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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 18:46:25 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t12905378826xof9dbomza62ea.htm/, Retrieved Fri, 26 Apr 2024 02:37:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99570, Retrieved Fri, 26 Apr 2024 02:37:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact214

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 15:10:25] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D    [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 15:44:14] [956e8df26b41c50d9c6c2ec1b6a122a8]
F    D      [Multiple Regression] [WS7 comp 4] [2010-11-23 09:27:22] [dc30d19c3bc2be07fe595ad36c2cf923]
-    D          [Multiple Regression] [ws 7] [2010-11-23 18:46:25] [7131fefee4115a2a717140ef0bdd6369] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	24	14	11	12	24
9	23	25	11	7	8	25
9	25	17	6	17	8	30
9	23	18	12	10	8	19
9	19	18	8	12	9	22
10	29	16	10	12	7	22
10	25	20	10	11	4	25
10	21	16	11	11	11	23
10	22	18	16	12	7	17
10	25	17	11	13	7	21
10	24	23	13	14	12	19
10	18	30	12	16	10	19
10	22	23	8	11	10	15
10	15	18	12	10	8	16
10	22	15	11	11	8	23
10	28	12	4	15	4	27
10	20	21	9	9	9	22
10	12	15	8	11	8	14
10	24	20	8	17	7	22
10	20	31	14	17	11	23
10	21	27	15	11	9	23
10	20	34	16	18	11	21
10	21	21	9	14	13	19
10	23	31	14	10	8	18
10	28	19	11	11	8	20
10	24	16	8	15	9	23
10	24	20	9	15	6	25
10	24	21	9	13	9	19
10	23	22	9	16	9	24
10	23	17	9	13	6	22
10	29	24	10	9	6	25
10	24	25	16	18	16	26
10	18	26	11	18	5	29
10	25	25	8	12	7	32
10	21	17	9	17	9	25
10	26	32	16	9	6	29
10	22	33	11	9	6	28
10	22	13	16	12	5	17
10	22	32	12	18	12	28
10	23	25	12	12	7	29
10	30	29	14	18	10	26
10	23	22	9	14	9	25
10	17	18	10	15	8	14
10	23	17	9	16	5	25
10	23	20	10	10	8	26
10	25	15	12	11	8	20
10	24	20	14	14	10	18
10	24	33	14	9	6	32
10	23	29	10	12	8	25
10	21	23	14	17	7	25
10	24	26	16	5	4	23
10	24	18	9	12	8	21
10	28	20	10	12	8	20
10	16	11	6	6	4	15
10	20	28	8	24	20	30
10	29	26	13	12	8	24
10	27	22	10	12	8	26
10	22	17	8	14	6	24
10	28	12	7	7	4	22
10	16	14	15	13	8	14
10	25	17	9	12	9	24
10	24	21	10	13	6	24
10	28	19	12	14	7	24
10	24	18	13	8	9	24
10	23	10	10	11	5	19
10	30	29	11	9	5	31
10	24	31	8	11	8	22
10	21	19	9	13	8	27
10	25	9	13	10	6	19
10	25	20	11	11	8	25
10	22	28	8	12	7	20
10	23	19	9	9	7	21
10	26	30	9	15	9	27
10	23	29	15	18	11	23
10	25	26	9	15	6	25
10	21	23	10	12	8	20
10	25	13	14	13	6	21
10	24	21	12	14	9	22
10	29	19	12	10	8	23
10	22	28	11	13	6	25
10	27	23	14	13	10	25
10	26	18	6	11	8	17
10	22	21	12	13	8	19
10	24	20	8	16	10	25
10	27	23	14	8	5	19
10	24	21	11	16	7	20
10	24	21	10	11	5	26
10	29	15	14	9	8	23
10	22	28	12	16	14	27
10	21	19	10	12	7	17
10	24	26	14	14	8	17
10	24	10	5	8	6	19
10	23	16	11	9	5	17
10	20	22	10	15	6	22
10	27	19	9	11	10	21
10	26	31	10	21	12	32
10	25	31	16	14	9	21
10	21	29	13	18	12	21
10	21	19	9	12	7	18
10	19	22	10	13	8	18
10	21	23	10	15	10	23
10	21	15	7	12	6	19
10	16	20	9	19	10	20
10	22	18	8	15	10	21
10	29	23	14	11	10	20
10	15	25	14	11	5	17
10	17	21	8	10	7	18
10	15	24	9	13	10	19
10	21	25	14	15	11	22
10	21	17	14	12	6	15
10	19	13	8	12	7	14
10	24	28	8	16	12	18
10	20	21	8	9	11	24
10	17	25	7	18	11	35
10	23	9	6	8	11	29
10	24	16	8	13	5	21
10	14	19	6	17	8	25
10	19	17	11	9	6	20
10	24	25	14	15	9	22
10	13	20	11	8	4	13
10	22	29	11	7	4	26
10	16	14	11	12	7	17
10	19	22	14	14	11	25
10	25	15	8	6	6	20
10	25	19	20	8	7	19
10	23	20	11	17	8	21
10	24	15	8	10	4	22
10	26	20	11	11	8	24
10	26	18	10	14	9	21
10	25	33	14	11	8	26
10	18	22	11	13	11	24
10	21	16	9	12	8	16
10	26	17	9	11	5	23
10	23	16	8	9	4	18
10	23	21	10	12	8	16
10	22	26	13	20	10	26
10	20	18	13	12	6	19
10	13	18	12	13	9	21
10	24	17	8	12	9	21
10	15	22	13	12	13	22
10	14	30	14	9	9	23
10	22	30	12	15	10	29
10	10	24	14	24	20	21
10	24	21	15	7	5	21
10	22	21	13	17	11	23
10	24	29	16	11	6	27
10	19	31	9	17	9	25
10	20	20	9	11	7	21
10	13	16	9	12	9	10
10	20	22	8	14	10	20
10	22	20	7	11	9	26
10	24	28	16	16	8	24
10	29	38	11	21	7	29
10	12	22	9	14	6	19
10	20	20	11	20	13	24
10	21	17	9	13	6	19
10	24	28	14	11	8	24
10	22	22	13	15	10	22
10	20	31	16	19	16	17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Parameter[t] = + 6.67822358754462 + 1.09089428266741Variable[t] -0.0608903034875088S.D.[t] + 0.214416815593605`T-STAT`[t] -0.142213186036547`2-tail`[t] -0.24081553440865`1-tail`[t] + 0.399817197705633MultipleLinearRegression[t] -0.0160680246338639t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Parameter[t] =  +  6.67822358754462 +  1.09089428266741Variable[t] -0.0608903034875088S.D.[t] +  0.214416815593605`T-STAT`[t] -0.142213186036547`2-tail`[t] -0.24081553440865`1-tail`[t] +  0.399817197705633MultipleLinearRegression[t] -0.0160680246338639t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Parameter[t] =  +  6.67822358754462 +  1.09089428266741Variable[t] -0.0608903034875088S.D.[t] +  0.214416815593605`T-STAT`[t] -0.142213186036547`2-tail`[t] -0.24081553440865`1-tail`[t] +  0.399817197705633MultipleLinearRegression[t] -0.0160680246338639t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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
Parameter[t] = + 6.67822358754462 + 1.09089428266741Variable[t] -0.0608903034875088S.D.[t] + 0.214416815593605`T-STAT`[t] -0.142213186036547`2-tail`[t] -0.24081553440865`1-tail`[t] + 0.399817197705633MultipleLinearRegression[t] -0.0160680246338639t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.6782235875446216.6400620.40130.6887420.344371
Variable1.090894282667411.6695450.65340.5144870.257243
S.D.-0.06089030348750880.062235-0.97840.3294440.164722
`T-STAT`0.2144168155936050.111071.93050.0554230.027711
`2-tail`-0.1422131860365470.103898-1.36880.1731020.086551
`1-tail`-0.240815534408650.129923-1.85350.0657590.03288
MultipleLinearRegression0.3998171977056330.0755515.29200
t-0.01606802463386390.006317-2.54380.0119710.005986

\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) & 6.67822358754462 & 16.640062 & 0.4013 & 0.688742 & 0.344371 \tabularnewline
Variable & 1.09089428266741 & 1.669545 & 0.6534 & 0.514487 & 0.257243 \tabularnewline
S.D. & -0.0608903034875088 & 0.062235 & -0.9784 & 0.329444 & 0.164722 \tabularnewline
`T-STAT` & 0.214416815593605 & 0.11107 & 1.9305 & 0.055423 & 0.027711 \tabularnewline
`2-tail` & -0.142213186036547 & 0.103898 & -1.3688 & 0.173102 & 0.086551 \tabularnewline
`1-tail` & -0.24081553440865 & 0.129923 & -1.8535 & 0.065759 & 0.03288 \tabularnewline
MultipleLinearRegression & 0.399817197705633 & 0.075551 & 5.292 & 0 & 0 \tabularnewline
t & -0.0160680246338639 & 0.006317 & -2.5438 & 0.011971 & 0.005986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&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]6.67822358754462[/C][C]16.640062[/C][C]0.4013[/C][C]0.688742[/C][C]0.344371[/C][/ROW]
[ROW][C]Variable[/C][C]1.09089428266741[/C][C]1.669545[/C][C]0.6534[/C][C]0.514487[/C][C]0.257243[/C][/ROW]
[ROW][C]S.D.[/C][C]-0.0608903034875088[/C][C]0.062235[/C][C]-0.9784[/C][C]0.329444[/C][C]0.164722[/C][/ROW]
[ROW][C]`T-STAT`[/C][C]0.214416815593605[/C][C]0.11107[/C][C]1.9305[/C][C]0.055423[/C][C]0.027711[/C][/ROW]
[ROW][C]`2-tail`[/C][C]-0.142213186036547[/C][C]0.103898[/C][C]-1.3688[/C][C]0.173102[/C][C]0.086551[/C][/ROW]
[ROW][C]`1-tail`[/C][C]-0.24081553440865[/C][C]0.129923[/C][C]-1.8535[/C][C]0.065759[/C][C]0.03288[/C][/ROW]
[ROW][C]MultipleLinearRegression[/C][C]0.399817197705633[/C][C]0.075551[/C][C]5.292[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.0160680246338639[/C][C]0.006317[/C][C]-2.5438[/C][C]0.011971[/C][C]0.005986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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)6.6782235875446216.6400620.40130.6887420.344371
Variable1.090894282667411.6695450.65340.5144870.257243
S.D.-0.06089030348750880.062235-0.97840.3294440.164722
`T-STAT`0.2144168155936050.111071.93050.0554230.027711
`2-tail`-0.1422131860365470.103898-1.36880.1731020.086551
`1-tail`-0.240815534408650.129923-1.85350.0657590.03288
MultipleLinearRegression0.3998171977056330.0755515.29200
t-0.01606802463386390.006317-2.54380.0119710.005986






Multiple Linear Regression - Regression Statistics
Multiple R0.504343958942387
R-squared0.25436282892168
Adjusted R-squared0.219796867348513
F-TEST (value)7.3587661776242
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.34744991164837e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.44924263771775
Sum Squared Residuals1796.48849085137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.504343958942387 \tabularnewline
R-squared & 0.25436282892168 \tabularnewline
Adjusted R-squared & 0.219796867348513 \tabularnewline
F-TEST (value) & 7.3587661776242 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 1.34744991164837e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.44924263771775 \tabularnewline
Sum Squared Residuals & 1796.48849085137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.504343958942387[/C][/ROW]
[ROW][C]R-squared[/C][C]0.25436282892168[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.219796867348513[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.3587661776242[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]1.34744991164837e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.44924263771775[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1796.48849085137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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 R0.504343958942387
R-squared0.25436282892168
Adjusted R-squared0.219796867348513
F-TEST (value)7.3587661776242
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.34744991164837e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.44924263771775
Sum Squared Residuals1796.48849085137






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12623.16215352715712.83784647284294
22324.3738768317413-1.3738768317413
32524.34980128520220.650198714797821
42322.15684697813630.843153021863694
51921.9573213777632-2.95732137776317
62924.06439294277634.93560705722375
72525.8688750865717-0.86887508657174
82123.8254419552097-2.82544195520971
92223.1818231669331-1.18182316693311
102523.61161697260471.38838302739529
112421.51311550474192.48688449525806
121821.0536032368461-3.05360323684612
132219.71789721361062.28210278638941
141521.8876094213482-6.88760942134818
152224.4963026894861-2.49630268948612
162825.15566605047052.84433394952951
172023.3137848280649-3.31378482806494
181220.206493389453-8.20649338945301
192422.4720478472161.52795215278396
202022.5092424378522-2.50924243785224
212124.2860626277986-3.2860626277986
222021.7814215278614-1.78142152786138
232120.34359701932750.656402980672482
242322.16382325627040.836176743729601
252822.89260963608055.10739036391946
262422.80574538969051.19425461030955
272424.2826139653374-0.282613965337373
282421.36873221982932.63126778017065
292322.86422032212650.135779677873502
302323.5020555808545-0.502055580854503
312925.04247658466483.95752341533523
322422.96376232939521.03623767060476
331825.6631423949179-7.66314239491789
342526.6358138675096-1.6358138675096
352123.3298677034299-2.32986770342994
362627.3607837179795-1.36078371797954
372225.8119241141845-3.81192411418451
382223.5019330388059-1.5019330388059
392223.3302833032171-1.33028330321707
402326.1976213889639-3.19762138896394
413023.59164846900516.40835153099488
422323.339579571665-0.339579571664994
431719.4821027501849-2.48210275018491
442324.2907308053963-1.29073080539632
452324.8370583965925-1.8370583965925
462523.0131591483131.98684085168696
472421.41356821509062.58643178490936
482427.8776950808154-3.87769508081536
492323.5405299968907-0.540529996890733
502124.2772206597823-3.27722065978226
512426.1366857961263-2.13668579612633
522422.34843365493561.6515663450644
532822.02518464121475.97481535878531
541621.5169173509197-5.5169173509197
552020.4789198645736-0.478919864573648
562923.85415798399145.1458420160086
572724.2380351219382.76196487806198
582223.4951552848874-1.49515528488743
592824.24661093775943.75338906224063
601620.8090179954004-4.80901799540038
612523.22334779542661.77665220457342
622423.75836878962570.241631210374307
632823.90988628270894.09011371729114
642424.5407734245581-0.540773424558093
652322.90611397204030.0938860279597222
663027.0297797412782.97022025872196
672421.64345290823862.3565470917614
682124.2871449575035-3.28714495750352
692523.4473802754011.55261972459896
702524.10774421259730.892255787402678
712221.06081967312650.93918032687349
722322.63363795128910.366362048710895
732623.01176958948992.98823041051014
742321.83555334415571.16444665584434
752523.14600696198691.85399303801315
762121.4729491641733-0.472949164173296
772523.66268651727531.33731348272467
782422.26581984199731.73418015800268
792923.58101790059895.41898209940106
802223.6571462351028-1.65714623510282
812723.62551803705273.37448196294728
822619.76608686435296.23391313564712
832221.36905684615630.630943153843704
842422.04684442194241.95315557805762
852723.07748635450943.92251364549055
862421.32042913066572.67957086933426
872424.6815444756721-0.681544475672105
882924.2510137100684.74898628993204
892222.1734213910241-0.173421391024074
902120.5329219745410.467078025459013
912420.42304718138723.57695281861276
922421.58601725265892.41398274734108
932321.79007625362251.20992374637753
942022.0992409303702-2.09924093037018
952721.25720040941125.74279959058881
962623.21909180412.78090819589998
972521.80947440374763.1905255962524
982119.98063719193581.0193628080642
992120.57371013494820.426289865051761
1001920.2063592950003-1.20635929500026
1012121.3624295145167-0.362429514516656
1022120.98086637592380.0191336240762447
1031619.5302432228548-3.53024322285476
1042220.39020893145411.60979106854587
1052921.52522582938497.47477417061509
1061521.3920032767024-6.39200327670238
1071720.3933948873818-3.3933948873818
1081519.6598038042491-4.65980380424906
1092121.3291392407309-0.329139240730865
1102120.63219049021050.367809509789468
1111918.93255005385080.0674499461492113
1122417.82946585153746.17053414846261
1132021.8748409742144-1.87484097421436
1141724.5188654204699-7.5188654204699
1152324.2858541101742-1.28585411017424
1162421.80767728693912.19232271306087
1171421.4880741641059-7.48807416410592
1181922.2861213929966-3.28612139299661
1192421.65009006320952.34990993679047
1201319.8964383041808-6.89643830418078
1212224.6721943043691-2.67219430436911
1221620.5376135192885-4.53761351928849
1231922.6285225854727-3.62852258547274
1242522.09488296349732.90511703650273
1252523.48319640784931.51680359215074
1262320.75538692605912.24461307394087
1272422.75909160967811.24090839032194
1282622.77598158612763.22401841387242
1292620.80037066723995.19962933276006
1302523.39515643371431.60484356628568
1311821.5991239299519-3.59912392995193
1322119.18568630267331.81431369732666
1332622.77210814775393.2278918522461
1342321.12866952896751.87133047103249
1352319.04744752692783.95255247307219
1362221.74901385158390.250986148416128
1372021.5223154968376-1.52231549683762
1381321.2268052627589-8.22680526275892
1392420.55617346527473.44382653472531
1401520.7442930612423-5.74429306124235
1411422.2452383177519-8.2452383177519
1422223.1051451975367-1.10514519753668
1431016.9966410249546-6.99664102495459
1442423.40751790512790.592482094872088
1452220.89522557790071.10477442209927
1462424.6919111512327-0.691911151232678
1471920.6777846956121-1.67778469561206
1482021.0671514035548-1.06715140355484
1491316.2728111632552-3.27281116325521
1502019.14991457267730.850085427322731
1512222.1075686181769-0.107568618176906
1522422.26424471480011.73575528519993
1532922.09602517007726.90397482992283
1541219.8635042296644-7.86350422966438
1552019.85814857464110.141851425358917
1562120.27803288387080.72196711612925
1572422.46613689062631.53386310937373
1582220.75087566294911.2491243370509
1592016.81721341458223.18278658541778

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 23.1621535271571 & 2.83784647284294 \tabularnewline
2 & 23 & 24.3738768317413 & -1.3738768317413 \tabularnewline
3 & 25 & 24.3498012852022 & 0.650198714797821 \tabularnewline
4 & 23 & 22.1568469781363 & 0.843153021863694 \tabularnewline
5 & 19 & 21.9573213777632 & -2.95732137776317 \tabularnewline
6 & 29 & 24.0643929427763 & 4.93560705722375 \tabularnewline
7 & 25 & 25.8688750865717 & -0.86887508657174 \tabularnewline
8 & 21 & 23.8254419552097 & -2.82544195520971 \tabularnewline
9 & 22 & 23.1818231669331 & -1.18182316693311 \tabularnewline
10 & 25 & 23.6116169726047 & 1.38838302739529 \tabularnewline
11 & 24 & 21.5131155047419 & 2.48688449525806 \tabularnewline
12 & 18 & 21.0536032368461 & -3.05360323684612 \tabularnewline
13 & 22 & 19.7178972136106 & 2.28210278638941 \tabularnewline
14 & 15 & 21.8876094213482 & -6.88760942134818 \tabularnewline
15 & 22 & 24.4963026894861 & -2.49630268948612 \tabularnewline
16 & 28 & 25.1556660504705 & 2.84433394952951 \tabularnewline
17 & 20 & 23.3137848280649 & -3.31378482806494 \tabularnewline
18 & 12 & 20.206493389453 & -8.20649338945301 \tabularnewline
19 & 24 & 22.472047847216 & 1.52795215278396 \tabularnewline
20 & 20 & 22.5092424378522 & -2.50924243785224 \tabularnewline
21 & 21 & 24.2860626277986 & -3.2860626277986 \tabularnewline
22 & 20 & 21.7814215278614 & -1.78142152786138 \tabularnewline
23 & 21 & 20.3435970193275 & 0.656402980672482 \tabularnewline
24 & 23 & 22.1638232562704 & 0.836176743729601 \tabularnewline
25 & 28 & 22.8926096360805 & 5.10739036391946 \tabularnewline
26 & 24 & 22.8057453896905 & 1.19425461030955 \tabularnewline
27 & 24 & 24.2826139653374 & -0.282613965337373 \tabularnewline
28 & 24 & 21.3687322198293 & 2.63126778017065 \tabularnewline
29 & 23 & 22.8642203221265 & 0.135779677873502 \tabularnewline
30 & 23 & 23.5020555808545 & -0.502055580854503 \tabularnewline
31 & 29 & 25.0424765846648 & 3.95752341533523 \tabularnewline
32 & 24 & 22.9637623293952 & 1.03623767060476 \tabularnewline
33 & 18 & 25.6631423949179 & -7.66314239491789 \tabularnewline
34 & 25 & 26.6358138675096 & -1.6358138675096 \tabularnewline
35 & 21 & 23.3298677034299 & -2.32986770342994 \tabularnewline
36 & 26 & 27.3607837179795 & -1.36078371797954 \tabularnewline
37 & 22 & 25.8119241141845 & -3.81192411418451 \tabularnewline
38 & 22 & 23.5019330388059 & -1.5019330388059 \tabularnewline
39 & 22 & 23.3302833032171 & -1.33028330321707 \tabularnewline
40 & 23 & 26.1976213889639 & -3.19762138896394 \tabularnewline
41 & 30 & 23.5916484690051 & 6.40835153099488 \tabularnewline
42 & 23 & 23.339579571665 & -0.339579571664994 \tabularnewline
43 & 17 & 19.4821027501849 & -2.48210275018491 \tabularnewline
44 & 23 & 24.2907308053963 & -1.29073080539632 \tabularnewline
45 & 23 & 24.8370583965925 & -1.8370583965925 \tabularnewline
46 & 25 & 23.013159148313 & 1.98684085168696 \tabularnewline
47 & 24 & 21.4135682150906 & 2.58643178490936 \tabularnewline
48 & 24 & 27.8776950808154 & -3.87769508081536 \tabularnewline
49 & 23 & 23.5405299968907 & -0.540529996890733 \tabularnewline
50 & 21 & 24.2772206597823 & -3.27722065978226 \tabularnewline
51 & 24 & 26.1366857961263 & -2.13668579612633 \tabularnewline
52 & 24 & 22.3484336549356 & 1.6515663450644 \tabularnewline
53 & 28 & 22.0251846412147 & 5.97481535878531 \tabularnewline
54 & 16 & 21.5169173509197 & -5.5169173509197 \tabularnewline
55 & 20 & 20.4789198645736 & -0.478919864573648 \tabularnewline
56 & 29 & 23.8541579839914 & 5.1458420160086 \tabularnewline
57 & 27 & 24.238035121938 & 2.76196487806198 \tabularnewline
58 & 22 & 23.4951552848874 & -1.49515528488743 \tabularnewline
59 & 28 & 24.2466109377594 & 3.75338906224063 \tabularnewline
60 & 16 & 20.8090179954004 & -4.80901799540038 \tabularnewline
61 & 25 & 23.2233477954266 & 1.77665220457342 \tabularnewline
62 & 24 & 23.7583687896257 & 0.241631210374307 \tabularnewline
63 & 28 & 23.9098862827089 & 4.09011371729114 \tabularnewline
64 & 24 & 24.5407734245581 & -0.540773424558093 \tabularnewline
65 & 23 & 22.9061139720403 & 0.0938860279597222 \tabularnewline
66 & 30 & 27.029779741278 & 2.97022025872196 \tabularnewline
67 & 24 & 21.6434529082386 & 2.3565470917614 \tabularnewline
68 & 21 & 24.2871449575035 & -3.28714495750352 \tabularnewline
69 & 25 & 23.447380275401 & 1.55261972459896 \tabularnewline
70 & 25 & 24.1077442125973 & 0.892255787402678 \tabularnewline
71 & 22 & 21.0608196731265 & 0.93918032687349 \tabularnewline
72 & 23 & 22.6336379512891 & 0.366362048710895 \tabularnewline
73 & 26 & 23.0117695894899 & 2.98823041051014 \tabularnewline
74 & 23 & 21.8355533441557 & 1.16444665584434 \tabularnewline
75 & 25 & 23.1460069619869 & 1.85399303801315 \tabularnewline
76 & 21 & 21.4729491641733 & -0.472949164173296 \tabularnewline
77 & 25 & 23.6626865172753 & 1.33731348272467 \tabularnewline
78 & 24 & 22.2658198419973 & 1.73418015800268 \tabularnewline
79 & 29 & 23.5810179005989 & 5.41898209940106 \tabularnewline
80 & 22 & 23.6571462351028 & -1.65714623510282 \tabularnewline
81 & 27 & 23.6255180370527 & 3.37448196294728 \tabularnewline
82 & 26 & 19.7660868643529 & 6.23391313564712 \tabularnewline
83 & 22 & 21.3690568461563 & 0.630943153843704 \tabularnewline
84 & 24 & 22.0468444219424 & 1.95315557805762 \tabularnewline
85 & 27 & 23.0774863545094 & 3.92251364549055 \tabularnewline
86 & 24 & 21.3204291306657 & 2.67957086933426 \tabularnewline
87 & 24 & 24.6815444756721 & -0.681544475672105 \tabularnewline
88 & 29 & 24.251013710068 & 4.74898628993204 \tabularnewline
89 & 22 & 22.1734213910241 & -0.173421391024074 \tabularnewline
90 & 21 & 20.532921974541 & 0.467078025459013 \tabularnewline
91 & 24 & 20.4230471813872 & 3.57695281861276 \tabularnewline
92 & 24 & 21.5860172526589 & 2.41398274734108 \tabularnewline
93 & 23 & 21.7900762536225 & 1.20992374637753 \tabularnewline
94 & 20 & 22.0992409303702 & -2.09924093037018 \tabularnewline
95 & 27 & 21.2572004094112 & 5.74279959058881 \tabularnewline
96 & 26 & 23.2190918041 & 2.78090819589998 \tabularnewline
97 & 25 & 21.8094744037476 & 3.1905255962524 \tabularnewline
98 & 21 & 19.9806371919358 & 1.0193628080642 \tabularnewline
99 & 21 & 20.5737101349482 & 0.426289865051761 \tabularnewline
100 & 19 & 20.2063592950003 & -1.20635929500026 \tabularnewline
101 & 21 & 21.3624295145167 & -0.362429514516656 \tabularnewline
102 & 21 & 20.9808663759238 & 0.0191336240762447 \tabularnewline
103 & 16 & 19.5302432228548 & -3.53024322285476 \tabularnewline
104 & 22 & 20.3902089314541 & 1.60979106854587 \tabularnewline
105 & 29 & 21.5252258293849 & 7.47477417061509 \tabularnewline
106 & 15 & 21.3920032767024 & -6.39200327670238 \tabularnewline
107 & 17 & 20.3933948873818 & -3.3933948873818 \tabularnewline
108 & 15 & 19.6598038042491 & -4.65980380424906 \tabularnewline
109 & 21 & 21.3291392407309 & -0.329139240730865 \tabularnewline
110 & 21 & 20.6321904902105 & 0.367809509789468 \tabularnewline
111 & 19 & 18.9325500538508 & 0.0674499461492113 \tabularnewline
112 & 24 & 17.8294658515374 & 6.17053414846261 \tabularnewline
113 & 20 & 21.8748409742144 & -1.87484097421436 \tabularnewline
114 & 17 & 24.5188654204699 & -7.5188654204699 \tabularnewline
115 & 23 & 24.2858541101742 & -1.28585411017424 \tabularnewline
116 & 24 & 21.8076772869391 & 2.19232271306087 \tabularnewline
117 & 14 & 21.4880741641059 & -7.48807416410592 \tabularnewline
118 & 19 & 22.2861213929966 & -3.28612139299661 \tabularnewline
119 & 24 & 21.6500900632095 & 2.34990993679047 \tabularnewline
120 & 13 & 19.8964383041808 & -6.89643830418078 \tabularnewline
121 & 22 & 24.6721943043691 & -2.67219430436911 \tabularnewline
122 & 16 & 20.5376135192885 & -4.53761351928849 \tabularnewline
123 & 19 & 22.6285225854727 & -3.62852258547274 \tabularnewline
124 & 25 & 22.0948829634973 & 2.90511703650273 \tabularnewline
125 & 25 & 23.4831964078493 & 1.51680359215074 \tabularnewline
126 & 23 & 20.7553869260591 & 2.24461307394087 \tabularnewline
127 & 24 & 22.7590916096781 & 1.24090839032194 \tabularnewline
128 & 26 & 22.7759815861276 & 3.22401841387242 \tabularnewline
129 & 26 & 20.8003706672399 & 5.19962933276006 \tabularnewline
130 & 25 & 23.3951564337143 & 1.60484356628568 \tabularnewline
131 & 18 & 21.5991239299519 & -3.59912392995193 \tabularnewline
132 & 21 & 19.1856863026733 & 1.81431369732666 \tabularnewline
133 & 26 & 22.7721081477539 & 3.2278918522461 \tabularnewline
134 & 23 & 21.1286695289675 & 1.87133047103249 \tabularnewline
135 & 23 & 19.0474475269278 & 3.95255247307219 \tabularnewline
136 & 22 & 21.7490138515839 & 0.250986148416128 \tabularnewline
137 & 20 & 21.5223154968376 & -1.52231549683762 \tabularnewline
138 & 13 & 21.2268052627589 & -8.22680526275892 \tabularnewline
139 & 24 & 20.5561734652747 & 3.44382653472531 \tabularnewline
140 & 15 & 20.7442930612423 & -5.74429306124235 \tabularnewline
141 & 14 & 22.2452383177519 & -8.2452383177519 \tabularnewline
142 & 22 & 23.1051451975367 & -1.10514519753668 \tabularnewline
143 & 10 & 16.9966410249546 & -6.99664102495459 \tabularnewline
144 & 24 & 23.4075179051279 & 0.592482094872088 \tabularnewline
145 & 22 & 20.8952255779007 & 1.10477442209927 \tabularnewline
146 & 24 & 24.6919111512327 & -0.691911151232678 \tabularnewline
147 & 19 & 20.6777846956121 & -1.67778469561206 \tabularnewline
148 & 20 & 21.0671514035548 & -1.06715140355484 \tabularnewline
149 & 13 & 16.2728111632552 & -3.27281116325521 \tabularnewline
150 & 20 & 19.1499145726773 & 0.850085427322731 \tabularnewline
151 & 22 & 22.1075686181769 & -0.107568618176906 \tabularnewline
152 & 24 & 22.2642447148001 & 1.73575528519993 \tabularnewline
153 & 29 & 22.0960251700772 & 6.90397482992283 \tabularnewline
154 & 12 & 19.8635042296644 & -7.86350422966438 \tabularnewline
155 & 20 & 19.8581485746411 & 0.141851425358917 \tabularnewline
156 & 21 & 20.2780328838708 & 0.72196711612925 \tabularnewline
157 & 24 & 22.4661368906263 & 1.53386310937373 \tabularnewline
158 & 22 & 20.7508756629491 & 1.2491243370509 \tabularnewline
159 & 20 & 16.8172134145822 & 3.18278658541778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&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]26[/C][C]23.1621535271571[/C][C]2.83784647284294[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]24.3738768317413[/C][C]-1.3738768317413[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]24.3498012852022[/C][C]0.650198714797821[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]22.1568469781363[/C][C]0.843153021863694[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]21.9573213777632[/C][C]-2.95732137776317[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.0643929427763[/C][C]4.93560705722375[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]25.8688750865717[/C][C]-0.86887508657174[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]23.8254419552097[/C][C]-2.82544195520971[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]23.1818231669331[/C][C]-1.18182316693311[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]23.6116169726047[/C][C]1.38838302739529[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]21.5131155047419[/C][C]2.48688449525806[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]21.0536032368461[/C][C]-3.05360323684612[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]19.7178972136106[/C][C]2.28210278638941[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]21.8876094213482[/C][C]-6.88760942134818[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]24.4963026894861[/C][C]-2.49630268948612[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]25.1556660504705[/C][C]2.84433394952951[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]23.3137848280649[/C][C]-3.31378482806494[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]20.206493389453[/C][C]-8.20649338945301[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]22.472047847216[/C][C]1.52795215278396[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]22.5092424378522[/C][C]-2.50924243785224[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]24.2860626277986[/C][C]-3.2860626277986[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]21.7814215278614[/C][C]-1.78142152786138[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.3435970193275[/C][C]0.656402980672482[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]22.1638232562704[/C][C]0.836176743729601[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]22.8926096360805[/C][C]5.10739036391946[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]22.8057453896905[/C][C]1.19425461030955[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]24.2826139653374[/C][C]-0.282613965337373[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]21.3687322198293[/C][C]2.63126778017065[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.8642203221265[/C][C]0.135779677873502[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]23.5020555808545[/C][C]-0.502055580854503[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]25.0424765846648[/C][C]3.95752341533523[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]22.9637623293952[/C][C]1.03623767060476[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.6631423949179[/C][C]-7.66314239491789[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]26.6358138675096[/C][C]-1.6358138675096[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]23.3298677034299[/C][C]-2.32986770342994[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]27.3607837179795[/C][C]-1.36078371797954[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.8119241141845[/C][C]-3.81192411418451[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]23.5019330388059[/C][C]-1.5019330388059[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]23.3302833032171[/C][C]-1.33028330321707[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]26.1976213889639[/C][C]-3.19762138896394[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]23.5916484690051[/C][C]6.40835153099488[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]23.339579571665[/C][C]-0.339579571664994[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]19.4821027501849[/C][C]-2.48210275018491[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]24.2907308053963[/C][C]-1.29073080539632[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.8370583965925[/C][C]-1.8370583965925[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]23.013159148313[/C][C]1.98684085168696[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]21.4135682150906[/C][C]2.58643178490936[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.8776950808154[/C][C]-3.87769508081536[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]23.5405299968907[/C][C]-0.540529996890733[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]24.2772206597823[/C][C]-3.27722065978226[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]26.1366857961263[/C][C]-2.13668579612633[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]22.3484336549356[/C][C]1.6515663450644[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]22.0251846412147[/C][C]5.97481535878531[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.5169173509197[/C][C]-5.5169173509197[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]20.4789198645736[/C][C]-0.478919864573648[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.8541579839914[/C][C]5.1458420160086[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]24.238035121938[/C][C]2.76196487806198[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.4951552848874[/C][C]-1.49515528488743[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]24.2466109377594[/C][C]3.75338906224063[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.8090179954004[/C][C]-4.80901799540038[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]23.2233477954266[/C][C]1.77665220457342[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.7583687896257[/C][C]0.241631210374307[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.9098862827089[/C][C]4.09011371729114[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.5407734245581[/C][C]-0.540773424558093[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.9061139720403[/C][C]0.0938860279597222[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]27.029779741278[/C][C]2.97022025872196[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.6434529082386[/C][C]2.3565470917614[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.2871449575035[/C][C]-3.28714495750352[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.447380275401[/C][C]1.55261972459896[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]24.1077442125973[/C][C]0.892255787402678[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]21.0608196731265[/C][C]0.93918032687349[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.6336379512891[/C][C]0.366362048710895[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]23.0117695894899[/C][C]2.98823041051014[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.8355533441557[/C][C]1.16444665584434[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.1460069619869[/C][C]1.85399303801315[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.4729491641733[/C][C]-0.472949164173296[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6626865172753[/C][C]1.33731348272467[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.2658198419973[/C][C]1.73418015800268[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5810179005989[/C][C]5.41898209940106[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.6571462351028[/C][C]-1.65714623510282[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.6255180370527[/C][C]3.37448196294728[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.7660868643529[/C][C]6.23391313564712[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3690568461563[/C][C]0.630943153843704[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.0468444219424[/C][C]1.95315557805762[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.0774863545094[/C][C]3.92251364549055[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.3204291306657[/C][C]2.67957086933426[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.6815444756721[/C][C]-0.681544475672105[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.251013710068[/C][C]4.74898628993204[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.1734213910241[/C][C]-0.173421391024074[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.532921974541[/C][C]0.467078025459013[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4230471813872[/C][C]3.57695281861276[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.5860172526589[/C][C]2.41398274734108[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.7900762536225[/C][C]1.20992374637753[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.0992409303702[/C][C]-2.09924093037018[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.2572004094112[/C][C]5.74279959058881[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.2190918041[/C][C]2.78090819589998[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.8094744037476[/C][C]3.1905255962524[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]19.9806371919358[/C][C]1.0193628080642[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.5737101349482[/C][C]0.426289865051761[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.2063592950003[/C][C]-1.20635929500026[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.3624295145167[/C][C]-0.362429514516656[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]20.9808663759238[/C][C]0.0191336240762447[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.5302432228548[/C][C]-3.53024322285476[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.3902089314541[/C][C]1.60979106854587[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.5252258293849[/C][C]7.47477417061509[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.3920032767024[/C][C]-6.39200327670238[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.3933948873818[/C][C]-3.3933948873818[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.6598038042491[/C][C]-4.65980380424906[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.3291392407309[/C][C]-0.329139240730865[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]20.6321904902105[/C][C]0.367809509789468[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]18.9325500538508[/C][C]0.0674499461492113[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]17.8294658515374[/C][C]6.17053414846261[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]21.8748409742144[/C][C]-1.87484097421436[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]24.5188654204699[/C][C]-7.5188654204699[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]24.2858541101742[/C][C]-1.28585411017424[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]21.8076772869391[/C][C]2.19232271306087[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]21.4880741641059[/C][C]-7.48807416410592[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.2861213929966[/C][C]-3.28612139299661[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]21.6500900632095[/C][C]2.34990993679047[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]19.8964383041808[/C][C]-6.89643830418078[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]24.6721943043691[/C][C]-2.67219430436911[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]20.5376135192885[/C][C]-4.53761351928849[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]22.6285225854727[/C][C]-3.62852258547274[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.0948829634973[/C][C]2.90511703650273[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]23.4831964078493[/C][C]1.51680359215074[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]20.7553869260591[/C][C]2.24461307394087[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]22.7590916096781[/C][C]1.24090839032194[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]22.7759815861276[/C][C]3.22401841387242[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]20.8003706672399[/C][C]5.19962933276006[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]23.3951564337143[/C][C]1.60484356628568[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]21.5991239299519[/C][C]-3.59912392995193[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.1856863026733[/C][C]1.81431369732666[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]22.7721081477539[/C][C]3.2278918522461[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.1286695289675[/C][C]1.87133047103249[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.0474475269278[/C][C]3.95255247307219[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]21.7490138515839[/C][C]0.250986148416128[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]21.5223154968376[/C][C]-1.52231549683762[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]21.2268052627589[/C][C]-8.22680526275892[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]20.5561734652747[/C][C]3.44382653472531[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]20.7442930612423[/C][C]-5.74429306124235[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]22.2452383177519[/C][C]-8.2452383177519[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]23.1051451975367[/C][C]-1.10514519753668[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]16.9966410249546[/C][C]-6.99664102495459[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]23.4075179051279[/C][C]0.592482094872088[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]20.8952255779007[/C][C]1.10477442209927[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]24.6919111512327[/C][C]-0.691911151232678[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]20.6777846956121[/C][C]-1.67778469561206[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]21.0671514035548[/C][C]-1.06715140355484[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]16.2728111632552[/C][C]-3.27281116325521[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]19.1499145726773[/C][C]0.850085427322731[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]22.1075686181769[/C][C]-0.107568618176906[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]22.2642447148001[/C][C]1.73575528519993[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]22.0960251700772[/C][C]6.90397482992283[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]19.8635042296644[/C][C]-7.86350422966438[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.8581485746411[/C][C]0.141851425358917[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.2780328838708[/C][C]0.72196711612925[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]22.4661368906263[/C][C]1.53386310937373[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]20.7508756629491[/C][C]1.2491243370509[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]16.8172134145822[/C][C]3.18278658541778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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
12623.16215352715712.83784647284294
22324.3738768317413-1.3738768317413
32524.34980128520220.650198714797821
42322.15684697813630.843153021863694
51921.9573213777632-2.95732137776317
62924.06439294277634.93560705722375
72525.8688750865717-0.86887508657174
82123.8254419552097-2.82544195520971
92223.1818231669331-1.18182316693311
102523.61161697260471.38838302739529
112421.51311550474192.48688449525806
121821.0536032368461-3.05360323684612
132219.71789721361062.28210278638941
141521.8876094213482-6.88760942134818
152224.4963026894861-2.49630268948612
162825.15566605047052.84433394952951
172023.3137848280649-3.31378482806494
181220.206493389453-8.20649338945301
192422.4720478472161.52795215278396
202022.5092424378522-2.50924243785224
212124.2860626277986-3.2860626277986
222021.7814215278614-1.78142152786138
232120.34359701932750.656402980672482
242322.16382325627040.836176743729601
252822.89260963608055.10739036391946
262422.80574538969051.19425461030955
272424.2826139653374-0.282613965337373
282421.36873221982932.63126778017065
292322.86422032212650.135779677873502
302323.5020555808545-0.502055580854503
312925.04247658466483.95752341533523
322422.96376232939521.03623767060476
331825.6631423949179-7.66314239491789
342526.6358138675096-1.6358138675096
352123.3298677034299-2.32986770342994
362627.3607837179795-1.36078371797954
372225.8119241141845-3.81192411418451
382223.5019330388059-1.5019330388059
392223.3302833032171-1.33028330321707
402326.1976213889639-3.19762138896394
413023.59164846900516.40835153099488
422323.339579571665-0.339579571664994
431719.4821027501849-2.48210275018491
442324.2907308053963-1.29073080539632
452324.8370583965925-1.8370583965925
462523.0131591483131.98684085168696
472421.41356821509062.58643178490936
482427.8776950808154-3.87769508081536
492323.5405299968907-0.540529996890733
502124.2772206597823-3.27722065978226
512426.1366857961263-2.13668579612633
522422.34843365493561.6515663450644
532822.02518464121475.97481535878531
541621.5169173509197-5.5169173509197
552020.4789198645736-0.478919864573648
562923.85415798399145.1458420160086
572724.2380351219382.76196487806198
582223.4951552848874-1.49515528488743
592824.24661093775943.75338906224063
601620.8090179954004-4.80901799540038
612523.22334779542661.77665220457342
622423.75836878962570.241631210374307
632823.90988628270894.09011371729114
642424.5407734245581-0.540773424558093
652322.90611397204030.0938860279597222
663027.0297797412782.97022025872196
672421.64345290823862.3565470917614
682124.2871449575035-3.28714495750352
692523.4473802754011.55261972459896
702524.10774421259730.892255787402678
712221.06081967312650.93918032687349
722322.63363795128910.366362048710895
732623.01176958948992.98823041051014
742321.83555334415571.16444665584434
752523.14600696198691.85399303801315
762121.4729491641733-0.472949164173296
772523.66268651727531.33731348272467
782422.26581984199731.73418015800268
792923.58101790059895.41898209940106
802223.6571462351028-1.65714623510282
812723.62551803705273.37448196294728
822619.76608686435296.23391313564712
832221.36905684615630.630943153843704
842422.04684442194241.95315557805762
852723.07748635450943.92251364549055
862421.32042913066572.67957086933426
872424.6815444756721-0.681544475672105
882924.2510137100684.74898628993204
892222.1734213910241-0.173421391024074
902120.5329219745410.467078025459013
912420.42304718138723.57695281861276
922421.58601725265892.41398274734108
932321.79007625362251.20992374637753
942022.0992409303702-2.09924093037018
952721.25720040941125.74279959058881
962623.21909180412.78090819589998
972521.80947440374763.1905255962524
982119.98063719193581.0193628080642
992120.57371013494820.426289865051761
1001920.2063592950003-1.20635929500026
1012121.3624295145167-0.362429514516656
1022120.98086637592380.0191336240762447
1031619.5302432228548-3.53024322285476
1042220.39020893145411.60979106854587
1052921.52522582938497.47477417061509
1061521.3920032767024-6.39200327670238
1071720.3933948873818-3.3933948873818
1081519.6598038042491-4.65980380424906
1092121.3291392407309-0.329139240730865
1102120.63219049021050.367809509789468
1111918.93255005385080.0674499461492113
1122417.82946585153746.17053414846261
1132021.8748409742144-1.87484097421436
1141724.5188654204699-7.5188654204699
1152324.2858541101742-1.28585411017424
1162421.80767728693912.19232271306087
1171421.4880741641059-7.48807416410592
1181922.2861213929966-3.28612139299661
1192421.65009006320952.34990993679047
1201319.8964383041808-6.89643830418078
1212224.6721943043691-2.67219430436911
1221620.5376135192885-4.53761351928849
1231922.6285225854727-3.62852258547274
1242522.09488296349732.90511703650273
1252523.48319640784931.51680359215074
1262320.75538692605912.24461307394087
1272422.75909160967811.24090839032194
1282622.77598158612763.22401841387242
1292620.80037066723995.19962933276006
1302523.39515643371431.60484356628568
1311821.5991239299519-3.59912392995193
1322119.18568630267331.81431369732666
1332622.77210814775393.2278918522461
1342321.12866952896751.87133047103249
1352319.04744752692783.95255247307219
1362221.74901385158390.250986148416128
1372021.5223154968376-1.52231549683762
1381321.2268052627589-8.22680526275892
1392420.55617346527473.44382653472531
1401520.7442930612423-5.74429306124235
1411422.2452383177519-8.2452383177519
1422223.1051451975367-1.10514519753668
1431016.9966410249546-6.99664102495459
1442423.40751790512790.592482094872088
1452220.89522557790071.10477442209927
1462424.6919111512327-0.691911151232678
1471920.6777846956121-1.67778469561206
1482021.0671514035548-1.06715140355484
1491316.2728111632552-3.27281116325521
1502019.14991457267730.850085427322731
1512222.1075686181769-0.107568618176906
1522422.26424471480011.73575528519993
1532922.09602517007726.90397482992283
1541219.8635042296644-7.86350422966438
1552019.85814857464110.141851425358917
1562120.27803288387080.72196711612925
1572422.46613689062631.53386310937373
1582220.75087566294911.2491243370509
1592016.81721341458223.18278658541778






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3549197886053450.709839577210690.645080211394655
120.4719164566980020.9438329133960050.528083543301998
130.4077372273622130.8154744547244270.592262772637786
140.3036743406512410.6073486813024820.696325659348759
150.5008357235950750.998328552809850.499164276404925
160.6004402485757070.7991195028485860.399559751424293
170.5061895913117720.9876208173764560.493810408688228
180.5955634918273280.8088730163453450.404436508172672
190.5904047334324860.8191905331350280.409595266567514
200.5091644093816690.9816711812366620.490835590618331
210.4500813580152640.9001627160305280.549918641984736
220.3747471741177460.7494943482354910.625252825882254
230.3870389998471780.7740779996943560.612961000152822
240.4872748084027210.9745496168054410.512725191597279
250.7001726677181120.5996546645637760.299827332281888
260.6390331318615220.7219337362769560.360966868138478
270.5736397528763110.8527204942473790.426360247123689
280.5579572989444640.8840854021110730.442042701055536
290.4918517531149060.9837035062298120.508148246885094
300.4270967743950210.8541935487900410.572903225604979
310.4263684220418660.8527368440837310.573631577958134
320.363574902249910.727149804499820.63642509775009
330.6185367063515680.7629265872968640.381463293648432
340.581391977051940.837216045896120.41860802294806
350.5447268223119730.9105463553760540.455273177688027
360.4887008817200250.977401763440050.511299118279975
370.4739740734102790.9479481468205580.526025926589721
380.4226611136591910.8453222273183830.577338886340809
390.372538606214630.745077212429260.62746139378537
400.3477060646928810.6954121293857620.652293935307119
410.524496840865870.951006318268260.47550315913413
420.4698436569480270.9396873138960550.530156343051973
430.4360078912459130.8720157824918270.563992108754087
440.387783054411890.7755661088237810.61221694558811
450.3472617260356180.6945234520712360.652738273964382
460.3230958270943180.6461916541886370.676904172905682
470.3014481512429850.602896302485970.698551848757015
480.2968788140391410.5937576280782810.703121185960859
490.2590555129926830.5181110259853660.740944487007317
500.256624033905840.5132480678116790.74337596609416
510.2361128904272420.4722257808544840.763887109572758
520.206391706240350.4127834124806990.79360829375965
530.2777687684555640.5555375369111270.722231231544436
540.3559677413940770.7119354827881540.644032258605923
550.3394362076862970.6788724153725930.660563792313703
560.4016344482217150.803268896443430.598365551778285
570.3779713644651720.7559427289303450.622028635534828
580.3465549066634640.6931098133269290.653445093336536
590.3461664627694190.6923329255388380.653833537230581
600.4234398175223220.8468796350446440.576560182477678
610.3791917105059450.758383421011890.620808289494055
620.3369521344662720.6739042689325440.663047865533728
630.3394454175331240.6788908350662470.660554582466876
640.3052883105343780.6105766210687550.694711689465622
650.2659873424675390.5319746849350780.734012657532461
660.2495279946974870.4990559893949740.750472005302513
670.2213893676464310.4427787352928620.778610632353569
680.2436382092156180.4872764184312360.756361790784382
690.210155767961260.420311535922520.78984423203874
700.1774716906975820.3549433813951640.822528309302418
710.148776901146770.297553802293540.85122309885323
720.1236831082826130.2473662165652260.876316891717387
730.1076579889894920.2153159779789830.892342011010508
740.08754289046573230.1750857809314650.912457109534268
750.07124854544520620.1424970908904120.928751454554794
760.05951155317531720.1190231063506340.940488446824683
770.04714125077915430.09428250155830860.952858749220846
780.03673011863325530.07346023726651050.963269881366745
790.04174200201368180.08348400402736360.958257997986318
800.03824101181685720.07648202363371430.961758988183143
810.03201733480635110.06403466961270230.967982665193649
820.04223284033593980.08446568067187960.95776715966406
830.03295245186569710.06590490373139430.967047548134303
840.02575891058130410.05151782116260820.974241089418696
850.02356509472913480.04713018945826960.976434905270865
860.01859405663974460.03718811327948930.981405943360255
870.0151210998071830.03024219961436610.984878900192817
880.01548032701830720.03096065403661450.984519672981693
890.0129878819700140.0259757639400280.987012118029986
900.009802251923316420.01960450384663280.990197748076684
910.008332817191194040.01666563438238810.991667182808806
920.006784351260456410.01356870252091280.993215648739544
930.00498570699695850.0099714139939170.995014293003041
940.004735011343198910.009470022686397820.9952649886568
950.007017622301945590.01403524460389120.992982377698054
960.005705680702298980.0114113614045980.994294319297701
970.004858297631387390.009716595262774780.995141702368613
980.003709824064891840.007419648129783680.996290175935108
990.002792435160482580.005584870320965160.997207564839517
1000.002312291502123110.004624583004246220.997687708497877
1010.001812016914379980.003624033828759970.99818798308562
1020.001325473903971470.002650947807942930.998674526096029
1030.0016113856524260.0032227713048520.998388614347574
1040.001252669475850970.002505338951701950.998747330524149
1050.005495307817376340.01099061563475270.994504692182624
1060.01312153206325580.02624306412651160.986878467936744
1070.01374202532511010.02748405065022030.98625797467489
1080.01857356976407310.03714713952814610.981426430235927
1090.01438446893145930.02876893786291860.98561553106854
1100.01039184141641690.02078368283283390.989608158583583
1110.007474239515151140.01494847903030230.992525760484849
1120.02186030425490210.04372060850980420.978139695745098
1130.02195896014113740.04391792028227470.978041039858863
1140.054676084978930.109352169957860.94532391502107
1150.04688926585099560.09377853170199120.953110734149004
1160.03935805691945930.07871611383891860.96064194308054
1170.1025239485141660.2050478970283310.897476051485834
1180.096328145671370.192656291342740.90367185432863
1190.08602058769620280.1720411753924060.913979412303797
1200.1491044011898810.2982088023797610.85089559881012
1210.1460654492191740.2921308984383480.853934550780826
1220.1850908034884050.370181606976810.814909196511595
1230.1841339221542560.3682678443085130.815866077845744
1240.1744011046192420.3488022092384840.825598895380758
1250.1509588145727630.3019176291455260.849041185427237
1260.1211508876999490.2423017753998990.878849112300051
1270.09477429155194780.1895485831038960.905225708448052
1280.09242629017886620.1848525803577320.907573709821134
1290.1302268451496470.2604536902992950.869773154850352
1300.1126165170909610.2252330341819220.887383482909039
1310.09419782492921260.1883956498584250.905802175070787
1320.0836187819287560.1672375638575120.916381218071244
1330.08194509887316450.1638901977463290.918054901126836
1340.0729533502478810.1459067004957620.92704664975212
1350.1715120388681580.3430240777363170.828487961131842
1360.1366285669904160.2732571339808330.863371433009584
1370.1142398417889620.2284796835779230.885760158211038
1380.1609553139341110.3219106278682210.83904468606589
1390.391954458218440.783908916436880.60804554178156
1400.3377506037674250.675501207534850.662249396232575
1410.4814081405487280.9628162810974570.518591859451272
1420.390283213132020.780566426264040.60971678686798
1430.5534319600366770.8931360799266450.446568039963323
1440.4997416852793470.9994833705586930.500258314720653
1450.4030767509351620.8061535018703230.596923249064838
1460.2986133808187430.5972267616374870.701386619181257
1470.2901804484839530.5803608969679060.709819551516047
1480.1729023858608620.3458047717217240.827097614139138

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.354919788605345 & 0.70983957721069 & 0.645080211394655 \tabularnewline
12 & 0.471916456698002 & 0.943832913396005 & 0.528083543301998 \tabularnewline
13 & 0.407737227362213 & 0.815474454724427 & 0.592262772637786 \tabularnewline
14 & 0.303674340651241 & 0.607348681302482 & 0.696325659348759 \tabularnewline
15 & 0.500835723595075 & 0.99832855280985 & 0.499164276404925 \tabularnewline
16 & 0.600440248575707 & 0.799119502848586 & 0.399559751424293 \tabularnewline
17 & 0.506189591311772 & 0.987620817376456 & 0.493810408688228 \tabularnewline
18 & 0.595563491827328 & 0.808873016345345 & 0.404436508172672 \tabularnewline
19 & 0.590404733432486 & 0.819190533135028 & 0.409595266567514 \tabularnewline
20 & 0.509164409381669 & 0.981671181236662 & 0.490835590618331 \tabularnewline
21 & 0.450081358015264 & 0.900162716030528 & 0.549918641984736 \tabularnewline
22 & 0.374747174117746 & 0.749494348235491 & 0.625252825882254 \tabularnewline
23 & 0.387038999847178 & 0.774077999694356 & 0.612961000152822 \tabularnewline
24 & 0.487274808402721 & 0.974549616805441 & 0.512725191597279 \tabularnewline
25 & 0.700172667718112 & 0.599654664563776 & 0.299827332281888 \tabularnewline
26 & 0.639033131861522 & 0.721933736276956 & 0.360966868138478 \tabularnewline
27 & 0.573639752876311 & 0.852720494247379 & 0.426360247123689 \tabularnewline
28 & 0.557957298944464 & 0.884085402111073 & 0.442042701055536 \tabularnewline
29 & 0.491851753114906 & 0.983703506229812 & 0.508148246885094 \tabularnewline
30 & 0.427096774395021 & 0.854193548790041 & 0.572903225604979 \tabularnewline
31 & 0.426368422041866 & 0.852736844083731 & 0.573631577958134 \tabularnewline
32 & 0.36357490224991 & 0.72714980449982 & 0.63642509775009 \tabularnewline
33 & 0.618536706351568 & 0.762926587296864 & 0.381463293648432 \tabularnewline
34 & 0.58139197705194 & 0.83721604589612 & 0.41860802294806 \tabularnewline
35 & 0.544726822311973 & 0.910546355376054 & 0.455273177688027 \tabularnewline
36 & 0.488700881720025 & 0.97740176344005 & 0.511299118279975 \tabularnewline
37 & 0.473974073410279 & 0.947948146820558 & 0.526025926589721 \tabularnewline
38 & 0.422661113659191 & 0.845322227318383 & 0.577338886340809 \tabularnewline
39 & 0.37253860621463 & 0.74507721242926 & 0.62746139378537 \tabularnewline
40 & 0.347706064692881 & 0.695412129385762 & 0.652293935307119 \tabularnewline
41 & 0.52449684086587 & 0.95100631826826 & 0.47550315913413 \tabularnewline
42 & 0.469843656948027 & 0.939687313896055 & 0.530156343051973 \tabularnewline
43 & 0.436007891245913 & 0.872015782491827 & 0.563992108754087 \tabularnewline
44 & 0.38778305441189 & 0.775566108823781 & 0.61221694558811 \tabularnewline
45 & 0.347261726035618 & 0.694523452071236 & 0.652738273964382 \tabularnewline
46 & 0.323095827094318 & 0.646191654188637 & 0.676904172905682 \tabularnewline
47 & 0.301448151242985 & 0.60289630248597 & 0.698551848757015 \tabularnewline
48 & 0.296878814039141 & 0.593757628078281 & 0.703121185960859 \tabularnewline
49 & 0.259055512992683 & 0.518111025985366 & 0.740944487007317 \tabularnewline
50 & 0.25662403390584 & 0.513248067811679 & 0.74337596609416 \tabularnewline
51 & 0.236112890427242 & 0.472225780854484 & 0.763887109572758 \tabularnewline
52 & 0.20639170624035 & 0.412783412480699 & 0.79360829375965 \tabularnewline
53 & 0.277768768455564 & 0.555537536911127 & 0.722231231544436 \tabularnewline
54 & 0.355967741394077 & 0.711935482788154 & 0.644032258605923 \tabularnewline
55 & 0.339436207686297 & 0.678872415372593 & 0.660563792313703 \tabularnewline
56 & 0.401634448221715 & 0.80326889644343 & 0.598365551778285 \tabularnewline
57 & 0.377971364465172 & 0.755942728930345 & 0.622028635534828 \tabularnewline
58 & 0.346554906663464 & 0.693109813326929 & 0.653445093336536 \tabularnewline
59 & 0.346166462769419 & 0.692332925538838 & 0.653833537230581 \tabularnewline
60 & 0.423439817522322 & 0.846879635044644 & 0.576560182477678 \tabularnewline
61 & 0.379191710505945 & 0.75838342101189 & 0.620808289494055 \tabularnewline
62 & 0.336952134466272 & 0.673904268932544 & 0.663047865533728 \tabularnewline
63 & 0.339445417533124 & 0.678890835066247 & 0.660554582466876 \tabularnewline
64 & 0.305288310534378 & 0.610576621068755 & 0.694711689465622 \tabularnewline
65 & 0.265987342467539 & 0.531974684935078 & 0.734012657532461 \tabularnewline
66 & 0.249527994697487 & 0.499055989394974 & 0.750472005302513 \tabularnewline
67 & 0.221389367646431 & 0.442778735292862 & 0.778610632353569 \tabularnewline
68 & 0.243638209215618 & 0.487276418431236 & 0.756361790784382 \tabularnewline
69 & 0.21015576796126 & 0.42031153592252 & 0.78984423203874 \tabularnewline
70 & 0.177471690697582 & 0.354943381395164 & 0.822528309302418 \tabularnewline
71 & 0.14877690114677 & 0.29755380229354 & 0.85122309885323 \tabularnewline
72 & 0.123683108282613 & 0.247366216565226 & 0.876316891717387 \tabularnewline
73 & 0.107657988989492 & 0.215315977978983 & 0.892342011010508 \tabularnewline
74 & 0.0875428904657323 & 0.175085780931465 & 0.912457109534268 \tabularnewline
75 & 0.0712485454452062 & 0.142497090890412 & 0.928751454554794 \tabularnewline
76 & 0.0595115531753172 & 0.119023106350634 & 0.940488446824683 \tabularnewline
77 & 0.0471412507791543 & 0.0942825015583086 & 0.952858749220846 \tabularnewline
78 & 0.0367301186332553 & 0.0734602372665105 & 0.963269881366745 \tabularnewline
79 & 0.0417420020136818 & 0.0834840040273636 & 0.958257997986318 \tabularnewline
80 & 0.0382410118168572 & 0.0764820236337143 & 0.961758988183143 \tabularnewline
81 & 0.0320173348063511 & 0.0640346696127023 & 0.967982665193649 \tabularnewline
82 & 0.0422328403359398 & 0.0844656806718796 & 0.95776715966406 \tabularnewline
83 & 0.0329524518656971 & 0.0659049037313943 & 0.967047548134303 \tabularnewline
84 & 0.0257589105813041 & 0.0515178211626082 & 0.974241089418696 \tabularnewline
85 & 0.0235650947291348 & 0.0471301894582696 & 0.976434905270865 \tabularnewline
86 & 0.0185940566397446 & 0.0371881132794893 & 0.981405943360255 \tabularnewline
87 & 0.015121099807183 & 0.0302421996143661 & 0.984878900192817 \tabularnewline
88 & 0.0154803270183072 & 0.0309606540366145 & 0.984519672981693 \tabularnewline
89 & 0.012987881970014 & 0.025975763940028 & 0.987012118029986 \tabularnewline
90 & 0.00980225192331642 & 0.0196045038466328 & 0.990197748076684 \tabularnewline
91 & 0.00833281719119404 & 0.0166656343823881 & 0.991667182808806 \tabularnewline
92 & 0.00678435126045641 & 0.0135687025209128 & 0.993215648739544 \tabularnewline
93 & 0.0049857069969585 & 0.009971413993917 & 0.995014293003041 \tabularnewline
94 & 0.00473501134319891 & 0.00947002268639782 & 0.9952649886568 \tabularnewline
95 & 0.00701762230194559 & 0.0140352446038912 & 0.992982377698054 \tabularnewline
96 & 0.00570568070229898 & 0.011411361404598 & 0.994294319297701 \tabularnewline
97 & 0.00485829763138739 & 0.00971659526277478 & 0.995141702368613 \tabularnewline
98 & 0.00370982406489184 & 0.00741964812978368 & 0.996290175935108 \tabularnewline
99 & 0.00279243516048258 & 0.00558487032096516 & 0.997207564839517 \tabularnewline
100 & 0.00231229150212311 & 0.00462458300424622 & 0.997687708497877 \tabularnewline
101 & 0.00181201691437998 & 0.00362403382875997 & 0.99818798308562 \tabularnewline
102 & 0.00132547390397147 & 0.00265094780794293 & 0.998674526096029 \tabularnewline
103 & 0.001611385652426 & 0.003222771304852 & 0.998388614347574 \tabularnewline
104 & 0.00125266947585097 & 0.00250533895170195 & 0.998747330524149 \tabularnewline
105 & 0.00549530781737634 & 0.0109906156347527 & 0.994504692182624 \tabularnewline
106 & 0.0131215320632558 & 0.0262430641265116 & 0.986878467936744 \tabularnewline
107 & 0.0137420253251101 & 0.0274840506502203 & 0.98625797467489 \tabularnewline
108 & 0.0185735697640731 & 0.0371471395281461 & 0.981426430235927 \tabularnewline
109 & 0.0143844689314593 & 0.0287689378629186 & 0.98561553106854 \tabularnewline
110 & 0.0103918414164169 & 0.0207836828328339 & 0.989608158583583 \tabularnewline
111 & 0.00747423951515114 & 0.0149484790303023 & 0.992525760484849 \tabularnewline
112 & 0.0218603042549021 & 0.0437206085098042 & 0.978139695745098 \tabularnewline
113 & 0.0219589601411374 & 0.0439179202822747 & 0.978041039858863 \tabularnewline
114 & 0.05467608497893 & 0.10935216995786 & 0.94532391502107 \tabularnewline
115 & 0.0468892658509956 & 0.0937785317019912 & 0.953110734149004 \tabularnewline
116 & 0.0393580569194593 & 0.0787161138389186 & 0.96064194308054 \tabularnewline
117 & 0.102523948514166 & 0.205047897028331 & 0.897476051485834 \tabularnewline
118 & 0.09632814567137 & 0.19265629134274 & 0.90367185432863 \tabularnewline
119 & 0.0860205876962028 & 0.172041175392406 & 0.913979412303797 \tabularnewline
120 & 0.149104401189881 & 0.298208802379761 & 0.85089559881012 \tabularnewline
121 & 0.146065449219174 & 0.292130898438348 & 0.853934550780826 \tabularnewline
122 & 0.185090803488405 & 0.37018160697681 & 0.814909196511595 \tabularnewline
123 & 0.184133922154256 & 0.368267844308513 & 0.815866077845744 \tabularnewline
124 & 0.174401104619242 & 0.348802209238484 & 0.825598895380758 \tabularnewline
125 & 0.150958814572763 & 0.301917629145526 & 0.849041185427237 \tabularnewline
126 & 0.121150887699949 & 0.242301775399899 & 0.878849112300051 \tabularnewline
127 & 0.0947742915519478 & 0.189548583103896 & 0.905225708448052 \tabularnewline
128 & 0.0924262901788662 & 0.184852580357732 & 0.907573709821134 \tabularnewline
129 & 0.130226845149647 & 0.260453690299295 & 0.869773154850352 \tabularnewline
130 & 0.112616517090961 & 0.225233034181922 & 0.887383482909039 \tabularnewline
131 & 0.0941978249292126 & 0.188395649858425 & 0.905802175070787 \tabularnewline
132 & 0.083618781928756 & 0.167237563857512 & 0.916381218071244 \tabularnewline
133 & 0.0819450988731645 & 0.163890197746329 & 0.918054901126836 \tabularnewline
134 & 0.072953350247881 & 0.145906700495762 & 0.92704664975212 \tabularnewline
135 & 0.171512038868158 & 0.343024077736317 & 0.828487961131842 \tabularnewline
136 & 0.136628566990416 & 0.273257133980833 & 0.863371433009584 \tabularnewline
137 & 0.114239841788962 & 0.228479683577923 & 0.885760158211038 \tabularnewline
138 & 0.160955313934111 & 0.321910627868221 & 0.83904468606589 \tabularnewline
139 & 0.39195445821844 & 0.78390891643688 & 0.60804554178156 \tabularnewline
140 & 0.337750603767425 & 0.67550120753485 & 0.662249396232575 \tabularnewline
141 & 0.481408140548728 & 0.962816281097457 & 0.518591859451272 \tabularnewline
142 & 0.39028321313202 & 0.78056642626404 & 0.60971678686798 \tabularnewline
143 & 0.553431960036677 & 0.893136079926645 & 0.446568039963323 \tabularnewline
144 & 0.499741685279347 & 0.999483370558693 & 0.500258314720653 \tabularnewline
145 & 0.403076750935162 & 0.806153501870323 & 0.596923249064838 \tabularnewline
146 & 0.298613380818743 & 0.597226761637487 & 0.701386619181257 \tabularnewline
147 & 0.290180448483953 & 0.580360896967906 & 0.709819551516047 \tabularnewline
148 & 0.172902385860862 & 0.345804771721724 & 0.827097614139138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.354919788605345[/C][C]0.70983957721069[/C][C]0.645080211394655[/C][/ROW]
[ROW][C]12[/C][C]0.471916456698002[/C][C]0.943832913396005[/C][C]0.528083543301998[/C][/ROW]
[ROW][C]13[/C][C]0.407737227362213[/C][C]0.815474454724427[/C][C]0.592262772637786[/C][/ROW]
[ROW][C]14[/C][C]0.303674340651241[/C][C]0.607348681302482[/C][C]0.696325659348759[/C][/ROW]
[ROW][C]15[/C][C]0.500835723595075[/C][C]0.99832855280985[/C][C]0.499164276404925[/C][/ROW]
[ROW][C]16[/C][C]0.600440248575707[/C][C]0.799119502848586[/C][C]0.399559751424293[/C][/ROW]
[ROW][C]17[/C][C]0.506189591311772[/C][C]0.987620817376456[/C][C]0.493810408688228[/C][/ROW]
[ROW][C]18[/C][C]0.595563491827328[/C][C]0.808873016345345[/C][C]0.404436508172672[/C][/ROW]
[ROW][C]19[/C][C]0.590404733432486[/C][C]0.819190533135028[/C][C]0.409595266567514[/C][/ROW]
[ROW][C]20[/C][C]0.509164409381669[/C][C]0.981671181236662[/C][C]0.490835590618331[/C][/ROW]
[ROW][C]21[/C][C]0.450081358015264[/C][C]0.900162716030528[/C][C]0.549918641984736[/C][/ROW]
[ROW][C]22[/C][C]0.374747174117746[/C][C]0.749494348235491[/C][C]0.625252825882254[/C][/ROW]
[ROW][C]23[/C][C]0.387038999847178[/C][C]0.774077999694356[/C][C]0.612961000152822[/C][/ROW]
[ROW][C]24[/C][C]0.487274808402721[/C][C]0.974549616805441[/C][C]0.512725191597279[/C][/ROW]
[ROW][C]25[/C][C]0.700172667718112[/C][C]0.599654664563776[/C][C]0.299827332281888[/C][/ROW]
[ROW][C]26[/C][C]0.639033131861522[/C][C]0.721933736276956[/C][C]0.360966868138478[/C][/ROW]
[ROW][C]27[/C][C]0.573639752876311[/C][C]0.852720494247379[/C][C]0.426360247123689[/C][/ROW]
[ROW][C]28[/C][C]0.557957298944464[/C][C]0.884085402111073[/C][C]0.442042701055536[/C][/ROW]
[ROW][C]29[/C][C]0.491851753114906[/C][C]0.983703506229812[/C][C]0.508148246885094[/C][/ROW]
[ROW][C]30[/C][C]0.427096774395021[/C][C]0.854193548790041[/C][C]0.572903225604979[/C][/ROW]
[ROW][C]31[/C][C]0.426368422041866[/C][C]0.852736844083731[/C][C]0.573631577958134[/C][/ROW]
[ROW][C]32[/C][C]0.36357490224991[/C][C]0.72714980449982[/C][C]0.63642509775009[/C][/ROW]
[ROW][C]33[/C][C]0.618536706351568[/C][C]0.762926587296864[/C][C]0.381463293648432[/C][/ROW]
[ROW][C]34[/C][C]0.58139197705194[/C][C]0.83721604589612[/C][C]0.41860802294806[/C][/ROW]
[ROW][C]35[/C][C]0.544726822311973[/C][C]0.910546355376054[/C][C]0.455273177688027[/C][/ROW]
[ROW][C]36[/C][C]0.488700881720025[/C][C]0.97740176344005[/C][C]0.511299118279975[/C][/ROW]
[ROW][C]37[/C][C]0.473974073410279[/C][C]0.947948146820558[/C][C]0.526025926589721[/C][/ROW]
[ROW][C]38[/C][C]0.422661113659191[/C][C]0.845322227318383[/C][C]0.577338886340809[/C][/ROW]
[ROW][C]39[/C][C]0.37253860621463[/C][C]0.74507721242926[/C][C]0.62746139378537[/C][/ROW]
[ROW][C]40[/C][C]0.347706064692881[/C][C]0.695412129385762[/C][C]0.652293935307119[/C][/ROW]
[ROW][C]41[/C][C]0.52449684086587[/C][C]0.95100631826826[/C][C]0.47550315913413[/C][/ROW]
[ROW][C]42[/C][C]0.469843656948027[/C][C]0.939687313896055[/C][C]0.530156343051973[/C][/ROW]
[ROW][C]43[/C][C]0.436007891245913[/C][C]0.872015782491827[/C][C]0.563992108754087[/C][/ROW]
[ROW][C]44[/C][C]0.38778305441189[/C][C]0.775566108823781[/C][C]0.61221694558811[/C][/ROW]
[ROW][C]45[/C][C]0.347261726035618[/C][C]0.694523452071236[/C][C]0.652738273964382[/C][/ROW]
[ROW][C]46[/C][C]0.323095827094318[/C][C]0.646191654188637[/C][C]0.676904172905682[/C][/ROW]
[ROW][C]47[/C][C]0.301448151242985[/C][C]0.60289630248597[/C][C]0.698551848757015[/C][/ROW]
[ROW][C]48[/C][C]0.296878814039141[/C][C]0.593757628078281[/C][C]0.703121185960859[/C][/ROW]
[ROW][C]49[/C][C]0.259055512992683[/C][C]0.518111025985366[/C][C]0.740944487007317[/C][/ROW]
[ROW][C]50[/C][C]0.25662403390584[/C][C]0.513248067811679[/C][C]0.74337596609416[/C][/ROW]
[ROW][C]51[/C][C]0.236112890427242[/C][C]0.472225780854484[/C][C]0.763887109572758[/C][/ROW]
[ROW][C]52[/C][C]0.20639170624035[/C][C]0.412783412480699[/C][C]0.79360829375965[/C][/ROW]
[ROW][C]53[/C][C]0.277768768455564[/C][C]0.555537536911127[/C][C]0.722231231544436[/C][/ROW]
[ROW][C]54[/C][C]0.355967741394077[/C][C]0.711935482788154[/C][C]0.644032258605923[/C][/ROW]
[ROW][C]55[/C][C]0.339436207686297[/C][C]0.678872415372593[/C][C]0.660563792313703[/C][/ROW]
[ROW][C]56[/C][C]0.401634448221715[/C][C]0.80326889644343[/C][C]0.598365551778285[/C][/ROW]
[ROW][C]57[/C][C]0.377971364465172[/C][C]0.755942728930345[/C][C]0.622028635534828[/C][/ROW]
[ROW][C]58[/C][C]0.346554906663464[/C][C]0.693109813326929[/C][C]0.653445093336536[/C][/ROW]
[ROW][C]59[/C][C]0.346166462769419[/C][C]0.692332925538838[/C][C]0.653833537230581[/C][/ROW]
[ROW][C]60[/C][C]0.423439817522322[/C][C]0.846879635044644[/C][C]0.576560182477678[/C][/ROW]
[ROW][C]61[/C][C]0.379191710505945[/C][C]0.75838342101189[/C][C]0.620808289494055[/C][/ROW]
[ROW][C]62[/C][C]0.336952134466272[/C][C]0.673904268932544[/C][C]0.663047865533728[/C][/ROW]
[ROW][C]63[/C][C]0.339445417533124[/C][C]0.678890835066247[/C][C]0.660554582466876[/C][/ROW]
[ROW][C]64[/C][C]0.305288310534378[/C][C]0.610576621068755[/C][C]0.694711689465622[/C][/ROW]
[ROW][C]65[/C][C]0.265987342467539[/C][C]0.531974684935078[/C][C]0.734012657532461[/C][/ROW]
[ROW][C]66[/C][C]0.249527994697487[/C][C]0.499055989394974[/C][C]0.750472005302513[/C][/ROW]
[ROW][C]67[/C][C]0.221389367646431[/C][C]0.442778735292862[/C][C]0.778610632353569[/C][/ROW]
[ROW][C]68[/C][C]0.243638209215618[/C][C]0.487276418431236[/C][C]0.756361790784382[/C][/ROW]
[ROW][C]69[/C][C]0.21015576796126[/C][C]0.42031153592252[/C][C]0.78984423203874[/C][/ROW]
[ROW][C]70[/C][C]0.177471690697582[/C][C]0.354943381395164[/C][C]0.822528309302418[/C][/ROW]
[ROW][C]71[/C][C]0.14877690114677[/C][C]0.29755380229354[/C][C]0.85122309885323[/C][/ROW]
[ROW][C]72[/C][C]0.123683108282613[/C][C]0.247366216565226[/C][C]0.876316891717387[/C][/ROW]
[ROW][C]73[/C][C]0.107657988989492[/C][C]0.215315977978983[/C][C]0.892342011010508[/C][/ROW]
[ROW][C]74[/C][C]0.0875428904657323[/C][C]0.175085780931465[/C][C]0.912457109534268[/C][/ROW]
[ROW][C]75[/C][C]0.0712485454452062[/C][C]0.142497090890412[/C][C]0.928751454554794[/C][/ROW]
[ROW][C]76[/C][C]0.0595115531753172[/C][C]0.119023106350634[/C][C]0.940488446824683[/C][/ROW]
[ROW][C]77[/C][C]0.0471412507791543[/C][C]0.0942825015583086[/C][C]0.952858749220846[/C][/ROW]
[ROW][C]78[/C][C]0.0367301186332553[/C][C]0.0734602372665105[/C][C]0.963269881366745[/C][/ROW]
[ROW][C]79[/C][C]0.0417420020136818[/C][C]0.0834840040273636[/C][C]0.958257997986318[/C][/ROW]
[ROW][C]80[/C][C]0.0382410118168572[/C][C]0.0764820236337143[/C][C]0.961758988183143[/C][/ROW]
[ROW][C]81[/C][C]0.0320173348063511[/C][C]0.0640346696127023[/C][C]0.967982665193649[/C][/ROW]
[ROW][C]82[/C][C]0.0422328403359398[/C][C]0.0844656806718796[/C][C]0.95776715966406[/C][/ROW]
[ROW][C]83[/C][C]0.0329524518656971[/C][C]0.0659049037313943[/C][C]0.967047548134303[/C][/ROW]
[ROW][C]84[/C][C]0.0257589105813041[/C][C]0.0515178211626082[/C][C]0.974241089418696[/C][/ROW]
[ROW][C]85[/C][C]0.0235650947291348[/C][C]0.0471301894582696[/C][C]0.976434905270865[/C][/ROW]
[ROW][C]86[/C][C]0.0185940566397446[/C][C]0.0371881132794893[/C][C]0.981405943360255[/C][/ROW]
[ROW][C]87[/C][C]0.015121099807183[/C][C]0.0302421996143661[/C][C]0.984878900192817[/C][/ROW]
[ROW][C]88[/C][C]0.0154803270183072[/C][C]0.0309606540366145[/C][C]0.984519672981693[/C][/ROW]
[ROW][C]89[/C][C]0.012987881970014[/C][C]0.025975763940028[/C][C]0.987012118029986[/C][/ROW]
[ROW][C]90[/C][C]0.00980225192331642[/C][C]0.0196045038466328[/C][C]0.990197748076684[/C][/ROW]
[ROW][C]91[/C][C]0.00833281719119404[/C][C]0.0166656343823881[/C][C]0.991667182808806[/C][/ROW]
[ROW][C]92[/C][C]0.00678435126045641[/C][C]0.0135687025209128[/C][C]0.993215648739544[/C][/ROW]
[ROW][C]93[/C][C]0.0049857069969585[/C][C]0.009971413993917[/C][C]0.995014293003041[/C][/ROW]
[ROW][C]94[/C][C]0.00473501134319891[/C][C]0.00947002268639782[/C][C]0.9952649886568[/C][/ROW]
[ROW][C]95[/C][C]0.00701762230194559[/C][C]0.0140352446038912[/C][C]0.992982377698054[/C][/ROW]
[ROW][C]96[/C][C]0.00570568070229898[/C][C]0.011411361404598[/C][C]0.994294319297701[/C][/ROW]
[ROW][C]97[/C][C]0.00485829763138739[/C][C]0.00971659526277478[/C][C]0.995141702368613[/C][/ROW]
[ROW][C]98[/C][C]0.00370982406489184[/C][C]0.00741964812978368[/C][C]0.996290175935108[/C][/ROW]
[ROW][C]99[/C][C]0.00279243516048258[/C][C]0.00558487032096516[/C][C]0.997207564839517[/C][/ROW]
[ROW][C]100[/C][C]0.00231229150212311[/C][C]0.00462458300424622[/C][C]0.997687708497877[/C][/ROW]
[ROW][C]101[/C][C]0.00181201691437998[/C][C]0.00362403382875997[/C][C]0.99818798308562[/C][/ROW]
[ROW][C]102[/C][C]0.00132547390397147[/C][C]0.00265094780794293[/C][C]0.998674526096029[/C][/ROW]
[ROW][C]103[/C][C]0.001611385652426[/C][C]0.003222771304852[/C][C]0.998388614347574[/C][/ROW]
[ROW][C]104[/C][C]0.00125266947585097[/C][C]0.00250533895170195[/C][C]0.998747330524149[/C][/ROW]
[ROW][C]105[/C][C]0.00549530781737634[/C][C]0.0109906156347527[/C][C]0.994504692182624[/C][/ROW]
[ROW][C]106[/C][C]0.0131215320632558[/C][C]0.0262430641265116[/C][C]0.986878467936744[/C][/ROW]
[ROW][C]107[/C][C]0.0137420253251101[/C][C]0.0274840506502203[/C][C]0.98625797467489[/C][/ROW]
[ROW][C]108[/C][C]0.0185735697640731[/C][C]0.0371471395281461[/C][C]0.981426430235927[/C][/ROW]
[ROW][C]109[/C][C]0.0143844689314593[/C][C]0.0287689378629186[/C][C]0.98561553106854[/C][/ROW]
[ROW][C]110[/C][C]0.0103918414164169[/C][C]0.0207836828328339[/C][C]0.989608158583583[/C][/ROW]
[ROW][C]111[/C][C]0.00747423951515114[/C][C]0.0149484790303023[/C][C]0.992525760484849[/C][/ROW]
[ROW][C]112[/C][C]0.0218603042549021[/C][C]0.0437206085098042[/C][C]0.978139695745098[/C][/ROW]
[ROW][C]113[/C][C]0.0219589601411374[/C][C]0.0439179202822747[/C][C]0.978041039858863[/C][/ROW]
[ROW][C]114[/C][C]0.05467608497893[/C][C]0.10935216995786[/C][C]0.94532391502107[/C][/ROW]
[ROW][C]115[/C][C]0.0468892658509956[/C][C]0.0937785317019912[/C][C]0.953110734149004[/C][/ROW]
[ROW][C]116[/C][C]0.0393580569194593[/C][C]0.0787161138389186[/C][C]0.96064194308054[/C][/ROW]
[ROW][C]117[/C][C]0.102523948514166[/C][C]0.205047897028331[/C][C]0.897476051485834[/C][/ROW]
[ROW][C]118[/C][C]0.09632814567137[/C][C]0.19265629134274[/C][C]0.90367185432863[/C][/ROW]
[ROW][C]119[/C][C]0.0860205876962028[/C][C]0.172041175392406[/C][C]0.913979412303797[/C][/ROW]
[ROW][C]120[/C][C]0.149104401189881[/C][C]0.298208802379761[/C][C]0.85089559881012[/C][/ROW]
[ROW][C]121[/C][C]0.146065449219174[/C][C]0.292130898438348[/C][C]0.853934550780826[/C][/ROW]
[ROW][C]122[/C][C]0.185090803488405[/C][C]0.37018160697681[/C][C]0.814909196511595[/C][/ROW]
[ROW][C]123[/C][C]0.184133922154256[/C][C]0.368267844308513[/C][C]0.815866077845744[/C][/ROW]
[ROW][C]124[/C][C]0.174401104619242[/C][C]0.348802209238484[/C][C]0.825598895380758[/C][/ROW]
[ROW][C]125[/C][C]0.150958814572763[/C][C]0.301917629145526[/C][C]0.849041185427237[/C][/ROW]
[ROW][C]126[/C][C]0.121150887699949[/C][C]0.242301775399899[/C][C]0.878849112300051[/C][/ROW]
[ROW][C]127[/C][C]0.0947742915519478[/C][C]0.189548583103896[/C][C]0.905225708448052[/C][/ROW]
[ROW][C]128[/C][C]0.0924262901788662[/C][C]0.184852580357732[/C][C]0.907573709821134[/C][/ROW]
[ROW][C]129[/C][C]0.130226845149647[/C][C]0.260453690299295[/C][C]0.869773154850352[/C][/ROW]
[ROW][C]130[/C][C]0.112616517090961[/C][C]0.225233034181922[/C][C]0.887383482909039[/C][/ROW]
[ROW][C]131[/C][C]0.0941978249292126[/C][C]0.188395649858425[/C][C]0.905802175070787[/C][/ROW]
[ROW][C]132[/C][C]0.083618781928756[/C][C]0.167237563857512[/C][C]0.916381218071244[/C][/ROW]
[ROW][C]133[/C][C]0.0819450988731645[/C][C]0.163890197746329[/C][C]0.918054901126836[/C][/ROW]
[ROW][C]134[/C][C]0.072953350247881[/C][C]0.145906700495762[/C][C]0.92704664975212[/C][/ROW]
[ROW][C]135[/C][C]0.171512038868158[/C][C]0.343024077736317[/C][C]0.828487961131842[/C][/ROW]
[ROW][C]136[/C][C]0.136628566990416[/C][C]0.273257133980833[/C][C]0.863371433009584[/C][/ROW]
[ROW][C]137[/C][C]0.114239841788962[/C][C]0.228479683577923[/C][C]0.885760158211038[/C][/ROW]
[ROW][C]138[/C][C]0.160955313934111[/C][C]0.321910627868221[/C][C]0.83904468606589[/C][/ROW]
[ROW][C]139[/C][C]0.39195445821844[/C][C]0.78390891643688[/C][C]0.60804554178156[/C][/ROW]
[ROW][C]140[/C][C]0.337750603767425[/C][C]0.67550120753485[/C][C]0.662249396232575[/C][/ROW]
[ROW][C]141[/C][C]0.481408140548728[/C][C]0.962816281097457[/C][C]0.518591859451272[/C][/ROW]
[ROW][C]142[/C][C]0.39028321313202[/C][C]0.78056642626404[/C][C]0.60971678686798[/C][/ROW]
[ROW][C]143[/C][C]0.553431960036677[/C][C]0.893136079926645[/C][C]0.446568039963323[/C][/ROW]
[ROW][C]144[/C][C]0.499741685279347[/C][C]0.999483370558693[/C][C]0.500258314720653[/C][/ROW]
[ROW][C]145[/C][C]0.403076750935162[/C][C]0.806153501870323[/C][C]0.596923249064838[/C][/ROW]
[ROW][C]146[/C][C]0.298613380818743[/C][C]0.597226761637487[/C][C]0.701386619181257[/C][/ROW]
[ROW][C]147[/C][C]0.290180448483953[/C][C]0.580360896967906[/C][C]0.709819551516047[/C][/ROW]
[ROW][C]148[/C][C]0.172902385860862[/C][C]0.345804771721724[/C][C]0.827097614139138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3549197886053450.709839577210690.645080211394655
120.4719164566980020.9438329133960050.528083543301998
130.4077372273622130.8154744547244270.592262772637786
140.3036743406512410.6073486813024820.696325659348759
150.5008357235950750.998328552809850.499164276404925
160.6004402485757070.7991195028485860.399559751424293
170.5061895913117720.9876208173764560.493810408688228
180.5955634918273280.8088730163453450.404436508172672
190.5904047334324860.8191905331350280.409595266567514
200.5091644093816690.9816711812366620.490835590618331
210.4500813580152640.9001627160305280.549918641984736
220.3747471741177460.7494943482354910.625252825882254
230.3870389998471780.7740779996943560.612961000152822
240.4872748084027210.9745496168054410.512725191597279
250.7001726677181120.5996546645637760.299827332281888
260.6390331318615220.7219337362769560.360966868138478
270.5736397528763110.8527204942473790.426360247123689
280.5579572989444640.8840854021110730.442042701055536
290.4918517531149060.9837035062298120.508148246885094
300.4270967743950210.8541935487900410.572903225604979
310.4263684220418660.8527368440837310.573631577958134
320.363574902249910.727149804499820.63642509775009
330.6185367063515680.7629265872968640.381463293648432
340.581391977051940.837216045896120.41860802294806
350.5447268223119730.9105463553760540.455273177688027
360.4887008817200250.977401763440050.511299118279975
370.4739740734102790.9479481468205580.526025926589721
380.4226611136591910.8453222273183830.577338886340809
390.372538606214630.745077212429260.62746139378537
400.3477060646928810.6954121293857620.652293935307119
410.524496840865870.951006318268260.47550315913413
420.4698436569480270.9396873138960550.530156343051973
430.4360078912459130.8720157824918270.563992108754087
440.387783054411890.7755661088237810.61221694558811
450.3472617260356180.6945234520712360.652738273964382
460.3230958270943180.6461916541886370.676904172905682
470.3014481512429850.602896302485970.698551848757015
480.2968788140391410.5937576280782810.703121185960859
490.2590555129926830.5181110259853660.740944487007317
500.256624033905840.5132480678116790.74337596609416
510.2361128904272420.4722257808544840.763887109572758
520.206391706240350.4127834124806990.79360829375965
530.2777687684555640.5555375369111270.722231231544436
540.3559677413940770.7119354827881540.644032258605923
550.3394362076862970.6788724153725930.660563792313703
560.4016344482217150.803268896443430.598365551778285
570.3779713644651720.7559427289303450.622028635534828
580.3465549066634640.6931098133269290.653445093336536
590.3461664627694190.6923329255388380.653833537230581
600.4234398175223220.8468796350446440.576560182477678
610.3791917105059450.758383421011890.620808289494055
620.3369521344662720.6739042689325440.663047865533728
630.3394454175331240.6788908350662470.660554582466876
640.3052883105343780.6105766210687550.694711689465622
650.2659873424675390.5319746849350780.734012657532461
660.2495279946974870.4990559893949740.750472005302513
670.2213893676464310.4427787352928620.778610632353569
680.2436382092156180.4872764184312360.756361790784382
690.210155767961260.420311535922520.78984423203874
700.1774716906975820.3549433813951640.822528309302418
710.148776901146770.297553802293540.85122309885323
720.1236831082826130.2473662165652260.876316891717387
730.1076579889894920.2153159779789830.892342011010508
740.08754289046573230.1750857809314650.912457109534268
750.07124854544520620.1424970908904120.928751454554794
760.05951155317531720.1190231063506340.940488446824683
770.04714125077915430.09428250155830860.952858749220846
780.03673011863325530.07346023726651050.963269881366745
790.04174200201368180.08348400402736360.958257997986318
800.03824101181685720.07648202363371430.961758988183143
810.03201733480635110.06403466961270230.967982665193649
820.04223284033593980.08446568067187960.95776715966406
830.03295245186569710.06590490373139430.967047548134303
840.02575891058130410.05151782116260820.974241089418696
850.02356509472913480.04713018945826960.976434905270865
860.01859405663974460.03718811327948930.981405943360255
870.0151210998071830.03024219961436610.984878900192817
880.01548032701830720.03096065403661450.984519672981693
890.0129878819700140.0259757639400280.987012118029986
900.009802251923316420.01960450384663280.990197748076684
910.008332817191194040.01666563438238810.991667182808806
920.006784351260456410.01356870252091280.993215648739544
930.00498570699695850.0099714139939170.995014293003041
940.004735011343198910.009470022686397820.9952649886568
950.007017622301945590.01403524460389120.992982377698054
960.005705680702298980.0114113614045980.994294319297701
970.004858297631387390.009716595262774780.995141702368613
980.003709824064891840.007419648129783680.996290175935108
990.002792435160482580.005584870320965160.997207564839517
1000.002312291502123110.004624583004246220.997687708497877
1010.001812016914379980.003624033828759970.99818798308562
1020.001325473903971470.002650947807942930.998674526096029
1030.0016113856524260.0032227713048520.998388614347574
1040.001252669475850970.002505338951701950.998747330524149
1050.005495307817376340.01099061563475270.994504692182624
1060.01312153206325580.02624306412651160.986878467936744
1070.01374202532511010.02748405065022030.98625797467489
1080.01857356976407310.03714713952814610.981426430235927
1090.01438446893145930.02876893786291860.98561553106854
1100.01039184141641690.02078368283283390.989608158583583
1110.007474239515151140.01494847903030230.992525760484849
1120.02186030425490210.04372060850980420.978139695745098
1130.02195896014113740.04391792028227470.978041039858863
1140.054676084978930.109352169957860.94532391502107
1150.04688926585099560.09377853170199120.953110734149004
1160.03935805691945930.07871611383891860.96064194308054
1170.1025239485141660.2050478970283310.897476051485834
1180.096328145671370.192656291342740.90367185432863
1190.08602058769620280.1720411753924060.913979412303797
1200.1491044011898810.2982088023797610.85089559881012
1210.1460654492191740.2921308984383480.853934550780826
1220.1850908034884050.370181606976810.814909196511595
1230.1841339221542560.3682678443085130.815866077845744
1240.1744011046192420.3488022092384840.825598895380758
1250.1509588145727630.3019176291455260.849041185427237
1260.1211508876999490.2423017753998990.878849112300051
1270.09477429155194780.1895485831038960.905225708448052
1280.09242629017886620.1848525803577320.907573709821134
1290.1302268451496470.2604536902992950.869773154850352
1300.1126165170909610.2252330341819220.887383482909039
1310.09419782492921260.1883956498584250.905802175070787
1320.0836187819287560.1672375638575120.916381218071244
1330.08194509887316450.1638901977463290.918054901126836
1340.0729533502478810.1459067004957620.92704664975212
1350.1715120388681580.3430240777363170.828487961131842
1360.1366285669904160.2732571339808330.863371433009584
1370.1142398417889620.2284796835779230.885760158211038
1380.1609553139341110.3219106278682210.83904468606589
1390.391954458218440.783908916436880.60804554178156
1400.3377506037674250.675501207534850.662249396232575
1410.4814081405487280.9628162810974570.518591859451272
1420.390283213132020.780566426264040.60971678686798
1430.5534319600366770.8931360799266450.446568039963323
1440.4997416852793470.9994833705586930.500258314720653
1450.4030767509351620.8061535018703230.596923249064838
1460.2986133808187430.5972267616374870.701386619181257
1470.2901804484839530.5803608969679060.709819551516047
1480.1729023858608620.3458047717217240.827097614139138






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.072463768115942NOK
5% type I error level290.210144927536232NOK
10% type I error level390.282608695652174NOK

\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 & 10 & 0.072463768115942 & NOK \tabularnewline
5% type I error level & 29 & 0.210144927536232 & NOK \tabularnewline
10% type I error level & 39 & 0.282608695652174 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99570&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]10[/C][C]0.072463768115942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.210144927536232[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.282608695652174[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99570&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99570&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 level100.072463768115942NOK
5% type I error level290.210144927536232NOK
10% type I error level390.282608695652174NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}