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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, 23 Nov 2010 13:01:30 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290517233c2qf0vauesx37dq.htm/, Retrieved Fri, 26 Apr 2024 21:59:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98987, Retrieved Fri, 26 Apr 2024 21:59:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-23 13:01:30] [c2e23af56713b360851e64c7775b3f2b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
38	23	10	11	35	37	12
36	15	10	11	35	37	12
23	25	10	11	35	37	12
30	18	10	11	35	37	12
26	21	10	11	35	37	12
26	19	10	11	35	37	12
30	15	13	12	38	34	12
27	22	10	11	35	37	12
34	19	10	11	35	37	14
28	20	13	9	34	32	12
36	26	10	11	35	37	12
42	26	10	11	35	37	12
31	21	10	11	35	37	14
26	19	10	11	35	37	12
16	19	13	12	38	34	12
23	19	10	11	35	37	14
45	28	10	11	35	37	12
30	27	10	11	35	37	15
45	18	10	11	35	37	12
30	19	10	11	35	37	15
24	24	10	11	35	37	12
29	21	13	12	38	34	12
30	22	13	9	34	32	12
31	25	10	11	35	37	14
34	15	10	11	35	37	14
41	34	10	11	35	37	12
37	23	10	11	35	37	12
33	19	10	11	35	37	12
48	15	10	11	35	37	14
44	15	10	11	35	37	15
29	17	10	11	35	37	14
44	30	13	9	34	32	12
43	28	10	11	35	37	14
31	23	10	11	35	37	14
28	23	10	11	35	37	12
26	21	10	11	35	37	14
30	18	10	11	35	37	12
27	19	15	11	33	36	12
34	24	10	11	35	37	12
47	15	10	11	35	37	12
37	24	13	16	34	36	12
27	20	10	11	35	37	12
30	20	10	11	35	37	12
36	44	10	11	35	37	14
39	20	10	11	35	37	12
32	20	10	11	35	37	12
25	20	10	11	35	37	12
19	11	10	11	35	37	12
29	21	10	11	35	37	12
26	21	13	9	34	32	12
31	19	13	12	38	34	12
31	21	10	11	35	37	12
31	17	10	11	35	37	15
39	19	10	11	35	37	12
28	21	10	11	35	37	12
22	16	10	11	35	37	12
31	19	10	11	35	37	12
36	19	10	11	35	37	14
28	16	10	11	35	37	12
39	24	10	11	35	37	12
35	21	10	11	35	37	12
33	20	10	11	35	37	12
27	19	10	11	35	37	12
33	23	10	11	35	37	12
31	18	10	11	35	37	12
39	19	10	11	35	37	14
37	23	10	11	35	37	14
24	19	10	11	35	37	15
28	26	13	12	38	34	12
37	13	13	12	38	34	12
32	23	10	11	35	37	14
31	16	13	12	38	34	12
29	17	13	12	38	34	12
40	30	10	11	35	37	12
40	22	10	11	35	37	14
15	14	10	11	35	37	12
27	14	13	9	34	32	12
32	21	13	9	34	32	12
28	21	10	11	35	37	12
41	33	10	11	35	37	14
47	23	10	11	35	37	12
42	30	10	11	35	37	12
32	21	11	17	36	35	12
33	25	10	11	35	37	15
29	29	10	11	35	37	12
37	21	10	11	35	37	14
39	16	10	11	35	37	15
29	17	10	11	35	37	12
33	23	10	11	35	37	12
31	18	13	9	34	32	12
21	19	10	11	35	37	15
36	28	10	11	35	37	14
32	29	10	11	35	37	14
15	19	10	11	35	37	12
25	25	13	9	34	32	12
28	15	10	11	35	37	12
39	24	10	11	35	37	12
31	12	13	9	34	32	12
40	11	10	11	35	37	12
25	19	10	11	35	37	12
36	25	10	11	35	37	14
23	12	10	11	35	37	14
39	15	10	11	35	37	12
31	25	10	11	35	37	14
23	14	10	11	35	37	12
31	19	10	11	35	37	14
28	23	13	9	34	32	12
47	19	13	9	34	32	12
25	20	10	11	35	37	15
26	16	13	9	34	32	12
24	13	12	18	32	35	12
30	22	10	11	35	37	15
25	21	13	16	34	36	12
44	18	15	13	34	31	12
38	44	10	11	35	37	15
36	12	10	11	35	37	12
34	28	13	12	38	34	12
45	17	13	16	34	36	12
29	18	10	11	35	37	14
25	21	10	11	35	37	12
30	24	10	11	35	37	12
27	20	10	11	35	37	16
44	24	10	11	35	37	14
31	33	10	11	35	37	12
35	25	10	11	35	37	12
47	35	10	11	35	37	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 12 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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 time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
CM+D[t] = + 44.1744764200473 + 0.355225335188637`PE+PC`[t] -0.223616457775059happiness[t] + 0.253261494075167depression[t] -0.457390995302612connected[t] -0.134163985832665separated[t] + 0.0731026269579095populariteit[t] + 0.00173563261092779t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CM+D[t] =  +  44.1744764200473 +  0.355225335188637`PE+PC`[t] -0.223616457775059happiness[t] +  0.253261494075167depression[t] -0.457390995302612connected[t] -0.134163985832665separated[t] +  0.0731026269579095populariteit[t] +  0.00173563261092779t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CM+D[t] =  +  44.1744764200473 +  0.355225335188637`PE+PC`[t] -0.223616457775059happiness[t] +  0.253261494075167depression[t] -0.457390995302612connected[t] -0.134163985832665separated[t] +  0.0731026269579095populariteit[t] +  0.00173563261092779t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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
CM+D[t] = + 44.1744764200473 + 0.355225335188637`PE+PC`[t] -0.223616457775059happiness[t] + 0.253261494075167depression[t] -0.457390995302612connected[t] -0.134163985832665separated[t] + 0.0731026269579095populariteit[t] + 0.00173563261092779t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)44.174476420047350.901410.86780.3872410.193621
`PE+PC`0.3552253351886370.1122633.16420.0019780.000989
happiness-0.2236164577750591.298405-0.17220.8635570.431778
depression0.2532614940751670.5991770.42270.6732960.336648
connected-0.4573909953026120.676092-0.67650.5000330.250017
separated-0.1341639858326651.036453-0.12940.8972260.448613
populariteit0.07310262695790950.5985370.12210.9029990.4515
t0.001735632610927790.0177240.09790.9221590.46108

\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) & 44.1744764200473 & 50.90141 & 0.8678 & 0.387241 & 0.193621 \tabularnewline
`PE+PC` & 0.355225335188637 & 0.112263 & 3.1642 & 0.001978 & 0.000989 \tabularnewline
happiness & -0.223616457775059 & 1.298405 & -0.1722 & 0.863557 & 0.431778 \tabularnewline
depression & 0.253261494075167 & 0.599177 & 0.4227 & 0.673296 & 0.336648 \tabularnewline
connected & -0.457390995302612 & 0.676092 & -0.6765 & 0.500033 & 0.250017 \tabularnewline
separated & -0.134163985832665 & 1.036453 & -0.1294 & 0.897226 & 0.448613 \tabularnewline
populariteit & 0.0731026269579095 & 0.598537 & 0.1221 & 0.902999 & 0.4515 \tabularnewline
t & 0.00173563261092779 & 0.017724 & 0.0979 & 0.922159 & 0.46108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&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]44.1744764200473[/C][C]50.90141[/C][C]0.8678[/C][C]0.387241[/C][C]0.193621[/C][/ROW]
[ROW][C]`PE+PC`[/C][C]0.355225335188637[/C][C]0.112263[/C][C]3.1642[/C][C]0.001978[/C][C]0.000989[/C][/ROW]
[ROW][C]happiness[/C][C]-0.223616457775059[/C][C]1.298405[/C][C]-0.1722[/C][C]0.863557[/C][C]0.431778[/C][/ROW]
[ROW][C]depression[/C][C]0.253261494075167[/C][C]0.599177[/C][C]0.4227[/C][C]0.673296[/C][C]0.336648[/C][/ROW]
[ROW][C]connected[/C][C]-0.457390995302612[/C][C]0.676092[/C][C]-0.6765[/C][C]0.500033[/C][C]0.250017[/C][/ROW]
[ROW][C]separated[/C][C]-0.134163985832665[/C][C]1.036453[/C][C]-0.1294[/C][C]0.897226[/C][C]0.448613[/C][/ROW]
[ROW][C]populariteit[/C][C]0.0731026269579095[/C][C]0.598537[/C][C]0.1221[/C][C]0.902999[/C][C]0.4515[/C][/ROW]
[ROW][C]t[/C][C]0.00173563261092779[/C][C]0.017724[/C][C]0.0979[/C][C]0.922159[/C][C]0.46108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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)44.174476420047350.901410.86780.3872410.193621
`PE+PC`0.3552253351886370.1122633.16420.0019780.000989
happiness-0.2236164577750591.298405-0.17220.8635570.431778
depression0.2532614940751670.5991770.42270.6732960.336648
connected-0.4573909953026120.676092-0.67650.5000330.250017
separated-0.1341639858326651.036453-0.12940.8972260.448613
populariteit0.07310262695790950.5985370.12210.9029990.4515
t0.001735632610927790.0177240.09790.9221590.46108






Multiple Linear Regression - Regression Statistics
Multiple R0.293957093886829
R-squared0.08641077304639
Adjusted R-squared0.0322148019559215
F-TEST (value)1.59441322496364
F-TEST (DF numerator)7
F-TEST (DF denominator)118
p-value0.143625117302426
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.94948698655691
Sum Squared Residuals5698.85358640621

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.293957093886829 \tabularnewline
R-squared & 0.08641077304639 \tabularnewline
Adjusted R-squared & 0.0322148019559215 \tabularnewline
F-TEST (value) & 1.59441322496364 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 118 \tabularnewline
p-value & 0.143625117302426 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.94948698655691 \tabularnewline
Sum Squared Residuals & 5698.85358640621 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.293957093886829[/C][/ROW]
[ROW][C]R-squared[/C][C]0.08641077304639[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0322148019559215[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.59441322496364[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]118[/C][/ROW]
[ROW][C]p-value[/C][C]0.143625117302426[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.94948698655691[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5698.85358640621[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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 R0.293957093886829
R-squared0.08641077304639
Adjusted R-squared0.0322148019559215
F-TEST (value)1.59441322496364
F-TEST (DF numerator)7
F-TEST (DF denominator)118
p-value0.143625117302426
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.94948698655691
Sum Squared Residuals5698.85358640621






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13832.8005858311685.19941416883201
23629.96051878226986.03948121773016
32333.5145077667671-10.5145077667671
43031.0296660530576-1.02966605305760
52632.0970776912344-6.09707769123444
62631.3883626534681-5.3883626534681
73028.58192803766461.41807196233537
82732.4575099242559-5.45750992425586
93431.53977480521672.46022519478330
102831.7013690820909-3.70136908209087
113633.88361816284322.11638183715681
124233.88535379545418.11464620454588
133132.2571680060377-1.25716800603768
142631.4022477143555-5.40224771435552
151630.0167144393066-14.0167144393066
162331.5519242334932-8.5519242334932
174534.60448262888610.3955173711140
183034.4703008071820-4.47030080718205
194531.055700542221513.9442994577785
203031.6319693908948-1.63196939089481
212433.1905238185752-9.1905238185752
222930.7393145379604-1.73931453796036
233032.4343829764102-2.43438297641020
243133.6971613055124-2.69716130551243
253430.1466435862373.85335641376300
264136.75145533351624.2485446664838
273732.84571227905214.15428772094788
283331.42654657090851.57345342909149
294830.153586116680717.8464138833193
304430.228424376249513.7715756237505
312930.8675080522798-1.86750805227983
324435.29180635141768.70819364858235
334334.77845800457678.2215419954233
343133.0040669612444-2.00406696124444
352832.8595973399395-4.85959733993954
362632.297087556089-6.29708755608902
373031.0869419292182-1.08694192921822
382731.3747665845804-4.37476658458038
393433.22176520557190.778234794428106
404730.026472821485116.9735271785149
413734.41224954897972.58775045102032
422731.8060707626501-4.80607076265013
433031.8078063952611-1.80780639526106
443640.4811553263151-4.48115532631508
453931.81127766048297.18872233951709
463231.81301329309380.186986706906158
472531.8147489257048-6.81474892570477
481928.6194565416180-9.61945654161797
492932.1734455261153-3.17344552611526
502632.1260197217166-6.12601972171661
513130.07919721330.920802786700005
523132.1786524239480-1.17865242394805
533130.97879459667820.0212054033218439
543931.47167301879267.52832698120737
552832.1838593217808-4.18385932178083
562230.4094682784486-8.40946827844857
573131.4768799166254-0.476879916625412
583631.62482080315224.37517919684784
592830.4146751762814-2.41467517628136
603933.25821349040145.74178650959862
613532.19427311744642.80572688255360
623331.84078341486871.15921658513131
632731.487293712291-4.48729371229098
643332.90993068565650.0900693143435477
653131.1355396423242-0.135539642324197
663931.63870586403967.36129413596042
673733.06134283740513.93865716259494
682431.7152797562193-7.71527975621935
692832.5970159466172-4.59701594661715
703727.98082222177589.0191777782242
713233.0682853678488-1.06828536784877
723129.04996949256361.95003050743643
732929.4069304603631-0.406930460363133
744035.41386435808624.58613564191381
754032.72000256310387.27999743689616
761529.7337302602899-14.7337302602899
772729.6863044558912-2.68630445589121
783232.1746174348226-0.174617434822594
792832.2255145044431-4.22551450444309
804136.63615941323354.36384058676652
814732.939436440042214.0605635599578
824235.42774941897366.57225058102639
833233.3393465179255-1.33934651792547
843333.874401889126-0.87440188912601
852935.0777309816178-6.07773098161775
863732.38386918663544.61613081336459
873930.68258077026118.31741922973894
882930.8202338571869-1.8202338571869
893332.95332150092960.0466784990703529
903131.1297690205878-0.129769020587818
912131.7551993062707-10.7551993062707
923634.88086032862141.11913967137857
933235.237821296421-3.23782129642100
941531.5410983232297-16.5410983232297
952533.6250245299629-8.62502452996291
962830.1236682476971-2.12366824769705
973933.32243189700575.6775681029943
983129.01230207034341.98769792965658
994028.707973804775311.2920261952247
1002531.5515121188953-6.5515121188953
1013633.83080501655392.16919498344613
1022329.2146112917125-6.21461129171252
1033930.13581767597358.86418232402646
1043133.8360119143867-2.83601191438666
1052329.7840636060068-6.78406360600676
1063131.7081311684767-0.708131168476692
1072832.9354014509168-4.93540145091677
1084731.516235742773215.4837642572268
1092532.141666028456-7.14166602845602
1102630.4540310024291-4.4540310024291
1112432.4053505670329-8.40535056703291
1123032.8573235966661-2.85732359666608
1132533.4715390914006-8.47153909140057
1144431.871401249833312.1285987501667
1153840.6774878686489-2.67748786864887
1163629.09270489434976.9072951056503
1173433.39077698231900.609223017681041
1184532.059315913700712.9406840862993
1192931.3754690572301-2.37546905723012
1202532.2966754414911-7.29667544149113
1213033.364087079668-3.36408707966797
1222732.237331879356-5.23733187935599
1234433.513763598805610.4862364011944
1243136.5663219941985-5.56632199419849
1253533.72625494530031.27374505469968
1264737.28024392979769.71975607020239

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 38 & 32.800585831168 & 5.19941416883201 \tabularnewline
2 & 36 & 29.9605187822698 & 6.03948121773016 \tabularnewline
3 & 23 & 33.5145077667671 & -10.5145077667671 \tabularnewline
4 & 30 & 31.0296660530576 & -1.02966605305760 \tabularnewline
5 & 26 & 32.0970776912344 & -6.09707769123444 \tabularnewline
6 & 26 & 31.3883626534681 & -5.3883626534681 \tabularnewline
7 & 30 & 28.5819280376646 & 1.41807196233537 \tabularnewline
8 & 27 & 32.4575099242559 & -5.45750992425586 \tabularnewline
9 & 34 & 31.5397748052167 & 2.46022519478330 \tabularnewline
10 & 28 & 31.7013690820909 & -3.70136908209087 \tabularnewline
11 & 36 & 33.8836181628432 & 2.11638183715681 \tabularnewline
12 & 42 & 33.8853537954541 & 8.11464620454588 \tabularnewline
13 & 31 & 32.2571680060377 & -1.25716800603768 \tabularnewline
14 & 26 & 31.4022477143555 & -5.40224771435552 \tabularnewline
15 & 16 & 30.0167144393066 & -14.0167144393066 \tabularnewline
16 & 23 & 31.5519242334932 & -8.5519242334932 \tabularnewline
17 & 45 & 34.604482628886 & 10.3955173711140 \tabularnewline
18 & 30 & 34.4703008071820 & -4.47030080718205 \tabularnewline
19 & 45 & 31.0557005422215 & 13.9442994577785 \tabularnewline
20 & 30 & 31.6319693908948 & -1.63196939089481 \tabularnewline
21 & 24 & 33.1905238185752 & -9.1905238185752 \tabularnewline
22 & 29 & 30.7393145379604 & -1.73931453796036 \tabularnewline
23 & 30 & 32.4343829764102 & -2.43438297641020 \tabularnewline
24 & 31 & 33.6971613055124 & -2.69716130551243 \tabularnewline
25 & 34 & 30.146643586237 & 3.85335641376300 \tabularnewline
26 & 41 & 36.7514553335162 & 4.2485446664838 \tabularnewline
27 & 37 & 32.8457122790521 & 4.15428772094788 \tabularnewline
28 & 33 & 31.4265465709085 & 1.57345342909149 \tabularnewline
29 & 48 & 30.1535861166807 & 17.8464138833193 \tabularnewline
30 & 44 & 30.2284243762495 & 13.7715756237505 \tabularnewline
31 & 29 & 30.8675080522798 & -1.86750805227983 \tabularnewline
32 & 44 & 35.2918063514176 & 8.70819364858235 \tabularnewline
33 & 43 & 34.7784580045767 & 8.2215419954233 \tabularnewline
34 & 31 & 33.0040669612444 & -2.00406696124444 \tabularnewline
35 & 28 & 32.8595973399395 & -4.85959733993954 \tabularnewline
36 & 26 & 32.297087556089 & -6.29708755608902 \tabularnewline
37 & 30 & 31.0869419292182 & -1.08694192921822 \tabularnewline
38 & 27 & 31.3747665845804 & -4.37476658458038 \tabularnewline
39 & 34 & 33.2217652055719 & 0.778234794428106 \tabularnewline
40 & 47 & 30.0264728214851 & 16.9735271785149 \tabularnewline
41 & 37 & 34.4122495489797 & 2.58775045102032 \tabularnewline
42 & 27 & 31.8060707626501 & -4.80607076265013 \tabularnewline
43 & 30 & 31.8078063952611 & -1.80780639526106 \tabularnewline
44 & 36 & 40.4811553263151 & -4.48115532631508 \tabularnewline
45 & 39 & 31.8112776604829 & 7.18872233951709 \tabularnewline
46 & 32 & 31.8130132930938 & 0.186986706906158 \tabularnewline
47 & 25 & 31.8147489257048 & -6.81474892570477 \tabularnewline
48 & 19 & 28.6194565416180 & -9.61945654161797 \tabularnewline
49 & 29 & 32.1734455261153 & -3.17344552611526 \tabularnewline
50 & 26 & 32.1260197217166 & -6.12601972171661 \tabularnewline
51 & 31 & 30.0791972133 & 0.920802786700005 \tabularnewline
52 & 31 & 32.1786524239480 & -1.17865242394805 \tabularnewline
53 & 31 & 30.9787945966782 & 0.0212054033218439 \tabularnewline
54 & 39 & 31.4716730187926 & 7.52832698120737 \tabularnewline
55 & 28 & 32.1838593217808 & -4.18385932178083 \tabularnewline
56 & 22 & 30.4094682784486 & -8.40946827844857 \tabularnewline
57 & 31 & 31.4768799166254 & -0.476879916625412 \tabularnewline
58 & 36 & 31.6248208031522 & 4.37517919684784 \tabularnewline
59 & 28 & 30.4146751762814 & -2.41467517628136 \tabularnewline
60 & 39 & 33.2582134904014 & 5.74178650959862 \tabularnewline
61 & 35 & 32.1942731174464 & 2.80572688255360 \tabularnewline
62 & 33 & 31.8407834148687 & 1.15921658513131 \tabularnewline
63 & 27 & 31.487293712291 & -4.48729371229098 \tabularnewline
64 & 33 & 32.9099306856565 & 0.0900693143435477 \tabularnewline
65 & 31 & 31.1355396423242 & -0.135539642324197 \tabularnewline
66 & 39 & 31.6387058640396 & 7.36129413596042 \tabularnewline
67 & 37 & 33.0613428374051 & 3.93865716259494 \tabularnewline
68 & 24 & 31.7152797562193 & -7.71527975621935 \tabularnewline
69 & 28 & 32.5970159466172 & -4.59701594661715 \tabularnewline
70 & 37 & 27.9808222217758 & 9.0191777782242 \tabularnewline
71 & 32 & 33.0682853678488 & -1.06828536784877 \tabularnewline
72 & 31 & 29.0499694925636 & 1.95003050743643 \tabularnewline
73 & 29 & 29.4069304603631 & -0.406930460363133 \tabularnewline
74 & 40 & 35.4138643580862 & 4.58613564191381 \tabularnewline
75 & 40 & 32.7200025631038 & 7.27999743689616 \tabularnewline
76 & 15 & 29.7337302602899 & -14.7337302602899 \tabularnewline
77 & 27 & 29.6863044558912 & -2.68630445589121 \tabularnewline
78 & 32 & 32.1746174348226 & -0.174617434822594 \tabularnewline
79 & 28 & 32.2255145044431 & -4.22551450444309 \tabularnewline
80 & 41 & 36.6361594132335 & 4.36384058676652 \tabularnewline
81 & 47 & 32.9394364400422 & 14.0605635599578 \tabularnewline
82 & 42 & 35.4277494189736 & 6.57225058102639 \tabularnewline
83 & 32 & 33.3393465179255 & -1.33934651792547 \tabularnewline
84 & 33 & 33.874401889126 & -0.87440188912601 \tabularnewline
85 & 29 & 35.0777309816178 & -6.07773098161775 \tabularnewline
86 & 37 & 32.3838691866354 & 4.61613081336459 \tabularnewline
87 & 39 & 30.6825807702611 & 8.31741922973894 \tabularnewline
88 & 29 & 30.8202338571869 & -1.8202338571869 \tabularnewline
89 & 33 & 32.9533215009296 & 0.0466784990703529 \tabularnewline
90 & 31 & 31.1297690205878 & -0.129769020587818 \tabularnewline
91 & 21 & 31.7551993062707 & -10.7551993062707 \tabularnewline
92 & 36 & 34.8808603286214 & 1.11913967137857 \tabularnewline
93 & 32 & 35.237821296421 & -3.23782129642100 \tabularnewline
94 & 15 & 31.5410983232297 & -16.5410983232297 \tabularnewline
95 & 25 & 33.6250245299629 & -8.62502452996291 \tabularnewline
96 & 28 & 30.1236682476971 & -2.12366824769705 \tabularnewline
97 & 39 & 33.3224318970057 & 5.6775681029943 \tabularnewline
98 & 31 & 29.0123020703434 & 1.98769792965658 \tabularnewline
99 & 40 & 28.7079738047753 & 11.2920261952247 \tabularnewline
100 & 25 & 31.5515121188953 & -6.5515121188953 \tabularnewline
101 & 36 & 33.8308050165539 & 2.16919498344613 \tabularnewline
102 & 23 & 29.2146112917125 & -6.21461129171252 \tabularnewline
103 & 39 & 30.1358176759735 & 8.86418232402646 \tabularnewline
104 & 31 & 33.8360119143867 & -2.83601191438666 \tabularnewline
105 & 23 & 29.7840636060068 & -6.78406360600676 \tabularnewline
106 & 31 & 31.7081311684767 & -0.708131168476692 \tabularnewline
107 & 28 & 32.9354014509168 & -4.93540145091677 \tabularnewline
108 & 47 & 31.5162357427732 & 15.4837642572268 \tabularnewline
109 & 25 & 32.141666028456 & -7.14166602845602 \tabularnewline
110 & 26 & 30.4540310024291 & -4.4540310024291 \tabularnewline
111 & 24 & 32.4053505670329 & -8.40535056703291 \tabularnewline
112 & 30 & 32.8573235966661 & -2.85732359666608 \tabularnewline
113 & 25 & 33.4715390914006 & -8.47153909140057 \tabularnewline
114 & 44 & 31.8714012498333 & 12.1285987501667 \tabularnewline
115 & 38 & 40.6774878686489 & -2.67748786864887 \tabularnewline
116 & 36 & 29.0927048943497 & 6.9072951056503 \tabularnewline
117 & 34 & 33.3907769823190 & 0.609223017681041 \tabularnewline
118 & 45 & 32.0593159137007 & 12.9406840862993 \tabularnewline
119 & 29 & 31.3754690572301 & -2.37546905723012 \tabularnewline
120 & 25 & 32.2966754414911 & -7.29667544149113 \tabularnewline
121 & 30 & 33.364087079668 & -3.36408707966797 \tabularnewline
122 & 27 & 32.237331879356 & -5.23733187935599 \tabularnewline
123 & 44 & 33.5137635988056 & 10.4862364011944 \tabularnewline
124 & 31 & 36.5663219941985 & -5.56632199419849 \tabularnewline
125 & 35 & 33.7262549453003 & 1.27374505469968 \tabularnewline
126 & 47 & 37.2802439297976 & 9.71975607020239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&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]38[/C][C]32.800585831168[/C][C]5.19941416883201[/C][/ROW]
[ROW][C]2[/C][C]36[/C][C]29.9605187822698[/C][C]6.03948121773016[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]33.5145077667671[/C][C]-10.5145077667671[/C][/ROW]
[ROW][C]4[/C][C]30[/C][C]31.0296660530576[/C][C]-1.02966605305760[/C][/ROW]
[ROW][C]5[/C][C]26[/C][C]32.0970776912344[/C][C]-6.09707769123444[/C][/ROW]
[ROW][C]6[/C][C]26[/C][C]31.3883626534681[/C][C]-5.3883626534681[/C][/ROW]
[ROW][C]7[/C][C]30[/C][C]28.5819280376646[/C][C]1.41807196233537[/C][/ROW]
[ROW][C]8[/C][C]27[/C][C]32.4575099242559[/C][C]-5.45750992425586[/C][/ROW]
[ROW][C]9[/C][C]34[/C][C]31.5397748052167[/C][C]2.46022519478330[/C][/ROW]
[ROW][C]10[/C][C]28[/C][C]31.7013690820909[/C][C]-3.70136908209087[/C][/ROW]
[ROW][C]11[/C][C]36[/C][C]33.8836181628432[/C][C]2.11638183715681[/C][/ROW]
[ROW][C]12[/C][C]42[/C][C]33.8853537954541[/C][C]8.11464620454588[/C][/ROW]
[ROW][C]13[/C][C]31[/C][C]32.2571680060377[/C][C]-1.25716800603768[/C][/ROW]
[ROW][C]14[/C][C]26[/C][C]31.4022477143555[/C][C]-5.40224771435552[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]30.0167144393066[/C][C]-14.0167144393066[/C][/ROW]
[ROW][C]16[/C][C]23[/C][C]31.5519242334932[/C][C]-8.5519242334932[/C][/ROW]
[ROW][C]17[/C][C]45[/C][C]34.604482628886[/C][C]10.3955173711140[/C][/ROW]
[ROW][C]18[/C][C]30[/C][C]34.4703008071820[/C][C]-4.47030080718205[/C][/ROW]
[ROW][C]19[/C][C]45[/C][C]31.0557005422215[/C][C]13.9442994577785[/C][/ROW]
[ROW][C]20[/C][C]30[/C][C]31.6319693908948[/C][C]-1.63196939089481[/C][/ROW]
[ROW][C]21[/C][C]24[/C][C]33.1905238185752[/C][C]-9.1905238185752[/C][/ROW]
[ROW][C]22[/C][C]29[/C][C]30.7393145379604[/C][C]-1.73931453796036[/C][/ROW]
[ROW][C]23[/C][C]30[/C][C]32.4343829764102[/C][C]-2.43438297641020[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]33.6971613055124[/C][C]-2.69716130551243[/C][/ROW]
[ROW][C]25[/C][C]34[/C][C]30.146643586237[/C][C]3.85335641376300[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]36.7514553335162[/C][C]4.2485446664838[/C][/ROW]
[ROW][C]27[/C][C]37[/C][C]32.8457122790521[/C][C]4.15428772094788[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]31.4265465709085[/C][C]1.57345342909149[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]30.1535861166807[/C][C]17.8464138833193[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]30.2284243762495[/C][C]13.7715756237505[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]30.8675080522798[/C][C]-1.86750805227983[/C][/ROW]
[ROW][C]32[/C][C]44[/C][C]35.2918063514176[/C][C]8.70819364858235[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]34.7784580045767[/C][C]8.2215419954233[/C][/ROW]
[ROW][C]34[/C][C]31[/C][C]33.0040669612444[/C][C]-2.00406696124444[/C][/ROW]
[ROW][C]35[/C][C]28[/C][C]32.8595973399395[/C][C]-4.85959733993954[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]32.297087556089[/C][C]-6.29708755608902[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]31.0869419292182[/C][C]-1.08694192921822[/C][/ROW]
[ROW][C]38[/C][C]27[/C][C]31.3747665845804[/C][C]-4.37476658458038[/C][/ROW]
[ROW][C]39[/C][C]34[/C][C]33.2217652055719[/C][C]0.778234794428106[/C][/ROW]
[ROW][C]40[/C][C]47[/C][C]30.0264728214851[/C][C]16.9735271785149[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.4122495489797[/C][C]2.58775045102032[/C][/ROW]
[ROW][C]42[/C][C]27[/C][C]31.8060707626501[/C][C]-4.80607076265013[/C][/ROW]
[ROW][C]43[/C][C]30[/C][C]31.8078063952611[/C][C]-1.80780639526106[/C][/ROW]
[ROW][C]44[/C][C]36[/C][C]40.4811553263151[/C][C]-4.48115532631508[/C][/ROW]
[ROW][C]45[/C][C]39[/C][C]31.8112776604829[/C][C]7.18872233951709[/C][/ROW]
[ROW][C]46[/C][C]32[/C][C]31.8130132930938[/C][C]0.186986706906158[/C][/ROW]
[ROW][C]47[/C][C]25[/C][C]31.8147489257048[/C][C]-6.81474892570477[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]28.6194565416180[/C][C]-9.61945654161797[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]32.1734455261153[/C][C]-3.17344552611526[/C][/ROW]
[ROW][C]50[/C][C]26[/C][C]32.1260197217166[/C][C]-6.12601972171661[/C][/ROW]
[ROW][C]51[/C][C]31[/C][C]30.0791972133[/C][C]0.920802786700005[/C][/ROW]
[ROW][C]52[/C][C]31[/C][C]32.1786524239480[/C][C]-1.17865242394805[/C][/ROW]
[ROW][C]53[/C][C]31[/C][C]30.9787945966782[/C][C]0.0212054033218439[/C][/ROW]
[ROW][C]54[/C][C]39[/C][C]31.4716730187926[/C][C]7.52832698120737[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]32.1838593217808[/C][C]-4.18385932178083[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]30.4094682784486[/C][C]-8.40946827844857[/C][/ROW]
[ROW][C]57[/C][C]31[/C][C]31.4768799166254[/C][C]-0.476879916625412[/C][/ROW]
[ROW][C]58[/C][C]36[/C][C]31.6248208031522[/C][C]4.37517919684784[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]30.4146751762814[/C][C]-2.41467517628136[/C][/ROW]
[ROW][C]60[/C][C]39[/C][C]33.2582134904014[/C][C]5.74178650959862[/C][/ROW]
[ROW][C]61[/C][C]35[/C][C]32.1942731174464[/C][C]2.80572688255360[/C][/ROW]
[ROW][C]62[/C][C]33[/C][C]31.8407834148687[/C][C]1.15921658513131[/C][/ROW]
[ROW][C]63[/C][C]27[/C][C]31.487293712291[/C][C]-4.48729371229098[/C][/ROW]
[ROW][C]64[/C][C]33[/C][C]32.9099306856565[/C][C]0.0900693143435477[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]31.1355396423242[/C][C]-0.135539642324197[/C][/ROW]
[ROW][C]66[/C][C]39[/C][C]31.6387058640396[/C][C]7.36129413596042[/C][/ROW]
[ROW][C]67[/C][C]37[/C][C]33.0613428374051[/C][C]3.93865716259494[/C][/ROW]
[ROW][C]68[/C][C]24[/C][C]31.7152797562193[/C][C]-7.71527975621935[/C][/ROW]
[ROW][C]69[/C][C]28[/C][C]32.5970159466172[/C][C]-4.59701594661715[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]27.9808222217758[/C][C]9.0191777782242[/C][/ROW]
[ROW][C]71[/C][C]32[/C][C]33.0682853678488[/C][C]-1.06828536784877[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]29.0499694925636[/C][C]1.95003050743643[/C][/ROW]
[ROW][C]73[/C][C]29[/C][C]29.4069304603631[/C][C]-0.406930460363133[/C][/ROW]
[ROW][C]74[/C][C]40[/C][C]35.4138643580862[/C][C]4.58613564191381[/C][/ROW]
[ROW][C]75[/C][C]40[/C][C]32.7200025631038[/C][C]7.27999743689616[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]29.7337302602899[/C][C]-14.7337302602899[/C][/ROW]
[ROW][C]77[/C][C]27[/C][C]29.6863044558912[/C][C]-2.68630445589121[/C][/ROW]
[ROW][C]78[/C][C]32[/C][C]32.1746174348226[/C][C]-0.174617434822594[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.2255145044431[/C][C]-4.22551450444309[/C][/ROW]
[ROW][C]80[/C][C]41[/C][C]36.6361594132335[/C][C]4.36384058676652[/C][/ROW]
[ROW][C]81[/C][C]47[/C][C]32.9394364400422[/C][C]14.0605635599578[/C][/ROW]
[ROW][C]82[/C][C]42[/C][C]35.4277494189736[/C][C]6.57225058102639[/C][/ROW]
[ROW][C]83[/C][C]32[/C][C]33.3393465179255[/C][C]-1.33934651792547[/C][/ROW]
[ROW][C]84[/C][C]33[/C][C]33.874401889126[/C][C]-0.87440188912601[/C][/ROW]
[ROW][C]85[/C][C]29[/C][C]35.0777309816178[/C][C]-6.07773098161775[/C][/ROW]
[ROW][C]86[/C][C]37[/C][C]32.3838691866354[/C][C]4.61613081336459[/C][/ROW]
[ROW][C]87[/C][C]39[/C][C]30.6825807702611[/C][C]8.31741922973894[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]30.8202338571869[/C][C]-1.8202338571869[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]32.9533215009296[/C][C]0.0466784990703529[/C][/ROW]
[ROW][C]90[/C][C]31[/C][C]31.1297690205878[/C][C]-0.129769020587818[/C][/ROW]
[ROW][C]91[/C][C]21[/C][C]31.7551993062707[/C][C]-10.7551993062707[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.8808603286214[/C][C]1.11913967137857[/C][/ROW]
[ROW][C]93[/C][C]32[/C][C]35.237821296421[/C][C]-3.23782129642100[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]31.5410983232297[/C][C]-16.5410983232297[/C][/ROW]
[ROW][C]95[/C][C]25[/C][C]33.6250245299629[/C][C]-8.62502452996291[/C][/ROW]
[ROW][C]96[/C][C]28[/C][C]30.1236682476971[/C][C]-2.12366824769705[/C][/ROW]
[ROW][C]97[/C][C]39[/C][C]33.3224318970057[/C][C]5.6775681029943[/C][/ROW]
[ROW][C]98[/C][C]31[/C][C]29.0123020703434[/C][C]1.98769792965658[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]28.7079738047753[/C][C]11.2920261952247[/C][/ROW]
[ROW][C]100[/C][C]25[/C][C]31.5515121188953[/C][C]-6.5515121188953[/C][/ROW]
[ROW][C]101[/C][C]36[/C][C]33.8308050165539[/C][C]2.16919498344613[/C][/ROW]
[ROW][C]102[/C][C]23[/C][C]29.2146112917125[/C][C]-6.21461129171252[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]30.1358176759735[/C][C]8.86418232402646[/C][/ROW]
[ROW][C]104[/C][C]31[/C][C]33.8360119143867[/C][C]-2.83601191438666[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]29.7840636060068[/C][C]-6.78406360600676[/C][/ROW]
[ROW][C]106[/C][C]31[/C][C]31.7081311684767[/C][C]-0.708131168476692[/C][/ROW]
[ROW][C]107[/C][C]28[/C][C]32.9354014509168[/C][C]-4.93540145091677[/C][/ROW]
[ROW][C]108[/C][C]47[/C][C]31.5162357427732[/C][C]15.4837642572268[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]32.141666028456[/C][C]-7.14166602845602[/C][/ROW]
[ROW][C]110[/C][C]26[/C][C]30.4540310024291[/C][C]-4.4540310024291[/C][/ROW]
[ROW][C]111[/C][C]24[/C][C]32.4053505670329[/C][C]-8.40535056703291[/C][/ROW]
[ROW][C]112[/C][C]30[/C][C]32.8573235966661[/C][C]-2.85732359666608[/C][/ROW]
[ROW][C]113[/C][C]25[/C][C]33.4715390914006[/C][C]-8.47153909140057[/C][/ROW]
[ROW][C]114[/C][C]44[/C][C]31.8714012498333[/C][C]12.1285987501667[/C][/ROW]
[ROW][C]115[/C][C]38[/C][C]40.6774878686489[/C][C]-2.67748786864887[/C][/ROW]
[ROW][C]116[/C][C]36[/C][C]29.0927048943497[/C][C]6.9072951056503[/C][/ROW]
[ROW][C]117[/C][C]34[/C][C]33.3907769823190[/C][C]0.609223017681041[/C][/ROW]
[ROW][C]118[/C][C]45[/C][C]32.0593159137007[/C][C]12.9406840862993[/C][/ROW]
[ROW][C]119[/C][C]29[/C][C]31.3754690572301[/C][C]-2.37546905723012[/C][/ROW]
[ROW][C]120[/C][C]25[/C][C]32.2966754414911[/C][C]-7.29667544149113[/C][/ROW]
[ROW][C]121[/C][C]30[/C][C]33.364087079668[/C][C]-3.36408707966797[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]32.237331879356[/C][C]-5.23733187935599[/C][/ROW]
[ROW][C]123[/C][C]44[/C][C]33.5137635988056[/C][C]10.4862364011944[/C][/ROW]
[ROW][C]124[/C][C]31[/C][C]36.5663219941985[/C][C]-5.56632199419849[/C][/ROW]
[ROW][C]125[/C][C]35[/C][C]33.7262549453003[/C][C]1.27374505469968[/C][/ROW]
[ROW][C]126[/C][C]47[/C][C]37.2802439297976[/C][C]9.71975607020239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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
13832.8005858311685.19941416883201
23629.96051878226986.03948121773016
32333.5145077667671-10.5145077667671
43031.0296660530576-1.02966605305760
52632.0970776912344-6.09707769123444
62631.3883626534681-5.3883626534681
73028.58192803766461.41807196233537
82732.4575099242559-5.45750992425586
93431.53977480521672.46022519478330
102831.7013690820909-3.70136908209087
113633.88361816284322.11638183715681
124233.88535379545418.11464620454588
133132.2571680060377-1.25716800603768
142631.4022477143555-5.40224771435552
151630.0167144393066-14.0167144393066
162331.5519242334932-8.5519242334932
174534.60448262888610.3955173711140
183034.4703008071820-4.47030080718205
194531.055700542221513.9442994577785
203031.6319693908948-1.63196939089481
212433.1905238185752-9.1905238185752
222930.7393145379604-1.73931453796036
233032.4343829764102-2.43438297641020
243133.6971613055124-2.69716130551243
253430.1466435862373.85335641376300
264136.75145533351624.2485446664838
273732.84571227905214.15428772094788
283331.42654657090851.57345342909149
294830.153586116680717.8464138833193
304430.228424376249513.7715756237505
312930.8675080522798-1.86750805227983
324435.29180635141768.70819364858235
334334.77845800457678.2215419954233
343133.0040669612444-2.00406696124444
352832.8595973399395-4.85959733993954
362632.297087556089-6.29708755608902
373031.0869419292182-1.08694192921822
382731.3747665845804-4.37476658458038
393433.22176520557190.778234794428106
404730.026472821485116.9735271785149
413734.41224954897972.58775045102032
422731.8060707626501-4.80607076265013
433031.8078063952611-1.80780639526106
443640.4811553263151-4.48115532631508
453931.81127766048297.18872233951709
463231.81301329309380.186986706906158
472531.8147489257048-6.81474892570477
481928.6194565416180-9.61945654161797
492932.1734455261153-3.17344552611526
502632.1260197217166-6.12601972171661
513130.07919721330.920802786700005
523132.1786524239480-1.17865242394805
533130.97879459667820.0212054033218439
543931.47167301879267.52832698120737
552832.1838593217808-4.18385932178083
562230.4094682784486-8.40946827844857
573131.4768799166254-0.476879916625412
583631.62482080315224.37517919684784
592830.4146751762814-2.41467517628136
603933.25821349040145.74178650959862
613532.19427311744642.80572688255360
623331.84078341486871.15921658513131
632731.487293712291-4.48729371229098
643332.90993068565650.0900693143435477
653131.1355396423242-0.135539642324197
663931.63870586403967.36129413596042
673733.06134283740513.93865716259494
682431.7152797562193-7.71527975621935
692832.5970159466172-4.59701594661715
703727.98082222177589.0191777782242
713233.0682853678488-1.06828536784877
723129.04996949256361.95003050743643
732929.4069304603631-0.406930460363133
744035.41386435808624.58613564191381
754032.72000256310387.27999743689616
761529.7337302602899-14.7337302602899
772729.6863044558912-2.68630445589121
783232.1746174348226-0.174617434822594
792832.2255145044431-4.22551450444309
804136.63615941323354.36384058676652
814732.939436440042214.0605635599578
824235.42774941897366.57225058102639
833233.3393465179255-1.33934651792547
843333.874401889126-0.87440188912601
852935.0777309816178-6.07773098161775
863732.38386918663544.61613081336459
873930.68258077026118.31741922973894
882930.8202338571869-1.8202338571869
893332.95332150092960.0466784990703529
903131.1297690205878-0.129769020587818
912131.7551993062707-10.7551993062707
923634.88086032862141.11913967137857
933235.237821296421-3.23782129642100
941531.5410983232297-16.5410983232297
952533.6250245299629-8.62502452996291
962830.1236682476971-2.12366824769705
973933.32243189700575.6775681029943
983129.01230207034341.98769792965658
994028.707973804775311.2920261952247
1002531.5515121188953-6.5515121188953
1013633.83080501655392.16919498344613
1022329.2146112917125-6.21461129171252
1033930.13581767597358.86418232402646
1043133.8360119143867-2.83601191438666
1052329.7840636060068-6.78406360600676
1063131.7081311684767-0.708131168476692
1072832.9354014509168-4.93540145091677
1084731.516235742773215.4837642572268
1092532.141666028456-7.14166602845602
1102630.4540310024291-4.4540310024291
1112432.4053505670329-8.40535056703291
1123032.8573235966661-2.85732359666608
1132533.4715390914006-8.47153909140057
1144431.871401249833312.1285987501667
1153840.6774878686489-2.67748786864887
1163629.09270489434976.9072951056503
1173433.39077698231900.609223017681041
1184532.059315913700712.9406840862993
1192931.3754690572301-2.37546905723012
1202532.2966754414911-7.29667544149113
1213033.364087079668-3.36408707966797
1222732.237331879356-5.23733187935599
1234433.513763598805610.4862364011944
1243136.5663219941985-5.56632199419849
1253533.72625494530031.27374505469968
1264737.28024392979769.71975607020239






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7741795510421040.4516408979157920.225820448957896
120.8149145520900870.3701708958198260.185085447909913
130.729755094369280.540489811261440.27024490563072
140.6795800198238570.6408399603522870.320419980176143
150.7603637058842260.4792725882315480.239636294115774
160.7352125131111030.5295749737777940.264787486888897
170.8353978488981680.3292043022036640.164602151101832
180.7808579137529480.4382841724941040.219142086247052
190.8578932390138640.2842135219722710.142106760986136
200.8052888542008960.3894222915982080.194711145799104
210.8766043737022560.2467912525954890.123395626297744
220.8409286242566530.3181427514866940.159071375743347
230.7919972538685190.4160054922629620.208002746131481
240.7380387401435440.5239225197129130.261961259856457
250.6823558993028250.6352882013943490.317644100697175
260.628397732480640.743204535038720.37160226751936
270.5606895323393610.8786209353212770.439310467660639
280.4993725420220450.998745084044090.500627457977955
290.712494630997280.575010738005440.28750536900272
300.7589564962590270.4820870074819460.241043503740973
310.7628083824437750.4743832351124510.237191617556225
320.7778945687454230.4442108625091540.222105431254577
330.764031850003810.4719362999923790.235968149996189
340.754397002799980.4912059944000390.245602997200020
350.7905180381004770.4189639237990450.209481961899523
360.8155544294973420.3688911410053160.184445570502658
370.7896373844112930.4207252311774150.210362615588707
380.7603812151786110.4792375696427780.239618784821389
390.7147045690093870.5705908619812260.285295430990613
400.8345937487057460.3308125025885080.165406251294254
410.7956537936076150.408692412784770.204346206392385
420.8107828480860150.3784343038279690.189217151913985
430.787891153546170.4242176929076590.212108846453829
440.7533709671292240.4932580657415520.246629032870776
450.7356458006680170.5287083986639650.264354199331983
460.6975717508985620.6048564982028770.302428249101438
470.7225661270982480.5548677458035050.277433872901752
480.7919149412724330.4161701174551350.208085058727567
490.7602613008875480.4794773982249040.239738699112452
500.7530226358098980.4939547283802040.246977364190102
510.7205585906136660.5588828187726680.279441409386334
520.674546944615520.650906110768960.32545305538448
530.6258850264867990.7482299470264020.374114973513201
540.6258987781054330.7482024437891350.374101221894567
550.5946853003074550.810629399385090.405314699692545
560.6210038184032790.7579923631934430.378996181596721
570.5685701710143480.8628596579713040.431429828985652
580.5318042796019890.9363914407960220.468195720398011
590.4846531007382080.9693062014764160.515346899261792
600.4652739070457570.9305478140915140.534726092954243
610.4192161746982320.8384323493964640.580783825301768
620.3681645393321070.7363290786642140.631835460667893
630.3389689412269120.6779378824538240.661031058773088
640.2907781865500270.5815563731000540.709221813449973
650.2461149123342440.4922298246684880.753885087665756
660.2465637890466220.4931275780932440.753436210953378
670.2185501569100420.4371003138200840.781449843089958
680.2301916793145330.4603833586290660.769808320685467
690.2165168481306240.4330336962612480.783483151869376
700.2376425059757140.4752850119514270.762357494024286
710.1990564218904210.3981128437808420.800943578109579
720.1652637641805250.3305275283610490.834736235819476
730.1378532133499850.2757064266999710.862146786650015
740.1202593067360450.2405186134720910.879740693263955
750.121986672590530.243973345181060.87801332740947
760.2385922037746780.4771844075493560.761407796225322
770.2046620996283550.409324199256710.795337900371645
780.1684648272293860.3369296544587710.831535172770614
790.1494033217441320.2988066434882640.850596678255868
800.1293022206634390.2586044413268780.870697779336561
810.2295857606627970.4591715213255950.770414239337203
820.2362929497024040.4725858994048080.763707050297596
830.2125691280665950.425138256133190.787430871933405
840.1794519760608260.3589039521216510.820548023939174
850.1596925693253290.3193851386506580.840307430674671
860.1523426088338210.3046852176676430.847657391166179
870.2034651835262830.4069303670525660.796534816473717
880.1663267419460950.3326534838921890.833673258053905
890.1380556974819640.2761113949639280.861944302518036
900.1075984544069100.2151969088138190.89240154559309
910.1186363424519630.2372726849039260.881363657548037
920.1067106758199590.2134213516399180.89328932418004
930.08751513262486150.1750302652497230.912484867375138
940.1881704979458090.3763409958916170.811829502054191
950.21023346831170.42046693662340.7897665316883
960.1695701361166150.3391402722332310.830429863883385
970.1596102763318970.3192205526637930.840389723668103
980.1259184669423680.2518369338847360.874081533057632
990.2004562881983540.4009125763967090.799543711801646
1000.1711946944271040.3423893888542080.828805305572896
1010.1556420411250330.3112840822500660.844357958874967
1020.1256872195845050.2513744391690100.874312780415495
1030.2045195674387530.4090391348775060.795480432561247
1040.1713360190501860.3426720381003730.828663980949814
1050.1307675358935260.2615350717870520.869232464106474
1060.1213400269227360.2426800538454710.878659973077264
1070.1190359892873440.2380719785746880.880964010712656
1080.2484814307765160.4969628615530320.751518569223484
1090.1888952655817880.3777905311635750.811104734418212
1100.1912281573872800.3824563147745590.80877184261272
1110.1316933933912590.2633867867825190.86830660660874
1120.0867317670122280.1734635340244560.913268232987772
1130.17305551589780.34611103179560.8269444841022
1140.1151740462331490.2303480924662970.884825953766851
1150.06585745660195570.1317149132039110.934142543398044

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.774179551042104 & 0.451640897915792 & 0.225820448957896 \tabularnewline
12 & 0.814914552090087 & 0.370170895819826 & 0.185085447909913 \tabularnewline
13 & 0.72975509436928 & 0.54048981126144 & 0.27024490563072 \tabularnewline
14 & 0.679580019823857 & 0.640839960352287 & 0.320419980176143 \tabularnewline
15 & 0.760363705884226 & 0.479272588231548 & 0.239636294115774 \tabularnewline
16 & 0.735212513111103 & 0.529574973777794 & 0.264787486888897 \tabularnewline
17 & 0.835397848898168 & 0.329204302203664 & 0.164602151101832 \tabularnewline
18 & 0.780857913752948 & 0.438284172494104 & 0.219142086247052 \tabularnewline
19 & 0.857893239013864 & 0.284213521972271 & 0.142106760986136 \tabularnewline
20 & 0.805288854200896 & 0.389422291598208 & 0.194711145799104 \tabularnewline
21 & 0.876604373702256 & 0.246791252595489 & 0.123395626297744 \tabularnewline
22 & 0.840928624256653 & 0.318142751486694 & 0.159071375743347 \tabularnewline
23 & 0.791997253868519 & 0.416005492262962 & 0.208002746131481 \tabularnewline
24 & 0.738038740143544 & 0.523922519712913 & 0.261961259856457 \tabularnewline
25 & 0.682355899302825 & 0.635288201394349 & 0.317644100697175 \tabularnewline
26 & 0.62839773248064 & 0.74320453503872 & 0.37160226751936 \tabularnewline
27 & 0.560689532339361 & 0.878620935321277 & 0.439310467660639 \tabularnewline
28 & 0.499372542022045 & 0.99874508404409 & 0.500627457977955 \tabularnewline
29 & 0.71249463099728 & 0.57501073800544 & 0.28750536900272 \tabularnewline
30 & 0.758956496259027 & 0.482087007481946 & 0.241043503740973 \tabularnewline
31 & 0.762808382443775 & 0.474383235112451 & 0.237191617556225 \tabularnewline
32 & 0.777894568745423 & 0.444210862509154 & 0.222105431254577 \tabularnewline
33 & 0.76403185000381 & 0.471936299992379 & 0.235968149996189 \tabularnewline
34 & 0.75439700279998 & 0.491205994400039 & 0.245602997200020 \tabularnewline
35 & 0.790518038100477 & 0.418963923799045 & 0.209481961899523 \tabularnewline
36 & 0.815554429497342 & 0.368891141005316 & 0.184445570502658 \tabularnewline
37 & 0.789637384411293 & 0.420725231177415 & 0.210362615588707 \tabularnewline
38 & 0.760381215178611 & 0.479237569642778 & 0.239618784821389 \tabularnewline
39 & 0.714704569009387 & 0.570590861981226 & 0.285295430990613 \tabularnewline
40 & 0.834593748705746 & 0.330812502588508 & 0.165406251294254 \tabularnewline
41 & 0.795653793607615 & 0.40869241278477 & 0.204346206392385 \tabularnewline
42 & 0.810782848086015 & 0.378434303827969 & 0.189217151913985 \tabularnewline
43 & 0.78789115354617 & 0.424217692907659 & 0.212108846453829 \tabularnewline
44 & 0.753370967129224 & 0.493258065741552 & 0.246629032870776 \tabularnewline
45 & 0.735645800668017 & 0.528708398663965 & 0.264354199331983 \tabularnewline
46 & 0.697571750898562 & 0.604856498202877 & 0.302428249101438 \tabularnewline
47 & 0.722566127098248 & 0.554867745803505 & 0.277433872901752 \tabularnewline
48 & 0.791914941272433 & 0.416170117455135 & 0.208085058727567 \tabularnewline
49 & 0.760261300887548 & 0.479477398224904 & 0.239738699112452 \tabularnewline
50 & 0.753022635809898 & 0.493954728380204 & 0.246977364190102 \tabularnewline
51 & 0.720558590613666 & 0.558882818772668 & 0.279441409386334 \tabularnewline
52 & 0.67454694461552 & 0.65090611076896 & 0.32545305538448 \tabularnewline
53 & 0.625885026486799 & 0.748229947026402 & 0.374114973513201 \tabularnewline
54 & 0.625898778105433 & 0.748202443789135 & 0.374101221894567 \tabularnewline
55 & 0.594685300307455 & 0.81062939938509 & 0.405314699692545 \tabularnewline
56 & 0.621003818403279 & 0.757992363193443 & 0.378996181596721 \tabularnewline
57 & 0.568570171014348 & 0.862859657971304 & 0.431429828985652 \tabularnewline
58 & 0.531804279601989 & 0.936391440796022 & 0.468195720398011 \tabularnewline
59 & 0.484653100738208 & 0.969306201476416 & 0.515346899261792 \tabularnewline
60 & 0.465273907045757 & 0.930547814091514 & 0.534726092954243 \tabularnewline
61 & 0.419216174698232 & 0.838432349396464 & 0.580783825301768 \tabularnewline
62 & 0.368164539332107 & 0.736329078664214 & 0.631835460667893 \tabularnewline
63 & 0.338968941226912 & 0.677937882453824 & 0.661031058773088 \tabularnewline
64 & 0.290778186550027 & 0.581556373100054 & 0.709221813449973 \tabularnewline
65 & 0.246114912334244 & 0.492229824668488 & 0.753885087665756 \tabularnewline
66 & 0.246563789046622 & 0.493127578093244 & 0.753436210953378 \tabularnewline
67 & 0.218550156910042 & 0.437100313820084 & 0.781449843089958 \tabularnewline
68 & 0.230191679314533 & 0.460383358629066 & 0.769808320685467 \tabularnewline
69 & 0.216516848130624 & 0.433033696261248 & 0.783483151869376 \tabularnewline
70 & 0.237642505975714 & 0.475285011951427 & 0.762357494024286 \tabularnewline
71 & 0.199056421890421 & 0.398112843780842 & 0.800943578109579 \tabularnewline
72 & 0.165263764180525 & 0.330527528361049 & 0.834736235819476 \tabularnewline
73 & 0.137853213349985 & 0.275706426699971 & 0.862146786650015 \tabularnewline
74 & 0.120259306736045 & 0.240518613472091 & 0.879740693263955 \tabularnewline
75 & 0.12198667259053 & 0.24397334518106 & 0.87801332740947 \tabularnewline
76 & 0.238592203774678 & 0.477184407549356 & 0.761407796225322 \tabularnewline
77 & 0.204662099628355 & 0.40932419925671 & 0.795337900371645 \tabularnewline
78 & 0.168464827229386 & 0.336929654458771 & 0.831535172770614 \tabularnewline
79 & 0.149403321744132 & 0.298806643488264 & 0.850596678255868 \tabularnewline
80 & 0.129302220663439 & 0.258604441326878 & 0.870697779336561 \tabularnewline
81 & 0.229585760662797 & 0.459171521325595 & 0.770414239337203 \tabularnewline
82 & 0.236292949702404 & 0.472585899404808 & 0.763707050297596 \tabularnewline
83 & 0.212569128066595 & 0.42513825613319 & 0.787430871933405 \tabularnewline
84 & 0.179451976060826 & 0.358903952121651 & 0.820548023939174 \tabularnewline
85 & 0.159692569325329 & 0.319385138650658 & 0.840307430674671 \tabularnewline
86 & 0.152342608833821 & 0.304685217667643 & 0.847657391166179 \tabularnewline
87 & 0.203465183526283 & 0.406930367052566 & 0.796534816473717 \tabularnewline
88 & 0.166326741946095 & 0.332653483892189 & 0.833673258053905 \tabularnewline
89 & 0.138055697481964 & 0.276111394963928 & 0.861944302518036 \tabularnewline
90 & 0.107598454406910 & 0.215196908813819 & 0.89240154559309 \tabularnewline
91 & 0.118636342451963 & 0.237272684903926 & 0.881363657548037 \tabularnewline
92 & 0.106710675819959 & 0.213421351639918 & 0.89328932418004 \tabularnewline
93 & 0.0875151326248615 & 0.175030265249723 & 0.912484867375138 \tabularnewline
94 & 0.188170497945809 & 0.376340995891617 & 0.811829502054191 \tabularnewline
95 & 0.2102334683117 & 0.4204669366234 & 0.7897665316883 \tabularnewline
96 & 0.169570136116615 & 0.339140272233231 & 0.830429863883385 \tabularnewline
97 & 0.159610276331897 & 0.319220552663793 & 0.840389723668103 \tabularnewline
98 & 0.125918466942368 & 0.251836933884736 & 0.874081533057632 \tabularnewline
99 & 0.200456288198354 & 0.400912576396709 & 0.799543711801646 \tabularnewline
100 & 0.171194694427104 & 0.342389388854208 & 0.828805305572896 \tabularnewline
101 & 0.155642041125033 & 0.311284082250066 & 0.844357958874967 \tabularnewline
102 & 0.125687219584505 & 0.251374439169010 & 0.874312780415495 \tabularnewline
103 & 0.204519567438753 & 0.409039134877506 & 0.795480432561247 \tabularnewline
104 & 0.171336019050186 & 0.342672038100373 & 0.828663980949814 \tabularnewline
105 & 0.130767535893526 & 0.261535071787052 & 0.869232464106474 \tabularnewline
106 & 0.121340026922736 & 0.242680053845471 & 0.878659973077264 \tabularnewline
107 & 0.119035989287344 & 0.238071978574688 & 0.880964010712656 \tabularnewline
108 & 0.248481430776516 & 0.496962861553032 & 0.751518569223484 \tabularnewline
109 & 0.188895265581788 & 0.377790531163575 & 0.811104734418212 \tabularnewline
110 & 0.191228157387280 & 0.382456314774559 & 0.80877184261272 \tabularnewline
111 & 0.131693393391259 & 0.263386786782519 & 0.86830660660874 \tabularnewline
112 & 0.086731767012228 & 0.173463534024456 & 0.913268232987772 \tabularnewline
113 & 0.1730555158978 & 0.3461110317956 & 0.8269444841022 \tabularnewline
114 & 0.115174046233149 & 0.230348092466297 & 0.884825953766851 \tabularnewline
115 & 0.0658574566019557 & 0.131714913203911 & 0.934142543398044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&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]11[/C][C]0.774179551042104[/C][C]0.451640897915792[/C][C]0.225820448957896[/C][/ROW]
[ROW][C]12[/C][C]0.814914552090087[/C][C]0.370170895819826[/C][C]0.185085447909913[/C][/ROW]
[ROW][C]13[/C][C]0.72975509436928[/C][C]0.54048981126144[/C][C]0.27024490563072[/C][/ROW]
[ROW][C]14[/C][C]0.679580019823857[/C][C]0.640839960352287[/C][C]0.320419980176143[/C][/ROW]
[ROW][C]15[/C][C]0.760363705884226[/C][C]0.479272588231548[/C][C]0.239636294115774[/C][/ROW]
[ROW][C]16[/C][C]0.735212513111103[/C][C]0.529574973777794[/C][C]0.264787486888897[/C][/ROW]
[ROW][C]17[/C][C]0.835397848898168[/C][C]0.329204302203664[/C][C]0.164602151101832[/C][/ROW]
[ROW][C]18[/C][C]0.780857913752948[/C][C]0.438284172494104[/C][C]0.219142086247052[/C][/ROW]
[ROW][C]19[/C][C]0.857893239013864[/C][C]0.284213521972271[/C][C]0.142106760986136[/C][/ROW]
[ROW][C]20[/C][C]0.805288854200896[/C][C]0.389422291598208[/C][C]0.194711145799104[/C][/ROW]
[ROW][C]21[/C][C]0.876604373702256[/C][C]0.246791252595489[/C][C]0.123395626297744[/C][/ROW]
[ROW][C]22[/C][C]0.840928624256653[/C][C]0.318142751486694[/C][C]0.159071375743347[/C][/ROW]
[ROW][C]23[/C][C]0.791997253868519[/C][C]0.416005492262962[/C][C]0.208002746131481[/C][/ROW]
[ROW][C]24[/C][C]0.738038740143544[/C][C]0.523922519712913[/C][C]0.261961259856457[/C][/ROW]
[ROW][C]25[/C][C]0.682355899302825[/C][C]0.635288201394349[/C][C]0.317644100697175[/C][/ROW]
[ROW][C]26[/C][C]0.62839773248064[/C][C]0.74320453503872[/C][C]0.37160226751936[/C][/ROW]
[ROW][C]27[/C][C]0.560689532339361[/C][C]0.878620935321277[/C][C]0.439310467660639[/C][/ROW]
[ROW][C]28[/C][C]0.499372542022045[/C][C]0.99874508404409[/C][C]0.500627457977955[/C][/ROW]
[ROW][C]29[/C][C]0.71249463099728[/C][C]0.57501073800544[/C][C]0.28750536900272[/C][/ROW]
[ROW][C]30[/C][C]0.758956496259027[/C][C]0.482087007481946[/C][C]0.241043503740973[/C][/ROW]
[ROW][C]31[/C][C]0.762808382443775[/C][C]0.474383235112451[/C][C]0.237191617556225[/C][/ROW]
[ROW][C]32[/C][C]0.777894568745423[/C][C]0.444210862509154[/C][C]0.222105431254577[/C][/ROW]
[ROW][C]33[/C][C]0.76403185000381[/C][C]0.471936299992379[/C][C]0.235968149996189[/C][/ROW]
[ROW][C]34[/C][C]0.75439700279998[/C][C]0.491205994400039[/C][C]0.245602997200020[/C][/ROW]
[ROW][C]35[/C][C]0.790518038100477[/C][C]0.418963923799045[/C][C]0.209481961899523[/C][/ROW]
[ROW][C]36[/C][C]0.815554429497342[/C][C]0.368891141005316[/C][C]0.184445570502658[/C][/ROW]
[ROW][C]37[/C][C]0.789637384411293[/C][C]0.420725231177415[/C][C]0.210362615588707[/C][/ROW]
[ROW][C]38[/C][C]0.760381215178611[/C][C]0.479237569642778[/C][C]0.239618784821389[/C][/ROW]
[ROW][C]39[/C][C]0.714704569009387[/C][C]0.570590861981226[/C][C]0.285295430990613[/C][/ROW]
[ROW][C]40[/C][C]0.834593748705746[/C][C]0.330812502588508[/C][C]0.165406251294254[/C][/ROW]
[ROW][C]41[/C][C]0.795653793607615[/C][C]0.40869241278477[/C][C]0.204346206392385[/C][/ROW]
[ROW][C]42[/C][C]0.810782848086015[/C][C]0.378434303827969[/C][C]0.189217151913985[/C][/ROW]
[ROW][C]43[/C][C]0.78789115354617[/C][C]0.424217692907659[/C][C]0.212108846453829[/C][/ROW]
[ROW][C]44[/C][C]0.753370967129224[/C][C]0.493258065741552[/C][C]0.246629032870776[/C][/ROW]
[ROW][C]45[/C][C]0.735645800668017[/C][C]0.528708398663965[/C][C]0.264354199331983[/C][/ROW]
[ROW][C]46[/C][C]0.697571750898562[/C][C]0.604856498202877[/C][C]0.302428249101438[/C][/ROW]
[ROW][C]47[/C][C]0.722566127098248[/C][C]0.554867745803505[/C][C]0.277433872901752[/C][/ROW]
[ROW][C]48[/C][C]0.791914941272433[/C][C]0.416170117455135[/C][C]0.208085058727567[/C][/ROW]
[ROW][C]49[/C][C]0.760261300887548[/C][C]0.479477398224904[/C][C]0.239738699112452[/C][/ROW]
[ROW][C]50[/C][C]0.753022635809898[/C][C]0.493954728380204[/C][C]0.246977364190102[/C][/ROW]
[ROW][C]51[/C][C]0.720558590613666[/C][C]0.558882818772668[/C][C]0.279441409386334[/C][/ROW]
[ROW][C]52[/C][C]0.67454694461552[/C][C]0.65090611076896[/C][C]0.32545305538448[/C][/ROW]
[ROW][C]53[/C][C]0.625885026486799[/C][C]0.748229947026402[/C][C]0.374114973513201[/C][/ROW]
[ROW][C]54[/C][C]0.625898778105433[/C][C]0.748202443789135[/C][C]0.374101221894567[/C][/ROW]
[ROW][C]55[/C][C]0.594685300307455[/C][C]0.81062939938509[/C][C]0.405314699692545[/C][/ROW]
[ROW][C]56[/C][C]0.621003818403279[/C][C]0.757992363193443[/C][C]0.378996181596721[/C][/ROW]
[ROW][C]57[/C][C]0.568570171014348[/C][C]0.862859657971304[/C][C]0.431429828985652[/C][/ROW]
[ROW][C]58[/C][C]0.531804279601989[/C][C]0.936391440796022[/C][C]0.468195720398011[/C][/ROW]
[ROW][C]59[/C][C]0.484653100738208[/C][C]0.969306201476416[/C][C]0.515346899261792[/C][/ROW]
[ROW][C]60[/C][C]0.465273907045757[/C][C]0.930547814091514[/C][C]0.534726092954243[/C][/ROW]
[ROW][C]61[/C][C]0.419216174698232[/C][C]0.838432349396464[/C][C]0.580783825301768[/C][/ROW]
[ROW][C]62[/C][C]0.368164539332107[/C][C]0.736329078664214[/C][C]0.631835460667893[/C][/ROW]
[ROW][C]63[/C][C]0.338968941226912[/C][C]0.677937882453824[/C][C]0.661031058773088[/C][/ROW]
[ROW][C]64[/C][C]0.290778186550027[/C][C]0.581556373100054[/C][C]0.709221813449973[/C][/ROW]
[ROW][C]65[/C][C]0.246114912334244[/C][C]0.492229824668488[/C][C]0.753885087665756[/C][/ROW]
[ROW][C]66[/C][C]0.246563789046622[/C][C]0.493127578093244[/C][C]0.753436210953378[/C][/ROW]
[ROW][C]67[/C][C]0.218550156910042[/C][C]0.437100313820084[/C][C]0.781449843089958[/C][/ROW]
[ROW][C]68[/C][C]0.230191679314533[/C][C]0.460383358629066[/C][C]0.769808320685467[/C][/ROW]
[ROW][C]69[/C][C]0.216516848130624[/C][C]0.433033696261248[/C][C]0.783483151869376[/C][/ROW]
[ROW][C]70[/C][C]0.237642505975714[/C][C]0.475285011951427[/C][C]0.762357494024286[/C][/ROW]
[ROW][C]71[/C][C]0.199056421890421[/C][C]0.398112843780842[/C][C]0.800943578109579[/C][/ROW]
[ROW][C]72[/C][C]0.165263764180525[/C][C]0.330527528361049[/C][C]0.834736235819476[/C][/ROW]
[ROW][C]73[/C][C]0.137853213349985[/C][C]0.275706426699971[/C][C]0.862146786650015[/C][/ROW]
[ROW][C]74[/C][C]0.120259306736045[/C][C]0.240518613472091[/C][C]0.879740693263955[/C][/ROW]
[ROW][C]75[/C][C]0.12198667259053[/C][C]0.24397334518106[/C][C]0.87801332740947[/C][/ROW]
[ROW][C]76[/C][C]0.238592203774678[/C][C]0.477184407549356[/C][C]0.761407796225322[/C][/ROW]
[ROW][C]77[/C][C]0.204662099628355[/C][C]0.40932419925671[/C][C]0.795337900371645[/C][/ROW]
[ROW][C]78[/C][C]0.168464827229386[/C][C]0.336929654458771[/C][C]0.831535172770614[/C][/ROW]
[ROW][C]79[/C][C]0.149403321744132[/C][C]0.298806643488264[/C][C]0.850596678255868[/C][/ROW]
[ROW][C]80[/C][C]0.129302220663439[/C][C]0.258604441326878[/C][C]0.870697779336561[/C][/ROW]
[ROW][C]81[/C][C]0.229585760662797[/C][C]0.459171521325595[/C][C]0.770414239337203[/C][/ROW]
[ROW][C]82[/C][C]0.236292949702404[/C][C]0.472585899404808[/C][C]0.763707050297596[/C][/ROW]
[ROW][C]83[/C][C]0.212569128066595[/C][C]0.42513825613319[/C][C]0.787430871933405[/C][/ROW]
[ROW][C]84[/C][C]0.179451976060826[/C][C]0.358903952121651[/C][C]0.820548023939174[/C][/ROW]
[ROW][C]85[/C][C]0.159692569325329[/C][C]0.319385138650658[/C][C]0.840307430674671[/C][/ROW]
[ROW][C]86[/C][C]0.152342608833821[/C][C]0.304685217667643[/C][C]0.847657391166179[/C][/ROW]
[ROW][C]87[/C][C]0.203465183526283[/C][C]0.406930367052566[/C][C]0.796534816473717[/C][/ROW]
[ROW][C]88[/C][C]0.166326741946095[/C][C]0.332653483892189[/C][C]0.833673258053905[/C][/ROW]
[ROW][C]89[/C][C]0.138055697481964[/C][C]0.276111394963928[/C][C]0.861944302518036[/C][/ROW]
[ROW][C]90[/C][C]0.107598454406910[/C][C]0.215196908813819[/C][C]0.89240154559309[/C][/ROW]
[ROW][C]91[/C][C]0.118636342451963[/C][C]0.237272684903926[/C][C]0.881363657548037[/C][/ROW]
[ROW][C]92[/C][C]0.106710675819959[/C][C]0.213421351639918[/C][C]0.89328932418004[/C][/ROW]
[ROW][C]93[/C][C]0.0875151326248615[/C][C]0.175030265249723[/C][C]0.912484867375138[/C][/ROW]
[ROW][C]94[/C][C]0.188170497945809[/C][C]0.376340995891617[/C][C]0.811829502054191[/C][/ROW]
[ROW][C]95[/C][C]0.2102334683117[/C][C]0.4204669366234[/C][C]0.7897665316883[/C][/ROW]
[ROW][C]96[/C][C]0.169570136116615[/C][C]0.339140272233231[/C][C]0.830429863883385[/C][/ROW]
[ROW][C]97[/C][C]0.159610276331897[/C][C]0.319220552663793[/C][C]0.840389723668103[/C][/ROW]
[ROW][C]98[/C][C]0.125918466942368[/C][C]0.251836933884736[/C][C]0.874081533057632[/C][/ROW]
[ROW][C]99[/C][C]0.200456288198354[/C][C]0.400912576396709[/C][C]0.799543711801646[/C][/ROW]
[ROW][C]100[/C][C]0.171194694427104[/C][C]0.342389388854208[/C][C]0.828805305572896[/C][/ROW]
[ROW][C]101[/C][C]0.155642041125033[/C][C]0.311284082250066[/C][C]0.844357958874967[/C][/ROW]
[ROW][C]102[/C][C]0.125687219584505[/C][C]0.251374439169010[/C][C]0.874312780415495[/C][/ROW]
[ROW][C]103[/C][C]0.204519567438753[/C][C]0.409039134877506[/C][C]0.795480432561247[/C][/ROW]
[ROW][C]104[/C][C]0.171336019050186[/C][C]0.342672038100373[/C][C]0.828663980949814[/C][/ROW]
[ROW][C]105[/C][C]0.130767535893526[/C][C]0.261535071787052[/C][C]0.869232464106474[/C][/ROW]
[ROW][C]106[/C][C]0.121340026922736[/C][C]0.242680053845471[/C][C]0.878659973077264[/C][/ROW]
[ROW][C]107[/C][C]0.119035989287344[/C][C]0.238071978574688[/C][C]0.880964010712656[/C][/ROW]
[ROW][C]108[/C][C]0.248481430776516[/C][C]0.496962861553032[/C][C]0.751518569223484[/C][/ROW]
[ROW][C]109[/C][C]0.188895265581788[/C][C]0.377790531163575[/C][C]0.811104734418212[/C][/ROW]
[ROW][C]110[/C][C]0.191228157387280[/C][C]0.382456314774559[/C][C]0.80877184261272[/C][/ROW]
[ROW][C]111[/C][C]0.131693393391259[/C][C]0.263386786782519[/C][C]0.86830660660874[/C][/ROW]
[ROW][C]112[/C][C]0.086731767012228[/C][C]0.173463534024456[/C][C]0.913268232987772[/C][/ROW]
[ROW][C]113[/C][C]0.1730555158978[/C][C]0.3461110317956[/C][C]0.8269444841022[/C][/ROW]
[ROW][C]114[/C][C]0.115174046233149[/C][C]0.230348092466297[/C][C]0.884825953766851[/C][/ROW]
[ROW][C]115[/C][C]0.0658574566019557[/C][C]0.131714913203911[/C][C]0.934142543398044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&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
110.7741795510421040.4516408979157920.225820448957896
120.8149145520900870.3701708958198260.185085447909913
130.729755094369280.540489811261440.27024490563072
140.6795800198238570.6408399603522870.320419980176143
150.7603637058842260.4792725882315480.239636294115774
160.7352125131111030.5295749737777940.264787486888897
170.8353978488981680.3292043022036640.164602151101832
180.7808579137529480.4382841724941040.219142086247052
190.8578932390138640.2842135219722710.142106760986136
200.8052888542008960.3894222915982080.194711145799104
210.8766043737022560.2467912525954890.123395626297744
220.8409286242566530.3181427514866940.159071375743347
230.7919972538685190.4160054922629620.208002746131481
240.7380387401435440.5239225197129130.261961259856457
250.6823558993028250.6352882013943490.317644100697175
260.628397732480640.743204535038720.37160226751936
270.5606895323393610.8786209353212770.439310467660639
280.4993725420220450.998745084044090.500627457977955
290.712494630997280.575010738005440.28750536900272
300.7589564962590270.4820870074819460.241043503740973
310.7628083824437750.4743832351124510.237191617556225
320.7778945687454230.4442108625091540.222105431254577
330.764031850003810.4719362999923790.235968149996189
340.754397002799980.4912059944000390.245602997200020
350.7905180381004770.4189639237990450.209481961899523
360.8155544294973420.3688911410053160.184445570502658
370.7896373844112930.4207252311774150.210362615588707
380.7603812151786110.4792375696427780.239618784821389
390.7147045690093870.5705908619812260.285295430990613
400.8345937487057460.3308125025885080.165406251294254
410.7956537936076150.408692412784770.204346206392385
420.8107828480860150.3784343038279690.189217151913985
430.787891153546170.4242176929076590.212108846453829
440.7533709671292240.4932580657415520.246629032870776
450.7356458006680170.5287083986639650.264354199331983
460.6975717508985620.6048564982028770.302428249101438
470.7225661270982480.5548677458035050.277433872901752
480.7919149412724330.4161701174551350.208085058727567
490.7602613008875480.4794773982249040.239738699112452
500.7530226358098980.4939547283802040.246977364190102
510.7205585906136660.5588828187726680.279441409386334
520.674546944615520.650906110768960.32545305538448
530.6258850264867990.7482299470264020.374114973513201
540.6258987781054330.7482024437891350.374101221894567
550.5946853003074550.810629399385090.405314699692545
560.6210038184032790.7579923631934430.378996181596721
570.5685701710143480.8628596579713040.431429828985652
580.5318042796019890.9363914407960220.468195720398011
590.4846531007382080.9693062014764160.515346899261792
600.4652739070457570.9305478140915140.534726092954243
610.4192161746982320.8384323493964640.580783825301768
620.3681645393321070.7363290786642140.631835460667893
630.3389689412269120.6779378824538240.661031058773088
640.2907781865500270.5815563731000540.709221813449973
650.2461149123342440.4922298246684880.753885087665756
660.2465637890466220.4931275780932440.753436210953378
670.2185501569100420.4371003138200840.781449843089958
680.2301916793145330.4603833586290660.769808320685467
690.2165168481306240.4330336962612480.783483151869376
700.2376425059757140.4752850119514270.762357494024286
710.1990564218904210.3981128437808420.800943578109579
720.1652637641805250.3305275283610490.834736235819476
730.1378532133499850.2757064266999710.862146786650015
740.1202593067360450.2405186134720910.879740693263955
750.121986672590530.243973345181060.87801332740947
760.2385922037746780.4771844075493560.761407796225322
770.2046620996283550.409324199256710.795337900371645
780.1684648272293860.3369296544587710.831535172770614
790.1494033217441320.2988066434882640.850596678255868
800.1293022206634390.2586044413268780.870697779336561
810.2295857606627970.4591715213255950.770414239337203
820.2362929497024040.4725858994048080.763707050297596
830.2125691280665950.425138256133190.787430871933405
840.1794519760608260.3589039521216510.820548023939174
850.1596925693253290.3193851386506580.840307430674671
860.1523426088338210.3046852176676430.847657391166179
870.2034651835262830.4069303670525660.796534816473717
880.1663267419460950.3326534838921890.833673258053905
890.1380556974819640.2761113949639280.861944302518036
900.1075984544069100.2151969088138190.89240154559309
910.1186363424519630.2372726849039260.881363657548037
920.1067106758199590.2134213516399180.89328932418004
930.08751513262486150.1750302652497230.912484867375138
940.1881704979458090.3763409958916170.811829502054191
950.21023346831170.42046693662340.7897665316883
960.1695701361166150.3391402722332310.830429863883385
970.1596102763318970.3192205526637930.840389723668103
980.1259184669423680.2518369338847360.874081533057632
990.2004562881983540.4009125763967090.799543711801646
1000.1711946944271040.3423893888542080.828805305572896
1010.1556420411250330.3112840822500660.844357958874967
1020.1256872195845050.2513744391690100.874312780415495
1030.2045195674387530.4090391348775060.795480432561247
1040.1713360190501860.3426720381003730.828663980949814
1050.1307675358935260.2615350717870520.869232464106474
1060.1213400269227360.2426800538454710.878659973077264
1070.1190359892873440.2380719785746880.880964010712656
1080.2484814307765160.4969628615530320.751518569223484
1090.1888952655817880.3777905311635750.811104734418212
1100.1912281573872800.3824563147745590.80877184261272
1110.1316933933912590.2633867867825190.86830660660874
1120.0867317670122280.1734635340244560.913268232987772
1130.17305551589780.34611103179560.8269444841022
1140.1151740462331490.2303480924662970.884825953766851
1150.06585745660195570.1317149132039110.934142543398044






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98987&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98987&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98987&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}