Free Statistics

of Irreproducible Research!

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, 23 Nov 2010 10:33:31 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290508372v6lg37u8bd4z7mi.htm/, Retrieved Fri, 29 Mar 2024 10:45:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98909, Retrieved Fri, 29 Mar 2024 10:45:02 +0000
QR Codes:

Original text written by user:
IsPrivate?This computation is/was private until 2010-11-24
User-defined keywords
Estimated Impact105
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Linear Regression...] [2010-11-23 10:33:31] [278a0539dc236556c5f30b5bc56ff9eb] [Current]
Feedback Forum

Post a new message
Dataseries X:
55	1
20	0
80	0
52	0
75	1
30	1
90	1
68	1
24	0
60	0
65	1
60	0
80	1
65	1
90	0
65	1
76	1
70	1
38	0
60	1
10	0
5	0
93	1
70	0
61	1
72	1
40	0
75	1
100	1
29	0
70	1
25	0
70	1
82	0
40	0
50	1
70	1
91	1
10	0
25	0
56	0
30	0
74	0
60	0
80	0
80	1
60	1
64	1
40	1
80	1
71	1
65	1
90	0
68	1
76	1
25	1
79	1
40	0
61	1
27	1
70	0
40	0
13	0
15	0
38	1
47	0
65	1
62	1
50	0
80	1
87	1
40	1
80	1
20	0
60	1
48	1
70	1
91	1
10	0
50	0
70	0
45	1
20	1
10	0
90	1
80	1
74	0
71	0
40	0
29	1
60	1
31	0
67	0
82	0
40	1
30	1
70	0
63	0
35	0
35	1
70	0
60	1
80	1
70	1
71	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Q1[t] = + 50.0313369097804 + 16.4922088057442gender[t] -0.0467799991133598t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Q1[t] =  +  50.0313369097804 +  16.4922088057442gender[t] -0.0467799991133598t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Q1[t] =  +  50.0313369097804 +  16.4922088057442gender[t] -0.0467799991133598t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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
Q1[t] = + 50.0313369097804 + 16.4922088057442gender[t] -0.0467799991133598t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)50.03133690978044.9190210.17100
gender16.49220880574424.3034113.83240.000220.00011
t-0.04677999911335980.070445-0.66410.5081470.254073

\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) & 50.0313369097804 & 4.91902 & 10.171 & 0 & 0 \tabularnewline
gender & 16.4922088057442 & 4.303411 & 3.8324 & 0.00022 & 0.00011 \tabularnewline
t & -0.0467799991133598 & 0.070445 & -0.6641 & 0.508147 & 0.254073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&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]50.0313369097804[/C][C]4.91902[/C][C]10.171[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gender[/C][C]16.4922088057442[/C][C]4.303411[/C][C]3.8324[/C][C]0.00022[/C][C]0.00011[/C][/ROW]
[ROW][C]t[/C][C]-0.0467799991133598[/C][C]0.070445[/C][C]-0.6641[/C][C]0.508147[/C][C]0.254073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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)50.03133690978044.9190210.17100
gender16.49220880574424.3034113.83240.000220.00011
t-0.04677999911335980.070445-0.66410.5081470.254073







Multiple Linear Regression - Regression Statistics
Multiple R0.358961497027954
R-squared0.128853356348550
Adjusted R-squared0.111772049610286
F-TEST (value)7.54353038224565
F-TEST (DF numerator)2
F-TEST (DF denominator)102
p-value0.000880348550419296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.8780400356169
Sum Squared Residuals48822.1608516056

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.358961497027954 \tabularnewline
R-squared & 0.128853356348550 \tabularnewline
Adjusted R-squared & 0.111772049610286 \tabularnewline
F-TEST (value) & 7.54353038224565 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 102 \tabularnewline
p-value & 0.000880348550419296 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 21.8780400356169 \tabularnewline
Sum Squared Residuals & 48822.1608516056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.358961497027954[/C][/ROW]
[ROW][C]R-squared[/C][C]0.128853356348550[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.111772049610286[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.54353038224565[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]102[/C][/ROW]
[ROW][C]p-value[/C][C]0.000880348550419296[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]21.8780400356169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]48822.1608516056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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 R0.358961497027954
R-squared0.128853356348550
Adjusted R-squared0.111772049610286
F-TEST (value)7.54353038224565
F-TEST (DF numerator)2
F-TEST (DF denominator)102
p-value0.000880348550419296
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.8780400356169
Sum Squared Residuals48822.1608516056







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15566.476765716411-11.4767657164110
22049.9377769115537-29.9377769115537
38049.890996912440330.1090030875597
45249.84421691332692.15578308667306
57566.28964571995788.71035428004222
63066.2428657208444-36.2428657208444
79066.19608572173123.8039142782689
86866.14930572261771.85069427738230
92449.6103169177601-25.6103169177601
106049.563536918646810.4364630813532
116566.0089657252776-1.00896572527762
126049.469976920420110.5300230795799
138065.915405727050914.0845942729491
146565.8686257279375-0.868625727937538
159049.3296369230840.67036307692
166565.7750657297108-0.775065729710819
177665.728285730597510.2717142694025
187065.68150573148414.3184942685159
193849.1425169266265-11.1425169266265
206065.5879457332574-5.58794573325738
211049.0489569283998-39.0489569283998
22549.0021769292865-44.0021769292865
239365.447605735917327.5523942640827
247048.908616931059721.0913830689403
256165.3540457376906-4.35404573769058
267265.30726573857726.69273426142278
274048.7682769337197-8.76827693371967
287565.21370574035059.7862942596495
2910065.166925741237234.8330742587629
302948.6279369363796-19.6279369363796
317065.07336574301044.92663425698958
322548.5343769381529-23.5343769381529
337064.97980574478375.0201942552163
348248.440816939926233.5591830600738
354048.3940369408128-8.39403694081279
365064.8394657474436-14.8394657474436
377064.79268574833035.20731425166974
389164.745905749216926.2540942507831
391048.2069169443594-38.2069169443594
402548.160136945246-23.160136945246
415648.11335694613267.88664305386737
423048.0665769470193-18.0665769470193
437448.019796947905925.9802030520941
446047.973016948792512.0269830512074
458047.926236949679232.0737630503208
468064.3716657563115.6283342436900
476064.3248857571967-4.32488575719667
486464.2781057580833-0.278105758083305
494064.23132575897-24.2313257589699
508064.184545759856615.8154542401434
517164.13776576074326.86223423925677
526564.09098576162990.909014238370135
539047.551996956772342.4480030432277
546863.99742576340314.00257423659685
557663.950645764289812.0493542357102
562563.9038657651764-38.9038657651764
577963.857085766063115.1429142339369
584047.3180969612055-7.31809696120551
596163.7635257678363-2.76352576783635
602763.716745768723-36.716745768723
617047.177756963865422.8222430361346
624047.1309769647521-7.13097696475207
631347.0841969656387-34.0841969656387
641547.0374169665254-32.0374169665254
653863.4828457731562-25.4828457731562
664746.94385696829860.0561430317013662
676563.38928577492951.61071422507053
686263.3425057758161-1.34250577581611
695046.80351697095863.19648302904144
708063.248945777589416.7510542224106
718763.20216577847623.7978342215240
724063.1553857793627-23.1553857793627
738063.108605780249316.8913942197507
742046.5696169753918-26.5696169753918
756063.0150457820226-3.01504578202259
764862.9682657829092-14.9682657829092
777062.92148578379597.07851421620413
789162.874705784682528.1252942153175
791046.335716979825-36.3357169798250
805046.28893698071163.7110630192884
817046.242156981598223.7578430184018
824562.6875857882291-17.6875857882291
832062.6408057891157-42.6408057891157
841046.1018169842582-36.1018169842582
859062.54724579088927.452754209111
868062.500465791775617.4995342082244
877445.961476986918128.0385230130819
887145.914696987804725.0853030121953
894045.8679169886914-5.86791698869136
902962.3133457953222-33.3133457953222
916062.2665657962088-2.26656579620883
923145.7275769913513-14.7275769913513
936745.680796992237921.3192030077621
948245.634016993124636.3659830068754
954062.0794457997554-22.0794457997554
963062.032665800642-32.032665800642
977045.493676995784524.5063230042155
986345.446896996671117.5531030033289
993545.4001169975578-10.4001169975578
1003561.8455458041886-26.8455458041886
1017045.30655699933124.6934430006690
1026061.7519858059619-1.75198580596188
1038061.705205806848518.2947941931515
1047061.65842580773528.34157419226484
1057161.61164580862189.3883541913782

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 55 & 66.476765716411 & -11.4767657164110 \tabularnewline
2 & 20 & 49.9377769115537 & -29.9377769115537 \tabularnewline
3 & 80 & 49.8909969124403 & 30.1090030875597 \tabularnewline
4 & 52 & 49.8442169133269 & 2.15578308667306 \tabularnewline
5 & 75 & 66.2896457199578 & 8.71035428004222 \tabularnewline
6 & 30 & 66.2428657208444 & -36.2428657208444 \tabularnewline
7 & 90 & 66.196085721731 & 23.8039142782689 \tabularnewline
8 & 68 & 66.1493057226177 & 1.85069427738230 \tabularnewline
9 & 24 & 49.6103169177601 & -25.6103169177601 \tabularnewline
10 & 60 & 49.5635369186468 & 10.4364630813532 \tabularnewline
11 & 65 & 66.0089657252776 & -1.00896572527762 \tabularnewline
12 & 60 & 49.4699769204201 & 10.5300230795799 \tabularnewline
13 & 80 & 65.9154057270509 & 14.0845942729491 \tabularnewline
14 & 65 & 65.8686257279375 & -0.868625727937538 \tabularnewline
15 & 90 & 49.32963692308 & 40.67036307692 \tabularnewline
16 & 65 & 65.7750657297108 & -0.775065729710819 \tabularnewline
17 & 76 & 65.7282857305975 & 10.2717142694025 \tabularnewline
18 & 70 & 65.6815057314841 & 4.3184942685159 \tabularnewline
19 & 38 & 49.1425169266265 & -11.1425169266265 \tabularnewline
20 & 60 & 65.5879457332574 & -5.58794573325738 \tabularnewline
21 & 10 & 49.0489569283998 & -39.0489569283998 \tabularnewline
22 & 5 & 49.0021769292865 & -44.0021769292865 \tabularnewline
23 & 93 & 65.4476057359173 & 27.5523942640827 \tabularnewline
24 & 70 & 48.9086169310597 & 21.0913830689403 \tabularnewline
25 & 61 & 65.3540457376906 & -4.35404573769058 \tabularnewline
26 & 72 & 65.3072657385772 & 6.69273426142278 \tabularnewline
27 & 40 & 48.7682769337197 & -8.76827693371967 \tabularnewline
28 & 75 & 65.2137057403505 & 9.7862942596495 \tabularnewline
29 & 100 & 65.1669257412372 & 34.8330742587629 \tabularnewline
30 & 29 & 48.6279369363796 & -19.6279369363796 \tabularnewline
31 & 70 & 65.0733657430104 & 4.92663425698958 \tabularnewline
32 & 25 & 48.5343769381529 & -23.5343769381529 \tabularnewline
33 & 70 & 64.9798057447837 & 5.0201942552163 \tabularnewline
34 & 82 & 48.4408169399262 & 33.5591830600738 \tabularnewline
35 & 40 & 48.3940369408128 & -8.39403694081279 \tabularnewline
36 & 50 & 64.8394657474436 & -14.8394657474436 \tabularnewline
37 & 70 & 64.7926857483303 & 5.20731425166974 \tabularnewline
38 & 91 & 64.7459057492169 & 26.2540942507831 \tabularnewline
39 & 10 & 48.2069169443594 & -38.2069169443594 \tabularnewline
40 & 25 & 48.160136945246 & -23.160136945246 \tabularnewline
41 & 56 & 48.1133569461326 & 7.88664305386737 \tabularnewline
42 & 30 & 48.0665769470193 & -18.0665769470193 \tabularnewline
43 & 74 & 48.0197969479059 & 25.9802030520941 \tabularnewline
44 & 60 & 47.9730169487925 & 12.0269830512074 \tabularnewline
45 & 80 & 47.9262369496792 & 32.0737630503208 \tabularnewline
46 & 80 & 64.37166575631 & 15.6283342436900 \tabularnewline
47 & 60 & 64.3248857571967 & -4.32488575719667 \tabularnewline
48 & 64 & 64.2781057580833 & -0.278105758083305 \tabularnewline
49 & 40 & 64.23132575897 & -24.2313257589699 \tabularnewline
50 & 80 & 64.1845457598566 & 15.8154542401434 \tabularnewline
51 & 71 & 64.1377657607432 & 6.86223423925677 \tabularnewline
52 & 65 & 64.0909857616299 & 0.909014238370135 \tabularnewline
53 & 90 & 47.5519969567723 & 42.4480030432277 \tabularnewline
54 & 68 & 63.9974257634031 & 4.00257423659685 \tabularnewline
55 & 76 & 63.9506457642898 & 12.0493542357102 \tabularnewline
56 & 25 & 63.9038657651764 & -38.9038657651764 \tabularnewline
57 & 79 & 63.8570857660631 & 15.1429142339369 \tabularnewline
58 & 40 & 47.3180969612055 & -7.31809696120551 \tabularnewline
59 & 61 & 63.7635257678363 & -2.76352576783635 \tabularnewline
60 & 27 & 63.716745768723 & -36.716745768723 \tabularnewline
61 & 70 & 47.1777569638654 & 22.8222430361346 \tabularnewline
62 & 40 & 47.1309769647521 & -7.13097696475207 \tabularnewline
63 & 13 & 47.0841969656387 & -34.0841969656387 \tabularnewline
64 & 15 & 47.0374169665254 & -32.0374169665254 \tabularnewline
65 & 38 & 63.4828457731562 & -25.4828457731562 \tabularnewline
66 & 47 & 46.9438569682986 & 0.0561430317013662 \tabularnewline
67 & 65 & 63.3892857749295 & 1.61071422507053 \tabularnewline
68 & 62 & 63.3425057758161 & -1.34250577581611 \tabularnewline
69 & 50 & 46.8035169709586 & 3.19648302904144 \tabularnewline
70 & 80 & 63.2489457775894 & 16.7510542224106 \tabularnewline
71 & 87 & 63.202165778476 & 23.7978342215240 \tabularnewline
72 & 40 & 63.1553857793627 & -23.1553857793627 \tabularnewline
73 & 80 & 63.1086057802493 & 16.8913942197507 \tabularnewline
74 & 20 & 46.5696169753918 & -26.5696169753918 \tabularnewline
75 & 60 & 63.0150457820226 & -3.01504578202259 \tabularnewline
76 & 48 & 62.9682657829092 & -14.9682657829092 \tabularnewline
77 & 70 & 62.9214857837959 & 7.07851421620413 \tabularnewline
78 & 91 & 62.8747057846825 & 28.1252942153175 \tabularnewline
79 & 10 & 46.335716979825 & -36.3357169798250 \tabularnewline
80 & 50 & 46.2889369807116 & 3.7110630192884 \tabularnewline
81 & 70 & 46.2421569815982 & 23.7578430184018 \tabularnewline
82 & 45 & 62.6875857882291 & -17.6875857882291 \tabularnewline
83 & 20 & 62.6408057891157 & -42.6408057891157 \tabularnewline
84 & 10 & 46.1018169842582 & -36.1018169842582 \tabularnewline
85 & 90 & 62.547245790889 & 27.452754209111 \tabularnewline
86 & 80 & 62.5004657917756 & 17.4995342082244 \tabularnewline
87 & 74 & 45.9614769869181 & 28.0385230130819 \tabularnewline
88 & 71 & 45.9146969878047 & 25.0853030121953 \tabularnewline
89 & 40 & 45.8679169886914 & -5.86791698869136 \tabularnewline
90 & 29 & 62.3133457953222 & -33.3133457953222 \tabularnewline
91 & 60 & 62.2665657962088 & -2.26656579620883 \tabularnewline
92 & 31 & 45.7275769913513 & -14.7275769913513 \tabularnewline
93 & 67 & 45.6807969922379 & 21.3192030077621 \tabularnewline
94 & 82 & 45.6340169931246 & 36.3659830068754 \tabularnewline
95 & 40 & 62.0794457997554 & -22.0794457997554 \tabularnewline
96 & 30 & 62.032665800642 & -32.032665800642 \tabularnewline
97 & 70 & 45.4936769957845 & 24.5063230042155 \tabularnewline
98 & 63 & 45.4468969966711 & 17.5531030033289 \tabularnewline
99 & 35 & 45.4001169975578 & -10.4001169975578 \tabularnewline
100 & 35 & 61.8455458041886 & -26.8455458041886 \tabularnewline
101 & 70 & 45.306556999331 & 24.6934430006690 \tabularnewline
102 & 60 & 61.7519858059619 & -1.75198580596188 \tabularnewline
103 & 80 & 61.7052058068485 & 18.2947941931515 \tabularnewline
104 & 70 & 61.6584258077352 & 8.34157419226484 \tabularnewline
105 & 71 & 61.6116458086218 & 9.3883541913782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&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]55[/C][C]66.476765716411[/C][C]-11.4767657164110[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]49.9377769115537[/C][C]-29.9377769115537[/C][/ROW]
[ROW][C]3[/C][C]80[/C][C]49.8909969124403[/C][C]30.1090030875597[/C][/ROW]
[ROW][C]4[/C][C]52[/C][C]49.8442169133269[/C][C]2.15578308667306[/C][/ROW]
[ROW][C]5[/C][C]75[/C][C]66.2896457199578[/C][C]8.71035428004222[/C][/ROW]
[ROW][C]6[/C][C]30[/C][C]66.2428657208444[/C][C]-36.2428657208444[/C][/ROW]
[ROW][C]7[/C][C]90[/C][C]66.196085721731[/C][C]23.8039142782689[/C][/ROW]
[ROW][C]8[/C][C]68[/C][C]66.1493057226177[/C][C]1.85069427738230[/C][/ROW]
[ROW][C]9[/C][C]24[/C][C]49.6103169177601[/C][C]-25.6103169177601[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]49.5635369186468[/C][C]10.4364630813532[/C][/ROW]
[ROW][C]11[/C][C]65[/C][C]66.0089657252776[/C][C]-1.00896572527762[/C][/ROW]
[ROW][C]12[/C][C]60[/C][C]49.4699769204201[/C][C]10.5300230795799[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]65.9154057270509[/C][C]14.0845942729491[/C][/ROW]
[ROW][C]14[/C][C]65[/C][C]65.8686257279375[/C][C]-0.868625727937538[/C][/ROW]
[ROW][C]15[/C][C]90[/C][C]49.32963692308[/C][C]40.67036307692[/C][/ROW]
[ROW][C]16[/C][C]65[/C][C]65.7750657297108[/C][C]-0.775065729710819[/C][/ROW]
[ROW][C]17[/C][C]76[/C][C]65.7282857305975[/C][C]10.2717142694025[/C][/ROW]
[ROW][C]18[/C][C]70[/C][C]65.6815057314841[/C][C]4.3184942685159[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]49.1425169266265[/C][C]-11.1425169266265[/C][/ROW]
[ROW][C]20[/C][C]60[/C][C]65.5879457332574[/C][C]-5.58794573325738[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]49.0489569283998[/C][C]-39.0489569283998[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]49.0021769292865[/C][C]-44.0021769292865[/C][/ROW]
[ROW][C]23[/C][C]93[/C][C]65.4476057359173[/C][C]27.5523942640827[/C][/ROW]
[ROW][C]24[/C][C]70[/C][C]48.9086169310597[/C][C]21.0913830689403[/C][/ROW]
[ROW][C]25[/C][C]61[/C][C]65.3540457376906[/C][C]-4.35404573769058[/C][/ROW]
[ROW][C]26[/C][C]72[/C][C]65.3072657385772[/C][C]6.69273426142278[/C][/ROW]
[ROW][C]27[/C][C]40[/C][C]48.7682769337197[/C][C]-8.76827693371967[/C][/ROW]
[ROW][C]28[/C][C]75[/C][C]65.2137057403505[/C][C]9.7862942596495[/C][/ROW]
[ROW][C]29[/C][C]100[/C][C]65.1669257412372[/C][C]34.8330742587629[/C][/ROW]
[ROW][C]30[/C][C]29[/C][C]48.6279369363796[/C][C]-19.6279369363796[/C][/ROW]
[ROW][C]31[/C][C]70[/C][C]65.0733657430104[/C][C]4.92663425698958[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]48.5343769381529[/C][C]-23.5343769381529[/C][/ROW]
[ROW][C]33[/C][C]70[/C][C]64.9798057447837[/C][C]5.0201942552163[/C][/ROW]
[ROW][C]34[/C][C]82[/C][C]48.4408169399262[/C][C]33.5591830600738[/C][/ROW]
[ROW][C]35[/C][C]40[/C][C]48.3940369408128[/C][C]-8.39403694081279[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]64.8394657474436[/C][C]-14.8394657474436[/C][/ROW]
[ROW][C]37[/C][C]70[/C][C]64.7926857483303[/C][C]5.20731425166974[/C][/ROW]
[ROW][C]38[/C][C]91[/C][C]64.7459057492169[/C][C]26.2540942507831[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]48.2069169443594[/C][C]-38.2069169443594[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]48.160136945246[/C][C]-23.160136945246[/C][/ROW]
[ROW][C]41[/C][C]56[/C][C]48.1133569461326[/C][C]7.88664305386737[/C][/ROW]
[ROW][C]42[/C][C]30[/C][C]48.0665769470193[/C][C]-18.0665769470193[/C][/ROW]
[ROW][C]43[/C][C]74[/C][C]48.0197969479059[/C][C]25.9802030520941[/C][/ROW]
[ROW][C]44[/C][C]60[/C][C]47.9730169487925[/C][C]12.0269830512074[/C][/ROW]
[ROW][C]45[/C][C]80[/C][C]47.9262369496792[/C][C]32.0737630503208[/C][/ROW]
[ROW][C]46[/C][C]80[/C][C]64.37166575631[/C][C]15.6283342436900[/C][/ROW]
[ROW][C]47[/C][C]60[/C][C]64.3248857571967[/C][C]-4.32488575719667[/C][/ROW]
[ROW][C]48[/C][C]64[/C][C]64.2781057580833[/C][C]-0.278105758083305[/C][/ROW]
[ROW][C]49[/C][C]40[/C][C]64.23132575897[/C][C]-24.2313257589699[/C][/ROW]
[ROW][C]50[/C][C]80[/C][C]64.1845457598566[/C][C]15.8154542401434[/C][/ROW]
[ROW][C]51[/C][C]71[/C][C]64.1377657607432[/C][C]6.86223423925677[/C][/ROW]
[ROW][C]52[/C][C]65[/C][C]64.0909857616299[/C][C]0.909014238370135[/C][/ROW]
[ROW][C]53[/C][C]90[/C][C]47.5519969567723[/C][C]42.4480030432277[/C][/ROW]
[ROW][C]54[/C][C]68[/C][C]63.9974257634031[/C][C]4.00257423659685[/C][/ROW]
[ROW][C]55[/C][C]76[/C][C]63.9506457642898[/C][C]12.0493542357102[/C][/ROW]
[ROW][C]56[/C][C]25[/C][C]63.9038657651764[/C][C]-38.9038657651764[/C][/ROW]
[ROW][C]57[/C][C]79[/C][C]63.8570857660631[/C][C]15.1429142339369[/C][/ROW]
[ROW][C]58[/C][C]40[/C][C]47.3180969612055[/C][C]-7.31809696120551[/C][/ROW]
[ROW][C]59[/C][C]61[/C][C]63.7635257678363[/C][C]-2.76352576783635[/C][/ROW]
[ROW][C]60[/C][C]27[/C][C]63.716745768723[/C][C]-36.716745768723[/C][/ROW]
[ROW][C]61[/C][C]70[/C][C]47.1777569638654[/C][C]22.8222430361346[/C][/ROW]
[ROW][C]62[/C][C]40[/C][C]47.1309769647521[/C][C]-7.13097696475207[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]47.0841969656387[/C][C]-34.0841969656387[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]47.0374169665254[/C][C]-32.0374169665254[/C][/ROW]
[ROW][C]65[/C][C]38[/C][C]63.4828457731562[/C][C]-25.4828457731562[/C][/ROW]
[ROW][C]66[/C][C]47[/C][C]46.9438569682986[/C][C]0.0561430317013662[/C][/ROW]
[ROW][C]67[/C][C]65[/C][C]63.3892857749295[/C][C]1.61071422507053[/C][/ROW]
[ROW][C]68[/C][C]62[/C][C]63.3425057758161[/C][C]-1.34250577581611[/C][/ROW]
[ROW][C]69[/C][C]50[/C][C]46.8035169709586[/C][C]3.19648302904144[/C][/ROW]
[ROW][C]70[/C][C]80[/C][C]63.2489457775894[/C][C]16.7510542224106[/C][/ROW]
[ROW][C]71[/C][C]87[/C][C]63.202165778476[/C][C]23.7978342215240[/C][/ROW]
[ROW][C]72[/C][C]40[/C][C]63.1553857793627[/C][C]-23.1553857793627[/C][/ROW]
[ROW][C]73[/C][C]80[/C][C]63.1086057802493[/C][C]16.8913942197507[/C][/ROW]
[ROW][C]74[/C][C]20[/C][C]46.5696169753918[/C][C]-26.5696169753918[/C][/ROW]
[ROW][C]75[/C][C]60[/C][C]63.0150457820226[/C][C]-3.01504578202259[/C][/ROW]
[ROW][C]76[/C][C]48[/C][C]62.9682657829092[/C][C]-14.9682657829092[/C][/ROW]
[ROW][C]77[/C][C]70[/C][C]62.9214857837959[/C][C]7.07851421620413[/C][/ROW]
[ROW][C]78[/C][C]91[/C][C]62.8747057846825[/C][C]28.1252942153175[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]46.335716979825[/C][C]-36.3357169798250[/C][/ROW]
[ROW][C]80[/C][C]50[/C][C]46.2889369807116[/C][C]3.7110630192884[/C][/ROW]
[ROW][C]81[/C][C]70[/C][C]46.2421569815982[/C][C]23.7578430184018[/C][/ROW]
[ROW][C]82[/C][C]45[/C][C]62.6875857882291[/C][C]-17.6875857882291[/C][/ROW]
[ROW][C]83[/C][C]20[/C][C]62.6408057891157[/C][C]-42.6408057891157[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]46.1018169842582[/C][C]-36.1018169842582[/C][/ROW]
[ROW][C]85[/C][C]90[/C][C]62.547245790889[/C][C]27.452754209111[/C][/ROW]
[ROW][C]86[/C][C]80[/C][C]62.5004657917756[/C][C]17.4995342082244[/C][/ROW]
[ROW][C]87[/C][C]74[/C][C]45.9614769869181[/C][C]28.0385230130819[/C][/ROW]
[ROW][C]88[/C][C]71[/C][C]45.9146969878047[/C][C]25.0853030121953[/C][/ROW]
[ROW][C]89[/C][C]40[/C][C]45.8679169886914[/C][C]-5.86791698869136[/C][/ROW]
[ROW][C]90[/C][C]29[/C][C]62.3133457953222[/C][C]-33.3133457953222[/C][/ROW]
[ROW][C]91[/C][C]60[/C][C]62.2665657962088[/C][C]-2.26656579620883[/C][/ROW]
[ROW][C]92[/C][C]31[/C][C]45.7275769913513[/C][C]-14.7275769913513[/C][/ROW]
[ROW][C]93[/C][C]67[/C][C]45.6807969922379[/C][C]21.3192030077621[/C][/ROW]
[ROW][C]94[/C][C]82[/C][C]45.6340169931246[/C][C]36.3659830068754[/C][/ROW]
[ROW][C]95[/C][C]40[/C][C]62.0794457997554[/C][C]-22.0794457997554[/C][/ROW]
[ROW][C]96[/C][C]30[/C][C]62.032665800642[/C][C]-32.032665800642[/C][/ROW]
[ROW][C]97[/C][C]70[/C][C]45.4936769957845[/C][C]24.5063230042155[/C][/ROW]
[ROW][C]98[/C][C]63[/C][C]45.4468969966711[/C][C]17.5531030033289[/C][/ROW]
[ROW][C]99[/C][C]35[/C][C]45.4001169975578[/C][C]-10.4001169975578[/C][/ROW]
[ROW][C]100[/C][C]35[/C][C]61.8455458041886[/C][C]-26.8455458041886[/C][/ROW]
[ROW][C]101[/C][C]70[/C][C]45.306556999331[/C][C]24.6934430006690[/C][/ROW]
[ROW][C]102[/C][C]60[/C][C]61.7519858059619[/C][C]-1.75198580596188[/C][/ROW]
[ROW][C]103[/C][C]80[/C][C]61.7052058068485[/C][C]18.2947941931515[/C][/ROW]
[ROW][C]104[/C][C]70[/C][C]61.6584258077352[/C][C]8.34157419226484[/C][/ROW]
[ROW][C]105[/C][C]71[/C][C]61.6116458086218[/C][C]9.3883541913782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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
15566.476765716411-11.4767657164110
22049.9377769115537-29.9377769115537
38049.890996912440330.1090030875597
45249.84421691332692.15578308667306
57566.28964571995788.71035428004222
63066.2428657208444-36.2428657208444
79066.19608572173123.8039142782689
86866.14930572261771.85069427738230
92449.6103169177601-25.6103169177601
106049.563536918646810.4364630813532
116566.0089657252776-1.00896572527762
126049.469976920420110.5300230795799
138065.915405727050914.0845942729491
146565.8686257279375-0.868625727937538
159049.3296369230840.67036307692
166565.7750657297108-0.775065729710819
177665.728285730597510.2717142694025
187065.68150573148414.3184942685159
193849.1425169266265-11.1425169266265
206065.5879457332574-5.58794573325738
211049.0489569283998-39.0489569283998
22549.0021769292865-44.0021769292865
239365.447605735917327.5523942640827
247048.908616931059721.0913830689403
256165.3540457376906-4.35404573769058
267265.30726573857726.69273426142278
274048.7682769337197-8.76827693371967
287565.21370574035059.7862942596495
2910065.166925741237234.8330742587629
302948.6279369363796-19.6279369363796
317065.07336574301044.92663425698958
322548.5343769381529-23.5343769381529
337064.97980574478375.0201942552163
348248.440816939926233.5591830600738
354048.3940369408128-8.39403694081279
365064.8394657474436-14.8394657474436
377064.79268574833035.20731425166974
389164.745905749216926.2540942507831
391048.2069169443594-38.2069169443594
402548.160136945246-23.160136945246
415648.11335694613267.88664305386737
423048.0665769470193-18.0665769470193
437448.019796947905925.9802030520941
446047.973016948792512.0269830512074
458047.926236949679232.0737630503208
468064.3716657563115.6283342436900
476064.3248857571967-4.32488575719667
486464.2781057580833-0.278105758083305
494064.23132575897-24.2313257589699
508064.184545759856615.8154542401434
517164.13776576074326.86223423925677
526564.09098576162990.909014238370135
539047.551996956772342.4480030432277
546863.99742576340314.00257423659685
557663.950645764289812.0493542357102
562563.9038657651764-38.9038657651764
577963.857085766063115.1429142339369
584047.3180969612055-7.31809696120551
596163.7635257678363-2.76352576783635
602763.716745768723-36.716745768723
617047.177756963865422.8222430361346
624047.1309769647521-7.13097696475207
631347.0841969656387-34.0841969656387
641547.0374169665254-32.0374169665254
653863.4828457731562-25.4828457731562
664746.94385696829860.0561430317013662
676563.38928577492951.61071422507053
686263.3425057758161-1.34250577581611
695046.80351697095863.19648302904144
708063.248945777589416.7510542224106
718763.20216577847623.7978342215240
724063.1553857793627-23.1553857793627
738063.108605780249316.8913942197507
742046.5696169753918-26.5696169753918
756063.0150457820226-3.01504578202259
764862.9682657829092-14.9682657829092
777062.92148578379597.07851421620413
789162.874705784682528.1252942153175
791046.335716979825-36.3357169798250
805046.28893698071163.7110630192884
817046.242156981598223.7578430184018
824562.6875857882291-17.6875857882291
832062.6408057891157-42.6408057891157
841046.1018169842582-36.1018169842582
859062.54724579088927.452754209111
868062.500465791775617.4995342082244
877445.961476986918128.0385230130819
887145.914696987804725.0853030121953
894045.8679169886914-5.86791698869136
902962.3133457953222-33.3133457953222
916062.2665657962088-2.26656579620883
923145.7275769913513-14.7275769913513
936745.680796992237921.3192030077621
948245.634016993124636.3659830068754
954062.0794457997554-22.0794457997554
963062.032665800642-32.032665800642
977045.493676995784524.5063230042155
986345.446896996671117.5531030033289
993545.4001169975578-10.4001169975578
1003561.8455458041886-26.8455458041886
1017045.30655699933124.6934430006690
1026061.7519858059619-1.75198580596188
1038061.705205806848518.2947941931515
1047061.65842580773528.34157419226484
1057161.61164580862189.3883541913782







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.8787957974612670.2424084050774660.121204202538733
70.8757827299085690.2484345401828620.124217270091431
80.7967264033285590.4065471933428820.203273596671441
90.8246227699742330.3507544600515340.175377230025767
100.7675834149417720.4648331701164550.232416585058228
110.6780790326507120.6438419346985750.321920967349287
120.592112324297180.815775351405640.40788767570282
130.5099203066970480.9801593866059050.490079693302952
140.425277711644380.850555423288760.57472228835562
150.4908356009382140.9816712018764280.509164399061786
160.4303284407047810.8606568814095620.569671559295219
170.350850710943320.701701421886640.64914928905668
180.2852787751612030.5705575503224050.714721224838797
190.3124818262245070.6249636524490150.687518173775493
200.2657218969298030.5314437938596050.734278103070197
210.4620451740218860.9240903480437730.537954825978114
220.609057525507160.781884948985680.39094247449284
230.6558848506259710.6882302987480580.344115149374029
240.6676184668332350.664763066333530.332381533166765
250.6084026690714490.7831946618571010.391597330928551
260.5447921928946750.910415614210650.455207807105325
270.4838754394110120.9677508788220250.516124560588988
280.4258783529017070.8517567058034130.574121647098293
290.4901354624427460.9802709248854930.509864537557254
300.472029680027730.944059360055460.52797031997227
310.4109056666727760.8218113333455510.589094333327224
320.4048237869773010.8096475739546010.595176213022699
330.346761186123320.693522372246640.65323881387668
340.4451414822189270.8902829644378530.554858517781073
350.3927787250368480.7855574500736960.607221274963152
360.3767372191856250.753474438371250.623262780814375
370.3227153970103650.6454307940207290.677284602989635
380.3335557982418350.667111596483670.666444201758165
390.4347312250520950.869462450104190.565268774947905
400.4285809085990560.8571618171981120.571419091400944
410.3890185159533990.7780370319067970.610981484046601
420.3667135978141660.7334271956283330.633286402185834
430.4037373815700180.8074747631400370.596262618429982
440.3672814197438420.7345628394876840.632718580256158
450.4277053146769180.8554106293538360.572294685323082
460.3943091296347310.7886182592694620.605690870365269
470.3530197941115700.7060395882231390.64698020588843
480.3073171962697440.6146343925394880.692682803730256
490.3301937788730950.660387557746190.669806221126905
500.3061097959302230.6122195918604470.693890204069777
510.2656495377022380.5312990754044760.734350462297762
520.2249531699874470.4499063399748940.775046830012553
530.3700888392769160.7401776785538320.629911160723084
540.3299584798740970.6599169597481930.670041520125903
550.3109071885379790.6218143770759580.689092811462021
560.4142637916015450.828527583203090.585736208398455
570.4071799896409920.8143599792819830.592820010359008
580.3565581502496020.7131163004992040.643441849750398
590.3131525675295030.6263051350590060.686847432470497
600.377681783793880.755363567587760.62231821620612
610.4035806403868800.8071612807737590.59641935961312
620.3513171678945950.702634335789190.648682832105405
630.3994031843021140.7988063686042270.600596815697886
640.4441216209005210.8882432418010430.555878379099479
650.4425109869491660.8850219738983320.557489013050834
660.386801140448670.773602280897340.61319885955133
670.3339162010152470.6678324020304950.666083798984753
680.2820327321631450.564065464326290.717967267836855
690.2362748916827980.4725497833655950.763725108317202
700.2301784413082290.4603568826164570.769821558691771
710.2681341408920070.5362682817840140.731865859107993
720.2484053633027910.4968107266055820.75159463669721
730.2588922366234650.5177844732469310.741107763376535
740.2709133407736410.5418266815472820.729086659226359
750.2248214272647560.4496428545295110.775178572735244
760.1864267373075740.3728534746151490.813573262692426
770.1650580101328990.3301160202657980.834941989867101
780.2786888643764400.5573777287528810.72131113562356
790.3686411875915060.7372823751830130.631358812408494
800.3102255493294490.6204510986588990.68977445067055
810.3198876021987210.6397752043974420.680112397801279
820.2686972541347730.5373945082695470.731302745865227
830.3375065916882120.6750131833764240.662493408311788
840.556601528139870.886796943720260.44339847186013
850.6694412340057330.6611175319885350.330558765994267
860.7799729279892750.440054144021450.220027072010725
870.8183886641196350.3632226717607300.181611335880365
880.8576006312409440.2847987375181120.142399368759056
890.8046088607228440.3907822785543130.195391139277156
900.7658253590772050.468349281845590.234174640922795
910.790415408461360.4191691830772810.209584591538640
920.7850156839395360.4299686321209280.214984316060464
930.746794800804590.506410398390820.25320519919541
940.9042903416208810.1914193167582380.0957096583791188
950.8655015495078160.2689969009843690.134498450492185
960.7893795456088820.4212409087822370.210620454391118
970.8456794027557690.3086411944884630.154320597244231
980.890310482953670.2193790340926600.109689517046330
990.857518995316260.2849620093674820.142481004683741

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.878795797461267 & 0.242408405077466 & 0.121204202538733 \tabularnewline
7 & 0.875782729908569 & 0.248434540182862 & 0.124217270091431 \tabularnewline
8 & 0.796726403328559 & 0.406547193342882 & 0.203273596671441 \tabularnewline
9 & 0.824622769974233 & 0.350754460051534 & 0.175377230025767 \tabularnewline
10 & 0.767583414941772 & 0.464833170116455 & 0.232416585058228 \tabularnewline
11 & 0.678079032650712 & 0.643841934698575 & 0.321920967349287 \tabularnewline
12 & 0.59211232429718 & 0.81577535140564 & 0.40788767570282 \tabularnewline
13 & 0.509920306697048 & 0.980159386605905 & 0.490079693302952 \tabularnewline
14 & 0.42527771164438 & 0.85055542328876 & 0.57472228835562 \tabularnewline
15 & 0.490835600938214 & 0.981671201876428 & 0.509164399061786 \tabularnewline
16 & 0.430328440704781 & 0.860656881409562 & 0.569671559295219 \tabularnewline
17 & 0.35085071094332 & 0.70170142188664 & 0.64914928905668 \tabularnewline
18 & 0.285278775161203 & 0.570557550322405 & 0.714721224838797 \tabularnewline
19 & 0.312481826224507 & 0.624963652449015 & 0.687518173775493 \tabularnewline
20 & 0.265721896929803 & 0.531443793859605 & 0.734278103070197 \tabularnewline
21 & 0.462045174021886 & 0.924090348043773 & 0.537954825978114 \tabularnewline
22 & 0.60905752550716 & 0.78188494898568 & 0.39094247449284 \tabularnewline
23 & 0.655884850625971 & 0.688230298748058 & 0.344115149374029 \tabularnewline
24 & 0.667618466833235 & 0.66476306633353 & 0.332381533166765 \tabularnewline
25 & 0.608402669071449 & 0.783194661857101 & 0.391597330928551 \tabularnewline
26 & 0.544792192894675 & 0.91041561421065 & 0.455207807105325 \tabularnewline
27 & 0.483875439411012 & 0.967750878822025 & 0.516124560588988 \tabularnewline
28 & 0.425878352901707 & 0.851756705803413 & 0.574121647098293 \tabularnewline
29 & 0.490135462442746 & 0.980270924885493 & 0.509864537557254 \tabularnewline
30 & 0.47202968002773 & 0.94405936005546 & 0.52797031997227 \tabularnewline
31 & 0.410905666672776 & 0.821811333345551 & 0.589094333327224 \tabularnewline
32 & 0.404823786977301 & 0.809647573954601 & 0.595176213022699 \tabularnewline
33 & 0.34676118612332 & 0.69352237224664 & 0.65323881387668 \tabularnewline
34 & 0.445141482218927 & 0.890282964437853 & 0.554858517781073 \tabularnewline
35 & 0.392778725036848 & 0.785557450073696 & 0.607221274963152 \tabularnewline
36 & 0.376737219185625 & 0.75347443837125 & 0.623262780814375 \tabularnewline
37 & 0.322715397010365 & 0.645430794020729 & 0.677284602989635 \tabularnewline
38 & 0.333555798241835 & 0.66711159648367 & 0.666444201758165 \tabularnewline
39 & 0.434731225052095 & 0.86946245010419 & 0.565268774947905 \tabularnewline
40 & 0.428580908599056 & 0.857161817198112 & 0.571419091400944 \tabularnewline
41 & 0.389018515953399 & 0.778037031906797 & 0.610981484046601 \tabularnewline
42 & 0.366713597814166 & 0.733427195628333 & 0.633286402185834 \tabularnewline
43 & 0.403737381570018 & 0.807474763140037 & 0.596262618429982 \tabularnewline
44 & 0.367281419743842 & 0.734562839487684 & 0.632718580256158 \tabularnewline
45 & 0.427705314676918 & 0.855410629353836 & 0.572294685323082 \tabularnewline
46 & 0.394309129634731 & 0.788618259269462 & 0.605690870365269 \tabularnewline
47 & 0.353019794111570 & 0.706039588223139 & 0.64698020588843 \tabularnewline
48 & 0.307317196269744 & 0.614634392539488 & 0.692682803730256 \tabularnewline
49 & 0.330193778873095 & 0.66038755774619 & 0.669806221126905 \tabularnewline
50 & 0.306109795930223 & 0.612219591860447 & 0.693890204069777 \tabularnewline
51 & 0.265649537702238 & 0.531299075404476 & 0.734350462297762 \tabularnewline
52 & 0.224953169987447 & 0.449906339974894 & 0.775046830012553 \tabularnewline
53 & 0.370088839276916 & 0.740177678553832 & 0.629911160723084 \tabularnewline
54 & 0.329958479874097 & 0.659916959748193 & 0.670041520125903 \tabularnewline
55 & 0.310907188537979 & 0.621814377075958 & 0.689092811462021 \tabularnewline
56 & 0.414263791601545 & 0.82852758320309 & 0.585736208398455 \tabularnewline
57 & 0.407179989640992 & 0.814359979281983 & 0.592820010359008 \tabularnewline
58 & 0.356558150249602 & 0.713116300499204 & 0.643441849750398 \tabularnewline
59 & 0.313152567529503 & 0.626305135059006 & 0.686847432470497 \tabularnewline
60 & 0.37768178379388 & 0.75536356758776 & 0.62231821620612 \tabularnewline
61 & 0.403580640386880 & 0.807161280773759 & 0.59641935961312 \tabularnewline
62 & 0.351317167894595 & 0.70263433578919 & 0.648682832105405 \tabularnewline
63 & 0.399403184302114 & 0.798806368604227 & 0.600596815697886 \tabularnewline
64 & 0.444121620900521 & 0.888243241801043 & 0.555878379099479 \tabularnewline
65 & 0.442510986949166 & 0.885021973898332 & 0.557489013050834 \tabularnewline
66 & 0.38680114044867 & 0.77360228089734 & 0.61319885955133 \tabularnewline
67 & 0.333916201015247 & 0.667832402030495 & 0.666083798984753 \tabularnewline
68 & 0.282032732163145 & 0.56406546432629 & 0.717967267836855 \tabularnewline
69 & 0.236274891682798 & 0.472549783365595 & 0.763725108317202 \tabularnewline
70 & 0.230178441308229 & 0.460356882616457 & 0.769821558691771 \tabularnewline
71 & 0.268134140892007 & 0.536268281784014 & 0.731865859107993 \tabularnewline
72 & 0.248405363302791 & 0.496810726605582 & 0.75159463669721 \tabularnewline
73 & 0.258892236623465 & 0.517784473246931 & 0.741107763376535 \tabularnewline
74 & 0.270913340773641 & 0.541826681547282 & 0.729086659226359 \tabularnewline
75 & 0.224821427264756 & 0.449642854529511 & 0.775178572735244 \tabularnewline
76 & 0.186426737307574 & 0.372853474615149 & 0.813573262692426 \tabularnewline
77 & 0.165058010132899 & 0.330116020265798 & 0.834941989867101 \tabularnewline
78 & 0.278688864376440 & 0.557377728752881 & 0.72131113562356 \tabularnewline
79 & 0.368641187591506 & 0.737282375183013 & 0.631358812408494 \tabularnewline
80 & 0.310225549329449 & 0.620451098658899 & 0.68977445067055 \tabularnewline
81 & 0.319887602198721 & 0.639775204397442 & 0.680112397801279 \tabularnewline
82 & 0.268697254134773 & 0.537394508269547 & 0.731302745865227 \tabularnewline
83 & 0.337506591688212 & 0.675013183376424 & 0.662493408311788 \tabularnewline
84 & 0.55660152813987 & 0.88679694372026 & 0.44339847186013 \tabularnewline
85 & 0.669441234005733 & 0.661117531988535 & 0.330558765994267 \tabularnewline
86 & 0.779972927989275 & 0.44005414402145 & 0.220027072010725 \tabularnewline
87 & 0.818388664119635 & 0.363222671760730 & 0.181611335880365 \tabularnewline
88 & 0.857600631240944 & 0.284798737518112 & 0.142399368759056 \tabularnewline
89 & 0.804608860722844 & 0.390782278554313 & 0.195391139277156 \tabularnewline
90 & 0.765825359077205 & 0.46834928184559 & 0.234174640922795 \tabularnewline
91 & 0.79041540846136 & 0.419169183077281 & 0.209584591538640 \tabularnewline
92 & 0.785015683939536 & 0.429968632120928 & 0.214984316060464 \tabularnewline
93 & 0.74679480080459 & 0.50641039839082 & 0.25320519919541 \tabularnewline
94 & 0.904290341620881 & 0.191419316758238 & 0.0957096583791188 \tabularnewline
95 & 0.865501549507816 & 0.268996900984369 & 0.134498450492185 \tabularnewline
96 & 0.789379545608882 & 0.421240908782237 & 0.210620454391118 \tabularnewline
97 & 0.845679402755769 & 0.308641194488463 & 0.154320597244231 \tabularnewline
98 & 0.89031048295367 & 0.219379034092660 & 0.109689517046330 \tabularnewline
99 & 0.85751899531626 & 0.284962009367482 & 0.142481004683741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98909&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]6[/C][C]0.878795797461267[/C][C]0.242408405077466[/C][C]0.121204202538733[/C][/ROW]
[ROW][C]7[/C][C]0.875782729908569[/C][C]0.248434540182862[/C][C]0.124217270091431[/C][/ROW]
[ROW][C]8[/C][C]0.796726403328559[/C][C]0.406547193342882[/C][C]0.203273596671441[/C][/ROW]
[ROW][C]9[/C][C]0.824622769974233[/C][C]0.350754460051534[/C][C]0.175377230025767[/C][/ROW]
[ROW][C]10[/C][C]0.767583414941772[/C][C]0.464833170116455[/C][C]0.232416585058228[/C][/ROW]
[ROW][C]11[/C][C]0.678079032650712[/C][C]0.643841934698575[/C][C]0.321920967349287[/C][/ROW]
[ROW][C]12[/C][C]0.59211232429718[/C][C]0.81577535140564[/C][C]0.40788767570282[/C][/ROW]
[ROW][C]13[/C][C]0.509920306697048[/C][C]0.980159386605905[/C][C]0.490079693302952[/C][/ROW]
[ROW][C]14[/C][C]0.42527771164438[/C][C]0.85055542328876[/C][C]0.57472228835562[/C][/ROW]
[ROW][C]15[/C][C]0.490835600938214[/C][C]0.981671201876428[/C][C]0.509164399061786[/C][/ROW]
[ROW][C]16[/C][C]0.430328440704781[/C][C]0.860656881409562[/C][C]0.569671559295219[/C][/ROW]
[ROW][C]17[/C][C]0.35085071094332[/C][C]0.70170142188664[/C][C]0.64914928905668[/C][/ROW]
[ROW][C]18[/C][C]0.285278775161203[/C][C]0.570557550322405[/C][C]0.714721224838797[/C][/ROW]
[ROW][C]19[/C][C]0.312481826224507[/C][C]0.624963652449015[/C][C]0.687518173775493[/C][/ROW]
[ROW][C]20[/C][C]0.265721896929803[/C][C]0.531443793859605[/C][C]0.734278103070197[/C][/ROW]
[ROW][C]21[/C][C]0.462045174021886[/C][C]0.924090348043773[/C][C]0.537954825978114[/C][/ROW]
[ROW][C]22[/C][C]0.60905752550716[/C][C]0.78188494898568[/C][C]0.39094247449284[/C][/ROW]
[ROW][C]23[/C][C]0.655884850625971[/C][C]0.688230298748058[/C][C]0.344115149374029[/C][/ROW]
[ROW][C]24[/C][C]0.667618466833235[/C][C]0.66476306633353[/C][C]0.332381533166765[/C][/ROW]
[ROW][C]25[/C][C]0.608402669071449[/C][C]0.783194661857101[/C][C]0.391597330928551[/C][/ROW]
[ROW][C]26[/C][C]0.544792192894675[/C][C]0.91041561421065[/C][C]0.455207807105325[/C][/ROW]
[ROW][C]27[/C][C]0.483875439411012[/C][C]0.967750878822025[/C][C]0.516124560588988[/C][/ROW]
[ROW][C]28[/C][C]0.425878352901707[/C][C]0.851756705803413[/C][C]0.574121647098293[/C][/ROW]
[ROW][C]29[/C][C]0.490135462442746[/C][C]0.980270924885493[/C][C]0.509864537557254[/C][/ROW]
[ROW][C]30[/C][C]0.47202968002773[/C][C]0.94405936005546[/C][C]0.52797031997227[/C][/ROW]
[ROW][C]31[/C][C]0.410905666672776[/C][C]0.821811333345551[/C][C]0.589094333327224[/C][/ROW]
[ROW][C]32[/C][C]0.404823786977301[/C][C]0.809647573954601[/C][C]0.595176213022699[/C][/ROW]
[ROW][C]33[/C][C]0.34676118612332[/C][C]0.69352237224664[/C][C]0.65323881387668[/C][/ROW]
[ROW][C]34[/C][C]0.445141482218927[/C][C]0.890282964437853[/C][C]0.554858517781073[/C][/ROW]
[ROW][C]35[/C][C]0.392778725036848[/C][C]0.785557450073696[/C][C]0.607221274963152[/C][/ROW]
[ROW][C]36[/C][C]0.376737219185625[/C][C]0.75347443837125[/C][C]0.623262780814375[/C][/ROW]
[ROW][C]37[/C][C]0.322715397010365[/C][C]0.645430794020729[/C][C]0.677284602989635[/C][/ROW]
[ROW][C]38[/C][C]0.333555798241835[/C][C]0.66711159648367[/C][C]0.666444201758165[/C][/ROW]
[ROW][C]39[/C][C]0.434731225052095[/C][C]0.86946245010419[/C][C]0.565268774947905[/C][/ROW]
[ROW][C]40[/C][C]0.428580908599056[/C][C]0.857161817198112[/C][C]0.571419091400944[/C][/ROW]
[ROW][C]41[/C][C]0.389018515953399[/C][C]0.778037031906797[/C][C]0.610981484046601[/C][/ROW]
[ROW][C]42[/C][C]0.366713597814166[/C][C]0.733427195628333[/C][C]0.633286402185834[/C][/ROW]
[ROW][C]43[/C][C]0.403737381570018[/C][C]0.807474763140037[/C][C]0.596262618429982[/C][/ROW]
[ROW][C]44[/C][C]0.367281419743842[/C][C]0.734562839487684[/C][C]0.632718580256158[/C][/ROW]
[ROW][C]45[/C][C]0.427705314676918[/C][C]0.855410629353836[/C][C]0.572294685323082[/C][/ROW]
[ROW][C]46[/C][C]0.394309129634731[/C][C]0.788618259269462[/C][C]0.605690870365269[/C][/ROW]
[ROW][C]47[/C][C]0.353019794111570[/C][C]0.706039588223139[/C][C]0.64698020588843[/C][/ROW]
[ROW][C]48[/C][C]0.307317196269744[/C][C]0.614634392539488[/C][C]0.692682803730256[/C][/ROW]
[ROW][C]49[/C][C]0.330193778873095[/C][C]0.66038755774619[/C][C]0.669806221126905[/C][/ROW]
[ROW][C]50[/C][C]0.306109795930223[/C][C]0.612219591860447[/C][C]0.693890204069777[/C][/ROW]
[ROW][C]51[/C][C]0.265649537702238[/C][C]0.531299075404476[/C][C]0.734350462297762[/C][/ROW]
[ROW][C]52[/C][C]0.224953169987447[/C][C]0.449906339974894[/C][C]0.775046830012553[/C][/ROW]
[ROW][C]53[/C][C]0.370088839276916[/C][C]0.740177678553832[/C][C]0.629911160723084[/C][/ROW]
[ROW][C]54[/C][C]0.329958479874097[/C][C]0.659916959748193[/C][C]0.670041520125903[/C][/ROW]
[ROW][C]55[/C][C]0.310907188537979[/C][C]0.621814377075958[/C][C]0.689092811462021[/C][/ROW]
[ROW][C]56[/C][C]0.414263791601545[/C][C]0.82852758320309[/C][C]0.585736208398455[/C][/ROW]
[ROW][C]57[/C][C]0.407179989640992[/C][C]0.814359979281983[/C][C]0.592820010359008[/C][/ROW]
[ROW][C]58[/C][C]0.356558150249602[/C][C]0.713116300499204[/C][C]0.643441849750398[/C][/ROW]
[ROW][C]59[/C][C]0.313152567529503[/C][C]0.626305135059006[/C][C]0.686847432470497[/C][/ROW]
[ROW][C]60[/C][C]0.37768178379388[/C][C]0.75536356758776[/C][C]0.62231821620612[/C][/ROW]
[ROW][C]61[/C][C]0.403580640386880[/C][C]0.807161280773759[/C][C]0.59641935961312[/C][/ROW]
[ROW][C]62[/C][C]0.351317167894595[/C][C]0.70263433578919[/C][C]0.648682832105405[/C][/ROW]
[ROW][C]63[/C][C]0.399403184302114[/C][C]0.798806368604227[/C][C]0.600596815697886[/C][/ROW]
[ROW][C]64[/C][C]0.444121620900521[/C][C]0.888243241801043[/C][C]0.555878379099479[/C][/ROW]
[ROW][C]65[/C][C]0.442510986949166[/C][C]0.885021973898332[/C][C]0.557489013050834[/C][/ROW]
[ROW][C]66[/C][C]0.38680114044867[/C][C]0.77360228089734[/C][C]0.61319885955133[/C][/ROW]
[ROW][C]67[/C][C]0.333916201015247[/C][C]0.667832402030495[/C][C]0.666083798984753[/C][/ROW]
[ROW][C]68[/C][C]0.282032732163145[/C][C]0.56406546432629[/C][C]0.717967267836855[/C][/ROW]
[ROW][C]69[/C][C]0.236274891682798[/C][C]0.472549783365595[/C][C]0.763725108317202[/C][/ROW]
[ROW][C]70[/C][C]0.230178441308229[/C][C]0.460356882616457[/C][C]0.769821558691771[/C][/ROW]
[ROW][C]71[/C][C]0.268134140892007[/C][C]0.536268281784014[/C][C]0.731865859107993[/C][/ROW]
[ROW][C]72[/C][C]0.248405363302791[/C][C]0.496810726605582[/C][C]0.75159463669721[/C][/ROW]
[ROW][C]73[/C][C]0.258892236623465[/C][C]0.517784473246931[/C][C]0.741107763376535[/C][/ROW]
[ROW][C]74[/C][C]0.270913340773641[/C][C]0.541826681547282[/C][C]0.729086659226359[/C][/ROW]
[ROW][C]75[/C][C]0.224821427264756[/C][C]0.449642854529511[/C][C]0.775178572735244[/C][/ROW]
[ROW][C]76[/C][C]0.186426737307574[/C][C]0.372853474615149[/C][C]0.813573262692426[/C][/ROW]
[ROW][C]77[/C][C]0.165058010132899[/C][C]0.330116020265798[/C][C]0.834941989867101[/C][/ROW]
[ROW][C]78[/C][C]0.278688864376440[/C][C]0.557377728752881[/C][C]0.72131113562356[/C][/ROW]
[ROW][C]79[/C][C]0.368641187591506[/C][C]0.737282375183013[/C][C]0.631358812408494[/C][/ROW]
[ROW][C]80[/C][C]0.310225549329449[/C][C]0.620451098658899[/C][C]0.68977445067055[/C][/ROW]
[ROW][C]81[/C][C]0.319887602198721[/C][C]0.639775204397442[/C][C]0.680112397801279[/C][/ROW]
[ROW][C]82[/C][C]0.268697254134773[/C][C]0.537394508269547[/C][C]0.731302745865227[/C][/ROW]
[ROW][C]83[/C][C]0.337506591688212[/C][C]0.675013183376424[/C][C]0.662493408311788[/C][/ROW]
[ROW][C]84[/C][C]0.55660152813987[/C][C]0.88679694372026[/C][C]0.44339847186013[/C][/ROW]
[ROW][C]85[/C][C]0.669441234005733[/C][C]0.661117531988535[/C][C]0.330558765994267[/C][/ROW]
[ROW][C]86[/C][C]0.779972927989275[/C][C]0.44005414402145[/C][C]0.220027072010725[/C][/ROW]
[ROW][C]87[/C][C]0.818388664119635[/C][C]0.363222671760730[/C][C]0.181611335880365[/C][/ROW]
[ROW][C]88[/C][C]0.857600631240944[/C][C]0.284798737518112[/C][C]0.142399368759056[/C][/ROW]
[ROW][C]89[/C][C]0.804608860722844[/C][C]0.390782278554313[/C][C]0.195391139277156[/C][/ROW]
[ROW][C]90[/C][C]0.765825359077205[/C][C]0.46834928184559[/C][C]0.234174640922795[/C][/ROW]
[ROW][C]91[/C][C]0.79041540846136[/C][C]0.419169183077281[/C][C]0.209584591538640[/C][/ROW]
[ROW][C]92[/C][C]0.785015683939536[/C][C]0.429968632120928[/C][C]0.214984316060464[/C][/ROW]
[ROW][C]93[/C][C]0.74679480080459[/C][C]0.50641039839082[/C][C]0.25320519919541[/C][/ROW]
[ROW][C]94[/C][C]0.904290341620881[/C][C]0.191419316758238[/C][C]0.0957096583791188[/C][/ROW]
[ROW][C]95[/C][C]0.865501549507816[/C][C]0.268996900984369[/C][C]0.134498450492185[/C][/ROW]
[ROW][C]96[/C][C]0.789379545608882[/C][C]0.421240908782237[/C][C]0.210620454391118[/C][/ROW]
[ROW][C]97[/C][C]0.845679402755769[/C][C]0.308641194488463[/C][C]0.154320597244231[/C][/ROW]
[ROW][C]98[/C][C]0.89031048295367[/C][C]0.219379034092660[/C][C]0.109689517046330[/C][/ROW]
[ROW][C]99[/C][C]0.85751899531626[/C][C]0.284962009367482[/C][C]0.142481004683741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98909&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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
60.8787957974612670.2424084050774660.121204202538733
70.8757827299085690.2484345401828620.124217270091431
80.7967264033285590.4065471933428820.203273596671441
90.8246227699742330.3507544600515340.175377230025767
100.7675834149417720.4648331701164550.232416585058228
110.6780790326507120.6438419346985750.321920967349287
120.592112324297180.815775351405640.40788767570282
130.5099203066970480.9801593866059050.490079693302952
140.425277711644380.850555423288760.57472228835562
150.4908356009382140.9816712018764280.509164399061786
160.4303284407047810.8606568814095620.569671559295219
170.350850710943320.701701421886640.64914928905668
180.2852787751612030.5705575503224050.714721224838797
190.3124818262245070.6249636524490150.687518173775493
200.2657218969298030.5314437938596050.734278103070197
210.4620451740218860.9240903480437730.537954825978114
220.609057525507160.781884948985680.39094247449284
230.6558848506259710.6882302987480580.344115149374029
240.6676184668332350.664763066333530.332381533166765
250.6084026690714490.7831946618571010.391597330928551
260.5447921928946750.910415614210650.455207807105325
270.4838754394110120.9677508788220250.516124560588988
280.4258783529017070.8517567058034130.574121647098293
290.4901354624427460.9802709248854930.509864537557254
300.472029680027730.944059360055460.52797031997227
310.4109056666727760.8218113333455510.589094333327224
320.4048237869773010.8096475739546010.595176213022699
330.346761186123320.693522372246640.65323881387668
340.4451414822189270.8902829644378530.554858517781073
350.3927787250368480.7855574500736960.607221274963152
360.3767372191856250.753474438371250.623262780814375
370.3227153970103650.6454307940207290.677284602989635
380.3335557982418350.667111596483670.666444201758165
390.4347312250520950.869462450104190.565268774947905
400.4285809085990560.8571618171981120.571419091400944
410.3890185159533990.7780370319067970.610981484046601
420.3667135978141660.7334271956283330.633286402185834
430.4037373815700180.8074747631400370.596262618429982
440.3672814197438420.7345628394876840.632718580256158
450.4277053146769180.8554106293538360.572294685323082
460.3943091296347310.7886182592694620.605690870365269
470.3530197941115700.7060395882231390.64698020588843
480.3073171962697440.6146343925394880.692682803730256
490.3301937788730950.660387557746190.669806221126905
500.3061097959302230.6122195918604470.693890204069777
510.2656495377022380.5312990754044760.734350462297762
520.2249531699874470.4499063399748940.775046830012553
530.3700888392769160.7401776785538320.629911160723084
540.3299584798740970.6599169597481930.670041520125903
550.3109071885379790.6218143770759580.689092811462021
560.4142637916015450.828527583203090.585736208398455
570.4071799896409920.8143599792819830.592820010359008
580.3565581502496020.7131163004992040.643441849750398
590.3131525675295030.6263051350590060.686847432470497
600.377681783793880.755363567587760.62231821620612
610.4035806403868800.8071612807737590.59641935961312
620.3513171678945950.702634335789190.648682832105405
630.3994031843021140.7988063686042270.600596815697886
640.4441216209005210.8882432418010430.555878379099479
650.4425109869491660.8850219738983320.557489013050834
660.386801140448670.773602280897340.61319885955133
670.3339162010152470.6678324020304950.666083798984753
680.2820327321631450.564065464326290.717967267836855
690.2362748916827980.4725497833655950.763725108317202
700.2301784413082290.4603568826164570.769821558691771
710.2681341408920070.5362682817840140.731865859107993
720.2484053633027910.4968107266055820.75159463669721
730.2588922366234650.5177844732469310.741107763376535
740.2709133407736410.5418266815472820.729086659226359
750.2248214272647560.4496428545295110.775178572735244
760.1864267373075740.3728534746151490.813573262692426
770.1650580101328990.3301160202657980.834941989867101
780.2786888643764400.5573777287528810.72131113562356
790.3686411875915060.7372823751830130.631358812408494
800.3102255493294490.6204510986588990.68977445067055
810.3198876021987210.6397752043974420.680112397801279
820.2686972541347730.5373945082695470.731302745865227
830.3375065916882120.6750131833764240.662493408311788
840.556601528139870.886796943720260.44339847186013
850.6694412340057330.6611175319885350.330558765994267
860.7799729279892750.440054144021450.220027072010725
870.8183886641196350.3632226717607300.181611335880365
880.8576006312409440.2847987375181120.142399368759056
890.8046088607228440.3907822785543130.195391139277156
900.7658253590772050.468349281845590.234174640922795
910.790415408461360.4191691830772810.209584591538640
920.7850156839395360.4299686321209280.214984316060464
930.746794800804590.506410398390820.25320519919541
940.9042903416208810.1914193167582380.0957096583791188
950.8655015495078160.2689969009843690.134498450492185
960.7893795456088820.4212409087822370.210620454391118
970.8456794027557690.3086411944884630.154320597244231
980.890310482953670.2193790340926600.109689517046330
990.857518995316260.2849620093674820.142481004683741







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=98909&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=98909&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98909&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 level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
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')
}