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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 15 Dec 2009 04:40:48 -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/Dec/15/t1260877317w2dxeuefg3j72p9.htm/, Retrieved Wed, 08 May 2024 17:44:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67845, Retrieved Wed, 08 May 2024 17:44:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-12-14 15:04:18] [69bbb86d5181c362d5647cae31af3dc7]
-   P         [Multiple Regression] [] [2009-12-15 11:40:48] [14869f38c4320b00c96ca15cc00142de] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.9	436	443	448	460	467
106.2	431	436	443	448	460
81	484	431	436	443	448
94.7	510	484	431	436	443
101	513	510	484	431	436
109.4	503	513	510	484	431
102.3	471	503	513	510	484
90.7	471	471	503	513	510
96.2	476	471	471	503	513
96.1	475	476	471	471	503
106	470	475	476	471	471
103.1	461	470	475	476	471
102	455	461	470	475	476
104.7	456	455	461	470	475
86	517	456	455	461	470
92.1	525	517	456	455	461
106.9	523	525	517	456	455
112.6	519	523	525	517	456
101.7	509	519	523	525	517
92	512	509	519	523	525
97.4	519	512	509	519	523
97	517	519	512	509	519
105.4	510	517	519	512	509
102.7	509	510	517	519	512
98.1	501	509	510	517	519
104.5	507	501	509	510	517
87.4	569	507	501	509	510
89.9	580	569	507	501	509
109.8	578	580	569	507	501
111.7	565	578	580	569	507
98.6	547	565	578	580	569
96.9	555	547	565	578	580
95.1	562	555	547	565	578
97	561	562	555	547	565
112.7	555	561	562	555	547
102.9	544	555	561	562	555
97.4	537	544	555	561	562
111.4	543	537	544	555	561
87.4	594	543	537	544	555
96.8	611	594	543	537	544
114.1	613	611	594	543	537
110.3	611	613	611	594	543
103.9	594	611	613	611	594
101.6	595	594	611	613	611
94.6	591	595	594	611	613
95.9	589	591	595	594	611
104.7	584	589	591	595	594
102.8	573	584	589	591	595
98.1	567	573	584	589	591
113.9	569	567	573	584	589
80.9	621	569	567	573	584
95.7	629	621	569	567	573
113.2	628	629	621	569	567
105.9	612	628	629	621	569
108.8	595	612	628	629	621
102.3	597	595	612	628	629
99	593	597	595	612	628
100.7	590	593	597	595	612
115.5	580	590	593	597	595
100.7	574	580	590	593	597
109.9	573	574	580	590	593
114.6	573	573	574	580	590
85.4	620	573	573	574	580
100.5	626	620	573	573	574
114.8	620	626	620	573	573
116.5	588	620	626	620	573
112.9	566	588	620	626	620
102	557	566	588	620	626

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 335.571157600404 -1.70477710726246X[t] + 0.89890147954134`Y(t-1)`[t] + 0.0936947988015468`Y(t-2)`[t] -0.208005305165498`Y(t-3)`[t] -0.142243480596650`Y(t-4)`[t] + 0.96939388495551t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  335.571157600404 -1.70477710726246X[t] +  0.89890147954134`Y(t-1)`[t] +  0.0936947988015468`Y(t-2)`[t] -0.208005305165498`Y(t-3)`[t] -0.142243480596650`Y(t-4)`[t] +  0.96939388495551t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  335.571157600404 -1.70477710726246X[t] +  0.89890147954134`Y(t-1)`[t] +  0.0936947988015468`Y(t-2)`[t] -0.208005305165498`Y(t-3)`[t] -0.142243480596650`Y(t-4)`[t] +  0.96939388495551t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 335.571157600404 -1.70477710726246X[t] + 0.89890147954134`Y(t-1)`[t] + 0.0936947988015468`Y(t-2)`[t] -0.208005305165498`Y(t-3)`[t] -0.142243480596650`Y(t-4)`[t] + 0.96939388495551t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)335.57115760040445.2601397.414300
X-1.704777107262460.227642-7.488800
`Y(t-1)`0.898901479541340.0966749.298300
`Y(t-2)`0.09369479880154680.1514810.61850.5385330.269267
`Y(t-3)`-0.2080053051654980.138109-1.50610.1372040.068602
`Y(t-4)`-0.1422434805966500.10325-1.37770.173340.08667
t0.969393884955510.2169964.46733.5e-051.7e-05

\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) & 335.571157600404 & 45.260139 & 7.4143 & 0 & 0 \tabularnewline
X & -1.70477710726246 & 0.227642 & -7.4888 & 0 & 0 \tabularnewline
`Y(t-1)` & 0.89890147954134 & 0.096674 & 9.2983 & 0 & 0 \tabularnewline
`Y(t-2)` & 0.0936947988015468 & 0.151481 & 0.6185 & 0.538533 & 0.269267 \tabularnewline
`Y(t-3)` & -0.208005305165498 & 0.138109 & -1.5061 & 0.137204 & 0.068602 \tabularnewline
`Y(t-4)` & -0.142243480596650 & 0.10325 & -1.3777 & 0.17334 & 0.08667 \tabularnewline
t & 0.96939388495551 & 0.216996 & 4.4673 & 3.5e-05 & 1.7e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&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]335.571157600404[/C][C]45.260139[/C][C]7.4143[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.70477710726246[/C][C]0.227642[/C][C]-7.4888[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]0.89890147954134[/C][C]0.096674[/C][C]9.2983[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]0.0936947988015468[/C][C]0.151481[/C][C]0.6185[/C][C]0.538533[/C][C]0.269267[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]-0.208005305165498[/C][C]0.138109[/C][C]-1.5061[/C][C]0.137204[/C][C]0.068602[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]-0.142243480596650[/C][C]0.10325[/C][C]-1.3777[/C][C]0.17334[/C][C]0.08667[/C][/ROW]
[ROW][C]t[/C][C]0.96939388495551[/C][C]0.216996[/C][C]4.4673[/C][C]3.5e-05[/C][C]1.7e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)335.57115760040445.2601397.414300
X-1.704777107262460.227642-7.488800
`Y(t-1)`0.898901479541340.0966749.298300
`Y(t-2)`0.09369479880154680.1514810.61850.5385330.269267
`Y(t-3)`-0.2080053051654980.138109-1.50610.1372040.068602
`Y(t-4)`-0.1422434805966500.10325-1.37770.173340.08667
t0.969393884955510.2169964.46733.5e-051.7e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.975397615345982
R-squared0.951400508022629
Adjusted R-squared0.946620230123215
F-TEST (value)199.026192209315
F-TEST (DF numerator)6
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.0710486924909
Sum Squared Residuals8888.32320872567

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.975397615345982 \tabularnewline
R-squared & 0.951400508022629 \tabularnewline
Adjusted R-squared & 0.946620230123215 \tabularnewline
F-TEST (value) & 199.026192209315 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 12.0710486924909 \tabularnewline
Sum Squared Residuals & 8888.32320872567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.975397615345982[/C][/ROW]
[ROW][C]R-squared[/C][C]0.951400508022629[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.946620230123215[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]199.026192209315[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/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]12.0710486924909[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8888.32320872567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.975397615345982
R-squared0.951400508022629
Adjusted R-squared0.946620230123215
F-TEST (value)199.026192209315
F-TEST (DF numerator)6
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.0710486924909
Sum Squared Residuals8888.32320872567






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1436440.902243740456-4.90224374045607
2431431.272079739549-0.272079739548576
3484472.79843403118811.2015659688121
4510499.75294050747310.2470594925274
5513520.355232311236-7.35523231123551
6503501.8242039318621.1757960681376
7471493.242542473447-22.2425424734472
8471479.9632090583-8.96320905829952
9476470.2114179015275.78858209847298
10475483.924401466178-8.92440146617794
11470472.137865882794-2.13786588279427
12461472.422884656475-11.4228846564752
13455466.205733951722-11.2057339517219
14456457.517837587031-1.5178375870309
15517493.28656121399923.7134387860008
16525541.31172295184-16.3117229518399
17523530.60246579095-7.6024657909504
18519507.97581850014811.0241814998525
19509513.403392580776-4.40339258077575
20512520.823393181115-8.82339318111535
21519515.4632553193173.5367446806826
22517526.336981774414-9.33698177441351
23510512.642727481362-2.64272748136249
24509509.852552023586-0.852552023585738
25501516.529461776951-15.5294617769509
26507501.0438996376465.95610036235386
27569537.01254221296731.9874577870325
28580591.82033977606-11.8203397760598
29578574.4515790409233.54842095907682
30565557.6649464459747.33505355402648
31547557.986657450614-10.9866574506140
32555543.30724572551911.6927542744807
33562555.8384997897956.16150021020511
34561566.203946658889-5.20394665888889
35555541.06164228130913.9383577186910
36544550.656763160455-6.65676316045531
37537549.764647008579-12.7646470085793
38543520.93448355984422.0655164401564
39594570.69759254513623.3024074548642
40611605.0689412939635.93105870603661
41613596.35312364754416.6468763524564
42611595.72955363178515.2704463682149
43594595.208599943498-1.20859994349809
44595581.79611664487713.2038833551227
45591594.136563829723-3.13656382972284
46589593.20841350488-4.20841350488008
47584579.2133205566154.78667944338537
48573579.429671690124-6.42967169012437
49567579.040112242969-12.0401122429686
50569547.97448965613321.0255103438671
51621609.43643800682711.5635619931732
52629634.918107355607-5.91810735560679
53628618.5546935107299.44530648927072
54612620.718854359773-8.71885435977258
55595593.2075727298541.79242727014585
56597587.547633339389.45236666061956
57593597.818111421003-4.81811142100308
58590598.29315378041-8.29315378041001
59580572.9624914038637.03750859613707
60574590.440021543954-16.4400215439536
61573570.5880990147142.41190098528601
62573564.590753716638.40924628336974
63620617.9164109718082.0835890281924
64626636.453506264288-10.4535062642883
65620622.983895416908-2.98389541690800
66588606.4476787923-18.4476787923001
67566576.293778706233-10.2937787062328
68557573.465747896203-16.4657478962033

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 436 & 440.902243740456 & -4.90224374045607 \tabularnewline
2 & 431 & 431.272079739549 & -0.272079739548576 \tabularnewline
3 & 484 & 472.798434031188 & 11.2015659688121 \tabularnewline
4 & 510 & 499.752940507473 & 10.2470594925274 \tabularnewline
5 & 513 & 520.355232311236 & -7.35523231123551 \tabularnewline
6 & 503 & 501.824203931862 & 1.1757960681376 \tabularnewline
7 & 471 & 493.242542473447 & -22.2425424734472 \tabularnewline
8 & 471 & 479.9632090583 & -8.96320905829952 \tabularnewline
9 & 476 & 470.211417901527 & 5.78858209847298 \tabularnewline
10 & 475 & 483.924401466178 & -8.92440146617794 \tabularnewline
11 & 470 & 472.137865882794 & -2.13786588279427 \tabularnewline
12 & 461 & 472.422884656475 & -11.4228846564752 \tabularnewline
13 & 455 & 466.205733951722 & -11.2057339517219 \tabularnewline
14 & 456 & 457.517837587031 & -1.5178375870309 \tabularnewline
15 & 517 & 493.286561213999 & 23.7134387860008 \tabularnewline
16 & 525 & 541.31172295184 & -16.3117229518399 \tabularnewline
17 & 523 & 530.60246579095 & -7.6024657909504 \tabularnewline
18 & 519 & 507.975818500148 & 11.0241814998525 \tabularnewline
19 & 509 & 513.403392580776 & -4.40339258077575 \tabularnewline
20 & 512 & 520.823393181115 & -8.82339318111535 \tabularnewline
21 & 519 & 515.463255319317 & 3.5367446806826 \tabularnewline
22 & 517 & 526.336981774414 & -9.33698177441351 \tabularnewline
23 & 510 & 512.642727481362 & -2.64272748136249 \tabularnewline
24 & 509 & 509.852552023586 & -0.852552023585738 \tabularnewline
25 & 501 & 516.529461776951 & -15.5294617769509 \tabularnewline
26 & 507 & 501.043899637646 & 5.95610036235386 \tabularnewline
27 & 569 & 537.012542212967 & 31.9874577870325 \tabularnewline
28 & 580 & 591.82033977606 & -11.8203397760598 \tabularnewline
29 & 578 & 574.451579040923 & 3.54842095907682 \tabularnewline
30 & 565 & 557.664946445974 & 7.33505355402648 \tabularnewline
31 & 547 & 557.986657450614 & -10.9866574506140 \tabularnewline
32 & 555 & 543.307245725519 & 11.6927542744807 \tabularnewline
33 & 562 & 555.838499789795 & 6.16150021020511 \tabularnewline
34 & 561 & 566.203946658889 & -5.20394665888889 \tabularnewline
35 & 555 & 541.061642281309 & 13.9383577186910 \tabularnewline
36 & 544 & 550.656763160455 & -6.65676316045531 \tabularnewline
37 & 537 & 549.764647008579 & -12.7646470085793 \tabularnewline
38 & 543 & 520.934483559844 & 22.0655164401564 \tabularnewline
39 & 594 & 570.697592545136 & 23.3024074548642 \tabularnewline
40 & 611 & 605.068941293963 & 5.93105870603661 \tabularnewline
41 & 613 & 596.353123647544 & 16.6468763524564 \tabularnewline
42 & 611 & 595.729553631785 & 15.2704463682149 \tabularnewline
43 & 594 & 595.208599943498 & -1.20859994349809 \tabularnewline
44 & 595 & 581.796116644877 & 13.2038833551227 \tabularnewline
45 & 591 & 594.136563829723 & -3.13656382972284 \tabularnewline
46 & 589 & 593.20841350488 & -4.20841350488008 \tabularnewline
47 & 584 & 579.213320556615 & 4.78667944338537 \tabularnewline
48 & 573 & 579.429671690124 & -6.42967169012437 \tabularnewline
49 & 567 & 579.040112242969 & -12.0401122429686 \tabularnewline
50 & 569 & 547.974489656133 & 21.0255103438671 \tabularnewline
51 & 621 & 609.436438006827 & 11.5635619931732 \tabularnewline
52 & 629 & 634.918107355607 & -5.91810735560679 \tabularnewline
53 & 628 & 618.554693510729 & 9.44530648927072 \tabularnewline
54 & 612 & 620.718854359773 & -8.71885435977258 \tabularnewline
55 & 595 & 593.207572729854 & 1.79242727014585 \tabularnewline
56 & 597 & 587.54763333938 & 9.45236666061956 \tabularnewline
57 & 593 & 597.818111421003 & -4.81811142100308 \tabularnewline
58 & 590 & 598.29315378041 & -8.29315378041001 \tabularnewline
59 & 580 & 572.962491403863 & 7.03750859613707 \tabularnewline
60 & 574 & 590.440021543954 & -16.4400215439536 \tabularnewline
61 & 573 & 570.588099014714 & 2.41190098528601 \tabularnewline
62 & 573 & 564.59075371663 & 8.40924628336974 \tabularnewline
63 & 620 & 617.916410971808 & 2.0835890281924 \tabularnewline
64 & 626 & 636.453506264288 & -10.4535062642883 \tabularnewline
65 & 620 & 622.983895416908 & -2.98389541690800 \tabularnewline
66 & 588 & 606.4476787923 & -18.4476787923001 \tabularnewline
67 & 566 & 576.293778706233 & -10.2937787062328 \tabularnewline
68 & 557 & 573.465747896203 & -16.4657478962033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&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]436[/C][C]440.902243740456[/C][C]-4.90224374045607[/C][/ROW]
[ROW][C]2[/C][C]431[/C][C]431.272079739549[/C][C]-0.272079739548576[/C][/ROW]
[ROW][C]3[/C][C]484[/C][C]472.798434031188[/C][C]11.2015659688121[/C][/ROW]
[ROW][C]4[/C][C]510[/C][C]499.752940507473[/C][C]10.2470594925274[/C][/ROW]
[ROW][C]5[/C][C]513[/C][C]520.355232311236[/C][C]-7.35523231123551[/C][/ROW]
[ROW][C]6[/C][C]503[/C][C]501.824203931862[/C][C]1.1757960681376[/C][/ROW]
[ROW][C]7[/C][C]471[/C][C]493.242542473447[/C][C]-22.2425424734472[/C][/ROW]
[ROW][C]8[/C][C]471[/C][C]479.9632090583[/C][C]-8.96320905829952[/C][/ROW]
[ROW][C]9[/C][C]476[/C][C]470.211417901527[/C][C]5.78858209847298[/C][/ROW]
[ROW][C]10[/C][C]475[/C][C]483.924401466178[/C][C]-8.92440146617794[/C][/ROW]
[ROW][C]11[/C][C]470[/C][C]472.137865882794[/C][C]-2.13786588279427[/C][/ROW]
[ROW][C]12[/C][C]461[/C][C]472.422884656475[/C][C]-11.4228846564752[/C][/ROW]
[ROW][C]13[/C][C]455[/C][C]466.205733951722[/C][C]-11.2057339517219[/C][/ROW]
[ROW][C]14[/C][C]456[/C][C]457.517837587031[/C][C]-1.5178375870309[/C][/ROW]
[ROW][C]15[/C][C]517[/C][C]493.286561213999[/C][C]23.7134387860008[/C][/ROW]
[ROW][C]16[/C][C]525[/C][C]541.31172295184[/C][C]-16.3117229518399[/C][/ROW]
[ROW][C]17[/C][C]523[/C][C]530.60246579095[/C][C]-7.6024657909504[/C][/ROW]
[ROW][C]18[/C][C]519[/C][C]507.975818500148[/C][C]11.0241814998525[/C][/ROW]
[ROW][C]19[/C][C]509[/C][C]513.403392580776[/C][C]-4.40339258077575[/C][/ROW]
[ROW][C]20[/C][C]512[/C][C]520.823393181115[/C][C]-8.82339318111535[/C][/ROW]
[ROW][C]21[/C][C]519[/C][C]515.463255319317[/C][C]3.5367446806826[/C][/ROW]
[ROW][C]22[/C][C]517[/C][C]526.336981774414[/C][C]-9.33698177441351[/C][/ROW]
[ROW][C]23[/C][C]510[/C][C]512.642727481362[/C][C]-2.64272748136249[/C][/ROW]
[ROW][C]24[/C][C]509[/C][C]509.852552023586[/C][C]-0.852552023585738[/C][/ROW]
[ROW][C]25[/C][C]501[/C][C]516.529461776951[/C][C]-15.5294617769509[/C][/ROW]
[ROW][C]26[/C][C]507[/C][C]501.043899637646[/C][C]5.95610036235386[/C][/ROW]
[ROW][C]27[/C][C]569[/C][C]537.012542212967[/C][C]31.9874577870325[/C][/ROW]
[ROW][C]28[/C][C]580[/C][C]591.82033977606[/C][C]-11.8203397760598[/C][/ROW]
[ROW][C]29[/C][C]578[/C][C]574.451579040923[/C][C]3.54842095907682[/C][/ROW]
[ROW][C]30[/C][C]565[/C][C]557.664946445974[/C][C]7.33505355402648[/C][/ROW]
[ROW][C]31[/C][C]547[/C][C]557.986657450614[/C][C]-10.9866574506140[/C][/ROW]
[ROW][C]32[/C][C]555[/C][C]543.307245725519[/C][C]11.6927542744807[/C][/ROW]
[ROW][C]33[/C][C]562[/C][C]555.838499789795[/C][C]6.16150021020511[/C][/ROW]
[ROW][C]34[/C][C]561[/C][C]566.203946658889[/C][C]-5.20394665888889[/C][/ROW]
[ROW][C]35[/C][C]555[/C][C]541.061642281309[/C][C]13.9383577186910[/C][/ROW]
[ROW][C]36[/C][C]544[/C][C]550.656763160455[/C][C]-6.65676316045531[/C][/ROW]
[ROW][C]37[/C][C]537[/C][C]549.764647008579[/C][C]-12.7646470085793[/C][/ROW]
[ROW][C]38[/C][C]543[/C][C]520.934483559844[/C][C]22.0655164401564[/C][/ROW]
[ROW][C]39[/C][C]594[/C][C]570.697592545136[/C][C]23.3024074548642[/C][/ROW]
[ROW][C]40[/C][C]611[/C][C]605.068941293963[/C][C]5.93105870603661[/C][/ROW]
[ROW][C]41[/C][C]613[/C][C]596.353123647544[/C][C]16.6468763524564[/C][/ROW]
[ROW][C]42[/C][C]611[/C][C]595.729553631785[/C][C]15.2704463682149[/C][/ROW]
[ROW][C]43[/C][C]594[/C][C]595.208599943498[/C][C]-1.20859994349809[/C][/ROW]
[ROW][C]44[/C][C]595[/C][C]581.796116644877[/C][C]13.2038833551227[/C][/ROW]
[ROW][C]45[/C][C]591[/C][C]594.136563829723[/C][C]-3.13656382972284[/C][/ROW]
[ROW][C]46[/C][C]589[/C][C]593.20841350488[/C][C]-4.20841350488008[/C][/ROW]
[ROW][C]47[/C][C]584[/C][C]579.213320556615[/C][C]4.78667944338537[/C][/ROW]
[ROW][C]48[/C][C]573[/C][C]579.429671690124[/C][C]-6.42967169012437[/C][/ROW]
[ROW][C]49[/C][C]567[/C][C]579.040112242969[/C][C]-12.0401122429686[/C][/ROW]
[ROW][C]50[/C][C]569[/C][C]547.974489656133[/C][C]21.0255103438671[/C][/ROW]
[ROW][C]51[/C][C]621[/C][C]609.436438006827[/C][C]11.5635619931732[/C][/ROW]
[ROW][C]52[/C][C]629[/C][C]634.918107355607[/C][C]-5.91810735560679[/C][/ROW]
[ROW][C]53[/C][C]628[/C][C]618.554693510729[/C][C]9.44530648927072[/C][/ROW]
[ROW][C]54[/C][C]612[/C][C]620.718854359773[/C][C]-8.71885435977258[/C][/ROW]
[ROW][C]55[/C][C]595[/C][C]593.207572729854[/C][C]1.79242727014585[/C][/ROW]
[ROW][C]56[/C][C]597[/C][C]587.54763333938[/C][C]9.45236666061956[/C][/ROW]
[ROW][C]57[/C][C]593[/C][C]597.818111421003[/C][C]-4.81811142100308[/C][/ROW]
[ROW][C]58[/C][C]590[/C][C]598.29315378041[/C][C]-8.29315378041001[/C][/ROW]
[ROW][C]59[/C][C]580[/C][C]572.962491403863[/C][C]7.03750859613707[/C][/ROW]
[ROW][C]60[/C][C]574[/C][C]590.440021543954[/C][C]-16.4400215439536[/C][/ROW]
[ROW][C]61[/C][C]573[/C][C]570.588099014714[/C][C]2.41190098528601[/C][/ROW]
[ROW][C]62[/C][C]573[/C][C]564.59075371663[/C][C]8.40924628336974[/C][/ROW]
[ROW][C]63[/C][C]620[/C][C]617.916410971808[/C][C]2.0835890281924[/C][/ROW]
[ROW][C]64[/C][C]626[/C][C]636.453506264288[/C][C]-10.4535062642883[/C][/ROW]
[ROW][C]65[/C][C]620[/C][C]622.983895416908[/C][C]-2.98389541690800[/C][/ROW]
[ROW][C]66[/C][C]588[/C][C]606.4476787923[/C][C]-18.4476787923001[/C][/ROW]
[ROW][C]67[/C][C]566[/C][C]576.293778706233[/C][C]-10.2937787062328[/C][/ROW]
[ROW][C]68[/C][C]557[/C][C]573.465747896203[/C][C]-16.4657478962033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1436440.902243740456-4.90224374045607
2431431.272079739549-0.272079739548576
3484472.79843403118811.2015659688121
4510499.75294050747310.2470594925274
5513520.355232311236-7.35523231123551
6503501.8242039318621.1757960681376
7471493.242542473447-22.2425424734472
8471479.9632090583-8.96320905829952
9476470.2114179015275.78858209847298
10475483.924401466178-8.92440146617794
11470472.137865882794-2.13786588279427
12461472.422884656475-11.4228846564752
13455466.205733951722-11.2057339517219
14456457.517837587031-1.5178375870309
15517493.28656121399923.7134387860008
16525541.31172295184-16.3117229518399
17523530.60246579095-7.6024657909504
18519507.97581850014811.0241814998525
19509513.403392580776-4.40339258077575
20512520.823393181115-8.82339318111535
21519515.4632553193173.5367446806826
22517526.336981774414-9.33698177441351
23510512.642727481362-2.64272748136249
24509509.852552023586-0.852552023585738
25501516.529461776951-15.5294617769509
26507501.0438996376465.95610036235386
27569537.01254221296731.9874577870325
28580591.82033977606-11.8203397760598
29578574.4515790409233.54842095907682
30565557.6649464459747.33505355402648
31547557.986657450614-10.9866574506140
32555543.30724572551911.6927542744807
33562555.8384997897956.16150021020511
34561566.203946658889-5.20394665888889
35555541.06164228130913.9383577186910
36544550.656763160455-6.65676316045531
37537549.764647008579-12.7646470085793
38543520.93448355984422.0655164401564
39594570.69759254513623.3024074548642
40611605.0689412939635.93105870603661
41613596.35312364754416.6468763524564
42611595.72955363178515.2704463682149
43594595.208599943498-1.20859994349809
44595581.79611664487713.2038833551227
45591594.136563829723-3.13656382972284
46589593.20841350488-4.20841350488008
47584579.2133205566154.78667944338537
48573579.429671690124-6.42967169012437
49567579.040112242969-12.0401122429686
50569547.97448965613321.0255103438671
51621609.43643800682711.5635619931732
52629634.918107355607-5.91810735560679
53628618.5546935107299.44530648927072
54612620.718854359773-8.71885435977258
55595593.2075727298541.79242727014585
56597587.547633339389.45236666061956
57593597.818111421003-4.81811142100308
58590598.29315378041-8.29315378041001
59580572.9624914038637.03750859613707
60574590.440021543954-16.4400215439536
61573570.5880990147142.41190098528601
62573564.590753716638.40924628336974
63620617.9164109718082.0835890281924
64626636.453506264288-10.4535062642883
65620622.983895416908-2.98389541690800
66588606.4476787923-18.4476787923001
67566576.293778706233-10.2937787062328
68557573.465747896203-16.4657478962033






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2365104756820150.473020951364030.763489524317985
110.1651780938148370.3303561876296750.834821906185162
120.2034232412397410.4068464824794820.796576758760259
130.1518874577930230.3037749155860470.848112542206977
140.09171503166316390.1834300633263280.908284968336836
150.1615031456963570.3230062913927150.838496854303643
160.3949739133387680.7899478266775350.605026086661232
170.3459932607469640.6919865214939270.654006739253036
180.3906042446121350.781208489224270.609395755387865
190.3407798927322960.6815597854645930.659220107267704
200.2774829621406130.5549659242812260.722517037859387
210.2565136330805380.5130272661610770.743486366919462
220.2163009453441940.4326018906883870.783699054655806
230.1755880118565620.3511760237131250.824411988143438
240.1337485455869700.2674970911739400.86625145441303
250.2205962778132330.4411925556264670.779403722186767
260.2194881558276340.4389763116552680.780511844172366
270.4643914198730610.9287828397461220.535608580126939
280.5002222388370070.9995555223259860.499777761162993
290.5569685970647260.8860628058705480.443031402935274
300.4948325090558340.9896650181116670.505167490944166
310.5104231042587590.9791537914824820.489576895741241
320.5414669102143980.9170661795712040.458533089785602
330.500195413798780.999609172402440.49980458620122
340.5198875650685560.9602248698628870.480112434931444
350.5169219643707070.9661560712585850.483078035629293
360.6070984864223180.7858030271553650.392901513577682
370.9039443648203160.1921112703593680.0960556351796842
380.907860554819050.1842788903618990.0921394451809495
390.8952935467500450.2094129064999100.104706453249955
400.8643221263202530.2713557473594940.135677873679747
410.8538729308550820.2922541382898360.146127069144918
420.8331258130068420.3337483739863170.166874186993159
430.7806290941007190.4387418117985630.219370905899281
440.7767448344059870.4465103311880260.223255165594013
450.7172950457865410.5654099084269190.282704954213459
460.6908606628657150.6182786742685710.309139337134285
470.6100824441390560.7798351117218880.389917555860944
480.6829615155564750.6340769688870510.317038484443526
490.9411154475338830.1177691049322350.0588845524661173
500.9092425260411990.1815149479176030.0907574739588014
510.867069145480490.2658617090390210.132930854519510
520.8475859285540380.3048281428919230.152414071445962
530.7696193125803660.4607613748392680.230380687419634
540.721703255999370.556593488001260.27829674400063
550.6089479247799730.7821041504400550.391052075220028
560.7510088722725480.4979822554549050.248991127727452
570.7205363125370670.5589273749258650.279463687462933
580.5749999531594690.8500000936810630.425000046840531

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.236510475682015 & 0.47302095136403 & 0.763489524317985 \tabularnewline
11 & 0.165178093814837 & 0.330356187629675 & 0.834821906185162 \tabularnewline
12 & 0.203423241239741 & 0.406846482479482 & 0.796576758760259 \tabularnewline
13 & 0.151887457793023 & 0.303774915586047 & 0.848112542206977 \tabularnewline
14 & 0.0917150316631639 & 0.183430063326328 & 0.908284968336836 \tabularnewline
15 & 0.161503145696357 & 0.323006291392715 & 0.838496854303643 \tabularnewline
16 & 0.394973913338768 & 0.789947826677535 & 0.605026086661232 \tabularnewline
17 & 0.345993260746964 & 0.691986521493927 & 0.654006739253036 \tabularnewline
18 & 0.390604244612135 & 0.78120848922427 & 0.609395755387865 \tabularnewline
19 & 0.340779892732296 & 0.681559785464593 & 0.659220107267704 \tabularnewline
20 & 0.277482962140613 & 0.554965924281226 & 0.722517037859387 \tabularnewline
21 & 0.256513633080538 & 0.513027266161077 & 0.743486366919462 \tabularnewline
22 & 0.216300945344194 & 0.432601890688387 & 0.783699054655806 \tabularnewline
23 & 0.175588011856562 & 0.351176023713125 & 0.824411988143438 \tabularnewline
24 & 0.133748545586970 & 0.267497091173940 & 0.86625145441303 \tabularnewline
25 & 0.220596277813233 & 0.441192555626467 & 0.779403722186767 \tabularnewline
26 & 0.219488155827634 & 0.438976311655268 & 0.780511844172366 \tabularnewline
27 & 0.464391419873061 & 0.928782839746122 & 0.535608580126939 \tabularnewline
28 & 0.500222238837007 & 0.999555522325986 & 0.499777761162993 \tabularnewline
29 & 0.556968597064726 & 0.886062805870548 & 0.443031402935274 \tabularnewline
30 & 0.494832509055834 & 0.989665018111667 & 0.505167490944166 \tabularnewline
31 & 0.510423104258759 & 0.979153791482482 & 0.489576895741241 \tabularnewline
32 & 0.541466910214398 & 0.917066179571204 & 0.458533089785602 \tabularnewline
33 & 0.50019541379878 & 0.99960917240244 & 0.49980458620122 \tabularnewline
34 & 0.519887565068556 & 0.960224869862887 & 0.480112434931444 \tabularnewline
35 & 0.516921964370707 & 0.966156071258585 & 0.483078035629293 \tabularnewline
36 & 0.607098486422318 & 0.785803027155365 & 0.392901513577682 \tabularnewline
37 & 0.903944364820316 & 0.192111270359368 & 0.0960556351796842 \tabularnewline
38 & 0.90786055481905 & 0.184278890361899 & 0.0921394451809495 \tabularnewline
39 & 0.895293546750045 & 0.209412906499910 & 0.104706453249955 \tabularnewline
40 & 0.864322126320253 & 0.271355747359494 & 0.135677873679747 \tabularnewline
41 & 0.853872930855082 & 0.292254138289836 & 0.146127069144918 \tabularnewline
42 & 0.833125813006842 & 0.333748373986317 & 0.166874186993159 \tabularnewline
43 & 0.780629094100719 & 0.438741811798563 & 0.219370905899281 \tabularnewline
44 & 0.776744834405987 & 0.446510331188026 & 0.223255165594013 \tabularnewline
45 & 0.717295045786541 & 0.565409908426919 & 0.282704954213459 \tabularnewline
46 & 0.690860662865715 & 0.618278674268571 & 0.309139337134285 \tabularnewline
47 & 0.610082444139056 & 0.779835111721888 & 0.389917555860944 \tabularnewline
48 & 0.682961515556475 & 0.634076968887051 & 0.317038484443526 \tabularnewline
49 & 0.941115447533883 & 0.117769104932235 & 0.0588845524661173 \tabularnewline
50 & 0.909242526041199 & 0.181514947917603 & 0.0907574739588014 \tabularnewline
51 & 0.86706914548049 & 0.265861709039021 & 0.132930854519510 \tabularnewline
52 & 0.847585928554038 & 0.304828142891923 & 0.152414071445962 \tabularnewline
53 & 0.769619312580366 & 0.460761374839268 & 0.230380687419634 \tabularnewline
54 & 0.72170325599937 & 0.55659348800126 & 0.27829674400063 \tabularnewline
55 & 0.608947924779973 & 0.782104150440055 & 0.391052075220028 \tabularnewline
56 & 0.751008872272548 & 0.497982255454905 & 0.248991127727452 \tabularnewline
57 & 0.720536312537067 & 0.558927374925865 & 0.279463687462933 \tabularnewline
58 & 0.574999953159469 & 0.850000093681063 & 0.425000046840531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.236510475682015[/C][C]0.47302095136403[/C][C]0.763489524317985[/C][/ROW]
[ROW][C]11[/C][C]0.165178093814837[/C][C]0.330356187629675[/C][C]0.834821906185162[/C][/ROW]
[ROW][C]12[/C][C]0.203423241239741[/C][C]0.406846482479482[/C][C]0.796576758760259[/C][/ROW]
[ROW][C]13[/C][C]0.151887457793023[/C][C]0.303774915586047[/C][C]0.848112542206977[/C][/ROW]
[ROW][C]14[/C][C]0.0917150316631639[/C][C]0.183430063326328[/C][C]0.908284968336836[/C][/ROW]
[ROW][C]15[/C][C]0.161503145696357[/C][C]0.323006291392715[/C][C]0.838496854303643[/C][/ROW]
[ROW][C]16[/C][C]0.394973913338768[/C][C]0.789947826677535[/C][C]0.605026086661232[/C][/ROW]
[ROW][C]17[/C][C]0.345993260746964[/C][C]0.691986521493927[/C][C]0.654006739253036[/C][/ROW]
[ROW][C]18[/C][C]0.390604244612135[/C][C]0.78120848922427[/C][C]0.609395755387865[/C][/ROW]
[ROW][C]19[/C][C]0.340779892732296[/C][C]0.681559785464593[/C][C]0.659220107267704[/C][/ROW]
[ROW][C]20[/C][C]0.277482962140613[/C][C]0.554965924281226[/C][C]0.722517037859387[/C][/ROW]
[ROW][C]21[/C][C]0.256513633080538[/C][C]0.513027266161077[/C][C]0.743486366919462[/C][/ROW]
[ROW][C]22[/C][C]0.216300945344194[/C][C]0.432601890688387[/C][C]0.783699054655806[/C][/ROW]
[ROW][C]23[/C][C]0.175588011856562[/C][C]0.351176023713125[/C][C]0.824411988143438[/C][/ROW]
[ROW][C]24[/C][C]0.133748545586970[/C][C]0.267497091173940[/C][C]0.86625145441303[/C][/ROW]
[ROW][C]25[/C][C]0.220596277813233[/C][C]0.441192555626467[/C][C]0.779403722186767[/C][/ROW]
[ROW][C]26[/C][C]0.219488155827634[/C][C]0.438976311655268[/C][C]0.780511844172366[/C][/ROW]
[ROW][C]27[/C][C]0.464391419873061[/C][C]0.928782839746122[/C][C]0.535608580126939[/C][/ROW]
[ROW][C]28[/C][C]0.500222238837007[/C][C]0.999555522325986[/C][C]0.499777761162993[/C][/ROW]
[ROW][C]29[/C][C]0.556968597064726[/C][C]0.886062805870548[/C][C]0.443031402935274[/C][/ROW]
[ROW][C]30[/C][C]0.494832509055834[/C][C]0.989665018111667[/C][C]0.505167490944166[/C][/ROW]
[ROW][C]31[/C][C]0.510423104258759[/C][C]0.979153791482482[/C][C]0.489576895741241[/C][/ROW]
[ROW][C]32[/C][C]0.541466910214398[/C][C]0.917066179571204[/C][C]0.458533089785602[/C][/ROW]
[ROW][C]33[/C][C]0.50019541379878[/C][C]0.99960917240244[/C][C]0.49980458620122[/C][/ROW]
[ROW][C]34[/C][C]0.519887565068556[/C][C]0.960224869862887[/C][C]0.480112434931444[/C][/ROW]
[ROW][C]35[/C][C]0.516921964370707[/C][C]0.966156071258585[/C][C]0.483078035629293[/C][/ROW]
[ROW][C]36[/C][C]0.607098486422318[/C][C]0.785803027155365[/C][C]0.392901513577682[/C][/ROW]
[ROW][C]37[/C][C]0.903944364820316[/C][C]0.192111270359368[/C][C]0.0960556351796842[/C][/ROW]
[ROW][C]38[/C][C]0.90786055481905[/C][C]0.184278890361899[/C][C]0.0921394451809495[/C][/ROW]
[ROW][C]39[/C][C]0.895293546750045[/C][C]0.209412906499910[/C][C]0.104706453249955[/C][/ROW]
[ROW][C]40[/C][C]0.864322126320253[/C][C]0.271355747359494[/C][C]0.135677873679747[/C][/ROW]
[ROW][C]41[/C][C]0.853872930855082[/C][C]0.292254138289836[/C][C]0.146127069144918[/C][/ROW]
[ROW][C]42[/C][C]0.833125813006842[/C][C]0.333748373986317[/C][C]0.166874186993159[/C][/ROW]
[ROW][C]43[/C][C]0.780629094100719[/C][C]0.438741811798563[/C][C]0.219370905899281[/C][/ROW]
[ROW][C]44[/C][C]0.776744834405987[/C][C]0.446510331188026[/C][C]0.223255165594013[/C][/ROW]
[ROW][C]45[/C][C]0.717295045786541[/C][C]0.565409908426919[/C][C]0.282704954213459[/C][/ROW]
[ROW][C]46[/C][C]0.690860662865715[/C][C]0.618278674268571[/C][C]0.309139337134285[/C][/ROW]
[ROW][C]47[/C][C]0.610082444139056[/C][C]0.779835111721888[/C][C]0.389917555860944[/C][/ROW]
[ROW][C]48[/C][C]0.682961515556475[/C][C]0.634076968887051[/C][C]0.317038484443526[/C][/ROW]
[ROW][C]49[/C][C]0.941115447533883[/C][C]0.117769104932235[/C][C]0.0588845524661173[/C][/ROW]
[ROW][C]50[/C][C]0.909242526041199[/C][C]0.181514947917603[/C][C]0.0907574739588014[/C][/ROW]
[ROW][C]51[/C][C]0.86706914548049[/C][C]0.265861709039021[/C][C]0.132930854519510[/C][/ROW]
[ROW][C]52[/C][C]0.847585928554038[/C][C]0.304828142891923[/C][C]0.152414071445962[/C][/ROW]
[ROW][C]53[/C][C]0.769619312580366[/C][C]0.460761374839268[/C][C]0.230380687419634[/C][/ROW]
[ROW][C]54[/C][C]0.72170325599937[/C][C]0.55659348800126[/C][C]0.27829674400063[/C][/ROW]
[ROW][C]55[/C][C]0.608947924779973[/C][C]0.782104150440055[/C][C]0.391052075220028[/C][/ROW]
[ROW][C]56[/C][C]0.751008872272548[/C][C]0.497982255454905[/C][C]0.248991127727452[/C][/ROW]
[ROW][C]57[/C][C]0.720536312537067[/C][C]0.558927374925865[/C][C]0.279463687462933[/C][/ROW]
[ROW][C]58[/C][C]0.574999953159469[/C][C]0.850000093681063[/C][C]0.425000046840531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2365104756820150.473020951364030.763489524317985
110.1651780938148370.3303561876296750.834821906185162
120.2034232412397410.4068464824794820.796576758760259
130.1518874577930230.3037749155860470.848112542206977
140.09171503166316390.1834300633263280.908284968336836
150.1615031456963570.3230062913927150.838496854303643
160.3949739133387680.7899478266775350.605026086661232
170.3459932607469640.6919865214939270.654006739253036
180.3906042446121350.781208489224270.609395755387865
190.3407798927322960.6815597854645930.659220107267704
200.2774829621406130.5549659242812260.722517037859387
210.2565136330805380.5130272661610770.743486366919462
220.2163009453441940.4326018906883870.783699054655806
230.1755880118565620.3511760237131250.824411988143438
240.1337485455869700.2674970911739400.86625145441303
250.2205962778132330.4411925556264670.779403722186767
260.2194881558276340.4389763116552680.780511844172366
270.4643914198730610.9287828397461220.535608580126939
280.5002222388370070.9995555223259860.499777761162993
290.5569685970647260.8860628058705480.443031402935274
300.4948325090558340.9896650181116670.505167490944166
310.5104231042587590.9791537914824820.489576895741241
320.5414669102143980.9170661795712040.458533089785602
330.500195413798780.999609172402440.49980458620122
340.5198875650685560.9602248698628870.480112434931444
350.5169219643707070.9661560712585850.483078035629293
360.6070984864223180.7858030271553650.392901513577682
370.9039443648203160.1921112703593680.0960556351796842
380.907860554819050.1842788903618990.0921394451809495
390.8952935467500450.2094129064999100.104706453249955
400.8643221263202530.2713557473594940.135677873679747
410.8538729308550820.2922541382898360.146127069144918
420.8331258130068420.3337483739863170.166874186993159
430.7806290941007190.4387418117985630.219370905899281
440.7767448344059870.4465103311880260.223255165594013
450.7172950457865410.5654099084269190.282704954213459
460.6908606628657150.6182786742685710.309139337134285
470.6100824441390560.7798351117218880.389917555860944
480.6829615155564750.6340769688870510.317038484443526
490.9411154475338830.1177691049322350.0588845524661173
500.9092425260411990.1815149479176030.0907574739588014
510.867069145480490.2658617090390210.132930854519510
520.8475859285540380.3048281428919230.152414071445962
530.7696193125803660.4607613748392680.230380687419634
540.721703255999370.556593488001260.27829674400063
550.6089479247799730.7821041504400550.391052075220028
560.7510088722725480.4979822554549050.248991127727452
570.7205363125370670.5589273749258650.279463687462933
580.5749999531594690.8500000936810630.425000046840531






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67845&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67845&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67845&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal 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')
}