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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 computationThu, 24 Nov 2011 12:27:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322155906gjhezluo1grf1qn.htm/, Retrieved Thu, 28 Mar 2024 12:18:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147114, Retrieved Thu, 28 Mar 2024 12:18:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Linear R...] [2011-11-23 18:09:50] [489eb911c8db04aca1fc54d886fc3144]
-   P   [Multiple Regression] [Multiple Linear R...] [2011-11-24 15:39:50] [489eb911c8db04aca1fc54d886fc3144]
-    D      [Multiple Regression] [Multiple Linear R...] [2011-11-24 17:27:18] [d160b678fd2d7bb562db2147d7efddc2] [Current]
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Dataseries X:
63047	13	6	10823
66751	26	7	44480
7176	0	0	1929
78306	37	12	30032
144655	47	15	27669
269638	84	16	114967
69266	21	12	29951
83529	36	15	38824
73226	35	15	26517
178519	40	13	63570
67250	35	6	27131
102982	46	16	41061
50001	20	7	18810
91093	24	12	27582
80112	19	9	37026
72961	15	10	24252
77159	52	16	32579
15629	0	5	0
71693	38	20	29666
19920	12	7	7533
39403	10	13	11892
104383	53	13	51557
56088	4	11	5737
62006	24	9	11203
81665	39	10	28714
69451	19	7	24268
88794	23	13	30749
90642	39	15	46643
207069	38	13	64530
99340	20	7	35346
56695	20	14	5766
108143	41	11	29217
64204	29	3	15912
29101	0	8	3728
113060	31	12	37494
0	0	0	0
65773	8	12	13214
67047	35	8	19576
41953	3	20	13632
113787	47	18	67378
86584	42	9	29387
59588	11	14	15936
40064	10	7	18156
74471	26	13	23750
60437	27	11	15559
55118	1	11	21713
40295	15	14	12023
103397	32	9	23588
78982	13	12	28661
67317	25	12	16874
39887	10	17	11804
59682	24	10	12949
132740	26	11	38340
104816	29	12	36573
101395	40	17	40068
72824	22	6	25561
76018	27	8	31287
33891	8	12	8383
63694	27	13	29178
33239	0	14	1237
35093	0	17	10241
35252	17	8	8219
36977	7	9	9348
42406	18	9	25242
56353	7	9	24267
58817	24	15	25902
81051	19	16	51849
70872	39	13	29065
42372	17	12	22417




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TijdInRFC[t] = + 16866.0613532482 + 592.162232583682Blogs[t] + 59.9880577996278Peerreviews[t] + 1.61250184118775Compendiumtijd[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TijdInRFC[t] =  +  16866.0613532482 +  592.162232583682Blogs[t] +  59.9880577996278Peerreviews[t] +  1.61250184118775Compendiumtijd[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TijdInRFC[t] =  +  16866.0613532482 +  592.162232583682Blogs[t] +  59.9880577996278Peerreviews[t] +  1.61250184118775Compendiumtijd[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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
TijdInRFC[t] = + 16866.0613532482 + 592.162232583682Blogs[t] + 59.9880577996278Peerreviews[t] + 1.61250184118775Compendiumtijd[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16866.06135324827048.4082962.39290.0196150.009807
Blogs592.162232583682241.4700342.45230.0168880.008444
Peerreviews59.9880577996278627.2553390.09560.9241040.462052
Compendiumtijd1.612501841187750.2101977.671400

\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) & 16866.0613532482 & 7048.408296 & 2.3929 & 0.019615 & 0.009807 \tabularnewline
Blogs & 592.162232583682 & 241.470034 & 2.4523 & 0.016888 & 0.008444 \tabularnewline
Peerreviews & 59.9880577996278 & 627.255339 & 0.0956 & 0.924104 & 0.462052 \tabularnewline
Compendiumtijd & 1.61250184118775 & 0.210197 & 7.6714 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&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]16866.0613532482[/C][C]7048.408296[/C][C]2.3929[/C][C]0.019615[/C][C]0.009807[/C][/ROW]
[ROW][C]Blogs[/C][C]592.162232583682[/C][C]241.470034[/C][C]2.4523[/C][C]0.016888[/C][C]0.008444[/C][/ROW]
[ROW][C]Peerreviews[/C][C]59.9880577996278[/C][C]627.255339[/C][C]0.0956[/C][C]0.924104[/C][C]0.462052[/C][/ROW]
[ROW][C]Compendiumtijd[/C][C]1.61250184118775[/C][C]0.210197[/C][C]7.6714[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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)16866.06135324827048.4082962.39290.0196150.009807
Blogs592.162232583682241.4700342.45230.0168880.008444
Peerreviews59.9880577996278627.2553390.09560.9241040.462052
Compendiumtijd1.612501841187750.2101977.671400







Multiple Linear Regression - Regression Statistics
Multiple R0.892528796741298
R-squared0.796607653012469
Adjusted R-squared0.787220313920737
F-TEST (value)84.8597930923867
F-TEST (DF numerator)3
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19612.2492686778
Sum Squared Residuals25001620889.4891

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.892528796741298 \tabularnewline
R-squared & 0.796607653012469 \tabularnewline
Adjusted R-squared & 0.787220313920737 \tabularnewline
F-TEST (value) & 84.8597930923867 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 65 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 19612.2492686778 \tabularnewline
Sum Squared Residuals & 25001620889.4891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.892528796741298[/C][/ROW]
[ROW][C]R-squared[/C][C]0.796607653012469[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.787220313920737[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.8597930923867[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]65[/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]19612.2492686778[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25001620889.4891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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 R0.892528796741298
R-squared0.796607653012469
Adjusted R-squared0.787220313920737
F-TEST (value)84.8597930923867
F-TEST (DF numerator)3
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19612.2492686778
Sum Squared Residuals25001620889.4891







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16304742376.20615080920670.793849191
266751104406.277701053-37655.2777010527
3717619976.5774048994-12800.5774048994
47830687922.5759469907-9616.57594699067
514465590213.820595499754441.1794045003
6269638252951.99699090416686.0030090958
76926678317.3675765155-9051.36757651555
883529101687.494075529-18158.4940755286
97322681250.2716834472-8024.27168344724
10178519143839.13745229634679.8625477037
116725081700.4552937399-14450.4552937399
12102982111276.271077902-8294.27107790208
135000159460.382042261-9459.38204226096
149109376273.837412492814819.1625875072
158011288361.5294643527-8249.52946435267
167296165454.77007248527506.22992751481
1777159101152.00385645-23993.0038564496
181562917166.0016422464-1537.00164224639
197169388404.4669680967-16711.4669680967
201992036538.9009185172-16618.9009185172
213940342743.400325885-3340.40032588502
22104383132166.261857696-27783.2618576956
235608829145.50198227326942.498017727
246200649682.705582279712323.2944177203
258166586861.6468698733-5196.64686987332
266945167669.254858881781.74514111996
278879480848.45656875047945.54343124962
2890642116072.132669527-25430.1326695267
29207069144202.81475466962866.1852453308
309934086124.712488141713215.2875118583
315669538846.824430405317848.1755695947
3210814388917.047818957819225.9521810422
336420459876.85956855354327.14043144652
342910123357.37267959325743.62732040678
3511306096402.091290431616657.9087095684
36016866.0613532482-16866.0613532482
376577343630.815236968222142.1847630318
386704769637.9799991656-2590.97999916564
394195341823.9343060633129.065693936673
40113787154424.620380623-40637.6203806232
418658489663.3592489441-3079.3592489441
425958849916.50806203169671.4919379684
434006452484.1835122873-12420.1835122873
447447171339.04288002833131.95711997167
456043758603.22641584391833.77358415615
465511853130.34469933761987.65530066244
474029545975.4372877987-5680.43728779865
4810339774390.838746059529006.1612539405
497898271499.94234071397482.05765928611
506731759599.3299296387717.67007036198
513988742841.452395059-2954.45239505901
525968252558.12185479317123.87814520686
5313274094745.468627358437994.5313726416
5410481693732.652629530311083.3473704697
55101395106182.0714119-4787.07141190017
567282471470.71837948721353.28162051277
577601883784.691200646-7766.69120064598
583389135840.8188421902-1949.81884219019
596369480683.8651065791-16989.8651065791
603323919700.558939992313538.4410600077
613509334399.4896914457693.510308554279
623525240665.87640229-5413.87640229003
633697736624.7567129538352.243287046189
644240668767.6455352125-26361.6455352125
655635360681.6716816339-4328.67168163392
665881773744.7984926963-14927.7984926963
6781051112683.560660876-31632.5606608762
687087287607.5991895291-16735.5991895291
694237263800.1297746723-21428.1297746723

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 63047 & 42376.206150809 & 20670.793849191 \tabularnewline
2 & 66751 & 104406.277701053 & -37655.2777010527 \tabularnewline
3 & 7176 & 19976.5774048994 & -12800.5774048994 \tabularnewline
4 & 78306 & 87922.5759469907 & -9616.57594699067 \tabularnewline
5 & 144655 & 90213.8205954997 & 54441.1794045003 \tabularnewline
6 & 269638 & 252951.996990904 & 16686.0030090958 \tabularnewline
7 & 69266 & 78317.3675765155 & -9051.36757651555 \tabularnewline
8 & 83529 & 101687.494075529 & -18158.4940755286 \tabularnewline
9 & 73226 & 81250.2716834472 & -8024.27168344724 \tabularnewline
10 & 178519 & 143839.137452296 & 34679.8625477037 \tabularnewline
11 & 67250 & 81700.4552937399 & -14450.4552937399 \tabularnewline
12 & 102982 & 111276.271077902 & -8294.27107790208 \tabularnewline
13 & 50001 & 59460.382042261 & -9459.38204226096 \tabularnewline
14 & 91093 & 76273.8374124928 & 14819.1625875072 \tabularnewline
15 & 80112 & 88361.5294643527 & -8249.52946435267 \tabularnewline
16 & 72961 & 65454.7700724852 & 7506.22992751481 \tabularnewline
17 & 77159 & 101152.00385645 & -23993.0038564496 \tabularnewline
18 & 15629 & 17166.0016422464 & -1537.00164224639 \tabularnewline
19 & 71693 & 88404.4669680967 & -16711.4669680967 \tabularnewline
20 & 19920 & 36538.9009185172 & -16618.9009185172 \tabularnewline
21 & 39403 & 42743.400325885 & -3340.40032588502 \tabularnewline
22 & 104383 & 132166.261857696 & -27783.2618576956 \tabularnewline
23 & 56088 & 29145.501982273 & 26942.498017727 \tabularnewline
24 & 62006 & 49682.7055822797 & 12323.2944177203 \tabularnewline
25 & 81665 & 86861.6468698733 & -5196.64686987332 \tabularnewline
26 & 69451 & 67669.25485888 & 1781.74514111996 \tabularnewline
27 & 88794 & 80848.4565687504 & 7945.54343124962 \tabularnewline
28 & 90642 & 116072.132669527 & -25430.1326695267 \tabularnewline
29 & 207069 & 144202.814754669 & 62866.1852453308 \tabularnewline
30 & 99340 & 86124.7124881417 & 13215.2875118583 \tabularnewline
31 & 56695 & 38846.8244304053 & 17848.1755695947 \tabularnewline
32 & 108143 & 88917.0478189578 & 19225.9521810422 \tabularnewline
33 & 64204 & 59876.8595685535 & 4327.14043144652 \tabularnewline
34 & 29101 & 23357.3726795932 & 5743.62732040678 \tabularnewline
35 & 113060 & 96402.0912904316 & 16657.9087095684 \tabularnewline
36 & 0 & 16866.0613532482 & -16866.0613532482 \tabularnewline
37 & 65773 & 43630.8152369682 & 22142.1847630318 \tabularnewline
38 & 67047 & 69637.9799991656 & -2590.97999916564 \tabularnewline
39 & 41953 & 41823.9343060633 & 129.065693936673 \tabularnewline
40 & 113787 & 154424.620380623 & -40637.6203806232 \tabularnewline
41 & 86584 & 89663.3592489441 & -3079.3592489441 \tabularnewline
42 & 59588 & 49916.5080620316 & 9671.4919379684 \tabularnewline
43 & 40064 & 52484.1835122873 & -12420.1835122873 \tabularnewline
44 & 74471 & 71339.0428800283 & 3131.95711997167 \tabularnewline
45 & 60437 & 58603.2264158439 & 1833.77358415615 \tabularnewline
46 & 55118 & 53130.3446993376 & 1987.65530066244 \tabularnewline
47 & 40295 & 45975.4372877987 & -5680.43728779865 \tabularnewline
48 & 103397 & 74390.8387460595 & 29006.1612539405 \tabularnewline
49 & 78982 & 71499.9423407139 & 7482.05765928611 \tabularnewline
50 & 67317 & 59599.329929638 & 7717.67007036198 \tabularnewline
51 & 39887 & 42841.452395059 & -2954.45239505901 \tabularnewline
52 & 59682 & 52558.1218547931 & 7123.87814520686 \tabularnewline
53 & 132740 & 94745.4686273584 & 37994.5313726416 \tabularnewline
54 & 104816 & 93732.6526295303 & 11083.3473704697 \tabularnewline
55 & 101395 & 106182.0714119 & -4787.07141190017 \tabularnewline
56 & 72824 & 71470.7183794872 & 1353.28162051277 \tabularnewline
57 & 76018 & 83784.691200646 & -7766.69120064598 \tabularnewline
58 & 33891 & 35840.8188421902 & -1949.81884219019 \tabularnewline
59 & 63694 & 80683.8651065791 & -16989.8651065791 \tabularnewline
60 & 33239 & 19700.5589399923 & 13538.4410600077 \tabularnewline
61 & 35093 & 34399.4896914457 & 693.510308554279 \tabularnewline
62 & 35252 & 40665.87640229 & -5413.87640229003 \tabularnewline
63 & 36977 & 36624.7567129538 & 352.243287046189 \tabularnewline
64 & 42406 & 68767.6455352125 & -26361.6455352125 \tabularnewline
65 & 56353 & 60681.6716816339 & -4328.67168163392 \tabularnewline
66 & 58817 & 73744.7984926963 & -14927.7984926963 \tabularnewline
67 & 81051 & 112683.560660876 & -31632.5606608762 \tabularnewline
68 & 70872 & 87607.5991895291 & -16735.5991895291 \tabularnewline
69 & 42372 & 63800.1297746723 & -21428.1297746723 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&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]63047[/C][C]42376.206150809[/C][C]20670.793849191[/C][/ROW]
[ROW][C]2[/C][C]66751[/C][C]104406.277701053[/C][C]-37655.2777010527[/C][/ROW]
[ROW][C]3[/C][C]7176[/C][C]19976.5774048994[/C][C]-12800.5774048994[/C][/ROW]
[ROW][C]4[/C][C]78306[/C][C]87922.5759469907[/C][C]-9616.57594699067[/C][/ROW]
[ROW][C]5[/C][C]144655[/C][C]90213.8205954997[/C][C]54441.1794045003[/C][/ROW]
[ROW][C]6[/C][C]269638[/C][C]252951.996990904[/C][C]16686.0030090958[/C][/ROW]
[ROW][C]7[/C][C]69266[/C][C]78317.3675765155[/C][C]-9051.36757651555[/C][/ROW]
[ROW][C]8[/C][C]83529[/C][C]101687.494075529[/C][C]-18158.4940755286[/C][/ROW]
[ROW][C]9[/C][C]73226[/C][C]81250.2716834472[/C][C]-8024.27168344724[/C][/ROW]
[ROW][C]10[/C][C]178519[/C][C]143839.137452296[/C][C]34679.8625477037[/C][/ROW]
[ROW][C]11[/C][C]67250[/C][C]81700.4552937399[/C][C]-14450.4552937399[/C][/ROW]
[ROW][C]12[/C][C]102982[/C][C]111276.271077902[/C][C]-8294.27107790208[/C][/ROW]
[ROW][C]13[/C][C]50001[/C][C]59460.382042261[/C][C]-9459.38204226096[/C][/ROW]
[ROW][C]14[/C][C]91093[/C][C]76273.8374124928[/C][C]14819.1625875072[/C][/ROW]
[ROW][C]15[/C][C]80112[/C][C]88361.5294643527[/C][C]-8249.52946435267[/C][/ROW]
[ROW][C]16[/C][C]72961[/C][C]65454.7700724852[/C][C]7506.22992751481[/C][/ROW]
[ROW][C]17[/C][C]77159[/C][C]101152.00385645[/C][C]-23993.0038564496[/C][/ROW]
[ROW][C]18[/C][C]15629[/C][C]17166.0016422464[/C][C]-1537.00164224639[/C][/ROW]
[ROW][C]19[/C][C]71693[/C][C]88404.4669680967[/C][C]-16711.4669680967[/C][/ROW]
[ROW][C]20[/C][C]19920[/C][C]36538.9009185172[/C][C]-16618.9009185172[/C][/ROW]
[ROW][C]21[/C][C]39403[/C][C]42743.400325885[/C][C]-3340.40032588502[/C][/ROW]
[ROW][C]22[/C][C]104383[/C][C]132166.261857696[/C][C]-27783.2618576956[/C][/ROW]
[ROW][C]23[/C][C]56088[/C][C]29145.501982273[/C][C]26942.498017727[/C][/ROW]
[ROW][C]24[/C][C]62006[/C][C]49682.7055822797[/C][C]12323.2944177203[/C][/ROW]
[ROW][C]25[/C][C]81665[/C][C]86861.6468698733[/C][C]-5196.64686987332[/C][/ROW]
[ROW][C]26[/C][C]69451[/C][C]67669.25485888[/C][C]1781.74514111996[/C][/ROW]
[ROW][C]27[/C][C]88794[/C][C]80848.4565687504[/C][C]7945.54343124962[/C][/ROW]
[ROW][C]28[/C][C]90642[/C][C]116072.132669527[/C][C]-25430.1326695267[/C][/ROW]
[ROW][C]29[/C][C]207069[/C][C]144202.814754669[/C][C]62866.1852453308[/C][/ROW]
[ROW][C]30[/C][C]99340[/C][C]86124.7124881417[/C][C]13215.2875118583[/C][/ROW]
[ROW][C]31[/C][C]56695[/C][C]38846.8244304053[/C][C]17848.1755695947[/C][/ROW]
[ROW][C]32[/C][C]108143[/C][C]88917.0478189578[/C][C]19225.9521810422[/C][/ROW]
[ROW][C]33[/C][C]64204[/C][C]59876.8595685535[/C][C]4327.14043144652[/C][/ROW]
[ROW][C]34[/C][C]29101[/C][C]23357.3726795932[/C][C]5743.62732040678[/C][/ROW]
[ROW][C]35[/C][C]113060[/C][C]96402.0912904316[/C][C]16657.9087095684[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]16866.0613532482[/C][C]-16866.0613532482[/C][/ROW]
[ROW][C]37[/C][C]65773[/C][C]43630.8152369682[/C][C]22142.1847630318[/C][/ROW]
[ROW][C]38[/C][C]67047[/C][C]69637.9799991656[/C][C]-2590.97999916564[/C][/ROW]
[ROW][C]39[/C][C]41953[/C][C]41823.9343060633[/C][C]129.065693936673[/C][/ROW]
[ROW][C]40[/C][C]113787[/C][C]154424.620380623[/C][C]-40637.6203806232[/C][/ROW]
[ROW][C]41[/C][C]86584[/C][C]89663.3592489441[/C][C]-3079.3592489441[/C][/ROW]
[ROW][C]42[/C][C]59588[/C][C]49916.5080620316[/C][C]9671.4919379684[/C][/ROW]
[ROW][C]43[/C][C]40064[/C][C]52484.1835122873[/C][C]-12420.1835122873[/C][/ROW]
[ROW][C]44[/C][C]74471[/C][C]71339.0428800283[/C][C]3131.95711997167[/C][/ROW]
[ROW][C]45[/C][C]60437[/C][C]58603.2264158439[/C][C]1833.77358415615[/C][/ROW]
[ROW][C]46[/C][C]55118[/C][C]53130.3446993376[/C][C]1987.65530066244[/C][/ROW]
[ROW][C]47[/C][C]40295[/C][C]45975.4372877987[/C][C]-5680.43728779865[/C][/ROW]
[ROW][C]48[/C][C]103397[/C][C]74390.8387460595[/C][C]29006.1612539405[/C][/ROW]
[ROW][C]49[/C][C]78982[/C][C]71499.9423407139[/C][C]7482.05765928611[/C][/ROW]
[ROW][C]50[/C][C]67317[/C][C]59599.329929638[/C][C]7717.67007036198[/C][/ROW]
[ROW][C]51[/C][C]39887[/C][C]42841.452395059[/C][C]-2954.45239505901[/C][/ROW]
[ROW][C]52[/C][C]59682[/C][C]52558.1218547931[/C][C]7123.87814520686[/C][/ROW]
[ROW][C]53[/C][C]132740[/C][C]94745.4686273584[/C][C]37994.5313726416[/C][/ROW]
[ROW][C]54[/C][C]104816[/C][C]93732.6526295303[/C][C]11083.3473704697[/C][/ROW]
[ROW][C]55[/C][C]101395[/C][C]106182.0714119[/C][C]-4787.07141190017[/C][/ROW]
[ROW][C]56[/C][C]72824[/C][C]71470.7183794872[/C][C]1353.28162051277[/C][/ROW]
[ROW][C]57[/C][C]76018[/C][C]83784.691200646[/C][C]-7766.69120064598[/C][/ROW]
[ROW][C]58[/C][C]33891[/C][C]35840.8188421902[/C][C]-1949.81884219019[/C][/ROW]
[ROW][C]59[/C][C]63694[/C][C]80683.8651065791[/C][C]-16989.8651065791[/C][/ROW]
[ROW][C]60[/C][C]33239[/C][C]19700.5589399923[/C][C]13538.4410600077[/C][/ROW]
[ROW][C]61[/C][C]35093[/C][C]34399.4896914457[/C][C]693.510308554279[/C][/ROW]
[ROW][C]62[/C][C]35252[/C][C]40665.87640229[/C][C]-5413.87640229003[/C][/ROW]
[ROW][C]63[/C][C]36977[/C][C]36624.7567129538[/C][C]352.243287046189[/C][/ROW]
[ROW][C]64[/C][C]42406[/C][C]68767.6455352125[/C][C]-26361.6455352125[/C][/ROW]
[ROW][C]65[/C][C]56353[/C][C]60681.6716816339[/C][C]-4328.67168163392[/C][/ROW]
[ROW][C]66[/C][C]58817[/C][C]73744.7984926963[/C][C]-14927.7984926963[/C][/ROW]
[ROW][C]67[/C][C]81051[/C][C]112683.560660876[/C][C]-31632.5606608762[/C][/ROW]
[ROW][C]68[/C][C]70872[/C][C]87607.5991895291[/C][C]-16735.5991895291[/C][/ROW]
[ROW][C]69[/C][C]42372[/C][C]63800.1297746723[/C][C]-21428.1297746723[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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
16304742376.20615080920670.793849191
266751104406.277701053-37655.2777010527
3717619976.5774048994-12800.5774048994
47830687922.5759469907-9616.57594699067
514465590213.820595499754441.1794045003
6269638252951.99699090416686.0030090958
76926678317.3675765155-9051.36757651555
883529101687.494075529-18158.4940755286
97322681250.2716834472-8024.27168344724
10178519143839.13745229634679.8625477037
116725081700.4552937399-14450.4552937399
12102982111276.271077902-8294.27107790208
135000159460.382042261-9459.38204226096
149109376273.837412492814819.1625875072
158011288361.5294643527-8249.52946435267
167296165454.77007248527506.22992751481
1777159101152.00385645-23993.0038564496
181562917166.0016422464-1537.00164224639
197169388404.4669680967-16711.4669680967
201992036538.9009185172-16618.9009185172
213940342743.400325885-3340.40032588502
22104383132166.261857696-27783.2618576956
235608829145.50198227326942.498017727
246200649682.705582279712323.2944177203
258166586861.6468698733-5196.64686987332
266945167669.254858881781.74514111996
278879480848.45656875047945.54343124962
2890642116072.132669527-25430.1326695267
29207069144202.81475466962866.1852453308
309934086124.712488141713215.2875118583
315669538846.824430405317848.1755695947
3210814388917.047818957819225.9521810422
336420459876.85956855354327.14043144652
342910123357.37267959325743.62732040678
3511306096402.091290431616657.9087095684
36016866.0613532482-16866.0613532482
376577343630.815236968222142.1847630318
386704769637.9799991656-2590.97999916564
394195341823.9343060633129.065693936673
40113787154424.620380623-40637.6203806232
418658489663.3592489441-3079.3592489441
425958849916.50806203169671.4919379684
434006452484.1835122873-12420.1835122873
447447171339.04288002833131.95711997167
456043758603.22641584391833.77358415615
465511853130.34469933761987.65530066244
474029545975.4372877987-5680.43728779865
4810339774390.838746059529006.1612539405
497898271499.94234071397482.05765928611
506731759599.3299296387717.67007036198
513988742841.452395059-2954.45239505901
525968252558.12185479317123.87814520686
5313274094745.468627358437994.5313726416
5410481693732.652629530311083.3473704697
55101395106182.0714119-4787.07141190017
567282471470.71837948721353.28162051277
577601883784.691200646-7766.69120064598
583389135840.8188421902-1949.81884219019
596369480683.8651065791-16989.8651065791
603323919700.558939992313538.4410600077
613509334399.4896914457693.510308554279
623525240665.87640229-5413.87640229003
633697736624.7567129538352.243287046189
644240668767.6455352125-26361.6455352125
655635360681.6716816339-4328.67168163392
665881773744.7984926963-14927.7984926963
6781051112683.560660876-31632.5606608762
687087287607.5991895291-16735.5991895291
694237263800.1297746723-21428.1297746723







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8827199747480580.2345600505038840.117280025251942
80.848595146154280.3028097076914410.151404853845721
90.8078126667059710.3843746665880570.192187333294029
100.978961703777840.04207659244431930.0210382962221596
110.9777208646441960.04455827071160710.0222791353558035
120.9714795868483670.05704082630326510.0285204131516326
130.9533980972960.0932038054080010.0466019027040005
140.9433719300553770.1132561398892450.0566280699446227
150.9141575292981320.1716849414037360.085842470701868
160.8862977262978720.2274045474042560.113702273702128
170.9208365528884760.1583268942230490.0791634471115243
180.8888562510098680.2222874979802640.111143748990132
190.8775203241185320.2449593517629350.122479675881468
200.8545688441141240.2908623117717510.145431155885876
210.8051905401994990.3896189196010020.194809459800501
220.8401584568863780.3196830862272430.159841543113622
230.8718514849750040.2562970300499930.128148515024996
240.8519810537490640.2960378925018720.148018946250936
250.8060116757655130.3879766484689740.193988324234487
260.7517290191165430.4965419617669140.248270980883457
270.6954884830839540.6090230338320930.304511516916046
280.736780497892190.5264390042156210.26321950210781
290.9901229280231090.0197541439537830.00987707197689148
300.9892945450318730.0214109099362550.0107054549681275
310.9867104479803520.02657910403929630.0132895520196481
320.988107502467410.02378499506518110.0118924975325906
330.982363243977940.03527351204411880.0176367560220594
340.972819640772040.054360718455920.02718035922796
350.9754796238970040.04904075220599150.0245203761029957
360.9805189352398240.03896212952035130.0194810647601756
370.9815382351238640.03692352975227220.0184617648761361
380.972313521584530.0553729568309410.0276864784154705
390.9616194011040240.0767611977919520.038380598895976
400.981503772048480.03699245590304120.0184962279515206
410.9714269894859370.05714602102812620.0285730105140631
420.9620012397682920.07599752046341570.0379987602317078
430.954135536933120.09172892613376170.0458644630668808
440.9328290860480890.1343418279038220.0671709139519112
450.9025603087471210.1948793825057580.0974396912528792
460.8668556215526770.2662887568946470.133144378447323
470.8221648412235450.355670317552910.177835158776455
480.881181068434160.2376378631316820.118818931565841
490.8541302271687940.2917395456624130.145869772831206
500.8111828411661290.3776343176677430.188817158833871
510.7463650854317690.5072698291364620.253634914568231
520.6817080743167090.6365838513665810.318291925683291
530.9775208355597210.04495832888055810.0224791644402791
540.995027837225290.009944325549418920.00497216277470946
550.9977427608757960.004514478248407360.00225723912420368
560.998134706358490.003730587283019090.00186529364150954
570.9989468896297510.002106220740498020.00105311037024901
580.9967426104564380.006514779087124630.00325738954356231
590.9909161948124930.01816761037501350.00908380518750674
600.9794767612741720.04104647745165580.0205232387258279
610.9433720193305860.1132559613388280.0566279806694141
620.8571218900286450.285756219942710.142878109971355

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.882719974748058 & 0.234560050503884 & 0.117280025251942 \tabularnewline
8 & 0.84859514615428 & 0.302809707691441 & 0.151404853845721 \tabularnewline
9 & 0.807812666705971 & 0.384374666588057 & 0.192187333294029 \tabularnewline
10 & 0.97896170377784 & 0.0420765924443193 & 0.0210382962221596 \tabularnewline
11 & 0.977720864644196 & 0.0445582707116071 & 0.0222791353558035 \tabularnewline
12 & 0.971479586848367 & 0.0570408263032651 & 0.0285204131516326 \tabularnewline
13 & 0.953398097296 & 0.093203805408001 & 0.0466019027040005 \tabularnewline
14 & 0.943371930055377 & 0.113256139889245 & 0.0566280699446227 \tabularnewline
15 & 0.914157529298132 & 0.171684941403736 & 0.085842470701868 \tabularnewline
16 & 0.886297726297872 & 0.227404547404256 & 0.113702273702128 \tabularnewline
17 & 0.920836552888476 & 0.158326894223049 & 0.0791634471115243 \tabularnewline
18 & 0.888856251009868 & 0.222287497980264 & 0.111143748990132 \tabularnewline
19 & 0.877520324118532 & 0.244959351762935 & 0.122479675881468 \tabularnewline
20 & 0.854568844114124 & 0.290862311771751 & 0.145431155885876 \tabularnewline
21 & 0.805190540199499 & 0.389618919601002 & 0.194809459800501 \tabularnewline
22 & 0.840158456886378 & 0.319683086227243 & 0.159841543113622 \tabularnewline
23 & 0.871851484975004 & 0.256297030049993 & 0.128148515024996 \tabularnewline
24 & 0.851981053749064 & 0.296037892501872 & 0.148018946250936 \tabularnewline
25 & 0.806011675765513 & 0.387976648468974 & 0.193988324234487 \tabularnewline
26 & 0.751729019116543 & 0.496541961766914 & 0.248270980883457 \tabularnewline
27 & 0.695488483083954 & 0.609023033832093 & 0.304511516916046 \tabularnewline
28 & 0.73678049789219 & 0.526439004215621 & 0.26321950210781 \tabularnewline
29 & 0.990122928023109 & 0.019754143953783 & 0.00987707197689148 \tabularnewline
30 & 0.989294545031873 & 0.021410909936255 & 0.0107054549681275 \tabularnewline
31 & 0.986710447980352 & 0.0265791040392963 & 0.0132895520196481 \tabularnewline
32 & 0.98810750246741 & 0.0237849950651811 & 0.0118924975325906 \tabularnewline
33 & 0.98236324397794 & 0.0352735120441188 & 0.0176367560220594 \tabularnewline
34 & 0.97281964077204 & 0.05436071845592 & 0.02718035922796 \tabularnewline
35 & 0.975479623897004 & 0.0490407522059915 & 0.0245203761029957 \tabularnewline
36 & 0.980518935239824 & 0.0389621295203513 & 0.0194810647601756 \tabularnewline
37 & 0.981538235123864 & 0.0369235297522722 & 0.0184617648761361 \tabularnewline
38 & 0.97231352158453 & 0.055372956830941 & 0.0276864784154705 \tabularnewline
39 & 0.961619401104024 & 0.076761197791952 & 0.038380598895976 \tabularnewline
40 & 0.98150377204848 & 0.0369924559030412 & 0.0184962279515206 \tabularnewline
41 & 0.971426989485937 & 0.0571460210281262 & 0.0285730105140631 \tabularnewline
42 & 0.962001239768292 & 0.0759975204634157 & 0.0379987602317078 \tabularnewline
43 & 0.95413553693312 & 0.0917289261337617 & 0.0458644630668808 \tabularnewline
44 & 0.932829086048089 & 0.134341827903822 & 0.0671709139519112 \tabularnewline
45 & 0.902560308747121 & 0.194879382505758 & 0.0974396912528792 \tabularnewline
46 & 0.866855621552677 & 0.266288756894647 & 0.133144378447323 \tabularnewline
47 & 0.822164841223545 & 0.35567031755291 & 0.177835158776455 \tabularnewline
48 & 0.88118106843416 & 0.237637863131682 & 0.118818931565841 \tabularnewline
49 & 0.854130227168794 & 0.291739545662413 & 0.145869772831206 \tabularnewline
50 & 0.811182841166129 & 0.377634317667743 & 0.188817158833871 \tabularnewline
51 & 0.746365085431769 & 0.507269829136462 & 0.253634914568231 \tabularnewline
52 & 0.681708074316709 & 0.636583851366581 & 0.318291925683291 \tabularnewline
53 & 0.977520835559721 & 0.0449583288805581 & 0.0224791644402791 \tabularnewline
54 & 0.99502783722529 & 0.00994432554941892 & 0.00497216277470946 \tabularnewline
55 & 0.997742760875796 & 0.00451447824840736 & 0.00225723912420368 \tabularnewline
56 & 0.99813470635849 & 0.00373058728301909 & 0.00186529364150954 \tabularnewline
57 & 0.998946889629751 & 0.00210622074049802 & 0.00105311037024901 \tabularnewline
58 & 0.996742610456438 & 0.00651477908712463 & 0.00325738954356231 \tabularnewline
59 & 0.990916194812493 & 0.0181676103750135 & 0.00908380518750674 \tabularnewline
60 & 0.979476761274172 & 0.0410464774516558 & 0.0205232387258279 \tabularnewline
61 & 0.943372019330586 & 0.113255961338828 & 0.0566279806694141 \tabularnewline
62 & 0.857121890028645 & 0.28575621994271 & 0.142878109971355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&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]7[/C][C]0.882719974748058[/C][C]0.234560050503884[/C][C]0.117280025251942[/C][/ROW]
[ROW][C]8[/C][C]0.84859514615428[/C][C]0.302809707691441[/C][C]0.151404853845721[/C][/ROW]
[ROW][C]9[/C][C]0.807812666705971[/C][C]0.384374666588057[/C][C]0.192187333294029[/C][/ROW]
[ROW][C]10[/C][C]0.97896170377784[/C][C]0.0420765924443193[/C][C]0.0210382962221596[/C][/ROW]
[ROW][C]11[/C][C]0.977720864644196[/C][C]0.0445582707116071[/C][C]0.0222791353558035[/C][/ROW]
[ROW][C]12[/C][C]0.971479586848367[/C][C]0.0570408263032651[/C][C]0.0285204131516326[/C][/ROW]
[ROW][C]13[/C][C]0.953398097296[/C][C]0.093203805408001[/C][C]0.0466019027040005[/C][/ROW]
[ROW][C]14[/C][C]0.943371930055377[/C][C]0.113256139889245[/C][C]0.0566280699446227[/C][/ROW]
[ROW][C]15[/C][C]0.914157529298132[/C][C]0.171684941403736[/C][C]0.085842470701868[/C][/ROW]
[ROW][C]16[/C][C]0.886297726297872[/C][C]0.227404547404256[/C][C]0.113702273702128[/C][/ROW]
[ROW][C]17[/C][C]0.920836552888476[/C][C]0.158326894223049[/C][C]0.0791634471115243[/C][/ROW]
[ROW][C]18[/C][C]0.888856251009868[/C][C]0.222287497980264[/C][C]0.111143748990132[/C][/ROW]
[ROW][C]19[/C][C]0.877520324118532[/C][C]0.244959351762935[/C][C]0.122479675881468[/C][/ROW]
[ROW][C]20[/C][C]0.854568844114124[/C][C]0.290862311771751[/C][C]0.145431155885876[/C][/ROW]
[ROW][C]21[/C][C]0.805190540199499[/C][C]0.389618919601002[/C][C]0.194809459800501[/C][/ROW]
[ROW][C]22[/C][C]0.840158456886378[/C][C]0.319683086227243[/C][C]0.159841543113622[/C][/ROW]
[ROW][C]23[/C][C]0.871851484975004[/C][C]0.256297030049993[/C][C]0.128148515024996[/C][/ROW]
[ROW][C]24[/C][C]0.851981053749064[/C][C]0.296037892501872[/C][C]0.148018946250936[/C][/ROW]
[ROW][C]25[/C][C]0.806011675765513[/C][C]0.387976648468974[/C][C]0.193988324234487[/C][/ROW]
[ROW][C]26[/C][C]0.751729019116543[/C][C]0.496541961766914[/C][C]0.248270980883457[/C][/ROW]
[ROW][C]27[/C][C]0.695488483083954[/C][C]0.609023033832093[/C][C]0.304511516916046[/C][/ROW]
[ROW][C]28[/C][C]0.73678049789219[/C][C]0.526439004215621[/C][C]0.26321950210781[/C][/ROW]
[ROW][C]29[/C][C]0.990122928023109[/C][C]0.019754143953783[/C][C]0.00987707197689148[/C][/ROW]
[ROW][C]30[/C][C]0.989294545031873[/C][C]0.021410909936255[/C][C]0.0107054549681275[/C][/ROW]
[ROW][C]31[/C][C]0.986710447980352[/C][C]0.0265791040392963[/C][C]0.0132895520196481[/C][/ROW]
[ROW][C]32[/C][C]0.98810750246741[/C][C]0.0237849950651811[/C][C]0.0118924975325906[/C][/ROW]
[ROW][C]33[/C][C]0.98236324397794[/C][C]0.0352735120441188[/C][C]0.0176367560220594[/C][/ROW]
[ROW][C]34[/C][C]0.97281964077204[/C][C]0.05436071845592[/C][C]0.02718035922796[/C][/ROW]
[ROW][C]35[/C][C]0.975479623897004[/C][C]0.0490407522059915[/C][C]0.0245203761029957[/C][/ROW]
[ROW][C]36[/C][C]0.980518935239824[/C][C]0.0389621295203513[/C][C]0.0194810647601756[/C][/ROW]
[ROW][C]37[/C][C]0.981538235123864[/C][C]0.0369235297522722[/C][C]0.0184617648761361[/C][/ROW]
[ROW][C]38[/C][C]0.97231352158453[/C][C]0.055372956830941[/C][C]0.0276864784154705[/C][/ROW]
[ROW][C]39[/C][C]0.961619401104024[/C][C]0.076761197791952[/C][C]0.038380598895976[/C][/ROW]
[ROW][C]40[/C][C]0.98150377204848[/C][C]0.0369924559030412[/C][C]0.0184962279515206[/C][/ROW]
[ROW][C]41[/C][C]0.971426989485937[/C][C]0.0571460210281262[/C][C]0.0285730105140631[/C][/ROW]
[ROW][C]42[/C][C]0.962001239768292[/C][C]0.0759975204634157[/C][C]0.0379987602317078[/C][/ROW]
[ROW][C]43[/C][C]0.95413553693312[/C][C]0.0917289261337617[/C][C]0.0458644630668808[/C][/ROW]
[ROW][C]44[/C][C]0.932829086048089[/C][C]0.134341827903822[/C][C]0.0671709139519112[/C][/ROW]
[ROW][C]45[/C][C]0.902560308747121[/C][C]0.194879382505758[/C][C]0.0974396912528792[/C][/ROW]
[ROW][C]46[/C][C]0.866855621552677[/C][C]0.266288756894647[/C][C]0.133144378447323[/C][/ROW]
[ROW][C]47[/C][C]0.822164841223545[/C][C]0.35567031755291[/C][C]0.177835158776455[/C][/ROW]
[ROW][C]48[/C][C]0.88118106843416[/C][C]0.237637863131682[/C][C]0.118818931565841[/C][/ROW]
[ROW][C]49[/C][C]0.854130227168794[/C][C]0.291739545662413[/C][C]0.145869772831206[/C][/ROW]
[ROW][C]50[/C][C]0.811182841166129[/C][C]0.377634317667743[/C][C]0.188817158833871[/C][/ROW]
[ROW][C]51[/C][C]0.746365085431769[/C][C]0.507269829136462[/C][C]0.253634914568231[/C][/ROW]
[ROW][C]52[/C][C]0.681708074316709[/C][C]0.636583851366581[/C][C]0.318291925683291[/C][/ROW]
[ROW][C]53[/C][C]0.977520835559721[/C][C]0.0449583288805581[/C][C]0.0224791644402791[/C][/ROW]
[ROW][C]54[/C][C]0.99502783722529[/C][C]0.00994432554941892[/C][C]0.00497216277470946[/C][/ROW]
[ROW][C]55[/C][C]0.997742760875796[/C][C]0.00451447824840736[/C][C]0.00225723912420368[/C][/ROW]
[ROW][C]56[/C][C]0.99813470635849[/C][C]0.00373058728301909[/C][C]0.00186529364150954[/C][/ROW]
[ROW][C]57[/C][C]0.998946889629751[/C][C]0.00210622074049802[/C][C]0.00105311037024901[/C][/ROW]
[ROW][C]58[/C][C]0.996742610456438[/C][C]0.00651477908712463[/C][C]0.00325738954356231[/C][/ROW]
[ROW][C]59[/C][C]0.990916194812493[/C][C]0.0181676103750135[/C][C]0.00908380518750674[/C][/ROW]
[ROW][C]60[/C][C]0.979476761274172[/C][C]0.0410464774516558[/C][C]0.0205232387258279[/C][/ROW]
[ROW][C]61[/C][C]0.943372019330586[/C][C]0.113255961338828[/C][C]0.0566279806694141[/C][/ROW]
[ROW][C]62[/C][C]0.857121890028645[/C][C]0.28575621994271[/C][C]0.142878109971355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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
70.8827199747480580.2345600505038840.117280025251942
80.848595146154280.3028097076914410.151404853845721
90.8078126667059710.3843746665880570.192187333294029
100.978961703777840.04207659244431930.0210382962221596
110.9777208646441960.04455827071160710.0222791353558035
120.9714795868483670.05704082630326510.0285204131516326
130.9533980972960.0932038054080010.0466019027040005
140.9433719300553770.1132561398892450.0566280699446227
150.9141575292981320.1716849414037360.085842470701868
160.8862977262978720.2274045474042560.113702273702128
170.9208365528884760.1583268942230490.0791634471115243
180.8888562510098680.2222874979802640.111143748990132
190.8775203241185320.2449593517629350.122479675881468
200.8545688441141240.2908623117717510.145431155885876
210.8051905401994990.3896189196010020.194809459800501
220.8401584568863780.3196830862272430.159841543113622
230.8718514849750040.2562970300499930.128148515024996
240.8519810537490640.2960378925018720.148018946250936
250.8060116757655130.3879766484689740.193988324234487
260.7517290191165430.4965419617669140.248270980883457
270.6954884830839540.6090230338320930.304511516916046
280.736780497892190.5264390042156210.26321950210781
290.9901229280231090.0197541439537830.00987707197689148
300.9892945450318730.0214109099362550.0107054549681275
310.9867104479803520.02657910403929630.0132895520196481
320.988107502467410.02378499506518110.0118924975325906
330.982363243977940.03527351204411880.0176367560220594
340.972819640772040.054360718455920.02718035922796
350.9754796238970040.04904075220599150.0245203761029957
360.9805189352398240.03896212952035130.0194810647601756
370.9815382351238640.03692352975227220.0184617648761361
380.972313521584530.0553729568309410.0276864784154705
390.9616194011040240.0767611977919520.038380598895976
400.981503772048480.03699245590304120.0184962279515206
410.9714269894859370.05714602102812620.0285730105140631
420.9620012397682920.07599752046341570.0379987602317078
430.954135536933120.09172892613376170.0458644630668808
440.9328290860480890.1343418279038220.0671709139519112
450.9025603087471210.1948793825057580.0974396912528792
460.8668556215526770.2662887568946470.133144378447323
470.8221648412235450.355670317552910.177835158776455
480.881181068434160.2376378631316820.118818931565841
490.8541302271687940.2917395456624130.145869772831206
500.8111828411661290.3776343176677430.188817158833871
510.7463650854317690.5072698291364620.253634914568231
520.6817080743167090.6365838513665810.318291925683291
530.9775208355597210.04495832888055810.0224791644402791
540.995027837225290.009944325549418920.00497216277470946
550.9977427608757960.004514478248407360.00225723912420368
560.998134706358490.003730587283019090.00186529364150954
570.9989468896297510.002106220740498020.00105311037024901
580.9967426104564380.006514779087124630.00325738954356231
590.9909161948124930.01816761037501350.00908380518750674
600.9794767612741720.04104647745165580.0205232387258279
610.9433720193305860.1132559613388280.0566279806694141
620.8571218900286450.285756219942710.142878109971355







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0892857142857143NOK
5% type I error level190.339285714285714NOK
10% type I error level270.482142857142857NOK

\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 & 5 & 0.0892857142857143 & NOK \tabularnewline
5% type I error level & 19 & 0.339285714285714 & NOK \tabularnewline
10% type I error level & 27 & 0.482142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147114&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]5[/C][C]0.0892857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.339285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.482142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147114&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147114&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 level50.0892857142857143NOK
5% type I error level190.339285714285714NOK
10% type I error level270.482142857142857NOK



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