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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 computationMon, 15 Dec 2014 12:44:13 +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/15/t14186474766iggsqgcupu1ztt.htm/, Retrieved Thu, 16 May 2024 22:33:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268270, Retrieved Thu, 16 May 2024 22:33:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-15 12:44:13] [4bf1efda48b6e8e35beb7b429a900cbb] [Current]
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Dataset

Dataseries X:
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12,9	0	26	50	21	86	96	149	2,1	7,5
7,4	0	51	68	26	62	75	152	1,5	2,5
12,2	1	57	62	22	70	70	139	2,0	6,0
12,8	0	37	54	22	71	88	148	2,0	6,5
7,4	1	67	71	18	108	114	158	2,1	1,0
6,7	1	43	54	23	64	69	128	2,0	1,0
12,6	1	52	65	12	119	176	224	2,3	5,5
14,8	0	52	73	20	97	114	159	2,1	8,5
13,3	1	43	52	22	129	121	105	2,1	6,5
11,1	1	84	84	21	153	110	159	2,2	4,5
8,2	1	67	42	19	78	158	167	2,1	2,0
11,4	1	49	66	22	80	116	165	2,1	5,0
6,4	1	70	65	15	99	181	159	2,1	0,5
10,6	1	52	78	20	68	77	119	2,0	5,0
12,0	0	58	73	19	147	141	176	2,3	5,0
6,3	0	68	75	18	40	35	54	1,8	2,5
11,9	1	43	66	20	120	152	163	2,2	5,5
9,3	0	56	70	21	71	97	124	2,0	3,5
10,0	0	74	81	15	68	84	121	2,0	4,0
6,4	1	65	71	16	55	68	153	1,8	0,5
13,8	1	63	69	23	137	101	148	2,2	6,5
10,8	0	58	71	21	79	107	221	2,2	4,5
13,8	1	57	72	18	116	88	188	1,7	7,5
11,7	1	63	68	25	101	112	149	2,1	5,5
10,9	1	53	70	9	111	171	244	2,3	4,0
9,9	1	64	67	23	81	66	150	2,0	4,0
11,5	0	53	76	16	63	93	153	2,0	5,5
8,3	0	29	70	16	69	105	94	1,9	2,5
11,7	0	54	60	19	71	131	156	2,0	5,5
6,1	1	51	77	25	70	89	146	2,0	0,5
9,0	1	58	72	25	64	102	132	2,0	3,5
9,7	1	43	69	18	143	161	161	2,1	2,5
10,8	1	51	71	23	85	120	105	2,0	4,5
10,3	1	53	62	21	86	127	97	1,8	4,5
10,4	0	54	70	10	55	77	151	2,0	4,5
9,3	1	61	58	22	120	85	166	2,2	2,5
11,8	0	47	76	26	96	168	157	2,1	5,0
5,9	1	39	52	23	60	48	111	1,8	0,0
11,4	1	48	59	23	95	152	145	1,9	5,0
13,0	1	50	68	24	100	75	162	2,1	6,5
10,8	1	35	76	24	68	107	163	2,0	5,0
11,3	0	68	67	23	105	121	187	2,2	4,5
11,8	1	49	59	15	85	124	109	2,0	5,5
12,7	0	67	76	16	57	40	105	1,7	7,5
10,9	1	43	60	19	49	126	148	2,0	5,0
13,3	1	62	63	18	93	148	125	2,0	7,0
10,1	1	57	70	27	58	146	116	1,9	4,5
14,3	1	54	66	13	74	97	138	2,0	8,5
9,3	1	61	64	28	107	118	164	2,1	3,5
12,5	0	56	70	23	65	58	162	2,0	6,0
7,6	0	41	75	21	58	63	99	1,9	1,5
15,9	1	43	61	19	107	139	202	2,2	9,0
9,2	0	53	60	19	70	50	186	2,1	3,5
11,1	0	66	73	18	136	152	183	2,3	4,0
13,0	1	58	61	19	126	142	214	2,3	6,5
14,5	1	46	66	17	95	94	188	2,2	7,5
12,3	0	51	59	25	136	127	177	2,2	5,0
11,4	0	51	64	19	58	67	126	1,9	5,5
7,3	1	45	66	26	110	96	157	1,8	1,0
12,6	0	37	78	14	82	128	139	2,0	6,5
13,0	0	42	67	16	102	146	162	2,1	6,5
13,2	0	66	66	20	90	186	159	2,1	7,0
7,7	1	53	71	24	83	85	110	2,0	1,5
4,35	1	52	51	23	34	41	48	0,75	0,5
12,7	1	16	56	22	61	146	50	1,5	7,5
18,1	1	46	67	21	70	182	150	3	9
17,85	1	56	69	25	69	192	154	2,25	9,5
17,1	1	50	55	27	120	439	194	3	8
19,1	0	59	63	23	147	214	158	3	10
16,1	1	60	67	23	215	341	159	3	7
13,35	0	52	65	18	24	58	67	0,75	8,5
18,4	0	44	47	18	84	292	147	3	9
14,7	1	67	76	23	30	85	39	2,25	9,5
10,6	1	52	64	19	77	200	100	1,5	4
12,6	1	55	68	15	46	158	111	1,5	6
16,2	1	37	64	20	61	199	138	2,25	8
13,6	1	54	65	16	178	297	101	3	5,5
14,1	1	51	63	25	57	108	101	1,5	7,5
14,5	1	48	60	25	42	86	114	2,25	7
16,15	0	60	68	19	163	302	165	2,25	7,5
14,75	1	50	72	19	75	148	114	1,5	8
14,8	1	63	70	16	94	178	111	2,25	7
12,45	1	33	61	19	45	120	75	1,5	7
12,65	1	67	61	19	78	207	82	2,25	6
17,35	1	46	62	23	47	157	121	2,25	10
8,6	1	54	71	21	29	128	32	3	2,5
18,4	0	59	71	22	97	296	150	3	9
16,1	1	61	51	19	116	323	117	3	8
17,75	1	47	70	20	50	70	165	3	8,5
15,25	1	69	73	3	118	146	154	3	6
17,65	1	52	76	23	66	246	126	2,25	9
15,6	0	55	59	14	48	145	138	1,5	8
16,35	0	55	68	23	86	196	149	2,25	8
17,65	0	41	48	20	89	199	145	2,25	9
13,6	1	73	52	15	76	127	120	3	5,5
11,7	0	51	59	13	39	91	138	0,75	5
14,35	0	52	60	16	75	153	109	2,25	7
14,75	0	50	59	7	57	299	132	3	5,5
18,25	1	51	57	24	72	228	172	3	9
9,9	0	60	79	17	60	190	169	1,5	2
16	1	56	60	24	109	180	114	2,25	8,5
18,25	1	56	60	24	76	212	156	3	9
16,85	0	29	59	19	65	269	172	2,25	8,5
18,95	1	73	61	28	123	243	167	2,25	10
15,6	0	55	71	23	71	190	113	1,5	9
17,1	0	43	58	19	93	157	173	3	8
16,1	1	61	59	23	19	96	2	3	10
15,4	1	56	58	25	49	222	165	2,25	7,5
15,4	1	56	60	25	49	222	165	2,25	7,5
13,35	1	47	55	20	86	165	118	2,25	6
19,1	0	25	62	16	69	249	158	3	10
7,6	0	46	69	20	52	122	49	1,5	3
19,1	1	51	68	25	94	274	155	3	10
14,75	0	48	72	25	87	268	151	3	5,5
19,25	1	47	19	23	121	255	220	3	10
13,6	0	58	68	17	58	132	141	1,5	6
12,75	1	51	79	20	50	92	122	1,5	5
9,85	1	55	71	16	64	171	44	2,25	4,5
15,25	1	57	71	23	56	117	152	1,5	7,5
11,9	0	60	74	12	102	219	107	1,5	5
16,35	0	56	75	24	100	279	154	2,25	8
12,4	1	49	53	11	67	148	103	1,5	5,5
14,35	0	43	50	14	62	130	154	0,75	7,5
18,15	1	59	70	23	55	181	175	2,25	9,5
17,75	0	58	78	18	86	234	143	3	8,5
12,35	1	53	59	29	43	85	110	0,75	6,5
15,6	1	48	72	16	23	66	131	3	6,5
19,3	0	51	70	19	77	236	167	3	10,5
17,1	0	59	63	16	26	135	137	3	8
18,4	1	62	74	23	94	218	121	3	10
19,05	0	51	67	19	62	199	149	3	9,5
18,55	0	64	66	4	74	112	168	3	9
19,1	0	52	62	20	114	278	140	3	10
12,85	1	50	73	20	64	113	168	2,25	4,5
9,5	1	54	67	4	31	84	94	0,75	4,5
4,5	1	58	61	24	38	86	51	0,75	0,5
13,6	1	63	74	16	105	222	145	3	4,5
11,7	1	31	32	3	64	167	66	2,25	5,5
13,35	0	71	69	24	65	207	109	2,25	6
17,75	1	54	60	23	48	85	128	3	8,5
17,6	0	43	57	17	71	237	164	3	8,5
14,05	1	41	60	20	76	102	119	3	5,5
16,1	0	63	68	22	63	221	126	3	7
13,35	1	63	68	19	46	128	132	2,25	5
11,85	1	56	73	24	53	91	142	2,25	3,5
11,95	0	51	69	19	74	198	83	3	5
13,2	1	41	65	27	56	138	166	2,25	5
7,7	0	66	81	22	52	196	93	2,25	1,5
14,6	0	44	55	23	68	139	117	1,5	8

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'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 2.00556 + 0.111937geslacht[t] + 0.00322054IM[t] + 0.00427172EM[t] -0.0138064Numeracy_tot[t] -0.00768254uren_rfc[t] + 0.00683382blogs[t] + 0.0100742zinvolle_teksten[t] + 1.04756PE[t] + 1.1289ruwe_examenscore[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  2.00556 +  0.111937geslacht[t] +  0.00322054IM[t] +  0.00427172EM[t] -0.0138064Numeracy_tot[t] -0.00768254uren_rfc[t] +  0.00683382blogs[t] +  0.0100742zinvolle_teksten[t] +  1.04756PE[t] +  1.1289ruwe_examenscore[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  2.00556 +  0.111937geslacht[t] +  0.00322054IM[t] +  0.00427172EM[t] -0.0138064Numeracy_tot[t] -0.00768254uren_rfc[t] +  0.00683382blogs[t] +  0.0100742zinvolle_teksten[t] +  1.04756PE[t] +  1.1289ruwe_examenscore[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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[t] = + 2.00556 + 0.111937geslacht[t] + 0.00322054IM[t] + 0.00427172EM[t] -0.0138064Numeracy_tot[t] -0.00768254uren_rfc[t] + 0.00683382blogs[t] + 0.0100742zinvolle_teksten[t] + 1.04756PE[t] + 1.1289ruwe_examenscore[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.005560.6143473.2650.001380440.00069022
geslacht0.1119370.1288540.86870.3864990.193249
IM0.003220540.006347710.50740.612710.306355
EM0.004271720.007311870.58420.5600210.28001
Numeracy_tot-0.01380640.0126068-1.0950.275340.13767
uren_rfc-0.007682540.00237246-3.2380.001504370.000752184
blogs0.006833820.001131226.0411.32665e-086.63326e-09
zinvolle_teksten0.01007420.001764865.7086.62977e-083.31488e-08
PE1.047560.128668.1421.98797e-139.93987e-14
ruwe_examenscore1.12890.027798840.615.55121e-792.7756e-79

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 2.00556 & 0.614347 & 3.265 & 0.00138044 & 0.00069022 \tabularnewline
geslacht & 0.111937 & 0.128854 & 0.8687 & 0.386499 & 0.193249 \tabularnewline
IM & 0.00322054 & 0.00634771 & 0.5074 & 0.61271 & 0.306355 \tabularnewline
EM & 0.00427172 & 0.00731187 & 0.5842 & 0.560021 & 0.28001 \tabularnewline
Numeracy_tot & -0.0138064 & 0.0126068 & -1.095 & 0.27534 & 0.13767 \tabularnewline
uren_rfc & -0.00768254 & 0.00237246 & -3.238 & 0.00150437 & 0.000752184 \tabularnewline
blogs & 0.00683382 & 0.00113122 & 6.041 & 1.32665e-08 & 6.63326e-09 \tabularnewline
zinvolle_teksten & 0.0100742 & 0.00176486 & 5.708 & 6.62977e-08 & 3.31488e-08 \tabularnewline
PE & 1.04756 & 0.12866 & 8.142 & 1.98797e-13 & 9.93987e-14 \tabularnewline
ruwe_examenscore & 1.1289 & 0.0277988 & 40.61 & 5.55121e-79 & 2.7756e-79 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]2.00556[/C][C]0.614347[/C][C]3.265[/C][C]0.00138044[/C][C]0.00069022[/C][/ROW]
[ROW][C]geslacht[/C][C]0.111937[/C][C]0.128854[/C][C]0.8687[/C][C]0.386499[/C][C]0.193249[/C][/ROW]
[ROW][C]IM[/C][C]0.00322054[/C][C]0.00634771[/C][C]0.5074[/C][C]0.61271[/C][C]0.306355[/C][/ROW]
[ROW][C]EM[/C][C]0.00427172[/C][C]0.00731187[/C][C]0.5842[/C][C]0.560021[/C][C]0.28001[/C][/ROW]
[ROW][C]Numeracy_tot[/C][C]-0.0138064[/C][C]0.0126068[/C][C]-1.095[/C][C]0.27534[/C][C]0.13767[/C][/ROW]
[ROW][C]uren_rfc[/C][C]-0.00768254[/C][C]0.00237246[/C][C]-3.238[/C][C]0.00150437[/C][C]0.000752184[/C][/ROW]
[ROW][C]blogs[/C][C]0.00683382[/C][C]0.00113122[/C][C]6.041[/C][C]1.32665e-08[/C][C]6.63326e-09[/C][/ROW]
[ROW][C]zinvolle_teksten[/C][C]0.0100742[/C][C]0.00176486[/C][C]5.708[/C][C]6.62977e-08[/C][C]3.31488e-08[/C][/ROW]
[ROW][C]PE[/C][C]1.04756[/C][C]0.12866[/C][C]8.142[/C][C]1.98797e-13[/C][C]9.93987e-14[/C][/ROW]
[ROW][C]ruwe_examenscore[/C][C]1.1289[/C][C]0.0277988[/C][C]40.61[/C][C]5.55121e-79[/C][C]2.7756e-79[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.005560.6143473.2650.001380440.00069022
geslacht0.1119370.1288540.86870.3864990.193249
IM0.003220540.006347710.50740.612710.306355
EM0.004271720.007311870.58420.5600210.28001
Numeracy_tot-0.01380640.0126068-1.0950.275340.13767
uren_rfc-0.007682540.00237246-3.2380.001504370.000752184
blogs0.006833820.001131226.0411.32665e-086.63326e-09
zinvolle_teksten0.01007420.001764865.7086.62977e-083.31488e-08
PE1.047560.128668.1421.98797e-139.93987e-14
ruwe_examenscore1.12890.027798840.615.55121e-792.7756e-79






Multiple Linear Regression - Regression Statistics
Multiple R0.980127
R-squared0.960649
Adjusted R-squared0.958102
F-TEST (value)377.039
F-TEST (DF numerator)9
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.725688
Sum Squared Residuals73.2007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.980127 \tabularnewline
R-squared & 0.960649 \tabularnewline
Adjusted R-squared & 0.958102 \tabularnewline
F-TEST (value) & 377.039 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.725688 \tabularnewline
Sum Squared Residuals & 73.2007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.980127[/C][/ROW]
[ROW][C]R-squared[/C][C]0.960649[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.958102[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]377.039[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/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]0.725688[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]73.2007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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 R0.980127
R-squared0.960649
Adjusted R-squared0.958102
F-TEST (value)377.039
F-TEST (DF numerator)9
F-TEST (DF denominator)139
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.725688
Sum Squared Residuals73.2007






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.176-1.276
27.48.06241-0.662413
312.212.4716-0.271615
412.813.0315-0.23154
57.47.257890.142108
66.76.662470.0375257
712.613.5605-0.960459
814.815.6399-0.839936
913.312.60570.694251
1011.111.01970.0803033
118.28.87094-0.670942
1211.411.9383-0.538251
136.47.25597-0.855974
1410.611.2843-0.684286
151211.90310.0969374
166.37.48017-1.18017
1711.912.5343-0.634315
189.39.6079-0.307895
191010.2641-0.26413
206.46.44279-0.0427925
2113.813.06880.731216
2210.811.9411-1.1411
2313.814.2119-0.411897
2411.712.1651-0.465058
2510.912.1619-1.26188
269.910.2429-0.342877
2711.512.277-0.776982
288.38.124120.175878
2911.712.3989-0.698883
306.16.46634-0.366341
3199.84813-0.848133
329.78.947930.752066
3310.810.66750.132496
3410.310.4132-0.113162
3510.411.1405-0.740478
369.38.716140.583856
3711.811.9592-0.159208
385.95.018590.881408
3911.411.6111-0.211105
401313.1517-0.151697
4110.811.814-1.01405
4211.311.482-0.182011
4311.811.9168-0.11679
4412.713.4659-0.765949
4510.911.9652-1.0652
4613.313.8914-0.591419
4710.111.0185-0.918499
4814.315.5693-1.26927
499.39.98832-0.688324
5012.512.5649-0.0649392
517.66.834050.76595
5215.916.8819-0.981855
539.29.99898-0.798981
5411.111.04390.0560706
551314.2081-1.20808
5614.514.8908-0.390761
5712.311.6320.667971
5811.411.6618-0.261834
597.36.592510.707485
6012.613.3427-0.742689
611313.59-0.590011
6213.214.5076-1.30758
637.77.099980.600019
644.354.037940.312064
6512.713.1754-0.475426
6618.117.78180.318174
6717.8517.66250.187548
6817.118.3471-1.24714
6919.118.50370.59632
7016.115.60480.495229
7113.3513.4705-0.120475
7218.418.23340.166625
7314.716.1653-1.46525
7410.610.16560.434387
7512.612.56730.0326501
7616.215.90370.296311
7713.613.37950.220547
7814.113.56150.538541
7914.513.85610.643943
8016.1515.52450.625466
8114.7514.510.239994
8214.814.27030.529664
8312.4512.9256-0.475598
8412.6513.1034-0.453399
8517.3517.7898-0.439791
868.69.244-0.644001
8718.418.28670.113337
8816.116.9382-0.838218
8917.7516.78660.963407
9015.2514.16891.08115
9117.6517.25260.397372
9215.614.85640.74362
9316.3515.72360.62636
9417.6516.72060.929402
9513.613.21220.387802
9611.710.3851.31496
9714.3514.03520.31477
9814.7514.60880.141182
9918.2518.23440.0155836
1009.98.670711.22929
1011615.71660.283368
10218.2517.96210.287925
10316.8517.113-0.263048
10418.9518.27070.679308
10515.615.7913-0.191254
10617.116.40470.695345
10716.117.2104-1.11036
10815.415.8271-0.427138
10915.415.8357-0.435681
11013.3513.01370.336257
11119.119.325-0.224977
1127.68.05824-0.45824
11319.119.3706-0.27058
11414.7514.15850.59152
11519.2519.4936-0.243551
11613.612.46981.13018
11712.7511.03261.71742
1189.8510.9342-1.08425
11915.2514.22551.02446
12011.911.3560.543995
12116.3516.2530.0970209
12212.411.66450.735541
12314.3513.38030.969705
12418.1517.94790.202059
12517.7517.39440.35559
12612.3511.62210.727938
12715.614.43331.16666
12819.319.9063-0.606286
12917.116.52070.579325
13018.418.734-0.334031
13119.0518.44560.604383
13218.5517.63050.919465
13319.119.02780.0721641
13412.8511.72431.12569
1359.59.67098-0.170979
1364.54.393180.106815
13713.612.80950.790464
13811.712.193-0.493045
13913.3513.34140.00863755
14017.7516.47011.27987
14117.617.6175-0.0175009
14214.0512.89341.15664
14316.115.53580.564189
14413.3512.20121.1488
14511.8510.23171.61826
14611.9512.6102-0.66017
14713.212.34110.858885
1487.78.18758-0.48758
14914.614.27340.326603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.176 & -1.276 \tabularnewline
2 & 7.4 & 8.06241 & -0.662413 \tabularnewline
3 & 12.2 & 12.4716 & -0.271615 \tabularnewline
4 & 12.8 & 13.0315 & -0.23154 \tabularnewline
5 & 7.4 & 7.25789 & 0.142108 \tabularnewline
6 & 6.7 & 6.66247 & 0.0375257 \tabularnewline
7 & 12.6 & 13.5605 & -0.960459 \tabularnewline
8 & 14.8 & 15.6399 & -0.839936 \tabularnewline
9 & 13.3 & 12.6057 & 0.694251 \tabularnewline
10 & 11.1 & 11.0197 & 0.0803033 \tabularnewline
11 & 8.2 & 8.87094 & -0.670942 \tabularnewline
12 & 11.4 & 11.9383 & -0.538251 \tabularnewline
13 & 6.4 & 7.25597 & -0.855974 \tabularnewline
14 & 10.6 & 11.2843 & -0.684286 \tabularnewline
15 & 12 & 11.9031 & 0.0969374 \tabularnewline
16 & 6.3 & 7.48017 & -1.18017 \tabularnewline
17 & 11.9 & 12.5343 & -0.634315 \tabularnewline
18 & 9.3 & 9.6079 & -0.307895 \tabularnewline
19 & 10 & 10.2641 & -0.26413 \tabularnewline
20 & 6.4 & 6.44279 & -0.0427925 \tabularnewline
21 & 13.8 & 13.0688 & 0.731216 \tabularnewline
22 & 10.8 & 11.9411 & -1.1411 \tabularnewline
23 & 13.8 & 14.2119 & -0.411897 \tabularnewline
24 & 11.7 & 12.1651 & -0.465058 \tabularnewline
25 & 10.9 & 12.1619 & -1.26188 \tabularnewline
26 & 9.9 & 10.2429 & -0.342877 \tabularnewline
27 & 11.5 & 12.277 & -0.776982 \tabularnewline
28 & 8.3 & 8.12412 & 0.175878 \tabularnewline
29 & 11.7 & 12.3989 & -0.698883 \tabularnewline
30 & 6.1 & 6.46634 & -0.366341 \tabularnewline
31 & 9 & 9.84813 & -0.848133 \tabularnewline
32 & 9.7 & 8.94793 & 0.752066 \tabularnewline
33 & 10.8 & 10.6675 & 0.132496 \tabularnewline
34 & 10.3 & 10.4132 & -0.113162 \tabularnewline
35 & 10.4 & 11.1405 & -0.740478 \tabularnewline
36 & 9.3 & 8.71614 & 0.583856 \tabularnewline
37 & 11.8 & 11.9592 & -0.159208 \tabularnewline
38 & 5.9 & 5.01859 & 0.881408 \tabularnewline
39 & 11.4 & 11.6111 & -0.211105 \tabularnewline
40 & 13 & 13.1517 & -0.151697 \tabularnewline
41 & 10.8 & 11.814 & -1.01405 \tabularnewline
42 & 11.3 & 11.482 & -0.182011 \tabularnewline
43 & 11.8 & 11.9168 & -0.11679 \tabularnewline
44 & 12.7 & 13.4659 & -0.765949 \tabularnewline
45 & 10.9 & 11.9652 & -1.0652 \tabularnewline
46 & 13.3 & 13.8914 & -0.591419 \tabularnewline
47 & 10.1 & 11.0185 & -0.918499 \tabularnewline
48 & 14.3 & 15.5693 & -1.26927 \tabularnewline
49 & 9.3 & 9.98832 & -0.688324 \tabularnewline
50 & 12.5 & 12.5649 & -0.0649392 \tabularnewline
51 & 7.6 & 6.83405 & 0.76595 \tabularnewline
52 & 15.9 & 16.8819 & -0.981855 \tabularnewline
53 & 9.2 & 9.99898 & -0.798981 \tabularnewline
54 & 11.1 & 11.0439 & 0.0560706 \tabularnewline
55 & 13 & 14.2081 & -1.20808 \tabularnewline
56 & 14.5 & 14.8908 & -0.390761 \tabularnewline
57 & 12.3 & 11.632 & 0.667971 \tabularnewline
58 & 11.4 & 11.6618 & -0.261834 \tabularnewline
59 & 7.3 & 6.59251 & 0.707485 \tabularnewline
60 & 12.6 & 13.3427 & -0.742689 \tabularnewline
61 & 13 & 13.59 & -0.590011 \tabularnewline
62 & 13.2 & 14.5076 & -1.30758 \tabularnewline
63 & 7.7 & 7.09998 & 0.600019 \tabularnewline
64 & 4.35 & 4.03794 & 0.312064 \tabularnewline
65 & 12.7 & 13.1754 & -0.475426 \tabularnewline
66 & 18.1 & 17.7818 & 0.318174 \tabularnewline
67 & 17.85 & 17.6625 & 0.187548 \tabularnewline
68 & 17.1 & 18.3471 & -1.24714 \tabularnewline
69 & 19.1 & 18.5037 & 0.59632 \tabularnewline
70 & 16.1 & 15.6048 & 0.495229 \tabularnewline
71 & 13.35 & 13.4705 & -0.120475 \tabularnewline
72 & 18.4 & 18.2334 & 0.166625 \tabularnewline
73 & 14.7 & 16.1653 & -1.46525 \tabularnewline
74 & 10.6 & 10.1656 & 0.434387 \tabularnewline
75 & 12.6 & 12.5673 & 0.0326501 \tabularnewline
76 & 16.2 & 15.9037 & 0.296311 \tabularnewline
77 & 13.6 & 13.3795 & 0.220547 \tabularnewline
78 & 14.1 & 13.5615 & 0.538541 \tabularnewline
79 & 14.5 & 13.8561 & 0.643943 \tabularnewline
80 & 16.15 & 15.5245 & 0.625466 \tabularnewline
81 & 14.75 & 14.51 & 0.239994 \tabularnewline
82 & 14.8 & 14.2703 & 0.529664 \tabularnewline
83 & 12.45 & 12.9256 & -0.475598 \tabularnewline
84 & 12.65 & 13.1034 & -0.453399 \tabularnewline
85 & 17.35 & 17.7898 & -0.439791 \tabularnewline
86 & 8.6 & 9.244 & -0.644001 \tabularnewline
87 & 18.4 & 18.2867 & 0.113337 \tabularnewline
88 & 16.1 & 16.9382 & -0.838218 \tabularnewline
89 & 17.75 & 16.7866 & 0.963407 \tabularnewline
90 & 15.25 & 14.1689 & 1.08115 \tabularnewline
91 & 17.65 & 17.2526 & 0.397372 \tabularnewline
92 & 15.6 & 14.8564 & 0.74362 \tabularnewline
93 & 16.35 & 15.7236 & 0.62636 \tabularnewline
94 & 17.65 & 16.7206 & 0.929402 \tabularnewline
95 & 13.6 & 13.2122 & 0.387802 \tabularnewline
96 & 11.7 & 10.385 & 1.31496 \tabularnewline
97 & 14.35 & 14.0352 & 0.31477 \tabularnewline
98 & 14.75 & 14.6088 & 0.141182 \tabularnewline
99 & 18.25 & 18.2344 & 0.0155836 \tabularnewline
100 & 9.9 & 8.67071 & 1.22929 \tabularnewline
101 & 16 & 15.7166 & 0.283368 \tabularnewline
102 & 18.25 & 17.9621 & 0.287925 \tabularnewline
103 & 16.85 & 17.113 & -0.263048 \tabularnewline
104 & 18.95 & 18.2707 & 0.679308 \tabularnewline
105 & 15.6 & 15.7913 & -0.191254 \tabularnewline
106 & 17.1 & 16.4047 & 0.695345 \tabularnewline
107 & 16.1 & 17.2104 & -1.11036 \tabularnewline
108 & 15.4 & 15.8271 & -0.427138 \tabularnewline
109 & 15.4 & 15.8357 & -0.435681 \tabularnewline
110 & 13.35 & 13.0137 & 0.336257 \tabularnewline
111 & 19.1 & 19.325 & -0.224977 \tabularnewline
112 & 7.6 & 8.05824 & -0.45824 \tabularnewline
113 & 19.1 & 19.3706 & -0.27058 \tabularnewline
114 & 14.75 & 14.1585 & 0.59152 \tabularnewline
115 & 19.25 & 19.4936 & -0.243551 \tabularnewline
116 & 13.6 & 12.4698 & 1.13018 \tabularnewline
117 & 12.75 & 11.0326 & 1.71742 \tabularnewline
118 & 9.85 & 10.9342 & -1.08425 \tabularnewline
119 & 15.25 & 14.2255 & 1.02446 \tabularnewline
120 & 11.9 & 11.356 & 0.543995 \tabularnewline
121 & 16.35 & 16.253 & 0.0970209 \tabularnewline
122 & 12.4 & 11.6645 & 0.735541 \tabularnewline
123 & 14.35 & 13.3803 & 0.969705 \tabularnewline
124 & 18.15 & 17.9479 & 0.202059 \tabularnewline
125 & 17.75 & 17.3944 & 0.35559 \tabularnewline
126 & 12.35 & 11.6221 & 0.727938 \tabularnewline
127 & 15.6 & 14.4333 & 1.16666 \tabularnewline
128 & 19.3 & 19.9063 & -0.606286 \tabularnewline
129 & 17.1 & 16.5207 & 0.579325 \tabularnewline
130 & 18.4 & 18.734 & -0.334031 \tabularnewline
131 & 19.05 & 18.4456 & 0.604383 \tabularnewline
132 & 18.55 & 17.6305 & 0.919465 \tabularnewline
133 & 19.1 & 19.0278 & 0.0721641 \tabularnewline
134 & 12.85 & 11.7243 & 1.12569 \tabularnewline
135 & 9.5 & 9.67098 & -0.170979 \tabularnewline
136 & 4.5 & 4.39318 & 0.106815 \tabularnewline
137 & 13.6 & 12.8095 & 0.790464 \tabularnewline
138 & 11.7 & 12.193 & -0.493045 \tabularnewline
139 & 13.35 & 13.3414 & 0.00863755 \tabularnewline
140 & 17.75 & 16.4701 & 1.27987 \tabularnewline
141 & 17.6 & 17.6175 & -0.0175009 \tabularnewline
142 & 14.05 & 12.8934 & 1.15664 \tabularnewline
143 & 16.1 & 15.5358 & 0.564189 \tabularnewline
144 & 13.35 & 12.2012 & 1.1488 \tabularnewline
145 & 11.85 & 10.2317 & 1.61826 \tabularnewline
146 & 11.95 & 12.6102 & -0.66017 \tabularnewline
147 & 13.2 & 12.3411 & 0.858885 \tabularnewline
148 & 7.7 & 8.18758 & -0.48758 \tabularnewline
149 & 14.6 & 14.2734 & 0.326603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&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]12.9[/C][C]14.176[/C][C]-1.276[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]8.06241[/C][C]-0.662413[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]12.4716[/C][C]-0.271615[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]13.0315[/C][C]-0.23154[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]7.25789[/C][C]0.142108[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]6.66247[/C][C]0.0375257[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]13.5605[/C][C]-0.960459[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]15.6399[/C][C]-0.839936[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]12.6057[/C][C]0.694251[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]11.0197[/C][C]0.0803033[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]8.87094[/C][C]-0.670942[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]11.9383[/C][C]-0.538251[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]7.25597[/C][C]-0.855974[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]11.2843[/C][C]-0.684286[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]11.9031[/C][C]0.0969374[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]7.48017[/C][C]-1.18017[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]12.5343[/C][C]-0.634315[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]9.6079[/C][C]-0.307895[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]10.2641[/C][C]-0.26413[/C][/ROW]
[ROW][C]20[/C][C]6.4[/C][C]6.44279[/C][C]-0.0427925[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]13.0688[/C][C]0.731216[/C][/ROW]
[ROW][C]22[/C][C]10.8[/C][C]11.9411[/C][C]-1.1411[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]14.2119[/C][C]-0.411897[/C][/ROW]
[ROW][C]24[/C][C]11.7[/C][C]12.1651[/C][C]-0.465058[/C][/ROW]
[ROW][C]25[/C][C]10.9[/C][C]12.1619[/C][C]-1.26188[/C][/ROW]
[ROW][C]26[/C][C]9.9[/C][C]10.2429[/C][C]-0.342877[/C][/ROW]
[ROW][C]27[/C][C]11.5[/C][C]12.277[/C][C]-0.776982[/C][/ROW]
[ROW][C]28[/C][C]8.3[/C][C]8.12412[/C][C]0.175878[/C][/ROW]
[ROW][C]29[/C][C]11.7[/C][C]12.3989[/C][C]-0.698883[/C][/ROW]
[ROW][C]30[/C][C]6.1[/C][C]6.46634[/C][C]-0.366341[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]9.84813[/C][C]-0.848133[/C][/ROW]
[ROW][C]32[/C][C]9.7[/C][C]8.94793[/C][C]0.752066[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]10.6675[/C][C]0.132496[/C][/ROW]
[ROW][C]34[/C][C]10.3[/C][C]10.4132[/C][C]-0.113162[/C][/ROW]
[ROW][C]35[/C][C]10.4[/C][C]11.1405[/C][C]-0.740478[/C][/ROW]
[ROW][C]36[/C][C]9.3[/C][C]8.71614[/C][C]0.583856[/C][/ROW]
[ROW][C]37[/C][C]11.8[/C][C]11.9592[/C][C]-0.159208[/C][/ROW]
[ROW][C]38[/C][C]5.9[/C][C]5.01859[/C][C]0.881408[/C][/ROW]
[ROW][C]39[/C][C]11.4[/C][C]11.6111[/C][C]-0.211105[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.1517[/C][C]-0.151697[/C][/ROW]
[ROW][C]41[/C][C]10.8[/C][C]11.814[/C][C]-1.01405[/C][/ROW]
[ROW][C]42[/C][C]11.3[/C][C]11.482[/C][C]-0.182011[/C][/ROW]
[ROW][C]43[/C][C]11.8[/C][C]11.9168[/C][C]-0.11679[/C][/ROW]
[ROW][C]44[/C][C]12.7[/C][C]13.4659[/C][C]-0.765949[/C][/ROW]
[ROW][C]45[/C][C]10.9[/C][C]11.9652[/C][C]-1.0652[/C][/ROW]
[ROW][C]46[/C][C]13.3[/C][C]13.8914[/C][C]-0.591419[/C][/ROW]
[ROW][C]47[/C][C]10.1[/C][C]11.0185[/C][C]-0.918499[/C][/ROW]
[ROW][C]48[/C][C]14.3[/C][C]15.5693[/C][C]-1.26927[/C][/ROW]
[ROW][C]49[/C][C]9.3[/C][C]9.98832[/C][C]-0.688324[/C][/ROW]
[ROW][C]50[/C][C]12.5[/C][C]12.5649[/C][C]-0.0649392[/C][/ROW]
[ROW][C]51[/C][C]7.6[/C][C]6.83405[/C][C]0.76595[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]16.8819[/C][C]-0.981855[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]9.99898[/C][C]-0.798981[/C][/ROW]
[ROW][C]54[/C][C]11.1[/C][C]11.0439[/C][C]0.0560706[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]14.2081[/C][C]-1.20808[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]14.8908[/C][C]-0.390761[/C][/ROW]
[ROW][C]57[/C][C]12.3[/C][C]11.632[/C][C]0.667971[/C][/ROW]
[ROW][C]58[/C][C]11.4[/C][C]11.6618[/C][C]-0.261834[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]6.59251[/C][C]0.707485[/C][/ROW]
[ROW][C]60[/C][C]12.6[/C][C]13.3427[/C][C]-0.742689[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]13.59[/C][C]-0.590011[/C][/ROW]
[ROW][C]62[/C][C]13.2[/C][C]14.5076[/C][C]-1.30758[/C][/ROW]
[ROW][C]63[/C][C]7.7[/C][C]7.09998[/C][C]0.600019[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]4.03794[/C][C]0.312064[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]13.1754[/C][C]-0.475426[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]17.7818[/C][C]0.318174[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]17.6625[/C][C]0.187548[/C][/ROW]
[ROW][C]68[/C][C]17.1[/C][C]18.3471[/C][C]-1.24714[/C][/ROW]
[ROW][C]69[/C][C]19.1[/C][C]18.5037[/C][C]0.59632[/C][/ROW]
[ROW][C]70[/C][C]16.1[/C][C]15.6048[/C][C]0.495229[/C][/ROW]
[ROW][C]71[/C][C]13.35[/C][C]13.4705[/C][C]-0.120475[/C][/ROW]
[ROW][C]72[/C][C]18.4[/C][C]18.2334[/C][C]0.166625[/C][/ROW]
[ROW][C]73[/C][C]14.7[/C][C]16.1653[/C][C]-1.46525[/C][/ROW]
[ROW][C]74[/C][C]10.6[/C][C]10.1656[/C][C]0.434387[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.5673[/C][C]0.0326501[/C][/ROW]
[ROW][C]76[/C][C]16.2[/C][C]15.9037[/C][C]0.296311[/C][/ROW]
[ROW][C]77[/C][C]13.6[/C][C]13.3795[/C][C]0.220547[/C][/ROW]
[ROW][C]78[/C][C]14.1[/C][C]13.5615[/C][C]0.538541[/C][/ROW]
[ROW][C]79[/C][C]14.5[/C][C]13.8561[/C][C]0.643943[/C][/ROW]
[ROW][C]80[/C][C]16.15[/C][C]15.5245[/C][C]0.625466[/C][/ROW]
[ROW][C]81[/C][C]14.75[/C][C]14.51[/C][C]0.239994[/C][/ROW]
[ROW][C]82[/C][C]14.8[/C][C]14.2703[/C][C]0.529664[/C][/ROW]
[ROW][C]83[/C][C]12.45[/C][C]12.9256[/C][C]-0.475598[/C][/ROW]
[ROW][C]84[/C][C]12.65[/C][C]13.1034[/C][C]-0.453399[/C][/ROW]
[ROW][C]85[/C][C]17.35[/C][C]17.7898[/C][C]-0.439791[/C][/ROW]
[ROW][C]86[/C][C]8.6[/C][C]9.244[/C][C]-0.644001[/C][/ROW]
[ROW][C]87[/C][C]18.4[/C][C]18.2867[/C][C]0.113337[/C][/ROW]
[ROW][C]88[/C][C]16.1[/C][C]16.9382[/C][C]-0.838218[/C][/ROW]
[ROW][C]89[/C][C]17.75[/C][C]16.7866[/C][C]0.963407[/C][/ROW]
[ROW][C]90[/C][C]15.25[/C][C]14.1689[/C][C]1.08115[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.2526[/C][C]0.397372[/C][/ROW]
[ROW][C]92[/C][C]15.6[/C][C]14.8564[/C][C]0.74362[/C][/ROW]
[ROW][C]93[/C][C]16.35[/C][C]15.7236[/C][C]0.62636[/C][/ROW]
[ROW][C]94[/C][C]17.65[/C][C]16.7206[/C][C]0.929402[/C][/ROW]
[ROW][C]95[/C][C]13.6[/C][C]13.2122[/C][C]0.387802[/C][/ROW]
[ROW][C]96[/C][C]11.7[/C][C]10.385[/C][C]1.31496[/C][/ROW]
[ROW][C]97[/C][C]14.35[/C][C]14.0352[/C][C]0.31477[/C][/ROW]
[ROW][C]98[/C][C]14.75[/C][C]14.6088[/C][C]0.141182[/C][/ROW]
[ROW][C]99[/C][C]18.25[/C][C]18.2344[/C][C]0.0155836[/C][/ROW]
[ROW][C]100[/C][C]9.9[/C][C]8.67071[/C][C]1.22929[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.7166[/C][C]0.283368[/C][/ROW]
[ROW][C]102[/C][C]18.25[/C][C]17.9621[/C][C]0.287925[/C][/ROW]
[ROW][C]103[/C][C]16.85[/C][C]17.113[/C][C]-0.263048[/C][/ROW]
[ROW][C]104[/C][C]18.95[/C][C]18.2707[/C][C]0.679308[/C][/ROW]
[ROW][C]105[/C][C]15.6[/C][C]15.7913[/C][C]-0.191254[/C][/ROW]
[ROW][C]106[/C][C]17.1[/C][C]16.4047[/C][C]0.695345[/C][/ROW]
[ROW][C]107[/C][C]16.1[/C][C]17.2104[/C][C]-1.11036[/C][/ROW]
[ROW][C]108[/C][C]15.4[/C][C]15.8271[/C][C]-0.427138[/C][/ROW]
[ROW][C]109[/C][C]15.4[/C][C]15.8357[/C][C]-0.435681[/C][/ROW]
[ROW][C]110[/C][C]13.35[/C][C]13.0137[/C][C]0.336257[/C][/ROW]
[ROW][C]111[/C][C]19.1[/C][C]19.325[/C][C]-0.224977[/C][/ROW]
[ROW][C]112[/C][C]7.6[/C][C]8.05824[/C][C]-0.45824[/C][/ROW]
[ROW][C]113[/C][C]19.1[/C][C]19.3706[/C][C]-0.27058[/C][/ROW]
[ROW][C]114[/C][C]14.75[/C][C]14.1585[/C][C]0.59152[/C][/ROW]
[ROW][C]115[/C][C]19.25[/C][C]19.4936[/C][C]-0.243551[/C][/ROW]
[ROW][C]116[/C][C]13.6[/C][C]12.4698[/C][C]1.13018[/C][/ROW]
[ROW][C]117[/C][C]12.75[/C][C]11.0326[/C][C]1.71742[/C][/ROW]
[ROW][C]118[/C][C]9.85[/C][C]10.9342[/C][C]-1.08425[/C][/ROW]
[ROW][C]119[/C][C]15.25[/C][C]14.2255[/C][C]1.02446[/C][/ROW]
[ROW][C]120[/C][C]11.9[/C][C]11.356[/C][C]0.543995[/C][/ROW]
[ROW][C]121[/C][C]16.35[/C][C]16.253[/C][C]0.0970209[/C][/ROW]
[ROW][C]122[/C][C]12.4[/C][C]11.6645[/C][C]0.735541[/C][/ROW]
[ROW][C]123[/C][C]14.35[/C][C]13.3803[/C][C]0.969705[/C][/ROW]
[ROW][C]124[/C][C]18.15[/C][C]17.9479[/C][C]0.202059[/C][/ROW]
[ROW][C]125[/C][C]17.75[/C][C]17.3944[/C][C]0.35559[/C][/ROW]
[ROW][C]126[/C][C]12.35[/C][C]11.6221[/C][C]0.727938[/C][/ROW]
[ROW][C]127[/C][C]15.6[/C][C]14.4333[/C][C]1.16666[/C][/ROW]
[ROW][C]128[/C][C]19.3[/C][C]19.9063[/C][C]-0.606286[/C][/ROW]
[ROW][C]129[/C][C]17.1[/C][C]16.5207[/C][C]0.579325[/C][/ROW]
[ROW][C]130[/C][C]18.4[/C][C]18.734[/C][C]-0.334031[/C][/ROW]
[ROW][C]131[/C][C]19.05[/C][C]18.4456[/C][C]0.604383[/C][/ROW]
[ROW][C]132[/C][C]18.55[/C][C]17.6305[/C][C]0.919465[/C][/ROW]
[ROW][C]133[/C][C]19.1[/C][C]19.0278[/C][C]0.0721641[/C][/ROW]
[ROW][C]134[/C][C]12.85[/C][C]11.7243[/C][C]1.12569[/C][/ROW]
[ROW][C]135[/C][C]9.5[/C][C]9.67098[/C][C]-0.170979[/C][/ROW]
[ROW][C]136[/C][C]4.5[/C][C]4.39318[/C][C]0.106815[/C][/ROW]
[ROW][C]137[/C][C]13.6[/C][C]12.8095[/C][C]0.790464[/C][/ROW]
[ROW][C]138[/C][C]11.7[/C][C]12.193[/C][C]-0.493045[/C][/ROW]
[ROW][C]139[/C][C]13.35[/C][C]13.3414[/C][C]0.00863755[/C][/ROW]
[ROW][C]140[/C][C]17.75[/C][C]16.4701[/C][C]1.27987[/C][/ROW]
[ROW][C]141[/C][C]17.6[/C][C]17.6175[/C][C]-0.0175009[/C][/ROW]
[ROW][C]142[/C][C]14.05[/C][C]12.8934[/C][C]1.15664[/C][/ROW]
[ROW][C]143[/C][C]16.1[/C][C]15.5358[/C][C]0.564189[/C][/ROW]
[ROW][C]144[/C][C]13.35[/C][C]12.2012[/C][C]1.1488[/C][/ROW]
[ROW][C]145[/C][C]11.85[/C][C]10.2317[/C][C]1.61826[/C][/ROW]
[ROW][C]146[/C][C]11.95[/C][C]12.6102[/C][C]-0.66017[/C][/ROW]
[ROW][C]147[/C][C]13.2[/C][C]12.3411[/C][C]0.858885[/C][/ROW]
[ROW][C]148[/C][C]7.7[/C][C]8.18758[/C][C]-0.48758[/C][/ROW]
[ROW][C]149[/C][C]14.6[/C][C]14.2734[/C][C]0.326603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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
112.914.176-1.276
27.48.06241-0.662413
312.212.4716-0.271615
412.813.0315-0.23154
57.47.257890.142108
66.76.662470.0375257
712.613.5605-0.960459
814.815.6399-0.839936
913.312.60570.694251
1011.111.01970.0803033
118.28.87094-0.670942
1211.411.9383-0.538251
136.47.25597-0.855974
1410.611.2843-0.684286
151211.90310.0969374
166.37.48017-1.18017
1711.912.5343-0.634315
189.39.6079-0.307895
191010.2641-0.26413
206.46.44279-0.0427925
2113.813.06880.731216
2210.811.9411-1.1411
2313.814.2119-0.411897
2411.712.1651-0.465058
2510.912.1619-1.26188
269.910.2429-0.342877
2711.512.277-0.776982
288.38.124120.175878
2911.712.3989-0.698883
306.16.46634-0.366341
3199.84813-0.848133
329.78.947930.752066
3310.810.66750.132496
3410.310.4132-0.113162
3510.411.1405-0.740478
369.38.716140.583856
3711.811.9592-0.159208
385.95.018590.881408
3911.411.6111-0.211105
401313.1517-0.151697
4110.811.814-1.01405
4211.311.482-0.182011
4311.811.9168-0.11679
4412.713.4659-0.765949
4510.911.9652-1.0652
4613.313.8914-0.591419
4710.111.0185-0.918499
4814.315.5693-1.26927
499.39.98832-0.688324
5012.512.5649-0.0649392
517.66.834050.76595
5215.916.8819-0.981855
539.29.99898-0.798981
5411.111.04390.0560706
551314.2081-1.20808
5614.514.8908-0.390761
5712.311.6320.667971
5811.411.6618-0.261834
597.36.592510.707485
6012.613.3427-0.742689
611313.59-0.590011
6213.214.5076-1.30758
637.77.099980.600019
644.354.037940.312064
6512.713.1754-0.475426
6618.117.78180.318174
6717.8517.66250.187548
6817.118.3471-1.24714
6919.118.50370.59632
7016.115.60480.495229
7113.3513.4705-0.120475
7218.418.23340.166625
7314.716.1653-1.46525
7410.610.16560.434387
7512.612.56730.0326501
7616.215.90370.296311
7713.613.37950.220547
7814.113.56150.538541
7914.513.85610.643943
8016.1515.52450.625466
8114.7514.510.239994
8214.814.27030.529664
8312.4512.9256-0.475598
8412.6513.1034-0.453399
8517.3517.7898-0.439791
868.69.244-0.644001
8718.418.28670.113337
8816.116.9382-0.838218
8917.7516.78660.963407
9015.2514.16891.08115
9117.6517.25260.397372
9215.614.85640.74362
9316.3515.72360.62636
9417.6516.72060.929402
9513.613.21220.387802
9611.710.3851.31496
9714.3514.03520.31477
9814.7514.60880.141182
9918.2518.23440.0155836
1009.98.670711.22929
1011615.71660.283368
10218.2517.96210.287925
10316.8517.113-0.263048
10418.9518.27070.679308
10515.615.7913-0.191254
10617.116.40470.695345
10716.117.2104-1.11036
10815.415.8271-0.427138
10915.415.8357-0.435681
11013.3513.01370.336257
11119.119.325-0.224977
1127.68.05824-0.45824
11319.119.3706-0.27058
11414.7514.15850.59152
11519.2519.4936-0.243551
11613.612.46981.13018
11712.7511.03261.71742
1189.8510.9342-1.08425
11915.2514.22551.02446
12011.911.3560.543995
12116.3516.2530.0970209
12212.411.66450.735541
12314.3513.38030.969705
12418.1517.94790.202059
12517.7517.39440.35559
12612.3511.62210.727938
12715.614.43331.16666
12819.319.9063-0.606286
12917.116.52070.579325
13018.418.734-0.334031
13119.0518.44560.604383
13218.5517.63050.919465
13319.119.02780.0721641
13412.8511.72431.12569
1359.59.67098-0.170979
1364.54.393180.106815
13713.612.80950.790464
13811.712.193-0.493045
13913.3513.34140.00863755
14017.7516.47011.27987
14117.617.6175-0.0175009
14214.0512.89341.15664
14316.115.53580.564189
14413.3512.20121.1488
14511.8510.23171.61826
14611.9512.6102-0.66017
14713.212.34110.858885
1487.78.18758-0.48758
14914.614.27340.326603






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.3399390.6798790.660061
140.2298070.4596130.770193
150.1577950.315590.842205
160.09422030.1884410.90578
170.07038790.1407760.929612
180.05550690.1110140.944493
190.08315850.1663170.916841
200.067640.135280.93236
210.04763680.09527350.952363
220.03562360.07124710.964376
230.02323080.04646150.976769
240.01490590.02981180.985094
250.01122850.02245690.988772
260.007410040.01482010.99259
270.005945810.01189160.994054
280.006756260.01351250.993244
290.00530680.01061360.994693
300.003187170.006374340.996813
310.002155290.004310580.997845
320.001708370.003416740.998292
330.001314420.002628830.998686
340.0007049950.001409990.999295
350.0005184190.001036840.999482
360.0002925790.0005851580.999707
370.000308770.000617540.999691
380.0002936440.0005872890.999706
390.0001647370.0003294740.999835
408.76827e-050.0001753650.999912
418.0875e-050.000161750.999919
425.66521e-050.0001133040.999943
433.30545e-056.6109e-050.999967
442.20592e-054.41185e-050.999978
451.83386e-053.66771e-050.999982
461.0602e-052.1204e-050.999989
477.65409e-061.53082e-050.999992
489.9314e-061.98628e-050.99999
492.48662e-054.97324e-050.999975
504.99829e-059.99659e-050.99995
517.11182e-050.0001422360.999929
528.97362e-050.0001794720.99991
530.0002278330.0004556660.999772
540.0001858860.0003717710.999814
550.001918290.003836580.998082
560.004752720.009505440.995247
570.004300960.008601910.995699
580.00530890.01061780.994691
590.004746190.009492380.995254
600.01135270.02270540.988647
610.04035660.08071330.959643
620.2385570.4771150.761443
630.2581530.5163070.741847
640.2229020.4458040.777098
650.1918670.3837350.808133
660.5284730.9430540.471527
670.7838910.4322180.216109
680.7829420.4341170.217058
690.7661990.4676020.233801
700.736510.526980.26349
710.820670.358660.17933
720.8735660.2528680.126434
730.9553510.0892970.0446485
740.9678630.06427340.0321367
750.9766460.04670820.0233541
760.9843760.03124810.015624
770.9831830.03363340.0168167
780.9855830.02883320.0144166
790.9887440.02251110.0112555
800.9879250.02415040.0120752
810.9867480.02650450.0132523
820.9872360.02552870.0127643
830.9848270.03034660.0151733
840.9801590.03968260.0198413
850.977370.04526040.0226302
860.9736280.05274340.0263717
870.9721890.05562180.0278109
880.9687310.06253720.0312686
890.9879840.02403170.0120158
900.9924360.01512790.00756394
910.9962110.007578370.00378918
920.9977360.004527540.00226377
930.9975250.004950160.00247508
940.9987280.002544590.00127229
950.9983740.003252830.00162641
960.9991780.001643240.000821618
970.9987170.002565440.00128272
980.9995650.0008709040.000435452
990.9993240.001351480.000675738
1000.9995360.0009278310.000463916
1010.9992440.001511840.000755918
1020.9989270.002145150.00107258
1030.9982780.003443860.00172193
1040.9978910.004218970.00210949
1050.9968410.006318480.00315924
1060.997280.00544070.00272035
1070.9964380.007124130.00356206
1080.9946810.01063720.00531862
1090.9927850.014430.007215
1100.989080.02183980.0109199
1110.9834520.03309630.0165482
1120.9856660.02866890.0143344
1130.9783950.04321060.0216053
1140.9780030.04399470.0219973
1150.9889770.02204510.0110225
1160.9873390.02532170.0126608
1170.998170.003660330.00183017
1180.9976790.004642520.00232126
1190.9966660.006668540.00333427
1200.9979240.004151580.00207579
1210.9971420.005715220.00285761
1220.997770.004460190.0022301
1230.9975970.004806940.00240347
1240.9971930.005613030.00280651
1250.9981340.003732770.00186639
1260.9961280.007744390.0038722
1270.9957230.00855370.00427685
1280.9970110.005978140.00298907
1290.9938090.0123820.00619099
1300.9981680.003663470.00183174
1310.9976490.004702090.00235104
1320.9967740.006452510.00322626
1330.9924490.01510140.0075507
1340.9868040.02639190.0131959
1350.9601060.07978880.0398944
1360.8901490.2197020.109851

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.339939 & 0.679879 & 0.660061 \tabularnewline
14 & 0.229807 & 0.459613 & 0.770193 \tabularnewline
15 & 0.157795 & 0.31559 & 0.842205 \tabularnewline
16 & 0.0942203 & 0.188441 & 0.90578 \tabularnewline
17 & 0.0703879 & 0.140776 & 0.929612 \tabularnewline
18 & 0.0555069 & 0.111014 & 0.944493 \tabularnewline
19 & 0.0831585 & 0.166317 & 0.916841 \tabularnewline
20 & 0.06764 & 0.13528 & 0.93236 \tabularnewline
21 & 0.0476368 & 0.0952735 & 0.952363 \tabularnewline
22 & 0.0356236 & 0.0712471 & 0.964376 \tabularnewline
23 & 0.0232308 & 0.0464615 & 0.976769 \tabularnewline
24 & 0.0149059 & 0.0298118 & 0.985094 \tabularnewline
25 & 0.0112285 & 0.0224569 & 0.988772 \tabularnewline
26 & 0.00741004 & 0.0148201 & 0.99259 \tabularnewline
27 & 0.00594581 & 0.0118916 & 0.994054 \tabularnewline
28 & 0.00675626 & 0.0135125 & 0.993244 \tabularnewline
29 & 0.0053068 & 0.0106136 & 0.994693 \tabularnewline
30 & 0.00318717 & 0.00637434 & 0.996813 \tabularnewline
31 & 0.00215529 & 0.00431058 & 0.997845 \tabularnewline
32 & 0.00170837 & 0.00341674 & 0.998292 \tabularnewline
33 & 0.00131442 & 0.00262883 & 0.998686 \tabularnewline
34 & 0.000704995 & 0.00140999 & 0.999295 \tabularnewline
35 & 0.000518419 & 0.00103684 & 0.999482 \tabularnewline
36 & 0.000292579 & 0.000585158 & 0.999707 \tabularnewline
37 & 0.00030877 & 0.00061754 & 0.999691 \tabularnewline
38 & 0.000293644 & 0.000587289 & 0.999706 \tabularnewline
39 & 0.000164737 & 0.000329474 & 0.999835 \tabularnewline
40 & 8.76827e-05 & 0.000175365 & 0.999912 \tabularnewline
41 & 8.0875e-05 & 0.00016175 & 0.999919 \tabularnewline
42 & 5.66521e-05 & 0.000113304 & 0.999943 \tabularnewline
43 & 3.30545e-05 & 6.6109e-05 & 0.999967 \tabularnewline
44 & 2.20592e-05 & 4.41185e-05 & 0.999978 \tabularnewline
45 & 1.83386e-05 & 3.66771e-05 & 0.999982 \tabularnewline
46 & 1.0602e-05 & 2.1204e-05 & 0.999989 \tabularnewline
47 & 7.65409e-06 & 1.53082e-05 & 0.999992 \tabularnewline
48 & 9.9314e-06 & 1.98628e-05 & 0.99999 \tabularnewline
49 & 2.48662e-05 & 4.97324e-05 & 0.999975 \tabularnewline
50 & 4.99829e-05 & 9.99659e-05 & 0.99995 \tabularnewline
51 & 7.11182e-05 & 0.000142236 & 0.999929 \tabularnewline
52 & 8.97362e-05 & 0.000179472 & 0.99991 \tabularnewline
53 & 0.000227833 & 0.000455666 & 0.999772 \tabularnewline
54 & 0.000185886 & 0.000371771 & 0.999814 \tabularnewline
55 & 0.00191829 & 0.00383658 & 0.998082 \tabularnewline
56 & 0.00475272 & 0.00950544 & 0.995247 \tabularnewline
57 & 0.00430096 & 0.00860191 & 0.995699 \tabularnewline
58 & 0.0053089 & 0.0106178 & 0.994691 \tabularnewline
59 & 0.00474619 & 0.00949238 & 0.995254 \tabularnewline
60 & 0.0113527 & 0.0227054 & 0.988647 \tabularnewline
61 & 0.0403566 & 0.0807133 & 0.959643 \tabularnewline
62 & 0.238557 & 0.477115 & 0.761443 \tabularnewline
63 & 0.258153 & 0.516307 & 0.741847 \tabularnewline
64 & 0.222902 & 0.445804 & 0.777098 \tabularnewline
65 & 0.191867 & 0.383735 & 0.808133 \tabularnewline
66 & 0.528473 & 0.943054 & 0.471527 \tabularnewline
67 & 0.783891 & 0.432218 & 0.216109 \tabularnewline
68 & 0.782942 & 0.434117 & 0.217058 \tabularnewline
69 & 0.766199 & 0.467602 & 0.233801 \tabularnewline
70 & 0.73651 & 0.52698 & 0.26349 \tabularnewline
71 & 0.82067 & 0.35866 & 0.17933 \tabularnewline
72 & 0.873566 & 0.252868 & 0.126434 \tabularnewline
73 & 0.955351 & 0.089297 & 0.0446485 \tabularnewline
74 & 0.967863 & 0.0642734 & 0.0321367 \tabularnewline
75 & 0.976646 & 0.0467082 & 0.0233541 \tabularnewline
76 & 0.984376 & 0.0312481 & 0.015624 \tabularnewline
77 & 0.983183 & 0.0336334 & 0.0168167 \tabularnewline
78 & 0.985583 & 0.0288332 & 0.0144166 \tabularnewline
79 & 0.988744 & 0.0225111 & 0.0112555 \tabularnewline
80 & 0.987925 & 0.0241504 & 0.0120752 \tabularnewline
81 & 0.986748 & 0.0265045 & 0.0132523 \tabularnewline
82 & 0.987236 & 0.0255287 & 0.0127643 \tabularnewline
83 & 0.984827 & 0.0303466 & 0.0151733 \tabularnewline
84 & 0.980159 & 0.0396826 & 0.0198413 \tabularnewline
85 & 0.97737 & 0.0452604 & 0.0226302 \tabularnewline
86 & 0.973628 & 0.0527434 & 0.0263717 \tabularnewline
87 & 0.972189 & 0.0556218 & 0.0278109 \tabularnewline
88 & 0.968731 & 0.0625372 & 0.0312686 \tabularnewline
89 & 0.987984 & 0.0240317 & 0.0120158 \tabularnewline
90 & 0.992436 & 0.0151279 & 0.00756394 \tabularnewline
91 & 0.996211 & 0.00757837 & 0.00378918 \tabularnewline
92 & 0.997736 & 0.00452754 & 0.00226377 \tabularnewline
93 & 0.997525 & 0.00495016 & 0.00247508 \tabularnewline
94 & 0.998728 & 0.00254459 & 0.00127229 \tabularnewline
95 & 0.998374 & 0.00325283 & 0.00162641 \tabularnewline
96 & 0.999178 & 0.00164324 & 0.000821618 \tabularnewline
97 & 0.998717 & 0.00256544 & 0.00128272 \tabularnewline
98 & 0.999565 & 0.000870904 & 0.000435452 \tabularnewline
99 & 0.999324 & 0.00135148 & 0.000675738 \tabularnewline
100 & 0.999536 & 0.000927831 & 0.000463916 \tabularnewline
101 & 0.999244 & 0.00151184 & 0.000755918 \tabularnewline
102 & 0.998927 & 0.00214515 & 0.00107258 \tabularnewline
103 & 0.998278 & 0.00344386 & 0.00172193 \tabularnewline
104 & 0.997891 & 0.00421897 & 0.00210949 \tabularnewline
105 & 0.996841 & 0.00631848 & 0.00315924 \tabularnewline
106 & 0.99728 & 0.0054407 & 0.00272035 \tabularnewline
107 & 0.996438 & 0.00712413 & 0.00356206 \tabularnewline
108 & 0.994681 & 0.0106372 & 0.00531862 \tabularnewline
109 & 0.992785 & 0.01443 & 0.007215 \tabularnewline
110 & 0.98908 & 0.0218398 & 0.0109199 \tabularnewline
111 & 0.983452 & 0.0330963 & 0.0165482 \tabularnewline
112 & 0.985666 & 0.0286689 & 0.0143344 \tabularnewline
113 & 0.978395 & 0.0432106 & 0.0216053 \tabularnewline
114 & 0.978003 & 0.0439947 & 0.0219973 \tabularnewline
115 & 0.988977 & 0.0220451 & 0.0110225 \tabularnewline
116 & 0.987339 & 0.0253217 & 0.0126608 \tabularnewline
117 & 0.99817 & 0.00366033 & 0.00183017 \tabularnewline
118 & 0.997679 & 0.00464252 & 0.00232126 \tabularnewline
119 & 0.996666 & 0.00666854 & 0.00333427 \tabularnewline
120 & 0.997924 & 0.00415158 & 0.00207579 \tabularnewline
121 & 0.997142 & 0.00571522 & 0.00285761 \tabularnewline
122 & 0.99777 & 0.00446019 & 0.0022301 \tabularnewline
123 & 0.997597 & 0.00480694 & 0.00240347 \tabularnewline
124 & 0.997193 & 0.00561303 & 0.00280651 \tabularnewline
125 & 0.998134 & 0.00373277 & 0.00186639 \tabularnewline
126 & 0.996128 & 0.00774439 & 0.0038722 \tabularnewline
127 & 0.995723 & 0.0085537 & 0.00427685 \tabularnewline
128 & 0.997011 & 0.00597814 & 0.00298907 \tabularnewline
129 & 0.993809 & 0.012382 & 0.00619099 \tabularnewline
130 & 0.998168 & 0.00366347 & 0.00183174 \tabularnewline
131 & 0.997649 & 0.00470209 & 0.00235104 \tabularnewline
132 & 0.996774 & 0.00645251 & 0.00322626 \tabularnewline
133 & 0.992449 & 0.0151014 & 0.0075507 \tabularnewline
134 & 0.986804 & 0.0263919 & 0.0131959 \tabularnewline
135 & 0.960106 & 0.0797888 & 0.0398944 \tabularnewline
136 & 0.890149 & 0.219702 & 0.109851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&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]13[/C][C]0.339939[/C][C]0.679879[/C][C]0.660061[/C][/ROW]
[ROW][C]14[/C][C]0.229807[/C][C]0.459613[/C][C]0.770193[/C][/ROW]
[ROW][C]15[/C][C]0.157795[/C][C]0.31559[/C][C]0.842205[/C][/ROW]
[ROW][C]16[/C][C]0.0942203[/C][C]0.188441[/C][C]0.90578[/C][/ROW]
[ROW][C]17[/C][C]0.0703879[/C][C]0.140776[/C][C]0.929612[/C][/ROW]
[ROW][C]18[/C][C]0.0555069[/C][C]0.111014[/C][C]0.944493[/C][/ROW]
[ROW][C]19[/C][C]0.0831585[/C][C]0.166317[/C][C]0.916841[/C][/ROW]
[ROW][C]20[/C][C]0.06764[/C][C]0.13528[/C][C]0.93236[/C][/ROW]
[ROW][C]21[/C][C]0.0476368[/C][C]0.0952735[/C][C]0.952363[/C][/ROW]
[ROW][C]22[/C][C]0.0356236[/C][C]0.0712471[/C][C]0.964376[/C][/ROW]
[ROW][C]23[/C][C]0.0232308[/C][C]0.0464615[/C][C]0.976769[/C][/ROW]
[ROW][C]24[/C][C]0.0149059[/C][C]0.0298118[/C][C]0.985094[/C][/ROW]
[ROW][C]25[/C][C]0.0112285[/C][C]0.0224569[/C][C]0.988772[/C][/ROW]
[ROW][C]26[/C][C]0.00741004[/C][C]0.0148201[/C][C]0.99259[/C][/ROW]
[ROW][C]27[/C][C]0.00594581[/C][C]0.0118916[/C][C]0.994054[/C][/ROW]
[ROW][C]28[/C][C]0.00675626[/C][C]0.0135125[/C][C]0.993244[/C][/ROW]
[ROW][C]29[/C][C]0.0053068[/C][C]0.0106136[/C][C]0.994693[/C][/ROW]
[ROW][C]30[/C][C]0.00318717[/C][C]0.00637434[/C][C]0.996813[/C][/ROW]
[ROW][C]31[/C][C]0.00215529[/C][C]0.00431058[/C][C]0.997845[/C][/ROW]
[ROW][C]32[/C][C]0.00170837[/C][C]0.00341674[/C][C]0.998292[/C][/ROW]
[ROW][C]33[/C][C]0.00131442[/C][C]0.00262883[/C][C]0.998686[/C][/ROW]
[ROW][C]34[/C][C]0.000704995[/C][C]0.00140999[/C][C]0.999295[/C][/ROW]
[ROW][C]35[/C][C]0.000518419[/C][C]0.00103684[/C][C]0.999482[/C][/ROW]
[ROW][C]36[/C][C]0.000292579[/C][C]0.000585158[/C][C]0.999707[/C][/ROW]
[ROW][C]37[/C][C]0.00030877[/C][C]0.00061754[/C][C]0.999691[/C][/ROW]
[ROW][C]38[/C][C]0.000293644[/C][C]0.000587289[/C][C]0.999706[/C][/ROW]
[ROW][C]39[/C][C]0.000164737[/C][C]0.000329474[/C][C]0.999835[/C][/ROW]
[ROW][C]40[/C][C]8.76827e-05[/C][C]0.000175365[/C][C]0.999912[/C][/ROW]
[ROW][C]41[/C][C]8.0875e-05[/C][C]0.00016175[/C][C]0.999919[/C][/ROW]
[ROW][C]42[/C][C]5.66521e-05[/C][C]0.000113304[/C][C]0.999943[/C][/ROW]
[ROW][C]43[/C][C]3.30545e-05[/C][C]6.6109e-05[/C][C]0.999967[/C][/ROW]
[ROW][C]44[/C][C]2.20592e-05[/C][C]4.41185e-05[/C][C]0.999978[/C][/ROW]
[ROW][C]45[/C][C]1.83386e-05[/C][C]3.66771e-05[/C][C]0.999982[/C][/ROW]
[ROW][C]46[/C][C]1.0602e-05[/C][C]2.1204e-05[/C][C]0.999989[/C][/ROW]
[ROW][C]47[/C][C]7.65409e-06[/C][C]1.53082e-05[/C][C]0.999992[/C][/ROW]
[ROW][C]48[/C][C]9.9314e-06[/C][C]1.98628e-05[/C][C]0.99999[/C][/ROW]
[ROW][C]49[/C][C]2.48662e-05[/C][C]4.97324e-05[/C][C]0.999975[/C][/ROW]
[ROW][C]50[/C][C]4.99829e-05[/C][C]9.99659e-05[/C][C]0.99995[/C][/ROW]
[ROW][C]51[/C][C]7.11182e-05[/C][C]0.000142236[/C][C]0.999929[/C][/ROW]
[ROW][C]52[/C][C]8.97362e-05[/C][C]0.000179472[/C][C]0.99991[/C][/ROW]
[ROW][C]53[/C][C]0.000227833[/C][C]0.000455666[/C][C]0.999772[/C][/ROW]
[ROW][C]54[/C][C]0.000185886[/C][C]0.000371771[/C][C]0.999814[/C][/ROW]
[ROW][C]55[/C][C]0.00191829[/C][C]0.00383658[/C][C]0.998082[/C][/ROW]
[ROW][C]56[/C][C]0.00475272[/C][C]0.00950544[/C][C]0.995247[/C][/ROW]
[ROW][C]57[/C][C]0.00430096[/C][C]0.00860191[/C][C]0.995699[/C][/ROW]
[ROW][C]58[/C][C]0.0053089[/C][C]0.0106178[/C][C]0.994691[/C][/ROW]
[ROW][C]59[/C][C]0.00474619[/C][C]0.00949238[/C][C]0.995254[/C][/ROW]
[ROW][C]60[/C][C]0.0113527[/C][C]0.0227054[/C][C]0.988647[/C][/ROW]
[ROW][C]61[/C][C]0.0403566[/C][C]0.0807133[/C][C]0.959643[/C][/ROW]
[ROW][C]62[/C][C]0.238557[/C][C]0.477115[/C][C]0.761443[/C][/ROW]
[ROW][C]63[/C][C]0.258153[/C][C]0.516307[/C][C]0.741847[/C][/ROW]
[ROW][C]64[/C][C]0.222902[/C][C]0.445804[/C][C]0.777098[/C][/ROW]
[ROW][C]65[/C][C]0.191867[/C][C]0.383735[/C][C]0.808133[/C][/ROW]
[ROW][C]66[/C][C]0.528473[/C][C]0.943054[/C][C]0.471527[/C][/ROW]
[ROW][C]67[/C][C]0.783891[/C][C]0.432218[/C][C]0.216109[/C][/ROW]
[ROW][C]68[/C][C]0.782942[/C][C]0.434117[/C][C]0.217058[/C][/ROW]
[ROW][C]69[/C][C]0.766199[/C][C]0.467602[/C][C]0.233801[/C][/ROW]
[ROW][C]70[/C][C]0.73651[/C][C]0.52698[/C][C]0.26349[/C][/ROW]
[ROW][C]71[/C][C]0.82067[/C][C]0.35866[/C][C]0.17933[/C][/ROW]
[ROW][C]72[/C][C]0.873566[/C][C]0.252868[/C][C]0.126434[/C][/ROW]
[ROW][C]73[/C][C]0.955351[/C][C]0.089297[/C][C]0.0446485[/C][/ROW]
[ROW][C]74[/C][C]0.967863[/C][C]0.0642734[/C][C]0.0321367[/C][/ROW]
[ROW][C]75[/C][C]0.976646[/C][C]0.0467082[/C][C]0.0233541[/C][/ROW]
[ROW][C]76[/C][C]0.984376[/C][C]0.0312481[/C][C]0.015624[/C][/ROW]
[ROW][C]77[/C][C]0.983183[/C][C]0.0336334[/C][C]0.0168167[/C][/ROW]
[ROW][C]78[/C][C]0.985583[/C][C]0.0288332[/C][C]0.0144166[/C][/ROW]
[ROW][C]79[/C][C]0.988744[/C][C]0.0225111[/C][C]0.0112555[/C][/ROW]
[ROW][C]80[/C][C]0.987925[/C][C]0.0241504[/C][C]0.0120752[/C][/ROW]
[ROW][C]81[/C][C]0.986748[/C][C]0.0265045[/C][C]0.0132523[/C][/ROW]
[ROW][C]82[/C][C]0.987236[/C][C]0.0255287[/C][C]0.0127643[/C][/ROW]
[ROW][C]83[/C][C]0.984827[/C][C]0.0303466[/C][C]0.0151733[/C][/ROW]
[ROW][C]84[/C][C]0.980159[/C][C]0.0396826[/C][C]0.0198413[/C][/ROW]
[ROW][C]85[/C][C]0.97737[/C][C]0.0452604[/C][C]0.0226302[/C][/ROW]
[ROW][C]86[/C][C]0.973628[/C][C]0.0527434[/C][C]0.0263717[/C][/ROW]
[ROW][C]87[/C][C]0.972189[/C][C]0.0556218[/C][C]0.0278109[/C][/ROW]
[ROW][C]88[/C][C]0.968731[/C][C]0.0625372[/C][C]0.0312686[/C][/ROW]
[ROW][C]89[/C][C]0.987984[/C][C]0.0240317[/C][C]0.0120158[/C][/ROW]
[ROW][C]90[/C][C]0.992436[/C][C]0.0151279[/C][C]0.00756394[/C][/ROW]
[ROW][C]91[/C][C]0.996211[/C][C]0.00757837[/C][C]0.00378918[/C][/ROW]
[ROW][C]92[/C][C]0.997736[/C][C]0.00452754[/C][C]0.00226377[/C][/ROW]
[ROW][C]93[/C][C]0.997525[/C][C]0.00495016[/C][C]0.00247508[/C][/ROW]
[ROW][C]94[/C][C]0.998728[/C][C]0.00254459[/C][C]0.00127229[/C][/ROW]
[ROW][C]95[/C][C]0.998374[/C][C]0.00325283[/C][C]0.00162641[/C][/ROW]
[ROW][C]96[/C][C]0.999178[/C][C]0.00164324[/C][C]0.000821618[/C][/ROW]
[ROW][C]97[/C][C]0.998717[/C][C]0.00256544[/C][C]0.00128272[/C][/ROW]
[ROW][C]98[/C][C]0.999565[/C][C]0.000870904[/C][C]0.000435452[/C][/ROW]
[ROW][C]99[/C][C]0.999324[/C][C]0.00135148[/C][C]0.000675738[/C][/ROW]
[ROW][C]100[/C][C]0.999536[/C][C]0.000927831[/C][C]0.000463916[/C][/ROW]
[ROW][C]101[/C][C]0.999244[/C][C]0.00151184[/C][C]0.000755918[/C][/ROW]
[ROW][C]102[/C][C]0.998927[/C][C]0.00214515[/C][C]0.00107258[/C][/ROW]
[ROW][C]103[/C][C]0.998278[/C][C]0.00344386[/C][C]0.00172193[/C][/ROW]
[ROW][C]104[/C][C]0.997891[/C][C]0.00421897[/C][C]0.00210949[/C][/ROW]
[ROW][C]105[/C][C]0.996841[/C][C]0.00631848[/C][C]0.00315924[/C][/ROW]
[ROW][C]106[/C][C]0.99728[/C][C]0.0054407[/C][C]0.00272035[/C][/ROW]
[ROW][C]107[/C][C]0.996438[/C][C]0.00712413[/C][C]0.00356206[/C][/ROW]
[ROW][C]108[/C][C]0.994681[/C][C]0.0106372[/C][C]0.00531862[/C][/ROW]
[ROW][C]109[/C][C]0.992785[/C][C]0.01443[/C][C]0.007215[/C][/ROW]
[ROW][C]110[/C][C]0.98908[/C][C]0.0218398[/C][C]0.0109199[/C][/ROW]
[ROW][C]111[/C][C]0.983452[/C][C]0.0330963[/C][C]0.0165482[/C][/ROW]
[ROW][C]112[/C][C]0.985666[/C][C]0.0286689[/C][C]0.0143344[/C][/ROW]
[ROW][C]113[/C][C]0.978395[/C][C]0.0432106[/C][C]0.0216053[/C][/ROW]
[ROW][C]114[/C][C]0.978003[/C][C]0.0439947[/C][C]0.0219973[/C][/ROW]
[ROW][C]115[/C][C]0.988977[/C][C]0.0220451[/C][C]0.0110225[/C][/ROW]
[ROW][C]116[/C][C]0.987339[/C][C]0.0253217[/C][C]0.0126608[/C][/ROW]
[ROW][C]117[/C][C]0.99817[/C][C]0.00366033[/C][C]0.00183017[/C][/ROW]
[ROW][C]118[/C][C]0.997679[/C][C]0.00464252[/C][C]0.00232126[/C][/ROW]
[ROW][C]119[/C][C]0.996666[/C][C]0.00666854[/C][C]0.00333427[/C][/ROW]
[ROW][C]120[/C][C]0.997924[/C][C]0.00415158[/C][C]0.00207579[/C][/ROW]
[ROW][C]121[/C][C]0.997142[/C][C]0.00571522[/C][C]0.00285761[/C][/ROW]
[ROW][C]122[/C][C]0.99777[/C][C]0.00446019[/C][C]0.0022301[/C][/ROW]
[ROW][C]123[/C][C]0.997597[/C][C]0.00480694[/C][C]0.00240347[/C][/ROW]
[ROW][C]124[/C][C]0.997193[/C][C]0.00561303[/C][C]0.00280651[/C][/ROW]
[ROW][C]125[/C][C]0.998134[/C][C]0.00373277[/C][C]0.00186639[/C][/ROW]
[ROW][C]126[/C][C]0.996128[/C][C]0.00774439[/C][C]0.0038722[/C][/ROW]
[ROW][C]127[/C][C]0.995723[/C][C]0.0085537[/C][C]0.00427685[/C][/ROW]
[ROW][C]128[/C][C]0.997011[/C][C]0.00597814[/C][C]0.00298907[/C][/ROW]
[ROW][C]129[/C][C]0.993809[/C][C]0.012382[/C][C]0.00619099[/C][/ROW]
[ROW][C]130[/C][C]0.998168[/C][C]0.00366347[/C][C]0.00183174[/C][/ROW]
[ROW][C]131[/C][C]0.997649[/C][C]0.00470209[/C][C]0.00235104[/C][/ROW]
[ROW][C]132[/C][C]0.996774[/C][C]0.00645251[/C][C]0.00322626[/C][/ROW]
[ROW][C]133[/C][C]0.992449[/C][C]0.0151014[/C][C]0.0075507[/C][/ROW]
[ROW][C]134[/C][C]0.986804[/C][C]0.0263919[/C][C]0.0131959[/C][/ROW]
[ROW][C]135[/C][C]0.960106[/C][C]0.0797888[/C][C]0.0398944[/C][/ROW]
[ROW][C]136[/C][C]0.890149[/C][C]0.219702[/C][C]0.109851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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
130.3399390.6798790.660061
140.2298070.4596130.770193
150.1577950.315590.842205
160.09422030.1884410.90578
170.07038790.1407760.929612
180.05550690.1110140.944493
190.08315850.1663170.916841
200.067640.135280.93236
210.04763680.09527350.952363
220.03562360.07124710.964376
230.02323080.04646150.976769
240.01490590.02981180.985094
250.01122850.02245690.988772
260.007410040.01482010.99259
270.005945810.01189160.994054
280.006756260.01351250.993244
290.00530680.01061360.994693
300.003187170.006374340.996813
310.002155290.004310580.997845
320.001708370.003416740.998292
330.001314420.002628830.998686
340.0007049950.001409990.999295
350.0005184190.001036840.999482
360.0002925790.0005851580.999707
370.000308770.000617540.999691
380.0002936440.0005872890.999706
390.0001647370.0003294740.999835
408.76827e-050.0001753650.999912
418.0875e-050.000161750.999919
425.66521e-050.0001133040.999943
433.30545e-056.6109e-050.999967
442.20592e-054.41185e-050.999978
451.83386e-053.66771e-050.999982
461.0602e-052.1204e-050.999989
477.65409e-061.53082e-050.999992
489.9314e-061.98628e-050.99999
492.48662e-054.97324e-050.999975
504.99829e-059.99659e-050.99995
517.11182e-050.0001422360.999929
528.97362e-050.0001794720.99991
530.0002278330.0004556660.999772
540.0001858860.0003717710.999814
550.001918290.003836580.998082
560.004752720.009505440.995247
570.004300960.008601910.995699
580.00530890.01061780.994691
590.004746190.009492380.995254
600.01135270.02270540.988647
610.04035660.08071330.959643
620.2385570.4771150.761443
630.2581530.5163070.741847
640.2229020.4458040.777098
650.1918670.3837350.808133
660.5284730.9430540.471527
670.7838910.4322180.216109
680.7829420.4341170.217058
690.7661990.4676020.233801
700.736510.526980.26349
710.820670.358660.17933
720.8735660.2528680.126434
730.9553510.0892970.0446485
740.9678630.06427340.0321367
750.9766460.04670820.0233541
760.9843760.03124810.015624
770.9831830.03363340.0168167
780.9855830.02883320.0144166
790.9887440.02251110.0112555
800.9879250.02415040.0120752
810.9867480.02650450.0132523
820.9872360.02552870.0127643
830.9848270.03034660.0151733
840.9801590.03968260.0198413
850.977370.04526040.0226302
860.9736280.05274340.0263717
870.9721890.05562180.0278109
880.9687310.06253720.0312686
890.9879840.02403170.0120158
900.9924360.01512790.00756394
910.9962110.007578370.00378918
920.9977360.004527540.00226377
930.9975250.004950160.00247508
940.9987280.002544590.00127229
950.9983740.003252830.00162641
960.9991780.001643240.000821618
970.9987170.002565440.00128272
980.9995650.0008709040.000435452
990.9993240.001351480.000675738
1000.9995360.0009278310.000463916
1010.9992440.001511840.000755918
1020.9989270.002145150.00107258
1030.9982780.003443860.00172193
1040.9978910.004218970.00210949
1050.9968410.006318480.00315924
1060.997280.00544070.00272035
1070.9964380.007124130.00356206
1080.9946810.01063720.00531862
1090.9927850.014430.007215
1100.989080.02183980.0109199
1110.9834520.03309630.0165482
1120.9856660.02866890.0143344
1130.9783950.04321060.0216053
1140.9780030.04399470.0219973
1150.9889770.02204510.0110225
1160.9873390.02532170.0126608
1170.998170.003660330.00183017
1180.9976790.004642520.00232126
1190.9966660.006668540.00333427
1200.9979240.004151580.00207579
1210.9971420.005715220.00285761
1220.997770.004460190.0022301
1230.9975970.004806940.00240347
1240.9971930.005613030.00280651
1250.9981340.003732770.00186639
1260.9961280.007744390.0038722
1270.9957230.00855370.00427685
1280.9970110.005978140.00298907
1290.9938090.0123820.00619099
1300.9981680.003663470.00183174
1310.9976490.004702090.00235104
1320.9967740.006452510.00322626
1330.9924490.01510140.0075507
1340.9868040.02639190.0131959
1350.9601060.07978880.0398944
1360.8901490.2197020.109851






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level610.491935NOK
5% type I error level950.766129NOK
10% type I error level1040.83871NOK

\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 & 61 & 0.491935 & NOK \tabularnewline
5% type I error level & 95 & 0.766129 & NOK \tabularnewline
10% type I error level & 104 & 0.83871 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268270&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]61[/C][C]0.491935[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]95[/C][C]0.766129[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]104[/C][C]0.83871[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268270&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268270&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 level610.491935NOK
5% type I error level950.766129NOK
10% type I error level1040.83871NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}