43.4700909886035[/C][C]-5.47009098860349[/C][/ROW]
[ROW][C]4[/C][C]28[/C][C]33.8124848549776[/C][C]-5.81248485497759[/C][/ROW]
[ROW][C]5[/C][C]35[/C][C]35.7779925241264[/C][C]-0.777992524126385[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]11.2850314523279[/C][C]3.71496854767208[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]15.8166613895576[/C][C]11.1833386104424[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]16.2094821097181[/C][C]19.7905178902819[/C][/ROW]
[ROW][C]9[/C][C]25[/C][C]26.012521920403[/C][C]-1.01252192040295[/C][/ROW]
[ROW][C]10[/C][C]30[/C][C]21.8692536379901[/C][C]8.13074636200994[/C][/ROW]
[ROW][C]11[/C][C]27[/C][C]24.3434641599845[/C][C]2.65653584001546[/C][/ROW]
[ROW][C]12[/C][C]33[/C][C]27.3371308455452[/C][C]5.66286915445479[/C][/ROW]
[ROW][C]13[/C][C]29[/C][C]22.9939020996607[/C][C]6.0060979003393[/C][/ROW]
[ROW][C]14[/C][C]30[/C][C]26.5585678425206[/C][C]3.44143215747942[/C][/ROW]
[ROW][C]15[/C][C]25[/C][C]23.5795366064658[/C][C]1.42046339353421[/C][/ROW]
[ROW][C]16[/C][C]23[/C][C]35.6867592077983[/C][C]-12.6867592077983[/C][/ROW]
[ROW][C]17[/C][C]26[/C][C]22.8207197554027[/C][C]3.17928024459731[/C][/ROW]
[ROW][C]18[/C][C]24[/C][C]17.0197563588552[/C][C]6.98024364114482[/C][/ROW]
[ROW][C]19[/C][C]35[/C][C]28.7023934637431[/C][C]6.29760653625693[/C][/ROW]
[ROW][C]20[/C][C]39[/C][C]22.4633352042144[/C][C]16.5366647957856[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]22.4367742752767[/C][C]0.563225724723339[/C][/ROW]
[ROW][C]22[/C][C]32[/C][C]23.7123883032342[/C][C]8.2876116967658[/C][/ROW]
[ROW][C]23[/C][C]29[/C][C]19.1520189561287[/C][C]9.84798104387127[/C][/ROW]
[ROW][C]24[/C][C]26[/C][C]19.8831193441958[/C][C]6.11688065580424[/C][/ROW]
[ROW][C]25[/C][C]21[/C][C]33.9815273841012[/C][C]-12.9815273841012[/C][/ROW]
[ROW][C]26[/C][C]35[/C][C]17.335990829213[/C][C]17.664009170787[/C][/ROW]
[ROW][C]27[/C][C]23[/C][C]25.4057514859759[/C][C]-2.40575148597589[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]28.5230450879891[/C][C]-7.52304508798912[/C][/ROW]
[ROW][C]29[/C][C]28[/C][C]35.7780259602269[/C][C]-7.77802596022694[/C][/ROW]
[ROW][C]30[/C][C]41[/C][C]25.7701639781102[/C][C]15.2298360218898[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]25.1126210933218[/C][C]8.88737890667822[/C][/ROW]
[ROW][C]32[/C][C]34[/C][C]12.0951804227501[/C][C]21.9048195772499[/C][/ROW]
[ROW][C]33[/C][C]36[/C][C]16.1940666085117[/C][C]19.8059333914883[/C][/ROW]
[ROW][C]34[/C][C]36[/C][C]34.5555605276701[/C][C]1.44443947232986[/C][/ROW]
[ROW][C]35[/C][C]26[/C][C]23.9103953680852[/C][C]2.08960463191481[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]28.1833250494164[/C][C]-2.18332504941643[/C][/ROW]
[ROW][C]37[/C][C]34[/C][C]27.5718773267478[/C][C]6.42812267325217[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]25.1769263339136[/C][C]7.8230736660864[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]21.8712483531659[/C][C]9.12875164683409[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]23.6372316542499[/C][C]9.36276834575006[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]27.6068327512708[/C][C]-5.6068327512708[/C][/ROW]
[ROW][C]42[/C][C]29[/C][C]29.8050986306249[/C][C]-0.805098630624858[/C][/ROW]
[ROW][C]43[/C][C]24[/C][C]25.4353701352059[/C][C]-1.43537013520592[/C][/ROW]
[ROW][C]44[/C][C]37[/C][C]34.9382478529768[/C][C]2.06175214702324[/C][/ROW]
[ROW][C]45[/C][C]50[/C][C]34.9661263275063[/C][C]15.0338736724937[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]36.1011883120994[/C][C]-11.1011883120994[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]34.682297047448[/C][C]12.317702952552[/C][/ROW]
[ROW][C]48[/C][C]47[/C][C]32.5474003688628[/C][C]14.4525996311372[/C][/ROW]
[ROW][C]49[/C][C]41[/C][C]41.7506616865069[/C][C]-0.750661686506878[/C][/ROW]
[ROW][C]50[/C][C]45[/C][C]37.036528588181[/C][C]7.96347141181904[/C][/ROW]
[ROW][C]51[/C][C]41[/C][C]42.0785958985905[/C][C]-1.07859589859045[/C][/ROW]
[ROW][C]52[/C][C]45[/C][C]32.1798846322191[/C][C]12.8201153677809[/C][/ROW]
[ROW][C]53[/C][C]40[/C][C]34.7920423625532[/C][C]5.20795763744681[/C][/ROW]
[ROW][C]54[/C][C]29[/C][C]39.046374803845[/C][C]-10.046374803845[/C][/ROW]
[ROW][C]55[/C][C]34[/C][C]34.1029920598119[/C][C]-0.102992059811904[/C][/ROW]
[ROW][C]56[/C][C]45[/C][C]36.2180261847556[/C][C]8.78197381524437[/C][/ROW]
[ROW][C]57[/C][C]52[/C][C]36.9749587045486[/C][C]15.0250412954514[/C][/ROW]
[ROW][C]58[/C][C]41[/C][C]38.7858518395974[/C][C]2.21414816040256[/C][/ROW]
[ROW][C]59[/C][C]48[/C][C]45.6205940475501[/C][C]2.37940595244995[/C][/ROW]
[ROW][C]60[/C][C]45[/C][C]38.7243566396743[/C][C]6.27564336032571[/C][/ROW]
[ROW][C]61[/C][C]54[/C][C]32.2480674278596[/C][C]21.7519325721404[/C][/ROW]
[ROW][C]62[/C][C]25[/C][C]38.6519987606336[/C][C]-13.6519987606336[/C][/ROW]
[ROW][C]63[/C][C]26[/C][C]35.4307443692423[/C][C]-9.43074436924228[/C][/ROW]
[ROW][C]64[/C][C]28[/C][C]33.5926395228081[/C][C]-5.59263952280808[/C][/ROW]
[ROW][C]65[/C][C]50[/C][C]40.4463909927611[/C][C]9.5536090072389[/C][/ROW]
[ROW][C]66[/C][C]48[/C][C]34.1661942258873[/C][C]13.8338057741127[/C][/ROW]
[ROW][C]67[/C][C]51[/C][C]34.1065071973854[/C][C]16.8934928026146[/C][/ROW]
[ROW][C]68[/C][C]53[/C][C]36.7269005856528[/C][C]16.2730994143472[/C][/ROW]
[ROW][C]69[/C][C]37[/C][C]34.4676237315608[/C][C]2.53237626843921[/C][/ROW]
[ROW][C]70[/C][C]56[/C][C]49.8684484073243[/C][C]6.13155159267575[/C][/ROW]
[ROW][C]71[/C][C]43[/C][C]41.1615281952559[/C][C]1.83847180474405[/C][/ROW]
[ROW][C]72[/C][C]34[/C][C]34.5386225249966[/C][C]-0.538622524996606[/C][/ROW]
[ROW][C]73[/C][C]42[/C][C]44.7650788261328[/C][C]-2.76507882613283[/C][/ROW]
[ROW][C]74[/C][C]32[/C][C]35.6497987584591[/C][C]-3.64979875845913[/C][/ROW]
[ROW][C]75[/C][C]31[/C][C]31.2451694013108[/C][C]-0.245169401310842[/C][/ROW]
[ROW][C]76[/C][C]46[/C][C]38.5745563275581[/C][C]7.42544367244187[/C][/ROW]
[ROW][C]77[/C][C]30[/C][C]44.703843978225[/C][C]-14.703843978225[/C][/ROW]
[ROW][C]78[/C][C]47[/C][C]27.4658060966868[/C][C]19.5341939033132[/C][/ROW]
[ROW][C]79[/C][C]33[/C][C]25.051008746873[/C][C]7.94899125312705[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]31.1538757106491[/C][C]-6.15387571064914[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]39.5294990328182[/C][C]-14.5294990328182[/C][/ROW]
[ROW][C]82[/C][C]21[/C][C]36.1234666806205[/C][C]-15.1234666806205[/C][/ROW]
[ROW][C]83[/C][C]36[/C][C]33.2657898805252[/C][C]2.73421011947483[/C][/ROW]
[ROW][C]84[/C][C]50[/C][C]43.1827162920628[/C][C]6.81728370793717[/C][/ROW]
[ROW][C]85[/C][C]48[/C][C]39.358808879254[/C][C]8.64119112074601[/C][/ROW]
[ROW][C]86[/C][C]48[/C][C]30.2534881778978[/C][C]17.7465118221022[/C][/ROW]
[ROW][C]87[/C][C]25[/C][C]40.5219061335348[/C][C]-15.5219061335348[/C][/ROW]
[ROW][C]88[/C][C]48[/C][C]41.6484818875072[/C][C]6.35151811249281[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]34.695092975994[/C][C]14.304907024006[/C][/ROW]
[ROW][C]90[/C][C]27[/C][C]30.1836863881547[/C][C]-3.18368638815467[/C][/ROW]
[ROW][C]91[/C][C]28[/C][C]30.0967677433173[/C][C]-2.09676774331727[/C][/ROW]
[ROW][C]92[/C][C]43[/C][C]30.3163324681325[/C][C]12.6836675318675[/C][/ROW]
[ROW][C]93[/C][C]48[/C][C]40.1913783095229[/C][C]7.8086216904771[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]31.3640727050642[/C][C]16.6359272949358[/C][/ROW]
[ROW][C]95[/C][C]25[/C][C]45.9924981336161[/C][C]-20.9924981336161[/C][/ROW]
[ROW][C]96[/C][C]49[/C][C]45.4532715018327[/C][C]3.5467284981673[/C][/ROW]
[ROW][C]97[/C][C]26[/C][C]37.0553285366894[/C][C]-11.0553285366894[/C][/ROW]
[ROW][C]98[/C][C]51[/C][C]42.0116404235593[/C][C]8.98835957644072[/C][/ROW]
[ROW][C]99[/C][C]25[/C][C]27.9960979307587[/C][C]-2.99609793075874[/C][/ROW]
[ROW][C]100[/C][C]29[/C][C]37.5064670741246[/C][C]-8.50646707412458[/C][/ROW]
[ROW][C]101[/C][C]29[/C][C]42.2527611638624[/C][C]-13.2527611638624[/C][/ROW]
[ROW][C]102[/C][C]43[/C][C]34.1791762394126[/C][C]8.82082376058744[/C][/ROW]
[ROW][C]103[/C][C]46[/C][C]46.1626339579283[/C][C]-0.162633957928265[/C][/ROW]
[ROW][C]104[/C][C]44[/C][C]30.7479034617849[/C][C]13.2520965382151[/C][/ROW]
[ROW][C]105[/C][C]25[/C][C]36.3060603054146[/C][C]-11.3060603054146[/C][/ROW]
[ROW][C]106[/C][C]51[/C][C]38.5673791613834[/C][C]12.4326208386166[/C][/ROW]
[ROW][C]107[/C][C]42[/C][C]37.5445385294616[/C][C]4.4554614705384[/C][/ROW]
[ROW][C]108[/C][C]53[/C][C]32.1664057170225[/C][C]20.8335942829775[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]34.6731417780003[/C][C]-9.6731417780003[/C][/ROW]
[ROW][C]110[/C][C]49[/C][C]32.594629812848[/C][C]16.405370187152[/C][/ROW]
[ROW][C]111[/C][C]51[/C][C]36.4986504576048[/C][C]14.5013495423952[/C][/ROW]
[ROW][C]112[/C][C]20[/C][C]44.0841045910609[/C][C]-24.0841045910609[/C][/ROW]
[ROW][C]113[/C][C]44[/C][C]40.8381302354604[/C][C]3.16186976453962[/C][/ROW]
[ROW][C]114[/C][C]38[/C][C]33.4086729349642[/C][C]4.5913270650358[/C][/ROW]
[ROW][C]115[/C][C]46[/C][C]32.3989968925344[/C][C]13.6010031074656[/C][/ROW]
[ROW][C]116[/C][C]42[/C][C]35.1068360738483[/C][C]6.8931639261517[/C][/ROW]
[ROW][C]117[/C][C]29[/C][C]19.3784644370256[/C][C]9.62153556297436[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]21.2967246445253[/C][C]-17.2967246445253[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]14.666621903141[/C][C]-12.666621903141[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]16.4396784459303[/C][C]-13.4396784459303[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]19.4273755534065[/C][C]-16.4273755534065[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]50.2730748361739[/C][C]-37.2730748361739[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]29.9084777145267[/C][C]-7.90847771452665[/C][/ROW]
[ROW][C]124[/C][C]29[/C][C]25.231695778222[/C][C]3.76830422177804[/C][/ROW]
[ROW][C]125[/C][C]30[/C][C]25.4872194130074[/C][C]4.51278058699261[/C][/ROW]
[ROW][C]126[/C][C]24[/C][C]27.6734778966619[/C][C]-3.67347789666189[/C][/ROW]
[ROW][C]127[/C][C]20[/C][C]23.0984776134085[/C][C]-3.09847761340853[/C][/ROW]
[ROW][C]128[/C][C]29[/C][C]22.0107919430443[/C][C]6.98920805695573[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]23.208771279855[/C][C]2.79122872014495[/C][/ROW]
[ROW][C]130[/C][C]20[/C][C]21.500645022831[/C][C]-1.50064502283098[/C][/ROW]
[ROW][C]131[/C][C]40[/C][C]18.8976352858518[/C][C]21.1023647141482[/C][/ROW]
[ROW][C]132[/C][C]29[/C][C]18.100695514524[/C][C]10.899304485476[/C][/ROW]
[ROW][C]133[/C][C]32[/C][C]17.4317527485975[/C][C]14.5682472514025[/C][/ROW]
[ROW][C]134[/C][C]33[/C][C]32.8095482731763[/C][C]0.190451726823726[/C][/ROW]
[ROW][C]135[/C][C]32[/C][C]21.1134735543895[/C][C]10.8865264456105[/C][/ROW]
[ROW][C]136[/C][C]34[/C][C]21.037311972095[/C][C]12.962688027905[/C][/ROW]
[ROW][C]137[/C][C]24[/C][C]24.016547310793[/C][C]-0.016547310792958[/C][/ROW]
[ROW][C]138[/C][C]25[/C][C]23.6611404462044[/C][C]1.33885955379561[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]34.5008553337865[/C][C]6.49914466621346[/C][/ROW]
[ROW][C]140[/C][C]39[/C][C]24.6773955478687[/C][C]14.3226044521313[/C][/ROW]
[ROW][C]141[/C][C]21[/C][C]23.9763709875974[/C][C]-2.97637098759737[/C][/ROW]
[ROW][C]142[/C][C]38[/C][C]26.4398026775625[/C][C]11.5601973224375[/C][/ROW]
[ROW][C]143[/C][C]28[/C][C]28.7780775037463[/C][C]-0.778077503746348[/C][/ROW]
[ROW][C]144[/C][C]37[/C][C]38.7140546792898[/C][C]-1.71405467928981[/C][/ROW]
[ROW][C]145[/C][C]46[/C][C]28.9112648957223[/C][C]17.0887351042777[/C][/ROW]
[ROW][C]146[/C][C]39[/C][C]14.7908535486701[/C][C]24.2091464513299[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]38.780305885236[/C][C]-17.780305885236[/C][/ROW]
[ROW][C]148[/C][C]31[/C][C]33.753746160025[/C][C]-2.75374616002501[/C][/ROW]
[ROW][C]149[/C][C]25[/C][C]28.1176105717317[/C][C]-3.11761057173165[/C][/ROW]
[ROW][C]150[/C][C]29[/C][C]13.7709962940994[/C][C]15.2290037059006[/C][/ROW]
[ROW][C]151[/C][C]31[/C][C]42.0360213842561[/C][C]-11.0360213842561[/C][/ROW]
[ROW][C]152[/C][C]40[/C][C]42.4240390559347[/C][C]-2.42403905593473[/C][/ROW]
[ROW][C]153[/C][C]49[/C][C]34.5702391824497[/C][C]14.4297608175503[/C][/ROW]
[ROW][C]154[/C][C]38[/C][C]42.0895052201835[/C][C]-4.08950522018346[/C][/ROW]
[ROW][C]155[/C][C]32[/C][C]38.6754747923305[/C][C]-6.67547479233046[/C][/ROW]
[ROW][C]156[/C][C]46[/C][C]41.868658812673[/C][C]4.13134118732701[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]32.5133332386866[/C][C]-0.513333238686616[/C][/ROW]
[ROW][C]158[/C][C]41[/C][C]47.3196832689716[/C][C]-6.31968326897162[/C][/ROW]
[ROW][C]159[/C][C]43[/C][C]40.0651727064665[/C][C]2.93482729353352[/C][/ROW]
[ROW][C]160[/C][C]44[/C][C]31.0776470938772[/C][C]12.9223529061228[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]16.7399385020929[/C][C]-11.7399385020929[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]26.7186080374193[/C][C]-23.7186080374193[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]21.9011043646141[/C][C]-20.9011043646141[/C][/ROW]
[ROW][C]164[/C][C]2[/C][C]21.3240454814979[/C][C]-19.3240454814979[/C][/ROW]
[ROW][C]165[/C][C]5[/C][C]9.57740478933114[/C][C]-4.57740478933114[/C][/ROW]
[ROW][C]166[/C][C]4[/C][C]19.5031923206486[/C][C]-15.5031923206486[/C][/ROW]
[ROW][C]167[/C][C]4[/C][C]27.321978216279[/C][C]-23.321978216279[/C][/ROW]
[ROW][C]168[/C][C]4[/C][C]9.37828119328644[/C][C]-5.37828119328644[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]19.9414576990592[/C][C]-16.9414576990592[/C][/ROW]
[ROW][C]170[/C][C]4[/C][C]13.4385706627882[/C][C]-9.43857066278816[/C][/ROW]
[ROW][C]171[/C][C]3[/C][C]22.4876931676216[/C][C]-19.4876931676216[/C][/ROW]
[ROW][C]172[/C][C]3[/C][C]17.7183904207536[/C][C]-14.7183904207536[/C][/ROW]
[ROW][C]173[/C][C]4[/C][C]15.8052158379184[/C][C]-11.8052158379184[/C][/ROW]
[ROW][C]174[/C][C]4[/C][C]12.9331713735988[/C][C]-8.93317137359878[/C][/ROW]
[ROW][C]175[/C][C]2[/C][C]18.2126673382932[/C][C]-16.2126673382932[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]10.8562759109007[/C][C]-7.85627591090074[/C][/ROW]
[ROW][C]177[/C][C]5[/C][C]13.9188822022708[/C][C]-8.91888220227084[/C][/ROW]
[ROW][C]178[/C][C]2[/C][C]12.0159889606893[/C][C]-10.0159889606893[/C][/ROW]
[ROW][C]179[/C][C]5[/C][C]18.5411390670181[/C][C]-13.5411390670181[/C][/ROW]
[ROW][C]180[/C][C]3[/C][C]14.396718147705[/C][C]-11.396718147705[/C][/ROW]
[ROW][C]181[/C][C]2[/C][C]5.36960708630196[/C][C]-3.36960708630196[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]6.38005898918652[/C][C]-3.38005898918652[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]1.97359655525689[/C][C]1.02640344474311[/C][/ROW]
[ROW][C]184[/C][C]4[/C][C]10.1361983489485[/C][C]-6.13619834894854[/C][/ROW]
[ROW][C]185[/C][C]4[/C][C]18.9183453140679[/C][C]-14.9183453140679[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]17.6287945328651[/C][C]-14.6287945328651[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]25.3155455445228[/C][C]-22.3155455445228[/C][/ROW]
[ROW][C]188[/C][C]4[/C][C]27.4724203861792[/C][C]-23.4724203861792[/C][/ROW]
[ROW][C]189[/C][C]4[/C][C]18.09587655163[/C][C]-14.09587655163[/C][/ROW]
[ROW][C]190[/C][C]3[/C][C]24.7491279382184[/C][C]-21.7491279382184[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]27.7905759433758[/C][C]-24.7905759433758[/C][/ROW]
[ROW][C]192[/C][C]4[/C][C]7.41100623324648[/C][C]-3.41100623324648[/C][/ROW]
[ROW][C]193[/C][C]5[/C][C]23.3665736085699[/C][C]-18.3665736085699[/C][/ROW]
[ROW][C]194[/C][C]5[/C][C]16.8557878146264[/C][C]-11.8557878146264[/C][/ROW]
[ROW][C]195[/C][C]4[/C][C]11.915572111654[/C][C]-7.91557211165402[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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
14032.53939102657647.46060897342359
24533.098719999583711.9012800004163
33843.4700909886035-5.47009098860349
42833.8124848549776-5.81248485497759
53535.7779925241264-0.777992524126385
61511.28503145232793.71496854767208
72715.816661389557611.1833386104424
83616.209482109718119.7905178902819
92526.012521920403-1.01252192040295
103021.86925363799018.13074636200994
112724.34346415998452.65653584001546
123327.33713084554525.66286915445479
132922.99390209966076.0060979003393
143026.55856784252063.44143215747942
152523.57953660646581.42046339353421
162335.6867592077983-12.6867592077983
172622.82071975540273.17928024459731
182417.01975635885526.98024364114482
193528.70239346374316.29760653625693
203922.463335204214416.5366647957856
212322.43677427527670.563225724723339
223223.71238830323428.2876116967658
232919.15201895612879.84798104387127
242619.88311934419586.11688065580424
252133.9815273841012-12.9815273841012
263517.33599082921317.664009170787
272325.4057514859759-2.40575148597589
282128.5230450879891-7.52304508798912
292835.7780259602269-7.77802596022694
304125.770163978110215.2298360218898
313425.11262109332188.88737890667822
323412.095180422750121.9048195772499
333616.194066608511719.8059333914883
343634.55556052767011.44443947232986
352623.91039536808522.08960463191481
362628.1833250494164-2.18332504941643
373427.57187732674786.42812267325217
383325.17692633391367.8230736660864
393121.87124835316599.12875164683409
403323.63723165424999.36276834575006
412227.6068327512708-5.6068327512708
422929.8050986306249-0.805098630624858
432425.4353701352059-1.43537013520592
443734.93824785297682.06175214702324
455034.966126327506315.0338736724937
462536.1011883120994-11.1011883120994
474734.68229704744812.317702952552
484732.547400368862814.4525996311372
494141.7506616865069-0.750661686506878
504537.0365285881817.96347141181904
514142.0785958985905-1.07859589859045
524532.179884632219112.8201153677809
534034.79204236255325.20795763744681
542939.046374803845-10.046374803845
553434.1029920598119-0.102992059811904
564536.21802618475568.78197381524437
575236.974958704548615.0250412954514
584138.78585183959742.21414816040256
594845.62059404755012.37940595244995
604538.72435663967436.27564336032571
615432.248067427859621.7519325721404
622538.6519987606336-13.6519987606336
632635.4307443692423-9.43074436924228
642833.5926395228081-5.59263952280808
655040.44639099276119.5536090072389
664834.166194225887313.8338057741127
675134.106507197385416.8934928026146
685336.726900585652816.2730994143472
693734.46762373156082.53237626843921
705649.86844840732436.13155159267575
714341.16152819525591.83847180474405
723434.5386225249966-0.538622524996606
734244.7650788261328-2.76507882613283
743235.6497987584591-3.64979875845913
753131.2451694013108-0.245169401310842
764638.57455632755817.42544367244187
773044.703843978225-14.703843978225
784727.465806096686819.5341939033132
793325.0510087468737.94899125312705
802531.1538757106491-6.15387571064914
812539.5294990328182-14.5294990328182
822136.1234666806205-15.1234666806205
833633.26578988052522.73421011947483
845043.18271629206286.81728370793717
854839.3588088792548.64119112074601
864830.253488177897817.7465118221022
872540.5219061335348-15.5219061335348
884841.64848188750726.35151811249281
894934.69509297599414.304907024006
902730.1836863881547-3.18368638815467
912830.0967677433173-2.09676774331727
924330.316332468132512.6836675318675
934840.19137830952297.8086216904771
944831.364072705064216.6359272949358
952545.9924981336161-20.9924981336161
964945.45327150183273.5467284981673
972637.0553285366894-11.0553285366894
985142.01164042355938.98835957644072
992527.9960979307587-2.99609793075874
1002937.5064670741246-8.50646707412458
1012942.2527611638624-13.2527611638624
1024334.17917623941268.82082376058744
1034646.1626339579283-0.162633957928265
1044430.747903461784913.2520965382151
1052536.3060603054146-11.3060603054146
1065138.567379161383412.4326208386166
1074237.54453852946164.4554614705384
1085332.166405717022520.8335942829775
1092534.6731417780003-9.6731417780003
1104932.59462981284816.405370187152
1115136.498650457604814.5013495423952
1122044.0841045910609-24.0841045910609
1134440.83813023546043.16186976453962
1143833.40867293496424.5913270650358
1154632.398996892534413.6010031074656
1164235.10683607384836.8931639261517
1172919.37846443702569.62153556297436
118421.2967246445253-17.2967246445253
119214.666621903141-12.666621903141
120316.4396784459303-13.4396784459303
121319.4273755534065-16.4273755534065
1221350.2730748361739-37.2730748361739
1232229.9084777145267-7.90847771452665
1242925.2316957782223.76830422177804
1253025.48721941300744.51278058699261
1262427.6734778966619-3.67347789666189
1272023.0984776134085-3.09847761340853
1282922.01079194304436.98920805695573
1292623.2087712798552.79122872014495
1302021.500645022831-1.50064502283098
1314018.897635285851821.1023647141482
1322918.10069551452410.899304485476
1333217.431752748597514.5682472514025
1343332.80954827317630.190451726823726
1353221.113473554389510.8865264456105
1363421.03731197209512.962688027905
1372424.016547310793-0.016547310792958
1382523.66114044620441.33885955379561
1394134.50085533378656.49914466621346
1403924.677395547868714.3226044521313
1412123.9763709875974-2.97637098759737
1423826.439802677562511.5601973224375
1432828.7780775037463-0.778077503746348
1443738.7140546792898-1.71405467928981
1454628.911264895722317.0887351042777
1463914.790853548670124.2091464513299
1472138.780305885236-17.780305885236
1483133.753746160025-2.75374616002501
1492528.1176105717317-3.11761057173165
1502913.770996294099415.2290037059006
1513142.0360213842561-11.0360213842561
1524042.4240390559347-2.42403905593473
1534934.570239182449714.4297608175503
1543842.0895052201835-4.08950522018346
1553238.6754747923305-6.67547479233046
1564641.8686588126734.13134118732701
1573232.5133332386866-0.513333238686616
1584147.3196832689716-6.31968326897162
1594340.06517270646652.93482729353352
1604431.077647093877212.9223529061228
161516.7399385020929-11.7399385020929
162326.7186080374193-23.7186080374193
163121.9011043646141-20.9011043646141
164221.3240454814979-19.3240454814979
16559.57740478933114-4.57740478933114
166419.5031923206486-15.5031923206486
167427.321978216279-23.321978216279
16849.37828119328644-5.37828119328644
169319.9414576990592-16.9414576990592
170413.4385706627882-9.43857066278816
171322.4876931676216-19.4876931676216
172317.7183904207536-14.7183904207536
173415.8052158379184-11.8052158379184
174412.9331713735988-8.93317137359878
175218.2126673382932-16.2126673382932
176310.8562759109007-7.85627591090074
177513.9188822022708-8.91888220227084
178212.0159889606893-10.0159889606893
179518.5411390670181-13.5411390670181
180314.396718147705-11.396718147705
18125.36960708630196-3.36960708630196
18236.38005898918652-3.38005898918652
18331.973596555256891.02640344474311
184410.1361983489485-6.13619834894854
185418.9183453140679-14.9183453140679
186317.6287945328651-14.6287945328651
187325.3155455445228-22.3155455445228
188427.4724203861792-23.4724203861792
189418.09587655163-14.09587655163
190324.7491279382184-21.7491279382184
191327.7905759433758-24.7905759433758
19247.41100623324648-3.41100623324648
193523.3665736085699-18.3665736085699
194516.8557878146264-11.8557878146264
195411.915572111654-7.91557211165402






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.06721371142636730.1344274228527350.932786288573633
100.03275321015199060.06550642030398110.96724678984801
110.02480131738878260.04960263477756520.975198682611217
120.01079721371462230.02159442742924450.989202786285378
130.003696940048824160.007393880097648330.996303059951176
140.00125073936903780.00250147873807560.998749260630962
150.000448999634291150.00089799926858230.99955100036571
160.0001357265551329320.0002714531102658640.999864273444867
170.0002604367106237470.0005208734212474940.999739563289376
180.000151194804344730.0003023896086894590.999848805195655
190.001096843667874930.002193687335749850.998903156332125
200.0006238798796513470.001247759759302690.999376120120349
210.0003012964824493050.0006025929648986090.99969870351755
220.0001354427345132430.0002708854690264850.999864557265487
235.63708273697442e-050.0001127416547394880.99994362917263
242.28557000142778e-054.57114000285557e-050.999977144299986
251.52932330628172e-053.05864661256344e-050.999984706766937
263.64338020357563e-057.28676040715126e-050.999963566197964
271.79571001928842e-053.59142003857683e-050.999982042899807
289.01006869843972e-061.80201373968794e-050.999990989931302
294.79425746253434e-069.58851492506868e-060.999995205742537
302.48574171451135e-064.9714834290227e-060.999997514258286
311.35879113787931e-062.71758227575862e-060.999998641208862
327.44895022277805e-071.48979004455561e-060.999999255104978
334.17058319103472e-078.34116638206943e-070.999999582941681
341.77808876021986e-073.55617752043973e-070.999999822191124
351.50370896498666e-073.00741792997332e-070.999999849629103
361.95733983287409e-073.91467966574818e-070.999999804266017
371.19836717999224e-072.39673435998448e-070.999999880163282
385.14557242066258e-081.02911448413252e-070.999999948544276
392.71737775985396e-085.43475551970791e-080.999999972826222
401.45296568553963e-082.90593137107926e-080.999999985470343
412.04367517711704e-084.08735035423407e-080.999999979563248
422.81220837939698e-085.62441675879396e-080.999999971877916
432.10222559048526e-084.20445118097051e-080.999999978977744
441.11290202599758e-082.22580405199516e-080.99999998887098
458.30664316587167e-091.66132863317433e-080.999999991693357
466.914092914075e-071.382818582815e-060.999999308590709
474.25345995243362e-078.50691990486723e-070.999999574654005
482.70133547472604e-075.40267094945209e-070.999999729866453
491.33386644511617e-072.66773289023234e-070.999999866613355
506.56522326325853e-081.31304465265171e-070.999999934347767
513.00367398771681e-086.00734797543361e-080.99999996996326
521.58173523240309e-083.16347046480617e-080.999999984182648
537.78054766117728e-091.55610953223546e-080.999999992219452
541.58450885611884e-083.16901771223767e-080.999999984154911
551.77703602262092e-083.55407204524185e-080.99999998222964
561.04752913865117e-082.09505827730234e-080.999999989524709
571.42172503032749e-082.84345006065498e-080.99999998578275
586.70933142702049e-091.3418662854041e-080.999999993290669
597.09423144472389e-091.41884628894478e-080.999999992905769
603.60374708149876e-097.20749416299753e-090.999999996396253
616.28648990382731e-091.25729798076546e-080.99999999371351
625.49367142736086e-081.09873428547217e-070.999999945063286
632.53612134218436e-075.07224268436872e-070.999999746387866
645.89716557741003e-071.17943311548201e-060.999999410283442
656.35911449346476e-071.27182289869295e-060.99999936408855
665.43201449942562e-071.08640289988512e-060.99999945679855
676.87915598153579e-071.37583119630716e-060.999999312084402
681.15881651738289e-062.31763303476577e-060.999998841183483
697.74171759185466e-071.54834351837093e-060.99999922582824
701.88674086463135e-063.77348172926271e-060.999998113259135
711.06535167015015e-062.1307033403003e-060.99999893464833
728.07317299838075e-071.61463459967615e-060.9999991926827
734.51221627025701e-079.02443254051402e-070.999999548778373
744.82529523692141e-079.65059047384282e-070.999999517470476
754.58540313524617e-079.17080627049233e-070.999999541459687
763.02354568271632e-076.04709136543264e-070.999999697645432
773.65491759163354e-077.30983518326707e-070.99999963450824
783.95007724882972e-077.90015449765944e-070.999999604992275
794.15731402250013e-078.31462804500027e-070.999999584268598
809.25968580811202e-071.8519371616224e-060.99999907403142
812.04690410281715e-064.0938082056343e-060.999997953095897
826.58064580461795e-061.31612916092359e-050.999993419354195
834.43950285612772e-068.87900571225544e-060.999995560497144
844.15252537449052e-068.30505074898105e-060.999995847474626
853.63129821268454e-067.26259642536908e-060.999996368701787
864.56078210356307e-069.12156420712615e-060.999995439217896
878.40716417992426e-061.68143283598485e-050.99999159283582
887.15150734065786e-061.43030146813157e-050.99999284849266
898.92039471454802e-061.7840789429096e-050.999991079605285
901.02366186803802e-052.04732373607604e-050.99998976338132
911.03604120976993e-052.07208241953985e-050.999989639587902
928.9622499205936e-061.79244998411872e-050.99999103775008
938.25009810997574e-061.65001962199515e-050.99999174990189
941.22786964859153e-052.45573929718306e-050.999987721303514
952.81423319013909e-055.62846638027818e-050.999971857668099
962.37131179505214e-054.74262359010429e-050.99997628688205
972.9932988455323e-055.98659769106461e-050.999970067011545
983.2653863801272e-056.5307727602544e-050.9999673461362
994.15028715719197e-058.30057431438393e-050.999958497128428
1003.91137076518404e-057.82274153036807e-050.999960886292348
1014.09953813490006e-058.19907626980013e-050.99995900461865
1023.36639360758372e-056.73278721516743e-050.999966336063924
1032.40067102914424e-054.80134205828848e-050.999975993289709
1042.61081031032069e-055.22162062064138e-050.999973891896897
1053.33895978984065e-056.6779195796813e-050.999966610402102
1064.35316166928938e-058.70632333857876e-050.999956468383307
1073.04681687699929e-056.09363375399857e-050.99996953183123
1080.0001043276353394490.0002086552706788990.99989567236466
1090.0001076195404034360.0002152390808068720.999892380459597
1100.0002035125991682830.0004070251983365670.999796487400832
1110.0003693496535133390.0007386993070266790.999630650346487
1120.000914225281968230.001828450563936460.999085774718032
1130.0007569304736153740.001513860947230750.999243069526385
1140.0006729903409333130.001345980681866630.999327009659067
1150.001199288598545990.002398577197091990.998800711401454
1160.001382978558311090.002765957116622190.998617021441689
1170.001530870113788460.003061740227576910.998469129886212
1180.004914987119118290.009829974238236580.995085012880882
1190.01094727201312670.02189454402625340.989052727986873
1200.01801815419036330.03603630838072650.981981845809637
1210.03130838665203350.06261677330406710.968691613347966
1220.1586562194960770.3173124389921540.841343780503923
1230.1492704372621020.2985408745242040.850729562737898
1240.1290272117852260.2580544235704520.870972788214774
1250.1106503271827550.221300654365510.889349672817245
1260.09440415973536750.1888083194707350.905595840264632
1270.08004923806550930.1600984761310190.91995076193449
1280.06788669986100880.1357733997220180.932113300138991
1290.05474359648855080.1094871929771020.94525640351145
1300.04485693602908050.0897138720581610.95514306397092
1310.07662501175818460.1532500235163690.923374988241815
1320.07507208245690080.1501441649138020.9249279175431
1330.09063181008708020.181263620174160.90936818991292
1340.07471402946423780.1494280589284760.925285970535762
1350.0764331605244410.1528663210488820.923566839475559
1360.09691893338041070.1938378667608210.90308106661959
1370.08215698920321060.1643139784064210.91784301079679
1380.06791236858695310.1358247371739060.932087631413047
1390.07233938344450290.1446787668890060.927660616555497
1400.1416198961564430.2832397923128870.858380103843557
1410.1390163554878180.2780327109756360.860983644512182
1420.4857951041848520.9715902083697040.514204895815148
1430.5434353304540460.9131293390919090.456564669545954
1440.6180265167522020.7639469664955960.381973483247798
1450.7325482511515170.5349034976969670.267451748848483
1460.8272322778080640.3455354443838710.172767722191936
1470.8339142253752980.3321715492494050.166085774624702
1480.8053818709372170.3892362581255660.194618129062783
1490.8031041479269770.3937917041460450.196895852073023
1500.9672955978852980.06540880422940470.0327044021147023
1510.9624559661076060.07508806778478890.0375440338923945
1520.9525891245647150.09482175087056970.0474108754352848
1530.9739318467053070.05213630658938540.0260681532946927
1540.967058352964910.06588329407018220.0329416470350911
1550.9637118043775130.07257639124497410.036288195622487
1560.9830883059595690.03382338808086250.0169116940404313
1570.9782423865756870.04351522684862640.0217576134243132
1580.9911270449071460.0177459101857070.00887295509285352
1590.9988892634977880.002221473004424550.00111073650221227
16013.92917506869403e-231.96458753434701e-23
16117.5713629963432e-233.7856814981716e-23
16213.53480096721305e-221.76740048360652e-22
16311.90048674271785e-229.50243371358923e-23
16414.7979147068331e-222.39895735341655e-22
16511.97369744228535e-219.86848721142673e-22
16611.33122940162832e-206.65614700814161e-21
16719.93309456159492e-204.96654728079746e-20
16811.00550669335647e-185.02753346678233e-19
16915.64378661186278e-182.82189330593139e-18
17015.21918766637356e-172.60959383318678e-17
17114.04668000188201e-162.023340000941e-16
1720.9999999999999983.62051334790331e-151.81025667395166e-15
1730.9999999999999872.63125022056261e-141.3156251102813e-14
1740.999999999999882.37978124191467e-131.18989062095734e-13
1750.9999999999995688.64322946759029e-134.32161473379515e-13
1760.9999999999961267.74716567708688e-123.87358283854344e-12
1770.9999999999843053.13895638437187e-111.56947819218593e-11
1780.9999999999501619.96775817252916e-114.98387908626458e-11
1790.9999999999130231.7395449689015e-108.6977248445075e-11
1800.999999998939232.12154161172487e-091.06077080586243e-09
1810.9999999953649449.27011244588052e-094.63505622294026e-09
1820.9999999589255438.21489142068034e-084.10744571034017e-08
1830.9999997544056534.9118869423603e-072.45594347118015e-07
1840.9999960295332327.94093353627038e-063.97046676813519e-06
1850.9999388207274230.0001223585451549416.11792725774705e-05
1860.9995062440010310.0009875119979374890.000493755998968744

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0672137114263673 & 0.134427422852735 & 0.932786288573633 \tabularnewline
10 & 0.0327532101519906 & 0.0655064203039811 & 0.96724678984801 \tabularnewline
11 & 0.0248013173887826 & 0.0496026347775652 & 0.975198682611217 \tabularnewline
12 & 0.0107972137146223 & 0.0215944274292445 & 0.989202786285378 \tabularnewline
13 & 0.00369694004882416 & 0.00739388009764833 & 0.996303059951176 \tabularnewline
14 & 0.0012507393690378 & 0.0025014787380756 & 0.998749260630962 \tabularnewline
15 & 0.00044899963429115 & 0.0008979992685823 & 0.99955100036571 \tabularnewline
16 & 0.000135726555132932 & 0.000271453110265864 & 0.999864273444867 \tabularnewline
17 & 0.000260436710623747 & 0.000520873421247494 & 0.999739563289376 \tabularnewline
18 & 0.00015119480434473 & 0.000302389608689459 & 0.999848805195655 \tabularnewline
19 & 0.00109684366787493 & 0.00219368733574985 & 0.998903156332125 \tabularnewline
20 & 0.000623879879651347 & 0.00124775975930269 & 0.999376120120349 \tabularnewline
21 & 0.000301296482449305 & 0.000602592964898609 & 0.99969870351755 \tabularnewline
22 & 0.000135442734513243 & 0.000270885469026485 & 0.999864557265487 \tabularnewline
23 & 5.63708273697442e-05 & 0.000112741654739488 & 0.99994362917263 \tabularnewline
24 & 2.28557000142778e-05 & 4.57114000285557e-05 & 0.999977144299986 \tabularnewline
25 & 1.52932330628172e-05 & 3.05864661256344e-05 & 0.999984706766937 \tabularnewline
26 & 3.64338020357563e-05 & 7.28676040715126e-05 & 0.999963566197964 \tabularnewline
27 & 1.79571001928842e-05 & 3.59142003857683e-05 & 0.999982042899807 \tabularnewline
28 & 9.01006869843972e-06 & 1.80201373968794e-05 & 0.999990989931302 \tabularnewline
29 & 4.79425746253434e-06 & 9.58851492506868e-06 & 0.999995205742537 \tabularnewline
30 & 2.48574171451135e-06 & 4.9714834290227e-06 & 0.999997514258286 \tabularnewline
31 & 1.35879113787931e-06 & 2.71758227575862e-06 & 0.999998641208862 \tabularnewline
32 & 7.44895022277805e-07 & 1.48979004455561e-06 & 0.999999255104978 \tabularnewline
33 & 4.17058319103472e-07 & 8.34116638206943e-07 & 0.999999582941681 \tabularnewline
34 & 1.77808876021986e-07 & 3.55617752043973e-07 & 0.999999822191124 \tabularnewline
35 & 1.50370896498666e-07 & 3.00741792997332e-07 & 0.999999849629103 \tabularnewline
36 & 1.95733983287409e-07 & 3.91467966574818e-07 & 0.999999804266017 \tabularnewline
37 & 1.19836717999224e-07 & 2.39673435998448e-07 & 0.999999880163282 \tabularnewline
38 & 5.14557242066258e-08 & 1.02911448413252e-07 & 0.999999948544276 \tabularnewline
39 & 2.71737775985396e-08 & 5.43475551970791e-08 & 0.999999972826222 \tabularnewline
40 & 1.45296568553963e-08 & 2.90593137107926e-08 & 0.999999985470343 \tabularnewline
41 & 2.04367517711704e-08 & 4.08735035423407e-08 & 0.999999979563248 \tabularnewline
42 & 2.81220837939698e-08 & 5.62441675879396e-08 & 0.999999971877916 \tabularnewline
43 & 2.10222559048526e-08 & 4.20445118097051e-08 & 0.999999978977744 \tabularnewline
44 & 1.11290202599758e-08 & 2.22580405199516e-08 & 0.99999998887098 \tabularnewline
45 & 8.30664316587167e-09 & 1.66132863317433e-08 & 0.999999991693357 \tabularnewline
46 & 6.914092914075e-07 & 1.382818582815e-06 & 0.999999308590709 \tabularnewline
47 & 4.25345995243362e-07 & 8.50691990486723e-07 & 0.999999574654005 \tabularnewline
48 & 2.70133547472604e-07 & 5.40267094945209e-07 & 0.999999729866453 \tabularnewline
49 & 1.33386644511617e-07 & 2.66773289023234e-07 & 0.999999866613355 \tabularnewline
50 & 6.56522326325853e-08 & 1.31304465265171e-07 & 0.999999934347767 \tabularnewline
51 & 3.00367398771681e-08 & 6.00734797543361e-08 & 0.99999996996326 \tabularnewline
52 & 1.58173523240309e-08 & 3.16347046480617e-08 & 0.999999984182648 \tabularnewline
53 & 7.78054766117728e-09 & 1.55610953223546e-08 & 0.999999992219452 \tabularnewline
54 & 1.58450885611884e-08 & 3.16901771223767e-08 & 0.999999984154911 \tabularnewline
55 & 1.77703602262092e-08 & 3.55407204524185e-08 & 0.99999998222964 \tabularnewline
56 & 1.04752913865117e-08 & 2.09505827730234e-08 & 0.999999989524709 \tabularnewline
57 & 1.42172503032749e-08 & 2.84345006065498e-08 & 0.99999998578275 \tabularnewline
58 & 6.70933142702049e-09 & 1.3418662854041e-08 & 0.999999993290669 \tabularnewline
59 & 7.09423144472389e-09 & 1.41884628894478e-08 & 0.999999992905769 \tabularnewline
60 & 3.60374708149876e-09 & 7.20749416299753e-09 & 0.999999996396253 \tabularnewline
61 & 6.28648990382731e-09 & 1.25729798076546e-08 & 0.99999999371351 \tabularnewline
62 & 5.49367142736086e-08 & 1.09873428547217e-07 & 0.999999945063286 \tabularnewline
63 & 2.53612134218436e-07 & 5.07224268436872e-07 & 0.999999746387866 \tabularnewline
64 & 5.89716557741003e-07 & 1.17943311548201e-06 & 0.999999410283442 \tabularnewline
65 & 6.35911449346476e-07 & 1.27182289869295e-06 & 0.99999936408855 \tabularnewline
66 & 5.43201449942562e-07 & 1.08640289988512e-06 & 0.99999945679855 \tabularnewline
67 & 6.87915598153579e-07 & 1.37583119630716e-06 & 0.999999312084402 \tabularnewline
68 & 1.15881651738289e-06 & 2.31763303476577e-06 & 0.999998841183483 \tabularnewline
69 & 7.74171759185466e-07 & 1.54834351837093e-06 & 0.99999922582824 \tabularnewline
70 & 1.88674086463135e-06 & 3.77348172926271e-06 & 0.999998113259135 \tabularnewline
71 & 1.06535167015015e-06 & 2.1307033403003e-06 & 0.99999893464833 \tabularnewline
72 & 8.07317299838075e-07 & 1.61463459967615e-06 & 0.9999991926827 \tabularnewline
73 & 4.51221627025701e-07 & 9.02443254051402e-07 & 0.999999548778373 \tabularnewline
74 & 4.82529523692141e-07 & 9.65059047384282e-07 & 0.999999517470476 \tabularnewline
75 & 4.58540313524617e-07 & 9.17080627049233e-07 & 0.999999541459687 \tabularnewline
76 & 3.02354568271632e-07 & 6.04709136543264e-07 & 0.999999697645432 \tabularnewline
77 & 3.65491759163354e-07 & 7.30983518326707e-07 & 0.99999963450824 \tabularnewline
78 & 3.95007724882972e-07 & 7.90015449765944e-07 & 0.999999604992275 \tabularnewline
79 & 4.15731402250013e-07 & 8.31462804500027e-07 & 0.999999584268598 \tabularnewline
80 & 9.25968580811202e-07 & 1.8519371616224e-06 & 0.99999907403142 \tabularnewline
81 & 2.04690410281715e-06 & 4.0938082056343e-06 & 0.999997953095897 \tabularnewline
82 & 6.58064580461795e-06 & 1.31612916092359e-05 & 0.999993419354195 \tabularnewline
83 & 4.43950285612772e-06 & 8.87900571225544e-06 & 0.999995560497144 \tabularnewline
84 & 4.15252537449052e-06 & 8.30505074898105e-06 & 0.999995847474626 \tabularnewline
85 & 3.63129821268454e-06 & 7.26259642536908e-06 & 0.999996368701787 \tabularnewline
86 & 4.56078210356307e-06 & 9.12156420712615e-06 & 0.999995439217896 \tabularnewline
87 & 8.40716417992426e-06 & 1.68143283598485e-05 & 0.99999159283582 \tabularnewline
88 & 7.15150734065786e-06 & 1.43030146813157e-05 & 0.99999284849266 \tabularnewline
89 & 8.92039471454802e-06 & 1.7840789429096e-05 & 0.999991079605285 \tabularnewline
90 & 1.02366186803802e-05 & 2.04732373607604e-05 & 0.99998976338132 \tabularnewline
91 & 1.03604120976993e-05 & 2.07208241953985e-05 & 0.999989639587902 \tabularnewline
92 & 8.9622499205936e-06 & 1.79244998411872e-05 & 0.99999103775008 \tabularnewline
93 & 8.25009810997574e-06 & 1.65001962199515e-05 & 0.99999174990189 \tabularnewline
94 & 1.22786964859153e-05 & 2.45573929718306e-05 & 0.999987721303514 \tabularnewline
95 & 2.81423319013909e-05 & 5.62846638027818e-05 & 0.999971857668099 \tabularnewline
96 & 2.37131179505214e-05 & 4.74262359010429e-05 & 0.99997628688205 \tabularnewline
97 & 2.9932988455323e-05 & 5.98659769106461e-05 & 0.999970067011545 \tabularnewline
98 & 3.2653863801272e-05 & 6.5307727602544e-05 & 0.9999673461362 \tabularnewline
99 & 4.15028715719197e-05 & 8.30057431438393e-05 & 0.999958497128428 \tabularnewline
100 & 3.91137076518404e-05 & 7.82274153036807e-05 & 0.999960886292348 \tabularnewline
101 & 4.09953813490006e-05 & 8.19907626980013e-05 & 0.99995900461865 \tabularnewline
102 & 3.36639360758372e-05 & 6.73278721516743e-05 & 0.999966336063924 \tabularnewline
103 & 2.40067102914424e-05 & 4.80134205828848e-05 & 0.999975993289709 \tabularnewline
104 & 2.61081031032069e-05 & 5.22162062064138e-05 & 0.999973891896897 \tabularnewline
105 & 3.33895978984065e-05 & 6.6779195796813e-05 & 0.999966610402102 \tabularnewline
106 & 4.35316166928938e-05 & 8.70632333857876e-05 & 0.999956468383307 \tabularnewline
107 & 3.04681687699929e-05 & 6.09363375399857e-05 & 0.99996953183123 \tabularnewline
108 & 0.000104327635339449 & 0.000208655270678899 & 0.99989567236466 \tabularnewline
109 & 0.000107619540403436 & 0.000215239080806872 & 0.999892380459597 \tabularnewline
110 & 0.000203512599168283 & 0.000407025198336567 & 0.999796487400832 \tabularnewline
111 & 0.000369349653513339 & 0.000738699307026679 & 0.999630650346487 \tabularnewline
112 & 0.00091422528196823 & 0.00182845056393646 & 0.999085774718032 \tabularnewline
113 & 0.000756930473615374 & 0.00151386094723075 & 0.999243069526385 \tabularnewline
114 & 0.000672990340933313 & 0.00134598068186663 & 0.999327009659067 \tabularnewline
115 & 0.00119928859854599 & 0.00239857719709199 & 0.998800711401454 \tabularnewline
116 & 0.00138297855831109 & 0.00276595711662219 & 0.998617021441689 \tabularnewline
117 & 0.00153087011378846 & 0.00306174022757691 & 0.998469129886212 \tabularnewline
118 & 0.00491498711911829 & 0.00982997423823658 & 0.995085012880882 \tabularnewline
119 & 0.0109472720131267 & 0.0218945440262534 & 0.989052727986873 \tabularnewline
120 & 0.0180181541903633 & 0.0360363083807265 & 0.981981845809637 \tabularnewline
121 & 0.0313083866520335 & 0.0626167733040671 & 0.968691613347966 \tabularnewline
122 & 0.158656219496077 & 0.317312438992154 & 0.841343780503923 \tabularnewline
123 & 0.149270437262102 & 0.298540874524204 & 0.850729562737898 \tabularnewline
124 & 0.129027211785226 & 0.258054423570452 & 0.870972788214774 \tabularnewline
125 & 0.110650327182755 & 0.22130065436551 & 0.889349672817245 \tabularnewline
126 & 0.0944041597353675 & 0.188808319470735 & 0.905595840264632 \tabularnewline
127 & 0.0800492380655093 & 0.160098476131019 & 0.91995076193449 \tabularnewline
128 & 0.0678866998610088 & 0.135773399722018 & 0.932113300138991 \tabularnewline
129 & 0.0547435964885508 & 0.109487192977102 & 0.94525640351145 \tabularnewline
130 & 0.0448569360290805 & 0.089713872058161 & 0.95514306397092 \tabularnewline
131 & 0.0766250117581846 & 0.153250023516369 & 0.923374988241815 \tabularnewline
132 & 0.0750720824569008 & 0.150144164913802 & 0.9249279175431 \tabularnewline
133 & 0.0906318100870802 & 0.18126362017416 & 0.90936818991292 \tabularnewline
134 & 0.0747140294642378 & 0.149428058928476 & 0.925285970535762 \tabularnewline
135 & 0.076433160524441 & 0.152866321048882 & 0.923566839475559 \tabularnewline
136 & 0.0969189333804107 & 0.193837866760821 & 0.90308106661959 \tabularnewline
137 & 0.0821569892032106 & 0.164313978406421 & 0.91784301079679 \tabularnewline
138 & 0.0679123685869531 & 0.135824737173906 & 0.932087631413047 \tabularnewline
139 & 0.0723393834445029 & 0.144678766889006 & 0.927660616555497 \tabularnewline
140 & 0.141619896156443 & 0.283239792312887 & 0.858380103843557 \tabularnewline
141 & 0.139016355487818 & 0.278032710975636 & 0.860983644512182 \tabularnewline
142 & 0.485795104184852 & 0.971590208369704 & 0.514204895815148 \tabularnewline
143 & 0.543435330454046 & 0.913129339091909 & 0.456564669545954 \tabularnewline
144 & 0.618026516752202 & 0.763946966495596 & 0.381973483247798 \tabularnewline
145 & 0.732548251151517 & 0.534903497696967 & 0.267451748848483 \tabularnewline
146 & 0.827232277808064 & 0.345535444383871 & 0.172767722191936 \tabularnewline
147 & 0.833914225375298 & 0.332171549249405 & 0.166085774624702 \tabularnewline
148 & 0.805381870937217 & 0.389236258125566 & 0.194618129062783 \tabularnewline
149 & 0.803104147926977 & 0.393791704146045 & 0.196895852073023 \tabularnewline
150 & 0.967295597885298 & 0.0654088042294047 & 0.0327044021147023 \tabularnewline
151 & 0.962455966107606 & 0.0750880677847889 & 0.0375440338923945 \tabularnewline
152 & 0.952589124564715 & 0.0948217508705697 & 0.0474108754352848 \tabularnewline
153 & 0.973931846705307 & 0.0521363065893854 & 0.0260681532946927 \tabularnewline
154 & 0.96705835296491 & 0.0658832940701822 & 0.0329416470350911 \tabularnewline
155 & 0.963711804377513 & 0.0725763912449741 & 0.036288195622487 \tabularnewline
156 & 0.983088305959569 & 0.0338233880808625 & 0.0169116940404313 \tabularnewline
157 & 0.978242386575687 & 0.0435152268486264 & 0.0217576134243132 \tabularnewline
158 & 0.991127044907146 & 0.017745910185707 & 0.00887295509285352 \tabularnewline
159 & 0.998889263497788 & 0.00222147300442455 & 0.00111073650221227 \tabularnewline
160 & 1 & 3.92917506869403e-23 & 1.96458753434701e-23 \tabularnewline
161 & 1 & 7.5713629963432e-23 & 3.7856814981716e-23 \tabularnewline
162 & 1 & 3.53480096721305e-22 & 1.76740048360652e-22 \tabularnewline
163 & 1 & 1.90048674271785e-22 & 9.50243371358923e-23 \tabularnewline
164 & 1 & 4.7979147068331e-22 & 2.39895735341655e-22 \tabularnewline
165 & 1 & 1.97369744228535e-21 & 9.86848721142673e-22 \tabularnewline
166 & 1 & 1.33122940162832e-20 & 6.65614700814161e-21 \tabularnewline
167 & 1 & 9.93309456159492e-20 & 4.96654728079746e-20 \tabularnewline
168 & 1 & 1.00550669335647e-18 & 5.02753346678233e-19 \tabularnewline
169 & 1 & 5.64378661186278e-18 & 2.82189330593139e-18 \tabularnewline
170 & 1 & 5.21918766637356e-17 & 2.60959383318678e-17 \tabularnewline
171 & 1 & 4.04668000188201e-16 & 2.023340000941e-16 \tabularnewline
172 & 0.999999999999998 & 3.62051334790331e-15 & 1.81025667395166e-15 \tabularnewline
173 & 0.999999999999987 & 2.63125022056261e-14 & 1.3156251102813e-14 \tabularnewline
174 & 0.99999999999988 & 2.37978124191467e-13 & 1.18989062095734e-13 \tabularnewline
175 & 0.999999999999568 & 8.64322946759029e-13 & 4.32161473379515e-13 \tabularnewline
176 & 0.999999999996126 & 7.74716567708688e-12 & 3.87358283854344e-12 \tabularnewline
177 & 0.999999999984305 & 3.13895638437187e-11 & 1.56947819218593e-11 \tabularnewline
178 & 0.999999999950161 & 9.96775817252916e-11 & 4.98387908626458e-11 \tabularnewline
179 & 0.999999999913023 & 1.7395449689015e-10 & 8.6977248445075e-11 \tabularnewline
180 & 0.99999999893923 & 2.12154161172487e-09 & 1.06077080586243e-09 \tabularnewline
181 & 0.999999995364944 & 9.27011244588052e-09 & 4.63505622294026e-09 \tabularnewline
182 & 0.999999958925543 & 8.21489142068034e-08 & 4.10744571034017e-08 \tabularnewline
183 & 0.999999754405653 & 4.9118869423603e-07 & 2.45594347118015e-07 \tabularnewline
184 & 0.999996029533232 & 7.94093353627038e-06 & 3.97046676813519e-06 \tabularnewline
185 & 0.999938820727423 & 0.000122358545154941 & 6.11792725774705e-05 \tabularnewline
186 & 0.999506244001031 & 0.000987511997937489 & 0.000493755998968744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&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]9[/C][C]0.0672137114263673[/C][C]0.134427422852735[/C][C]0.932786288573633[/C][/ROW]
[ROW][C]10[/C][C]0.0327532101519906[/C][C]0.0655064203039811[/C][C]0.96724678984801[/C][/ROW]
[ROW][C]11[/C][C]0.0248013173887826[/C][C]0.0496026347775652[/C][C]0.975198682611217[/C][/ROW]
[ROW][C]12[/C][C]0.0107972137146223[/C][C]0.0215944274292445[/C][C]0.989202786285378[/C][/ROW]
[ROW][C]13[/C][C]0.00369694004882416[/C][C]0.00739388009764833[/C][C]0.996303059951176[/C][/ROW]
[ROW][C]14[/C][C]0.0012507393690378[/C][C]0.0025014787380756[/C][C]0.998749260630962[/C][/ROW]
[ROW][C]15[/C][C]0.00044899963429115[/C][C]0.0008979992685823[/C][C]0.99955100036571[/C][/ROW]
[ROW][C]16[/C][C]0.000135726555132932[/C][C]0.000271453110265864[/C][C]0.999864273444867[/C][/ROW]
[ROW][C]17[/C][C]0.000260436710623747[/C][C]0.000520873421247494[/C][C]0.999739563289376[/C][/ROW]
[ROW][C]18[/C][C]0.00015119480434473[/C][C]0.000302389608689459[/C][C]0.999848805195655[/C][/ROW]
[ROW][C]19[/C][C]0.00109684366787493[/C][C]0.00219368733574985[/C][C]0.998903156332125[/C][/ROW]
[ROW][C]20[/C][C]0.000623879879651347[/C][C]0.00124775975930269[/C][C]0.999376120120349[/C][/ROW]
[ROW][C]21[/C][C]0.000301296482449305[/C][C]0.000602592964898609[/C][C]0.99969870351755[/C][/ROW]
[ROW][C]22[/C][C]0.000135442734513243[/C][C]0.000270885469026485[/C][C]0.999864557265487[/C][/ROW]
[ROW][C]23[/C][C]5.63708273697442e-05[/C][C]0.000112741654739488[/C][C]0.99994362917263[/C][/ROW]
[ROW][C]24[/C][C]2.28557000142778e-05[/C][C]4.57114000285557e-05[/C][C]0.999977144299986[/C][/ROW]
[ROW][C]25[/C][C]1.52932330628172e-05[/C][C]3.05864661256344e-05[/C][C]0.999984706766937[/C][/ROW]
[ROW][C]26[/C][C]3.64338020357563e-05[/C][C]7.28676040715126e-05[/C][C]0.999963566197964[/C][/ROW]
[ROW][C]27[/C][C]1.79571001928842e-05[/C][C]3.59142003857683e-05[/C][C]0.999982042899807[/C][/ROW]
[ROW][C]28[/C][C]9.01006869843972e-06[/C][C]1.80201373968794e-05[/C][C]0.999990989931302[/C][/ROW]
[ROW][C]29[/C][C]4.79425746253434e-06[/C][C]9.58851492506868e-06[/C][C]0.999995205742537[/C][/ROW]
[ROW][C]30[/C][C]2.48574171451135e-06[/C][C]4.9714834290227e-06[/C][C]0.999997514258286[/C][/ROW]
[ROW][C]31[/C][C]1.35879113787931e-06[/C][C]2.71758227575862e-06[/C][C]0.999998641208862[/C][/ROW]
[ROW][C]32[/C][C]7.44895022277805e-07[/C][C]1.48979004455561e-06[/C][C]0.999999255104978[/C][/ROW]
[ROW][C]33[/C][C]4.17058319103472e-07[/C][C]8.34116638206943e-07[/C][C]0.999999582941681[/C][/ROW]
[ROW][C]34[/C][C]1.77808876021986e-07[/C][C]3.55617752043973e-07[/C][C]0.999999822191124[/C][/ROW]
[ROW][C]35[/C][C]1.50370896498666e-07[/C][C]3.00741792997332e-07[/C][C]0.999999849629103[/C][/ROW]
[ROW][C]36[/C][C]1.95733983287409e-07[/C][C]3.91467966574818e-07[/C][C]0.999999804266017[/C][/ROW]
[ROW][C]37[/C][C]1.19836717999224e-07[/C][C]2.39673435998448e-07[/C][C]0.999999880163282[/C][/ROW]
[ROW][C]38[/C][C]5.14557242066258e-08[/C][C]1.02911448413252e-07[/C][C]0.999999948544276[/C][/ROW]
[ROW][C]39[/C][C]2.71737775985396e-08[/C][C]5.43475551970791e-08[/C][C]0.999999972826222[/C][/ROW]
[ROW][C]40[/C][C]1.45296568553963e-08[/C][C]2.90593137107926e-08[/C][C]0.999999985470343[/C][/ROW]
[ROW][C]41[/C][C]2.04367517711704e-08[/C][C]4.08735035423407e-08[/C][C]0.999999979563248[/C][/ROW]
[ROW][C]42[/C][C]2.81220837939698e-08[/C][C]5.62441675879396e-08[/C][C]0.999999971877916[/C][/ROW]
[ROW][C]43[/C][C]2.10222559048526e-08[/C][C]4.20445118097051e-08[/C][C]0.999999978977744[/C][/ROW]
[ROW][C]44[/C][C]1.11290202599758e-08[/C][C]2.22580405199516e-08[/C][C]0.99999998887098[/C][/ROW]
[ROW][C]45[/C][C]8.30664316587167e-09[/C][C]1.66132863317433e-08[/C][C]0.999999991693357[/C][/ROW]
[ROW][C]46[/C][C]6.914092914075e-07[/C][C]1.382818582815e-06[/C][C]0.999999308590709[/C][/ROW]
[ROW][C]47[/C][C]4.25345995243362e-07[/C][C]8.50691990486723e-07[/C][C]0.999999574654005[/C][/ROW]
[ROW][C]48[/C][C]2.70133547472604e-07[/C][C]5.40267094945209e-07[/C][C]0.999999729866453[/C][/ROW]
[ROW][C]49[/C][C]1.33386644511617e-07[/C][C]2.66773289023234e-07[/C][C]0.999999866613355[/C][/ROW]
[ROW][C]50[/C][C]6.56522326325853e-08[/C][C]1.31304465265171e-07[/C][C]0.999999934347767[/C][/ROW]
[ROW][C]51[/C][C]3.00367398771681e-08[/C][C]6.00734797543361e-08[/C][C]0.99999996996326[/C][/ROW]
[ROW][C]52[/C][C]1.58173523240309e-08[/C][C]3.16347046480617e-08[/C][C]0.999999984182648[/C][/ROW]
[ROW][C]53[/C][C]7.78054766117728e-09[/C][C]1.55610953223546e-08[/C][C]0.999999992219452[/C][/ROW]
[ROW][C]54[/C][C]1.58450885611884e-08[/C][C]3.16901771223767e-08[/C][C]0.999999984154911[/C][/ROW]
[ROW][C]55[/C][C]1.77703602262092e-08[/C][C]3.55407204524185e-08[/C][C]0.99999998222964[/C][/ROW]
[ROW][C]56[/C][C]1.04752913865117e-08[/C][C]2.09505827730234e-08[/C][C]0.999999989524709[/C][/ROW]
[ROW][C]57[/C][C]1.42172503032749e-08[/C][C]2.84345006065498e-08[/C][C]0.99999998578275[/C][/ROW]
[ROW][C]58[/C][C]6.70933142702049e-09[/C][C]1.3418662854041e-08[/C][C]0.999999993290669[/C][/ROW]
[ROW][C]59[/C][C]7.09423144472389e-09[/C][C]1.41884628894478e-08[/C][C]0.999999992905769[/C][/ROW]
[ROW][C]60[/C][C]3.60374708149876e-09[/C][C]7.20749416299753e-09[/C][C]0.999999996396253[/C][/ROW]
[ROW][C]61[/C][C]6.28648990382731e-09[/C][C]1.25729798076546e-08[/C][C]0.99999999371351[/C][/ROW]
[ROW][C]62[/C][C]5.49367142736086e-08[/C][C]1.09873428547217e-07[/C][C]0.999999945063286[/C][/ROW]
[ROW][C]63[/C][C]2.53612134218436e-07[/C][C]5.07224268436872e-07[/C][C]0.999999746387866[/C][/ROW]
[ROW][C]64[/C][C]5.89716557741003e-07[/C][C]1.17943311548201e-06[/C][C]0.999999410283442[/C][/ROW]
[ROW][C]65[/C][C]6.35911449346476e-07[/C][C]1.27182289869295e-06[/C][C]0.99999936408855[/C][/ROW]
[ROW][C]66[/C][C]5.43201449942562e-07[/C][C]1.08640289988512e-06[/C][C]0.99999945679855[/C][/ROW]
[ROW][C]67[/C][C]6.87915598153579e-07[/C][C]1.37583119630716e-06[/C][C]0.999999312084402[/C][/ROW]
[ROW][C]68[/C][C]1.15881651738289e-06[/C][C]2.31763303476577e-06[/C][C]0.999998841183483[/C][/ROW]
[ROW][C]69[/C][C]7.74171759185466e-07[/C][C]1.54834351837093e-06[/C][C]0.99999922582824[/C][/ROW]
[ROW][C]70[/C][C]1.88674086463135e-06[/C][C]3.77348172926271e-06[/C][C]0.999998113259135[/C][/ROW]
[ROW][C]71[/C][C]1.06535167015015e-06[/C][C]2.1307033403003e-06[/C][C]0.99999893464833[/C][/ROW]
[ROW][C]72[/C][C]8.07317299838075e-07[/C][C]1.61463459967615e-06[/C][C]0.9999991926827[/C][/ROW]
[ROW][C]73[/C][C]4.51221627025701e-07[/C][C]9.02443254051402e-07[/C][C]0.999999548778373[/C][/ROW]
[ROW][C]74[/C][C]4.82529523692141e-07[/C][C]9.65059047384282e-07[/C][C]0.999999517470476[/C][/ROW]
[ROW][C]75[/C][C]4.58540313524617e-07[/C][C]9.17080627049233e-07[/C][C]0.999999541459687[/C][/ROW]
[ROW][C]76[/C][C]3.02354568271632e-07[/C][C]6.04709136543264e-07[/C][C]0.999999697645432[/C][/ROW]
[ROW][C]77[/C][C]3.65491759163354e-07[/C][C]7.30983518326707e-07[/C][C]0.99999963450824[/C][/ROW]
[ROW][C]78[/C][C]3.95007724882972e-07[/C][C]7.90015449765944e-07[/C][C]0.999999604992275[/C][/ROW]
[ROW][C]79[/C][C]4.15731402250013e-07[/C][C]8.31462804500027e-07[/C][C]0.999999584268598[/C][/ROW]
[ROW][C]80[/C][C]9.25968580811202e-07[/C][C]1.8519371616224e-06[/C][C]0.99999907403142[/C][/ROW]
[ROW][C]81[/C][C]2.04690410281715e-06[/C][C]4.0938082056343e-06[/C][C]0.999997953095897[/C][/ROW]
[ROW][C]82[/C][C]6.58064580461795e-06[/C][C]1.31612916092359e-05[/C][C]0.999993419354195[/C][/ROW]
[ROW][C]83[/C][C]4.43950285612772e-06[/C][C]8.87900571225544e-06[/C][C]0.999995560497144[/C][/ROW]
[ROW][C]84[/C][C]4.15252537449052e-06[/C][C]8.30505074898105e-06[/C][C]0.999995847474626[/C][/ROW]
[ROW][C]85[/C][C]3.63129821268454e-06[/C][C]7.26259642536908e-06[/C][C]0.999996368701787[/C][/ROW]
[ROW][C]86[/C][C]4.56078210356307e-06[/C][C]9.12156420712615e-06[/C][C]0.999995439217896[/C][/ROW]
[ROW][C]87[/C][C]8.40716417992426e-06[/C][C]1.68143283598485e-05[/C][C]0.99999159283582[/C][/ROW]
[ROW][C]88[/C][C]7.15150734065786e-06[/C][C]1.43030146813157e-05[/C][C]0.99999284849266[/C][/ROW]
[ROW][C]89[/C][C]8.92039471454802e-06[/C][C]1.7840789429096e-05[/C][C]0.999991079605285[/C][/ROW]
[ROW][C]90[/C][C]1.02366186803802e-05[/C][C]2.04732373607604e-05[/C][C]0.99998976338132[/C][/ROW]
[ROW][C]91[/C][C]1.03604120976993e-05[/C][C]2.07208241953985e-05[/C][C]0.999989639587902[/C][/ROW]
[ROW][C]92[/C][C]8.9622499205936e-06[/C][C]1.79244998411872e-05[/C][C]0.99999103775008[/C][/ROW]
[ROW][C]93[/C][C]8.25009810997574e-06[/C][C]1.65001962199515e-05[/C][C]0.99999174990189[/C][/ROW]
[ROW][C]94[/C][C]1.22786964859153e-05[/C][C]2.45573929718306e-05[/C][C]0.999987721303514[/C][/ROW]
[ROW][C]95[/C][C]2.81423319013909e-05[/C][C]5.62846638027818e-05[/C][C]0.999971857668099[/C][/ROW]
[ROW][C]96[/C][C]2.37131179505214e-05[/C][C]4.74262359010429e-05[/C][C]0.99997628688205[/C][/ROW]
[ROW][C]97[/C][C]2.9932988455323e-05[/C][C]5.98659769106461e-05[/C][C]0.999970067011545[/C][/ROW]
[ROW][C]98[/C][C]3.2653863801272e-05[/C][C]6.5307727602544e-05[/C][C]0.9999673461362[/C][/ROW]
[ROW][C]99[/C][C]4.15028715719197e-05[/C][C]8.30057431438393e-05[/C][C]0.999958497128428[/C][/ROW]
[ROW][C]100[/C][C]3.91137076518404e-05[/C][C]7.82274153036807e-05[/C][C]0.999960886292348[/C][/ROW]
[ROW][C]101[/C][C]4.09953813490006e-05[/C][C]8.19907626980013e-05[/C][C]0.99995900461865[/C][/ROW]
[ROW][C]102[/C][C]3.36639360758372e-05[/C][C]6.73278721516743e-05[/C][C]0.999966336063924[/C][/ROW]
[ROW][C]103[/C][C]2.40067102914424e-05[/C][C]4.80134205828848e-05[/C][C]0.999975993289709[/C][/ROW]
[ROW][C]104[/C][C]2.61081031032069e-05[/C][C]5.22162062064138e-05[/C][C]0.999973891896897[/C][/ROW]
[ROW][C]105[/C][C]3.33895978984065e-05[/C][C]6.6779195796813e-05[/C][C]0.999966610402102[/C][/ROW]
[ROW][C]106[/C][C]4.35316166928938e-05[/C][C]8.70632333857876e-05[/C][C]0.999956468383307[/C][/ROW]
[ROW][C]107[/C][C]3.04681687699929e-05[/C][C]6.09363375399857e-05[/C][C]0.99996953183123[/C][/ROW]
[ROW][C]108[/C][C]0.000104327635339449[/C][C]0.000208655270678899[/C][C]0.99989567236466[/C][/ROW]
[ROW][C]109[/C][C]0.000107619540403436[/C][C]0.000215239080806872[/C][C]0.999892380459597[/C][/ROW]
[ROW][C]110[/C][C]0.000203512599168283[/C][C]0.000407025198336567[/C][C]0.999796487400832[/C][/ROW]
[ROW][C]111[/C][C]0.000369349653513339[/C][C]0.000738699307026679[/C][C]0.999630650346487[/C][/ROW]
[ROW][C]112[/C][C]0.00091422528196823[/C][C]0.00182845056393646[/C][C]0.999085774718032[/C][/ROW]
[ROW][C]113[/C][C]0.000756930473615374[/C][C]0.00151386094723075[/C][C]0.999243069526385[/C][/ROW]
[ROW][C]114[/C][C]0.000672990340933313[/C][C]0.00134598068186663[/C][C]0.999327009659067[/C][/ROW]
[ROW][C]115[/C][C]0.00119928859854599[/C][C]0.00239857719709199[/C][C]0.998800711401454[/C][/ROW]
[ROW][C]116[/C][C]0.00138297855831109[/C][C]0.00276595711662219[/C][C]0.998617021441689[/C][/ROW]
[ROW][C]117[/C][C]0.00153087011378846[/C][C]0.00306174022757691[/C][C]0.998469129886212[/C][/ROW]
[ROW][C]118[/C][C]0.00491498711911829[/C][C]0.00982997423823658[/C][C]0.995085012880882[/C][/ROW]
[ROW][C]119[/C][C]0.0109472720131267[/C][C]0.0218945440262534[/C][C]0.989052727986873[/C][/ROW]
[ROW][C]120[/C][C]0.0180181541903633[/C][C]0.0360363083807265[/C][C]0.981981845809637[/C][/ROW]
[ROW][C]121[/C][C]0.0313083866520335[/C][C]0.0626167733040671[/C][C]0.968691613347966[/C][/ROW]
[ROW][C]122[/C][C]0.158656219496077[/C][C]0.317312438992154[/C][C]0.841343780503923[/C][/ROW]
[ROW][C]123[/C][C]0.149270437262102[/C][C]0.298540874524204[/C][C]0.850729562737898[/C][/ROW]
[ROW][C]124[/C][C]0.129027211785226[/C][C]0.258054423570452[/C][C]0.870972788214774[/C][/ROW]
[ROW][C]125[/C][C]0.110650327182755[/C][C]0.22130065436551[/C][C]0.889349672817245[/C][/ROW]
[ROW][C]126[/C][C]0.0944041597353675[/C][C]0.188808319470735[/C][C]0.905595840264632[/C][/ROW]
[ROW][C]127[/C][C]0.0800492380655093[/C][C]0.160098476131019[/C][C]0.91995076193449[/C][/ROW]
[ROW][C]128[/C][C]0.0678866998610088[/C][C]0.135773399722018[/C][C]0.932113300138991[/C][/ROW]
[ROW][C]129[/C][C]0.0547435964885508[/C][C]0.109487192977102[/C][C]0.94525640351145[/C][/ROW]
[ROW][C]130[/C][C]0.0448569360290805[/C][C]0.089713872058161[/C][C]0.95514306397092[/C][/ROW]
[ROW][C]131[/C][C]0.0766250117581846[/C][C]0.153250023516369[/C][C]0.923374988241815[/C][/ROW]
[ROW][C]132[/C][C]0.0750720824569008[/C][C]0.150144164913802[/C][C]0.9249279175431[/C][/ROW]
[ROW][C]133[/C][C]0.0906318100870802[/C][C]0.18126362017416[/C][C]0.90936818991292[/C][/ROW]
[ROW][C]134[/C][C]0.0747140294642378[/C][C]0.149428058928476[/C][C]0.925285970535762[/C][/ROW]
[ROW][C]135[/C][C]0.076433160524441[/C][C]0.152866321048882[/C][C]0.923566839475559[/C][/ROW]
[ROW][C]136[/C][C]0.0969189333804107[/C][C]0.193837866760821[/C][C]0.90308106661959[/C][/ROW]
[ROW][C]137[/C][C]0.0821569892032106[/C][C]0.164313978406421[/C][C]0.91784301079679[/C][/ROW]
[ROW][C]138[/C][C]0.0679123685869531[/C][C]0.135824737173906[/C][C]0.932087631413047[/C][/ROW]
[ROW][C]139[/C][C]0.0723393834445029[/C][C]0.144678766889006[/C][C]0.927660616555497[/C][/ROW]
[ROW][C]140[/C][C]0.141619896156443[/C][C]0.283239792312887[/C][C]0.858380103843557[/C][/ROW]
[ROW][C]141[/C][C]0.139016355487818[/C][C]0.278032710975636[/C][C]0.860983644512182[/C][/ROW]
[ROW][C]142[/C][C]0.485795104184852[/C][C]0.971590208369704[/C][C]0.514204895815148[/C][/ROW]
[ROW][C]143[/C][C]0.543435330454046[/C][C]0.913129339091909[/C][C]0.456564669545954[/C][/ROW]
[ROW][C]144[/C][C]0.618026516752202[/C][C]0.763946966495596[/C][C]0.381973483247798[/C][/ROW]
[ROW][C]145[/C][C]0.732548251151517[/C][C]0.534903497696967[/C][C]0.267451748848483[/C][/ROW]
[ROW][C]146[/C][C]0.827232277808064[/C][C]0.345535444383871[/C][C]0.172767722191936[/C][/ROW]
[ROW][C]147[/C][C]0.833914225375298[/C][C]0.332171549249405[/C][C]0.166085774624702[/C][/ROW]
[ROW][C]148[/C][C]0.805381870937217[/C][C]0.389236258125566[/C][C]0.194618129062783[/C][/ROW]
[ROW][C]149[/C][C]0.803104147926977[/C][C]0.393791704146045[/C][C]0.196895852073023[/C][/ROW]
[ROW][C]150[/C][C]0.967295597885298[/C][C]0.0654088042294047[/C][C]0.0327044021147023[/C][/ROW]
[ROW][C]151[/C][C]0.962455966107606[/C][C]0.0750880677847889[/C][C]0.0375440338923945[/C][/ROW]
[ROW][C]152[/C][C]0.952589124564715[/C][C]0.0948217508705697[/C][C]0.0474108754352848[/C][/ROW]
[ROW][C]153[/C][C]0.973931846705307[/C][C]0.0521363065893854[/C][C]0.0260681532946927[/C][/ROW]
[ROW][C]154[/C][C]0.96705835296491[/C][C]0.0658832940701822[/C][C]0.0329416470350911[/C][/ROW]
[ROW][C]155[/C][C]0.963711804377513[/C][C]0.0725763912449741[/C][C]0.036288195622487[/C][/ROW]
[ROW][C]156[/C][C]0.983088305959569[/C][C]0.0338233880808625[/C][C]0.0169116940404313[/C][/ROW]
[ROW][C]157[/C][C]0.978242386575687[/C][C]0.0435152268486264[/C][C]0.0217576134243132[/C][/ROW]
[ROW][C]158[/C][C]0.991127044907146[/C][C]0.017745910185707[/C][C]0.00887295509285352[/C][/ROW]
[ROW][C]159[/C][C]0.998889263497788[/C][C]0.00222147300442455[/C][C]0.00111073650221227[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]3.92917506869403e-23[/C][C]1.96458753434701e-23[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]7.5713629963432e-23[/C][C]3.7856814981716e-23[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]3.53480096721305e-22[/C][C]1.76740048360652e-22[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]1.90048674271785e-22[/C][C]9.50243371358923e-23[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]4.7979147068331e-22[/C][C]2.39895735341655e-22[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.97369744228535e-21[/C][C]9.86848721142673e-22[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]1.33122940162832e-20[/C][C]6.65614700814161e-21[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]9.93309456159492e-20[/C][C]4.96654728079746e-20[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]1.00550669335647e-18[/C][C]5.02753346678233e-19[/C][/ROW]
[ROW][C]169[/C][C]1[/C][C]5.64378661186278e-18[/C][C]2.82189330593139e-18[/C][/ROW]
[ROW][C]170[/C][C]1[/C][C]5.21918766637356e-17[/C][C]2.60959383318678e-17[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]4.04668000188201e-16[/C][C]2.023340000941e-16[/C][/ROW]
[ROW][C]172[/C][C]0.999999999999998[/C][C]3.62051334790331e-15[/C][C]1.81025667395166e-15[/C][/ROW]
[ROW][C]173[/C][C]0.999999999999987[/C][C]2.63125022056261e-14[/C][C]1.3156251102813e-14[/C][/ROW]
[ROW][C]174[/C][C]0.99999999999988[/C][C]2.37978124191467e-13[/C][C]1.18989062095734e-13[/C][/ROW]
[ROW][C]175[/C][C]0.999999999999568[/C][C]8.64322946759029e-13[/C][C]4.32161473379515e-13[/C][/ROW]
[ROW][C]176[/C][C]0.999999999996126[/C][C]7.74716567708688e-12[/C][C]3.87358283854344e-12[/C][/ROW]
[ROW][C]177[/C][C]0.999999999984305[/C][C]3.13895638437187e-11[/C][C]1.56947819218593e-11[/C][/ROW]
[ROW][C]178[/C][C]0.999999999950161[/C][C]9.96775817252916e-11[/C][C]4.98387908626458e-11[/C][/ROW]
[ROW][C]179[/C][C]0.999999999913023[/C][C]1.7395449689015e-10[/C][C]8.6977248445075e-11[/C][/ROW]
[ROW][C]180[/C][C]0.99999999893923[/C][C]2.12154161172487e-09[/C][C]1.06077080586243e-09[/C][/ROW]
[ROW][C]181[/C][C]0.999999995364944[/C][C]9.27011244588052e-09[/C][C]4.63505622294026e-09[/C][/ROW]
[ROW][C]182[/C][C]0.999999958925543[/C][C]8.21489142068034e-08[/C][C]4.10744571034017e-08[/C][/ROW]
[ROW][C]183[/C][C]0.999999754405653[/C][C]4.9118869423603e-07[/C][C]2.45594347118015e-07[/C][/ROW]
[ROW][C]184[/C][C]0.999996029533232[/C][C]7.94093353627038e-06[/C][C]3.97046676813519e-06[/C][/ROW]
[ROW][C]185[/C][C]0.999938820727423[/C][C]0.000122358545154941[/C][C]6.11792725774705e-05[/C][/ROW]
[ROW][C]186[/C][C]0.999506244001031[/C][C]0.000987511997937489[/C][C]0.000493755998968744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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
90.06721371142636730.1344274228527350.932786288573633
100.03275321015199060.06550642030398110.96724678984801
110.02480131738878260.04960263477756520.975198682611217
120.01079721371462230.02159442742924450.989202786285378
130.003696940048824160.007393880097648330.996303059951176
140.00125073936903780.00250147873807560.998749260630962
150.000448999634291150.00089799926858230.99955100036571
160.0001357265551329320.0002714531102658640.999864273444867
170.0002604367106237470.0005208734212474940.999739563289376
180.000151194804344730.0003023896086894590.999848805195655
190.001096843667874930.002193687335749850.998903156332125
200.0006238798796513470.001247759759302690.999376120120349
210.0003012964824493050.0006025929648986090.99969870351755
220.0001354427345132430.0002708854690264850.999864557265487
235.63708273697442e-050.0001127416547394880.99994362917263
242.28557000142778e-054.57114000285557e-050.999977144299986
251.52932330628172e-053.05864661256344e-050.999984706766937
263.64338020357563e-057.28676040715126e-050.999963566197964
271.79571001928842e-053.59142003857683e-050.999982042899807
289.01006869843972e-061.80201373968794e-050.999990989931302
294.79425746253434e-069.58851492506868e-060.999995205742537
302.48574171451135e-064.9714834290227e-060.999997514258286
311.35879113787931e-062.71758227575862e-060.999998641208862
327.44895022277805e-071.48979004455561e-060.999999255104978
334.17058319103472e-078.34116638206943e-070.999999582941681
341.77808876021986e-073.55617752043973e-070.999999822191124
351.50370896498666e-073.00741792997332e-070.999999849629103
361.95733983287409e-073.91467966574818e-070.999999804266017
371.19836717999224e-072.39673435998448e-070.999999880163282
385.14557242066258e-081.02911448413252e-070.999999948544276
392.71737775985396e-085.43475551970791e-080.999999972826222
401.45296568553963e-082.90593137107926e-080.999999985470343
412.04367517711704e-084.08735035423407e-080.999999979563248
422.81220837939698e-085.62441675879396e-080.999999971877916
432.10222559048526e-084.20445118097051e-080.999999978977744
441.11290202599758e-082.22580405199516e-080.99999998887098
458.30664316587167e-091.66132863317433e-080.999999991693357
466.914092914075e-071.382818582815e-060.999999308590709
474.25345995243362e-078.50691990486723e-070.999999574654005
482.70133547472604e-075.40267094945209e-070.999999729866453
491.33386644511617e-072.66773289023234e-070.999999866613355
506.56522326325853e-081.31304465265171e-070.999999934347767
513.00367398771681e-086.00734797543361e-080.99999996996326
521.58173523240309e-083.16347046480617e-080.999999984182648
537.78054766117728e-091.55610953223546e-080.999999992219452
541.58450885611884e-083.16901771223767e-080.999999984154911
551.77703602262092e-083.55407204524185e-080.99999998222964
561.04752913865117e-082.09505827730234e-080.999999989524709
571.42172503032749e-082.84345006065498e-080.99999998578275
586.70933142702049e-091.3418662854041e-080.999999993290669
597.09423144472389e-091.41884628894478e-080.999999992905769
603.60374708149876e-097.20749416299753e-090.999999996396253
616.28648990382731e-091.25729798076546e-080.99999999371351
625.49367142736086e-081.09873428547217e-070.999999945063286
632.53612134218436e-075.07224268436872e-070.999999746387866
645.89716557741003e-071.17943311548201e-060.999999410283442
656.35911449346476e-071.27182289869295e-060.99999936408855
665.43201449942562e-071.08640289988512e-060.99999945679855
676.87915598153579e-071.37583119630716e-060.999999312084402
681.15881651738289e-062.31763303476577e-060.999998841183483
697.74171759185466e-071.54834351837093e-060.99999922582824
701.88674086463135e-063.77348172926271e-060.999998113259135
711.06535167015015e-062.1307033403003e-060.99999893464833
728.07317299838075e-071.61463459967615e-060.9999991926827
734.51221627025701e-079.02443254051402e-070.999999548778373
744.82529523692141e-079.65059047384282e-070.999999517470476
754.58540313524617e-079.17080627049233e-070.999999541459687
763.02354568271632e-076.04709136543264e-070.999999697645432
773.65491759163354e-077.30983518326707e-070.99999963450824
783.95007724882972e-077.90015449765944e-070.999999604992275
794.15731402250013e-078.31462804500027e-070.999999584268598
809.25968580811202e-071.8519371616224e-060.99999907403142
812.04690410281715e-064.0938082056343e-060.999997953095897
826.58064580461795e-061.31612916092359e-050.999993419354195
834.43950285612772e-068.87900571225544e-060.999995560497144
844.15252537449052e-068.30505074898105e-060.999995847474626
853.63129821268454e-067.26259642536908e-060.999996368701787
864.56078210356307e-069.12156420712615e-060.999995439217896
878.40716417992426e-061.68143283598485e-050.99999159283582
887.15150734065786e-061.43030146813157e-050.99999284849266
898.92039471454802e-061.7840789429096e-050.999991079605285
901.02366186803802e-052.04732373607604e-050.99998976338132
911.03604120976993e-052.07208241953985e-050.999989639587902
928.9622499205936e-061.79244998411872e-050.99999103775008
938.25009810997574e-061.65001962199515e-050.99999174990189
941.22786964859153e-052.45573929718306e-050.999987721303514
952.81423319013909e-055.62846638027818e-050.999971857668099
962.37131179505214e-054.74262359010429e-050.99997628688205
972.9932988455323e-055.98659769106461e-050.999970067011545
983.2653863801272e-056.5307727602544e-050.9999673461362
994.15028715719197e-058.30057431438393e-050.999958497128428
1003.91137076518404e-057.82274153036807e-050.999960886292348
1014.09953813490006e-058.19907626980013e-050.99995900461865
1023.36639360758372e-056.73278721516743e-050.999966336063924
1032.40067102914424e-054.80134205828848e-050.999975993289709
1042.61081031032069e-055.22162062064138e-050.999973891896897
1053.33895978984065e-056.6779195796813e-050.999966610402102
1064.35316166928938e-058.70632333857876e-050.999956468383307
1073.04681687699929e-056.09363375399857e-050.99996953183123
1080.0001043276353394490.0002086552706788990.99989567236466
1090.0001076195404034360.0002152390808068720.999892380459597
1100.0002035125991682830.0004070251983365670.999796487400832
1110.0003693496535133390.0007386993070266790.999630650346487
1120.000914225281968230.001828450563936460.999085774718032
1130.0007569304736153740.001513860947230750.999243069526385
1140.0006729903409333130.001345980681866630.999327009659067
1150.001199288598545990.002398577197091990.998800711401454
1160.001382978558311090.002765957116622190.998617021441689
1170.001530870113788460.003061740227576910.998469129886212
1180.004914987119118290.009829974238236580.995085012880882
1190.01094727201312670.02189454402625340.989052727986873
1200.01801815419036330.03603630838072650.981981845809637
1210.03130838665203350.06261677330406710.968691613347966
1220.1586562194960770.3173124389921540.841343780503923
1230.1492704372621020.2985408745242040.850729562737898
1240.1290272117852260.2580544235704520.870972788214774
1250.1106503271827550.221300654365510.889349672817245
1260.09440415973536750.1888083194707350.905595840264632
1270.08004923806550930.1600984761310190.91995076193449
1280.06788669986100880.1357733997220180.932113300138991
1290.05474359648855080.1094871929771020.94525640351145
1300.04485693602908050.0897138720581610.95514306397092
1310.07662501175818460.1532500235163690.923374988241815
1320.07507208245690080.1501441649138020.9249279175431
1330.09063181008708020.181263620174160.90936818991292
1340.07471402946423780.1494280589284760.925285970535762
1350.0764331605244410.1528663210488820.923566839475559
1360.09691893338041070.1938378667608210.90308106661959
1370.08215698920321060.1643139784064210.91784301079679
1380.06791236858695310.1358247371739060.932087631413047
1390.07233938344450290.1446787668890060.927660616555497
1400.1416198961564430.2832397923128870.858380103843557
1410.1390163554878180.2780327109756360.860983644512182
1420.4857951041848520.9715902083697040.514204895815148
1430.5434353304540460.9131293390919090.456564669545954
1440.6180265167522020.7639469664955960.381973483247798
1450.7325482511515170.5349034976969670.267451748848483
1460.8272322778080640.3455354443838710.172767722191936
1470.8339142253752980.3321715492494050.166085774624702
1480.8053818709372170.3892362581255660.194618129062783
1490.8031041479269770.3937917041460450.196895852073023
1500.9672955978852980.06540880422940470.0327044021147023
1510.9624559661076060.07508806778478890.0375440338923945
1520.9525891245647150.09482175087056970.0474108754352848
1530.9739318467053070.05213630658938540.0260681532946927
1540.967058352964910.06588329407018220.0329416470350911
1550.9637118043775130.07257639124497410.036288195622487
1560.9830883059595690.03382338808086250.0169116940404313
1570.9782423865756870.04351522684862640.0217576134243132
1580.9911270449071460.0177459101857070.00887295509285352
1590.9988892634977880.002221473004424550.00111073650221227
16013.92917506869403e-231.96458753434701e-23
16117.5713629963432e-233.7856814981716e-23
16213.53480096721305e-221.76740048360652e-22
16311.90048674271785e-229.50243371358923e-23
16414.7979147068331e-222.39895735341655e-22
16511.97369744228535e-219.86848721142673e-22
16611.33122940162832e-206.65614700814161e-21
16719.93309456159492e-204.96654728079746e-20
16811.00550669335647e-185.02753346678233e-19
16915.64378661186278e-182.82189330593139e-18
17015.21918766637356e-172.60959383318678e-17
17114.04668000188201e-162.023340000941e-16
1720.9999999999999983.62051334790331e-151.81025667395166e-15
1730.9999999999999872.63125022056261e-141.3156251102813e-14
1740.999999999999882.37978124191467e-131.18989062095734e-13
1750.9999999999995688.64322946759029e-134.32161473379515e-13
1760.9999999999961267.74716567708688e-123.87358283854344e-12
1770.9999999999843053.13895638437187e-111.56947819218593e-11
1780.9999999999501619.96775817252916e-114.98387908626458e-11
1790.9999999999130231.7395449689015e-108.6977248445075e-11
1800.999999998939232.12154161172487e-091.06077080586243e-09
1810.9999999953649449.27011244588052e-094.63505622294026e-09
1820.9999999589255438.21489142068034e-084.10744571034017e-08
1830.9999997544056534.9118869423603e-072.45594347118015e-07
1840.9999960295332327.94093353627038e-063.97046676813519e-06
1850.9999388207274230.0001223585451549416.11792725774705e-05
1860.9995062440010310.0009875119979374890.000493755998968744






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1340.752808988764045NOK
5% type I error level1410.792134831460674NOK
10% type I error level1500.842696629213483NOK

\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 & 134 & 0.752808988764045 & NOK \tabularnewline
5% type I error level & 141 & 0.792134831460674 & NOK \tabularnewline
10% type I error level & 150 & 0.842696629213483 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116295&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]134[/C][C]0.752808988764045[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]141[/C][C]0.792134831460674[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]150[/C][C]0.842696629213483[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116295&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116295&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 level1340.752808988764045NOK
5% type I error level1410.792134831460674NOK
10% type I error level1500.842696629213483NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}