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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, 18 Dec 2014 16:47:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418921478gcbwumkyr8spozg.htm/, Retrieved Sun, 19 May 2024 17:45:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271135, Retrieved Sun, 19 May 2024 17:45:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 16:47:55] [a3de03a8fa2b95b1b988206b9ba33408] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	23	12	34	2,1
50	146	16	45	61	2,7
150	182	33	37	70	2,1
154	192	32	37	69	2,1
109	263	37	108	145	2,1
68	35	14	10	23	2,1
194	439	52	68	120	2,1
158	214	75	72	147	2,1
159	341	72	143	215	2,1
67	58	15	9	24	2,1
147	292	29	55	84	2,4
39	85	13	17	30	1,95
100	200	40	37	77	2,1
111	158	19	27	46	2,1
138	199	24	37	61	1,95
101	297	121	58	178	2,1
131	227	93	66	160	2,4
101	108	36	21	57	2,1
114	86	23	19	42	2,25
165	302	85	78	163	2,4
114	148	41	35	75	2,25
111	178	46	48	94	2,55
75	120	18	27	45	1,95
82	207	35	43	78	2,4
121	157	17	30	47	2,1
32	128	4	25	29	2,1
150	296	28	69	97	2,4
117	323	44	72	116	2,1
71	79	10	23	32	2,1
165	70	38	13	50	2,25
154	146	57	61	118	2,25
126	246	23	43	66	2,4
149	196	36	51	86	2,1
145	199	22	67	89	2,4
120	127	40	36	76	2,1
109	153	31	44	75	2,1
132	299	11	45	57	2,25
172	228	38	34	72	2,25
169	190	24	36	60	2,4
114	180	37	72	109	2,25
156	212	37	39	76	2,25
172	269	22	43	65	2,1
68	130	15	25	40	2,1
89	179	2	56	58	2,1
167	243	43	80	123	2,7
113	190	31	40	71	2,1
115	299	29	73	102	2,1
78	121	45	34	80	2,25
118	137	25	72	97	2,7
87	305	4	42	46	2,4
173	157	31	61	93	2,1
2	96	-4	23	19	2,1
162	183	66	74	140	2,4
49	52	61	16	78	1,95
122	238	32	66	98	2,7
96	40	31	9	40	2,1
100	226	39	41	80	2,25
82	190	19	57	76	2,1
100	214	31	48	79	2,7
115	145	36	51	87	2,1
141	119	42	53	95	2,1
165	222	21	29	49	1,65
165	222	21	29	49	1,65
110	159	25	55	80	2,1
118	165	32	54	86	2,1
158	249	26	43	69	2,1
146	125	28	51	79	2,1
49	122	32	20	52	2,1
90	186	41	79	120	2,4
121	148	29	39	69	2,4
155	274	33	61	94	2,1
104	172	17	55	72	2,25
147	84	13	30	43	2,4
110	168	32	55	87	2,1
108	102	30	22	52	2,1
113	106	34	37	71	2,4
115	2	59	2	61	2,4
61	139	13	38	51	2,1
60	95	23	27	50	2,1
109	130	10	56	67	2,4
68	72	5	25	30	2,1
111	141	31	39	70	2,7
77	113	19	33	52	2,1
73	206	32	43	75	2,1
151	268	30	57	87	2,25
89	175	25	43	69	2,1
78	77	48	23	72	2,4
110	125	35	44	79	2,25
220	255	67	54	121	2,25
65	111	15	28	43	2,1
141	132	22	36	58	2,1
117	211	18	39	57	2,4
122	92	33	16	50	2,25
63	76	46	23	69	2,1
44	171	24	40	64	2,1
52	83	14	24	38	1,65
131	266	12	78	90	2,7
101	186	38	57	96	2,1
42	50	12	37	49	1,95
152	117	28	27	56	2,25
107	219	41	61	102	2,4
77	246	12	27	40	1,95
154	279	31	69	100	2,1
103	148	33	34	67	2,4
96	137	34	44	78	2,1
175	181	21	34	55	2,4
57	98	20	39	59	2,4
112	226	44	51	96	2,4
143	234	52	34	86	2,25
49	138	7	31	38	2,4
110	85	29	13	43	2,1
131	66	11	12	23	2,1
167	236	26	51	77	1,8
56	106	24	24	48	2,7
137	135	7	19	26	2,1
86	122	60	30	91	2,1
121	218	13	81	94	2,4
149	199	20	42	62	2,55
168	112	52	22	74	2,55
140	278	28	85	114	2,1
88	94	25	27	52	2,1
168	113	39	25	64	2,1
94	84	9	22	31	2,25
51	86	19	19	38	2,25
48	62	13	14	27	2,1
145	222	60	45	105	2,1
66	167	19	45	64	1,95
85	82	34	28	62	2,4
109	207	14	51	65	2,1
63	184	17	41	58	2,4
102	83	45	31	76	2,4
162	183	66	74	140	2,4
86	89	48	19	68	1,95
114	225	29	51	80	2,1
164	237	-2	73	71	2,1
119	102	51	24	76	2,55
126	221	2	61	63	2,1
132	128	24	23	46	2,1
142	91	40	14	53	2,1
83	198	20	54	74	1,95
94	204	19	51	70	2,25
81	158	16	62	78	2,4
166	138	20	36	56	1,95
110	226	40	59	100	2,1
64	44	27	24	51	2,1
93	196	25	26	52	1,95
104	83	49	54	102	2,1
105	79	39	39	78	2,1
49	52	61	16	78	1,95
88	105	19	36	55	2,1
95	116	67	31	98	1,95
102	83	45	31	76	2,4
99	196	30	42	73	2,4
63	153	8	39	47	2,4
76	157	19	25	45	1,95
109	75	52	31	83	2,7
117	106	22	38	60	2,1
57	58	17	31	48	1,95
120	75	33	17	50	2,1
73	74	34	22	56	1,95
91	185	22	55	77	2,1
108	265	30	62	91	2,25
105	131	25	51	76	2,7
117	139	38	30	68	2,1
119	196	26	49	74	2,4
31	78	13	16	29	1,35

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 2.05746 + 0.000669951LFM[t] -0.000520608B[t] -0.0318921PRH[t] -0.02899CH[t] + 0.0322952H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  2.05746 +  0.000669951LFM[t] -0.000520608B[t] -0.0318921PRH[t] -0.02899CH[t] +  0.0322952H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  2.05746 +  0.000669951LFM[t] -0.000520608B[t] -0.0318921PRH[t] -0.02899CH[t] +  0.0322952H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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
PA[t] = + 2.05746 + 0.000669951LFM[t] -0.000520608B[t] -0.0318921PRH[t] -0.02899CH[t] + 0.0322952H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.057460.05450537.751.41611e-817.08054e-82
LFM0.0006699510.0005271071.2710.2055750.102787
B-0.0005206080.000351847-1.480.1409350.0704673
PRH-0.03189210.0354688-0.89920.3699190.18496
CH-0.028990.0353686-0.81970.4136320.206816
H0.03229520.03535440.91350.3623690.181185

\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) & 2.05746 & 0.054505 & 37.75 & 1.41611e-81 & 7.08054e-82 \tabularnewline
LFM & 0.000669951 & 0.000527107 & 1.271 & 0.205575 & 0.102787 \tabularnewline
B & -0.000520608 & 0.000351847 & -1.48 & 0.140935 & 0.0704673 \tabularnewline
PRH & -0.0318921 & 0.0354688 & -0.8992 & 0.369919 & 0.18496 \tabularnewline
CH & -0.02899 & 0.0353686 & -0.8197 & 0.413632 & 0.206816 \tabularnewline
H & 0.0322952 & 0.0353544 & 0.9135 & 0.362369 & 0.181185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&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]2.05746[/C][C]0.054505[/C][C]37.75[/C][C]1.41611e-81[/C][C]7.08054e-82[/C][/ROW]
[ROW][C]LFM[/C][C]0.000669951[/C][C]0.000527107[/C][C]1.271[/C][C]0.205575[/C][C]0.102787[/C][/ROW]
[ROW][C]B[/C][C]-0.000520608[/C][C]0.000351847[/C][C]-1.48[/C][C]0.140935[/C][C]0.0704673[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0318921[/C][C]0.0354688[/C][C]-0.8992[/C][C]0.369919[/C][C]0.18496[/C][/ROW]
[ROW][C]CH[/C][C]-0.02899[/C][C]0.0353686[/C][C]-0.8197[/C][C]0.413632[/C][C]0.206816[/C][/ROW]
[ROW][C]H[/C][C]0.0322952[/C][C]0.0353544[/C][C]0.9135[/C][C]0.362369[/C][C]0.181185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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)2.057460.05450537.751.41611e-817.08054e-82
LFM0.0006699510.0005271071.2710.2055750.102787
B-0.0005206080.000351847-1.480.1409350.0704673
PRH-0.03189210.0354688-0.89920.3699190.18496
CH-0.028990.0353686-0.81970.4136320.206816
H0.03229520.03535440.91350.3623690.181185






Multiple Linear Regression - Regression Statistics
Multiple R0.28204
R-squared0.0795467
Adjusted R-squared0.0507825
F-TEST (value)2.76548
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0.0200238
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.213886
Sum Squared Residuals7.31956

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.28204 \tabularnewline
R-squared & 0.0795467 \tabularnewline
Adjusted R-squared & 0.0507825 \tabularnewline
F-TEST (value) & 2.76548 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0.0200238 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.213886 \tabularnewline
Sum Squared Residuals & 7.31956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.28204[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0795467[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0507825[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.76548[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0.0200238[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.213886[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.31956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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 R0.28204
R-squared0.0795467
Adjusted R-squared0.0507825
F-TEST (value)2.76548
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0.0200238
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.213886
Sum Squared Residuals7.31956






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.12.084910.0150894
22.72.170130.529869
32.12.1988-0.0987966
42.12.19587-0.0958672
52.12.36544-0.265441
62.12.091190.00880552
72.12.2046-0.104599
82.12.32011-0.220111
92.12.48812-0.388123
102.12.10794-0.00794372
112.42.19740.202599
121.952.10076-0.150764
132.12.15875-0.0587502
142.12.14647-0.0464669
151.952.17828-0.228278
162.12.17869-0.078691
172.42.314980.0850244
182.12.15282-0.0528204
192.252.161130.0888684
202.42.302850.0971498
212.252.15670.0933013
222.552.216350.333651
231.952.14173-0.191729
242.42.160860.239138
252.12.1628-0.0627963
262.12.09650.00349876
272.42.24320.156802
282.12.2234-0.123399
292.12.11165-0.0116525
302.252.157550.0924501
312.252.30922-0.0592188
322.42.16520.234802
332.12.20603-0.106025
342.42.281320.118682
352.12.20685-0.106848
362.12.20876-0.108756
372.252.175690.0743064
382.252.181690.0683122
392.42.200430.199573
402.252.29301-0.0430141
412.252.195420.0545789
422.12.18364-0.0836391
432.12.12401-0.024013
442.12.20979-0.109792
452.72.324580.375416
462.12.17895-0.0789528
472.12.23181-0.131812
482.252.209530.0404652
492.72.313240.386758
502.42.097390.302611
512.12.33803-0.238034
522.12.083230.0167735
532.42.341910.0580881
541.952.17299-0.222985
552.72.246330.453669
562.12.14319-0.0431938
572.252.158030.0919679
582.12.20953-0.109535
592.72.184190.51581
602.12.24209-0.142093
612.12.28208-0.182077
621.652.12445-0.474447
631.652.12445-0.474447
642.12.24024-0.140241
652.12.24199-0.141994
662.12.18628-0.0862846
672.12.27005-0.170048
682.12.10578-0.00577655
692.42.298560.101439
702.42.234360.165638
712.12.23358-0.133575
722.252.226230.0237721
732.42.216610.183393
742.12.23838-0.138377
752.12.16152-0.0615199
762.42.213980.186022
772.42.163860.236142
782.12.1568-0.0567991
792.12.14671-0.0467104
802.42.284220.115779
812.12.15018-0.0501767
822.72.199820.500182
832.12.16695-0.0669471
842.12.15414-0.0541438
852.252.219590.0304113
862.12.21047-0.110475
872.42.197290.202706
882.252.225620.0243841
892.252.27758-0.0275846
902.12.14181-0.0418103
912.12.21106-0.111058
922.42.162150.237846
932.252.189780.0602212
942.12.15466-0.0546637
952.12.1398-0.0397956
961.652.13405-0.484054
972.72.269380.430617
982.12.2643-0.164301
991.952.1867-0.236696
1002.252.23120.0187965
1012.42.233280.166724
1021.952.10735-0.157348
1032.12.25594-0.155937
1042.42.175090.224906
1052.12.20959-0.109586
1062.42.201310.198687
1072.42.181590.218408
1082.42.233430.166566
1092.252.164780.0852202
1102.42.123720.276275
1112.12.17386-0.0738554
1122.12.15496-0.0549587
1131.82.22552-0.425524
1142.72.128790.571209
1152.12.14458-0.0445802
1162.12.2072-0.1072
1172.42.297990.102008
1182.552.200560.349439
1192.552.205380.34462
1202.12.33105-0.231048
1212.12.1668-0.0667959
1222.12.20953-0.109534
1232.252.153050.0969548
1242.252.117310.132688
1252.12.10885-0.00885188
1262.12.21195-0.111949
1271.952.17113-0.221127
1282.42.177970.222033
1292.12.19693-0.0969261
1302.42.146240.25376
1312.42.203190.196814
1322.42.341910.0580881
1331.952.18319-0.233185
1342.12.19695-0.0969523
1352.12.28442-0.184419
1362.552.216260.333739
1372.12.22924-0.129241
1382.12.13265-0.0326539
1392.12.13532-0.0353198
1401.952.19653-0.246527
1412.252.190450.0595455
1422.42.240840.159159
1431.952.22388-0.273876
1442.12.25692-0.156923
1452.12.16764-0.0676382
1461.952.14603-0.196034
1472.12.24986-0.149863
1482.12.2313-0.131301
1491.952.17299-0.222985
1502.12.1884-0.088397
1511.952.19019-0.240186
1522.42.203190.196814
1532.42.204950.195048
1542.42.152140.24786
1551.952.14922-0.199224
1562.72.214860.485137
1572.12.21512-0.115125
1581.952.17476-0.224765
1592.12.1683-0.0682992
1601.952.15426-0.204261
1612.12.21277-0.112766
1622.252.176570.0734266
1632.72.238250.461753
1642.12.17795-0.0779538
1652.42.175280.224715
1661.352.09574-0.745743

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.1 & 2.08491 & 0.0150894 \tabularnewline
2 & 2.7 & 2.17013 & 0.529869 \tabularnewline
3 & 2.1 & 2.1988 & -0.0987966 \tabularnewline
4 & 2.1 & 2.19587 & -0.0958672 \tabularnewline
5 & 2.1 & 2.36544 & -0.265441 \tabularnewline
6 & 2.1 & 2.09119 & 0.00880552 \tabularnewline
7 & 2.1 & 2.2046 & -0.104599 \tabularnewline
8 & 2.1 & 2.32011 & -0.220111 \tabularnewline
9 & 2.1 & 2.48812 & -0.388123 \tabularnewline
10 & 2.1 & 2.10794 & -0.00794372 \tabularnewline
11 & 2.4 & 2.1974 & 0.202599 \tabularnewline
12 & 1.95 & 2.10076 & -0.150764 \tabularnewline
13 & 2.1 & 2.15875 & -0.0587502 \tabularnewline
14 & 2.1 & 2.14647 & -0.0464669 \tabularnewline
15 & 1.95 & 2.17828 & -0.228278 \tabularnewline
16 & 2.1 & 2.17869 & -0.078691 \tabularnewline
17 & 2.4 & 2.31498 & 0.0850244 \tabularnewline
18 & 2.1 & 2.15282 & -0.0528204 \tabularnewline
19 & 2.25 & 2.16113 & 0.0888684 \tabularnewline
20 & 2.4 & 2.30285 & 0.0971498 \tabularnewline
21 & 2.25 & 2.1567 & 0.0933013 \tabularnewline
22 & 2.55 & 2.21635 & 0.333651 \tabularnewline
23 & 1.95 & 2.14173 & -0.191729 \tabularnewline
24 & 2.4 & 2.16086 & 0.239138 \tabularnewline
25 & 2.1 & 2.1628 & -0.0627963 \tabularnewline
26 & 2.1 & 2.0965 & 0.00349876 \tabularnewline
27 & 2.4 & 2.2432 & 0.156802 \tabularnewline
28 & 2.1 & 2.2234 & -0.123399 \tabularnewline
29 & 2.1 & 2.11165 & -0.0116525 \tabularnewline
30 & 2.25 & 2.15755 & 0.0924501 \tabularnewline
31 & 2.25 & 2.30922 & -0.0592188 \tabularnewline
32 & 2.4 & 2.1652 & 0.234802 \tabularnewline
33 & 2.1 & 2.20603 & -0.106025 \tabularnewline
34 & 2.4 & 2.28132 & 0.118682 \tabularnewline
35 & 2.1 & 2.20685 & -0.106848 \tabularnewline
36 & 2.1 & 2.20876 & -0.108756 \tabularnewline
37 & 2.25 & 2.17569 & 0.0743064 \tabularnewline
38 & 2.25 & 2.18169 & 0.0683122 \tabularnewline
39 & 2.4 & 2.20043 & 0.199573 \tabularnewline
40 & 2.25 & 2.29301 & -0.0430141 \tabularnewline
41 & 2.25 & 2.19542 & 0.0545789 \tabularnewline
42 & 2.1 & 2.18364 & -0.0836391 \tabularnewline
43 & 2.1 & 2.12401 & -0.024013 \tabularnewline
44 & 2.1 & 2.20979 & -0.109792 \tabularnewline
45 & 2.7 & 2.32458 & 0.375416 \tabularnewline
46 & 2.1 & 2.17895 & -0.0789528 \tabularnewline
47 & 2.1 & 2.23181 & -0.131812 \tabularnewline
48 & 2.25 & 2.20953 & 0.0404652 \tabularnewline
49 & 2.7 & 2.31324 & 0.386758 \tabularnewline
50 & 2.4 & 2.09739 & 0.302611 \tabularnewline
51 & 2.1 & 2.33803 & -0.238034 \tabularnewline
52 & 2.1 & 2.08323 & 0.0167735 \tabularnewline
53 & 2.4 & 2.34191 & 0.0580881 \tabularnewline
54 & 1.95 & 2.17299 & -0.222985 \tabularnewline
55 & 2.7 & 2.24633 & 0.453669 \tabularnewline
56 & 2.1 & 2.14319 & -0.0431938 \tabularnewline
57 & 2.25 & 2.15803 & 0.0919679 \tabularnewline
58 & 2.1 & 2.20953 & -0.109535 \tabularnewline
59 & 2.7 & 2.18419 & 0.51581 \tabularnewline
60 & 2.1 & 2.24209 & -0.142093 \tabularnewline
61 & 2.1 & 2.28208 & -0.182077 \tabularnewline
62 & 1.65 & 2.12445 & -0.474447 \tabularnewline
63 & 1.65 & 2.12445 & -0.474447 \tabularnewline
64 & 2.1 & 2.24024 & -0.140241 \tabularnewline
65 & 2.1 & 2.24199 & -0.141994 \tabularnewline
66 & 2.1 & 2.18628 & -0.0862846 \tabularnewline
67 & 2.1 & 2.27005 & -0.170048 \tabularnewline
68 & 2.1 & 2.10578 & -0.00577655 \tabularnewline
69 & 2.4 & 2.29856 & 0.101439 \tabularnewline
70 & 2.4 & 2.23436 & 0.165638 \tabularnewline
71 & 2.1 & 2.23358 & -0.133575 \tabularnewline
72 & 2.25 & 2.22623 & 0.0237721 \tabularnewline
73 & 2.4 & 2.21661 & 0.183393 \tabularnewline
74 & 2.1 & 2.23838 & -0.138377 \tabularnewline
75 & 2.1 & 2.16152 & -0.0615199 \tabularnewline
76 & 2.4 & 2.21398 & 0.186022 \tabularnewline
77 & 2.4 & 2.16386 & 0.236142 \tabularnewline
78 & 2.1 & 2.1568 & -0.0567991 \tabularnewline
79 & 2.1 & 2.14671 & -0.0467104 \tabularnewline
80 & 2.4 & 2.28422 & 0.115779 \tabularnewline
81 & 2.1 & 2.15018 & -0.0501767 \tabularnewline
82 & 2.7 & 2.19982 & 0.500182 \tabularnewline
83 & 2.1 & 2.16695 & -0.0669471 \tabularnewline
84 & 2.1 & 2.15414 & -0.0541438 \tabularnewline
85 & 2.25 & 2.21959 & 0.0304113 \tabularnewline
86 & 2.1 & 2.21047 & -0.110475 \tabularnewline
87 & 2.4 & 2.19729 & 0.202706 \tabularnewline
88 & 2.25 & 2.22562 & 0.0243841 \tabularnewline
89 & 2.25 & 2.27758 & -0.0275846 \tabularnewline
90 & 2.1 & 2.14181 & -0.0418103 \tabularnewline
91 & 2.1 & 2.21106 & -0.111058 \tabularnewline
92 & 2.4 & 2.16215 & 0.237846 \tabularnewline
93 & 2.25 & 2.18978 & 0.0602212 \tabularnewline
94 & 2.1 & 2.15466 & -0.0546637 \tabularnewline
95 & 2.1 & 2.1398 & -0.0397956 \tabularnewline
96 & 1.65 & 2.13405 & -0.484054 \tabularnewline
97 & 2.7 & 2.26938 & 0.430617 \tabularnewline
98 & 2.1 & 2.2643 & -0.164301 \tabularnewline
99 & 1.95 & 2.1867 & -0.236696 \tabularnewline
100 & 2.25 & 2.2312 & 0.0187965 \tabularnewline
101 & 2.4 & 2.23328 & 0.166724 \tabularnewline
102 & 1.95 & 2.10735 & -0.157348 \tabularnewline
103 & 2.1 & 2.25594 & -0.155937 \tabularnewline
104 & 2.4 & 2.17509 & 0.224906 \tabularnewline
105 & 2.1 & 2.20959 & -0.109586 \tabularnewline
106 & 2.4 & 2.20131 & 0.198687 \tabularnewline
107 & 2.4 & 2.18159 & 0.218408 \tabularnewline
108 & 2.4 & 2.23343 & 0.166566 \tabularnewline
109 & 2.25 & 2.16478 & 0.0852202 \tabularnewline
110 & 2.4 & 2.12372 & 0.276275 \tabularnewline
111 & 2.1 & 2.17386 & -0.0738554 \tabularnewline
112 & 2.1 & 2.15496 & -0.0549587 \tabularnewline
113 & 1.8 & 2.22552 & -0.425524 \tabularnewline
114 & 2.7 & 2.12879 & 0.571209 \tabularnewline
115 & 2.1 & 2.14458 & -0.0445802 \tabularnewline
116 & 2.1 & 2.2072 & -0.1072 \tabularnewline
117 & 2.4 & 2.29799 & 0.102008 \tabularnewline
118 & 2.55 & 2.20056 & 0.349439 \tabularnewline
119 & 2.55 & 2.20538 & 0.34462 \tabularnewline
120 & 2.1 & 2.33105 & -0.231048 \tabularnewline
121 & 2.1 & 2.1668 & -0.0667959 \tabularnewline
122 & 2.1 & 2.20953 & -0.109534 \tabularnewline
123 & 2.25 & 2.15305 & 0.0969548 \tabularnewline
124 & 2.25 & 2.11731 & 0.132688 \tabularnewline
125 & 2.1 & 2.10885 & -0.00885188 \tabularnewline
126 & 2.1 & 2.21195 & -0.111949 \tabularnewline
127 & 1.95 & 2.17113 & -0.221127 \tabularnewline
128 & 2.4 & 2.17797 & 0.222033 \tabularnewline
129 & 2.1 & 2.19693 & -0.0969261 \tabularnewline
130 & 2.4 & 2.14624 & 0.25376 \tabularnewline
131 & 2.4 & 2.20319 & 0.196814 \tabularnewline
132 & 2.4 & 2.34191 & 0.0580881 \tabularnewline
133 & 1.95 & 2.18319 & -0.233185 \tabularnewline
134 & 2.1 & 2.19695 & -0.0969523 \tabularnewline
135 & 2.1 & 2.28442 & -0.184419 \tabularnewline
136 & 2.55 & 2.21626 & 0.333739 \tabularnewline
137 & 2.1 & 2.22924 & -0.129241 \tabularnewline
138 & 2.1 & 2.13265 & -0.0326539 \tabularnewline
139 & 2.1 & 2.13532 & -0.0353198 \tabularnewline
140 & 1.95 & 2.19653 & -0.246527 \tabularnewline
141 & 2.25 & 2.19045 & 0.0595455 \tabularnewline
142 & 2.4 & 2.24084 & 0.159159 \tabularnewline
143 & 1.95 & 2.22388 & -0.273876 \tabularnewline
144 & 2.1 & 2.25692 & -0.156923 \tabularnewline
145 & 2.1 & 2.16764 & -0.0676382 \tabularnewline
146 & 1.95 & 2.14603 & -0.196034 \tabularnewline
147 & 2.1 & 2.24986 & -0.149863 \tabularnewline
148 & 2.1 & 2.2313 & -0.131301 \tabularnewline
149 & 1.95 & 2.17299 & -0.222985 \tabularnewline
150 & 2.1 & 2.1884 & -0.088397 \tabularnewline
151 & 1.95 & 2.19019 & -0.240186 \tabularnewline
152 & 2.4 & 2.20319 & 0.196814 \tabularnewline
153 & 2.4 & 2.20495 & 0.195048 \tabularnewline
154 & 2.4 & 2.15214 & 0.24786 \tabularnewline
155 & 1.95 & 2.14922 & -0.199224 \tabularnewline
156 & 2.7 & 2.21486 & 0.485137 \tabularnewline
157 & 2.1 & 2.21512 & -0.115125 \tabularnewline
158 & 1.95 & 2.17476 & -0.224765 \tabularnewline
159 & 2.1 & 2.1683 & -0.0682992 \tabularnewline
160 & 1.95 & 2.15426 & -0.204261 \tabularnewline
161 & 2.1 & 2.21277 & -0.112766 \tabularnewline
162 & 2.25 & 2.17657 & 0.0734266 \tabularnewline
163 & 2.7 & 2.23825 & 0.461753 \tabularnewline
164 & 2.1 & 2.17795 & -0.0779538 \tabularnewline
165 & 2.4 & 2.17528 & 0.224715 \tabularnewline
166 & 1.35 & 2.09574 & -0.745743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&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]2.1[/C][C]2.08491[/C][C]0.0150894[/C][/ROW]
[ROW][C]2[/C][C]2.7[/C][C]2.17013[/C][C]0.529869[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.1988[/C][C]-0.0987966[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]2.19587[/C][C]-0.0958672[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]2.36544[/C][C]-0.265441[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.09119[/C][C]0.00880552[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.2046[/C][C]-0.104599[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.32011[/C][C]-0.220111[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.48812[/C][C]-0.388123[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.10794[/C][C]-0.00794372[/C][/ROW]
[ROW][C]11[/C][C]2.4[/C][C]2.1974[/C][C]0.202599[/C][/ROW]
[ROW][C]12[/C][C]1.95[/C][C]2.10076[/C][C]-0.150764[/C][/ROW]
[ROW][C]13[/C][C]2.1[/C][C]2.15875[/C][C]-0.0587502[/C][/ROW]
[ROW][C]14[/C][C]2.1[/C][C]2.14647[/C][C]-0.0464669[/C][/ROW]
[ROW][C]15[/C][C]1.95[/C][C]2.17828[/C][C]-0.228278[/C][/ROW]
[ROW][C]16[/C][C]2.1[/C][C]2.17869[/C][C]-0.078691[/C][/ROW]
[ROW][C]17[/C][C]2.4[/C][C]2.31498[/C][C]0.0850244[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.15282[/C][C]-0.0528204[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]2.16113[/C][C]0.0888684[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.30285[/C][C]0.0971498[/C][/ROW]
[ROW][C]21[/C][C]2.25[/C][C]2.1567[/C][C]0.0933013[/C][/ROW]
[ROW][C]22[/C][C]2.55[/C][C]2.21635[/C][C]0.333651[/C][/ROW]
[ROW][C]23[/C][C]1.95[/C][C]2.14173[/C][C]-0.191729[/C][/ROW]
[ROW][C]24[/C][C]2.4[/C][C]2.16086[/C][C]0.239138[/C][/ROW]
[ROW][C]25[/C][C]2.1[/C][C]2.1628[/C][C]-0.0627963[/C][/ROW]
[ROW][C]26[/C][C]2.1[/C][C]2.0965[/C][C]0.00349876[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.2432[/C][C]0.156802[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.2234[/C][C]-0.123399[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]2.11165[/C][C]-0.0116525[/C][/ROW]
[ROW][C]30[/C][C]2.25[/C][C]2.15755[/C][C]0.0924501[/C][/ROW]
[ROW][C]31[/C][C]2.25[/C][C]2.30922[/C][C]-0.0592188[/C][/ROW]
[ROW][C]32[/C][C]2.4[/C][C]2.1652[/C][C]0.234802[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.20603[/C][C]-0.106025[/C][/ROW]
[ROW][C]34[/C][C]2.4[/C][C]2.28132[/C][C]0.118682[/C][/ROW]
[ROW][C]35[/C][C]2.1[/C][C]2.20685[/C][C]-0.106848[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.20876[/C][C]-0.108756[/C][/ROW]
[ROW][C]37[/C][C]2.25[/C][C]2.17569[/C][C]0.0743064[/C][/ROW]
[ROW][C]38[/C][C]2.25[/C][C]2.18169[/C][C]0.0683122[/C][/ROW]
[ROW][C]39[/C][C]2.4[/C][C]2.20043[/C][C]0.199573[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.29301[/C][C]-0.0430141[/C][/ROW]
[ROW][C]41[/C][C]2.25[/C][C]2.19542[/C][C]0.0545789[/C][/ROW]
[ROW][C]42[/C][C]2.1[/C][C]2.18364[/C][C]-0.0836391[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]2.12401[/C][C]-0.024013[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.20979[/C][C]-0.109792[/C][/ROW]
[ROW][C]45[/C][C]2.7[/C][C]2.32458[/C][C]0.375416[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.17895[/C][C]-0.0789528[/C][/ROW]
[ROW][C]47[/C][C]2.1[/C][C]2.23181[/C][C]-0.131812[/C][/ROW]
[ROW][C]48[/C][C]2.25[/C][C]2.20953[/C][C]0.0404652[/C][/ROW]
[ROW][C]49[/C][C]2.7[/C][C]2.31324[/C][C]0.386758[/C][/ROW]
[ROW][C]50[/C][C]2.4[/C][C]2.09739[/C][C]0.302611[/C][/ROW]
[ROW][C]51[/C][C]2.1[/C][C]2.33803[/C][C]-0.238034[/C][/ROW]
[ROW][C]52[/C][C]2.1[/C][C]2.08323[/C][C]0.0167735[/C][/ROW]
[ROW][C]53[/C][C]2.4[/C][C]2.34191[/C][C]0.0580881[/C][/ROW]
[ROW][C]54[/C][C]1.95[/C][C]2.17299[/C][C]-0.222985[/C][/ROW]
[ROW][C]55[/C][C]2.7[/C][C]2.24633[/C][C]0.453669[/C][/ROW]
[ROW][C]56[/C][C]2.1[/C][C]2.14319[/C][C]-0.0431938[/C][/ROW]
[ROW][C]57[/C][C]2.25[/C][C]2.15803[/C][C]0.0919679[/C][/ROW]
[ROW][C]58[/C][C]2.1[/C][C]2.20953[/C][C]-0.109535[/C][/ROW]
[ROW][C]59[/C][C]2.7[/C][C]2.18419[/C][C]0.51581[/C][/ROW]
[ROW][C]60[/C][C]2.1[/C][C]2.24209[/C][C]-0.142093[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.28208[/C][C]-0.182077[/C][/ROW]
[ROW][C]62[/C][C]1.65[/C][C]2.12445[/C][C]-0.474447[/C][/ROW]
[ROW][C]63[/C][C]1.65[/C][C]2.12445[/C][C]-0.474447[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]2.24024[/C][C]-0.140241[/C][/ROW]
[ROW][C]65[/C][C]2.1[/C][C]2.24199[/C][C]-0.141994[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.18628[/C][C]-0.0862846[/C][/ROW]
[ROW][C]67[/C][C]2.1[/C][C]2.27005[/C][C]-0.170048[/C][/ROW]
[ROW][C]68[/C][C]2.1[/C][C]2.10578[/C][C]-0.00577655[/C][/ROW]
[ROW][C]69[/C][C]2.4[/C][C]2.29856[/C][C]0.101439[/C][/ROW]
[ROW][C]70[/C][C]2.4[/C][C]2.23436[/C][C]0.165638[/C][/ROW]
[ROW][C]71[/C][C]2.1[/C][C]2.23358[/C][C]-0.133575[/C][/ROW]
[ROW][C]72[/C][C]2.25[/C][C]2.22623[/C][C]0.0237721[/C][/ROW]
[ROW][C]73[/C][C]2.4[/C][C]2.21661[/C][C]0.183393[/C][/ROW]
[ROW][C]74[/C][C]2.1[/C][C]2.23838[/C][C]-0.138377[/C][/ROW]
[ROW][C]75[/C][C]2.1[/C][C]2.16152[/C][C]-0.0615199[/C][/ROW]
[ROW][C]76[/C][C]2.4[/C][C]2.21398[/C][C]0.186022[/C][/ROW]
[ROW][C]77[/C][C]2.4[/C][C]2.16386[/C][C]0.236142[/C][/ROW]
[ROW][C]78[/C][C]2.1[/C][C]2.1568[/C][C]-0.0567991[/C][/ROW]
[ROW][C]79[/C][C]2.1[/C][C]2.14671[/C][C]-0.0467104[/C][/ROW]
[ROW][C]80[/C][C]2.4[/C][C]2.28422[/C][C]0.115779[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.15018[/C][C]-0.0501767[/C][/ROW]
[ROW][C]82[/C][C]2.7[/C][C]2.19982[/C][C]0.500182[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]2.16695[/C][C]-0.0669471[/C][/ROW]
[ROW][C]84[/C][C]2.1[/C][C]2.15414[/C][C]-0.0541438[/C][/ROW]
[ROW][C]85[/C][C]2.25[/C][C]2.21959[/C][C]0.0304113[/C][/ROW]
[ROW][C]86[/C][C]2.1[/C][C]2.21047[/C][C]-0.110475[/C][/ROW]
[ROW][C]87[/C][C]2.4[/C][C]2.19729[/C][C]0.202706[/C][/ROW]
[ROW][C]88[/C][C]2.25[/C][C]2.22562[/C][C]0.0243841[/C][/ROW]
[ROW][C]89[/C][C]2.25[/C][C]2.27758[/C][C]-0.0275846[/C][/ROW]
[ROW][C]90[/C][C]2.1[/C][C]2.14181[/C][C]-0.0418103[/C][/ROW]
[ROW][C]91[/C][C]2.1[/C][C]2.21106[/C][C]-0.111058[/C][/ROW]
[ROW][C]92[/C][C]2.4[/C][C]2.16215[/C][C]0.237846[/C][/ROW]
[ROW][C]93[/C][C]2.25[/C][C]2.18978[/C][C]0.0602212[/C][/ROW]
[ROW][C]94[/C][C]2.1[/C][C]2.15466[/C][C]-0.0546637[/C][/ROW]
[ROW][C]95[/C][C]2.1[/C][C]2.1398[/C][C]-0.0397956[/C][/ROW]
[ROW][C]96[/C][C]1.65[/C][C]2.13405[/C][C]-0.484054[/C][/ROW]
[ROW][C]97[/C][C]2.7[/C][C]2.26938[/C][C]0.430617[/C][/ROW]
[ROW][C]98[/C][C]2.1[/C][C]2.2643[/C][C]-0.164301[/C][/ROW]
[ROW][C]99[/C][C]1.95[/C][C]2.1867[/C][C]-0.236696[/C][/ROW]
[ROW][C]100[/C][C]2.25[/C][C]2.2312[/C][C]0.0187965[/C][/ROW]
[ROW][C]101[/C][C]2.4[/C][C]2.23328[/C][C]0.166724[/C][/ROW]
[ROW][C]102[/C][C]1.95[/C][C]2.10735[/C][C]-0.157348[/C][/ROW]
[ROW][C]103[/C][C]2.1[/C][C]2.25594[/C][C]-0.155937[/C][/ROW]
[ROW][C]104[/C][C]2.4[/C][C]2.17509[/C][C]0.224906[/C][/ROW]
[ROW][C]105[/C][C]2.1[/C][C]2.20959[/C][C]-0.109586[/C][/ROW]
[ROW][C]106[/C][C]2.4[/C][C]2.20131[/C][C]0.198687[/C][/ROW]
[ROW][C]107[/C][C]2.4[/C][C]2.18159[/C][C]0.218408[/C][/ROW]
[ROW][C]108[/C][C]2.4[/C][C]2.23343[/C][C]0.166566[/C][/ROW]
[ROW][C]109[/C][C]2.25[/C][C]2.16478[/C][C]0.0852202[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.12372[/C][C]0.276275[/C][/ROW]
[ROW][C]111[/C][C]2.1[/C][C]2.17386[/C][C]-0.0738554[/C][/ROW]
[ROW][C]112[/C][C]2.1[/C][C]2.15496[/C][C]-0.0549587[/C][/ROW]
[ROW][C]113[/C][C]1.8[/C][C]2.22552[/C][C]-0.425524[/C][/ROW]
[ROW][C]114[/C][C]2.7[/C][C]2.12879[/C][C]0.571209[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]2.14458[/C][C]-0.0445802[/C][/ROW]
[ROW][C]116[/C][C]2.1[/C][C]2.2072[/C][C]-0.1072[/C][/ROW]
[ROW][C]117[/C][C]2.4[/C][C]2.29799[/C][C]0.102008[/C][/ROW]
[ROW][C]118[/C][C]2.55[/C][C]2.20056[/C][C]0.349439[/C][/ROW]
[ROW][C]119[/C][C]2.55[/C][C]2.20538[/C][C]0.34462[/C][/ROW]
[ROW][C]120[/C][C]2.1[/C][C]2.33105[/C][C]-0.231048[/C][/ROW]
[ROW][C]121[/C][C]2.1[/C][C]2.1668[/C][C]-0.0667959[/C][/ROW]
[ROW][C]122[/C][C]2.1[/C][C]2.20953[/C][C]-0.109534[/C][/ROW]
[ROW][C]123[/C][C]2.25[/C][C]2.15305[/C][C]0.0969548[/C][/ROW]
[ROW][C]124[/C][C]2.25[/C][C]2.11731[/C][C]0.132688[/C][/ROW]
[ROW][C]125[/C][C]2.1[/C][C]2.10885[/C][C]-0.00885188[/C][/ROW]
[ROW][C]126[/C][C]2.1[/C][C]2.21195[/C][C]-0.111949[/C][/ROW]
[ROW][C]127[/C][C]1.95[/C][C]2.17113[/C][C]-0.221127[/C][/ROW]
[ROW][C]128[/C][C]2.4[/C][C]2.17797[/C][C]0.222033[/C][/ROW]
[ROW][C]129[/C][C]2.1[/C][C]2.19693[/C][C]-0.0969261[/C][/ROW]
[ROW][C]130[/C][C]2.4[/C][C]2.14624[/C][C]0.25376[/C][/ROW]
[ROW][C]131[/C][C]2.4[/C][C]2.20319[/C][C]0.196814[/C][/ROW]
[ROW][C]132[/C][C]2.4[/C][C]2.34191[/C][C]0.0580881[/C][/ROW]
[ROW][C]133[/C][C]1.95[/C][C]2.18319[/C][C]-0.233185[/C][/ROW]
[ROW][C]134[/C][C]2.1[/C][C]2.19695[/C][C]-0.0969523[/C][/ROW]
[ROW][C]135[/C][C]2.1[/C][C]2.28442[/C][C]-0.184419[/C][/ROW]
[ROW][C]136[/C][C]2.55[/C][C]2.21626[/C][C]0.333739[/C][/ROW]
[ROW][C]137[/C][C]2.1[/C][C]2.22924[/C][C]-0.129241[/C][/ROW]
[ROW][C]138[/C][C]2.1[/C][C]2.13265[/C][C]-0.0326539[/C][/ROW]
[ROW][C]139[/C][C]2.1[/C][C]2.13532[/C][C]-0.0353198[/C][/ROW]
[ROW][C]140[/C][C]1.95[/C][C]2.19653[/C][C]-0.246527[/C][/ROW]
[ROW][C]141[/C][C]2.25[/C][C]2.19045[/C][C]0.0595455[/C][/ROW]
[ROW][C]142[/C][C]2.4[/C][C]2.24084[/C][C]0.159159[/C][/ROW]
[ROW][C]143[/C][C]1.95[/C][C]2.22388[/C][C]-0.273876[/C][/ROW]
[ROW][C]144[/C][C]2.1[/C][C]2.25692[/C][C]-0.156923[/C][/ROW]
[ROW][C]145[/C][C]2.1[/C][C]2.16764[/C][C]-0.0676382[/C][/ROW]
[ROW][C]146[/C][C]1.95[/C][C]2.14603[/C][C]-0.196034[/C][/ROW]
[ROW][C]147[/C][C]2.1[/C][C]2.24986[/C][C]-0.149863[/C][/ROW]
[ROW][C]148[/C][C]2.1[/C][C]2.2313[/C][C]-0.131301[/C][/ROW]
[ROW][C]149[/C][C]1.95[/C][C]2.17299[/C][C]-0.222985[/C][/ROW]
[ROW][C]150[/C][C]2.1[/C][C]2.1884[/C][C]-0.088397[/C][/ROW]
[ROW][C]151[/C][C]1.95[/C][C]2.19019[/C][C]-0.240186[/C][/ROW]
[ROW][C]152[/C][C]2.4[/C][C]2.20319[/C][C]0.196814[/C][/ROW]
[ROW][C]153[/C][C]2.4[/C][C]2.20495[/C][C]0.195048[/C][/ROW]
[ROW][C]154[/C][C]2.4[/C][C]2.15214[/C][C]0.24786[/C][/ROW]
[ROW][C]155[/C][C]1.95[/C][C]2.14922[/C][C]-0.199224[/C][/ROW]
[ROW][C]156[/C][C]2.7[/C][C]2.21486[/C][C]0.485137[/C][/ROW]
[ROW][C]157[/C][C]2.1[/C][C]2.21512[/C][C]-0.115125[/C][/ROW]
[ROW][C]158[/C][C]1.95[/C][C]2.17476[/C][C]-0.224765[/C][/ROW]
[ROW][C]159[/C][C]2.1[/C][C]2.1683[/C][C]-0.0682992[/C][/ROW]
[ROW][C]160[/C][C]1.95[/C][C]2.15426[/C][C]-0.204261[/C][/ROW]
[ROW][C]161[/C][C]2.1[/C][C]2.21277[/C][C]-0.112766[/C][/ROW]
[ROW][C]162[/C][C]2.25[/C][C]2.17657[/C][C]0.0734266[/C][/ROW]
[ROW][C]163[/C][C]2.7[/C][C]2.23825[/C][C]0.461753[/C][/ROW]
[ROW][C]164[/C][C]2.1[/C][C]2.17795[/C][C]-0.0779538[/C][/ROW]
[ROW][C]165[/C][C]2.4[/C][C]2.17528[/C][C]0.224715[/C][/ROW]
[ROW][C]166[/C][C]1.35[/C][C]2.09574[/C][C]-0.745743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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
12.12.084910.0150894
22.72.170130.529869
32.12.1988-0.0987966
42.12.19587-0.0958672
52.12.36544-0.265441
62.12.091190.00880552
72.12.2046-0.104599
82.12.32011-0.220111
92.12.48812-0.388123
102.12.10794-0.00794372
112.42.19740.202599
121.952.10076-0.150764
132.12.15875-0.0587502
142.12.14647-0.0464669
151.952.17828-0.228278
162.12.17869-0.078691
172.42.314980.0850244
182.12.15282-0.0528204
192.252.161130.0888684
202.42.302850.0971498
212.252.15670.0933013
222.552.216350.333651
231.952.14173-0.191729
242.42.160860.239138
252.12.1628-0.0627963
262.12.09650.00349876
272.42.24320.156802
282.12.2234-0.123399
292.12.11165-0.0116525
302.252.157550.0924501
312.252.30922-0.0592188
322.42.16520.234802
332.12.20603-0.106025
342.42.281320.118682
352.12.20685-0.106848
362.12.20876-0.108756
372.252.175690.0743064
382.252.181690.0683122
392.42.200430.199573
402.252.29301-0.0430141
412.252.195420.0545789
422.12.18364-0.0836391
432.12.12401-0.024013
442.12.20979-0.109792
452.72.324580.375416
462.12.17895-0.0789528
472.12.23181-0.131812
482.252.209530.0404652
492.72.313240.386758
502.42.097390.302611
512.12.33803-0.238034
522.12.083230.0167735
532.42.341910.0580881
541.952.17299-0.222985
552.72.246330.453669
562.12.14319-0.0431938
572.252.158030.0919679
582.12.20953-0.109535
592.72.184190.51581
602.12.24209-0.142093
612.12.28208-0.182077
621.652.12445-0.474447
631.652.12445-0.474447
642.12.24024-0.140241
652.12.24199-0.141994
662.12.18628-0.0862846
672.12.27005-0.170048
682.12.10578-0.00577655
692.42.298560.101439
702.42.234360.165638
712.12.23358-0.133575
722.252.226230.0237721
732.42.216610.183393
742.12.23838-0.138377
752.12.16152-0.0615199
762.42.213980.186022
772.42.163860.236142
782.12.1568-0.0567991
792.12.14671-0.0467104
802.42.284220.115779
812.12.15018-0.0501767
822.72.199820.500182
832.12.16695-0.0669471
842.12.15414-0.0541438
852.252.219590.0304113
862.12.21047-0.110475
872.42.197290.202706
882.252.225620.0243841
892.252.27758-0.0275846
902.12.14181-0.0418103
912.12.21106-0.111058
922.42.162150.237846
932.252.189780.0602212
942.12.15466-0.0546637
952.12.1398-0.0397956
961.652.13405-0.484054
972.72.269380.430617
982.12.2643-0.164301
991.952.1867-0.236696
1002.252.23120.0187965
1012.42.233280.166724
1021.952.10735-0.157348
1032.12.25594-0.155937
1042.42.175090.224906
1052.12.20959-0.109586
1062.42.201310.198687
1072.42.181590.218408
1082.42.233430.166566
1092.252.164780.0852202
1102.42.123720.276275
1112.12.17386-0.0738554
1122.12.15496-0.0549587
1131.82.22552-0.425524
1142.72.128790.571209
1152.12.14458-0.0445802
1162.12.2072-0.1072
1172.42.297990.102008
1182.552.200560.349439
1192.552.205380.34462
1202.12.33105-0.231048
1212.12.1668-0.0667959
1222.12.20953-0.109534
1232.252.153050.0969548
1242.252.117310.132688
1252.12.10885-0.00885188
1262.12.21195-0.111949
1271.952.17113-0.221127
1282.42.177970.222033
1292.12.19693-0.0969261
1302.42.146240.25376
1312.42.203190.196814
1322.42.341910.0580881
1331.952.18319-0.233185
1342.12.19695-0.0969523
1352.12.28442-0.184419
1362.552.216260.333739
1372.12.22924-0.129241
1382.12.13265-0.0326539
1392.12.13532-0.0353198
1401.952.19653-0.246527
1412.252.190450.0595455
1422.42.240840.159159
1431.952.22388-0.273876
1442.12.25692-0.156923
1452.12.16764-0.0676382
1461.952.14603-0.196034
1472.12.24986-0.149863
1482.12.2313-0.131301
1491.952.17299-0.222985
1502.12.1884-0.088397
1511.952.19019-0.240186
1522.42.203190.196814
1532.42.204950.195048
1542.42.152140.24786
1551.952.14922-0.199224
1562.72.214860.485137
1572.12.21512-0.115125
1581.952.17476-0.224765
1592.12.1683-0.0682992
1601.952.15426-0.204261
1612.12.21277-0.112766
1622.252.176570.0734266
1632.72.238250.461753
1642.12.17795-0.0779538
1652.42.175280.224715
1661.352.09574-0.745743






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2123590.4247180.787641
100.4800170.9600330.519983
110.4646730.9293460.535327
120.7356620.5286760.264338
130.6416040.7167920.358396
140.5491720.9016560.450828
150.5230530.9538940.476947
160.4286720.8573450.571328
170.40470.8093990.5953
180.3212030.6424050.678797
190.2873070.5746130.712693
200.3168940.6337880.683106
210.3118150.6236310.688185
220.4427150.885430.557285
230.466530.933060.53347
240.4426230.8852460.557377
250.3760820.7521640.623918
260.3339860.6679720.666014
270.3330810.6661620.666919
280.3012730.6025450.698727
290.2458230.4916460.754177
300.2260850.4521710.773915
310.1825580.3651170.817442
320.1877640.3755290.812236
330.1509680.3019370.849032
340.1435040.2870080.856496
350.1194270.2388530.880573
360.0984020.1968040.901598
370.07564060.1512810.924359
380.05775530.1155110.942245
390.0567210.1134420.943279
400.04269630.08539250.957304
410.03141580.06283150.968584
420.02528640.05057280.974714
430.0188050.03760990.981195
440.01429130.02858250.985709
450.0450150.090030.954985
460.03577940.07155890.964221
470.02942570.05885140.970574
480.02149020.04298050.97851
490.0516110.1032220.948389
500.06538540.1307710.934615
510.07261010.145220.92739
520.0574570.1149140.942543
530.04724870.09449740.952751
540.04767940.09535890.952321
550.1114250.222850.888575
560.09000050.1800010.909999
570.07402540.1480510.925975
580.06379070.1275810.936209
590.1738760.3477510.826124
600.1575880.3151750.842412
610.1493480.2986960.850652
620.2987650.5975290.701235
630.4542150.908430.545785
640.4286530.8573070.571347
650.403140.8062810.59686
660.3649890.7299780.635011
670.3465410.6930830.653459
680.3060160.6120330.693984
690.272420.5448410.72758
700.2538940.5077880.746106
710.2327670.4655340.767233
720.1988720.3977430.801128
730.1958750.391750.804125
740.1794970.3589950.820503
750.1528890.3057780.847111
760.1469250.2938490.853075
770.1544380.3088770.845562
780.1318420.2636840.868158
790.1107560.2215130.889244
800.09534860.1906970.904651
810.07871820.1574360.921282
820.1791090.3582180.820891
830.1541920.3083840.845808
840.1312520.2625050.868748
850.1087350.2174710.891265
860.09750850.1950170.902491
870.09346490.186930.906535
880.07586540.1517310.924135
890.06296460.1259290.937035
900.05073710.1014740.949263
910.04264630.08529270.957354
920.04426440.08852890.955736
930.03572470.07144940.964275
940.0282830.0565660.971717
950.02201630.04403250.977984
960.05895780.1179160.941042
970.1103340.2206680.889666
980.1023050.204610.897695
990.1048010.2096010.895199
1000.08634720.1726940.913653
1010.0782330.1564660.921767
1020.07088610.1417720.929114
1030.06328360.1265670.936716
1040.06350220.1270040.936498
1050.0534330.1068660.946567
1060.0520750.104150.947925
1070.05155880.1031180.948441
1080.04804650.0960930.951954
1090.03899390.07798770.961006
1100.04728230.09456460.952718
1110.03768030.07536060.96232
1120.02912970.05825940.97087
1130.05694480.113890.943055
1140.2140350.428070.785965
1150.1805530.3611070.819447
1160.1552240.3104490.844776
1170.1329680.2659360.867032
1180.180560.3611210.81944
1190.22680.4536010.7732
1200.2292410.4584820.770759
1210.1936710.3873430.806329
1220.1670330.3340660.832967
1230.1500930.3001870.849907
1240.1478550.295710.852145
1250.1277830.2555670.872217
1260.1144060.2288130.885594
1270.1051590.2103180.894841
1280.1149990.2299980.885001
1290.09276490.185530.907235
1300.1277170.2554330.872283
1310.1237570.2475140.876243
1320.1170630.2341260.882937
1330.1099120.2198230.890088
1340.08924430.1784890.910756
1350.1040670.2081350.895933
1360.1388450.2776890.861155
1370.129930.2598590.87007
1380.1005750.201150.899425
1390.07692190.1538440.923078
1400.08319470.1663890.916805
1410.06164060.1232810.938359
1420.04612580.09225170.953874
1430.105530.211060.89447
1440.159210.318420.84079
1450.1393420.2786840.860658
1460.12490.2497990.8751
1470.1436190.2872390.856381
1480.1889070.3778150.811093
1490.1454170.2908340.854583
1500.1103790.2207580.889621
1510.1531630.3063270.846837
1520.1063320.2126640.893668
1530.0693530.1387060.930647
1540.4599040.9198070.540096
1550.7852540.4294920.214746
1560.6710860.6578290.328914
1570.7775060.4449880.222494

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.212359 & 0.424718 & 0.787641 \tabularnewline
10 & 0.480017 & 0.960033 & 0.519983 \tabularnewline
11 & 0.464673 & 0.929346 & 0.535327 \tabularnewline
12 & 0.735662 & 0.528676 & 0.264338 \tabularnewline
13 & 0.641604 & 0.716792 & 0.358396 \tabularnewline
14 & 0.549172 & 0.901656 & 0.450828 \tabularnewline
15 & 0.523053 & 0.953894 & 0.476947 \tabularnewline
16 & 0.428672 & 0.857345 & 0.571328 \tabularnewline
17 & 0.4047 & 0.809399 & 0.5953 \tabularnewline
18 & 0.321203 & 0.642405 & 0.678797 \tabularnewline
19 & 0.287307 & 0.574613 & 0.712693 \tabularnewline
20 & 0.316894 & 0.633788 & 0.683106 \tabularnewline
21 & 0.311815 & 0.623631 & 0.688185 \tabularnewline
22 & 0.442715 & 0.88543 & 0.557285 \tabularnewline
23 & 0.46653 & 0.93306 & 0.53347 \tabularnewline
24 & 0.442623 & 0.885246 & 0.557377 \tabularnewline
25 & 0.376082 & 0.752164 & 0.623918 \tabularnewline
26 & 0.333986 & 0.667972 & 0.666014 \tabularnewline
27 & 0.333081 & 0.666162 & 0.666919 \tabularnewline
28 & 0.301273 & 0.602545 & 0.698727 \tabularnewline
29 & 0.245823 & 0.491646 & 0.754177 \tabularnewline
30 & 0.226085 & 0.452171 & 0.773915 \tabularnewline
31 & 0.182558 & 0.365117 & 0.817442 \tabularnewline
32 & 0.187764 & 0.375529 & 0.812236 \tabularnewline
33 & 0.150968 & 0.301937 & 0.849032 \tabularnewline
34 & 0.143504 & 0.287008 & 0.856496 \tabularnewline
35 & 0.119427 & 0.238853 & 0.880573 \tabularnewline
36 & 0.098402 & 0.196804 & 0.901598 \tabularnewline
37 & 0.0756406 & 0.151281 & 0.924359 \tabularnewline
38 & 0.0577553 & 0.115511 & 0.942245 \tabularnewline
39 & 0.056721 & 0.113442 & 0.943279 \tabularnewline
40 & 0.0426963 & 0.0853925 & 0.957304 \tabularnewline
41 & 0.0314158 & 0.0628315 & 0.968584 \tabularnewline
42 & 0.0252864 & 0.0505728 & 0.974714 \tabularnewline
43 & 0.018805 & 0.0376099 & 0.981195 \tabularnewline
44 & 0.0142913 & 0.0285825 & 0.985709 \tabularnewline
45 & 0.045015 & 0.09003 & 0.954985 \tabularnewline
46 & 0.0357794 & 0.0715589 & 0.964221 \tabularnewline
47 & 0.0294257 & 0.0588514 & 0.970574 \tabularnewline
48 & 0.0214902 & 0.0429805 & 0.97851 \tabularnewline
49 & 0.051611 & 0.103222 & 0.948389 \tabularnewline
50 & 0.0653854 & 0.130771 & 0.934615 \tabularnewline
51 & 0.0726101 & 0.14522 & 0.92739 \tabularnewline
52 & 0.057457 & 0.114914 & 0.942543 \tabularnewline
53 & 0.0472487 & 0.0944974 & 0.952751 \tabularnewline
54 & 0.0476794 & 0.0953589 & 0.952321 \tabularnewline
55 & 0.111425 & 0.22285 & 0.888575 \tabularnewline
56 & 0.0900005 & 0.180001 & 0.909999 \tabularnewline
57 & 0.0740254 & 0.148051 & 0.925975 \tabularnewline
58 & 0.0637907 & 0.127581 & 0.936209 \tabularnewline
59 & 0.173876 & 0.347751 & 0.826124 \tabularnewline
60 & 0.157588 & 0.315175 & 0.842412 \tabularnewline
61 & 0.149348 & 0.298696 & 0.850652 \tabularnewline
62 & 0.298765 & 0.597529 & 0.701235 \tabularnewline
63 & 0.454215 & 0.90843 & 0.545785 \tabularnewline
64 & 0.428653 & 0.857307 & 0.571347 \tabularnewline
65 & 0.40314 & 0.806281 & 0.59686 \tabularnewline
66 & 0.364989 & 0.729978 & 0.635011 \tabularnewline
67 & 0.346541 & 0.693083 & 0.653459 \tabularnewline
68 & 0.306016 & 0.612033 & 0.693984 \tabularnewline
69 & 0.27242 & 0.544841 & 0.72758 \tabularnewline
70 & 0.253894 & 0.507788 & 0.746106 \tabularnewline
71 & 0.232767 & 0.465534 & 0.767233 \tabularnewline
72 & 0.198872 & 0.397743 & 0.801128 \tabularnewline
73 & 0.195875 & 0.39175 & 0.804125 \tabularnewline
74 & 0.179497 & 0.358995 & 0.820503 \tabularnewline
75 & 0.152889 & 0.305778 & 0.847111 \tabularnewline
76 & 0.146925 & 0.293849 & 0.853075 \tabularnewline
77 & 0.154438 & 0.308877 & 0.845562 \tabularnewline
78 & 0.131842 & 0.263684 & 0.868158 \tabularnewline
79 & 0.110756 & 0.221513 & 0.889244 \tabularnewline
80 & 0.0953486 & 0.190697 & 0.904651 \tabularnewline
81 & 0.0787182 & 0.157436 & 0.921282 \tabularnewline
82 & 0.179109 & 0.358218 & 0.820891 \tabularnewline
83 & 0.154192 & 0.308384 & 0.845808 \tabularnewline
84 & 0.131252 & 0.262505 & 0.868748 \tabularnewline
85 & 0.108735 & 0.217471 & 0.891265 \tabularnewline
86 & 0.0975085 & 0.195017 & 0.902491 \tabularnewline
87 & 0.0934649 & 0.18693 & 0.906535 \tabularnewline
88 & 0.0758654 & 0.151731 & 0.924135 \tabularnewline
89 & 0.0629646 & 0.125929 & 0.937035 \tabularnewline
90 & 0.0507371 & 0.101474 & 0.949263 \tabularnewline
91 & 0.0426463 & 0.0852927 & 0.957354 \tabularnewline
92 & 0.0442644 & 0.0885289 & 0.955736 \tabularnewline
93 & 0.0357247 & 0.0714494 & 0.964275 \tabularnewline
94 & 0.028283 & 0.056566 & 0.971717 \tabularnewline
95 & 0.0220163 & 0.0440325 & 0.977984 \tabularnewline
96 & 0.0589578 & 0.117916 & 0.941042 \tabularnewline
97 & 0.110334 & 0.220668 & 0.889666 \tabularnewline
98 & 0.102305 & 0.20461 & 0.897695 \tabularnewline
99 & 0.104801 & 0.209601 & 0.895199 \tabularnewline
100 & 0.0863472 & 0.172694 & 0.913653 \tabularnewline
101 & 0.078233 & 0.156466 & 0.921767 \tabularnewline
102 & 0.0708861 & 0.141772 & 0.929114 \tabularnewline
103 & 0.0632836 & 0.126567 & 0.936716 \tabularnewline
104 & 0.0635022 & 0.127004 & 0.936498 \tabularnewline
105 & 0.053433 & 0.106866 & 0.946567 \tabularnewline
106 & 0.052075 & 0.10415 & 0.947925 \tabularnewline
107 & 0.0515588 & 0.103118 & 0.948441 \tabularnewline
108 & 0.0480465 & 0.096093 & 0.951954 \tabularnewline
109 & 0.0389939 & 0.0779877 & 0.961006 \tabularnewline
110 & 0.0472823 & 0.0945646 & 0.952718 \tabularnewline
111 & 0.0376803 & 0.0753606 & 0.96232 \tabularnewline
112 & 0.0291297 & 0.0582594 & 0.97087 \tabularnewline
113 & 0.0569448 & 0.11389 & 0.943055 \tabularnewline
114 & 0.214035 & 0.42807 & 0.785965 \tabularnewline
115 & 0.180553 & 0.361107 & 0.819447 \tabularnewline
116 & 0.155224 & 0.310449 & 0.844776 \tabularnewline
117 & 0.132968 & 0.265936 & 0.867032 \tabularnewline
118 & 0.18056 & 0.361121 & 0.81944 \tabularnewline
119 & 0.2268 & 0.453601 & 0.7732 \tabularnewline
120 & 0.229241 & 0.458482 & 0.770759 \tabularnewline
121 & 0.193671 & 0.387343 & 0.806329 \tabularnewline
122 & 0.167033 & 0.334066 & 0.832967 \tabularnewline
123 & 0.150093 & 0.300187 & 0.849907 \tabularnewline
124 & 0.147855 & 0.29571 & 0.852145 \tabularnewline
125 & 0.127783 & 0.255567 & 0.872217 \tabularnewline
126 & 0.114406 & 0.228813 & 0.885594 \tabularnewline
127 & 0.105159 & 0.210318 & 0.894841 \tabularnewline
128 & 0.114999 & 0.229998 & 0.885001 \tabularnewline
129 & 0.0927649 & 0.18553 & 0.907235 \tabularnewline
130 & 0.127717 & 0.255433 & 0.872283 \tabularnewline
131 & 0.123757 & 0.247514 & 0.876243 \tabularnewline
132 & 0.117063 & 0.234126 & 0.882937 \tabularnewline
133 & 0.109912 & 0.219823 & 0.890088 \tabularnewline
134 & 0.0892443 & 0.178489 & 0.910756 \tabularnewline
135 & 0.104067 & 0.208135 & 0.895933 \tabularnewline
136 & 0.138845 & 0.277689 & 0.861155 \tabularnewline
137 & 0.12993 & 0.259859 & 0.87007 \tabularnewline
138 & 0.100575 & 0.20115 & 0.899425 \tabularnewline
139 & 0.0769219 & 0.153844 & 0.923078 \tabularnewline
140 & 0.0831947 & 0.166389 & 0.916805 \tabularnewline
141 & 0.0616406 & 0.123281 & 0.938359 \tabularnewline
142 & 0.0461258 & 0.0922517 & 0.953874 \tabularnewline
143 & 0.10553 & 0.21106 & 0.89447 \tabularnewline
144 & 0.15921 & 0.31842 & 0.84079 \tabularnewline
145 & 0.139342 & 0.278684 & 0.860658 \tabularnewline
146 & 0.1249 & 0.249799 & 0.8751 \tabularnewline
147 & 0.143619 & 0.287239 & 0.856381 \tabularnewline
148 & 0.188907 & 0.377815 & 0.811093 \tabularnewline
149 & 0.145417 & 0.290834 & 0.854583 \tabularnewline
150 & 0.110379 & 0.220758 & 0.889621 \tabularnewline
151 & 0.153163 & 0.306327 & 0.846837 \tabularnewline
152 & 0.106332 & 0.212664 & 0.893668 \tabularnewline
153 & 0.069353 & 0.138706 & 0.930647 \tabularnewline
154 & 0.459904 & 0.919807 & 0.540096 \tabularnewline
155 & 0.785254 & 0.429492 & 0.214746 \tabularnewline
156 & 0.671086 & 0.657829 & 0.328914 \tabularnewline
157 & 0.777506 & 0.444988 & 0.222494 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.212359[/C][C]0.424718[/C][C]0.787641[/C][/ROW]
[ROW][C]10[/C][C]0.480017[/C][C]0.960033[/C][C]0.519983[/C][/ROW]
[ROW][C]11[/C][C]0.464673[/C][C]0.929346[/C][C]0.535327[/C][/ROW]
[ROW][C]12[/C][C]0.735662[/C][C]0.528676[/C][C]0.264338[/C][/ROW]
[ROW][C]13[/C][C]0.641604[/C][C]0.716792[/C][C]0.358396[/C][/ROW]
[ROW][C]14[/C][C]0.549172[/C][C]0.901656[/C][C]0.450828[/C][/ROW]
[ROW][C]15[/C][C]0.523053[/C][C]0.953894[/C][C]0.476947[/C][/ROW]
[ROW][C]16[/C][C]0.428672[/C][C]0.857345[/C][C]0.571328[/C][/ROW]
[ROW][C]17[/C][C]0.4047[/C][C]0.809399[/C][C]0.5953[/C][/ROW]
[ROW][C]18[/C][C]0.321203[/C][C]0.642405[/C][C]0.678797[/C][/ROW]
[ROW][C]19[/C][C]0.287307[/C][C]0.574613[/C][C]0.712693[/C][/ROW]
[ROW][C]20[/C][C]0.316894[/C][C]0.633788[/C][C]0.683106[/C][/ROW]
[ROW][C]21[/C][C]0.311815[/C][C]0.623631[/C][C]0.688185[/C][/ROW]
[ROW][C]22[/C][C]0.442715[/C][C]0.88543[/C][C]0.557285[/C][/ROW]
[ROW][C]23[/C][C]0.46653[/C][C]0.93306[/C][C]0.53347[/C][/ROW]
[ROW][C]24[/C][C]0.442623[/C][C]0.885246[/C][C]0.557377[/C][/ROW]
[ROW][C]25[/C][C]0.376082[/C][C]0.752164[/C][C]0.623918[/C][/ROW]
[ROW][C]26[/C][C]0.333986[/C][C]0.667972[/C][C]0.666014[/C][/ROW]
[ROW][C]27[/C][C]0.333081[/C][C]0.666162[/C][C]0.666919[/C][/ROW]
[ROW][C]28[/C][C]0.301273[/C][C]0.602545[/C][C]0.698727[/C][/ROW]
[ROW][C]29[/C][C]0.245823[/C][C]0.491646[/C][C]0.754177[/C][/ROW]
[ROW][C]30[/C][C]0.226085[/C][C]0.452171[/C][C]0.773915[/C][/ROW]
[ROW][C]31[/C][C]0.182558[/C][C]0.365117[/C][C]0.817442[/C][/ROW]
[ROW][C]32[/C][C]0.187764[/C][C]0.375529[/C][C]0.812236[/C][/ROW]
[ROW][C]33[/C][C]0.150968[/C][C]0.301937[/C][C]0.849032[/C][/ROW]
[ROW][C]34[/C][C]0.143504[/C][C]0.287008[/C][C]0.856496[/C][/ROW]
[ROW][C]35[/C][C]0.119427[/C][C]0.238853[/C][C]0.880573[/C][/ROW]
[ROW][C]36[/C][C]0.098402[/C][C]0.196804[/C][C]0.901598[/C][/ROW]
[ROW][C]37[/C][C]0.0756406[/C][C]0.151281[/C][C]0.924359[/C][/ROW]
[ROW][C]38[/C][C]0.0577553[/C][C]0.115511[/C][C]0.942245[/C][/ROW]
[ROW][C]39[/C][C]0.056721[/C][C]0.113442[/C][C]0.943279[/C][/ROW]
[ROW][C]40[/C][C]0.0426963[/C][C]0.0853925[/C][C]0.957304[/C][/ROW]
[ROW][C]41[/C][C]0.0314158[/C][C]0.0628315[/C][C]0.968584[/C][/ROW]
[ROW][C]42[/C][C]0.0252864[/C][C]0.0505728[/C][C]0.974714[/C][/ROW]
[ROW][C]43[/C][C]0.018805[/C][C]0.0376099[/C][C]0.981195[/C][/ROW]
[ROW][C]44[/C][C]0.0142913[/C][C]0.0285825[/C][C]0.985709[/C][/ROW]
[ROW][C]45[/C][C]0.045015[/C][C]0.09003[/C][C]0.954985[/C][/ROW]
[ROW][C]46[/C][C]0.0357794[/C][C]0.0715589[/C][C]0.964221[/C][/ROW]
[ROW][C]47[/C][C]0.0294257[/C][C]0.0588514[/C][C]0.970574[/C][/ROW]
[ROW][C]48[/C][C]0.0214902[/C][C]0.0429805[/C][C]0.97851[/C][/ROW]
[ROW][C]49[/C][C]0.051611[/C][C]0.103222[/C][C]0.948389[/C][/ROW]
[ROW][C]50[/C][C]0.0653854[/C][C]0.130771[/C][C]0.934615[/C][/ROW]
[ROW][C]51[/C][C]0.0726101[/C][C]0.14522[/C][C]0.92739[/C][/ROW]
[ROW][C]52[/C][C]0.057457[/C][C]0.114914[/C][C]0.942543[/C][/ROW]
[ROW][C]53[/C][C]0.0472487[/C][C]0.0944974[/C][C]0.952751[/C][/ROW]
[ROW][C]54[/C][C]0.0476794[/C][C]0.0953589[/C][C]0.952321[/C][/ROW]
[ROW][C]55[/C][C]0.111425[/C][C]0.22285[/C][C]0.888575[/C][/ROW]
[ROW][C]56[/C][C]0.0900005[/C][C]0.180001[/C][C]0.909999[/C][/ROW]
[ROW][C]57[/C][C]0.0740254[/C][C]0.148051[/C][C]0.925975[/C][/ROW]
[ROW][C]58[/C][C]0.0637907[/C][C]0.127581[/C][C]0.936209[/C][/ROW]
[ROW][C]59[/C][C]0.173876[/C][C]0.347751[/C][C]0.826124[/C][/ROW]
[ROW][C]60[/C][C]0.157588[/C][C]0.315175[/C][C]0.842412[/C][/ROW]
[ROW][C]61[/C][C]0.149348[/C][C]0.298696[/C][C]0.850652[/C][/ROW]
[ROW][C]62[/C][C]0.298765[/C][C]0.597529[/C][C]0.701235[/C][/ROW]
[ROW][C]63[/C][C]0.454215[/C][C]0.90843[/C][C]0.545785[/C][/ROW]
[ROW][C]64[/C][C]0.428653[/C][C]0.857307[/C][C]0.571347[/C][/ROW]
[ROW][C]65[/C][C]0.40314[/C][C]0.806281[/C][C]0.59686[/C][/ROW]
[ROW][C]66[/C][C]0.364989[/C][C]0.729978[/C][C]0.635011[/C][/ROW]
[ROW][C]67[/C][C]0.346541[/C][C]0.693083[/C][C]0.653459[/C][/ROW]
[ROW][C]68[/C][C]0.306016[/C][C]0.612033[/C][C]0.693984[/C][/ROW]
[ROW][C]69[/C][C]0.27242[/C][C]0.544841[/C][C]0.72758[/C][/ROW]
[ROW][C]70[/C][C]0.253894[/C][C]0.507788[/C][C]0.746106[/C][/ROW]
[ROW][C]71[/C][C]0.232767[/C][C]0.465534[/C][C]0.767233[/C][/ROW]
[ROW][C]72[/C][C]0.198872[/C][C]0.397743[/C][C]0.801128[/C][/ROW]
[ROW][C]73[/C][C]0.195875[/C][C]0.39175[/C][C]0.804125[/C][/ROW]
[ROW][C]74[/C][C]0.179497[/C][C]0.358995[/C][C]0.820503[/C][/ROW]
[ROW][C]75[/C][C]0.152889[/C][C]0.305778[/C][C]0.847111[/C][/ROW]
[ROW][C]76[/C][C]0.146925[/C][C]0.293849[/C][C]0.853075[/C][/ROW]
[ROW][C]77[/C][C]0.154438[/C][C]0.308877[/C][C]0.845562[/C][/ROW]
[ROW][C]78[/C][C]0.131842[/C][C]0.263684[/C][C]0.868158[/C][/ROW]
[ROW][C]79[/C][C]0.110756[/C][C]0.221513[/C][C]0.889244[/C][/ROW]
[ROW][C]80[/C][C]0.0953486[/C][C]0.190697[/C][C]0.904651[/C][/ROW]
[ROW][C]81[/C][C]0.0787182[/C][C]0.157436[/C][C]0.921282[/C][/ROW]
[ROW][C]82[/C][C]0.179109[/C][C]0.358218[/C][C]0.820891[/C][/ROW]
[ROW][C]83[/C][C]0.154192[/C][C]0.308384[/C][C]0.845808[/C][/ROW]
[ROW][C]84[/C][C]0.131252[/C][C]0.262505[/C][C]0.868748[/C][/ROW]
[ROW][C]85[/C][C]0.108735[/C][C]0.217471[/C][C]0.891265[/C][/ROW]
[ROW][C]86[/C][C]0.0975085[/C][C]0.195017[/C][C]0.902491[/C][/ROW]
[ROW][C]87[/C][C]0.0934649[/C][C]0.18693[/C][C]0.906535[/C][/ROW]
[ROW][C]88[/C][C]0.0758654[/C][C]0.151731[/C][C]0.924135[/C][/ROW]
[ROW][C]89[/C][C]0.0629646[/C][C]0.125929[/C][C]0.937035[/C][/ROW]
[ROW][C]90[/C][C]0.0507371[/C][C]0.101474[/C][C]0.949263[/C][/ROW]
[ROW][C]91[/C][C]0.0426463[/C][C]0.0852927[/C][C]0.957354[/C][/ROW]
[ROW][C]92[/C][C]0.0442644[/C][C]0.0885289[/C][C]0.955736[/C][/ROW]
[ROW][C]93[/C][C]0.0357247[/C][C]0.0714494[/C][C]0.964275[/C][/ROW]
[ROW][C]94[/C][C]0.028283[/C][C]0.056566[/C][C]0.971717[/C][/ROW]
[ROW][C]95[/C][C]0.0220163[/C][C]0.0440325[/C][C]0.977984[/C][/ROW]
[ROW][C]96[/C][C]0.0589578[/C][C]0.117916[/C][C]0.941042[/C][/ROW]
[ROW][C]97[/C][C]0.110334[/C][C]0.220668[/C][C]0.889666[/C][/ROW]
[ROW][C]98[/C][C]0.102305[/C][C]0.20461[/C][C]0.897695[/C][/ROW]
[ROW][C]99[/C][C]0.104801[/C][C]0.209601[/C][C]0.895199[/C][/ROW]
[ROW][C]100[/C][C]0.0863472[/C][C]0.172694[/C][C]0.913653[/C][/ROW]
[ROW][C]101[/C][C]0.078233[/C][C]0.156466[/C][C]0.921767[/C][/ROW]
[ROW][C]102[/C][C]0.0708861[/C][C]0.141772[/C][C]0.929114[/C][/ROW]
[ROW][C]103[/C][C]0.0632836[/C][C]0.126567[/C][C]0.936716[/C][/ROW]
[ROW][C]104[/C][C]0.0635022[/C][C]0.127004[/C][C]0.936498[/C][/ROW]
[ROW][C]105[/C][C]0.053433[/C][C]0.106866[/C][C]0.946567[/C][/ROW]
[ROW][C]106[/C][C]0.052075[/C][C]0.10415[/C][C]0.947925[/C][/ROW]
[ROW][C]107[/C][C]0.0515588[/C][C]0.103118[/C][C]0.948441[/C][/ROW]
[ROW][C]108[/C][C]0.0480465[/C][C]0.096093[/C][C]0.951954[/C][/ROW]
[ROW][C]109[/C][C]0.0389939[/C][C]0.0779877[/C][C]0.961006[/C][/ROW]
[ROW][C]110[/C][C]0.0472823[/C][C]0.0945646[/C][C]0.952718[/C][/ROW]
[ROW][C]111[/C][C]0.0376803[/C][C]0.0753606[/C][C]0.96232[/C][/ROW]
[ROW][C]112[/C][C]0.0291297[/C][C]0.0582594[/C][C]0.97087[/C][/ROW]
[ROW][C]113[/C][C]0.0569448[/C][C]0.11389[/C][C]0.943055[/C][/ROW]
[ROW][C]114[/C][C]0.214035[/C][C]0.42807[/C][C]0.785965[/C][/ROW]
[ROW][C]115[/C][C]0.180553[/C][C]0.361107[/C][C]0.819447[/C][/ROW]
[ROW][C]116[/C][C]0.155224[/C][C]0.310449[/C][C]0.844776[/C][/ROW]
[ROW][C]117[/C][C]0.132968[/C][C]0.265936[/C][C]0.867032[/C][/ROW]
[ROW][C]118[/C][C]0.18056[/C][C]0.361121[/C][C]0.81944[/C][/ROW]
[ROW][C]119[/C][C]0.2268[/C][C]0.453601[/C][C]0.7732[/C][/ROW]
[ROW][C]120[/C][C]0.229241[/C][C]0.458482[/C][C]0.770759[/C][/ROW]
[ROW][C]121[/C][C]0.193671[/C][C]0.387343[/C][C]0.806329[/C][/ROW]
[ROW][C]122[/C][C]0.167033[/C][C]0.334066[/C][C]0.832967[/C][/ROW]
[ROW][C]123[/C][C]0.150093[/C][C]0.300187[/C][C]0.849907[/C][/ROW]
[ROW][C]124[/C][C]0.147855[/C][C]0.29571[/C][C]0.852145[/C][/ROW]
[ROW][C]125[/C][C]0.127783[/C][C]0.255567[/C][C]0.872217[/C][/ROW]
[ROW][C]126[/C][C]0.114406[/C][C]0.228813[/C][C]0.885594[/C][/ROW]
[ROW][C]127[/C][C]0.105159[/C][C]0.210318[/C][C]0.894841[/C][/ROW]
[ROW][C]128[/C][C]0.114999[/C][C]0.229998[/C][C]0.885001[/C][/ROW]
[ROW][C]129[/C][C]0.0927649[/C][C]0.18553[/C][C]0.907235[/C][/ROW]
[ROW][C]130[/C][C]0.127717[/C][C]0.255433[/C][C]0.872283[/C][/ROW]
[ROW][C]131[/C][C]0.123757[/C][C]0.247514[/C][C]0.876243[/C][/ROW]
[ROW][C]132[/C][C]0.117063[/C][C]0.234126[/C][C]0.882937[/C][/ROW]
[ROW][C]133[/C][C]0.109912[/C][C]0.219823[/C][C]0.890088[/C][/ROW]
[ROW][C]134[/C][C]0.0892443[/C][C]0.178489[/C][C]0.910756[/C][/ROW]
[ROW][C]135[/C][C]0.104067[/C][C]0.208135[/C][C]0.895933[/C][/ROW]
[ROW][C]136[/C][C]0.138845[/C][C]0.277689[/C][C]0.861155[/C][/ROW]
[ROW][C]137[/C][C]0.12993[/C][C]0.259859[/C][C]0.87007[/C][/ROW]
[ROW][C]138[/C][C]0.100575[/C][C]0.20115[/C][C]0.899425[/C][/ROW]
[ROW][C]139[/C][C]0.0769219[/C][C]0.153844[/C][C]0.923078[/C][/ROW]
[ROW][C]140[/C][C]0.0831947[/C][C]0.166389[/C][C]0.916805[/C][/ROW]
[ROW][C]141[/C][C]0.0616406[/C][C]0.123281[/C][C]0.938359[/C][/ROW]
[ROW][C]142[/C][C]0.0461258[/C][C]0.0922517[/C][C]0.953874[/C][/ROW]
[ROW][C]143[/C][C]0.10553[/C][C]0.21106[/C][C]0.89447[/C][/ROW]
[ROW][C]144[/C][C]0.15921[/C][C]0.31842[/C][C]0.84079[/C][/ROW]
[ROW][C]145[/C][C]0.139342[/C][C]0.278684[/C][C]0.860658[/C][/ROW]
[ROW][C]146[/C][C]0.1249[/C][C]0.249799[/C][C]0.8751[/C][/ROW]
[ROW][C]147[/C][C]0.143619[/C][C]0.287239[/C][C]0.856381[/C][/ROW]
[ROW][C]148[/C][C]0.188907[/C][C]0.377815[/C][C]0.811093[/C][/ROW]
[ROW][C]149[/C][C]0.145417[/C][C]0.290834[/C][C]0.854583[/C][/ROW]
[ROW][C]150[/C][C]0.110379[/C][C]0.220758[/C][C]0.889621[/C][/ROW]
[ROW][C]151[/C][C]0.153163[/C][C]0.306327[/C][C]0.846837[/C][/ROW]
[ROW][C]152[/C][C]0.106332[/C][C]0.212664[/C][C]0.893668[/C][/ROW]
[ROW][C]153[/C][C]0.069353[/C][C]0.138706[/C][C]0.930647[/C][/ROW]
[ROW][C]154[/C][C]0.459904[/C][C]0.919807[/C][C]0.540096[/C][/ROW]
[ROW][C]155[/C][C]0.785254[/C][C]0.429492[/C][C]0.214746[/C][/ROW]
[ROW][C]156[/C][C]0.671086[/C][C]0.657829[/C][C]0.328914[/C][/ROW]
[ROW][C]157[/C][C]0.777506[/C][C]0.444988[/C][C]0.222494[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&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
90.2123590.4247180.787641
100.4800170.9600330.519983
110.4646730.9293460.535327
120.7356620.5286760.264338
130.6416040.7167920.358396
140.5491720.9016560.450828
150.5230530.9538940.476947
160.4286720.8573450.571328
170.40470.8093990.5953
180.3212030.6424050.678797
190.2873070.5746130.712693
200.3168940.6337880.683106
210.3118150.6236310.688185
220.4427150.885430.557285
230.466530.933060.53347
240.4426230.8852460.557377
250.3760820.7521640.623918
260.3339860.6679720.666014
270.3330810.6661620.666919
280.3012730.6025450.698727
290.2458230.4916460.754177
300.2260850.4521710.773915
310.1825580.3651170.817442
320.1877640.3755290.812236
330.1509680.3019370.849032
340.1435040.2870080.856496
350.1194270.2388530.880573
360.0984020.1968040.901598
370.07564060.1512810.924359
380.05775530.1155110.942245
390.0567210.1134420.943279
400.04269630.08539250.957304
410.03141580.06283150.968584
420.02528640.05057280.974714
430.0188050.03760990.981195
440.01429130.02858250.985709
450.0450150.090030.954985
460.03577940.07155890.964221
470.02942570.05885140.970574
480.02149020.04298050.97851
490.0516110.1032220.948389
500.06538540.1307710.934615
510.07261010.145220.92739
520.0574570.1149140.942543
530.04724870.09449740.952751
540.04767940.09535890.952321
550.1114250.222850.888575
560.09000050.1800010.909999
570.07402540.1480510.925975
580.06379070.1275810.936209
590.1738760.3477510.826124
600.1575880.3151750.842412
610.1493480.2986960.850652
620.2987650.5975290.701235
630.4542150.908430.545785
640.4286530.8573070.571347
650.403140.8062810.59686
660.3649890.7299780.635011
670.3465410.6930830.653459
680.3060160.6120330.693984
690.272420.5448410.72758
700.2538940.5077880.746106
710.2327670.4655340.767233
720.1988720.3977430.801128
730.1958750.391750.804125
740.1794970.3589950.820503
750.1528890.3057780.847111
760.1469250.2938490.853075
770.1544380.3088770.845562
780.1318420.2636840.868158
790.1107560.2215130.889244
800.09534860.1906970.904651
810.07871820.1574360.921282
820.1791090.3582180.820891
830.1541920.3083840.845808
840.1312520.2625050.868748
850.1087350.2174710.891265
860.09750850.1950170.902491
870.09346490.186930.906535
880.07586540.1517310.924135
890.06296460.1259290.937035
900.05073710.1014740.949263
910.04264630.08529270.957354
920.04426440.08852890.955736
930.03572470.07144940.964275
940.0282830.0565660.971717
950.02201630.04403250.977984
960.05895780.1179160.941042
970.1103340.2206680.889666
980.1023050.204610.897695
990.1048010.2096010.895199
1000.08634720.1726940.913653
1010.0782330.1564660.921767
1020.07088610.1417720.929114
1030.06328360.1265670.936716
1040.06350220.1270040.936498
1050.0534330.1068660.946567
1060.0520750.104150.947925
1070.05155880.1031180.948441
1080.04804650.0960930.951954
1090.03899390.07798770.961006
1100.04728230.09456460.952718
1110.03768030.07536060.96232
1120.02912970.05825940.97087
1130.05694480.113890.943055
1140.2140350.428070.785965
1150.1805530.3611070.819447
1160.1552240.3104490.844776
1170.1329680.2659360.867032
1180.180560.3611210.81944
1190.22680.4536010.7732
1200.2292410.4584820.770759
1210.1936710.3873430.806329
1220.1670330.3340660.832967
1230.1500930.3001870.849907
1240.1478550.295710.852145
1250.1277830.2555670.872217
1260.1144060.2288130.885594
1270.1051590.2103180.894841
1280.1149990.2299980.885001
1290.09276490.185530.907235
1300.1277170.2554330.872283
1310.1237570.2475140.876243
1320.1170630.2341260.882937
1330.1099120.2198230.890088
1340.08924430.1784890.910756
1350.1040670.2081350.895933
1360.1388450.2776890.861155
1370.129930.2598590.87007
1380.1005750.201150.899425
1390.07692190.1538440.923078
1400.08319470.1663890.916805
1410.06164060.1232810.938359
1420.04612580.09225170.953874
1430.105530.211060.89447
1440.159210.318420.84079
1450.1393420.2786840.860658
1460.12490.2497990.8751
1470.1436190.2872390.856381
1480.1889070.3778150.811093
1490.1454170.2908340.854583
1500.1103790.2207580.889621
1510.1531630.3063270.846837
1520.1063320.2126640.893668
1530.0693530.1387060.930647
1540.4599040.9198070.540096
1550.7852540.4294920.214746
1560.6710860.6578290.328914
1570.7775060.4449880.222494






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0268456OK
10% type I error level220.147651NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 4 & 0.0268456 & OK \tabularnewline
10% type I error level & 22 & 0.147651 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271135&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0268456[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.147651[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271135&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271135&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0268456OK
10% type I error level220.147651NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}