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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 12:29:25 +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/t1418646638hffepapbcyqjhd7.htm/, Retrieved Thu, 16 May 2024 15:48:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268242, Retrieved Thu, 16 May 2024 15:48:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact49

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 12:29:25] [4bf1efda48b6e8e35beb7b429a900cbb] [Current]
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Dataset

Dataseries X:
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Boxplots 
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
16,6	0	52	57	30	145	263	109	3	8,5
12,6	1	55	56	17	23	35	68	1,5	7
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
18,9	1	72	71	24	160	227	131	3	9,5
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
11,6	1	33	56	20	32	79	71	1,5	6
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
14,6	1	66	62	25	40	130	68	1,5	9
13,85	1	66	59	20	58	179	89	2,25	7,5
18,95	1	73	61	28	123	243	167	2,25	10
15,6	0	55	71	23	71	190	113	1,5	9
14,85	0	64	57	27	102	299	115	2,25	7,5
11,75	0	40	66	18	80	121	78	1,5	6
18,45	0	46	63	28	97	137	118	2,25	10,5
15,9	1	58	69	21	46	305	87	3	8,5
17,1	0	43	58	19	93	157	173	3	8
16,1	1	61	59	23	19	96	2	3	10
19,9	0	51	48	27	140	183	162	3	10,5
10,95	1	50	66	22	78	52	49	1,5	6,5
18,45	0	52	73	28	98	238	122	2,25	9,5
15,1	1	54	67	25	40	40	96	1,5	8,5
15	0	66	61	21	80	226	100	2,25	7,5
11,35	0	61	68	22	76	190	82	2,25	5
15,95	1	80	75	28	79	214	100	2,25	8
18,1	0	51	62	20	87	145	115	3	10
14,6	1	56	69	29	95	119	141	1,5	7
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
17,6	1	53	74	20	80	159	110	3	9,5
13,35	1	47	55	20	86	165	118	2,25	6
19,1	0	25	62	16	69	249	158	3	10
15,35	1	47	63	20	79	125	146	2,25	7
7,6	0	46	69	20	52	122	49	1,5	3
13,4	0	50	58	23	120	186	90	3	6
13,9	0	39	58	18	69	148	121	1,5	7
19,1	1	51	68	25	94	274	155	3	10
15,25	0	58	72	18	72	172	104	3	7
12,9	1	35	62	19	43	84	147	3	3,5
16,1	0	58	62	25	87	168	110	3	8
17,35	0	60	65	25	52	102	108	2,25	10
13,15	0	62	69	25	71	106	113	2,25	5,5
12,15	0	63	66	24	61	2	115	0,75	6
12,6	1	53	72	19	51	139	61	3	6,5
10,35	1	46	62	26	50	95	60	0,75	6,5
15,4	1	67	75	10	67	130	109	1,5	8,5
9,6	1	59	58	17	30	72	68	1,5	4
18,2	0	64	66	13	70	141	111	3	9,5
13,6	0	38	55	17	52	113	77	1,5	8
14,85	1	50	47	30	75	206	73	2,25	8,5
14,75	0	48	72	25	87	268	151	3	5,5
14,1	0	48	62	4	69	175	89	3	7
14,9	0	47	64	16	72	77	78	1,5	9
16,25	0	66	64	21	79	125	110	3	8
19,25	1	47	19	23	121	255	220	3	10
13,6	1	63	50	22	43	111	65	1,5	8
13,6	0	58	68	17	58	132	141	1,5	6
15,65	0	44	70	20	57	211	117	2,25	8
12,75	1	51	79	20	50	92	122	1,5	5
14,6	0	43	69	22	69	76	63	1,5	9
9,85	1	55	71	16	64	171	44	2,25	4,5
12,65	1	38	48	23	38	83	52	1,5	8,5
11,9	1	56	66	16	53	119	62	2,25	7
19,2	0	45	73	0	90	266	131	3	9,5
16,6	1	50	74	18	96	186	101	3	8,5
11,2	1	54	66	25	49	50	42	0,75	7,5
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
13,2	0	55	78	18	40	246	77	2,25	7
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
15,85	1	37	60	18	78	137	96	2,25	8,5
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
11,15	1	46	69	24	59	98	57	0,75	7
15,65	0	51	65	29	96	226	112	2,25	8
17,75	0	58	78	18	86	234	143	3	8,5
7,65	0	64	78	15	38	138	49	0,75	3,5
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
15,2	0	47	63	22	48	106	56	3	8,5
17,1	0	59	63	16	26	135	137	3	8
15,6	1	62	71	23	91	122	86	1,5	10
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
13,1	1	67	80	24	52	94	88	1,5	7,5
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
11,85	0	56	73	22	27	62	48	2,25	6,5
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
12,4	1	65	69	15	62	82	85	3	5
13,35	0	71	69	24	65	207	109	2,25	6
11,4	0	50	84	17	58	184	63	3	4
14,9	1	57	64	20	76	83	102	1,5	8
19,9	0	47	58	27	140	183	162	3	10,5
17,75	1	54	60	23	48	85	128	3	8,5
11,2	1	47	59	26	68	89	86	0,75	6,5
14,6	1	57	78	23	80	225	114	1,5	8
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
14,75	1	50	67	23	70	204	94	1,5	9
15,15	0	22	60	15	78	158	81	2,25	8,5
13,2	1	41	65	27	56	138	166	2,25	5
16,85	0	59	66	26	100	226	110	2,25	9,5
7,85	1	56	74	22	51	44	64	0,75	3
7,7	0	66	81	22	52	196	93	2,25	1,5
12,6	0	53	72	18	102	83	104	1,5	6
7,85	1	42	55	15	78	79	105	2,25	0,5
10,95	1	52	49	22	78	52	49	1,5	6,5
12,35	0	54	74	27	55	105	88	0,75	7,5
9,95	1	44	53	10	98	116	95	1,5	4,5
14,9	1	62	64	20	76	83	102	1,5	8
16,65	0	53	65	17	73	196	99	2,25	9
13,4	1	50	57	23	47	153	63	1,5	7,5
13,95	0	36	51	19	45	157	76	1,5	8,5
15,7	0	76	80	13	83	75	109	3	7
16,85	1	66	67	27	60	106	117	2,25	9,5
10,95	1	62	70	23	48	58	57	1,5	6,5
15,35	0	59	74	16	50	75	120	0,75	9,5
12,2	1	47	75	25	56	74	73	2,25	6
15,1	0	55	70	2	77	185	91	3	8
17,75	0	58	69	26	91	265	108	3	9,5
15,2	1	60	65	20	76	131	105	1,5	8
14,6	0	44	55	23	68	139	117	1,5	8
16,65	0	57	71	22	74	196	119	2,25	9
8,1	1	45	65	24	29	78	31	0,75	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 1.36449 -0.0798793geslacht[t] + 0.0031986IM[t] + 0.0107415EM[t] -0.00869917Numeracy_tot[t] + 0.00118352uren_rfc[t] -0.000714387blogs[t] + 0.0251669zinvolle_teksten[t] + 1.05579PE[t] + 1.02818ruwe_examenscore[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  1.36449 -0.0798793geslacht[t] +  0.0031986IM[t] +  0.0107415EM[t] -0.00869917Numeracy_tot[t] +  0.00118352uren_rfc[t] -0.000714387blogs[t] +  0.0251669zinvolle_teksten[t] +  1.05579PE[t] +  1.02818ruwe_examenscore[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  1.36449 -0.0798793geslacht[t] +  0.0031986IM[t] +  0.0107415EM[t] -0.00869917Numeracy_tot[t] +  0.00118352uren_rfc[t] -0.000714387blogs[t] +  0.0251669zinvolle_teksten[t] +  1.05579PE[t] +  1.02818ruwe_examenscore[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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] = + 1.36449 -0.0798793geslacht[t] + 0.0031986IM[t] + 0.0107415EM[t] -0.00869917Numeracy_tot[t] + 0.00118352uren_rfc[t] -0.000714387blogs[t] + 0.0251669zinvolle_teksten[t] + 1.05579PE[t] + 1.02818ruwe_examenscore[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.364490.3197914.2673.37335e-051.68668e-05
geslacht-0.07987930.0692739-1.1530.2505810.125291
IM0.00319860.003634170.88010.3800920.190046
EM0.01074150.004073332.6370.009182690.00459135
Numeracy_tot-0.008699170.00620114-1.4030.1625920.0812958
uren_rfc0.001183520.001389520.85170.3956190.19781
blogs-0.0007143870.000621542-1.1490.2521050.126052
zinvolle_teksten0.02516690.0010150924.797.5431e-573.77155e-57
PE1.055790.054232819.473.68053e-441.84027e-44
ruwe_examenscore1.028180.017320259.362.28413e-1111.14206e-111

\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) & 1.36449 & 0.319791 & 4.267 & 3.37335e-05 & 1.68668e-05 \tabularnewline
geslacht & -0.0798793 & 0.0692739 & -1.153 & 0.250581 & 0.125291 \tabularnewline
IM & 0.0031986 & 0.00363417 & 0.8801 & 0.380092 & 0.190046 \tabularnewline
EM & 0.0107415 & 0.00407333 & 2.637 & 0.00918269 & 0.00459135 \tabularnewline
Numeracy_tot & -0.00869917 & 0.00620114 & -1.403 & 0.162592 & 0.0812958 \tabularnewline
uren_rfc & 0.00118352 & 0.00138952 & 0.8517 & 0.395619 & 0.19781 \tabularnewline
blogs & -0.000714387 & 0.000621542 & -1.149 & 0.252105 & 0.126052 \tabularnewline
zinvolle_teksten & 0.0251669 & 0.00101509 & 24.79 & 7.5431e-57 & 3.77155e-57 \tabularnewline
PE & 1.05579 & 0.0542328 & 19.47 & 3.68053e-44 & 1.84027e-44 \tabularnewline
ruwe_examenscore & 1.02818 & 0.0173202 & 59.36 & 2.28413e-111 & 1.14206e-111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&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]1.36449[/C][C]0.319791[/C][C]4.267[/C][C]3.37335e-05[/C][C]1.68668e-05[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.0798793[/C][C]0.0692739[/C][C]-1.153[/C][C]0.250581[/C][C]0.125291[/C][/ROW]
[ROW][C]IM[/C][C]0.0031986[/C][C]0.00363417[/C][C]0.8801[/C][C]0.380092[/C][C]0.190046[/C][/ROW]
[ROW][C]EM[/C][C]0.0107415[/C][C]0.00407333[/C][C]2.637[/C][C]0.00918269[/C][C]0.00459135[/C][/ROW]
[ROW][C]Numeracy_tot[/C][C]-0.00869917[/C][C]0.00620114[/C][C]-1.403[/C][C]0.162592[/C][C]0.0812958[/C][/ROW]
[ROW][C]uren_rfc[/C][C]0.00118352[/C][C]0.00138952[/C][C]0.8517[/C][C]0.395619[/C][C]0.19781[/C][/ROW]
[ROW][C]blogs[/C][C]-0.000714387[/C][C]0.000621542[/C][C]-1.149[/C][C]0.252105[/C][C]0.126052[/C][/ROW]
[ROW][C]zinvolle_teksten[/C][C]0.0251669[/C][C]0.00101509[/C][C]24.79[/C][C]7.5431e-57[/C][C]3.77155e-57[/C][/ROW]
[ROW][C]PE[/C][C]1.05579[/C][C]0.0542328[/C][C]19.47[/C][C]3.68053e-44[/C][C]1.84027e-44[/C][/ROW]
[ROW][C]ruwe_examenscore[/C][C]1.02818[/C][C]0.0173202[/C][C]59.36[/C][C]2.28413e-111[/C][C]1.14206e-111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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)1.364490.3197914.2673.37335e-051.68668e-05
geslacht-0.07987930.0692739-1.1530.2505810.125291
IM0.00319860.003634170.88010.3800920.190046
EM0.01074150.004073332.6370.009182690.00459135
Numeracy_tot-0.008699170.00620114-1.4030.1625920.0812958
uren_rfc0.001183520.001389520.85170.3956190.19781
blogs-0.0007143870.000621542-1.1490.2521050.126052
zinvolle_teksten0.02516690.0010150924.797.5431e-573.77155e-57
PE1.055790.054232819.473.68053e-441.84027e-44
ruwe_examenscore1.028180.017320259.362.28413e-1111.14206e-111






Multiple Linear Regression - Regression Statistics
Multiple R0.990809
R-squared0.981702
Adjusted R-squared0.980679
F-TEST (value)959.759
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.420971
Sum Squared Residuals28.5318

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990809 \tabularnewline
R-squared & 0.981702 \tabularnewline
Adjusted R-squared & 0.980679 \tabularnewline
F-TEST (value) & 959.759 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.420971 \tabularnewline
Sum Squared Residuals & 28.5318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990809[/C][/ROW]
[ROW][C]R-squared[/C][C]0.981702[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980679[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]959.759[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]161[/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]0.420971[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.5318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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 R0.990809
R-squared0.981702
Adjusted R-squared0.980679
F-TEST (value)959.759
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.420971
Sum Squared Residuals28.5318






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.354.323570.0264302
212.712.26720.432774
318.118.1176-0.0176218
417.8517.9509-0.100884
516.616.51590.0840506
612.612.40870.191297
717.117.9041-0.804063
819.119.4765-0.376507
916.116.3732-0.273164
1013.3513.2770.0730215
1118.417.86490.535143
1214.715.2147-0.514746
1310.610.13450.465529
1412.612.54830.0516547
1516.215.92050.279515
1613.613.37910.220928
1718.918.32790.572056
1814.113.73420.365806
1914.514.29530.204742
2016.1516.3382-0.18815
2114.7514.71390.0361462
2214.814.44930.350736
2312.4512.5161-0.066126
2412.6512.54160.108389
2517.3517.5437-0.193655
268.68.523350.076652
2718.418.22390.176135
2816.116.1062-0.00615768
2911.611.33880.261227
3017.7518.0815-0.331497
3115.2515.5109-0.260871
3217.6516.76980.880214
3315.615.28780.312225
3416.3516.3834-0.033375
3517.6517.07880.571216
3613.613.7878-0.187802
3711.711.43520.264784
3814.3514.33160.0184177
3914.7514.09550.654461
4018.2518.5233-0.273279
419.910.0856-0.185635
421615.8840.116036
4318.2518.1850.0650095
4416.8517.2543-0.40426
4514.614.44740.152638
4613.8514.223-0.373004
4718.9518.7620.188033
4815.615.7325-0.132465
4914.8514.83480.0152027
5011.7511.7688-0.0188294
5118.4518.10280.347166
5215.915.9616-0.0616078
5317.117.7044-0.604367
5416.115.36680.733171
5519.919.88360.0163812
5610.9511.5173-0.567316
5718.4517.2311.21904
5815.114.71760.382437
591514.5850.415035
6011.3511.633-0.282987
6115.9515.16080.789166
6218.118.3624-0.262379
6314.614.30950.29046
6415.416.0081-0.608095
6515.416.0296-0.629578
6617.617.7596-0.159583
6713.3513.352.86481e-05
6819.119.3006-0.200589
6915.3515.18910.16095
707.67.95461-0.354606
7113.413.558-0.15798
7213.913.75780.142249
7319.119.2263-0.126258
7415.2515.11120.138842
7512.912.35370.546322
7616.116.1426-0.0426425
7717.3517.4012-0.0511798
7813.1512.96920.180815
7912.1511.99210.15794
8012.613.409-0.809039
8110.3510.8479-0.497895
8215.415.27040.129605
839.69.340280.259715
8418.217.87580.3242
8513.613.6567-0.0567491
8614.8514.58230.26772
8714.7514.6080.14198
8814.114.7103-0.610348
8914.914.89360.00635255
9016.2516.24580.00423846
9119.2520.3859-1.1359
9213.613.24840.351595
9313.613.40820.191795
9415.6515.54540.104608
9512.7511.91070.839251
9614.614.5020.097974
979.8510.1473-0.297273
9812.6513.3393-0.689265
9911.913.1444-1.24435
10019.218.4410.758984
10116.616.51230.0876509
10211.211.5313-0.331292
10315.2515.13260.117384
10411.911.62860.271386
10513.213.6039-0.403925
10616.3516.5362-0.186175
10712.411.71940.680601
10815.8515.41640.433589
10914.3514.27670.0732594
11018.1518.5084-0.358413
11111.1511.3876-0.237584
11215.6515.34540.304611
11317.7517.67170.0783326
1147.657.8466-0.1966
11512.3512.06920.280832
11615.615.19980.400162
11719.320.2029-0.902936
11815.215.2975-0.0975102
11917.116.86580.234239
12015.616.0959-0.495901
12118.418.5276-0.127621
12219.0518.69820.351796
12318.5518.9-0.349966
12419.118.93170.168308
12513.113.6536-0.553591
12612.8513.2801-0.430097
1279.59.90325-0.403255
1284.54.489570.010435
12911.8512.3907-0.540749
13013.613.55090.0491237
13111.711.34940.350612
13212.412.5655-0.165465
13313.3513.34080.00915855
13411.411.08160.318366
13514.914.38720.512772
13619.919.9782-0.0782398
13717.7517.02610.723884
13811.211.4988-0.298799
13914.614.7168-0.116806
14017.617.9154-0.315424
14114.0513.72060.329424
14216.115.55740.542597
14313.3512.85270.497267
14411.8511.58470.265333
14511.9512.4468-0.496767
14614.7515.1043-0.354271
14715.1515.08190.0680971
14813.213.5409-0.340912
14916.8516.9045-0.0544913
1507.857.583220.266778
1517.78.43413-0.734126
15212.612.58240.0176159
1537.857.447270.402731
15410.9511.3411-0.391107
15512.3512.8052-0.455192
1569.9510.5421-0.592137
15714.914.40320.496779
15816.6516.15140.4986
15913.412.68360.71638
16013.9514.0392-0.0891941
16115.715.50630.193691
16216.8517.0636-0.213598
16310.9511.7515-0.80151
16415.3515.7941-0.444096
16512.212.4183-0.218299
16615.115.9169-0.816909
16717.7517.63650.113487
16815.214.44880.751224
16914.614.6308-0.0307841
17016.6516.6897-0.0396693
1718.18.6095-0.509496

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 4.32357 & 0.0264302 \tabularnewline
2 & 12.7 & 12.2672 & 0.432774 \tabularnewline
3 & 18.1 & 18.1176 & -0.0176218 \tabularnewline
4 & 17.85 & 17.9509 & -0.100884 \tabularnewline
5 & 16.6 & 16.5159 & 0.0840506 \tabularnewline
6 & 12.6 & 12.4087 & 0.191297 \tabularnewline
7 & 17.1 & 17.9041 & -0.804063 \tabularnewline
8 & 19.1 & 19.4765 & -0.376507 \tabularnewline
9 & 16.1 & 16.3732 & -0.273164 \tabularnewline
10 & 13.35 & 13.277 & 0.0730215 \tabularnewline
11 & 18.4 & 17.8649 & 0.535143 \tabularnewline
12 & 14.7 & 15.2147 & -0.514746 \tabularnewline
13 & 10.6 & 10.1345 & 0.465529 \tabularnewline
14 & 12.6 & 12.5483 & 0.0516547 \tabularnewline
15 & 16.2 & 15.9205 & 0.279515 \tabularnewline
16 & 13.6 & 13.3791 & 0.220928 \tabularnewline
17 & 18.9 & 18.3279 & 0.572056 \tabularnewline
18 & 14.1 & 13.7342 & 0.365806 \tabularnewline
19 & 14.5 & 14.2953 & 0.204742 \tabularnewline
20 & 16.15 & 16.3382 & -0.18815 \tabularnewline
21 & 14.75 & 14.7139 & 0.0361462 \tabularnewline
22 & 14.8 & 14.4493 & 0.350736 \tabularnewline
23 & 12.45 & 12.5161 & -0.066126 \tabularnewline
24 & 12.65 & 12.5416 & 0.108389 \tabularnewline
25 & 17.35 & 17.5437 & -0.193655 \tabularnewline
26 & 8.6 & 8.52335 & 0.076652 \tabularnewline
27 & 18.4 & 18.2239 & 0.176135 \tabularnewline
28 & 16.1 & 16.1062 & -0.00615768 \tabularnewline
29 & 11.6 & 11.3388 & 0.261227 \tabularnewline
30 & 17.75 & 18.0815 & -0.331497 \tabularnewline
31 & 15.25 & 15.5109 & -0.260871 \tabularnewline
32 & 17.65 & 16.7698 & 0.880214 \tabularnewline
33 & 15.6 & 15.2878 & 0.312225 \tabularnewline
34 & 16.35 & 16.3834 & -0.033375 \tabularnewline
35 & 17.65 & 17.0788 & 0.571216 \tabularnewline
36 & 13.6 & 13.7878 & -0.187802 \tabularnewline
37 & 11.7 & 11.4352 & 0.264784 \tabularnewline
38 & 14.35 & 14.3316 & 0.0184177 \tabularnewline
39 & 14.75 & 14.0955 & 0.654461 \tabularnewline
40 & 18.25 & 18.5233 & -0.273279 \tabularnewline
41 & 9.9 & 10.0856 & -0.185635 \tabularnewline
42 & 16 & 15.884 & 0.116036 \tabularnewline
43 & 18.25 & 18.185 & 0.0650095 \tabularnewline
44 & 16.85 & 17.2543 & -0.40426 \tabularnewline
45 & 14.6 & 14.4474 & 0.152638 \tabularnewline
46 & 13.85 & 14.223 & -0.373004 \tabularnewline
47 & 18.95 & 18.762 & 0.188033 \tabularnewline
48 & 15.6 & 15.7325 & -0.132465 \tabularnewline
49 & 14.85 & 14.8348 & 0.0152027 \tabularnewline
50 & 11.75 & 11.7688 & -0.0188294 \tabularnewline
51 & 18.45 & 18.1028 & 0.347166 \tabularnewline
52 & 15.9 & 15.9616 & -0.0616078 \tabularnewline
53 & 17.1 & 17.7044 & -0.604367 \tabularnewline
54 & 16.1 & 15.3668 & 0.733171 \tabularnewline
55 & 19.9 & 19.8836 & 0.0163812 \tabularnewline
56 & 10.95 & 11.5173 & -0.567316 \tabularnewline
57 & 18.45 & 17.231 & 1.21904 \tabularnewline
58 & 15.1 & 14.7176 & 0.382437 \tabularnewline
59 & 15 & 14.585 & 0.415035 \tabularnewline
60 & 11.35 & 11.633 & -0.282987 \tabularnewline
61 & 15.95 & 15.1608 & 0.789166 \tabularnewline
62 & 18.1 & 18.3624 & -0.262379 \tabularnewline
63 & 14.6 & 14.3095 & 0.29046 \tabularnewline
64 & 15.4 & 16.0081 & -0.608095 \tabularnewline
65 & 15.4 & 16.0296 & -0.629578 \tabularnewline
66 & 17.6 & 17.7596 & -0.159583 \tabularnewline
67 & 13.35 & 13.35 & 2.86481e-05 \tabularnewline
68 & 19.1 & 19.3006 & -0.200589 \tabularnewline
69 & 15.35 & 15.1891 & 0.16095 \tabularnewline
70 & 7.6 & 7.95461 & -0.354606 \tabularnewline
71 & 13.4 & 13.558 & -0.15798 \tabularnewline
72 & 13.9 & 13.7578 & 0.142249 \tabularnewline
73 & 19.1 & 19.2263 & -0.126258 \tabularnewline
74 & 15.25 & 15.1112 & 0.138842 \tabularnewline
75 & 12.9 & 12.3537 & 0.546322 \tabularnewline
76 & 16.1 & 16.1426 & -0.0426425 \tabularnewline
77 & 17.35 & 17.4012 & -0.0511798 \tabularnewline
78 & 13.15 & 12.9692 & 0.180815 \tabularnewline
79 & 12.15 & 11.9921 & 0.15794 \tabularnewline
80 & 12.6 & 13.409 & -0.809039 \tabularnewline
81 & 10.35 & 10.8479 & -0.497895 \tabularnewline
82 & 15.4 & 15.2704 & 0.129605 \tabularnewline
83 & 9.6 & 9.34028 & 0.259715 \tabularnewline
84 & 18.2 & 17.8758 & 0.3242 \tabularnewline
85 & 13.6 & 13.6567 & -0.0567491 \tabularnewline
86 & 14.85 & 14.5823 & 0.26772 \tabularnewline
87 & 14.75 & 14.608 & 0.14198 \tabularnewline
88 & 14.1 & 14.7103 & -0.610348 \tabularnewline
89 & 14.9 & 14.8936 & 0.00635255 \tabularnewline
90 & 16.25 & 16.2458 & 0.00423846 \tabularnewline
91 & 19.25 & 20.3859 & -1.1359 \tabularnewline
92 & 13.6 & 13.2484 & 0.351595 \tabularnewline
93 & 13.6 & 13.4082 & 0.191795 \tabularnewline
94 & 15.65 & 15.5454 & 0.104608 \tabularnewline
95 & 12.75 & 11.9107 & 0.839251 \tabularnewline
96 & 14.6 & 14.502 & 0.097974 \tabularnewline
97 & 9.85 & 10.1473 & -0.297273 \tabularnewline
98 & 12.65 & 13.3393 & -0.689265 \tabularnewline
99 & 11.9 & 13.1444 & -1.24435 \tabularnewline
100 & 19.2 & 18.441 & 0.758984 \tabularnewline
101 & 16.6 & 16.5123 & 0.0876509 \tabularnewline
102 & 11.2 & 11.5313 & -0.331292 \tabularnewline
103 & 15.25 & 15.1326 & 0.117384 \tabularnewline
104 & 11.9 & 11.6286 & 0.271386 \tabularnewline
105 & 13.2 & 13.6039 & -0.403925 \tabularnewline
106 & 16.35 & 16.5362 & -0.186175 \tabularnewline
107 & 12.4 & 11.7194 & 0.680601 \tabularnewline
108 & 15.85 & 15.4164 & 0.433589 \tabularnewline
109 & 14.35 & 14.2767 & 0.0732594 \tabularnewline
110 & 18.15 & 18.5084 & -0.358413 \tabularnewline
111 & 11.15 & 11.3876 & -0.237584 \tabularnewline
112 & 15.65 & 15.3454 & 0.304611 \tabularnewline
113 & 17.75 & 17.6717 & 0.0783326 \tabularnewline
114 & 7.65 & 7.8466 & -0.1966 \tabularnewline
115 & 12.35 & 12.0692 & 0.280832 \tabularnewline
116 & 15.6 & 15.1998 & 0.400162 \tabularnewline
117 & 19.3 & 20.2029 & -0.902936 \tabularnewline
118 & 15.2 & 15.2975 & -0.0975102 \tabularnewline
119 & 17.1 & 16.8658 & 0.234239 \tabularnewline
120 & 15.6 & 16.0959 & -0.495901 \tabularnewline
121 & 18.4 & 18.5276 & -0.127621 \tabularnewline
122 & 19.05 & 18.6982 & 0.351796 \tabularnewline
123 & 18.55 & 18.9 & -0.349966 \tabularnewline
124 & 19.1 & 18.9317 & 0.168308 \tabularnewline
125 & 13.1 & 13.6536 & -0.553591 \tabularnewline
126 & 12.85 & 13.2801 & -0.430097 \tabularnewline
127 & 9.5 & 9.90325 & -0.403255 \tabularnewline
128 & 4.5 & 4.48957 & 0.010435 \tabularnewline
129 & 11.85 & 12.3907 & -0.540749 \tabularnewline
130 & 13.6 & 13.5509 & 0.0491237 \tabularnewline
131 & 11.7 & 11.3494 & 0.350612 \tabularnewline
132 & 12.4 & 12.5655 & -0.165465 \tabularnewline
133 & 13.35 & 13.3408 & 0.00915855 \tabularnewline
134 & 11.4 & 11.0816 & 0.318366 \tabularnewline
135 & 14.9 & 14.3872 & 0.512772 \tabularnewline
136 & 19.9 & 19.9782 & -0.0782398 \tabularnewline
137 & 17.75 & 17.0261 & 0.723884 \tabularnewline
138 & 11.2 & 11.4988 & -0.298799 \tabularnewline
139 & 14.6 & 14.7168 & -0.116806 \tabularnewline
140 & 17.6 & 17.9154 & -0.315424 \tabularnewline
141 & 14.05 & 13.7206 & 0.329424 \tabularnewline
142 & 16.1 & 15.5574 & 0.542597 \tabularnewline
143 & 13.35 & 12.8527 & 0.497267 \tabularnewline
144 & 11.85 & 11.5847 & 0.265333 \tabularnewline
145 & 11.95 & 12.4468 & -0.496767 \tabularnewline
146 & 14.75 & 15.1043 & -0.354271 \tabularnewline
147 & 15.15 & 15.0819 & 0.0680971 \tabularnewline
148 & 13.2 & 13.5409 & -0.340912 \tabularnewline
149 & 16.85 & 16.9045 & -0.0544913 \tabularnewline
150 & 7.85 & 7.58322 & 0.266778 \tabularnewline
151 & 7.7 & 8.43413 & -0.734126 \tabularnewline
152 & 12.6 & 12.5824 & 0.0176159 \tabularnewline
153 & 7.85 & 7.44727 & 0.402731 \tabularnewline
154 & 10.95 & 11.3411 & -0.391107 \tabularnewline
155 & 12.35 & 12.8052 & -0.455192 \tabularnewline
156 & 9.95 & 10.5421 & -0.592137 \tabularnewline
157 & 14.9 & 14.4032 & 0.496779 \tabularnewline
158 & 16.65 & 16.1514 & 0.4986 \tabularnewline
159 & 13.4 & 12.6836 & 0.71638 \tabularnewline
160 & 13.95 & 14.0392 & -0.0891941 \tabularnewline
161 & 15.7 & 15.5063 & 0.193691 \tabularnewline
162 & 16.85 & 17.0636 & -0.213598 \tabularnewline
163 & 10.95 & 11.7515 & -0.80151 \tabularnewline
164 & 15.35 & 15.7941 & -0.444096 \tabularnewline
165 & 12.2 & 12.4183 & -0.218299 \tabularnewline
166 & 15.1 & 15.9169 & -0.816909 \tabularnewline
167 & 17.75 & 17.6365 & 0.113487 \tabularnewline
168 & 15.2 & 14.4488 & 0.751224 \tabularnewline
169 & 14.6 & 14.6308 & -0.0307841 \tabularnewline
170 & 16.65 & 16.6897 & -0.0396693 \tabularnewline
171 & 8.1 & 8.6095 & -0.509496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&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]4.32357[/C][C]0.0264302[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.2672[/C][C]0.432774[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]18.1176[/C][C]-0.0176218[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.9509[/C][C]-0.100884[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.5159[/C][C]0.0840506[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.4087[/C][C]0.191297[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]17.9041[/C][C]-0.804063[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]19.4765[/C][C]-0.376507[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.3732[/C][C]-0.273164[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]13.277[/C][C]0.0730215[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.8649[/C][C]0.535143[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]15.2147[/C][C]-0.514746[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1345[/C][C]0.465529[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]12.5483[/C][C]0.0516547[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.9205[/C][C]0.279515[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3791[/C][C]0.220928[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]18.3279[/C][C]0.572056[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.7342[/C][C]0.365806[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.2953[/C][C]0.204742[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]16.3382[/C][C]-0.18815[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]14.7139[/C][C]0.0361462[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]14.4493[/C][C]0.350736[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.5161[/C][C]-0.066126[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.5416[/C][C]0.108389[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]17.5437[/C][C]-0.193655[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]8.52335[/C][C]0.076652[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]18.2239[/C][C]0.176135[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]16.1062[/C][C]-0.00615768[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.3388[/C][C]0.261227[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]18.0815[/C][C]-0.331497[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]15.5109[/C][C]-0.260871[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]16.7698[/C][C]0.880214[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]15.2878[/C][C]0.312225[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.3834[/C][C]-0.033375[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]17.0788[/C][C]0.571216[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.7878[/C][C]-0.187802[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]11.4352[/C][C]0.264784[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]14.3316[/C][C]0.0184177[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]14.0955[/C][C]0.654461[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]18.5233[/C][C]-0.273279[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]10.0856[/C][C]-0.185635[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.884[/C][C]0.116036[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]18.185[/C][C]0.0650095[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.2543[/C][C]-0.40426[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]14.4474[/C][C]0.152638[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.223[/C][C]-0.373004[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.762[/C][C]0.188033[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]15.7325[/C][C]-0.132465[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]14.8348[/C][C]0.0152027[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]11.7688[/C][C]-0.0188294[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]18.1028[/C][C]0.347166[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]15.9616[/C][C]-0.0616078[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.7044[/C][C]-0.604367[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]15.3668[/C][C]0.733171[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]19.8836[/C][C]0.0163812[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]11.5173[/C][C]-0.567316[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.231[/C][C]1.21904[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]14.7176[/C][C]0.382437[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.585[/C][C]0.415035[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]11.633[/C][C]-0.282987[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]15.1608[/C][C]0.789166[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]18.3624[/C][C]-0.262379[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]14.3095[/C][C]0.29046[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.0081[/C][C]-0.608095[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.0296[/C][C]-0.629578[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]17.7596[/C][C]-0.159583[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]13.35[/C][C]2.86481e-05[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]19.3006[/C][C]-0.200589[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]15.1891[/C][C]0.16095[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]7.95461[/C][C]-0.354606[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]13.558[/C][C]-0.15798[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]13.7578[/C][C]0.142249[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]19.2263[/C][C]-0.126258[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]15.1112[/C][C]0.138842[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]12.3537[/C][C]0.546322[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]16.1426[/C][C]-0.0426425[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]17.4012[/C][C]-0.0511798[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]12.9692[/C][C]0.180815[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]11.9921[/C][C]0.15794[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]13.409[/C][C]-0.809039[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]10.8479[/C][C]-0.497895[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.2704[/C][C]0.129605[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]9.34028[/C][C]0.259715[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]17.8758[/C][C]0.3242[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.6567[/C][C]-0.0567491[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]14.5823[/C][C]0.26772[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]14.608[/C][C]0.14198[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]14.7103[/C][C]-0.610348[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]14.8936[/C][C]0.00635255[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]16.2458[/C][C]0.00423846[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]20.3859[/C][C]-1.1359[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.2484[/C][C]0.351595[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]13.4082[/C][C]0.191795[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]15.5454[/C][C]0.104608[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]11.9107[/C][C]0.839251[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]14.502[/C][C]0.097974[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]10.1473[/C][C]-0.297273[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]13.3393[/C][C]-0.689265[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]13.1444[/C][C]-1.24435[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]18.441[/C][C]0.758984[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]16.5123[/C][C]0.0876509[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.5313[/C][C]-0.331292[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.1326[/C][C]0.117384[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]11.6286[/C][C]0.271386[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.6039[/C][C]-0.403925[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]16.5362[/C][C]-0.186175[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]11.7194[/C][C]0.680601[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]15.4164[/C][C]0.433589[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]14.2767[/C][C]0.0732594[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]18.5084[/C][C]-0.358413[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]11.3876[/C][C]-0.237584[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]15.3454[/C][C]0.304611[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]17.6717[/C][C]0.0783326[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]7.8466[/C][C]-0.1966[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]12.0692[/C][C]0.280832[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]15.1998[/C][C]0.400162[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]20.2029[/C][C]-0.902936[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]15.2975[/C][C]-0.0975102[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]16.8658[/C][C]0.234239[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]16.0959[/C][C]-0.495901[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]18.5276[/C][C]-0.127621[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]18.6982[/C][C]0.351796[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]18.9[/C][C]-0.349966[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.9317[/C][C]0.168308[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.6536[/C][C]-0.553591[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]13.2801[/C][C]-0.430097[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]9.90325[/C][C]-0.403255[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]4.48957[/C][C]0.010435[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]12.3907[/C][C]-0.540749[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]13.5509[/C][C]0.0491237[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.3494[/C][C]0.350612[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.5655[/C][C]-0.165465[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]13.3408[/C][C]0.00915855[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]11.0816[/C][C]0.318366[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]14.3872[/C][C]0.512772[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]19.9782[/C][C]-0.0782398[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]17.0261[/C][C]0.723884[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]11.4988[/C][C]-0.298799[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]14.7168[/C][C]-0.116806[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]17.9154[/C][C]-0.315424[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.7206[/C][C]0.329424[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]15.5574[/C][C]0.542597[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]12.8527[/C][C]0.497267[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]11.5847[/C][C]0.265333[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]12.4468[/C][C]-0.496767[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]15.1043[/C][C]-0.354271[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]15.0819[/C][C]0.0680971[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]13.5409[/C][C]-0.340912[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.9045[/C][C]-0.0544913[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]7.58322[/C][C]0.266778[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]8.43413[/C][C]-0.734126[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]12.5824[/C][C]0.0176159[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]7.44727[/C][C]0.402731[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]11.3411[/C][C]-0.391107[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]12.8052[/C][C]-0.455192[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]10.5421[/C][C]-0.592137[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]14.4032[/C][C]0.496779[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]16.1514[/C][C]0.4986[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]12.6836[/C][C]0.71638[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]14.0392[/C][C]-0.0891941[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]15.5063[/C][C]0.193691[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]17.0636[/C][C]-0.213598[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]11.7515[/C][C]-0.80151[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]15.7941[/C][C]-0.444096[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.4183[/C][C]-0.218299[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]15.9169[/C][C]-0.816909[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]17.6365[/C][C]0.113487[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.4488[/C][C]0.751224[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.6308[/C][C]-0.0307841[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]16.6897[/C][C]-0.0396693[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]8.6095[/C][C]-0.509496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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
14.354.323570.0264302
212.712.26720.432774
318.118.1176-0.0176218
417.8517.9509-0.100884
516.616.51590.0840506
612.612.40870.191297
717.117.9041-0.804063
819.119.4765-0.376507
916.116.3732-0.273164
1013.3513.2770.0730215
1118.417.86490.535143
1214.715.2147-0.514746
1310.610.13450.465529
1412.612.54830.0516547
1516.215.92050.279515
1613.613.37910.220928
1718.918.32790.572056
1814.113.73420.365806
1914.514.29530.204742
2016.1516.3382-0.18815
2114.7514.71390.0361462
2214.814.44930.350736
2312.4512.5161-0.066126
2412.6512.54160.108389
2517.3517.5437-0.193655
268.68.523350.076652
2718.418.22390.176135
2816.116.1062-0.00615768
2911.611.33880.261227
3017.7518.0815-0.331497
3115.2515.5109-0.260871
3217.6516.76980.880214
3315.615.28780.312225
3416.3516.3834-0.033375
3517.6517.07880.571216
3613.613.7878-0.187802
3711.711.43520.264784
3814.3514.33160.0184177
3914.7514.09550.654461
4018.2518.5233-0.273279
419.910.0856-0.185635
421615.8840.116036
4318.2518.1850.0650095
4416.8517.2543-0.40426
4514.614.44740.152638
4613.8514.223-0.373004
4718.9518.7620.188033
4815.615.7325-0.132465
4914.8514.83480.0152027
5011.7511.7688-0.0188294
5118.4518.10280.347166
5215.915.9616-0.0616078
5317.117.7044-0.604367
5416.115.36680.733171
5519.919.88360.0163812
5610.9511.5173-0.567316
5718.4517.2311.21904
5815.114.71760.382437
591514.5850.415035
6011.3511.633-0.282987
6115.9515.16080.789166
6218.118.3624-0.262379
6314.614.30950.29046
6415.416.0081-0.608095
6515.416.0296-0.629578
6617.617.7596-0.159583
6713.3513.352.86481e-05
6819.119.3006-0.200589
6915.3515.18910.16095
707.67.95461-0.354606
7113.413.558-0.15798
7213.913.75780.142249
7319.119.2263-0.126258
7415.2515.11120.138842
7512.912.35370.546322
7616.116.1426-0.0426425
7717.3517.4012-0.0511798
7813.1512.96920.180815
7912.1511.99210.15794
8012.613.409-0.809039
8110.3510.8479-0.497895
8215.415.27040.129605
839.69.340280.259715
8418.217.87580.3242
8513.613.6567-0.0567491
8614.8514.58230.26772
8714.7514.6080.14198
8814.114.7103-0.610348
8914.914.89360.00635255
9016.2516.24580.00423846
9119.2520.3859-1.1359
9213.613.24840.351595
9313.613.40820.191795
9415.6515.54540.104608
9512.7511.91070.839251
9614.614.5020.097974
979.8510.1473-0.297273
9812.6513.3393-0.689265
9911.913.1444-1.24435
10019.218.4410.758984
10116.616.51230.0876509
10211.211.5313-0.331292
10315.2515.13260.117384
10411.911.62860.271386
10513.213.6039-0.403925
10616.3516.5362-0.186175
10712.411.71940.680601
10815.8515.41640.433589
10914.3514.27670.0732594
11018.1518.5084-0.358413
11111.1511.3876-0.237584
11215.6515.34540.304611
11317.7517.67170.0783326
1147.657.8466-0.1966
11512.3512.06920.280832
11615.615.19980.400162
11719.320.2029-0.902936
11815.215.2975-0.0975102
11917.116.86580.234239
12015.616.0959-0.495901
12118.418.5276-0.127621
12219.0518.69820.351796
12318.5518.9-0.349966
12419.118.93170.168308
12513.113.6536-0.553591
12612.8513.2801-0.430097
1279.59.90325-0.403255
1284.54.489570.010435
12911.8512.3907-0.540749
13013.613.55090.0491237
13111.711.34940.350612
13212.412.5655-0.165465
13313.3513.34080.00915855
13411.411.08160.318366
13514.914.38720.512772
13619.919.9782-0.0782398
13717.7517.02610.723884
13811.211.4988-0.298799
13914.614.7168-0.116806
14017.617.9154-0.315424
14114.0513.72060.329424
14216.115.55740.542597
14313.3512.85270.497267
14411.8511.58470.265333
14511.9512.4468-0.496767
14614.7515.1043-0.354271
14715.1515.08190.0680971
14813.213.5409-0.340912
14916.8516.9045-0.0544913
1507.857.583220.266778
1517.78.43413-0.734126
15212.612.58240.0176159
1537.857.447270.402731
15410.9511.3411-0.391107
15512.3512.8052-0.455192
1569.9510.5421-0.592137
15714.914.40320.496779
15816.6516.15140.4986
15913.412.68360.71638
16013.9514.0392-0.0891941
16115.715.50630.193691
16216.8517.0636-0.213598
16310.9511.7515-0.80151
16415.3515.7941-0.444096
16512.212.4183-0.218299
16615.115.9169-0.816909
16717.7517.63650.113487
16815.214.44880.751224
16914.614.6308-0.0307841
17016.6516.6897-0.0396693
1718.18.6095-0.509496






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.6502130.6995730.349787
140.5015820.9968370.498418
150.359460.7189210.64054
160.2426880.4853750.757312
170.652040.6959190.34796
180.6147220.7705560.385278
190.5201720.9596560.479828
200.429240.858480.57076
210.3402270.6804550.659773
220.2878070.5756130.712193
230.2754940.5509880.724506
240.2109190.4218370.789081
250.1732410.3464820.826759
260.1270810.2541630.872919
270.1263590.2527180.873641
280.09064780.1812960.909352
290.06475060.1295010.935249
300.07129930.1425990.928701
310.0742740.1485480.925726
320.221860.4437210.77814
330.1892180.3784360.810782
340.1480440.2960880.851956
350.1597610.3195220.840239
360.1254490.2508980.874551
370.09795840.1959170.902042
380.07674270.1534850.923257
390.0788120.1576240.921188
400.06154040.1230810.93846
410.04836640.09673280.951634
420.03617580.07235150.963824
430.02788990.05577980.97211
440.04053750.0810750.959463
450.02975090.05950180.970249
460.03291960.06583910.96708
470.03005010.06010020.96995
480.02485010.04970010.97515
490.01781590.03563170.982184
500.01414270.02828550.985857
510.01242320.02484640.987577
520.008937820.01787560.991062
530.0129660.02593190.987034
540.01833770.03667550.981662
550.01324030.02648060.98676
560.02608410.05216820.973916
570.151580.3031590.84842
580.1457780.2915570.854222
590.1342440.2684870.865756
600.1301750.260350.869825
610.2036020.4072040.796398
620.1905690.3811380.809431
630.1753010.3506030.824699
640.2010410.4020820.798959
650.2278080.4556170.772192
660.2003540.4007070.799646
670.1687320.3374640.831268
680.1476120.2952230.852388
690.1310940.2621870.868906
700.1362750.2725510.863725
710.1142120.2284230.885788
720.0936260.1872520.906374
730.07649290.1529860.923507
740.0627540.1255080.937246
750.1021250.204250.897875
760.08305090.1661020.916949
770.06788010.135760.93212
780.05630680.1126140.943693
790.04499910.08999820.955001
800.08718630.1743730.912814
810.1063330.2126650.893667
820.08785490.175710.912145
830.07665270.1533050.923347
840.06943480.138870.930565
850.05759570.1151910.942404
860.05137620.1027520.948624
870.04200860.08401720.957991
880.05536860.1107370.944631
890.04425310.08850610.955747
900.03468590.06937180.965314
910.1338390.2676790.866161
920.1271250.254250.872875
930.1075740.2151470.892426
940.08902150.1780430.910978
950.1585780.3171560.841422
960.1448080.2896160.855192
970.1352010.2704010.864799
980.1797490.3594980.820251
990.5043560.9912870.495644
1000.6385670.7228660.361433
1010.5979370.8041260.402063
1020.5804210.8391570.419579
1030.5392060.9215880.460794
1040.5403150.919370.459685
1050.5401590.9196820.459841
1060.5056750.9886490.494325
1070.5721990.8556020.427801
1080.5822950.8354110.417705
1090.538460.9230810.46154
1100.533870.9322610.46613
1110.4994230.9988460.500577
1120.4758550.9517090.524145
1130.4399090.8798170.560091
1140.4192280.8384560.580772
1150.3852830.7705650.614717
1160.3812040.7624080.618796
1170.549890.900220.45011
1180.50160.9967990.4984
1190.4581180.9162360.541882
1200.4640830.9281660.535917
1210.4280740.8561480.571926
1220.4099660.8199320.590034
1230.3904880.7809770.609512
1240.3432410.6864820.656759
1250.3600730.7201460.639927
1260.3655760.7311520.634424
1270.34020.6804010.6598
1280.2948850.589770.705115
1290.2940770.5881540.705923
1300.2485880.4971750.751412
1310.2222880.4445750.777712
1320.2043240.4086470.795676
1330.165830.3316590.83417
1340.2024040.4048080.797596
1350.2020550.4041090.797945
1360.1795320.3590640.820468
1370.1851290.3702590.814871
1380.1634540.3269080.836546
1390.1282770.2565550.871723
1400.133740.2674810.86626
1410.1077860.2155720.892214
1420.1123070.2246130.887693
1430.1132650.2265290.886735
1440.09732280.1946460.902677
1450.08194890.1638980.918051
1460.07466520.149330.925335
1470.06029470.1205890.939705
1480.07127950.1425590.92872
1490.05227820.1045560.947722
1500.06717290.1343460.932827
1510.08750120.1750020.912499
1520.08380660.1676130.916193
1530.06056440.1211290.939436
1540.04782260.09564520.952177
1550.03200070.06400130.967999
1560.07912890.1582580.920871
1570.04393090.08786170.956069
1580.04509430.09018860.954906

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.650213 & 0.699573 & 0.349787 \tabularnewline
14 & 0.501582 & 0.996837 & 0.498418 \tabularnewline
15 & 0.35946 & 0.718921 & 0.64054 \tabularnewline
16 & 0.242688 & 0.485375 & 0.757312 \tabularnewline
17 & 0.65204 & 0.695919 & 0.34796 \tabularnewline
18 & 0.614722 & 0.770556 & 0.385278 \tabularnewline
19 & 0.520172 & 0.959656 & 0.479828 \tabularnewline
20 & 0.42924 & 0.85848 & 0.57076 \tabularnewline
21 & 0.340227 & 0.680455 & 0.659773 \tabularnewline
22 & 0.287807 & 0.575613 & 0.712193 \tabularnewline
23 & 0.275494 & 0.550988 & 0.724506 \tabularnewline
24 & 0.210919 & 0.421837 & 0.789081 \tabularnewline
25 & 0.173241 & 0.346482 & 0.826759 \tabularnewline
26 & 0.127081 & 0.254163 & 0.872919 \tabularnewline
27 & 0.126359 & 0.252718 & 0.873641 \tabularnewline
28 & 0.0906478 & 0.181296 & 0.909352 \tabularnewline
29 & 0.0647506 & 0.129501 & 0.935249 \tabularnewline
30 & 0.0712993 & 0.142599 & 0.928701 \tabularnewline
31 & 0.074274 & 0.148548 & 0.925726 \tabularnewline
32 & 0.22186 & 0.443721 & 0.77814 \tabularnewline
33 & 0.189218 & 0.378436 & 0.810782 \tabularnewline
34 & 0.148044 & 0.296088 & 0.851956 \tabularnewline
35 & 0.159761 & 0.319522 & 0.840239 \tabularnewline
36 & 0.125449 & 0.250898 & 0.874551 \tabularnewline
37 & 0.0979584 & 0.195917 & 0.902042 \tabularnewline
38 & 0.0767427 & 0.153485 & 0.923257 \tabularnewline
39 & 0.078812 & 0.157624 & 0.921188 \tabularnewline
40 & 0.0615404 & 0.123081 & 0.93846 \tabularnewline
41 & 0.0483664 & 0.0967328 & 0.951634 \tabularnewline
42 & 0.0361758 & 0.0723515 & 0.963824 \tabularnewline
43 & 0.0278899 & 0.0557798 & 0.97211 \tabularnewline
44 & 0.0405375 & 0.081075 & 0.959463 \tabularnewline
45 & 0.0297509 & 0.0595018 & 0.970249 \tabularnewline
46 & 0.0329196 & 0.0658391 & 0.96708 \tabularnewline
47 & 0.0300501 & 0.0601002 & 0.96995 \tabularnewline
48 & 0.0248501 & 0.0497001 & 0.97515 \tabularnewline
49 & 0.0178159 & 0.0356317 & 0.982184 \tabularnewline
50 & 0.0141427 & 0.0282855 & 0.985857 \tabularnewline
51 & 0.0124232 & 0.0248464 & 0.987577 \tabularnewline
52 & 0.00893782 & 0.0178756 & 0.991062 \tabularnewline
53 & 0.012966 & 0.0259319 & 0.987034 \tabularnewline
54 & 0.0183377 & 0.0366755 & 0.981662 \tabularnewline
55 & 0.0132403 & 0.0264806 & 0.98676 \tabularnewline
56 & 0.0260841 & 0.0521682 & 0.973916 \tabularnewline
57 & 0.15158 & 0.303159 & 0.84842 \tabularnewline
58 & 0.145778 & 0.291557 & 0.854222 \tabularnewline
59 & 0.134244 & 0.268487 & 0.865756 \tabularnewline
60 & 0.130175 & 0.26035 & 0.869825 \tabularnewline
61 & 0.203602 & 0.407204 & 0.796398 \tabularnewline
62 & 0.190569 & 0.381138 & 0.809431 \tabularnewline
63 & 0.175301 & 0.350603 & 0.824699 \tabularnewline
64 & 0.201041 & 0.402082 & 0.798959 \tabularnewline
65 & 0.227808 & 0.455617 & 0.772192 \tabularnewline
66 & 0.200354 & 0.400707 & 0.799646 \tabularnewline
67 & 0.168732 & 0.337464 & 0.831268 \tabularnewline
68 & 0.147612 & 0.295223 & 0.852388 \tabularnewline
69 & 0.131094 & 0.262187 & 0.868906 \tabularnewline
70 & 0.136275 & 0.272551 & 0.863725 \tabularnewline
71 & 0.114212 & 0.228423 & 0.885788 \tabularnewline
72 & 0.093626 & 0.187252 & 0.906374 \tabularnewline
73 & 0.0764929 & 0.152986 & 0.923507 \tabularnewline
74 & 0.062754 & 0.125508 & 0.937246 \tabularnewline
75 & 0.102125 & 0.20425 & 0.897875 \tabularnewline
76 & 0.0830509 & 0.166102 & 0.916949 \tabularnewline
77 & 0.0678801 & 0.13576 & 0.93212 \tabularnewline
78 & 0.0563068 & 0.112614 & 0.943693 \tabularnewline
79 & 0.0449991 & 0.0899982 & 0.955001 \tabularnewline
80 & 0.0871863 & 0.174373 & 0.912814 \tabularnewline
81 & 0.106333 & 0.212665 & 0.893667 \tabularnewline
82 & 0.0878549 & 0.17571 & 0.912145 \tabularnewline
83 & 0.0766527 & 0.153305 & 0.923347 \tabularnewline
84 & 0.0694348 & 0.13887 & 0.930565 \tabularnewline
85 & 0.0575957 & 0.115191 & 0.942404 \tabularnewline
86 & 0.0513762 & 0.102752 & 0.948624 \tabularnewline
87 & 0.0420086 & 0.0840172 & 0.957991 \tabularnewline
88 & 0.0553686 & 0.110737 & 0.944631 \tabularnewline
89 & 0.0442531 & 0.0885061 & 0.955747 \tabularnewline
90 & 0.0346859 & 0.0693718 & 0.965314 \tabularnewline
91 & 0.133839 & 0.267679 & 0.866161 \tabularnewline
92 & 0.127125 & 0.25425 & 0.872875 \tabularnewline
93 & 0.107574 & 0.215147 & 0.892426 \tabularnewline
94 & 0.0890215 & 0.178043 & 0.910978 \tabularnewline
95 & 0.158578 & 0.317156 & 0.841422 \tabularnewline
96 & 0.144808 & 0.289616 & 0.855192 \tabularnewline
97 & 0.135201 & 0.270401 & 0.864799 \tabularnewline
98 & 0.179749 & 0.359498 & 0.820251 \tabularnewline
99 & 0.504356 & 0.991287 & 0.495644 \tabularnewline
100 & 0.638567 & 0.722866 & 0.361433 \tabularnewline
101 & 0.597937 & 0.804126 & 0.402063 \tabularnewline
102 & 0.580421 & 0.839157 & 0.419579 \tabularnewline
103 & 0.539206 & 0.921588 & 0.460794 \tabularnewline
104 & 0.540315 & 0.91937 & 0.459685 \tabularnewline
105 & 0.540159 & 0.919682 & 0.459841 \tabularnewline
106 & 0.505675 & 0.988649 & 0.494325 \tabularnewline
107 & 0.572199 & 0.855602 & 0.427801 \tabularnewline
108 & 0.582295 & 0.835411 & 0.417705 \tabularnewline
109 & 0.53846 & 0.923081 & 0.46154 \tabularnewline
110 & 0.53387 & 0.932261 & 0.46613 \tabularnewline
111 & 0.499423 & 0.998846 & 0.500577 \tabularnewline
112 & 0.475855 & 0.951709 & 0.524145 \tabularnewline
113 & 0.439909 & 0.879817 & 0.560091 \tabularnewline
114 & 0.419228 & 0.838456 & 0.580772 \tabularnewline
115 & 0.385283 & 0.770565 & 0.614717 \tabularnewline
116 & 0.381204 & 0.762408 & 0.618796 \tabularnewline
117 & 0.54989 & 0.90022 & 0.45011 \tabularnewline
118 & 0.5016 & 0.996799 & 0.4984 \tabularnewline
119 & 0.458118 & 0.916236 & 0.541882 \tabularnewline
120 & 0.464083 & 0.928166 & 0.535917 \tabularnewline
121 & 0.428074 & 0.856148 & 0.571926 \tabularnewline
122 & 0.409966 & 0.819932 & 0.590034 \tabularnewline
123 & 0.390488 & 0.780977 & 0.609512 \tabularnewline
124 & 0.343241 & 0.686482 & 0.656759 \tabularnewline
125 & 0.360073 & 0.720146 & 0.639927 \tabularnewline
126 & 0.365576 & 0.731152 & 0.634424 \tabularnewline
127 & 0.3402 & 0.680401 & 0.6598 \tabularnewline
128 & 0.294885 & 0.58977 & 0.705115 \tabularnewline
129 & 0.294077 & 0.588154 & 0.705923 \tabularnewline
130 & 0.248588 & 0.497175 & 0.751412 \tabularnewline
131 & 0.222288 & 0.444575 & 0.777712 \tabularnewline
132 & 0.204324 & 0.408647 & 0.795676 \tabularnewline
133 & 0.16583 & 0.331659 & 0.83417 \tabularnewline
134 & 0.202404 & 0.404808 & 0.797596 \tabularnewline
135 & 0.202055 & 0.404109 & 0.797945 \tabularnewline
136 & 0.179532 & 0.359064 & 0.820468 \tabularnewline
137 & 0.185129 & 0.370259 & 0.814871 \tabularnewline
138 & 0.163454 & 0.326908 & 0.836546 \tabularnewline
139 & 0.128277 & 0.256555 & 0.871723 \tabularnewline
140 & 0.13374 & 0.267481 & 0.86626 \tabularnewline
141 & 0.107786 & 0.215572 & 0.892214 \tabularnewline
142 & 0.112307 & 0.224613 & 0.887693 \tabularnewline
143 & 0.113265 & 0.226529 & 0.886735 \tabularnewline
144 & 0.0973228 & 0.194646 & 0.902677 \tabularnewline
145 & 0.0819489 & 0.163898 & 0.918051 \tabularnewline
146 & 0.0746652 & 0.14933 & 0.925335 \tabularnewline
147 & 0.0602947 & 0.120589 & 0.939705 \tabularnewline
148 & 0.0712795 & 0.142559 & 0.92872 \tabularnewline
149 & 0.0522782 & 0.104556 & 0.947722 \tabularnewline
150 & 0.0671729 & 0.134346 & 0.932827 \tabularnewline
151 & 0.0875012 & 0.175002 & 0.912499 \tabularnewline
152 & 0.0838066 & 0.167613 & 0.916193 \tabularnewline
153 & 0.0605644 & 0.121129 & 0.939436 \tabularnewline
154 & 0.0478226 & 0.0956452 & 0.952177 \tabularnewline
155 & 0.0320007 & 0.0640013 & 0.967999 \tabularnewline
156 & 0.0791289 & 0.158258 & 0.920871 \tabularnewline
157 & 0.0439309 & 0.0878617 & 0.956069 \tabularnewline
158 & 0.0450943 & 0.0901886 & 0.954906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&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]0.650213[/C][C]0.699573[/C][C]0.349787[/C][/ROW]
[ROW][C]14[/C][C]0.501582[/C][C]0.996837[/C][C]0.498418[/C][/ROW]
[ROW][C]15[/C][C]0.35946[/C][C]0.718921[/C][C]0.64054[/C][/ROW]
[ROW][C]16[/C][C]0.242688[/C][C]0.485375[/C][C]0.757312[/C][/ROW]
[ROW][C]17[/C][C]0.65204[/C][C]0.695919[/C][C]0.34796[/C][/ROW]
[ROW][C]18[/C][C]0.614722[/C][C]0.770556[/C][C]0.385278[/C][/ROW]
[ROW][C]19[/C][C]0.520172[/C][C]0.959656[/C][C]0.479828[/C][/ROW]
[ROW][C]20[/C][C]0.42924[/C][C]0.85848[/C][C]0.57076[/C][/ROW]
[ROW][C]21[/C][C]0.340227[/C][C]0.680455[/C][C]0.659773[/C][/ROW]
[ROW][C]22[/C][C]0.287807[/C][C]0.575613[/C][C]0.712193[/C][/ROW]
[ROW][C]23[/C][C]0.275494[/C][C]0.550988[/C][C]0.724506[/C][/ROW]
[ROW][C]24[/C][C]0.210919[/C][C]0.421837[/C][C]0.789081[/C][/ROW]
[ROW][C]25[/C][C]0.173241[/C][C]0.346482[/C][C]0.826759[/C][/ROW]
[ROW][C]26[/C][C]0.127081[/C][C]0.254163[/C][C]0.872919[/C][/ROW]
[ROW][C]27[/C][C]0.126359[/C][C]0.252718[/C][C]0.873641[/C][/ROW]
[ROW][C]28[/C][C]0.0906478[/C][C]0.181296[/C][C]0.909352[/C][/ROW]
[ROW][C]29[/C][C]0.0647506[/C][C]0.129501[/C][C]0.935249[/C][/ROW]
[ROW][C]30[/C][C]0.0712993[/C][C]0.142599[/C][C]0.928701[/C][/ROW]
[ROW][C]31[/C][C]0.074274[/C][C]0.148548[/C][C]0.925726[/C][/ROW]
[ROW][C]32[/C][C]0.22186[/C][C]0.443721[/C][C]0.77814[/C][/ROW]
[ROW][C]33[/C][C]0.189218[/C][C]0.378436[/C][C]0.810782[/C][/ROW]
[ROW][C]34[/C][C]0.148044[/C][C]0.296088[/C][C]0.851956[/C][/ROW]
[ROW][C]35[/C][C]0.159761[/C][C]0.319522[/C][C]0.840239[/C][/ROW]
[ROW][C]36[/C][C]0.125449[/C][C]0.250898[/C][C]0.874551[/C][/ROW]
[ROW][C]37[/C][C]0.0979584[/C][C]0.195917[/C][C]0.902042[/C][/ROW]
[ROW][C]38[/C][C]0.0767427[/C][C]0.153485[/C][C]0.923257[/C][/ROW]
[ROW][C]39[/C][C]0.078812[/C][C]0.157624[/C][C]0.921188[/C][/ROW]
[ROW][C]40[/C][C]0.0615404[/C][C]0.123081[/C][C]0.93846[/C][/ROW]
[ROW][C]41[/C][C]0.0483664[/C][C]0.0967328[/C][C]0.951634[/C][/ROW]
[ROW][C]42[/C][C]0.0361758[/C][C]0.0723515[/C][C]0.963824[/C][/ROW]
[ROW][C]43[/C][C]0.0278899[/C][C]0.0557798[/C][C]0.97211[/C][/ROW]
[ROW][C]44[/C][C]0.0405375[/C][C]0.081075[/C][C]0.959463[/C][/ROW]
[ROW][C]45[/C][C]0.0297509[/C][C]0.0595018[/C][C]0.970249[/C][/ROW]
[ROW][C]46[/C][C]0.0329196[/C][C]0.0658391[/C][C]0.96708[/C][/ROW]
[ROW][C]47[/C][C]0.0300501[/C][C]0.0601002[/C][C]0.96995[/C][/ROW]
[ROW][C]48[/C][C]0.0248501[/C][C]0.0497001[/C][C]0.97515[/C][/ROW]
[ROW][C]49[/C][C]0.0178159[/C][C]0.0356317[/C][C]0.982184[/C][/ROW]
[ROW][C]50[/C][C]0.0141427[/C][C]0.0282855[/C][C]0.985857[/C][/ROW]
[ROW][C]51[/C][C]0.0124232[/C][C]0.0248464[/C][C]0.987577[/C][/ROW]
[ROW][C]52[/C][C]0.00893782[/C][C]0.0178756[/C][C]0.991062[/C][/ROW]
[ROW][C]53[/C][C]0.012966[/C][C]0.0259319[/C][C]0.987034[/C][/ROW]
[ROW][C]54[/C][C]0.0183377[/C][C]0.0366755[/C][C]0.981662[/C][/ROW]
[ROW][C]55[/C][C]0.0132403[/C][C]0.0264806[/C][C]0.98676[/C][/ROW]
[ROW][C]56[/C][C]0.0260841[/C][C]0.0521682[/C][C]0.973916[/C][/ROW]
[ROW][C]57[/C][C]0.15158[/C][C]0.303159[/C][C]0.84842[/C][/ROW]
[ROW][C]58[/C][C]0.145778[/C][C]0.291557[/C][C]0.854222[/C][/ROW]
[ROW][C]59[/C][C]0.134244[/C][C]0.268487[/C][C]0.865756[/C][/ROW]
[ROW][C]60[/C][C]0.130175[/C][C]0.26035[/C][C]0.869825[/C][/ROW]
[ROW][C]61[/C][C]0.203602[/C][C]0.407204[/C][C]0.796398[/C][/ROW]
[ROW][C]62[/C][C]0.190569[/C][C]0.381138[/C][C]0.809431[/C][/ROW]
[ROW][C]63[/C][C]0.175301[/C][C]0.350603[/C][C]0.824699[/C][/ROW]
[ROW][C]64[/C][C]0.201041[/C][C]0.402082[/C][C]0.798959[/C][/ROW]
[ROW][C]65[/C][C]0.227808[/C][C]0.455617[/C][C]0.772192[/C][/ROW]
[ROW][C]66[/C][C]0.200354[/C][C]0.400707[/C][C]0.799646[/C][/ROW]
[ROW][C]67[/C][C]0.168732[/C][C]0.337464[/C][C]0.831268[/C][/ROW]
[ROW][C]68[/C][C]0.147612[/C][C]0.295223[/C][C]0.852388[/C][/ROW]
[ROW][C]69[/C][C]0.131094[/C][C]0.262187[/C][C]0.868906[/C][/ROW]
[ROW][C]70[/C][C]0.136275[/C][C]0.272551[/C][C]0.863725[/C][/ROW]
[ROW][C]71[/C][C]0.114212[/C][C]0.228423[/C][C]0.885788[/C][/ROW]
[ROW][C]72[/C][C]0.093626[/C][C]0.187252[/C][C]0.906374[/C][/ROW]
[ROW][C]73[/C][C]0.0764929[/C][C]0.152986[/C][C]0.923507[/C][/ROW]
[ROW][C]74[/C][C]0.062754[/C][C]0.125508[/C][C]0.937246[/C][/ROW]
[ROW][C]75[/C][C]0.102125[/C][C]0.20425[/C][C]0.897875[/C][/ROW]
[ROW][C]76[/C][C]0.0830509[/C][C]0.166102[/C][C]0.916949[/C][/ROW]
[ROW][C]77[/C][C]0.0678801[/C][C]0.13576[/C][C]0.93212[/C][/ROW]
[ROW][C]78[/C][C]0.0563068[/C][C]0.112614[/C][C]0.943693[/C][/ROW]
[ROW][C]79[/C][C]0.0449991[/C][C]0.0899982[/C][C]0.955001[/C][/ROW]
[ROW][C]80[/C][C]0.0871863[/C][C]0.174373[/C][C]0.912814[/C][/ROW]
[ROW][C]81[/C][C]0.106333[/C][C]0.212665[/C][C]0.893667[/C][/ROW]
[ROW][C]82[/C][C]0.0878549[/C][C]0.17571[/C][C]0.912145[/C][/ROW]
[ROW][C]83[/C][C]0.0766527[/C][C]0.153305[/C][C]0.923347[/C][/ROW]
[ROW][C]84[/C][C]0.0694348[/C][C]0.13887[/C][C]0.930565[/C][/ROW]
[ROW][C]85[/C][C]0.0575957[/C][C]0.115191[/C][C]0.942404[/C][/ROW]
[ROW][C]86[/C][C]0.0513762[/C][C]0.102752[/C][C]0.948624[/C][/ROW]
[ROW][C]87[/C][C]0.0420086[/C][C]0.0840172[/C][C]0.957991[/C][/ROW]
[ROW][C]88[/C][C]0.0553686[/C][C]0.110737[/C][C]0.944631[/C][/ROW]
[ROW][C]89[/C][C]0.0442531[/C][C]0.0885061[/C][C]0.955747[/C][/ROW]
[ROW][C]90[/C][C]0.0346859[/C][C]0.0693718[/C][C]0.965314[/C][/ROW]
[ROW][C]91[/C][C]0.133839[/C][C]0.267679[/C][C]0.866161[/C][/ROW]
[ROW][C]92[/C][C]0.127125[/C][C]0.25425[/C][C]0.872875[/C][/ROW]
[ROW][C]93[/C][C]0.107574[/C][C]0.215147[/C][C]0.892426[/C][/ROW]
[ROW][C]94[/C][C]0.0890215[/C][C]0.178043[/C][C]0.910978[/C][/ROW]
[ROW][C]95[/C][C]0.158578[/C][C]0.317156[/C][C]0.841422[/C][/ROW]
[ROW][C]96[/C][C]0.144808[/C][C]0.289616[/C][C]0.855192[/C][/ROW]
[ROW][C]97[/C][C]0.135201[/C][C]0.270401[/C][C]0.864799[/C][/ROW]
[ROW][C]98[/C][C]0.179749[/C][C]0.359498[/C][C]0.820251[/C][/ROW]
[ROW][C]99[/C][C]0.504356[/C][C]0.991287[/C][C]0.495644[/C][/ROW]
[ROW][C]100[/C][C]0.638567[/C][C]0.722866[/C][C]0.361433[/C][/ROW]
[ROW][C]101[/C][C]0.597937[/C][C]0.804126[/C][C]0.402063[/C][/ROW]
[ROW][C]102[/C][C]0.580421[/C][C]0.839157[/C][C]0.419579[/C][/ROW]
[ROW][C]103[/C][C]0.539206[/C][C]0.921588[/C][C]0.460794[/C][/ROW]
[ROW][C]104[/C][C]0.540315[/C][C]0.91937[/C][C]0.459685[/C][/ROW]
[ROW][C]105[/C][C]0.540159[/C][C]0.919682[/C][C]0.459841[/C][/ROW]
[ROW][C]106[/C][C]0.505675[/C][C]0.988649[/C][C]0.494325[/C][/ROW]
[ROW][C]107[/C][C]0.572199[/C][C]0.855602[/C][C]0.427801[/C][/ROW]
[ROW][C]108[/C][C]0.582295[/C][C]0.835411[/C][C]0.417705[/C][/ROW]
[ROW][C]109[/C][C]0.53846[/C][C]0.923081[/C][C]0.46154[/C][/ROW]
[ROW][C]110[/C][C]0.53387[/C][C]0.932261[/C][C]0.46613[/C][/ROW]
[ROW][C]111[/C][C]0.499423[/C][C]0.998846[/C][C]0.500577[/C][/ROW]
[ROW][C]112[/C][C]0.475855[/C][C]0.951709[/C][C]0.524145[/C][/ROW]
[ROW][C]113[/C][C]0.439909[/C][C]0.879817[/C][C]0.560091[/C][/ROW]
[ROW][C]114[/C][C]0.419228[/C][C]0.838456[/C][C]0.580772[/C][/ROW]
[ROW][C]115[/C][C]0.385283[/C][C]0.770565[/C][C]0.614717[/C][/ROW]
[ROW][C]116[/C][C]0.381204[/C][C]0.762408[/C][C]0.618796[/C][/ROW]
[ROW][C]117[/C][C]0.54989[/C][C]0.90022[/C][C]0.45011[/C][/ROW]
[ROW][C]118[/C][C]0.5016[/C][C]0.996799[/C][C]0.4984[/C][/ROW]
[ROW][C]119[/C][C]0.458118[/C][C]0.916236[/C][C]0.541882[/C][/ROW]
[ROW][C]120[/C][C]0.464083[/C][C]0.928166[/C][C]0.535917[/C][/ROW]
[ROW][C]121[/C][C]0.428074[/C][C]0.856148[/C][C]0.571926[/C][/ROW]
[ROW][C]122[/C][C]0.409966[/C][C]0.819932[/C][C]0.590034[/C][/ROW]
[ROW][C]123[/C][C]0.390488[/C][C]0.780977[/C][C]0.609512[/C][/ROW]
[ROW][C]124[/C][C]0.343241[/C][C]0.686482[/C][C]0.656759[/C][/ROW]
[ROW][C]125[/C][C]0.360073[/C][C]0.720146[/C][C]0.639927[/C][/ROW]
[ROW][C]126[/C][C]0.365576[/C][C]0.731152[/C][C]0.634424[/C][/ROW]
[ROW][C]127[/C][C]0.3402[/C][C]0.680401[/C][C]0.6598[/C][/ROW]
[ROW][C]128[/C][C]0.294885[/C][C]0.58977[/C][C]0.705115[/C][/ROW]
[ROW][C]129[/C][C]0.294077[/C][C]0.588154[/C][C]0.705923[/C][/ROW]
[ROW][C]130[/C][C]0.248588[/C][C]0.497175[/C][C]0.751412[/C][/ROW]
[ROW][C]131[/C][C]0.222288[/C][C]0.444575[/C][C]0.777712[/C][/ROW]
[ROW][C]132[/C][C]0.204324[/C][C]0.408647[/C][C]0.795676[/C][/ROW]
[ROW][C]133[/C][C]0.16583[/C][C]0.331659[/C][C]0.83417[/C][/ROW]
[ROW][C]134[/C][C]0.202404[/C][C]0.404808[/C][C]0.797596[/C][/ROW]
[ROW][C]135[/C][C]0.202055[/C][C]0.404109[/C][C]0.797945[/C][/ROW]
[ROW][C]136[/C][C]0.179532[/C][C]0.359064[/C][C]0.820468[/C][/ROW]
[ROW][C]137[/C][C]0.185129[/C][C]0.370259[/C][C]0.814871[/C][/ROW]
[ROW][C]138[/C][C]0.163454[/C][C]0.326908[/C][C]0.836546[/C][/ROW]
[ROW][C]139[/C][C]0.128277[/C][C]0.256555[/C][C]0.871723[/C][/ROW]
[ROW][C]140[/C][C]0.13374[/C][C]0.267481[/C][C]0.86626[/C][/ROW]
[ROW][C]141[/C][C]0.107786[/C][C]0.215572[/C][C]0.892214[/C][/ROW]
[ROW][C]142[/C][C]0.112307[/C][C]0.224613[/C][C]0.887693[/C][/ROW]
[ROW][C]143[/C][C]0.113265[/C][C]0.226529[/C][C]0.886735[/C][/ROW]
[ROW][C]144[/C][C]0.0973228[/C][C]0.194646[/C][C]0.902677[/C][/ROW]
[ROW][C]145[/C][C]0.0819489[/C][C]0.163898[/C][C]0.918051[/C][/ROW]
[ROW][C]146[/C][C]0.0746652[/C][C]0.14933[/C][C]0.925335[/C][/ROW]
[ROW][C]147[/C][C]0.0602947[/C][C]0.120589[/C][C]0.939705[/C][/ROW]
[ROW][C]148[/C][C]0.0712795[/C][C]0.142559[/C][C]0.92872[/C][/ROW]
[ROW][C]149[/C][C]0.0522782[/C][C]0.104556[/C][C]0.947722[/C][/ROW]
[ROW][C]150[/C][C]0.0671729[/C][C]0.134346[/C][C]0.932827[/C][/ROW]
[ROW][C]151[/C][C]0.0875012[/C][C]0.175002[/C][C]0.912499[/C][/ROW]
[ROW][C]152[/C][C]0.0838066[/C][C]0.167613[/C][C]0.916193[/C][/ROW]
[ROW][C]153[/C][C]0.0605644[/C][C]0.121129[/C][C]0.939436[/C][/ROW]
[ROW][C]154[/C][C]0.0478226[/C][C]0.0956452[/C][C]0.952177[/C][/ROW]
[ROW][C]155[/C][C]0.0320007[/C][C]0.0640013[/C][C]0.967999[/C][/ROW]
[ROW][C]156[/C][C]0.0791289[/C][C]0.158258[/C][C]0.920871[/C][/ROW]
[ROW][C]157[/C][C]0.0439309[/C][C]0.0878617[/C][C]0.956069[/C][/ROW]
[ROW][C]158[/C][C]0.0450943[/C][C]0.0901886[/C][C]0.954906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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
130.6502130.6995730.349787
140.5015820.9968370.498418
150.359460.7189210.64054
160.2426880.4853750.757312
170.652040.6959190.34796
180.6147220.7705560.385278
190.5201720.9596560.479828
200.429240.858480.57076
210.3402270.6804550.659773
220.2878070.5756130.712193
230.2754940.5509880.724506
240.2109190.4218370.789081
250.1732410.3464820.826759
260.1270810.2541630.872919
270.1263590.2527180.873641
280.09064780.1812960.909352
290.06475060.1295010.935249
300.07129930.1425990.928701
310.0742740.1485480.925726
320.221860.4437210.77814
330.1892180.3784360.810782
340.1480440.2960880.851956
350.1597610.3195220.840239
360.1254490.2508980.874551
370.09795840.1959170.902042
380.07674270.1534850.923257
390.0788120.1576240.921188
400.06154040.1230810.93846
410.04836640.09673280.951634
420.03617580.07235150.963824
430.02788990.05577980.97211
440.04053750.0810750.959463
450.02975090.05950180.970249
460.03291960.06583910.96708
470.03005010.06010020.96995
480.02485010.04970010.97515
490.01781590.03563170.982184
500.01414270.02828550.985857
510.01242320.02484640.987577
520.008937820.01787560.991062
530.0129660.02593190.987034
540.01833770.03667550.981662
550.01324030.02648060.98676
560.02608410.05216820.973916
570.151580.3031590.84842
580.1457780.2915570.854222
590.1342440.2684870.865756
600.1301750.260350.869825
610.2036020.4072040.796398
620.1905690.3811380.809431
630.1753010.3506030.824699
640.2010410.4020820.798959
650.2278080.4556170.772192
660.2003540.4007070.799646
670.1687320.3374640.831268
680.1476120.2952230.852388
690.1310940.2621870.868906
700.1362750.2725510.863725
710.1142120.2284230.885788
720.0936260.1872520.906374
730.07649290.1529860.923507
740.0627540.1255080.937246
750.1021250.204250.897875
760.08305090.1661020.916949
770.06788010.135760.93212
780.05630680.1126140.943693
790.04499910.08999820.955001
800.08718630.1743730.912814
810.1063330.2126650.893667
820.08785490.175710.912145
830.07665270.1533050.923347
840.06943480.138870.930565
850.05759570.1151910.942404
860.05137620.1027520.948624
870.04200860.08401720.957991
880.05536860.1107370.944631
890.04425310.08850610.955747
900.03468590.06937180.965314
910.1338390.2676790.866161
920.1271250.254250.872875
930.1075740.2151470.892426
940.08902150.1780430.910978
950.1585780.3171560.841422
960.1448080.2896160.855192
970.1352010.2704010.864799
980.1797490.3594980.820251
990.5043560.9912870.495644
1000.6385670.7228660.361433
1010.5979370.8041260.402063
1020.5804210.8391570.419579
1030.5392060.9215880.460794
1040.5403150.919370.459685
1050.5401590.9196820.459841
1060.5056750.9886490.494325
1070.5721990.8556020.427801
1080.5822950.8354110.417705
1090.538460.9230810.46154
1100.533870.9322610.46613
1110.4994230.9988460.500577
1120.4758550.9517090.524145
1130.4399090.8798170.560091
1140.4192280.8384560.580772
1150.3852830.7705650.614717
1160.3812040.7624080.618796
1170.549890.900220.45011
1180.50160.9967990.4984
1190.4581180.9162360.541882
1200.4640830.9281660.535917
1210.4280740.8561480.571926
1220.4099660.8199320.590034
1230.3904880.7809770.609512
1240.3432410.6864820.656759
1250.3600730.7201460.639927
1260.3655760.7311520.634424
1270.34020.6804010.6598
1280.2948850.589770.705115
1290.2940770.5881540.705923
1300.2485880.4971750.751412
1310.2222880.4445750.777712
1320.2043240.4086470.795676
1330.165830.3316590.83417
1340.2024040.4048080.797596
1350.2020550.4041090.797945
1360.1795320.3590640.820468
1370.1851290.3702590.814871
1380.1634540.3269080.836546
1390.1282770.2565550.871723
1400.133740.2674810.86626
1410.1077860.2155720.892214
1420.1123070.2246130.887693
1430.1132650.2265290.886735
1440.09732280.1946460.902677
1450.08194890.1638980.918051
1460.07466520.149330.925335
1470.06029470.1205890.939705
1480.07127950.1425590.92872
1490.05227820.1045560.947722
1500.06717290.1343460.932827
1510.08750120.1750020.912499
1520.08380660.1676130.916193
1530.06056440.1211290.939436
1540.04782260.09564520.952177
1550.03200070.06400130.967999
1560.07912890.1582580.920871
1570.04393090.08786170.956069
1580.04509430.09018860.954906






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0547945NOK
10% type I error level240.164384NOK

\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 & 8 & 0.0547945 & NOK \tabularnewline
10% type I error level & 24 & 0.164384 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268242&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]8[/C][C]0.0547945[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.164384[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268242&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268242&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 level80.0547945NOK
10% type I error level240.164384NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 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')
}