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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 10:52:30 -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/2012/Dec/21/t1356105168pnf7kxnuvz5ebsy.htm/, Retrieved Thu, 31 Oct 2024 23:00:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203853, Retrieved Thu, 31 Oct 2024 23:00:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact196

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R PD      [Multiple Regression] [] [2011-11-22 00:46:31] [bdca8f3e7c3554be8c1291e54f61d441]
- RM            [Multiple Regression] [] [2012-12-21 15:52:30] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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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 time9 seconds
R Server'George Udny Yule' @ yule.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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
KansOverwinning[t] = + 3.27412322335457 -0.0365847634432943GeboekteOverwinning[t] + 0.1417360817956Gevoel[t] + 0.406011472206854EigenGevoel[t] + 0.519932408798465Beste[t] -0.0971170265764618`2deBeste`[t] + 0.0512747686924238`3debeste`[t] -0.00292551178459613t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
KansOverwinning[t] =  +  3.27412322335457 -0.0365847634432943GeboekteOverwinning[t] +  0.1417360817956Gevoel[t] +  0.406011472206854EigenGevoel[t] +  0.519932408798465Beste[t] -0.0971170265764618`2deBeste`[t] +  0.0512747686924238`3debeste`[t] -0.00292551178459613t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]KansOverwinning[t] =  +  3.27412322335457 -0.0365847634432943GeboekteOverwinning[t] +  0.1417360817956Gevoel[t] +  0.406011472206854EigenGevoel[t] +  0.519932408798465Beste[t] -0.0971170265764618`2deBeste`[t] +  0.0512747686924238`3debeste`[t] -0.00292551178459613t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&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] = + 3.27412322335457 -0.0365847634432943GeboekteOverwinning[t] + 0.1417360817956Gevoel[t] + 0.406011472206854EigenGevoel[t] + 0.519932408798465Beste[t] -0.0971170265764618`2deBeste`[t] + 0.0512747686924238`3debeste`[t] -0.00292551178459613t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.274123223354572.2656351.44510.1522340.076117
GeboekteOverwinning-0.03658476344329430.125769-0.29090.771870.385935
Gevoel0.14173608179560.0935681.51480.1336690.066834
EigenGevoel0.4060114722068540.1454032.79230.006510.003255
Beste0.5199324087984650.2484542.09270.0394710.019735
`2deBeste`-0.09711702657646180.055824-1.73970.0856620.042831
`3debeste`0.05127476869242380.0736590.69610.4883260.244163
t-0.002925511784596130.009271-0.31560.7531450.376573

\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) & 3.27412322335457 & 2.265635 & 1.4451 & 0.152234 & 0.076117 \tabularnewline
GeboekteOverwinning & -0.0365847634432943 & 0.125769 & -0.2909 & 0.77187 & 0.385935 \tabularnewline
Gevoel & 0.1417360817956 & 0.093568 & 1.5148 & 0.133669 & 0.066834 \tabularnewline
EigenGevoel & 0.406011472206854 & 0.145403 & 2.7923 & 0.00651 & 0.003255 \tabularnewline
Beste & 0.519932408798465 & 0.248454 & 2.0927 & 0.039471 & 0.019735 \tabularnewline
`2deBeste` & -0.0971170265764618 & 0.055824 & -1.7397 & 0.085662 & 0.042831 \tabularnewline
`3debeste` & 0.0512747686924238 & 0.073659 & 0.6961 & 0.488326 & 0.244163 \tabularnewline
t & -0.00292551178459613 & 0.009271 & -0.3156 & 0.753145 & 0.376573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&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]3.27412322335457[/C][C]2.265635[/C][C]1.4451[/C][C]0.152234[/C][C]0.076117[/C][/ROW]
[ROW][C]GeboekteOverwinning[/C][C]-0.0365847634432943[/C][C]0.125769[/C][C]-0.2909[/C][C]0.77187[/C][C]0.385935[/C][/ROW]
[ROW][C]Gevoel[/C][C]0.1417360817956[/C][C]0.093568[/C][C]1.5148[/C][C]0.133669[/C][C]0.066834[/C][/ROW]
[ROW][C]EigenGevoel[/C][C]0.406011472206854[/C][C]0.145403[/C][C]2.7923[/C][C]0.00651[/C][C]0.003255[/C][/ROW]
[ROW][C]Beste[/C][C]0.519932408798465[/C][C]0.248454[/C][C]2.0927[/C][C]0.039471[/C][C]0.019735[/C][/ROW]
[ROW][C]`2deBeste`[/C][C]-0.0971170265764618[/C][C]0.055824[/C][C]-1.7397[/C][C]0.085662[/C][C]0.042831[/C][/ROW]
[ROW][C]`3debeste`[/C][C]0.0512747686924238[/C][C]0.073659[/C][C]0.6961[/C][C]0.488326[/C][C]0.244163[/C][/ROW]
[ROW][C]t[/C][C]-0.00292551178459613[/C][C]0.009271[/C][C]-0.3156[/C][C]0.753145[/C][C]0.376573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&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)3.274123223354572.2656351.44510.1522340.076117
GeboekteOverwinning-0.03658476344329430.125769-0.29090.771870.385935
Gevoel0.14173608179560.0935681.51480.1336690.066834
EigenGevoel0.4060114722068540.1454032.79230.006510.003255
Beste0.5199324087984650.2484542.09270.0394710.019735
`2deBeste`-0.09711702657646180.055824-1.73970.0856620.042831
`3debeste`0.05127476869242380.0736590.69610.4883260.244163
t-0.002925511784596130.009271-0.31560.7531450.376573






Multiple Linear Regression - Regression Statistics
Multiple R0.641706523426841
R-squared0.411787262208563
Adjusted R-squared0.361573979714172
F-TEST (value)8.20076365759521
F-TEST (DF numerator)7
F-TEST (DF denominator)82
p-value1.5149123733238e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26284766532345
Sum Squared Residuals419.879323629701

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.641706523426841 \tabularnewline
R-squared & 0.411787262208563 \tabularnewline
Adjusted R-squared & 0.361573979714172 \tabularnewline
F-TEST (value) & 8.20076365759521 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 82 \tabularnewline
p-value & 1.5149123733238e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.26284766532345 \tabularnewline
Sum Squared Residuals & 419.879323629701 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.641706523426841[/C][/ROW]
[ROW][C]R-squared[/C][C]0.411787262208563[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.361573979714172[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.20076365759521[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]82[/C][/ROW]
[ROW][C]p-value[/C][C]1.5149123733238e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.26284766532345[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]419.879323629701[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&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 R0.641706523426841
R-squared0.411787262208563
Adjusted R-squared0.361573979714172
F-TEST (value)8.20076365759521
F-TEST (DF numerator)7
F-TEST (DF denominator)82
p-value1.5149123733238e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26284766532345
Sum Squared Residuals419.879323629701






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.43447826549391.56552173450609
21211.61174358609190.388256413908108
31514.58046593487090.419534065129146
41211.60701221349730.392987786502711
5109.685397887346870.314602112653129
6129.378452239202932.62154776079707
71516.478508558673-1.47850855867301
8910.7899929449438-1.78999294494379
91212.1902337841296-0.190233784129594
10119.197028579721661.80297142027834
111113.5750115435071-2.57501154350706
121112.5100857565302-1.51008575653019
131511.98385568773983.01614431226024
14711.0419188265824-4.04191882658238
151111.8328964751244-0.832896475124381
161110.75870993452880.241290065471165
171011.9236210597652-1.92362105976521
181415.0953285785478-1.09532857854785
19109.466704575852840.533295424147158
20610.1578883026548-4.15788830265483
21119.187443631711761.81255636828824
221514.54306369928120.456936300718764
231110.49239002962750.507609970372465
241210.47302401974561.52697598025438
251412.9081500011451.09184999885504
261514.25451429067090.745485709329117
27913.2134168235069-4.21341682350693
281313.1381138604073-0.138113860407333
291312.39642439555960.603575604440443
301611.56430011480054.43569988519953
31139.21477282908653.7852271709135
321213.4819965570027-1.48199655700274
331413.83488886724780.165111132752197
341110.9557323116150.0442676883850103
35911.0077877172975-2.00778771729747
361613.39159304990152.60840695009853
371213.2273277278192-1.22732772781925
38109.625560215237210.374439784762785
391312.32773115361840.672268846381576
401614.51093395853811.4890660414619
411413.23900913119330.760990868806653
42159.030597807102725.96940219289728
4359.83904999621332-4.83904999621332
44811.3903003132839-3.39030031328388
451110.62487226654890.375127733451107
461613.7821445779882.21785542201202
471713.30331247031033.69668752968974
4898.55568386348020.444316136519805
49910.6668604820787-1.66686048207868
501313.5662382876034-0.566238287603411
511010.4031458639492-0.403145863949191
52612.3319916036467-6.33199160364673
531212.6405973592364-0.640597359236439
54811.4415672298019-3.44156722980189
551411.88327828519772.11672171480234
561212.1006908929036-0.100690892903634
571110.96695137421750.0330486257825075
581613.90896232711862.09103767288136
59810.2861781046951-2.28617810469506
601515.0826977204439-0.0826977204438767
6179.93655482149613-2.93655482149613
621613.75785176865672.24214823134325
631413.23235702632980.76764297367022
641613.20957558787772.79042441212227
65910.4646714026967-1.4646714026967
661411.84727267193472.15272732806526
671112.9269919571766-1.92699195717657
681311.5887590161.41124098399997
691512.44468055033912.55531944966089
7056.25615700634952-1.25615700634952
711512.38856904293972.61143095706027
721312.52040380887920.479596191120832
731112.5631878101519-1.5631878101519
741114.0835827158939-3.08358271589387
751212.4207007150317-0.420700715031715
761212.5297376107104-0.529737610710368
771210.94239475060761.05760524939237
781212.4440907171751-0.444090717175073
791411.2212834733942.77871652660605
8068.4369102527637-2.4369102527637
81710.0270296233869-3.02702962338688
821412.82457820828461.17542179171542
831414.1710902523429-0.171090252342856
841011.1674343292838-1.16743432928377
85139.109373466473983.89062653352602
861211.53012847444260.469871525557389
8799.13569814246753-0.135698142467526
881212.6994850854823-0.6994850854823
891615.07208589328150.927914106718544
901010.9547338725112-0.954733872511203

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.4344782654939 & 1.56552173450609 \tabularnewline
2 & 12 & 11.6117435860919 & 0.388256413908108 \tabularnewline
3 & 15 & 14.5804659348709 & 0.419534065129146 \tabularnewline
4 & 12 & 11.6070122134973 & 0.392987786502711 \tabularnewline
5 & 10 & 9.68539788734687 & 0.314602112653129 \tabularnewline
6 & 12 & 9.37845223920293 & 2.62154776079707 \tabularnewline
7 & 15 & 16.478508558673 & -1.47850855867301 \tabularnewline
8 & 9 & 10.7899929449438 & -1.78999294494379 \tabularnewline
9 & 12 & 12.1902337841296 & -0.190233784129594 \tabularnewline
10 & 11 & 9.19702857972166 & 1.80297142027834 \tabularnewline
11 & 11 & 13.5750115435071 & -2.57501154350706 \tabularnewline
12 & 11 & 12.5100857565302 & -1.51008575653019 \tabularnewline
13 & 15 & 11.9838556877398 & 3.01614431226024 \tabularnewline
14 & 7 & 11.0419188265824 & -4.04191882658238 \tabularnewline
15 & 11 & 11.8328964751244 & -0.832896475124381 \tabularnewline
16 & 11 & 10.7587099345288 & 0.241290065471165 \tabularnewline
17 & 10 & 11.9236210597652 & -1.92362105976521 \tabularnewline
18 & 14 & 15.0953285785478 & -1.09532857854785 \tabularnewline
19 & 10 & 9.46670457585284 & 0.533295424147158 \tabularnewline
20 & 6 & 10.1578883026548 & -4.15788830265483 \tabularnewline
21 & 11 & 9.18744363171176 & 1.81255636828824 \tabularnewline
22 & 15 & 14.5430636992812 & 0.456936300718764 \tabularnewline
23 & 11 & 10.4923900296275 & 0.507609970372465 \tabularnewline
24 & 12 & 10.4730240197456 & 1.52697598025438 \tabularnewline
25 & 14 & 12.908150001145 & 1.09184999885504 \tabularnewline
26 & 15 & 14.2545142906709 & 0.745485709329117 \tabularnewline
27 & 9 & 13.2134168235069 & -4.21341682350693 \tabularnewline
28 & 13 & 13.1381138604073 & -0.138113860407333 \tabularnewline
29 & 13 & 12.3964243955596 & 0.603575604440443 \tabularnewline
30 & 16 & 11.5643001148005 & 4.43569988519953 \tabularnewline
31 & 13 & 9.2147728290865 & 3.7852271709135 \tabularnewline
32 & 12 & 13.4819965570027 & -1.48199655700274 \tabularnewline
33 & 14 & 13.8348888672478 & 0.165111132752197 \tabularnewline
34 & 11 & 10.955732311615 & 0.0442676883850103 \tabularnewline
35 & 9 & 11.0077877172975 & -2.00778771729747 \tabularnewline
36 & 16 & 13.3915930499015 & 2.60840695009853 \tabularnewline
37 & 12 & 13.2273277278192 & -1.22732772781925 \tabularnewline
38 & 10 & 9.62556021523721 & 0.374439784762785 \tabularnewline
39 & 13 & 12.3277311536184 & 0.672268846381576 \tabularnewline
40 & 16 & 14.5109339585381 & 1.4890660414619 \tabularnewline
41 & 14 & 13.2390091311933 & 0.760990868806653 \tabularnewline
42 & 15 & 9.03059780710272 & 5.96940219289728 \tabularnewline
43 & 5 & 9.83904999621332 & -4.83904999621332 \tabularnewline
44 & 8 & 11.3903003132839 & -3.39030031328388 \tabularnewline
45 & 11 & 10.6248722665489 & 0.375127733451107 \tabularnewline
46 & 16 & 13.782144577988 & 2.21785542201202 \tabularnewline
47 & 17 & 13.3033124703103 & 3.69668752968974 \tabularnewline
48 & 9 & 8.5556838634802 & 0.444316136519805 \tabularnewline
49 & 9 & 10.6668604820787 & -1.66686048207868 \tabularnewline
50 & 13 & 13.5662382876034 & -0.566238287603411 \tabularnewline
51 & 10 & 10.4031458639492 & -0.403145863949191 \tabularnewline
52 & 6 & 12.3319916036467 & -6.33199160364673 \tabularnewline
53 & 12 & 12.6405973592364 & -0.640597359236439 \tabularnewline
54 & 8 & 11.4415672298019 & -3.44156722980189 \tabularnewline
55 & 14 & 11.8832782851977 & 2.11672171480234 \tabularnewline
56 & 12 & 12.1006908929036 & -0.100690892903634 \tabularnewline
57 & 11 & 10.9669513742175 & 0.0330486257825075 \tabularnewline
58 & 16 & 13.9089623271186 & 2.09103767288136 \tabularnewline
59 & 8 & 10.2861781046951 & -2.28617810469506 \tabularnewline
60 & 15 & 15.0826977204439 & -0.0826977204438767 \tabularnewline
61 & 7 & 9.93655482149613 & -2.93655482149613 \tabularnewline
62 & 16 & 13.7578517686567 & 2.24214823134325 \tabularnewline
63 & 14 & 13.2323570263298 & 0.76764297367022 \tabularnewline
64 & 16 & 13.2095755878777 & 2.79042441212227 \tabularnewline
65 & 9 & 10.4646714026967 & -1.4646714026967 \tabularnewline
66 & 14 & 11.8472726719347 & 2.15272732806526 \tabularnewline
67 & 11 & 12.9269919571766 & -1.92699195717657 \tabularnewline
68 & 13 & 11.588759016 & 1.41124098399997 \tabularnewline
69 & 15 & 12.4446805503391 & 2.55531944966089 \tabularnewline
70 & 5 & 6.25615700634952 & -1.25615700634952 \tabularnewline
71 & 15 & 12.3885690429397 & 2.61143095706027 \tabularnewline
72 & 13 & 12.5204038088792 & 0.479596191120832 \tabularnewline
73 & 11 & 12.5631878101519 & -1.5631878101519 \tabularnewline
74 & 11 & 14.0835827158939 & -3.08358271589387 \tabularnewline
75 & 12 & 12.4207007150317 & -0.420700715031715 \tabularnewline
76 & 12 & 12.5297376107104 & -0.529737610710368 \tabularnewline
77 & 12 & 10.9423947506076 & 1.05760524939237 \tabularnewline
78 & 12 & 12.4440907171751 & -0.444090717175073 \tabularnewline
79 & 14 & 11.221283473394 & 2.77871652660605 \tabularnewline
80 & 6 & 8.4369102527637 & -2.4369102527637 \tabularnewline
81 & 7 & 10.0270296233869 & -3.02702962338688 \tabularnewline
82 & 14 & 12.8245782082846 & 1.17542179171542 \tabularnewline
83 & 14 & 14.1710902523429 & -0.171090252342856 \tabularnewline
84 & 10 & 11.1674343292838 & -1.16743432928377 \tabularnewline
85 & 13 & 9.10937346647398 & 3.89062653352602 \tabularnewline
86 & 12 & 11.5301284744426 & 0.469871525557389 \tabularnewline
87 & 9 & 9.13569814246753 & -0.135698142467526 \tabularnewline
88 & 12 & 12.6994850854823 & -0.6994850854823 \tabularnewline
89 & 16 & 15.0720858932815 & 0.927914106718544 \tabularnewline
90 & 10 & 10.9547338725112 & -0.954733872511203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&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]11.4344782654939[/C][C]1.56552173450609[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.6117435860919[/C][C]0.388256413908108[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]14.5804659348709[/C][C]0.419534065129146[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.6070122134973[/C][C]0.392987786502711[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]9.68539788734687[/C][C]0.314602112653129[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.37845223920293[/C][C]2.62154776079707[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.478508558673[/C][C]-1.47850855867301[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.7899929449438[/C][C]-1.78999294494379[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.1902337841296[/C][C]-0.190233784129594[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]9.19702857972166[/C][C]1.80297142027834[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.5750115435071[/C][C]-2.57501154350706[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.5100857565302[/C][C]-1.51008575653019[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]11.9838556877398[/C][C]3.01614431226024[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.0419188265824[/C][C]-4.04191882658238[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.8328964751244[/C][C]-0.832896475124381[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.7587099345288[/C][C]0.241290065471165[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.9236210597652[/C][C]-1.92362105976521[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]15.0953285785478[/C][C]-1.09532857854785[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]9.46670457585284[/C][C]0.533295424147158[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]10.1578883026548[/C][C]-4.15788830265483[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]9.18744363171176[/C][C]1.81255636828824[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.5430636992812[/C][C]0.456936300718764[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]10.4923900296275[/C][C]0.507609970372465[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.4730240197456[/C][C]1.52697598025438[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.908150001145[/C][C]1.09184999885504[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.2545142906709[/C][C]0.745485709329117[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]13.2134168235069[/C][C]-4.21341682350693[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.1381138604073[/C][C]-0.138113860407333[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]12.3964243955596[/C][C]0.603575604440443[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.5643001148005[/C][C]4.43569988519953[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]9.2147728290865[/C][C]3.7852271709135[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.4819965570027[/C][C]-1.48199655700274[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.8348888672478[/C][C]0.165111132752197[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.955732311615[/C][C]0.0442676883850103[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]11.0077877172975[/C][C]-2.00778771729747[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.3915930499015[/C][C]2.60840695009853[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.2273277278192[/C][C]-1.22732772781925[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.62556021523721[/C][C]0.374439784762785[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.3277311536184[/C][C]0.672268846381576[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5109339585381[/C][C]1.4890660414619[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.2390091311933[/C][C]0.760990868806653[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]9.03059780710272[/C][C]5.96940219289728[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.83904999621332[/C][C]-4.83904999621332[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]11.3903003132839[/C][C]-3.39030031328388[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.6248722665489[/C][C]0.375127733451107[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.782144577988[/C][C]2.21785542201202[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.3033124703103[/C][C]3.69668752968974[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.5556838634802[/C][C]0.444316136519805[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]10.6668604820787[/C][C]-1.66686048207868[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.5662382876034[/C][C]-0.566238287603411[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.4031458639492[/C][C]-0.403145863949191[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.3319916036467[/C][C]-6.33199160364673[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.6405973592364[/C][C]-0.640597359236439[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]11.4415672298019[/C][C]-3.44156722980189[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8832782851977[/C][C]2.11672171480234[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.1006908929036[/C][C]-0.100690892903634[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.9669513742175[/C][C]0.0330486257825075[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.9089623271186[/C][C]2.09103767288136[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2861781046951[/C][C]-2.28617810469506[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.0826977204439[/C][C]-0.0826977204438767[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.93655482149613[/C][C]-2.93655482149613[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.7578517686567[/C][C]2.24214823134325[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.2323570263298[/C][C]0.76764297367022[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.2095755878777[/C][C]2.79042441212227[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.4646714026967[/C][C]-1.4646714026967[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]11.8472726719347[/C][C]2.15272732806526[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.9269919571766[/C][C]-1.92699195717657[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.588759016[/C][C]1.41124098399997[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.4446805503391[/C][C]2.55531944966089[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.25615700634952[/C][C]-1.25615700634952[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.3885690429397[/C][C]2.61143095706027[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.5204038088792[/C][C]0.479596191120832[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.5631878101519[/C][C]-1.5631878101519[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.0835827158939[/C][C]-3.08358271589387[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4207007150317[/C][C]-0.420700715031715[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.5297376107104[/C][C]-0.529737610710368[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.9423947506076[/C][C]1.05760524939237[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.4440907171751[/C][C]-0.444090717175073[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.221283473394[/C][C]2.77871652660605[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.4369102527637[/C][C]-2.4369102527637[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]10.0270296233869[/C][C]-3.02702962338688[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.8245782082846[/C][C]1.17542179171542[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.1710902523429[/C][C]-0.171090252342856[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1674343292838[/C][C]-1.16743432928377[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.10937346647398[/C][C]3.89062653352602[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.5301284744426[/C][C]0.469871525557389[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.13569814246753[/C][C]-0.135698142467526[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.6994850854823[/C][C]-0.6994850854823[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0720858932815[/C][C]0.927914106718544[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.9547338725112[/C][C]-0.954733872511203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&T=4

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QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.43447826549391.56552173450609
21211.61174358609190.388256413908108
31514.58046593487090.419534065129146
41211.60701221349730.392987786502711
5109.685397887346870.314602112653129
6129.378452239202932.62154776079707
71516.478508558673-1.47850855867301
8910.7899929449438-1.78999294494379
91212.1902337841296-0.190233784129594
10119.197028579721661.80297142027834
111113.5750115435071-2.57501154350706
121112.5100857565302-1.51008575653019
131511.98385568773983.01614431226024
14711.0419188265824-4.04191882658238
151111.8328964751244-0.832896475124381
161110.75870993452880.241290065471165
171011.9236210597652-1.92362105976521
181415.0953285785478-1.09532857854785
19109.466704575852840.533295424147158
20610.1578883026548-4.15788830265483
21119.187443631711761.81255636828824
221514.54306369928120.456936300718764
231110.49239002962750.507609970372465
241210.47302401974561.52697598025438
251412.9081500011451.09184999885504
261514.25451429067090.745485709329117
27913.2134168235069-4.21341682350693
281313.1381138604073-0.138113860407333
291312.39642439555960.603575604440443
301611.56430011480054.43569988519953
31139.21477282908653.7852271709135
321213.4819965570027-1.48199655700274
331413.83488886724780.165111132752197
341110.9557323116150.0442676883850103
35911.0077877172975-2.00778771729747
361613.39159304990152.60840695009853
371213.2273277278192-1.22732772781925
38109.625560215237210.374439784762785
391312.32773115361840.672268846381576
401614.51093395853811.4890660414619
411413.23900913119330.760990868806653
42159.030597807102725.96940219289728
4359.83904999621332-4.83904999621332
44811.3903003132839-3.39030031328388
451110.62487226654890.375127733451107
461613.7821445779882.21785542201202
471713.30331247031033.69668752968974
4898.55568386348020.444316136519805
49910.6668604820787-1.66686048207868
501313.5662382876034-0.566238287603411
511010.4031458639492-0.403145863949191
52612.3319916036467-6.33199160364673
531212.6405973592364-0.640597359236439
54811.4415672298019-3.44156722980189
551411.88327828519772.11672171480234
561212.1006908929036-0.100690892903634
571110.96695137421750.0330486257825075
581613.90896232711862.09103767288136
59810.2861781046951-2.28617810469506
601515.0826977204439-0.0826977204438767
6179.93655482149613-2.93655482149613
621613.75785176865672.24214823134325
631413.23235702632980.76764297367022
641613.20957558787772.79042441212227
65910.4646714026967-1.4646714026967
661411.84727267193472.15272732806526
671112.9269919571766-1.92699195717657
681311.5887590161.41124098399997
691512.44468055033912.55531944966089
7056.25615700634952-1.25615700634952
711512.38856904293972.61143095706027
721312.52040380887920.479596191120832
731112.5631878101519-1.5631878101519
741114.0835827158939-3.08358271589387
751212.4207007150317-0.420700715031715
761212.5297376107104-0.529737610710368
771210.94239475060761.05760524939237
781212.4440907171751-0.444090717175073
791411.2212834733942.77871652660605
8068.4369102527637-2.4369102527637
81710.0270296233869-3.02702962338688
821412.82457820828461.17542179171542
831414.1710902523429-0.171090252342856
841011.1674343292838-1.16743432928377
85139.109373466473983.89062653352602
861211.53012847444260.469871525557389
8799.13569814246753-0.135698142467526
881212.6994850854823-0.6994850854823
891615.07208589328150.927914106718544
901010.9547338725112-0.954733872511203






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1563745919148940.3127491838297870.843625408085106
120.0685573732561650.137114746512330.931442626743835
130.4065509483761720.8131018967523430.593449051623828
140.5642542739235060.8714914521529890.435745726076494
150.5143364551139250.9713270897721490.485663544886075
160.4457212559720590.8914425119441190.554278744027941
170.3494295789454340.6988591578908670.650570421054566
180.2935771015604150.5871542031208290.706422898439586
190.2211609299057040.4423218598114070.778839070094296
200.2309246347479280.4618492694958560.769075365252072
210.3052956539524310.6105913079048630.694704346047569
220.3539142549980020.7078285099960040.646085745001998
230.2998177497319590.5996354994639180.700182250268041
240.2674112761425690.5348225522851380.732588723857431
250.2632461550800480.5264923101600960.736753844919952
260.2236917594013840.4473835188027680.776308240598616
270.2980992286111950.5961984572223910.701900771388805
280.236199451424810.472398902849620.76380054857519
290.2096122072149780.4192244144299560.790387792785022
300.3163977620596430.6327955241192860.683602237940357
310.3793341799014610.7586683598029220.620665820098539
320.3339350486101550.667870097220310.666064951389845
330.2793857645489840.5587715290979670.720614235451016
340.2370793537778790.4741587075557590.762920646222121
350.2723513728660390.5447027457320780.727648627133961
360.298162801629550.5963256032591010.70183719837045
370.267615170145480.5352303402909610.73238482985452
380.2161004138180880.4322008276361760.783899586181912
390.1733376155313920.3466752310627850.826662384468608
400.1478777564512510.2957555129025020.852122243548749
410.1143085727145060.2286171454290120.885691427285494
420.411249586363840.8224991727276810.58875041363616
430.751021767544610.497956464910780.24897823245539
440.8039367276393320.3921265447213350.196063272360668
450.7635803315440610.4728393369118780.236419668455939
460.7654620185955580.4690759628088830.234537981404442
470.8486726924182110.3026546151635790.151327307581789
480.8158710059446450.368257988110710.184128994055355
490.7900450112912590.4199099774174810.209954988708741
500.7516673427579610.4966653144840790.248332657242039
510.6958442513615490.6083114972769030.304155748638451
520.9327825950888310.1344348098223380.0672174049111689
530.9094896418974210.1810207162051580.0905103581025788
540.9377888598243420.1244222803513160.0622111401756582
550.929461607245150.1410767855096990.0705383927548496
560.9026328206038820.1947343587922350.0973671793961177
570.8710295389291050.257940922141790.128970461070895
580.8641461888138080.2717076223723830.135853811186192
590.85497588235530.29004823528940.1450241176447
600.8180602845849140.3638794308301730.181939715415086
610.8545694256095440.2908611487809110.145430574390456
620.8306479600042130.3387040799915740.169352039995787
630.7881804419045710.4236391161908570.211819558095429
640.7884425046151360.4231149907697270.211557495384864
650.7438767290618260.5122465418763490.256123270938174
660.7314190797278010.5371618405443970.268580920272199
670.73377834934170.53244330131660.2662216506583
680.6978433904027350.6043132191945310.302156609597265
690.6867098505442460.6265802989115070.313290149455754
700.6246181931758840.7507636136482320.375381806824116
710.6704700601953450.6590598796093110.329529939804655
720.5844376665646290.8311246668707420.415562333435371
730.5101975210592070.9796049578815860.489802478940793
740.5179939699191950.9640120601616090.482006030080805
750.4254483373678670.8508966747357340.574551662632133
760.3215072472574470.6430144945148930.678492752742553
770.3221200869702560.6442401739405110.677879913029744
780.2132635699237850.426527139847570.786736430076215
790.1874249993231180.3748499986462350.812575000676882

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.156374591914894 & 0.312749183829787 & 0.843625408085106 \tabularnewline
12 & 0.068557373256165 & 0.13711474651233 & 0.931442626743835 \tabularnewline
13 & 0.406550948376172 & 0.813101896752343 & 0.593449051623828 \tabularnewline
14 & 0.564254273923506 & 0.871491452152989 & 0.435745726076494 \tabularnewline
15 & 0.514336455113925 & 0.971327089772149 & 0.485663544886075 \tabularnewline
16 & 0.445721255972059 & 0.891442511944119 & 0.554278744027941 \tabularnewline
17 & 0.349429578945434 & 0.698859157890867 & 0.650570421054566 \tabularnewline
18 & 0.293577101560415 & 0.587154203120829 & 0.706422898439586 \tabularnewline
19 & 0.221160929905704 & 0.442321859811407 & 0.778839070094296 \tabularnewline
20 & 0.230924634747928 & 0.461849269495856 & 0.769075365252072 \tabularnewline
21 & 0.305295653952431 & 0.610591307904863 & 0.694704346047569 \tabularnewline
22 & 0.353914254998002 & 0.707828509996004 & 0.646085745001998 \tabularnewline
23 & 0.299817749731959 & 0.599635499463918 & 0.700182250268041 \tabularnewline
24 & 0.267411276142569 & 0.534822552285138 & 0.732588723857431 \tabularnewline
25 & 0.263246155080048 & 0.526492310160096 & 0.736753844919952 \tabularnewline
26 & 0.223691759401384 & 0.447383518802768 & 0.776308240598616 \tabularnewline
27 & 0.298099228611195 & 0.596198457222391 & 0.701900771388805 \tabularnewline
28 & 0.23619945142481 & 0.47239890284962 & 0.76380054857519 \tabularnewline
29 & 0.209612207214978 & 0.419224414429956 & 0.790387792785022 \tabularnewline
30 & 0.316397762059643 & 0.632795524119286 & 0.683602237940357 \tabularnewline
31 & 0.379334179901461 & 0.758668359802922 & 0.620665820098539 \tabularnewline
32 & 0.333935048610155 & 0.66787009722031 & 0.666064951389845 \tabularnewline
33 & 0.279385764548984 & 0.558771529097967 & 0.720614235451016 \tabularnewline
34 & 0.237079353777879 & 0.474158707555759 & 0.762920646222121 \tabularnewline
35 & 0.272351372866039 & 0.544702745732078 & 0.727648627133961 \tabularnewline
36 & 0.29816280162955 & 0.596325603259101 & 0.70183719837045 \tabularnewline
37 & 0.26761517014548 & 0.535230340290961 & 0.73238482985452 \tabularnewline
38 & 0.216100413818088 & 0.432200827636176 & 0.783899586181912 \tabularnewline
39 & 0.173337615531392 & 0.346675231062785 & 0.826662384468608 \tabularnewline
40 & 0.147877756451251 & 0.295755512902502 & 0.852122243548749 \tabularnewline
41 & 0.114308572714506 & 0.228617145429012 & 0.885691427285494 \tabularnewline
42 & 0.41124958636384 & 0.822499172727681 & 0.58875041363616 \tabularnewline
43 & 0.75102176754461 & 0.49795646491078 & 0.24897823245539 \tabularnewline
44 & 0.803936727639332 & 0.392126544721335 & 0.196063272360668 \tabularnewline
45 & 0.763580331544061 & 0.472839336911878 & 0.236419668455939 \tabularnewline
46 & 0.765462018595558 & 0.469075962808883 & 0.234537981404442 \tabularnewline
47 & 0.848672692418211 & 0.302654615163579 & 0.151327307581789 \tabularnewline
48 & 0.815871005944645 & 0.36825798811071 & 0.184128994055355 \tabularnewline
49 & 0.790045011291259 & 0.419909977417481 & 0.209954988708741 \tabularnewline
50 & 0.751667342757961 & 0.496665314484079 & 0.248332657242039 \tabularnewline
51 & 0.695844251361549 & 0.608311497276903 & 0.304155748638451 \tabularnewline
52 & 0.932782595088831 & 0.134434809822338 & 0.0672174049111689 \tabularnewline
53 & 0.909489641897421 & 0.181020716205158 & 0.0905103581025788 \tabularnewline
54 & 0.937788859824342 & 0.124422280351316 & 0.0622111401756582 \tabularnewline
55 & 0.92946160724515 & 0.141076785509699 & 0.0705383927548496 \tabularnewline
56 & 0.902632820603882 & 0.194734358792235 & 0.0973671793961177 \tabularnewline
57 & 0.871029538929105 & 0.25794092214179 & 0.128970461070895 \tabularnewline
58 & 0.864146188813808 & 0.271707622372383 & 0.135853811186192 \tabularnewline
59 & 0.8549758823553 & 0.2900482352894 & 0.1450241176447 \tabularnewline
60 & 0.818060284584914 & 0.363879430830173 & 0.181939715415086 \tabularnewline
61 & 0.854569425609544 & 0.290861148780911 & 0.145430574390456 \tabularnewline
62 & 0.830647960004213 & 0.338704079991574 & 0.169352039995787 \tabularnewline
63 & 0.788180441904571 & 0.423639116190857 & 0.211819558095429 \tabularnewline
64 & 0.788442504615136 & 0.423114990769727 & 0.211557495384864 \tabularnewline
65 & 0.743876729061826 & 0.512246541876349 & 0.256123270938174 \tabularnewline
66 & 0.731419079727801 & 0.537161840544397 & 0.268580920272199 \tabularnewline
67 & 0.7337783493417 & 0.5324433013166 & 0.2662216506583 \tabularnewline
68 & 0.697843390402735 & 0.604313219194531 & 0.302156609597265 \tabularnewline
69 & 0.686709850544246 & 0.626580298911507 & 0.313290149455754 \tabularnewline
70 & 0.624618193175884 & 0.750763613648232 & 0.375381806824116 \tabularnewline
71 & 0.670470060195345 & 0.659059879609311 & 0.329529939804655 \tabularnewline
72 & 0.584437666564629 & 0.831124666870742 & 0.415562333435371 \tabularnewline
73 & 0.510197521059207 & 0.979604957881586 & 0.489802478940793 \tabularnewline
74 & 0.517993969919195 & 0.964012060161609 & 0.482006030080805 \tabularnewline
75 & 0.425448337367867 & 0.850896674735734 & 0.574551662632133 \tabularnewline
76 & 0.321507247257447 & 0.643014494514893 & 0.678492752742553 \tabularnewline
77 & 0.322120086970256 & 0.644240173940511 & 0.677879913029744 \tabularnewline
78 & 0.213263569923785 & 0.42652713984757 & 0.786736430076215 \tabularnewline
79 & 0.187424999323118 & 0.374849998646235 & 0.812575000676882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.156374591914894[/C][C]0.312749183829787[/C][C]0.843625408085106[/C][/ROW]
[ROW][C]12[/C][C]0.068557373256165[/C][C]0.13711474651233[/C][C]0.931442626743835[/C][/ROW]
[ROW][C]13[/C][C]0.406550948376172[/C][C]0.813101896752343[/C][C]0.593449051623828[/C][/ROW]
[ROW][C]14[/C][C]0.564254273923506[/C][C]0.871491452152989[/C][C]0.435745726076494[/C][/ROW]
[ROW][C]15[/C][C]0.514336455113925[/C][C]0.971327089772149[/C][C]0.485663544886075[/C][/ROW]
[ROW][C]16[/C][C]0.445721255972059[/C][C]0.891442511944119[/C][C]0.554278744027941[/C][/ROW]
[ROW][C]17[/C][C]0.349429578945434[/C][C]0.698859157890867[/C][C]0.650570421054566[/C][/ROW]
[ROW][C]18[/C][C]0.293577101560415[/C][C]0.587154203120829[/C][C]0.706422898439586[/C][/ROW]
[ROW][C]19[/C][C]0.221160929905704[/C][C]0.442321859811407[/C][C]0.778839070094296[/C][/ROW]
[ROW][C]20[/C][C]0.230924634747928[/C][C]0.461849269495856[/C][C]0.769075365252072[/C][/ROW]
[ROW][C]21[/C][C]0.305295653952431[/C][C]0.610591307904863[/C][C]0.694704346047569[/C][/ROW]
[ROW][C]22[/C][C]0.353914254998002[/C][C]0.707828509996004[/C][C]0.646085745001998[/C][/ROW]
[ROW][C]23[/C][C]0.299817749731959[/C][C]0.599635499463918[/C][C]0.700182250268041[/C][/ROW]
[ROW][C]24[/C][C]0.267411276142569[/C][C]0.534822552285138[/C][C]0.732588723857431[/C][/ROW]
[ROW][C]25[/C][C]0.263246155080048[/C][C]0.526492310160096[/C][C]0.736753844919952[/C][/ROW]
[ROW][C]26[/C][C]0.223691759401384[/C][C]0.447383518802768[/C][C]0.776308240598616[/C][/ROW]
[ROW][C]27[/C][C]0.298099228611195[/C][C]0.596198457222391[/C][C]0.701900771388805[/C][/ROW]
[ROW][C]28[/C][C]0.23619945142481[/C][C]0.47239890284962[/C][C]0.76380054857519[/C][/ROW]
[ROW][C]29[/C][C]0.209612207214978[/C][C]0.419224414429956[/C][C]0.790387792785022[/C][/ROW]
[ROW][C]30[/C][C]0.316397762059643[/C][C]0.632795524119286[/C][C]0.683602237940357[/C][/ROW]
[ROW][C]31[/C][C]0.379334179901461[/C][C]0.758668359802922[/C][C]0.620665820098539[/C][/ROW]
[ROW][C]32[/C][C]0.333935048610155[/C][C]0.66787009722031[/C][C]0.666064951389845[/C][/ROW]
[ROW][C]33[/C][C]0.279385764548984[/C][C]0.558771529097967[/C][C]0.720614235451016[/C][/ROW]
[ROW][C]34[/C][C]0.237079353777879[/C][C]0.474158707555759[/C][C]0.762920646222121[/C][/ROW]
[ROW][C]35[/C][C]0.272351372866039[/C][C]0.544702745732078[/C][C]0.727648627133961[/C][/ROW]
[ROW][C]36[/C][C]0.29816280162955[/C][C]0.596325603259101[/C][C]0.70183719837045[/C][/ROW]
[ROW][C]37[/C][C]0.26761517014548[/C][C]0.535230340290961[/C][C]0.73238482985452[/C][/ROW]
[ROW][C]38[/C][C]0.216100413818088[/C][C]0.432200827636176[/C][C]0.783899586181912[/C][/ROW]
[ROW][C]39[/C][C]0.173337615531392[/C][C]0.346675231062785[/C][C]0.826662384468608[/C][/ROW]
[ROW][C]40[/C][C]0.147877756451251[/C][C]0.295755512902502[/C][C]0.852122243548749[/C][/ROW]
[ROW][C]41[/C][C]0.114308572714506[/C][C]0.228617145429012[/C][C]0.885691427285494[/C][/ROW]
[ROW][C]42[/C][C]0.41124958636384[/C][C]0.822499172727681[/C][C]0.58875041363616[/C][/ROW]
[ROW][C]43[/C][C]0.75102176754461[/C][C]0.49795646491078[/C][C]0.24897823245539[/C][/ROW]
[ROW][C]44[/C][C]0.803936727639332[/C][C]0.392126544721335[/C][C]0.196063272360668[/C][/ROW]
[ROW][C]45[/C][C]0.763580331544061[/C][C]0.472839336911878[/C][C]0.236419668455939[/C][/ROW]
[ROW][C]46[/C][C]0.765462018595558[/C][C]0.469075962808883[/C][C]0.234537981404442[/C][/ROW]
[ROW][C]47[/C][C]0.848672692418211[/C][C]0.302654615163579[/C][C]0.151327307581789[/C][/ROW]
[ROW][C]48[/C][C]0.815871005944645[/C][C]0.36825798811071[/C][C]0.184128994055355[/C][/ROW]
[ROW][C]49[/C][C]0.790045011291259[/C][C]0.419909977417481[/C][C]0.209954988708741[/C][/ROW]
[ROW][C]50[/C][C]0.751667342757961[/C][C]0.496665314484079[/C][C]0.248332657242039[/C][/ROW]
[ROW][C]51[/C][C]0.695844251361549[/C][C]0.608311497276903[/C][C]0.304155748638451[/C][/ROW]
[ROW][C]52[/C][C]0.932782595088831[/C][C]0.134434809822338[/C][C]0.0672174049111689[/C][/ROW]
[ROW][C]53[/C][C]0.909489641897421[/C][C]0.181020716205158[/C][C]0.0905103581025788[/C][/ROW]
[ROW][C]54[/C][C]0.937788859824342[/C][C]0.124422280351316[/C][C]0.0622111401756582[/C][/ROW]
[ROW][C]55[/C][C]0.92946160724515[/C][C]0.141076785509699[/C][C]0.0705383927548496[/C][/ROW]
[ROW][C]56[/C][C]0.902632820603882[/C][C]0.194734358792235[/C][C]0.0973671793961177[/C][/ROW]
[ROW][C]57[/C][C]0.871029538929105[/C][C]0.25794092214179[/C][C]0.128970461070895[/C][/ROW]
[ROW][C]58[/C][C]0.864146188813808[/C][C]0.271707622372383[/C][C]0.135853811186192[/C][/ROW]
[ROW][C]59[/C][C]0.8549758823553[/C][C]0.2900482352894[/C][C]0.1450241176447[/C][/ROW]
[ROW][C]60[/C][C]0.818060284584914[/C][C]0.363879430830173[/C][C]0.181939715415086[/C][/ROW]
[ROW][C]61[/C][C]0.854569425609544[/C][C]0.290861148780911[/C][C]0.145430574390456[/C][/ROW]
[ROW][C]62[/C][C]0.830647960004213[/C][C]0.338704079991574[/C][C]0.169352039995787[/C][/ROW]
[ROW][C]63[/C][C]0.788180441904571[/C][C]0.423639116190857[/C][C]0.211819558095429[/C][/ROW]
[ROW][C]64[/C][C]0.788442504615136[/C][C]0.423114990769727[/C][C]0.211557495384864[/C][/ROW]
[ROW][C]65[/C][C]0.743876729061826[/C][C]0.512246541876349[/C][C]0.256123270938174[/C][/ROW]
[ROW][C]66[/C][C]0.731419079727801[/C][C]0.537161840544397[/C][C]0.268580920272199[/C][/ROW]
[ROW][C]67[/C][C]0.7337783493417[/C][C]0.5324433013166[/C][C]0.2662216506583[/C][/ROW]
[ROW][C]68[/C][C]0.697843390402735[/C][C]0.604313219194531[/C][C]0.302156609597265[/C][/ROW]
[ROW][C]69[/C][C]0.686709850544246[/C][C]0.626580298911507[/C][C]0.313290149455754[/C][/ROW]
[ROW][C]70[/C][C]0.624618193175884[/C][C]0.750763613648232[/C][C]0.375381806824116[/C][/ROW]
[ROW][C]71[/C][C]0.670470060195345[/C][C]0.659059879609311[/C][C]0.329529939804655[/C][/ROW]
[ROW][C]72[/C][C]0.584437666564629[/C][C]0.831124666870742[/C][C]0.415562333435371[/C][/ROW]
[ROW][C]73[/C][C]0.510197521059207[/C][C]0.979604957881586[/C][C]0.489802478940793[/C][/ROW]
[ROW][C]74[/C][C]0.517993969919195[/C][C]0.964012060161609[/C][C]0.482006030080805[/C][/ROW]
[ROW][C]75[/C][C]0.425448337367867[/C][C]0.850896674735734[/C][C]0.574551662632133[/C][/ROW]
[ROW][C]76[/C][C]0.321507247257447[/C][C]0.643014494514893[/C][C]0.678492752742553[/C][/ROW]
[ROW][C]77[/C][C]0.322120086970256[/C][C]0.644240173940511[/C][C]0.677879913029744[/C][/ROW]
[ROW][C]78[/C][C]0.213263569923785[/C][C]0.42652713984757[/C][C]0.786736430076215[/C][/ROW]
[ROW][C]79[/C][C]0.187424999323118[/C][C]0.374849998646235[/C][C]0.812575000676882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1563745919148940.3127491838297870.843625408085106
120.0685573732561650.137114746512330.931442626743835
130.4065509483761720.8131018967523430.593449051623828
140.5642542739235060.8714914521529890.435745726076494
150.5143364551139250.9713270897721490.485663544886075
160.4457212559720590.8914425119441190.554278744027941
170.3494295789454340.6988591578908670.650570421054566
180.2935771015604150.5871542031208290.706422898439586
190.2211609299057040.4423218598114070.778839070094296
200.2309246347479280.4618492694958560.769075365252072
210.3052956539524310.6105913079048630.694704346047569
220.3539142549980020.7078285099960040.646085745001998
230.2998177497319590.5996354994639180.700182250268041
240.2674112761425690.5348225522851380.732588723857431
250.2632461550800480.5264923101600960.736753844919952
260.2236917594013840.4473835188027680.776308240598616
270.2980992286111950.5961984572223910.701900771388805
280.236199451424810.472398902849620.76380054857519
290.2096122072149780.4192244144299560.790387792785022
300.3163977620596430.6327955241192860.683602237940357
310.3793341799014610.7586683598029220.620665820098539
320.3339350486101550.667870097220310.666064951389845
330.2793857645489840.5587715290979670.720614235451016
340.2370793537778790.4741587075557590.762920646222121
350.2723513728660390.5447027457320780.727648627133961
360.298162801629550.5963256032591010.70183719837045
370.267615170145480.5352303402909610.73238482985452
380.2161004138180880.4322008276361760.783899586181912
390.1733376155313920.3466752310627850.826662384468608
400.1478777564512510.2957555129025020.852122243548749
410.1143085727145060.2286171454290120.885691427285494
420.411249586363840.8224991727276810.58875041363616
430.751021767544610.497956464910780.24897823245539
440.8039367276393320.3921265447213350.196063272360668
450.7635803315440610.4728393369118780.236419668455939
460.7654620185955580.4690759628088830.234537981404442
470.8486726924182110.3026546151635790.151327307581789
480.8158710059446450.368257988110710.184128994055355
490.7900450112912590.4199099774174810.209954988708741
500.7516673427579610.4966653144840790.248332657242039
510.6958442513615490.6083114972769030.304155748638451
520.9327825950888310.1344348098223380.0672174049111689
530.9094896418974210.1810207162051580.0905103581025788
540.9377888598243420.1244222803513160.0622111401756582
550.929461607245150.1410767855096990.0705383927548496
560.9026328206038820.1947343587922350.0973671793961177
570.8710295389291050.257940922141790.128970461070895
580.8641461888138080.2717076223723830.135853811186192
590.85497588235530.29004823528940.1450241176447
600.8180602845849140.3638794308301730.181939715415086
610.8545694256095440.2908611487809110.145430574390456
620.8306479600042130.3387040799915740.169352039995787
630.7881804419045710.4236391161908570.211819558095429
640.7884425046151360.4231149907697270.211557495384864
650.7438767290618260.5122465418763490.256123270938174
660.7314190797278010.5371618405443970.268580920272199
670.73377834934170.53244330131660.2662216506583
680.6978433904027350.6043132191945310.302156609597265
690.6867098505442460.6265802989115070.313290149455754
700.6246181931758840.7507636136482320.375381806824116
710.6704700601953450.6590598796093110.329529939804655
720.5844376665646290.8311246668707420.415562333435371
730.5101975210592070.9796049578815860.489802478940793
740.5179939699191950.9640120601616090.482006030080805
750.4254483373678670.8508966747357340.574551662632133
760.3215072472574470.6430144945148930.678492752742553
770.3221200869702560.6442401739405110.677879913029744
780.2132635699237850.426527139847570.786736430076215
790.1874249993231180.3748499986462350.812575000676882






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203853&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203853&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203853&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 level00OK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}