Free Statistics

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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 computationThu, 17 Nov 2011 10:16:59 -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/17/t13215431137ldf23gk0mtvawj.htm/, Retrieved Thu, 31 Oct 2024 23:12:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=144884, Retrieved Thu, 31 Oct 2024 23:12:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS 7] [2011-11-17 14:51:59] [088a244c534fec2347300624359db3c1]
-    D      [Multiple Regression] [WS7] [2011-11-17 15:16:59] [84449ea5bbe6e767918d59f07903f9b5] [Current]
- R  D        [Multiple Regression] [] [2011-12-18 16:46:31] [06c08141d7d783218a8164fd2ea166f2]
-   PD          [Multiple Regression] [] [2011-12-18 18:19:05] [06c08141d7d783218a8164fd2ea166f2]
- R  D        [Multiple Regression] [] [2011-12-18 18:05:50] [06c08141d7d783218a8164fd2ea166f2]
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Dataseries X:
65	146455	1	95556	114468	127
54	84944	4	54565	88594	90
58	113337	9	63016	74151	68
75	128655	2	79774	77921	111
41	74398	1	31258	53212	51
0	35523	2	52491	34956	33
111	293403	0	91256	149703	123
1	32750	0	22807	6853	5
36	106539	5	77411	58907	63
60	130539	0	48821	67067	66
63	154991	0	52295	110563	99
71	126683	7	63262	58126	72
38	100672	6	50466	57113	55
76	179562	3	62932	77993	116
61	125971	4	38439	68091	71
125	234509	0	70817	124676	125
84	158980	4	105965	109522	123
69	184217	3	73795	75865	74
77	107342	0	82043	79746	116
95	141371	5	74349	77844	117
78	154730	0	82204	98681	98
76	264020	1	55709	105531	101
40	90938	3	37137	51428	43
81	101324	5	70780	65703	103
102	130232	0	55027	72562	107
70	137793	0	56699	81728	77
75	161678	4	65911	95580	87
93	151503	0	56316	98278	99
42	105324	0	26982	46629	46
95	175914	0	54628	115189	96
87	181853	3	96750	124865	92
44	114928	4	53009	59392	96
84	190410	1	64664	127818	96
28	61499	4	36990	17821	15
87	223004	1	85224	154076	147
71	167131	0	37048	64881	56
68	233482	0	59635	136506	81
50	121185	2	42051	66524	69
30	78776	1	26998	45988	34
86	188967	2	63717	107445	98
75	199512	8	55071	102772	82
46	102531	5	40001	46657	64
52	118958	3	54506	97563	61
31	68948	4	35838	36663	45
30	93125	1	50838	55369	37
70	277108	2	86997	77921	64
20	78800	2	33032	56968	21
84	157250	0	61704	77519	104
81	210554	6	117986	129805	126
79	127324	3	56733	72761	104
70	114397	0	55064	81278	87
8	24188	0	5950	15049	7
67	246209	6	84607	113935	130
21	65029	5	32551	25109	21
30	98030	3	31701	45824	35
70	173587	1	71170	89644	97
87	172684	5	101773	109011	103
87	191381	5	101653	134245	210
112	191276	0	81493	136692	151
54	134043	9	55901	50741	57
96	233406	6	109104	149510	117
93	195304	6	114425	147888	152
49	127619	5	36311	54987	52
49	162810	6	70027	74467	83
38	129100	2	73713	100033	87
64	108715	0	40671	85505	80
62	106469	3	89041	62426	88
66	142069	8	57231	82932	83
98	143937	2	78792	79169	140
97	84256	5	59155	65469	76
56	118807	11	55827	63572	70
22	69471	6	22618	23824	26
51	122433	5	58425	73831	66
56	131122	1	65724	63551	89
94	94763	0	56979	56756	100
98	188780	3	72369	81399	98
76	191467	3	79194	117881	109
57	105615	6	202316	70711	51
75	89318	1	44970	50495	82
48	107335	0	49319	53845	65
48	98599	1	36252	51390	46
109	260646	0	75741	104953	104
27	131876	5	38417	65983	36
83	119291	2	64102	76839	123
49	80953	0	56622	55792	59
24	99768	0	15430	25155	27
43	84572	5	72571	55291	84
44	202373	1	67271	84279	61
49	166790	0	43460	99692	46
106	99946	1	99501	59633	125
42	116900	1	28340	63249	58
108	142146	2	76013	82928	152
27	99246	4	37361	50000	52
79	156833	1	48204	69455	85
49	175078	4	76168	84068	95
64	130533	0	85168	76195	78
75	142339	2	125410	114634	144
115	176789	0	123328	139357	149
92	181379	7	83038	110044	101
106	228548	7	120087	155118	205
73	142141	6	91939	83061	61
105	167845	0	103646	127122	145
30	103012	0	29467	45653	28
13	43287	4	43750	19630	49
69	125366	4	34497	67229	68
72	118372	0	66477	86060	142
80	135171	0	71181	88003	82
106	175568	0	74482	95815	105
28	74112	0	174949	85499	52
70	88817	0	46765	27220	56
51	164767	4	90257	109882	81
90	141933	0	51370	72579	100
12	22938	0	1168	5841	11
84	115199	0	51360	68369	87
23	61857	4	25162	24610	31
57	91185	0	21067	30995	67
84	213765	1	58233	150662	150
4	21054	0	855	6622	4
56	167105	5	85903	93694	75
18	31414	0	14116	13155	39
86	178863	1	57637	111908	88
39	126681	7	94137	57550	67
16	64320	5	62147	16356	24
18	67746	2	62832	40174	58
16	38214	0	8773	13983	16
42	90961	1	63785	52316	49
75	181510	0	65196	99585	109
30	116775	0	73087	86271	124
104	223914	2	72631	131012	115
121	185139	0	86281	130274	128
106	242879	2	162365	159051	159
57	139144	0	56530	76506	75
28	75812	0	35606	49145	30
56	178218	4	70111	66398	83
81	246834	4	92046	127546	135
2	50999	8	63989	6802	8
88	223842	0	104911	99509	115
41	93577	4	43448	43106	60
83	155383	0	60029	108303	99
55	111664	1	38650	64167	98
3	75426	0	47261	8579	36
54	243551	9	73586	97811	93
89	136548	0	83042	84365	158
41	173260	3	37238	10901	16
94	185039	7	63958	91346	100
101	67507	5	78956	33660	49
70	139350	2	99518	93634	89
111	172964	1	111436	109348	153
0	0	9	0	0	0
4	14688	0	6023	7953	5
0	98	0	0	0	0
0	455	0	0	0	0
0	0	1	0	0	0
0	0	0	0	0	0
42	128066	2	42564	63538	80
97	176460	1	38885	108281	122
0	0	0	0	0	0
0	203	0	0	0	0
7	7199	0	1644	4245	6
12	46660	0	6179	21509	13
0	17547	0	3926	7670	3
37	73567	0	23238	10641	18
0	969	0	0	0	0
39	101060	2	49288	41243	49




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
BlogdComputations[t] = + 4.73148067023909 + 0.00015703077847098TotalTime[t] -0.859116985736638Shared[t] + 3.15058900167502e-05Caracters[t] -2.08877716524734e-06Writing[t] + 0.44407158237694Hyperlink[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BlogdComputations[t] =  +  4.73148067023909 +  0.00015703077847098TotalTime[t] -0.859116985736638Shared[t] +  3.15058900167502e-05Caracters[t] -2.08877716524734e-06Writing[t] +  0.44407158237694Hyperlink[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BlogdComputations[t] =  +  4.73148067023909 +  0.00015703077847098TotalTime[t] -0.859116985736638Shared[t] +  3.15058900167502e-05Caracters[t] -2.08877716524734e-06Writing[t] +  0.44407158237694Hyperlink[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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
BlogdComputations[t] = + 4.73148067023909 + 0.00015703077847098TotalTime[t] -0.859116985736638Shared[t] + 3.15058900167502e-05Caracters[t] -2.08877716524734e-06Writing[t] + 0.44407158237694Hyperlink[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.731480670239092.9010471.6310.1048910.052446
TotalTime0.000157030778470984e-053.95890.0001145.7e-05
Shared-0.8591169857366380.488721-1.75790.0807030.040351
Caracters3.15058900167502e-055.6e-050.56260.5744960.287248
Writing-2.08877716524734e-068.5e-05-0.02450.9805130.490256
Hyperlink0.444071582376940.0561447.909600

\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) & 4.73148067023909 & 2.901047 & 1.631 & 0.104891 & 0.052446 \tabularnewline
TotalTime & 0.00015703077847098 & 4e-05 & 3.9589 & 0.000114 & 5.7e-05 \tabularnewline
Shared & -0.859116985736638 & 0.488721 & -1.7579 & 0.080703 & 0.040351 \tabularnewline
Caracters & 3.15058900167502e-05 & 5.6e-05 & 0.5626 & 0.574496 & 0.287248 \tabularnewline
Writing & -2.08877716524734e-06 & 8.5e-05 & -0.0245 & 0.980513 & 0.490256 \tabularnewline
Hyperlink & 0.44407158237694 & 0.056144 & 7.9096 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&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]4.73148067023909[/C][C]2.901047[/C][C]1.631[/C][C]0.104891[/C][C]0.052446[/C][/ROW]
[ROW][C]TotalTime[/C][C]0.00015703077847098[/C][C]4e-05[/C][C]3.9589[/C][C]0.000114[/C][C]5.7e-05[/C][/ROW]
[ROW][C]Shared[/C][C]-0.859116985736638[/C][C]0.488721[/C][C]-1.7579[/C][C]0.080703[/C][C]0.040351[/C][/ROW]
[ROW][C]Caracters[/C][C]3.15058900167502e-05[/C][C]5.6e-05[/C][C]0.5626[/C][C]0.574496[/C][C]0.287248[/C][/ROW]
[ROW][C]Writing[/C][C]-2.08877716524734e-06[/C][C]8.5e-05[/C][C]-0.0245[/C][C]0.980513[/C][C]0.490256[/C][/ROW]
[ROW][C]Hyperlink[/C][C]0.44407158237694[/C][C]0.056144[/C][C]7.9096[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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)4.731480670239092.9010471.6310.1048910.052446
TotalTime0.000157030778470984e-053.95890.0001145.7e-05
Shared-0.8591169857366380.488721-1.75790.0807030.040351
Caracters3.15058900167502e-055.6e-050.56260.5744960.287248
Writing-2.08877716524734e-068.5e-05-0.02450.9805130.490256
Hyperlink0.444071582376940.0561447.909600







Multiple Linear Regression - Regression Statistics
Multiple R0.882249715161768
R-squared0.778364559903021
Adjusted R-squared0.771350780153117
F-TEST (value)110.976475974118
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.4173704212632
Sum Squared Residuals37555.8590916178

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.882249715161768 \tabularnewline
R-squared & 0.778364559903021 \tabularnewline
Adjusted R-squared & 0.771350780153117 \tabularnewline
F-TEST (value) & 110.976475974118 \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 & 15.4173704212632 \tabularnewline
Sum Squared Residuals & 37555.8590916178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.882249715161768[/C][/ROW]
[ROW][C]R-squared[/C][C]0.778364559903021[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.771350780153117[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]110.976475974118[/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]15.4173704212632[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37555.8590916178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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 R0.882249715161768
R-squared0.778364559903021
Adjusted R-squared0.771350780153117
F-TEST (value)110.976475974118
F-TEST (DF numerator)5
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.4173704212632
Sum Squared Residuals37555.8590916178







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16586.0388759892302-21.0388759892302
25456.1343433522421-2.13434335224208
35846.82418298952211.175817010478
47574.8585784114930.141421588506966
54139.07645334203681.92354665796321
6024.8265736391103-24.8265736391103
7111107.9879920897233.01200791027686
8112.798837020747-11.798837020747
93647.4581663944363-11.4581663944363
106055.93690693630654.06309306369352
116374.4495837602565-11.4495837602565
127152.455659162993918.5443408370061
133841.279998232128-3.27999823212798
147683.6830125856453-7.68301258564528
156153.67424725121817.32575274878192
1612599.036291527269425.9637084727306
178483.99032510390520.00967489609476042
186966.10967780065582.89032219934418
197775.51804815942051.48195184057954
209576.771701710082618.2282982899174
217874.93165565948713.06834434051293
227691.7175905203989-15.7175905203989
234036.59188529332933.40811470667073
248164.179003293471616.8209967065284
2510274.279681087691327.7203189123087
267062.17837544901367.82162455098643
277567.19460199115717.80539800884292
289374.054206216182118.9457937838179
294242.4505775052494-0.450577505249447
309576.466764549316618.5332354506834
318774.352613147372712.6473868526273
324463.5191570140911-19.5191570140911
338478.17377967368355.82622032631649
342818.74150108199119.25849891800886
3587106.53260555633-19.5326055563299
367156.875908582063214.1240914179368
376878.9588621971622-10.9588621971622
385053.8698611407338-3.86986114073381
393032.0955914245451-2.09559142454514
408677.98892901870888.01107098129115
417567.12233227584297.87766772415715
424646.1198107914492-0.119810791449168
435249.43603625805332.56396374194671
443133.1577192974828-2.15771929748278
453036.4125464113682-6.41254641136819
467077.5264712397202-7.52647123972022
472025.6344843736783-5.63448437367825
488477.39013467252326.60986532747683
498192.0393768850737-11.0393768850737
507969.96680326126979.03319673873031
517062.89462699922297.1053730007771
52811.7942682545736-3.79426825457358
536798.3961094117077-31.3961094117077
542120.9460545847540.0539454152459745
553033.9934144043329-3.99341440433293
567076.2608367697983-6.26083676979828
578776.270720932977310.7292790670227
5887126.665895802592-39.665895802592
59112104.1044991588357.89550084116481
605445.0156087483658.98439125163499
619691.31020532413014.68979467586986
6293101.040554823363-8.04055482336292
634944.59688372525754.40311627474249
644964.051619127004-15.051619127004
653864.0335948907966-26.0335948907966
666458.43158350322365.56841649677637
676260.62626086489041.37373913510964
686658.6555789119067.34442108809403
699890.10285307911917.89714692088085
709749.143102044763547.8568979552365
715646.64865187133969.35134812866042
722222.6945623019911-0.694562301991146
735150.65688459631270.343115403687335
745665.9228734885542-9.92287348855418
759465.695970038652228.3040299613478
769877.427440527869120.5725594721309
777682.8729945665887-6.87299456658872
785745.035681247752711.9643187522472
797555.623255581976619.3767444180234
804851.8924009151962-3.89240091519616
814840.8175434666677.182456533333
8210994.011433708722314.9885662912777
832738.2036016418437-11.2036016418437
848378.22550093896444.77449906103564
854945.31120608896473.68879391103528
862432.8216527942758-8.82165279427584
874353.1892390242284-10.1892390242284
884464.6829126176103-20.6829126176103
894952.5109486077232-3.51094860772318
9010678.08621718054227.913782819458
914248.7461773217725-6.74617732177248
9210895.055063361680212.9449366383198
932741.0440643496777-14.0440643496777
947967.619690170836711.3803098291633
954973.1985889983118-24.1985889983118
966462.39090196663341.60909803336663
977593.0228673232656-18.0228673232656
98115102.25393342407812.7460665759224
999274.437305859279817.5626941407202
100106129.100847393463-23.1008473934635
1017351.708781265583521.2912187344165
10210598.47862107323256.5213789267675
1033034.1745646458445-4.17456464584452
1041331.1892915639149-18.1892915639149
1056952.124633190582716.8753668094173
1067288.2923495647337-16.2923495647337
1078064.43015988225815.569840117742
10810681.075062050550224.9249379494498
1092844.7944036015702-16.7944036015702
1107044.96300836725.036991633
1115165.7524092789227-14.7524092789227
1129072.893344600938617.1066553990614
1131213.2428384050701-1.24283840507011
1148462.930831891360821.0691681086392
1152325.5160310434218-2.51603104342181
1165749.40212116111647.59787883888361
11784105.570768548967-21.5707685489674
11849.82699866325091-5.82699866325091
1195662.4926372386077-6.49263723860769
1201827.4004965376947-9.40049653769472
1218672.61981317121513.380186828785
1223951.2089346794666-12.2089346794666
1231623.1176658974121-7.11766589741207
1241841.3032691426191-23.3032691426191
1251618.0845939587754-2.08459395877544
1264241.81587459001260.184125409987366
1277583.483986881124-8.48398688112398
1283080.2560961297595-50.2560961297595
12910491.257517826498112.7424821735019
13012193.091410849935927.9085891500641
131106116.543338476612-10.5433384766123
1325761.5079639649181-4.50796396491808
1332830.9775912851395-2.97759128513954
1345668.2088841708659-12.2088841708659
13581102.638787501448-21.6387875014484
136211.4213526486068-9.42135264860681
1378894.0472584586989-6.04725845869893
1384143.9126059078504-2.91260590785042
1398374.75952701520018.24047298479988
1405566.009735689411-11.009735689411
141334.033341381542-31.033341381542
1425478.657175126513-24.6571751265129
1438998.7771218576758-9.77712185767583
1444137.61687428150753.38312571849245
1459474.005890500022919.9941094999771
14610135.213350853046765.7866491469533
1477067.35767911184072.64232088815932
148111102.258474110078.74152588993008
1490-3.000572201390663.00057220139066
15049.43145458708121-5.43145458708121
15104.74686968652924-4.74686968652924
15204.80292967444338-4.80292967444338
15303.87236368450244-3.87236368450244
15404.73148067023908-4.73148067023908
1554259.857576943733-17.857576943733
1569786.757679556549410.2423204434506
15704.73148067023908-4.73148067023908
15804.76335791826869-4.76335791826869
15978.56930356283437-1.56930356283437
1601217.9812147509614-5.98121475096142
16108.9267856905485-8.9267856905485
1623724.986959627192412.0130403728076
16304.88364349457746-4.88364349457746
1643942.1089995780324-3.10899957803238

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 65 & 86.0388759892302 & -21.0388759892302 \tabularnewline
2 & 54 & 56.1343433522421 & -2.13434335224208 \tabularnewline
3 & 58 & 46.824182989522 & 11.175817010478 \tabularnewline
4 & 75 & 74.858578411493 & 0.141421588506966 \tabularnewline
5 & 41 & 39.0764533420368 & 1.92354665796321 \tabularnewline
6 & 0 & 24.8265736391103 & -24.8265736391103 \tabularnewline
7 & 111 & 107.987992089723 & 3.01200791027686 \tabularnewline
8 & 1 & 12.798837020747 & -11.798837020747 \tabularnewline
9 & 36 & 47.4581663944363 & -11.4581663944363 \tabularnewline
10 & 60 & 55.9369069363065 & 4.06309306369352 \tabularnewline
11 & 63 & 74.4495837602565 & -11.4495837602565 \tabularnewline
12 & 71 & 52.4556591629939 & 18.5443408370061 \tabularnewline
13 & 38 & 41.279998232128 & -3.27999823212798 \tabularnewline
14 & 76 & 83.6830125856453 & -7.68301258564528 \tabularnewline
15 & 61 & 53.6742472512181 & 7.32575274878192 \tabularnewline
16 & 125 & 99.0362915272694 & 25.9637084727306 \tabularnewline
17 & 84 & 83.9903251039052 & 0.00967489609476042 \tabularnewline
18 & 69 & 66.1096778006558 & 2.89032219934418 \tabularnewline
19 & 77 & 75.5180481594205 & 1.48195184057954 \tabularnewline
20 & 95 & 76.7717017100826 & 18.2282982899174 \tabularnewline
21 & 78 & 74.9316556594871 & 3.06834434051293 \tabularnewline
22 & 76 & 91.7175905203989 & -15.7175905203989 \tabularnewline
23 & 40 & 36.5918852933293 & 3.40811470667073 \tabularnewline
24 & 81 & 64.1790032934716 & 16.8209967065284 \tabularnewline
25 & 102 & 74.2796810876913 & 27.7203189123087 \tabularnewline
26 & 70 & 62.1783754490136 & 7.82162455098643 \tabularnewline
27 & 75 & 67.1946019911571 & 7.80539800884292 \tabularnewline
28 & 93 & 74.0542062161821 & 18.9457937838179 \tabularnewline
29 & 42 & 42.4505775052494 & -0.450577505249447 \tabularnewline
30 & 95 & 76.4667645493166 & 18.5332354506834 \tabularnewline
31 & 87 & 74.3526131473727 & 12.6473868526273 \tabularnewline
32 & 44 & 63.5191570140911 & -19.5191570140911 \tabularnewline
33 & 84 & 78.1737796736835 & 5.82622032631649 \tabularnewline
34 & 28 & 18.7415010819911 & 9.25849891800886 \tabularnewline
35 & 87 & 106.53260555633 & -19.5326055563299 \tabularnewline
36 & 71 & 56.8759085820632 & 14.1240914179368 \tabularnewline
37 & 68 & 78.9588621971622 & -10.9588621971622 \tabularnewline
38 & 50 & 53.8698611407338 & -3.86986114073381 \tabularnewline
39 & 30 & 32.0955914245451 & -2.09559142454514 \tabularnewline
40 & 86 & 77.9889290187088 & 8.01107098129115 \tabularnewline
41 & 75 & 67.1223322758429 & 7.87766772415715 \tabularnewline
42 & 46 & 46.1198107914492 & -0.119810791449168 \tabularnewline
43 & 52 & 49.4360362580533 & 2.56396374194671 \tabularnewline
44 & 31 & 33.1577192974828 & -2.15771929748278 \tabularnewline
45 & 30 & 36.4125464113682 & -6.41254641136819 \tabularnewline
46 & 70 & 77.5264712397202 & -7.52647123972022 \tabularnewline
47 & 20 & 25.6344843736783 & -5.63448437367825 \tabularnewline
48 & 84 & 77.3901346725232 & 6.60986532747683 \tabularnewline
49 & 81 & 92.0393768850737 & -11.0393768850737 \tabularnewline
50 & 79 & 69.9668032612697 & 9.03319673873031 \tabularnewline
51 & 70 & 62.8946269992229 & 7.1053730007771 \tabularnewline
52 & 8 & 11.7942682545736 & -3.79426825457358 \tabularnewline
53 & 67 & 98.3961094117077 & -31.3961094117077 \tabularnewline
54 & 21 & 20.946054584754 & 0.0539454152459745 \tabularnewline
55 & 30 & 33.9934144043329 & -3.99341440433293 \tabularnewline
56 & 70 & 76.2608367697983 & -6.26083676979828 \tabularnewline
57 & 87 & 76.2707209329773 & 10.7292790670227 \tabularnewline
58 & 87 & 126.665895802592 & -39.665895802592 \tabularnewline
59 & 112 & 104.104499158835 & 7.89550084116481 \tabularnewline
60 & 54 & 45.015608748365 & 8.98439125163499 \tabularnewline
61 & 96 & 91.3102053241301 & 4.68979467586986 \tabularnewline
62 & 93 & 101.040554823363 & -8.04055482336292 \tabularnewline
63 & 49 & 44.5968837252575 & 4.40311627474249 \tabularnewline
64 & 49 & 64.051619127004 & -15.051619127004 \tabularnewline
65 & 38 & 64.0335948907966 & -26.0335948907966 \tabularnewline
66 & 64 & 58.4315835032236 & 5.56841649677637 \tabularnewline
67 & 62 & 60.6262608648904 & 1.37373913510964 \tabularnewline
68 & 66 & 58.655578911906 & 7.34442108809403 \tabularnewline
69 & 98 & 90.1028530791191 & 7.89714692088085 \tabularnewline
70 & 97 & 49.1431020447635 & 47.8568979552365 \tabularnewline
71 & 56 & 46.6486518713396 & 9.35134812866042 \tabularnewline
72 & 22 & 22.6945623019911 & -0.694562301991146 \tabularnewline
73 & 51 & 50.6568845963127 & 0.343115403687335 \tabularnewline
74 & 56 & 65.9228734885542 & -9.92287348855418 \tabularnewline
75 & 94 & 65.6959700386522 & 28.3040299613478 \tabularnewline
76 & 98 & 77.4274405278691 & 20.5725594721309 \tabularnewline
77 & 76 & 82.8729945665887 & -6.87299456658872 \tabularnewline
78 & 57 & 45.0356812477527 & 11.9643187522472 \tabularnewline
79 & 75 & 55.6232555819766 & 19.3767444180234 \tabularnewline
80 & 48 & 51.8924009151962 & -3.89240091519616 \tabularnewline
81 & 48 & 40.817543466667 & 7.182456533333 \tabularnewline
82 & 109 & 94.0114337087223 & 14.9885662912777 \tabularnewline
83 & 27 & 38.2036016418437 & -11.2036016418437 \tabularnewline
84 & 83 & 78.2255009389644 & 4.77449906103564 \tabularnewline
85 & 49 & 45.3112060889647 & 3.68879391103528 \tabularnewline
86 & 24 & 32.8216527942758 & -8.82165279427584 \tabularnewline
87 & 43 & 53.1892390242284 & -10.1892390242284 \tabularnewline
88 & 44 & 64.6829126176103 & -20.6829126176103 \tabularnewline
89 & 49 & 52.5109486077232 & -3.51094860772318 \tabularnewline
90 & 106 & 78.086217180542 & 27.913782819458 \tabularnewline
91 & 42 & 48.7461773217725 & -6.74617732177248 \tabularnewline
92 & 108 & 95.0550633616802 & 12.9449366383198 \tabularnewline
93 & 27 & 41.0440643496777 & -14.0440643496777 \tabularnewline
94 & 79 & 67.6196901708367 & 11.3803098291633 \tabularnewline
95 & 49 & 73.1985889983118 & -24.1985889983118 \tabularnewline
96 & 64 & 62.3909019666334 & 1.60909803336663 \tabularnewline
97 & 75 & 93.0228673232656 & -18.0228673232656 \tabularnewline
98 & 115 & 102.253933424078 & 12.7460665759224 \tabularnewline
99 & 92 & 74.4373058592798 & 17.5626941407202 \tabularnewline
100 & 106 & 129.100847393463 & -23.1008473934635 \tabularnewline
101 & 73 & 51.7087812655835 & 21.2912187344165 \tabularnewline
102 & 105 & 98.4786210732325 & 6.5213789267675 \tabularnewline
103 & 30 & 34.1745646458445 & -4.17456464584452 \tabularnewline
104 & 13 & 31.1892915639149 & -18.1892915639149 \tabularnewline
105 & 69 & 52.1246331905827 & 16.8753668094173 \tabularnewline
106 & 72 & 88.2923495647337 & -16.2923495647337 \tabularnewline
107 & 80 & 64.430159882258 & 15.569840117742 \tabularnewline
108 & 106 & 81.0750620505502 & 24.9249379494498 \tabularnewline
109 & 28 & 44.7944036015702 & -16.7944036015702 \tabularnewline
110 & 70 & 44.963008367 & 25.036991633 \tabularnewline
111 & 51 & 65.7524092789227 & -14.7524092789227 \tabularnewline
112 & 90 & 72.8933446009386 & 17.1066553990614 \tabularnewline
113 & 12 & 13.2428384050701 & -1.24283840507011 \tabularnewline
114 & 84 & 62.9308318913608 & 21.0691681086392 \tabularnewline
115 & 23 & 25.5160310434218 & -2.51603104342181 \tabularnewline
116 & 57 & 49.4021211611164 & 7.59787883888361 \tabularnewline
117 & 84 & 105.570768548967 & -21.5707685489674 \tabularnewline
118 & 4 & 9.82699866325091 & -5.82699866325091 \tabularnewline
119 & 56 & 62.4926372386077 & -6.49263723860769 \tabularnewline
120 & 18 & 27.4004965376947 & -9.40049653769472 \tabularnewline
121 & 86 & 72.619813171215 & 13.380186828785 \tabularnewline
122 & 39 & 51.2089346794666 & -12.2089346794666 \tabularnewline
123 & 16 & 23.1176658974121 & -7.11766589741207 \tabularnewline
124 & 18 & 41.3032691426191 & -23.3032691426191 \tabularnewline
125 & 16 & 18.0845939587754 & -2.08459395877544 \tabularnewline
126 & 42 & 41.8158745900126 & 0.184125409987366 \tabularnewline
127 & 75 & 83.483986881124 & -8.48398688112398 \tabularnewline
128 & 30 & 80.2560961297595 & -50.2560961297595 \tabularnewline
129 & 104 & 91.2575178264981 & 12.7424821735019 \tabularnewline
130 & 121 & 93.0914108499359 & 27.9085891500641 \tabularnewline
131 & 106 & 116.543338476612 & -10.5433384766123 \tabularnewline
132 & 57 & 61.5079639649181 & -4.50796396491808 \tabularnewline
133 & 28 & 30.9775912851395 & -2.97759128513954 \tabularnewline
134 & 56 & 68.2088841708659 & -12.2088841708659 \tabularnewline
135 & 81 & 102.638787501448 & -21.6387875014484 \tabularnewline
136 & 2 & 11.4213526486068 & -9.42135264860681 \tabularnewline
137 & 88 & 94.0472584586989 & -6.04725845869893 \tabularnewline
138 & 41 & 43.9126059078504 & -2.91260590785042 \tabularnewline
139 & 83 & 74.7595270152001 & 8.24047298479988 \tabularnewline
140 & 55 & 66.009735689411 & -11.009735689411 \tabularnewline
141 & 3 & 34.033341381542 & -31.033341381542 \tabularnewline
142 & 54 & 78.657175126513 & -24.6571751265129 \tabularnewline
143 & 89 & 98.7771218576758 & -9.77712185767583 \tabularnewline
144 & 41 & 37.6168742815075 & 3.38312571849245 \tabularnewline
145 & 94 & 74.0058905000229 & 19.9941094999771 \tabularnewline
146 & 101 & 35.2133508530467 & 65.7866491469533 \tabularnewline
147 & 70 & 67.3576791118407 & 2.64232088815932 \tabularnewline
148 & 111 & 102.25847411007 & 8.74152588993008 \tabularnewline
149 & 0 & -3.00057220139066 & 3.00057220139066 \tabularnewline
150 & 4 & 9.43145458708121 & -5.43145458708121 \tabularnewline
151 & 0 & 4.74686968652924 & -4.74686968652924 \tabularnewline
152 & 0 & 4.80292967444338 & -4.80292967444338 \tabularnewline
153 & 0 & 3.87236368450244 & -3.87236368450244 \tabularnewline
154 & 0 & 4.73148067023908 & -4.73148067023908 \tabularnewline
155 & 42 & 59.857576943733 & -17.857576943733 \tabularnewline
156 & 97 & 86.7576795565494 & 10.2423204434506 \tabularnewline
157 & 0 & 4.73148067023908 & -4.73148067023908 \tabularnewline
158 & 0 & 4.76335791826869 & -4.76335791826869 \tabularnewline
159 & 7 & 8.56930356283437 & -1.56930356283437 \tabularnewline
160 & 12 & 17.9812147509614 & -5.98121475096142 \tabularnewline
161 & 0 & 8.9267856905485 & -8.9267856905485 \tabularnewline
162 & 37 & 24.9869596271924 & 12.0130403728076 \tabularnewline
163 & 0 & 4.88364349457746 & -4.88364349457746 \tabularnewline
164 & 39 & 42.1089995780324 & -3.10899957803238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&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]65[/C][C]86.0388759892302[/C][C]-21.0388759892302[/C][/ROW]
[ROW][C]2[/C][C]54[/C][C]56.1343433522421[/C][C]-2.13434335224208[/C][/ROW]
[ROW][C]3[/C][C]58[/C][C]46.824182989522[/C][C]11.175817010478[/C][/ROW]
[ROW][C]4[/C][C]75[/C][C]74.858578411493[/C][C]0.141421588506966[/C][/ROW]
[ROW][C]5[/C][C]41[/C][C]39.0764533420368[/C][C]1.92354665796321[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]24.8265736391103[/C][C]-24.8265736391103[/C][/ROW]
[ROW][C]7[/C][C]111[/C][C]107.987992089723[/C][C]3.01200791027686[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]12.798837020747[/C][C]-11.798837020747[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]47.4581663944363[/C][C]-11.4581663944363[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]55.9369069363065[/C][C]4.06309306369352[/C][/ROW]
[ROW][C]11[/C][C]63[/C][C]74.4495837602565[/C][C]-11.4495837602565[/C][/ROW]
[ROW][C]12[/C][C]71[/C][C]52.4556591629939[/C][C]18.5443408370061[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]41.279998232128[/C][C]-3.27999823212798[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]83.6830125856453[/C][C]-7.68301258564528[/C][/ROW]
[ROW][C]15[/C][C]61[/C][C]53.6742472512181[/C][C]7.32575274878192[/C][/ROW]
[ROW][C]16[/C][C]125[/C][C]99.0362915272694[/C][C]25.9637084727306[/C][/ROW]
[ROW][C]17[/C][C]84[/C][C]83.9903251039052[/C][C]0.00967489609476042[/C][/ROW]
[ROW][C]18[/C][C]69[/C][C]66.1096778006558[/C][C]2.89032219934418[/C][/ROW]
[ROW][C]19[/C][C]77[/C][C]75.5180481594205[/C][C]1.48195184057954[/C][/ROW]
[ROW][C]20[/C][C]95[/C][C]76.7717017100826[/C][C]18.2282982899174[/C][/ROW]
[ROW][C]21[/C][C]78[/C][C]74.9316556594871[/C][C]3.06834434051293[/C][/ROW]
[ROW][C]22[/C][C]76[/C][C]91.7175905203989[/C][C]-15.7175905203989[/C][/ROW]
[ROW][C]23[/C][C]40[/C][C]36.5918852933293[/C][C]3.40811470667073[/C][/ROW]
[ROW][C]24[/C][C]81[/C][C]64.1790032934716[/C][C]16.8209967065284[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]74.2796810876913[/C][C]27.7203189123087[/C][/ROW]
[ROW][C]26[/C][C]70[/C][C]62.1783754490136[/C][C]7.82162455098643[/C][/ROW]
[ROW][C]27[/C][C]75[/C][C]67.1946019911571[/C][C]7.80539800884292[/C][/ROW]
[ROW][C]28[/C][C]93[/C][C]74.0542062161821[/C][C]18.9457937838179[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]42.4505775052494[/C][C]-0.450577505249447[/C][/ROW]
[ROW][C]30[/C][C]95[/C][C]76.4667645493166[/C][C]18.5332354506834[/C][/ROW]
[ROW][C]31[/C][C]87[/C][C]74.3526131473727[/C][C]12.6473868526273[/C][/ROW]
[ROW][C]32[/C][C]44[/C][C]63.5191570140911[/C][C]-19.5191570140911[/C][/ROW]
[ROW][C]33[/C][C]84[/C][C]78.1737796736835[/C][C]5.82622032631649[/C][/ROW]
[ROW][C]34[/C][C]28[/C][C]18.7415010819911[/C][C]9.25849891800886[/C][/ROW]
[ROW][C]35[/C][C]87[/C][C]106.53260555633[/C][C]-19.5326055563299[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]56.8759085820632[/C][C]14.1240914179368[/C][/ROW]
[ROW][C]37[/C][C]68[/C][C]78.9588621971622[/C][C]-10.9588621971622[/C][/ROW]
[ROW][C]38[/C][C]50[/C][C]53.8698611407338[/C][C]-3.86986114073381[/C][/ROW]
[ROW][C]39[/C][C]30[/C][C]32.0955914245451[/C][C]-2.09559142454514[/C][/ROW]
[ROW][C]40[/C][C]86[/C][C]77.9889290187088[/C][C]8.01107098129115[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]67.1223322758429[/C][C]7.87766772415715[/C][/ROW]
[ROW][C]42[/C][C]46[/C][C]46.1198107914492[/C][C]-0.119810791449168[/C][/ROW]
[ROW][C]43[/C][C]52[/C][C]49.4360362580533[/C][C]2.56396374194671[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]33.1577192974828[/C][C]-2.15771929748278[/C][/ROW]
[ROW][C]45[/C][C]30[/C][C]36.4125464113682[/C][C]-6.41254641136819[/C][/ROW]
[ROW][C]46[/C][C]70[/C][C]77.5264712397202[/C][C]-7.52647123972022[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]25.6344843736783[/C][C]-5.63448437367825[/C][/ROW]
[ROW][C]48[/C][C]84[/C][C]77.3901346725232[/C][C]6.60986532747683[/C][/ROW]
[ROW][C]49[/C][C]81[/C][C]92.0393768850737[/C][C]-11.0393768850737[/C][/ROW]
[ROW][C]50[/C][C]79[/C][C]69.9668032612697[/C][C]9.03319673873031[/C][/ROW]
[ROW][C]51[/C][C]70[/C][C]62.8946269992229[/C][C]7.1053730007771[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]11.7942682545736[/C][C]-3.79426825457358[/C][/ROW]
[ROW][C]53[/C][C]67[/C][C]98.3961094117077[/C][C]-31.3961094117077[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]20.946054584754[/C][C]0.0539454152459745[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]33.9934144043329[/C][C]-3.99341440433293[/C][/ROW]
[ROW][C]56[/C][C]70[/C][C]76.2608367697983[/C][C]-6.26083676979828[/C][/ROW]
[ROW][C]57[/C][C]87[/C][C]76.2707209329773[/C][C]10.7292790670227[/C][/ROW]
[ROW][C]58[/C][C]87[/C][C]126.665895802592[/C][C]-39.665895802592[/C][/ROW]
[ROW][C]59[/C][C]112[/C][C]104.104499158835[/C][C]7.89550084116481[/C][/ROW]
[ROW][C]60[/C][C]54[/C][C]45.015608748365[/C][C]8.98439125163499[/C][/ROW]
[ROW][C]61[/C][C]96[/C][C]91.3102053241301[/C][C]4.68979467586986[/C][/ROW]
[ROW][C]62[/C][C]93[/C][C]101.040554823363[/C][C]-8.04055482336292[/C][/ROW]
[ROW][C]63[/C][C]49[/C][C]44.5968837252575[/C][C]4.40311627474249[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]64.051619127004[/C][C]-15.051619127004[/C][/ROW]
[ROW][C]65[/C][C]38[/C][C]64.0335948907966[/C][C]-26.0335948907966[/C][/ROW]
[ROW][C]66[/C][C]64[/C][C]58.4315835032236[/C][C]5.56841649677637[/C][/ROW]
[ROW][C]67[/C][C]62[/C][C]60.6262608648904[/C][C]1.37373913510964[/C][/ROW]
[ROW][C]68[/C][C]66[/C][C]58.655578911906[/C][C]7.34442108809403[/C][/ROW]
[ROW][C]69[/C][C]98[/C][C]90.1028530791191[/C][C]7.89714692088085[/C][/ROW]
[ROW][C]70[/C][C]97[/C][C]49.1431020447635[/C][C]47.8568979552365[/C][/ROW]
[ROW][C]71[/C][C]56[/C][C]46.6486518713396[/C][C]9.35134812866042[/C][/ROW]
[ROW][C]72[/C][C]22[/C][C]22.6945623019911[/C][C]-0.694562301991146[/C][/ROW]
[ROW][C]73[/C][C]51[/C][C]50.6568845963127[/C][C]0.343115403687335[/C][/ROW]
[ROW][C]74[/C][C]56[/C][C]65.9228734885542[/C][C]-9.92287348855418[/C][/ROW]
[ROW][C]75[/C][C]94[/C][C]65.6959700386522[/C][C]28.3040299613478[/C][/ROW]
[ROW][C]76[/C][C]98[/C][C]77.4274405278691[/C][C]20.5725594721309[/C][/ROW]
[ROW][C]77[/C][C]76[/C][C]82.8729945665887[/C][C]-6.87299456658872[/C][/ROW]
[ROW][C]78[/C][C]57[/C][C]45.0356812477527[/C][C]11.9643187522472[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]55.6232555819766[/C][C]19.3767444180234[/C][/ROW]
[ROW][C]80[/C][C]48[/C][C]51.8924009151962[/C][C]-3.89240091519616[/C][/ROW]
[ROW][C]81[/C][C]48[/C][C]40.817543466667[/C][C]7.182456533333[/C][/ROW]
[ROW][C]82[/C][C]109[/C][C]94.0114337087223[/C][C]14.9885662912777[/C][/ROW]
[ROW][C]83[/C][C]27[/C][C]38.2036016418437[/C][C]-11.2036016418437[/C][/ROW]
[ROW][C]84[/C][C]83[/C][C]78.2255009389644[/C][C]4.77449906103564[/C][/ROW]
[ROW][C]85[/C][C]49[/C][C]45.3112060889647[/C][C]3.68879391103528[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]32.8216527942758[/C][C]-8.82165279427584[/C][/ROW]
[ROW][C]87[/C][C]43[/C][C]53.1892390242284[/C][C]-10.1892390242284[/C][/ROW]
[ROW][C]88[/C][C]44[/C][C]64.6829126176103[/C][C]-20.6829126176103[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]52.5109486077232[/C][C]-3.51094860772318[/C][/ROW]
[ROW][C]90[/C][C]106[/C][C]78.086217180542[/C][C]27.913782819458[/C][/ROW]
[ROW][C]91[/C][C]42[/C][C]48.7461773217725[/C][C]-6.74617732177248[/C][/ROW]
[ROW][C]92[/C][C]108[/C][C]95.0550633616802[/C][C]12.9449366383198[/C][/ROW]
[ROW][C]93[/C][C]27[/C][C]41.0440643496777[/C][C]-14.0440643496777[/C][/ROW]
[ROW][C]94[/C][C]79[/C][C]67.6196901708367[/C][C]11.3803098291633[/C][/ROW]
[ROW][C]95[/C][C]49[/C][C]73.1985889983118[/C][C]-24.1985889983118[/C][/ROW]
[ROW][C]96[/C][C]64[/C][C]62.3909019666334[/C][C]1.60909803336663[/C][/ROW]
[ROW][C]97[/C][C]75[/C][C]93.0228673232656[/C][C]-18.0228673232656[/C][/ROW]
[ROW][C]98[/C][C]115[/C][C]102.253933424078[/C][C]12.7460665759224[/C][/ROW]
[ROW][C]99[/C][C]92[/C][C]74.4373058592798[/C][C]17.5626941407202[/C][/ROW]
[ROW][C]100[/C][C]106[/C][C]129.100847393463[/C][C]-23.1008473934635[/C][/ROW]
[ROW][C]101[/C][C]73[/C][C]51.7087812655835[/C][C]21.2912187344165[/C][/ROW]
[ROW][C]102[/C][C]105[/C][C]98.4786210732325[/C][C]6.5213789267675[/C][/ROW]
[ROW][C]103[/C][C]30[/C][C]34.1745646458445[/C][C]-4.17456464584452[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]31.1892915639149[/C][C]-18.1892915639149[/C][/ROW]
[ROW][C]105[/C][C]69[/C][C]52.1246331905827[/C][C]16.8753668094173[/C][/ROW]
[ROW][C]106[/C][C]72[/C][C]88.2923495647337[/C][C]-16.2923495647337[/C][/ROW]
[ROW][C]107[/C][C]80[/C][C]64.430159882258[/C][C]15.569840117742[/C][/ROW]
[ROW][C]108[/C][C]106[/C][C]81.0750620505502[/C][C]24.9249379494498[/C][/ROW]
[ROW][C]109[/C][C]28[/C][C]44.7944036015702[/C][C]-16.7944036015702[/C][/ROW]
[ROW][C]110[/C][C]70[/C][C]44.963008367[/C][C]25.036991633[/C][/ROW]
[ROW][C]111[/C][C]51[/C][C]65.7524092789227[/C][C]-14.7524092789227[/C][/ROW]
[ROW][C]112[/C][C]90[/C][C]72.8933446009386[/C][C]17.1066553990614[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]13.2428384050701[/C][C]-1.24283840507011[/C][/ROW]
[ROW][C]114[/C][C]84[/C][C]62.9308318913608[/C][C]21.0691681086392[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]25.5160310434218[/C][C]-2.51603104342181[/C][/ROW]
[ROW][C]116[/C][C]57[/C][C]49.4021211611164[/C][C]7.59787883888361[/C][/ROW]
[ROW][C]117[/C][C]84[/C][C]105.570768548967[/C][C]-21.5707685489674[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]9.82699866325091[/C][C]-5.82699866325091[/C][/ROW]
[ROW][C]119[/C][C]56[/C][C]62.4926372386077[/C][C]-6.49263723860769[/C][/ROW]
[ROW][C]120[/C][C]18[/C][C]27.4004965376947[/C][C]-9.40049653769472[/C][/ROW]
[ROW][C]121[/C][C]86[/C][C]72.619813171215[/C][C]13.380186828785[/C][/ROW]
[ROW][C]122[/C][C]39[/C][C]51.2089346794666[/C][C]-12.2089346794666[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]23.1176658974121[/C][C]-7.11766589741207[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]41.3032691426191[/C][C]-23.3032691426191[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]18.0845939587754[/C][C]-2.08459395877544[/C][/ROW]
[ROW][C]126[/C][C]42[/C][C]41.8158745900126[/C][C]0.184125409987366[/C][/ROW]
[ROW][C]127[/C][C]75[/C][C]83.483986881124[/C][C]-8.48398688112398[/C][/ROW]
[ROW][C]128[/C][C]30[/C][C]80.2560961297595[/C][C]-50.2560961297595[/C][/ROW]
[ROW][C]129[/C][C]104[/C][C]91.2575178264981[/C][C]12.7424821735019[/C][/ROW]
[ROW][C]130[/C][C]121[/C][C]93.0914108499359[/C][C]27.9085891500641[/C][/ROW]
[ROW][C]131[/C][C]106[/C][C]116.543338476612[/C][C]-10.5433384766123[/C][/ROW]
[ROW][C]132[/C][C]57[/C][C]61.5079639649181[/C][C]-4.50796396491808[/C][/ROW]
[ROW][C]133[/C][C]28[/C][C]30.9775912851395[/C][C]-2.97759128513954[/C][/ROW]
[ROW][C]134[/C][C]56[/C][C]68.2088841708659[/C][C]-12.2088841708659[/C][/ROW]
[ROW][C]135[/C][C]81[/C][C]102.638787501448[/C][C]-21.6387875014484[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]11.4213526486068[/C][C]-9.42135264860681[/C][/ROW]
[ROW][C]137[/C][C]88[/C][C]94.0472584586989[/C][C]-6.04725845869893[/C][/ROW]
[ROW][C]138[/C][C]41[/C][C]43.9126059078504[/C][C]-2.91260590785042[/C][/ROW]
[ROW][C]139[/C][C]83[/C][C]74.7595270152001[/C][C]8.24047298479988[/C][/ROW]
[ROW][C]140[/C][C]55[/C][C]66.009735689411[/C][C]-11.009735689411[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]34.033341381542[/C][C]-31.033341381542[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]78.657175126513[/C][C]-24.6571751265129[/C][/ROW]
[ROW][C]143[/C][C]89[/C][C]98.7771218576758[/C][C]-9.77712185767583[/C][/ROW]
[ROW][C]144[/C][C]41[/C][C]37.6168742815075[/C][C]3.38312571849245[/C][/ROW]
[ROW][C]145[/C][C]94[/C][C]74.0058905000229[/C][C]19.9941094999771[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]35.2133508530467[/C][C]65.7866491469533[/C][/ROW]
[ROW][C]147[/C][C]70[/C][C]67.3576791118407[/C][C]2.64232088815932[/C][/ROW]
[ROW][C]148[/C][C]111[/C][C]102.25847411007[/C][C]8.74152588993008[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-3.00057220139066[/C][C]3.00057220139066[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]9.43145458708121[/C][C]-5.43145458708121[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]4.74686968652924[/C][C]-4.74686968652924[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]4.80292967444338[/C][C]-4.80292967444338[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3.87236368450244[/C][C]-3.87236368450244[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]4.73148067023908[/C][C]-4.73148067023908[/C][/ROW]
[ROW][C]155[/C][C]42[/C][C]59.857576943733[/C][C]-17.857576943733[/C][/ROW]
[ROW][C]156[/C][C]97[/C][C]86.7576795565494[/C][C]10.2423204434506[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]4.73148067023908[/C][C]-4.73148067023908[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]4.76335791826869[/C][C]-4.76335791826869[/C][/ROW]
[ROW][C]159[/C][C]7[/C][C]8.56930356283437[/C][C]-1.56930356283437[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]17.9812147509614[/C][C]-5.98121475096142[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]8.9267856905485[/C][C]-8.9267856905485[/C][/ROW]
[ROW][C]162[/C][C]37[/C][C]24.9869596271924[/C][C]12.0130403728076[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]4.88364349457746[/C][C]-4.88364349457746[/C][/ROW]
[ROW][C]164[/C][C]39[/C][C]42.1089995780324[/C][C]-3.10899957803238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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
16586.0388759892302-21.0388759892302
25456.1343433522421-2.13434335224208
35846.82418298952211.175817010478
47574.8585784114930.141421588506966
54139.07645334203681.92354665796321
6024.8265736391103-24.8265736391103
7111107.9879920897233.01200791027686
8112.798837020747-11.798837020747
93647.4581663944363-11.4581663944363
106055.93690693630654.06309306369352
116374.4495837602565-11.4495837602565
127152.455659162993918.5443408370061
133841.279998232128-3.27999823212798
147683.6830125856453-7.68301258564528
156153.67424725121817.32575274878192
1612599.036291527269425.9637084727306
178483.99032510390520.00967489609476042
186966.10967780065582.89032219934418
197775.51804815942051.48195184057954
209576.771701710082618.2282982899174
217874.93165565948713.06834434051293
227691.7175905203989-15.7175905203989
234036.59188529332933.40811470667073
248164.179003293471616.8209967065284
2510274.279681087691327.7203189123087
267062.17837544901367.82162455098643
277567.19460199115717.80539800884292
289374.054206216182118.9457937838179
294242.4505775052494-0.450577505249447
309576.466764549316618.5332354506834
318774.352613147372712.6473868526273
324463.5191570140911-19.5191570140911
338478.17377967368355.82622032631649
342818.74150108199119.25849891800886
3587106.53260555633-19.5326055563299
367156.875908582063214.1240914179368
376878.9588621971622-10.9588621971622
385053.8698611407338-3.86986114073381
393032.0955914245451-2.09559142454514
408677.98892901870888.01107098129115
417567.12233227584297.87766772415715
424646.1198107914492-0.119810791449168
435249.43603625805332.56396374194671
443133.1577192974828-2.15771929748278
453036.4125464113682-6.41254641136819
467077.5264712397202-7.52647123972022
472025.6344843736783-5.63448437367825
488477.39013467252326.60986532747683
498192.0393768850737-11.0393768850737
507969.96680326126979.03319673873031
517062.89462699922297.1053730007771
52811.7942682545736-3.79426825457358
536798.3961094117077-31.3961094117077
542120.9460545847540.0539454152459745
553033.9934144043329-3.99341440433293
567076.2608367697983-6.26083676979828
578776.270720932977310.7292790670227
5887126.665895802592-39.665895802592
59112104.1044991588357.89550084116481
605445.0156087483658.98439125163499
619691.31020532413014.68979467586986
6293101.040554823363-8.04055482336292
634944.59688372525754.40311627474249
644964.051619127004-15.051619127004
653864.0335948907966-26.0335948907966
666458.43158350322365.56841649677637
676260.62626086489041.37373913510964
686658.6555789119067.34442108809403
699890.10285307911917.89714692088085
709749.143102044763547.8568979552365
715646.64865187133969.35134812866042
722222.6945623019911-0.694562301991146
735150.65688459631270.343115403687335
745665.9228734885542-9.92287348855418
759465.695970038652228.3040299613478
769877.427440527869120.5725594721309
777682.8729945665887-6.87299456658872
785745.035681247752711.9643187522472
797555.623255581976619.3767444180234
804851.8924009151962-3.89240091519616
814840.8175434666677.182456533333
8210994.011433708722314.9885662912777
832738.2036016418437-11.2036016418437
848378.22550093896444.77449906103564
854945.31120608896473.68879391103528
862432.8216527942758-8.82165279427584
874353.1892390242284-10.1892390242284
884464.6829126176103-20.6829126176103
894952.5109486077232-3.51094860772318
9010678.08621718054227.913782819458
914248.7461773217725-6.74617732177248
9210895.055063361680212.9449366383198
932741.0440643496777-14.0440643496777
947967.619690170836711.3803098291633
954973.1985889983118-24.1985889983118
966462.39090196663341.60909803336663
977593.0228673232656-18.0228673232656
98115102.25393342407812.7460665759224
999274.437305859279817.5626941407202
100106129.100847393463-23.1008473934635
1017351.708781265583521.2912187344165
10210598.47862107323256.5213789267675
1033034.1745646458445-4.17456464584452
1041331.1892915639149-18.1892915639149
1056952.124633190582716.8753668094173
1067288.2923495647337-16.2923495647337
1078064.43015988225815.569840117742
10810681.075062050550224.9249379494498
1092844.7944036015702-16.7944036015702
1107044.96300836725.036991633
1115165.7524092789227-14.7524092789227
1129072.893344600938617.1066553990614
1131213.2428384050701-1.24283840507011
1148462.930831891360821.0691681086392
1152325.5160310434218-2.51603104342181
1165749.40212116111647.59787883888361
11784105.570768548967-21.5707685489674
11849.82699866325091-5.82699866325091
1195662.4926372386077-6.49263723860769
1201827.4004965376947-9.40049653769472
1218672.61981317121513.380186828785
1223951.2089346794666-12.2089346794666
1231623.1176658974121-7.11766589741207
1241841.3032691426191-23.3032691426191
1251618.0845939587754-2.08459395877544
1264241.81587459001260.184125409987366
1277583.483986881124-8.48398688112398
1283080.2560961297595-50.2560961297595
12910491.257517826498112.7424821735019
13012193.091410849935927.9085891500641
131106116.543338476612-10.5433384766123
1325761.5079639649181-4.50796396491808
1332830.9775912851395-2.97759128513954
1345668.2088841708659-12.2088841708659
13581102.638787501448-21.6387875014484
136211.4213526486068-9.42135264860681
1378894.0472584586989-6.04725845869893
1384143.9126059078504-2.91260590785042
1398374.75952701520018.24047298479988
1405566.009735689411-11.009735689411
141334.033341381542-31.033341381542
1425478.657175126513-24.6571751265129
1438998.7771218576758-9.77712185767583
1444137.61687428150753.38312571849245
1459474.005890500022919.9941094999771
14610135.213350853046765.7866491469533
1477067.35767911184072.64232088815932
148111102.258474110078.74152588993008
1490-3.000572201390663.00057220139066
15049.43145458708121-5.43145458708121
15104.74686968652924-4.74686968652924
15204.80292967444338-4.80292967444338
15303.87236368450244-3.87236368450244
15404.73148067023908-4.73148067023908
1554259.857576943733-17.857576943733
1569786.757679556549410.2423204434506
15704.73148067023908-4.73148067023908
15804.76335791826869-4.76335791826869
15978.56930356283437-1.56930356283437
1601217.9812147509614-5.98121475096142
16108.9267856905485-8.9267856905485
1623724.986959627192412.0130403728076
16304.88364349457746-4.88364349457746
1643942.1089995780324-3.10899957803238







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.0009745992614719250.001949198522943850.999025400738528
100.0005533759972565650.001106751994513130.999446624002743
110.008493462122568650.01698692424513730.991506537877431
120.00259176300932360.00518352601864720.997408236990676
130.005611293584334920.01122258716866980.994388706415665
140.08962740173826490.179254803476530.910372598261735
150.051077645416890.102155290833780.94892235458311
160.1232226542056240.2464453084112490.876777345794376
170.0993525845288260.1987051690576520.900647415471174
180.06321765592722620.1264353118544520.936782344072774
190.06878365431469230.1375673086293850.931216345685308
200.05598661057085960.1119732211417190.94401338942914
210.04924286280995880.09848572561991760.950757137190041
220.1460858684002680.2921717368005370.853914131599732
230.1124480034232330.2248960068464650.887551996576767
240.09955895565353120.1991179113070620.900441044346469
250.1627991318638420.3255982637276840.837200868136158
260.1474378989192990.2948757978385980.852562101080701
270.1162638747383270.2325277494766530.883736125261673
280.1264367025133490.2528734050266980.873563297486651
290.09423236147008050.1884647229401610.905767638529919
300.09807872563344450.1961574512668890.901921274366555
310.1105973042413190.2211946084826370.889402695758681
320.1914252290086470.3828504580172930.808574770991353
330.1533122055118440.3066244110236880.846687794488156
340.1505770470025270.3011540940050530.849422952997473
350.2351657850948570.4703315701897150.764834214905143
360.2160286403483010.4320572806966020.783971359651699
370.2042552718189280.4085105436378560.795744728181072
380.1721247137039460.3442494274078920.827875286296054
390.1379887588028840.2759775176057680.862011241197116
400.112467142238860.2249342844777210.88753285776114
410.08947882888576430.1789576577715290.910521171114236
420.07093110907414220.1418622181482840.929068890925858
430.0546369228335020.1092738456670040.945363077166498
440.04161552245970180.08323104491940360.958384477540298
450.03134989051098250.06269978102196510.968650109489017
460.02484806160755330.04969612321510660.975151938392447
470.01823076275029980.03646152550059950.9817692372497
480.01342567821483840.02685135642967680.986574321785162
490.01151803965842370.02303607931684730.988481960341576
500.00843571985462930.01687143970925860.991564280145371
510.006273105429200990.0125462108584020.993726894570799
520.004376620863383990.008753241726767980.995623379136616
530.02451653447109890.04903306894219770.975483465528901
540.01797248072135190.03594496144270380.982027519278648
550.01342430902099050.02684861804198110.986575690979009
560.01023525536158640.02047051072317270.989764744638414
570.009573856017174290.01914771203434860.990426143982826
580.06074640985930160.1214928197186030.939253590140698
590.0514648310961430.1029296621922860.948535168903857
600.04358354884492420.08716709768984840.956416451155076
610.03451222231265060.06902444462530120.965487777687349
620.02748931591166120.05497863182332240.972510684088339
630.02087863955895340.04175727911790680.979121360441047
640.02062888791042090.04125777582084190.979371112089579
650.03516354518687870.07032709037375740.964836454813121
660.02745805488413260.05491610976826510.972541945115867
670.02129850801029810.04259701602059620.978701491989702
680.01684120547652970.03368241095305940.98315879452347
690.01413465039570260.02826930079140510.985865349604297
700.1237019248789180.2474038497578360.876298075121082
710.1083062926336860.2166125852673730.891693707366314
720.08990303156204430.1798060631240890.910096968437956
730.0726622260720430.1453244521440860.927337773927957
740.06324998068562340.1264999613712470.936750019314377
750.1035892755167720.2071785510335450.896410724483228
760.1209907996614720.2419815993229450.879009200338528
770.102494432103110.204988864206220.89750556789689
780.09833863764026750.1966772752805350.901661362359732
790.106504296912030.2130085938240610.89349570308797
800.08915309020754680.1783061804150940.910846909792453
810.07461955406466460.1492391081293290.925380445935335
820.07304748514456060.1460949702891210.926952514855439
830.06605623485387330.1321124697077470.933943765146127
840.05359711862562890.1071942372512580.946402881374371
850.04261072166561430.08522144333122860.957389278334386
860.03746459507668630.07492919015337260.962535404923314
870.03291850127701550.06583700255403090.967081498722985
880.04069276992169550.08138553984339110.959307230078304
890.03213811504160570.06427623008321150.967861884958394
900.05635804483335760.1127160896667150.943641955166642
910.04717508819107710.09435017638215410.952824911808923
920.04807869638056760.09615739276113520.951921303619432
930.04640694499935650.0928138899987130.953593055000644
940.0410530267610160.0821060535220320.958946973238984
950.05756094435949440.1151218887189890.942439055640506
960.04551405332090040.09102810664180070.9544859466791
970.047555504466850.09511100893369990.95244449553315
980.0447945457826690.0895890915653380.955205454217331
990.04864866908558760.09729733817117520.951351330914412
1000.05563887141132220.1112777428226440.944361128588678
1010.06745397621636710.1349079524327340.932546023783633
1020.05679731633059160.1135946326611830.943202683669408
1030.04616504885073530.09233009770147060.953834951149265
1040.04884346909704340.09768693819408690.951156530902957
1050.05087870547520310.1017574109504060.949121294524797
1060.0491983423983990.09839668479679810.950801657601601
1070.04896653018621510.09793306037243030.951033469813785
1080.07141031947415390.1428206389483080.928589680525846
1090.07259589687836740.1451917937567350.927404103121633
1100.1058907917244640.2117815834489280.894109208275536
1110.1059071900259880.2118143800519760.894092809974012
1120.1218295176444490.2436590352888980.878170482355551
1130.1000752321215220.2001504642430430.899924767878479
1140.130884422670990.261768845341980.86911557732901
1150.1070979042491350.2141958084982710.892902095750865
1160.1082391969679770.2164783939359550.891760803032023
1170.1252513697010960.2505027394021920.874748630298904
1180.1043586196591580.2087172393183160.895641380340842
1190.09210150691051730.1842030138210350.907898493089483
1200.07722875132050580.1544575026410120.922771248679494
1210.06706006844731450.1341201368946290.932939931552686
1220.06165280622315010.12330561244630.93834719377685
1230.05029545753171460.1005909150634290.949704542468285
1240.06541841600794820.1308368320158960.934581583992052
1250.05056743314213480.101134866284270.949432566857865
1260.03805859061035940.07611718122071880.961941409389641
1270.0292714998073410.0585429996146820.970728500192659
1280.2148642187311570.4297284374623140.785135781268843
1290.2026404615016280.4052809230032550.797359538498372
1300.3015123987906980.6030247975813960.698487601209302
1310.3189344730275530.6378689460551060.681065526972447
1320.2688829787142360.5377659574284730.731117021285764
1330.2240016668183150.448003333636630.775998333181685
1340.1933452534510030.3866905069020060.806654746548997
1350.2252789071010050.4505578142020110.774721092898994
1360.2979703211215640.5959406422431280.702029678878436
1370.2551849410480420.5103698820960840.744815058951958
1380.2098496730211160.4196993460422310.790150326978884
1390.1762185066118140.3524370132236290.823781493388186
1400.1393681162411040.2787362324822070.860631883758896
1410.3283529976377480.6567059952754960.671647002362252
1420.6134605813365010.7730788373269980.386539418663499
1430.6166562797806310.7666874404387380.383343720219369
1440.5720591832882190.8558816334235630.427940816711781
1450.5124773469182380.9750453061635250.487522653081762
1460.9970182025352280.00596359492954490.00298179746477245
1470.9946237271141190.01075254577176290.00537627288588145
1480.9970570139079150.005885972184169120.00294298609208456
1490.9972227723365570.005554455326886190.00277722766344309
1500.9929773772448150.01404524551036910.00702262275518457
1510.9828208613554980.03435827728900410.0171791386445021
1520.9604563863294570.07908722734108650.0395436136705433
1530.9995888705674750.0008222588650508430.000411129432525422
1540.9974789991035340.005042001792932310.00252100089646616
1550.9995581035299330.0008837929401340530.000441896470067027

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.000974599261471925 & 0.00194919852294385 & 0.999025400738528 \tabularnewline
10 & 0.000553375997256565 & 0.00110675199451313 & 0.999446624002743 \tabularnewline
11 & 0.00849346212256865 & 0.0169869242451373 & 0.991506537877431 \tabularnewline
12 & 0.0025917630093236 & 0.0051835260186472 & 0.997408236990676 \tabularnewline
13 & 0.00561129358433492 & 0.0112225871686698 & 0.994388706415665 \tabularnewline
14 & 0.0896274017382649 & 0.17925480347653 & 0.910372598261735 \tabularnewline
15 & 0.05107764541689 & 0.10215529083378 & 0.94892235458311 \tabularnewline
16 & 0.123222654205624 & 0.246445308411249 & 0.876777345794376 \tabularnewline
17 & 0.099352584528826 & 0.198705169057652 & 0.900647415471174 \tabularnewline
18 & 0.0632176559272262 & 0.126435311854452 & 0.936782344072774 \tabularnewline
19 & 0.0687836543146923 & 0.137567308629385 & 0.931216345685308 \tabularnewline
20 & 0.0559866105708596 & 0.111973221141719 & 0.94401338942914 \tabularnewline
21 & 0.0492428628099588 & 0.0984857256199176 & 0.950757137190041 \tabularnewline
22 & 0.146085868400268 & 0.292171736800537 & 0.853914131599732 \tabularnewline
23 & 0.112448003423233 & 0.224896006846465 & 0.887551996576767 \tabularnewline
24 & 0.0995589556535312 & 0.199117911307062 & 0.900441044346469 \tabularnewline
25 & 0.162799131863842 & 0.325598263727684 & 0.837200868136158 \tabularnewline
26 & 0.147437898919299 & 0.294875797838598 & 0.852562101080701 \tabularnewline
27 & 0.116263874738327 & 0.232527749476653 & 0.883736125261673 \tabularnewline
28 & 0.126436702513349 & 0.252873405026698 & 0.873563297486651 \tabularnewline
29 & 0.0942323614700805 & 0.188464722940161 & 0.905767638529919 \tabularnewline
30 & 0.0980787256334445 & 0.196157451266889 & 0.901921274366555 \tabularnewline
31 & 0.110597304241319 & 0.221194608482637 & 0.889402695758681 \tabularnewline
32 & 0.191425229008647 & 0.382850458017293 & 0.808574770991353 \tabularnewline
33 & 0.153312205511844 & 0.306624411023688 & 0.846687794488156 \tabularnewline
34 & 0.150577047002527 & 0.301154094005053 & 0.849422952997473 \tabularnewline
35 & 0.235165785094857 & 0.470331570189715 & 0.764834214905143 \tabularnewline
36 & 0.216028640348301 & 0.432057280696602 & 0.783971359651699 \tabularnewline
37 & 0.204255271818928 & 0.408510543637856 & 0.795744728181072 \tabularnewline
38 & 0.172124713703946 & 0.344249427407892 & 0.827875286296054 \tabularnewline
39 & 0.137988758802884 & 0.275977517605768 & 0.862011241197116 \tabularnewline
40 & 0.11246714223886 & 0.224934284477721 & 0.88753285776114 \tabularnewline
41 & 0.0894788288857643 & 0.178957657771529 & 0.910521171114236 \tabularnewline
42 & 0.0709311090741422 & 0.141862218148284 & 0.929068890925858 \tabularnewline
43 & 0.054636922833502 & 0.109273845667004 & 0.945363077166498 \tabularnewline
44 & 0.0416155224597018 & 0.0832310449194036 & 0.958384477540298 \tabularnewline
45 & 0.0313498905109825 & 0.0626997810219651 & 0.968650109489017 \tabularnewline
46 & 0.0248480616075533 & 0.0496961232151066 & 0.975151938392447 \tabularnewline
47 & 0.0182307627502998 & 0.0364615255005995 & 0.9817692372497 \tabularnewline
48 & 0.0134256782148384 & 0.0268513564296768 & 0.986574321785162 \tabularnewline
49 & 0.0115180396584237 & 0.0230360793168473 & 0.988481960341576 \tabularnewline
50 & 0.0084357198546293 & 0.0168714397092586 & 0.991564280145371 \tabularnewline
51 & 0.00627310542920099 & 0.012546210858402 & 0.993726894570799 \tabularnewline
52 & 0.00437662086338399 & 0.00875324172676798 & 0.995623379136616 \tabularnewline
53 & 0.0245165344710989 & 0.0490330689421977 & 0.975483465528901 \tabularnewline
54 & 0.0179724807213519 & 0.0359449614427038 & 0.982027519278648 \tabularnewline
55 & 0.0134243090209905 & 0.0268486180419811 & 0.986575690979009 \tabularnewline
56 & 0.0102352553615864 & 0.0204705107231727 & 0.989764744638414 \tabularnewline
57 & 0.00957385601717429 & 0.0191477120343486 & 0.990426143982826 \tabularnewline
58 & 0.0607464098593016 & 0.121492819718603 & 0.939253590140698 \tabularnewline
59 & 0.051464831096143 & 0.102929662192286 & 0.948535168903857 \tabularnewline
60 & 0.0435835488449242 & 0.0871670976898484 & 0.956416451155076 \tabularnewline
61 & 0.0345122223126506 & 0.0690244446253012 & 0.965487777687349 \tabularnewline
62 & 0.0274893159116612 & 0.0549786318233224 & 0.972510684088339 \tabularnewline
63 & 0.0208786395589534 & 0.0417572791179068 & 0.979121360441047 \tabularnewline
64 & 0.0206288879104209 & 0.0412577758208419 & 0.979371112089579 \tabularnewline
65 & 0.0351635451868787 & 0.0703270903737574 & 0.964836454813121 \tabularnewline
66 & 0.0274580548841326 & 0.0549161097682651 & 0.972541945115867 \tabularnewline
67 & 0.0212985080102981 & 0.0425970160205962 & 0.978701491989702 \tabularnewline
68 & 0.0168412054765297 & 0.0336824109530594 & 0.98315879452347 \tabularnewline
69 & 0.0141346503957026 & 0.0282693007914051 & 0.985865349604297 \tabularnewline
70 & 0.123701924878918 & 0.247403849757836 & 0.876298075121082 \tabularnewline
71 & 0.108306292633686 & 0.216612585267373 & 0.891693707366314 \tabularnewline
72 & 0.0899030315620443 & 0.179806063124089 & 0.910096968437956 \tabularnewline
73 & 0.072662226072043 & 0.145324452144086 & 0.927337773927957 \tabularnewline
74 & 0.0632499806856234 & 0.126499961371247 & 0.936750019314377 \tabularnewline
75 & 0.103589275516772 & 0.207178551033545 & 0.896410724483228 \tabularnewline
76 & 0.120990799661472 & 0.241981599322945 & 0.879009200338528 \tabularnewline
77 & 0.10249443210311 & 0.20498886420622 & 0.89750556789689 \tabularnewline
78 & 0.0983386376402675 & 0.196677275280535 & 0.901661362359732 \tabularnewline
79 & 0.10650429691203 & 0.213008593824061 & 0.89349570308797 \tabularnewline
80 & 0.0891530902075468 & 0.178306180415094 & 0.910846909792453 \tabularnewline
81 & 0.0746195540646646 & 0.149239108129329 & 0.925380445935335 \tabularnewline
82 & 0.0730474851445606 & 0.146094970289121 & 0.926952514855439 \tabularnewline
83 & 0.0660562348538733 & 0.132112469707747 & 0.933943765146127 \tabularnewline
84 & 0.0535971186256289 & 0.107194237251258 & 0.946402881374371 \tabularnewline
85 & 0.0426107216656143 & 0.0852214433312286 & 0.957389278334386 \tabularnewline
86 & 0.0374645950766863 & 0.0749291901533726 & 0.962535404923314 \tabularnewline
87 & 0.0329185012770155 & 0.0658370025540309 & 0.967081498722985 \tabularnewline
88 & 0.0406927699216955 & 0.0813855398433911 & 0.959307230078304 \tabularnewline
89 & 0.0321381150416057 & 0.0642762300832115 & 0.967861884958394 \tabularnewline
90 & 0.0563580448333576 & 0.112716089666715 & 0.943641955166642 \tabularnewline
91 & 0.0471750881910771 & 0.0943501763821541 & 0.952824911808923 \tabularnewline
92 & 0.0480786963805676 & 0.0961573927611352 & 0.951921303619432 \tabularnewline
93 & 0.0464069449993565 & 0.092813889998713 & 0.953593055000644 \tabularnewline
94 & 0.041053026761016 & 0.082106053522032 & 0.958946973238984 \tabularnewline
95 & 0.0575609443594944 & 0.115121888718989 & 0.942439055640506 \tabularnewline
96 & 0.0455140533209004 & 0.0910281066418007 & 0.9544859466791 \tabularnewline
97 & 0.04755550446685 & 0.0951110089336999 & 0.95244449553315 \tabularnewline
98 & 0.044794545782669 & 0.089589091565338 & 0.955205454217331 \tabularnewline
99 & 0.0486486690855876 & 0.0972973381711752 & 0.951351330914412 \tabularnewline
100 & 0.0556388714113222 & 0.111277742822644 & 0.944361128588678 \tabularnewline
101 & 0.0674539762163671 & 0.134907952432734 & 0.932546023783633 \tabularnewline
102 & 0.0567973163305916 & 0.113594632661183 & 0.943202683669408 \tabularnewline
103 & 0.0461650488507353 & 0.0923300977014706 & 0.953834951149265 \tabularnewline
104 & 0.0488434690970434 & 0.0976869381940869 & 0.951156530902957 \tabularnewline
105 & 0.0508787054752031 & 0.101757410950406 & 0.949121294524797 \tabularnewline
106 & 0.049198342398399 & 0.0983966847967981 & 0.950801657601601 \tabularnewline
107 & 0.0489665301862151 & 0.0979330603724303 & 0.951033469813785 \tabularnewline
108 & 0.0714103194741539 & 0.142820638948308 & 0.928589680525846 \tabularnewline
109 & 0.0725958968783674 & 0.145191793756735 & 0.927404103121633 \tabularnewline
110 & 0.105890791724464 & 0.211781583448928 & 0.894109208275536 \tabularnewline
111 & 0.105907190025988 & 0.211814380051976 & 0.894092809974012 \tabularnewline
112 & 0.121829517644449 & 0.243659035288898 & 0.878170482355551 \tabularnewline
113 & 0.100075232121522 & 0.200150464243043 & 0.899924767878479 \tabularnewline
114 & 0.13088442267099 & 0.26176884534198 & 0.86911557732901 \tabularnewline
115 & 0.107097904249135 & 0.214195808498271 & 0.892902095750865 \tabularnewline
116 & 0.108239196967977 & 0.216478393935955 & 0.891760803032023 \tabularnewline
117 & 0.125251369701096 & 0.250502739402192 & 0.874748630298904 \tabularnewline
118 & 0.104358619659158 & 0.208717239318316 & 0.895641380340842 \tabularnewline
119 & 0.0921015069105173 & 0.184203013821035 & 0.907898493089483 \tabularnewline
120 & 0.0772287513205058 & 0.154457502641012 & 0.922771248679494 \tabularnewline
121 & 0.0670600684473145 & 0.134120136894629 & 0.932939931552686 \tabularnewline
122 & 0.0616528062231501 & 0.1233056124463 & 0.93834719377685 \tabularnewline
123 & 0.0502954575317146 & 0.100590915063429 & 0.949704542468285 \tabularnewline
124 & 0.0654184160079482 & 0.130836832015896 & 0.934581583992052 \tabularnewline
125 & 0.0505674331421348 & 0.10113486628427 & 0.949432566857865 \tabularnewline
126 & 0.0380585906103594 & 0.0761171812207188 & 0.961941409389641 \tabularnewline
127 & 0.029271499807341 & 0.058542999614682 & 0.970728500192659 \tabularnewline
128 & 0.214864218731157 & 0.429728437462314 & 0.785135781268843 \tabularnewline
129 & 0.202640461501628 & 0.405280923003255 & 0.797359538498372 \tabularnewline
130 & 0.301512398790698 & 0.603024797581396 & 0.698487601209302 \tabularnewline
131 & 0.318934473027553 & 0.637868946055106 & 0.681065526972447 \tabularnewline
132 & 0.268882978714236 & 0.537765957428473 & 0.731117021285764 \tabularnewline
133 & 0.224001666818315 & 0.44800333363663 & 0.775998333181685 \tabularnewline
134 & 0.193345253451003 & 0.386690506902006 & 0.806654746548997 \tabularnewline
135 & 0.225278907101005 & 0.450557814202011 & 0.774721092898994 \tabularnewline
136 & 0.297970321121564 & 0.595940642243128 & 0.702029678878436 \tabularnewline
137 & 0.255184941048042 & 0.510369882096084 & 0.744815058951958 \tabularnewline
138 & 0.209849673021116 & 0.419699346042231 & 0.790150326978884 \tabularnewline
139 & 0.176218506611814 & 0.352437013223629 & 0.823781493388186 \tabularnewline
140 & 0.139368116241104 & 0.278736232482207 & 0.860631883758896 \tabularnewline
141 & 0.328352997637748 & 0.656705995275496 & 0.671647002362252 \tabularnewline
142 & 0.613460581336501 & 0.773078837326998 & 0.386539418663499 \tabularnewline
143 & 0.616656279780631 & 0.766687440438738 & 0.383343720219369 \tabularnewline
144 & 0.572059183288219 & 0.855881633423563 & 0.427940816711781 \tabularnewline
145 & 0.512477346918238 & 0.975045306163525 & 0.487522653081762 \tabularnewline
146 & 0.997018202535228 & 0.0059635949295449 & 0.00298179746477245 \tabularnewline
147 & 0.994623727114119 & 0.0107525457717629 & 0.00537627288588145 \tabularnewline
148 & 0.997057013907915 & 0.00588597218416912 & 0.00294298609208456 \tabularnewline
149 & 0.997222772336557 & 0.00555445532688619 & 0.00277722766344309 \tabularnewline
150 & 0.992977377244815 & 0.0140452455103691 & 0.00702262275518457 \tabularnewline
151 & 0.982820861355498 & 0.0343582772890041 & 0.0171791386445021 \tabularnewline
152 & 0.960456386329457 & 0.0790872273410865 & 0.0395436136705433 \tabularnewline
153 & 0.999588870567475 & 0.000822258865050843 & 0.000411129432525422 \tabularnewline
154 & 0.997478999103534 & 0.00504200179293231 & 0.00252100089646616 \tabularnewline
155 & 0.999558103529933 & 0.000883792940134053 & 0.000441896470067027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&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]0.000974599261471925[/C][C]0.00194919852294385[/C][C]0.999025400738528[/C][/ROW]
[ROW][C]10[/C][C]0.000553375997256565[/C][C]0.00110675199451313[/C][C]0.999446624002743[/C][/ROW]
[ROW][C]11[/C][C]0.00849346212256865[/C][C]0.0169869242451373[/C][C]0.991506537877431[/C][/ROW]
[ROW][C]12[/C][C]0.0025917630093236[/C][C]0.0051835260186472[/C][C]0.997408236990676[/C][/ROW]
[ROW][C]13[/C][C]0.00561129358433492[/C][C]0.0112225871686698[/C][C]0.994388706415665[/C][/ROW]
[ROW][C]14[/C][C]0.0896274017382649[/C][C]0.17925480347653[/C][C]0.910372598261735[/C][/ROW]
[ROW][C]15[/C][C]0.05107764541689[/C][C]0.10215529083378[/C][C]0.94892235458311[/C][/ROW]
[ROW][C]16[/C][C]0.123222654205624[/C][C]0.246445308411249[/C][C]0.876777345794376[/C][/ROW]
[ROW][C]17[/C][C]0.099352584528826[/C][C]0.198705169057652[/C][C]0.900647415471174[/C][/ROW]
[ROW][C]18[/C][C]0.0632176559272262[/C][C]0.126435311854452[/C][C]0.936782344072774[/C][/ROW]
[ROW][C]19[/C][C]0.0687836543146923[/C][C]0.137567308629385[/C][C]0.931216345685308[/C][/ROW]
[ROW][C]20[/C][C]0.0559866105708596[/C][C]0.111973221141719[/C][C]0.94401338942914[/C][/ROW]
[ROW][C]21[/C][C]0.0492428628099588[/C][C]0.0984857256199176[/C][C]0.950757137190041[/C][/ROW]
[ROW][C]22[/C][C]0.146085868400268[/C][C]0.292171736800537[/C][C]0.853914131599732[/C][/ROW]
[ROW][C]23[/C][C]0.112448003423233[/C][C]0.224896006846465[/C][C]0.887551996576767[/C][/ROW]
[ROW][C]24[/C][C]0.0995589556535312[/C][C]0.199117911307062[/C][C]0.900441044346469[/C][/ROW]
[ROW][C]25[/C][C]0.162799131863842[/C][C]0.325598263727684[/C][C]0.837200868136158[/C][/ROW]
[ROW][C]26[/C][C]0.147437898919299[/C][C]0.294875797838598[/C][C]0.852562101080701[/C][/ROW]
[ROW][C]27[/C][C]0.116263874738327[/C][C]0.232527749476653[/C][C]0.883736125261673[/C][/ROW]
[ROW][C]28[/C][C]0.126436702513349[/C][C]0.252873405026698[/C][C]0.873563297486651[/C][/ROW]
[ROW][C]29[/C][C]0.0942323614700805[/C][C]0.188464722940161[/C][C]0.905767638529919[/C][/ROW]
[ROW][C]30[/C][C]0.0980787256334445[/C][C]0.196157451266889[/C][C]0.901921274366555[/C][/ROW]
[ROW][C]31[/C][C]0.110597304241319[/C][C]0.221194608482637[/C][C]0.889402695758681[/C][/ROW]
[ROW][C]32[/C][C]0.191425229008647[/C][C]0.382850458017293[/C][C]0.808574770991353[/C][/ROW]
[ROW][C]33[/C][C]0.153312205511844[/C][C]0.306624411023688[/C][C]0.846687794488156[/C][/ROW]
[ROW][C]34[/C][C]0.150577047002527[/C][C]0.301154094005053[/C][C]0.849422952997473[/C][/ROW]
[ROW][C]35[/C][C]0.235165785094857[/C][C]0.470331570189715[/C][C]0.764834214905143[/C][/ROW]
[ROW][C]36[/C][C]0.216028640348301[/C][C]0.432057280696602[/C][C]0.783971359651699[/C][/ROW]
[ROW][C]37[/C][C]0.204255271818928[/C][C]0.408510543637856[/C][C]0.795744728181072[/C][/ROW]
[ROW][C]38[/C][C]0.172124713703946[/C][C]0.344249427407892[/C][C]0.827875286296054[/C][/ROW]
[ROW][C]39[/C][C]0.137988758802884[/C][C]0.275977517605768[/C][C]0.862011241197116[/C][/ROW]
[ROW][C]40[/C][C]0.11246714223886[/C][C]0.224934284477721[/C][C]0.88753285776114[/C][/ROW]
[ROW][C]41[/C][C]0.0894788288857643[/C][C]0.178957657771529[/C][C]0.910521171114236[/C][/ROW]
[ROW][C]42[/C][C]0.0709311090741422[/C][C]0.141862218148284[/C][C]0.929068890925858[/C][/ROW]
[ROW][C]43[/C][C]0.054636922833502[/C][C]0.109273845667004[/C][C]0.945363077166498[/C][/ROW]
[ROW][C]44[/C][C]0.0416155224597018[/C][C]0.0832310449194036[/C][C]0.958384477540298[/C][/ROW]
[ROW][C]45[/C][C]0.0313498905109825[/C][C]0.0626997810219651[/C][C]0.968650109489017[/C][/ROW]
[ROW][C]46[/C][C]0.0248480616075533[/C][C]0.0496961232151066[/C][C]0.975151938392447[/C][/ROW]
[ROW][C]47[/C][C]0.0182307627502998[/C][C]0.0364615255005995[/C][C]0.9817692372497[/C][/ROW]
[ROW][C]48[/C][C]0.0134256782148384[/C][C]0.0268513564296768[/C][C]0.986574321785162[/C][/ROW]
[ROW][C]49[/C][C]0.0115180396584237[/C][C]0.0230360793168473[/C][C]0.988481960341576[/C][/ROW]
[ROW][C]50[/C][C]0.0084357198546293[/C][C]0.0168714397092586[/C][C]0.991564280145371[/C][/ROW]
[ROW][C]51[/C][C]0.00627310542920099[/C][C]0.012546210858402[/C][C]0.993726894570799[/C][/ROW]
[ROW][C]52[/C][C]0.00437662086338399[/C][C]0.00875324172676798[/C][C]0.995623379136616[/C][/ROW]
[ROW][C]53[/C][C]0.0245165344710989[/C][C]0.0490330689421977[/C][C]0.975483465528901[/C][/ROW]
[ROW][C]54[/C][C]0.0179724807213519[/C][C]0.0359449614427038[/C][C]0.982027519278648[/C][/ROW]
[ROW][C]55[/C][C]0.0134243090209905[/C][C]0.0268486180419811[/C][C]0.986575690979009[/C][/ROW]
[ROW][C]56[/C][C]0.0102352553615864[/C][C]0.0204705107231727[/C][C]0.989764744638414[/C][/ROW]
[ROW][C]57[/C][C]0.00957385601717429[/C][C]0.0191477120343486[/C][C]0.990426143982826[/C][/ROW]
[ROW][C]58[/C][C]0.0607464098593016[/C][C]0.121492819718603[/C][C]0.939253590140698[/C][/ROW]
[ROW][C]59[/C][C]0.051464831096143[/C][C]0.102929662192286[/C][C]0.948535168903857[/C][/ROW]
[ROW][C]60[/C][C]0.0435835488449242[/C][C]0.0871670976898484[/C][C]0.956416451155076[/C][/ROW]
[ROW][C]61[/C][C]0.0345122223126506[/C][C]0.0690244446253012[/C][C]0.965487777687349[/C][/ROW]
[ROW][C]62[/C][C]0.0274893159116612[/C][C]0.0549786318233224[/C][C]0.972510684088339[/C][/ROW]
[ROW][C]63[/C][C]0.0208786395589534[/C][C]0.0417572791179068[/C][C]0.979121360441047[/C][/ROW]
[ROW][C]64[/C][C]0.0206288879104209[/C][C]0.0412577758208419[/C][C]0.979371112089579[/C][/ROW]
[ROW][C]65[/C][C]0.0351635451868787[/C][C]0.0703270903737574[/C][C]0.964836454813121[/C][/ROW]
[ROW][C]66[/C][C]0.0274580548841326[/C][C]0.0549161097682651[/C][C]0.972541945115867[/C][/ROW]
[ROW][C]67[/C][C]0.0212985080102981[/C][C]0.0425970160205962[/C][C]0.978701491989702[/C][/ROW]
[ROW][C]68[/C][C]0.0168412054765297[/C][C]0.0336824109530594[/C][C]0.98315879452347[/C][/ROW]
[ROW][C]69[/C][C]0.0141346503957026[/C][C]0.0282693007914051[/C][C]0.985865349604297[/C][/ROW]
[ROW][C]70[/C][C]0.123701924878918[/C][C]0.247403849757836[/C][C]0.876298075121082[/C][/ROW]
[ROW][C]71[/C][C]0.108306292633686[/C][C]0.216612585267373[/C][C]0.891693707366314[/C][/ROW]
[ROW][C]72[/C][C]0.0899030315620443[/C][C]0.179806063124089[/C][C]0.910096968437956[/C][/ROW]
[ROW][C]73[/C][C]0.072662226072043[/C][C]0.145324452144086[/C][C]0.927337773927957[/C][/ROW]
[ROW][C]74[/C][C]0.0632499806856234[/C][C]0.126499961371247[/C][C]0.936750019314377[/C][/ROW]
[ROW][C]75[/C][C]0.103589275516772[/C][C]0.207178551033545[/C][C]0.896410724483228[/C][/ROW]
[ROW][C]76[/C][C]0.120990799661472[/C][C]0.241981599322945[/C][C]0.879009200338528[/C][/ROW]
[ROW][C]77[/C][C]0.10249443210311[/C][C]0.20498886420622[/C][C]0.89750556789689[/C][/ROW]
[ROW][C]78[/C][C]0.0983386376402675[/C][C]0.196677275280535[/C][C]0.901661362359732[/C][/ROW]
[ROW][C]79[/C][C]0.10650429691203[/C][C]0.213008593824061[/C][C]0.89349570308797[/C][/ROW]
[ROW][C]80[/C][C]0.0891530902075468[/C][C]0.178306180415094[/C][C]0.910846909792453[/C][/ROW]
[ROW][C]81[/C][C]0.0746195540646646[/C][C]0.149239108129329[/C][C]0.925380445935335[/C][/ROW]
[ROW][C]82[/C][C]0.0730474851445606[/C][C]0.146094970289121[/C][C]0.926952514855439[/C][/ROW]
[ROW][C]83[/C][C]0.0660562348538733[/C][C]0.132112469707747[/C][C]0.933943765146127[/C][/ROW]
[ROW][C]84[/C][C]0.0535971186256289[/C][C]0.107194237251258[/C][C]0.946402881374371[/C][/ROW]
[ROW][C]85[/C][C]0.0426107216656143[/C][C]0.0852214433312286[/C][C]0.957389278334386[/C][/ROW]
[ROW][C]86[/C][C]0.0374645950766863[/C][C]0.0749291901533726[/C][C]0.962535404923314[/C][/ROW]
[ROW][C]87[/C][C]0.0329185012770155[/C][C]0.0658370025540309[/C][C]0.967081498722985[/C][/ROW]
[ROW][C]88[/C][C]0.0406927699216955[/C][C]0.0813855398433911[/C][C]0.959307230078304[/C][/ROW]
[ROW][C]89[/C][C]0.0321381150416057[/C][C]0.0642762300832115[/C][C]0.967861884958394[/C][/ROW]
[ROW][C]90[/C][C]0.0563580448333576[/C][C]0.112716089666715[/C][C]0.943641955166642[/C][/ROW]
[ROW][C]91[/C][C]0.0471750881910771[/C][C]0.0943501763821541[/C][C]0.952824911808923[/C][/ROW]
[ROW][C]92[/C][C]0.0480786963805676[/C][C]0.0961573927611352[/C][C]0.951921303619432[/C][/ROW]
[ROW][C]93[/C][C]0.0464069449993565[/C][C]0.092813889998713[/C][C]0.953593055000644[/C][/ROW]
[ROW][C]94[/C][C]0.041053026761016[/C][C]0.082106053522032[/C][C]0.958946973238984[/C][/ROW]
[ROW][C]95[/C][C]0.0575609443594944[/C][C]0.115121888718989[/C][C]0.942439055640506[/C][/ROW]
[ROW][C]96[/C][C]0.0455140533209004[/C][C]0.0910281066418007[/C][C]0.9544859466791[/C][/ROW]
[ROW][C]97[/C][C]0.04755550446685[/C][C]0.0951110089336999[/C][C]0.95244449553315[/C][/ROW]
[ROW][C]98[/C][C]0.044794545782669[/C][C]0.089589091565338[/C][C]0.955205454217331[/C][/ROW]
[ROW][C]99[/C][C]0.0486486690855876[/C][C]0.0972973381711752[/C][C]0.951351330914412[/C][/ROW]
[ROW][C]100[/C][C]0.0556388714113222[/C][C]0.111277742822644[/C][C]0.944361128588678[/C][/ROW]
[ROW][C]101[/C][C]0.0674539762163671[/C][C]0.134907952432734[/C][C]0.932546023783633[/C][/ROW]
[ROW][C]102[/C][C]0.0567973163305916[/C][C]0.113594632661183[/C][C]0.943202683669408[/C][/ROW]
[ROW][C]103[/C][C]0.0461650488507353[/C][C]0.0923300977014706[/C][C]0.953834951149265[/C][/ROW]
[ROW][C]104[/C][C]0.0488434690970434[/C][C]0.0976869381940869[/C][C]0.951156530902957[/C][/ROW]
[ROW][C]105[/C][C]0.0508787054752031[/C][C]0.101757410950406[/C][C]0.949121294524797[/C][/ROW]
[ROW][C]106[/C][C]0.049198342398399[/C][C]0.0983966847967981[/C][C]0.950801657601601[/C][/ROW]
[ROW][C]107[/C][C]0.0489665301862151[/C][C]0.0979330603724303[/C][C]0.951033469813785[/C][/ROW]
[ROW][C]108[/C][C]0.0714103194741539[/C][C]0.142820638948308[/C][C]0.928589680525846[/C][/ROW]
[ROW][C]109[/C][C]0.0725958968783674[/C][C]0.145191793756735[/C][C]0.927404103121633[/C][/ROW]
[ROW][C]110[/C][C]0.105890791724464[/C][C]0.211781583448928[/C][C]0.894109208275536[/C][/ROW]
[ROW][C]111[/C][C]0.105907190025988[/C][C]0.211814380051976[/C][C]0.894092809974012[/C][/ROW]
[ROW][C]112[/C][C]0.121829517644449[/C][C]0.243659035288898[/C][C]0.878170482355551[/C][/ROW]
[ROW][C]113[/C][C]0.100075232121522[/C][C]0.200150464243043[/C][C]0.899924767878479[/C][/ROW]
[ROW][C]114[/C][C]0.13088442267099[/C][C]0.26176884534198[/C][C]0.86911557732901[/C][/ROW]
[ROW][C]115[/C][C]0.107097904249135[/C][C]0.214195808498271[/C][C]0.892902095750865[/C][/ROW]
[ROW][C]116[/C][C]0.108239196967977[/C][C]0.216478393935955[/C][C]0.891760803032023[/C][/ROW]
[ROW][C]117[/C][C]0.125251369701096[/C][C]0.250502739402192[/C][C]0.874748630298904[/C][/ROW]
[ROW][C]118[/C][C]0.104358619659158[/C][C]0.208717239318316[/C][C]0.895641380340842[/C][/ROW]
[ROW][C]119[/C][C]0.0921015069105173[/C][C]0.184203013821035[/C][C]0.907898493089483[/C][/ROW]
[ROW][C]120[/C][C]0.0772287513205058[/C][C]0.154457502641012[/C][C]0.922771248679494[/C][/ROW]
[ROW][C]121[/C][C]0.0670600684473145[/C][C]0.134120136894629[/C][C]0.932939931552686[/C][/ROW]
[ROW][C]122[/C][C]0.0616528062231501[/C][C]0.1233056124463[/C][C]0.93834719377685[/C][/ROW]
[ROW][C]123[/C][C]0.0502954575317146[/C][C]0.100590915063429[/C][C]0.949704542468285[/C][/ROW]
[ROW][C]124[/C][C]0.0654184160079482[/C][C]0.130836832015896[/C][C]0.934581583992052[/C][/ROW]
[ROW][C]125[/C][C]0.0505674331421348[/C][C]0.10113486628427[/C][C]0.949432566857865[/C][/ROW]
[ROW][C]126[/C][C]0.0380585906103594[/C][C]0.0761171812207188[/C][C]0.961941409389641[/C][/ROW]
[ROW][C]127[/C][C]0.029271499807341[/C][C]0.058542999614682[/C][C]0.970728500192659[/C][/ROW]
[ROW][C]128[/C][C]0.214864218731157[/C][C]0.429728437462314[/C][C]0.785135781268843[/C][/ROW]
[ROW][C]129[/C][C]0.202640461501628[/C][C]0.405280923003255[/C][C]0.797359538498372[/C][/ROW]
[ROW][C]130[/C][C]0.301512398790698[/C][C]0.603024797581396[/C][C]0.698487601209302[/C][/ROW]
[ROW][C]131[/C][C]0.318934473027553[/C][C]0.637868946055106[/C][C]0.681065526972447[/C][/ROW]
[ROW][C]132[/C][C]0.268882978714236[/C][C]0.537765957428473[/C][C]0.731117021285764[/C][/ROW]
[ROW][C]133[/C][C]0.224001666818315[/C][C]0.44800333363663[/C][C]0.775998333181685[/C][/ROW]
[ROW][C]134[/C][C]0.193345253451003[/C][C]0.386690506902006[/C][C]0.806654746548997[/C][/ROW]
[ROW][C]135[/C][C]0.225278907101005[/C][C]0.450557814202011[/C][C]0.774721092898994[/C][/ROW]
[ROW][C]136[/C][C]0.297970321121564[/C][C]0.595940642243128[/C][C]0.702029678878436[/C][/ROW]
[ROW][C]137[/C][C]0.255184941048042[/C][C]0.510369882096084[/C][C]0.744815058951958[/C][/ROW]
[ROW][C]138[/C][C]0.209849673021116[/C][C]0.419699346042231[/C][C]0.790150326978884[/C][/ROW]
[ROW][C]139[/C][C]0.176218506611814[/C][C]0.352437013223629[/C][C]0.823781493388186[/C][/ROW]
[ROW][C]140[/C][C]0.139368116241104[/C][C]0.278736232482207[/C][C]0.860631883758896[/C][/ROW]
[ROW][C]141[/C][C]0.328352997637748[/C][C]0.656705995275496[/C][C]0.671647002362252[/C][/ROW]
[ROW][C]142[/C][C]0.613460581336501[/C][C]0.773078837326998[/C][C]0.386539418663499[/C][/ROW]
[ROW][C]143[/C][C]0.616656279780631[/C][C]0.766687440438738[/C][C]0.383343720219369[/C][/ROW]
[ROW][C]144[/C][C]0.572059183288219[/C][C]0.855881633423563[/C][C]0.427940816711781[/C][/ROW]
[ROW][C]145[/C][C]0.512477346918238[/C][C]0.975045306163525[/C][C]0.487522653081762[/C][/ROW]
[ROW][C]146[/C][C]0.997018202535228[/C][C]0.0059635949295449[/C][C]0.00298179746477245[/C][/ROW]
[ROW][C]147[/C][C]0.994623727114119[/C][C]0.0107525457717629[/C][C]0.00537627288588145[/C][/ROW]
[ROW][C]148[/C][C]0.997057013907915[/C][C]0.00588597218416912[/C][C]0.00294298609208456[/C][/ROW]
[ROW][C]149[/C][C]0.997222772336557[/C][C]0.00555445532688619[/C][C]0.00277722766344309[/C][/ROW]
[ROW][C]150[/C][C]0.992977377244815[/C][C]0.0140452455103691[/C][C]0.00702262275518457[/C][/ROW]
[ROW][C]151[/C][C]0.982820861355498[/C][C]0.0343582772890041[/C][C]0.0171791386445021[/C][/ROW]
[ROW][C]152[/C][C]0.960456386329457[/C][C]0.0790872273410865[/C][C]0.0395436136705433[/C][/ROW]
[ROW][C]153[/C][C]0.999588870567475[/C][C]0.000822258865050843[/C][C]0.000411129432525422[/C][/ROW]
[ROW][C]154[/C][C]0.997478999103534[/C][C]0.00504200179293231[/C][C]0.00252100089646616[/C][/ROW]
[ROW][C]155[/C][C]0.999558103529933[/C][C]0.000883792940134053[/C][C]0.000441896470067027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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
90.0009745992614719250.001949198522943850.999025400738528
100.0005533759972565650.001106751994513130.999446624002743
110.008493462122568650.01698692424513730.991506537877431
120.00259176300932360.00518352601864720.997408236990676
130.005611293584334920.01122258716866980.994388706415665
140.08962740173826490.179254803476530.910372598261735
150.051077645416890.102155290833780.94892235458311
160.1232226542056240.2464453084112490.876777345794376
170.0993525845288260.1987051690576520.900647415471174
180.06321765592722620.1264353118544520.936782344072774
190.06878365431469230.1375673086293850.931216345685308
200.05598661057085960.1119732211417190.94401338942914
210.04924286280995880.09848572561991760.950757137190041
220.1460858684002680.2921717368005370.853914131599732
230.1124480034232330.2248960068464650.887551996576767
240.09955895565353120.1991179113070620.900441044346469
250.1627991318638420.3255982637276840.837200868136158
260.1474378989192990.2948757978385980.852562101080701
270.1162638747383270.2325277494766530.883736125261673
280.1264367025133490.2528734050266980.873563297486651
290.09423236147008050.1884647229401610.905767638529919
300.09807872563344450.1961574512668890.901921274366555
310.1105973042413190.2211946084826370.889402695758681
320.1914252290086470.3828504580172930.808574770991353
330.1533122055118440.3066244110236880.846687794488156
340.1505770470025270.3011540940050530.849422952997473
350.2351657850948570.4703315701897150.764834214905143
360.2160286403483010.4320572806966020.783971359651699
370.2042552718189280.4085105436378560.795744728181072
380.1721247137039460.3442494274078920.827875286296054
390.1379887588028840.2759775176057680.862011241197116
400.112467142238860.2249342844777210.88753285776114
410.08947882888576430.1789576577715290.910521171114236
420.07093110907414220.1418622181482840.929068890925858
430.0546369228335020.1092738456670040.945363077166498
440.04161552245970180.08323104491940360.958384477540298
450.03134989051098250.06269978102196510.968650109489017
460.02484806160755330.04969612321510660.975151938392447
470.01823076275029980.03646152550059950.9817692372497
480.01342567821483840.02685135642967680.986574321785162
490.01151803965842370.02303607931684730.988481960341576
500.00843571985462930.01687143970925860.991564280145371
510.006273105429200990.0125462108584020.993726894570799
520.004376620863383990.008753241726767980.995623379136616
530.02451653447109890.04903306894219770.975483465528901
540.01797248072135190.03594496144270380.982027519278648
550.01342430902099050.02684861804198110.986575690979009
560.01023525536158640.02047051072317270.989764744638414
570.009573856017174290.01914771203434860.990426143982826
580.06074640985930160.1214928197186030.939253590140698
590.0514648310961430.1029296621922860.948535168903857
600.04358354884492420.08716709768984840.956416451155076
610.03451222231265060.06902444462530120.965487777687349
620.02748931591166120.05497863182332240.972510684088339
630.02087863955895340.04175727911790680.979121360441047
640.02062888791042090.04125777582084190.979371112089579
650.03516354518687870.07032709037375740.964836454813121
660.02745805488413260.05491610976826510.972541945115867
670.02129850801029810.04259701602059620.978701491989702
680.01684120547652970.03368241095305940.98315879452347
690.01413465039570260.02826930079140510.985865349604297
700.1237019248789180.2474038497578360.876298075121082
710.1083062926336860.2166125852673730.891693707366314
720.08990303156204430.1798060631240890.910096968437956
730.0726622260720430.1453244521440860.927337773927957
740.06324998068562340.1264999613712470.936750019314377
750.1035892755167720.2071785510335450.896410724483228
760.1209907996614720.2419815993229450.879009200338528
770.102494432103110.204988864206220.89750556789689
780.09833863764026750.1966772752805350.901661362359732
790.106504296912030.2130085938240610.89349570308797
800.08915309020754680.1783061804150940.910846909792453
810.07461955406466460.1492391081293290.925380445935335
820.07304748514456060.1460949702891210.926952514855439
830.06605623485387330.1321124697077470.933943765146127
840.05359711862562890.1071942372512580.946402881374371
850.04261072166561430.08522144333122860.957389278334386
860.03746459507668630.07492919015337260.962535404923314
870.03291850127701550.06583700255403090.967081498722985
880.04069276992169550.08138553984339110.959307230078304
890.03213811504160570.06427623008321150.967861884958394
900.05635804483335760.1127160896667150.943641955166642
910.04717508819107710.09435017638215410.952824911808923
920.04807869638056760.09615739276113520.951921303619432
930.04640694499935650.0928138899987130.953593055000644
940.0410530267610160.0821060535220320.958946973238984
950.05756094435949440.1151218887189890.942439055640506
960.04551405332090040.09102810664180070.9544859466791
970.047555504466850.09511100893369990.95244449553315
980.0447945457826690.0895890915653380.955205454217331
990.04864866908558760.09729733817117520.951351330914412
1000.05563887141132220.1112777428226440.944361128588678
1010.06745397621636710.1349079524327340.932546023783633
1020.05679731633059160.1135946326611830.943202683669408
1030.04616504885073530.09233009770147060.953834951149265
1040.04884346909704340.09768693819408690.951156530902957
1050.05087870547520310.1017574109504060.949121294524797
1060.0491983423983990.09839668479679810.950801657601601
1070.04896653018621510.09793306037243030.951033469813785
1080.07141031947415390.1428206389483080.928589680525846
1090.07259589687836740.1451917937567350.927404103121633
1100.1058907917244640.2117815834489280.894109208275536
1110.1059071900259880.2118143800519760.894092809974012
1120.1218295176444490.2436590352888980.878170482355551
1130.1000752321215220.2001504642430430.899924767878479
1140.130884422670990.261768845341980.86911557732901
1150.1070979042491350.2141958084982710.892902095750865
1160.1082391969679770.2164783939359550.891760803032023
1170.1252513697010960.2505027394021920.874748630298904
1180.1043586196591580.2087172393183160.895641380340842
1190.09210150691051730.1842030138210350.907898493089483
1200.07722875132050580.1544575026410120.922771248679494
1210.06706006844731450.1341201368946290.932939931552686
1220.06165280622315010.12330561244630.93834719377685
1230.05029545753171460.1005909150634290.949704542468285
1240.06541841600794820.1308368320158960.934581583992052
1250.05056743314213480.101134866284270.949432566857865
1260.03805859061035940.07611718122071880.961941409389641
1270.0292714998073410.0585429996146820.970728500192659
1280.2148642187311570.4297284374623140.785135781268843
1290.2026404615016280.4052809230032550.797359538498372
1300.3015123987906980.6030247975813960.698487601209302
1310.3189344730275530.6378689460551060.681065526972447
1320.2688829787142360.5377659574284730.731117021285764
1330.2240016668183150.448003333636630.775998333181685
1340.1933452534510030.3866905069020060.806654746548997
1350.2252789071010050.4505578142020110.774721092898994
1360.2979703211215640.5959406422431280.702029678878436
1370.2551849410480420.5103698820960840.744815058951958
1380.2098496730211160.4196993460422310.790150326978884
1390.1762185066118140.3524370132236290.823781493388186
1400.1393681162411040.2787362324822070.860631883758896
1410.3283529976377480.6567059952754960.671647002362252
1420.6134605813365010.7730788373269980.386539418663499
1430.6166562797806310.7666874404387380.383343720219369
1440.5720591832882190.8558816334235630.427940816711781
1450.5124773469182380.9750453061635250.487522653081762
1460.9970182025352280.00596359492954490.00298179746477245
1470.9946237271141190.01075254577176290.00537627288588145
1480.9970570139079150.005885972184169120.00294298609208456
1490.9972227723365570.005554455326886190.00277722766344309
1500.9929773772448150.01404524551036910.00702262275518457
1510.9828208613554980.03435827728900410.0171791386445021
1520.9604563863294570.07908722734108650.0395436136705433
1530.9995888705674750.0008222588650508430.000411129432525422
1540.9974789991035340.005042001792932310.00252100089646616
1550.9995581035299330.0008837929401340530.000441896470067027







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0680272108843537NOK
5% type I error level310.210884353741497NOK
10% type I error level590.401360544217687NOK

\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 & 10 & 0.0680272108843537 & NOK \tabularnewline
5% type I error level & 31 & 0.210884353741497 & NOK \tabularnewline
10% type I error level & 59 & 0.401360544217687 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=144884&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]10[/C][C]0.0680272108843537[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.210884353741497[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.401360544217687[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=144884&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=144884&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 level100.0680272108843537NOK
5% type I error level310.210884353741497NOK
10% type I error level590.401360544217687NOK



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