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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 15:30:04 -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/21/t1321907617ystfgcuef1711vr.htm/, Retrieved Thu, 31 Oct 2024 23:05:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145971, Retrieved Thu, 31 Oct 2024 23:05:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact394

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [Tutorial 2-1] [2011-11-21 19:39:55] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD      [Multiple Regression] [Tutorial2-1] [2011-11-21 20:30:04] [5ae3d23a633522d14794d358c652ae9c] [Current]
-   PD        [Multiple Regression] [Statistiek Paper 3.1] [2011-12-15 07:45:19] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD          [Multiple Regression] [Paper statistiek 3.1] [2011-12-15 07:48:26] [9e469a83342941fcd5c6dffbf184cd3a]
-  M          [Multiple Regression] [] [2011-12-17 13:36:03] [4c0148be6b1ebc4ef8d5b4e23a77fcfa]
- R PD        [Multiple Regression] [] [2011-12-19 08:43:54] [d0c153a232569da05656a074c1bdec10]
- R PD        [Multiple Regression] [multiple regressi...] [2011-12-19 21:00:38] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP           [Kendall tau Correlation Matrix] [pearson correlatie] [2011-12-21 13:23:03] [2e8e2c135ae7a1d1ed044e87454acf31]
- R P             [Kendall tau Correlation Matrix] [kendall'tau] [2011-12-21 14:00:53] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP             [Recursive Partitioning (Regression Trees)] [Regression trees] [2011-12-21 14:21:32] [2e8e2c135ae7a1d1ed044e87454acf31]
- R P               [Recursive Partitioning (Regression Trees)] [regression trees ] [2011-12-21 14:30:40] [2e8e2c135ae7a1d1ed044e87454acf31]
- RM            [Multiple Regression] [] [2011-12-21 15:30:16] [2e8e2c135ae7a1d1ed044e87454acf31]
-    D        [Multiple Regression] [] [2011-12-21 17:36:12] [ff74c68cc78961a8924de2f2c00accbc]
-               [Multiple Regression] [] [2011-12-21 17:48:59] [ff74c68cc78961a8924de2f2c00accbc]
-    D        [Multiple Regression] [Multiple regression] [2011-12-22 18:36:40] [c035d973aa8488be257660c2dc4ec375]
- R  D        [Multiple Regression] [Workshop 7 Tutorial] [2012-11-03 14:50:24] [bc2c61a583a6186666a33616ccc196e4]
- R  D        [Multiple Regression] [] [2012-11-05 15:06:35] [8fcd082199f7dbedf65d69a953eb5ad7]
-  M          [Multiple Regression] [] [2012-11-05 16:12:46] [60d1ad8da4696c30bdea6b2c1b52db5e]
-  M          [Multiple Regression] [] [2012-11-05 16:13:54] [60d1ad8da4696c30bdea6b2c1b52db5e]
-   PD        [Multiple Regression] [multiple regression] [2012-12-17 13:56:18] [dbdfdab7c884aa7a69290945f2923e51]
-    D        [Multiple Regression] [] [2012-12-17 16:23:19] [edf0418499cd31d27dbea8ea1d30b3db]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
46	26	95556	47,38555556
48	20	54565	24,06138889
37	24	63016	31,4825
75	25	79774	42,36388889
31	15	31258	23,94611111
18	16	52491	10,34916667
79	20	91256	85,01527778
16	18	22807	9,097222222
38	19	77411	32,36166667
24	20	48821	36,26083333
65	30	52295	44,96555556
74	37	63262	35,63166667
43	23	50466	28,43055556
42	36	62932	53,61777778
55	29	38439	39,32611111
121	35	70817	70,43305556
42	24	105965	50,30833333
102	22	73795	55,12
36	19	82043	31,62583333
50	30	74349	44,42777778
48	27	82204	46,33944444
56	26	55709	79,63194444
19	15	37137	25,46027778
32	30	70780	30,07722222
77	28	55027	40,65055556
90	24	56699	40,31722222
81	21	65911	44,92777778
55	27	56316	44,69583333
34	21	26982	29,69111111
38	30	54628	52,26388889
53	30	96750	52,61138889
48	33	53009	35,96777778
63	30	64664	56,675
25	20	36990	17,42527778
56	27	85224	67,67361111
37	25	37048	46,45972222
83	30	59635	73,48
50	20	42051	33,89555556
26	8	26998	22,49
108	24	63717	58,27638889
55	25	55071	62,27916667
41	25	40001	32,21416667
49	21	54506	38,38638889
31	21	35838	22,52944444
49	21	50838	25,86805556
96	26	86997	84,93222222
42	26	33032	21,88888889
55	30	61704	44,12083333
70	34	117986	61,59583333
39	30	56733	36,41888889
53	18	55064	35,75944444
24	4	5950	6,718888889
209	31	84607	71,57277778
17	18	32551	18,06361111
58	14	31701	27,24055556
27	20	71170	48,21861111
58	36	101773	50,01166667
114	24	101653	54,79611111
75	26	81493	58,90555556
51	22	55901	39,32833333
86	31	109104	68,08527778
77	21	114425	57,46638889
62	31	36311	40,47111111
60	26	70027	47,39861111
39	24	73713	39,46222222
35	15	40671	31,89444444
86	19	89041	31,51694444
102	28	57231	40,35694444
49	24	68608	41,94416667
35	18	59155	25,50333333
33	25	55827	33,00194444
28	20	22618	19,2975
44	25	58425	35,175
37	24	65724	40,53
33	23	56979	27,33138889
45	25	72369	53,035
57	20	79194	55,22138889
58	23	202316	29,49805556
36	22	44970	24,81055556
42	25	49319	33,43388889
30	18	36252	27,44194444
67	30	75741	76,37583333
53	22	38417	36,88833333
59	25	64102	37,56972222
25	8	56622	22,48694444
39	21	15430	30,34361111
36	22	72571	26,84277778
114	24	67271	62,83083333
54	30	43460	47,57944444
70	27	99501	32,72638889
51	24	28340	37,10027778
49	25	76013	42,27583333
42	21	37361	31,11222222
51	24	48204	47,11472222
51	24	76168	52,07861111
27	20	85168	36,25916667
29	20	125410	39,53861111
54	24	123328	52,71222222
92	40	83038	56,00083333
72	22	120087	68,565
63	31	91939	43,31861111
41	26	103646	50,71694444
111	20	29467	29,54194444
14	19	43750	12,02416667
45	15	34497	35,41472222
91	21	66477	35,53611111
29	22	71181	41,39055556
64	24	74482	52,12583333
32	19	174949	20,58666667
65	24	46765	26,11277778
42	23	90257	49,0625
55	27	51370	39,42583333
10	1	1168	6,371666667
53	24	51360	34,97972222
25	11	25162	17,1825
33	27	21067	25,35833333
66	22	58233	70,86111111
16	0	855	5,848333333
35	17	85903	46,97027778
19	8	14116	8,726111111
76	24	57637	52,41694444
35	31	94137	38,20666667
46	24	62147	21,435
29	20	62832	20,71305556
34	8	8773	10,615
25	22	63785	25,26694444
48	33	65196	53,95111111
38	33	73087	37,5725
50	31	72631	67,85333333
65	33	86281	56,04111111
72	35	162365	71,22277778
23	21	56530	38,65111111
29	20	35606	21,24166667
194	24	70111	52,63944444
114	29	92046	77,87055556
15	20	63989	14,16638889
86	27	104911	70,35388889
50	24	43448	28,6775
33	26	60029	46,68305556
50	26	38650	35,76888889
72	12	47261	21,04055556
81	21	73586	69,23111111
54	24	83042	42,32388889
63	21	37238	48,12777778
69	30	63958	54,77694444
39	32	78956	18,75194444
49	24	99518	38,72472222
67	29	111436	51,49055556
0	0	0	0
10	0	6023	4,08
1	0	0	0,027222222
2	0	0	0,126388889
0	0	0	0
0	0	0	0
58	20	42564	38,30138889
72	27	38885	51,46888889
0	0	0	0
4	0	0	0,056388889
5	0	1644	1,999722222
20	5	6179	12,96111111
5	1	3926	4,874166667
27	23	23238	20,43527778
2	0	0	0,269166667
33	16	49288	29,29916667

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'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=145971&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=145971&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
AantalurenRFC[t] = -0.0992742116382974 + 0.246966684905508`#logins`[t] + 0.80493782844676`otaal#peer_reviews`[t] + 0.000133114330761128`totaal#karakterscompendium`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
AantalurenRFC[t] =  -0.0992742116382974 +  0.246966684905508`#logins`[t] +  0.80493782844676`otaal#peer_reviews`[t] +  0.000133114330761128`totaal#karakterscompendium`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]AantalurenRFC[t] =  -0.0992742116382974 +  0.246966684905508`#logins`[t] +  0.80493782844676`otaal#peer_reviews`[t] +  0.000133114330761128`totaal#karakterscompendium`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
AantalurenRFC[t] = -0.0992742116382974 + 0.246966684905508`#logins`[t] + 0.80493782844676`otaal#peer_reviews`[t] + 0.000133114330761128`totaal#karakterscompendium`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09927421163829742.355258-0.04220.9664320.483216
`#logins`0.2469666849055080.0342727.206100
`otaal#peer_reviews`0.804937828446760.135945.921300
`totaal#karakterscompendium`0.0001331143307611283.3e-054.04458.1e-054.1e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.0992742116382974 & 2.355258 & -0.0422 & 0.966432 & 0.483216 \tabularnewline
`#logins` & 0.246966684905508 & 0.034272 & 7.2061 & 0 & 0 \tabularnewline
`otaal#peer_reviews` & 0.80493782844676 & 0.13594 & 5.9213 & 0 & 0 \tabularnewline
`totaal#karakterscompendium` & 0.000133114330761128 & 3.3e-05 & 4.0445 & 8.1e-05 & 4.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&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]-0.0992742116382974[/C][C]2.355258[/C][C]-0.0422[/C][C]0.966432[/C][C]0.483216[/C][/ROW]
[ROW][C]`#logins`[/C][C]0.246966684905508[/C][C]0.034272[/C][C]7.2061[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`otaal#peer_reviews`[/C][C]0.80493782844676[/C][C]0.13594[/C][C]5.9213[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`totaal#karakterscompendium`[/C][C]0.000133114330761128[/C][C]3.3e-05[/C][C]4.0445[/C][C]8.1e-05[/C][C]4.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09927421163829742.355258-0.04220.9664320.483216
`#logins`0.2469666849055080.0342727.206100
`otaal#peer_reviews`0.804937828446760.135945.921300
`totaal#karakterscompendium`0.0001331143307611283.3e-054.04458.1e-054.1e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.823788024047727
R-squared0.678626708564458
Adjusted R-squared0.672600959350041
F-TEST (value)112.62113380704
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.0474264069462
Sum Squared Residuals19527.3008347026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.823788024047727 \tabularnewline
R-squared & 0.678626708564458 \tabularnewline
Adjusted R-squared & 0.672600959350041 \tabularnewline
F-TEST (value) & 112.62113380704 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.0474264069462 \tabularnewline
Sum Squared Residuals & 19527.3008347026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.823788024047727[/C][/ROW]
[ROW][C]R-squared[/C][C]0.678626708564458[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.672600959350041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]112.62113380704[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/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]11.0474264069462[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19527.3008347026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.823788024047727
R-squared0.678626708564458
Adjusted R-squared0.672600959350041
F-TEST (value)112.62113380704
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.0474264069462
Sum Squared Residuals19527.3008347026






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.3855555644.90944982384122.47610573615882
224.0613888935.1172666907422-11.0558778007422
331.482536.745333679831-5.26283367983099
442.3638888949.165735489582-6.80184659958204
523.9461111123.79164819806520.154462911934808
610.3491666724.2124357077914-13.8632690377914
785.0152777847.657331832769537.3579459472305
89.09722222221.3770122005606-12.2797899785606
932.3616666734.8837920138091-2.52212534380914
1036.2608333328.42545753711817.83537579288187
1144.9655555647.0629090877757-2.09735352777571
1235.6316666756.3800389165099-20.7483722465099
1328.4305555635.7516111097651-7.32105554976512
1453.6177777847.62823944193575.98953833806429
1539.3261111141.9438722432477-2.61776113324769
1670.4330555667.38327621907563.04977934092442
1750.3083333343.69729449621826.61103883378178
1855.1252.62313191306972.49686808693031
1931.6258333335.0064442240837-3.38061089408367
2044.4277777846.294112264799-1.86633448479901
2146.3394444444.43097847777641.90846596222362
2279.6319444442.074909935057637.5570345049424
2325.4602777821.61062712974383.84965065025623
2430.0772222241.3736268900134-11.2964046700134
2540.6505555648.7803020013877-8.12974644138769
2640.3172222248.9936847524049-8.67646253240486
2744.9277777845.5824203178865-0.654642537886524
2844.6958333342.71368147737081.98215185262915
2929.6911111128.79297834512770.898132764872311
3052.2638888940.705364328992711.5585245610073
3152.6113888950.01690644289562.59448244710443
3235.9677777845.3743325618858-9.4065547818858
3356.67548.21546687514918.45953312485091
3417.4252777827.0975485747887-9.67227079478873
3567.6736111146.80871723591920.8648938740809
3646.4597222234.093558567072812.3661636529272
3773.4852.485368603861520.9946313961385
3833.8955555633.9454073254085-0.0498517654085003
3922.4916.35518292536796.13481707463207
4058.2763888954.37328145398563.9031074360144
4162.2791666740.938078478679721.3410881913203
4232.2141666735.4745119254324-3.26034525543242
4338.3863888936.16131745857962.2250714314204
4422.5294444429.2309388036317-6.70149436363172
4525.8680555635.6730540933478-9.80499853334778
4684.9322222256.118458512132128.8137637078679
4721.8888888935.5987426677104-13.7098537777104
4844.1208333345.8457149768521-1.72488164685208
4961.5958333360.26190732811961.33392600188044
5036.4188888941.2325366801504-4.8136477901504
5135.7594444434.80864850942610.950795930573938
526.7188888899.83970780790965-3.12081891890965
5371.5727777887.7322397981692-16.1594620181692
5418.0636111122.9210449244025-4.8574338144025
5527.2405555629.7137805105943-2.47322495059433
5648.2186111132.141329770015116.0772813399849
5750.0116666756.7500001215168-6.73833345151682
5854.7961111160.9049068151728-6.10879570517281
5958.9055555650.19949685260728.70605870739283
6039.3283333337.64588314824921.68245018175085
6168.0852777860.61623931544717.46903846455293
6257.4663888951.05246222080996.41392666919013
6340.4711111144.9992473986201-4.52813628862007
6447.3986111144.96870766251752.42990344748254
6539.4622222238.66319104579380.799031174206211
6631.8944444426.03252013314175.86192430685828
6731.5169444448.2863125560254-16.7693681160254
6840.3569444455.2478531090229-14.8909086690229
6941.9441666740.45330923631331.49085743368669
7025.5033333330.9078189082707-5.4044855782707
7133.0019444435.605445844814-2.60350140481396
7219.297525.9253294678063-6.62782946780632
7335.17538.667910410092-3.49291041009197
7440.5337.10580728753213.42419271246788
7527.3313888934.1489178969573-6.81752900695726
7653.03540.771023323130612.2639766768694
7755.2213888940.618439707207614.6029491827924
7829.4980555659.669522509425-30.171466949425
7924.8105555632.4863101251166-7.67575456511664
8033.4338888936.9618379443701-3.52794905437011
8127.4419444426.6242679663210.817676473678963
8276.3758333350.677841056612125.6979922733879
8336.8883333335.81244555903261.0758877709674
8437.5697222243.1281007394055-5.5583785194055
8522.4869444420.05159517493012.43534926506992
8630.3436111128.49007502070271.85353608929732
8726.8427777836.1603987684545-9.31762098845454
8862.8308333356.32816989494376.50266343505629
8947.5794444443.17021044154064.40923399845944
9032.7263888952.1667241248728-19.4403352348728
9137.1002777835.58699473503521.51328304496478
9242.2758333342.24395868404620.0318746459537728
9331.1122222232.1503054633415-1.0380832433415
9447.1147222238.23117780127438.88354441872573
9552.0786111141.953586946678510.1250241633215
9636.2591666734.00466417200942.25450249799062
9739.5386111139.8553844403097-0.316773330309714
9852.7122222248.97215884008983.74006337991022
9956.0008333365.8727217352814-9.87188840528139
10068.56551.376259965498617.1887400345014
10143.3186111152.6510980751056-9.33248696510562
10250.7169444444.75151133517125.96543310482883
10329.5419444447.3352643663465-17.7933199263464
10412.0241666724.4758300883266-12.4516634183266
10535.4147222227.68033910407767.7343831159224
10635.5361111148.1274298781524-12.5913187681524
10741.3905555634.2466030543587.14395250564199
10852.1258333344.93972308878687.1861102412132
10920.5866666746.385697498155-25.799030828155
11026.1127777841.4971598679861-15.3843820879861
11149.062540.80139676017578.26110323982434
11239.4258333342.0552979974263-2.62946466742631
1136.3716666673.330808004192543.04085866280746
11434.9797222239.1452199989674-4.16549777896741
11517.182518.2786318145253-1.09613181452526
11625.3583333332.5882673644507-7.22993403445067
11770.8611111141.660806041166729.2003050688333
1185.8483333333.96600549965061.8823278333494
11946.9702777833.663423199022613.3068545809774
1208.72611111112.9116373221645-4.18552621116452
12152.4169444445.66101240598176.75593203401831
12238.2066666746.0286161967644-7.82194952676436
12321.43538.8523574905491-17.4173574905491
12420.7130555631.5253558499398-10.8123002899398
12510.61515.9049077264904-5.28990772649043
12625.2669444432.2742227244267-7.00727828442668
12753.9511111146.99659691087176.95451419912833
12837.572545.5773352458527-8.00483524585265
12967.8533333346.870359672998220.9829736570018
13056.0411111154.00174621836372.03936489163631
13171.2227777867.46825941122543.75451836877456
13238.6511111130.00960705649698.64150405350309
13321.2416666727.9011850806374-6.65951841063736
13452.6394444476.463549386746-23.824104946746
13577.8705555663.650766581784514.2197889782155
13614.1663888928.2218355419533-14.0554466519533
13770.3538888956.838339612778613.5155492772214
13828.677537.3511193592688-8.67361935926884
13946.6830555636.9697300911199.71332546888102
14035.7688888938.3223124571705-2.55342356717046
14121.0405555633.6326974290211-12.5921418690211
14269.2311111146.604072806478222.6270383035218
14342.3238888943.609514911047-1.28562602104698
14448.1277777837.320232783673610.8075449963264
14554.7769444449.60328826706485.17365617293522
14618.7519444445.8006121095485-27.0486676695485
14738.7247222244.5678732001398-5.84315098013978
14851.4905555654.6244192646839-3.13386370468385
1490-0.09927421163829430.0992742116382943
1504.083.172140251591060.907859748408937
1510.0272222220.147692473267216-0.120470251267216
1520.1263888890.394659158172725-0.268270269172725
1530-0.09927421163829430.0992742116382943
1540-0.09927421163829430.0992742116382943
15538.3013888935.9894284563332.31196043366698
15651.4688888944.59179922126736.87708966873274
1570-0.09927421163829430.0992742116382943
1580.0563888890.888592527983742-0.832203638983742
1591.9997222221.354399172660540.645323049339463
16012.961111119.687262078478683.27384903152132
1614.8741666672.46310390390422.4110627630958
16220.4352777828.175707153313-7.74042937331299
1630.2691666670.394659158172722-0.125492491172722
16429.2991666727.49057077994611.80859589005389

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 47.38555556 & 44.9094498238412 & 2.47610573615882 \tabularnewline
2 & 24.06138889 & 35.1172666907422 & -11.0558778007422 \tabularnewline
3 & 31.4825 & 36.745333679831 & -5.26283367983099 \tabularnewline
4 & 42.36388889 & 49.165735489582 & -6.80184659958204 \tabularnewline
5 & 23.94611111 & 23.7916481980652 & 0.154462911934808 \tabularnewline
6 & 10.34916667 & 24.2124357077914 & -13.8632690377914 \tabularnewline
7 & 85.01527778 & 47.6573318327695 & 37.3579459472305 \tabularnewline
8 & 9.097222222 & 21.3770122005606 & -12.2797899785606 \tabularnewline
9 & 32.36166667 & 34.8837920138091 & -2.52212534380914 \tabularnewline
10 & 36.26083333 & 28.4254575371181 & 7.83537579288187 \tabularnewline
11 & 44.96555556 & 47.0629090877757 & -2.09735352777571 \tabularnewline
12 & 35.63166667 & 56.3800389165099 & -20.7483722465099 \tabularnewline
13 & 28.43055556 & 35.7516111097651 & -7.32105554976512 \tabularnewline
14 & 53.61777778 & 47.6282394419357 & 5.98953833806429 \tabularnewline
15 & 39.32611111 & 41.9438722432477 & -2.61776113324769 \tabularnewline
16 & 70.43305556 & 67.3832762190756 & 3.04977934092442 \tabularnewline
17 & 50.30833333 & 43.6972944962182 & 6.61103883378178 \tabularnewline
18 & 55.12 & 52.6231319130697 & 2.49686808693031 \tabularnewline
19 & 31.62583333 & 35.0064442240837 & -3.38061089408367 \tabularnewline
20 & 44.42777778 & 46.294112264799 & -1.86633448479901 \tabularnewline
21 & 46.33944444 & 44.4309784777764 & 1.90846596222362 \tabularnewline
22 & 79.63194444 & 42.0749099350576 & 37.5570345049424 \tabularnewline
23 & 25.46027778 & 21.6106271297438 & 3.84965065025623 \tabularnewline
24 & 30.07722222 & 41.3736268900134 & -11.2964046700134 \tabularnewline
25 & 40.65055556 & 48.7803020013877 & -8.12974644138769 \tabularnewline
26 & 40.31722222 & 48.9936847524049 & -8.67646253240486 \tabularnewline
27 & 44.92777778 & 45.5824203178865 & -0.654642537886524 \tabularnewline
28 & 44.69583333 & 42.7136814773708 & 1.98215185262915 \tabularnewline
29 & 29.69111111 & 28.7929783451277 & 0.898132764872311 \tabularnewline
30 & 52.26388889 & 40.7053643289927 & 11.5585245610073 \tabularnewline
31 & 52.61138889 & 50.0169064428956 & 2.59448244710443 \tabularnewline
32 & 35.96777778 & 45.3743325618858 & -9.4065547818858 \tabularnewline
33 & 56.675 & 48.2154668751491 & 8.45953312485091 \tabularnewline
34 & 17.42527778 & 27.0975485747887 & -9.67227079478873 \tabularnewline
35 & 67.67361111 & 46.808717235919 & 20.8648938740809 \tabularnewline
36 & 46.45972222 & 34.0935585670728 & 12.3661636529272 \tabularnewline
37 & 73.48 & 52.4853686038615 & 20.9946313961385 \tabularnewline
38 & 33.89555556 & 33.9454073254085 & -0.0498517654085003 \tabularnewline
39 & 22.49 & 16.3551829253679 & 6.13481707463207 \tabularnewline
40 & 58.27638889 & 54.3732814539856 & 3.9031074360144 \tabularnewline
41 & 62.27916667 & 40.9380784786797 & 21.3410881913203 \tabularnewline
42 & 32.21416667 & 35.4745119254324 & -3.26034525543242 \tabularnewline
43 & 38.38638889 & 36.1613174585796 & 2.2250714314204 \tabularnewline
44 & 22.52944444 & 29.2309388036317 & -6.70149436363172 \tabularnewline
45 & 25.86805556 & 35.6730540933478 & -9.80499853334778 \tabularnewline
46 & 84.93222222 & 56.1184585121321 & 28.8137637078679 \tabularnewline
47 & 21.88888889 & 35.5987426677104 & -13.7098537777104 \tabularnewline
48 & 44.12083333 & 45.8457149768521 & -1.72488164685208 \tabularnewline
49 & 61.59583333 & 60.2619073281196 & 1.33392600188044 \tabularnewline
50 & 36.41888889 & 41.2325366801504 & -4.8136477901504 \tabularnewline
51 & 35.75944444 & 34.8086485094261 & 0.950795930573938 \tabularnewline
52 & 6.718888889 & 9.83970780790965 & -3.12081891890965 \tabularnewline
53 & 71.57277778 & 87.7322397981692 & -16.1594620181692 \tabularnewline
54 & 18.06361111 & 22.9210449244025 & -4.8574338144025 \tabularnewline
55 & 27.24055556 & 29.7137805105943 & -2.47322495059433 \tabularnewline
56 & 48.21861111 & 32.1413297700151 & 16.0772813399849 \tabularnewline
57 & 50.01166667 & 56.7500001215168 & -6.73833345151682 \tabularnewline
58 & 54.79611111 & 60.9049068151728 & -6.10879570517281 \tabularnewline
59 & 58.90555556 & 50.1994968526072 & 8.70605870739283 \tabularnewline
60 & 39.32833333 & 37.6458831482492 & 1.68245018175085 \tabularnewline
61 & 68.08527778 & 60.6162393154471 & 7.46903846455293 \tabularnewline
62 & 57.46638889 & 51.0524622208099 & 6.41392666919013 \tabularnewline
63 & 40.47111111 & 44.9992473986201 & -4.52813628862007 \tabularnewline
64 & 47.39861111 & 44.9687076625175 & 2.42990344748254 \tabularnewline
65 & 39.46222222 & 38.6631910457938 & 0.799031174206211 \tabularnewline
66 & 31.89444444 & 26.0325201331417 & 5.86192430685828 \tabularnewline
67 & 31.51694444 & 48.2863125560254 & -16.7693681160254 \tabularnewline
68 & 40.35694444 & 55.2478531090229 & -14.8909086690229 \tabularnewline
69 & 41.94416667 & 40.4533092363133 & 1.49085743368669 \tabularnewline
70 & 25.50333333 & 30.9078189082707 & -5.4044855782707 \tabularnewline
71 & 33.00194444 & 35.605445844814 & -2.60350140481396 \tabularnewline
72 & 19.2975 & 25.9253294678063 & -6.62782946780632 \tabularnewline
73 & 35.175 & 38.667910410092 & -3.49291041009197 \tabularnewline
74 & 40.53 & 37.1058072875321 & 3.42419271246788 \tabularnewline
75 & 27.33138889 & 34.1489178969573 & -6.81752900695726 \tabularnewline
76 & 53.035 & 40.7710233231306 & 12.2639766768694 \tabularnewline
77 & 55.22138889 & 40.6184397072076 & 14.6029491827924 \tabularnewline
78 & 29.49805556 & 59.669522509425 & -30.171466949425 \tabularnewline
79 & 24.81055556 & 32.4863101251166 & -7.67575456511664 \tabularnewline
80 & 33.43388889 & 36.9618379443701 & -3.52794905437011 \tabularnewline
81 & 27.44194444 & 26.624267966321 & 0.817676473678963 \tabularnewline
82 & 76.37583333 & 50.6778410566121 & 25.6979922733879 \tabularnewline
83 & 36.88833333 & 35.8124455590326 & 1.0758877709674 \tabularnewline
84 & 37.56972222 & 43.1281007394055 & -5.5583785194055 \tabularnewline
85 & 22.48694444 & 20.0515951749301 & 2.43534926506992 \tabularnewline
86 & 30.34361111 & 28.4900750207027 & 1.85353608929732 \tabularnewline
87 & 26.84277778 & 36.1603987684545 & -9.31762098845454 \tabularnewline
88 & 62.83083333 & 56.3281698949437 & 6.50266343505629 \tabularnewline
89 & 47.57944444 & 43.1702104415406 & 4.40923399845944 \tabularnewline
90 & 32.72638889 & 52.1667241248728 & -19.4403352348728 \tabularnewline
91 & 37.10027778 & 35.5869947350352 & 1.51328304496478 \tabularnewline
92 & 42.27583333 & 42.2439586840462 & 0.0318746459537728 \tabularnewline
93 & 31.11222222 & 32.1503054633415 & -1.0380832433415 \tabularnewline
94 & 47.11472222 & 38.2311778012743 & 8.88354441872573 \tabularnewline
95 & 52.07861111 & 41.9535869466785 & 10.1250241633215 \tabularnewline
96 & 36.25916667 & 34.0046641720094 & 2.25450249799062 \tabularnewline
97 & 39.53861111 & 39.8553844403097 & -0.316773330309714 \tabularnewline
98 & 52.71222222 & 48.9721588400898 & 3.74006337991022 \tabularnewline
99 & 56.00083333 & 65.8727217352814 & -9.87188840528139 \tabularnewline
100 & 68.565 & 51.3762599654986 & 17.1887400345014 \tabularnewline
101 & 43.31861111 & 52.6510980751056 & -9.33248696510562 \tabularnewline
102 & 50.71694444 & 44.7515113351712 & 5.96543310482883 \tabularnewline
103 & 29.54194444 & 47.3352643663465 & -17.7933199263464 \tabularnewline
104 & 12.02416667 & 24.4758300883266 & -12.4516634183266 \tabularnewline
105 & 35.41472222 & 27.6803391040776 & 7.7343831159224 \tabularnewline
106 & 35.53611111 & 48.1274298781524 & -12.5913187681524 \tabularnewline
107 & 41.39055556 & 34.246603054358 & 7.14395250564199 \tabularnewline
108 & 52.12583333 & 44.9397230887868 & 7.1861102412132 \tabularnewline
109 & 20.58666667 & 46.385697498155 & -25.799030828155 \tabularnewline
110 & 26.11277778 & 41.4971598679861 & -15.3843820879861 \tabularnewline
111 & 49.0625 & 40.8013967601757 & 8.26110323982434 \tabularnewline
112 & 39.42583333 & 42.0552979974263 & -2.62946466742631 \tabularnewline
113 & 6.371666667 & 3.33080800419254 & 3.04085866280746 \tabularnewline
114 & 34.97972222 & 39.1452199989674 & -4.16549777896741 \tabularnewline
115 & 17.1825 & 18.2786318145253 & -1.09613181452526 \tabularnewline
116 & 25.35833333 & 32.5882673644507 & -7.22993403445067 \tabularnewline
117 & 70.86111111 & 41.6608060411667 & 29.2003050688333 \tabularnewline
118 & 5.848333333 & 3.9660054996506 & 1.8823278333494 \tabularnewline
119 & 46.97027778 & 33.6634231990226 & 13.3068545809774 \tabularnewline
120 & 8.726111111 & 12.9116373221645 & -4.18552621116452 \tabularnewline
121 & 52.41694444 & 45.6610124059817 & 6.75593203401831 \tabularnewline
122 & 38.20666667 & 46.0286161967644 & -7.82194952676436 \tabularnewline
123 & 21.435 & 38.8523574905491 & -17.4173574905491 \tabularnewline
124 & 20.71305556 & 31.5253558499398 & -10.8123002899398 \tabularnewline
125 & 10.615 & 15.9049077264904 & -5.28990772649043 \tabularnewline
126 & 25.26694444 & 32.2742227244267 & -7.00727828442668 \tabularnewline
127 & 53.95111111 & 46.9965969108717 & 6.95451419912833 \tabularnewline
128 & 37.5725 & 45.5773352458527 & -8.00483524585265 \tabularnewline
129 & 67.85333333 & 46.8703596729982 & 20.9829736570018 \tabularnewline
130 & 56.04111111 & 54.0017462183637 & 2.03936489163631 \tabularnewline
131 & 71.22277778 & 67.4682594112254 & 3.75451836877456 \tabularnewline
132 & 38.65111111 & 30.0096070564969 & 8.64150405350309 \tabularnewline
133 & 21.24166667 & 27.9011850806374 & -6.65951841063736 \tabularnewline
134 & 52.63944444 & 76.463549386746 & -23.824104946746 \tabularnewline
135 & 77.87055556 & 63.6507665817845 & 14.2197889782155 \tabularnewline
136 & 14.16638889 & 28.2218355419533 & -14.0554466519533 \tabularnewline
137 & 70.35388889 & 56.8383396127786 & 13.5155492772214 \tabularnewline
138 & 28.6775 & 37.3511193592688 & -8.67361935926884 \tabularnewline
139 & 46.68305556 & 36.969730091119 & 9.71332546888102 \tabularnewline
140 & 35.76888889 & 38.3223124571705 & -2.55342356717046 \tabularnewline
141 & 21.04055556 & 33.6326974290211 & -12.5921418690211 \tabularnewline
142 & 69.23111111 & 46.6040728064782 & 22.6270383035218 \tabularnewline
143 & 42.32388889 & 43.609514911047 & -1.28562602104698 \tabularnewline
144 & 48.12777778 & 37.3202327836736 & 10.8075449963264 \tabularnewline
145 & 54.77694444 & 49.6032882670648 & 5.17365617293522 \tabularnewline
146 & 18.75194444 & 45.8006121095485 & -27.0486676695485 \tabularnewline
147 & 38.72472222 & 44.5678732001398 & -5.84315098013978 \tabularnewline
148 & 51.49055556 & 54.6244192646839 & -3.13386370468385 \tabularnewline
149 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
150 & 4.08 & 3.17214025159106 & 0.907859748408937 \tabularnewline
151 & 0.027222222 & 0.147692473267216 & -0.120470251267216 \tabularnewline
152 & 0.126388889 & 0.394659158172725 & -0.268270269172725 \tabularnewline
153 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
154 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
155 & 38.30138889 & 35.989428456333 & 2.31196043366698 \tabularnewline
156 & 51.46888889 & 44.5917992212673 & 6.87708966873274 \tabularnewline
157 & 0 & -0.0992742116382943 & 0.0992742116382943 \tabularnewline
158 & 0.056388889 & 0.888592527983742 & -0.832203638983742 \tabularnewline
159 & 1.999722222 & 1.35439917266054 & 0.645323049339463 \tabularnewline
160 & 12.96111111 & 9.68726207847868 & 3.27384903152132 \tabularnewline
161 & 4.874166667 & 2.4631039039042 & 2.4110627630958 \tabularnewline
162 & 20.43527778 & 28.175707153313 & -7.74042937331299 \tabularnewline
163 & 0.269166667 & 0.394659158172722 & -0.125492491172722 \tabularnewline
164 & 29.29916667 & 27.4905707799461 & 1.80859589005389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&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]47.38555556[/C][C]44.9094498238412[/C][C]2.47610573615882[/C][/ROW]
[ROW][C]2[/C][C]24.06138889[/C][C]35.1172666907422[/C][C]-11.0558778007422[/C][/ROW]
[ROW][C]3[/C][C]31.4825[/C][C]36.745333679831[/C][C]-5.26283367983099[/C][/ROW]
[ROW][C]4[/C][C]42.36388889[/C][C]49.165735489582[/C][C]-6.80184659958204[/C][/ROW]
[ROW][C]5[/C][C]23.94611111[/C][C]23.7916481980652[/C][C]0.154462911934808[/C][/ROW]
[ROW][C]6[/C][C]10.34916667[/C][C]24.2124357077914[/C][C]-13.8632690377914[/C][/ROW]
[ROW][C]7[/C][C]85.01527778[/C][C]47.6573318327695[/C][C]37.3579459472305[/C][/ROW]
[ROW][C]8[/C][C]9.097222222[/C][C]21.3770122005606[/C][C]-12.2797899785606[/C][/ROW]
[ROW][C]9[/C][C]32.36166667[/C][C]34.8837920138091[/C][C]-2.52212534380914[/C][/ROW]
[ROW][C]10[/C][C]36.26083333[/C][C]28.4254575371181[/C][C]7.83537579288187[/C][/ROW]
[ROW][C]11[/C][C]44.96555556[/C][C]47.0629090877757[/C][C]-2.09735352777571[/C][/ROW]
[ROW][C]12[/C][C]35.63166667[/C][C]56.3800389165099[/C][C]-20.7483722465099[/C][/ROW]
[ROW][C]13[/C][C]28.43055556[/C][C]35.7516111097651[/C][C]-7.32105554976512[/C][/ROW]
[ROW][C]14[/C][C]53.61777778[/C][C]47.6282394419357[/C][C]5.98953833806429[/C][/ROW]
[ROW][C]15[/C][C]39.32611111[/C][C]41.9438722432477[/C][C]-2.61776113324769[/C][/ROW]
[ROW][C]16[/C][C]70.43305556[/C][C]67.3832762190756[/C][C]3.04977934092442[/C][/ROW]
[ROW][C]17[/C][C]50.30833333[/C][C]43.6972944962182[/C][C]6.61103883378178[/C][/ROW]
[ROW][C]18[/C][C]55.12[/C][C]52.6231319130697[/C][C]2.49686808693031[/C][/ROW]
[ROW][C]19[/C][C]31.62583333[/C][C]35.0064442240837[/C][C]-3.38061089408367[/C][/ROW]
[ROW][C]20[/C][C]44.42777778[/C][C]46.294112264799[/C][C]-1.86633448479901[/C][/ROW]
[ROW][C]21[/C][C]46.33944444[/C][C]44.4309784777764[/C][C]1.90846596222362[/C][/ROW]
[ROW][C]22[/C][C]79.63194444[/C][C]42.0749099350576[/C][C]37.5570345049424[/C][/ROW]
[ROW][C]23[/C][C]25.46027778[/C][C]21.6106271297438[/C][C]3.84965065025623[/C][/ROW]
[ROW][C]24[/C][C]30.07722222[/C][C]41.3736268900134[/C][C]-11.2964046700134[/C][/ROW]
[ROW][C]25[/C][C]40.65055556[/C][C]48.7803020013877[/C][C]-8.12974644138769[/C][/ROW]
[ROW][C]26[/C][C]40.31722222[/C][C]48.9936847524049[/C][C]-8.67646253240486[/C][/ROW]
[ROW][C]27[/C][C]44.92777778[/C][C]45.5824203178865[/C][C]-0.654642537886524[/C][/ROW]
[ROW][C]28[/C][C]44.69583333[/C][C]42.7136814773708[/C][C]1.98215185262915[/C][/ROW]
[ROW][C]29[/C][C]29.69111111[/C][C]28.7929783451277[/C][C]0.898132764872311[/C][/ROW]
[ROW][C]30[/C][C]52.26388889[/C][C]40.7053643289927[/C][C]11.5585245610073[/C][/ROW]
[ROW][C]31[/C][C]52.61138889[/C][C]50.0169064428956[/C][C]2.59448244710443[/C][/ROW]
[ROW][C]32[/C][C]35.96777778[/C][C]45.3743325618858[/C][C]-9.4065547818858[/C][/ROW]
[ROW][C]33[/C][C]56.675[/C][C]48.2154668751491[/C][C]8.45953312485091[/C][/ROW]
[ROW][C]34[/C][C]17.42527778[/C][C]27.0975485747887[/C][C]-9.67227079478873[/C][/ROW]
[ROW][C]35[/C][C]67.67361111[/C][C]46.808717235919[/C][C]20.8648938740809[/C][/ROW]
[ROW][C]36[/C][C]46.45972222[/C][C]34.0935585670728[/C][C]12.3661636529272[/C][/ROW]
[ROW][C]37[/C][C]73.48[/C][C]52.4853686038615[/C][C]20.9946313961385[/C][/ROW]
[ROW][C]38[/C][C]33.89555556[/C][C]33.9454073254085[/C][C]-0.0498517654085003[/C][/ROW]
[ROW][C]39[/C][C]22.49[/C][C]16.3551829253679[/C][C]6.13481707463207[/C][/ROW]
[ROW][C]40[/C][C]58.27638889[/C][C]54.3732814539856[/C][C]3.9031074360144[/C][/ROW]
[ROW][C]41[/C][C]62.27916667[/C][C]40.9380784786797[/C][C]21.3410881913203[/C][/ROW]
[ROW][C]42[/C][C]32.21416667[/C][C]35.4745119254324[/C][C]-3.26034525543242[/C][/ROW]
[ROW][C]43[/C][C]38.38638889[/C][C]36.1613174585796[/C][C]2.2250714314204[/C][/ROW]
[ROW][C]44[/C][C]22.52944444[/C][C]29.2309388036317[/C][C]-6.70149436363172[/C][/ROW]
[ROW][C]45[/C][C]25.86805556[/C][C]35.6730540933478[/C][C]-9.80499853334778[/C][/ROW]
[ROW][C]46[/C][C]84.93222222[/C][C]56.1184585121321[/C][C]28.8137637078679[/C][/ROW]
[ROW][C]47[/C][C]21.88888889[/C][C]35.5987426677104[/C][C]-13.7098537777104[/C][/ROW]
[ROW][C]48[/C][C]44.12083333[/C][C]45.8457149768521[/C][C]-1.72488164685208[/C][/ROW]
[ROW][C]49[/C][C]61.59583333[/C][C]60.2619073281196[/C][C]1.33392600188044[/C][/ROW]
[ROW][C]50[/C][C]36.41888889[/C][C]41.2325366801504[/C][C]-4.8136477901504[/C][/ROW]
[ROW][C]51[/C][C]35.75944444[/C][C]34.8086485094261[/C][C]0.950795930573938[/C][/ROW]
[ROW][C]52[/C][C]6.718888889[/C][C]9.83970780790965[/C][C]-3.12081891890965[/C][/ROW]
[ROW][C]53[/C][C]71.57277778[/C][C]87.7322397981692[/C][C]-16.1594620181692[/C][/ROW]
[ROW][C]54[/C][C]18.06361111[/C][C]22.9210449244025[/C][C]-4.8574338144025[/C][/ROW]
[ROW][C]55[/C][C]27.24055556[/C][C]29.7137805105943[/C][C]-2.47322495059433[/C][/ROW]
[ROW][C]56[/C][C]48.21861111[/C][C]32.1413297700151[/C][C]16.0772813399849[/C][/ROW]
[ROW][C]57[/C][C]50.01166667[/C][C]56.7500001215168[/C][C]-6.73833345151682[/C][/ROW]
[ROW][C]58[/C][C]54.79611111[/C][C]60.9049068151728[/C][C]-6.10879570517281[/C][/ROW]
[ROW][C]59[/C][C]58.90555556[/C][C]50.1994968526072[/C][C]8.70605870739283[/C][/ROW]
[ROW][C]60[/C][C]39.32833333[/C][C]37.6458831482492[/C][C]1.68245018175085[/C][/ROW]
[ROW][C]61[/C][C]68.08527778[/C][C]60.6162393154471[/C][C]7.46903846455293[/C][/ROW]
[ROW][C]62[/C][C]57.46638889[/C][C]51.0524622208099[/C][C]6.41392666919013[/C][/ROW]
[ROW][C]63[/C][C]40.47111111[/C][C]44.9992473986201[/C][C]-4.52813628862007[/C][/ROW]
[ROW][C]64[/C][C]47.39861111[/C][C]44.9687076625175[/C][C]2.42990344748254[/C][/ROW]
[ROW][C]65[/C][C]39.46222222[/C][C]38.6631910457938[/C][C]0.799031174206211[/C][/ROW]
[ROW][C]66[/C][C]31.89444444[/C][C]26.0325201331417[/C][C]5.86192430685828[/C][/ROW]
[ROW][C]67[/C][C]31.51694444[/C][C]48.2863125560254[/C][C]-16.7693681160254[/C][/ROW]
[ROW][C]68[/C][C]40.35694444[/C][C]55.2478531090229[/C][C]-14.8909086690229[/C][/ROW]
[ROW][C]69[/C][C]41.94416667[/C][C]40.4533092363133[/C][C]1.49085743368669[/C][/ROW]
[ROW][C]70[/C][C]25.50333333[/C][C]30.9078189082707[/C][C]-5.4044855782707[/C][/ROW]
[ROW][C]71[/C][C]33.00194444[/C][C]35.605445844814[/C][C]-2.60350140481396[/C][/ROW]
[ROW][C]72[/C][C]19.2975[/C][C]25.9253294678063[/C][C]-6.62782946780632[/C][/ROW]
[ROW][C]73[/C][C]35.175[/C][C]38.667910410092[/C][C]-3.49291041009197[/C][/ROW]
[ROW][C]74[/C][C]40.53[/C][C]37.1058072875321[/C][C]3.42419271246788[/C][/ROW]
[ROW][C]75[/C][C]27.33138889[/C][C]34.1489178969573[/C][C]-6.81752900695726[/C][/ROW]
[ROW][C]76[/C][C]53.035[/C][C]40.7710233231306[/C][C]12.2639766768694[/C][/ROW]
[ROW][C]77[/C][C]55.22138889[/C][C]40.6184397072076[/C][C]14.6029491827924[/C][/ROW]
[ROW][C]78[/C][C]29.49805556[/C][C]59.669522509425[/C][C]-30.171466949425[/C][/ROW]
[ROW][C]79[/C][C]24.81055556[/C][C]32.4863101251166[/C][C]-7.67575456511664[/C][/ROW]
[ROW][C]80[/C][C]33.43388889[/C][C]36.9618379443701[/C][C]-3.52794905437011[/C][/ROW]
[ROW][C]81[/C][C]27.44194444[/C][C]26.624267966321[/C][C]0.817676473678963[/C][/ROW]
[ROW][C]82[/C][C]76.37583333[/C][C]50.6778410566121[/C][C]25.6979922733879[/C][/ROW]
[ROW][C]83[/C][C]36.88833333[/C][C]35.8124455590326[/C][C]1.0758877709674[/C][/ROW]
[ROW][C]84[/C][C]37.56972222[/C][C]43.1281007394055[/C][C]-5.5583785194055[/C][/ROW]
[ROW][C]85[/C][C]22.48694444[/C][C]20.0515951749301[/C][C]2.43534926506992[/C][/ROW]
[ROW][C]86[/C][C]30.34361111[/C][C]28.4900750207027[/C][C]1.85353608929732[/C][/ROW]
[ROW][C]87[/C][C]26.84277778[/C][C]36.1603987684545[/C][C]-9.31762098845454[/C][/ROW]
[ROW][C]88[/C][C]62.83083333[/C][C]56.3281698949437[/C][C]6.50266343505629[/C][/ROW]
[ROW][C]89[/C][C]47.57944444[/C][C]43.1702104415406[/C][C]4.40923399845944[/C][/ROW]
[ROW][C]90[/C][C]32.72638889[/C][C]52.1667241248728[/C][C]-19.4403352348728[/C][/ROW]
[ROW][C]91[/C][C]37.10027778[/C][C]35.5869947350352[/C][C]1.51328304496478[/C][/ROW]
[ROW][C]92[/C][C]42.27583333[/C][C]42.2439586840462[/C][C]0.0318746459537728[/C][/ROW]
[ROW][C]93[/C][C]31.11222222[/C][C]32.1503054633415[/C][C]-1.0380832433415[/C][/ROW]
[ROW][C]94[/C][C]47.11472222[/C][C]38.2311778012743[/C][C]8.88354441872573[/C][/ROW]
[ROW][C]95[/C][C]52.07861111[/C][C]41.9535869466785[/C][C]10.1250241633215[/C][/ROW]
[ROW][C]96[/C][C]36.25916667[/C][C]34.0046641720094[/C][C]2.25450249799062[/C][/ROW]
[ROW][C]97[/C][C]39.53861111[/C][C]39.8553844403097[/C][C]-0.316773330309714[/C][/ROW]
[ROW][C]98[/C][C]52.71222222[/C][C]48.9721588400898[/C][C]3.74006337991022[/C][/ROW]
[ROW][C]99[/C][C]56.00083333[/C][C]65.8727217352814[/C][C]-9.87188840528139[/C][/ROW]
[ROW][C]100[/C][C]68.565[/C][C]51.3762599654986[/C][C]17.1887400345014[/C][/ROW]
[ROW][C]101[/C][C]43.31861111[/C][C]52.6510980751056[/C][C]-9.33248696510562[/C][/ROW]
[ROW][C]102[/C][C]50.71694444[/C][C]44.7515113351712[/C][C]5.96543310482883[/C][/ROW]
[ROW][C]103[/C][C]29.54194444[/C][C]47.3352643663465[/C][C]-17.7933199263464[/C][/ROW]
[ROW][C]104[/C][C]12.02416667[/C][C]24.4758300883266[/C][C]-12.4516634183266[/C][/ROW]
[ROW][C]105[/C][C]35.41472222[/C][C]27.6803391040776[/C][C]7.7343831159224[/C][/ROW]
[ROW][C]106[/C][C]35.53611111[/C][C]48.1274298781524[/C][C]-12.5913187681524[/C][/ROW]
[ROW][C]107[/C][C]41.39055556[/C][C]34.246603054358[/C][C]7.14395250564199[/C][/ROW]
[ROW][C]108[/C][C]52.12583333[/C][C]44.9397230887868[/C][C]7.1861102412132[/C][/ROW]
[ROW][C]109[/C][C]20.58666667[/C][C]46.385697498155[/C][C]-25.799030828155[/C][/ROW]
[ROW][C]110[/C][C]26.11277778[/C][C]41.4971598679861[/C][C]-15.3843820879861[/C][/ROW]
[ROW][C]111[/C][C]49.0625[/C][C]40.8013967601757[/C][C]8.26110323982434[/C][/ROW]
[ROW][C]112[/C][C]39.42583333[/C][C]42.0552979974263[/C][C]-2.62946466742631[/C][/ROW]
[ROW][C]113[/C][C]6.371666667[/C][C]3.33080800419254[/C][C]3.04085866280746[/C][/ROW]
[ROW][C]114[/C][C]34.97972222[/C][C]39.1452199989674[/C][C]-4.16549777896741[/C][/ROW]
[ROW][C]115[/C][C]17.1825[/C][C]18.2786318145253[/C][C]-1.09613181452526[/C][/ROW]
[ROW][C]116[/C][C]25.35833333[/C][C]32.5882673644507[/C][C]-7.22993403445067[/C][/ROW]
[ROW][C]117[/C][C]70.86111111[/C][C]41.6608060411667[/C][C]29.2003050688333[/C][/ROW]
[ROW][C]118[/C][C]5.848333333[/C][C]3.9660054996506[/C][C]1.8823278333494[/C][/ROW]
[ROW][C]119[/C][C]46.97027778[/C][C]33.6634231990226[/C][C]13.3068545809774[/C][/ROW]
[ROW][C]120[/C][C]8.726111111[/C][C]12.9116373221645[/C][C]-4.18552621116452[/C][/ROW]
[ROW][C]121[/C][C]52.41694444[/C][C]45.6610124059817[/C][C]6.75593203401831[/C][/ROW]
[ROW][C]122[/C][C]38.20666667[/C][C]46.0286161967644[/C][C]-7.82194952676436[/C][/ROW]
[ROW][C]123[/C][C]21.435[/C][C]38.8523574905491[/C][C]-17.4173574905491[/C][/ROW]
[ROW][C]124[/C][C]20.71305556[/C][C]31.5253558499398[/C][C]-10.8123002899398[/C][/ROW]
[ROW][C]125[/C][C]10.615[/C][C]15.9049077264904[/C][C]-5.28990772649043[/C][/ROW]
[ROW][C]126[/C][C]25.26694444[/C][C]32.2742227244267[/C][C]-7.00727828442668[/C][/ROW]
[ROW][C]127[/C][C]53.95111111[/C][C]46.9965969108717[/C][C]6.95451419912833[/C][/ROW]
[ROW][C]128[/C][C]37.5725[/C][C]45.5773352458527[/C][C]-8.00483524585265[/C][/ROW]
[ROW][C]129[/C][C]67.85333333[/C][C]46.8703596729982[/C][C]20.9829736570018[/C][/ROW]
[ROW][C]130[/C][C]56.04111111[/C][C]54.0017462183637[/C][C]2.03936489163631[/C][/ROW]
[ROW][C]131[/C][C]71.22277778[/C][C]67.4682594112254[/C][C]3.75451836877456[/C][/ROW]
[ROW][C]132[/C][C]38.65111111[/C][C]30.0096070564969[/C][C]8.64150405350309[/C][/ROW]
[ROW][C]133[/C][C]21.24166667[/C][C]27.9011850806374[/C][C]-6.65951841063736[/C][/ROW]
[ROW][C]134[/C][C]52.63944444[/C][C]76.463549386746[/C][C]-23.824104946746[/C][/ROW]
[ROW][C]135[/C][C]77.87055556[/C][C]63.6507665817845[/C][C]14.2197889782155[/C][/ROW]
[ROW][C]136[/C][C]14.16638889[/C][C]28.2218355419533[/C][C]-14.0554466519533[/C][/ROW]
[ROW][C]137[/C][C]70.35388889[/C][C]56.8383396127786[/C][C]13.5155492772214[/C][/ROW]
[ROW][C]138[/C][C]28.6775[/C][C]37.3511193592688[/C][C]-8.67361935926884[/C][/ROW]
[ROW][C]139[/C][C]46.68305556[/C][C]36.969730091119[/C][C]9.71332546888102[/C][/ROW]
[ROW][C]140[/C][C]35.76888889[/C][C]38.3223124571705[/C][C]-2.55342356717046[/C][/ROW]
[ROW][C]141[/C][C]21.04055556[/C][C]33.6326974290211[/C][C]-12.5921418690211[/C][/ROW]
[ROW][C]142[/C][C]69.23111111[/C][C]46.6040728064782[/C][C]22.6270383035218[/C][/ROW]
[ROW][C]143[/C][C]42.32388889[/C][C]43.609514911047[/C][C]-1.28562602104698[/C][/ROW]
[ROW][C]144[/C][C]48.12777778[/C][C]37.3202327836736[/C][C]10.8075449963264[/C][/ROW]
[ROW][C]145[/C][C]54.77694444[/C][C]49.6032882670648[/C][C]5.17365617293522[/C][/ROW]
[ROW][C]146[/C][C]18.75194444[/C][C]45.8006121095485[/C][C]-27.0486676695485[/C][/ROW]
[ROW][C]147[/C][C]38.72472222[/C][C]44.5678732001398[/C][C]-5.84315098013978[/C][/ROW]
[ROW][C]148[/C][C]51.49055556[/C][C]54.6244192646839[/C][C]-3.13386370468385[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]150[/C][C]4.08[/C][C]3.17214025159106[/C][C]0.907859748408937[/C][/ROW]
[ROW][C]151[/C][C]0.027222222[/C][C]0.147692473267216[/C][C]-0.120470251267216[/C][/ROW]
[ROW][C]152[/C][C]0.126388889[/C][C]0.394659158172725[/C][C]-0.268270269172725[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]155[/C][C]38.30138889[/C][C]35.989428456333[/C][C]2.31196043366698[/C][/ROW]
[ROW][C]156[/C][C]51.46888889[/C][C]44.5917992212673[/C][C]6.87708966873274[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-0.0992742116382943[/C][C]0.0992742116382943[/C][/ROW]
[ROW][C]158[/C][C]0.056388889[/C][C]0.888592527983742[/C][C]-0.832203638983742[/C][/ROW]
[ROW][C]159[/C][C]1.999722222[/C][C]1.35439917266054[/C][C]0.645323049339463[/C][/ROW]
[ROW][C]160[/C][C]12.96111111[/C][C]9.68726207847868[/C][C]3.27384903152132[/C][/ROW]
[ROW][C]161[/C][C]4.874166667[/C][C]2.4631039039042[/C][C]2.4110627630958[/C][/ROW]
[ROW][C]162[/C][C]20.43527778[/C][C]28.175707153313[/C][C]-7.74042937331299[/C][/ROW]
[ROW][C]163[/C][C]0.269166667[/C][C]0.394659158172722[/C][C]-0.125492491172722[/C][/ROW]
[ROW][C]164[/C][C]29.29916667[/C][C]27.4905707799461[/C][C]1.80859589005389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.3855555644.90944982384122.47610573615882
224.0613888935.1172666907422-11.0558778007422
331.482536.745333679831-5.26283367983099
442.3638888949.165735489582-6.80184659958204
523.9461111123.79164819806520.154462911934808
610.3491666724.2124357077914-13.8632690377914
785.0152777847.657331832769537.3579459472305
89.09722222221.3770122005606-12.2797899785606
932.3616666734.8837920138091-2.52212534380914
1036.2608333328.42545753711817.83537579288187
1144.9655555647.0629090877757-2.09735352777571
1235.6316666756.3800389165099-20.7483722465099
1328.4305555635.7516111097651-7.32105554976512
1453.6177777847.62823944193575.98953833806429
1539.3261111141.9438722432477-2.61776113324769
1670.4330555667.38327621907563.04977934092442
1750.3083333343.69729449621826.61103883378178
1855.1252.62313191306972.49686808693031
1931.6258333335.0064442240837-3.38061089408367
2044.4277777846.294112264799-1.86633448479901
2146.3394444444.43097847777641.90846596222362
2279.6319444442.074909935057637.5570345049424
2325.4602777821.61062712974383.84965065025623
2430.0772222241.3736268900134-11.2964046700134
2540.6505555648.7803020013877-8.12974644138769
2640.3172222248.9936847524049-8.67646253240486
2744.9277777845.5824203178865-0.654642537886524
2844.6958333342.71368147737081.98215185262915
2929.6911111128.79297834512770.898132764872311
3052.2638888940.705364328992711.5585245610073
3152.6113888950.01690644289562.59448244710443
3235.9677777845.3743325618858-9.4065547818858
3356.67548.21546687514918.45953312485091
3417.4252777827.0975485747887-9.67227079478873
3567.6736111146.80871723591920.8648938740809
3646.4597222234.093558567072812.3661636529272
3773.4852.485368603861520.9946313961385
3833.8955555633.9454073254085-0.0498517654085003
3922.4916.35518292536796.13481707463207
4058.2763888954.37328145398563.9031074360144
4162.2791666740.938078478679721.3410881913203
4232.2141666735.4745119254324-3.26034525543242
4338.3863888936.16131745857962.2250714314204
4422.5294444429.2309388036317-6.70149436363172
4525.8680555635.6730540933478-9.80499853334778
4684.9322222256.118458512132128.8137637078679
4721.8888888935.5987426677104-13.7098537777104
4844.1208333345.8457149768521-1.72488164685208
4961.5958333360.26190732811961.33392600188044
5036.4188888941.2325366801504-4.8136477901504
5135.7594444434.80864850942610.950795930573938
526.7188888899.83970780790965-3.12081891890965
5371.5727777887.7322397981692-16.1594620181692
5418.0636111122.9210449244025-4.8574338144025
5527.2405555629.7137805105943-2.47322495059433
5648.2186111132.141329770015116.0772813399849
5750.0116666756.7500001215168-6.73833345151682
5854.7961111160.9049068151728-6.10879570517281
5958.9055555650.19949685260728.70605870739283
6039.3283333337.64588314824921.68245018175085
6168.0852777860.61623931544717.46903846455293
6257.4663888951.05246222080996.41392666919013
6340.4711111144.9992473986201-4.52813628862007
6447.3986111144.96870766251752.42990344748254
6539.4622222238.66319104579380.799031174206211
6631.8944444426.03252013314175.86192430685828
6731.5169444448.2863125560254-16.7693681160254
6840.3569444455.2478531090229-14.8909086690229
6941.9441666740.45330923631331.49085743368669
7025.5033333330.9078189082707-5.4044855782707
7133.0019444435.605445844814-2.60350140481396
7219.297525.9253294678063-6.62782946780632
7335.17538.667910410092-3.49291041009197
7440.5337.10580728753213.42419271246788
7527.3313888934.1489178969573-6.81752900695726
7653.03540.771023323130612.2639766768694
7755.2213888940.618439707207614.6029491827924
7829.4980555659.669522509425-30.171466949425
7924.8105555632.4863101251166-7.67575456511664
8033.4338888936.9618379443701-3.52794905437011
8127.4419444426.6242679663210.817676473678963
8276.3758333350.677841056612125.6979922733879
8336.8883333335.81244555903261.0758877709674
8437.5697222243.1281007394055-5.5583785194055
8522.4869444420.05159517493012.43534926506992
8630.3436111128.49007502070271.85353608929732
8726.8427777836.1603987684545-9.31762098845454
8862.8308333356.32816989494376.50266343505629
8947.5794444443.17021044154064.40923399845944
9032.7263888952.1667241248728-19.4403352348728
9137.1002777835.58699473503521.51328304496478
9242.2758333342.24395868404620.0318746459537728
9331.1122222232.1503054633415-1.0380832433415
9447.1147222238.23117780127438.88354441872573
9552.0786111141.953586946678510.1250241633215
9636.2591666734.00466417200942.25450249799062
9739.5386111139.8553844403097-0.316773330309714
9852.7122222248.97215884008983.74006337991022
9956.0008333365.8727217352814-9.87188840528139
10068.56551.376259965498617.1887400345014
10143.3186111152.6510980751056-9.33248696510562
10250.7169444444.75151133517125.96543310482883
10329.5419444447.3352643663465-17.7933199263464
10412.0241666724.4758300883266-12.4516634183266
10535.4147222227.68033910407767.7343831159224
10635.5361111148.1274298781524-12.5913187681524
10741.3905555634.2466030543587.14395250564199
10852.1258333344.93972308878687.1861102412132
10920.5866666746.385697498155-25.799030828155
11026.1127777841.4971598679861-15.3843820879861
11149.062540.80139676017578.26110323982434
11239.4258333342.0552979974263-2.62946466742631
1136.3716666673.330808004192543.04085866280746
11434.9797222239.1452199989674-4.16549777896741
11517.182518.2786318145253-1.09613181452526
11625.3583333332.5882673644507-7.22993403445067
11770.8611111141.660806041166729.2003050688333
1185.8483333333.96600549965061.8823278333494
11946.9702777833.663423199022613.3068545809774
1208.72611111112.9116373221645-4.18552621116452
12152.4169444445.66101240598176.75593203401831
12238.2066666746.0286161967644-7.82194952676436
12321.43538.8523574905491-17.4173574905491
12420.7130555631.5253558499398-10.8123002899398
12510.61515.9049077264904-5.28990772649043
12625.2669444432.2742227244267-7.00727828442668
12753.9511111146.99659691087176.95451419912833
12837.572545.5773352458527-8.00483524585265
12967.8533333346.870359672998220.9829736570018
13056.0411111154.00174621836372.03936489163631
13171.2227777867.46825941122543.75451836877456
13238.6511111130.00960705649698.64150405350309
13321.2416666727.9011850806374-6.65951841063736
13452.6394444476.463549386746-23.824104946746
13577.8705555663.650766581784514.2197889782155
13614.1663888928.2218355419533-14.0554466519533
13770.3538888956.838339612778613.5155492772214
13828.677537.3511193592688-8.67361935926884
13946.6830555636.9697300911199.71332546888102
14035.7688888938.3223124571705-2.55342356717046
14121.0405555633.6326974290211-12.5921418690211
14269.2311111146.604072806478222.6270383035218
14342.3238888943.609514911047-1.28562602104698
14448.1277777837.320232783673610.8075449963264
14554.7769444449.60328826706485.17365617293522
14618.7519444445.8006121095485-27.0486676695485
14738.7247222244.5678732001398-5.84315098013978
14851.4905555654.6244192646839-3.13386370468385
1490-0.09927421163829430.0992742116382943
1504.083.172140251591060.907859748408937
1510.0272222220.147692473267216-0.120470251267216
1520.1263888890.394659158172725-0.268270269172725
1530-0.09927421163829430.0992742116382943
1540-0.09927421163829430.0992742116382943
15538.3013888935.9894284563332.31196043366698
15651.4688888944.59179922126736.87708966873274
1570-0.09927421163829430.0992742116382943
1580.0563888890.888592527983742-0.832203638983742
1591.9997222221.354399172660540.645323049339463
16012.961111119.687262078478683.27384903152132
1614.8741666672.46310390390422.4110627630958
16220.4352777828.175707153313-7.74042937331299
1630.2691666670.394659158172722-0.125492491172722
16429.2991666727.49057077994611.80859589005389






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8007617534472560.3984764931054870.199238246552744
80.7872238394987350.425552321002530.212776160501265
90.7282221835412060.5435556329175870.271777816458794
100.8415321733094670.3169356533810650.158467826690533
110.7889459359336360.4221081281327290.211054064066364
120.7506922168421630.4986155663156750.249307783157837
130.66829957161610.6634008567678010.3317004283839
140.859786594458910.2804268110821790.14021340554109
150.8243343320888930.3513313358222140.175665667911107
160.7659644720820970.4680710558358060.234035527917903
170.7015700816653970.5968598366692070.298429918334603
180.6538599723877310.6922800552245380.346140027612269
190.6084040140961480.7831919718077040.391595985903852
200.5336196185323120.9327607629353750.466380381467688
210.4594880264074760.9189760528149520.540511973592524
220.9594478426540850.08110431469182990.0405521573459149
230.9480791551650110.1038416896699780.0519208448349888
240.9383358591781540.1233282816436920.0616641408218459
250.9284205449502920.1431589100994160.071579455049708
260.9263289143050190.1473421713899630.0736710856949813
270.9055216308758980.1889567382482030.0944783691241017
280.8807678497467060.2384643005065870.119232150253294
290.857052793233530.285894413532940.14294720676647
300.8761479272285080.2477041455429840.123852072771492
310.8438760416860280.3122479166279450.156123958313972
320.819297334026730.3614053319465390.18070266597327
330.8037497432583960.3925005134832080.196250256741604
340.7789852284715440.4420295430569120.221014771528456
350.8371694688310780.3256610623378450.162830531168922
360.8637167359563640.2725665280872730.136283264043636
370.9138818328065530.1722363343868950.0861181671934473
380.8906896153765560.2186207692468890.109310384623444
390.8728671681224740.2542656637550520.127132831877526
400.846164290290430.3076714194191410.15383570970957
410.9060902541118720.1878194917762570.0939097458881283
420.8833912711172380.2332174577655230.116608728882761
430.8566030485676520.2867939028646970.143396951432348
440.83320650327060.33358699345880.1667934967294
450.8288706260007470.3422587479985070.171129373999253
460.917255927339450.1654881453211010.0827440726605503
470.9178813706149130.1642372587701740.0821186293850868
480.8980177442144090.2039645115711820.101982255785591
490.8826809866823450.234638026635310.117319013317655
500.8596143421856590.2807713156286820.140385657814341
510.8318064470478680.3363871059042640.168193552952132
520.8004504057394280.3990991885211440.199549594260572
530.8862268548763390.2275462902473210.113773145123661
540.8649135646553990.2701728706892020.135086435344601
550.8377646237919880.3244707524160250.162235376208012
560.8518889316634960.2962221366730090.148111068336504
570.8460840255680210.3078319488639590.15391597443198
580.8433181151590820.3133637696818350.156681884840918
590.8268794451118750.346241109776250.173120554888125
600.7955817778983260.4088364442033480.204418222101674
610.7712036051966920.4575927896066170.228796394803308
620.7480856050313710.5038287899372570.251914394968629
630.7134948892754930.5730102214490130.286505110724507
640.674073387685610.651853224628780.32592661231439
650.6328796351557260.7342407296885470.367120364844274
660.599884618413280.800230763173440.40011538158672
670.68700118463260.6259976307347990.3129988153674
680.7090145285673630.5819709428652750.290985471432637
690.6690451062358750.6619097875282510.330954893764125
700.6412070794255410.7175858411489170.358792920574459
710.6004628097269330.7990743805461330.399537190273067
720.5678231632828290.8643536734343430.432176836717171
730.5275719574017950.9448560851964110.472428042598205
740.4848870232080490.9697740464160980.515112976791951
750.459063187596950.9181263751938990.54093681240305
760.4623467274349260.9246934548698520.537653272565074
770.4855660601646180.9711321203292370.514433939835382
780.8169812370753940.3660375258492110.183018762924606
790.8004838164185020.3990323671629950.199516183581498
800.7705504392371020.4588991215257960.229449560762898
810.7347985324278290.5304029351443430.265201467572171
820.8684232111688180.2631535776623630.131576788831182
830.8429668218506120.3140663562987770.157033178149388
840.8210903156407870.3578193687184260.178909684359213
850.7913612675966320.4172774648067360.208638732403368
860.7583694022795350.4832611954409310.241630597720465
870.7468307590925610.5063384818148780.253169240907439
880.7238556075885680.5522887848228640.276144392411432
890.6917531258283820.6164937483432350.308246874171618
900.7663646885139560.4672706229720880.233635311486044
910.7310267131504540.5379465736990920.268973286849546
920.69179650966680.6164069806664010.3082034903332
930.6504762508235360.6990474983529290.349523749176464
940.6369885467679770.7260229064640460.363011453232023
950.6311803293862390.7376393412275220.368819670613761
960.588674692426390.8226506151472190.41132530757361
970.5433124380788650.9133751238422710.456687561921135
980.5021485743548040.9957028512903920.497851425645196
990.4862614484522260.9725228969044530.513738551547774
1000.5556985924482540.8886028151034930.444301407551746
1010.5377549740750970.9244900518498070.462245025924903
1020.5073233574047920.9853532851904170.492676642595208
1030.5796252506618420.8407494986763160.420374749338158
1040.5893772235703030.8212455528593950.410622776429697
1050.5651922173964630.8696155652070730.434807782603537
1060.5768780544170350.846243891165930.423121945582965
1070.5522787902233980.8954424195532040.447721209776602
1080.5260358148688340.9479283702623310.473964185131166
1090.741978954630520.5160420907389610.25802104536948
1100.7712423830019560.4575152339960880.228757616998044
1110.7484764446970270.5030471106059470.251523555302973
1120.7085746818972480.5828506362055040.291425318102752
1130.6676576443059160.6646847113881680.332342355694084
1140.6264914251790470.7470171496419050.373508574820953
1150.5783407226142820.8433185547714360.421659277385718
1160.5415941604403420.9168116791193160.458405839559658
1170.8096585765369290.3806828469261420.190341423463071
1180.7737083337954130.4525833324091730.226291666204587
1190.7863688555719030.4272622888561940.213631144428097
1200.7510043278020360.4979913443959290.248995672197964
1210.7279152136282230.5441695727435530.272084786371777
1220.7053562991525880.5892874016948240.294643700847412
1230.7709307128862860.4581385742274270.229069287113714
1240.7749412437906380.4501175124187250.225058756209362
1250.7396951158124450.520609768375110.260304884187555
1260.7184963310399380.5630073379201240.281503668960062
1270.687871865331330.6242562693373410.31212813466867
1280.670941144065180.6581177118696410.32905885593482
1290.7941118821595730.4117762356808530.205888117840427
1300.7520417708037520.4959164583924970.247958229196248
1310.7038655726302350.5922688547395290.296134427369765
1320.6850629278731910.6298741442536190.314937072126809
1330.6443180993570880.7113638012858240.355681900642912
1340.9835197961687760.03296040766244760.0164802038312238
1350.9774269226154540.04514615476909280.0225730773845464
1360.9712104424174190.05757911516516260.0287895575825813
1370.9711735699926370.05765286001472590.028826430007363
1380.9688619015638750.06227619687224950.0311380984361248
1390.9884475821178370.02310483576432590.0115524178821629
1400.9815145859073580.03697082818528360.0184854140926418
1410.9999999518628049.62743925794442e-084.81371962897221e-08
1420.999999942303811.15392379214597e-075.76961896072986e-08
1430.9999998067054323.86589135712995e-071.93294567856497e-07
1440.9999993748451761.25030964758685e-066.25154823793426e-07
1450.9999992646998611.47060027886255e-067.35300139431275e-07
1460.9999999998102123.79575812322054e-101.89787906161027e-10
1470.9999999989086692.1826611102213e-091.09133055511065e-09
1480.9999999982260163.54796820221849e-091.77398410110924e-09
1490.9999999869969742.60060515242326e-081.30030257621163e-08
1500.9999999499156771.0016864700133e-075.00843235006651e-08
1510.9999996149046067.70190787247421e-073.8509539362371e-07
1520.9999973310479725.33790405516175e-062.66895202758087e-06
1530.9999819844649353.60310701309463e-051.80155350654732e-05
1540.9998862056627450.000227588674510980.00011379433725549
1550.9999637099253337.25801493348042e-053.62900746674021e-05
1560.9996425525517450.0007148948965100250.000357447448255013
1570.9971417039050350.005716592189930060.00285829609496503

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.800761753447256 & 0.398476493105487 & 0.199238246552744 \tabularnewline
8 & 0.787223839498735 & 0.42555232100253 & 0.212776160501265 \tabularnewline
9 & 0.728222183541206 & 0.543555632917587 & 0.271777816458794 \tabularnewline
10 & 0.841532173309467 & 0.316935653381065 & 0.158467826690533 \tabularnewline
11 & 0.788945935933636 & 0.422108128132729 & 0.211054064066364 \tabularnewline
12 & 0.750692216842163 & 0.498615566315675 & 0.249307783157837 \tabularnewline
13 & 0.6682995716161 & 0.663400856767801 & 0.3317004283839 \tabularnewline
14 & 0.85978659445891 & 0.280426811082179 & 0.14021340554109 \tabularnewline
15 & 0.824334332088893 & 0.351331335822214 & 0.175665667911107 \tabularnewline
16 & 0.765964472082097 & 0.468071055835806 & 0.234035527917903 \tabularnewline
17 & 0.701570081665397 & 0.596859836669207 & 0.298429918334603 \tabularnewline
18 & 0.653859972387731 & 0.692280055224538 & 0.346140027612269 \tabularnewline
19 & 0.608404014096148 & 0.783191971807704 & 0.391595985903852 \tabularnewline
20 & 0.533619618532312 & 0.932760762935375 & 0.466380381467688 \tabularnewline
21 & 0.459488026407476 & 0.918976052814952 & 0.540511973592524 \tabularnewline
22 & 0.959447842654085 & 0.0811043146918299 & 0.0405521573459149 \tabularnewline
23 & 0.948079155165011 & 0.103841689669978 & 0.0519208448349888 \tabularnewline
24 & 0.938335859178154 & 0.123328281643692 & 0.0616641408218459 \tabularnewline
25 & 0.928420544950292 & 0.143158910099416 & 0.071579455049708 \tabularnewline
26 & 0.926328914305019 & 0.147342171389963 & 0.0736710856949813 \tabularnewline
27 & 0.905521630875898 & 0.188956738248203 & 0.0944783691241017 \tabularnewline
28 & 0.880767849746706 & 0.238464300506587 & 0.119232150253294 \tabularnewline
29 & 0.85705279323353 & 0.28589441353294 & 0.14294720676647 \tabularnewline
30 & 0.876147927228508 & 0.247704145542984 & 0.123852072771492 \tabularnewline
31 & 0.843876041686028 & 0.312247916627945 & 0.156123958313972 \tabularnewline
32 & 0.81929733402673 & 0.361405331946539 & 0.18070266597327 \tabularnewline
33 & 0.803749743258396 & 0.392500513483208 & 0.196250256741604 \tabularnewline
34 & 0.778985228471544 & 0.442029543056912 & 0.221014771528456 \tabularnewline
35 & 0.837169468831078 & 0.325661062337845 & 0.162830531168922 \tabularnewline
36 & 0.863716735956364 & 0.272566528087273 & 0.136283264043636 \tabularnewline
37 & 0.913881832806553 & 0.172236334386895 & 0.0861181671934473 \tabularnewline
38 & 0.890689615376556 & 0.218620769246889 & 0.109310384623444 \tabularnewline
39 & 0.872867168122474 & 0.254265663755052 & 0.127132831877526 \tabularnewline
40 & 0.84616429029043 & 0.307671419419141 & 0.15383570970957 \tabularnewline
41 & 0.906090254111872 & 0.187819491776257 & 0.0939097458881283 \tabularnewline
42 & 0.883391271117238 & 0.233217457765523 & 0.116608728882761 \tabularnewline
43 & 0.856603048567652 & 0.286793902864697 & 0.143396951432348 \tabularnewline
44 & 0.8332065032706 & 0.3335869934588 & 0.1667934967294 \tabularnewline
45 & 0.828870626000747 & 0.342258747998507 & 0.171129373999253 \tabularnewline
46 & 0.91725592733945 & 0.165488145321101 & 0.0827440726605503 \tabularnewline
47 & 0.917881370614913 & 0.164237258770174 & 0.0821186293850868 \tabularnewline
48 & 0.898017744214409 & 0.203964511571182 & 0.101982255785591 \tabularnewline
49 & 0.882680986682345 & 0.23463802663531 & 0.117319013317655 \tabularnewline
50 & 0.859614342185659 & 0.280771315628682 & 0.140385657814341 \tabularnewline
51 & 0.831806447047868 & 0.336387105904264 & 0.168193552952132 \tabularnewline
52 & 0.800450405739428 & 0.399099188521144 & 0.199549594260572 \tabularnewline
53 & 0.886226854876339 & 0.227546290247321 & 0.113773145123661 \tabularnewline
54 & 0.864913564655399 & 0.270172870689202 & 0.135086435344601 \tabularnewline
55 & 0.837764623791988 & 0.324470752416025 & 0.162235376208012 \tabularnewline
56 & 0.851888931663496 & 0.296222136673009 & 0.148111068336504 \tabularnewline
57 & 0.846084025568021 & 0.307831948863959 & 0.15391597443198 \tabularnewline
58 & 0.843318115159082 & 0.313363769681835 & 0.156681884840918 \tabularnewline
59 & 0.826879445111875 & 0.34624110977625 & 0.173120554888125 \tabularnewline
60 & 0.795581777898326 & 0.408836444203348 & 0.204418222101674 \tabularnewline
61 & 0.771203605196692 & 0.457592789606617 & 0.228796394803308 \tabularnewline
62 & 0.748085605031371 & 0.503828789937257 & 0.251914394968629 \tabularnewline
63 & 0.713494889275493 & 0.573010221449013 & 0.286505110724507 \tabularnewline
64 & 0.67407338768561 & 0.65185322462878 & 0.32592661231439 \tabularnewline
65 & 0.632879635155726 & 0.734240729688547 & 0.367120364844274 \tabularnewline
66 & 0.59988461841328 & 0.80023076317344 & 0.40011538158672 \tabularnewline
67 & 0.6870011846326 & 0.625997630734799 & 0.3129988153674 \tabularnewline
68 & 0.709014528567363 & 0.581970942865275 & 0.290985471432637 \tabularnewline
69 & 0.669045106235875 & 0.661909787528251 & 0.330954893764125 \tabularnewline
70 & 0.641207079425541 & 0.717585841148917 & 0.358792920574459 \tabularnewline
71 & 0.600462809726933 & 0.799074380546133 & 0.399537190273067 \tabularnewline
72 & 0.567823163282829 & 0.864353673434343 & 0.432176836717171 \tabularnewline
73 & 0.527571957401795 & 0.944856085196411 & 0.472428042598205 \tabularnewline
74 & 0.484887023208049 & 0.969774046416098 & 0.515112976791951 \tabularnewline
75 & 0.45906318759695 & 0.918126375193899 & 0.54093681240305 \tabularnewline
76 & 0.462346727434926 & 0.924693454869852 & 0.537653272565074 \tabularnewline
77 & 0.485566060164618 & 0.971132120329237 & 0.514433939835382 \tabularnewline
78 & 0.816981237075394 & 0.366037525849211 & 0.183018762924606 \tabularnewline
79 & 0.800483816418502 & 0.399032367162995 & 0.199516183581498 \tabularnewline
80 & 0.770550439237102 & 0.458899121525796 & 0.229449560762898 \tabularnewline
81 & 0.734798532427829 & 0.530402935144343 & 0.265201467572171 \tabularnewline
82 & 0.868423211168818 & 0.263153577662363 & 0.131576788831182 \tabularnewline
83 & 0.842966821850612 & 0.314066356298777 & 0.157033178149388 \tabularnewline
84 & 0.821090315640787 & 0.357819368718426 & 0.178909684359213 \tabularnewline
85 & 0.791361267596632 & 0.417277464806736 & 0.208638732403368 \tabularnewline
86 & 0.758369402279535 & 0.483261195440931 & 0.241630597720465 \tabularnewline
87 & 0.746830759092561 & 0.506338481814878 & 0.253169240907439 \tabularnewline
88 & 0.723855607588568 & 0.552288784822864 & 0.276144392411432 \tabularnewline
89 & 0.691753125828382 & 0.616493748343235 & 0.308246874171618 \tabularnewline
90 & 0.766364688513956 & 0.467270622972088 & 0.233635311486044 \tabularnewline
91 & 0.731026713150454 & 0.537946573699092 & 0.268973286849546 \tabularnewline
92 & 0.6917965096668 & 0.616406980666401 & 0.3082034903332 \tabularnewline
93 & 0.650476250823536 & 0.699047498352929 & 0.349523749176464 \tabularnewline
94 & 0.636988546767977 & 0.726022906464046 & 0.363011453232023 \tabularnewline
95 & 0.631180329386239 & 0.737639341227522 & 0.368819670613761 \tabularnewline
96 & 0.58867469242639 & 0.822650615147219 & 0.41132530757361 \tabularnewline
97 & 0.543312438078865 & 0.913375123842271 & 0.456687561921135 \tabularnewline
98 & 0.502148574354804 & 0.995702851290392 & 0.497851425645196 \tabularnewline
99 & 0.486261448452226 & 0.972522896904453 & 0.513738551547774 \tabularnewline
100 & 0.555698592448254 & 0.888602815103493 & 0.444301407551746 \tabularnewline
101 & 0.537754974075097 & 0.924490051849807 & 0.462245025924903 \tabularnewline
102 & 0.507323357404792 & 0.985353285190417 & 0.492676642595208 \tabularnewline
103 & 0.579625250661842 & 0.840749498676316 & 0.420374749338158 \tabularnewline
104 & 0.589377223570303 & 0.821245552859395 & 0.410622776429697 \tabularnewline
105 & 0.565192217396463 & 0.869615565207073 & 0.434807782603537 \tabularnewline
106 & 0.576878054417035 & 0.84624389116593 & 0.423121945582965 \tabularnewline
107 & 0.552278790223398 & 0.895442419553204 & 0.447721209776602 \tabularnewline
108 & 0.526035814868834 & 0.947928370262331 & 0.473964185131166 \tabularnewline
109 & 0.74197895463052 & 0.516042090738961 & 0.25802104536948 \tabularnewline
110 & 0.771242383001956 & 0.457515233996088 & 0.228757616998044 \tabularnewline
111 & 0.748476444697027 & 0.503047110605947 & 0.251523555302973 \tabularnewline
112 & 0.708574681897248 & 0.582850636205504 & 0.291425318102752 \tabularnewline
113 & 0.667657644305916 & 0.664684711388168 & 0.332342355694084 \tabularnewline
114 & 0.626491425179047 & 0.747017149641905 & 0.373508574820953 \tabularnewline
115 & 0.578340722614282 & 0.843318554771436 & 0.421659277385718 \tabularnewline
116 & 0.541594160440342 & 0.916811679119316 & 0.458405839559658 \tabularnewline
117 & 0.809658576536929 & 0.380682846926142 & 0.190341423463071 \tabularnewline
118 & 0.773708333795413 & 0.452583332409173 & 0.226291666204587 \tabularnewline
119 & 0.786368855571903 & 0.427262288856194 & 0.213631144428097 \tabularnewline
120 & 0.751004327802036 & 0.497991344395929 & 0.248995672197964 \tabularnewline
121 & 0.727915213628223 & 0.544169572743553 & 0.272084786371777 \tabularnewline
122 & 0.705356299152588 & 0.589287401694824 & 0.294643700847412 \tabularnewline
123 & 0.770930712886286 & 0.458138574227427 & 0.229069287113714 \tabularnewline
124 & 0.774941243790638 & 0.450117512418725 & 0.225058756209362 \tabularnewline
125 & 0.739695115812445 & 0.52060976837511 & 0.260304884187555 \tabularnewline
126 & 0.718496331039938 & 0.563007337920124 & 0.281503668960062 \tabularnewline
127 & 0.68787186533133 & 0.624256269337341 & 0.31212813466867 \tabularnewline
128 & 0.67094114406518 & 0.658117711869641 & 0.32905885593482 \tabularnewline
129 & 0.794111882159573 & 0.411776235680853 & 0.205888117840427 \tabularnewline
130 & 0.752041770803752 & 0.495916458392497 & 0.247958229196248 \tabularnewline
131 & 0.703865572630235 & 0.592268854739529 & 0.296134427369765 \tabularnewline
132 & 0.685062927873191 & 0.629874144253619 & 0.314937072126809 \tabularnewline
133 & 0.644318099357088 & 0.711363801285824 & 0.355681900642912 \tabularnewline
134 & 0.983519796168776 & 0.0329604076624476 & 0.0164802038312238 \tabularnewline
135 & 0.977426922615454 & 0.0451461547690928 & 0.0225730773845464 \tabularnewline
136 & 0.971210442417419 & 0.0575791151651626 & 0.0287895575825813 \tabularnewline
137 & 0.971173569992637 & 0.0576528600147259 & 0.028826430007363 \tabularnewline
138 & 0.968861901563875 & 0.0622761968722495 & 0.0311380984361248 \tabularnewline
139 & 0.988447582117837 & 0.0231048357643259 & 0.0115524178821629 \tabularnewline
140 & 0.981514585907358 & 0.0369708281852836 & 0.0184854140926418 \tabularnewline
141 & 0.999999951862804 & 9.62743925794442e-08 & 4.81371962897221e-08 \tabularnewline
142 & 0.99999994230381 & 1.15392379214597e-07 & 5.76961896072986e-08 \tabularnewline
143 & 0.999999806705432 & 3.86589135712995e-07 & 1.93294567856497e-07 \tabularnewline
144 & 0.999999374845176 & 1.25030964758685e-06 & 6.25154823793426e-07 \tabularnewline
145 & 0.999999264699861 & 1.47060027886255e-06 & 7.35300139431275e-07 \tabularnewline
146 & 0.999999999810212 & 3.79575812322054e-10 & 1.89787906161027e-10 \tabularnewline
147 & 0.999999998908669 & 2.1826611102213e-09 & 1.09133055511065e-09 \tabularnewline
148 & 0.999999998226016 & 3.54796820221849e-09 & 1.77398410110924e-09 \tabularnewline
149 & 0.999999986996974 & 2.60060515242326e-08 & 1.30030257621163e-08 \tabularnewline
150 & 0.999999949915677 & 1.0016864700133e-07 & 5.00843235006651e-08 \tabularnewline
151 & 0.999999614904606 & 7.70190787247421e-07 & 3.8509539362371e-07 \tabularnewline
152 & 0.999997331047972 & 5.33790405516175e-06 & 2.66895202758087e-06 \tabularnewline
153 & 0.999981984464935 & 3.60310701309463e-05 & 1.80155350654732e-05 \tabularnewline
154 & 0.999886205662745 & 0.00022758867451098 & 0.00011379433725549 \tabularnewline
155 & 0.999963709925333 & 7.25801493348042e-05 & 3.62900746674021e-05 \tabularnewline
156 & 0.999642552551745 & 0.000714894896510025 & 0.000357447448255013 \tabularnewline
157 & 0.997141703905035 & 0.00571659218993006 & 0.00285829609496503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&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]0.800761753447256[/C][C]0.398476493105487[/C][C]0.199238246552744[/C][/ROW]
[ROW][C]8[/C][C]0.787223839498735[/C][C]0.42555232100253[/C][C]0.212776160501265[/C][/ROW]
[ROW][C]9[/C][C]0.728222183541206[/C][C]0.543555632917587[/C][C]0.271777816458794[/C][/ROW]
[ROW][C]10[/C][C]0.841532173309467[/C][C]0.316935653381065[/C][C]0.158467826690533[/C][/ROW]
[ROW][C]11[/C][C]0.788945935933636[/C][C]0.422108128132729[/C][C]0.211054064066364[/C][/ROW]
[ROW][C]12[/C][C]0.750692216842163[/C][C]0.498615566315675[/C][C]0.249307783157837[/C][/ROW]
[ROW][C]13[/C][C]0.6682995716161[/C][C]0.663400856767801[/C][C]0.3317004283839[/C][/ROW]
[ROW][C]14[/C][C]0.85978659445891[/C][C]0.280426811082179[/C][C]0.14021340554109[/C][/ROW]
[ROW][C]15[/C][C]0.824334332088893[/C][C]0.351331335822214[/C][C]0.175665667911107[/C][/ROW]
[ROW][C]16[/C][C]0.765964472082097[/C][C]0.468071055835806[/C][C]0.234035527917903[/C][/ROW]
[ROW][C]17[/C][C]0.701570081665397[/C][C]0.596859836669207[/C][C]0.298429918334603[/C][/ROW]
[ROW][C]18[/C][C]0.653859972387731[/C][C]0.692280055224538[/C][C]0.346140027612269[/C][/ROW]
[ROW][C]19[/C][C]0.608404014096148[/C][C]0.783191971807704[/C][C]0.391595985903852[/C][/ROW]
[ROW][C]20[/C][C]0.533619618532312[/C][C]0.932760762935375[/C][C]0.466380381467688[/C][/ROW]
[ROW][C]21[/C][C]0.459488026407476[/C][C]0.918976052814952[/C][C]0.540511973592524[/C][/ROW]
[ROW][C]22[/C][C]0.959447842654085[/C][C]0.0811043146918299[/C][C]0.0405521573459149[/C][/ROW]
[ROW][C]23[/C][C]0.948079155165011[/C][C]0.103841689669978[/C][C]0.0519208448349888[/C][/ROW]
[ROW][C]24[/C][C]0.938335859178154[/C][C]0.123328281643692[/C][C]0.0616641408218459[/C][/ROW]
[ROW][C]25[/C][C]0.928420544950292[/C][C]0.143158910099416[/C][C]0.071579455049708[/C][/ROW]
[ROW][C]26[/C][C]0.926328914305019[/C][C]0.147342171389963[/C][C]0.0736710856949813[/C][/ROW]
[ROW][C]27[/C][C]0.905521630875898[/C][C]0.188956738248203[/C][C]0.0944783691241017[/C][/ROW]
[ROW][C]28[/C][C]0.880767849746706[/C][C]0.238464300506587[/C][C]0.119232150253294[/C][/ROW]
[ROW][C]29[/C][C]0.85705279323353[/C][C]0.28589441353294[/C][C]0.14294720676647[/C][/ROW]
[ROW][C]30[/C][C]0.876147927228508[/C][C]0.247704145542984[/C][C]0.123852072771492[/C][/ROW]
[ROW][C]31[/C][C]0.843876041686028[/C][C]0.312247916627945[/C][C]0.156123958313972[/C][/ROW]
[ROW][C]32[/C][C]0.81929733402673[/C][C]0.361405331946539[/C][C]0.18070266597327[/C][/ROW]
[ROW][C]33[/C][C]0.803749743258396[/C][C]0.392500513483208[/C][C]0.196250256741604[/C][/ROW]
[ROW][C]34[/C][C]0.778985228471544[/C][C]0.442029543056912[/C][C]0.221014771528456[/C][/ROW]
[ROW][C]35[/C][C]0.837169468831078[/C][C]0.325661062337845[/C][C]0.162830531168922[/C][/ROW]
[ROW][C]36[/C][C]0.863716735956364[/C][C]0.272566528087273[/C][C]0.136283264043636[/C][/ROW]
[ROW][C]37[/C][C]0.913881832806553[/C][C]0.172236334386895[/C][C]0.0861181671934473[/C][/ROW]
[ROW][C]38[/C][C]0.890689615376556[/C][C]0.218620769246889[/C][C]0.109310384623444[/C][/ROW]
[ROW][C]39[/C][C]0.872867168122474[/C][C]0.254265663755052[/C][C]0.127132831877526[/C][/ROW]
[ROW][C]40[/C][C]0.84616429029043[/C][C]0.307671419419141[/C][C]0.15383570970957[/C][/ROW]
[ROW][C]41[/C][C]0.906090254111872[/C][C]0.187819491776257[/C][C]0.0939097458881283[/C][/ROW]
[ROW][C]42[/C][C]0.883391271117238[/C][C]0.233217457765523[/C][C]0.116608728882761[/C][/ROW]
[ROW][C]43[/C][C]0.856603048567652[/C][C]0.286793902864697[/C][C]0.143396951432348[/C][/ROW]
[ROW][C]44[/C][C]0.8332065032706[/C][C]0.3335869934588[/C][C]0.1667934967294[/C][/ROW]
[ROW][C]45[/C][C]0.828870626000747[/C][C]0.342258747998507[/C][C]0.171129373999253[/C][/ROW]
[ROW][C]46[/C][C]0.91725592733945[/C][C]0.165488145321101[/C][C]0.0827440726605503[/C][/ROW]
[ROW][C]47[/C][C]0.917881370614913[/C][C]0.164237258770174[/C][C]0.0821186293850868[/C][/ROW]
[ROW][C]48[/C][C]0.898017744214409[/C][C]0.203964511571182[/C][C]0.101982255785591[/C][/ROW]
[ROW][C]49[/C][C]0.882680986682345[/C][C]0.23463802663531[/C][C]0.117319013317655[/C][/ROW]
[ROW][C]50[/C][C]0.859614342185659[/C][C]0.280771315628682[/C][C]0.140385657814341[/C][/ROW]
[ROW][C]51[/C][C]0.831806447047868[/C][C]0.336387105904264[/C][C]0.168193552952132[/C][/ROW]
[ROW][C]52[/C][C]0.800450405739428[/C][C]0.399099188521144[/C][C]0.199549594260572[/C][/ROW]
[ROW][C]53[/C][C]0.886226854876339[/C][C]0.227546290247321[/C][C]0.113773145123661[/C][/ROW]
[ROW][C]54[/C][C]0.864913564655399[/C][C]0.270172870689202[/C][C]0.135086435344601[/C][/ROW]
[ROW][C]55[/C][C]0.837764623791988[/C][C]0.324470752416025[/C][C]0.162235376208012[/C][/ROW]
[ROW][C]56[/C][C]0.851888931663496[/C][C]0.296222136673009[/C][C]0.148111068336504[/C][/ROW]
[ROW][C]57[/C][C]0.846084025568021[/C][C]0.307831948863959[/C][C]0.15391597443198[/C][/ROW]
[ROW][C]58[/C][C]0.843318115159082[/C][C]0.313363769681835[/C][C]0.156681884840918[/C][/ROW]
[ROW][C]59[/C][C]0.826879445111875[/C][C]0.34624110977625[/C][C]0.173120554888125[/C][/ROW]
[ROW][C]60[/C][C]0.795581777898326[/C][C]0.408836444203348[/C][C]0.204418222101674[/C][/ROW]
[ROW][C]61[/C][C]0.771203605196692[/C][C]0.457592789606617[/C][C]0.228796394803308[/C][/ROW]
[ROW][C]62[/C][C]0.748085605031371[/C][C]0.503828789937257[/C][C]0.251914394968629[/C][/ROW]
[ROW][C]63[/C][C]0.713494889275493[/C][C]0.573010221449013[/C][C]0.286505110724507[/C][/ROW]
[ROW][C]64[/C][C]0.67407338768561[/C][C]0.65185322462878[/C][C]0.32592661231439[/C][/ROW]
[ROW][C]65[/C][C]0.632879635155726[/C][C]0.734240729688547[/C][C]0.367120364844274[/C][/ROW]
[ROW][C]66[/C][C]0.59988461841328[/C][C]0.80023076317344[/C][C]0.40011538158672[/C][/ROW]
[ROW][C]67[/C][C]0.6870011846326[/C][C]0.625997630734799[/C][C]0.3129988153674[/C][/ROW]
[ROW][C]68[/C][C]0.709014528567363[/C][C]0.581970942865275[/C][C]0.290985471432637[/C][/ROW]
[ROW][C]69[/C][C]0.669045106235875[/C][C]0.661909787528251[/C][C]0.330954893764125[/C][/ROW]
[ROW][C]70[/C][C]0.641207079425541[/C][C]0.717585841148917[/C][C]0.358792920574459[/C][/ROW]
[ROW][C]71[/C][C]0.600462809726933[/C][C]0.799074380546133[/C][C]0.399537190273067[/C][/ROW]
[ROW][C]72[/C][C]0.567823163282829[/C][C]0.864353673434343[/C][C]0.432176836717171[/C][/ROW]
[ROW][C]73[/C][C]0.527571957401795[/C][C]0.944856085196411[/C][C]0.472428042598205[/C][/ROW]
[ROW][C]74[/C][C]0.484887023208049[/C][C]0.969774046416098[/C][C]0.515112976791951[/C][/ROW]
[ROW][C]75[/C][C]0.45906318759695[/C][C]0.918126375193899[/C][C]0.54093681240305[/C][/ROW]
[ROW][C]76[/C][C]0.462346727434926[/C][C]0.924693454869852[/C][C]0.537653272565074[/C][/ROW]
[ROW][C]77[/C][C]0.485566060164618[/C][C]0.971132120329237[/C][C]0.514433939835382[/C][/ROW]
[ROW][C]78[/C][C]0.816981237075394[/C][C]0.366037525849211[/C][C]0.183018762924606[/C][/ROW]
[ROW][C]79[/C][C]0.800483816418502[/C][C]0.399032367162995[/C][C]0.199516183581498[/C][/ROW]
[ROW][C]80[/C][C]0.770550439237102[/C][C]0.458899121525796[/C][C]0.229449560762898[/C][/ROW]
[ROW][C]81[/C][C]0.734798532427829[/C][C]0.530402935144343[/C][C]0.265201467572171[/C][/ROW]
[ROW][C]82[/C][C]0.868423211168818[/C][C]0.263153577662363[/C][C]0.131576788831182[/C][/ROW]
[ROW][C]83[/C][C]0.842966821850612[/C][C]0.314066356298777[/C][C]0.157033178149388[/C][/ROW]
[ROW][C]84[/C][C]0.821090315640787[/C][C]0.357819368718426[/C][C]0.178909684359213[/C][/ROW]
[ROW][C]85[/C][C]0.791361267596632[/C][C]0.417277464806736[/C][C]0.208638732403368[/C][/ROW]
[ROW][C]86[/C][C]0.758369402279535[/C][C]0.483261195440931[/C][C]0.241630597720465[/C][/ROW]
[ROW][C]87[/C][C]0.746830759092561[/C][C]0.506338481814878[/C][C]0.253169240907439[/C][/ROW]
[ROW][C]88[/C][C]0.723855607588568[/C][C]0.552288784822864[/C][C]0.276144392411432[/C][/ROW]
[ROW][C]89[/C][C]0.691753125828382[/C][C]0.616493748343235[/C][C]0.308246874171618[/C][/ROW]
[ROW][C]90[/C][C]0.766364688513956[/C][C]0.467270622972088[/C][C]0.233635311486044[/C][/ROW]
[ROW][C]91[/C][C]0.731026713150454[/C][C]0.537946573699092[/C][C]0.268973286849546[/C][/ROW]
[ROW][C]92[/C][C]0.6917965096668[/C][C]0.616406980666401[/C][C]0.3082034903332[/C][/ROW]
[ROW][C]93[/C][C]0.650476250823536[/C][C]0.699047498352929[/C][C]0.349523749176464[/C][/ROW]
[ROW][C]94[/C][C]0.636988546767977[/C][C]0.726022906464046[/C][C]0.363011453232023[/C][/ROW]
[ROW][C]95[/C][C]0.631180329386239[/C][C]0.737639341227522[/C][C]0.368819670613761[/C][/ROW]
[ROW][C]96[/C][C]0.58867469242639[/C][C]0.822650615147219[/C][C]0.41132530757361[/C][/ROW]
[ROW][C]97[/C][C]0.543312438078865[/C][C]0.913375123842271[/C][C]0.456687561921135[/C][/ROW]
[ROW][C]98[/C][C]0.502148574354804[/C][C]0.995702851290392[/C][C]0.497851425645196[/C][/ROW]
[ROW][C]99[/C][C]0.486261448452226[/C][C]0.972522896904453[/C][C]0.513738551547774[/C][/ROW]
[ROW][C]100[/C][C]0.555698592448254[/C][C]0.888602815103493[/C][C]0.444301407551746[/C][/ROW]
[ROW][C]101[/C][C]0.537754974075097[/C][C]0.924490051849807[/C][C]0.462245025924903[/C][/ROW]
[ROW][C]102[/C][C]0.507323357404792[/C][C]0.985353285190417[/C][C]0.492676642595208[/C][/ROW]
[ROW][C]103[/C][C]0.579625250661842[/C][C]0.840749498676316[/C][C]0.420374749338158[/C][/ROW]
[ROW][C]104[/C][C]0.589377223570303[/C][C]0.821245552859395[/C][C]0.410622776429697[/C][/ROW]
[ROW][C]105[/C][C]0.565192217396463[/C][C]0.869615565207073[/C][C]0.434807782603537[/C][/ROW]
[ROW][C]106[/C][C]0.576878054417035[/C][C]0.84624389116593[/C][C]0.423121945582965[/C][/ROW]
[ROW][C]107[/C][C]0.552278790223398[/C][C]0.895442419553204[/C][C]0.447721209776602[/C][/ROW]
[ROW][C]108[/C][C]0.526035814868834[/C][C]0.947928370262331[/C][C]0.473964185131166[/C][/ROW]
[ROW][C]109[/C][C]0.74197895463052[/C][C]0.516042090738961[/C][C]0.25802104536948[/C][/ROW]
[ROW][C]110[/C][C]0.771242383001956[/C][C]0.457515233996088[/C][C]0.228757616998044[/C][/ROW]
[ROW][C]111[/C][C]0.748476444697027[/C][C]0.503047110605947[/C][C]0.251523555302973[/C][/ROW]
[ROW][C]112[/C][C]0.708574681897248[/C][C]0.582850636205504[/C][C]0.291425318102752[/C][/ROW]
[ROW][C]113[/C][C]0.667657644305916[/C][C]0.664684711388168[/C][C]0.332342355694084[/C][/ROW]
[ROW][C]114[/C][C]0.626491425179047[/C][C]0.747017149641905[/C][C]0.373508574820953[/C][/ROW]
[ROW][C]115[/C][C]0.578340722614282[/C][C]0.843318554771436[/C][C]0.421659277385718[/C][/ROW]
[ROW][C]116[/C][C]0.541594160440342[/C][C]0.916811679119316[/C][C]0.458405839559658[/C][/ROW]
[ROW][C]117[/C][C]0.809658576536929[/C][C]0.380682846926142[/C][C]0.190341423463071[/C][/ROW]
[ROW][C]118[/C][C]0.773708333795413[/C][C]0.452583332409173[/C][C]0.226291666204587[/C][/ROW]
[ROW][C]119[/C][C]0.786368855571903[/C][C]0.427262288856194[/C][C]0.213631144428097[/C][/ROW]
[ROW][C]120[/C][C]0.751004327802036[/C][C]0.497991344395929[/C][C]0.248995672197964[/C][/ROW]
[ROW][C]121[/C][C]0.727915213628223[/C][C]0.544169572743553[/C][C]0.272084786371777[/C][/ROW]
[ROW][C]122[/C][C]0.705356299152588[/C][C]0.589287401694824[/C][C]0.294643700847412[/C][/ROW]
[ROW][C]123[/C][C]0.770930712886286[/C][C]0.458138574227427[/C][C]0.229069287113714[/C][/ROW]
[ROW][C]124[/C][C]0.774941243790638[/C][C]0.450117512418725[/C][C]0.225058756209362[/C][/ROW]
[ROW][C]125[/C][C]0.739695115812445[/C][C]0.52060976837511[/C][C]0.260304884187555[/C][/ROW]
[ROW][C]126[/C][C]0.718496331039938[/C][C]0.563007337920124[/C][C]0.281503668960062[/C][/ROW]
[ROW][C]127[/C][C]0.68787186533133[/C][C]0.624256269337341[/C][C]0.31212813466867[/C][/ROW]
[ROW][C]128[/C][C]0.67094114406518[/C][C]0.658117711869641[/C][C]0.32905885593482[/C][/ROW]
[ROW][C]129[/C][C]0.794111882159573[/C][C]0.411776235680853[/C][C]0.205888117840427[/C][/ROW]
[ROW][C]130[/C][C]0.752041770803752[/C][C]0.495916458392497[/C][C]0.247958229196248[/C][/ROW]
[ROW][C]131[/C][C]0.703865572630235[/C][C]0.592268854739529[/C][C]0.296134427369765[/C][/ROW]
[ROW][C]132[/C][C]0.685062927873191[/C][C]0.629874144253619[/C][C]0.314937072126809[/C][/ROW]
[ROW][C]133[/C][C]0.644318099357088[/C][C]0.711363801285824[/C][C]0.355681900642912[/C][/ROW]
[ROW][C]134[/C][C]0.983519796168776[/C][C]0.0329604076624476[/C][C]0.0164802038312238[/C][/ROW]
[ROW][C]135[/C][C]0.977426922615454[/C][C]0.0451461547690928[/C][C]0.0225730773845464[/C][/ROW]
[ROW][C]136[/C][C]0.971210442417419[/C][C]0.0575791151651626[/C][C]0.0287895575825813[/C][/ROW]
[ROW][C]137[/C][C]0.971173569992637[/C][C]0.0576528600147259[/C][C]0.028826430007363[/C][/ROW]
[ROW][C]138[/C][C]0.968861901563875[/C][C]0.0622761968722495[/C][C]0.0311380984361248[/C][/ROW]
[ROW][C]139[/C][C]0.988447582117837[/C][C]0.0231048357643259[/C][C]0.0115524178821629[/C][/ROW]
[ROW][C]140[/C][C]0.981514585907358[/C][C]0.0369708281852836[/C][C]0.0184854140926418[/C][/ROW]
[ROW][C]141[/C][C]0.999999951862804[/C][C]9.62743925794442e-08[/C][C]4.81371962897221e-08[/C][/ROW]
[ROW][C]142[/C][C]0.99999994230381[/C][C]1.15392379214597e-07[/C][C]5.76961896072986e-08[/C][/ROW]
[ROW][C]143[/C][C]0.999999806705432[/C][C]3.86589135712995e-07[/C][C]1.93294567856497e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999374845176[/C][C]1.25030964758685e-06[/C][C]6.25154823793426e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999999264699861[/C][C]1.47060027886255e-06[/C][C]7.35300139431275e-07[/C][/ROW]
[ROW][C]146[/C][C]0.999999999810212[/C][C]3.79575812322054e-10[/C][C]1.89787906161027e-10[/C][/ROW]
[ROW][C]147[/C][C]0.999999998908669[/C][C]2.1826611102213e-09[/C][C]1.09133055511065e-09[/C][/ROW]
[ROW][C]148[/C][C]0.999999998226016[/C][C]3.54796820221849e-09[/C][C]1.77398410110924e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999986996974[/C][C]2.60060515242326e-08[/C][C]1.30030257621163e-08[/C][/ROW]
[ROW][C]150[/C][C]0.999999949915677[/C][C]1.0016864700133e-07[/C][C]5.00843235006651e-08[/C][/ROW]
[ROW][C]151[/C][C]0.999999614904606[/C][C]7.70190787247421e-07[/C][C]3.8509539362371e-07[/C][/ROW]
[ROW][C]152[/C][C]0.999997331047972[/C][C]5.33790405516175e-06[/C][C]2.66895202758087e-06[/C][/ROW]
[ROW][C]153[/C][C]0.999981984464935[/C][C]3.60310701309463e-05[/C][C]1.80155350654732e-05[/C][/ROW]
[ROW][C]154[/C][C]0.999886205662745[/C][C]0.00022758867451098[/C][C]0.00011379433725549[/C][/ROW]
[ROW][C]155[/C][C]0.999963709925333[/C][C]7.25801493348042e-05[/C][C]3.62900746674021e-05[/C][/ROW]
[ROW][C]156[/C][C]0.999642552551745[/C][C]0.000714894896510025[/C][C]0.000357447448255013[/C][/ROW]
[ROW][C]157[/C][C]0.997141703905035[/C][C]0.00571659218993006[/C][C]0.00285829609496503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.8007617534472560.3984764931054870.199238246552744
80.7872238394987350.425552321002530.212776160501265
90.7282221835412060.5435556329175870.271777816458794
100.8415321733094670.3169356533810650.158467826690533
110.7889459359336360.4221081281327290.211054064066364
120.7506922168421630.4986155663156750.249307783157837
130.66829957161610.6634008567678010.3317004283839
140.859786594458910.2804268110821790.14021340554109
150.8243343320888930.3513313358222140.175665667911107
160.7659644720820970.4680710558358060.234035527917903
170.7015700816653970.5968598366692070.298429918334603
180.6538599723877310.6922800552245380.346140027612269
190.6084040140961480.7831919718077040.391595985903852
200.5336196185323120.9327607629353750.466380381467688
210.4594880264074760.9189760528149520.540511973592524
220.9594478426540850.08110431469182990.0405521573459149
230.9480791551650110.1038416896699780.0519208448349888
240.9383358591781540.1233282816436920.0616641408218459
250.9284205449502920.1431589100994160.071579455049708
260.9263289143050190.1473421713899630.0736710856949813
270.9055216308758980.1889567382482030.0944783691241017
280.8807678497467060.2384643005065870.119232150253294
290.857052793233530.285894413532940.14294720676647
300.8761479272285080.2477041455429840.123852072771492
310.8438760416860280.3122479166279450.156123958313972
320.819297334026730.3614053319465390.18070266597327
330.8037497432583960.3925005134832080.196250256741604
340.7789852284715440.4420295430569120.221014771528456
350.8371694688310780.3256610623378450.162830531168922
360.8637167359563640.2725665280872730.136283264043636
370.9138818328065530.1722363343868950.0861181671934473
380.8906896153765560.2186207692468890.109310384623444
390.8728671681224740.2542656637550520.127132831877526
400.846164290290430.3076714194191410.15383570970957
410.9060902541118720.1878194917762570.0939097458881283
420.8833912711172380.2332174577655230.116608728882761
430.8566030485676520.2867939028646970.143396951432348
440.83320650327060.33358699345880.1667934967294
450.8288706260007470.3422587479985070.171129373999253
460.917255927339450.1654881453211010.0827440726605503
470.9178813706149130.1642372587701740.0821186293850868
480.8980177442144090.2039645115711820.101982255785591
490.8826809866823450.234638026635310.117319013317655
500.8596143421856590.2807713156286820.140385657814341
510.8318064470478680.3363871059042640.168193552952132
520.8004504057394280.3990991885211440.199549594260572
530.8862268548763390.2275462902473210.113773145123661
540.8649135646553990.2701728706892020.135086435344601
550.8377646237919880.3244707524160250.162235376208012
560.8518889316634960.2962221366730090.148111068336504
570.8460840255680210.3078319488639590.15391597443198
580.8433181151590820.3133637696818350.156681884840918
590.8268794451118750.346241109776250.173120554888125
600.7955817778983260.4088364442033480.204418222101674
610.7712036051966920.4575927896066170.228796394803308
620.7480856050313710.5038287899372570.251914394968629
630.7134948892754930.5730102214490130.286505110724507
640.674073387685610.651853224628780.32592661231439
650.6328796351557260.7342407296885470.367120364844274
660.599884618413280.800230763173440.40011538158672
670.68700118463260.6259976307347990.3129988153674
680.7090145285673630.5819709428652750.290985471432637
690.6690451062358750.6619097875282510.330954893764125
700.6412070794255410.7175858411489170.358792920574459
710.6004628097269330.7990743805461330.399537190273067
720.5678231632828290.8643536734343430.432176836717171
730.5275719574017950.9448560851964110.472428042598205
740.4848870232080490.9697740464160980.515112976791951
750.459063187596950.9181263751938990.54093681240305
760.4623467274349260.9246934548698520.537653272565074
770.4855660601646180.9711321203292370.514433939835382
780.8169812370753940.3660375258492110.183018762924606
790.8004838164185020.3990323671629950.199516183581498
800.7705504392371020.4588991215257960.229449560762898
810.7347985324278290.5304029351443430.265201467572171
820.8684232111688180.2631535776623630.131576788831182
830.8429668218506120.3140663562987770.157033178149388
840.8210903156407870.3578193687184260.178909684359213
850.7913612675966320.4172774648067360.208638732403368
860.7583694022795350.4832611954409310.241630597720465
870.7468307590925610.5063384818148780.253169240907439
880.7238556075885680.5522887848228640.276144392411432
890.6917531258283820.6164937483432350.308246874171618
900.7663646885139560.4672706229720880.233635311486044
910.7310267131504540.5379465736990920.268973286849546
920.69179650966680.6164069806664010.3082034903332
930.6504762508235360.6990474983529290.349523749176464
940.6369885467679770.7260229064640460.363011453232023
950.6311803293862390.7376393412275220.368819670613761
960.588674692426390.8226506151472190.41132530757361
970.5433124380788650.9133751238422710.456687561921135
980.5021485743548040.9957028512903920.497851425645196
990.4862614484522260.9725228969044530.513738551547774
1000.5556985924482540.8886028151034930.444301407551746
1010.5377549740750970.9244900518498070.462245025924903
1020.5073233574047920.9853532851904170.492676642595208
1030.5796252506618420.8407494986763160.420374749338158
1040.5893772235703030.8212455528593950.410622776429697
1050.5651922173964630.8696155652070730.434807782603537
1060.5768780544170350.846243891165930.423121945582965
1070.5522787902233980.8954424195532040.447721209776602
1080.5260358148688340.9479283702623310.473964185131166
1090.741978954630520.5160420907389610.25802104536948
1100.7712423830019560.4575152339960880.228757616998044
1110.7484764446970270.5030471106059470.251523555302973
1120.7085746818972480.5828506362055040.291425318102752
1130.6676576443059160.6646847113881680.332342355694084
1140.6264914251790470.7470171496419050.373508574820953
1150.5783407226142820.8433185547714360.421659277385718
1160.5415941604403420.9168116791193160.458405839559658
1170.8096585765369290.3806828469261420.190341423463071
1180.7737083337954130.4525833324091730.226291666204587
1190.7863688555719030.4272622888561940.213631144428097
1200.7510043278020360.4979913443959290.248995672197964
1210.7279152136282230.5441695727435530.272084786371777
1220.7053562991525880.5892874016948240.294643700847412
1230.7709307128862860.4581385742274270.229069287113714
1240.7749412437906380.4501175124187250.225058756209362
1250.7396951158124450.520609768375110.260304884187555
1260.7184963310399380.5630073379201240.281503668960062
1270.687871865331330.6242562693373410.31212813466867
1280.670941144065180.6581177118696410.32905885593482
1290.7941118821595730.4117762356808530.205888117840427
1300.7520417708037520.4959164583924970.247958229196248
1310.7038655726302350.5922688547395290.296134427369765
1320.6850629278731910.6298741442536190.314937072126809
1330.6443180993570880.7113638012858240.355681900642912
1340.9835197961687760.03296040766244760.0164802038312238
1350.9774269226154540.04514615476909280.0225730773845464
1360.9712104424174190.05757911516516260.0287895575825813
1370.9711735699926370.05765286001472590.028826430007363
1380.9688619015638750.06227619687224950.0311380984361248
1390.9884475821178370.02310483576432590.0115524178821629
1400.9815145859073580.03697082818528360.0184854140926418
1410.9999999518628049.62743925794442e-084.81371962897221e-08
1420.999999942303811.15392379214597e-075.76961896072986e-08
1430.9999998067054323.86589135712995e-071.93294567856497e-07
1440.9999993748451761.25030964758685e-066.25154823793426e-07
1450.9999992646998611.47060027886255e-067.35300139431275e-07
1460.9999999998102123.79575812322054e-101.89787906161027e-10
1470.9999999989086692.1826611102213e-091.09133055511065e-09
1480.9999999982260163.54796820221849e-091.77398410110924e-09
1490.9999999869969742.60060515242326e-081.30030257621163e-08
1500.9999999499156771.0016864700133e-075.00843235006651e-08
1510.9999996149046067.70190787247421e-073.8509539362371e-07
1520.9999973310479725.33790405516175e-062.66895202758087e-06
1530.9999819844649353.60310701309463e-051.80155350654732e-05
1540.9998862056627450.000227588674510980.00011379433725549
1550.9999637099253337.25801493348042e-053.62900746674021e-05
1560.9996425525517450.0007148948965100250.000357447448255013
1570.9971417039050350.005716592189930060.00285829609496503






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.112582781456954NOK
5% type I error level210.139072847682119NOK
10% type I error level250.165562913907285NOK

\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 & 17 & 0.112582781456954 & NOK \tabularnewline
5% type I error level & 21 & 0.139072847682119 & NOK \tabularnewline
10% type I error level & 25 & 0.165562913907285 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145971&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]17[/C][C]0.112582781456954[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.139072847682119[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.165562913907285[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145971&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145971&T=6

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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 level170.112582781456954NOK
5% type I error level210.139072847682119NOK
10% type I error level250.165562913907285NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}