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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 16:32:56 +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/t1290529891u1r7jin2rw08es1.htm/, Retrieved Thu, 06 Nov 2025 04:29:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99394, Retrieved Thu, 06 Nov 2025 04:29:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact422

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 - Incl...] [2010-11-19 12:30:30] [6f0e7a2d1a07390e3505a2db8288f975]
-   P       [Multiple Regression] [W7] [2010-11-23 16:32:56] [476d588d86fe88306e0383abd6004235] [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
9	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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Organization[t] = + 18.2651799335285 -0.080823679576389Month[t] -0.0591540614026579ConcernOverMistakes[t] + 0.216924456733213DoubtsAboutActions[t] -0.132556183679637ParentalExpectations[t] -0.254001352868105ParentalCriticism[t] + 0.395674100791331PersonalStandards[t] -0.0147661337424251t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Organization[t] =  +  18.2651799335285 -0.080823679576389Month[t] -0.0591540614026579ConcernOverMistakes[t] +  0.216924456733213DoubtsAboutActions[t] -0.132556183679637ParentalExpectations[t] -0.254001352868105ParentalCriticism[t] +  0.395674100791331PersonalStandards[t] -0.0147661337424251t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Organization[t] =  +  18.2651799335285 -0.080823679576389Month[t] -0.0591540614026579ConcernOverMistakes[t] +  0.216924456733213DoubtsAboutActions[t] -0.132556183679637ParentalExpectations[t] -0.254001352868105ParentalCriticism[t] +  0.395674100791331PersonalStandards[t] -0.0147661337424251t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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
Organization[t] = + 18.2651799335285 -0.080823679576389Month[t] -0.0591540614026579ConcernOverMistakes[t] + 0.216924456733213DoubtsAboutActions[t] -0.132556183679637ParentalExpectations[t] -0.254001352868105ParentalCriticism[t] + 0.395674100791331PersonalStandards[t] -0.0147661337424251t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.265179933528515.3497621.18990.235940.11797
Month-0.0808236795763891.54324-0.05240.9583010.47915
ConcernOverMistakes-0.05915406140265790.06241-0.94780.3447320.172366
DoubtsAboutActions0.2169244567332130.1111961.95080.0529290.026464
ParentalExpectations-0.1325561836796370.103796-1.27710.2035340.101767
ParentalCriticism-0.2540013528681050.129774-1.95730.052160.02608
PersonalStandards0.3956741007913310.0756655.22931e-060
t-0.01476613374242510.006382-2.31380.022030.011015

\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) & 18.2651799335285 & 15.349762 & 1.1899 & 0.23594 & 0.11797 \tabularnewline
Month & -0.080823679576389 & 1.54324 & -0.0524 & 0.958301 & 0.47915 \tabularnewline
ConcernOverMistakes & -0.0591540614026579 & 0.06241 & -0.9478 & 0.344732 & 0.172366 \tabularnewline
DoubtsAboutActions & 0.216924456733213 & 0.111196 & 1.9508 & 0.052929 & 0.026464 \tabularnewline
ParentalExpectations & -0.132556183679637 & 0.103796 & -1.2771 & 0.203534 & 0.101767 \tabularnewline
ParentalCriticism & -0.254001352868105 & 0.129774 & -1.9573 & 0.05216 & 0.02608 \tabularnewline
PersonalStandards & 0.395674100791331 & 0.075665 & 5.2293 & 1e-06 & 0 \tabularnewline
t & -0.0147661337424251 & 0.006382 & -2.3138 & 0.02203 & 0.011015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&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]18.2651799335285[/C][C]15.349762[/C][C]1.1899[/C][C]0.23594[/C][C]0.11797[/C][/ROW]
[ROW][C]Month[/C][C]-0.080823679576389[/C][C]1.54324[/C][C]-0.0524[/C][C]0.958301[/C][C]0.47915[/C][/ROW]
[ROW][C]ConcernOverMistakes[/C][C]-0.0591540614026579[/C][C]0.06241[/C][C]-0.9478[/C][C]0.344732[/C][C]0.172366[/C][/ROW]
[ROW][C]DoubtsAboutActions[/C][C]0.216924456733213[/C][C]0.111196[/C][C]1.9508[/C][C]0.052929[/C][C]0.026464[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]-0.132556183679637[/C][C]0.103796[/C][C]-1.2771[/C][C]0.203534[/C][C]0.101767[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]-0.254001352868105[/C][C]0.129774[/C][C]-1.9573[/C][C]0.05216[/C][C]0.02608[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.395674100791331[/C][C]0.075665[/C][C]5.2293[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.0147661337424251[/C][C]0.006382[/C][C]-2.3138[/C][C]0.02203[/C][C]0.011015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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)18.265179933528515.3497621.18990.235940.11797
Month-0.0808236795763891.54324-0.05240.9583010.47915
ConcernOverMistakes-0.05915406140265790.06241-0.94780.3447320.172366
DoubtsAboutActions0.2169244567332130.1111961.95080.0529290.026464
ParentalExpectations-0.1325561836796370.103796-1.27710.2035340.101767
ParentalCriticism-0.2540013528681050.129774-1.95730.052160.02608
PersonalStandards0.3956741007913310.0756655.22931e-060
t-0.01476613374242510.006382-2.31380.022030.011015






Multiple Linear Regression - Regression Statistics
Multiple R0.502263051134702
R-squared0.252268172535141
Adjusted R-squared0.217605107685776
F-TEST (value)7.2777226604579
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.63300429179003e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.45408407493565
Sum Squared Residuals1801.53521630534

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.502263051134702 \tabularnewline
R-squared & 0.252268172535141 \tabularnewline
Adjusted R-squared & 0.217605107685776 \tabularnewline
F-TEST (value) & 7.2777226604579 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 1.63300429179003e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.45408407493565 \tabularnewline
Sum Squared Residuals & 1801.53521630534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.502263051134702[/C][/ROW]
[ROW][C]R-squared[/C][C]0.252268172535141[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.217605107685776[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.2777226604579[/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]1.63300429179003e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.45408407493565[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1801.53521630534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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 R0.502263051134702
R-squared0.252268172535141
Adjusted R-squared0.217605107685776
F-TEST (value)7.2777226604579
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value1.63300429179003e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.45408407493565
Sum Squared Residuals1801.53521630534






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.13028976829841.86971023170162
22325.347500449936-2.34750044993599
32525.3741531909091-0.374153190909052
42323.1772579132161-0.177257913216066
51922.9627025346874-3.9627025346874
62924.00809614295294.99190385704707
72525.7574726286814-0.75747262868142
82123.6268895256234-2.62688952562345
92223.0878421757866-1.08784217578657
102523.49774803926641.50225196073357
112421.36799530097172.63200469902834
121820.9651166190543-2.96511661905435
132219.57681560343052.42318439656946
141521.7617505938414-6.76175059384144
152224.3446847094334-2.34468470943345
162825.05738664268572.9426133573143
172023.1418160737661-3.14181607376606
181220.0885460308846-8.08854603088456
192422.40206664724981.59793335275023
202022.4178212677963-2.41782126779630
212124.1599356442117-3.15993564421175
222021.7207713443076-1.7207713443076
232120.18741063906690.812589360933083
242322.07028357323170.929716426768281
252822.77338482402465.22661517597543
262422.68810371907781.31189628092218
272424.2069980566450-0.206998056644954
282421.26214156550682.73785843449315
292322.76892332327950.231076676720495
302323.4182519046109-0.418251904610935
312924.92357883487574.07642116512434
322422.81386029912341.18613970087660
331825.6363550042354-7.6363550042354
342526.5043262604116-1.50432626041160
352123.2392147449499-2.23921474494994
362627.2607588185069-1.26075881850687
372225.7065422389044-3.70654223890439
382223.4633973100057-1.46339731000574
392223.2360747192300-1.23607471923005
402326.0964049825159-3.09640498251590
413023.53450805357316.46549194642686
422323.2377500527786-0.237750052778584
431719.4455546818638-2.44555468186384
442324.2548811364202-1.25488113642017
452324.7085844194678-1.70858441946782
462522.91683671777752.08316328222251
472421.34312973213042.65687026786958
482427.7775845411027-3.77758454110267
492323.4563468637236-0.456346863723595
502124.2554233597999-3.25542335979989
512426.0583740164932-2.05837401649321
522422.26312227802701.73687772197298
532821.95129837742126.04870162257884
541621.4341929789634-5.4341929789634
552020.3327352745890-0.332735274589025
562923.78554538114295.2144546188571
572724.14797032439412.85202967560587
582223.4466677209928-1.44666772099284
592824.15529522744153.84470477255850
601620.7808813048786-4.78088130487858
612523.12240208525371.87759791474626
622423.71739203755860.282607962441426
632823.86822540354014.13177459645985
642424.4168721842752-0.416872184275207
652322.86453152803130.135468471968737
663026.95596426122683.04403573877321
672421.58393330139382.41606669860615
682124.2091984978139-3.20919849781391
692523.39394925547541.60605074452461
702524.02812524816940.97187475183056
712221.03242791823790.967572081762072
722322.56031544568290.439684554317121
732622.96555943344523.03444056655483
742321.82312644156421.17687355843577
752523.14329926859261.85670073140740
762121.4342151171374-0.434215117137413
772523.64980804720231.35019195279768
782422.22907436727961.77092563272041
792923.51251654472055.48748345527954
802223.6501217579009-1.65012175790086
812723.56589388989893.43410611010106
822619.71922467606896.28077532393105
832221.35477893274120.64522106725879
842421.99984238144152.00015761855855
852723.06557243292003.93442756708003
862421.34556297740122.65443702259875
872424.658700615808-0.658700615807986
882924.18264268379534.81735731620467
892222.0958198385512-0.0958198385511574
902120.53108454084820.468915459151794
912420.45082408399263.54917591600735
922421.52489083149052.47510916850947
932321.78684403733721.21315596266276
942022.1292611274564-2.12926112745638
952721.19357794364355.80642205635649
962623.20473809597452.79526190402554
972521.82900093828843.17099906171155
982119.98954076382881.01045923617116
992120.57693898122450.423061018775502
1001920.2150775834596-1.21507758345957
1012121.3464128191757-0.346412819175655
1022120.98508336580090.0149166341991357
1031619.5601712420730-3.56017124207303
1042220.37268760991261.62731239008742
1052921.49824854348347.50175145651664
1061521.4481587489022-6.44815874890216
1071720.3886896991058-3.38868969910584
1081519.6493873290368-4.64938732903676
1092121.3279979997044-0.327997999704354
1102120.68442096702330.315579032976685
1111918.95504888483280.04495111516719
1122417.83543673415686.16456326584324
1132021.7906882736065-1.79068827360649
1141724.4817908931081-7.48179089310812
1152324.1480825171234-1.14808251712340
1162421.84892125950862.15107874049140
1171421.5133116379342-7.51331163793423
1181922.2915575818798-3.29155758187984
1192421.68833936801632.31166063198369
1201319.9554033140635-6.95540331406355
1212224.6845701216641-2.68457012166414
1221620.5712630248371-4.57126302483710
1231922.618312797572-3.61831279757200
1242522.06816408306992.93183591693012
1252523.50508736349671.49491263650334
1262320.82318825335052.17681174664951
1272422.79299185444291.20700814555708
1282622.77601539031753.22398460968255
1292620.82394071636615.17605928363388
1302523.41960189638031.58039810361965
1311821.5862924403213-3.58629244032127
1322119.22176919748171.77823080251832
1332622.81212795015993.18787204984014
1342321.18033463735761.81966536264239
1352319.09862494597433.90137505402567
1362221.82715070815820.172849291841754
1372021.5923532410073-1.59235324100729
1381321.2574506098304-8.25745060983036
1392420.56669689423743.43330310576262
1401520.7204514264666-5.72045142646664
1411422.2587253215388-8.25872532153883
1422223.1348164241320-1.13481642413203
1431017.0104115841435-7.01041158414355
1442423.45350750691770.546492493082285
1452220.94667070728651.05332929271347
1462424.7574857221061-0.757485722106145
1471920.7572509061611-1.75725090616112
1482021.1138228524966-1.11382285249664
1491316.3426989662444-3.34269896624436
1502019.19371129503870.806288704961296
1512222.1060433360234-0.106043336023380
1522422.37023705454591.62976294545413
1532922.24889896153746.75110103846263
1541219.9719025274833-7.9719025274833
1552019.91431736181470.085682638185284
1562120.37069675069140.629303249308619
1572422.52533839076551.4746616092345
1582220.81899652666841.18100347333161
1592016.89001385461793.10998614538215

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.1302897682984 & 1.86971023170162 \tabularnewline
2 & 23 & 25.347500449936 & -2.34750044993599 \tabularnewline
3 & 25 & 25.3741531909091 & -0.374153190909052 \tabularnewline
4 & 23 & 23.1772579132161 & -0.177257913216066 \tabularnewline
5 & 19 & 22.9627025346874 & -3.9627025346874 \tabularnewline
6 & 29 & 24.0080961429529 & 4.99190385704707 \tabularnewline
7 & 25 & 25.7574726286814 & -0.75747262868142 \tabularnewline
8 & 21 & 23.6268895256234 & -2.62688952562345 \tabularnewline
9 & 22 & 23.0878421757866 & -1.08784217578657 \tabularnewline
10 & 25 & 23.4977480392664 & 1.50225196073357 \tabularnewline
11 & 24 & 21.3679953009717 & 2.63200469902834 \tabularnewline
12 & 18 & 20.9651166190543 & -2.96511661905435 \tabularnewline
13 & 22 & 19.5768156034305 & 2.42318439656946 \tabularnewline
14 & 15 & 21.7617505938414 & -6.76175059384144 \tabularnewline
15 & 22 & 24.3446847094334 & -2.34468470943345 \tabularnewline
16 & 28 & 25.0573866426857 & 2.9426133573143 \tabularnewline
17 & 20 & 23.1418160737661 & -3.14181607376606 \tabularnewline
18 & 12 & 20.0885460308846 & -8.08854603088456 \tabularnewline
19 & 24 & 22.4020666472498 & 1.59793335275023 \tabularnewline
20 & 20 & 22.4178212677963 & -2.41782126779630 \tabularnewline
21 & 21 & 24.1599356442117 & -3.15993564421175 \tabularnewline
22 & 20 & 21.7207713443076 & -1.7207713443076 \tabularnewline
23 & 21 & 20.1874106390669 & 0.812589360933083 \tabularnewline
24 & 23 & 22.0702835732317 & 0.929716426768281 \tabularnewline
25 & 28 & 22.7733848240246 & 5.22661517597543 \tabularnewline
26 & 24 & 22.6881037190778 & 1.31189628092218 \tabularnewline
27 & 24 & 24.2069980566450 & -0.206998056644954 \tabularnewline
28 & 24 & 21.2621415655068 & 2.73785843449315 \tabularnewline
29 & 23 & 22.7689233232795 & 0.231076676720495 \tabularnewline
30 & 23 & 23.4182519046109 & -0.418251904610935 \tabularnewline
31 & 29 & 24.9235788348757 & 4.07642116512434 \tabularnewline
32 & 24 & 22.8138602991234 & 1.18613970087660 \tabularnewline
33 & 18 & 25.6363550042354 & -7.6363550042354 \tabularnewline
34 & 25 & 26.5043262604116 & -1.50432626041160 \tabularnewline
35 & 21 & 23.2392147449499 & -2.23921474494994 \tabularnewline
36 & 26 & 27.2607588185069 & -1.26075881850687 \tabularnewline
37 & 22 & 25.7065422389044 & -3.70654223890439 \tabularnewline
38 & 22 & 23.4633973100057 & -1.46339731000574 \tabularnewline
39 & 22 & 23.2360747192300 & -1.23607471923005 \tabularnewline
40 & 23 & 26.0964049825159 & -3.09640498251590 \tabularnewline
41 & 30 & 23.5345080535731 & 6.46549194642686 \tabularnewline
42 & 23 & 23.2377500527786 & -0.237750052778584 \tabularnewline
43 & 17 & 19.4455546818638 & -2.44555468186384 \tabularnewline
44 & 23 & 24.2548811364202 & -1.25488113642017 \tabularnewline
45 & 23 & 24.7085844194678 & -1.70858441946782 \tabularnewline
46 & 25 & 22.9168367177775 & 2.08316328222251 \tabularnewline
47 & 24 & 21.3431297321304 & 2.65687026786958 \tabularnewline
48 & 24 & 27.7775845411027 & -3.77758454110267 \tabularnewline
49 & 23 & 23.4563468637236 & -0.456346863723595 \tabularnewline
50 & 21 & 24.2554233597999 & -3.25542335979989 \tabularnewline
51 & 24 & 26.0583740164932 & -2.05837401649321 \tabularnewline
52 & 24 & 22.2631222780270 & 1.73687772197298 \tabularnewline
53 & 28 & 21.9512983774212 & 6.04870162257884 \tabularnewline
54 & 16 & 21.4341929789634 & -5.4341929789634 \tabularnewline
55 & 20 & 20.3327352745890 & -0.332735274589025 \tabularnewline
56 & 29 & 23.7855453811429 & 5.2144546188571 \tabularnewline
57 & 27 & 24.1479703243941 & 2.85202967560587 \tabularnewline
58 & 22 & 23.4466677209928 & -1.44666772099284 \tabularnewline
59 & 28 & 24.1552952274415 & 3.84470477255850 \tabularnewline
60 & 16 & 20.7808813048786 & -4.78088130487858 \tabularnewline
61 & 25 & 23.1224020852537 & 1.87759791474626 \tabularnewline
62 & 24 & 23.7173920375586 & 0.282607962441426 \tabularnewline
63 & 28 & 23.8682254035401 & 4.13177459645985 \tabularnewline
64 & 24 & 24.4168721842752 & -0.416872184275207 \tabularnewline
65 & 23 & 22.8645315280313 & 0.135468471968737 \tabularnewline
66 & 30 & 26.9559642612268 & 3.04403573877321 \tabularnewline
67 & 24 & 21.5839333013938 & 2.41606669860615 \tabularnewline
68 & 21 & 24.2091984978139 & -3.20919849781391 \tabularnewline
69 & 25 & 23.3939492554754 & 1.60605074452461 \tabularnewline
70 & 25 & 24.0281252481694 & 0.97187475183056 \tabularnewline
71 & 22 & 21.0324279182379 & 0.967572081762072 \tabularnewline
72 & 23 & 22.5603154456829 & 0.439684554317121 \tabularnewline
73 & 26 & 22.9655594334452 & 3.03444056655483 \tabularnewline
74 & 23 & 21.8231264415642 & 1.17687355843577 \tabularnewline
75 & 25 & 23.1432992685926 & 1.85670073140740 \tabularnewline
76 & 21 & 21.4342151171374 & -0.434215117137413 \tabularnewline
77 & 25 & 23.6498080472023 & 1.35019195279768 \tabularnewline
78 & 24 & 22.2290743672796 & 1.77092563272041 \tabularnewline
79 & 29 & 23.5125165447205 & 5.48748345527954 \tabularnewline
80 & 22 & 23.6501217579009 & -1.65012175790086 \tabularnewline
81 & 27 & 23.5658938898989 & 3.43410611010106 \tabularnewline
82 & 26 & 19.7192246760689 & 6.28077532393105 \tabularnewline
83 & 22 & 21.3547789327412 & 0.64522106725879 \tabularnewline
84 & 24 & 21.9998423814415 & 2.00015761855855 \tabularnewline
85 & 27 & 23.0655724329200 & 3.93442756708003 \tabularnewline
86 & 24 & 21.3455629774012 & 2.65443702259875 \tabularnewline
87 & 24 & 24.658700615808 & -0.658700615807986 \tabularnewline
88 & 29 & 24.1826426837953 & 4.81735731620467 \tabularnewline
89 & 22 & 22.0958198385512 & -0.0958198385511574 \tabularnewline
90 & 21 & 20.5310845408482 & 0.468915459151794 \tabularnewline
91 & 24 & 20.4508240839926 & 3.54917591600735 \tabularnewline
92 & 24 & 21.5248908314905 & 2.47510916850947 \tabularnewline
93 & 23 & 21.7868440373372 & 1.21315596266276 \tabularnewline
94 & 20 & 22.1292611274564 & -2.12926112745638 \tabularnewline
95 & 27 & 21.1935779436435 & 5.80642205635649 \tabularnewline
96 & 26 & 23.2047380959745 & 2.79526190402554 \tabularnewline
97 & 25 & 21.8290009382884 & 3.17099906171155 \tabularnewline
98 & 21 & 19.9895407638288 & 1.01045923617116 \tabularnewline
99 & 21 & 20.5769389812245 & 0.423061018775502 \tabularnewline
100 & 19 & 20.2150775834596 & -1.21507758345957 \tabularnewline
101 & 21 & 21.3464128191757 & -0.346412819175655 \tabularnewline
102 & 21 & 20.9850833658009 & 0.0149166341991357 \tabularnewline
103 & 16 & 19.5601712420730 & -3.56017124207303 \tabularnewline
104 & 22 & 20.3726876099126 & 1.62731239008742 \tabularnewline
105 & 29 & 21.4982485434834 & 7.50175145651664 \tabularnewline
106 & 15 & 21.4481587489022 & -6.44815874890216 \tabularnewline
107 & 17 & 20.3886896991058 & -3.38868969910584 \tabularnewline
108 & 15 & 19.6493873290368 & -4.64938732903676 \tabularnewline
109 & 21 & 21.3279979997044 & -0.327997999704354 \tabularnewline
110 & 21 & 20.6844209670233 & 0.315579032976685 \tabularnewline
111 & 19 & 18.9550488848328 & 0.04495111516719 \tabularnewline
112 & 24 & 17.8354367341568 & 6.16456326584324 \tabularnewline
113 & 20 & 21.7906882736065 & -1.79068827360649 \tabularnewline
114 & 17 & 24.4817908931081 & -7.48179089310812 \tabularnewline
115 & 23 & 24.1480825171234 & -1.14808251712340 \tabularnewline
116 & 24 & 21.8489212595086 & 2.15107874049140 \tabularnewline
117 & 14 & 21.5133116379342 & -7.51331163793423 \tabularnewline
118 & 19 & 22.2915575818798 & -3.29155758187984 \tabularnewline
119 & 24 & 21.6883393680163 & 2.31166063198369 \tabularnewline
120 & 13 & 19.9554033140635 & -6.95540331406355 \tabularnewline
121 & 22 & 24.6845701216641 & -2.68457012166414 \tabularnewline
122 & 16 & 20.5712630248371 & -4.57126302483710 \tabularnewline
123 & 19 & 22.618312797572 & -3.61831279757200 \tabularnewline
124 & 25 & 22.0681640830699 & 2.93183591693012 \tabularnewline
125 & 25 & 23.5050873634967 & 1.49491263650334 \tabularnewline
126 & 23 & 20.8231882533505 & 2.17681174664951 \tabularnewline
127 & 24 & 22.7929918544429 & 1.20700814555708 \tabularnewline
128 & 26 & 22.7760153903175 & 3.22398460968255 \tabularnewline
129 & 26 & 20.8239407163661 & 5.17605928363388 \tabularnewline
130 & 25 & 23.4196018963803 & 1.58039810361965 \tabularnewline
131 & 18 & 21.5862924403213 & -3.58629244032127 \tabularnewline
132 & 21 & 19.2217691974817 & 1.77823080251832 \tabularnewline
133 & 26 & 22.8121279501599 & 3.18787204984014 \tabularnewline
134 & 23 & 21.1803346373576 & 1.81966536264239 \tabularnewline
135 & 23 & 19.0986249459743 & 3.90137505402567 \tabularnewline
136 & 22 & 21.8271507081582 & 0.172849291841754 \tabularnewline
137 & 20 & 21.5923532410073 & -1.59235324100729 \tabularnewline
138 & 13 & 21.2574506098304 & -8.25745060983036 \tabularnewline
139 & 24 & 20.5666968942374 & 3.43330310576262 \tabularnewline
140 & 15 & 20.7204514264666 & -5.72045142646664 \tabularnewline
141 & 14 & 22.2587253215388 & -8.25872532153883 \tabularnewline
142 & 22 & 23.1348164241320 & -1.13481642413203 \tabularnewline
143 & 10 & 17.0104115841435 & -7.01041158414355 \tabularnewline
144 & 24 & 23.4535075069177 & 0.546492493082285 \tabularnewline
145 & 22 & 20.9466707072865 & 1.05332929271347 \tabularnewline
146 & 24 & 24.7574857221061 & -0.757485722106145 \tabularnewline
147 & 19 & 20.7572509061611 & -1.75725090616112 \tabularnewline
148 & 20 & 21.1138228524966 & -1.11382285249664 \tabularnewline
149 & 13 & 16.3426989662444 & -3.34269896624436 \tabularnewline
150 & 20 & 19.1937112950387 & 0.806288704961296 \tabularnewline
151 & 22 & 22.1060433360234 & -0.106043336023380 \tabularnewline
152 & 24 & 22.3702370545459 & 1.62976294545413 \tabularnewline
153 & 29 & 22.2488989615374 & 6.75110103846263 \tabularnewline
154 & 12 & 19.9719025274833 & -7.9719025274833 \tabularnewline
155 & 20 & 19.9143173618147 & 0.085682638185284 \tabularnewline
156 & 21 & 20.3706967506914 & 0.629303249308619 \tabularnewline
157 & 24 & 22.5253383907655 & 1.4746616092345 \tabularnewline
158 & 22 & 20.8189965266684 & 1.18100347333161 \tabularnewline
159 & 20 & 16.8900138546179 & 3.10998614538215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&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]24.1302897682984[/C][C]1.86971023170162[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.347500449936[/C][C]-2.34750044993599[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]25.3741531909091[/C][C]-0.374153190909052[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]23.1772579132161[/C][C]-0.177257913216066[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]22.9627025346874[/C][C]-3.9627025346874[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.0080961429529[/C][C]4.99190385704707[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]25.7574726286814[/C][C]-0.75747262868142[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]23.6268895256234[/C][C]-2.62688952562345[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]23.0878421757866[/C][C]-1.08784217578657[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]23.4977480392664[/C][C]1.50225196073357[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]21.3679953009717[/C][C]2.63200469902834[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]20.9651166190543[/C][C]-2.96511661905435[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]19.5768156034305[/C][C]2.42318439656946[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]21.7617505938414[/C][C]-6.76175059384144[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]24.3446847094334[/C][C]-2.34468470943345[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]25.0573866426857[/C][C]2.9426133573143[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]23.1418160737661[/C][C]-3.14181607376606[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]20.0885460308846[/C][C]-8.08854603088456[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]22.4020666472498[/C][C]1.59793335275023[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]22.4178212677963[/C][C]-2.41782126779630[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]24.1599356442117[/C][C]-3.15993564421175[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]21.7207713443076[/C][C]-1.7207713443076[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.1874106390669[/C][C]0.812589360933083[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]22.0702835732317[/C][C]0.929716426768281[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]22.7733848240246[/C][C]5.22661517597543[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]22.6881037190778[/C][C]1.31189628092218[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]24.2069980566450[/C][C]-0.206998056644954[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]21.2621415655068[/C][C]2.73785843449315[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.7689233232795[/C][C]0.231076676720495[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]23.4182519046109[/C][C]-0.418251904610935[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.9235788348757[/C][C]4.07642116512434[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]22.8138602991234[/C][C]1.18613970087660[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.6363550042354[/C][C]-7.6363550042354[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]26.5043262604116[/C][C]-1.50432626041160[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]23.2392147449499[/C][C]-2.23921474494994[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]27.2607588185069[/C][C]-1.26075881850687[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.7065422389044[/C][C]-3.70654223890439[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]23.4633973100057[/C][C]-1.46339731000574[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]23.2360747192300[/C][C]-1.23607471923005[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]26.0964049825159[/C][C]-3.09640498251590[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]23.5345080535731[/C][C]6.46549194642686[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]23.2377500527786[/C][C]-0.237750052778584[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]19.4455546818638[/C][C]-2.44555468186384[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]24.2548811364202[/C][C]-1.25488113642017[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.7085844194678[/C][C]-1.70858441946782[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.9168367177775[/C][C]2.08316328222251[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]21.3431297321304[/C][C]2.65687026786958[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.7775845411027[/C][C]-3.77758454110267[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]23.4563468637236[/C][C]-0.456346863723595[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]24.2554233597999[/C][C]-3.25542335979989[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]26.0583740164932[/C][C]-2.05837401649321[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]22.2631222780270[/C][C]1.73687772197298[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.9512983774212[/C][C]6.04870162257884[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.4341929789634[/C][C]-5.4341929789634[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]20.3327352745890[/C][C]-0.332735274589025[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.7855453811429[/C][C]5.2144546188571[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]24.1479703243941[/C][C]2.85202967560587[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.4466677209928[/C][C]-1.44666772099284[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]24.1552952274415[/C][C]3.84470477255850[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.7808813048786[/C][C]-4.78088130487858[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]23.1224020852537[/C][C]1.87759791474626[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.7173920375586[/C][C]0.282607962441426[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.8682254035401[/C][C]4.13177459645985[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.4168721842752[/C][C]-0.416872184275207[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.8645315280313[/C][C]0.135468471968737[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.9559642612268[/C][C]3.04403573877321[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.5839333013938[/C][C]2.41606669860615[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.2091984978139[/C][C]-3.20919849781391[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.3939492554754[/C][C]1.60605074452461[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]24.0281252481694[/C][C]0.97187475183056[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]21.0324279182379[/C][C]0.967572081762072[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.5603154456829[/C][C]0.439684554317121[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]22.9655594334452[/C][C]3.03444056655483[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.8231264415642[/C][C]1.17687355843577[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.1432992685926[/C][C]1.85670073140740[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.4342151171374[/C][C]-0.434215117137413[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6498080472023[/C][C]1.35019195279768[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.2290743672796[/C][C]1.77092563272041[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5125165447205[/C][C]5.48748345527954[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.6501217579009[/C][C]-1.65012175790086[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.5658938898989[/C][C]3.43410611010106[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.7192246760689[/C][C]6.28077532393105[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3547789327412[/C][C]0.64522106725879[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]21.9998423814415[/C][C]2.00015761855855[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.0655724329200[/C][C]3.93442756708003[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.3455629774012[/C][C]2.65443702259875[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.658700615808[/C][C]-0.658700615807986[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.1826426837953[/C][C]4.81735731620467[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.0958198385512[/C][C]-0.0958198385511574[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.5310845408482[/C][C]0.468915459151794[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4508240839926[/C][C]3.54917591600735[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.5248908314905[/C][C]2.47510916850947[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.7868440373372[/C][C]1.21315596266276[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.1292611274564[/C][C]-2.12926112745638[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.1935779436435[/C][C]5.80642205635649[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.2047380959745[/C][C]2.79526190402554[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.8290009382884[/C][C]3.17099906171155[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]19.9895407638288[/C][C]1.01045923617116[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.5769389812245[/C][C]0.423061018775502[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.2150775834596[/C][C]-1.21507758345957[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.3464128191757[/C][C]-0.346412819175655[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]20.9850833658009[/C][C]0.0149166341991357[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.5601712420730[/C][C]-3.56017124207303[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.3726876099126[/C][C]1.62731239008742[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.4982485434834[/C][C]7.50175145651664[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.4481587489022[/C][C]-6.44815874890216[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.3886896991058[/C][C]-3.38868969910584[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.6493873290368[/C][C]-4.64938732903676[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.3279979997044[/C][C]-0.327997999704354[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]20.6844209670233[/C][C]0.315579032976685[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]18.9550488848328[/C][C]0.04495111516719[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]17.8354367341568[/C][C]6.16456326584324[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]21.7906882736065[/C][C]-1.79068827360649[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]24.4817908931081[/C][C]-7.48179089310812[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]24.1480825171234[/C][C]-1.14808251712340[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]21.8489212595086[/C][C]2.15107874049140[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]21.5133116379342[/C][C]-7.51331163793423[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.2915575818798[/C][C]-3.29155758187984[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]21.6883393680163[/C][C]2.31166063198369[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]19.9554033140635[/C][C]-6.95540331406355[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]24.6845701216641[/C][C]-2.68457012166414[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]20.5712630248371[/C][C]-4.57126302483710[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]22.618312797572[/C][C]-3.61831279757200[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.0681640830699[/C][C]2.93183591693012[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]23.5050873634967[/C][C]1.49491263650334[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]20.8231882533505[/C][C]2.17681174664951[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]22.7929918544429[/C][C]1.20700814555708[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]22.7760153903175[/C][C]3.22398460968255[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]20.8239407163661[/C][C]5.17605928363388[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]23.4196018963803[/C][C]1.58039810361965[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]21.5862924403213[/C][C]-3.58629244032127[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.2217691974817[/C][C]1.77823080251832[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]22.8121279501599[/C][C]3.18787204984014[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.1803346373576[/C][C]1.81966536264239[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.0986249459743[/C][C]3.90137505402567[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]21.8271507081582[/C][C]0.172849291841754[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]21.5923532410073[/C][C]-1.59235324100729[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]21.2574506098304[/C][C]-8.25745060983036[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]20.5666968942374[/C][C]3.43330310576262[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]20.7204514264666[/C][C]-5.72045142646664[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]22.2587253215388[/C][C]-8.25872532153883[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]23.1348164241320[/C][C]-1.13481642413203[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]17.0104115841435[/C][C]-7.01041158414355[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]23.4535075069177[/C][C]0.546492493082285[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]20.9466707072865[/C][C]1.05332929271347[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]24.7574857221061[/C][C]-0.757485722106145[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]20.7572509061611[/C][C]-1.75725090616112[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]21.1138228524966[/C][C]-1.11382285249664[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]16.3426989662444[/C][C]-3.34269896624436[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]19.1937112950387[/C][C]0.806288704961296[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]22.1060433360234[/C][C]-0.106043336023380[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]22.3702370545459[/C][C]1.62976294545413[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]22.2488989615374[/C][C]6.75110103846263[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]19.9719025274833[/C][C]-7.9719025274833[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.9143173618147[/C][C]0.085682638185284[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.3706967506914[/C][C]0.629303249308619[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]22.5253383907655[/C][C]1.4746616092345[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]20.8189965266684[/C][C]1.18100347333161[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]16.8900138546179[/C][C]3.10998614538215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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
12624.13028976829841.86971023170162
22325.347500449936-2.34750044993599
32525.3741531909091-0.374153190909052
42323.1772579132161-0.177257913216066
51922.9627025346874-3.9627025346874
62924.00809614295294.99190385704707
72525.7574726286814-0.75747262868142
82123.6268895256234-2.62688952562345
92223.0878421757866-1.08784217578657
102523.49774803926641.50225196073357
112421.36799530097172.63200469902834
121820.9651166190543-2.96511661905435
132219.57681560343052.42318439656946
141521.7617505938414-6.76175059384144
152224.3446847094334-2.34468470943345
162825.05738664268572.9426133573143
172023.1418160737661-3.14181607376606
181220.0885460308846-8.08854603088456
192422.40206664724981.59793335275023
202022.4178212677963-2.41782126779630
212124.1599356442117-3.15993564421175
222021.7207713443076-1.7207713443076
232120.18741063906690.812589360933083
242322.07028357323170.929716426768281
252822.77338482402465.22661517597543
262422.68810371907781.31189628092218
272424.2069980566450-0.206998056644954
282421.26214156550682.73785843449315
292322.76892332327950.231076676720495
302323.4182519046109-0.418251904610935
312924.92357883487574.07642116512434
322422.81386029912341.18613970087660
331825.6363550042354-7.6363550042354
342526.5043262604116-1.50432626041160
352123.2392147449499-2.23921474494994
362627.2607588185069-1.26075881850687
372225.7065422389044-3.70654223890439
382223.4633973100057-1.46339731000574
392223.2360747192300-1.23607471923005
402326.0964049825159-3.09640498251590
413023.53450805357316.46549194642686
422323.2377500527786-0.237750052778584
431719.4455546818638-2.44555468186384
442324.2548811364202-1.25488113642017
452324.7085844194678-1.70858441946782
462522.91683671777752.08316328222251
472421.34312973213042.65687026786958
482427.7775845411027-3.77758454110267
492323.4563468637236-0.456346863723595
502124.2554233597999-3.25542335979989
512426.0583740164932-2.05837401649321
522422.26312227802701.73687772197298
532821.95129837742126.04870162257884
541621.4341929789634-5.4341929789634
552020.3327352745890-0.332735274589025
562923.78554538114295.2144546188571
572724.14797032439412.85202967560587
582223.4466677209928-1.44666772099284
592824.15529522744153.84470477255850
601620.7808813048786-4.78088130487858
612523.12240208525371.87759791474626
622423.71739203755860.282607962441426
632823.86822540354014.13177459645985
642424.4168721842752-0.416872184275207
652322.86453152803130.135468471968737
663026.95596426122683.04403573877321
672421.58393330139382.41606669860615
682124.2091984978139-3.20919849781391
692523.39394925547541.60605074452461
702524.02812524816940.97187475183056
712221.03242791823790.967572081762072
722322.56031544568290.439684554317121
732622.96555943344523.03444056655483
742321.82312644156421.17687355843577
752523.14329926859261.85670073140740
762121.4342151171374-0.434215117137413
772523.64980804720231.35019195279768
782422.22907436727961.77092563272041
792923.51251654472055.48748345527954
802223.6501217579009-1.65012175790086
812723.56589388989893.43410611010106
822619.71922467606896.28077532393105
832221.35477893274120.64522106725879
842421.99984238144152.00015761855855
852723.06557243292003.93442756708003
862421.34556297740122.65443702259875
872424.658700615808-0.658700615807986
882924.18264268379534.81735731620467
892222.0958198385512-0.0958198385511574
902120.53108454084820.468915459151794
912420.45082408399263.54917591600735
922421.52489083149052.47510916850947
932321.78684403733721.21315596266276
942022.1292611274564-2.12926112745638
952721.19357794364355.80642205635649
962623.20473809597452.79526190402554
972521.82900093828843.17099906171155
982119.98954076382881.01045923617116
992120.57693898122450.423061018775502
1001920.2150775834596-1.21507758345957
1012121.3464128191757-0.346412819175655
1022120.98508336580090.0149166341991357
1031619.5601712420730-3.56017124207303
1042220.37268760991261.62731239008742
1052921.49824854348347.50175145651664
1061521.4481587489022-6.44815874890216
1071720.3886896991058-3.38868969910584
1081519.6493873290368-4.64938732903676
1092121.3279979997044-0.327997999704354
1102120.68442096702330.315579032976685
1111918.95504888483280.04495111516719
1122417.83543673415686.16456326584324
1132021.7906882736065-1.79068827360649
1141724.4817908931081-7.48179089310812
1152324.1480825171234-1.14808251712340
1162421.84892125950862.15107874049140
1171421.5133116379342-7.51331163793423
1181922.2915575818798-3.29155758187984
1192421.68833936801632.31166063198369
1201319.9554033140635-6.95540331406355
1212224.6845701216641-2.68457012166414
1221620.5712630248371-4.57126302483710
1231922.618312797572-3.61831279757200
1242522.06816408306992.93183591693012
1252523.50508736349671.49491263650334
1262320.82318825335052.17681174664951
1272422.79299185444291.20700814555708
1282622.77601539031753.22398460968255
1292620.82394071636615.17605928363388
1302523.41960189638031.58039810361965
1311821.5862924403213-3.58629244032127
1322119.22176919748171.77823080251832
1332622.81212795015993.18787204984014
1342321.18033463735761.81966536264239
1352319.09862494597433.90137505402567
1362221.82715070815820.172849291841754
1372021.5923532410073-1.59235324100729
1381321.2574506098304-8.25745060983036
1392420.56669689423743.43330310576262
1401520.7204514264666-5.72045142646664
1411422.2587253215388-8.25872532153883
1422223.1348164241320-1.13481642413203
1431017.0104115841435-7.01041158414355
1442423.45350750691770.546492493082285
1452220.94667070728651.05332929271347
1462424.7574857221061-0.757485722106145
1471920.7572509061611-1.75725090616112
1482021.1138228524966-1.11382285249664
1491316.3426989662444-3.34269896624436
1502019.19371129503870.806288704961296
1512222.1060433360234-0.106043336023380
1522422.37023705454591.62976294545413
1532922.24889896153746.75110103846263
1541219.9719025274833-7.9719025274833
1552019.91431736181470.085682638185284
1562120.37069675069140.629303249308619
1572422.52533839076551.4746616092345
1582220.81899652666841.18100347333161
1592016.89001385461793.10998614538215






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3888119205789580.7776238411579160.611188079421042
120.3271609506185950.654321901237190.672839049381405
130.6054959526754520.7890080946490960.394504047324548
140.7989457399692450.402108520061510.201054260030755
150.7358930750394960.5282138499210080.264106924960504
160.7287997300884690.5424005398230610.271200269911531
170.6462548381272970.7074903237454050.353745161872703
180.7705510485505740.4588979028988530.229448951449426
190.7125536777042180.5748926445915630.287446322295782
200.6585370735619950.6829258528760110.341462926438005
210.5902127871100680.8195744257798630.409787212889932
220.5146036738187430.9707926523625140.485396326181257
230.5019114708571620.9961770582856770.498088529142838
240.5416610351044230.9166779297911540.458338964895577
250.6751287524351910.6497424951296180.324871247564809
260.6082066760991980.7835866478016030.391793323900802
270.5496778760248140.9006442479503710.450322123975186
280.5132061612499260.9735876775001480.486793838750074
290.4510428306991730.9020856613983470.548957169300827
300.394921652974090.789843305948180.60507834702591
310.3911365887191210.7822731774382430.608863411280879
320.3303106556751820.6606213113503640.669689344324818
330.6219342241277720.7561315517444560.378065775872228
340.5776768708595110.8446462582809780.422323129140489
350.55322236800280.89355526399440.4467776319972
360.4970716997907720.9941433995815440.502928300209228
370.4830148942217290.9660297884434570.516985105778272
380.4319044530522560.8638089061045120.568095546947744
390.3825548415100720.7651096830201440.617445158489928
400.3595262734836140.7190525469672270.640473726516386
410.5097023993446720.9805952013106560.490297600655328
420.4559547596677270.9119095193354530.544045240332273
430.4370202503257670.8740405006515330.562979749674233
440.3904145028563260.7808290057126520.609585497143674
450.3509042471563130.7018084943126260.649095752843687
460.3201695611115520.6403391222231050.679830438888448
470.2906118007357920.5812236014715850.709388199264208
480.2871705649557130.5743411299114260.712829435044287
490.2487347569393150.497469513878630.751265243060685
500.252832407244920.505664814489840.74716759275508
510.2320680949045100.4641361898090210.76793190509549
520.1997209588839840.3994419177679670.800279041116016
530.2581902658740270.5163805317480530.741809734125973
540.3448581922029280.6897163844058560.655141807797072
550.3309725494094380.6619450988188760.669027450590562
560.3834857686921670.7669715373843350.616514231307833
570.3579419193720680.7158838387441360.642058080627932
580.3295963100279610.6591926200559220.670403689972039
590.3280651805548360.6561303611096720.671934819445164
600.4179175113748390.8358350227496770.582082488625161
610.3733737209164060.7467474418328120.626626279083594
620.3306895682581530.6613791365163050.669310431741847
630.3284689447310430.6569378894620870.671531055268957
640.2945056332805400.5890112665610790.70549436671946
650.2562162389695010.5124324779390020.743783761030499
660.2383317882999490.4766635765998980.761668211700051
670.2078796577639050.415759315527810.792120342236095
680.2316081701556450.463216340311290.768391829844355
690.1985057940658820.3970115881317640.801494205934118
700.1669370510447580.3338741020895160.833062948955242
710.1389054926604550.2778109853209090.861094507339545
720.1151865074430960.2303730148861920.884813492556904
730.0986535742556520.1973071485113040.901346425744348
740.07964463577530870.1592892715506170.920355364224691
750.06393846797183660.1278769359436730.936061532028163
760.05377455811341810.1075491162268360.946225441886582
770.04218418428591640.08436836857183280.957815815714084
780.03257149363446550.0651429872689310.967428506365535
790.03677726196256640.07355452392513270.963222738037434
800.03435947671592320.06871895343184640.965640523284077
810.02847976954018190.05695953908036390.971520230459818
820.03699074989941210.07398149979882420.963009250100588
830.02891479061725920.05782958123451840.97108520938274
840.02252112476736540.04504224953473080.977478875232635
850.02017250079394490.04034500158788970.979827499206055
860.0156704633593250.031340926718650.984329536640675
870.01281653732293350.02563307464586710.987183462677066
880.01311180702030990.02622361404061970.98688819297969
890.01109681059684280.02219362119368560.988903189403157
900.008435632673667810.01687126534733560.991564367326332
910.007009882311874630.01401976462374930.992990117688125
920.005684285655983080.01136857131196620.994315714344017
930.004172385724984800.008344771449969610.995827614275015
940.004107159012126370.008214318024252740.995892840987874
950.006095032815711120.01219006563142220.993904967184289
960.0049235407924840.0098470815849680.995076459207516
970.004132592073915490.008265184147830980.995867407926085
980.003183519546736040.006367039093472080.996816480453264
990.002416002782183520.004832005564367040.997583997217816
1000.002045476543347740.004090953086695490.997954523456652
1010.001621383270851660.003242766541703320.998378616729148
1020.001194824776916670.002389649553833340.998805175223083
1030.001514341498144370.003028682996288740.998485658501856
1040.001178049046612250.002356098093224490.998821950953388
1050.005144787914121130.01028957582824230.994855212085879
1060.01297522783279850.02595045566559690.987024772167202
1070.01376218603548950.02752437207097910.98623781396451
1080.01884532676345050.03769065352690110.98115467323655
1090.01468696022822920.02937392045645840.98531303977177
1100.01063908807700780.02127817615401560.989360911922992
1110.007680369153452230.01536073830690450.992319630846548
1120.02212775385329220.04425550770658440.977872246146708
1130.02211825922263240.04423651844526480.977881740777368
1140.05532848596498740.1106569719299750.944671514035013
1150.04713639170565680.09427278341131360.952863608294343
1160.03942747995681460.07885495991362920.960572520043185
1170.1037658932952070.2075317865904140.896234106704793
1180.0979247994118950.195849598823790.902075200588105
1190.08724583162094140.1744916632418830.912754168379059
1200.1523436132538380.3046872265076760.847656386746162
1210.1495101123288110.2990202246576220.850489887671189
1220.1898212314671350.3796424629342710.810178768532865
1230.1892017623483830.3784035246967670.810798237651617
1240.179172696914950.35834539382990.82082730308505
1250.1551863820878950.3103727641757900.844813617912105
1260.1243628190448060.2487256380896110.875637180955194
1270.09731904476253530.1946380895250710.902680955237465
1280.09474043605050620.1894808721010120.905259563949494
1290.132633760853390.265267521706780.86736623914661
1300.1146561010108530.2293122020217070.885343898989147
1310.09610701241889340.1922140248377870.903892987581107
1320.08526733619917320.1705346723983460.914732663800827
1330.08332499376890050.1666499875378010.9166750062311
1340.07409555307210050.1481911061442010.9259044469279
1350.1731010135385920.3462020270771850.826898986461408
1360.1380309681349250.2760619362698500.861969031865075
1370.1156667783362390.2313335566724780.884333221663761
1380.163171768623360.326343537246720.83682823137664
1390.3949034503525630.7898069007051260.605096549647437
1400.3404830589815740.6809661179631480.659516941018426
1410.4844937380520160.9689874761040310.515506261947984
1420.3932680680070860.7865361360141710.606731931992914
1430.5564286642972340.8871426714055310.443571335702766
1440.5025856130784580.9948287738430850.497414386921542
1450.4056433860077280.8112867720154570.594356613992272
1460.3008476352833820.6016952705667640.699152364716618
1470.292262080086770.584524160173540.70773791991323
1480.1744211088474480.3488422176948960.825578891152552

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.388811920578958 & 0.777623841157916 & 0.611188079421042 \tabularnewline
12 & 0.327160950618595 & 0.65432190123719 & 0.672839049381405 \tabularnewline
13 & 0.605495952675452 & 0.789008094649096 & 0.394504047324548 \tabularnewline
14 & 0.798945739969245 & 0.40210852006151 & 0.201054260030755 \tabularnewline
15 & 0.735893075039496 & 0.528213849921008 & 0.264106924960504 \tabularnewline
16 & 0.728799730088469 & 0.542400539823061 & 0.271200269911531 \tabularnewline
17 & 0.646254838127297 & 0.707490323745405 & 0.353745161872703 \tabularnewline
18 & 0.770551048550574 & 0.458897902898853 & 0.229448951449426 \tabularnewline
19 & 0.712553677704218 & 0.574892644591563 & 0.287446322295782 \tabularnewline
20 & 0.658537073561995 & 0.682925852876011 & 0.341462926438005 \tabularnewline
21 & 0.590212787110068 & 0.819574425779863 & 0.409787212889932 \tabularnewline
22 & 0.514603673818743 & 0.970792652362514 & 0.485396326181257 \tabularnewline
23 & 0.501911470857162 & 0.996177058285677 & 0.498088529142838 \tabularnewline
24 & 0.541661035104423 & 0.916677929791154 & 0.458338964895577 \tabularnewline
25 & 0.675128752435191 & 0.649742495129618 & 0.324871247564809 \tabularnewline
26 & 0.608206676099198 & 0.783586647801603 & 0.391793323900802 \tabularnewline
27 & 0.549677876024814 & 0.900644247950371 & 0.450322123975186 \tabularnewline
28 & 0.513206161249926 & 0.973587677500148 & 0.486793838750074 \tabularnewline
29 & 0.451042830699173 & 0.902085661398347 & 0.548957169300827 \tabularnewline
30 & 0.39492165297409 & 0.78984330594818 & 0.60507834702591 \tabularnewline
31 & 0.391136588719121 & 0.782273177438243 & 0.608863411280879 \tabularnewline
32 & 0.330310655675182 & 0.660621311350364 & 0.669689344324818 \tabularnewline
33 & 0.621934224127772 & 0.756131551744456 & 0.378065775872228 \tabularnewline
34 & 0.577676870859511 & 0.844646258280978 & 0.422323129140489 \tabularnewline
35 & 0.5532223680028 & 0.8935552639944 & 0.4467776319972 \tabularnewline
36 & 0.497071699790772 & 0.994143399581544 & 0.502928300209228 \tabularnewline
37 & 0.483014894221729 & 0.966029788443457 & 0.516985105778272 \tabularnewline
38 & 0.431904453052256 & 0.863808906104512 & 0.568095546947744 \tabularnewline
39 & 0.382554841510072 & 0.765109683020144 & 0.617445158489928 \tabularnewline
40 & 0.359526273483614 & 0.719052546967227 & 0.640473726516386 \tabularnewline
41 & 0.509702399344672 & 0.980595201310656 & 0.490297600655328 \tabularnewline
42 & 0.455954759667727 & 0.911909519335453 & 0.544045240332273 \tabularnewline
43 & 0.437020250325767 & 0.874040500651533 & 0.562979749674233 \tabularnewline
44 & 0.390414502856326 & 0.780829005712652 & 0.609585497143674 \tabularnewline
45 & 0.350904247156313 & 0.701808494312626 & 0.649095752843687 \tabularnewline
46 & 0.320169561111552 & 0.640339122223105 & 0.679830438888448 \tabularnewline
47 & 0.290611800735792 & 0.581223601471585 & 0.709388199264208 \tabularnewline
48 & 0.287170564955713 & 0.574341129911426 & 0.712829435044287 \tabularnewline
49 & 0.248734756939315 & 0.49746951387863 & 0.751265243060685 \tabularnewline
50 & 0.25283240724492 & 0.50566481448984 & 0.74716759275508 \tabularnewline
51 & 0.232068094904510 & 0.464136189809021 & 0.76793190509549 \tabularnewline
52 & 0.199720958883984 & 0.399441917767967 & 0.800279041116016 \tabularnewline
53 & 0.258190265874027 & 0.516380531748053 & 0.741809734125973 \tabularnewline
54 & 0.344858192202928 & 0.689716384405856 & 0.655141807797072 \tabularnewline
55 & 0.330972549409438 & 0.661945098818876 & 0.669027450590562 \tabularnewline
56 & 0.383485768692167 & 0.766971537384335 & 0.616514231307833 \tabularnewline
57 & 0.357941919372068 & 0.715883838744136 & 0.642058080627932 \tabularnewline
58 & 0.329596310027961 & 0.659192620055922 & 0.670403689972039 \tabularnewline
59 & 0.328065180554836 & 0.656130361109672 & 0.671934819445164 \tabularnewline
60 & 0.417917511374839 & 0.835835022749677 & 0.582082488625161 \tabularnewline
61 & 0.373373720916406 & 0.746747441832812 & 0.626626279083594 \tabularnewline
62 & 0.330689568258153 & 0.661379136516305 & 0.669310431741847 \tabularnewline
63 & 0.328468944731043 & 0.656937889462087 & 0.671531055268957 \tabularnewline
64 & 0.294505633280540 & 0.589011266561079 & 0.70549436671946 \tabularnewline
65 & 0.256216238969501 & 0.512432477939002 & 0.743783761030499 \tabularnewline
66 & 0.238331788299949 & 0.476663576599898 & 0.761668211700051 \tabularnewline
67 & 0.207879657763905 & 0.41575931552781 & 0.792120342236095 \tabularnewline
68 & 0.231608170155645 & 0.46321634031129 & 0.768391829844355 \tabularnewline
69 & 0.198505794065882 & 0.397011588131764 & 0.801494205934118 \tabularnewline
70 & 0.166937051044758 & 0.333874102089516 & 0.833062948955242 \tabularnewline
71 & 0.138905492660455 & 0.277810985320909 & 0.861094507339545 \tabularnewline
72 & 0.115186507443096 & 0.230373014886192 & 0.884813492556904 \tabularnewline
73 & 0.098653574255652 & 0.197307148511304 & 0.901346425744348 \tabularnewline
74 & 0.0796446357753087 & 0.159289271550617 & 0.920355364224691 \tabularnewline
75 & 0.0639384679718366 & 0.127876935943673 & 0.936061532028163 \tabularnewline
76 & 0.0537745581134181 & 0.107549116226836 & 0.946225441886582 \tabularnewline
77 & 0.0421841842859164 & 0.0843683685718328 & 0.957815815714084 \tabularnewline
78 & 0.0325714936344655 & 0.065142987268931 & 0.967428506365535 \tabularnewline
79 & 0.0367772619625664 & 0.0735545239251327 & 0.963222738037434 \tabularnewline
80 & 0.0343594767159232 & 0.0687189534318464 & 0.965640523284077 \tabularnewline
81 & 0.0284797695401819 & 0.0569595390803639 & 0.971520230459818 \tabularnewline
82 & 0.0369907498994121 & 0.0739814997988242 & 0.963009250100588 \tabularnewline
83 & 0.0289147906172592 & 0.0578295812345184 & 0.97108520938274 \tabularnewline
84 & 0.0225211247673654 & 0.0450422495347308 & 0.977478875232635 \tabularnewline
85 & 0.0201725007939449 & 0.0403450015878897 & 0.979827499206055 \tabularnewline
86 & 0.015670463359325 & 0.03134092671865 & 0.984329536640675 \tabularnewline
87 & 0.0128165373229335 & 0.0256330746458671 & 0.987183462677066 \tabularnewline
88 & 0.0131118070203099 & 0.0262236140406197 & 0.98688819297969 \tabularnewline
89 & 0.0110968105968428 & 0.0221936211936856 & 0.988903189403157 \tabularnewline
90 & 0.00843563267366781 & 0.0168712653473356 & 0.991564367326332 \tabularnewline
91 & 0.00700988231187463 & 0.0140197646237493 & 0.992990117688125 \tabularnewline
92 & 0.00568428565598308 & 0.0113685713119662 & 0.994315714344017 \tabularnewline
93 & 0.00417238572498480 & 0.00834477144996961 & 0.995827614275015 \tabularnewline
94 & 0.00410715901212637 & 0.00821431802425274 & 0.995892840987874 \tabularnewline
95 & 0.00609503281571112 & 0.0121900656314222 & 0.993904967184289 \tabularnewline
96 & 0.004923540792484 & 0.009847081584968 & 0.995076459207516 \tabularnewline
97 & 0.00413259207391549 & 0.00826518414783098 & 0.995867407926085 \tabularnewline
98 & 0.00318351954673604 & 0.00636703909347208 & 0.996816480453264 \tabularnewline
99 & 0.00241600278218352 & 0.00483200556436704 & 0.997583997217816 \tabularnewline
100 & 0.00204547654334774 & 0.00409095308669549 & 0.997954523456652 \tabularnewline
101 & 0.00162138327085166 & 0.00324276654170332 & 0.998378616729148 \tabularnewline
102 & 0.00119482477691667 & 0.00238964955383334 & 0.998805175223083 \tabularnewline
103 & 0.00151434149814437 & 0.00302868299628874 & 0.998485658501856 \tabularnewline
104 & 0.00117804904661225 & 0.00235609809322449 & 0.998821950953388 \tabularnewline
105 & 0.00514478791412113 & 0.0102895758282423 & 0.994855212085879 \tabularnewline
106 & 0.0129752278327985 & 0.0259504556655969 & 0.987024772167202 \tabularnewline
107 & 0.0137621860354895 & 0.0275243720709791 & 0.98623781396451 \tabularnewline
108 & 0.0188453267634505 & 0.0376906535269011 & 0.98115467323655 \tabularnewline
109 & 0.0146869602282292 & 0.0293739204564584 & 0.98531303977177 \tabularnewline
110 & 0.0106390880770078 & 0.0212781761540156 & 0.989360911922992 \tabularnewline
111 & 0.00768036915345223 & 0.0153607383069045 & 0.992319630846548 \tabularnewline
112 & 0.0221277538532922 & 0.0442555077065844 & 0.977872246146708 \tabularnewline
113 & 0.0221182592226324 & 0.0442365184452648 & 0.977881740777368 \tabularnewline
114 & 0.0553284859649874 & 0.110656971929975 & 0.944671514035013 \tabularnewline
115 & 0.0471363917056568 & 0.0942727834113136 & 0.952863608294343 \tabularnewline
116 & 0.0394274799568146 & 0.0788549599136292 & 0.960572520043185 \tabularnewline
117 & 0.103765893295207 & 0.207531786590414 & 0.896234106704793 \tabularnewline
118 & 0.097924799411895 & 0.19584959882379 & 0.902075200588105 \tabularnewline
119 & 0.0872458316209414 & 0.174491663241883 & 0.912754168379059 \tabularnewline
120 & 0.152343613253838 & 0.304687226507676 & 0.847656386746162 \tabularnewline
121 & 0.149510112328811 & 0.299020224657622 & 0.850489887671189 \tabularnewline
122 & 0.189821231467135 & 0.379642462934271 & 0.810178768532865 \tabularnewline
123 & 0.189201762348383 & 0.378403524696767 & 0.810798237651617 \tabularnewline
124 & 0.17917269691495 & 0.3583453938299 & 0.82082730308505 \tabularnewline
125 & 0.155186382087895 & 0.310372764175790 & 0.844813617912105 \tabularnewline
126 & 0.124362819044806 & 0.248725638089611 & 0.875637180955194 \tabularnewline
127 & 0.0973190447625353 & 0.194638089525071 & 0.902680955237465 \tabularnewline
128 & 0.0947404360505062 & 0.189480872101012 & 0.905259563949494 \tabularnewline
129 & 0.13263376085339 & 0.26526752170678 & 0.86736623914661 \tabularnewline
130 & 0.114656101010853 & 0.229312202021707 & 0.885343898989147 \tabularnewline
131 & 0.0961070124188934 & 0.192214024837787 & 0.903892987581107 \tabularnewline
132 & 0.0852673361991732 & 0.170534672398346 & 0.914732663800827 \tabularnewline
133 & 0.0833249937689005 & 0.166649987537801 & 0.9166750062311 \tabularnewline
134 & 0.0740955530721005 & 0.148191106144201 & 0.9259044469279 \tabularnewline
135 & 0.173101013538592 & 0.346202027077185 & 0.826898986461408 \tabularnewline
136 & 0.138030968134925 & 0.276061936269850 & 0.861969031865075 \tabularnewline
137 & 0.115666778336239 & 0.231333556672478 & 0.884333221663761 \tabularnewline
138 & 0.16317176862336 & 0.32634353724672 & 0.83682823137664 \tabularnewline
139 & 0.394903450352563 & 0.789806900705126 & 0.605096549647437 \tabularnewline
140 & 0.340483058981574 & 0.680966117963148 & 0.659516941018426 \tabularnewline
141 & 0.484493738052016 & 0.968987476104031 & 0.515506261947984 \tabularnewline
142 & 0.393268068007086 & 0.786536136014171 & 0.606731931992914 \tabularnewline
143 & 0.556428664297234 & 0.887142671405531 & 0.443571335702766 \tabularnewline
144 & 0.502585613078458 & 0.994828773843085 & 0.497414386921542 \tabularnewline
145 & 0.405643386007728 & 0.811286772015457 & 0.594356613992272 \tabularnewline
146 & 0.300847635283382 & 0.601695270566764 & 0.699152364716618 \tabularnewline
147 & 0.29226208008677 & 0.58452416017354 & 0.70773791991323 \tabularnewline
148 & 0.174421108847448 & 0.348842217694896 & 0.825578891152552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&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]0.388811920578958[/C][C]0.777623841157916[/C][C]0.611188079421042[/C][/ROW]
[ROW][C]12[/C][C]0.327160950618595[/C][C]0.65432190123719[/C][C]0.672839049381405[/C][/ROW]
[ROW][C]13[/C][C]0.605495952675452[/C][C]0.789008094649096[/C][C]0.394504047324548[/C][/ROW]
[ROW][C]14[/C][C]0.798945739969245[/C][C]0.40210852006151[/C][C]0.201054260030755[/C][/ROW]
[ROW][C]15[/C][C]0.735893075039496[/C][C]0.528213849921008[/C][C]0.264106924960504[/C][/ROW]
[ROW][C]16[/C][C]0.728799730088469[/C][C]0.542400539823061[/C][C]0.271200269911531[/C][/ROW]
[ROW][C]17[/C][C]0.646254838127297[/C][C]0.707490323745405[/C][C]0.353745161872703[/C][/ROW]
[ROW][C]18[/C][C]0.770551048550574[/C][C]0.458897902898853[/C][C]0.229448951449426[/C][/ROW]
[ROW][C]19[/C][C]0.712553677704218[/C][C]0.574892644591563[/C][C]0.287446322295782[/C][/ROW]
[ROW][C]20[/C][C]0.658537073561995[/C][C]0.682925852876011[/C][C]0.341462926438005[/C][/ROW]
[ROW][C]21[/C][C]0.590212787110068[/C][C]0.819574425779863[/C][C]0.409787212889932[/C][/ROW]
[ROW][C]22[/C][C]0.514603673818743[/C][C]0.970792652362514[/C][C]0.485396326181257[/C][/ROW]
[ROW][C]23[/C][C]0.501911470857162[/C][C]0.996177058285677[/C][C]0.498088529142838[/C][/ROW]
[ROW][C]24[/C][C]0.541661035104423[/C][C]0.916677929791154[/C][C]0.458338964895577[/C][/ROW]
[ROW][C]25[/C][C]0.675128752435191[/C][C]0.649742495129618[/C][C]0.324871247564809[/C][/ROW]
[ROW][C]26[/C][C]0.608206676099198[/C][C]0.783586647801603[/C][C]0.391793323900802[/C][/ROW]
[ROW][C]27[/C][C]0.549677876024814[/C][C]0.900644247950371[/C][C]0.450322123975186[/C][/ROW]
[ROW][C]28[/C][C]0.513206161249926[/C][C]0.973587677500148[/C][C]0.486793838750074[/C][/ROW]
[ROW][C]29[/C][C]0.451042830699173[/C][C]0.902085661398347[/C][C]0.548957169300827[/C][/ROW]
[ROW][C]30[/C][C]0.39492165297409[/C][C]0.78984330594818[/C][C]0.60507834702591[/C][/ROW]
[ROW][C]31[/C][C]0.391136588719121[/C][C]0.782273177438243[/C][C]0.608863411280879[/C][/ROW]
[ROW][C]32[/C][C]0.330310655675182[/C][C]0.660621311350364[/C][C]0.669689344324818[/C][/ROW]
[ROW][C]33[/C][C]0.621934224127772[/C][C]0.756131551744456[/C][C]0.378065775872228[/C][/ROW]
[ROW][C]34[/C][C]0.577676870859511[/C][C]0.844646258280978[/C][C]0.422323129140489[/C][/ROW]
[ROW][C]35[/C][C]0.5532223680028[/C][C]0.8935552639944[/C][C]0.4467776319972[/C][/ROW]
[ROW][C]36[/C][C]0.497071699790772[/C][C]0.994143399581544[/C][C]0.502928300209228[/C][/ROW]
[ROW][C]37[/C][C]0.483014894221729[/C][C]0.966029788443457[/C][C]0.516985105778272[/C][/ROW]
[ROW][C]38[/C][C]0.431904453052256[/C][C]0.863808906104512[/C][C]0.568095546947744[/C][/ROW]
[ROW][C]39[/C][C]0.382554841510072[/C][C]0.765109683020144[/C][C]0.617445158489928[/C][/ROW]
[ROW][C]40[/C][C]0.359526273483614[/C][C]0.719052546967227[/C][C]0.640473726516386[/C][/ROW]
[ROW][C]41[/C][C]0.509702399344672[/C][C]0.980595201310656[/C][C]0.490297600655328[/C][/ROW]
[ROW][C]42[/C][C]0.455954759667727[/C][C]0.911909519335453[/C][C]0.544045240332273[/C][/ROW]
[ROW][C]43[/C][C]0.437020250325767[/C][C]0.874040500651533[/C][C]0.562979749674233[/C][/ROW]
[ROW][C]44[/C][C]0.390414502856326[/C][C]0.780829005712652[/C][C]0.609585497143674[/C][/ROW]
[ROW][C]45[/C][C]0.350904247156313[/C][C]0.701808494312626[/C][C]0.649095752843687[/C][/ROW]
[ROW][C]46[/C][C]0.320169561111552[/C][C]0.640339122223105[/C][C]0.679830438888448[/C][/ROW]
[ROW][C]47[/C][C]0.290611800735792[/C][C]0.581223601471585[/C][C]0.709388199264208[/C][/ROW]
[ROW][C]48[/C][C]0.287170564955713[/C][C]0.574341129911426[/C][C]0.712829435044287[/C][/ROW]
[ROW][C]49[/C][C]0.248734756939315[/C][C]0.49746951387863[/C][C]0.751265243060685[/C][/ROW]
[ROW][C]50[/C][C]0.25283240724492[/C][C]0.50566481448984[/C][C]0.74716759275508[/C][/ROW]
[ROW][C]51[/C][C]0.232068094904510[/C][C]0.464136189809021[/C][C]0.76793190509549[/C][/ROW]
[ROW][C]52[/C][C]0.199720958883984[/C][C]0.399441917767967[/C][C]0.800279041116016[/C][/ROW]
[ROW][C]53[/C][C]0.258190265874027[/C][C]0.516380531748053[/C][C]0.741809734125973[/C][/ROW]
[ROW][C]54[/C][C]0.344858192202928[/C][C]0.689716384405856[/C][C]0.655141807797072[/C][/ROW]
[ROW][C]55[/C][C]0.330972549409438[/C][C]0.661945098818876[/C][C]0.669027450590562[/C][/ROW]
[ROW][C]56[/C][C]0.383485768692167[/C][C]0.766971537384335[/C][C]0.616514231307833[/C][/ROW]
[ROW][C]57[/C][C]0.357941919372068[/C][C]0.715883838744136[/C][C]0.642058080627932[/C][/ROW]
[ROW][C]58[/C][C]0.329596310027961[/C][C]0.659192620055922[/C][C]0.670403689972039[/C][/ROW]
[ROW][C]59[/C][C]0.328065180554836[/C][C]0.656130361109672[/C][C]0.671934819445164[/C][/ROW]
[ROW][C]60[/C][C]0.417917511374839[/C][C]0.835835022749677[/C][C]0.582082488625161[/C][/ROW]
[ROW][C]61[/C][C]0.373373720916406[/C][C]0.746747441832812[/C][C]0.626626279083594[/C][/ROW]
[ROW][C]62[/C][C]0.330689568258153[/C][C]0.661379136516305[/C][C]0.669310431741847[/C][/ROW]
[ROW][C]63[/C][C]0.328468944731043[/C][C]0.656937889462087[/C][C]0.671531055268957[/C][/ROW]
[ROW][C]64[/C][C]0.294505633280540[/C][C]0.589011266561079[/C][C]0.70549436671946[/C][/ROW]
[ROW][C]65[/C][C]0.256216238969501[/C][C]0.512432477939002[/C][C]0.743783761030499[/C][/ROW]
[ROW][C]66[/C][C]0.238331788299949[/C][C]0.476663576599898[/C][C]0.761668211700051[/C][/ROW]
[ROW][C]67[/C][C]0.207879657763905[/C][C]0.41575931552781[/C][C]0.792120342236095[/C][/ROW]
[ROW][C]68[/C][C]0.231608170155645[/C][C]0.46321634031129[/C][C]0.768391829844355[/C][/ROW]
[ROW][C]69[/C][C]0.198505794065882[/C][C]0.397011588131764[/C][C]0.801494205934118[/C][/ROW]
[ROW][C]70[/C][C]0.166937051044758[/C][C]0.333874102089516[/C][C]0.833062948955242[/C][/ROW]
[ROW][C]71[/C][C]0.138905492660455[/C][C]0.277810985320909[/C][C]0.861094507339545[/C][/ROW]
[ROW][C]72[/C][C]0.115186507443096[/C][C]0.230373014886192[/C][C]0.884813492556904[/C][/ROW]
[ROW][C]73[/C][C]0.098653574255652[/C][C]0.197307148511304[/C][C]0.901346425744348[/C][/ROW]
[ROW][C]74[/C][C]0.0796446357753087[/C][C]0.159289271550617[/C][C]0.920355364224691[/C][/ROW]
[ROW][C]75[/C][C]0.0639384679718366[/C][C]0.127876935943673[/C][C]0.936061532028163[/C][/ROW]
[ROW][C]76[/C][C]0.0537745581134181[/C][C]0.107549116226836[/C][C]0.946225441886582[/C][/ROW]
[ROW][C]77[/C][C]0.0421841842859164[/C][C]0.0843683685718328[/C][C]0.957815815714084[/C][/ROW]
[ROW][C]78[/C][C]0.0325714936344655[/C][C]0.065142987268931[/C][C]0.967428506365535[/C][/ROW]
[ROW][C]79[/C][C]0.0367772619625664[/C][C]0.0735545239251327[/C][C]0.963222738037434[/C][/ROW]
[ROW][C]80[/C][C]0.0343594767159232[/C][C]0.0687189534318464[/C][C]0.965640523284077[/C][/ROW]
[ROW][C]81[/C][C]0.0284797695401819[/C][C]0.0569595390803639[/C][C]0.971520230459818[/C][/ROW]
[ROW][C]82[/C][C]0.0369907498994121[/C][C]0.0739814997988242[/C][C]0.963009250100588[/C][/ROW]
[ROW][C]83[/C][C]0.0289147906172592[/C][C]0.0578295812345184[/C][C]0.97108520938274[/C][/ROW]
[ROW][C]84[/C][C]0.0225211247673654[/C][C]0.0450422495347308[/C][C]0.977478875232635[/C][/ROW]
[ROW][C]85[/C][C]0.0201725007939449[/C][C]0.0403450015878897[/C][C]0.979827499206055[/C][/ROW]
[ROW][C]86[/C][C]0.015670463359325[/C][C]0.03134092671865[/C][C]0.984329536640675[/C][/ROW]
[ROW][C]87[/C][C]0.0128165373229335[/C][C]0.0256330746458671[/C][C]0.987183462677066[/C][/ROW]
[ROW][C]88[/C][C]0.0131118070203099[/C][C]0.0262236140406197[/C][C]0.98688819297969[/C][/ROW]
[ROW][C]89[/C][C]0.0110968105968428[/C][C]0.0221936211936856[/C][C]0.988903189403157[/C][/ROW]
[ROW][C]90[/C][C]0.00843563267366781[/C][C]0.0168712653473356[/C][C]0.991564367326332[/C][/ROW]
[ROW][C]91[/C][C]0.00700988231187463[/C][C]0.0140197646237493[/C][C]0.992990117688125[/C][/ROW]
[ROW][C]92[/C][C]0.00568428565598308[/C][C]0.0113685713119662[/C][C]0.994315714344017[/C][/ROW]
[ROW][C]93[/C][C]0.00417238572498480[/C][C]0.00834477144996961[/C][C]0.995827614275015[/C][/ROW]
[ROW][C]94[/C][C]0.00410715901212637[/C][C]0.00821431802425274[/C][C]0.995892840987874[/C][/ROW]
[ROW][C]95[/C][C]0.00609503281571112[/C][C]0.0121900656314222[/C][C]0.993904967184289[/C][/ROW]
[ROW][C]96[/C][C]0.004923540792484[/C][C]0.009847081584968[/C][C]0.995076459207516[/C][/ROW]
[ROW][C]97[/C][C]0.00413259207391549[/C][C]0.00826518414783098[/C][C]0.995867407926085[/C][/ROW]
[ROW][C]98[/C][C]0.00318351954673604[/C][C]0.00636703909347208[/C][C]0.996816480453264[/C][/ROW]
[ROW][C]99[/C][C]0.00241600278218352[/C][C]0.00483200556436704[/C][C]0.997583997217816[/C][/ROW]
[ROW][C]100[/C][C]0.00204547654334774[/C][C]0.00409095308669549[/C][C]0.997954523456652[/C][/ROW]
[ROW][C]101[/C][C]0.00162138327085166[/C][C]0.00324276654170332[/C][C]0.998378616729148[/C][/ROW]
[ROW][C]102[/C][C]0.00119482477691667[/C][C]0.00238964955383334[/C][C]0.998805175223083[/C][/ROW]
[ROW][C]103[/C][C]0.00151434149814437[/C][C]0.00302868299628874[/C][C]0.998485658501856[/C][/ROW]
[ROW][C]104[/C][C]0.00117804904661225[/C][C]0.00235609809322449[/C][C]0.998821950953388[/C][/ROW]
[ROW][C]105[/C][C]0.00514478791412113[/C][C]0.0102895758282423[/C][C]0.994855212085879[/C][/ROW]
[ROW][C]106[/C][C]0.0129752278327985[/C][C]0.0259504556655969[/C][C]0.987024772167202[/C][/ROW]
[ROW][C]107[/C][C]0.0137621860354895[/C][C]0.0275243720709791[/C][C]0.98623781396451[/C][/ROW]
[ROW][C]108[/C][C]0.0188453267634505[/C][C]0.0376906535269011[/C][C]0.98115467323655[/C][/ROW]
[ROW][C]109[/C][C]0.0146869602282292[/C][C]0.0293739204564584[/C][C]0.98531303977177[/C][/ROW]
[ROW][C]110[/C][C]0.0106390880770078[/C][C]0.0212781761540156[/C][C]0.989360911922992[/C][/ROW]
[ROW][C]111[/C][C]0.00768036915345223[/C][C]0.0153607383069045[/C][C]0.992319630846548[/C][/ROW]
[ROW][C]112[/C][C]0.0221277538532922[/C][C]0.0442555077065844[/C][C]0.977872246146708[/C][/ROW]
[ROW][C]113[/C][C]0.0221182592226324[/C][C]0.0442365184452648[/C][C]0.977881740777368[/C][/ROW]
[ROW][C]114[/C][C]0.0553284859649874[/C][C]0.110656971929975[/C][C]0.944671514035013[/C][/ROW]
[ROW][C]115[/C][C]0.0471363917056568[/C][C]0.0942727834113136[/C][C]0.952863608294343[/C][/ROW]
[ROW][C]116[/C][C]0.0394274799568146[/C][C]0.0788549599136292[/C][C]0.960572520043185[/C][/ROW]
[ROW][C]117[/C][C]0.103765893295207[/C][C]0.207531786590414[/C][C]0.896234106704793[/C][/ROW]
[ROW][C]118[/C][C]0.097924799411895[/C][C]0.19584959882379[/C][C]0.902075200588105[/C][/ROW]
[ROW][C]119[/C][C]0.0872458316209414[/C][C]0.174491663241883[/C][C]0.912754168379059[/C][/ROW]
[ROW][C]120[/C][C]0.152343613253838[/C][C]0.304687226507676[/C][C]0.847656386746162[/C][/ROW]
[ROW][C]121[/C][C]0.149510112328811[/C][C]0.299020224657622[/C][C]0.850489887671189[/C][/ROW]
[ROW][C]122[/C][C]0.189821231467135[/C][C]0.379642462934271[/C][C]0.810178768532865[/C][/ROW]
[ROW][C]123[/C][C]0.189201762348383[/C][C]0.378403524696767[/C][C]0.810798237651617[/C][/ROW]
[ROW][C]124[/C][C]0.17917269691495[/C][C]0.3583453938299[/C][C]0.82082730308505[/C][/ROW]
[ROW][C]125[/C][C]0.155186382087895[/C][C]0.310372764175790[/C][C]0.844813617912105[/C][/ROW]
[ROW][C]126[/C][C]0.124362819044806[/C][C]0.248725638089611[/C][C]0.875637180955194[/C][/ROW]
[ROW][C]127[/C][C]0.0973190447625353[/C][C]0.194638089525071[/C][C]0.902680955237465[/C][/ROW]
[ROW][C]128[/C][C]0.0947404360505062[/C][C]0.189480872101012[/C][C]0.905259563949494[/C][/ROW]
[ROW][C]129[/C][C]0.13263376085339[/C][C]0.26526752170678[/C][C]0.86736623914661[/C][/ROW]
[ROW][C]130[/C][C]0.114656101010853[/C][C]0.229312202021707[/C][C]0.885343898989147[/C][/ROW]
[ROW][C]131[/C][C]0.0961070124188934[/C][C]0.192214024837787[/C][C]0.903892987581107[/C][/ROW]
[ROW][C]132[/C][C]0.0852673361991732[/C][C]0.170534672398346[/C][C]0.914732663800827[/C][/ROW]
[ROW][C]133[/C][C]0.0833249937689005[/C][C]0.166649987537801[/C][C]0.9166750062311[/C][/ROW]
[ROW][C]134[/C][C]0.0740955530721005[/C][C]0.148191106144201[/C][C]0.9259044469279[/C][/ROW]
[ROW][C]135[/C][C]0.173101013538592[/C][C]0.346202027077185[/C][C]0.826898986461408[/C][/ROW]
[ROW][C]136[/C][C]0.138030968134925[/C][C]0.276061936269850[/C][C]0.861969031865075[/C][/ROW]
[ROW][C]137[/C][C]0.115666778336239[/C][C]0.231333556672478[/C][C]0.884333221663761[/C][/ROW]
[ROW][C]138[/C][C]0.16317176862336[/C][C]0.32634353724672[/C][C]0.83682823137664[/C][/ROW]
[ROW][C]139[/C][C]0.394903450352563[/C][C]0.789806900705126[/C][C]0.605096549647437[/C][/ROW]
[ROW][C]140[/C][C]0.340483058981574[/C][C]0.680966117963148[/C][C]0.659516941018426[/C][/ROW]
[ROW][C]141[/C][C]0.484493738052016[/C][C]0.968987476104031[/C][C]0.515506261947984[/C][/ROW]
[ROW][C]142[/C][C]0.393268068007086[/C][C]0.786536136014171[/C][C]0.606731931992914[/C][/ROW]
[ROW][C]143[/C][C]0.556428664297234[/C][C]0.887142671405531[/C][C]0.443571335702766[/C][/ROW]
[ROW][C]144[/C][C]0.502585613078458[/C][C]0.994828773843085[/C][C]0.497414386921542[/C][/ROW]
[ROW][C]145[/C][C]0.405643386007728[/C][C]0.811286772015457[/C][C]0.594356613992272[/C][/ROW]
[ROW][C]146[/C][C]0.300847635283382[/C][C]0.601695270566764[/C][C]0.699152364716618[/C][/ROW]
[ROW][C]147[/C][C]0.29226208008677[/C][C]0.58452416017354[/C][C]0.70773791991323[/C][/ROW]
[ROW][C]148[/C][C]0.174421108847448[/C][C]0.348842217694896[/C][C]0.825578891152552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99394&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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
110.3888119205789580.7776238411579160.611188079421042
120.3271609506185950.654321901237190.672839049381405
130.6054959526754520.7890080946490960.394504047324548
140.7989457399692450.402108520061510.201054260030755
150.7358930750394960.5282138499210080.264106924960504
160.7287997300884690.5424005398230610.271200269911531
170.6462548381272970.7074903237454050.353745161872703
180.7705510485505740.4588979028988530.229448951449426
190.7125536777042180.5748926445915630.287446322295782
200.6585370735619950.6829258528760110.341462926438005
210.5902127871100680.8195744257798630.409787212889932
220.5146036738187430.9707926523625140.485396326181257
230.5019114708571620.9961770582856770.498088529142838
240.5416610351044230.9166779297911540.458338964895577
250.6751287524351910.6497424951296180.324871247564809
260.6082066760991980.7835866478016030.391793323900802
270.5496778760248140.9006442479503710.450322123975186
280.5132061612499260.9735876775001480.486793838750074
290.4510428306991730.9020856613983470.548957169300827
300.394921652974090.789843305948180.60507834702591
310.3911365887191210.7822731774382430.608863411280879
320.3303106556751820.6606213113503640.669689344324818
330.6219342241277720.7561315517444560.378065775872228
340.5776768708595110.8446462582809780.422323129140489
350.55322236800280.89355526399440.4467776319972
360.4970716997907720.9941433995815440.502928300209228
370.4830148942217290.9660297884434570.516985105778272
380.4319044530522560.8638089061045120.568095546947744
390.3825548415100720.7651096830201440.617445158489928
400.3595262734836140.7190525469672270.640473726516386
410.5097023993446720.9805952013106560.490297600655328
420.4559547596677270.9119095193354530.544045240332273
430.4370202503257670.8740405006515330.562979749674233
440.3904145028563260.7808290057126520.609585497143674
450.3509042471563130.7018084943126260.649095752843687
460.3201695611115520.6403391222231050.679830438888448
470.2906118007357920.5812236014715850.709388199264208
480.2871705649557130.5743411299114260.712829435044287
490.2487347569393150.497469513878630.751265243060685
500.252832407244920.505664814489840.74716759275508
510.2320680949045100.4641361898090210.76793190509549
520.1997209588839840.3994419177679670.800279041116016
530.2581902658740270.5163805317480530.741809734125973
540.3448581922029280.6897163844058560.655141807797072
550.3309725494094380.6619450988188760.669027450590562
560.3834857686921670.7669715373843350.616514231307833
570.3579419193720680.7158838387441360.642058080627932
580.3295963100279610.6591926200559220.670403689972039
590.3280651805548360.6561303611096720.671934819445164
600.4179175113748390.8358350227496770.582082488625161
610.3733737209164060.7467474418328120.626626279083594
620.3306895682581530.6613791365163050.669310431741847
630.3284689447310430.6569378894620870.671531055268957
640.2945056332805400.5890112665610790.70549436671946
650.2562162389695010.5124324779390020.743783761030499
660.2383317882999490.4766635765998980.761668211700051
670.2078796577639050.415759315527810.792120342236095
680.2316081701556450.463216340311290.768391829844355
690.1985057940658820.3970115881317640.801494205934118
700.1669370510447580.3338741020895160.833062948955242
710.1389054926604550.2778109853209090.861094507339545
720.1151865074430960.2303730148861920.884813492556904
730.0986535742556520.1973071485113040.901346425744348
740.07964463577530870.1592892715506170.920355364224691
750.06393846797183660.1278769359436730.936061532028163
760.05377455811341810.1075491162268360.946225441886582
770.04218418428591640.08436836857183280.957815815714084
780.03257149363446550.0651429872689310.967428506365535
790.03677726196256640.07355452392513270.963222738037434
800.03435947671592320.06871895343184640.965640523284077
810.02847976954018190.05695953908036390.971520230459818
820.03699074989941210.07398149979882420.963009250100588
830.02891479061725920.05782958123451840.97108520938274
840.02252112476736540.04504224953473080.977478875232635
850.02017250079394490.04034500158788970.979827499206055
860.0156704633593250.031340926718650.984329536640675
870.01281653732293350.02563307464586710.987183462677066
880.01311180702030990.02622361404061970.98688819297969
890.01109681059684280.02219362119368560.988903189403157
900.008435632673667810.01687126534733560.991564367326332
910.007009882311874630.01401976462374930.992990117688125
920.005684285655983080.01136857131196620.994315714344017
930.004172385724984800.008344771449969610.995827614275015
940.004107159012126370.008214318024252740.995892840987874
950.006095032815711120.01219006563142220.993904967184289
960.0049235407924840.0098470815849680.995076459207516
970.004132592073915490.008265184147830980.995867407926085
980.003183519546736040.006367039093472080.996816480453264
990.002416002782183520.004832005564367040.997583997217816
1000.002045476543347740.004090953086695490.997954523456652
1010.001621383270851660.003242766541703320.998378616729148
1020.001194824776916670.002389649553833340.998805175223083
1030.001514341498144370.003028682996288740.998485658501856
1040.001178049046612250.002356098093224490.998821950953388
1050.005144787914121130.01028957582824230.994855212085879
1060.01297522783279850.02595045566559690.987024772167202
1070.01376218603548950.02752437207097910.98623781396451
1080.01884532676345050.03769065352690110.98115467323655
1090.01468696022822920.02937392045645840.98531303977177
1100.01063908807700780.02127817615401560.989360911922992
1110.007680369153452230.01536073830690450.992319630846548
1120.02212775385329220.04425550770658440.977872246146708
1130.02211825922263240.04423651844526480.977881740777368
1140.05532848596498740.1106569719299750.944671514035013
1150.04713639170565680.09427278341131360.952863608294343
1160.03942747995681460.07885495991362920.960572520043185
1170.1037658932952070.2075317865904140.896234106704793
1180.0979247994118950.195849598823790.902075200588105
1190.08724583162094140.1744916632418830.912754168379059
1200.1523436132538380.3046872265076760.847656386746162
1210.1495101123288110.2990202246576220.850489887671189
1220.1898212314671350.3796424629342710.810178768532865
1230.1892017623483830.3784035246967670.810798237651617
1240.179172696914950.35834539382990.82082730308505
1250.1551863820878950.3103727641757900.844813617912105
1260.1243628190448060.2487256380896110.875637180955194
1270.09731904476253530.1946380895250710.902680955237465
1280.09474043605050620.1894808721010120.905259563949494
1290.132633760853390.265267521706780.86736623914661
1300.1146561010108530.2293122020217070.885343898989147
1310.09610701241889340.1922140248377870.903892987581107
1320.08526733619917320.1705346723983460.914732663800827
1330.08332499376890050.1666499875378010.9166750062311
1340.07409555307210050.1481911061442010.9259044469279
1350.1731010135385920.3462020270771850.826898986461408
1360.1380309681349250.2760619362698500.861969031865075
1370.1156667783362390.2313335566724780.884333221663761
1380.163171768623360.326343537246720.83682823137664
1390.3949034503525630.7898069007051260.605096549647437
1400.3404830589815740.6809661179631480.659516941018426
1410.4844937380520160.9689874761040310.515506261947984
1420.3932680680070860.7865361360141710.606731931992914
1430.5564286642972340.8871426714055310.443571335702766
1440.5025856130784580.9948287738430850.497414386921542
1450.4056433860077280.8112867720154570.594356613992272
1460.3008476352833820.6016952705667640.699152364716618
1470.292262080086770.584524160173540.70773791991323
1480.1744211088474480.3488422176948960.825578891152552






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.0797101449275362NOK
5% type I error level300.217391304347826NOK
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 & 11 & 0.0797101449275362 & NOK \tabularnewline
5% type I error level & 30 & 0.217391304347826 & NOK \tabularnewline
10% type I error level & 39 & 0.282608695652174 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99394&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]11[/C][C]0.0797101449275362[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.217391304347826[/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=99394&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99394&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 level110.0797101449275362NOK
5% type I error level300.217391304347826NOK
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')
}