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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 12:23:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t13219826348e0w8pyibwyho3u.htm/, Retrieved Fri, 29 Mar 2024 14:46:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146331, Retrieved Fri, 29 Mar 2024 14:46:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [Multiple Regressi...] [2011-11-22 17:23:37] [e889f2ef2eeddd5259af4a52678400a6] [Current]
-           [Multiple Regression] [WS7: Model 3] [2012-11-19 10:35:24] [000f7cd337b3382b4028da000456dfed]
- R           [Multiple Regression] [model ] [2012-11-20 20:49:41] [c5e00e3d2459b4cd6380f7873395bbc5]
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Dataseries X:
170588	46	65	26	99
86621	48	54	20	77
113337	37	58	24	90
144530	72	77	25	96
81530	31	41	15	41
35523	17	0	16	64
305115	78	111	20	76
32750	16	1	18	67
115885	37	37	19	72
130539	24	60	20	75
156990	63	64	26	97
128274	74	71	37	139
102350	43	38	23	76
192887	42	76	36	123
129796	55	61	28	106
245478	120	125	35	133
169569	42	85	20	76
185279	100	69	22	83
109598	36	77	19	72
155012	49	100	28	107
154730	46	78	27	99
280379	56	76	25	88
90938	17	40	15	56
101324	31	81	26	104
139502	77	102	27	103
145120	90	70	24	90
161729	80	75	21	78
160905	54	93	27	103
106888	34	42	21	81
187289	38	95	30	114
181853	53	87	25	95
129340	47	44	33	118
196862	63	87	30	113
62731	25	28	20	75
234863	55	87	27	103
167255	37	71	25	93
264528	83	70	30	114
121976	49	50	20	76
80964	26	30	8	27
209631	107	87	24	92
213310	55	78	22	84
115911	40	48	25	92
131337	46	52	20	72
81106	31	31	21	79
93125	49	30	21	57
305708	95	70	26	99
78800	42	20	26	82
157566	54	84	30	113
213487	68	81	26	97
131108	39	79	30	110
128734	53	72	18	78
24188	24	8	4	12
257662	200	67	31	114
65029	17	21	18	67
98066	58	30	14	52
173587	27	70	20	76
179571	58	87	35	134
197067	113	87	24	92
208823	74	116	26	93
134088	50	54	20	75
245107	86	96	31	118
201409	76	93	21	77
141760	61	49	31	122
170635	59	49	26	99
129100	38	38	19	72
108811	34	64	15	58
113450	85	64	19	73
142286	100	66	28	103
143937	49	98	20	76
89882	35	99	17	65
118807	33	56	25	95
69471	28	22	20	76
126630	44	51	25	95
145908	37	61	24	92
96981	33	94	22	84
189066	44	98	25	95
191467	55	76	20	76
106193	58	57	23	87
89318	36	75	22	84
120362	42	48	25	95
98791	30	48	18	69
274949	66	109	30	115
132798	53	27	22	83
128075	57	83	25	47
80953	25	49	8	28
109237	39	24	21	79
96634	35	46	22	83
226183	114	44	24	92
167226	53	49	30	98
117805	70	108	27	103
121630	48	42	21	77
152193	49	110	25	95
112004	42	28	21	78
169613	51	79	24	92
176577	51	49	20	76
130533	27	64	20	76
142339	29	75	20	67
189764	54	118	24	92
201603	92	95	40	151
243180	72	106	22	83
155931	63	73	31	118
182557	40	108	26	98
106351	108	30	20	76
43287	14	13	19	71
127394	44	69	15	57
127930	91	75	21	79
135306	29	80	22	83
175663	63	106	24	92
74112	32	28	19	75
89059	65	70	20	79
166142	41	51	23	88
141933	55	90	27	99
22938	10	12	1	0
125927	53	87	24	91
61857	25	23	11	32
91185	31	57	27	101
236316	64	85	22	84
21054	16	4	0	0
169093	35	56	17	60
31414	19	18	8	25
183059	74	86	23	86
137544	35	40	31	115
75032	45	16	23	88
71908	28	18	17	59
38214	34	16	8	27
90961	25	42	22	83
193662	48	78	33	126
127185	38	30	33	125
242153	49	104	31	119
201748	65	121	33	127
254599	71	111	35	133
139144	23	57	21	79
76470	29	28	20	76
183260	190	56	24	92
280039	113	82	29	109
50999	15	2	20	76
253056	85	91	27	100
98466	48	41	24	87
168059	33	84	26	97
128768	50	55	26	95
75746	72	3	12	48
244909	79	54	21	80
152366	54	93	24	91
173260	63	41	21	79
197033	68	94	30	114
67507	39	101	32	120
139409	49	70	24	89
185366	67	114	29	111
0	0	0	0	0
14688	10	4	0	0
98	1	0	0	0
455	2	0	0	0
0	0	0	0	0
0	0	0	0	0
128873	57	42	20	74
185288	71	97	27	107
0	0	0	0	0
203	4	0	0	0
7199	5	7	0	0
46660	20	12	5	15
17547	5	0	1	4
73567	27	37	23	82
969	2	0	0	0
105477	33	39	16	54




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC_in_seconds[t] = + 6297.40017501913 + 795.474404186852Number_of_Logins[t] + 922.703453004308Total_Number_of_Blogged_Computations[t] + 886.938059635362Total_Number_of_Reviewed_Compendiums[t] + 204.456317980793Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -22.6485546279842t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_spent_in_RFC_in_seconds[t] =  +  6297.40017501913 +  795.474404186852Number_of_Logins[t] +  922.703453004308Total_Number_of_Blogged_Computations[t] +  886.938059635362Total_Number_of_Reviewed_Compendiums[t] +  204.456317980793Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -22.6485546279842t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC_in_seconds[t] =  +  6297.40017501913 +  795.474404186852Number_of_Logins[t] +  922.703453004308Total_Number_of_Blogged_Computations[t] +  886.938059635362Total_Number_of_Reviewed_Compendiums[t] +  204.456317980793Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -22.6485546279842t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Total_Time_spent_in_RFC_in_seconds[t] = + 6297.40017501913 + 795.474404186852Number_of_Logins[t] + 922.703453004308Total_Number_of_Blogged_Computations[t] + 886.938059635362Total_Number_of_Reviewed_Compendiums[t] + 204.456317980793Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[t] -22.6485546279842t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6297.4001750191310688.928430.58920.5566010.2783
Number_of_Logins795.474404186852113.906796.983600
Total_Number_of_Blogged_Computations922.703453004308122.8811077.508900
Total_Number_of_Reviewed_Compendiums886.9380596353622033.1072840.43620.6632530.331626
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews204.456317980793535.3759130.38190.7030540.351527
t-22.648554627984261.937078-0.36570.71510.35755

\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) & 6297.40017501913 & 10688.92843 & 0.5892 & 0.556601 & 0.2783 \tabularnewline
Number_of_Logins & 795.474404186852 & 113.90679 & 6.9836 & 0 & 0 \tabularnewline
Total_Number_of_Blogged_Computations & 922.703453004308 & 122.881107 & 7.5089 & 0 & 0 \tabularnewline
Total_Number_of_Reviewed_Compendiums & 886.938059635362 & 2033.107284 & 0.4362 & 0.663253 & 0.331626 \tabularnewline
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews & 204.456317980793 & 535.375913 & 0.3819 & 0.703054 & 0.351527 \tabularnewline
t & -22.6485546279842 & 61.937078 & -0.3657 & 0.7151 & 0.35755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&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]6297.40017501913[/C][C]10688.92843[/C][C]0.5892[/C][C]0.556601[/C][C]0.2783[/C][/ROW]
[ROW][C]Number_of_Logins[/C][C]795.474404186852[/C][C]113.90679[/C][C]6.9836[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Blogged_Computations[/C][C]922.703453004308[/C][C]122.881107[/C][C]7.5089[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Total_Number_of_Reviewed_Compendiums[/C][C]886.938059635362[/C][C]2033.107284[/C][C]0.4362[/C][C]0.663253[/C][C]0.331626[/C][/ROW]
[ROW][C]Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews[/C][C]204.456317980793[/C][C]535.375913[/C][C]0.3819[/C][C]0.703054[/C][C]0.351527[/C][/ROW]
[ROW][C]t[/C][C]-22.6485546279842[/C][C]61.937078[/C][C]-0.3657[/C][C]0.7151[/C][C]0.35755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6297.4001750191310688.928430.58920.5566010.2783
Number_of_Logins795.474404186852113.906796.983600
Total_Number_of_Blogged_Computations922.703453004308122.8811077.508900
Total_Number_of_Reviewed_Compendiums886.9380596353622033.1072840.43620.6632530.331626
Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews204.456317980793535.3759130.38190.7030540.351527
t-22.648554627984261.937078-0.36570.71510.35755







Multiple Linear Regression - Regression Statistics
Multiple R0.861020118507569
R-squared0.741355644474789
Adjusted R-squared0.73317069651513
F-TEST (value)90.5754866284713
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35309.2241360739
Sum Squared Residuals196985126836.458

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.861020118507569 \tabularnewline
R-squared & 0.741355644474789 \tabularnewline
Adjusted R-squared & 0.73317069651513 \tabularnewline
F-TEST (value) & 90.5754866284713 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 35309.2241360739 \tabularnewline
Sum Squared Residuals & 196985126836.458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.861020118507569[/C][/ROW]
[ROW][C]R-squared[/C][C]0.741355644474789[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.73317069651513[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]90.5754866284713[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/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]35309.2241360739[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]196985126836.458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.861020118507569
R-squared0.741355644474789
Adjusted R-squared0.73317069651513
F-TEST (value)90.5754866284713
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation35309.2241360739
Sum Squared Residuals196985126836.458







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1170588146143.86368888424444.1363111157
286621127742.758606193-41121.7586061931
3113337128866.389789819-15529.3897898186
4144530176330.386956332-31800.3869563325
58153090361.4854365912-8831.48543659122
63552346960.7870233643-11437.7870233643
7305115203883.388461923101231.611538077
83275049429.9640361389-16679.9640361389
9115885101238.82192712914646.1780728708
10130539113597.49255074916941.507449251
11156990158108.826924815-1118.82692481506
12128274191638.804998455-63364.8049984549
13102350111209.355097207-8859.3550972073
14192887166593.60507291326293.3949270869
15129796152500.310094893-22704.3100948932
16245478274965.405807615-29487.4058076153
17169569151029.53458680518539.4654131951
18185279185586.216572082-307.216572081706
19109598137125.000096835-27527.0000968348
20155012183804.111881781-28792.111881781
21154730158572.975545016-3842.97554501599
22280379160636.768509188119742.231490812
239093880961.31111137929976.68888862084
24101324149476.367707611-48152.3677076108
25139502206104.796000323-66602.796000323
26145120181578.05789133-36458.0578913299
27161729173099.892565179-11370.8925651793
28160905179436.607963103-18531.6079631027
29106888106626.927868128261.072131871643
30187289173418.96096956113870.0390304395
31181853169627.44051388912225.559486111
32129340136953.696845596-7613.69684559579
33196862185651.79146833311210.2085316674
346273184322.8911477263-21591.8911477263
35234863174537.32176686860325.6782331322
36167255141614.43938972925640.5606102711
37264528185989.18295046578538.8170495347
38121976123827.614913774-1851.61491377442
398096466393.369706079514570.6302939205
40209631210878.914334749-1247.91433474943
41213310157777.73902224955532.2609777507
42115911122438.335537442-6527.3355374417
43131337122355.5305621598981.46943784062
448110693342.1257171391-12236.1257171391
4593125102217.273989293-9092.27398929267
46305708188716.441800802116991.558199198
477880096922.7197683823-18122.7197683823
48157566175384.683152218-17818.6831522183
49213487176911.51257095936575.4874290408
50131108158180.383761196-27072.3837611956
51128734145649.593803144-16915.5938031437
522418837593.9167131939-13405.9167131939
53257662276816.139066902-19154.1390669017
546502967637.6739875246-2608.67398752461
5598066101919.210073343-3853.210073343
56173587124373.57309844649213.4269015539
57179571189859.1271121-10288.1271121002
58197067215244.086776567-18177.0867765668
59208823212934.669033028-4111.66903302804
60134088127611.1786101826476.82138981794
61245107213527.09396162531579.9060383752
62201409185529.50137254915879.4986274508
63141760151045.699728418-9285.6997284182
64170635140294.91675368130340.0832463185
65129100101688.68072515327411.3192748468
66108811116064.283641617-7253.2836416173
67113450163225.426708772-49775.4267087721
68142286191096.433199098-48810.4331990976
69143937167415.275464514-23478.2754645137
7089882152268.855027579-62386.8550275793
71118807124208.203201899-5401.20320189903
726947180516.9048843784-11045.9048843784
73126630128299.607273677-1669.6072736769
74145908130435.36540620615472.6345937937
7596981154270.506520856-57289.506520856
76189066171598.72390099517467.2760990046
77191467151707.45748651639759.5425134838
78106193141449.700214062-35256.7002140617
7989318139034.969907823-49716.9699078228
80120362123782.008223894-3420.00822389438
8198791102689.236134076-3898.23613407602
82274949207636.82410617867312.1758938217
83132798107973.218498324824.7815017003
84128075158104.247660258-30029.2476602579
858095382291.883714068-1338.88371406799
8610923792295.757485228416941.2425147716
8796634111095.450611506-14461.4506115063
88226183175683.85606272950499.1439372711
89167226138299.15238342128926.8476165786
90117805204600.539838222-86795.5398382219
91121630115541.5338678866088.46613211391
92152193186286.160483934-34093.1604839336
9311200498009.998309429513994.0016905705
94169613157727.6981263411885.3018736599
95176577123204.89265534953372.1073446513
96130533117931.41019530112601.5898046991
97142339127809.34157026714529.6584297331
98189764196008.961787557-6244.9617875567
99201603231246.092887963-29643.0928879626
100243180195595.77953651547584.2204634855
101155931173103.061061109-17172.0610611087
102182557178555.3054075414001.69459245876
103106351150834.379649894-44483.3796498936
1044328758441.9587510889-15154.9587510889
105127394127544.795000035-150.795000035186
106127930180265.331513605-52335.3315136047
107135306137241.550495972-1935.55049597197
108175663191869.304442907-16206.3044429068
1097411287305.6323203001-13193.6323203001
11089059153991.947461578-64932.9474615778
111166142121847.46864011744294.5313598833
112141933174743.668147603-32810.6681476028
1132293823652.2370396125-714.237039612506
114125927166042.847148208-40115.8471482078
1156185761100.7767479457756.223252054295
11691185125521.386915426-34336.3869154258
117236316169674.64267923466641.3573207657
1182105420043.27500792391010.72499207613
119169093110460.54578171958632.4542182812
1203141447509.1618798915-16095.1618798915
121183059179757.3466511923301.65334880786
122137544119291.57519360518252.4248063953
1237503292462.9627461775-17430.9627461775
1247190869511.79464712652396.20535287355
1253821457891.5408995073-19677.5408995073
1269096198566.5991271292-7605.59912712917
127193662168605.12650611725056.873493883
128127185116133.51184743311051.4881525671
129242153190140.52323402452012.4767659764
130201748221940.950510576-20192.9505105755
131254599220464.72787818134134.2721218189
132139144108975.54745449430168.4525455064
1337647085467.0381742841-8997.0381742841
134183260246170.518704094-62910.5187040941
135280039216797.07850904163241.9214909592
1365099950272.1610736722726.838926327795
137253056199168.84617849453887.1538215061
13898466118259.725706081-19793.7257060806
139168059149799.64886691418259.3511330863
140128768136132.752410376-7364.75241037566
1417574683603.3814116421-7857.38141164208
142244909150731.97450164594177.0254983546
143152366171717.734186209-19351.7341862089
144173260125759.48571836347500.514281637
145197033193755.9058599443277.09414005639
14667507180124.037782083-112617.037782083
147139409146018.675891702-6609.67589170206
148185366209846.247838381-24480.2478383812
14902922.76553544949-2922.76553544949
1501468814545.6748347073142.325165292729
151983672.94283038038-3574.94283038038
1524554445.76867993924-3990.76867993924
15302832.17131693756-2832.17131693756
15402809.52276230957-2809.52276230957
155128873119750.9889957999122.01100420096
156185288194569.296925838-9281.29692583762
15702741.57709842562-2741.57709842562
1582035900.82616054505-5697.82616054505
159719913132.5761811341-5933.57618113407
1604666037157.09602221919502.90397778088
161175478333.118232406489213.88176759352
1627356795411.1644455285-21844.1644455285
1639694196.63457903142-3227.63457903142
16410547790050.777346492515426.2226535075

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 170588 & 146143.863688884 & 24444.1363111157 \tabularnewline
2 & 86621 & 127742.758606193 & -41121.7586061931 \tabularnewline
3 & 113337 & 128866.389789819 & -15529.3897898186 \tabularnewline
4 & 144530 & 176330.386956332 & -31800.3869563325 \tabularnewline
5 & 81530 & 90361.4854365912 & -8831.48543659122 \tabularnewline
6 & 35523 & 46960.7870233643 & -11437.7870233643 \tabularnewline
7 & 305115 & 203883.388461923 & 101231.611538077 \tabularnewline
8 & 32750 & 49429.9640361389 & -16679.9640361389 \tabularnewline
9 & 115885 & 101238.821927129 & 14646.1780728708 \tabularnewline
10 & 130539 & 113597.492550749 & 16941.507449251 \tabularnewline
11 & 156990 & 158108.826924815 & -1118.82692481506 \tabularnewline
12 & 128274 & 191638.804998455 & -63364.8049984549 \tabularnewline
13 & 102350 & 111209.355097207 & -8859.3550972073 \tabularnewline
14 & 192887 & 166593.605072913 & 26293.3949270869 \tabularnewline
15 & 129796 & 152500.310094893 & -22704.3100948932 \tabularnewline
16 & 245478 & 274965.405807615 & -29487.4058076153 \tabularnewline
17 & 169569 & 151029.534586805 & 18539.4654131951 \tabularnewline
18 & 185279 & 185586.216572082 & -307.216572081706 \tabularnewline
19 & 109598 & 137125.000096835 & -27527.0000968348 \tabularnewline
20 & 155012 & 183804.111881781 & -28792.111881781 \tabularnewline
21 & 154730 & 158572.975545016 & -3842.97554501599 \tabularnewline
22 & 280379 & 160636.768509188 & 119742.231490812 \tabularnewline
23 & 90938 & 80961.3111113792 & 9976.68888862084 \tabularnewline
24 & 101324 & 149476.367707611 & -48152.3677076108 \tabularnewline
25 & 139502 & 206104.796000323 & -66602.796000323 \tabularnewline
26 & 145120 & 181578.05789133 & -36458.0578913299 \tabularnewline
27 & 161729 & 173099.892565179 & -11370.8925651793 \tabularnewline
28 & 160905 & 179436.607963103 & -18531.6079631027 \tabularnewline
29 & 106888 & 106626.927868128 & 261.072131871643 \tabularnewline
30 & 187289 & 173418.960969561 & 13870.0390304395 \tabularnewline
31 & 181853 & 169627.440513889 & 12225.559486111 \tabularnewline
32 & 129340 & 136953.696845596 & -7613.69684559579 \tabularnewline
33 & 196862 & 185651.791468333 & 11210.2085316674 \tabularnewline
34 & 62731 & 84322.8911477263 & -21591.8911477263 \tabularnewline
35 & 234863 & 174537.321766868 & 60325.6782331322 \tabularnewline
36 & 167255 & 141614.439389729 & 25640.5606102711 \tabularnewline
37 & 264528 & 185989.182950465 & 78538.8170495347 \tabularnewline
38 & 121976 & 123827.614913774 & -1851.61491377442 \tabularnewline
39 & 80964 & 66393.3697060795 & 14570.6302939205 \tabularnewline
40 & 209631 & 210878.914334749 & -1247.91433474943 \tabularnewline
41 & 213310 & 157777.739022249 & 55532.2609777507 \tabularnewline
42 & 115911 & 122438.335537442 & -6527.3355374417 \tabularnewline
43 & 131337 & 122355.530562159 & 8981.46943784062 \tabularnewline
44 & 81106 & 93342.1257171391 & -12236.1257171391 \tabularnewline
45 & 93125 & 102217.273989293 & -9092.27398929267 \tabularnewline
46 & 305708 & 188716.441800802 & 116991.558199198 \tabularnewline
47 & 78800 & 96922.7197683823 & -18122.7197683823 \tabularnewline
48 & 157566 & 175384.683152218 & -17818.6831522183 \tabularnewline
49 & 213487 & 176911.512570959 & 36575.4874290408 \tabularnewline
50 & 131108 & 158180.383761196 & -27072.3837611956 \tabularnewline
51 & 128734 & 145649.593803144 & -16915.5938031437 \tabularnewline
52 & 24188 & 37593.9167131939 & -13405.9167131939 \tabularnewline
53 & 257662 & 276816.139066902 & -19154.1390669017 \tabularnewline
54 & 65029 & 67637.6739875246 & -2608.67398752461 \tabularnewline
55 & 98066 & 101919.210073343 & -3853.210073343 \tabularnewline
56 & 173587 & 124373.573098446 & 49213.4269015539 \tabularnewline
57 & 179571 & 189859.1271121 & -10288.1271121002 \tabularnewline
58 & 197067 & 215244.086776567 & -18177.0867765668 \tabularnewline
59 & 208823 & 212934.669033028 & -4111.66903302804 \tabularnewline
60 & 134088 & 127611.178610182 & 6476.82138981794 \tabularnewline
61 & 245107 & 213527.093961625 & 31579.9060383752 \tabularnewline
62 & 201409 & 185529.501372549 & 15879.4986274508 \tabularnewline
63 & 141760 & 151045.699728418 & -9285.6997284182 \tabularnewline
64 & 170635 & 140294.916753681 & 30340.0832463185 \tabularnewline
65 & 129100 & 101688.680725153 & 27411.3192748468 \tabularnewline
66 & 108811 & 116064.283641617 & -7253.2836416173 \tabularnewline
67 & 113450 & 163225.426708772 & -49775.4267087721 \tabularnewline
68 & 142286 & 191096.433199098 & -48810.4331990976 \tabularnewline
69 & 143937 & 167415.275464514 & -23478.2754645137 \tabularnewline
70 & 89882 & 152268.855027579 & -62386.8550275793 \tabularnewline
71 & 118807 & 124208.203201899 & -5401.20320189903 \tabularnewline
72 & 69471 & 80516.9048843784 & -11045.9048843784 \tabularnewline
73 & 126630 & 128299.607273677 & -1669.6072736769 \tabularnewline
74 & 145908 & 130435.365406206 & 15472.6345937937 \tabularnewline
75 & 96981 & 154270.506520856 & -57289.506520856 \tabularnewline
76 & 189066 & 171598.723900995 & 17467.2760990046 \tabularnewline
77 & 191467 & 151707.457486516 & 39759.5425134838 \tabularnewline
78 & 106193 & 141449.700214062 & -35256.7002140617 \tabularnewline
79 & 89318 & 139034.969907823 & -49716.9699078228 \tabularnewline
80 & 120362 & 123782.008223894 & -3420.00822389438 \tabularnewline
81 & 98791 & 102689.236134076 & -3898.23613407602 \tabularnewline
82 & 274949 & 207636.824106178 & 67312.1758938217 \tabularnewline
83 & 132798 & 107973.2184983 & 24824.7815017003 \tabularnewline
84 & 128075 & 158104.247660258 & -30029.2476602579 \tabularnewline
85 & 80953 & 82291.883714068 & -1338.88371406799 \tabularnewline
86 & 109237 & 92295.7574852284 & 16941.2425147716 \tabularnewline
87 & 96634 & 111095.450611506 & -14461.4506115063 \tabularnewline
88 & 226183 & 175683.856062729 & 50499.1439372711 \tabularnewline
89 & 167226 & 138299.152383421 & 28926.8476165786 \tabularnewline
90 & 117805 & 204600.539838222 & -86795.5398382219 \tabularnewline
91 & 121630 & 115541.533867886 & 6088.46613211391 \tabularnewline
92 & 152193 & 186286.160483934 & -34093.1604839336 \tabularnewline
93 & 112004 & 98009.9983094295 & 13994.0016905705 \tabularnewline
94 & 169613 & 157727.69812634 & 11885.3018736599 \tabularnewline
95 & 176577 & 123204.892655349 & 53372.1073446513 \tabularnewline
96 & 130533 & 117931.410195301 & 12601.5898046991 \tabularnewline
97 & 142339 & 127809.341570267 & 14529.6584297331 \tabularnewline
98 & 189764 & 196008.961787557 & -6244.9617875567 \tabularnewline
99 & 201603 & 231246.092887963 & -29643.0928879626 \tabularnewline
100 & 243180 & 195595.779536515 & 47584.2204634855 \tabularnewline
101 & 155931 & 173103.061061109 & -17172.0610611087 \tabularnewline
102 & 182557 & 178555.305407541 & 4001.69459245876 \tabularnewline
103 & 106351 & 150834.379649894 & -44483.3796498936 \tabularnewline
104 & 43287 & 58441.9587510889 & -15154.9587510889 \tabularnewline
105 & 127394 & 127544.795000035 & -150.795000035186 \tabularnewline
106 & 127930 & 180265.331513605 & -52335.3315136047 \tabularnewline
107 & 135306 & 137241.550495972 & -1935.55049597197 \tabularnewline
108 & 175663 & 191869.304442907 & -16206.3044429068 \tabularnewline
109 & 74112 & 87305.6323203001 & -13193.6323203001 \tabularnewline
110 & 89059 & 153991.947461578 & -64932.9474615778 \tabularnewline
111 & 166142 & 121847.468640117 & 44294.5313598833 \tabularnewline
112 & 141933 & 174743.668147603 & -32810.6681476028 \tabularnewline
113 & 22938 & 23652.2370396125 & -714.237039612506 \tabularnewline
114 & 125927 & 166042.847148208 & -40115.8471482078 \tabularnewline
115 & 61857 & 61100.7767479457 & 756.223252054295 \tabularnewline
116 & 91185 & 125521.386915426 & -34336.3869154258 \tabularnewline
117 & 236316 & 169674.642679234 & 66641.3573207657 \tabularnewline
118 & 21054 & 20043.2750079239 & 1010.72499207613 \tabularnewline
119 & 169093 & 110460.545781719 & 58632.4542182812 \tabularnewline
120 & 31414 & 47509.1618798915 & -16095.1618798915 \tabularnewline
121 & 183059 & 179757.346651192 & 3301.65334880786 \tabularnewline
122 & 137544 & 119291.575193605 & 18252.4248063953 \tabularnewline
123 & 75032 & 92462.9627461775 & -17430.9627461775 \tabularnewline
124 & 71908 & 69511.7946471265 & 2396.20535287355 \tabularnewline
125 & 38214 & 57891.5408995073 & -19677.5408995073 \tabularnewline
126 & 90961 & 98566.5991271292 & -7605.59912712917 \tabularnewline
127 & 193662 & 168605.126506117 & 25056.873493883 \tabularnewline
128 & 127185 & 116133.511847433 & 11051.4881525671 \tabularnewline
129 & 242153 & 190140.523234024 & 52012.4767659764 \tabularnewline
130 & 201748 & 221940.950510576 & -20192.9505105755 \tabularnewline
131 & 254599 & 220464.727878181 & 34134.2721218189 \tabularnewline
132 & 139144 & 108975.547454494 & 30168.4525455064 \tabularnewline
133 & 76470 & 85467.0381742841 & -8997.0381742841 \tabularnewline
134 & 183260 & 246170.518704094 & -62910.5187040941 \tabularnewline
135 & 280039 & 216797.078509041 & 63241.9214909592 \tabularnewline
136 & 50999 & 50272.1610736722 & 726.838926327795 \tabularnewline
137 & 253056 & 199168.846178494 & 53887.1538215061 \tabularnewline
138 & 98466 & 118259.725706081 & -19793.7257060806 \tabularnewline
139 & 168059 & 149799.648866914 & 18259.3511330863 \tabularnewline
140 & 128768 & 136132.752410376 & -7364.75241037566 \tabularnewline
141 & 75746 & 83603.3814116421 & -7857.38141164208 \tabularnewline
142 & 244909 & 150731.974501645 & 94177.0254983546 \tabularnewline
143 & 152366 & 171717.734186209 & -19351.7341862089 \tabularnewline
144 & 173260 & 125759.485718363 & 47500.514281637 \tabularnewline
145 & 197033 & 193755.905859944 & 3277.09414005639 \tabularnewline
146 & 67507 & 180124.037782083 & -112617.037782083 \tabularnewline
147 & 139409 & 146018.675891702 & -6609.67589170206 \tabularnewline
148 & 185366 & 209846.247838381 & -24480.2478383812 \tabularnewline
149 & 0 & 2922.76553544949 & -2922.76553544949 \tabularnewline
150 & 14688 & 14545.6748347073 & 142.325165292729 \tabularnewline
151 & 98 & 3672.94283038038 & -3574.94283038038 \tabularnewline
152 & 455 & 4445.76867993924 & -3990.76867993924 \tabularnewline
153 & 0 & 2832.17131693756 & -2832.17131693756 \tabularnewline
154 & 0 & 2809.52276230957 & -2809.52276230957 \tabularnewline
155 & 128873 & 119750.988995799 & 9122.01100420096 \tabularnewline
156 & 185288 & 194569.296925838 & -9281.29692583762 \tabularnewline
157 & 0 & 2741.57709842562 & -2741.57709842562 \tabularnewline
158 & 203 & 5900.82616054505 & -5697.82616054505 \tabularnewline
159 & 7199 & 13132.5761811341 & -5933.57618113407 \tabularnewline
160 & 46660 & 37157.0960222191 & 9502.90397778088 \tabularnewline
161 & 17547 & 8333.11823240648 & 9213.88176759352 \tabularnewline
162 & 73567 & 95411.1644455285 & -21844.1644455285 \tabularnewline
163 & 969 & 4196.63457903142 & -3227.63457903142 \tabularnewline
164 & 105477 & 90050.7773464925 & 15426.2226535075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&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]170588[/C][C]146143.863688884[/C][C]24444.1363111157[/C][/ROW]
[ROW][C]2[/C][C]86621[/C][C]127742.758606193[/C][C]-41121.7586061931[/C][/ROW]
[ROW][C]3[/C][C]113337[/C][C]128866.389789819[/C][C]-15529.3897898186[/C][/ROW]
[ROW][C]4[/C][C]144530[/C][C]176330.386956332[/C][C]-31800.3869563325[/C][/ROW]
[ROW][C]5[/C][C]81530[/C][C]90361.4854365912[/C][C]-8831.48543659122[/C][/ROW]
[ROW][C]6[/C][C]35523[/C][C]46960.7870233643[/C][C]-11437.7870233643[/C][/ROW]
[ROW][C]7[/C][C]305115[/C][C]203883.388461923[/C][C]101231.611538077[/C][/ROW]
[ROW][C]8[/C][C]32750[/C][C]49429.9640361389[/C][C]-16679.9640361389[/C][/ROW]
[ROW][C]9[/C][C]115885[/C][C]101238.821927129[/C][C]14646.1780728708[/C][/ROW]
[ROW][C]10[/C][C]130539[/C][C]113597.492550749[/C][C]16941.507449251[/C][/ROW]
[ROW][C]11[/C][C]156990[/C][C]158108.826924815[/C][C]-1118.82692481506[/C][/ROW]
[ROW][C]12[/C][C]128274[/C][C]191638.804998455[/C][C]-63364.8049984549[/C][/ROW]
[ROW][C]13[/C][C]102350[/C][C]111209.355097207[/C][C]-8859.3550972073[/C][/ROW]
[ROW][C]14[/C][C]192887[/C][C]166593.605072913[/C][C]26293.3949270869[/C][/ROW]
[ROW][C]15[/C][C]129796[/C][C]152500.310094893[/C][C]-22704.3100948932[/C][/ROW]
[ROW][C]16[/C][C]245478[/C][C]274965.405807615[/C][C]-29487.4058076153[/C][/ROW]
[ROW][C]17[/C][C]169569[/C][C]151029.534586805[/C][C]18539.4654131951[/C][/ROW]
[ROW][C]18[/C][C]185279[/C][C]185586.216572082[/C][C]-307.216572081706[/C][/ROW]
[ROW][C]19[/C][C]109598[/C][C]137125.000096835[/C][C]-27527.0000968348[/C][/ROW]
[ROW][C]20[/C][C]155012[/C][C]183804.111881781[/C][C]-28792.111881781[/C][/ROW]
[ROW][C]21[/C][C]154730[/C][C]158572.975545016[/C][C]-3842.97554501599[/C][/ROW]
[ROW][C]22[/C][C]280379[/C][C]160636.768509188[/C][C]119742.231490812[/C][/ROW]
[ROW][C]23[/C][C]90938[/C][C]80961.3111113792[/C][C]9976.68888862084[/C][/ROW]
[ROW][C]24[/C][C]101324[/C][C]149476.367707611[/C][C]-48152.3677076108[/C][/ROW]
[ROW][C]25[/C][C]139502[/C][C]206104.796000323[/C][C]-66602.796000323[/C][/ROW]
[ROW][C]26[/C][C]145120[/C][C]181578.05789133[/C][C]-36458.0578913299[/C][/ROW]
[ROW][C]27[/C][C]161729[/C][C]173099.892565179[/C][C]-11370.8925651793[/C][/ROW]
[ROW][C]28[/C][C]160905[/C][C]179436.607963103[/C][C]-18531.6079631027[/C][/ROW]
[ROW][C]29[/C][C]106888[/C][C]106626.927868128[/C][C]261.072131871643[/C][/ROW]
[ROW][C]30[/C][C]187289[/C][C]173418.960969561[/C][C]13870.0390304395[/C][/ROW]
[ROW][C]31[/C][C]181853[/C][C]169627.440513889[/C][C]12225.559486111[/C][/ROW]
[ROW][C]32[/C][C]129340[/C][C]136953.696845596[/C][C]-7613.69684559579[/C][/ROW]
[ROW][C]33[/C][C]196862[/C][C]185651.791468333[/C][C]11210.2085316674[/C][/ROW]
[ROW][C]34[/C][C]62731[/C][C]84322.8911477263[/C][C]-21591.8911477263[/C][/ROW]
[ROW][C]35[/C][C]234863[/C][C]174537.321766868[/C][C]60325.6782331322[/C][/ROW]
[ROW][C]36[/C][C]167255[/C][C]141614.439389729[/C][C]25640.5606102711[/C][/ROW]
[ROW][C]37[/C][C]264528[/C][C]185989.182950465[/C][C]78538.8170495347[/C][/ROW]
[ROW][C]38[/C][C]121976[/C][C]123827.614913774[/C][C]-1851.61491377442[/C][/ROW]
[ROW][C]39[/C][C]80964[/C][C]66393.3697060795[/C][C]14570.6302939205[/C][/ROW]
[ROW][C]40[/C][C]209631[/C][C]210878.914334749[/C][C]-1247.91433474943[/C][/ROW]
[ROW][C]41[/C][C]213310[/C][C]157777.739022249[/C][C]55532.2609777507[/C][/ROW]
[ROW][C]42[/C][C]115911[/C][C]122438.335537442[/C][C]-6527.3355374417[/C][/ROW]
[ROW][C]43[/C][C]131337[/C][C]122355.530562159[/C][C]8981.46943784062[/C][/ROW]
[ROW][C]44[/C][C]81106[/C][C]93342.1257171391[/C][C]-12236.1257171391[/C][/ROW]
[ROW][C]45[/C][C]93125[/C][C]102217.273989293[/C][C]-9092.27398929267[/C][/ROW]
[ROW][C]46[/C][C]305708[/C][C]188716.441800802[/C][C]116991.558199198[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]96922.7197683823[/C][C]-18122.7197683823[/C][/ROW]
[ROW][C]48[/C][C]157566[/C][C]175384.683152218[/C][C]-17818.6831522183[/C][/ROW]
[ROW][C]49[/C][C]213487[/C][C]176911.512570959[/C][C]36575.4874290408[/C][/ROW]
[ROW][C]50[/C][C]131108[/C][C]158180.383761196[/C][C]-27072.3837611956[/C][/ROW]
[ROW][C]51[/C][C]128734[/C][C]145649.593803144[/C][C]-16915.5938031437[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]37593.9167131939[/C][C]-13405.9167131939[/C][/ROW]
[ROW][C]53[/C][C]257662[/C][C]276816.139066902[/C][C]-19154.1390669017[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]67637.6739875246[/C][C]-2608.67398752461[/C][/ROW]
[ROW][C]55[/C][C]98066[/C][C]101919.210073343[/C][C]-3853.210073343[/C][/ROW]
[ROW][C]56[/C][C]173587[/C][C]124373.573098446[/C][C]49213.4269015539[/C][/ROW]
[ROW][C]57[/C][C]179571[/C][C]189859.1271121[/C][C]-10288.1271121002[/C][/ROW]
[ROW][C]58[/C][C]197067[/C][C]215244.086776567[/C][C]-18177.0867765668[/C][/ROW]
[ROW][C]59[/C][C]208823[/C][C]212934.669033028[/C][C]-4111.66903302804[/C][/ROW]
[ROW][C]60[/C][C]134088[/C][C]127611.178610182[/C][C]6476.82138981794[/C][/ROW]
[ROW][C]61[/C][C]245107[/C][C]213527.093961625[/C][C]31579.9060383752[/C][/ROW]
[ROW][C]62[/C][C]201409[/C][C]185529.501372549[/C][C]15879.4986274508[/C][/ROW]
[ROW][C]63[/C][C]141760[/C][C]151045.699728418[/C][C]-9285.6997284182[/C][/ROW]
[ROW][C]64[/C][C]170635[/C][C]140294.916753681[/C][C]30340.0832463185[/C][/ROW]
[ROW][C]65[/C][C]129100[/C][C]101688.680725153[/C][C]27411.3192748468[/C][/ROW]
[ROW][C]66[/C][C]108811[/C][C]116064.283641617[/C][C]-7253.2836416173[/C][/ROW]
[ROW][C]67[/C][C]113450[/C][C]163225.426708772[/C][C]-49775.4267087721[/C][/ROW]
[ROW][C]68[/C][C]142286[/C][C]191096.433199098[/C][C]-48810.4331990976[/C][/ROW]
[ROW][C]69[/C][C]143937[/C][C]167415.275464514[/C][C]-23478.2754645137[/C][/ROW]
[ROW][C]70[/C][C]89882[/C][C]152268.855027579[/C][C]-62386.8550275793[/C][/ROW]
[ROW][C]71[/C][C]118807[/C][C]124208.203201899[/C][C]-5401.20320189903[/C][/ROW]
[ROW][C]72[/C][C]69471[/C][C]80516.9048843784[/C][C]-11045.9048843784[/C][/ROW]
[ROW][C]73[/C][C]126630[/C][C]128299.607273677[/C][C]-1669.6072736769[/C][/ROW]
[ROW][C]74[/C][C]145908[/C][C]130435.365406206[/C][C]15472.6345937937[/C][/ROW]
[ROW][C]75[/C][C]96981[/C][C]154270.506520856[/C][C]-57289.506520856[/C][/ROW]
[ROW][C]76[/C][C]189066[/C][C]171598.723900995[/C][C]17467.2760990046[/C][/ROW]
[ROW][C]77[/C][C]191467[/C][C]151707.457486516[/C][C]39759.5425134838[/C][/ROW]
[ROW][C]78[/C][C]106193[/C][C]141449.700214062[/C][C]-35256.7002140617[/C][/ROW]
[ROW][C]79[/C][C]89318[/C][C]139034.969907823[/C][C]-49716.9699078228[/C][/ROW]
[ROW][C]80[/C][C]120362[/C][C]123782.008223894[/C][C]-3420.00822389438[/C][/ROW]
[ROW][C]81[/C][C]98791[/C][C]102689.236134076[/C][C]-3898.23613407602[/C][/ROW]
[ROW][C]82[/C][C]274949[/C][C]207636.824106178[/C][C]67312.1758938217[/C][/ROW]
[ROW][C]83[/C][C]132798[/C][C]107973.2184983[/C][C]24824.7815017003[/C][/ROW]
[ROW][C]84[/C][C]128075[/C][C]158104.247660258[/C][C]-30029.2476602579[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]82291.883714068[/C][C]-1338.88371406799[/C][/ROW]
[ROW][C]86[/C][C]109237[/C][C]92295.7574852284[/C][C]16941.2425147716[/C][/ROW]
[ROW][C]87[/C][C]96634[/C][C]111095.450611506[/C][C]-14461.4506115063[/C][/ROW]
[ROW][C]88[/C][C]226183[/C][C]175683.856062729[/C][C]50499.1439372711[/C][/ROW]
[ROW][C]89[/C][C]167226[/C][C]138299.152383421[/C][C]28926.8476165786[/C][/ROW]
[ROW][C]90[/C][C]117805[/C][C]204600.539838222[/C][C]-86795.5398382219[/C][/ROW]
[ROW][C]91[/C][C]121630[/C][C]115541.533867886[/C][C]6088.46613211391[/C][/ROW]
[ROW][C]92[/C][C]152193[/C][C]186286.160483934[/C][C]-34093.1604839336[/C][/ROW]
[ROW][C]93[/C][C]112004[/C][C]98009.9983094295[/C][C]13994.0016905705[/C][/ROW]
[ROW][C]94[/C][C]169613[/C][C]157727.69812634[/C][C]11885.3018736599[/C][/ROW]
[ROW][C]95[/C][C]176577[/C][C]123204.892655349[/C][C]53372.1073446513[/C][/ROW]
[ROW][C]96[/C][C]130533[/C][C]117931.410195301[/C][C]12601.5898046991[/C][/ROW]
[ROW][C]97[/C][C]142339[/C][C]127809.341570267[/C][C]14529.6584297331[/C][/ROW]
[ROW][C]98[/C][C]189764[/C][C]196008.961787557[/C][C]-6244.9617875567[/C][/ROW]
[ROW][C]99[/C][C]201603[/C][C]231246.092887963[/C][C]-29643.0928879626[/C][/ROW]
[ROW][C]100[/C][C]243180[/C][C]195595.779536515[/C][C]47584.2204634855[/C][/ROW]
[ROW][C]101[/C][C]155931[/C][C]173103.061061109[/C][C]-17172.0610611087[/C][/ROW]
[ROW][C]102[/C][C]182557[/C][C]178555.305407541[/C][C]4001.69459245876[/C][/ROW]
[ROW][C]103[/C][C]106351[/C][C]150834.379649894[/C][C]-44483.3796498936[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]58441.9587510889[/C][C]-15154.9587510889[/C][/ROW]
[ROW][C]105[/C][C]127394[/C][C]127544.795000035[/C][C]-150.795000035186[/C][/ROW]
[ROW][C]106[/C][C]127930[/C][C]180265.331513605[/C][C]-52335.3315136047[/C][/ROW]
[ROW][C]107[/C][C]135306[/C][C]137241.550495972[/C][C]-1935.55049597197[/C][/ROW]
[ROW][C]108[/C][C]175663[/C][C]191869.304442907[/C][C]-16206.3044429068[/C][/ROW]
[ROW][C]109[/C][C]74112[/C][C]87305.6323203001[/C][C]-13193.6323203001[/C][/ROW]
[ROW][C]110[/C][C]89059[/C][C]153991.947461578[/C][C]-64932.9474615778[/C][/ROW]
[ROW][C]111[/C][C]166142[/C][C]121847.468640117[/C][C]44294.5313598833[/C][/ROW]
[ROW][C]112[/C][C]141933[/C][C]174743.668147603[/C][C]-32810.6681476028[/C][/ROW]
[ROW][C]113[/C][C]22938[/C][C]23652.2370396125[/C][C]-714.237039612506[/C][/ROW]
[ROW][C]114[/C][C]125927[/C][C]166042.847148208[/C][C]-40115.8471482078[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]61100.7767479457[/C][C]756.223252054295[/C][/ROW]
[ROW][C]116[/C][C]91185[/C][C]125521.386915426[/C][C]-34336.3869154258[/C][/ROW]
[ROW][C]117[/C][C]236316[/C][C]169674.642679234[/C][C]66641.3573207657[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]20043.2750079239[/C][C]1010.72499207613[/C][/ROW]
[ROW][C]119[/C][C]169093[/C][C]110460.545781719[/C][C]58632.4542182812[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]47509.1618798915[/C][C]-16095.1618798915[/C][/ROW]
[ROW][C]121[/C][C]183059[/C][C]179757.346651192[/C][C]3301.65334880786[/C][/ROW]
[ROW][C]122[/C][C]137544[/C][C]119291.575193605[/C][C]18252.4248063953[/C][/ROW]
[ROW][C]123[/C][C]75032[/C][C]92462.9627461775[/C][C]-17430.9627461775[/C][/ROW]
[ROW][C]124[/C][C]71908[/C][C]69511.7946471265[/C][C]2396.20535287355[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]57891.5408995073[/C][C]-19677.5408995073[/C][/ROW]
[ROW][C]126[/C][C]90961[/C][C]98566.5991271292[/C][C]-7605.59912712917[/C][/ROW]
[ROW][C]127[/C][C]193662[/C][C]168605.126506117[/C][C]25056.873493883[/C][/ROW]
[ROW][C]128[/C][C]127185[/C][C]116133.511847433[/C][C]11051.4881525671[/C][/ROW]
[ROW][C]129[/C][C]242153[/C][C]190140.523234024[/C][C]52012.4767659764[/C][/ROW]
[ROW][C]130[/C][C]201748[/C][C]221940.950510576[/C][C]-20192.9505105755[/C][/ROW]
[ROW][C]131[/C][C]254599[/C][C]220464.727878181[/C][C]34134.2721218189[/C][/ROW]
[ROW][C]132[/C][C]139144[/C][C]108975.547454494[/C][C]30168.4525455064[/C][/ROW]
[ROW][C]133[/C][C]76470[/C][C]85467.0381742841[/C][C]-8997.0381742841[/C][/ROW]
[ROW][C]134[/C][C]183260[/C][C]246170.518704094[/C][C]-62910.5187040941[/C][/ROW]
[ROW][C]135[/C][C]280039[/C][C]216797.078509041[/C][C]63241.9214909592[/C][/ROW]
[ROW][C]136[/C][C]50999[/C][C]50272.1610736722[/C][C]726.838926327795[/C][/ROW]
[ROW][C]137[/C][C]253056[/C][C]199168.846178494[/C][C]53887.1538215061[/C][/ROW]
[ROW][C]138[/C][C]98466[/C][C]118259.725706081[/C][C]-19793.7257060806[/C][/ROW]
[ROW][C]139[/C][C]168059[/C][C]149799.648866914[/C][C]18259.3511330863[/C][/ROW]
[ROW][C]140[/C][C]128768[/C][C]136132.752410376[/C][C]-7364.75241037566[/C][/ROW]
[ROW][C]141[/C][C]75746[/C][C]83603.3814116421[/C][C]-7857.38141164208[/C][/ROW]
[ROW][C]142[/C][C]244909[/C][C]150731.974501645[/C][C]94177.0254983546[/C][/ROW]
[ROW][C]143[/C][C]152366[/C][C]171717.734186209[/C][C]-19351.7341862089[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]125759.485718363[/C][C]47500.514281637[/C][/ROW]
[ROW][C]145[/C][C]197033[/C][C]193755.905859944[/C][C]3277.09414005639[/C][/ROW]
[ROW][C]146[/C][C]67507[/C][C]180124.037782083[/C][C]-112617.037782083[/C][/ROW]
[ROW][C]147[/C][C]139409[/C][C]146018.675891702[/C][C]-6609.67589170206[/C][/ROW]
[ROW][C]148[/C][C]185366[/C][C]209846.247838381[/C][C]-24480.2478383812[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]2922.76553544949[/C][C]-2922.76553544949[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]14545.6748347073[/C][C]142.325165292729[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]3672.94283038038[/C][C]-3574.94283038038[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]4445.76867993924[/C][C]-3990.76867993924[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]2832.17131693756[/C][C]-2832.17131693756[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]2809.52276230957[/C][C]-2809.52276230957[/C][/ROW]
[ROW][C]155[/C][C]128873[/C][C]119750.988995799[/C][C]9122.01100420096[/C][/ROW]
[ROW][C]156[/C][C]185288[/C][C]194569.296925838[/C][C]-9281.29692583762[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]2741.57709842562[/C][C]-2741.57709842562[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]5900.82616054505[/C][C]-5697.82616054505[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]13132.5761811341[/C][C]-5933.57618113407[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]37157.0960222191[/C][C]9502.90397778088[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]8333.11823240648[/C][C]9213.88176759352[/C][/ROW]
[ROW][C]162[/C][C]73567[/C][C]95411.1644455285[/C][C]-21844.1644455285[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]4196.63457903142[/C][C]-3227.63457903142[/C][/ROW]
[ROW][C]164[/C][C]105477[/C][C]90050.7773464925[/C][C]15426.2226535075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1170588146143.86368888424444.1363111157
286621127742.758606193-41121.7586061931
3113337128866.389789819-15529.3897898186
4144530176330.386956332-31800.3869563325
58153090361.4854365912-8831.48543659122
63552346960.7870233643-11437.7870233643
7305115203883.388461923101231.611538077
83275049429.9640361389-16679.9640361389
9115885101238.82192712914646.1780728708
10130539113597.49255074916941.507449251
11156990158108.826924815-1118.82692481506
12128274191638.804998455-63364.8049984549
13102350111209.355097207-8859.3550972073
14192887166593.60507291326293.3949270869
15129796152500.310094893-22704.3100948932
16245478274965.405807615-29487.4058076153
17169569151029.53458680518539.4654131951
18185279185586.216572082-307.216572081706
19109598137125.000096835-27527.0000968348
20155012183804.111881781-28792.111881781
21154730158572.975545016-3842.97554501599
22280379160636.768509188119742.231490812
239093880961.31111137929976.68888862084
24101324149476.367707611-48152.3677076108
25139502206104.796000323-66602.796000323
26145120181578.05789133-36458.0578913299
27161729173099.892565179-11370.8925651793
28160905179436.607963103-18531.6079631027
29106888106626.927868128261.072131871643
30187289173418.96096956113870.0390304395
31181853169627.44051388912225.559486111
32129340136953.696845596-7613.69684559579
33196862185651.79146833311210.2085316674
346273184322.8911477263-21591.8911477263
35234863174537.32176686860325.6782331322
36167255141614.43938972925640.5606102711
37264528185989.18295046578538.8170495347
38121976123827.614913774-1851.61491377442
398096466393.369706079514570.6302939205
40209631210878.914334749-1247.91433474943
41213310157777.73902224955532.2609777507
42115911122438.335537442-6527.3355374417
43131337122355.5305621598981.46943784062
448110693342.1257171391-12236.1257171391
4593125102217.273989293-9092.27398929267
46305708188716.441800802116991.558199198
477880096922.7197683823-18122.7197683823
48157566175384.683152218-17818.6831522183
49213487176911.51257095936575.4874290408
50131108158180.383761196-27072.3837611956
51128734145649.593803144-16915.5938031437
522418837593.9167131939-13405.9167131939
53257662276816.139066902-19154.1390669017
546502967637.6739875246-2608.67398752461
5598066101919.210073343-3853.210073343
56173587124373.57309844649213.4269015539
57179571189859.1271121-10288.1271121002
58197067215244.086776567-18177.0867765668
59208823212934.669033028-4111.66903302804
60134088127611.1786101826476.82138981794
61245107213527.09396162531579.9060383752
62201409185529.50137254915879.4986274508
63141760151045.699728418-9285.6997284182
64170635140294.91675368130340.0832463185
65129100101688.68072515327411.3192748468
66108811116064.283641617-7253.2836416173
67113450163225.426708772-49775.4267087721
68142286191096.433199098-48810.4331990976
69143937167415.275464514-23478.2754645137
7089882152268.855027579-62386.8550275793
71118807124208.203201899-5401.20320189903
726947180516.9048843784-11045.9048843784
73126630128299.607273677-1669.6072736769
74145908130435.36540620615472.6345937937
7596981154270.506520856-57289.506520856
76189066171598.72390099517467.2760990046
77191467151707.45748651639759.5425134838
78106193141449.700214062-35256.7002140617
7989318139034.969907823-49716.9699078228
80120362123782.008223894-3420.00822389438
8198791102689.236134076-3898.23613407602
82274949207636.82410617867312.1758938217
83132798107973.218498324824.7815017003
84128075158104.247660258-30029.2476602579
858095382291.883714068-1338.88371406799
8610923792295.757485228416941.2425147716
8796634111095.450611506-14461.4506115063
88226183175683.85606272950499.1439372711
89167226138299.15238342128926.8476165786
90117805204600.539838222-86795.5398382219
91121630115541.5338678866088.46613211391
92152193186286.160483934-34093.1604839336
9311200498009.998309429513994.0016905705
94169613157727.6981263411885.3018736599
95176577123204.89265534953372.1073446513
96130533117931.41019530112601.5898046991
97142339127809.34157026714529.6584297331
98189764196008.961787557-6244.9617875567
99201603231246.092887963-29643.0928879626
100243180195595.77953651547584.2204634855
101155931173103.061061109-17172.0610611087
102182557178555.3054075414001.69459245876
103106351150834.379649894-44483.3796498936
1044328758441.9587510889-15154.9587510889
105127394127544.795000035-150.795000035186
106127930180265.331513605-52335.3315136047
107135306137241.550495972-1935.55049597197
108175663191869.304442907-16206.3044429068
1097411287305.6323203001-13193.6323203001
11089059153991.947461578-64932.9474615778
111166142121847.46864011744294.5313598833
112141933174743.668147603-32810.6681476028
1132293823652.2370396125-714.237039612506
114125927166042.847148208-40115.8471482078
1156185761100.7767479457756.223252054295
11691185125521.386915426-34336.3869154258
117236316169674.64267923466641.3573207657
1182105420043.27500792391010.72499207613
119169093110460.54578171958632.4542182812
1203141447509.1618798915-16095.1618798915
121183059179757.3466511923301.65334880786
122137544119291.57519360518252.4248063953
1237503292462.9627461775-17430.9627461775
1247190869511.79464712652396.20535287355
1253821457891.5408995073-19677.5408995073
1269096198566.5991271292-7605.59912712917
127193662168605.12650611725056.873493883
128127185116133.51184743311051.4881525671
129242153190140.52323402452012.4767659764
130201748221940.950510576-20192.9505105755
131254599220464.72787818134134.2721218189
132139144108975.54745449430168.4525455064
1337647085467.0381742841-8997.0381742841
134183260246170.518704094-62910.5187040941
135280039216797.07850904163241.9214909592
1365099950272.1610736722726.838926327795
137253056199168.84617849453887.1538215061
13898466118259.725706081-19793.7257060806
139168059149799.64886691418259.3511330863
140128768136132.752410376-7364.75241037566
1417574683603.3814116421-7857.38141164208
142244909150731.97450164594177.0254983546
143152366171717.734186209-19351.7341862089
144173260125759.48571836347500.514281637
145197033193755.9058599443277.09414005639
14667507180124.037782083-112617.037782083
147139409146018.675891702-6609.67589170206
148185366209846.247838381-24480.2478383812
14902922.76553544949-2922.76553544949
1501468814545.6748347073142.325165292729
151983672.94283038038-3574.94283038038
1524554445.76867993924-3990.76867993924
15302832.17131693756-2832.17131693756
15402809.52276230957-2809.52276230957
155128873119750.9889957999122.01100420096
156185288194569.296925838-9281.29692583762
15702741.57709842562-2741.57709842562
1582035900.82616054505-5697.82616054505
159719913132.5761811341-5933.57618113407
1604666037157.09602221919502.90397778088
161175478333.118232406489213.88176759352
1627356795411.1644455285-21844.1644455285
1639694196.63457903142-3227.63457903142
16410547790050.777346492515426.2226535075







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5438550260333370.9122899479333260.456144973966663
100.7110073030745860.5779853938508290.288992696925414
110.5804621581951790.8390756836096420.419537841804821
120.4759023604252990.9518047208505970.524097639574701
130.378344368189090.756688736378180.62165563181091
140.3887330700558160.7774661401116320.611266929944184
150.3559602103103390.7119204206206780.644039789689661
160.3040797666963830.6081595333927650.695920233303617
170.3613788786310580.7227577572621150.638621121368942
180.3150893558378680.6301787116757350.684910644162132
190.4578614091821530.9157228183643050.542138590817848
200.4369253238077350.8738506476154690.563074676192265
210.3622877451577570.7245754903155140.637712254842243
220.8969274125941410.2061451748117180.103072587405859
230.8627960361404530.2744079277190940.137203963859547
240.8643199675077090.2713600649845830.135680032492291
250.9262992821900580.1474014356198840.0737007178099421
260.9110231117505370.1779537764989270.0889768882494633
270.8830530686266790.2338938627466430.116946931373321
280.8513946752305120.2972106495389750.148605324769488
290.8329783978940570.3340432042118870.167021602105943
300.8101146245231310.3797707509537380.189885375476869
310.7743462293228840.4513075413542310.225653770677116
320.7398203223432160.5203593553135680.260179677656784
330.7075437129576210.5849125740847570.292456287042379
340.6644100618941560.6711798762116870.335589938105844
350.761392331718160.477215336563680.23860766828184
360.7270635873102980.5458728253794040.272936412689702
370.8970483157066470.2059033685867050.102951684293353
380.8726845678984230.2546308642031530.127315432101577
390.8488331892457740.3023336215084520.151166810754226
400.8164690318314580.3670619363370830.183530968168542
410.8333107998026360.3333784003947290.166689200197364
420.8035142532076140.3929714935847730.196485746792386
430.7688757754618940.4622484490762120.231124224538106
440.7335221082584640.5329557834830720.266477891741536
450.7370571960559010.5258856078881980.262942803944099
460.9580278272456420.08394434550871610.0419721727543581
470.951064605136050.09787078972790070.0489353948639504
480.944876551054360.110246897891280.0551234489456401
490.9391869030022730.1216261939954550.0608130969977275
500.9367595655182120.1264808689635770.0632404344817884
510.9302586937251620.1394826125496760.0697413062748381
520.921838579649260.156322840701480.07816142035074
530.9110300973609410.1779398052781180.088969902639059
540.889812842400510.2203743151989790.11018715759949
550.8664445039551860.2671109920896280.133555496044814
560.8747185977418020.2505628045163950.125281402258198
570.8510383793525790.2979232412948430.148961620647421
580.8384874919626290.3230250160747420.161512508037371
590.8267771720672660.3464456558654680.173222827932734
600.795616746722660.4087665065546790.20438325327734
610.7849771530959850.430045693808030.215022846904015
620.7596651617791370.4806696764417260.240334838220863
630.7229692944066540.5540614111866920.277030705593346
640.7145316835235040.5709366329529910.285468316476496
650.6962061456718080.6075877086563840.303793854328192
660.6697806937235340.6604386125529320.330219306276466
670.7162805046149030.5674389907701940.283719495385097
680.7457134963917280.5085730072165440.254286503608272
690.7392607672936550.521478465412690.260739232706345
700.8211325511566960.3577348976866080.178867448843304
710.7895288479920540.4209423040158920.210471152007946
720.7570128598063910.4859742803872180.242987140193609
730.7196504354357620.5606991291284750.280349564564238
740.6883028951665070.6233942096669870.311697104833493
750.7478345082565080.5043309834869840.252165491743492
760.7194377202573950.561124559485210.280562279742605
770.7305641823617240.5388716352765530.269435817638276
780.7255185536257310.5489628927485370.274481446374268
790.7566812404651580.4866375190696840.243318759534842
800.7208754176008030.5582491647983950.279124582399197
810.6820792951849840.6358414096300330.317920704815016
820.7786678502786250.4426642994427510.221332149721375
830.7632329490034630.4735341019930750.236767050996537
840.767094617920710.465810764158580.23290538207929
850.7303151105657720.5393697788684560.269684889434228
860.7023867399842240.5952265200315520.297613260015776
870.6662432554452880.6675134891094230.333756744554712
880.7176671116876340.5646657766247310.282332888312366
890.6939712757893080.6120574484213830.306028724210692
900.8514558359636120.2970883280727750.148544164036388
910.8239319530164150.3521360939671690.176068046983585
920.8204524117248310.3590951765503390.179547588275169
930.7944942142883510.4110115714232980.205505785711649
940.7648196661543460.4703606676913090.235180333845654
950.816310726320280.3673785473594390.18368927367972
960.790019312111520.419961375776960.20998068788848
970.7588122814294270.4823754371411450.241187718570573
980.721029459352780.5579410812944410.27897054064722
990.7029926226429180.5940147547141640.297007377357082
1000.7430066943248030.5139866113503930.256993305675197
1010.7087177662809540.5825644674380910.291282233719046
1020.6680090183759560.6639819632480880.331990981624044
1030.6793048875190970.6413902249618060.320695112480903
1040.6410771908832440.7178456182335120.358922809116756
1050.5960349841663870.8079300316672260.403965015833613
1060.6399722033893240.7200555932213520.360027796610676
1070.5932523033588050.8134953932823910.406747696641196
1080.5530583055397630.8938833889204730.446941694460237
1090.5088712358996010.9822575282007990.491128764100399
1100.6317371986437820.7365256027124370.368262801356218
1110.6483870748597390.7032258502805220.351612925140261
1120.6529979349559080.6940041300881830.347002065044092
1130.6072554491507050.7854891016985910.392744550849295
1140.6509455362471580.6981089275056830.349054463752842
1150.6077758427217090.7844483145565810.392224157278291
1160.6304216205901130.7391567588197740.369578379409887
1170.7038912260420060.5922175479159870.296108773957994
1180.6600846729519870.6798306540960260.339915327048013
1190.7127774446368420.5744451107263170.287222555363158
1200.6839298886305580.6321402227388840.316070111369442
1210.6370895715067260.7258208569865480.362910428493274
1220.5940446103987250.8119107792025490.405955389601275
1230.5622386174255390.8755227651489210.437761382574461
1240.5106946804182370.9786106391635270.489305319581763
1250.5201576733627170.9596846532745650.479842326637283
1260.4907119151584570.9814238303169130.509288084841543
1270.4480980958695850.8961961917391710.551901904130415
1280.3952210125467850.7904420250935710.604778987453215
1290.4296001904677650.859200380935530.570399809532235
1300.3984996052187020.7969992104374050.601500394781298
1310.379754121558760.759508243117520.62024587844124
1320.3479329585491860.6958659170983720.652067041450814
1330.2980231856806540.5960463713613070.701976814319347
1340.9386941163362470.1226117673275070.0613058836637533
1350.9268737645947560.1462524708104880.073126235405244
1360.9496897741222750.1006204517554510.0503102258777253
1370.9341005142221130.1317989715557740.0658994857778871
1380.921062639484630.157874721030740.0789373605153702
1390.9740834654501220.05183306909975530.0259165345498776
1400.9614481110116930.07710377797661350.0385518889883068
1410.999987099937092.5800125820577e-051.29000629102885e-05
1420.9999898586166462.02827667075954e-051.01413833537977e-05
1430.999973764991745.24700165191888e-052.62350082595944e-05
1440.9999351823124780.0001296353750444996.48176875222496e-05
1450.9999519488952499.61022095019256e-054.80511047509628e-05
1460.9999999846601263.06797490404688e-081.53398745202344e-08
1470.9999999488116161.02376767827398e-075.11883839136989e-08
1480.999999822656023.54687960504775e-071.77343980252388e-07
1490.9999990014253231.99714935372723e-069.98574676863614e-07
1500.9999943546830161.12906339684674e-055.64531698423369e-06
1510.9999651229837576.97540324869982e-053.48770162434991e-05
1520.9997936091370160.0004127817259688520.000206390862984426
1530.998982386546950.002035226906099910.00101761345304996
1540.9962456424784660.00750871504306810.00375435752153405
1550.9809053058518090.0381893882963820.019094694148191

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.543855026033337 & 0.912289947933326 & 0.456144973966663 \tabularnewline
10 & 0.711007303074586 & 0.577985393850829 & 0.288992696925414 \tabularnewline
11 & 0.580462158195179 & 0.839075683609642 & 0.419537841804821 \tabularnewline
12 & 0.475902360425299 & 0.951804720850597 & 0.524097639574701 \tabularnewline
13 & 0.37834436818909 & 0.75668873637818 & 0.62165563181091 \tabularnewline
14 & 0.388733070055816 & 0.777466140111632 & 0.611266929944184 \tabularnewline
15 & 0.355960210310339 & 0.711920420620678 & 0.644039789689661 \tabularnewline
16 & 0.304079766696383 & 0.608159533392765 & 0.695920233303617 \tabularnewline
17 & 0.361378878631058 & 0.722757757262115 & 0.638621121368942 \tabularnewline
18 & 0.315089355837868 & 0.630178711675735 & 0.684910644162132 \tabularnewline
19 & 0.457861409182153 & 0.915722818364305 & 0.542138590817848 \tabularnewline
20 & 0.436925323807735 & 0.873850647615469 & 0.563074676192265 \tabularnewline
21 & 0.362287745157757 & 0.724575490315514 & 0.637712254842243 \tabularnewline
22 & 0.896927412594141 & 0.206145174811718 & 0.103072587405859 \tabularnewline
23 & 0.862796036140453 & 0.274407927719094 & 0.137203963859547 \tabularnewline
24 & 0.864319967507709 & 0.271360064984583 & 0.135680032492291 \tabularnewline
25 & 0.926299282190058 & 0.147401435619884 & 0.0737007178099421 \tabularnewline
26 & 0.911023111750537 & 0.177953776498927 & 0.0889768882494633 \tabularnewline
27 & 0.883053068626679 & 0.233893862746643 & 0.116946931373321 \tabularnewline
28 & 0.851394675230512 & 0.297210649538975 & 0.148605324769488 \tabularnewline
29 & 0.832978397894057 & 0.334043204211887 & 0.167021602105943 \tabularnewline
30 & 0.810114624523131 & 0.379770750953738 & 0.189885375476869 \tabularnewline
31 & 0.774346229322884 & 0.451307541354231 & 0.225653770677116 \tabularnewline
32 & 0.739820322343216 & 0.520359355313568 & 0.260179677656784 \tabularnewline
33 & 0.707543712957621 & 0.584912574084757 & 0.292456287042379 \tabularnewline
34 & 0.664410061894156 & 0.671179876211687 & 0.335589938105844 \tabularnewline
35 & 0.76139233171816 & 0.47721533656368 & 0.23860766828184 \tabularnewline
36 & 0.727063587310298 & 0.545872825379404 & 0.272936412689702 \tabularnewline
37 & 0.897048315706647 & 0.205903368586705 & 0.102951684293353 \tabularnewline
38 & 0.872684567898423 & 0.254630864203153 & 0.127315432101577 \tabularnewline
39 & 0.848833189245774 & 0.302333621508452 & 0.151166810754226 \tabularnewline
40 & 0.816469031831458 & 0.367061936337083 & 0.183530968168542 \tabularnewline
41 & 0.833310799802636 & 0.333378400394729 & 0.166689200197364 \tabularnewline
42 & 0.803514253207614 & 0.392971493584773 & 0.196485746792386 \tabularnewline
43 & 0.768875775461894 & 0.462248449076212 & 0.231124224538106 \tabularnewline
44 & 0.733522108258464 & 0.532955783483072 & 0.266477891741536 \tabularnewline
45 & 0.737057196055901 & 0.525885607888198 & 0.262942803944099 \tabularnewline
46 & 0.958027827245642 & 0.0839443455087161 & 0.0419721727543581 \tabularnewline
47 & 0.95106460513605 & 0.0978707897279007 & 0.0489353948639504 \tabularnewline
48 & 0.94487655105436 & 0.11024689789128 & 0.0551234489456401 \tabularnewline
49 & 0.939186903002273 & 0.121626193995455 & 0.0608130969977275 \tabularnewline
50 & 0.936759565518212 & 0.126480868963577 & 0.0632404344817884 \tabularnewline
51 & 0.930258693725162 & 0.139482612549676 & 0.0697413062748381 \tabularnewline
52 & 0.92183857964926 & 0.15632284070148 & 0.07816142035074 \tabularnewline
53 & 0.911030097360941 & 0.177939805278118 & 0.088969902639059 \tabularnewline
54 & 0.88981284240051 & 0.220374315198979 & 0.11018715759949 \tabularnewline
55 & 0.866444503955186 & 0.267110992089628 & 0.133555496044814 \tabularnewline
56 & 0.874718597741802 & 0.250562804516395 & 0.125281402258198 \tabularnewline
57 & 0.851038379352579 & 0.297923241294843 & 0.148961620647421 \tabularnewline
58 & 0.838487491962629 & 0.323025016074742 & 0.161512508037371 \tabularnewline
59 & 0.826777172067266 & 0.346445655865468 & 0.173222827932734 \tabularnewline
60 & 0.79561674672266 & 0.408766506554679 & 0.20438325327734 \tabularnewline
61 & 0.784977153095985 & 0.43004569380803 & 0.215022846904015 \tabularnewline
62 & 0.759665161779137 & 0.480669676441726 & 0.240334838220863 \tabularnewline
63 & 0.722969294406654 & 0.554061411186692 & 0.277030705593346 \tabularnewline
64 & 0.714531683523504 & 0.570936632952991 & 0.285468316476496 \tabularnewline
65 & 0.696206145671808 & 0.607587708656384 & 0.303793854328192 \tabularnewline
66 & 0.669780693723534 & 0.660438612552932 & 0.330219306276466 \tabularnewline
67 & 0.716280504614903 & 0.567438990770194 & 0.283719495385097 \tabularnewline
68 & 0.745713496391728 & 0.508573007216544 & 0.254286503608272 \tabularnewline
69 & 0.739260767293655 & 0.52147846541269 & 0.260739232706345 \tabularnewline
70 & 0.821132551156696 & 0.357734897686608 & 0.178867448843304 \tabularnewline
71 & 0.789528847992054 & 0.420942304015892 & 0.210471152007946 \tabularnewline
72 & 0.757012859806391 & 0.485974280387218 & 0.242987140193609 \tabularnewline
73 & 0.719650435435762 & 0.560699129128475 & 0.280349564564238 \tabularnewline
74 & 0.688302895166507 & 0.623394209666987 & 0.311697104833493 \tabularnewline
75 & 0.747834508256508 & 0.504330983486984 & 0.252165491743492 \tabularnewline
76 & 0.719437720257395 & 0.56112455948521 & 0.280562279742605 \tabularnewline
77 & 0.730564182361724 & 0.538871635276553 & 0.269435817638276 \tabularnewline
78 & 0.725518553625731 & 0.548962892748537 & 0.274481446374268 \tabularnewline
79 & 0.756681240465158 & 0.486637519069684 & 0.243318759534842 \tabularnewline
80 & 0.720875417600803 & 0.558249164798395 & 0.279124582399197 \tabularnewline
81 & 0.682079295184984 & 0.635841409630033 & 0.317920704815016 \tabularnewline
82 & 0.778667850278625 & 0.442664299442751 & 0.221332149721375 \tabularnewline
83 & 0.763232949003463 & 0.473534101993075 & 0.236767050996537 \tabularnewline
84 & 0.76709461792071 & 0.46581076415858 & 0.23290538207929 \tabularnewline
85 & 0.730315110565772 & 0.539369778868456 & 0.269684889434228 \tabularnewline
86 & 0.702386739984224 & 0.595226520031552 & 0.297613260015776 \tabularnewline
87 & 0.666243255445288 & 0.667513489109423 & 0.333756744554712 \tabularnewline
88 & 0.717667111687634 & 0.564665776624731 & 0.282332888312366 \tabularnewline
89 & 0.693971275789308 & 0.612057448421383 & 0.306028724210692 \tabularnewline
90 & 0.851455835963612 & 0.297088328072775 & 0.148544164036388 \tabularnewline
91 & 0.823931953016415 & 0.352136093967169 & 0.176068046983585 \tabularnewline
92 & 0.820452411724831 & 0.359095176550339 & 0.179547588275169 \tabularnewline
93 & 0.794494214288351 & 0.411011571423298 & 0.205505785711649 \tabularnewline
94 & 0.764819666154346 & 0.470360667691309 & 0.235180333845654 \tabularnewline
95 & 0.81631072632028 & 0.367378547359439 & 0.18368927367972 \tabularnewline
96 & 0.79001931211152 & 0.41996137577696 & 0.20998068788848 \tabularnewline
97 & 0.758812281429427 & 0.482375437141145 & 0.241187718570573 \tabularnewline
98 & 0.72102945935278 & 0.557941081294441 & 0.27897054064722 \tabularnewline
99 & 0.702992622642918 & 0.594014754714164 & 0.297007377357082 \tabularnewline
100 & 0.743006694324803 & 0.513986611350393 & 0.256993305675197 \tabularnewline
101 & 0.708717766280954 & 0.582564467438091 & 0.291282233719046 \tabularnewline
102 & 0.668009018375956 & 0.663981963248088 & 0.331990981624044 \tabularnewline
103 & 0.679304887519097 & 0.641390224961806 & 0.320695112480903 \tabularnewline
104 & 0.641077190883244 & 0.717845618233512 & 0.358922809116756 \tabularnewline
105 & 0.596034984166387 & 0.807930031667226 & 0.403965015833613 \tabularnewline
106 & 0.639972203389324 & 0.720055593221352 & 0.360027796610676 \tabularnewline
107 & 0.593252303358805 & 0.813495393282391 & 0.406747696641196 \tabularnewline
108 & 0.553058305539763 & 0.893883388920473 & 0.446941694460237 \tabularnewline
109 & 0.508871235899601 & 0.982257528200799 & 0.491128764100399 \tabularnewline
110 & 0.631737198643782 & 0.736525602712437 & 0.368262801356218 \tabularnewline
111 & 0.648387074859739 & 0.703225850280522 & 0.351612925140261 \tabularnewline
112 & 0.652997934955908 & 0.694004130088183 & 0.347002065044092 \tabularnewline
113 & 0.607255449150705 & 0.785489101698591 & 0.392744550849295 \tabularnewline
114 & 0.650945536247158 & 0.698108927505683 & 0.349054463752842 \tabularnewline
115 & 0.607775842721709 & 0.784448314556581 & 0.392224157278291 \tabularnewline
116 & 0.630421620590113 & 0.739156758819774 & 0.369578379409887 \tabularnewline
117 & 0.703891226042006 & 0.592217547915987 & 0.296108773957994 \tabularnewline
118 & 0.660084672951987 & 0.679830654096026 & 0.339915327048013 \tabularnewline
119 & 0.712777444636842 & 0.574445110726317 & 0.287222555363158 \tabularnewline
120 & 0.683929888630558 & 0.632140222738884 & 0.316070111369442 \tabularnewline
121 & 0.637089571506726 & 0.725820856986548 & 0.362910428493274 \tabularnewline
122 & 0.594044610398725 & 0.811910779202549 & 0.405955389601275 \tabularnewline
123 & 0.562238617425539 & 0.875522765148921 & 0.437761382574461 \tabularnewline
124 & 0.510694680418237 & 0.978610639163527 & 0.489305319581763 \tabularnewline
125 & 0.520157673362717 & 0.959684653274565 & 0.479842326637283 \tabularnewline
126 & 0.490711915158457 & 0.981423830316913 & 0.509288084841543 \tabularnewline
127 & 0.448098095869585 & 0.896196191739171 & 0.551901904130415 \tabularnewline
128 & 0.395221012546785 & 0.790442025093571 & 0.604778987453215 \tabularnewline
129 & 0.429600190467765 & 0.85920038093553 & 0.570399809532235 \tabularnewline
130 & 0.398499605218702 & 0.796999210437405 & 0.601500394781298 \tabularnewline
131 & 0.37975412155876 & 0.75950824311752 & 0.62024587844124 \tabularnewline
132 & 0.347932958549186 & 0.695865917098372 & 0.652067041450814 \tabularnewline
133 & 0.298023185680654 & 0.596046371361307 & 0.701976814319347 \tabularnewline
134 & 0.938694116336247 & 0.122611767327507 & 0.0613058836637533 \tabularnewline
135 & 0.926873764594756 & 0.146252470810488 & 0.073126235405244 \tabularnewline
136 & 0.949689774122275 & 0.100620451755451 & 0.0503102258777253 \tabularnewline
137 & 0.934100514222113 & 0.131798971555774 & 0.0658994857778871 \tabularnewline
138 & 0.92106263948463 & 0.15787472103074 & 0.0789373605153702 \tabularnewline
139 & 0.974083465450122 & 0.0518330690997553 & 0.0259165345498776 \tabularnewline
140 & 0.961448111011693 & 0.0771037779766135 & 0.0385518889883068 \tabularnewline
141 & 0.99998709993709 & 2.5800125820577e-05 & 1.29000629102885e-05 \tabularnewline
142 & 0.999989858616646 & 2.02827667075954e-05 & 1.01413833537977e-05 \tabularnewline
143 & 0.99997376499174 & 5.24700165191888e-05 & 2.62350082595944e-05 \tabularnewline
144 & 0.999935182312478 & 0.000129635375044499 & 6.48176875222496e-05 \tabularnewline
145 & 0.999951948895249 & 9.61022095019256e-05 & 4.80511047509628e-05 \tabularnewline
146 & 0.999999984660126 & 3.06797490404688e-08 & 1.53398745202344e-08 \tabularnewline
147 & 0.999999948811616 & 1.02376767827398e-07 & 5.11883839136989e-08 \tabularnewline
148 & 0.99999982265602 & 3.54687960504775e-07 & 1.77343980252388e-07 \tabularnewline
149 & 0.999999001425323 & 1.99714935372723e-06 & 9.98574676863614e-07 \tabularnewline
150 & 0.999994354683016 & 1.12906339684674e-05 & 5.64531698423369e-06 \tabularnewline
151 & 0.999965122983757 & 6.97540324869982e-05 & 3.48770162434991e-05 \tabularnewline
152 & 0.999793609137016 & 0.000412781725968852 & 0.000206390862984426 \tabularnewline
153 & 0.99898238654695 & 0.00203522690609991 & 0.00101761345304996 \tabularnewline
154 & 0.996245642478466 & 0.0075087150430681 & 0.00375435752153405 \tabularnewline
155 & 0.980905305851809 & 0.038189388296382 & 0.019094694148191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&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]9[/C][C]0.543855026033337[/C][C]0.912289947933326[/C][C]0.456144973966663[/C][/ROW]
[ROW][C]10[/C][C]0.711007303074586[/C][C]0.577985393850829[/C][C]0.288992696925414[/C][/ROW]
[ROW][C]11[/C][C]0.580462158195179[/C][C]0.839075683609642[/C][C]0.419537841804821[/C][/ROW]
[ROW][C]12[/C][C]0.475902360425299[/C][C]0.951804720850597[/C][C]0.524097639574701[/C][/ROW]
[ROW][C]13[/C][C]0.37834436818909[/C][C]0.75668873637818[/C][C]0.62165563181091[/C][/ROW]
[ROW][C]14[/C][C]0.388733070055816[/C][C]0.777466140111632[/C][C]0.611266929944184[/C][/ROW]
[ROW][C]15[/C][C]0.355960210310339[/C][C]0.711920420620678[/C][C]0.644039789689661[/C][/ROW]
[ROW][C]16[/C][C]0.304079766696383[/C][C]0.608159533392765[/C][C]0.695920233303617[/C][/ROW]
[ROW][C]17[/C][C]0.361378878631058[/C][C]0.722757757262115[/C][C]0.638621121368942[/C][/ROW]
[ROW][C]18[/C][C]0.315089355837868[/C][C]0.630178711675735[/C][C]0.684910644162132[/C][/ROW]
[ROW][C]19[/C][C]0.457861409182153[/C][C]0.915722818364305[/C][C]0.542138590817848[/C][/ROW]
[ROW][C]20[/C][C]0.436925323807735[/C][C]0.873850647615469[/C][C]0.563074676192265[/C][/ROW]
[ROW][C]21[/C][C]0.362287745157757[/C][C]0.724575490315514[/C][C]0.637712254842243[/C][/ROW]
[ROW][C]22[/C][C]0.896927412594141[/C][C]0.206145174811718[/C][C]0.103072587405859[/C][/ROW]
[ROW][C]23[/C][C]0.862796036140453[/C][C]0.274407927719094[/C][C]0.137203963859547[/C][/ROW]
[ROW][C]24[/C][C]0.864319967507709[/C][C]0.271360064984583[/C][C]0.135680032492291[/C][/ROW]
[ROW][C]25[/C][C]0.926299282190058[/C][C]0.147401435619884[/C][C]0.0737007178099421[/C][/ROW]
[ROW][C]26[/C][C]0.911023111750537[/C][C]0.177953776498927[/C][C]0.0889768882494633[/C][/ROW]
[ROW][C]27[/C][C]0.883053068626679[/C][C]0.233893862746643[/C][C]0.116946931373321[/C][/ROW]
[ROW][C]28[/C][C]0.851394675230512[/C][C]0.297210649538975[/C][C]0.148605324769488[/C][/ROW]
[ROW][C]29[/C][C]0.832978397894057[/C][C]0.334043204211887[/C][C]0.167021602105943[/C][/ROW]
[ROW][C]30[/C][C]0.810114624523131[/C][C]0.379770750953738[/C][C]0.189885375476869[/C][/ROW]
[ROW][C]31[/C][C]0.774346229322884[/C][C]0.451307541354231[/C][C]0.225653770677116[/C][/ROW]
[ROW][C]32[/C][C]0.739820322343216[/C][C]0.520359355313568[/C][C]0.260179677656784[/C][/ROW]
[ROW][C]33[/C][C]0.707543712957621[/C][C]0.584912574084757[/C][C]0.292456287042379[/C][/ROW]
[ROW][C]34[/C][C]0.664410061894156[/C][C]0.671179876211687[/C][C]0.335589938105844[/C][/ROW]
[ROW][C]35[/C][C]0.76139233171816[/C][C]0.47721533656368[/C][C]0.23860766828184[/C][/ROW]
[ROW][C]36[/C][C]0.727063587310298[/C][C]0.545872825379404[/C][C]0.272936412689702[/C][/ROW]
[ROW][C]37[/C][C]0.897048315706647[/C][C]0.205903368586705[/C][C]0.102951684293353[/C][/ROW]
[ROW][C]38[/C][C]0.872684567898423[/C][C]0.254630864203153[/C][C]0.127315432101577[/C][/ROW]
[ROW][C]39[/C][C]0.848833189245774[/C][C]0.302333621508452[/C][C]0.151166810754226[/C][/ROW]
[ROW][C]40[/C][C]0.816469031831458[/C][C]0.367061936337083[/C][C]0.183530968168542[/C][/ROW]
[ROW][C]41[/C][C]0.833310799802636[/C][C]0.333378400394729[/C][C]0.166689200197364[/C][/ROW]
[ROW][C]42[/C][C]0.803514253207614[/C][C]0.392971493584773[/C][C]0.196485746792386[/C][/ROW]
[ROW][C]43[/C][C]0.768875775461894[/C][C]0.462248449076212[/C][C]0.231124224538106[/C][/ROW]
[ROW][C]44[/C][C]0.733522108258464[/C][C]0.532955783483072[/C][C]0.266477891741536[/C][/ROW]
[ROW][C]45[/C][C]0.737057196055901[/C][C]0.525885607888198[/C][C]0.262942803944099[/C][/ROW]
[ROW][C]46[/C][C]0.958027827245642[/C][C]0.0839443455087161[/C][C]0.0419721727543581[/C][/ROW]
[ROW][C]47[/C][C]0.95106460513605[/C][C]0.0978707897279007[/C][C]0.0489353948639504[/C][/ROW]
[ROW][C]48[/C][C]0.94487655105436[/C][C]0.11024689789128[/C][C]0.0551234489456401[/C][/ROW]
[ROW][C]49[/C][C]0.939186903002273[/C][C]0.121626193995455[/C][C]0.0608130969977275[/C][/ROW]
[ROW][C]50[/C][C]0.936759565518212[/C][C]0.126480868963577[/C][C]0.0632404344817884[/C][/ROW]
[ROW][C]51[/C][C]0.930258693725162[/C][C]0.139482612549676[/C][C]0.0697413062748381[/C][/ROW]
[ROW][C]52[/C][C]0.92183857964926[/C][C]0.15632284070148[/C][C]0.07816142035074[/C][/ROW]
[ROW][C]53[/C][C]0.911030097360941[/C][C]0.177939805278118[/C][C]0.088969902639059[/C][/ROW]
[ROW][C]54[/C][C]0.88981284240051[/C][C]0.220374315198979[/C][C]0.11018715759949[/C][/ROW]
[ROW][C]55[/C][C]0.866444503955186[/C][C]0.267110992089628[/C][C]0.133555496044814[/C][/ROW]
[ROW][C]56[/C][C]0.874718597741802[/C][C]0.250562804516395[/C][C]0.125281402258198[/C][/ROW]
[ROW][C]57[/C][C]0.851038379352579[/C][C]0.297923241294843[/C][C]0.148961620647421[/C][/ROW]
[ROW][C]58[/C][C]0.838487491962629[/C][C]0.323025016074742[/C][C]0.161512508037371[/C][/ROW]
[ROW][C]59[/C][C]0.826777172067266[/C][C]0.346445655865468[/C][C]0.173222827932734[/C][/ROW]
[ROW][C]60[/C][C]0.79561674672266[/C][C]0.408766506554679[/C][C]0.20438325327734[/C][/ROW]
[ROW][C]61[/C][C]0.784977153095985[/C][C]0.43004569380803[/C][C]0.215022846904015[/C][/ROW]
[ROW][C]62[/C][C]0.759665161779137[/C][C]0.480669676441726[/C][C]0.240334838220863[/C][/ROW]
[ROW][C]63[/C][C]0.722969294406654[/C][C]0.554061411186692[/C][C]0.277030705593346[/C][/ROW]
[ROW][C]64[/C][C]0.714531683523504[/C][C]0.570936632952991[/C][C]0.285468316476496[/C][/ROW]
[ROW][C]65[/C][C]0.696206145671808[/C][C]0.607587708656384[/C][C]0.303793854328192[/C][/ROW]
[ROW][C]66[/C][C]0.669780693723534[/C][C]0.660438612552932[/C][C]0.330219306276466[/C][/ROW]
[ROW][C]67[/C][C]0.716280504614903[/C][C]0.567438990770194[/C][C]0.283719495385097[/C][/ROW]
[ROW][C]68[/C][C]0.745713496391728[/C][C]0.508573007216544[/C][C]0.254286503608272[/C][/ROW]
[ROW][C]69[/C][C]0.739260767293655[/C][C]0.52147846541269[/C][C]0.260739232706345[/C][/ROW]
[ROW][C]70[/C][C]0.821132551156696[/C][C]0.357734897686608[/C][C]0.178867448843304[/C][/ROW]
[ROW][C]71[/C][C]0.789528847992054[/C][C]0.420942304015892[/C][C]0.210471152007946[/C][/ROW]
[ROW][C]72[/C][C]0.757012859806391[/C][C]0.485974280387218[/C][C]0.242987140193609[/C][/ROW]
[ROW][C]73[/C][C]0.719650435435762[/C][C]0.560699129128475[/C][C]0.280349564564238[/C][/ROW]
[ROW][C]74[/C][C]0.688302895166507[/C][C]0.623394209666987[/C][C]0.311697104833493[/C][/ROW]
[ROW][C]75[/C][C]0.747834508256508[/C][C]0.504330983486984[/C][C]0.252165491743492[/C][/ROW]
[ROW][C]76[/C][C]0.719437720257395[/C][C]0.56112455948521[/C][C]0.280562279742605[/C][/ROW]
[ROW][C]77[/C][C]0.730564182361724[/C][C]0.538871635276553[/C][C]0.269435817638276[/C][/ROW]
[ROW][C]78[/C][C]0.725518553625731[/C][C]0.548962892748537[/C][C]0.274481446374268[/C][/ROW]
[ROW][C]79[/C][C]0.756681240465158[/C][C]0.486637519069684[/C][C]0.243318759534842[/C][/ROW]
[ROW][C]80[/C][C]0.720875417600803[/C][C]0.558249164798395[/C][C]0.279124582399197[/C][/ROW]
[ROW][C]81[/C][C]0.682079295184984[/C][C]0.635841409630033[/C][C]0.317920704815016[/C][/ROW]
[ROW][C]82[/C][C]0.778667850278625[/C][C]0.442664299442751[/C][C]0.221332149721375[/C][/ROW]
[ROW][C]83[/C][C]0.763232949003463[/C][C]0.473534101993075[/C][C]0.236767050996537[/C][/ROW]
[ROW][C]84[/C][C]0.76709461792071[/C][C]0.46581076415858[/C][C]0.23290538207929[/C][/ROW]
[ROW][C]85[/C][C]0.730315110565772[/C][C]0.539369778868456[/C][C]0.269684889434228[/C][/ROW]
[ROW][C]86[/C][C]0.702386739984224[/C][C]0.595226520031552[/C][C]0.297613260015776[/C][/ROW]
[ROW][C]87[/C][C]0.666243255445288[/C][C]0.667513489109423[/C][C]0.333756744554712[/C][/ROW]
[ROW][C]88[/C][C]0.717667111687634[/C][C]0.564665776624731[/C][C]0.282332888312366[/C][/ROW]
[ROW][C]89[/C][C]0.693971275789308[/C][C]0.612057448421383[/C][C]0.306028724210692[/C][/ROW]
[ROW][C]90[/C][C]0.851455835963612[/C][C]0.297088328072775[/C][C]0.148544164036388[/C][/ROW]
[ROW][C]91[/C][C]0.823931953016415[/C][C]0.352136093967169[/C][C]0.176068046983585[/C][/ROW]
[ROW][C]92[/C][C]0.820452411724831[/C][C]0.359095176550339[/C][C]0.179547588275169[/C][/ROW]
[ROW][C]93[/C][C]0.794494214288351[/C][C]0.411011571423298[/C][C]0.205505785711649[/C][/ROW]
[ROW][C]94[/C][C]0.764819666154346[/C][C]0.470360667691309[/C][C]0.235180333845654[/C][/ROW]
[ROW][C]95[/C][C]0.81631072632028[/C][C]0.367378547359439[/C][C]0.18368927367972[/C][/ROW]
[ROW][C]96[/C][C]0.79001931211152[/C][C]0.41996137577696[/C][C]0.20998068788848[/C][/ROW]
[ROW][C]97[/C][C]0.758812281429427[/C][C]0.482375437141145[/C][C]0.241187718570573[/C][/ROW]
[ROW][C]98[/C][C]0.72102945935278[/C][C]0.557941081294441[/C][C]0.27897054064722[/C][/ROW]
[ROW][C]99[/C][C]0.702992622642918[/C][C]0.594014754714164[/C][C]0.297007377357082[/C][/ROW]
[ROW][C]100[/C][C]0.743006694324803[/C][C]0.513986611350393[/C][C]0.256993305675197[/C][/ROW]
[ROW][C]101[/C][C]0.708717766280954[/C][C]0.582564467438091[/C][C]0.291282233719046[/C][/ROW]
[ROW][C]102[/C][C]0.668009018375956[/C][C]0.663981963248088[/C][C]0.331990981624044[/C][/ROW]
[ROW][C]103[/C][C]0.679304887519097[/C][C]0.641390224961806[/C][C]0.320695112480903[/C][/ROW]
[ROW][C]104[/C][C]0.641077190883244[/C][C]0.717845618233512[/C][C]0.358922809116756[/C][/ROW]
[ROW][C]105[/C][C]0.596034984166387[/C][C]0.807930031667226[/C][C]0.403965015833613[/C][/ROW]
[ROW][C]106[/C][C]0.639972203389324[/C][C]0.720055593221352[/C][C]0.360027796610676[/C][/ROW]
[ROW][C]107[/C][C]0.593252303358805[/C][C]0.813495393282391[/C][C]0.406747696641196[/C][/ROW]
[ROW][C]108[/C][C]0.553058305539763[/C][C]0.893883388920473[/C][C]0.446941694460237[/C][/ROW]
[ROW][C]109[/C][C]0.508871235899601[/C][C]0.982257528200799[/C][C]0.491128764100399[/C][/ROW]
[ROW][C]110[/C][C]0.631737198643782[/C][C]0.736525602712437[/C][C]0.368262801356218[/C][/ROW]
[ROW][C]111[/C][C]0.648387074859739[/C][C]0.703225850280522[/C][C]0.351612925140261[/C][/ROW]
[ROW][C]112[/C][C]0.652997934955908[/C][C]0.694004130088183[/C][C]0.347002065044092[/C][/ROW]
[ROW][C]113[/C][C]0.607255449150705[/C][C]0.785489101698591[/C][C]0.392744550849295[/C][/ROW]
[ROW][C]114[/C][C]0.650945536247158[/C][C]0.698108927505683[/C][C]0.349054463752842[/C][/ROW]
[ROW][C]115[/C][C]0.607775842721709[/C][C]0.784448314556581[/C][C]0.392224157278291[/C][/ROW]
[ROW][C]116[/C][C]0.630421620590113[/C][C]0.739156758819774[/C][C]0.369578379409887[/C][/ROW]
[ROW][C]117[/C][C]0.703891226042006[/C][C]0.592217547915987[/C][C]0.296108773957994[/C][/ROW]
[ROW][C]118[/C][C]0.660084672951987[/C][C]0.679830654096026[/C][C]0.339915327048013[/C][/ROW]
[ROW][C]119[/C][C]0.712777444636842[/C][C]0.574445110726317[/C][C]0.287222555363158[/C][/ROW]
[ROW][C]120[/C][C]0.683929888630558[/C][C]0.632140222738884[/C][C]0.316070111369442[/C][/ROW]
[ROW][C]121[/C][C]0.637089571506726[/C][C]0.725820856986548[/C][C]0.362910428493274[/C][/ROW]
[ROW][C]122[/C][C]0.594044610398725[/C][C]0.811910779202549[/C][C]0.405955389601275[/C][/ROW]
[ROW][C]123[/C][C]0.562238617425539[/C][C]0.875522765148921[/C][C]0.437761382574461[/C][/ROW]
[ROW][C]124[/C][C]0.510694680418237[/C][C]0.978610639163527[/C][C]0.489305319581763[/C][/ROW]
[ROW][C]125[/C][C]0.520157673362717[/C][C]0.959684653274565[/C][C]0.479842326637283[/C][/ROW]
[ROW][C]126[/C][C]0.490711915158457[/C][C]0.981423830316913[/C][C]0.509288084841543[/C][/ROW]
[ROW][C]127[/C][C]0.448098095869585[/C][C]0.896196191739171[/C][C]0.551901904130415[/C][/ROW]
[ROW][C]128[/C][C]0.395221012546785[/C][C]0.790442025093571[/C][C]0.604778987453215[/C][/ROW]
[ROW][C]129[/C][C]0.429600190467765[/C][C]0.85920038093553[/C][C]0.570399809532235[/C][/ROW]
[ROW][C]130[/C][C]0.398499605218702[/C][C]0.796999210437405[/C][C]0.601500394781298[/C][/ROW]
[ROW][C]131[/C][C]0.37975412155876[/C][C]0.75950824311752[/C][C]0.62024587844124[/C][/ROW]
[ROW][C]132[/C][C]0.347932958549186[/C][C]0.695865917098372[/C][C]0.652067041450814[/C][/ROW]
[ROW][C]133[/C][C]0.298023185680654[/C][C]0.596046371361307[/C][C]0.701976814319347[/C][/ROW]
[ROW][C]134[/C][C]0.938694116336247[/C][C]0.122611767327507[/C][C]0.0613058836637533[/C][/ROW]
[ROW][C]135[/C][C]0.926873764594756[/C][C]0.146252470810488[/C][C]0.073126235405244[/C][/ROW]
[ROW][C]136[/C][C]0.949689774122275[/C][C]0.100620451755451[/C][C]0.0503102258777253[/C][/ROW]
[ROW][C]137[/C][C]0.934100514222113[/C][C]0.131798971555774[/C][C]0.0658994857778871[/C][/ROW]
[ROW][C]138[/C][C]0.92106263948463[/C][C]0.15787472103074[/C][C]0.0789373605153702[/C][/ROW]
[ROW][C]139[/C][C]0.974083465450122[/C][C]0.0518330690997553[/C][C]0.0259165345498776[/C][/ROW]
[ROW][C]140[/C][C]0.961448111011693[/C][C]0.0771037779766135[/C][C]0.0385518889883068[/C][/ROW]
[ROW][C]141[/C][C]0.99998709993709[/C][C]2.5800125820577e-05[/C][C]1.29000629102885e-05[/C][/ROW]
[ROW][C]142[/C][C]0.999989858616646[/C][C]2.02827667075954e-05[/C][C]1.01413833537977e-05[/C][/ROW]
[ROW][C]143[/C][C]0.99997376499174[/C][C]5.24700165191888e-05[/C][C]2.62350082595944e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999935182312478[/C][C]0.000129635375044499[/C][C]6.48176875222496e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999951948895249[/C][C]9.61022095019256e-05[/C][C]4.80511047509628e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999999984660126[/C][C]3.06797490404688e-08[/C][C]1.53398745202344e-08[/C][/ROW]
[ROW][C]147[/C][C]0.999999948811616[/C][C]1.02376767827398e-07[/C][C]5.11883839136989e-08[/C][/ROW]
[ROW][C]148[/C][C]0.99999982265602[/C][C]3.54687960504775e-07[/C][C]1.77343980252388e-07[/C][/ROW]
[ROW][C]149[/C][C]0.999999001425323[/C][C]1.99714935372723e-06[/C][C]9.98574676863614e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999994354683016[/C][C]1.12906339684674e-05[/C][C]5.64531698423369e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999965122983757[/C][C]6.97540324869982e-05[/C][C]3.48770162434991e-05[/C][/ROW]
[ROW][C]152[/C][C]0.999793609137016[/C][C]0.000412781725968852[/C][C]0.000206390862984426[/C][/ROW]
[ROW][C]153[/C][C]0.99898238654695[/C][C]0.00203522690609991[/C][C]0.00101761345304996[/C][/ROW]
[ROW][C]154[/C][C]0.996245642478466[/C][C]0.0075087150430681[/C][C]0.00375435752153405[/C][/ROW]
[ROW][C]155[/C][C]0.980905305851809[/C][C]0.038189388296382[/C][C]0.019094694148191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5438550260333370.9122899479333260.456144973966663
100.7110073030745860.5779853938508290.288992696925414
110.5804621581951790.8390756836096420.419537841804821
120.4759023604252990.9518047208505970.524097639574701
130.378344368189090.756688736378180.62165563181091
140.3887330700558160.7774661401116320.611266929944184
150.3559602103103390.7119204206206780.644039789689661
160.3040797666963830.6081595333927650.695920233303617
170.3613788786310580.7227577572621150.638621121368942
180.3150893558378680.6301787116757350.684910644162132
190.4578614091821530.9157228183643050.542138590817848
200.4369253238077350.8738506476154690.563074676192265
210.3622877451577570.7245754903155140.637712254842243
220.8969274125941410.2061451748117180.103072587405859
230.8627960361404530.2744079277190940.137203963859547
240.8643199675077090.2713600649845830.135680032492291
250.9262992821900580.1474014356198840.0737007178099421
260.9110231117505370.1779537764989270.0889768882494633
270.8830530686266790.2338938627466430.116946931373321
280.8513946752305120.2972106495389750.148605324769488
290.8329783978940570.3340432042118870.167021602105943
300.8101146245231310.3797707509537380.189885375476869
310.7743462293228840.4513075413542310.225653770677116
320.7398203223432160.5203593553135680.260179677656784
330.7075437129576210.5849125740847570.292456287042379
340.6644100618941560.6711798762116870.335589938105844
350.761392331718160.477215336563680.23860766828184
360.7270635873102980.5458728253794040.272936412689702
370.8970483157066470.2059033685867050.102951684293353
380.8726845678984230.2546308642031530.127315432101577
390.8488331892457740.3023336215084520.151166810754226
400.8164690318314580.3670619363370830.183530968168542
410.8333107998026360.3333784003947290.166689200197364
420.8035142532076140.3929714935847730.196485746792386
430.7688757754618940.4622484490762120.231124224538106
440.7335221082584640.5329557834830720.266477891741536
450.7370571960559010.5258856078881980.262942803944099
460.9580278272456420.08394434550871610.0419721727543581
470.951064605136050.09787078972790070.0489353948639504
480.944876551054360.110246897891280.0551234489456401
490.9391869030022730.1216261939954550.0608130969977275
500.9367595655182120.1264808689635770.0632404344817884
510.9302586937251620.1394826125496760.0697413062748381
520.921838579649260.156322840701480.07816142035074
530.9110300973609410.1779398052781180.088969902639059
540.889812842400510.2203743151989790.11018715759949
550.8664445039551860.2671109920896280.133555496044814
560.8747185977418020.2505628045163950.125281402258198
570.8510383793525790.2979232412948430.148961620647421
580.8384874919626290.3230250160747420.161512508037371
590.8267771720672660.3464456558654680.173222827932734
600.795616746722660.4087665065546790.20438325327734
610.7849771530959850.430045693808030.215022846904015
620.7596651617791370.4806696764417260.240334838220863
630.7229692944066540.5540614111866920.277030705593346
640.7145316835235040.5709366329529910.285468316476496
650.6962061456718080.6075877086563840.303793854328192
660.6697806937235340.6604386125529320.330219306276466
670.7162805046149030.5674389907701940.283719495385097
680.7457134963917280.5085730072165440.254286503608272
690.7392607672936550.521478465412690.260739232706345
700.8211325511566960.3577348976866080.178867448843304
710.7895288479920540.4209423040158920.210471152007946
720.7570128598063910.4859742803872180.242987140193609
730.7196504354357620.5606991291284750.280349564564238
740.6883028951665070.6233942096669870.311697104833493
750.7478345082565080.5043309834869840.252165491743492
760.7194377202573950.561124559485210.280562279742605
770.7305641823617240.5388716352765530.269435817638276
780.7255185536257310.5489628927485370.274481446374268
790.7566812404651580.4866375190696840.243318759534842
800.7208754176008030.5582491647983950.279124582399197
810.6820792951849840.6358414096300330.317920704815016
820.7786678502786250.4426642994427510.221332149721375
830.7632329490034630.4735341019930750.236767050996537
840.767094617920710.465810764158580.23290538207929
850.7303151105657720.5393697788684560.269684889434228
860.7023867399842240.5952265200315520.297613260015776
870.6662432554452880.6675134891094230.333756744554712
880.7176671116876340.5646657766247310.282332888312366
890.6939712757893080.6120574484213830.306028724210692
900.8514558359636120.2970883280727750.148544164036388
910.8239319530164150.3521360939671690.176068046983585
920.8204524117248310.3590951765503390.179547588275169
930.7944942142883510.4110115714232980.205505785711649
940.7648196661543460.4703606676913090.235180333845654
950.816310726320280.3673785473594390.18368927367972
960.790019312111520.419961375776960.20998068788848
970.7588122814294270.4823754371411450.241187718570573
980.721029459352780.5579410812944410.27897054064722
990.7029926226429180.5940147547141640.297007377357082
1000.7430066943248030.5139866113503930.256993305675197
1010.7087177662809540.5825644674380910.291282233719046
1020.6680090183759560.6639819632480880.331990981624044
1030.6793048875190970.6413902249618060.320695112480903
1040.6410771908832440.7178456182335120.358922809116756
1050.5960349841663870.8079300316672260.403965015833613
1060.6399722033893240.7200555932213520.360027796610676
1070.5932523033588050.8134953932823910.406747696641196
1080.5530583055397630.8938833889204730.446941694460237
1090.5088712358996010.9822575282007990.491128764100399
1100.6317371986437820.7365256027124370.368262801356218
1110.6483870748597390.7032258502805220.351612925140261
1120.6529979349559080.6940041300881830.347002065044092
1130.6072554491507050.7854891016985910.392744550849295
1140.6509455362471580.6981089275056830.349054463752842
1150.6077758427217090.7844483145565810.392224157278291
1160.6304216205901130.7391567588197740.369578379409887
1170.7038912260420060.5922175479159870.296108773957994
1180.6600846729519870.6798306540960260.339915327048013
1190.7127774446368420.5744451107263170.287222555363158
1200.6839298886305580.6321402227388840.316070111369442
1210.6370895715067260.7258208569865480.362910428493274
1220.5940446103987250.8119107792025490.405955389601275
1230.5622386174255390.8755227651489210.437761382574461
1240.5106946804182370.9786106391635270.489305319581763
1250.5201576733627170.9596846532745650.479842326637283
1260.4907119151584570.9814238303169130.509288084841543
1270.4480980958695850.8961961917391710.551901904130415
1280.3952210125467850.7904420250935710.604778987453215
1290.4296001904677650.859200380935530.570399809532235
1300.3984996052187020.7969992104374050.601500394781298
1310.379754121558760.759508243117520.62024587844124
1320.3479329585491860.6958659170983720.652067041450814
1330.2980231856806540.5960463713613070.701976814319347
1340.9386941163362470.1226117673275070.0613058836637533
1350.9268737645947560.1462524708104880.073126235405244
1360.9496897741222750.1006204517554510.0503102258777253
1370.9341005142221130.1317989715557740.0658994857778871
1380.921062639484630.157874721030740.0789373605153702
1390.9740834654501220.05183306909975530.0259165345498776
1400.9614481110116930.07710377797661350.0385518889883068
1410.999987099937092.5800125820577e-051.29000629102885e-05
1420.9999898586166462.02827667075954e-051.01413833537977e-05
1430.999973764991745.24700165191888e-052.62350082595944e-05
1440.9999351823124780.0001296353750444996.48176875222496e-05
1450.9999519488952499.61022095019256e-054.80511047509628e-05
1460.9999999846601263.06797490404688e-081.53398745202344e-08
1470.9999999488116161.02376767827398e-075.11883839136989e-08
1480.999999822656023.54687960504775e-071.77343980252388e-07
1490.9999990014253231.99714935372723e-069.98574676863614e-07
1500.9999943546830161.12906339684674e-055.64531698423369e-06
1510.9999651229837576.97540324869982e-053.48770162434991e-05
1520.9997936091370160.0004127817259688520.000206390862984426
1530.998982386546950.002035226906099910.00101761345304996
1540.9962456424784660.00750871504306810.00375435752153405
1550.9809053058518090.0381893882963820.019094694148191







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0952380952380952NOK
5% type I error level150.102040816326531NOK
10% type I error level190.129251700680272NOK

\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 & 14 & 0.0952380952380952 & NOK \tabularnewline
5% type I error level & 15 & 0.102040816326531 & NOK \tabularnewline
10% type I error level & 19 & 0.129251700680272 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146331&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]14[/C][C]0.0952380952380952[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.102040816326531[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.129251700680272[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146331&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146331&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0952380952380952NOK
5% type I error level150.102040816326531NOK
10% type I error level190.129251700680272NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}