Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 14:12:09 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321902760j5oze86rtbqpm43.htm/, Retrieved Thu, 28 Mar 2024 08:08:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145922, Retrieved Thu, 28 Mar 2024 08:08:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-11-18 14:53:14] [06c08141d7d783218a8164fd2ea166f2]
- R  D    [Multiple Regression] [] [2011-11-21 19:12:09] [ce4468323d272130d499477f5e05a6d2] [Current]
-   P       [Multiple Regression] [] [2011-11-21 19:17:03] [06c08141d7d783218a8164fd2ea166f2]
Feedback Forum

Post a new message
Dataseries X:
9	13	13	14	13	3
9	12	12	8	13	5
9	8	10	12	16	6
9	12	9	7	12	6
9	10	10	10	11	5
9	12	12	7	12	3
9	15	13	16	18	8
9	9	12	11	11	4
9	12	15	14	14	4
9	11	6	6	9	4
9	11	5	16	14	6
9	11	12	11	12	6
9	15	11	16	11	5
9	7	14	12	12	4
9	11	14	7	13	6
9	11	12	13	11	4
9	10	12	11	12	6
9	14	11	15	16	6
9	10	11	7	9	4
9	6	7	9	11	4
9	11	9	7	13	2
9	15	11	14	15	7
9	11	11	15	10	5
9	12	12	7	11	4
9	14	12	15	13	6
9	15	11	17	16	6
9	9	11	15	15	7
9	13	8	14	14	5
9	13	9	14	14	6
9	16	12	8	14	4
9	13	10	8	8	4
9	12	10	14	13	7
9	14	12	14	15	7
9	11	8	8	13	4
9	9	12	11	11	4
9	16	11	16	15	6
9	12	12	10	15	6
9	10	7	8	9	5
9	13	11	14	13	6
9	16	11	16	16	7
9	14	12	13	13	6
9	15	9	5	11	3
9	5	15	8	12	3
9	8	11	10	12	4
9	11	11	8	12	6
9	16	11	13	14	7
9	17	11	15	14	5
9	9	15	6	8	4
9	9	11	12	13	5
9	13	12	16	16	6
9	10	12	5	13	6
9	6	9	15	11	6
9	12	12	12	14	5
9	8	12	8	13	4
9	14	13	13	13	5
9	12	11	14	13	5
10	11	9	12	12	4
10	16	9	16	16	6
10	8	11	10	15	2
10	15	11	15	15	8
10	7	12	8	12	3
10	16	12	16	14	6
10	14	9	19	12	6
10	16	11	14	15	6
10	9	9	6	12	5
10	14	12	13	13	5
10	11	12	15	12	6
10	13	12	7	12	5
10	15	12	13	13	6
10	5	14	4	5	2
10	15	11	14	13	5
10	13	12	13	13	5
10	11	11	11	14	5
10	11	6	14	17	6
10	12	10	12	13	6
10	12	12	15	13	6
10	12	13	14	12	5
10	12	8	13	13	5
10	14	12	8	14	4
10	6	12	6	11	2
10	7	12	7	12	4
10	14	6	13	12	6
10	14	11	13	16	6
10	10	10	11	12	5
10	13	12	5	12	3
10	12	13	12	12	6
10	9	11	8	10	4
10	12	7	11	15	5
10	16	11	14	15	8
10	10	11	9	12	4
10	14	11	10	16	6
10	10	11	13	15	6
10	16	12	16	16	7
10	15	10	16	13	6
10	12	11	11	12	5
10	10	12	8	11	4
10	8	7	4	13	6
10	8	13	7	10	3
10	11	8	14	15	5
10	13	12	11	13	6
10	16	11	17	16	7
10	16	12	15	15	7
10	14	14	17	18	6
10	11	10	5	13	3
10	4	10	4	10	2
10	14	13	10	16	8
10	9	10	11	13	3
10	14	11	15	15	8
10	8	10	10	14	3
10	8	7	9	15	4
10	11	10	12	14	5
10	12	8	15	13	7
10	11	12	7	13	6
10	14	12	13	15	6
10	15	12	12	16	7
10	16	11	14	14	6
10	16	12	14	14	6
10	11	12	8	16	6
10	14	12	15	14	6
10	14	11	12	12	4
10	12	12	12	13	4
10	14	11	16	12	5
10	8	11	9	12	4
10	13	13	15	14	6
10	16	12	15	14	6
10	12	12	6	14	5
10	16	12	14	16	8
10	12	12	15	13	6
10	11	8	10	14	5
10	4	8	6	4	4
10	16	12	14	16	8
10	15	11	12	13	6
10	10	12	8	16	4
10	13	13	11	15	6
10	15	12	13	14	6
10	12	12	9	13	4
10	14	11	15	14	6
10	7	12	13	12	3
10	19	12	15	15	6
10	12	10	14	14	5
10	12	11	16	13	4
10	13	12	14	14	6
10	15	12	14	16	4
10	8	10	10	6	4
10	12	12	10	13	4
10	10	13	4	13	6
10	8	12	8	14	5
10	10	15	15	15	6
10	15	11	16	14	6
10	16	12	12	15	8
10	13	11	12	13	7
10	16	12	15	16	7
10	9	11	9	12	4
10	14	10	12	15	6
10	14	11	14	12	6
10	12	11	11	14	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Month[t] = + 9.2442566528024 + 0.00720870390552251Depressie[t] -0.00228637185263363belasting[t] -0.00650125206351354autonomie[t] + 0.0370683252745271conformistisch[t] -0.0149286826042584agressief[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Month[t] =  +  9.2442566528024 +  0.00720870390552251Depressie[t] -0.00228637185263363belasting[t] -0.00650125206351354autonomie[t] +  0.0370683252745271conformistisch[t] -0.0149286826042584agressief[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Month[t] =  +  9.2442566528024 +  0.00720870390552251Depressie[t] -0.00228637185263363belasting[t] -0.00650125206351354autonomie[t] +  0.0370683252745271conformistisch[t] -0.0149286826042584agressief[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Month[t] = + 9.2442566528024 + 0.00720870390552251Depressie[t] -0.00228637185263363belasting[t] -0.00650125206351354autonomie[t] + 0.0370683252745271conformistisch[t] -0.0149286826042584agressief[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.24425665280240.32482628.459100
Depressie0.007208703905522510.0183090.39370.6943460.347173
belasting-0.002286371852633630.0218-0.10490.9166120.458306
autonomie-0.006501252063513540.014826-0.43850.6616580.330829
conformistisch0.03706832527452710.0228891.61950.1074480.053724
agressief-0.01492868260425840.03762-0.39680.6920550.346028

\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) & 9.2442566528024 & 0.324826 & 28.4591 & 0 & 0 \tabularnewline
Depressie & 0.00720870390552251 & 0.018309 & 0.3937 & 0.694346 & 0.347173 \tabularnewline
belasting & -0.00228637185263363 & 0.0218 & -0.1049 & 0.916612 & 0.458306 \tabularnewline
autonomie & -0.00650125206351354 & 0.014826 & -0.4385 & 0.661658 & 0.330829 \tabularnewline
conformistisch & 0.0370683252745271 & 0.022889 & 1.6195 & 0.107448 & 0.053724 \tabularnewline
agressief & -0.0149286826042584 & 0.03762 & -0.3968 & 0.692055 & 0.346028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&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]9.2442566528024[/C][C]0.324826[/C][C]28.4591[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depressie[/C][C]0.00720870390552251[/C][C]0.018309[/C][C]0.3937[/C][C]0.694346[/C][C]0.347173[/C][/ROW]
[ROW][C]belasting[/C][C]-0.00228637185263363[/C][C]0.0218[/C][C]-0.1049[/C][C]0.916612[/C][C]0.458306[/C][/ROW]
[ROW][C]autonomie[/C][C]-0.00650125206351354[/C][C]0.014826[/C][C]-0.4385[/C][C]0.661658[/C][C]0.330829[/C][/ROW]
[ROW][C]conformistisch[/C][C]0.0370683252745271[/C][C]0.022889[/C][C]1.6195[/C][C]0.107448[/C][C]0.053724[/C][/ROW]
[ROW][C]agressief[/C][C]-0.0149286826042584[/C][C]0.03762[/C][C]-0.3968[/C][C]0.692055[/C][C]0.346028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=2

As an alternative you can also use a 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)9.24425665280240.32482628.459100
Depressie0.007208703905522510.0183090.39370.6943460.347173
belasting-0.002286371852633630.0218-0.10490.9166120.458306
autonomie-0.006501252063513540.014826-0.43850.6616580.330829
conformistisch0.03706832527452710.0228891.61950.1074480.053724
agressief-0.01492868260425840.03762-0.39680.6920550.346028







Multiple Linear Regression - Regression Statistics
Multiple R0.154693587622328
R-squared0.0239301060514669
Adjusted R-squared-0.00860555708015109
F-TEST (value)0.735503867084604
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.597942012156677
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483310848553561
Sum Squared Residuals35.0384064494345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.154693587622328 \tabularnewline
R-squared & 0.0239301060514669 \tabularnewline
Adjusted R-squared & -0.00860555708015109 \tabularnewline
F-TEST (value) & 0.735503867084604 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0.597942012156677 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.483310848553561 \tabularnewline
Sum Squared Residuals & 35.0384064494345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.154693587622328[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0239301060514669[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00860555708015109[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.735503867084604[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0.597942012156677[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.483310848553561[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.0384064494345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.154693587622328
R-squared0.0239301060514669
Adjusted R-squared-0.00860555708015109
F-TEST (value)0.735503867084604
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.597942012156677
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483310848553561
Sum Squared Residuals35.0384064494345







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
199.65433162135685-0.654331621356851
299.65855943647652-0.658559436476517
399.70456864952496-0.704568649524963
499.61992279621915-0.619922796219146
599.56157561769466-0.561575617694658
699.65784972847402-0.65784972847402
799.7664447383922-0.766444738392203
899.55822160062461-0.558221600624613
999.66468981641632-0.664689816416321
1099.54472684931997-0.544726849319974
1199.63748496170159-0.63748496170159
1299.57984996850167-0.579849968501669
1399.55632525298856-0.556325252988556
1499.56979852231932-0.569798522319315
1599.63835055832498-0.638350558324983
1699.55963650430863-0.559636504308631
1799.57264126459615-0.572641264596146
1899.72603074491492-0.726030744914924
1999.51958503408777-0.51958503408777
2099.56102985229824-0.561029852298241
2199.70949714800518-0.709497148005184
2299.68774369300517-0.687743693005174
2399.49692336415545-0.496923364155452
2499.60585272059524-0.605852720595235
2599.61253939723871-0.612539397238709
2699.72023694469342-0.720236944693419
2799.63799021750853-0.637990217508526
2899.67297444068602-0.67297444068602
2999.65575938622913-0.655759386229128
3099.73939125997739-0.739391259977393
3199.49992794031893-0.49992794031893
3299.59426730259219-0.594267302592186
3399.67824861724702-0.678248617247018
3499.67542490258579-0.675424902585788
3599.55822160062461-0.558221600624613
3699.69687857538793-0.696878575387928
3799.70476490029429-0.704764900294286
3899.50730058683053-0.507300586830532
3999.61411831724933-0.614118317249334
4099.7190182180582-0.719018218058197
4199.62554190136574-0.625541901365736
4299.66226913460099-0.662269134600989
4399.59402843351395-0.594028433513949
4499.59686884590977-0.596868845909765
4599.60164009654484-0.601640096544843
4699.66438532369968-0.664385323699684
4799.6884488886867-0.688448888686696
4899.4726637695607-0.472663769560699
4999.61321468835853-0.613214688358529
5099.71003441709325-0.710034417093254
5199.64871710225176-0.648717102251754
5299.48759223100338-0.487592231003376
5399.66962275349699-0.66962275349699
5499.64465330345869-0.644653303458686
5599.63818421211736-0.638184212117361
5699.62183829594807-0.62183829594807
57109.610065197204570.389934802795427
58109.738519644367720.261480355632277
59109.737931186941860.262068813058137
60109.66631375833740.333686241662597
61109.615304956882890.384695043117105
62109.657523878260770.342476121739232
63109.556325179268030.443674820731971
64109.709881079514960.290118920485045
65109.619726619170350.380273380829649
66109.640470583969990.359529416030005
67109.553844960247610.446155039752385
68109.635201067171030.364798932828974
69109.632750605271260.367249394728741
70109.37777021930320.622229780696794
71109.643464407664640.356535592335363
72109.633261880064470.366738119935528
73109.671201673507610.328798326492385
74109.759406069799570.240593930200435
75109.622198489323470.377801510676528
76109.598121989427660.401878010572336
77109.580197226968280.419802773031725
78109.635198663569480.364801336430516
79109.724973852166350.275026147833652
80109.598959114434130.40104088556587
81109.606877526342150.39312247365785
82109.602191807207010.397808192792989
83109.739033249041950.260966750958049
84109.592142690905670.407857309094328
85109.678060936506570.32193906349343
86109.578271048491040.421728951508956
87109.542943403393260.457056596606739
88109.72462419009820.275375809901801
89109.680023714306440.319976285693561
90109.617787505784320.382212494215676
91109.758537005232490.241462994767508
92109.673130108145330.326869891854666
93109.716731846205560.283268153794437
94109.617819592785980.382180407214015
95109.604273726864080.395726273135917
96109.584934060720680.415065939279323
97109.652232805767390.347767194232609
98109.552591890450240.447408109549757
99109.69562535814950.304374641850498
100109.631335701587240.368664298412759
101109.712516965994680.287483034005316
102109.686164772994550.31383522700545
103109.780305775779050.21969422422095
104109.705284597675320.294715402324681
105109.565048629180850.434951370819147
106109.724106896318710.275893103681292
107109.651859677483190.348140322516807
108109.659105054431880.34089494556812
109109.688220550915710.311779449084289
110109.723720561207390.276279438792606
111109.666986793296730.333013206703265
112109.592338794233940.40766120576606
113109.642923302030250.35707669796975
114109.699678551914790.30032144808521
115109.73552815055410.264471849445905
116109.672812754240430.327187245759572
117109.670526382387790.329473617612205
118109.747627025790320.252372974209682
119109.649607722513240.350392277486764
120109.627118565215870.372881434784127
121109.647483110826720.352516889173278
122109.586184874357560.413815125642439
123109.603370097973280.396629902026721
124109.640112646755080.35988735324492
125109.664025130324280.335974869675719
126109.708630265878070.291369734121929
127109.714805667728330.285194332271668
128109.598121989427660.401878010572336
129109.684562041129030.315437958870971
130109.304351551903410.695648448096586
131109.714805667728330.285194332271668
132109.641538229187410.358461770812594
133109.770275687093310.229724312906688
134109.703185980283660.296814019716339
135109.669818930545790.330181069454214
136109.666986867017260.333013132982738
137109.651894094365870.34810590563413
138109.582798696565330.417201303434673
139109.722719567315380.277280432684624
140109.661192993075230.33880700692477
141109.62376447442530.376235525574699
142109.648900270671230.351099729328773
143109.767311694239840.232688305760157
144109.376745266115240.623254733884764
145109.660485614953750.339514385046251
146109.652931982462630.347068017537366
147109.666792946128950.333207053871046
148109.650982116607770.349017883392228
149109.652601546207880.347398453792121
150109.690739846580830.309260153419168
151109.61219213877210.387807861227897
152109.723233098269080.276766901730923
153109.61057880187880.389421198121199
154109.710752547683570.289247452316429
155109.584258695880330.415741304119671
156109.723196425225910.276803574774087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 9.65433162135685 & -0.654331621356851 \tabularnewline
2 & 9 & 9.65855943647652 & -0.658559436476517 \tabularnewline
3 & 9 & 9.70456864952496 & -0.704568649524963 \tabularnewline
4 & 9 & 9.61992279621915 & -0.619922796219146 \tabularnewline
5 & 9 & 9.56157561769466 & -0.561575617694658 \tabularnewline
6 & 9 & 9.65784972847402 & -0.65784972847402 \tabularnewline
7 & 9 & 9.7664447383922 & -0.766444738392203 \tabularnewline
8 & 9 & 9.55822160062461 & -0.558221600624613 \tabularnewline
9 & 9 & 9.66468981641632 & -0.664689816416321 \tabularnewline
10 & 9 & 9.54472684931997 & -0.544726849319974 \tabularnewline
11 & 9 & 9.63748496170159 & -0.63748496170159 \tabularnewline
12 & 9 & 9.57984996850167 & -0.579849968501669 \tabularnewline
13 & 9 & 9.55632525298856 & -0.556325252988556 \tabularnewline
14 & 9 & 9.56979852231932 & -0.569798522319315 \tabularnewline
15 & 9 & 9.63835055832498 & -0.638350558324983 \tabularnewline
16 & 9 & 9.55963650430863 & -0.559636504308631 \tabularnewline
17 & 9 & 9.57264126459615 & -0.572641264596146 \tabularnewline
18 & 9 & 9.72603074491492 & -0.726030744914924 \tabularnewline
19 & 9 & 9.51958503408777 & -0.51958503408777 \tabularnewline
20 & 9 & 9.56102985229824 & -0.561029852298241 \tabularnewline
21 & 9 & 9.70949714800518 & -0.709497148005184 \tabularnewline
22 & 9 & 9.68774369300517 & -0.687743693005174 \tabularnewline
23 & 9 & 9.49692336415545 & -0.496923364155452 \tabularnewline
24 & 9 & 9.60585272059524 & -0.605852720595235 \tabularnewline
25 & 9 & 9.61253939723871 & -0.612539397238709 \tabularnewline
26 & 9 & 9.72023694469342 & -0.720236944693419 \tabularnewline
27 & 9 & 9.63799021750853 & -0.637990217508526 \tabularnewline
28 & 9 & 9.67297444068602 & -0.67297444068602 \tabularnewline
29 & 9 & 9.65575938622913 & -0.655759386229128 \tabularnewline
30 & 9 & 9.73939125997739 & -0.739391259977393 \tabularnewline
31 & 9 & 9.49992794031893 & -0.49992794031893 \tabularnewline
32 & 9 & 9.59426730259219 & -0.594267302592186 \tabularnewline
33 & 9 & 9.67824861724702 & -0.678248617247018 \tabularnewline
34 & 9 & 9.67542490258579 & -0.675424902585788 \tabularnewline
35 & 9 & 9.55822160062461 & -0.558221600624613 \tabularnewline
36 & 9 & 9.69687857538793 & -0.696878575387928 \tabularnewline
37 & 9 & 9.70476490029429 & -0.704764900294286 \tabularnewline
38 & 9 & 9.50730058683053 & -0.507300586830532 \tabularnewline
39 & 9 & 9.61411831724933 & -0.614118317249334 \tabularnewline
40 & 9 & 9.7190182180582 & -0.719018218058197 \tabularnewline
41 & 9 & 9.62554190136574 & -0.625541901365736 \tabularnewline
42 & 9 & 9.66226913460099 & -0.662269134600989 \tabularnewline
43 & 9 & 9.59402843351395 & -0.594028433513949 \tabularnewline
44 & 9 & 9.59686884590977 & -0.596868845909765 \tabularnewline
45 & 9 & 9.60164009654484 & -0.601640096544843 \tabularnewline
46 & 9 & 9.66438532369968 & -0.664385323699684 \tabularnewline
47 & 9 & 9.6884488886867 & -0.688448888686696 \tabularnewline
48 & 9 & 9.4726637695607 & -0.472663769560699 \tabularnewline
49 & 9 & 9.61321468835853 & -0.613214688358529 \tabularnewline
50 & 9 & 9.71003441709325 & -0.710034417093254 \tabularnewline
51 & 9 & 9.64871710225176 & -0.648717102251754 \tabularnewline
52 & 9 & 9.48759223100338 & -0.487592231003376 \tabularnewline
53 & 9 & 9.66962275349699 & -0.66962275349699 \tabularnewline
54 & 9 & 9.64465330345869 & -0.644653303458686 \tabularnewline
55 & 9 & 9.63818421211736 & -0.638184212117361 \tabularnewline
56 & 9 & 9.62183829594807 & -0.62183829594807 \tabularnewline
57 & 10 & 9.61006519720457 & 0.389934802795427 \tabularnewline
58 & 10 & 9.73851964436772 & 0.261480355632277 \tabularnewline
59 & 10 & 9.73793118694186 & 0.262068813058137 \tabularnewline
60 & 10 & 9.6663137583374 & 0.333686241662597 \tabularnewline
61 & 10 & 9.61530495688289 & 0.384695043117105 \tabularnewline
62 & 10 & 9.65752387826077 & 0.342476121739232 \tabularnewline
63 & 10 & 9.55632517926803 & 0.443674820731971 \tabularnewline
64 & 10 & 9.70988107951496 & 0.290118920485045 \tabularnewline
65 & 10 & 9.61972661917035 & 0.380273380829649 \tabularnewline
66 & 10 & 9.64047058396999 & 0.359529416030005 \tabularnewline
67 & 10 & 9.55384496024761 & 0.446155039752385 \tabularnewline
68 & 10 & 9.63520106717103 & 0.364798932828974 \tabularnewline
69 & 10 & 9.63275060527126 & 0.367249394728741 \tabularnewline
70 & 10 & 9.3777702193032 & 0.622229780696794 \tabularnewline
71 & 10 & 9.64346440766464 & 0.356535592335363 \tabularnewline
72 & 10 & 9.63326188006447 & 0.366738119935528 \tabularnewline
73 & 10 & 9.67120167350761 & 0.328798326492385 \tabularnewline
74 & 10 & 9.75940606979957 & 0.240593930200435 \tabularnewline
75 & 10 & 9.62219848932347 & 0.377801510676528 \tabularnewline
76 & 10 & 9.59812198942766 & 0.401878010572336 \tabularnewline
77 & 10 & 9.58019722696828 & 0.419802773031725 \tabularnewline
78 & 10 & 9.63519866356948 & 0.364801336430516 \tabularnewline
79 & 10 & 9.72497385216635 & 0.275026147833652 \tabularnewline
80 & 10 & 9.59895911443413 & 0.40104088556587 \tabularnewline
81 & 10 & 9.60687752634215 & 0.39312247365785 \tabularnewline
82 & 10 & 9.60219180720701 & 0.397808192792989 \tabularnewline
83 & 10 & 9.73903324904195 & 0.260966750958049 \tabularnewline
84 & 10 & 9.59214269090567 & 0.407857309094328 \tabularnewline
85 & 10 & 9.67806093650657 & 0.32193906349343 \tabularnewline
86 & 10 & 9.57827104849104 & 0.421728951508956 \tabularnewline
87 & 10 & 9.54294340339326 & 0.457056596606739 \tabularnewline
88 & 10 & 9.7246241900982 & 0.275375809901801 \tabularnewline
89 & 10 & 9.68002371430644 & 0.319976285693561 \tabularnewline
90 & 10 & 9.61778750578432 & 0.382212494215676 \tabularnewline
91 & 10 & 9.75853700523249 & 0.241462994767508 \tabularnewline
92 & 10 & 9.67313010814533 & 0.326869891854666 \tabularnewline
93 & 10 & 9.71673184620556 & 0.283268153794437 \tabularnewline
94 & 10 & 9.61781959278598 & 0.382180407214015 \tabularnewline
95 & 10 & 9.60427372686408 & 0.395726273135917 \tabularnewline
96 & 10 & 9.58493406072068 & 0.415065939279323 \tabularnewline
97 & 10 & 9.65223280576739 & 0.347767194232609 \tabularnewline
98 & 10 & 9.55259189045024 & 0.447408109549757 \tabularnewline
99 & 10 & 9.6956253581495 & 0.304374641850498 \tabularnewline
100 & 10 & 9.63133570158724 & 0.368664298412759 \tabularnewline
101 & 10 & 9.71251696599468 & 0.287483034005316 \tabularnewline
102 & 10 & 9.68616477299455 & 0.31383522700545 \tabularnewline
103 & 10 & 9.78030577577905 & 0.21969422422095 \tabularnewline
104 & 10 & 9.70528459767532 & 0.294715402324681 \tabularnewline
105 & 10 & 9.56504862918085 & 0.434951370819147 \tabularnewline
106 & 10 & 9.72410689631871 & 0.275893103681292 \tabularnewline
107 & 10 & 9.65185967748319 & 0.348140322516807 \tabularnewline
108 & 10 & 9.65910505443188 & 0.34089494556812 \tabularnewline
109 & 10 & 9.68822055091571 & 0.311779449084289 \tabularnewline
110 & 10 & 9.72372056120739 & 0.276279438792606 \tabularnewline
111 & 10 & 9.66698679329673 & 0.333013206703265 \tabularnewline
112 & 10 & 9.59233879423394 & 0.40766120576606 \tabularnewline
113 & 10 & 9.64292330203025 & 0.35707669796975 \tabularnewline
114 & 10 & 9.69967855191479 & 0.30032144808521 \tabularnewline
115 & 10 & 9.7355281505541 & 0.264471849445905 \tabularnewline
116 & 10 & 9.67281275424043 & 0.327187245759572 \tabularnewline
117 & 10 & 9.67052638238779 & 0.329473617612205 \tabularnewline
118 & 10 & 9.74762702579032 & 0.252372974209682 \tabularnewline
119 & 10 & 9.64960772251324 & 0.350392277486764 \tabularnewline
120 & 10 & 9.62711856521587 & 0.372881434784127 \tabularnewline
121 & 10 & 9.64748311082672 & 0.352516889173278 \tabularnewline
122 & 10 & 9.58618487435756 & 0.413815125642439 \tabularnewline
123 & 10 & 9.60337009797328 & 0.396629902026721 \tabularnewline
124 & 10 & 9.64011264675508 & 0.35988735324492 \tabularnewline
125 & 10 & 9.66402513032428 & 0.335974869675719 \tabularnewline
126 & 10 & 9.70863026587807 & 0.291369734121929 \tabularnewline
127 & 10 & 9.71480566772833 & 0.285194332271668 \tabularnewline
128 & 10 & 9.59812198942766 & 0.401878010572336 \tabularnewline
129 & 10 & 9.68456204112903 & 0.315437958870971 \tabularnewline
130 & 10 & 9.30435155190341 & 0.695648448096586 \tabularnewline
131 & 10 & 9.71480566772833 & 0.285194332271668 \tabularnewline
132 & 10 & 9.64153822918741 & 0.358461770812594 \tabularnewline
133 & 10 & 9.77027568709331 & 0.229724312906688 \tabularnewline
134 & 10 & 9.70318598028366 & 0.296814019716339 \tabularnewline
135 & 10 & 9.66981893054579 & 0.330181069454214 \tabularnewline
136 & 10 & 9.66698686701726 & 0.333013132982738 \tabularnewline
137 & 10 & 9.65189409436587 & 0.34810590563413 \tabularnewline
138 & 10 & 9.58279869656533 & 0.417201303434673 \tabularnewline
139 & 10 & 9.72271956731538 & 0.277280432684624 \tabularnewline
140 & 10 & 9.66119299307523 & 0.33880700692477 \tabularnewline
141 & 10 & 9.6237644744253 & 0.376235525574699 \tabularnewline
142 & 10 & 9.64890027067123 & 0.351099729328773 \tabularnewline
143 & 10 & 9.76731169423984 & 0.232688305760157 \tabularnewline
144 & 10 & 9.37674526611524 & 0.623254733884764 \tabularnewline
145 & 10 & 9.66048561495375 & 0.339514385046251 \tabularnewline
146 & 10 & 9.65293198246263 & 0.347068017537366 \tabularnewline
147 & 10 & 9.66679294612895 & 0.333207053871046 \tabularnewline
148 & 10 & 9.65098211660777 & 0.349017883392228 \tabularnewline
149 & 10 & 9.65260154620788 & 0.347398453792121 \tabularnewline
150 & 10 & 9.69073984658083 & 0.309260153419168 \tabularnewline
151 & 10 & 9.6121921387721 & 0.387807861227897 \tabularnewline
152 & 10 & 9.72323309826908 & 0.276766901730923 \tabularnewline
153 & 10 & 9.6105788018788 & 0.389421198121199 \tabularnewline
154 & 10 & 9.71075254768357 & 0.289247452316429 \tabularnewline
155 & 10 & 9.58425869588033 & 0.415741304119671 \tabularnewline
156 & 10 & 9.72319642522591 & 0.276803574774087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&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]9[/C][C]9.65433162135685[/C][C]-0.654331621356851[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]9.65855943647652[/C][C]-0.658559436476517[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]9.70456864952496[/C][C]-0.704568649524963[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]9.61992279621915[/C][C]-0.619922796219146[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.56157561769466[/C][C]-0.561575617694658[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]9.65784972847402[/C][C]-0.65784972847402[/C][/ROW]
[ROW][C]7[/C][C]9[/C][C]9.7664447383922[/C][C]-0.766444738392203[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.55822160062461[/C][C]-0.558221600624613[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.66468981641632[/C][C]-0.664689816416321[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]9.54472684931997[/C][C]-0.544726849319974[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]9.63748496170159[/C][C]-0.63748496170159[/C][/ROW]
[ROW][C]12[/C][C]9[/C][C]9.57984996850167[/C][C]-0.579849968501669[/C][/ROW]
[ROW][C]13[/C][C]9[/C][C]9.55632525298856[/C][C]-0.556325252988556[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]9.56979852231932[/C][C]-0.569798522319315[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]9.63835055832498[/C][C]-0.638350558324983[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]9.55963650430863[/C][C]-0.559636504308631[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]9.57264126459615[/C][C]-0.572641264596146[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]9.72603074491492[/C][C]-0.726030744914924[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]9.51958503408777[/C][C]-0.51958503408777[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.56102985229824[/C][C]-0.561029852298241[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.70949714800518[/C][C]-0.709497148005184[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.68774369300517[/C][C]-0.687743693005174[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.49692336415545[/C][C]-0.496923364155452[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]9.60585272059524[/C][C]-0.605852720595235[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.61253939723871[/C][C]-0.612539397238709[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]9.72023694469342[/C][C]-0.720236944693419[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.63799021750853[/C][C]-0.637990217508526[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.67297444068602[/C][C]-0.67297444068602[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]9.65575938622913[/C][C]-0.655759386229128[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.73939125997739[/C][C]-0.739391259977393[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]9.49992794031893[/C][C]-0.49992794031893[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]9.59426730259219[/C][C]-0.594267302592186[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]9.67824861724702[/C][C]-0.678248617247018[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]9.67542490258579[/C][C]-0.675424902585788[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.55822160062461[/C][C]-0.558221600624613[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.69687857538793[/C][C]-0.696878575387928[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]9.70476490029429[/C][C]-0.704764900294286[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]9.50730058683053[/C][C]-0.507300586830532[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]9.61411831724933[/C][C]-0.614118317249334[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]9.7190182180582[/C][C]-0.719018218058197[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.62554190136574[/C][C]-0.625541901365736[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]9.66226913460099[/C][C]-0.662269134600989[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]9.59402843351395[/C][C]-0.594028433513949[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]9.59686884590977[/C][C]-0.596868845909765[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]9.60164009654484[/C][C]-0.601640096544843[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]9.66438532369968[/C][C]-0.664385323699684[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]9.6884488886867[/C][C]-0.688448888686696[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]9.4726637695607[/C][C]-0.472663769560699[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]9.61321468835853[/C][C]-0.613214688358529[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]9.71003441709325[/C][C]-0.710034417093254[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]9.64871710225176[/C][C]-0.648717102251754[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]9.48759223100338[/C][C]-0.487592231003376[/C][/ROW]
[ROW][C]53[/C][C]9[/C][C]9.66962275349699[/C][C]-0.66962275349699[/C][/ROW]
[ROW][C]54[/C][C]9[/C][C]9.64465330345869[/C][C]-0.644653303458686[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]9.63818421211736[/C][C]-0.638184212117361[/C][/ROW]
[ROW][C]56[/C][C]9[/C][C]9.62183829594807[/C][C]-0.62183829594807[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]9.61006519720457[/C][C]0.389934802795427[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]9.73851964436772[/C][C]0.261480355632277[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]9.73793118694186[/C][C]0.262068813058137[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]9.6663137583374[/C][C]0.333686241662597[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]9.61530495688289[/C][C]0.384695043117105[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]9.65752387826077[/C][C]0.342476121739232[/C][/ROW]
[ROW][C]63[/C][C]10[/C][C]9.55632517926803[/C][C]0.443674820731971[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]9.70988107951496[/C][C]0.290118920485045[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]9.61972661917035[/C][C]0.380273380829649[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]9.64047058396999[/C][C]0.359529416030005[/C][/ROW]
[ROW][C]67[/C][C]10[/C][C]9.55384496024761[/C][C]0.446155039752385[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]9.63520106717103[/C][C]0.364798932828974[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.63275060527126[/C][C]0.367249394728741[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]9.3777702193032[/C][C]0.622229780696794[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]9.64346440766464[/C][C]0.356535592335363[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]9.63326188006447[/C][C]0.366738119935528[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]9.67120167350761[/C][C]0.328798326492385[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]9.75940606979957[/C][C]0.240593930200435[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.62219848932347[/C][C]0.377801510676528[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]9.59812198942766[/C][C]0.401878010572336[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]9.58019722696828[/C][C]0.419802773031725[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]9.63519866356948[/C][C]0.364801336430516[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]9.72497385216635[/C][C]0.275026147833652[/C][/ROW]
[ROW][C]80[/C][C]10[/C][C]9.59895911443413[/C][C]0.40104088556587[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]9.60687752634215[/C][C]0.39312247365785[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]9.60219180720701[/C][C]0.397808192792989[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]9.73903324904195[/C][C]0.260966750958049[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]9.59214269090567[/C][C]0.407857309094328[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]9.67806093650657[/C][C]0.32193906349343[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]9.57827104849104[/C][C]0.421728951508956[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.54294340339326[/C][C]0.457056596606739[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]9.7246241900982[/C][C]0.275375809901801[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]9.68002371430644[/C][C]0.319976285693561[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]9.61778750578432[/C][C]0.382212494215676[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]9.75853700523249[/C][C]0.241462994767508[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.67313010814533[/C][C]0.326869891854666[/C][/ROW]
[ROW][C]93[/C][C]10[/C][C]9.71673184620556[/C][C]0.283268153794437[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]9.61781959278598[/C][C]0.382180407214015[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]9.60427372686408[/C][C]0.395726273135917[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.58493406072068[/C][C]0.415065939279323[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]9.65223280576739[/C][C]0.347767194232609[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]9.55259189045024[/C][C]0.447408109549757[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]9.6956253581495[/C][C]0.304374641850498[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]9.63133570158724[/C][C]0.368664298412759[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]9.71251696599468[/C][C]0.287483034005316[/C][/ROW]
[ROW][C]102[/C][C]10[/C][C]9.68616477299455[/C][C]0.31383522700545[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]9.78030577577905[/C][C]0.21969422422095[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]9.70528459767532[/C][C]0.294715402324681[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]9.56504862918085[/C][C]0.434951370819147[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]9.72410689631871[/C][C]0.275893103681292[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]9.65185967748319[/C][C]0.348140322516807[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]9.65910505443188[/C][C]0.34089494556812[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.68822055091571[/C][C]0.311779449084289[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.72372056120739[/C][C]0.276279438792606[/C][/ROW]
[ROW][C]111[/C][C]10[/C][C]9.66698679329673[/C][C]0.333013206703265[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]9.59233879423394[/C][C]0.40766120576606[/C][/ROW]
[ROW][C]113[/C][C]10[/C][C]9.64292330203025[/C][C]0.35707669796975[/C][/ROW]
[ROW][C]114[/C][C]10[/C][C]9.69967855191479[/C][C]0.30032144808521[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.7355281505541[/C][C]0.264471849445905[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]9.67281275424043[/C][C]0.327187245759572[/C][/ROW]
[ROW][C]117[/C][C]10[/C][C]9.67052638238779[/C][C]0.329473617612205[/C][/ROW]
[ROW][C]118[/C][C]10[/C][C]9.74762702579032[/C][C]0.252372974209682[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]9.64960772251324[/C][C]0.350392277486764[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]9.62711856521587[/C][C]0.372881434784127[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]9.64748311082672[/C][C]0.352516889173278[/C][/ROW]
[ROW][C]122[/C][C]10[/C][C]9.58618487435756[/C][C]0.413815125642439[/C][/ROW]
[ROW][C]123[/C][C]10[/C][C]9.60337009797328[/C][C]0.396629902026721[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]9.64011264675508[/C][C]0.35988735324492[/C][/ROW]
[ROW][C]125[/C][C]10[/C][C]9.66402513032428[/C][C]0.335974869675719[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]9.70863026587807[/C][C]0.291369734121929[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]9.71480566772833[/C][C]0.285194332271668[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]9.59812198942766[/C][C]0.401878010572336[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]9.68456204112903[/C][C]0.315437958870971[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]9.30435155190341[/C][C]0.695648448096586[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]9.71480566772833[/C][C]0.285194332271668[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]9.64153822918741[/C][C]0.358461770812594[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]9.77027568709331[/C][C]0.229724312906688[/C][/ROW]
[ROW][C]134[/C][C]10[/C][C]9.70318598028366[/C][C]0.296814019716339[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]9.66981893054579[/C][C]0.330181069454214[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]9.66698686701726[/C][C]0.333013132982738[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.65189409436587[/C][C]0.34810590563413[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]9.58279869656533[/C][C]0.417201303434673[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.72271956731538[/C][C]0.277280432684624[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]9.66119299307523[/C][C]0.33880700692477[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]9.6237644744253[/C][C]0.376235525574699[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]9.64890027067123[/C][C]0.351099729328773[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]9.76731169423984[/C][C]0.232688305760157[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]9.37674526611524[/C][C]0.623254733884764[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]9.66048561495375[/C][C]0.339514385046251[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]9.65293198246263[/C][C]0.347068017537366[/C][/ROW]
[ROW][C]147[/C][C]10[/C][C]9.66679294612895[/C][C]0.333207053871046[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]9.65098211660777[/C][C]0.349017883392228[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]9.65260154620788[/C][C]0.347398453792121[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]9.69073984658083[/C][C]0.309260153419168[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]9.6121921387721[/C][C]0.387807861227897[/C][/ROW]
[ROW][C]152[/C][C]10[/C][C]9.72323309826908[/C][C]0.276766901730923[/C][/ROW]
[ROW][C]153[/C][C]10[/C][C]9.6105788018788[/C][C]0.389421198121199[/C][/ROW]
[ROW][C]154[/C][C]10[/C][C]9.71075254768357[/C][C]0.289247452316429[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]9.58425869588033[/C][C]0.415741304119671[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]9.72319642522591[/C][C]0.276803574774087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
199.65433162135685-0.654331621356851
299.65855943647652-0.658559436476517
399.70456864952496-0.704568649524963
499.61992279621915-0.619922796219146
599.56157561769466-0.561575617694658
699.65784972847402-0.65784972847402
799.7664447383922-0.766444738392203
899.55822160062461-0.558221600624613
999.66468981641632-0.664689816416321
1099.54472684931997-0.544726849319974
1199.63748496170159-0.63748496170159
1299.57984996850167-0.579849968501669
1399.55632525298856-0.556325252988556
1499.56979852231932-0.569798522319315
1599.63835055832498-0.638350558324983
1699.55963650430863-0.559636504308631
1799.57264126459615-0.572641264596146
1899.72603074491492-0.726030744914924
1999.51958503408777-0.51958503408777
2099.56102985229824-0.561029852298241
2199.70949714800518-0.709497148005184
2299.68774369300517-0.687743693005174
2399.49692336415545-0.496923364155452
2499.60585272059524-0.605852720595235
2599.61253939723871-0.612539397238709
2699.72023694469342-0.720236944693419
2799.63799021750853-0.637990217508526
2899.67297444068602-0.67297444068602
2999.65575938622913-0.655759386229128
3099.73939125997739-0.739391259977393
3199.49992794031893-0.49992794031893
3299.59426730259219-0.594267302592186
3399.67824861724702-0.678248617247018
3499.67542490258579-0.675424902585788
3599.55822160062461-0.558221600624613
3699.69687857538793-0.696878575387928
3799.70476490029429-0.704764900294286
3899.50730058683053-0.507300586830532
3999.61411831724933-0.614118317249334
4099.7190182180582-0.719018218058197
4199.62554190136574-0.625541901365736
4299.66226913460099-0.662269134600989
4399.59402843351395-0.594028433513949
4499.59686884590977-0.596868845909765
4599.60164009654484-0.601640096544843
4699.66438532369968-0.664385323699684
4799.6884488886867-0.688448888686696
4899.4726637695607-0.472663769560699
4999.61321468835853-0.613214688358529
5099.71003441709325-0.710034417093254
5199.64871710225176-0.648717102251754
5299.48759223100338-0.487592231003376
5399.66962275349699-0.66962275349699
5499.64465330345869-0.644653303458686
5599.63818421211736-0.638184212117361
5699.62183829594807-0.62183829594807
57109.610065197204570.389934802795427
58109.738519644367720.261480355632277
59109.737931186941860.262068813058137
60109.66631375833740.333686241662597
61109.615304956882890.384695043117105
62109.657523878260770.342476121739232
63109.556325179268030.443674820731971
64109.709881079514960.290118920485045
65109.619726619170350.380273380829649
66109.640470583969990.359529416030005
67109.553844960247610.446155039752385
68109.635201067171030.364798932828974
69109.632750605271260.367249394728741
70109.37777021930320.622229780696794
71109.643464407664640.356535592335363
72109.633261880064470.366738119935528
73109.671201673507610.328798326492385
74109.759406069799570.240593930200435
75109.622198489323470.377801510676528
76109.598121989427660.401878010572336
77109.580197226968280.419802773031725
78109.635198663569480.364801336430516
79109.724973852166350.275026147833652
80109.598959114434130.40104088556587
81109.606877526342150.39312247365785
82109.602191807207010.397808192792989
83109.739033249041950.260966750958049
84109.592142690905670.407857309094328
85109.678060936506570.32193906349343
86109.578271048491040.421728951508956
87109.542943403393260.457056596606739
88109.72462419009820.275375809901801
89109.680023714306440.319976285693561
90109.617787505784320.382212494215676
91109.758537005232490.241462994767508
92109.673130108145330.326869891854666
93109.716731846205560.283268153794437
94109.617819592785980.382180407214015
95109.604273726864080.395726273135917
96109.584934060720680.415065939279323
97109.652232805767390.347767194232609
98109.552591890450240.447408109549757
99109.69562535814950.304374641850498
100109.631335701587240.368664298412759
101109.712516965994680.287483034005316
102109.686164772994550.31383522700545
103109.780305775779050.21969422422095
104109.705284597675320.294715402324681
105109.565048629180850.434951370819147
106109.724106896318710.275893103681292
107109.651859677483190.348140322516807
108109.659105054431880.34089494556812
109109.688220550915710.311779449084289
110109.723720561207390.276279438792606
111109.666986793296730.333013206703265
112109.592338794233940.40766120576606
113109.642923302030250.35707669796975
114109.699678551914790.30032144808521
115109.73552815055410.264471849445905
116109.672812754240430.327187245759572
117109.670526382387790.329473617612205
118109.747627025790320.252372974209682
119109.649607722513240.350392277486764
120109.627118565215870.372881434784127
121109.647483110826720.352516889173278
122109.586184874357560.413815125642439
123109.603370097973280.396629902026721
124109.640112646755080.35988735324492
125109.664025130324280.335974869675719
126109.708630265878070.291369734121929
127109.714805667728330.285194332271668
128109.598121989427660.401878010572336
129109.684562041129030.315437958870971
130109.304351551903410.695648448096586
131109.714805667728330.285194332271668
132109.641538229187410.358461770812594
133109.770275687093310.229724312906688
134109.703185980283660.296814019716339
135109.669818930545790.330181069454214
136109.666986867017260.333013132982738
137109.651894094365870.34810590563413
138109.582798696565330.417201303434673
139109.722719567315380.277280432684624
140109.661192993075230.33880700692477
141109.62376447442530.376235525574699
142109.648900270671230.351099729328773
143109.767311694239840.232688305760157
144109.376745266115240.623254733884764
145109.660485614953750.339514385046251
146109.652931982462630.347068017537366
147109.666792946128950.333207053871046
148109.650982116607770.349017883392228
149109.652601546207880.347398453792121
150109.690739846580830.309260153419168
151109.61219213877210.387807861227897
152109.723233098269080.276766901730923
153109.61057880187880.389421198121199
154109.710752547683570.289247452316429
155109.584258695880330.415741304119671
156109.723196425225910.276803574774087







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
94.27908391874747e-468.55816783749493e-461
104.18400225730214e-618.36800451460429e-611
112.6809022400441e-755.3618044800882e-751
121.98805066332483e-873.97610132664967e-871
135.3780415845907e-1041.07560831691814e-1031
141.52771599706326e-1143.05543199412651e-1141
157.62254172268797e-1291.52450834453759e-1281
16001
173.52545953197209e-1587.05091906394418e-1581
182.67269723690182e-1745.34539447380364e-1741
198.60315688855638e-1901.72063137771128e-1891
202.02809010063348e-2134.05618020126696e-2131
212.6961985325324e-2215.39239706506481e-2211
222.59868596528599e-2335.19737193057198e-2331
232.66615837434989e-2445.33231674869978e-2441
245.22885657905049e-2681.0457713158101e-2671
253.00005092131741e-2816.00010184263482e-2811
261.95136361485614e-2993.90272722971228e-2991
276.47485724264592e-3091.29497144852918e-3081
289.98012604599318e-3221.99602520919864e-3211
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
561.63739958814161e-193.27479917628323e-191
57100
58100
59100
60100
61100
62100
63100
64100
65100
66100
67100
68100
69100
70100
71100
72100
73100
74100
75100
76100
77100
78100
79100
80100
81100
82100
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
12911.3514184537407e-3116.75709226870352e-312
13017.58435177576793e-2943.79217588788397e-294
13115.7088389430528e-2702.8544194715264e-270
13211.98001888649211e-2779.90009443246057e-278
13311.12868467724482e-2425.6434233862241e-243
13413.41340177775943e-2311.70670088887971e-231
13517.68806768738786e-2173.84403384369393e-217
13614.16561168200178e-2122.08280584100089e-212
13712.08640119981463e-1901.04320059990731e-190
13812.62665423644272e-1821.31332711822136e-182
13919.60893019772673e-1594.80446509886337e-159
140100
14118.53280397306393e-1314.26640198653197e-131
14211.08470546683471e-1165.42352733417355e-117
14312.41125256215207e-1081.20562628107604e-108
14413.45858546032533e-861.72929273016267e-86
14512.38011135111482e-741.19005567555741e-74
14611.95048712871268e-669.75243564356342e-67
14712.66286098189341e-461.33143049094671e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 4.27908391874747e-46 & 8.55816783749493e-46 & 1 \tabularnewline
10 & 4.18400225730214e-61 & 8.36800451460429e-61 & 1 \tabularnewline
11 & 2.6809022400441e-75 & 5.3618044800882e-75 & 1 \tabularnewline
12 & 1.98805066332483e-87 & 3.97610132664967e-87 & 1 \tabularnewline
13 & 5.3780415845907e-104 & 1.07560831691814e-103 & 1 \tabularnewline
14 & 1.52771599706326e-114 & 3.05543199412651e-114 & 1 \tabularnewline
15 & 7.62254172268797e-129 & 1.52450834453759e-128 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 3.52545953197209e-158 & 7.05091906394418e-158 & 1 \tabularnewline
18 & 2.67269723690182e-174 & 5.34539447380364e-174 & 1 \tabularnewline
19 & 8.60315688855638e-190 & 1.72063137771128e-189 & 1 \tabularnewline
20 & 2.02809010063348e-213 & 4.05618020126696e-213 & 1 \tabularnewline
21 & 2.6961985325324e-221 & 5.39239706506481e-221 & 1 \tabularnewline
22 & 2.59868596528599e-233 & 5.19737193057198e-233 & 1 \tabularnewline
23 & 2.66615837434989e-244 & 5.33231674869978e-244 & 1 \tabularnewline
24 & 5.22885657905049e-268 & 1.0457713158101e-267 & 1 \tabularnewline
25 & 3.00005092131741e-281 & 6.00010184263482e-281 & 1 \tabularnewline
26 & 1.95136361485614e-299 & 3.90272722971228e-299 & 1 \tabularnewline
27 & 6.47485724264592e-309 & 1.29497144852918e-308 & 1 \tabularnewline
28 & 9.98012604599318e-322 & 1.99602520919864e-321 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 1.63739958814161e-19 & 3.27479917628323e-19 & 1 \tabularnewline
57 & 1 & 0 & 0 \tabularnewline
58 & 1 & 0 & 0 \tabularnewline
59 & 1 & 0 & 0 \tabularnewline
60 & 1 & 0 & 0 \tabularnewline
61 & 1 & 0 & 0 \tabularnewline
62 & 1 & 0 & 0 \tabularnewline
63 & 1 & 0 & 0 \tabularnewline
64 & 1 & 0 & 0 \tabularnewline
65 & 1 & 0 & 0 \tabularnewline
66 & 1 & 0 & 0 \tabularnewline
67 & 1 & 0 & 0 \tabularnewline
68 & 1 & 0 & 0 \tabularnewline
69 & 1 & 0 & 0 \tabularnewline
70 & 1 & 0 & 0 \tabularnewline
71 & 1 & 0 & 0 \tabularnewline
72 & 1 & 0 & 0 \tabularnewline
73 & 1 & 0 & 0 \tabularnewline
74 & 1 & 0 & 0 \tabularnewline
75 & 1 & 0 & 0 \tabularnewline
76 & 1 & 0 & 0 \tabularnewline
77 & 1 & 0 & 0 \tabularnewline
78 & 1 & 0 & 0 \tabularnewline
79 & 1 & 0 & 0 \tabularnewline
80 & 1 & 0 & 0 \tabularnewline
81 & 1 & 0 & 0 \tabularnewline
82 & 1 & 0 & 0 \tabularnewline
83 & 1 & 0 & 0 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 1.3514184537407e-311 & 6.75709226870352e-312 \tabularnewline
130 & 1 & 7.58435177576793e-294 & 3.79217588788397e-294 \tabularnewline
131 & 1 & 5.7088389430528e-270 & 2.8544194715264e-270 \tabularnewline
132 & 1 & 1.98001888649211e-277 & 9.90009443246057e-278 \tabularnewline
133 & 1 & 1.12868467724482e-242 & 5.6434233862241e-243 \tabularnewline
134 & 1 & 3.41340177775943e-231 & 1.70670088887971e-231 \tabularnewline
135 & 1 & 7.68806768738786e-217 & 3.84403384369393e-217 \tabularnewline
136 & 1 & 4.16561168200178e-212 & 2.08280584100089e-212 \tabularnewline
137 & 1 & 2.08640119981463e-190 & 1.04320059990731e-190 \tabularnewline
138 & 1 & 2.62665423644272e-182 & 1.31332711822136e-182 \tabularnewline
139 & 1 & 9.60893019772673e-159 & 4.80446509886337e-159 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 8.53280397306393e-131 & 4.26640198653197e-131 \tabularnewline
142 & 1 & 1.08470546683471e-116 & 5.42352733417355e-117 \tabularnewline
143 & 1 & 2.41125256215207e-108 & 1.20562628107604e-108 \tabularnewline
144 & 1 & 3.45858546032533e-86 & 1.72929273016267e-86 \tabularnewline
145 & 1 & 2.38011135111482e-74 & 1.19005567555741e-74 \tabularnewline
146 & 1 & 1.95048712871268e-66 & 9.75243564356342e-67 \tabularnewline
147 & 1 & 2.66286098189341e-46 & 1.33143049094671e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&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]9[/C][C]4.27908391874747e-46[/C][C]8.55816783749493e-46[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]4.18400225730214e-61[/C][C]8.36800451460429e-61[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]2.6809022400441e-75[/C][C]5.3618044800882e-75[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]1.98805066332483e-87[/C][C]3.97610132664967e-87[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]5.3780415845907e-104[/C][C]1.07560831691814e-103[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]1.52771599706326e-114[/C][C]3.05543199412651e-114[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]7.62254172268797e-129[/C][C]1.52450834453759e-128[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]3.52545953197209e-158[/C][C]7.05091906394418e-158[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]2.67269723690182e-174[/C][C]5.34539447380364e-174[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]8.60315688855638e-190[/C][C]1.72063137771128e-189[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]2.02809010063348e-213[/C][C]4.05618020126696e-213[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]2.6961985325324e-221[/C][C]5.39239706506481e-221[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]2.59868596528599e-233[/C][C]5.19737193057198e-233[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]2.66615837434989e-244[/C][C]5.33231674869978e-244[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]5.22885657905049e-268[/C][C]1.0457713158101e-267[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]3.00005092131741e-281[/C][C]6.00010184263482e-281[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]1.95136361485614e-299[/C][C]3.90272722971228e-299[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]6.47485724264592e-309[/C][C]1.29497144852918e-308[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]9.98012604599318e-322[/C][C]1.99602520919864e-321[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]1.63739958814161e-19[/C][C]3.27479917628323e-19[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.3514184537407e-311[/C][C]6.75709226870352e-312[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]7.58435177576793e-294[/C][C]3.79217588788397e-294[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]5.7088389430528e-270[/C][C]2.8544194715264e-270[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.98001888649211e-277[/C][C]9.90009443246057e-278[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.12868467724482e-242[/C][C]5.6434233862241e-243[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]3.41340177775943e-231[/C][C]1.70670088887971e-231[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]7.68806768738786e-217[/C][C]3.84403384369393e-217[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]4.16561168200178e-212[/C][C]2.08280584100089e-212[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]2.08640119981463e-190[/C][C]1.04320059990731e-190[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]2.62665423644272e-182[/C][C]1.31332711822136e-182[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]9.60893019772673e-159[/C][C]4.80446509886337e-159[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]8.53280397306393e-131[/C][C]4.26640198653197e-131[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.08470546683471e-116[/C][C]5.42352733417355e-117[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]2.41125256215207e-108[/C][C]1.20562628107604e-108[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]3.45858546032533e-86[/C][C]1.72929273016267e-86[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]2.38011135111482e-74[/C][C]1.19005567555741e-74[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.95048712871268e-66[/C][C]9.75243564356342e-67[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]2.66286098189341e-46[/C][C]1.33143049094671e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
94.27908391874747e-468.55816783749493e-461
104.18400225730214e-618.36800451460429e-611
112.6809022400441e-755.3618044800882e-751
121.98805066332483e-873.97610132664967e-871
135.3780415845907e-1041.07560831691814e-1031
141.52771599706326e-1143.05543199412651e-1141
157.62254172268797e-1291.52450834453759e-1281
16001
173.52545953197209e-1587.05091906394418e-1581
182.67269723690182e-1745.34539447380364e-1741
198.60315688855638e-1901.72063137771128e-1891
202.02809010063348e-2134.05618020126696e-2131
212.6961985325324e-2215.39239706506481e-2211
222.59868596528599e-2335.19737193057198e-2331
232.66615837434989e-2445.33231674869978e-2441
245.22885657905049e-2681.0457713158101e-2671
253.00005092131741e-2816.00010184263482e-2811
261.95136361485614e-2993.90272722971228e-2991
276.47485724264592e-3091.29497144852918e-3081
289.98012604599318e-3221.99602520919864e-3211
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
561.63739958814161e-193.27479917628323e-191
57100
58100
59100
60100
61100
62100
63100
64100
65100
66100
67100
68100
69100
70100
71100
72100
73100
74100
75100
76100
77100
78100
79100
80100
81100
82100
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
12911.3514184537407e-3116.75709226870352e-312
13017.58435177576793e-2943.79217588788397e-294
13115.7088389430528e-2702.8544194715264e-270
13211.98001888649211e-2779.90009443246057e-278
13311.12868467724482e-2425.6434233862241e-243
13413.41340177775943e-2311.70670088887971e-231
13517.68806768738786e-2173.84403384369393e-217
13614.16561168200178e-2122.08280584100089e-212
13712.08640119981463e-1901.04320059990731e-190
13812.62665423644272e-1821.31332711822136e-182
13919.60893019772673e-1594.80446509886337e-159
140100
14118.53280397306393e-1314.26640198653197e-131
14211.08470546683471e-1165.42352733417355e-117
14312.41125256215207e-1081.20562628107604e-108
14413.45858546032533e-861.72929273016267e-86
14512.38011135111482e-741.19005567555741e-74
14611.95048712871268e-669.75243564356342e-67
14712.66286098189341e-461.33143049094671e-46







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1391NOK
5% type I error level1391NOK
10% type I error level1391NOK

\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 & 139 & 1 & NOK \tabularnewline
5% type I error level & 139 & 1 & NOK \tabularnewline
10% type I error level & 139 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145922&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]139[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]139[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]139[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145922&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145922&T=6

As an alternative you can also use a 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 level1391NOK
5% type I error level1391NOK
10% type I error level1391NOK



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