FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 10:11:31 -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/t1321892385rk8115qlj2su3sv.htm/, Retrieved Sat, 20 Apr 2024 10:02:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145788, Retrieved Sat, 20 Apr 2024 10:02:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-21 15:11:31] [fe5ec8748c528a1557751a5a0f6a19ab] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	4	4
12	12	8	13	5	5	5
15	10	12	16	6	1	9
12	9	7	12	6	9	11
10	10	10	11	5	19	3
12	12	7	12	3	11	4
15	13	16	18	8	3	5
9	12	11	11	4	5	7
12	12	14	14	4	8	8
11	6	6	9	4	9	9
11	5	16	14	6	11	10
11	12	11	12	6	1	5
15	11	16	11	5	4	4
7	14	12	12	4	5	3
11	14	7	13	6	6	6
11	12	13	11	4	8	7
10	12	11	12	6	9	9
14	11	15	16	6	4	18
10	11	7	9	4	5	8
6	7	9	11	4	8	3
11	9	7	13	2	13	5
15	11	14	15	7	4	8
11	11	15	10	5	15	7
12	12	7	11	4	3	9
14	12	15	13	6	6	4
15	11	17	16	6	9	6
9	11	15	15	7	19	8
13	8	14	14	5	4	7
13	9	14	14	6	15	4
16	12	8	14	4	4	6
13	10	8	8	4	7	12
12	10	14	13	7	4	3
14	12	14	15	7	9	5
11	8	8	13	4	8	7
9	12	11	11	4	3	9
16	11	16	15	6	13	8
12	12	10	15	6	5	7
10	7	8	9	5	9	4
13	11	14	13	6	11	5
16	11	16	16	7	13	12
14	12	13	13	6	5	15
15	9	5	11	3	7	3
5	15	8	12	3	6	5
8	11	10	12	4	4	13
11	11	8	12	6	17	8
16	11	13	14	7	6	9
17	11	15	14	5	1	5
9	15	6	8	4	9	13
9	11	12	13	5	19	4
13	12	16	16	6	13	5
10	12	5	13	6	18	7
6	9	15	11	6	6	8
12	12	12	14	5	5	9
8	12	8	13	4	3	11
14	13	13	13	5	7	4
12	11	14	13	5	8	6
11	9	12	12	4	9	8
16	9	16	16	6	13	10
8	11	10	15	2	12	4
15	11	15	15	8	2	4
7	12	8	12	3	4	2
16	12	16	14	6	6	12
14	9	19	12	6	8	11
16	11	14	15	6	9	4
9	9	6	12	5	10	7
14	12	13	13	5	9	7
11	12	15	12	6	3	9
13	12	7	12	5	5	19
15	12	13	13	6	6	3
5	14	4	5	2	2	5
15	11	14	13	5	3	3
13	12	13	13	5	4	11
11	11	11	14	5	2	5
11	6	14	17	6	11	6
12	10	12	13	6	8	8
12	12	15	13	6	11	9
12	13	14	12	5	17	11
12	8	13	13	5	4	7
14	12	8	14	4	5	4
6	12	6	11	2	8	5
7	12	7	12	4	9	7
14	6	13	12	6	4	11
14	11	13	16	6	6	13
10	10	11	12	5	7	3
13	12	5	12	3	9	5
12	13	12	12	6	11	7
9	11	8	10	4	12	8
12	7	11	15	5	9	11
16	11	14	15	8	4	12
10	11	9	12	4	3	8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
KansOverwinning[t] = + 3.1669540137257 -0.0364824329886247GeboekteOverwinning[t] + 0.142677588756209Gevoel[t] + 0.403677872812312EigenGevoel[t] + 0.522701285058165Beste[t] -0.0971236960285132`2deBeste`[t] + 0.0482753220205221`3debeste`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
KansOverwinning[t] =  +  3.1669540137257 -0.0364824329886247GeboekteOverwinning[t] +  0.142677588756209Gevoel[t] +  0.403677872812312EigenGevoel[t] +  0.522701285058165Beste[t] -0.0971236960285132`2deBeste`[t] +  0.0482753220205221`3debeste`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]KansOverwinning[t] =  +  3.1669540137257 -0.0364824329886247GeboekteOverwinning[t] +  0.142677588756209Gevoel[t] +  0.403677872812312EigenGevoel[t] +  0.522701285058165Beste[t] -0.0971236960285132`2deBeste`[t] +  0.0482753220205221`3debeste`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&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
KansOverwinning[t] = + 3.1669540137257 -0.0364824329886247GeboekteOverwinning[t] + 0.142677588756209Gevoel[t] + 0.403677872812312EigenGevoel[t] + 0.522701285058165Beste[t] -0.0971236960285132`2deBeste`[t] + 0.0482753220205221`3debeste`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.16695401372572.2278521.42150.158910.079455
GeboekteOverwinning-0.03648243298862470.125084-0.29170.7712720.385636
Gevoel0.1426775887562090.0930111.5340.1288380.064419
EigenGevoel0.4036778728123120.1444252.79510.0064440.003222
Beste0.5227012850581650.2469492.11660.0372850.018643
`2deBeste`-0.09712369602851320.05552-1.74930.0839280.041964
`3debeste`0.04827532202052210.0726450.66450.5081910.254096

\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) & 3.1669540137257 & 2.227852 & 1.4215 & 0.15891 & 0.079455 \tabularnewline
GeboekteOverwinning & -0.0364824329886247 & 0.125084 & -0.2917 & 0.771272 & 0.385636 \tabularnewline
Gevoel & 0.142677588756209 & 0.093011 & 1.534 & 0.128838 & 0.064419 \tabularnewline
EigenGevoel & 0.403677872812312 & 0.144425 & 2.7951 & 0.006444 & 0.003222 \tabularnewline
Beste & 0.522701285058165 & 0.246949 & 2.1166 & 0.037285 & 0.018643 \tabularnewline
`2deBeste` & -0.0971236960285132 & 0.05552 & -1.7493 & 0.083928 & 0.041964 \tabularnewline
`3debeste` & 0.0482753220205221 & 0.072645 & 0.6645 & 0.508191 & 0.254096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&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]3.1669540137257[/C][C]2.227852[/C][C]1.4215[/C][C]0.15891[/C][C]0.079455[/C][/ROW]
[ROW][C]GeboekteOverwinning[/C][C]-0.0364824329886247[/C][C]0.125084[/C][C]-0.2917[/C][C]0.771272[/C][C]0.385636[/C][/ROW]
[ROW][C]Gevoel[/C][C]0.142677588756209[/C][C]0.093011[/C][C]1.534[/C][C]0.128838[/C][C]0.064419[/C][/ROW]
[ROW][C]EigenGevoel[/C][C]0.403677872812312[/C][C]0.144425[/C][C]2.7951[/C][C]0.006444[/C][C]0.003222[/C][/ROW]
[ROW][C]Beste[/C][C]0.522701285058165[/C][C]0.246949[/C][C]2.1166[/C][C]0.037285[/C][C]0.018643[/C][/ROW]
[ROW][C]`2deBeste`[/C][C]-0.0971236960285132[/C][C]0.05552[/C][C]-1.7493[/C][C]0.083928[/C][C]0.041964[/C][/ROW]
[ROW][C]`3debeste`[/C][C]0.0482753220205221[/C][C]0.072645[/C][C]0.6645[/C][C]0.508191[/C][C]0.254096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&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)3.16695401372572.2278521.42150.158910.079455
GeboekteOverwinning-0.03648243298862470.125084-0.29170.7712720.385636
Gevoel0.1426775887562090.0930111.5340.1288380.064419
EigenGevoel0.4036778728123120.1444252.79510.0064440.003222
Beste0.5227012850581650.2469492.11660.0372850.018643
`2deBeste`-0.09712369602851320.05552-1.74930.0839280.041964
`3debeste`0.04827532202052210.0726450.66450.5081910.254096






Multiple Linear Regression - Regression Statistics
Multiple R0.641149746228267
R-squared0.411072997088571
Adjusted R-squared0.368499960733528
F-TEST (value)9.65571244814151
F-TEST (DF numerator)6
F-TEST (DF denominator)83
p-value4.79153297039403e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25053991365421
Sum Squared Residuals420.38918194491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.641149746228267 \tabularnewline
R-squared & 0.411072997088571 \tabularnewline
Adjusted R-squared & 0.368499960733528 \tabularnewline
F-TEST (value) & 9.65571244814151 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 83 \tabularnewline
p-value & 4.79153297039403e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.25053991365421 \tabularnewline
Sum Squared Residuals & 420.38918194491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.641149746228267[/C][/ROW]
[ROW][C]R-squared[/C][C]0.411072997088571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.368499960733528[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.65571244814151[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]83[/C][/ROW]
[ROW][C]p-value[/C][C]4.79153297039403e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.25053991365421[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]420.38918194491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&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 R0.641149746228267
R-squared0.411072997088571
Adjusted R-squared0.368499960733528
F-TEST (value)9.65571244814151
F-TEST (DF numerator)6
F-TEST (DF denominator)83
p-value4.79153297039403e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25053991365421
Sum Squared Residuals420.38918194491






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.31069133316311.68930866683689
21211.48766242972280.512337570277194
31514.44666862641610.553331373583867
41211.47461270018740.525387299812597
5109.582344339147620.417655660852379
6129.264886899846352.73511310015365
71516.3733413180769-1.37334131807693
8910.6821888093497-1.68218880934969
91212.0781594279902-0.0781594279902341
10119.088395577802761.91160442219724
111113.4694737624949-2.46947376249486
121112.4232133923513-1.42321339235134
131511.90705820114453.09294179885553
14710.9624781168589-3.96247811685887
151111.7458728860395-0.745872886039522
161110.67617289877660.323827101223434
171011.8393251122053-1.83932511220532
181414.9813257697953-0.981325769795295
19109.388880463709380.611119536290625
20610.0947734206128-4.09477342061277
21119.109338716529871.89066128347013
221514.47491837307970.525081626920283
231110.43716804932390.562831950676131
241210.40227649042291.59772350957708
251412.86370781802541.1362921819746
261514.20175860291890.798241397081118
27913.1607405214082-4.16074052140823
281313.0870099070964-0.0870099070964283
291312.36004213679080.639957863209243
301611.5140380355264.48596196447401
31139.163216508666963.83678349133304
321213.4626684503411-1.46266845034111
331413.8079914938870.19200850611304
341110.91607043257460.0839295674253545
35910.9729868454478-1.97298684544776
361613.36345900127742.63654099872265
371213.1996252819591-1.19962528195906
38109.618592997282110.381407002717894
391312.32016950413580.67983049586423
401614.48293944722991.51706055277008
411413.20650487876720.793495121232764
42159.025520610581035.97447938941897
4359.83201099179978-4.83201099179978
44811.3664471545461-3.36644715454606
451110.62250988867670.377490111323308
461613.78259084147472.21740915852531
471713.31506064093133.68493935906874
4898.549477096174910.450522903825087
49910.6868481513166-1.68684815131656
501313.5858284750395-0.585828475039473
511010.4162735441827-0.416273544182717
52612.3589006594487-6.35890065944873
531212.655151945642-0.655151945642043
54811.4488604688448-3.4488604688448
551411.92204522643772.07795477356232
561212.1371146291837-0.137114629183667
571110.99777210779060.00222789220944707
581613.9366523841082.06334761589204
59810.3206107364539-2.32061073645387
601515.141443350869-0.141443350869029
6179.99087971676111-2.99087971676111
621613.79626585575812.2037341442419
631413.28386746129040.716132538709576
641613.27349731979692.7265026802031
65910.5190088422624-1.51900884226243
661411.90910623343082.09089376656916
671112.9927776434012-1.99277764340124
681311.61716147644161.38283852355841
691512.53007731849252.46992268150754
7056.33783145913327-1.33783145913327
711512.47790714326472.52209285673533
721312.58782600165550.412173998344504
731112.6472265898779-1.64722658987791
741114.1655684823487-3.16556848234867
751212.5074938137591-0.507493813759083
761212.6194659479854-0.619465947985442
771211.02773523624010.972264763759903
781212.5406544455279-0.540654445527907
791411.32036369545642.67963630454357
8068.53547656332573-2.53547656332573
81710.1266615430231-3.12666154302311
821412.92574401183311.0742559881669
831414.2603465901232-0.260346590123241
841011.2941841530583-1.2941841530583
85139.222054436411493.77794556358851
861211.65472223187480.345277768125165
8799.2553700530783-0.255370053078304
881212.8066202545683-0.806620254568261
891615.190720946220.80927905378003
901011.0795166517158-1.07951665171576

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3106913331631 & 1.68930866683689 \tabularnewline
2 & 12 & 11.4876624297228 & 0.512337570277194 \tabularnewline
3 & 15 & 14.4466686264161 & 0.553331373583867 \tabularnewline
4 & 12 & 11.4746127001874 & 0.525387299812597 \tabularnewline
5 & 10 & 9.58234433914762 & 0.417655660852379 \tabularnewline
6 & 12 & 9.26488689984635 & 2.73511310015365 \tabularnewline
7 & 15 & 16.3733413180769 & -1.37334131807693 \tabularnewline
8 & 9 & 10.6821888093497 & -1.68218880934969 \tabularnewline
9 & 12 & 12.0781594279902 & -0.0781594279902341 \tabularnewline
10 & 11 & 9.08839557780276 & 1.91160442219724 \tabularnewline
11 & 11 & 13.4694737624949 & -2.46947376249486 \tabularnewline
12 & 11 & 12.4232133923513 & -1.42321339235134 \tabularnewline
13 & 15 & 11.9070582011445 & 3.09294179885553 \tabularnewline
14 & 7 & 10.9624781168589 & -3.96247811685887 \tabularnewline
15 & 11 & 11.7458728860395 & -0.745872886039522 \tabularnewline
16 & 11 & 10.6761728987766 & 0.323827101223434 \tabularnewline
17 & 10 & 11.8393251122053 & -1.83932511220532 \tabularnewline
18 & 14 & 14.9813257697953 & -0.981325769795295 \tabularnewline
19 & 10 & 9.38888046370938 & 0.611119536290625 \tabularnewline
20 & 6 & 10.0947734206128 & -4.09477342061277 \tabularnewline
21 & 11 & 9.10933871652987 & 1.89066128347013 \tabularnewline
22 & 15 & 14.4749183730797 & 0.525081626920283 \tabularnewline
23 & 11 & 10.4371680493239 & 0.562831950676131 \tabularnewline
24 & 12 & 10.4022764904229 & 1.59772350957708 \tabularnewline
25 & 14 & 12.8637078180254 & 1.1362921819746 \tabularnewline
26 & 15 & 14.2017586029189 & 0.798241397081118 \tabularnewline
27 & 9 & 13.1607405214082 & -4.16074052140823 \tabularnewline
28 & 13 & 13.0870099070964 & -0.0870099070964283 \tabularnewline
29 & 13 & 12.3600421367908 & 0.639957863209243 \tabularnewline
30 & 16 & 11.514038035526 & 4.48596196447401 \tabularnewline
31 & 13 & 9.16321650866696 & 3.83678349133304 \tabularnewline
32 & 12 & 13.4626684503411 & -1.46266845034111 \tabularnewline
33 & 14 & 13.807991493887 & 0.19200850611304 \tabularnewline
34 & 11 & 10.9160704325746 & 0.0839295674253545 \tabularnewline
35 & 9 & 10.9729868454478 & -1.97298684544776 \tabularnewline
36 & 16 & 13.3634590012774 & 2.63654099872265 \tabularnewline
37 & 12 & 13.1996252819591 & -1.19962528195906 \tabularnewline
38 & 10 & 9.61859299728211 & 0.381407002717894 \tabularnewline
39 & 13 & 12.3201695041358 & 0.67983049586423 \tabularnewline
40 & 16 & 14.4829394472299 & 1.51706055277008 \tabularnewline
41 & 14 & 13.2065048787672 & 0.793495121232764 \tabularnewline
42 & 15 & 9.02552061058103 & 5.97447938941897 \tabularnewline
43 & 5 & 9.83201099179978 & -4.83201099179978 \tabularnewline
44 & 8 & 11.3664471545461 & -3.36644715454606 \tabularnewline
45 & 11 & 10.6225098886767 & 0.377490111323308 \tabularnewline
46 & 16 & 13.7825908414747 & 2.21740915852531 \tabularnewline
47 & 17 & 13.3150606409313 & 3.68493935906874 \tabularnewline
48 & 9 & 8.54947709617491 & 0.450522903825087 \tabularnewline
49 & 9 & 10.6868481513166 & -1.68684815131656 \tabularnewline
50 & 13 & 13.5858284750395 & -0.585828475039473 \tabularnewline
51 & 10 & 10.4162735441827 & -0.416273544182717 \tabularnewline
52 & 6 & 12.3589006594487 & -6.35890065944873 \tabularnewline
53 & 12 & 12.655151945642 & -0.655151945642043 \tabularnewline
54 & 8 & 11.4488604688448 & -3.4488604688448 \tabularnewline
55 & 14 & 11.9220452264377 & 2.07795477356232 \tabularnewline
56 & 12 & 12.1371146291837 & -0.137114629183667 \tabularnewline
57 & 11 & 10.9977721077906 & 0.00222789220944707 \tabularnewline
58 & 16 & 13.936652384108 & 2.06334761589204 \tabularnewline
59 & 8 & 10.3206107364539 & -2.32061073645387 \tabularnewline
60 & 15 & 15.141443350869 & -0.141443350869029 \tabularnewline
61 & 7 & 9.99087971676111 & -2.99087971676111 \tabularnewline
62 & 16 & 13.7962658557581 & 2.2037341442419 \tabularnewline
63 & 14 & 13.2838674612904 & 0.716132538709576 \tabularnewline
64 & 16 & 13.2734973197969 & 2.7265026802031 \tabularnewline
65 & 9 & 10.5190088422624 & -1.51900884226243 \tabularnewline
66 & 14 & 11.9091062334308 & 2.09089376656916 \tabularnewline
67 & 11 & 12.9927776434012 & -1.99277764340124 \tabularnewline
68 & 13 & 11.6171614764416 & 1.38283852355841 \tabularnewline
69 & 15 & 12.5300773184925 & 2.46992268150754 \tabularnewline
70 & 5 & 6.33783145913327 & -1.33783145913327 \tabularnewline
71 & 15 & 12.4779071432647 & 2.52209285673533 \tabularnewline
72 & 13 & 12.5878260016555 & 0.412173998344504 \tabularnewline
73 & 11 & 12.6472265898779 & -1.64722658987791 \tabularnewline
74 & 11 & 14.1655684823487 & -3.16556848234867 \tabularnewline
75 & 12 & 12.5074938137591 & -0.507493813759083 \tabularnewline
76 & 12 & 12.6194659479854 & -0.619465947985442 \tabularnewline
77 & 12 & 11.0277352362401 & 0.972264763759903 \tabularnewline
78 & 12 & 12.5406544455279 & -0.540654445527907 \tabularnewline
79 & 14 & 11.3203636954564 & 2.67963630454357 \tabularnewline
80 & 6 & 8.53547656332573 & -2.53547656332573 \tabularnewline
81 & 7 & 10.1266615430231 & -3.12666154302311 \tabularnewline
82 & 14 & 12.9257440118331 & 1.0742559881669 \tabularnewline
83 & 14 & 14.2603465901232 & -0.260346590123241 \tabularnewline
84 & 10 & 11.2941841530583 & -1.2941841530583 \tabularnewline
85 & 13 & 9.22205443641149 & 3.77794556358851 \tabularnewline
86 & 12 & 11.6547222318748 & 0.345277768125165 \tabularnewline
87 & 9 & 9.2553700530783 & -0.255370053078304 \tabularnewline
88 & 12 & 12.8066202545683 & -0.806620254568261 \tabularnewline
89 & 16 & 15.19072094622 & 0.80927905378003 \tabularnewline
90 & 10 & 11.0795166517158 & -1.07951665171576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&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]13[/C][C]11.3106913331631[/C][C]1.68930866683689[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.4876624297228[/C][C]0.512337570277194[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]14.4466686264161[/C][C]0.553331373583867[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.4746127001874[/C][C]0.525387299812597[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]9.58234433914762[/C][C]0.417655660852379[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.26488689984635[/C][C]2.73511310015365[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.3733413180769[/C][C]-1.37334131807693[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.6821888093497[/C][C]-1.68218880934969[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.0781594279902[/C][C]-0.0781594279902341[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]9.08839557780276[/C][C]1.91160442219724[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.4694737624949[/C][C]-2.46947376249486[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.4232133923513[/C][C]-1.42321339235134[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]11.9070582011445[/C][C]3.09294179885553[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]10.9624781168589[/C][C]-3.96247811685887[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.7458728860395[/C][C]-0.745872886039522[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.6761728987766[/C][C]0.323827101223434[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.8393251122053[/C][C]-1.83932511220532[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.9813257697953[/C][C]-0.981325769795295[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]9.38888046370938[/C][C]0.611119536290625[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]10.0947734206128[/C][C]-4.09477342061277[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]9.10933871652987[/C][C]1.89066128347013[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4749183730797[/C][C]0.525081626920283[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]10.4371680493239[/C][C]0.562831950676131[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.4022764904229[/C][C]1.59772350957708[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.8637078180254[/C][C]1.1362921819746[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.2017586029189[/C][C]0.798241397081118[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]13.1607405214082[/C][C]-4.16074052140823[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.0870099070964[/C][C]-0.0870099070964283[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]12.3600421367908[/C][C]0.639957863209243[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.514038035526[/C][C]4.48596196447401[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]9.16321650866696[/C][C]3.83678349133304[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.4626684503411[/C][C]-1.46266845034111[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.807991493887[/C][C]0.19200850611304[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.9160704325746[/C][C]0.0839295674253545[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.9729868454478[/C][C]-1.97298684544776[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.3634590012774[/C][C]2.63654099872265[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.1996252819591[/C][C]-1.19962528195906[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.61859299728211[/C][C]0.381407002717894[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.3201695041358[/C][C]0.67983049586423[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4829394472299[/C][C]1.51706055277008[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.2065048787672[/C][C]0.793495121232764[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]9.02552061058103[/C][C]5.97447938941897[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.83201099179978[/C][C]-4.83201099179978[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]11.3664471545461[/C][C]-3.36644715454606[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.6225098886767[/C][C]0.377490111323308[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7825908414747[/C][C]2.21740915852531[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.3150606409313[/C][C]3.68493935906874[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.54947709617491[/C][C]0.450522903825087[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]10.6868481513166[/C][C]-1.68684815131656[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.5858284750395[/C][C]-0.585828475039473[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.4162735441827[/C][C]-0.416273544182717[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.3589006594487[/C][C]-6.35890065944873[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.655151945642[/C][C]-0.655151945642043[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]11.4488604688448[/C][C]-3.4488604688448[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.9220452264377[/C][C]2.07795477356232[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.1371146291837[/C][C]-0.137114629183667[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.9977721077906[/C][C]0.00222789220944707[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.936652384108[/C][C]2.06334761589204[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.3206107364539[/C][C]-2.32061073645387[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.141443350869[/C][C]-0.141443350869029[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.99087971676111[/C][C]-2.99087971676111[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.7962658557581[/C][C]2.2037341442419[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.2838674612904[/C][C]0.716132538709576[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.2734973197969[/C][C]2.7265026802031[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.5190088422624[/C][C]-1.51900884226243[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]11.9091062334308[/C][C]2.09089376656916[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.9927776434012[/C][C]-1.99277764340124[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.6171614764416[/C][C]1.38283852355841[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.5300773184925[/C][C]2.46992268150754[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.33783145913327[/C][C]-1.33783145913327[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4779071432647[/C][C]2.52209285673533[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.5878260016555[/C][C]0.412173998344504[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.6472265898779[/C][C]-1.64722658987791[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1655684823487[/C][C]-3.16556848234867[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.5074938137591[/C][C]-0.507493813759083[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.6194659479854[/C][C]-0.619465947985442[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.0277352362401[/C][C]0.972264763759903[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.5406544455279[/C][C]-0.540654445527907[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.3203636954564[/C][C]2.67963630454357[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.53547656332573[/C][C]-2.53547656332573[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]10.1266615430231[/C][C]-3.12666154302311[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.9257440118331[/C][C]1.0742559881669[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.2603465901232[/C][C]-0.260346590123241[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2941841530583[/C][C]-1.2941841530583[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.22205443641149[/C][C]3.77794556358851[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.6547222318748[/C][C]0.345277768125165[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.2553700530783[/C][C]-0.255370053078304[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8066202545683[/C][C]-0.806620254568261[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.19072094622[/C][C]0.80927905378003[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]11.0795166517158[/C][C]-1.07951665171576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&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
11311.31069133316311.68930866683689
21211.48766242972280.512337570277194
31514.44666862641610.553331373583867
41211.47461270018740.525387299812597
5109.582344339147620.417655660852379
6129.264886899846352.73511310015365
71516.3733413180769-1.37334131807693
8910.6821888093497-1.68218880934969
91212.0781594279902-0.0781594279902341
10119.088395577802761.91160442219724
111113.4694737624949-2.46947376249486
121112.4232133923513-1.42321339235134
131511.90705820114453.09294179885553
14710.9624781168589-3.96247811685887
151111.7458728860395-0.745872886039522
161110.67617289877660.323827101223434
171011.8393251122053-1.83932511220532
181414.9813257697953-0.981325769795295
19109.388880463709380.611119536290625
20610.0947734206128-4.09477342061277
21119.109338716529871.89066128347013
221514.47491837307970.525081626920283
231110.43716804932390.562831950676131
241210.40227649042291.59772350957708
251412.86370781802541.1362921819746
261514.20175860291890.798241397081118
27913.1607405214082-4.16074052140823
281313.0870099070964-0.0870099070964283
291312.36004213679080.639957863209243
301611.5140380355264.48596196447401
31139.163216508666963.83678349133304
321213.4626684503411-1.46266845034111
331413.8079914938870.19200850611304
341110.91607043257460.0839295674253545
35910.9729868454478-1.97298684544776
361613.36345900127742.63654099872265
371213.1996252819591-1.19962528195906
38109.618592997282110.381407002717894
391312.32016950413580.67983049586423
401614.48293944722991.51706055277008
411413.20650487876720.793495121232764
42159.025520610581035.97447938941897
4359.83201099179978-4.83201099179978
44811.3664471545461-3.36644715454606
451110.62250988867670.377490111323308
461613.78259084147472.21740915852531
471713.31506064093133.68493935906874
4898.549477096174910.450522903825087
49910.6868481513166-1.68684815131656
501313.5858284750395-0.585828475039473
511010.4162735441827-0.416273544182717
52612.3589006594487-6.35890065944873
531212.655151945642-0.655151945642043
54811.4488604688448-3.4488604688448
551411.92204522643772.07795477356232
561212.1371146291837-0.137114629183667
571110.99777210779060.00222789220944707
581613.9366523841082.06334761589204
59810.3206107364539-2.32061073645387
601515.141443350869-0.141443350869029
6179.99087971676111-2.99087971676111
621613.79626585575812.2037341442419
631413.28386746129040.716132538709576
641613.27349731979692.7265026802031
65910.5190088422624-1.51900884226243
661411.90910623343082.09089376656916
671112.9927776434012-1.99277764340124
681311.61716147644161.38283852355841
691512.53007731849252.46992268150754
7056.33783145913327-1.33783145913327
711512.47790714326472.52209285673533
721312.58782600165550.412173998344504
731112.6472265898779-1.64722658987791
741114.1655684823487-3.16556848234867
751212.5074938137591-0.507493813759083
761212.6194659479854-0.619465947985442
771211.02773523624010.972264763759903
781212.5406544455279-0.540654445527907
791411.32036369545642.67963630454357
8068.53547656332573-2.53547656332573
81710.1266615430231-3.12666154302311
821412.92574401183311.0742559881669
831414.2603465901232-0.260346590123241
841011.2941841530583-1.2941841530583
85139.222054436411493.77794556358851
861211.65472223187480.345277768125165
8799.2553700530783-0.255370053078304
881212.8066202545683-0.806620254568261
891615.190720946220.80927905378003
901011.0795166517158-1.07951665171576






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.06744272663703930.1348854532740790.932557273362961
110.08429937969049680.1685987593809940.915700620309503
120.03425461319869540.06850922639739080.965745386801305
130.3014717549819530.6029435099639050.698528245018047
140.6431783103426810.7136433793146370.356821689657319
150.5366477375746730.9267045248506540.463352262425327
160.4458550467941940.8917100935883870.554144953205806
170.3546035963789520.7092071927579030.645396403621048
180.2895003658494190.5790007316988370.710499634150581
190.2136928410803130.4273856821606250.786307158919687
200.4807198027809150.961439605561830.519280197219085
210.4148303932395170.8296607864790330.585169606760483
220.372836874753280.745673749506560.62716312524672
230.3057575598753150.6115151197506290.694242440124685
240.2554014306048350.510802861209670.744598569395165
250.2312450423134980.4624900846269960.768754957686502
260.1905274918896290.3810549837792580.809472508110371
270.2660560916967690.5321121833935390.733943908303231
280.207622207998020.415244415996040.79237779200198
290.1789538326939240.3579076653878480.821046167306076
300.2966273857580740.5932547715161490.703372614241926
310.3858027430051160.7716054860102320.614197256994884
320.3365347156410.6730694312819990.663465284359
330.2931770428648270.5863540857296530.706822957135173
340.2409014865122210.4818029730244410.759098513487779
350.2577131323032650.5154262646065290.742286867696735
360.2979486246083590.5958972492167180.702051375391641
370.2608241569506830.5216483139013650.739175843049317
380.2123249754095580.4246499508191170.787675024590442
390.174204235936070.3484084718721410.82579576406393
400.1553064274738110.3106128549476210.844693572526189
410.1213787920500730.2427575841001470.878621207949927
420.4336985652539540.8673971305079070.566301434746046
430.7352822693386290.5294354613227430.264717730661371
440.7980447181577830.4039105636844340.201955281842217
450.7535526640537260.4928946718925480.246447335946274
460.7487020099840520.5025959800318950.251297990015948
470.8183143416847780.3633713166304450.181685658315222
480.7743962383587580.4512075232824840.225603761641242
490.7488369026972610.5023261946054780.251163097302739
500.7167757879646520.5664484240706970.283224212035348
510.6679070221087140.6641859557825720.332092977891286
520.9370774063539270.1258451872921470.0629225936460733
530.9165138012784650.1669723974430690.0834861987215346
540.9464298755448820.1071402489102350.0535701244551176
550.9386893431739070.1226213136521860.0613106568260932
560.9148174318284790.1703651363430410.0851825681715206
570.8868237821815920.2263524356368150.113176217818408
580.8784606835087810.2430786329824380.121539316491219
590.8746888650378810.2506222699242390.125311134962119
600.8419080661802020.3161838676395950.158091933819798
610.8732528318016510.2534943363966980.126747168198349
620.8531826353311290.2936347293377410.146817364668871
630.8173735174429230.3652529651141540.182626482557077
640.8198546815252870.3602906369494250.180145318474713
650.779899475737110.440201048525780.22010052426289
660.7713737918291490.4572524163417020.228626208170851
670.7769276080888920.4461447838222150.223072391911108
680.7358696503141060.5282606993717880.264130349685894
690.7128184458767620.5743631082464760.287181554123238
700.6799591642875050.640081671424990.320040835712495
710.7305233882247380.5389532235505250.269476611775262
720.6502367190590380.6995265618819240.349763280940962
730.5879194544370820.8241610911258360.412080545562918
740.6037440745862340.7925118508275320.396255925413766
750.510186978485560.9796260430288810.48981302151444
760.4040637105012190.8081274210024380.595936289498781
770.4280172129038380.8560344258076760.571982787096162
780.312823354234530.625646708469060.68717664576547
790.2797916470042420.5595832940084850.720208352995758
800.1814574250286840.3629148500573680.818542574971316

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0674427266370393 & 0.134885453274079 & 0.932557273362961 \tabularnewline
11 & 0.0842993796904968 & 0.168598759380994 & 0.915700620309503 \tabularnewline
12 & 0.0342546131986954 & 0.0685092263973908 & 0.965745386801305 \tabularnewline
13 & 0.301471754981953 & 0.602943509963905 & 0.698528245018047 \tabularnewline
14 & 0.643178310342681 & 0.713643379314637 & 0.356821689657319 \tabularnewline
15 & 0.536647737574673 & 0.926704524850654 & 0.463352262425327 \tabularnewline
16 & 0.445855046794194 & 0.891710093588387 & 0.554144953205806 \tabularnewline
17 & 0.354603596378952 & 0.709207192757903 & 0.645396403621048 \tabularnewline
18 & 0.289500365849419 & 0.579000731698837 & 0.710499634150581 \tabularnewline
19 & 0.213692841080313 & 0.427385682160625 & 0.786307158919687 \tabularnewline
20 & 0.480719802780915 & 0.96143960556183 & 0.519280197219085 \tabularnewline
21 & 0.414830393239517 & 0.829660786479033 & 0.585169606760483 \tabularnewline
22 & 0.37283687475328 & 0.74567374950656 & 0.62716312524672 \tabularnewline
23 & 0.305757559875315 & 0.611515119750629 & 0.694242440124685 \tabularnewline
24 & 0.255401430604835 & 0.51080286120967 & 0.744598569395165 \tabularnewline
25 & 0.231245042313498 & 0.462490084626996 & 0.768754957686502 \tabularnewline
26 & 0.190527491889629 & 0.381054983779258 & 0.809472508110371 \tabularnewline
27 & 0.266056091696769 & 0.532112183393539 & 0.733943908303231 \tabularnewline
28 & 0.20762220799802 & 0.41524441599604 & 0.79237779200198 \tabularnewline
29 & 0.178953832693924 & 0.357907665387848 & 0.821046167306076 \tabularnewline
30 & 0.296627385758074 & 0.593254771516149 & 0.703372614241926 \tabularnewline
31 & 0.385802743005116 & 0.771605486010232 & 0.614197256994884 \tabularnewline
32 & 0.336534715641 & 0.673069431281999 & 0.663465284359 \tabularnewline
33 & 0.293177042864827 & 0.586354085729653 & 0.706822957135173 \tabularnewline
34 & 0.240901486512221 & 0.481802973024441 & 0.759098513487779 \tabularnewline
35 & 0.257713132303265 & 0.515426264606529 & 0.742286867696735 \tabularnewline
36 & 0.297948624608359 & 0.595897249216718 & 0.702051375391641 \tabularnewline
37 & 0.260824156950683 & 0.521648313901365 & 0.739175843049317 \tabularnewline
38 & 0.212324975409558 & 0.424649950819117 & 0.787675024590442 \tabularnewline
39 & 0.17420423593607 & 0.348408471872141 & 0.82579576406393 \tabularnewline
40 & 0.155306427473811 & 0.310612854947621 & 0.844693572526189 \tabularnewline
41 & 0.121378792050073 & 0.242757584100147 & 0.878621207949927 \tabularnewline
42 & 0.433698565253954 & 0.867397130507907 & 0.566301434746046 \tabularnewline
43 & 0.735282269338629 & 0.529435461322743 & 0.264717730661371 \tabularnewline
44 & 0.798044718157783 & 0.403910563684434 & 0.201955281842217 \tabularnewline
45 & 0.753552664053726 & 0.492894671892548 & 0.246447335946274 \tabularnewline
46 & 0.748702009984052 & 0.502595980031895 & 0.251297990015948 \tabularnewline
47 & 0.818314341684778 & 0.363371316630445 & 0.181685658315222 \tabularnewline
48 & 0.774396238358758 & 0.451207523282484 & 0.225603761641242 \tabularnewline
49 & 0.748836902697261 & 0.502326194605478 & 0.251163097302739 \tabularnewline
50 & 0.716775787964652 & 0.566448424070697 & 0.283224212035348 \tabularnewline
51 & 0.667907022108714 & 0.664185955782572 & 0.332092977891286 \tabularnewline
52 & 0.937077406353927 & 0.125845187292147 & 0.0629225936460733 \tabularnewline
53 & 0.916513801278465 & 0.166972397443069 & 0.0834861987215346 \tabularnewline
54 & 0.946429875544882 & 0.107140248910235 & 0.0535701244551176 \tabularnewline
55 & 0.938689343173907 & 0.122621313652186 & 0.0613106568260932 \tabularnewline
56 & 0.914817431828479 & 0.170365136343041 & 0.0851825681715206 \tabularnewline
57 & 0.886823782181592 & 0.226352435636815 & 0.113176217818408 \tabularnewline
58 & 0.878460683508781 & 0.243078632982438 & 0.121539316491219 \tabularnewline
59 & 0.874688865037881 & 0.250622269924239 & 0.125311134962119 \tabularnewline
60 & 0.841908066180202 & 0.316183867639595 & 0.158091933819798 \tabularnewline
61 & 0.873252831801651 & 0.253494336396698 & 0.126747168198349 \tabularnewline
62 & 0.853182635331129 & 0.293634729337741 & 0.146817364668871 \tabularnewline
63 & 0.817373517442923 & 0.365252965114154 & 0.182626482557077 \tabularnewline
64 & 0.819854681525287 & 0.360290636949425 & 0.180145318474713 \tabularnewline
65 & 0.77989947573711 & 0.44020104852578 & 0.22010052426289 \tabularnewline
66 & 0.771373791829149 & 0.457252416341702 & 0.228626208170851 \tabularnewline
67 & 0.776927608088892 & 0.446144783822215 & 0.223072391911108 \tabularnewline
68 & 0.735869650314106 & 0.528260699371788 & 0.264130349685894 \tabularnewline
69 & 0.712818445876762 & 0.574363108246476 & 0.287181554123238 \tabularnewline
70 & 0.679959164287505 & 0.64008167142499 & 0.320040835712495 \tabularnewline
71 & 0.730523388224738 & 0.538953223550525 & 0.269476611775262 \tabularnewline
72 & 0.650236719059038 & 0.699526561881924 & 0.349763280940962 \tabularnewline
73 & 0.587919454437082 & 0.824161091125836 & 0.412080545562918 \tabularnewline
74 & 0.603744074586234 & 0.792511850827532 & 0.396255925413766 \tabularnewline
75 & 0.51018697848556 & 0.979626043028881 & 0.48981302151444 \tabularnewline
76 & 0.404063710501219 & 0.808127421002438 & 0.595936289498781 \tabularnewline
77 & 0.428017212903838 & 0.856034425807676 & 0.571982787096162 \tabularnewline
78 & 0.31282335423453 & 0.62564670846906 & 0.68717664576547 \tabularnewline
79 & 0.279791647004242 & 0.559583294008485 & 0.720208352995758 \tabularnewline
80 & 0.181457425028684 & 0.362914850057368 & 0.818542574971316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0674427266370393[/C][C]0.134885453274079[/C][C]0.932557273362961[/C][/ROW]
[ROW][C]11[/C][C]0.0842993796904968[/C][C]0.168598759380994[/C][C]0.915700620309503[/C][/ROW]
[ROW][C]12[/C][C]0.0342546131986954[/C][C]0.0685092263973908[/C][C]0.965745386801305[/C][/ROW]
[ROW][C]13[/C][C]0.301471754981953[/C][C]0.602943509963905[/C][C]0.698528245018047[/C][/ROW]
[ROW][C]14[/C][C]0.643178310342681[/C][C]0.713643379314637[/C][C]0.356821689657319[/C][/ROW]
[ROW][C]15[/C][C]0.536647737574673[/C][C]0.926704524850654[/C][C]0.463352262425327[/C][/ROW]
[ROW][C]16[/C][C]0.445855046794194[/C][C]0.891710093588387[/C][C]0.554144953205806[/C][/ROW]
[ROW][C]17[/C][C]0.354603596378952[/C][C]0.709207192757903[/C][C]0.645396403621048[/C][/ROW]
[ROW][C]18[/C][C]0.289500365849419[/C][C]0.579000731698837[/C][C]0.710499634150581[/C][/ROW]
[ROW][C]19[/C][C]0.213692841080313[/C][C]0.427385682160625[/C][C]0.786307158919687[/C][/ROW]
[ROW][C]20[/C][C]0.480719802780915[/C][C]0.96143960556183[/C][C]0.519280197219085[/C][/ROW]
[ROW][C]21[/C][C]0.414830393239517[/C][C]0.829660786479033[/C][C]0.585169606760483[/C][/ROW]
[ROW][C]22[/C][C]0.37283687475328[/C][C]0.74567374950656[/C][C]0.62716312524672[/C][/ROW]
[ROW][C]23[/C][C]0.305757559875315[/C][C]0.611515119750629[/C][C]0.694242440124685[/C][/ROW]
[ROW][C]24[/C][C]0.255401430604835[/C][C]0.51080286120967[/C][C]0.744598569395165[/C][/ROW]
[ROW][C]25[/C][C]0.231245042313498[/C][C]0.462490084626996[/C][C]0.768754957686502[/C][/ROW]
[ROW][C]26[/C][C]0.190527491889629[/C][C]0.381054983779258[/C][C]0.809472508110371[/C][/ROW]
[ROW][C]27[/C][C]0.266056091696769[/C][C]0.532112183393539[/C][C]0.733943908303231[/C][/ROW]
[ROW][C]28[/C][C]0.20762220799802[/C][C]0.41524441599604[/C][C]0.79237779200198[/C][/ROW]
[ROW][C]29[/C][C]0.178953832693924[/C][C]0.357907665387848[/C][C]0.821046167306076[/C][/ROW]
[ROW][C]30[/C][C]0.296627385758074[/C][C]0.593254771516149[/C][C]0.703372614241926[/C][/ROW]
[ROW][C]31[/C][C]0.385802743005116[/C][C]0.771605486010232[/C][C]0.614197256994884[/C][/ROW]
[ROW][C]32[/C][C]0.336534715641[/C][C]0.673069431281999[/C][C]0.663465284359[/C][/ROW]
[ROW][C]33[/C][C]0.293177042864827[/C][C]0.586354085729653[/C][C]0.706822957135173[/C][/ROW]
[ROW][C]34[/C][C]0.240901486512221[/C][C]0.481802973024441[/C][C]0.759098513487779[/C][/ROW]
[ROW][C]35[/C][C]0.257713132303265[/C][C]0.515426264606529[/C][C]0.742286867696735[/C][/ROW]
[ROW][C]36[/C][C]0.297948624608359[/C][C]0.595897249216718[/C][C]0.702051375391641[/C][/ROW]
[ROW][C]37[/C][C]0.260824156950683[/C][C]0.521648313901365[/C][C]0.739175843049317[/C][/ROW]
[ROW][C]38[/C][C]0.212324975409558[/C][C]0.424649950819117[/C][C]0.787675024590442[/C][/ROW]
[ROW][C]39[/C][C]0.17420423593607[/C][C]0.348408471872141[/C][C]0.82579576406393[/C][/ROW]
[ROW][C]40[/C][C]0.155306427473811[/C][C]0.310612854947621[/C][C]0.844693572526189[/C][/ROW]
[ROW][C]41[/C][C]0.121378792050073[/C][C]0.242757584100147[/C][C]0.878621207949927[/C][/ROW]
[ROW][C]42[/C][C]0.433698565253954[/C][C]0.867397130507907[/C][C]0.566301434746046[/C][/ROW]
[ROW][C]43[/C][C]0.735282269338629[/C][C]0.529435461322743[/C][C]0.264717730661371[/C][/ROW]
[ROW][C]44[/C][C]0.798044718157783[/C][C]0.403910563684434[/C][C]0.201955281842217[/C][/ROW]
[ROW][C]45[/C][C]0.753552664053726[/C][C]0.492894671892548[/C][C]0.246447335946274[/C][/ROW]
[ROW][C]46[/C][C]0.748702009984052[/C][C]0.502595980031895[/C][C]0.251297990015948[/C][/ROW]
[ROW][C]47[/C][C]0.818314341684778[/C][C]0.363371316630445[/C][C]0.181685658315222[/C][/ROW]
[ROW][C]48[/C][C]0.774396238358758[/C][C]0.451207523282484[/C][C]0.225603761641242[/C][/ROW]
[ROW][C]49[/C][C]0.748836902697261[/C][C]0.502326194605478[/C][C]0.251163097302739[/C][/ROW]
[ROW][C]50[/C][C]0.716775787964652[/C][C]0.566448424070697[/C][C]0.283224212035348[/C][/ROW]
[ROW][C]51[/C][C]0.667907022108714[/C][C]0.664185955782572[/C][C]0.332092977891286[/C][/ROW]
[ROW][C]52[/C][C]0.937077406353927[/C][C]0.125845187292147[/C][C]0.0629225936460733[/C][/ROW]
[ROW][C]53[/C][C]0.916513801278465[/C][C]0.166972397443069[/C][C]0.0834861987215346[/C][/ROW]
[ROW][C]54[/C][C]0.946429875544882[/C][C]0.107140248910235[/C][C]0.0535701244551176[/C][/ROW]
[ROW][C]55[/C][C]0.938689343173907[/C][C]0.122621313652186[/C][C]0.0613106568260932[/C][/ROW]
[ROW][C]56[/C][C]0.914817431828479[/C][C]0.170365136343041[/C][C]0.0851825681715206[/C][/ROW]
[ROW][C]57[/C][C]0.886823782181592[/C][C]0.226352435636815[/C][C]0.113176217818408[/C][/ROW]
[ROW][C]58[/C][C]0.878460683508781[/C][C]0.243078632982438[/C][C]0.121539316491219[/C][/ROW]
[ROW][C]59[/C][C]0.874688865037881[/C][C]0.250622269924239[/C][C]0.125311134962119[/C][/ROW]
[ROW][C]60[/C][C]0.841908066180202[/C][C]0.316183867639595[/C][C]0.158091933819798[/C][/ROW]
[ROW][C]61[/C][C]0.873252831801651[/C][C]0.253494336396698[/C][C]0.126747168198349[/C][/ROW]
[ROW][C]62[/C][C]0.853182635331129[/C][C]0.293634729337741[/C][C]0.146817364668871[/C][/ROW]
[ROW][C]63[/C][C]0.817373517442923[/C][C]0.365252965114154[/C][C]0.182626482557077[/C][/ROW]
[ROW][C]64[/C][C]0.819854681525287[/C][C]0.360290636949425[/C][C]0.180145318474713[/C][/ROW]
[ROW][C]65[/C][C]0.77989947573711[/C][C]0.44020104852578[/C][C]0.22010052426289[/C][/ROW]
[ROW][C]66[/C][C]0.771373791829149[/C][C]0.457252416341702[/C][C]0.228626208170851[/C][/ROW]
[ROW][C]67[/C][C]0.776927608088892[/C][C]0.446144783822215[/C][C]0.223072391911108[/C][/ROW]
[ROW][C]68[/C][C]0.735869650314106[/C][C]0.528260699371788[/C][C]0.264130349685894[/C][/ROW]
[ROW][C]69[/C][C]0.712818445876762[/C][C]0.574363108246476[/C][C]0.287181554123238[/C][/ROW]
[ROW][C]70[/C][C]0.679959164287505[/C][C]0.64008167142499[/C][C]0.320040835712495[/C][/ROW]
[ROW][C]71[/C][C]0.730523388224738[/C][C]0.538953223550525[/C][C]0.269476611775262[/C][/ROW]
[ROW][C]72[/C][C]0.650236719059038[/C][C]0.699526561881924[/C][C]0.349763280940962[/C][/ROW]
[ROW][C]73[/C][C]0.587919454437082[/C][C]0.824161091125836[/C][C]0.412080545562918[/C][/ROW]
[ROW][C]74[/C][C]0.603744074586234[/C][C]0.792511850827532[/C][C]0.396255925413766[/C][/ROW]
[ROW][C]75[/C][C]0.51018697848556[/C][C]0.979626043028881[/C][C]0.48981302151444[/C][/ROW]
[ROW][C]76[/C][C]0.404063710501219[/C][C]0.808127421002438[/C][C]0.595936289498781[/C][/ROW]
[ROW][C]77[/C][C]0.428017212903838[/C][C]0.856034425807676[/C][C]0.571982787096162[/C][/ROW]
[ROW][C]78[/C][C]0.31282335423453[/C][C]0.62564670846906[/C][C]0.68717664576547[/C][/ROW]
[ROW][C]79[/C][C]0.279791647004242[/C][C]0.559583294008485[/C][C]0.720208352995758[/C][/ROW]
[ROW][C]80[/C][C]0.181457425028684[/C][C]0.362914850057368[/C][C]0.818542574971316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.06744272663703930.1348854532740790.932557273362961
110.08429937969049680.1685987593809940.915700620309503
120.03425461319869540.06850922639739080.965745386801305
130.3014717549819530.6029435099639050.698528245018047
140.6431783103426810.7136433793146370.356821689657319
150.5366477375746730.9267045248506540.463352262425327
160.4458550467941940.8917100935883870.554144953205806
170.3546035963789520.7092071927579030.645396403621048
180.2895003658494190.5790007316988370.710499634150581
190.2136928410803130.4273856821606250.786307158919687
200.4807198027809150.961439605561830.519280197219085
210.4148303932395170.8296607864790330.585169606760483
220.372836874753280.745673749506560.62716312524672
230.3057575598753150.6115151197506290.694242440124685
240.2554014306048350.510802861209670.744598569395165
250.2312450423134980.4624900846269960.768754957686502
260.1905274918896290.3810549837792580.809472508110371
270.2660560916967690.5321121833935390.733943908303231
280.207622207998020.415244415996040.79237779200198
290.1789538326939240.3579076653878480.821046167306076
300.2966273857580740.5932547715161490.703372614241926
310.3858027430051160.7716054860102320.614197256994884
320.3365347156410.6730694312819990.663465284359
330.2931770428648270.5863540857296530.706822957135173
340.2409014865122210.4818029730244410.759098513487779
350.2577131323032650.5154262646065290.742286867696735
360.2979486246083590.5958972492167180.702051375391641
370.2608241569506830.5216483139013650.739175843049317
380.2123249754095580.4246499508191170.787675024590442
390.174204235936070.3484084718721410.82579576406393
400.1553064274738110.3106128549476210.844693572526189
410.1213787920500730.2427575841001470.878621207949927
420.4336985652539540.8673971305079070.566301434746046
430.7352822693386290.5294354613227430.264717730661371
440.7980447181577830.4039105636844340.201955281842217
450.7535526640537260.4928946718925480.246447335946274
460.7487020099840520.5025959800318950.251297990015948
470.8183143416847780.3633713166304450.181685658315222
480.7743962383587580.4512075232824840.225603761641242
490.7488369026972610.5023261946054780.251163097302739
500.7167757879646520.5664484240706970.283224212035348
510.6679070221087140.6641859557825720.332092977891286
520.9370774063539270.1258451872921470.0629225936460733
530.9165138012784650.1669723974430690.0834861987215346
540.9464298755448820.1071402489102350.0535701244551176
550.9386893431739070.1226213136521860.0613106568260932
560.9148174318284790.1703651363430410.0851825681715206
570.8868237821815920.2263524356368150.113176217818408
580.8784606835087810.2430786329824380.121539316491219
590.8746888650378810.2506222699242390.125311134962119
600.8419080661802020.3161838676395950.158091933819798
610.8732528318016510.2534943363966980.126747168198349
620.8531826353311290.2936347293377410.146817364668871
630.8173735174429230.3652529651141540.182626482557077
640.8198546815252870.3602906369494250.180145318474713
650.779899475737110.440201048525780.22010052426289
660.7713737918291490.4572524163417020.228626208170851
670.7769276080888920.4461447838222150.223072391911108
680.7358696503141060.5282606993717880.264130349685894
690.7128184458767620.5743631082464760.287181554123238
700.6799591642875050.640081671424990.320040835712495
710.7305233882247380.5389532235505250.269476611775262
720.6502367190590380.6995265618819240.349763280940962
730.5879194544370820.8241610911258360.412080545562918
740.6037440745862340.7925118508275320.396255925413766
750.510186978485560.9796260430288810.48981302151444
760.4040637105012190.8081274210024380.595936289498781
770.4280172129038380.8560344258076760.571982787096162
780.312823354234530.625646708469060.68717664576547
790.2797916470042420.5595832940084850.720208352995758
800.1814574250286840.3629148500573680.818542574971316






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0140845070422535OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0140845070422535 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145788&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0140845070422535[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145788&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145788&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0140845070422535OK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 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')
}