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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 15:41:52 +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/t129052680665fbtkblnxrpiyo.htm/, Retrieved Fri, 29 Mar 2024 06:53:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99302, Retrieved Fri, 29 Mar 2024 06:53:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [] [2010-11-20 13:46:38] [7f2363d2c77d3bf71367965cc53be730]
-   PD    [Multiple Regression] [] [2010-11-20 14:43:40] [7f2363d2c77d3bf71367965cc53be730]
-           [Multiple Regression] [] [2010-11-20 15:13:49] [7f2363d2c77d3bf71367965cc53be730]
-    D        [Multiple Regression] [] [2010-11-20 15:21:49] [7f2363d2c77d3bf71367965cc53be730]
-    D          [Multiple Regression] [] [2010-11-23 15:33:51] [7f2363d2c77d3bf71367965cc53be730]
-   PD              [Multiple Regression] [] [2010-11-23 15:41:52] [4dba6678eac10ee5c3460d144a14bd5c] [Current]
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Dataseries X:
14	24	11	12	24	26	237.588
11	25	7	8	25	23	164.083
6	17	17	8	30	25	278.261
12	18	10	8	19	23	220.36
8	18	12	9	22	19	253.967
10	16	12	7	22	29	422.31
10	20	11	4	25	25	136.921
11	16	11	11	23	21	143.495
16	18	12	7	17	22	189.785
11	17	13	7	21	25	219.529
13	23	14	12	19	24	217.761
12	30	16	10	19	18	221.754
8	23	11	10	15	22	159.854
12	18	10	8	16	15	209.464
11	15	11	8	23	22	174.283
4	12	15	4	27	28	154.55
9	21	9	9	22	20	153.024
8	15	11	8	14	12	162.49
8	20	17	7	22	24	154.462
14	31	17	11	23	20	249.671
15	27	11	9	23	21	259.473
16	34	18	11	21	20	155.337
9	21	14	13	19	21	151.289
14	31	10	8	18	23	276.614
11	19	11	8	20	28	188.214
8	16	15	9	23	24	181.098
9	20	15	6	25	24	240.898
9	21	13	9	19	24	244.551
9	22	16	9	24	23	250.238
9	17	13	6	22	23	183.129
10	24	9	6	25	29	310.331
16	25	18	16	26	24	281.942
11	26	18	5	29	18	230.343
8	25	12	7	32	25	161.563
9	17	17	9	25	21	392.527
16	32	9	6	29	26	1077.414
11	33	9	6	28	22	248.275
16	13	12	5	17	22	557.386
12	32	18	12	28	22	731.874
12	25	12	7	29	23	301.429
14	29	18	10	26	30	226.36
9	22	14	9	25	23	215.018
10	18	15	8	14	17	157.672
9	17	16	5	25	23	219.118
10	20	10	8	26	23	213.019
12	15	11	8	20	25	390.642
14	20	14	10	18	24	157.124
14	33	9	6	32	24	227.652
10	29	12	8	25	23	239.266
14	23	17	7	25	21	506.343
16	26	5	4	23	24	149.219
9	18	12	8	21	24	213.351
10	20	12	8	20	28	174.517
6	11	6	4	15	16	172.531
8	28	24	20	30	20	320.656
13	26	12	8	24	29	305.011
10	22	12	8	26	27	266.495
8	17	14	6	24	22	361.511
7	12	7	4	22	28	361.019
15	14	13	8	14	16	382.187
9	17	12	9	24	25	196.763
10	21	13	6	24	24	273.212
12	19	14	7	24	28	186.397
13	18	8	9	24	24	294.205
10	10	11	5	19	23	364.685
11	29	9	5	31	30	230.501
8	31	11	8	22	24	217.51
9	19	13	8	27	21	262.297
13	9	10	6	19	25	169.246
11	20	11	8	25	25	260.428
8	28	12	7	20	22	348.187
9	19	9	7	21	23	512.937
9	30	15	9	27	26	164.496
15	29	18	11	23	23	111.187
9	26	15	6	25	25	169.999
10	23	12	8	20	21	240.187
14	13	13	6	21	25	187.158
12	21	14	9	22	24	194.096
12	19	10	8	23	29	265.846
11	28	13	6	25	22	283.319
14	23	13	10	25	27	356.938
6	18	11	8	17	26	240.802
12	21	13	8	19	22	326.662
8	20	16	10	25	24	249.266
14	23	8	5	19	27	277.368
11	21	16	7	20	24	394.618
10	21	11	5	26	24	235.686
14	15	9	8	23	29	227.641
12	28	16	14	27	22	159.593
10	19	12	7	17	21	268.866
14	26	14	8	17	24	206.466
5	10	8	6	19	24	233.064
11	16	9	5	17	23	133.824
10	22	15	6	22	20	486.783
9	19	11	10	21	27	228.859
10	31	21	12	32	26	155.238
16	31	14	9	21	25	2042.451
13	29	18	12	21	21	205.218
9	19	12	7	18	21	373.648
10	22	13	8	18	19	229.151
10	23	15	10	23	21	199.156
7	15	12	6	19	21	234.41
9	20	19	10	20	16	56.519
8	18	15	10	21	22	289.239
14	23	11	10	20	29	199.227
14	25	11	5	17	15	274.513
8	21	10	7	18	17	174.499
9	24	13	10	19	15	217.714
14	25	15	11	22	21	239.717
14	17	12	6	15	21	241.529
8	13	12	7	14	19	155.561
8	28	16	12	18	24	204.107
8	21	9	11	24	20	745.97
7	25	18	11	35	17	241.772
6	9	8	11	29	23	110.267
8	16	13	5	21	24	186.58
6	19	17	8	25	14	227.906
11	17	9	6	20	19	197.518
14	25	15	9	22	24	254.094
11	20	8	4	13	13	173.942
11	29	7	4	26	22	294.42
11	14	12	7	17	16	211.924
14	22	14	11	25	19	262.479
8	15	6	6	20	25	193.495
20	19	8	7	19	25	165.972
11	20	17	8	21	23	237.352
8	15	10	4	22	24	205.814
11	20	11	8	24	26	227.526
10	18	14	9	21	26	250.439
14	33	11	8	26	25	470.849
11	22	13	11	24	18	176.469
9	16	12	8	16	21	298.691
9	17	11	5	23	26	193.922
8	16	9	4	18	23	212.422
10	21	12	8	16	23	203.284
13	26	20	10	26	22	240.56
13	18	12	6	19	20	445.327
12	18	13	9	21	13	248.984
8	17	12	9	21	24	174.44
13	22	12	13	22	15	165.024
14	30	9	9	23	14	249.681
12	30	15	10	29	22	238.312
14	24	24	20	21	10	250.437
15	21	7	5	21	24	174.75
13	21	17	11	23	22	4941.633
16	29	11	6	27	24	138.936
9	31	17	9	25	19	203.181
9	20	11	7	21	20	187.747
9	16	12	9	10	13	270.95
8	22	14	10	20	20	307.688
7	20	11	9	26	22	184.477
16	28	16	8	24	24	230.916
11	38	21	7	29	29	187.286
9	22	14	6	19	12	169.376
11	20	20	13	24	20	182.838
9	17	13	6	19	21	176.081
14	28	11	8	24	24	248.056
13	22	15	10	22	22	235.24
16	31	19	16	17	20	76.347




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time21 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 & 21 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&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]21 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=99302&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time21 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Doubts[t] = + 7.37200247283272 + 0.246240482864769Concern[t] -0.112243025930271Expectations[t] + 0.144715731741287Criticism[t] -0.191116446549504Standards[t] + 0.107741225800187Organization[t] + 0.000776734503210637Time[t] + 0.000750661646830256t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Doubts[t] =  +  7.37200247283272 +  0.246240482864769Concern[t] -0.112243025930271Expectations[t] +  0.144715731741287Criticism[t] -0.191116446549504Standards[t] +  0.107741225800187Organization[t] +  0.000776734503210637Time[t] +  0.000750661646830256t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Doubts[t] =  +  7.37200247283272 +  0.246240482864769Concern[t] -0.112243025930271Expectations[t] +  0.144715731741287Criticism[t] -0.191116446549504Standards[t] +  0.107741225800187Organization[t] +  0.000776734503210637Time[t] +  0.000750661646830256t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99302&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Doubts[t] = + 7.37200247283272 + 0.246240482864769Concern[t] -0.112243025930271Expectations[t] + 0.144715731741287Criticism[t] -0.191116446549504Standards[t] + 0.107741225800187Organization[t] + 0.000776734503210637Time[t] + 0.000750661646830256t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.372002472832721.6890274.36462.4e-051.2e-05
Concern0.2462404828647690.0400736.144800
Expectations-0.1122430259302710.073895-1.5190.1308630.065432
Criticism0.1447157317412870.0927011.56110.1205920.060296
Standards-0.1911164465495040.056737-3.36850.0009590.00048
Organization0.1077412258001870.0577121.86690.063860.03193
Time0.0007767345032106370.0004791.62130.1070490.053525
t0.0007506616468302560.0044430.1690.8660540.433027

\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) & 7.37200247283272 & 1.689027 & 4.3646 & 2.4e-05 & 1.2e-05 \tabularnewline
Concern & 0.246240482864769 & 0.040073 & 6.1448 & 0 & 0 \tabularnewline
Expectations & -0.112243025930271 & 0.073895 & -1.519 & 0.130863 & 0.065432 \tabularnewline
Criticism & 0.144715731741287 & 0.092701 & 1.5611 & 0.120592 & 0.060296 \tabularnewline
Standards & -0.191116446549504 & 0.056737 & -3.3685 & 0.000959 & 0.00048 \tabularnewline
Organization & 0.107741225800187 & 0.057712 & 1.8669 & 0.06386 & 0.03193 \tabularnewline
Time & 0.000776734503210637 & 0.000479 & 1.6213 & 0.107049 & 0.053525 \tabularnewline
t & 0.000750661646830256 & 0.004443 & 0.169 & 0.866054 & 0.433027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&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]7.37200247283272[/C][C]1.689027[/C][C]4.3646[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Concern[/C][C]0.246240482864769[/C][C]0.040073[/C][C]6.1448[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Expectations[/C][C]-0.112243025930271[/C][C]0.073895[/C][C]-1.519[/C][C]0.130863[/C][C]0.065432[/C][/ROW]
[ROW][C]Criticism[/C][C]0.144715731741287[/C][C]0.092701[/C][C]1.5611[/C][C]0.120592[/C][C]0.060296[/C][/ROW]
[ROW][C]Standards[/C][C]-0.191116446549504[/C][C]0.056737[/C][C]-3.3685[/C][C]0.000959[/C][C]0.00048[/C][/ROW]
[ROW][C]Organization[/C][C]0.107741225800187[/C][C]0.057712[/C][C]1.8669[/C][C]0.06386[/C][C]0.03193[/C][/ROW]
[ROW][C]Time[/C][C]0.000776734503210637[/C][C]0.000479[/C][C]1.6213[/C][C]0.107049[/C][C]0.053525[/C][/ROW]
[ROW][C]t[/C][C]0.000750661646830256[/C][C]0.004443[/C][C]0.169[/C][C]0.866054[/C][C]0.433027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99302&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.372002472832721.6890274.36462.4e-051.2e-05
Concern0.2462404828647690.0400736.144800
Expectations-0.1122430259302710.073895-1.5190.1308630.065432
Criticism0.1447157317412870.0927011.56110.1205920.060296
Standards-0.1911164465495040.056737-3.36850.0009590.00048
Organization0.1077412258001870.0577121.86690.063860.03193
Time0.0007767345032106370.0004791.62130.1070490.053525
t0.0007506616468302560.0044430.1690.8660540.433027







Multiple Linear Regression - Regression Statistics
Multiple R0.503046862684808
R-squared0.253056146057028
Adjusted R-squared0.218429609781526
F-TEST (value)7.30815649719095
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.51923045033087e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47584311691768
Sum Squared Residuals925.599670077885

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.503046862684808 \tabularnewline
R-squared & 0.253056146057028 \tabularnewline
Adjusted R-squared & 0.218429609781526 \tabularnewline
F-TEST (value) & 7.30815649719095 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 1.51923045033087e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.47584311691768 \tabularnewline
Sum Squared Residuals & 925.599670077885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.503046862684808[/C][/ROW]
[ROW][C]R-squared[/C][C]0.253056146057028[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.218429609781526[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.30815649719095[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]1.51923045033087e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.47584311691768[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]925.599670077885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99302&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.503046862684808
R-squared0.253056146057028
Adjusted R-squared0.218429609781526
F-TEST (value)7.30815649719095
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.51923045033087e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47584311691768
Sum Squared Residuals925.599670077885







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.18346016966211.81653983033794
21111.7291264973210-0.729126497321018
367.98610924770742-1.98610924770742
41210.86062632970471.13937367029531
589.8033961448324-1.80339614483241
61010.2304044517430-0.230404451742978
7109.668227149569260.331772850430744
8119.653400244468391.34659975553161
91610.74592086420025.25407913579983
10119.9700490993181.02995090068200
111312.33269669162680.667303308373247
121212.8998673640540-0.89986736405405
13812.8855005989489-4.88550059894887
141210.57108918027301.42891081972697
15119.10992252559261.89007747440740
1647.21077097461015-3.21077097461015
17910.9171899258221-1.91718992582208
1889.7456502414804-1.7456502414804
1989.9171569427426-1.91715694274260
201412.65728660843301.34271339156703
211512.17245680812042.82754319187957
221613.59422677486382.4057732251362
23911.6190846241025-2.61908462410249
241414.3115767091764-0.311576709176407
251111.3330084563338-0.333008456333843
2689.28093981183246-1.28093981183246
2799.4967210399075-0.496721039907497
28911.5518815219408-2.55188152194075
29910.4032374192336-1.40323741923359
3099.40547456644657-0.405474566446572
311011.7507809092979-1.75078090929788
321611.68286884648784.31713115351218
33119.078111523764471.92188847623553
3489.9229267634327-1.92292676343270
3598.768217826437920.231782173562084
361613.23258844977792.76741155022207
371112.5957102683807-1.59571026838072
38169.532584554266566.46741544573344
391212.5847062949031-0.58470629490306
401210.39393636938791.60606363061212
411412.40956723996431.59043276003568
42910.4190090367503-1.41900903675034
431010.5891299496888-0.589129949688844
4498.389143578357630.610856421642369
451010.0403672891197-0.0403672891196706
461210.19781955407361.80218044592644
471411.47558519531412.52441480468592
481412.03896561623851.9610343837615
491012.2465516266846-2.24655162668464
501410.05589399906383.94410600093617
511612.13620126301933.86379873698067
52910.3922362374602-1.39223623746017
531011.4773855068891-1.47738550688911
5469.0177119797914-3.0177119797914
55811.1789000945013-3.17890009450128
561312.40171502088240.598284979117642
571010.7898717002451-0.789871700245069
5888.96283140188007-0.962831401880073
5979.25694746175693-2.25694746175693
60159.908062639274575.09193736072543
6199.81897485537058-0.818974855370582
621010.2099365775581-0.209936577558119
631210.11421167659091.88578832340906
641310.48438476455862.51561523544141
65108.502204813264921.49779518673508
661111.7625765806314-0.762576580631354
67813.5289794575842-5.52897945758425
6899.10633997104052-0.106339971040520
69138.579603971686424.42039602831358
701110.39031390857270.609686091427253
71812.8045536740804-4.80455367408039
72910.9704608563897-1.97046085638975
73912.2017089885203-3.20170898852031
741512.30875672215992.69124327784007
75911.0628672224010-2.06286722240102
761011.5301917475851-1.53019174758505
77148.865522093851835.13447790614817
781210.8646320993441.13536790065601
791211.08047855029790.91952144970213
801111.5483834247285-0.54838342472852
811411.49268314540942.50731685459055
82611.5282694894414-5.52826948944138
831211.29674817596790.703251824032116
84810.0126283691346-2.01262836913456
851412.41821617781821.58178382218176
861110.89470512632730.105294873672674
87109.897092806781640.102907193218359
881410.18484045789543.81515954210463
891211.89780100944620.102198990553771
901011.0066486559064-1.0066486559064
911412.92606782188761.07393217811242
9259.00942414101454-4.00942414101454
931110.42806747737860.571932522621375
941010.3728681357444-0.372868135744375
95911.4076989366278-2.40769893662784
961011.2631304881262-1.26313048812616
971615.07583827431300.924161725686956
981312.71127589699520.288724103004823
99910.9036759588938-1.90367595889379
1001011.3479025178351-1.34790251783514
1011010.8964411413978-0.89644114139782
10279.47698287532637-2.47698287532637
10399.63410104368921-0.634101043689212
104810.2274354051664-2.22743540516638
1051412.78375018590601.21624981409404
1061411.67685256683052.32314743316949
107811.0409974668779-3.04099746687787
108911.5048553779584-2.50485537795842
1091411.76226470677752.23773529322248
1101410.74546449335693.25453550664308
11189.81482863846308-1.81482863846308
112813.5957407940626-5.59574079406258
113811.3570136310421-3.35701363104209
11478.51540641828066-1.51540641828066
11567.38974117664732-1.38974117664732
11689.38061343658819-1.38061343658819
11768.2954819242119-2.29548192421190
118119.882949317753681.11705068224632
1191411.81473064911652.18526935088353
1201111.1190391304871-0.119039130487054
1211112.0269338103830-1.02693381038302
122119.216532469198371.78346753080163
1231410.37514378668233.6248562133177
124810.3750239515906-2.37502395159064
1252011.45070460739498.54929539260508
1261110.28995221441520.710047785584832
12789.15846684277333-1.15846684277333
1281110.70715383781400.292846162186044
1291010.6145568450023-0.614556845002299
1301413.60880468897510.391195311024858
1311110.50996039191540.490039608084616
132910.6584532813280-1.65845328132801
13399.70305356253355-0.703053562533552
134810.1840622050912-2.18406220509122
1351012.0332842234449-2.03328422344490
1361310.66677241950242.33322758049756
1371310.29806276697322.70193723302678
138129.331789741649442.66821025835056
139810.3257955333565-2.32579553335650
1401310.96851032545892.03148967454110
1411412.46394934133821.53605065866184
1421211.64235801168210.357641988317876
1431410.8480906288263.15190937117401
1441511.29710376343193.70289623656807
1451314.1486057103926-1.14860571039262
1461611.78975592886894.21024407113107
147911.9371046681444-2.93710466814438
148910.4734556020534-1.47345560205345
149911.0791517421074-2.07915174210738
150811.3491347681088-3.34913476810879
151710.0224993475039-3.02249934750387
1521611.92102912897024.07897087102981
1531113.2274887277504-2.22748872775040
15499.9950294252709-0.9950294252709
155119.759655061331771.24034493866823
15699.8524503972167-0.852450397216694
1571412.49931079624071.50068920375927
1581311.01987373256591.98012626743408
1591614.27279313245081.72720686754919

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 12.1834601696621 & 1.81653983033794 \tabularnewline
2 & 11 & 11.7291264973210 & -0.729126497321018 \tabularnewline
3 & 6 & 7.98610924770742 & -1.98610924770742 \tabularnewline
4 & 12 & 10.8606263297047 & 1.13937367029531 \tabularnewline
5 & 8 & 9.8033961448324 & -1.80339614483241 \tabularnewline
6 & 10 & 10.2304044517430 & -0.230404451742978 \tabularnewline
7 & 10 & 9.66822714956926 & 0.331772850430744 \tabularnewline
8 & 11 & 9.65340024446839 & 1.34659975553161 \tabularnewline
9 & 16 & 10.7459208642002 & 5.25407913579983 \tabularnewline
10 & 11 & 9.970049099318 & 1.02995090068200 \tabularnewline
11 & 13 & 12.3326966916268 & 0.667303308373247 \tabularnewline
12 & 12 & 12.8998673640540 & -0.89986736405405 \tabularnewline
13 & 8 & 12.8855005989489 & -4.88550059894887 \tabularnewline
14 & 12 & 10.5710891802730 & 1.42891081972697 \tabularnewline
15 & 11 & 9.1099225255926 & 1.89007747440740 \tabularnewline
16 & 4 & 7.21077097461015 & -3.21077097461015 \tabularnewline
17 & 9 & 10.9171899258221 & -1.91718992582208 \tabularnewline
18 & 8 & 9.7456502414804 & -1.7456502414804 \tabularnewline
19 & 8 & 9.9171569427426 & -1.91715694274260 \tabularnewline
20 & 14 & 12.6572866084330 & 1.34271339156703 \tabularnewline
21 & 15 & 12.1724568081204 & 2.82754319187957 \tabularnewline
22 & 16 & 13.5942267748638 & 2.4057732251362 \tabularnewline
23 & 9 & 11.6190846241025 & -2.61908462410249 \tabularnewline
24 & 14 & 14.3115767091764 & -0.311576709176407 \tabularnewline
25 & 11 & 11.3330084563338 & -0.333008456333843 \tabularnewline
26 & 8 & 9.28093981183246 & -1.28093981183246 \tabularnewline
27 & 9 & 9.4967210399075 & -0.496721039907497 \tabularnewline
28 & 9 & 11.5518815219408 & -2.55188152194075 \tabularnewline
29 & 9 & 10.4032374192336 & -1.40323741923359 \tabularnewline
30 & 9 & 9.40547456644657 & -0.405474566446572 \tabularnewline
31 & 10 & 11.7507809092979 & -1.75078090929788 \tabularnewline
32 & 16 & 11.6828688464878 & 4.31713115351218 \tabularnewline
33 & 11 & 9.07811152376447 & 1.92188847623553 \tabularnewline
34 & 8 & 9.9229267634327 & -1.92292676343270 \tabularnewline
35 & 9 & 8.76821782643792 & 0.231782173562084 \tabularnewline
36 & 16 & 13.2325884497779 & 2.76741155022207 \tabularnewline
37 & 11 & 12.5957102683807 & -1.59571026838072 \tabularnewline
38 & 16 & 9.53258455426656 & 6.46741544573344 \tabularnewline
39 & 12 & 12.5847062949031 & -0.58470629490306 \tabularnewline
40 & 12 & 10.3939363693879 & 1.60606363061212 \tabularnewline
41 & 14 & 12.4095672399643 & 1.59043276003568 \tabularnewline
42 & 9 & 10.4190090367503 & -1.41900903675034 \tabularnewline
43 & 10 & 10.5891299496888 & -0.589129949688844 \tabularnewline
44 & 9 & 8.38914357835763 & 0.610856421642369 \tabularnewline
45 & 10 & 10.0403672891197 & -0.0403672891196706 \tabularnewline
46 & 12 & 10.1978195540736 & 1.80218044592644 \tabularnewline
47 & 14 & 11.4755851953141 & 2.52441480468592 \tabularnewline
48 & 14 & 12.0389656162385 & 1.9610343837615 \tabularnewline
49 & 10 & 12.2465516266846 & -2.24655162668464 \tabularnewline
50 & 14 & 10.0558939990638 & 3.94410600093617 \tabularnewline
51 & 16 & 12.1362012630193 & 3.86379873698067 \tabularnewline
52 & 9 & 10.3922362374602 & -1.39223623746017 \tabularnewline
53 & 10 & 11.4773855068891 & -1.47738550688911 \tabularnewline
54 & 6 & 9.0177119797914 & -3.0177119797914 \tabularnewline
55 & 8 & 11.1789000945013 & -3.17890009450128 \tabularnewline
56 & 13 & 12.4017150208824 & 0.598284979117642 \tabularnewline
57 & 10 & 10.7898717002451 & -0.789871700245069 \tabularnewline
58 & 8 & 8.96283140188007 & -0.962831401880073 \tabularnewline
59 & 7 & 9.25694746175693 & -2.25694746175693 \tabularnewline
60 & 15 & 9.90806263927457 & 5.09193736072543 \tabularnewline
61 & 9 & 9.81897485537058 & -0.818974855370582 \tabularnewline
62 & 10 & 10.2099365775581 & -0.209936577558119 \tabularnewline
63 & 12 & 10.1142116765909 & 1.88578832340906 \tabularnewline
64 & 13 & 10.4843847645586 & 2.51561523544141 \tabularnewline
65 & 10 & 8.50220481326492 & 1.49779518673508 \tabularnewline
66 & 11 & 11.7625765806314 & -0.762576580631354 \tabularnewline
67 & 8 & 13.5289794575842 & -5.52897945758425 \tabularnewline
68 & 9 & 9.10633997104052 & -0.106339971040520 \tabularnewline
69 & 13 & 8.57960397168642 & 4.42039602831358 \tabularnewline
70 & 11 & 10.3903139085727 & 0.609686091427253 \tabularnewline
71 & 8 & 12.8045536740804 & -4.80455367408039 \tabularnewline
72 & 9 & 10.9704608563897 & -1.97046085638975 \tabularnewline
73 & 9 & 12.2017089885203 & -3.20170898852031 \tabularnewline
74 & 15 & 12.3087567221599 & 2.69124327784007 \tabularnewline
75 & 9 & 11.0628672224010 & -2.06286722240102 \tabularnewline
76 & 10 & 11.5301917475851 & -1.53019174758505 \tabularnewline
77 & 14 & 8.86552209385183 & 5.13447790614817 \tabularnewline
78 & 12 & 10.864632099344 & 1.13536790065601 \tabularnewline
79 & 12 & 11.0804785502979 & 0.91952144970213 \tabularnewline
80 & 11 & 11.5483834247285 & -0.54838342472852 \tabularnewline
81 & 14 & 11.4926831454094 & 2.50731685459055 \tabularnewline
82 & 6 & 11.5282694894414 & -5.52826948944138 \tabularnewline
83 & 12 & 11.2967481759679 & 0.703251824032116 \tabularnewline
84 & 8 & 10.0126283691346 & -2.01262836913456 \tabularnewline
85 & 14 & 12.4182161778182 & 1.58178382218176 \tabularnewline
86 & 11 & 10.8947051263273 & 0.105294873672674 \tabularnewline
87 & 10 & 9.89709280678164 & 0.102907193218359 \tabularnewline
88 & 14 & 10.1848404578954 & 3.81515954210463 \tabularnewline
89 & 12 & 11.8978010094462 & 0.102198990553771 \tabularnewline
90 & 10 & 11.0066486559064 & -1.0066486559064 \tabularnewline
91 & 14 & 12.9260678218876 & 1.07393217811242 \tabularnewline
92 & 5 & 9.00942414101454 & -4.00942414101454 \tabularnewline
93 & 11 & 10.4280674773786 & 0.571932522621375 \tabularnewline
94 & 10 & 10.3728681357444 & -0.372868135744375 \tabularnewline
95 & 9 & 11.4076989366278 & -2.40769893662784 \tabularnewline
96 & 10 & 11.2631304881262 & -1.26313048812616 \tabularnewline
97 & 16 & 15.0758382743130 & 0.924161725686956 \tabularnewline
98 & 13 & 12.7112758969952 & 0.288724103004823 \tabularnewline
99 & 9 & 10.9036759588938 & -1.90367595889379 \tabularnewline
100 & 10 & 11.3479025178351 & -1.34790251783514 \tabularnewline
101 & 10 & 10.8964411413978 & -0.89644114139782 \tabularnewline
102 & 7 & 9.47698287532637 & -2.47698287532637 \tabularnewline
103 & 9 & 9.63410104368921 & -0.634101043689212 \tabularnewline
104 & 8 & 10.2274354051664 & -2.22743540516638 \tabularnewline
105 & 14 & 12.7837501859060 & 1.21624981409404 \tabularnewline
106 & 14 & 11.6768525668305 & 2.32314743316949 \tabularnewline
107 & 8 & 11.0409974668779 & -3.04099746687787 \tabularnewline
108 & 9 & 11.5048553779584 & -2.50485537795842 \tabularnewline
109 & 14 & 11.7622647067775 & 2.23773529322248 \tabularnewline
110 & 14 & 10.7454644933569 & 3.25453550664308 \tabularnewline
111 & 8 & 9.81482863846308 & -1.81482863846308 \tabularnewline
112 & 8 & 13.5957407940626 & -5.59574079406258 \tabularnewline
113 & 8 & 11.3570136310421 & -3.35701363104209 \tabularnewline
114 & 7 & 8.51540641828066 & -1.51540641828066 \tabularnewline
115 & 6 & 7.38974117664732 & -1.38974117664732 \tabularnewline
116 & 8 & 9.38061343658819 & -1.38061343658819 \tabularnewline
117 & 6 & 8.2954819242119 & -2.29548192421190 \tabularnewline
118 & 11 & 9.88294931775368 & 1.11705068224632 \tabularnewline
119 & 14 & 11.8147306491165 & 2.18526935088353 \tabularnewline
120 & 11 & 11.1190391304871 & -0.119039130487054 \tabularnewline
121 & 11 & 12.0269338103830 & -1.02693381038302 \tabularnewline
122 & 11 & 9.21653246919837 & 1.78346753080163 \tabularnewline
123 & 14 & 10.3751437866823 & 3.6248562133177 \tabularnewline
124 & 8 & 10.3750239515906 & -2.37502395159064 \tabularnewline
125 & 20 & 11.4507046073949 & 8.54929539260508 \tabularnewline
126 & 11 & 10.2899522144152 & 0.710047785584832 \tabularnewline
127 & 8 & 9.15846684277333 & -1.15846684277333 \tabularnewline
128 & 11 & 10.7071538378140 & 0.292846162186044 \tabularnewline
129 & 10 & 10.6145568450023 & -0.614556845002299 \tabularnewline
130 & 14 & 13.6088046889751 & 0.391195311024858 \tabularnewline
131 & 11 & 10.5099603919154 & 0.490039608084616 \tabularnewline
132 & 9 & 10.6584532813280 & -1.65845328132801 \tabularnewline
133 & 9 & 9.70305356253355 & -0.703053562533552 \tabularnewline
134 & 8 & 10.1840622050912 & -2.18406220509122 \tabularnewline
135 & 10 & 12.0332842234449 & -2.03328422344490 \tabularnewline
136 & 13 & 10.6667724195024 & 2.33322758049756 \tabularnewline
137 & 13 & 10.2980627669732 & 2.70193723302678 \tabularnewline
138 & 12 & 9.33178974164944 & 2.66821025835056 \tabularnewline
139 & 8 & 10.3257955333565 & -2.32579553335650 \tabularnewline
140 & 13 & 10.9685103254589 & 2.03148967454110 \tabularnewline
141 & 14 & 12.4639493413382 & 1.53605065866184 \tabularnewline
142 & 12 & 11.6423580116821 & 0.357641988317876 \tabularnewline
143 & 14 & 10.848090628826 & 3.15190937117401 \tabularnewline
144 & 15 & 11.2971037634319 & 3.70289623656807 \tabularnewline
145 & 13 & 14.1486057103926 & -1.14860571039262 \tabularnewline
146 & 16 & 11.7897559288689 & 4.21024407113107 \tabularnewline
147 & 9 & 11.9371046681444 & -2.93710466814438 \tabularnewline
148 & 9 & 10.4734556020534 & -1.47345560205345 \tabularnewline
149 & 9 & 11.0791517421074 & -2.07915174210738 \tabularnewline
150 & 8 & 11.3491347681088 & -3.34913476810879 \tabularnewline
151 & 7 & 10.0224993475039 & -3.02249934750387 \tabularnewline
152 & 16 & 11.9210291289702 & 4.07897087102981 \tabularnewline
153 & 11 & 13.2274887277504 & -2.22748872775040 \tabularnewline
154 & 9 & 9.9950294252709 & -0.9950294252709 \tabularnewline
155 & 11 & 9.75965506133177 & 1.24034493866823 \tabularnewline
156 & 9 & 9.8524503972167 & -0.852450397216694 \tabularnewline
157 & 14 & 12.4993107962407 & 1.50068920375927 \tabularnewline
158 & 13 & 11.0198737325659 & 1.98012626743408 \tabularnewline
159 & 16 & 14.2727931324508 & 1.72720686754919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]12.1834601696621[/C][C]1.81653983033794[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.7291264973210[/C][C]-0.729126497321018[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]7.98610924770742[/C][C]-1.98610924770742[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8606263297047[/C][C]1.13937367029531[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]9.8033961448324[/C][C]-1.80339614483241[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.2304044517430[/C][C]-0.230404451742978[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]9.66822714956926[/C][C]0.331772850430744[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.65340024446839[/C][C]1.34659975553161[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]10.7459208642002[/C][C]5.25407913579983[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]9.970049099318[/C][C]1.02995090068200[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]12.3326966916268[/C][C]0.667303308373247[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.8998673640540[/C][C]-0.89986736405405[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]12.8855005989489[/C][C]-4.88550059894887[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.5710891802730[/C][C]1.42891081972697[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]9.1099225255926[/C][C]1.89007747440740[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.21077097461015[/C][C]-3.21077097461015[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]10.9171899258221[/C][C]-1.91718992582208[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]9.7456502414804[/C][C]-1.7456502414804[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]9.9171569427426[/C][C]-1.91715694274260[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]12.6572866084330[/C][C]1.34271339156703[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]12.1724568081204[/C][C]2.82754319187957[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]13.5942267748638[/C][C]2.4057732251362[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]11.6190846241025[/C][C]-2.61908462410249[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.3115767091764[/C][C]-0.311576709176407[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.3330084563338[/C][C]-0.333008456333843[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]9.28093981183246[/C][C]-1.28093981183246[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.4967210399075[/C][C]-0.496721039907497[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]11.5518815219408[/C][C]-2.55188152194075[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4032374192336[/C][C]-1.40323741923359[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.40547456644657[/C][C]-0.405474566446572[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]11.7507809092979[/C][C]-1.75078090929788[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]11.6828688464878[/C][C]4.31713115351218[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]9.07811152376447[/C][C]1.92188847623553[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]9.9229267634327[/C][C]-1.92292676343270[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.76821782643792[/C][C]0.231782173562084[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.2325884497779[/C][C]2.76741155022207[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.5957102683807[/C][C]-1.59571026838072[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]9.53258455426656[/C][C]6.46741544573344[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]12.5847062949031[/C][C]-0.58470629490306[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]10.3939363693879[/C][C]1.60606363061212[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.4095672399643[/C][C]1.59043276003568[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.4190090367503[/C][C]-1.41900903675034[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]10.5891299496888[/C][C]-0.589129949688844[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]8.38914357835763[/C][C]0.610856421642369[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.0403672891197[/C][C]-0.0403672891196706[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.1978195540736[/C][C]1.80218044592644[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]11.4755851953141[/C][C]2.52441480468592[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]12.0389656162385[/C][C]1.9610343837615[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]12.2465516266846[/C][C]-2.24655162668464[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]10.0558939990638[/C][C]3.94410600093617[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]12.1362012630193[/C][C]3.86379873698067[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.3922362374602[/C][C]-1.39223623746017[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.4773855068891[/C][C]-1.47738550688911[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]9.0177119797914[/C][C]-3.0177119797914[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]11.1789000945013[/C][C]-3.17890009450128[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.4017150208824[/C][C]0.598284979117642[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.7898717002451[/C][C]-0.789871700245069[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.96283140188007[/C][C]-0.962831401880073[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.25694746175693[/C][C]-2.25694746175693[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]9.90806263927457[/C][C]5.09193736072543[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.81897485537058[/C][C]-0.818974855370582[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.2099365775581[/C][C]-0.209936577558119[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.1142116765909[/C][C]1.88578832340906[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]10.4843847645586[/C][C]2.51561523544141[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]8.50220481326492[/C][C]1.49779518673508[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.7625765806314[/C][C]-0.762576580631354[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]13.5289794575842[/C][C]-5.52897945758425[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]9.10633997104052[/C][C]-0.106339971040520[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.57960397168642[/C][C]4.42039602831358[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.3903139085727[/C][C]0.609686091427253[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]12.8045536740804[/C][C]-4.80455367408039[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.9704608563897[/C][C]-1.97046085638975[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]12.2017089885203[/C][C]-3.20170898852031[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.3087567221599[/C][C]2.69124327784007[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]11.0628672224010[/C][C]-2.06286722240102[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]11.5301917475851[/C][C]-1.53019174758505[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]8.86552209385183[/C][C]5.13447790614817[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.864632099344[/C][C]1.13536790065601[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.0804785502979[/C][C]0.91952144970213[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.5483834247285[/C][C]-0.54838342472852[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]11.4926831454094[/C][C]2.50731685459055[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]11.5282694894414[/C][C]-5.52826948944138[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.2967481759679[/C][C]0.703251824032116[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.0126283691346[/C][C]-2.01262836913456[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]12.4182161778182[/C][C]1.58178382218176[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.8947051263273[/C][C]0.105294873672674[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.89709280678164[/C][C]0.102907193218359[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]10.1848404578954[/C][C]3.81515954210463[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8978010094462[/C][C]0.102198990553771[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]11.0066486559064[/C][C]-1.0066486559064[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.9260678218876[/C][C]1.07393217811242[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]9.00942414101454[/C][C]-4.00942414101454[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]10.4280674773786[/C][C]0.571932522621375[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.3728681357444[/C][C]-0.372868135744375[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]11.4076989366278[/C][C]-2.40769893662784[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.2631304881262[/C][C]-1.26313048812616[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.0758382743130[/C][C]0.924161725686956[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.7112758969952[/C][C]0.288724103004823[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.9036759588938[/C][C]-1.90367595889379[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.3479025178351[/C][C]-1.34790251783514[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.8964411413978[/C][C]-0.89644114139782[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]9.47698287532637[/C][C]-2.47698287532637[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]9.63410104368921[/C][C]-0.634101043689212[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]10.2274354051664[/C][C]-2.22743540516638[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.7837501859060[/C][C]1.21624981409404[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]11.6768525668305[/C][C]2.32314743316949[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]11.0409974668779[/C][C]-3.04099746687787[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]11.5048553779584[/C][C]-2.50485537795842[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.7622647067775[/C][C]2.23773529322248[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]10.7454644933569[/C][C]3.25453550664308[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]9.81482863846308[/C][C]-1.81482863846308[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]13.5957407940626[/C][C]-5.59574079406258[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]11.3570136310421[/C][C]-3.35701363104209[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]8.51540641828066[/C][C]-1.51540641828066[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]7.38974117664732[/C][C]-1.38974117664732[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]9.38061343658819[/C][C]-1.38061343658819[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]8.2954819242119[/C][C]-2.29548192421190[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]9.88294931775368[/C][C]1.11705068224632[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]11.8147306491165[/C][C]2.18526935088353[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]11.1190391304871[/C][C]-0.119039130487054[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.0269338103830[/C][C]-1.02693381038302[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]9.21653246919837[/C][C]1.78346753080163[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]10.3751437866823[/C][C]3.6248562133177[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.3750239515906[/C][C]-2.37502395159064[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]11.4507046073949[/C][C]8.54929539260508[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.2899522144152[/C][C]0.710047785584832[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.15846684277333[/C][C]-1.15846684277333[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.7071538378140[/C][C]0.292846162186044[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.6145568450023[/C][C]-0.614556845002299[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.6088046889751[/C][C]0.391195311024858[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.5099603919154[/C][C]0.490039608084616[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.6584532813280[/C][C]-1.65845328132801[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]9.70305356253355[/C][C]-0.703053562533552[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.1840622050912[/C][C]-2.18406220509122[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]12.0332842234449[/C][C]-2.03328422344490[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.6667724195024[/C][C]2.33322758049756[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]10.2980627669732[/C][C]2.70193723302678[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]9.33178974164944[/C][C]2.66821025835056[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]10.3257955333565[/C][C]-2.32579553335650[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]10.9685103254589[/C][C]2.03148967454110[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]12.4639493413382[/C][C]1.53605065866184[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.6423580116821[/C][C]0.357641988317876[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.848090628826[/C][C]3.15190937117401[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]11.2971037634319[/C][C]3.70289623656807[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]14.1486057103926[/C][C]-1.14860571039262[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]11.7897559288689[/C][C]4.21024407113107[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]11.9371046681444[/C][C]-2.93710466814438[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.4734556020534[/C][C]-1.47345560205345[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.0791517421074[/C][C]-2.07915174210738[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]11.3491347681088[/C][C]-3.34913476810879[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]10.0224993475039[/C][C]-3.02249934750387[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]11.9210291289702[/C][C]4.07897087102981[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]13.2274887277504[/C][C]-2.22748872775040[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.9950294252709[/C][C]-0.9950294252709[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]9.75965506133177[/C][C]1.24034493866823[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.8524503972167[/C][C]-0.852450397216694[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.4993107962407[/C][C]1.50068920375927[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.0198737325659[/C][C]1.98012626743408[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.2727931324508[/C][C]1.72720686754919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99302&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.18346016966211.81653983033794
21111.7291264973210-0.729126497321018
367.98610924770742-1.98610924770742
41210.86062632970471.13937367029531
589.8033961448324-1.80339614483241
61010.2304044517430-0.230404451742978
7109.668227149569260.331772850430744
8119.653400244468391.34659975553161
91610.74592086420025.25407913579983
10119.9700490993181.02995090068200
111312.33269669162680.667303308373247
121212.8998673640540-0.89986736405405
13812.8855005989489-4.88550059894887
141210.57108918027301.42891081972697
15119.10992252559261.89007747440740
1647.21077097461015-3.21077097461015
17910.9171899258221-1.91718992582208
1889.7456502414804-1.7456502414804
1989.9171569427426-1.91715694274260
201412.65728660843301.34271339156703
211512.17245680812042.82754319187957
221613.59422677486382.4057732251362
23911.6190846241025-2.61908462410249
241414.3115767091764-0.311576709176407
251111.3330084563338-0.333008456333843
2689.28093981183246-1.28093981183246
2799.4967210399075-0.496721039907497
28911.5518815219408-2.55188152194075
29910.4032374192336-1.40323741923359
3099.40547456644657-0.405474566446572
311011.7507809092979-1.75078090929788
321611.68286884648784.31713115351218
33119.078111523764471.92188847623553
3489.9229267634327-1.92292676343270
3598.768217826437920.231782173562084
361613.23258844977792.76741155022207
371112.5957102683807-1.59571026838072
38169.532584554266566.46741544573344
391212.5847062949031-0.58470629490306
401210.39393636938791.60606363061212
411412.40956723996431.59043276003568
42910.4190090367503-1.41900903675034
431010.5891299496888-0.589129949688844
4498.389143578357630.610856421642369
451010.0403672891197-0.0403672891196706
461210.19781955407361.80218044592644
471411.47558519531412.52441480468592
481412.03896561623851.9610343837615
491012.2465516266846-2.24655162668464
501410.05589399906383.94410600093617
511612.13620126301933.86379873698067
52910.3922362374602-1.39223623746017
531011.4773855068891-1.47738550688911
5469.0177119797914-3.0177119797914
55811.1789000945013-3.17890009450128
561312.40171502088240.598284979117642
571010.7898717002451-0.789871700245069
5888.96283140188007-0.962831401880073
5979.25694746175693-2.25694746175693
60159.908062639274575.09193736072543
6199.81897485537058-0.818974855370582
621010.2099365775581-0.209936577558119
631210.11421167659091.88578832340906
641310.48438476455862.51561523544141
65108.502204813264921.49779518673508
661111.7625765806314-0.762576580631354
67813.5289794575842-5.52897945758425
6899.10633997104052-0.106339971040520
69138.579603971686424.42039602831358
701110.39031390857270.609686091427253
71812.8045536740804-4.80455367408039
72910.9704608563897-1.97046085638975
73912.2017089885203-3.20170898852031
741512.30875672215992.69124327784007
75911.0628672224010-2.06286722240102
761011.5301917475851-1.53019174758505
77148.865522093851835.13447790614817
781210.8646320993441.13536790065601
791211.08047855029790.91952144970213
801111.5483834247285-0.54838342472852
811411.49268314540942.50731685459055
82611.5282694894414-5.52826948944138
831211.29674817596790.703251824032116
84810.0126283691346-2.01262836913456
851412.41821617781821.58178382218176
861110.89470512632730.105294873672674
87109.897092806781640.102907193218359
881410.18484045789543.81515954210463
891211.89780100944620.102198990553771
901011.0066486559064-1.0066486559064
911412.92606782188761.07393217811242
9259.00942414101454-4.00942414101454
931110.42806747737860.571932522621375
941010.3728681357444-0.372868135744375
95911.4076989366278-2.40769893662784
961011.2631304881262-1.26313048812616
971615.07583827431300.924161725686956
981312.71127589699520.288724103004823
99910.9036759588938-1.90367595889379
1001011.3479025178351-1.34790251783514
1011010.8964411413978-0.89644114139782
10279.47698287532637-2.47698287532637
10399.63410104368921-0.634101043689212
104810.2274354051664-2.22743540516638
1051412.78375018590601.21624981409404
1061411.67685256683052.32314743316949
107811.0409974668779-3.04099746687787
108911.5048553779584-2.50485537795842
1091411.76226470677752.23773529322248
1101410.74546449335693.25453550664308
11189.81482863846308-1.81482863846308
112813.5957407940626-5.59574079406258
113811.3570136310421-3.35701363104209
11478.51540641828066-1.51540641828066
11567.38974117664732-1.38974117664732
11689.38061343658819-1.38061343658819
11768.2954819242119-2.29548192421190
118119.882949317753681.11705068224632
1191411.81473064911652.18526935088353
1201111.1190391304871-0.119039130487054
1211112.0269338103830-1.02693381038302
122119.216532469198371.78346753080163
1231410.37514378668233.6248562133177
124810.3750239515906-2.37502395159064
1252011.45070460739498.54929539260508
1261110.28995221441520.710047785584832
12789.15846684277333-1.15846684277333
1281110.70715383781400.292846162186044
1291010.6145568450023-0.614556845002299
1301413.60880468897510.391195311024858
1311110.50996039191540.490039608084616
132910.6584532813280-1.65845328132801
13399.70305356253355-0.703053562533552
134810.1840622050912-2.18406220509122
1351012.0332842234449-2.03328422344490
1361310.66677241950242.33322758049756
1371310.29806276697322.70193723302678
138129.331789741649442.66821025835056
139810.3257955333565-2.32579553335650
1401310.96851032545892.03148967454110
1411412.46394934133821.53605065866184
1421211.64235801168210.357641988317876
1431410.8480906288263.15190937117401
1441511.29710376343193.70289623656807
1451314.1486057103926-1.14860571039262
1461611.78975592886894.21024407113107
147911.9371046681444-2.93710466814438
148910.4734556020534-1.47345560205345
149911.0791517421074-2.07915174210738
150811.3491347681088-3.34913476810879
151710.0224993475039-3.02249934750387
1521611.92102912897024.07897087102981
1531113.2274887277504-2.22748872775040
15499.9950294252709-0.9950294252709
155119.759655061331771.24034493866823
15699.8524503972167-0.852450397216694
1571412.49931079624071.50068920375927
1581311.01987373256591.98012626743408
1591614.27279313245081.72720686754919







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3204326073753530.6408652147507060.679567392624647
120.1935141710419890.3870283420839790.80648582895801
130.8379448510243020.3241102979513960.162055148975698
140.8205878536940020.3588242926119950.179412146305998
150.7853479177986520.4293041644026960.214652082201348
160.7842430211402150.4315139577195690.215756978859784
170.7081170771753940.5837658456492130.291882922824606
180.6853511225399590.6292977549200820.314648877460041
190.6056321113102440.7887357773795130.394367888689756
200.6810979027679410.6378041944641180.318902097232059
210.6870663282876680.6258673434246640.312933671712332
220.6723764544035280.6552470911929440.327623545596472
230.6481451338978870.7037097322042250.351854866102113
240.5980726613422010.8038546773155980.401927338657799
250.5281737323489020.9436525353021960.471826267651098
260.4577688607607470.9155377215214940.542231139239253
270.3879551745653970.7759103491307950.612044825434603
280.3616560167363440.7233120334726890.638343983263656
290.3031297967037670.6062595934075340.696870203296233
300.253564103276060.507128206552120.74643589672394
310.2154950502048490.4309901004096990.78450494979515
320.3247068143943730.6494136287887450.675293185605627
330.2950686174662050.590137234932410.704931382533795
340.2668203713444310.5336407426888620.733179628655569
350.2182767964080350.4365535928160690.781723203591965
360.1861745255211950.3723490510423910.813825474478805
370.1573916545140990.3147833090281990.8426083454859
380.3736011694098190.7472023388196380.626398830590181
390.4017666608684050.803533321736810.598233339131595
400.3787251640057740.7574503280115490.621274835994226
410.3590314646157450.718062929231490.640968535384255
420.3208534430737320.6417068861474640.679146556926268
430.2768069729459490.5536139458918990.72319302705405
440.2365515763983010.4731031527966010.7634484236017
450.1967290904049250.393458180809850.803270909595075
460.1700875783969440.3401751567938890.829912421603056
470.1697931743421430.3395863486842860.830206825657857
480.1670265412722590.3340530825445170.832973458727741
490.1663451013831630.3326902027663260.833654898616837
500.1846577189560650.369315437912130.815342281043935
510.2470504165386270.4941008330772540.752949583461373
520.231795051027020.463590102054040.76820494897298
530.2102030848938550.420406169787710.789796915106145
540.2360834299117460.4721668598234910.763916570088254
550.2531352138819970.5062704277639940.746864786118003
560.2161741244182290.4323482488364590.78382587558177
570.183178292879220.366356585758440.81682170712078
580.1583483446582170.3166966893164330.841651655341783
590.1548026394428180.3096052788856370.845197360557182
600.2482174600585790.4964349201171580.751782539941421
610.2114974772311500.4229949544622990.78850252276885
620.1780273734662120.3560547469324240.821972626533788
630.1730799666080470.3461599332160940.826920033391953
640.1807424640546430.3614849281092850.819257535945357
650.1583743209035490.3167486418070980.84162567909645
660.1316088847827230.2632177695654450.868391115217277
670.257495083954970.514990167909940.74250491604503
680.2203662721328680.4407325442657350.779633727867132
690.3240036073389520.6480072146779040.675996392661048
700.2871020974684460.5742041949368930.712897902531553
710.4167211303237420.8334422606474840.583278869676258
720.4174932412080760.8349864824161510.582506758791924
730.4225878301127120.8451756602254250.577412169887288
740.4625460159315660.9250920318631310.537453984068434
750.4347100121081210.8694200242162420.565289987891879
760.4004957432539370.8009914865078750.599504256746062
770.5897441237994220.8205117524011550.410255876200578
780.560563310831190.878873378337620.43943668916881
790.5233754405809440.9532491188381120.476624559419056
800.4766481055791090.9532962111582180.523351894420891
810.4822027763124220.9644055526248440.517797223687578
820.6505881278503540.6988237442992920.349411872149646
830.6126544157907540.7746911684184910.387345584209246
840.585845396566670.8283092068666610.414154603433331
850.5620318331751670.8759363336496660.437968166824833
860.5202279460742370.9595441078515250.479772053925763
870.4763998627855020.9527997255710030.523600137214498
880.5765311541396460.8469376917207080.423468845860354
890.5324607731595420.9350784536809150.467539226840458
900.4899273244343270.9798546488686540.510072675565673
910.4603145831793470.9206291663586950.539685416820653
920.5064400208099370.9871199583801260.493559979190063
930.472142260543570.944284521087140.52785773945643
940.4306246926261630.8612493852523260.569375307373837
950.4112892373292930.8225784746585860.588710762670707
960.3669170684299770.7338341368599540.633082931570023
970.3530284766776610.7060569533553210.646971523322339
980.3134251207948560.6268502415897130.686574879205143
990.2843840312625960.5687680625251930.715615968737404
1000.2478986165910040.4957972331820080.752101383408996
1010.2111131646227650.4222263292455300.788886835377235
1020.1944807652737160.3889615305474310.805519234726284
1030.1621883831874410.3243767663748830.837811616812559
1040.1461509662671200.2923019325342400.85384903373288
1050.1288083622543560.2576167245087120.871191637745644
1060.1358255600331860.2716511200663720.864174439966814
1070.134839615749290.269679231498580.86516038425071
1080.1288994477032200.2577988954064410.87110055229678
1090.1286923445368060.2573846890736110.871307655463194
1100.1690079854073830.3380159708147650.830992014592617
1110.1432436485190620.2864872970381230.856756351480938
1120.2993493908245460.5986987816490920.700650609175454
1130.3827495901722320.7654991803444650.617250409827768
1140.3514716574334640.7029433148669290.648528342566536
1150.3406236687865280.6812473375730550.659376331213472
1160.2999778204211480.5999556408422950.700022179578852
1170.3007749821086670.6015499642173340.699225017891333
1180.2627109896107030.5254219792214070.737289010389297
1190.2385129836369450.477025967273890.761487016363055
1200.1979825930025820.3959651860051650.802017406997418
1210.1889458467158920.3778916934317850.811054153284108
1220.1683711400298510.3367422800597020.83162885997015
1230.1741868844067680.3483737688135370.825813115593232
1240.1882003940888420.3764007881776850.811799605911158
1250.7343472534054690.5313054931890620.265652746594531
1260.6951935082597180.6096129834805630.304806491740282
1270.6394749396361110.7210501207277780.360525060363889
1280.5775164227470.8449671545060.422483577253
1290.5123487845308040.9753024309383930.487651215469196
1300.4499309984285780.8998619968571560.550069001571422
1310.3927132939458550.7854265878917110.607286706054145
1320.338930841221450.67786168244290.66106915877855
1330.2785618766095880.5571237532191770.721438123390412
1340.2437850797807720.4875701595615450.756214920219228
1350.2378485700929950.475697140185990.762151429907005
1360.2027804648023160.4055609296046320.797219535197684
1370.2189495807604200.4378991615208390.78105041923958
1380.2369032896769960.4738065793539920.763096710323004
1390.2285321341370170.4570642682740340.771467865862983
1400.1749468959603840.3498937919207670.825053104039617
1410.1268082131930470.2536164263860940.873191786806953
1420.09133362535390540.1826672507078110.908666374646095
1430.1209166309238120.2418332618476250.879083369076188
1440.1319735812173820.2639471624347640.868026418782618
1450.1101588667377370.2203177334754750.889841133262263
1460.3063150293443640.6126300586887280.693684970655636
1470.2029418450686560.4058836901373120.797058154931344
1480.1401024783599680.2802049567199350.859897521640032

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.320432607375353 & 0.640865214750706 & 0.679567392624647 \tabularnewline
12 & 0.193514171041989 & 0.387028342083979 & 0.80648582895801 \tabularnewline
13 & 0.837944851024302 & 0.324110297951396 & 0.162055148975698 \tabularnewline
14 & 0.820587853694002 & 0.358824292611995 & 0.179412146305998 \tabularnewline
15 & 0.785347917798652 & 0.429304164402696 & 0.214652082201348 \tabularnewline
16 & 0.784243021140215 & 0.431513957719569 & 0.215756978859784 \tabularnewline
17 & 0.708117077175394 & 0.583765845649213 & 0.291882922824606 \tabularnewline
18 & 0.685351122539959 & 0.629297754920082 & 0.314648877460041 \tabularnewline
19 & 0.605632111310244 & 0.788735777379513 & 0.394367888689756 \tabularnewline
20 & 0.681097902767941 & 0.637804194464118 & 0.318902097232059 \tabularnewline
21 & 0.687066328287668 & 0.625867343424664 & 0.312933671712332 \tabularnewline
22 & 0.672376454403528 & 0.655247091192944 & 0.327623545596472 \tabularnewline
23 & 0.648145133897887 & 0.703709732204225 & 0.351854866102113 \tabularnewline
24 & 0.598072661342201 & 0.803854677315598 & 0.401927338657799 \tabularnewline
25 & 0.528173732348902 & 0.943652535302196 & 0.471826267651098 \tabularnewline
26 & 0.457768860760747 & 0.915537721521494 & 0.542231139239253 \tabularnewline
27 & 0.387955174565397 & 0.775910349130795 & 0.612044825434603 \tabularnewline
28 & 0.361656016736344 & 0.723312033472689 & 0.638343983263656 \tabularnewline
29 & 0.303129796703767 & 0.606259593407534 & 0.696870203296233 \tabularnewline
30 & 0.25356410327606 & 0.50712820655212 & 0.74643589672394 \tabularnewline
31 & 0.215495050204849 & 0.430990100409699 & 0.78450494979515 \tabularnewline
32 & 0.324706814394373 & 0.649413628788745 & 0.675293185605627 \tabularnewline
33 & 0.295068617466205 & 0.59013723493241 & 0.704931382533795 \tabularnewline
34 & 0.266820371344431 & 0.533640742688862 & 0.733179628655569 \tabularnewline
35 & 0.218276796408035 & 0.436553592816069 & 0.781723203591965 \tabularnewline
36 & 0.186174525521195 & 0.372349051042391 & 0.813825474478805 \tabularnewline
37 & 0.157391654514099 & 0.314783309028199 & 0.8426083454859 \tabularnewline
38 & 0.373601169409819 & 0.747202338819638 & 0.626398830590181 \tabularnewline
39 & 0.401766660868405 & 0.80353332173681 & 0.598233339131595 \tabularnewline
40 & 0.378725164005774 & 0.757450328011549 & 0.621274835994226 \tabularnewline
41 & 0.359031464615745 & 0.71806292923149 & 0.640968535384255 \tabularnewline
42 & 0.320853443073732 & 0.641706886147464 & 0.679146556926268 \tabularnewline
43 & 0.276806972945949 & 0.553613945891899 & 0.72319302705405 \tabularnewline
44 & 0.236551576398301 & 0.473103152796601 & 0.7634484236017 \tabularnewline
45 & 0.196729090404925 & 0.39345818080985 & 0.803270909595075 \tabularnewline
46 & 0.170087578396944 & 0.340175156793889 & 0.829912421603056 \tabularnewline
47 & 0.169793174342143 & 0.339586348684286 & 0.830206825657857 \tabularnewline
48 & 0.167026541272259 & 0.334053082544517 & 0.832973458727741 \tabularnewline
49 & 0.166345101383163 & 0.332690202766326 & 0.833654898616837 \tabularnewline
50 & 0.184657718956065 & 0.36931543791213 & 0.815342281043935 \tabularnewline
51 & 0.247050416538627 & 0.494100833077254 & 0.752949583461373 \tabularnewline
52 & 0.23179505102702 & 0.46359010205404 & 0.76820494897298 \tabularnewline
53 & 0.210203084893855 & 0.42040616978771 & 0.789796915106145 \tabularnewline
54 & 0.236083429911746 & 0.472166859823491 & 0.763916570088254 \tabularnewline
55 & 0.253135213881997 & 0.506270427763994 & 0.746864786118003 \tabularnewline
56 & 0.216174124418229 & 0.432348248836459 & 0.78382587558177 \tabularnewline
57 & 0.18317829287922 & 0.36635658575844 & 0.81682170712078 \tabularnewline
58 & 0.158348344658217 & 0.316696689316433 & 0.841651655341783 \tabularnewline
59 & 0.154802639442818 & 0.309605278885637 & 0.845197360557182 \tabularnewline
60 & 0.248217460058579 & 0.496434920117158 & 0.751782539941421 \tabularnewline
61 & 0.211497477231150 & 0.422994954462299 & 0.78850252276885 \tabularnewline
62 & 0.178027373466212 & 0.356054746932424 & 0.821972626533788 \tabularnewline
63 & 0.173079966608047 & 0.346159933216094 & 0.826920033391953 \tabularnewline
64 & 0.180742464054643 & 0.361484928109285 & 0.819257535945357 \tabularnewline
65 & 0.158374320903549 & 0.316748641807098 & 0.84162567909645 \tabularnewline
66 & 0.131608884782723 & 0.263217769565445 & 0.868391115217277 \tabularnewline
67 & 0.25749508395497 & 0.51499016790994 & 0.74250491604503 \tabularnewline
68 & 0.220366272132868 & 0.440732544265735 & 0.779633727867132 \tabularnewline
69 & 0.324003607338952 & 0.648007214677904 & 0.675996392661048 \tabularnewline
70 & 0.287102097468446 & 0.574204194936893 & 0.712897902531553 \tabularnewline
71 & 0.416721130323742 & 0.833442260647484 & 0.583278869676258 \tabularnewline
72 & 0.417493241208076 & 0.834986482416151 & 0.582506758791924 \tabularnewline
73 & 0.422587830112712 & 0.845175660225425 & 0.577412169887288 \tabularnewline
74 & 0.462546015931566 & 0.925092031863131 & 0.537453984068434 \tabularnewline
75 & 0.434710012108121 & 0.869420024216242 & 0.565289987891879 \tabularnewline
76 & 0.400495743253937 & 0.800991486507875 & 0.599504256746062 \tabularnewline
77 & 0.589744123799422 & 0.820511752401155 & 0.410255876200578 \tabularnewline
78 & 0.56056331083119 & 0.87887337833762 & 0.43943668916881 \tabularnewline
79 & 0.523375440580944 & 0.953249118838112 & 0.476624559419056 \tabularnewline
80 & 0.476648105579109 & 0.953296211158218 & 0.523351894420891 \tabularnewline
81 & 0.482202776312422 & 0.964405552624844 & 0.517797223687578 \tabularnewline
82 & 0.650588127850354 & 0.698823744299292 & 0.349411872149646 \tabularnewline
83 & 0.612654415790754 & 0.774691168418491 & 0.387345584209246 \tabularnewline
84 & 0.58584539656667 & 0.828309206866661 & 0.414154603433331 \tabularnewline
85 & 0.562031833175167 & 0.875936333649666 & 0.437968166824833 \tabularnewline
86 & 0.520227946074237 & 0.959544107851525 & 0.479772053925763 \tabularnewline
87 & 0.476399862785502 & 0.952799725571003 & 0.523600137214498 \tabularnewline
88 & 0.576531154139646 & 0.846937691720708 & 0.423468845860354 \tabularnewline
89 & 0.532460773159542 & 0.935078453680915 & 0.467539226840458 \tabularnewline
90 & 0.489927324434327 & 0.979854648868654 & 0.510072675565673 \tabularnewline
91 & 0.460314583179347 & 0.920629166358695 & 0.539685416820653 \tabularnewline
92 & 0.506440020809937 & 0.987119958380126 & 0.493559979190063 \tabularnewline
93 & 0.47214226054357 & 0.94428452108714 & 0.52785773945643 \tabularnewline
94 & 0.430624692626163 & 0.861249385252326 & 0.569375307373837 \tabularnewline
95 & 0.411289237329293 & 0.822578474658586 & 0.588710762670707 \tabularnewline
96 & 0.366917068429977 & 0.733834136859954 & 0.633082931570023 \tabularnewline
97 & 0.353028476677661 & 0.706056953355321 & 0.646971523322339 \tabularnewline
98 & 0.313425120794856 & 0.626850241589713 & 0.686574879205143 \tabularnewline
99 & 0.284384031262596 & 0.568768062525193 & 0.715615968737404 \tabularnewline
100 & 0.247898616591004 & 0.495797233182008 & 0.752101383408996 \tabularnewline
101 & 0.211113164622765 & 0.422226329245530 & 0.788886835377235 \tabularnewline
102 & 0.194480765273716 & 0.388961530547431 & 0.805519234726284 \tabularnewline
103 & 0.162188383187441 & 0.324376766374883 & 0.837811616812559 \tabularnewline
104 & 0.146150966267120 & 0.292301932534240 & 0.85384903373288 \tabularnewline
105 & 0.128808362254356 & 0.257616724508712 & 0.871191637745644 \tabularnewline
106 & 0.135825560033186 & 0.271651120066372 & 0.864174439966814 \tabularnewline
107 & 0.13483961574929 & 0.26967923149858 & 0.86516038425071 \tabularnewline
108 & 0.128899447703220 & 0.257798895406441 & 0.87110055229678 \tabularnewline
109 & 0.128692344536806 & 0.257384689073611 & 0.871307655463194 \tabularnewline
110 & 0.169007985407383 & 0.338015970814765 & 0.830992014592617 \tabularnewline
111 & 0.143243648519062 & 0.286487297038123 & 0.856756351480938 \tabularnewline
112 & 0.299349390824546 & 0.598698781649092 & 0.700650609175454 \tabularnewline
113 & 0.382749590172232 & 0.765499180344465 & 0.617250409827768 \tabularnewline
114 & 0.351471657433464 & 0.702943314866929 & 0.648528342566536 \tabularnewline
115 & 0.340623668786528 & 0.681247337573055 & 0.659376331213472 \tabularnewline
116 & 0.299977820421148 & 0.599955640842295 & 0.700022179578852 \tabularnewline
117 & 0.300774982108667 & 0.601549964217334 & 0.699225017891333 \tabularnewline
118 & 0.262710989610703 & 0.525421979221407 & 0.737289010389297 \tabularnewline
119 & 0.238512983636945 & 0.47702596727389 & 0.761487016363055 \tabularnewline
120 & 0.197982593002582 & 0.395965186005165 & 0.802017406997418 \tabularnewline
121 & 0.188945846715892 & 0.377891693431785 & 0.811054153284108 \tabularnewline
122 & 0.168371140029851 & 0.336742280059702 & 0.83162885997015 \tabularnewline
123 & 0.174186884406768 & 0.348373768813537 & 0.825813115593232 \tabularnewline
124 & 0.188200394088842 & 0.376400788177685 & 0.811799605911158 \tabularnewline
125 & 0.734347253405469 & 0.531305493189062 & 0.265652746594531 \tabularnewline
126 & 0.695193508259718 & 0.609612983480563 & 0.304806491740282 \tabularnewline
127 & 0.639474939636111 & 0.721050120727778 & 0.360525060363889 \tabularnewline
128 & 0.577516422747 & 0.844967154506 & 0.422483577253 \tabularnewline
129 & 0.512348784530804 & 0.975302430938393 & 0.487651215469196 \tabularnewline
130 & 0.449930998428578 & 0.899861996857156 & 0.550069001571422 \tabularnewline
131 & 0.392713293945855 & 0.785426587891711 & 0.607286706054145 \tabularnewline
132 & 0.33893084122145 & 0.6778616824429 & 0.66106915877855 \tabularnewline
133 & 0.278561876609588 & 0.557123753219177 & 0.721438123390412 \tabularnewline
134 & 0.243785079780772 & 0.487570159561545 & 0.756214920219228 \tabularnewline
135 & 0.237848570092995 & 0.47569714018599 & 0.762151429907005 \tabularnewline
136 & 0.202780464802316 & 0.405560929604632 & 0.797219535197684 \tabularnewline
137 & 0.218949580760420 & 0.437899161520839 & 0.78105041923958 \tabularnewline
138 & 0.236903289676996 & 0.473806579353992 & 0.763096710323004 \tabularnewline
139 & 0.228532134137017 & 0.457064268274034 & 0.771467865862983 \tabularnewline
140 & 0.174946895960384 & 0.349893791920767 & 0.825053104039617 \tabularnewline
141 & 0.126808213193047 & 0.253616426386094 & 0.873191786806953 \tabularnewline
142 & 0.0913336253539054 & 0.182667250707811 & 0.908666374646095 \tabularnewline
143 & 0.120916630923812 & 0.241833261847625 & 0.879083369076188 \tabularnewline
144 & 0.131973581217382 & 0.263947162434764 & 0.868026418782618 \tabularnewline
145 & 0.110158866737737 & 0.220317733475475 & 0.889841133262263 \tabularnewline
146 & 0.306315029344364 & 0.612630058688728 & 0.693684970655636 \tabularnewline
147 & 0.202941845068656 & 0.405883690137312 & 0.797058154931344 \tabularnewline
148 & 0.140102478359968 & 0.280204956719935 & 0.859897521640032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99302&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]0.320432607375353[/C][C]0.640865214750706[/C][C]0.679567392624647[/C][/ROW]
[ROW][C]12[/C][C]0.193514171041989[/C][C]0.387028342083979[/C][C]0.80648582895801[/C][/ROW]
[ROW][C]13[/C][C]0.837944851024302[/C][C]0.324110297951396[/C][C]0.162055148975698[/C][/ROW]
[ROW][C]14[/C][C]0.820587853694002[/C][C]0.358824292611995[/C][C]0.179412146305998[/C][/ROW]
[ROW][C]15[/C][C]0.785347917798652[/C][C]0.429304164402696[/C][C]0.214652082201348[/C][/ROW]
[ROW][C]16[/C][C]0.784243021140215[/C][C]0.431513957719569[/C][C]0.215756978859784[/C][/ROW]
[ROW][C]17[/C][C]0.708117077175394[/C][C]0.583765845649213[/C][C]0.291882922824606[/C][/ROW]
[ROW][C]18[/C][C]0.685351122539959[/C][C]0.629297754920082[/C][C]0.314648877460041[/C][/ROW]
[ROW][C]19[/C][C]0.605632111310244[/C][C]0.788735777379513[/C][C]0.394367888689756[/C][/ROW]
[ROW][C]20[/C][C]0.681097902767941[/C][C]0.637804194464118[/C][C]0.318902097232059[/C][/ROW]
[ROW][C]21[/C][C]0.687066328287668[/C][C]0.625867343424664[/C][C]0.312933671712332[/C][/ROW]
[ROW][C]22[/C][C]0.672376454403528[/C][C]0.655247091192944[/C][C]0.327623545596472[/C][/ROW]
[ROW][C]23[/C][C]0.648145133897887[/C][C]0.703709732204225[/C][C]0.351854866102113[/C][/ROW]
[ROW][C]24[/C][C]0.598072661342201[/C][C]0.803854677315598[/C][C]0.401927338657799[/C][/ROW]
[ROW][C]25[/C][C]0.528173732348902[/C][C]0.943652535302196[/C][C]0.471826267651098[/C][/ROW]
[ROW][C]26[/C][C]0.457768860760747[/C][C]0.915537721521494[/C][C]0.542231139239253[/C][/ROW]
[ROW][C]27[/C][C]0.387955174565397[/C][C]0.775910349130795[/C][C]0.612044825434603[/C][/ROW]
[ROW][C]28[/C][C]0.361656016736344[/C][C]0.723312033472689[/C][C]0.638343983263656[/C][/ROW]
[ROW][C]29[/C][C]0.303129796703767[/C][C]0.606259593407534[/C][C]0.696870203296233[/C][/ROW]
[ROW][C]30[/C][C]0.25356410327606[/C][C]0.50712820655212[/C][C]0.74643589672394[/C][/ROW]
[ROW][C]31[/C][C]0.215495050204849[/C][C]0.430990100409699[/C][C]0.78450494979515[/C][/ROW]
[ROW][C]32[/C][C]0.324706814394373[/C][C]0.649413628788745[/C][C]0.675293185605627[/C][/ROW]
[ROW][C]33[/C][C]0.295068617466205[/C][C]0.59013723493241[/C][C]0.704931382533795[/C][/ROW]
[ROW][C]34[/C][C]0.266820371344431[/C][C]0.533640742688862[/C][C]0.733179628655569[/C][/ROW]
[ROW][C]35[/C][C]0.218276796408035[/C][C]0.436553592816069[/C][C]0.781723203591965[/C][/ROW]
[ROW][C]36[/C][C]0.186174525521195[/C][C]0.372349051042391[/C][C]0.813825474478805[/C][/ROW]
[ROW][C]37[/C][C]0.157391654514099[/C][C]0.314783309028199[/C][C]0.8426083454859[/C][/ROW]
[ROW][C]38[/C][C]0.373601169409819[/C][C]0.747202338819638[/C][C]0.626398830590181[/C][/ROW]
[ROW][C]39[/C][C]0.401766660868405[/C][C]0.80353332173681[/C][C]0.598233339131595[/C][/ROW]
[ROW][C]40[/C][C]0.378725164005774[/C][C]0.757450328011549[/C][C]0.621274835994226[/C][/ROW]
[ROW][C]41[/C][C]0.359031464615745[/C][C]0.71806292923149[/C][C]0.640968535384255[/C][/ROW]
[ROW][C]42[/C][C]0.320853443073732[/C][C]0.641706886147464[/C][C]0.679146556926268[/C][/ROW]
[ROW][C]43[/C][C]0.276806972945949[/C][C]0.553613945891899[/C][C]0.72319302705405[/C][/ROW]
[ROW][C]44[/C][C]0.236551576398301[/C][C]0.473103152796601[/C][C]0.7634484236017[/C][/ROW]
[ROW][C]45[/C][C]0.196729090404925[/C][C]0.39345818080985[/C][C]0.803270909595075[/C][/ROW]
[ROW][C]46[/C][C]0.170087578396944[/C][C]0.340175156793889[/C][C]0.829912421603056[/C][/ROW]
[ROW][C]47[/C][C]0.169793174342143[/C][C]0.339586348684286[/C][C]0.830206825657857[/C][/ROW]
[ROW][C]48[/C][C]0.167026541272259[/C][C]0.334053082544517[/C][C]0.832973458727741[/C][/ROW]
[ROW][C]49[/C][C]0.166345101383163[/C][C]0.332690202766326[/C][C]0.833654898616837[/C][/ROW]
[ROW][C]50[/C][C]0.184657718956065[/C][C]0.36931543791213[/C][C]0.815342281043935[/C][/ROW]
[ROW][C]51[/C][C]0.247050416538627[/C][C]0.494100833077254[/C][C]0.752949583461373[/C][/ROW]
[ROW][C]52[/C][C]0.23179505102702[/C][C]0.46359010205404[/C][C]0.76820494897298[/C][/ROW]
[ROW][C]53[/C][C]0.210203084893855[/C][C]0.42040616978771[/C][C]0.789796915106145[/C][/ROW]
[ROW][C]54[/C][C]0.236083429911746[/C][C]0.472166859823491[/C][C]0.763916570088254[/C][/ROW]
[ROW][C]55[/C][C]0.253135213881997[/C][C]0.506270427763994[/C][C]0.746864786118003[/C][/ROW]
[ROW][C]56[/C][C]0.216174124418229[/C][C]0.432348248836459[/C][C]0.78382587558177[/C][/ROW]
[ROW][C]57[/C][C]0.18317829287922[/C][C]0.36635658575844[/C][C]0.81682170712078[/C][/ROW]
[ROW][C]58[/C][C]0.158348344658217[/C][C]0.316696689316433[/C][C]0.841651655341783[/C][/ROW]
[ROW][C]59[/C][C]0.154802639442818[/C][C]0.309605278885637[/C][C]0.845197360557182[/C][/ROW]
[ROW][C]60[/C][C]0.248217460058579[/C][C]0.496434920117158[/C][C]0.751782539941421[/C][/ROW]
[ROW][C]61[/C][C]0.211497477231150[/C][C]0.422994954462299[/C][C]0.78850252276885[/C][/ROW]
[ROW][C]62[/C][C]0.178027373466212[/C][C]0.356054746932424[/C][C]0.821972626533788[/C][/ROW]
[ROW][C]63[/C][C]0.173079966608047[/C][C]0.346159933216094[/C][C]0.826920033391953[/C][/ROW]
[ROW][C]64[/C][C]0.180742464054643[/C][C]0.361484928109285[/C][C]0.819257535945357[/C][/ROW]
[ROW][C]65[/C][C]0.158374320903549[/C][C]0.316748641807098[/C][C]0.84162567909645[/C][/ROW]
[ROW][C]66[/C][C]0.131608884782723[/C][C]0.263217769565445[/C][C]0.868391115217277[/C][/ROW]
[ROW][C]67[/C][C]0.25749508395497[/C][C]0.51499016790994[/C][C]0.74250491604503[/C][/ROW]
[ROW][C]68[/C][C]0.220366272132868[/C][C]0.440732544265735[/C][C]0.779633727867132[/C][/ROW]
[ROW][C]69[/C][C]0.324003607338952[/C][C]0.648007214677904[/C][C]0.675996392661048[/C][/ROW]
[ROW][C]70[/C][C]0.287102097468446[/C][C]0.574204194936893[/C][C]0.712897902531553[/C][/ROW]
[ROW][C]71[/C][C]0.416721130323742[/C][C]0.833442260647484[/C][C]0.583278869676258[/C][/ROW]
[ROW][C]72[/C][C]0.417493241208076[/C][C]0.834986482416151[/C][C]0.582506758791924[/C][/ROW]
[ROW][C]73[/C][C]0.422587830112712[/C][C]0.845175660225425[/C][C]0.577412169887288[/C][/ROW]
[ROW][C]74[/C][C]0.462546015931566[/C][C]0.925092031863131[/C][C]0.537453984068434[/C][/ROW]
[ROW][C]75[/C][C]0.434710012108121[/C][C]0.869420024216242[/C][C]0.565289987891879[/C][/ROW]
[ROW][C]76[/C][C]0.400495743253937[/C][C]0.800991486507875[/C][C]0.599504256746062[/C][/ROW]
[ROW][C]77[/C][C]0.589744123799422[/C][C]0.820511752401155[/C][C]0.410255876200578[/C][/ROW]
[ROW][C]78[/C][C]0.56056331083119[/C][C]0.87887337833762[/C][C]0.43943668916881[/C][/ROW]
[ROW][C]79[/C][C]0.523375440580944[/C][C]0.953249118838112[/C][C]0.476624559419056[/C][/ROW]
[ROW][C]80[/C][C]0.476648105579109[/C][C]0.953296211158218[/C][C]0.523351894420891[/C][/ROW]
[ROW][C]81[/C][C]0.482202776312422[/C][C]0.964405552624844[/C][C]0.517797223687578[/C][/ROW]
[ROW][C]82[/C][C]0.650588127850354[/C][C]0.698823744299292[/C][C]0.349411872149646[/C][/ROW]
[ROW][C]83[/C][C]0.612654415790754[/C][C]0.774691168418491[/C][C]0.387345584209246[/C][/ROW]
[ROW][C]84[/C][C]0.58584539656667[/C][C]0.828309206866661[/C][C]0.414154603433331[/C][/ROW]
[ROW][C]85[/C][C]0.562031833175167[/C][C]0.875936333649666[/C][C]0.437968166824833[/C][/ROW]
[ROW][C]86[/C][C]0.520227946074237[/C][C]0.959544107851525[/C][C]0.479772053925763[/C][/ROW]
[ROW][C]87[/C][C]0.476399862785502[/C][C]0.952799725571003[/C][C]0.523600137214498[/C][/ROW]
[ROW][C]88[/C][C]0.576531154139646[/C][C]0.846937691720708[/C][C]0.423468845860354[/C][/ROW]
[ROW][C]89[/C][C]0.532460773159542[/C][C]0.935078453680915[/C][C]0.467539226840458[/C][/ROW]
[ROW][C]90[/C][C]0.489927324434327[/C][C]0.979854648868654[/C][C]0.510072675565673[/C][/ROW]
[ROW][C]91[/C][C]0.460314583179347[/C][C]0.920629166358695[/C][C]0.539685416820653[/C][/ROW]
[ROW][C]92[/C][C]0.506440020809937[/C][C]0.987119958380126[/C][C]0.493559979190063[/C][/ROW]
[ROW][C]93[/C][C]0.47214226054357[/C][C]0.94428452108714[/C][C]0.52785773945643[/C][/ROW]
[ROW][C]94[/C][C]0.430624692626163[/C][C]0.861249385252326[/C][C]0.569375307373837[/C][/ROW]
[ROW][C]95[/C][C]0.411289237329293[/C][C]0.822578474658586[/C][C]0.588710762670707[/C][/ROW]
[ROW][C]96[/C][C]0.366917068429977[/C][C]0.733834136859954[/C][C]0.633082931570023[/C][/ROW]
[ROW][C]97[/C][C]0.353028476677661[/C][C]0.706056953355321[/C][C]0.646971523322339[/C][/ROW]
[ROW][C]98[/C][C]0.313425120794856[/C][C]0.626850241589713[/C][C]0.686574879205143[/C][/ROW]
[ROW][C]99[/C][C]0.284384031262596[/C][C]0.568768062525193[/C][C]0.715615968737404[/C][/ROW]
[ROW][C]100[/C][C]0.247898616591004[/C][C]0.495797233182008[/C][C]0.752101383408996[/C][/ROW]
[ROW][C]101[/C][C]0.211113164622765[/C][C]0.422226329245530[/C][C]0.788886835377235[/C][/ROW]
[ROW][C]102[/C][C]0.194480765273716[/C][C]0.388961530547431[/C][C]0.805519234726284[/C][/ROW]
[ROW][C]103[/C][C]0.162188383187441[/C][C]0.324376766374883[/C][C]0.837811616812559[/C][/ROW]
[ROW][C]104[/C][C]0.146150966267120[/C][C]0.292301932534240[/C][C]0.85384903373288[/C][/ROW]
[ROW][C]105[/C][C]0.128808362254356[/C][C]0.257616724508712[/C][C]0.871191637745644[/C][/ROW]
[ROW][C]106[/C][C]0.135825560033186[/C][C]0.271651120066372[/C][C]0.864174439966814[/C][/ROW]
[ROW][C]107[/C][C]0.13483961574929[/C][C]0.26967923149858[/C][C]0.86516038425071[/C][/ROW]
[ROW][C]108[/C][C]0.128899447703220[/C][C]0.257798895406441[/C][C]0.87110055229678[/C][/ROW]
[ROW][C]109[/C][C]0.128692344536806[/C][C]0.257384689073611[/C][C]0.871307655463194[/C][/ROW]
[ROW][C]110[/C][C]0.169007985407383[/C][C]0.338015970814765[/C][C]0.830992014592617[/C][/ROW]
[ROW][C]111[/C][C]0.143243648519062[/C][C]0.286487297038123[/C][C]0.856756351480938[/C][/ROW]
[ROW][C]112[/C][C]0.299349390824546[/C][C]0.598698781649092[/C][C]0.700650609175454[/C][/ROW]
[ROW][C]113[/C][C]0.382749590172232[/C][C]0.765499180344465[/C][C]0.617250409827768[/C][/ROW]
[ROW][C]114[/C][C]0.351471657433464[/C][C]0.702943314866929[/C][C]0.648528342566536[/C][/ROW]
[ROW][C]115[/C][C]0.340623668786528[/C][C]0.681247337573055[/C][C]0.659376331213472[/C][/ROW]
[ROW][C]116[/C][C]0.299977820421148[/C][C]0.599955640842295[/C][C]0.700022179578852[/C][/ROW]
[ROW][C]117[/C][C]0.300774982108667[/C][C]0.601549964217334[/C][C]0.699225017891333[/C][/ROW]
[ROW][C]118[/C][C]0.262710989610703[/C][C]0.525421979221407[/C][C]0.737289010389297[/C][/ROW]
[ROW][C]119[/C][C]0.238512983636945[/C][C]0.47702596727389[/C][C]0.761487016363055[/C][/ROW]
[ROW][C]120[/C][C]0.197982593002582[/C][C]0.395965186005165[/C][C]0.802017406997418[/C][/ROW]
[ROW][C]121[/C][C]0.188945846715892[/C][C]0.377891693431785[/C][C]0.811054153284108[/C][/ROW]
[ROW][C]122[/C][C]0.168371140029851[/C][C]0.336742280059702[/C][C]0.83162885997015[/C][/ROW]
[ROW][C]123[/C][C]0.174186884406768[/C][C]0.348373768813537[/C][C]0.825813115593232[/C][/ROW]
[ROW][C]124[/C][C]0.188200394088842[/C][C]0.376400788177685[/C][C]0.811799605911158[/C][/ROW]
[ROW][C]125[/C][C]0.734347253405469[/C][C]0.531305493189062[/C][C]0.265652746594531[/C][/ROW]
[ROW][C]126[/C][C]0.695193508259718[/C][C]0.609612983480563[/C][C]0.304806491740282[/C][/ROW]
[ROW][C]127[/C][C]0.639474939636111[/C][C]0.721050120727778[/C][C]0.360525060363889[/C][/ROW]
[ROW][C]128[/C][C]0.577516422747[/C][C]0.844967154506[/C][C]0.422483577253[/C][/ROW]
[ROW][C]129[/C][C]0.512348784530804[/C][C]0.975302430938393[/C][C]0.487651215469196[/C][/ROW]
[ROW][C]130[/C][C]0.449930998428578[/C][C]0.899861996857156[/C][C]0.550069001571422[/C][/ROW]
[ROW][C]131[/C][C]0.392713293945855[/C][C]0.785426587891711[/C][C]0.607286706054145[/C][/ROW]
[ROW][C]132[/C][C]0.33893084122145[/C][C]0.6778616824429[/C][C]0.66106915877855[/C][/ROW]
[ROW][C]133[/C][C]0.278561876609588[/C][C]0.557123753219177[/C][C]0.721438123390412[/C][/ROW]
[ROW][C]134[/C][C]0.243785079780772[/C][C]0.487570159561545[/C][C]0.756214920219228[/C][/ROW]
[ROW][C]135[/C][C]0.237848570092995[/C][C]0.47569714018599[/C][C]0.762151429907005[/C][/ROW]
[ROW][C]136[/C][C]0.202780464802316[/C][C]0.405560929604632[/C][C]0.797219535197684[/C][/ROW]
[ROW][C]137[/C][C]0.218949580760420[/C][C]0.437899161520839[/C][C]0.78105041923958[/C][/ROW]
[ROW][C]138[/C][C]0.236903289676996[/C][C]0.473806579353992[/C][C]0.763096710323004[/C][/ROW]
[ROW][C]139[/C][C]0.228532134137017[/C][C]0.457064268274034[/C][C]0.771467865862983[/C][/ROW]
[ROW][C]140[/C][C]0.174946895960384[/C][C]0.349893791920767[/C][C]0.825053104039617[/C][/ROW]
[ROW][C]141[/C][C]0.126808213193047[/C][C]0.253616426386094[/C][C]0.873191786806953[/C][/ROW]
[ROW][C]142[/C][C]0.0913336253539054[/C][C]0.182667250707811[/C][C]0.908666374646095[/C][/ROW]
[ROW][C]143[/C][C]0.120916630923812[/C][C]0.241833261847625[/C][C]0.879083369076188[/C][/ROW]
[ROW][C]144[/C][C]0.131973581217382[/C][C]0.263947162434764[/C][C]0.868026418782618[/C][/ROW]
[ROW][C]145[/C][C]0.110158866737737[/C][C]0.220317733475475[/C][C]0.889841133262263[/C][/ROW]
[ROW][C]146[/C][C]0.306315029344364[/C][C]0.612630058688728[/C][C]0.693684970655636[/C][/ROW]
[ROW][C]147[/C][C]0.202941845068656[/C][C]0.405883690137312[/C][C]0.797058154931344[/C][/ROW]
[ROW][C]148[/C][C]0.140102478359968[/C][C]0.280204956719935[/C][C]0.859897521640032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99302&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3204326073753530.6408652147507060.679567392624647
120.1935141710419890.3870283420839790.80648582895801
130.8379448510243020.3241102979513960.162055148975698
140.8205878536940020.3588242926119950.179412146305998
150.7853479177986520.4293041644026960.214652082201348
160.7842430211402150.4315139577195690.215756978859784
170.7081170771753940.5837658456492130.291882922824606
180.6853511225399590.6292977549200820.314648877460041
190.6056321113102440.7887357773795130.394367888689756
200.6810979027679410.6378041944641180.318902097232059
210.6870663282876680.6258673434246640.312933671712332
220.6723764544035280.6552470911929440.327623545596472
230.6481451338978870.7037097322042250.351854866102113
240.5980726613422010.8038546773155980.401927338657799
250.5281737323489020.9436525353021960.471826267651098
260.4577688607607470.9155377215214940.542231139239253
270.3879551745653970.7759103491307950.612044825434603
280.3616560167363440.7233120334726890.638343983263656
290.3031297967037670.6062595934075340.696870203296233
300.253564103276060.507128206552120.74643589672394
310.2154950502048490.4309901004096990.78450494979515
320.3247068143943730.6494136287887450.675293185605627
330.2950686174662050.590137234932410.704931382533795
340.2668203713444310.5336407426888620.733179628655569
350.2182767964080350.4365535928160690.781723203591965
360.1861745255211950.3723490510423910.813825474478805
370.1573916545140990.3147833090281990.8426083454859
380.3736011694098190.7472023388196380.626398830590181
390.4017666608684050.803533321736810.598233339131595
400.3787251640057740.7574503280115490.621274835994226
410.3590314646157450.718062929231490.640968535384255
420.3208534430737320.6417068861474640.679146556926268
430.2768069729459490.5536139458918990.72319302705405
440.2365515763983010.4731031527966010.7634484236017
450.1967290904049250.393458180809850.803270909595075
460.1700875783969440.3401751567938890.829912421603056
470.1697931743421430.3395863486842860.830206825657857
480.1670265412722590.3340530825445170.832973458727741
490.1663451013831630.3326902027663260.833654898616837
500.1846577189560650.369315437912130.815342281043935
510.2470504165386270.4941008330772540.752949583461373
520.231795051027020.463590102054040.76820494897298
530.2102030848938550.420406169787710.789796915106145
540.2360834299117460.4721668598234910.763916570088254
550.2531352138819970.5062704277639940.746864786118003
560.2161741244182290.4323482488364590.78382587558177
570.183178292879220.366356585758440.81682170712078
580.1583483446582170.3166966893164330.841651655341783
590.1548026394428180.3096052788856370.845197360557182
600.2482174600585790.4964349201171580.751782539941421
610.2114974772311500.4229949544622990.78850252276885
620.1780273734662120.3560547469324240.821972626533788
630.1730799666080470.3461599332160940.826920033391953
640.1807424640546430.3614849281092850.819257535945357
650.1583743209035490.3167486418070980.84162567909645
660.1316088847827230.2632177695654450.868391115217277
670.257495083954970.514990167909940.74250491604503
680.2203662721328680.4407325442657350.779633727867132
690.3240036073389520.6480072146779040.675996392661048
700.2871020974684460.5742041949368930.712897902531553
710.4167211303237420.8334422606474840.583278869676258
720.4174932412080760.8349864824161510.582506758791924
730.4225878301127120.8451756602254250.577412169887288
740.4625460159315660.9250920318631310.537453984068434
750.4347100121081210.8694200242162420.565289987891879
760.4004957432539370.8009914865078750.599504256746062
770.5897441237994220.8205117524011550.410255876200578
780.560563310831190.878873378337620.43943668916881
790.5233754405809440.9532491188381120.476624559419056
800.4766481055791090.9532962111582180.523351894420891
810.4822027763124220.9644055526248440.517797223687578
820.6505881278503540.6988237442992920.349411872149646
830.6126544157907540.7746911684184910.387345584209246
840.585845396566670.8283092068666610.414154603433331
850.5620318331751670.8759363336496660.437968166824833
860.5202279460742370.9595441078515250.479772053925763
870.4763998627855020.9527997255710030.523600137214498
880.5765311541396460.8469376917207080.423468845860354
890.5324607731595420.9350784536809150.467539226840458
900.4899273244343270.9798546488686540.510072675565673
910.4603145831793470.9206291663586950.539685416820653
920.5064400208099370.9871199583801260.493559979190063
930.472142260543570.944284521087140.52785773945643
940.4306246926261630.8612493852523260.569375307373837
950.4112892373292930.8225784746585860.588710762670707
960.3669170684299770.7338341368599540.633082931570023
970.3530284766776610.7060569533553210.646971523322339
980.3134251207948560.6268502415897130.686574879205143
990.2843840312625960.5687680625251930.715615968737404
1000.2478986165910040.4957972331820080.752101383408996
1010.2111131646227650.4222263292455300.788886835377235
1020.1944807652737160.3889615305474310.805519234726284
1030.1621883831874410.3243767663748830.837811616812559
1040.1461509662671200.2923019325342400.85384903373288
1050.1288083622543560.2576167245087120.871191637745644
1060.1358255600331860.2716511200663720.864174439966814
1070.134839615749290.269679231498580.86516038425071
1080.1288994477032200.2577988954064410.87110055229678
1090.1286923445368060.2573846890736110.871307655463194
1100.1690079854073830.3380159708147650.830992014592617
1110.1432436485190620.2864872970381230.856756351480938
1120.2993493908245460.5986987816490920.700650609175454
1130.3827495901722320.7654991803444650.617250409827768
1140.3514716574334640.7029433148669290.648528342566536
1150.3406236687865280.6812473375730550.659376331213472
1160.2999778204211480.5999556408422950.700022179578852
1170.3007749821086670.6015499642173340.699225017891333
1180.2627109896107030.5254219792214070.737289010389297
1190.2385129836369450.477025967273890.761487016363055
1200.1979825930025820.3959651860051650.802017406997418
1210.1889458467158920.3778916934317850.811054153284108
1220.1683711400298510.3367422800597020.83162885997015
1230.1741868844067680.3483737688135370.825813115593232
1240.1882003940888420.3764007881776850.811799605911158
1250.7343472534054690.5313054931890620.265652746594531
1260.6951935082597180.6096129834805630.304806491740282
1270.6394749396361110.7210501207277780.360525060363889
1280.5775164227470.8449671545060.422483577253
1290.5123487845308040.9753024309383930.487651215469196
1300.4499309984285780.8998619968571560.550069001571422
1310.3927132939458550.7854265878917110.607286706054145
1320.338930841221450.67786168244290.66106915877855
1330.2785618766095880.5571237532191770.721438123390412
1340.2437850797807720.4875701595615450.756214920219228
1350.2378485700929950.475697140185990.762151429907005
1360.2027804648023160.4055609296046320.797219535197684
1370.2189495807604200.4378991615208390.78105041923958
1380.2369032896769960.4738065793539920.763096710323004
1390.2285321341370170.4570642682740340.771467865862983
1400.1749468959603840.3498937919207670.825053104039617
1410.1268082131930470.2536164263860940.873191786806953
1420.09133362535390540.1826672507078110.908666374646095
1430.1209166309238120.2418332618476250.879083369076188
1440.1319735812173820.2639471624347640.868026418782618
1450.1101588667377370.2203177334754750.889841133262263
1460.3063150293443640.6126300586887280.693684970655636
1470.2029418450686560.4058836901373120.797058154931344
1480.1401024783599680.2802049567199350.859897521640032







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=99302&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=99302&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99302&T=6

As an alternative you can also use a QR Code:  

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

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



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')
}