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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Dec 2011 11:16:29 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t1323965828wrr9b114fx214tf.htm/, Retrieved Wed, 08 May 2024 11:18:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155547, Retrieved Wed, 08 May 2024 11:18:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Pearson Corr Matrix] [2011-12-15 16:11:32] [15a5dd358825f04074b70fc847ec6454]
- RMP       [Multiple Regression] [Multiple regression ] [2011-12-15 16:16:29] [614dd89c388120cee0dd25886939832b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
70	500	20	18	129404	22622	28
68	329	38	17	130358	73570	39
0	72	0	0	7215	1929	0
68	584	49	22	112861	36294	54
120	1100	76	30	219904	62378	80
120	1585	104	31	397355	167760	144
72	442	37	19	117604	52443	36
96	321	53	25	126737	57283	48
109	406	42	30	99729	36614	42
104	818	62	26	256310	93268	71
54	568	50	20	113066	35439	49
98	556	65	25	157228	72405	74
49	494	28	15	69952	24044	27
88	818	48	22	152673	55909	83
57	338	42	16	130642	44689	31
74	419	47	19	125769	49319	28
112	364	71	28	123467	62075	98
45	284	0	12	56232	2341	2
110	674	50	28	108330	40551	43
39	188	12	13	22762	11621	24
55	286	16	14	48554	18741	16
102	640	77	27	182081	84202	95
96	520	29	25	140857	15334	22
86	532	38	30	93773	28024	33
78	547	50	21	133398	53306	45
64	428	33	17	113933	37918	59
82	561	49	22	153851	54819	66
100	266	59	28	140711	89058	70
99	783	55	26	303804	103354	56
67	746	40	17	161651	70239	55
87	394	40	23	123344	33045	27
65	482	51	20	157640	63852	37
43	568	41	11	91279	30905	48
80	746	73	20	189374	24242	26
84	668	51	21	178768	78907	64
0	0	0	0	0	0	0
105	835	46	27	175403	36005	21
51	464	44	14	92342	31972	44
98	418	31	29	100023	35853	36
124	607	71	31	178277	115301	89
75	539	61	19	145062	47689	101
120	519	28	30	110980	34223	31
84	309	21	23	86039	43431	65
82	647	42	21	125481	52220	71
87	321	44	22	95535	33863	102
78	261	40	21	126456	46879	53
97	180	15	32	61554	23228	41
76	576	46	19	164752	42827	46
104	544	43	26	159121	65765	37
93	758	47	25	129362	38167	51
82	205	12	22	48188	14812	14
73	317	46	19	95461	32615	40
87	709	56	24	229864	82188	77
95	590	47	26	191094	51763	51
105	526	48	27	150640	59325	43
37	443	35	10	111388	48976	33
96	419	44	26	165098	43384	47
88	205	25	23	63205	26692	31
83	310	47	21	109102	53279	31
124	785	28	34	137303	20652	40
116	434	48	29	125304	38338	42
76	576	32	19	85332	36735	35
65	317	28	19	95808	42764	40
86	288	31	23	83419	44331	30
85	285	13	22	101723	41354	11
107	391	38	29	94982	47879	41
124	446	39	31	129700	103793	53
78	715	68	21	113325	52235	82
83	208	32	21	81518	49825	41
78	101	5	21	31970	4105	6
59	858	53	15	192268	58687	81
33	302	33	9	91086	40745	47
92	360	54	23	80820	33187	100
52	411	36	18	83261	14063	46
121	561	52	31	116290	37407	38
92	292	0	25	56544	7190	0
99	492	52	24	116173	49562	45
86	669	45	22	111488	76324	56
75	253	16	21	60138	21928	18
96	366	33	26	73422	27860	54
81	192	48	22	67751	28078	37
104	616	33	26	213351	49577	40
76	221	24	20	51185	28145	37
90	438	37	25	97181	36241	36
75	247	17	19	45100	10824	34
86	388	32	22	115801	46892	49
100	541	55	25	186310	61264	82
88	233	39	22	71960	22933	36
80	333	31	21	80105	20787	33
73	422	26	20	103613	43978	55
88	452	37	23	98707	51305	50
79	584	66	22	136234	55593	71
81	366	35	21	136781	51648	31
48	406	24	12	105863	30552	42
33	254	18	9	38775	23470	31
120	606	37	32	179997	77530	51
90	491	86	24	169406	57299	64
2	67	13	1	19349	9604	14
96	607	21	24	153069	34684	37
86	597	32	25	109510	41094	37
15	240	8	4	43803	3439	8
48	219	38	15	47062	25171	38
81	349	45	21	110845	23437	23
84	241	24	23	92517	34086	22
46	136	23	12	58660	24649	18
59	194	2	16	27676	2342	1
96	222	52	24	98550	45571	48
29	153	5	9	43646	3255	5
0	0	0	0	0	0	0
83	251	43	23	67312	30002	46
63	240	18	17	57359	19360	33
68	358	44	18	104330	43320	41
84	302	45	21	70369	35513	57
54	267	29	17	65494	23536	49
0	14	0	0	3616	0	0
0	0	0	0	0	0	0
75	287	32	20	143931	54438	45
87	476	65	26	117946	56812	78
104	509	26	26	131175	33838	46
80	243	24	20	84336	32366	25
3	292	7	1	43410	13	1
93	410	62	24	136250	55082	59
55	217	30	14	79015	31334	29
96	422	49	26	92937	16612	26
48	160	3	12	57586	5084	4
8	75	10	2	19764	9927	10
60	412	42	16	105757	47413	43
84	309	18	22	97213	27389	36
112	417	40	28	113402	30425	41
8	79	1	2	11796	0	0
0	25	0	0	7627	0	0
52	431	29	17	121085	33510	32
4	11	0	1	6836	0	0
57	564	46	17	139563	40389	53
0	6	5	0	5118	0	0
14	183	8	4	40248	6012	6
0	0	0	0	0	0	0
91	295	21	25	95079	22205	18
89	230	21	26	80763	17231	26
0	27	0	0	7131	0	0
0	14	0	0	4194	0	0
54	240	15	15	60378	11017	16
77	251	47	20	109173	46741	84
76	347	17	19	83484	39869	22

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
FMPeerReview[t] = -0.751517150912109 -0.00291827310409937CompendiumViews[t] -0.00402103809330103BloggedComputations[t] + 3.62928267451594ReviewedCompendiums[t] + 2.64125157782592e-05TimeRFC[t] + 5.14535883528436e-05CWSeconds[t] -0.00898959884570436CWBlogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FMPeerReview[t] =  -0.751517150912109 -0.00291827310409937CompendiumViews[t] -0.00402103809330103BloggedComputations[t] +  3.62928267451594ReviewedCompendiums[t] +  2.64125157782592e-05TimeRFC[t] +  5.14535883528436e-05CWSeconds[t] -0.00898959884570436CWBlogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FMPeerReview[t] =  -0.751517150912109 -0.00291827310409937CompendiumViews[t] -0.00402103809330103BloggedComputations[t] +  3.62928267451594ReviewedCompendiums[t] +  2.64125157782592e-05TimeRFC[t] +  5.14535883528436e-05CWSeconds[t] -0.00898959884570436CWBlogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
FMPeerReview[t] = -0.751517150912109 -0.00291827310409937CompendiumViews[t] -0.00402103809330103BloggedComputations[t] + 3.62928267451594ReviewedCompendiums[t] + 2.64125157782592e-05TimeRFC[t] + 5.14535883528436e-05CWSeconds[t] -0.00898959884570436CWBlogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.7515171509121091.188456-0.63230.5282140.264107
CompendiumViews-0.002918273104099370.004375-0.6670.5059010.25295
BloggedComputations-0.004021038093301030.050059-0.08030.9360950.468047
ReviewedCompendiums3.629282674515940.07635747.530400
TimeRFC2.64125157782592e-052.3e-051.17030.2439090.121954
CWSeconds5.14535883528436e-054e-051.27620.2040510.102026
CWBlogs-0.008989598845704360.03605-0.24940.8034510.401726

\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) & -0.751517150912109 & 1.188456 & -0.6323 & 0.528214 & 0.264107 \tabularnewline
CompendiumViews & -0.00291827310409937 & 0.004375 & -0.667 & 0.505901 & 0.25295 \tabularnewline
BloggedComputations & -0.00402103809330103 & 0.050059 & -0.0803 & 0.936095 & 0.468047 \tabularnewline
ReviewedCompendiums & 3.62928267451594 & 0.076357 & 47.5304 & 0 & 0 \tabularnewline
TimeRFC & 2.64125157782592e-05 & 2.3e-05 & 1.1703 & 0.243909 & 0.121954 \tabularnewline
CWSeconds & 5.14535883528436e-05 & 4e-05 & 1.2762 & 0.204051 & 0.102026 \tabularnewline
CWBlogs & -0.00898959884570436 & 0.03605 & -0.2494 & 0.803451 & 0.401726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&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]-0.751517150912109[/C][C]1.188456[/C][C]-0.6323[/C][C]0.528214[/C][C]0.264107[/C][/ROW]
[ROW][C]CompendiumViews[/C][C]-0.00291827310409937[/C][C]0.004375[/C][C]-0.667[/C][C]0.505901[/C][C]0.25295[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]-0.00402103809330103[/C][C]0.050059[/C][C]-0.0803[/C][C]0.936095[/C][C]0.468047[/C][/ROW]
[ROW][C]ReviewedCompendiums[/C][C]3.62928267451594[/C][C]0.076357[/C][C]47.5304[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]TimeRFC[/C][C]2.64125157782592e-05[/C][C]2.3e-05[/C][C]1.1703[/C][C]0.243909[/C][C]0.121954[/C][/ROW]
[ROW][C]CWSeconds[/C][C]5.14535883528436e-05[/C][C]4e-05[/C][C]1.2762[/C][C]0.204051[/C][C]0.102026[/C][/ROW]
[ROW][C]CWBlogs[/C][C]-0.00898959884570436[/C][C]0.03605[/C][C]-0.2494[/C][C]0.803451[/C][C]0.401726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.7515171509121091.188456-0.63230.5282140.264107
CompendiumViews-0.002918273104099370.004375-0.6670.5059010.25295
BloggedComputations-0.004021038093301030.050059-0.08030.9360950.468047
ReviewedCompendiums3.629282674515940.07635747.530400
TimeRFC2.64125157782592e-052.3e-051.17030.2439090.121954
CWSeconds5.14535883528436e-054e-051.27620.2040510.102026
CWBlogs-0.008989598845704360.03605-0.24940.8034510.401726






Multiple Linear Regression - Regression Statistics
Multiple R0.985596188089823
R-squared0.97139984597719
Adjusted R-squared0.97014728448714
F-TEST (value)775.530665515148
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.60538915243775
Sum Squared Residuals4304.59309438654

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985596188089823 \tabularnewline
R-squared & 0.97139984597719 \tabularnewline
Adjusted R-squared & 0.97014728448714 \tabularnewline
F-TEST (value) & 775.530665515148 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.60538915243775 \tabularnewline
Sum Squared Residuals & 4304.59309438654 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985596188089823[/C][/ROW]
[ROW][C]R-squared[/C][C]0.97139984597719[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.97014728448714[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]775.530665515148[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/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]5.60538915243775[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4304.59309438654[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.985596188089823
R-squared0.97139984597719
Adjusted R-squared0.97014728448714
F-TEST (value)775.530665515148
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.60538915243775
Sum Squared Residuals4304.59309438654






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17067.36617317626732.63382682373267
26866.71130588902341.28869411097665
30-0.6718125411344830.671812541134483
46881.5543604703331-13.554360470333
5120112.9098856712867.09011432871431
6120124.545091882743-4.54509188274317
77272.247171024556-0.247171024555994
89694.6940271958371.30597280416298
9109110.913712922865-1.91371292286468
10104101.9038843051382.09611569486188
115474.3448361947949-20.3448361947949
129895.30967617093232.69032382906765
134954.9755462000321-5.97554620003209
148882.67560444924685.32439555075322
155761.6330614642255-4.63306146422551
167472.40091489709811.59908510290189
17112105.0947275167366.90527248326413
184543.55878762160071.44121237839933
19110103.2616393038966.73836069610399
203946.8156632792011-7.8156632792011
215551.26236898393293.73763101606715
22102103.349500782776-1.34950078277606
239692.65804347732443.34195652267564
2486110.043701785806-24.0437017858065
257879.5277015337835-1.52770153378353
266463.99446116165650.00553883834354191
278283.5494323115662-1.54943231156618
28100107.524509102342-7.52450910234202
2999103.94241202962-4.94241202961969
306765.99764530034181.0023546996582
318786.12673324064670.873266759353275
326577.338924113972-12.338924113972
334340.9177310149842.08226898501595
348075.37902090479114.62097909520894
358481.51536622830662.48463377169341
360-0.7515171509121030.751517150912103
37105100.9140286447424.08597135525809
385152.2159522055059-1.21595220550591
3998107.316189082102-9.31618908210238
40124119.5406802490744.45931975092627
417571.76389419345843.23610580654163
42120110.913269867989.08673013202004
438485.6586594896558-1.6586594896558
448278.7693264737643.23067352623598
458781.3277638209245.67223617907598
467879.8165733348469-1.81657333484692
4797117.252310097262-20.2523100972616
487672.48055668562233.51944331437765
4910499.10340318374654.89659681625348
509392.50164534033330.498354659666737
518280.35524572214391.6447542778561
527370.93473333760772.06526666239229
538793.6649882087173-6.66498820871729
549598.9512583076067-3.95125830760666
55105102.1558063352882.8441936647122
563739.2731497646351-2.27314976463515
579698.3805551410903-2.38055514109026
588884.78733710014393.21266289985614
598379.71414202734073.28585797265927
60124124.570213536037-0.570213536036642
61116107.9427984502238.05720154977657
627670.22459954287645.77540045712356
636571.5384796344552-6.5384796344552
648685.9714762416870.0285237583129557
658582.924406806512.07559319348996
66107107.807522556525-0.807522556524531
67124118.587652322545.4123476774603
687878.0471525869815-0.0471525869814686
698379.07594193750313.92405806249689
707876.15025575648731.84974424351267
715958.34051044138050.659489558619532
723334.9777899090815-1.97778990908148
739284.39755986573497.60244013426512
745265.7406061155485-13.7406061155485
75121114.5646316500636.43536834993741
769290.99183455801231.00816544198768
779989.92041468279679.07958531720334
788683.32783496876722.6721650312328
797577.2156166891442-2.2156166891442
809695.29636854063670.703631459363319
818181.2409564769461-0.240956476946063
8210499.50594914704644.49405085295357
837673.56016377617152.43983622382848
849092.6614663158359-2.6614663158359
857568.85817430176896.1418256982311
868682.86260556630273.13739443369729
8710095.51662821368554.48337178631449
888881.01292778819876.98707221180128
898077.25566544496512.74433455503492
907375.0031560684514-2.00315606845141
918886.05159311353161.94840688646838
927982.9435221752768-3.94352217527677
938180.24612241225370.753877587746279
944845.5091061864462.49089381355399
953333.0514903193371-0.0514903193371479
96120121.753177289466-1.75317728946611
979091.4199291481702-1.41992914817017
9823.50932937691066-1.50932937691066
999689.99037194212426.00962805787576
1008692.7839206552114-6.78392065521141
1011514.29503922563630.704960774363704
1024855.092381043333-7.09238104333296
1038178.1908472746092.80915272539096
1048485.9218581908742-1.92185819087417
1054644.9663308206151.03366917938496
1065957.58532607471691.41467392528312
1079690.01056058768995.98943941231008
1082932.7207600438556-3.72076004385557
1090-0.7515171509121030.751517150912103
1108384.7246614487395-1.72466144873953
1116362.38800428632370.61199571367634
1126867.96991720891920.0300827910808357
1138477.57464029405596.42535970594412
1145462.550881912773-8.55088191277301
1150-0.6968653173153060.696865317315306
1160-0.7515171509121030.751517150912103
1177577.0659970427207-2.06599704272066
1188797.296610050406-10.296610050406
11910496.81211111908437.18788888091568
1208074.69662386103225.3033761389678
12133.13572911829083-0.135729118290835
1229390.80795619954772.19204380045225
1235552.74307719106432.25692280893575
1249695.2570076886140.742992311386033
1254844.06751091375333.9324890862467
12687.190868079342470.809131920657531
1276060.7921181879049-0.792118187904869
1288481.77185328289512.22814671710486
129112103.682770314648.3172296853596
13086.584045620922981.41595437907702
1310-0.6230257206738060.623025720673806
1325264.2066045589382-12.2066045589382
13343.026220477318930.973779522681071
1345764.4033347135769-7.40333471357692
1350-0.6539527242500760.653952724250076
1361414.5178535795015-0.517853579501526
1370-0.7515171509121030.751517150912103
1389192.5272070841512-1.52720708415119
1398995.6402089953194-6.64020899531935
1400-0.6419628747080220.641962874708022
1410-0.6815988831954730.681598883195473
1425454.9447873294554-0.944787329455439
1437775.44606045511371.55393954488632
1447671.18250965684854.81749034315155

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 70 & 67.3661731762673 & 2.63382682373267 \tabularnewline
2 & 68 & 66.7113058890234 & 1.28869411097665 \tabularnewline
3 & 0 & -0.671812541134483 & 0.671812541134483 \tabularnewline
4 & 68 & 81.5543604703331 & -13.554360470333 \tabularnewline
5 & 120 & 112.909885671286 & 7.09011432871431 \tabularnewline
6 & 120 & 124.545091882743 & -4.54509188274317 \tabularnewline
7 & 72 & 72.247171024556 & -0.247171024555994 \tabularnewline
8 & 96 & 94.694027195837 & 1.30597280416298 \tabularnewline
9 & 109 & 110.913712922865 & -1.91371292286468 \tabularnewline
10 & 104 & 101.903884305138 & 2.09611569486188 \tabularnewline
11 & 54 & 74.3448361947949 & -20.3448361947949 \tabularnewline
12 & 98 & 95.3096761709323 & 2.69032382906765 \tabularnewline
13 & 49 & 54.9755462000321 & -5.97554620003209 \tabularnewline
14 & 88 & 82.6756044492468 & 5.32439555075322 \tabularnewline
15 & 57 & 61.6330614642255 & -4.63306146422551 \tabularnewline
16 & 74 & 72.4009148970981 & 1.59908510290189 \tabularnewline
17 & 112 & 105.094727516736 & 6.90527248326413 \tabularnewline
18 & 45 & 43.5587876216007 & 1.44121237839933 \tabularnewline
19 & 110 & 103.261639303896 & 6.73836069610399 \tabularnewline
20 & 39 & 46.8156632792011 & -7.8156632792011 \tabularnewline
21 & 55 & 51.2623689839329 & 3.73763101606715 \tabularnewline
22 & 102 & 103.349500782776 & -1.34950078277606 \tabularnewline
23 & 96 & 92.6580434773244 & 3.34195652267564 \tabularnewline
24 & 86 & 110.043701785806 & -24.0437017858065 \tabularnewline
25 & 78 & 79.5277015337835 & -1.52770153378353 \tabularnewline
26 & 64 & 63.9944611616565 & 0.00553883834354191 \tabularnewline
27 & 82 & 83.5494323115662 & -1.54943231156618 \tabularnewline
28 & 100 & 107.524509102342 & -7.52450910234202 \tabularnewline
29 & 99 & 103.94241202962 & -4.94241202961969 \tabularnewline
30 & 67 & 65.9976453003418 & 1.0023546996582 \tabularnewline
31 & 87 & 86.1267332406467 & 0.873266759353275 \tabularnewline
32 & 65 & 77.338924113972 & -12.338924113972 \tabularnewline
33 & 43 & 40.917731014984 & 2.08226898501595 \tabularnewline
34 & 80 & 75.3790209047911 & 4.62097909520894 \tabularnewline
35 & 84 & 81.5153662283066 & 2.48463377169341 \tabularnewline
36 & 0 & -0.751517150912103 & 0.751517150912103 \tabularnewline
37 & 105 & 100.914028644742 & 4.08597135525809 \tabularnewline
38 & 51 & 52.2159522055059 & -1.21595220550591 \tabularnewline
39 & 98 & 107.316189082102 & -9.31618908210238 \tabularnewline
40 & 124 & 119.540680249074 & 4.45931975092627 \tabularnewline
41 & 75 & 71.7638941934584 & 3.23610580654163 \tabularnewline
42 & 120 & 110.91326986798 & 9.08673013202004 \tabularnewline
43 & 84 & 85.6586594896558 & -1.6586594896558 \tabularnewline
44 & 82 & 78.769326473764 & 3.23067352623598 \tabularnewline
45 & 87 & 81.327763820924 & 5.67223617907598 \tabularnewline
46 & 78 & 79.8165733348469 & -1.81657333484692 \tabularnewline
47 & 97 & 117.252310097262 & -20.2523100972616 \tabularnewline
48 & 76 & 72.4805566856223 & 3.51944331437765 \tabularnewline
49 & 104 & 99.1034031837465 & 4.89659681625348 \tabularnewline
50 & 93 & 92.5016453403333 & 0.498354659666737 \tabularnewline
51 & 82 & 80.3552457221439 & 1.6447542778561 \tabularnewline
52 & 73 & 70.9347333376077 & 2.06526666239229 \tabularnewline
53 & 87 & 93.6649882087173 & -6.66498820871729 \tabularnewline
54 & 95 & 98.9512583076067 & -3.95125830760666 \tabularnewline
55 & 105 & 102.155806335288 & 2.8441936647122 \tabularnewline
56 & 37 & 39.2731497646351 & -2.27314976463515 \tabularnewline
57 & 96 & 98.3805551410903 & -2.38055514109026 \tabularnewline
58 & 88 & 84.7873371001439 & 3.21266289985614 \tabularnewline
59 & 83 & 79.7141420273407 & 3.28585797265927 \tabularnewline
60 & 124 & 124.570213536037 & -0.570213536036642 \tabularnewline
61 & 116 & 107.942798450223 & 8.05720154977657 \tabularnewline
62 & 76 & 70.2245995428764 & 5.77540045712356 \tabularnewline
63 & 65 & 71.5384796344552 & -6.5384796344552 \tabularnewline
64 & 86 & 85.971476241687 & 0.0285237583129557 \tabularnewline
65 & 85 & 82.92440680651 & 2.07559319348996 \tabularnewline
66 & 107 & 107.807522556525 & -0.807522556524531 \tabularnewline
67 & 124 & 118.58765232254 & 5.4123476774603 \tabularnewline
68 & 78 & 78.0471525869815 & -0.0471525869814686 \tabularnewline
69 & 83 & 79.0759419375031 & 3.92405806249689 \tabularnewline
70 & 78 & 76.1502557564873 & 1.84974424351267 \tabularnewline
71 & 59 & 58.3405104413805 & 0.659489558619532 \tabularnewline
72 & 33 & 34.9777899090815 & -1.97778990908148 \tabularnewline
73 & 92 & 84.3975598657349 & 7.60244013426512 \tabularnewline
74 & 52 & 65.7406061155485 & -13.7406061155485 \tabularnewline
75 & 121 & 114.564631650063 & 6.43536834993741 \tabularnewline
76 & 92 & 90.9918345580123 & 1.00816544198768 \tabularnewline
77 & 99 & 89.9204146827967 & 9.07958531720334 \tabularnewline
78 & 86 & 83.3278349687672 & 2.6721650312328 \tabularnewline
79 & 75 & 77.2156166891442 & -2.2156166891442 \tabularnewline
80 & 96 & 95.2963685406367 & 0.703631459363319 \tabularnewline
81 & 81 & 81.2409564769461 & -0.240956476946063 \tabularnewline
82 & 104 & 99.5059491470464 & 4.49405085295357 \tabularnewline
83 & 76 & 73.5601637761715 & 2.43983622382848 \tabularnewline
84 & 90 & 92.6614663158359 & -2.6614663158359 \tabularnewline
85 & 75 & 68.8581743017689 & 6.1418256982311 \tabularnewline
86 & 86 & 82.8626055663027 & 3.13739443369729 \tabularnewline
87 & 100 & 95.5166282136855 & 4.48337178631449 \tabularnewline
88 & 88 & 81.0129277881987 & 6.98707221180128 \tabularnewline
89 & 80 & 77.2556654449651 & 2.74433455503492 \tabularnewline
90 & 73 & 75.0031560684514 & -2.00315606845141 \tabularnewline
91 & 88 & 86.0515931135316 & 1.94840688646838 \tabularnewline
92 & 79 & 82.9435221752768 & -3.94352217527677 \tabularnewline
93 & 81 & 80.2461224122537 & 0.753877587746279 \tabularnewline
94 & 48 & 45.509106186446 & 2.49089381355399 \tabularnewline
95 & 33 & 33.0514903193371 & -0.0514903193371479 \tabularnewline
96 & 120 & 121.753177289466 & -1.75317728946611 \tabularnewline
97 & 90 & 91.4199291481702 & -1.41992914817017 \tabularnewline
98 & 2 & 3.50932937691066 & -1.50932937691066 \tabularnewline
99 & 96 & 89.9903719421242 & 6.00962805787576 \tabularnewline
100 & 86 & 92.7839206552114 & -6.78392065521141 \tabularnewline
101 & 15 & 14.2950392256363 & 0.704960774363704 \tabularnewline
102 & 48 & 55.092381043333 & -7.09238104333296 \tabularnewline
103 & 81 & 78.190847274609 & 2.80915272539096 \tabularnewline
104 & 84 & 85.9218581908742 & -1.92185819087417 \tabularnewline
105 & 46 & 44.966330820615 & 1.03366917938496 \tabularnewline
106 & 59 & 57.5853260747169 & 1.41467392528312 \tabularnewline
107 & 96 & 90.0105605876899 & 5.98943941231008 \tabularnewline
108 & 29 & 32.7207600438556 & -3.72076004385557 \tabularnewline
109 & 0 & -0.751517150912103 & 0.751517150912103 \tabularnewline
110 & 83 & 84.7246614487395 & -1.72466144873953 \tabularnewline
111 & 63 & 62.3880042863237 & 0.61199571367634 \tabularnewline
112 & 68 & 67.9699172089192 & 0.0300827910808357 \tabularnewline
113 & 84 & 77.5746402940559 & 6.42535970594412 \tabularnewline
114 & 54 & 62.550881912773 & -8.55088191277301 \tabularnewline
115 & 0 & -0.696865317315306 & 0.696865317315306 \tabularnewline
116 & 0 & -0.751517150912103 & 0.751517150912103 \tabularnewline
117 & 75 & 77.0659970427207 & -2.06599704272066 \tabularnewline
118 & 87 & 97.296610050406 & -10.296610050406 \tabularnewline
119 & 104 & 96.8121111190843 & 7.18788888091568 \tabularnewline
120 & 80 & 74.6966238610322 & 5.3033761389678 \tabularnewline
121 & 3 & 3.13572911829083 & -0.135729118290835 \tabularnewline
122 & 93 & 90.8079561995477 & 2.19204380045225 \tabularnewline
123 & 55 & 52.7430771910643 & 2.25692280893575 \tabularnewline
124 & 96 & 95.257007688614 & 0.742992311386033 \tabularnewline
125 & 48 & 44.0675109137533 & 3.9324890862467 \tabularnewline
126 & 8 & 7.19086807934247 & 0.809131920657531 \tabularnewline
127 & 60 & 60.7921181879049 & -0.792118187904869 \tabularnewline
128 & 84 & 81.7718532828951 & 2.22814671710486 \tabularnewline
129 & 112 & 103.68277031464 & 8.3172296853596 \tabularnewline
130 & 8 & 6.58404562092298 & 1.41595437907702 \tabularnewline
131 & 0 & -0.623025720673806 & 0.623025720673806 \tabularnewline
132 & 52 & 64.2066045589382 & -12.2066045589382 \tabularnewline
133 & 4 & 3.02622047731893 & 0.973779522681071 \tabularnewline
134 & 57 & 64.4033347135769 & -7.40333471357692 \tabularnewline
135 & 0 & -0.653952724250076 & 0.653952724250076 \tabularnewline
136 & 14 & 14.5178535795015 & -0.517853579501526 \tabularnewline
137 & 0 & -0.751517150912103 & 0.751517150912103 \tabularnewline
138 & 91 & 92.5272070841512 & -1.52720708415119 \tabularnewline
139 & 89 & 95.6402089953194 & -6.64020899531935 \tabularnewline
140 & 0 & -0.641962874708022 & 0.641962874708022 \tabularnewline
141 & 0 & -0.681598883195473 & 0.681598883195473 \tabularnewline
142 & 54 & 54.9447873294554 & -0.944787329455439 \tabularnewline
143 & 77 & 75.4460604551137 & 1.55393954488632 \tabularnewline
144 & 76 & 71.1825096568485 & 4.81749034315155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&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]70[/C][C]67.3661731762673[/C][C]2.63382682373267[/C][/ROW]
[ROW][C]2[/C][C]68[/C][C]66.7113058890234[/C][C]1.28869411097665[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.671812541134483[/C][C]0.671812541134483[/C][/ROW]
[ROW][C]4[/C][C]68[/C][C]81.5543604703331[/C][C]-13.554360470333[/C][/ROW]
[ROW][C]5[/C][C]120[/C][C]112.909885671286[/C][C]7.09011432871431[/C][/ROW]
[ROW][C]6[/C][C]120[/C][C]124.545091882743[/C][C]-4.54509188274317[/C][/ROW]
[ROW][C]7[/C][C]72[/C][C]72.247171024556[/C][C]-0.247171024555994[/C][/ROW]
[ROW][C]8[/C][C]96[/C][C]94.694027195837[/C][C]1.30597280416298[/C][/ROW]
[ROW][C]9[/C][C]109[/C][C]110.913712922865[/C][C]-1.91371292286468[/C][/ROW]
[ROW][C]10[/C][C]104[/C][C]101.903884305138[/C][C]2.09611569486188[/C][/ROW]
[ROW][C]11[/C][C]54[/C][C]74.3448361947949[/C][C]-20.3448361947949[/C][/ROW]
[ROW][C]12[/C][C]98[/C][C]95.3096761709323[/C][C]2.69032382906765[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]54.9755462000321[/C][C]-5.97554620003209[/C][/ROW]
[ROW][C]14[/C][C]88[/C][C]82.6756044492468[/C][C]5.32439555075322[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]61.6330614642255[/C][C]-4.63306146422551[/C][/ROW]
[ROW][C]16[/C][C]74[/C][C]72.4009148970981[/C][C]1.59908510290189[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]105.094727516736[/C][C]6.90527248326413[/C][/ROW]
[ROW][C]18[/C][C]45[/C][C]43.5587876216007[/C][C]1.44121237839933[/C][/ROW]
[ROW][C]19[/C][C]110[/C][C]103.261639303896[/C][C]6.73836069610399[/C][/ROW]
[ROW][C]20[/C][C]39[/C][C]46.8156632792011[/C][C]-7.8156632792011[/C][/ROW]
[ROW][C]21[/C][C]55[/C][C]51.2623689839329[/C][C]3.73763101606715[/C][/ROW]
[ROW][C]22[/C][C]102[/C][C]103.349500782776[/C][C]-1.34950078277606[/C][/ROW]
[ROW][C]23[/C][C]96[/C][C]92.6580434773244[/C][C]3.34195652267564[/C][/ROW]
[ROW][C]24[/C][C]86[/C][C]110.043701785806[/C][C]-24.0437017858065[/C][/ROW]
[ROW][C]25[/C][C]78[/C][C]79.5277015337835[/C][C]-1.52770153378353[/C][/ROW]
[ROW][C]26[/C][C]64[/C][C]63.9944611616565[/C][C]0.00553883834354191[/C][/ROW]
[ROW][C]27[/C][C]82[/C][C]83.5494323115662[/C][C]-1.54943231156618[/C][/ROW]
[ROW][C]28[/C][C]100[/C][C]107.524509102342[/C][C]-7.52450910234202[/C][/ROW]
[ROW][C]29[/C][C]99[/C][C]103.94241202962[/C][C]-4.94241202961969[/C][/ROW]
[ROW][C]30[/C][C]67[/C][C]65.9976453003418[/C][C]1.0023546996582[/C][/ROW]
[ROW][C]31[/C][C]87[/C][C]86.1267332406467[/C][C]0.873266759353275[/C][/ROW]
[ROW][C]32[/C][C]65[/C][C]77.338924113972[/C][C]-12.338924113972[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]40.917731014984[/C][C]2.08226898501595[/C][/ROW]
[ROW][C]34[/C][C]80[/C][C]75.3790209047911[/C][C]4.62097909520894[/C][/ROW]
[ROW][C]35[/C][C]84[/C][C]81.5153662283066[/C][C]2.48463377169341[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.751517150912103[/C][C]0.751517150912103[/C][/ROW]
[ROW][C]37[/C][C]105[/C][C]100.914028644742[/C][C]4.08597135525809[/C][/ROW]
[ROW][C]38[/C][C]51[/C][C]52.2159522055059[/C][C]-1.21595220550591[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]107.316189082102[/C][C]-9.31618908210238[/C][/ROW]
[ROW][C]40[/C][C]124[/C][C]119.540680249074[/C][C]4.45931975092627[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]71.7638941934584[/C][C]3.23610580654163[/C][/ROW]
[ROW][C]42[/C][C]120[/C][C]110.91326986798[/C][C]9.08673013202004[/C][/ROW]
[ROW][C]43[/C][C]84[/C][C]85.6586594896558[/C][C]-1.6586594896558[/C][/ROW]
[ROW][C]44[/C][C]82[/C][C]78.769326473764[/C][C]3.23067352623598[/C][/ROW]
[ROW][C]45[/C][C]87[/C][C]81.327763820924[/C][C]5.67223617907598[/C][/ROW]
[ROW][C]46[/C][C]78[/C][C]79.8165733348469[/C][C]-1.81657333484692[/C][/ROW]
[ROW][C]47[/C][C]97[/C][C]117.252310097262[/C][C]-20.2523100972616[/C][/ROW]
[ROW][C]48[/C][C]76[/C][C]72.4805566856223[/C][C]3.51944331437765[/C][/ROW]
[ROW][C]49[/C][C]104[/C][C]99.1034031837465[/C][C]4.89659681625348[/C][/ROW]
[ROW][C]50[/C][C]93[/C][C]92.5016453403333[/C][C]0.498354659666737[/C][/ROW]
[ROW][C]51[/C][C]82[/C][C]80.3552457221439[/C][C]1.6447542778561[/C][/ROW]
[ROW][C]52[/C][C]73[/C][C]70.9347333376077[/C][C]2.06526666239229[/C][/ROW]
[ROW][C]53[/C][C]87[/C][C]93.6649882087173[/C][C]-6.66498820871729[/C][/ROW]
[ROW][C]54[/C][C]95[/C][C]98.9512583076067[/C][C]-3.95125830760666[/C][/ROW]
[ROW][C]55[/C][C]105[/C][C]102.155806335288[/C][C]2.8441936647122[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]39.2731497646351[/C][C]-2.27314976463515[/C][/ROW]
[ROW][C]57[/C][C]96[/C][C]98.3805551410903[/C][C]-2.38055514109026[/C][/ROW]
[ROW][C]58[/C][C]88[/C][C]84.7873371001439[/C][C]3.21266289985614[/C][/ROW]
[ROW][C]59[/C][C]83[/C][C]79.7141420273407[/C][C]3.28585797265927[/C][/ROW]
[ROW][C]60[/C][C]124[/C][C]124.570213536037[/C][C]-0.570213536036642[/C][/ROW]
[ROW][C]61[/C][C]116[/C][C]107.942798450223[/C][C]8.05720154977657[/C][/ROW]
[ROW][C]62[/C][C]76[/C][C]70.2245995428764[/C][C]5.77540045712356[/C][/ROW]
[ROW][C]63[/C][C]65[/C][C]71.5384796344552[/C][C]-6.5384796344552[/C][/ROW]
[ROW][C]64[/C][C]86[/C][C]85.971476241687[/C][C]0.0285237583129557[/C][/ROW]
[ROW][C]65[/C][C]85[/C][C]82.92440680651[/C][C]2.07559319348996[/C][/ROW]
[ROW][C]66[/C][C]107[/C][C]107.807522556525[/C][C]-0.807522556524531[/C][/ROW]
[ROW][C]67[/C][C]124[/C][C]118.58765232254[/C][C]5.4123476774603[/C][/ROW]
[ROW][C]68[/C][C]78[/C][C]78.0471525869815[/C][C]-0.0471525869814686[/C][/ROW]
[ROW][C]69[/C][C]83[/C][C]79.0759419375031[/C][C]3.92405806249689[/C][/ROW]
[ROW][C]70[/C][C]78[/C][C]76.1502557564873[/C][C]1.84974424351267[/C][/ROW]
[ROW][C]71[/C][C]59[/C][C]58.3405104413805[/C][C]0.659489558619532[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]34.9777899090815[/C][C]-1.97778990908148[/C][/ROW]
[ROW][C]73[/C][C]92[/C][C]84.3975598657349[/C][C]7.60244013426512[/C][/ROW]
[ROW][C]74[/C][C]52[/C][C]65.7406061155485[/C][C]-13.7406061155485[/C][/ROW]
[ROW][C]75[/C][C]121[/C][C]114.564631650063[/C][C]6.43536834993741[/C][/ROW]
[ROW][C]76[/C][C]92[/C][C]90.9918345580123[/C][C]1.00816544198768[/C][/ROW]
[ROW][C]77[/C][C]99[/C][C]89.9204146827967[/C][C]9.07958531720334[/C][/ROW]
[ROW][C]78[/C][C]86[/C][C]83.3278349687672[/C][C]2.6721650312328[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]77.2156166891442[/C][C]-2.2156166891442[/C][/ROW]
[ROW][C]80[/C][C]96[/C][C]95.2963685406367[/C][C]0.703631459363319[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]81.2409564769461[/C][C]-0.240956476946063[/C][/ROW]
[ROW][C]82[/C][C]104[/C][C]99.5059491470464[/C][C]4.49405085295357[/C][/ROW]
[ROW][C]83[/C][C]76[/C][C]73.5601637761715[/C][C]2.43983622382848[/C][/ROW]
[ROW][C]84[/C][C]90[/C][C]92.6614663158359[/C][C]-2.6614663158359[/C][/ROW]
[ROW][C]85[/C][C]75[/C][C]68.8581743017689[/C][C]6.1418256982311[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]82.8626055663027[/C][C]3.13739443369729[/C][/ROW]
[ROW][C]87[/C][C]100[/C][C]95.5166282136855[/C][C]4.48337178631449[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]81.0129277881987[/C][C]6.98707221180128[/C][/ROW]
[ROW][C]89[/C][C]80[/C][C]77.2556654449651[/C][C]2.74433455503492[/C][/ROW]
[ROW][C]90[/C][C]73[/C][C]75.0031560684514[/C][C]-2.00315606845141[/C][/ROW]
[ROW][C]91[/C][C]88[/C][C]86.0515931135316[/C][C]1.94840688646838[/C][/ROW]
[ROW][C]92[/C][C]79[/C][C]82.9435221752768[/C][C]-3.94352217527677[/C][/ROW]
[ROW][C]93[/C][C]81[/C][C]80.2461224122537[/C][C]0.753877587746279[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]45.509106186446[/C][C]2.49089381355399[/C][/ROW]
[ROW][C]95[/C][C]33[/C][C]33.0514903193371[/C][C]-0.0514903193371479[/C][/ROW]
[ROW][C]96[/C][C]120[/C][C]121.753177289466[/C][C]-1.75317728946611[/C][/ROW]
[ROW][C]97[/C][C]90[/C][C]91.4199291481702[/C][C]-1.41992914817017[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]3.50932937691066[/C][C]-1.50932937691066[/C][/ROW]
[ROW][C]99[/C][C]96[/C][C]89.9903719421242[/C][C]6.00962805787576[/C][/ROW]
[ROW][C]100[/C][C]86[/C][C]92.7839206552114[/C][C]-6.78392065521141[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]14.2950392256363[/C][C]0.704960774363704[/C][/ROW]
[ROW][C]102[/C][C]48[/C][C]55.092381043333[/C][C]-7.09238104333296[/C][/ROW]
[ROW][C]103[/C][C]81[/C][C]78.190847274609[/C][C]2.80915272539096[/C][/ROW]
[ROW][C]104[/C][C]84[/C][C]85.9218581908742[/C][C]-1.92185819087417[/C][/ROW]
[ROW][C]105[/C][C]46[/C][C]44.966330820615[/C][C]1.03366917938496[/C][/ROW]
[ROW][C]106[/C][C]59[/C][C]57.5853260747169[/C][C]1.41467392528312[/C][/ROW]
[ROW][C]107[/C][C]96[/C][C]90.0105605876899[/C][C]5.98943941231008[/C][/ROW]
[ROW][C]108[/C][C]29[/C][C]32.7207600438556[/C][C]-3.72076004385557[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.751517150912103[/C][C]0.751517150912103[/C][/ROW]
[ROW][C]110[/C][C]83[/C][C]84.7246614487395[/C][C]-1.72466144873953[/C][/ROW]
[ROW][C]111[/C][C]63[/C][C]62.3880042863237[/C][C]0.61199571367634[/C][/ROW]
[ROW][C]112[/C][C]68[/C][C]67.9699172089192[/C][C]0.0300827910808357[/C][/ROW]
[ROW][C]113[/C][C]84[/C][C]77.5746402940559[/C][C]6.42535970594412[/C][/ROW]
[ROW][C]114[/C][C]54[/C][C]62.550881912773[/C][C]-8.55088191277301[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-0.696865317315306[/C][C]0.696865317315306[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.751517150912103[/C][C]0.751517150912103[/C][/ROW]
[ROW][C]117[/C][C]75[/C][C]77.0659970427207[/C][C]-2.06599704272066[/C][/ROW]
[ROW][C]118[/C][C]87[/C][C]97.296610050406[/C][C]-10.296610050406[/C][/ROW]
[ROW][C]119[/C][C]104[/C][C]96.8121111190843[/C][C]7.18788888091568[/C][/ROW]
[ROW][C]120[/C][C]80[/C][C]74.6966238610322[/C][C]5.3033761389678[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.13572911829083[/C][C]-0.135729118290835[/C][/ROW]
[ROW][C]122[/C][C]93[/C][C]90.8079561995477[/C][C]2.19204380045225[/C][/ROW]
[ROW][C]123[/C][C]55[/C][C]52.7430771910643[/C][C]2.25692280893575[/C][/ROW]
[ROW][C]124[/C][C]96[/C][C]95.257007688614[/C][C]0.742992311386033[/C][/ROW]
[ROW][C]125[/C][C]48[/C][C]44.0675109137533[/C][C]3.9324890862467[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]7.19086807934247[/C][C]0.809131920657531[/C][/ROW]
[ROW][C]127[/C][C]60[/C][C]60.7921181879049[/C][C]-0.792118187904869[/C][/ROW]
[ROW][C]128[/C][C]84[/C][C]81.7718532828951[/C][C]2.22814671710486[/C][/ROW]
[ROW][C]129[/C][C]112[/C][C]103.68277031464[/C][C]8.3172296853596[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]6.58404562092298[/C][C]1.41595437907702[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.623025720673806[/C][C]0.623025720673806[/C][/ROW]
[ROW][C]132[/C][C]52[/C][C]64.2066045589382[/C][C]-12.2066045589382[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]3.02622047731893[/C][C]0.973779522681071[/C][/ROW]
[ROW][C]134[/C][C]57[/C][C]64.4033347135769[/C][C]-7.40333471357692[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.653952724250076[/C][C]0.653952724250076[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]14.5178535795015[/C][C]-0.517853579501526[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-0.751517150912103[/C][C]0.751517150912103[/C][/ROW]
[ROW][C]138[/C][C]91[/C][C]92.5272070841512[/C][C]-1.52720708415119[/C][/ROW]
[ROW][C]139[/C][C]89[/C][C]95.6402089953194[/C][C]-6.64020899531935[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.641962874708022[/C][C]0.641962874708022[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]-0.681598883195473[/C][C]0.681598883195473[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]54.9447873294554[/C][C]-0.944787329455439[/C][/ROW]
[ROW][C]143[/C][C]77[/C][C]75.4460604551137[/C][C]1.55393954488632[/C][/ROW]
[ROW][C]144[/C][C]76[/C][C]71.1825096568485[/C][C]4.81749034315155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17067.36617317626732.63382682373267
26866.71130588902341.28869411097665
30-0.6718125411344830.671812541134483
46881.5543604703331-13.554360470333
5120112.9098856712867.09011432871431
6120124.545091882743-4.54509188274317
77272.247171024556-0.247171024555994
89694.6940271958371.30597280416298
9109110.913712922865-1.91371292286468
10104101.9038843051382.09611569486188
115474.3448361947949-20.3448361947949
129895.30967617093232.69032382906765
134954.9755462000321-5.97554620003209
148882.67560444924685.32439555075322
155761.6330614642255-4.63306146422551
167472.40091489709811.59908510290189
17112105.0947275167366.90527248326413
184543.55878762160071.44121237839933
19110103.2616393038966.73836069610399
203946.8156632792011-7.8156632792011
215551.26236898393293.73763101606715
22102103.349500782776-1.34950078277606
239692.65804347732443.34195652267564
2486110.043701785806-24.0437017858065
257879.5277015337835-1.52770153378353
266463.99446116165650.00553883834354191
278283.5494323115662-1.54943231156618
28100107.524509102342-7.52450910234202
2999103.94241202962-4.94241202961969
306765.99764530034181.0023546996582
318786.12673324064670.873266759353275
326577.338924113972-12.338924113972
334340.9177310149842.08226898501595
348075.37902090479114.62097909520894
358481.51536622830662.48463377169341
360-0.7515171509121030.751517150912103
37105100.9140286447424.08597135525809
385152.2159522055059-1.21595220550591
3998107.316189082102-9.31618908210238
40124119.5406802490744.45931975092627
417571.76389419345843.23610580654163
42120110.913269867989.08673013202004
438485.6586594896558-1.6586594896558
448278.7693264737643.23067352623598
458781.3277638209245.67223617907598
467879.8165733348469-1.81657333484692
4797117.252310097262-20.2523100972616
487672.48055668562233.51944331437765
4910499.10340318374654.89659681625348
509392.50164534033330.498354659666737
518280.35524572214391.6447542778561
527370.93473333760772.06526666239229
538793.6649882087173-6.66498820871729
549598.9512583076067-3.95125830760666
55105102.1558063352882.8441936647122
563739.2731497646351-2.27314976463515
579698.3805551410903-2.38055514109026
588884.78733710014393.21266289985614
598379.71414202734073.28585797265927
60124124.570213536037-0.570213536036642
61116107.9427984502238.05720154977657
627670.22459954287645.77540045712356
636571.5384796344552-6.5384796344552
648685.9714762416870.0285237583129557
658582.924406806512.07559319348996
66107107.807522556525-0.807522556524531
67124118.587652322545.4123476774603
687878.0471525869815-0.0471525869814686
698379.07594193750313.92405806249689
707876.15025575648731.84974424351267
715958.34051044138050.659489558619532
723334.9777899090815-1.97778990908148
739284.39755986573497.60244013426512
745265.7406061155485-13.7406061155485
75121114.5646316500636.43536834993741
769290.99183455801231.00816544198768
779989.92041468279679.07958531720334
788683.32783496876722.6721650312328
797577.2156166891442-2.2156166891442
809695.29636854063670.703631459363319
818181.2409564769461-0.240956476946063
8210499.50594914704644.49405085295357
837673.56016377617152.43983622382848
849092.6614663158359-2.6614663158359
857568.85817430176896.1418256982311
868682.86260556630273.13739443369729
8710095.51662821368554.48337178631449
888881.01292778819876.98707221180128
898077.25566544496512.74433455503492
907375.0031560684514-2.00315606845141
918886.05159311353161.94840688646838
927982.9435221752768-3.94352217527677
938180.24612241225370.753877587746279
944845.5091061864462.49089381355399
953333.0514903193371-0.0514903193371479
96120121.753177289466-1.75317728946611
979091.4199291481702-1.41992914817017
9823.50932937691066-1.50932937691066
999689.99037194212426.00962805787576
1008692.7839206552114-6.78392065521141
1011514.29503922563630.704960774363704
1024855.092381043333-7.09238104333296
1038178.1908472746092.80915272539096
1048485.9218581908742-1.92185819087417
1054644.9663308206151.03366917938496
1065957.58532607471691.41467392528312
1079690.01056058768995.98943941231008
1082932.7207600438556-3.72076004385557
1090-0.7515171509121030.751517150912103
1108384.7246614487395-1.72466144873953
1116362.38800428632370.61199571367634
1126867.96991720891920.0300827910808357
1138477.57464029405596.42535970594412
1145462.550881912773-8.55088191277301
1150-0.6968653173153060.696865317315306
1160-0.7515171509121030.751517150912103
1177577.0659970427207-2.06599704272066
1188797.296610050406-10.296610050406
11910496.81211111908437.18788888091568
1208074.69662386103225.3033761389678
12133.13572911829083-0.135729118290835
1229390.80795619954772.19204380045225
1235552.74307719106432.25692280893575
1249695.2570076886140.742992311386033
1254844.06751091375333.9324890862467
12687.190868079342470.809131920657531
1276060.7921181879049-0.792118187904869
1288481.77185328289512.22814671710486
129112103.682770314648.3172296853596
13086.584045620922981.41595437907702
1310-0.6230257206738060.623025720673806
1325264.2066045589382-12.2066045589382
13343.026220477318930.973779522681071
1345764.4033347135769-7.40333471357692
1350-0.6539527242500760.653952724250076
1361414.5178535795015-0.517853579501526
1370-0.7515171509121030.751517150912103
1389192.5272070841512-1.52720708415119
1398995.6402089953194-6.64020899531935
1400-0.6419628747080220.641962874708022
1410-0.6815988831954730.681598883195473
1425454.9447873294554-0.944787329455439
1437775.44606045511371.55393954488632
1447671.18250965684854.81749034315155






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6674858453950450.665028309209910.332514154604955
110.9783780005546910.04324399889061740.0216219994453087
120.9940254673629620.01194906527407530.00597453263703764
130.9885502878128740.02289942437425150.0114497121871258
140.991872822211950.0162543555761010.0081271777880505
150.985727530662650.02854493867469970.0142724693373499
160.9842301501348450.03153969973030970.0157698498651548
170.9817450451403140.03650990971937280.0182549548596864
180.9701858492515620.05962830149687630.0298141507484381
190.9821402885912370.03571942281752590.017859711408763
200.9854288355233930.02914232895321390.014571164476607
210.9820564263606720.03588714727865670.0179435736393284
220.9724251027488190.05514979450236240.0275748972511812
230.9598513903621140.08029721927577280.0401486096378864
240.999927203311750.0001455933764990157.27966882495076e-05
250.9998688071544120.0002623856911770860.000131192845588543
260.9997693152898070.0004613694203868730.000230684710193437
270.9996152439495980.0007695121008038510.000384756050401926
280.9996431818537350.0007136362925309530.000356818146265476
290.9996347539162240.0007304921675517630.000365246083775881
300.9994288930990720.001142213801855350.000571106900927676
310.9991216660211440.001756667957712220.000878333978856111
320.9997319762721110.0005360474557775670.000268023727888783
330.9995727155213580.0008545689572847470.000427284478642373
340.9994098742405860.00118025151882830.000590125759414152
350.9991925937180840.001614812563832790.000807406281916394
360.9987064993584760.002587001283048460.00129350064152423
370.9986824623171840.002635075365632670.00131753768281633
380.9979936662775120.004012667444976830.00200633372248841
390.9984380580659260.00312388386814860.0015619419340743
400.9988527279746050.002294544050790410.0011472720253952
410.9983692955616120.003261408876774970.00163070443838749
420.9994355224526980.001128955094604680.000564477547302339
430.99912168234360.001756635312800010.000878317656400005
440.9988049306142860.002390138771427930.00119506938571397
450.9988152626614540.0023694746770910.0011847373385455
460.9982395283827950.003520943234410940.00176047161720547
470.999987537650472.49246990605957e-051.24623495302978e-05
480.9999832322690433.35354619146719e-051.6767730957336e-05
490.9999852468315882.95063368246484e-051.47531684123242e-05
500.9999749499543855.01000912296262e-052.50500456148131e-05
510.999966714329456.65713411007444e-053.32856705503722e-05
520.9999483381161540.0001033237676917495.16618838458743e-05
530.9999562812665448.7437466912019e-054.37187334560095e-05
540.9999413430641790.0001173138716411775.86569358205887e-05
550.9999215110613750.0001569778772507137.84889386253564e-05
560.9998808532356550.0002382935286901730.000119146764345086
570.9998262963553960.0003474072892079870.000173703644603993
580.9997851480775140.0004297038449714430.000214851922485722
590.9997141940838760.0005716118322477450.000285805916123872
600.9995684724742170.0008630550515670060.000431527525783503
610.9997469557381440.0005060885237122170.000253044261856108
620.999776194571450.0004476108571002550.000223805428550127
630.9998294589067030.0003410821865936960.000170541093296848
640.9997417751318810.0005164497362386890.000258224868119344
650.9996461032822190.000707793435561330.000353896717780665
660.9994864798971910.001027040205617590.000513520102808796
670.9994306146062710.001138770787458090.000569385393729044
680.9991579715948880.00168405681022460.000842028405112302
690.998903542692340.00219291461531990.00109645730765995
700.998446612734070.003106774531859660.00155338726592983
710.997897280815080.004205438369839950.00210271918491998
720.9970083360788730.005983327842254030.00299166392112702
730.9984025885940420.003194822811916140.00159741140595807
740.9997981653694660.000403669261067170.000201834630533585
750.9998292582441390.0003414835117228890.000170741755861445
760.999739765201050.0005204695978996720.000260234798949836
770.9999148860021160.0001702279957679088.51139978839541e-05
780.9999280010508630.0001439978982731157.19989491365573e-05
790.999898614131540.000202771736919040.00010138586845952
800.9998328784668190.0003342430663618930.000167121533180947
810.9997299057391390.000540188521722190.000270094260861095
820.9996576163251650.0006847673496691010.000342383674834551
830.9994904391360890.001019121727821470.000509560863910733
840.9992607530340940.001478493931812890.000739246965906447
850.9993126002476820.001374799504636290.000687399752318147
860.9990759155080790.001848168983842270.000924084491921136
870.9990273175207410.001945364958518780.000972682479259388
880.9992388491920550.001522301615890490.000761150807945244
890.9989498121404560.002100375719088680.00105018785954434
900.9983941240928190.003211751814361110.00160587590718056
910.9979313439758330.004137312048333150.00206865602416658
920.997059297843990.005881404312019610.00294070215600981
930.9955742904797610.0088514190404780.004425709520239
940.9946093419959040.01078131600819240.00539065800409618
950.9924228617200710.01515427655985710.00757713827992855
960.9894320054932540.0211359890134920.010567994506746
970.9849648079565670.03007038408686690.0150351920434334
980.9789087555087440.04218248898251160.0210912444912558
990.9849627165761160.03007456684776740.0150372834238837
1000.9845536701428580.03089265971428310.0154463298571416
1010.9791761813741440.04164763725171160.0208238186258558
1020.9848784735490720.03024305290185540.0151215264509277
1030.9811000666445180.03779986671096430.0188999333554822
1040.9794295488212410.04114090235751760.0205704511787588
1050.9709244587339290.05815108253214120.0290755412660706
1060.9603011883978840.07939762320423250.0396988116021163
1070.957491890632560.08501621873488030.0425081093674402
1080.9510188808783930.0979622382432140.048981119121607
1090.9328659202309320.1342681595381350.0671340797690677
1100.9161663528732990.1676672942534030.0838336471267013
1110.8886830180452430.2226339639095140.111316981954757
1120.8548514461370440.2902971077259110.145148553862956
1130.8791636252900170.2416727494199670.120836374709983
1140.9153781622497810.1692436755004380.0846218377502189
1150.8852206221355760.2295587557288480.114779377864424
1160.8477643426799270.3044713146401450.152235657320073
1170.8087479508305670.3825040983388670.191252049169433
1180.9492151991979810.1015696016040390.0507848008020193
1190.9716826616597010.0566346766805990.0283173383402995
1200.965648836061380.06870232787724090.0343511639386204
1210.9594112367812230.08117752643755480.0405887632187774
1220.9448665089309980.1102669821380040.0551334910690018
1230.9372261023518720.1255477952962550.0627738976481277
1240.904128658961050.19174268207790.0958713410389498
1250.9773757499671510.04524850006569790.022624250032849
1260.9602648355895350.07947032882092940.0397351644104647
1270.9385953789630910.1228092420738180.0614046210369089
1280.9777935925491760.04441281490164850.0222064074508242
1290.9904275025489580.01914499490208450.00957249745104224
1300.9774979335957080.04500413280858350.0225020664042918
1310.9504951425045870.09900971499082550.0495048574954128
1320.9852366983260260.02952660334794850.0147633016739743
1330.9572845734627980.08543085307440340.0427154265372017
1340.9786821463914930.04263570721701430.0213178536085071

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.667485845395045 & 0.66502830920991 & 0.332514154604955 \tabularnewline
11 & 0.978378000554691 & 0.0432439988906174 & 0.0216219994453087 \tabularnewline
12 & 0.994025467362962 & 0.0119490652740753 & 0.00597453263703764 \tabularnewline
13 & 0.988550287812874 & 0.0228994243742515 & 0.0114497121871258 \tabularnewline
14 & 0.99187282221195 & 0.016254355576101 & 0.0081271777880505 \tabularnewline
15 & 0.98572753066265 & 0.0285449386746997 & 0.0142724693373499 \tabularnewline
16 & 0.984230150134845 & 0.0315396997303097 & 0.0157698498651548 \tabularnewline
17 & 0.981745045140314 & 0.0365099097193728 & 0.0182549548596864 \tabularnewline
18 & 0.970185849251562 & 0.0596283014968763 & 0.0298141507484381 \tabularnewline
19 & 0.982140288591237 & 0.0357194228175259 & 0.017859711408763 \tabularnewline
20 & 0.985428835523393 & 0.0291423289532139 & 0.014571164476607 \tabularnewline
21 & 0.982056426360672 & 0.0358871472786567 & 0.0179435736393284 \tabularnewline
22 & 0.972425102748819 & 0.0551497945023624 & 0.0275748972511812 \tabularnewline
23 & 0.959851390362114 & 0.0802972192757728 & 0.0401486096378864 \tabularnewline
24 & 0.99992720331175 & 0.000145593376499015 & 7.27966882495076e-05 \tabularnewline
25 & 0.999868807154412 & 0.000262385691177086 & 0.000131192845588543 \tabularnewline
26 & 0.999769315289807 & 0.000461369420386873 & 0.000230684710193437 \tabularnewline
27 & 0.999615243949598 & 0.000769512100803851 & 0.000384756050401926 \tabularnewline
28 & 0.999643181853735 & 0.000713636292530953 & 0.000356818146265476 \tabularnewline
29 & 0.999634753916224 & 0.000730492167551763 & 0.000365246083775881 \tabularnewline
30 & 0.999428893099072 & 0.00114221380185535 & 0.000571106900927676 \tabularnewline
31 & 0.999121666021144 & 0.00175666795771222 & 0.000878333978856111 \tabularnewline
32 & 0.999731976272111 & 0.000536047455777567 & 0.000268023727888783 \tabularnewline
33 & 0.999572715521358 & 0.000854568957284747 & 0.000427284478642373 \tabularnewline
34 & 0.999409874240586 & 0.0011802515188283 & 0.000590125759414152 \tabularnewline
35 & 0.999192593718084 & 0.00161481256383279 & 0.000807406281916394 \tabularnewline
36 & 0.998706499358476 & 0.00258700128304846 & 0.00129350064152423 \tabularnewline
37 & 0.998682462317184 & 0.00263507536563267 & 0.00131753768281633 \tabularnewline
38 & 0.997993666277512 & 0.00401266744497683 & 0.00200633372248841 \tabularnewline
39 & 0.998438058065926 & 0.0031238838681486 & 0.0015619419340743 \tabularnewline
40 & 0.998852727974605 & 0.00229454405079041 & 0.0011472720253952 \tabularnewline
41 & 0.998369295561612 & 0.00326140887677497 & 0.00163070443838749 \tabularnewline
42 & 0.999435522452698 & 0.00112895509460468 & 0.000564477547302339 \tabularnewline
43 & 0.9991216823436 & 0.00175663531280001 & 0.000878317656400005 \tabularnewline
44 & 0.998804930614286 & 0.00239013877142793 & 0.00119506938571397 \tabularnewline
45 & 0.998815262661454 & 0.002369474677091 & 0.0011847373385455 \tabularnewline
46 & 0.998239528382795 & 0.00352094323441094 & 0.00176047161720547 \tabularnewline
47 & 0.99998753765047 & 2.49246990605957e-05 & 1.24623495302978e-05 \tabularnewline
48 & 0.999983232269043 & 3.35354619146719e-05 & 1.6767730957336e-05 \tabularnewline
49 & 0.999985246831588 & 2.95063368246484e-05 & 1.47531684123242e-05 \tabularnewline
50 & 0.999974949954385 & 5.01000912296262e-05 & 2.50500456148131e-05 \tabularnewline
51 & 0.99996671432945 & 6.65713411007444e-05 & 3.32856705503722e-05 \tabularnewline
52 & 0.999948338116154 & 0.000103323767691749 & 5.16618838458743e-05 \tabularnewline
53 & 0.999956281266544 & 8.7437466912019e-05 & 4.37187334560095e-05 \tabularnewline
54 & 0.999941343064179 & 0.000117313871641177 & 5.86569358205887e-05 \tabularnewline
55 & 0.999921511061375 & 0.000156977877250713 & 7.84889386253564e-05 \tabularnewline
56 & 0.999880853235655 & 0.000238293528690173 & 0.000119146764345086 \tabularnewline
57 & 0.999826296355396 & 0.000347407289207987 & 0.000173703644603993 \tabularnewline
58 & 0.999785148077514 & 0.000429703844971443 & 0.000214851922485722 \tabularnewline
59 & 0.999714194083876 & 0.000571611832247745 & 0.000285805916123872 \tabularnewline
60 & 0.999568472474217 & 0.000863055051567006 & 0.000431527525783503 \tabularnewline
61 & 0.999746955738144 & 0.000506088523712217 & 0.000253044261856108 \tabularnewline
62 & 0.99977619457145 & 0.000447610857100255 & 0.000223805428550127 \tabularnewline
63 & 0.999829458906703 & 0.000341082186593696 & 0.000170541093296848 \tabularnewline
64 & 0.999741775131881 & 0.000516449736238689 & 0.000258224868119344 \tabularnewline
65 & 0.999646103282219 & 0.00070779343556133 & 0.000353896717780665 \tabularnewline
66 & 0.999486479897191 & 0.00102704020561759 & 0.000513520102808796 \tabularnewline
67 & 0.999430614606271 & 0.00113877078745809 & 0.000569385393729044 \tabularnewline
68 & 0.999157971594888 & 0.0016840568102246 & 0.000842028405112302 \tabularnewline
69 & 0.99890354269234 & 0.0021929146153199 & 0.00109645730765995 \tabularnewline
70 & 0.99844661273407 & 0.00310677453185966 & 0.00155338726592983 \tabularnewline
71 & 0.99789728081508 & 0.00420543836983995 & 0.00210271918491998 \tabularnewline
72 & 0.997008336078873 & 0.00598332784225403 & 0.00299166392112702 \tabularnewline
73 & 0.998402588594042 & 0.00319482281191614 & 0.00159741140595807 \tabularnewline
74 & 0.999798165369466 & 0.00040366926106717 & 0.000201834630533585 \tabularnewline
75 & 0.999829258244139 & 0.000341483511722889 & 0.000170741755861445 \tabularnewline
76 & 0.99973976520105 & 0.000520469597899672 & 0.000260234798949836 \tabularnewline
77 & 0.999914886002116 & 0.000170227995767908 & 8.51139978839541e-05 \tabularnewline
78 & 0.999928001050863 & 0.000143997898273115 & 7.19989491365573e-05 \tabularnewline
79 & 0.99989861413154 & 0.00020277173691904 & 0.00010138586845952 \tabularnewline
80 & 0.999832878466819 & 0.000334243066361893 & 0.000167121533180947 \tabularnewline
81 & 0.999729905739139 & 0.00054018852172219 & 0.000270094260861095 \tabularnewline
82 & 0.999657616325165 & 0.000684767349669101 & 0.000342383674834551 \tabularnewline
83 & 0.999490439136089 & 0.00101912172782147 & 0.000509560863910733 \tabularnewline
84 & 0.999260753034094 & 0.00147849393181289 & 0.000739246965906447 \tabularnewline
85 & 0.999312600247682 & 0.00137479950463629 & 0.000687399752318147 \tabularnewline
86 & 0.999075915508079 & 0.00184816898384227 & 0.000924084491921136 \tabularnewline
87 & 0.999027317520741 & 0.00194536495851878 & 0.000972682479259388 \tabularnewline
88 & 0.999238849192055 & 0.00152230161589049 & 0.000761150807945244 \tabularnewline
89 & 0.998949812140456 & 0.00210037571908868 & 0.00105018785954434 \tabularnewline
90 & 0.998394124092819 & 0.00321175181436111 & 0.00160587590718056 \tabularnewline
91 & 0.997931343975833 & 0.00413731204833315 & 0.00206865602416658 \tabularnewline
92 & 0.99705929784399 & 0.00588140431201961 & 0.00294070215600981 \tabularnewline
93 & 0.995574290479761 & 0.008851419040478 & 0.004425709520239 \tabularnewline
94 & 0.994609341995904 & 0.0107813160081924 & 0.00539065800409618 \tabularnewline
95 & 0.992422861720071 & 0.0151542765598571 & 0.00757713827992855 \tabularnewline
96 & 0.989432005493254 & 0.021135989013492 & 0.010567994506746 \tabularnewline
97 & 0.984964807956567 & 0.0300703840868669 & 0.0150351920434334 \tabularnewline
98 & 0.978908755508744 & 0.0421824889825116 & 0.0210912444912558 \tabularnewline
99 & 0.984962716576116 & 0.0300745668477674 & 0.0150372834238837 \tabularnewline
100 & 0.984553670142858 & 0.0308926597142831 & 0.0154463298571416 \tabularnewline
101 & 0.979176181374144 & 0.0416476372517116 & 0.0208238186258558 \tabularnewline
102 & 0.984878473549072 & 0.0302430529018554 & 0.0151215264509277 \tabularnewline
103 & 0.981100066644518 & 0.0377998667109643 & 0.0188999333554822 \tabularnewline
104 & 0.979429548821241 & 0.0411409023575176 & 0.0205704511787588 \tabularnewline
105 & 0.970924458733929 & 0.0581510825321412 & 0.0290755412660706 \tabularnewline
106 & 0.960301188397884 & 0.0793976232042325 & 0.0396988116021163 \tabularnewline
107 & 0.95749189063256 & 0.0850162187348803 & 0.0425081093674402 \tabularnewline
108 & 0.951018880878393 & 0.097962238243214 & 0.048981119121607 \tabularnewline
109 & 0.932865920230932 & 0.134268159538135 & 0.0671340797690677 \tabularnewline
110 & 0.916166352873299 & 0.167667294253403 & 0.0838336471267013 \tabularnewline
111 & 0.888683018045243 & 0.222633963909514 & 0.111316981954757 \tabularnewline
112 & 0.854851446137044 & 0.290297107725911 & 0.145148553862956 \tabularnewline
113 & 0.879163625290017 & 0.241672749419967 & 0.120836374709983 \tabularnewline
114 & 0.915378162249781 & 0.169243675500438 & 0.0846218377502189 \tabularnewline
115 & 0.885220622135576 & 0.229558755728848 & 0.114779377864424 \tabularnewline
116 & 0.847764342679927 & 0.304471314640145 & 0.152235657320073 \tabularnewline
117 & 0.808747950830567 & 0.382504098338867 & 0.191252049169433 \tabularnewline
118 & 0.949215199197981 & 0.101569601604039 & 0.0507848008020193 \tabularnewline
119 & 0.971682661659701 & 0.056634676680599 & 0.0283173383402995 \tabularnewline
120 & 0.96564883606138 & 0.0687023278772409 & 0.0343511639386204 \tabularnewline
121 & 0.959411236781223 & 0.0811775264375548 & 0.0405887632187774 \tabularnewline
122 & 0.944866508930998 & 0.110266982138004 & 0.0551334910690018 \tabularnewline
123 & 0.937226102351872 & 0.125547795296255 & 0.0627738976481277 \tabularnewline
124 & 0.90412865896105 & 0.1917426820779 & 0.0958713410389498 \tabularnewline
125 & 0.977375749967151 & 0.0452485000656979 & 0.022624250032849 \tabularnewline
126 & 0.960264835589535 & 0.0794703288209294 & 0.0397351644104647 \tabularnewline
127 & 0.938595378963091 & 0.122809242073818 & 0.0614046210369089 \tabularnewline
128 & 0.977793592549176 & 0.0444128149016485 & 0.0222064074508242 \tabularnewline
129 & 0.990427502548958 & 0.0191449949020845 & 0.00957249745104224 \tabularnewline
130 & 0.977497933595708 & 0.0450041328085835 & 0.0225020664042918 \tabularnewline
131 & 0.950495142504587 & 0.0990097149908255 & 0.0495048574954128 \tabularnewline
132 & 0.985236698326026 & 0.0295266033479485 & 0.0147633016739743 \tabularnewline
133 & 0.957284573462798 & 0.0854308530744034 & 0.0427154265372017 \tabularnewline
134 & 0.978682146391493 & 0.0426357072170143 & 0.0213178536085071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.667485845395045[/C][C]0.66502830920991[/C][C]0.332514154604955[/C][/ROW]
[ROW][C]11[/C][C]0.978378000554691[/C][C]0.0432439988906174[/C][C]0.0216219994453087[/C][/ROW]
[ROW][C]12[/C][C]0.994025467362962[/C][C]0.0119490652740753[/C][C]0.00597453263703764[/C][/ROW]
[ROW][C]13[/C][C]0.988550287812874[/C][C]0.0228994243742515[/C][C]0.0114497121871258[/C][/ROW]
[ROW][C]14[/C][C]0.99187282221195[/C][C]0.016254355576101[/C][C]0.0081271777880505[/C][/ROW]
[ROW][C]15[/C][C]0.98572753066265[/C][C]0.0285449386746997[/C][C]0.0142724693373499[/C][/ROW]
[ROW][C]16[/C][C]0.984230150134845[/C][C]0.0315396997303097[/C][C]0.0157698498651548[/C][/ROW]
[ROW][C]17[/C][C]0.981745045140314[/C][C]0.0365099097193728[/C][C]0.0182549548596864[/C][/ROW]
[ROW][C]18[/C][C]0.970185849251562[/C][C]0.0596283014968763[/C][C]0.0298141507484381[/C][/ROW]
[ROW][C]19[/C][C]0.982140288591237[/C][C]0.0357194228175259[/C][C]0.017859711408763[/C][/ROW]
[ROW][C]20[/C][C]0.985428835523393[/C][C]0.0291423289532139[/C][C]0.014571164476607[/C][/ROW]
[ROW][C]21[/C][C]0.982056426360672[/C][C]0.0358871472786567[/C][C]0.0179435736393284[/C][/ROW]
[ROW][C]22[/C][C]0.972425102748819[/C][C]0.0551497945023624[/C][C]0.0275748972511812[/C][/ROW]
[ROW][C]23[/C][C]0.959851390362114[/C][C]0.0802972192757728[/C][C]0.0401486096378864[/C][/ROW]
[ROW][C]24[/C][C]0.99992720331175[/C][C]0.000145593376499015[/C][C]7.27966882495076e-05[/C][/ROW]
[ROW][C]25[/C][C]0.999868807154412[/C][C]0.000262385691177086[/C][C]0.000131192845588543[/C][/ROW]
[ROW][C]26[/C][C]0.999769315289807[/C][C]0.000461369420386873[/C][C]0.000230684710193437[/C][/ROW]
[ROW][C]27[/C][C]0.999615243949598[/C][C]0.000769512100803851[/C][C]0.000384756050401926[/C][/ROW]
[ROW][C]28[/C][C]0.999643181853735[/C][C]0.000713636292530953[/C][C]0.000356818146265476[/C][/ROW]
[ROW][C]29[/C][C]0.999634753916224[/C][C]0.000730492167551763[/C][C]0.000365246083775881[/C][/ROW]
[ROW][C]30[/C][C]0.999428893099072[/C][C]0.00114221380185535[/C][C]0.000571106900927676[/C][/ROW]
[ROW][C]31[/C][C]0.999121666021144[/C][C]0.00175666795771222[/C][C]0.000878333978856111[/C][/ROW]
[ROW][C]32[/C][C]0.999731976272111[/C][C]0.000536047455777567[/C][C]0.000268023727888783[/C][/ROW]
[ROW][C]33[/C][C]0.999572715521358[/C][C]0.000854568957284747[/C][C]0.000427284478642373[/C][/ROW]
[ROW][C]34[/C][C]0.999409874240586[/C][C]0.0011802515188283[/C][C]0.000590125759414152[/C][/ROW]
[ROW][C]35[/C][C]0.999192593718084[/C][C]0.00161481256383279[/C][C]0.000807406281916394[/C][/ROW]
[ROW][C]36[/C][C]0.998706499358476[/C][C]0.00258700128304846[/C][C]0.00129350064152423[/C][/ROW]
[ROW][C]37[/C][C]0.998682462317184[/C][C]0.00263507536563267[/C][C]0.00131753768281633[/C][/ROW]
[ROW][C]38[/C][C]0.997993666277512[/C][C]0.00401266744497683[/C][C]0.00200633372248841[/C][/ROW]
[ROW][C]39[/C][C]0.998438058065926[/C][C]0.0031238838681486[/C][C]0.0015619419340743[/C][/ROW]
[ROW][C]40[/C][C]0.998852727974605[/C][C]0.00229454405079041[/C][C]0.0011472720253952[/C][/ROW]
[ROW][C]41[/C][C]0.998369295561612[/C][C]0.00326140887677497[/C][C]0.00163070443838749[/C][/ROW]
[ROW][C]42[/C][C]0.999435522452698[/C][C]0.00112895509460468[/C][C]0.000564477547302339[/C][/ROW]
[ROW][C]43[/C][C]0.9991216823436[/C][C]0.00175663531280001[/C][C]0.000878317656400005[/C][/ROW]
[ROW][C]44[/C][C]0.998804930614286[/C][C]0.00239013877142793[/C][C]0.00119506938571397[/C][/ROW]
[ROW][C]45[/C][C]0.998815262661454[/C][C]0.002369474677091[/C][C]0.0011847373385455[/C][/ROW]
[ROW][C]46[/C][C]0.998239528382795[/C][C]0.00352094323441094[/C][C]0.00176047161720547[/C][/ROW]
[ROW][C]47[/C][C]0.99998753765047[/C][C]2.49246990605957e-05[/C][C]1.24623495302978e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999983232269043[/C][C]3.35354619146719e-05[/C][C]1.6767730957336e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999985246831588[/C][C]2.95063368246484e-05[/C][C]1.47531684123242e-05[/C][/ROW]
[ROW][C]50[/C][C]0.999974949954385[/C][C]5.01000912296262e-05[/C][C]2.50500456148131e-05[/C][/ROW]
[ROW][C]51[/C][C]0.99996671432945[/C][C]6.65713411007444e-05[/C][C]3.32856705503722e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999948338116154[/C][C]0.000103323767691749[/C][C]5.16618838458743e-05[/C][/ROW]
[ROW][C]53[/C][C]0.999956281266544[/C][C]8.7437466912019e-05[/C][C]4.37187334560095e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999941343064179[/C][C]0.000117313871641177[/C][C]5.86569358205887e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999921511061375[/C][C]0.000156977877250713[/C][C]7.84889386253564e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999880853235655[/C][C]0.000238293528690173[/C][C]0.000119146764345086[/C][/ROW]
[ROW][C]57[/C][C]0.999826296355396[/C][C]0.000347407289207987[/C][C]0.000173703644603993[/C][/ROW]
[ROW][C]58[/C][C]0.999785148077514[/C][C]0.000429703844971443[/C][C]0.000214851922485722[/C][/ROW]
[ROW][C]59[/C][C]0.999714194083876[/C][C]0.000571611832247745[/C][C]0.000285805916123872[/C][/ROW]
[ROW][C]60[/C][C]0.999568472474217[/C][C]0.000863055051567006[/C][C]0.000431527525783503[/C][/ROW]
[ROW][C]61[/C][C]0.999746955738144[/C][C]0.000506088523712217[/C][C]0.000253044261856108[/C][/ROW]
[ROW][C]62[/C][C]0.99977619457145[/C][C]0.000447610857100255[/C][C]0.000223805428550127[/C][/ROW]
[ROW][C]63[/C][C]0.999829458906703[/C][C]0.000341082186593696[/C][C]0.000170541093296848[/C][/ROW]
[ROW][C]64[/C][C]0.999741775131881[/C][C]0.000516449736238689[/C][C]0.000258224868119344[/C][/ROW]
[ROW][C]65[/C][C]0.999646103282219[/C][C]0.00070779343556133[/C][C]0.000353896717780665[/C][/ROW]
[ROW][C]66[/C][C]0.999486479897191[/C][C]0.00102704020561759[/C][C]0.000513520102808796[/C][/ROW]
[ROW][C]67[/C][C]0.999430614606271[/C][C]0.00113877078745809[/C][C]0.000569385393729044[/C][/ROW]
[ROW][C]68[/C][C]0.999157971594888[/C][C]0.0016840568102246[/C][C]0.000842028405112302[/C][/ROW]
[ROW][C]69[/C][C]0.99890354269234[/C][C]0.0021929146153199[/C][C]0.00109645730765995[/C][/ROW]
[ROW][C]70[/C][C]0.99844661273407[/C][C]0.00310677453185966[/C][C]0.00155338726592983[/C][/ROW]
[ROW][C]71[/C][C]0.99789728081508[/C][C]0.00420543836983995[/C][C]0.00210271918491998[/C][/ROW]
[ROW][C]72[/C][C]0.997008336078873[/C][C]0.00598332784225403[/C][C]0.00299166392112702[/C][/ROW]
[ROW][C]73[/C][C]0.998402588594042[/C][C]0.00319482281191614[/C][C]0.00159741140595807[/C][/ROW]
[ROW][C]74[/C][C]0.999798165369466[/C][C]0.00040366926106717[/C][C]0.000201834630533585[/C][/ROW]
[ROW][C]75[/C][C]0.999829258244139[/C][C]0.000341483511722889[/C][C]0.000170741755861445[/C][/ROW]
[ROW][C]76[/C][C]0.99973976520105[/C][C]0.000520469597899672[/C][C]0.000260234798949836[/C][/ROW]
[ROW][C]77[/C][C]0.999914886002116[/C][C]0.000170227995767908[/C][C]8.51139978839541e-05[/C][/ROW]
[ROW][C]78[/C][C]0.999928001050863[/C][C]0.000143997898273115[/C][C]7.19989491365573e-05[/C][/ROW]
[ROW][C]79[/C][C]0.99989861413154[/C][C]0.00020277173691904[/C][C]0.00010138586845952[/C][/ROW]
[ROW][C]80[/C][C]0.999832878466819[/C][C]0.000334243066361893[/C][C]0.000167121533180947[/C][/ROW]
[ROW][C]81[/C][C]0.999729905739139[/C][C]0.00054018852172219[/C][C]0.000270094260861095[/C][/ROW]
[ROW][C]82[/C][C]0.999657616325165[/C][C]0.000684767349669101[/C][C]0.000342383674834551[/C][/ROW]
[ROW][C]83[/C][C]0.999490439136089[/C][C]0.00101912172782147[/C][C]0.000509560863910733[/C][/ROW]
[ROW][C]84[/C][C]0.999260753034094[/C][C]0.00147849393181289[/C][C]0.000739246965906447[/C][/ROW]
[ROW][C]85[/C][C]0.999312600247682[/C][C]0.00137479950463629[/C][C]0.000687399752318147[/C][/ROW]
[ROW][C]86[/C][C]0.999075915508079[/C][C]0.00184816898384227[/C][C]0.000924084491921136[/C][/ROW]
[ROW][C]87[/C][C]0.999027317520741[/C][C]0.00194536495851878[/C][C]0.000972682479259388[/C][/ROW]
[ROW][C]88[/C][C]0.999238849192055[/C][C]0.00152230161589049[/C][C]0.000761150807945244[/C][/ROW]
[ROW][C]89[/C][C]0.998949812140456[/C][C]0.00210037571908868[/C][C]0.00105018785954434[/C][/ROW]
[ROW][C]90[/C][C]0.998394124092819[/C][C]0.00321175181436111[/C][C]0.00160587590718056[/C][/ROW]
[ROW][C]91[/C][C]0.997931343975833[/C][C]0.00413731204833315[/C][C]0.00206865602416658[/C][/ROW]
[ROW][C]92[/C][C]0.99705929784399[/C][C]0.00588140431201961[/C][C]0.00294070215600981[/C][/ROW]
[ROW][C]93[/C][C]0.995574290479761[/C][C]0.008851419040478[/C][C]0.004425709520239[/C][/ROW]
[ROW][C]94[/C][C]0.994609341995904[/C][C]0.0107813160081924[/C][C]0.00539065800409618[/C][/ROW]
[ROW][C]95[/C][C]0.992422861720071[/C][C]0.0151542765598571[/C][C]0.00757713827992855[/C][/ROW]
[ROW][C]96[/C][C]0.989432005493254[/C][C]0.021135989013492[/C][C]0.010567994506746[/C][/ROW]
[ROW][C]97[/C][C]0.984964807956567[/C][C]0.0300703840868669[/C][C]0.0150351920434334[/C][/ROW]
[ROW][C]98[/C][C]0.978908755508744[/C][C]0.0421824889825116[/C][C]0.0210912444912558[/C][/ROW]
[ROW][C]99[/C][C]0.984962716576116[/C][C]0.0300745668477674[/C][C]0.0150372834238837[/C][/ROW]
[ROW][C]100[/C][C]0.984553670142858[/C][C]0.0308926597142831[/C][C]0.0154463298571416[/C][/ROW]
[ROW][C]101[/C][C]0.979176181374144[/C][C]0.0416476372517116[/C][C]0.0208238186258558[/C][/ROW]
[ROW][C]102[/C][C]0.984878473549072[/C][C]0.0302430529018554[/C][C]0.0151215264509277[/C][/ROW]
[ROW][C]103[/C][C]0.981100066644518[/C][C]0.0377998667109643[/C][C]0.0188999333554822[/C][/ROW]
[ROW][C]104[/C][C]0.979429548821241[/C][C]0.0411409023575176[/C][C]0.0205704511787588[/C][/ROW]
[ROW][C]105[/C][C]0.970924458733929[/C][C]0.0581510825321412[/C][C]0.0290755412660706[/C][/ROW]
[ROW][C]106[/C][C]0.960301188397884[/C][C]0.0793976232042325[/C][C]0.0396988116021163[/C][/ROW]
[ROW][C]107[/C][C]0.95749189063256[/C][C]0.0850162187348803[/C][C]0.0425081093674402[/C][/ROW]
[ROW][C]108[/C][C]0.951018880878393[/C][C]0.097962238243214[/C][C]0.048981119121607[/C][/ROW]
[ROW][C]109[/C][C]0.932865920230932[/C][C]0.134268159538135[/C][C]0.0671340797690677[/C][/ROW]
[ROW][C]110[/C][C]0.916166352873299[/C][C]0.167667294253403[/C][C]0.0838336471267013[/C][/ROW]
[ROW][C]111[/C][C]0.888683018045243[/C][C]0.222633963909514[/C][C]0.111316981954757[/C][/ROW]
[ROW][C]112[/C][C]0.854851446137044[/C][C]0.290297107725911[/C][C]0.145148553862956[/C][/ROW]
[ROW][C]113[/C][C]0.879163625290017[/C][C]0.241672749419967[/C][C]0.120836374709983[/C][/ROW]
[ROW][C]114[/C][C]0.915378162249781[/C][C]0.169243675500438[/C][C]0.0846218377502189[/C][/ROW]
[ROW][C]115[/C][C]0.885220622135576[/C][C]0.229558755728848[/C][C]0.114779377864424[/C][/ROW]
[ROW][C]116[/C][C]0.847764342679927[/C][C]0.304471314640145[/C][C]0.152235657320073[/C][/ROW]
[ROW][C]117[/C][C]0.808747950830567[/C][C]0.382504098338867[/C][C]0.191252049169433[/C][/ROW]
[ROW][C]118[/C][C]0.949215199197981[/C][C]0.101569601604039[/C][C]0.0507848008020193[/C][/ROW]
[ROW][C]119[/C][C]0.971682661659701[/C][C]0.056634676680599[/C][C]0.0283173383402995[/C][/ROW]
[ROW][C]120[/C][C]0.96564883606138[/C][C]0.0687023278772409[/C][C]0.0343511639386204[/C][/ROW]
[ROW][C]121[/C][C]0.959411236781223[/C][C]0.0811775264375548[/C][C]0.0405887632187774[/C][/ROW]
[ROW][C]122[/C][C]0.944866508930998[/C][C]0.110266982138004[/C][C]0.0551334910690018[/C][/ROW]
[ROW][C]123[/C][C]0.937226102351872[/C][C]0.125547795296255[/C][C]0.0627738976481277[/C][/ROW]
[ROW][C]124[/C][C]0.90412865896105[/C][C]0.1917426820779[/C][C]0.0958713410389498[/C][/ROW]
[ROW][C]125[/C][C]0.977375749967151[/C][C]0.0452485000656979[/C][C]0.022624250032849[/C][/ROW]
[ROW][C]126[/C][C]0.960264835589535[/C][C]0.0794703288209294[/C][C]0.0397351644104647[/C][/ROW]
[ROW][C]127[/C][C]0.938595378963091[/C][C]0.122809242073818[/C][C]0.0614046210369089[/C][/ROW]
[ROW][C]128[/C][C]0.977793592549176[/C][C]0.0444128149016485[/C][C]0.0222064074508242[/C][/ROW]
[ROW][C]129[/C][C]0.990427502548958[/C][C]0.0191449949020845[/C][C]0.00957249745104224[/C][/ROW]
[ROW][C]130[/C][C]0.977497933595708[/C][C]0.0450041328085835[/C][C]0.0225020664042918[/C][/ROW]
[ROW][C]131[/C][C]0.950495142504587[/C][C]0.0990097149908255[/C][C]0.0495048574954128[/C][/ROW]
[ROW][C]132[/C][C]0.985236698326026[/C][C]0.0295266033479485[/C][C]0.0147633016739743[/C][/ROW]
[ROW][C]133[/C][C]0.957284573462798[/C][C]0.0854308530744034[/C][C]0.0427154265372017[/C][/ROW]
[ROW][C]134[/C][C]0.978682146391493[/C][C]0.0426357072170143[/C][C]0.0213178536085071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6674858453950450.665028309209910.332514154604955
110.9783780005546910.04324399889061740.0216219994453087
120.9940254673629620.01194906527407530.00597453263703764
130.9885502878128740.02289942437425150.0114497121871258
140.991872822211950.0162543555761010.0081271777880505
150.985727530662650.02854493867469970.0142724693373499
160.9842301501348450.03153969973030970.0157698498651548
170.9817450451403140.03650990971937280.0182549548596864
180.9701858492515620.05962830149687630.0298141507484381
190.9821402885912370.03571942281752590.017859711408763
200.9854288355233930.02914232895321390.014571164476607
210.9820564263606720.03588714727865670.0179435736393284
220.9724251027488190.05514979450236240.0275748972511812
230.9598513903621140.08029721927577280.0401486096378864
240.999927203311750.0001455933764990157.27966882495076e-05
250.9998688071544120.0002623856911770860.000131192845588543
260.9997693152898070.0004613694203868730.000230684710193437
270.9996152439495980.0007695121008038510.000384756050401926
280.9996431818537350.0007136362925309530.000356818146265476
290.9996347539162240.0007304921675517630.000365246083775881
300.9994288930990720.001142213801855350.000571106900927676
310.9991216660211440.001756667957712220.000878333978856111
320.9997319762721110.0005360474557775670.000268023727888783
330.9995727155213580.0008545689572847470.000427284478642373
340.9994098742405860.00118025151882830.000590125759414152
350.9991925937180840.001614812563832790.000807406281916394
360.9987064993584760.002587001283048460.00129350064152423
370.9986824623171840.002635075365632670.00131753768281633
380.9979936662775120.004012667444976830.00200633372248841
390.9984380580659260.00312388386814860.0015619419340743
400.9988527279746050.002294544050790410.0011472720253952
410.9983692955616120.003261408876774970.00163070443838749
420.9994355224526980.001128955094604680.000564477547302339
430.99912168234360.001756635312800010.000878317656400005
440.9988049306142860.002390138771427930.00119506938571397
450.9988152626614540.0023694746770910.0011847373385455
460.9982395283827950.003520943234410940.00176047161720547
470.999987537650472.49246990605957e-051.24623495302978e-05
480.9999832322690433.35354619146719e-051.6767730957336e-05
490.9999852468315882.95063368246484e-051.47531684123242e-05
500.9999749499543855.01000912296262e-052.50500456148131e-05
510.999966714329456.65713411007444e-053.32856705503722e-05
520.9999483381161540.0001033237676917495.16618838458743e-05
530.9999562812665448.7437466912019e-054.37187334560095e-05
540.9999413430641790.0001173138716411775.86569358205887e-05
550.9999215110613750.0001569778772507137.84889386253564e-05
560.9998808532356550.0002382935286901730.000119146764345086
570.9998262963553960.0003474072892079870.000173703644603993
580.9997851480775140.0004297038449714430.000214851922485722
590.9997141940838760.0005716118322477450.000285805916123872
600.9995684724742170.0008630550515670060.000431527525783503
610.9997469557381440.0005060885237122170.000253044261856108
620.999776194571450.0004476108571002550.000223805428550127
630.9998294589067030.0003410821865936960.000170541093296848
640.9997417751318810.0005164497362386890.000258224868119344
650.9996461032822190.000707793435561330.000353896717780665
660.9994864798971910.001027040205617590.000513520102808796
670.9994306146062710.001138770787458090.000569385393729044
680.9991579715948880.00168405681022460.000842028405112302
690.998903542692340.00219291461531990.00109645730765995
700.998446612734070.003106774531859660.00155338726592983
710.997897280815080.004205438369839950.00210271918491998
720.9970083360788730.005983327842254030.00299166392112702
730.9984025885940420.003194822811916140.00159741140595807
740.9997981653694660.000403669261067170.000201834630533585
750.9998292582441390.0003414835117228890.000170741755861445
760.999739765201050.0005204695978996720.000260234798949836
770.9999148860021160.0001702279957679088.51139978839541e-05
780.9999280010508630.0001439978982731157.19989491365573e-05
790.999898614131540.000202771736919040.00010138586845952
800.9998328784668190.0003342430663618930.000167121533180947
810.9997299057391390.000540188521722190.000270094260861095
820.9996576163251650.0006847673496691010.000342383674834551
830.9994904391360890.001019121727821470.000509560863910733
840.9992607530340940.001478493931812890.000739246965906447
850.9993126002476820.001374799504636290.000687399752318147
860.9990759155080790.001848168983842270.000924084491921136
870.9990273175207410.001945364958518780.000972682479259388
880.9992388491920550.001522301615890490.000761150807945244
890.9989498121404560.002100375719088680.00105018785954434
900.9983941240928190.003211751814361110.00160587590718056
910.9979313439758330.004137312048333150.00206865602416658
920.997059297843990.005881404312019610.00294070215600981
930.9955742904797610.0088514190404780.004425709520239
940.9946093419959040.01078131600819240.00539065800409618
950.9924228617200710.01515427655985710.00757713827992855
960.9894320054932540.0211359890134920.010567994506746
970.9849648079565670.03007038408686690.0150351920434334
980.9789087555087440.04218248898251160.0210912444912558
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1000.9845536701428580.03089265971428310.0154463298571416
1010.9791761813741440.04164763725171160.0208238186258558
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1290.9904275025489580.01914499490208450.00957249745104224
1300.9774979335957080.04500413280858350.0225020664042918
1310.9504951425045870.09900971499082550.0495048574954128
1320.9852366983260260.02952660334794850.0147633016739743
1330.9572845734627980.08543085307440340.0427154265372017
1340.9786821463914930.04263570721701430.0213178536085071






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level700.56NOK
5% type I error level970.776NOK
10% type I error level1100.88NOK

\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 & 70 & 0.56 & NOK \tabularnewline
5% type I error level & 97 & 0.776 & NOK \tabularnewline
10% type I error level & 110 & 0.88 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155547&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]70[/C][C]0.56[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]97[/C][C]0.776[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]110[/C][C]0.88[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155547&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155547&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level700.56NOK
5% type I error level970.776NOK
10% type I error level1100.88NOK


Figures (Output of Computation)

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Input Parameters & R Code

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