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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 computationThu, 24 Nov 2011 11:02:18 -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/t1322150669dqcsd9189z22uzt.htm/, Retrieved Thu, 31 Oct 2024 23:40:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147023, Retrieved Thu, 31 Oct 2024 23:40:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 16:02:18] [fc803cbaf0eb62e67cf40ee2236375c4] [Current]
-    D    [Multiple Regression] [] [2011-11-24 16:09:49] [8b1cc7b14f6109a921afd5b897efe79f]
- RMPD      [Univariate Explorative Data Analysis] [] [2011-11-24 16:45:34] [8b1cc7b14f6109a921afd5b897efe79f]
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Dataseries X:
10345	3010	13	13
17607	4344	27	24
1423	603	0	0
20050	6792	37	37
21212	7843	39	38
93979	13738	99	96
15524	4120	21	21
16182	4174	33	33
19238	6202	36	35
28909	8535	44	40
22357	5818	33	33
25560	9834	47	47
9954	4145	19	19
18490	4719	41	40
17777	3981	22	22
25268	3264	17	17
37525	11276	46	46
6023	1	0	0
25042	9480	31	31
35713	1953	20	20
7039	1801	10	10
40841	7352	55	55
9214	761	6	6
17446	1147	17	17
10295	3536	33	33
13206	3146	33	33
26093	6764	32	32
20744	7038	37	36
68013	8298	44	39
12840	5718	22	22
12672	2493	15	15
10872	4226	18	18
21325	3553	25	24
24542	58	7	7
16401	4425	35	34
0	0	0	0
12821	3705	14	7
14662	4968	31	31
22190	2320	9	9
37929	9820	59	52
18009	3606	62	60
11076	3987	12	11
24981	2138	23	20
30691	2299	31	31
29164	3308	57	56
13985	4721	23	23
7588	1369	14	14
20023	4118	31	30
25524	5396	17	17
14717	3704	24	24
6832	1801	11	11
9624	3814	16	16
24300	5010	32	30
21790	5369	36	35
16493	3952	37	37
9269	3264	25	25
20105	4177	30	30
11216	2352	10	9
15569	5624	16	16
21799	176	3	3
3772	2356	0	0
6057	1700	17	19
20828	1262	9	9
9976	2766	22	18
14055	2536	5	5
17455	4931	23	22
39553	9606	16	16
14818	4097	53	53
17065	4537	23	23
1536	516	0	0
11938	2643	51	50
24589	1277	25	25
21332	3230	51	48
13229	3356	46	46
11331	2204	16	16
853	342	0	0
19821	6783	25	25
34666	4213	34	33
15051	2822	14	14
27969	5199	32	30
17897	4780	24	23
6031	2341	16	16
7153	1825	19	19
13365	4653	27	27
11197	1524	24	24
25291	2685	12	12
28994	9230	43	43
10461	2490	13	13
16415	4718	19	19
8495	2937	24	24
18318	3599	27	27
25143	4487	26	26
20471	2149	14	14
14561	1921	26	26
16902	2896	15	15
12994	5815	30	29
29697	4679	33	33
3895	786	14	14
9807	4006	11	11
10711	2686	12	11
2325	593	8	8
19000	2454	22	22
22418	4061	12	11
7872	2856	6	6
5650	1678	10	10
3979	460	1	0
14956	5054	31	30
3738	999	5	5
0	0	0	0
10586	3685	35	34
18122	503	15	15
17899	3595	36	34
10913	3367	27	28
18060	1330	36	36
0	0	0	0
0	0	0	0
15452	6878	29	29
33996	3080	19	19
8877	1349	16	15
18708	3339	15	15
2781	4	1	1
20854	3446	36	36
8179	1467	22	22
7139	255	16	16
13798	424	1	1
5619	2374	10	10
13050	3519	31	31
11297	2650	22	22
16170	2757	22	21
0	0	0	0
0	0	0	0
20539	459	10	10
0	0	0	0
10056	549	9	9
0	0	0	0
2418	206	0	0
0	0	0	0
11806	2885	7	7
15924	1034	2	2
0	0	0	0
0	0	0	0
7084	2558	16	16
14831	5086	25	25
6585	1392	6	6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
x[t] = + 3171.06815055654 + 1.96330144662449y[t] + 1411.54206862072z[t] -1151.72798629381t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
x[t] =  +  3171.06815055654 +  1.96330144662449y[t] +  1411.54206862072z[t] -1151.72798629381t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]x[t] =  +  3171.06815055654 +  1.96330144662449y[t] +  1411.54206862072z[t] -1151.72798629381t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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
x[t] = + 3171.06815055654 + 1.96330144662449y[t] + 1411.54206862072z[t] -1151.72798629381t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3171.068150556541151.1018182.75480.0066530.003327
y1.963301446624490.3882385.0571e-061e-06
z1411.54206862072596.7692572.36530.0193870.009693
t-1151.72798629381606.987429-1.89740.0598280.029914

\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) & 3171.06815055654 & 1151.101818 & 2.7548 & 0.006653 & 0.003327 \tabularnewline
y & 1.96330144662449 & 0.388238 & 5.057 & 1e-06 & 1e-06 \tabularnewline
z & 1411.54206862072 & 596.769257 & 2.3653 & 0.019387 & 0.009693 \tabularnewline
t & -1151.72798629381 & 606.987429 & -1.8974 & 0.059828 & 0.029914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&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]3171.06815055654[/C][C]1151.101818[/C][C]2.7548[/C][C]0.006653[/C][C]0.003327[/C][/ROW]
[ROW][C]y[/C][C]1.96330144662449[/C][C]0.388238[/C][C]5.057[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]z[/C][C]1411.54206862072[/C][C]596.769257[/C][C]2.3653[/C][C]0.019387[/C][C]0.009693[/C][/ROW]
[ROW][C]t[/C][C]-1151.72798629381[/C][C]606.987429[/C][C]-1.8974[/C][C]0.059828[/C][C]0.029914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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)3171.068150556541151.1018182.75480.0066530.003327
y1.963301446624490.3882385.0571e-061e-06
z1411.54206862072596.7692572.36530.0193870.009693
t-1151.72798629381606.987429-1.89740.0598280.029914







Multiple Linear Regression - Regression Statistics
Multiple R0.762271548187973
R-squared0.581057913176889
Adjusted R-squared0.572080582744965
F-TEST (value)64.7250223864568
F-TEST (DF numerator)3
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7943.0584651562
Sum Squared Residuals8832904889.32454

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.762271548187973 \tabularnewline
R-squared & 0.581057913176889 \tabularnewline
Adjusted R-squared & 0.572080582744965 \tabularnewline
F-TEST (value) & 64.7250223864568 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7943.0584651562 \tabularnewline
Sum Squared Residuals & 8832904889.32454 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.762271548187973[/C][/ROW]
[ROW][C]R-squared[/C][C]0.581057913176889[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.572080582744965[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]64.7250223864568[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/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]7943.0584651562[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8832904889.32454[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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 R0.762271548187973
R-squared0.581057913176889
Adjusted R-squared0.572080582744965
F-TEST (value)64.7250223864568
F-TEST (DF numerator)3
F-TEST (DF denominator)140
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7943.0584651562
Sum Squared Residuals8832904889.32454







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11034512458.1885751461-2113.18857514607
21760722169.8138164013-4562.8138164013
314234354.93892287111-2931.93892287111
42005026118.9326221257-6068.93262212572
52121229853.7185934757-8641.71859347569
69397959319.681533529234659.3184664708
71552416715.9658395145-1191.96583951454
81618219939.7531055552-3757.75310555518
91923825852.4986725842-6614.49867258418
102890935966.5775650558-7057.57756505581
112235723167.4206838058-810.420683805837
122556034689.4364460265-9129.43644602651
13995416245.4202110263-6291.42021102633
141849024239.9930388746-5749.99303887461
151777716702.88102076061074.11897923935
162526813996.123471896311271.8765281037
173752537260.7030497321264.29695026788
1860233173.031452003172849.96854799683
192504229837.4024166909-4795.4024166909
203571312201.677522352423511.3224776476
2170399305.11487919635-2266.11487919635
224084131895.03491411988945.96508588019
2392146224.025045399242989.97495460076
24174469839.81430939237606.1856906077
251029518687.1667826088-8392.16678260876
261320617921.4792184252-4715.4792184252
272609324764.88976998571328.11023001431
282074427753.6327642892-7009.63276428916
296801336653.003108499631359.9968915004
301284020113.1356335474-7273.13563354739
311267211962.789891895709.210108104959
321087216144.633545876-5272.63354587601
332132517793.75823487993531.2417651201
34245425103.6382107491319438.3617892509
351640122103.8979196056-5702.89791960555
3603171.06815055654-3171.06815055654
371282122144.5930669337-9323.59306693367
381466220978.9862895212-6316.9862895212
392219010064.254247667512125.7457523325
403792945841.8151177533-7912.81511775333
411800928662.6622439405-10653.6622439405
421107615268.2479924651-4192.24799246511
432498116799.514495848181.48550415995
443069115738.934728480414952.0652715196
452916425626.8000149183537.199985082
461398518415.5381735897-4430.53817358968
4775889496.22498356221-1908.22498356221
482002320461.9080461842-438.908046184195
492552418181.88215609977342.11784390025
501471716678.6746846995-1961.67468469948
5168329564.92896152326-2732.92896152326
52962414816.1251852129-5192.1251852129
532430023624.715005194675.284994806043
542179024217.068567546-2427.06856754598
551649320543.1565137122-4050.15651371218
56926916074.6361305116-6805.63613051162
572010519166.2007629143938.799237085678
581121611538.6219625802-322.621962580248
591556918369.7008036032-2800.70080360322
60217994296.0514521431817502.9485478568
6137727796.60635880384-4024.60635880384
6260578622.06403678802-2565.06403678802
63208287987.0813171388412840.9186828612
64997618924.3817082871-8948.38170828712
65140559449.07103083084605.9289691692
661745519979.5594636746-2524.55946367463
673955326187.567164061913365.4328359381
681481824984.8605407033-10166.8605407033
691706518054.2907074108-989.290707410772
7015364184.13169701478-2648.13169701478
711193822762.3200589513-10824.3200589513
722458912173.556156068812415.4438439312
732133226218.2339807075-4886.23398070746
741322921711.3555924662-8482.35559246617
751133111655.2098561475-324.209856147473
768533842.51724530212-2989.51724530212
771982122983.4939211832-3162.49392118319
783466621427.863930594213238.1360694058
791505112348.90198550762702.09801449241
802796923995.7789786063973.22102139401
811789719942.9150275612-2045.91502756124
82603111924.182154335-5893.18215433503
83715311690.5608548575-4537.56085485752
841336519321.2900045269-5956.29000452685
851119712398.6775310581-1201.6775310581
862529111560.301522666213730.6984773338
872899432464.3460429577-3470.34604295768
881046111437.2718229013-976.271822901348
891641517370.3919399422-955.391939942166
90849515172.8224751385-6677.8224751385
911831817251.97027978461066.02972021536
922514318735.56788206036407.43211793972
932047111027.60011192939443.39988807069
941456113697.7363700218863.263629978159
951690212754.00037488474147.99962511529
961299423533.816518779-10539.816518779
972969720931.22033610058765.77966389946
9838958351.62024018013-4456.62024018013
99980713894.0086513303-4087.00865133025
1001071112713.9928104066-2002.99281040665
10123256413.81856702015-4088.81856702015
1021900013704.91971176515295.08028823495
1032241815413.53229951537004.46770048468
104787210337.1415760775-2465.14157607755
10556509063.62880126154-3413.62880126154
10639795485.72888462453-1506.72888462453
1071495622299.5582002247-7343.55820022472
10837386431.47670736896-2693.47670736896
10903171.06815055654-3171.06815055654
1101058620651.0548491034-10065.0548491034
111181228055.8200131123110066.1799868877
1121789921885.8997875279-3986.89978752794
1131091315644.756357874-4731.75635787395
1141806015135.56603833592924.43396166414
11503171.06815055654-3171.06815055654
11603171.06815055654-3171.06815055654
1171545224209.2638879202-8757.26388792016
1183399614154.504170371319841.4958296287
119887711128.3151055773-2251.31510557734
1201870813623.74291573945084.25708426064
12127813438.73543866995-657.735438669953
1222085419289.91189939331564.08810060672
123817911767.1411839467-3588.14118394668
12471397828.73533667634-689.735336676344
125137984263.322046252249534.67795374776
126561910430.0866081122-4811.08660811218
1271305018134.1624933623-5084.16249336232
1281129714089.7267953035-2792.72679530345
1291617015451.5280363861718.47196361392
13003171.06815055654-3171.06815055654
13103171.06815055654-3171.06815055654
132205396670.3643378262813868.6356621737
13303171.06815055654-3171.06815055654
134100566587.247385695583468.75261430442
13503171.06815055654-3171.06815055654
13624183575.50824856119-1157.50824856119
13703171.06815055654-3171.06815055654
1381180610653.89140035661152.10859964343
139159245720.7500110200910203.2499889799
14003171.06815055654-3171.06815055654
14103171.06815055654-3171.06815055654
142708412350.2185682525-5266.21856825254
1431483119651.7713662614-4820.77136626144
14465857462.86825821929-877.868258219294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10345 & 12458.1885751461 & -2113.18857514607 \tabularnewline
2 & 17607 & 22169.8138164013 & -4562.8138164013 \tabularnewline
3 & 1423 & 4354.93892287111 & -2931.93892287111 \tabularnewline
4 & 20050 & 26118.9326221257 & -6068.93262212572 \tabularnewline
5 & 21212 & 29853.7185934757 & -8641.71859347569 \tabularnewline
6 & 93979 & 59319.6815335292 & 34659.3184664708 \tabularnewline
7 & 15524 & 16715.9658395145 & -1191.96583951454 \tabularnewline
8 & 16182 & 19939.7531055552 & -3757.75310555518 \tabularnewline
9 & 19238 & 25852.4986725842 & -6614.49867258418 \tabularnewline
10 & 28909 & 35966.5775650558 & -7057.57756505581 \tabularnewline
11 & 22357 & 23167.4206838058 & -810.420683805837 \tabularnewline
12 & 25560 & 34689.4364460265 & -9129.43644602651 \tabularnewline
13 & 9954 & 16245.4202110263 & -6291.42021102633 \tabularnewline
14 & 18490 & 24239.9930388746 & -5749.99303887461 \tabularnewline
15 & 17777 & 16702.8810207606 & 1074.11897923935 \tabularnewline
16 & 25268 & 13996.1234718963 & 11271.8765281037 \tabularnewline
17 & 37525 & 37260.7030497321 & 264.29695026788 \tabularnewline
18 & 6023 & 3173.03145200317 & 2849.96854799683 \tabularnewline
19 & 25042 & 29837.4024166909 & -4795.4024166909 \tabularnewline
20 & 35713 & 12201.6775223524 & 23511.3224776476 \tabularnewline
21 & 7039 & 9305.11487919635 & -2266.11487919635 \tabularnewline
22 & 40841 & 31895.0349141198 & 8945.96508588019 \tabularnewline
23 & 9214 & 6224.02504539924 & 2989.97495460076 \tabularnewline
24 & 17446 & 9839.8143093923 & 7606.1856906077 \tabularnewline
25 & 10295 & 18687.1667826088 & -8392.16678260876 \tabularnewline
26 & 13206 & 17921.4792184252 & -4715.4792184252 \tabularnewline
27 & 26093 & 24764.8897699857 & 1328.11023001431 \tabularnewline
28 & 20744 & 27753.6327642892 & -7009.63276428916 \tabularnewline
29 & 68013 & 36653.0031084996 & 31359.9968915004 \tabularnewline
30 & 12840 & 20113.1356335474 & -7273.13563354739 \tabularnewline
31 & 12672 & 11962.789891895 & 709.210108104959 \tabularnewline
32 & 10872 & 16144.633545876 & -5272.63354587601 \tabularnewline
33 & 21325 & 17793.7582348799 & 3531.2417651201 \tabularnewline
34 & 24542 & 5103.63821074913 & 19438.3617892509 \tabularnewline
35 & 16401 & 22103.8979196056 & -5702.89791960555 \tabularnewline
36 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
37 & 12821 & 22144.5930669337 & -9323.59306693367 \tabularnewline
38 & 14662 & 20978.9862895212 & -6316.9862895212 \tabularnewline
39 & 22190 & 10064.2542476675 & 12125.7457523325 \tabularnewline
40 & 37929 & 45841.8151177533 & -7912.81511775333 \tabularnewline
41 & 18009 & 28662.6622439405 & -10653.6622439405 \tabularnewline
42 & 11076 & 15268.2479924651 & -4192.24799246511 \tabularnewline
43 & 24981 & 16799.51449584 & 8181.48550415995 \tabularnewline
44 & 30691 & 15738.9347284804 & 14952.0652715196 \tabularnewline
45 & 29164 & 25626.800014918 & 3537.199985082 \tabularnewline
46 & 13985 & 18415.5381735897 & -4430.53817358968 \tabularnewline
47 & 7588 & 9496.22498356221 & -1908.22498356221 \tabularnewline
48 & 20023 & 20461.9080461842 & -438.908046184195 \tabularnewline
49 & 25524 & 18181.8821560997 & 7342.11784390025 \tabularnewline
50 & 14717 & 16678.6746846995 & -1961.67468469948 \tabularnewline
51 & 6832 & 9564.92896152326 & -2732.92896152326 \tabularnewline
52 & 9624 & 14816.1251852129 & -5192.1251852129 \tabularnewline
53 & 24300 & 23624.715005194 & 675.284994806043 \tabularnewline
54 & 21790 & 24217.068567546 & -2427.06856754598 \tabularnewline
55 & 16493 & 20543.1565137122 & -4050.15651371218 \tabularnewline
56 & 9269 & 16074.6361305116 & -6805.63613051162 \tabularnewline
57 & 20105 & 19166.2007629143 & 938.799237085678 \tabularnewline
58 & 11216 & 11538.6219625802 & -322.621962580248 \tabularnewline
59 & 15569 & 18369.7008036032 & -2800.70080360322 \tabularnewline
60 & 21799 & 4296.05145214318 & 17502.9485478568 \tabularnewline
61 & 3772 & 7796.60635880384 & -4024.60635880384 \tabularnewline
62 & 6057 & 8622.06403678802 & -2565.06403678802 \tabularnewline
63 & 20828 & 7987.08131713884 & 12840.9186828612 \tabularnewline
64 & 9976 & 18924.3817082871 & -8948.38170828712 \tabularnewline
65 & 14055 & 9449.0710308308 & 4605.9289691692 \tabularnewline
66 & 17455 & 19979.5594636746 & -2524.55946367463 \tabularnewline
67 & 39553 & 26187.5671640619 & 13365.4328359381 \tabularnewline
68 & 14818 & 24984.8605407033 & -10166.8605407033 \tabularnewline
69 & 17065 & 18054.2907074108 & -989.290707410772 \tabularnewline
70 & 1536 & 4184.13169701478 & -2648.13169701478 \tabularnewline
71 & 11938 & 22762.3200589513 & -10824.3200589513 \tabularnewline
72 & 24589 & 12173.5561560688 & 12415.4438439312 \tabularnewline
73 & 21332 & 26218.2339807075 & -4886.23398070746 \tabularnewline
74 & 13229 & 21711.3555924662 & -8482.35559246617 \tabularnewline
75 & 11331 & 11655.2098561475 & -324.209856147473 \tabularnewline
76 & 853 & 3842.51724530212 & -2989.51724530212 \tabularnewline
77 & 19821 & 22983.4939211832 & -3162.49392118319 \tabularnewline
78 & 34666 & 21427.8639305942 & 13238.1360694058 \tabularnewline
79 & 15051 & 12348.9019855076 & 2702.09801449241 \tabularnewline
80 & 27969 & 23995.778978606 & 3973.22102139401 \tabularnewline
81 & 17897 & 19942.9150275612 & -2045.91502756124 \tabularnewline
82 & 6031 & 11924.182154335 & -5893.18215433503 \tabularnewline
83 & 7153 & 11690.5608548575 & -4537.56085485752 \tabularnewline
84 & 13365 & 19321.2900045269 & -5956.29000452685 \tabularnewline
85 & 11197 & 12398.6775310581 & -1201.6775310581 \tabularnewline
86 & 25291 & 11560.3015226662 & 13730.6984773338 \tabularnewline
87 & 28994 & 32464.3460429577 & -3470.34604295768 \tabularnewline
88 & 10461 & 11437.2718229013 & -976.271822901348 \tabularnewline
89 & 16415 & 17370.3919399422 & -955.391939942166 \tabularnewline
90 & 8495 & 15172.8224751385 & -6677.8224751385 \tabularnewline
91 & 18318 & 17251.9702797846 & 1066.02972021536 \tabularnewline
92 & 25143 & 18735.5678820603 & 6407.43211793972 \tabularnewline
93 & 20471 & 11027.6001119293 & 9443.39988807069 \tabularnewline
94 & 14561 & 13697.7363700218 & 863.263629978159 \tabularnewline
95 & 16902 & 12754.0003748847 & 4147.99962511529 \tabularnewline
96 & 12994 & 23533.816518779 & -10539.816518779 \tabularnewline
97 & 29697 & 20931.2203361005 & 8765.77966389946 \tabularnewline
98 & 3895 & 8351.62024018013 & -4456.62024018013 \tabularnewline
99 & 9807 & 13894.0086513303 & -4087.00865133025 \tabularnewline
100 & 10711 & 12713.9928104066 & -2002.99281040665 \tabularnewline
101 & 2325 & 6413.81856702015 & -4088.81856702015 \tabularnewline
102 & 19000 & 13704.9197117651 & 5295.08028823495 \tabularnewline
103 & 22418 & 15413.5322995153 & 7004.46770048468 \tabularnewline
104 & 7872 & 10337.1415760775 & -2465.14157607755 \tabularnewline
105 & 5650 & 9063.62880126154 & -3413.62880126154 \tabularnewline
106 & 3979 & 5485.72888462453 & -1506.72888462453 \tabularnewline
107 & 14956 & 22299.5582002247 & -7343.55820022472 \tabularnewline
108 & 3738 & 6431.47670736896 & -2693.47670736896 \tabularnewline
109 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
110 & 10586 & 20651.0548491034 & -10065.0548491034 \tabularnewline
111 & 18122 & 8055.82001311231 & 10066.1799868877 \tabularnewline
112 & 17899 & 21885.8997875279 & -3986.89978752794 \tabularnewline
113 & 10913 & 15644.756357874 & -4731.75635787395 \tabularnewline
114 & 18060 & 15135.5660383359 & 2924.43396166414 \tabularnewline
115 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
116 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
117 & 15452 & 24209.2638879202 & -8757.26388792016 \tabularnewline
118 & 33996 & 14154.5041703713 & 19841.4958296287 \tabularnewline
119 & 8877 & 11128.3151055773 & -2251.31510557734 \tabularnewline
120 & 18708 & 13623.7429157394 & 5084.25708426064 \tabularnewline
121 & 2781 & 3438.73543866995 & -657.735438669953 \tabularnewline
122 & 20854 & 19289.9118993933 & 1564.08810060672 \tabularnewline
123 & 8179 & 11767.1411839467 & -3588.14118394668 \tabularnewline
124 & 7139 & 7828.73533667634 & -689.735336676344 \tabularnewline
125 & 13798 & 4263.32204625224 & 9534.67795374776 \tabularnewline
126 & 5619 & 10430.0866081122 & -4811.08660811218 \tabularnewline
127 & 13050 & 18134.1624933623 & -5084.16249336232 \tabularnewline
128 & 11297 & 14089.7267953035 & -2792.72679530345 \tabularnewline
129 & 16170 & 15451.5280363861 & 718.47196361392 \tabularnewline
130 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
131 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
132 & 20539 & 6670.36433782628 & 13868.6356621737 \tabularnewline
133 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
134 & 10056 & 6587.24738569558 & 3468.75261430442 \tabularnewline
135 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
136 & 2418 & 3575.50824856119 & -1157.50824856119 \tabularnewline
137 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
138 & 11806 & 10653.8914003566 & 1152.10859964343 \tabularnewline
139 & 15924 & 5720.75001102009 & 10203.2499889799 \tabularnewline
140 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
141 & 0 & 3171.06815055654 & -3171.06815055654 \tabularnewline
142 & 7084 & 12350.2185682525 & -5266.21856825254 \tabularnewline
143 & 14831 & 19651.7713662614 & -4820.77136626144 \tabularnewline
144 & 6585 & 7462.86825821929 & -877.868258219294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&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]10345[/C][C]12458.1885751461[/C][C]-2113.18857514607[/C][/ROW]
[ROW][C]2[/C][C]17607[/C][C]22169.8138164013[/C][C]-4562.8138164013[/C][/ROW]
[ROW][C]3[/C][C]1423[/C][C]4354.93892287111[/C][C]-2931.93892287111[/C][/ROW]
[ROW][C]4[/C][C]20050[/C][C]26118.9326221257[/C][C]-6068.93262212572[/C][/ROW]
[ROW][C]5[/C][C]21212[/C][C]29853.7185934757[/C][C]-8641.71859347569[/C][/ROW]
[ROW][C]6[/C][C]93979[/C][C]59319.6815335292[/C][C]34659.3184664708[/C][/ROW]
[ROW][C]7[/C][C]15524[/C][C]16715.9658395145[/C][C]-1191.96583951454[/C][/ROW]
[ROW][C]8[/C][C]16182[/C][C]19939.7531055552[/C][C]-3757.75310555518[/C][/ROW]
[ROW][C]9[/C][C]19238[/C][C]25852.4986725842[/C][C]-6614.49867258418[/C][/ROW]
[ROW][C]10[/C][C]28909[/C][C]35966.5775650558[/C][C]-7057.57756505581[/C][/ROW]
[ROW][C]11[/C][C]22357[/C][C]23167.4206838058[/C][C]-810.420683805837[/C][/ROW]
[ROW][C]12[/C][C]25560[/C][C]34689.4364460265[/C][C]-9129.43644602651[/C][/ROW]
[ROW][C]13[/C][C]9954[/C][C]16245.4202110263[/C][C]-6291.42021102633[/C][/ROW]
[ROW][C]14[/C][C]18490[/C][C]24239.9930388746[/C][C]-5749.99303887461[/C][/ROW]
[ROW][C]15[/C][C]17777[/C][C]16702.8810207606[/C][C]1074.11897923935[/C][/ROW]
[ROW][C]16[/C][C]25268[/C][C]13996.1234718963[/C][C]11271.8765281037[/C][/ROW]
[ROW][C]17[/C][C]37525[/C][C]37260.7030497321[/C][C]264.29695026788[/C][/ROW]
[ROW][C]18[/C][C]6023[/C][C]3173.03145200317[/C][C]2849.96854799683[/C][/ROW]
[ROW][C]19[/C][C]25042[/C][C]29837.4024166909[/C][C]-4795.4024166909[/C][/ROW]
[ROW][C]20[/C][C]35713[/C][C]12201.6775223524[/C][C]23511.3224776476[/C][/ROW]
[ROW][C]21[/C][C]7039[/C][C]9305.11487919635[/C][C]-2266.11487919635[/C][/ROW]
[ROW][C]22[/C][C]40841[/C][C]31895.0349141198[/C][C]8945.96508588019[/C][/ROW]
[ROW][C]23[/C][C]9214[/C][C]6224.02504539924[/C][C]2989.97495460076[/C][/ROW]
[ROW][C]24[/C][C]17446[/C][C]9839.8143093923[/C][C]7606.1856906077[/C][/ROW]
[ROW][C]25[/C][C]10295[/C][C]18687.1667826088[/C][C]-8392.16678260876[/C][/ROW]
[ROW][C]26[/C][C]13206[/C][C]17921.4792184252[/C][C]-4715.4792184252[/C][/ROW]
[ROW][C]27[/C][C]26093[/C][C]24764.8897699857[/C][C]1328.11023001431[/C][/ROW]
[ROW][C]28[/C][C]20744[/C][C]27753.6327642892[/C][C]-7009.63276428916[/C][/ROW]
[ROW][C]29[/C][C]68013[/C][C]36653.0031084996[/C][C]31359.9968915004[/C][/ROW]
[ROW][C]30[/C][C]12840[/C][C]20113.1356335474[/C][C]-7273.13563354739[/C][/ROW]
[ROW][C]31[/C][C]12672[/C][C]11962.789891895[/C][C]709.210108104959[/C][/ROW]
[ROW][C]32[/C][C]10872[/C][C]16144.633545876[/C][C]-5272.63354587601[/C][/ROW]
[ROW][C]33[/C][C]21325[/C][C]17793.7582348799[/C][C]3531.2417651201[/C][/ROW]
[ROW][C]34[/C][C]24542[/C][C]5103.63821074913[/C][C]19438.3617892509[/C][/ROW]
[ROW][C]35[/C][C]16401[/C][C]22103.8979196056[/C][C]-5702.89791960555[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]37[/C][C]12821[/C][C]22144.5930669337[/C][C]-9323.59306693367[/C][/ROW]
[ROW][C]38[/C][C]14662[/C][C]20978.9862895212[/C][C]-6316.9862895212[/C][/ROW]
[ROW][C]39[/C][C]22190[/C][C]10064.2542476675[/C][C]12125.7457523325[/C][/ROW]
[ROW][C]40[/C][C]37929[/C][C]45841.8151177533[/C][C]-7912.81511775333[/C][/ROW]
[ROW][C]41[/C][C]18009[/C][C]28662.6622439405[/C][C]-10653.6622439405[/C][/ROW]
[ROW][C]42[/C][C]11076[/C][C]15268.2479924651[/C][C]-4192.24799246511[/C][/ROW]
[ROW][C]43[/C][C]24981[/C][C]16799.51449584[/C][C]8181.48550415995[/C][/ROW]
[ROW][C]44[/C][C]30691[/C][C]15738.9347284804[/C][C]14952.0652715196[/C][/ROW]
[ROW][C]45[/C][C]29164[/C][C]25626.800014918[/C][C]3537.199985082[/C][/ROW]
[ROW][C]46[/C][C]13985[/C][C]18415.5381735897[/C][C]-4430.53817358968[/C][/ROW]
[ROW][C]47[/C][C]7588[/C][C]9496.22498356221[/C][C]-1908.22498356221[/C][/ROW]
[ROW][C]48[/C][C]20023[/C][C]20461.9080461842[/C][C]-438.908046184195[/C][/ROW]
[ROW][C]49[/C][C]25524[/C][C]18181.8821560997[/C][C]7342.11784390025[/C][/ROW]
[ROW][C]50[/C][C]14717[/C][C]16678.6746846995[/C][C]-1961.67468469948[/C][/ROW]
[ROW][C]51[/C][C]6832[/C][C]9564.92896152326[/C][C]-2732.92896152326[/C][/ROW]
[ROW][C]52[/C][C]9624[/C][C]14816.1251852129[/C][C]-5192.1251852129[/C][/ROW]
[ROW][C]53[/C][C]24300[/C][C]23624.715005194[/C][C]675.284994806043[/C][/ROW]
[ROW][C]54[/C][C]21790[/C][C]24217.068567546[/C][C]-2427.06856754598[/C][/ROW]
[ROW][C]55[/C][C]16493[/C][C]20543.1565137122[/C][C]-4050.15651371218[/C][/ROW]
[ROW][C]56[/C][C]9269[/C][C]16074.6361305116[/C][C]-6805.63613051162[/C][/ROW]
[ROW][C]57[/C][C]20105[/C][C]19166.2007629143[/C][C]938.799237085678[/C][/ROW]
[ROW][C]58[/C][C]11216[/C][C]11538.6219625802[/C][C]-322.621962580248[/C][/ROW]
[ROW][C]59[/C][C]15569[/C][C]18369.7008036032[/C][C]-2800.70080360322[/C][/ROW]
[ROW][C]60[/C][C]21799[/C][C]4296.05145214318[/C][C]17502.9485478568[/C][/ROW]
[ROW][C]61[/C][C]3772[/C][C]7796.60635880384[/C][C]-4024.60635880384[/C][/ROW]
[ROW][C]62[/C][C]6057[/C][C]8622.06403678802[/C][C]-2565.06403678802[/C][/ROW]
[ROW][C]63[/C][C]20828[/C][C]7987.08131713884[/C][C]12840.9186828612[/C][/ROW]
[ROW][C]64[/C][C]9976[/C][C]18924.3817082871[/C][C]-8948.38170828712[/C][/ROW]
[ROW][C]65[/C][C]14055[/C][C]9449.0710308308[/C][C]4605.9289691692[/C][/ROW]
[ROW][C]66[/C][C]17455[/C][C]19979.5594636746[/C][C]-2524.55946367463[/C][/ROW]
[ROW][C]67[/C][C]39553[/C][C]26187.5671640619[/C][C]13365.4328359381[/C][/ROW]
[ROW][C]68[/C][C]14818[/C][C]24984.8605407033[/C][C]-10166.8605407033[/C][/ROW]
[ROW][C]69[/C][C]17065[/C][C]18054.2907074108[/C][C]-989.290707410772[/C][/ROW]
[ROW][C]70[/C][C]1536[/C][C]4184.13169701478[/C][C]-2648.13169701478[/C][/ROW]
[ROW][C]71[/C][C]11938[/C][C]22762.3200589513[/C][C]-10824.3200589513[/C][/ROW]
[ROW][C]72[/C][C]24589[/C][C]12173.5561560688[/C][C]12415.4438439312[/C][/ROW]
[ROW][C]73[/C][C]21332[/C][C]26218.2339807075[/C][C]-4886.23398070746[/C][/ROW]
[ROW][C]74[/C][C]13229[/C][C]21711.3555924662[/C][C]-8482.35559246617[/C][/ROW]
[ROW][C]75[/C][C]11331[/C][C]11655.2098561475[/C][C]-324.209856147473[/C][/ROW]
[ROW][C]76[/C][C]853[/C][C]3842.51724530212[/C][C]-2989.51724530212[/C][/ROW]
[ROW][C]77[/C][C]19821[/C][C]22983.4939211832[/C][C]-3162.49392118319[/C][/ROW]
[ROW][C]78[/C][C]34666[/C][C]21427.8639305942[/C][C]13238.1360694058[/C][/ROW]
[ROW][C]79[/C][C]15051[/C][C]12348.9019855076[/C][C]2702.09801449241[/C][/ROW]
[ROW][C]80[/C][C]27969[/C][C]23995.778978606[/C][C]3973.22102139401[/C][/ROW]
[ROW][C]81[/C][C]17897[/C][C]19942.9150275612[/C][C]-2045.91502756124[/C][/ROW]
[ROW][C]82[/C][C]6031[/C][C]11924.182154335[/C][C]-5893.18215433503[/C][/ROW]
[ROW][C]83[/C][C]7153[/C][C]11690.5608548575[/C][C]-4537.56085485752[/C][/ROW]
[ROW][C]84[/C][C]13365[/C][C]19321.2900045269[/C][C]-5956.29000452685[/C][/ROW]
[ROW][C]85[/C][C]11197[/C][C]12398.6775310581[/C][C]-1201.6775310581[/C][/ROW]
[ROW][C]86[/C][C]25291[/C][C]11560.3015226662[/C][C]13730.6984773338[/C][/ROW]
[ROW][C]87[/C][C]28994[/C][C]32464.3460429577[/C][C]-3470.34604295768[/C][/ROW]
[ROW][C]88[/C][C]10461[/C][C]11437.2718229013[/C][C]-976.271822901348[/C][/ROW]
[ROW][C]89[/C][C]16415[/C][C]17370.3919399422[/C][C]-955.391939942166[/C][/ROW]
[ROW][C]90[/C][C]8495[/C][C]15172.8224751385[/C][C]-6677.8224751385[/C][/ROW]
[ROW][C]91[/C][C]18318[/C][C]17251.9702797846[/C][C]1066.02972021536[/C][/ROW]
[ROW][C]92[/C][C]25143[/C][C]18735.5678820603[/C][C]6407.43211793972[/C][/ROW]
[ROW][C]93[/C][C]20471[/C][C]11027.6001119293[/C][C]9443.39988807069[/C][/ROW]
[ROW][C]94[/C][C]14561[/C][C]13697.7363700218[/C][C]863.263629978159[/C][/ROW]
[ROW][C]95[/C][C]16902[/C][C]12754.0003748847[/C][C]4147.99962511529[/C][/ROW]
[ROW][C]96[/C][C]12994[/C][C]23533.816518779[/C][C]-10539.816518779[/C][/ROW]
[ROW][C]97[/C][C]29697[/C][C]20931.2203361005[/C][C]8765.77966389946[/C][/ROW]
[ROW][C]98[/C][C]3895[/C][C]8351.62024018013[/C][C]-4456.62024018013[/C][/ROW]
[ROW][C]99[/C][C]9807[/C][C]13894.0086513303[/C][C]-4087.00865133025[/C][/ROW]
[ROW][C]100[/C][C]10711[/C][C]12713.9928104066[/C][C]-2002.99281040665[/C][/ROW]
[ROW][C]101[/C][C]2325[/C][C]6413.81856702015[/C][C]-4088.81856702015[/C][/ROW]
[ROW][C]102[/C][C]19000[/C][C]13704.9197117651[/C][C]5295.08028823495[/C][/ROW]
[ROW][C]103[/C][C]22418[/C][C]15413.5322995153[/C][C]7004.46770048468[/C][/ROW]
[ROW][C]104[/C][C]7872[/C][C]10337.1415760775[/C][C]-2465.14157607755[/C][/ROW]
[ROW][C]105[/C][C]5650[/C][C]9063.62880126154[/C][C]-3413.62880126154[/C][/ROW]
[ROW][C]106[/C][C]3979[/C][C]5485.72888462453[/C][C]-1506.72888462453[/C][/ROW]
[ROW][C]107[/C][C]14956[/C][C]22299.5582002247[/C][C]-7343.55820022472[/C][/ROW]
[ROW][C]108[/C][C]3738[/C][C]6431.47670736896[/C][C]-2693.47670736896[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]110[/C][C]10586[/C][C]20651.0548491034[/C][C]-10065.0548491034[/C][/ROW]
[ROW][C]111[/C][C]18122[/C][C]8055.82001311231[/C][C]10066.1799868877[/C][/ROW]
[ROW][C]112[/C][C]17899[/C][C]21885.8997875279[/C][C]-3986.89978752794[/C][/ROW]
[ROW][C]113[/C][C]10913[/C][C]15644.756357874[/C][C]-4731.75635787395[/C][/ROW]
[ROW][C]114[/C][C]18060[/C][C]15135.5660383359[/C][C]2924.43396166414[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]117[/C][C]15452[/C][C]24209.2638879202[/C][C]-8757.26388792016[/C][/ROW]
[ROW][C]118[/C][C]33996[/C][C]14154.5041703713[/C][C]19841.4958296287[/C][/ROW]
[ROW][C]119[/C][C]8877[/C][C]11128.3151055773[/C][C]-2251.31510557734[/C][/ROW]
[ROW][C]120[/C][C]18708[/C][C]13623.7429157394[/C][C]5084.25708426064[/C][/ROW]
[ROW][C]121[/C][C]2781[/C][C]3438.73543866995[/C][C]-657.735438669953[/C][/ROW]
[ROW][C]122[/C][C]20854[/C][C]19289.9118993933[/C][C]1564.08810060672[/C][/ROW]
[ROW][C]123[/C][C]8179[/C][C]11767.1411839467[/C][C]-3588.14118394668[/C][/ROW]
[ROW][C]124[/C][C]7139[/C][C]7828.73533667634[/C][C]-689.735336676344[/C][/ROW]
[ROW][C]125[/C][C]13798[/C][C]4263.32204625224[/C][C]9534.67795374776[/C][/ROW]
[ROW][C]126[/C][C]5619[/C][C]10430.0866081122[/C][C]-4811.08660811218[/C][/ROW]
[ROW][C]127[/C][C]13050[/C][C]18134.1624933623[/C][C]-5084.16249336232[/C][/ROW]
[ROW][C]128[/C][C]11297[/C][C]14089.7267953035[/C][C]-2792.72679530345[/C][/ROW]
[ROW][C]129[/C][C]16170[/C][C]15451.5280363861[/C][C]718.47196361392[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]132[/C][C]20539[/C][C]6670.36433782628[/C][C]13868.6356621737[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]134[/C][C]10056[/C][C]6587.24738569558[/C][C]3468.75261430442[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]136[/C][C]2418[/C][C]3575.50824856119[/C][C]-1157.50824856119[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]138[/C][C]11806[/C][C]10653.8914003566[/C][C]1152.10859964343[/C][/ROW]
[ROW][C]139[/C][C]15924[/C][C]5720.75001102009[/C][C]10203.2499889799[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]3171.06815055654[/C][C]-3171.06815055654[/C][/ROW]
[ROW][C]142[/C][C]7084[/C][C]12350.2185682525[/C][C]-5266.21856825254[/C][/ROW]
[ROW][C]143[/C][C]14831[/C][C]19651.7713662614[/C][C]-4820.77136626144[/C][/ROW]
[ROW][C]144[/C][C]6585[/C][C]7462.86825821929[/C][C]-877.868258219294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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
11034512458.1885751461-2113.18857514607
21760722169.8138164013-4562.8138164013
314234354.93892287111-2931.93892287111
42005026118.9326221257-6068.93262212572
52121229853.7185934757-8641.71859347569
69397959319.681533529234659.3184664708
71552416715.9658395145-1191.96583951454
81618219939.7531055552-3757.75310555518
91923825852.4986725842-6614.49867258418
102890935966.5775650558-7057.57756505581
112235723167.4206838058-810.420683805837
122556034689.4364460265-9129.43644602651
13995416245.4202110263-6291.42021102633
141849024239.9930388746-5749.99303887461
151777716702.88102076061074.11897923935
162526813996.123471896311271.8765281037
173752537260.7030497321264.29695026788
1860233173.031452003172849.96854799683
192504229837.4024166909-4795.4024166909
203571312201.677522352423511.3224776476
2170399305.11487919635-2266.11487919635
224084131895.03491411988945.96508588019
2392146224.025045399242989.97495460076
24174469839.81430939237606.1856906077
251029518687.1667826088-8392.16678260876
261320617921.4792184252-4715.4792184252
272609324764.88976998571328.11023001431
282074427753.6327642892-7009.63276428916
296801336653.003108499631359.9968915004
301284020113.1356335474-7273.13563354739
311267211962.789891895709.210108104959
321087216144.633545876-5272.63354587601
332132517793.75823487993531.2417651201
34245425103.6382107491319438.3617892509
351640122103.8979196056-5702.89791960555
3603171.06815055654-3171.06815055654
371282122144.5930669337-9323.59306693367
381466220978.9862895212-6316.9862895212
392219010064.254247667512125.7457523325
403792945841.8151177533-7912.81511775333
411800928662.6622439405-10653.6622439405
421107615268.2479924651-4192.24799246511
432498116799.514495848181.48550415995
443069115738.934728480414952.0652715196
452916425626.8000149183537.199985082
461398518415.5381735897-4430.53817358968
4775889496.22498356221-1908.22498356221
482002320461.9080461842-438.908046184195
492552418181.88215609977342.11784390025
501471716678.6746846995-1961.67468469948
5168329564.92896152326-2732.92896152326
52962414816.1251852129-5192.1251852129
532430023624.715005194675.284994806043
542179024217.068567546-2427.06856754598
551649320543.1565137122-4050.15651371218
56926916074.6361305116-6805.63613051162
572010519166.2007629143938.799237085678
581121611538.6219625802-322.621962580248
591556918369.7008036032-2800.70080360322
60217994296.0514521431817502.9485478568
6137727796.60635880384-4024.60635880384
6260578622.06403678802-2565.06403678802
63208287987.0813171388412840.9186828612
64997618924.3817082871-8948.38170828712
65140559449.07103083084605.9289691692
661745519979.5594636746-2524.55946367463
673955326187.567164061913365.4328359381
681481824984.8605407033-10166.8605407033
691706518054.2907074108-989.290707410772
7015364184.13169701478-2648.13169701478
711193822762.3200589513-10824.3200589513
722458912173.556156068812415.4438439312
732133226218.2339807075-4886.23398070746
741322921711.3555924662-8482.35559246617
751133111655.2098561475-324.209856147473
768533842.51724530212-2989.51724530212
771982122983.4939211832-3162.49392118319
783466621427.863930594213238.1360694058
791505112348.90198550762702.09801449241
802796923995.7789786063973.22102139401
811789719942.9150275612-2045.91502756124
82603111924.182154335-5893.18215433503
83715311690.5608548575-4537.56085485752
841336519321.2900045269-5956.29000452685
851119712398.6775310581-1201.6775310581
862529111560.301522666213730.6984773338
872899432464.3460429577-3470.34604295768
881046111437.2718229013-976.271822901348
891641517370.3919399422-955.391939942166
90849515172.8224751385-6677.8224751385
911831817251.97027978461066.02972021536
922514318735.56788206036407.43211793972
932047111027.60011192939443.39988807069
941456113697.7363700218863.263629978159
951690212754.00037488474147.99962511529
961299423533.816518779-10539.816518779
972969720931.22033610058765.77966389946
9838958351.62024018013-4456.62024018013
99980713894.0086513303-4087.00865133025
1001071112713.9928104066-2002.99281040665
10123256413.81856702015-4088.81856702015
1021900013704.91971176515295.08028823495
1032241815413.53229951537004.46770048468
104787210337.1415760775-2465.14157607755
10556509063.62880126154-3413.62880126154
10639795485.72888462453-1506.72888462453
1071495622299.5582002247-7343.55820022472
10837386431.47670736896-2693.47670736896
10903171.06815055654-3171.06815055654
1101058620651.0548491034-10065.0548491034
111181228055.8200131123110066.1799868877
1121789921885.8997875279-3986.89978752794
1131091315644.756357874-4731.75635787395
1141806015135.56603833592924.43396166414
11503171.06815055654-3171.06815055654
11603171.06815055654-3171.06815055654
1171545224209.2638879202-8757.26388792016
1183399614154.504170371319841.4958296287
119887711128.3151055773-2251.31510557734
1201870813623.74291573945084.25708426064
12127813438.73543866995-657.735438669953
1222085419289.91189939331564.08810060672
123817911767.1411839467-3588.14118394668
12471397828.73533667634-689.735336676344
125137984263.322046252249534.67795374776
126561910430.0866081122-4811.08660811218
1271305018134.1624933623-5084.16249336232
1281129714089.7267953035-2792.72679530345
1291617015451.5280363861718.47196361392
13003171.06815055654-3171.06815055654
13103171.06815055654-3171.06815055654
132205396670.3643378262813868.6356621737
13303171.06815055654-3171.06815055654
134100566587.247385695583468.75261430442
13503171.06815055654-3171.06815055654
13624183575.50824856119-1157.50824856119
13703171.06815055654-3171.06815055654
1381180610653.89140035661152.10859964343
139159245720.7500110200910203.2499889799
14003171.06815055654-3171.06815055654
14103171.06815055654-3171.06815055654
142708412350.2185682525-5266.21856825254
1431483119651.7713662614-4820.77136626144
14465857462.86825821929-877.868258219294







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2046110848358470.4092221696716950.795388915164153
80.7551846595756410.4896306808487170.244815340424359
90.7083672751404860.5832654497190280.291632724859514
100.6027114819010870.7945770361978270.397288518098913
110.4865325600291290.9730651200582580.513467439970871
120.3828477444532180.7656954889064360.617152255546782
130.295328520834570.5906570416691410.70467147916543
140.5905534589222730.8188930821554540.409446541077727
150.5563228787778460.8873542424443070.443677121222153
160.8010323347455380.3979353305089230.198967665254462
170.7843708707742570.4312582584514860.215629129225743
180.796648855558420.406702288883160.20335114444158
190.7616077131966740.4767845736066510.238392286803326
200.9535345250188790.09293094996224180.0464654749811209
210.9341848229896450.131630354020710.0658151770103549
220.923238152933470.153523694133060.0767618470665301
230.9020859195043360.1958281609913290.0979140804956643
240.8779082119007290.2441835761985420.122091788099271
250.9402219878351250.1195560243297510.0597780121648755
260.9509447797681340.09811044046373160.0490552202318658
270.9355490868774440.1289018262451120.064450913122556
280.926850999512680.146298000974640.07314900048732
290.9990141561092860.001971687781428040.000985843890714022
300.9986269693505330.002746061298933270.00137303064946664
310.9978751433024660.00424971339506760.0021248566975338
320.9969640016101850.006071996779629180.00303599838981459
330.9956106094250310.008778781149937070.00438939057496853
340.9993884844127230.001223031174553880.000611515587276941
350.9994908576611970.001018284677606390.000509142338803194
360.999241101029820.001517797940359830.000758898970179916
370.9996411914338430.0007176171323136180.000358808566156809
380.9995829911571980.0008340176856049270.000417008842802463
390.9998345420977090.0003309158045816220.000165457902290811
400.9998824233630650.0002351532738699370.000117576636934969
410.9999777736880824.44526238365927e-052.22263119182963e-05
420.9999650789104476.98421791060513e-053.49210895530256e-05
430.9999672926027436.54147945149833e-053.27073972574917e-05
440.9999894567586662.10864826678604e-051.05432413339302e-05
450.9999878981777432.42036445138185e-051.21018222569093e-05
460.9999818347312673.63305374651922e-051.81652687325961e-05
470.999970029295585.99414088403962e-052.99707044201981e-05
480.9999505695414569.88609170884175e-054.94304585442087e-05
490.9999510999883259.78000233503461e-054.8900011675173e-05
500.9999212044863550.0001575910272895747.87955136447869e-05
510.9998787830549880.0002424338900247730.000121216945012387
520.9998384796640770.0003230406718463170.000161520335923158
530.9997537933418490.000492413316301670.000246206658150835
540.9996282489749970.0007435020500060340.000371751025003017
550.9994984873442750.001003025311450770.000501512655725387
560.9994347511272160.001130497745567940.000565248872783968
570.9991455711925690.001708857614862960.00085442880743148
580.9987119574719670.002576085056065280.00128804252803264
590.9981920965669790.003615806866042120.00180790343302106
600.9996481981138340.0007036037723312880.000351801886165644
610.9995355347796780.0009289304406444090.000464465220322205
620.9993584591779780.001283081644042980.000641540822021491
630.9996916124410270.0006167751179458310.000308387558972916
640.9996803261949140.0006393476101715730.000319673805085786
650.9995701032844190.0008597934311622020.000429896715581101
660.9993594799843890.001281040031221520.000640520015610761
670.9997504503845650.0004990992308692480.000249549615434624
680.9998062886793750.0003874226412492790.00019371132062464
690.9996945266199470.0006109467601068920.000305473380053446
700.9995540744036510.0008918511926989760.000445925596349488
710.9996832475263210.0006335049473572750.000316752473678637
720.9998525733865810.0002948532268373170.000147426613418658
730.9997904688528980.000419062294204540.00020953114710227
740.9998130164296750.0003739671406499520.000186983570324976
750.9997019874234560.0005960251530890450.000298012576544523
760.999564216954320.0008715660913594170.000435783045679709
770.9993512047367130.001297590526573280.000648795263286638
780.9997895097597050.0004209804805898850.000210490240294942
790.999686751228760.0006264975424800540.000313248771240027
800.9996660755785590.0006678488428818520.000333924421440926
810.9994814386880770.001037122623845010.000518561311922503
820.9993840667256650.00123186654866930.000615933274334649
830.9991950409920030.001609918015993360.000804959007996678
840.9990233627939950.001953274412009390.000976637206004693
850.9985332626314250.002933474737150880.00146673736857544
860.9995311549380940.0009376901238129080.000468845061906454
870.9992867248449920.001426550310014960.000713275155007482
880.9988937004972510.002212599005498720.00110629950274936
890.9983031025403950.003393794919210150.00169689745960507
900.9981905844861810.003618831027637240.00180941551381862
910.9972793909039510.005441218192097050.00272060909604852
920.997154258866560.005691482266879470.00284574113343974
930.9979075453602530.004184909279493590.0020924546397468
940.996828989688990.006342020622019660.00317101031100983
950.9960080423860660.007983915227867610.0039919576139338
960.9963747995248090.007250400950382860.00362520047519143
970.9974444746228370.005111050754325670.00255552537716284
980.9967164626926310.006567074614737780.00328353730736889
990.9953169215496680.009366156900663940.00468307845033197
1000.9930432068925430.01391358621491440.00695679310745718
1010.9910514180234720.01789716395305640.00894858197652821
1020.9896996158918520.02060076821629660.0103003841081483
1030.9930360834849780.01392783303004390.00696391651502197
1040.9897217410783350.02055651784333040.0102782589216652
1050.9858468707748360.02830625845032710.0141531292251636
1060.9796010586541790.04079788269164270.0203989413458214
1070.9739569231494390.0520861537011220.026043076850561
1080.9648344464151280.07033110716974350.0351655535848718
1090.9550490521781170.08990189564376540.0449509478218827
1100.9601181577995620.07976368440087530.0398818422004376
1110.9675489961207420.06490200775851680.0324510038792584
1120.9578925115185280.08421497696294450.0421074884814723
1130.9476207263768130.1047585472463740.0523792736231868
1140.9286016952015610.1427966095968780.0713983047984388
1150.9091878347500750.1816243304998490.0908121652499247
1160.886588702481390.226822595037220.11341129751861
1170.8949202769879030.2101594460241950.105079723012097
1180.9935233663749120.0129532672501760.00647663362508802
1190.989921152104080.02015769579183980.0100788478959199
1200.9898149519231560.0203700961536880.010185048076844
1210.9833565736874090.03328685262518120.0166434263125906
1220.9772774763670910.04544504726581820.0227225236329091
1230.9660348878505950.06793022429881020.0339651121494051
1240.9479271547674160.1041456904651690.0520728452325843
1250.9671976309669110.06560473806617840.0328023690330892
1260.9516571703999090.09668565920018250.0483428296000913
1270.9377349787441940.1245300425116130.0622650212558063
1280.9182367722385360.1635264555229290.0817632277614644
1290.8746851957460070.2506296085079850.125314804253993
1300.8264251419245150.347149716150970.173574858075485
1310.7677853381232780.4644293237534450.232214661876722
1320.9434101997696630.1131796004606730.0565898002303367
1330.9071115685899950.185776862820010.0928884314100049
1340.9517340866843080.09653182663138360.0482659133156918
1350.9047828425716490.1904343148567020.0952171574283509
1360.8151856202532430.3696287594935150.184814379746757
1370.6866494588325710.6267010823348590.313350541167429

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.204611084835847 & 0.409222169671695 & 0.795388915164153 \tabularnewline
8 & 0.755184659575641 & 0.489630680848717 & 0.244815340424359 \tabularnewline
9 & 0.708367275140486 & 0.583265449719028 & 0.291632724859514 \tabularnewline
10 & 0.602711481901087 & 0.794577036197827 & 0.397288518098913 \tabularnewline
11 & 0.486532560029129 & 0.973065120058258 & 0.513467439970871 \tabularnewline
12 & 0.382847744453218 & 0.765695488906436 & 0.617152255546782 \tabularnewline
13 & 0.29532852083457 & 0.590657041669141 & 0.70467147916543 \tabularnewline
14 & 0.590553458922273 & 0.818893082155454 & 0.409446541077727 \tabularnewline
15 & 0.556322878777846 & 0.887354242444307 & 0.443677121222153 \tabularnewline
16 & 0.801032334745538 & 0.397935330508923 & 0.198967665254462 \tabularnewline
17 & 0.784370870774257 & 0.431258258451486 & 0.215629129225743 \tabularnewline
18 & 0.79664885555842 & 0.40670228888316 & 0.20335114444158 \tabularnewline
19 & 0.761607713196674 & 0.476784573606651 & 0.238392286803326 \tabularnewline
20 & 0.953534525018879 & 0.0929309499622418 & 0.0464654749811209 \tabularnewline
21 & 0.934184822989645 & 0.13163035402071 & 0.0658151770103549 \tabularnewline
22 & 0.92323815293347 & 0.15352369413306 & 0.0767618470665301 \tabularnewline
23 & 0.902085919504336 & 0.195828160991329 & 0.0979140804956643 \tabularnewline
24 & 0.877908211900729 & 0.244183576198542 & 0.122091788099271 \tabularnewline
25 & 0.940221987835125 & 0.119556024329751 & 0.0597780121648755 \tabularnewline
26 & 0.950944779768134 & 0.0981104404637316 & 0.0490552202318658 \tabularnewline
27 & 0.935549086877444 & 0.128901826245112 & 0.064450913122556 \tabularnewline
28 & 0.92685099951268 & 0.14629800097464 & 0.07314900048732 \tabularnewline
29 & 0.999014156109286 & 0.00197168778142804 & 0.000985843890714022 \tabularnewline
30 & 0.998626969350533 & 0.00274606129893327 & 0.00137303064946664 \tabularnewline
31 & 0.997875143302466 & 0.0042497133950676 & 0.0021248566975338 \tabularnewline
32 & 0.996964001610185 & 0.00607199677962918 & 0.00303599838981459 \tabularnewline
33 & 0.995610609425031 & 0.00877878114993707 & 0.00438939057496853 \tabularnewline
34 & 0.999388484412723 & 0.00122303117455388 & 0.000611515587276941 \tabularnewline
35 & 0.999490857661197 & 0.00101828467760639 & 0.000509142338803194 \tabularnewline
36 & 0.99924110102982 & 0.00151779794035983 & 0.000758898970179916 \tabularnewline
37 & 0.999641191433843 & 0.000717617132313618 & 0.000358808566156809 \tabularnewline
38 & 0.999582991157198 & 0.000834017685604927 & 0.000417008842802463 \tabularnewline
39 & 0.999834542097709 & 0.000330915804581622 & 0.000165457902290811 \tabularnewline
40 & 0.999882423363065 & 0.000235153273869937 & 0.000117576636934969 \tabularnewline
41 & 0.999977773688082 & 4.44526238365927e-05 & 2.22263119182963e-05 \tabularnewline
42 & 0.999965078910447 & 6.98421791060513e-05 & 3.49210895530256e-05 \tabularnewline
43 & 0.999967292602743 & 6.54147945149833e-05 & 3.27073972574917e-05 \tabularnewline
44 & 0.999989456758666 & 2.10864826678604e-05 & 1.05432413339302e-05 \tabularnewline
45 & 0.999987898177743 & 2.42036445138185e-05 & 1.21018222569093e-05 \tabularnewline
46 & 0.999981834731267 & 3.63305374651922e-05 & 1.81652687325961e-05 \tabularnewline
47 & 0.99997002929558 & 5.99414088403962e-05 & 2.99707044201981e-05 \tabularnewline
48 & 0.999950569541456 & 9.88609170884175e-05 & 4.94304585442087e-05 \tabularnewline
49 & 0.999951099988325 & 9.78000233503461e-05 & 4.8900011675173e-05 \tabularnewline
50 & 0.999921204486355 & 0.000157591027289574 & 7.87955136447869e-05 \tabularnewline
51 & 0.999878783054988 & 0.000242433890024773 & 0.000121216945012387 \tabularnewline
52 & 0.999838479664077 & 0.000323040671846317 & 0.000161520335923158 \tabularnewline
53 & 0.999753793341849 & 0.00049241331630167 & 0.000246206658150835 \tabularnewline
54 & 0.999628248974997 & 0.000743502050006034 & 0.000371751025003017 \tabularnewline
55 & 0.999498487344275 & 0.00100302531145077 & 0.000501512655725387 \tabularnewline
56 & 0.999434751127216 & 0.00113049774556794 & 0.000565248872783968 \tabularnewline
57 & 0.999145571192569 & 0.00170885761486296 & 0.00085442880743148 \tabularnewline
58 & 0.998711957471967 & 0.00257608505606528 & 0.00128804252803264 \tabularnewline
59 & 0.998192096566979 & 0.00361580686604212 & 0.00180790343302106 \tabularnewline
60 & 0.999648198113834 & 0.000703603772331288 & 0.000351801886165644 \tabularnewline
61 & 0.999535534779678 & 0.000928930440644409 & 0.000464465220322205 \tabularnewline
62 & 0.999358459177978 & 0.00128308164404298 & 0.000641540822021491 \tabularnewline
63 & 0.999691612441027 & 0.000616775117945831 & 0.000308387558972916 \tabularnewline
64 & 0.999680326194914 & 0.000639347610171573 & 0.000319673805085786 \tabularnewline
65 & 0.999570103284419 & 0.000859793431162202 & 0.000429896715581101 \tabularnewline
66 & 0.999359479984389 & 0.00128104003122152 & 0.000640520015610761 \tabularnewline
67 & 0.999750450384565 & 0.000499099230869248 & 0.000249549615434624 \tabularnewline
68 & 0.999806288679375 & 0.000387422641249279 & 0.00019371132062464 \tabularnewline
69 & 0.999694526619947 & 0.000610946760106892 & 0.000305473380053446 \tabularnewline
70 & 0.999554074403651 & 0.000891851192698976 & 0.000445925596349488 \tabularnewline
71 & 0.999683247526321 & 0.000633504947357275 & 0.000316752473678637 \tabularnewline
72 & 0.999852573386581 & 0.000294853226837317 & 0.000147426613418658 \tabularnewline
73 & 0.999790468852898 & 0.00041906229420454 & 0.00020953114710227 \tabularnewline
74 & 0.999813016429675 & 0.000373967140649952 & 0.000186983570324976 \tabularnewline
75 & 0.999701987423456 & 0.000596025153089045 & 0.000298012576544523 \tabularnewline
76 & 0.99956421695432 & 0.000871566091359417 & 0.000435783045679709 \tabularnewline
77 & 0.999351204736713 & 0.00129759052657328 & 0.000648795263286638 \tabularnewline
78 & 0.999789509759705 & 0.000420980480589885 & 0.000210490240294942 \tabularnewline
79 & 0.99968675122876 & 0.000626497542480054 & 0.000313248771240027 \tabularnewline
80 & 0.999666075578559 & 0.000667848842881852 & 0.000333924421440926 \tabularnewline
81 & 0.999481438688077 & 0.00103712262384501 & 0.000518561311922503 \tabularnewline
82 & 0.999384066725665 & 0.0012318665486693 & 0.000615933274334649 \tabularnewline
83 & 0.999195040992003 & 0.00160991801599336 & 0.000804959007996678 \tabularnewline
84 & 0.999023362793995 & 0.00195327441200939 & 0.000976637206004693 \tabularnewline
85 & 0.998533262631425 & 0.00293347473715088 & 0.00146673736857544 \tabularnewline
86 & 0.999531154938094 & 0.000937690123812908 & 0.000468845061906454 \tabularnewline
87 & 0.999286724844992 & 0.00142655031001496 & 0.000713275155007482 \tabularnewline
88 & 0.998893700497251 & 0.00221259900549872 & 0.00110629950274936 \tabularnewline
89 & 0.998303102540395 & 0.00339379491921015 & 0.00169689745960507 \tabularnewline
90 & 0.998190584486181 & 0.00361883102763724 & 0.00180941551381862 \tabularnewline
91 & 0.997279390903951 & 0.00544121819209705 & 0.00272060909604852 \tabularnewline
92 & 0.99715425886656 & 0.00569148226687947 & 0.00284574113343974 \tabularnewline
93 & 0.997907545360253 & 0.00418490927949359 & 0.0020924546397468 \tabularnewline
94 & 0.99682898968899 & 0.00634202062201966 & 0.00317101031100983 \tabularnewline
95 & 0.996008042386066 & 0.00798391522786761 & 0.0039919576139338 \tabularnewline
96 & 0.996374799524809 & 0.00725040095038286 & 0.00362520047519143 \tabularnewline
97 & 0.997444474622837 & 0.00511105075432567 & 0.00255552537716284 \tabularnewline
98 & 0.996716462692631 & 0.00656707461473778 & 0.00328353730736889 \tabularnewline
99 & 0.995316921549668 & 0.00936615690066394 & 0.00468307845033197 \tabularnewline
100 & 0.993043206892543 & 0.0139135862149144 & 0.00695679310745718 \tabularnewline
101 & 0.991051418023472 & 0.0178971639530564 & 0.00894858197652821 \tabularnewline
102 & 0.989699615891852 & 0.0206007682162966 & 0.0103003841081483 \tabularnewline
103 & 0.993036083484978 & 0.0139278330300439 & 0.00696391651502197 \tabularnewline
104 & 0.989721741078335 & 0.0205565178433304 & 0.0102782589216652 \tabularnewline
105 & 0.985846870774836 & 0.0283062584503271 & 0.0141531292251636 \tabularnewline
106 & 0.979601058654179 & 0.0407978826916427 & 0.0203989413458214 \tabularnewline
107 & 0.973956923149439 & 0.052086153701122 & 0.026043076850561 \tabularnewline
108 & 0.964834446415128 & 0.0703311071697435 & 0.0351655535848718 \tabularnewline
109 & 0.955049052178117 & 0.0899018956437654 & 0.0449509478218827 \tabularnewline
110 & 0.960118157799562 & 0.0797636844008753 & 0.0398818422004376 \tabularnewline
111 & 0.967548996120742 & 0.0649020077585168 & 0.0324510038792584 \tabularnewline
112 & 0.957892511518528 & 0.0842149769629445 & 0.0421074884814723 \tabularnewline
113 & 0.947620726376813 & 0.104758547246374 & 0.0523792736231868 \tabularnewline
114 & 0.928601695201561 & 0.142796609596878 & 0.0713983047984388 \tabularnewline
115 & 0.909187834750075 & 0.181624330499849 & 0.0908121652499247 \tabularnewline
116 & 0.88658870248139 & 0.22682259503722 & 0.11341129751861 \tabularnewline
117 & 0.894920276987903 & 0.210159446024195 & 0.105079723012097 \tabularnewline
118 & 0.993523366374912 & 0.012953267250176 & 0.00647663362508802 \tabularnewline
119 & 0.98992115210408 & 0.0201576957918398 & 0.0100788478959199 \tabularnewline
120 & 0.989814951923156 & 0.020370096153688 & 0.010185048076844 \tabularnewline
121 & 0.983356573687409 & 0.0332868526251812 & 0.0166434263125906 \tabularnewline
122 & 0.977277476367091 & 0.0454450472658182 & 0.0227225236329091 \tabularnewline
123 & 0.966034887850595 & 0.0679302242988102 & 0.0339651121494051 \tabularnewline
124 & 0.947927154767416 & 0.104145690465169 & 0.0520728452325843 \tabularnewline
125 & 0.967197630966911 & 0.0656047380661784 & 0.0328023690330892 \tabularnewline
126 & 0.951657170399909 & 0.0966856592001825 & 0.0483428296000913 \tabularnewline
127 & 0.937734978744194 & 0.124530042511613 & 0.0622650212558063 \tabularnewline
128 & 0.918236772238536 & 0.163526455522929 & 0.0817632277614644 \tabularnewline
129 & 0.874685195746007 & 0.250629608507985 & 0.125314804253993 \tabularnewline
130 & 0.826425141924515 & 0.34714971615097 & 0.173574858075485 \tabularnewline
131 & 0.767785338123278 & 0.464429323753445 & 0.232214661876722 \tabularnewline
132 & 0.943410199769663 & 0.113179600460673 & 0.0565898002303367 \tabularnewline
133 & 0.907111568589995 & 0.18577686282001 & 0.0928884314100049 \tabularnewline
134 & 0.951734086684308 & 0.0965318266313836 & 0.0482659133156918 \tabularnewline
135 & 0.904782842571649 & 0.190434314856702 & 0.0952171574283509 \tabularnewline
136 & 0.815185620253243 & 0.369628759493515 & 0.184814379746757 \tabularnewline
137 & 0.686649458832571 & 0.626701082334859 & 0.313350541167429 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&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]0.204611084835847[/C][C]0.409222169671695[/C][C]0.795388915164153[/C][/ROW]
[ROW][C]8[/C][C]0.755184659575641[/C][C]0.489630680848717[/C][C]0.244815340424359[/C][/ROW]
[ROW][C]9[/C][C]0.708367275140486[/C][C]0.583265449719028[/C][C]0.291632724859514[/C][/ROW]
[ROW][C]10[/C][C]0.602711481901087[/C][C]0.794577036197827[/C][C]0.397288518098913[/C][/ROW]
[ROW][C]11[/C][C]0.486532560029129[/C][C]0.973065120058258[/C][C]0.513467439970871[/C][/ROW]
[ROW][C]12[/C][C]0.382847744453218[/C][C]0.765695488906436[/C][C]0.617152255546782[/C][/ROW]
[ROW][C]13[/C][C]0.29532852083457[/C][C]0.590657041669141[/C][C]0.70467147916543[/C][/ROW]
[ROW][C]14[/C][C]0.590553458922273[/C][C]0.818893082155454[/C][C]0.409446541077727[/C][/ROW]
[ROW][C]15[/C][C]0.556322878777846[/C][C]0.887354242444307[/C][C]0.443677121222153[/C][/ROW]
[ROW][C]16[/C][C]0.801032334745538[/C][C]0.397935330508923[/C][C]0.198967665254462[/C][/ROW]
[ROW][C]17[/C][C]0.784370870774257[/C][C]0.431258258451486[/C][C]0.215629129225743[/C][/ROW]
[ROW][C]18[/C][C]0.79664885555842[/C][C]0.40670228888316[/C][C]0.20335114444158[/C][/ROW]
[ROW][C]19[/C][C]0.761607713196674[/C][C]0.476784573606651[/C][C]0.238392286803326[/C][/ROW]
[ROW][C]20[/C][C]0.953534525018879[/C][C]0.0929309499622418[/C][C]0.0464654749811209[/C][/ROW]
[ROW][C]21[/C][C]0.934184822989645[/C][C]0.13163035402071[/C][C]0.0658151770103549[/C][/ROW]
[ROW][C]22[/C][C]0.92323815293347[/C][C]0.15352369413306[/C][C]0.0767618470665301[/C][/ROW]
[ROW][C]23[/C][C]0.902085919504336[/C][C]0.195828160991329[/C][C]0.0979140804956643[/C][/ROW]
[ROW][C]24[/C][C]0.877908211900729[/C][C]0.244183576198542[/C][C]0.122091788099271[/C][/ROW]
[ROW][C]25[/C][C]0.940221987835125[/C][C]0.119556024329751[/C][C]0.0597780121648755[/C][/ROW]
[ROW][C]26[/C][C]0.950944779768134[/C][C]0.0981104404637316[/C][C]0.0490552202318658[/C][/ROW]
[ROW][C]27[/C][C]0.935549086877444[/C][C]0.128901826245112[/C][C]0.064450913122556[/C][/ROW]
[ROW][C]28[/C][C]0.92685099951268[/C][C]0.14629800097464[/C][C]0.07314900048732[/C][/ROW]
[ROW][C]29[/C][C]0.999014156109286[/C][C]0.00197168778142804[/C][C]0.000985843890714022[/C][/ROW]
[ROW][C]30[/C][C]0.998626969350533[/C][C]0.00274606129893327[/C][C]0.00137303064946664[/C][/ROW]
[ROW][C]31[/C][C]0.997875143302466[/C][C]0.0042497133950676[/C][C]0.0021248566975338[/C][/ROW]
[ROW][C]32[/C][C]0.996964001610185[/C][C]0.00607199677962918[/C][C]0.00303599838981459[/C][/ROW]
[ROW][C]33[/C][C]0.995610609425031[/C][C]0.00877878114993707[/C][C]0.00438939057496853[/C][/ROW]
[ROW][C]34[/C][C]0.999388484412723[/C][C]0.00122303117455388[/C][C]0.000611515587276941[/C][/ROW]
[ROW][C]35[/C][C]0.999490857661197[/C][C]0.00101828467760639[/C][C]0.000509142338803194[/C][/ROW]
[ROW][C]36[/C][C]0.99924110102982[/C][C]0.00151779794035983[/C][C]0.000758898970179916[/C][/ROW]
[ROW][C]37[/C][C]0.999641191433843[/C][C]0.000717617132313618[/C][C]0.000358808566156809[/C][/ROW]
[ROW][C]38[/C][C]0.999582991157198[/C][C]0.000834017685604927[/C][C]0.000417008842802463[/C][/ROW]
[ROW][C]39[/C][C]0.999834542097709[/C][C]0.000330915804581622[/C][C]0.000165457902290811[/C][/ROW]
[ROW][C]40[/C][C]0.999882423363065[/C][C]0.000235153273869937[/C][C]0.000117576636934969[/C][/ROW]
[ROW][C]41[/C][C]0.999977773688082[/C][C]4.44526238365927e-05[/C][C]2.22263119182963e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999965078910447[/C][C]6.98421791060513e-05[/C][C]3.49210895530256e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999967292602743[/C][C]6.54147945149833e-05[/C][C]3.27073972574917e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999989456758666[/C][C]2.10864826678604e-05[/C][C]1.05432413339302e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999987898177743[/C][C]2.42036445138185e-05[/C][C]1.21018222569093e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999981834731267[/C][C]3.63305374651922e-05[/C][C]1.81652687325961e-05[/C][/ROW]
[ROW][C]47[/C][C]0.99997002929558[/C][C]5.99414088403962e-05[/C][C]2.99707044201981e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999950569541456[/C][C]9.88609170884175e-05[/C][C]4.94304585442087e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999951099988325[/C][C]9.78000233503461e-05[/C][C]4.8900011675173e-05[/C][/ROW]
[ROW][C]50[/C][C]0.999921204486355[/C][C]0.000157591027289574[/C][C]7.87955136447869e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999878783054988[/C][C]0.000242433890024773[/C][C]0.000121216945012387[/C][/ROW]
[ROW][C]52[/C][C]0.999838479664077[/C][C]0.000323040671846317[/C][C]0.000161520335923158[/C][/ROW]
[ROW][C]53[/C][C]0.999753793341849[/C][C]0.00049241331630167[/C][C]0.000246206658150835[/C][/ROW]
[ROW][C]54[/C][C]0.999628248974997[/C][C]0.000743502050006034[/C][C]0.000371751025003017[/C][/ROW]
[ROW][C]55[/C][C]0.999498487344275[/C][C]0.00100302531145077[/C][C]0.000501512655725387[/C][/ROW]
[ROW][C]56[/C][C]0.999434751127216[/C][C]0.00113049774556794[/C][C]0.000565248872783968[/C][/ROW]
[ROW][C]57[/C][C]0.999145571192569[/C][C]0.00170885761486296[/C][C]0.00085442880743148[/C][/ROW]
[ROW][C]58[/C][C]0.998711957471967[/C][C]0.00257608505606528[/C][C]0.00128804252803264[/C][/ROW]
[ROW][C]59[/C][C]0.998192096566979[/C][C]0.00361580686604212[/C][C]0.00180790343302106[/C][/ROW]
[ROW][C]60[/C][C]0.999648198113834[/C][C]0.000703603772331288[/C][C]0.000351801886165644[/C][/ROW]
[ROW][C]61[/C][C]0.999535534779678[/C][C]0.000928930440644409[/C][C]0.000464465220322205[/C][/ROW]
[ROW][C]62[/C][C]0.999358459177978[/C][C]0.00128308164404298[/C][C]0.000641540822021491[/C][/ROW]
[ROW][C]63[/C][C]0.999691612441027[/C][C]0.000616775117945831[/C][C]0.000308387558972916[/C][/ROW]
[ROW][C]64[/C][C]0.999680326194914[/C][C]0.000639347610171573[/C][C]0.000319673805085786[/C][/ROW]
[ROW][C]65[/C][C]0.999570103284419[/C][C]0.000859793431162202[/C][C]0.000429896715581101[/C][/ROW]
[ROW][C]66[/C][C]0.999359479984389[/C][C]0.00128104003122152[/C][C]0.000640520015610761[/C][/ROW]
[ROW][C]67[/C][C]0.999750450384565[/C][C]0.000499099230869248[/C][C]0.000249549615434624[/C][/ROW]
[ROW][C]68[/C][C]0.999806288679375[/C][C]0.000387422641249279[/C][C]0.00019371132062464[/C][/ROW]
[ROW][C]69[/C][C]0.999694526619947[/C][C]0.000610946760106892[/C][C]0.000305473380053446[/C][/ROW]
[ROW][C]70[/C][C]0.999554074403651[/C][C]0.000891851192698976[/C][C]0.000445925596349488[/C][/ROW]
[ROW][C]71[/C][C]0.999683247526321[/C][C]0.000633504947357275[/C][C]0.000316752473678637[/C][/ROW]
[ROW][C]72[/C][C]0.999852573386581[/C][C]0.000294853226837317[/C][C]0.000147426613418658[/C][/ROW]
[ROW][C]73[/C][C]0.999790468852898[/C][C]0.00041906229420454[/C][C]0.00020953114710227[/C][/ROW]
[ROW][C]74[/C][C]0.999813016429675[/C][C]0.000373967140649952[/C][C]0.000186983570324976[/C][/ROW]
[ROW][C]75[/C][C]0.999701987423456[/C][C]0.000596025153089045[/C][C]0.000298012576544523[/C][/ROW]
[ROW][C]76[/C][C]0.99956421695432[/C][C]0.000871566091359417[/C][C]0.000435783045679709[/C][/ROW]
[ROW][C]77[/C][C]0.999351204736713[/C][C]0.00129759052657328[/C][C]0.000648795263286638[/C][/ROW]
[ROW][C]78[/C][C]0.999789509759705[/C][C]0.000420980480589885[/C][C]0.000210490240294942[/C][/ROW]
[ROW][C]79[/C][C]0.99968675122876[/C][C]0.000626497542480054[/C][C]0.000313248771240027[/C][/ROW]
[ROW][C]80[/C][C]0.999666075578559[/C][C]0.000667848842881852[/C][C]0.000333924421440926[/C][/ROW]
[ROW][C]81[/C][C]0.999481438688077[/C][C]0.00103712262384501[/C][C]0.000518561311922503[/C][/ROW]
[ROW][C]82[/C][C]0.999384066725665[/C][C]0.0012318665486693[/C][C]0.000615933274334649[/C][/ROW]
[ROW][C]83[/C][C]0.999195040992003[/C][C]0.00160991801599336[/C][C]0.000804959007996678[/C][/ROW]
[ROW][C]84[/C][C]0.999023362793995[/C][C]0.00195327441200939[/C][C]0.000976637206004693[/C][/ROW]
[ROW][C]85[/C][C]0.998533262631425[/C][C]0.00293347473715088[/C][C]0.00146673736857544[/C][/ROW]
[ROW][C]86[/C][C]0.999531154938094[/C][C]0.000937690123812908[/C][C]0.000468845061906454[/C][/ROW]
[ROW][C]87[/C][C]0.999286724844992[/C][C]0.00142655031001496[/C][C]0.000713275155007482[/C][/ROW]
[ROW][C]88[/C][C]0.998893700497251[/C][C]0.00221259900549872[/C][C]0.00110629950274936[/C][/ROW]
[ROW][C]89[/C][C]0.998303102540395[/C][C]0.00339379491921015[/C][C]0.00169689745960507[/C][/ROW]
[ROW][C]90[/C][C]0.998190584486181[/C][C]0.00361883102763724[/C][C]0.00180941551381862[/C][/ROW]
[ROW][C]91[/C][C]0.997279390903951[/C][C]0.00544121819209705[/C][C]0.00272060909604852[/C][/ROW]
[ROW][C]92[/C][C]0.99715425886656[/C][C]0.00569148226687947[/C][C]0.00284574113343974[/C][/ROW]
[ROW][C]93[/C][C]0.997907545360253[/C][C]0.00418490927949359[/C][C]0.0020924546397468[/C][/ROW]
[ROW][C]94[/C][C]0.99682898968899[/C][C]0.00634202062201966[/C][C]0.00317101031100983[/C][/ROW]
[ROW][C]95[/C][C]0.996008042386066[/C][C]0.00798391522786761[/C][C]0.0039919576139338[/C][/ROW]
[ROW][C]96[/C][C]0.996374799524809[/C][C]0.00725040095038286[/C][C]0.00362520047519143[/C][/ROW]
[ROW][C]97[/C][C]0.997444474622837[/C][C]0.00511105075432567[/C][C]0.00255552537716284[/C][/ROW]
[ROW][C]98[/C][C]0.996716462692631[/C][C]0.00656707461473778[/C][C]0.00328353730736889[/C][/ROW]
[ROW][C]99[/C][C]0.995316921549668[/C][C]0.00936615690066394[/C][C]0.00468307845033197[/C][/ROW]
[ROW][C]100[/C][C]0.993043206892543[/C][C]0.0139135862149144[/C][C]0.00695679310745718[/C][/ROW]
[ROW][C]101[/C][C]0.991051418023472[/C][C]0.0178971639530564[/C][C]0.00894858197652821[/C][/ROW]
[ROW][C]102[/C][C]0.989699615891852[/C][C]0.0206007682162966[/C][C]0.0103003841081483[/C][/ROW]
[ROW][C]103[/C][C]0.993036083484978[/C][C]0.0139278330300439[/C][C]0.00696391651502197[/C][/ROW]
[ROW][C]104[/C][C]0.989721741078335[/C][C]0.0205565178433304[/C][C]0.0102782589216652[/C][/ROW]
[ROW][C]105[/C][C]0.985846870774836[/C][C]0.0283062584503271[/C][C]0.0141531292251636[/C][/ROW]
[ROW][C]106[/C][C]0.979601058654179[/C][C]0.0407978826916427[/C][C]0.0203989413458214[/C][/ROW]
[ROW][C]107[/C][C]0.973956923149439[/C][C]0.052086153701122[/C][C]0.026043076850561[/C][/ROW]
[ROW][C]108[/C][C]0.964834446415128[/C][C]0.0703311071697435[/C][C]0.0351655535848718[/C][/ROW]
[ROW][C]109[/C][C]0.955049052178117[/C][C]0.0899018956437654[/C][C]0.0449509478218827[/C][/ROW]
[ROW][C]110[/C][C]0.960118157799562[/C][C]0.0797636844008753[/C][C]0.0398818422004376[/C][/ROW]
[ROW][C]111[/C][C]0.967548996120742[/C][C]0.0649020077585168[/C][C]0.0324510038792584[/C][/ROW]
[ROW][C]112[/C][C]0.957892511518528[/C][C]0.0842149769629445[/C][C]0.0421074884814723[/C][/ROW]
[ROW][C]113[/C][C]0.947620726376813[/C][C]0.104758547246374[/C][C]0.0523792736231868[/C][/ROW]
[ROW][C]114[/C][C]0.928601695201561[/C][C]0.142796609596878[/C][C]0.0713983047984388[/C][/ROW]
[ROW][C]115[/C][C]0.909187834750075[/C][C]0.181624330499849[/C][C]0.0908121652499247[/C][/ROW]
[ROW][C]116[/C][C]0.88658870248139[/C][C]0.22682259503722[/C][C]0.11341129751861[/C][/ROW]
[ROW][C]117[/C][C]0.894920276987903[/C][C]0.210159446024195[/C][C]0.105079723012097[/C][/ROW]
[ROW][C]118[/C][C]0.993523366374912[/C][C]0.012953267250176[/C][C]0.00647663362508802[/C][/ROW]
[ROW][C]119[/C][C]0.98992115210408[/C][C]0.0201576957918398[/C][C]0.0100788478959199[/C][/ROW]
[ROW][C]120[/C][C]0.989814951923156[/C][C]0.020370096153688[/C][C]0.010185048076844[/C][/ROW]
[ROW][C]121[/C][C]0.983356573687409[/C][C]0.0332868526251812[/C][C]0.0166434263125906[/C][/ROW]
[ROW][C]122[/C][C]0.977277476367091[/C][C]0.0454450472658182[/C][C]0.0227225236329091[/C][/ROW]
[ROW][C]123[/C][C]0.966034887850595[/C][C]0.0679302242988102[/C][C]0.0339651121494051[/C][/ROW]
[ROW][C]124[/C][C]0.947927154767416[/C][C]0.104145690465169[/C][C]0.0520728452325843[/C][/ROW]
[ROW][C]125[/C][C]0.967197630966911[/C][C]0.0656047380661784[/C][C]0.0328023690330892[/C][/ROW]
[ROW][C]126[/C][C]0.951657170399909[/C][C]0.0966856592001825[/C][C]0.0483428296000913[/C][/ROW]
[ROW][C]127[/C][C]0.937734978744194[/C][C]0.124530042511613[/C][C]0.0622650212558063[/C][/ROW]
[ROW][C]128[/C][C]0.918236772238536[/C][C]0.163526455522929[/C][C]0.0817632277614644[/C][/ROW]
[ROW][C]129[/C][C]0.874685195746007[/C][C]0.250629608507985[/C][C]0.125314804253993[/C][/ROW]
[ROW][C]130[/C][C]0.826425141924515[/C][C]0.34714971615097[/C][C]0.173574858075485[/C][/ROW]
[ROW][C]131[/C][C]0.767785338123278[/C][C]0.464429323753445[/C][C]0.232214661876722[/C][/ROW]
[ROW][C]132[/C][C]0.943410199769663[/C][C]0.113179600460673[/C][C]0.0565898002303367[/C][/ROW]
[ROW][C]133[/C][C]0.907111568589995[/C][C]0.18577686282001[/C][C]0.0928884314100049[/C][/ROW]
[ROW][C]134[/C][C]0.951734086684308[/C][C]0.0965318266313836[/C][C]0.0482659133156918[/C][/ROW]
[ROW][C]135[/C][C]0.904782842571649[/C][C]0.190434314856702[/C][C]0.0952171574283509[/C][/ROW]
[ROW][C]136[/C][C]0.815185620253243[/C][C]0.369628759493515[/C][C]0.184814379746757[/C][/ROW]
[ROW][C]137[/C][C]0.686649458832571[/C][C]0.626701082334859[/C][C]0.313350541167429[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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
70.2046110848358470.4092221696716950.795388915164153
80.7551846595756410.4896306808487170.244815340424359
90.7083672751404860.5832654497190280.291632724859514
100.6027114819010870.7945770361978270.397288518098913
110.4865325600291290.9730651200582580.513467439970871
120.3828477444532180.7656954889064360.617152255546782
130.295328520834570.5906570416691410.70467147916543
140.5905534589222730.8188930821554540.409446541077727
150.5563228787778460.8873542424443070.443677121222153
160.8010323347455380.3979353305089230.198967665254462
170.7843708707742570.4312582584514860.215629129225743
180.796648855558420.406702288883160.20335114444158
190.7616077131966740.4767845736066510.238392286803326
200.9535345250188790.09293094996224180.0464654749811209
210.9341848229896450.131630354020710.0658151770103549
220.923238152933470.153523694133060.0767618470665301
230.9020859195043360.1958281609913290.0979140804956643
240.8779082119007290.2441835761985420.122091788099271
250.9402219878351250.1195560243297510.0597780121648755
260.9509447797681340.09811044046373160.0490552202318658
270.9355490868774440.1289018262451120.064450913122556
280.926850999512680.146298000974640.07314900048732
290.9990141561092860.001971687781428040.000985843890714022
300.9986269693505330.002746061298933270.00137303064946664
310.9978751433024660.00424971339506760.0021248566975338
320.9969640016101850.006071996779629180.00303599838981459
330.9956106094250310.008778781149937070.00438939057496853
340.9993884844127230.001223031174553880.000611515587276941
350.9994908576611970.001018284677606390.000509142338803194
360.999241101029820.001517797940359830.000758898970179916
370.9996411914338430.0007176171323136180.000358808566156809
380.9995829911571980.0008340176856049270.000417008842802463
390.9998345420977090.0003309158045816220.000165457902290811
400.9998824233630650.0002351532738699370.000117576636934969
410.9999777736880824.44526238365927e-052.22263119182963e-05
420.9999650789104476.98421791060513e-053.49210895530256e-05
430.9999672926027436.54147945149833e-053.27073972574917e-05
440.9999894567586662.10864826678604e-051.05432413339302e-05
450.9999878981777432.42036445138185e-051.21018222569093e-05
460.9999818347312673.63305374651922e-051.81652687325961e-05
470.999970029295585.99414088403962e-052.99707044201981e-05
480.9999505695414569.88609170884175e-054.94304585442087e-05
490.9999510999883259.78000233503461e-054.8900011675173e-05
500.9999212044863550.0001575910272895747.87955136447869e-05
510.9998787830549880.0002424338900247730.000121216945012387
520.9998384796640770.0003230406718463170.000161520335923158
530.9997537933418490.000492413316301670.000246206658150835
540.9996282489749970.0007435020500060340.000371751025003017
550.9994984873442750.001003025311450770.000501512655725387
560.9994347511272160.001130497745567940.000565248872783968
570.9991455711925690.001708857614862960.00085442880743148
580.9987119574719670.002576085056065280.00128804252803264
590.9981920965669790.003615806866042120.00180790343302106
600.9996481981138340.0007036037723312880.000351801886165644
610.9995355347796780.0009289304406444090.000464465220322205
620.9993584591779780.001283081644042980.000641540822021491
630.9996916124410270.0006167751179458310.000308387558972916
640.9996803261949140.0006393476101715730.000319673805085786
650.9995701032844190.0008597934311622020.000429896715581101
660.9993594799843890.001281040031221520.000640520015610761
670.9997504503845650.0004990992308692480.000249549615434624
680.9998062886793750.0003874226412492790.00019371132062464
690.9996945266199470.0006109467601068920.000305473380053446
700.9995540744036510.0008918511926989760.000445925596349488
710.9996832475263210.0006335049473572750.000316752473678637
720.9998525733865810.0002948532268373170.000147426613418658
730.9997904688528980.000419062294204540.00020953114710227
740.9998130164296750.0003739671406499520.000186983570324976
750.9997019874234560.0005960251530890450.000298012576544523
760.999564216954320.0008715660913594170.000435783045679709
770.9993512047367130.001297590526573280.000648795263286638
780.9997895097597050.0004209804805898850.000210490240294942
790.999686751228760.0006264975424800540.000313248771240027
800.9996660755785590.0006678488428818520.000333924421440926
810.9994814386880770.001037122623845010.000518561311922503
820.9993840667256650.00123186654866930.000615933274334649
830.9991950409920030.001609918015993360.000804959007996678
840.9990233627939950.001953274412009390.000976637206004693
850.9985332626314250.002933474737150880.00146673736857544
860.9995311549380940.0009376901238129080.000468845061906454
870.9992867248449920.001426550310014960.000713275155007482
880.9988937004972510.002212599005498720.00110629950274936
890.9983031025403950.003393794919210150.00169689745960507
900.9981905844861810.003618831027637240.00180941551381862
910.9972793909039510.005441218192097050.00272060909604852
920.997154258866560.005691482266879470.00284574113343974
930.9979075453602530.004184909279493590.0020924546397468
940.996828989688990.006342020622019660.00317101031100983
950.9960080423860660.007983915227867610.0039919576139338
960.9963747995248090.007250400950382860.00362520047519143
970.9974444746228370.005111050754325670.00255552537716284
980.9967164626926310.006567074614737780.00328353730736889
990.9953169215496680.009366156900663940.00468307845033197
1000.9930432068925430.01391358621491440.00695679310745718
1010.9910514180234720.01789716395305640.00894858197652821
1020.9896996158918520.02060076821629660.0103003841081483
1030.9930360834849780.01392783303004390.00696391651502197
1040.9897217410783350.02055651784333040.0102782589216652
1050.9858468707748360.02830625845032710.0141531292251636
1060.9796010586541790.04079788269164270.0203989413458214
1070.9739569231494390.0520861537011220.026043076850561
1080.9648344464151280.07033110716974350.0351655535848718
1090.9550490521781170.08990189564376540.0449509478218827
1100.9601181577995620.07976368440087530.0398818422004376
1110.9675489961207420.06490200775851680.0324510038792584
1120.9578925115185280.08421497696294450.0421074884814723
1130.9476207263768130.1047585472463740.0523792736231868
1140.9286016952015610.1427966095968780.0713983047984388
1150.9091878347500750.1816243304998490.0908121652499247
1160.886588702481390.226822595037220.11341129751861
1170.8949202769879030.2101594460241950.105079723012097
1180.9935233663749120.0129532672501760.00647663362508802
1190.989921152104080.02015769579183980.0100788478959199
1200.9898149519231560.0203700961536880.010185048076844
1210.9833565736874090.03328685262518120.0166434263125906
1220.9772774763670910.04544504726581820.0227225236329091
1230.9660348878505950.06793022429881020.0339651121494051
1240.9479271547674160.1041456904651690.0520728452325843
1250.9671976309669110.06560473806617840.0328023690330892
1260.9516571703999090.09668565920018250.0483428296000913
1270.9377349787441940.1245300425116130.0622650212558063
1280.9182367722385360.1635264555229290.0817632277614644
1290.8746851957460070.2506296085079850.125314804253993
1300.8264251419245150.347149716150970.173574858075485
1310.7677853381232780.4644293237534450.232214661876722
1320.9434101997696630.1131796004606730.0565898002303367
1330.9071115685899950.185776862820010.0928884314100049
1340.9517340866843080.09653182663138360.0482659133156918
1350.9047828425716490.1904343148567020.0952171574283509
1360.8151856202532430.3696287594935150.184814379746757
1370.6866494588325710.6267010823348590.313350541167429







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level710.541984732824427NOK
5% type I error level830.633587786259542NOK
10% type I error level950.725190839694656NOK

\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 & 71 & 0.541984732824427 & NOK \tabularnewline
5% type I error level & 83 & 0.633587786259542 & NOK \tabularnewline
10% type I error level & 95 & 0.725190839694656 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147023&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]71[/C][C]0.541984732824427[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]83[/C][C]0.633587786259542[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]95[/C][C]0.725190839694656[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147023&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147023&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 level710.541984732824427NOK
5% type I error level830.633587786259542NOK
10% type I error level950.725190839694656NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}