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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 computationFri, 20 Nov 2009 12:07:22 -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/20/t1258744075f3q2p0ddolavx54.htm/, Retrieved Fri, 29 Mar 2024 07:50:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58428, Retrieved Fri, 29 Mar 2024 07:50:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
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-   PD      [Multiple Regression] [Seatbelt Law part 5] [2009-11-20 19:07:22] [befe6dd6a614b6d3a2a74a47a0a4f514] [Current]
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Dataseries X:
500857	1.1	509127	509933
506971	1.6	500857	509127
569323	1.5	506971	500857
579714	1.6	569323	506971
577992	1.7	579714	569323
565464	1.6	577992	579714
547344	1.7	565464	577992
554788	1.6	547344	565464
562325	1.6	554788	547344
560854	1.3	562325	554788
555332	1.1	560854	562325
543599	1.6	555332	560854
536662	1.9	543599	555332
542722	1.6	536662	543599
593530	1.7	542722	536662
610763	1.6	593530	542722
612613	1.4	610763	593530
611324	2.1	612613	610763
594167	1.9	611324	612613
595454	1.7	594167	611324
590865	1.8	595454	594167
589379	2	590865	595454
584428	2.5	589379	590865
573100	2.1	584428	589379
567456	2.1	573100	584428
569028	2.3	567456	573100
620735	2.4	569028	567456
628884	2.4	620735	569028
628232	2.3	628884	620735
612117	1.7	628232	628884
595404	2	612117	628232
597141	2.3	595404	612117
593408	2	597141	595404
590072	2	593408	597141
579799	1.3	590072	593408
574205	1.7	579799	590072
572775	1.9	574205	579799
572942	1.7	572775	574205
619567	1.6	572942	572775
625809	1.7	619567	572942
619916	1.8	625809	619567
587625	1.9	619916	625809
565742	1.9	587625	619916
557274	1.9	565742	587625
560576	2	557274	565742
548854	2.1	560576	557274
531673	1.9	548854	560576
525919	1.9	531673	548854
511038	1.3	525919	531673
498662	1.3	511038	525919
555362	1.4	498662	511038
564591	1.2	555362	498662
541657	1.3	564591	555362
527070	1.8	541657	564591
509846	2.2	527070	541657
514258	2.6	509846	527070
516922	2.8	514258	509846
507561	3.1	516922	514258
492622	3.9	507561	516922
490243	3.7	492622	507561
469357	4.6	490243	492622
477580	5.1	469357	490243
528379	5.2	477580	469357
533590	4.9	528379	477580
517945	5.1	533590	528379
506174	4.8	517945	533590
501866	3.9	506174	517945
516141	3.5	501866	506174




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=58428&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=58428&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
TWIB[t] = + 17449.4878192846 + 1308.17632173413GI[t] + 0.99428281596959TWIB1[t] -0.0329312886467168`TWIB2 `[t] -3736.91327370935M1[t] + 7320.3659768742M2[t] + 58676.1756270611M3[t] + 15535.1254686653M4[t] + 557.825793580474M5[t] -6334.55395907056M6[t] -7567.63201795877M7[t] + 11374.1603938179M8[t] + 8282.24218097304M9[t] + 1910.72164777117M10[t] -3066.56991435644M11[t] -170.214815449318t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TWIB[t] =  +  17449.4878192846 +  1308.17632173413GI[t] +  0.99428281596959TWIB1[t] -0.0329312886467168`TWIB2
`[t] -3736.91327370935M1[t] +  7320.3659768742M2[t] +  58676.1756270611M3[t] +  15535.1254686653M4[t] +  557.825793580474M5[t] -6334.55395907056M6[t] -7567.63201795877M7[t] +  11374.1603938179M8[t] +  8282.24218097304M9[t] +  1910.72164777117M10[t] -3066.56991435644M11[t] -170.214815449318t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TWIB[t] =  +  17449.4878192846 +  1308.17632173413GI[t] +  0.99428281596959TWIB1[t] -0.0329312886467168`TWIB2
`[t] -3736.91327370935M1[t] +  7320.3659768742M2[t] +  58676.1756270611M3[t] +  15535.1254686653M4[t] +  557.825793580474M5[t] -6334.55395907056M6[t] -7567.63201795877M7[t] +  11374.1603938179M8[t] +  8282.24218097304M9[t] +  1910.72164777117M10[t] -3066.56991435644M11[t] -170.214815449318t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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
TWIB[t] = + 17449.4878192846 + 1308.17632173413GI[t] + 0.99428281596959TWIB1[t] -0.0329312886467168`TWIB2 `[t] -3736.91327370935M1[t] + 7320.3659768742M2[t] + 58676.1756270611M3[t] + 15535.1254686653M4[t] + 557.825793580474M5[t] -6334.55395907056M6[t] -7567.63201795877M7[t] + 11374.1603938179M8[t] + 8282.24218097304M9[t] + 1910.72164777117M10[t] -3066.56991435644M11[t] -170.214815449318t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17449.487819284617152.6374541.01730.3137180.156859
GI1308.176321734131078.721321.21270.2307210.115361
TWIB10.994282815969590.1435486.926500
`TWIB2 `-0.03293128864671680.140787-0.23390.8159750.407988
M1-3736.913273709353956.796504-0.94440.3493180.174659
M27320.36597687423956.3019521.85030.0699550.034977
M358676.17562706114268.73467913.745600
M415535.12546866539776.5785521.5890.1181190.05906
M5557.8257935804744928.7167260.11320.9103250.455162
M6-6334.553959070564043.672772-1.56650.1232890.061645
M7-7567.632017958773979.157759-1.90180.0627410.031371
M811374.16039381793961.4070362.87120.0059020.002951
M98282.242180973044393.454071.88510.0650050.032503
M101910.721647771174389.1245880.43530.6651240.332562
M11-3066.569914356444134.419778-0.74170.4615960.230798
t-170.21481544931858.669167-2.90130.0054370.002719

\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) & 17449.4878192846 & 17152.637454 & 1.0173 & 0.313718 & 0.156859 \tabularnewline
GI & 1308.17632173413 & 1078.72132 & 1.2127 & 0.230721 & 0.115361 \tabularnewline
TWIB1 & 0.99428281596959 & 0.143548 & 6.9265 & 0 & 0 \tabularnewline
`TWIB2
` & -0.0329312886467168 & 0.140787 & -0.2339 & 0.815975 & 0.407988 \tabularnewline
M1 & -3736.91327370935 & 3956.796504 & -0.9444 & 0.349318 & 0.174659 \tabularnewline
M2 & 7320.3659768742 & 3956.301952 & 1.8503 & 0.069955 & 0.034977 \tabularnewline
M3 & 58676.1756270611 & 4268.734679 & 13.7456 & 0 & 0 \tabularnewline
M4 & 15535.1254686653 & 9776.578552 & 1.589 & 0.118119 & 0.05906 \tabularnewline
M5 & 557.825793580474 & 4928.716726 & 0.1132 & 0.910325 & 0.455162 \tabularnewline
M6 & -6334.55395907056 & 4043.672772 & -1.5665 & 0.123289 & 0.061645 \tabularnewline
M7 & -7567.63201795877 & 3979.157759 & -1.9018 & 0.062741 & 0.031371 \tabularnewline
M8 & 11374.1603938179 & 3961.407036 & 2.8712 & 0.005902 & 0.002951 \tabularnewline
M9 & 8282.24218097304 & 4393.45407 & 1.8851 & 0.065005 & 0.032503 \tabularnewline
M10 & 1910.72164777117 & 4389.124588 & 0.4353 & 0.665124 & 0.332562 \tabularnewline
M11 & -3066.56991435644 & 4134.419778 & -0.7417 & 0.461596 & 0.230798 \tabularnewline
t & -170.214815449318 & 58.669167 & -2.9013 & 0.005437 & 0.002719 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&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]17449.4878192846[/C][C]17152.637454[/C][C]1.0173[/C][C]0.313718[/C][C]0.156859[/C][/ROW]
[ROW][C]GI[/C][C]1308.17632173413[/C][C]1078.72132[/C][C]1.2127[/C][C]0.230721[/C][C]0.115361[/C][/ROW]
[ROW][C]TWIB1[/C][C]0.99428281596959[/C][C]0.143548[/C][C]6.9265[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`TWIB2
`[/C][C]-0.0329312886467168[/C][C]0.140787[/C][C]-0.2339[/C][C]0.815975[/C][C]0.407988[/C][/ROW]
[ROW][C]M1[/C][C]-3736.91327370935[/C][C]3956.796504[/C][C]-0.9444[/C][C]0.349318[/C][C]0.174659[/C][/ROW]
[ROW][C]M2[/C][C]7320.3659768742[/C][C]3956.301952[/C][C]1.8503[/C][C]0.069955[/C][C]0.034977[/C][/ROW]
[ROW][C]M3[/C][C]58676.1756270611[/C][C]4268.734679[/C][C]13.7456[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]15535.1254686653[/C][C]9776.578552[/C][C]1.589[/C][C]0.118119[/C][C]0.05906[/C][/ROW]
[ROW][C]M5[/C][C]557.825793580474[/C][C]4928.716726[/C][C]0.1132[/C][C]0.910325[/C][C]0.455162[/C][/ROW]
[ROW][C]M6[/C][C]-6334.55395907056[/C][C]4043.672772[/C][C]-1.5665[/C][C]0.123289[/C][C]0.061645[/C][/ROW]
[ROW][C]M7[/C][C]-7567.63201795877[/C][C]3979.157759[/C][C]-1.9018[/C][C]0.062741[/C][C]0.031371[/C][/ROW]
[ROW][C]M8[/C][C]11374.1603938179[/C][C]3961.407036[/C][C]2.8712[/C][C]0.005902[/C][C]0.002951[/C][/ROW]
[ROW][C]M9[/C][C]8282.24218097304[/C][C]4393.45407[/C][C]1.8851[/C][C]0.065005[/C][C]0.032503[/C][/ROW]
[ROW][C]M10[/C][C]1910.72164777117[/C][C]4389.124588[/C][C]0.4353[/C][C]0.665124[/C][C]0.332562[/C][/ROW]
[ROW][C]M11[/C][C]-3066.56991435644[/C][C]4134.419778[/C][C]-0.7417[/C][C]0.461596[/C][C]0.230798[/C][/ROW]
[ROW][C]t[/C][C]-170.214815449318[/C][C]58.669167[/C][C]-2.9013[/C][C]0.005437[/C][C]0.002719[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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)17449.487819284617152.6374541.01730.3137180.156859
GI1308.176321734131078.721321.21270.2307210.115361
TWIB10.994282815969590.1435486.926500
`TWIB2 `-0.03293128864671680.140787-0.23390.8159750.407988
M1-3736.913273709353956.796504-0.94440.3493180.174659
M27320.36597687423956.3019521.85030.0699550.034977
M358676.17562706114268.73467913.745600
M415535.12546866539776.5785521.5890.1181190.05906
M5557.8257935804744928.7167260.11320.9103250.455162
M6-6334.553959070564043.672772-1.56650.1232890.061645
M7-7567.632017958773979.157759-1.90180.0627410.031371
M811374.16039381793961.4070362.87120.0059020.002951
M98282.242180973044393.454071.88510.0650050.032503
M101910.721647771174389.1245880.43530.6651240.332562
M11-3066.569914356444134.419778-0.74170.4615960.230798
t-170.21481544931858.669167-2.90130.0054370.002719







Multiple Linear Regression - Regression Statistics
Multiple R0.990297707438577
R-squared0.980689549358102
Adjusted R-squared0.975119227057554
F-TEST (value)176.056159131347
F-TEST (DF numerator)15
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6427.4395375485
Sum Squared Residuals2148222908.45976

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990297707438577 \tabularnewline
R-squared & 0.980689549358102 \tabularnewline
Adjusted R-squared & 0.975119227057554 \tabularnewline
F-TEST (value) & 176.056159131347 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6427.4395375485 \tabularnewline
Sum Squared Residuals & 2148222908.45976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990297707438577[/C][/ROW]
[ROW][C]R-squared[/C][C]0.980689549358102[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.975119227057554[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]176.056159131347[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6427.4395375485[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2148222908.45976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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 R0.990297707438577
R-squared0.980689549358102
Adjusted R-squared0.975119227057554
F-TEST (value)176.056159131347
F-TEST (DF numerator)15
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6427.4395375485
Sum Squared Residuals2148222908.45976







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1500857504404.830116698-3547.83011669796
2506971507749.806443279-778.8064432789
3569323565155.970539794167.02946021052
4579714583769.703440668-4055.7034406678
5577992577031.267613347960.732386653092
6565464567783.511383645-2319.51138364549
7547344554111.368702064-6767.36870206405
8554788555148.287225015-360.287225015
9562325559884.3104290772440.68957092303
10560854560198.891255182655.108744817821
11555332553078.9564684372253.04353156315
12543599551187.411944026-7588.4119440263
13536662536188.663047524473.336952476172
14542722540172.3175014492549.68249855130
15593530597742.528182478-4212.52818247766
16610763604618.4032810436144.59671895693
17612613604670.5563802047942.44361979637
18611324599795.60354961211528.3964503880
19594167596788.121977146-2621.12197714644
20595454598281.602466602-2827.60246660231
21590865596994.931173946-6129.93117394614
22589379586109.6846786693269.31532133100
23584428580289.8838810284138.11611897189
24573100577789.210124305-4689.21012430516
25567456562781.8891059334674.11089406713
26569028568691.902229872336.097770128443
27620735621757.191476609-1022.19147660879
28628884629805.54008235-921.540082350759
29628232620926.8404849247305.15951507644
30612117612162.710656588-45.7106565884505
31595404595150.474299619253.525700381135
32597141598227.743805708-1086.7438057085
33593408596850.607759386-3442.60775938586
34590072586540.0130103413531.98698965915
35579799577282.7882339942516.21176600635
36574205570598.0052720643606.99472793573
37572775561728.79750298611046.2024970137
38572942571116.6198756271825.38012437311
39619567622384.534051223-2817.53405122274
40625809625557.023478929251.976521070690
41619916615211.2186246984704.78137530246
42587625602214.575950529-14589.575950529
43565742568898.960749713-3156.96074971251
44557274566976.031725868-9702.03172586842
45560576556145.7648335734430.23516642672
46548854553296.831127687-4442.8311276875
47531673536123.967201857-4450.96720185673
48525919522323.5698051073595.43019489287
49511038512476.225070058-1438.2250700582
50498662508756.853555622-10094.8535556222
51555362548258.0723984457103.92760155475
52564591561468.5654540213122.43454597903
53541657553760.900637975-12103.9006379747
54527070524245.5892663742824.41073362574
55509846509617.209658006228.79034199423
56514258512266.8992682561991.10073174381
57516922514220.3858040182701.61419598226
58507561510574.57992812-3013.57992812047
59492622497078.404214685-4456.40421468465
60490243485167.8028544975075.19714550285
61469357480564.595156801-11207.5951568009
62477580471417.5003941526162.49960584819
63528379531597.703351456-3218.70335145607
64533590538131.764262988-4541.76426298808
65517945526754.216258854-8809.21625885367
66506174503572.0091932512601.99080674921
67501866489802.86461345212063.1353865476
68516141504155.4355085511985.5644914504

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 500857 & 504404.830116698 & -3547.83011669796 \tabularnewline
2 & 506971 & 507749.806443279 & -778.8064432789 \tabularnewline
3 & 569323 & 565155.97053979 & 4167.02946021052 \tabularnewline
4 & 579714 & 583769.703440668 & -4055.7034406678 \tabularnewline
5 & 577992 & 577031.267613347 & 960.732386653092 \tabularnewline
6 & 565464 & 567783.511383645 & -2319.51138364549 \tabularnewline
7 & 547344 & 554111.368702064 & -6767.36870206405 \tabularnewline
8 & 554788 & 555148.287225015 & -360.287225015 \tabularnewline
9 & 562325 & 559884.310429077 & 2440.68957092303 \tabularnewline
10 & 560854 & 560198.891255182 & 655.108744817821 \tabularnewline
11 & 555332 & 553078.956468437 & 2253.04353156315 \tabularnewline
12 & 543599 & 551187.411944026 & -7588.4119440263 \tabularnewline
13 & 536662 & 536188.663047524 & 473.336952476172 \tabularnewline
14 & 542722 & 540172.317501449 & 2549.68249855130 \tabularnewline
15 & 593530 & 597742.528182478 & -4212.52818247766 \tabularnewline
16 & 610763 & 604618.403281043 & 6144.59671895693 \tabularnewline
17 & 612613 & 604670.556380204 & 7942.44361979637 \tabularnewline
18 & 611324 & 599795.603549612 & 11528.3964503880 \tabularnewline
19 & 594167 & 596788.121977146 & -2621.12197714644 \tabularnewline
20 & 595454 & 598281.602466602 & -2827.60246660231 \tabularnewline
21 & 590865 & 596994.931173946 & -6129.93117394614 \tabularnewline
22 & 589379 & 586109.684678669 & 3269.31532133100 \tabularnewline
23 & 584428 & 580289.883881028 & 4138.11611897189 \tabularnewline
24 & 573100 & 577789.210124305 & -4689.21012430516 \tabularnewline
25 & 567456 & 562781.889105933 & 4674.11089406713 \tabularnewline
26 & 569028 & 568691.902229872 & 336.097770128443 \tabularnewline
27 & 620735 & 621757.191476609 & -1022.19147660879 \tabularnewline
28 & 628884 & 629805.54008235 & -921.540082350759 \tabularnewline
29 & 628232 & 620926.840484924 & 7305.15951507644 \tabularnewline
30 & 612117 & 612162.710656588 & -45.7106565884505 \tabularnewline
31 & 595404 & 595150.474299619 & 253.525700381135 \tabularnewline
32 & 597141 & 598227.743805708 & -1086.7438057085 \tabularnewline
33 & 593408 & 596850.607759386 & -3442.60775938586 \tabularnewline
34 & 590072 & 586540.013010341 & 3531.98698965915 \tabularnewline
35 & 579799 & 577282.788233994 & 2516.21176600635 \tabularnewline
36 & 574205 & 570598.005272064 & 3606.99472793573 \tabularnewline
37 & 572775 & 561728.797502986 & 11046.2024970137 \tabularnewline
38 & 572942 & 571116.619875627 & 1825.38012437311 \tabularnewline
39 & 619567 & 622384.534051223 & -2817.53405122274 \tabularnewline
40 & 625809 & 625557.023478929 & 251.976521070690 \tabularnewline
41 & 619916 & 615211.218624698 & 4704.78137530246 \tabularnewline
42 & 587625 & 602214.575950529 & -14589.575950529 \tabularnewline
43 & 565742 & 568898.960749713 & -3156.96074971251 \tabularnewline
44 & 557274 & 566976.031725868 & -9702.03172586842 \tabularnewline
45 & 560576 & 556145.764833573 & 4430.23516642672 \tabularnewline
46 & 548854 & 553296.831127687 & -4442.8311276875 \tabularnewline
47 & 531673 & 536123.967201857 & -4450.96720185673 \tabularnewline
48 & 525919 & 522323.569805107 & 3595.43019489287 \tabularnewline
49 & 511038 & 512476.225070058 & -1438.2250700582 \tabularnewline
50 & 498662 & 508756.853555622 & -10094.8535556222 \tabularnewline
51 & 555362 & 548258.072398445 & 7103.92760155475 \tabularnewline
52 & 564591 & 561468.565454021 & 3122.43454597903 \tabularnewline
53 & 541657 & 553760.900637975 & -12103.9006379747 \tabularnewline
54 & 527070 & 524245.589266374 & 2824.41073362574 \tabularnewline
55 & 509846 & 509617.209658006 & 228.79034199423 \tabularnewline
56 & 514258 & 512266.899268256 & 1991.10073174381 \tabularnewline
57 & 516922 & 514220.385804018 & 2701.61419598226 \tabularnewline
58 & 507561 & 510574.57992812 & -3013.57992812047 \tabularnewline
59 & 492622 & 497078.404214685 & -4456.40421468465 \tabularnewline
60 & 490243 & 485167.802854497 & 5075.19714550285 \tabularnewline
61 & 469357 & 480564.595156801 & -11207.5951568009 \tabularnewline
62 & 477580 & 471417.500394152 & 6162.49960584819 \tabularnewline
63 & 528379 & 531597.703351456 & -3218.70335145607 \tabularnewline
64 & 533590 & 538131.764262988 & -4541.76426298808 \tabularnewline
65 & 517945 & 526754.216258854 & -8809.21625885367 \tabularnewline
66 & 506174 & 503572.009193251 & 2601.99080674921 \tabularnewline
67 & 501866 & 489802.864613452 & 12063.1353865476 \tabularnewline
68 & 516141 & 504155.43550855 & 11985.5644914504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&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]500857[/C][C]504404.830116698[/C][C]-3547.83011669796[/C][/ROW]
[ROW][C]2[/C][C]506971[/C][C]507749.806443279[/C][C]-778.8064432789[/C][/ROW]
[ROW][C]3[/C][C]569323[/C][C]565155.97053979[/C][C]4167.02946021052[/C][/ROW]
[ROW][C]4[/C][C]579714[/C][C]583769.703440668[/C][C]-4055.7034406678[/C][/ROW]
[ROW][C]5[/C][C]577992[/C][C]577031.267613347[/C][C]960.732386653092[/C][/ROW]
[ROW][C]6[/C][C]565464[/C][C]567783.511383645[/C][C]-2319.51138364549[/C][/ROW]
[ROW][C]7[/C][C]547344[/C][C]554111.368702064[/C][C]-6767.36870206405[/C][/ROW]
[ROW][C]8[/C][C]554788[/C][C]555148.287225015[/C][C]-360.287225015[/C][/ROW]
[ROW][C]9[/C][C]562325[/C][C]559884.310429077[/C][C]2440.68957092303[/C][/ROW]
[ROW][C]10[/C][C]560854[/C][C]560198.891255182[/C][C]655.108744817821[/C][/ROW]
[ROW][C]11[/C][C]555332[/C][C]553078.956468437[/C][C]2253.04353156315[/C][/ROW]
[ROW][C]12[/C][C]543599[/C][C]551187.411944026[/C][C]-7588.4119440263[/C][/ROW]
[ROW][C]13[/C][C]536662[/C][C]536188.663047524[/C][C]473.336952476172[/C][/ROW]
[ROW][C]14[/C][C]542722[/C][C]540172.317501449[/C][C]2549.68249855130[/C][/ROW]
[ROW][C]15[/C][C]593530[/C][C]597742.528182478[/C][C]-4212.52818247766[/C][/ROW]
[ROW][C]16[/C][C]610763[/C][C]604618.403281043[/C][C]6144.59671895693[/C][/ROW]
[ROW][C]17[/C][C]612613[/C][C]604670.556380204[/C][C]7942.44361979637[/C][/ROW]
[ROW][C]18[/C][C]611324[/C][C]599795.603549612[/C][C]11528.3964503880[/C][/ROW]
[ROW][C]19[/C][C]594167[/C][C]596788.121977146[/C][C]-2621.12197714644[/C][/ROW]
[ROW][C]20[/C][C]595454[/C][C]598281.602466602[/C][C]-2827.60246660231[/C][/ROW]
[ROW][C]21[/C][C]590865[/C][C]596994.931173946[/C][C]-6129.93117394614[/C][/ROW]
[ROW][C]22[/C][C]589379[/C][C]586109.684678669[/C][C]3269.31532133100[/C][/ROW]
[ROW][C]23[/C][C]584428[/C][C]580289.883881028[/C][C]4138.11611897189[/C][/ROW]
[ROW][C]24[/C][C]573100[/C][C]577789.210124305[/C][C]-4689.21012430516[/C][/ROW]
[ROW][C]25[/C][C]567456[/C][C]562781.889105933[/C][C]4674.11089406713[/C][/ROW]
[ROW][C]26[/C][C]569028[/C][C]568691.902229872[/C][C]336.097770128443[/C][/ROW]
[ROW][C]27[/C][C]620735[/C][C]621757.191476609[/C][C]-1022.19147660879[/C][/ROW]
[ROW][C]28[/C][C]628884[/C][C]629805.54008235[/C][C]-921.540082350759[/C][/ROW]
[ROW][C]29[/C][C]628232[/C][C]620926.840484924[/C][C]7305.15951507644[/C][/ROW]
[ROW][C]30[/C][C]612117[/C][C]612162.710656588[/C][C]-45.7106565884505[/C][/ROW]
[ROW][C]31[/C][C]595404[/C][C]595150.474299619[/C][C]253.525700381135[/C][/ROW]
[ROW][C]32[/C][C]597141[/C][C]598227.743805708[/C][C]-1086.7438057085[/C][/ROW]
[ROW][C]33[/C][C]593408[/C][C]596850.607759386[/C][C]-3442.60775938586[/C][/ROW]
[ROW][C]34[/C][C]590072[/C][C]586540.013010341[/C][C]3531.98698965915[/C][/ROW]
[ROW][C]35[/C][C]579799[/C][C]577282.788233994[/C][C]2516.21176600635[/C][/ROW]
[ROW][C]36[/C][C]574205[/C][C]570598.005272064[/C][C]3606.99472793573[/C][/ROW]
[ROW][C]37[/C][C]572775[/C][C]561728.797502986[/C][C]11046.2024970137[/C][/ROW]
[ROW][C]38[/C][C]572942[/C][C]571116.619875627[/C][C]1825.38012437311[/C][/ROW]
[ROW][C]39[/C][C]619567[/C][C]622384.534051223[/C][C]-2817.53405122274[/C][/ROW]
[ROW][C]40[/C][C]625809[/C][C]625557.023478929[/C][C]251.976521070690[/C][/ROW]
[ROW][C]41[/C][C]619916[/C][C]615211.218624698[/C][C]4704.78137530246[/C][/ROW]
[ROW][C]42[/C][C]587625[/C][C]602214.575950529[/C][C]-14589.575950529[/C][/ROW]
[ROW][C]43[/C][C]565742[/C][C]568898.960749713[/C][C]-3156.96074971251[/C][/ROW]
[ROW][C]44[/C][C]557274[/C][C]566976.031725868[/C][C]-9702.03172586842[/C][/ROW]
[ROW][C]45[/C][C]560576[/C][C]556145.764833573[/C][C]4430.23516642672[/C][/ROW]
[ROW][C]46[/C][C]548854[/C][C]553296.831127687[/C][C]-4442.8311276875[/C][/ROW]
[ROW][C]47[/C][C]531673[/C][C]536123.967201857[/C][C]-4450.96720185673[/C][/ROW]
[ROW][C]48[/C][C]525919[/C][C]522323.569805107[/C][C]3595.43019489287[/C][/ROW]
[ROW][C]49[/C][C]511038[/C][C]512476.225070058[/C][C]-1438.2250700582[/C][/ROW]
[ROW][C]50[/C][C]498662[/C][C]508756.853555622[/C][C]-10094.8535556222[/C][/ROW]
[ROW][C]51[/C][C]555362[/C][C]548258.072398445[/C][C]7103.92760155475[/C][/ROW]
[ROW][C]52[/C][C]564591[/C][C]561468.565454021[/C][C]3122.43454597903[/C][/ROW]
[ROW][C]53[/C][C]541657[/C][C]553760.900637975[/C][C]-12103.9006379747[/C][/ROW]
[ROW][C]54[/C][C]527070[/C][C]524245.589266374[/C][C]2824.41073362574[/C][/ROW]
[ROW][C]55[/C][C]509846[/C][C]509617.209658006[/C][C]228.79034199423[/C][/ROW]
[ROW][C]56[/C][C]514258[/C][C]512266.899268256[/C][C]1991.10073174381[/C][/ROW]
[ROW][C]57[/C][C]516922[/C][C]514220.385804018[/C][C]2701.61419598226[/C][/ROW]
[ROW][C]58[/C][C]507561[/C][C]510574.57992812[/C][C]-3013.57992812047[/C][/ROW]
[ROW][C]59[/C][C]492622[/C][C]497078.404214685[/C][C]-4456.40421468465[/C][/ROW]
[ROW][C]60[/C][C]490243[/C][C]485167.802854497[/C][C]5075.19714550285[/C][/ROW]
[ROW][C]61[/C][C]469357[/C][C]480564.595156801[/C][C]-11207.5951568009[/C][/ROW]
[ROW][C]62[/C][C]477580[/C][C]471417.500394152[/C][C]6162.49960584819[/C][/ROW]
[ROW][C]63[/C][C]528379[/C][C]531597.703351456[/C][C]-3218.70335145607[/C][/ROW]
[ROW][C]64[/C][C]533590[/C][C]538131.764262988[/C][C]-4541.76426298808[/C][/ROW]
[ROW][C]65[/C][C]517945[/C][C]526754.216258854[/C][C]-8809.21625885367[/C][/ROW]
[ROW][C]66[/C][C]506174[/C][C]503572.009193251[/C][C]2601.99080674921[/C][/ROW]
[ROW][C]67[/C][C]501866[/C][C]489802.864613452[/C][C]12063.1353865476[/C][/ROW]
[ROW][C]68[/C][C]516141[/C][C]504155.43550855[/C][C]11985.5644914504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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
1500857504404.830116698-3547.83011669796
2506971507749.806443279-778.8064432789
3569323565155.970539794167.02946021052
4579714583769.703440668-4055.7034406678
5577992577031.267613347960.732386653092
6565464567783.511383645-2319.51138364549
7547344554111.368702064-6767.36870206405
8554788555148.287225015-360.287225015
9562325559884.3104290772440.68957092303
10560854560198.891255182655.108744817821
11555332553078.9564684372253.04353156315
12543599551187.411944026-7588.4119440263
13536662536188.663047524473.336952476172
14542722540172.3175014492549.68249855130
15593530597742.528182478-4212.52818247766
16610763604618.4032810436144.59671895693
17612613604670.5563802047942.44361979637
18611324599795.60354961211528.3964503880
19594167596788.121977146-2621.12197714644
20595454598281.602466602-2827.60246660231
21590865596994.931173946-6129.93117394614
22589379586109.6846786693269.31532133100
23584428580289.8838810284138.11611897189
24573100577789.210124305-4689.21012430516
25567456562781.8891059334674.11089406713
26569028568691.902229872336.097770128443
27620735621757.191476609-1022.19147660879
28628884629805.54008235-921.540082350759
29628232620926.8404849247305.15951507644
30612117612162.710656588-45.7106565884505
31595404595150.474299619253.525700381135
32597141598227.743805708-1086.7438057085
33593408596850.607759386-3442.60775938586
34590072586540.0130103413531.98698965915
35579799577282.7882339942516.21176600635
36574205570598.0052720643606.99472793573
37572775561728.79750298611046.2024970137
38572942571116.6198756271825.38012437311
39619567622384.534051223-2817.53405122274
40625809625557.023478929251.976521070690
41619916615211.2186246984704.78137530246
42587625602214.575950529-14589.575950529
43565742568898.960749713-3156.96074971251
44557274566976.031725868-9702.03172586842
45560576556145.7648335734430.23516642672
46548854553296.831127687-4442.8311276875
47531673536123.967201857-4450.96720185673
48525919522323.5698051073595.43019489287
49511038512476.225070058-1438.2250700582
50498662508756.853555622-10094.8535556222
51555362548258.0723984457103.92760155475
52564591561468.5654540213122.43454597903
53541657553760.900637975-12103.9006379747
54527070524245.5892663742824.41073362574
55509846509617.209658006228.79034199423
56514258512266.8992682561991.10073174381
57516922514220.3858040182701.61419598226
58507561510574.57992812-3013.57992812047
59492622497078.404214685-4456.40421468465
60490243485167.8028544975075.19714550285
61469357480564.595156801-11207.5951568009
62477580471417.5003941526162.49960584819
63528379531597.703351456-3218.70335145607
64533590538131.764262988-4541.76426298808
65517945526754.216258854-8809.21625885367
66506174503572.0091932512601.99080674921
67501866489802.86461345212063.1353865476
68516141504155.4355085511985.5644914504







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.3942366880232680.7884733760465350.605763311976732
200.2778161851683780.5556323703367570.722183814831622
210.1967067160127890.3934134320255780.80329328398721
220.1240757497641290.2481514995282580.875924250235871
230.1341136041115810.2682272082231630.865886395888419
240.09634967384319980.1926993476864000.9036503261568
250.05371423184788460.1074284636957690.946285768152115
260.04655472326854740.09310944653709490.953445276731453
270.0379323691744640.0758647383489280.962067630825536
280.03507441922170130.07014883844340250.96492558077830
290.0261446276067420.0522892552134840.973855372393258
300.0288038605591770.0576077211183540.971196139440823
310.01672665314679570.03345330629359130.983273346853204
320.009317009338924440.01863401867784890.990682990661076
330.005860575929144630.01172115185828930.994139424070855
340.003396606705680670.006793213411361330.99660339329432
350.002056732355642020.004113464711284040.997943267644358
360.002035184920235810.004070369840471610.997964815079764
370.01079393580595130.02158787161190270.989206064194049
380.01232043205315010.02464086410630030.98767956794685
390.008401168947790040.01680233789558010.99159883105221
400.005019934945642980.01003986989128600.994980065054357
410.1344507777709780.2689015555419560.865549222229022
420.3252742289404370.6505484578808740.674725771059563
430.2585247340776810.5170494681553630.741475265922318
440.3156043269185170.6312086538370340.684395673081483
450.2710089379266460.5420178758532920.728991062073354
460.2153724961495840.4307449922991680.784627503850416
470.1513713933679720.3027427867359440.848628606632028
480.1798326456922580.3596652913845160.820167354307742
490.8885964391531020.2228071216937970.111403560846898

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.394236688023268 & 0.788473376046535 & 0.605763311976732 \tabularnewline
20 & 0.277816185168378 & 0.555632370336757 & 0.722183814831622 \tabularnewline
21 & 0.196706716012789 & 0.393413432025578 & 0.80329328398721 \tabularnewline
22 & 0.124075749764129 & 0.248151499528258 & 0.875924250235871 \tabularnewline
23 & 0.134113604111581 & 0.268227208223163 & 0.865886395888419 \tabularnewline
24 & 0.0963496738431998 & 0.192699347686400 & 0.9036503261568 \tabularnewline
25 & 0.0537142318478846 & 0.107428463695769 & 0.946285768152115 \tabularnewline
26 & 0.0465547232685474 & 0.0931094465370949 & 0.953445276731453 \tabularnewline
27 & 0.037932369174464 & 0.075864738348928 & 0.962067630825536 \tabularnewline
28 & 0.0350744192217013 & 0.0701488384434025 & 0.96492558077830 \tabularnewline
29 & 0.026144627606742 & 0.052289255213484 & 0.973855372393258 \tabularnewline
30 & 0.028803860559177 & 0.057607721118354 & 0.971196139440823 \tabularnewline
31 & 0.0167266531467957 & 0.0334533062935913 & 0.983273346853204 \tabularnewline
32 & 0.00931700933892444 & 0.0186340186778489 & 0.990682990661076 \tabularnewline
33 & 0.00586057592914463 & 0.0117211518582893 & 0.994139424070855 \tabularnewline
34 & 0.00339660670568067 & 0.00679321341136133 & 0.99660339329432 \tabularnewline
35 & 0.00205673235564202 & 0.00411346471128404 & 0.997943267644358 \tabularnewline
36 & 0.00203518492023581 & 0.00407036984047161 & 0.997964815079764 \tabularnewline
37 & 0.0107939358059513 & 0.0215878716119027 & 0.989206064194049 \tabularnewline
38 & 0.0123204320531501 & 0.0246408641063003 & 0.98767956794685 \tabularnewline
39 & 0.00840116894779004 & 0.0168023378955801 & 0.99159883105221 \tabularnewline
40 & 0.00501993494564298 & 0.0100398698912860 & 0.994980065054357 \tabularnewline
41 & 0.134450777770978 & 0.268901555541956 & 0.865549222229022 \tabularnewline
42 & 0.325274228940437 & 0.650548457880874 & 0.674725771059563 \tabularnewline
43 & 0.258524734077681 & 0.517049468155363 & 0.741475265922318 \tabularnewline
44 & 0.315604326918517 & 0.631208653837034 & 0.684395673081483 \tabularnewline
45 & 0.271008937926646 & 0.542017875853292 & 0.728991062073354 \tabularnewline
46 & 0.215372496149584 & 0.430744992299168 & 0.784627503850416 \tabularnewline
47 & 0.151371393367972 & 0.302742786735944 & 0.848628606632028 \tabularnewline
48 & 0.179832645692258 & 0.359665291384516 & 0.820167354307742 \tabularnewline
49 & 0.888596439153102 & 0.222807121693797 & 0.111403560846898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&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]19[/C][C]0.394236688023268[/C][C]0.788473376046535[/C][C]0.605763311976732[/C][/ROW]
[ROW][C]20[/C][C]0.277816185168378[/C][C]0.555632370336757[/C][C]0.722183814831622[/C][/ROW]
[ROW][C]21[/C][C]0.196706716012789[/C][C]0.393413432025578[/C][C]0.80329328398721[/C][/ROW]
[ROW][C]22[/C][C]0.124075749764129[/C][C]0.248151499528258[/C][C]0.875924250235871[/C][/ROW]
[ROW][C]23[/C][C]0.134113604111581[/C][C]0.268227208223163[/C][C]0.865886395888419[/C][/ROW]
[ROW][C]24[/C][C]0.0963496738431998[/C][C]0.192699347686400[/C][C]0.9036503261568[/C][/ROW]
[ROW][C]25[/C][C]0.0537142318478846[/C][C]0.107428463695769[/C][C]0.946285768152115[/C][/ROW]
[ROW][C]26[/C][C]0.0465547232685474[/C][C]0.0931094465370949[/C][C]0.953445276731453[/C][/ROW]
[ROW][C]27[/C][C]0.037932369174464[/C][C]0.075864738348928[/C][C]0.962067630825536[/C][/ROW]
[ROW][C]28[/C][C]0.0350744192217013[/C][C]0.0701488384434025[/C][C]0.96492558077830[/C][/ROW]
[ROW][C]29[/C][C]0.026144627606742[/C][C]0.052289255213484[/C][C]0.973855372393258[/C][/ROW]
[ROW][C]30[/C][C]0.028803860559177[/C][C]0.057607721118354[/C][C]0.971196139440823[/C][/ROW]
[ROW][C]31[/C][C]0.0167266531467957[/C][C]0.0334533062935913[/C][C]0.983273346853204[/C][/ROW]
[ROW][C]32[/C][C]0.00931700933892444[/C][C]0.0186340186778489[/C][C]0.990682990661076[/C][/ROW]
[ROW][C]33[/C][C]0.00586057592914463[/C][C]0.0117211518582893[/C][C]0.994139424070855[/C][/ROW]
[ROW][C]34[/C][C]0.00339660670568067[/C][C]0.00679321341136133[/C][C]0.99660339329432[/C][/ROW]
[ROW][C]35[/C][C]0.00205673235564202[/C][C]0.00411346471128404[/C][C]0.997943267644358[/C][/ROW]
[ROW][C]36[/C][C]0.00203518492023581[/C][C]0.00407036984047161[/C][C]0.997964815079764[/C][/ROW]
[ROW][C]37[/C][C]0.0107939358059513[/C][C]0.0215878716119027[/C][C]0.989206064194049[/C][/ROW]
[ROW][C]38[/C][C]0.0123204320531501[/C][C]0.0246408641063003[/C][C]0.98767956794685[/C][/ROW]
[ROW][C]39[/C][C]0.00840116894779004[/C][C]0.0168023378955801[/C][C]0.99159883105221[/C][/ROW]
[ROW][C]40[/C][C]0.00501993494564298[/C][C]0.0100398698912860[/C][C]0.994980065054357[/C][/ROW]
[ROW][C]41[/C][C]0.134450777770978[/C][C]0.268901555541956[/C][C]0.865549222229022[/C][/ROW]
[ROW][C]42[/C][C]0.325274228940437[/C][C]0.650548457880874[/C][C]0.674725771059563[/C][/ROW]
[ROW][C]43[/C][C]0.258524734077681[/C][C]0.517049468155363[/C][C]0.741475265922318[/C][/ROW]
[ROW][C]44[/C][C]0.315604326918517[/C][C]0.631208653837034[/C][C]0.684395673081483[/C][/ROW]
[ROW][C]45[/C][C]0.271008937926646[/C][C]0.542017875853292[/C][C]0.728991062073354[/C][/ROW]
[ROW][C]46[/C][C]0.215372496149584[/C][C]0.430744992299168[/C][C]0.784627503850416[/C][/ROW]
[ROW][C]47[/C][C]0.151371393367972[/C][C]0.302742786735944[/C][C]0.848628606632028[/C][/ROW]
[ROW][C]48[/C][C]0.179832645692258[/C][C]0.359665291384516[/C][C]0.820167354307742[/C][/ROW]
[ROW][C]49[/C][C]0.888596439153102[/C][C]0.222807121693797[/C][C]0.111403560846898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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
190.3942366880232680.7884733760465350.605763311976732
200.2778161851683780.5556323703367570.722183814831622
210.1967067160127890.3934134320255780.80329328398721
220.1240757497641290.2481514995282580.875924250235871
230.1341136041115810.2682272082231630.865886395888419
240.09634967384319980.1926993476864000.9036503261568
250.05371423184788460.1074284636957690.946285768152115
260.04655472326854740.09310944653709490.953445276731453
270.0379323691744640.0758647383489280.962067630825536
280.03507441922170130.07014883844340250.96492558077830
290.0261446276067420.0522892552134840.973855372393258
300.0288038605591770.0576077211183540.971196139440823
310.01672665314679570.03345330629359130.983273346853204
320.009317009338924440.01863401867784890.990682990661076
330.005860575929144630.01172115185828930.994139424070855
340.003396606705680670.006793213411361330.99660339329432
350.002056732355642020.004113464711284040.997943267644358
360.002035184920235810.004070369840471610.997964815079764
370.01079393580595130.02158787161190270.989206064194049
380.01232043205315010.02464086410630030.98767956794685
390.008401168947790040.01680233789558010.99159883105221
400.005019934945642980.01003986989128600.994980065054357
410.1344507777709780.2689015555419560.865549222229022
420.3252742289404370.6505484578808740.674725771059563
430.2585247340776810.5170494681553630.741475265922318
440.3156043269185170.6312086538370340.684395673081483
450.2710089379266460.5420178758532920.728991062073354
460.2153724961495840.4307449922991680.784627503850416
470.1513713933679720.3027427867359440.848628606632028
480.1798326456922580.3596652913845160.820167354307742
490.8885964391531020.2228071216937970.111403560846898







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0967741935483871NOK
5% type I error level100.32258064516129NOK
10% type I error level150.483870967741935NOK

\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 & 3 & 0.0967741935483871 & NOK \tabularnewline
5% type I error level & 10 & 0.32258064516129 & NOK \tabularnewline
10% type I error level & 15 & 0.483870967741935 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58428&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]3[/C][C]0.0967741935483871[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.32258064516129[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.483870967741935[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58428&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58428&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 level30.0967741935483871NOK
5% type I error level100.32258064516129NOK
10% type I error level150.483870967741935NOK



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