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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 computationTue, 16 Dec 2014 16:47:32 +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/16/t1418748955lj680u74jp363de.htm/, Retrieved Thu, 16 May 2024 13:13:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269837, Retrieved Thu, 16 May 2024 13:13:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Percentiles] [Intrinsic Motivat...] [2010-10-12 12:10:58] [b98453cac15ba1066b407e146608df68]
- R  D  [Percentiles] [] [2011-10-18 22:34:54] [b98453cac15ba1066b407e146608df68]
- RMPD    [Percentiles] [] [2014-12-14 10:18:21] [2fea329c6e322b1612c5dc504f90c0ef]
-    D      [Percentiles] [] [2014-12-14 10:24:25] [2fea329c6e322b1612c5dc504f90c0ef]
-    D        [Percentiles] [] [2014-12-14 10:27:52] [2fea329c6e322b1612c5dc504f90c0ef]
-   PD          [Percentiles] [] [2014-12-16 14:14:51] [2fea329c6e322b1612c5dc504f90c0ef]
-    D            [Percentiles] [] [2014-12-16 14:30:23] [2fea329c6e322b1612c5dc504f90c0ef]
- RM D              [Multiple Regression] [] [2014-12-16 16:28:02] [2fea329c6e322b1612c5dc504f90c0ef]
- R  D                  [Multiple Regression] [] [2014-12-16 16:47:32] [4bf1efda48b6e8e35beb7b429a900cbb] [Current]
- R PD                    [Multiple Regression] [] [2014-12-17 15:01:44] [95c11abf048d3a1e472aeccb09199113]
-    D                      [Multiple Regression] [] [2014-12-17 15:21:27] [95c11abf048d3a1e472aeccb09199113]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,35	103	23	0	34	48	41	23	12
12,7	72	22	0	61	50	146	16	45
18,1	113	21	0	70	150	182	33	37
17,85	125	25	0	69	154	192	32	37
16,6	109	30	1	145	109	263	37	108
12,6	111	17	1	23	68	35	14	10
17,1	105	27	0	120	194	439	52	68
19,1	122	23	0	147	158	214	75	72
16,1	127	23	0	215	159	341	72	143
13,35	117	18	0	24	67	58	15	9
18,4	91	18	0	84	147	292	29	55
14,7	143	23	0	30	39	85	13	17
10,6	116	19	0	77	100	200	40	37
12,6	123	15	0	46	111	158	19	27
16,2	101	20	0	61	138	199	24	37
13,6	119	16	0	178	101	297	121	58
18,9	143	24	1	160	131	227	93	66
14,1	114	25	0	57	101	108	36	21
14,5	108	25	0	42	114	86	23	19
16,15	128	19	0	163	165	302	85	78
14,75	122	19	0	75	114	148	41	35
14,8	133	16	0	94	111	178	46	48
12,45	94	19	0	45	75	120	18	27
12,65	128	19	0	78	82	207	35	43
17,35	108	23	0	47	121	157	17	30
8,6	125	21	0	29	32	128	4	25
18,4	130	22	0	97	150	296	28	69
16,1	112	19	0	116	117	323	44	72
11,6	89	20	1	32	71	79	10	23
17,75	117	20	0	50	165	70	38	13
15,25	142	3	0	118	154	146	57	61
17,65	128	23	0	66	126	246	23	43
15,6	114	14	0	48	138	145	26	22
16,35	123	23	0	86	149	196	36	51
17,65	89	20	0	89	145	199	22	67
13,6	125	15	0	76	120	127	40	36
11,7	110	13	0	39	138	91	18	21
14,35	112	16	0	75	109	153	31	44
14,75	109	7	0	57	132	299	11	45
18,25	108	24	0	72	172	228	38	34
9,9	139	17	0	60	169	190	24	36
16	116	24	0	109	114	180	37	72
18,25	116	24	0	76	156	212	37	39
16,85	88	19	0	65	172	269	22	43
14,6	128	25	1	40	68	130	15	25
13,85	125	20	1	58	89	179	2	56
18,95	134	28	0	123	167	243	43	80
15,6	126	23	0	71	113	190	31	40
14,85	121	27	1	102	115	299	29	73
11,75	106	18	1	80	78	121	45	34
18,45	109	28	1	97	118	137	25	72
15,9	127	21	1	46	87	305	4	42
17,1	101	19	0	93	173	157	31	61
16,1	120	23	0	19	2	96	-4	23
19,9	99	27	1	140	162	183	66	74
10,95	116	22	1	78	49	52	61	16
18,45	125	28	1	98	122	238	32	66
15,1	121	25	1	40	96	40	31	9
15	127	21	1	80	100	226	39	41
11,35	129	22	1	76	82	190	19	57
15,95	155	28	1	79	100	214	31	48
18,1	113	20	1	87	115	145	36	51
14,6	125	29	1	95	141	119	42	53
15,4	114	25	0	49	165	222	21	29
15,4	116	25	0	49	165	222	21	29
17,6	127	20	1	80	110	159	25	55
13,35	102	20	0	86	118	165	32	54
19,1	87	16	0	69	158	249	26	43
15,35	110	20	1	79	146	125	28	51
7,6	115	20	0	52	49	122	32	20
13,4	108	23	1	120	90	186	41	79
13,9	97	18	1	69	121	148	29	39
19,1	119	25	0	94	155	274	33	61
15,25	130	18	1	72	104	172	17	55
12,9	97	19	1	43	147	84	13	30
16,1	120	25	1	87	110	168	32	55
17,35	125	25	1	52	108	102	30	22
13,15	131	25	1	71	113	106	34	37
12,15	129	24	1	61	115	2	59	2
12,6	125	19	1	51	61	139	13	38
10,35	108	26	1	50	60	95	23	27
15,4	142	10	1	67	109	130	10	56
9,6	117	17	1	30	68	72	5	25
18,2	130	13	1	70	111	141	31	39
13,6	93	17	1	52	77	113	19	33
14,85	97	30	1	75	73	206	32	43
14,75	120	25	0	87	151	268	30	57
14,1	110	4	1	69	89	175	25	43
14,9	111	16	1	72	78	77	48	23
16,25	130	21	1	79	110	125	35	44
19,25	66	23	0	121	220	255	67	54
13,6	113	22	1	43	65	111	15	28
13,6	126	17	0	58	141	132	22	36
15,65	114	20	1	57	117	211	18	39
12,75	130	20	0	50	122	92	33	16
14,6	112	22	1	69	63	76	46	23
9,85	126	16	0	64	44	171	24	40
12,65	86	23	1	38	52	83	14	24
11,9	122	16	1	53	62	119	23	29
19,2	118	0	1	90	131	266	12	78
16,6	124	18	1	96	101	186	38	57
11,2	120	25	1	49	42	50	12	37
15,25	128	23	0	56	152	117	28	27
11,9	134	12	0	102	107	219	41	61
13,2	133	18	1	40	77	246	12	27
16,35	131	24	0	100	154	279	31	69
12,4	102	11	0	67	103	148	33	34
15,85	97	18	1	78	96	137	34	44
14,35	93	14	0	62	154	130	41	21
18,15	129	23	0	55	175	181	21	34
11,15	115	24	1	59	57	98	20	39
15,65	116	29	1	96	112	226	44	51
17,75	136	18	0	86	143	234	52	34
7,65	142	15	1	38	49	138	7	31
12,35	112	29	0	43	110	85	29	13
15,6	120	16	0	23	131	66	11	12
19,3	121	19	0	77	167	236	26	51
15,2	110	22	1	48	56	106	24	24
17,1	122	16	0	26	137	135	7	19
15,6	133	23	1	91	86	122	60	30
18,4	136	23	0	94	121	218	13	81
19,05	118	19	0	62	149	199	20	42
18,55	130	4	0	74	168	112	52	22
19,1	114	20	0	114	140	278	28	85
13,1	147	24	1	52	88	94	25	27
12,85	123	20	0	64	168	113	39	25
9,5	121	4	0	31	94	84	9	22
4,5	119	24	0	38	51	86	19	19
11,85	129	22	1	27	48	62	13	14
13,6	137	16	0	105	145	222	60	45
11,7	63	3	0	64	66	167	19	45
12,4	134	15	1	62	85	82	34	28
13,35	140	24	0	65	109	207	14	51
11,4	134	17	1	58	63	184	17	41
14,9	121	20	1	76	102	83	45	31
19,9	105	27	1	140	162	183	66	74
17,75	114	23	0	48	128	85	24	24
11,2	106	26	1	68	86	89	48	19
14,6	135	23	1	80	114	225	29	51
17,6	100	17	0	71	164	237	-2	73
14,05	101	20	0	76	119	102	51	24
16,1	131	22	0	63	126	221	2	61
13,35	131	19	0	46	132	128	24	23
11,85	129	24	0	53	142	91	40	14
11,95	120	19	0	74	83	198	20	54
14,75	117	23	1	70	94	204	19	51
15,15	82	15	1	78	81	158	16	62
13,2	106	27	0	56	166	138	20	36
16,85	125	26	1	100	110	226	40	59
7,85	130	22	1	51	64	44	27	24
7,7	147	22	0	52	93	196	25	26
12,6	125	18	1	102	104	83	49	54
7,85	97	15	1	78	105	79	39	39
10,95	101	22	1	78	49	52	61	16
12,35	128	27	1	55	88	105	19	36
9,95	97	10	1	98	95	116	67	31
14,9	126	20	1	76	102	83	45	31
16,65	118	17	1	73	99	196	30	42
13,4	107	23	1	47	63	153	8	39
13,95	87	19	1	45	76	157	19	25
15,7	156	13	1	83	109	75	52	31
16,85	133	27	1	60	117	106	22	38
10,95	132	23	1	48	57	58	17	31
15,35	133	16	1	50	120	75	33	17
12,2	122	25	1	56	73	74	34	22
15,1	125	2	1	77	91	185	22	55
17,75	127	26	1	91	108	265	30	62
15,2	125	20	1	76	105	131	25	51
14,6	99	23	0	68	117	139	38	30
16,65	128	22	1	74	119	196	26	49
8,1	110	24	1	29	31	78	13	16

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
TOT.SCORE[t] = + 6.42395 -0.00199958TOT.AMS[t] + 0.0379787NUMERACY[t] + 1.21267BA_of_SCH[t] + 0.275482H[t] + 0.0489303zinvolle_teksten[t] + 0.00615412B[t] -0.286673PRH[t] -0.253138CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT.SCORE[t] =  +  6.42395 -0.00199958TOT.AMS[t] +  0.0379787NUMERACY[t] +  1.21267BA_of_SCH[t] +  0.275482H[t] +  0.0489303zinvolle_teksten[t] +  0.00615412B[t] -0.286673PRH[t] -0.253138CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT.SCORE[t] =  +  6.42395 -0.00199958TOT.AMS[t] +  0.0379787NUMERACY[t] +  1.21267BA_of_SCH[t] +  0.275482H[t] +  0.0489303zinvolle_teksten[t] +  0.00615412B[t] -0.286673PRH[t] -0.253138CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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
TOT.SCORE[t] = + 6.42395 -0.00199958TOT.AMS[t] + 0.0379787NUMERACY[t] + 1.21267BA_of_SCH[t] + 0.275482H[t] + 0.0489303zinvolle_teksten[t] + 0.00615412B[t] -0.286673PRH[t] -0.253138CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.423951.486144.3232.68391e-051.34195e-05
TOT.AMS-0.001999580.010643-0.18790.8512070.425604
NUMERACY0.03797870.03040671.2490.2134590.106729
BA_of_SCH1.212670.4007413.0260.002882590.00144129
H0.2754820.3589150.76750.4438780.221939
zinvolle_teksten0.04893030.005630028.6913.7747e-151.88735e-15
B0.006154120.003692561.6670.09752070.0487603
PRH-0.2866730.360013-0.79630.4270320.213516
CH-0.2531380.35852-0.70610.4811620.240581

\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) & 6.42395 & 1.48614 & 4.323 & 2.68391e-05 & 1.34195e-05 \tabularnewline
TOT.AMS & -0.00199958 & 0.010643 & -0.1879 & 0.851207 & 0.425604 \tabularnewline
NUMERACY & 0.0379787 & 0.0304067 & 1.249 & 0.213459 & 0.106729 \tabularnewline
BA_of_SCH & 1.21267 & 0.400741 & 3.026 & 0.00288259 & 0.00144129 \tabularnewline
H & 0.275482 & 0.358915 & 0.7675 & 0.443878 & 0.221939 \tabularnewline
zinvolle_teksten & 0.0489303 & 0.00563002 & 8.691 & 3.7747e-15 & 1.88735e-15 \tabularnewline
B & 0.00615412 & 0.00369256 & 1.667 & 0.0975207 & 0.0487603 \tabularnewline
PRH & -0.286673 & 0.360013 & -0.7963 & 0.427032 & 0.213516 \tabularnewline
CH & -0.253138 & 0.35852 & -0.7061 & 0.481162 & 0.240581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&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]6.42395[/C][C]1.48614[/C][C]4.323[/C][C]2.68391e-05[/C][C]1.34195e-05[/C][/ROW]
[ROW][C]TOT.AMS[/C][C]-0.00199958[/C][C]0.010643[/C][C]-0.1879[/C][C]0.851207[/C][C]0.425604[/C][/ROW]
[ROW][C]NUMERACY[/C][C]0.0379787[/C][C]0.0304067[/C][C]1.249[/C][C]0.213459[/C][C]0.106729[/C][/ROW]
[ROW][C]BA_of_SCH[/C][C]1.21267[/C][C]0.400741[/C][C]3.026[/C][C]0.00288259[/C][C]0.00144129[/C][/ROW]
[ROW][C]H[/C][C]0.275482[/C][C]0.358915[/C][C]0.7675[/C][C]0.443878[/C][C]0.221939[/C][/ROW]
[ROW][C]zinvolle_teksten[/C][C]0.0489303[/C][C]0.00563002[/C][C]8.691[/C][C]3.7747e-15[/C][C]1.88735e-15[/C][/ROW]
[ROW][C]B[/C][C]0.00615412[/C][C]0.00369256[/C][C]1.667[/C][C]0.0975207[/C][C]0.0487603[/C][/ROW]
[ROW][C]PRH[/C][C]-0.286673[/C][C]0.360013[/C][C]-0.7963[/C][C]0.427032[/C][C]0.213516[/C][/ROW]
[ROW][C]CH[/C][C]-0.253138[/C][C]0.35852[/C][C]-0.7061[/C][C]0.481162[/C][C]0.240581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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)6.423951.486144.3232.68391e-051.34195e-05
TOT.AMS-0.001999580.010643-0.18790.8512070.425604
NUMERACY0.03797870.03040671.2490.2134590.106729
BA_of_SCH1.212670.4007413.0260.002882590.00144129
H0.2754820.3589150.76750.4438780.221939
zinvolle_teksten0.04893030.005630028.6913.7747e-151.88735e-15
B0.006154120.003692561.6670.09752070.0487603
PRH-0.2866730.360013-0.79630.4270320.213516
CH-0.2531380.35852-0.70610.4811620.240581






Multiple Linear Regression - Regression Statistics
Multiple R0.717098
R-squared0.514229
Adjusted R-squared0.49024
F-TEST (value)21.4363
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.16233
Sum Squared Residuals757.46

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.717098 \tabularnewline
R-squared & 0.514229 \tabularnewline
Adjusted R-squared & 0.49024 \tabularnewline
F-TEST (value) & 21.4363 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.16233 \tabularnewline
Sum Squared Residuals & 757.46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.717098[/C][/ROW]
[ROW][C]R-squared[/C][C]0.514229[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.49024[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.4363[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/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]2.16233[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]757.46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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 R0.717098
R-squared0.514229
Adjusted R-squared0.49024
F-TEST (value)21.4363
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.16233
Sum Squared Residuals757.46






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.42771-5.07771
212.711.28691.41308
318.115.91252.18747
417.8516.30891.5411
516.617.509-0.908978
612.611.39421.20577
717.120.371-3.27095
819.116.87082.22916
916.119.3113-3.21132
1013.3510.54212.8079
1118.416.81971.5803
1214.79.677255.02275
1310.613.4165-2.81649
1412.613.5419-0.941931
1516.215.51670.683271
1613.613.22960.370353
1718.916.77842.12164
1814.112.81841.28162
1914.513.43191.06811
2016.1517.6131-1.46315
2114.7513.43811.31186
2214.813.850.949964
2312.4511.76770.682322
2412.6512.7449-0.0948515
2517.3514.44832.90168
268.69.83887-1.23887
2718.417.3891.01096
2816.115.75050.349478
2911.612.305-0.704962
3017.7515.04362.70644
3115.2515.4127-0.162746
3217.6515.4242.22599
3315.614.5731.027
3416.3516.00950.340526
3517.6516.57591.07407
3613.613.7536-0.153593
3711.714.2779-2.57789
3814.3513.71880.631186
3914.7515.9286-1.17856
4018.2517.2730.976968
419.916.7659-6.86592
421614.98391.01607
4318.2516.49861.75141
4416.8517.7556-0.905608
4514.612.84821.75185
4613.8514.8315-0.981488
4718.9518.19250.757536
4815.614.29071.30928
4914.8517.1937-2.34369
5011.7513.201-1.45103
5118.4516.42792.02209
5215.915.20780.692167
5317.117.6662-0.566224
5416.18.304847.79516
5519.918.43181.46823
5610.9510.90810.0419131
5718.4517.00081.4492
5815.113.14181.95824
591514.94370.0563009
6011.3514.4567-3.10672
6115.9515.32570.624349
6218.115.42622.6738
6314.616.8337-2.23373
6415.416.7226-1.32264
6515.416.7186-1.31864
6617.615.45222.14781
6713.3514.6172-1.26719
6819.116.79082.3092
6915.3516.9155-1.56548
707.610.1907-2.5907
7113.415.1489-1.74885
7213.915.78-1.87998
7319.117.39951.70047
7415.2515.24620.00381023
7512.916.3988-3.49877
7616.115.63310.466873
7717.3514.40412.94586
7813.1514.9518-1.8018
7912.1513.3138-1.16383
8012.612.652-0.0520036
8110.3512.2744-1.92444
8215.415.28070.119289
839.612.3213-2.7213
8418.214.69383.50615
8513.613.0840.515957
8614.8514.02430.825677
8714.7517.1091-2.35908
8814.113.95680.143187
8914.912.56492.33506
9016.2514.91721.33277
9119.2519.9562-0.706164
9213.612.56751.03248
9313.615.0873-1.48731
9415.6515.8616-0.211556
9512.7513.7229-0.972907
9614.611.79762.80239
979.8510.6101-0.760093
9812.6511.77290.877103
9911.912.4324-0.532387
10019.217.0562.14397
10116.615.28271.3173
10211.211.4013-0.201329
10315.2515.7643-0.514342
10411.914.0991-2.19914
10513.214.0803-0.880298
10616.3517.5205-1.17052
10712.412.9787-0.578745
10815.8514.26931.5807
10914.3515.1153-0.765348
11018.1517.24090.909132
11111.1512.3579-1.20785
11215.6516.2996-0.649647
11317.7515.45042.29957
1147.6511.7835-4.13352
11512.3513.4482-1.0982
11615.613.75271.84729
11719.317.37591.92413
11815.211.91233.28772
11917.114.66812.4319
12015.613.47732.12272
12118.415.9522.44798
12219.0516.13952.91052
12318.5515.13513.41494
12419.117.3781.72202
12513.113.462-0.362009
12612.8515.9754-3.12539
1279.511.8411-2.34114
1284.510.3341-5.83407
12911.8511.11170.738266
13013.615.5527-1.95273
13111.711.46190.23813
13212.412.8472-0.447174
13313.3514.6456-1.29564
13411.412.9551-1.55511
13514.913.8451.05505
13619.918.41981.48022
13717.7514.12333.62667
13811.213.3306-2.13064
13914.616.0179-1.41788
14017.618.0062-0.406189
14114.0513.67290.377056
14216.115.86340.236611
14313.3514.0999-0.749947
14411.8514.1753-2.32528
14511.9513.168-1.21805
14614.7515.0579-0.307949
14715.1514.18430.965724
14813.216.7897-3.58965
14916.8516.29340.556631
1507.8511.8486-3.9986
1517.713.2989-5.59889
15212.614.0525-1.45251
1537.8514.0711-6.22112
15410.9510.93810.0119193
15512.3513.9499-1.59987
1569.9513.1275-3.17751
15714.913.8351.06504
15816.6514.97481.67522
15913.413.10220.297769
16013.9513.49060.459418
16115.713.72411.97595
16216.8515.37611.47388
16310.9511.8945-0.944545
16415.3514.3221.02795
16512.212.4805-0.280498
16615.114.03651.06352
16717.7516.05951.6905
16815.214.94980.250151
16914.613.92480.675207
17016.6515.77350.876511
1718.110.537-2.43702

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 9.42771 & -5.07771 \tabularnewline
2 & 12.7 & 11.2869 & 1.41308 \tabularnewline
3 & 18.1 & 15.9125 & 2.18747 \tabularnewline
4 & 17.85 & 16.3089 & 1.5411 \tabularnewline
5 & 16.6 & 17.509 & -0.908978 \tabularnewline
6 & 12.6 & 11.3942 & 1.20577 \tabularnewline
7 & 17.1 & 20.371 & -3.27095 \tabularnewline
8 & 19.1 & 16.8708 & 2.22916 \tabularnewline
9 & 16.1 & 19.3113 & -3.21132 \tabularnewline
10 & 13.35 & 10.5421 & 2.8079 \tabularnewline
11 & 18.4 & 16.8197 & 1.5803 \tabularnewline
12 & 14.7 & 9.67725 & 5.02275 \tabularnewline
13 & 10.6 & 13.4165 & -2.81649 \tabularnewline
14 & 12.6 & 13.5419 & -0.941931 \tabularnewline
15 & 16.2 & 15.5167 & 0.683271 \tabularnewline
16 & 13.6 & 13.2296 & 0.370353 \tabularnewline
17 & 18.9 & 16.7784 & 2.12164 \tabularnewline
18 & 14.1 & 12.8184 & 1.28162 \tabularnewline
19 & 14.5 & 13.4319 & 1.06811 \tabularnewline
20 & 16.15 & 17.6131 & -1.46315 \tabularnewline
21 & 14.75 & 13.4381 & 1.31186 \tabularnewline
22 & 14.8 & 13.85 & 0.949964 \tabularnewline
23 & 12.45 & 11.7677 & 0.682322 \tabularnewline
24 & 12.65 & 12.7449 & -0.0948515 \tabularnewline
25 & 17.35 & 14.4483 & 2.90168 \tabularnewline
26 & 8.6 & 9.83887 & -1.23887 \tabularnewline
27 & 18.4 & 17.389 & 1.01096 \tabularnewline
28 & 16.1 & 15.7505 & 0.349478 \tabularnewline
29 & 11.6 & 12.305 & -0.704962 \tabularnewline
30 & 17.75 & 15.0436 & 2.70644 \tabularnewline
31 & 15.25 & 15.4127 & -0.162746 \tabularnewline
32 & 17.65 & 15.424 & 2.22599 \tabularnewline
33 & 15.6 & 14.573 & 1.027 \tabularnewline
34 & 16.35 & 16.0095 & 0.340526 \tabularnewline
35 & 17.65 & 16.5759 & 1.07407 \tabularnewline
36 & 13.6 & 13.7536 & -0.153593 \tabularnewline
37 & 11.7 & 14.2779 & -2.57789 \tabularnewline
38 & 14.35 & 13.7188 & 0.631186 \tabularnewline
39 & 14.75 & 15.9286 & -1.17856 \tabularnewline
40 & 18.25 & 17.273 & 0.976968 \tabularnewline
41 & 9.9 & 16.7659 & -6.86592 \tabularnewline
42 & 16 & 14.9839 & 1.01607 \tabularnewline
43 & 18.25 & 16.4986 & 1.75141 \tabularnewline
44 & 16.85 & 17.7556 & -0.905608 \tabularnewline
45 & 14.6 & 12.8482 & 1.75185 \tabularnewline
46 & 13.85 & 14.8315 & -0.981488 \tabularnewline
47 & 18.95 & 18.1925 & 0.757536 \tabularnewline
48 & 15.6 & 14.2907 & 1.30928 \tabularnewline
49 & 14.85 & 17.1937 & -2.34369 \tabularnewline
50 & 11.75 & 13.201 & -1.45103 \tabularnewline
51 & 18.45 & 16.4279 & 2.02209 \tabularnewline
52 & 15.9 & 15.2078 & 0.692167 \tabularnewline
53 & 17.1 & 17.6662 & -0.566224 \tabularnewline
54 & 16.1 & 8.30484 & 7.79516 \tabularnewline
55 & 19.9 & 18.4318 & 1.46823 \tabularnewline
56 & 10.95 & 10.9081 & 0.0419131 \tabularnewline
57 & 18.45 & 17.0008 & 1.4492 \tabularnewline
58 & 15.1 & 13.1418 & 1.95824 \tabularnewline
59 & 15 & 14.9437 & 0.0563009 \tabularnewline
60 & 11.35 & 14.4567 & -3.10672 \tabularnewline
61 & 15.95 & 15.3257 & 0.624349 \tabularnewline
62 & 18.1 & 15.4262 & 2.6738 \tabularnewline
63 & 14.6 & 16.8337 & -2.23373 \tabularnewline
64 & 15.4 & 16.7226 & -1.32264 \tabularnewline
65 & 15.4 & 16.7186 & -1.31864 \tabularnewline
66 & 17.6 & 15.4522 & 2.14781 \tabularnewline
67 & 13.35 & 14.6172 & -1.26719 \tabularnewline
68 & 19.1 & 16.7908 & 2.3092 \tabularnewline
69 & 15.35 & 16.9155 & -1.56548 \tabularnewline
70 & 7.6 & 10.1907 & -2.5907 \tabularnewline
71 & 13.4 & 15.1489 & -1.74885 \tabularnewline
72 & 13.9 & 15.78 & -1.87998 \tabularnewline
73 & 19.1 & 17.3995 & 1.70047 \tabularnewline
74 & 15.25 & 15.2462 & 0.00381023 \tabularnewline
75 & 12.9 & 16.3988 & -3.49877 \tabularnewline
76 & 16.1 & 15.6331 & 0.466873 \tabularnewline
77 & 17.35 & 14.4041 & 2.94586 \tabularnewline
78 & 13.15 & 14.9518 & -1.8018 \tabularnewline
79 & 12.15 & 13.3138 & -1.16383 \tabularnewline
80 & 12.6 & 12.652 & -0.0520036 \tabularnewline
81 & 10.35 & 12.2744 & -1.92444 \tabularnewline
82 & 15.4 & 15.2807 & 0.119289 \tabularnewline
83 & 9.6 & 12.3213 & -2.7213 \tabularnewline
84 & 18.2 & 14.6938 & 3.50615 \tabularnewline
85 & 13.6 & 13.084 & 0.515957 \tabularnewline
86 & 14.85 & 14.0243 & 0.825677 \tabularnewline
87 & 14.75 & 17.1091 & -2.35908 \tabularnewline
88 & 14.1 & 13.9568 & 0.143187 \tabularnewline
89 & 14.9 & 12.5649 & 2.33506 \tabularnewline
90 & 16.25 & 14.9172 & 1.33277 \tabularnewline
91 & 19.25 & 19.9562 & -0.706164 \tabularnewline
92 & 13.6 & 12.5675 & 1.03248 \tabularnewline
93 & 13.6 & 15.0873 & -1.48731 \tabularnewline
94 & 15.65 & 15.8616 & -0.211556 \tabularnewline
95 & 12.75 & 13.7229 & -0.972907 \tabularnewline
96 & 14.6 & 11.7976 & 2.80239 \tabularnewline
97 & 9.85 & 10.6101 & -0.760093 \tabularnewline
98 & 12.65 & 11.7729 & 0.877103 \tabularnewline
99 & 11.9 & 12.4324 & -0.532387 \tabularnewline
100 & 19.2 & 17.056 & 2.14397 \tabularnewline
101 & 16.6 & 15.2827 & 1.3173 \tabularnewline
102 & 11.2 & 11.4013 & -0.201329 \tabularnewline
103 & 15.25 & 15.7643 & -0.514342 \tabularnewline
104 & 11.9 & 14.0991 & -2.19914 \tabularnewline
105 & 13.2 & 14.0803 & -0.880298 \tabularnewline
106 & 16.35 & 17.5205 & -1.17052 \tabularnewline
107 & 12.4 & 12.9787 & -0.578745 \tabularnewline
108 & 15.85 & 14.2693 & 1.5807 \tabularnewline
109 & 14.35 & 15.1153 & -0.765348 \tabularnewline
110 & 18.15 & 17.2409 & 0.909132 \tabularnewline
111 & 11.15 & 12.3579 & -1.20785 \tabularnewline
112 & 15.65 & 16.2996 & -0.649647 \tabularnewline
113 & 17.75 & 15.4504 & 2.29957 \tabularnewline
114 & 7.65 & 11.7835 & -4.13352 \tabularnewline
115 & 12.35 & 13.4482 & -1.0982 \tabularnewline
116 & 15.6 & 13.7527 & 1.84729 \tabularnewline
117 & 19.3 & 17.3759 & 1.92413 \tabularnewline
118 & 15.2 & 11.9123 & 3.28772 \tabularnewline
119 & 17.1 & 14.6681 & 2.4319 \tabularnewline
120 & 15.6 & 13.4773 & 2.12272 \tabularnewline
121 & 18.4 & 15.952 & 2.44798 \tabularnewline
122 & 19.05 & 16.1395 & 2.91052 \tabularnewline
123 & 18.55 & 15.1351 & 3.41494 \tabularnewline
124 & 19.1 & 17.378 & 1.72202 \tabularnewline
125 & 13.1 & 13.462 & -0.362009 \tabularnewline
126 & 12.85 & 15.9754 & -3.12539 \tabularnewline
127 & 9.5 & 11.8411 & -2.34114 \tabularnewline
128 & 4.5 & 10.3341 & -5.83407 \tabularnewline
129 & 11.85 & 11.1117 & 0.738266 \tabularnewline
130 & 13.6 & 15.5527 & -1.95273 \tabularnewline
131 & 11.7 & 11.4619 & 0.23813 \tabularnewline
132 & 12.4 & 12.8472 & -0.447174 \tabularnewline
133 & 13.35 & 14.6456 & -1.29564 \tabularnewline
134 & 11.4 & 12.9551 & -1.55511 \tabularnewline
135 & 14.9 & 13.845 & 1.05505 \tabularnewline
136 & 19.9 & 18.4198 & 1.48022 \tabularnewline
137 & 17.75 & 14.1233 & 3.62667 \tabularnewline
138 & 11.2 & 13.3306 & -2.13064 \tabularnewline
139 & 14.6 & 16.0179 & -1.41788 \tabularnewline
140 & 17.6 & 18.0062 & -0.406189 \tabularnewline
141 & 14.05 & 13.6729 & 0.377056 \tabularnewline
142 & 16.1 & 15.8634 & 0.236611 \tabularnewline
143 & 13.35 & 14.0999 & -0.749947 \tabularnewline
144 & 11.85 & 14.1753 & -2.32528 \tabularnewline
145 & 11.95 & 13.168 & -1.21805 \tabularnewline
146 & 14.75 & 15.0579 & -0.307949 \tabularnewline
147 & 15.15 & 14.1843 & 0.965724 \tabularnewline
148 & 13.2 & 16.7897 & -3.58965 \tabularnewline
149 & 16.85 & 16.2934 & 0.556631 \tabularnewline
150 & 7.85 & 11.8486 & -3.9986 \tabularnewline
151 & 7.7 & 13.2989 & -5.59889 \tabularnewline
152 & 12.6 & 14.0525 & -1.45251 \tabularnewline
153 & 7.85 & 14.0711 & -6.22112 \tabularnewline
154 & 10.95 & 10.9381 & 0.0119193 \tabularnewline
155 & 12.35 & 13.9499 & -1.59987 \tabularnewline
156 & 9.95 & 13.1275 & -3.17751 \tabularnewline
157 & 14.9 & 13.835 & 1.06504 \tabularnewline
158 & 16.65 & 14.9748 & 1.67522 \tabularnewline
159 & 13.4 & 13.1022 & 0.297769 \tabularnewline
160 & 13.95 & 13.4906 & 0.459418 \tabularnewline
161 & 15.7 & 13.7241 & 1.97595 \tabularnewline
162 & 16.85 & 15.3761 & 1.47388 \tabularnewline
163 & 10.95 & 11.8945 & -0.944545 \tabularnewline
164 & 15.35 & 14.322 & 1.02795 \tabularnewline
165 & 12.2 & 12.4805 & -0.280498 \tabularnewline
166 & 15.1 & 14.0365 & 1.06352 \tabularnewline
167 & 17.75 & 16.0595 & 1.6905 \tabularnewline
168 & 15.2 & 14.9498 & 0.250151 \tabularnewline
169 & 14.6 & 13.9248 & 0.675207 \tabularnewline
170 & 16.65 & 15.7735 & 0.876511 \tabularnewline
171 & 8.1 & 10.537 & -2.43702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&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]4.35[/C][C]9.42771[/C][C]-5.07771[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.2869[/C][C]1.41308[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.9125[/C][C]2.18747[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.3089[/C][C]1.5411[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.509[/C][C]-0.908978[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.3942[/C][C]1.20577[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]20.371[/C][C]-3.27095[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]16.8708[/C][C]2.22916[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]19.3113[/C][C]-3.21132[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.5421[/C][C]2.8079[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.8197[/C][C]1.5803[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.67725[/C][C]5.02275[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.4165[/C][C]-2.81649[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.5419[/C][C]-0.941931[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.5167[/C][C]0.683271[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.2296[/C][C]0.370353[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.7784[/C][C]2.12164[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.8184[/C][C]1.28162[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.4319[/C][C]1.06811[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.6131[/C][C]-1.46315[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.4381[/C][C]1.31186[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.85[/C][C]0.949964[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.7677[/C][C]0.682322[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.7449[/C][C]-0.0948515[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.4483[/C][C]2.90168[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.83887[/C][C]-1.23887[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.389[/C][C]1.01096[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.7505[/C][C]0.349478[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.305[/C][C]-0.704962[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.0436[/C][C]2.70644[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]15.4127[/C][C]-0.162746[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.424[/C][C]2.22599[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.573[/C][C]1.027[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.0095[/C][C]0.340526[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]16.5759[/C][C]1.07407[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.7536[/C][C]-0.153593[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.2779[/C][C]-2.57789[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]13.7188[/C][C]0.631186[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.9286[/C][C]-1.17856[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]17.273[/C][C]0.976968[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.7659[/C][C]-6.86592[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.9839[/C][C]1.01607[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.4986[/C][C]1.75141[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.7556[/C][C]-0.905608[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.8482[/C][C]1.75185[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.8315[/C][C]-0.981488[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.1925[/C][C]0.757536[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.2907[/C][C]1.30928[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]17.1937[/C][C]-2.34369[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.201[/C][C]-1.45103[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]16.4279[/C][C]2.02209[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]15.2078[/C][C]0.692167[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.6662[/C][C]-0.566224[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.30484[/C][C]7.79516[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]18.4318[/C][C]1.46823[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.9081[/C][C]0.0419131[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.0008[/C][C]1.4492[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]13.1418[/C][C]1.95824[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.9437[/C][C]0.0563009[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]14.4567[/C][C]-3.10672[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]15.3257[/C][C]0.624349[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.4262[/C][C]2.6738[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]16.8337[/C][C]-2.23373[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.7226[/C][C]-1.32264[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.7186[/C][C]-1.31864[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.4522[/C][C]2.14781[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]14.6172[/C][C]-1.26719[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.7908[/C][C]2.3092[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.9155[/C][C]-1.56548[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]10.1907[/C][C]-2.5907[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.1489[/C][C]-1.74885[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.78[/C][C]-1.87998[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.3995[/C][C]1.70047[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]15.2462[/C][C]0.00381023[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]16.3988[/C][C]-3.49877[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.6331[/C][C]0.466873[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.4041[/C][C]2.94586[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]14.9518[/C][C]-1.8018[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.3138[/C][C]-1.16383[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.652[/C][C]-0.0520036[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]12.2744[/C][C]-1.92444[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.2807[/C][C]0.119289[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]12.3213[/C][C]-2.7213[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.6938[/C][C]3.50615[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.084[/C][C]0.515957[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]14.0243[/C][C]0.825677[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.1091[/C][C]-2.35908[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]13.9568[/C][C]0.143187[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.5649[/C][C]2.33506[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]14.9172[/C][C]1.33277[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.9562[/C][C]-0.706164[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.5675[/C][C]1.03248[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.0873[/C][C]-1.48731[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]15.8616[/C][C]-0.211556[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.7229[/C][C]-0.972907[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]11.7976[/C][C]2.80239[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]10.6101[/C][C]-0.760093[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.7729[/C][C]0.877103[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.4324[/C][C]-0.532387[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.056[/C][C]2.14397[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.2827[/C][C]1.3173[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.4013[/C][C]-0.201329[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.7643[/C][C]-0.514342[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.0991[/C][C]-2.19914[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]14.0803[/C][C]-0.880298[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.5205[/C][C]-1.17052[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]12.9787[/C][C]-0.578745[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]14.2693[/C][C]1.5807[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.1153[/C][C]-0.765348[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]17.2409[/C][C]0.909132[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]12.3579[/C][C]-1.20785[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.2996[/C][C]-0.649647[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.4504[/C][C]2.29957[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]11.7835[/C][C]-4.13352[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.4482[/C][C]-1.0982[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.7527[/C][C]1.84729[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.3759[/C][C]1.92413[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]11.9123[/C][C]3.28772[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.6681[/C][C]2.4319[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.4773[/C][C]2.12272[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.952[/C][C]2.44798[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.1395[/C][C]2.91052[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.1351[/C][C]3.41494[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.378[/C][C]1.72202[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.462[/C][C]-0.362009[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]15.9754[/C][C]-3.12539[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.8411[/C][C]-2.34114[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.3341[/C][C]-5.83407[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]11.1117[/C][C]0.738266[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.5527[/C][C]-1.95273[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.4619[/C][C]0.23813[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.8472[/C][C]-0.447174[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]14.6456[/C][C]-1.29564[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.9551[/C][C]-1.55511[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.845[/C][C]1.05505[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]18.4198[/C][C]1.48022[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.1233[/C][C]3.62667[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]13.3306[/C][C]-2.13064[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]16.0179[/C][C]-1.41788[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]18.0062[/C][C]-0.406189[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.6729[/C][C]0.377056[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]15.8634[/C][C]0.236611[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.0999[/C][C]-0.749947[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.1753[/C][C]-2.32528[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]13.168[/C][C]-1.21805[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]15.0579[/C][C]-0.307949[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.1843[/C][C]0.965724[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.7897[/C][C]-3.58965[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.2934[/C][C]0.556631[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]11.8486[/C][C]-3.9986[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]13.2989[/C][C]-5.59889[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]14.0525[/C][C]-1.45251[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]14.0711[/C][C]-6.22112[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.9381[/C][C]0.0119193[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]13.9499[/C][C]-1.59987[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.1275[/C][C]-3.17751[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.835[/C][C]1.06504[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]14.9748[/C][C]1.67522[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]13.1022[/C][C]0.297769[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.4906[/C][C]0.459418[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.7241[/C][C]1.97595[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.3761[/C][C]1.47388[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]11.8945[/C][C]-0.944545[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]14.322[/C][C]1.02795[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.4805[/C][C]-0.280498[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.0365[/C][C]1.06352[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.0595[/C][C]1.6905[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.9498[/C][C]0.250151[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]13.9248[/C][C]0.675207[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]15.7735[/C][C]0.876511[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]10.537[/C][C]-2.43702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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
14.359.42771-5.07771
212.711.28691.41308
318.115.91252.18747
417.8516.30891.5411
516.617.509-0.908978
612.611.39421.20577
717.120.371-3.27095
819.116.87082.22916
916.119.3113-3.21132
1013.3510.54212.8079
1118.416.81971.5803
1214.79.677255.02275
1310.613.4165-2.81649
1412.613.5419-0.941931
1516.215.51670.683271
1613.613.22960.370353
1718.916.77842.12164
1814.112.81841.28162
1914.513.43191.06811
2016.1517.6131-1.46315
2114.7513.43811.31186
2214.813.850.949964
2312.4511.76770.682322
2412.6512.7449-0.0948515
2517.3514.44832.90168
268.69.83887-1.23887
2718.417.3891.01096
2816.115.75050.349478
2911.612.305-0.704962
3017.7515.04362.70644
3115.2515.4127-0.162746
3217.6515.4242.22599
3315.614.5731.027
3416.3516.00950.340526
3517.6516.57591.07407
3613.613.7536-0.153593
3711.714.2779-2.57789
3814.3513.71880.631186
3914.7515.9286-1.17856
4018.2517.2730.976968
419.916.7659-6.86592
421614.98391.01607
4318.2516.49861.75141
4416.8517.7556-0.905608
4514.612.84821.75185
4613.8514.8315-0.981488
4718.9518.19250.757536
4815.614.29071.30928
4914.8517.1937-2.34369
5011.7513.201-1.45103
5118.4516.42792.02209
5215.915.20780.692167
5317.117.6662-0.566224
5416.18.304847.79516
5519.918.43181.46823
5610.9510.90810.0419131
5718.4517.00081.4492
5815.113.14181.95824
591514.94370.0563009
6011.3514.4567-3.10672
6115.9515.32570.624349
6218.115.42622.6738
6314.616.8337-2.23373
6415.416.7226-1.32264
6515.416.7186-1.31864
6617.615.45222.14781
6713.3514.6172-1.26719
6819.116.79082.3092
6915.3516.9155-1.56548
707.610.1907-2.5907
7113.415.1489-1.74885
7213.915.78-1.87998
7319.117.39951.70047
7415.2515.24620.00381023
7512.916.3988-3.49877
7616.115.63310.466873
7717.3514.40412.94586
7813.1514.9518-1.8018
7912.1513.3138-1.16383
8012.612.652-0.0520036
8110.3512.2744-1.92444
8215.415.28070.119289
839.612.3213-2.7213
8418.214.69383.50615
8513.613.0840.515957
8614.8514.02430.825677
8714.7517.1091-2.35908
8814.113.95680.143187
8914.912.56492.33506
9016.2514.91721.33277
9119.2519.9562-0.706164
9213.612.56751.03248
9313.615.0873-1.48731
9415.6515.8616-0.211556
9512.7513.7229-0.972907
9614.611.79762.80239
979.8510.6101-0.760093
9812.6511.77290.877103
9911.912.4324-0.532387
10019.217.0562.14397
10116.615.28271.3173
10211.211.4013-0.201329
10315.2515.7643-0.514342
10411.914.0991-2.19914
10513.214.0803-0.880298
10616.3517.5205-1.17052
10712.412.9787-0.578745
10815.8514.26931.5807
10914.3515.1153-0.765348
11018.1517.24090.909132
11111.1512.3579-1.20785
11215.6516.2996-0.649647
11317.7515.45042.29957
1147.6511.7835-4.13352
11512.3513.4482-1.0982
11615.613.75271.84729
11719.317.37591.92413
11815.211.91233.28772
11917.114.66812.4319
12015.613.47732.12272
12118.415.9522.44798
12219.0516.13952.91052
12318.5515.13513.41494
12419.117.3781.72202
12513.113.462-0.362009
12612.8515.9754-3.12539
1279.511.8411-2.34114
1284.510.3341-5.83407
12911.8511.11170.738266
13013.615.5527-1.95273
13111.711.46190.23813
13212.412.8472-0.447174
13313.3514.6456-1.29564
13411.412.9551-1.55511
13514.913.8451.05505
13619.918.41981.48022
13717.7514.12333.62667
13811.213.3306-2.13064
13914.616.0179-1.41788
14017.618.0062-0.406189
14114.0513.67290.377056
14216.115.86340.236611
14313.3514.0999-0.749947
14411.8514.1753-2.32528
14511.9513.168-1.21805
14614.7515.0579-0.307949
14715.1514.18430.965724
14813.216.7897-3.58965
14916.8516.29340.556631
1507.8511.8486-3.9986
1517.713.2989-5.59889
15212.614.0525-1.45251
1537.8514.0711-6.22112
15410.9510.93810.0119193
15512.3513.9499-1.59987
1569.9513.1275-3.17751
15714.913.8351.06504
15816.6514.97481.67522
15913.413.10220.297769
16013.9513.49060.459418
16115.713.72411.97595
16216.8515.37611.47388
16310.9511.8945-0.944545
16415.3514.3221.02795
16512.212.4805-0.280498
16615.114.03651.06352
16717.7516.05951.6905
16815.214.94980.250151
16914.613.92480.675207
17016.6515.77350.876511
1718.110.537-2.43702






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.4885870.9771740.511413
130.8786890.2426220.121311
140.9179960.1640080.0820041
150.8683670.2632650.131633
160.8974630.2050740.102537
170.9573740.08525160.0426258
180.9326840.1346310.0673155
190.8996710.2006580.100329
200.8736250.2527490.126375
210.8668080.2663830.133192
220.8202290.3595430.179771
230.7659770.4680460.234023
240.7087470.5825060.291253
250.7086070.5827860.291393
260.6810440.6379120.318956
270.6462890.7074230.353711
280.5986090.8027810.401391
290.5338970.9322060.466103
300.5065450.9869110.493455
310.4868670.9737330.513133
320.4647930.9295860.535207
330.4087950.8175890.591205
340.3522830.7045670.647717
350.3115910.6231830.688409
360.2796240.5592480.720376
370.3897770.7795530.610223
380.336350.6726990.66365
390.293090.586180.70691
400.2477140.4954280.752286
410.7701730.4596540.229827
420.7311920.5376170.268808
430.7000390.5999210.299961
440.6550630.6898730.344937
450.6179160.7641670.382084
460.5704070.8591850.429593
470.5201810.9596390.479819
480.4775780.9551570.522422
490.4720960.9441910.527904
500.4889350.9778690.511065
510.4704140.9408270.529586
520.4489810.8979610.551019
530.4088160.8176330.591184
540.8478010.3043990.152199
550.8309640.3380710.169036
560.8212030.3575930.178797
570.7972910.4054180.202709
580.7807020.4385950.219298
590.743470.5130610.25653
600.7882020.4235960.211798
610.7530370.4939260.246963
620.7806130.4387730.219387
630.8072730.3854550.192727
640.7864550.4270890.213545
650.763230.473540.23677
660.7675670.4648660.232433
670.7453570.5092860.254643
680.7664310.4671390.233569
690.7509050.498190.249095
700.791850.4162990.20815
710.7802780.4394430.219722
720.7796920.4406160.220308
730.7631450.473710.236855
740.7275810.5448370.272419
750.7981470.4037060.201853
760.7649090.4701810.235091
770.7842490.4315010.215751
780.7852990.4294010.214701
790.7698160.4603670.230184
800.7339720.5320550.266028
810.7322360.5355290.267764
820.7005930.5988130.299407
830.7208310.5583380.279169
840.7912790.4174410.208721
850.7613550.477290.238645
860.7343030.5313940.265697
870.7432620.5134770.256738
880.7122090.5755810.287791
890.7128720.5742550.287128
900.6846560.6306890.315344
910.6668680.6662640.333132
920.638280.723440.36172
930.6173190.7653630.382681
940.5781210.8437580.421879
950.5523530.8952940.447647
960.6084280.7831430.391572
970.5903170.8193660.409683
980.5633080.8733850.436692
990.5220430.9559150.477957
1000.5286840.9426310.471316
1010.4907260.9814530.509274
1020.4589170.9178340.541083
1030.4192130.8384250.580787
1040.4077480.8154970.592252
1050.3758120.7516240.624188
1060.3511180.7022370.648882
1070.3098640.6197280.690136
1080.288150.5762990.71185
1090.2574130.5148270.742587
1100.2238630.4477250.776137
1110.1981860.3963730.801814
1120.175240.3504790.82476
1130.1780860.3561720.821914
1140.2522170.5044340.747783
1150.2234830.4469670.776517
1160.2167180.4334350.783282
1170.1989680.3979360.801032
1180.2950250.590050.704975
1190.3155410.6310830.684459
1200.3244190.6488380.675581
1210.3456080.6912160.654392
1220.3898850.779770.610115
1230.481810.963620.51819
1240.4616720.9233450.538328
1250.4124230.8248450.587577
1260.4417840.8835690.558216
1270.4182210.8364410.581779
1280.5660690.8678620.433931
1290.5443290.9113430.455671
1300.5149230.9701550.485077
1310.48240.9647990.5176
1320.4266730.8533460.573327
1330.376190.752380.62381
1340.3351760.6703520.664824
1350.3023720.6047430.697628
1360.259190.518380.74081
1370.489220.978440.51078
1380.4570110.9140220.542989
1390.4611130.9222270.538887
1400.3976660.7953320.602334
1410.430510.8610190.56949
1420.3859860.7719720.614014
1430.3438570.6877140.656143
1440.2950010.5900020.704999
1450.2685320.5370640.731468
1460.2215520.4431050.778448
1470.218530.437060.78147
1480.1986110.3972220.801389
1490.1555380.3110770.844462
1500.1752270.3504550.824773
1510.9509030.09819450.0490972
1520.9560740.08785170.0439258
1530.9830290.03394240.0169712
1540.9853480.02930330.0146516
1550.9956470.008705650.00435282
1560.9985750.002849050.00142453
1570.9951330.009734960.00486748
1580.9938170.01236540.00618269
1590.9953630.009273990.00463699

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.488587 & 0.977174 & 0.511413 \tabularnewline
13 & 0.878689 & 0.242622 & 0.121311 \tabularnewline
14 & 0.917996 & 0.164008 & 0.0820041 \tabularnewline
15 & 0.868367 & 0.263265 & 0.131633 \tabularnewline
16 & 0.897463 & 0.205074 & 0.102537 \tabularnewline
17 & 0.957374 & 0.0852516 & 0.0426258 \tabularnewline
18 & 0.932684 & 0.134631 & 0.0673155 \tabularnewline
19 & 0.899671 & 0.200658 & 0.100329 \tabularnewline
20 & 0.873625 & 0.252749 & 0.126375 \tabularnewline
21 & 0.866808 & 0.266383 & 0.133192 \tabularnewline
22 & 0.820229 & 0.359543 & 0.179771 \tabularnewline
23 & 0.765977 & 0.468046 & 0.234023 \tabularnewline
24 & 0.708747 & 0.582506 & 0.291253 \tabularnewline
25 & 0.708607 & 0.582786 & 0.291393 \tabularnewline
26 & 0.681044 & 0.637912 & 0.318956 \tabularnewline
27 & 0.646289 & 0.707423 & 0.353711 \tabularnewline
28 & 0.598609 & 0.802781 & 0.401391 \tabularnewline
29 & 0.533897 & 0.932206 & 0.466103 \tabularnewline
30 & 0.506545 & 0.986911 & 0.493455 \tabularnewline
31 & 0.486867 & 0.973733 & 0.513133 \tabularnewline
32 & 0.464793 & 0.929586 & 0.535207 \tabularnewline
33 & 0.408795 & 0.817589 & 0.591205 \tabularnewline
34 & 0.352283 & 0.704567 & 0.647717 \tabularnewline
35 & 0.311591 & 0.623183 & 0.688409 \tabularnewline
36 & 0.279624 & 0.559248 & 0.720376 \tabularnewline
37 & 0.389777 & 0.779553 & 0.610223 \tabularnewline
38 & 0.33635 & 0.672699 & 0.66365 \tabularnewline
39 & 0.29309 & 0.58618 & 0.70691 \tabularnewline
40 & 0.247714 & 0.495428 & 0.752286 \tabularnewline
41 & 0.770173 & 0.459654 & 0.229827 \tabularnewline
42 & 0.731192 & 0.537617 & 0.268808 \tabularnewline
43 & 0.700039 & 0.599921 & 0.299961 \tabularnewline
44 & 0.655063 & 0.689873 & 0.344937 \tabularnewline
45 & 0.617916 & 0.764167 & 0.382084 \tabularnewline
46 & 0.570407 & 0.859185 & 0.429593 \tabularnewline
47 & 0.520181 & 0.959639 & 0.479819 \tabularnewline
48 & 0.477578 & 0.955157 & 0.522422 \tabularnewline
49 & 0.472096 & 0.944191 & 0.527904 \tabularnewline
50 & 0.488935 & 0.977869 & 0.511065 \tabularnewline
51 & 0.470414 & 0.940827 & 0.529586 \tabularnewline
52 & 0.448981 & 0.897961 & 0.551019 \tabularnewline
53 & 0.408816 & 0.817633 & 0.591184 \tabularnewline
54 & 0.847801 & 0.304399 & 0.152199 \tabularnewline
55 & 0.830964 & 0.338071 & 0.169036 \tabularnewline
56 & 0.821203 & 0.357593 & 0.178797 \tabularnewline
57 & 0.797291 & 0.405418 & 0.202709 \tabularnewline
58 & 0.780702 & 0.438595 & 0.219298 \tabularnewline
59 & 0.74347 & 0.513061 & 0.25653 \tabularnewline
60 & 0.788202 & 0.423596 & 0.211798 \tabularnewline
61 & 0.753037 & 0.493926 & 0.246963 \tabularnewline
62 & 0.780613 & 0.438773 & 0.219387 \tabularnewline
63 & 0.807273 & 0.385455 & 0.192727 \tabularnewline
64 & 0.786455 & 0.427089 & 0.213545 \tabularnewline
65 & 0.76323 & 0.47354 & 0.23677 \tabularnewline
66 & 0.767567 & 0.464866 & 0.232433 \tabularnewline
67 & 0.745357 & 0.509286 & 0.254643 \tabularnewline
68 & 0.766431 & 0.467139 & 0.233569 \tabularnewline
69 & 0.750905 & 0.49819 & 0.249095 \tabularnewline
70 & 0.79185 & 0.416299 & 0.20815 \tabularnewline
71 & 0.780278 & 0.439443 & 0.219722 \tabularnewline
72 & 0.779692 & 0.440616 & 0.220308 \tabularnewline
73 & 0.763145 & 0.47371 & 0.236855 \tabularnewline
74 & 0.727581 & 0.544837 & 0.272419 \tabularnewline
75 & 0.798147 & 0.403706 & 0.201853 \tabularnewline
76 & 0.764909 & 0.470181 & 0.235091 \tabularnewline
77 & 0.784249 & 0.431501 & 0.215751 \tabularnewline
78 & 0.785299 & 0.429401 & 0.214701 \tabularnewline
79 & 0.769816 & 0.460367 & 0.230184 \tabularnewline
80 & 0.733972 & 0.532055 & 0.266028 \tabularnewline
81 & 0.732236 & 0.535529 & 0.267764 \tabularnewline
82 & 0.700593 & 0.598813 & 0.299407 \tabularnewline
83 & 0.720831 & 0.558338 & 0.279169 \tabularnewline
84 & 0.791279 & 0.417441 & 0.208721 \tabularnewline
85 & 0.761355 & 0.47729 & 0.238645 \tabularnewline
86 & 0.734303 & 0.531394 & 0.265697 \tabularnewline
87 & 0.743262 & 0.513477 & 0.256738 \tabularnewline
88 & 0.712209 & 0.575581 & 0.287791 \tabularnewline
89 & 0.712872 & 0.574255 & 0.287128 \tabularnewline
90 & 0.684656 & 0.630689 & 0.315344 \tabularnewline
91 & 0.666868 & 0.666264 & 0.333132 \tabularnewline
92 & 0.63828 & 0.72344 & 0.36172 \tabularnewline
93 & 0.617319 & 0.765363 & 0.382681 \tabularnewline
94 & 0.578121 & 0.843758 & 0.421879 \tabularnewline
95 & 0.552353 & 0.895294 & 0.447647 \tabularnewline
96 & 0.608428 & 0.783143 & 0.391572 \tabularnewline
97 & 0.590317 & 0.819366 & 0.409683 \tabularnewline
98 & 0.563308 & 0.873385 & 0.436692 \tabularnewline
99 & 0.522043 & 0.955915 & 0.477957 \tabularnewline
100 & 0.528684 & 0.942631 & 0.471316 \tabularnewline
101 & 0.490726 & 0.981453 & 0.509274 \tabularnewline
102 & 0.458917 & 0.917834 & 0.541083 \tabularnewline
103 & 0.419213 & 0.838425 & 0.580787 \tabularnewline
104 & 0.407748 & 0.815497 & 0.592252 \tabularnewline
105 & 0.375812 & 0.751624 & 0.624188 \tabularnewline
106 & 0.351118 & 0.702237 & 0.648882 \tabularnewline
107 & 0.309864 & 0.619728 & 0.690136 \tabularnewline
108 & 0.28815 & 0.576299 & 0.71185 \tabularnewline
109 & 0.257413 & 0.514827 & 0.742587 \tabularnewline
110 & 0.223863 & 0.447725 & 0.776137 \tabularnewline
111 & 0.198186 & 0.396373 & 0.801814 \tabularnewline
112 & 0.17524 & 0.350479 & 0.82476 \tabularnewline
113 & 0.178086 & 0.356172 & 0.821914 \tabularnewline
114 & 0.252217 & 0.504434 & 0.747783 \tabularnewline
115 & 0.223483 & 0.446967 & 0.776517 \tabularnewline
116 & 0.216718 & 0.433435 & 0.783282 \tabularnewline
117 & 0.198968 & 0.397936 & 0.801032 \tabularnewline
118 & 0.295025 & 0.59005 & 0.704975 \tabularnewline
119 & 0.315541 & 0.631083 & 0.684459 \tabularnewline
120 & 0.324419 & 0.648838 & 0.675581 \tabularnewline
121 & 0.345608 & 0.691216 & 0.654392 \tabularnewline
122 & 0.389885 & 0.77977 & 0.610115 \tabularnewline
123 & 0.48181 & 0.96362 & 0.51819 \tabularnewline
124 & 0.461672 & 0.923345 & 0.538328 \tabularnewline
125 & 0.412423 & 0.824845 & 0.587577 \tabularnewline
126 & 0.441784 & 0.883569 & 0.558216 \tabularnewline
127 & 0.418221 & 0.836441 & 0.581779 \tabularnewline
128 & 0.566069 & 0.867862 & 0.433931 \tabularnewline
129 & 0.544329 & 0.911343 & 0.455671 \tabularnewline
130 & 0.514923 & 0.970155 & 0.485077 \tabularnewline
131 & 0.4824 & 0.964799 & 0.5176 \tabularnewline
132 & 0.426673 & 0.853346 & 0.573327 \tabularnewline
133 & 0.37619 & 0.75238 & 0.62381 \tabularnewline
134 & 0.335176 & 0.670352 & 0.664824 \tabularnewline
135 & 0.302372 & 0.604743 & 0.697628 \tabularnewline
136 & 0.25919 & 0.51838 & 0.74081 \tabularnewline
137 & 0.48922 & 0.97844 & 0.51078 \tabularnewline
138 & 0.457011 & 0.914022 & 0.542989 \tabularnewline
139 & 0.461113 & 0.922227 & 0.538887 \tabularnewline
140 & 0.397666 & 0.795332 & 0.602334 \tabularnewline
141 & 0.43051 & 0.861019 & 0.56949 \tabularnewline
142 & 0.385986 & 0.771972 & 0.614014 \tabularnewline
143 & 0.343857 & 0.687714 & 0.656143 \tabularnewline
144 & 0.295001 & 0.590002 & 0.704999 \tabularnewline
145 & 0.268532 & 0.537064 & 0.731468 \tabularnewline
146 & 0.221552 & 0.443105 & 0.778448 \tabularnewline
147 & 0.21853 & 0.43706 & 0.78147 \tabularnewline
148 & 0.198611 & 0.397222 & 0.801389 \tabularnewline
149 & 0.155538 & 0.311077 & 0.844462 \tabularnewline
150 & 0.175227 & 0.350455 & 0.824773 \tabularnewline
151 & 0.950903 & 0.0981945 & 0.0490972 \tabularnewline
152 & 0.956074 & 0.0878517 & 0.0439258 \tabularnewline
153 & 0.983029 & 0.0339424 & 0.0169712 \tabularnewline
154 & 0.985348 & 0.0293033 & 0.0146516 \tabularnewline
155 & 0.995647 & 0.00870565 & 0.00435282 \tabularnewline
156 & 0.998575 & 0.00284905 & 0.00142453 \tabularnewline
157 & 0.995133 & 0.00973496 & 0.00486748 \tabularnewline
158 & 0.993817 & 0.0123654 & 0.00618269 \tabularnewline
159 & 0.995363 & 0.00927399 & 0.00463699 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&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]12[/C][C]0.488587[/C][C]0.977174[/C][C]0.511413[/C][/ROW]
[ROW][C]13[/C][C]0.878689[/C][C]0.242622[/C][C]0.121311[/C][/ROW]
[ROW][C]14[/C][C]0.917996[/C][C]0.164008[/C][C]0.0820041[/C][/ROW]
[ROW][C]15[/C][C]0.868367[/C][C]0.263265[/C][C]0.131633[/C][/ROW]
[ROW][C]16[/C][C]0.897463[/C][C]0.205074[/C][C]0.102537[/C][/ROW]
[ROW][C]17[/C][C]0.957374[/C][C]0.0852516[/C][C]0.0426258[/C][/ROW]
[ROW][C]18[/C][C]0.932684[/C][C]0.134631[/C][C]0.0673155[/C][/ROW]
[ROW][C]19[/C][C]0.899671[/C][C]0.200658[/C][C]0.100329[/C][/ROW]
[ROW][C]20[/C][C]0.873625[/C][C]0.252749[/C][C]0.126375[/C][/ROW]
[ROW][C]21[/C][C]0.866808[/C][C]0.266383[/C][C]0.133192[/C][/ROW]
[ROW][C]22[/C][C]0.820229[/C][C]0.359543[/C][C]0.179771[/C][/ROW]
[ROW][C]23[/C][C]0.765977[/C][C]0.468046[/C][C]0.234023[/C][/ROW]
[ROW][C]24[/C][C]0.708747[/C][C]0.582506[/C][C]0.291253[/C][/ROW]
[ROW][C]25[/C][C]0.708607[/C][C]0.582786[/C][C]0.291393[/C][/ROW]
[ROW][C]26[/C][C]0.681044[/C][C]0.637912[/C][C]0.318956[/C][/ROW]
[ROW][C]27[/C][C]0.646289[/C][C]0.707423[/C][C]0.353711[/C][/ROW]
[ROW][C]28[/C][C]0.598609[/C][C]0.802781[/C][C]0.401391[/C][/ROW]
[ROW][C]29[/C][C]0.533897[/C][C]0.932206[/C][C]0.466103[/C][/ROW]
[ROW][C]30[/C][C]0.506545[/C][C]0.986911[/C][C]0.493455[/C][/ROW]
[ROW][C]31[/C][C]0.486867[/C][C]0.973733[/C][C]0.513133[/C][/ROW]
[ROW][C]32[/C][C]0.464793[/C][C]0.929586[/C][C]0.535207[/C][/ROW]
[ROW][C]33[/C][C]0.408795[/C][C]0.817589[/C][C]0.591205[/C][/ROW]
[ROW][C]34[/C][C]0.352283[/C][C]0.704567[/C][C]0.647717[/C][/ROW]
[ROW][C]35[/C][C]0.311591[/C][C]0.623183[/C][C]0.688409[/C][/ROW]
[ROW][C]36[/C][C]0.279624[/C][C]0.559248[/C][C]0.720376[/C][/ROW]
[ROW][C]37[/C][C]0.389777[/C][C]0.779553[/C][C]0.610223[/C][/ROW]
[ROW][C]38[/C][C]0.33635[/C][C]0.672699[/C][C]0.66365[/C][/ROW]
[ROW][C]39[/C][C]0.29309[/C][C]0.58618[/C][C]0.70691[/C][/ROW]
[ROW][C]40[/C][C]0.247714[/C][C]0.495428[/C][C]0.752286[/C][/ROW]
[ROW][C]41[/C][C]0.770173[/C][C]0.459654[/C][C]0.229827[/C][/ROW]
[ROW][C]42[/C][C]0.731192[/C][C]0.537617[/C][C]0.268808[/C][/ROW]
[ROW][C]43[/C][C]0.700039[/C][C]0.599921[/C][C]0.299961[/C][/ROW]
[ROW][C]44[/C][C]0.655063[/C][C]0.689873[/C][C]0.344937[/C][/ROW]
[ROW][C]45[/C][C]0.617916[/C][C]0.764167[/C][C]0.382084[/C][/ROW]
[ROW][C]46[/C][C]0.570407[/C][C]0.859185[/C][C]0.429593[/C][/ROW]
[ROW][C]47[/C][C]0.520181[/C][C]0.959639[/C][C]0.479819[/C][/ROW]
[ROW][C]48[/C][C]0.477578[/C][C]0.955157[/C][C]0.522422[/C][/ROW]
[ROW][C]49[/C][C]0.472096[/C][C]0.944191[/C][C]0.527904[/C][/ROW]
[ROW][C]50[/C][C]0.488935[/C][C]0.977869[/C][C]0.511065[/C][/ROW]
[ROW][C]51[/C][C]0.470414[/C][C]0.940827[/C][C]0.529586[/C][/ROW]
[ROW][C]52[/C][C]0.448981[/C][C]0.897961[/C][C]0.551019[/C][/ROW]
[ROW][C]53[/C][C]0.408816[/C][C]0.817633[/C][C]0.591184[/C][/ROW]
[ROW][C]54[/C][C]0.847801[/C][C]0.304399[/C][C]0.152199[/C][/ROW]
[ROW][C]55[/C][C]0.830964[/C][C]0.338071[/C][C]0.169036[/C][/ROW]
[ROW][C]56[/C][C]0.821203[/C][C]0.357593[/C][C]0.178797[/C][/ROW]
[ROW][C]57[/C][C]0.797291[/C][C]0.405418[/C][C]0.202709[/C][/ROW]
[ROW][C]58[/C][C]0.780702[/C][C]0.438595[/C][C]0.219298[/C][/ROW]
[ROW][C]59[/C][C]0.74347[/C][C]0.513061[/C][C]0.25653[/C][/ROW]
[ROW][C]60[/C][C]0.788202[/C][C]0.423596[/C][C]0.211798[/C][/ROW]
[ROW][C]61[/C][C]0.753037[/C][C]0.493926[/C][C]0.246963[/C][/ROW]
[ROW][C]62[/C][C]0.780613[/C][C]0.438773[/C][C]0.219387[/C][/ROW]
[ROW][C]63[/C][C]0.807273[/C][C]0.385455[/C][C]0.192727[/C][/ROW]
[ROW][C]64[/C][C]0.786455[/C][C]0.427089[/C][C]0.213545[/C][/ROW]
[ROW][C]65[/C][C]0.76323[/C][C]0.47354[/C][C]0.23677[/C][/ROW]
[ROW][C]66[/C][C]0.767567[/C][C]0.464866[/C][C]0.232433[/C][/ROW]
[ROW][C]67[/C][C]0.745357[/C][C]0.509286[/C][C]0.254643[/C][/ROW]
[ROW][C]68[/C][C]0.766431[/C][C]0.467139[/C][C]0.233569[/C][/ROW]
[ROW][C]69[/C][C]0.750905[/C][C]0.49819[/C][C]0.249095[/C][/ROW]
[ROW][C]70[/C][C]0.79185[/C][C]0.416299[/C][C]0.20815[/C][/ROW]
[ROW][C]71[/C][C]0.780278[/C][C]0.439443[/C][C]0.219722[/C][/ROW]
[ROW][C]72[/C][C]0.779692[/C][C]0.440616[/C][C]0.220308[/C][/ROW]
[ROW][C]73[/C][C]0.763145[/C][C]0.47371[/C][C]0.236855[/C][/ROW]
[ROW][C]74[/C][C]0.727581[/C][C]0.544837[/C][C]0.272419[/C][/ROW]
[ROW][C]75[/C][C]0.798147[/C][C]0.403706[/C][C]0.201853[/C][/ROW]
[ROW][C]76[/C][C]0.764909[/C][C]0.470181[/C][C]0.235091[/C][/ROW]
[ROW][C]77[/C][C]0.784249[/C][C]0.431501[/C][C]0.215751[/C][/ROW]
[ROW][C]78[/C][C]0.785299[/C][C]0.429401[/C][C]0.214701[/C][/ROW]
[ROW][C]79[/C][C]0.769816[/C][C]0.460367[/C][C]0.230184[/C][/ROW]
[ROW][C]80[/C][C]0.733972[/C][C]0.532055[/C][C]0.266028[/C][/ROW]
[ROW][C]81[/C][C]0.732236[/C][C]0.535529[/C][C]0.267764[/C][/ROW]
[ROW][C]82[/C][C]0.700593[/C][C]0.598813[/C][C]0.299407[/C][/ROW]
[ROW][C]83[/C][C]0.720831[/C][C]0.558338[/C][C]0.279169[/C][/ROW]
[ROW][C]84[/C][C]0.791279[/C][C]0.417441[/C][C]0.208721[/C][/ROW]
[ROW][C]85[/C][C]0.761355[/C][C]0.47729[/C][C]0.238645[/C][/ROW]
[ROW][C]86[/C][C]0.734303[/C][C]0.531394[/C][C]0.265697[/C][/ROW]
[ROW][C]87[/C][C]0.743262[/C][C]0.513477[/C][C]0.256738[/C][/ROW]
[ROW][C]88[/C][C]0.712209[/C][C]0.575581[/C][C]0.287791[/C][/ROW]
[ROW][C]89[/C][C]0.712872[/C][C]0.574255[/C][C]0.287128[/C][/ROW]
[ROW][C]90[/C][C]0.684656[/C][C]0.630689[/C][C]0.315344[/C][/ROW]
[ROW][C]91[/C][C]0.666868[/C][C]0.666264[/C][C]0.333132[/C][/ROW]
[ROW][C]92[/C][C]0.63828[/C][C]0.72344[/C][C]0.36172[/C][/ROW]
[ROW][C]93[/C][C]0.617319[/C][C]0.765363[/C][C]0.382681[/C][/ROW]
[ROW][C]94[/C][C]0.578121[/C][C]0.843758[/C][C]0.421879[/C][/ROW]
[ROW][C]95[/C][C]0.552353[/C][C]0.895294[/C][C]0.447647[/C][/ROW]
[ROW][C]96[/C][C]0.608428[/C][C]0.783143[/C][C]0.391572[/C][/ROW]
[ROW][C]97[/C][C]0.590317[/C][C]0.819366[/C][C]0.409683[/C][/ROW]
[ROW][C]98[/C][C]0.563308[/C][C]0.873385[/C][C]0.436692[/C][/ROW]
[ROW][C]99[/C][C]0.522043[/C][C]0.955915[/C][C]0.477957[/C][/ROW]
[ROW][C]100[/C][C]0.528684[/C][C]0.942631[/C][C]0.471316[/C][/ROW]
[ROW][C]101[/C][C]0.490726[/C][C]0.981453[/C][C]0.509274[/C][/ROW]
[ROW][C]102[/C][C]0.458917[/C][C]0.917834[/C][C]0.541083[/C][/ROW]
[ROW][C]103[/C][C]0.419213[/C][C]0.838425[/C][C]0.580787[/C][/ROW]
[ROW][C]104[/C][C]0.407748[/C][C]0.815497[/C][C]0.592252[/C][/ROW]
[ROW][C]105[/C][C]0.375812[/C][C]0.751624[/C][C]0.624188[/C][/ROW]
[ROW][C]106[/C][C]0.351118[/C][C]0.702237[/C][C]0.648882[/C][/ROW]
[ROW][C]107[/C][C]0.309864[/C][C]0.619728[/C][C]0.690136[/C][/ROW]
[ROW][C]108[/C][C]0.28815[/C][C]0.576299[/C][C]0.71185[/C][/ROW]
[ROW][C]109[/C][C]0.257413[/C][C]0.514827[/C][C]0.742587[/C][/ROW]
[ROW][C]110[/C][C]0.223863[/C][C]0.447725[/C][C]0.776137[/C][/ROW]
[ROW][C]111[/C][C]0.198186[/C][C]0.396373[/C][C]0.801814[/C][/ROW]
[ROW][C]112[/C][C]0.17524[/C][C]0.350479[/C][C]0.82476[/C][/ROW]
[ROW][C]113[/C][C]0.178086[/C][C]0.356172[/C][C]0.821914[/C][/ROW]
[ROW][C]114[/C][C]0.252217[/C][C]0.504434[/C][C]0.747783[/C][/ROW]
[ROW][C]115[/C][C]0.223483[/C][C]0.446967[/C][C]0.776517[/C][/ROW]
[ROW][C]116[/C][C]0.216718[/C][C]0.433435[/C][C]0.783282[/C][/ROW]
[ROW][C]117[/C][C]0.198968[/C][C]0.397936[/C][C]0.801032[/C][/ROW]
[ROW][C]118[/C][C]0.295025[/C][C]0.59005[/C][C]0.704975[/C][/ROW]
[ROW][C]119[/C][C]0.315541[/C][C]0.631083[/C][C]0.684459[/C][/ROW]
[ROW][C]120[/C][C]0.324419[/C][C]0.648838[/C][C]0.675581[/C][/ROW]
[ROW][C]121[/C][C]0.345608[/C][C]0.691216[/C][C]0.654392[/C][/ROW]
[ROW][C]122[/C][C]0.389885[/C][C]0.77977[/C][C]0.610115[/C][/ROW]
[ROW][C]123[/C][C]0.48181[/C][C]0.96362[/C][C]0.51819[/C][/ROW]
[ROW][C]124[/C][C]0.461672[/C][C]0.923345[/C][C]0.538328[/C][/ROW]
[ROW][C]125[/C][C]0.412423[/C][C]0.824845[/C][C]0.587577[/C][/ROW]
[ROW][C]126[/C][C]0.441784[/C][C]0.883569[/C][C]0.558216[/C][/ROW]
[ROW][C]127[/C][C]0.418221[/C][C]0.836441[/C][C]0.581779[/C][/ROW]
[ROW][C]128[/C][C]0.566069[/C][C]0.867862[/C][C]0.433931[/C][/ROW]
[ROW][C]129[/C][C]0.544329[/C][C]0.911343[/C][C]0.455671[/C][/ROW]
[ROW][C]130[/C][C]0.514923[/C][C]0.970155[/C][C]0.485077[/C][/ROW]
[ROW][C]131[/C][C]0.4824[/C][C]0.964799[/C][C]0.5176[/C][/ROW]
[ROW][C]132[/C][C]0.426673[/C][C]0.853346[/C][C]0.573327[/C][/ROW]
[ROW][C]133[/C][C]0.37619[/C][C]0.75238[/C][C]0.62381[/C][/ROW]
[ROW][C]134[/C][C]0.335176[/C][C]0.670352[/C][C]0.664824[/C][/ROW]
[ROW][C]135[/C][C]0.302372[/C][C]0.604743[/C][C]0.697628[/C][/ROW]
[ROW][C]136[/C][C]0.25919[/C][C]0.51838[/C][C]0.74081[/C][/ROW]
[ROW][C]137[/C][C]0.48922[/C][C]0.97844[/C][C]0.51078[/C][/ROW]
[ROW][C]138[/C][C]0.457011[/C][C]0.914022[/C][C]0.542989[/C][/ROW]
[ROW][C]139[/C][C]0.461113[/C][C]0.922227[/C][C]0.538887[/C][/ROW]
[ROW][C]140[/C][C]0.397666[/C][C]0.795332[/C][C]0.602334[/C][/ROW]
[ROW][C]141[/C][C]0.43051[/C][C]0.861019[/C][C]0.56949[/C][/ROW]
[ROW][C]142[/C][C]0.385986[/C][C]0.771972[/C][C]0.614014[/C][/ROW]
[ROW][C]143[/C][C]0.343857[/C][C]0.687714[/C][C]0.656143[/C][/ROW]
[ROW][C]144[/C][C]0.295001[/C][C]0.590002[/C][C]0.704999[/C][/ROW]
[ROW][C]145[/C][C]0.268532[/C][C]0.537064[/C][C]0.731468[/C][/ROW]
[ROW][C]146[/C][C]0.221552[/C][C]0.443105[/C][C]0.778448[/C][/ROW]
[ROW][C]147[/C][C]0.21853[/C][C]0.43706[/C][C]0.78147[/C][/ROW]
[ROW][C]148[/C][C]0.198611[/C][C]0.397222[/C][C]0.801389[/C][/ROW]
[ROW][C]149[/C][C]0.155538[/C][C]0.311077[/C][C]0.844462[/C][/ROW]
[ROW][C]150[/C][C]0.175227[/C][C]0.350455[/C][C]0.824773[/C][/ROW]
[ROW][C]151[/C][C]0.950903[/C][C]0.0981945[/C][C]0.0490972[/C][/ROW]
[ROW][C]152[/C][C]0.956074[/C][C]0.0878517[/C][C]0.0439258[/C][/ROW]
[ROW][C]153[/C][C]0.983029[/C][C]0.0339424[/C][C]0.0169712[/C][/ROW]
[ROW][C]154[/C][C]0.985348[/C][C]0.0293033[/C][C]0.0146516[/C][/ROW]
[ROW][C]155[/C][C]0.995647[/C][C]0.00870565[/C][C]0.00435282[/C][/ROW]
[ROW][C]156[/C][C]0.998575[/C][C]0.00284905[/C][C]0.00142453[/C][/ROW]
[ROW][C]157[/C][C]0.995133[/C][C]0.00973496[/C][C]0.00486748[/C][/ROW]
[ROW][C]158[/C][C]0.993817[/C][C]0.0123654[/C][C]0.00618269[/C][/ROW]
[ROW][C]159[/C][C]0.995363[/C][C]0.00927399[/C][C]0.00463699[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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
120.4885870.9771740.511413
130.8786890.2426220.121311
140.9179960.1640080.0820041
150.8683670.2632650.131633
160.8974630.2050740.102537
170.9573740.08525160.0426258
180.9326840.1346310.0673155
190.8996710.2006580.100329
200.8736250.2527490.126375
210.8668080.2663830.133192
220.8202290.3595430.179771
230.7659770.4680460.234023
240.7087470.5825060.291253
250.7086070.5827860.291393
260.6810440.6379120.318956
270.6462890.7074230.353711
280.5986090.8027810.401391
290.5338970.9322060.466103
300.5065450.9869110.493455
310.4868670.9737330.513133
320.4647930.9295860.535207
330.4087950.8175890.591205
340.3522830.7045670.647717
350.3115910.6231830.688409
360.2796240.5592480.720376
370.3897770.7795530.610223
380.336350.6726990.66365
390.293090.586180.70691
400.2477140.4954280.752286
410.7701730.4596540.229827
420.7311920.5376170.268808
430.7000390.5999210.299961
440.6550630.6898730.344937
450.6179160.7641670.382084
460.5704070.8591850.429593
470.5201810.9596390.479819
480.4775780.9551570.522422
490.4720960.9441910.527904
500.4889350.9778690.511065
510.4704140.9408270.529586
520.4489810.8979610.551019
530.4088160.8176330.591184
540.8478010.3043990.152199
550.8309640.3380710.169036
560.8212030.3575930.178797
570.7972910.4054180.202709
580.7807020.4385950.219298
590.743470.5130610.25653
600.7882020.4235960.211798
610.7530370.4939260.246963
620.7806130.4387730.219387
630.8072730.3854550.192727
640.7864550.4270890.213545
650.763230.473540.23677
660.7675670.4648660.232433
670.7453570.5092860.254643
680.7664310.4671390.233569
690.7509050.498190.249095
700.791850.4162990.20815
710.7802780.4394430.219722
720.7796920.4406160.220308
730.7631450.473710.236855
740.7275810.5448370.272419
750.7981470.4037060.201853
760.7649090.4701810.235091
770.7842490.4315010.215751
780.7852990.4294010.214701
790.7698160.4603670.230184
800.7339720.5320550.266028
810.7322360.5355290.267764
820.7005930.5988130.299407
830.7208310.5583380.279169
840.7912790.4174410.208721
850.7613550.477290.238645
860.7343030.5313940.265697
870.7432620.5134770.256738
880.7122090.5755810.287791
890.7128720.5742550.287128
900.6846560.6306890.315344
910.6668680.6662640.333132
920.638280.723440.36172
930.6173190.7653630.382681
940.5781210.8437580.421879
950.5523530.8952940.447647
960.6084280.7831430.391572
970.5903170.8193660.409683
980.5633080.8733850.436692
990.5220430.9559150.477957
1000.5286840.9426310.471316
1010.4907260.9814530.509274
1020.4589170.9178340.541083
1030.4192130.8384250.580787
1040.4077480.8154970.592252
1050.3758120.7516240.624188
1060.3511180.7022370.648882
1070.3098640.6197280.690136
1080.288150.5762990.71185
1090.2574130.5148270.742587
1100.2238630.4477250.776137
1110.1981860.3963730.801814
1120.175240.3504790.82476
1130.1780860.3561720.821914
1140.2522170.5044340.747783
1150.2234830.4469670.776517
1160.2167180.4334350.783282
1170.1989680.3979360.801032
1180.2950250.590050.704975
1190.3155410.6310830.684459
1200.3244190.6488380.675581
1210.3456080.6912160.654392
1220.3898850.779770.610115
1230.481810.963620.51819
1240.4616720.9233450.538328
1250.4124230.8248450.587577
1260.4417840.8835690.558216
1270.4182210.8364410.581779
1280.5660690.8678620.433931
1290.5443290.9113430.455671
1300.5149230.9701550.485077
1310.48240.9647990.5176
1320.4266730.8533460.573327
1330.376190.752380.62381
1340.3351760.6703520.664824
1350.3023720.6047430.697628
1360.259190.518380.74081
1370.489220.978440.51078
1380.4570110.9140220.542989
1390.4611130.9222270.538887
1400.3976660.7953320.602334
1410.430510.8610190.56949
1420.3859860.7719720.614014
1430.3438570.6877140.656143
1440.2950010.5900020.704999
1450.2685320.5370640.731468
1460.2215520.4431050.778448
1470.218530.437060.78147
1480.1986110.3972220.801389
1490.1555380.3110770.844462
1500.1752270.3504550.824773
1510.9509030.09819450.0490972
1520.9560740.08785170.0439258
1530.9830290.03394240.0169712
1540.9853480.02930330.0146516
1550.9956470.008705650.00435282
1560.9985750.002849050.00142453
1570.9951330.009734960.00486748
1580.9938170.01236540.00618269
1590.9953630.009273990.00463699






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.027027NOK
5% type I error level70.0472973OK
10% type I error level100.0675676OK

\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 & 4 & 0.027027 & NOK \tabularnewline
5% type I error level & 7 & 0.0472973 & OK \tabularnewline
10% type I error level & 10 & 0.0675676 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269837&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]4[/C][C]0.027027[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.0472973[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0675676[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269837&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269837&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 level40.027027NOK
5% type I error level70.0472973OK
10% type I error level100.0675676OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ;
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}