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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 computationThu, 08 Dec 2011 06:41:05 -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/08/t1323344521uovpitnv1zy20qa.htm/, Retrieved Fri, 03 May 2024 06:49:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152835, Retrieved Fri, 03 May 2024 06:49:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Paper: Multiple r...] [2011-12-08 11:41:05] [e889f2ef2eeddd5259af4a52678400a6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	236496	61	85	34	131	124252
10	130631	58	58	30	117	98956
12	198514	62	62	38	146	98073
11	189326	94	108	34	132	106816
12	137449	43	55	25	80	41449
10	65295	27	8	31	117	76173
10	439387	103	134	29	112	177551
11	33186	19	1	18	67	22807
10	174859	50	63	30	116	126938
10	186657	38	77	29	107	61680
12	261949	95	86	38	140	72117
10	190794	94	93	49	186	79738
11	138866	57	44	33	109	57793
11	296878	65	106	46	159	91677
10	192648	71	63	38	146	64631
12	333348	161	160	52	201	106385
10	242212	57	104	32	124	161961
10	263451	130	86	35	131	112669
12	150733	47	92	25	96	114029
10	223226	67	119	42	163	124550
11	240028	63	107	40	151	105416
10	384138	86	86	35	128	72875
10	156540	34	50	25	89	81964
12	148421	43	92	46	184	104880
11	176502	96	123	36	136	76302
12	191441	105	81	35	134	96740
11	249735	120	93	38	146	93071
12	236812	76	113	35	130	78912
11	142329	45	52	28	105	35224
10	259667	53	113	37	142	90694
11	228871	64	109	40	155	125369
12	176054	66	44	42	154	80849
11	286683	79	123	44	169	104434
10	87485	33	38	33	125	65702
12	322865	82	111	35	135	108179
12	247013	50	77	37	139	63583
11	340093	103	92	37	139	95066
12	191653	72	74	32	124	62486
10	114673	31	33	17	55	31081
11	284210	160	105	34	131	94584
10	284195	72	108	33	125	87408
11	155363	59	66	35	128	68966
12	174198	65	69	32	107	88766
10	142986	48	62	35	130	57139
10	140319	73	50	45	73	90586
10	392666	132	91	34	125	109249
11	78800	42	20	26	82	33032
12	201970	69	101	45	173	96056
12	302674	99	129	44	169	146648
11	164733	50	93	40	145	80613
11	194221	68	89	33	134	87026
10	24188	24	8	4	12	5950
11	340411	274	79	41	151	131106
12	65029	17	21	18	67	32551
11	101097	64	30	14	52	31701
12	243889	45	86	33	121	91072
10	273003	74	116	49	186	159803
12	282220	160	106	32	120	143950
10	273495	118	127	37	135	112368
10	214872	74	75	32	123	82124
12	333165	122	138	41	158	144068
12	260981	105	114	25	90	162627
11	184474	87	55	38	149	55062
12	222366	76	67	34	131	95329
11	205675	60	43	33	125	105612
10	201345	60	88	28	110	62853
11	163043	110	67	31	121	125976
10	204250	128	75	40	151	79146
12	197760	66	114	32	123	108461
10	127260	57	119	25	92	99971
10	216092	59	86	42	162	77826
12	73566	32	22	23	88	22618
11	213198	67	67	42	163	84892
11	177949	48	77	34	120	92059
10	148698	49	105	34	132	77993
11	300103	69	119	38	144	104155
10	251437	78	88	32	124	109840
10	191971	99	75	37	140	238712
11	154651	53	112	34	132	67486
12	155473	56	66	33	122	68007
11	132672	41	58	25	97	48194
10	376465	99	132	40	155	134796
11	145869	65	30	26	99	38692
11	223666	85	100	40	106	93587
10	80953	25	49	8	28	56622
11	130789	46	26	27	101	15986
12	135042	47	67	32	120	113402
10	300074	152	57	33	127	97967
12	271757	93	95	50	178	74844
12	150949	95	139	37	141	136051
10	216802	77	70	33	122	50548
10	197389	67	134	34	127	112215
11	156583	56	37	28	102	59591
12	222599	65	98	32	124	59938
10	261601	70	58	32	124	137639
10	178489	35	78	32	124	143372
12	200657	43	88	31	111	138599
12	259084	67	142	35	129	174110
10	302789	129	127	52	199	135062
12	342025	98	139	27	102	175681
11	246440	104	108	45	174	130307
12	251306	56	128	37	141	139141
12	159965	156	62	32	122	44244
11	43287	14	13	19	71	43750
10	172212	67	89	22	81	48029
10	181781	117	83	35	131	95216
12	227681	43	116	36	139	92288
12	260464	80	157	36	137	94588
11	106288	54	28	23	91	197426
11	109632	76	83	36	142	151244
12	268905	58	72	36	133	139206
10	266568	77	134	42	155	106271
12	23623	11	12	1	0	1168
11	152474	65	106	32	123	71764
10	61857	25	23	11	32	25162
11	144889	43	83	40	149	45635
11	330910	95	120	34	128	101817
11	21054	16	4	0	0	855
10	223718	44	71	27	99	100174
10	31414	19	18	8	25	14116
10	259747	103	98	35	132	85008
12	190495	55	66	40	151	124254
11	154984	73	44	40	151	105793
11	112933	45	29	28	103	117129
12	38214	34	16	8	27	8773
11	158671	33	56	35	131	94747
11	299775	68	112	45	170	107549
10	172783	54	46	43	165	97392
10	348678	69	129	41	159	126893
11	266701	89	139	43	167	118850
10	358933	99	136	47	178	234853
11	172464	31	66	35	135	74783
10	94381	35	42	32	118	66089
11	243875	274	70	36	140	95684
11	382487	153	97	42	158	139537
12	111853	39	49	35	132	144253
10	334926	117	113	37	136	153824
10	147979	72	55	34	123	63995
11	216638	44	100	36	134	84891
10	192853	71	80	36	129	61263
11	173710	103	29	27	107	106221
10	336678	103	95	33	128	113587
10	212961	75	114	35	129	113864
12	173260	63	41	21	79	37238
10	271773	89	128	40	154	119906
12	127096	51	142	47	180	135096
11	203606	73	88	33	122	151611
12	230177	87	132	39	144	144645
10	1	0	0	0	0	0
10	14688	10	4	0	0	6023
11	98	1	0	0	0	0
12	455	2	0	0	0	0
12	0	0	0	0	0	0
10	0	0	0	0	0	0
11	195765	75	56	33	120	77457
10	306514	117	111	42	168	62464
12	0	0	0	0	0	0
10	203	4	0	0	0	0
10	7199	5	7	0	0	1644
10	46660	20	12	5	15	6179
11	17547	5	0	1	4	3926
11	105044	37	37	38	133	42087
12	969	2	0	0	0	0
10	165838	56	46	28	101	87656

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=152835&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=152835&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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
Total_Time_spent_in_RFC[t] = + 109814.604797363 -8639.89178051661Maand[t] + 730.176100253657Number_of_Logins[t] + 1032.7370661353Total_Number_of_Blogged_Computations[t] + 379.130788167338Total_Number_of_Reviewed_Compendiums[t] + 168.930078899488Total_Number_of_Feedback_Messages_PeerReviews[t] + 0.254271143539059Total_number_of_characters[t] -92.391534518519t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_spent_in_RFC[t] =  +  109814.604797363 -8639.89178051661Maand[t] +  730.176100253657Number_of_Logins[t] +  1032.7370661353Total_Number_of_Blogged_Computations[t] +  379.130788167338Total_Number_of_Reviewed_Compendiums[t] +  168.930078899488Total_Number_of_Feedback_Messages_PeerReviews[t] +  0.254271143539059Total_number_of_characters[t] -92.391534518519t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC[t] =  +  109814.604797363 -8639.89178051661Maand[t] +  730.176100253657Number_of_Logins[t] +  1032.7370661353Total_Number_of_Blogged_Computations[t] +  379.130788167338Total_Number_of_Reviewed_Compendiums[t] +  168.930078899488Total_Number_of_Feedback_Messages_PeerReviews[t] +  0.254271143539059Total_number_of_characters[t] -92.391534518519t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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
Total_Time_spent_in_RFC[t] = + 109814.604797363 -8639.89178051661Maand[t] + 730.176100253657Number_of_Logins[t] + 1032.7370661353Total_Number_of_Blogged_Computations[t] + 379.130788167338Total_Number_of_Reviewed_Compendiums[t] + 168.930078899488Total_Number_of_Feedback_Messages_PeerReviews[t] + 0.254271143539059Total_number_of_characters[t] -92.391534518519t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)109814.60479736350756.3617822.16360.032020.01601
Maand-8639.891780516614460.351546-1.9370.0545470.027273
Number_of_Logins730.176100253657108.9652786.70100
Total_Number_of_Blogged_Computations1032.7370661353148.9642586.932800
Total_Number_of_Reviewed_Compendiums379.1307881673381498.9880270.25290.800660.40033
Total_Number_of_Feedback_Messages_PeerReviews168.930078899488398.5063090.42390.6722170.336108
Total_number_of_characters0.2542711435390590.1141132.22820.0272940.013647
t-92.39153451851981.250058-1.13710.2572290.128615

\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) & 109814.604797363 & 50756.361782 & 2.1636 & 0.03202 & 0.01601 \tabularnewline
Maand & -8639.89178051661 & 4460.351546 & -1.937 & 0.054547 & 0.027273 \tabularnewline
Number_of_Logins & 730.176100253657 & 108.965278 & 6.701 & 0 & 0 \tabularnewline
Total_Number_of_Blogged_Computations & 1032.7370661353 & 148.964258 & 6.9328 & 0 & 0 \tabularnewline
Total_Number_of_Reviewed_Compendiums & 379.130788167338 & 1498.988027 & 0.2529 & 0.80066 & 0.40033 \tabularnewline
Total_Number_of_Feedback_Messages_PeerReviews & 168.930078899488 & 398.506309 & 0.4239 & 0.672217 & 0.336108 \tabularnewline
Total_number_of_characters & 0.254271143539059 & 0.114113 & 2.2282 & 0.027294 & 0.013647 \tabularnewline
t & -92.391534518519 & 81.250058 & -1.1371 & 0.257229 & 0.128615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&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]109814.604797363[/C][C]50756.361782[/C][C]2.1636[/C][C]0.03202[/C][C]0.01601[/C][/ROW]
[ROW][C]Maand[/C][C]-8639.89178051661[/C][C]4460.351546[/C][C]-1.937[/C][C]0.054547[/C][C]0.027273[/C][/ROW]
[ROW][C]Number_of_Logins[/C][C]730.176100253657[/C][C]108.965278[/C][C]6.701[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations[/C][C]1032.7370661353[/C][C]148.964258[/C][C]6.9328[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums[/C][C]379.130788167338[/C][C]1498.988027[/C][C]0.2529[/C][C]0.80066[/C][C]0.40033[/C][/ROW]
[ROW][C]Total_Number_of_Feedback_Messages_PeerReviews[/C][C]168.930078899488[/C][C]398.506309[/C][C]0.4239[/C][C]0.672217[/C][C]0.336108[/C][/ROW]
[ROW][C]Total_number_of_characters[/C][C]0.254271143539059[/C][C]0.114113[/C][C]2.2282[/C][C]0.027294[/C][C]0.013647[/C][/ROW]
[ROW][C]t[/C][C]-92.391534518519[/C][C]81.250058[/C][C]-1.1371[/C][C]0.257229[/C][C]0.128615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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)109814.60479736350756.3617822.16360.032020.01601
Maand-8639.891780516614460.351546-1.9370.0545470.027273
Number_of_Logins730.176100253657108.9652786.70100
Total_Number_of_Blogged_Computations1032.7370661353148.9642586.932800
Total_Number_of_Reviewed_Compendiums379.1307881673381498.9880270.25290.800660.40033
Total_Number_of_Feedback_Messages_PeerReviews168.930078899488398.5063090.42390.6722170.336108
Total_number_of_characters0.2542711435390590.1141132.22820.0272940.013647
t-92.39153451851981.250058-1.13710.2572290.128615






Multiple Linear Regression - Regression Statistics
Multiple R0.882286447462723
R-squared0.778429375376391
Adjusted R-squared0.768487103758665
F-TEST (value)78.2949214532153
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation46222.4482843564
Sum Squared Residuals333296297162.401

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.882286447462723 \tabularnewline
R-squared & 0.778429375376391 \tabularnewline
Adjusted R-squared & 0.768487103758665 \tabularnewline
F-TEST (value) & 78.2949214532153 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 46222.4482843564 \tabularnewline
Sum Squared Residuals & 333296297162.401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.882286447462723[/C][/ROW]
[ROW][C]R-squared[/C][C]0.778429375376391[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.768487103758665[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.2949214532153[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/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]46222.4482843564[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]333296297162.401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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 R0.882286447462723
R-squared0.778429375376391
Adjusted R-squared0.768487103758665
F-TEST (value)78.2949214532153
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation46222.4482843564
Sum Squared Residuals333296297162.401






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1236496213620.78167467322875.218325327
2130631181780.265730031-51149.2657300308
3198514179167.24047371419346.7595262863
4189326256927.829320753-67601.8293207526
5137449127404.01735161310044.9826483872
665295101724.458502245-36429.4585022451
7439387311424.908938923127962.091061077
83318652884.5773504598-19698.5773504598
9174859187401.886576659-12542.8865766589
10186657174512.97298165612144.0270183439
11261949219696.16681808742252.8331819132
12190794257261.564891423-66467.5648914233
13138866146254.96069547-7388.96069547034
14296878238024.60368219758853.3963178028
15192648194439.31200671-1791.31200670971
16333348368174.304050256-34826.304050256
17242212245131.247181533-2919.24718153287
18263451269538.813484567-6087.8134845669
19150733188400.392576831-37667.3925768312
20223226268513.932780357-45287.9327803571
21240028236817.4536870793210.54631292122
22384138226416.24281264157721.75718736
23156540143107.64914893413432.3508510664
24148421205518.997305654-57097.9973056541
25176502265614.167506382-89112.1675063824
26191441224558.315001632-33117.3150016324
27249735258682.93403071-8947.93403071002
28236812231037.1448789575774.85512104327
29142329135966.4597742456362.54022575508
30259667237119.34020142522547.6597985755
31228871244438.381017051-15567.3810170507
32176054159307.72079080416746.2792091957
33286683268222.93624498518460.0637550153
3487485133947.893184841-46462.8931848407
35322865240992.39055903381872.6094409665
36247013172515.80954246274497.1904575377
37340093243258.91750597496834.0824940259
38191653180588.14891180911064.8510881915
39114673100167.57858695714505.4214130431
40284210295415.970789752-11205.9707897521
41284195239588.82442423444606.1755757662
42155363174565.078412104-19202.0784121042
43174198173661.707517679536.292482321011
44142986168187.897099415-25201.8970994148
45140319176623.963599968-36304.9635999676
46392666271313.569476764121352.430523236
477880093864.2819981604-15064.2819981604
48201970227099.762452372-25129.7624523721
49302674289638.52636741513035.4736325854
50164733202867.223310254-38134.2233102537
51194221208905.54777421-14684.5477742101
522418849454.0475359245-25266.0475359245
53340411365922.800349905-25511.800349905
546502961666.68212693473362.31787306531
55101097109560.487871903-8463.48787190253
56243889178744.12683792765144.8731620735
57273003282611.595461584-9608.59546158361
58282220296081.625281758-13861.6252817576
59273495300688.313355539-27193.3133555386
60214872203152.85461799811719.1453820025
61333165310966.86907151722198.1309284829
62260981260841.474422538139.525577462091
63184474182859.2173084531614.78269154653
64222366184169.31324827938196.6867517211
65205675157470.26521041948204.7347895809
66201345197188.948481594156.05151840975
67163043222323.972416409-59280.972416409
68204250248849.100804612-44599.1008046118
69197760226173.623130933-28413.6231309334
70127260231903.60561414-104643.60561414
71216092211830.73754584261.26245420019
727356674906.523404494-1340.52340449396
73213198191191.07722085122006.9227791489
74177949179078.032030594-1129.03203059393
75148698215722.929270408-67024.9292704077
76300103246248.41264307253854.587356928
77251437225144.79388518426292.2061148157
78191971264327.484609628-72356.4846096278
79154651214191.708310614-59540.7083106138
80155473158208.091942737-2735.09194273728
81132672135246.881711083-2574.88171108282
82376465300072.43463725676392.5653627445
83145869121970.59373640323898.4062635968
84223666229221.874847645-5555.87484764491
8580953106581.354509087-25628.3545090869
8613078998632.635324255432156.3646757446
87135042162848.150979949-27806.1509799494
88300074244013.62911168356060.3708883167
89271757231986.21838497239770.7816150277
90150949283178.670678053-132229.670678053
91216802189496.89509888627305.1049011136
92197389265101.834585779-67712.8345857787
93156583128283.31739373828299.6826062618
94222599194440.79699050628158.2030094944
95261601193726.70899700567874.2910029951
96178489190190.631742224-11701.6317422239
97200657185198.37812808215458.6218719181
98259084271984.701722033-12900.7017220332
99302789327293.505281141-24504.5052811407
100342025284142.46809332957882.5319066714
101246440272506.196891488-26066.1968914881
102251306243018.6944599868287.30554001419
103159965218548.37243755-58583.3724375498
1044328759137.0059919412-15850.0059919412
105172212188786.575954367-16574.5759543667
106181781244380.063677047-62599.0636770466
107227681208041.2458562719639.7541437298
108260464277554.553215025-17090.5532150253
109106288147343.645306692-41055.645306692
110109632221917.050934324-112285.050934324
111268905184100.20335121684804.7966487838
112266568276807.465735273-10239.4657352731
1132362316796.56141080576826.4385891943
114152474212332.835074328-59858.8350743277
1156185770772.1872583746-8915.18725837458
116144889173112.602925792-28223.6029257915
117330910257663.78505188873246.2149481124
1182105419904.76183482051149.2381651795
119223718170305.1411157153412.8588842904
1203141455636.9056852527-24222.9056852527
121259747245836.111493413910.8885066001
122190495175452.34820860415042.6517913962
123154984169728.703223316-14744.7032233165
124112933123924.529327641-10991.5293276413
1253821445761.6212610309-7547.62126103087
126158671144554.39483287914116.6051671206
127299775241486.20264915658288.7973508437
128172783167465.0471509155317.95284908518
129348678269771.88456524678906.1154347544
130266701286035.093316683-19334.0933166825
131358933331657.11285133727275.8871486635
132172464148466.51529259823997.4847074018
13394381128929.193324642-34548.1933246417
134243875336383.775203516-92508.7752035155
135382487292290.05493077190196.945069229
136111853144899.362156697-33046.3621566966
137334926289003.27324240145922.726757599
138147979169979.801417455-22000.8014174547
139216638195205.49753102721432.5024689729
140192853195960.442187129-3107.44218712949
141173710162227.08694924511482.9130507549
142336678246630.51118937590047.4888106247
143212961246712.81786632-33751.8178663196
144173260111950.60811570661309.3918842943
145271773278864.031289315-7091.03128931493
146127096259111.959548935-132015.959548935
147203606217049.044754242-13443.0447542415
148230177268199.651431611-38022.6514316106
14919649.3483489374-9648.3483489374
1501468822521.1411790324-7833.14117903243
151981554.8495996374-1456.8495996374
152455-6447.257615144056902.25761514405
1530-8000.001350169888000.00135016988
15409187.3906763448-9187.3906763448
155195765165529.59602647730235.4039735235
156306514269253.5647461737260.4352538297
1570-8369.567488243968369.56748824396
15820311738.5289392854-11535.5289392854
159719920023.4947279458-12824.4947279458
1604666041630.15478818735029.84521181267
161175475604.7582687664711942.2417312335
162105044112612.333838191-7568.33383819067
163969-7463.564494847768432.56449484776
164165838146625.23338318219212.7666168181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 236496 & 213620.781674673 & 22875.218325327 \tabularnewline
2 & 130631 & 181780.265730031 & -51149.2657300308 \tabularnewline
3 & 198514 & 179167.240473714 & 19346.7595262863 \tabularnewline
4 & 189326 & 256927.829320753 & -67601.8293207526 \tabularnewline
5 & 137449 & 127404.017351613 & 10044.9826483872 \tabularnewline
6 & 65295 & 101724.458502245 & -36429.4585022451 \tabularnewline
7 & 439387 & 311424.908938923 & 127962.091061077 \tabularnewline
8 & 33186 & 52884.5773504598 & -19698.5773504598 \tabularnewline
9 & 174859 & 187401.886576659 & -12542.8865766589 \tabularnewline
10 & 186657 & 174512.972981656 & 12144.0270183439 \tabularnewline
11 & 261949 & 219696.166818087 & 42252.8331819132 \tabularnewline
12 & 190794 & 257261.564891423 & -66467.5648914233 \tabularnewline
13 & 138866 & 146254.96069547 & -7388.96069547034 \tabularnewline
14 & 296878 & 238024.603682197 & 58853.3963178028 \tabularnewline
15 & 192648 & 194439.31200671 & -1791.31200670971 \tabularnewline
16 & 333348 & 368174.304050256 & -34826.304050256 \tabularnewline
17 & 242212 & 245131.247181533 & -2919.24718153287 \tabularnewline
18 & 263451 & 269538.813484567 & -6087.8134845669 \tabularnewline
19 & 150733 & 188400.392576831 & -37667.3925768312 \tabularnewline
20 & 223226 & 268513.932780357 & -45287.9327803571 \tabularnewline
21 & 240028 & 236817.453687079 & 3210.54631292122 \tabularnewline
22 & 384138 & 226416.24281264 & 157721.75718736 \tabularnewline
23 & 156540 & 143107.649148934 & 13432.3508510664 \tabularnewline
24 & 148421 & 205518.997305654 & -57097.9973056541 \tabularnewline
25 & 176502 & 265614.167506382 & -89112.1675063824 \tabularnewline
26 & 191441 & 224558.315001632 & -33117.3150016324 \tabularnewline
27 & 249735 & 258682.93403071 & -8947.93403071002 \tabularnewline
28 & 236812 & 231037.144878957 & 5774.85512104327 \tabularnewline
29 & 142329 & 135966.459774245 & 6362.54022575508 \tabularnewline
30 & 259667 & 237119.340201425 & 22547.6597985755 \tabularnewline
31 & 228871 & 244438.381017051 & -15567.3810170507 \tabularnewline
32 & 176054 & 159307.720790804 & 16746.2792091957 \tabularnewline
33 & 286683 & 268222.936244985 & 18460.0637550153 \tabularnewline
34 & 87485 & 133947.893184841 & -46462.8931848407 \tabularnewline
35 & 322865 & 240992.390559033 & 81872.6094409665 \tabularnewline
36 & 247013 & 172515.809542462 & 74497.1904575377 \tabularnewline
37 & 340093 & 243258.917505974 & 96834.0824940259 \tabularnewline
38 & 191653 & 180588.148911809 & 11064.8510881915 \tabularnewline
39 & 114673 & 100167.578586957 & 14505.4214130431 \tabularnewline
40 & 284210 & 295415.970789752 & -11205.9707897521 \tabularnewline
41 & 284195 & 239588.824424234 & 44606.1755757662 \tabularnewline
42 & 155363 & 174565.078412104 & -19202.0784121042 \tabularnewline
43 & 174198 & 173661.707517679 & 536.292482321011 \tabularnewline
44 & 142986 & 168187.897099415 & -25201.8970994148 \tabularnewline
45 & 140319 & 176623.963599968 & -36304.9635999676 \tabularnewline
46 & 392666 & 271313.569476764 & 121352.430523236 \tabularnewline
47 & 78800 & 93864.2819981604 & -15064.2819981604 \tabularnewline
48 & 201970 & 227099.762452372 & -25129.7624523721 \tabularnewline
49 & 302674 & 289638.526367415 & 13035.4736325854 \tabularnewline
50 & 164733 & 202867.223310254 & -38134.2233102537 \tabularnewline
51 & 194221 & 208905.54777421 & -14684.5477742101 \tabularnewline
52 & 24188 & 49454.0475359245 & -25266.0475359245 \tabularnewline
53 & 340411 & 365922.800349905 & -25511.800349905 \tabularnewline
54 & 65029 & 61666.6821269347 & 3362.31787306531 \tabularnewline
55 & 101097 & 109560.487871903 & -8463.48787190253 \tabularnewline
56 & 243889 & 178744.126837927 & 65144.8731620735 \tabularnewline
57 & 273003 & 282611.595461584 & -9608.59546158361 \tabularnewline
58 & 282220 & 296081.625281758 & -13861.6252817576 \tabularnewline
59 & 273495 & 300688.313355539 & -27193.3133555386 \tabularnewline
60 & 214872 & 203152.854617998 & 11719.1453820025 \tabularnewline
61 & 333165 & 310966.869071517 & 22198.1309284829 \tabularnewline
62 & 260981 & 260841.474422538 & 139.525577462091 \tabularnewline
63 & 184474 & 182859.217308453 & 1614.78269154653 \tabularnewline
64 & 222366 & 184169.313248279 & 38196.6867517211 \tabularnewline
65 & 205675 & 157470.265210419 & 48204.7347895809 \tabularnewline
66 & 201345 & 197188.94848159 & 4156.05151840975 \tabularnewline
67 & 163043 & 222323.972416409 & -59280.972416409 \tabularnewline
68 & 204250 & 248849.100804612 & -44599.1008046118 \tabularnewline
69 & 197760 & 226173.623130933 & -28413.6231309334 \tabularnewline
70 & 127260 & 231903.60561414 & -104643.60561414 \tabularnewline
71 & 216092 & 211830.7375458 & 4261.26245420019 \tabularnewline
72 & 73566 & 74906.523404494 & -1340.52340449396 \tabularnewline
73 & 213198 & 191191.077220851 & 22006.9227791489 \tabularnewline
74 & 177949 & 179078.032030594 & -1129.03203059393 \tabularnewline
75 & 148698 & 215722.929270408 & -67024.9292704077 \tabularnewline
76 & 300103 & 246248.412643072 & 53854.587356928 \tabularnewline
77 & 251437 & 225144.793885184 & 26292.2061148157 \tabularnewline
78 & 191971 & 264327.484609628 & -72356.4846096278 \tabularnewline
79 & 154651 & 214191.708310614 & -59540.7083106138 \tabularnewline
80 & 155473 & 158208.091942737 & -2735.09194273728 \tabularnewline
81 & 132672 & 135246.881711083 & -2574.88171108282 \tabularnewline
82 & 376465 & 300072.434637256 & 76392.5653627445 \tabularnewline
83 & 145869 & 121970.593736403 & 23898.4062635968 \tabularnewline
84 & 223666 & 229221.874847645 & -5555.87484764491 \tabularnewline
85 & 80953 & 106581.354509087 & -25628.3545090869 \tabularnewline
86 & 130789 & 98632.6353242554 & 32156.3646757446 \tabularnewline
87 & 135042 & 162848.150979949 & -27806.1509799494 \tabularnewline
88 & 300074 & 244013.629111683 & 56060.3708883167 \tabularnewline
89 & 271757 & 231986.218384972 & 39770.7816150277 \tabularnewline
90 & 150949 & 283178.670678053 & -132229.670678053 \tabularnewline
91 & 216802 & 189496.895098886 & 27305.1049011136 \tabularnewline
92 & 197389 & 265101.834585779 & -67712.8345857787 \tabularnewline
93 & 156583 & 128283.317393738 & 28299.6826062618 \tabularnewline
94 & 222599 & 194440.796990506 & 28158.2030094944 \tabularnewline
95 & 261601 & 193726.708997005 & 67874.2910029951 \tabularnewline
96 & 178489 & 190190.631742224 & -11701.6317422239 \tabularnewline
97 & 200657 & 185198.378128082 & 15458.6218719181 \tabularnewline
98 & 259084 & 271984.701722033 & -12900.7017220332 \tabularnewline
99 & 302789 & 327293.505281141 & -24504.5052811407 \tabularnewline
100 & 342025 & 284142.468093329 & 57882.5319066714 \tabularnewline
101 & 246440 & 272506.196891488 & -26066.1968914881 \tabularnewline
102 & 251306 & 243018.694459986 & 8287.30554001419 \tabularnewline
103 & 159965 & 218548.37243755 & -58583.3724375498 \tabularnewline
104 & 43287 & 59137.0059919412 & -15850.0059919412 \tabularnewline
105 & 172212 & 188786.575954367 & -16574.5759543667 \tabularnewline
106 & 181781 & 244380.063677047 & -62599.0636770466 \tabularnewline
107 & 227681 & 208041.24585627 & 19639.7541437298 \tabularnewline
108 & 260464 & 277554.553215025 & -17090.5532150253 \tabularnewline
109 & 106288 & 147343.645306692 & -41055.645306692 \tabularnewline
110 & 109632 & 221917.050934324 & -112285.050934324 \tabularnewline
111 & 268905 & 184100.203351216 & 84804.7966487838 \tabularnewline
112 & 266568 & 276807.465735273 & -10239.4657352731 \tabularnewline
113 & 23623 & 16796.5614108057 & 6826.4385891943 \tabularnewline
114 & 152474 & 212332.835074328 & -59858.8350743277 \tabularnewline
115 & 61857 & 70772.1872583746 & -8915.18725837458 \tabularnewline
116 & 144889 & 173112.602925792 & -28223.6029257915 \tabularnewline
117 & 330910 & 257663.785051888 & 73246.2149481124 \tabularnewline
118 & 21054 & 19904.7618348205 & 1149.2381651795 \tabularnewline
119 & 223718 & 170305.14111571 & 53412.8588842904 \tabularnewline
120 & 31414 & 55636.9056852527 & -24222.9056852527 \tabularnewline
121 & 259747 & 245836.1114934 & 13910.8885066001 \tabularnewline
122 & 190495 & 175452.348208604 & 15042.6517913962 \tabularnewline
123 & 154984 & 169728.703223316 & -14744.7032233165 \tabularnewline
124 & 112933 & 123924.529327641 & -10991.5293276413 \tabularnewline
125 & 38214 & 45761.6212610309 & -7547.62126103087 \tabularnewline
126 & 158671 & 144554.394832879 & 14116.6051671206 \tabularnewline
127 & 299775 & 241486.202649156 & 58288.7973508437 \tabularnewline
128 & 172783 & 167465.047150915 & 5317.95284908518 \tabularnewline
129 & 348678 & 269771.884565246 & 78906.1154347544 \tabularnewline
130 & 266701 & 286035.093316683 & -19334.0933166825 \tabularnewline
131 & 358933 & 331657.112851337 & 27275.8871486635 \tabularnewline
132 & 172464 & 148466.515292598 & 23997.4847074018 \tabularnewline
133 & 94381 & 128929.193324642 & -34548.1933246417 \tabularnewline
134 & 243875 & 336383.775203516 & -92508.7752035155 \tabularnewline
135 & 382487 & 292290.054930771 & 90196.945069229 \tabularnewline
136 & 111853 & 144899.362156697 & -33046.3621566966 \tabularnewline
137 & 334926 & 289003.273242401 & 45922.726757599 \tabularnewline
138 & 147979 & 169979.801417455 & -22000.8014174547 \tabularnewline
139 & 216638 & 195205.497531027 & 21432.5024689729 \tabularnewline
140 & 192853 & 195960.442187129 & -3107.44218712949 \tabularnewline
141 & 173710 & 162227.086949245 & 11482.9130507549 \tabularnewline
142 & 336678 & 246630.511189375 & 90047.4888106247 \tabularnewline
143 & 212961 & 246712.81786632 & -33751.8178663196 \tabularnewline
144 & 173260 & 111950.608115706 & 61309.3918842943 \tabularnewline
145 & 271773 & 278864.031289315 & -7091.03128931493 \tabularnewline
146 & 127096 & 259111.959548935 & -132015.959548935 \tabularnewline
147 & 203606 & 217049.044754242 & -13443.0447542415 \tabularnewline
148 & 230177 & 268199.651431611 & -38022.6514316106 \tabularnewline
149 & 1 & 9649.3483489374 & -9648.3483489374 \tabularnewline
150 & 14688 & 22521.1411790324 & -7833.14117903243 \tabularnewline
151 & 98 & 1554.8495996374 & -1456.8495996374 \tabularnewline
152 & 455 & -6447.25761514405 & 6902.25761514405 \tabularnewline
153 & 0 & -8000.00135016988 & 8000.00135016988 \tabularnewline
154 & 0 & 9187.3906763448 & -9187.3906763448 \tabularnewline
155 & 195765 & 165529.596026477 & 30235.4039735235 \tabularnewline
156 & 306514 & 269253.56474617 & 37260.4352538297 \tabularnewline
157 & 0 & -8369.56748824396 & 8369.56748824396 \tabularnewline
158 & 203 & 11738.5289392854 & -11535.5289392854 \tabularnewline
159 & 7199 & 20023.4947279458 & -12824.4947279458 \tabularnewline
160 & 46660 & 41630.1547881873 & 5029.84521181267 \tabularnewline
161 & 17547 & 5604.75826876647 & 11942.2417312335 \tabularnewline
162 & 105044 & 112612.333838191 & -7568.33383819067 \tabularnewline
163 & 969 & -7463.56449484776 & 8432.56449484776 \tabularnewline
164 & 165838 & 146625.233383182 & 19212.7666168181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&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]236496[/C][C]213620.781674673[/C][C]22875.218325327[/C][/ROW]
[ROW][C]2[/C][C]130631[/C][C]181780.265730031[/C][C]-51149.2657300308[/C][/ROW]
[ROW][C]3[/C][C]198514[/C][C]179167.240473714[/C][C]19346.7595262863[/C][/ROW]
[ROW][C]4[/C][C]189326[/C][C]256927.829320753[/C][C]-67601.8293207526[/C][/ROW]
[ROW][C]5[/C][C]137449[/C][C]127404.017351613[/C][C]10044.9826483872[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]101724.458502245[/C][C]-36429.4585022451[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]311424.908938923[/C][C]127962.091061077[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]52884.5773504598[/C][C]-19698.5773504598[/C][/ROW]
[ROW][C]9[/C][C]174859[/C][C]187401.886576659[/C][C]-12542.8865766589[/C][/ROW]
[ROW][C]10[/C][C]186657[/C][C]174512.972981656[/C][C]12144.0270183439[/C][/ROW]
[ROW][C]11[/C][C]261949[/C][C]219696.166818087[/C][C]42252.8331819132[/C][/ROW]
[ROW][C]12[/C][C]190794[/C][C]257261.564891423[/C][C]-66467.5648914233[/C][/ROW]
[ROW][C]13[/C][C]138866[/C][C]146254.96069547[/C][C]-7388.96069547034[/C][/ROW]
[ROW][C]14[/C][C]296878[/C][C]238024.603682197[/C][C]58853.3963178028[/C][/ROW]
[ROW][C]15[/C][C]192648[/C][C]194439.31200671[/C][C]-1791.31200670971[/C][/ROW]
[ROW][C]16[/C][C]333348[/C][C]368174.304050256[/C][C]-34826.304050256[/C][/ROW]
[ROW][C]17[/C][C]242212[/C][C]245131.247181533[/C][C]-2919.24718153287[/C][/ROW]
[ROW][C]18[/C][C]263451[/C][C]269538.813484567[/C][C]-6087.8134845669[/C][/ROW]
[ROW][C]19[/C][C]150733[/C][C]188400.392576831[/C][C]-37667.3925768312[/C][/ROW]
[ROW][C]20[/C][C]223226[/C][C]268513.932780357[/C][C]-45287.9327803571[/C][/ROW]
[ROW][C]21[/C][C]240028[/C][C]236817.453687079[/C][C]3210.54631292122[/C][/ROW]
[ROW][C]22[/C][C]384138[/C][C]226416.24281264[/C][C]157721.75718736[/C][/ROW]
[ROW][C]23[/C][C]156540[/C][C]143107.649148934[/C][C]13432.3508510664[/C][/ROW]
[ROW][C]24[/C][C]148421[/C][C]205518.997305654[/C][C]-57097.9973056541[/C][/ROW]
[ROW][C]25[/C][C]176502[/C][C]265614.167506382[/C][C]-89112.1675063824[/C][/ROW]
[ROW][C]26[/C][C]191441[/C][C]224558.315001632[/C][C]-33117.3150016324[/C][/ROW]
[ROW][C]27[/C][C]249735[/C][C]258682.93403071[/C][C]-8947.93403071002[/C][/ROW]
[ROW][C]28[/C][C]236812[/C][C]231037.144878957[/C][C]5774.85512104327[/C][/ROW]
[ROW][C]29[/C][C]142329[/C][C]135966.459774245[/C][C]6362.54022575508[/C][/ROW]
[ROW][C]30[/C][C]259667[/C][C]237119.340201425[/C][C]22547.6597985755[/C][/ROW]
[ROW][C]31[/C][C]228871[/C][C]244438.381017051[/C][C]-15567.3810170507[/C][/ROW]
[ROW][C]32[/C][C]176054[/C][C]159307.720790804[/C][C]16746.2792091957[/C][/ROW]
[ROW][C]33[/C][C]286683[/C][C]268222.936244985[/C][C]18460.0637550153[/C][/ROW]
[ROW][C]34[/C][C]87485[/C][C]133947.893184841[/C][C]-46462.8931848407[/C][/ROW]
[ROW][C]35[/C][C]322865[/C][C]240992.390559033[/C][C]81872.6094409665[/C][/ROW]
[ROW][C]36[/C][C]247013[/C][C]172515.809542462[/C][C]74497.1904575377[/C][/ROW]
[ROW][C]37[/C][C]340093[/C][C]243258.917505974[/C][C]96834.0824940259[/C][/ROW]
[ROW][C]38[/C][C]191653[/C][C]180588.148911809[/C][C]11064.8510881915[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]100167.578586957[/C][C]14505.4214130431[/C][/ROW]
[ROW][C]40[/C][C]284210[/C][C]295415.970789752[/C][C]-11205.9707897521[/C][/ROW]
[ROW][C]41[/C][C]284195[/C][C]239588.824424234[/C][C]44606.1755757662[/C][/ROW]
[ROW][C]42[/C][C]155363[/C][C]174565.078412104[/C][C]-19202.0784121042[/C][/ROW]
[ROW][C]43[/C][C]174198[/C][C]173661.707517679[/C][C]536.292482321011[/C][/ROW]
[ROW][C]44[/C][C]142986[/C][C]168187.897099415[/C][C]-25201.8970994148[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]176623.963599968[/C][C]-36304.9635999676[/C][/ROW]
[ROW][C]46[/C][C]392666[/C][C]271313.569476764[/C][C]121352.430523236[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]93864.2819981604[/C][C]-15064.2819981604[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]227099.762452372[/C][C]-25129.7624523721[/C][/ROW]
[ROW][C]49[/C][C]302674[/C][C]289638.526367415[/C][C]13035.4736325854[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]202867.223310254[/C][C]-38134.2233102537[/C][/ROW]
[ROW][C]51[/C][C]194221[/C][C]208905.54777421[/C][C]-14684.5477742101[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]49454.0475359245[/C][C]-25266.0475359245[/C][/ROW]
[ROW][C]53[/C][C]340411[/C][C]365922.800349905[/C][C]-25511.800349905[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]61666.6821269347[/C][C]3362.31787306531[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]109560.487871903[/C][C]-8463.48787190253[/C][/ROW]
[ROW][C]56[/C][C]243889[/C][C]178744.126837927[/C][C]65144.8731620735[/C][/ROW]
[ROW][C]57[/C][C]273003[/C][C]282611.595461584[/C][C]-9608.59546158361[/C][/ROW]
[ROW][C]58[/C][C]282220[/C][C]296081.625281758[/C][C]-13861.6252817576[/C][/ROW]
[ROW][C]59[/C][C]273495[/C][C]300688.313355539[/C][C]-27193.3133555386[/C][/ROW]
[ROW][C]60[/C][C]214872[/C][C]203152.854617998[/C][C]11719.1453820025[/C][/ROW]
[ROW][C]61[/C][C]333165[/C][C]310966.869071517[/C][C]22198.1309284829[/C][/ROW]
[ROW][C]62[/C][C]260981[/C][C]260841.474422538[/C][C]139.525577462091[/C][/ROW]
[ROW][C]63[/C][C]184474[/C][C]182859.217308453[/C][C]1614.78269154653[/C][/ROW]
[ROW][C]64[/C][C]222366[/C][C]184169.313248279[/C][C]38196.6867517211[/C][/ROW]
[ROW][C]65[/C][C]205675[/C][C]157470.265210419[/C][C]48204.7347895809[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]197188.94848159[/C][C]4156.05151840975[/C][/ROW]
[ROW][C]67[/C][C]163043[/C][C]222323.972416409[/C][C]-59280.972416409[/C][/ROW]
[ROW][C]68[/C][C]204250[/C][C]248849.100804612[/C][C]-44599.1008046118[/C][/ROW]
[ROW][C]69[/C][C]197760[/C][C]226173.623130933[/C][C]-28413.6231309334[/C][/ROW]
[ROW][C]70[/C][C]127260[/C][C]231903.60561414[/C][C]-104643.60561414[/C][/ROW]
[ROW][C]71[/C][C]216092[/C][C]211830.7375458[/C][C]4261.26245420019[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]74906.523404494[/C][C]-1340.52340449396[/C][/ROW]
[ROW][C]73[/C][C]213198[/C][C]191191.077220851[/C][C]22006.9227791489[/C][/ROW]
[ROW][C]74[/C][C]177949[/C][C]179078.032030594[/C][C]-1129.03203059393[/C][/ROW]
[ROW][C]75[/C][C]148698[/C][C]215722.929270408[/C][C]-67024.9292704077[/C][/ROW]
[ROW][C]76[/C][C]300103[/C][C]246248.412643072[/C][C]53854.587356928[/C][/ROW]
[ROW][C]77[/C][C]251437[/C][C]225144.793885184[/C][C]26292.2061148157[/C][/ROW]
[ROW][C]78[/C][C]191971[/C][C]264327.484609628[/C][C]-72356.4846096278[/C][/ROW]
[ROW][C]79[/C][C]154651[/C][C]214191.708310614[/C][C]-59540.7083106138[/C][/ROW]
[ROW][C]80[/C][C]155473[/C][C]158208.091942737[/C][C]-2735.09194273728[/C][/ROW]
[ROW][C]81[/C][C]132672[/C][C]135246.881711083[/C][C]-2574.88171108282[/C][/ROW]
[ROW][C]82[/C][C]376465[/C][C]300072.434637256[/C][C]76392.5653627445[/C][/ROW]
[ROW][C]83[/C][C]145869[/C][C]121970.593736403[/C][C]23898.4062635968[/C][/ROW]
[ROW][C]84[/C][C]223666[/C][C]229221.874847645[/C][C]-5555.87484764491[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]106581.354509087[/C][C]-25628.3545090869[/C][/ROW]
[ROW][C]86[/C][C]130789[/C][C]98632.6353242554[/C][C]32156.3646757446[/C][/ROW]
[ROW][C]87[/C][C]135042[/C][C]162848.150979949[/C][C]-27806.1509799494[/C][/ROW]
[ROW][C]88[/C][C]300074[/C][C]244013.629111683[/C][C]56060.3708883167[/C][/ROW]
[ROW][C]89[/C][C]271757[/C][C]231986.218384972[/C][C]39770.7816150277[/C][/ROW]
[ROW][C]90[/C][C]150949[/C][C]283178.670678053[/C][C]-132229.670678053[/C][/ROW]
[ROW][C]91[/C][C]216802[/C][C]189496.895098886[/C][C]27305.1049011136[/C][/ROW]
[ROW][C]92[/C][C]197389[/C][C]265101.834585779[/C][C]-67712.8345857787[/C][/ROW]
[ROW][C]93[/C][C]156583[/C][C]128283.317393738[/C][C]28299.6826062618[/C][/ROW]
[ROW][C]94[/C][C]222599[/C][C]194440.796990506[/C][C]28158.2030094944[/C][/ROW]
[ROW][C]95[/C][C]261601[/C][C]193726.708997005[/C][C]67874.2910029951[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]190190.631742224[/C][C]-11701.6317422239[/C][/ROW]
[ROW][C]97[/C][C]200657[/C][C]185198.378128082[/C][C]15458.6218719181[/C][/ROW]
[ROW][C]98[/C][C]259084[/C][C]271984.701722033[/C][C]-12900.7017220332[/C][/ROW]
[ROW][C]99[/C][C]302789[/C][C]327293.505281141[/C][C]-24504.5052811407[/C][/ROW]
[ROW][C]100[/C][C]342025[/C][C]284142.468093329[/C][C]57882.5319066714[/C][/ROW]
[ROW][C]101[/C][C]246440[/C][C]272506.196891488[/C][C]-26066.1968914881[/C][/ROW]
[ROW][C]102[/C][C]251306[/C][C]243018.694459986[/C][C]8287.30554001419[/C][/ROW]
[ROW][C]103[/C][C]159965[/C][C]218548.37243755[/C][C]-58583.3724375498[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]59137.0059919412[/C][C]-15850.0059919412[/C][/ROW]
[ROW][C]105[/C][C]172212[/C][C]188786.575954367[/C][C]-16574.5759543667[/C][/ROW]
[ROW][C]106[/C][C]181781[/C][C]244380.063677047[/C][C]-62599.0636770466[/C][/ROW]
[ROW][C]107[/C][C]227681[/C][C]208041.24585627[/C][C]19639.7541437298[/C][/ROW]
[ROW][C]108[/C][C]260464[/C][C]277554.553215025[/C][C]-17090.5532150253[/C][/ROW]
[ROW][C]109[/C][C]106288[/C][C]147343.645306692[/C][C]-41055.645306692[/C][/ROW]
[ROW][C]110[/C][C]109632[/C][C]221917.050934324[/C][C]-112285.050934324[/C][/ROW]
[ROW][C]111[/C][C]268905[/C][C]184100.203351216[/C][C]84804.7966487838[/C][/ROW]
[ROW][C]112[/C][C]266568[/C][C]276807.465735273[/C][C]-10239.4657352731[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]16796.5614108057[/C][C]6826.4385891943[/C][/ROW]
[ROW][C]114[/C][C]152474[/C][C]212332.835074328[/C][C]-59858.8350743277[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]70772.1872583746[/C][C]-8915.18725837458[/C][/ROW]
[ROW][C]116[/C][C]144889[/C][C]173112.602925792[/C][C]-28223.6029257915[/C][/ROW]
[ROW][C]117[/C][C]330910[/C][C]257663.785051888[/C][C]73246.2149481124[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]19904.7618348205[/C][C]1149.2381651795[/C][/ROW]
[ROW][C]119[/C][C]223718[/C][C]170305.14111571[/C][C]53412.8588842904[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]55636.9056852527[/C][C]-24222.9056852527[/C][/ROW]
[ROW][C]121[/C][C]259747[/C][C]245836.1114934[/C][C]13910.8885066001[/C][/ROW]
[ROW][C]122[/C][C]190495[/C][C]175452.348208604[/C][C]15042.6517913962[/C][/ROW]
[ROW][C]123[/C][C]154984[/C][C]169728.703223316[/C][C]-14744.7032233165[/C][/ROW]
[ROW][C]124[/C][C]112933[/C][C]123924.529327641[/C][C]-10991.5293276413[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]45761.6212610309[/C][C]-7547.62126103087[/C][/ROW]
[ROW][C]126[/C][C]158671[/C][C]144554.394832879[/C][C]14116.6051671206[/C][/ROW]
[ROW][C]127[/C][C]299775[/C][C]241486.202649156[/C][C]58288.7973508437[/C][/ROW]
[ROW][C]128[/C][C]172783[/C][C]167465.047150915[/C][C]5317.95284908518[/C][/ROW]
[ROW][C]129[/C][C]348678[/C][C]269771.884565246[/C][C]78906.1154347544[/C][/ROW]
[ROW][C]130[/C][C]266701[/C][C]286035.093316683[/C][C]-19334.0933166825[/C][/ROW]
[ROW][C]131[/C][C]358933[/C][C]331657.112851337[/C][C]27275.8871486635[/C][/ROW]
[ROW][C]132[/C][C]172464[/C][C]148466.515292598[/C][C]23997.4847074018[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]128929.193324642[/C][C]-34548.1933246417[/C][/ROW]
[ROW][C]134[/C][C]243875[/C][C]336383.775203516[/C][C]-92508.7752035155[/C][/ROW]
[ROW][C]135[/C][C]382487[/C][C]292290.054930771[/C][C]90196.945069229[/C][/ROW]
[ROW][C]136[/C][C]111853[/C][C]144899.362156697[/C][C]-33046.3621566966[/C][/ROW]
[ROW][C]137[/C][C]334926[/C][C]289003.273242401[/C][C]45922.726757599[/C][/ROW]
[ROW][C]138[/C][C]147979[/C][C]169979.801417455[/C][C]-22000.8014174547[/C][/ROW]
[ROW][C]139[/C][C]216638[/C][C]195205.497531027[/C][C]21432.5024689729[/C][/ROW]
[ROW][C]140[/C][C]192853[/C][C]195960.442187129[/C][C]-3107.44218712949[/C][/ROW]
[ROW][C]141[/C][C]173710[/C][C]162227.086949245[/C][C]11482.9130507549[/C][/ROW]
[ROW][C]142[/C][C]336678[/C][C]246630.511189375[/C][C]90047.4888106247[/C][/ROW]
[ROW][C]143[/C][C]212961[/C][C]246712.81786632[/C][C]-33751.8178663196[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]111950.608115706[/C][C]61309.3918842943[/C][/ROW]
[ROW][C]145[/C][C]271773[/C][C]278864.031289315[/C][C]-7091.03128931493[/C][/ROW]
[ROW][C]146[/C][C]127096[/C][C]259111.959548935[/C][C]-132015.959548935[/C][/ROW]
[ROW][C]147[/C][C]203606[/C][C]217049.044754242[/C][C]-13443.0447542415[/C][/ROW]
[ROW][C]148[/C][C]230177[/C][C]268199.651431611[/C][C]-38022.6514316106[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]9649.3483489374[/C][C]-9648.3483489374[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]22521.1411790324[/C][C]-7833.14117903243[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]1554.8495996374[/C][C]-1456.8495996374[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-6447.25761514405[/C][C]6902.25761514405[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-8000.00135016988[/C][C]8000.00135016988[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]9187.3906763448[/C][C]-9187.3906763448[/C][/ROW]
[ROW][C]155[/C][C]195765[/C][C]165529.596026477[/C][C]30235.4039735235[/C][/ROW]
[ROW][C]156[/C][C]306514[/C][C]269253.56474617[/C][C]37260.4352538297[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-8369.56748824396[/C][C]8369.56748824396[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]11738.5289392854[/C][C]-11535.5289392854[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]20023.4947279458[/C][C]-12824.4947279458[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]41630.1547881873[/C][C]5029.84521181267[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]5604.75826876647[/C][C]11942.2417312335[/C][/ROW]
[ROW][C]162[/C][C]105044[/C][C]112612.333838191[/C][C]-7568.33383819067[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-7463.56449484776[/C][C]8432.56449484776[/C][/ROW]
[ROW][C]164[/C][C]165838[/C][C]146625.233383182[/C][C]19212.7666168181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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
1236496213620.78167467322875.218325327
2130631181780.265730031-51149.2657300308
3198514179167.24047371419346.7595262863
4189326256927.829320753-67601.8293207526
5137449127404.01735161310044.9826483872
665295101724.458502245-36429.4585022451
7439387311424.908938923127962.091061077
83318652884.5773504598-19698.5773504598
9174859187401.886576659-12542.8865766589
10186657174512.97298165612144.0270183439
11261949219696.16681808742252.8331819132
12190794257261.564891423-66467.5648914233
13138866146254.96069547-7388.96069547034
14296878238024.60368219758853.3963178028
15192648194439.31200671-1791.31200670971
16333348368174.304050256-34826.304050256
17242212245131.247181533-2919.24718153287
18263451269538.813484567-6087.8134845669
19150733188400.392576831-37667.3925768312
20223226268513.932780357-45287.9327803571
21240028236817.4536870793210.54631292122
22384138226416.24281264157721.75718736
23156540143107.64914893413432.3508510664
24148421205518.997305654-57097.9973056541
25176502265614.167506382-89112.1675063824
26191441224558.315001632-33117.3150016324
27249735258682.93403071-8947.93403071002
28236812231037.1448789575774.85512104327
29142329135966.4597742456362.54022575508
30259667237119.34020142522547.6597985755
31228871244438.381017051-15567.3810170507
32176054159307.72079080416746.2792091957
33286683268222.93624498518460.0637550153
3487485133947.893184841-46462.8931848407
35322865240992.39055903381872.6094409665
36247013172515.80954246274497.1904575377
37340093243258.91750597496834.0824940259
38191653180588.14891180911064.8510881915
39114673100167.57858695714505.4214130431
40284210295415.970789752-11205.9707897521
41284195239588.82442423444606.1755757662
42155363174565.078412104-19202.0784121042
43174198173661.707517679536.292482321011
44142986168187.897099415-25201.8970994148
45140319176623.963599968-36304.9635999676
46392666271313.569476764121352.430523236
477880093864.2819981604-15064.2819981604
48201970227099.762452372-25129.7624523721
49302674289638.52636741513035.4736325854
50164733202867.223310254-38134.2233102537
51194221208905.54777421-14684.5477742101
522418849454.0475359245-25266.0475359245
53340411365922.800349905-25511.800349905
546502961666.68212693473362.31787306531
55101097109560.487871903-8463.48787190253
56243889178744.12683792765144.8731620735
57273003282611.595461584-9608.59546158361
58282220296081.625281758-13861.6252817576
59273495300688.313355539-27193.3133555386
60214872203152.85461799811719.1453820025
61333165310966.86907151722198.1309284829
62260981260841.474422538139.525577462091
63184474182859.2173084531614.78269154653
64222366184169.31324827938196.6867517211
65205675157470.26521041948204.7347895809
66201345197188.948481594156.05151840975
67163043222323.972416409-59280.972416409
68204250248849.100804612-44599.1008046118
69197760226173.623130933-28413.6231309334
70127260231903.60561414-104643.60561414
71216092211830.73754584261.26245420019
727356674906.523404494-1340.52340449396
73213198191191.07722085122006.9227791489
74177949179078.032030594-1129.03203059393
75148698215722.929270408-67024.9292704077
76300103246248.41264307253854.587356928
77251437225144.79388518426292.2061148157
78191971264327.484609628-72356.4846096278
79154651214191.708310614-59540.7083106138
80155473158208.091942737-2735.09194273728
81132672135246.881711083-2574.88171108282
82376465300072.43463725676392.5653627445
83145869121970.59373640323898.4062635968
84223666229221.874847645-5555.87484764491
8580953106581.354509087-25628.3545090869
8613078998632.635324255432156.3646757446
87135042162848.150979949-27806.1509799494
88300074244013.62911168356060.3708883167
89271757231986.21838497239770.7816150277
90150949283178.670678053-132229.670678053
91216802189496.89509888627305.1049011136
92197389265101.834585779-67712.8345857787
93156583128283.31739373828299.6826062618
94222599194440.79699050628158.2030094944
95261601193726.70899700567874.2910029951
96178489190190.631742224-11701.6317422239
97200657185198.37812808215458.6218719181
98259084271984.701722033-12900.7017220332
99302789327293.505281141-24504.5052811407
100342025284142.46809332957882.5319066714
101246440272506.196891488-26066.1968914881
102251306243018.6944599868287.30554001419
103159965218548.37243755-58583.3724375498
1044328759137.0059919412-15850.0059919412
105172212188786.575954367-16574.5759543667
106181781244380.063677047-62599.0636770466
107227681208041.2458562719639.7541437298
108260464277554.553215025-17090.5532150253
109106288147343.645306692-41055.645306692
110109632221917.050934324-112285.050934324
111268905184100.20335121684804.7966487838
112266568276807.465735273-10239.4657352731
1132362316796.56141080576826.4385891943
114152474212332.835074328-59858.8350743277
1156185770772.1872583746-8915.18725837458
116144889173112.602925792-28223.6029257915
117330910257663.78505188873246.2149481124
1182105419904.76183482051149.2381651795
119223718170305.1411157153412.8588842904
1203141455636.9056852527-24222.9056852527
121259747245836.111493413910.8885066001
122190495175452.34820860415042.6517913962
123154984169728.703223316-14744.7032233165
124112933123924.529327641-10991.5293276413
1253821445761.6212610309-7547.62126103087
126158671144554.39483287914116.6051671206
127299775241486.20264915658288.7973508437
128172783167465.0471509155317.95284908518
129348678269771.88456524678906.1154347544
130266701286035.093316683-19334.0933166825
131358933331657.11285133727275.8871486635
132172464148466.51529259823997.4847074018
13394381128929.193324642-34548.1933246417
134243875336383.775203516-92508.7752035155
135382487292290.05493077190196.945069229
136111853144899.362156697-33046.3621566966
137334926289003.27324240145922.726757599
138147979169979.801417455-22000.8014174547
139216638195205.49753102721432.5024689729
140192853195960.442187129-3107.44218712949
141173710162227.08694924511482.9130507549
142336678246630.51118937590047.4888106247
143212961246712.81786632-33751.8178663196
144173260111950.60811570661309.3918842943
145271773278864.031289315-7091.03128931493
146127096259111.959548935-132015.959548935
147203606217049.044754242-13443.0447542415
148230177268199.651431611-38022.6514316106
14919649.3483489374-9648.3483489374
1501468822521.1411790324-7833.14117903243
151981554.8495996374-1456.8495996374
152455-6447.257615144056902.25761514405
1530-8000.001350169888000.00135016988
15409187.3906763448-9187.3906763448
155195765165529.59602647730235.4039735235
156306514269253.5647461737260.4352538297
1570-8369.567488243968369.56748824396
15820311738.5289392854-11535.5289392854
159719920023.4947279458-12824.4947279458
1604666041630.15478818735029.84521181267
161175475604.7582687664711942.2417312335
162105044112612.333838191-7568.33383819067
163969-7463.564494847768432.56449484776
164165838146625.23338318219212.7666168181






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7831097652225140.4337804695549720.216890234777486
120.658663224512460.6826735509750810.34133677548754
130.5739874921461680.8520250157076630.426012507853832
140.4507409617937080.9014819235874160.549259038206292
150.3684176660688240.7368353321376470.631582333931176
160.4002979615088780.8005959230177550.599702038491122
170.639123694928930.7217526101421390.36087630507107
180.5463135018403120.9073729963193760.453686498159688
190.5690245253956070.8619509492087850.430975474604393
200.4988622183330180.9977244366660360.501137781666982
210.4367839555999360.8735679111998710.563216044400064
220.9379754172151740.1240491655696530.0620245827848265
230.9144615720120340.1710768559759330.0855384279879664
240.8976924416404810.2046151167190370.102307558359519
250.9497245895969140.1005508208061730.0502754104030865
260.9331552390232280.1336895219535450.0668447609767725
270.9094068483219390.1811863033561220.0905931516780611
280.883399489149620.2332010217007590.11660051085038
290.8661018568647760.2677962862704480.133898143135224
300.8410963537247140.3178072925505720.158903646275286
310.801419667464360.397160665071280.19858033253564
320.7655359907233710.4689280185532580.234464009276629
330.7302401548135080.5395196903729830.269759845186492
340.7146495220434920.5707009559130160.285350477956508
350.798314716102120.4033705677957610.20168528389788
360.8538461507994270.2923076984011460.146153849200573
370.8946184410259160.2107631179481670.105381558974084
380.8671799025090180.2656401949819640.132820097490982
390.8524926254608020.2950147490783960.147507374539198
400.8383755878376320.3232488243247350.161624412162368
410.8181149184587670.3637701630824660.181885081541233
420.8107409897997540.3785180204004910.189259010200246
430.8295489716369710.3409020567260590.170451028363029
440.8066141392223930.3867717215552140.193385860777607
450.8793120774673110.2413758450653790.120687922532689
460.9529029297992780.09419414040144460.0470970702007223
470.9417684566608570.1164630866782870.0582315433391433
480.9301468900936110.1397062198127790.0698531099063895
490.9132190924594140.1735618150811730.0867809075405865
500.9060313127877060.1879373744245890.0939686872122943
510.889927720765920.2201445584681590.11007227923408
520.8866291674249350.226741665150130.113370832575065
530.8814052523536120.2371894952927760.118594747646388
540.8549591865369140.2900816269261720.145040813463086
550.8283003083902610.3433993832194780.171699691609739
560.8459088234810520.3081823530378960.154091176518948
570.8172795096062990.3654409807874020.182720490393701
580.7996174872918130.4007650254163740.200382512708187
590.7875272515025770.4249454969948460.212472748497423
600.7539787963414210.4920424073171590.246021203658579
610.7241738948136080.5516522103727840.275826105186392
620.6999319119685850.6001361760628310.300068088031415
630.6602410219613320.6795179560773350.339758978038668
640.6510996859585860.6978006280828280.348900314041414
650.6596521418906340.6806957162187310.340347858109366
660.6187665133447020.7624669733105950.381233486655298
670.642250987383340.7154980252333190.35774901261666
680.6252298222727950.749540355454410.374770177727205
690.6057689576264340.7884620847471310.394231042373566
700.7686110947336360.4627778105327280.231388905266364
710.7348872983763270.5302254032473450.265112701623673
720.6952779037064430.6094441925871130.304722096293557
730.6679987722762770.6640024554474470.332001227723723
740.6243740800732210.7512518398535580.375625919926779
750.6554488762303730.6891022475392540.344551123769627
760.6760759644757150.647848071048570.323924035524285
770.6511923429901050.697615314019790.348807657009895
780.6974615894381290.6050768211237420.302538410561871
790.7135706794489750.5728586411020510.286429320551025
800.6725843402647780.6548313194704440.327415659735222
810.629740313110290.740519373779420.37025968688971
820.7111103078638280.5777793842723430.288889692136172
830.6860042486467150.6279915027065690.313995751353285
840.6497526831380930.7004946337238140.350247316861907
850.6157934190765120.7684131618469750.384206580923488
860.5989692934727080.8020614130545840.401030706527292
870.5642725548651520.8714548902696960.435727445134848
880.5990895110706530.8018209778586930.400910488929347
890.5876821051513070.8246357896973860.412317894848693
900.8310715153012780.3378569693974450.168928484698722
910.8160749710755020.3678500578489950.183925028924498
920.8463828845823330.3072342308353340.153617115417667
930.8340623264780220.3318753470439550.165937673521977
940.8242780339558570.3514439320882850.175721966044143
950.8714358692620660.2571282614758690.128564130737934
960.844875916469890.310248167060220.15512408353011
970.8190450583336380.3619098833327230.180954941666362
980.7920008019782490.4159983960435020.207999198021751
990.7605704410047330.4788591179905340.239429558995267
1000.7777800717299470.4444398565401070.222219928270053
1010.7454143792238120.5091712415523760.254585620776188
1020.7080285997410550.583942800517890.291971400258945
1030.707487381644480.5850252367110410.29251261835552
1040.6653083469769190.6693833060461610.334691653023081
1050.6225727096795880.7548545806408250.377427290320412
1060.6494570789184090.7010858421631820.350542921081591
1070.6170910434595520.7658179130808950.382908956540448
1080.5729254334218910.8541491331562170.427074566578109
1090.5568452992473790.8863094015052410.443154700752621
1100.7997472420098270.4005055159803450.200252757990173
1110.8712646899863460.2574706200273090.128735310013654
1120.8457978833790170.3084042332419650.154202116620983
1130.8161643038170040.3676713923659920.183835696182996
1140.8485060713947480.3029878572105050.151493928605252
1150.8187887806030230.3624224387939550.181211219396977
1160.799619384332110.400761231335780.20038061566789
1170.8410893953047390.3178212093905230.158910604695261
1180.8062386160391060.3875227679217880.193761383960894
1190.8073981035948550.3852037928102910.192601896405145
1200.7849793699877880.4300412600244250.215020630012213
1210.7442890783813770.5114218432372450.255710921618623
1220.7042632123234350.591473575353130.295736787676565
1230.6613140528108370.6773718943783260.338685947189163
1240.6160008146422460.7679983707155090.383999185357754
1250.56071664059980.8785667188004010.4392833594002
1260.505388686229440.989222627541120.49461131377056
1270.5336166644413350.932766671117330.466383335558665
1280.4788084086069940.9576168172139870.521191591393006
1290.563710877006240.872578245987520.43628912299376
1300.5056447317373270.9887105365253450.494355268262673
1310.4539752178050460.9079504356100920.546024782194954
1320.4182337990146230.8364675980292470.581766200985377
1330.3943595588761780.7887191177523570.605640441123822
1340.9944899086439280.01102018271214310.00551009135607156
1350.9925517585267780.01489648294644460.00744824147322228
1360.991267502013020.01746499597395950.00873249798697975
1370.9868676215581620.02626475688367560.0131323784418378
1380.9891065016544910.02178699669101870.0108934983455094
1390.9987700222224670.002459955555066020.00122997777753301
1400.9979680477389240.004063904522152690.00203195226107634
1410.9999962007030017.59859399831354e-063.79929699915677e-06
1420.9999998631579952.73684010658372e-071.36842005329186e-07
1430.9999994580736041.0838527921151e-065.41926396057549e-07
1440.9999980296965043.94060699216152e-061.97030349608076e-06
1450.9999999968960846.20783147015761e-093.10391573507881e-09
1460.9999999974761185.04776478517752e-092.52388239258876e-09
1470.9999999811352283.77295442422001e-081.88647721211e-08
1480.9999998989873212.02025358829961e-071.0101267941498e-07
1490.9999992871479831.42570403296472e-067.12852016482361e-07
1500.9999942353823241.15292353510999e-055.76461767554997e-06
1510.9999436585859920.0001126828280161755.63414140080875e-05
1520.9994827632754140.001034473449172540.000517236724586271
1530.99600420702780.007991585944400290.00399579297220015

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.783109765222514 & 0.433780469554972 & 0.216890234777486 \tabularnewline
12 & 0.65866322451246 & 0.682673550975081 & 0.34133677548754 \tabularnewline
13 & 0.573987492146168 & 0.852025015707663 & 0.426012507853832 \tabularnewline
14 & 0.450740961793708 & 0.901481923587416 & 0.549259038206292 \tabularnewline
15 & 0.368417666068824 & 0.736835332137647 & 0.631582333931176 \tabularnewline
16 & 0.400297961508878 & 0.800595923017755 & 0.599702038491122 \tabularnewline
17 & 0.63912369492893 & 0.721752610142139 & 0.36087630507107 \tabularnewline
18 & 0.546313501840312 & 0.907372996319376 & 0.453686498159688 \tabularnewline
19 & 0.569024525395607 & 0.861950949208785 & 0.430975474604393 \tabularnewline
20 & 0.498862218333018 & 0.997724436666036 & 0.501137781666982 \tabularnewline
21 & 0.436783955599936 & 0.873567911199871 & 0.563216044400064 \tabularnewline
22 & 0.937975417215174 & 0.124049165569653 & 0.0620245827848265 \tabularnewline
23 & 0.914461572012034 & 0.171076855975933 & 0.0855384279879664 \tabularnewline
24 & 0.897692441640481 & 0.204615116719037 & 0.102307558359519 \tabularnewline
25 & 0.949724589596914 & 0.100550820806173 & 0.0502754104030865 \tabularnewline
26 & 0.933155239023228 & 0.133689521953545 & 0.0668447609767725 \tabularnewline
27 & 0.909406848321939 & 0.181186303356122 & 0.0905931516780611 \tabularnewline
28 & 0.88339948914962 & 0.233201021700759 & 0.11660051085038 \tabularnewline
29 & 0.866101856864776 & 0.267796286270448 & 0.133898143135224 \tabularnewline
30 & 0.841096353724714 & 0.317807292550572 & 0.158903646275286 \tabularnewline
31 & 0.80141966746436 & 0.39716066507128 & 0.19858033253564 \tabularnewline
32 & 0.765535990723371 & 0.468928018553258 & 0.234464009276629 \tabularnewline
33 & 0.730240154813508 & 0.539519690372983 & 0.269759845186492 \tabularnewline
34 & 0.714649522043492 & 0.570700955913016 & 0.285350477956508 \tabularnewline
35 & 0.79831471610212 & 0.403370567795761 & 0.20168528389788 \tabularnewline
36 & 0.853846150799427 & 0.292307698401146 & 0.146153849200573 \tabularnewline
37 & 0.894618441025916 & 0.210763117948167 & 0.105381558974084 \tabularnewline
38 & 0.867179902509018 & 0.265640194981964 & 0.132820097490982 \tabularnewline
39 & 0.852492625460802 & 0.295014749078396 & 0.147507374539198 \tabularnewline
40 & 0.838375587837632 & 0.323248824324735 & 0.161624412162368 \tabularnewline
41 & 0.818114918458767 & 0.363770163082466 & 0.181885081541233 \tabularnewline
42 & 0.810740989799754 & 0.378518020400491 & 0.189259010200246 \tabularnewline
43 & 0.829548971636971 & 0.340902056726059 & 0.170451028363029 \tabularnewline
44 & 0.806614139222393 & 0.386771721555214 & 0.193385860777607 \tabularnewline
45 & 0.879312077467311 & 0.241375845065379 & 0.120687922532689 \tabularnewline
46 & 0.952902929799278 & 0.0941941404014446 & 0.0470970702007223 \tabularnewline
47 & 0.941768456660857 & 0.116463086678287 & 0.0582315433391433 \tabularnewline
48 & 0.930146890093611 & 0.139706219812779 & 0.0698531099063895 \tabularnewline
49 & 0.913219092459414 & 0.173561815081173 & 0.0867809075405865 \tabularnewline
50 & 0.906031312787706 & 0.187937374424589 & 0.0939686872122943 \tabularnewline
51 & 0.88992772076592 & 0.220144558468159 & 0.11007227923408 \tabularnewline
52 & 0.886629167424935 & 0.22674166515013 & 0.113370832575065 \tabularnewline
53 & 0.881405252353612 & 0.237189495292776 & 0.118594747646388 \tabularnewline
54 & 0.854959186536914 & 0.290081626926172 & 0.145040813463086 \tabularnewline
55 & 0.828300308390261 & 0.343399383219478 & 0.171699691609739 \tabularnewline
56 & 0.845908823481052 & 0.308182353037896 & 0.154091176518948 \tabularnewline
57 & 0.817279509606299 & 0.365440980787402 & 0.182720490393701 \tabularnewline
58 & 0.799617487291813 & 0.400765025416374 & 0.200382512708187 \tabularnewline
59 & 0.787527251502577 & 0.424945496994846 & 0.212472748497423 \tabularnewline
60 & 0.753978796341421 & 0.492042407317159 & 0.246021203658579 \tabularnewline
61 & 0.724173894813608 & 0.551652210372784 & 0.275826105186392 \tabularnewline
62 & 0.699931911968585 & 0.600136176062831 & 0.300068088031415 \tabularnewline
63 & 0.660241021961332 & 0.679517956077335 & 0.339758978038668 \tabularnewline
64 & 0.651099685958586 & 0.697800628082828 & 0.348900314041414 \tabularnewline
65 & 0.659652141890634 & 0.680695716218731 & 0.340347858109366 \tabularnewline
66 & 0.618766513344702 & 0.762466973310595 & 0.381233486655298 \tabularnewline
67 & 0.64225098738334 & 0.715498025233319 & 0.35774901261666 \tabularnewline
68 & 0.625229822272795 & 0.74954035545441 & 0.374770177727205 \tabularnewline
69 & 0.605768957626434 & 0.788462084747131 & 0.394231042373566 \tabularnewline
70 & 0.768611094733636 & 0.462777810532728 & 0.231388905266364 \tabularnewline
71 & 0.734887298376327 & 0.530225403247345 & 0.265112701623673 \tabularnewline
72 & 0.695277903706443 & 0.609444192587113 & 0.304722096293557 \tabularnewline
73 & 0.667998772276277 & 0.664002455447447 & 0.332001227723723 \tabularnewline
74 & 0.624374080073221 & 0.751251839853558 & 0.375625919926779 \tabularnewline
75 & 0.655448876230373 & 0.689102247539254 & 0.344551123769627 \tabularnewline
76 & 0.676075964475715 & 0.64784807104857 & 0.323924035524285 \tabularnewline
77 & 0.651192342990105 & 0.69761531401979 & 0.348807657009895 \tabularnewline
78 & 0.697461589438129 & 0.605076821123742 & 0.302538410561871 \tabularnewline
79 & 0.713570679448975 & 0.572858641102051 & 0.286429320551025 \tabularnewline
80 & 0.672584340264778 & 0.654831319470444 & 0.327415659735222 \tabularnewline
81 & 0.62974031311029 & 0.74051937377942 & 0.37025968688971 \tabularnewline
82 & 0.711110307863828 & 0.577779384272343 & 0.288889692136172 \tabularnewline
83 & 0.686004248646715 & 0.627991502706569 & 0.313995751353285 \tabularnewline
84 & 0.649752683138093 & 0.700494633723814 & 0.350247316861907 \tabularnewline
85 & 0.615793419076512 & 0.768413161846975 & 0.384206580923488 \tabularnewline
86 & 0.598969293472708 & 0.802061413054584 & 0.401030706527292 \tabularnewline
87 & 0.564272554865152 & 0.871454890269696 & 0.435727445134848 \tabularnewline
88 & 0.599089511070653 & 0.801820977858693 & 0.400910488929347 \tabularnewline
89 & 0.587682105151307 & 0.824635789697386 & 0.412317894848693 \tabularnewline
90 & 0.831071515301278 & 0.337856969397445 & 0.168928484698722 \tabularnewline
91 & 0.816074971075502 & 0.367850057848995 & 0.183925028924498 \tabularnewline
92 & 0.846382884582333 & 0.307234230835334 & 0.153617115417667 \tabularnewline
93 & 0.834062326478022 & 0.331875347043955 & 0.165937673521977 \tabularnewline
94 & 0.824278033955857 & 0.351443932088285 & 0.175721966044143 \tabularnewline
95 & 0.871435869262066 & 0.257128261475869 & 0.128564130737934 \tabularnewline
96 & 0.84487591646989 & 0.31024816706022 & 0.15512408353011 \tabularnewline
97 & 0.819045058333638 & 0.361909883332723 & 0.180954941666362 \tabularnewline
98 & 0.792000801978249 & 0.415998396043502 & 0.207999198021751 \tabularnewline
99 & 0.760570441004733 & 0.478859117990534 & 0.239429558995267 \tabularnewline
100 & 0.777780071729947 & 0.444439856540107 & 0.222219928270053 \tabularnewline
101 & 0.745414379223812 & 0.509171241552376 & 0.254585620776188 \tabularnewline
102 & 0.708028599741055 & 0.58394280051789 & 0.291971400258945 \tabularnewline
103 & 0.70748738164448 & 0.585025236711041 & 0.29251261835552 \tabularnewline
104 & 0.665308346976919 & 0.669383306046161 & 0.334691653023081 \tabularnewline
105 & 0.622572709679588 & 0.754854580640825 & 0.377427290320412 \tabularnewline
106 & 0.649457078918409 & 0.701085842163182 & 0.350542921081591 \tabularnewline
107 & 0.617091043459552 & 0.765817913080895 & 0.382908956540448 \tabularnewline
108 & 0.572925433421891 & 0.854149133156217 & 0.427074566578109 \tabularnewline
109 & 0.556845299247379 & 0.886309401505241 & 0.443154700752621 \tabularnewline
110 & 0.799747242009827 & 0.400505515980345 & 0.200252757990173 \tabularnewline
111 & 0.871264689986346 & 0.257470620027309 & 0.128735310013654 \tabularnewline
112 & 0.845797883379017 & 0.308404233241965 & 0.154202116620983 \tabularnewline
113 & 0.816164303817004 & 0.367671392365992 & 0.183835696182996 \tabularnewline
114 & 0.848506071394748 & 0.302987857210505 & 0.151493928605252 \tabularnewline
115 & 0.818788780603023 & 0.362422438793955 & 0.181211219396977 \tabularnewline
116 & 0.79961938433211 & 0.40076123133578 & 0.20038061566789 \tabularnewline
117 & 0.841089395304739 & 0.317821209390523 & 0.158910604695261 \tabularnewline
118 & 0.806238616039106 & 0.387522767921788 & 0.193761383960894 \tabularnewline
119 & 0.807398103594855 & 0.385203792810291 & 0.192601896405145 \tabularnewline
120 & 0.784979369987788 & 0.430041260024425 & 0.215020630012213 \tabularnewline
121 & 0.744289078381377 & 0.511421843237245 & 0.255710921618623 \tabularnewline
122 & 0.704263212323435 & 0.59147357535313 & 0.295736787676565 \tabularnewline
123 & 0.661314052810837 & 0.677371894378326 & 0.338685947189163 \tabularnewline
124 & 0.616000814642246 & 0.767998370715509 & 0.383999185357754 \tabularnewline
125 & 0.5607166405998 & 0.878566718800401 & 0.4392833594002 \tabularnewline
126 & 0.50538868622944 & 0.98922262754112 & 0.49461131377056 \tabularnewline
127 & 0.533616664441335 & 0.93276667111733 & 0.466383335558665 \tabularnewline
128 & 0.478808408606994 & 0.957616817213987 & 0.521191591393006 \tabularnewline
129 & 0.56371087700624 & 0.87257824598752 & 0.43628912299376 \tabularnewline
130 & 0.505644731737327 & 0.988710536525345 & 0.494355268262673 \tabularnewline
131 & 0.453975217805046 & 0.907950435610092 & 0.546024782194954 \tabularnewline
132 & 0.418233799014623 & 0.836467598029247 & 0.581766200985377 \tabularnewline
133 & 0.394359558876178 & 0.788719117752357 & 0.605640441123822 \tabularnewline
134 & 0.994489908643928 & 0.0110201827121431 & 0.00551009135607156 \tabularnewline
135 & 0.992551758526778 & 0.0148964829464446 & 0.00744824147322228 \tabularnewline
136 & 0.99126750201302 & 0.0174649959739595 & 0.00873249798697975 \tabularnewline
137 & 0.986867621558162 & 0.0262647568836756 & 0.0131323784418378 \tabularnewline
138 & 0.989106501654491 & 0.0217869966910187 & 0.0108934983455094 \tabularnewline
139 & 0.998770022222467 & 0.00245995555506602 & 0.00122997777753301 \tabularnewline
140 & 0.997968047738924 & 0.00406390452215269 & 0.00203195226107634 \tabularnewline
141 & 0.999996200703001 & 7.59859399831354e-06 & 3.79929699915677e-06 \tabularnewline
142 & 0.999999863157995 & 2.73684010658372e-07 & 1.36842005329186e-07 \tabularnewline
143 & 0.999999458073604 & 1.0838527921151e-06 & 5.41926396057549e-07 \tabularnewline
144 & 0.999998029696504 & 3.94060699216152e-06 & 1.97030349608076e-06 \tabularnewline
145 & 0.999999996896084 & 6.20783147015761e-09 & 3.10391573507881e-09 \tabularnewline
146 & 0.999999997476118 & 5.04776478517752e-09 & 2.52388239258876e-09 \tabularnewline
147 & 0.999999981135228 & 3.77295442422001e-08 & 1.88647721211e-08 \tabularnewline
148 & 0.999999898987321 & 2.02025358829961e-07 & 1.0101267941498e-07 \tabularnewline
149 & 0.999999287147983 & 1.42570403296472e-06 & 7.12852016482361e-07 \tabularnewline
150 & 0.999994235382324 & 1.15292353510999e-05 & 5.76461767554997e-06 \tabularnewline
151 & 0.999943658585992 & 0.000112682828016175 & 5.63414140080875e-05 \tabularnewline
152 & 0.999482763275414 & 0.00103447344917254 & 0.000517236724586271 \tabularnewline
153 & 0.9960042070278 & 0.00799158594440029 & 0.00399579297220015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&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]11[/C][C]0.783109765222514[/C][C]0.433780469554972[/C][C]0.216890234777486[/C][/ROW]
[ROW][C]12[/C][C]0.65866322451246[/C][C]0.682673550975081[/C][C]0.34133677548754[/C][/ROW]
[ROW][C]13[/C][C]0.573987492146168[/C][C]0.852025015707663[/C][C]0.426012507853832[/C][/ROW]
[ROW][C]14[/C][C]0.450740961793708[/C][C]0.901481923587416[/C][C]0.549259038206292[/C][/ROW]
[ROW][C]15[/C][C]0.368417666068824[/C][C]0.736835332137647[/C][C]0.631582333931176[/C][/ROW]
[ROW][C]16[/C][C]0.400297961508878[/C][C]0.800595923017755[/C][C]0.599702038491122[/C][/ROW]
[ROW][C]17[/C][C]0.63912369492893[/C][C]0.721752610142139[/C][C]0.36087630507107[/C][/ROW]
[ROW][C]18[/C][C]0.546313501840312[/C][C]0.907372996319376[/C][C]0.453686498159688[/C][/ROW]
[ROW][C]19[/C][C]0.569024525395607[/C][C]0.861950949208785[/C][C]0.430975474604393[/C][/ROW]
[ROW][C]20[/C][C]0.498862218333018[/C][C]0.997724436666036[/C][C]0.501137781666982[/C][/ROW]
[ROW][C]21[/C][C]0.436783955599936[/C][C]0.873567911199871[/C][C]0.563216044400064[/C][/ROW]
[ROW][C]22[/C][C]0.937975417215174[/C][C]0.124049165569653[/C][C]0.0620245827848265[/C][/ROW]
[ROW][C]23[/C][C]0.914461572012034[/C][C]0.171076855975933[/C][C]0.0855384279879664[/C][/ROW]
[ROW][C]24[/C][C]0.897692441640481[/C][C]0.204615116719037[/C][C]0.102307558359519[/C][/ROW]
[ROW][C]25[/C][C]0.949724589596914[/C][C]0.100550820806173[/C][C]0.0502754104030865[/C][/ROW]
[ROW][C]26[/C][C]0.933155239023228[/C][C]0.133689521953545[/C][C]0.0668447609767725[/C][/ROW]
[ROW][C]27[/C][C]0.909406848321939[/C][C]0.181186303356122[/C][C]0.0905931516780611[/C][/ROW]
[ROW][C]28[/C][C]0.88339948914962[/C][C]0.233201021700759[/C][C]0.11660051085038[/C][/ROW]
[ROW][C]29[/C][C]0.866101856864776[/C][C]0.267796286270448[/C][C]0.133898143135224[/C][/ROW]
[ROW][C]30[/C][C]0.841096353724714[/C][C]0.317807292550572[/C][C]0.158903646275286[/C][/ROW]
[ROW][C]31[/C][C]0.80141966746436[/C][C]0.39716066507128[/C][C]0.19858033253564[/C][/ROW]
[ROW][C]32[/C][C]0.765535990723371[/C][C]0.468928018553258[/C][C]0.234464009276629[/C][/ROW]
[ROW][C]33[/C][C]0.730240154813508[/C][C]0.539519690372983[/C][C]0.269759845186492[/C][/ROW]
[ROW][C]34[/C][C]0.714649522043492[/C][C]0.570700955913016[/C][C]0.285350477956508[/C][/ROW]
[ROW][C]35[/C][C]0.79831471610212[/C][C]0.403370567795761[/C][C]0.20168528389788[/C][/ROW]
[ROW][C]36[/C][C]0.853846150799427[/C][C]0.292307698401146[/C][C]0.146153849200573[/C][/ROW]
[ROW][C]37[/C][C]0.894618441025916[/C][C]0.210763117948167[/C][C]0.105381558974084[/C][/ROW]
[ROW][C]38[/C][C]0.867179902509018[/C][C]0.265640194981964[/C][C]0.132820097490982[/C][/ROW]
[ROW][C]39[/C][C]0.852492625460802[/C][C]0.295014749078396[/C][C]0.147507374539198[/C][/ROW]
[ROW][C]40[/C][C]0.838375587837632[/C][C]0.323248824324735[/C][C]0.161624412162368[/C][/ROW]
[ROW][C]41[/C][C]0.818114918458767[/C][C]0.363770163082466[/C][C]0.181885081541233[/C][/ROW]
[ROW][C]42[/C][C]0.810740989799754[/C][C]0.378518020400491[/C][C]0.189259010200246[/C][/ROW]
[ROW][C]43[/C][C]0.829548971636971[/C][C]0.340902056726059[/C][C]0.170451028363029[/C][/ROW]
[ROW][C]44[/C][C]0.806614139222393[/C][C]0.386771721555214[/C][C]0.193385860777607[/C][/ROW]
[ROW][C]45[/C][C]0.879312077467311[/C][C]0.241375845065379[/C][C]0.120687922532689[/C][/ROW]
[ROW][C]46[/C][C]0.952902929799278[/C][C]0.0941941404014446[/C][C]0.0470970702007223[/C][/ROW]
[ROW][C]47[/C][C]0.941768456660857[/C][C]0.116463086678287[/C][C]0.0582315433391433[/C][/ROW]
[ROW][C]48[/C][C]0.930146890093611[/C][C]0.139706219812779[/C][C]0.0698531099063895[/C][/ROW]
[ROW][C]49[/C][C]0.913219092459414[/C][C]0.173561815081173[/C][C]0.0867809075405865[/C][/ROW]
[ROW][C]50[/C][C]0.906031312787706[/C][C]0.187937374424589[/C][C]0.0939686872122943[/C][/ROW]
[ROW][C]51[/C][C]0.88992772076592[/C][C]0.220144558468159[/C][C]0.11007227923408[/C][/ROW]
[ROW][C]52[/C][C]0.886629167424935[/C][C]0.22674166515013[/C][C]0.113370832575065[/C][/ROW]
[ROW][C]53[/C][C]0.881405252353612[/C][C]0.237189495292776[/C][C]0.118594747646388[/C][/ROW]
[ROW][C]54[/C][C]0.854959186536914[/C][C]0.290081626926172[/C][C]0.145040813463086[/C][/ROW]
[ROW][C]55[/C][C]0.828300308390261[/C][C]0.343399383219478[/C][C]0.171699691609739[/C][/ROW]
[ROW][C]56[/C][C]0.845908823481052[/C][C]0.308182353037896[/C][C]0.154091176518948[/C][/ROW]
[ROW][C]57[/C][C]0.817279509606299[/C][C]0.365440980787402[/C][C]0.182720490393701[/C][/ROW]
[ROW][C]58[/C][C]0.799617487291813[/C][C]0.400765025416374[/C][C]0.200382512708187[/C][/ROW]
[ROW][C]59[/C][C]0.787527251502577[/C][C]0.424945496994846[/C][C]0.212472748497423[/C][/ROW]
[ROW][C]60[/C][C]0.753978796341421[/C][C]0.492042407317159[/C][C]0.246021203658579[/C][/ROW]
[ROW][C]61[/C][C]0.724173894813608[/C][C]0.551652210372784[/C][C]0.275826105186392[/C][/ROW]
[ROW][C]62[/C][C]0.699931911968585[/C][C]0.600136176062831[/C][C]0.300068088031415[/C][/ROW]
[ROW][C]63[/C][C]0.660241021961332[/C][C]0.679517956077335[/C][C]0.339758978038668[/C][/ROW]
[ROW][C]64[/C][C]0.651099685958586[/C][C]0.697800628082828[/C][C]0.348900314041414[/C][/ROW]
[ROW][C]65[/C][C]0.659652141890634[/C][C]0.680695716218731[/C][C]0.340347858109366[/C][/ROW]
[ROW][C]66[/C][C]0.618766513344702[/C][C]0.762466973310595[/C][C]0.381233486655298[/C][/ROW]
[ROW][C]67[/C][C]0.64225098738334[/C][C]0.715498025233319[/C][C]0.35774901261666[/C][/ROW]
[ROW][C]68[/C][C]0.625229822272795[/C][C]0.74954035545441[/C][C]0.374770177727205[/C][/ROW]
[ROW][C]69[/C][C]0.605768957626434[/C][C]0.788462084747131[/C][C]0.394231042373566[/C][/ROW]
[ROW][C]70[/C][C]0.768611094733636[/C][C]0.462777810532728[/C][C]0.231388905266364[/C][/ROW]
[ROW][C]71[/C][C]0.734887298376327[/C][C]0.530225403247345[/C][C]0.265112701623673[/C][/ROW]
[ROW][C]72[/C][C]0.695277903706443[/C][C]0.609444192587113[/C][C]0.304722096293557[/C][/ROW]
[ROW][C]73[/C][C]0.667998772276277[/C][C]0.664002455447447[/C][C]0.332001227723723[/C][/ROW]
[ROW][C]74[/C][C]0.624374080073221[/C][C]0.751251839853558[/C][C]0.375625919926779[/C][/ROW]
[ROW][C]75[/C][C]0.655448876230373[/C][C]0.689102247539254[/C][C]0.344551123769627[/C][/ROW]
[ROW][C]76[/C][C]0.676075964475715[/C][C]0.64784807104857[/C][C]0.323924035524285[/C][/ROW]
[ROW][C]77[/C][C]0.651192342990105[/C][C]0.69761531401979[/C][C]0.348807657009895[/C][/ROW]
[ROW][C]78[/C][C]0.697461589438129[/C][C]0.605076821123742[/C][C]0.302538410561871[/C][/ROW]
[ROW][C]79[/C][C]0.713570679448975[/C][C]0.572858641102051[/C][C]0.286429320551025[/C][/ROW]
[ROW][C]80[/C][C]0.672584340264778[/C][C]0.654831319470444[/C][C]0.327415659735222[/C][/ROW]
[ROW][C]81[/C][C]0.62974031311029[/C][C]0.74051937377942[/C][C]0.37025968688971[/C][/ROW]
[ROW][C]82[/C][C]0.711110307863828[/C][C]0.577779384272343[/C][C]0.288889692136172[/C][/ROW]
[ROW][C]83[/C][C]0.686004248646715[/C][C]0.627991502706569[/C][C]0.313995751353285[/C][/ROW]
[ROW][C]84[/C][C]0.649752683138093[/C][C]0.700494633723814[/C][C]0.350247316861907[/C][/ROW]
[ROW][C]85[/C][C]0.615793419076512[/C][C]0.768413161846975[/C][C]0.384206580923488[/C][/ROW]
[ROW][C]86[/C][C]0.598969293472708[/C][C]0.802061413054584[/C][C]0.401030706527292[/C][/ROW]
[ROW][C]87[/C][C]0.564272554865152[/C][C]0.871454890269696[/C][C]0.435727445134848[/C][/ROW]
[ROW][C]88[/C][C]0.599089511070653[/C][C]0.801820977858693[/C][C]0.400910488929347[/C][/ROW]
[ROW][C]89[/C][C]0.587682105151307[/C][C]0.824635789697386[/C][C]0.412317894848693[/C][/ROW]
[ROW][C]90[/C][C]0.831071515301278[/C][C]0.337856969397445[/C][C]0.168928484698722[/C][/ROW]
[ROW][C]91[/C][C]0.816074971075502[/C][C]0.367850057848995[/C][C]0.183925028924498[/C][/ROW]
[ROW][C]92[/C][C]0.846382884582333[/C][C]0.307234230835334[/C][C]0.153617115417667[/C][/ROW]
[ROW][C]93[/C][C]0.834062326478022[/C][C]0.331875347043955[/C][C]0.165937673521977[/C][/ROW]
[ROW][C]94[/C][C]0.824278033955857[/C][C]0.351443932088285[/C][C]0.175721966044143[/C][/ROW]
[ROW][C]95[/C][C]0.871435869262066[/C][C]0.257128261475869[/C][C]0.128564130737934[/C][/ROW]
[ROW][C]96[/C][C]0.84487591646989[/C][C]0.31024816706022[/C][C]0.15512408353011[/C][/ROW]
[ROW][C]97[/C][C]0.819045058333638[/C][C]0.361909883332723[/C][C]0.180954941666362[/C][/ROW]
[ROW][C]98[/C][C]0.792000801978249[/C][C]0.415998396043502[/C][C]0.207999198021751[/C][/ROW]
[ROW][C]99[/C][C]0.760570441004733[/C][C]0.478859117990534[/C][C]0.239429558995267[/C][/ROW]
[ROW][C]100[/C][C]0.777780071729947[/C][C]0.444439856540107[/C][C]0.222219928270053[/C][/ROW]
[ROW][C]101[/C][C]0.745414379223812[/C][C]0.509171241552376[/C][C]0.254585620776188[/C][/ROW]
[ROW][C]102[/C][C]0.708028599741055[/C][C]0.58394280051789[/C][C]0.291971400258945[/C][/ROW]
[ROW][C]103[/C][C]0.70748738164448[/C][C]0.585025236711041[/C][C]0.29251261835552[/C][/ROW]
[ROW][C]104[/C][C]0.665308346976919[/C][C]0.669383306046161[/C][C]0.334691653023081[/C][/ROW]
[ROW][C]105[/C][C]0.622572709679588[/C][C]0.754854580640825[/C][C]0.377427290320412[/C][/ROW]
[ROW][C]106[/C][C]0.649457078918409[/C][C]0.701085842163182[/C][C]0.350542921081591[/C][/ROW]
[ROW][C]107[/C][C]0.617091043459552[/C][C]0.765817913080895[/C][C]0.382908956540448[/C][/ROW]
[ROW][C]108[/C][C]0.572925433421891[/C][C]0.854149133156217[/C][C]0.427074566578109[/C][/ROW]
[ROW][C]109[/C][C]0.556845299247379[/C][C]0.886309401505241[/C][C]0.443154700752621[/C][/ROW]
[ROW][C]110[/C][C]0.799747242009827[/C][C]0.400505515980345[/C][C]0.200252757990173[/C][/ROW]
[ROW][C]111[/C][C]0.871264689986346[/C][C]0.257470620027309[/C][C]0.128735310013654[/C][/ROW]
[ROW][C]112[/C][C]0.845797883379017[/C][C]0.308404233241965[/C][C]0.154202116620983[/C][/ROW]
[ROW][C]113[/C][C]0.816164303817004[/C][C]0.367671392365992[/C][C]0.183835696182996[/C][/ROW]
[ROW][C]114[/C][C]0.848506071394748[/C][C]0.302987857210505[/C][C]0.151493928605252[/C][/ROW]
[ROW][C]115[/C][C]0.818788780603023[/C][C]0.362422438793955[/C][C]0.181211219396977[/C][/ROW]
[ROW][C]116[/C][C]0.79961938433211[/C][C]0.40076123133578[/C][C]0.20038061566789[/C][/ROW]
[ROW][C]117[/C][C]0.841089395304739[/C][C]0.317821209390523[/C][C]0.158910604695261[/C][/ROW]
[ROW][C]118[/C][C]0.806238616039106[/C][C]0.387522767921788[/C][C]0.193761383960894[/C][/ROW]
[ROW][C]119[/C][C]0.807398103594855[/C][C]0.385203792810291[/C][C]0.192601896405145[/C][/ROW]
[ROW][C]120[/C][C]0.784979369987788[/C][C]0.430041260024425[/C][C]0.215020630012213[/C][/ROW]
[ROW][C]121[/C][C]0.744289078381377[/C][C]0.511421843237245[/C][C]0.255710921618623[/C][/ROW]
[ROW][C]122[/C][C]0.704263212323435[/C][C]0.59147357535313[/C][C]0.295736787676565[/C][/ROW]
[ROW][C]123[/C][C]0.661314052810837[/C][C]0.677371894378326[/C][C]0.338685947189163[/C][/ROW]
[ROW][C]124[/C][C]0.616000814642246[/C][C]0.767998370715509[/C][C]0.383999185357754[/C][/ROW]
[ROW][C]125[/C][C]0.5607166405998[/C][C]0.878566718800401[/C][C]0.4392833594002[/C][/ROW]
[ROW][C]126[/C][C]0.50538868622944[/C][C]0.98922262754112[/C][C]0.49461131377056[/C][/ROW]
[ROW][C]127[/C][C]0.533616664441335[/C][C]0.93276667111733[/C][C]0.466383335558665[/C][/ROW]
[ROW][C]128[/C][C]0.478808408606994[/C][C]0.957616817213987[/C][C]0.521191591393006[/C][/ROW]
[ROW][C]129[/C][C]0.56371087700624[/C][C]0.87257824598752[/C][C]0.43628912299376[/C][/ROW]
[ROW][C]130[/C][C]0.505644731737327[/C][C]0.988710536525345[/C][C]0.494355268262673[/C][/ROW]
[ROW][C]131[/C][C]0.453975217805046[/C][C]0.907950435610092[/C][C]0.546024782194954[/C][/ROW]
[ROW][C]132[/C][C]0.418233799014623[/C][C]0.836467598029247[/C][C]0.581766200985377[/C][/ROW]
[ROW][C]133[/C][C]0.394359558876178[/C][C]0.788719117752357[/C][C]0.605640441123822[/C][/ROW]
[ROW][C]134[/C][C]0.994489908643928[/C][C]0.0110201827121431[/C][C]0.00551009135607156[/C][/ROW]
[ROW][C]135[/C][C]0.992551758526778[/C][C]0.0148964829464446[/C][C]0.00744824147322228[/C][/ROW]
[ROW][C]136[/C][C]0.99126750201302[/C][C]0.0174649959739595[/C][C]0.00873249798697975[/C][/ROW]
[ROW][C]137[/C][C]0.986867621558162[/C][C]0.0262647568836756[/C][C]0.0131323784418378[/C][/ROW]
[ROW][C]138[/C][C]0.989106501654491[/C][C]0.0217869966910187[/C][C]0.0108934983455094[/C][/ROW]
[ROW][C]139[/C][C]0.998770022222467[/C][C]0.00245995555506602[/C][C]0.00122997777753301[/C][/ROW]
[ROW][C]140[/C][C]0.997968047738924[/C][C]0.00406390452215269[/C][C]0.00203195226107634[/C][/ROW]
[ROW][C]141[/C][C]0.999996200703001[/C][C]7.59859399831354e-06[/C][C]3.79929699915677e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999999863157995[/C][C]2.73684010658372e-07[/C][C]1.36842005329186e-07[/C][/ROW]
[ROW][C]143[/C][C]0.999999458073604[/C][C]1.0838527921151e-06[/C][C]5.41926396057549e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999998029696504[/C][C]3.94060699216152e-06[/C][C]1.97030349608076e-06[/C][/ROW]
[ROW][C]145[/C][C]0.999999996896084[/C][C]6.20783147015761e-09[/C][C]3.10391573507881e-09[/C][/ROW]
[ROW][C]146[/C][C]0.999999997476118[/C][C]5.04776478517752e-09[/C][C]2.52388239258876e-09[/C][/ROW]
[ROW][C]147[/C][C]0.999999981135228[/C][C]3.77295442422001e-08[/C][C]1.88647721211e-08[/C][/ROW]
[ROW][C]148[/C][C]0.999999898987321[/C][C]2.02025358829961e-07[/C][C]1.0101267941498e-07[/C][/ROW]
[ROW][C]149[/C][C]0.999999287147983[/C][C]1.42570403296472e-06[/C][C]7.12852016482361e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999994235382324[/C][C]1.15292353510999e-05[/C][C]5.76461767554997e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999943658585992[/C][C]0.000112682828016175[/C][C]5.63414140080875e-05[/C][/ROW]
[ROW][C]152[/C][C]0.999482763275414[/C][C]0.00103447344917254[/C][C]0.000517236724586271[/C][/ROW]
[ROW][C]153[/C][C]0.9960042070278[/C][C]0.00799158594440029[/C][C]0.00399579297220015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&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
110.7831097652225140.4337804695549720.216890234777486
120.658663224512460.6826735509750810.34133677548754
130.5739874921461680.8520250157076630.426012507853832
140.4507409617937080.9014819235874160.549259038206292
150.3684176660688240.7368353321376470.631582333931176
160.4002979615088780.8005959230177550.599702038491122
170.639123694928930.7217526101421390.36087630507107
180.5463135018403120.9073729963193760.453686498159688
190.5690245253956070.8619509492087850.430975474604393
200.4988622183330180.9977244366660360.501137781666982
210.4367839555999360.8735679111998710.563216044400064
220.9379754172151740.1240491655696530.0620245827848265
230.9144615720120340.1710768559759330.0855384279879664
240.8976924416404810.2046151167190370.102307558359519
250.9497245895969140.1005508208061730.0502754104030865
260.9331552390232280.1336895219535450.0668447609767725
270.9094068483219390.1811863033561220.0905931516780611
280.883399489149620.2332010217007590.11660051085038
290.8661018568647760.2677962862704480.133898143135224
300.8410963537247140.3178072925505720.158903646275286
310.801419667464360.397160665071280.19858033253564
320.7655359907233710.4689280185532580.234464009276629
330.7302401548135080.5395196903729830.269759845186492
340.7146495220434920.5707009559130160.285350477956508
350.798314716102120.4033705677957610.20168528389788
360.8538461507994270.2923076984011460.146153849200573
370.8946184410259160.2107631179481670.105381558974084
380.8671799025090180.2656401949819640.132820097490982
390.8524926254608020.2950147490783960.147507374539198
400.8383755878376320.3232488243247350.161624412162368
410.8181149184587670.3637701630824660.181885081541233
420.8107409897997540.3785180204004910.189259010200246
430.8295489716369710.3409020567260590.170451028363029
440.8066141392223930.3867717215552140.193385860777607
450.8793120774673110.2413758450653790.120687922532689
460.9529029297992780.09419414040144460.0470970702007223
470.9417684566608570.1164630866782870.0582315433391433
480.9301468900936110.1397062198127790.0698531099063895
490.9132190924594140.1735618150811730.0867809075405865
500.9060313127877060.1879373744245890.0939686872122943
510.889927720765920.2201445584681590.11007227923408
520.8866291674249350.226741665150130.113370832575065
530.8814052523536120.2371894952927760.118594747646388
540.8549591865369140.2900816269261720.145040813463086
550.8283003083902610.3433993832194780.171699691609739
560.8459088234810520.3081823530378960.154091176518948
570.8172795096062990.3654409807874020.182720490393701
580.7996174872918130.4007650254163740.200382512708187
590.7875272515025770.4249454969948460.212472748497423
600.7539787963414210.4920424073171590.246021203658579
610.7241738948136080.5516522103727840.275826105186392
620.6999319119685850.6001361760628310.300068088031415
630.6602410219613320.6795179560773350.339758978038668
640.6510996859585860.6978006280828280.348900314041414
650.6596521418906340.6806957162187310.340347858109366
660.6187665133447020.7624669733105950.381233486655298
670.642250987383340.7154980252333190.35774901261666
680.6252298222727950.749540355454410.374770177727205
690.6057689576264340.7884620847471310.394231042373566
700.7686110947336360.4627778105327280.231388905266364
710.7348872983763270.5302254032473450.265112701623673
720.6952779037064430.6094441925871130.304722096293557
730.6679987722762770.6640024554474470.332001227723723
740.6243740800732210.7512518398535580.375625919926779
750.6554488762303730.6891022475392540.344551123769627
760.6760759644757150.647848071048570.323924035524285
770.6511923429901050.697615314019790.348807657009895
780.6974615894381290.6050768211237420.302538410561871
790.7135706794489750.5728586411020510.286429320551025
800.6725843402647780.6548313194704440.327415659735222
810.629740313110290.740519373779420.37025968688971
820.7111103078638280.5777793842723430.288889692136172
830.6860042486467150.6279915027065690.313995751353285
840.6497526831380930.7004946337238140.350247316861907
850.6157934190765120.7684131618469750.384206580923488
860.5989692934727080.8020614130545840.401030706527292
870.5642725548651520.8714548902696960.435727445134848
880.5990895110706530.8018209778586930.400910488929347
890.5876821051513070.8246357896973860.412317894848693
900.8310715153012780.3378569693974450.168928484698722
910.8160749710755020.3678500578489950.183925028924498
920.8463828845823330.3072342308353340.153617115417667
930.8340623264780220.3318753470439550.165937673521977
940.8242780339558570.3514439320882850.175721966044143
950.8714358692620660.2571282614758690.128564130737934
960.844875916469890.310248167060220.15512408353011
970.8190450583336380.3619098833327230.180954941666362
980.7920008019782490.4159983960435020.207999198021751
990.7605704410047330.4788591179905340.239429558995267
1000.7777800717299470.4444398565401070.222219928270053
1010.7454143792238120.5091712415523760.254585620776188
1020.7080285997410550.583942800517890.291971400258945
1030.707487381644480.5850252367110410.29251261835552
1040.6653083469769190.6693833060461610.334691653023081
1050.6225727096795880.7548545806408250.377427290320412
1060.6494570789184090.7010858421631820.350542921081591
1070.6170910434595520.7658179130808950.382908956540448
1080.5729254334218910.8541491331562170.427074566578109
1090.5568452992473790.8863094015052410.443154700752621
1100.7997472420098270.4005055159803450.200252757990173
1110.8712646899863460.2574706200273090.128735310013654
1120.8457978833790170.3084042332419650.154202116620983
1130.8161643038170040.3676713923659920.183835696182996
1140.8485060713947480.3029878572105050.151493928605252
1150.8187887806030230.3624224387939550.181211219396977
1160.799619384332110.400761231335780.20038061566789
1170.8410893953047390.3178212093905230.158910604695261
1180.8062386160391060.3875227679217880.193761383960894
1190.8073981035948550.3852037928102910.192601896405145
1200.7849793699877880.4300412600244250.215020630012213
1210.7442890783813770.5114218432372450.255710921618623
1220.7042632123234350.591473575353130.295736787676565
1230.6613140528108370.6773718943783260.338685947189163
1240.6160008146422460.7679983707155090.383999185357754
1250.56071664059980.8785667188004010.4392833594002
1260.505388686229440.989222627541120.49461131377056
1270.5336166644413350.932766671117330.466383335558665
1280.4788084086069940.9576168172139870.521191591393006
1290.563710877006240.872578245987520.43628912299376
1300.5056447317373270.9887105365253450.494355268262673
1310.4539752178050460.9079504356100920.546024782194954
1320.4182337990146230.8364675980292470.581766200985377
1330.3943595588761780.7887191177523570.605640441123822
1340.9944899086439280.01102018271214310.00551009135607156
1350.9925517585267780.01489648294644460.00744824147322228
1360.991267502013020.01746499597395950.00873249798697975
1370.9868676215581620.02626475688367560.0131323784418378
1380.9891065016544910.02178699669101870.0108934983455094
1390.9987700222224670.002459955555066020.00122997777753301
1400.9979680477389240.004063904522152690.00203195226107634
1410.9999962007030017.59859399831354e-063.79929699915677e-06
1420.9999998631579952.73684010658372e-071.36842005329186e-07
1430.9999994580736041.0838527921151e-065.41926396057549e-07
1440.9999980296965043.94060699216152e-061.97030349608076e-06
1450.9999999968960846.20783147015761e-093.10391573507881e-09
1460.9999999974761185.04776478517752e-092.52388239258876e-09
1470.9999999811352283.77295442422001e-081.88647721211e-08
1480.9999998989873212.02025358829961e-071.0101267941498e-07
1490.9999992871479831.42570403296472e-067.12852016482361e-07
1500.9999942353823241.15292353510999e-055.76461767554997e-06
1510.9999436585859920.0001126828280161755.63414140080875e-05
1520.9994827632754140.001034473449172540.000517236724586271
1530.99600420702780.007991585944400290.00399579297220015






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.104895104895105NOK
5% type I error level200.13986013986014NOK
10% type I error level210.146853146853147NOK

\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 & 15 & 0.104895104895105 & NOK \tabularnewline
5% type I error level & 20 & 0.13986013986014 & NOK \tabularnewline
10% type I error level & 21 & 0.146853146853147 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152835&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]15[/C][C]0.104895104895105[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.13986013986014[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.146853146853147[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152835&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152835&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.104895104895105NOK
5% type I error level200.13986013986014NOK
10% type I error level210.146853146853147NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}