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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Dec 2011 10:10:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t1323961865w6q1xubbrqcwoew.htm/, Retrieved Wed, 08 May 2024 20:54:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155478, Retrieved Wed, 08 May 2024 20:54:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:24:09] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-           [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:51:47] [2e1e44f0ae3cb9513dc28781dfdb387b]
- RMPD          [Multiple Regression] [Workshop 10] [2011-12-15 15:10:33] [b625935f05df8270d3a5abfea0142dde] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14	1
2	39	32	16	11	18	1
2	30	35	19	15	11	0
1	31	33	15	6	12	0
2	34	37	14	13	16	1
2	35	29	13	10	18	1
2	39	31	19	12	14	1
2	34	36	15	14	14	0
2	36	35	14	12	15	0
2	37	38	15	6	15	1
1	38	31	16	10	17	0
2	36	34	16	12	19	0
1	38	35	16	12	10	0
2	39	38	16	11	16	0
2	33	37	17	15	18	0
1	32	33	15	12	14	1
1	36	32	15	10	14	1
2	38	38	20	12	17	1
1	39	38	18	11	14	0
2	32	32	16	12	16	0
1	32	33	16	11	18	1
2	31	31	16	12	11	1
2	39	38	19	13	14	0
2	37	39	16	11	12	1
1	39	32	17	9	17	1
2	41	32	17	13	9	1
1	36	35	16	10	16	1
2	33	37	15	14	14	0
2	33	33	16	12	15	0
1	34	33	14	10	11	1
2	31	28	15	12	16	1
1	27	32	12	8	13	1
2	37	31	14	10	17	0
2	34	37	16	12	15	0
1	34	30	14	12	14	0
1	32	33	7	7	16	1
1	29	31	10	6	9	1
1	36	33	14	12	15	0
2	29	31	16	10	17	0
1	35	33	16	10	13	1
1	37	32	16	10	15	1
2	34	33	14	12	16	1
1	38	32	20	15	16	1
1	35	33	14	10	12	1
2	38	28	14	10	12	0
2	37	35	11	12	11	0
2	38	39	14	13	15	0
2	33	34	15	11	15	1
2	36	38	16	11	17	1
1	38	32	14	12	13	0
2	32	38	16	14	16	0
1	32	30	14	10	14	1
1	32	33	12	12	11	0
2	34	38	16	13	12	0
1	32	32	9	5	12	0
2	37	32	14	6	15	0
2	39	34	16	12	16	0
2	29	34	16	12	15	0
1	37	36	15	11	12	0
2	35	34	16	10	12	0
1	30	28	12	7	8	0
1	38	34	16	12	13	1
2	34	35	16	14	11	1
2	31	35	14	11	14	1
2	34	31	16	12	15	0
1	35	37	17	13	10	0
2	36	35	18	14	11	0
1	30	27	18	11	12	0
2	39	40	12	12	15	1
1	35	37	16	12	15	1
1	38	36	10	8	14	0
2	31	38	14	11	16	0
2	34	39	18	14	15	0
1	38	41	18	14	15	0
1	34	27	16	12	13	1
2	39	30	17	9	12	1
2	37	37	16	13	17	0
2	34	31	16	11	13	1
1	28	31	13	12	15	1
1	37	27	16	12	13	1
1	33	36	16	12	15	0
1	37	38	20	12	16	0
2	35	37	16	12	15	0
1	37	33	15	12	16	1
2	32	34	15	11	15	0
2	33	31	16	10	14	0
1	38	39	14	9	15	1
2	33	34	16	12	14	1
2	29	32	16	12	13	0
2	33	33	15	12	7	0
2	31	36	12	9	17	1
2	36	32	17	15	13	1
2	35	41	16	12	15	1
2	32	28	15	12	14	0
2	29	30	13	12	13	1
2	39	36	16	10	16	1
2	37	35	16	13	12	1
2	35	31	16	9	14	1
1	37	34	16	12	17	0
1	32	36	14	10	15	0
2	38	36	16	14	17	1
1	37	35	16	11	12	0
2	36	37	20	15	16	0
1	32	28	15	11	11	0
2	33	39	16	11	15	0
1	40	32	13	12	9	1
2	38	35	17	12	16	1
1	41	39	16	12	15	0
1	36	35	16	11	10	0
2	43	42	12	7	10	1
2	30	34	16	12	15	1
2	31	33	16	14	11	1
2	32	41	17	11	13	0
1	32	33	13	11	14	0
2	37	34	12	10	18	1
1	37	32	18	13	16	0
2	33	40	14	13	14	0
2	34	40	14	8	14	0
2	33	35	13	11	14	0
2	38	36	16	12	14	0
2	33	37	13	11	12	1
2	31	27	16	13	14	1
2	38	39	13	12	15	1
2	37	38	16	14	15	0
2	33	31	15	13	15	0
2	31	33	16	15	13	1
1	39	32	15	10	17	1
2	44	39	17	11	17	0
2	33	36	15	9	19	1
2	35	33	12	11	15	1
1	32	33	16	10	13	1
1	28	32	10	11	9	1
2	40	37	16	8	15	0
1	27	30	12	11	15	0
1	37	38	14	12	15	1
2	32	29	15	12	16	1
1	28	22	13	9	11	1
1	34	35	15	11	14	0
2	30	35	11	10	11	0
2	35	34	12	8	15	0
1	31	35	8	9	13	1
2	32	34	16	8	15	1
1	30	34	15	9	16	0
2	30	35	17	15	14	0
1	31	23	16	11	15	1
2	40	31	10	8	16	1
2	32	27	18	13	16	1
1	36	36	13	12	11	1
1	32	31	16	12	12	1
1	35	32	13	9	9	0
2	38	39	10	7	16	0
2	42	37	15	13	13	0
1	34	38	16	9	16	1
2	35	39	16	6	12	1
2	35	34	14	8	9	0
2	33	31	10	8	13	0
2	36	32	17	15	13	1
2	32	37	13	6	14	0
2	33	36	15	9	19	0
2	34	32	16	11	13	0
2	32	35	12	8	12	0
2	34	36	13	8	13	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.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 & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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 time6 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 17.7776309431389 -0.28835113027119Gender[t] + 0.358509478841511Separate[t] + 0.344545056420027Learning[t] -0.129904848873766Software[t] + 0.0748566273482812Happiness[t] + 0.685191817204791Population[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  17.7776309431389 -0.28835113027119Gender[t] +  0.358509478841511Separate[t] +  0.344545056420027Learning[t] -0.129904848873766Software[t] +  0.0748566273482812Happiness[t] +  0.685191817204791Population[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  17.7776309431389 -0.28835113027119Gender[t] +  0.358509478841511Separate[t] +  0.344545056420027Learning[t] -0.129904848873766Software[t] +  0.0748566273482812Happiness[t] +  0.685191817204791Population[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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
Connected[t] = + 17.7776309431389 -0.28835113027119Gender[t] + 0.358509478841511Separate[t] + 0.344545056420027Learning[t] -0.129904848873766Software[t] + 0.0748566273482812Happiness[t] + 0.685191817204791Population[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.77763094313893.0071765.911700
Gender-0.288351130271190.529048-0.5450.5865110.293255
Separate0.3585094788415110.0725944.93862e-061e-06
Learning0.3445450564200270.1311912.62630.0094980.004749
Software-0.1299048488737660.137094-0.94760.3448280.172414
Happiness0.07485662734828120.108850.68770.4926670.246333
Population0.6851918172047910.4994591.37190.1720870.086043

\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) & 17.7776309431389 & 3.007176 & 5.9117 & 0 & 0 \tabularnewline
Gender & -0.28835113027119 & 0.529048 & -0.545 & 0.586511 & 0.293255 \tabularnewline
Separate & 0.358509478841511 & 0.072594 & 4.9386 & 2e-06 & 1e-06 \tabularnewline
Learning & 0.344545056420027 & 0.131191 & 2.6263 & 0.009498 & 0.004749 \tabularnewline
Software & -0.129904848873766 & 0.137094 & -0.9476 & 0.344828 & 0.172414 \tabularnewline
Happiness & 0.0748566273482812 & 0.10885 & 0.6877 & 0.492667 & 0.246333 \tabularnewline
Population & 0.685191817204791 & 0.499459 & 1.3719 & 0.172087 & 0.086043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&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]17.7776309431389[/C][C]3.007176[/C][C]5.9117[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]-0.28835113027119[/C][C]0.529048[/C][C]-0.545[/C][C]0.586511[/C][C]0.293255[/C][/ROW]
[ROW][C]Separate[/C][C]0.358509478841511[/C][C]0.072594[/C][C]4.9386[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.344545056420027[/C][C]0.131191[/C][C]2.6263[/C][C]0.009498[/C][C]0.004749[/C][/ROW]
[ROW][C]Software[/C][C]-0.129904848873766[/C][C]0.137094[/C][C]-0.9476[/C][C]0.344828[/C][C]0.172414[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0748566273482812[/C][C]0.10885[/C][C]0.6877[/C][C]0.492667[/C][C]0.246333[/C][/ROW]
[ROW][C]Population[/C][C]0.685191817204791[/C][C]0.499459[/C][C]1.3719[/C][C]0.172087[/C][C]0.086043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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)17.77763094313893.0071765.911700
Gender-0.288351130271190.529048-0.5450.5865110.293255
Separate0.3585094788415110.0725944.93862e-061e-06
Learning0.3445450564200270.1311912.62630.0094980.004749
Software-0.1299048488737660.137094-0.94760.3448280.172414
Happiness0.07485662734828120.108850.68770.4926670.246333
Population0.6851918172047910.4994591.37190.1720870.086043






Multiple Linear Regression - Regression Statistics
Multiple R0.435849630153994
R-squared0.189964900105373
Adjusted R-squared0.158608702690097
F-TEST (value)6.05828881574866
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.01781375027032e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.09591757651607
Sum Squared Residuals1485.62937429008

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.435849630153994 \tabularnewline
R-squared & 0.189964900105373 \tabularnewline
Adjusted R-squared & 0.158608702690097 \tabularnewline
F-TEST (value) & 6.05828881574866 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.01781375027032e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.09591757651607 \tabularnewline
Sum Squared Residuals & 1485.62937429008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.435849630153994[/C][/ROW]
[ROW][C]R-squared[/C][C]0.189964900105373[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.158608702690097[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.05828881574866[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.01781375027032e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.09591757651607[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1485.62937429008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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 R0.435849630153994
R-squared0.189964900105373
Adjusted R-squared0.158608702690097
F-TEST (value)6.05828881574866
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.01781375027032e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.09591757651607
Sum Squared Residuals1485.62937429008






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.47770102562995.52229897437008
23934.78961068010784.21038931989224
33035.1699666817546-5.16996668175455
43134.6071188958748-3.60711889587478
53435.4835450090312-1.48354500903117
63532.81035192319692.18964807680309
73935.03540501225943.96459498774056
83434.5047706658346-0.504770665834566
93634.13638245566881.86361754433116
103737.0210768590608-0.0210768590607845
113834.08930873585813.91069126414187
123634.76638959906051.23361040093949
133834.73954056203873.26045943796132
143936.10576248125552.89423751874453
153335.7218919180355-2.72189191803549
163234.6625948745335-2.66259487453355
173634.56389509343961.43610490656043
183838.1140863026149-0.114086302614887
193936.93349046967022.06650953032985
203233.8248007593326-1.82480075933264
213235.4364712892205-3.43647128922046
223133.7771999609545-2.77719996095452
233936.72987469807152.27012530192854
243736.85003726790870.149962732091349
253935.60745993719823.39254006280177
264134.20063639264576.79936360735427
273636.1336818410807-0.133681841080688
283334.8632801446761-1.86328014467608
293334.1084536108259-1.10845361082587
303434.3532896338162-0.353289633816206
313132.7314096047514-1.73140960475136
322733.7152129945787-6.71521299457874
333733.11186749274693.88813250725311
343435.5424915261919-1.54249152619192
353432.55732956438421.44267043561581
363232.7054719222387-0.705471922238722
372932.6279965912516-3.62799659125158
383633.7077146282572.29228537174299
392933.8009576055869-4.80095760558694
403535.1920930013528-0.192093001352822
413734.98329677720792.01670322279213
423434.1794119425389-0.179411942538891
433835.78680938586742.21319061413257
443534.42814626116450.571853738835512
453831.6620559194816.33794408051905
463732.80332077701564.19667922298436
473835.44051552216112.55948447783888
483334.9375146993259-1.93751469932591
493636.8658109258085-0.865810925808546
503833.19949189471894.80050810528106
513235.7160479346342-3.71604793463418
523233.5023310793365-1.50233107933652
533232.7191980060238-0.719198006023831
543435.5465262741148-1.54652627411482
553232.3112439273869-0.311243927386879
563733.84028311238693.1597168876131
573934.54181971701574.45818028298434
582934.4669630896674-5.46696308966738
593735.03312308803051.96687691196951
603534.50220290537010.497797094629929
613031.3516049741403-1.35160497414026
623835.29079278244682.7092072175532
633434.951428178573-0.95142817857303
643134.8766224943991-3.87662249439912
653433.39143465314290.60856534685715
663535.671199727268-0.671199727267961
673634.95532647420831.04467352579171
683032.840172947717-2.84017294771697
693935.92503155424113.07496844575887
703536.5160344736679-1.5160344736679
713833.84982560724824.15017439275178
723135.4166723684154-4.41667236841542
733436.6887908989675-2.68879089896746
743837.69416098692170.305839013078328
753432.78122643055621.21877356944377
763934.22780671250264.77219328749739
773735.56229993201471.43770006798529
783434.0568180645248-0.0568180645248449
792833.3313424313587-5.33134243135875
803732.78122643055624.21877356944377
813335.4723331776216-2.47233317762159
823737.642388988333-0.642388988333005
833535.5424915261919-0.542491526191916
843734.81230812923012.18769187076989
853234.2523228821211-2.25232288212112
863333.5763877235421-0.5763877235421
873836.93367786513221.06632213486784
883335.0772982795239-2.07729827952389
892933.6002308772878-4.6002308772878
903333.1650555356196-0.165055535619596
913135.0304214401929-4.03042144019295
923634.24025320429131.75974679570868
933537.6617212587628-2.66172125876275
943231.896504532850.10349546714999
952932.5347685675495-3.53476856754949
963936.2038401896512.79615981034899
973735.15618965479511.84381034520492
983534.39148438962070.608515610379343
993734.90502747463512.09497252536486
1003235.0430527625291-3.04305276252907
1013835.75907742150422.24092257849577
1023735.0191586656091.98084133439099
1033636.605813832599-0.605813832599007
1043232.0901906299501-0.0901906299501221
1053336.3894153327487-3.3894153327487
1064033.24071214611066.75928785388943
1073835.9300660694822.06993393051801
1084136.54786161414614.45213838585387
1093634.86944541091241.13055458908756
1104336.91729161955166.08270838044842
1113035.1521549068722-5.15215490687217
1123134.23440922089-3.23440922089001
1133237.3012660921552-5.30126609215519
1143233.4182177933625-1.41821779336247
1153734.25835426098442.74164573901556
1163734.67233715357012.32766284642988
1173335.7241683736544-2.72416837365435
1183436.3736926180232-2.37369261802318
1193333.8468856207743-0.846885620774297
1203835.10912542000212.89087457999788
1213335.0993831409655-2.09938314096555
1223132.4378270787596-1.43782707875955
1233835.91106713181962.08893286818035
1243735.64119130728591.3588086927141
1253332.91698474784910.0830152521509419
1263134.2542176267128-3.2542176267128
1273934.78846497548444.21153502451559
1284436.88367364386537.11632635613471
1293336.2137698641496-3.21376986414959
1303533.54537005122431.45462994877568
1313235.1920930013528-3.19209300135282
1322832.3369818257243-4.33698182572426
1334036.0621109216873.93788907831302
1342732.0730009277662-5.07300092776619
1353736.18545383966940.814546160330646
1363233.0899190835929-1.08991908359287
1372830.1950451590133-2.19504515901333
1383434.8243268638855-0.824326863885542
1393033.0631304747632-3.06313047476317
1403533.60840225948231.39159774051766
1413133.2826563565494-2.2826563565494
1423235.6717743023672-3.67177430236724
1433034.8753403374881-4.87534033748812
1443034.7054464509593-4.70544645095934
1453131.6268066187605-0.62680661876051
1464032.60383215467087.39616784532918
1473233.2766304462962-1.27663044629617
1483634.82446331617321.17553668382682
1493234.140407718574-2.14040771857399
1503532.94523487552712.05476512447292
1513834.91662101707193.08337898292811
1524234.91832836620167.08167163379844
1533437.339115126479-3.33911512647899
1543537.4995615122775-2.49956151227748
1553533.84835260823271.1516473917673
1563331.69407045542121.30592954457881
1573634.24025320429131.75974679570868
1583235.2134288228261-3.21342882282615
1593335.5285780469448-2.5285780469448
1603433.73013572616160.269864273838436
1613233.742341856279-1.742341856279
1623434.5202530188888-0.520253018888824

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.4777010256299 & 5.52229897437008 \tabularnewline
2 & 39 & 34.7896106801078 & 4.21038931989224 \tabularnewline
3 & 30 & 35.1699666817546 & -5.16996668175455 \tabularnewline
4 & 31 & 34.6071188958748 & -3.60711889587478 \tabularnewline
5 & 34 & 35.4835450090312 & -1.48354500903117 \tabularnewline
6 & 35 & 32.8103519231969 & 2.18964807680309 \tabularnewline
7 & 39 & 35.0354050122594 & 3.96459498774056 \tabularnewline
8 & 34 & 34.5047706658346 & -0.504770665834566 \tabularnewline
9 & 36 & 34.1363824556688 & 1.86361754433116 \tabularnewline
10 & 37 & 37.0210768590608 & -0.0210768590607845 \tabularnewline
11 & 38 & 34.0893087358581 & 3.91069126414187 \tabularnewline
12 & 36 & 34.7663895990605 & 1.23361040093949 \tabularnewline
13 & 38 & 34.7395405620387 & 3.26045943796132 \tabularnewline
14 & 39 & 36.1057624812555 & 2.89423751874453 \tabularnewline
15 & 33 & 35.7218919180355 & -2.72189191803549 \tabularnewline
16 & 32 & 34.6625948745335 & -2.66259487453355 \tabularnewline
17 & 36 & 34.5638950934396 & 1.43610490656043 \tabularnewline
18 & 38 & 38.1140863026149 & -0.114086302614887 \tabularnewline
19 & 39 & 36.9334904696702 & 2.06650953032985 \tabularnewline
20 & 32 & 33.8248007593326 & -1.82480075933264 \tabularnewline
21 & 32 & 35.4364712892205 & -3.43647128922046 \tabularnewline
22 & 31 & 33.7771999609545 & -2.77719996095452 \tabularnewline
23 & 39 & 36.7298746980715 & 2.27012530192854 \tabularnewline
24 & 37 & 36.8500372679087 & 0.149962732091349 \tabularnewline
25 & 39 & 35.6074599371982 & 3.39254006280177 \tabularnewline
26 & 41 & 34.2006363926457 & 6.79936360735427 \tabularnewline
27 & 36 & 36.1336818410807 & -0.133681841080688 \tabularnewline
28 & 33 & 34.8632801446761 & -1.86328014467608 \tabularnewline
29 & 33 & 34.1084536108259 & -1.10845361082587 \tabularnewline
30 & 34 & 34.3532896338162 & -0.353289633816206 \tabularnewline
31 & 31 & 32.7314096047514 & -1.73140960475136 \tabularnewline
32 & 27 & 33.7152129945787 & -6.71521299457874 \tabularnewline
33 & 37 & 33.1118674927469 & 3.88813250725311 \tabularnewline
34 & 34 & 35.5424915261919 & -1.54249152619192 \tabularnewline
35 & 34 & 32.5573295643842 & 1.44267043561581 \tabularnewline
36 & 32 & 32.7054719222387 & -0.705471922238722 \tabularnewline
37 & 29 & 32.6279965912516 & -3.62799659125158 \tabularnewline
38 & 36 & 33.707714628257 & 2.29228537174299 \tabularnewline
39 & 29 & 33.8009576055869 & -4.80095760558694 \tabularnewline
40 & 35 & 35.1920930013528 & -0.192093001352822 \tabularnewline
41 & 37 & 34.9832967772079 & 2.01670322279213 \tabularnewline
42 & 34 & 34.1794119425389 & -0.179411942538891 \tabularnewline
43 & 38 & 35.7868093858674 & 2.21319061413257 \tabularnewline
44 & 35 & 34.4281462611645 & 0.571853738835512 \tabularnewline
45 & 38 & 31.662055919481 & 6.33794408051905 \tabularnewline
46 & 37 & 32.8033207770156 & 4.19667922298436 \tabularnewline
47 & 38 & 35.4405155221611 & 2.55948447783888 \tabularnewline
48 & 33 & 34.9375146993259 & -1.93751469932591 \tabularnewline
49 & 36 & 36.8658109258085 & -0.865810925808546 \tabularnewline
50 & 38 & 33.1994918947189 & 4.80050810528106 \tabularnewline
51 & 32 & 35.7160479346342 & -3.71604793463418 \tabularnewline
52 & 32 & 33.5023310793365 & -1.50233107933652 \tabularnewline
53 & 32 & 32.7191980060238 & -0.719198006023831 \tabularnewline
54 & 34 & 35.5465262741148 & -1.54652627411482 \tabularnewline
55 & 32 & 32.3112439273869 & -0.311243927386879 \tabularnewline
56 & 37 & 33.8402831123869 & 3.1597168876131 \tabularnewline
57 & 39 & 34.5418197170157 & 4.45818028298434 \tabularnewline
58 & 29 & 34.4669630896674 & -5.46696308966738 \tabularnewline
59 & 37 & 35.0331230880305 & 1.96687691196951 \tabularnewline
60 & 35 & 34.5022029053701 & 0.497797094629929 \tabularnewline
61 & 30 & 31.3516049741403 & -1.35160497414026 \tabularnewline
62 & 38 & 35.2907927824468 & 2.7092072175532 \tabularnewline
63 & 34 & 34.951428178573 & -0.95142817857303 \tabularnewline
64 & 31 & 34.8766224943991 & -3.87662249439912 \tabularnewline
65 & 34 & 33.3914346531429 & 0.60856534685715 \tabularnewline
66 & 35 & 35.671199727268 & -0.671199727267961 \tabularnewline
67 & 36 & 34.9553264742083 & 1.04467352579171 \tabularnewline
68 & 30 & 32.840172947717 & -2.84017294771697 \tabularnewline
69 & 39 & 35.9250315542411 & 3.07496844575887 \tabularnewline
70 & 35 & 36.5160344736679 & -1.5160344736679 \tabularnewline
71 & 38 & 33.8498256072482 & 4.15017439275178 \tabularnewline
72 & 31 & 35.4166723684154 & -4.41667236841542 \tabularnewline
73 & 34 & 36.6887908989675 & -2.68879089896746 \tabularnewline
74 & 38 & 37.6941609869217 & 0.305839013078328 \tabularnewline
75 & 34 & 32.7812264305562 & 1.21877356944377 \tabularnewline
76 & 39 & 34.2278067125026 & 4.77219328749739 \tabularnewline
77 & 37 & 35.5622999320147 & 1.43770006798529 \tabularnewline
78 & 34 & 34.0568180645248 & -0.0568180645248449 \tabularnewline
79 & 28 & 33.3313424313587 & -5.33134243135875 \tabularnewline
80 & 37 & 32.7812264305562 & 4.21877356944377 \tabularnewline
81 & 33 & 35.4723331776216 & -2.47233317762159 \tabularnewline
82 & 37 & 37.642388988333 & -0.642388988333005 \tabularnewline
83 & 35 & 35.5424915261919 & -0.542491526191916 \tabularnewline
84 & 37 & 34.8123081292301 & 2.18769187076989 \tabularnewline
85 & 32 & 34.2523228821211 & -2.25232288212112 \tabularnewline
86 & 33 & 33.5763877235421 & -0.5763877235421 \tabularnewline
87 & 38 & 36.9336778651322 & 1.06632213486784 \tabularnewline
88 & 33 & 35.0772982795239 & -2.07729827952389 \tabularnewline
89 & 29 & 33.6002308772878 & -4.6002308772878 \tabularnewline
90 & 33 & 33.1650555356196 & -0.165055535619596 \tabularnewline
91 & 31 & 35.0304214401929 & -4.03042144019295 \tabularnewline
92 & 36 & 34.2402532042913 & 1.75974679570868 \tabularnewline
93 & 35 & 37.6617212587628 & -2.66172125876275 \tabularnewline
94 & 32 & 31.89650453285 & 0.10349546714999 \tabularnewline
95 & 29 & 32.5347685675495 & -3.53476856754949 \tabularnewline
96 & 39 & 36.203840189651 & 2.79615981034899 \tabularnewline
97 & 37 & 35.1561896547951 & 1.84381034520492 \tabularnewline
98 & 35 & 34.3914843896207 & 0.608515610379343 \tabularnewline
99 & 37 & 34.9050274746351 & 2.09497252536486 \tabularnewline
100 & 32 & 35.0430527625291 & -3.04305276252907 \tabularnewline
101 & 38 & 35.7590774215042 & 2.24092257849577 \tabularnewline
102 & 37 & 35.019158665609 & 1.98084133439099 \tabularnewline
103 & 36 & 36.605813832599 & -0.605813832599007 \tabularnewline
104 & 32 & 32.0901906299501 & -0.0901906299501221 \tabularnewline
105 & 33 & 36.3894153327487 & -3.3894153327487 \tabularnewline
106 & 40 & 33.2407121461106 & 6.75928785388943 \tabularnewline
107 & 38 & 35.930066069482 & 2.06993393051801 \tabularnewline
108 & 41 & 36.5478616141461 & 4.45213838585387 \tabularnewline
109 & 36 & 34.8694454109124 & 1.13055458908756 \tabularnewline
110 & 43 & 36.9172916195516 & 6.08270838044842 \tabularnewline
111 & 30 & 35.1521549068722 & -5.15215490687217 \tabularnewline
112 & 31 & 34.23440922089 & -3.23440922089001 \tabularnewline
113 & 32 & 37.3012660921552 & -5.30126609215519 \tabularnewline
114 & 32 & 33.4182177933625 & -1.41821779336247 \tabularnewline
115 & 37 & 34.2583542609844 & 2.74164573901556 \tabularnewline
116 & 37 & 34.6723371535701 & 2.32766284642988 \tabularnewline
117 & 33 & 35.7241683736544 & -2.72416837365435 \tabularnewline
118 & 34 & 36.3736926180232 & -2.37369261802318 \tabularnewline
119 & 33 & 33.8468856207743 & -0.846885620774297 \tabularnewline
120 & 38 & 35.1091254200021 & 2.89087457999788 \tabularnewline
121 & 33 & 35.0993831409655 & -2.09938314096555 \tabularnewline
122 & 31 & 32.4378270787596 & -1.43782707875955 \tabularnewline
123 & 38 & 35.9110671318196 & 2.08893286818035 \tabularnewline
124 & 37 & 35.6411913072859 & 1.3588086927141 \tabularnewline
125 & 33 & 32.9169847478491 & 0.0830152521509419 \tabularnewline
126 & 31 & 34.2542176267128 & -3.2542176267128 \tabularnewline
127 & 39 & 34.7884649754844 & 4.21153502451559 \tabularnewline
128 & 44 & 36.8836736438653 & 7.11632635613471 \tabularnewline
129 & 33 & 36.2137698641496 & -3.21376986414959 \tabularnewline
130 & 35 & 33.5453700512243 & 1.45462994877568 \tabularnewline
131 & 32 & 35.1920930013528 & -3.19209300135282 \tabularnewline
132 & 28 & 32.3369818257243 & -4.33698182572426 \tabularnewline
133 & 40 & 36.062110921687 & 3.93788907831302 \tabularnewline
134 & 27 & 32.0730009277662 & -5.07300092776619 \tabularnewline
135 & 37 & 36.1854538396694 & 0.814546160330646 \tabularnewline
136 & 32 & 33.0899190835929 & -1.08991908359287 \tabularnewline
137 & 28 & 30.1950451590133 & -2.19504515901333 \tabularnewline
138 & 34 & 34.8243268638855 & -0.824326863885542 \tabularnewline
139 & 30 & 33.0631304747632 & -3.06313047476317 \tabularnewline
140 & 35 & 33.6084022594823 & 1.39159774051766 \tabularnewline
141 & 31 & 33.2826563565494 & -2.2826563565494 \tabularnewline
142 & 32 & 35.6717743023672 & -3.67177430236724 \tabularnewline
143 & 30 & 34.8753403374881 & -4.87534033748812 \tabularnewline
144 & 30 & 34.7054464509593 & -4.70544645095934 \tabularnewline
145 & 31 & 31.6268066187605 & -0.62680661876051 \tabularnewline
146 & 40 & 32.6038321546708 & 7.39616784532918 \tabularnewline
147 & 32 & 33.2766304462962 & -1.27663044629617 \tabularnewline
148 & 36 & 34.8244633161732 & 1.17553668382682 \tabularnewline
149 & 32 & 34.140407718574 & -2.14040771857399 \tabularnewline
150 & 35 & 32.9452348755271 & 2.05476512447292 \tabularnewline
151 & 38 & 34.9166210170719 & 3.08337898292811 \tabularnewline
152 & 42 & 34.9183283662016 & 7.08167163379844 \tabularnewline
153 & 34 & 37.339115126479 & -3.33911512647899 \tabularnewline
154 & 35 & 37.4995615122775 & -2.49956151227748 \tabularnewline
155 & 35 & 33.8483526082327 & 1.1516473917673 \tabularnewline
156 & 33 & 31.6940704554212 & 1.30592954457881 \tabularnewline
157 & 36 & 34.2402532042913 & 1.75974679570868 \tabularnewline
158 & 32 & 35.2134288228261 & -3.21342882282615 \tabularnewline
159 & 33 & 35.5285780469448 & -2.5285780469448 \tabularnewline
160 & 34 & 33.7301357261616 & 0.269864273838436 \tabularnewline
161 & 32 & 33.742341856279 & -1.742341856279 \tabularnewline
162 & 34 & 34.5202530188888 & -0.520253018888824 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&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]41[/C][C]35.4777010256299[/C][C]5.52229897437008[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.7896106801078[/C][C]4.21038931989224[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.1699666817546[/C][C]-5.16996668175455[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.6071188958748[/C][C]-3.60711889587478[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.4835450090312[/C][C]-1.48354500903117[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.8103519231969[/C][C]2.18964807680309[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]35.0354050122594[/C][C]3.96459498774056[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.5047706658346[/C][C]-0.504770665834566[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.1363824556688[/C][C]1.86361754433116[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]37.0210768590608[/C][C]-0.0210768590607845[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.0893087358581[/C][C]3.91069126414187[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7663895990605[/C][C]1.23361040093949[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.7395405620387[/C][C]3.26045943796132[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.1057624812555[/C][C]2.89423751874453[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]35.7218919180355[/C][C]-2.72189191803549[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.6625948745335[/C][C]-2.66259487453355[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.5638950934396[/C][C]1.43610490656043[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]38.1140863026149[/C][C]-0.114086302614887[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.9334904696702[/C][C]2.06650953032985[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.8248007593326[/C][C]-1.82480075933264[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.4364712892205[/C][C]-3.43647128922046[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.7771999609545[/C][C]-2.77719996095452[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.7298746980715[/C][C]2.27012530192854[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.8500372679087[/C][C]0.149962732091349[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.6074599371982[/C][C]3.39254006280177[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.2006363926457[/C][C]6.79936360735427[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]36.1336818410807[/C][C]-0.133681841080688[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]34.8632801446761[/C][C]-1.86328014467608[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.1084536108259[/C][C]-1.10845361082587[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.3532896338162[/C][C]-0.353289633816206[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.7314096047514[/C][C]-1.73140960475136[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.7152129945787[/C][C]-6.71521299457874[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.1118674927469[/C][C]3.88813250725311[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.5424915261919[/C][C]-1.54249152619192[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.5573295643842[/C][C]1.44267043561581[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.7054719222387[/C][C]-0.705471922238722[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.6279965912516[/C][C]-3.62799659125158[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.707714628257[/C][C]2.29228537174299[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.8009576055869[/C][C]-4.80095760558694[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]35.1920930013528[/C][C]-0.192093001352822[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.9832967772079[/C][C]2.01670322279213[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.1794119425389[/C][C]-0.179411942538891[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.7868093858674[/C][C]2.21319061413257[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.4281462611645[/C][C]0.571853738835512[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]31.662055919481[/C][C]6.33794408051905[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]32.8033207770156[/C][C]4.19667922298436[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.4405155221611[/C][C]2.55948447783888[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.9375146993259[/C][C]-1.93751469932591[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.8658109258085[/C][C]-0.865810925808546[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.1994918947189[/C][C]4.80050810528106[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.7160479346342[/C][C]-3.71604793463418[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.5023310793365[/C][C]-1.50233107933652[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.7191980060238[/C][C]-0.719198006023831[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.5465262741148[/C][C]-1.54652627411482[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.3112439273869[/C][C]-0.311243927386879[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.8402831123869[/C][C]3.1597168876131[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.5418197170157[/C][C]4.45818028298434[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.4669630896674[/C][C]-5.46696308966738[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.0331230880305[/C][C]1.96687691196951[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.5022029053701[/C][C]0.497797094629929[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.3516049741403[/C][C]-1.35160497414026[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]35.2907927824468[/C][C]2.7092072175532[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.951428178573[/C][C]-0.95142817857303[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.8766224943991[/C][C]-3.87662249439912[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.3914346531429[/C][C]0.60856534685715[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.671199727268[/C][C]-0.671199727267961[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.9553264742083[/C][C]1.04467352579171[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]32.840172947717[/C][C]-2.84017294771697[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.9250315542411[/C][C]3.07496844575887[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]36.5160344736679[/C][C]-1.5160344736679[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.8498256072482[/C][C]4.15017439275178[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.4166723684154[/C][C]-4.41667236841542[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.6887908989675[/C][C]-2.68879089896746[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6941609869217[/C][C]0.305839013078328[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7812264305562[/C][C]1.21877356944377[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.2278067125026[/C][C]4.77219328749739[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.5622999320147[/C][C]1.43770006798529[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]34.0568180645248[/C][C]-0.0568180645248449[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.3313424313587[/C][C]-5.33134243135875[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.7812264305562[/C][C]4.21877356944377[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.4723331776216[/C][C]-2.47233317762159[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.642388988333[/C][C]-0.642388988333005[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.5424915261919[/C][C]-0.542491526191916[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.8123081292301[/C][C]2.18769187076989[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.2523228821211[/C][C]-2.25232288212112[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.5763877235421[/C][C]-0.5763877235421[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.9336778651322[/C][C]1.06632213486784[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]35.0772982795239[/C][C]-2.07729827952389[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.6002308772878[/C][C]-4.6002308772878[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.1650555356196[/C][C]-0.165055535619596[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]35.0304214401929[/C][C]-4.03042144019295[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.2402532042913[/C][C]1.75974679570868[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.6617212587628[/C][C]-2.66172125876275[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]31.89650453285[/C][C]0.10349546714999[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.5347685675495[/C][C]-3.53476856754949[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.203840189651[/C][C]2.79615981034899[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]35.1561896547951[/C][C]1.84381034520492[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.3914843896207[/C][C]0.608515610379343[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]34.9050274746351[/C][C]2.09497252536486[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.0430527625291[/C][C]-3.04305276252907[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.7590774215042[/C][C]2.24092257849577[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.019158665609[/C][C]1.98084133439099[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.605813832599[/C][C]-0.605813832599007[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.0901906299501[/C][C]-0.0901906299501221[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.3894153327487[/C][C]-3.3894153327487[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.2407121461106[/C][C]6.75928785388943[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.930066069482[/C][C]2.06993393051801[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.5478616141461[/C][C]4.45213838585387[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.8694454109124[/C][C]1.13055458908756[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.9172916195516[/C][C]6.08270838044842[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.1521549068722[/C][C]-5.15215490687217[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.23440922089[/C][C]-3.23440922089001[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.3012660921552[/C][C]-5.30126609215519[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.4182177933625[/C][C]-1.41821779336247[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.2583542609844[/C][C]2.74164573901556[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.6723371535701[/C][C]2.32766284642988[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.7241683736544[/C][C]-2.72416837365435[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.3736926180232[/C][C]-2.37369261802318[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]33.8468856207743[/C][C]-0.846885620774297[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.1091254200021[/C][C]2.89087457999788[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]35.0993831409655[/C][C]-2.09938314096555[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.4378270787596[/C][C]-1.43782707875955[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.9110671318196[/C][C]2.08893286818035[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]35.6411913072859[/C][C]1.3588086927141[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]32.9169847478491[/C][C]0.0830152521509419[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2542176267128[/C][C]-3.2542176267128[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.7884649754844[/C][C]4.21153502451559[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.8836736438653[/C][C]7.11632635613471[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]36.2137698641496[/C][C]-3.21376986414959[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.5453700512243[/C][C]1.45462994877568[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]35.1920930013528[/C][C]-3.19209300135282[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]32.3369818257243[/C][C]-4.33698182572426[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.062110921687[/C][C]3.93788907831302[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.0730009277662[/C][C]-5.07300092776619[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]36.1854538396694[/C][C]0.814546160330646[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.0899190835929[/C][C]-1.08991908359287[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]30.1950451590133[/C][C]-2.19504515901333[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.8243268638855[/C][C]-0.824326863885542[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.0631304747632[/C][C]-3.06313047476317[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.6084022594823[/C][C]1.39159774051766[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]33.2826563565494[/C][C]-2.2826563565494[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.6717743023672[/C][C]-3.67177430236724[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.8753403374881[/C][C]-4.87534033748812[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.7054464509593[/C][C]-4.70544645095934[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.6268066187605[/C][C]-0.62680661876051[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.6038321546708[/C][C]7.39616784532918[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.2766304462962[/C][C]-1.27663044629617[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.8244633161732[/C][C]1.17553668382682[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]34.140407718574[/C][C]-2.14040771857399[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]32.9452348755271[/C][C]2.05476512447292[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]34.9166210170719[/C][C]3.08337898292811[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]34.9183283662016[/C][C]7.08167163379844[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]37.339115126479[/C][C]-3.33911512647899[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.4995615122775[/C][C]-2.49956151227748[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.8483526082327[/C][C]1.1516473917673[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]31.6940704554212[/C][C]1.30592954457881[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]34.2402532042913[/C][C]1.75974679570868[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.2134288228261[/C][C]-3.21342882282615[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.5285780469448[/C][C]-2.5285780469448[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.7301357261616[/C][C]0.269864273838436[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.742341856279[/C][C]-1.742341856279[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.5202530188888[/C][C]-0.520253018888824[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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
14135.47770102562995.52229897437008
23934.78961068010784.21038931989224
33035.1699666817546-5.16996668175455
43134.6071188958748-3.60711889587478
53435.4835450090312-1.48354500903117
63532.81035192319692.18964807680309
73935.03540501225943.96459498774056
83434.5047706658346-0.504770665834566
93634.13638245566881.86361754433116
103737.0210768590608-0.0210768590607845
113834.08930873585813.91069126414187
123634.76638959906051.23361040093949
133834.73954056203873.26045943796132
143936.10576248125552.89423751874453
153335.7218919180355-2.72189191803549
163234.6625948745335-2.66259487453355
173634.56389509343961.43610490656043
183838.1140863026149-0.114086302614887
193936.93349046967022.06650953032985
203233.8248007593326-1.82480075933264
213235.4364712892205-3.43647128922046
223133.7771999609545-2.77719996095452
233936.72987469807152.27012530192854
243736.85003726790870.149962732091349
253935.60745993719823.39254006280177
264134.20063639264576.79936360735427
273636.1336818410807-0.133681841080688
283334.8632801446761-1.86328014467608
293334.1084536108259-1.10845361082587
303434.3532896338162-0.353289633816206
313132.7314096047514-1.73140960475136
322733.7152129945787-6.71521299457874
333733.11186749274693.88813250725311
343435.5424915261919-1.54249152619192
353432.55732956438421.44267043561581
363232.7054719222387-0.705471922238722
372932.6279965912516-3.62799659125158
383633.7077146282572.29228537174299
392933.8009576055869-4.80095760558694
403535.1920930013528-0.192093001352822
413734.98329677720792.01670322279213
423434.1794119425389-0.179411942538891
433835.78680938586742.21319061413257
443534.42814626116450.571853738835512
453831.6620559194816.33794408051905
463732.80332077701564.19667922298436
473835.44051552216112.55948447783888
483334.9375146993259-1.93751469932591
493636.8658109258085-0.865810925808546
503833.19949189471894.80050810528106
513235.7160479346342-3.71604793463418
523233.5023310793365-1.50233107933652
533232.7191980060238-0.719198006023831
543435.5465262741148-1.54652627411482
553232.3112439273869-0.311243927386879
563733.84028311238693.1597168876131
573934.54181971701574.45818028298434
582934.4669630896674-5.46696308966738
593735.03312308803051.96687691196951
603534.50220290537010.497797094629929
613031.3516049741403-1.35160497414026
623835.29079278244682.7092072175532
633434.951428178573-0.95142817857303
643134.8766224943991-3.87662249439912
653433.39143465314290.60856534685715
663535.671199727268-0.671199727267961
673634.95532647420831.04467352579171
683032.840172947717-2.84017294771697
693935.92503155424113.07496844575887
703536.5160344736679-1.5160344736679
713833.84982560724824.15017439275178
723135.4166723684154-4.41667236841542
733436.6887908989675-2.68879089896746
743837.69416098692170.305839013078328
753432.78122643055621.21877356944377
763934.22780671250264.77219328749739
773735.56229993201471.43770006798529
783434.0568180645248-0.0568180645248449
792833.3313424313587-5.33134243135875
803732.78122643055624.21877356944377
813335.4723331776216-2.47233317762159
823737.642388988333-0.642388988333005
833535.5424915261919-0.542491526191916
843734.81230812923012.18769187076989
853234.2523228821211-2.25232288212112
863333.5763877235421-0.5763877235421
873836.93367786513221.06632213486784
883335.0772982795239-2.07729827952389
892933.6002308772878-4.6002308772878
903333.1650555356196-0.165055535619596
913135.0304214401929-4.03042144019295
923634.24025320429131.75974679570868
933537.6617212587628-2.66172125876275
943231.896504532850.10349546714999
952932.5347685675495-3.53476856754949
963936.2038401896512.79615981034899
973735.15618965479511.84381034520492
983534.39148438962070.608515610379343
993734.90502747463512.09497252536486
1003235.0430527625291-3.04305276252907
1013835.75907742150422.24092257849577
1023735.0191586656091.98084133439099
1033636.605813832599-0.605813832599007
1043232.0901906299501-0.0901906299501221
1053336.3894153327487-3.3894153327487
1064033.24071214611066.75928785388943
1073835.9300660694822.06993393051801
1084136.54786161414614.45213838585387
1093634.86944541091241.13055458908756
1104336.91729161955166.08270838044842
1113035.1521549068722-5.15215490687217
1123134.23440922089-3.23440922089001
1133237.3012660921552-5.30126609215519
1143233.4182177933625-1.41821779336247
1153734.25835426098442.74164573901556
1163734.67233715357012.32766284642988
1173335.7241683736544-2.72416837365435
1183436.3736926180232-2.37369261802318
1193333.8468856207743-0.846885620774297
1203835.10912542000212.89087457999788
1213335.0993831409655-2.09938314096555
1223132.4378270787596-1.43782707875955
1233835.91106713181962.08893286818035
1243735.64119130728591.3588086927141
1253332.91698474784910.0830152521509419
1263134.2542176267128-3.2542176267128
1273934.78846497548444.21153502451559
1284436.88367364386537.11632635613471
1293336.2137698641496-3.21376986414959
1303533.54537005122431.45462994877568
1313235.1920930013528-3.19209300135282
1322832.3369818257243-4.33698182572426
1334036.0621109216873.93788907831302
1342732.0730009277662-5.07300092776619
1353736.18545383966940.814546160330646
1363233.0899190835929-1.08991908359287
1372830.1950451590133-2.19504515901333
1383434.8243268638855-0.824326863885542
1393033.0631304747632-3.06313047476317
1403533.60840225948231.39159774051766
1413133.2826563565494-2.2826563565494
1423235.6717743023672-3.67177430236724
1433034.8753403374881-4.87534033748812
1443034.7054464509593-4.70544645095934
1453131.6268066187605-0.62680661876051
1464032.60383215467087.39616784532918
1473233.2766304462962-1.27663044629617
1483634.82446331617321.17553668382682
1493234.140407718574-2.14040771857399
1503532.94523487552712.05476512447292
1513834.91662101707193.08337898292811
1524234.91832836620167.08167163379844
1533437.339115126479-3.33911512647899
1543537.4995615122775-2.49956151227748
1553533.84835260823271.1516473917673
1563331.69407045542121.30592954457881
1573634.24025320429131.75974679570868
1583235.2134288228261-3.21342882282615
1593335.5285780469448-2.5285780469448
1603433.73013572616160.269864273838436
1613233.742341856279-1.742341856279
1623434.5202530188888-0.520253018888824






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8593100875826430.2813798248347130.140689912417357
110.8468225129257460.3063549741485090.153177487074255
120.7584738879709310.4830522240581380.241526112029069
130.7525814975585180.4948370048829650.247418502441482
140.7488131631455430.5023736737089140.251186836854457
150.7910800549768650.417839890046270.208919945023135
160.8635285978388460.2729428043223070.136471402161154
170.8079545041144970.3840909917710060.192045495885503
180.7411670726140970.5176658547718060.258832927385903
190.7185069148464160.5629861703071670.281493085153583
200.6816343528375410.6367312943249180.318365647162459
210.7473465210382540.5053069579234910.252653478961746
220.7321733125403150.535653374919370.267826687459685
230.7046419857438070.5907160285123860.295358014256193
240.6385832927035450.722833414592910.361416707296455
250.6160562277898740.7678875444202510.383943772210126
260.7958906755313010.4082186489373980.204109324468699
270.7471376632270470.5057246735459060.252862336772953
280.7098808879219610.5802382241560770.290119112078039
290.6646549976283240.6706900047433510.335345002371676
300.6104748473129060.7790503053741890.389525152687094
310.5913840826164180.8172318347671630.408615917383582
320.7647819093873560.4704361812252890.235218090612644
330.7765205611619520.4469588776760960.223479438838048
340.7453186945882250.509362610823550.254681305411775
350.7170261221481740.5659477557036530.282973877851826
360.66786156114790.66427687770420.3321384388521
370.6589136338029940.6821727323940130.341086366197007
380.6429174868055930.7141650263888140.357082513194407
390.7269318484339480.5461363031321030.273068151566052
400.6789971591036160.6420056817927680.321002840896384
410.6433112770995650.713377445800870.356688722900435
420.5920775569564070.8158448860871850.407922443043593
430.5496760871368410.9006478257263180.450323912863159
440.4981604873860780.9963209747721550.501839512613922
450.6499031586587330.7001936826825350.350096841341267
460.6853254534766440.6293490930467110.314674546523356
470.6652735303018990.6694529393962020.334726469698101
480.6415795290890190.7168409418219630.358420470910981
490.595741652378870.808516695242260.40425834762113
500.6392518216650230.7214963566699550.360748178334977
510.6671606870685260.6656786258629470.332839312931473
520.6350815604238870.7298368791522270.364918439576113
530.5930044378769350.813991124246130.406995562123065
540.5594159543867150.881168091226570.440584045613285
550.5101078643893880.9797842712212230.489892135610611
560.500570396761840.9988592064763210.49942960323816
570.5355664703904150.928867059219170.464433529609585
580.6534544155612260.6930911688775480.346545584438774
590.6246663270143450.750667345971310.375333672985655
600.5794953894837570.8410092210324860.420504610516243
610.5485516861522370.9028966276955260.451448313847763
620.5339199071540330.9321601856919340.466080092845967
630.4920443843238950.984088768647790.507955615676105
640.5134999373344190.9730001253311620.486500062665581
650.4691035971003660.9382071942007310.530896402899634
660.4251825440003210.8503650880006420.574817455999679
670.3834991301131650.7669982602263310.616500869886835
680.3919403084958380.7838806169916760.608059691504162
690.3912280143686950.782456028737390.608771985631305
700.3567775914017910.7135551828035810.64322240859821
710.386721853431170.7734437068623410.61327814656883
720.4350155453451770.8700310906903550.564984454654823
730.4220475940078910.8440951880157820.577952405992109
740.3773186152538940.7546372305077870.622681384746106
750.3390281315022020.6780562630044040.660971868497798
760.3931515823676270.7863031647352540.606848417632373
770.3578945005274790.7157890010549580.642105499472521
780.3163736343877270.6327472687754540.683626365612273
790.3986994463995960.7973988927991910.601300553600404
800.4362496592126710.8724993184253410.563750340787329
810.4170238474991370.8340476949982740.582976152500863
820.3729709435967910.7459418871935810.62702905640321
830.3310151658835460.6620303317670910.668984834116454
840.3100659287947340.6201318575894680.689934071205266
850.2909435871832430.5818871743664860.709056412816757
860.255101990021550.51020398004310.74489800997845
870.2242028449380150.448405689876030.775797155061985
880.2050612686237160.4101225372474320.794938731376284
890.2439337636858440.4878675273716890.756066236314156
900.2088075972795530.4176151945591060.791192402720447
910.2302102640177690.4604205280355380.769789735982231
920.2064568518559020.4129137037118030.793543148144098
930.1970731347499020.3941462694998040.802926865250098
940.1674074828392590.3348149656785170.832592517160741
950.174813474643020.349626949286040.82518652535698
960.1690201630151360.3380403260302710.830979836984864
970.1504715890783230.3009431781566470.849528410921677
980.127328002457430.254656004914860.87267199754257
990.1142698423064460.2285396846128920.885730157693554
1000.1117108715914590.2234217431829180.88828912840854
1010.1001414675994470.2002829351988940.899858532400553
1020.0894073936863080.1788147873726160.910592606313692
1030.0719853422057580.1439706844115160.928014657794242
1040.05770282227475460.1154056445495090.942297177725245
1050.05862846217097640.1172569243419530.941371537829024
1060.1402804776856540.2805609553713080.859719522314346
1070.1270821214380790.2541642428761580.872917878561921
1080.1599466121620680.3198932243241360.840053387837932
1090.1469436002114640.2938872004229270.853056399788537
1100.2700246583929660.5400493167859330.729975341607034
1110.3324756737094240.6649513474188490.667524326290576
1120.3198642124049720.6397284248099440.680135787595028
1130.3894365570229820.7788731140459650.610563442977017
1140.3468769118797290.6937538237594590.65312308812027
1150.3244704008300810.6489408016601620.675529599169919
1160.3161665533663370.6323331067326750.683833446633663
1170.3195356613940440.6390713227880870.680464338605956
1180.3033516385691650.606703277138330.696648361430835
1190.2671180491520620.5342360983041230.732881950847938
1200.2537842575032850.5075685150065690.746215742496716
1210.232543566879580.4650871337591610.76745643312042
1220.1984905977404560.3969811954809110.801509402259544
1230.1695583239148550.3391166478297110.830441676085145
1240.1392423726937160.2784847453874320.860757627306284
1250.1107044294598930.2214088589197870.889295570540107
1260.1182854408456820.2365708816913650.881714559154318
1270.1890303818825780.3780607637651550.810969618117422
1280.3952335287957760.7904670575915510.604766471204224
1290.3874625130357820.7749250260715650.612537486964218
1300.3327374447542910.6654748895085830.667262555245709
1310.2940394094244970.5880788188489940.705960590575503
1320.3501238900702790.7002477801405590.64987610992972
1330.4734757282971720.9469514565943430.526524271702828
1340.5364997491175730.9270005017648530.463500250882427
1350.4857459931852230.9714919863704470.514254006814777
1360.4354961281326230.8709922562652460.564503871867377
1370.3991172199410230.7982344398820470.600882780058977
1380.3497759982424820.6995519964849630.650224001757518
1390.4901811449168620.9803622898337240.509818855083138
1400.4203095664973510.8406191329947030.579690433502649
1410.6646294490686850.670741101862630.335370550931315
1420.6214638006380260.7570723987239480.378536199361974
1430.5598627489971330.8802745020057330.440137251002867
1440.754256792118770.4914864157624590.245743207881229
1450.7067039860173450.586592027965310.293296013982655
1460.954846587995240.0903068240095210.0451534120047605
1470.9514938298733770.0970123402532450.0485061701266225
1480.9513444239886890.09731115202262290.0486555760113114
1490.9143490307748540.1713019384502920.0856509692251459
1500.8607960332492140.2784079335015720.139203966750786
1510.7959151444866930.4081697110266140.204084855513307
1520.950361844760510.099276310478980.04963815523949

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.859310087582643 & 0.281379824834713 & 0.140689912417357 \tabularnewline
11 & 0.846822512925746 & 0.306354974148509 & 0.153177487074255 \tabularnewline
12 & 0.758473887970931 & 0.483052224058138 & 0.241526112029069 \tabularnewline
13 & 0.752581497558518 & 0.494837004882965 & 0.247418502441482 \tabularnewline
14 & 0.748813163145543 & 0.502373673708914 & 0.251186836854457 \tabularnewline
15 & 0.791080054976865 & 0.41783989004627 & 0.208919945023135 \tabularnewline
16 & 0.863528597838846 & 0.272942804322307 & 0.136471402161154 \tabularnewline
17 & 0.807954504114497 & 0.384090991771006 & 0.192045495885503 \tabularnewline
18 & 0.741167072614097 & 0.517665854771806 & 0.258832927385903 \tabularnewline
19 & 0.718506914846416 & 0.562986170307167 & 0.281493085153583 \tabularnewline
20 & 0.681634352837541 & 0.636731294324918 & 0.318365647162459 \tabularnewline
21 & 0.747346521038254 & 0.505306957923491 & 0.252653478961746 \tabularnewline
22 & 0.732173312540315 & 0.53565337491937 & 0.267826687459685 \tabularnewline
23 & 0.704641985743807 & 0.590716028512386 & 0.295358014256193 \tabularnewline
24 & 0.638583292703545 & 0.72283341459291 & 0.361416707296455 \tabularnewline
25 & 0.616056227789874 & 0.767887544420251 & 0.383943772210126 \tabularnewline
26 & 0.795890675531301 & 0.408218648937398 & 0.204109324468699 \tabularnewline
27 & 0.747137663227047 & 0.505724673545906 & 0.252862336772953 \tabularnewline
28 & 0.709880887921961 & 0.580238224156077 & 0.290119112078039 \tabularnewline
29 & 0.664654997628324 & 0.670690004743351 & 0.335345002371676 \tabularnewline
30 & 0.610474847312906 & 0.779050305374189 & 0.389525152687094 \tabularnewline
31 & 0.591384082616418 & 0.817231834767163 & 0.408615917383582 \tabularnewline
32 & 0.764781909387356 & 0.470436181225289 & 0.235218090612644 \tabularnewline
33 & 0.776520561161952 & 0.446958877676096 & 0.223479438838048 \tabularnewline
34 & 0.745318694588225 & 0.50936261082355 & 0.254681305411775 \tabularnewline
35 & 0.717026122148174 & 0.565947755703653 & 0.282973877851826 \tabularnewline
36 & 0.6678615611479 & 0.6642768777042 & 0.3321384388521 \tabularnewline
37 & 0.658913633802994 & 0.682172732394013 & 0.341086366197007 \tabularnewline
38 & 0.642917486805593 & 0.714165026388814 & 0.357082513194407 \tabularnewline
39 & 0.726931848433948 & 0.546136303132103 & 0.273068151566052 \tabularnewline
40 & 0.678997159103616 & 0.642005681792768 & 0.321002840896384 \tabularnewline
41 & 0.643311277099565 & 0.71337744580087 & 0.356688722900435 \tabularnewline
42 & 0.592077556956407 & 0.815844886087185 & 0.407922443043593 \tabularnewline
43 & 0.549676087136841 & 0.900647825726318 & 0.450323912863159 \tabularnewline
44 & 0.498160487386078 & 0.996320974772155 & 0.501839512613922 \tabularnewline
45 & 0.649903158658733 & 0.700193682682535 & 0.350096841341267 \tabularnewline
46 & 0.685325453476644 & 0.629349093046711 & 0.314674546523356 \tabularnewline
47 & 0.665273530301899 & 0.669452939396202 & 0.334726469698101 \tabularnewline
48 & 0.641579529089019 & 0.716840941821963 & 0.358420470910981 \tabularnewline
49 & 0.59574165237887 & 0.80851669524226 & 0.40425834762113 \tabularnewline
50 & 0.639251821665023 & 0.721496356669955 & 0.360748178334977 \tabularnewline
51 & 0.667160687068526 & 0.665678625862947 & 0.332839312931473 \tabularnewline
52 & 0.635081560423887 & 0.729836879152227 & 0.364918439576113 \tabularnewline
53 & 0.593004437876935 & 0.81399112424613 & 0.406995562123065 \tabularnewline
54 & 0.559415954386715 & 0.88116809122657 & 0.440584045613285 \tabularnewline
55 & 0.510107864389388 & 0.979784271221223 & 0.489892135610611 \tabularnewline
56 & 0.50057039676184 & 0.998859206476321 & 0.49942960323816 \tabularnewline
57 & 0.535566470390415 & 0.92886705921917 & 0.464433529609585 \tabularnewline
58 & 0.653454415561226 & 0.693091168877548 & 0.346545584438774 \tabularnewline
59 & 0.624666327014345 & 0.75066734597131 & 0.375333672985655 \tabularnewline
60 & 0.579495389483757 & 0.841009221032486 & 0.420504610516243 \tabularnewline
61 & 0.548551686152237 & 0.902896627695526 & 0.451448313847763 \tabularnewline
62 & 0.533919907154033 & 0.932160185691934 & 0.466080092845967 \tabularnewline
63 & 0.492044384323895 & 0.98408876864779 & 0.507955615676105 \tabularnewline
64 & 0.513499937334419 & 0.973000125331162 & 0.486500062665581 \tabularnewline
65 & 0.469103597100366 & 0.938207194200731 & 0.530896402899634 \tabularnewline
66 & 0.425182544000321 & 0.850365088000642 & 0.574817455999679 \tabularnewline
67 & 0.383499130113165 & 0.766998260226331 & 0.616500869886835 \tabularnewline
68 & 0.391940308495838 & 0.783880616991676 & 0.608059691504162 \tabularnewline
69 & 0.391228014368695 & 0.78245602873739 & 0.608771985631305 \tabularnewline
70 & 0.356777591401791 & 0.713555182803581 & 0.64322240859821 \tabularnewline
71 & 0.38672185343117 & 0.773443706862341 & 0.61327814656883 \tabularnewline
72 & 0.435015545345177 & 0.870031090690355 & 0.564984454654823 \tabularnewline
73 & 0.422047594007891 & 0.844095188015782 & 0.577952405992109 \tabularnewline
74 & 0.377318615253894 & 0.754637230507787 & 0.622681384746106 \tabularnewline
75 & 0.339028131502202 & 0.678056263004404 & 0.660971868497798 \tabularnewline
76 & 0.393151582367627 & 0.786303164735254 & 0.606848417632373 \tabularnewline
77 & 0.357894500527479 & 0.715789001054958 & 0.642105499472521 \tabularnewline
78 & 0.316373634387727 & 0.632747268775454 & 0.683626365612273 \tabularnewline
79 & 0.398699446399596 & 0.797398892799191 & 0.601300553600404 \tabularnewline
80 & 0.436249659212671 & 0.872499318425341 & 0.563750340787329 \tabularnewline
81 & 0.417023847499137 & 0.834047694998274 & 0.582976152500863 \tabularnewline
82 & 0.372970943596791 & 0.745941887193581 & 0.62702905640321 \tabularnewline
83 & 0.331015165883546 & 0.662030331767091 & 0.668984834116454 \tabularnewline
84 & 0.310065928794734 & 0.620131857589468 & 0.689934071205266 \tabularnewline
85 & 0.290943587183243 & 0.581887174366486 & 0.709056412816757 \tabularnewline
86 & 0.25510199002155 & 0.5102039800431 & 0.74489800997845 \tabularnewline
87 & 0.224202844938015 & 0.44840568987603 & 0.775797155061985 \tabularnewline
88 & 0.205061268623716 & 0.410122537247432 & 0.794938731376284 \tabularnewline
89 & 0.243933763685844 & 0.487867527371689 & 0.756066236314156 \tabularnewline
90 & 0.208807597279553 & 0.417615194559106 & 0.791192402720447 \tabularnewline
91 & 0.230210264017769 & 0.460420528035538 & 0.769789735982231 \tabularnewline
92 & 0.206456851855902 & 0.412913703711803 & 0.793543148144098 \tabularnewline
93 & 0.197073134749902 & 0.394146269499804 & 0.802926865250098 \tabularnewline
94 & 0.167407482839259 & 0.334814965678517 & 0.832592517160741 \tabularnewline
95 & 0.17481347464302 & 0.34962694928604 & 0.82518652535698 \tabularnewline
96 & 0.169020163015136 & 0.338040326030271 & 0.830979836984864 \tabularnewline
97 & 0.150471589078323 & 0.300943178156647 & 0.849528410921677 \tabularnewline
98 & 0.12732800245743 & 0.25465600491486 & 0.87267199754257 \tabularnewline
99 & 0.114269842306446 & 0.228539684612892 & 0.885730157693554 \tabularnewline
100 & 0.111710871591459 & 0.223421743182918 & 0.88828912840854 \tabularnewline
101 & 0.100141467599447 & 0.200282935198894 & 0.899858532400553 \tabularnewline
102 & 0.089407393686308 & 0.178814787372616 & 0.910592606313692 \tabularnewline
103 & 0.071985342205758 & 0.143970684411516 & 0.928014657794242 \tabularnewline
104 & 0.0577028222747546 & 0.115405644549509 & 0.942297177725245 \tabularnewline
105 & 0.0586284621709764 & 0.117256924341953 & 0.941371537829024 \tabularnewline
106 & 0.140280477685654 & 0.280560955371308 & 0.859719522314346 \tabularnewline
107 & 0.127082121438079 & 0.254164242876158 & 0.872917878561921 \tabularnewline
108 & 0.159946612162068 & 0.319893224324136 & 0.840053387837932 \tabularnewline
109 & 0.146943600211464 & 0.293887200422927 & 0.853056399788537 \tabularnewline
110 & 0.270024658392966 & 0.540049316785933 & 0.729975341607034 \tabularnewline
111 & 0.332475673709424 & 0.664951347418849 & 0.667524326290576 \tabularnewline
112 & 0.319864212404972 & 0.639728424809944 & 0.680135787595028 \tabularnewline
113 & 0.389436557022982 & 0.778873114045965 & 0.610563442977017 \tabularnewline
114 & 0.346876911879729 & 0.693753823759459 & 0.65312308812027 \tabularnewline
115 & 0.324470400830081 & 0.648940801660162 & 0.675529599169919 \tabularnewline
116 & 0.316166553366337 & 0.632333106732675 & 0.683833446633663 \tabularnewline
117 & 0.319535661394044 & 0.639071322788087 & 0.680464338605956 \tabularnewline
118 & 0.303351638569165 & 0.60670327713833 & 0.696648361430835 \tabularnewline
119 & 0.267118049152062 & 0.534236098304123 & 0.732881950847938 \tabularnewline
120 & 0.253784257503285 & 0.507568515006569 & 0.746215742496716 \tabularnewline
121 & 0.23254356687958 & 0.465087133759161 & 0.76745643312042 \tabularnewline
122 & 0.198490597740456 & 0.396981195480911 & 0.801509402259544 \tabularnewline
123 & 0.169558323914855 & 0.339116647829711 & 0.830441676085145 \tabularnewline
124 & 0.139242372693716 & 0.278484745387432 & 0.860757627306284 \tabularnewline
125 & 0.110704429459893 & 0.221408858919787 & 0.889295570540107 \tabularnewline
126 & 0.118285440845682 & 0.236570881691365 & 0.881714559154318 \tabularnewline
127 & 0.189030381882578 & 0.378060763765155 & 0.810969618117422 \tabularnewline
128 & 0.395233528795776 & 0.790467057591551 & 0.604766471204224 \tabularnewline
129 & 0.387462513035782 & 0.774925026071565 & 0.612537486964218 \tabularnewline
130 & 0.332737444754291 & 0.665474889508583 & 0.667262555245709 \tabularnewline
131 & 0.294039409424497 & 0.588078818848994 & 0.705960590575503 \tabularnewline
132 & 0.350123890070279 & 0.700247780140559 & 0.64987610992972 \tabularnewline
133 & 0.473475728297172 & 0.946951456594343 & 0.526524271702828 \tabularnewline
134 & 0.536499749117573 & 0.927000501764853 & 0.463500250882427 \tabularnewline
135 & 0.485745993185223 & 0.971491986370447 & 0.514254006814777 \tabularnewline
136 & 0.435496128132623 & 0.870992256265246 & 0.564503871867377 \tabularnewline
137 & 0.399117219941023 & 0.798234439882047 & 0.600882780058977 \tabularnewline
138 & 0.349775998242482 & 0.699551996484963 & 0.650224001757518 \tabularnewline
139 & 0.490181144916862 & 0.980362289833724 & 0.509818855083138 \tabularnewline
140 & 0.420309566497351 & 0.840619132994703 & 0.579690433502649 \tabularnewline
141 & 0.664629449068685 & 0.67074110186263 & 0.335370550931315 \tabularnewline
142 & 0.621463800638026 & 0.757072398723948 & 0.378536199361974 \tabularnewline
143 & 0.559862748997133 & 0.880274502005733 & 0.440137251002867 \tabularnewline
144 & 0.75425679211877 & 0.491486415762459 & 0.245743207881229 \tabularnewline
145 & 0.706703986017345 & 0.58659202796531 & 0.293296013982655 \tabularnewline
146 & 0.95484658799524 & 0.090306824009521 & 0.0451534120047605 \tabularnewline
147 & 0.951493829873377 & 0.097012340253245 & 0.0485061701266225 \tabularnewline
148 & 0.951344423988689 & 0.0973111520226229 & 0.0486555760113114 \tabularnewline
149 & 0.914349030774854 & 0.171301938450292 & 0.0856509692251459 \tabularnewline
150 & 0.860796033249214 & 0.278407933501572 & 0.139203966750786 \tabularnewline
151 & 0.795915144486693 & 0.408169711026614 & 0.204084855513307 \tabularnewline
152 & 0.95036184476051 & 0.09927631047898 & 0.04963815523949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.859310087582643[/C][C]0.281379824834713[/C][C]0.140689912417357[/C][/ROW]
[ROW][C]11[/C][C]0.846822512925746[/C][C]0.306354974148509[/C][C]0.153177487074255[/C][/ROW]
[ROW][C]12[/C][C]0.758473887970931[/C][C]0.483052224058138[/C][C]0.241526112029069[/C][/ROW]
[ROW][C]13[/C][C]0.752581497558518[/C][C]0.494837004882965[/C][C]0.247418502441482[/C][/ROW]
[ROW][C]14[/C][C]0.748813163145543[/C][C]0.502373673708914[/C][C]0.251186836854457[/C][/ROW]
[ROW][C]15[/C][C]0.791080054976865[/C][C]0.41783989004627[/C][C]0.208919945023135[/C][/ROW]
[ROW][C]16[/C][C]0.863528597838846[/C][C]0.272942804322307[/C][C]0.136471402161154[/C][/ROW]
[ROW][C]17[/C][C]0.807954504114497[/C][C]0.384090991771006[/C][C]0.192045495885503[/C][/ROW]
[ROW][C]18[/C][C]0.741167072614097[/C][C]0.517665854771806[/C][C]0.258832927385903[/C][/ROW]
[ROW][C]19[/C][C]0.718506914846416[/C][C]0.562986170307167[/C][C]0.281493085153583[/C][/ROW]
[ROW][C]20[/C][C]0.681634352837541[/C][C]0.636731294324918[/C][C]0.318365647162459[/C][/ROW]
[ROW][C]21[/C][C]0.747346521038254[/C][C]0.505306957923491[/C][C]0.252653478961746[/C][/ROW]
[ROW][C]22[/C][C]0.732173312540315[/C][C]0.53565337491937[/C][C]0.267826687459685[/C][/ROW]
[ROW][C]23[/C][C]0.704641985743807[/C][C]0.590716028512386[/C][C]0.295358014256193[/C][/ROW]
[ROW][C]24[/C][C]0.638583292703545[/C][C]0.72283341459291[/C][C]0.361416707296455[/C][/ROW]
[ROW][C]25[/C][C]0.616056227789874[/C][C]0.767887544420251[/C][C]0.383943772210126[/C][/ROW]
[ROW][C]26[/C][C]0.795890675531301[/C][C]0.408218648937398[/C][C]0.204109324468699[/C][/ROW]
[ROW][C]27[/C][C]0.747137663227047[/C][C]0.505724673545906[/C][C]0.252862336772953[/C][/ROW]
[ROW][C]28[/C][C]0.709880887921961[/C][C]0.580238224156077[/C][C]0.290119112078039[/C][/ROW]
[ROW][C]29[/C][C]0.664654997628324[/C][C]0.670690004743351[/C][C]0.335345002371676[/C][/ROW]
[ROW][C]30[/C][C]0.610474847312906[/C][C]0.779050305374189[/C][C]0.389525152687094[/C][/ROW]
[ROW][C]31[/C][C]0.591384082616418[/C][C]0.817231834767163[/C][C]0.408615917383582[/C][/ROW]
[ROW][C]32[/C][C]0.764781909387356[/C][C]0.470436181225289[/C][C]0.235218090612644[/C][/ROW]
[ROW][C]33[/C][C]0.776520561161952[/C][C]0.446958877676096[/C][C]0.223479438838048[/C][/ROW]
[ROW][C]34[/C][C]0.745318694588225[/C][C]0.50936261082355[/C][C]0.254681305411775[/C][/ROW]
[ROW][C]35[/C][C]0.717026122148174[/C][C]0.565947755703653[/C][C]0.282973877851826[/C][/ROW]
[ROW][C]36[/C][C]0.6678615611479[/C][C]0.6642768777042[/C][C]0.3321384388521[/C][/ROW]
[ROW][C]37[/C][C]0.658913633802994[/C][C]0.682172732394013[/C][C]0.341086366197007[/C][/ROW]
[ROW][C]38[/C][C]0.642917486805593[/C][C]0.714165026388814[/C][C]0.357082513194407[/C][/ROW]
[ROW][C]39[/C][C]0.726931848433948[/C][C]0.546136303132103[/C][C]0.273068151566052[/C][/ROW]
[ROW][C]40[/C][C]0.678997159103616[/C][C]0.642005681792768[/C][C]0.321002840896384[/C][/ROW]
[ROW][C]41[/C][C]0.643311277099565[/C][C]0.71337744580087[/C][C]0.356688722900435[/C][/ROW]
[ROW][C]42[/C][C]0.592077556956407[/C][C]0.815844886087185[/C][C]0.407922443043593[/C][/ROW]
[ROW][C]43[/C][C]0.549676087136841[/C][C]0.900647825726318[/C][C]0.450323912863159[/C][/ROW]
[ROW][C]44[/C][C]0.498160487386078[/C][C]0.996320974772155[/C][C]0.501839512613922[/C][/ROW]
[ROW][C]45[/C][C]0.649903158658733[/C][C]0.700193682682535[/C][C]0.350096841341267[/C][/ROW]
[ROW][C]46[/C][C]0.685325453476644[/C][C]0.629349093046711[/C][C]0.314674546523356[/C][/ROW]
[ROW][C]47[/C][C]0.665273530301899[/C][C]0.669452939396202[/C][C]0.334726469698101[/C][/ROW]
[ROW][C]48[/C][C]0.641579529089019[/C][C]0.716840941821963[/C][C]0.358420470910981[/C][/ROW]
[ROW][C]49[/C][C]0.59574165237887[/C][C]0.80851669524226[/C][C]0.40425834762113[/C][/ROW]
[ROW][C]50[/C][C]0.639251821665023[/C][C]0.721496356669955[/C][C]0.360748178334977[/C][/ROW]
[ROW][C]51[/C][C]0.667160687068526[/C][C]0.665678625862947[/C][C]0.332839312931473[/C][/ROW]
[ROW][C]52[/C][C]0.635081560423887[/C][C]0.729836879152227[/C][C]0.364918439576113[/C][/ROW]
[ROW][C]53[/C][C]0.593004437876935[/C][C]0.81399112424613[/C][C]0.406995562123065[/C][/ROW]
[ROW][C]54[/C][C]0.559415954386715[/C][C]0.88116809122657[/C][C]0.440584045613285[/C][/ROW]
[ROW][C]55[/C][C]0.510107864389388[/C][C]0.979784271221223[/C][C]0.489892135610611[/C][/ROW]
[ROW][C]56[/C][C]0.50057039676184[/C][C]0.998859206476321[/C][C]0.49942960323816[/C][/ROW]
[ROW][C]57[/C][C]0.535566470390415[/C][C]0.92886705921917[/C][C]0.464433529609585[/C][/ROW]
[ROW][C]58[/C][C]0.653454415561226[/C][C]0.693091168877548[/C][C]0.346545584438774[/C][/ROW]
[ROW][C]59[/C][C]0.624666327014345[/C][C]0.75066734597131[/C][C]0.375333672985655[/C][/ROW]
[ROW][C]60[/C][C]0.579495389483757[/C][C]0.841009221032486[/C][C]0.420504610516243[/C][/ROW]
[ROW][C]61[/C][C]0.548551686152237[/C][C]0.902896627695526[/C][C]0.451448313847763[/C][/ROW]
[ROW][C]62[/C][C]0.533919907154033[/C][C]0.932160185691934[/C][C]0.466080092845967[/C][/ROW]
[ROW][C]63[/C][C]0.492044384323895[/C][C]0.98408876864779[/C][C]0.507955615676105[/C][/ROW]
[ROW][C]64[/C][C]0.513499937334419[/C][C]0.973000125331162[/C][C]0.486500062665581[/C][/ROW]
[ROW][C]65[/C][C]0.469103597100366[/C][C]0.938207194200731[/C][C]0.530896402899634[/C][/ROW]
[ROW][C]66[/C][C]0.425182544000321[/C][C]0.850365088000642[/C][C]0.574817455999679[/C][/ROW]
[ROW][C]67[/C][C]0.383499130113165[/C][C]0.766998260226331[/C][C]0.616500869886835[/C][/ROW]
[ROW][C]68[/C][C]0.391940308495838[/C][C]0.783880616991676[/C][C]0.608059691504162[/C][/ROW]
[ROW][C]69[/C][C]0.391228014368695[/C][C]0.78245602873739[/C][C]0.608771985631305[/C][/ROW]
[ROW][C]70[/C][C]0.356777591401791[/C][C]0.713555182803581[/C][C]0.64322240859821[/C][/ROW]
[ROW][C]71[/C][C]0.38672185343117[/C][C]0.773443706862341[/C][C]0.61327814656883[/C][/ROW]
[ROW][C]72[/C][C]0.435015545345177[/C][C]0.870031090690355[/C][C]0.564984454654823[/C][/ROW]
[ROW][C]73[/C][C]0.422047594007891[/C][C]0.844095188015782[/C][C]0.577952405992109[/C][/ROW]
[ROW][C]74[/C][C]0.377318615253894[/C][C]0.754637230507787[/C][C]0.622681384746106[/C][/ROW]
[ROW][C]75[/C][C]0.339028131502202[/C][C]0.678056263004404[/C][C]0.660971868497798[/C][/ROW]
[ROW][C]76[/C][C]0.393151582367627[/C][C]0.786303164735254[/C][C]0.606848417632373[/C][/ROW]
[ROW][C]77[/C][C]0.357894500527479[/C][C]0.715789001054958[/C][C]0.642105499472521[/C][/ROW]
[ROW][C]78[/C][C]0.316373634387727[/C][C]0.632747268775454[/C][C]0.683626365612273[/C][/ROW]
[ROW][C]79[/C][C]0.398699446399596[/C][C]0.797398892799191[/C][C]0.601300553600404[/C][/ROW]
[ROW][C]80[/C][C]0.436249659212671[/C][C]0.872499318425341[/C][C]0.563750340787329[/C][/ROW]
[ROW][C]81[/C][C]0.417023847499137[/C][C]0.834047694998274[/C][C]0.582976152500863[/C][/ROW]
[ROW][C]82[/C][C]0.372970943596791[/C][C]0.745941887193581[/C][C]0.62702905640321[/C][/ROW]
[ROW][C]83[/C][C]0.331015165883546[/C][C]0.662030331767091[/C][C]0.668984834116454[/C][/ROW]
[ROW][C]84[/C][C]0.310065928794734[/C][C]0.620131857589468[/C][C]0.689934071205266[/C][/ROW]
[ROW][C]85[/C][C]0.290943587183243[/C][C]0.581887174366486[/C][C]0.709056412816757[/C][/ROW]
[ROW][C]86[/C][C]0.25510199002155[/C][C]0.5102039800431[/C][C]0.74489800997845[/C][/ROW]
[ROW][C]87[/C][C]0.224202844938015[/C][C]0.44840568987603[/C][C]0.775797155061985[/C][/ROW]
[ROW][C]88[/C][C]0.205061268623716[/C][C]0.410122537247432[/C][C]0.794938731376284[/C][/ROW]
[ROW][C]89[/C][C]0.243933763685844[/C][C]0.487867527371689[/C][C]0.756066236314156[/C][/ROW]
[ROW][C]90[/C][C]0.208807597279553[/C][C]0.417615194559106[/C][C]0.791192402720447[/C][/ROW]
[ROW][C]91[/C][C]0.230210264017769[/C][C]0.460420528035538[/C][C]0.769789735982231[/C][/ROW]
[ROW][C]92[/C][C]0.206456851855902[/C][C]0.412913703711803[/C][C]0.793543148144098[/C][/ROW]
[ROW][C]93[/C][C]0.197073134749902[/C][C]0.394146269499804[/C][C]0.802926865250098[/C][/ROW]
[ROW][C]94[/C][C]0.167407482839259[/C][C]0.334814965678517[/C][C]0.832592517160741[/C][/ROW]
[ROW][C]95[/C][C]0.17481347464302[/C][C]0.34962694928604[/C][C]0.82518652535698[/C][/ROW]
[ROW][C]96[/C][C]0.169020163015136[/C][C]0.338040326030271[/C][C]0.830979836984864[/C][/ROW]
[ROW][C]97[/C][C]0.150471589078323[/C][C]0.300943178156647[/C][C]0.849528410921677[/C][/ROW]
[ROW][C]98[/C][C]0.12732800245743[/C][C]0.25465600491486[/C][C]0.87267199754257[/C][/ROW]
[ROW][C]99[/C][C]0.114269842306446[/C][C]0.228539684612892[/C][C]0.885730157693554[/C][/ROW]
[ROW][C]100[/C][C]0.111710871591459[/C][C]0.223421743182918[/C][C]0.88828912840854[/C][/ROW]
[ROW][C]101[/C][C]0.100141467599447[/C][C]0.200282935198894[/C][C]0.899858532400553[/C][/ROW]
[ROW][C]102[/C][C]0.089407393686308[/C][C]0.178814787372616[/C][C]0.910592606313692[/C][/ROW]
[ROW][C]103[/C][C]0.071985342205758[/C][C]0.143970684411516[/C][C]0.928014657794242[/C][/ROW]
[ROW][C]104[/C][C]0.0577028222747546[/C][C]0.115405644549509[/C][C]0.942297177725245[/C][/ROW]
[ROW][C]105[/C][C]0.0586284621709764[/C][C]0.117256924341953[/C][C]0.941371537829024[/C][/ROW]
[ROW][C]106[/C][C]0.140280477685654[/C][C]0.280560955371308[/C][C]0.859719522314346[/C][/ROW]
[ROW][C]107[/C][C]0.127082121438079[/C][C]0.254164242876158[/C][C]0.872917878561921[/C][/ROW]
[ROW][C]108[/C][C]0.159946612162068[/C][C]0.319893224324136[/C][C]0.840053387837932[/C][/ROW]
[ROW][C]109[/C][C]0.146943600211464[/C][C]0.293887200422927[/C][C]0.853056399788537[/C][/ROW]
[ROW][C]110[/C][C]0.270024658392966[/C][C]0.540049316785933[/C][C]0.729975341607034[/C][/ROW]
[ROW][C]111[/C][C]0.332475673709424[/C][C]0.664951347418849[/C][C]0.667524326290576[/C][/ROW]
[ROW][C]112[/C][C]0.319864212404972[/C][C]0.639728424809944[/C][C]0.680135787595028[/C][/ROW]
[ROW][C]113[/C][C]0.389436557022982[/C][C]0.778873114045965[/C][C]0.610563442977017[/C][/ROW]
[ROW][C]114[/C][C]0.346876911879729[/C][C]0.693753823759459[/C][C]0.65312308812027[/C][/ROW]
[ROW][C]115[/C][C]0.324470400830081[/C][C]0.648940801660162[/C][C]0.675529599169919[/C][/ROW]
[ROW][C]116[/C][C]0.316166553366337[/C][C]0.632333106732675[/C][C]0.683833446633663[/C][/ROW]
[ROW][C]117[/C][C]0.319535661394044[/C][C]0.639071322788087[/C][C]0.680464338605956[/C][/ROW]
[ROW][C]118[/C][C]0.303351638569165[/C][C]0.60670327713833[/C][C]0.696648361430835[/C][/ROW]
[ROW][C]119[/C][C]0.267118049152062[/C][C]0.534236098304123[/C][C]0.732881950847938[/C][/ROW]
[ROW][C]120[/C][C]0.253784257503285[/C][C]0.507568515006569[/C][C]0.746215742496716[/C][/ROW]
[ROW][C]121[/C][C]0.23254356687958[/C][C]0.465087133759161[/C][C]0.76745643312042[/C][/ROW]
[ROW][C]122[/C][C]0.198490597740456[/C][C]0.396981195480911[/C][C]0.801509402259544[/C][/ROW]
[ROW][C]123[/C][C]0.169558323914855[/C][C]0.339116647829711[/C][C]0.830441676085145[/C][/ROW]
[ROW][C]124[/C][C]0.139242372693716[/C][C]0.278484745387432[/C][C]0.860757627306284[/C][/ROW]
[ROW][C]125[/C][C]0.110704429459893[/C][C]0.221408858919787[/C][C]0.889295570540107[/C][/ROW]
[ROW][C]126[/C][C]0.118285440845682[/C][C]0.236570881691365[/C][C]0.881714559154318[/C][/ROW]
[ROW][C]127[/C][C]0.189030381882578[/C][C]0.378060763765155[/C][C]0.810969618117422[/C][/ROW]
[ROW][C]128[/C][C]0.395233528795776[/C][C]0.790467057591551[/C][C]0.604766471204224[/C][/ROW]
[ROW][C]129[/C][C]0.387462513035782[/C][C]0.774925026071565[/C][C]0.612537486964218[/C][/ROW]
[ROW][C]130[/C][C]0.332737444754291[/C][C]0.665474889508583[/C][C]0.667262555245709[/C][/ROW]
[ROW][C]131[/C][C]0.294039409424497[/C][C]0.588078818848994[/C][C]0.705960590575503[/C][/ROW]
[ROW][C]132[/C][C]0.350123890070279[/C][C]0.700247780140559[/C][C]0.64987610992972[/C][/ROW]
[ROW][C]133[/C][C]0.473475728297172[/C][C]0.946951456594343[/C][C]0.526524271702828[/C][/ROW]
[ROW][C]134[/C][C]0.536499749117573[/C][C]0.927000501764853[/C][C]0.463500250882427[/C][/ROW]
[ROW][C]135[/C][C]0.485745993185223[/C][C]0.971491986370447[/C][C]0.514254006814777[/C][/ROW]
[ROW][C]136[/C][C]0.435496128132623[/C][C]0.870992256265246[/C][C]0.564503871867377[/C][/ROW]
[ROW][C]137[/C][C]0.399117219941023[/C][C]0.798234439882047[/C][C]0.600882780058977[/C][/ROW]
[ROW][C]138[/C][C]0.349775998242482[/C][C]0.699551996484963[/C][C]0.650224001757518[/C][/ROW]
[ROW][C]139[/C][C]0.490181144916862[/C][C]0.980362289833724[/C][C]0.509818855083138[/C][/ROW]
[ROW][C]140[/C][C]0.420309566497351[/C][C]0.840619132994703[/C][C]0.579690433502649[/C][/ROW]
[ROW][C]141[/C][C]0.664629449068685[/C][C]0.67074110186263[/C][C]0.335370550931315[/C][/ROW]
[ROW][C]142[/C][C]0.621463800638026[/C][C]0.757072398723948[/C][C]0.378536199361974[/C][/ROW]
[ROW][C]143[/C][C]0.559862748997133[/C][C]0.880274502005733[/C][C]0.440137251002867[/C][/ROW]
[ROW][C]144[/C][C]0.75425679211877[/C][C]0.491486415762459[/C][C]0.245743207881229[/C][/ROW]
[ROW][C]145[/C][C]0.706703986017345[/C][C]0.58659202796531[/C][C]0.293296013982655[/C][/ROW]
[ROW][C]146[/C][C]0.95484658799524[/C][C]0.090306824009521[/C][C]0.0451534120047605[/C][/ROW]
[ROW][C]147[/C][C]0.951493829873377[/C][C]0.097012340253245[/C][C]0.0485061701266225[/C][/ROW]
[ROW][C]148[/C][C]0.951344423988689[/C][C]0.0973111520226229[/C][C]0.0486555760113114[/C][/ROW]
[ROW][C]149[/C][C]0.914349030774854[/C][C]0.171301938450292[/C][C]0.0856509692251459[/C][/ROW]
[ROW][C]150[/C][C]0.860796033249214[/C][C]0.278407933501572[/C][C]0.139203966750786[/C][/ROW]
[ROW][C]151[/C][C]0.795915144486693[/C][C]0.408169711026614[/C][C]0.204084855513307[/C][/ROW]
[ROW][C]152[/C][C]0.95036184476051[/C][C]0.09927631047898[/C][C]0.04963815523949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8593100875826430.2813798248347130.140689912417357
110.8468225129257460.3063549741485090.153177487074255
120.7584738879709310.4830522240581380.241526112029069
130.7525814975585180.4948370048829650.247418502441482
140.7488131631455430.5023736737089140.251186836854457
150.7910800549768650.417839890046270.208919945023135
160.8635285978388460.2729428043223070.136471402161154
170.8079545041144970.3840909917710060.192045495885503
180.7411670726140970.5176658547718060.258832927385903
190.7185069148464160.5629861703071670.281493085153583
200.6816343528375410.6367312943249180.318365647162459
210.7473465210382540.5053069579234910.252653478961746
220.7321733125403150.535653374919370.267826687459685
230.7046419857438070.5907160285123860.295358014256193
240.6385832927035450.722833414592910.361416707296455
250.6160562277898740.7678875444202510.383943772210126
260.7958906755313010.4082186489373980.204109324468699
270.7471376632270470.5057246735459060.252862336772953
280.7098808879219610.5802382241560770.290119112078039
290.6646549976283240.6706900047433510.335345002371676
300.6104748473129060.7790503053741890.389525152687094
310.5913840826164180.8172318347671630.408615917383582
320.7647819093873560.4704361812252890.235218090612644
330.7765205611619520.4469588776760960.223479438838048
340.7453186945882250.509362610823550.254681305411775
350.7170261221481740.5659477557036530.282973877851826
360.66786156114790.66427687770420.3321384388521
370.6589136338029940.6821727323940130.341086366197007
380.6429174868055930.7141650263888140.357082513194407
390.7269318484339480.5461363031321030.273068151566052
400.6789971591036160.6420056817927680.321002840896384
410.6433112770995650.713377445800870.356688722900435
420.5920775569564070.8158448860871850.407922443043593
430.5496760871368410.9006478257263180.450323912863159
440.4981604873860780.9963209747721550.501839512613922
450.6499031586587330.7001936826825350.350096841341267
460.6853254534766440.6293490930467110.314674546523356
470.6652735303018990.6694529393962020.334726469698101
480.6415795290890190.7168409418219630.358420470910981
490.595741652378870.808516695242260.40425834762113
500.6392518216650230.7214963566699550.360748178334977
510.6671606870685260.6656786258629470.332839312931473
520.6350815604238870.7298368791522270.364918439576113
530.5930044378769350.813991124246130.406995562123065
540.5594159543867150.881168091226570.440584045613285
550.5101078643893880.9797842712212230.489892135610611
560.500570396761840.9988592064763210.49942960323816
570.5355664703904150.928867059219170.464433529609585
580.6534544155612260.6930911688775480.346545584438774
590.6246663270143450.750667345971310.375333672985655
600.5794953894837570.8410092210324860.420504610516243
610.5485516861522370.9028966276955260.451448313847763
620.5339199071540330.9321601856919340.466080092845967
630.4920443843238950.984088768647790.507955615676105
640.5134999373344190.9730001253311620.486500062665581
650.4691035971003660.9382071942007310.530896402899634
660.4251825440003210.8503650880006420.574817455999679
670.3834991301131650.7669982602263310.616500869886835
680.3919403084958380.7838806169916760.608059691504162
690.3912280143686950.782456028737390.608771985631305
700.3567775914017910.7135551828035810.64322240859821
710.386721853431170.7734437068623410.61327814656883
720.4350155453451770.8700310906903550.564984454654823
730.4220475940078910.8440951880157820.577952405992109
740.3773186152538940.7546372305077870.622681384746106
750.3390281315022020.6780562630044040.660971868497798
760.3931515823676270.7863031647352540.606848417632373
770.3578945005274790.7157890010549580.642105499472521
780.3163736343877270.6327472687754540.683626365612273
790.3986994463995960.7973988927991910.601300553600404
800.4362496592126710.8724993184253410.563750340787329
810.4170238474991370.8340476949982740.582976152500863
820.3729709435967910.7459418871935810.62702905640321
830.3310151658835460.6620303317670910.668984834116454
840.3100659287947340.6201318575894680.689934071205266
850.2909435871832430.5818871743664860.709056412816757
860.255101990021550.51020398004310.74489800997845
870.2242028449380150.448405689876030.775797155061985
880.2050612686237160.4101225372474320.794938731376284
890.2439337636858440.4878675273716890.756066236314156
900.2088075972795530.4176151945591060.791192402720447
910.2302102640177690.4604205280355380.769789735982231
920.2064568518559020.4129137037118030.793543148144098
930.1970731347499020.3941462694998040.802926865250098
940.1674074828392590.3348149656785170.832592517160741
950.174813474643020.349626949286040.82518652535698
960.1690201630151360.3380403260302710.830979836984864
970.1504715890783230.3009431781566470.849528410921677
980.127328002457430.254656004914860.87267199754257
990.1142698423064460.2285396846128920.885730157693554
1000.1117108715914590.2234217431829180.88828912840854
1010.1001414675994470.2002829351988940.899858532400553
1020.0894073936863080.1788147873726160.910592606313692
1030.0719853422057580.1439706844115160.928014657794242
1040.05770282227475460.1154056445495090.942297177725245
1050.05862846217097640.1172569243419530.941371537829024
1060.1402804776856540.2805609553713080.859719522314346
1070.1270821214380790.2541642428761580.872917878561921
1080.1599466121620680.3198932243241360.840053387837932
1090.1469436002114640.2938872004229270.853056399788537
1100.2700246583929660.5400493167859330.729975341607034
1110.3324756737094240.6649513474188490.667524326290576
1120.3198642124049720.6397284248099440.680135787595028
1130.3894365570229820.7788731140459650.610563442977017
1140.3468769118797290.6937538237594590.65312308812027
1150.3244704008300810.6489408016601620.675529599169919
1160.3161665533663370.6323331067326750.683833446633663
1170.3195356613940440.6390713227880870.680464338605956
1180.3033516385691650.606703277138330.696648361430835
1190.2671180491520620.5342360983041230.732881950847938
1200.2537842575032850.5075685150065690.746215742496716
1210.232543566879580.4650871337591610.76745643312042
1220.1984905977404560.3969811954809110.801509402259544
1230.1695583239148550.3391166478297110.830441676085145
1240.1392423726937160.2784847453874320.860757627306284
1250.1107044294598930.2214088589197870.889295570540107
1260.1182854408456820.2365708816913650.881714559154318
1270.1890303818825780.3780607637651550.810969618117422
1280.3952335287957760.7904670575915510.604766471204224
1290.3874625130357820.7749250260715650.612537486964218
1300.3327374447542910.6654748895085830.667262555245709
1310.2940394094244970.5880788188489940.705960590575503
1320.3501238900702790.7002477801405590.64987610992972
1330.4734757282971720.9469514565943430.526524271702828
1340.5364997491175730.9270005017648530.463500250882427
1350.4857459931852230.9714919863704470.514254006814777
1360.4354961281326230.8709922562652460.564503871867377
1370.3991172199410230.7982344398820470.600882780058977
1380.3497759982424820.6995519964849630.650224001757518
1390.4901811449168620.9803622898337240.509818855083138
1400.4203095664973510.8406191329947030.579690433502649
1410.6646294490686850.670741101862630.335370550931315
1420.6214638006380260.7570723987239480.378536199361974
1430.5598627489971330.8802745020057330.440137251002867
1440.754256792118770.4914864157624590.245743207881229
1450.7067039860173450.586592027965310.293296013982655
1460.954846587995240.0903068240095210.0451534120047605
1470.9514938298733770.0970123402532450.0485061701266225
1480.9513444239886890.09731115202262290.0486555760113114
1490.9143490307748540.1713019384502920.0856509692251459
1500.8607960332492140.2784079335015720.139203966750786
1510.7959151444866930.4081697110266140.204084855513307
1520.950361844760510.099276310478980.04963815523949






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.027972027972028 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155478&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.027972027972028[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155478&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155478&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 level00OK
5% type I error level00OK
10% type I error level40.027972027972028OK


Figures (Output of Computation)

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

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