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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 18:00:31 -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/2012/Nov/05/t1352156457sfnfksrujqfxdtk.htm/, Retrieved Mon, 06 Feb 2023 00:37:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186357, Retrieved Mon, 06 Feb 2023 00:37:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS 7] [2011-11-24 14:05:53] [2e8e2c135ae7a1d1ed044e87454acf31]
- R PD    [Multiple Regression] [WS7] [2012-11-05 23:00:31] [951f0bbf00246852608bf7fcd40f4937] [Current]
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Dataseries X:
1	21221	6086	7
2	7106	2273	6
3	4794	2601	6
4	6776	2708	6
5	7122	3321	6
6	12025	2106	6
7	18945	6353	5
8	24380	7912	5
9	6942	1905	5
10	7370	3841	5
11	13143	2737	5
12	11831	4449	5
13	3102	1253	5
14	7355	2372	5
15	7749	3303	5
16	10306	2876	5
17	7860	2939	4
18	9138	2149	4
19	6545	2545	4
20	9829	3734	4
21	9460	1911	4
22	30631	875	4
23	4807	1466	4
24	4618	1562	4
25	7304	1203	4
26	12621	2860	4
27	9320	3245	4
28	9569	2668	4
29	8480	2111	4
30	4718	942	4
31	7342	2690	4
32	8230	3675	4
33	10007	3798	4
34	9176	672	4
35	2334	523	4
36	5459	2352	3
37	5440	1476	3
38	9027	2646	3
39	9852	2500	3
40	3122	720	3
41	8863	2483	3
42	4286	1550	3
43	9119	2551	3
44	7297	2544	3
45	8009	2870	3
46	5049	1288	3
47	5057	1474	3
48	4372	1521	3
49	3393	1331	3
50	7586	1685	3
51	7440	2755	3
52	8636	1268	3
53	10846	1266	3
54	6843	1564	3
55	5102	1062	3
56	4183	1306	3
57	10083	4566	3
58	5273	1655	3
59	6433	1423	3
60	6116	474	3
61	5887	725	3
62	7415	1061	2
63	7953	687	2
64	8447	2411	2
65	4733	1705	2
66	11478	2422	2
67	4234	1900	2
68	11089	2848	2
69	6390	1398	2
70	7737	2625	2




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' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186357&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' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186357&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Ranking[t] = + 96.4872254866104 -0.000114095407653338Characters[t] -0.000387566505631692Revisions[t] -15.9835092599923`Hours\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ranking[t] =  +  96.4872254866104 -0.000114095407653338Characters[t] -0.000387566505631692Revisions[t] -15.9835092599923`Hours\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ranking[t] =  +  96.4872254866104 -0.000114095407653338Characters[t] -0.000387566505631692Revisions[t] -15.9835092599923`Hours\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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
Ranking[t] = + 96.4872254866104 -0.000114095407653338Characters[t] -0.000387566505631692Revisions[t] -15.9835092599923`Hours\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)96.48722548661042.92058833.036900
Characters-0.0001140954076533380.000224-0.50880.612580.30629
Revisions-0.0003875665056316920.000823-0.47090.6392590.319629
`Hours\r`-15.98350925999230.814517-19.623300

\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) & 96.4872254866104 & 2.920588 & 33.0369 & 0 & 0 \tabularnewline
Characters & -0.000114095407653338 & 0.000224 & -0.5088 & 0.61258 & 0.30629 \tabularnewline
Revisions & -0.000387566505631692 & 0.000823 & -0.4709 & 0.639259 & 0.319629 \tabularnewline
`Hours\r` & -15.9835092599923 & 0.814517 & -19.6233 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&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]96.4872254866104[/C][C]2.920588[/C][C]33.0369[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Characters[/C][C]-0.000114095407653338[/C][C]0.000224[/C][C]-0.5088[/C][C]0.61258[/C][C]0.30629[/C][/ROW]
[ROW][C]Revisions[/C][C]-0.000387566505631692[/C][C]0.000823[/C][C]-0.4709[/C][C]0.639259[/C][C]0.319629[/C][/ROW]
[ROW][C]`Hours\r`[/C][C]-15.9835092599923[/C][C]0.814517[/C][C]-19.6233[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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)96.48722548661042.92058833.036900
Characters-0.0001140954076533380.000224-0.50880.612580.30629
Revisions-0.0003875665056316920.000823-0.47090.6392590.319629
`Hours\r`-15.98350925999230.814517-19.623300







Multiple Linear Regression - Regression Statistics
Multiple R0.940386567189182
R-squared0.884326895749854
Adjusted R-squared0.879069027374847
F-TEST (value)168.191143763414
F-TEST (DF numerator)3
F-TEST (DF denominator)66
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.07711649112247
Sum Squared Residuals3305.64813670856

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.940386567189182 \tabularnewline
R-squared & 0.884326895749854 \tabularnewline
Adjusted R-squared & 0.879069027374847 \tabularnewline
F-TEST (value) & 168.191143763414 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.07711649112247 \tabularnewline
Sum Squared Residuals & 3305.64813670856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.940386567189182[/C][/ROW]
[ROW][C]R-squared[/C][C]0.884326895749854[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.879069027374847[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]168.191143763414[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/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]7.07711649112247[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3305.64813670856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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 R0.940386567189182
R-squared0.884326895749854
Adjusted R-squared0.879069027374847
F-TEST (value)168.191143763414
F-TEST (DF numerator)3
F-TEST (DF denominator)66
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.07711649112247
Sum Squared Residuals3305.64813670856







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11-20.177287732421721.1772877324217
22-1.105530707428883.10553070742888
33-0.9688639387815593.96886393878156
44-1.236470652853065.23647065285306
55-1.513525931853356.51352593185335
66-1.602042411235157.60204241123515
7711.9459316783783-4.94593167837826
8810.7216069555026-2.72160695550257
9915.039314673491-6.03931467349104
101014.2401530841125-4.24015308411245
111114.0093537179471-3.00935371794713
121213.4955330351468-1.49553303514685
131315.7301344005517-2.73013440055172
141414.8111997120002-0.811199712000209
151514.40542170464170.59457829535831
161614.27917064517681.72082935482316
171730.5173405824344-13.5173405824344
181830.6777041909025-12.6777041909025
191930.8200772467174-11.8200772467174
202029.9845713527878-9.98457135278779
212130.7332062979784-9.73320629797845
222228.7192113223841-6.71921132238407
232331.4365593247955-8.43655932479553
242431.4209169723014-7.42091697230137
252531.2535930828663-6.25359308286628
262630.0047501005418-4.00475010054177
272730.2321659365372-3.23216593653724
282830.427382053781-2.42738205378104
292930.7675064963524-1.76750649635238
303031.6497986650277-1.64979866502768
313130.67294606350110.327053936498871
323230.18987633345781.81012366654225
333329.93945811386513.06054188613493
343431.24580429422972.75419570577034
353532.08419248273292.91580751726708
363647.0022944550082-11.0022944550082
373747.343970526687-10.343970526687
383846.4812574878454-8.48125748784536
393946.4437134863536-7.44371348635358
404047.901443959885-7.90144395988495
414146.5631424751185-5.56314247511847
424247.4469567057022-5.44695670570216
434346.5075795283763-3.50757952837626
444446.7181743266601-2.71817432666006
454546.510591715575-1.51059171557496
464647.4614443341382-1.46144433413817
474747.3884442008294-0.388444200829446
484847.44838392930730.551616070692703
494947.63372096946991.36627903053007
505047.01812038218592.98187961781413
515146.62008215067734.37991784932265
525247.05993543699834.94006456300172
535346.80855971909576.19144028090433
545447.14978881725376.85021118274627
555547.54298730780537.4570126921947
565647.55327476006468.44672523993541
575745.616645046550611.3833549534494
585847.29365005525710.706349944743
595947.251214811685711.7487851883143
606047.655183669756312.3448163302437
616147.584032325195313.4159676748047
626263.2629814564011-1.26298145640107
636363.3465480001898-0.346548000189826
646462.62202021311.37797978689996
656563.31939251010051.68060748989949
666662.27193380094083.72806619905917
676763.30075064992133.69924935007865
686862.15121358311895.84878641688112
696963.24931933684795.75068066315213
707062.62008872032877.37991127967128

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & -20.1772877324217 & 21.1772877324217 \tabularnewline
2 & 2 & -1.10553070742888 & 3.10553070742888 \tabularnewline
3 & 3 & -0.968863938781559 & 3.96886393878156 \tabularnewline
4 & 4 & -1.23647065285306 & 5.23647065285306 \tabularnewline
5 & 5 & -1.51352593185335 & 6.51352593185335 \tabularnewline
6 & 6 & -1.60204241123515 & 7.60204241123515 \tabularnewline
7 & 7 & 11.9459316783783 & -4.94593167837826 \tabularnewline
8 & 8 & 10.7216069555026 & -2.72160695550257 \tabularnewline
9 & 9 & 15.039314673491 & -6.03931467349104 \tabularnewline
10 & 10 & 14.2401530841125 & -4.24015308411245 \tabularnewline
11 & 11 & 14.0093537179471 & -3.00935371794713 \tabularnewline
12 & 12 & 13.4955330351468 & -1.49553303514685 \tabularnewline
13 & 13 & 15.7301344005517 & -2.73013440055172 \tabularnewline
14 & 14 & 14.8111997120002 & -0.811199712000209 \tabularnewline
15 & 15 & 14.4054217046417 & 0.59457829535831 \tabularnewline
16 & 16 & 14.2791706451768 & 1.72082935482316 \tabularnewline
17 & 17 & 30.5173405824344 & -13.5173405824344 \tabularnewline
18 & 18 & 30.6777041909025 & -12.6777041909025 \tabularnewline
19 & 19 & 30.8200772467174 & -11.8200772467174 \tabularnewline
20 & 20 & 29.9845713527878 & -9.98457135278779 \tabularnewline
21 & 21 & 30.7332062979784 & -9.73320629797845 \tabularnewline
22 & 22 & 28.7192113223841 & -6.71921132238407 \tabularnewline
23 & 23 & 31.4365593247955 & -8.43655932479553 \tabularnewline
24 & 24 & 31.4209169723014 & -7.42091697230137 \tabularnewline
25 & 25 & 31.2535930828663 & -6.25359308286628 \tabularnewline
26 & 26 & 30.0047501005418 & -4.00475010054177 \tabularnewline
27 & 27 & 30.2321659365372 & -3.23216593653724 \tabularnewline
28 & 28 & 30.427382053781 & -2.42738205378104 \tabularnewline
29 & 29 & 30.7675064963524 & -1.76750649635238 \tabularnewline
30 & 30 & 31.6497986650277 & -1.64979866502768 \tabularnewline
31 & 31 & 30.6729460635011 & 0.327053936498871 \tabularnewline
32 & 32 & 30.1898763334578 & 1.81012366654225 \tabularnewline
33 & 33 & 29.9394581138651 & 3.06054188613493 \tabularnewline
34 & 34 & 31.2458042942297 & 2.75419570577034 \tabularnewline
35 & 35 & 32.0841924827329 & 2.91580751726708 \tabularnewline
36 & 36 & 47.0022944550082 & -11.0022944550082 \tabularnewline
37 & 37 & 47.343970526687 & -10.343970526687 \tabularnewline
38 & 38 & 46.4812574878454 & -8.48125748784536 \tabularnewline
39 & 39 & 46.4437134863536 & -7.44371348635358 \tabularnewline
40 & 40 & 47.901443959885 & -7.90144395988495 \tabularnewline
41 & 41 & 46.5631424751185 & -5.56314247511847 \tabularnewline
42 & 42 & 47.4469567057022 & -5.44695670570216 \tabularnewline
43 & 43 & 46.5075795283763 & -3.50757952837626 \tabularnewline
44 & 44 & 46.7181743266601 & -2.71817432666006 \tabularnewline
45 & 45 & 46.510591715575 & -1.51059171557496 \tabularnewline
46 & 46 & 47.4614443341382 & -1.46144433413817 \tabularnewline
47 & 47 & 47.3884442008294 & -0.388444200829446 \tabularnewline
48 & 48 & 47.4483839293073 & 0.551616070692703 \tabularnewline
49 & 49 & 47.6337209694699 & 1.36627903053007 \tabularnewline
50 & 50 & 47.0181203821859 & 2.98187961781413 \tabularnewline
51 & 51 & 46.6200821506773 & 4.37991784932265 \tabularnewline
52 & 52 & 47.0599354369983 & 4.94006456300172 \tabularnewline
53 & 53 & 46.8085597190957 & 6.19144028090433 \tabularnewline
54 & 54 & 47.1497888172537 & 6.85021118274627 \tabularnewline
55 & 55 & 47.5429873078053 & 7.4570126921947 \tabularnewline
56 & 56 & 47.5532747600646 & 8.44672523993541 \tabularnewline
57 & 57 & 45.6166450465506 & 11.3833549534494 \tabularnewline
58 & 58 & 47.293650055257 & 10.706349944743 \tabularnewline
59 & 59 & 47.2512148116857 & 11.7487851883143 \tabularnewline
60 & 60 & 47.6551836697563 & 12.3448163302437 \tabularnewline
61 & 61 & 47.5840323251953 & 13.4159676748047 \tabularnewline
62 & 62 & 63.2629814564011 & -1.26298145640107 \tabularnewline
63 & 63 & 63.3465480001898 & -0.346548000189826 \tabularnewline
64 & 64 & 62.6220202131 & 1.37797978689996 \tabularnewline
65 & 65 & 63.3193925101005 & 1.68060748989949 \tabularnewline
66 & 66 & 62.2719338009408 & 3.72806619905917 \tabularnewline
67 & 67 & 63.3007506499213 & 3.69924935007865 \tabularnewline
68 & 68 & 62.1512135831189 & 5.84878641688112 \tabularnewline
69 & 69 & 63.2493193368479 & 5.75068066315213 \tabularnewline
70 & 70 & 62.6200887203287 & 7.37991127967128 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&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]1[/C][C]-20.1772877324217[/C][C]21.1772877324217[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]-1.10553070742888[/C][C]3.10553070742888[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]-0.968863938781559[/C][C]3.96886393878156[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-1.23647065285306[/C][C]5.23647065285306[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]-1.51352593185335[/C][C]6.51352593185335[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]-1.60204241123515[/C][C]7.60204241123515[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]11.9459316783783[/C][C]-4.94593167837826[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]10.7216069555026[/C][C]-2.72160695550257[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]15.039314673491[/C][C]-6.03931467349104[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]14.2401530841125[/C][C]-4.24015308411245[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]14.0093537179471[/C][C]-3.00935371794713[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.4955330351468[/C][C]-1.49553303514685[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]15.7301344005517[/C][C]-2.73013440055172[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]14.8111997120002[/C][C]-0.811199712000209[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]14.4054217046417[/C][C]0.59457829535831[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]14.2791706451768[/C][C]1.72082935482316[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]30.5173405824344[/C][C]-13.5173405824344[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]30.6777041909025[/C][C]-12.6777041909025[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]30.8200772467174[/C][C]-11.8200772467174[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]29.9845713527878[/C][C]-9.98457135278779[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]30.7332062979784[/C][C]-9.73320629797845[/C][/ROW]
[ROW][C]22[/C][C]22[/C][C]28.7192113223841[/C][C]-6.71921132238407[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]31.4365593247955[/C][C]-8.43655932479553[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]31.4209169723014[/C][C]-7.42091697230137[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]31.2535930828663[/C][C]-6.25359308286628[/C][/ROW]
[ROW][C]26[/C][C]26[/C][C]30.0047501005418[/C][C]-4.00475010054177[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]30.2321659365372[/C][C]-3.23216593653724[/C][/ROW]
[ROW][C]28[/C][C]28[/C][C]30.427382053781[/C][C]-2.42738205378104[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]30.7675064963524[/C][C]-1.76750649635238[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]31.6497986650277[/C][C]-1.64979866502768[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]30.6729460635011[/C][C]0.327053936498871[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]30.1898763334578[/C][C]1.81012366654225[/C][/ROW]
[ROW][C]33[/C][C]33[/C][C]29.9394581138651[/C][C]3.06054188613493[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]31.2458042942297[/C][C]2.75419570577034[/C][/ROW]
[ROW][C]35[/C][C]35[/C][C]32.0841924827329[/C][C]2.91580751726708[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]47.0022944550082[/C][C]-11.0022944550082[/C][/ROW]
[ROW][C]37[/C][C]37[/C][C]47.343970526687[/C][C]-10.343970526687[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]46.4812574878454[/C][C]-8.48125748784536[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]46.4437134863536[/C][C]-7.44371348635358[/C][/ROW]
[ROW][C]40[/C][C]40[/C][C]47.901443959885[/C][C]-7.90144395988495[/C][/ROW]
[ROW][C]41[/C][C]41[/C][C]46.5631424751185[/C][C]-5.56314247511847[/C][/ROW]
[ROW][C]42[/C][C]42[/C][C]47.4469567057022[/C][C]-5.44695670570216[/C][/ROW]
[ROW][C]43[/C][C]43[/C][C]46.5075795283763[/C][C]-3.50757952837626[/C][/ROW]
[ROW][C]44[/C][C]44[/C][C]46.7181743266601[/C][C]-2.71817432666006[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]46.510591715575[/C][C]-1.51059171557496[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]47.4614443341382[/C][C]-1.46144433413817[/C][/ROW]
[ROW][C]47[/C][C]47[/C][C]47.3884442008294[/C][C]-0.388444200829446[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]47.4483839293073[/C][C]0.551616070692703[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]47.6337209694699[/C][C]1.36627903053007[/C][/ROW]
[ROW][C]50[/C][C]50[/C][C]47.0181203821859[/C][C]2.98187961781413[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]46.6200821506773[/C][C]4.37991784932265[/C][/ROW]
[ROW][C]52[/C][C]52[/C][C]47.0599354369983[/C][C]4.94006456300172[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]46.8085597190957[/C][C]6.19144028090433[/C][/ROW]
[ROW][C]54[/C][C]54[/C][C]47.1497888172537[/C][C]6.85021118274627[/C][/ROW]
[ROW][C]55[/C][C]55[/C][C]47.5429873078053[/C][C]7.4570126921947[/C][/ROW]
[ROW][C]56[/C][C]56[/C][C]47.5532747600646[/C][C]8.44672523993541[/C][/ROW]
[ROW][C]57[/C][C]57[/C][C]45.6166450465506[/C][C]11.3833549534494[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]47.293650055257[/C][C]10.706349944743[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]47.2512148116857[/C][C]11.7487851883143[/C][/ROW]
[ROW][C]60[/C][C]60[/C][C]47.6551836697563[/C][C]12.3448163302437[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]47.5840323251953[/C][C]13.4159676748047[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]63.2629814564011[/C][C]-1.26298145640107[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]63.3465480001898[/C][C]-0.346548000189826[/C][/ROW]
[ROW][C]64[/C][C]64[/C][C]62.6220202131[/C][C]1.37797978689996[/C][/ROW]
[ROW][C]65[/C][C]65[/C][C]63.3193925101005[/C][C]1.68060748989949[/C][/ROW]
[ROW][C]66[/C][C]66[/C][C]62.2719338009408[/C][C]3.72806619905917[/C][/ROW]
[ROW][C]67[/C][C]67[/C][C]63.3007506499213[/C][C]3.69924935007865[/C][/ROW]
[ROW][C]68[/C][C]68[/C][C]62.1512135831189[/C][C]5.84878641688112[/C][/ROW]
[ROW][C]69[/C][C]69[/C][C]63.2493193368479[/C][C]5.75068066315213[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]62.6200887203287[/C][C]7.37991127967128[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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
11-20.177287732421721.1772877324217
22-1.105530707428883.10553070742888
33-0.9688639387815593.96886393878156
44-1.236470652853065.23647065285306
55-1.513525931853356.51352593185335
66-1.602042411235157.60204241123515
7711.9459316783783-4.94593167837826
8810.7216069555026-2.72160695550257
9915.039314673491-6.03931467349104
101014.2401530841125-4.24015308411245
111114.0093537179471-3.00935371794713
121213.4955330351468-1.49553303514685
131315.7301344005517-2.73013440055172
141414.8111997120002-0.811199712000209
151514.40542170464170.59457829535831
161614.27917064517681.72082935482316
171730.5173405824344-13.5173405824344
181830.6777041909025-12.6777041909025
191930.8200772467174-11.8200772467174
202029.9845713527878-9.98457135278779
212130.7332062979784-9.73320629797845
222228.7192113223841-6.71921132238407
232331.4365593247955-8.43655932479553
242431.4209169723014-7.42091697230137
252531.2535930828663-6.25359308286628
262630.0047501005418-4.00475010054177
272730.2321659365372-3.23216593653724
282830.427382053781-2.42738205378104
292930.7675064963524-1.76750649635238
303031.6497986650277-1.64979866502768
313130.67294606350110.327053936498871
323230.18987633345781.81012366654225
333329.93945811386513.06054188613493
343431.24580429422972.75419570577034
353532.08419248273292.91580751726708
363647.0022944550082-11.0022944550082
373747.343970526687-10.343970526687
383846.4812574878454-8.48125748784536
393946.4437134863536-7.44371348635358
404047.901443959885-7.90144395988495
414146.5631424751185-5.56314247511847
424247.4469567057022-5.44695670570216
434346.5075795283763-3.50757952837626
444446.7181743266601-2.71817432666006
454546.510591715575-1.51059171557496
464647.4614443341382-1.46144433413817
474747.3884442008294-0.388444200829446
484847.44838392930730.551616070692703
494947.63372096946991.36627903053007
505047.01812038218592.98187961781413
515146.62008215067734.37991784932265
525247.05993543699834.94006456300172
535346.80855971909576.19144028090433
545447.14978881725376.85021118274627
555547.54298730780537.4570126921947
565647.55327476006468.44672523993541
575745.616645046550611.3833549534494
585847.29365005525710.706349944743
595947.251214811685711.7487851883143
606047.655183669756312.3448163302437
616147.584032325195313.4159676748047
626263.2629814564011-1.26298145640107
636363.3465480001898-0.346548000189826
646462.62202021311.37797978689996
656563.31939251010051.68060748989949
666662.27193380094083.72806619905917
676763.30075064992133.69924935007865
686862.15121358311895.84878641688112
696963.24931933684795.75068066315213
707062.62008872032877.37991127967128







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.02275576917603010.04551153835206030.97724423082397
80.004970907017657190.009941814035314380.995029092982343
90.001244508241528320.002489016483056630.998755491758472
100.001034343834676020.002068687669352050.998965656165324
110.0003527938916767810.0007055877833535610.999647206108323
120.0004800417787743120.0009600835575486230.999519958221226
130.00040743840460620.00081487680921240.999592561595394
140.0004400187301089530.0008800374602179070.999559981269891
150.0008518153466859940.001703630693371990.999148184653314
160.001370306651626240.002740613303252490.998629693348374
170.0006898755094161440.001379751018832290.999310124490584
180.0003446193147082210.0006892386294164420.999655380685292
190.0001883772601607740.0003767545203215480.999811622739839
200.0001265984033005260.0002531968066010530.999873401596699
217.30015312906287e-050.0001460030625812570.999926998468709
223.28405956059066e-056.56811912118132e-050.999967159404394
233.73035889236213e-057.46071778472426e-050.999962696411076
245.15446557484041e-050.0001030893114968080.999948455344252
256.93366079667634e-050.0001386732159335270.999930663392033
260.0001665333357823250.0003330666715646510.999833466664218
270.0004697769901051160.0009395539802102310.999530223009895
280.001006258186795040.002012516373590090.998993741813205
290.001889052838861770.003778105677723540.998110947161138
300.002850130807056530.005700261614113060.997149869192943
310.005856679915164890.01171335983032980.994143320084835
320.01257658237818360.02515316475636720.987423417621816
330.02394820543701450.0478964108740290.976051794562986
340.03355121212143810.06710242424287610.966448787878562
350.04218798830081690.08437597660163380.957812011699183
360.04941091892099740.09882183784199480.950589081079003
370.06904510362425850.1380902072485170.930954896375742
380.08868656152979710.1773731230595940.911313438470203
390.1273516601639570.2547033203279150.872648339836043
400.1828943228576160.3657886457152310.817105677142384
410.2544047983455130.5088095966910270.745595201654487
420.3426535720113840.6853071440227680.657346427988616
430.4564098070785980.9128196141571960.543590192921402
440.5753944958380060.8492110083239880.424605504161994
450.7099182392954780.5801635214090430.290081760704522
460.8034369538909930.3931260922180150.196563046109007
470.8787510331188070.2424979337623870.121248966881193
480.9333188737321270.1333622525357470.0666811262678735
490.9728300039172490.05433999216550180.0271699960827509
500.9864385927840340.02712281443193140.0135614072159657
510.9951479773230110.009704045353978920.00485202267698946
520.9965313605622990.00693727887540180.0034686394377009
530.9968985571183220.0062028857633570.0031014428816785
540.9976234963161420.004753007367715960.00237650368385798
550.9976615955194780.004676808961043290.00233840448052165
560.9974022833084460.005195433383108420.00259771669155421
570.9983774459589790.00324510808204250.00162255404102125
580.9983444659150160.00331106816996740.0016555340849837
590.9978445710921420.004310857815716030.00215542890785801
600.9942542344631360.01149153107372880.00574576553686438
610.9847021901646630.03059561967067410.0152978098353371
620.9655082782715990.06898344345680120.0344917217284006
630.9112089404780990.1775821190438020.0887910595219009

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0227557691760301 & 0.0455115383520603 & 0.97724423082397 \tabularnewline
8 & 0.00497090701765719 & 0.00994181403531438 & 0.995029092982343 \tabularnewline
9 & 0.00124450824152832 & 0.00248901648305663 & 0.998755491758472 \tabularnewline
10 & 0.00103434383467602 & 0.00206868766935205 & 0.998965656165324 \tabularnewline
11 & 0.000352793891676781 & 0.000705587783353561 & 0.999647206108323 \tabularnewline
12 & 0.000480041778774312 & 0.000960083557548623 & 0.999519958221226 \tabularnewline
13 & 0.0004074384046062 & 0.0008148768092124 & 0.999592561595394 \tabularnewline
14 & 0.000440018730108953 & 0.000880037460217907 & 0.999559981269891 \tabularnewline
15 & 0.000851815346685994 & 0.00170363069337199 & 0.999148184653314 \tabularnewline
16 & 0.00137030665162624 & 0.00274061330325249 & 0.998629693348374 \tabularnewline
17 & 0.000689875509416144 & 0.00137975101883229 & 0.999310124490584 \tabularnewline
18 & 0.000344619314708221 & 0.000689238629416442 & 0.999655380685292 \tabularnewline
19 & 0.000188377260160774 & 0.000376754520321548 & 0.999811622739839 \tabularnewline
20 & 0.000126598403300526 & 0.000253196806601053 & 0.999873401596699 \tabularnewline
21 & 7.30015312906287e-05 & 0.000146003062581257 & 0.999926998468709 \tabularnewline
22 & 3.28405956059066e-05 & 6.56811912118132e-05 & 0.999967159404394 \tabularnewline
23 & 3.73035889236213e-05 & 7.46071778472426e-05 & 0.999962696411076 \tabularnewline
24 & 5.15446557484041e-05 & 0.000103089311496808 & 0.999948455344252 \tabularnewline
25 & 6.93366079667634e-05 & 0.000138673215933527 & 0.999930663392033 \tabularnewline
26 & 0.000166533335782325 & 0.000333066671564651 & 0.999833466664218 \tabularnewline
27 & 0.000469776990105116 & 0.000939553980210231 & 0.999530223009895 \tabularnewline
28 & 0.00100625818679504 & 0.00201251637359009 & 0.998993741813205 \tabularnewline
29 & 0.00188905283886177 & 0.00377810567772354 & 0.998110947161138 \tabularnewline
30 & 0.00285013080705653 & 0.00570026161411306 & 0.997149869192943 \tabularnewline
31 & 0.00585667991516489 & 0.0117133598303298 & 0.994143320084835 \tabularnewline
32 & 0.0125765823781836 & 0.0251531647563672 & 0.987423417621816 \tabularnewline
33 & 0.0239482054370145 & 0.047896410874029 & 0.976051794562986 \tabularnewline
34 & 0.0335512121214381 & 0.0671024242428761 & 0.966448787878562 \tabularnewline
35 & 0.0421879883008169 & 0.0843759766016338 & 0.957812011699183 \tabularnewline
36 & 0.0494109189209974 & 0.0988218378419948 & 0.950589081079003 \tabularnewline
37 & 0.0690451036242585 & 0.138090207248517 & 0.930954896375742 \tabularnewline
38 & 0.0886865615297971 & 0.177373123059594 & 0.911313438470203 \tabularnewline
39 & 0.127351660163957 & 0.254703320327915 & 0.872648339836043 \tabularnewline
40 & 0.182894322857616 & 0.365788645715231 & 0.817105677142384 \tabularnewline
41 & 0.254404798345513 & 0.508809596691027 & 0.745595201654487 \tabularnewline
42 & 0.342653572011384 & 0.685307144022768 & 0.657346427988616 \tabularnewline
43 & 0.456409807078598 & 0.912819614157196 & 0.543590192921402 \tabularnewline
44 & 0.575394495838006 & 0.849211008323988 & 0.424605504161994 \tabularnewline
45 & 0.709918239295478 & 0.580163521409043 & 0.290081760704522 \tabularnewline
46 & 0.803436953890993 & 0.393126092218015 & 0.196563046109007 \tabularnewline
47 & 0.878751033118807 & 0.242497933762387 & 0.121248966881193 \tabularnewline
48 & 0.933318873732127 & 0.133362252535747 & 0.0666811262678735 \tabularnewline
49 & 0.972830003917249 & 0.0543399921655018 & 0.0271699960827509 \tabularnewline
50 & 0.986438592784034 & 0.0271228144319314 & 0.0135614072159657 \tabularnewline
51 & 0.995147977323011 & 0.00970404535397892 & 0.00485202267698946 \tabularnewline
52 & 0.996531360562299 & 0.0069372788754018 & 0.0034686394377009 \tabularnewline
53 & 0.996898557118322 & 0.006202885763357 & 0.0031014428816785 \tabularnewline
54 & 0.997623496316142 & 0.00475300736771596 & 0.00237650368385798 \tabularnewline
55 & 0.997661595519478 & 0.00467680896104329 & 0.00233840448052165 \tabularnewline
56 & 0.997402283308446 & 0.00519543338310842 & 0.00259771669155421 \tabularnewline
57 & 0.998377445958979 & 0.0032451080820425 & 0.00162255404102125 \tabularnewline
58 & 0.998344465915016 & 0.0033110681699674 & 0.0016555340849837 \tabularnewline
59 & 0.997844571092142 & 0.00431085781571603 & 0.00215542890785801 \tabularnewline
60 & 0.994254234463136 & 0.0114915310737288 & 0.00574576553686438 \tabularnewline
61 & 0.984702190164663 & 0.0305956196706741 & 0.0152978098353371 \tabularnewline
62 & 0.965508278271599 & 0.0689834434568012 & 0.0344917217284006 \tabularnewline
63 & 0.911208940478099 & 0.177582119043802 & 0.0887910595219009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&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]0.0227557691760301[/C][C]0.0455115383520603[/C][C]0.97724423082397[/C][/ROW]
[ROW][C]8[/C][C]0.00497090701765719[/C][C]0.00994181403531438[/C][C]0.995029092982343[/C][/ROW]
[ROW][C]9[/C][C]0.00124450824152832[/C][C]0.00248901648305663[/C][C]0.998755491758472[/C][/ROW]
[ROW][C]10[/C][C]0.00103434383467602[/C][C]0.00206868766935205[/C][C]0.998965656165324[/C][/ROW]
[ROW][C]11[/C][C]0.000352793891676781[/C][C]0.000705587783353561[/C][C]0.999647206108323[/C][/ROW]
[ROW][C]12[/C][C]0.000480041778774312[/C][C]0.000960083557548623[/C][C]0.999519958221226[/C][/ROW]
[ROW][C]13[/C][C]0.0004074384046062[/C][C]0.0008148768092124[/C][C]0.999592561595394[/C][/ROW]
[ROW][C]14[/C][C]0.000440018730108953[/C][C]0.000880037460217907[/C][C]0.999559981269891[/C][/ROW]
[ROW][C]15[/C][C]0.000851815346685994[/C][C]0.00170363069337199[/C][C]0.999148184653314[/C][/ROW]
[ROW][C]16[/C][C]0.00137030665162624[/C][C]0.00274061330325249[/C][C]0.998629693348374[/C][/ROW]
[ROW][C]17[/C][C]0.000689875509416144[/C][C]0.00137975101883229[/C][C]0.999310124490584[/C][/ROW]
[ROW][C]18[/C][C]0.000344619314708221[/C][C]0.000689238629416442[/C][C]0.999655380685292[/C][/ROW]
[ROW][C]19[/C][C]0.000188377260160774[/C][C]0.000376754520321548[/C][C]0.999811622739839[/C][/ROW]
[ROW][C]20[/C][C]0.000126598403300526[/C][C]0.000253196806601053[/C][C]0.999873401596699[/C][/ROW]
[ROW][C]21[/C][C]7.30015312906287e-05[/C][C]0.000146003062581257[/C][C]0.999926998468709[/C][/ROW]
[ROW][C]22[/C][C]3.28405956059066e-05[/C][C]6.56811912118132e-05[/C][C]0.999967159404394[/C][/ROW]
[ROW][C]23[/C][C]3.73035889236213e-05[/C][C]7.46071778472426e-05[/C][C]0.999962696411076[/C][/ROW]
[ROW][C]24[/C][C]5.15446557484041e-05[/C][C]0.000103089311496808[/C][C]0.999948455344252[/C][/ROW]
[ROW][C]25[/C][C]6.93366079667634e-05[/C][C]0.000138673215933527[/C][C]0.999930663392033[/C][/ROW]
[ROW][C]26[/C][C]0.000166533335782325[/C][C]0.000333066671564651[/C][C]0.999833466664218[/C][/ROW]
[ROW][C]27[/C][C]0.000469776990105116[/C][C]0.000939553980210231[/C][C]0.999530223009895[/C][/ROW]
[ROW][C]28[/C][C]0.00100625818679504[/C][C]0.00201251637359009[/C][C]0.998993741813205[/C][/ROW]
[ROW][C]29[/C][C]0.00188905283886177[/C][C]0.00377810567772354[/C][C]0.998110947161138[/C][/ROW]
[ROW][C]30[/C][C]0.00285013080705653[/C][C]0.00570026161411306[/C][C]0.997149869192943[/C][/ROW]
[ROW][C]31[/C][C]0.00585667991516489[/C][C]0.0117133598303298[/C][C]0.994143320084835[/C][/ROW]
[ROW][C]32[/C][C]0.0125765823781836[/C][C]0.0251531647563672[/C][C]0.987423417621816[/C][/ROW]
[ROW][C]33[/C][C]0.0239482054370145[/C][C]0.047896410874029[/C][C]0.976051794562986[/C][/ROW]
[ROW][C]34[/C][C]0.0335512121214381[/C][C]0.0671024242428761[/C][C]0.966448787878562[/C][/ROW]
[ROW][C]35[/C][C]0.0421879883008169[/C][C]0.0843759766016338[/C][C]0.957812011699183[/C][/ROW]
[ROW][C]36[/C][C]0.0494109189209974[/C][C]0.0988218378419948[/C][C]0.950589081079003[/C][/ROW]
[ROW][C]37[/C][C]0.0690451036242585[/C][C]0.138090207248517[/C][C]0.930954896375742[/C][/ROW]
[ROW][C]38[/C][C]0.0886865615297971[/C][C]0.177373123059594[/C][C]0.911313438470203[/C][/ROW]
[ROW][C]39[/C][C]0.127351660163957[/C][C]0.254703320327915[/C][C]0.872648339836043[/C][/ROW]
[ROW][C]40[/C][C]0.182894322857616[/C][C]0.365788645715231[/C][C]0.817105677142384[/C][/ROW]
[ROW][C]41[/C][C]0.254404798345513[/C][C]0.508809596691027[/C][C]0.745595201654487[/C][/ROW]
[ROW][C]42[/C][C]0.342653572011384[/C][C]0.685307144022768[/C][C]0.657346427988616[/C][/ROW]
[ROW][C]43[/C][C]0.456409807078598[/C][C]0.912819614157196[/C][C]0.543590192921402[/C][/ROW]
[ROW][C]44[/C][C]0.575394495838006[/C][C]0.849211008323988[/C][C]0.424605504161994[/C][/ROW]
[ROW][C]45[/C][C]0.709918239295478[/C][C]0.580163521409043[/C][C]0.290081760704522[/C][/ROW]
[ROW][C]46[/C][C]0.803436953890993[/C][C]0.393126092218015[/C][C]0.196563046109007[/C][/ROW]
[ROW][C]47[/C][C]0.878751033118807[/C][C]0.242497933762387[/C][C]0.121248966881193[/C][/ROW]
[ROW][C]48[/C][C]0.933318873732127[/C][C]0.133362252535747[/C][C]0.0666811262678735[/C][/ROW]
[ROW][C]49[/C][C]0.972830003917249[/C][C]0.0543399921655018[/C][C]0.0271699960827509[/C][/ROW]
[ROW][C]50[/C][C]0.986438592784034[/C][C]0.0271228144319314[/C][C]0.0135614072159657[/C][/ROW]
[ROW][C]51[/C][C]0.995147977323011[/C][C]0.00970404535397892[/C][C]0.00485202267698946[/C][/ROW]
[ROW][C]52[/C][C]0.996531360562299[/C][C]0.0069372788754018[/C][C]0.0034686394377009[/C][/ROW]
[ROW][C]53[/C][C]0.996898557118322[/C][C]0.006202885763357[/C][C]0.0031014428816785[/C][/ROW]
[ROW][C]54[/C][C]0.997623496316142[/C][C]0.00475300736771596[/C][C]0.00237650368385798[/C][/ROW]
[ROW][C]55[/C][C]0.997661595519478[/C][C]0.00467680896104329[/C][C]0.00233840448052165[/C][/ROW]
[ROW][C]56[/C][C]0.997402283308446[/C][C]0.00519543338310842[/C][C]0.00259771669155421[/C][/ROW]
[ROW][C]57[/C][C]0.998377445958979[/C][C]0.0032451080820425[/C][C]0.00162255404102125[/C][/ROW]
[ROW][C]58[/C][C]0.998344465915016[/C][C]0.0033110681699674[/C][C]0.0016555340849837[/C][/ROW]
[ROW][C]59[/C][C]0.997844571092142[/C][C]0.00431085781571603[/C][C]0.00215542890785801[/C][/ROW]
[ROW][C]60[/C][C]0.994254234463136[/C][C]0.0114915310737288[/C][C]0.00574576553686438[/C][/ROW]
[ROW][C]61[/C][C]0.984702190164663[/C][C]0.0305956196706741[/C][C]0.0152978098353371[/C][/ROW]
[ROW][C]62[/C][C]0.965508278271599[/C][C]0.0689834434568012[/C][C]0.0344917217284006[/C][/ROW]
[ROW][C]63[/C][C]0.911208940478099[/C][C]0.177582119043802[/C][C]0.0887910595219009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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
70.02275576917603010.04551153835206030.97724423082397
80.004970907017657190.009941814035314380.995029092982343
90.001244508241528320.002489016483056630.998755491758472
100.001034343834676020.002068687669352050.998965656165324
110.0003527938916767810.0007055877833535610.999647206108323
120.0004800417787743120.0009600835575486230.999519958221226
130.00040743840460620.00081487680921240.999592561595394
140.0004400187301089530.0008800374602179070.999559981269891
150.0008518153466859940.001703630693371990.999148184653314
160.001370306651626240.002740613303252490.998629693348374
170.0006898755094161440.001379751018832290.999310124490584
180.0003446193147082210.0006892386294164420.999655380685292
190.0001883772601607740.0003767545203215480.999811622739839
200.0001265984033005260.0002531968066010530.999873401596699
217.30015312906287e-050.0001460030625812570.999926998468709
223.28405956059066e-056.56811912118132e-050.999967159404394
233.73035889236213e-057.46071778472426e-050.999962696411076
245.15446557484041e-050.0001030893114968080.999948455344252
256.93366079667634e-050.0001386732159335270.999930663392033
260.0001665333357823250.0003330666715646510.999833466664218
270.0004697769901051160.0009395539802102310.999530223009895
280.001006258186795040.002012516373590090.998993741813205
290.001889052838861770.003778105677723540.998110947161138
300.002850130807056530.005700261614113060.997149869192943
310.005856679915164890.01171335983032980.994143320084835
320.01257658237818360.02515316475636720.987423417621816
330.02394820543701450.0478964108740290.976051794562986
340.03355121212143810.06710242424287610.966448787878562
350.04218798830081690.08437597660163380.957812011699183
360.04941091892099740.09882183784199480.950589081079003
370.06904510362425850.1380902072485170.930954896375742
380.08868656152979710.1773731230595940.911313438470203
390.1273516601639570.2547033203279150.872648339836043
400.1828943228576160.3657886457152310.817105677142384
410.2544047983455130.5088095966910270.745595201654487
420.3426535720113840.6853071440227680.657346427988616
430.4564098070785980.9128196141571960.543590192921402
440.5753944958380060.8492110083239880.424605504161994
450.7099182392954780.5801635214090430.290081760704522
460.8034369538909930.3931260922180150.196563046109007
470.8787510331188070.2424979337623870.121248966881193
480.9333188737321270.1333622525357470.0666811262678735
490.9728300039172490.05433999216550180.0271699960827509
500.9864385927840340.02712281443193140.0135614072159657
510.9951479773230110.009704045353978920.00485202267698946
520.9965313605622990.00693727887540180.0034686394377009
530.9968985571183220.0062028857633570.0031014428816785
540.9976234963161420.004753007367715960.00237650368385798
550.9976615955194780.004676808961043290.00233840448052165
560.9974022833084460.005195433383108420.00259771669155421
570.9983774459589790.00324510808204250.00162255404102125
580.9983444659150160.00331106816996740.0016555340849837
590.9978445710921420.004310857815716030.00215542890785801
600.9942542344631360.01149153107372880.00574576553686438
610.9847021901646630.03059561967067410.0152978098353371
620.9655082782715990.06898344345680120.0344917217284006
630.9112089404780990.1775821190438020.0887910595219009







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level320.56140350877193NOK
5% type I error level390.684210526315789NOK
10% type I error level440.771929824561403NOK

\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 & 32 & 0.56140350877193 & NOK \tabularnewline
5% type I error level & 39 & 0.684210526315789 & NOK \tabularnewline
10% type I error level & 44 & 0.771929824561403 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186357&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]32[/C][C]0.56140350877193[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]39[/C][C]0.684210526315789[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.771929824561403[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186357&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186357&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 level320.56140350877193NOK
5% type I error level390.684210526315789NOK
10% type I error level440.771929824561403NOK



Parameters (Session):
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')
}