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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 14:16:56 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t135214303466lsd2n60jiogn4.htm/, Retrieved Wed, 01 Feb 2023 13:13:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186238, Retrieved Wed, 01 Feb 2023 13:13:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 19:16:56] [5822c3aa9d6681e7e6dd0478dad183d5] [Current]
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Dataseries X:
12	41	38	13	12	53	32
11	39	32	16	11	86	51
14	30	35	19	15	66	42
12	31	33	15	6	67	41
21	34	37	14	13	76	46
12	35	29	13	10	78	47
22	39	31	19	12	53	37
11	34	36	15	14	80	49
10	36	35	14	12	74	45
13	37	38	15	6	76	47
10	38	31	16	10	79	49
8	36	34	16	12	54	33
15	38	35	16	12	67	42
14	39	38	16	11	54	33
10	33	37	17	15	87	53
14	32	33	15	12	58	36
14	36	32	15	10	75	45
11	38	38	20	12	88	54
10	39	38	18	11	64	41
13	32	32	16	12	57	36
7	32	33	16	11	66	41
14	31	31	16	12	68	44
12	39	38	19	13	54	33
14	37	39	16	11	56	37
11	39	32	17	9	86	52
9	41	32	17	13	80	47
11	36	35	16	10	76	43
15	33	37	15	14	69	44
14	33	33	16	12	78	45
13	34	33	14	10	67	44
9	31	28	15	12	80	49
15	27	32	12	8	54	33
10	37	31	14	10	71	43
11	34	37	16	12	84	54
13	34	30	14	12	74	42
8	32	33	7	7	71	44
20	29	31	10	6	63	37
12	36	33	14	12	71	43
10	29	31	16	10	76	46
10	35	33	16	10	69	42
9	37	32	16	10	74	45
14	34	33	14	12	75	44
8	38	32	20	15	54	33
14	35	33	14	10	52	31
11	38	28	14	10	69	42
13	37	35	11	12	68	40
9	38	39	14	13	65	43
11	33	34	15	11	75	46
15	36	38	16	11	74	42
11	38	32	14	12	75	45
10	32	38	16	14	72	44
14	32	30	14	10	67	40
18	32	33	12	12	63	37
14	34	38	16	13	62	46
11	32	32	9	5	63	36
12	37	32	14	6	76	47
13	39	34	16	12	74	45
9	29	34	16	12	67	42
10	37	36	15	11	73	43
15	35	34	16	10	70	43
20	30	28	12	7	53	32
12	38	34	16	12	77	45
12	34	35	16	14	77	45
14	31	35	14	11	52	31
13	34	31	16	12	54	33
11	35	37	17	13	80	49
17	36	35	18	14	66	42
12	30	27	18	11	73	41
13	39	40	12	12	63	38
14	35	37	16	12	69	42
13	38	36	10	8	67	44
15	31	38	14	11	54	33
13	34	39	18	14	81	48
10	38	41	18	14	69	40
11	34	27	16	12	84	50
19	39	30	17	9	80	49
13	37	37	16	13	70	43
17	34	31	16	11	69	44
13	28	31	13	12	77	47
9	37	27	16	12	54	33
11	33	36	16	12	79	46
10	37	38	20	12	30	0
9	35	37	16	12	71	45
12	37	33	15	12	73	43
12	32	34	15	11	72	44
13	33	31	16	10	77	47
13	38	39	14	9	75	45
12	33	34	16	12	69	42
15	29	32	16	12	54	33
22	33	33	15	12	70	43
13	31	36	12	9	73	46
15	36	32	17	15	54	33
13	35	41	16	12	77	46
15	32	28	15	12	82	48
10	29	30	13	12	80	47
11	39	36	16	10	80	47
16	37	35	16	13	69	43
11	35	31	16	9	78	46
11	37	34	16	12	81	48
10	32	36	14	10	76	46
10	38	36	16	14	76	45
16	37	35	16	11	73	45
12	36	37	20	15	85	52
11	32	28	15	11	66	42
16	33	39	16	11	79	47
19	40	32	13	12	68	41
11	38	35	17	12	76	47
16	41	39	16	12	71	43
15	36	35	16	11	54	33
24	43	42	12	7	46	30
14	30	34	16	12	82	49
15	31	33	16	14	74	44
11	32	41	17	11	88	55
15	32	33	13	11	38	11
12	37	34	12	10	76	47
10	37	32	18	13	86	53
14	33	40	14	13	54	33
13	34	40	14	8	70	44
9	33	35	13	11	69	42
15	38	36	16	12	90	55
15	33	37	13	11	54	33
14	31	27	16	13	76	46
11	38	39	13	12	89	54
8	37	38	16	14	76	47
11	33	31	15	13	73	45
11	31	33	16	15	79	47
8	39	32	15	10	90	55
10	44	39	17	11	74	44
11	33	36	15	9	81	53
13	35	33	12	11	72	44
11	32	33	16	10	71	42
20	28	32	10	11	66	40
10	40	37	16	8	77	46
15	27	30	12	11	65	40
12	37	38	14	12	74	46
14	32	29	15	12	82	53
23	28	22	13	9	54	33
14	34	35	15	11	63	42
16	30	35	11	10	54	35
11	35	34	12	8	64	40
12	31	35	8	9	69	41
10	32	34	16	8	54	33
14	30	34	15	9	84	51
12	30	35	17	15	86	53
12	31	23	16	11	77	46
11	40	31	10	8	89	55
12	32	27	18	13	76	47
13	36	36	13	12	60	38
11	32	31	16	12	75	46
19	35	32	13	9	73	46
12	38	39	10	7	85	53
17	42	37	15	13	79	47
9	34	38	16	9	71	41
12	35	39	16	6	72	44
19	35	34	14	8	69	43
18	33	31	10	8	78	51
15	36	32	17	15	54	33
14	32	37	13	6	69	43
11	33	36	15	9	81	53
9	34	32	16	11	84	51
18	32	35	12	8	84	50
16	34	36	13	8	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186238&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186238&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
depression[t] = + 25.7053808259285 -0.032400412503554connected[t] -0.0572463805845197separate[t] -0.240771499295298learning[t] -0.0219056272594492software[t] -0.205751622268116belonging[t] + 0.201504996803279`belonging_final\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
depression[t] =  +  25.7053808259285 -0.032400412503554connected[t] -0.0572463805845197separate[t] -0.240771499295298learning[t] -0.0219056272594492software[t] -0.205751622268116belonging[t] +  0.201504996803279`belonging_final\r\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]depression[t] =  +  25.7053808259285 -0.032400412503554connected[t] -0.0572463805845197separate[t] -0.240771499295298learning[t] -0.0219056272594492software[t] -0.205751622268116belonging[t] +  0.201504996803279`belonging_final\r\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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
depression[t] = + 25.7053808259285 -0.032400412503554connected[t] -0.0572463805845197separate[t] -0.240771499295298learning[t] -0.0219056272594492software[t] -0.205751622268116belonging[t] + 0.201504996803279`belonging_final\r\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.70538082592853.3650357.63900
connected-0.0324004125035540.076932-0.42120.6742230.337111
separate-0.05724638058451970.071532-0.80030.4247680.212384
learning-0.2407714992952980.12814-1.8790.0621250.031062
software-0.02190562725944920.131424-0.16670.867840.43392
belonging-0.2057516222681160.069607-2.95590.0036050.001803
`belonging_final\r\r`0.2015049968032790.1018911.97760.0497420.024871

\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) & 25.7053808259285 & 3.365035 & 7.639 & 0 & 0 \tabularnewline
connected & -0.032400412503554 & 0.076932 & -0.4212 & 0.674223 & 0.337111 \tabularnewline
separate & -0.0572463805845197 & 0.071532 & -0.8003 & 0.424768 & 0.212384 \tabularnewline
learning & -0.240771499295298 & 0.12814 & -1.879 & 0.062125 & 0.031062 \tabularnewline
software & -0.0219056272594492 & 0.131424 & -0.1667 & 0.86784 & 0.43392 \tabularnewline
belonging & -0.205751622268116 & 0.069607 & -2.9559 & 0.003605 & 0.001803 \tabularnewline
`belonging_final\r\r` & 0.201504996803279 & 0.101891 & 1.9776 & 0.049742 & 0.024871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&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]25.7053808259285[/C][C]3.365035[/C][C]7.639[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]connected[/C][C]-0.032400412503554[/C][C]0.076932[/C][C]-0.4212[/C][C]0.674223[/C][C]0.337111[/C][/ROW]
[ROW][C]separate[/C][C]-0.0572463805845197[/C][C]0.071532[/C][C]-0.8003[/C][C]0.424768[/C][C]0.212384[/C][/ROW]
[ROW][C]learning[/C][C]-0.240771499295298[/C][C]0.12814[/C][C]-1.879[/C][C]0.062125[/C][C]0.031062[/C][/ROW]
[ROW][C]software[/C][C]-0.0219056272594492[/C][C]0.131424[/C][C]-0.1667[/C][C]0.86784[/C][C]0.43392[/C][/ROW]
[ROW][C]belonging[/C][C]-0.205751622268116[/C][C]0.069607[/C][C]-2.9559[/C][C]0.003605[/C][C]0.001803[/C][/ROW]
[ROW][C]`belonging_final\r\r`[/C][C]0.201504996803279[/C][C]0.101891[/C][C]1.9776[/C][C]0.049742[/C][C]0.024871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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)25.70538082592853.3650357.63900
connected-0.0324004125035540.076932-0.42120.6742230.337111
separate-0.05724638058451970.071532-0.80030.4247680.212384
learning-0.2407714992952980.12814-1.8790.0621250.031062
software-0.02190562725944920.131424-0.16670.867840.43392
belonging-0.2057516222681160.069607-2.95590.0036050.001803
`belonging_final\r\r`0.2015049968032790.1018911.97760.0497420.024871







Multiple Linear Regression - Regression Statistics
Multiple R0.382456737564587
R-squared0.146273156108547
Adjusted R-squared0.113225665377265
F-TEST (value)4.42615015154806
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000368187229362937
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98153266129728
Sum Squared Residuals1377.87823660928

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.382456737564587 \tabularnewline
R-squared & 0.146273156108547 \tabularnewline
Adjusted R-squared & 0.113225665377265 \tabularnewline
F-TEST (value) & 4.42615015154806 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.000368187229362937 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.98153266129728 \tabularnewline
Sum Squared Residuals & 1377.87823660928 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.382456737564587[/C][/ROW]
[ROW][C]R-squared[/C][C]0.146273156108547[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.113225665377265[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.42615015154806[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.000368187229362937[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.98153266129728[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1377.87823660928[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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 R0.382456737564587
R-squared0.146273156108547
Adjusted R-squared0.113225665377265
F-TEST (value)4.42615015154806
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000368187229362937
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98153266129728
Sum Squared Residuals1377.87823660928







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.3520283506136-2.35202835061359
21111.0986899929158-0.0986899929157827
31412.71010503090331.28989496909667
41213.5451774030137-1.54517740301365
52112.46218313524748.53781686475259
61212.9842439007607-0.984243900760713
72214.38044982795697.61955017204312
81112.0382608906146-1.03826089061456
91012.7437789464018-2.74377894640175
101312.42180840547640.578191594523639
111012.2474937755336-2.24749377553356
12814.0164548121186-6.01645481211865
131513.0331814882711.96681851172898
141413.71217367952940.287826320470643
151010.8757249280199-0.875724928019855
161414.2255828433501-0.225582843350055
171412.51280622111081.48719377888921
18119.995632243345181.00436775665482
191012.7851544326838-2.78515443268383
201314.2478093469074-1.24780934690739
21713.3682289771857-6.36822897718567
221413.68622826947240.313771730527578
231212.9460479271246-0.946047927124565
241414.1142448666288-0.114244866628829
251111.1032347449427-0.103234744942663
26911.1777961604901-2.17779616049006
271111.4915339641873-0.491533964187264
281513.26915778346651.73084221653352
291411.65092345741842.34907654258161
301314.2056401461703-1.20564014617033
31912.6372444273203-3.63724442732028
321515.4732597920387-0.47325979203866
331013.198420183953-3.19842018395296
341111.9685727601976-0.968572760197574
351312.49029668392160.50970331607838
36815.1985518589504-7.1985518589504
372014.94531498752565.05468501247436
381213.0725165807686-1.07251658076858
391012.5518373644601-2.55183736446006
401012.8771834969334-2.87718349693339
41912.4453859315801-3.44538593158006
421412.51581591350651.4841840864935
43813.037343869321-5.03734386932104
441414.6399491092459-0.639949109245891
451113.5477571609359-2.54775716093592
461313.6606777813764-0.66067778137639
47913.8768415786036-4.8768415786036
481112.6751140669962-1.67511406699625
491511.50788744290723.49211255709279
501112.6449656408801-1.64496564088009
511012.3862854492859-2.38628544928587
521413.63616012571790.363839874282119
531814.12064422669863.87935577330136
541414.8039164678259-0.803916467825926
551114.8520394991819-3.85203949918192
561213.0060581882788-1.00605818827878
571312.2222810908850.777718909114987
58913.3820315813875-4.38203158138753
591012.2380079099394-2.23800790993939
601512.8156904908842.18430950911597
612015.63119632959044.36880367040964
621211.63742663658420.362573363415781
631211.6659706514950.334029348504983
641414.6331523708316-0.633152370831617
651314.2529947788793-1.25299477887932
661111.4889767261953-0.488976726195343
671712.77837968243684.22162031756325
681211.85470373123250.145296268767508
691313.6946216718854-0.694621671885408
701412.60438672007641.39561327992359
711314.9111966061026-1.91119660610264
721514.45291997814840.547080021851616
731310.73695063190372.26304936809628
741011.3498357135116-1.34983571351162
751111.7350165788297-0.735016578829656
761911.84772224931067.15227775068944
771312.5134336423450.486566357654981
781713.40518103695313.59481896304691
791313.2584943948658-0.258494394865768
80914.3847790637067-5.38477906370673
811111.4749376902-0.474937690199997
821011.0803569200224-1.08035692002241
83912.79739846595-3.79739846595002
841212.3878414244335-0.387841424433496
851212.9217593526976-0.921759352697591
861312.4179890889810.582010911018996
871312.30995785856680.690042141433205
881212.8409266868371-0.84092668683708
891514.35775046081260.642249539187434
902213.13469794125218.86530205874794
911313.8030511277753-0.803051127775338
921513.8244591922141.17554080778596
931311.53540820680651.46459179319348
941511.99183577347723.00816422652278
951012.6660854961424-2.66608549614239
961111.3200998442327-0.320099844232734
971612.83367802578223.16632197421783
981111.967837272162-0.967837272161952
991111.4513355504251-0.451335550425146
1001012.6499472226174-2.64994722261739
1011011.6848742431644-1.6848742431644
1021612.45749278483523.54250721516483
1031210.26620744035631.73379255964375
1041114.0967373762068-3.09673737620685
1051611.52660917250924.47339082749084
1061913.4551776838325.54482231616803
1071111.9481703725791-0.948170372579076
1081612.08549323615313.9145067638469
1091513.98111405879361.01888594120642
1102415.44579300113118.55420699886886
1111411.67389181248522.32610818751481
1121512.29341452017582.70658547982421
1131110.96402069856160.0359793014384302
1141513.80643899448041.19356100551955
1151213.2854859166625-1.28548591666254
1161011.04114655842-1.04114655841996
1171414.2298151374533-0.229815137453338
1181313.2314718697932-0.231471869793242
119913.5279004313979-4.5279004313979
1201510.86321275396254.13678724603754
1211514.68613703302110.313862966978907
1221412.65030518001271.34969481998732
1231111.4180347355596-0.418034735559631
124812.0057918881055-4.00579188810547
1251113.0130402019639-2.01304020196386
1261111.8472657719856-0.847265771985595
127811.3443806176112-3.34438061761118
1281011.3536762566055-1.35367625660552
1291112.7804578043604-1.78045780436036
1301313.6041189936573-0.60411899365734
1311112.5628814899078-1.56288148990782
1322014.79820100675295.20179899324709
1331011.6900141756646-1.69001417566463
1341514.6693028041030.330697195896972
1351212.7411443889479-0.741144388947915
1361412.94211437690911.05788562309091
1372315.75064605882567.24935394117444
1381414.2484667539124-0.248466753912449
1391615.80428965115740.195710348842603
1401114.452582485783-3.45258248578298
1411214.6388650105971-2.63886501059712
1421014.2336789711707-4.23367897117066
1431411.97188694262922.02811305737084
1441211.29317054896770.706829451032327
1451212.7173503346015-0.717350334601543
1461112.8226469589556-1.82264695895565
1471212.3378667657218-0.337866765721811
1481314.3972917992432-1.3972917992432
1491112.6165764946986-1.61657649469861
1501913.66166350080395.3383364991961
1511212.871378862012-0.871378862011954
1521711.54645846592265.45354153407738
153912.0322493924343-3.03224939243428
1541212.4070828492663-0.407082849266279
1551913.54679636626165.45320363373836
1561814.50669770421673.49330229578331
1571513.8244591922141.17554080778596
1581413.75684121583290.243158784167058
1591112.7804578043604-1.78045780436036
160911.6721952999698-2.67219529996978
1611812.39255486537965.60744513462041
1621614.30999050730131.69000949269871

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.3520283506136 & -2.35202835061359 \tabularnewline
2 & 11 & 11.0986899929158 & -0.0986899929157827 \tabularnewline
3 & 14 & 12.7101050309033 & 1.28989496909667 \tabularnewline
4 & 12 & 13.5451774030137 & -1.54517740301365 \tabularnewline
5 & 21 & 12.4621831352474 & 8.53781686475259 \tabularnewline
6 & 12 & 12.9842439007607 & -0.984243900760713 \tabularnewline
7 & 22 & 14.3804498279569 & 7.61955017204312 \tabularnewline
8 & 11 & 12.0382608906146 & -1.03826089061456 \tabularnewline
9 & 10 & 12.7437789464018 & -2.74377894640175 \tabularnewline
10 & 13 & 12.4218084054764 & 0.578191594523639 \tabularnewline
11 & 10 & 12.2474937755336 & -2.24749377553356 \tabularnewline
12 & 8 & 14.0164548121186 & -6.01645481211865 \tabularnewline
13 & 15 & 13.033181488271 & 1.96681851172898 \tabularnewline
14 & 14 & 13.7121736795294 & 0.287826320470643 \tabularnewline
15 & 10 & 10.8757249280199 & -0.875724928019855 \tabularnewline
16 & 14 & 14.2255828433501 & -0.225582843350055 \tabularnewline
17 & 14 & 12.5128062211108 & 1.48719377888921 \tabularnewline
18 & 11 & 9.99563224334518 & 1.00436775665482 \tabularnewline
19 & 10 & 12.7851544326838 & -2.78515443268383 \tabularnewline
20 & 13 & 14.2478093469074 & -1.24780934690739 \tabularnewline
21 & 7 & 13.3682289771857 & -6.36822897718567 \tabularnewline
22 & 14 & 13.6862282694724 & 0.313771730527578 \tabularnewline
23 & 12 & 12.9460479271246 & -0.946047927124565 \tabularnewline
24 & 14 & 14.1142448666288 & -0.114244866628829 \tabularnewline
25 & 11 & 11.1032347449427 & -0.103234744942663 \tabularnewline
26 & 9 & 11.1777961604901 & -2.17779616049006 \tabularnewline
27 & 11 & 11.4915339641873 & -0.491533964187264 \tabularnewline
28 & 15 & 13.2691577834665 & 1.73084221653352 \tabularnewline
29 & 14 & 11.6509234574184 & 2.34907654258161 \tabularnewline
30 & 13 & 14.2056401461703 & -1.20564014617033 \tabularnewline
31 & 9 & 12.6372444273203 & -3.63724442732028 \tabularnewline
32 & 15 & 15.4732597920387 & -0.47325979203866 \tabularnewline
33 & 10 & 13.198420183953 & -3.19842018395296 \tabularnewline
34 & 11 & 11.9685727601976 & -0.968572760197574 \tabularnewline
35 & 13 & 12.4902966839216 & 0.50970331607838 \tabularnewline
36 & 8 & 15.1985518589504 & -7.1985518589504 \tabularnewline
37 & 20 & 14.9453149875256 & 5.05468501247436 \tabularnewline
38 & 12 & 13.0725165807686 & -1.07251658076858 \tabularnewline
39 & 10 & 12.5518373644601 & -2.55183736446006 \tabularnewline
40 & 10 & 12.8771834969334 & -2.87718349693339 \tabularnewline
41 & 9 & 12.4453859315801 & -3.44538593158006 \tabularnewline
42 & 14 & 12.5158159135065 & 1.4841840864935 \tabularnewline
43 & 8 & 13.037343869321 & -5.03734386932104 \tabularnewline
44 & 14 & 14.6399491092459 & -0.639949109245891 \tabularnewline
45 & 11 & 13.5477571609359 & -2.54775716093592 \tabularnewline
46 & 13 & 13.6606777813764 & -0.66067778137639 \tabularnewline
47 & 9 & 13.8768415786036 & -4.8768415786036 \tabularnewline
48 & 11 & 12.6751140669962 & -1.67511406699625 \tabularnewline
49 & 15 & 11.5078874429072 & 3.49211255709279 \tabularnewline
50 & 11 & 12.6449656408801 & -1.64496564088009 \tabularnewline
51 & 10 & 12.3862854492859 & -2.38628544928587 \tabularnewline
52 & 14 & 13.6361601257179 & 0.363839874282119 \tabularnewline
53 & 18 & 14.1206442266986 & 3.87935577330136 \tabularnewline
54 & 14 & 14.8039164678259 & -0.803916467825926 \tabularnewline
55 & 11 & 14.8520394991819 & -3.85203949918192 \tabularnewline
56 & 12 & 13.0060581882788 & -1.00605818827878 \tabularnewline
57 & 13 & 12.222281090885 & 0.777718909114987 \tabularnewline
58 & 9 & 13.3820315813875 & -4.38203158138753 \tabularnewline
59 & 10 & 12.2380079099394 & -2.23800790993939 \tabularnewline
60 & 15 & 12.815690490884 & 2.18430950911597 \tabularnewline
61 & 20 & 15.6311963295904 & 4.36880367040964 \tabularnewline
62 & 12 & 11.6374266365842 & 0.362573363415781 \tabularnewline
63 & 12 & 11.665970651495 & 0.334029348504983 \tabularnewline
64 & 14 & 14.6331523708316 & -0.633152370831617 \tabularnewline
65 & 13 & 14.2529947788793 & -1.25299477887932 \tabularnewline
66 & 11 & 11.4889767261953 & -0.488976726195343 \tabularnewline
67 & 17 & 12.7783796824368 & 4.22162031756325 \tabularnewline
68 & 12 & 11.8547037312325 & 0.145296268767508 \tabularnewline
69 & 13 & 13.6946216718854 & -0.694621671885408 \tabularnewline
70 & 14 & 12.6043867200764 & 1.39561327992359 \tabularnewline
71 & 13 & 14.9111966061026 & -1.91119660610264 \tabularnewline
72 & 15 & 14.4529199781484 & 0.547080021851616 \tabularnewline
73 & 13 & 10.7369506319037 & 2.26304936809628 \tabularnewline
74 & 10 & 11.3498357135116 & -1.34983571351162 \tabularnewline
75 & 11 & 11.7350165788297 & -0.735016578829656 \tabularnewline
76 & 19 & 11.8477222493106 & 7.15227775068944 \tabularnewline
77 & 13 & 12.513433642345 & 0.486566357654981 \tabularnewline
78 & 17 & 13.4051810369531 & 3.59481896304691 \tabularnewline
79 & 13 & 13.2584943948658 & -0.258494394865768 \tabularnewline
80 & 9 & 14.3847790637067 & -5.38477906370673 \tabularnewline
81 & 11 & 11.4749376902 & -0.474937690199997 \tabularnewline
82 & 10 & 11.0803569200224 & -1.08035692002241 \tabularnewline
83 & 9 & 12.79739846595 & -3.79739846595002 \tabularnewline
84 & 12 & 12.3878414244335 & -0.387841424433496 \tabularnewline
85 & 12 & 12.9217593526976 & -0.921759352697591 \tabularnewline
86 & 13 & 12.417989088981 & 0.582010911018996 \tabularnewline
87 & 13 & 12.3099578585668 & 0.690042141433205 \tabularnewline
88 & 12 & 12.8409266868371 & -0.84092668683708 \tabularnewline
89 & 15 & 14.3577504608126 & 0.642249539187434 \tabularnewline
90 & 22 & 13.1346979412521 & 8.86530205874794 \tabularnewline
91 & 13 & 13.8030511277753 & -0.803051127775338 \tabularnewline
92 & 15 & 13.824459192214 & 1.17554080778596 \tabularnewline
93 & 13 & 11.5354082068065 & 1.46459179319348 \tabularnewline
94 & 15 & 11.9918357734772 & 3.00816422652278 \tabularnewline
95 & 10 & 12.6660854961424 & -2.66608549614239 \tabularnewline
96 & 11 & 11.3200998442327 & -0.320099844232734 \tabularnewline
97 & 16 & 12.8336780257822 & 3.16632197421783 \tabularnewline
98 & 11 & 11.967837272162 & -0.967837272161952 \tabularnewline
99 & 11 & 11.4513355504251 & -0.451335550425146 \tabularnewline
100 & 10 & 12.6499472226174 & -2.64994722261739 \tabularnewline
101 & 10 & 11.6848742431644 & -1.6848742431644 \tabularnewline
102 & 16 & 12.4574927848352 & 3.54250721516483 \tabularnewline
103 & 12 & 10.2662074403563 & 1.73379255964375 \tabularnewline
104 & 11 & 14.0967373762068 & -3.09673737620685 \tabularnewline
105 & 16 & 11.5266091725092 & 4.47339082749084 \tabularnewline
106 & 19 & 13.455177683832 & 5.54482231616803 \tabularnewline
107 & 11 & 11.9481703725791 & -0.948170372579076 \tabularnewline
108 & 16 & 12.0854932361531 & 3.9145067638469 \tabularnewline
109 & 15 & 13.9811140587936 & 1.01888594120642 \tabularnewline
110 & 24 & 15.4457930011311 & 8.55420699886886 \tabularnewline
111 & 14 & 11.6738918124852 & 2.32610818751481 \tabularnewline
112 & 15 & 12.2934145201758 & 2.70658547982421 \tabularnewline
113 & 11 & 10.9640206985616 & 0.0359793014384302 \tabularnewline
114 & 15 & 13.8064389944804 & 1.19356100551955 \tabularnewline
115 & 12 & 13.2854859166625 & -1.28548591666254 \tabularnewline
116 & 10 & 11.04114655842 & -1.04114655841996 \tabularnewline
117 & 14 & 14.2298151374533 & -0.229815137453338 \tabularnewline
118 & 13 & 13.2314718697932 & -0.231471869793242 \tabularnewline
119 & 9 & 13.5279004313979 & -4.5279004313979 \tabularnewline
120 & 15 & 10.8632127539625 & 4.13678724603754 \tabularnewline
121 & 15 & 14.6861370330211 & 0.313862966978907 \tabularnewline
122 & 14 & 12.6503051800127 & 1.34969481998732 \tabularnewline
123 & 11 & 11.4180347355596 & -0.418034735559631 \tabularnewline
124 & 8 & 12.0057918881055 & -4.00579188810547 \tabularnewline
125 & 11 & 13.0130402019639 & -2.01304020196386 \tabularnewline
126 & 11 & 11.8472657719856 & -0.847265771985595 \tabularnewline
127 & 8 & 11.3443806176112 & -3.34438061761118 \tabularnewline
128 & 10 & 11.3536762566055 & -1.35367625660552 \tabularnewline
129 & 11 & 12.7804578043604 & -1.78045780436036 \tabularnewline
130 & 13 & 13.6041189936573 & -0.60411899365734 \tabularnewline
131 & 11 & 12.5628814899078 & -1.56288148990782 \tabularnewline
132 & 20 & 14.7982010067529 & 5.20179899324709 \tabularnewline
133 & 10 & 11.6900141756646 & -1.69001417566463 \tabularnewline
134 & 15 & 14.669302804103 & 0.330697195896972 \tabularnewline
135 & 12 & 12.7411443889479 & -0.741144388947915 \tabularnewline
136 & 14 & 12.9421143769091 & 1.05788562309091 \tabularnewline
137 & 23 & 15.7506460588256 & 7.24935394117444 \tabularnewline
138 & 14 & 14.2484667539124 & -0.248466753912449 \tabularnewline
139 & 16 & 15.8042896511574 & 0.195710348842603 \tabularnewline
140 & 11 & 14.452582485783 & -3.45258248578298 \tabularnewline
141 & 12 & 14.6388650105971 & -2.63886501059712 \tabularnewline
142 & 10 & 14.2336789711707 & -4.23367897117066 \tabularnewline
143 & 14 & 11.9718869426292 & 2.02811305737084 \tabularnewline
144 & 12 & 11.2931705489677 & 0.706829451032327 \tabularnewline
145 & 12 & 12.7173503346015 & -0.717350334601543 \tabularnewline
146 & 11 & 12.8226469589556 & -1.82264695895565 \tabularnewline
147 & 12 & 12.3378667657218 & -0.337866765721811 \tabularnewline
148 & 13 & 14.3972917992432 & -1.3972917992432 \tabularnewline
149 & 11 & 12.6165764946986 & -1.61657649469861 \tabularnewline
150 & 19 & 13.6616635008039 & 5.3383364991961 \tabularnewline
151 & 12 & 12.871378862012 & -0.871378862011954 \tabularnewline
152 & 17 & 11.5464584659226 & 5.45354153407738 \tabularnewline
153 & 9 & 12.0322493924343 & -3.03224939243428 \tabularnewline
154 & 12 & 12.4070828492663 & -0.407082849266279 \tabularnewline
155 & 19 & 13.5467963662616 & 5.45320363373836 \tabularnewline
156 & 18 & 14.5066977042167 & 3.49330229578331 \tabularnewline
157 & 15 & 13.824459192214 & 1.17554080778596 \tabularnewline
158 & 14 & 13.7568412158329 & 0.243158784167058 \tabularnewline
159 & 11 & 12.7804578043604 & -1.78045780436036 \tabularnewline
160 & 9 & 11.6721952999698 & -2.67219529996978 \tabularnewline
161 & 18 & 12.3925548653796 & 5.60744513462041 \tabularnewline
162 & 16 & 14.3099905073013 & 1.69000949269871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&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]12[/C][C]14.3520283506136[/C][C]-2.35202835061359[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.0986899929158[/C][C]-0.0986899929157827[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.7101050309033[/C][C]1.28989496909667[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.5451774030137[/C][C]-1.54517740301365[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.4621831352474[/C][C]8.53781686475259[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.9842439007607[/C][C]-0.984243900760713[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]14.3804498279569[/C][C]7.61955017204312[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.0382608906146[/C][C]-1.03826089061456[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.7437789464018[/C][C]-2.74377894640175[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.4218084054764[/C][C]0.578191594523639[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.2474937755336[/C][C]-2.24749377553356[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]14.0164548121186[/C][C]-6.01645481211865[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.033181488271[/C][C]1.96681851172898[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]13.7121736795294[/C][C]0.287826320470643[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.8757249280199[/C][C]-0.875724928019855[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.2255828433501[/C][C]-0.225582843350055[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.5128062211108[/C][C]1.48719377888921[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.99563224334518[/C][C]1.00436775665482[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.7851544326838[/C][C]-2.78515443268383[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]14.2478093469074[/C][C]-1.24780934690739[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]13.3682289771857[/C][C]-6.36822897718567[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]13.6862282694724[/C][C]0.313771730527578[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.9460479271246[/C][C]-0.946047927124565[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.1142448666288[/C][C]-0.114244866628829[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.1032347449427[/C][C]-0.103234744942663[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]11.1777961604901[/C][C]-2.17779616049006[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.4915339641873[/C][C]-0.491533964187264[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.2691577834665[/C][C]1.73084221653352[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.6509234574184[/C][C]2.34907654258161[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]14.2056401461703[/C][C]-1.20564014617033[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.6372444273203[/C][C]-3.63724442732028[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]15.4732597920387[/C][C]-0.47325979203866[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]13.198420183953[/C][C]-3.19842018395296[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.9685727601976[/C][C]-0.968572760197574[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.4902966839216[/C][C]0.50970331607838[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]15.1985518589504[/C][C]-7.1985518589504[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]14.9453149875256[/C][C]5.05468501247436[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]13.0725165807686[/C][C]-1.07251658076858[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.5518373644601[/C][C]-2.55183736446006[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8771834969334[/C][C]-2.87718349693339[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.4453859315801[/C][C]-3.44538593158006[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.5158159135065[/C][C]1.4841840864935[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]13.037343869321[/C][C]-5.03734386932104[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.6399491092459[/C][C]-0.639949109245891[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]13.5477571609359[/C][C]-2.54775716093592[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]13.6606777813764[/C][C]-0.66067778137639[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]13.8768415786036[/C][C]-4.8768415786036[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.6751140669962[/C][C]-1.67511406699625[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]11.5078874429072[/C][C]3.49211255709279[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.6449656408801[/C][C]-1.64496564088009[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.3862854492859[/C][C]-2.38628544928587[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.6361601257179[/C][C]0.363839874282119[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.1206442266986[/C][C]3.87935577330136[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.8039164678259[/C][C]-0.803916467825926[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]14.8520394991819[/C][C]-3.85203949918192[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.0060581882788[/C][C]-1.00605818827878[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.222281090885[/C][C]0.777718909114987[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.3820315813875[/C][C]-4.38203158138753[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]12.2380079099394[/C][C]-2.23800790993939[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.815690490884[/C][C]2.18430950911597[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]15.6311963295904[/C][C]4.36880367040964[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.6374266365842[/C][C]0.362573363415781[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]11.665970651495[/C][C]0.334029348504983[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6331523708316[/C][C]-0.633152370831617[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]14.2529947788793[/C][C]-1.25299477887932[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.4889767261953[/C][C]-0.488976726195343[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.7783796824368[/C][C]4.22162031756325[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]11.8547037312325[/C][C]0.145296268767508[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.6946216718854[/C][C]-0.694621671885408[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.6043867200764[/C][C]1.39561327992359[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.9111966061026[/C][C]-1.91119660610264[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]14.4529199781484[/C][C]0.547080021851616[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]10.7369506319037[/C][C]2.26304936809628[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.3498357135116[/C][C]-1.34983571351162[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]11.7350165788297[/C][C]-0.735016578829656[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]11.8477222493106[/C][C]7.15227775068944[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.513433642345[/C][C]0.486566357654981[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.4051810369531[/C][C]3.59481896304691[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.2584943948658[/C][C]-0.258494394865768[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.3847790637067[/C][C]-5.38477906370673[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.4749376902[/C][C]-0.474937690199997[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]11.0803569200224[/C][C]-1.08035692002241[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.79739846595[/C][C]-3.79739846595002[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.3878414244335[/C][C]-0.387841424433496[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.9217593526976[/C][C]-0.921759352697591[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.417989088981[/C][C]0.582010911018996[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.3099578585668[/C][C]0.690042141433205[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8409266868371[/C][C]-0.84092668683708[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.3577504608126[/C][C]0.642249539187434[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.1346979412521[/C][C]8.86530205874794[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]13.8030511277753[/C][C]-0.803051127775338[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.824459192214[/C][C]1.17554080778596[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.5354082068065[/C][C]1.46459179319348[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]11.9918357734772[/C][C]3.00816422652278[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]12.6660854961424[/C][C]-2.66608549614239[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.3200998442327[/C][C]-0.320099844232734[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.8336780257822[/C][C]3.16632197421783[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]11.967837272162[/C][C]-0.967837272161952[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]11.4513355504251[/C][C]-0.451335550425146[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.6499472226174[/C][C]-2.64994722261739[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.6848742431644[/C][C]-1.6848742431644[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]12.4574927848352[/C][C]3.54250721516483[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.2662074403563[/C][C]1.73379255964375[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0967373762068[/C][C]-3.09673737620685[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.5266091725092[/C][C]4.47339082749084[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]13.455177683832[/C][C]5.54482231616803[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.9481703725791[/C][C]-0.948170372579076[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.0854932361531[/C][C]3.9145067638469[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.9811140587936[/C][C]1.01888594120642[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]15.4457930011311[/C][C]8.55420699886886[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.6738918124852[/C][C]2.32610818751481[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.2934145201758[/C][C]2.70658547982421[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]10.9640206985616[/C][C]0.0359793014384302[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.8064389944804[/C][C]1.19356100551955[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.2854859166625[/C][C]-1.28548591666254[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]11.04114655842[/C][C]-1.04114655841996[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.2298151374533[/C][C]-0.229815137453338[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.2314718697932[/C][C]-0.231471869793242[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.5279004313979[/C][C]-4.5279004313979[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]10.8632127539625[/C][C]4.13678724603754[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.6861370330211[/C][C]0.313862966978907[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.6503051800127[/C][C]1.34969481998732[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.4180347355596[/C][C]-0.418034735559631[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.0057918881055[/C][C]-4.00579188810547[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.0130402019639[/C][C]-2.01304020196386[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]11.8472657719856[/C][C]-0.847265771985595[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]11.3443806176112[/C][C]-3.34438061761118[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]11.3536762566055[/C][C]-1.35367625660552[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]12.7804578043604[/C][C]-1.78045780436036[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.6041189936573[/C][C]-0.60411899365734[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.5628814899078[/C][C]-1.56288148990782[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]14.7982010067529[/C][C]5.20179899324709[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.6900141756646[/C][C]-1.69001417566463[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.669302804103[/C][C]0.330697195896972[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.7411443889479[/C][C]-0.741144388947915[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.9421143769091[/C][C]1.05788562309091[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.7506460588256[/C][C]7.24935394117444[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]14.2484667539124[/C][C]-0.248466753912449[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.8042896511574[/C][C]0.195710348842603[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]14.452582485783[/C][C]-3.45258248578298[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.6388650105971[/C][C]-2.63886501059712[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]14.2336789711707[/C][C]-4.23367897117066[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.9718869426292[/C][C]2.02811305737084[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.2931705489677[/C][C]0.706829451032327[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.7173503346015[/C][C]-0.717350334601543[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.8226469589556[/C][C]-1.82264695895565[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.3378667657218[/C][C]-0.337866765721811[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.3972917992432[/C][C]-1.3972917992432[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]12.6165764946986[/C][C]-1.61657649469861[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.6616635008039[/C][C]5.3383364991961[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.871378862012[/C][C]-0.871378862011954[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]11.5464584659226[/C][C]5.45354153407738[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.0322493924343[/C][C]-3.03224939243428[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]12.4070828492663[/C][C]-0.407082849266279[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.5467963662616[/C][C]5.45320363373836[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.5066977042167[/C][C]3.49330229578331[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.824459192214[/C][C]1.17554080778596[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.7568412158329[/C][C]0.243158784167058[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.7804578043604[/C][C]-1.78045780436036[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]11.6721952999698[/C][C]-2.67219529996978[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]12.3925548653796[/C][C]5.60744513462041[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.3099905073013[/C][C]1.69000949269871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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
11214.3520283506136-2.35202835061359
21111.0986899929158-0.0986899929157827
31412.71010503090331.28989496909667
41213.5451774030137-1.54517740301365
52112.46218313524748.53781686475259
61212.9842439007607-0.984243900760713
72214.38044982795697.61955017204312
81112.0382608906146-1.03826089061456
91012.7437789464018-2.74377894640175
101312.42180840547640.578191594523639
111012.2474937755336-2.24749377553356
12814.0164548121186-6.01645481211865
131513.0331814882711.96681851172898
141413.71217367952940.287826320470643
151010.8757249280199-0.875724928019855
161414.2255828433501-0.225582843350055
171412.51280622111081.48719377888921
18119.995632243345181.00436775665482
191012.7851544326838-2.78515443268383
201314.2478093469074-1.24780934690739
21713.3682289771857-6.36822897718567
221413.68622826947240.313771730527578
231212.9460479271246-0.946047927124565
241414.1142448666288-0.114244866628829
251111.1032347449427-0.103234744942663
26911.1777961604901-2.17779616049006
271111.4915339641873-0.491533964187264
281513.26915778346651.73084221653352
291411.65092345741842.34907654258161
301314.2056401461703-1.20564014617033
31912.6372444273203-3.63724442732028
321515.4732597920387-0.47325979203866
331013.198420183953-3.19842018395296
341111.9685727601976-0.968572760197574
351312.49029668392160.50970331607838
36815.1985518589504-7.1985518589504
372014.94531498752565.05468501247436
381213.0725165807686-1.07251658076858
391012.5518373644601-2.55183736446006
401012.8771834969334-2.87718349693339
41912.4453859315801-3.44538593158006
421412.51581591350651.4841840864935
43813.037343869321-5.03734386932104
441414.6399491092459-0.639949109245891
451113.5477571609359-2.54775716093592
461313.6606777813764-0.66067778137639
47913.8768415786036-4.8768415786036
481112.6751140669962-1.67511406699625
491511.50788744290723.49211255709279
501112.6449656408801-1.64496564088009
511012.3862854492859-2.38628544928587
521413.63616012571790.363839874282119
531814.12064422669863.87935577330136
541414.8039164678259-0.803916467825926
551114.8520394991819-3.85203949918192
561213.0060581882788-1.00605818827878
571312.2222810908850.777718909114987
58913.3820315813875-4.38203158138753
591012.2380079099394-2.23800790993939
601512.8156904908842.18430950911597
612015.63119632959044.36880367040964
621211.63742663658420.362573363415781
631211.6659706514950.334029348504983
641414.6331523708316-0.633152370831617
651314.2529947788793-1.25299477887932
661111.4889767261953-0.488976726195343
671712.77837968243684.22162031756325
681211.85470373123250.145296268767508
691313.6946216718854-0.694621671885408
701412.60438672007641.39561327992359
711314.9111966061026-1.91119660610264
721514.45291997814840.547080021851616
731310.73695063190372.26304936809628
741011.3498357135116-1.34983571351162
751111.7350165788297-0.735016578829656
761911.84772224931067.15227775068944
771312.5134336423450.486566357654981
781713.40518103695313.59481896304691
791313.2584943948658-0.258494394865768
80914.3847790637067-5.38477906370673
811111.4749376902-0.474937690199997
821011.0803569200224-1.08035692002241
83912.79739846595-3.79739846595002
841212.3878414244335-0.387841424433496
851212.9217593526976-0.921759352697591
861312.4179890889810.582010911018996
871312.30995785856680.690042141433205
881212.8409266868371-0.84092668683708
891514.35775046081260.642249539187434
902213.13469794125218.86530205874794
911313.8030511277753-0.803051127775338
921513.8244591922141.17554080778596
931311.53540820680651.46459179319348
941511.99183577347723.00816422652278
951012.6660854961424-2.66608549614239
961111.3200998442327-0.320099844232734
971612.83367802578223.16632197421783
981111.967837272162-0.967837272161952
991111.4513355504251-0.451335550425146
1001012.6499472226174-2.64994722261739
1011011.6848742431644-1.6848742431644
1021612.45749278483523.54250721516483
1031210.26620744035631.73379255964375
1041114.0967373762068-3.09673737620685
1051611.52660917250924.47339082749084
1061913.4551776838325.54482231616803
1071111.9481703725791-0.948170372579076
1081612.08549323615313.9145067638469
1091513.98111405879361.01888594120642
1102415.44579300113118.55420699886886
1111411.67389181248522.32610818751481
1121512.29341452017582.70658547982421
1131110.96402069856160.0359793014384302
1141513.80643899448041.19356100551955
1151213.2854859166625-1.28548591666254
1161011.04114655842-1.04114655841996
1171414.2298151374533-0.229815137453338
1181313.2314718697932-0.231471869793242
119913.5279004313979-4.5279004313979
1201510.86321275396254.13678724603754
1211514.68613703302110.313862966978907
1221412.65030518001271.34969481998732
1231111.4180347355596-0.418034735559631
124812.0057918881055-4.00579188810547
1251113.0130402019639-2.01304020196386
1261111.8472657719856-0.847265771985595
127811.3443806176112-3.34438061761118
1281011.3536762566055-1.35367625660552
1291112.7804578043604-1.78045780436036
1301313.6041189936573-0.60411899365734
1311112.5628814899078-1.56288148990782
1322014.79820100675295.20179899324709
1331011.6900141756646-1.69001417566463
1341514.6693028041030.330697195896972
1351212.7411443889479-0.741144388947915
1361412.94211437690911.05788562309091
1372315.75064605882567.24935394117444
1381414.2484667539124-0.248466753912449
1391615.80428965115740.195710348842603
1401114.452582485783-3.45258248578298
1411214.6388650105971-2.63886501059712
1421014.2336789711707-4.23367897117066
1431411.97188694262922.02811305737084
1441211.29317054896770.706829451032327
1451212.7173503346015-0.717350334601543
1461112.8226469589556-1.82264695895565
1471212.3378667657218-0.337866765721811
1481314.3972917992432-1.3972917992432
1491112.6165764946986-1.61657649469861
1501913.66166350080395.3383364991961
1511212.871378862012-0.871378862011954
1521711.54645846592265.45354153407738
153912.0322493924343-3.03224939243428
1541212.4070828492663-0.407082849266279
1551913.54679636626165.45320363373836
1561814.50669770421673.49330229578331
1571513.8244591922141.17554080778596
1581413.75684121583290.243158784167058
1591112.7804578043604-1.78045780436036
160911.6721952999698-2.67219529996978
1611812.39255486537965.60744513462041
1621614.30999050730131.69000949269871







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9833152453106040.03336950937879180.0166847546893959
110.9801163586231060.03976728275378870.0198836413768943
120.9772044622584990.04559107548300240.0227955377415012
130.9584908696269990.08301826074600120.0415091303730006
140.9442978792363460.1114042415273080.0557021207636539
150.9313269057057230.1373461885885540.0686730942942772
160.8955066635497370.2089866729005260.104493336450263
170.8978589803673170.2042820392653660.102141019632683
180.8542100305062370.2915799389875260.145789969493763
190.8883015598552820.2233968802894350.111698440144718
200.8512849905423750.297430018915250.148715009457625
210.9275105460242480.1449789079515040.072489453975752
220.9051734843295970.1896530313408070.0948265156704033
230.8774919231546190.2450161536907630.122508076845381
240.8510093838225740.2979812323548520.148990616177426
250.8065328666828350.386934266634330.193467133317165
260.7627028150864580.4745943698270840.237297184913542
270.7653698597631590.4692602804736820.234630140236841
280.7153579419487360.5692841161025280.284642058051264
290.7536358943340960.4927282113318080.246364105665904
300.7197412347930620.5605175304138760.280258765206938
310.7241732745927660.5516534508144680.275826725407234
320.6885725505952870.6228548988094250.311427449404713
330.6639172981330120.6721654037339760.336082701866988
340.6367076017546660.7265847964906680.363292398245334
350.6053813703497270.7892372593005460.394618629650273
360.6988694283211560.6022611433576880.301130571678844
370.8656493605019590.2687012789960820.134350639498041
380.8343074378335460.3313851243329080.165692562166454
390.8227382508832640.3545234982334720.177261749116736
400.8110709519562360.3778580960875280.188929048043764
410.8076463458636190.3847073082727630.192353654136381
420.7866805351336610.4266389297326790.213319464866339
430.8381211721285540.3237576557428920.161878827871446
440.8062708726997030.3874582546005940.193729127300297
450.7832697869520810.4334604260958370.216730213047919
460.7451781322261940.5096437355476120.254821867773806
470.7941830735381420.4116338529237170.205816926461858
480.7642515781969610.4714968436060780.235748421803039
490.7689870825811550.462025834837690.231012917418845
500.737006494029650.5259870119407010.26299350597035
510.7248309185613370.5503381628773260.275169081438663
520.6870508763004370.6258982473991260.312949123699563
530.7309151638668850.538169672266230.269084836133115
540.6950748937835520.6098502124328960.304925106216448
550.7058718727480990.5882562545038020.294128127251901
560.6696742985870830.6606514028258340.330325701412917
570.6336471463750690.7327057072498620.366352853624931
580.6811647124532870.6376705750934260.318835287546713
590.6601278241858080.6797443516283850.339872175814192
600.6456411634039020.7087176731921950.354358836596098
610.7125272321646220.5749455356707560.287472767835378
620.6724395791309780.6551208417380450.327560420869022
630.6286882905914850.742623418817030.371311709408515
640.58554616377220.82890767245560.4144538362278
650.549433088903650.9011338221927010.45056691109635
660.5028906345207710.9942187309584580.497109365479229
670.5528695920825390.8942608158349220.447130407917461
680.5059601130438870.9880797739122270.494039886956113
690.4630693188253810.9261386376507620.536930681174619
700.4266915361233930.8533830722467860.573308463876607
710.4022974764063320.8045949528126640.597702523593668
720.3582416678353720.7164833356707440.641758332164628
730.3381505252890750.6763010505781510.661849474710925
740.3092084155858910.6184168311717820.690791584414109
750.2716652110117510.5433304220235030.728334788988249
760.4861604312522060.9723208625044130.513839568747794
770.4419373328189360.8838746656378720.558062667181064
780.4643529507990860.9287059015981720.535647049200914
790.4193659280561430.8387318561122870.580634071943857
800.5374586735594860.9250826528810280.462541326440514
810.4925072968321660.9850145936643320.507492703167834
820.4552158393331680.9104316786663360.544784160666832
830.4882175219044890.9764350438089780.511782478095511
840.4458542569120430.8917085138240860.554145743087957
850.405072782360740.8101455647214790.59492721763926
860.3616428701753360.7232857403506730.638357129824664
870.3213541665903390.6427083331806770.678645833409661
880.2851961956661280.5703923913322550.714803804333872
890.2490844988192030.4981689976384060.750915501180797
900.5862938261941650.8274123476116690.413706173805834
910.5436126041466130.9127747917067750.456387395853387
920.5047248533382320.9905502933235350.495275146661768
930.4701956148876830.9403912297753650.529804385112317
940.4692068101678490.9384136203356970.530793189832151
950.4582052722455040.9164105444910080.541794727754496
960.4121542995612830.8243085991225660.587845700438717
970.4113161680680710.8226323361361430.588683831931929
980.3713242305857570.7426484611715150.628675769414243
990.3284726008283080.6569452016566150.671527399171692
1000.318567674261560.637135348523120.68143232573844
1010.2932973599016370.5865947198032730.706702640098363
1020.3038754753476110.6077509506952220.696124524652389
1030.2818374627845480.5636749255690970.718162537215452
1040.2961038002227990.5922076004455980.703896199777201
1050.368150753272450.73630150654490.63184924672755
1060.4556381551471830.9112763102943650.544361844852817
1070.4117623847484520.8235247694969040.588237615251548
1080.4479325846363240.8958651692726470.552067415363676
1090.4027395309831620.8054790619663240.597260469016838
1100.7560805345952630.4878389308094750.243919465404737
1110.7370666333724720.5258667332550550.262933366627528
1120.7312079247622320.5375841504755370.268792075237768
1130.6974065270082870.6051869459834260.302593472991713
1140.6567902929063080.6864194141873830.343209707093692
1150.6213996212565560.7572007574868890.378600378743444
1160.5727056337317550.854588732536490.427294366268245
1170.5239052617469040.9521894765061930.476094738253096
1180.4740608987610660.9481217975221310.525939101238934
1190.5455175181092080.9089649637815830.454482481890792
1200.6359188759517410.7281622480965180.364081124048259
1210.5832947535993780.8334104928012450.416705246400622
1220.5351289059737730.9297421880524550.464871094026227
1230.4811027303836150.9622054607672290.518897269616385
1240.4829316020605470.9658632041210930.517068397939453
1250.4604060860674470.9208121721348950.539593913932553
1260.4080028084495250.816005616899050.591997191550475
1270.4256805203868930.8513610407737870.574319479613107
1280.372992659894530.7459853197890610.62700734010547
1290.3320121299201330.6640242598402660.667987870079867
1300.2914238933680040.5828477867360070.708576106631996
1310.2518939741817050.503787948363410.748106025818295
1320.2976941783660930.5953883567321860.702305821633907
1330.2556785150560070.5113570301120140.744321484943993
1340.2077992728618440.4155985457236880.792200727138156
1350.164897148861340.3297942977226790.83510285113866
1360.1286618416457790.2573236832915580.871338158354221
1370.3019806942627110.6039613885254210.698019305737289
1380.2453552623801730.4907105247603460.754644737619827
1390.1968270182265790.3936540364531580.803172981773421
1400.2031606693701470.4063213387402940.796839330629853
1410.222684828870950.4453696577419010.77731517112905
1420.2618144222774670.5236288445549350.738185577722533
1430.220100935746890.4402018714937810.77989906425311
1440.1759080961916930.3518161923833850.824091903808307
1450.136704957227240.2734099144544790.86329504277276
1460.4733828507887880.9467657015775770.526617149211212
1470.3896317223850590.7792634447701170.610368277614942
1480.3417819238454030.6835638476908060.658218076154597
1490.2641365035465170.5282730070930350.735863496453482
1500.1938261703633140.3876523407266280.806173829636686
1510.4708681410208840.9417362820417680.529131858979116
1520.35130262927810.7026052585561990.6486973707219

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.983315245310604 & 0.0333695093787918 & 0.0166847546893959 \tabularnewline
11 & 0.980116358623106 & 0.0397672827537887 & 0.0198836413768943 \tabularnewline
12 & 0.977204462258499 & 0.0455910754830024 & 0.0227955377415012 \tabularnewline
13 & 0.958490869626999 & 0.0830182607460012 & 0.0415091303730006 \tabularnewline
14 & 0.944297879236346 & 0.111404241527308 & 0.0557021207636539 \tabularnewline
15 & 0.931326905705723 & 0.137346188588554 & 0.0686730942942772 \tabularnewline
16 & 0.895506663549737 & 0.208986672900526 & 0.104493336450263 \tabularnewline
17 & 0.897858980367317 & 0.204282039265366 & 0.102141019632683 \tabularnewline
18 & 0.854210030506237 & 0.291579938987526 & 0.145789969493763 \tabularnewline
19 & 0.888301559855282 & 0.223396880289435 & 0.111698440144718 \tabularnewline
20 & 0.851284990542375 & 0.29743001891525 & 0.148715009457625 \tabularnewline
21 & 0.927510546024248 & 0.144978907951504 & 0.072489453975752 \tabularnewline
22 & 0.905173484329597 & 0.189653031340807 & 0.0948265156704033 \tabularnewline
23 & 0.877491923154619 & 0.245016153690763 & 0.122508076845381 \tabularnewline
24 & 0.851009383822574 & 0.297981232354852 & 0.148990616177426 \tabularnewline
25 & 0.806532866682835 & 0.38693426663433 & 0.193467133317165 \tabularnewline
26 & 0.762702815086458 & 0.474594369827084 & 0.237297184913542 \tabularnewline
27 & 0.765369859763159 & 0.469260280473682 & 0.234630140236841 \tabularnewline
28 & 0.715357941948736 & 0.569284116102528 & 0.284642058051264 \tabularnewline
29 & 0.753635894334096 & 0.492728211331808 & 0.246364105665904 \tabularnewline
30 & 0.719741234793062 & 0.560517530413876 & 0.280258765206938 \tabularnewline
31 & 0.724173274592766 & 0.551653450814468 & 0.275826725407234 \tabularnewline
32 & 0.688572550595287 & 0.622854898809425 & 0.311427449404713 \tabularnewline
33 & 0.663917298133012 & 0.672165403733976 & 0.336082701866988 \tabularnewline
34 & 0.636707601754666 & 0.726584796490668 & 0.363292398245334 \tabularnewline
35 & 0.605381370349727 & 0.789237259300546 & 0.394618629650273 \tabularnewline
36 & 0.698869428321156 & 0.602261143357688 & 0.301130571678844 \tabularnewline
37 & 0.865649360501959 & 0.268701278996082 & 0.134350639498041 \tabularnewline
38 & 0.834307437833546 & 0.331385124332908 & 0.165692562166454 \tabularnewline
39 & 0.822738250883264 & 0.354523498233472 & 0.177261749116736 \tabularnewline
40 & 0.811070951956236 & 0.377858096087528 & 0.188929048043764 \tabularnewline
41 & 0.807646345863619 & 0.384707308272763 & 0.192353654136381 \tabularnewline
42 & 0.786680535133661 & 0.426638929732679 & 0.213319464866339 \tabularnewline
43 & 0.838121172128554 & 0.323757655742892 & 0.161878827871446 \tabularnewline
44 & 0.806270872699703 & 0.387458254600594 & 0.193729127300297 \tabularnewline
45 & 0.783269786952081 & 0.433460426095837 & 0.216730213047919 \tabularnewline
46 & 0.745178132226194 & 0.509643735547612 & 0.254821867773806 \tabularnewline
47 & 0.794183073538142 & 0.411633852923717 & 0.205816926461858 \tabularnewline
48 & 0.764251578196961 & 0.471496843606078 & 0.235748421803039 \tabularnewline
49 & 0.768987082581155 & 0.46202583483769 & 0.231012917418845 \tabularnewline
50 & 0.73700649402965 & 0.525987011940701 & 0.26299350597035 \tabularnewline
51 & 0.724830918561337 & 0.550338162877326 & 0.275169081438663 \tabularnewline
52 & 0.687050876300437 & 0.625898247399126 & 0.312949123699563 \tabularnewline
53 & 0.730915163866885 & 0.53816967226623 & 0.269084836133115 \tabularnewline
54 & 0.695074893783552 & 0.609850212432896 & 0.304925106216448 \tabularnewline
55 & 0.705871872748099 & 0.588256254503802 & 0.294128127251901 \tabularnewline
56 & 0.669674298587083 & 0.660651402825834 & 0.330325701412917 \tabularnewline
57 & 0.633647146375069 & 0.732705707249862 & 0.366352853624931 \tabularnewline
58 & 0.681164712453287 & 0.637670575093426 & 0.318835287546713 \tabularnewline
59 & 0.660127824185808 & 0.679744351628385 & 0.339872175814192 \tabularnewline
60 & 0.645641163403902 & 0.708717673192195 & 0.354358836596098 \tabularnewline
61 & 0.712527232164622 & 0.574945535670756 & 0.287472767835378 \tabularnewline
62 & 0.672439579130978 & 0.655120841738045 & 0.327560420869022 \tabularnewline
63 & 0.628688290591485 & 0.74262341881703 & 0.371311709408515 \tabularnewline
64 & 0.5855461637722 & 0.8289076724556 & 0.4144538362278 \tabularnewline
65 & 0.54943308890365 & 0.901133822192701 & 0.45056691109635 \tabularnewline
66 & 0.502890634520771 & 0.994218730958458 & 0.497109365479229 \tabularnewline
67 & 0.552869592082539 & 0.894260815834922 & 0.447130407917461 \tabularnewline
68 & 0.505960113043887 & 0.988079773912227 & 0.494039886956113 \tabularnewline
69 & 0.463069318825381 & 0.926138637650762 & 0.536930681174619 \tabularnewline
70 & 0.426691536123393 & 0.853383072246786 & 0.573308463876607 \tabularnewline
71 & 0.402297476406332 & 0.804594952812664 & 0.597702523593668 \tabularnewline
72 & 0.358241667835372 & 0.716483335670744 & 0.641758332164628 \tabularnewline
73 & 0.338150525289075 & 0.676301050578151 & 0.661849474710925 \tabularnewline
74 & 0.309208415585891 & 0.618416831171782 & 0.690791584414109 \tabularnewline
75 & 0.271665211011751 & 0.543330422023503 & 0.728334788988249 \tabularnewline
76 & 0.486160431252206 & 0.972320862504413 & 0.513839568747794 \tabularnewline
77 & 0.441937332818936 & 0.883874665637872 & 0.558062667181064 \tabularnewline
78 & 0.464352950799086 & 0.928705901598172 & 0.535647049200914 \tabularnewline
79 & 0.419365928056143 & 0.838731856112287 & 0.580634071943857 \tabularnewline
80 & 0.537458673559486 & 0.925082652881028 & 0.462541326440514 \tabularnewline
81 & 0.492507296832166 & 0.985014593664332 & 0.507492703167834 \tabularnewline
82 & 0.455215839333168 & 0.910431678666336 & 0.544784160666832 \tabularnewline
83 & 0.488217521904489 & 0.976435043808978 & 0.511782478095511 \tabularnewline
84 & 0.445854256912043 & 0.891708513824086 & 0.554145743087957 \tabularnewline
85 & 0.40507278236074 & 0.810145564721479 & 0.59492721763926 \tabularnewline
86 & 0.361642870175336 & 0.723285740350673 & 0.638357129824664 \tabularnewline
87 & 0.321354166590339 & 0.642708333180677 & 0.678645833409661 \tabularnewline
88 & 0.285196195666128 & 0.570392391332255 & 0.714803804333872 \tabularnewline
89 & 0.249084498819203 & 0.498168997638406 & 0.750915501180797 \tabularnewline
90 & 0.586293826194165 & 0.827412347611669 & 0.413706173805834 \tabularnewline
91 & 0.543612604146613 & 0.912774791706775 & 0.456387395853387 \tabularnewline
92 & 0.504724853338232 & 0.990550293323535 & 0.495275146661768 \tabularnewline
93 & 0.470195614887683 & 0.940391229775365 & 0.529804385112317 \tabularnewline
94 & 0.469206810167849 & 0.938413620335697 & 0.530793189832151 \tabularnewline
95 & 0.458205272245504 & 0.916410544491008 & 0.541794727754496 \tabularnewline
96 & 0.412154299561283 & 0.824308599122566 & 0.587845700438717 \tabularnewline
97 & 0.411316168068071 & 0.822632336136143 & 0.588683831931929 \tabularnewline
98 & 0.371324230585757 & 0.742648461171515 & 0.628675769414243 \tabularnewline
99 & 0.328472600828308 & 0.656945201656615 & 0.671527399171692 \tabularnewline
100 & 0.31856767426156 & 0.63713534852312 & 0.68143232573844 \tabularnewline
101 & 0.293297359901637 & 0.586594719803273 & 0.706702640098363 \tabularnewline
102 & 0.303875475347611 & 0.607750950695222 & 0.696124524652389 \tabularnewline
103 & 0.281837462784548 & 0.563674925569097 & 0.718162537215452 \tabularnewline
104 & 0.296103800222799 & 0.592207600445598 & 0.703896199777201 \tabularnewline
105 & 0.36815075327245 & 0.7363015065449 & 0.63184924672755 \tabularnewline
106 & 0.455638155147183 & 0.911276310294365 & 0.544361844852817 \tabularnewline
107 & 0.411762384748452 & 0.823524769496904 & 0.588237615251548 \tabularnewline
108 & 0.447932584636324 & 0.895865169272647 & 0.552067415363676 \tabularnewline
109 & 0.402739530983162 & 0.805479061966324 & 0.597260469016838 \tabularnewline
110 & 0.756080534595263 & 0.487838930809475 & 0.243919465404737 \tabularnewline
111 & 0.737066633372472 & 0.525866733255055 & 0.262933366627528 \tabularnewline
112 & 0.731207924762232 & 0.537584150475537 & 0.268792075237768 \tabularnewline
113 & 0.697406527008287 & 0.605186945983426 & 0.302593472991713 \tabularnewline
114 & 0.656790292906308 & 0.686419414187383 & 0.343209707093692 \tabularnewline
115 & 0.621399621256556 & 0.757200757486889 & 0.378600378743444 \tabularnewline
116 & 0.572705633731755 & 0.85458873253649 & 0.427294366268245 \tabularnewline
117 & 0.523905261746904 & 0.952189476506193 & 0.476094738253096 \tabularnewline
118 & 0.474060898761066 & 0.948121797522131 & 0.525939101238934 \tabularnewline
119 & 0.545517518109208 & 0.908964963781583 & 0.454482481890792 \tabularnewline
120 & 0.635918875951741 & 0.728162248096518 & 0.364081124048259 \tabularnewline
121 & 0.583294753599378 & 0.833410492801245 & 0.416705246400622 \tabularnewline
122 & 0.535128905973773 & 0.929742188052455 & 0.464871094026227 \tabularnewline
123 & 0.481102730383615 & 0.962205460767229 & 0.518897269616385 \tabularnewline
124 & 0.482931602060547 & 0.965863204121093 & 0.517068397939453 \tabularnewline
125 & 0.460406086067447 & 0.920812172134895 & 0.539593913932553 \tabularnewline
126 & 0.408002808449525 & 0.81600561689905 & 0.591997191550475 \tabularnewline
127 & 0.425680520386893 & 0.851361040773787 & 0.574319479613107 \tabularnewline
128 & 0.37299265989453 & 0.745985319789061 & 0.62700734010547 \tabularnewline
129 & 0.332012129920133 & 0.664024259840266 & 0.667987870079867 \tabularnewline
130 & 0.291423893368004 & 0.582847786736007 & 0.708576106631996 \tabularnewline
131 & 0.251893974181705 & 0.50378794836341 & 0.748106025818295 \tabularnewline
132 & 0.297694178366093 & 0.595388356732186 & 0.702305821633907 \tabularnewline
133 & 0.255678515056007 & 0.511357030112014 & 0.744321484943993 \tabularnewline
134 & 0.207799272861844 & 0.415598545723688 & 0.792200727138156 \tabularnewline
135 & 0.16489714886134 & 0.329794297722679 & 0.83510285113866 \tabularnewline
136 & 0.128661841645779 & 0.257323683291558 & 0.871338158354221 \tabularnewline
137 & 0.301980694262711 & 0.603961388525421 & 0.698019305737289 \tabularnewline
138 & 0.245355262380173 & 0.490710524760346 & 0.754644737619827 \tabularnewline
139 & 0.196827018226579 & 0.393654036453158 & 0.803172981773421 \tabularnewline
140 & 0.203160669370147 & 0.406321338740294 & 0.796839330629853 \tabularnewline
141 & 0.22268482887095 & 0.445369657741901 & 0.77731517112905 \tabularnewline
142 & 0.261814422277467 & 0.523628844554935 & 0.738185577722533 \tabularnewline
143 & 0.22010093574689 & 0.440201871493781 & 0.77989906425311 \tabularnewline
144 & 0.175908096191693 & 0.351816192383385 & 0.824091903808307 \tabularnewline
145 & 0.13670495722724 & 0.273409914454479 & 0.86329504277276 \tabularnewline
146 & 0.473382850788788 & 0.946765701577577 & 0.526617149211212 \tabularnewline
147 & 0.389631722385059 & 0.779263444770117 & 0.610368277614942 \tabularnewline
148 & 0.341781923845403 & 0.683563847690806 & 0.658218076154597 \tabularnewline
149 & 0.264136503546517 & 0.528273007093035 & 0.735863496453482 \tabularnewline
150 & 0.193826170363314 & 0.387652340726628 & 0.806173829636686 \tabularnewline
151 & 0.470868141020884 & 0.941736282041768 & 0.529131858979116 \tabularnewline
152 & 0.3513026292781 & 0.702605258556199 & 0.6486973707219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.983315245310604[/C][C]0.0333695093787918[/C][C]0.0166847546893959[/C][/ROW]
[ROW][C]11[/C][C]0.980116358623106[/C][C]0.0397672827537887[/C][C]0.0198836413768943[/C][/ROW]
[ROW][C]12[/C][C]0.977204462258499[/C][C]0.0455910754830024[/C][C]0.0227955377415012[/C][/ROW]
[ROW][C]13[/C][C]0.958490869626999[/C][C]0.0830182607460012[/C][C]0.0415091303730006[/C][/ROW]
[ROW][C]14[/C][C]0.944297879236346[/C][C]0.111404241527308[/C][C]0.0557021207636539[/C][/ROW]
[ROW][C]15[/C][C]0.931326905705723[/C][C]0.137346188588554[/C][C]0.0686730942942772[/C][/ROW]
[ROW][C]16[/C][C]0.895506663549737[/C][C]0.208986672900526[/C][C]0.104493336450263[/C][/ROW]
[ROW][C]17[/C][C]0.897858980367317[/C][C]0.204282039265366[/C][C]0.102141019632683[/C][/ROW]
[ROW][C]18[/C][C]0.854210030506237[/C][C]0.291579938987526[/C][C]0.145789969493763[/C][/ROW]
[ROW][C]19[/C][C]0.888301559855282[/C][C]0.223396880289435[/C][C]0.111698440144718[/C][/ROW]
[ROW][C]20[/C][C]0.851284990542375[/C][C]0.29743001891525[/C][C]0.148715009457625[/C][/ROW]
[ROW][C]21[/C][C]0.927510546024248[/C][C]0.144978907951504[/C][C]0.072489453975752[/C][/ROW]
[ROW][C]22[/C][C]0.905173484329597[/C][C]0.189653031340807[/C][C]0.0948265156704033[/C][/ROW]
[ROW][C]23[/C][C]0.877491923154619[/C][C]0.245016153690763[/C][C]0.122508076845381[/C][/ROW]
[ROW][C]24[/C][C]0.851009383822574[/C][C]0.297981232354852[/C][C]0.148990616177426[/C][/ROW]
[ROW][C]25[/C][C]0.806532866682835[/C][C]0.38693426663433[/C][C]0.193467133317165[/C][/ROW]
[ROW][C]26[/C][C]0.762702815086458[/C][C]0.474594369827084[/C][C]0.237297184913542[/C][/ROW]
[ROW][C]27[/C][C]0.765369859763159[/C][C]0.469260280473682[/C][C]0.234630140236841[/C][/ROW]
[ROW][C]28[/C][C]0.715357941948736[/C][C]0.569284116102528[/C][C]0.284642058051264[/C][/ROW]
[ROW][C]29[/C][C]0.753635894334096[/C][C]0.492728211331808[/C][C]0.246364105665904[/C][/ROW]
[ROW][C]30[/C][C]0.719741234793062[/C][C]0.560517530413876[/C][C]0.280258765206938[/C][/ROW]
[ROW][C]31[/C][C]0.724173274592766[/C][C]0.551653450814468[/C][C]0.275826725407234[/C][/ROW]
[ROW][C]32[/C][C]0.688572550595287[/C][C]0.622854898809425[/C][C]0.311427449404713[/C][/ROW]
[ROW][C]33[/C][C]0.663917298133012[/C][C]0.672165403733976[/C][C]0.336082701866988[/C][/ROW]
[ROW][C]34[/C][C]0.636707601754666[/C][C]0.726584796490668[/C][C]0.363292398245334[/C][/ROW]
[ROW][C]35[/C][C]0.605381370349727[/C][C]0.789237259300546[/C][C]0.394618629650273[/C][/ROW]
[ROW][C]36[/C][C]0.698869428321156[/C][C]0.602261143357688[/C][C]0.301130571678844[/C][/ROW]
[ROW][C]37[/C][C]0.865649360501959[/C][C]0.268701278996082[/C][C]0.134350639498041[/C][/ROW]
[ROW][C]38[/C][C]0.834307437833546[/C][C]0.331385124332908[/C][C]0.165692562166454[/C][/ROW]
[ROW][C]39[/C][C]0.822738250883264[/C][C]0.354523498233472[/C][C]0.177261749116736[/C][/ROW]
[ROW][C]40[/C][C]0.811070951956236[/C][C]0.377858096087528[/C][C]0.188929048043764[/C][/ROW]
[ROW][C]41[/C][C]0.807646345863619[/C][C]0.384707308272763[/C][C]0.192353654136381[/C][/ROW]
[ROW][C]42[/C][C]0.786680535133661[/C][C]0.426638929732679[/C][C]0.213319464866339[/C][/ROW]
[ROW][C]43[/C][C]0.838121172128554[/C][C]0.323757655742892[/C][C]0.161878827871446[/C][/ROW]
[ROW][C]44[/C][C]0.806270872699703[/C][C]0.387458254600594[/C][C]0.193729127300297[/C][/ROW]
[ROW][C]45[/C][C]0.783269786952081[/C][C]0.433460426095837[/C][C]0.216730213047919[/C][/ROW]
[ROW][C]46[/C][C]0.745178132226194[/C][C]0.509643735547612[/C][C]0.254821867773806[/C][/ROW]
[ROW][C]47[/C][C]0.794183073538142[/C][C]0.411633852923717[/C][C]0.205816926461858[/C][/ROW]
[ROW][C]48[/C][C]0.764251578196961[/C][C]0.471496843606078[/C][C]0.235748421803039[/C][/ROW]
[ROW][C]49[/C][C]0.768987082581155[/C][C]0.46202583483769[/C][C]0.231012917418845[/C][/ROW]
[ROW][C]50[/C][C]0.73700649402965[/C][C]0.525987011940701[/C][C]0.26299350597035[/C][/ROW]
[ROW][C]51[/C][C]0.724830918561337[/C][C]0.550338162877326[/C][C]0.275169081438663[/C][/ROW]
[ROW][C]52[/C][C]0.687050876300437[/C][C]0.625898247399126[/C][C]0.312949123699563[/C][/ROW]
[ROW][C]53[/C][C]0.730915163866885[/C][C]0.53816967226623[/C][C]0.269084836133115[/C][/ROW]
[ROW][C]54[/C][C]0.695074893783552[/C][C]0.609850212432896[/C][C]0.304925106216448[/C][/ROW]
[ROW][C]55[/C][C]0.705871872748099[/C][C]0.588256254503802[/C][C]0.294128127251901[/C][/ROW]
[ROW][C]56[/C][C]0.669674298587083[/C][C]0.660651402825834[/C][C]0.330325701412917[/C][/ROW]
[ROW][C]57[/C][C]0.633647146375069[/C][C]0.732705707249862[/C][C]0.366352853624931[/C][/ROW]
[ROW][C]58[/C][C]0.681164712453287[/C][C]0.637670575093426[/C][C]0.318835287546713[/C][/ROW]
[ROW][C]59[/C][C]0.660127824185808[/C][C]0.679744351628385[/C][C]0.339872175814192[/C][/ROW]
[ROW][C]60[/C][C]0.645641163403902[/C][C]0.708717673192195[/C][C]0.354358836596098[/C][/ROW]
[ROW][C]61[/C][C]0.712527232164622[/C][C]0.574945535670756[/C][C]0.287472767835378[/C][/ROW]
[ROW][C]62[/C][C]0.672439579130978[/C][C]0.655120841738045[/C][C]0.327560420869022[/C][/ROW]
[ROW][C]63[/C][C]0.628688290591485[/C][C]0.74262341881703[/C][C]0.371311709408515[/C][/ROW]
[ROW][C]64[/C][C]0.5855461637722[/C][C]0.8289076724556[/C][C]0.4144538362278[/C][/ROW]
[ROW][C]65[/C][C]0.54943308890365[/C][C]0.901133822192701[/C][C]0.45056691109635[/C][/ROW]
[ROW][C]66[/C][C]0.502890634520771[/C][C]0.994218730958458[/C][C]0.497109365479229[/C][/ROW]
[ROW][C]67[/C][C]0.552869592082539[/C][C]0.894260815834922[/C][C]0.447130407917461[/C][/ROW]
[ROW][C]68[/C][C]0.505960113043887[/C][C]0.988079773912227[/C][C]0.494039886956113[/C][/ROW]
[ROW][C]69[/C][C]0.463069318825381[/C][C]0.926138637650762[/C][C]0.536930681174619[/C][/ROW]
[ROW][C]70[/C][C]0.426691536123393[/C][C]0.853383072246786[/C][C]0.573308463876607[/C][/ROW]
[ROW][C]71[/C][C]0.402297476406332[/C][C]0.804594952812664[/C][C]0.597702523593668[/C][/ROW]
[ROW][C]72[/C][C]0.358241667835372[/C][C]0.716483335670744[/C][C]0.641758332164628[/C][/ROW]
[ROW][C]73[/C][C]0.338150525289075[/C][C]0.676301050578151[/C][C]0.661849474710925[/C][/ROW]
[ROW][C]74[/C][C]0.309208415585891[/C][C]0.618416831171782[/C][C]0.690791584414109[/C][/ROW]
[ROW][C]75[/C][C]0.271665211011751[/C][C]0.543330422023503[/C][C]0.728334788988249[/C][/ROW]
[ROW][C]76[/C][C]0.486160431252206[/C][C]0.972320862504413[/C][C]0.513839568747794[/C][/ROW]
[ROW][C]77[/C][C]0.441937332818936[/C][C]0.883874665637872[/C][C]0.558062667181064[/C][/ROW]
[ROW][C]78[/C][C]0.464352950799086[/C][C]0.928705901598172[/C][C]0.535647049200914[/C][/ROW]
[ROW][C]79[/C][C]0.419365928056143[/C][C]0.838731856112287[/C][C]0.580634071943857[/C][/ROW]
[ROW][C]80[/C][C]0.537458673559486[/C][C]0.925082652881028[/C][C]0.462541326440514[/C][/ROW]
[ROW][C]81[/C][C]0.492507296832166[/C][C]0.985014593664332[/C][C]0.507492703167834[/C][/ROW]
[ROW][C]82[/C][C]0.455215839333168[/C][C]0.910431678666336[/C][C]0.544784160666832[/C][/ROW]
[ROW][C]83[/C][C]0.488217521904489[/C][C]0.976435043808978[/C][C]0.511782478095511[/C][/ROW]
[ROW][C]84[/C][C]0.445854256912043[/C][C]0.891708513824086[/C][C]0.554145743087957[/C][/ROW]
[ROW][C]85[/C][C]0.40507278236074[/C][C]0.810145564721479[/C][C]0.59492721763926[/C][/ROW]
[ROW][C]86[/C][C]0.361642870175336[/C][C]0.723285740350673[/C][C]0.638357129824664[/C][/ROW]
[ROW][C]87[/C][C]0.321354166590339[/C][C]0.642708333180677[/C][C]0.678645833409661[/C][/ROW]
[ROW][C]88[/C][C]0.285196195666128[/C][C]0.570392391332255[/C][C]0.714803804333872[/C][/ROW]
[ROW][C]89[/C][C]0.249084498819203[/C][C]0.498168997638406[/C][C]0.750915501180797[/C][/ROW]
[ROW][C]90[/C][C]0.586293826194165[/C][C]0.827412347611669[/C][C]0.413706173805834[/C][/ROW]
[ROW][C]91[/C][C]0.543612604146613[/C][C]0.912774791706775[/C][C]0.456387395853387[/C][/ROW]
[ROW][C]92[/C][C]0.504724853338232[/C][C]0.990550293323535[/C][C]0.495275146661768[/C][/ROW]
[ROW][C]93[/C][C]0.470195614887683[/C][C]0.940391229775365[/C][C]0.529804385112317[/C][/ROW]
[ROW][C]94[/C][C]0.469206810167849[/C][C]0.938413620335697[/C][C]0.530793189832151[/C][/ROW]
[ROW][C]95[/C][C]0.458205272245504[/C][C]0.916410544491008[/C][C]0.541794727754496[/C][/ROW]
[ROW][C]96[/C][C]0.412154299561283[/C][C]0.824308599122566[/C][C]0.587845700438717[/C][/ROW]
[ROW][C]97[/C][C]0.411316168068071[/C][C]0.822632336136143[/C][C]0.588683831931929[/C][/ROW]
[ROW][C]98[/C][C]0.371324230585757[/C][C]0.742648461171515[/C][C]0.628675769414243[/C][/ROW]
[ROW][C]99[/C][C]0.328472600828308[/C][C]0.656945201656615[/C][C]0.671527399171692[/C][/ROW]
[ROW][C]100[/C][C]0.31856767426156[/C][C]0.63713534852312[/C][C]0.68143232573844[/C][/ROW]
[ROW][C]101[/C][C]0.293297359901637[/C][C]0.586594719803273[/C][C]0.706702640098363[/C][/ROW]
[ROW][C]102[/C][C]0.303875475347611[/C][C]0.607750950695222[/C][C]0.696124524652389[/C][/ROW]
[ROW][C]103[/C][C]0.281837462784548[/C][C]0.563674925569097[/C][C]0.718162537215452[/C][/ROW]
[ROW][C]104[/C][C]0.296103800222799[/C][C]0.592207600445598[/C][C]0.703896199777201[/C][/ROW]
[ROW][C]105[/C][C]0.36815075327245[/C][C]0.7363015065449[/C][C]0.63184924672755[/C][/ROW]
[ROW][C]106[/C][C]0.455638155147183[/C][C]0.911276310294365[/C][C]0.544361844852817[/C][/ROW]
[ROW][C]107[/C][C]0.411762384748452[/C][C]0.823524769496904[/C][C]0.588237615251548[/C][/ROW]
[ROW][C]108[/C][C]0.447932584636324[/C][C]0.895865169272647[/C][C]0.552067415363676[/C][/ROW]
[ROW][C]109[/C][C]0.402739530983162[/C][C]0.805479061966324[/C][C]0.597260469016838[/C][/ROW]
[ROW][C]110[/C][C]0.756080534595263[/C][C]0.487838930809475[/C][C]0.243919465404737[/C][/ROW]
[ROW][C]111[/C][C]0.737066633372472[/C][C]0.525866733255055[/C][C]0.262933366627528[/C][/ROW]
[ROW][C]112[/C][C]0.731207924762232[/C][C]0.537584150475537[/C][C]0.268792075237768[/C][/ROW]
[ROW][C]113[/C][C]0.697406527008287[/C][C]0.605186945983426[/C][C]0.302593472991713[/C][/ROW]
[ROW][C]114[/C][C]0.656790292906308[/C][C]0.686419414187383[/C][C]0.343209707093692[/C][/ROW]
[ROW][C]115[/C][C]0.621399621256556[/C][C]0.757200757486889[/C][C]0.378600378743444[/C][/ROW]
[ROW][C]116[/C][C]0.572705633731755[/C][C]0.85458873253649[/C][C]0.427294366268245[/C][/ROW]
[ROW][C]117[/C][C]0.523905261746904[/C][C]0.952189476506193[/C][C]0.476094738253096[/C][/ROW]
[ROW][C]118[/C][C]0.474060898761066[/C][C]0.948121797522131[/C][C]0.525939101238934[/C][/ROW]
[ROW][C]119[/C][C]0.545517518109208[/C][C]0.908964963781583[/C][C]0.454482481890792[/C][/ROW]
[ROW][C]120[/C][C]0.635918875951741[/C][C]0.728162248096518[/C][C]0.364081124048259[/C][/ROW]
[ROW][C]121[/C][C]0.583294753599378[/C][C]0.833410492801245[/C][C]0.416705246400622[/C][/ROW]
[ROW][C]122[/C][C]0.535128905973773[/C][C]0.929742188052455[/C][C]0.464871094026227[/C][/ROW]
[ROW][C]123[/C][C]0.481102730383615[/C][C]0.962205460767229[/C][C]0.518897269616385[/C][/ROW]
[ROW][C]124[/C][C]0.482931602060547[/C][C]0.965863204121093[/C][C]0.517068397939453[/C][/ROW]
[ROW][C]125[/C][C]0.460406086067447[/C][C]0.920812172134895[/C][C]0.539593913932553[/C][/ROW]
[ROW][C]126[/C][C]0.408002808449525[/C][C]0.81600561689905[/C][C]0.591997191550475[/C][/ROW]
[ROW][C]127[/C][C]0.425680520386893[/C][C]0.851361040773787[/C][C]0.574319479613107[/C][/ROW]
[ROW][C]128[/C][C]0.37299265989453[/C][C]0.745985319789061[/C][C]0.62700734010547[/C][/ROW]
[ROW][C]129[/C][C]0.332012129920133[/C][C]0.664024259840266[/C][C]0.667987870079867[/C][/ROW]
[ROW][C]130[/C][C]0.291423893368004[/C][C]0.582847786736007[/C][C]0.708576106631996[/C][/ROW]
[ROW][C]131[/C][C]0.251893974181705[/C][C]0.50378794836341[/C][C]0.748106025818295[/C][/ROW]
[ROW][C]132[/C][C]0.297694178366093[/C][C]0.595388356732186[/C][C]0.702305821633907[/C][/ROW]
[ROW][C]133[/C][C]0.255678515056007[/C][C]0.511357030112014[/C][C]0.744321484943993[/C][/ROW]
[ROW][C]134[/C][C]0.207799272861844[/C][C]0.415598545723688[/C][C]0.792200727138156[/C][/ROW]
[ROW][C]135[/C][C]0.16489714886134[/C][C]0.329794297722679[/C][C]0.83510285113866[/C][/ROW]
[ROW][C]136[/C][C]0.128661841645779[/C][C]0.257323683291558[/C][C]0.871338158354221[/C][/ROW]
[ROW][C]137[/C][C]0.301980694262711[/C][C]0.603961388525421[/C][C]0.698019305737289[/C][/ROW]
[ROW][C]138[/C][C]0.245355262380173[/C][C]0.490710524760346[/C][C]0.754644737619827[/C][/ROW]
[ROW][C]139[/C][C]0.196827018226579[/C][C]0.393654036453158[/C][C]0.803172981773421[/C][/ROW]
[ROW][C]140[/C][C]0.203160669370147[/C][C]0.406321338740294[/C][C]0.796839330629853[/C][/ROW]
[ROW][C]141[/C][C]0.22268482887095[/C][C]0.445369657741901[/C][C]0.77731517112905[/C][/ROW]
[ROW][C]142[/C][C]0.261814422277467[/C][C]0.523628844554935[/C][C]0.738185577722533[/C][/ROW]
[ROW][C]143[/C][C]0.22010093574689[/C][C]0.440201871493781[/C][C]0.77989906425311[/C][/ROW]
[ROW][C]144[/C][C]0.175908096191693[/C][C]0.351816192383385[/C][C]0.824091903808307[/C][/ROW]
[ROW][C]145[/C][C]0.13670495722724[/C][C]0.273409914454479[/C][C]0.86329504277276[/C][/ROW]
[ROW][C]146[/C][C]0.473382850788788[/C][C]0.946765701577577[/C][C]0.526617149211212[/C][/ROW]
[ROW][C]147[/C][C]0.389631722385059[/C][C]0.779263444770117[/C][C]0.610368277614942[/C][/ROW]
[ROW][C]148[/C][C]0.341781923845403[/C][C]0.683563847690806[/C][C]0.658218076154597[/C][/ROW]
[ROW][C]149[/C][C]0.264136503546517[/C][C]0.528273007093035[/C][C]0.735863496453482[/C][/ROW]
[ROW][C]150[/C][C]0.193826170363314[/C][C]0.387652340726628[/C][C]0.806173829636686[/C][/ROW]
[ROW][C]151[/C][C]0.470868141020884[/C][C]0.941736282041768[/C][C]0.529131858979116[/C][/ROW]
[ROW][C]152[/C][C]0.3513026292781[/C][C]0.702605258556199[/C][C]0.6486973707219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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
100.9833152453106040.03336950937879180.0166847546893959
110.9801163586231060.03976728275378870.0198836413768943
120.9772044622584990.04559107548300240.0227955377415012
130.9584908696269990.08301826074600120.0415091303730006
140.9442978792363460.1114042415273080.0557021207636539
150.9313269057057230.1373461885885540.0686730942942772
160.8955066635497370.2089866729005260.104493336450263
170.8978589803673170.2042820392653660.102141019632683
180.8542100305062370.2915799389875260.145789969493763
190.8883015598552820.2233968802894350.111698440144718
200.8512849905423750.297430018915250.148715009457625
210.9275105460242480.1449789079515040.072489453975752
220.9051734843295970.1896530313408070.0948265156704033
230.8774919231546190.2450161536907630.122508076845381
240.8510093838225740.2979812323548520.148990616177426
250.8065328666828350.386934266634330.193467133317165
260.7627028150864580.4745943698270840.237297184913542
270.7653698597631590.4692602804736820.234630140236841
280.7153579419487360.5692841161025280.284642058051264
290.7536358943340960.4927282113318080.246364105665904
300.7197412347930620.5605175304138760.280258765206938
310.7241732745927660.5516534508144680.275826725407234
320.6885725505952870.6228548988094250.311427449404713
330.6639172981330120.6721654037339760.336082701866988
340.6367076017546660.7265847964906680.363292398245334
350.6053813703497270.7892372593005460.394618629650273
360.6988694283211560.6022611433576880.301130571678844
370.8656493605019590.2687012789960820.134350639498041
380.8343074378335460.3313851243329080.165692562166454
390.8227382508832640.3545234982334720.177261749116736
400.8110709519562360.3778580960875280.188929048043764
410.8076463458636190.3847073082727630.192353654136381
420.7866805351336610.4266389297326790.213319464866339
430.8381211721285540.3237576557428920.161878827871446
440.8062708726997030.3874582546005940.193729127300297
450.7832697869520810.4334604260958370.216730213047919
460.7451781322261940.5096437355476120.254821867773806
470.7941830735381420.4116338529237170.205816926461858
480.7642515781969610.4714968436060780.235748421803039
490.7689870825811550.462025834837690.231012917418845
500.737006494029650.5259870119407010.26299350597035
510.7248309185613370.5503381628773260.275169081438663
520.6870508763004370.6258982473991260.312949123699563
530.7309151638668850.538169672266230.269084836133115
540.6950748937835520.6098502124328960.304925106216448
550.7058718727480990.5882562545038020.294128127251901
560.6696742985870830.6606514028258340.330325701412917
570.6336471463750690.7327057072498620.366352853624931
580.6811647124532870.6376705750934260.318835287546713
590.6601278241858080.6797443516283850.339872175814192
600.6456411634039020.7087176731921950.354358836596098
610.7125272321646220.5749455356707560.287472767835378
620.6724395791309780.6551208417380450.327560420869022
630.6286882905914850.742623418817030.371311709408515
640.58554616377220.82890767245560.4144538362278
650.549433088903650.9011338221927010.45056691109635
660.5028906345207710.9942187309584580.497109365479229
670.5528695920825390.8942608158349220.447130407917461
680.5059601130438870.9880797739122270.494039886956113
690.4630693188253810.9261386376507620.536930681174619
700.4266915361233930.8533830722467860.573308463876607
710.4022974764063320.8045949528126640.597702523593668
720.3582416678353720.7164833356707440.641758332164628
730.3381505252890750.6763010505781510.661849474710925
740.3092084155858910.6184168311717820.690791584414109
750.2716652110117510.5433304220235030.728334788988249
760.4861604312522060.9723208625044130.513839568747794
770.4419373328189360.8838746656378720.558062667181064
780.4643529507990860.9287059015981720.535647049200914
790.4193659280561430.8387318561122870.580634071943857
800.5374586735594860.9250826528810280.462541326440514
810.4925072968321660.9850145936643320.507492703167834
820.4552158393331680.9104316786663360.544784160666832
830.4882175219044890.9764350438089780.511782478095511
840.4458542569120430.8917085138240860.554145743087957
850.405072782360740.8101455647214790.59492721763926
860.3616428701753360.7232857403506730.638357129824664
870.3213541665903390.6427083331806770.678645833409661
880.2851961956661280.5703923913322550.714803804333872
890.2490844988192030.4981689976384060.750915501180797
900.5862938261941650.8274123476116690.413706173805834
910.5436126041466130.9127747917067750.456387395853387
920.5047248533382320.9905502933235350.495275146661768
930.4701956148876830.9403912297753650.529804385112317
940.4692068101678490.9384136203356970.530793189832151
950.4582052722455040.9164105444910080.541794727754496
960.4121542995612830.8243085991225660.587845700438717
970.4113161680680710.8226323361361430.588683831931929
980.3713242305857570.7426484611715150.628675769414243
990.3284726008283080.6569452016566150.671527399171692
1000.318567674261560.637135348523120.68143232573844
1010.2932973599016370.5865947198032730.706702640098363
1020.3038754753476110.6077509506952220.696124524652389
1030.2818374627845480.5636749255690970.718162537215452
1040.2961038002227990.5922076004455980.703896199777201
1050.368150753272450.73630150654490.63184924672755
1060.4556381551471830.9112763102943650.544361844852817
1070.4117623847484520.8235247694969040.588237615251548
1080.4479325846363240.8958651692726470.552067415363676
1090.4027395309831620.8054790619663240.597260469016838
1100.7560805345952630.4878389308094750.243919465404737
1110.7370666333724720.5258667332550550.262933366627528
1120.7312079247622320.5375841504755370.268792075237768
1130.6974065270082870.6051869459834260.302593472991713
1140.6567902929063080.6864194141873830.343209707093692
1150.6213996212565560.7572007574868890.378600378743444
1160.5727056337317550.854588732536490.427294366268245
1170.5239052617469040.9521894765061930.476094738253096
1180.4740608987610660.9481217975221310.525939101238934
1190.5455175181092080.9089649637815830.454482481890792
1200.6359188759517410.7281622480965180.364081124048259
1210.5832947535993780.8334104928012450.416705246400622
1220.5351289059737730.9297421880524550.464871094026227
1230.4811027303836150.9622054607672290.518897269616385
1240.4829316020605470.9658632041210930.517068397939453
1250.4604060860674470.9208121721348950.539593913932553
1260.4080028084495250.816005616899050.591997191550475
1270.4256805203868930.8513610407737870.574319479613107
1280.372992659894530.7459853197890610.62700734010547
1290.3320121299201330.6640242598402660.667987870079867
1300.2914238933680040.5828477867360070.708576106631996
1310.2518939741817050.503787948363410.748106025818295
1320.2976941783660930.5953883567321860.702305821633907
1330.2556785150560070.5113570301120140.744321484943993
1340.2077992728618440.4155985457236880.792200727138156
1350.164897148861340.3297942977226790.83510285113866
1360.1286618416457790.2573236832915580.871338158354221
1370.3019806942627110.6039613885254210.698019305737289
1380.2453552623801730.4907105247603460.754644737619827
1390.1968270182265790.3936540364531580.803172981773421
1400.2031606693701470.4063213387402940.796839330629853
1410.222684828870950.4453696577419010.77731517112905
1420.2618144222774670.5236288445549350.738185577722533
1430.220100935746890.4402018714937810.77989906425311
1440.1759080961916930.3518161923833850.824091903808307
1450.136704957227240.2734099144544790.86329504277276
1460.4733828507887880.9467657015775770.526617149211212
1470.3896317223850590.7792634447701170.610368277614942
1480.3417819238454030.6835638476908060.658218076154597
1490.2641365035465170.5282730070930350.735863496453482
1500.1938261703633140.3876523407266280.806173829636686
1510.4708681410208840.9417362820417680.529131858979116
1520.35130262927810.7026052585561990.6486973707219







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.020979020979021OK
10% type I error level40.027972027972028OK

\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 & 3 & 0.020979020979021 & OK \tabularnewline
10% type I error level & 4 & 0.027972027972028 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186238&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]3[/C][C]0.020979020979021[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.027972027972028[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186238&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186238&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 level30.020979020979021OK
10% type I error level40.027972027972028OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}