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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 computationSun, 16 Dec 2012 09:29:57 -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/Dec/16/t1355668209t3img63fk82vuqk.htm/, Retrieved Thu, 28 Mar 2024 17:33:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200381, Retrieved Thu, 28 Mar 2024 17:33:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Workshop 7] [2012-11-04 10:18:25] [9d44b52ac7f20a3e9be7c3c8470fe2cd]
- R P     [Multiple Regression] [paper] [2012-12-16 14:29:57] [97e5c69206415429213a02c19f23a896] [Current]
-   PD      [Multiple Regression] [paper] [2012-12-21 13:44:36] [fa543719fe3f8358943b948de15add90]
-   PD      [Multiple Regression] [paper] [2012-12-21 13:46:25] [fa543719fe3f8358943b948de15add90]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.5104574109139 -0.0476502811001375Connected[t] + 0.033615813974464Separate[t] + 0.528535975192473Learning[t] -0.0406097034253699Happiness[t] -0.0224269391840146Depression[t] + 0.00391007197576475Belonging[t] -0.00626369092094975Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.5104574109139 -0.0476502811001375Connected[t] +  0.033615813974464Separate[t] +  0.528535975192473Learning[t] -0.0406097034253699Happiness[t] -0.0224269391840146Depression[t] +  0.00391007197576475Belonging[t] -0.00626369092094975Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.5104574109139 -0.0476502811001375Connected[t] +  0.033615813974464Separate[t] +  0.528535975192473Learning[t] -0.0406097034253699Happiness[t] -0.0224269391840146Depression[t] +  0.00391007197576475Belonging[t] -0.00626369092094975Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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
Software[t] = + 4.5104574109139 -0.0476502811001375Connected[t] + 0.033615813974464Separate[t] + 0.528535975192473Learning[t] -0.0406097034253699Happiness[t] -0.0224269391840146Depression[t] + 0.00391007197576475Belonging[t] -0.00626369092094975Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.51045741091392.5747431.75180.0817970.040899
Connected-0.04765028110013750.047004-1.01380.3122890.156145
Separate0.0336158139744640.0441760.7610.4478440.223922
Learning0.5285359751924730.0671697.868800
Happiness-0.04060970342536990.075466-0.53810.5912710.295635
Depression-0.02242693918401460.055865-0.40140.6886470.344324
Belonging0.003910071975764750.0440420.08880.9293720.464686
Belonging_Final-0.006263690920949750.06326-0.0990.9212550.460628

\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) & 4.5104574109139 & 2.574743 & 1.7518 & 0.081797 & 0.040899 \tabularnewline
Connected & -0.0476502811001375 & 0.047004 & -1.0138 & 0.312289 & 0.156145 \tabularnewline
Separate & 0.033615813974464 & 0.044176 & 0.761 & 0.447844 & 0.223922 \tabularnewline
Learning & 0.528535975192473 & 0.067169 & 7.8688 & 0 & 0 \tabularnewline
Happiness & -0.0406097034253699 & 0.075466 & -0.5381 & 0.591271 & 0.295635 \tabularnewline
Depression & -0.0224269391840146 & 0.055865 & -0.4014 & 0.688647 & 0.344324 \tabularnewline
Belonging & 0.00391007197576475 & 0.044042 & 0.0888 & 0.929372 & 0.464686 \tabularnewline
Belonging_Final & -0.00626369092094975 & 0.06326 & -0.099 & 0.921255 & 0.460628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&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]4.5104574109139[/C][C]2.574743[/C][C]1.7518[/C][C]0.081797[/C][C]0.040899[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0476502811001375[/C][C]0.047004[/C][C]-1.0138[/C][C]0.312289[/C][C]0.156145[/C][/ROW]
[ROW][C]Separate[/C][C]0.033615813974464[/C][C]0.044176[/C][C]0.761[/C][C]0.447844[/C][C]0.223922[/C][/ROW]
[ROW][C]Learning[/C][C]0.528535975192473[/C][C]0.067169[/C][C]7.8688[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0406097034253699[/C][C]0.075466[/C][C]-0.5381[/C][C]0.591271[/C][C]0.295635[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0224269391840146[/C][C]0.055865[/C][C]-0.4014[/C][C]0.688647[/C][C]0.344324[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00391007197576475[/C][C]0.044042[/C][C]0.0888[/C][C]0.929372[/C][C]0.464686[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.00626369092094975[/C][C]0.06326[/C][C]-0.099[/C][C]0.921255[/C][C]0.460628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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)4.51045741091392.5747431.75180.0817970.040899
Connected-0.04765028110013750.047004-1.01380.3122890.156145
Separate0.0336158139744640.0441760.7610.4478440.223922
Learning0.5285359751924730.0671697.868800
Happiness-0.04060970342536990.075466-0.53810.5912710.295635
Depression-0.02242693918401460.055865-0.40140.6886470.344324
Belonging0.003910071975764750.0440420.08880.9293720.464686
Belonging_Final-0.006263690920949750.06326-0.0990.9212550.460628







Multiple Linear Regression - Regression Statistics
Multiple R0.551834021342622
R-squared0.30452078711117
Adjusted R-squared0.272908095616223
F-TEST (value)9.63286492577976
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value6.54508891528849e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82623628063756
Sum Squared Residuals513.611398718401

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.551834021342622 \tabularnewline
R-squared & 0.30452078711117 \tabularnewline
Adjusted R-squared & 0.272908095616223 \tabularnewline
F-TEST (value) & 9.63286492577976 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 6.54508891528849e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.82623628063756 \tabularnewline
Sum Squared Residuals & 513.611398718401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.551834021342622[/C][/ROW]
[ROW][C]R-squared[/C][C]0.30452078711117[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.272908095616223[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.63286492577976[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]6.54508891528849e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.82623628063756[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]513.611398718401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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 R0.551834021342622
R-squared0.30452078711117
Adjusted R-squared0.272908095616223
F-TEST (value)9.63286492577976
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value6.54508891528849e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82623628063756
Sum Squared Residuals513.611398718401







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.874301081421832.12569891857817
21111.2235250585375-0.223525058537466
31513.53399184113831.46600815886169
4611.3193839691587-5.31938396915873
51310.42195133338322.57804866661678
6109.699018064130870.300981935869128
71212.6499189505108-0.6499189505108
81411.21920950843232.78079049156767
91210.5451687246531.45483127534703
10611.0549138052263-5.05491380522629
111011.2857530462841-1.28575304628407
121211.44800277726610.551997222733896
131211.58927450297690.410725497023086
141111.4267826650356-0.42678266503557
151512.2198514195212.78014858047904
161211.14178820966270.858211790337311
171010.9276692765871-0.927669276587128
181213.6166528988683-1.61665289886833
191112.6437729713974-1.64377297139736
201211.57400583123820.425994168761792
211111.6648360666432-0.664836066643154
221211.76156314067250.23843685932747
231313.1384638758318-0.138463875831758
241111.7009032351795-0.700903235179519
25911.7864070462344-2.78640704623436
261312.06869601255520.931303987444767
271011.559551558222-1.55955155822196
281411.19907550964222.80092449035779
291211.60389242155640.396107578443598
301010.6472888421444-0.64728884214435
311211.05686831145430.943131688545682
3289.78214942471264-1.78214942471264
331010.2826329467188-0.282632946718843
341211.72507342747220.274926572527795
351210.46451017561711.5354898243829
3676.96756404469360.0324359553064027
3768.65660110003244-2.65660110003244
381210.43388038425061.56611961574938
391011.7216664330209-1.72166643302086
401011.6631194479239-1.66311944792389
411011.4761698911984-1.4761698911984
421210.45309396163961.5469060383604
431513.52144359816291.4785564018371
441010.5593788207707-0.559378820770704
451010.3132003486076-0.313200348607569
46129.014926536875012.98507346312499
471310.58409509159462.41590490840542
481111.1582591710422-0.158259171042193
491111.5285250869533-0.528525086953345
501210.41172326017181.58827673982821
511411.85152308490592.14847691509409
521010.5225406766449-0.522540676644908
53129.60158830658322.39841169341681
541311.77732547807151.22267452192849
5558.10500712881499-3.10500712881499
56610.347109885371-4.34710988537104
571211.31778349678560.682216503214374
581211.91602433688090.0839756631190745
591111.190116652862-0.190116652862015
601011.6228566504585-1.62285665045847
6179.59800276566633-2.59800276566633
621211.52142004327320.478579956726817
631411.82685638849892.17314361150106
641110.73599216626940.264007833730558
651211.49276001532440.507239984675606
661312.42468580539310.575314194606921
671412.6522733617931.34772663820703
681112.7744077238441-1.77440772384409
69129.446779228001122.55322077199888
701211.62665554023490.373344459765051
7188.32156214862119-0.321562148621191
721110.72848602426770.271513975732291
731412.83037505703651.16962494296355
741412.77747504179531.2225249582047
751211.49518945826210.504810541737896
76911.7381390628484-2.73813906284843
771311.47020889142281.52979110857724
781111.4740221449451-0.4740221449451
791210.19529375889711.80470624110291
801211.38627307971290.613726920287076
811211.76966706208670.230332937913348
821213.8387949577148-1.83879495771478
831211.72781930734370.272180692656298
841210.88197651886911.11802348113089
851111.1842796788729-0.184279678872921
861011.5832599823992-1.58325998239919
87910.5209606727743-1.52096067277427
881211.70657224230520.293427757694769
891211.80099276328230.199007236717744
901211.20206536633060.797934633669447
9199.60129000644368-0.601290006443678
921511.99597677077383.00402322922623
931211.78977154743910.210228452560861
941210.96995963697331.03004036302669
951210.27425810415261.72574189584743
961011.4408020531153-1.44080205311527
971311.53483489107291.46516510892712
98911.5429870614637-2.54298706146366
991211.42590766499610.574092335003935
1001010.7709421160586-0.770942116058564
1011411.46715666391292.5328433360871
1021111.537947797134-0.537947797134037
1031513.69731757725011.3026824227499
1041111.1565174978989-0.156517497898942
1051111.752116117169-0.752116117169022
106129.768594282861862.23140571713815
1071211.96773220753040.0322677924696399
1081211.36468805624560.635311943754397
1091111.6901173477808-0.690117347780795
11079.26340022143179-2.26340022143179
1111211.77104460305050.228955396949462
1121411.8298282612922.17017173870797
1131112.5739692245957-1.57396922459566
1141110.14068015360220.859319846397823
115109.235440452658910.764559547341086
1161312.46701653531550.532983464684458
1171310.80406343605112.19593656394888
118810.7725006456168-2.77250064561678
1191110.22186094825430.77813905174568
1201211.46895517672010.531044823279859
1211110.2334724866020.766527513398023
1221311.52402396846281.47597603153722
1231210.0356463653221.96435363467801
1241411.69558447633882.30441552366117
1251311.05585527608821.94414472391178
1261511.83907589829333.16092410170667
1271010.7934651285417-0.793465128541668
1281111.8090819413974-0.809081941397355
129911.0426665806976-2.04266658069758
130119.399678156836611.60032184316339
1311011.7914634959918-1.79146349599182
132118.730806338271492.26919366172851
133811.4843377035926-3.48433770359264
134119.632863345199741.36713665480026
1351210.54724831618721.45275168381275
1361210.91346452867631.0865354713237
13799.82868087243896-0.828680872438958
1381111.0956874897645-0.0956874897644672
139109.257775133967970.742224866032029
14089.47192203705678-1.47192203705678
14197.654074211286511.34592578871349
142811.7561888370001-3.75618883700011
143911.1971916865423-2.19719168654233
1441512.40924549823012.59075450176987
1451111.3977149594834-0.397714959483352
14688.03893797140226-0.0389379714022622
1471312.49081641834390.509183581656068
1481210.13451138837031.86548861162966
1491211.75542709568750.244572904312479
15099.99507759363662-0.995077593636617
15178.37762520015308-1.37762520015308
1521310.78628845179312.21371154820689
153911.7935304626767-2.79353046267673
154611.8597729909134-5.85977299091341
155810.5939959816378-2.59399598163781
15688.31937444704162-0.31937444704162
1571511.99597677077383.00402322922623
158610.2183444704624-4.21834447046237
159911.0426665806976-2.04266658069758
1601111.7018587155815-0.701858715581503
16189.62889376062547-1.62889376062547
162810.0663928465821-2.06639284658212

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.87430108142183 & 2.12569891857817 \tabularnewline
2 & 11 & 11.2235250585375 & -0.223525058537466 \tabularnewline
3 & 15 & 13.5339918411383 & 1.46600815886169 \tabularnewline
4 & 6 & 11.3193839691587 & -5.31938396915873 \tabularnewline
5 & 13 & 10.4219513333832 & 2.57804866661678 \tabularnewline
6 & 10 & 9.69901806413087 & 0.300981935869128 \tabularnewline
7 & 12 & 12.6499189505108 & -0.6499189505108 \tabularnewline
8 & 14 & 11.2192095084323 & 2.78079049156767 \tabularnewline
9 & 12 & 10.545168724653 & 1.45483127534703 \tabularnewline
10 & 6 & 11.0549138052263 & -5.05491380522629 \tabularnewline
11 & 10 & 11.2857530462841 & -1.28575304628407 \tabularnewline
12 & 12 & 11.4480027772661 & 0.551997222733896 \tabularnewline
13 & 12 & 11.5892745029769 & 0.410725497023086 \tabularnewline
14 & 11 & 11.4267826650356 & -0.42678266503557 \tabularnewline
15 & 15 & 12.219851419521 & 2.78014858047904 \tabularnewline
16 & 12 & 11.1417882096627 & 0.858211790337311 \tabularnewline
17 & 10 & 10.9276692765871 & -0.927669276587128 \tabularnewline
18 & 12 & 13.6166528988683 & -1.61665289886833 \tabularnewline
19 & 11 & 12.6437729713974 & -1.64377297139736 \tabularnewline
20 & 12 & 11.5740058312382 & 0.425994168761792 \tabularnewline
21 & 11 & 11.6648360666432 & -0.664836066643154 \tabularnewline
22 & 12 & 11.7615631406725 & 0.23843685932747 \tabularnewline
23 & 13 & 13.1384638758318 & -0.138463875831758 \tabularnewline
24 & 11 & 11.7009032351795 & -0.700903235179519 \tabularnewline
25 & 9 & 11.7864070462344 & -2.78640704623436 \tabularnewline
26 & 13 & 12.0686960125552 & 0.931303987444767 \tabularnewline
27 & 10 & 11.559551558222 & -1.55955155822196 \tabularnewline
28 & 14 & 11.1990755096422 & 2.80092449035779 \tabularnewline
29 & 12 & 11.6038924215564 & 0.396107578443598 \tabularnewline
30 & 10 & 10.6472888421444 & -0.64728884214435 \tabularnewline
31 & 12 & 11.0568683114543 & 0.943131688545682 \tabularnewline
32 & 8 & 9.78214942471264 & -1.78214942471264 \tabularnewline
33 & 10 & 10.2826329467188 & -0.282632946718843 \tabularnewline
34 & 12 & 11.7250734274722 & 0.274926572527795 \tabularnewline
35 & 12 & 10.4645101756171 & 1.5354898243829 \tabularnewline
36 & 7 & 6.9675640446936 & 0.0324359553064027 \tabularnewline
37 & 6 & 8.65660110003244 & -2.65660110003244 \tabularnewline
38 & 12 & 10.4338803842506 & 1.56611961574938 \tabularnewline
39 & 10 & 11.7216664330209 & -1.72166643302086 \tabularnewline
40 & 10 & 11.6631194479239 & -1.66311944792389 \tabularnewline
41 & 10 & 11.4761698911984 & -1.4761698911984 \tabularnewline
42 & 12 & 10.4530939616396 & 1.5469060383604 \tabularnewline
43 & 15 & 13.5214435981629 & 1.4785564018371 \tabularnewline
44 & 10 & 10.5593788207707 & -0.559378820770704 \tabularnewline
45 & 10 & 10.3132003486076 & -0.313200348607569 \tabularnewline
46 & 12 & 9.01492653687501 & 2.98507346312499 \tabularnewline
47 & 13 & 10.5840950915946 & 2.41590490840542 \tabularnewline
48 & 11 & 11.1582591710422 & -0.158259171042193 \tabularnewline
49 & 11 & 11.5285250869533 & -0.528525086953345 \tabularnewline
50 & 12 & 10.4117232601718 & 1.58827673982821 \tabularnewline
51 & 14 & 11.8515230849059 & 2.14847691509409 \tabularnewline
52 & 10 & 10.5225406766449 & -0.522540676644908 \tabularnewline
53 & 12 & 9.6015883065832 & 2.39841169341681 \tabularnewline
54 & 13 & 11.7773254780715 & 1.22267452192849 \tabularnewline
55 & 5 & 8.10500712881499 & -3.10500712881499 \tabularnewline
56 & 6 & 10.347109885371 & -4.34710988537104 \tabularnewline
57 & 12 & 11.3177834967856 & 0.682216503214374 \tabularnewline
58 & 12 & 11.9160243368809 & 0.0839756631190745 \tabularnewline
59 & 11 & 11.190116652862 & -0.190116652862015 \tabularnewline
60 & 10 & 11.6228566504585 & -1.62285665045847 \tabularnewline
61 & 7 & 9.59800276566633 & -2.59800276566633 \tabularnewline
62 & 12 & 11.5214200432732 & 0.478579956726817 \tabularnewline
63 & 14 & 11.8268563884989 & 2.17314361150106 \tabularnewline
64 & 11 & 10.7359921662694 & 0.264007833730558 \tabularnewline
65 & 12 & 11.4927600153244 & 0.507239984675606 \tabularnewline
66 & 13 & 12.4246858053931 & 0.575314194606921 \tabularnewline
67 & 14 & 12.652273361793 & 1.34772663820703 \tabularnewline
68 & 11 & 12.7744077238441 & -1.77440772384409 \tabularnewline
69 & 12 & 9.44677922800112 & 2.55322077199888 \tabularnewline
70 & 12 & 11.6266555402349 & 0.373344459765051 \tabularnewline
71 & 8 & 8.32156214862119 & -0.321562148621191 \tabularnewline
72 & 11 & 10.7284860242677 & 0.271513975732291 \tabularnewline
73 & 14 & 12.8303750570365 & 1.16962494296355 \tabularnewline
74 & 14 & 12.7774750417953 & 1.2225249582047 \tabularnewline
75 & 12 & 11.4951894582621 & 0.504810541737896 \tabularnewline
76 & 9 & 11.7381390628484 & -2.73813906284843 \tabularnewline
77 & 13 & 11.4702088914228 & 1.52979110857724 \tabularnewline
78 & 11 & 11.4740221449451 & -0.4740221449451 \tabularnewline
79 & 12 & 10.1952937588971 & 1.80470624110291 \tabularnewline
80 & 12 & 11.3862730797129 & 0.613726920287076 \tabularnewline
81 & 12 & 11.7696670620867 & 0.230332937913348 \tabularnewline
82 & 12 & 13.8387949577148 & -1.83879495771478 \tabularnewline
83 & 12 & 11.7278193073437 & 0.272180692656298 \tabularnewline
84 & 12 & 10.8819765188691 & 1.11802348113089 \tabularnewline
85 & 11 & 11.1842796788729 & -0.184279678872921 \tabularnewline
86 & 10 & 11.5832599823992 & -1.58325998239919 \tabularnewline
87 & 9 & 10.5209606727743 & -1.52096067277427 \tabularnewline
88 & 12 & 11.7065722423052 & 0.293427757694769 \tabularnewline
89 & 12 & 11.8009927632823 & 0.199007236717744 \tabularnewline
90 & 12 & 11.2020653663306 & 0.797934633669447 \tabularnewline
91 & 9 & 9.60129000644368 & -0.601290006443678 \tabularnewline
92 & 15 & 11.9959767707738 & 3.00402322922623 \tabularnewline
93 & 12 & 11.7897715474391 & 0.210228452560861 \tabularnewline
94 & 12 & 10.9699596369733 & 1.03004036302669 \tabularnewline
95 & 12 & 10.2742581041526 & 1.72574189584743 \tabularnewline
96 & 10 & 11.4408020531153 & -1.44080205311527 \tabularnewline
97 & 13 & 11.5348348910729 & 1.46516510892712 \tabularnewline
98 & 9 & 11.5429870614637 & -2.54298706146366 \tabularnewline
99 & 12 & 11.4259076649961 & 0.574092335003935 \tabularnewline
100 & 10 & 10.7709421160586 & -0.770942116058564 \tabularnewline
101 & 14 & 11.4671566639129 & 2.5328433360871 \tabularnewline
102 & 11 & 11.537947797134 & -0.537947797134037 \tabularnewline
103 & 15 & 13.6973175772501 & 1.3026824227499 \tabularnewline
104 & 11 & 11.1565174978989 & -0.156517497898942 \tabularnewline
105 & 11 & 11.752116117169 & -0.752116117169022 \tabularnewline
106 & 12 & 9.76859428286186 & 2.23140571713815 \tabularnewline
107 & 12 & 11.9677322075304 & 0.0322677924696399 \tabularnewline
108 & 12 & 11.3646880562456 & 0.635311943754397 \tabularnewline
109 & 11 & 11.6901173477808 & -0.690117347780795 \tabularnewline
110 & 7 & 9.26340022143179 & -2.26340022143179 \tabularnewline
111 & 12 & 11.7710446030505 & 0.228955396949462 \tabularnewline
112 & 14 & 11.829828261292 & 2.17017173870797 \tabularnewline
113 & 11 & 12.5739692245957 & -1.57396922459566 \tabularnewline
114 & 11 & 10.1406801536022 & 0.859319846397823 \tabularnewline
115 & 10 & 9.23544045265891 & 0.764559547341086 \tabularnewline
116 & 13 & 12.4670165353155 & 0.532983464684458 \tabularnewline
117 & 13 & 10.8040634360511 & 2.19593656394888 \tabularnewline
118 & 8 & 10.7725006456168 & -2.77250064561678 \tabularnewline
119 & 11 & 10.2218609482543 & 0.77813905174568 \tabularnewline
120 & 12 & 11.4689551767201 & 0.531044823279859 \tabularnewline
121 & 11 & 10.233472486602 & 0.766527513398023 \tabularnewline
122 & 13 & 11.5240239684628 & 1.47597603153722 \tabularnewline
123 & 12 & 10.035646365322 & 1.96435363467801 \tabularnewline
124 & 14 & 11.6955844763388 & 2.30441552366117 \tabularnewline
125 & 13 & 11.0558552760882 & 1.94414472391178 \tabularnewline
126 & 15 & 11.8390758982933 & 3.16092410170667 \tabularnewline
127 & 10 & 10.7934651285417 & -0.793465128541668 \tabularnewline
128 & 11 & 11.8090819413974 & -0.809081941397355 \tabularnewline
129 & 9 & 11.0426665806976 & -2.04266658069758 \tabularnewline
130 & 11 & 9.39967815683661 & 1.60032184316339 \tabularnewline
131 & 10 & 11.7914634959918 & -1.79146349599182 \tabularnewline
132 & 11 & 8.73080633827149 & 2.26919366172851 \tabularnewline
133 & 8 & 11.4843377035926 & -3.48433770359264 \tabularnewline
134 & 11 & 9.63286334519974 & 1.36713665480026 \tabularnewline
135 & 12 & 10.5472483161872 & 1.45275168381275 \tabularnewline
136 & 12 & 10.9134645286763 & 1.0865354713237 \tabularnewline
137 & 9 & 9.82868087243896 & -0.828680872438958 \tabularnewline
138 & 11 & 11.0956874897645 & -0.0956874897644672 \tabularnewline
139 & 10 & 9.25777513396797 & 0.742224866032029 \tabularnewline
140 & 8 & 9.47192203705678 & -1.47192203705678 \tabularnewline
141 & 9 & 7.65407421128651 & 1.34592578871349 \tabularnewline
142 & 8 & 11.7561888370001 & -3.75618883700011 \tabularnewline
143 & 9 & 11.1971916865423 & -2.19719168654233 \tabularnewline
144 & 15 & 12.4092454982301 & 2.59075450176987 \tabularnewline
145 & 11 & 11.3977149594834 & -0.397714959483352 \tabularnewline
146 & 8 & 8.03893797140226 & -0.0389379714022622 \tabularnewline
147 & 13 & 12.4908164183439 & 0.509183581656068 \tabularnewline
148 & 12 & 10.1345113883703 & 1.86548861162966 \tabularnewline
149 & 12 & 11.7554270956875 & 0.244572904312479 \tabularnewline
150 & 9 & 9.99507759363662 & -0.995077593636617 \tabularnewline
151 & 7 & 8.37762520015308 & -1.37762520015308 \tabularnewline
152 & 13 & 10.7862884517931 & 2.21371154820689 \tabularnewline
153 & 9 & 11.7935304626767 & -2.79353046267673 \tabularnewline
154 & 6 & 11.8597729909134 & -5.85977299091341 \tabularnewline
155 & 8 & 10.5939959816378 & -2.59399598163781 \tabularnewline
156 & 8 & 8.31937444704162 & -0.31937444704162 \tabularnewline
157 & 15 & 11.9959767707738 & 3.00402322922623 \tabularnewline
158 & 6 & 10.2183444704624 & -4.21834447046237 \tabularnewline
159 & 9 & 11.0426665806976 & -2.04266658069758 \tabularnewline
160 & 11 & 11.7018587155815 & -0.701858715581503 \tabularnewline
161 & 8 & 9.62889376062547 & -1.62889376062547 \tabularnewline
162 & 8 & 10.0663928465821 & -2.06639284658212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&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]9.87430108142183[/C][C]2.12569891857817[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.2235250585375[/C][C]-0.223525058537466[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5339918411383[/C][C]1.46600815886169[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.3193839691587[/C][C]-5.31938396915873[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.4219513333832[/C][C]2.57804866661678[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.69901806413087[/C][C]0.300981935869128[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.6499189505108[/C][C]-0.6499189505108[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.2192095084323[/C][C]2.78079049156767[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.545168724653[/C][C]1.45483127534703[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0549138052263[/C][C]-5.05491380522629[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.2857530462841[/C][C]-1.28575304628407[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.4480027772661[/C][C]0.551997222733896[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.5892745029769[/C][C]0.410725497023086[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.4267826650356[/C][C]-0.42678266503557[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.219851419521[/C][C]2.78014858047904[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.1417882096627[/C][C]0.858211790337311[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.9276692765871[/C][C]-0.927669276587128[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6166528988683[/C][C]-1.61665289886833[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.6437729713974[/C][C]-1.64377297139736[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5740058312382[/C][C]0.425994168761792[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6648360666432[/C][C]-0.664836066643154[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.7615631406725[/C][C]0.23843685932747[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1384638758318[/C][C]-0.138463875831758[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.7009032351795[/C][C]-0.700903235179519[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.7864070462344[/C][C]-2.78640704623436[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.0686960125552[/C][C]0.931303987444767[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.559551558222[/C][C]-1.55955155822196[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.1990755096422[/C][C]2.80092449035779[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.6038924215564[/C][C]0.396107578443598[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.6472888421444[/C][C]-0.64728884214435[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0568683114543[/C][C]0.943131688545682[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.78214942471264[/C][C]-1.78214942471264[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.2826329467188[/C][C]-0.282632946718843[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7250734274722[/C][C]0.274926572527795[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.4645101756171[/C][C]1.5354898243829[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.9675640446936[/C][C]0.0324359553064027[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.65660110003244[/C][C]-2.65660110003244[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.4338803842506[/C][C]1.56611961574938[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.7216664330209[/C][C]-1.72166643302086[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.6631194479239[/C][C]-1.66311944792389[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4761698911984[/C][C]-1.4761698911984[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.4530939616396[/C][C]1.5469060383604[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.5214435981629[/C][C]1.4785564018371[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5593788207707[/C][C]-0.559378820770704[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.3132003486076[/C][C]-0.313200348607569[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.01492653687501[/C][C]2.98507346312499[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.5840950915946[/C][C]2.41590490840542[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1582591710422[/C][C]-0.158259171042193[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.5285250869533[/C][C]-0.528525086953345[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.4117232601718[/C][C]1.58827673982821[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8515230849059[/C][C]2.14847691509409[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5225406766449[/C][C]-0.522540676644908[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.6015883065832[/C][C]2.39841169341681[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7773254780715[/C][C]1.22267452192849[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.10500712881499[/C][C]-3.10500712881499[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.347109885371[/C][C]-4.34710988537104[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3177834967856[/C][C]0.682216503214374[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9160243368809[/C][C]0.0839756631190745[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.190116652862[/C][C]-0.190116652862015[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.6228566504585[/C][C]-1.62285665045847[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.59800276566633[/C][C]-2.59800276566633[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.5214200432732[/C][C]0.478579956726817[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.8268563884989[/C][C]2.17314361150106[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7359921662694[/C][C]0.264007833730558[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.4927600153244[/C][C]0.507239984675606[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.4246858053931[/C][C]0.575314194606921[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.652273361793[/C][C]1.34772663820703[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.7744077238441[/C][C]-1.77440772384409[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.44677922800112[/C][C]2.55322077199888[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6266555402349[/C][C]0.373344459765051[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.32156214862119[/C][C]-0.321562148621191[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7284860242677[/C][C]0.271513975732291[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8303750570365[/C][C]1.16962494296355[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.7774750417953[/C][C]1.2225249582047[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4951894582621[/C][C]0.504810541737896[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7381390628484[/C][C]-2.73813906284843[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.4702088914228[/C][C]1.52979110857724[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4740221449451[/C][C]-0.4740221449451[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.1952937588971[/C][C]1.80470624110291[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.3862730797129[/C][C]0.613726920287076[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7696670620867[/C][C]0.230332937913348[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.8387949577148[/C][C]-1.83879495771478[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7278193073437[/C][C]0.272180692656298[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.8819765188691[/C][C]1.11802348113089[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1842796788729[/C][C]-0.184279678872921[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5832599823992[/C][C]-1.58325998239919[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5209606727743[/C][C]-1.52096067277427[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.7065722423052[/C][C]0.293427757694769[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8009927632823[/C][C]0.199007236717744[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.2020653663306[/C][C]0.797934633669447[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.60129000644368[/C][C]-0.601290006443678[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]11.9959767707738[/C][C]3.00402322922623[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.7897715474391[/C][C]0.210228452560861[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9699596369733[/C][C]1.03004036302669[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.2742581041526[/C][C]1.72574189584743[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4408020531153[/C][C]-1.44080205311527[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5348348910729[/C][C]1.46516510892712[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.5429870614637[/C][C]-2.54298706146366[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4259076649961[/C][C]0.574092335003935[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7709421160586[/C][C]-0.770942116058564[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.4671566639129[/C][C]2.5328433360871[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.537947797134[/C][C]-0.537947797134037[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.6973175772501[/C][C]1.3026824227499[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.1565174978989[/C][C]-0.156517497898942[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.752116117169[/C][C]-0.752116117169022[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.76859428286186[/C][C]2.23140571713815[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.9677322075304[/C][C]0.0322677924696399[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.3646880562456[/C][C]0.635311943754397[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.6901173477808[/C][C]-0.690117347780795[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.26340022143179[/C][C]-2.26340022143179[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.7710446030505[/C][C]0.228955396949462[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.829828261292[/C][C]2.17017173870797[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5739692245957[/C][C]-1.57396922459566[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.1406801536022[/C][C]0.859319846397823[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.23544045265891[/C][C]0.764559547341086[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4670165353155[/C][C]0.532983464684458[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.8040634360511[/C][C]2.19593656394888[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7725006456168[/C][C]-2.77250064561678[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.2218609482543[/C][C]0.77813905174568[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4689551767201[/C][C]0.531044823279859[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.233472486602[/C][C]0.766527513398023[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.5240239684628[/C][C]1.47597603153722[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.035646365322[/C][C]1.96435363467801[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.6955844763388[/C][C]2.30441552366117[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.0558552760882[/C][C]1.94414472391178[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.8390758982933[/C][C]3.16092410170667[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.7934651285417[/C][C]-0.793465128541668[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8090819413974[/C][C]-0.809081941397355[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.0426665806976[/C][C]-2.04266658069758[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.39967815683661[/C][C]1.60032184316339[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.7914634959918[/C][C]-1.79146349599182[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.73080633827149[/C][C]2.26919366172851[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.4843377035926[/C][C]-3.48433770359264[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.63286334519974[/C][C]1.36713665480026[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.5472483161872[/C][C]1.45275168381275[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.9134645286763[/C][C]1.0865354713237[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.82868087243896[/C][C]-0.828680872438958[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.0956874897645[/C][C]-0.0956874897644672[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.25777513396797[/C][C]0.742224866032029[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.47192203705678[/C][C]-1.47192203705678[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.65407421128651[/C][C]1.34592578871349[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.7561888370001[/C][C]-3.75618883700011[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.1971916865423[/C][C]-2.19719168654233[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.4092454982301[/C][C]2.59075450176987[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.3977149594834[/C][C]-0.397714959483352[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.03893797140226[/C][C]-0.0389379714022622[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.4908164183439[/C][C]0.509183581656068[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.1345113883703[/C][C]1.86548861162966[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.7554270956875[/C][C]0.244572904312479[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.99507759363662[/C][C]-0.995077593636617[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.37762520015308[/C][C]-1.37762520015308[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.7862884517931[/C][C]2.21371154820689[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.7935304626767[/C][C]-2.79353046267673[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.8597729909134[/C][C]-5.85977299091341[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.5939959816378[/C][C]-2.59399598163781[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.31937444704162[/C][C]-0.31937444704162[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]11.9959767707738[/C][C]3.00402322922623[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2183444704624[/C][C]-4.21834447046237[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.0426665806976[/C][C]-2.04266658069758[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.7018587155815[/C][C]-0.701858715581503[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.62889376062547[/C][C]-1.62889376062547[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.0663928465821[/C][C]-2.06639284658212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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
1129.874301081421832.12569891857817
21111.2235250585375-0.223525058537466
31513.53399184113831.46600815886169
4611.3193839691587-5.31938396915873
51310.42195133338322.57804866661678
6109.699018064130870.300981935869128
71212.6499189505108-0.6499189505108
81411.21920950843232.78079049156767
91210.5451687246531.45483127534703
10611.0549138052263-5.05491380522629
111011.2857530462841-1.28575304628407
121211.44800277726610.551997222733896
131211.58927450297690.410725497023086
141111.4267826650356-0.42678266503557
151512.2198514195212.78014858047904
161211.14178820966270.858211790337311
171010.9276692765871-0.927669276587128
181213.6166528988683-1.61665289886833
191112.6437729713974-1.64377297139736
201211.57400583123820.425994168761792
211111.6648360666432-0.664836066643154
221211.76156314067250.23843685932747
231313.1384638758318-0.138463875831758
241111.7009032351795-0.700903235179519
25911.7864070462344-2.78640704623436
261312.06869601255520.931303987444767
271011.559551558222-1.55955155822196
281411.19907550964222.80092449035779
291211.60389242155640.396107578443598
301010.6472888421444-0.64728884214435
311211.05686831145430.943131688545682
3289.78214942471264-1.78214942471264
331010.2826329467188-0.282632946718843
341211.72507342747220.274926572527795
351210.46451017561711.5354898243829
3676.96756404469360.0324359553064027
3768.65660110003244-2.65660110003244
381210.43388038425061.56611961574938
391011.7216664330209-1.72166643302086
401011.6631194479239-1.66311944792389
411011.4761698911984-1.4761698911984
421210.45309396163961.5469060383604
431513.52144359816291.4785564018371
441010.5593788207707-0.559378820770704
451010.3132003486076-0.313200348607569
46129.014926536875012.98507346312499
471310.58409509159462.41590490840542
481111.1582591710422-0.158259171042193
491111.5285250869533-0.528525086953345
501210.41172326017181.58827673982821
511411.85152308490592.14847691509409
521010.5225406766449-0.522540676644908
53129.60158830658322.39841169341681
541311.77732547807151.22267452192849
5558.10500712881499-3.10500712881499
56610.347109885371-4.34710988537104
571211.31778349678560.682216503214374
581211.91602433688090.0839756631190745
591111.190116652862-0.190116652862015
601011.6228566504585-1.62285665045847
6179.59800276566633-2.59800276566633
621211.52142004327320.478579956726817
631411.82685638849892.17314361150106
641110.73599216626940.264007833730558
651211.49276001532440.507239984675606
661312.42468580539310.575314194606921
671412.6522733617931.34772663820703
681112.7744077238441-1.77440772384409
69129.446779228001122.55322077199888
701211.62665554023490.373344459765051
7188.32156214862119-0.321562148621191
721110.72848602426770.271513975732291
731412.83037505703651.16962494296355
741412.77747504179531.2225249582047
751211.49518945826210.504810541737896
76911.7381390628484-2.73813906284843
771311.47020889142281.52979110857724
781111.4740221449451-0.4740221449451
791210.19529375889711.80470624110291
801211.38627307971290.613726920287076
811211.76966706208670.230332937913348
821213.8387949577148-1.83879495771478
831211.72781930734370.272180692656298
841210.88197651886911.11802348113089
851111.1842796788729-0.184279678872921
861011.5832599823992-1.58325998239919
87910.5209606727743-1.52096067277427
881211.70657224230520.293427757694769
891211.80099276328230.199007236717744
901211.20206536633060.797934633669447
9199.60129000644368-0.601290006443678
921511.99597677077383.00402322922623
931211.78977154743910.210228452560861
941210.96995963697331.03004036302669
951210.27425810415261.72574189584743
961011.4408020531153-1.44080205311527
971311.53483489107291.46516510892712
98911.5429870614637-2.54298706146366
991211.42590766499610.574092335003935
1001010.7709421160586-0.770942116058564
1011411.46715666391292.5328433360871
1021111.537947797134-0.537947797134037
1031513.69731757725011.3026824227499
1041111.1565174978989-0.156517497898942
1051111.752116117169-0.752116117169022
106129.768594282861862.23140571713815
1071211.96773220753040.0322677924696399
1081211.36468805624560.635311943754397
1091111.6901173477808-0.690117347780795
11079.26340022143179-2.26340022143179
1111211.77104460305050.228955396949462
1121411.8298282612922.17017173870797
1131112.5739692245957-1.57396922459566
1141110.14068015360220.859319846397823
115109.235440452658910.764559547341086
1161312.46701653531550.532983464684458
1171310.80406343605112.19593656394888
118810.7725006456168-2.77250064561678
1191110.22186094825430.77813905174568
1201211.46895517672010.531044823279859
1211110.2334724866020.766527513398023
1221311.52402396846281.47597603153722
1231210.0356463653221.96435363467801
1241411.69558447633882.30441552366117
1251311.05585527608821.94414472391178
1261511.83907589829333.16092410170667
1271010.7934651285417-0.793465128541668
1281111.8090819413974-0.809081941397355
129911.0426665806976-2.04266658069758
130119.399678156836611.60032184316339
1311011.7914634959918-1.79146349599182
132118.730806338271492.26919366172851
133811.4843377035926-3.48433770359264
134119.632863345199741.36713665480026
1351210.54724831618721.45275168381275
1361210.91346452867631.0865354713237
13799.82868087243896-0.828680872438958
1381111.0956874897645-0.0956874897644672
139109.257775133967970.742224866032029
14089.47192203705678-1.47192203705678
14197.654074211286511.34592578871349
142811.7561888370001-3.75618883700011
143911.1971916865423-2.19719168654233
1441512.40924549823012.59075450176987
1451111.3977149594834-0.397714959483352
14688.03893797140226-0.0389379714022622
1471312.49081641834390.509183581656068
1481210.13451138837031.86548861162966
1491211.75542709568750.244572904312479
15099.99507759363662-0.995077593636617
15178.37762520015308-1.37762520015308
1521310.78628845179312.21371154820689
153911.7935304626767-2.79353046267673
154611.8597729909134-5.85977299091341
155810.5939959816378-2.59399598163781
15688.31937444704162-0.31937444704162
1571511.99597677077383.00402322922623
158610.2183444704624-4.21834447046237
159911.0426665806976-2.04266658069758
1601111.7018587155815-0.701858715581503
16189.62889376062547-1.62889376062547
162810.0663928465821-2.06639284658212







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.999845277639330.0003094447213392090.000154722360669604
120.9997387761658580.0005224476682831490.000261223834141575
130.99937620894040.001247582119200550.000623791059600274
140.9989786116943470.002042776611306560.00102138830565328
150.9988277124633210.00234457507335890.00117228753667945
160.9978884357510840.004223128497832780.00211156424891639
170.996138793580410.007722412839180730.00386120641959036
180.9952399477167480.009520104566504420.00476005228325221
190.9924909046690810.01501819066183870.00750909533091937
200.9873317133469310.02533657330613760.0126682866530688
210.9804492441022580.03910151179548340.0195507558977417
220.9721117340166760.05577653196664860.0278882659833243
230.958661872229080.082676255541840.04133812777092
240.9414552039860340.1170895920279310.0585447960139657
250.9397248320608970.1205503358782060.0602751679391029
260.9438080052345620.1123839895308750.0561919947654377
270.944348473289650.1113030534207010.0556515267103503
280.9510299361597010.0979401276805970.0489700638402985
290.9326026622233590.1347946755532820.0673973377766411
300.9124649382166250.1750701235667510.0875350617833753
310.895878221702280.208243556595440.10412177829772
320.9127524137851460.1744951724297080.0872475862148541
330.8863934760461950.2272130479076090.113606523953805
340.8548625780582710.2902748438834590.145137421941729
350.8409881868778980.3180236262442040.159011813122102
360.8041023307379570.3917953385240860.195897669262043
370.8453924300134460.3092151399731080.154607569986554
380.836623689300350.3267526213992990.16337631069965
390.8263998343062820.3472003313874350.173600165693718
400.8083401264615410.3833197470769170.191659873538459
410.7845053023636840.4309893952726310.215494697636316
420.7684564944507260.4630870110985470.231543505549274
430.7666212685709690.4667574628580630.233378731429031
440.7255091689857550.548981662028490.274490831014245
450.6823671044687630.6352657910624750.317632895531237
460.7434928648364650.513014270327070.256507135163535
470.7527181077850840.4945637844298330.247281892214916
480.7097225707995760.5805548584008480.290277429200424
490.6726482035869450.6547035928261090.327351796413055
500.6583408940175140.6833182119649730.341659105982486
510.6629418169940120.6741163660119760.337058183005988
520.6166776116118510.7666447767762990.383322388388149
530.6424040766553650.7151918466892690.357595923344635
540.6085561217334350.7828877565331290.391443878266565
550.6984517466766860.6030965066466280.301548253323314
560.8535492249161070.2929015501677870.146450775083893
570.8271720422627930.3456559154744150.172827957737207
580.7941093066880780.4117813866238430.205890693311922
590.7583736916009220.4832526167981560.241626308399078
600.7463733902446110.5072532195107780.253626609755389
610.7632503831573560.4734992336852870.236749616842644
620.7276953077934560.5446093844130870.272304692206544
630.7405496089027450.5189007821945110.259450391097255
640.7005464281188410.5989071437623180.299453571881159
650.6660181231306240.6679637537387510.333981876869376
660.6243940201928260.7512119596143470.375605979807174
670.6049522144077080.7900955711845850.395047785592292
680.5896277001180020.8207445997639950.410372299881998
690.6070929514849940.7858140970300110.392907048515006
700.5634563878275350.873087224344930.436543612172465
710.5218566368108020.9562867263783960.478143363189198
720.4792235036068120.9584470072136240.520776496393188
730.4491229282731520.8982458565463050.550877071726848
740.4243533932696170.8487067865392340.575646606730383
750.398045207823040.7960904156460810.60195479217696
760.4481081238777490.8962162477554980.551891876122251
770.4355511060253780.8711022120507550.564448893974622
780.393364936598240.7867298731964790.60663506340176
790.397659851617910.7953197032358210.60234014838209
800.3740504173604110.7481008347208220.625949582639589
810.3323503331245540.6647006662491080.667649666875446
820.3406255592939210.6812511185878420.659374440706079
830.3041549275409210.6083098550818420.695845072459079
840.2772077453144890.5544154906289780.722792254685511
850.239892116531760.479784233063520.76010788346824
860.2316142165585570.4632284331171140.768385783441443
870.2310498283038540.4620996566077080.768950171696146
880.1976353714580380.3952707429160760.802364628541962
890.1679542073286480.3359084146572970.832045792671352
900.146381774512210.2927635490244210.85361822548779
910.1258818510426020.2517637020852040.874118148957398
920.1774113677312090.3548227354624190.822588632268791
930.1529604767553270.3059209535106540.847039523244673
940.1373460080312960.2746920160625930.862653991968704
950.1310683844360880.2621367688721770.868931615563912
960.1239992590621380.2479985181242760.876000740937862
970.1158073313360320.2316146626720630.884192668663969
980.1480222398527110.2960444797054210.851977760147289
990.1235477044532570.2470954089065140.876452295546743
1000.1050612365163520.2101224730327040.894938763483648
1010.1241389341100990.2482778682201980.875861065889901
1020.1030544766326450.206108953265290.896945523367355
1030.09777733595538720.1955546719107740.902222664044613
1040.08026767979018530.1605353595803710.919732320209815
1050.06659951585948310.1331990317189660.933400484140517
1060.06764694106284230.1352938821256850.932353058937158
1070.05352294377041080.1070458875408220.946477056229589
1080.04632711695731860.09265423391463720.953672883042681
1090.03664093497805990.07328186995611980.96335906502194
1100.03731473175627370.07462946351254740.962685268243726
1110.0287122086855360.0574244173710720.971287791314464
1120.03125569254557010.06251138509114030.96874430745443
1130.02799829950783610.05599659901567230.972001700492164
1140.02217299218749120.04434598437498240.977827007812509
1150.01717442833991090.03434885667982180.982825571660089
1160.01298181897238550.0259636379447710.987018181027615
1170.0197856320743810.0395712641487620.980214367925619
1180.02252945226342350.04505890452684690.977470547736576
1190.01715942700424040.03431885400848080.98284057299576
1200.01315916809705030.02631833619410060.98684083190295
1210.01081306854810780.02162613709621560.989186931451892
1220.008922272125821960.01784454425164390.991077727874178
1230.01043890517058070.02087781034116150.989561094829419
1240.01657781242633410.03315562485266830.983422187573666
1250.01742680042418930.03485360084837860.982573199575811
1260.04159219282934970.08318438565869950.95840780717065
1270.03138627933699590.06277255867399180.968613720663004
1280.0237770813098520.04755416261970390.976222918690148
1290.01977977503775590.03955955007551190.980220224962244
1300.01856987637374460.03713975274748920.981430123626255
1310.01477222379204070.02954444758408150.985227776207959
1320.01716368217780340.03432736435560690.982836317822197
1330.0263944701552070.0527889403104140.973605529844793
1340.02901193043455730.05802386086911450.970988069565443
1350.03211530956849590.06423061913699190.967884690431504
1360.0262638251580680.0525276503161360.973736174841932
1370.02235021937266320.04470043874532650.977649780627337
1380.01626861010196460.03253722020392910.983731389898035
1390.01648063884255920.03296127768511840.983519361157441
1400.01139772995980.02279545991960.9886022700402
1410.03625612954502040.07251225909004070.96374387045498
1420.03815195549699650.07630391099399290.961848044503004
1430.0269842296513140.05396845930262790.973015770348686
1440.29507049969640.59014099939280.7049295003036
1450.3189114083975660.6378228167951320.681088591602434
1460.6378085465169780.7243829069660430.362191453483022
1470.6427798045536870.7144403908926270.357220195446313
1480.9113118865711810.1773762268576380.088688113428819
1490.9184880601176740.1630238797646520.0815119398823261
1500.839795699479520.320408601040960.16020430052048
1510.726040678729630.547918642540740.27395932127037

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.99984527763933 & 0.000309444721339209 & 0.000154722360669604 \tabularnewline
12 & 0.999738776165858 & 0.000522447668283149 & 0.000261223834141575 \tabularnewline
13 & 0.9993762089404 & 0.00124758211920055 & 0.000623791059600274 \tabularnewline
14 & 0.998978611694347 & 0.00204277661130656 & 0.00102138830565328 \tabularnewline
15 & 0.998827712463321 & 0.0023445750733589 & 0.00117228753667945 \tabularnewline
16 & 0.997888435751084 & 0.00422312849783278 & 0.00211156424891639 \tabularnewline
17 & 0.99613879358041 & 0.00772241283918073 & 0.00386120641959036 \tabularnewline
18 & 0.995239947716748 & 0.00952010456650442 & 0.00476005228325221 \tabularnewline
19 & 0.992490904669081 & 0.0150181906618387 & 0.00750909533091937 \tabularnewline
20 & 0.987331713346931 & 0.0253365733061376 & 0.0126682866530688 \tabularnewline
21 & 0.980449244102258 & 0.0391015117954834 & 0.0195507558977417 \tabularnewline
22 & 0.972111734016676 & 0.0557765319666486 & 0.0278882659833243 \tabularnewline
23 & 0.95866187222908 & 0.08267625554184 & 0.04133812777092 \tabularnewline
24 & 0.941455203986034 & 0.117089592027931 & 0.0585447960139657 \tabularnewline
25 & 0.939724832060897 & 0.120550335878206 & 0.0602751679391029 \tabularnewline
26 & 0.943808005234562 & 0.112383989530875 & 0.0561919947654377 \tabularnewline
27 & 0.94434847328965 & 0.111303053420701 & 0.0556515267103503 \tabularnewline
28 & 0.951029936159701 & 0.097940127680597 & 0.0489700638402985 \tabularnewline
29 & 0.932602662223359 & 0.134794675553282 & 0.0673973377766411 \tabularnewline
30 & 0.912464938216625 & 0.175070123566751 & 0.0875350617833753 \tabularnewline
31 & 0.89587822170228 & 0.20824355659544 & 0.10412177829772 \tabularnewline
32 & 0.912752413785146 & 0.174495172429708 & 0.0872475862148541 \tabularnewline
33 & 0.886393476046195 & 0.227213047907609 & 0.113606523953805 \tabularnewline
34 & 0.854862578058271 & 0.290274843883459 & 0.145137421941729 \tabularnewline
35 & 0.840988186877898 & 0.318023626244204 & 0.159011813122102 \tabularnewline
36 & 0.804102330737957 & 0.391795338524086 & 0.195897669262043 \tabularnewline
37 & 0.845392430013446 & 0.309215139973108 & 0.154607569986554 \tabularnewline
38 & 0.83662368930035 & 0.326752621399299 & 0.16337631069965 \tabularnewline
39 & 0.826399834306282 & 0.347200331387435 & 0.173600165693718 \tabularnewline
40 & 0.808340126461541 & 0.383319747076917 & 0.191659873538459 \tabularnewline
41 & 0.784505302363684 & 0.430989395272631 & 0.215494697636316 \tabularnewline
42 & 0.768456494450726 & 0.463087011098547 & 0.231543505549274 \tabularnewline
43 & 0.766621268570969 & 0.466757462858063 & 0.233378731429031 \tabularnewline
44 & 0.725509168985755 & 0.54898166202849 & 0.274490831014245 \tabularnewline
45 & 0.682367104468763 & 0.635265791062475 & 0.317632895531237 \tabularnewline
46 & 0.743492864836465 & 0.51301427032707 & 0.256507135163535 \tabularnewline
47 & 0.752718107785084 & 0.494563784429833 & 0.247281892214916 \tabularnewline
48 & 0.709722570799576 & 0.580554858400848 & 0.290277429200424 \tabularnewline
49 & 0.672648203586945 & 0.654703592826109 & 0.327351796413055 \tabularnewline
50 & 0.658340894017514 & 0.683318211964973 & 0.341659105982486 \tabularnewline
51 & 0.662941816994012 & 0.674116366011976 & 0.337058183005988 \tabularnewline
52 & 0.616677611611851 & 0.766644776776299 & 0.383322388388149 \tabularnewline
53 & 0.642404076655365 & 0.715191846689269 & 0.357595923344635 \tabularnewline
54 & 0.608556121733435 & 0.782887756533129 & 0.391443878266565 \tabularnewline
55 & 0.698451746676686 & 0.603096506646628 & 0.301548253323314 \tabularnewline
56 & 0.853549224916107 & 0.292901550167787 & 0.146450775083893 \tabularnewline
57 & 0.827172042262793 & 0.345655915474415 & 0.172827957737207 \tabularnewline
58 & 0.794109306688078 & 0.411781386623843 & 0.205890693311922 \tabularnewline
59 & 0.758373691600922 & 0.483252616798156 & 0.241626308399078 \tabularnewline
60 & 0.746373390244611 & 0.507253219510778 & 0.253626609755389 \tabularnewline
61 & 0.763250383157356 & 0.473499233685287 & 0.236749616842644 \tabularnewline
62 & 0.727695307793456 & 0.544609384413087 & 0.272304692206544 \tabularnewline
63 & 0.740549608902745 & 0.518900782194511 & 0.259450391097255 \tabularnewline
64 & 0.700546428118841 & 0.598907143762318 & 0.299453571881159 \tabularnewline
65 & 0.666018123130624 & 0.667963753738751 & 0.333981876869376 \tabularnewline
66 & 0.624394020192826 & 0.751211959614347 & 0.375605979807174 \tabularnewline
67 & 0.604952214407708 & 0.790095571184585 & 0.395047785592292 \tabularnewline
68 & 0.589627700118002 & 0.820744599763995 & 0.410372299881998 \tabularnewline
69 & 0.607092951484994 & 0.785814097030011 & 0.392907048515006 \tabularnewline
70 & 0.563456387827535 & 0.87308722434493 & 0.436543612172465 \tabularnewline
71 & 0.521856636810802 & 0.956286726378396 & 0.478143363189198 \tabularnewline
72 & 0.479223503606812 & 0.958447007213624 & 0.520776496393188 \tabularnewline
73 & 0.449122928273152 & 0.898245856546305 & 0.550877071726848 \tabularnewline
74 & 0.424353393269617 & 0.848706786539234 & 0.575646606730383 \tabularnewline
75 & 0.39804520782304 & 0.796090415646081 & 0.60195479217696 \tabularnewline
76 & 0.448108123877749 & 0.896216247755498 & 0.551891876122251 \tabularnewline
77 & 0.435551106025378 & 0.871102212050755 & 0.564448893974622 \tabularnewline
78 & 0.39336493659824 & 0.786729873196479 & 0.60663506340176 \tabularnewline
79 & 0.39765985161791 & 0.795319703235821 & 0.60234014838209 \tabularnewline
80 & 0.374050417360411 & 0.748100834720822 & 0.625949582639589 \tabularnewline
81 & 0.332350333124554 & 0.664700666249108 & 0.667649666875446 \tabularnewline
82 & 0.340625559293921 & 0.681251118587842 & 0.659374440706079 \tabularnewline
83 & 0.304154927540921 & 0.608309855081842 & 0.695845072459079 \tabularnewline
84 & 0.277207745314489 & 0.554415490628978 & 0.722792254685511 \tabularnewline
85 & 0.23989211653176 & 0.47978423306352 & 0.76010788346824 \tabularnewline
86 & 0.231614216558557 & 0.463228433117114 & 0.768385783441443 \tabularnewline
87 & 0.231049828303854 & 0.462099656607708 & 0.768950171696146 \tabularnewline
88 & 0.197635371458038 & 0.395270742916076 & 0.802364628541962 \tabularnewline
89 & 0.167954207328648 & 0.335908414657297 & 0.832045792671352 \tabularnewline
90 & 0.14638177451221 & 0.292763549024421 & 0.85361822548779 \tabularnewline
91 & 0.125881851042602 & 0.251763702085204 & 0.874118148957398 \tabularnewline
92 & 0.177411367731209 & 0.354822735462419 & 0.822588632268791 \tabularnewline
93 & 0.152960476755327 & 0.305920953510654 & 0.847039523244673 \tabularnewline
94 & 0.137346008031296 & 0.274692016062593 & 0.862653991968704 \tabularnewline
95 & 0.131068384436088 & 0.262136768872177 & 0.868931615563912 \tabularnewline
96 & 0.123999259062138 & 0.247998518124276 & 0.876000740937862 \tabularnewline
97 & 0.115807331336032 & 0.231614662672063 & 0.884192668663969 \tabularnewline
98 & 0.148022239852711 & 0.296044479705421 & 0.851977760147289 \tabularnewline
99 & 0.123547704453257 & 0.247095408906514 & 0.876452295546743 \tabularnewline
100 & 0.105061236516352 & 0.210122473032704 & 0.894938763483648 \tabularnewline
101 & 0.124138934110099 & 0.248277868220198 & 0.875861065889901 \tabularnewline
102 & 0.103054476632645 & 0.20610895326529 & 0.896945523367355 \tabularnewline
103 & 0.0977773359553872 & 0.195554671910774 & 0.902222664044613 \tabularnewline
104 & 0.0802676797901853 & 0.160535359580371 & 0.919732320209815 \tabularnewline
105 & 0.0665995158594831 & 0.133199031718966 & 0.933400484140517 \tabularnewline
106 & 0.0676469410628423 & 0.135293882125685 & 0.932353058937158 \tabularnewline
107 & 0.0535229437704108 & 0.107045887540822 & 0.946477056229589 \tabularnewline
108 & 0.0463271169573186 & 0.0926542339146372 & 0.953672883042681 \tabularnewline
109 & 0.0366409349780599 & 0.0732818699561198 & 0.96335906502194 \tabularnewline
110 & 0.0373147317562737 & 0.0746294635125474 & 0.962685268243726 \tabularnewline
111 & 0.028712208685536 & 0.057424417371072 & 0.971287791314464 \tabularnewline
112 & 0.0312556925455701 & 0.0625113850911403 & 0.96874430745443 \tabularnewline
113 & 0.0279982995078361 & 0.0559965990156723 & 0.972001700492164 \tabularnewline
114 & 0.0221729921874912 & 0.0443459843749824 & 0.977827007812509 \tabularnewline
115 & 0.0171744283399109 & 0.0343488566798218 & 0.982825571660089 \tabularnewline
116 & 0.0129818189723855 & 0.025963637944771 & 0.987018181027615 \tabularnewline
117 & 0.019785632074381 & 0.039571264148762 & 0.980214367925619 \tabularnewline
118 & 0.0225294522634235 & 0.0450589045268469 & 0.977470547736576 \tabularnewline
119 & 0.0171594270042404 & 0.0343188540084808 & 0.98284057299576 \tabularnewline
120 & 0.0131591680970503 & 0.0263183361941006 & 0.98684083190295 \tabularnewline
121 & 0.0108130685481078 & 0.0216261370962156 & 0.989186931451892 \tabularnewline
122 & 0.00892227212582196 & 0.0178445442516439 & 0.991077727874178 \tabularnewline
123 & 0.0104389051705807 & 0.0208778103411615 & 0.989561094829419 \tabularnewline
124 & 0.0165778124263341 & 0.0331556248526683 & 0.983422187573666 \tabularnewline
125 & 0.0174268004241893 & 0.0348536008483786 & 0.982573199575811 \tabularnewline
126 & 0.0415921928293497 & 0.0831843856586995 & 0.95840780717065 \tabularnewline
127 & 0.0313862793369959 & 0.0627725586739918 & 0.968613720663004 \tabularnewline
128 & 0.023777081309852 & 0.0475541626197039 & 0.976222918690148 \tabularnewline
129 & 0.0197797750377559 & 0.0395595500755119 & 0.980220224962244 \tabularnewline
130 & 0.0185698763737446 & 0.0371397527474892 & 0.981430123626255 \tabularnewline
131 & 0.0147722237920407 & 0.0295444475840815 & 0.985227776207959 \tabularnewline
132 & 0.0171636821778034 & 0.0343273643556069 & 0.982836317822197 \tabularnewline
133 & 0.026394470155207 & 0.052788940310414 & 0.973605529844793 \tabularnewline
134 & 0.0290119304345573 & 0.0580238608691145 & 0.970988069565443 \tabularnewline
135 & 0.0321153095684959 & 0.0642306191369919 & 0.967884690431504 \tabularnewline
136 & 0.026263825158068 & 0.052527650316136 & 0.973736174841932 \tabularnewline
137 & 0.0223502193726632 & 0.0447004387453265 & 0.977649780627337 \tabularnewline
138 & 0.0162686101019646 & 0.0325372202039291 & 0.983731389898035 \tabularnewline
139 & 0.0164806388425592 & 0.0329612776851184 & 0.983519361157441 \tabularnewline
140 & 0.0113977299598 & 0.0227954599196 & 0.9886022700402 \tabularnewline
141 & 0.0362561295450204 & 0.0725122590900407 & 0.96374387045498 \tabularnewline
142 & 0.0381519554969965 & 0.0763039109939929 & 0.961848044503004 \tabularnewline
143 & 0.026984229651314 & 0.0539684593026279 & 0.973015770348686 \tabularnewline
144 & 0.2950704996964 & 0.5901409993928 & 0.7049295003036 \tabularnewline
145 & 0.318911408397566 & 0.637822816795132 & 0.681088591602434 \tabularnewline
146 & 0.637808546516978 & 0.724382906966043 & 0.362191453483022 \tabularnewline
147 & 0.642779804553687 & 0.714440390892627 & 0.357220195446313 \tabularnewline
148 & 0.911311886571181 & 0.177376226857638 & 0.088688113428819 \tabularnewline
149 & 0.918488060117674 & 0.163023879764652 & 0.0815119398823261 \tabularnewline
150 & 0.83979569947952 & 0.32040860104096 & 0.16020430052048 \tabularnewline
151 & 0.72604067872963 & 0.54791864254074 & 0.27395932127037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.99984527763933[/C][C]0.000309444721339209[/C][C]0.000154722360669604[/C][/ROW]
[ROW][C]12[/C][C]0.999738776165858[/C][C]0.000522447668283149[/C][C]0.000261223834141575[/C][/ROW]
[ROW][C]13[/C][C]0.9993762089404[/C][C]0.00124758211920055[/C][C]0.000623791059600274[/C][/ROW]
[ROW][C]14[/C][C]0.998978611694347[/C][C]0.00204277661130656[/C][C]0.00102138830565328[/C][/ROW]
[ROW][C]15[/C][C]0.998827712463321[/C][C]0.0023445750733589[/C][C]0.00117228753667945[/C][/ROW]
[ROW][C]16[/C][C]0.997888435751084[/C][C]0.00422312849783278[/C][C]0.00211156424891639[/C][/ROW]
[ROW][C]17[/C][C]0.99613879358041[/C][C]0.00772241283918073[/C][C]0.00386120641959036[/C][/ROW]
[ROW][C]18[/C][C]0.995239947716748[/C][C]0.00952010456650442[/C][C]0.00476005228325221[/C][/ROW]
[ROW][C]19[/C][C]0.992490904669081[/C][C]0.0150181906618387[/C][C]0.00750909533091937[/C][/ROW]
[ROW][C]20[/C][C]0.987331713346931[/C][C]0.0253365733061376[/C][C]0.0126682866530688[/C][/ROW]
[ROW][C]21[/C][C]0.980449244102258[/C][C]0.0391015117954834[/C][C]0.0195507558977417[/C][/ROW]
[ROW][C]22[/C][C]0.972111734016676[/C][C]0.0557765319666486[/C][C]0.0278882659833243[/C][/ROW]
[ROW][C]23[/C][C]0.95866187222908[/C][C]0.08267625554184[/C][C]0.04133812777092[/C][/ROW]
[ROW][C]24[/C][C]0.941455203986034[/C][C]0.117089592027931[/C][C]0.0585447960139657[/C][/ROW]
[ROW][C]25[/C][C]0.939724832060897[/C][C]0.120550335878206[/C][C]0.0602751679391029[/C][/ROW]
[ROW][C]26[/C][C]0.943808005234562[/C][C]0.112383989530875[/C][C]0.0561919947654377[/C][/ROW]
[ROW][C]27[/C][C]0.94434847328965[/C][C]0.111303053420701[/C][C]0.0556515267103503[/C][/ROW]
[ROW][C]28[/C][C]0.951029936159701[/C][C]0.097940127680597[/C][C]0.0489700638402985[/C][/ROW]
[ROW][C]29[/C][C]0.932602662223359[/C][C]0.134794675553282[/C][C]0.0673973377766411[/C][/ROW]
[ROW][C]30[/C][C]0.912464938216625[/C][C]0.175070123566751[/C][C]0.0875350617833753[/C][/ROW]
[ROW][C]31[/C][C]0.89587822170228[/C][C]0.20824355659544[/C][C]0.10412177829772[/C][/ROW]
[ROW][C]32[/C][C]0.912752413785146[/C][C]0.174495172429708[/C][C]0.0872475862148541[/C][/ROW]
[ROW][C]33[/C][C]0.886393476046195[/C][C]0.227213047907609[/C][C]0.113606523953805[/C][/ROW]
[ROW][C]34[/C][C]0.854862578058271[/C][C]0.290274843883459[/C][C]0.145137421941729[/C][/ROW]
[ROW][C]35[/C][C]0.840988186877898[/C][C]0.318023626244204[/C][C]0.159011813122102[/C][/ROW]
[ROW][C]36[/C][C]0.804102330737957[/C][C]0.391795338524086[/C][C]0.195897669262043[/C][/ROW]
[ROW][C]37[/C][C]0.845392430013446[/C][C]0.309215139973108[/C][C]0.154607569986554[/C][/ROW]
[ROW][C]38[/C][C]0.83662368930035[/C][C]0.326752621399299[/C][C]0.16337631069965[/C][/ROW]
[ROW][C]39[/C][C]0.826399834306282[/C][C]0.347200331387435[/C][C]0.173600165693718[/C][/ROW]
[ROW][C]40[/C][C]0.808340126461541[/C][C]0.383319747076917[/C][C]0.191659873538459[/C][/ROW]
[ROW][C]41[/C][C]0.784505302363684[/C][C]0.430989395272631[/C][C]0.215494697636316[/C][/ROW]
[ROW][C]42[/C][C]0.768456494450726[/C][C]0.463087011098547[/C][C]0.231543505549274[/C][/ROW]
[ROW][C]43[/C][C]0.766621268570969[/C][C]0.466757462858063[/C][C]0.233378731429031[/C][/ROW]
[ROW][C]44[/C][C]0.725509168985755[/C][C]0.54898166202849[/C][C]0.274490831014245[/C][/ROW]
[ROW][C]45[/C][C]0.682367104468763[/C][C]0.635265791062475[/C][C]0.317632895531237[/C][/ROW]
[ROW][C]46[/C][C]0.743492864836465[/C][C]0.51301427032707[/C][C]0.256507135163535[/C][/ROW]
[ROW][C]47[/C][C]0.752718107785084[/C][C]0.494563784429833[/C][C]0.247281892214916[/C][/ROW]
[ROW][C]48[/C][C]0.709722570799576[/C][C]0.580554858400848[/C][C]0.290277429200424[/C][/ROW]
[ROW][C]49[/C][C]0.672648203586945[/C][C]0.654703592826109[/C][C]0.327351796413055[/C][/ROW]
[ROW][C]50[/C][C]0.658340894017514[/C][C]0.683318211964973[/C][C]0.341659105982486[/C][/ROW]
[ROW][C]51[/C][C]0.662941816994012[/C][C]0.674116366011976[/C][C]0.337058183005988[/C][/ROW]
[ROW][C]52[/C][C]0.616677611611851[/C][C]0.766644776776299[/C][C]0.383322388388149[/C][/ROW]
[ROW][C]53[/C][C]0.642404076655365[/C][C]0.715191846689269[/C][C]0.357595923344635[/C][/ROW]
[ROW][C]54[/C][C]0.608556121733435[/C][C]0.782887756533129[/C][C]0.391443878266565[/C][/ROW]
[ROW][C]55[/C][C]0.698451746676686[/C][C]0.603096506646628[/C][C]0.301548253323314[/C][/ROW]
[ROW][C]56[/C][C]0.853549224916107[/C][C]0.292901550167787[/C][C]0.146450775083893[/C][/ROW]
[ROW][C]57[/C][C]0.827172042262793[/C][C]0.345655915474415[/C][C]0.172827957737207[/C][/ROW]
[ROW][C]58[/C][C]0.794109306688078[/C][C]0.411781386623843[/C][C]0.205890693311922[/C][/ROW]
[ROW][C]59[/C][C]0.758373691600922[/C][C]0.483252616798156[/C][C]0.241626308399078[/C][/ROW]
[ROW][C]60[/C][C]0.746373390244611[/C][C]0.507253219510778[/C][C]0.253626609755389[/C][/ROW]
[ROW][C]61[/C][C]0.763250383157356[/C][C]0.473499233685287[/C][C]0.236749616842644[/C][/ROW]
[ROW][C]62[/C][C]0.727695307793456[/C][C]0.544609384413087[/C][C]0.272304692206544[/C][/ROW]
[ROW][C]63[/C][C]0.740549608902745[/C][C]0.518900782194511[/C][C]0.259450391097255[/C][/ROW]
[ROW][C]64[/C][C]0.700546428118841[/C][C]0.598907143762318[/C][C]0.299453571881159[/C][/ROW]
[ROW][C]65[/C][C]0.666018123130624[/C][C]0.667963753738751[/C][C]0.333981876869376[/C][/ROW]
[ROW][C]66[/C][C]0.624394020192826[/C][C]0.751211959614347[/C][C]0.375605979807174[/C][/ROW]
[ROW][C]67[/C][C]0.604952214407708[/C][C]0.790095571184585[/C][C]0.395047785592292[/C][/ROW]
[ROW][C]68[/C][C]0.589627700118002[/C][C]0.820744599763995[/C][C]0.410372299881998[/C][/ROW]
[ROW][C]69[/C][C]0.607092951484994[/C][C]0.785814097030011[/C][C]0.392907048515006[/C][/ROW]
[ROW][C]70[/C][C]0.563456387827535[/C][C]0.87308722434493[/C][C]0.436543612172465[/C][/ROW]
[ROW][C]71[/C][C]0.521856636810802[/C][C]0.956286726378396[/C][C]0.478143363189198[/C][/ROW]
[ROW][C]72[/C][C]0.479223503606812[/C][C]0.958447007213624[/C][C]0.520776496393188[/C][/ROW]
[ROW][C]73[/C][C]0.449122928273152[/C][C]0.898245856546305[/C][C]0.550877071726848[/C][/ROW]
[ROW][C]74[/C][C]0.424353393269617[/C][C]0.848706786539234[/C][C]0.575646606730383[/C][/ROW]
[ROW][C]75[/C][C]0.39804520782304[/C][C]0.796090415646081[/C][C]0.60195479217696[/C][/ROW]
[ROW][C]76[/C][C]0.448108123877749[/C][C]0.896216247755498[/C][C]0.551891876122251[/C][/ROW]
[ROW][C]77[/C][C]0.435551106025378[/C][C]0.871102212050755[/C][C]0.564448893974622[/C][/ROW]
[ROW][C]78[/C][C]0.39336493659824[/C][C]0.786729873196479[/C][C]0.60663506340176[/C][/ROW]
[ROW][C]79[/C][C]0.39765985161791[/C][C]0.795319703235821[/C][C]0.60234014838209[/C][/ROW]
[ROW][C]80[/C][C]0.374050417360411[/C][C]0.748100834720822[/C][C]0.625949582639589[/C][/ROW]
[ROW][C]81[/C][C]0.332350333124554[/C][C]0.664700666249108[/C][C]0.667649666875446[/C][/ROW]
[ROW][C]82[/C][C]0.340625559293921[/C][C]0.681251118587842[/C][C]0.659374440706079[/C][/ROW]
[ROW][C]83[/C][C]0.304154927540921[/C][C]0.608309855081842[/C][C]0.695845072459079[/C][/ROW]
[ROW][C]84[/C][C]0.277207745314489[/C][C]0.554415490628978[/C][C]0.722792254685511[/C][/ROW]
[ROW][C]85[/C][C]0.23989211653176[/C][C]0.47978423306352[/C][C]0.76010788346824[/C][/ROW]
[ROW][C]86[/C][C]0.231614216558557[/C][C]0.463228433117114[/C][C]0.768385783441443[/C][/ROW]
[ROW][C]87[/C][C]0.231049828303854[/C][C]0.462099656607708[/C][C]0.768950171696146[/C][/ROW]
[ROW][C]88[/C][C]0.197635371458038[/C][C]0.395270742916076[/C][C]0.802364628541962[/C][/ROW]
[ROW][C]89[/C][C]0.167954207328648[/C][C]0.335908414657297[/C][C]0.832045792671352[/C][/ROW]
[ROW][C]90[/C][C]0.14638177451221[/C][C]0.292763549024421[/C][C]0.85361822548779[/C][/ROW]
[ROW][C]91[/C][C]0.125881851042602[/C][C]0.251763702085204[/C][C]0.874118148957398[/C][/ROW]
[ROW][C]92[/C][C]0.177411367731209[/C][C]0.354822735462419[/C][C]0.822588632268791[/C][/ROW]
[ROW][C]93[/C][C]0.152960476755327[/C][C]0.305920953510654[/C][C]0.847039523244673[/C][/ROW]
[ROW][C]94[/C][C]0.137346008031296[/C][C]0.274692016062593[/C][C]0.862653991968704[/C][/ROW]
[ROW][C]95[/C][C]0.131068384436088[/C][C]0.262136768872177[/C][C]0.868931615563912[/C][/ROW]
[ROW][C]96[/C][C]0.123999259062138[/C][C]0.247998518124276[/C][C]0.876000740937862[/C][/ROW]
[ROW][C]97[/C][C]0.115807331336032[/C][C]0.231614662672063[/C][C]0.884192668663969[/C][/ROW]
[ROW][C]98[/C][C]0.148022239852711[/C][C]0.296044479705421[/C][C]0.851977760147289[/C][/ROW]
[ROW][C]99[/C][C]0.123547704453257[/C][C]0.247095408906514[/C][C]0.876452295546743[/C][/ROW]
[ROW][C]100[/C][C]0.105061236516352[/C][C]0.210122473032704[/C][C]0.894938763483648[/C][/ROW]
[ROW][C]101[/C][C]0.124138934110099[/C][C]0.248277868220198[/C][C]0.875861065889901[/C][/ROW]
[ROW][C]102[/C][C]0.103054476632645[/C][C]0.20610895326529[/C][C]0.896945523367355[/C][/ROW]
[ROW][C]103[/C][C]0.0977773359553872[/C][C]0.195554671910774[/C][C]0.902222664044613[/C][/ROW]
[ROW][C]104[/C][C]0.0802676797901853[/C][C]0.160535359580371[/C][C]0.919732320209815[/C][/ROW]
[ROW][C]105[/C][C]0.0665995158594831[/C][C]0.133199031718966[/C][C]0.933400484140517[/C][/ROW]
[ROW][C]106[/C][C]0.0676469410628423[/C][C]0.135293882125685[/C][C]0.932353058937158[/C][/ROW]
[ROW][C]107[/C][C]0.0535229437704108[/C][C]0.107045887540822[/C][C]0.946477056229589[/C][/ROW]
[ROW][C]108[/C][C]0.0463271169573186[/C][C]0.0926542339146372[/C][C]0.953672883042681[/C][/ROW]
[ROW][C]109[/C][C]0.0366409349780599[/C][C]0.0732818699561198[/C][C]0.96335906502194[/C][/ROW]
[ROW][C]110[/C][C]0.0373147317562737[/C][C]0.0746294635125474[/C][C]0.962685268243726[/C][/ROW]
[ROW][C]111[/C][C]0.028712208685536[/C][C]0.057424417371072[/C][C]0.971287791314464[/C][/ROW]
[ROW][C]112[/C][C]0.0312556925455701[/C][C]0.0625113850911403[/C][C]0.96874430745443[/C][/ROW]
[ROW][C]113[/C][C]0.0279982995078361[/C][C]0.0559965990156723[/C][C]0.972001700492164[/C][/ROW]
[ROW][C]114[/C][C]0.0221729921874912[/C][C]0.0443459843749824[/C][C]0.977827007812509[/C][/ROW]
[ROW][C]115[/C][C]0.0171744283399109[/C][C]0.0343488566798218[/C][C]0.982825571660089[/C][/ROW]
[ROW][C]116[/C][C]0.0129818189723855[/C][C]0.025963637944771[/C][C]0.987018181027615[/C][/ROW]
[ROW][C]117[/C][C]0.019785632074381[/C][C]0.039571264148762[/C][C]0.980214367925619[/C][/ROW]
[ROW][C]118[/C][C]0.0225294522634235[/C][C]0.0450589045268469[/C][C]0.977470547736576[/C][/ROW]
[ROW][C]119[/C][C]0.0171594270042404[/C][C]0.0343188540084808[/C][C]0.98284057299576[/C][/ROW]
[ROW][C]120[/C][C]0.0131591680970503[/C][C]0.0263183361941006[/C][C]0.98684083190295[/C][/ROW]
[ROW][C]121[/C][C]0.0108130685481078[/C][C]0.0216261370962156[/C][C]0.989186931451892[/C][/ROW]
[ROW][C]122[/C][C]0.00892227212582196[/C][C]0.0178445442516439[/C][C]0.991077727874178[/C][/ROW]
[ROW][C]123[/C][C]0.0104389051705807[/C][C]0.0208778103411615[/C][C]0.989561094829419[/C][/ROW]
[ROW][C]124[/C][C]0.0165778124263341[/C][C]0.0331556248526683[/C][C]0.983422187573666[/C][/ROW]
[ROW][C]125[/C][C]0.0174268004241893[/C][C]0.0348536008483786[/C][C]0.982573199575811[/C][/ROW]
[ROW][C]126[/C][C]0.0415921928293497[/C][C]0.0831843856586995[/C][C]0.95840780717065[/C][/ROW]
[ROW][C]127[/C][C]0.0313862793369959[/C][C]0.0627725586739918[/C][C]0.968613720663004[/C][/ROW]
[ROW][C]128[/C][C]0.023777081309852[/C][C]0.0475541626197039[/C][C]0.976222918690148[/C][/ROW]
[ROW][C]129[/C][C]0.0197797750377559[/C][C]0.0395595500755119[/C][C]0.980220224962244[/C][/ROW]
[ROW][C]130[/C][C]0.0185698763737446[/C][C]0.0371397527474892[/C][C]0.981430123626255[/C][/ROW]
[ROW][C]131[/C][C]0.0147722237920407[/C][C]0.0295444475840815[/C][C]0.985227776207959[/C][/ROW]
[ROW][C]132[/C][C]0.0171636821778034[/C][C]0.0343273643556069[/C][C]0.982836317822197[/C][/ROW]
[ROW][C]133[/C][C]0.026394470155207[/C][C]0.052788940310414[/C][C]0.973605529844793[/C][/ROW]
[ROW][C]134[/C][C]0.0290119304345573[/C][C]0.0580238608691145[/C][C]0.970988069565443[/C][/ROW]
[ROW][C]135[/C][C]0.0321153095684959[/C][C]0.0642306191369919[/C][C]0.967884690431504[/C][/ROW]
[ROW][C]136[/C][C]0.026263825158068[/C][C]0.052527650316136[/C][C]0.973736174841932[/C][/ROW]
[ROW][C]137[/C][C]0.0223502193726632[/C][C]0.0447004387453265[/C][C]0.977649780627337[/C][/ROW]
[ROW][C]138[/C][C]0.0162686101019646[/C][C]0.0325372202039291[/C][C]0.983731389898035[/C][/ROW]
[ROW][C]139[/C][C]0.0164806388425592[/C][C]0.0329612776851184[/C][C]0.983519361157441[/C][/ROW]
[ROW][C]140[/C][C]0.0113977299598[/C][C]0.0227954599196[/C][C]0.9886022700402[/C][/ROW]
[ROW][C]141[/C][C]0.0362561295450204[/C][C]0.0725122590900407[/C][C]0.96374387045498[/C][/ROW]
[ROW][C]142[/C][C]0.0381519554969965[/C][C]0.0763039109939929[/C][C]0.961848044503004[/C][/ROW]
[ROW][C]143[/C][C]0.026984229651314[/C][C]0.0539684593026279[/C][C]0.973015770348686[/C][/ROW]
[ROW][C]144[/C][C]0.2950704996964[/C][C]0.5901409993928[/C][C]0.7049295003036[/C][/ROW]
[ROW][C]145[/C][C]0.318911408397566[/C][C]0.637822816795132[/C][C]0.681088591602434[/C][/ROW]
[ROW][C]146[/C][C]0.637808546516978[/C][C]0.724382906966043[/C][C]0.362191453483022[/C][/ROW]
[ROW][C]147[/C][C]0.642779804553687[/C][C]0.714440390892627[/C][C]0.357220195446313[/C][/ROW]
[ROW][C]148[/C][C]0.911311886571181[/C][C]0.177376226857638[/C][C]0.088688113428819[/C][/ROW]
[ROW][C]149[/C][C]0.918488060117674[/C][C]0.163023879764652[/C][C]0.0815119398823261[/C][/ROW]
[ROW][C]150[/C][C]0.83979569947952[/C][C]0.32040860104096[/C][C]0.16020430052048[/C][/ROW]
[ROW][C]151[/C][C]0.72604067872963[/C][C]0.54791864254074[/C][C]0.27395932127037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.999845277639330.0003094447213392090.000154722360669604
120.9997387761658580.0005224476682831490.000261223834141575
130.99937620894040.001247582119200550.000623791059600274
140.9989786116943470.002042776611306560.00102138830565328
150.9988277124633210.00234457507335890.00117228753667945
160.9978884357510840.004223128497832780.00211156424891639
170.996138793580410.007722412839180730.00386120641959036
180.9952399477167480.009520104566504420.00476005228325221
190.9924909046690810.01501819066183870.00750909533091937
200.9873317133469310.02533657330613760.0126682866530688
210.9804492441022580.03910151179548340.0195507558977417
220.9721117340166760.05577653196664860.0278882659833243
230.958661872229080.082676255541840.04133812777092
240.9414552039860340.1170895920279310.0585447960139657
250.9397248320608970.1205503358782060.0602751679391029
260.9438080052345620.1123839895308750.0561919947654377
270.944348473289650.1113030534207010.0556515267103503
280.9510299361597010.0979401276805970.0489700638402985
290.9326026622233590.1347946755532820.0673973377766411
300.9124649382166250.1750701235667510.0875350617833753
310.895878221702280.208243556595440.10412177829772
320.9127524137851460.1744951724297080.0872475862148541
330.8863934760461950.2272130479076090.113606523953805
340.8548625780582710.2902748438834590.145137421941729
350.8409881868778980.3180236262442040.159011813122102
360.8041023307379570.3917953385240860.195897669262043
370.8453924300134460.3092151399731080.154607569986554
380.836623689300350.3267526213992990.16337631069965
390.8263998343062820.3472003313874350.173600165693718
400.8083401264615410.3833197470769170.191659873538459
410.7845053023636840.4309893952726310.215494697636316
420.7684564944507260.4630870110985470.231543505549274
430.7666212685709690.4667574628580630.233378731429031
440.7255091689857550.548981662028490.274490831014245
450.6823671044687630.6352657910624750.317632895531237
460.7434928648364650.513014270327070.256507135163535
470.7527181077850840.4945637844298330.247281892214916
480.7097225707995760.5805548584008480.290277429200424
490.6726482035869450.6547035928261090.327351796413055
500.6583408940175140.6833182119649730.341659105982486
510.6629418169940120.6741163660119760.337058183005988
520.6166776116118510.7666447767762990.383322388388149
530.6424040766553650.7151918466892690.357595923344635
540.6085561217334350.7828877565331290.391443878266565
550.6984517466766860.6030965066466280.301548253323314
560.8535492249161070.2929015501677870.146450775083893
570.8271720422627930.3456559154744150.172827957737207
580.7941093066880780.4117813866238430.205890693311922
590.7583736916009220.4832526167981560.241626308399078
600.7463733902446110.5072532195107780.253626609755389
610.7632503831573560.4734992336852870.236749616842644
620.7276953077934560.5446093844130870.272304692206544
630.7405496089027450.5189007821945110.259450391097255
640.7005464281188410.5989071437623180.299453571881159
650.6660181231306240.6679637537387510.333981876869376
660.6243940201928260.7512119596143470.375605979807174
670.6049522144077080.7900955711845850.395047785592292
680.5896277001180020.8207445997639950.410372299881998
690.6070929514849940.7858140970300110.392907048515006
700.5634563878275350.873087224344930.436543612172465
710.5218566368108020.9562867263783960.478143363189198
720.4792235036068120.9584470072136240.520776496393188
730.4491229282731520.8982458565463050.550877071726848
740.4243533932696170.8487067865392340.575646606730383
750.398045207823040.7960904156460810.60195479217696
760.4481081238777490.8962162477554980.551891876122251
770.4355511060253780.8711022120507550.564448893974622
780.393364936598240.7867298731964790.60663506340176
790.397659851617910.7953197032358210.60234014838209
800.3740504173604110.7481008347208220.625949582639589
810.3323503331245540.6647006662491080.667649666875446
820.3406255592939210.6812511185878420.659374440706079
830.3041549275409210.6083098550818420.695845072459079
840.2772077453144890.5544154906289780.722792254685511
850.239892116531760.479784233063520.76010788346824
860.2316142165585570.4632284331171140.768385783441443
870.2310498283038540.4620996566077080.768950171696146
880.1976353714580380.3952707429160760.802364628541962
890.1679542073286480.3359084146572970.832045792671352
900.146381774512210.2927635490244210.85361822548779
910.1258818510426020.2517637020852040.874118148957398
920.1774113677312090.3548227354624190.822588632268791
930.1529604767553270.3059209535106540.847039523244673
940.1373460080312960.2746920160625930.862653991968704
950.1310683844360880.2621367688721770.868931615563912
960.1239992590621380.2479985181242760.876000740937862
970.1158073313360320.2316146626720630.884192668663969
980.1480222398527110.2960444797054210.851977760147289
990.1235477044532570.2470954089065140.876452295546743
1000.1050612365163520.2101224730327040.894938763483648
1010.1241389341100990.2482778682201980.875861065889901
1020.1030544766326450.206108953265290.896945523367355
1030.09777733595538720.1955546719107740.902222664044613
1040.08026767979018530.1605353595803710.919732320209815
1050.06659951585948310.1331990317189660.933400484140517
1060.06764694106284230.1352938821256850.932353058937158
1070.05352294377041080.1070458875408220.946477056229589
1080.04632711695731860.09265423391463720.953672883042681
1090.03664093497805990.07328186995611980.96335906502194
1100.03731473175627370.07462946351254740.962685268243726
1110.0287122086855360.0574244173710720.971287791314464
1120.03125569254557010.06251138509114030.96874430745443
1130.02799829950783610.05599659901567230.972001700492164
1140.02217299218749120.04434598437498240.977827007812509
1150.01717442833991090.03434885667982180.982825571660089
1160.01298181897238550.0259636379447710.987018181027615
1170.0197856320743810.0395712641487620.980214367925619
1180.02252945226342350.04505890452684690.977470547736576
1190.01715942700424040.03431885400848080.98284057299576
1200.01315916809705030.02631833619410060.98684083190295
1210.01081306854810780.02162613709621560.989186931451892
1220.008922272125821960.01784454425164390.991077727874178
1230.01043890517058070.02087781034116150.989561094829419
1240.01657781242633410.03315562485266830.983422187573666
1250.01742680042418930.03485360084837860.982573199575811
1260.04159219282934970.08318438565869950.95840780717065
1270.03138627933699590.06277255867399180.968613720663004
1280.0237770813098520.04755416261970390.976222918690148
1290.01977977503775590.03955955007551190.980220224962244
1300.01856987637374460.03713975274748920.981430123626255
1310.01477222379204070.02954444758408150.985227776207959
1320.01716368217780340.03432736435560690.982836317822197
1330.0263944701552070.0527889403104140.973605529844793
1340.02901193043455730.05802386086911450.970988069565443
1350.03211530956849590.06423061913699190.967884690431504
1360.0262638251580680.0525276503161360.973736174841932
1370.02235021937266320.04470043874532650.977649780627337
1380.01626861010196460.03253722020392910.983731389898035
1390.01648063884255920.03296127768511840.983519361157441
1400.01139772995980.02279545991960.9886022700402
1410.03625612954502040.07251225909004070.96374387045498
1420.03815195549699650.07630391099399290.961848044503004
1430.0269842296513140.05396845930262790.973015770348686
1440.29507049969640.59014099939280.7049295003036
1450.3189114083975660.6378228167951320.681088591602434
1460.6378085465169780.7243829069660430.362191453483022
1470.6427798045536870.7144403908926270.357220195446313
1480.9113118865711810.1773762268576380.088688113428819
1490.9184880601176740.1630238797646520.0815119398823261
1500.839795699479520.320408601040960.16020430052048
1510.726040678729630.547918642540740.27395932127037







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0567375886524823NOK
5% type I error level320.226950354609929NOK
10% type I error level500.354609929078014NOK

\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 & 8 & 0.0567375886524823 & NOK \tabularnewline
5% type I error level & 32 & 0.226950354609929 & NOK \tabularnewline
10% type I error level & 50 & 0.354609929078014 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200381&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]8[/C][C]0.0567375886524823[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.226950354609929[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.354609929078014[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200381&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200381&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 level80.0567375886524823NOK
5% type I error level320.226950354609929NOK
10% type I error level500.354609929078014NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}