Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Dec 2012 13:24:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t1355163891aa4jmfmzdqshqsp.htm/, Retrieved Thu, 28 Mar 2024 08:42:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198275, Retrieved Thu, 28 Mar 2024 08:42:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS 10: Multiple ...] [2011-12-10 19:48:31] [a9a952c1cbc7081c25fad93a34aab827]
-           [Multiple Regression] [] [2012-12-10 18:24:30] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
0	255202	64	92	34
0	135248	59	58	30
0	207223	64	62	42
1	189326	95	108	34
1	141365	46	55	25
0	65295	27	8	31
0	439387	103	134	29
0	33186	19	1	18
0	183696	51	64	30
0	186657	38	77	29
1	276696	99	86	42
1	194414	98	96	50
0	141409	59	44	33
1	306730	68	108	46
1	192691	74	63	38
1	333497	164	160	52
0	261835	59	109	32
1	263451	130	86	35
1	157448	49	93	25
1	232190	73	126	42
0	245725	64	110	40
0	388603	92	86	35
0	156540	34	50	25
0	156189	47	92	46
0	189726	106	123	39
0	192167	106	81	35
1	249893	122	93	38
1	236812	76	113	35
1	143160	47	52	28
0	259667	54	113	37
0	243020	68	113	40
0	176062	67	44	42
0	286683	79	123	44
1	87485	33	38	33
0	329737	88	111	38
1	247082	51	77	37
0	378463	108	102	41
1	191653	75	74	32
0	114673	31	33	17
0	301596	167	107	39
0	284195	73	108	33
1	155568	60	66	35
1	177306	67	69	32
1	144595	51	62	35
0	140319	73	50	45
1	405267	135	91	38
1	78800	42	20	26
1	201970	69	101	45
1	302705	101	129	44
1	164733	50	93	40
1	194221	68	89	33
0	24188	24	8	4
0	346142	288	80	41
0	65029	17	21	18
0	101097	64	30	14
1	253745	51	86	36
0	273513	77	116	49
1	282220	160	106	32
1	280928	120	132	37
1	214872	74	75	32
0	342048	127	139	43
0	273924	108	121	25
1	195726	92	57	42
1	231162	80	67	37
0	209798	61	45	33
1	201345	60	88	28
0	180231	118	79	31
1	204441	129	75	40
0	197813	67	114	32
1	136421	60	127	25
1	216092	59	86	42
1	73566	32	22	23
0	213998	70	67	42
1	181728	50	77	38
0	148758	51	105	34
0	308343	71	121	39
1	251437	78	88	32
0	202388	102	78	37
0	173286	56	122	34
0	155529	58	66	33
0	132672	41	58	25
1	390163	102	134	45
0	145905	66	30	26
0	228012	88	103	40
1	80953	25	49	8
0	130805	47	26	27
1	135163	49	67	32
1	333790	168	59	37
1	271806	95	95	50
1	164235	99	156	41
1	234092	80	74	37
0	207158	69	137	38
0	156583	57	37	28
0	242395	68	111	36
1	261601	70	58	32
1	178489	35	78	32
0	204221	44	88	33
1	268066	69	152	35
1	327622	133	130	58
1	361799	101	145	27
0	247131	107	108	45
1	265849	58	138	37
0	162336	162	62	32
1	43287	14	13	19
0	172244	68	89	22
0	189021	121	86	35
0	227681	43	116	36
0	269329	81	157	36
0	106503	56	28	23
1	117891	77	83	40
1	287201	59	72	40
0	266805	78	134	42
0	23623	11	12	1
1	174954	69	120	36
0	61857	25	23	11
1	144889	43	83	40
1	347988	103	126	34
0	21054	16	4	0
1	224051	46	71	27
1	31414	19	18	8
1	278660	107	98	35
0	209481	58	68	44
0	156870	75	44	40
1	112933	46	29	28
0	38214	34	16	8
0	166011	35	61	36
1	316044	73	117	47
1	181578	56	46	48
1	358903	72	129	45
1	275578	91	139	48
1	368796	106	136	49
1	172464	31	66	35
1	94381	35	42	32
1	250563	290	75	36
1	382499	154	97	42
1	118010	42	49	35
1	365575	122	127	42
1	147989	72	55	34
1	231681	46	101	41
0	193119	77	80	36
0	189020	108	29	32
0	341958	106	95	33
1	222060	79	120	35
0	173260	63	41	21
0	274787	91	128	42
1	130908	52	142	49
0	204009	75	88	33
0	262412	94	170	39
0	1	0	0	0
0	14688	10	4	0
0	98	1	0	0
0	455	2	0	0
1	0	0	0	0
0	0	0	0	0
1	195765	75	56	33
0	334258	129	121	47
0	0	0	0	0
0	203	4	0	0
0	7199	5	7	0
1	46660	20	12	5
1	17547	5	0	1
0	107465	38	37	38
1	969	2	0	0
1	179994	58	47	28




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Time_in_RFC[t] = + 5060.78809216539 -1704.67570366525Geslacht[t] + 754.508242023966Logins[t] + 1081.23649471056Blogged_computations[t] + 1694.1075558023Reviewed_compendiums[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time_in_RFC[t] =  +  5060.78809216539 -1704.67570366525Geslacht[t] +  754.508242023966Logins[t] +  1081.23649471056Blogged_computations[t] +  1694.1075558023Reviewed_compendiums[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time_in_RFC[t] =  +  5060.78809216539 -1704.67570366525Geslacht[t] +  754.508242023966Logins[t] +  1081.23649471056Blogged_computations[t] +  1694.1075558023Reviewed_compendiums[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Time_in_RFC[t] = + 5060.78809216539 -1704.67570366525Geslacht[t] + 754.508242023966Logins[t] + 1081.23649471056Blogged_computations[t] + 1694.1075558023Reviewed_compendiums[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5060.7880921653910410.0271190.48610.6275330.313767
Geslacht-1704.675703665257803.15188-0.21850.8273510.413675
Logins754.508242023966109.4143966.895900
Blogged_computations1081.23649471056135.7356767.965800
Reviewed_compendiums1694.1075558023466.9373773.62810.0003840.000192

\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) & 5060.78809216539 & 10410.027119 & 0.4861 & 0.627533 & 0.313767 \tabularnewline
Geslacht & -1704.67570366525 & 7803.15188 & -0.2185 & 0.827351 & 0.413675 \tabularnewline
Logins & 754.508242023966 & 109.414396 & 6.8959 & 0 & 0 \tabularnewline
Blogged_computations & 1081.23649471056 & 135.735676 & 7.9658 & 0 & 0 \tabularnewline
Reviewed_compendiums & 1694.1075558023 & 466.937377 & 3.6281 & 0.000384 & 0.000192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&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]5060.78809216539[/C][C]10410.027119[/C][C]0.4861[/C][C]0.627533[/C][C]0.313767[/C][/ROW]
[ROW][C]Geslacht[/C][C]-1704.67570366525[/C][C]7803.15188[/C][C]-0.2185[/C][C]0.827351[/C][C]0.413675[/C][/ROW]
[ROW][C]Logins[/C][C]754.508242023966[/C][C]109.414396[/C][C]6.8959[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Blogged_computations[/C][C]1081.23649471056[/C][C]135.735676[/C][C]7.9658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Reviewed_compendiums[/C][C]1694.1075558023[/C][C]466.937377[/C][C]3.6281[/C][C]0.000384[/C][C]0.000192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5060.7880921653910410.0271190.48610.6275330.313767
Geslacht-1704.675703665257803.15188-0.21850.8273510.413675
Logins754.508242023966109.4143966.895900
Blogged_computations1081.23649471056135.7356767.965800
Reviewed_compendiums1694.1075558023466.9373773.62810.0003840.000192







Multiple Linear Regression - Regression Statistics
Multiple R0.87486396823506
R-squared0.765386962915996
Adjusted R-squared0.759484748146587
F-TEST (value)129.677924782234
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation48908.7744357889
Sum Squared Residuals380338846472.929

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.87486396823506 \tabularnewline
R-squared & 0.765386962915996 \tabularnewline
Adjusted R-squared & 0.759484748146587 \tabularnewline
F-TEST (value) & 129.677924782234 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 48908.7744357889 \tabularnewline
Sum Squared Residuals & 380338846472.929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.87486396823506[/C][/ROW]
[ROW][C]R-squared[/C][C]0.765386962915996[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.759484748146587[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]129.677924782234[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/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]48908.7744357889[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]380338846472.929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.87486396823506
R-squared0.765386962915996
Adjusted R-squared0.759484748146587
F-TEST (value)129.677924782234
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation48908.7744357889
Sum Squared Residuals380338846472.929







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1255202210422.72999234944779.2700076509
2135248163111.717738861-27863.717738861
3207223191538.49559745115684.5044025493
4189326249407.593706796-60081.593706796
5141365139884.1876257411480.81237425897
66529586599.7368143683-21304.7368143683
7439387276789.946430116162597.053569884
83318650971.6171897727-17785.6171897727
9183696163563.07077093320132.9292290673
10186657166116.43050005620540.5694999438
11276696242191.28423767834504.7157623222
12194414265802.001389178-71388.0013891779
13141409153056.72948032-11647.72948032
14306730249365.16184177657364.8381582235
15192691191683.7085855271007.2914144735
16333497388186.89613584-54689.8961358403
17261835221642.99408070440192.0059192956
18263451253722.2868498059728.71315019535
19157448183234.699150814-25786.6991508144
20232190265823.529733477-33633.5297334772
21245725240049.6322319535675.36776804683
22388603226755.649356559161847.350643441
23156540127128.58195156629411.4180484341
24156189217925.380547569-61736.3805475694
25189726284100.945272395-94374.9452723948
26192167231912.582271342-39745.5822713419
27249893260337.199043994-10444.1990439938
28236812242172.227137696-5360.2271376957
29143160142477.30905104682.690948959784
30259667230665.93662843829001.0633715617
31243020246311.374684181-3291.37468418073
32176062174339.7634187331722.23658126751
33286683272199.76051675914483.2394832408
3487485125247.420515768-37762.4205157683
35329737255850.85142363473886.1485763657
36247082187773.22238912159308.7776108791
37378463266292.210479125112170.789520875
38191653194167.172934553-2514.17293455286
3911467392931.17636899621741.823631004
40301596312826.164120488-11230.1641204877
41284195232818.98053013251376.0194698684
42155568179281.980013916-23713.9800139158
43177306182724.924524808-5418.92452480831
44144595168166.459856858-23571.4598568578
45140319190436.554506547-50117.5545065466
46405267267983.333200884137283.666799116
4778800100716.984898578-21916.9848985778
48201970240856.907065024-38886.9070650242
49302705293581.6851058859123.31489411542
50164733209400.820729873-44667.8207298728
51194221206798.270216846-12577.2702168459
522418838595.3080816343-14407.3080816343
53346142378316.491159807-32174.4911598069
546502971087.330599936-6058.33059993603
55101097109503.916204248-8406.91620424827
56253745195810.24328571457934.7567142864
57273513271592.6263487491920.37365125118
58282220292899.941337328-10679.941337328
59280928299302.298297856-18374.2982978555
60214872194493.90118723920378.0988127605
61342048324021.83249347618026.1675065237
62273924259729.98298578914194.0170142106
63195726205553.868196904-9827.86819690371
64231162198841.5964607132320.4035392897
65209798155646.98245907954151.0175409214
66201345191210.43000693210134.5699930679
67180231232027.777962999-51796.7779629991
68204441249544.714944976-45103.714944976
69197813233085.242490449-35272.2424904489
70136421228296.330633237-91875.3306332371
71216092212010.9545567194081.04544328083
727356690252.0528003523-16686.0528003523
73213998201471.72752314712526.2724768527
74181728188712.821702899-6984.82170289921
75148758214670.197277275-65912.197277275
76308343255530.68381213552812.3161878652
77251437211568.00858657339868.9914134274
78202388229039.054930719-26651.054930719
79173286236823.758897474-63537.7588974744
80155529176089.424121928-20560.4241219285
81132672141060.031603418-8388.03160341813
82390163301436.48337726488726.5166227364
83145905131342.22335792414562.7766420762
84228012250589.174577554-22577.1745775544
858095388752.2671263353-7799.26712633526
86130805114375.72833642816429.2716635715
87135163166981.303178956-31818.3031789558
88333790256588.42980113577201.5701988653
89271806262457.2401683959348.75983160458
90164235316183.731311615-151948.731311615
91234092206410.25192368427681.7480763158
92207158269627.343687654-62469.3436876536
93156583135508.51975428721074.4802457133
94242395237372.471471555022.5285284496
95261601173094.84780906488506.152190936
96178489168311.78923243610177.2107675635
97204221189313.51161722514907.4883827746
98268066279058.89273724-10992.8927372399
99327622342524.691126594-14902.6911265943
100361799282081.64057261579717.3594273855
101247131278801.551428574-31670.5514285741
102265849259010.2062606336838.793739367
103162336248539.227757776-86203.2277577764
1044328760163.3457683167-16876.3457683167
105172244189867.762806686-17623.7628066858
106189021248636.388375254-59615.3883752542
107227681223915.9478945043765.05210549592
108269329296917.957374548-27588.9573745479
109106503116552.345280856-10049.3452808561
110117891218960.178317414-101069.178317414
111287201193485.42851916793715.5714808333
112266805279950.638604947-13145.6386049468
1132362328029.324246758-4406.32424675803
114174954246153.432462304-71199.4324623042
1156185767427.1166349328-5570.11663493278
116144889193306.898088599-48417.8980885994
117347988274905.91654777873082.0834522222
1182105421457.8659433911-403.865943391068
119224051160572.18665271563478.8133472854
1203141450706.886338164-19292.886338164
121278660249343.4352197829316.5647802198
122209481196887.08022517512593.9197748251
123156870176987.61424332-20117.6142433196
124112933116854.361430673-3921.36143067329
1253821461566.7126827676-23352.7126827676
126166011158411.8747492317599.12525076865
127316044264562.93906009451481.0609399063
128181578176662.6153770394915.38462296145
129358903273395.05364299285507.9463570081
130275578303625.39785596-28047.3978559598
131368796313393.4195579955402.5804420102
132172464157401.24099522115062.7590047792
13394381129387.275422856-35006.2754228562
134250563364244.111687625-113681.111687625
135382499295582.83899081286916.1610091879
136118010147319.811247405-29309.8112474048
137365575303875.67008736261699.3299126379
138147989174748.369920585-26759.3699205849
139231681216726.78727526414954.2127247362
140193119210644.714313739-17525.7143137386
141189020172114.97836303416905.0216369664
142341958243661.67808568598296.3219143148
143222060252004.407326742-29944.4073267415
144173260132501.76229465740758.2377053434
145274787283271.826782995-8484.82678299499
146130908279137.393456959-148229.393456959
147204009212703.267119968-8694.2671199683
148262412325864.961619504-63452.9616195037
14915060.78809216535-5059.78809216535
1501468816930.8164912473-2242.81649124727
151985815.29633418932-5717.29633418932
1524556569.80457621329-6114.80457621329
15303356.11238850009-3356.11238850009
15405060.78809216535-5060.78809216535
155195765176399.02358556519365.976414435
156334258312845.02229594321412.9777040567
15705060.78809216535-5060.78809216535
1582038078.82106026122-7875.82106026122
159719916401.9847652591-9202.98476525913
1604666039891.65294451776768.34705548232
161175478822.761154422228724.23884557778
162107465138113.938713854-30648.9387138543
1639694865.12887254801-3896.12887254801
164179994145370.71723975134623.282760249

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 255202 & 210422.729992349 & 44779.2700076509 \tabularnewline
2 & 135248 & 163111.717738861 & -27863.717738861 \tabularnewline
3 & 207223 & 191538.495597451 & 15684.5044025493 \tabularnewline
4 & 189326 & 249407.593706796 & -60081.593706796 \tabularnewline
5 & 141365 & 139884.187625741 & 1480.81237425897 \tabularnewline
6 & 65295 & 86599.7368143683 & -21304.7368143683 \tabularnewline
7 & 439387 & 276789.946430116 & 162597.053569884 \tabularnewline
8 & 33186 & 50971.6171897727 & -17785.6171897727 \tabularnewline
9 & 183696 & 163563.070770933 & 20132.9292290673 \tabularnewline
10 & 186657 & 166116.430500056 & 20540.5694999438 \tabularnewline
11 & 276696 & 242191.284237678 & 34504.7157623222 \tabularnewline
12 & 194414 & 265802.001389178 & -71388.0013891779 \tabularnewline
13 & 141409 & 153056.72948032 & -11647.72948032 \tabularnewline
14 & 306730 & 249365.161841776 & 57364.8381582235 \tabularnewline
15 & 192691 & 191683.708585527 & 1007.2914144735 \tabularnewline
16 & 333497 & 388186.89613584 & -54689.8961358403 \tabularnewline
17 & 261835 & 221642.994080704 & 40192.0059192956 \tabularnewline
18 & 263451 & 253722.286849805 & 9728.71315019535 \tabularnewline
19 & 157448 & 183234.699150814 & -25786.6991508144 \tabularnewline
20 & 232190 & 265823.529733477 & -33633.5297334772 \tabularnewline
21 & 245725 & 240049.632231953 & 5675.36776804683 \tabularnewline
22 & 388603 & 226755.649356559 & 161847.350643441 \tabularnewline
23 & 156540 & 127128.581951566 & 29411.4180484341 \tabularnewline
24 & 156189 & 217925.380547569 & -61736.3805475694 \tabularnewline
25 & 189726 & 284100.945272395 & -94374.9452723948 \tabularnewline
26 & 192167 & 231912.582271342 & -39745.5822713419 \tabularnewline
27 & 249893 & 260337.199043994 & -10444.1990439938 \tabularnewline
28 & 236812 & 242172.227137696 & -5360.2271376957 \tabularnewline
29 & 143160 & 142477.30905104 & 682.690948959784 \tabularnewline
30 & 259667 & 230665.936628438 & 29001.0633715617 \tabularnewline
31 & 243020 & 246311.374684181 & -3291.37468418073 \tabularnewline
32 & 176062 & 174339.763418733 & 1722.23658126751 \tabularnewline
33 & 286683 & 272199.760516759 & 14483.2394832408 \tabularnewline
34 & 87485 & 125247.420515768 & -37762.4205157683 \tabularnewline
35 & 329737 & 255850.851423634 & 73886.1485763657 \tabularnewline
36 & 247082 & 187773.222389121 & 59308.7776108791 \tabularnewline
37 & 378463 & 266292.210479125 & 112170.789520875 \tabularnewline
38 & 191653 & 194167.172934553 & -2514.17293455286 \tabularnewline
39 & 114673 & 92931.176368996 & 21741.823631004 \tabularnewline
40 & 301596 & 312826.164120488 & -11230.1641204877 \tabularnewline
41 & 284195 & 232818.980530132 & 51376.0194698684 \tabularnewline
42 & 155568 & 179281.980013916 & -23713.9800139158 \tabularnewline
43 & 177306 & 182724.924524808 & -5418.92452480831 \tabularnewline
44 & 144595 & 168166.459856858 & -23571.4598568578 \tabularnewline
45 & 140319 & 190436.554506547 & -50117.5545065466 \tabularnewline
46 & 405267 & 267983.333200884 & 137283.666799116 \tabularnewline
47 & 78800 & 100716.984898578 & -21916.9848985778 \tabularnewline
48 & 201970 & 240856.907065024 & -38886.9070650242 \tabularnewline
49 & 302705 & 293581.685105885 & 9123.31489411542 \tabularnewline
50 & 164733 & 209400.820729873 & -44667.8207298728 \tabularnewline
51 & 194221 & 206798.270216846 & -12577.2702168459 \tabularnewline
52 & 24188 & 38595.3080816343 & -14407.3080816343 \tabularnewline
53 & 346142 & 378316.491159807 & -32174.4911598069 \tabularnewline
54 & 65029 & 71087.330599936 & -6058.33059993603 \tabularnewline
55 & 101097 & 109503.916204248 & -8406.91620424827 \tabularnewline
56 & 253745 & 195810.243285714 & 57934.7567142864 \tabularnewline
57 & 273513 & 271592.626348749 & 1920.37365125118 \tabularnewline
58 & 282220 & 292899.941337328 & -10679.941337328 \tabularnewline
59 & 280928 & 299302.298297856 & -18374.2982978555 \tabularnewline
60 & 214872 & 194493.901187239 & 20378.0988127605 \tabularnewline
61 & 342048 & 324021.832493476 & 18026.1675065237 \tabularnewline
62 & 273924 & 259729.982985789 & 14194.0170142106 \tabularnewline
63 & 195726 & 205553.868196904 & -9827.86819690371 \tabularnewline
64 & 231162 & 198841.59646071 & 32320.4035392897 \tabularnewline
65 & 209798 & 155646.982459079 & 54151.0175409214 \tabularnewline
66 & 201345 & 191210.430006932 & 10134.5699930679 \tabularnewline
67 & 180231 & 232027.777962999 & -51796.7779629991 \tabularnewline
68 & 204441 & 249544.714944976 & -45103.714944976 \tabularnewline
69 & 197813 & 233085.242490449 & -35272.2424904489 \tabularnewline
70 & 136421 & 228296.330633237 & -91875.3306332371 \tabularnewline
71 & 216092 & 212010.954556719 & 4081.04544328083 \tabularnewline
72 & 73566 & 90252.0528003523 & -16686.0528003523 \tabularnewline
73 & 213998 & 201471.727523147 & 12526.2724768527 \tabularnewline
74 & 181728 & 188712.821702899 & -6984.82170289921 \tabularnewline
75 & 148758 & 214670.197277275 & -65912.197277275 \tabularnewline
76 & 308343 & 255530.683812135 & 52812.3161878652 \tabularnewline
77 & 251437 & 211568.008586573 & 39868.9914134274 \tabularnewline
78 & 202388 & 229039.054930719 & -26651.054930719 \tabularnewline
79 & 173286 & 236823.758897474 & -63537.7588974744 \tabularnewline
80 & 155529 & 176089.424121928 & -20560.4241219285 \tabularnewline
81 & 132672 & 141060.031603418 & -8388.03160341813 \tabularnewline
82 & 390163 & 301436.483377264 & 88726.5166227364 \tabularnewline
83 & 145905 & 131342.223357924 & 14562.7766420762 \tabularnewline
84 & 228012 & 250589.174577554 & -22577.1745775544 \tabularnewline
85 & 80953 & 88752.2671263353 & -7799.26712633526 \tabularnewline
86 & 130805 & 114375.728336428 & 16429.2716635715 \tabularnewline
87 & 135163 & 166981.303178956 & -31818.3031789558 \tabularnewline
88 & 333790 & 256588.429801135 & 77201.5701988653 \tabularnewline
89 & 271806 & 262457.240168395 & 9348.75983160458 \tabularnewline
90 & 164235 & 316183.731311615 & -151948.731311615 \tabularnewline
91 & 234092 & 206410.251923684 & 27681.7480763158 \tabularnewline
92 & 207158 & 269627.343687654 & -62469.3436876536 \tabularnewline
93 & 156583 & 135508.519754287 & 21074.4802457133 \tabularnewline
94 & 242395 & 237372.47147155 & 5022.5285284496 \tabularnewline
95 & 261601 & 173094.847809064 & 88506.152190936 \tabularnewline
96 & 178489 & 168311.789232436 & 10177.2107675635 \tabularnewline
97 & 204221 & 189313.511617225 & 14907.4883827746 \tabularnewline
98 & 268066 & 279058.89273724 & -10992.8927372399 \tabularnewline
99 & 327622 & 342524.691126594 & -14902.6911265943 \tabularnewline
100 & 361799 & 282081.640572615 & 79717.3594273855 \tabularnewline
101 & 247131 & 278801.551428574 & -31670.5514285741 \tabularnewline
102 & 265849 & 259010.206260633 & 6838.793739367 \tabularnewline
103 & 162336 & 248539.227757776 & -86203.2277577764 \tabularnewline
104 & 43287 & 60163.3457683167 & -16876.3457683167 \tabularnewline
105 & 172244 & 189867.762806686 & -17623.7628066858 \tabularnewline
106 & 189021 & 248636.388375254 & -59615.3883752542 \tabularnewline
107 & 227681 & 223915.947894504 & 3765.05210549592 \tabularnewline
108 & 269329 & 296917.957374548 & -27588.9573745479 \tabularnewline
109 & 106503 & 116552.345280856 & -10049.3452808561 \tabularnewline
110 & 117891 & 218960.178317414 & -101069.178317414 \tabularnewline
111 & 287201 & 193485.428519167 & 93715.5714808333 \tabularnewline
112 & 266805 & 279950.638604947 & -13145.6386049468 \tabularnewline
113 & 23623 & 28029.324246758 & -4406.32424675803 \tabularnewline
114 & 174954 & 246153.432462304 & -71199.4324623042 \tabularnewline
115 & 61857 & 67427.1166349328 & -5570.11663493278 \tabularnewline
116 & 144889 & 193306.898088599 & -48417.8980885994 \tabularnewline
117 & 347988 & 274905.916547778 & 73082.0834522222 \tabularnewline
118 & 21054 & 21457.8659433911 & -403.865943391068 \tabularnewline
119 & 224051 & 160572.186652715 & 63478.8133472854 \tabularnewline
120 & 31414 & 50706.886338164 & -19292.886338164 \tabularnewline
121 & 278660 & 249343.43521978 & 29316.5647802198 \tabularnewline
122 & 209481 & 196887.080225175 & 12593.9197748251 \tabularnewline
123 & 156870 & 176987.61424332 & -20117.6142433196 \tabularnewline
124 & 112933 & 116854.361430673 & -3921.36143067329 \tabularnewline
125 & 38214 & 61566.7126827676 & -23352.7126827676 \tabularnewline
126 & 166011 & 158411.874749231 & 7599.12525076865 \tabularnewline
127 & 316044 & 264562.939060094 & 51481.0609399063 \tabularnewline
128 & 181578 & 176662.615377039 & 4915.38462296145 \tabularnewline
129 & 358903 & 273395.053642992 & 85507.9463570081 \tabularnewline
130 & 275578 & 303625.39785596 & -28047.3978559598 \tabularnewline
131 & 368796 & 313393.41955799 & 55402.5804420102 \tabularnewline
132 & 172464 & 157401.240995221 & 15062.7590047792 \tabularnewline
133 & 94381 & 129387.275422856 & -35006.2754228562 \tabularnewline
134 & 250563 & 364244.111687625 & -113681.111687625 \tabularnewline
135 & 382499 & 295582.838990812 & 86916.1610091879 \tabularnewline
136 & 118010 & 147319.811247405 & -29309.8112474048 \tabularnewline
137 & 365575 & 303875.670087362 & 61699.3299126379 \tabularnewline
138 & 147989 & 174748.369920585 & -26759.3699205849 \tabularnewline
139 & 231681 & 216726.787275264 & 14954.2127247362 \tabularnewline
140 & 193119 & 210644.714313739 & -17525.7143137386 \tabularnewline
141 & 189020 & 172114.978363034 & 16905.0216369664 \tabularnewline
142 & 341958 & 243661.678085685 & 98296.3219143148 \tabularnewline
143 & 222060 & 252004.407326742 & -29944.4073267415 \tabularnewline
144 & 173260 & 132501.762294657 & 40758.2377053434 \tabularnewline
145 & 274787 & 283271.826782995 & -8484.82678299499 \tabularnewline
146 & 130908 & 279137.393456959 & -148229.393456959 \tabularnewline
147 & 204009 & 212703.267119968 & -8694.2671199683 \tabularnewline
148 & 262412 & 325864.961619504 & -63452.9616195037 \tabularnewline
149 & 1 & 5060.78809216535 & -5059.78809216535 \tabularnewline
150 & 14688 & 16930.8164912473 & -2242.81649124727 \tabularnewline
151 & 98 & 5815.29633418932 & -5717.29633418932 \tabularnewline
152 & 455 & 6569.80457621329 & -6114.80457621329 \tabularnewline
153 & 0 & 3356.11238850009 & -3356.11238850009 \tabularnewline
154 & 0 & 5060.78809216535 & -5060.78809216535 \tabularnewline
155 & 195765 & 176399.023585565 & 19365.976414435 \tabularnewline
156 & 334258 & 312845.022295943 & 21412.9777040567 \tabularnewline
157 & 0 & 5060.78809216535 & -5060.78809216535 \tabularnewline
158 & 203 & 8078.82106026122 & -7875.82106026122 \tabularnewline
159 & 7199 & 16401.9847652591 & -9202.98476525913 \tabularnewline
160 & 46660 & 39891.6529445177 & 6768.34705548232 \tabularnewline
161 & 17547 & 8822.76115442222 & 8724.23884557778 \tabularnewline
162 & 107465 & 138113.938713854 & -30648.9387138543 \tabularnewline
163 & 969 & 4865.12887254801 & -3896.12887254801 \tabularnewline
164 & 179994 & 145370.717239751 & 34623.282760249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&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]255202[/C][C]210422.729992349[/C][C]44779.2700076509[/C][/ROW]
[ROW][C]2[/C][C]135248[/C][C]163111.717738861[/C][C]-27863.717738861[/C][/ROW]
[ROW][C]3[/C][C]207223[/C][C]191538.495597451[/C][C]15684.5044025493[/C][/ROW]
[ROW][C]4[/C][C]189326[/C][C]249407.593706796[/C][C]-60081.593706796[/C][/ROW]
[ROW][C]5[/C][C]141365[/C][C]139884.187625741[/C][C]1480.81237425897[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]86599.7368143683[/C][C]-21304.7368143683[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]276789.946430116[/C][C]162597.053569884[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]50971.6171897727[/C][C]-17785.6171897727[/C][/ROW]
[ROW][C]9[/C][C]183696[/C][C]163563.070770933[/C][C]20132.9292290673[/C][/ROW]
[ROW][C]10[/C][C]186657[/C][C]166116.430500056[/C][C]20540.5694999438[/C][/ROW]
[ROW][C]11[/C][C]276696[/C][C]242191.284237678[/C][C]34504.7157623222[/C][/ROW]
[ROW][C]12[/C][C]194414[/C][C]265802.001389178[/C][C]-71388.0013891779[/C][/ROW]
[ROW][C]13[/C][C]141409[/C][C]153056.72948032[/C][C]-11647.72948032[/C][/ROW]
[ROW][C]14[/C][C]306730[/C][C]249365.161841776[/C][C]57364.8381582235[/C][/ROW]
[ROW][C]15[/C][C]192691[/C][C]191683.708585527[/C][C]1007.2914144735[/C][/ROW]
[ROW][C]16[/C][C]333497[/C][C]388186.89613584[/C][C]-54689.8961358403[/C][/ROW]
[ROW][C]17[/C][C]261835[/C][C]221642.994080704[/C][C]40192.0059192956[/C][/ROW]
[ROW][C]18[/C][C]263451[/C][C]253722.286849805[/C][C]9728.71315019535[/C][/ROW]
[ROW][C]19[/C][C]157448[/C][C]183234.699150814[/C][C]-25786.6991508144[/C][/ROW]
[ROW][C]20[/C][C]232190[/C][C]265823.529733477[/C][C]-33633.5297334772[/C][/ROW]
[ROW][C]21[/C][C]245725[/C][C]240049.632231953[/C][C]5675.36776804683[/C][/ROW]
[ROW][C]22[/C][C]388603[/C][C]226755.649356559[/C][C]161847.350643441[/C][/ROW]
[ROW][C]23[/C][C]156540[/C][C]127128.581951566[/C][C]29411.4180484341[/C][/ROW]
[ROW][C]24[/C][C]156189[/C][C]217925.380547569[/C][C]-61736.3805475694[/C][/ROW]
[ROW][C]25[/C][C]189726[/C][C]284100.945272395[/C][C]-94374.9452723948[/C][/ROW]
[ROW][C]26[/C][C]192167[/C][C]231912.582271342[/C][C]-39745.5822713419[/C][/ROW]
[ROW][C]27[/C][C]249893[/C][C]260337.199043994[/C][C]-10444.1990439938[/C][/ROW]
[ROW][C]28[/C][C]236812[/C][C]242172.227137696[/C][C]-5360.2271376957[/C][/ROW]
[ROW][C]29[/C][C]143160[/C][C]142477.30905104[/C][C]682.690948959784[/C][/ROW]
[ROW][C]30[/C][C]259667[/C][C]230665.936628438[/C][C]29001.0633715617[/C][/ROW]
[ROW][C]31[/C][C]243020[/C][C]246311.374684181[/C][C]-3291.37468418073[/C][/ROW]
[ROW][C]32[/C][C]176062[/C][C]174339.763418733[/C][C]1722.23658126751[/C][/ROW]
[ROW][C]33[/C][C]286683[/C][C]272199.760516759[/C][C]14483.2394832408[/C][/ROW]
[ROW][C]34[/C][C]87485[/C][C]125247.420515768[/C][C]-37762.4205157683[/C][/ROW]
[ROW][C]35[/C][C]329737[/C][C]255850.851423634[/C][C]73886.1485763657[/C][/ROW]
[ROW][C]36[/C][C]247082[/C][C]187773.222389121[/C][C]59308.7776108791[/C][/ROW]
[ROW][C]37[/C][C]378463[/C][C]266292.210479125[/C][C]112170.789520875[/C][/ROW]
[ROW][C]38[/C][C]191653[/C][C]194167.172934553[/C][C]-2514.17293455286[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]92931.176368996[/C][C]21741.823631004[/C][/ROW]
[ROW][C]40[/C][C]301596[/C][C]312826.164120488[/C][C]-11230.1641204877[/C][/ROW]
[ROW][C]41[/C][C]284195[/C][C]232818.980530132[/C][C]51376.0194698684[/C][/ROW]
[ROW][C]42[/C][C]155568[/C][C]179281.980013916[/C][C]-23713.9800139158[/C][/ROW]
[ROW][C]43[/C][C]177306[/C][C]182724.924524808[/C][C]-5418.92452480831[/C][/ROW]
[ROW][C]44[/C][C]144595[/C][C]168166.459856858[/C][C]-23571.4598568578[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]190436.554506547[/C][C]-50117.5545065466[/C][/ROW]
[ROW][C]46[/C][C]405267[/C][C]267983.333200884[/C][C]137283.666799116[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]100716.984898578[/C][C]-21916.9848985778[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]240856.907065024[/C][C]-38886.9070650242[/C][/ROW]
[ROW][C]49[/C][C]302705[/C][C]293581.685105885[/C][C]9123.31489411542[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]209400.820729873[/C][C]-44667.8207298728[/C][/ROW]
[ROW][C]51[/C][C]194221[/C][C]206798.270216846[/C][C]-12577.2702168459[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]38595.3080816343[/C][C]-14407.3080816343[/C][/ROW]
[ROW][C]53[/C][C]346142[/C][C]378316.491159807[/C][C]-32174.4911598069[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]71087.330599936[/C][C]-6058.33059993603[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]109503.916204248[/C][C]-8406.91620424827[/C][/ROW]
[ROW][C]56[/C][C]253745[/C][C]195810.243285714[/C][C]57934.7567142864[/C][/ROW]
[ROW][C]57[/C][C]273513[/C][C]271592.626348749[/C][C]1920.37365125118[/C][/ROW]
[ROW][C]58[/C][C]282220[/C][C]292899.941337328[/C][C]-10679.941337328[/C][/ROW]
[ROW][C]59[/C][C]280928[/C][C]299302.298297856[/C][C]-18374.2982978555[/C][/ROW]
[ROW][C]60[/C][C]214872[/C][C]194493.901187239[/C][C]20378.0988127605[/C][/ROW]
[ROW][C]61[/C][C]342048[/C][C]324021.832493476[/C][C]18026.1675065237[/C][/ROW]
[ROW][C]62[/C][C]273924[/C][C]259729.982985789[/C][C]14194.0170142106[/C][/ROW]
[ROW][C]63[/C][C]195726[/C][C]205553.868196904[/C][C]-9827.86819690371[/C][/ROW]
[ROW][C]64[/C][C]231162[/C][C]198841.59646071[/C][C]32320.4035392897[/C][/ROW]
[ROW][C]65[/C][C]209798[/C][C]155646.982459079[/C][C]54151.0175409214[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]191210.430006932[/C][C]10134.5699930679[/C][/ROW]
[ROW][C]67[/C][C]180231[/C][C]232027.777962999[/C][C]-51796.7779629991[/C][/ROW]
[ROW][C]68[/C][C]204441[/C][C]249544.714944976[/C][C]-45103.714944976[/C][/ROW]
[ROW][C]69[/C][C]197813[/C][C]233085.242490449[/C][C]-35272.2424904489[/C][/ROW]
[ROW][C]70[/C][C]136421[/C][C]228296.330633237[/C][C]-91875.3306332371[/C][/ROW]
[ROW][C]71[/C][C]216092[/C][C]212010.954556719[/C][C]4081.04544328083[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]90252.0528003523[/C][C]-16686.0528003523[/C][/ROW]
[ROW][C]73[/C][C]213998[/C][C]201471.727523147[/C][C]12526.2724768527[/C][/ROW]
[ROW][C]74[/C][C]181728[/C][C]188712.821702899[/C][C]-6984.82170289921[/C][/ROW]
[ROW][C]75[/C][C]148758[/C][C]214670.197277275[/C][C]-65912.197277275[/C][/ROW]
[ROW][C]76[/C][C]308343[/C][C]255530.683812135[/C][C]52812.3161878652[/C][/ROW]
[ROW][C]77[/C][C]251437[/C][C]211568.008586573[/C][C]39868.9914134274[/C][/ROW]
[ROW][C]78[/C][C]202388[/C][C]229039.054930719[/C][C]-26651.054930719[/C][/ROW]
[ROW][C]79[/C][C]173286[/C][C]236823.758897474[/C][C]-63537.7588974744[/C][/ROW]
[ROW][C]80[/C][C]155529[/C][C]176089.424121928[/C][C]-20560.4241219285[/C][/ROW]
[ROW][C]81[/C][C]132672[/C][C]141060.031603418[/C][C]-8388.03160341813[/C][/ROW]
[ROW][C]82[/C][C]390163[/C][C]301436.483377264[/C][C]88726.5166227364[/C][/ROW]
[ROW][C]83[/C][C]145905[/C][C]131342.223357924[/C][C]14562.7766420762[/C][/ROW]
[ROW][C]84[/C][C]228012[/C][C]250589.174577554[/C][C]-22577.1745775544[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]88752.2671263353[/C][C]-7799.26712633526[/C][/ROW]
[ROW][C]86[/C][C]130805[/C][C]114375.728336428[/C][C]16429.2716635715[/C][/ROW]
[ROW][C]87[/C][C]135163[/C][C]166981.303178956[/C][C]-31818.3031789558[/C][/ROW]
[ROW][C]88[/C][C]333790[/C][C]256588.429801135[/C][C]77201.5701988653[/C][/ROW]
[ROW][C]89[/C][C]271806[/C][C]262457.240168395[/C][C]9348.75983160458[/C][/ROW]
[ROW][C]90[/C][C]164235[/C][C]316183.731311615[/C][C]-151948.731311615[/C][/ROW]
[ROW][C]91[/C][C]234092[/C][C]206410.251923684[/C][C]27681.7480763158[/C][/ROW]
[ROW][C]92[/C][C]207158[/C][C]269627.343687654[/C][C]-62469.3436876536[/C][/ROW]
[ROW][C]93[/C][C]156583[/C][C]135508.519754287[/C][C]21074.4802457133[/C][/ROW]
[ROW][C]94[/C][C]242395[/C][C]237372.47147155[/C][C]5022.5285284496[/C][/ROW]
[ROW][C]95[/C][C]261601[/C][C]173094.847809064[/C][C]88506.152190936[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]168311.789232436[/C][C]10177.2107675635[/C][/ROW]
[ROW][C]97[/C][C]204221[/C][C]189313.511617225[/C][C]14907.4883827746[/C][/ROW]
[ROW][C]98[/C][C]268066[/C][C]279058.89273724[/C][C]-10992.8927372399[/C][/ROW]
[ROW][C]99[/C][C]327622[/C][C]342524.691126594[/C][C]-14902.6911265943[/C][/ROW]
[ROW][C]100[/C][C]361799[/C][C]282081.640572615[/C][C]79717.3594273855[/C][/ROW]
[ROW][C]101[/C][C]247131[/C][C]278801.551428574[/C][C]-31670.5514285741[/C][/ROW]
[ROW][C]102[/C][C]265849[/C][C]259010.206260633[/C][C]6838.793739367[/C][/ROW]
[ROW][C]103[/C][C]162336[/C][C]248539.227757776[/C][C]-86203.2277577764[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]60163.3457683167[/C][C]-16876.3457683167[/C][/ROW]
[ROW][C]105[/C][C]172244[/C][C]189867.762806686[/C][C]-17623.7628066858[/C][/ROW]
[ROW][C]106[/C][C]189021[/C][C]248636.388375254[/C][C]-59615.3883752542[/C][/ROW]
[ROW][C]107[/C][C]227681[/C][C]223915.947894504[/C][C]3765.05210549592[/C][/ROW]
[ROW][C]108[/C][C]269329[/C][C]296917.957374548[/C][C]-27588.9573745479[/C][/ROW]
[ROW][C]109[/C][C]106503[/C][C]116552.345280856[/C][C]-10049.3452808561[/C][/ROW]
[ROW][C]110[/C][C]117891[/C][C]218960.178317414[/C][C]-101069.178317414[/C][/ROW]
[ROW][C]111[/C][C]287201[/C][C]193485.428519167[/C][C]93715.5714808333[/C][/ROW]
[ROW][C]112[/C][C]266805[/C][C]279950.638604947[/C][C]-13145.6386049468[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]28029.324246758[/C][C]-4406.32424675803[/C][/ROW]
[ROW][C]114[/C][C]174954[/C][C]246153.432462304[/C][C]-71199.4324623042[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]67427.1166349328[/C][C]-5570.11663493278[/C][/ROW]
[ROW][C]116[/C][C]144889[/C][C]193306.898088599[/C][C]-48417.8980885994[/C][/ROW]
[ROW][C]117[/C][C]347988[/C][C]274905.916547778[/C][C]73082.0834522222[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]21457.8659433911[/C][C]-403.865943391068[/C][/ROW]
[ROW][C]119[/C][C]224051[/C][C]160572.186652715[/C][C]63478.8133472854[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]50706.886338164[/C][C]-19292.886338164[/C][/ROW]
[ROW][C]121[/C][C]278660[/C][C]249343.43521978[/C][C]29316.5647802198[/C][/ROW]
[ROW][C]122[/C][C]209481[/C][C]196887.080225175[/C][C]12593.9197748251[/C][/ROW]
[ROW][C]123[/C][C]156870[/C][C]176987.61424332[/C][C]-20117.6142433196[/C][/ROW]
[ROW][C]124[/C][C]112933[/C][C]116854.361430673[/C][C]-3921.36143067329[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]61566.7126827676[/C][C]-23352.7126827676[/C][/ROW]
[ROW][C]126[/C][C]166011[/C][C]158411.874749231[/C][C]7599.12525076865[/C][/ROW]
[ROW][C]127[/C][C]316044[/C][C]264562.939060094[/C][C]51481.0609399063[/C][/ROW]
[ROW][C]128[/C][C]181578[/C][C]176662.615377039[/C][C]4915.38462296145[/C][/ROW]
[ROW][C]129[/C][C]358903[/C][C]273395.053642992[/C][C]85507.9463570081[/C][/ROW]
[ROW][C]130[/C][C]275578[/C][C]303625.39785596[/C][C]-28047.3978559598[/C][/ROW]
[ROW][C]131[/C][C]368796[/C][C]313393.41955799[/C][C]55402.5804420102[/C][/ROW]
[ROW][C]132[/C][C]172464[/C][C]157401.240995221[/C][C]15062.7590047792[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]129387.275422856[/C][C]-35006.2754228562[/C][/ROW]
[ROW][C]134[/C][C]250563[/C][C]364244.111687625[/C][C]-113681.111687625[/C][/ROW]
[ROW][C]135[/C][C]382499[/C][C]295582.838990812[/C][C]86916.1610091879[/C][/ROW]
[ROW][C]136[/C][C]118010[/C][C]147319.811247405[/C][C]-29309.8112474048[/C][/ROW]
[ROW][C]137[/C][C]365575[/C][C]303875.670087362[/C][C]61699.3299126379[/C][/ROW]
[ROW][C]138[/C][C]147989[/C][C]174748.369920585[/C][C]-26759.3699205849[/C][/ROW]
[ROW][C]139[/C][C]231681[/C][C]216726.787275264[/C][C]14954.2127247362[/C][/ROW]
[ROW][C]140[/C][C]193119[/C][C]210644.714313739[/C][C]-17525.7143137386[/C][/ROW]
[ROW][C]141[/C][C]189020[/C][C]172114.978363034[/C][C]16905.0216369664[/C][/ROW]
[ROW][C]142[/C][C]341958[/C][C]243661.678085685[/C][C]98296.3219143148[/C][/ROW]
[ROW][C]143[/C][C]222060[/C][C]252004.407326742[/C][C]-29944.4073267415[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]132501.762294657[/C][C]40758.2377053434[/C][/ROW]
[ROW][C]145[/C][C]274787[/C][C]283271.826782995[/C][C]-8484.82678299499[/C][/ROW]
[ROW][C]146[/C][C]130908[/C][C]279137.393456959[/C][C]-148229.393456959[/C][/ROW]
[ROW][C]147[/C][C]204009[/C][C]212703.267119968[/C][C]-8694.2671199683[/C][/ROW]
[ROW][C]148[/C][C]262412[/C][C]325864.961619504[/C][C]-63452.9616195037[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]5060.78809216535[/C][C]-5059.78809216535[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]16930.8164912473[/C][C]-2242.81649124727[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]5815.29633418932[/C][C]-5717.29633418932[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]6569.80457621329[/C][C]-6114.80457621329[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3356.11238850009[/C][C]-3356.11238850009[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]5060.78809216535[/C][C]-5060.78809216535[/C][/ROW]
[ROW][C]155[/C][C]195765[/C][C]176399.023585565[/C][C]19365.976414435[/C][/ROW]
[ROW][C]156[/C][C]334258[/C][C]312845.022295943[/C][C]21412.9777040567[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]5060.78809216535[/C][C]-5060.78809216535[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]8078.82106026122[/C][C]-7875.82106026122[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]16401.9847652591[/C][C]-9202.98476525913[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]39891.6529445177[/C][C]6768.34705548232[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]8822.76115442222[/C][C]8724.23884557778[/C][/ROW]
[ROW][C]162[/C][C]107465[/C][C]138113.938713854[/C][C]-30648.9387138543[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]4865.12887254801[/C][C]-3896.12887254801[/C][/ROW]
[ROW][C]164[/C][C]179994[/C][C]145370.717239751[/C][C]34623.282760249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1255202210422.72999234944779.2700076509
2135248163111.717738861-27863.717738861
3207223191538.49559745115684.5044025493
4189326249407.593706796-60081.593706796
5141365139884.1876257411480.81237425897
66529586599.7368143683-21304.7368143683
7439387276789.946430116162597.053569884
83318650971.6171897727-17785.6171897727
9183696163563.07077093320132.9292290673
10186657166116.43050005620540.5694999438
11276696242191.28423767834504.7157623222
12194414265802.001389178-71388.0013891779
13141409153056.72948032-11647.72948032
14306730249365.16184177657364.8381582235
15192691191683.7085855271007.2914144735
16333497388186.89613584-54689.8961358403
17261835221642.99408070440192.0059192956
18263451253722.2868498059728.71315019535
19157448183234.699150814-25786.6991508144
20232190265823.529733477-33633.5297334772
21245725240049.6322319535675.36776804683
22388603226755.649356559161847.350643441
23156540127128.58195156629411.4180484341
24156189217925.380547569-61736.3805475694
25189726284100.945272395-94374.9452723948
26192167231912.582271342-39745.5822713419
27249893260337.199043994-10444.1990439938
28236812242172.227137696-5360.2271376957
29143160142477.30905104682.690948959784
30259667230665.93662843829001.0633715617
31243020246311.374684181-3291.37468418073
32176062174339.7634187331722.23658126751
33286683272199.76051675914483.2394832408
3487485125247.420515768-37762.4205157683
35329737255850.85142363473886.1485763657
36247082187773.22238912159308.7776108791
37378463266292.210479125112170.789520875
38191653194167.172934553-2514.17293455286
3911467392931.17636899621741.823631004
40301596312826.164120488-11230.1641204877
41284195232818.98053013251376.0194698684
42155568179281.980013916-23713.9800139158
43177306182724.924524808-5418.92452480831
44144595168166.459856858-23571.4598568578
45140319190436.554506547-50117.5545065466
46405267267983.333200884137283.666799116
4778800100716.984898578-21916.9848985778
48201970240856.907065024-38886.9070650242
49302705293581.6851058859123.31489411542
50164733209400.820729873-44667.8207298728
51194221206798.270216846-12577.2702168459
522418838595.3080816343-14407.3080816343
53346142378316.491159807-32174.4911598069
546502971087.330599936-6058.33059993603
55101097109503.916204248-8406.91620424827
56253745195810.24328571457934.7567142864
57273513271592.6263487491920.37365125118
58282220292899.941337328-10679.941337328
59280928299302.298297856-18374.2982978555
60214872194493.90118723920378.0988127605
61342048324021.83249347618026.1675065237
62273924259729.98298578914194.0170142106
63195726205553.868196904-9827.86819690371
64231162198841.5964607132320.4035392897
65209798155646.98245907954151.0175409214
66201345191210.43000693210134.5699930679
67180231232027.777962999-51796.7779629991
68204441249544.714944976-45103.714944976
69197813233085.242490449-35272.2424904489
70136421228296.330633237-91875.3306332371
71216092212010.9545567194081.04544328083
727356690252.0528003523-16686.0528003523
73213998201471.72752314712526.2724768527
74181728188712.821702899-6984.82170289921
75148758214670.197277275-65912.197277275
76308343255530.68381213552812.3161878652
77251437211568.00858657339868.9914134274
78202388229039.054930719-26651.054930719
79173286236823.758897474-63537.7588974744
80155529176089.424121928-20560.4241219285
81132672141060.031603418-8388.03160341813
82390163301436.48337726488726.5166227364
83145905131342.22335792414562.7766420762
84228012250589.174577554-22577.1745775544
858095388752.2671263353-7799.26712633526
86130805114375.72833642816429.2716635715
87135163166981.303178956-31818.3031789558
88333790256588.42980113577201.5701988653
89271806262457.2401683959348.75983160458
90164235316183.731311615-151948.731311615
91234092206410.25192368427681.7480763158
92207158269627.343687654-62469.3436876536
93156583135508.51975428721074.4802457133
94242395237372.471471555022.5285284496
95261601173094.84780906488506.152190936
96178489168311.78923243610177.2107675635
97204221189313.51161722514907.4883827746
98268066279058.89273724-10992.8927372399
99327622342524.691126594-14902.6911265943
100361799282081.64057261579717.3594273855
101247131278801.551428574-31670.5514285741
102265849259010.2062606336838.793739367
103162336248539.227757776-86203.2277577764
1044328760163.3457683167-16876.3457683167
105172244189867.762806686-17623.7628066858
106189021248636.388375254-59615.3883752542
107227681223915.9478945043765.05210549592
108269329296917.957374548-27588.9573745479
109106503116552.345280856-10049.3452808561
110117891218960.178317414-101069.178317414
111287201193485.42851916793715.5714808333
112266805279950.638604947-13145.6386049468
1132362328029.324246758-4406.32424675803
114174954246153.432462304-71199.4324623042
1156185767427.1166349328-5570.11663493278
116144889193306.898088599-48417.8980885994
117347988274905.91654777873082.0834522222
1182105421457.8659433911-403.865943391068
119224051160572.18665271563478.8133472854
1203141450706.886338164-19292.886338164
121278660249343.4352197829316.5647802198
122209481196887.08022517512593.9197748251
123156870176987.61424332-20117.6142433196
124112933116854.361430673-3921.36143067329
1253821461566.7126827676-23352.7126827676
126166011158411.8747492317599.12525076865
127316044264562.93906009451481.0609399063
128181578176662.6153770394915.38462296145
129358903273395.05364299285507.9463570081
130275578303625.39785596-28047.3978559598
131368796313393.4195579955402.5804420102
132172464157401.24099522115062.7590047792
13394381129387.275422856-35006.2754228562
134250563364244.111687625-113681.111687625
135382499295582.83899081286916.1610091879
136118010147319.811247405-29309.8112474048
137365575303875.67008736261699.3299126379
138147989174748.369920585-26759.3699205849
139231681216726.78727526414954.2127247362
140193119210644.714313739-17525.7143137386
141189020172114.97836303416905.0216369664
142341958243661.67808568598296.3219143148
143222060252004.407326742-29944.4073267415
144173260132501.76229465740758.2377053434
145274787283271.826782995-8484.82678299499
146130908279137.393456959-148229.393456959
147204009212703.267119968-8694.2671199683
148262412325864.961619504-63452.9616195037
14915060.78809216535-5059.78809216535
1501468816930.8164912473-2242.81649124727
151985815.29633418932-5717.29633418932
1524556569.80457621329-6114.80457621329
15303356.11238850009-3356.11238850009
15405060.78809216535-5060.78809216535
155195765176399.02358556519365.976414435
156334258312845.02229594321412.9777040567
15705060.78809216535-5060.78809216535
1582038078.82106026122-7875.82106026122
159719916401.9847652591-9202.98476525913
1604666039891.65294451776768.34705548232
161175478822.761154422228724.23884557778
162107465138113.938713854-30648.9387138543
1639694865.12887254801-3896.12887254801
164179994145370.71723975134623.282760249







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7341225454806630.5317549090386740.265877454519337
90.623535338421640.752929323156720.37646466157836
100.5679412430393930.8641175139212150.432058756960607
110.6269797425761160.7460405148477670.373020257423884
120.5875745850381790.8248508299236410.412425414961821
130.4978769196208750.995753839241750.502123080379125
140.600961229418140.798077541163720.39903877058186
150.5555109236755450.888978152648910.444489076324455
160.6163306347369190.7673387305261620.383669365263081
170.5807442109758980.8385115780482050.419255789024102
180.5538396648210460.8923206703579080.446160335178954
190.5524620706797360.8950758586405290.447537929320264
200.520286524130410.9594269517391810.47971347586959
210.4668793052099010.9337586104198020.533120694790099
220.8314083052810460.3371833894379080.168591694718954
230.7868939832222470.4262120335555060.213106016777753
240.8047061917614330.3905876164771340.195293808238567
250.964025059648780.07194988070244070.0359749403512204
260.9670459287732490.0659081424535020.032954071226751
270.953943921214460.09211215757108020.0460560787855401
280.9371974425858880.1256051148282230.0628025574141116
290.9168653815329360.1662692369341270.0831346184670637
300.8945777665561540.2108444668876920.105422233443846
310.8670942395283850.2658115209432290.132905760471615
320.8385122175660340.3229755648679330.161487782433966
330.8019003864080690.3961992271838610.198099613591931
340.7672080935034640.4655838129930710.232791906496536
350.783673927728430.4326521445431410.21632607227157
360.830406463702630.339187072594740.16959353629737
370.9147813010850480.1704373978299050.0852186989149525
380.8915401709022810.2169196581954380.108459829097719
390.868103993648510.2637920127029790.13189600635149
400.8549568387780340.2900863224439310.145043161221966
410.8401752383890320.3196495232219360.159824761610968
420.8097953082728220.3804093834543560.190204691727178
430.7725210205655680.4549579588688630.227478979434432
440.7358493366254470.5283013267491060.264150663374553
450.7172062493751970.5655875012496070.282793750624803
460.9231476060164890.1537047879670230.0768523939835114
470.9065989863145620.1868020273708760.093401013685438
480.8917061125860490.2165877748279020.108293887413951
490.8677093972516920.2645812054966150.132290602748308
500.8546172821161760.2907654357676470.145382717883824
510.8273603834802720.3452792330394560.172639616519728
520.8278830125686180.3442339748627640.172116987431382
530.8311920448928740.3376159102142520.168807955107126
540.8017520238394540.3964959523210910.198247976160546
550.7756416988699510.4487166022600980.224358301130049
560.791957656946660.416084686106680.20804234305334
570.7567611058883220.4864777882233560.243238894111678
580.7239209116348110.5521581767303780.276079088365189
590.6979594982148370.6040810035703250.302040501785163
600.6641048375450150.671790324909970.335895162454985
610.6295343766411590.7409312467176810.370465623358841
620.6056031760011940.7887936479976120.394396823998806
630.5660433567762090.8679132864475810.433956643223791
640.5490705348259890.9018589303480220.450929465174011
650.5616903203661890.8766193592676230.438309679633811
660.5162424909272790.9675150181454420.483757509072721
670.5336772725901990.9326454548196030.466322727409801
680.5182183237367660.9635633525264690.481781676263234
690.5210485675352270.9579028649295450.478951432464773
700.6598466400241730.6803067199516540.340153359975827
710.6181248186041320.7637503627917350.381875181395868
720.579895189348610.8402096213027790.42010481065139
730.5377242023976830.9245515952046350.462275797602317
740.4935261674491450.9870523348982890.506473832550855
750.5425987147615130.9148025704769730.457401285238487
760.5499883797422510.9000232405154980.450011620257749
770.5351865681432590.9296268637134820.464813431856741
780.5037061737547750.992587652490450.496293826245225
790.5420076193905020.9159847612189960.457992380609498
800.5047155765156410.9905688469687190.495284423484359
810.4612585335450930.9225170670901860.538741466454907
820.5639934625085890.8720130749828220.436006537491411
830.5234576361441990.9530847277116020.476542363855801
840.4879681299474570.9759362598949130.512031870052543
850.4438612475102170.8877224950204340.556138752489783
860.4048992512615470.8097985025230930.595100748738453
870.3799788565070160.7599577130140320.620021143492984
880.4492797650312390.8985595300624780.550720234968761
890.4061469963417210.8122939926834430.593853003658279
900.770211849046770.459576301906460.22978815095323
910.7453825920313590.5092348159372820.254617407968641
920.7637855817131590.4724288365736810.236214418286841
930.7368097273295280.5263805453409440.263190272670472
940.6986761370165710.6026477259668580.301323862983429
950.7830678504444330.4338642991111330.216932149555566
960.748824220934520.502351558130960.25117577906548
970.7152505852356160.5694988295287680.284749414764384
980.6800231016437550.639953796712490.319976898356245
990.639469376765030.7210612464699410.36053062323497
1000.7024947170237280.5950105659525430.297505282976272
1010.6730373345220490.6539253309559020.326962665477951
1020.6300575260166250.7398849479667490.369942473983375
1030.7033255003994540.5933489992010910.296674499600546
1040.6675447584810010.6649104830379990.332455241518999
1050.6277059723493240.7445880553013530.372294027650676
1060.6423194854401770.7153610291196450.357680514559823
1070.5965661816521170.8068676366957650.403433818347883
1080.5622780200462470.8754439599075060.437721979953753
1090.5156389881255850.968722023748830.484361011874415
1100.6736340981809180.6527318036381650.326365901819082
1110.7758349583244060.4483300833511870.224165041675594
1120.7399267923742130.5201464152515750.260073207625787
1130.6979908428562010.6040183142875980.302009157143799
1140.7570389064168010.4859221871663980.242961093583199
1150.7157911539153160.5684176921693690.284208846084684
1160.7240336910693360.5519326178613280.275966308930664
1170.7638628898418540.4722742203162920.236137110158146
1180.7219999901169730.5560000197660530.278000009883027
1190.7466115531574560.5067768936850890.253388446842545
1200.70934152928020.58131694143960.2906584707198
1210.6792690196738230.6414619606523550.320730980326177
1220.6317755665165240.7364488669669520.368224433483476
1230.5902086593272670.8195826813454670.409791340672733
1240.5371385141294860.9257229717410280.462861485870514
1250.4932835108905720.9865670217811430.506716489109428
1260.4384703547325610.8769407094651220.561529645267439
1270.4376893296054280.8753786592108560.562310670394572
1280.3828058843527140.7656117687054290.617194115647286
1290.50793033060810.98413933878380.4920696693919
1300.4596485452120880.9192970904241750.540351454787912
1310.5028122826968640.9943754346062710.497187717303136
1320.4547936195700540.9095872391401080.545206380429946
1330.4195936498166390.8391872996332790.580406350183361
1340.9981170031980950.00376599360380970.00188299680190485
1350.9973653774778630.005269245044273670.00263462252213683
1360.9955654348159840.008869130368031270.00443456518401563
1370.9951527494109140.00969450117817130.00484725058908565
1380.9951308385224120.009738322955176020.00486916147758801
1390.9996356932704980.0007286134590047930.000364306729502397
1400.9992827714643960.001434457071207560.000717228535603781
1410.999999612643417.74713180659723e-073.87356590329862e-07
1420.9999999953036829.39263547749333e-094.69631773874666e-09
1430.9999999804555363.90889280316553e-081.95444640158277e-08
1440.9999999176333781.64733244345589e-078.23666221727945e-08
1450.9999999855670682.88658637917459e-081.44329318958729e-08
1460.9999999988345312.33093771757632e-091.16546885878816e-09
1470.999999992198271.5603459296075e-087.80172964803749e-09
1480.999999949728841.00542319741233e-075.02711598706167e-08
1490.9999997154328455.69134310521702e-072.84567155260851e-07
1500.9999982901601673.41967966699336e-061.70983983349668e-06
1510.9999898986655472.02026689060243e-051.01013344530122e-05
1520.9999413716574670.0001172566850651855.86283425325927e-05
1530.9996995162428140.0006009675143717790.000300483757185889
1540.9986575208525580.002684958294883480.00134247914744174
1550.9961545861863820.007690827627235450.00384541381361773
1560.9914724110993770.01705517780124690.00852758890062346

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.734122545480663 & 0.531754909038674 & 0.265877454519337 \tabularnewline
9 & 0.62353533842164 & 0.75292932315672 & 0.37646466157836 \tabularnewline
10 & 0.567941243039393 & 0.864117513921215 & 0.432058756960607 \tabularnewline
11 & 0.626979742576116 & 0.746040514847767 & 0.373020257423884 \tabularnewline
12 & 0.587574585038179 & 0.824850829923641 & 0.412425414961821 \tabularnewline
13 & 0.497876919620875 & 0.99575383924175 & 0.502123080379125 \tabularnewline
14 & 0.60096122941814 & 0.79807754116372 & 0.39903877058186 \tabularnewline
15 & 0.555510923675545 & 0.88897815264891 & 0.444489076324455 \tabularnewline
16 & 0.616330634736919 & 0.767338730526162 & 0.383669365263081 \tabularnewline
17 & 0.580744210975898 & 0.838511578048205 & 0.419255789024102 \tabularnewline
18 & 0.553839664821046 & 0.892320670357908 & 0.446160335178954 \tabularnewline
19 & 0.552462070679736 & 0.895075858640529 & 0.447537929320264 \tabularnewline
20 & 0.52028652413041 & 0.959426951739181 & 0.47971347586959 \tabularnewline
21 & 0.466879305209901 & 0.933758610419802 & 0.533120694790099 \tabularnewline
22 & 0.831408305281046 & 0.337183389437908 & 0.168591694718954 \tabularnewline
23 & 0.786893983222247 & 0.426212033555506 & 0.213106016777753 \tabularnewline
24 & 0.804706191761433 & 0.390587616477134 & 0.195293808238567 \tabularnewline
25 & 0.96402505964878 & 0.0719498807024407 & 0.0359749403512204 \tabularnewline
26 & 0.967045928773249 & 0.065908142453502 & 0.032954071226751 \tabularnewline
27 & 0.95394392121446 & 0.0921121575710802 & 0.0460560787855401 \tabularnewline
28 & 0.937197442585888 & 0.125605114828223 & 0.0628025574141116 \tabularnewline
29 & 0.916865381532936 & 0.166269236934127 & 0.0831346184670637 \tabularnewline
30 & 0.894577766556154 & 0.210844466887692 & 0.105422233443846 \tabularnewline
31 & 0.867094239528385 & 0.265811520943229 & 0.132905760471615 \tabularnewline
32 & 0.838512217566034 & 0.322975564867933 & 0.161487782433966 \tabularnewline
33 & 0.801900386408069 & 0.396199227183861 & 0.198099613591931 \tabularnewline
34 & 0.767208093503464 & 0.465583812993071 & 0.232791906496536 \tabularnewline
35 & 0.78367392772843 & 0.432652144543141 & 0.21632607227157 \tabularnewline
36 & 0.83040646370263 & 0.33918707259474 & 0.16959353629737 \tabularnewline
37 & 0.914781301085048 & 0.170437397829905 & 0.0852186989149525 \tabularnewline
38 & 0.891540170902281 & 0.216919658195438 & 0.108459829097719 \tabularnewline
39 & 0.86810399364851 & 0.263792012702979 & 0.13189600635149 \tabularnewline
40 & 0.854956838778034 & 0.290086322443931 & 0.145043161221966 \tabularnewline
41 & 0.840175238389032 & 0.319649523221936 & 0.159824761610968 \tabularnewline
42 & 0.809795308272822 & 0.380409383454356 & 0.190204691727178 \tabularnewline
43 & 0.772521020565568 & 0.454957958868863 & 0.227478979434432 \tabularnewline
44 & 0.735849336625447 & 0.528301326749106 & 0.264150663374553 \tabularnewline
45 & 0.717206249375197 & 0.565587501249607 & 0.282793750624803 \tabularnewline
46 & 0.923147606016489 & 0.153704787967023 & 0.0768523939835114 \tabularnewline
47 & 0.906598986314562 & 0.186802027370876 & 0.093401013685438 \tabularnewline
48 & 0.891706112586049 & 0.216587774827902 & 0.108293887413951 \tabularnewline
49 & 0.867709397251692 & 0.264581205496615 & 0.132290602748308 \tabularnewline
50 & 0.854617282116176 & 0.290765435767647 & 0.145382717883824 \tabularnewline
51 & 0.827360383480272 & 0.345279233039456 & 0.172639616519728 \tabularnewline
52 & 0.827883012568618 & 0.344233974862764 & 0.172116987431382 \tabularnewline
53 & 0.831192044892874 & 0.337615910214252 & 0.168807955107126 \tabularnewline
54 & 0.801752023839454 & 0.396495952321091 & 0.198247976160546 \tabularnewline
55 & 0.775641698869951 & 0.448716602260098 & 0.224358301130049 \tabularnewline
56 & 0.79195765694666 & 0.41608468610668 & 0.20804234305334 \tabularnewline
57 & 0.756761105888322 & 0.486477788223356 & 0.243238894111678 \tabularnewline
58 & 0.723920911634811 & 0.552158176730378 & 0.276079088365189 \tabularnewline
59 & 0.697959498214837 & 0.604081003570325 & 0.302040501785163 \tabularnewline
60 & 0.664104837545015 & 0.67179032490997 & 0.335895162454985 \tabularnewline
61 & 0.629534376641159 & 0.740931246717681 & 0.370465623358841 \tabularnewline
62 & 0.605603176001194 & 0.788793647997612 & 0.394396823998806 \tabularnewline
63 & 0.566043356776209 & 0.867913286447581 & 0.433956643223791 \tabularnewline
64 & 0.549070534825989 & 0.901858930348022 & 0.450929465174011 \tabularnewline
65 & 0.561690320366189 & 0.876619359267623 & 0.438309679633811 \tabularnewline
66 & 0.516242490927279 & 0.967515018145442 & 0.483757509072721 \tabularnewline
67 & 0.533677272590199 & 0.932645454819603 & 0.466322727409801 \tabularnewline
68 & 0.518218323736766 & 0.963563352526469 & 0.481781676263234 \tabularnewline
69 & 0.521048567535227 & 0.957902864929545 & 0.478951432464773 \tabularnewline
70 & 0.659846640024173 & 0.680306719951654 & 0.340153359975827 \tabularnewline
71 & 0.618124818604132 & 0.763750362791735 & 0.381875181395868 \tabularnewline
72 & 0.57989518934861 & 0.840209621302779 & 0.42010481065139 \tabularnewline
73 & 0.537724202397683 & 0.924551595204635 & 0.462275797602317 \tabularnewline
74 & 0.493526167449145 & 0.987052334898289 & 0.506473832550855 \tabularnewline
75 & 0.542598714761513 & 0.914802570476973 & 0.457401285238487 \tabularnewline
76 & 0.549988379742251 & 0.900023240515498 & 0.450011620257749 \tabularnewline
77 & 0.535186568143259 & 0.929626863713482 & 0.464813431856741 \tabularnewline
78 & 0.503706173754775 & 0.99258765249045 & 0.496293826245225 \tabularnewline
79 & 0.542007619390502 & 0.915984761218996 & 0.457992380609498 \tabularnewline
80 & 0.504715576515641 & 0.990568846968719 & 0.495284423484359 \tabularnewline
81 & 0.461258533545093 & 0.922517067090186 & 0.538741466454907 \tabularnewline
82 & 0.563993462508589 & 0.872013074982822 & 0.436006537491411 \tabularnewline
83 & 0.523457636144199 & 0.953084727711602 & 0.476542363855801 \tabularnewline
84 & 0.487968129947457 & 0.975936259894913 & 0.512031870052543 \tabularnewline
85 & 0.443861247510217 & 0.887722495020434 & 0.556138752489783 \tabularnewline
86 & 0.404899251261547 & 0.809798502523093 & 0.595100748738453 \tabularnewline
87 & 0.379978856507016 & 0.759957713014032 & 0.620021143492984 \tabularnewline
88 & 0.449279765031239 & 0.898559530062478 & 0.550720234968761 \tabularnewline
89 & 0.406146996341721 & 0.812293992683443 & 0.593853003658279 \tabularnewline
90 & 0.77021184904677 & 0.45957630190646 & 0.22978815095323 \tabularnewline
91 & 0.745382592031359 & 0.509234815937282 & 0.254617407968641 \tabularnewline
92 & 0.763785581713159 & 0.472428836573681 & 0.236214418286841 \tabularnewline
93 & 0.736809727329528 & 0.526380545340944 & 0.263190272670472 \tabularnewline
94 & 0.698676137016571 & 0.602647725966858 & 0.301323862983429 \tabularnewline
95 & 0.783067850444433 & 0.433864299111133 & 0.216932149555566 \tabularnewline
96 & 0.74882422093452 & 0.50235155813096 & 0.25117577906548 \tabularnewline
97 & 0.715250585235616 & 0.569498829528768 & 0.284749414764384 \tabularnewline
98 & 0.680023101643755 & 0.63995379671249 & 0.319976898356245 \tabularnewline
99 & 0.63946937676503 & 0.721061246469941 & 0.36053062323497 \tabularnewline
100 & 0.702494717023728 & 0.595010565952543 & 0.297505282976272 \tabularnewline
101 & 0.673037334522049 & 0.653925330955902 & 0.326962665477951 \tabularnewline
102 & 0.630057526016625 & 0.739884947966749 & 0.369942473983375 \tabularnewline
103 & 0.703325500399454 & 0.593348999201091 & 0.296674499600546 \tabularnewline
104 & 0.667544758481001 & 0.664910483037999 & 0.332455241518999 \tabularnewline
105 & 0.627705972349324 & 0.744588055301353 & 0.372294027650676 \tabularnewline
106 & 0.642319485440177 & 0.715361029119645 & 0.357680514559823 \tabularnewline
107 & 0.596566181652117 & 0.806867636695765 & 0.403433818347883 \tabularnewline
108 & 0.562278020046247 & 0.875443959907506 & 0.437721979953753 \tabularnewline
109 & 0.515638988125585 & 0.96872202374883 & 0.484361011874415 \tabularnewline
110 & 0.673634098180918 & 0.652731803638165 & 0.326365901819082 \tabularnewline
111 & 0.775834958324406 & 0.448330083351187 & 0.224165041675594 \tabularnewline
112 & 0.739926792374213 & 0.520146415251575 & 0.260073207625787 \tabularnewline
113 & 0.697990842856201 & 0.604018314287598 & 0.302009157143799 \tabularnewline
114 & 0.757038906416801 & 0.485922187166398 & 0.242961093583199 \tabularnewline
115 & 0.715791153915316 & 0.568417692169369 & 0.284208846084684 \tabularnewline
116 & 0.724033691069336 & 0.551932617861328 & 0.275966308930664 \tabularnewline
117 & 0.763862889841854 & 0.472274220316292 & 0.236137110158146 \tabularnewline
118 & 0.721999990116973 & 0.556000019766053 & 0.278000009883027 \tabularnewline
119 & 0.746611553157456 & 0.506776893685089 & 0.253388446842545 \tabularnewline
120 & 0.7093415292802 & 0.5813169414396 & 0.2906584707198 \tabularnewline
121 & 0.679269019673823 & 0.641461960652355 & 0.320730980326177 \tabularnewline
122 & 0.631775566516524 & 0.736448866966952 & 0.368224433483476 \tabularnewline
123 & 0.590208659327267 & 0.819582681345467 & 0.409791340672733 \tabularnewline
124 & 0.537138514129486 & 0.925722971741028 & 0.462861485870514 \tabularnewline
125 & 0.493283510890572 & 0.986567021781143 & 0.506716489109428 \tabularnewline
126 & 0.438470354732561 & 0.876940709465122 & 0.561529645267439 \tabularnewline
127 & 0.437689329605428 & 0.875378659210856 & 0.562310670394572 \tabularnewline
128 & 0.382805884352714 & 0.765611768705429 & 0.617194115647286 \tabularnewline
129 & 0.5079303306081 & 0.9841393387838 & 0.4920696693919 \tabularnewline
130 & 0.459648545212088 & 0.919297090424175 & 0.540351454787912 \tabularnewline
131 & 0.502812282696864 & 0.994375434606271 & 0.497187717303136 \tabularnewline
132 & 0.454793619570054 & 0.909587239140108 & 0.545206380429946 \tabularnewline
133 & 0.419593649816639 & 0.839187299633279 & 0.580406350183361 \tabularnewline
134 & 0.998117003198095 & 0.0037659936038097 & 0.00188299680190485 \tabularnewline
135 & 0.997365377477863 & 0.00526924504427367 & 0.00263462252213683 \tabularnewline
136 & 0.995565434815984 & 0.00886913036803127 & 0.00443456518401563 \tabularnewline
137 & 0.995152749410914 & 0.0096945011781713 & 0.00484725058908565 \tabularnewline
138 & 0.995130838522412 & 0.00973832295517602 & 0.00486916147758801 \tabularnewline
139 & 0.999635693270498 & 0.000728613459004793 & 0.000364306729502397 \tabularnewline
140 & 0.999282771464396 & 0.00143445707120756 & 0.000717228535603781 \tabularnewline
141 & 0.99999961264341 & 7.74713180659723e-07 & 3.87356590329862e-07 \tabularnewline
142 & 0.999999995303682 & 9.39263547749333e-09 & 4.69631773874666e-09 \tabularnewline
143 & 0.999999980455536 & 3.90889280316553e-08 & 1.95444640158277e-08 \tabularnewline
144 & 0.999999917633378 & 1.64733244345589e-07 & 8.23666221727945e-08 \tabularnewline
145 & 0.999999985567068 & 2.88658637917459e-08 & 1.44329318958729e-08 \tabularnewline
146 & 0.999999998834531 & 2.33093771757632e-09 & 1.16546885878816e-09 \tabularnewline
147 & 0.99999999219827 & 1.5603459296075e-08 & 7.80172964803749e-09 \tabularnewline
148 & 0.99999994972884 & 1.00542319741233e-07 & 5.02711598706167e-08 \tabularnewline
149 & 0.999999715432845 & 5.69134310521702e-07 & 2.84567155260851e-07 \tabularnewline
150 & 0.999998290160167 & 3.41967966699336e-06 & 1.70983983349668e-06 \tabularnewline
151 & 0.999989898665547 & 2.02026689060243e-05 & 1.01013344530122e-05 \tabularnewline
152 & 0.999941371657467 & 0.000117256685065185 & 5.86283425325927e-05 \tabularnewline
153 & 0.999699516242814 & 0.000600967514371779 & 0.000300483757185889 \tabularnewline
154 & 0.998657520852558 & 0.00268495829488348 & 0.00134247914744174 \tabularnewline
155 & 0.996154586186382 & 0.00769082762723545 & 0.00384541381361773 \tabularnewline
156 & 0.991472411099377 & 0.0170551778012469 & 0.00852758890062346 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&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]8[/C][C]0.734122545480663[/C][C]0.531754909038674[/C][C]0.265877454519337[/C][/ROW]
[ROW][C]9[/C][C]0.62353533842164[/C][C]0.75292932315672[/C][C]0.37646466157836[/C][/ROW]
[ROW][C]10[/C][C]0.567941243039393[/C][C]0.864117513921215[/C][C]0.432058756960607[/C][/ROW]
[ROW][C]11[/C][C]0.626979742576116[/C][C]0.746040514847767[/C][C]0.373020257423884[/C][/ROW]
[ROW][C]12[/C][C]0.587574585038179[/C][C]0.824850829923641[/C][C]0.412425414961821[/C][/ROW]
[ROW][C]13[/C][C]0.497876919620875[/C][C]0.99575383924175[/C][C]0.502123080379125[/C][/ROW]
[ROW][C]14[/C][C]0.60096122941814[/C][C]0.79807754116372[/C][C]0.39903877058186[/C][/ROW]
[ROW][C]15[/C][C]0.555510923675545[/C][C]0.88897815264891[/C][C]0.444489076324455[/C][/ROW]
[ROW][C]16[/C][C]0.616330634736919[/C][C]0.767338730526162[/C][C]0.383669365263081[/C][/ROW]
[ROW][C]17[/C][C]0.580744210975898[/C][C]0.838511578048205[/C][C]0.419255789024102[/C][/ROW]
[ROW][C]18[/C][C]0.553839664821046[/C][C]0.892320670357908[/C][C]0.446160335178954[/C][/ROW]
[ROW][C]19[/C][C]0.552462070679736[/C][C]0.895075858640529[/C][C]0.447537929320264[/C][/ROW]
[ROW][C]20[/C][C]0.52028652413041[/C][C]0.959426951739181[/C][C]0.47971347586959[/C][/ROW]
[ROW][C]21[/C][C]0.466879305209901[/C][C]0.933758610419802[/C][C]0.533120694790099[/C][/ROW]
[ROW][C]22[/C][C]0.831408305281046[/C][C]0.337183389437908[/C][C]0.168591694718954[/C][/ROW]
[ROW][C]23[/C][C]0.786893983222247[/C][C]0.426212033555506[/C][C]0.213106016777753[/C][/ROW]
[ROW][C]24[/C][C]0.804706191761433[/C][C]0.390587616477134[/C][C]0.195293808238567[/C][/ROW]
[ROW][C]25[/C][C]0.96402505964878[/C][C]0.0719498807024407[/C][C]0.0359749403512204[/C][/ROW]
[ROW][C]26[/C][C]0.967045928773249[/C][C]0.065908142453502[/C][C]0.032954071226751[/C][/ROW]
[ROW][C]27[/C][C]0.95394392121446[/C][C]0.0921121575710802[/C][C]0.0460560787855401[/C][/ROW]
[ROW][C]28[/C][C]0.937197442585888[/C][C]0.125605114828223[/C][C]0.0628025574141116[/C][/ROW]
[ROW][C]29[/C][C]0.916865381532936[/C][C]0.166269236934127[/C][C]0.0831346184670637[/C][/ROW]
[ROW][C]30[/C][C]0.894577766556154[/C][C]0.210844466887692[/C][C]0.105422233443846[/C][/ROW]
[ROW][C]31[/C][C]0.867094239528385[/C][C]0.265811520943229[/C][C]0.132905760471615[/C][/ROW]
[ROW][C]32[/C][C]0.838512217566034[/C][C]0.322975564867933[/C][C]0.161487782433966[/C][/ROW]
[ROW][C]33[/C][C]0.801900386408069[/C][C]0.396199227183861[/C][C]0.198099613591931[/C][/ROW]
[ROW][C]34[/C][C]0.767208093503464[/C][C]0.465583812993071[/C][C]0.232791906496536[/C][/ROW]
[ROW][C]35[/C][C]0.78367392772843[/C][C]0.432652144543141[/C][C]0.21632607227157[/C][/ROW]
[ROW][C]36[/C][C]0.83040646370263[/C][C]0.33918707259474[/C][C]0.16959353629737[/C][/ROW]
[ROW][C]37[/C][C]0.914781301085048[/C][C]0.170437397829905[/C][C]0.0852186989149525[/C][/ROW]
[ROW][C]38[/C][C]0.891540170902281[/C][C]0.216919658195438[/C][C]0.108459829097719[/C][/ROW]
[ROW][C]39[/C][C]0.86810399364851[/C][C]0.263792012702979[/C][C]0.13189600635149[/C][/ROW]
[ROW][C]40[/C][C]0.854956838778034[/C][C]0.290086322443931[/C][C]0.145043161221966[/C][/ROW]
[ROW][C]41[/C][C]0.840175238389032[/C][C]0.319649523221936[/C][C]0.159824761610968[/C][/ROW]
[ROW][C]42[/C][C]0.809795308272822[/C][C]0.380409383454356[/C][C]0.190204691727178[/C][/ROW]
[ROW][C]43[/C][C]0.772521020565568[/C][C]0.454957958868863[/C][C]0.227478979434432[/C][/ROW]
[ROW][C]44[/C][C]0.735849336625447[/C][C]0.528301326749106[/C][C]0.264150663374553[/C][/ROW]
[ROW][C]45[/C][C]0.717206249375197[/C][C]0.565587501249607[/C][C]0.282793750624803[/C][/ROW]
[ROW][C]46[/C][C]0.923147606016489[/C][C]0.153704787967023[/C][C]0.0768523939835114[/C][/ROW]
[ROW][C]47[/C][C]0.906598986314562[/C][C]0.186802027370876[/C][C]0.093401013685438[/C][/ROW]
[ROW][C]48[/C][C]0.891706112586049[/C][C]0.216587774827902[/C][C]0.108293887413951[/C][/ROW]
[ROW][C]49[/C][C]0.867709397251692[/C][C]0.264581205496615[/C][C]0.132290602748308[/C][/ROW]
[ROW][C]50[/C][C]0.854617282116176[/C][C]0.290765435767647[/C][C]0.145382717883824[/C][/ROW]
[ROW][C]51[/C][C]0.827360383480272[/C][C]0.345279233039456[/C][C]0.172639616519728[/C][/ROW]
[ROW][C]52[/C][C]0.827883012568618[/C][C]0.344233974862764[/C][C]0.172116987431382[/C][/ROW]
[ROW][C]53[/C][C]0.831192044892874[/C][C]0.337615910214252[/C][C]0.168807955107126[/C][/ROW]
[ROW][C]54[/C][C]0.801752023839454[/C][C]0.396495952321091[/C][C]0.198247976160546[/C][/ROW]
[ROW][C]55[/C][C]0.775641698869951[/C][C]0.448716602260098[/C][C]0.224358301130049[/C][/ROW]
[ROW][C]56[/C][C]0.79195765694666[/C][C]0.41608468610668[/C][C]0.20804234305334[/C][/ROW]
[ROW][C]57[/C][C]0.756761105888322[/C][C]0.486477788223356[/C][C]0.243238894111678[/C][/ROW]
[ROW][C]58[/C][C]0.723920911634811[/C][C]0.552158176730378[/C][C]0.276079088365189[/C][/ROW]
[ROW][C]59[/C][C]0.697959498214837[/C][C]0.604081003570325[/C][C]0.302040501785163[/C][/ROW]
[ROW][C]60[/C][C]0.664104837545015[/C][C]0.67179032490997[/C][C]0.335895162454985[/C][/ROW]
[ROW][C]61[/C][C]0.629534376641159[/C][C]0.740931246717681[/C][C]0.370465623358841[/C][/ROW]
[ROW][C]62[/C][C]0.605603176001194[/C][C]0.788793647997612[/C][C]0.394396823998806[/C][/ROW]
[ROW][C]63[/C][C]0.566043356776209[/C][C]0.867913286447581[/C][C]0.433956643223791[/C][/ROW]
[ROW][C]64[/C][C]0.549070534825989[/C][C]0.901858930348022[/C][C]0.450929465174011[/C][/ROW]
[ROW][C]65[/C][C]0.561690320366189[/C][C]0.876619359267623[/C][C]0.438309679633811[/C][/ROW]
[ROW][C]66[/C][C]0.516242490927279[/C][C]0.967515018145442[/C][C]0.483757509072721[/C][/ROW]
[ROW][C]67[/C][C]0.533677272590199[/C][C]0.932645454819603[/C][C]0.466322727409801[/C][/ROW]
[ROW][C]68[/C][C]0.518218323736766[/C][C]0.963563352526469[/C][C]0.481781676263234[/C][/ROW]
[ROW][C]69[/C][C]0.521048567535227[/C][C]0.957902864929545[/C][C]0.478951432464773[/C][/ROW]
[ROW][C]70[/C][C]0.659846640024173[/C][C]0.680306719951654[/C][C]0.340153359975827[/C][/ROW]
[ROW][C]71[/C][C]0.618124818604132[/C][C]0.763750362791735[/C][C]0.381875181395868[/C][/ROW]
[ROW][C]72[/C][C]0.57989518934861[/C][C]0.840209621302779[/C][C]0.42010481065139[/C][/ROW]
[ROW][C]73[/C][C]0.537724202397683[/C][C]0.924551595204635[/C][C]0.462275797602317[/C][/ROW]
[ROW][C]74[/C][C]0.493526167449145[/C][C]0.987052334898289[/C][C]0.506473832550855[/C][/ROW]
[ROW][C]75[/C][C]0.542598714761513[/C][C]0.914802570476973[/C][C]0.457401285238487[/C][/ROW]
[ROW][C]76[/C][C]0.549988379742251[/C][C]0.900023240515498[/C][C]0.450011620257749[/C][/ROW]
[ROW][C]77[/C][C]0.535186568143259[/C][C]0.929626863713482[/C][C]0.464813431856741[/C][/ROW]
[ROW][C]78[/C][C]0.503706173754775[/C][C]0.99258765249045[/C][C]0.496293826245225[/C][/ROW]
[ROW][C]79[/C][C]0.542007619390502[/C][C]0.915984761218996[/C][C]0.457992380609498[/C][/ROW]
[ROW][C]80[/C][C]0.504715576515641[/C][C]0.990568846968719[/C][C]0.495284423484359[/C][/ROW]
[ROW][C]81[/C][C]0.461258533545093[/C][C]0.922517067090186[/C][C]0.538741466454907[/C][/ROW]
[ROW][C]82[/C][C]0.563993462508589[/C][C]0.872013074982822[/C][C]0.436006537491411[/C][/ROW]
[ROW][C]83[/C][C]0.523457636144199[/C][C]0.953084727711602[/C][C]0.476542363855801[/C][/ROW]
[ROW][C]84[/C][C]0.487968129947457[/C][C]0.975936259894913[/C][C]0.512031870052543[/C][/ROW]
[ROW][C]85[/C][C]0.443861247510217[/C][C]0.887722495020434[/C][C]0.556138752489783[/C][/ROW]
[ROW][C]86[/C][C]0.404899251261547[/C][C]0.809798502523093[/C][C]0.595100748738453[/C][/ROW]
[ROW][C]87[/C][C]0.379978856507016[/C][C]0.759957713014032[/C][C]0.620021143492984[/C][/ROW]
[ROW][C]88[/C][C]0.449279765031239[/C][C]0.898559530062478[/C][C]0.550720234968761[/C][/ROW]
[ROW][C]89[/C][C]0.406146996341721[/C][C]0.812293992683443[/C][C]0.593853003658279[/C][/ROW]
[ROW][C]90[/C][C]0.77021184904677[/C][C]0.45957630190646[/C][C]0.22978815095323[/C][/ROW]
[ROW][C]91[/C][C]0.745382592031359[/C][C]0.509234815937282[/C][C]0.254617407968641[/C][/ROW]
[ROW][C]92[/C][C]0.763785581713159[/C][C]0.472428836573681[/C][C]0.236214418286841[/C][/ROW]
[ROW][C]93[/C][C]0.736809727329528[/C][C]0.526380545340944[/C][C]0.263190272670472[/C][/ROW]
[ROW][C]94[/C][C]0.698676137016571[/C][C]0.602647725966858[/C][C]0.301323862983429[/C][/ROW]
[ROW][C]95[/C][C]0.783067850444433[/C][C]0.433864299111133[/C][C]0.216932149555566[/C][/ROW]
[ROW][C]96[/C][C]0.74882422093452[/C][C]0.50235155813096[/C][C]0.25117577906548[/C][/ROW]
[ROW][C]97[/C][C]0.715250585235616[/C][C]0.569498829528768[/C][C]0.284749414764384[/C][/ROW]
[ROW][C]98[/C][C]0.680023101643755[/C][C]0.63995379671249[/C][C]0.319976898356245[/C][/ROW]
[ROW][C]99[/C][C]0.63946937676503[/C][C]0.721061246469941[/C][C]0.36053062323497[/C][/ROW]
[ROW][C]100[/C][C]0.702494717023728[/C][C]0.595010565952543[/C][C]0.297505282976272[/C][/ROW]
[ROW][C]101[/C][C]0.673037334522049[/C][C]0.653925330955902[/C][C]0.326962665477951[/C][/ROW]
[ROW][C]102[/C][C]0.630057526016625[/C][C]0.739884947966749[/C][C]0.369942473983375[/C][/ROW]
[ROW][C]103[/C][C]0.703325500399454[/C][C]0.593348999201091[/C][C]0.296674499600546[/C][/ROW]
[ROW][C]104[/C][C]0.667544758481001[/C][C]0.664910483037999[/C][C]0.332455241518999[/C][/ROW]
[ROW][C]105[/C][C]0.627705972349324[/C][C]0.744588055301353[/C][C]0.372294027650676[/C][/ROW]
[ROW][C]106[/C][C]0.642319485440177[/C][C]0.715361029119645[/C][C]0.357680514559823[/C][/ROW]
[ROW][C]107[/C][C]0.596566181652117[/C][C]0.806867636695765[/C][C]0.403433818347883[/C][/ROW]
[ROW][C]108[/C][C]0.562278020046247[/C][C]0.875443959907506[/C][C]0.437721979953753[/C][/ROW]
[ROW][C]109[/C][C]0.515638988125585[/C][C]0.96872202374883[/C][C]0.484361011874415[/C][/ROW]
[ROW][C]110[/C][C]0.673634098180918[/C][C]0.652731803638165[/C][C]0.326365901819082[/C][/ROW]
[ROW][C]111[/C][C]0.775834958324406[/C][C]0.448330083351187[/C][C]0.224165041675594[/C][/ROW]
[ROW][C]112[/C][C]0.739926792374213[/C][C]0.520146415251575[/C][C]0.260073207625787[/C][/ROW]
[ROW][C]113[/C][C]0.697990842856201[/C][C]0.604018314287598[/C][C]0.302009157143799[/C][/ROW]
[ROW][C]114[/C][C]0.757038906416801[/C][C]0.485922187166398[/C][C]0.242961093583199[/C][/ROW]
[ROW][C]115[/C][C]0.715791153915316[/C][C]0.568417692169369[/C][C]0.284208846084684[/C][/ROW]
[ROW][C]116[/C][C]0.724033691069336[/C][C]0.551932617861328[/C][C]0.275966308930664[/C][/ROW]
[ROW][C]117[/C][C]0.763862889841854[/C][C]0.472274220316292[/C][C]0.236137110158146[/C][/ROW]
[ROW][C]118[/C][C]0.721999990116973[/C][C]0.556000019766053[/C][C]0.278000009883027[/C][/ROW]
[ROW][C]119[/C][C]0.746611553157456[/C][C]0.506776893685089[/C][C]0.253388446842545[/C][/ROW]
[ROW][C]120[/C][C]0.7093415292802[/C][C]0.5813169414396[/C][C]0.2906584707198[/C][/ROW]
[ROW][C]121[/C][C]0.679269019673823[/C][C]0.641461960652355[/C][C]0.320730980326177[/C][/ROW]
[ROW][C]122[/C][C]0.631775566516524[/C][C]0.736448866966952[/C][C]0.368224433483476[/C][/ROW]
[ROW][C]123[/C][C]0.590208659327267[/C][C]0.819582681345467[/C][C]0.409791340672733[/C][/ROW]
[ROW][C]124[/C][C]0.537138514129486[/C][C]0.925722971741028[/C][C]0.462861485870514[/C][/ROW]
[ROW][C]125[/C][C]0.493283510890572[/C][C]0.986567021781143[/C][C]0.506716489109428[/C][/ROW]
[ROW][C]126[/C][C]0.438470354732561[/C][C]0.876940709465122[/C][C]0.561529645267439[/C][/ROW]
[ROW][C]127[/C][C]0.437689329605428[/C][C]0.875378659210856[/C][C]0.562310670394572[/C][/ROW]
[ROW][C]128[/C][C]0.382805884352714[/C][C]0.765611768705429[/C][C]0.617194115647286[/C][/ROW]
[ROW][C]129[/C][C]0.5079303306081[/C][C]0.9841393387838[/C][C]0.4920696693919[/C][/ROW]
[ROW][C]130[/C][C]0.459648545212088[/C][C]0.919297090424175[/C][C]0.540351454787912[/C][/ROW]
[ROW][C]131[/C][C]0.502812282696864[/C][C]0.994375434606271[/C][C]0.497187717303136[/C][/ROW]
[ROW][C]132[/C][C]0.454793619570054[/C][C]0.909587239140108[/C][C]0.545206380429946[/C][/ROW]
[ROW][C]133[/C][C]0.419593649816639[/C][C]0.839187299633279[/C][C]0.580406350183361[/C][/ROW]
[ROW][C]134[/C][C]0.998117003198095[/C][C]0.0037659936038097[/C][C]0.00188299680190485[/C][/ROW]
[ROW][C]135[/C][C]0.997365377477863[/C][C]0.00526924504427367[/C][C]0.00263462252213683[/C][/ROW]
[ROW][C]136[/C][C]0.995565434815984[/C][C]0.00886913036803127[/C][C]0.00443456518401563[/C][/ROW]
[ROW][C]137[/C][C]0.995152749410914[/C][C]0.0096945011781713[/C][C]0.00484725058908565[/C][/ROW]
[ROW][C]138[/C][C]0.995130838522412[/C][C]0.00973832295517602[/C][C]0.00486916147758801[/C][/ROW]
[ROW][C]139[/C][C]0.999635693270498[/C][C]0.000728613459004793[/C][C]0.000364306729502397[/C][/ROW]
[ROW][C]140[/C][C]0.999282771464396[/C][C]0.00143445707120756[/C][C]0.000717228535603781[/C][/ROW]
[ROW][C]141[/C][C]0.99999961264341[/C][C]7.74713180659723e-07[/C][C]3.87356590329862e-07[/C][/ROW]
[ROW][C]142[/C][C]0.999999995303682[/C][C]9.39263547749333e-09[/C][C]4.69631773874666e-09[/C][/ROW]
[ROW][C]143[/C][C]0.999999980455536[/C][C]3.90889280316553e-08[/C][C]1.95444640158277e-08[/C][/ROW]
[ROW][C]144[/C][C]0.999999917633378[/C][C]1.64733244345589e-07[/C][C]8.23666221727945e-08[/C][/ROW]
[ROW][C]145[/C][C]0.999999985567068[/C][C]2.88658637917459e-08[/C][C]1.44329318958729e-08[/C][/ROW]
[ROW][C]146[/C][C]0.999999998834531[/C][C]2.33093771757632e-09[/C][C]1.16546885878816e-09[/C][/ROW]
[ROW][C]147[/C][C]0.99999999219827[/C][C]1.5603459296075e-08[/C][C]7.80172964803749e-09[/C][/ROW]
[ROW][C]148[/C][C]0.99999994972884[/C][C]1.00542319741233e-07[/C][C]5.02711598706167e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999715432845[/C][C]5.69134310521702e-07[/C][C]2.84567155260851e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999998290160167[/C][C]3.41967966699336e-06[/C][C]1.70983983349668e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999989898665547[/C][C]2.02026689060243e-05[/C][C]1.01013344530122e-05[/C][/ROW]
[ROW][C]152[/C][C]0.999941371657467[/C][C]0.000117256685065185[/C][C]5.86283425325927e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999699516242814[/C][C]0.000600967514371779[/C][C]0.000300483757185889[/C][/ROW]
[ROW][C]154[/C][C]0.998657520852558[/C][C]0.00268495829488348[/C][C]0.00134247914744174[/C][/ROW]
[ROW][C]155[/C][C]0.996154586186382[/C][C]0.00769082762723545[/C][C]0.00384541381361773[/C][/ROW]
[ROW][C]156[/C][C]0.991472411099377[/C][C]0.0170551778012469[/C][C]0.00852758890062346[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7341225454806630.5317549090386740.265877454519337
90.623535338421640.752929323156720.37646466157836
100.5679412430393930.8641175139212150.432058756960607
110.6269797425761160.7460405148477670.373020257423884
120.5875745850381790.8248508299236410.412425414961821
130.4978769196208750.995753839241750.502123080379125
140.600961229418140.798077541163720.39903877058186
150.5555109236755450.888978152648910.444489076324455
160.6163306347369190.7673387305261620.383669365263081
170.5807442109758980.8385115780482050.419255789024102
180.5538396648210460.8923206703579080.446160335178954
190.5524620706797360.8950758586405290.447537929320264
200.520286524130410.9594269517391810.47971347586959
210.4668793052099010.9337586104198020.533120694790099
220.8314083052810460.3371833894379080.168591694718954
230.7868939832222470.4262120335555060.213106016777753
240.8047061917614330.3905876164771340.195293808238567
250.964025059648780.07194988070244070.0359749403512204
260.9670459287732490.0659081424535020.032954071226751
270.953943921214460.09211215757108020.0460560787855401
280.9371974425858880.1256051148282230.0628025574141116
290.9168653815329360.1662692369341270.0831346184670637
300.8945777665561540.2108444668876920.105422233443846
310.8670942395283850.2658115209432290.132905760471615
320.8385122175660340.3229755648679330.161487782433966
330.8019003864080690.3961992271838610.198099613591931
340.7672080935034640.4655838129930710.232791906496536
350.783673927728430.4326521445431410.21632607227157
360.830406463702630.339187072594740.16959353629737
370.9147813010850480.1704373978299050.0852186989149525
380.8915401709022810.2169196581954380.108459829097719
390.868103993648510.2637920127029790.13189600635149
400.8549568387780340.2900863224439310.145043161221966
410.8401752383890320.3196495232219360.159824761610968
420.8097953082728220.3804093834543560.190204691727178
430.7725210205655680.4549579588688630.227478979434432
440.7358493366254470.5283013267491060.264150663374553
450.7172062493751970.5655875012496070.282793750624803
460.9231476060164890.1537047879670230.0768523939835114
470.9065989863145620.1868020273708760.093401013685438
480.8917061125860490.2165877748279020.108293887413951
490.8677093972516920.2645812054966150.132290602748308
500.8546172821161760.2907654357676470.145382717883824
510.8273603834802720.3452792330394560.172639616519728
520.8278830125686180.3442339748627640.172116987431382
530.8311920448928740.3376159102142520.168807955107126
540.8017520238394540.3964959523210910.198247976160546
550.7756416988699510.4487166022600980.224358301130049
560.791957656946660.416084686106680.20804234305334
570.7567611058883220.4864777882233560.243238894111678
580.7239209116348110.5521581767303780.276079088365189
590.6979594982148370.6040810035703250.302040501785163
600.6641048375450150.671790324909970.335895162454985
610.6295343766411590.7409312467176810.370465623358841
620.6056031760011940.7887936479976120.394396823998806
630.5660433567762090.8679132864475810.433956643223791
640.5490705348259890.9018589303480220.450929465174011
650.5616903203661890.8766193592676230.438309679633811
660.5162424909272790.9675150181454420.483757509072721
670.5336772725901990.9326454548196030.466322727409801
680.5182183237367660.9635633525264690.481781676263234
690.5210485675352270.9579028649295450.478951432464773
700.6598466400241730.6803067199516540.340153359975827
710.6181248186041320.7637503627917350.381875181395868
720.579895189348610.8402096213027790.42010481065139
730.5377242023976830.9245515952046350.462275797602317
740.4935261674491450.9870523348982890.506473832550855
750.5425987147615130.9148025704769730.457401285238487
760.5499883797422510.9000232405154980.450011620257749
770.5351865681432590.9296268637134820.464813431856741
780.5037061737547750.992587652490450.496293826245225
790.5420076193905020.9159847612189960.457992380609498
800.5047155765156410.9905688469687190.495284423484359
810.4612585335450930.9225170670901860.538741466454907
820.5639934625085890.8720130749828220.436006537491411
830.5234576361441990.9530847277116020.476542363855801
840.4879681299474570.9759362598949130.512031870052543
850.4438612475102170.8877224950204340.556138752489783
860.4048992512615470.8097985025230930.595100748738453
870.3799788565070160.7599577130140320.620021143492984
880.4492797650312390.8985595300624780.550720234968761
890.4061469963417210.8122939926834430.593853003658279
900.770211849046770.459576301906460.22978815095323
910.7453825920313590.5092348159372820.254617407968641
920.7637855817131590.4724288365736810.236214418286841
930.7368097273295280.5263805453409440.263190272670472
940.6986761370165710.6026477259668580.301323862983429
950.7830678504444330.4338642991111330.216932149555566
960.748824220934520.502351558130960.25117577906548
970.7152505852356160.5694988295287680.284749414764384
980.6800231016437550.639953796712490.319976898356245
990.639469376765030.7210612464699410.36053062323497
1000.7024947170237280.5950105659525430.297505282976272
1010.6730373345220490.6539253309559020.326962665477951
1020.6300575260166250.7398849479667490.369942473983375
1030.7033255003994540.5933489992010910.296674499600546
1040.6675447584810010.6649104830379990.332455241518999
1050.6277059723493240.7445880553013530.372294027650676
1060.6423194854401770.7153610291196450.357680514559823
1070.5965661816521170.8068676366957650.403433818347883
1080.5622780200462470.8754439599075060.437721979953753
1090.5156389881255850.968722023748830.484361011874415
1100.6736340981809180.6527318036381650.326365901819082
1110.7758349583244060.4483300833511870.224165041675594
1120.7399267923742130.5201464152515750.260073207625787
1130.6979908428562010.6040183142875980.302009157143799
1140.7570389064168010.4859221871663980.242961093583199
1150.7157911539153160.5684176921693690.284208846084684
1160.7240336910693360.5519326178613280.275966308930664
1170.7638628898418540.4722742203162920.236137110158146
1180.7219999901169730.5560000197660530.278000009883027
1190.7466115531574560.5067768936850890.253388446842545
1200.70934152928020.58131694143960.2906584707198
1210.6792690196738230.6414619606523550.320730980326177
1220.6317755665165240.7364488669669520.368224433483476
1230.5902086593272670.8195826813454670.409791340672733
1240.5371385141294860.9257229717410280.462861485870514
1250.4932835108905720.9865670217811430.506716489109428
1260.4384703547325610.8769407094651220.561529645267439
1270.4376893296054280.8753786592108560.562310670394572
1280.3828058843527140.7656117687054290.617194115647286
1290.50793033060810.98413933878380.4920696693919
1300.4596485452120880.9192970904241750.540351454787912
1310.5028122826968640.9943754346062710.497187717303136
1320.4547936195700540.9095872391401080.545206380429946
1330.4195936498166390.8391872996332790.580406350183361
1340.9981170031980950.00376599360380970.00188299680190485
1350.9973653774778630.005269245044273670.00263462252213683
1360.9955654348159840.008869130368031270.00443456518401563
1370.9951527494109140.00969450117817130.00484725058908565
1380.9951308385224120.009738322955176020.00486916147758801
1390.9996356932704980.0007286134590047930.000364306729502397
1400.9992827714643960.001434457071207560.000717228535603781
1410.999999612643417.74713180659723e-073.87356590329862e-07
1420.9999999953036829.39263547749333e-094.69631773874666e-09
1430.9999999804555363.90889280316553e-081.95444640158277e-08
1440.9999999176333781.64733244345589e-078.23666221727945e-08
1450.9999999855670682.88658637917459e-081.44329318958729e-08
1460.9999999988345312.33093771757632e-091.16546885878816e-09
1470.999999992198271.5603459296075e-087.80172964803749e-09
1480.999999949728841.00542319741233e-075.02711598706167e-08
1490.9999997154328455.69134310521702e-072.84567155260851e-07
1500.9999982901601673.41967966699336e-061.70983983349668e-06
1510.9999898986655472.02026689060243e-051.01013344530122e-05
1520.9999413716574670.0001172566850651855.86283425325927e-05
1530.9996995162428140.0006009675143717790.000300483757185889
1540.9986575208525580.002684958294883480.00134247914744174
1550.9961545861863820.007690827627235450.00384541381361773
1560.9914724110993770.01705517780124690.00852758890062346







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.147651006711409NOK
5% type I error level230.154362416107383NOK
10% type I error level260.174496644295302NOK

\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 & 22 & 0.147651006711409 & NOK \tabularnewline
5% type I error level & 23 & 0.154362416107383 & NOK \tabularnewline
10% type I error level & 26 & 0.174496644295302 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198275&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]22[/C][C]0.147651006711409[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.154362416107383[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.174496644295302[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198275&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198275&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.147651006711409NOK
5% type I error level230.154362416107383NOK
10% type I error level260.174496644295302NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}