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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 02:40:11 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258625101p3071rm00i725kw.htm/, Retrieved Fri, 29 Mar 2024 11:52:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57678, Retrieved Fri, 29 Mar 2024 11:52:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-19 09:40:11] [fd7715938ba69fff5a3edaf7913b7ba1] [Current]
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Dataseries X:
96.8	610763
114.1	612613
110.3	611324
103.9	594167
101.6	595454
94.6	590865
95.9	589379
104.7	584428
102.8	573100
98.1	567456
113.9	569028
80.9	620735
95.7	628884
113.2	628232
105.9	612117
108.8	595404
102.3	597141
99	593408
100.7	590072
115.5	579799
100.7	574205
109.9	572775
114.6	572942
85.4	619567
100.5	625809
114.8	619916
116.5	587625
112.9	565742
102	557274
106	560576
105.3	548854
118.8	531673
106.1	525919
109.3	511038
117.2	498662
92.5	555362
104.2	564591
112.5	541657
122.4	527070
113.3	509846
100	514258
110.7	516922
112.8	507561
109.8	492622
117.3	490243
109.1	469357
115.9	477580
96	528379
99.8	533590
116.8	517945
115.7	506174
99.4	501866
94.3	516141
91	528222
93.2	532638
103.1	536322
94.1	536535
91.8	523597
102.7	536214
82.6	586570
89.1	596594




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Tot_nietwerkende_werkzoekenden[t] = + 760951.910845173 -2044.23080527175Tot_ind_productie[t] + 32107.2016497893M1[t] + 56735.3855812829M2[t] + 41279.4778846503M3[t] + 12534.9776503839M4[t] -393.461085786818M5[t] + 2001.26969137297M6[t] + 401.854354331684M7[t] + 9659.08544072309M8[t] -7942.66093585633M9[t] -20243.2301868085M10[t] + 645.177837797036M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tot_nietwerkende_werkzoekenden[t] =  +  760951.910845173 -2044.23080527175Tot_ind_productie[t] +  32107.2016497893M1[t] +  56735.3855812829M2[t] +  41279.4778846503M3[t] +  12534.9776503839M4[t] -393.461085786818M5[t] +  2001.26969137297M6[t] +  401.854354331684M7[t] +  9659.08544072309M8[t] -7942.66093585633M9[t] -20243.2301868085M10[t] +  645.177837797036M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tot_nietwerkende_werkzoekenden[t] =  +  760951.910845173 -2044.23080527175Tot_ind_productie[t] +  32107.2016497893M1[t] +  56735.3855812829M2[t] +  41279.4778846503M3[t] +  12534.9776503839M4[t] -393.461085786818M5[t] +  2001.26969137297M6[t] +  401.854354331684M7[t] +  9659.08544072309M8[t] -7942.66093585633M9[t] -20243.2301868085M10[t] +  645.177837797036M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Tot_nietwerkende_werkzoekenden[t] = + 760951.910845173 -2044.23080527175Tot_ind_productie[t] + 32107.2016497893M1[t] + 56735.3855812829M2[t] + 41279.4778846503M3[t] + 12534.9776503839M4[t] -393.461085786818M5[t] + 2001.26969137297M6[t] + 401.854354331684M7[t] + 9659.08544072309M8[t] -7942.66093585633M9[t] -20243.2301868085M10[t] + 645.177837797036M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)760951.91084517378419.256739.703600
Tot_ind_productie-2044.23080527175873.385413-2.34060.0234570.011728
M132107.201649789325524.4582091.25790.2145130.107257
M256735.385581282934234.0002931.65730.1039850.051993
M341279.477884650334162.4269841.20830.232840.11642
M412534.977650383930573.3467580.410.6836320.341816
M5-393.46108578681827284.181946-0.01440.9885540.494277
M62001.2696913729727362.0001740.07310.9419980.470999
M7401.85435433168427852.1900720.01440.9885480.494274
M89659.0854407230932001.7632160.30180.7640860.382043
M9-7942.6609358563328936.819784-0.27450.7848910.392446
M10-20243.230186808528693.103999-0.70550.4839040.241952
M11645.17783779703633398.3008290.01930.9846680.492334

\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) & 760951.910845173 & 78419.25673 & 9.7036 & 0 & 0 \tabularnewline
Tot_ind_productie & -2044.23080527175 & 873.385413 & -2.3406 & 0.023457 & 0.011728 \tabularnewline
M1 & 32107.2016497893 & 25524.458209 & 1.2579 & 0.214513 & 0.107257 \tabularnewline
M2 & 56735.3855812829 & 34234.000293 & 1.6573 & 0.103985 & 0.051993 \tabularnewline
M3 & 41279.4778846503 & 34162.426984 & 1.2083 & 0.23284 & 0.11642 \tabularnewline
M4 & 12534.9776503839 & 30573.346758 & 0.41 & 0.683632 & 0.341816 \tabularnewline
M5 & -393.461085786818 & 27284.181946 & -0.0144 & 0.988554 & 0.494277 \tabularnewline
M6 & 2001.26969137297 & 27362.000174 & 0.0731 & 0.941998 & 0.470999 \tabularnewline
M7 & 401.854354331684 & 27852.190072 & 0.0144 & 0.988548 & 0.494274 \tabularnewline
M8 & 9659.08544072309 & 32001.763216 & 0.3018 & 0.764086 & 0.382043 \tabularnewline
M9 & -7942.66093585633 & 28936.819784 & -0.2745 & 0.784891 & 0.392446 \tabularnewline
M10 & -20243.2301868085 & 28693.103999 & -0.7055 & 0.483904 & 0.241952 \tabularnewline
M11 & 645.177837797036 & 33398.300829 & 0.0193 & 0.984668 & 0.492334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&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]760951.910845173[/C][C]78419.25673[/C][C]9.7036[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tot_ind_productie[/C][C]-2044.23080527175[/C][C]873.385413[/C][C]-2.3406[/C][C]0.023457[/C][C]0.011728[/C][/ROW]
[ROW][C]M1[/C][C]32107.2016497893[/C][C]25524.458209[/C][C]1.2579[/C][C]0.214513[/C][C]0.107257[/C][/ROW]
[ROW][C]M2[/C][C]56735.3855812829[/C][C]34234.000293[/C][C]1.6573[/C][C]0.103985[/C][C]0.051993[/C][/ROW]
[ROW][C]M3[/C][C]41279.4778846503[/C][C]34162.426984[/C][C]1.2083[/C][C]0.23284[/C][C]0.11642[/C][/ROW]
[ROW][C]M4[/C][C]12534.9776503839[/C][C]30573.346758[/C][C]0.41[/C][C]0.683632[/C][C]0.341816[/C][/ROW]
[ROW][C]M5[/C][C]-393.461085786818[/C][C]27284.181946[/C][C]-0.0144[/C][C]0.988554[/C][C]0.494277[/C][/ROW]
[ROW][C]M6[/C][C]2001.26969137297[/C][C]27362.000174[/C][C]0.0731[/C][C]0.941998[/C][C]0.470999[/C][/ROW]
[ROW][C]M7[/C][C]401.854354331684[/C][C]27852.190072[/C][C]0.0144[/C][C]0.988548[/C][C]0.494274[/C][/ROW]
[ROW][C]M8[/C][C]9659.08544072309[/C][C]32001.763216[/C][C]0.3018[/C][C]0.764086[/C][C]0.382043[/C][/ROW]
[ROW][C]M9[/C][C]-7942.66093585633[/C][C]28936.819784[/C][C]-0.2745[/C][C]0.784891[/C][C]0.392446[/C][/ROW]
[ROW][C]M10[/C][C]-20243.2301868085[/C][C]28693.103999[/C][C]-0.7055[/C][C]0.483904[/C][C]0.241952[/C][/ROW]
[ROW][C]M11[/C][C]645.177837797036[/C][C]33398.300829[/C][C]0.0193[/C][C]0.984668[/C][C]0.492334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)760951.91084517378419.256739.703600
Tot_ind_productie-2044.23080527175873.385413-2.34060.0234570.011728
M132107.201649789325524.4582091.25790.2145130.107257
M256735.385581282934234.0002931.65730.1039850.051993
M341279.477884650334162.4269841.20830.232840.11642
M412534.977650383930573.3467580.410.6836320.341816
M5-393.46108578681827284.181946-0.01440.9885540.494277
M62001.2696913729727362.0001740.07310.9419980.470999
M7401.85435433168427852.1900720.01440.9885480.494274
M89659.0854407230932001.7632160.30180.7640860.382043
M9-7942.6609358563328936.819784-0.27450.7848910.392446
M10-20243.230186808528693.103999-0.70550.4839040.241952
M11645.17783779703633398.3008290.01930.9846680.492334







Multiple Linear Regression - Regression Statistics
Multiple R0.556241731077129
R-squared0.309404863391681
Adjusted R-squared0.136756079239601
F-TEST (value)1.79210566069865
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.0766682421656164
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39499.7408316648
Sum Squared Residuals74891017236.897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.556241731077129 \tabularnewline
R-squared & 0.309404863391681 \tabularnewline
Adjusted R-squared & 0.136756079239601 \tabularnewline
F-TEST (value) & 1.79210566069865 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.0766682421656164 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39499.7408316648 \tabularnewline
Sum Squared Residuals & 74891017236.897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.556241731077129[/C][/ROW]
[ROW][C]R-squared[/C][C]0.309404863391681[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.136756079239601[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.79210566069865[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.0766682421656164[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39499.7408316648[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74891017236.897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.556241731077129
R-squared0.309404863391681
Adjusted R-squared0.136756079239601
F-TEST (value)1.79210566069865
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.0766682421656164
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39499.7408316648
Sum Squared Residuals74891017236.897







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1610763595177.57054465715585.4294553426
2612613584440.56154494928172.4384550511
3611324576752.73090834934571.2690916511
4594167561091.30782782233075.6921721782
5595454552864.59994377642589.4000562240
6590865569568.94635783821296.0536421619
7589379565312.03097394424066.9690260565
8584428556580.03097394427847.9690260565
9573100542862.3231273830237.6768726195
10567456540169.63866120627286.3613387945
11569028528759.19996251740268.8000374826
12620735595573.63869868825161.3613013119
13628884597426.22443045531457.7755695445
14628232586280.36926969441951.6307303065
15612117585747.34645154526369.6535484553
16595404551074.5768819944329.4231180098
17597141551433.63838008645707.3616199142
18593408560574.33081464232833.6691853576
19590072555499.72310863934572.2768913609
20579799534502.33827700945296.6617229914
21574205547155.20781845127049.7921815489
22572775516047.71515899956727.2848410012
23572942527328.23839882745613.7616011728
24619567586374.60007496533192.3999250348
25625809587613.91656515138195.0834348489
26619916583009.59998125936906.4000187413
27587625564078.49991566423546.5000843359
28565742542693.23058037623048.769419624
29557274552046.9076216675227.09237833263
30560576546264.7151777414311.2848222599
31548854546096.2614043892757.73859561091
32531673527756.3766196123916.62338038815
33525919536116.361469984-10197.3614699837
34511038517274.253642162-6236.25364216188
35498662522013.23830512-23351.2383051206
36555362571860.561357536-16498.5613575358
37564591580050.262585646-15459.2625856456
38541657587711.330833384-46054.3308333837
39527070552017.538164561-24947.5381645607
40509846541875.538258267-32029.5382582673
41514258556135.369232211-41877.3692322109
42516922536656.830392963-19734.8303929629
43507561530764.530364851-23203.5303648510
44492622546154.453867058-53532.4538670576
45490243513220.97645094-22977.9764509400
46469357517683.099803216-48326.0998032162
47477580524670.738351974-47090.7383519738
48528379564705.753539085-36326.7535390847
49533590589044.878128841-55454.8781288413
50517945578921.138370715-60976.1383707152
51506174565713.884559882-59539.8845598815
52501866570290.346451545-68424.3464515447
53516141567787.48482226-51646.4848222599
54528222576928.177256816-48706.1772568164
55532638570831.454148177-38193.4541481772
56536322559850.800262378-23528.8002623783
57536535560647.131133245-24112.1311332447
58523597553048.292734418-29451.2927344175
59536214551654.584981561-15440.5849815610
60586570592098.446329726-5528.44632972616
61596594610918.147745249-14324.1477452491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 610763 & 595177.570544657 & 15585.4294553426 \tabularnewline
2 & 612613 & 584440.561544949 & 28172.4384550511 \tabularnewline
3 & 611324 & 576752.730908349 & 34571.2690916511 \tabularnewline
4 & 594167 & 561091.307827822 & 33075.6921721782 \tabularnewline
5 & 595454 & 552864.599943776 & 42589.4000562240 \tabularnewline
6 & 590865 & 569568.946357838 & 21296.0536421619 \tabularnewline
7 & 589379 & 565312.030973944 & 24066.9690260565 \tabularnewline
8 & 584428 & 556580.030973944 & 27847.9690260565 \tabularnewline
9 & 573100 & 542862.32312738 & 30237.6768726195 \tabularnewline
10 & 567456 & 540169.638661206 & 27286.3613387945 \tabularnewline
11 & 569028 & 528759.199962517 & 40268.8000374826 \tabularnewline
12 & 620735 & 595573.638698688 & 25161.3613013119 \tabularnewline
13 & 628884 & 597426.224430455 & 31457.7755695445 \tabularnewline
14 & 628232 & 586280.369269694 & 41951.6307303065 \tabularnewline
15 & 612117 & 585747.346451545 & 26369.6535484553 \tabularnewline
16 & 595404 & 551074.57688199 & 44329.4231180098 \tabularnewline
17 & 597141 & 551433.638380086 & 45707.3616199142 \tabularnewline
18 & 593408 & 560574.330814642 & 32833.6691853576 \tabularnewline
19 & 590072 & 555499.723108639 & 34572.2768913609 \tabularnewline
20 & 579799 & 534502.338277009 & 45296.6617229914 \tabularnewline
21 & 574205 & 547155.207818451 & 27049.7921815489 \tabularnewline
22 & 572775 & 516047.715158999 & 56727.2848410012 \tabularnewline
23 & 572942 & 527328.238398827 & 45613.7616011728 \tabularnewline
24 & 619567 & 586374.600074965 & 33192.3999250348 \tabularnewline
25 & 625809 & 587613.916565151 & 38195.0834348489 \tabularnewline
26 & 619916 & 583009.599981259 & 36906.4000187413 \tabularnewline
27 & 587625 & 564078.499915664 & 23546.5000843359 \tabularnewline
28 & 565742 & 542693.230580376 & 23048.769419624 \tabularnewline
29 & 557274 & 552046.907621667 & 5227.09237833263 \tabularnewline
30 & 560576 & 546264.71517774 & 14311.2848222599 \tabularnewline
31 & 548854 & 546096.261404389 & 2757.73859561091 \tabularnewline
32 & 531673 & 527756.376619612 & 3916.62338038815 \tabularnewline
33 & 525919 & 536116.361469984 & -10197.3614699837 \tabularnewline
34 & 511038 & 517274.253642162 & -6236.25364216188 \tabularnewline
35 & 498662 & 522013.23830512 & -23351.2383051206 \tabularnewline
36 & 555362 & 571860.561357536 & -16498.5613575358 \tabularnewline
37 & 564591 & 580050.262585646 & -15459.2625856456 \tabularnewline
38 & 541657 & 587711.330833384 & -46054.3308333837 \tabularnewline
39 & 527070 & 552017.538164561 & -24947.5381645607 \tabularnewline
40 & 509846 & 541875.538258267 & -32029.5382582673 \tabularnewline
41 & 514258 & 556135.369232211 & -41877.3692322109 \tabularnewline
42 & 516922 & 536656.830392963 & -19734.8303929629 \tabularnewline
43 & 507561 & 530764.530364851 & -23203.5303648510 \tabularnewline
44 & 492622 & 546154.453867058 & -53532.4538670576 \tabularnewline
45 & 490243 & 513220.97645094 & -22977.9764509400 \tabularnewline
46 & 469357 & 517683.099803216 & -48326.0998032162 \tabularnewline
47 & 477580 & 524670.738351974 & -47090.7383519738 \tabularnewline
48 & 528379 & 564705.753539085 & -36326.7535390847 \tabularnewline
49 & 533590 & 589044.878128841 & -55454.8781288413 \tabularnewline
50 & 517945 & 578921.138370715 & -60976.1383707152 \tabularnewline
51 & 506174 & 565713.884559882 & -59539.8845598815 \tabularnewline
52 & 501866 & 570290.346451545 & -68424.3464515447 \tabularnewline
53 & 516141 & 567787.48482226 & -51646.4848222599 \tabularnewline
54 & 528222 & 576928.177256816 & -48706.1772568164 \tabularnewline
55 & 532638 & 570831.454148177 & -38193.4541481772 \tabularnewline
56 & 536322 & 559850.800262378 & -23528.8002623783 \tabularnewline
57 & 536535 & 560647.131133245 & -24112.1311332447 \tabularnewline
58 & 523597 & 553048.292734418 & -29451.2927344175 \tabularnewline
59 & 536214 & 551654.584981561 & -15440.5849815610 \tabularnewline
60 & 586570 & 592098.446329726 & -5528.44632972616 \tabularnewline
61 & 596594 & 610918.147745249 & -14324.1477452491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&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]610763[/C][C]595177.570544657[/C][C]15585.4294553426[/C][/ROW]
[ROW][C]2[/C][C]612613[/C][C]584440.561544949[/C][C]28172.4384550511[/C][/ROW]
[ROW][C]3[/C][C]611324[/C][C]576752.730908349[/C][C]34571.2690916511[/C][/ROW]
[ROW][C]4[/C][C]594167[/C][C]561091.307827822[/C][C]33075.6921721782[/C][/ROW]
[ROW][C]5[/C][C]595454[/C][C]552864.599943776[/C][C]42589.4000562240[/C][/ROW]
[ROW][C]6[/C][C]590865[/C][C]569568.946357838[/C][C]21296.0536421619[/C][/ROW]
[ROW][C]7[/C][C]589379[/C][C]565312.030973944[/C][C]24066.9690260565[/C][/ROW]
[ROW][C]8[/C][C]584428[/C][C]556580.030973944[/C][C]27847.9690260565[/C][/ROW]
[ROW][C]9[/C][C]573100[/C][C]542862.32312738[/C][C]30237.6768726195[/C][/ROW]
[ROW][C]10[/C][C]567456[/C][C]540169.638661206[/C][C]27286.3613387945[/C][/ROW]
[ROW][C]11[/C][C]569028[/C][C]528759.199962517[/C][C]40268.8000374826[/C][/ROW]
[ROW][C]12[/C][C]620735[/C][C]595573.638698688[/C][C]25161.3613013119[/C][/ROW]
[ROW][C]13[/C][C]628884[/C][C]597426.224430455[/C][C]31457.7755695445[/C][/ROW]
[ROW][C]14[/C][C]628232[/C][C]586280.369269694[/C][C]41951.6307303065[/C][/ROW]
[ROW][C]15[/C][C]612117[/C][C]585747.346451545[/C][C]26369.6535484553[/C][/ROW]
[ROW][C]16[/C][C]595404[/C][C]551074.57688199[/C][C]44329.4231180098[/C][/ROW]
[ROW][C]17[/C][C]597141[/C][C]551433.638380086[/C][C]45707.3616199142[/C][/ROW]
[ROW][C]18[/C][C]593408[/C][C]560574.330814642[/C][C]32833.6691853576[/C][/ROW]
[ROW][C]19[/C][C]590072[/C][C]555499.723108639[/C][C]34572.2768913609[/C][/ROW]
[ROW][C]20[/C][C]579799[/C][C]534502.338277009[/C][C]45296.6617229914[/C][/ROW]
[ROW][C]21[/C][C]574205[/C][C]547155.207818451[/C][C]27049.7921815489[/C][/ROW]
[ROW][C]22[/C][C]572775[/C][C]516047.715158999[/C][C]56727.2848410012[/C][/ROW]
[ROW][C]23[/C][C]572942[/C][C]527328.238398827[/C][C]45613.7616011728[/C][/ROW]
[ROW][C]24[/C][C]619567[/C][C]586374.600074965[/C][C]33192.3999250348[/C][/ROW]
[ROW][C]25[/C][C]625809[/C][C]587613.916565151[/C][C]38195.0834348489[/C][/ROW]
[ROW][C]26[/C][C]619916[/C][C]583009.599981259[/C][C]36906.4000187413[/C][/ROW]
[ROW][C]27[/C][C]587625[/C][C]564078.499915664[/C][C]23546.5000843359[/C][/ROW]
[ROW][C]28[/C][C]565742[/C][C]542693.230580376[/C][C]23048.769419624[/C][/ROW]
[ROW][C]29[/C][C]557274[/C][C]552046.907621667[/C][C]5227.09237833263[/C][/ROW]
[ROW][C]30[/C][C]560576[/C][C]546264.71517774[/C][C]14311.2848222599[/C][/ROW]
[ROW][C]31[/C][C]548854[/C][C]546096.261404389[/C][C]2757.73859561091[/C][/ROW]
[ROW][C]32[/C][C]531673[/C][C]527756.376619612[/C][C]3916.62338038815[/C][/ROW]
[ROW][C]33[/C][C]525919[/C][C]536116.361469984[/C][C]-10197.3614699837[/C][/ROW]
[ROW][C]34[/C][C]511038[/C][C]517274.253642162[/C][C]-6236.25364216188[/C][/ROW]
[ROW][C]35[/C][C]498662[/C][C]522013.23830512[/C][C]-23351.2383051206[/C][/ROW]
[ROW][C]36[/C][C]555362[/C][C]571860.561357536[/C][C]-16498.5613575358[/C][/ROW]
[ROW][C]37[/C][C]564591[/C][C]580050.262585646[/C][C]-15459.2625856456[/C][/ROW]
[ROW][C]38[/C][C]541657[/C][C]587711.330833384[/C][C]-46054.3308333837[/C][/ROW]
[ROW][C]39[/C][C]527070[/C][C]552017.538164561[/C][C]-24947.5381645607[/C][/ROW]
[ROW][C]40[/C][C]509846[/C][C]541875.538258267[/C][C]-32029.5382582673[/C][/ROW]
[ROW][C]41[/C][C]514258[/C][C]556135.369232211[/C][C]-41877.3692322109[/C][/ROW]
[ROW][C]42[/C][C]516922[/C][C]536656.830392963[/C][C]-19734.8303929629[/C][/ROW]
[ROW][C]43[/C][C]507561[/C][C]530764.530364851[/C][C]-23203.5303648510[/C][/ROW]
[ROW][C]44[/C][C]492622[/C][C]546154.453867058[/C][C]-53532.4538670576[/C][/ROW]
[ROW][C]45[/C][C]490243[/C][C]513220.97645094[/C][C]-22977.9764509400[/C][/ROW]
[ROW][C]46[/C][C]469357[/C][C]517683.099803216[/C][C]-48326.0998032162[/C][/ROW]
[ROW][C]47[/C][C]477580[/C][C]524670.738351974[/C][C]-47090.7383519738[/C][/ROW]
[ROW][C]48[/C][C]528379[/C][C]564705.753539085[/C][C]-36326.7535390847[/C][/ROW]
[ROW][C]49[/C][C]533590[/C][C]589044.878128841[/C][C]-55454.8781288413[/C][/ROW]
[ROW][C]50[/C][C]517945[/C][C]578921.138370715[/C][C]-60976.1383707152[/C][/ROW]
[ROW][C]51[/C][C]506174[/C][C]565713.884559882[/C][C]-59539.8845598815[/C][/ROW]
[ROW][C]52[/C][C]501866[/C][C]570290.346451545[/C][C]-68424.3464515447[/C][/ROW]
[ROW][C]53[/C][C]516141[/C][C]567787.48482226[/C][C]-51646.4848222599[/C][/ROW]
[ROW][C]54[/C][C]528222[/C][C]576928.177256816[/C][C]-48706.1772568164[/C][/ROW]
[ROW][C]55[/C][C]532638[/C][C]570831.454148177[/C][C]-38193.4541481772[/C][/ROW]
[ROW][C]56[/C][C]536322[/C][C]559850.800262378[/C][C]-23528.8002623783[/C][/ROW]
[ROW][C]57[/C][C]536535[/C][C]560647.131133245[/C][C]-24112.1311332447[/C][/ROW]
[ROW][C]58[/C][C]523597[/C][C]553048.292734418[/C][C]-29451.2927344175[/C][/ROW]
[ROW][C]59[/C][C]536214[/C][C]551654.584981561[/C][C]-15440.5849815610[/C][/ROW]
[ROW][C]60[/C][C]586570[/C][C]592098.446329726[/C][C]-5528.44632972616[/C][/ROW]
[ROW][C]61[/C][C]596594[/C][C]610918.147745249[/C][C]-14324.1477452491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1610763595177.57054465715585.4294553426
2612613584440.56154494928172.4384550511
3611324576752.73090834934571.2690916511
4594167561091.30782782233075.6921721782
5595454552864.59994377642589.4000562240
6590865569568.94635783821296.0536421619
7589379565312.03097394424066.9690260565
8584428556580.03097394427847.9690260565
9573100542862.3231273830237.6768726195
10567456540169.63866120627286.3613387945
11569028528759.19996251740268.8000374826
12620735595573.63869868825161.3613013119
13628884597426.22443045531457.7755695445
14628232586280.36926969441951.6307303065
15612117585747.34645154526369.6535484553
16595404551074.5768819944329.4231180098
17597141551433.63838008645707.3616199142
18593408560574.33081464232833.6691853576
19590072555499.72310863934572.2768913609
20579799534502.33827700945296.6617229914
21574205547155.20781845127049.7921815489
22572775516047.71515899956727.2848410012
23572942527328.23839882745613.7616011728
24619567586374.60007496533192.3999250348
25625809587613.91656515138195.0834348489
26619916583009.59998125936906.4000187413
27587625564078.49991566423546.5000843359
28565742542693.23058037623048.769419624
29557274552046.9076216675227.09237833263
30560576546264.7151777414311.2848222599
31548854546096.2614043892757.73859561091
32531673527756.3766196123916.62338038815
33525919536116.361469984-10197.3614699837
34511038517274.253642162-6236.25364216188
35498662522013.23830512-23351.2383051206
36555362571860.561357536-16498.5613575358
37564591580050.262585646-15459.2625856456
38541657587711.330833384-46054.3308333837
39527070552017.538164561-24947.5381645607
40509846541875.538258267-32029.5382582673
41514258556135.369232211-41877.3692322109
42516922536656.830392963-19734.8303929629
43507561530764.530364851-23203.5303648510
44492622546154.453867058-53532.4538670576
45490243513220.97645094-22977.9764509400
46469357517683.099803216-48326.0998032162
47477580524670.738351974-47090.7383519738
48528379564705.753539085-36326.7535390847
49533590589044.878128841-55454.8781288413
50517945578921.138370715-60976.1383707152
51506174565713.884559882-59539.8845598815
52501866570290.346451545-68424.3464515447
53516141567787.48482226-51646.4848222599
54528222576928.177256816-48706.1772568164
55532638570831.454148177-38193.4541481772
56536322559850.800262378-23528.8002623783
57536535560647.131133245-24112.1311332447
58523597553048.292734418-29451.2927344175
59536214551654.584981561-15440.5849815610
60586570592098.446329726-5528.44632972616
61596594610918.147745249-14324.1477452491







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.01941949445029630.03883898890059250.980580505549704
170.004449829673593780.008899659347187570.995550170326406
180.0009865369189316430.001973073837863290.999013463081068
190.0002021812594400840.0004043625188801680.99979781874056
204.33620109211557e-058.67240218423115e-050.999956637989079
218.28313972603116e-061.65662794520623e-050.999991716860274
223.20550616958073e-066.41101233916146e-060.99999679449383
231.05581314821786e-062.11162629643571e-060.999998944186852
242.71478086110986e-075.42956172221972e-070.999999728521914
251.33274674714065e-072.66549349428130e-070.999999866725325
261.27310894771374e-072.54621789542748e-070.999999872689105
276.25531859213443e-061.25106371842689e-050.999993744681408
280.0001701019178975380.0003402038357950750.999829898082103
290.007694673610283570.01538934722056710.992305326389716
300.01814120592341730.03628241184683450.981858794076583
310.04441660920192660.08883321840385330.955583390798073
320.1206304505719650.241260901143930.879369549428035
330.1809586363039730.3619172726079470.819041363696027
340.3561489247959290.7122978495918570.643851075204071
350.556121656636080.887756686727840.44387834336392
360.5197101254456690.9605797491086620.480289874554331
370.521689977740980.956620044518040.47831002225902
380.7246840933463680.5506318133072640.275315906653632
390.7442087903103480.5115824193793040.255791209689652
400.8298587753157690.3402824493684610.170141224684231
410.8380352343998110.3239295312003770.161964765600189
420.8718815663177380.2562368673645240.128118433682262
430.890618875888550.2187622482229010.109381124111450
440.8879515014437880.2240969971124250.112048498556212
450.9454169794356240.1091660411287520.0545830205643761

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0194194944502963 & 0.0388389889005925 & 0.980580505549704 \tabularnewline
17 & 0.00444982967359378 & 0.00889965934718757 & 0.995550170326406 \tabularnewline
18 & 0.000986536918931643 & 0.00197307383786329 & 0.999013463081068 \tabularnewline
19 & 0.000202181259440084 & 0.000404362518880168 & 0.99979781874056 \tabularnewline
20 & 4.33620109211557e-05 & 8.67240218423115e-05 & 0.999956637989079 \tabularnewline
21 & 8.28313972603116e-06 & 1.65662794520623e-05 & 0.999991716860274 \tabularnewline
22 & 3.20550616958073e-06 & 6.41101233916146e-06 & 0.99999679449383 \tabularnewline
23 & 1.05581314821786e-06 & 2.11162629643571e-06 & 0.999998944186852 \tabularnewline
24 & 2.71478086110986e-07 & 5.42956172221972e-07 & 0.999999728521914 \tabularnewline
25 & 1.33274674714065e-07 & 2.66549349428130e-07 & 0.999999866725325 \tabularnewline
26 & 1.27310894771374e-07 & 2.54621789542748e-07 & 0.999999872689105 \tabularnewline
27 & 6.25531859213443e-06 & 1.25106371842689e-05 & 0.999993744681408 \tabularnewline
28 & 0.000170101917897538 & 0.000340203835795075 & 0.999829898082103 \tabularnewline
29 & 0.00769467361028357 & 0.0153893472205671 & 0.992305326389716 \tabularnewline
30 & 0.0181412059234173 & 0.0362824118468345 & 0.981858794076583 \tabularnewline
31 & 0.0444166092019266 & 0.0888332184038533 & 0.955583390798073 \tabularnewline
32 & 0.120630450571965 & 0.24126090114393 & 0.879369549428035 \tabularnewline
33 & 0.180958636303973 & 0.361917272607947 & 0.819041363696027 \tabularnewline
34 & 0.356148924795929 & 0.712297849591857 & 0.643851075204071 \tabularnewline
35 & 0.55612165663608 & 0.88775668672784 & 0.44387834336392 \tabularnewline
36 & 0.519710125445669 & 0.960579749108662 & 0.480289874554331 \tabularnewline
37 & 0.52168997774098 & 0.95662004451804 & 0.47831002225902 \tabularnewline
38 & 0.724684093346368 & 0.550631813307264 & 0.275315906653632 \tabularnewline
39 & 0.744208790310348 & 0.511582419379304 & 0.255791209689652 \tabularnewline
40 & 0.829858775315769 & 0.340282449368461 & 0.170141224684231 \tabularnewline
41 & 0.838035234399811 & 0.323929531200377 & 0.161964765600189 \tabularnewline
42 & 0.871881566317738 & 0.256236867364524 & 0.128118433682262 \tabularnewline
43 & 0.89061887588855 & 0.218762248222901 & 0.109381124111450 \tabularnewline
44 & 0.887951501443788 & 0.224096997112425 & 0.112048498556212 \tabularnewline
45 & 0.945416979435624 & 0.109166041128752 & 0.0545830205643761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&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]16[/C][C]0.0194194944502963[/C][C]0.0388389889005925[/C][C]0.980580505549704[/C][/ROW]
[ROW][C]17[/C][C]0.00444982967359378[/C][C]0.00889965934718757[/C][C]0.995550170326406[/C][/ROW]
[ROW][C]18[/C][C]0.000986536918931643[/C][C]0.00197307383786329[/C][C]0.999013463081068[/C][/ROW]
[ROW][C]19[/C][C]0.000202181259440084[/C][C]0.000404362518880168[/C][C]0.99979781874056[/C][/ROW]
[ROW][C]20[/C][C]4.33620109211557e-05[/C][C]8.67240218423115e-05[/C][C]0.999956637989079[/C][/ROW]
[ROW][C]21[/C][C]8.28313972603116e-06[/C][C]1.65662794520623e-05[/C][C]0.999991716860274[/C][/ROW]
[ROW][C]22[/C][C]3.20550616958073e-06[/C][C]6.41101233916146e-06[/C][C]0.99999679449383[/C][/ROW]
[ROW][C]23[/C][C]1.05581314821786e-06[/C][C]2.11162629643571e-06[/C][C]0.999998944186852[/C][/ROW]
[ROW][C]24[/C][C]2.71478086110986e-07[/C][C]5.42956172221972e-07[/C][C]0.999999728521914[/C][/ROW]
[ROW][C]25[/C][C]1.33274674714065e-07[/C][C]2.66549349428130e-07[/C][C]0.999999866725325[/C][/ROW]
[ROW][C]26[/C][C]1.27310894771374e-07[/C][C]2.54621789542748e-07[/C][C]0.999999872689105[/C][/ROW]
[ROW][C]27[/C][C]6.25531859213443e-06[/C][C]1.25106371842689e-05[/C][C]0.999993744681408[/C][/ROW]
[ROW][C]28[/C][C]0.000170101917897538[/C][C]0.000340203835795075[/C][C]0.999829898082103[/C][/ROW]
[ROW][C]29[/C][C]0.00769467361028357[/C][C]0.0153893472205671[/C][C]0.992305326389716[/C][/ROW]
[ROW][C]30[/C][C]0.0181412059234173[/C][C]0.0362824118468345[/C][C]0.981858794076583[/C][/ROW]
[ROW][C]31[/C][C]0.0444166092019266[/C][C]0.0888332184038533[/C][C]0.955583390798073[/C][/ROW]
[ROW][C]32[/C][C]0.120630450571965[/C][C]0.24126090114393[/C][C]0.879369549428035[/C][/ROW]
[ROW][C]33[/C][C]0.180958636303973[/C][C]0.361917272607947[/C][C]0.819041363696027[/C][/ROW]
[ROW][C]34[/C][C]0.356148924795929[/C][C]0.712297849591857[/C][C]0.643851075204071[/C][/ROW]
[ROW][C]35[/C][C]0.55612165663608[/C][C]0.88775668672784[/C][C]0.44387834336392[/C][/ROW]
[ROW][C]36[/C][C]0.519710125445669[/C][C]0.960579749108662[/C][C]0.480289874554331[/C][/ROW]
[ROW][C]37[/C][C]0.52168997774098[/C][C]0.95662004451804[/C][C]0.47831002225902[/C][/ROW]
[ROW][C]38[/C][C]0.724684093346368[/C][C]0.550631813307264[/C][C]0.275315906653632[/C][/ROW]
[ROW][C]39[/C][C]0.744208790310348[/C][C]0.511582419379304[/C][C]0.255791209689652[/C][/ROW]
[ROW][C]40[/C][C]0.829858775315769[/C][C]0.340282449368461[/C][C]0.170141224684231[/C][/ROW]
[ROW][C]41[/C][C]0.838035234399811[/C][C]0.323929531200377[/C][C]0.161964765600189[/C][/ROW]
[ROW][C]42[/C][C]0.871881566317738[/C][C]0.256236867364524[/C][C]0.128118433682262[/C][/ROW]
[ROW][C]43[/C][C]0.89061887588855[/C][C]0.218762248222901[/C][C]0.109381124111450[/C][/ROW]
[ROW][C]44[/C][C]0.887951501443788[/C][C]0.224096997112425[/C][C]0.112048498556212[/C][/ROW]
[ROW][C]45[/C][C]0.945416979435624[/C][C]0.109166041128752[/C][C]0.0545830205643761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.01941949445029630.03883898890059250.980580505549704
170.004449829673593780.008899659347187570.995550170326406
180.0009865369189316430.001973073837863290.999013463081068
190.0002021812594400840.0004043625188801680.99979781874056
204.33620109211557e-058.67240218423115e-050.999956637989079
218.28313972603116e-061.65662794520623e-050.999991716860274
223.20550616958073e-066.41101233916146e-060.99999679449383
231.05581314821786e-062.11162629643571e-060.999998944186852
242.71478086110986e-075.42956172221972e-070.999999728521914
251.33274674714065e-072.66549349428130e-070.999999866725325
261.27310894771374e-072.54621789542748e-070.999999872689105
276.25531859213443e-061.25106371842689e-050.999993744681408
280.0001701019178975380.0003402038357950750.999829898082103
290.007694673610283570.01538934722056710.992305326389716
300.01814120592341730.03628241184683450.981858794076583
310.04441660920192660.08883321840385330.955583390798073
320.1206304505719650.241260901143930.879369549428035
330.1809586363039730.3619172726079470.819041363696027
340.3561489247959290.7122978495918570.643851075204071
350.556121656636080.887756686727840.44387834336392
360.5197101254456690.9605797491086620.480289874554331
370.521689977740980.956620044518040.47831002225902
380.7246840933463680.5506318133072640.275315906653632
390.7442087903103480.5115824193793040.255791209689652
400.8298587753157690.3402824493684610.170141224684231
410.8380352343998110.3239295312003770.161964765600189
420.8718815663177380.2562368673645240.128118433682262
430.890618875888550.2187622482229010.109381124111450
440.8879515014437880.2240969971124250.112048498556212
450.9454169794356240.1091660411287520.0545830205643761







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.4NOK
5% type I error level150.5NOK
10% type I error level160.533333333333333NOK

\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 & 12 & 0.4 & NOK \tabularnewline
5% type I error level & 15 & 0.5 & NOK \tabularnewline
10% type I error level & 16 & 0.533333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57678&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]12[/C][C]0.4[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.533333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57678&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57678&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.4NOK
5% type I error level150.5NOK
10% type I error level160.533333333333333NOK



Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Include Monthly 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')
}