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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 11:45:00 -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/2011/Dec/13/t1323794860twoo3un3654au2c.htm/, Retrieved Thu, 02 May 2024 14:37:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154526, Retrieved Thu, 02 May 2024 14:37:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2011-12-13 16:45:00] [fdaf10f0fcbe7b8f79ecbd42ec74e6ad] [Current]
-   PD      [Multiple Regression] [] [2011-12-13 22:11:10] [a1957df0bc37aec4aa3c994e6a08412c]
-   PD        [Multiple Regression] [] [2011-12-19 12:16:46] [a1957df0bc37aec4aa3c994e6a08412c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	7	41	38	14	12	53
0	5	39	32	18	11	86
0	5	30	35	11	14	66
1	5	31	33	12	12	67
0	8	34	37	16	21	76
0	6	35	29	18	12	78
0	5	39	31	14	22	53
0	6	34	36	14	11	80
0	5	36	35	15	10	74
0	4	37	38	15	13	76
1	6	38	31	17	10	79
0	5	36	34	19	8	54
1	5	38	35	10	15	67
0	6	39	38	16	14	54
0	7	33	37	18	10	87
1	6	32	33	14	14	58
1	7	36	32	14	14	75
0	6	38	38	17	11	88
1	8	39	38	14	10	64
0	7	32	32	16	13	57
1	5	32	33	18	7	66
0	5	31	31	11	14	68
0	7	39	38	14	12	54
0	7	37	39	12	14	56
1	5	39	32	17	11	86
0	4	41	32	9	9	80
1	10	36	35	16	11	76
0	6	33	37	14	15	69
0	5	33	33	15	14	78
1	5	34	33	11	13	67
0	5	31	28	16	9	80
1	5	27	32	13	15	54
0	6	37	31	17	10	71
0	5	34	37	15	11	84
1	5	34	30	14	13	74
1	5	32	33	16	8	71
1	5	29	31	9	20	63
1	5	36	33	15	12	71
0	5	29	31	17	10	76
1	5	35	33	13	10	69
1	5	37	32	15	9	74
0	7	34	33	16	14	75
1	5	38	32	16	8	54
1	6	35	33	12	14	52
0	7	38	28	12	11	69
0	7	37	35	11	13	68
0	5	38	39	15	9	65
0	5	33	34	15	11	75
0	4	36	38	17	15	74
1	5	38	32	13	11	75
0	4	32	38	16	10	72
1	5	32	30	14	14	67
1	5	32	33	11	18	63
0	7	34	38	12	14	62
1	5	32	32	12	11	63
0	5	37	32	15	12	76
0	6	39	34	16	13	74
0	4	29	34	15	9	67
1	6	37	36	12	10	73
0	6	35	34	12	15	70
1	5	30	28	8	20	53
1	7	38	34	13	12	77
0	6	34	35	11	12	77
0	8	31	35	14	14	52
0	7	34	31	15	13	54
1	5	35	37	10	11	80
0	6	36	35	11	17	66
1	6	30	27	12	12	73
0	5	39	40	15	13	63
1	5	35	37	15	14	69
1	5	38	36	14	13	67
0	5	31	38	16	15	54
0	4	34	39	15	13	81
1	6	38	41	15	10	69
1	6	34	27	13	11	84
0	6	39	30	12	19	80
0	6	37	37	17	13	70
0	7	34	31	13	17	69
1	5	28	31	15	13	77
1	7	37	27	13	9	54
1	6	33	36	15	11	79
1	5	37	38	16	10	30
0	5	35	37	15	9	71
1	4	37	33	16	12	73
0	8	32	34	15	12	72
0	8	33	31	14	13	77
1	5	38	39	15	13	75
0	5	33	34	14	12	69
0	6	29	32	13	15	54
0	4	33	33	7	22	70
0	5	31	36	17	13	73
0	5	36	32	13	15	54
0	5	35	41	15	13	77
0	5	32	28	14	15	82
0	6	29	30	13	10	80
0	6	39	36	16	11	80
0	5	37	35	12	16	69
0	6	35	31	14	11	78
1	5	37	34	17	11	81
1	7	32	36	15	10	76
0	5	38	36	17	10	76
1	6	37	35	12	16	73
0	6	36	37	16	12	85
1	6	32	28	11	11	66
0	4	33	39	15	16	79
1	5	40	32	9	19	68
0	5	38	35	16	11	76
1	7	41	39	15	16	71
1	6	36	35	10	15	54
0	9	43	42	10	24	46
0	6	30	34	15	14	82
0	6	31	33	11	15	74
0	5	32	41	13	11	88
1	6	32	33	14	15	38
0	5	37	34	18	12	76
1	8	37	32	16	10	86
0	7	33	40	14	14	54
0	5	34	40	14	13	70
0	7	33	35	14	9	69
0	6	38	36	14	15	90
0	6	33	37	12	15	54
0	9	31	27	14	14	76
0	7	38	39	15	11	89
0	6	37	38	15	8	76
0	5	33	31	15	11	73
0	5	31	33	13	11	79
1	6	39	32	17	8	90
0	6	44	39	17	10	74
0	7	33	36	19	11	81
0	5	35	33	15	13	72
1	5	32	33	13	11	71
1	5	28	32	9	20	66
0	6	40	37	15	10	77
1	4	27	30	15	15	65
1	5	37	38	15	12	74
0	7	32	29	16	14	82
1	5	28	22	11	23	54
1	7	34	35	14	14	63
0	7	30	35	11	16	54
0	6	35	34	15	11	64
1	5	31	35	13	12	69
0	8	32	34	15	10	54
1	5	30	34	16	14	84
0	5	30	35	14	12	86
1	5	31	23	15	12	77
0	6	40	31	16	11	89
0	4	32	27	16	12	76
1	5	36	36	11	13	60
1	5	32	31	12	11	75
1	7	35	32	9	19	73
0	6	38	39	16	12	85
0	7	42	37	13	17	79
1	10	34	38	16	9	71
0	6	35	39	12	12	72
0	8	35	34	9	19	69
0	4	33	31	13	18	78
0	5	36	32	13	15	54
0	6	32	37	14	14	69
0	7	33	36	19	11	81
0	7	34	32	13	9	84
0	6	32	35	12	18	84
0	6	34	36	13	16	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'George Udny Yule' @ yule.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 & 17 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 28.0482789971253 -1.09780036996708Gender[t] + 0.0686300079408777Age[t] -0.0349275288819376Connected[t] -0.027094811952049Separate[t] -0.709795434689005Happiness[t] -0.042654067919869Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  28.0482789971253 -1.09780036996708Gender[t] +  0.0686300079408777Age[t] -0.0349275288819376Connected[t] -0.027094811952049Separate[t] -0.709795434689005Happiness[t] -0.042654067919869Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  28.0482789971253 -1.09780036996708Gender[t] +  0.0686300079408777Age[t] -0.0349275288819376Connected[t] -0.027094811952049Separate[t] -0.709795434689005Happiness[t] -0.042654067919869Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 28.0482789971253 -1.09780036996708Gender[t] + 0.0686300079408777Age[t] -0.0349275288819376Connected[t] -0.027094811952049Separate[t] -0.709795434689005Happiness[t] -0.042654067919869Belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.04827899712533.0801229.106200
Gender-1.097800369967080.447651-2.45240.0153020.007651
Age0.06863000794087770.1813060.37850.7055540.352777
Connected-0.03492752888193760.066929-0.52190.6025120.301256
Separate-0.0270948119520490.064957-0.41710.6771680.338584
Happiness-0.7097954346890050.0944-7.51900
Belonging-0.0426540679198690.020426-2.08820.0384160.019208

\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) & 28.0482789971253 & 3.080122 & 9.1062 & 0 & 0 \tabularnewline
Gender & -1.09780036996708 & 0.447651 & -2.4524 & 0.015302 & 0.007651 \tabularnewline
Age & 0.0686300079408777 & 0.181306 & 0.3785 & 0.705554 & 0.352777 \tabularnewline
Connected & -0.0349275288819376 & 0.066929 & -0.5219 & 0.602512 & 0.301256 \tabularnewline
Separate & -0.027094811952049 & 0.064957 & -0.4171 & 0.677168 & 0.338584 \tabularnewline
Happiness & -0.709795434689005 & 0.0944 & -7.519 & 0 & 0 \tabularnewline
Belonging & -0.042654067919869 & 0.020426 & -2.0882 & 0.038416 & 0.019208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&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]28.0482789971253[/C][C]3.080122[/C][C]9.1062[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]-1.09780036996708[/C][C]0.447651[/C][C]-2.4524[/C][C]0.015302[/C][C]0.007651[/C][/ROW]
[ROW][C]Age[/C][C]0.0686300079408777[/C][C]0.181306[/C][C]0.3785[/C][C]0.705554[/C][C]0.352777[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0349275288819376[/C][C]0.066929[/C][C]-0.5219[/C][C]0.602512[/C][C]0.301256[/C][/ROW]
[ROW][C]Separate[/C][C]-0.027094811952049[/C][C]0.064957[/C][C]-0.4171[/C][C]0.677168[/C][C]0.338584[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.709795434689005[/C][C]0.0944[/C][C]-7.519[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.042654067919869[/C][C]0.020426[/C][C]-2.0882[/C][C]0.038416[/C][C]0.019208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.04827899712533.0801229.106200
Gender-1.097800369967080.447651-2.45240.0153020.007651
Age0.06863000794087770.1813060.37850.7055540.352777
Connected-0.03492752888193760.066929-0.52190.6025120.301256
Separate-0.0270948119520490.064957-0.41710.6771680.338584
Happiness-0.7097954346890050.0944-7.51900
Belonging-0.0426540679198690.020426-2.08820.0384160.019208






Multiple Linear Regression - Regression Statistics
Multiple R0.583078979256039
R-squared0.339981096050264
Adjusted R-squared0.314431977187694
F-TEST (value)13.3069597381825
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value3.92830212803119e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.62155087273153
Sum Squared Residuals1065.24199163952

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.583078979256039 \tabularnewline
R-squared & 0.339981096050264 \tabularnewline
Adjusted R-squared & 0.314431977187694 \tabularnewline
F-TEST (value) & 13.3069597381825 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 3.92830212803119e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.62155087273153 \tabularnewline
Sum Squared Residuals & 1065.24199163952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.583078979256039[/C][/ROW]
[ROW][C]R-squared[/C][C]0.339981096050264[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.314431977187694[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.3069597381825[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]3.92830212803119e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.62155087273153[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1065.24199163952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.583078979256039
R-squared0.339981096050264
Adjusted R-squared0.314431977187694
F-TEST (value)13.3069597381825
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value3.92830212803119e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.62155087273153
Sum Squared Residuals1065.24199163952






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.8692558289751-1.8692558289751
2119.717653762457741.28234623754226
31415.7723664877594-1.77236648775944
41213.9413787102057-1.94137871020565
52111.80883891950659.19116108049349
61210.34851086514151.65148913485854
72213.99151455452158.00848544547852
81112.9476483132753-1.94764831327534
91012.3823870323528-2.38238703235285
101312.11223692383410.887763076165854
11109.758879651393610.241120348606387
12810.4233814639463-2.42338146394625
131515.062287253506-0.0622872535060025
141412.40823594150011.59176405849986
15109.8863508239510.113649176048996
161412.93937693116541.0606230688346
171412.17027248089281.82972751910719
181110.28312972641750.716870273582474
191012.4407457775941-2.44074577759414
201312.75596531956730.244034680432735
2179.69033264110955-2.69033264110955
221415.760510070846-1.76051007084596
231213.896456818819-1.89645681881903
241415.2734997981691-1.27349979816912
25119.329648827179671.67035117282033
26916.2232520164732-7.22325201647324
271110.83263313156140.167366868438584
281513.42467577732381.57532422267621
291412.37074297122331.62925702877672
301314.5463915582488-1.54639155824885
31911.7809685182187-2.78096851821866
321513.95289108595471.04710891404526
331011.2328400936016-1.23284009360158
341111.9715117870139-0.971511787013934
351312.19971121459890.800288785401105
36810.8966531708882-2.89665317088822
372016.36542596762013.63457403237988
381211.46673849004950.533261509950525
391011.2303599771169-1.23035997711686
401013.0065650241492-3.00656502414916
41911.33094356936-2.33094356935998
421411.89124222729372.1087577727063
43811.4393019641864-3.43930196418641
441414.5101096214168-0.510109621416815
451114.9821123178014-3.98211231780143
461315.5798256656279-2.5798256656279
47912.5880393380596-3.5880393380596
481112.4716103630308-1.47161036303084
491510.81288171917784.18711828082219
501112.6729528419362-1.67295284193618
511011.7476954052343-1.7476954052343
521412.56814474780191.43185525219815
531814.78686288769223.2131371123078
541415.1494527892478-1.14945278924777
551114.1041622649552-3.10416226495524
561212.3434358034873-0.343435803487318
571311.6635338309111.33646616908905
58912.8839230139767-3.88392301397666
591013.4632347014795-3.46323470147954
601514.81304195687420.1869580431258
612017.5481189884822.45188101151798
621212.6707150980741-0.670715098074103
631215.232091633054-3.23209163305401
641414.4110996295113-0.411099629511288
651313.5509627122041-0.550962712204051
661114.5583773332894-3.55837733328942
671715.63143132240871.3685686775913
681213.9515807112215-1.95158071122155
691312.61132513306530.388674866934654
701411.4785949069632.52140509303705
711312.19601070279790.803989297202067
721512.61902616461482.38097383538524
731311.97665435892861.02334564107144
741011.3340630804498-1.33406308044982
751112.6328804138862-1.63288041388624
761914.3551704099564.64482959004404
771311.11292528980921.88707471019085
781714.3307425627842.66925743721597
791311.54442393748991.45557606251014
80913.8763498727774-4.87634987277737
811111.2176341054211-0.217634105421067
821012.3353582514329-2.33535825143291
83912.4910871410903-3.49108714109029
841210.56807738269791.43192261730208
851212.840390119495-0.840390119495016
861313.3832721215589-0.383272121558886
871311.06369828889381.93630171110617
881213.4373302052391-1.43733020523906
891515.0494664060988-0.0494664060988223
902218.32170898415343.6782910158466
911311.15299306335231.84700693664766
921514.73634369598440.263656304015618
931312.12678348576290.873216514237117
941513.0803237228751.91967627712501
951013.9946502640863-3.99465026408633
961111.3534197994876-0.353419799487643
971614.69011614713731.30988385286273
981113.1335029799934-2.13350297999339
99119.558584600638791.44141539936121
1001011.4491538460035-1.44915384600349
1011010.7805381574192-0.780538157419174
1021613.49032951343162.50967048656841
1031211.21783723458210.782162765417938
1041114.8630047516337-3.86300475163371
1051612.09689002365023.90310997634976
1061915.74085799836743.25914200163259
1071111.5174284040602-0.517428404060228
1081611.26679198980924.73320801019075
1091515.7552752021691-0.75527520216905
1102416.96604175347987.0339582465202
1111412.34644448217841.65355551782155
1121515.5190260473635-0.519026047363529
1131113.1819621946681-2.18196219466815
1141513.79245828956281.20754171043722
1151210.15985987551621.8401401244838
1161010.3151893434552-0.31518934345518
1171414.0518323682066-0.0518323682065531
1181313.197179736725-0.197179736724956
119913.5474954091688-4.54749540916876
1201512.38139751854892.6186024814511
1211515.4840776654998-0.484077665499832
1221413.67279050299170.327209497008296
1231111.7016017238645-0.701601723864496
124812.2494969397159-4.2494969397159
1251112.6382029347267-1.63820293472672
1261113.8175348304453-2.8175348304453
12789.22766256344107-1.22766256344107
1281010.643626692052-0.643626692052017
129119.459574608733261.54042539126674
1301312.55681232097860.443187679021381
1311113.0260394749552-2.02603947495524
1322016.24529648079043.7547035192096
1331012.1291550971023-2.12915509710227
1341512.04966508542142.9503349145786
1351211.16837469764770.831625302352315
1361411.77089805742672.22910194257331
1372315.60850254597137.3914974540287
1381412.6706919178391.32930808216104
1391616.4214753186796-0.421475318679625
1401112.9395800603264-1.9395800603264
1411213.0920855157728-1.09208551577281
1421013.6081633420527-3.60816334205266
1431410.38491053374173.61508946625825
1441212.789898825295-0.789898825295049
1451211.65639984646040.343600153539561
1461111.0700797190871-0.0700797190871305
1471211.87512206502740.124877934972633
1481314.6938305400679-1.69383054006791
1491113.6194082618689-2.61940826186886
1501915.83948531905953.16051468094049
1511211.09379255291410.906207447085911
1521713.46221278081753.53778721918246
153911.0344740930685-2.03447409306849
1541214.5922597612742-2.59225976127422
1551917.12234234474281.87765765525716
1561813.77589345656454.22410654343549
1571514.73634369598440.263656304015618
1581413.45960330620570.540396693794274
159119.459574608733261.54042539126674
160913.6638367320339-4.66383673203394
1611814.29357278068983.7064272193102
1621614.12663849508291.8733615049171

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.8692558289751 & -1.8692558289751 \tabularnewline
2 & 11 & 9.71765376245774 & 1.28234623754226 \tabularnewline
3 & 14 & 15.7723664877594 & -1.77236648775944 \tabularnewline
4 & 12 & 13.9413787102057 & -1.94137871020565 \tabularnewline
5 & 21 & 11.8088389195065 & 9.19116108049349 \tabularnewline
6 & 12 & 10.3485108651415 & 1.65148913485854 \tabularnewline
7 & 22 & 13.9915145545215 & 8.00848544547852 \tabularnewline
8 & 11 & 12.9476483132753 & -1.94764831327534 \tabularnewline
9 & 10 & 12.3823870323528 & -2.38238703235285 \tabularnewline
10 & 13 & 12.1122369238341 & 0.887763076165854 \tabularnewline
11 & 10 & 9.75887965139361 & 0.241120348606387 \tabularnewline
12 & 8 & 10.4233814639463 & -2.42338146394625 \tabularnewline
13 & 15 & 15.062287253506 & -0.0622872535060025 \tabularnewline
14 & 14 & 12.4082359415001 & 1.59176405849986 \tabularnewline
15 & 10 & 9.886350823951 & 0.113649176048996 \tabularnewline
16 & 14 & 12.9393769311654 & 1.0606230688346 \tabularnewline
17 & 14 & 12.1702724808928 & 1.82972751910719 \tabularnewline
18 & 11 & 10.2831297264175 & 0.716870273582474 \tabularnewline
19 & 10 & 12.4407457775941 & -2.44074577759414 \tabularnewline
20 & 13 & 12.7559653195673 & 0.244034680432735 \tabularnewline
21 & 7 & 9.69033264110955 & -2.69033264110955 \tabularnewline
22 & 14 & 15.760510070846 & -1.76051007084596 \tabularnewline
23 & 12 & 13.896456818819 & -1.89645681881903 \tabularnewline
24 & 14 & 15.2734997981691 & -1.27349979816912 \tabularnewline
25 & 11 & 9.32964882717967 & 1.67035117282033 \tabularnewline
26 & 9 & 16.2232520164732 & -7.22325201647324 \tabularnewline
27 & 11 & 10.8326331315614 & 0.167366868438584 \tabularnewline
28 & 15 & 13.4246757773238 & 1.57532422267621 \tabularnewline
29 & 14 & 12.3707429712233 & 1.62925702877672 \tabularnewline
30 & 13 & 14.5463915582488 & -1.54639155824885 \tabularnewline
31 & 9 & 11.7809685182187 & -2.78096851821866 \tabularnewline
32 & 15 & 13.9528910859547 & 1.04710891404526 \tabularnewline
33 & 10 & 11.2328400936016 & -1.23284009360158 \tabularnewline
34 & 11 & 11.9715117870139 & -0.971511787013934 \tabularnewline
35 & 13 & 12.1997112145989 & 0.800288785401105 \tabularnewline
36 & 8 & 10.8966531708882 & -2.89665317088822 \tabularnewline
37 & 20 & 16.3654259676201 & 3.63457403237988 \tabularnewline
38 & 12 & 11.4667384900495 & 0.533261509950525 \tabularnewline
39 & 10 & 11.2303599771169 & -1.23035997711686 \tabularnewline
40 & 10 & 13.0065650241492 & -3.00656502414916 \tabularnewline
41 & 9 & 11.33094356936 & -2.33094356935998 \tabularnewline
42 & 14 & 11.8912422272937 & 2.1087577727063 \tabularnewline
43 & 8 & 11.4393019641864 & -3.43930196418641 \tabularnewline
44 & 14 & 14.5101096214168 & -0.510109621416815 \tabularnewline
45 & 11 & 14.9821123178014 & -3.98211231780143 \tabularnewline
46 & 13 & 15.5798256656279 & -2.5798256656279 \tabularnewline
47 & 9 & 12.5880393380596 & -3.5880393380596 \tabularnewline
48 & 11 & 12.4716103630308 & -1.47161036303084 \tabularnewline
49 & 15 & 10.8128817191778 & 4.18711828082219 \tabularnewline
50 & 11 & 12.6729528419362 & -1.67295284193618 \tabularnewline
51 & 10 & 11.7476954052343 & -1.7476954052343 \tabularnewline
52 & 14 & 12.5681447478019 & 1.43185525219815 \tabularnewline
53 & 18 & 14.7868628876922 & 3.2131371123078 \tabularnewline
54 & 14 & 15.1494527892478 & -1.14945278924777 \tabularnewline
55 & 11 & 14.1041622649552 & -3.10416226495524 \tabularnewline
56 & 12 & 12.3434358034873 & -0.343435803487318 \tabularnewline
57 & 13 & 11.663533830911 & 1.33646616908905 \tabularnewline
58 & 9 & 12.8839230139767 & -3.88392301397666 \tabularnewline
59 & 10 & 13.4632347014795 & -3.46323470147954 \tabularnewline
60 & 15 & 14.8130419568742 & 0.1869580431258 \tabularnewline
61 & 20 & 17.548118988482 & 2.45188101151798 \tabularnewline
62 & 12 & 12.6707150980741 & -0.670715098074103 \tabularnewline
63 & 12 & 15.232091633054 & -3.23209163305401 \tabularnewline
64 & 14 & 14.4110996295113 & -0.411099629511288 \tabularnewline
65 & 13 & 13.5509627122041 & -0.550962712204051 \tabularnewline
66 & 11 & 14.5583773332894 & -3.55837733328942 \tabularnewline
67 & 17 & 15.6314313224087 & 1.3685686775913 \tabularnewline
68 & 12 & 13.9515807112215 & -1.95158071122155 \tabularnewline
69 & 13 & 12.6113251330653 & 0.388674866934654 \tabularnewline
70 & 14 & 11.478594906963 & 2.52140509303705 \tabularnewline
71 & 13 & 12.1960107027979 & 0.803989297202067 \tabularnewline
72 & 15 & 12.6190261646148 & 2.38097383538524 \tabularnewline
73 & 13 & 11.9766543589286 & 1.02334564107144 \tabularnewline
74 & 10 & 11.3340630804498 & -1.33406308044982 \tabularnewline
75 & 11 & 12.6328804138862 & -1.63288041388624 \tabularnewline
76 & 19 & 14.355170409956 & 4.64482959004404 \tabularnewline
77 & 13 & 11.1129252898092 & 1.88707471019085 \tabularnewline
78 & 17 & 14.330742562784 & 2.66925743721597 \tabularnewline
79 & 13 & 11.5444239374899 & 1.45557606251014 \tabularnewline
80 & 9 & 13.8763498727774 & -4.87634987277737 \tabularnewline
81 & 11 & 11.2176341054211 & -0.217634105421067 \tabularnewline
82 & 10 & 12.3353582514329 & -2.33535825143291 \tabularnewline
83 & 9 & 12.4910871410903 & -3.49108714109029 \tabularnewline
84 & 12 & 10.5680773826979 & 1.43192261730208 \tabularnewline
85 & 12 & 12.840390119495 & -0.840390119495016 \tabularnewline
86 & 13 & 13.3832721215589 & -0.383272121558886 \tabularnewline
87 & 13 & 11.0636982888938 & 1.93630171110617 \tabularnewline
88 & 12 & 13.4373302052391 & -1.43733020523906 \tabularnewline
89 & 15 & 15.0494664060988 & -0.0494664060988223 \tabularnewline
90 & 22 & 18.3217089841534 & 3.6782910158466 \tabularnewline
91 & 13 & 11.1529930633523 & 1.84700693664766 \tabularnewline
92 & 15 & 14.7363436959844 & 0.263656304015618 \tabularnewline
93 & 13 & 12.1267834857629 & 0.873216514237117 \tabularnewline
94 & 15 & 13.080323722875 & 1.91967627712501 \tabularnewline
95 & 10 & 13.9946502640863 & -3.99465026408633 \tabularnewline
96 & 11 & 11.3534197994876 & -0.353419799487643 \tabularnewline
97 & 16 & 14.6901161471373 & 1.30988385286273 \tabularnewline
98 & 11 & 13.1335029799934 & -2.13350297999339 \tabularnewline
99 & 11 & 9.55858460063879 & 1.44141539936121 \tabularnewline
100 & 10 & 11.4491538460035 & -1.44915384600349 \tabularnewline
101 & 10 & 10.7805381574192 & -0.780538157419174 \tabularnewline
102 & 16 & 13.4903295134316 & 2.50967048656841 \tabularnewline
103 & 12 & 11.2178372345821 & 0.782162765417938 \tabularnewline
104 & 11 & 14.8630047516337 & -3.86300475163371 \tabularnewline
105 & 16 & 12.0968900236502 & 3.90310997634976 \tabularnewline
106 & 19 & 15.7408579983674 & 3.25914200163259 \tabularnewline
107 & 11 & 11.5174284040602 & -0.517428404060228 \tabularnewline
108 & 16 & 11.2667919898092 & 4.73320801019075 \tabularnewline
109 & 15 & 15.7552752021691 & -0.75527520216905 \tabularnewline
110 & 24 & 16.9660417534798 & 7.0339582465202 \tabularnewline
111 & 14 & 12.3464444821784 & 1.65355551782155 \tabularnewline
112 & 15 & 15.5190260473635 & -0.519026047363529 \tabularnewline
113 & 11 & 13.1819621946681 & -2.18196219466815 \tabularnewline
114 & 15 & 13.7924582895628 & 1.20754171043722 \tabularnewline
115 & 12 & 10.1598598755162 & 1.8401401244838 \tabularnewline
116 & 10 & 10.3151893434552 & -0.31518934345518 \tabularnewline
117 & 14 & 14.0518323682066 & -0.0518323682065531 \tabularnewline
118 & 13 & 13.197179736725 & -0.197179736724956 \tabularnewline
119 & 9 & 13.5474954091688 & -4.54749540916876 \tabularnewline
120 & 15 & 12.3813975185489 & 2.6186024814511 \tabularnewline
121 & 15 & 15.4840776654998 & -0.484077665499832 \tabularnewline
122 & 14 & 13.6727905029917 & 0.327209497008296 \tabularnewline
123 & 11 & 11.7016017238645 & -0.701601723864496 \tabularnewline
124 & 8 & 12.2494969397159 & -4.2494969397159 \tabularnewline
125 & 11 & 12.6382029347267 & -1.63820293472672 \tabularnewline
126 & 11 & 13.8175348304453 & -2.8175348304453 \tabularnewline
127 & 8 & 9.22766256344107 & -1.22766256344107 \tabularnewline
128 & 10 & 10.643626692052 & -0.643626692052017 \tabularnewline
129 & 11 & 9.45957460873326 & 1.54042539126674 \tabularnewline
130 & 13 & 12.5568123209786 & 0.443187679021381 \tabularnewline
131 & 11 & 13.0260394749552 & -2.02603947495524 \tabularnewline
132 & 20 & 16.2452964807904 & 3.7547035192096 \tabularnewline
133 & 10 & 12.1291550971023 & -2.12915509710227 \tabularnewline
134 & 15 & 12.0496650854214 & 2.9503349145786 \tabularnewline
135 & 12 & 11.1683746976477 & 0.831625302352315 \tabularnewline
136 & 14 & 11.7708980574267 & 2.22910194257331 \tabularnewline
137 & 23 & 15.6085025459713 & 7.3914974540287 \tabularnewline
138 & 14 & 12.670691917839 & 1.32930808216104 \tabularnewline
139 & 16 & 16.4214753186796 & -0.421475318679625 \tabularnewline
140 & 11 & 12.9395800603264 & -1.9395800603264 \tabularnewline
141 & 12 & 13.0920855157728 & -1.09208551577281 \tabularnewline
142 & 10 & 13.6081633420527 & -3.60816334205266 \tabularnewline
143 & 14 & 10.3849105337417 & 3.61508946625825 \tabularnewline
144 & 12 & 12.789898825295 & -0.789898825295049 \tabularnewline
145 & 12 & 11.6563998464604 & 0.343600153539561 \tabularnewline
146 & 11 & 11.0700797190871 & -0.0700797190871305 \tabularnewline
147 & 12 & 11.8751220650274 & 0.124877934972633 \tabularnewline
148 & 13 & 14.6938305400679 & -1.69383054006791 \tabularnewline
149 & 11 & 13.6194082618689 & -2.61940826186886 \tabularnewline
150 & 19 & 15.8394853190595 & 3.16051468094049 \tabularnewline
151 & 12 & 11.0937925529141 & 0.906207447085911 \tabularnewline
152 & 17 & 13.4622127808175 & 3.53778721918246 \tabularnewline
153 & 9 & 11.0344740930685 & -2.03447409306849 \tabularnewline
154 & 12 & 14.5922597612742 & -2.59225976127422 \tabularnewline
155 & 19 & 17.1223423447428 & 1.87765765525716 \tabularnewline
156 & 18 & 13.7758934565645 & 4.22410654343549 \tabularnewline
157 & 15 & 14.7363436959844 & 0.263656304015618 \tabularnewline
158 & 14 & 13.4596033062057 & 0.540396693794274 \tabularnewline
159 & 11 & 9.45957460873326 & 1.54042539126674 \tabularnewline
160 & 9 & 13.6638367320339 & -4.66383673203394 \tabularnewline
161 & 18 & 14.2935727806898 & 3.7064272193102 \tabularnewline
162 & 16 & 14.1266384950829 & 1.8733615049171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&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]13.8692558289751[/C][C]-1.8692558289751[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.71765376245774[/C][C]1.28234623754226[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.7723664877594[/C][C]-1.77236648775944[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.9413787102057[/C][C]-1.94137871020565[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.8088389195065[/C][C]9.19116108049349[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3485108651415[/C][C]1.65148913485854[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.9915145545215[/C][C]8.00848544547852[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.9476483132753[/C][C]-1.94764831327534[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.3823870323528[/C][C]-2.38238703235285[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.1122369238341[/C][C]0.887763076165854[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]9.75887965139361[/C][C]0.241120348606387[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.4233814639463[/C][C]-2.42338146394625[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.062287253506[/C][C]-0.0622872535060025[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.4082359415001[/C][C]1.59176405849986[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.886350823951[/C][C]0.113649176048996[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9393769311654[/C][C]1.0606230688346[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.1702724808928[/C][C]1.82972751910719[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.2831297264175[/C][C]0.716870273582474[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.4407457775941[/C][C]-2.44074577759414[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.7559653195673[/C][C]0.244034680432735[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]9.69033264110955[/C][C]-2.69033264110955[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.760510070846[/C][C]-1.76051007084596[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.896456818819[/C][C]-1.89645681881903[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]15.2734997981691[/C][C]-1.27349979816912[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.32964882717967[/C][C]1.67035117282033[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.2232520164732[/C][C]-7.22325201647324[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.8326331315614[/C][C]0.167366868438584[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.4246757773238[/C][C]1.57532422267621[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.3707429712233[/C][C]1.62925702877672[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]14.5463915582488[/C][C]-1.54639155824885[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.7809685182187[/C][C]-2.78096851821866[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]13.9528910859547[/C][C]1.04710891404526[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.2328400936016[/C][C]-1.23284009360158[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.9715117870139[/C][C]-0.971511787013934[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.1997112145989[/C][C]0.800288785401105[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]10.8966531708882[/C][C]-2.89665317088822[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]16.3654259676201[/C][C]3.63457403237988[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.4667384900495[/C][C]0.533261509950525[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.2303599771169[/C][C]-1.23035997711686[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.0065650241492[/C][C]-3.00656502414916[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.33094356936[/C][C]-2.33094356935998[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.8912422272937[/C][C]2.1087577727063[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.4393019641864[/C][C]-3.43930196418641[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.5101096214168[/C][C]-0.510109621416815[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.9821123178014[/C][C]-3.98211231780143[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.5798256656279[/C][C]-2.5798256656279[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5880393380596[/C][C]-3.5880393380596[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.4716103630308[/C][C]-1.47161036303084[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.8128817191778[/C][C]4.18711828082219[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.6729528419362[/C][C]-1.67295284193618[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.7476954052343[/C][C]-1.7476954052343[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.5681447478019[/C][C]1.43185525219815[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.7868628876922[/C][C]3.2131371123078[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]15.1494527892478[/C][C]-1.14945278924777[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]14.1041622649552[/C][C]-3.10416226495524[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.3434358034873[/C][C]-0.343435803487318[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.663533830911[/C][C]1.33646616908905[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.8839230139767[/C][C]-3.88392301397666[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.4632347014795[/C][C]-3.46323470147954[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.8130419568742[/C][C]0.1869580431258[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.548118988482[/C][C]2.45188101151798[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.6707150980741[/C][C]-0.670715098074103[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]15.232091633054[/C][C]-3.23209163305401[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4110996295113[/C][C]-0.411099629511288[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.5509627122041[/C][C]-0.550962712204051[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.5583773332894[/C][C]-3.55837733328942[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.6314313224087[/C][C]1.3685686775913[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.9515807112215[/C][C]-1.95158071122155[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.6113251330653[/C][C]0.388674866934654[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]11.478594906963[/C][C]2.52140509303705[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.1960107027979[/C][C]0.803989297202067[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.6190261646148[/C][C]2.38097383538524[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.9766543589286[/C][C]1.02334564107144[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.3340630804498[/C][C]-1.33406308044982[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.6328804138862[/C][C]-1.63288041388624[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.355170409956[/C][C]4.64482959004404[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.1129252898092[/C][C]1.88707471019085[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.330742562784[/C][C]2.66925743721597[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.5444239374899[/C][C]1.45557606251014[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.8763498727774[/C][C]-4.87634987277737[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.2176341054211[/C][C]-0.217634105421067[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.3353582514329[/C][C]-2.33535825143291[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.4910871410903[/C][C]-3.49108714109029[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.5680773826979[/C][C]1.43192261730208[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.840390119495[/C][C]-0.840390119495016[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.3832721215589[/C][C]-0.383272121558886[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]11.0636982888938[/C][C]1.93630171110617[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.4373302052391[/C][C]-1.43733020523906[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]15.0494664060988[/C][C]-0.0494664060988223[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]18.3217089841534[/C][C]3.6782910158466[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.1529930633523[/C][C]1.84700693664766[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.7363436959844[/C][C]0.263656304015618[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.1267834857629[/C][C]0.873216514237117[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.080323722875[/C][C]1.91967627712501[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.9946502640863[/C][C]-3.99465026408633[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.3534197994876[/C][C]-0.353419799487643[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.6901161471373[/C][C]1.30988385286273[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]13.1335029799934[/C][C]-2.13350297999339[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]9.55858460063879[/C][C]1.44141539936121[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.4491538460035[/C][C]-1.44915384600349[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.7805381574192[/C][C]-0.780538157419174[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.4903295134316[/C][C]2.50967048656841[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]11.2178372345821[/C][C]0.782162765417938[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.8630047516337[/C][C]-3.86300475163371[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.0968900236502[/C][C]3.90310997634976[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]15.7408579983674[/C][C]3.25914200163259[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.5174284040602[/C][C]-0.517428404060228[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.2667919898092[/C][C]4.73320801019075[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.7552752021691[/C][C]-0.75527520216905[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]16.9660417534798[/C][C]7.0339582465202[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.3464444821784[/C][C]1.65355551782155[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]15.5190260473635[/C][C]-0.519026047363529[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.1819621946681[/C][C]-2.18196219466815[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.7924582895628[/C][C]1.20754171043722[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.1598598755162[/C][C]1.8401401244838[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.3151893434552[/C][C]-0.31518934345518[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0518323682066[/C][C]-0.0518323682065531[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.197179736725[/C][C]-0.197179736724956[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.5474954091688[/C][C]-4.54749540916876[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.3813975185489[/C][C]2.6186024814511[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.4840776654998[/C][C]-0.484077665499832[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.6727905029917[/C][C]0.327209497008296[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.7016017238645[/C][C]-0.701601723864496[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.2494969397159[/C][C]-4.2494969397159[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.6382029347267[/C][C]-1.63820293472672[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.8175348304453[/C][C]-2.8175348304453[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.22766256344107[/C][C]-1.22766256344107[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.643626692052[/C][C]-0.643626692052017[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.45957460873326[/C][C]1.54042539126674[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.5568123209786[/C][C]0.443187679021381[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.0260394749552[/C][C]-2.02603947495524[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]16.2452964807904[/C][C]3.7547035192096[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.1291550971023[/C][C]-2.12915509710227[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.0496650854214[/C][C]2.9503349145786[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.1683746976477[/C][C]0.831625302352315[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.7708980574267[/C][C]2.22910194257331[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.6085025459713[/C][C]7.3914974540287[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.670691917839[/C][C]1.32930808216104[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]16.4214753186796[/C][C]-0.421475318679625[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.9395800603264[/C][C]-1.9395800603264[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.0920855157728[/C][C]-1.09208551577281[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]13.6081633420527[/C][C]-3.60816334205266[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.3849105337417[/C][C]3.61508946625825[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.789898825295[/C][C]-0.789898825295049[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.6563998464604[/C][C]0.343600153539561[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.0700797190871[/C][C]-0.0700797190871305[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.8751220650274[/C][C]0.124877934972633[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.6938305400679[/C][C]-1.69383054006791[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.6194082618689[/C][C]-2.61940826186886[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]15.8394853190595[/C][C]3.16051468094049[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.0937925529141[/C][C]0.906207447085911[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.4622127808175[/C][C]3.53778721918246[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.0344740930685[/C][C]-2.03447409306849[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.5922597612742[/C][C]-2.59225976127422[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]17.1223423447428[/C][C]1.87765765525716[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.7758934565645[/C][C]4.22410654343549[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]14.7363436959844[/C][C]0.263656304015618[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4596033062057[/C][C]0.540396693794274[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.45957460873326[/C][C]1.54042539126674[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]13.6638367320339[/C][C]-4.66383673203394[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.2935727806898[/C][C]3.7064272193102[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.1266384950829[/C][C]1.8733615049171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.8692558289751-1.8692558289751
2119.717653762457741.28234623754226
31415.7723664877594-1.77236648775944
41213.9413787102057-1.94137871020565
52111.80883891950659.19116108049349
61210.34851086514151.65148913485854
72213.99151455452158.00848544547852
81112.9476483132753-1.94764831327534
91012.3823870323528-2.38238703235285
101312.11223692383410.887763076165854
11109.758879651393610.241120348606387
12810.4233814639463-2.42338146394625
131515.062287253506-0.0622872535060025
141412.40823594150011.59176405849986
15109.8863508239510.113649176048996
161412.93937693116541.0606230688346
171412.17027248089281.82972751910719
181110.28312972641750.716870273582474
191012.4407457775941-2.44074577759414
201312.75596531956730.244034680432735
2179.69033264110955-2.69033264110955
221415.760510070846-1.76051007084596
231213.896456818819-1.89645681881903
241415.2734997981691-1.27349979816912
25119.329648827179671.67035117282033
26916.2232520164732-7.22325201647324
271110.83263313156140.167366868438584
281513.42467577732381.57532422267621
291412.37074297122331.62925702877672
301314.5463915582488-1.54639155824885
31911.7809685182187-2.78096851821866
321513.95289108595471.04710891404526
331011.2328400936016-1.23284009360158
341111.9715117870139-0.971511787013934
351312.19971121459890.800288785401105
36810.8966531708882-2.89665317088822
372016.36542596762013.63457403237988
381211.46673849004950.533261509950525
391011.2303599771169-1.23035997711686
401013.0065650241492-3.00656502414916
41911.33094356936-2.33094356935998
421411.89124222729372.1087577727063
43811.4393019641864-3.43930196418641
441414.5101096214168-0.510109621416815
451114.9821123178014-3.98211231780143
461315.5798256656279-2.5798256656279
47912.5880393380596-3.5880393380596
481112.4716103630308-1.47161036303084
491510.81288171917784.18711828082219
501112.6729528419362-1.67295284193618
511011.7476954052343-1.7476954052343
521412.56814474780191.43185525219815
531814.78686288769223.2131371123078
541415.1494527892478-1.14945278924777
551114.1041622649552-3.10416226495524
561212.3434358034873-0.343435803487318
571311.6635338309111.33646616908905
58912.8839230139767-3.88392301397666
591013.4632347014795-3.46323470147954
601514.81304195687420.1869580431258
612017.5481189884822.45188101151798
621212.6707150980741-0.670715098074103
631215.232091633054-3.23209163305401
641414.4110996295113-0.411099629511288
651313.5509627122041-0.550962712204051
661114.5583773332894-3.55837733328942
671715.63143132240871.3685686775913
681213.9515807112215-1.95158071122155
691312.61132513306530.388674866934654
701411.4785949069632.52140509303705
711312.19601070279790.803989297202067
721512.61902616461482.38097383538524
731311.97665435892861.02334564107144
741011.3340630804498-1.33406308044982
751112.6328804138862-1.63288041388624
761914.3551704099564.64482959004404
771311.11292528980921.88707471019085
781714.3307425627842.66925743721597
791311.54442393748991.45557606251014
80913.8763498727774-4.87634987277737
811111.2176341054211-0.217634105421067
821012.3353582514329-2.33535825143291
83912.4910871410903-3.49108714109029
841210.56807738269791.43192261730208
851212.840390119495-0.840390119495016
861313.3832721215589-0.383272121558886
871311.06369828889381.93630171110617
881213.4373302052391-1.43733020523906
891515.0494664060988-0.0494664060988223
902218.32170898415343.6782910158466
911311.15299306335231.84700693664766
921514.73634369598440.263656304015618
931312.12678348576290.873216514237117
941513.0803237228751.91967627712501
951013.9946502640863-3.99465026408633
961111.3534197994876-0.353419799487643
971614.69011614713731.30988385286273
981113.1335029799934-2.13350297999339
99119.558584600638791.44141539936121
1001011.4491538460035-1.44915384600349
1011010.7805381574192-0.780538157419174
1021613.49032951343162.50967048656841
1031211.21783723458210.782162765417938
1041114.8630047516337-3.86300475163371
1051612.09689002365023.90310997634976
1061915.74085799836743.25914200163259
1071111.5174284040602-0.517428404060228
1081611.26679198980924.73320801019075
1091515.7552752021691-0.75527520216905
1102416.96604175347987.0339582465202
1111412.34644448217841.65355551782155
1121515.5190260473635-0.519026047363529
1131113.1819621946681-2.18196219466815
1141513.79245828956281.20754171043722
1151210.15985987551621.8401401244838
1161010.3151893434552-0.31518934345518
1171414.0518323682066-0.0518323682065531
1181313.197179736725-0.197179736724956
119913.5474954091688-4.54749540916876
1201512.38139751854892.6186024814511
1211515.4840776654998-0.484077665499832
1221413.67279050299170.327209497008296
1231111.7016017238645-0.701601723864496
124812.2494969397159-4.2494969397159
1251112.6382029347267-1.63820293472672
1261113.8175348304453-2.8175348304453
12789.22766256344107-1.22766256344107
1281010.643626692052-0.643626692052017
129119.459574608733261.54042539126674
1301312.55681232097860.443187679021381
1311113.0260394749552-2.02603947495524
1322016.24529648079043.7547035192096
1331012.1291550971023-2.12915509710227
1341512.04966508542142.9503349145786
1351211.16837469764770.831625302352315
1361411.77089805742672.22910194257331
1372315.60850254597137.3914974540287
1381412.6706919178391.32930808216104
1391616.4214753186796-0.421475318679625
1401112.9395800603264-1.9395800603264
1411213.0920855157728-1.09208551577281
1421013.6081633420527-3.60816334205266
1431410.38491053374173.61508946625825
1441212.789898825295-0.789898825295049
1451211.65639984646040.343600153539561
1461111.0700797190871-0.0700797190871305
1471211.87512206502740.124877934972633
1481314.6938305400679-1.69383054006791
1491113.6194082618689-2.61940826186886
1501915.83948531905953.16051468094049
1511211.09379255291410.906207447085911
1521713.46221278081753.53778721918246
153911.0344740930685-2.03447409306849
1541214.5922597612742-2.59225976127422
1551917.12234234474281.87765765525716
1561813.77589345656454.22410654343549
1571514.73634369598440.263656304015618
1581413.45960330620570.540396693794274
159119.459574608733261.54042539126674
160913.6638367320339-4.66383673203394
1611814.29357278068983.7064272193102
1621614.12663849508291.8733615049171






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9986728923889460.002654215222108060.00132710761105403
110.9965060324537520.006987935092496950.00349396754624848
120.9981386159492780.003722768101443230.00186138405072161
130.9959968727393650.008006254521269580.00400312726063479
140.9928140817606280.01437183647874320.0071859182393716
150.9868016591579140.02639668168417220.0131983408420861
160.979696835763890.04060632847222190.020303164236111
170.9679735109000170.06405297819996640.0320264890999832
180.950742835251130.0985143294977420.049257164748871
190.9545074670583580.09098506588328380.0454925329416419
200.9472620630533320.1054758738933360.0527379369466679
210.9268498013500330.1463003972999350.0731501986499674
220.9267044335422070.1465911329155850.0732955664577927
230.9254523698476710.1490952603046580.0745476301523291
240.9030029830373790.1939940339252420.096997016962621
250.8762075173364730.2475849653270540.123792482663527
260.9747747011038220.05045059779235660.0252252988961783
270.97125929834590.05748140330820070.0287407016541003
280.9651059286124040.06978814277519190.034894071387596
290.9556516412854840.08869671742903240.0443483587145162
300.941483341918660.1170333161626820.058516658081341
310.9500210071816710.09995798563665710.0499789928183286
320.9395310858345080.1209378283309850.0604689141654924
330.9294540342599830.1410919314800340.0705459657400172
340.9086258831135620.1827482337728760.0913741168864379
350.8883491010713310.2233017978573380.111650898928669
360.8874122348317930.2251755303364140.112587765168207
370.9239637300903340.1520725398193320.0760362699096659
380.9045623670977920.1908752658044160.095437632902208
390.88726955082010.2254608983598010.1127304491799
400.8838093613011860.2323812773976290.116190638698814
410.8696303184394740.2607393631210520.130369681560526
420.8502480411928880.2995039176142230.149751958807112
430.8579063423499970.2841873153000070.142093657650003
440.8266881919486130.3466236161027740.173311808051387
450.8679249793358220.2641500413283560.132075020664178
460.8603031770712720.2793936458574560.139696822928728
470.8713848306467730.2572303387064550.128615169353227
480.8493962566572120.3012074866855760.150603743342788
490.8967257514699280.2065484970601450.103274248530072
500.8798031209621980.2403937580756040.120196879037802
510.8648565099783770.2702869800432460.135143490021623
520.8477193606723540.3045612786552920.152280639327646
530.8713990610951960.2572018778096090.128600938904804
540.8484140555013030.3031718889973940.151585944498697
550.8539932017053640.2920135965892730.146006798294636
560.8261689377915750.3476621244168510.173831062208425
570.8041743960240410.3916512079519180.195825603975959
580.8384143183468180.3231713633063650.161585681653182
590.8512720754785070.2974558490429860.148727924521493
600.8244162219909880.3511675560180230.175583778009012
610.8309886113862120.3380227772275770.169011388613788
620.8008628021634930.3982743956730130.199137197836507
630.8108508267909520.3782983464180950.189149173209048
640.7826374020618790.4347251958762430.217362597938121
650.749966168577070.5000676628458590.250033831422929
660.7762675446353560.4474649107292880.223732455364644
670.7593656466613510.4812687066772980.240634353338649
680.7459963071220850.508007385755830.254003692877915
690.7123086634971840.5753826730056320.287691336502816
700.712717844142860.574564311714280.28728215585714
710.679255826657830.641488346684340.32074417334217
720.6711496541706760.6577006916586490.328850345829324
730.6382587788268350.723482442346330.361741221173165
740.6080314454603190.7839371090793620.391968554539681
750.5850898505412140.8298202989175710.414910149458786
760.6940206217407630.6119587565184750.305979378259237
770.6758410815891970.6483178368216060.324158918410803
780.6775601823961640.6448796352076720.322439817603836
790.6467256956916250.706548608616750.353274304308375
800.7590845710042440.4818308579915130.240915428995756
810.7220475033343240.5559049933313510.277952496665676
820.7199913175223270.5600173649553460.280008682477673
830.753562911320740.4928741773585210.24643708867926
840.72822431264070.54355137471860.2717756873593
850.6945122532787730.6109754934424540.305487746721227
860.6539192758832730.6921614482334540.346080724116727
870.6303705006886750.7392589986226490.369629499311325
880.6010602463733610.7978795072532780.398939753626639
890.5551676636880570.8896646726238850.444832336311943
900.6006666711116870.7986666577766250.399333328888312
910.5801865076734960.8396269846530090.419813492326504
920.5376496138625050.924700772274990.462350386137495
930.4956473289281360.9912946578562720.504352671071864
940.4714312644326110.9428625288652210.528568735567389
950.5285826094805510.9428347810388970.471417390519449
960.4825822655262340.9651645310524680.517417734473766
970.4446119996230290.8892239992460580.555388000376971
980.4334588372603680.8669176745207360.566541162739632
990.3963574415019660.7927148830039320.603642558498034
1000.3638263883453220.7276527766906440.636173611654678
1010.3251508410695980.6503016821391950.674849158930402
1020.3124989229327960.6249978458655910.687501077067204
1030.2746944851210030.5493889702420060.725305514878997
1040.36236592490830.72473184981660.6376340750917
1050.4341201833825730.8682403667651460.565879816617427
1060.4324681432615380.8649362865230770.567531856738462
1070.387020603116510.774041206233020.61297939688349
1080.4748236457210420.9496472914420850.525176354278958
1090.4513392978322310.9026785956644620.548660702167769
1100.7531145383937310.4937709232125370.246885461606269
1110.7282243338540750.543551332291850.271775666145925
1120.6890486324508230.6219027350983550.310951367549177
1130.671285420019660.6574291599606790.328714579980339
1140.6349090124717640.7301819750564730.365090987528236
1150.6193774542650470.7612450914699060.380622545734953
1160.5697292160291470.8605415679417060.430270783970853
1170.5322370604347560.9355258791304870.467762939565244
1180.4818628510751560.9637257021503130.518137148924844
1190.5737404139759560.8525191720480890.426259586024044
1200.5719478408238790.8561043183522430.428052159176121
1210.517280446081610.965439107836780.48271955391839
1220.4661356922374710.9322713844749410.533864307762529
1230.4139856061402390.8279712122804790.58601439385976
1240.4712588458163920.9425176916327830.528741154183608
1250.4508904700416550.901780940083310.549109529958345
1260.5109719666175630.9780560667648750.489028033382437
1270.4739154676391130.9478309352782250.526084532360887
1280.4235624507228490.8471249014456970.576437549277151
1290.4035108116462820.8070216232925650.596489188353718
1300.3459281715176520.6918563430353040.654071828482348
1310.3652351913305880.7304703826611750.634764808669412
1320.3477656755328850.695531351065770.652234324467115
1330.3170226964985580.6340453929971160.682977303501442
1340.2937010552647960.5874021105295910.706298944735204
1350.2447765777053220.4895531554106440.755223422294678
1360.2111960264270540.4223920528541090.788803973572946
1370.5335200063195980.9329599873608050.466479993680402
1380.5092518305073860.9814963389852280.490748169492614
1390.444522280984390.8890445619687790.55547771901561
1400.3888288538905650.777657707781130.611171146109435
1410.3288685520408730.6577371040817460.671131447959127
1420.2956134147651050.591226829530210.704386585234895
1430.3723806773813640.7447613547627280.627619322618636
1440.3106044736716650.6212089473433290.689395526328335
1450.2487591838579850.497518367715970.751240816142015
1460.1991512241350080.3983024482700170.800848775864992
1470.14176085830390.2835217166078010.8582391416961
1480.1023933151985530.2047866303971060.897606684801447
1490.1447068687514010.2894137375028010.8552931312486
1500.09606659147960970.1921331829592190.90393340852039
1510.05652937583752980.113058751675060.94347062416247
1520.121137828296490.242275656592980.87886217170351

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.998672892388946 & 0.00265421522210806 & 0.00132710761105403 \tabularnewline
11 & 0.996506032453752 & 0.00698793509249695 & 0.00349396754624848 \tabularnewline
12 & 0.998138615949278 & 0.00372276810144323 & 0.00186138405072161 \tabularnewline
13 & 0.995996872739365 & 0.00800625452126958 & 0.00400312726063479 \tabularnewline
14 & 0.992814081760628 & 0.0143718364787432 & 0.0071859182393716 \tabularnewline
15 & 0.986801659157914 & 0.0263966816841722 & 0.0131983408420861 \tabularnewline
16 & 0.97969683576389 & 0.0406063284722219 & 0.020303164236111 \tabularnewline
17 & 0.967973510900017 & 0.0640529781999664 & 0.0320264890999832 \tabularnewline
18 & 0.95074283525113 & 0.098514329497742 & 0.049257164748871 \tabularnewline
19 & 0.954507467058358 & 0.0909850658832838 & 0.0454925329416419 \tabularnewline
20 & 0.947262063053332 & 0.105475873893336 & 0.0527379369466679 \tabularnewline
21 & 0.926849801350033 & 0.146300397299935 & 0.0731501986499674 \tabularnewline
22 & 0.926704433542207 & 0.146591132915585 & 0.0732955664577927 \tabularnewline
23 & 0.925452369847671 & 0.149095260304658 & 0.0745476301523291 \tabularnewline
24 & 0.903002983037379 & 0.193994033925242 & 0.096997016962621 \tabularnewline
25 & 0.876207517336473 & 0.247584965327054 & 0.123792482663527 \tabularnewline
26 & 0.974774701103822 & 0.0504505977923566 & 0.0252252988961783 \tabularnewline
27 & 0.9712592983459 & 0.0574814033082007 & 0.0287407016541003 \tabularnewline
28 & 0.965105928612404 & 0.0697881427751919 & 0.034894071387596 \tabularnewline
29 & 0.955651641285484 & 0.0886967174290324 & 0.0443483587145162 \tabularnewline
30 & 0.94148334191866 & 0.117033316162682 & 0.058516658081341 \tabularnewline
31 & 0.950021007181671 & 0.0999579856366571 & 0.0499789928183286 \tabularnewline
32 & 0.939531085834508 & 0.120937828330985 & 0.0604689141654924 \tabularnewline
33 & 0.929454034259983 & 0.141091931480034 & 0.0705459657400172 \tabularnewline
34 & 0.908625883113562 & 0.182748233772876 & 0.0913741168864379 \tabularnewline
35 & 0.888349101071331 & 0.223301797857338 & 0.111650898928669 \tabularnewline
36 & 0.887412234831793 & 0.225175530336414 & 0.112587765168207 \tabularnewline
37 & 0.923963730090334 & 0.152072539819332 & 0.0760362699096659 \tabularnewline
38 & 0.904562367097792 & 0.190875265804416 & 0.095437632902208 \tabularnewline
39 & 0.8872695508201 & 0.225460898359801 & 0.1127304491799 \tabularnewline
40 & 0.883809361301186 & 0.232381277397629 & 0.116190638698814 \tabularnewline
41 & 0.869630318439474 & 0.260739363121052 & 0.130369681560526 \tabularnewline
42 & 0.850248041192888 & 0.299503917614223 & 0.149751958807112 \tabularnewline
43 & 0.857906342349997 & 0.284187315300007 & 0.142093657650003 \tabularnewline
44 & 0.826688191948613 & 0.346623616102774 & 0.173311808051387 \tabularnewline
45 & 0.867924979335822 & 0.264150041328356 & 0.132075020664178 \tabularnewline
46 & 0.860303177071272 & 0.279393645857456 & 0.139696822928728 \tabularnewline
47 & 0.871384830646773 & 0.257230338706455 & 0.128615169353227 \tabularnewline
48 & 0.849396256657212 & 0.301207486685576 & 0.150603743342788 \tabularnewline
49 & 0.896725751469928 & 0.206548497060145 & 0.103274248530072 \tabularnewline
50 & 0.879803120962198 & 0.240393758075604 & 0.120196879037802 \tabularnewline
51 & 0.864856509978377 & 0.270286980043246 & 0.135143490021623 \tabularnewline
52 & 0.847719360672354 & 0.304561278655292 & 0.152280639327646 \tabularnewline
53 & 0.871399061095196 & 0.257201877809609 & 0.128600938904804 \tabularnewline
54 & 0.848414055501303 & 0.303171888997394 & 0.151585944498697 \tabularnewline
55 & 0.853993201705364 & 0.292013596589273 & 0.146006798294636 \tabularnewline
56 & 0.826168937791575 & 0.347662124416851 & 0.173831062208425 \tabularnewline
57 & 0.804174396024041 & 0.391651207951918 & 0.195825603975959 \tabularnewline
58 & 0.838414318346818 & 0.323171363306365 & 0.161585681653182 \tabularnewline
59 & 0.851272075478507 & 0.297455849042986 & 0.148727924521493 \tabularnewline
60 & 0.824416221990988 & 0.351167556018023 & 0.175583778009012 \tabularnewline
61 & 0.830988611386212 & 0.338022777227577 & 0.169011388613788 \tabularnewline
62 & 0.800862802163493 & 0.398274395673013 & 0.199137197836507 \tabularnewline
63 & 0.810850826790952 & 0.378298346418095 & 0.189149173209048 \tabularnewline
64 & 0.782637402061879 & 0.434725195876243 & 0.217362597938121 \tabularnewline
65 & 0.74996616857707 & 0.500067662845859 & 0.250033831422929 \tabularnewline
66 & 0.776267544635356 & 0.447464910729288 & 0.223732455364644 \tabularnewline
67 & 0.759365646661351 & 0.481268706677298 & 0.240634353338649 \tabularnewline
68 & 0.745996307122085 & 0.50800738575583 & 0.254003692877915 \tabularnewline
69 & 0.712308663497184 & 0.575382673005632 & 0.287691336502816 \tabularnewline
70 & 0.71271784414286 & 0.57456431171428 & 0.28728215585714 \tabularnewline
71 & 0.67925582665783 & 0.64148834668434 & 0.32074417334217 \tabularnewline
72 & 0.671149654170676 & 0.657700691658649 & 0.328850345829324 \tabularnewline
73 & 0.638258778826835 & 0.72348244234633 & 0.361741221173165 \tabularnewline
74 & 0.608031445460319 & 0.783937109079362 & 0.391968554539681 \tabularnewline
75 & 0.585089850541214 & 0.829820298917571 & 0.414910149458786 \tabularnewline
76 & 0.694020621740763 & 0.611958756518475 & 0.305979378259237 \tabularnewline
77 & 0.675841081589197 & 0.648317836821606 & 0.324158918410803 \tabularnewline
78 & 0.677560182396164 & 0.644879635207672 & 0.322439817603836 \tabularnewline
79 & 0.646725695691625 & 0.70654860861675 & 0.353274304308375 \tabularnewline
80 & 0.759084571004244 & 0.481830857991513 & 0.240915428995756 \tabularnewline
81 & 0.722047503334324 & 0.555904993331351 & 0.277952496665676 \tabularnewline
82 & 0.719991317522327 & 0.560017364955346 & 0.280008682477673 \tabularnewline
83 & 0.75356291132074 & 0.492874177358521 & 0.24643708867926 \tabularnewline
84 & 0.7282243126407 & 0.5435513747186 & 0.2717756873593 \tabularnewline
85 & 0.694512253278773 & 0.610975493442454 & 0.305487746721227 \tabularnewline
86 & 0.653919275883273 & 0.692161448233454 & 0.346080724116727 \tabularnewline
87 & 0.630370500688675 & 0.739258998622649 & 0.369629499311325 \tabularnewline
88 & 0.601060246373361 & 0.797879507253278 & 0.398939753626639 \tabularnewline
89 & 0.555167663688057 & 0.889664672623885 & 0.444832336311943 \tabularnewline
90 & 0.600666671111687 & 0.798666657776625 & 0.399333328888312 \tabularnewline
91 & 0.580186507673496 & 0.839626984653009 & 0.419813492326504 \tabularnewline
92 & 0.537649613862505 & 0.92470077227499 & 0.462350386137495 \tabularnewline
93 & 0.495647328928136 & 0.991294657856272 & 0.504352671071864 \tabularnewline
94 & 0.471431264432611 & 0.942862528865221 & 0.528568735567389 \tabularnewline
95 & 0.528582609480551 & 0.942834781038897 & 0.471417390519449 \tabularnewline
96 & 0.482582265526234 & 0.965164531052468 & 0.517417734473766 \tabularnewline
97 & 0.444611999623029 & 0.889223999246058 & 0.555388000376971 \tabularnewline
98 & 0.433458837260368 & 0.866917674520736 & 0.566541162739632 \tabularnewline
99 & 0.396357441501966 & 0.792714883003932 & 0.603642558498034 \tabularnewline
100 & 0.363826388345322 & 0.727652776690644 & 0.636173611654678 \tabularnewline
101 & 0.325150841069598 & 0.650301682139195 & 0.674849158930402 \tabularnewline
102 & 0.312498922932796 & 0.624997845865591 & 0.687501077067204 \tabularnewline
103 & 0.274694485121003 & 0.549388970242006 & 0.725305514878997 \tabularnewline
104 & 0.3623659249083 & 0.7247318498166 & 0.6376340750917 \tabularnewline
105 & 0.434120183382573 & 0.868240366765146 & 0.565879816617427 \tabularnewline
106 & 0.432468143261538 & 0.864936286523077 & 0.567531856738462 \tabularnewline
107 & 0.38702060311651 & 0.77404120623302 & 0.61297939688349 \tabularnewline
108 & 0.474823645721042 & 0.949647291442085 & 0.525176354278958 \tabularnewline
109 & 0.451339297832231 & 0.902678595664462 & 0.548660702167769 \tabularnewline
110 & 0.753114538393731 & 0.493770923212537 & 0.246885461606269 \tabularnewline
111 & 0.728224333854075 & 0.54355133229185 & 0.271775666145925 \tabularnewline
112 & 0.689048632450823 & 0.621902735098355 & 0.310951367549177 \tabularnewline
113 & 0.67128542001966 & 0.657429159960679 & 0.328714579980339 \tabularnewline
114 & 0.634909012471764 & 0.730181975056473 & 0.365090987528236 \tabularnewline
115 & 0.619377454265047 & 0.761245091469906 & 0.380622545734953 \tabularnewline
116 & 0.569729216029147 & 0.860541567941706 & 0.430270783970853 \tabularnewline
117 & 0.532237060434756 & 0.935525879130487 & 0.467762939565244 \tabularnewline
118 & 0.481862851075156 & 0.963725702150313 & 0.518137148924844 \tabularnewline
119 & 0.573740413975956 & 0.852519172048089 & 0.426259586024044 \tabularnewline
120 & 0.571947840823879 & 0.856104318352243 & 0.428052159176121 \tabularnewline
121 & 0.51728044608161 & 0.96543910783678 & 0.48271955391839 \tabularnewline
122 & 0.466135692237471 & 0.932271384474941 & 0.533864307762529 \tabularnewline
123 & 0.413985606140239 & 0.827971212280479 & 0.58601439385976 \tabularnewline
124 & 0.471258845816392 & 0.942517691632783 & 0.528741154183608 \tabularnewline
125 & 0.450890470041655 & 0.90178094008331 & 0.549109529958345 \tabularnewline
126 & 0.510971966617563 & 0.978056066764875 & 0.489028033382437 \tabularnewline
127 & 0.473915467639113 & 0.947830935278225 & 0.526084532360887 \tabularnewline
128 & 0.423562450722849 & 0.847124901445697 & 0.576437549277151 \tabularnewline
129 & 0.403510811646282 & 0.807021623292565 & 0.596489188353718 \tabularnewline
130 & 0.345928171517652 & 0.691856343035304 & 0.654071828482348 \tabularnewline
131 & 0.365235191330588 & 0.730470382661175 & 0.634764808669412 \tabularnewline
132 & 0.347765675532885 & 0.69553135106577 & 0.652234324467115 \tabularnewline
133 & 0.317022696498558 & 0.634045392997116 & 0.682977303501442 \tabularnewline
134 & 0.293701055264796 & 0.587402110529591 & 0.706298944735204 \tabularnewline
135 & 0.244776577705322 & 0.489553155410644 & 0.755223422294678 \tabularnewline
136 & 0.211196026427054 & 0.422392052854109 & 0.788803973572946 \tabularnewline
137 & 0.533520006319598 & 0.932959987360805 & 0.466479993680402 \tabularnewline
138 & 0.509251830507386 & 0.981496338985228 & 0.490748169492614 \tabularnewline
139 & 0.44452228098439 & 0.889044561968779 & 0.55547771901561 \tabularnewline
140 & 0.388828853890565 & 0.77765770778113 & 0.611171146109435 \tabularnewline
141 & 0.328868552040873 & 0.657737104081746 & 0.671131447959127 \tabularnewline
142 & 0.295613414765105 & 0.59122682953021 & 0.704386585234895 \tabularnewline
143 & 0.372380677381364 & 0.744761354762728 & 0.627619322618636 \tabularnewline
144 & 0.310604473671665 & 0.621208947343329 & 0.689395526328335 \tabularnewline
145 & 0.248759183857985 & 0.49751836771597 & 0.751240816142015 \tabularnewline
146 & 0.199151224135008 & 0.398302448270017 & 0.800848775864992 \tabularnewline
147 & 0.1417608583039 & 0.283521716607801 & 0.8582391416961 \tabularnewline
148 & 0.102393315198553 & 0.204786630397106 & 0.897606684801447 \tabularnewline
149 & 0.144706868751401 & 0.289413737502801 & 0.8552931312486 \tabularnewline
150 & 0.0960665914796097 & 0.192133182959219 & 0.90393340852039 \tabularnewline
151 & 0.0565293758375298 & 0.11305875167506 & 0.94347062416247 \tabularnewline
152 & 0.12113782829649 & 0.24227565659298 & 0.87886217170351 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.998672892388946[/C][C]0.00265421522210806[/C][C]0.00132710761105403[/C][/ROW]
[ROW][C]11[/C][C]0.996506032453752[/C][C]0.00698793509249695[/C][C]0.00349396754624848[/C][/ROW]
[ROW][C]12[/C][C]0.998138615949278[/C][C]0.00372276810144323[/C][C]0.00186138405072161[/C][/ROW]
[ROW][C]13[/C][C]0.995996872739365[/C][C]0.00800625452126958[/C][C]0.00400312726063479[/C][/ROW]
[ROW][C]14[/C][C]0.992814081760628[/C][C]0.0143718364787432[/C][C]0.0071859182393716[/C][/ROW]
[ROW][C]15[/C][C]0.986801659157914[/C][C]0.0263966816841722[/C][C]0.0131983408420861[/C][/ROW]
[ROW][C]16[/C][C]0.97969683576389[/C][C]0.0406063284722219[/C][C]0.020303164236111[/C][/ROW]
[ROW][C]17[/C][C]0.967973510900017[/C][C]0.0640529781999664[/C][C]0.0320264890999832[/C][/ROW]
[ROW][C]18[/C][C]0.95074283525113[/C][C]0.098514329497742[/C][C]0.049257164748871[/C][/ROW]
[ROW][C]19[/C][C]0.954507467058358[/C][C]0.0909850658832838[/C][C]0.0454925329416419[/C][/ROW]
[ROW][C]20[/C][C]0.947262063053332[/C][C]0.105475873893336[/C][C]0.0527379369466679[/C][/ROW]
[ROW][C]21[/C][C]0.926849801350033[/C][C]0.146300397299935[/C][C]0.0731501986499674[/C][/ROW]
[ROW][C]22[/C][C]0.926704433542207[/C][C]0.146591132915585[/C][C]0.0732955664577927[/C][/ROW]
[ROW][C]23[/C][C]0.925452369847671[/C][C]0.149095260304658[/C][C]0.0745476301523291[/C][/ROW]
[ROW][C]24[/C][C]0.903002983037379[/C][C]0.193994033925242[/C][C]0.096997016962621[/C][/ROW]
[ROW][C]25[/C][C]0.876207517336473[/C][C]0.247584965327054[/C][C]0.123792482663527[/C][/ROW]
[ROW][C]26[/C][C]0.974774701103822[/C][C]0.0504505977923566[/C][C]0.0252252988961783[/C][/ROW]
[ROW][C]27[/C][C]0.9712592983459[/C][C]0.0574814033082007[/C][C]0.0287407016541003[/C][/ROW]
[ROW][C]28[/C][C]0.965105928612404[/C][C]0.0697881427751919[/C][C]0.034894071387596[/C][/ROW]
[ROW][C]29[/C][C]0.955651641285484[/C][C]0.0886967174290324[/C][C]0.0443483587145162[/C][/ROW]
[ROW][C]30[/C][C]0.94148334191866[/C][C]0.117033316162682[/C][C]0.058516658081341[/C][/ROW]
[ROW][C]31[/C][C]0.950021007181671[/C][C]0.0999579856366571[/C][C]0.0499789928183286[/C][/ROW]
[ROW][C]32[/C][C]0.939531085834508[/C][C]0.120937828330985[/C][C]0.0604689141654924[/C][/ROW]
[ROW][C]33[/C][C]0.929454034259983[/C][C]0.141091931480034[/C][C]0.0705459657400172[/C][/ROW]
[ROW][C]34[/C][C]0.908625883113562[/C][C]0.182748233772876[/C][C]0.0913741168864379[/C][/ROW]
[ROW][C]35[/C][C]0.888349101071331[/C][C]0.223301797857338[/C][C]0.111650898928669[/C][/ROW]
[ROW][C]36[/C][C]0.887412234831793[/C][C]0.225175530336414[/C][C]0.112587765168207[/C][/ROW]
[ROW][C]37[/C][C]0.923963730090334[/C][C]0.152072539819332[/C][C]0.0760362699096659[/C][/ROW]
[ROW][C]38[/C][C]0.904562367097792[/C][C]0.190875265804416[/C][C]0.095437632902208[/C][/ROW]
[ROW][C]39[/C][C]0.8872695508201[/C][C]0.225460898359801[/C][C]0.1127304491799[/C][/ROW]
[ROW][C]40[/C][C]0.883809361301186[/C][C]0.232381277397629[/C][C]0.116190638698814[/C][/ROW]
[ROW][C]41[/C][C]0.869630318439474[/C][C]0.260739363121052[/C][C]0.130369681560526[/C][/ROW]
[ROW][C]42[/C][C]0.850248041192888[/C][C]0.299503917614223[/C][C]0.149751958807112[/C][/ROW]
[ROW][C]43[/C][C]0.857906342349997[/C][C]0.284187315300007[/C][C]0.142093657650003[/C][/ROW]
[ROW][C]44[/C][C]0.826688191948613[/C][C]0.346623616102774[/C][C]0.173311808051387[/C][/ROW]
[ROW][C]45[/C][C]0.867924979335822[/C][C]0.264150041328356[/C][C]0.132075020664178[/C][/ROW]
[ROW][C]46[/C][C]0.860303177071272[/C][C]0.279393645857456[/C][C]0.139696822928728[/C][/ROW]
[ROW][C]47[/C][C]0.871384830646773[/C][C]0.257230338706455[/C][C]0.128615169353227[/C][/ROW]
[ROW][C]48[/C][C]0.849396256657212[/C][C]0.301207486685576[/C][C]0.150603743342788[/C][/ROW]
[ROW][C]49[/C][C]0.896725751469928[/C][C]0.206548497060145[/C][C]0.103274248530072[/C][/ROW]
[ROW][C]50[/C][C]0.879803120962198[/C][C]0.240393758075604[/C][C]0.120196879037802[/C][/ROW]
[ROW][C]51[/C][C]0.864856509978377[/C][C]0.270286980043246[/C][C]0.135143490021623[/C][/ROW]
[ROW][C]52[/C][C]0.847719360672354[/C][C]0.304561278655292[/C][C]0.152280639327646[/C][/ROW]
[ROW][C]53[/C][C]0.871399061095196[/C][C]0.257201877809609[/C][C]0.128600938904804[/C][/ROW]
[ROW][C]54[/C][C]0.848414055501303[/C][C]0.303171888997394[/C][C]0.151585944498697[/C][/ROW]
[ROW][C]55[/C][C]0.853993201705364[/C][C]0.292013596589273[/C][C]0.146006798294636[/C][/ROW]
[ROW][C]56[/C][C]0.826168937791575[/C][C]0.347662124416851[/C][C]0.173831062208425[/C][/ROW]
[ROW][C]57[/C][C]0.804174396024041[/C][C]0.391651207951918[/C][C]0.195825603975959[/C][/ROW]
[ROW][C]58[/C][C]0.838414318346818[/C][C]0.323171363306365[/C][C]0.161585681653182[/C][/ROW]
[ROW][C]59[/C][C]0.851272075478507[/C][C]0.297455849042986[/C][C]0.148727924521493[/C][/ROW]
[ROW][C]60[/C][C]0.824416221990988[/C][C]0.351167556018023[/C][C]0.175583778009012[/C][/ROW]
[ROW][C]61[/C][C]0.830988611386212[/C][C]0.338022777227577[/C][C]0.169011388613788[/C][/ROW]
[ROW][C]62[/C][C]0.800862802163493[/C][C]0.398274395673013[/C][C]0.199137197836507[/C][/ROW]
[ROW][C]63[/C][C]0.810850826790952[/C][C]0.378298346418095[/C][C]0.189149173209048[/C][/ROW]
[ROW][C]64[/C][C]0.782637402061879[/C][C]0.434725195876243[/C][C]0.217362597938121[/C][/ROW]
[ROW][C]65[/C][C]0.74996616857707[/C][C]0.500067662845859[/C][C]0.250033831422929[/C][/ROW]
[ROW][C]66[/C][C]0.776267544635356[/C][C]0.447464910729288[/C][C]0.223732455364644[/C][/ROW]
[ROW][C]67[/C][C]0.759365646661351[/C][C]0.481268706677298[/C][C]0.240634353338649[/C][/ROW]
[ROW][C]68[/C][C]0.745996307122085[/C][C]0.50800738575583[/C][C]0.254003692877915[/C][/ROW]
[ROW][C]69[/C][C]0.712308663497184[/C][C]0.575382673005632[/C][C]0.287691336502816[/C][/ROW]
[ROW][C]70[/C][C]0.71271784414286[/C][C]0.57456431171428[/C][C]0.28728215585714[/C][/ROW]
[ROW][C]71[/C][C]0.67925582665783[/C][C]0.64148834668434[/C][C]0.32074417334217[/C][/ROW]
[ROW][C]72[/C][C]0.671149654170676[/C][C]0.657700691658649[/C][C]0.328850345829324[/C][/ROW]
[ROW][C]73[/C][C]0.638258778826835[/C][C]0.72348244234633[/C][C]0.361741221173165[/C][/ROW]
[ROW][C]74[/C][C]0.608031445460319[/C][C]0.783937109079362[/C][C]0.391968554539681[/C][/ROW]
[ROW][C]75[/C][C]0.585089850541214[/C][C]0.829820298917571[/C][C]0.414910149458786[/C][/ROW]
[ROW][C]76[/C][C]0.694020621740763[/C][C]0.611958756518475[/C][C]0.305979378259237[/C][/ROW]
[ROW][C]77[/C][C]0.675841081589197[/C][C]0.648317836821606[/C][C]0.324158918410803[/C][/ROW]
[ROW][C]78[/C][C]0.677560182396164[/C][C]0.644879635207672[/C][C]0.322439817603836[/C][/ROW]
[ROW][C]79[/C][C]0.646725695691625[/C][C]0.70654860861675[/C][C]0.353274304308375[/C][/ROW]
[ROW][C]80[/C][C]0.759084571004244[/C][C]0.481830857991513[/C][C]0.240915428995756[/C][/ROW]
[ROW][C]81[/C][C]0.722047503334324[/C][C]0.555904993331351[/C][C]0.277952496665676[/C][/ROW]
[ROW][C]82[/C][C]0.719991317522327[/C][C]0.560017364955346[/C][C]0.280008682477673[/C][/ROW]
[ROW][C]83[/C][C]0.75356291132074[/C][C]0.492874177358521[/C][C]0.24643708867926[/C][/ROW]
[ROW][C]84[/C][C]0.7282243126407[/C][C]0.5435513747186[/C][C]0.2717756873593[/C][/ROW]
[ROW][C]85[/C][C]0.694512253278773[/C][C]0.610975493442454[/C][C]0.305487746721227[/C][/ROW]
[ROW][C]86[/C][C]0.653919275883273[/C][C]0.692161448233454[/C][C]0.346080724116727[/C][/ROW]
[ROW][C]87[/C][C]0.630370500688675[/C][C]0.739258998622649[/C][C]0.369629499311325[/C][/ROW]
[ROW][C]88[/C][C]0.601060246373361[/C][C]0.797879507253278[/C][C]0.398939753626639[/C][/ROW]
[ROW][C]89[/C][C]0.555167663688057[/C][C]0.889664672623885[/C][C]0.444832336311943[/C][/ROW]
[ROW][C]90[/C][C]0.600666671111687[/C][C]0.798666657776625[/C][C]0.399333328888312[/C][/ROW]
[ROW][C]91[/C][C]0.580186507673496[/C][C]0.839626984653009[/C][C]0.419813492326504[/C][/ROW]
[ROW][C]92[/C][C]0.537649613862505[/C][C]0.92470077227499[/C][C]0.462350386137495[/C][/ROW]
[ROW][C]93[/C][C]0.495647328928136[/C][C]0.991294657856272[/C][C]0.504352671071864[/C][/ROW]
[ROW][C]94[/C][C]0.471431264432611[/C][C]0.942862528865221[/C][C]0.528568735567389[/C][/ROW]
[ROW][C]95[/C][C]0.528582609480551[/C][C]0.942834781038897[/C][C]0.471417390519449[/C][/ROW]
[ROW][C]96[/C][C]0.482582265526234[/C][C]0.965164531052468[/C][C]0.517417734473766[/C][/ROW]
[ROW][C]97[/C][C]0.444611999623029[/C][C]0.889223999246058[/C][C]0.555388000376971[/C][/ROW]
[ROW][C]98[/C][C]0.433458837260368[/C][C]0.866917674520736[/C][C]0.566541162739632[/C][/ROW]
[ROW][C]99[/C][C]0.396357441501966[/C][C]0.792714883003932[/C][C]0.603642558498034[/C][/ROW]
[ROW][C]100[/C][C]0.363826388345322[/C][C]0.727652776690644[/C][C]0.636173611654678[/C][/ROW]
[ROW][C]101[/C][C]0.325150841069598[/C][C]0.650301682139195[/C][C]0.674849158930402[/C][/ROW]
[ROW][C]102[/C][C]0.312498922932796[/C][C]0.624997845865591[/C][C]0.687501077067204[/C][/ROW]
[ROW][C]103[/C][C]0.274694485121003[/C][C]0.549388970242006[/C][C]0.725305514878997[/C][/ROW]
[ROW][C]104[/C][C]0.3623659249083[/C][C]0.7247318498166[/C][C]0.6376340750917[/C][/ROW]
[ROW][C]105[/C][C]0.434120183382573[/C][C]0.868240366765146[/C][C]0.565879816617427[/C][/ROW]
[ROW][C]106[/C][C]0.432468143261538[/C][C]0.864936286523077[/C][C]0.567531856738462[/C][/ROW]
[ROW][C]107[/C][C]0.38702060311651[/C][C]0.77404120623302[/C][C]0.61297939688349[/C][/ROW]
[ROW][C]108[/C][C]0.474823645721042[/C][C]0.949647291442085[/C][C]0.525176354278958[/C][/ROW]
[ROW][C]109[/C][C]0.451339297832231[/C][C]0.902678595664462[/C][C]0.548660702167769[/C][/ROW]
[ROW][C]110[/C][C]0.753114538393731[/C][C]0.493770923212537[/C][C]0.246885461606269[/C][/ROW]
[ROW][C]111[/C][C]0.728224333854075[/C][C]0.54355133229185[/C][C]0.271775666145925[/C][/ROW]
[ROW][C]112[/C][C]0.689048632450823[/C][C]0.621902735098355[/C][C]0.310951367549177[/C][/ROW]
[ROW][C]113[/C][C]0.67128542001966[/C][C]0.657429159960679[/C][C]0.328714579980339[/C][/ROW]
[ROW][C]114[/C][C]0.634909012471764[/C][C]0.730181975056473[/C][C]0.365090987528236[/C][/ROW]
[ROW][C]115[/C][C]0.619377454265047[/C][C]0.761245091469906[/C][C]0.380622545734953[/C][/ROW]
[ROW][C]116[/C][C]0.569729216029147[/C][C]0.860541567941706[/C][C]0.430270783970853[/C][/ROW]
[ROW][C]117[/C][C]0.532237060434756[/C][C]0.935525879130487[/C][C]0.467762939565244[/C][/ROW]
[ROW][C]118[/C][C]0.481862851075156[/C][C]0.963725702150313[/C][C]0.518137148924844[/C][/ROW]
[ROW][C]119[/C][C]0.573740413975956[/C][C]0.852519172048089[/C][C]0.426259586024044[/C][/ROW]
[ROW][C]120[/C][C]0.571947840823879[/C][C]0.856104318352243[/C][C]0.428052159176121[/C][/ROW]
[ROW][C]121[/C][C]0.51728044608161[/C][C]0.96543910783678[/C][C]0.48271955391839[/C][/ROW]
[ROW][C]122[/C][C]0.466135692237471[/C][C]0.932271384474941[/C][C]0.533864307762529[/C][/ROW]
[ROW][C]123[/C][C]0.413985606140239[/C][C]0.827971212280479[/C][C]0.58601439385976[/C][/ROW]
[ROW][C]124[/C][C]0.471258845816392[/C][C]0.942517691632783[/C][C]0.528741154183608[/C][/ROW]
[ROW][C]125[/C][C]0.450890470041655[/C][C]0.90178094008331[/C][C]0.549109529958345[/C][/ROW]
[ROW][C]126[/C][C]0.510971966617563[/C][C]0.978056066764875[/C][C]0.489028033382437[/C][/ROW]
[ROW][C]127[/C][C]0.473915467639113[/C][C]0.947830935278225[/C][C]0.526084532360887[/C][/ROW]
[ROW][C]128[/C][C]0.423562450722849[/C][C]0.847124901445697[/C][C]0.576437549277151[/C][/ROW]
[ROW][C]129[/C][C]0.403510811646282[/C][C]0.807021623292565[/C][C]0.596489188353718[/C][/ROW]
[ROW][C]130[/C][C]0.345928171517652[/C][C]0.691856343035304[/C][C]0.654071828482348[/C][/ROW]
[ROW][C]131[/C][C]0.365235191330588[/C][C]0.730470382661175[/C][C]0.634764808669412[/C][/ROW]
[ROW][C]132[/C][C]0.347765675532885[/C][C]0.69553135106577[/C][C]0.652234324467115[/C][/ROW]
[ROW][C]133[/C][C]0.317022696498558[/C][C]0.634045392997116[/C][C]0.682977303501442[/C][/ROW]
[ROW][C]134[/C][C]0.293701055264796[/C][C]0.587402110529591[/C][C]0.706298944735204[/C][/ROW]
[ROW][C]135[/C][C]0.244776577705322[/C][C]0.489553155410644[/C][C]0.755223422294678[/C][/ROW]
[ROW][C]136[/C][C]0.211196026427054[/C][C]0.422392052854109[/C][C]0.788803973572946[/C][/ROW]
[ROW][C]137[/C][C]0.533520006319598[/C][C]0.932959987360805[/C][C]0.466479993680402[/C][/ROW]
[ROW][C]138[/C][C]0.509251830507386[/C][C]0.981496338985228[/C][C]0.490748169492614[/C][/ROW]
[ROW][C]139[/C][C]0.44452228098439[/C][C]0.889044561968779[/C][C]0.55547771901561[/C][/ROW]
[ROW][C]140[/C][C]0.388828853890565[/C][C]0.77765770778113[/C][C]0.611171146109435[/C][/ROW]
[ROW][C]141[/C][C]0.328868552040873[/C][C]0.657737104081746[/C][C]0.671131447959127[/C][/ROW]
[ROW][C]142[/C][C]0.295613414765105[/C][C]0.59122682953021[/C][C]0.704386585234895[/C][/ROW]
[ROW][C]143[/C][C]0.372380677381364[/C][C]0.744761354762728[/C][C]0.627619322618636[/C][/ROW]
[ROW][C]144[/C][C]0.310604473671665[/C][C]0.621208947343329[/C][C]0.689395526328335[/C][/ROW]
[ROW][C]145[/C][C]0.248759183857985[/C][C]0.49751836771597[/C][C]0.751240816142015[/C][/ROW]
[ROW][C]146[/C][C]0.199151224135008[/C][C]0.398302448270017[/C][C]0.800848775864992[/C][/ROW]
[ROW][C]147[/C][C]0.1417608583039[/C][C]0.283521716607801[/C][C]0.8582391416961[/C][/ROW]
[ROW][C]148[/C][C]0.102393315198553[/C][C]0.204786630397106[/C][C]0.897606684801447[/C][/ROW]
[ROW][C]149[/C][C]0.144706868751401[/C][C]0.289413737502801[/C][C]0.8552931312486[/C][/ROW]
[ROW][C]150[/C][C]0.0960665914796097[/C][C]0.192133182959219[/C][C]0.90393340852039[/C][/ROW]
[ROW][C]151[/C][C]0.0565293758375298[/C][C]0.11305875167506[/C][C]0.94347062416247[/C][/ROW]
[ROW][C]152[/C][C]0.12113782829649[/C][C]0.24227565659298[/C][C]0.87886217170351[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9986728923889460.002654215222108060.00132710761105403
110.9965060324537520.006987935092496950.00349396754624848
120.9981386159492780.003722768101443230.00186138405072161
130.9959968727393650.008006254521269580.00400312726063479
140.9928140817606280.01437183647874320.0071859182393716
150.9868016591579140.02639668168417220.0131983408420861
160.979696835763890.04060632847222190.020303164236111
170.9679735109000170.06405297819996640.0320264890999832
180.950742835251130.0985143294977420.049257164748871
190.9545074670583580.09098506588328380.0454925329416419
200.9472620630533320.1054758738933360.0527379369466679
210.9268498013500330.1463003972999350.0731501986499674
220.9267044335422070.1465911329155850.0732955664577927
230.9254523698476710.1490952603046580.0745476301523291
240.9030029830373790.1939940339252420.096997016962621
250.8762075173364730.2475849653270540.123792482663527
260.9747747011038220.05045059779235660.0252252988961783
270.97125929834590.05748140330820070.0287407016541003
280.9651059286124040.06978814277519190.034894071387596
290.9556516412854840.08869671742903240.0443483587145162
300.941483341918660.1170333161626820.058516658081341
310.9500210071816710.09995798563665710.0499789928183286
320.9395310858345080.1209378283309850.0604689141654924
330.9294540342599830.1410919314800340.0705459657400172
340.9086258831135620.1827482337728760.0913741168864379
350.8883491010713310.2233017978573380.111650898928669
360.8874122348317930.2251755303364140.112587765168207
370.9239637300903340.1520725398193320.0760362699096659
380.9045623670977920.1908752658044160.095437632902208
390.88726955082010.2254608983598010.1127304491799
400.8838093613011860.2323812773976290.116190638698814
410.8696303184394740.2607393631210520.130369681560526
420.8502480411928880.2995039176142230.149751958807112
430.8579063423499970.2841873153000070.142093657650003
440.8266881919486130.3466236161027740.173311808051387
450.8679249793358220.2641500413283560.132075020664178
460.8603031770712720.2793936458574560.139696822928728
470.8713848306467730.2572303387064550.128615169353227
480.8493962566572120.3012074866855760.150603743342788
490.8967257514699280.2065484970601450.103274248530072
500.8798031209621980.2403937580756040.120196879037802
510.8648565099783770.2702869800432460.135143490021623
520.8477193606723540.3045612786552920.152280639327646
530.8713990610951960.2572018778096090.128600938904804
540.8484140555013030.3031718889973940.151585944498697
550.8539932017053640.2920135965892730.146006798294636
560.8261689377915750.3476621244168510.173831062208425
570.8041743960240410.3916512079519180.195825603975959
580.8384143183468180.3231713633063650.161585681653182
590.8512720754785070.2974558490429860.148727924521493
600.8244162219909880.3511675560180230.175583778009012
610.8309886113862120.3380227772275770.169011388613788
620.8008628021634930.3982743956730130.199137197836507
630.8108508267909520.3782983464180950.189149173209048
640.7826374020618790.4347251958762430.217362597938121
650.749966168577070.5000676628458590.250033831422929
660.7762675446353560.4474649107292880.223732455364644
670.7593656466613510.4812687066772980.240634353338649
680.7459963071220850.508007385755830.254003692877915
690.7123086634971840.5753826730056320.287691336502816
700.712717844142860.574564311714280.28728215585714
710.679255826657830.641488346684340.32074417334217
720.6711496541706760.6577006916586490.328850345829324
730.6382587788268350.723482442346330.361741221173165
740.6080314454603190.7839371090793620.391968554539681
750.5850898505412140.8298202989175710.414910149458786
760.6940206217407630.6119587565184750.305979378259237
770.6758410815891970.6483178368216060.324158918410803
780.6775601823961640.6448796352076720.322439817603836
790.6467256956916250.706548608616750.353274304308375
800.7590845710042440.4818308579915130.240915428995756
810.7220475033343240.5559049933313510.277952496665676
820.7199913175223270.5600173649553460.280008682477673
830.753562911320740.4928741773585210.24643708867926
840.72822431264070.54355137471860.2717756873593
850.6945122532787730.6109754934424540.305487746721227
860.6539192758832730.6921614482334540.346080724116727
870.6303705006886750.7392589986226490.369629499311325
880.6010602463733610.7978795072532780.398939753626639
890.5551676636880570.8896646726238850.444832336311943
900.6006666711116870.7986666577766250.399333328888312
910.5801865076734960.8396269846530090.419813492326504
920.5376496138625050.924700772274990.462350386137495
930.4956473289281360.9912946578562720.504352671071864
940.4714312644326110.9428625288652210.528568735567389
950.5285826094805510.9428347810388970.471417390519449
960.4825822655262340.9651645310524680.517417734473766
970.4446119996230290.8892239992460580.555388000376971
980.4334588372603680.8669176745207360.566541162739632
990.3963574415019660.7927148830039320.603642558498034
1000.3638263883453220.7276527766906440.636173611654678
1010.3251508410695980.6503016821391950.674849158930402
1020.3124989229327960.6249978458655910.687501077067204
1030.2746944851210030.5493889702420060.725305514878997
1040.36236592490830.72473184981660.6376340750917
1050.4341201833825730.8682403667651460.565879816617427
1060.4324681432615380.8649362865230770.567531856738462
1070.387020603116510.774041206233020.61297939688349
1080.4748236457210420.9496472914420850.525176354278958
1090.4513392978322310.9026785956644620.548660702167769
1100.7531145383937310.4937709232125370.246885461606269
1110.7282243338540750.543551332291850.271775666145925
1120.6890486324508230.6219027350983550.310951367549177
1130.671285420019660.6574291599606790.328714579980339
1140.6349090124717640.7301819750564730.365090987528236
1150.6193774542650470.7612450914699060.380622545734953
1160.5697292160291470.8605415679417060.430270783970853
1170.5322370604347560.9355258791304870.467762939565244
1180.4818628510751560.9637257021503130.518137148924844
1190.5737404139759560.8525191720480890.426259586024044
1200.5719478408238790.8561043183522430.428052159176121
1210.517280446081610.965439107836780.48271955391839
1220.4661356922374710.9322713844749410.533864307762529
1230.4139856061402390.8279712122804790.58601439385976
1240.4712588458163920.9425176916327830.528741154183608
1250.4508904700416550.901780940083310.549109529958345
1260.5109719666175630.9780560667648750.489028033382437
1270.4739154676391130.9478309352782250.526084532360887
1280.4235624507228490.8471249014456970.576437549277151
1290.4035108116462820.8070216232925650.596489188353718
1300.3459281715176520.6918563430353040.654071828482348
1310.3652351913305880.7304703826611750.634764808669412
1320.3477656755328850.695531351065770.652234324467115
1330.3170226964985580.6340453929971160.682977303501442
1340.2937010552647960.5874021105295910.706298944735204
1350.2447765777053220.4895531554106440.755223422294678
1360.2111960264270540.4223920528541090.788803973572946
1370.5335200063195980.9329599873608050.466479993680402
1380.5092518305073860.9814963389852280.490748169492614
1390.444522280984390.8890445619687790.55547771901561
1400.3888288538905650.777657707781130.611171146109435
1410.3288685520408730.6577371040817460.671131447959127
1420.2956134147651050.591226829530210.704386585234895
1430.3723806773813640.7447613547627280.627619322618636
1440.3106044736716650.6212089473433290.689395526328335
1450.2487591838579850.497518367715970.751240816142015
1460.1991512241350080.3983024482700170.800848775864992
1470.14176085830390.2835217166078010.8582391416961
1480.1023933151985530.2047866303971060.897606684801447
1490.1447068687514010.2894137375028010.8552931312486
1500.09606659147960970.1921331829592190.90393340852039
1510.05652937583752980.113058751675060.94347062416247
1520.121137828296490.242275656592980.87886217170351






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.027972027972028NOK
5% type I error level70.048951048951049OK
10% type I error level150.104895104895105NOK

\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 & 4 & 0.027972027972028 & NOK \tabularnewline
5% type I error level & 7 & 0.048951048951049 & OK \tabularnewline
10% type I error level & 15 & 0.104895104895105 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154526&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]4[/C][C]0.027972027972028[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.048951048951049[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.104895104895105[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154526&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154526&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.027972027972028NOK
5% type I error level70.048951048951049OK
10% type I error level150.104895104895105NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}