FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 18 Nov 2012 13:11:36 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/18/t1353262338j4jxfyu426u7bq3.htm/, Retrieved Mon, 29 Apr 2024 19:51:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190298, Retrieved Mon, 29 Apr 2024 19:51:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R     [Multiple Regression] [ws 7 berekening 2] [2012-11-18 17:27:26] [4d16aec667f4ecb2462f7b90319797ea]
-    D    [Multiple Regression] [ws 7 berekening 4] [2012-11-18 18:08:14] [4d16aec667f4ecb2462f7b90319797ea]
-             [Multiple Regression] [ws 7 berekening 4] [2012-11-18 18:11:36] [5948b95c00a54abd73f88aac58cf0e09] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53
39	32	16	11	18	11	86
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	6	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	7	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	9	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	28	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	7	7	16	8	71
29	31	10	6	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	12	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	32	14	6	15	12	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	77
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
37	38	20	12	16	10	30
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	10	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	82
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	70
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
33	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	82
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	8	9	13	12	69
32	34	16	8	15	10	54
30	34	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
35	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	35	12	8	12	18	84
34	36	13	8	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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.72314643181462 -0.051400667958957Connected[t] + 0.0363745010698428Separate[t] + 0.521164448664763Learning[t] -0.0438633036984478Happiness[t] -0.0193625948220412Depression[t] + 0.00201574459344998Belonging[t] -0.00241000758858926t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.72314643181462 -0.051400667958957Connected[t] +  0.0363745010698428Separate[t] +  0.521164448664763Learning[t] -0.0438633036984478Happiness[t] -0.0193625948220412Depression[t] +  0.00201574459344998Belonging[t] -0.00241000758858926t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.72314643181462 -0.051400667958957Connected[t] +  0.0363745010698428Separate[t] +  0.521164448664763Learning[t] -0.0438633036984478Happiness[t] -0.0193625948220412Depression[t] +  0.00201574459344998Belonging[t] -0.00241000758858926t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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
Software[t] = + 4.72314643181462 -0.051400667958957Connected[t] + 0.0363745010698428Separate[t] + 0.521164448664763Learning[t] -0.0438633036984478Happiness[t] -0.0193625948220412Depression[t] + 0.00201574459344998Belonging[t] -0.00241000758858926t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.723146431814622.5685711.83880.0678670.033934
Connected-0.0514006679589570.047191-1.08920.2777690.138885
Separate0.03637450106984280.0441510.82390.4112860.205643
Learning0.5211644486647630.0676537.703500
Happiness-0.04386330369844780.075403-0.58170.5616080.280804
Depression-0.01936259482204120.05561-0.34820.7281780.364089
Belonging0.002015744593449980.0145090.13890.889690.444845
t-0.002410007588589260.003221-0.74820.4554630.227732

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 4.72314643181462 & 2.568571 & 1.8388 & 0.067867 & 0.033934 \tabularnewline
Connected & -0.051400667958957 & 0.047191 & -1.0892 & 0.277769 & 0.138885 \tabularnewline
Separate & 0.0363745010698428 & 0.044151 & 0.8239 & 0.411286 & 0.205643 \tabularnewline
Learning & 0.521164448664763 & 0.067653 & 7.7035 & 0 & 0 \tabularnewline
Happiness & -0.0438633036984478 & 0.075403 & -0.5817 & 0.561608 & 0.280804 \tabularnewline
Depression & -0.0193625948220412 & 0.05561 & -0.3482 & 0.728178 & 0.364089 \tabularnewline
Belonging & 0.00201574459344998 & 0.014509 & 0.1389 & 0.88969 & 0.444845 \tabularnewline
t & -0.00241000758858926 & 0.003221 & -0.7482 & 0.455463 & 0.227732 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]4.72314643181462[/C][C]2.568571[/C][C]1.8388[/C][C]0.067867[/C][C]0.033934[/C][/ROW]
[ROW][C]Connected[/C][C]-0.051400667958957[/C][C]0.047191[/C][C]-1.0892[/C][C]0.277769[/C][C]0.138885[/C][/ROW]
[ROW][C]Separate[/C][C]0.0363745010698428[/C][C]0.044151[/C][C]0.8239[/C][C]0.411286[/C][C]0.205643[/C][/ROW]
[ROW][C]Learning[/C][C]0.521164448664763[/C][C]0.067653[/C][C]7.7035[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0438633036984478[/C][C]0.075403[/C][C]-0.5817[/C][C]0.561608[/C][C]0.280804[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0193625948220412[/C][C]0.05561[/C][C]-0.3482[/C][C]0.728178[/C][C]0.364089[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00201574459344998[/C][C]0.014509[/C][C]0.1389[/C][C]0.88969[/C][C]0.444845[/C][/ROW]
[ROW][C]t[/C][C]-0.00241000758858926[/C][C]0.003221[/C][C]-0.7482[/C][C]0.455463[/C][C]0.227732[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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)4.723146431814622.5685711.83880.0678670.033934
Connected-0.0514006679589570.047191-1.08920.2777690.138885
Separate0.03637450106984280.0441510.82390.4112860.205643
Learning0.5211644486647630.0676537.703500
Happiness-0.04386330369844780.075403-0.58170.5616080.280804
Depression-0.01936259482204120.05561-0.34820.7281780.364089
Belonging0.002015744593449980.0145090.13890.889690.444845
t-0.002410007588589260.003221-0.74820.4554630.227732






Multiple Linear Regression - Regression Statistics
Multiple R0.554072050165754
R-squared0.306995836774882
Adjusted R-squared0.275495647537377
F-TEST (value)9.74584103162661
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.07121344917039e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82298380808959
Sum Squared Residuals511.783574541749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.554072050165754 \tabularnewline
R-squared & 0.306995836774882 \tabularnewline
Adjusted R-squared & 0.275495647537377 \tabularnewline
F-TEST (value) & 9.74584103162661 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 5.07121344917039e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.82298380808959 \tabularnewline
Sum Squared Residuals & 511.783574541749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.554072050165754[/C][/ROW]
[ROW][C]R-squared[/C][C]0.306995836774882[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.275495647537377[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.74584103162661[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]5.07121344917039e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.82298380808959[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]511.783574541749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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 R0.554072050165754
R-squared0.306995836774882
Adjusted R-squared0.275495647537377
F-TEST (value)9.74584103162661
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.07121344917039e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82298380808959
Sum Squared Residuals511.783574541749






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.03107498501481.96892501498517
21111.3871416045315-0.387141604531483
31513.72859490733131.27140509266866
4611.5142550655241-5.51425506552414
51310.65040174282222.34959825717783
6109.87499884523950.125001154760504
71212.7981555116805-0.798155511680472
81411.41737720164242.58262279835757
91210.71803173196421.28196826803578
10611.2404527130117-5.24045271301174
111011.4295933894896-1.42959338948963
121211.53971318843940.460286811560576
131211.75631259524940.243687404750644
141111.5416035158278-0.541603515827842
151512.3886308070632.61136919293697
161211.28934080812010.710659191879918
171011.0792212857145-1.07922128571447
181213.7507817450365-1.75078174503647
191112.757216807834-1.75721680783398
201211.69411096819230.30588903180766
211111.77466612455-0.774666124549995
221211.92644423410240.0735557658975766
231313.2098585905658-0.209858590565806
241111.8361639809104-0.836163980910398
25911.8967391823571-2.89673918235714
261312.16906499052160.830935009478397
271011.6577860833655-1.65778608336555
281411.35732864908332.64267135091667
291211.72422607834480.275773921655221
301010.8007291245556-0.80072912455559
311211.17615160467020.823848395329804
3289.92435390993591-1.92435390993591
331010.3695190364225-0.369519036422502
341211.87645562874910.123544371250929
351210.56207588446191.43792411553808
3677.12647870828039-0.126478708280395
3768.82758107970032-2.82758107970032
381210.5306200863311.46937991366899
391011.8186719548594-1.8186719548594
401011.7419499442964-1.74194994429639
411011.5420788101124-1.54207881011244
421210.54925587692581.45074412307416
431513.505700320891.49429967911005
441010.6221262829341-0.622126282934144
451010.3759972086742-0.375997208674243
46129.119238400000142.88076159999986
471310.67036900544032.32963099455973
481111.2456865372524-0.245686537252437
491111.5885442474526-0.5885442474526
501210.47767633887291.52232366112708
511411.9259716927332.074028307267
521010.5904342843976-0.590434284397627
53129.700895436122422.29910456387758
541311.89378572362041.10621427637955
5558.18788243393695-3.18788243393695
56610.4095435036749-4.40954350367485
571211.35215267193020.647847328069826
581211.97095281476360.0290471852363824
591111.2332438008123-0.233243800812297
601011.6791903677761-1.67919036777614
6179.67525148149917-2.67525148149917
621211.54850304164390.45149695835608
631411.87579681435792.1242031856421
641110.7645511977410.235448802259023
651211.48430085963620.515699140363835
661312.48035270673850.51964729326154
671412.6866981807771.313301819223
681112.7687560549493-1.76875605494931
69129.478511905797612.52148809420239
701211.6499707342330.350029265766973
7188.38919193904274-0.389191939042736
721110.75133692720980.248663072790245
731412.85277080883891.14722919116113
741412.75140598089891.24859401910114
751211.50162691431540.498373085684622
76911.7534010855546-2.75340108555461
771311.46395107721361.53604892278642
781111.493483157995-0.493483157994985
791210.24183354080471.75816645919528
801211.31362772433610.686372275663856
811211.76813271600720.23186728399276
821213.5942546394261-1.5942546394261
831211.71948509887850.280514901121527
841210.84969170345021.15030829654984
851111.1825070958312-0.1825070958312
861011.5753167975825-1.57531679758255
87910.516675768543-1.51667576854304
881211.68285692368930.317143076310664
891211.76883993612750.231160063872536
901211.23593088103970.764069118960275
9199.62362991677853-0.623629916778532
921511.92296968631383.07703031368624
931211.77552711554450.224472884455513
941210.94850298628171.05149701371834
951210.26735987600191.73264012399814
961011.3817310353197-1.38173103531966
971311.50221491273481.49778508726522
98911.4843363048391-2.48433630483909
991211.36270578722710.637294212772876
1001010.7447297034952-0.744729703495166
1011411.38851797808552.61148202191453
1021111.4982278531656-0.498227853165632
1031513.63081140995051.36918859004949
1041111.1011912872841-0.10119128728408
1051111.7226030629804-0.72260306298042
106129.725192373392562.27480762660744
1071211.88334858902530.116651410974744
1081211.28804173979540.711958260204604
1091111.6015485229479-0.601548522947852
11079.21890824233044-2.21890824233044
1111211.7252449394010.274755060599026
1121411.77502442600772.22497557399227
1131112.5513184038833-1.55131840388326
114119.951153680417771.04884631958223
115109.166183249662910.833816750337095
1161312.36462017489870.635379825101315
1171310.71992345416392.28007654583605
118810.7177272869336-2.71772728693364
1191110.13911562798470.860884372015253
1201211.40572519519570.594274804804293
1211110.14835948451020.851640515489847
1221311.42448151661641.57551848338362
123129.975696660641442.02430333935855
1241411.58368927068752.41631072931247
1251310.94696096053462.05303903946536
1261511.7410868146263.258913185374
1271010.6747402738314-0.674740273831436
1281111.6413002281272-0.641300228127205
129910.9598661774833-1.9598661774833
130119.300624308581641.69937569141836
1311011.6615101519765-1.6615101519765
132118.692452761593492.30754723840651
133811.3347132523929-3.33471325239288
134119.540230716891181.45976928310882
1351210.43336842140851.56663157859154
1361210.81529315605591.1847068439441
13799.71014773196199-0.710147731961985
1381110.97534627150120.0246537284987859
139109.168604161199610.831395838800387
14089.33549796666207-1.33549796666207
14197.568850072862211.43114992713779
142811.5687428989084-3.56874289890836
143911.0871284333898-2.08712843338981
1441512.29390511042852.70609488957153
1451111.2204309683385-0.220430968338543
14687.91911249193450.0808875080655009
1471312.30615813851940.693841861480593
148129.987395735574762.01260426442524
1491211.59730729531450.402692704685543
15099.88623410225667-0.886234102256665
15178.27343422527237-1.27343422527237
1521310.62117725645652.37882274354348
153911.5946964330076-2.59469643300758
154611.696641433451-5.69664143345099
155810.4600345367444-2.46003453674437
15688.22869564857441-0.228695648574412
1571511.76631919305553.23368080694454
158610.0724620280182-4.0724620280182
159910.8875659498256-1.88756594982562
1601111.5173739642786-0.517373964278588
16189.51183095145847-1.51183095145847
16289.92878427473045-1.92878427473045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 10.0310749850148 & 1.96892501498517 \tabularnewline
2 & 11 & 11.3871416045315 & -0.387141604531483 \tabularnewline
3 & 15 & 13.7285949073313 & 1.27140509266866 \tabularnewline
4 & 6 & 11.5142550655241 & -5.51425506552414 \tabularnewline
5 & 13 & 10.6504017428222 & 2.34959825717783 \tabularnewline
6 & 10 & 9.8749988452395 & 0.125001154760504 \tabularnewline
7 & 12 & 12.7981555116805 & -0.798155511680472 \tabularnewline
8 & 14 & 11.4173772016424 & 2.58262279835757 \tabularnewline
9 & 12 & 10.7180317319642 & 1.28196826803578 \tabularnewline
10 & 6 & 11.2404527130117 & -5.24045271301174 \tabularnewline
11 & 10 & 11.4295933894896 & -1.42959338948963 \tabularnewline
12 & 12 & 11.5397131884394 & 0.460286811560576 \tabularnewline
13 & 12 & 11.7563125952494 & 0.243687404750644 \tabularnewline
14 & 11 & 11.5416035158278 & -0.541603515827842 \tabularnewline
15 & 15 & 12.388630807063 & 2.61136919293697 \tabularnewline
16 & 12 & 11.2893408081201 & 0.710659191879918 \tabularnewline
17 & 10 & 11.0792212857145 & -1.07922128571447 \tabularnewline
18 & 12 & 13.7507817450365 & -1.75078174503647 \tabularnewline
19 & 11 & 12.757216807834 & -1.75721680783398 \tabularnewline
20 & 12 & 11.6941109681923 & 0.30588903180766 \tabularnewline
21 & 11 & 11.77466612455 & -0.774666124549995 \tabularnewline
22 & 12 & 11.9264442341024 & 0.0735557658975766 \tabularnewline
23 & 13 & 13.2098585905658 & -0.209858590565806 \tabularnewline
24 & 11 & 11.8361639809104 & -0.836163980910398 \tabularnewline
25 & 9 & 11.8967391823571 & -2.89673918235714 \tabularnewline
26 & 13 & 12.1690649905216 & 0.830935009478397 \tabularnewline
27 & 10 & 11.6577860833655 & -1.65778608336555 \tabularnewline
28 & 14 & 11.3573286490833 & 2.64267135091667 \tabularnewline
29 & 12 & 11.7242260783448 & 0.275773921655221 \tabularnewline
30 & 10 & 10.8007291245556 & -0.80072912455559 \tabularnewline
31 & 12 & 11.1761516046702 & 0.823848395329804 \tabularnewline
32 & 8 & 9.92435390993591 & -1.92435390993591 \tabularnewline
33 & 10 & 10.3695190364225 & -0.369519036422502 \tabularnewline
34 & 12 & 11.8764556287491 & 0.123544371250929 \tabularnewline
35 & 12 & 10.5620758844619 & 1.43792411553808 \tabularnewline
36 & 7 & 7.12647870828039 & -0.126478708280395 \tabularnewline
37 & 6 & 8.82758107970032 & -2.82758107970032 \tabularnewline
38 & 12 & 10.530620086331 & 1.46937991366899 \tabularnewline
39 & 10 & 11.8186719548594 & -1.8186719548594 \tabularnewline
40 & 10 & 11.7419499442964 & -1.74194994429639 \tabularnewline
41 & 10 & 11.5420788101124 & -1.54207881011244 \tabularnewline
42 & 12 & 10.5492558769258 & 1.45074412307416 \tabularnewline
43 & 15 & 13.50570032089 & 1.49429967911005 \tabularnewline
44 & 10 & 10.6221262829341 & -0.622126282934144 \tabularnewline
45 & 10 & 10.3759972086742 & -0.375997208674243 \tabularnewline
46 & 12 & 9.11923840000014 & 2.88076159999986 \tabularnewline
47 & 13 & 10.6703690054403 & 2.32963099455973 \tabularnewline
48 & 11 & 11.2456865372524 & -0.245686537252437 \tabularnewline
49 & 11 & 11.5885442474526 & -0.5885442474526 \tabularnewline
50 & 12 & 10.4776763388729 & 1.52232366112708 \tabularnewline
51 & 14 & 11.925971692733 & 2.074028307267 \tabularnewline
52 & 10 & 10.5904342843976 & -0.590434284397627 \tabularnewline
53 & 12 & 9.70089543612242 & 2.29910456387758 \tabularnewline
54 & 13 & 11.8937857236204 & 1.10621427637955 \tabularnewline
55 & 5 & 8.18788243393695 & -3.18788243393695 \tabularnewline
56 & 6 & 10.4095435036749 & -4.40954350367485 \tabularnewline
57 & 12 & 11.3521526719302 & 0.647847328069826 \tabularnewline
58 & 12 & 11.9709528147636 & 0.0290471852363824 \tabularnewline
59 & 11 & 11.2332438008123 & -0.233243800812297 \tabularnewline
60 & 10 & 11.6791903677761 & -1.67919036777614 \tabularnewline
61 & 7 & 9.67525148149917 & -2.67525148149917 \tabularnewline
62 & 12 & 11.5485030416439 & 0.45149695835608 \tabularnewline
63 & 14 & 11.8757968143579 & 2.1242031856421 \tabularnewline
64 & 11 & 10.764551197741 & 0.235448802259023 \tabularnewline
65 & 12 & 11.4843008596362 & 0.515699140363835 \tabularnewline
66 & 13 & 12.4803527067385 & 0.51964729326154 \tabularnewline
67 & 14 & 12.686698180777 & 1.313301819223 \tabularnewline
68 & 11 & 12.7687560549493 & -1.76875605494931 \tabularnewline
69 & 12 & 9.47851190579761 & 2.52148809420239 \tabularnewline
70 & 12 & 11.649970734233 & 0.350029265766973 \tabularnewline
71 & 8 & 8.38919193904274 & -0.389191939042736 \tabularnewline
72 & 11 & 10.7513369272098 & 0.248663072790245 \tabularnewline
73 & 14 & 12.8527708088389 & 1.14722919116113 \tabularnewline
74 & 14 & 12.7514059808989 & 1.24859401910114 \tabularnewline
75 & 12 & 11.5016269143154 & 0.498373085684622 \tabularnewline
76 & 9 & 11.7534010855546 & -2.75340108555461 \tabularnewline
77 & 13 & 11.4639510772136 & 1.53604892278642 \tabularnewline
78 & 11 & 11.493483157995 & -0.493483157994985 \tabularnewline
79 & 12 & 10.2418335408047 & 1.75816645919528 \tabularnewline
80 & 12 & 11.3136277243361 & 0.686372275663856 \tabularnewline
81 & 12 & 11.7681327160072 & 0.23186728399276 \tabularnewline
82 & 12 & 13.5942546394261 & -1.5942546394261 \tabularnewline
83 & 12 & 11.7194850988785 & 0.280514901121527 \tabularnewline
84 & 12 & 10.8496917034502 & 1.15030829654984 \tabularnewline
85 & 11 & 11.1825070958312 & -0.1825070958312 \tabularnewline
86 & 10 & 11.5753167975825 & -1.57531679758255 \tabularnewline
87 & 9 & 10.516675768543 & -1.51667576854304 \tabularnewline
88 & 12 & 11.6828569236893 & 0.317143076310664 \tabularnewline
89 & 12 & 11.7688399361275 & 0.231160063872536 \tabularnewline
90 & 12 & 11.2359308810397 & 0.764069118960275 \tabularnewline
91 & 9 & 9.62362991677853 & -0.623629916778532 \tabularnewline
92 & 15 & 11.9229696863138 & 3.07703031368624 \tabularnewline
93 & 12 & 11.7755271155445 & 0.224472884455513 \tabularnewline
94 & 12 & 10.9485029862817 & 1.05149701371834 \tabularnewline
95 & 12 & 10.2673598760019 & 1.73264012399814 \tabularnewline
96 & 10 & 11.3817310353197 & -1.38173103531966 \tabularnewline
97 & 13 & 11.5022149127348 & 1.49778508726522 \tabularnewline
98 & 9 & 11.4843363048391 & -2.48433630483909 \tabularnewline
99 & 12 & 11.3627057872271 & 0.637294212772876 \tabularnewline
100 & 10 & 10.7447297034952 & -0.744729703495166 \tabularnewline
101 & 14 & 11.3885179780855 & 2.61148202191453 \tabularnewline
102 & 11 & 11.4982278531656 & -0.498227853165632 \tabularnewline
103 & 15 & 13.6308114099505 & 1.36918859004949 \tabularnewline
104 & 11 & 11.1011912872841 & -0.10119128728408 \tabularnewline
105 & 11 & 11.7226030629804 & -0.72260306298042 \tabularnewline
106 & 12 & 9.72519237339256 & 2.27480762660744 \tabularnewline
107 & 12 & 11.8833485890253 & 0.116651410974744 \tabularnewline
108 & 12 & 11.2880417397954 & 0.711958260204604 \tabularnewline
109 & 11 & 11.6015485229479 & -0.601548522947852 \tabularnewline
110 & 7 & 9.21890824233044 & -2.21890824233044 \tabularnewline
111 & 12 & 11.725244939401 & 0.274755060599026 \tabularnewline
112 & 14 & 11.7750244260077 & 2.22497557399227 \tabularnewline
113 & 11 & 12.5513184038833 & -1.55131840388326 \tabularnewline
114 & 11 & 9.95115368041777 & 1.04884631958223 \tabularnewline
115 & 10 & 9.16618324966291 & 0.833816750337095 \tabularnewline
116 & 13 & 12.3646201748987 & 0.635379825101315 \tabularnewline
117 & 13 & 10.7199234541639 & 2.28007654583605 \tabularnewline
118 & 8 & 10.7177272869336 & -2.71772728693364 \tabularnewline
119 & 11 & 10.1391156279847 & 0.860884372015253 \tabularnewline
120 & 12 & 11.4057251951957 & 0.594274804804293 \tabularnewline
121 & 11 & 10.1483594845102 & 0.851640515489847 \tabularnewline
122 & 13 & 11.4244815166164 & 1.57551848338362 \tabularnewline
123 & 12 & 9.97569666064144 & 2.02430333935855 \tabularnewline
124 & 14 & 11.5836892706875 & 2.41631072931247 \tabularnewline
125 & 13 & 10.9469609605346 & 2.05303903946536 \tabularnewline
126 & 15 & 11.741086814626 & 3.258913185374 \tabularnewline
127 & 10 & 10.6747402738314 & -0.674740273831436 \tabularnewline
128 & 11 & 11.6413002281272 & -0.641300228127205 \tabularnewline
129 & 9 & 10.9598661774833 & -1.9598661774833 \tabularnewline
130 & 11 & 9.30062430858164 & 1.69937569141836 \tabularnewline
131 & 10 & 11.6615101519765 & -1.6615101519765 \tabularnewline
132 & 11 & 8.69245276159349 & 2.30754723840651 \tabularnewline
133 & 8 & 11.3347132523929 & -3.33471325239288 \tabularnewline
134 & 11 & 9.54023071689118 & 1.45976928310882 \tabularnewline
135 & 12 & 10.4333684214085 & 1.56663157859154 \tabularnewline
136 & 12 & 10.8152931560559 & 1.1847068439441 \tabularnewline
137 & 9 & 9.71014773196199 & -0.710147731961985 \tabularnewline
138 & 11 & 10.9753462715012 & 0.0246537284987859 \tabularnewline
139 & 10 & 9.16860416119961 & 0.831395838800387 \tabularnewline
140 & 8 & 9.33549796666207 & -1.33549796666207 \tabularnewline
141 & 9 & 7.56885007286221 & 1.43114992713779 \tabularnewline
142 & 8 & 11.5687428989084 & -3.56874289890836 \tabularnewline
143 & 9 & 11.0871284333898 & -2.08712843338981 \tabularnewline
144 & 15 & 12.2939051104285 & 2.70609488957153 \tabularnewline
145 & 11 & 11.2204309683385 & -0.220430968338543 \tabularnewline
146 & 8 & 7.9191124919345 & 0.0808875080655009 \tabularnewline
147 & 13 & 12.3061581385194 & 0.693841861480593 \tabularnewline
148 & 12 & 9.98739573557476 & 2.01260426442524 \tabularnewline
149 & 12 & 11.5973072953145 & 0.402692704685543 \tabularnewline
150 & 9 & 9.88623410225667 & -0.886234102256665 \tabularnewline
151 & 7 & 8.27343422527237 & -1.27343422527237 \tabularnewline
152 & 13 & 10.6211772564565 & 2.37882274354348 \tabularnewline
153 & 9 & 11.5946964330076 & -2.59469643300758 \tabularnewline
154 & 6 & 11.696641433451 & -5.69664143345099 \tabularnewline
155 & 8 & 10.4600345367444 & -2.46003453674437 \tabularnewline
156 & 8 & 8.22869564857441 & -0.228695648574412 \tabularnewline
157 & 15 & 11.7663191930555 & 3.23368080694454 \tabularnewline
158 & 6 & 10.0724620280182 & -4.0724620280182 \tabularnewline
159 & 9 & 10.8875659498256 & -1.88756594982562 \tabularnewline
160 & 11 & 11.5173739642786 & -0.517373964278588 \tabularnewline
161 & 8 & 9.51183095145847 & -1.51183095145847 \tabularnewline
162 & 8 & 9.92878427473045 & -1.92878427473045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&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]10.0310749850148[/C][C]1.96892501498517[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.3871416045315[/C][C]-0.387141604531483[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.7285949073313[/C][C]1.27140509266866[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.5142550655241[/C][C]-5.51425506552414[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.6504017428222[/C][C]2.34959825717783[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.8749988452395[/C][C]0.125001154760504[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.7981555116805[/C][C]-0.798155511680472[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.4173772016424[/C][C]2.58262279835757[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.7180317319642[/C][C]1.28196826803578[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.2404527130117[/C][C]-5.24045271301174[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.4295933894896[/C][C]-1.42959338948963[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.5397131884394[/C][C]0.460286811560576[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.7563125952494[/C][C]0.243687404750644[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.5416035158278[/C][C]-0.541603515827842[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.388630807063[/C][C]2.61136919293697[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.2893408081201[/C][C]0.710659191879918[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.0792212857145[/C][C]-1.07922128571447[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.7507817450365[/C][C]-1.75078174503647[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.757216807834[/C][C]-1.75721680783398[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.6941109681923[/C][C]0.30588903180766[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.77466612455[/C][C]-0.774666124549995[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.9264442341024[/C][C]0.0735557658975766[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.2098585905658[/C][C]-0.209858590565806[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.8361639809104[/C][C]-0.836163980910398[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.8967391823571[/C][C]-2.89673918235714[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.1690649905216[/C][C]0.830935009478397[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.6577860833655[/C][C]-1.65778608336555[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.3573286490833[/C][C]2.64267135091667[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.7242260783448[/C][C]0.275773921655221[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.8007291245556[/C][C]-0.80072912455559[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.1761516046702[/C][C]0.823848395329804[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.92435390993591[/C][C]-1.92435390993591[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.3695190364225[/C][C]-0.369519036422502[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.8764556287491[/C][C]0.123544371250929[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.5620758844619[/C][C]1.43792411553808[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.12647870828039[/C][C]-0.126478708280395[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.82758107970032[/C][C]-2.82758107970032[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.530620086331[/C][C]1.46937991366899[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.8186719548594[/C][C]-1.8186719548594[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.7419499442964[/C][C]-1.74194994429639[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.5420788101124[/C][C]-1.54207881011244[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.5492558769258[/C][C]1.45074412307416[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.50570032089[/C][C]1.49429967911005[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.6221262829341[/C][C]-0.622126282934144[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.3759972086742[/C][C]-0.375997208674243[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.11923840000014[/C][C]2.88076159999986[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.6703690054403[/C][C]2.32963099455973[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.2456865372524[/C][C]-0.245686537252437[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.5885442474526[/C][C]-0.5885442474526[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.4776763388729[/C][C]1.52232366112708[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.925971692733[/C][C]2.074028307267[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5904342843976[/C][C]-0.590434284397627[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.70089543612242[/C][C]2.29910456387758[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.8937857236204[/C][C]1.10621427637955[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.18788243393695[/C][C]-3.18788243393695[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.4095435036749[/C][C]-4.40954350367485[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3521526719302[/C][C]0.647847328069826[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9709528147636[/C][C]0.0290471852363824[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.2332438008123[/C][C]-0.233243800812297[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.6791903677761[/C][C]-1.67919036777614[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.67525148149917[/C][C]-2.67525148149917[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.5485030416439[/C][C]0.45149695835608[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.8757968143579[/C][C]2.1242031856421[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.764551197741[/C][C]0.235448802259023[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.4843008596362[/C][C]0.515699140363835[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.4803527067385[/C][C]0.51964729326154[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.686698180777[/C][C]1.313301819223[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.7687560549493[/C][C]-1.76875605494931[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.47851190579761[/C][C]2.52148809420239[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.649970734233[/C][C]0.350029265766973[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.38919193904274[/C][C]-0.389191939042736[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7513369272098[/C][C]0.248663072790245[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8527708088389[/C][C]1.14722919116113[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.7514059808989[/C][C]1.24859401910114[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.5016269143154[/C][C]0.498373085684622[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7534010855546[/C][C]-2.75340108555461[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.4639510772136[/C][C]1.53604892278642[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.493483157995[/C][C]-0.493483157994985[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.2418335408047[/C][C]1.75816645919528[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.3136277243361[/C][C]0.686372275663856[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7681327160072[/C][C]0.23186728399276[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.5942546394261[/C][C]-1.5942546394261[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7194850988785[/C][C]0.280514901121527[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.8496917034502[/C][C]1.15030829654984[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1825070958312[/C][C]-0.1825070958312[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5753167975825[/C][C]-1.57531679758255[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.516675768543[/C][C]-1.51667576854304[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.6828569236893[/C][C]0.317143076310664[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.7688399361275[/C][C]0.231160063872536[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.2359308810397[/C][C]0.764069118960275[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.62362991677853[/C][C]-0.623629916778532[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]11.9229696863138[/C][C]3.07703031368624[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.7755271155445[/C][C]0.224472884455513[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9485029862817[/C][C]1.05149701371834[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.2673598760019[/C][C]1.73264012399814[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.3817310353197[/C][C]-1.38173103531966[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5022149127348[/C][C]1.49778508726522[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.4843363048391[/C][C]-2.48433630483909[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.3627057872271[/C][C]0.637294212772876[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7447297034952[/C][C]-0.744729703495166[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.3885179780855[/C][C]2.61148202191453[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.4982278531656[/C][C]-0.498227853165632[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.6308114099505[/C][C]1.36918859004949[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.1011912872841[/C][C]-0.10119128728408[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.7226030629804[/C][C]-0.72260306298042[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.72519237339256[/C][C]2.27480762660744[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.8833485890253[/C][C]0.116651410974744[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.2880417397954[/C][C]0.711958260204604[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.6015485229479[/C][C]-0.601548522947852[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.21890824233044[/C][C]-2.21890824233044[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.725244939401[/C][C]0.274755060599026[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.7750244260077[/C][C]2.22497557399227[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5513184038833[/C][C]-1.55131840388326[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.95115368041777[/C][C]1.04884631958223[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.16618324966291[/C][C]0.833816750337095[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.3646201748987[/C][C]0.635379825101315[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.7199234541639[/C][C]2.28007654583605[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7177272869336[/C][C]-2.71772728693364[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.1391156279847[/C][C]0.860884372015253[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4057251951957[/C][C]0.594274804804293[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.1483594845102[/C][C]0.851640515489847[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.4244815166164[/C][C]1.57551848338362[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]9.97569666064144[/C][C]2.02430333935855[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.5836892706875[/C][C]2.41631072931247[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]10.9469609605346[/C][C]2.05303903946536[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.741086814626[/C][C]3.258913185374[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.6747402738314[/C][C]-0.674740273831436[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.6413002281272[/C][C]-0.641300228127205[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]10.9598661774833[/C][C]-1.9598661774833[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.30062430858164[/C][C]1.69937569141836[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.6615101519765[/C][C]-1.6615101519765[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.69245276159349[/C][C]2.30754723840651[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.3347132523929[/C][C]-3.33471325239288[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.54023071689118[/C][C]1.45976928310882[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.4333684214085[/C][C]1.56663157859154[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.8152931560559[/C][C]1.1847068439441[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.71014773196199[/C][C]-0.710147731961985[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.9753462715012[/C][C]0.0246537284987859[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.16860416119961[/C][C]0.831395838800387[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.33549796666207[/C][C]-1.33549796666207[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.56885007286221[/C][C]1.43114992713779[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.5687428989084[/C][C]-3.56874289890836[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.0871284333898[/C][C]-2.08712843338981[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.2939051104285[/C][C]2.70609488957153[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.2204309683385[/C][C]-0.220430968338543[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]7.9191124919345[/C][C]0.0808875080655009[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.3061581385194[/C][C]0.693841861480593[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]9.98739573557476[/C][C]2.01260426442524[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.5973072953145[/C][C]0.402692704685543[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.88623410225667[/C][C]-0.886234102256665[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.27343422527237[/C][C]-1.27343422527237[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.6211772564565[/C][C]2.37882274354348[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.5946964330076[/C][C]-2.59469643300758[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.696641433451[/C][C]-5.69664143345099[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.4600345367444[/C][C]-2.46003453674437[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.22869564857441[/C][C]-0.228695648574412[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]11.7663191930555[/C][C]3.23368080694454[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.0724620280182[/C][C]-4.0724620280182[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]10.8875659498256[/C][C]-1.88756594982562[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.5173739642786[/C][C]-0.517373964278588[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.51183095145847[/C][C]-1.51183095145847[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]9.92878427473045[/C][C]-1.92878427473045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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
11210.03107498501481.96892501498517
21111.3871416045315-0.387141604531483
31513.72859490733131.27140509266866
4611.5142550655241-5.51425506552414
51310.65040174282222.34959825717783
6109.87499884523950.125001154760504
71212.7981555116805-0.798155511680472
81411.41737720164242.58262279835757
91210.71803173196421.28196826803578
10611.2404527130117-5.24045271301174
111011.4295933894896-1.42959338948963
121211.53971318843940.460286811560576
131211.75631259524940.243687404750644
141111.5416035158278-0.541603515827842
151512.3886308070632.61136919293697
161211.28934080812010.710659191879918
171011.0792212857145-1.07922128571447
181213.7507817450365-1.75078174503647
191112.757216807834-1.75721680783398
201211.69411096819230.30588903180766
211111.77466612455-0.774666124549995
221211.92644423410240.0735557658975766
231313.2098585905658-0.209858590565806
241111.8361639809104-0.836163980910398
25911.8967391823571-2.89673918235714
261312.16906499052160.830935009478397
271011.6577860833655-1.65778608336555
281411.35732864908332.64267135091667
291211.72422607834480.275773921655221
301010.8007291245556-0.80072912455559
311211.17615160467020.823848395329804
3289.92435390993591-1.92435390993591
331010.3695190364225-0.369519036422502
341211.87645562874910.123544371250929
351210.56207588446191.43792411553808
3677.12647870828039-0.126478708280395
3768.82758107970032-2.82758107970032
381210.5306200863311.46937991366899
391011.8186719548594-1.8186719548594
401011.7419499442964-1.74194994429639
411011.5420788101124-1.54207881011244
421210.54925587692581.45074412307416
431513.505700320891.49429967911005
441010.6221262829341-0.622126282934144
451010.3759972086742-0.375997208674243
46129.119238400000142.88076159999986
471310.67036900544032.32963099455973
481111.2456865372524-0.245686537252437
491111.5885442474526-0.5885442474526
501210.47767633887291.52232366112708
511411.9259716927332.074028307267
521010.5904342843976-0.590434284397627
53129.700895436122422.29910456387758
541311.89378572362041.10621427637955
5558.18788243393695-3.18788243393695
56610.4095435036749-4.40954350367485
571211.35215267193020.647847328069826
581211.97095281476360.0290471852363824
591111.2332438008123-0.233243800812297
601011.6791903677761-1.67919036777614
6179.67525148149917-2.67525148149917
621211.54850304164390.45149695835608
631411.87579681435792.1242031856421
641110.7645511977410.235448802259023
651211.48430085963620.515699140363835
661312.48035270673850.51964729326154
671412.6866981807771.313301819223
681112.7687560549493-1.76875605494931
69129.478511905797612.52148809420239
701211.6499707342330.350029265766973
7188.38919193904274-0.389191939042736
721110.75133692720980.248663072790245
731412.85277080883891.14722919116113
741412.75140598089891.24859401910114
751211.50162691431540.498373085684622
76911.7534010855546-2.75340108555461
771311.46395107721361.53604892278642
781111.493483157995-0.493483157994985
791210.24183354080471.75816645919528
801211.31362772433610.686372275663856
811211.76813271600720.23186728399276
821213.5942546394261-1.5942546394261
831211.71948509887850.280514901121527
841210.84969170345021.15030829654984
851111.1825070958312-0.1825070958312
861011.5753167975825-1.57531679758255
87910.516675768543-1.51667576854304
881211.68285692368930.317143076310664
891211.76883993612750.231160063872536
901211.23593088103970.764069118960275
9199.62362991677853-0.623629916778532
921511.92296968631383.07703031368624
931211.77552711554450.224472884455513
941210.94850298628171.05149701371834
951210.26735987600191.73264012399814
961011.3817310353197-1.38173103531966
971311.50221491273481.49778508726522
98911.4843363048391-2.48433630483909
991211.36270578722710.637294212772876
1001010.7447297034952-0.744729703495166
1011411.38851797808552.61148202191453
1021111.4982278531656-0.498227853165632
1031513.63081140995051.36918859004949
1041111.1011912872841-0.10119128728408
1051111.7226030629804-0.72260306298042
106129.725192373392562.27480762660744
1071211.88334858902530.116651410974744
1081211.28804173979540.711958260204604
1091111.6015485229479-0.601548522947852
11079.21890824233044-2.21890824233044
1111211.7252449394010.274755060599026
1121411.77502442600772.22497557399227
1131112.5513184038833-1.55131840388326
114119.951153680417771.04884631958223
115109.166183249662910.833816750337095
1161312.36462017489870.635379825101315
1171310.71992345416392.28007654583605
118810.7177272869336-2.71772728693364
1191110.13911562798470.860884372015253
1201211.40572519519570.594274804804293
1211110.14835948451020.851640515489847
1221311.42448151661641.57551848338362
123129.975696660641442.02430333935855
1241411.58368927068752.41631072931247
1251310.94696096053462.05303903946536
1261511.7410868146263.258913185374
1271010.6747402738314-0.674740273831436
1281111.6413002281272-0.641300228127205
129910.9598661774833-1.9598661774833
130119.300624308581641.69937569141836
1311011.6615101519765-1.6615101519765
132118.692452761593492.30754723840651
133811.3347132523929-3.33471325239288
134119.540230716891181.45976928310882
1351210.43336842140851.56663157859154
1361210.81529315605591.1847068439441
13799.71014773196199-0.710147731961985
1381110.97534627150120.0246537284987859
139109.168604161199610.831395838800387
14089.33549796666207-1.33549796666207
14197.568850072862211.43114992713779
142811.5687428989084-3.56874289890836
143911.0871284333898-2.08712843338981
1441512.29390511042852.70609488957153
1451111.2204309683385-0.220430968338543
14687.91911249193450.0808875080655009
1471312.30615813851940.693841861480593
148129.987395735574762.01260426442524
1491211.59730729531450.402692704685543
15099.88623410225667-0.886234102256665
15178.27343422527237-1.27343422527237
1521310.62117725645652.37882274354348
153911.5946964330076-2.59469643300758
154611.696641433451-5.69664143345099
155810.4600345367444-2.46003453674437
15688.22869564857441-0.228695648574412
1571511.76631919305553.23368080694454
158610.0724620280182-4.0724620280182
159910.8875659498256-1.88756594982562
1601111.5173739642786-0.517373964278588
16189.51183095145847-1.51183095145847
16289.92878427473045-1.92878427473045






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9997983617085550.0004032765828900090.000201638291445004
120.9995788466884410.0008423066231172190.000421153311558609
130.9995344246759260.0009311506481481040.000465575324074052
140.9989650323483610.002069935303277720.00103496765163886
150.9988438455601680.002312308879664840.00115615443983242
160.9980761223991220.003847755201756090.00192387760087804
170.9963475035972270.007304992805546490.00365249640277324
180.9952403428519860.009519314296028490.00475965714801425
190.9924537252338060.01509254953238720.0075462747661936
200.9873927117021560.02521457659568830.0126072882978441
210.9804650476380160.03906990472396770.0195349523619839
220.9730503350532490.05389932989350190.0269496649467509
230.9611725914106750.077654817178650.038827408589325
240.9456931065084990.1086137869830030.0543068934915013
250.9404497205934850.1191005588130310.0595502794065153
260.9586356845697010.0827286308605970.0413643154302985
270.948891745216060.102216509567880.05110825478394
280.955117563081480.08976487383703960.0448824369185198
290.9379662375555360.1240675248889270.0620337624444636
300.9214289546206950.1571420907586090.0785710453793046
310.9047960082192570.1904079835614870.0952039917807433
320.9187907362725190.1624185274549630.0812092637274814
330.8948586577286880.2102826845426250.105141342271312
340.8650998904698920.2698002190602160.134900109530108
350.854914615976020.290170768047960.14508538402398
360.821827359013990.3563452819720190.17817264098601
370.8602754655637470.2794490688725060.139724534436253
380.8528303429180120.2943393141639770.147169657081988
390.8463288735992060.3073422528015880.153671126400794
400.8313418134561320.3373163730877350.168658186543868
410.8111622067628850.377675586474230.188837793237115
420.7984688700570140.4030622598859720.201531129942986
430.7998570612915090.4002858774169810.200142938708491
440.7642949679991080.4714100640017830.235705032000891
450.7268280453720150.546343909255970.273171954627985
460.7728405152869880.4543189694260230.227159484713011
470.7643593163233830.4712813673532350.235640683676617
480.7255984373554380.5488031252891230.274401562644562
490.6999428250765290.6001143498469410.300057174923471
500.6762212097992560.6475575804014880.323778790200744
510.6640945720189060.6718108559621890.335905427981094
520.6230343550319490.7539312899361020.376965644968051
530.6271833813665340.7456332372669330.372816618633466
540.5836773462218520.8326453075562960.416322653778148
550.7044565843538870.5910868312922250.295543415646113
560.8864184788666780.2271630422666430.113581521133322
570.8619030560180680.2761938879638650.138096943981932
580.8339750810088970.3320498379822060.166024918991103
590.8059512888151560.3880974223696870.194048711184844
600.8079571618858150.3840856762283690.192042838114185
610.8467225510522650.306554897895470.153277448947735
620.819131546289640.3617369074207190.18086845371036
630.8186667661183070.3626664677633870.181333233881693
640.786442793898440.4271144122031190.21355720610156
650.7552617119094180.4894765761811640.244738288090582
660.7161627561103760.5676744877792480.283837243889624
670.6868740964556730.6262518070886530.313125903544327
680.6954771177394520.6090457645210960.304522882260548
690.685049986101550.62990002779690.31495001389845
700.6437584816841590.7124830366316810.356241518315841
710.6201356279674970.7597287440650060.379864372032503
720.5777470063722640.8445059872554720.422252993627736
730.5385535238295590.9228929523408810.461446476170441
740.5036199029253330.9927601941493330.496380097074667
750.4756551379478040.9513102758956080.524344862052196
760.5734117882684240.8531764234631530.426588211731576
770.5422820315172760.9154359369654490.457717968482724
780.5140117302372780.9719765395254450.485988269762723
790.4956817634452990.9913635268905990.504318236554701
800.4708226445247350.9416452890494710.529177355475265
810.4269062620299280.8538125240598550.573093737970072
820.4372312709864880.8744625419729760.562768729013512
830.393525136180310.7870502723606190.60647486381969
840.354352886142570.7087057722851410.64564711385743
850.3189561392469710.6379122784939430.681043860753029
860.3432694182673340.6865388365346680.656730581732666
870.3699570969680120.7399141939360240.630042903031988
880.3295510881963090.6591021763926180.670448911803691
890.2943915652094960.5887831304189910.705608434790505
900.2636868653150640.5273737306301270.736313134684936
910.2469872273999450.4939744547998890.753012772600055
920.2895385639389060.5790771278778120.710461436061094
930.2543766947654220.5087533895308430.745623305234578
940.2298213696813210.4596427393626420.770178630318679
950.208331845154140.4166636903082790.79166815484586
960.2192620150832490.4385240301664970.780737984916751
970.1931438743967650.3862877487935310.806856125603235
980.2963989200271870.5927978400543750.703601079972813
990.2589628874821560.5179257749643130.741037112517844
1000.2541754336601120.5083508673202230.745824566339888
1010.2575476582911030.5150953165822050.742452341708897
1020.240294187285710.480588374571420.75970581271429
1030.2151498671594750.430299734318950.784850132840525
1040.2301022614675650.4602045229351290.769897738532435
1050.2072530833642620.4145061667285230.792746916635738
1060.1905915226399640.3811830452799290.809408477360036
1070.1623420722682880.3246841445365760.837657927731712
1080.1369053976105660.2738107952211320.863094602389434
1090.1309152828438690.2618305656877380.869084717156131
1100.1732222404375060.3464444808750110.826777759562494
1110.1444967349272130.2889934698544260.855503265072787
1120.1298458720741470.2596917441482940.870154127925853
1130.1393567485002260.2787134970004530.860643251499774
1140.1146446363921060.2292892727842120.885355363607894
1150.09207025962910480.184140519258210.907929740370895
1160.0743282837334260.1486565674668520.925671716266574
1170.07845329532634760.1569065906526950.921546704673652
1180.1265901016866050.2531802033732110.873409898313395
1190.1024555476524490.2049110953048980.897544452347551
1200.08421896423020130.1684379284604030.915781035769799
1210.06553557721519130.1310711544303830.934464422784809
1220.05284327259137360.1056865451827470.947156727408626
1230.04364679245452430.08729358490904870.956353207545476
1240.04456854813118750.0891370962623750.955431451868813
1250.03795431665430560.07590863330861120.962045683345694
1260.05410386846586740.1082077369317350.945896131534133
1270.04584796765494280.09169593530988560.954152032345057
1280.03544570867566810.07089141735133620.964554291324332
1290.03482488057557720.06964976115115450.965175119424423
1300.02743386976936030.05486773953872060.97256613023064
1310.0276698259262740.0553396518525480.972330174073726
1320.02381921233237470.04763842466474940.976180787667625
1330.07905352550678210.1581070510135640.920946474493218
1340.07239049792644920.1447809958528980.927609502073551
1350.05608889552746250.1121777910549250.943911104472537
1360.04078154934852860.08156309869705720.959218450651471
1370.04066048257032070.08132096514064150.959339517429679
1380.02789289060316350.0557857812063270.972107109396837
1390.02213248598693130.04426497197386260.977867514013069
1400.01776254444565470.03552508889130940.982237455554345
1410.03654163039457010.07308326078914010.96345836960543
1420.05483279894996440.1096655978999290.945167201050036
1430.0503989341749640.1007978683499280.949601065825036
1440.3158321074611990.6316642149223980.684167892538801
1450.3036877215256040.6073754430512090.696312278474396
1460.5608446864883710.8783106270232580.439155313511629
1470.5184515109709190.9630969780581630.481548489029081
1480.7395152104826680.5209695790346630.260484789517332
1490.7823702383860120.4352595232279760.217629761613988
1500.6554168785422350.689166242915530.344583121457765
1510.5172930192458620.9654139615082750.482706980754138

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.999798361708555 & 0.000403276582890009 & 0.000201638291445004 \tabularnewline
12 & 0.999578846688441 & 0.000842306623117219 & 0.000421153311558609 \tabularnewline
13 & 0.999534424675926 & 0.000931150648148104 & 0.000465575324074052 \tabularnewline
14 & 0.998965032348361 & 0.00206993530327772 & 0.00103496765163886 \tabularnewline
15 & 0.998843845560168 & 0.00231230887966484 & 0.00115615443983242 \tabularnewline
16 & 0.998076122399122 & 0.00384775520175609 & 0.00192387760087804 \tabularnewline
17 & 0.996347503597227 & 0.00730499280554649 & 0.00365249640277324 \tabularnewline
18 & 0.995240342851986 & 0.00951931429602849 & 0.00475965714801425 \tabularnewline
19 & 0.992453725233806 & 0.0150925495323872 & 0.0075462747661936 \tabularnewline
20 & 0.987392711702156 & 0.0252145765956883 & 0.0126072882978441 \tabularnewline
21 & 0.980465047638016 & 0.0390699047239677 & 0.0195349523619839 \tabularnewline
22 & 0.973050335053249 & 0.0538993298935019 & 0.0269496649467509 \tabularnewline
23 & 0.961172591410675 & 0.07765481717865 & 0.038827408589325 \tabularnewline
24 & 0.945693106508499 & 0.108613786983003 & 0.0543068934915013 \tabularnewline
25 & 0.940449720593485 & 0.119100558813031 & 0.0595502794065153 \tabularnewline
26 & 0.958635684569701 & 0.082728630860597 & 0.0413643154302985 \tabularnewline
27 & 0.94889174521606 & 0.10221650956788 & 0.05110825478394 \tabularnewline
28 & 0.95511756308148 & 0.0897648738370396 & 0.0448824369185198 \tabularnewline
29 & 0.937966237555536 & 0.124067524888927 & 0.0620337624444636 \tabularnewline
30 & 0.921428954620695 & 0.157142090758609 & 0.0785710453793046 \tabularnewline
31 & 0.904796008219257 & 0.190407983561487 & 0.0952039917807433 \tabularnewline
32 & 0.918790736272519 & 0.162418527454963 & 0.0812092637274814 \tabularnewline
33 & 0.894858657728688 & 0.210282684542625 & 0.105141342271312 \tabularnewline
34 & 0.865099890469892 & 0.269800219060216 & 0.134900109530108 \tabularnewline
35 & 0.85491461597602 & 0.29017076804796 & 0.14508538402398 \tabularnewline
36 & 0.82182735901399 & 0.356345281972019 & 0.17817264098601 \tabularnewline
37 & 0.860275465563747 & 0.279449068872506 & 0.139724534436253 \tabularnewline
38 & 0.852830342918012 & 0.294339314163977 & 0.147169657081988 \tabularnewline
39 & 0.846328873599206 & 0.307342252801588 & 0.153671126400794 \tabularnewline
40 & 0.831341813456132 & 0.337316373087735 & 0.168658186543868 \tabularnewline
41 & 0.811162206762885 & 0.37767558647423 & 0.188837793237115 \tabularnewline
42 & 0.798468870057014 & 0.403062259885972 & 0.201531129942986 \tabularnewline
43 & 0.799857061291509 & 0.400285877416981 & 0.200142938708491 \tabularnewline
44 & 0.764294967999108 & 0.471410064001783 & 0.235705032000891 \tabularnewline
45 & 0.726828045372015 & 0.54634390925597 & 0.273171954627985 \tabularnewline
46 & 0.772840515286988 & 0.454318969426023 & 0.227159484713011 \tabularnewline
47 & 0.764359316323383 & 0.471281367353235 & 0.235640683676617 \tabularnewline
48 & 0.725598437355438 & 0.548803125289123 & 0.274401562644562 \tabularnewline
49 & 0.699942825076529 & 0.600114349846941 & 0.300057174923471 \tabularnewline
50 & 0.676221209799256 & 0.647557580401488 & 0.323778790200744 \tabularnewline
51 & 0.664094572018906 & 0.671810855962189 & 0.335905427981094 \tabularnewline
52 & 0.623034355031949 & 0.753931289936102 & 0.376965644968051 \tabularnewline
53 & 0.627183381366534 & 0.745633237266933 & 0.372816618633466 \tabularnewline
54 & 0.583677346221852 & 0.832645307556296 & 0.416322653778148 \tabularnewline
55 & 0.704456584353887 & 0.591086831292225 & 0.295543415646113 \tabularnewline
56 & 0.886418478866678 & 0.227163042266643 & 0.113581521133322 \tabularnewline
57 & 0.861903056018068 & 0.276193887963865 & 0.138096943981932 \tabularnewline
58 & 0.833975081008897 & 0.332049837982206 & 0.166024918991103 \tabularnewline
59 & 0.805951288815156 & 0.388097422369687 & 0.194048711184844 \tabularnewline
60 & 0.807957161885815 & 0.384085676228369 & 0.192042838114185 \tabularnewline
61 & 0.846722551052265 & 0.30655489789547 & 0.153277448947735 \tabularnewline
62 & 0.81913154628964 & 0.361736907420719 & 0.18086845371036 \tabularnewline
63 & 0.818666766118307 & 0.362666467763387 & 0.181333233881693 \tabularnewline
64 & 0.78644279389844 & 0.427114412203119 & 0.21355720610156 \tabularnewline
65 & 0.755261711909418 & 0.489476576181164 & 0.244738288090582 \tabularnewline
66 & 0.716162756110376 & 0.567674487779248 & 0.283837243889624 \tabularnewline
67 & 0.686874096455673 & 0.626251807088653 & 0.313125903544327 \tabularnewline
68 & 0.695477117739452 & 0.609045764521096 & 0.304522882260548 \tabularnewline
69 & 0.68504998610155 & 0.6299000277969 & 0.31495001389845 \tabularnewline
70 & 0.643758481684159 & 0.712483036631681 & 0.356241518315841 \tabularnewline
71 & 0.620135627967497 & 0.759728744065006 & 0.379864372032503 \tabularnewline
72 & 0.577747006372264 & 0.844505987255472 & 0.422252993627736 \tabularnewline
73 & 0.538553523829559 & 0.922892952340881 & 0.461446476170441 \tabularnewline
74 & 0.503619902925333 & 0.992760194149333 & 0.496380097074667 \tabularnewline
75 & 0.475655137947804 & 0.951310275895608 & 0.524344862052196 \tabularnewline
76 & 0.573411788268424 & 0.853176423463153 & 0.426588211731576 \tabularnewline
77 & 0.542282031517276 & 0.915435936965449 & 0.457717968482724 \tabularnewline
78 & 0.514011730237278 & 0.971976539525445 & 0.485988269762723 \tabularnewline
79 & 0.495681763445299 & 0.991363526890599 & 0.504318236554701 \tabularnewline
80 & 0.470822644524735 & 0.941645289049471 & 0.529177355475265 \tabularnewline
81 & 0.426906262029928 & 0.853812524059855 & 0.573093737970072 \tabularnewline
82 & 0.437231270986488 & 0.874462541972976 & 0.562768729013512 \tabularnewline
83 & 0.39352513618031 & 0.787050272360619 & 0.60647486381969 \tabularnewline
84 & 0.35435288614257 & 0.708705772285141 & 0.64564711385743 \tabularnewline
85 & 0.318956139246971 & 0.637912278493943 & 0.681043860753029 \tabularnewline
86 & 0.343269418267334 & 0.686538836534668 & 0.656730581732666 \tabularnewline
87 & 0.369957096968012 & 0.739914193936024 & 0.630042903031988 \tabularnewline
88 & 0.329551088196309 & 0.659102176392618 & 0.670448911803691 \tabularnewline
89 & 0.294391565209496 & 0.588783130418991 & 0.705608434790505 \tabularnewline
90 & 0.263686865315064 & 0.527373730630127 & 0.736313134684936 \tabularnewline
91 & 0.246987227399945 & 0.493974454799889 & 0.753012772600055 \tabularnewline
92 & 0.289538563938906 & 0.579077127877812 & 0.710461436061094 \tabularnewline
93 & 0.254376694765422 & 0.508753389530843 & 0.745623305234578 \tabularnewline
94 & 0.229821369681321 & 0.459642739362642 & 0.770178630318679 \tabularnewline
95 & 0.20833184515414 & 0.416663690308279 & 0.79166815484586 \tabularnewline
96 & 0.219262015083249 & 0.438524030166497 & 0.780737984916751 \tabularnewline
97 & 0.193143874396765 & 0.386287748793531 & 0.806856125603235 \tabularnewline
98 & 0.296398920027187 & 0.592797840054375 & 0.703601079972813 \tabularnewline
99 & 0.258962887482156 & 0.517925774964313 & 0.741037112517844 \tabularnewline
100 & 0.254175433660112 & 0.508350867320223 & 0.745824566339888 \tabularnewline
101 & 0.257547658291103 & 0.515095316582205 & 0.742452341708897 \tabularnewline
102 & 0.24029418728571 & 0.48058837457142 & 0.75970581271429 \tabularnewline
103 & 0.215149867159475 & 0.43029973431895 & 0.784850132840525 \tabularnewline
104 & 0.230102261467565 & 0.460204522935129 & 0.769897738532435 \tabularnewline
105 & 0.207253083364262 & 0.414506166728523 & 0.792746916635738 \tabularnewline
106 & 0.190591522639964 & 0.381183045279929 & 0.809408477360036 \tabularnewline
107 & 0.162342072268288 & 0.324684144536576 & 0.837657927731712 \tabularnewline
108 & 0.136905397610566 & 0.273810795221132 & 0.863094602389434 \tabularnewline
109 & 0.130915282843869 & 0.261830565687738 & 0.869084717156131 \tabularnewline
110 & 0.173222240437506 & 0.346444480875011 & 0.826777759562494 \tabularnewline
111 & 0.144496734927213 & 0.288993469854426 & 0.855503265072787 \tabularnewline
112 & 0.129845872074147 & 0.259691744148294 & 0.870154127925853 \tabularnewline
113 & 0.139356748500226 & 0.278713497000453 & 0.860643251499774 \tabularnewline
114 & 0.114644636392106 & 0.229289272784212 & 0.885355363607894 \tabularnewline
115 & 0.0920702596291048 & 0.18414051925821 & 0.907929740370895 \tabularnewline
116 & 0.074328283733426 & 0.148656567466852 & 0.925671716266574 \tabularnewline
117 & 0.0784532953263476 & 0.156906590652695 & 0.921546704673652 \tabularnewline
118 & 0.126590101686605 & 0.253180203373211 & 0.873409898313395 \tabularnewline
119 & 0.102455547652449 & 0.204911095304898 & 0.897544452347551 \tabularnewline
120 & 0.0842189642302013 & 0.168437928460403 & 0.915781035769799 \tabularnewline
121 & 0.0655355772151913 & 0.131071154430383 & 0.934464422784809 \tabularnewline
122 & 0.0528432725913736 & 0.105686545182747 & 0.947156727408626 \tabularnewline
123 & 0.0436467924545243 & 0.0872935849090487 & 0.956353207545476 \tabularnewline
124 & 0.0445685481311875 & 0.089137096262375 & 0.955431451868813 \tabularnewline
125 & 0.0379543166543056 & 0.0759086333086112 & 0.962045683345694 \tabularnewline
126 & 0.0541038684658674 & 0.108207736931735 & 0.945896131534133 \tabularnewline
127 & 0.0458479676549428 & 0.0916959353098856 & 0.954152032345057 \tabularnewline
128 & 0.0354457086756681 & 0.0708914173513362 & 0.964554291324332 \tabularnewline
129 & 0.0348248805755772 & 0.0696497611511545 & 0.965175119424423 \tabularnewline
130 & 0.0274338697693603 & 0.0548677395387206 & 0.97256613023064 \tabularnewline
131 & 0.027669825926274 & 0.055339651852548 & 0.972330174073726 \tabularnewline
132 & 0.0238192123323747 & 0.0476384246647494 & 0.976180787667625 \tabularnewline
133 & 0.0790535255067821 & 0.158107051013564 & 0.920946474493218 \tabularnewline
134 & 0.0723904979264492 & 0.144780995852898 & 0.927609502073551 \tabularnewline
135 & 0.0560888955274625 & 0.112177791054925 & 0.943911104472537 \tabularnewline
136 & 0.0407815493485286 & 0.0815630986970572 & 0.959218450651471 \tabularnewline
137 & 0.0406604825703207 & 0.0813209651406415 & 0.959339517429679 \tabularnewline
138 & 0.0278928906031635 & 0.055785781206327 & 0.972107109396837 \tabularnewline
139 & 0.0221324859869313 & 0.0442649719738626 & 0.977867514013069 \tabularnewline
140 & 0.0177625444456547 & 0.0355250888913094 & 0.982237455554345 \tabularnewline
141 & 0.0365416303945701 & 0.0730832607891401 & 0.96345836960543 \tabularnewline
142 & 0.0548327989499644 & 0.109665597899929 & 0.945167201050036 \tabularnewline
143 & 0.050398934174964 & 0.100797868349928 & 0.949601065825036 \tabularnewline
144 & 0.315832107461199 & 0.631664214922398 & 0.684167892538801 \tabularnewline
145 & 0.303687721525604 & 0.607375443051209 & 0.696312278474396 \tabularnewline
146 & 0.560844686488371 & 0.878310627023258 & 0.439155313511629 \tabularnewline
147 & 0.518451510970919 & 0.963096978058163 & 0.481548489029081 \tabularnewline
148 & 0.739515210482668 & 0.520969579034663 & 0.260484789517332 \tabularnewline
149 & 0.782370238386012 & 0.435259523227976 & 0.217629761613988 \tabularnewline
150 & 0.655416878542235 & 0.68916624291553 & 0.344583121457765 \tabularnewline
151 & 0.517293019245862 & 0.965413961508275 & 0.482706980754138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.999798361708555[/C][C]0.000403276582890009[/C][C]0.000201638291445004[/C][/ROW]
[ROW][C]12[/C][C]0.999578846688441[/C][C]0.000842306623117219[/C][C]0.000421153311558609[/C][/ROW]
[ROW][C]13[/C][C]0.999534424675926[/C][C]0.000931150648148104[/C][C]0.000465575324074052[/C][/ROW]
[ROW][C]14[/C][C]0.998965032348361[/C][C]0.00206993530327772[/C][C]0.00103496765163886[/C][/ROW]
[ROW][C]15[/C][C]0.998843845560168[/C][C]0.00231230887966484[/C][C]0.00115615443983242[/C][/ROW]
[ROW][C]16[/C][C]0.998076122399122[/C][C]0.00384775520175609[/C][C]0.00192387760087804[/C][/ROW]
[ROW][C]17[/C][C]0.996347503597227[/C][C]0.00730499280554649[/C][C]0.00365249640277324[/C][/ROW]
[ROW][C]18[/C][C]0.995240342851986[/C][C]0.00951931429602849[/C][C]0.00475965714801425[/C][/ROW]
[ROW][C]19[/C][C]0.992453725233806[/C][C]0.0150925495323872[/C][C]0.0075462747661936[/C][/ROW]
[ROW][C]20[/C][C]0.987392711702156[/C][C]0.0252145765956883[/C][C]0.0126072882978441[/C][/ROW]
[ROW][C]21[/C][C]0.980465047638016[/C][C]0.0390699047239677[/C][C]0.0195349523619839[/C][/ROW]
[ROW][C]22[/C][C]0.973050335053249[/C][C]0.0538993298935019[/C][C]0.0269496649467509[/C][/ROW]
[ROW][C]23[/C][C]0.961172591410675[/C][C]0.07765481717865[/C][C]0.038827408589325[/C][/ROW]
[ROW][C]24[/C][C]0.945693106508499[/C][C]0.108613786983003[/C][C]0.0543068934915013[/C][/ROW]
[ROW][C]25[/C][C]0.940449720593485[/C][C]0.119100558813031[/C][C]0.0595502794065153[/C][/ROW]
[ROW][C]26[/C][C]0.958635684569701[/C][C]0.082728630860597[/C][C]0.0413643154302985[/C][/ROW]
[ROW][C]27[/C][C]0.94889174521606[/C][C]0.10221650956788[/C][C]0.05110825478394[/C][/ROW]
[ROW][C]28[/C][C]0.95511756308148[/C][C]0.0897648738370396[/C][C]0.0448824369185198[/C][/ROW]
[ROW][C]29[/C][C]0.937966237555536[/C][C]0.124067524888927[/C][C]0.0620337624444636[/C][/ROW]
[ROW][C]30[/C][C]0.921428954620695[/C][C]0.157142090758609[/C][C]0.0785710453793046[/C][/ROW]
[ROW][C]31[/C][C]0.904796008219257[/C][C]0.190407983561487[/C][C]0.0952039917807433[/C][/ROW]
[ROW][C]32[/C][C]0.918790736272519[/C][C]0.162418527454963[/C][C]0.0812092637274814[/C][/ROW]
[ROW][C]33[/C][C]0.894858657728688[/C][C]0.210282684542625[/C][C]0.105141342271312[/C][/ROW]
[ROW][C]34[/C][C]0.865099890469892[/C][C]0.269800219060216[/C][C]0.134900109530108[/C][/ROW]
[ROW][C]35[/C][C]0.85491461597602[/C][C]0.29017076804796[/C][C]0.14508538402398[/C][/ROW]
[ROW][C]36[/C][C]0.82182735901399[/C][C]0.356345281972019[/C][C]0.17817264098601[/C][/ROW]
[ROW][C]37[/C][C]0.860275465563747[/C][C]0.279449068872506[/C][C]0.139724534436253[/C][/ROW]
[ROW][C]38[/C][C]0.852830342918012[/C][C]0.294339314163977[/C][C]0.147169657081988[/C][/ROW]
[ROW][C]39[/C][C]0.846328873599206[/C][C]0.307342252801588[/C][C]0.153671126400794[/C][/ROW]
[ROW][C]40[/C][C]0.831341813456132[/C][C]0.337316373087735[/C][C]0.168658186543868[/C][/ROW]
[ROW][C]41[/C][C]0.811162206762885[/C][C]0.37767558647423[/C][C]0.188837793237115[/C][/ROW]
[ROW][C]42[/C][C]0.798468870057014[/C][C]0.403062259885972[/C][C]0.201531129942986[/C][/ROW]
[ROW][C]43[/C][C]0.799857061291509[/C][C]0.400285877416981[/C][C]0.200142938708491[/C][/ROW]
[ROW][C]44[/C][C]0.764294967999108[/C][C]0.471410064001783[/C][C]0.235705032000891[/C][/ROW]
[ROW][C]45[/C][C]0.726828045372015[/C][C]0.54634390925597[/C][C]0.273171954627985[/C][/ROW]
[ROW][C]46[/C][C]0.772840515286988[/C][C]0.454318969426023[/C][C]0.227159484713011[/C][/ROW]
[ROW][C]47[/C][C]0.764359316323383[/C][C]0.471281367353235[/C][C]0.235640683676617[/C][/ROW]
[ROW][C]48[/C][C]0.725598437355438[/C][C]0.548803125289123[/C][C]0.274401562644562[/C][/ROW]
[ROW][C]49[/C][C]0.699942825076529[/C][C]0.600114349846941[/C][C]0.300057174923471[/C][/ROW]
[ROW][C]50[/C][C]0.676221209799256[/C][C]0.647557580401488[/C][C]0.323778790200744[/C][/ROW]
[ROW][C]51[/C][C]0.664094572018906[/C][C]0.671810855962189[/C][C]0.335905427981094[/C][/ROW]
[ROW][C]52[/C][C]0.623034355031949[/C][C]0.753931289936102[/C][C]0.376965644968051[/C][/ROW]
[ROW][C]53[/C][C]0.627183381366534[/C][C]0.745633237266933[/C][C]0.372816618633466[/C][/ROW]
[ROW][C]54[/C][C]0.583677346221852[/C][C]0.832645307556296[/C][C]0.416322653778148[/C][/ROW]
[ROW][C]55[/C][C]0.704456584353887[/C][C]0.591086831292225[/C][C]0.295543415646113[/C][/ROW]
[ROW][C]56[/C][C]0.886418478866678[/C][C]0.227163042266643[/C][C]0.113581521133322[/C][/ROW]
[ROW][C]57[/C][C]0.861903056018068[/C][C]0.276193887963865[/C][C]0.138096943981932[/C][/ROW]
[ROW][C]58[/C][C]0.833975081008897[/C][C]0.332049837982206[/C][C]0.166024918991103[/C][/ROW]
[ROW][C]59[/C][C]0.805951288815156[/C][C]0.388097422369687[/C][C]0.194048711184844[/C][/ROW]
[ROW][C]60[/C][C]0.807957161885815[/C][C]0.384085676228369[/C][C]0.192042838114185[/C][/ROW]
[ROW][C]61[/C][C]0.846722551052265[/C][C]0.30655489789547[/C][C]0.153277448947735[/C][/ROW]
[ROW][C]62[/C][C]0.81913154628964[/C][C]0.361736907420719[/C][C]0.18086845371036[/C][/ROW]
[ROW][C]63[/C][C]0.818666766118307[/C][C]0.362666467763387[/C][C]0.181333233881693[/C][/ROW]
[ROW][C]64[/C][C]0.78644279389844[/C][C]0.427114412203119[/C][C]0.21355720610156[/C][/ROW]
[ROW][C]65[/C][C]0.755261711909418[/C][C]0.489476576181164[/C][C]0.244738288090582[/C][/ROW]
[ROW][C]66[/C][C]0.716162756110376[/C][C]0.567674487779248[/C][C]0.283837243889624[/C][/ROW]
[ROW][C]67[/C][C]0.686874096455673[/C][C]0.626251807088653[/C][C]0.313125903544327[/C][/ROW]
[ROW][C]68[/C][C]0.695477117739452[/C][C]0.609045764521096[/C][C]0.304522882260548[/C][/ROW]
[ROW][C]69[/C][C]0.68504998610155[/C][C]0.6299000277969[/C][C]0.31495001389845[/C][/ROW]
[ROW][C]70[/C][C]0.643758481684159[/C][C]0.712483036631681[/C][C]0.356241518315841[/C][/ROW]
[ROW][C]71[/C][C]0.620135627967497[/C][C]0.759728744065006[/C][C]0.379864372032503[/C][/ROW]
[ROW][C]72[/C][C]0.577747006372264[/C][C]0.844505987255472[/C][C]0.422252993627736[/C][/ROW]
[ROW][C]73[/C][C]0.538553523829559[/C][C]0.922892952340881[/C][C]0.461446476170441[/C][/ROW]
[ROW][C]74[/C][C]0.503619902925333[/C][C]0.992760194149333[/C][C]0.496380097074667[/C][/ROW]
[ROW][C]75[/C][C]0.475655137947804[/C][C]0.951310275895608[/C][C]0.524344862052196[/C][/ROW]
[ROW][C]76[/C][C]0.573411788268424[/C][C]0.853176423463153[/C][C]0.426588211731576[/C][/ROW]
[ROW][C]77[/C][C]0.542282031517276[/C][C]0.915435936965449[/C][C]0.457717968482724[/C][/ROW]
[ROW][C]78[/C][C]0.514011730237278[/C][C]0.971976539525445[/C][C]0.485988269762723[/C][/ROW]
[ROW][C]79[/C][C]0.495681763445299[/C][C]0.991363526890599[/C][C]0.504318236554701[/C][/ROW]
[ROW][C]80[/C][C]0.470822644524735[/C][C]0.941645289049471[/C][C]0.529177355475265[/C][/ROW]
[ROW][C]81[/C][C]0.426906262029928[/C][C]0.853812524059855[/C][C]0.573093737970072[/C][/ROW]
[ROW][C]82[/C][C]0.437231270986488[/C][C]0.874462541972976[/C][C]0.562768729013512[/C][/ROW]
[ROW][C]83[/C][C]0.39352513618031[/C][C]0.787050272360619[/C][C]0.60647486381969[/C][/ROW]
[ROW][C]84[/C][C]0.35435288614257[/C][C]0.708705772285141[/C][C]0.64564711385743[/C][/ROW]
[ROW][C]85[/C][C]0.318956139246971[/C][C]0.637912278493943[/C][C]0.681043860753029[/C][/ROW]
[ROW][C]86[/C][C]0.343269418267334[/C][C]0.686538836534668[/C][C]0.656730581732666[/C][/ROW]
[ROW][C]87[/C][C]0.369957096968012[/C][C]0.739914193936024[/C][C]0.630042903031988[/C][/ROW]
[ROW][C]88[/C][C]0.329551088196309[/C][C]0.659102176392618[/C][C]0.670448911803691[/C][/ROW]
[ROW][C]89[/C][C]0.294391565209496[/C][C]0.588783130418991[/C][C]0.705608434790505[/C][/ROW]
[ROW][C]90[/C][C]0.263686865315064[/C][C]0.527373730630127[/C][C]0.736313134684936[/C][/ROW]
[ROW][C]91[/C][C]0.246987227399945[/C][C]0.493974454799889[/C][C]0.753012772600055[/C][/ROW]
[ROW][C]92[/C][C]0.289538563938906[/C][C]0.579077127877812[/C][C]0.710461436061094[/C][/ROW]
[ROW][C]93[/C][C]0.254376694765422[/C][C]0.508753389530843[/C][C]0.745623305234578[/C][/ROW]
[ROW][C]94[/C][C]0.229821369681321[/C][C]0.459642739362642[/C][C]0.770178630318679[/C][/ROW]
[ROW][C]95[/C][C]0.20833184515414[/C][C]0.416663690308279[/C][C]0.79166815484586[/C][/ROW]
[ROW][C]96[/C][C]0.219262015083249[/C][C]0.438524030166497[/C][C]0.780737984916751[/C][/ROW]
[ROW][C]97[/C][C]0.193143874396765[/C][C]0.386287748793531[/C][C]0.806856125603235[/C][/ROW]
[ROW][C]98[/C][C]0.296398920027187[/C][C]0.592797840054375[/C][C]0.703601079972813[/C][/ROW]
[ROW][C]99[/C][C]0.258962887482156[/C][C]0.517925774964313[/C][C]0.741037112517844[/C][/ROW]
[ROW][C]100[/C][C]0.254175433660112[/C][C]0.508350867320223[/C][C]0.745824566339888[/C][/ROW]
[ROW][C]101[/C][C]0.257547658291103[/C][C]0.515095316582205[/C][C]0.742452341708897[/C][/ROW]
[ROW][C]102[/C][C]0.24029418728571[/C][C]0.48058837457142[/C][C]0.75970581271429[/C][/ROW]
[ROW][C]103[/C][C]0.215149867159475[/C][C]0.43029973431895[/C][C]0.784850132840525[/C][/ROW]
[ROW][C]104[/C][C]0.230102261467565[/C][C]0.460204522935129[/C][C]0.769897738532435[/C][/ROW]
[ROW][C]105[/C][C]0.207253083364262[/C][C]0.414506166728523[/C][C]0.792746916635738[/C][/ROW]
[ROW][C]106[/C][C]0.190591522639964[/C][C]0.381183045279929[/C][C]0.809408477360036[/C][/ROW]
[ROW][C]107[/C][C]0.162342072268288[/C][C]0.324684144536576[/C][C]0.837657927731712[/C][/ROW]
[ROW][C]108[/C][C]0.136905397610566[/C][C]0.273810795221132[/C][C]0.863094602389434[/C][/ROW]
[ROW][C]109[/C][C]0.130915282843869[/C][C]0.261830565687738[/C][C]0.869084717156131[/C][/ROW]
[ROW][C]110[/C][C]0.173222240437506[/C][C]0.346444480875011[/C][C]0.826777759562494[/C][/ROW]
[ROW][C]111[/C][C]0.144496734927213[/C][C]0.288993469854426[/C][C]0.855503265072787[/C][/ROW]
[ROW][C]112[/C][C]0.129845872074147[/C][C]0.259691744148294[/C][C]0.870154127925853[/C][/ROW]
[ROW][C]113[/C][C]0.139356748500226[/C][C]0.278713497000453[/C][C]0.860643251499774[/C][/ROW]
[ROW][C]114[/C][C]0.114644636392106[/C][C]0.229289272784212[/C][C]0.885355363607894[/C][/ROW]
[ROW][C]115[/C][C]0.0920702596291048[/C][C]0.18414051925821[/C][C]0.907929740370895[/C][/ROW]
[ROW][C]116[/C][C]0.074328283733426[/C][C]0.148656567466852[/C][C]0.925671716266574[/C][/ROW]
[ROW][C]117[/C][C]0.0784532953263476[/C][C]0.156906590652695[/C][C]0.921546704673652[/C][/ROW]
[ROW][C]118[/C][C]0.126590101686605[/C][C]0.253180203373211[/C][C]0.873409898313395[/C][/ROW]
[ROW][C]119[/C][C]0.102455547652449[/C][C]0.204911095304898[/C][C]0.897544452347551[/C][/ROW]
[ROW][C]120[/C][C]0.0842189642302013[/C][C]0.168437928460403[/C][C]0.915781035769799[/C][/ROW]
[ROW][C]121[/C][C]0.0655355772151913[/C][C]0.131071154430383[/C][C]0.934464422784809[/C][/ROW]
[ROW][C]122[/C][C]0.0528432725913736[/C][C]0.105686545182747[/C][C]0.947156727408626[/C][/ROW]
[ROW][C]123[/C][C]0.0436467924545243[/C][C]0.0872935849090487[/C][C]0.956353207545476[/C][/ROW]
[ROW][C]124[/C][C]0.0445685481311875[/C][C]0.089137096262375[/C][C]0.955431451868813[/C][/ROW]
[ROW][C]125[/C][C]0.0379543166543056[/C][C]0.0759086333086112[/C][C]0.962045683345694[/C][/ROW]
[ROW][C]126[/C][C]0.0541038684658674[/C][C]0.108207736931735[/C][C]0.945896131534133[/C][/ROW]
[ROW][C]127[/C][C]0.0458479676549428[/C][C]0.0916959353098856[/C][C]0.954152032345057[/C][/ROW]
[ROW][C]128[/C][C]0.0354457086756681[/C][C]0.0708914173513362[/C][C]0.964554291324332[/C][/ROW]
[ROW][C]129[/C][C]0.0348248805755772[/C][C]0.0696497611511545[/C][C]0.965175119424423[/C][/ROW]
[ROW][C]130[/C][C]0.0274338697693603[/C][C]0.0548677395387206[/C][C]0.97256613023064[/C][/ROW]
[ROW][C]131[/C][C]0.027669825926274[/C][C]0.055339651852548[/C][C]0.972330174073726[/C][/ROW]
[ROW][C]132[/C][C]0.0238192123323747[/C][C]0.0476384246647494[/C][C]0.976180787667625[/C][/ROW]
[ROW][C]133[/C][C]0.0790535255067821[/C][C]0.158107051013564[/C][C]0.920946474493218[/C][/ROW]
[ROW][C]134[/C][C]0.0723904979264492[/C][C]0.144780995852898[/C][C]0.927609502073551[/C][/ROW]
[ROW][C]135[/C][C]0.0560888955274625[/C][C]0.112177791054925[/C][C]0.943911104472537[/C][/ROW]
[ROW][C]136[/C][C]0.0407815493485286[/C][C]0.0815630986970572[/C][C]0.959218450651471[/C][/ROW]
[ROW][C]137[/C][C]0.0406604825703207[/C][C]0.0813209651406415[/C][C]0.959339517429679[/C][/ROW]
[ROW][C]138[/C][C]0.0278928906031635[/C][C]0.055785781206327[/C][C]0.972107109396837[/C][/ROW]
[ROW][C]139[/C][C]0.0221324859869313[/C][C]0.0442649719738626[/C][C]0.977867514013069[/C][/ROW]
[ROW][C]140[/C][C]0.0177625444456547[/C][C]0.0355250888913094[/C][C]0.982237455554345[/C][/ROW]
[ROW][C]141[/C][C]0.0365416303945701[/C][C]0.0730832607891401[/C][C]0.96345836960543[/C][/ROW]
[ROW][C]142[/C][C]0.0548327989499644[/C][C]0.109665597899929[/C][C]0.945167201050036[/C][/ROW]
[ROW][C]143[/C][C]0.050398934174964[/C][C]0.100797868349928[/C][C]0.949601065825036[/C][/ROW]
[ROW][C]144[/C][C]0.315832107461199[/C][C]0.631664214922398[/C][C]0.684167892538801[/C][/ROW]
[ROW][C]145[/C][C]0.303687721525604[/C][C]0.607375443051209[/C][C]0.696312278474396[/C][/ROW]
[ROW][C]146[/C][C]0.560844686488371[/C][C]0.878310627023258[/C][C]0.439155313511629[/C][/ROW]
[ROW][C]147[/C][C]0.518451510970919[/C][C]0.963096978058163[/C][C]0.481548489029081[/C][/ROW]
[ROW][C]148[/C][C]0.739515210482668[/C][C]0.520969579034663[/C][C]0.260484789517332[/C][/ROW]
[ROW][C]149[/C][C]0.782370238386012[/C][C]0.435259523227976[/C][C]0.217629761613988[/C][/ROW]
[ROW][C]150[/C][C]0.655416878542235[/C][C]0.68916624291553[/C][C]0.344583121457765[/C][/ROW]
[ROW][C]151[/C][C]0.517293019245862[/C][C]0.965413961508275[/C][C]0.482706980754138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9997983617085550.0004032765828900090.000201638291445004
120.9995788466884410.0008423066231172190.000421153311558609
130.9995344246759260.0009311506481481040.000465575324074052
140.9989650323483610.002069935303277720.00103496765163886
150.9988438455601680.002312308879664840.00115615443983242
160.9980761223991220.003847755201756090.00192387760087804
170.9963475035972270.007304992805546490.00365249640277324
180.9952403428519860.009519314296028490.00475965714801425
190.9924537252338060.01509254953238720.0075462747661936
200.9873927117021560.02521457659568830.0126072882978441
210.9804650476380160.03906990472396770.0195349523619839
220.9730503350532490.05389932989350190.0269496649467509
230.9611725914106750.077654817178650.038827408589325
240.9456931065084990.1086137869830030.0543068934915013
250.9404497205934850.1191005588130310.0595502794065153
260.9586356845697010.0827286308605970.0413643154302985
270.948891745216060.102216509567880.05110825478394
280.955117563081480.08976487383703960.0448824369185198
290.9379662375555360.1240675248889270.0620337624444636
300.9214289546206950.1571420907586090.0785710453793046
310.9047960082192570.1904079835614870.0952039917807433
320.9187907362725190.1624185274549630.0812092637274814
330.8948586577286880.2102826845426250.105141342271312
340.8650998904698920.2698002190602160.134900109530108
350.854914615976020.290170768047960.14508538402398
360.821827359013990.3563452819720190.17817264098601
370.8602754655637470.2794490688725060.139724534436253
380.8528303429180120.2943393141639770.147169657081988
390.8463288735992060.3073422528015880.153671126400794
400.8313418134561320.3373163730877350.168658186543868
410.8111622067628850.377675586474230.188837793237115
420.7984688700570140.4030622598859720.201531129942986
430.7998570612915090.4002858774169810.200142938708491
440.7642949679991080.4714100640017830.235705032000891
450.7268280453720150.546343909255970.273171954627985
460.7728405152869880.4543189694260230.227159484713011
470.7643593163233830.4712813673532350.235640683676617
480.7255984373554380.5488031252891230.274401562644562
490.6999428250765290.6001143498469410.300057174923471
500.6762212097992560.6475575804014880.323778790200744
510.6640945720189060.6718108559621890.335905427981094
520.6230343550319490.7539312899361020.376965644968051
530.6271833813665340.7456332372669330.372816618633466
540.5836773462218520.8326453075562960.416322653778148
550.7044565843538870.5910868312922250.295543415646113
560.8864184788666780.2271630422666430.113581521133322
570.8619030560180680.2761938879638650.138096943981932
580.8339750810088970.3320498379822060.166024918991103
590.8059512888151560.3880974223696870.194048711184844
600.8079571618858150.3840856762283690.192042838114185
610.8467225510522650.306554897895470.153277448947735
620.819131546289640.3617369074207190.18086845371036
630.8186667661183070.3626664677633870.181333233881693
640.786442793898440.4271144122031190.21355720610156
650.7552617119094180.4894765761811640.244738288090582
660.7161627561103760.5676744877792480.283837243889624
670.6868740964556730.6262518070886530.313125903544327
680.6954771177394520.6090457645210960.304522882260548
690.685049986101550.62990002779690.31495001389845
700.6437584816841590.7124830366316810.356241518315841
710.6201356279674970.7597287440650060.379864372032503
720.5777470063722640.8445059872554720.422252993627736
730.5385535238295590.9228929523408810.461446476170441
740.5036199029253330.9927601941493330.496380097074667
750.4756551379478040.9513102758956080.524344862052196
760.5734117882684240.8531764234631530.426588211731576
770.5422820315172760.9154359369654490.457717968482724
780.5140117302372780.9719765395254450.485988269762723
790.4956817634452990.9913635268905990.504318236554701
800.4708226445247350.9416452890494710.529177355475265
810.4269062620299280.8538125240598550.573093737970072
820.4372312709864880.8744625419729760.562768729013512
830.393525136180310.7870502723606190.60647486381969
840.354352886142570.7087057722851410.64564711385743
850.3189561392469710.6379122784939430.681043860753029
860.3432694182673340.6865388365346680.656730581732666
870.3699570969680120.7399141939360240.630042903031988
880.3295510881963090.6591021763926180.670448911803691
890.2943915652094960.5887831304189910.705608434790505
900.2636868653150640.5273737306301270.736313134684936
910.2469872273999450.4939744547998890.753012772600055
920.2895385639389060.5790771278778120.710461436061094
930.2543766947654220.5087533895308430.745623305234578
940.2298213696813210.4596427393626420.770178630318679
950.208331845154140.4166636903082790.79166815484586
960.2192620150832490.4385240301664970.780737984916751
970.1931438743967650.3862877487935310.806856125603235
980.2963989200271870.5927978400543750.703601079972813
990.2589628874821560.5179257749643130.741037112517844
1000.2541754336601120.5083508673202230.745824566339888
1010.2575476582911030.5150953165822050.742452341708897
1020.240294187285710.480588374571420.75970581271429
1030.2151498671594750.430299734318950.784850132840525
1040.2301022614675650.4602045229351290.769897738532435
1050.2072530833642620.4145061667285230.792746916635738
1060.1905915226399640.3811830452799290.809408477360036
1070.1623420722682880.3246841445365760.837657927731712
1080.1369053976105660.2738107952211320.863094602389434
1090.1309152828438690.2618305656877380.869084717156131
1100.1732222404375060.3464444808750110.826777759562494
1110.1444967349272130.2889934698544260.855503265072787
1120.1298458720741470.2596917441482940.870154127925853
1130.1393567485002260.2787134970004530.860643251499774
1140.1146446363921060.2292892727842120.885355363607894
1150.09207025962910480.184140519258210.907929740370895
1160.0743282837334260.1486565674668520.925671716266574
1170.07845329532634760.1569065906526950.921546704673652
1180.1265901016866050.2531802033732110.873409898313395
1190.1024555476524490.2049110953048980.897544452347551
1200.08421896423020130.1684379284604030.915781035769799
1210.06553557721519130.1310711544303830.934464422784809
1220.05284327259137360.1056865451827470.947156727408626
1230.04364679245452430.08729358490904870.956353207545476
1240.04456854813118750.0891370962623750.955431451868813
1250.03795431665430560.07590863330861120.962045683345694
1260.05410386846586740.1082077369317350.945896131534133
1270.04584796765494280.09169593530988560.954152032345057
1280.03544570867566810.07089141735133620.964554291324332
1290.03482488057557720.06964976115115450.965175119424423
1300.02743386976936030.05486773953872060.97256613023064
1310.0276698259262740.0553396518525480.972330174073726
1320.02381921233237470.04763842466474940.976180787667625
1330.07905352550678210.1581070510135640.920946474493218
1340.07239049792644920.1447809958528980.927609502073551
1350.05608889552746250.1121777910549250.943911104472537
1360.04078154934852860.08156309869705720.959218450651471
1370.04066048257032070.08132096514064150.959339517429679
1380.02789289060316350.0557857812063270.972107109396837
1390.02213248598693130.04426497197386260.977867514013069
1400.01776254444565470.03552508889130940.982237455554345
1410.03654163039457010.07308326078914010.96345836960543
1420.05483279894996440.1096655978999290.945167201050036
1430.0503989341749640.1007978683499280.949601065825036
1440.3158321074611990.6316642149223980.684167892538801
1450.3036877215256040.6073754430512090.696312278474396
1460.5608446864883710.8783106270232580.439155313511629
1470.5184515109709190.9630969780581630.481548489029081
1480.7395152104826680.5209695790346630.260484789517332
1490.7823702383860120.4352595232279760.217629761613988
1500.6554168785422350.689166242915530.344583121457765
1510.5172930192458620.9654139615082750.482706980754138






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0567375886524823NOK
5% type I error level140.099290780141844NOK
10% type I error level300.212765957446809NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 8 & 0.0567375886524823 & NOK \tabularnewline
5% type I error level & 14 & 0.099290780141844 & NOK \tabularnewline
10% type I error level & 30 & 0.212765957446809 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190298&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]8[/C][C]0.0567375886524823[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.099290780141844[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.212765957446809[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190298&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190298&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 level80.0567375886524823NOK
5% type I error level140.099290780141844NOK
10% type I error level300.212765957446809NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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