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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 06:16:39 -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/t13233430611k908ch9d7otuwh.htm/, Retrieved Fri, 03 May 2024 09:28:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152833, Retrieved Fri, 03 May 2024 09:28:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [Paper: multiple r...] [2011-12-08 11:16:39] [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=152833&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=152833&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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] = + 96656.8400850789 -8462.40398727396Maand[t] + 731.120354520719Number_of_Logins[t] + 1034.59843261906Total_Number_of_Blogged_Computations[t] + 581.646759452756Total_Number_of_Reviewed_Compendiums[t] + 151.524522830365Total_Number_of_Feedback_Messages_PeerReviews[t] + 0.24425575027551Total_number_of_characters[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_spent_in_RFC[t] =  +  96656.8400850789 -8462.40398727396Maand[t] +  731.120354520719Number_of_Logins[t] +  1034.59843261906Total_Number_of_Blogged_Computations[t] +  581.646759452756Total_Number_of_Reviewed_Compendiums[t] +  151.524522830365Total_Number_of_Feedback_Messages_PeerReviews[t] +  0.24425575027551Total_number_of_characters[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC[t] =  +  96656.8400850789 -8462.40398727396Maand[t] +  731.120354520719Number_of_Logins[t] +  1034.59843261906Total_Number_of_Blogged_Computations[t] +  581.646759452756Total_Number_of_Reviewed_Compendiums[t] +  151.524522830365Total_Number_of_Feedback_Messages_PeerReviews[t] +  0.24425575027551Total_number_of_characters[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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] = + 96656.8400850789 -8462.40398727396Maand[t] + 731.120354520719Number_of_Logins[t] + 1034.59843261906Total_Number_of_Blogged_Computations[t] + 581.646759452756Total_Number_of_Reviewed_Compendiums[t] + 151.524522830365Total_Number_of_Feedback_Messages_PeerReviews[t] + 0.24425575027551Total_number_of_characters[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)96656.840085078949465.9211621.9540.0524770.026239
Maand-8462.403987273964461.778055-1.89660.0597120.029856
Number_of_Logins731.120354520719109.0637616.703600
Total_Number_of_Blogged_Computations1034.59843261906149.0942186.939200
Total_Number_of_Reviewed_Compendiums581.6467594527561489.7591680.39040.6967480.348374
Total_Number_of_Feedback_Messages_PeerReviews151.524522830365398.5837140.38020.7043420.352171
Total_number_of_characters0.244255750275510.1138782.14490.03350.01675

\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) & 96656.8400850789 & 49465.921162 & 1.954 & 0.052477 & 0.026239 \tabularnewline
Maand & -8462.40398727396 & 4461.778055 & -1.8966 & 0.059712 & 0.029856 \tabularnewline
Number_of_Logins & 731.120354520719 & 109.063761 & 6.7036 & 0 & 0 \tabularnewline
Total_Number_of_Blogged_Computations & 1034.59843261906 & 149.094218 & 6.9392 & 0 & 0 \tabularnewline
Total_Number_of_Reviewed_Compendiums & 581.646759452756 & 1489.759168 & 0.3904 & 0.696748 & 0.348374 \tabularnewline
Total_Number_of_Feedback_Messages_PeerReviews & 151.524522830365 & 398.583714 & 0.3802 & 0.704342 & 0.352171 \tabularnewline
Total_number_of_characters & 0.24425575027551 & 0.113878 & 2.1449 & 0.0335 & 0.01675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&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]96656.8400850789[/C][C]49465.921162[/C][C]1.954[/C][C]0.052477[/C][C]0.026239[/C][/ROW]
[ROW][C]Maand[/C][C]-8462.40398727396[/C][C]4461.778055[/C][C]-1.8966[/C][C]0.059712[/C][C]0.029856[/C][/ROW]
[ROW][C]Number_of_Logins[/C][C]731.120354520719[/C][C]109.063761[/C][C]6.7036[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations[/C][C]1034.59843261906[/C][C]149.094218[/C][C]6.9392[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums[/C][C]581.646759452756[/C][C]1489.759168[/C][C]0.3904[/C][C]0.696748[/C][C]0.348374[/C][/ROW]
[ROW][C]Total_Number_of_Feedback_Messages_PeerReviews[/C][C]151.524522830365[/C][C]398.583714[/C][C]0.3802[/C][C]0.704342[/C][C]0.352171[/C][/ROW]
[ROW][C]Total_number_of_characters[/C][C]0.24425575027551[/C][C]0.113878[/C][C]2.1449[/C][C]0.0335[/C][C]0.01675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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)96656.840085078949465.9211621.9540.0524770.026239
Maand-8462.403987273964461.778055-1.89660.0597120.029856
Number_of_Logins731.120354520719109.0637616.703600
Total_Number_of_Blogged_Computations1034.59843261906149.0942186.939200
Total_Number_of_Reviewed_Compendiums581.6467594527561489.7591680.39040.6967480.348374
Total_Number_of_Feedback_Messages_PeerReviews151.524522830365398.5837140.38020.7043420.352171
Total_number_of_characters0.244255750275510.1138782.14490.03350.01675






Multiple Linear Regression - Regression Statistics
Multiple R0.881245038359046
R-squared0.776592817632436
Adjusted R-squared0.768054963529218
F-TEST (value)90.9587828796045
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation46265.5674092966
Sum Squared Residuals336058928249.554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.881245038359046 \tabularnewline
R-squared & 0.776592817632436 \tabularnewline
Adjusted R-squared & 0.768054963529218 \tabularnewline
F-TEST (value) & 90.9587828796045 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 46265.5674092966 \tabularnewline
Sum Squared Residuals & 336058928249.554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.881245038359046[/C][/ROW]
[ROW][C]R-squared[/C][C]0.776592817632436[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.768054963529218[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]90.9587828796045[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/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]46265.5674092966[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]336058928249.554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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 R0.881245038359046
R-squared0.776592817632436
Adjusted R-squared0.768054963529218
F-TEST (value)90.9587828796045
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation46265.5674092966
Sum Squared Residuals336058928249.554






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1236496206084.57241885430411.4275811463
2130631173792.833845445-43161.8338454454
3198514172762.60842966625751.391570334
4189326249899.989329302-60573.9893293024
5137449120236.36868214917212.6313178515
66529594414.9492252758-29119.9492252758
7439387303180.741997226136206.258002774
83318644688.8069898957-11502.8069898958
9174859179800.103053754-4941.10305375393
10186657167626.02763976719030.9723602331
11261949214470.89312057447478.1068794261
12190794253136.485245961-62342.4852459613
13138866140593.376094108-1727.37609410841
14296878234001.43760939562876.5623906045
15192648189133.6972268063514.30277319417
16333348365041.327108184-31693.327108184
17242212238266.5401162283945.45988377186
18263451263781.311704688-330.311704687915
19150733181593.466827419-30860.466827419
20223226263684.792262076-40458.7922620759
21240028232227.5483466497800.45165335113
22384138221437.529210822162700.470789178
23156540136667.843730819872.1562691998
24148421201983.029507645-53562.0295076455
25176502261197.37817395-84695.3781739499
26191441219969.326426379-28528.3264263792
27249735254480.777127454-4745.77712745451
28236812226913.2963818559898.7036181449
29142329131069.77938425911259.2206157409
30259667232881.74524504226785.2547549576
31228871240507.598642976-11636.5986429764
32176054156396.04023831419657.9597616857
33286683265293.21824287221389.7817571276
3487485129657.512071386-42172.5120713856
35322865237137.07730120685727.9226987936
36247013169441.44141843677571.5585815644
37340093239862.104470518100230.895529482
38191653176973.24372226314679.7562777372
3911467394652.836119590820020.1638804092
40284210291910.876569612-7700.87656961238
41284195235894.91149650848300.0885034918
42155363171588.11127128-16225.1112712799
43174198170525.5333066473672.46669335335
44142986165284.033915502-22298.033915502
45140319176496.053459753-36177.053459753
46392666268090.396014447124575.603985553
477880090585.5023282791-11785.5023282791
48201970225899.815367738-23929.8153677379
49302674287971.82418385814702.1758161424
50164733201271.183170147-36538.183170147
51194221206121.070880257-11900.0708802572
522418843454.6792077038-19266.6792077038
53340411364381.764021218-23970.7640212176
546502957836.15897664627192.84102335384
55101097105165.533251965-4068.53325196483
56243889176757.5434099767131.4565900305
57273003281866.178751594-8863.17875159403
58282220293710.937113019-11490.9371130188
59273495299122.273817215-25627.2738172149
60214872201040.06073955113831.9392604487
61333165310057.08836620323107.9116337974
62260981257720.8067221473260.19327785281
63184474182209.7217450162264.27825498436
64222366182901.59289702939464.4071029711
65205675155856.59681276249818.4031872381
66201345195250.6970021446094.30299785607
67163043220447.609410039-57404.6094100389
68204250248689.026974222-44439.026974222
69197760225048.592495987-27288.5924959872
70127260229723.790599194-102463.790599194
71216092212129.9509507783962.04904922216
727356673501.619136290864.3808637091813
73213198191736.57523418521461.4247658154
74177949178773.125229378-824.125229378382
75148698215317.998575096-66619.9985750959
76300103246497.47998538653605.5200146136
77251437224335.63867914827101.3613208516
78191971263049.739712091-71078.7397120908
79154651214455.869866093-59804.8698660934
80155473158625.664300042-3152.66430004191
81132672134563.749181963-1891.74918196273
82376465300657.57794656175807.4220534388
83145869121705.65924312224163.3407568781
84223666231362.102320396-7696.10232039585
8580953103732.242080664-22779.2420806636
8613078999014.603516109731774.3964838903
87135042163283.473520618-28241.4735206178
88300074244502.16530741355571.8346925866
89271757235723.81671709236033.1832829077
90150949284490.734950876-133541.734950876
91216802190777.93230594126024.067694059
92197389266082.817174198-68693.8171741983
93156583129090.33309317327492.6669068267
94222599196063.39997177326535.6000282267
95261601194238.7884663267362.2115336804
96178489190741.862926805-12252.8629268052
97200657186294.70386230114362.2961376988
98259084273437.702129019-14353.7021290191
99302789331130.00856663-28341.0085666299
100342025284638.02741294557386.9725870549
101246440275711.149017087-29271.1490170873
102251306245351.208634115954.79136589015
103159965221213.409868388-61248.4098683879
1044328759751.5794375151-16464.5794375151
105172212189898.198955527-17686.1989555268
106181781246909.956188503-65128.9561885029
107227681211102.6523611416578.3476388602
108260464280831.380395761-20367.3803957609
109106288147408.694281387-41120.6942813874
110109632224405.195352902-114773.195352902
111268905187097.97079815681807.029201844
112266568280838.02525464-14270.0252546401
1132362316432.43480472277190.5651952773
114152474215539.635399928-63065.6353999275
1156185771499.4352985802-9642.43529858015
116144889177869.876820496-32980.876820496
117330910261219.15828830369690.8417116967
1182105419615.55429435881438.44570564124
119223718172832.05032073450885.9496792661
1203141456436.0600526464-25022.0600526464
121259747249851.4095385179895.59046148327
122190495180098.93560951810396.0643904823
123154984174451.135054709-19467.135054709
124112933126976.733614676-14043.7336146755
1253821447406.8511026099-9192.85110260989
126158671148984.7287938959686.27120610538
127299775247364.33952872652410.6604712739
128172783172905.740211246-122.740211246229
129348678274877.56366942973800.4363305707
130266701291794.491800843-25093.4918008429
131358933336792.06062362222140.9393763782
132172464153598.24870386518865.751296135
13394381133710.355066995-39329.3550669946
134243875341843.347392989-97968.347392989
135382487298240.61156119284246.3884388083
136111853149910.507601381-38057.5076013813
137334926294184.16631227940741.8336877208
138147979175621.032400289-27642.0324002894
139216638201178.2193820915459.7806179103
140192853202160.00680738-9307.00680738026
141173710166741.8237847416968.17621525928
142336678251958.80771755684719.1922824436
143212961252527.284895301-39566.2848953006
144173260116867.72519084456392.2748091555
145271773285419.488026445-13646.4880264451
146127096266917.89439322-139821.89439322
147203606222699.037577821-19093.0375778212
148230177275116.584091641-44939.5840916414
149112032.8002123394-12031.8002123394
1501468824953.5498719322-10265.5498719322
151984301.51657958614-4203.51657958614
152455-3429.767053167073884.76705316707
1530-4892.007762208514892.00776220851
154012032.8002123394-12032.8002123394
155195765172638.53849146223126.4615085383
156306514277556.78262970628957.2173702941
1570-4892.007762208514892.00776220851
15820314957.2816304223-14754.2816304223
159719923332.1474667294-16133.1474667294
1604666045760.7464148541899.253585145851
161175479372.690924024898174.30907597511
162105044121437.321506726-16393.321506726
163969-3429.767053167074398.76705316707
164165838153567.63608267112270.3639173294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 236496 & 206084.572418854 & 30411.4275811463 \tabularnewline
2 & 130631 & 173792.833845445 & -43161.8338454454 \tabularnewline
3 & 198514 & 172762.608429666 & 25751.391570334 \tabularnewline
4 & 189326 & 249899.989329302 & -60573.9893293024 \tabularnewline
5 & 137449 & 120236.368682149 & 17212.6313178515 \tabularnewline
6 & 65295 & 94414.9492252758 & -29119.9492252758 \tabularnewline
7 & 439387 & 303180.741997226 & 136206.258002774 \tabularnewline
8 & 33186 & 44688.8069898957 & -11502.8069898958 \tabularnewline
9 & 174859 & 179800.103053754 & -4941.10305375393 \tabularnewline
10 & 186657 & 167626.027639767 & 19030.9723602331 \tabularnewline
11 & 261949 & 214470.893120574 & 47478.1068794261 \tabularnewline
12 & 190794 & 253136.485245961 & -62342.4852459613 \tabularnewline
13 & 138866 & 140593.376094108 & -1727.37609410841 \tabularnewline
14 & 296878 & 234001.437609395 & 62876.5623906045 \tabularnewline
15 & 192648 & 189133.697226806 & 3514.30277319417 \tabularnewline
16 & 333348 & 365041.327108184 & -31693.327108184 \tabularnewline
17 & 242212 & 238266.540116228 & 3945.45988377186 \tabularnewline
18 & 263451 & 263781.311704688 & -330.311704687915 \tabularnewline
19 & 150733 & 181593.466827419 & -30860.466827419 \tabularnewline
20 & 223226 & 263684.792262076 & -40458.7922620759 \tabularnewline
21 & 240028 & 232227.548346649 & 7800.45165335113 \tabularnewline
22 & 384138 & 221437.529210822 & 162700.470789178 \tabularnewline
23 & 156540 & 136667.8437308 & 19872.1562691998 \tabularnewline
24 & 148421 & 201983.029507645 & -53562.0295076455 \tabularnewline
25 & 176502 & 261197.37817395 & -84695.3781739499 \tabularnewline
26 & 191441 & 219969.326426379 & -28528.3264263792 \tabularnewline
27 & 249735 & 254480.777127454 & -4745.77712745451 \tabularnewline
28 & 236812 & 226913.296381855 & 9898.7036181449 \tabularnewline
29 & 142329 & 131069.779384259 & 11259.2206157409 \tabularnewline
30 & 259667 & 232881.745245042 & 26785.2547549576 \tabularnewline
31 & 228871 & 240507.598642976 & -11636.5986429764 \tabularnewline
32 & 176054 & 156396.040238314 & 19657.9597616857 \tabularnewline
33 & 286683 & 265293.218242872 & 21389.7817571276 \tabularnewline
34 & 87485 & 129657.512071386 & -42172.5120713856 \tabularnewline
35 & 322865 & 237137.077301206 & 85727.9226987936 \tabularnewline
36 & 247013 & 169441.441418436 & 77571.5585815644 \tabularnewline
37 & 340093 & 239862.104470518 & 100230.895529482 \tabularnewline
38 & 191653 & 176973.243722263 & 14679.7562777372 \tabularnewline
39 & 114673 & 94652.8361195908 & 20020.1638804092 \tabularnewline
40 & 284210 & 291910.876569612 & -7700.87656961238 \tabularnewline
41 & 284195 & 235894.911496508 & 48300.0885034918 \tabularnewline
42 & 155363 & 171588.11127128 & -16225.1112712799 \tabularnewline
43 & 174198 & 170525.533306647 & 3672.46669335335 \tabularnewline
44 & 142986 & 165284.033915502 & -22298.033915502 \tabularnewline
45 & 140319 & 176496.053459753 & -36177.053459753 \tabularnewline
46 & 392666 & 268090.396014447 & 124575.603985553 \tabularnewline
47 & 78800 & 90585.5023282791 & -11785.5023282791 \tabularnewline
48 & 201970 & 225899.815367738 & -23929.8153677379 \tabularnewline
49 & 302674 & 287971.824183858 & 14702.1758161424 \tabularnewline
50 & 164733 & 201271.183170147 & -36538.183170147 \tabularnewline
51 & 194221 & 206121.070880257 & -11900.0708802572 \tabularnewline
52 & 24188 & 43454.6792077038 & -19266.6792077038 \tabularnewline
53 & 340411 & 364381.764021218 & -23970.7640212176 \tabularnewline
54 & 65029 & 57836.1589766462 & 7192.84102335384 \tabularnewline
55 & 101097 & 105165.533251965 & -4068.53325196483 \tabularnewline
56 & 243889 & 176757.54340997 & 67131.4565900305 \tabularnewline
57 & 273003 & 281866.178751594 & -8863.17875159403 \tabularnewline
58 & 282220 & 293710.937113019 & -11490.9371130188 \tabularnewline
59 & 273495 & 299122.273817215 & -25627.2738172149 \tabularnewline
60 & 214872 & 201040.060739551 & 13831.9392604487 \tabularnewline
61 & 333165 & 310057.088366203 & 23107.9116337974 \tabularnewline
62 & 260981 & 257720.806722147 & 3260.19327785281 \tabularnewline
63 & 184474 & 182209.721745016 & 2264.27825498436 \tabularnewline
64 & 222366 & 182901.592897029 & 39464.4071029711 \tabularnewline
65 & 205675 & 155856.596812762 & 49818.4031872381 \tabularnewline
66 & 201345 & 195250.697002144 & 6094.30299785607 \tabularnewline
67 & 163043 & 220447.609410039 & -57404.6094100389 \tabularnewline
68 & 204250 & 248689.026974222 & -44439.026974222 \tabularnewline
69 & 197760 & 225048.592495987 & -27288.5924959872 \tabularnewline
70 & 127260 & 229723.790599194 & -102463.790599194 \tabularnewline
71 & 216092 & 212129.950950778 & 3962.04904922216 \tabularnewline
72 & 73566 & 73501.6191362908 & 64.3808637091813 \tabularnewline
73 & 213198 & 191736.575234185 & 21461.4247658154 \tabularnewline
74 & 177949 & 178773.125229378 & -824.125229378382 \tabularnewline
75 & 148698 & 215317.998575096 & -66619.9985750959 \tabularnewline
76 & 300103 & 246497.479985386 & 53605.5200146136 \tabularnewline
77 & 251437 & 224335.638679148 & 27101.3613208516 \tabularnewline
78 & 191971 & 263049.739712091 & -71078.7397120908 \tabularnewline
79 & 154651 & 214455.869866093 & -59804.8698660934 \tabularnewline
80 & 155473 & 158625.664300042 & -3152.66430004191 \tabularnewline
81 & 132672 & 134563.749181963 & -1891.74918196273 \tabularnewline
82 & 376465 & 300657.577946561 & 75807.4220534388 \tabularnewline
83 & 145869 & 121705.659243122 & 24163.3407568781 \tabularnewline
84 & 223666 & 231362.102320396 & -7696.10232039585 \tabularnewline
85 & 80953 & 103732.242080664 & -22779.2420806636 \tabularnewline
86 & 130789 & 99014.6035161097 & 31774.3964838903 \tabularnewline
87 & 135042 & 163283.473520618 & -28241.4735206178 \tabularnewline
88 & 300074 & 244502.165307413 & 55571.8346925866 \tabularnewline
89 & 271757 & 235723.816717092 & 36033.1832829077 \tabularnewline
90 & 150949 & 284490.734950876 & -133541.734950876 \tabularnewline
91 & 216802 & 190777.932305941 & 26024.067694059 \tabularnewline
92 & 197389 & 266082.817174198 & -68693.8171741983 \tabularnewline
93 & 156583 & 129090.333093173 & 27492.6669068267 \tabularnewline
94 & 222599 & 196063.399971773 & 26535.6000282267 \tabularnewline
95 & 261601 & 194238.78846632 & 67362.2115336804 \tabularnewline
96 & 178489 & 190741.862926805 & -12252.8629268052 \tabularnewline
97 & 200657 & 186294.703862301 & 14362.2961376988 \tabularnewline
98 & 259084 & 273437.702129019 & -14353.7021290191 \tabularnewline
99 & 302789 & 331130.00856663 & -28341.0085666299 \tabularnewline
100 & 342025 & 284638.027412945 & 57386.9725870549 \tabularnewline
101 & 246440 & 275711.149017087 & -29271.1490170873 \tabularnewline
102 & 251306 & 245351.20863411 & 5954.79136589015 \tabularnewline
103 & 159965 & 221213.409868388 & -61248.4098683879 \tabularnewline
104 & 43287 & 59751.5794375151 & -16464.5794375151 \tabularnewline
105 & 172212 & 189898.198955527 & -17686.1989555268 \tabularnewline
106 & 181781 & 246909.956188503 & -65128.9561885029 \tabularnewline
107 & 227681 & 211102.65236114 & 16578.3476388602 \tabularnewline
108 & 260464 & 280831.380395761 & -20367.3803957609 \tabularnewline
109 & 106288 & 147408.694281387 & -41120.6942813874 \tabularnewline
110 & 109632 & 224405.195352902 & -114773.195352902 \tabularnewline
111 & 268905 & 187097.970798156 & 81807.029201844 \tabularnewline
112 & 266568 & 280838.02525464 & -14270.0252546401 \tabularnewline
113 & 23623 & 16432.4348047227 & 7190.5651952773 \tabularnewline
114 & 152474 & 215539.635399928 & -63065.6353999275 \tabularnewline
115 & 61857 & 71499.4352985802 & -9642.43529858015 \tabularnewline
116 & 144889 & 177869.876820496 & -32980.876820496 \tabularnewline
117 & 330910 & 261219.158288303 & 69690.8417116967 \tabularnewline
118 & 21054 & 19615.5542943588 & 1438.44570564124 \tabularnewline
119 & 223718 & 172832.050320734 & 50885.9496792661 \tabularnewline
120 & 31414 & 56436.0600526464 & -25022.0600526464 \tabularnewline
121 & 259747 & 249851.409538517 & 9895.59046148327 \tabularnewline
122 & 190495 & 180098.935609518 & 10396.0643904823 \tabularnewline
123 & 154984 & 174451.135054709 & -19467.135054709 \tabularnewline
124 & 112933 & 126976.733614676 & -14043.7336146755 \tabularnewline
125 & 38214 & 47406.8511026099 & -9192.85110260989 \tabularnewline
126 & 158671 & 148984.728793895 & 9686.27120610538 \tabularnewline
127 & 299775 & 247364.339528726 & 52410.6604712739 \tabularnewline
128 & 172783 & 172905.740211246 & -122.740211246229 \tabularnewline
129 & 348678 & 274877.563669429 & 73800.4363305707 \tabularnewline
130 & 266701 & 291794.491800843 & -25093.4918008429 \tabularnewline
131 & 358933 & 336792.060623622 & 22140.9393763782 \tabularnewline
132 & 172464 & 153598.248703865 & 18865.751296135 \tabularnewline
133 & 94381 & 133710.355066995 & -39329.3550669946 \tabularnewline
134 & 243875 & 341843.347392989 & -97968.347392989 \tabularnewline
135 & 382487 & 298240.611561192 & 84246.3884388083 \tabularnewline
136 & 111853 & 149910.507601381 & -38057.5076013813 \tabularnewline
137 & 334926 & 294184.166312279 & 40741.8336877208 \tabularnewline
138 & 147979 & 175621.032400289 & -27642.0324002894 \tabularnewline
139 & 216638 & 201178.21938209 & 15459.7806179103 \tabularnewline
140 & 192853 & 202160.00680738 & -9307.00680738026 \tabularnewline
141 & 173710 & 166741.823784741 & 6968.17621525928 \tabularnewline
142 & 336678 & 251958.807717556 & 84719.1922824436 \tabularnewline
143 & 212961 & 252527.284895301 & -39566.2848953006 \tabularnewline
144 & 173260 & 116867.725190844 & 56392.2748091555 \tabularnewline
145 & 271773 & 285419.488026445 & -13646.4880264451 \tabularnewline
146 & 127096 & 266917.89439322 & -139821.89439322 \tabularnewline
147 & 203606 & 222699.037577821 & -19093.0375778212 \tabularnewline
148 & 230177 & 275116.584091641 & -44939.5840916414 \tabularnewline
149 & 1 & 12032.8002123394 & -12031.8002123394 \tabularnewline
150 & 14688 & 24953.5498719322 & -10265.5498719322 \tabularnewline
151 & 98 & 4301.51657958614 & -4203.51657958614 \tabularnewline
152 & 455 & -3429.76705316707 & 3884.76705316707 \tabularnewline
153 & 0 & -4892.00776220851 & 4892.00776220851 \tabularnewline
154 & 0 & 12032.8002123394 & -12032.8002123394 \tabularnewline
155 & 195765 & 172638.538491462 & 23126.4615085383 \tabularnewline
156 & 306514 & 277556.782629706 & 28957.2173702941 \tabularnewline
157 & 0 & -4892.00776220851 & 4892.00776220851 \tabularnewline
158 & 203 & 14957.2816304223 & -14754.2816304223 \tabularnewline
159 & 7199 & 23332.1474667294 & -16133.1474667294 \tabularnewline
160 & 46660 & 45760.7464148541 & 899.253585145851 \tabularnewline
161 & 17547 & 9372.69092402489 & 8174.30907597511 \tabularnewline
162 & 105044 & 121437.321506726 & -16393.321506726 \tabularnewline
163 & 969 & -3429.76705316707 & 4398.76705316707 \tabularnewline
164 & 165838 & 153567.636082671 & 12270.3639173294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&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]206084.572418854[/C][C]30411.4275811463[/C][/ROW]
[ROW][C]2[/C][C]130631[/C][C]173792.833845445[/C][C]-43161.8338454454[/C][/ROW]
[ROW][C]3[/C][C]198514[/C][C]172762.608429666[/C][C]25751.391570334[/C][/ROW]
[ROW][C]4[/C][C]189326[/C][C]249899.989329302[/C][C]-60573.9893293024[/C][/ROW]
[ROW][C]5[/C][C]137449[/C][C]120236.368682149[/C][C]17212.6313178515[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]94414.9492252758[/C][C]-29119.9492252758[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]303180.741997226[/C][C]136206.258002774[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]44688.8069898957[/C][C]-11502.8069898958[/C][/ROW]
[ROW][C]9[/C][C]174859[/C][C]179800.103053754[/C][C]-4941.10305375393[/C][/ROW]
[ROW][C]10[/C][C]186657[/C][C]167626.027639767[/C][C]19030.9723602331[/C][/ROW]
[ROW][C]11[/C][C]261949[/C][C]214470.893120574[/C][C]47478.1068794261[/C][/ROW]
[ROW][C]12[/C][C]190794[/C][C]253136.485245961[/C][C]-62342.4852459613[/C][/ROW]
[ROW][C]13[/C][C]138866[/C][C]140593.376094108[/C][C]-1727.37609410841[/C][/ROW]
[ROW][C]14[/C][C]296878[/C][C]234001.437609395[/C][C]62876.5623906045[/C][/ROW]
[ROW][C]15[/C][C]192648[/C][C]189133.697226806[/C][C]3514.30277319417[/C][/ROW]
[ROW][C]16[/C][C]333348[/C][C]365041.327108184[/C][C]-31693.327108184[/C][/ROW]
[ROW][C]17[/C][C]242212[/C][C]238266.540116228[/C][C]3945.45988377186[/C][/ROW]
[ROW][C]18[/C][C]263451[/C][C]263781.311704688[/C][C]-330.311704687915[/C][/ROW]
[ROW][C]19[/C][C]150733[/C][C]181593.466827419[/C][C]-30860.466827419[/C][/ROW]
[ROW][C]20[/C][C]223226[/C][C]263684.792262076[/C][C]-40458.7922620759[/C][/ROW]
[ROW][C]21[/C][C]240028[/C][C]232227.548346649[/C][C]7800.45165335113[/C][/ROW]
[ROW][C]22[/C][C]384138[/C][C]221437.529210822[/C][C]162700.470789178[/C][/ROW]
[ROW][C]23[/C][C]156540[/C][C]136667.8437308[/C][C]19872.1562691998[/C][/ROW]
[ROW][C]24[/C][C]148421[/C][C]201983.029507645[/C][C]-53562.0295076455[/C][/ROW]
[ROW][C]25[/C][C]176502[/C][C]261197.37817395[/C][C]-84695.3781739499[/C][/ROW]
[ROW][C]26[/C][C]191441[/C][C]219969.326426379[/C][C]-28528.3264263792[/C][/ROW]
[ROW][C]27[/C][C]249735[/C][C]254480.777127454[/C][C]-4745.77712745451[/C][/ROW]
[ROW][C]28[/C][C]236812[/C][C]226913.296381855[/C][C]9898.7036181449[/C][/ROW]
[ROW][C]29[/C][C]142329[/C][C]131069.779384259[/C][C]11259.2206157409[/C][/ROW]
[ROW][C]30[/C][C]259667[/C][C]232881.745245042[/C][C]26785.2547549576[/C][/ROW]
[ROW][C]31[/C][C]228871[/C][C]240507.598642976[/C][C]-11636.5986429764[/C][/ROW]
[ROW][C]32[/C][C]176054[/C][C]156396.040238314[/C][C]19657.9597616857[/C][/ROW]
[ROW][C]33[/C][C]286683[/C][C]265293.218242872[/C][C]21389.7817571276[/C][/ROW]
[ROW][C]34[/C][C]87485[/C][C]129657.512071386[/C][C]-42172.5120713856[/C][/ROW]
[ROW][C]35[/C][C]322865[/C][C]237137.077301206[/C][C]85727.9226987936[/C][/ROW]
[ROW][C]36[/C][C]247013[/C][C]169441.441418436[/C][C]77571.5585815644[/C][/ROW]
[ROW][C]37[/C][C]340093[/C][C]239862.104470518[/C][C]100230.895529482[/C][/ROW]
[ROW][C]38[/C][C]191653[/C][C]176973.243722263[/C][C]14679.7562777372[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]94652.8361195908[/C][C]20020.1638804092[/C][/ROW]
[ROW][C]40[/C][C]284210[/C][C]291910.876569612[/C][C]-7700.87656961238[/C][/ROW]
[ROW][C]41[/C][C]284195[/C][C]235894.911496508[/C][C]48300.0885034918[/C][/ROW]
[ROW][C]42[/C][C]155363[/C][C]171588.11127128[/C][C]-16225.1112712799[/C][/ROW]
[ROW][C]43[/C][C]174198[/C][C]170525.533306647[/C][C]3672.46669335335[/C][/ROW]
[ROW][C]44[/C][C]142986[/C][C]165284.033915502[/C][C]-22298.033915502[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]176496.053459753[/C][C]-36177.053459753[/C][/ROW]
[ROW][C]46[/C][C]392666[/C][C]268090.396014447[/C][C]124575.603985553[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]90585.5023282791[/C][C]-11785.5023282791[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]225899.815367738[/C][C]-23929.8153677379[/C][/ROW]
[ROW][C]49[/C][C]302674[/C][C]287971.824183858[/C][C]14702.1758161424[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]201271.183170147[/C][C]-36538.183170147[/C][/ROW]
[ROW][C]51[/C][C]194221[/C][C]206121.070880257[/C][C]-11900.0708802572[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]43454.6792077038[/C][C]-19266.6792077038[/C][/ROW]
[ROW][C]53[/C][C]340411[/C][C]364381.764021218[/C][C]-23970.7640212176[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]57836.1589766462[/C][C]7192.84102335384[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]105165.533251965[/C][C]-4068.53325196483[/C][/ROW]
[ROW][C]56[/C][C]243889[/C][C]176757.54340997[/C][C]67131.4565900305[/C][/ROW]
[ROW][C]57[/C][C]273003[/C][C]281866.178751594[/C][C]-8863.17875159403[/C][/ROW]
[ROW][C]58[/C][C]282220[/C][C]293710.937113019[/C][C]-11490.9371130188[/C][/ROW]
[ROW][C]59[/C][C]273495[/C][C]299122.273817215[/C][C]-25627.2738172149[/C][/ROW]
[ROW][C]60[/C][C]214872[/C][C]201040.060739551[/C][C]13831.9392604487[/C][/ROW]
[ROW][C]61[/C][C]333165[/C][C]310057.088366203[/C][C]23107.9116337974[/C][/ROW]
[ROW][C]62[/C][C]260981[/C][C]257720.806722147[/C][C]3260.19327785281[/C][/ROW]
[ROW][C]63[/C][C]184474[/C][C]182209.721745016[/C][C]2264.27825498436[/C][/ROW]
[ROW][C]64[/C][C]222366[/C][C]182901.592897029[/C][C]39464.4071029711[/C][/ROW]
[ROW][C]65[/C][C]205675[/C][C]155856.596812762[/C][C]49818.4031872381[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]195250.697002144[/C][C]6094.30299785607[/C][/ROW]
[ROW][C]67[/C][C]163043[/C][C]220447.609410039[/C][C]-57404.6094100389[/C][/ROW]
[ROW][C]68[/C][C]204250[/C][C]248689.026974222[/C][C]-44439.026974222[/C][/ROW]
[ROW][C]69[/C][C]197760[/C][C]225048.592495987[/C][C]-27288.5924959872[/C][/ROW]
[ROW][C]70[/C][C]127260[/C][C]229723.790599194[/C][C]-102463.790599194[/C][/ROW]
[ROW][C]71[/C][C]216092[/C][C]212129.950950778[/C][C]3962.04904922216[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]73501.6191362908[/C][C]64.3808637091813[/C][/ROW]
[ROW][C]73[/C][C]213198[/C][C]191736.575234185[/C][C]21461.4247658154[/C][/ROW]
[ROW][C]74[/C][C]177949[/C][C]178773.125229378[/C][C]-824.125229378382[/C][/ROW]
[ROW][C]75[/C][C]148698[/C][C]215317.998575096[/C][C]-66619.9985750959[/C][/ROW]
[ROW][C]76[/C][C]300103[/C][C]246497.479985386[/C][C]53605.5200146136[/C][/ROW]
[ROW][C]77[/C][C]251437[/C][C]224335.638679148[/C][C]27101.3613208516[/C][/ROW]
[ROW][C]78[/C][C]191971[/C][C]263049.739712091[/C][C]-71078.7397120908[/C][/ROW]
[ROW][C]79[/C][C]154651[/C][C]214455.869866093[/C][C]-59804.8698660934[/C][/ROW]
[ROW][C]80[/C][C]155473[/C][C]158625.664300042[/C][C]-3152.66430004191[/C][/ROW]
[ROW][C]81[/C][C]132672[/C][C]134563.749181963[/C][C]-1891.74918196273[/C][/ROW]
[ROW][C]82[/C][C]376465[/C][C]300657.577946561[/C][C]75807.4220534388[/C][/ROW]
[ROW][C]83[/C][C]145869[/C][C]121705.659243122[/C][C]24163.3407568781[/C][/ROW]
[ROW][C]84[/C][C]223666[/C][C]231362.102320396[/C][C]-7696.10232039585[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]103732.242080664[/C][C]-22779.2420806636[/C][/ROW]
[ROW][C]86[/C][C]130789[/C][C]99014.6035161097[/C][C]31774.3964838903[/C][/ROW]
[ROW][C]87[/C][C]135042[/C][C]163283.473520618[/C][C]-28241.4735206178[/C][/ROW]
[ROW][C]88[/C][C]300074[/C][C]244502.165307413[/C][C]55571.8346925866[/C][/ROW]
[ROW][C]89[/C][C]271757[/C][C]235723.816717092[/C][C]36033.1832829077[/C][/ROW]
[ROW][C]90[/C][C]150949[/C][C]284490.734950876[/C][C]-133541.734950876[/C][/ROW]
[ROW][C]91[/C][C]216802[/C][C]190777.932305941[/C][C]26024.067694059[/C][/ROW]
[ROW][C]92[/C][C]197389[/C][C]266082.817174198[/C][C]-68693.8171741983[/C][/ROW]
[ROW][C]93[/C][C]156583[/C][C]129090.333093173[/C][C]27492.6669068267[/C][/ROW]
[ROW][C]94[/C][C]222599[/C][C]196063.399971773[/C][C]26535.6000282267[/C][/ROW]
[ROW][C]95[/C][C]261601[/C][C]194238.78846632[/C][C]67362.2115336804[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]190741.862926805[/C][C]-12252.8629268052[/C][/ROW]
[ROW][C]97[/C][C]200657[/C][C]186294.703862301[/C][C]14362.2961376988[/C][/ROW]
[ROW][C]98[/C][C]259084[/C][C]273437.702129019[/C][C]-14353.7021290191[/C][/ROW]
[ROW][C]99[/C][C]302789[/C][C]331130.00856663[/C][C]-28341.0085666299[/C][/ROW]
[ROW][C]100[/C][C]342025[/C][C]284638.027412945[/C][C]57386.9725870549[/C][/ROW]
[ROW][C]101[/C][C]246440[/C][C]275711.149017087[/C][C]-29271.1490170873[/C][/ROW]
[ROW][C]102[/C][C]251306[/C][C]245351.20863411[/C][C]5954.79136589015[/C][/ROW]
[ROW][C]103[/C][C]159965[/C][C]221213.409868388[/C][C]-61248.4098683879[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]59751.5794375151[/C][C]-16464.5794375151[/C][/ROW]
[ROW][C]105[/C][C]172212[/C][C]189898.198955527[/C][C]-17686.1989555268[/C][/ROW]
[ROW][C]106[/C][C]181781[/C][C]246909.956188503[/C][C]-65128.9561885029[/C][/ROW]
[ROW][C]107[/C][C]227681[/C][C]211102.65236114[/C][C]16578.3476388602[/C][/ROW]
[ROW][C]108[/C][C]260464[/C][C]280831.380395761[/C][C]-20367.3803957609[/C][/ROW]
[ROW][C]109[/C][C]106288[/C][C]147408.694281387[/C][C]-41120.6942813874[/C][/ROW]
[ROW][C]110[/C][C]109632[/C][C]224405.195352902[/C][C]-114773.195352902[/C][/ROW]
[ROW][C]111[/C][C]268905[/C][C]187097.970798156[/C][C]81807.029201844[/C][/ROW]
[ROW][C]112[/C][C]266568[/C][C]280838.02525464[/C][C]-14270.0252546401[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]16432.4348047227[/C][C]7190.5651952773[/C][/ROW]
[ROW][C]114[/C][C]152474[/C][C]215539.635399928[/C][C]-63065.6353999275[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]71499.4352985802[/C][C]-9642.43529858015[/C][/ROW]
[ROW][C]116[/C][C]144889[/C][C]177869.876820496[/C][C]-32980.876820496[/C][/ROW]
[ROW][C]117[/C][C]330910[/C][C]261219.158288303[/C][C]69690.8417116967[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]19615.5542943588[/C][C]1438.44570564124[/C][/ROW]
[ROW][C]119[/C][C]223718[/C][C]172832.050320734[/C][C]50885.9496792661[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]56436.0600526464[/C][C]-25022.0600526464[/C][/ROW]
[ROW][C]121[/C][C]259747[/C][C]249851.409538517[/C][C]9895.59046148327[/C][/ROW]
[ROW][C]122[/C][C]190495[/C][C]180098.935609518[/C][C]10396.0643904823[/C][/ROW]
[ROW][C]123[/C][C]154984[/C][C]174451.135054709[/C][C]-19467.135054709[/C][/ROW]
[ROW][C]124[/C][C]112933[/C][C]126976.733614676[/C][C]-14043.7336146755[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]47406.8511026099[/C][C]-9192.85110260989[/C][/ROW]
[ROW][C]126[/C][C]158671[/C][C]148984.728793895[/C][C]9686.27120610538[/C][/ROW]
[ROW][C]127[/C][C]299775[/C][C]247364.339528726[/C][C]52410.6604712739[/C][/ROW]
[ROW][C]128[/C][C]172783[/C][C]172905.740211246[/C][C]-122.740211246229[/C][/ROW]
[ROW][C]129[/C][C]348678[/C][C]274877.563669429[/C][C]73800.4363305707[/C][/ROW]
[ROW][C]130[/C][C]266701[/C][C]291794.491800843[/C][C]-25093.4918008429[/C][/ROW]
[ROW][C]131[/C][C]358933[/C][C]336792.060623622[/C][C]22140.9393763782[/C][/ROW]
[ROW][C]132[/C][C]172464[/C][C]153598.248703865[/C][C]18865.751296135[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]133710.355066995[/C][C]-39329.3550669946[/C][/ROW]
[ROW][C]134[/C][C]243875[/C][C]341843.347392989[/C][C]-97968.347392989[/C][/ROW]
[ROW][C]135[/C][C]382487[/C][C]298240.611561192[/C][C]84246.3884388083[/C][/ROW]
[ROW][C]136[/C][C]111853[/C][C]149910.507601381[/C][C]-38057.5076013813[/C][/ROW]
[ROW][C]137[/C][C]334926[/C][C]294184.166312279[/C][C]40741.8336877208[/C][/ROW]
[ROW][C]138[/C][C]147979[/C][C]175621.032400289[/C][C]-27642.0324002894[/C][/ROW]
[ROW][C]139[/C][C]216638[/C][C]201178.21938209[/C][C]15459.7806179103[/C][/ROW]
[ROW][C]140[/C][C]192853[/C][C]202160.00680738[/C][C]-9307.00680738026[/C][/ROW]
[ROW][C]141[/C][C]173710[/C][C]166741.823784741[/C][C]6968.17621525928[/C][/ROW]
[ROW][C]142[/C][C]336678[/C][C]251958.807717556[/C][C]84719.1922824436[/C][/ROW]
[ROW][C]143[/C][C]212961[/C][C]252527.284895301[/C][C]-39566.2848953006[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]116867.725190844[/C][C]56392.2748091555[/C][/ROW]
[ROW][C]145[/C][C]271773[/C][C]285419.488026445[/C][C]-13646.4880264451[/C][/ROW]
[ROW][C]146[/C][C]127096[/C][C]266917.89439322[/C][C]-139821.89439322[/C][/ROW]
[ROW][C]147[/C][C]203606[/C][C]222699.037577821[/C][C]-19093.0375778212[/C][/ROW]
[ROW][C]148[/C][C]230177[/C][C]275116.584091641[/C][C]-44939.5840916414[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]12032.8002123394[/C][C]-12031.8002123394[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]24953.5498719322[/C][C]-10265.5498719322[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]4301.51657958614[/C][C]-4203.51657958614[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-3429.76705316707[/C][C]3884.76705316707[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-4892.00776220851[/C][C]4892.00776220851[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]12032.8002123394[/C][C]-12032.8002123394[/C][/ROW]
[ROW][C]155[/C][C]195765[/C][C]172638.538491462[/C][C]23126.4615085383[/C][/ROW]
[ROW][C]156[/C][C]306514[/C][C]277556.782629706[/C][C]28957.2173702941[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-4892.00776220851[/C][C]4892.00776220851[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]14957.2816304223[/C][C]-14754.2816304223[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]23332.1474667294[/C][C]-16133.1474667294[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]45760.7464148541[/C][C]899.253585145851[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]9372.69092402489[/C][C]8174.30907597511[/C][/ROW]
[ROW][C]162[/C][C]105044[/C][C]121437.321506726[/C][C]-16393.321506726[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-3429.76705316707[/C][C]4398.76705316707[/C][/ROW]
[ROW][C]164[/C][C]165838[/C][C]153567.636082671[/C][C]12270.3639173294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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
1236496206084.57241885430411.4275811463
2130631173792.833845445-43161.8338454454
3198514172762.60842966625751.391570334
4189326249899.989329302-60573.9893293024
5137449120236.36868214917212.6313178515
66529594414.9492252758-29119.9492252758
7439387303180.741997226136206.258002774
83318644688.8069898957-11502.8069898958
9174859179800.103053754-4941.10305375393
10186657167626.02763976719030.9723602331
11261949214470.89312057447478.1068794261
12190794253136.485245961-62342.4852459613
13138866140593.376094108-1727.37609410841
14296878234001.43760939562876.5623906045
15192648189133.6972268063514.30277319417
16333348365041.327108184-31693.327108184
17242212238266.5401162283945.45988377186
18263451263781.311704688-330.311704687915
19150733181593.466827419-30860.466827419
20223226263684.792262076-40458.7922620759
21240028232227.5483466497800.45165335113
22384138221437.529210822162700.470789178
23156540136667.843730819872.1562691998
24148421201983.029507645-53562.0295076455
25176502261197.37817395-84695.3781739499
26191441219969.326426379-28528.3264263792
27249735254480.777127454-4745.77712745451
28236812226913.2963818559898.7036181449
29142329131069.77938425911259.2206157409
30259667232881.74524504226785.2547549576
31228871240507.598642976-11636.5986429764
32176054156396.04023831419657.9597616857
33286683265293.21824287221389.7817571276
3487485129657.512071386-42172.5120713856
35322865237137.07730120685727.9226987936
36247013169441.44141843677571.5585815644
37340093239862.104470518100230.895529482
38191653176973.24372226314679.7562777372
3911467394652.836119590820020.1638804092
40284210291910.876569612-7700.87656961238
41284195235894.91149650848300.0885034918
42155363171588.11127128-16225.1112712799
43174198170525.5333066473672.46669335335
44142986165284.033915502-22298.033915502
45140319176496.053459753-36177.053459753
46392666268090.396014447124575.603985553
477880090585.5023282791-11785.5023282791
48201970225899.815367738-23929.8153677379
49302674287971.82418385814702.1758161424
50164733201271.183170147-36538.183170147
51194221206121.070880257-11900.0708802572
522418843454.6792077038-19266.6792077038
53340411364381.764021218-23970.7640212176
546502957836.15897664627192.84102335384
55101097105165.533251965-4068.53325196483
56243889176757.5434099767131.4565900305
57273003281866.178751594-8863.17875159403
58282220293710.937113019-11490.9371130188
59273495299122.273817215-25627.2738172149
60214872201040.06073955113831.9392604487
61333165310057.08836620323107.9116337974
62260981257720.8067221473260.19327785281
63184474182209.7217450162264.27825498436
64222366182901.59289702939464.4071029711
65205675155856.59681276249818.4031872381
66201345195250.6970021446094.30299785607
67163043220447.609410039-57404.6094100389
68204250248689.026974222-44439.026974222
69197760225048.592495987-27288.5924959872
70127260229723.790599194-102463.790599194
71216092212129.9509507783962.04904922216
727356673501.619136290864.3808637091813
73213198191736.57523418521461.4247658154
74177949178773.125229378-824.125229378382
75148698215317.998575096-66619.9985750959
76300103246497.47998538653605.5200146136
77251437224335.63867914827101.3613208516
78191971263049.739712091-71078.7397120908
79154651214455.869866093-59804.8698660934
80155473158625.664300042-3152.66430004191
81132672134563.749181963-1891.74918196273
82376465300657.57794656175807.4220534388
83145869121705.65924312224163.3407568781
84223666231362.102320396-7696.10232039585
8580953103732.242080664-22779.2420806636
8613078999014.603516109731774.3964838903
87135042163283.473520618-28241.4735206178
88300074244502.16530741355571.8346925866
89271757235723.81671709236033.1832829077
90150949284490.734950876-133541.734950876
91216802190777.93230594126024.067694059
92197389266082.817174198-68693.8171741983
93156583129090.33309317327492.6669068267
94222599196063.39997177326535.6000282267
95261601194238.7884663267362.2115336804
96178489190741.862926805-12252.8629268052
97200657186294.70386230114362.2961376988
98259084273437.702129019-14353.7021290191
99302789331130.00856663-28341.0085666299
100342025284638.02741294557386.9725870549
101246440275711.149017087-29271.1490170873
102251306245351.208634115954.79136589015
103159965221213.409868388-61248.4098683879
1044328759751.5794375151-16464.5794375151
105172212189898.198955527-17686.1989555268
106181781246909.956188503-65128.9561885029
107227681211102.6523611416578.3476388602
108260464280831.380395761-20367.3803957609
109106288147408.694281387-41120.6942813874
110109632224405.195352902-114773.195352902
111268905187097.97079815681807.029201844
112266568280838.02525464-14270.0252546401
1132362316432.43480472277190.5651952773
114152474215539.635399928-63065.6353999275
1156185771499.4352985802-9642.43529858015
116144889177869.876820496-32980.876820496
117330910261219.15828830369690.8417116967
1182105419615.55429435881438.44570564124
119223718172832.05032073450885.9496792661
1203141456436.0600526464-25022.0600526464
121259747249851.4095385179895.59046148327
122190495180098.93560951810396.0643904823
123154984174451.135054709-19467.135054709
124112933126976.733614676-14043.7336146755
1253821447406.8511026099-9192.85110260989
126158671148984.7287938959686.27120610538
127299775247364.33952872652410.6604712739
128172783172905.740211246-122.740211246229
129348678274877.56366942973800.4363305707
130266701291794.491800843-25093.4918008429
131358933336792.06062362222140.9393763782
132172464153598.24870386518865.751296135
13394381133710.355066995-39329.3550669946
134243875341843.347392989-97968.347392989
135382487298240.61156119284246.3884388083
136111853149910.507601381-38057.5076013813
137334926294184.16631227940741.8336877208
138147979175621.032400289-27642.0324002894
139216638201178.2193820915459.7806179103
140192853202160.00680738-9307.00680738026
141173710166741.8237847416968.17621525928
142336678251958.80771755684719.1922824436
143212961252527.284895301-39566.2848953006
144173260116867.72519084456392.2748091555
145271773285419.488026445-13646.4880264451
146127096266917.89439322-139821.89439322
147203606222699.037577821-19093.0375778212
148230177275116.584091641-44939.5840916414
149112032.8002123394-12031.8002123394
1501468824953.5498719322-10265.5498719322
151984301.51657958614-4203.51657958614
152455-3429.767053167073884.76705316707
1530-4892.007762208514892.00776220851
154012032.8002123394-12032.8002123394
155195765172638.53849146223126.4615085383
156306514277556.78262970628957.2173702941
1570-4892.007762208514892.00776220851
15820314957.2816304223-14754.2816304223
159719923332.1474667294-16133.1474667294
1604666045760.7464148541899.253585145851
161175479372.690924024898174.30907597511
162105044121437.321506726-16393.321506726
163969-3429.767053167074398.76705316707
164165838153567.63608267112270.3639173294






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7447786582544650.510442683491070.255221341745535
110.8386840379191730.3226319241616540.161315962080827
120.7502859153373810.4994281693252390.249714084662619
130.6627264783093520.6745470433812960.337273521690648
140.5751877613431380.8496244773137240.424812238656862
150.6167120088512390.7665759822975220.383287991148761
160.5425071035310820.9149857929378370.457492896468918
170.5555624237245850.8888751525508310.444437576275415
180.4628287922045950.9256575844091890.537171207795405
190.5352754445870070.9294491108259860.464724555412993
200.4887861833322490.9775723666644970.511213816667751
210.4110439542174120.8220879084348240.588956045782588
220.9175642251585780.1648715496828430.0824357748414215
230.8893590783351690.2212818433296620.110640921664831
240.8710330067134650.257933986573070.128966993286535
250.932852235662630.1342955286747390.0671477643373697
260.9137809498807270.1724381002385470.0862190501192734
270.8848452382075350.230309523584930.115154761792465
280.853586047811740.292827904376520.14641395218826
290.8330489025963360.3339021948073270.166951097403664
300.8071713513865120.3856572972269760.192828648613488
310.7621964989451430.4756070021097150.237803501054857
320.7292755273186820.5414489453626350.270724472681318
330.6984453788958980.6031092422082050.301554621104102
340.6700997596098670.6598004807802670.329900240390133
350.7849351254652660.4301297490694670.215064874534734
360.8639414378414420.2721171243171150.136058562158558
370.9284903194636630.1430193610726730.0715096805363367
380.9124208878374650.175158224325070.087579112162535
390.8908594114348220.2182811771303570.109140588565178
400.8679624512632610.2640750974734790.132037548736739
410.8599517908669390.2800964182661230.140048209133062
420.8370820615135730.3258358769728540.162917938486427
430.8320447349469540.3359105301060930.167955265053047
440.8035149187518570.3929701624962860.196485081248143
450.8708052160820820.2583895678358370.129194783917918
460.9562344542829680.0875310914340640.043765545717032
470.9443304492303140.1113391015393720.0556695507696859
480.9310901003472570.1378197993054850.0689098996527426
490.9139922787421960.1720154425156070.0860077212578037
500.9037503099905340.1924993800189330.0962496900094664
510.8847606885830080.2304786228339840.115239311416992
520.878763960546650.2424720789067010.12123603945335
530.8728801868818790.2542396262362420.127119813118121
540.8454735782948630.3090528434102750.154526421705137
550.8180417368686480.3639165262627040.181958263131352
560.8380142609576750.323971478084650.161985739042325
570.8079642663960520.3840714672078960.192035733603948
580.7898161260527160.4203677478945680.210183873947284
590.7793552421337220.4412895157325550.220644757866278
600.7446779607112620.5106440785774760.255322039288738
610.7141169034240620.5717661931518750.285883096575938
620.690669432521240.6186611349575190.30933056747876
630.6496135013353690.7007729973292630.350386498664631
640.6373917337298210.7252165325403580.362608266270179
650.6436018597625970.7127962804748060.356398140237403
660.6003366032806420.7993267934387160.399663396719358
670.6287865691720330.7424268616559340.371213430827967
680.6163821649036230.7672356701927540.383617835096377
690.6019525890750790.7960948218498430.398047410924921
700.7829734242350160.4340531515299690.217026575764984
710.7476400786207390.5047198427585210.252359921379261
720.7090016871308360.5819966257383280.290998312869164
730.6772949977140130.6454100045719740.322705002285987
740.6347859260940490.7304281478119020.365214073905951
750.6771376298274770.6457247403450470.322862370172523
760.689068171866570.621863656266860.31093182813343
770.6605062059981970.6789875880036050.339493794001803
780.7166331575609720.5667336848780550.283366842439028
790.7419807494418130.5160385011163730.258019250558187
800.7040861171174990.5918277657650010.295913882882501
810.6631486578811570.6737026842376860.336851342118843
820.7305360555357750.5389278889284490.269463944464225
830.7023293714891780.5953412570216430.297670628510822
840.668369970616550.66326005876690.33163002938345
850.6382469656180650.7235060687638690.361753034381935
860.6164339354673830.7671321290652340.383566064532617
870.5870146935147230.8259706129705530.412985306485277
880.6118834976701560.7762330046596890.388116502329844
890.5939961008666490.8120077982667020.406003899133351
900.8493047872972420.3013904254055150.150695212702758
910.8316937718228910.3366124563542180.168306228177109
920.8673553658233760.2652892683532490.132644634176624
930.8528469470873690.2943061058252620.147153052912631
940.8396531145261270.3206937709477450.160346885473873
950.8725591610203330.2548816779593340.127440838979667
960.8481094695204280.3037810609591440.151890530479572
970.8205865386903890.3588269226192230.179413461309611
980.7961710017887620.4076579964224760.203828998211238
990.7695258475077310.4609483049845370.230474152492269
1000.7795790618326060.4408418763347880.220420938167394
1010.7519382145764730.4961235708470540.248061785423527
1020.713249048592960.573501902814080.28675095140704
1030.7254292048666360.5491415902667270.274570795133364
1040.688211717198440.623576565603120.31178828280156
1050.65111216639040.6977756672191990.3488878336096
1060.6904893853458840.6190212293082330.309510614654116
1070.656263645999590.6874727080008210.34373635400041
1080.6161378152725880.7677243694548230.383862184727412
1090.6064796591677860.7870406816644270.393520340832214
1100.8333664697609250.3332670604781510.166633530239075
1110.8959384127351520.2081231745296970.104061587264849
1120.8733680748250260.2532638503499470.126631925174974
1130.8493083617006580.3013832765986840.150691638299342
1140.8750669460405870.2498661079188270.124933053959413
1150.8466811212663560.3066377574672890.153318878733644
1160.8256107628701050.348778474259790.174389237129895
1170.8697441627620210.2605116744759580.130255837237979
1180.8391012286015280.3217975427969430.160898771398472
1190.8409006469997830.3181987060004340.159099353000217
1200.8164048092621330.3671903814757340.183595190737867
1210.7788008072108880.4423983855782240.221199192789112
1220.7426858036151090.5146283927697820.257314196384891
1230.7022558771852510.5954882456294990.297744122814749
1240.656453789636070.6870924207278590.34354621036393
1250.605181431886110.7896371362277810.39481856811389
1260.5514126937909120.8971746124181760.448587306209088
1270.5837343256541730.8325313486916530.416265674345827
1280.5287720343112430.9424559313775140.471227965688757
1290.6089208121577250.782158375684550.391079187842275
1300.5547172379208360.8905655241583270.445282762079164
1310.5024775946091710.9950448107816580.497522405390829
1320.4611369567465020.9222739134930030.538863043253498
1330.4455921088775180.8911842177550370.554407891122482
1340.9966816528317080.006636694336584820.00331834716829241
1350.9953272389599810.009345522080038570.00467276104001928
1360.9946165452236080.01076690955278360.00538345477639181
1370.9916561054391340.01668778912173190.00834389456086595
1380.9929843016746660.0140313966506680.00701569832533402
1390.999225629083080.001548741833840550.000774370916920274
1400.9989465403086330.002106919382733880.00105345969136694
1410.999997913969334.17206133928116e-062.08603066964058e-06
1420.9999999647850357.04299304456358e-083.52149652228179e-08
1430.999999866978842.66042320893792e-071.33021160446896e-07
1440.999999528829939.42340140775738e-074.71170070387869e-07
1450.9999999992698971.46020546930838e-097.3010273465419e-10
1460.9999999991600361.67992851108301e-098.39964255541506e-10
1470.9999999954857429.02851576047743e-094.51425788023872e-09
1480.9999999853706492.92587022796569e-081.46293511398285e-08
1490.9999998599367252.80126549439366e-071.40063274719683e-07
1500.9999992683400661.46331986893346e-067.31659934466728e-07
1510.9999924747515241.50504969514007e-057.52524847570036e-06
1520.9999340337941330.0001319324117345126.59662058672558e-05
1530.9994087522912230.001182495417554630.000591247708777314
1540.9952989123424930.009402175315014070.00470108765750704

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.744778658254465 & 0.51044268349107 & 0.255221341745535 \tabularnewline
11 & 0.838684037919173 & 0.322631924161654 & 0.161315962080827 \tabularnewline
12 & 0.750285915337381 & 0.499428169325239 & 0.249714084662619 \tabularnewline
13 & 0.662726478309352 & 0.674547043381296 & 0.337273521690648 \tabularnewline
14 & 0.575187761343138 & 0.849624477313724 & 0.424812238656862 \tabularnewline
15 & 0.616712008851239 & 0.766575982297522 & 0.383287991148761 \tabularnewline
16 & 0.542507103531082 & 0.914985792937837 & 0.457492896468918 \tabularnewline
17 & 0.555562423724585 & 0.888875152550831 & 0.444437576275415 \tabularnewline
18 & 0.462828792204595 & 0.925657584409189 & 0.537171207795405 \tabularnewline
19 & 0.535275444587007 & 0.929449110825986 & 0.464724555412993 \tabularnewline
20 & 0.488786183332249 & 0.977572366664497 & 0.511213816667751 \tabularnewline
21 & 0.411043954217412 & 0.822087908434824 & 0.588956045782588 \tabularnewline
22 & 0.917564225158578 & 0.164871549682843 & 0.0824357748414215 \tabularnewline
23 & 0.889359078335169 & 0.221281843329662 & 0.110640921664831 \tabularnewline
24 & 0.871033006713465 & 0.25793398657307 & 0.128966993286535 \tabularnewline
25 & 0.93285223566263 & 0.134295528674739 & 0.0671477643373697 \tabularnewline
26 & 0.913780949880727 & 0.172438100238547 & 0.0862190501192734 \tabularnewline
27 & 0.884845238207535 & 0.23030952358493 & 0.115154761792465 \tabularnewline
28 & 0.85358604781174 & 0.29282790437652 & 0.14641395218826 \tabularnewline
29 & 0.833048902596336 & 0.333902194807327 & 0.166951097403664 \tabularnewline
30 & 0.807171351386512 & 0.385657297226976 & 0.192828648613488 \tabularnewline
31 & 0.762196498945143 & 0.475607002109715 & 0.237803501054857 \tabularnewline
32 & 0.729275527318682 & 0.541448945362635 & 0.270724472681318 \tabularnewline
33 & 0.698445378895898 & 0.603109242208205 & 0.301554621104102 \tabularnewline
34 & 0.670099759609867 & 0.659800480780267 & 0.329900240390133 \tabularnewline
35 & 0.784935125465266 & 0.430129749069467 & 0.215064874534734 \tabularnewline
36 & 0.863941437841442 & 0.272117124317115 & 0.136058562158558 \tabularnewline
37 & 0.928490319463663 & 0.143019361072673 & 0.0715096805363367 \tabularnewline
38 & 0.912420887837465 & 0.17515822432507 & 0.087579112162535 \tabularnewline
39 & 0.890859411434822 & 0.218281177130357 & 0.109140588565178 \tabularnewline
40 & 0.867962451263261 & 0.264075097473479 & 0.132037548736739 \tabularnewline
41 & 0.859951790866939 & 0.280096418266123 & 0.140048209133062 \tabularnewline
42 & 0.837082061513573 & 0.325835876972854 & 0.162917938486427 \tabularnewline
43 & 0.832044734946954 & 0.335910530106093 & 0.167955265053047 \tabularnewline
44 & 0.803514918751857 & 0.392970162496286 & 0.196485081248143 \tabularnewline
45 & 0.870805216082082 & 0.258389567835837 & 0.129194783917918 \tabularnewline
46 & 0.956234454282968 & 0.087531091434064 & 0.043765545717032 \tabularnewline
47 & 0.944330449230314 & 0.111339101539372 & 0.0556695507696859 \tabularnewline
48 & 0.931090100347257 & 0.137819799305485 & 0.0689098996527426 \tabularnewline
49 & 0.913992278742196 & 0.172015442515607 & 0.0860077212578037 \tabularnewline
50 & 0.903750309990534 & 0.192499380018933 & 0.0962496900094664 \tabularnewline
51 & 0.884760688583008 & 0.230478622833984 & 0.115239311416992 \tabularnewline
52 & 0.87876396054665 & 0.242472078906701 & 0.12123603945335 \tabularnewline
53 & 0.872880186881879 & 0.254239626236242 & 0.127119813118121 \tabularnewline
54 & 0.845473578294863 & 0.309052843410275 & 0.154526421705137 \tabularnewline
55 & 0.818041736868648 & 0.363916526262704 & 0.181958263131352 \tabularnewline
56 & 0.838014260957675 & 0.32397147808465 & 0.161985739042325 \tabularnewline
57 & 0.807964266396052 & 0.384071467207896 & 0.192035733603948 \tabularnewline
58 & 0.789816126052716 & 0.420367747894568 & 0.210183873947284 \tabularnewline
59 & 0.779355242133722 & 0.441289515732555 & 0.220644757866278 \tabularnewline
60 & 0.744677960711262 & 0.510644078577476 & 0.255322039288738 \tabularnewline
61 & 0.714116903424062 & 0.571766193151875 & 0.285883096575938 \tabularnewline
62 & 0.69066943252124 & 0.618661134957519 & 0.30933056747876 \tabularnewline
63 & 0.649613501335369 & 0.700772997329263 & 0.350386498664631 \tabularnewline
64 & 0.637391733729821 & 0.725216532540358 & 0.362608266270179 \tabularnewline
65 & 0.643601859762597 & 0.712796280474806 & 0.356398140237403 \tabularnewline
66 & 0.600336603280642 & 0.799326793438716 & 0.399663396719358 \tabularnewline
67 & 0.628786569172033 & 0.742426861655934 & 0.371213430827967 \tabularnewline
68 & 0.616382164903623 & 0.767235670192754 & 0.383617835096377 \tabularnewline
69 & 0.601952589075079 & 0.796094821849843 & 0.398047410924921 \tabularnewline
70 & 0.782973424235016 & 0.434053151529969 & 0.217026575764984 \tabularnewline
71 & 0.747640078620739 & 0.504719842758521 & 0.252359921379261 \tabularnewline
72 & 0.709001687130836 & 0.581996625738328 & 0.290998312869164 \tabularnewline
73 & 0.677294997714013 & 0.645410004571974 & 0.322705002285987 \tabularnewline
74 & 0.634785926094049 & 0.730428147811902 & 0.365214073905951 \tabularnewline
75 & 0.677137629827477 & 0.645724740345047 & 0.322862370172523 \tabularnewline
76 & 0.68906817186657 & 0.62186365626686 & 0.31093182813343 \tabularnewline
77 & 0.660506205998197 & 0.678987588003605 & 0.339493794001803 \tabularnewline
78 & 0.716633157560972 & 0.566733684878055 & 0.283366842439028 \tabularnewline
79 & 0.741980749441813 & 0.516038501116373 & 0.258019250558187 \tabularnewline
80 & 0.704086117117499 & 0.591827765765001 & 0.295913882882501 \tabularnewline
81 & 0.663148657881157 & 0.673702684237686 & 0.336851342118843 \tabularnewline
82 & 0.730536055535775 & 0.538927888928449 & 0.269463944464225 \tabularnewline
83 & 0.702329371489178 & 0.595341257021643 & 0.297670628510822 \tabularnewline
84 & 0.66836997061655 & 0.6632600587669 & 0.33163002938345 \tabularnewline
85 & 0.638246965618065 & 0.723506068763869 & 0.361753034381935 \tabularnewline
86 & 0.616433935467383 & 0.767132129065234 & 0.383566064532617 \tabularnewline
87 & 0.587014693514723 & 0.825970612970553 & 0.412985306485277 \tabularnewline
88 & 0.611883497670156 & 0.776233004659689 & 0.388116502329844 \tabularnewline
89 & 0.593996100866649 & 0.812007798266702 & 0.406003899133351 \tabularnewline
90 & 0.849304787297242 & 0.301390425405515 & 0.150695212702758 \tabularnewline
91 & 0.831693771822891 & 0.336612456354218 & 0.168306228177109 \tabularnewline
92 & 0.867355365823376 & 0.265289268353249 & 0.132644634176624 \tabularnewline
93 & 0.852846947087369 & 0.294306105825262 & 0.147153052912631 \tabularnewline
94 & 0.839653114526127 & 0.320693770947745 & 0.160346885473873 \tabularnewline
95 & 0.872559161020333 & 0.254881677959334 & 0.127440838979667 \tabularnewline
96 & 0.848109469520428 & 0.303781060959144 & 0.151890530479572 \tabularnewline
97 & 0.820586538690389 & 0.358826922619223 & 0.179413461309611 \tabularnewline
98 & 0.796171001788762 & 0.407657996422476 & 0.203828998211238 \tabularnewline
99 & 0.769525847507731 & 0.460948304984537 & 0.230474152492269 \tabularnewline
100 & 0.779579061832606 & 0.440841876334788 & 0.220420938167394 \tabularnewline
101 & 0.751938214576473 & 0.496123570847054 & 0.248061785423527 \tabularnewline
102 & 0.71324904859296 & 0.57350190281408 & 0.28675095140704 \tabularnewline
103 & 0.725429204866636 & 0.549141590266727 & 0.274570795133364 \tabularnewline
104 & 0.68821171719844 & 0.62357656560312 & 0.31178828280156 \tabularnewline
105 & 0.6511121663904 & 0.697775667219199 & 0.3488878336096 \tabularnewline
106 & 0.690489385345884 & 0.619021229308233 & 0.309510614654116 \tabularnewline
107 & 0.65626364599959 & 0.687472708000821 & 0.34373635400041 \tabularnewline
108 & 0.616137815272588 & 0.767724369454823 & 0.383862184727412 \tabularnewline
109 & 0.606479659167786 & 0.787040681664427 & 0.393520340832214 \tabularnewline
110 & 0.833366469760925 & 0.333267060478151 & 0.166633530239075 \tabularnewline
111 & 0.895938412735152 & 0.208123174529697 & 0.104061587264849 \tabularnewline
112 & 0.873368074825026 & 0.253263850349947 & 0.126631925174974 \tabularnewline
113 & 0.849308361700658 & 0.301383276598684 & 0.150691638299342 \tabularnewline
114 & 0.875066946040587 & 0.249866107918827 & 0.124933053959413 \tabularnewline
115 & 0.846681121266356 & 0.306637757467289 & 0.153318878733644 \tabularnewline
116 & 0.825610762870105 & 0.34877847425979 & 0.174389237129895 \tabularnewline
117 & 0.869744162762021 & 0.260511674475958 & 0.130255837237979 \tabularnewline
118 & 0.839101228601528 & 0.321797542796943 & 0.160898771398472 \tabularnewline
119 & 0.840900646999783 & 0.318198706000434 & 0.159099353000217 \tabularnewline
120 & 0.816404809262133 & 0.367190381475734 & 0.183595190737867 \tabularnewline
121 & 0.778800807210888 & 0.442398385578224 & 0.221199192789112 \tabularnewline
122 & 0.742685803615109 & 0.514628392769782 & 0.257314196384891 \tabularnewline
123 & 0.702255877185251 & 0.595488245629499 & 0.297744122814749 \tabularnewline
124 & 0.65645378963607 & 0.687092420727859 & 0.34354621036393 \tabularnewline
125 & 0.60518143188611 & 0.789637136227781 & 0.39481856811389 \tabularnewline
126 & 0.551412693790912 & 0.897174612418176 & 0.448587306209088 \tabularnewline
127 & 0.583734325654173 & 0.832531348691653 & 0.416265674345827 \tabularnewline
128 & 0.528772034311243 & 0.942455931377514 & 0.471227965688757 \tabularnewline
129 & 0.608920812157725 & 0.78215837568455 & 0.391079187842275 \tabularnewline
130 & 0.554717237920836 & 0.890565524158327 & 0.445282762079164 \tabularnewline
131 & 0.502477594609171 & 0.995044810781658 & 0.497522405390829 \tabularnewline
132 & 0.461136956746502 & 0.922273913493003 & 0.538863043253498 \tabularnewline
133 & 0.445592108877518 & 0.891184217755037 & 0.554407891122482 \tabularnewline
134 & 0.996681652831708 & 0.00663669433658482 & 0.00331834716829241 \tabularnewline
135 & 0.995327238959981 & 0.00934552208003857 & 0.00467276104001928 \tabularnewline
136 & 0.994616545223608 & 0.0107669095527836 & 0.00538345477639181 \tabularnewline
137 & 0.991656105439134 & 0.0166877891217319 & 0.00834389456086595 \tabularnewline
138 & 0.992984301674666 & 0.014031396650668 & 0.00701569832533402 \tabularnewline
139 & 0.99922562908308 & 0.00154874183384055 & 0.000774370916920274 \tabularnewline
140 & 0.998946540308633 & 0.00210691938273388 & 0.00105345969136694 \tabularnewline
141 & 0.99999791396933 & 4.17206133928116e-06 & 2.08603066964058e-06 \tabularnewline
142 & 0.999999964785035 & 7.04299304456358e-08 & 3.52149652228179e-08 \tabularnewline
143 & 0.99999986697884 & 2.66042320893792e-07 & 1.33021160446896e-07 \tabularnewline
144 & 0.99999952882993 & 9.42340140775738e-07 & 4.71170070387869e-07 \tabularnewline
145 & 0.999999999269897 & 1.46020546930838e-09 & 7.3010273465419e-10 \tabularnewline
146 & 0.999999999160036 & 1.67992851108301e-09 & 8.39964255541506e-10 \tabularnewline
147 & 0.999999995485742 & 9.02851576047743e-09 & 4.51425788023872e-09 \tabularnewline
148 & 0.999999985370649 & 2.92587022796569e-08 & 1.46293511398285e-08 \tabularnewline
149 & 0.999999859936725 & 2.80126549439366e-07 & 1.40063274719683e-07 \tabularnewline
150 & 0.999999268340066 & 1.46331986893346e-06 & 7.31659934466728e-07 \tabularnewline
151 & 0.999992474751524 & 1.50504969514007e-05 & 7.52524847570036e-06 \tabularnewline
152 & 0.999934033794133 & 0.000131932411734512 & 6.59662058672558e-05 \tabularnewline
153 & 0.999408752291223 & 0.00118249541755463 & 0.000591247708777314 \tabularnewline
154 & 0.995298912342493 & 0.00940217531501407 & 0.00470108765750704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&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]10[/C][C]0.744778658254465[/C][C]0.51044268349107[/C][C]0.255221341745535[/C][/ROW]
[ROW][C]11[/C][C]0.838684037919173[/C][C]0.322631924161654[/C][C]0.161315962080827[/C][/ROW]
[ROW][C]12[/C][C]0.750285915337381[/C][C]0.499428169325239[/C][C]0.249714084662619[/C][/ROW]
[ROW][C]13[/C][C]0.662726478309352[/C][C]0.674547043381296[/C][C]0.337273521690648[/C][/ROW]
[ROW][C]14[/C][C]0.575187761343138[/C][C]0.849624477313724[/C][C]0.424812238656862[/C][/ROW]
[ROW][C]15[/C][C]0.616712008851239[/C][C]0.766575982297522[/C][C]0.383287991148761[/C][/ROW]
[ROW][C]16[/C][C]0.542507103531082[/C][C]0.914985792937837[/C][C]0.457492896468918[/C][/ROW]
[ROW][C]17[/C][C]0.555562423724585[/C][C]0.888875152550831[/C][C]0.444437576275415[/C][/ROW]
[ROW][C]18[/C][C]0.462828792204595[/C][C]0.925657584409189[/C][C]0.537171207795405[/C][/ROW]
[ROW][C]19[/C][C]0.535275444587007[/C][C]0.929449110825986[/C][C]0.464724555412993[/C][/ROW]
[ROW][C]20[/C][C]0.488786183332249[/C][C]0.977572366664497[/C][C]0.511213816667751[/C][/ROW]
[ROW][C]21[/C][C]0.411043954217412[/C][C]0.822087908434824[/C][C]0.588956045782588[/C][/ROW]
[ROW][C]22[/C][C]0.917564225158578[/C][C]0.164871549682843[/C][C]0.0824357748414215[/C][/ROW]
[ROW][C]23[/C][C]0.889359078335169[/C][C]0.221281843329662[/C][C]0.110640921664831[/C][/ROW]
[ROW][C]24[/C][C]0.871033006713465[/C][C]0.25793398657307[/C][C]0.128966993286535[/C][/ROW]
[ROW][C]25[/C][C]0.93285223566263[/C][C]0.134295528674739[/C][C]0.0671477643373697[/C][/ROW]
[ROW][C]26[/C][C]0.913780949880727[/C][C]0.172438100238547[/C][C]0.0862190501192734[/C][/ROW]
[ROW][C]27[/C][C]0.884845238207535[/C][C]0.23030952358493[/C][C]0.115154761792465[/C][/ROW]
[ROW][C]28[/C][C]0.85358604781174[/C][C]0.29282790437652[/C][C]0.14641395218826[/C][/ROW]
[ROW][C]29[/C][C]0.833048902596336[/C][C]0.333902194807327[/C][C]0.166951097403664[/C][/ROW]
[ROW][C]30[/C][C]0.807171351386512[/C][C]0.385657297226976[/C][C]0.192828648613488[/C][/ROW]
[ROW][C]31[/C][C]0.762196498945143[/C][C]0.475607002109715[/C][C]0.237803501054857[/C][/ROW]
[ROW][C]32[/C][C]0.729275527318682[/C][C]0.541448945362635[/C][C]0.270724472681318[/C][/ROW]
[ROW][C]33[/C][C]0.698445378895898[/C][C]0.603109242208205[/C][C]0.301554621104102[/C][/ROW]
[ROW][C]34[/C][C]0.670099759609867[/C][C]0.659800480780267[/C][C]0.329900240390133[/C][/ROW]
[ROW][C]35[/C][C]0.784935125465266[/C][C]0.430129749069467[/C][C]0.215064874534734[/C][/ROW]
[ROW][C]36[/C][C]0.863941437841442[/C][C]0.272117124317115[/C][C]0.136058562158558[/C][/ROW]
[ROW][C]37[/C][C]0.928490319463663[/C][C]0.143019361072673[/C][C]0.0715096805363367[/C][/ROW]
[ROW][C]38[/C][C]0.912420887837465[/C][C]0.17515822432507[/C][C]0.087579112162535[/C][/ROW]
[ROW][C]39[/C][C]0.890859411434822[/C][C]0.218281177130357[/C][C]0.109140588565178[/C][/ROW]
[ROW][C]40[/C][C]0.867962451263261[/C][C]0.264075097473479[/C][C]0.132037548736739[/C][/ROW]
[ROW][C]41[/C][C]0.859951790866939[/C][C]0.280096418266123[/C][C]0.140048209133062[/C][/ROW]
[ROW][C]42[/C][C]0.837082061513573[/C][C]0.325835876972854[/C][C]0.162917938486427[/C][/ROW]
[ROW][C]43[/C][C]0.832044734946954[/C][C]0.335910530106093[/C][C]0.167955265053047[/C][/ROW]
[ROW][C]44[/C][C]0.803514918751857[/C][C]0.392970162496286[/C][C]0.196485081248143[/C][/ROW]
[ROW][C]45[/C][C]0.870805216082082[/C][C]0.258389567835837[/C][C]0.129194783917918[/C][/ROW]
[ROW][C]46[/C][C]0.956234454282968[/C][C]0.087531091434064[/C][C]0.043765545717032[/C][/ROW]
[ROW][C]47[/C][C]0.944330449230314[/C][C]0.111339101539372[/C][C]0.0556695507696859[/C][/ROW]
[ROW][C]48[/C][C]0.931090100347257[/C][C]0.137819799305485[/C][C]0.0689098996527426[/C][/ROW]
[ROW][C]49[/C][C]0.913992278742196[/C][C]0.172015442515607[/C][C]0.0860077212578037[/C][/ROW]
[ROW][C]50[/C][C]0.903750309990534[/C][C]0.192499380018933[/C][C]0.0962496900094664[/C][/ROW]
[ROW][C]51[/C][C]0.884760688583008[/C][C]0.230478622833984[/C][C]0.115239311416992[/C][/ROW]
[ROW][C]52[/C][C]0.87876396054665[/C][C]0.242472078906701[/C][C]0.12123603945335[/C][/ROW]
[ROW][C]53[/C][C]0.872880186881879[/C][C]0.254239626236242[/C][C]0.127119813118121[/C][/ROW]
[ROW][C]54[/C][C]0.845473578294863[/C][C]0.309052843410275[/C][C]0.154526421705137[/C][/ROW]
[ROW][C]55[/C][C]0.818041736868648[/C][C]0.363916526262704[/C][C]0.181958263131352[/C][/ROW]
[ROW][C]56[/C][C]0.838014260957675[/C][C]0.32397147808465[/C][C]0.161985739042325[/C][/ROW]
[ROW][C]57[/C][C]0.807964266396052[/C][C]0.384071467207896[/C][C]0.192035733603948[/C][/ROW]
[ROW][C]58[/C][C]0.789816126052716[/C][C]0.420367747894568[/C][C]0.210183873947284[/C][/ROW]
[ROW][C]59[/C][C]0.779355242133722[/C][C]0.441289515732555[/C][C]0.220644757866278[/C][/ROW]
[ROW][C]60[/C][C]0.744677960711262[/C][C]0.510644078577476[/C][C]0.255322039288738[/C][/ROW]
[ROW][C]61[/C][C]0.714116903424062[/C][C]0.571766193151875[/C][C]0.285883096575938[/C][/ROW]
[ROW][C]62[/C][C]0.69066943252124[/C][C]0.618661134957519[/C][C]0.30933056747876[/C][/ROW]
[ROW][C]63[/C][C]0.649613501335369[/C][C]0.700772997329263[/C][C]0.350386498664631[/C][/ROW]
[ROW][C]64[/C][C]0.637391733729821[/C][C]0.725216532540358[/C][C]0.362608266270179[/C][/ROW]
[ROW][C]65[/C][C]0.643601859762597[/C][C]0.712796280474806[/C][C]0.356398140237403[/C][/ROW]
[ROW][C]66[/C][C]0.600336603280642[/C][C]0.799326793438716[/C][C]0.399663396719358[/C][/ROW]
[ROW][C]67[/C][C]0.628786569172033[/C][C]0.742426861655934[/C][C]0.371213430827967[/C][/ROW]
[ROW][C]68[/C][C]0.616382164903623[/C][C]0.767235670192754[/C][C]0.383617835096377[/C][/ROW]
[ROW][C]69[/C][C]0.601952589075079[/C][C]0.796094821849843[/C][C]0.398047410924921[/C][/ROW]
[ROW][C]70[/C][C]0.782973424235016[/C][C]0.434053151529969[/C][C]0.217026575764984[/C][/ROW]
[ROW][C]71[/C][C]0.747640078620739[/C][C]0.504719842758521[/C][C]0.252359921379261[/C][/ROW]
[ROW][C]72[/C][C]0.709001687130836[/C][C]0.581996625738328[/C][C]0.290998312869164[/C][/ROW]
[ROW][C]73[/C][C]0.677294997714013[/C][C]0.645410004571974[/C][C]0.322705002285987[/C][/ROW]
[ROW][C]74[/C][C]0.634785926094049[/C][C]0.730428147811902[/C][C]0.365214073905951[/C][/ROW]
[ROW][C]75[/C][C]0.677137629827477[/C][C]0.645724740345047[/C][C]0.322862370172523[/C][/ROW]
[ROW][C]76[/C][C]0.68906817186657[/C][C]0.62186365626686[/C][C]0.31093182813343[/C][/ROW]
[ROW][C]77[/C][C]0.660506205998197[/C][C]0.678987588003605[/C][C]0.339493794001803[/C][/ROW]
[ROW][C]78[/C][C]0.716633157560972[/C][C]0.566733684878055[/C][C]0.283366842439028[/C][/ROW]
[ROW][C]79[/C][C]0.741980749441813[/C][C]0.516038501116373[/C][C]0.258019250558187[/C][/ROW]
[ROW][C]80[/C][C]0.704086117117499[/C][C]0.591827765765001[/C][C]0.295913882882501[/C][/ROW]
[ROW][C]81[/C][C]0.663148657881157[/C][C]0.673702684237686[/C][C]0.336851342118843[/C][/ROW]
[ROW][C]82[/C][C]0.730536055535775[/C][C]0.538927888928449[/C][C]0.269463944464225[/C][/ROW]
[ROW][C]83[/C][C]0.702329371489178[/C][C]0.595341257021643[/C][C]0.297670628510822[/C][/ROW]
[ROW][C]84[/C][C]0.66836997061655[/C][C]0.6632600587669[/C][C]0.33163002938345[/C][/ROW]
[ROW][C]85[/C][C]0.638246965618065[/C][C]0.723506068763869[/C][C]0.361753034381935[/C][/ROW]
[ROW][C]86[/C][C]0.616433935467383[/C][C]0.767132129065234[/C][C]0.383566064532617[/C][/ROW]
[ROW][C]87[/C][C]0.587014693514723[/C][C]0.825970612970553[/C][C]0.412985306485277[/C][/ROW]
[ROW][C]88[/C][C]0.611883497670156[/C][C]0.776233004659689[/C][C]0.388116502329844[/C][/ROW]
[ROW][C]89[/C][C]0.593996100866649[/C][C]0.812007798266702[/C][C]0.406003899133351[/C][/ROW]
[ROW][C]90[/C][C]0.849304787297242[/C][C]0.301390425405515[/C][C]0.150695212702758[/C][/ROW]
[ROW][C]91[/C][C]0.831693771822891[/C][C]0.336612456354218[/C][C]0.168306228177109[/C][/ROW]
[ROW][C]92[/C][C]0.867355365823376[/C][C]0.265289268353249[/C][C]0.132644634176624[/C][/ROW]
[ROW][C]93[/C][C]0.852846947087369[/C][C]0.294306105825262[/C][C]0.147153052912631[/C][/ROW]
[ROW][C]94[/C][C]0.839653114526127[/C][C]0.320693770947745[/C][C]0.160346885473873[/C][/ROW]
[ROW][C]95[/C][C]0.872559161020333[/C][C]0.254881677959334[/C][C]0.127440838979667[/C][/ROW]
[ROW][C]96[/C][C]0.848109469520428[/C][C]0.303781060959144[/C][C]0.151890530479572[/C][/ROW]
[ROW][C]97[/C][C]0.820586538690389[/C][C]0.358826922619223[/C][C]0.179413461309611[/C][/ROW]
[ROW][C]98[/C][C]0.796171001788762[/C][C]0.407657996422476[/C][C]0.203828998211238[/C][/ROW]
[ROW][C]99[/C][C]0.769525847507731[/C][C]0.460948304984537[/C][C]0.230474152492269[/C][/ROW]
[ROW][C]100[/C][C]0.779579061832606[/C][C]0.440841876334788[/C][C]0.220420938167394[/C][/ROW]
[ROW][C]101[/C][C]0.751938214576473[/C][C]0.496123570847054[/C][C]0.248061785423527[/C][/ROW]
[ROW][C]102[/C][C]0.71324904859296[/C][C]0.57350190281408[/C][C]0.28675095140704[/C][/ROW]
[ROW][C]103[/C][C]0.725429204866636[/C][C]0.549141590266727[/C][C]0.274570795133364[/C][/ROW]
[ROW][C]104[/C][C]0.68821171719844[/C][C]0.62357656560312[/C][C]0.31178828280156[/C][/ROW]
[ROW][C]105[/C][C]0.6511121663904[/C][C]0.697775667219199[/C][C]0.3488878336096[/C][/ROW]
[ROW][C]106[/C][C]0.690489385345884[/C][C]0.619021229308233[/C][C]0.309510614654116[/C][/ROW]
[ROW][C]107[/C][C]0.65626364599959[/C][C]0.687472708000821[/C][C]0.34373635400041[/C][/ROW]
[ROW][C]108[/C][C]0.616137815272588[/C][C]0.767724369454823[/C][C]0.383862184727412[/C][/ROW]
[ROW][C]109[/C][C]0.606479659167786[/C][C]0.787040681664427[/C][C]0.393520340832214[/C][/ROW]
[ROW][C]110[/C][C]0.833366469760925[/C][C]0.333267060478151[/C][C]0.166633530239075[/C][/ROW]
[ROW][C]111[/C][C]0.895938412735152[/C][C]0.208123174529697[/C][C]0.104061587264849[/C][/ROW]
[ROW][C]112[/C][C]0.873368074825026[/C][C]0.253263850349947[/C][C]0.126631925174974[/C][/ROW]
[ROW][C]113[/C][C]0.849308361700658[/C][C]0.301383276598684[/C][C]0.150691638299342[/C][/ROW]
[ROW][C]114[/C][C]0.875066946040587[/C][C]0.249866107918827[/C][C]0.124933053959413[/C][/ROW]
[ROW][C]115[/C][C]0.846681121266356[/C][C]0.306637757467289[/C][C]0.153318878733644[/C][/ROW]
[ROW][C]116[/C][C]0.825610762870105[/C][C]0.34877847425979[/C][C]0.174389237129895[/C][/ROW]
[ROW][C]117[/C][C]0.869744162762021[/C][C]0.260511674475958[/C][C]0.130255837237979[/C][/ROW]
[ROW][C]118[/C][C]0.839101228601528[/C][C]0.321797542796943[/C][C]0.160898771398472[/C][/ROW]
[ROW][C]119[/C][C]0.840900646999783[/C][C]0.318198706000434[/C][C]0.159099353000217[/C][/ROW]
[ROW][C]120[/C][C]0.816404809262133[/C][C]0.367190381475734[/C][C]0.183595190737867[/C][/ROW]
[ROW][C]121[/C][C]0.778800807210888[/C][C]0.442398385578224[/C][C]0.221199192789112[/C][/ROW]
[ROW][C]122[/C][C]0.742685803615109[/C][C]0.514628392769782[/C][C]0.257314196384891[/C][/ROW]
[ROW][C]123[/C][C]0.702255877185251[/C][C]0.595488245629499[/C][C]0.297744122814749[/C][/ROW]
[ROW][C]124[/C][C]0.65645378963607[/C][C]0.687092420727859[/C][C]0.34354621036393[/C][/ROW]
[ROW][C]125[/C][C]0.60518143188611[/C][C]0.789637136227781[/C][C]0.39481856811389[/C][/ROW]
[ROW][C]126[/C][C]0.551412693790912[/C][C]0.897174612418176[/C][C]0.448587306209088[/C][/ROW]
[ROW][C]127[/C][C]0.583734325654173[/C][C]0.832531348691653[/C][C]0.416265674345827[/C][/ROW]
[ROW][C]128[/C][C]0.528772034311243[/C][C]0.942455931377514[/C][C]0.471227965688757[/C][/ROW]
[ROW][C]129[/C][C]0.608920812157725[/C][C]0.78215837568455[/C][C]0.391079187842275[/C][/ROW]
[ROW][C]130[/C][C]0.554717237920836[/C][C]0.890565524158327[/C][C]0.445282762079164[/C][/ROW]
[ROW][C]131[/C][C]0.502477594609171[/C][C]0.995044810781658[/C][C]0.497522405390829[/C][/ROW]
[ROW][C]132[/C][C]0.461136956746502[/C][C]0.922273913493003[/C][C]0.538863043253498[/C][/ROW]
[ROW][C]133[/C][C]0.445592108877518[/C][C]0.891184217755037[/C][C]0.554407891122482[/C][/ROW]
[ROW][C]134[/C][C]0.996681652831708[/C][C]0.00663669433658482[/C][C]0.00331834716829241[/C][/ROW]
[ROW][C]135[/C][C]0.995327238959981[/C][C]0.00934552208003857[/C][C]0.00467276104001928[/C][/ROW]
[ROW][C]136[/C][C]0.994616545223608[/C][C]0.0107669095527836[/C][C]0.00538345477639181[/C][/ROW]
[ROW][C]137[/C][C]0.991656105439134[/C][C]0.0166877891217319[/C][C]0.00834389456086595[/C][/ROW]
[ROW][C]138[/C][C]0.992984301674666[/C][C]0.014031396650668[/C][C]0.00701569832533402[/C][/ROW]
[ROW][C]139[/C][C]0.99922562908308[/C][C]0.00154874183384055[/C][C]0.000774370916920274[/C][/ROW]
[ROW][C]140[/C][C]0.998946540308633[/C][C]0.00210691938273388[/C][C]0.00105345969136694[/C][/ROW]
[ROW][C]141[/C][C]0.99999791396933[/C][C]4.17206133928116e-06[/C][C]2.08603066964058e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999999964785035[/C][C]7.04299304456358e-08[/C][C]3.52149652228179e-08[/C][/ROW]
[ROW][C]143[/C][C]0.99999986697884[/C][C]2.66042320893792e-07[/C][C]1.33021160446896e-07[/C][/ROW]
[ROW][C]144[/C][C]0.99999952882993[/C][C]9.42340140775738e-07[/C][C]4.71170070387869e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999999999269897[/C][C]1.46020546930838e-09[/C][C]7.3010273465419e-10[/C][/ROW]
[ROW][C]146[/C][C]0.999999999160036[/C][C]1.67992851108301e-09[/C][C]8.39964255541506e-10[/C][/ROW]
[ROW][C]147[/C][C]0.999999995485742[/C][C]9.02851576047743e-09[/C][C]4.51425788023872e-09[/C][/ROW]
[ROW][C]148[/C][C]0.999999985370649[/C][C]2.92587022796569e-08[/C][C]1.46293511398285e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999859936725[/C][C]2.80126549439366e-07[/C][C]1.40063274719683e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999999268340066[/C][C]1.46331986893346e-06[/C][C]7.31659934466728e-07[/C][/ROW]
[ROW][C]151[/C][C]0.999992474751524[/C][C]1.50504969514007e-05[/C][C]7.52524847570036e-06[/C][/ROW]
[ROW][C]152[/C][C]0.999934033794133[/C][C]0.000131932411734512[/C][C]6.59662058672558e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999408752291223[/C][C]0.00118249541755463[/C][C]0.000591247708777314[/C][/ROW]
[ROW][C]154[/C][C]0.995298912342493[/C][C]0.00940217531501407[/C][C]0.00470108765750704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&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
100.7447786582544650.510442683491070.255221341745535
110.8386840379191730.3226319241616540.161315962080827
120.7502859153373810.4994281693252390.249714084662619
130.6627264783093520.6745470433812960.337273521690648
140.5751877613431380.8496244773137240.424812238656862
150.6167120088512390.7665759822975220.383287991148761
160.5425071035310820.9149857929378370.457492896468918
170.5555624237245850.8888751525508310.444437576275415
180.4628287922045950.9256575844091890.537171207795405
190.5352754445870070.9294491108259860.464724555412993
200.4887861833322490.9775723666644970.511213816667751
210.4110439542174120.8220879084348240.588956045782588
220.9175642251585780.1648715496828430.0824357748414215
230.8893590783351690.2212818433296620.110640921664831
240.8710330067134650.257933986573070.128966993286535
250.932852235662630.1342955286747390.0671477643373697
260.9137809498807270.1724381002385470.0862190501192734
270.8848452382075350.230309523584930.115154761792465
280.853586047811740.292827904376520.14641395218826
290.8330489025963360.3339021948073270.166951097403664
300.8071713513865120.3856572972269760.192828648613488
310.7621964989451430.4756070021097150.237803501054857
320.7292755273186820.5414489453626350.270724472681318
330.6984453788958980.6031092422082050.301554621104102
340.6700997596098670.6598004807802670.329900240390133
350.7849351254652660.4301297490694670.215064874534734
360.8639414378414420.2721171243171150.136058562158558
370.9284903194636630.1430193610726730.0715096805363367
380.9124208878374650.175158224325070.087579112162535
390.8908594114348220.2182811771303570.109140588565178
400.8679624512632610.2640750974734790.132037548736739
410.8599517908669390.2800964182661230.140048209133062
420.8370820615135730.3258358769728540.162917938486427
430.8320447349469540.3359105301060930.167955265053047
440.8035149187518570.3929701624962860.196485081248143
450.8708052160820820.2583895678358370.129194783917918
460.9562344542829680.0875310914340640.043765545717032
470.9443304492303140.1113391015393720.0556695507696859
480.9310901003472570.1378197993054850.0689098996527426
490.9139922787421960.1720154425156070.0860077212578037
500.9037503099905340.1924993800189330.0962496900094664
510.8847606885830080.2304786228339840.115239311416992
520.878763960546650.2424720789067010.12123603945335
530.8728801868818790.2542396262362420.127119813118121
540.8454735782948630.3090528434102750.154526421705137
550.8180417368686480.3639165262627040.181958263131352
560.8380142609576750.323971478084650.161985739042325
570.8079642663960520.3840714672078960.192035733603948
580.7898161260527160.4203677478945680.210183873947284
590.7793552421337220.4412895157325550.220644757866278
600.7446779607112620.5106440785774760.255322039288738
610.7141169034240620.5717661931518750.285883096575938
620.690669432521240.6186611349575190.30933056747876
630.6496135013353690.7007729973292630.350386498664631
640.6373917337298210.7252165325403580.362608266270179
650.6436018597625970.7127962804748060.356398140237403
660.6003366032806420.7993267934387160.399663396719358
670.6287865691720330.7424268616559340.371213430827967
680.6163821649036230.7672356701927540.383617835096377
690.6019525890750790.7960948218498430.398047410924921
700.7829734242350160.4340531515299690.217026575764984
710.7476400786207390.5047198427585210.252359921379261
720.7090016871308360.5819966257383280.290998312869164
730.6772949977140130.6454100045719740.322705002285987
740.6347859260940490.7304281478119020.365214073905951
750.6771376298274770.6457247403450470.322862370172523
760.689068171866570.621863656266860.31093182813343
770.6605062059981970.6789875880036050.339493794001803
780.7166331575609720.5667336848780550.283366842439028
790.7419807494418130.5160385011163730.258019250558187
800.7040861171174990.5918277657650010.295913882882501
810.6631486578811570.6737026842376860.336851342118843
820.7305360555357750.5389278889284490.269463944464225
830.7023293714891780.5953412570216430.297670628510822
840.668369970616550.66326005876690.33163002938345
850.6382469656180650.7235060687638690.361753034381935
860.6164339354673830.7671321290652340.383566064532617
870.5870146935147230.8259706129705530.412985306485277
880.6118834976701560.7762330046596890.388116502329844
890.5939961008666490.8120077982667020.406003899133351
900.8493047872972420.3013904254055150.150695212702758
910.8316937718228910.3366124563542180.168306228177109
920.8673553658233760.2652892683532490.132644634176624
930.8528469470873690.2943061058252620.147153052912631
940.8396531145261270.3206937709477450.160346885473873
950.8725591610203330.2548816779593340.127440838979667
960.8481094695204280.3037810609591440.151890530479572
970.8205865386903890.3588269226192230.179413461309611
980.7961710017887620.4076579964224760.203828998211238
990.7695258475077310.4609483049845370.230474152492269
1000.7795790618326060.4408418763347880.220420938167394
1010.7519382145764730.4961235708470540.248061785423527
1020.713249048592960.573501902814080.28675095140704
1030.7254292048666360.5491415902667270.274570795133364
1040.688211717198440.623576565603120.31178828280156
1050.65111216639040.6977756672191990.3488878336096
1060.6904893853458840.6190212293082330.309510614654116
1070.656263645999590.6874727080008210.34373635400041
1080.6161378152725880.7677243694548230.383862184727412
1090.6064796591677860.7870406816644270.393520340832214
1100.8333664697609250.3332670604781510.166633530239075
1110.8959384127351520.2081231745296970.104061587264849
1120.8733680748250260.2532638503499470.126631925174974
1130.8493083617006580.3013832765986840.150691638299342
1140.8750669460405870.2498661079188270.124933053959413
1150.8466811212663560.3066377574672890.153318878733644
1160.8256107628701050.348778474259790.174389237129895
1170.8697441627620210.2605116744759580.130255837237979
1180.8391012286015280.3217975427969430.160898771398472
1190.8409006469997830.3181987060004340.159099353000217
1200.8164048092621330.3671903814757340.183595190737867
1210.7788008072108880.4423983855782240.221199192789112
1220.7426858036151090.5146283927697820.257314196384891
1230.7022558771852510.5954882456294990.297744122814749
1240.656453789636070.6870924207278590.34354621036393
1250.605181431886110.7896371362277810.39481856811389
1260.5514126937909120.8971746124181760.448587306209088
1270.5837343256541730.8325313486916530.416265674345827
1280.5287720343112430.9424559313775140.471227965688757
1290.6089208121577250.782158375684550.391079187842275
1300.5547172379208360.8905655241583270.445282762079164
1310.5024775946091710.9950448107816580.497522405390829
1320.4611369567465020.9222739134930030.538863043253498
1330.4455921088775180.8911842177550370.554407891122482
1340.9966816528317080.006636694336584820.00331834716829241
1350.9953272389599810.009345522080038570.00467276104001928
1360.9946165452236080.01076690955278360.00538345477639181
1370.9916561054391340.01668778912173190.00834389456086595
1380.9929843016746660.0140313966506680.00701569832533402
1390.999225629083080.001548741833840550.000774370916920274
1400.9989465403086330.002106919382733880.00105345969136694
1410.999997913969334.17206133928116e-062.08603066964058e-06
1420.9999999647850357.04299304456358e-083.52149652228179e-08
1430.999999866978842.66042320893792e-071.33021160446896e-07
1440.999999528829939.42340140775738e-074.71170070387869e-07
1450.9999999992698971.46020546930838e-097.3010273465419e-10
1460.9999999991600361.67992851108301e-098.39964255541506e-10
1470.9999999954857429.02851576047743e-094.51425788023872e-09
1480.9999999853706492.92587022796569e-081.46293511398285e-08
1490.9999998599367252.80126549439366e-071.40063274719683e-07
1500.9999992683400661.46331986893346e-067.31659934466728e-07
1510.9999924747515241.50504969514007e-057.52524847570036e-06
1520.9999340337941330.0001319324117345126.59662058672558e-05
1530.9994087522912230.001182495417554630.000591247708777314
1540.9952989123424930.009402175315014070.00470108765750704






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.124137931034483NOK
5% type I error level210.144827586206897NOK
10% type I error level220.151724137931034NOK

\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 & 18 & 0.124137931034483 & NOK \tabularnewline
5% type I error level & 21 & 0.144827586206897 & NOK \tabularnewline
10% type I error level & 22 & 0.151724137931034 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152833&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]18[/C][C]0.124137931034483[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.144827586206897[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.151724137931034[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152833&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152833&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.124137931034483NOK
5% type I error level210.144827586206897NOK
10% type I error level220.151724137931034NOK


Figures (Output of Computation)

Figure 1
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Input Parameters & R Code

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