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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 18 Dec 2011 08:02:22 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/18/t1324213373528qr8hjo1oczda.htm/, Retrieved Sun, 05 May 2024 15:11:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156810, Retrieved Sun, 05 May 2024 15:11:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-18 13:02:22] [9afaf62527660fe62ab98f95fea710a5] [Current]
- RMP     [Multiple Regression] [paper] [2011-12-18 13:03:45] [a9dc51245fb8ca00f931d89893d090c8]
- RMP     [Recursive Partitioning (Regression Trees)] [] [2011-12-18 13:11:57] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P       [Recursive Partitioning (Regression Trees)] [] [2011-12-18 13:13:17] [6a3e51c0c7ab195427042dfaef1df5a0]
-    D        [Recursive Partitioning (Regression Trees)] [paper] [2011-12-19 15:43:16] [a9dc51245fb8ca00f931d89893d090c8]
-   P           [Recursive Partitioning (Regression Trees)] [paper] [2011-12-19 16:18:31] [a9dc51245fb8ca00f931d89893d090c8]
-   P             [Recursive Partitioning (Regression Trees)] [] [2011-12-20 16:48:29] [74be16979710d4c4e7c6647856088456]
-                   [Recursive Partitioning (Regression Trees)] [paper Deel 3] [2011-12-21 16:01:43] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P                 [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:46:13] [6a3e51c0c7ab195427042dfaef1df5a0]
- RM                    [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:46:46] [6a3e51c0c7ab195427042dfaef1df5a0]
-  MP             [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-20 18:36:06] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P             [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:51:57] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P               [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-21 16:53:25] [6a3e51c0c7ab195427042dfaef1df5a0]
- R             [Recursive Partitioning (Regression Trees)] [Paper Deel 3] [2011-12-20 17:29:08] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P     [Multiple Regression] [paper] [2011-12-18 13:25:55] [a9dc51245fb8ca00f931d89893d090c8]
- R PD      [Multiple Regression] [paper] [2011-12-19 14:39:48] [a9dc51245fb8ca00f931d89893d090c8]
- R           [Multiple Regression] [paper] [2011-12-19 14:51:07] [a9dc51245fb8ca00f931d89893d090c8]
- R  D          [Multiple Regression] [Paper Deel 3] [2011-12-20 16:40:34] [6a3e51c0c7ab195427042dfaef1df5a0]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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238146	2746	145	837	27269	134904	9	148	44	167
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73566	893	32	385	3007	23824	6	67	23	88
242622	2195	84	718	20065	111563	7	171	45	175
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342846	2695	92	1077	30927	165278	3	133	36	140
207650	1809	107	524	18487	181712	6	125	41	154
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280366	2308	104	656	26759	136323	2	92	44	122
80953	870	25	437	7905	55792	0	27	8	28
150216	1966	54	822	4527	25157	0	77	27	101
167878	1459	59	423	41517	100922	6	137	38	139
369718	3795	205	1489	21261	118845	1	137	37	143
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179797	3085	104	1044	39039	81716	1	159	45	171
262883	2367	91	792	13841	115750	1	85	37	138
262793	2209	77	678	23841	105590	3	138	40	148
189142	1829	63	597	8589	92795	9	90	31	114
275997	3087	74	1099	15049	82390	1	135	36	140
328875	2559	82	966	39038	135599	4	147	40	156
189252	1624	36	555	30391	111542	3	139	36	140
222504	1607	51	552	39932	162519	5	127	35	127
287386	2109	79	778	43840	211381	0	104	39	141
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43287	602	14	214	7409	19630	4	64	19	71
185468	2146	80	716	18213	88634	4	36	23	84
235352	2325	146	795	45873	139292	0	98	44	167
268077	2617	48	1170	39844	128602	0	125	40	155
305195	2688	90	1048	28317	135848	0	124	40	150
143356	1207	72	399	24797	178377	0	83	30	112
154165	3102	88	906	7471	106330	0	127	41	161
307000	1869	68	609	27259	178303	4	143	40	149
298039	2304	88	688	23201	116938	0	115	45	164
23623	398	11	156	238	5841	0	0	1	0
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61857	530	25	192	3913	24610	4	30	11	32
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316105	2387	97	820	21849	231257	0	145	48	187
397297	3334	124	1153	49809	258287	3	175	50	186
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407378	4104	170	1447	27195	195791	4	163	46	172
143860	1759	54	546	1037	18284	15	149	42	160
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258751	1787	48	590	34245	147341	2	135	41	151
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217665	1816	113	613	20954	100187	0	45	32	125
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168994	1915	57	720	21166	75767	5	179	50	192
253330	2648	86	1023	34672	134969	2	118	36	132
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1	2	0	0	0	0	9	0	0	0
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
X1[t] = -6621.20686091456 + 18.6792902874244X2[t] + 129.718229755785X3[t] + 96.5728322044372X4[t] -0.307030148761099X5[t] + 0.737535481670461X6[t] -145.873125893583X7[t] + 56.4219266694894X8[t] -1008.86288892542X9[t] + 487.392762192666X10[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  -6621.20686091456 +  18.6792902874244X2[t] +  129.718229755785X3[t] +  96.5728322044372X4[t] -0.307030148761099X5[t] +  0.737535481670461X6[t] -145.873125893583X7[t] +  56.4219266694894X8[t] -1008.86288892542X9[t] +  487.392762192666X10[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  -6621.20686091456 +  18.6792902874244X2[t] +  129.718229755785X3[t] +  96.5728322044372X4[t] -0.307030148761099X5[t] +  0.737535481670461X6[t] -145.873125893583X7[t] +  56.4219266694894X8[t] -1008.86288892542X9[t] +  487.392762192666X10[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
X1[t] = -6621.20686091456 + 18.6792902874244X2[t] + 129.718229755785X3[t] + 96.5728322044372X4[t] -0.307030148761099X5[t] + 0.737535481670461X6[t] -145.873125893583X7[t] + 56.4219266694894X8[t] -1008.86288892542X9[t] + 487.392762192666X10[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6621.206860914566902.866313-0.95920.3389630.169482
X218.679290287424412.6003411.48240.1402660.070133
X3129.71822975578579.1490261.63890.1032730.051637
X496.572832204437227.3286293.53380.0005410.000271
X5-0.3070301487610990.35517-0.86450.388680.19434
X60.7375354816704610.0825788.931400
X7-145.873125893583727.693613-0.20050.8413860.420693
X856.4219266694894150.3897520.37520.7080490.354025
X9-1008.862888925421036.329162-0.97350.3318330.165917
X10487.392762192666310.842371.5680.118940.05947

\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) & -6621.20686091456 & 6902.866313 & -0.9592 & 0.338963 & 0.169482 \tabularnewline
X2 & 18.6792902874244 & 12.600341 & 1.4824 & 0.140266 & 0.070133 \tabularnewline
X3 & 129.718229755785 & 79.149026 & 1.6389 & 0.103273 & 0.051637 \tabularnewline
X4 & 96.5728322044372 & 27.328629 & 3.5338 & 0.000541 & 0.000271 \tabularnewline
X5 & -0.307030148761099 & 0.35517 & -0.8645 & 0.38868 & 0.19434 \tabularnewline
X6 & 0.737535481670461 & 0.082578 & 8.9314 & 0 & 0 \tabularnewline
X7 & -145.873125893583 & 727.693613 & -0.2005 & 0.841386 & 0.420693 \tabularnewline
X8 & 56.4219266694894 & 150.389752 & 0.3752 & 0.708049 & 0.354025 \tabularnewline
X9 & -1008.86288892542 & 1036.329162 & -0.9735 & 0.331833 & 0.165917 \tabularnewline
X10 & 487.392762192666 & 310.84237 & 1.568 & 0.11894 & 0.05947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&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]-6621.20686091456[/C][C]6902.866313[/C][C]-0.9592[/C][C]0.338963[/C][C]0.169482[/C][/ROW]
[ROW][C]X2[/C][C]18.6792902874244[/C][C]12.600341[/C][C]1.4824[/C][C]0.140266[/C][C]0.070133[/C][/ROW]
[ROW][C]X3[/C][C]129.718229755785[/C][C]79.149026[/C][C]1.6389[/C][C]0.103273[/C][C]0.051637[/C][/ROW]
[ROW][C]X4[/C][C]96.5728322044372[/C][C]27.328629[/C][C]3.5338[/C][C]0.000541[/C][C]0.000271[/C][/ROW]
[ROW][C]X5[/C][C]-0.307030148761099[/C][C]0.35517[/C][C]-0.8645[/C][C]0.38868[/C][C]0.19434[/C][/ROW]
[ROW][C]X6[/C][C]0.737535481670461[/C][C]0.082578[/C][C]8.9314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X7[/C][C]-145.873125893583[/C][C]727.693613[/C][C]-0.2005[/C][C]0.841386[/C][C]0.420693[/C][/ROW]
[ROW][C]X8[/C][C]56.4219266694894[/C][C]150.389752[/C][C]0.3752[/C][C]0.708049[/C][C]0.354025[/C][/ROW]
[ROW][C]X9[/C][C]-1008.86288892542[/C][C]1036.329162[/C][C]-0.9735[/C][C]0.331833[/C][C]0.165917[/C][/ROW]
[ROW][C]X10[/C][C]487.392762192666[/C][C]310.84237[/C][C]1.568[/C][C]0.11894[/C][C]0.05947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6621.206860914566902.866313-0.95920.3389630.169482
X218.679290287424412.6003411.48240.1402660.070133
X3129.71822975578579.1490261.63890.1032730.051637
X496.572832204437227.3286293.53380.0005410.000271
X5-0.3070301487610990.35517-0.86450.388680.19434
X60.7375354816704610.0825788.931400
X7-145.873125893583727.693613-0.20050.8413860.420693
X856.4219266694894150.3897520.37520.7080490.354025
X9-1008.862888925421036.329162-0.97350.3318330.165917
X10487.392762192666310.842371.5680.118940.05947






Multiple Linear Regression - Regression Statistics
Multiple R0.961291904346723
R-squared0.924082125362549
Adjusted R-squared0.919645366455165
F-TEST (value)208.27864318359
F-TEST (DF numerator)9
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32512.3635923793
Sum Squared Residuals162786283099.913

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.961291904346723 \tabularnewline
R-squared & 0.924082125362549 \tabularnewline
Adjusted R-squared & 0.919645366455165 \tabularnewline
F-TEST (value) & 208.27864318359 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 32512.3635923793 \tabularnewline
Sum Squared Residuals & 162786283099.913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.961291904346723[/C][/ROW]
[ROW][C]R-squared[/C][C]0.924082125362549[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.919645366455165[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]208.27864318359[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/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]32512.3635923793[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]162786283099.913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.961291904346723
R-squared0.924082125362549
Adjusted R-squared0.919645366455165
F-TEST (value)208.27864318359
F-TEST (DF numerator)9
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32512.3635923793
Sum Squared Residuals162786283099.913






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1272545246981.36001979325563.6399802069
2179444180427.722185863-983.722185863332
3222373210569.24411634711803.7558836529
4218443275090.521928017-56647.5219280169
5167843149243.93488550718599.0651144934
67084990225.0870277869-19376.0870277869
7506574520973.271635155-14399.2716351552
83318634080.9942684387-894.994268438681
9216660221239.722640759-4579.72264075899
10213274194758.17306355118515.8269364493
11307153286264.37243447520888.6275655248
12237633231497.7086445746135.29135542582
13164292171751.977828388-7459.97782838828
14364402286897.97940883777504.0205911631
15244103227051.60265694517051.3973430554
16384448443556.251697253-59108.2516972526
17325587271409.29468685254177.7053131476
18323652310456.82635641413195.1736435865
19176082178313.03383888-2231.03383888004
20266736262505.3830696274230.61693037346
21278265272306.5102935125958.48970648825
22442703327687.75696088115015.24303912
23180393170003.31369855110389.6863014488
24189897217867.965212247-27970.9652122474
25234247297107.669179762-62860.6691797623
26237452279029.887881729-41577.8878817288
27267268317101.434641015-49833.4346410155
28270787246163.0490970824623.9509029198
29155915156452.88504463-537.885044629872
30342564364674.570287242-22110.5702872417
31282172307251.97262375-25079.9726237502
32216584229592.661680374-13008.6616803738
33318563305767.75084657912795.2491534211
3498672109915.002693819-11243.002693819
35386258357830.4523581828427.54764182
36273950281937.240892453-7987.24089245255
37425120377172.44843265547947.5515673447
38227636209021.04764543718614.9523545633
3911565894090.684981122821567.3150188772
40349863355274.626176781-5411.62617678116
41324178335809.994573646-11631.9945736464
42178083187880.390258584-9797.39025858405
43195153211235.804040916-16082.8040409159
44177694160224.60004128517469.3999587146
45153778228983.775199136-75205.7751991358
46455168407686.25400778547481.7459922147
4778800104591.091285669-25791.0912856685
48208051235518.620734115-27467.6207341146
49348077399505.120136979-51428.1201369785
50175523234982.72346565-59459.7234656501
51224591249001.121259301-24410.1212593012
522418839290.0833765472-15102.0833765472
53372238339285.6543086232952.3456913798
546502968730.0131587009-3701.01315870092
55101097112478.298737029-11381.2987370294
56279012284594.818062011-5582.81806201133
57317644321009.560883594-3365.56088359386
58340471363441.206620576-22970.2066205763
59358958337391.00668426521566.9933157345
60252529213527.41592897539001.5840710252
61370628396670.273882499-26042.2738824994
62304468343578.453050353-39110.4530503532
63265870287374.19404573-21504.1940457296
64264889271054.950723871-6165.95072387126
65228595221190.8543359067404.14566409417
66216027205417.36181947110609.6381805285
67198798219173.639196576-20375.6391965756
68238146279479.02081826-41333.0208182598
69234891241141.742138938-6250.74213893781
70175816181911.936569726-6095.93656972596
71239314248742.57888322-9428.57888321955
727356690630.4757338055-17064.4757338055
73242622239258.5121102643363.48788973588
74187167191050.676129491-3883.67612949122
75209049211330.474428436-2281.47442843567
76360592253058.895893248107533.104106752
77342846311047.78538922131798.2146107794
78207650259869.23402324-52219.2340232395
79206500206703.444630804-203.444630804052
80182357182049.112237271307.887762729431
81153613177117.398268151-23504.3982681515
82456979368302.09152164788676.908478353
83145943165459.168738754-19516.1687387537
84280366225631.3195368354734.6804631701
8580953100896.051623848-19943.0516238477
86150216159986.044285987-9770.04428598738
87167878167087.515805511790.484194489356
88369718355733.68928965713984.3107103428
89322454312513.6292444159940.37075558485
90179797260369.98138238-80572.9813823796
91262883241585.10791082321297.8920891766
92262793219790.71479298743002.2852070132
93189142187225.1080498371916.89195016292
94275997262306.51515723813690.4848427619
95328875276517.86462764252357.1353723582
96189252194237.919291058-4985.91929105784
97222504223948.34551428-1444.34551428028
98287386295840.213065021-8454.2130650211
99389104411477.853075969-22373.8530759685
100397681394051.9732451093629.0267548907
101287748272187.45782397315560.5421760269
102294320303115.620176152-8795.62017615172
103186856206041.105991991-19185.1059919908
1044328757773.4043628349-14486.4043628349
105185468191951.778553158-6483.77855315828
106235352263704.77749001-28352.7774900099
107268077286338.516711824-18261.5167118241
108305195287720.95933035317474.0406696466
109143356216748.03173448-73392.0317344801
110154165270632.908285133-116467.908285133
111307000258811.3843052448188.6156947604
112298039234417.81297559263621.1870244083
1132362320531.42170879283091.57829120717
114195817229610.948890009-33793.9488900086
1156185747621.337658870814235.6623411292
116163766168874.604839511-5108.60483951079
117414506379825.5483154434680.4516845602
1182105421562.9874275997-508.987427599667
119252805240770.75313084112034.246869159
1203196137836.9277675673-5875.92776756732
121317367341530.319163854-24163.3191638538
122240153216151.40295738724001.5970426131
123175083146153.11418727728929.8858127228
124152043183352.866207239-31309.8662072392
1253821451059.9991696589-12845.9991696589
126216299204515.0961946111783.9038053903
127357602314137.73662097643464.263379024
128198104212929.940367908-14825.9403679081
129410803404898.8756054365904.12439456418
130316105344488.798142186-28383.7981421859
131397297407940.172364413-10643.1723644132
132187992191641.118423603-3649.1184236033
133102424134264.223651173-31840.2236511728
134286327283191.3948805633135.6051194365
135407378413921.856083284-6543.85608328389
136143860140965.2948290062894.70517099356
137391854396666.353361697-4812.35336169667
138157429188283.09703442-30854.0970344196
139258751227676.24136583631074.7586341639
140282399256406.31985102125992.6801489787
141217665199795.11749834717869.882501653
142366774358145.7488642278628.25113577327
143236660246332.90958245-9672.90958244952
144173260137169.39837372736090.6016262734
145323545295023.08664463128521.9133553693
146168994207964.688172917-38970.6881729174
147253330276073.227735387-22743.2277353869
148301703290129.20405940911573.7959405914
1491-7896.706413381897897.70641338189
1501468811982.88169205092705.11830794912
15198-6398.09217972166496.0921797216
152455-6212.336079103546667.33607910354
1530-6767.079986808096767.07998680809
1540-6621.206860914516621.20686091451
155246435218941.77727977827493.2227202223
156382374354812.90420433927561.0957956612
1570-6621.206860914516621.20686091451
158203-6027.616780741666230.61678074166
15971996954.4760613738244.523938626199
1604666048061.8316008438-1401.83160084383
161175479331.896516026078215.10348397393
16211667891900.842898916824777.1571010832
163969-5820.070983067626789.07098306762
164206501174276.75076719432224.249232806

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 272545 & 246981.360019793 & 25563.6399802069 \tabularnewline
2 & 179444 & 180427.722185863 & -983.722185863332 \tabularnewline
3 & 222373 & 210569.244116347 & 11803.7558836529 \tabularnewline
4 & 218443 & 275090.521928017 & -56647.5219280169 \tabularnewline
5 & 167843 & 149243.934885507 & 18599.0651144934 \tabularnewline
6 & 70849 & 90225.0870277869 & -19376.0870277869 \tabularnewline
7 & 506574 & 520973.271635155 & -14399.2716351552 \tabularnewline
8 & 33186 & 34080.9942684387 & -894.994268438681 \tabularnewline
9 & 216660 & 221239.722640759 & -4579.72264075899 \tabularnewline
10 & 213274 & 194758.173063551 & 18515.8269364493 \tabularnewline
11 & 307153 & 286264.372434475 & 20888.6275655248 \tabularnewline
12 & 237633 & 231497.708644574 & 6135.29135542582 \tabularnewline
13 & 164292 & 171751.977828388 & -7459.97782838828 \tabularnewline
14 & 364402 & 286897.979408837 & 77504.0205911631 \tabularnewline
15 & 244103 & 227051.602656945 & 17051.3973430554 \tabularnewline
16 & 384448 & 443556.251697253 & -59108.2516972526 \tabularnewline
17 & 325587 & 271409.294686852 & 54177.7053131476 \tabularnewline
18 & 323652 & 310456.826356414 & 13195.1736435865 \tabularnewline
19 & 176082 & 178313.03383888 & -2231.03383888004 \tabularnewline
20 & 266736 & 262505.383069627 & 4230.61693037346 \tabularnewline
21 & 278265 & 272306.510293512 & 5958.48970648825 \tabularnewline
22 & 442703 & 327687.75696088 & 115015.24303912 \tabularnewline
23 & 180393 & 170003.313698551 & 10389.6863014488 \tabularnewline
24 & 189897 & 217867.965212247 & -27970.9652122474 \tabularnewline
25 & 234247 & 297107.669179762 & -62860.6691797623 \tabularnewline
26 & 237452 & 279029.887881729 & -41577.8878817288 \tabularnewline
27 & 267268 & 317101.434641015 & -49833.4346410155 \tabularnewline
28 & 270787 & 246163.04909708 & 24623.9509029198 \tabularnewline
29 & 155915 & 156452.88504463 & -537.885044629872 \tabularnewline
30 & 342564 & 364674.570287242 & -22110.5702872417 \tabularnewline
31 & 282172 & 307251.97262375 & -25079.9726237502 \tabularnewline
32 & 216584 & 229592.661680374 & -13008.6616803738 \tabularnewline
33 & 318563 & 305767.750846579 & 12795.2491534211 \tabularnewline
34 & 98672 & 109915.002693819 & -11243.002693819 \tabularnewline
35 & 386258 & 357830.45235818 & 28427.54764182 \tabularnewline
36 & 273950 & 281937.240892453 & -7987.24089245255 \tabularnewline
37 & 425120 & 377172.448432655 & 47947.5515673447 \tabularnewline
38 & 227636 & 209021.047645437 & 18614.9523545633 \tabularnewline
39 & 115658 & 94090.6849811228 & 21567.3150188772 \tabularnewline
40 & 349863 & 355274.626176781 & -5411.62617678116 \tabularnewline
41 & 324178 & 335809.994573646 & -11631.9945736464 \tabularnewline
42 & 178083 & 187880.390258584 & -9797.39025858405 \tabularnewline
43 & 195153 & 211235.804040916 & -16082.8040409159 \tabularnewline
44 & 177694 & 160224.600041285 & 17469.3999587146 \tabularnewline
45 & 153778 & 228983.775199136 & -75205.7751991358 \tabularnewline
46 & 455168 & 407686.254007785 & 47481.7459922147 \tabularnewline
47 & 78800 & 104591.091285669 & -25791.0912856685 \tabularnewline
48 & 208051 & 235518.620734115 & -27467.6207341146 \tabularnewline
49 & 348077 & 399505.120136979 & -51428.1201369785 \tabularnewline
50 & 175523 & 234982.72346565 & -59459.7234656501 \tabularnewline
51 & 224591 & 249001.121259301 & -24410.1212593012 \tabularnewline
52 & 24188 & 39290.0833765472 & -15102.0833765472 \tabularnewline
53 & 372238 & 339285.65430862 & 32952.3456913798 \tabularnewline
54 & 65029 & 68730.0131587009 & -3701.01315870092 \tabularnewline
55 & 101097 & 112478.298737029 & -11381.2987370294 \tabularnewline
56 & 279012 & 284594.818062011 & -5582.81806201133 \tabularnewline
57 & 317644 & 321009.560883594 & -3365.56088359386 \tabularnewline
58 & 340471 & 363441.206620576 & -22970.2066205763 \tabularnewline
59 & 358958 & 337391.006684265 & 21566.9933157345 \tabularnewline
60 & 252529 & 213527.415928975 & 39001.5840710252 \tabularnewline
61 & 370628 & 396670.273882499 & -26042.2738824994 \tabularnewline
62 & 304468 & 343578.453050353 & -39110.4530503532 \tabularnewline
63 & 265870 & 287374.19404573 & -21504.1940457296 \tabularnewline
64 & 264889 & 271054.950723871 & -6165.95072387126 \tabularnewline
65 & 228595 & 221190.854335906 & 7404.14566409417 \tabularnewline
66 & 216027 & 205417.361819471 & 10609.6381805285 \tabularnewline
67 & 198798 & 219173.639196576 & -20375.6391965756 \tabularnewline
68 & 238146 & 279479.02081826 & -41333.0208182598 \tabularnewline
69 & 234891 & 241141.742138938 & -6250.74213893781 \tabularnewline
70 & 175816 & 181911.936569726 & -6095.93656972596 \tabularnewline
71 & 239314 & 248742.57888322 & -9428.57888321955 \tabularnewline
72 & 73566 & 90630.4757338055 & -17064.4757338055 \tabularnewline
73 & 242622 & 239258.512110264 & 3363.48788973588 \tabularnewline
74 & 187167 & 191050.676129491 & -3883.67612949122 \tabularnewline
75 & 209049 & 211330.474428436 & -2281.47442843567 \tabularnewline
76 & 360592 & 253058.895893248 & 107533.104106752 \tabularnewline
77 & 342846 & 311047.785389221 & 31798.2146107794 \tabularnewline
78 & 207650 & 259869.23402324 & -52219.2340232395 \tabularnewline
79 & 206500 & 206703.444630804 & -203.444630804052 \tabularnewline
80 & 182357 & 182049.112237271 & 307.887762729431 \tabularnewline
81 & 153613 & 177117.398268151 & -23504.3982681515 \tabularnewline
82 & 456979 & 368302.091521647 & 88676.908478353 \tabularnewline
83 & 145943 & 165459.168738754 & -19516.1687387537 \tabularnewline
84 & 280366 & 225631.31953683 & 54734.6804631701 \tabularnewline
85 & 80953 & 100896.051623848 & -19943.0516238477 \tabularnewline
86 & 150216 & 159986.044285987 & -9770.04428598738 \tabularnewline
87 & 167878 & 167087.515805511 & 790.484194489356 \tabularnewline
88 & 369718 & 355733.689289657 & 13984.3107103428 \tabularnewline
89 & 322454 & 312513.629244415 & 9940.37075558485 \tabularnewline
90 & 179797 & 260369.98138238 & -80572.9813823796 \tabularnewline
91 & 262883 & 241585.107910823 & 21297.8920891766 \tabularnewline
92 & 262793 & 219790.714792987 & 43002.2852070132 \tabularnewline
93 & 189142 & 187225.108049837 & 1916.89195016292 \tabularnewline
94 & 275997 & 262306.515157238 & 13690.4848427619 \tabularnewline
95 & 328875 & 276517.864627642 & 52357.1353723582 \tabularnewline
96 & 189252 & 194237.919291058 & -4985.91929105784 \tabularnewline
97 & 222504 & 223948.34551428 & -1444.34551428028 \tabularnewline
98 & 287386 & 295840.213065021 & -8454.2130650211 \tabularnewline
99 & 389104 & 411477.853075969 & -22373.8530759685 \tabularnewline
100 & 397681 & 394051.973245109 & 3629.0267548907 \tabularnewline
101 & 287748 & 272187.457823973 & 15560.5421760269 \tabularnewline
102 & 294320 & 303115.620176152 & -8795.62017615172 \tabularnewline
103 & 186856 & 206041.105991991 & -19185.1059919908 \tabularnewline
104 & 43287 & 57773.4043628349 & -14486.4043628349 \tabularnewline
105 & 185468 & 191951.778553158 & -6483.77855315828 \tabularnewline
106 & 235352 & 263704.77749001 & -28352.7774900099 \tabularnewline
107 & 268077 & 286338.516711824 & -18261.5167118241 \tabularnewline
108 & 305195 & 287720.959330353 & 17474.0406696466 \tabularnewline
109 & 143356 & 216748.03173448 & -73392.0317344801 \tabularnewline
110 & 154165 & 270632.908285133 & -116467.908285133 \tabularnewline
111 & 307000 & 258811.38430524 & 48188.6156947604 \tabularnewline
112 & 298039 & 234417.812975592 & 63621.1870244083 \tabularnewline
113 & 23623 & 20531.4217087928 & 3091.57829120717 \tabularnewline
114 & 195817 & 229610.948890009 & -33793.9488900086 \tabularnewline
115 & 61857 & 47621.3376588708 & 14235.6623411292 \tabularnewline
116 & 163766 & 168874.604839511 & -5108.60483951079 \tabularnewline
117 & 414506 & 379825.54831544 & 34680.4516845602 \tabularnewline
118 & 21054 & 21562.9874275997 & -508.987427599667 \tabularnewline
119 & 252805 & 240770.753130841 & 12034.246869159 \tabularnewline
120 & 31961 & 37836.9277675673 & -5875.92776756732 \tabularnewline
121 & 317367 & 341530.319163854 & -24163.3191638538 \tabularnewline
122 & 240153 & 216151.402957387 & 24001.5970426131 \tabularnewline
123 & 175083 & 146153.114187277 & 28929.8858127228 \tabularnewline
124 & 152043 & 183352.866207239 & -31309.8662072392 \tabularnewline
125 & 38214 & 51059.9991696589 & -12845.9991696589 \tabularnewline
126 & 216299 & 204515.09619461 & 11783.9038053903 \tabularnewline
127 & 357602 & 314137.736620976 & 43464.263379024 \tabularnewline
128 & 198104 & 212929.940367908 & -14825.9403679081 \tabularnewline
129 & 410803 & 404898.875605436 & 5904.12439456418 \tabularnewline
130 & 316105 & 344488.798142186 & -28383.7981421859 \tabularnewline
131 & 397297 & 407940.172364413 & -10643.1723644132 \tabularnewline
132 & 187992 & 191641.118423603 & -3649.1184236033 \tabularnewline
133 & 102424 & 134264.223651173 & -31840.2236511728 \tabularnewline
134 & 286327 & 283191.394880563 & 3135.6051194365 \tabularnewline
135 & 407378 & 413921.856083284 & -6543.85608328389 \tabularnewline
136 & 143860 & 140965.294829006 & 2894.70517099356 \tabularnewline
137 & 391854 & 396666.353361697 & -4812.35336169667 \tabularnewline
138 & 157429 & 188283.09703442 & -30854.0970344196 \tabularnewline
139 & 258751 & 227676.241365836 & 31074.7586341639 \tabularnewline
140 & 282399 & 256406.319851021 & 25992.6801489787 \tabularnewline
141 & 217665 & 199795.117498347 & 17869.882501653 \tabularnewline
142 & 366774 & 358145.748864227 & 8628.25113577327 \tabularnewline
143 & 236660 & 246332.90958245 & -9672.90958244952 \tabularnewline
144 & 173260 & 137169.398373727 & 36090.6016262734 \tabularnewline
145 & 323545 & 295023.086644631 & 28521.9133553693 \tabularnewline
146 & 168994 & 207964.688172917 & -38970.6881729174 \tabularnewline
147 & 253330 & 276073.227735387 & -22743.2277353869 \tabularnewline
148 & 301703 & 290129.204059409 & 11573.7959405914 \tabularnewline
149 & 1 & -7896.70641338189 & 7897.70641338189 \tabularnewline
150 & 14688 & 11982.8816920509 & 2705.11830794912 \tabularnewline
151 & 98 & -6398.0921797216 & 6496.0921797216 \tabularnewline
152 & 455 & -6212.33607910354 & 6667.33607910354 \tabularnewline
153 & 0 & -6767.07998680809 & 6767.07998680809 \tabularnewline
154 & 0 & -6621.20686091451 & 6621.20686091451 \tabularnewline
155 & 246435 & 218941.777279778 & 27493.2227202223 \tabularnewline
156 & 382374 & 354812.904204339 & 27561.0957956612 \tabularnewline
157 & 0 & -6621.20686091451 & 6621.20686091451 \tabularnewline
158 & 203 & -6027.61678074166 & 6230.61678074166 \tabularnewline
159 & 7199 & 6954.4760613738 & 244.523938626199 \tabularnewline
160 & 46660 & 48061.8316008438 & -1401.83160084383 \tabularnewline
161 & 17547 & 9331.89651602607 & 8215.10348397393 \tabularnewline
162 & 116678 & 91900.8428989168 & 24777.1571010832 \tabularnewline
163 & 969 & -5820.07098306762 & 6789.07098306762 \tabularnewline
164 & 206501 & 174276.750767194 & 32224.249232806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&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]272545[/C][C]246981.360019793[/C][C]25563.6399802069[/C][/ROW]
[ROW][C]2[/C][C]179444[/C][C]180427.722185863[/C][C]-983.722185863332[/C][/ROW]
[ROW][C]3[/C][C]222373[/C][C]210569.244116347[/C][C]11803.7558836529[/C][/ROW]
[ROW][C]4[/C][C]218443[/C][C]275090.521928017[/C][C]-56647.5219280169[/C][/ROW]
[ROW][C]5[/C][C]167843[/C][C]149243.934885507[/C][C]18599.0651144934[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]90225.0870277869[/C][C]-19376.0870277869[/C][/ROW]
[ROW][C]7[/C][C]506574[/C][C]520973.271635155[/C][C]-14399.2716351552[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]34080.9942684387[/C][C]-894.994268438681[/C][/ROW]
[ROW][C]9[/C][C]216660[/C][C]221239.722640759[/C][C]-4579.72264075899[/C][/ROW]
[ROW][C]10[/C][C]213274[/C][C]194758.173063551[/C][C]18515.8269364493[/C][/ROW]
[ROW][C]11[/C][C]307153[/C][C]286264.372434475[/C][C]20888.6275655248[/C][/ROW]
[ROW][C]12[/C][C]237633[/C][C]231497.708644574[/C][C]6135.29135542582[/C][/ROW]
[ROW][C]13[/C][C]164292[/C][C]171751.977828388[/C][C]-7459.97782838828[/C][/ROW]
[ROW][C]14[/C][C]364402[/C][C]286897.979408837[/C][C]77504.0205911631[/C][/ROW]
[ROW][C]15[/C][C]244103[/C][C]227051.602656945[/C][C]17051.3973430554[/C][/ROW]
[ROW][C]16[/C][C]384448[/C][C]443556.251697253[/C][C]-59108.2516972526[/C][/ROW]
[ROW][C]17[/C][C]325587[/C][C]271409.294686852[/C][C]54177.7053131476[/C][/ROW]
[ROW][C]18[/C][C]323652[/C][C]310456.826356414[/C][C]13195.1736435865[/C][/ROW]
[ROW][C]19[/C][C]176082[/C][C]178313.03383888[/C][C]-2231.03383888004[/C][/ROW]
[ROW][C]20[/C][C]266736[/C][C]262505.383069627[/C][C]4230.61693037346[/C][/ROW]
[ROW][C]21[/C][C]278265[/C][C]272306.510293512[/C][C]5958.48970648825[/C][/ROW]
[ROW][C]22[/C][C]442703[/C][C]327687.75696088[/C][C]115015.24303912[/C][/ROW]
[ROW][C]23[/C][C]180393[/C][C]170003.313698551[/C][C]10389.6863014488[/C][/ROW]
[ROW][C]24[/C][C]189897[/C][C]217867.965212247[/C][C]-27970.9652122474[/C][/ROW]
[ROW][C]25[/C][C]234247[/C][C]297107.669179762[/C][C]-62860.6691797623[/C][/ROW]
[ROW][C]26[/C][C]237452[/C][C]279029.887881729[/C][C]-41577.8878817288[/C][/ROW]
[ROW][C]27[/C][C]267268[/C][C]317101.434641015[/C][C]-49833.4346410155[/C][/ROW]
[ROW][C]28[/C][C]270787[/C][C]246163.04909708[/C][C]24623.9509029198[/C][/ROW]
[ROW][C]29[/C][C]155915[/C][C]156452.88504463[/C][C]-537.885044629872[/C][/ROW]
[ROW][C]30[/C][C]342564[/C][C]364674.570287242[/C][C]-22110.5702872417[/C][/ROW]
[ROW][C]31[/C][C]282172[/C][C]307251.97262375[/C][C]-25079.9726237502[/C][/ROW]
[ROW][C]32[/C][C]216584[/C][C]229592.661680374[/C][C]-13008.6616803738[/C][/ROW]
[ROW][C]33[/C][C]318563[/C][C]305767.750846579[/C][C]12795.2491534211[/C][/ROW]
[ROW][C]34[/C][C]98672[/C][C]109915.002693819[/C][C]-11243.002693819[/C][/ROW]
[ROW][C]35[/C][C]386258[/C][C]357830.45235818[/C][C]28427.54764182[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]281937.240892453[/C][C]-7987.24089245255[/C][/ROW]
[ROW][C]37[/C][C]425120[/C][C]377172.448432655[/C][C]47947.5515673447[/C][/ROW]
[ROW][C]38[/C][C]227636[/C][C]209021.047645437[/C][C]18614.9523545633[/C][/ROW]
[ROW][C]39[/C][C]115658[/C][C]94090.6849811228[/C][C]21567.3150188772[/C][/ROW]
[ROW][C]40[/C][C]349863[/C][C]355274.626176781[/C][C]-5411.62617678116[/C][/ROW]
[ROW][C]41[/C][C]324178[/C][C]335809.994573646[/C][C]-11631.9945736464[/C][/ROW]
[ROW][C]42[/C][C]178083[/C][C]187880.390258584[/C][C]-9797.39025858405[/C][/ROW]
[ROW][C]43[/C][C]195153[/C][C]211235.804040916[/C][C]-16082.8040409159[/C][/ROW]
[ROW][C]44[/C][C]177694[/C][C]160224.600041285[/C][C]17469.3999587146[/C][/ROW]
[ROW][C]45[/C][C]153778[/C][C]228983.775199136[/C][C]-75205.7751991358[/C][/ROW]
[ROW][C]46[/C][C]455168[/C][C]407686.254007785[/C][C]47481.7459922147[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]104591.091285669[/C][C]-25791.0912856685[/C][/ROW]
[ROW][C]48[/C][C]208051[/C][C]235518.620734115[/C][C]-27467.6207341146[/C][/ROW]
[ROW][C]49[/C][C]348077[/C][C]399505.120136979[/C][C]-51428.1201369785[/C][/ROW]
[ROW][C]50[/C][C]175523[/C][C]234982.72346565[/C][C]-59459.7234656501[/C][/ROW]
[ROW][C]51[/C][C]224591[/C][C]249001.121259301[/C][C]-24410.1212593012[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]39290.0833765472[/C][C]-15102.0833765472[/C][/ROW]
[ROW][C]53[/C][C]372238[/C][C]339285.65430862[/C][C]32952.3456913798[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]68730.0131587009[/C][C]-3701.01315870092[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]112478.298737029[/C][C]-11381.2987370294[/C][/ROW]
[ROW][C]56[/C][C]279012[/C][C]284594.818062011[/C][C]-5582.81806201133[/C][/ROW]
[ROW][C]57[/C][C]317644[/C][C]321009.560883594[/C][C]-3365.56088359386[/C][/ROW]
[ROW][C]58[/C][C]340471[/C][C]363441.206620576[/C][C]-22970.2066205763[/C][/ROW]
[ROW][C]59[/C][C]358958[/C][C]337391.006684265[/C][C]21566.9933157345[/C][/ROW]
[ROW][C]60[/C][C]252529[/C][C]213527.415928975[/C][C]39001.5840710252[/C][/ROW]
[ROW][C]61[/C][C]370628[/C][C]396670.273882499[/C][C]-26042.2738824994[/C][/ROW]
[ROW][C]62[/C][C]304468[/C][C]343578.453050353[/C][C]-39110.4530503532[/C][/ROW]
[ROW][C]63[/C][C]265870[/C][C]287374.19404573[/C][C]-21504.1940457296[/C][/ROW]
[ROW][C]64[/C][C]264889[/C][C]271054.950723871[/C][C]-6165.95072387126[/C][/ROW]
[ROW][C]65[/C][C]228595[/C][C]221190.854335906[/C][C]7404.14566409417[/C][/ROW]
[ROW][C]66[/C][C]216027[/C][C]205417.361819471[/C][C]10609.6381805285[/C][/ROW]
[ROW][C]67[/C][C]198798[/C][C]219173.639196576[/C][C]-20375.6391965756[/C][/ROW]
[ROW][C]68[/C][C]238146[/C][C]279479.02081826[/C][C]-41333.0208182598[/C][/ROW]
[ROW][C]69[/C][C]234891[/C][C]241141.742138938[/C][C]-6250.74213893781[/C][/ROW]
[ROW][C]70[/C][C]175816[/C][C]181911.936569726[/C][C]-6095.93656972596[/C][/ROW]
[ROW][C]71[/C][C]239314[/C][C]248742.57888322[/C][C]-9428.57888321955[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]90630.4757338055[/C][C]-17064.4757338055[/C][/ROW]
[ROW][C]73[/C][C]242622[/C][C]239258.512110264[/C][C]3363.48788973588[/C][/ROW]
[ROW][C]74[/C][C]187167[/C][C]191050.676129491[/C][C]-3883.67612949122[/C][/ROW]
[ROW][C]75[/C][C]209049[/C][C]211330.474428436[/C][C]-2281.47442843567[/C][/ROW]
[ROW][C]76[/C][C]360592[/C][C]253058.895893248[/C][C]107533.104106752[/C][/ROW]
[ROW][C]77[/C][C]342846[/C][C]311047.785389221[/C][C]31798.2146107794[/C][/ROW]
[ROW][C]78[/C][C]207650[/C][C]259869.23402324[/C][C]-52219.2340232395[/C][/ROW]
[ROW][C]79[/C][C]206500[/C][C]206703.444630804[/C][C]-203.444630804052[/C][/ROW]
[ROW][C]80[/C][C]182357[/C][C]182049.112237271[/C][C]307.887762729431[/C][/ROW]
[ROW][C]81[/C][C]153613[/C][C]177117.398268151[/C][C]-23504.3982681515[/C][/ROW]
[ROW][C]82[/C][C]456979[/C][C]368302.091521647[/C][C]88676.908478353[/C][/ROW]
[ROW][C]83[/C][C]145943[/C][C]165459.168738754[/C][C]-19516.1687387537[/C][/ROW]
[ROW][C]84[/C][C]280366[/C][C]225631.31953683[/C][C]54734.6804631701[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]100896.051623848[/C][C]-19943.0516238477[/C][/ROW]
[ROW][C]86[/C][C]150216[/C][C]159986.044285987[/C][C]-9770.04428598738[/C][/ROW]
[ROW][C]87[/C][C]167878[/C][C]167087.515805511[/C][C]790.484194489356[/C][/ROW]
[ROW][C]88[/C][C]369718[/C][C]355733.689289657[/C][C]13984.3107103428[/C][/ROW]
[ROW][C]89[/C][C]322454[/C][C]312513.629244415[/C][C]9940.37075558485[/C][/ROW]
[ROW][C]90[/C][C]179797[/C][C]260369.98138238[/C][C]-80572.9813823796[/C][/ROW]
[ROW][C]91[/C][C]262883[/C][C]241585.107910823[/C][C]21297.8920891766[/C][/ROW]
[ROW][C]92[/C][C]262793[/C][C]219790.714792987[/C][C]43002.2852070132[/C][/ROW]
[ROW][C]93[/C][C]189142[/C][C]187225.108049837[/C][C]1916.89195016292[/C][/ROW]
[ROW][C]94[/C][C]275997[/C][C]262306.515157238[/C][C]13690.4848427619[/C][/ROW]
[ROW][C]95[/C][C]328875[/C][C]276517.864627642[/C][C]52357.1353723582[/C][/ROW]
[ROW][C]96[/C][C]189252[/C][C]194237.919291058[/C][C]-4985.91929105784[/C][/ROW]
[ROW][C]97[/C][C]222504[/C][C]223948.34551428[/C][C]-1444.34551428028[/C][/ROW]
[ROW][C]98[/C][C]287386[/C][C]295840.213065021[/C][C]-8454.2130650211[/C][/ROW]
[ROW][C]99[/C][C]389104[/C][C]411477.853075969[/C][C]-22373.8530759685[/C][/ROW]
[ROW][C]100[/C][C]397681[/C][C]394051.973245109[/C][C]3629.0267548907[/C][/ROW]
[ROW][C]101[/C][C]287748[/C][C]272187.457823973[/C][C]15560.5421760269[/C][/ROW]
[ROW][C]102[/C][C]294320[/C][C]303115.620176152[/C][C]-8795.62017615172[/C][/ROW]
[ROW][C]103[/C][C]186856[/C][C]206041.105991991[/C][C]-19185.1059919908[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]57773.4043628349[/C][C]-14486.4043628349[/C][/ROW]
[ROW][C]105[/C][C]185468[/C][C]191951.778553158[/C][C]-6483.77855315828[/C][/ROW]
[ROW][C]106[/C][C]235352[/C][C]263704.77749001[/C][C]-28352.7774900099[/C][/ROW]
[ROW][C]107[/C][C]268077[/C][C]286338.516711824[/C][C]-18261.5167118241[/C][/ROW]
[ROW][C]108[/C][C]305195[/C][C]287720.959330353[/C][C]17474.0406696466[/C][/ROW]
[ROW][C]109[/C][C]143356[/C][C]216748.03173448[/C][C]-73392.0317344801[/C][/ROW]
[ROW][C]110[/C][C]154165[/C][C]270632.908285133[/C][C]-116467.908285133[/C][/ROW]
[ROW][C]111[/C][C]307000[/C][C]258811.38430524[/C][C]48188.6156947604[/C][/ROW]
[ROW][C]112[/C][C]298039[/C][C]234417.812975592[/C][C]63621.1870244083[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]20531.4217087928[/C][C]3091.57829120717[/C][/ROW]
[ROW][C]114[/C][C]195817[/C][C]229610.948890009[/C][C]-33793.9488900086[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]47621.3376588708[/C][C]14235.6623411292[/C][/ROW]
[ROW][C]116[/C][C]163766[/C][C]168874.604839511[/C][C]-5108.60483951079[/C][/ROW]
[ROW][C]117[/C][C]414506[/C][C]379825.54831544[/C][C]34680.4516845602[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]21562.9874275997[/C][C]-508.987427599667[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]240770.753130841[/C][C]12034.246869159[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]37836.9277675673[/C][C]-5875.92776756732[/C][/ROW]
[ROW][C]121[/C][C]317367[/C][C]341530.319163854[/C][C]-24163.3191638538[/C][/ROW]
[ROW][C]122[/C][C]240153[/C][C]216151.402957387[/C][C]24001.5970426131[/C][/ROW]
[ROW][C]123[/C][C]175083[/C][C]146153.114187277[/C][C]28929.8858127228[/C][/ROW]
[ROW][C]124[/C][C]152043[/C][C]183352.866207239[/C][C]-31309.8662072392[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]51059.9991696589[/C][C]-12845.9991696589[/C][/ROW]
[ROW][C]126[/C][C]216299[/C][C]204515.09619461[/C][C]11783.9038053903[/C][/ROW]
[ROW][C]127[/C][C]357602[/C][C]314137.736620976[/C][C]43464.263379024[/C][/ROW]
[ROW][C]128[/C][C]198104[/C][C]212929.940367908[/C][C]-14825.9403679081[/C][/ROW]
[ROW][C]129[/C][C]410803[/C][C]404898.875605436[/C][C]5904.12439456418[/C][/ROW]
[ROW][C]130[/C][C]316105[/C][C]344488.798142186[/C][C]-28383.7981421859[/C][/ROW]
[ROW][C]131[/C][C]397297[/C][C]407940.172364413[/C][C]-10643.1723644132[/C][/ROW]
[ROW][C]132[/C][C]187992[/C][C]191641.118423603[/C][C]-3649.1184236033[/C][/ROW]
[ROW][C]133[/C][C]102424[/C][C]134264.223651173[/C][C]-31840.2236511728[/C][/ROW]
[ROW][C]134[/C][C]286327[/C][C]283191.394880563[/C][C]3135.6051194365[/C][/ROW]
[ROW][C]135[/C][C]407378[/C][C]413921.856083284[/C][C]-6543.85608328389[/C][/ROW]
[ROW][C]136[/C][C]143860[/C][C]140965.294829006[/C][C]2894.70517099356[/C][/ROW]
[ROW][C]137[/C][C]391854[/C][C]396666.353361697[/C][C]-4812.35336169667[/C][/ROW]
[ROW][C]138[/C][C]157429[/C][C]188283.09703442[/C][C]-30854.0970344196[/C][/ROW]
[ROW][C]139[/C][C]258751[/C][C]227676.241365836[/C][C]31074.7586341639[/C][/ROW]
[ROW][C]140[/C][C]282399[/C][C]256406.319851021[/C][C]25992.6801489787[/C][/ROW]
[ROW][C]141[/C][C]217665[/C][C]199795.117498347[/C][C]17869.882501653[/C][/ROW]
[ROW][C]142[/C][C]366774[/C][C]358145.748864227[/C][C]8628.25113577327[/C][/ROW]
[ROW][C]143[/C][C]236660[/C][C]246332.90958245[/C][C]-9672.90958244952[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]137169.398373727[/C][C]36090.6016262734[/C][/ROW]
[ROW][C]145[/C][C]323545[/C][C]295023.086644631[/C][C]28521.9133553693[/C][/ROW]
[ROW][C]146[/C][C]168994[/C][C]207964.688172917[/C][C]-38970.6881729174[/C][/ROW]
[ROW][C]147[/C][C]253330[/C][C]276073.227735387[/C][C]-22743.2277353869[/C][/ROW]
[ROW][C]148[/C][C]301703[/C][C]290129.204059409[/C][C]11573.7959405914[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]-7896.70641338189[/C][C]7897.70641338189[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]11982.8816920509[/C][C]2705.11830794912[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]-6398.0921797216[/C][C]6496.0921797216[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-6212.33607910354[/C][C]6667.33607910354[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-6767.07998680809[/C][C]6767.07998680809[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-6621.20686091451[/C][C]6621.20686091451[/C][/ROW]
[ROW][C]155[/C][C]246435[/C][C]218941.777279778[/C][C]27493.2227202223[/C][/ROW]
[ROW][C]156[/C][C]382374[/C][C]354812.904204339[/C][C]27561.0957956612[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-6621.20686091451[/C][C]6621.20686091451[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]-6027.61678074166[/C][C]6230.61678074166[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]6954.4760613738[/C][C]244.523938626199[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]48061.8316008438[/C][C]-1401.83160084383[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]9331.89651602607[/C][C]8215.10348397393[/C][/ROW]
[ROW][C]162[/C][C]116678[/C][C]91900.8428989168[/C][C]24777.1571010832[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-5820.07098306762[/C][C]6789.07098306762[/C][/ROW]
[ROW][C]164[/C][C]206501[/C][C]174276.750767194[/C][C]32224.249232806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1272545246981.36001979325563.6399802069
2179444180427.722185863-983.722185863332
3222373210569.24411634711803.7558836529
4218443275090.521928017-56647.5219280169
5167843149243.93488550718599.0651144934
67084990225.0870277869-19376.0870277869
7506574520973.271635155-14399.2716351552
83318634080.9942684387-894.994268438681
9216660221239.722640759-4579.72264075899
10213274194758.17306355118515.8269364493
11307153286264.37243447520888.6275655248
12237633231497.7086445746135.29135542582
13164292171751.977828388-7459.97782838828
14364402286897.97940883777504.0205911631
15244103227051.60265694517051.3973430554
16384448443556.251697253-59108.2516972526
17325587271409.29468685254177.7053131476
18323652310456.82635641413195.1736435865
19176082178313.03383888-2231.03383888004
20266736262505.3830696274230.61693037346
21278265272306.5102935125958.48970648825
22442703327687.75696088115015.24303912
23180393170003.31369855110389.6863014488
24189897217867.965212247-27970.9652122474
25234247297107.669179762-62860.6691797623
26237452279029.887881729-41577.8878817288
27267268317101.434641015-49833.4346410155
28270787246163.0490970824623.9509029198
29155915156452.88504463-537.885044629872
30342564364674.570287242-22110.5702872417
31282172307251.97262375-25079.9726237502
32216584229592.661680374-13008.6616803738
33318563305767.75084657912795.2491534211
3498672109915.002693819-11243.002693819
35386258357830.4523581828427.54764182
36273950281937.240892453-7987.24089245255
37425120377172.44843265547947.5515673447
38227636209021.04764543718614.9523545633
3911565894090.684981122821567.3150188772
40349863355274.626176781-5411.62617678116
41324178335809.994573646-11631.9945736464
42178083187880.390258584-9797.39025858405
43195153211235.804040916-16082.8040409159
44177694160224.60004128517469.3999587146
45153778228983.775199136-75205.7751991358
46455168407686.25400778547481.7459922147
4778800104591.091285669-25791.0912856685
48208051235518.620734115-27467.6207341146
49348077399505.120136979-51428.1201369785
50175523234982.72346565-59459.7234656501
51224591249001.121259301-24410.1212593012
522418839290.0833765472-15102.0833765472
53372238339285.6543086232952.3456913798
546502968730.0131587009-3701.01315870092
55101097112478.298737029-11381.2987370294
56279012284594.818062011-5582.81806201133
57317644321009.560883594-3365.56088359386
58340471363441.206620576-22970.2066205763
59358958337391.00668426521566.9933157345
60252529213527.41592897539001.5840710252
61370628396670.273882499-26042.2738824994
62304468343578.453050353-39110.4530503532
63265870287374.19404573-21504.1940457296
64264889271054.950723871-6165.95072387126
65228595221190.8543359067404.14566409417
66216027205417.36181947110609.6381805285
67198798219173.639196576-20375.6391965756
68238146279479.02081826-41333.0208182598
69234891241141.742138938-6250.74213893781
70175816181911.936569726-6095.93656972596
71239314248742.57888322-9428.57888321955
727356690630.4757338055-17064.4757338055
73242622239258.5121102643363.48788973588
74187167191050.676129491-3883.67612949122
75209049211330.474428436-2281.47442843567
76360592253058.895893248107533.104106752
77342846311047.78538922131798.2146107794
78207650259869.23402324-52219.2340232395
79206500206703.444630804-203.444630804052
80182357182049.112237271307.887762729431
81153613177117.398268151-23504.3982681515
82456979368302.09152164788676.908478353
83145943165459.168738754-19516.1687387537
84280366225631.3195368354734.6804631701
8580953100896.051623848-19943.0516238477
86150216159986.044285987-9770.04428598738
87167878167087.515805511790.484194489356
88369718355733.68928965713984.3107103428
89322454312513.6292444159940.37075558485
90179797260369.98138238-80572.9813823796
91262883241585.10791082321297.8920891766
92262793219790.71479298743002.2852070132
93189142187225.1080498371916.89195016292
94275997262306.51515723813690.4848427619
95328875276517.86462764252357.1353723582
96189252194237.919291058-4985.91929105784
97222504223948.34551428-1444.34551428028
98287386295840.213065021-8454.2130650211
99389104411477.853075969-22373.8530759685
100397681394051.9732451093629.0267548907
101287748272187.45782397315560.5421760269
102294320303115.620176152-8795.62017615172
103186856206041.105991991-19185.1059919908
1044328757773.4043628349-14486.4043628349
105185468191951.778553158-6483.77855315828
106235352263704.77749001-28352.7774900099
107268077286338.516711824-18261.5167118241
108305195287720.95933035317474.0406696466
109143356216748.03173448-73392.0317344801
110154165270632.908285133-116467.908285133
111307000258811.3843052448188.6156947604
112298039234417.81297559263621.1870244083
1132362320531.42170879283091.57829120717
114195817229610.948890009-33793.9488900086
1156185747621.337658870814235.6623411292
116163766168874.604839511-5108.60483951079
117414506379825.5483154434680.4516845602
1182105421562.9874275997-508.987427599667
119252805240770.75313084112034.246869159
1203196137836.9277675673-5875.92776756732
121317367341530.319163854-24163.3191638538
122240153216151.40295738724001.5970426131
123175083146153.11418727728929.8858127228
124152043183352.866207239-31309.8662072392
1253821451059.9991696589-12845.9991696589
126216299204515.0961946111783.9038053903
127357602314137.73662097643464.263379024
128198104212929.940367908-14825.9403679081
129410803404898.8756054365904.12439456418
130316105344488.798142186-28383.7981421859
131397297407940.172364413-10643.1723644132
132187992191641.118423603-3649.1184236033
133102424134264.223651173-31840.2236511728
134286327283191.3948805633135.6051194365
135407378413921.856083284-6543.85608328389
136143860140965.2948290062894.70517099356
137391854396666.353361697-4812.35336169667
138157429188283.09703442-30854.0970344196
139258751227676.24136583631074.7586341639
140282399256406.31985102125992.6801489787
141217665199795.11749834717869.882501653
142366774358145.7488642278628.25113577327
143236660246332.90958245-9672.90958244952
144173260137169.39837372736090.6016262734
145323545295023.08664463128521.9133553693
146168994207964.688172917-38970.6881729174
147253330276073.227735387-22743.2277353869
148301703290129.20405940911573.7959405914
1491-7896.706413381897897.70641338189
1501468811982.88169205092705.11830794912
15198-6398.09217972166496.0921797216
152455-6212.336079103546667.33607910354
1530-6767.079986808096767.07998680809
1540-6621.206860914516621.20686091451
155246435218941.77727977827493.2227202223
156382374354812.90420433927561.0957956612
1570-6621.206860914516621.20686091451
158203-6027.616780741666230.61678074166
15971996954.4760613738244.523938626199
1604666048061.8316008438-1401.83160084383
161175479331.896516026078215.10348397393
16211667891900.842898916824777.1571010832
163969-5820.070983067626789.07098306762
164206501174276.75076719432224.249232806






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.06277624072583350.1255524814516670.937223759274166
140.2181873239908260.4363746479816520.781812676009174
150.1419034206370580.2838068412741160.858096579362942
160.2940377708493190.5880755416986370.705962229150681
170.3495573717516390.6991147435032780.650442628248361
180.6255020535755960.7489958928488070.374497946424404
190.5270490001360220.9459019997279550.472950999863978
200.4295713254008110.8591426508016220.570428674599189
210.3418033929804880.6836067859609750.658196607019512
220.8800509889869310.2398980220261380.119949011013069
230.840924210713720.318151578572560.15907578928628
240.854239156904690.2915216861906210.14576084309531
250.8585552031753370.2828895936493260.141444796824663
260.8293646923093530.3412706153812940.170635307690647
270.8733040075654270.2533919848691470.126695992434573
280.8396077469575540.3207845060848920.160392253042446
290.7952868825072790.4094262349854420.204713117492721
300.8693762806377770.2612474387244460.130623719362223
310.8677421129216920.2645157741566150.132257887078308
320.8594464107865450.2811071784269090.140553589213455
330.8235380489870990.3529239020258020.176461951012901
340.7827482647841480.4345034704317040.217251735215852
350.7695768149409040.4608463701181920.230423185059096
360.7207144839571510.5585710320856970.279285516042849
370.7557943568209760.4884112863580480.244205643179024
380.7671909696534030.4656180606931930.232809030346597
390.7270769577696330.5458460844607340.272923042230367
400.696501063338550.6069978733228990.30349893666145
410.6608991593492880.6782016813014230.339100840650712
420.6269709385039070.7460581229921850.373029061496093
430.7107838904259730.5784322191480540.289216109574027
440.6857449874910070.6285100250179860.314255012508993
450.9103677636937330.1792644726125340.0896322363062668
460.9205130034760880.1589739930478240.0794869965239118
470.9184626509594490.1630746980811030.0815373490405515
480.9122713280938510.1754573438122970.0877286719061486
490.9573219888128010.08535602237439730.0426780111871987
500.9763906675006840.04721866499863230.0236093324993161
510.972578400104940.05484319979011980.0274215998950599
520.9685776894920450.06284462101591020.0314223105079551
530.965524143331310.06895171333737910.0344758566686895
540.9555940086514450.0888119826971110.0444059913485555
550.9459146474068960.1081707051862070.0540853525931036
560.9330725877741830.1338548244516340.066927412225817
570.9154436277061450.1691127445877110.0845563722938553
580.9035741379447850.1928517241104310.0964258620552154
590.8917279839050360.2165440321899280.108272016094964
600.9029708119671660.1940583760656680.0970291880328341
610.8975288801986020.2049422396027960.102471119801398
620.9028503780625850.1942992438748290.0971496219374146
630.8878146425638220.2243707148723570.112185357436178
640.8633326925712610.2733346148574780.136667307428739
650.8383319303315190.3233361393369610.161668069668481
660.8138938400800650.3722123198398690.186106159919935
670.7908716092996610.4182567814006780.209128390700339
680.8059655332058280.3880689335883450.194034466794172
690.7727020579377440.4545958841245110.227297942062256
700.7383647792876470.5232704414247050.261635220712353
710.7031498247827420.5937003504345160.296850175217258
720.6755062826992890.6489874346014210.324493717300711
730.6327794102406470.7344411795187050.367220589759353
740.5972059140570480.8055881718859040.402794085942952
750.5506219729203730.8987560541592550.449378027079627
760.9003647759649330.1992704480701340.0996352240350669
770.9031778703208880.1936442593582240.0968221296791118
780.9316902217624070.1366195564751860.0683097782375928
790.9147989538473780.1704020923052430.0852010461526216
800.8948139986250.210372002750.105186001375
810.8821328670742560.2357342658514870.117867132925744
820.9809132708444030.03817345831119440.0190867291555972
830.978014153858350.04397169228329920.0219858461416496
840.9817227170351030.03655456592979360.0182772829648968
850.9781726456692810.04365470866143880.0218273543307194
860.971807611270670.056384777458660.02819238872933
870.9637306253368230.07253874932635390.036269374663177
880.957226361410550.08554727717889940.0427736385894497
890.9452039984212720.1095920031574560.0547960015787281
900.9869652853769480.02606942924610450.0130347146230523
910.984532938777170.03093412244566090.0154670612228305
920.9870123820622640.02597523587547210.0129876179377361
930.982610621993570.03477875601285980.0173893780064299
940.9798420709866170.04031585802676550.0201579290133828
950.9899453894606180.02010922107876350.0100546105393818
960.9861867926352060.02762641472958790.013813207364794
970.9821281850911450.03574362981770960.0178718149088548
980.9780812394431940.04383752111361140.0219187605568057
990.9745228496680080.05095430066398350.0254771503319917
1000.9691258723659780.06174825526804480.0308741276340224
1010.9612699500310450.077460099937910.038730049968955
1020.9503131962869140.09937360742617180.0496868037130859
1030.9398300253833570.1203399492332850.0601699746166426
1040.9288425822711530.1423148354576950.0711574177288473
1050.9164402204415190.1671195591169620.0835597795584812
1060.9300676356190150.1398647287619690.0699323643809846
1070.9173812447932490.1652375104135020.082618755206751
1080.9036476007911970.1927047984176070.0963523992088035
1090.9817879245706690.03642415085866230.0182120754293311
1100.9998958954302330.000208209139534170.000104104569767085
1110.9999590046986428.19906027161464e-054.09953013580732e-05
1120.9999878370743592.43258512818242e-051.21629256409121e-05
1130.9999775087017614.49825964778065e-052.24912982389032e-05
1140.9999929696414621.40607170760881e-057.03035853804407e-06
1150.9999880015792022.39968415951582e-051.19984207975791e-05
1160.9999827274738743.45450522511714e-051.72725261255857e-05
1170.999994726733221.05465335605906e-055.27326678029528e-06
1180.9999898349678162.03300643684355e-051.01650321842178e-05
1190.9999867298579222.65402841569086e-051.32701420784543e-05
1200.9999767343573114.6531285378864e-052.3265642689432e-05
1210.9999879161916052.41676167899087e-051.20838083949544e-05
1220.9999799225953654.0154809270809e-052.00774046354045e-05
1230.9999807268938353.85462123299292e-051.92731061649646e-05
1240.9999851701911052.96596177895425e-051.48298088947712e-05
1250.9999724766250785.50467498435296e-052.75233749217648e-05
1260.9999439848287410.0001120303425183855.60151712591923e-05
1270.9999933074872051.33850255906132e-056.69251279530658e-06
1280.9999871172963882.57654072233543e-051.28827036116771e-05
1290.9999978452692914.30946141891017e-062.15473070945508e-06
1300.9999952663434829.46731303689551e-064.73365651844775e-06
1310.9999903129237561.93741524874791e-059.68707624373954e-06
1320.9999966832620216.63347595810964e-063.31673797905482e-06
1330.9999962746680757.45066385002322e-063.72533192501161e-06
1340.999999741410445.17179119486438e-072.58589559743219e-07
1350.9999995232999789.53400044809929e-074.76700022404964e-07
1360.9999989200650032.15986999417394e-061.07993499708697e-06
1370.9999997572366084.85526784817392e-072.42763392408696e-07
1380.9999994521435411.09571291828221e-065.47856459141104e-07
1390.9999999997086855.82629926352331e-102.91314963176165e-10
1400.9999999990119091.97618209953892e-099.88091049769462e-10
1410.999999995259439.48114004977495e-094.74057002488748e-09
1420.9999999730154975.39690057493964e-082.69845028746982e-08
1430.9999998577927522.84414495896922e-071.42207247948461e-07
1440.9999993863540551.22729188915719e-066.13645944578594e-07
1450.9999986422767752.7154464490024e-061.3577232245012e-06
1460.999999999987532.49400752434153e-111.24700376217077e-11
1470.9999999996903136.19374800899537e-103.09687400449769e-10
1480.9999999927219781.45560433564566e-087.27802167822831e-09
1490.9999997782405464.43518908773532e-072.21759454386766e-07
1500.9999975813870994.83722580276344e-062.41861290138172e-06
1510.9999221223658040.000155755268391517.7877634195755e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.0627762407258335 & 0.125552481451667 & 0.937223759274166 \tabularnewline
14 & 0.218187323990826 & 0.436374647981652 & 0.781812676009174 \tabularnewline
15 & 0.141903420637058 & 0.283806841274116 & 0.858096579362942 \tabularnewline
16 & 0.294037770849319 & 0.588075541698637 & 0.705962229150681 \tabularnewline
17 & 0.349557371751639 & 0.699114743503278 & 0.650442628248361 \tabularnewline
18 & 0.625502053575596 & 0.748995892848807 & 0.374497946424404 \tabularnewline
19 & 0.527049000136022 & 0.945901999727955 & 0.472950999863978 \tabularnewline
20 & 0.429571325400811 & 0.859142650801622 & 0.570428674599189 \tabularnewline
21 & 0.341803392980488 & 0.683606785960975 & 0.658196607019512 \tabularnewline
22 & 0.880050988986931 & 0.239898022026138 & 0.119949011013069 \tabularnewline
23 & 0.84092421071372 & 0.31815157857256 & 0.15907578928628 \tabularnewline
24 & 0.85423915690469 & 0.291521686190621 & 0.14576084309531 \tabularnewline
25 & 0.858555203175337 & 0.282889593649326 & 0.141444796824663 \tabularnewline
26 & 0.829364692309353 & 0.341270615381294 & 0.170635307690647 \tabularnewline
27 & 0.873304007565427 & 0.253391984869147 & 0.126695992434573 \tabularnewline
28 & 0.839607746957554 & 0.320784506084892 & 0.160392253042446 \tabularnewline
29 & 0.795286882507279 & 0.409426234985442 & 0.204713117492721 \tabularnewline
30 & 0.869376280637777 & 0.261247438724446 & 0.130623719362223 \tabularnewline
31 & 0.867742112921692 & 0.264515774156615 & 0.132257887078308 \tabularnewline
32 & 0.859446410786545 & 0.281107178426909 & 0.140553589213455 \tabularnewline
33 & 0.823538048987099 & 0.352923902025802 & 0.176461951012901 \tabularnewline
34 & 0.782748264784148 & 0.434503470431704 & 0.217251735215852 \tabularnewline
35 & 0.769576814940904 & 0.460846370118192 & 0.230423185059096 \tabularnewline
36 & 0.720714483957151 & 0.558571032085697 & 0.279285516042849 \tabularnewline
37 & 0.755794356820976 & 0.488411286358048 & 0.244205643179024 \tabularnewline
38 & 0.767190969653403 & 0.465618060693193 & 0.232809030346597 \tabularnewline
39 & 0.727076957769633 & 0.545846084460734 & 0.272923042230367 \tabularnewline
40 & 0.69650106333855 & 0.606997873322899 & 0.30349893666145 \tabularnewline
41 & 0.660899159349288 & 0.678201681301423 & 0.339100840650712 \tabularnewline
42 & 0.626970938503907 & 0.746058122992185 & 0.373029061496093 \tabularnewline
43 & 0.710783890425973 & 0.578432219148054 & 0.289216109574027 \tabularnewline
44 & 0.685744987491007 & 0.628510025017986 & 0.314255012508993 \tabularnewline
45 & 0.910367763693733 & 0.179264472612534 & 0.0896322363062668 \tabularnewline
46 & 0.920513003476088 & 0.158973993047824 & 0.0794869965239118 \tabularnewline
47 & 0.918462650959449 & 0.163074698081103 & 0.0815373490405515 \tabularnewline
48 & 0.912271328093851 & 0.175457343812297 & 0.0877286719061486 \tabularnewline
49 & 0.957321988812801 & 0.0853560223743973 & 0.0426780111871987 \tabularnewline
50 & 0.976390667500684 & 0.0472186649986323 & 0.0236093324993161 \tabularnewline
51 & 0.97257840010494 & 0.0548431997901198 & 0.0274215998950599 \tabularnewline
52 & 0.968577689492045 & 0.0628446210159102 & 0.0314223105079551 \tabularnewline
53 & 0.96552414333131 & 0.0689517133373791 & 0.0344758566686895 \tabularnewline
54 & 0.955594008651445 & 0.088811982697111 & 0.0444059913485555 \tabularnewline
55 & 0.945914647406896 & 0.108170705186207 & 0.0540853525931036 \tabularnewline
56 & 0.933072587774183 & 0.133854824451634 & 0.066927412225817 \tabularnewline
57 & 0.915443627706145 & 0.169112744587711 & 0.0845563722938553 \tabularnewline
58 & 0.903574137944785 & 0.192851724110431 & 0.0964258620552154 \tabularnewline
59 & 0.891727983905036 & 0.216544032189928 & 0.108272016094964 \tabularnewline
60 & 0.902970811967166 & 0.194058376065668 & 0.0970291880328341 \tabularnewline
61 & 0.897528880198602 & 0.204942239602796 & 0.102471119801398 \tabularnewline
62 & 0.902850378062585 & 0.194299243874829 & 0.0971496219374146 \tabularnewline
63 & 0.887814642563822 & 0.224370714872357 & 0.112185357436178 \tabularnewline
64 & 0.863332692571261 & 0.273334614857478 & 0.136667307428739 \tabularnewline
65 & 0.838331930331519 & 0.323336139336961 & 0.161668069668481 \tabularnewline
66 & 0.813893840080065 & 0.372212319839869 & 0.186106159919935 \tabularnewline
67 & 0.790871609299661 & 0.418256781400678 & 0.209128390700339 \tabularnewline
68 & 0.805965533205828 & 0.388068933588345 & 0.194034466794172 \tabularnewline
69 & 0.772702057937744 & 0.454595884124511 & 0.227297942062256 \tabularnewline
70 & 0.738364779287647 & 0.523270441424705 & 0.261635220712353 \tabularnewline
71 & 0.703149824782742 & 0.593700350434516 & 0.296850175217258 \tabularnewline
72 & 0.675506282699289 & 0.648987434601421 & 0.324493717300711 \tabularnewline
73 & 0.632779410240647 & 0.734441179518705 & 0.367220589759353 \tabularnewline
74 & 0.597205914057048 & 0.805588171885904 & 0.402794085942952 \tabularnewline
75 & 0.550621972920373 & 0.898756054159255 & 0.449378027079627 \tabularnewline
76 & 0.900364775964933 & 0.199270448070134 & 0.0996352240350669 \tabularnewline
77 & 0.903177870320888 & 0.193644259358224 & 0.0968221296791118 \tabularnewline
78 & 0.931690221762407 & 0.136619556475186 & 0.0683097782375928 \tabularnewline
79 & 0.914798953847378 & 0.170402092305243 & 0.0852010461526216 \tabularnewline
80 & 0.894813998625 & 0.21037200275 & 0.105186001375 \tabularnewline
81 & 0.882132867074256 & 0.235734265851487 & 0.117867132925744 \tabularnewline
82 & 0.980913270844403 & 0.0381734583111944 & 0.0190867291555972 \tabularnewline
83 & 0.97801415385835 & 0.0439716922832992 & 0.0219858461416496 \tabularnewline
84 & 0.981722717035103 & 0.0365545659297936 & 0.0182772829648968 \tabularnewline
85 & 0.978172645669281 & 0.0436547086614388 & 0.0218273543307194 \tabularnewline
86 & 0.97180761127067 & 0.05638477745866 & 0.02819238872933 \tabularnewline
87 & 0.963730625336823 & 0.0725387493263539 & 0.036269374663177 \tabularnewline
88 & 0.95722636141055 & 0.0855472771788994 & 0.0427736385894497 \tabularnewline
89 & 0.945203998421272 & 0.109592003157456 & 0.0547960015787281 \tabularnewline
90 & 0.986965285376948 & 0.0260694292461045 & 0.0130347146230523 \tabularnewline
91 & 0.98453293877717 & 0.0309341224456609 & 0.0154670612228305 \tabularnewline
92 & 0.987012382062264 & 0.0259752358754721 & 0.0129876179377361 \tabularnewline
93 & 0.98261062199357 & 0.0347787560128598 & 0.0173893780064299 \tabularnewline
94 & 0.979842070986617 & 0.0403158580267655 & 0.0201579290133828 \tabularnewline
95 & 0.989945389460618 & 0.0201092210787635 & 0.0100546105393818 \tabularnewline
96 & 0.986186792635206 & 0.0276264147295879 & 0.013813207364794 \tabularnewline
97 & 0.982128185091145 & 0.0357436298177096 & 0.0178718149088548 \tabularnewline
98 & 0.978081239443194 & 0.0438375211136114 & 0.0219187605568057 \tabularnewline
99 & 0.974522849668008 & 0.0509543006639835 & 0.0254771503319917 \tabularnewline
100 & 0.969125872365978 & 0.0617482552680448 & 0.0308741276340224 \tabularnewline
101 & 0.961269950031045 & 0.07746009993791 & 0.038730049968955 \tabularnewline
102 & 0.950313196286914 & 0.0993736074261718 & 0.0496868037130859 \tabularnewline
103 & 0.939830025383357 & 0.120339949233285 & 0.0601699746166426 \tabularnewline
104 & 0.928842582271153 & 0.142314835457695 & 0.0711574177288473 \tabularnewline
105 & 0.916440220441519 & 0.167119559116962 & 0.0835597795584812 \tabularnewline
106 & 0.930067635619015 & 0.139864728761969 & 0.0699323643809846 \tabularnewline
107 & 0.917381244793249 & 0.165237510413502 & 0.082618755206751 \tabularnewline
108 & 0.903647600791197 & 0.192704798417607 & 0.0963523992088035 \tabularnewline
109 & 0.981787924570669 & 0.0364241508586623 & 0.0182120754293311 \tabularnewline
110 & 0.999895895430233 & 0.00020820913953417 & 0.000104104569767085 \tabularnewline
111 & 0.999959004698642 & 8.19906027161464e-05 & 4.09953013580732e-05 \tabularnewline
112 & 0.999987837074359 & 2.43258512818242e-05 & 1.21629256409121e-05 \tabularnewline
113 & 0.999977508701761 & 4.49825964778065e-05 & 2.24912982389032e-05 \tabularnewline
114 & 0.999992969641462 & 1.40607170760881e-05 & 7.03035853804407e-06 \tabularnewline
115 & 0.999988001579202 & 2.39968415951582e-05 & 1.19984207975791e-05 \tabularnewline
116 & 0.999982727473874 & 3.45450522511714e-05 & 1.72725261255857e-05 \tabularnewline
117 & 0.99999472673322 & 1.05465335605906e-05 & 5.27326678029528e-06 \tabularnewline
118 & 0.999989834967816 & 2.03300643684355e-05 & 1.01650321842178e-05 \tabularnewline
119 & 0.999986729857922 & 2.65402841569086e-05 & 1.32701420784543e-05 \tabularnewline
120 & 0.999976734357311 & 4.6531285378864e-05 & 2.3265642689432e-05 \tabularnewline
121 & 0.999987916191605 & 2.41676167899087e-05 & 1.20838083949544e-05 \tabularnewline
122 & 0.999979922595365 & 4.0154809270809e-05 & 2.00774046354045e-05 \tabularnewline
123 & 0.999980726893835 & 3.85462123299292e-05 & 1.92731061649646e-05 \tabularnewline
124 & 0.999985170191105 & 2.96596177895425e-05 & 1.48298088947712e-05 \tabularnewline
125 & 0.999972476625078 & 5.50467498435296e-05 & 2.75233749217648e-05 \tabularnewline
126 & 0.999943984828741 & 0.000112030342518385 & 5.60151712591923e-05 \tabularnewline
127 & 0.999993307487205 & 1.33850255906132e-05 & 6.69251279530658e-06 \tabularnewline
128 & 0.999987117296388 & 2.57654072233543e-05 & 1.28827036116771e-05 \tabularnewline
129 & 0.999997845269291 & 4.30946141891017e-06 & 2.15473070945508e-06 \tabularnewline
130 & 0.999995266343482 & 9.46731303689551e-06 & 4.73365651844775e-06 \tabularnewline
131 & 0.999990312923756 & 1.93741524874791e-05 & 9.68707624373954e-06 \tabularnewline
132 & 0.999996683262021 & 6.63347595810964e-06 & 3.31673797905482e-06 \tabularnewline
133 & 0.999996274668075 & 7.45066385002322e-06 & 3.72533192501161e-06 \tabularnewline
134 & 0.99999974141044 & 5.17179119486438e-07 & 2.58589559743219e-07 \tabularnewline
135 & 0.999999523299978 & 9.53400044809929e-07 & 4.76700022404964e-07 \tabularnewline
136 & 0.999998920065003 & 2.15986999417394e-06 & 1.07993499708697e-06 \tabularnewline
137 & 0.999999757236608 & 4.85526784817392e-07 & 2.42763392408696e-07 \tabularnewline
138 & 0.999999452143541 & 1.09571291828221e-06 & 5.47856459141104e-07 \tabularnewline
139 & 0.999999999708685 & 5.82629926352331e-10 & 2.91314963176165e-10 \tabularnewline
140 & 0.999999999011909 & 1.97618209953892e-09 & 9.88091049769462e-10 \tabularnewline
141 & 0.99999999525943 & 9.48114004977495e-09 & 4.74057002488748e-09 \tabularnewline
142 & 0.999999973015497 & 5.39690057493964e-08 & 2.69845028746982e-08 \tabularnewline
143 & 0.999999857792752 & 2.84414495896922e-07 & 1.42207247948461e-07 \tabularnewline
144 & 0.999999386354055 & 1.22729188915719e-06 & 6.13645944578594e-07 \tabularnewline
145 & 0.999998642276775 & 2.7154464490024e-06 & 1.3577232245012e-06 \tabularnewline
146 & 0.99999999998753 & 2.49400752434153e-11 & 1.24700376217077e-11 \tabularnewline
147 & 0.999999999690313 & 6.19374800899537e-10 & 3.09687400449769e-10 \tabularnewline
148 & 0.999999992721978 & 1.45560433564566e-08 & 7.27802167822831e-09 \tabularnewline
149 & 0.999999778240546 & 4.43518908773532e-07 & 2.21759454386766e-07 \tabularnewline
150 & 0.999997581387099 & 4.83722580276344e-06 & 2.41861290138172e-06 \tabularnewline
151 & 0.999922122365804 & 0.00015575526839151 & 7.7877634195755e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&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]13[/C][C]0.0627762407258335[/C][C]0.125552481451667[/C][C]0.937223759274166[/C][/ROW]
[ROW][C]14[/C][C]0.218187323990826[/C][C]0.436374647981652[/C][C]0.781812676009174[/C][/ROW]
[ROW][C]15[/C][C]0.141903420637058[/C][C]0.283806841274116[/C][C]0.858096579362942[/C][/ROW]
[ROW][C]16[/C][C]0.294037770849319[/C][C]0.588075541698637[/C][C]0.705962229150681[/C][/ROW]
[ROW][C]17[/C][C]0.349557371751639[/C][C]0.699114743503278[/C][C]0.650442628248361[/C][/ROW]
[ROW][C]18[/C][C]0.625502053575596[/C][C]0.748995892848807[/C][C]0.374497946424404[/C][/ROW]
[ROW][C]19[/C][C]0.527049000136022[/C][C]0.945901999727955[/C][C]0.472950999863978[/C][/ROW]
[ROW][C]20[/C][C]0.429571325400811[/C][C]0.859142650801622[/C][C]0.570428674599189[/C][/ROW]
[ROW][C]21[/C][C]0.341803392980488[/C][C]0.683606785960975[/C][C]0.658196607019512[/C][/ROW]
[ROW][C]22[/C][C]0.880050988986931[/C][C]0.239898022026138[/C][C]0.119949011013069[/C][/ROW]
[ROW][C]23[/C][C]0.84092421071372[/C][C]0.31815157857256[/C][C]0.15907578928628[/C][/ROW]
[ROW][C]24[/C][C]0.85423915690469[/C][C]0.291521686190621[/C][C]0.14576084309531[/C][/ROW]
[ROW][C]25[/C][C]0.858555203175337[/C][C]0.282889593649326[/C][C]0.141444796824663[/C][/ROW]
[ROW][C]26[/C][C]0.829364692309353[/C][C]0.341270615381294[/C][C]0.170635307690647[/C][/ROW]
[ROW][C]27[/C][C]0.873304007565427[/C][C]0.253391984869147[/C][C]0.126695992434573[/C][/ROW]
[ROW][C]28[/C][C]0.839607746957554[/C][C]0.320784506084892[/C][C]0.160392253042446[/C][/ROW]
[ROW][C]29[/C][C]0.795286882507279[/C][C]0.409426234985442[/C][C]0.204713117492721[/C][/ROW]
[ROW][C]30[/C][C]0.869376280637777[/C][C]0.261247438724446[/C][C]0.130623719362223[/C][/ROW]
[ROW][C]31[/C][C]0.867742112921692[/C][C]0.264515774156615[/C][C]0.132257887078308[/C][/ROW]
[ROW][C]32[/C][C]0.859446410786545[/C][C]0.281107178426909[/C][C]0.140553589213455[/C][/ROW]
[ROW][C]33[/C][C]0.823538048987099[/C][C]0.352923902025802[/C][C]0.176461951012901[/C][/ROW]
[ROW][C]34[/C][C]0.782748264784148[/C][C]0.434503470431704[/C][C]0.217251735215852[/C][/ROW]
[ROW][C]35[/C][C]0.769576814940904[/C][C]0.460846370118192[/C][C]0.230423185059096[/C][/ROW]
[ROW][C]36[/C][C]0.720714483957151[/C][C]0.558571032085697[/C][C]0.279285516042849[/C][/ROW]
[ROW][C]37[/C][C]0.755794356820976[/C][C]0.488411286358048[/C][C]0.244205643179024[/C][/ROW]
[ROW][C]38[/C][C]0.767190969653403[/C][C]0.465618060693193[/C][C]0.232809030346597[/C][/ROW]
[ROW][C]39[/C][C]0.727076957769633[/C][C]0.545846084460734[/C][C]0.272923042230367[/C][/ROW]
[ROW][C]40[/C][C]0.69650106333855[/C][C]0.606997873322899[/C][C]0.30349893666145[/C][/ROW]
[ROW][C]41[/C][C]0.660899159349288[/C][C]0.678201681301423[/C][C]0.339100840650712[/C][/ROW]
[ROW][C]42[/C][C]0.626970938503907[/C][C]0.746058122992185[/C][C]0.373029061496093[/C][/ROW]
[ROW][C]43[/C][C]0.710783890425973[/C][C]0.578432219148054[/C][C]0.289216109574027[/C][/ROW]
[ROW][C]44[/C][C]0.685744987491007[/C][C]0.628510025017986[/C][C]0.314255012508993[/C][/ROW]
[ROW][C]45[/C][C]0.910367763693733[/C][C]0.179264472612534[/C][C]0.0896322363062668[/C][/ROW]
[ROW][C]46[/C][C]0.920513003476088[/C][C]0.158973993047824[/C][C]0.0794869965239118[/C][/ROW]
[ROW][C]47[/C][C]0.918462650959449[/C][C]0.163074698081103[/C][C]0.0815373490405515[/C][/ROW]
[ROW][C]48[/C][C]0.912271328093851[/C][C]0.175457343812297[/C][C]0.0877286719061486[/C][/ROW]
[ROW][C]49[/C][C]0.957321988812801[/C][C]0.0853560223743973[/C][C]0.0426780111871987[/C][/ROW]
[ROW][C]50[/C][C]0.976390667500684[/C][C]0.0472186649986323[/C][C]0.0236093324993161[/C][/ROW]
[ROW][C]51[/C][C]0.97257840010494[/C][C]0.0548431997901198[/C][C]0.0274215998950599[/C][/ROW]
[ROW][C]52[/C][C]0.968577689492045[/C][C]0.0628446210159102[/C][C]0.0314223105079551[/C][/ROW]
[ROW][C]53[/C][C]0.96552414333131[/C][C]0.0689517133373791[/C][C]0.0344758566686895[/C][/ROW]
[ROW][C]54[/C][C]0.955594008651445[/C][C]0.088811982697111[/C][C]0.0444059913485555[/C][/ROW]
[ROW][C]55[/C][C]0.945914647406896[/C][C]0.108170705186207[/C][C]0.0540853525931036[/C][/ROW]
[ROW][C]56[/C][C]0.933072587774183[/C][C]0.133854824451634[/C][C]0.066927412225817[/C][/ROW]
[ROW][C]57[/C][C]0.915443627706145[/C][C]0.169112744587711[/C][C]0.0845563722938553[/C][/ROW]
[ROW][C]58[/C][C]0.903574137944785[/C][C]0.192851724110431[/C][C]0.0964258620552154[/C][/ROW]
[ROW][C]59[/C][C]0.891727983905036[/C][C]0.216544032189928[/C][C]0.108272016094964[/C][/ROW]
[ROW][C]60[/C][C]0.902970811967166[/C][C]0.194058376065668[/C][C]0.0970291880328341[/C][/ROW]
[ROW][C]61[/C][C]0.897528880198602[/C][C]0.204942239602796[/C][C]0.102471119801398[/C][/ROW]
[ROW][C]62[/C][C]0.902850378062585[/C][C]0.194299243874829[/C][C]0.0971496219374146[/C][/ROW]
[ROW][C]63[/C][C]0.887814642563822[/C][C]0.224370714872357[/C][C]0.112185357436178[/C][/ROW]
[ROW][C]64[/C][C]0.863332692571261[/C][C]0.273334614857478[/C][C]0.136667307428739[/C][/ROW]
[ROW][C]65[/C][C]0.838331930331519[/C][C]0.323336139336961[/C][C]0.161668069668481[/C][/ROW]
[ROW][C]66[/C][C]0.813893840080065[/C][C]0.372212319839869[/C][C]0.186106159919935[/C][/ROW]
[ROW][C]67[/C][C]0.790871609299661[/C][C]0.418256781400678[/C][C]0.209128390700339[/C][/ROW]
[ROW][C]68[/C][C]0.805965533205828[/C][C]0.388068933588345[/C][C]0.194034466794172[/C][/ROW]
[ROW][C]69[/C][C]0.772702057937744[/C][C]0.454595884124511[/C][C]0.227297942062256[/C][/ROW]
[ROW][C]70[/C][C]0.738364779287647[/C][C]0.523270441424705[/C][C]0.261635220712353[/C][/ROW]
[ROW][C]71[/C][C]0.703149824782742[/C][C]0.593700350434516[/C][C]0.296850175217258[/C][/ROW]
[ROW][C]72[/C][C]0.675506282699289[/C][C]0.648987434601421[/C][C]0.324493717300711[/C][/ROW]
[ROW][C]73[/C][C]0.632779410240647[/C][C]0.734441179518705[/C][C]0.367220589759353[/C][/ROW]
[ROW][C]74[/C][C]0.597205914057048[/C][C]0.805588171885904[/C][C]0.402794085942952[/C][/ROW]
[ROW][C]75[/C][C]0.550621972920373[/C][C]0.898756054159255[/C][C]0.449378027079627[/C][/ROW]
[ROW][C]76[/C][C]0.900364775964933[/C][C]0.199270448070134[/C][C]0.0996352240350669[/C][/ROW]
[ROW][C]77[/C][C]0.903177870320888[/C][C]0.193644259358224[/C][C]0.0968221296791118[/C][/ROW]
[ROW][C]78[/C][C]0.931690221762407[/C][C]0.136619556475186[/C][C]0.0683097782375928[/C][/ROW]
[ROW][C]79[/C][C]0.914798953847378[/C][C]0.170402092305243[/C][C]0.0852010461526216[/C][/ROW]
[ROW][C]80[/C][C]0.894813998625[/C][C]0.21037200275[/C][C]0.105186001375[/C][/ROW]
[ROW][C]81[/C][C]0.882132867074256[/C][C]0.235734265851487[/C][C]0.117867132925744[/C][/ROW]
[ROW][C]82[/C][C]0.980913270844403[/C][C]0.0381734583111944[/C][C]0.0190867291555972[/C][/ROW]
[ROW][C]83[/C][C]0.97801415385835[/C][C]0.0439716922832992[/C][C]0.0219858461416496[/C][/ROW]
[ROW][C]84[/C][C]0.981722717035103[/C][C]0.0365545659297936[/C][C]0.0182772829648968[/C][/ROW]
[ROW][C]85[/C][C]0.978172645669281[/C][C]0.0436547086614388[/C][C]0.0218273543307194[/C][/ROW]
[ROW][C]86[/C][C]0.97180761127067[/C][C]0.05638477745866[/C][C]0.02819238872933[/C][/ROW]
[ROW][C]87[/C][C]0.963730625336823[/C][C]0.0725387493263539[/C][C]0.036269374663177[/C][/ROW]
[ROW][C]88[/C][C]0.95722636141055[/C][C]0.0855472771788994[/C][C]0.0427736385894497[/C][/ROW]
[ROW][C]89[/C][C]0.945203998421272[/C][C]0.109592003157456[/C][C]0.0547960015787281[/C][/ROW]
[ROW][C]90[/C][C]0.986965285376948[/C][C]0.0260694292461045[/C][C]0.0130347146230523[/C][/ROW]
[ROW][C]91[/C][C]0.98453293877717[/C][C]0.0309341224456609[/C][C]0.0154670612228305[/C][/ROW]
[ROW][C]92[/C][C]0.987012382062264[/C][C]0.0259752358754721[/C][C]0.0129876179377361[/C][/ROW]
[ROW][C]93[/C][C]0.98261062199357[/C][C]0.0347787560128598[/C][C]0.0173893780064299[/C][/ROW]
[ROW][C]94[/C][C]0.979842070986617[/C][C]0.0403158580267655[/C][C]0.0201579290133828[/C][/ROW]
[ROW][C]95[/C][C]0.989945389460618[/C][C]0.0201092210787635[/C][C]0.0100546105393818[/C][/ROW]
[ROW][C]96[/C][C]0.986186792635206[/C][C]0.0276264147295879[/C][C]0.013813207364794[/C][/ROW]
[ROW][C]97[/C][C]0.982128185091145[/C][C]0.0357436298177096[/C][C]0.0178718149088548[/C][/ROW]
[ROW][C]98[/C][C]0.978081239443194[/C][C]0.0438375211136114[/C][C]0.0219187605568057[/C][/ROW]
[ROW][C]99[/C][C]0.974522849668008[/C][C]0.0509543006639835[/C][C]0.0254771503319917[/C][/ROW]
[ROW][C]100[/C][C]0.969125872365978[/C][C]0.0617482552680448[/C][C]0.0308741276340224[/C][/ROW]
[ROW][C]101[/C][C]0.961269950031045[/C][C]0.07746009993791[/C][C]0.038730049968955[/C][/ROW]
[ROW][C]102[/C][C]0.950313196286914[/C][C]0.0993736074261718[/C][C]0.0496868037130859[/C][/ROW]
[ROW][C]103[/C][C]0.939830025383357[/C][C]0.120339949233285[/C][C]0.0601699746166426[/C][/ROW]
[ROW][C]104[/C][C]0.928842582271153[/C][C]0.142314835457695[/C][C]0.0711574177288473[/C][/ROW]
[ROW][C]105[/C][C]0.916440220441519[/C][C]0.167119559116962[/C][C]0.0835597795584812[/C][/ROW]
[ROW][C]106[/C][C]0.930067635619015[/C][C]0.139864728761969[/C][C]0.0699323643809846[/C][/ROW]
[ROW][C]107[/C][C]0.917381244793249[/C][C]0.165237510413502[/C][C]0.082618755206751[/C][/ROW]
[ROW][C]108[/C][C]0.903647600791197[/C][C]0.192704798417607[/C][C]0.0963523992088035[/C][/ROW]
[ROW][C]109[/C][C]0.981787924570669[/C][C]0.0364241508586623[/C][C]0.0182120754293311[/C][/ROW]
[ROW][C]110[/C][C]0.999895895430233[/C][C]0.00020820913953417[/C][C]0.000104104569767085[/C][/ROW]
[ROW][C]111[/C][C]0.999959004698642[/C][C]8.19906027161464e-05[/C][C]4.09953013580732e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999987837074359[/C][C]2.43258512818242e-05[/C][C]1.21629256409121e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999977508701761[/C][C]4.49825964778065e-05[/C][C]2.24912982389032e-05[/C][/ROW]
[ROW][C]114[/C][C]0.999992969641462[/C][C]1.40607170760881e-05[/C][C]7.03035853804407e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999988001579202[/C][C]2.39968415951582e-05[/C][C]1.19984207975791e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999982727473874[/C][C]3.45450522511714e-05[/C][C]1.72725261255857e-05[/C][/ROW]
[ROW][C]117[/C][C]0.99999472673322[/C][C]1.05465335605906e-05[/C][C]5.27326678029528e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999989834967816[/C][C]2.03300643684355e-05[/C][C]1.01650321842178e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999986729857922[/C][C]2.65402841569086e-05[/C][C]1.32701420784543e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999976734357311[/C][C]4.6531285378864e-05[/C][C]2.3265642689432e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999987916191605[/C][C]2.41676167899087e-05[/C][C]1.20838083949544e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999979922595365[/C][C]4.0154809270809e-05[/C][C]2.00774046354045e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999980726893835[/C][C]3.85462123299292e-05[/C][C]1.92731061649646e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999985170191105[/C][C]2.96596177895425e-05[/C][C]1.48298088947712e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999972476625078[/C][C]5.50467498435296e-05[/C][C]2.75233749217648e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999943984828741[/C][C]0.000112030342518385[/C][C]5.60151712591923e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999993307487205[/C][C]1.33850255906132e-05[/C][C]6.69251279530658e-06[/C][/ROW]
[ROW][C]128[/C][C]0.999987117296388[/C][C]2.57654072233543e-05[/C][C]1.28827036116771e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999997845269291[/C][C]4.30946141891017e-06[/C][C]2.15473070945508e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999995266343482[/C][C]9.46731303689551e-06[/C][C]4.73365651844775e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999990312923756[/C][C]1.93741524874791e-05[/C][C]9.68707624373954e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999996683262021[/C][C]6.63347595810964e-06[/C][C]3.31673797905482e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999996274668075[/C][C]7.45066385002322e-06[/C][C]3.72533192501161e-06[/C][/ROW]
[ROW][C]134[/C][C]0.99999974141044[/C][C]5.17179119486438e-07[/C][C]2.58589559743219e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999999523299978[/C][C]9.53400044809929e-07[/C][C]4.76700022404964e-07[/C][/ROW]
[ROW][C]136[/C][C]0.999998920065003[/C][C]2.15986999417394e-06[/C][C]1.07993499708697e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999999757236608[/C][C]4.85526784817392e-07[/C][C]2.42763392408696e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999452143541[/C][C]1.09571291828221e-06[/C][C]5.47856459141104e-07[/C][/ROW]
[ROW][C]139[/C][C]0.999999999708685[/C][C]5.82629926352331e-10[/C][C]2.91314963176165e-10[/C][/ROW]
[ROW][C]140[/C][C]0.999999999011909[/C][C]1.97618209953892e-09[/C][C]9.88091049769462e-10[/C][/ROW]
[ROW][C]141[/C][C]0.99999999525943[/C][C]9.48114004977495e-09[/C][C]4.74057002488748e-09[/C][/ROW]
[ROW][C]142[/C][C]0.999999973015497[/C][C]5.39690057493964e-08[/C][C]2.69845028746982e-08[/C][/ROW]
[ROW][C]143[/C][C]0.999999857792752[/C][C]2.84414495896922e-07[/C][C]1.42207247948461e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999386354055[/C][C]1.22729188915719e-06[/C][C]6.13645944578594e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999998642276775[/C][C]2.7154464490024e-06[/C][C]1.3577232245012e-06[/C][/ROW]
[ROW][C]146[/C][C]0.99999999998753[/C][C]2.49400752434153e-11[/C][C]1.24700376217077e-11[/C][/ROW]
[ROW][C]147[/C][C]0.999999999690313[/C][C]6.19374800899537e-10[/C][C]3.09687400449769e-10[/C][/ROW]
[ROW][C]148[/C][C]0.999999992721978[/C][C]1.45560433564566e-08[/C][C]7.27802167822831e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999778240546[/C][C]4.43518908773532e-07[/C][C]2.21759454386766e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999997581387099[/C][C]4.83722580276344e-06[/C][C]2.41861290138172e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999922122365804[/C][C]0.00015575526839151[/C][C]7.7877634195755e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.06277624072583350.1255524814516670.937223759274166
140.2181873239908260.4363746479816520.781812676009174
150.1419034206370580.2838068412741160.858096579362942
160.2940377708493190.5880755416986370.705962229150681
170.3495573717516390.6991147435032780.650442628248361
180.6255020535755960.7489958928488070.374497946424404
190.5270490001360220.9459019997279550.472950999863978
200.4295713254008110.8591426508016220.570428674599189
210.3418033929804880.6836067859609750.658196607019512
220.8800509889869310.2398980220261380.119949011013069
230.840924210713720.318151578572560.15907578928628
240.854239156904690.2915216861906210.14576084309531
250.8585552031753370.2828895936493260.141444796824663
260.8293646923093530.3412706153812940.170635307690647
270.8733040075654270.2533919848691470.126695992434573
280.8396077469575540.3207845060848920.160392253042446
290.7952868825072790.4094262349854420.204713117492721
300.8693762806377770.2612474387244460.130623719362223
310.8677421129216920.2645157741566150.132257887078308
320.8594464107865450.2811071784269090.140553589213455
330.8235380489870990.3529239020258020.176461951012901
340.7827482647841480.4345034704317040.217251735215852
350.7695768149409040.4608463701181920.230423185059096
360.7207144839571510.5585710320856970.279285516042849
370.7557943568209760.4884112863580480.244205643179024
380.7671909696534030.4656180606931930.232809030346597
390.7270769577696330.5458460844607340.272923042230367
400.696501063338550.6069978733228990.30349893666145
410.6608991593492880.6782016813014230.339100840650712
420.6269709385039070.7460581229921850.373029061496093
430.7107838904259730.5784322191480540.289216109574027
440.6857449874910070.6285100250179860.314255012508993
450.9103677636937330.1792644726125340.0896322363062668
460.9205130034760880.1589739930478240.0794869965239118
470.9184626509594490.1630746980811030.0815373490405515
480.9122713280938510.1754573438122970.0877286719061486
490.9573219888128010.08535602237439730.0426780111871987
500.9763906675006840.04721866499863230.0236093324993161
510.972578400104940.05484319979011980.0274215998950599
520.9685776894920450.06284462101591020.0314223105079551
530.965524143331310.06895171333737910.0344758566686895
540.9555940086514450.0888119826971110.0444059913485555
550.9459146474068960.1081707051862070.0540853525931036
560.9330725877741830.1338548244516340.066927412225817
570.9154436277061450.1691127445877110.0845563722938553
580.9035741379447850.1928517241104310.0964258620552154
590.8917279839050360.2165440321899280.108272016094964
600.9029708119671660.1940583760656680.0970291880328341
610.8975288801986020.2049422396027960.102471119801398
620.9028503780625850.1942992438748290.0971496219374146
630.8878146425638220.2243707148723570.112185357436178
640.8633326925712610.2733346148574780.136667307428739
650.8383319303315190.3233361393369610.161668069668481
660.8138938400800650.3722123198398690.186106159919935
670.7908716092996610.4182567814006780.209128390700339
680.8059655332058280.3880689335883450.194034466794172
690.7727020579377440.4545958841245110.227297942062256
700.7383647792876470.5232704414247050.261635220712353
710.7031498247827420.5937003504345160.296850175217258
720.6755062826992890.6489874346014210.324493717300711
730.6327794102406470.7344411795187050.367220589759353
740.5972059140570480.8055881718859040.402794085942952
750.5506219729203730.8987560541592550.449378027079627
760.9003647759649330.1992704480701340.0996352240350669
770.9031778703208880.1936442593582240.0968221296791118
780.9316902217624070.1366195564751860.0683097782375928
790.9147989538473780.1704020923052430.0852010461526216
800.8948139986250.210372002750.105186001375
810.8821328670742560.2357342658514870.117867132925744
820.9809132708444030.03817345831119440.0190867291555972
830.978014153858350.04397169228329920.0219858461416496
840.9817227170351030.03655456592979360.0182772829648968
850.9781726456692810.04365470866143880.0218273543307194
860.971807611270670.056384777458660.02819238872933
870.9637306253368230.07253874932635390.036269374663177
880.957226361410550.08554727717889940.0427736385894497
890.9452039984212720.1095920031574560.0547960015787281
900.9869652853769480.02606942924610450.0130347146230523
910.984532938777170.03093412244566090.0154670612228305
920.9870123820622640.02597523587547210.0129876179377361
930.982610621993570.03477875601285980.0173893780064299
940.9798420709866170.04031585802676550.0201579290133828
950.9899453894606180.02010922107876350.0100546105393818
960.9861867926352060.02762641472958790.013813207364794
970.9821281850911450.03574362981770960.0178718149088548
980.9780812394431940.04383752111361140.0219187605568057
990.9745228496680080.05095430066398350.0254771503319917
1000.9691258723659780.06174825526804480.0308741276340224
1010.9612699500310450.077460099937910.038730049968955
1020.9503131962869140.09937360742617180.0496868037130859
1030.9398300253833570.1203399492332850.0601699746166426
1040.9288425822711530.1423148354576950.0711574177288473
1050.9164402204415190.1671195591169620.0835597795584812
1060.9300676356190150.1398647287619690.0699323643809846
1070.9173812447932490.1652375104135020.082618755206751
1080.9036476007911970.1927047984176070.0963523992088035
1090.9817879245706690.03642415085866230.0182120754293311
1100.9998958954302330.000208209139534170.000104104569767085
1110.9999590046986428.19906027161464e-054.09953013580732e-05
1120.9999878370743592.43258512818242e-051.21629256409121e-05
1130.9999775087017614.49825964778065e-052.24912982389032e-05
1140.9999929696414621.40607170760881e-057.03035853804407e-06
1150.9999880015792022.39968415951582e-051.19984207975791e-05
1160.9999827274738743.45450522511714e-051.72725261255857e-05
1170.999994726733221.05465335605906e-055.27326678029528e-06
1180.9999898349678162.03300643684355e-051.01650321842178e-05
1190.9999867298579222.65402841569086e-051.32701420784543e-05
1200.9999767343573114.6531285378864e-052.3265642689432e-05
1210.9999879161916052.41676167899087e-051.20838083949544e-05
1220.9999799225953654.0154809270809e-052.00774046354045e-05
1230.9999807268938353.85462123299292e-051.92731061649646e-05
1240.9999851701911052.96596177895425e-051.48298088947712e-05
1250.9999724766250785.50467498435296e-052.75233749217648e-05
1260.9999439848287410.0001120303425183855.60151712591923e-05
1270.9999933074872051.33850255906132e-056.69251279530658e-06
1280.9999871172963882.57654072233543e-051.28827036116771e-05
1290.9999978452692914.30946141891017e-062.15473070945508e-06
1300.9999952663434829.46731303689551e-064.73365651844775e-06
1310.9999903129237561.93741524874791e-059.68707624373954e-06
1320.9999966832620216.63347595810964e-063.31673797905482e-06
1330.9999962746680757.45066385002322e-063.72533192501161e-06
1340.999999741410445.17179119486438e-072.58589559743219e-07
1350.9999995232999789.53400044809929e-074.76700022404964e-07
1360.9999989200650032.15986999417394e-061.07993499708697e-06
1370.9999997572366084.85526784817392e-072.42763392408696e-07
1380.9999994521435411.09571291828221e-065.47856459141104e-07
1390.9999999997086855.82629926352331e-102.91314963176165e-10
1400.9999999990119091.97618209953892e-099.88091049769462e-10
1410.999999995259439.48114004977495e-094.74057002488748e-09
1420.9999999730154975.39690057493964e-082.69845028746982e-08
1430.9999998577927522.84414495896922e-071.42207247948461e-07
1440.9999993863540551.22729188915719e-066.13645944578594e-07
1450.9999986422767752.7154464490024e-061.3577232245012e-06
1460.999999999987532.49400752434153e-111.24700376217077e-11
1470.9999999996903136.19374800899537e-103.09687400449769e-10
1480.9999999927219781.45560433564566e-087.27802167822831e-09
1490.9999997782405464.43518908773532e-072.21759454386766e-07
1500.9999975813870994.83722580276344e-062.41861290138172e-06
1510.9999221223658040.000155755268391517.7877634195755e-05






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level420.302158273381295NOK
5% type I error level570.410071942446043NOK
10% type I error level690.496402877697842NOK

\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 & 42 & 0.302158273381295 & NOK \tabularnewline
5% type I error level & 57 & 0.410071942446043 & NOK \tabularnewline
10% type I error level & 69 & 0.496402877697842 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156810&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]42[/C][C]0.302158273381295[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.410071942446043[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.496402877697842[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156810&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156810&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level420.302158273381295NOK
5% type I error level570.410071942446043NOK
10% type I error level690.496402877697842NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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