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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, 04 Dec 2014 14:12:34 +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/04/t1417704196wc29v7mm30sb1pn.htm/, Retrieved Thu, 16 May 2024 22:13:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263296, Retrieved Thu, 16 May 2024 22:13:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper statistiek ...] [2014-12-04 14:12:34] [0a6fc2c777821367d2239c664b701a36] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
0	1	52	51	16	9	23	48	41	34	4.35
0	1	16	56	16	11	22	50	146	61	12.7
0	1	46	67	16	12	21	150	182	70	18.1
0	1	56	69	16	12	25	154	192	69	17.85
1	0	52	57	12	7	30	109	263	145	16.6
1	1	55	56	15	12	17	68	35	23	12.6
0	1	50	55	14	12	27	194	439	120	17.1
0	0	59	63	15	12	23	158	214	147	19.1
0	1	60	67	16	10	23	159	341	215	16.1
0	0	52	65	13	15	18	67	58	24	13.35
0	0	44	47	10	10	18	147	292	84	18.4
0	1	67	76	17	15	23	39	85	30	14.7
0	1	52	64	15	10	19	100	200	77	10.6
0	1	55	68	18	15	15	111	158	46	12.6
0	1	37	64	16	9	20	138	199	61	16.2
0	1	54	65	20	15	16	101	297	178	13.6
1	1	72	71	16	12	24	131	227	160	18.9
0	1	51	63	17	13	25	101	108	57	14.1
0	1	48	60	16	12	25	114	86	42	14.5
0	0	60	68	15	12	19	165	302	163	16.15
0	1	50	72	13	8	19	114	148	75	14.75
0	1	63	70	16	9	16	111	178	94	14.8
0	1	33	61	16	15	19	75	120	45	12.45
0	1	67	61	16	12	19	82	207	78	12.65
0	1	46	62	17	12	23	121	157	47	17.35
0	1	54	71	20	15	21	32	128	29	8.6
0	0	59	71	14	11	22	150	296	97	18.4
0	1	61	51	17	12	19	117	323	116	16.1
1	1	33	56	6	6	20	71	79	32	11.6
0	1	47	70	16	14	20	165	70	50	17.75
0	1	69	73	15	12	3	154	146	118	15.25
0	1	52	76	16	12	23	126	246	66	17.65
0	0	55	68	16	12	23	149	196	86	16.35
0	0	41	48	14	11	20	145	199	89	17.65
0	1	73	52	16	12	15	120	127	76	13.6
0	0	52	60	16	12	16	109	153	75	14.35
0	0	50	59	16	12	7	132	299	57	14.75
0	1	51	57	14	12	24	172	228	72	18.25
0	0	60	79	14	8	17	169	190	60	9.9
0	1	56	60	16	8	24	114	180	109	16
0	1	56	60	16	12	24	156	212	76	18.25
0	0	29	59	15	12	19	172	269	65	16.85
1	1	66	62	16	11	25	68	130	40	14.6
1	1	66	59	16	10	20	89	179	58	13.85
0	1	73	61	18	11	28	167	243	123	18.95
0	0	55	71	15	12	23	113	190	71	15.6
1	0	64	57	16	13	27	115	299	102	14.85
1	0	40	66	16	12	18	78	121	80	11.75
1	0	46	63	16	12	28	118	137	97	18.45
1	1	58	69	17	10	21	87	305	46	15.9
0	0	43	58	14	10	19	173	157	93	17.1
0	1	61	59	18	11	23	2	96	19	16.1
1	0	51	48	9	8	27	162	183	140	19.9
1	1	50	66	15	12	22	49	52	78	10.95
1	0	52	73	14	9	28	122	238	98	18.45
1	1	54	67	15	12	25	96	40	40	15.1
1	0	66	61	13	9	21	100	226	80	15
1	0	61	68	16	11	22	82	190	76	11.35
1	1	80	75	20	15	28	100	214	79	15.95
1	0	51	62	14	8	20	115	145	87	18.1
1	1	56	69	12	8	29	141	119	95	14.6
0	1	56	58	15	11	25	165	222	49	15.4
0	1	56	60	15	11	25	165	222	49	15.4
1	1	53	74	15	11	20	110	159	80	17.6
0	1	47	55	16	13	20	118	165	86	13.35
0	0	25	62	11	7	16	158	249	69	19.1
1	1	47	63	16	12	20	146	125	79	15.35
0	0	46	69	7	8	20	49	122	52	7.6
1	0	50	58	11	8	23	90	186	120	13.4
1	0	39	58	9	4	18	121	148	69	13.9
0	1	51	68	15	11	25	155	274	94	19.1
1	0	58	72	16	10	18	104	172	72	15.25
1	1	35	62	14	7	19	147	84	43	12.9
1	0	58	62	15	12	25	110	168	87	16.1
1	0	60	65	13	11	25	108	102	52	17.35
1	0	62	69	13	9	25	113	106	71	13.15
1	0	63	66	12	10	24	115	2	61	12.15
1	1	53	72	16	8	19	61	139	51	12.6
1	1	46	62	14	8	26	60	95	50	10.35
1	1	67	75	16	11	10	109	130	67	15.4
1	1	59	58	14	12	17	68	72	30	9.6
1	0	64	66	15	10	13	111	141	70	18.2
1	0	38	55	10	10	17	77	113	52	13.6
1	1	50	47	16	12	30	73	206	75	14.85
0	0	48	72	14	8	25	151	268	87	14.75
1	0	48	62	16	11	4	89	175	69	14.1
1	0	47	64	12	8	16	78	77	72	14.9
1	0	66	64	16	10	21	110	125	79	16.25
0	1	47	19	16	14	23	220	255	121	19.25
1	1	63	50	15	9	22	65	111	43	13.6
0	0	58	68	14	9	17	141	132	58	13.6
1	0	44	70	16	10	20	117	211	57	15.65
0	1	51	79	11	13	20	122	92	50	12.75
1	0	43	69	15	12	22	63	76	69	14.6
0	1	55	71	18	13	16	44	171	64	9.85
1	1	38	48	13	8	23	52	83	38	12.65
1	0	45	73	7	3	0	131	266	90	19.2
1	1	50	74	7	8	18	101	186	96	16.6
1	1	54	66	17	12	25	42	50	49	11.2
0	1	57	71	18	11	23	152	117	56	15.25
0	0	60	74	15	9	12	107	219	102	11.9
1	0	55	78	8	12	18	77	246	40	13.2
0	0	56	75	13	12	24	154	279	100	16.35
0	1	49	53	13	12	11	103	148	67	12.4
1	1	37	60	15	10	18	96	137	78	15.85
0	1	59	70	18	13	23	175	181	55	18.15
1	1	46	69	16	9	24	57	98	59	11.15
1	0	51	65	14	12	29	112	226	96	15.65
0	0	58	78	15	11	18	143	234	86	17.75
1	0	64	78	19	14	15	49	138	38	7.65
0	1	53	59	16	11	29	110	85	43	12.35
0	1	48	72	12	9	16	131	66	23	15.6
0	0	51	70	16	12	19	167	236	77	19.3
1	0	47	63	11	8	22	56	106	48	15.2
0	0	59	63	16	15	16	137	135	26	17.1
1	1	62	71	15	12	23	86	122	91	15.6
0	1	62	74	19	14	23	121	218	94	18.4
0	0	51	67	15	12	19	149	199	62	19.05
0	0	64	66	14	9	4	168	112	74	18.55
0	0	52	62	14	9	20	140	278	114	19.1
1	1	67	80	17	13	24	88	94	52	13.1
0	1	50	73	16	13	20	168	113	64	12.85
0	1	54	67	20	15	4	94	84	31	9.5
0	1	58	61	16	11	24	51	86	38	4.5
1	0	56	73	9	7	22	48	62	27	11.85
0	1	63	74	13	10	16	145	222	105	13.6
0	1	31	32	15	11	3	66	167	64	11.7
1	1	65	69	19	14	15	85	82	62	12.4
0	0	71	69	16	14	24	109	207	65	13.35
1	0	50	84	17	13	17	63	184	58	11.4
1	1	57	64	16	12	20	102	83	76	14.9
1	0	47	58	9	8	27	162	183	140	19.9
1	1	47	59	11	13	26	86	89	68	11.2
1	1	57	78	14	9	23	114	225	80	14.6
0	0	43	57	19	12	17	164	237	71	17.6
0	1	41	60	13	13	20	119	102	76	14.05
0	0	63	68	14	11	22	126	221	63	16.1
0	1	63	68	15	11	19	132	128	46	13.35
0	1	56	73	15	13	24	142	91	53	11.85
0	0	51	69	14	12	19	83	198	74	11.95
1	1	50	67	16	12	23	94	204	70	14.75
1	0	22	60	17	10	15	81	158	78	15.15
0	1	41	65	12	9	27	166	138	56	13.2
1	0	59	66	15	10	26	110	226	100	16.85
1	1	56	74	17	13	22	64	44	51	7.85
0	0	66	81	15	13	22	93	196	52	7.7
1	0	53	72	10	9	18	104	83	102	12.6
1	1	42	55	16	11	15	105	79	78	7.85
1	1	52	49	15	12	22	49	52	78	10.95
1	0	54	74	11	8	27	88	105	55	12.35
1	1	44	53	16	12	10	95	116	98	9.95
1	1	62	64	16	12	20	102	83	76	14.9
1	0	53	65	16	12	17	99	196	73	16.65
1	1	50	57	14	9	23	63	153	47	13.4
1	0	36	51	14	12	19	76	157	45	13.95
1	0	76	80	16	12	13	109	75	83	15.7
1	1	66	67	16	11	27	117	106	60	16.85
1	1	62	70	18	12	23	57	58	48	10.95
1	0	59	74	14	6	16	120	75	50	15.35
1	1	47	75	20	7	25	73	74	56	12.2
1	0	55	70	15	10	2	91	185	77	15.1
1	0	58	69	16	12	26	108	265	91	17.75
1	1	60	65	16	10	20	105	131	76	15.2
0	0	44	55	16	12	23	117	139	68	14.6
1	0	57	71	12	9	22	119	196	74	16.65
1	1	45	65	8	3	24	31	78	29	8.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
[t] = + 6.61044 + 1.37721group[t] -0.620334gender[t] + 0.00166449AMS.I[t] -0.0167917AMS.E[t] + 0.0546739CONFSTATTOT[t] + 0.0165454CONTSSOFTTOT[t] + 0.0352007NUMERACYTOT[t] + 0.0475933LFM[t] + 0.00932479B[t] + 0.00197078H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  +  6.61044 +  1.37721group[t] -0.620334gender[t] +  0.00166449AMS.I[t] -0.0167917AMS.E[t] +  0.0546739CONFSTATTOT[t] +  0.0165454CONTSSOFTTOT[t] +  0.0352007NUMERACYTOT[t] +  0.0475933LFM[t] +  0.00932479B[t] +  0.00197078H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  +  6.61044 +  1.37721group[t] -0.620334gender[t] +  0.00166449AMS.I[t] -0.0167917AMS.E[t] +  0.0546739CONFSTATTOT[t] +  0.0165454CONTSSOFTTOT[t] +  0.0352007NUMERACYTOT[t] +  0.0475933LFM[t] +  0.00932479B[t] +  0.00197078H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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
[t] = + 6.61044 + 1.37721group[t] -0.620334gender[t] + 0.00166449AMS.I[t] -0.0167917AMS.E[t] + 0.0546739CONFSTATTOT[t] + 0.0165454CONTSSOFTTOT[t] + 0.0352007NUMERACYTOT[t] + 0.0475933LFM[t] + 0.00932479B[t] + 0.00197078H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.610441.894533.4890.0006308330.000315417
group1.377210.4311853.1940.001699780.000849891
gender-0.6203340.390158-1.590.1138820.0569409
AMS.I0.001664490.01983620.083910.9332350.466617
AMS.E-0.01679170.0214069-0.78440.4339990.217
CONFSTATTOT0.05467390.08822240.61970.5363460.268173
CONTSSOFTTOT0.01654540.1009180.16390.8699850.434993
NUMERACYTOT0.03520070.0315891.1140.2668610.133431
LFM0.04759330.005815318.1849.36312e-144.68156e-14
B0.009324790.003197872.9160.004072630.00203632
H0.001970780.00750840.26250.7933020.396651

\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) & 6.61044 & 1.89453 & 3.489 & 0.000630833 & 0.000315417 \tabularnewline
group & 1.37721 & 0.431185 & 3.194 & 0.00169978 & 0.000849891 \tabularnewline
gender & -0.620334 & 0.390158 & -1.59 & 0.113882 & 0.0569409 \tabularnewline
AMS.I & 0.00166449 & 0.0198362 & 0.08391 & 0.933235 & 0.466617 \tabularnewline
AMS.E & -0.0167917 & 0.0214069 & -0.7844 & 0.433999 & 0.217 \tabularnewline
CONFSTATTOT & 0.0546739 & 0.0882224 & 0.6197 & 0.536346 & 0.268173 \tabularnewline
CONTSSOFTTOT & 0.0165454 & 0.100918 & 0.1639 & 0.869985 & 0.434993 \tabularnewline
NUMERACYTOT & 0.0352007 & 0.031589 & 1.114 & 0.266861 & 0.133431 \tabularnewline
LFM & 0.0475933 & 0.00581531 & 8.184 & 9.36312e-14 & 4.68156e-14 \tabularnewline
B & 0.00932479 & 0.00319787 & 2.916 & 0.00407263 & 0.00203632 \tabularnewline
H & 0.00197078 & 0.0075084 & 0.2625 & 0.793302 & 0.396651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&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]6.61044[/C][C]1.89453[/C][C]3.489[/C][C]0.000630833[/C][C]0.000315417[/C][/ROW]
[ROW][C]group[/C][C]1.37721[/C][C]0.431185[/C][C]3.194[/C][C]0.00169978[/C][C]0.000849891[/C][/ROW]
[ROW][C]gender[/C][C]-0.620334[/C][C]0.390158[/C][C]-1.59[/C][C]0.113882[/C][C]0.0569409[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00166449[/C][C]0.0198362[/C][C]0.08391[/C][C]0.933235[/C][C]0.466617[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0167917[/C][C]0.0214069[/C][C]-0.7844[/C][C]0.433999[/C][C]0.217[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.0546739[/C][C]0.0882224[/C][C]0.6197[/C][C]0.536346[/C][C]0.268173[/C][/ROW]
[ROW][C]CONTSSOFTTOT[/C][C]0.0165454[/C][C]0.100918[/C][C]0.1639[/C][C]0.869985[/C][C]0.434993[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0352007[/C][C]0.031589[/C][C]1.114[/C][C]0.266861[/C][C]0.133431[/C][/ROW]
[ROW][C]LFM[/C][C]0.0475933[/C][C]0.00581531[/C][C]8.184[/C][C]9.36312e-14[/C][C]4.68156e-14[/C][/ROW]
[ROW][C]B[/C][C]0.00932479[/C][C]0.00319787[/C][C]2.916[/C][C]0.00407263[/C][C]0.00203632[/C][/ROW]
[ROW][C]H[/C][C]0.00197078[/C][C]0.0075084[/C][C]0.2625[/C][C]0.793302[/C][C]0.396651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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)6.610441.894533.4890.0006308330.000315417
group1.377210.4311853.1940.001699780.000849891
gender-0.6203340.390158-1.590.1138820.0569409
AMS.I0.001664490.01983620.083910.9332350.466617
AMS.E-0.01679170.0214069-0.78440.4339990.217
CONFSTATTOT0.05467390.08822240.61970.5363460.268173
CONTSSOFTTOT0.01654540.1009180.16390.8699850.434993
NUMERACYTOT0.03520070.0315891.1140.2668610.133431
LFM0.04759330.005815318.1849.36312e-144.68156e-14
B0.009324790.003197872.9160.004072630.00203632
H0.001970780.00750840.26250.7933020.396651






Multiple Linear Regression - Regression Statistics
Multiple R0.719307
R-squared0.517403
Adjusted R-squared0.486267
F-TEST (value)16.6179
F-TEST (DF numerator)10
F-TEST (DF denominator)155
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18463
Sum Squared Residuals739.757

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.719307 \tabularnewline
R-squared & 0.517403 \tabularnewline
Adjusted R-squared & 0.486267 \tabularnewline
F-TEST (value) & 16.6179 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.18463 \tabularnewline
Sum Squared Residuals & 739.757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.719307[/C][/ROW]
[ROW][C]R-squared[/C][C]0.517403[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.486267[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.6179[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/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]2.18463[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]739.757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.719307
R-squared0.517403
Adjusted R-squared0.486267
F-TEST (value)16.6179
F-TEST (DF numerator)10
F-TEST (DF denominator)155
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18463
Sum Squared Residuals739.757






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.78739-5.43739
212.710.76891.9311
318.115.72822.37177
417.8516.13371.71625
516.616.8709-0.270858
612.611.74360.856365
717.120.6274-3.52736
819.117.2841.81601
916.117.9856-1.88559
1013.3510.9752.37503
1118.417.12491.27513
1214.79.520585.17942
1310.613.4324-2.8324
1412.613.547-0.946968
1516.215.24850.951545
1613.614.8206-1.22058
1718.916.87992.02013
1814.112.9681.13198
1914.513.32621.17382
2016.1518.2462-2.09616
2114.7513.32981.42022
2214.813.63441.16563
2312.4511.58970.860327
2412.6512.8061-0.156076
2517.3514.27863.07139
268.69.74236-1.14236
2718.417.32861.07142
2816.115.8410.258989
2911.611.7921-0.192088
3017.7515.30752.44248
3115.2514.92680.323243
3217.6515.10422.54584
3316.3516.5316-0.181643
3417.6516.45621.19381
3513.613.885-0.285006
3614.3514.08820.261799
3714.7516.2054-1.45545
3818.2517.38070.869339
399.916.8132-6.9132
401614.24671.75331
4118.2516.54511.70485
4216.8518.178-1.32798
4314.612.90031.69971
4413.8514.25-0.399961
4518.9517.69551.25452
4615.614.62770.972276
4714.8517.6397-2.78971
4811.7513.6512-1.90116
4918.4516.152.30004
5015.915.21470.68531
5117.117.1887-0.0887131
5216.18.104487.99552
5319.918.53381.36615
5410.9511.106-0.15604
5518.4516.96721.48281
5615.113.25161.84839
571515.6965-0.696494
5811.3514.6027-3.25269
5915.9515.47890.471095
6018.115.63012.46995
6114.616.1187-1.5187
6215.417.0111-1.61109
6315.416.9775-1.57751
6417.614.79462.80536
6513.3514.2628-0.912767
6619.116.8692.23099
6715.3516.4349-1.08493
687.610.3196-2.71965
6913.414.8947-1.49465
7013.915.5454-1.64535
7119.116.93252.16751
7215.2515.24450.00549675
7312.915.7988-2.8988
7416.115.91510.184936
7517.3514.96252.38748
7613.1515.1783-2.02831
7712.1514.2627-2.11272
7812.612.22230.377661
7910.3512.0558-1.70581
8015.414.16021.23981
819.612.0208-2.42085
8218.215.16473.03529
8313.613.25880.34117
8414.8514.33370.516293
8514.7517.1162-2.36624
8614.114.2277-0.127673
8714.912.91511.98494
8816.2515.35880.891155
8919.2520.7521-1.50214
9013.612.58941.01061
9113.615.1337-1.53373
9215.6516.278-0.628003
9312.7513.0318-0.281769
9414.612.53672.06329
959.8510.4666-0.616646
9612.6511.6011.04899
9719.216.16163.0384
9816.614.08722.51282
9911.210.91870.281308
10015.2515.3041-0.054063
10111.914.1948-2.29478
10213.214.0764-0.876409
10316.3517.3265-0.97646
10412.412.8924-0.49244
10515.8514.04081.80925
10618.1517.04671.10326
10711.1511.8967-0.746688
10815.6516.5929-0.942928
10917.7516.19031.55972
1107.6512.2767-4.62666
11112.3513.2778-0.927829
11215.613.12472.47531
11319.317.56251.73747
11415.212.26442.93555
11517.115.16731.93269
11615.613.51662.08344
11718.414.90763.49238
11819.0516.3272.72302
11918.5515.84982.70025
12019.116.75432.34571
12113.113.2921-0.192087
12212.8515.8169-2.96693
1239.511.7556-2.25555
1244.510.268-5.76803
12511.8511.15320.696807
12613.615.4699-1.86987
12711.711.43660.263393
12812.413.0476-0.647585
12913.3514.7672-1.41723
13011.413.2318-1.83178
13114.913.94310.95688
13219.918.35931.54073
13311.213.2435-2.0435
13414.615.5578-0.957778
13517.617.7158-0.11585
13614.0513.44520.604777
13716.115.4770.62299
13813.3514.1906-0.840599
13911.8514.4488-2.59879
14011.9513.1119-1.16188
14114.7514.72240.0275759
14215.1514.12181.02821
14313.216.02-2.81998
14416.8516.41810.43187
1457.8511.6937-3.84368
1467.713.5261-5.8261
14712.614.1208-1.52081
1487.8513.9862-6.13615
14910.9511.3948-0.444828
15012.3513.7949-1.44485
1519.9513.7721-3.82211
15214.913.95140.948557
15316.6515.33941.31059
15413.412.73510.664928
15513.9513.9938-0.0437578
15615.714.35241.34764
15716.8515.03441.81558
15810.9511.6356-0.685641
15915.3514.78030.569715
16012.212.5502-0.350194
16115.114.16760.932429
16217.7516.70461.0454
16315.214.48860.711399
16414.614.6417-0.0416523
16516.6516.10680.543172
1668.19.94248-1.84248

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 9.78739 & -5.43739 \tabularnewline
2 & 12.7 & 10.7689 & 1.9311 \tabularnewline
3 & 18.1 & 15.7282 & 2.37177 \tabularnewline
4 & 17.85 & 16.1337 & 1.71625 \tabularnewline
5 & 16.6 & 16.8709 & -0.270858 \tabularnewline
6 & 12.6 & 11.7436 & 0.856365 \tabularnewline
7 & 17.1 & 20.6274 & -3.52736 \tabularnewline
8 & 19.1 & 17.284 & 1.81601 \tabularnewline
9 & 16.1 & 17.9856 & -1.88559 \tabularnewline
10 & 13.35 & 10.975 & 2.37503 \tabularnewline
11 & 18.4 & 17.1249 & 1.27513 \tabularnewline
12 & 14.7 & 9.52058 & 5.17942 \tabularnewline
13 & 10.6 & 13.4324 & -2.8324 \tabularnewline
14 & 12.6 & 13.547 & -0.946968 \tabularnewline
15 & 16.2 & 15.2485 & 0.951545 \tabularnewline
16 & 13.6 & 14.8206 & -1.22058 \tabularnewline
17 & 18.9 & 16.8799 & 2.02013 \tabularnewline
18 & 14.1 & 12.968 & 1.13198 \tabularnewline
19 & 14.5 & 13.3262 & 1.17382 \tabularnewline
20 & 16.15 & 18.2462 & -2.09616 \tabularnewline
21 & 14.75 & 13.3298 & 1.42022 \tabularnewline
22 & 14.8 & 13.6344 & 1.16563 \tabularnewline
23 & 12.45 & 11.5897 & 0.860327 \tabularnewline
24 & 12.65 & 12.8061 & -0.156076 \tabularnewline
25 & 17.35 & 14.2786 & 3.07139 \tabularnewline
26 & 8.6 & 9.74236 & -1.14236 \tabularnewline
27 & 18.4 & 17.3286 & 1.07142 \tabularnewline
28 & 16.1 & 15.841 & 0.258989 \tabularnewline
29 & 11.6 & 11.7921 & -0.192088 \tabularnewline
30 & 17.75 & 15.3075 & 2.44248 \tabularnewline
31 & 15.25 & 14.9268 & 0.323243 \tabularnewline
32 & 17.65 & 15.1042 & 2.54584 \tabularnewline
33 & 16.35 & 16.5316 & -0.181643 \tabularnewline
34 & 17.65 & 16.4562 & 1.19381 \tabularnewline
35 & 13.6 & 13.885 & -0.285006 \tabularnewline
36 & 14.35 & 14.0882 & 0.261799 \tabularnewline
37 & 14.75 & 16.2054 & -1.45545 \tabularnewline
38 & 18.25 & 17.3807 & 0.869339 \tabularnewline
39 & 9.9 & 16.8132 & -6.9132 \tabularnewline
40 & 16 & 14.2467 & 1.75331 \tabularnewline
41 & 18.25 & 16.5451 & 1.70485 \tabularnewline
42 & 16.85 & 18.178 & -1.32798 \tabularnewline
43 & 14.6 & 12.9003 & 1.69971 \tabularnewline
44 & 13.85 & 14.25 & -0.399961 \tabularnewline
45 & 18.95 & 17.6955 & 1.25452 \tabularnewline
46 & 15.6 & 14.6277 & 0.972276 \tabularnewline
47 & 14.85 & 17.6397 & -2.78971 \tabularnewline
48 & 11.75 & 13.6512 & -1.90116 \tabularnewline
49 & 18.45 & 16.15 & 2.30004 \tabularnewline
50 & 15.9 & 15.2147 & 0.68531 \tabularnewline
51 & 17.1 & 17.1887 & -0.0887131 \tabularnewline
52 & 16.1 & 8.10448 & 7.99552 \tabularnewline
53 & 19.9 & 18.5338 & 1.36615 \tabularnewline
54 & 10.95 & 11.106 & -0.15604 \tabularnewline
55 & 18.45 & 16.9672 & 1.48281 \tabularnewline
56 & 15.1 & 13.2516 & 1.84839 \tabularnewline
57 & 15 & 15.6965 & -0.696494 \tabularnewline
58 & 11.35 & 14.6027 & -3.25269 \tabularnewline
59 & 15.95 & 15.4789 & 0.471095 \tabularnewline
60 & 18.1 & 15.6301 & 2.46995 \tabularnewline
61 & 14.6 & 16.1187 & -1.5187 \tabularnewline
62 & 15.4 & 17.0111 & -1.61109 \tabularnewline
63 & 15.4 & 16.9775 & -1.57751 \tabularnewline
64 & 17.6 & 14.7946 & 2.80536 \tabularnewline
65 & 13.35 & 14.2628 & -0.912767 \tabularnewline
66 & 19.1 & 16.869 & 2.23099 \tabularnewline
67 & 15.35 & 16.4349 & -1.08493 \tabularnewline
68 & 7.6 & 10.3196 & -2.71965 \tabularnewline
69 & 13.4 & 14.8947 & -1.49465 \tabularnewline
70 & 13.9 & 15.5454 & -1.64535 \tabularnewline
71 & 19.1 & 16.9325 & 2.16751 \tabularnewline
72 & 15.25 & 15.2445 & 0.00549675 \tabularnewline
73 & 12.9 & 15.7988 & -2.8988 \tabularnewline
74 & 16.1 & 15.9151 & 0.184936 \tabularnewline
75 & 17.35 & 14.9625 & 2.38748 \tabularnewline
76 & 13.15 & 15.1783 & -2.02831 \tabularnewline
77 & 12.15 & 14.2627 & -2.11272 \tabularnewline
78 & 12.6 & 12.2223 & 0.377661 \tabularnewline
79 & 10.35 & 12.0558 & -1.70581 \tabularnewline
80 & 15.4 & 14.1602 & 1.23981 \tabularnewline
81 & 9.6 & 12.0208 & -2.42085 \tabularnewline
82 & 18.2 & 15.1647 & 3.03529 \tabularnewline
83 & 13.6 & 13.2588 & 0.34117 \tabularnewline
84 & 14.85 & 14.3337 & 0.516293 \tabularnewline
85 & 14.75 & 17.1162 & -2.36624 \tabularnewline
86 & 14.1 & 14.2277 & -0.127673 \tabularnewline
87 & 14.9 & 12.9151 & 1.98494 \tabularnewline
88 & 16.25 & 15.3588 & 0.891155 \tabularnewline
89 & 19.25 & 20.7521 & -1.50214 \tabularnewline
90 & 13.6 & 12.5894 & 1.01061 \tabularnewline
91 & 13.6 & 15.1337 & -1.53373 \tabularnewline
92 & 15.65 & 16.278 & -0.628003 \tabularnewline
93 & 12.75 & 13.0318 & -0.281769 \tabularnewline
94 & 14.6 & 12.5367 & 2.06329 \tabularnewline
95 & 9.85 & 10.4666 & -0.616646 \tabularnewline
96 & 12.65 & 11.601 & 1.04899 \tabularnewline
97 & 19.2 & 16.1616 & 3.0384 \tabularnewline
98 & 16.6 & 14.0872 & 2.51282 \tabularnewline
99 & 11.2 & 10.9187 & 0.281308 \tabularnewline
100 & 15.25 & 15.3041 & -0.054063 \tabularnewline
101 & 11.9 & 14.1948 & -2.29478 \tabularnewline
102 & 13.2 & 14.0764 & -0.876409 \tabularnewline
103 & 16.35 & 17.3265 & -0.97646 \tabularnewline
104 & 12.4 & 12.8924 & -0.49244 \tabularnewline
105 & 15.85 & 14.0408 & 1.80925 \tabularnewline
106 & 18.15 & 17.0467 & 1.10326 \tabularnewline
107 & 11.15 & 11.8967 & -0.746688 \tabularnewline
108 & 15.65 & 16.5929 & -0.942928 \tabularnewline
109 & 17.75 & 16.1903 & 1.55972 \tabularnewline
110 & 7.65 & 12.2767 & -4.62666 \tabularnewline
111 & 12.35 & 13.2778 & -0.927829 \tabularnewline
112 & 15.6 & 13.1247 & 2.47531 \tabularnewline
113 & 19.3 & 17.5625 & 1.73747 \tabularnewline
114 & 15.2 & 12.2644 & 2.93555 \tabularnewline
115 & 17.1 & 15.1673 & 1.93269 \tabularnewline
116 & 15.6 & 13.5166 & 2.08344 \tabularnewline
117 & 18.4 & 14.9076 & 3.49238 \tabularnewline
118 & 19.05 & 16.327 & 2.72302 \tabularnewline
119 & 18.55 & 15.8498 & 2.70025 \tabularnewline
120 & 19.1 & 16.7543 & 2.34571 \tabularnewline
121 & 13.1 & 13.2921 & -0.192087 \tabularnewline
122 & 12.85 & 15.8169 & -2.96693 \tabularnewline
123 & 9.5 & 11.7556 & -2.25555 \tabularnewline
124 & 4.5 & 10.268 & -5.76803 \tabularnewline
125 & 11.85 & 11.1532 & 0.696807 \tabularnewline
126 & 13.6 & 15.4699 & -1.86987 \tabularnewline
127 & 11.7 & 11.4366 & 0.263393 \tabularnewline
128 & 12.4 & 13.0476 & -0.647585 \tabularnewline
129 & 13.35 & 14.7672 & -1.41723 \tabularnewline
130 & 11.4 & 13.2318 & -1.83178 \tabularnewline
131 & 14.9 & 13.9431 & 0.95688 \tabularnewline
132 & 19.9 & 18.3593 & 1.54073 \tabularnewline
133 & 11.2 & 13.2435 & -2.0435 \tabularnewline
134 & 14.6 & 15.5578 & -0.957778 \tabularnewline
135 & 17.6 & 17.7158 & -0.11585 \tabularnewline
136 & 14.05 & 13.4452 & 0.604777 \tabularnewline
137 & 16.1 & 15.477 & 0.62299 \tabularnewline
138 & 13.35 & 14.1906 & -0.840599 \tabularnewline
139 & 11.85 & 14.4488 & -2.59879 \tabularnewline
140 & 11.95 & 13.1119 & -1.16188 \tabularnewline
141 & 14.75 & 14.7224 & 0.0275759 \tabularnewline
142 & 15.15 & 14.1218 & 1.02821 \tabularnewline
143 & 13.2 & 16.02 & -2.81998 \tabularnewline
144 & 16.85 & 16.4181 & 0.43187 \tabularnewline
145 & 7.85 & 11.6937 & -3.84368 \tabularnewline
146 & 7.7 & 13.5261 & -5.8261 \tabularnewline
147 & 12.6 & 14.1208 & -1.52081 \tabularnewline
148 & 7.85 & 13.9862 & -6.13615 \tabularnewline
149 & 10.95 & 11.3948 & -0.444828 \tabularnewline
150 & 12.35 & 13.7949 & -1.44485 \tabularnewline
151 & 9.95 & 13.7721 & -3.82211 \tabularnewline
152 & 14.9 & 13.9514 & 0.948557 \tabularnewline
153 & 16.65 & 15.3394 & 1.31059 \tabularnewline
154 & 13.4 & 12.7351 & 0.664928 \tabularnewline
155 & 13.95 & 13.9938 & -0.0437578 \tabularnewline
156 & 15.7 & 14.3524 & 1.34764 \tabularnewline
157 & 16.85 & 15.0344 & 1.81558 \tabularnewline
158 & 10.95 & 11.6356 & -0.685641 \tabularnewline
159 & 15.35 & 14.7803 & 0.569715 \tabularnewline
160 & 12.2 & 12.5502 & -0.350194 \tabularnewline
161 & 15.1 & 14.1676 & 0.932429 \tabularnewline
162 & 17.75 & 16.7046 & 1.0454 \tabularnewline
163 & 15.2 & 14.4886 & 0.711399 \tabularnewline
164 & 14.6 & 14.6417 & -0.0416523 \tabularnewline
165 & 16.65 & 16.1068 & 0.543172 \tabularnewline
166 & 8.1 & 9.94248 & -1.84248 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&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]9.78739[/C][C]-5.43739[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.7689[/C][C]1.9311[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.7282[/C][C]2.37177[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.1337[/C][C]1.71625[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.8709[/C][C]-0.270858[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.7436[/C][C]0.856365[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]20.6274[/C][C]-3.52736[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.284[/C][C]1.81601[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]17.9856[/C][C]-1.88559[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.975[/C][C]2.37503[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.1249[/C][C]1.27513[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.52058[/C][C]5.17942[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.4324[/C][C]-2.8324[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.547[/C][C]-0.946968[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.2485[/C][C]0.951545[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]14.8206[/C][C]-1.22058[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.8799[/C][C]2.02013[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.968[/C][C]1.13198[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.3262[/C][C]1.17382[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]18.2462[/C][C]-2.09616[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.3298[/C][C]1.42022[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.6344[/C][C]1.16563[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.5897[/C][C]0.860327[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.8061[/C][C]-0.156076[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.2786[/C][C]3.07139[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.74236[/C][C]-1.14236[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.3286[/C][C]1.07142[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.841[/C][C]0.258989[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.7921[/C][C]-0.192088[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.3075[/C][C]2.44248[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.9268[/C][C]0.323243[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.1042[/C][C]2.54584[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.5316[/C][C]-0.181643[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.4562[/C][C]1.19381[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]13.885[/C][C]-0.285006[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.0882[/C][C]0.261799[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]16.2054[/C][C]-1.45545[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.3807[/C][C]0.869339[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]16.8132[/C][C]-6.9132[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2467[/C][C]1.75331[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.5451[/C][C]1.70485[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.178[/C][C]-1.32798[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]12.9003[/C][C]1.69971[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.25[/C][C]-0.399961[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.6955[/C][C]1.25452[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.6277[/C][C]0.972276[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]17.6397[/C][C]-2.78971[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]13.6512[/C][C]-1.90116[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.15[/C][C]2.30004[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]15.2147[/C][C]0.68531[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]17.1887[/C][C]-0.0887131[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]8.10448[/C][C]7.99552[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.5338[/C][C]1.36615[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]11.106[/C][C]-0.15604[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.9672[/C][C]1.48281[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]13.2516[/C][C]1.84839[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.6965[/C][C]-0.696494[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]14.6027[/C][C]-3.25269[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]15.4789[/C][C]0.471095[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6301[/C][C]2.46995[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.1187[/C][C]-1.5187[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]17.0111[/C][C]-1.61109[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.9775[/C][C]-1.57751[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.7946[/C][C]2.80536[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.2628[/C][C]-0.912767[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.869[/C][C]2.23099[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]16.4349[/C][C]-1.08493[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.3196[/C][C]-2.71965[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.8947[/C][C]-1.49465[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]15.5454[/C][C]-1.64535[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.9325[/C][C]2.16751[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.2445[/C][C]0.00549675[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]15.7988[/C][C]-2.8988[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.9151[/C][C]0.184936[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]14.9625[/C][C]2.38748[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.1783[/C][C]-2.02831[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]14.2627[/C][C]-2.11272[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]12.2223[/C][C]0.377661[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.0558[/C][C]-1.70581[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]14.1602[/C][C]1.23981[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.0208[/C][C]-2.42085[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.1647[/C][C]3.03529[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.2588[/C][C]0.34117[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]14.3337[/C][C]0.516293[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]17.1162[/C][C]-2.36624[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]14.2277[/C][C]-0.127673[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]12.9151[/C][C]1.98494[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.3588[/C][C]0.891155[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]20.7521[/C][C]-1.50214[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]12.5894[/C][C]1.01061[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]15.1337[/C][C]-1.53373[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.278[/C][C]-0.628003[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.0318[/C][C]-0.281769[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]12.5367[/C][C]2.06329[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]10.4666[/C][C]-0.616646[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.601[/C][C]1.04899[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]16.1616[/C][C]3.0384[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]14.0872[/C][C]2.51282[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]10.9187[/C][C]0.281308[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]15.3041[/C][C]-0.054063[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]14.1948[/C][C]-2.29478[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]14.0764[/C][C]-0.876409[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]17.3265[/C][C]-0.97646[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]12.8924[/C][C]-0.49244[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0408[/C][C]1.80925[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.0467[/C][C]1.10326[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.8967[/C][C]-0.746688[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.5929[/C][C]-0.942928[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]16.1903[/C][C]1.55972[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]12.2767[/C][C]-4.62666[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]13.2778[/C][C]-0.927829[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]13.1247[/C][C]2.47531[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.5625[/C][C]1.73747[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]12.2644[/C][C]2.93555[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]15.1673[/C][C]1.93269[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.5166[/C][C]2.08344[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.9076[/C][C]3.49238[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.327[/C][C]2.72302[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]15.8498[/C][C]2.70025[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.7543[/C][C]2.34571[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.2921[/C][C]-0.192087[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]15.8169[/C][C]-2.96693[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]11.7556[/C][C]-2.25555[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.268[/C][C]-5.76803[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.1532[/C][C]0.696807[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]15.4699[/C][C]-1.86987[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.4366[/C][C]0.263393[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.0476[/C][C]-0.647585[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.7672[/C][C]-1.41723[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]13.2318[/C][C]-1.83178[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]13.9431[/C][C]0.95688[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]18.3593[/C][C]1.54073[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.2435[/C][C]-2.0435[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]15.5578[/C][C]-0.957778[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.7158[/C][C]-0.11585[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.4452[/C][C]0.604777[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.477[/C][C]0.62299[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.1906[/C][C]-0.840599[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]14.4488[/C][C]-2.59879[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]13.1119[/C][C]-1.16188[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]14.7224[/C][C]0.0275759[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]14.1218[/C][C]1.02821[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]16.02[/C][C]-2.81998[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.4181[/C][C]0.43187[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]11.6937[/C][C]-3.84368[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]13.5261[/C][C]-5.8261[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.1208[/C][C]-1.52081[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]13.9862[/C][C]-6.13615[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.3948[/C][C]-0.444828[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]13.7949[/C][C]-1.44485[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.7721[/C][C]-3.82211[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]13.9514[/C][C]0.948557[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.3394[/C][C]1.31059[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.7351[/C][C]0.664928[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]13.9938[/C][C]-0.0437578[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]14.3524[/C][C]1.34764[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.0344[/C][C]1.81558[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]11.6356[/C][C]-0.685641[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]14.7803[/C][C]0.569715[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]12.5502[/C][C]-0.350194[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]14.1676[/C][C]0.932429[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]16.7046[/C][C]1.0454[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.4886[/C][C]0.711399[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]14.6417[/C][C]-0.0416523[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]16.1068[/C][C]0.543172[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.94248[/C][C]-1.84248[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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
14.359.78739-5.43739
212.710.76891.9311
318.115.72822.37177
417.8516.13371.71625
516.616.8709-0.270858
612.611.74360.856365
717.120.6274-3.52736
819.117.2841.81601
916.117.9856-1.88559
1013.3510.9752.37503
1118.417.12491.27513
1214.79.520585.17942
1310.613.4324-2.8324
1412.613.547-0.946968
1516.215.24850.951545
1613.614.8206-1.22058
1718.916.87992.02013
1814.112.9681.13198
1914.513.32621.17382
2016.1518.2462-2.09616
2114.7513.32981.42022
2214.813.63441.16563
2312.4511.58970.860327
2412.6512.8061-0.156076
2517.3514.27863.07139
268.69.74236-1.14236
2718.417.32861.07142
2816.115.8410.258989
2911.611.7921-0.192088
3017.7515.30752.44248
3115.2514.92680.323243
3217.6515.10422.54584
3316.3516.5316-0.181643
3417.6516.45621.19381
3513.613.885-0.285006
3614.3514.08820.261799
3714.7516.2054-1.45545
3818.2517.38070.869339
399.916.8132-6.9132
401614.24671.75331
4118.2516.54511.70485
4216.8518.178-1.32798
4314.612.90031.69971
4413.8514.25-0.399961
4518.9517.69551.25452
4615.614.62770.972276
4714.8517.6397-2.78971
4811.7513.6512-1.90116
4918.4516.152.30004
5015.915.21470.68531
5117.117.1887-0.0887131
5216.18.104487.99552
5319.918.53381.36615
5410.9511.106-0.15604
5518.4516.96721.48281
5615.113.25161.84839
571515.6965-0.696494
5811.3514.6027-3.25269
5915.9515.47890.471095
6018.115.63012.46995
6114.616.1187-1.5187
6215.417.0111-1.61109
6315.416.9775-1.57751
6417.614.79462.80536
6513.3514.2628-0.912767
6619.116.8692.23099
6715.3516.4349-1.08493
687.610.3196-2.71965
6913.414.8947-1.49465
7013.915.5454-1.64535
7119.116.93252.16751
7215.2515.24450.00549675
7312.915.7988-2.8988
7416.115.91510.184936
7517.3514.96252.38748
7613.1515.1783-2.02831
7712.1514.2627-2.11272
7812.612.22230.377661
7910.3512.0558-1.70581
8015.414.16021.23981
819.612.0208-2.42085
8218.215.16473.03529
8313.613.25880.34117
8414.8514.33370.516293
8514.7517.1162-2.36624
8614.114.2277-0.127673
8714.912.91511.98494
8816.2515.35880.891155
8919.2520.7521-1.50214
9013.612.58941.01061
9113.615.1337-1.53373
9215.6516.278-0.628003
9312.7513.0318-0.281769
9414.612.53672.06329
959.8510.4666-0.616646
9612.6511.6011.04899
9719.216.16163.0384
9816.614.08722.51282
9911.210.91870.281308
10015.2515.3041-0.054063
10111.914.1948-2.29478
10213.214.0764-0.876409
10316.3517.3265-0.97646
10412.412.8924-0.49244
10515.8514.04081.80925
10618.1517.04671.10326
10711.1511.8967-0.746688
10815.6516.5929-0.942928
10917.7516.19031.55972
1107.6512.2767-4.62666
11112.3513.2778-0.927829
11215.613.12472.47531
11319.317.56251.73747
11415.212.26442.93555
11517.115.16731.93269
11615.613.51662.08344
11718.414.90763.49238
11819.0516.3272.72302
11918.5515.84982.70025
12019.116.75432.34571
12113.113.2921-0.192087
12212.8515.8169-2.96693
1239.511.7556-2.25555
1244.510.268-5.76803
12511.8511.15320.696807
12613.615.4699-1.86987
12711.711.43660.263393
12812.413.0476-0.647585
12913.3514.7672-1.41723
13011.413.2318-1.83178
13114.913.94310.95688
13219.918.35931.54073
13311.213.2435-2.0435
13414.615.5578-0.957778
13517.617.7158-0.11585
13614.0513.44520.604777
13716.115.4770.62299
13813.3514.1906-0.840599
13911.8514.4488-2.59879
14011.9513.1119-1.16188
14114.7514.72240.0275759
14215.1514.12181.02821
14313.216.02-2.81998
14416.8516.41810.43187
1457.8511.6937-3.84368
1467.713.5261-5.8261
14712.614.1208-1.52081
1487.8513.9862-6.13615
14910.9511.3948-0.444828
15012.3513.7949-1.44485
1519.9513.7721-3.82211
15214.913.95140.948557
15316.6515.33941.31059
15413.412.73510.664928
15513.9513.9938-0.0437578
15615.714.35241.34764
15716.8515.03441.81558
15810.9511.6356-0.685641
15915.3514.78030.569715
16012.212.5502-0.350194
16115.114.16760.932429
16217.7516.70461.0454
16315.214.48860.711399
16414.614.6417-0.0416523
16516.6516.10680.543172
1668.19.94248-1.84248






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9092960.1814080.090704
150.8481070.3037850.151893
160.7808790.4382420.219121
170.6836740.6326510.316326
180.5806570.8386860.419343
190.4771330.9542660.522867
200.5127950.974410.487205
210.4226990.8453970.577301
220.5134790.9730420.486521
230.481880.9637590.51812
240.4667580.9335170.533242
250.5242940.9514120.475706
260.5092990.9814020.490701
270.4332590.8665180.566741
280.6011190.7977620.398881
290.5792910.8414170.420709
300.5372950.9254110.462705
310.4676990.9353970.532301
320.4212450.842490.578755
330.3910260.7820520.608974
340.3712590.7425180.628741
350.3223120.6446250.677688
360.2669930.5339860.733007
370.224790.449580.77521
380.1823370.3646740.817663
390.6738550.652290.326145
400.7030590.5938830.296941
410.6694970.6610060.330503
420.6273590.7452820.372641
430.5942690.8114610.405731
440.5403950.919210.459605
450.4968720.9937440.503128
460.4485190.8970390.551481
470.4882030.9764060.511797
480.4599650.9199290.540035
490.4494060.8988120.550594
500.4448650.889730.555135
510.3936460.7872930.606354
520.9069440.1861110.0930557
530.8908090.2183820.109191
540.8805570.2388870.119443
550.8659860.2680290.134014
560.8546520.2906950.145348
570.829050.34190.17095
580.8637490.2725010.136251
590.8380070.3239850.161993
600.8667770.2664470.133223
610.871960.2560810.12804
620.8668660.2662690.133134
630.8588430.2823140.141157
640.8740910.2518170.125909
650.8571830.2856350.142817
660.8736480.2527040.126352
670.8542430.2915140.145757
680.8819730.2360540.118027
690.8711940.2576110.128806
700.8648480.2703040.135152
710.862890.2742210.13711
720.8364950.3270110.163505
730.8647810.2704390.135219
740.8371940.3256130.162806
750.8370820.3258370.162918
760.8426670.3146670.157333
770.8517820.2964350.148218
780.8248470.3503070.175153
790.8166960.3666070.183304
800.7990760.4018480.200924
810.807720.384560.19228
820.8415390.3169220.158461
830.812750.37450.18725
840.7830380.4339240.216962
850.7950410.4099180.204959
860.7636060.4727880.236394
870.7568970.4862060.243103
880.7223690.5552610.277631
890.7385090.5229830.261491
900.704120.591760.29588
910.6891150.6217690.310885
920.6578480.6843050.342152
930.6330220.7339560.366978
940.6669490.6661020.333051
950.6625770.6748450.337423
960.6277720.7444570.372228
970.6674380.6651240.332562
980.6877020.6245960.312298
990.6796010.6407980.320399
1000.634850.73030.36515
1010.6342080.7315850.365792
1020.5970790.8058420.402921
1030.5608020.8783950.439198
1040.515030.9699390.48497
1050.5101280.9797430.489872
1060.4672670.9345340.532733
1070.4331860.8663710.566814
1080.4021740.8043490.597826
1090.3780760.7561530.621924
1100.5174020.9651950.482598
1110.4734720.9469450.526528
1120.5830630.8338740.416937
1130.5574280.8851430.442572
1140.6315560.7368870.368444
1150.6342910.7314180.365709
1160.66290.67420.3371
1170.8435730.3128550.156427
1180.8777360.2445270.122264
1190.8762090.2475820.123791
1200.8854530.2290950.114547
1210.8693050.261390.130695
1220.8597880.2804250.140212
1230.8398660.3202690.160134
1240.9279120.1441770.0720884
1250.9186880.1626230.0813117
1260.8986260.2027490.101374
1270.8774220.2451570.122578
1280.844250.3114990.15575
1290.8190480.3619040.180952
1300.7809730.4380540.219027
1310.7600290.4799420.239971
1320.7097380.5805230.290262
1330.6642630.6714740.335737
1340.6028510.7942990.397149
1350.5494670.9010660.450533
1360.7666180.4667640.233382
1370.7091570.5816860.290843
1380.6559330.6881330.344067
1390.6369610.7260780.363039
1400.6354860.7290270.364514
1410.5993320.8013350.400668
1420.7040520.5918960.295948
1430.7662410.4675180.233759
1440.8094370.3811260.190563
1450.7791740.4416520.220826
1460.9306290.1387430.0693714
1470.9571070.08578670.0428934
1480.9906210.0187570.00937852
1490.9845140.03097160.0154858
1500.9630820.07383520.0369176
1510.9932030.01359410.00679705
1520.9703860.05922710.0296135

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.909296 & 0.181408 & 0.090704 \tabularnewline
15 & 0.848107 & 0.303785 & 0.151893 \tabularnewline
16 & 0.780879 & 0.438242 & 0.219121 \tabularnewline
17 & 0.683674 & 0.632651 & 0.316326 \tabularnewline
18 & 0.580657 & 0.838686 & 0.419343 \tabularnewline
19 & 0.477133 & 0.954266 & 0.522867 \tabularnewline
20 & 0.512795 & 0.97441 & 0.487205 \tabularnewline
21 & 0.422699 & 0.845397 & 0.577301 \tabularnewline
22 & 0.513479 & 0.973042 & 0.486521 \tabularnewline
23 & 0.48188 & 0.963759 & 0.51812 \tabularnewline
24 & 0.466758 & 0.933517 & 0.533242 \tabularnewline
25 & 0.524294 & 0.951412 & 0.475706 \tabularnewline
26 & 0.509299 & 0.981402 & 0.490701 \tabularnewline
27 & 0.433259 & 0.866518 & 0.566741 \tabularnewline
28 & 0.601119 & 0.797762 & 0.398881 \tabularnewline
29 & 0.579291 & 0.841417 & 0.420709 \tabularnewline
30 & 0.537295 & 0.925411 & 0.462705 \tabularnewline
31 & 0.467699 & 0.935397 & 0.532301 \tabularnewline
32 & 0.421245 & 0.84249 & 0.578755 \tabularnewline
33 & 0.391026 & 0.782052 & 0.608974 \tabularnewline
34 & 0.371259 & 0.742518 & 0.628741 \tabularnewline
35 & 0.322312 & 0.644625 & 0.677688 \tabularnewline
36 & 0.266993 & 0.533986 & 0.733007 \tabularnewline
37 & 0.22479 & 0.44958 & 0.77521 \tabularnewline
38 & 0.182337 & 0.364674 & 0.817663 \tabularnewline
39 & 0.673855 & 0.65229 & 0.326145 \tabularnewline
40 & 0.703059 & 0.593883 & 0.296941 \tabularnewline
41 & 0.669497 & 0.661006 & 0.330503 \tabularnewline
42 & 0.627359 & 0.745282 & 0.372641 \tabularnewline
43 & 0.594269 & 0.811461 & 0.405731 \tabularnewline
44 & 0.540395 & 0.91921 & 0.459605 \tabularnewline
45 & 0.496872 & 0.993744 & 0.503128 \tabularnewline
46 & 0.448519 & 0.897039 & 0.551481 \tabularnewline
47 & 0.488203 & 0.976406 & 0.511797 \tabularnewline
48 & 0.459965 & 0.919929 & 0.540035 \tabularnewline
49 & 0.449406 & 0.898812 & 0.550594 \tabularnewline
50 & 0.444865 & 0.88973 & 0.555135 \tabularnewline
51 & 0.393646 & 0.787293 & 0.606354 \tabularnewline
52 & 0.906944 & 0.186111 & 0.0930557 \tabularnewline
53 & 0.890809 & 0.218382 & 0.109191 \tabularnewline
54 & 0.880557 & 0.238887 & 0.119443 \tabularnewline
55 & 0.865986 & 0.268029 & 0.134014 \tabularnewline
56 & 0.854652 & 0.290695 & 0.145348 \tabularnewline
57 & 0.82905 & 0.3419 & 0.17095 \tabularnewline
58 & 0.863749 & 0.272501 & 0.136251 \tabularnewline
59 & 0.838007 & 0.323985 & 0.161993 \tabularnewline
60 & 0.866777 & 0.266447 & 0.133223 \tabularnewline
61 & 0.87196 & 0.256081 & 0.12804 \tabularnewline
62 & 0.866866 & 0.266269 & 0.133134 \tabularnewline
63 & 0.858843 & 0.282314 & 0.141157 \tabularnewline
64 & 0.874091 & 0.251817 & 0.125909 \tabularnewline
65 & 0.857183 & 0.285635 & 0.142817 \tabularnewline
66 & 0.873648 & 0.252704 & 0.126352 \tabularnewline
67 & 0.854243 & 0.291514 & 0.145757 \tabularnewline
68 & 0.881973 & 0.236054 & 0.118027 \tabularnewline
69 & 0.871194 & 0.257611 & 0.128806 \tabularnewline
70 & 0.864848 & 0.270304 & 0.135152 \tabularnewline
71 & 0.86289 & 0.274221 & 0.13711 \tabularnewline
72 & 0.836495 & 0.327011 & 0.163505 \tabularnewline
73 & 0.864781 & 0.270439 & 0.135219 \tabularnewline
74 & 0.837194 & 0.325613 & 0.162806 \tabularnewline
75 & 0.837082 & 0.325837 & 0.162918 \tabularnewline
76 & 0.842667 & 0.314667 & 0.157333 \tabularnewline
77 & 0.851782 & 0.296435 & 0.148218 \tabularnewline
78 & 0.824847 & 0.350307 & 0.175153 \tabularnewline
79 & 0.816696 & 0.366607 & 0.183304 \tabularnewline
80 & 0.799076 & 0.401848 & 0.200924 \tabularnewline
81 & 0.80772 & 0.38456 & 0.19228 \tabularnewline
82 & 0.841539 & 0.316922 & 0.158461 \tabularnewline
83 & 0.81275 & 0.3745 & 0.18725 \tabularnewline
84 & 0.783038 & 0.433924 & 0.216962 \tabularnewline
85 & 0.795041 & 0.409918 & 0.204959 \tabularnewline
86 & 0.763606 & 0.472788 & 0.236394 \tabularnewline
87 & 0.756897 & 0.486206 & 0.243103 \tabularnewline
88 & 0.722369 & 0.555261 & 0.277631 \tabularnewline
89 & 0.738509 & 0.522983 & 0.261491 \tabularnewline
90 & 0.70412 & 0.59176 & 0.29588 \tabularnewline
91 & 0.689115 & 0.621769 & 0.310885 \tabularnewline
92 & 0.657848 & 0.684305 & 0.342152 \tabularnewline
93 & 0.633022 & 0.733956 & 0.366978 \tabularnewline
94 & 0.666949 & 0.666102 & 0.333051 \tabularnewline
95 & 0.662577 & 0.674845 & 0.337423 \tabularnewline
96 & 0.627772 & 0.744457 & 0.372228 \tabularnewline
97 & 0.667438 & 0.665124 & 0.332562 \tabularnewline
98 & 0.687702 & 0.624596 & 0.312298 \tabularnewline
99 & 0.679601 & 0.640798 & 0.320399 \tabularnewline
100 & 0.63485 & 0.7303 & 0.36515 \tabularnewline
101 & 0.634208 & 0.731585 & 0.365792 \tabularnewline
102 & 0.597079 & 0.805842 & 0.402921 \tabularnewline
103 & 0.560802 & 0.878395 & 0.439198 \tabularnewline
104 & 0.51503 & 0.969939 & 0.48497 \tabularnewline
105 & 0.510128 & 0.979743 & 0.489872 \tabularnewline
106 & 0.467267 & 0.934534 & 0.532733 \tabularnewline
107 & 0.433186 & 0.866371 & 0.566814 \tabularnewline
108 & 0.402174 & 0.804349 & 0.597826 \tabularnewline
109 & 0.378076 & 0.756153 & 0.621924 \tabularnewline
110 & 0.517402 & 0.965195 & 0.482598 \tabularnewline
111 & 0.473472 & 0.946945 & 0.526528 \tabularnewline
112 & 0.583063 & 0.833874 & 0.416937 \tabularnewline
113 & 0.557428 & 0.885143 & 0.442572 \tabularnewline
114 & 0.631556 & 0.736887 & 0.368444 \tabularnewline
115 & 0.634291 & 0.731418 & 0.365709 \tabularnewline
116 & 0.6629 & 0.6742 & 0.3371 \tabularnewline
117 & 0.843573 & 0.312855 & 0.156427 \tabularnewline
118 & 0.877736 & 0.244527 & 0.122264 \tabularnewline
119 & 0.876209 & 0.247582 & 0.123791 \tabularnewline
120 & 0.885453 & 0.229095 & 0.114547 \tabularnewline
121 & 0.869305 & 0.26139 & 0.130695 \tabularnewline
122 & 0.859788 & 0.280425 & 0.140212 \tabularnewline
123 & 0.839866 & 0.320269 & 0.160134 \tabularnewline
124 & 0.927912 & 0.144177 & 0.0720884 \tabularnewline
125 & 0.918688 & 0.162623 & 0.0813117 \tabularnewline
126 & 0.898626 & 0.202749 & 0.101374 \tabularnewline
127 & 0.877422 & 0.245157 & 0.122578 \tabularnewline
128 & 0.84425 & 0.311499 & 0.15575 \tabularnewline
129 & 0.819048 & 0.361904 & 0.180952 \tabularnewline
130 & 0.780973 & 0.438054 & 0.219027 \tabularnewline
131 & 0.760029 & 0.479942 & 0.239971 \tabularnewline
132 & 0.709738 & 0.580523 & 0.290262 \tabularnewline
133 & 0.664263 & 0.671474 & 0.335737 \tabularnewline
134 & 0.602851 & 0.794299 & 0.397149 \tabularnewline
135 & 0.549467 & 0.901066 & 0.450533 \tabularnewline
136 & 0.766618 & 0.466764 & 0.233382 \tabularnewline
137 & 0.709157 & 0.581686 & 0.290843 \tabularnewline
138 & 0.655933 & 0.688133 & 0.344067 \tabularnewline
139 & 0.636961 & 0.726078 & 0.363039 \tabularnewline
140 & 0.635486 & 0.729027 & 0.364514 \tabularnewline
141 & 0.599332 & 0.801335 & 0.400668 \tabularnewline
142 & 0.704052 & 0.591896 & 0.295948 \tabularnewline
143 & 0.766241 & 0.467518 & 0.233759 \tabularnewline
144 & 0.809437 & 0.381126 & 0.190563 \tabularnewline
145 & 0.779174 & 0.441652 & 0.220826 \tabularnewline
146 & 0.930629 & 0.138743 & 0.0693714 \tabularnewline
147 & 0.957107 & 0.0857867 & 0.0428934 \tabularnewline
148 & 0.990621 & 0.018757 & 0.00937852 \tabularnewline
149 & 0.984514 & 0.0309716 & 0.0154858 \tabularnewline
150 & 0.963082 & 0.0738352 & 0.0369176 \tabularnewline
151 & 0.993203 & 0.0135941 & 0.00679705 \tabularnewline
152 & 0.970386 & 0.0592271 & 0.0296135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&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]14[/C][C]0.909296[/C][C]0.181408[/C][C]0.090704[/C][/ROW]
[ROW][C]15[/C][C]0.848107[/C][C]0.303785[/C][C]0.151893[/C][/ROW]
[ROW][C]16[/C][C]0.780879[/C][C]0.438242[/C][C]0.219121[/C][/ROW]
[ROW][C]17[/C][C]0.683674[/C][C]0.632651[/C][C]0.316326[/C][/ROW]
[ROW][C]18[/C][C]0.580657[/C][C]0.838686[/C][C]0.419343[/C][/ROW]
[ROW][C]19[/C][C]0.477133[/C][C]0.954266[/C][C]0.522867[/C][/ROW]
[ROW][C]20[/C][C]0.512795[/C][C]0.97441[/C][C]0.487205[/C][/ROW]
[ROW][C]21[/C][C]0.422699[/C][C]0.845397[/C][C]0.577301[/C][/ROW]
[ROW][C]22[/C][C]0.513479[/C][C]0.973042[/C][C]0.486521[/C][/ROW]
[ROW][C]23[/C][C]0.48188[/C][C]0.963759[/C][C]0.51812[/C][/ROW]
[ROW][C]24[/C][C]0.466758[/C][C]0.933517[/C][C]0.533242[/C][/ROW]
[ROW][C]25[/C][C]0.524294[/C][C]0.951412[/C][C]0.475706[/C][/ROW]
[ROW][C]26[/C][C]0.509299[/C][C]0.981402[/C][C]0.490701[/C][/ROW]
[ROW][C]27[/C][C]0.433259[/C][C]0.866518[/C][C]0.566741[/C][/ROW]
[ROW][C]28[/C][C]0.601119[/C][C]0.797762[/C][C]0.398881[/C][/ROW]
[ROW][C]29[/C][C]0.579291[/C][C]0.841417[/C][C]0.420709[/C][/ROW]
[ROW][C]30[/C][C]0.537295[/C][C]0.925411[/C][C]0.462705[/C][/ROW]
[ROW][C]31[/C][C]0.467699[/C][C]0.935397[/C][C]0.532301[/C][/ROW]
[ROW][C]32[/C][C]0.421245[/C][C]0.84249[/C][C]0.578755[/C][/ROW]
[ROW][C]33[/C][C]0.391026[/C][C]0.782052[/C][C]0.608974[/C][/ROW]
[ROW][C]34[/C][C]0.371259[/C][C]0.742518[/C][C]0.628741[/C][/ROW]
[ROW][C]35[/C][C]0.322312[/C][C]0.644625[/C][C]0.677688[/C][/ROW]
[ROW][C]36[/C][C]0.266993[/C][C]0.533986[/C][C]0.733007[/C][/ROW]
[ROW][C]37[/C][C]0.22479[/C][C]0.44958[/C][C]0.77521[/C][/ROW]
[ROW][C]38[/C][C]0.182337[/C][C]0.364674[/C][C]0.817663[/C][/ROW]
[ROW][C]39[/C][C]0.673855[/C][C]0.65229[/C][C]0.326145[/C][/ROW]
[ROW][C]40[/C][C]0.703059[/C][C]0.593883[/C][C]0.296941[/C][/ROW]
[ROW][C]41[/C][C]0.669497[/C][C]0.661006[/C][C]0.330503[/C][/ROW]
[ROW][C]42[/C][C]0.627359[/C][C]0.745282[/C][C]0.372641[/C][/ROW]
[ROW][C]43[/C][C]0.594269[/C][C]0.811461[/C][C]0.405731[/C][/ROW]
[ROW][C]44[/C][C]0.540395[/C][C]0.91921[/C][C]0.459605[/C][/ROW]
[ROW][C]45[/C][C]0.496872[/C][C]0.993744[/C][C]0.503128[/C][/ROW]
[ROW][C]46[/C][C]0.448519[/C][C]0.897039[/C][C]0.551481[/C][/ROW]
[ROW][C]47[/C][C]0.488203[/C][C]0.976406[/C][C]0.511797[/C][/ROW]
[ROW][C]48[/C][C]0.459965[/C][C]0.919929[/C][C]0.540035[/C][/ROW]
[ROW][C]49[/C][C]0.449406[/C][C]0.898812[/C][C]0.550594[/C][/ROW]
[ROW][C]50[/C][C]0.444865[/C][C]0.88973[/C][C]0.555135[/C][/ROW]
[ROW][C]51[/C][C]0.393646[/C][C]0.787293[/C][C]0.606354[/C][/ROW]
[ROW][C]52[/C][C]0.906944[/C][C]0.186111[/C][C]0.0930557[/C][/ROW]
[ROW][C]53[/C][C]0.890809[/C][C]0.218382[/C][C]0.109191[/C][/ROW]
[ROW][C]54[/C][C]0.880557[/C][C]0.238887[/C][C]0.119443[/C][/ROW]
[ROW][C]55[/C][C]0.865986[/C][C]0.268029[/C][C]0.134014[/C][/ROW]
[ROW][C]56[/C][C]0.854652[/C][C]0.290695[/C][C]0.145348[/C][/ROW]
[ROW][C]57[/C][C]0.82905[/C][C]0.3419[/C][C]0.17095[/C][/ROW]
[ROW][C]58[/C][C]0.863749[/C][C]0.272501[/C][C]0.136251[/C][/ROW]
[ROW][C]59[/C][C]0.838007[/C][C]0.323985[/C][C]0.161993[/C][/ROW]
[ROW][C]60[/C][C]0.866777[/C][C]0.266447[/C][C]0.133223[/C][/ROW]
[ROW][C]61[/C][C]0.87196[/C][C]0.256081[/C][C]0.12804[/C][/ROW]
[ROW][C]62[/C][C]0.866866[/C][C]0.266269[/C][C]0.133134[/C][/ROW]
[ROW][C]63[/C][C]0.858843[/C][C]0.282314[/C][C]0.141157[/C][/ROW]
[ROW][C]64[/C][C]0.874091[/C][C]0.251817[/C][C]0.125909[/C][/ROW]
[ROW][C]65[/C][C]0.857183[/C][C]0.285635[/C][C]0.142817[/C][/ROW]
[ROW][C]66[/C][C]0.873648[/C][C]0.252704[/C][C]0.126352[/C][/ROW]
[ROW][C]67[/C][C]0.854243[/C][C]0.291514[/C][C]0.145757[/C][/ROW]
[ROW][C]68[/C][C]0.881973[/C][C]0.236054[/C][C]0.118027[/C][/ROW]
[ROW][C]69[/C][C]0.871194[/C][C]0.257611[/C][C]0.128806[/C][/ROW]
[ROW][C]70[/C][C]0.864848[/C][C]0.270304[/C][C]0.135152[/C][/ROW]
[ROW][C]71[/C][C]0.86289[/C][C]0.274221[/C][C]0.13711[/C][/ROW]
[ROW][C]72[/C][C]0.836495[/C][C]0.327011[/C][C]0.163505[/C][/ROW]
[ROW][C]73[/C][C]0.864781[/C][C]0.270439[/C][C]0.135219[/C][/ROW]
[ROW][C]74[/C][C]0.837194[/C][C]0.325613[/C][C]0.162806[/C][/ROW]
[ROW][C]75[/C][C]0.837082[/C][C]0.325837[/C][C]0.162918[/C][/ROW]
[ROW][C]76[/C][C]0.842667[/C][C]0.314667[/C][C]0.157333[/C][/ROW]
[ROW][C]77[/C][C]0.851782[/C][C]0.296435[/C][C]0.148218[/C][/ROW]
[ROW][C]78[/C][C]0.824847[/C][C]0.350307[/C][C]0.175153[/C][/ROW]
[ROW][C]79[/C][C]0.816696[/C][C]0.366607[/C][C]0.183304[/C][/ROW]
[ROW][C]80[/C][C]0.799076[/C][C]0.401848[/C][C]0.200924[/C][/ROW]
[ROW][C]81[/C][C]0.80772[/C][C]0.38456[/C][C]0.19228[/C][/ROW]
[ROW][C]82[/C][C]0.841539[/C][C]0.316922[/C][C]0.158461[/C][/ROW]
[ROW][C]83[/C][C]0.81275[/C][C]0.3745[/C][C]0.18725[/C][/ROW]
[ROW][C]84[/C][C]0.783038[/C][C]0.433924[/C][C]0.216962[/C][/ROW]
[ROW][C]85[/C][C]0.795041[/C][C]0.409918[/C][C]0.204959[/C][/ROW]
[ROW][C]86[/C][C]0.763606[/C][C]0.472788[/C][C]0.236394[/C][/ROW]
[ROW][C]87[/C][C]0.756897[/C][C]0.486206[/C][C]0.243103[/C][/ROW]
[ROW][C]88[/C][C]0.722369[/C][C]0.555261[/C][C]0.277631[/C][/ROW]
[ROW][C]89[/C][C]0.738509[/C][C]0.522983[/C][C]0.261491[/C][/ROW]
[ROW][C]90[/C][C]0.70412[/C][C]0.59176[/C][C]0.29588[/C][/ROW]
[ROW][C]91[/C][C]0.689115[/C][C]0.621769[/C][C]0.310885[/C][/ROW]
[ROW][C]92[/C][C]0.657848[/C][C]0.684305[/C][C]0.342152[/C][/ROW]
[ROW][C]93[/C][C]0.633022[/C][C]0.733956[/C][C]0.366978[/C][/ROW]
[ROW][C]94[/C][C]0.666949[/C][C]0.666102[/C][C]0.333051[/C][/ROW]
[ROW][C]95[/C][C]0.662577[/C][C]0.674845[/C][C]0.337423[/C][/ROW]
[ROW][C]96[/C][C]0.627772[/C][C]0.744457[/C][C]0.372228[/C][/ROW]
[ROW][C]97[/C][C]0.667438[/C][C]0.665124[/C][C]0.332562[/C][/ROW]
[ROW][C]98[/C][C]0.687702[/C][C]0.624596[/C][C]0.312298[/C][/ROW]
[ROW][C]99[/C][C]0.679601[/C][C]0.640798[/C][C]0.320399[/C][/ROW]
[ROW][C]100[/C][C]0.63485[/C][C]0.7303[/C][C]0.36515[/C][/ROW]
[ROW][C]101[/C][C]0.634208[/C][C]0.731585[/C][C]0.365792[/C][/ROW]
[ROW][C]102[/C][C]0.597079[/C][C]0.805842[/C][C]0.402921[/C][/ROW]
[ROW][C]103[/C][C]0.560802[/C][C]0.878395[/C][C]0.439198[/C][/ROW]
[ROW][C]104[/C][C]0.51503[/C][C]0.969939[/C][C]0.48497[/C][/ROW]
[ROW][C]105[/C][C]0.510128[/C][C]0.979743[/C][C]0.489872[/C][/ROW]
[ROW][C]106[/C][C]0.467267[/C][C]0.934534[/C][C]0.532733[/C][/ROW]
[ROW][C]107[/C][C]0.433186[/C][C]0.866371[/C][C]0.566814[/C][/ROW]
[ROW][C]108[/C][C]0.402174[/C][C]0.804349[/C][C]0.597826[/C][/ROW]
[ROW][C]109[/C][C]0.378076[/C][C]0.756153[/C][C]0.621924[/C][/ROW]
[ROW][C]110[/C][C]0.517402[/C][C]0.965195[/C][C]0.482598[/C][/ROW]
[ROW][C]111[/C][C]0.473472[/C][C]0.946945[/C][C]0.526528[/C][/ROW]
[ROW][C]112[/C][C]0.583063[/C][C]0.833874[/C][C]0.416937[/C][/ROW]
[ROW][C]113[/C][C]0.557428[/C][C]0.885143[/C][C]0.442572[/C][/ROW]
[ROW][C]114[/C][C]0.631556[/C][C]0.736887[/C][C]0.368444[/C][/ROW]
[ROW][C]115[/C][C]0.634291[/C][C]0.731418[/C][C]0.365709[/C][/ROW]
[ROW][C]116[/C][C]0.6629[/C][C]0.6742[/C][C]0.3371[/C][/ROW]
[ROW][C]117[/C][C]0.843573[/C][C]0.312855[/C][C]0.156427[/C][/ROW]
[ROW][C]118[/C][C]0.877736[/C][C]0.244527[/C][C]0.122264[/C][/ROW]
[ROW][C]119[/C][C]0.876209[/C][C]0.247582[/C][C]0.123791[/C][/ROW]
[ROW][C]120[/C][C]0.885453[/C][C]0.229095[/C][C]0.114547[/C][/ROW]
[ROW][C]121[/C][C]0.869305[/C][C]0.26139[/C][C]0.130695[/C][/ROW]
[ROW][C]122[/C][C]0.859788[/C][C]0.280425[/C][C]0.140212[/C][/ROW]
[ROW][C]123[/C][C]0.839866[/C][C]0.320269[/C][C]0.160134[/C][/ROW]
[ROW][C]124[/C][C]0.927912[/C][C]0.144177[/C][C]0.0720884[/C][/ROW]
[ROW][C]125[/C][C]0.918688[/C][C]0.162623[/C][C]0.0813117[/C][/ROW]
[ROW][C]126[/C][C]0.898626[/C][C]0.202749[/C][C]0.101374[/C][/ROW]
[ROW][C]127[/C][C]0.877422[/C][C]0.245157[/C][C]0.122578[/C][/ROW]
[ROW][C]128[/C][C]0.84425[/C][C]0.311499[/C][C]0.15575[/C][/ROW]
[ROW][C]129[/C][C]0.819048[/C][C]0.361904[/C][C]0.180952[/C][/ROW]
[ROW][C]130[/C][C]0.780973[/C][C]0.438054[/C][C]0.219027[/C][/ROW]
[ROW][C]131[/C][C]0.760029[/C][C]0.479942[/C][C]0.239971[/C][/ROW]
[ROW][C]132[/C][C]0.709738[/C][C]0.580523[/C][C]0.290262[/C][/ROW]
[ROW][C]133[/C][C]0.664263[/C][C]0.671474[/C][C]0.335737[/C][/ROW]
[ROW][C]134[/C][C]0.602851[/C][C]0.794299[/C][C]0.397149[/C][/ROW]
[ROW][C]135[/C][C]0.549467[/C][C]0.901066[/C][C]0.450533[/C][/ROW]
[ROW][C]136[/C][C]0.766618[/C][C]0.466764[/C][C]0.233382[/C][/ROW]
[ROW][C]137[/C][C]0.709157[/C][C]0.581686[/C][C]0.290843[/C][/ROW]
[ROW][C]138[/C][C]0.655933[/C][C]0.688133[/C][C]0.344067[/C][/ROW]
[ROW][C]139[/C][C]0.636961[/C][C]0.726078[/C][C]0.363039[/C][/ROW]
[ROW][C]140[/C][C]0.635486[/C][C]0.729027[/C][C]0.364514[/C][/ROW]
[ROW][C]141[/C][C]0.599332[/C][C]0.801335[/C][C]0.400668[/C][/ROW]
[ROW][C]142[/C][C]0.704052[/C][C]0.591896[/C][C]0.295948[/C][/ROW]
[ROW][C]143[/C][C]0.766241[/C][C]0.467518[/C][C]0.233759[/C][/ROW]
[ROW][C]144[/C][C]0.809437[/C][C]0.381126[/C][C]0.190563[/C][/ROW]
[ROW][C]145[/C][C]0.779174[/C][C]0.441652[/C][C]0.220826[/C][/ROW]
[ROW][C]146[/C][C]0.930629[/C][C]0.138743[/C][C]0.0693714[/C][/ROW]
[ROW][C]147[/C][C]0.957107[/C][C]0.0857867[/C][C]0.0428934[/C][/ROW]
[ROW][C]148[/C][C]0.990621[/C][C]0.018757[/C][C]0.00937852[/C][/ROW]
[ROW][C]149[/C][C]0.984514[/C][C]0.0309716[/C][C]0.0154858[/C][/ROW]
[ROW][C]150[/C][C]0.963082[/C][C]0.0738352[/C][C]0.0369176[/C][/ROW]
[ROW][C]151[/C][C]0.993203[/C][C]0.0135941[/C][C]0.00679705[/C][/ROW]
[ROW][C]152[/C][C]0.970386[/C][C]0.0592271[/C][C]0.0296135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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
140.9092960.1814080.090704
150.8481070.3037850.151893
160.7808790.4382420.219121
170.6836740.6326510.316326
180.5806570.8386860.419343
190.4771330.9542660.522867
200.5127950.974410.487205
210.4226990.8453970.577301
220.5134790.9730420.486521
230.481880.9637590.51812
240.4667580.9335170.533242
250.5242940.9514120.475706
260.5092990.9814020.490701
270.4332590.8665180.566741
280.6011190.7977620.398881
290.5792910.8414170.420709
300.5372950.9254110.462705
310.4676990.9353970.532301
320.4212450.842490.578755
330.3910260.7820520.608974
340.3712590.7425180.628741
350.3223120.6446250.677688
360.2669930.5339860.733007
370.224790.449580.77521
380.1823370.3646740.817663
390.6738550.652290.326145
400.7030590.5938830.296941
410.6694970.6610060.330503
420.6273590.7452820.372641
430.5942690.8114610.405731
440.5403950.919210.459605
450.4968720.9937440.503128
460.4485190.8970390.551481
470.4882030.9764060.511797
480.4599650.9199290.540035
490.4494060.8988120.550594
500.4448650.889730.555135
510.3936460.7872930.606354
520.9069440.1861110.0930557
530.8908090.2183820.109191
540.8805570.2388870.119443
550.8659860.2680290.134014
560.8546520.2906950.145348
570.829050.34190.17095
580.8637490.2725010.136251
590.8380070.3239850.161993
600.8667770.2664470.133223
610.871960.2560810.12804
620.8668660.2662690.133134
630.8588430.2823140.141157
640.8740910.2518170.125909
650.8571830.2856350.142817
660.8736480.2527040.126352
670.8542430.2915140.145757
680.8819730.2360540.118027
690.8711940.2576110.128806
700.8648480.2703040.135152
710.862890.2742210.13711
720.8364950.3270110.163505
730.8647810.2704390.135219
740.8371940.3256130.162806
750.8370820.3258370.162918
760.8426670.3146670.157333
770.8517820.2964350.148218
780.8248470.3503070.175153
790.8166960.3666070.183304
800.7990760.4018480.200924
810.807720.384560.19228
820.8415390.3169220.158461
830.812750.37450.18725
840.7830380.4339240.216962
850.7950410.4099180.204959
860.7636060.4727880.236394
870.7568970.4862060.243103
880.7223690.5552610.277631
890.7385090.5229830.261491
900.704120.591760.29588
910.6891150.6217690.310885
920.6578480.6843050.342152
930.6330220.7339560.366978
940.6669490.6661020.333051
950.6625770.6748450.337423
960.6277720.7444570.372228
970.6674380.6651240.332562
980.6877020.6245960.312298
990.6796010.6407980.320399
1000.634850.73030.36515
1010.6342080.7315850.365792
1020.5970790.8058420.402921
1030.5608020.8783950.439198
1040.515030.9699390.48497
1050.5101280.9797430.489872
1060.4672670.9345340.532733
1070.4331860.8663710.566814
1080.4021740.8043490.597826
1090.3780760.7561530.621924
1100.5174020.9651950.482598
1110.4734720.9469450.526528
1120.5830630.8338740.416937
1130.5574280.8851430.442572
1140.6315560.7368870.368444
1150.6342910.7314180.365709
1160.66290.67420.3371
1170.8435730.3128550.156427
1180.8777360.2445270.122264
1190.8762090.2475820.123791
1200.8854530.2290950.114547
1210.8693050.261390.130695
1220.8597880.2804250.140212
1230.8398660.3202690.160134
1240.9279120.1441770.0720884
1250.9186880.1626230.0813117
1260.8986260.2027490.101374
1270.8774220.2451570.122578
1280.844250.3114990.15575
1290.8190480.3619040.180952
1300.7809730.4380540.219027
1310.7600290.4799420.239971
1320.7097380.5805230.290262
1330.6642630.6714740.335737
1340.6028510.7942990.397149
1350.5494670.9010660.450533
1360.7666180.4667640.233382
1370.7091570.5816860.290843
1380.6559330.6881330.344067
1390.6369610.7260780.363039
1400.6354860.7290270.364514
1410.5993320.8013350.400668
1420.7040520.5918960.295948
1430.7662410.4675180.233759
1440.8094370.3811260.190563
1450.7791740.4416520.220826
1460.9306290.1387430.0693714
1470.9571070.08578670.0428934
1480.9906210.0187570.00937852
1490.9845140.03097160.0154858
1500.9630820.07383520.0369176
1510.9932030.01359410.00679705
1520.9703860.05922710.0296135






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0215827OK
10% type I error level60.0431655OK

\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 & 3 & 0.0215827 & OK \tabularnewline
10% type I error level & 6 & 0.0431655 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263296&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]3[/C][C]0.0215827[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0431655[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263296&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263296&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 level30.0215827OK
10% type I error level60.0431655OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '11'
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')
}