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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 12:43:08 +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/t14186474067m6t1a3ibvjdpcl.htm/, Retrieved Thu, 16 May 2024 21:19:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268265, Retrieved Thu, 16 May 2024 21:19:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
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11,3	0	62	72	15	57	80	91	2,0	5,0
9,6	1	56	61	21	84	99	137	2,1	3,0
16,1	1	57	68	30	189	137	148	2,7	7,5
13,4	0	51	61	20	66	77	92	1,9	7,0
12,7	1	56	64	14	69	108	131	2,2	6,0
12,3	1	30	65	18	57	62	59	1,9	6,0
7,9	1	61	69	19	103	72	90	2,0	1,0
12,3	1	47	63	25	51	58	83	2,0	6,0
11,6	1	56	75	23	69	97	116	2,2	5,0
6,7	1	50	63	17	41	88	42	1,7	1,0
12,1	1	67	73	21	50	104	155	2,2	6,5
5,7	0	41	75	21	54	80	128	2,0	0,0
8,0	0	45	63	8	15	25	49	1,6	3,5
13,3	1	48	63	29	69	99	96	2,1	7,5
9,1	1	44	62	20	53	60	66	1,8	3,5
12,2	0	37	64	19	69	66	104	2,1	6,0
8,8	0	56	60	22	59	90	76	1,8	3,5
14,6	1	66	56	23	118	75	99	2,1	7,5
12,6	1	38	59	24	65	69	108	2,1	6,5
9,9	0	34	68	12	64	81	74	1,8	3,5
10,5	0	49	66	22	70	54	96	2,1	4,0
13,4	0	55	73	12	50	46	116	1,9	7,5
10,9	0	49	72	22	77	106	87	2,1	4,5
4,3	1	59	71	20	37	34	97	1,0	0,0
10,3	0	40	59	10	81	60	127	2,2	3,5
11,8	1	58	64	23	101	95	106	2,1	5,5
11,2	1	60	66	17	79	57	80	1,9	5,0
11,4	0	63	78	22	71	62	74	2,0	4,5
8,6	0	56	68	24	60	36	91	1,9	2,5
13,2	0	54	73	18	55	56	133	2,0	7,5
12,6	1	52	62	21	44	54	74	1,8	7,0
5,6	1	34	65	20	40	64	114	2,0	0,0
9,9	1	69	68	20	56	76	140	2,0	4,5
8,8	0	32	65	22	43	98	95	2,0	3,0
7,7	1	48	60	19	45	88	98	1,8	1,5
9,0	0	67	71	20	32	35	121	2,0	3,5
7,3	1	58	65	26	56	102	126	1,1	2,5
11,4	1	57	68	23	40	61	98	1,8	5,5
13,6	1	42	64	24	34	80	95	1,8	8,0
7,9	1	64	74	21	89	49	110	2,0	1,0
10,7	1	58	69	21	50	78	70	1,9	5,0
10,3	0	66	76	19	56	90	102	2,1	4,5
8,3	1	26	68	8	46	45	86	1,6	3,0
9,6	1	61	72	17	76	55	130	2,2	3,0
14,2	1	52	67	20	64	96	96	1,9	8,0
8,5	0	51	63	11	74	43	102	2,0	2,5
13,5	0	55	59	8	57	52	100	2,1	7,0
4,9	0	50	73	15	45	60	94	1,3	0,0
6,4	0	60	66	18	30	54	52	1,8	1,0
9,6	0	56	62	18	62	51	98	1,9	3,5
11,6	0	63	69	19	51	51	118	2,1	5,5
11,1	1	61	66	19	36	38	99	1,8	5,5
16,6	0	52	57	30	145	263	109	3	8,5
12,6	1	55	56	17	23	35	68	1,5	7
18,9	1	72	71	24	160	227	131	3	9,5
11,6	1	33	56	20	32	79	71	1,5	6
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
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
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
17,6	1	53	74	20	80	159	110	3	9,5
15,35	1	47	63	20	79	125	146	2,25	7
13,4	0	50	58	23	120	186	90	3	6
13,9	0	39	58	18	69	148	121	1,5	7
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
14,1	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
13,6	1	63	50	22	43	111	65	1,5	8
15,65	0	44	70	20	57	211	117	2,25	8
14,6	0	43	69	22	69	76	63	1,5	9
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
13,2	0	55	78	18	40	246	77	2,25	7
15,85	1	37	60	18	78	137	96	2,25	8,5
11,15	1	46	69	24	59	98	57	0,75	7
15,65	0	51	65	29	96	226	112	2,25	8
7,65	0	64	78	15	38	138	49	0,75	3,5
15,2	0	47	63	22	48	106	56	3	8,5
15,6	1	62	71	23	91	122	86	1,5	10
13,1	1	67	80	24	52	94	88	1,5	7,5
11,85	0	56	73	22	27	62	48	2,25	6,5
12,4	1	65	69	15	62	82	85	3	5
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
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
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
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
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
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 time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 1.54271 -0.0652082geslacht[t] + 0.000102092IM[t] + 0.00598415EM[t] -0.00949689Numeracy_tot[t] + 0.00944464uren_rfc[t] + 0.00182496blogs[t] + 0.0150016zinvolle_teksten[t] + 1.01996PE[t] + 1.06538ruwe_examenscore[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  1.54271 -0.0652082geslacht[t] +  0.000102092IM[t] +  0.00598415EM[t] -0.00949689Numeracy_tot[t] +  0.00944464uren_rfc[t] +  0.00182496blogs[t] +  0.0150016zinvolle_teksten[t] +  1.01996PE[t] +  1.06538ruwe_examenscore[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  1.54271 -0.0652082geslacht[t] +  0.000102092IM[t] +  0.00598415EM[t] -0.00949689Numeracy_tot[t] +  0.00944464uren_rfc[t] +  0.00182496blogs[t] +  0.0150016zinvolle_teksten[t] +  1.01996PE[t] +  1.06538ruwe_examenscore[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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] = + 1.54271 -0.0652082geslacht[t] + 0.000102092IM[t] + 0.00598415EM[t] -0.00949689Numeracy_tot[t] + 0.00944464uren_rfc[t] + 0.00182496blogs[t] + 0.0150016zinvolle_teksten[t] + 1.01996PE[t] + 1.06538ruwe_examenscore[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.542710.5707462.7030.007812990.00390649
geslacht-0.06520820.105606-0.61750.5380310.269015
IM0.0001020920.00547970.018630.9851650.492582
EM0.005984150.007864440.76090.448120.22406
Numeracy_tot-0.009496890.0101556-0.93510.3514920.175746
uren_rfc0.009444640.002468123.8270.0002028120.000101406
blogs0.001824960.001060961.720.08784860.0439243
zinvolle_teksten0.01500160.002231916.7215.46737e-102.73368e-10
PE1.019960.1030359.8991.79523e-178.97617e-18
ruwe_examenscore1.065380.023331245.661.14233e-805.71163e-81

\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) & 1.54271 & 0.570746 & 2.703 & 0.00781299 & 0.00390649 \tabularnewline
geslacht & -0.0652082 & 0.105606 & -0.6175 & 0.538031 & 0.269015 \tabularnewline
IM & 0.000102092 & 0.0054797 & 0.01863 & 0.985165 & 0.492582 \tabularnewline
EM & 0.00598415 & 0.00786444 & 0.7609 & 0.44812 & 0.22406 \tabularnewline
Numeracy_tot & -0.00949689 & 0.0101556 & -0.9351 & 0.351492 & 0.175746 \tabularnewline
uren_rfc & 0.00944464 & 0.00246812 & 3.827 & 0.000202812 & 0.000101406 \tabularnewline
blogs & 0.00182496 & 0.00106096 & 1.72 & 0.0878486 & 0.0439243 \tabularnewline
zinvolle_teksten & 0.0150016 & 0.00223191 & 6.721 & 5.46737e-10 & 2.73368e-10 \tabularnewline
PE & 1.01996 & 0.103035 & 9.899 & 1.79523e-17 & 8.97617e-18 \tabularnewline
ruwe_examenscore & 1.06538 & 0.0233312 & 45.66 & 1.14233e-80 & 5.71163e-81 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&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]1.54271[/C][C]0.570746[/C][C]2.703[/C][C]0.00781299[/C][C]0.00390649[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.0652082[/C][C]0.105606[/C][C]-0.6175[/C][C]0.538031[/C][C]0.269015[/C][/ROW]
[ROW][C]IM[/C][C]0.000102092[/C][C]0.0054797[/C][C]0.01863[/C][C]0.985165[/C][C]0.492582[/C][/ROW]
[ROW][C]EM[/C][C]0.00598415[/C][C]0.00786444[/C][C]0.7609[/C][C]0.44812[/C][C]0.22406[/C][/ROW]
[ROW][C]Numeracy_tot[/C][C]-0.00949689[/C][C]0.0101556[/C][C]-0.9351[/C][C]0.351492[/C][C]0.175746[/C][/ROW]
[ROW][C]uren_rfc[/C][C]0.00944464[/C][C]0.00246812[/C][C]3.827[/C][C]0.000202812[/C][C]0.000101406[/C][/ROW]
[ROW][C]blogs[/C][C]0.00182496[/C][C]0.00106096[/C][C]1.72[/C][C]0.0878486[/C][C]0.0439243[/C][/ROW]
[ROW][C]zinvolle_teksten[/C][C]0.0150016[/C][C]0.00223191[/C][C]6.721[/C][C]5.46737e-10[/C][C]2.73368e-10[/C][/ROW]
[ROW][C]PE[/C][C]1.01996[/C][C]0.103035[/C][C]9.899[/C][C]1.79523e-17[/C][C]8.97617e-18[/C][/ROW]
[ROW][C]ruwe_examenscore[/C][C]1.06538[/C][C]0.0233312[/C][C]45.66[/C][C]1.14233e-80[/C][C]5.71163e-81[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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)1.542710.5707462.7030.007812990.00390649
geslacht-0.06520820.105606-0.61750.5380310.269015
IM0.0001020920.00547970.018630.9851650.492582
EM0.005984150.007864440.76090.448120.22406
Numeracy_tot-0.009496890.0101556-0.93510.3514920.175746
uren_rfc0.009444640.002468123.8270.0002028120.000101406
blogs0.001824960.001060961.720.08784860.0439243
zinvolle_teksten0.01500160.002231916.7215.46737e-102.73368e-10
PE1.019960.1030359.8991.79523e-178.97617e-18
ruwe_examenscore1.065380.023331245.661.14233e-805.71163e-81






Multiple Linear Regression - Regression Statistics
Multiple R0.985561
R-squared0.971331
Adjusted R-squared0.969299
F-TEST (value)478.095
F-TEST (DF numerator)9
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.575011
Sum Squared Residuals41.9911

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985561 \tabularnewline
R-squared & 0.971331 \tabularnewline
Adjusted R-squared & 0.969299 \tabularnewline
F-TEST (value) & 478.095 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 127 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.575011 \tabularnewline
Sum Squared Residuals & 41.9911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985561[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971331[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.969299[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]478.095[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]127[/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]0.575011[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41.9911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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 R0.985561
R-squared0.971331
Adjusted R-squared0.969299
F-TEST (value)478.095
F-TEST (DF numerator)9
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.575011
Sum Squared Residuals41.9911






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.311.25380.0462488
29.610.0161-0.416118
316.116.6049-0.504868
413.413.26260.137395
512.713.1834-0.483423
612.311.56540.734623
77.97.275840.624158
812.311.88670.413267
911.611.8533-0.253301
106.75.675371.02463
1112.113.8779-1.7779
125.76.41242-0.712415
1388.13147-0.131469
1413.313.9888-0.688758
159.18.8280.271998
1612.212.6155-0.415519
178.89.12491-0.324906
1814.614.46970.130319
1912.613.0334-0.433399
209.99.26630.633703
2110.510.3370.163001
2213.414.0958-0.695838
2310.910.9316-0.0315889
244.34.60508-0.305083
2510.310.5574-0.257352
2611.812.3669-0.566934
2711.211.03220.167766
2811.410.53490.86506
298.68.326320.273675
3013.214.4613-1.26125
3112.612.57220.027795
325.65.9247-0.324701
339.911.3035-1.40348
348.88.9722-0.172197
357.77.150780.549223
3699.73454-0.734535
377.38.01613-0.716128
3811.411.32660.0734053
3913.613.8881-0.288076
407.97.412910.487092
4110.710.62640.0735917
4210.310.9832-0.683239
438.38.4459-0.145895
449.69.96157-0.361571
4514.214.3746-0.174573
468.58.83137-0.331367
4713.513.5584-0.0583881
484.95.11282-0.212816
496.45.836130.563869
509.69.564050.0359462
5111.612.1281-0.52805
5211.111.2883-0.188271
5316.617.2044-0.604427
5412.611.94560.654417
5518.918.75340.146596
5611.611.05980.540226
5714.614.37130.228678
5813.8514.1422-0.292221
5914.8515.1534-0.30338
6011.7511.8395-0.0895342
6118.4518.07620.373779
6215.916.1087-0.208707
6319.919.9116-0.0115778
6410.9511.6902-0.74019
6518.4517.32511.12493
6615.114.12310.976873
671514.66850.331526
6811.3511.6634-0.313405
6915.9515.12330.826661
7018.118.2542-0.154156
7114.613.83790.762056
7217.617.6127-0.0127018
7315.3514.58640.76359
7413.413.9516-0.551572
7513.913.44740.452612
7615.2514.88020.36984
7712.911.2251.67498
7816.116.04360.0564032
7917.3516.94650.403478
8013.1512.43820.711781
8112.1511.17840.971632
8212.613.3686-0.768625
8310.3510.8419-0.491925
8415.414.92910.470947
859.68.895460.704537
8618.217.58530.614656
8713.613.6197-0.0197054
8814.8515.009-0.158994
8914.114.7044-0.604371
9014.914.88760.0124482
9116.2515.94030.309661
9213.613.2110.389027
9315.6515.27270.377303
9414.614.6049-0.00490001
9512.6513.4263-0.776302
9611.913.1266-1.22662
9719.218.46580.734202
9816.616.6314-0.0313854
9911.211.58-0.379956
10013.213.5786-0.37856
10115.8515.44690.403128
10211.1511.481-0.330968
10315.6515.47870.171274
1047.657.71317-0.0631703
10515.215.3181-0.118062
10615.616.2462-0.646248
10713.113.2382-0.138238
10811.8512.0844-0.234445
10912.412.15170.248275
11011.411.03910.360874
11114.914.12880.771234
11219.919.971-0.071011
11311.211.3732-0.17317
11414.614.661-0.0609962
11514.7515.227-0.477031
11615.1515.3523-0.202339
11716.8517.1199-0.26986
1187.857.20030.649701
11912.612.40520.194759
1207.856.95210.897903
12110.9511.5887-0.638663
12212.3512.5212-0.171158
1239.9510.5908-0.640755
12414.914.12930.770724
12516.6516.19130.458726
12613.412.79380.606227
12713.9514.1084-0.158444
12815.714.97920.720768
12916.8516.56010.289917
13010.9511.5535-0.603478
13115.3515.1350.215031
13212.212.13990.0600591
13315.115.9611-0.86114
13417.7517.8589-0.108852
13515.214.26770.93234
13616.6516.48960.16042
1378.18.61629-0.516294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.3 & 11.2538 & 0.0462488 \tabularnewline
2 & 9.6 & 10.0161 & -0.416118 \tabularnewline
3 & 16.1 & 16.6049 & -0.504868 \tabularnewline
4 & 13.4 & 13.2626 & 0.137395 \tabularnewline
5 & 12.7 & 13.1834 & -0.483423 \tabularnewline
6 & 12.3 & 11.5654 & 0.734623 \tabularnewline
7 & 7.9 & 7.27584 & 0.624158 \tabularnewline
8 & 12.3 & 11.8867 & 0.413267 \tabularnewline
9 & 11.6 & 11.8533 & -0.253301 \tabularnewline
10 & 6.7 & 5.67537 & 1.02463 \tabularnewline
11 & 12.1 & 13.8779 & -1.7779 \tabularnewline
12 & 5.7 & 6.41242 & -0.712415 \tabularnewline
13 & 8 & 8.13147 & -0.131469 \tabularnewline
14 & 13.3 & 13.9888 & -0.688758 \tabularnewline
15 & 9.1 & 8.828 & 0.271998 \tabularnewline
16 & 12.2 & 12.6155 & -0.415519 \tabularnewline
17 & 8.8 & 9.12491 & -0.324906 \tabularnewline
18 & 14.6 & 14.4697 & 0.130319 \tabularnewline
19 & 12.6 & 13.0334 & -0.433399 \tabularnewline
20 & 9.9 & 9.2663 & 0.633703 \tabularnewline
21 & 10.5 & 10.337 & 0.163001 \tabularnewline
22 & 13.4 & 14.0958 & -0.695838 \tabularnewline
23 & 10.9 & 10.9316 & -0.0315889 \tabularnewline
24 & 4.3 & 4.60508 & -0.305083 \tabularnewline
25 & 10.3 & 10.5574 & -0.257352 \tabularnewline
26 & 11.8 & 12.3669 & -0.566934 \tabularnewline
27 & 11.2 & 11.0322 & 0.167766 \tabularnewline
28 & 11.4 & 10.5349 & 0.86506 \tabularnewline
29 & 8.6 & 8.32632 & 0.273675 \tabularnewline
30 & 13.2 & 14.4613 & -1.26125 \tabularnewline
31 & 12.6 & 12.5722 & 0.027795 \tabularnewline
32 & 5.6 & 5.9247 & -0.324701 \tabularnewline
33 & 9.9 & 11.3035 & -1.40348 \tabularnewline
34 & 8.8 & 8.9722 & -0.172197 \tabularnewline
35 & 7.7 & 7.15078 & 0.549223 \tabularnewline
36 & 9 & 9.73454 & -0.734535 \tabularnewline
37 & 7.3 & 8.01613 & -0.716128 \tabularnewline
38 & 11.4 & 11.3266 & 0.0734053 \tabularnewline
39 & 13.6 & 13.8881 & -0.288076 \tabularnewline
40 & 7.9 & 7.41291 & 0.487092 \tabularnewline
41 & 10.7 & 10.6264 & 0.0735917 \tabularnewline
42 & 10.3 & 10.9832 & -0.683239 \tabularnewline
43 & 8.3 & 8.4459 & -0.145895 \tabularnewline
44 & 9.6 & 9.96157 & -0.361571 \tabularnewline
45 & 14.2 & 14.3746 & -0.174573 \tabularnewline
46 & 8.5 & 8.83137 & -0.331367 \tabularnewline
47 & 13.5 & 13.5584 & -0.0583881 \tabularnewline
48 & 4.9 & 5.11282 & -0.212816 \tabularnewline
49 & 6.4 & 5.83613 & 0.563869 \tabularnewline
50 & 9.6 & 9.56405 & 0.0359462 \tabularnewline
51 & 11.6 & 12.1281 & -0.52805 \tabularnewline
52 & 11.1 & 11.2883 & -0.188271 \tabularnewline
53 & 16.6 & 17.2044 & -0.604427 \tabularnewline
54 & 12.6 & 11.9456 & 0.654417 \tabularnewline
55 & 18.9 & 18.7534 & 0.146596 \tabularnewline
56 & 11.6 & 11.0598 & 0.540226 \tabularnewline
57 & 14.6 & 14.3713 & 0.228678 \tabularnewline
58 & 13.85 & 14.1422 & -0.292221 \tabularnewline
59 & 14.85 & 15.1534 & -0.30338 \tabularnewline
60 & 11.75 & 11.8395 & -0.0895342 \tabularnewline
61 & 18.45 & 18.0762 & 0.373779 \tabularnewline
62 & 15.9 & 16.1087 & -0.208707 \tabularnewline
63 & 19.9 & 19.9116 & -0.0115778 \tabularnewline
64 & 10.95 & 11.6902 & -0.74019 \tabularnewline
65 & 18.45 & 17.3251 & 1.12493 \tabularnewline
66 & 15.1 & 14.1231 & 0.976873 \tabularnewline
67 & 15 & 14.6685 & 0.331526 \tabularnewline
68 & 11.35 & 11.6634 & -0.313405 \tabularnewline
69 & 15.95 & 15.1233 & 0.826661 \tabularnewline
70 & 18.1 & 18.2542 & -0.154156 \tabularnewline
71 & 14.6 & 13.8379 & 0.762056 \tabularnewline
72 & 17.6 & 17.6127 & -0.0127018 \tabularnewline
73 & 15.35 & 14.5864 & 0.76359 \tabularnewline
74 & 13.4 & 13.9516 & -0.551572 \tabularnewline
75 & 13.9 & 13.4474 & 0.452612 \tabularnewline
76 & 15.25 & 14.8802 & 0.36984 \tabularnewline
77 & 12.9 & 11.225 & 1.67498 \tabularnewline
78 & 16.1 & 16.0436 & 0.0564032 \tabularnewline
79 & 17.35 & 16.9465 & 0.403478 \tabularnewline
80 & 13.15 & 12.4382 & 0.711781 \tabularnewline
81 & 12.15 & 11.1784 & 0.971632 \tabularnewline
82 & 12.6 & 13.3686 & -0.768625 \tabularnewline
83 & 10.35 & 10.8419 & -0.491925 \tabularnewline
84 & 15.4 & 14.9291 & 0.470947 \tabularnewline
85 & 9.6 & 8.89546 & 0.704537 \tabularnewline
86 & 18.2 & 17.5853 & 0.614656 \tabularnewline
87 & 13.6 & 13.6197 & -0.0197054 \tabularnewline
88 & 14.85 & 15.009 & -0.158994 \tabularnewline
89 & 14.1 & 14.7044 & -0.604371 \tabularnewline
90 & 14.9 & 14.8876 & 0.0124482 \tabularnewline
91 & 16.25 & 15.9403 & 0.309661 \tabularnewline
92 & 13.6 & 13.211 & 0.389027 \tabularnewline
93 & 15.65 & 15.2727 & 0.377303 \tabularnewline
94 & 14.6 & 14.6049 & -0.00490001 \tabularnewline
95 & 12.65 & 13.4263 & -0.776302 \tabularnewline
96 & 11.9 & 13.1266 & -1.22662 \tabularnewline
97 & 19.2 & 18.4658 & 0.734202 \tabularnewline
98 & 16.6 & 16.6314 & -0.0313854 \tabularnewline
99 & 11.2 & 11.58 & -0.379956 \tabularnewline
100 & 13.2 & 13.5786 & -0.37856 \tabularnewline
101 & 15.85 & 15.4469 & 0.403128 \tabularnewline
102 & 11.15 & 11.481 & -0.330968 \tabularnewline
103 & 15.65 & 15.4787 & 0.171274 \tabularnewline
104 & 7.65 & 7.71317 & -0.0631703 \tabularnewline
105 & 15.2 & 15.3181 & -0.118062 \tabularnewline
106 & 15.6 & 16.2462 & -0.646248 \tabularnewline
107 & 13.1 & 13.2382 & -0.138238 \tabularnewline
108 & 11.85 & 12.0844 & -0.234445 \tabularnewline
109 & 12.4 & 12.1517 & 0.248275 \tabularnewline
110 & 11.4 & 11.0391 & 0.360874 \tabularnewline
111 & 14.9 & 14.1288 & 0.771234 \tabularnewline
112 & 19.9 & 19.971 & -0.071011 \tabularnewline
113 & 11.2 & 11.3732 & -0.17317 \tabularnewline
114 & 14.6 & 14.661 & -0.0609962 \tabularnewline
115 & 14.75 & 15.227 & -0.477031 \tabularnewline
116 & 15.15 & 15.3523 & -0.202339 \tabularnewline
117 & 16.85 & 17.1199 & -0.26986 \tabularnewline
118 & 7.85 & 7.2003 & 0.649701 \tabularnewline
119 & 12.6 & 12.4052 & 0.194759 \tabularnewline
120 & 7.85 & 6.9521 & 0.897903 \tabularnewline
121 & 10.95 & 11.5887 & -0.638663 \tabularnewline
122 & 12.35 & 12.5212 & -0.171158 \tabularnewline
123 & 9.95 & 10.5908 & -0.640755 \tabularnewline
124 & 14.9 & 14.1293 & 0.770724 \tabularnewline
125 & 16.65 & 16.1913 & 0.458726 \tabularnewline
126 & 13.4 & 12.7938 & 0.606227 \tabularnewline
127 & 13.95 & 14.1084 & -0.158444 \tabularnewline
128 & 15.7 & 14.9792 & 0.720768 \tabularnewline
129 & 16.85 & 16.5601 & 0.289917 \tabularnewline
130 & 10.95 & 11.5535 & -0.603478 \tabularnewline
131 & 15.35 & 15.135 & 0.215031 \tabularnewline
132 & 12.2 & 12.1399 & 0.0600591 \tabularnewline
133 & 15.1 & 15.9611 & -0.86114 \tabularnewline
134 & 17.75 & 17.8589 & -0.108852 \tabularnewline
135 & 15.2 & 14.2677 & 0.93234 \tabularnewline
136 & 16.65 & 16.4896 & 0.16042 \tabularnewline
137 & 8.1 & 8.61629 & -0.516294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&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]11.3[/C][C]11.2538[/C][C]0.0462488[/C][/ROW]
[ROW][C]2[/C][C]9.6[/C][C]10.0161[/C][C]-0.416118[/C][/ROW]
[ROW][C]3[/C][C]16.1[/C][C]16.6049[/C][C]-0.504868[/C][/ROW]
[ROW][C]4[/C][C]13.4[/C][C]13.2626[/C][C]0.137395[/C][/ROW]
[ROW][C]5[/C][C]12.7[/C][C]13.1834[/C][C]-0.483423[/C][/ROW]
[ROW][C]6[/C][C]12.3[/C][C]11.5654[/C][C]0.734623[/C][/ROW]
[ROW][C]7[/C][C]7.9[/C][C]7.27584[/C][C]0.624158[/C][/ROW]
[ROW][C]8[/C][C]12.3[/C][C]11.8867[/C][C]0.413267[/C][/ROW]
[ROW][C]9[/C][C]11.6[/C][C]11.8533[/C][C]-0.253301[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]5.67537[/C][C]1.02463[/C][/ROW]
[ROW][C]11[/C][C]12.1[/C][C]13.8779[/C][C]-1.7779[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]6.41242[/C][C]-0.712415[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]8.13147[/C][C]-0.131469[/C][/ROW]
[ROW][C]14[/C][C]13.3[/C][C]13.9888[/C][C]-0.688758[/C][/ROW]
[ROW][C]15[/C][C]9.1[/C][C]8.828[/C][C]0.271998[/C][/ROW]
[ROW][C]16[/C][C]12.2[/C][C]12.6155[/C][C]-0.415519[/C][/ROW]
[ROW][C]17[/C][C]8.8[/C][C]9.12491[/C][C]-0.324906[/C][/ROW]
[ROW][C]18[/C][C]14.6[/C][C]14.4697[/C][C]0.130319[/C][/ROW]
[ROW][C]19[/C][C]12.6[/C][C]13.0334[/C][C]-0.433399[/C][/ROW]
[ROW][C]20[/C][C]9.9[/C][C]9.2663[/C][C]0.633703[/C][/ROW]
[ROW][C]21[/C][C]10.5[/C][C]10.337[/C][C]0.163001[/C][/ROW]
[ROW][C]22[/C][C]13.4[/C][C]14.0958[/C][C]-0.695838[/C][/ROW]
[ROW][C]23[/C][C]10.9[/C][C]10.9316[/C][C]-0.0315889[/C][/ROW]
[ROW][C]24[/C][C]4.3[/C][C]4.60508[/C][C]-0.305083[/C][/ROW]
[ROW][C]25[/C][C]10.3[/C][C]10.5574[/C][C]-0.257352[/C][/ROW]
[ROW][C]26[/C][C]11.8[/C][C]12.3669[/C][C]-0.566934[/C][/ROW]
[ROW][C]27[/C][C]11.2[/C][C]11.0322[/C][C]0.167766[/C][/ROW]
[ROW][C]28[/C][C]11.4[/C][C]10.5349[/C][C]0.86506[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]8.32632[/C][C]0.273675[/C][/ROW]
[ROW][C]30[/C][C]13.2[/C][C]14.4613[/C][C]-1.26125[/C][/ROW]
[ROW][C]31[/C][C]12.6[/C][C]12.5722[/C][C]0.027795[/C][/ROW]
[ROW][C]32[/C][C]5.6[/C][C]5.9247[/C][C]-0.324701[/C][/ROW]
[ROW][C]33[/C][C]9.9[/C][C]11.3035[/C][C]-1.40348[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]8.9722[/C][C]-0.172197[/C][/ROW]
[ROW][C]35[/C][C]7.7[/C][C]7.15078[/C][C]0.549223[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.73454[/C][C]-0.734535[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]8.01613[/C][C]-0.716128[/C][/ROW]
[ROW][C]38[/C][C]11.4[/C][C]11.3266[/C][C]0.0734053[/C][/ROW]
[ROW][C]39[/C][C]13.6[/C][C]13.8881[/C][C]-0.288076[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]7.41291[/C][C]0.487092[/C][/ROW]
[ROW][C]41[/C][C]10.7[/C][C]10.6264[/C][C]0.0735917[/C][/ROW]
[ROW][C]42[/C][C]10.3[/C][C]10.9832[/C][C]-0.683239[/C][/ROW]
[ROW][C]43[/C][C]8.3[/C][C]8.4459[/C][C]-0.145895[/C][/ROW]
[ROW][C]44[/C][C]9.6[/C][C]9.96157[/C][C]-0.361571[/C][/ROW]
[ROW][C]45[/C][C]14.2[/C][C]14.3746[/C][C]-0.174573[/C][/ROW]
[ROW][C]46[/C][C]8.5[/C][C]8.83137[/C][C]-0.331367[/C][/ROW]
[ROW][C]47[/C][C]13.5[/C][C]13.5584[/C][C]-0.0583881[/C][/ROW]
[ROW][C]48[/C][C]4.9[/C][C]5.11282[/C][C]-0.212816[/C][/ROW]
[ROW][C]49[/C][C]6.4[/C][C]5.83613[/C][C]0.563869[/C][/ROW]
[ROW][C]50[/C][C]9.6[/C][C]9.56405[/C][C]0.0359462[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]12.1281[/C][C]-0.52805[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]11.2883[/C][C]-0.188271[/C][/ROW]
[ROW][C]53[/C][C]16.6[/C][C]17.2044[/C][C]-0.604427[/C][/ROW]
[ROW][C]54[/C][C]12.6[/C][C]11.9456[/C][C]0.654417[/C][/ROW]
[ROW][C]55[/C][C]18.9[/C][C]18.7534[/C][C]0.146596[/C][/ROW]
[ROW][C]56[/C][C]11.6[/C][C]11.0598[/C][C]0.540226[/C][/ROW]
[ROW][C]57[/C][C]14.6[/C][C]14.3713[/C][C]0.228678[/C][/ROW]
[ROW][C]58[/C][C]13.85[/C][C]14.1422[/C][C]-0.292221[/C][/ROW]
[ROW][C]59[/C][C]14.85[/C][C]15.1534[/C][C]-0.30338[/C][/ROW]
[ROW][C]60[/C][C]11.75[/C][C]11.8395[/C][C]-0.0895342[/C][/ROW]
[ROW][C]61[/C][C]18.45[/C][C]18.0762[/C][C]0.373779[/C][/ROW]
[ROW][C]62[/C][C]15.9[/C][C]16.1087[/C][C]-0.208707[/C][/ROW]
[ROW][C]63[/C][C]19.9[/C][C]19.9116[/C][C]-0.0115778[/C][/ROW]
[ROW][C]64[/C][C]10.95[/C][C]11.6902[/C][C]-0.74019[/C][/ROW]
[ROW][C]65[/C][C]18.45[/C][C]17.3251[/C][C]1.12493[/C][/ROW]
[ROW][C]66[/C][C]15.1[/C][C]14.1231[/C][C]0.976873[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]14.6685[/C][C]0.331526[/C][/ROW]
[ROW][C]68[/C][C]11.35[/C][C]11.6634[/C][C]-0.313405[/C][/ROW]
[ROW][C]69[/C][C]15.95[/C][C]15.1233[/C][C]0.826661[/C][/ROW]
[ROW][C]70[/C][C]18.1[/C][C]18.2542[/C][C]-0.154156[/C][/ROW]
[ROW][C]71[/C][C]14.6[/C][C]13.8379[/C][C]0.762056[/C][/ROW]
[ROW][C]72[/C][C]17.6[/C][C]17.6127[/C][C]-0.0127018[/C][/ROW]
[ROW][C]73[/C][C]15.35[/C][C]14.5864[/C][C]0.76359[/C][/ROW]
[ROW][C]74[/C][C]13.4[/C][C]13.9516[/C][C]-0.551572[/C][/ROW]
[ROW][C]75[/C][C]13.9[/C][C]13.4474[/C][C]0.452612[/C][/ROW]
[ROW][C]76[/C][C]15.25[/C][C]14.8802[/C][C]0.36984[/C][/ROW]
[ROW][C]77[/C][C]12.9[/C][C]11.225[/C][C]1.67498[/C][/ROW]
[ROW][C]78[/C][C]16.1[/C][C]16.0436[/C][C]0.0564032[/C][/ROW]
[ROW][C]79[/C][C]17.35[/C][C]16.9465[/C][C]0.403478[/C][/ROW]
[ROW][C]80[/C][C]13.15[/C][C]12.4382[/C][C]0.711781[/C][/ROW]
[ROW][C]81[/C][C]12.15[/C][C]11.1784[/C][C]0.971632[/C][/ROW]
[ROW][C]82[/C][C]12.6[/C][C]13.3686[/C][C]-0.768625[/C][/ROW]
[ROW][C]83[/C][C]10.35[/C][C]10.8419[/C][C]-0.491925[/C][/ROW]
[ROW][C]84[/C][C]15.4[/C][C]14.9291[/C][C]0.470947[/C][/ROW]
[ROW][C]85[/C][C]9.6[/C][C]8.89546[/C][C]0.704537[/C][/ROW]
[ROW][C]86[/C][C]18.2[/C][C]17.5853[/C][C]0.614656[/C][/ROW]
[ROW][C]87[/C][C]13.6[/C][C]13.6197[/C][C]-0.0197054[/C][/ROW]
[ROW][C]88[/C][C]14.85[/C][C]15.009[/C][C]-0.158994[/C][/ROW]
[ROW][C]89[/C][C]14.1[/C][C]14.7044[/C][C]-0.604371[/C][/ROW]
[ROW][C]90[/C][C]14.9[/C][C]14.8876[/C][C]0.0124482[/C][/ROW]
[ROW][C]91[/C][C]16.25[/C][C]15.9403[/C][C]0.309661[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.211[/C][C]0.389027[/C][/ROW]
[ROW][C]93[/C][C]15.65[/C][C]15.2727[/C][C]0.377303[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]14.6049[/C][C]-0.00490001[/C][/ROW]
[ROW][C]95[/C][C]12.65[/C][C]13.4263[/C][C]-0.776302[/C][/ROW]
[ROW][C]96[/C][C]11.9[/C][C]13.1266[/C][C]-1.22662[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]18.4658[/C][C]0.734202[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]16.6314[/C][C]-0.0313854[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.58[/C][C]-0.379956[/C][/ROW]
[ROW][C]100[/C][C]13.2[/C][C]13.5786[/C][C]-0.37856[/C][/ROW]
[ROW][C]101[/C][C]15.85[/C][C]15.4469[/C][C]0.403128[/C][/ROW]
[ROW][C]102[/C][C]11.15[/C][C]11.481[/C][C]-0.330968[/C][/ROW]
[ROW][C]103[/C][C]15.65[/C][C]15.4787[/C][C]0.171274[/C][/ROW]
[ROW][C]104[/C][C]7.65[/C][C]7.71317[/C][C]-0.0631703[/C][/ROW]
[ROW][C]105[/C][C]15.2[/C][C]15.3181[/C][C]-0.118062[/C][/ROW]
[ROW][C]106[/C][C]15.6[/C][C]16.2462[/C][C]-0.646248[/C][/ROW]
[ROW][C]107[/C][C]13.1[/C][C]13.2382[/C][C]-0.138238[/C][/ROW]
[ROW][C]108[/C][C]11.85[/C][C]12.0844[/C][C]-0.234445[/C][/ROW]
[ROW][C]109[/C][C]12.4[/C][C]12.1517[/C][C]0.248275[/C][/ROW]
[ROW][C]110[/C][C]11.4[/C][C]11.0391[/C][C]0.360874[/C][/ROW]
[ROW][C]111[/C][C]14.9[/C][C]14.1288[/C][C]0.771234[/C][/ROW]
[ROW][C]112[/C][C]19.9[/C][C]19.971[/C][C]-0.071011[/C][/ROW]
[ROW][C]113[/C][C]11.2[/C][C]11.3732[/C][C]-0.17317[/C][/ROW]
[ROW][C]114[/C][C]14.6[/C][C]14.661[/C][C]-0.0609962[/C][/ROW]
[ROW][C]115[/C][C]14.75[/C][C]15.227[/C][C]-0.477031[/C][/ROW]
[ROW][C]116[/C][C]15.15[/C][C]15.3523[/C][C]-0.202339[/C][/ROW]
[ROW][C]117[/C][C]16.85[/C][C]17.1199[/C][C]-0.26986[/C][/ROW]
[ROW][C]118[/C][C]7.85[/C][C]7.2003[/C][C]0.649701[/C][/ROW]
[ROW][C]119[/C][C]12.6[/C][C]12.4052[/C][C]0.194759[/C][/ROW]
[ROW][C]120[/C][C]7.85[/C][C]6.9521[/C][C]0.897903[/C][/ROW]
[ROW][C]121[/C][C]10.95[/C][C]11.5887[/C][C]-0.638663[/C][/ROW]
[ROW][C]122[/C][C]12.35[/C][C]12.5212[/C][C]-0.171158[/C][/ROW]
[ROW][C]123[/C][C]9.95[/C][C]10.5908[/C][C]-0.640755[/C][/ROW]
[ROW][C]124[/C][C]14.9[/C][C]14.1293[/C][C]0.770724[/C][/ROW]
[ROW][C]125[/C][C]16.65[/C][C]16.1913[/C][C]0.458726[/C][/ROW]
[ROW][C]126[/C][C]13.4[/C][C]12.7938[/C][C]0.606227[/C][/ROW]
[ROW][C]127[/C][C]13.95[/C][C]14.1084[/C][C]-0.158444[/C][/ROW]
[ROW][C]128[/C][C]15.7[/C][C]14.9792[/C][C]0.720768[/C][/ROW]
[ROW][C]129[/C][C]16.85[/C][C]16.5601[/C][C]0.289917[/C][/ROW]
[ROW][C]130[/C][C]10.95[/C][C]11.5535[/C][C]-0.603478[/C][/ROW]
[ROW][C]131[/C][C]15.35[/C][C]15.135[/C][C]0.215031[/C][/ROW]
[ROW][C]132[/C][C]12.2[/C][C]12.1399[/C][C]0.0600591[/C][/ROW]
[ROW][C]133[/C][C]15.1[/C][C]15.9611[/C][C]-0.86114[/C][/ROW]
[ROW][C]134[/C][C]17.75[/C][C]17.8589[/C][C]-0.108852[/C][/ROW]
[ROW][C]135[/C][C]15.2[/C][C]14.2677[/C][C]0.93234[/C][/ROW]
[ROW][C]136[/C][C]16.65[/C][C]16.4896[/C][C]0.16042[/C][/ROW]
[ROW][C]137[/C][C]8.1[/C][C]8.61629[/C][C]-0.516294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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
111.311.25380.0462488
29.610.0161-0.416118
316.116.6049-0.504868
413.413.26260.137395
512.713.1834-0.483423
612.311.56540.734623
77.97.275840.624158
812.311.88670.413267
911.611.8533-0.253301
106.75.675371.02463
1112.113.8779-1.7779
125.76.41242-0.712415
1388.13147-0.131469
1413.313.9888-0.688758
159.18.8280.271998
1612.212.6155-0.415519
178.89.12491-0.324906
1814.614.46970.130319
1912.613.0334-0.433399
209.99.26630.633703
2110.510.3370.163001
2213.414.0958-0.695838
2310.910.9316-0.0315889
244.34.60508-0.305083
2510.310.5574-0.257352
2611.812.3669-0.566934
2711.211.03220.167766
2811.410.53490.86506
298.68.326320.273675
3013.214.4613-1.26125
3112.612.57220.027795
325.65.9247-0.324701
339.911.3035-1.40348
348.88.9722-0.172197
357.77.150780.549223
3699.73454-0.734535
377.38.01613-0.716128
3811.411.32660.0734053
3913.613.8881-0.288076
407.97.412910.487092
4110.710.62640.0735917
4210.310.9832-0.683239
438.38.4459-0.145895
449.69.96157-0.361571
4514.214.3746-0.174573
468.58.83137-0.331367
4713.513.5584-0.0583881
484.95.11282-0.212816
496.45.836130.563869
509.69.564050.0359462
5111.612.1281-0.52805
5211.111.2883-0.188271
5316.617.2044-0.604427
5412.611.94560.654417
5518.918.75340.146596
5611.611.05980.540226
5714.614.37130.228678
5813.8514.1422-0.292221
5914.8515.1534-0.30338
6011.7511.8395-0.0895342
6118.4518.07620.373779
6215.916.1087-0.208707
6319.919.9116-0.0115778
6410.9511.6902-0.74019
6518.4517.32511.12493
6615.114.12310.976873
671514.66850.331526
6811.3511.6634-0.313405
6915.9515.12330.826661
7018.118.2542-0.154156
7114.613.83790.762056
7217.617.6127-0.0127018
7315.3514.58640.76359
7413.413.9516-0.551572
7513.913.44740.452612
7615.2514.88020.36984
7712.911.2251.67498
7816.116.04360.0564032
7917.3516.94650.403478
8013.1512.43820.711781
8112.1511.17840.971632
8212.613.3686-0.768625
8310.3510.8419-0.491925
8415.414.92910.470947
859.68.895460.704537
8618.217.58530.614656
8713.613.6197-0.0197054
8814.8515.009-0.158994
8914.114.7044-0.604371
9014.914.88760.0124482
9116.2515.94030.309661
9213.613.2110.389027
9315.6515.27270.377303
9414.614.6049-0.00490001
9512.6513.4263-0.776302
9611.913.1266-1.22662
9719.218.46580.734202
9816.616.6314-0.0313854
9911.211.58-0.379956
10013.213.5786-0.37856
10115.8515.44690.403128
10211.1511.481-0.330968
10315.6515.47870.171274
1047.657.71317-0.0631703
10515.215.3181-0.118062
10615.616.2462-0.646248
10713.113.2382-0.138238
10811.8512.0844-0.234445
10912.412.15170.248275
11011.411.03910.360874
11114.914.12880.771234
11219.919.971-0.071011
11311.211.3732-0.17317
11414.614.661-0.0609962
11514.7515.227-0.477031
11615.1515.3523-0.202339
11716.8517.1199-0.26986
1187.857.20030.649701
11912.612.40520.194759
1207.856.95210.897903
12110.9511.5887-0.638663
12212.3512.5212-0.171158
1239.9510.5908-0.640755
12414.914.12930.770724
12516.6516.19130.458726
12613.412.79380.606227
12713.9514.1084-0.158444
12815.714.97920.720768
12916.8516.56010.289917
13010.9511.5535-0.603478
13115.3515.1350.215031
13212.212.13990.0600591
13315.115.9611-0.86114
13417.7517.8589-0.108852
13515.214.26770.93234
13616.6516.48960.16042
1378.18.61629-0.516294






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.5376080.9247830.462392
140.6114330.7771340.388567
150.4926070.9852140.507393
160.3678990.7357970.632101
170.3086930.6173860.691307
180.2141310.4282620.785869
190.1465240.2930470.853476
200.1091310.2182620.890869
210.087510.175020.91249
220.05918570.1183710.940814
230.03595230.07190470.964048
240.02153650.04307310.978463
250.01237560.02475130.987624
260.01472880.02945770.985271
270.009226560.01845310.990773
280.01400460.02800930.985995
290.009015330.01803070.990985
300.01065160.02130310.989348
310.006316210.01263240.993684
320.004029090.008058170.995971
330.008818620.01763720.991181
340.005640330.01128070.99436
350.01634710.03269430.983653
360.01792010.03584020.98208
370.02733390.05466780.972666
380.03151680.06303370.968483
390.02855730.05711470.971443
400.02455090.04910180.975449
410.01893930.03787870.981061
420.02378760.04757520.976212
430.02240320.04480640.977597
440.02535680.05071360.974643
450.02057630.04115260.979424
460.02059370.04118750.979406
470.01922920.03845830.980771
480.01943690.03887390.980563
490.01546590.03093170.984534
500.01223120.02446240.987769
510.0278320.0556640.972168
520.0385440.07708790.961456
530.02996460.05992920.970035
540.04086650.08173290.959134
550.06863980.137280.93136
560.0760880.1521760.923912
570.06710650.1342130.932893
580.05778240.1155650.942218
590.06048610.1209720.939514
600.04948680.09897360.950513
610.1254010.2508020.874599
620.1039160.2078330.896084
630.1684010.3368030.831599
640.4472840.8945670.552716
650.8709880.2580240.129012
660.9398750.120250.0601251
670.9313650.1372710.0686355
680.929570.1408590.0704296
690.9608980.07820430.0391021
700.9519280.09614440.0480722
710.9668160.06636710.0331835
720.9568510.08629890.0431494
730.975980.04804070.0240204
740.9747350.05053020.0252651
750.9700240.05995250.0299762
760.9669550.06608930.0330447
770.9936030.01279440.0063972
780.9910340.01793290.00896645
790.9886150.02276960.0113848
800.9878380.02432370.0121619
810.989740.02052050.0102602
820.9919640.01607190.00803594
830.9922450.01550950.00775477
840.9908880.0182240.00911198
850.9894360.02112880.0105644
860.9890560.02188710.0109436
870.9845220.03095530.0154776
880.9802070.03958580.0197929
890.9818060.03638890.0181945
900.9757330.04853390.024267
910.9673070.06538610.032693
920.9695930.06081440.0304072
930.959780.08044050.0402202
940.9551660.0896680.044834
950.9581630.0836750.0418375
960.9888570.02228550.0111427
970.9916990.01660210.00830106
980.9874390.02512280.0125614
990.9822980.03540470.0177023
1000.9790960.04180870.0209043
1010.977850.04430.02215
1020.9688660.06226730.0311337
1030.95510.08980080.0449004
1040.9360650.127870.0639348
1050.913160.1736810.0868404
1060.8882820.2234360.111718
1070.8705040.2589930.129496
1080.8408410.3183180.159159
1090.8066920.3866160.193308
1100.7793790.4412430.220621
1110.7968930.4062140.203107
1120.7568280.4863440.243172
1130.7092210.5815590.290779
1140.635340.729320.36466
1150.601390.7972190.39861
1160.6013120.7973750.398688
1170.5396860.9206280.460314
1180.4835470.9670940.516453
1190.4395340.8790680.560466
1200.3571130.7142260.642887
1210.2935490.5870970.706451
1220.2091110.4182230.790889
1230.5142350.971530.485765
1240.3783930.7567870.621607

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.537608 & 0.924783 & 0.462392 \tabularnewline
14 & 0.611433 & 0.777134 & 0.388567 \tabularnewline
15 & 0.492607 & 0.985214 & 0.507393 \tabularnewline
16 & 0.367899 & 0.735797 & 0.632101 \tabularnewline
17 & 0.308693 & 0.617386 & 0.691307 \tabularnewline
18 & 0.214131 & 0.428262 & 0.785869 \tabularnewline
19 & 0.146524 & 0.293047 & 0.853476 \tabularnewline
20 & 0.109131 & 0.218262 & 0.890869 \tabularnewline
21 & 0.08751 & 0.17502 & 0.91249 \tabularnewline
22 & 0.0591857 & 0.118371 & 0.940814 \tabularnewline
23 & 0.0359523 & 0.0719047 & 0.964048 \tabularnewline
24 & 0.0215365 & 0.0430731 & 0.978463 \tabularnewline
25 & 0.0123756 & 0.0247513 & 0.987624 \tabularnewline
26 & 0.0147288 & 0.0294577 & 0.985271 \tabularnewline
27 & 0.00922656 & 0.0184531 & 0.990773 \tabularnewline
28 & 0.0140046 & 0.0280093 & 0.985995 \tabularnewline
29 & 0.00901533 & 0.0180307 & 0.990985 \tabularnewline
30 & 0.0106516 & 0.0213031 & 0.989348 \tabularnewline
31 & 0.00631621 & 0.0126324 & 0.993684 \tabularnewline
32 & 0.00402909 & 0.00805817 & 0.995971 \tabularnewline
33 & 0.00881862 & 0.0176372 & 0.991181 \tabularnewline
34 & 0.00564033 & 0.0112807 & 0.99436 \tabularnewline
35 & 0.0163471 & 0.0326943 & 0.983653 \tabularnewline
36 & 0.0179201 & 0.0358402 & 0.98208 \tabularnewline
37 & 0.0273339 & 0.0546678 & 0.972666 \tabularnewline
38 & 0.0315168 & 0.0630337 & 0.968483 \tabularnewline
39 & 0.0285573 & 0.0571147 & 0.971443 \tabularnewline
40 & 0.0245509 & 0.0491018 & 0.975449 \tabularnewline
41 & 0.0189393 & 0.0378787 & 0.981061 \tabularnewline
42 & 0.0237876 & 0.0475752 & 0.976212 \tabularnewline
43 & 0.0224032 & 0.0448064 & 0.977597 \tabularnewline
44 & 0.0253568 & 0.0507136 & 0.974643 \tabularnewline
45 & 0.0205763 & 0.0411526 & 0.979424 \tabularnewline
46 & 0.0205937 & 0.0411875 & 0.979406 \tabularnewline
47 & 0.0192292 & 0.0384583 & 0.980771 \tabularnewline
48 & 0.0194369 & 0.0388739 & 0.980563 \tabularnewline
49 & 0.0154659 & 0.0309317 & 0.984534 \tabularnewline
50 & 0.0122312 & 0.0244624 & 0.987769 \tabularnewline
51 & 0.027832 & 0.055664 & 0.972168 \tabularnewline
52 & 0.038544 & 0.0770879 & 0.961456 \tabularnewline
53 & 0.0299646 & 0.0599292 & 0.970035 \tabularnewline
54 & 0.0408665 & 0.0817329 & 0.959134 \tabularnewline
55 & 0.0686398 & 0.13728 & 0.93136 \tabularnewline
56 & 0.076088 & 0.152176 & 0.923912 \tabularnewline
57 & 0.0671065 & 0.134213 & 0.932893 \tabularnewline
58 & 0.0577824 & 0.115565 & 0.942218 \tabularnewline
59 & 0.0604861 & 0.120972 & 0.939514 \tabularnewline
60 & 0.0494868 & 0.0989736 & 0.950513 \tabularnewline
61 & 0.125401 & 0.250802 & 0.874599 \tabularnewline
62 & 0.103916 & 0.207833 & 0.896084 \tabularnewline
63 & 0.168401 & 0.336803 & 0.831599 \tabularnewline
64 & 0.447284 & 0.894567 & 0.552716 \tabularnewline
65 & 0.870988 & 0.258024 & 0.129012 \tabularnewline
66 & 0.939875 & 0.12025 & 0.0601251 \tabularnewline
67 & 0.931365 & 0.137271 & 0.0686355 \tabularnewline
68 & 0.92957 & 0.140859 & 0.0704296 \tabularnewline
69 & 0.960898 & 0.0782043 & 0.0391021 \tabularnewline
70 & 0.951928 & 0.0961444 & 0.0480722 \tabularnewline
71 & 0.966816 & 0.0663671 & 0.0331835 \tabularnewline
72 & 0.956851 & 0.0862989 & 0.0431494 \tabularnewline
73 & 0.97598 & 0.0480407 & 0.0240204 \tabularnewline
74 & 0.974735 & 0.0505302 & 0.0252651 \tabularnewline
75 & 0.970024 & 0.0599525 & 0.0299762 \tabularnewline
76 & 0.966955 & 0.0660893 & 0.0330447 \tabularnewline
77 & 0.993603 & 0.0127944 & 0.0063972 \tabularnewline
78 & 0.991034 & 0.0179329 & 0.00896645 \tabularnewline
79 & 0.988615 & 0.0227696 & 0.0113848 \tabularnewline
80 & 0.987838 & 0.0243237 & 0.0121619 \tabularnewline
81 & 0.98974 & 0.0205205 & 0.0102602 \tabularnewline
82 & 0.991964 & 0.0160719 & 0.00803594 \tabularnewline
83 & 0.992245 & 0.0155095 & 0.00775477 \tabularnewline
84 & 0.990888 & 0.018224 & 0.00911198 \tabularnewline
85 & 0.989436 & 0.0211288 & 0.0105644 \tabularnewline
86 & 0.989056 & 0.0218871 & 0.0109436 \tabularnewline
87 & 0.984522 & 0.0309553 & 0.0154776 \tabularnewline
88 & 0.980207 & 0.0395858 & 0.0197929 \tabularnewline
89 & 0.981806 & 0.0363889 & 0.0181945 \tabularnewline
90 & 0.975733 & 0.0485339 & 0.024267 \tabularnewline
91 & 0.967307 & 0.0653861 & 0.032693 \tabularnewline
92 & 0.969593 & 0.0608144 & 0.0304072 \tabularnewline
93 & 0.95978 & 0.0804405 & 0.0402202 \tabularnewline
94 & 0.955166 & 0.089668 & 0.044834 \tabularnewline
95 & 0.958163 & 0.083675 & 0.0418375 \tabularnewline
96 & 0.988857 & 0.0222855 & 0.0111427 \tabularnewline
97 & 0.991699 & 0.0166021 & 0.00830106 \tabularnewline
98 & 0.987439 & 0.0251228 & 0.0125614 \tabularnewline
99 & 0.982298 & 0.0354047 & 0.0177023 \tabularnewline
100 & 0.979096 & 0.0418087 & 0.0209043 \tabularnewline
101 & 0.97785 & 0.0443 & 0.02215 \tabularnewline
102 & 0.968866 & 0.0622673 & 0.0311337 \tabularnewline
103 & 0.9551 & 0.0898008 & 0.0449004 \tabularnewline
104 & 0.936065 & 0.12787 & 0.0639348 \tabularnewline
105 & 0.91316 & 0.173681 & 0.0868404 \tabularnewline
106 & 0.888282 & 0.223436 & 0.111718 \tabularnewline
107 & 0.870504 & 0.258993 & 0.129496 \tabularnewline
108 & 0.840841 & 0.318318 & 0.159159 \tabularnewline
109 & 0.806692 & 0.386616 & 0.193308 \tabularnewline
110 & 0.779379 & 0.441243 & 0.220621 \tabularnewline
111 & 0.796893 & 0.406214 & 0.203107 \tabularnewline
112 & 0.756828 & 0.486344 & 0.243172 \tabularnewline
113 & 0.709221 & 0.581559 & 0.290779 \tabularnewline
114 & 0.63534 & 0.72932 & 0.36466 \tabularnewline
115 & 0.60139 & 0.797219 & 0.39861 \tabularnewline
116 & 0.601312 & 0.797375 & 0.398688 \tabularnewline
117 & 0.539686 & 0.920628 & 0.460314 \tabularnewline
118 & 0.483547 & 0.967094 & 0.516453 \tabularnewline
119 & 0.439534 & 0.879068 & 0.560466 \tabularnewline
120 & 0.357113 & 0.714226 & 0.642887 \tabularnewline
121 & 0.293549 & 0.587097 & 0.706451 \tabularnewline
122 & 0.209111 & 0.418223 & 0.790889 \tabularnewline
123 & 0.514235 & 0.97153 & 0.485765 \tabularnewline
124 & 0.378393 & 0.756787 & 0.621607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&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]0.537608[/C][C]0.924783[/C][C]0.462392[/C][/ROW]
[ROW][C]14[/C][C]0.611433[/C][C]0.777134[/C][C]0.388567[/C][/ROW]
[ROW][C]15[/C][C]0.492607[/C][C]0.985214[/C][C]0.507393[/C][/ROW]
[ROW][C]16[/C][C]0.367899[/C][C]0.735797[/C][C]0.632101[/C][/ROW]
[ROW][C]17[/C][C]0.308693[/C][C]0.617386[/C][C]0.691307[/C][/ROW]
[ROW][C]18[/C][C]0.214131[/C][C]0.428262[/C][C]0.785869[/C][/ROW]
[ROW][C]19[/C][C]0.146524[/C][C]0.293047[/C][C]0.853476[/C][/ROW]
[ROW][C]20[/C][C]0.109131[/C][C]0.218262[/C][C]0.890869[/C][/ROW]
[ROW][C]21[/C][C]0.08751[/C][C]0.17502[/C][C]0.91249[/C][/ROW]
[ROW][C]22[/C][C]0.0591857[/C][C]0.118371[/C][C]0.940814[/C][/ROW]
[ROW][C]23[/C][C]0.0359523[/C][C]0.0719047[/C][C]0.964048[/C][/ROW]
[ROW][C]24[/C][C]0.0215365[/C][C]0.0430731[/C][C]0.978463[/C][/ROW]
[ROW][C]25[/C][C]0.0123756[/C][C]0.0247513[/C][C]0.987624[/C][/ROW]
[ROW][C]26[/C][C]0.0147288[/C][C]0.0294577[/C][C]0.985271[/C][/ROW]
[ROW][C]27[/C][C]0.00922656[/C][C]0.0184531[/C][C]0.990773[/C][/ROW]
[ROW][C]28[/C][C]0.0140046[/C][C]0.0280093[/C][C]0.985995[/C][/ROW]
[ROW][C]29[/C][C]0.00901533[/C][C]0.0180307[/C][C]0.990985[/C][/ROW]
[ROW][C]30[/C][C]0.0106516[/C][C]0.0213031[/C][C]0.989348[/C][/ROW]
[ROW][C]31[/C][C]0.00631621[/C][C]0.0126324[/C][C]0.993684[/C][/ROW]
[ROW][C]32[/C][C]0.00402909[/C][C]0.00805817[/C][C]0.995971[/C][/ROW]
[ROW][C]33[/C][C]0.00881862[/C][C]0.0176372[/C][C]0.991181[/C][/ROW]
[ROW][C]34[/C][C]0.00564033[/C][C]0.0112807[/C][C]0.99436[/C][/ROW]
[ROW][C]35[/C][C]0.0163471[/C][C]0.0326943[/C][C]0.983653[/C][/ROW]
[ROW][C]36[/C][C]0.0179201[/C][C]0.0358402[/C][C]0.98208[/C][/ROW]
[ROW][C]37[/C][C]0.0273339[/C][C]0.0546678[/C][C]0.972666[/C][/ROW]
[ROW][C]38[/C][C]0.0315168[/C][C]0.0630337[/C][C]0.968483[/C][/ROW]
[ROW][C]39[/C][C]0.0285573[/C][C]0.0571147[/C][C]0.971443[/C][/ROW]
[ROW][C]40[/C][C]0.0245509[/C][C]0.0491018[/C][C]0.975449[/C][/ROW]
[ROW][C]41[/C][C]0.0189393[/C][C]0.0378787[/C][C]0.981061[/C][/ROW]
[ROW][C]42[/C][C]0.0237876[/C][C]0.0475752[/C][C]0.976212[/C][/ROW]
[ROW][C]43[/C][C]0.0224032[/C][C]0.0448064[/C][C]0.977597[/C][/ROW]
[ROW][C]44[/C][C]0.0253568[/C][C]0.0507136[/C][C]0.974643[/C][/ROW]
[ROW][C]45[/C][C]0.0205763[/C][C]0.0411526[/C][C]0.979424[/C][/ROW]
[ROW][C]46[/C][C]0.0205937[/C][C]0.0411875[/C][C]0.979406[/C][/ROW]
[ROW][C]47[/C][C]0.0192292[/C][C]0.0384583[/C][C]0.980771[/C][/ROW]
[ROW][C]48[/C][C]0.0194369[/C][C]0.0388739[/C][C]0.980563[/C][/ROW]
[ROW][C]49[/C][C]0.0154659[/C][C]0.0309317[/C][C]0.984534[/C][/ROW]
[ROW][C]50[/C][C]0.0122312[/C][C]0.0244624[/C][C]0.987769[/C][/ROW]
[ROW][C]51[/C][C]0.027832[/C][C]0.055664[/C][C]0.972168[/C][/ROW]
[ROW][C]52[/C][C]0.038544[/C][C]0.0770879[/C][C]0.961456[/C][/ROW]
[ROW][C]53[/C][C]0.0299646[/C][C]0.0599292[/C][C]0.970035[/C][/ROW]
[ROW][C]54[/C][C]0.0408665[/C][C]0.0817329[/C][C]0.959134[/C][/ROW]
[ROW][C]55[/C][C]0.0686398[/C][C]0.13728[/C][C]0.93136[/C][/ROW]
[ROW][C]56[/C][C]0.076088[/C][C]0.152176[/C][C]0.923912[/C][/ROW]
[ROW][C]57[/C][C]0.0671065[/C][C]0.134213[/C][C]0.932893[/C][/ROW]
[ROW][C]58[/C][C]0.0577824[/C][C]0.115565[/C][C]0.942218[/C][/ROW]
[ROW][C]59[/C][C]0.0604861[/C][C]0.120972[/C][C]0.939514[/C][/ROW]
[ROW][C]60[/C][C]0.0494868[/C][C]0.0989736[/C][C]0.950513[/C][/ROW]
[ROW][C]61[/C][C]0.125401[/C][C]0.250802[/C][C]0.874599[/C][/ROW]
[ROW][C]62[/C][C]0.103916[/C][C]0.207833[/C][C]0.896084[/C][/ROW]
[ROW][C]63[/C][C]0.168401[/C][C]0.336803[/C][C]0.831599[/C][/ROW]
[ROW][C]64[/C][C]0.447284[/C][C]0.894567[/C][C]0.552716[/C][/ROW]
[ROW][C]65[/C][C]0.870988[/C][C]0.258024[/C][C]0.129012[/C][/ROW]
[ROW][C]66[/C][C]0.939875[/C][C]0.12025[/C][C]0.0601251[/C][/ROW]
[ROW][C]67[/C][C]0.931365[/C][C]0.137271[/C][C]0.0686355[/C][/ROW]
[ROW][C]68[/C][C]0.92957[/C][C]0.140859[/C][C]0.0704296[/C][/ROW]
[ROW][C]69[/C][C]0.960898[/C][C]0.0782043[/C][C]0.0391021[/C][/ROW]
[ROW][C]70[/C][C]0.951928[/C][C]0.0961444[/C][C]0.0480722[/C][/ROW]
[ROW][C]71[/C][C]0.966816[/C][C]0.0663671[/C][C]0.0331835[/C][/ROW]
[ROW][C]72[/C][C]0.956851[/C][C]0.0862989[/C][C]0.0431494[/C][/ROW]
[ROW][C]73[/C][C]0.97598[/C][C]0.0480407[/C][C]0.0240204[/C][/ROW]
[ROW][C]74[/C][C]0.974735[/C][C]0.0505302[/C][C]0.0252651[/C][/ROW]
[ROW][C]75[/C][C]0.970024[/C][C]0.0599525[/C][C]0.0299762[/C][/ROW]
[ROW][C]76[/C][C]0.966955[/C][C]0.0660893[/C][C]0.0330447[/C][/ROW]
[ROW][C]77[/C][C]0.993603[/C][C]0.0127944[/C][C]0.0063972[/C][/ROW]
[ROW][C]78[/C][C]0.991034[/C][C]0.0179329[/C][C]0.00896645[/C][/ROW]
[ROW][C]79[/C][C]0.988615[/C][C]0.0227696[/C][C]0.0113848[/C][/ROW]
[ROW][C]80[/C][C]0.987838[/C][C]0.0243237[/C][C]0.0121619[/C][/ROW]
[ROW][C]81[/C][C]0.98974[/C][C]0.0205205[/C][C]0.0102602[/C][/ROW]
[ROW][C]82[/C][C]0.991964[/C][C]0.0160719[/C][C]0.00803594[/C][/ROW]
[ROW][C]83[/C][C]0.992245[/C][C]0.0155095[/C][C]0.00775477[/C][/ROW]
[ROW][C]84[/C][C]0.990888[/C][C]0.018224[/C][C]0.00911198[/C][/ROW]
[ROW][C]85[/C][C]0.989436[/C][C]0.0211288[/C][C]0.0105644[/C][/ROW]
[ROW][C]86[/C][C]0.989056[/C][C]0.0218871[/C][C]0.0109436[/C][/ROW]
[ROW][C]87[/C][C]0.984522[/C][C]0.0309553[/C][C]0.0154776[/C][/ROW]
[ROW][C]88[/C][C]0.980207[/C][C]0.0395858[/C][C]0.0197929[/C][/ROW]
[ROW][C]89[/C][C]0.981806[/C][C]0.0363889[/C][C]0.0181945[/C][/ROW]
[ROW][C]90[/C][C]0.975733[/C][C]0.0485339[/C][C]0.024267[/C][/ROW]
[ROW][C]91[/C][C]0.967307[/C][C]0.0653861[/C][C]0.032693[/C][/ROW]
[ROW][C]92[/C][C]0.969593[/C][C]0.0608144[/C][C]0.0304072[/C][/ROW]
[ROW][C]93[/C][C]0.95978[/C][C]0.0804405[/C][C]0.0402202[/C][/ROW]
[ROW][C]94[/C][C]0.955166[/C][C]0.089668[/C][C]0.044834[/C][/ROW]
[ROW][C]95[/C][C]0.958163[/C][C]0.083675[/C][C]0.0418375[/C][/ROW]
[ROW][C]96[/C][C]0.988857[/C][C]0.0222855[/C][C]0.0111427[/C][/ROW]
[ROW][C]97[/C][C]0.991699[/C][C]0.0166021[/C][C]0.00830106[/C][/ROW]
[ROW][C]98[/C][C]0.987439[/C][C]0.0251228[/C][C]0.0125614[/C][/ROW]
[ROW][C]99[/C][C]0.982298[/C][C]0.0354047[/C][C]0.0177023[/C][/ROW]
[ROW][C]100[/C][C]0.979096[/C][C]0.0418087[/C][C]0.0209043[/C][/ROW]
[ROW][C]101[/C][C]0.97785[/C][C]0.0443[/C][C]0.02215[/C][/ROW]
[ROW][C]102[/C][C]0.968866[/C][C]0.0622673[/C][C]0.0311337[/C][/ROW]
[ROW][C]103[/C][C]0.9551[/C][C]0.0898008[/C][C]0.0449004[/C][/ROW]
[ROW][C]104[/C][C]0.936065[/C][C]0.12787[/C][C]0.0639348[/C][/ROW]
[ROW][C]105[/C][C]0.91316[/C][C]0.173681[/C][C]0.0868404[/C][/ROW]
[ROW][C]106[/C][C]0.888282[/C][C]0.223436[/C][C]0.111718[/C][/ROW]
[ROW][C]107[/C][C]0.870504[/C][C]0.258993[/C][C]0.129496[/C][/ROW]
[ROW][C]108[/C][C]0.840841[/C][C]0.318318[/C][C]0.159159[/C][/ROW]
[ROW][C]109[/C][C]0.806692[/C][C]0.386616[/C][C]0.193308[/C][/ROW]
[ROW][C]110[/C][C]0.779379[/C][C]0.441243[/C][C]0.220621[/C][/ROW]
[ROW][C]111[/C][C]0.796893[/C][C]0.406214[/C][C]0.203107[/C][/ROW]
[ROW][C]112[/C][C]0.756828[/C][C]0.486344[/C][C]0.243172[/C][/ROW]
[ROW][C]113[/C][C]0.709221[/C][C]0.581559[/C][C]0.290779[/C][/ROW]
[ROW][C]114[/C][C]0.63534[/C][C]0.72932[/C][C]0.36466[/C][/ROW]
[ROW][C]115[/C][C]0.60139[/C][C]0.797219[/C][C]0.39861[/C][/ROW]
[ROW][C]116[/C][C]0.601312[/C][C]0.797375[/C][C]0.398688[/C][/ROW]
[ROW][C]117[/C][C]0.539686[/C][C]0.920628[/C][C]0.460314[/C][/ROW]
[ROW][C]118[/C][C]0.483547[/C][C]0.967094[/C][C]0.516453[/C][/ROW]
[ROW][C]119[/C][C]0.439534[/C][C]0.879068[/C][C]0.560466[/C][/ROW]
[ROW][C]120[/C][C]0.357113[/C][C]0.714226[/C][C]0.642887[/C][/ROW]
[ROW][C]121[/C][C]0.293549[/C][C]0.587097[/C][C]0.706451[/C][/ROW]
[ROW][C]122[/C][C]0.209111[/C][C]0.418223[/C][C]0.790889[/C][/ROW]
[ROW][C]123[/C][C]0.514235[/C][C]0.97153[/C][C]0.485765[/C][/ROW]
[ROW][C]124[/C][C]0.378393[/C][C]0.756787[/C][C]0.621607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&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
130.5376080.9247830.462392
140.6114330.7771340.388567
150.4926070.9852140.507393
160.3678990.7357970.632101
170.3086930.6173860.691307
180.2141310.4282620.785869
190.1465240.2930470.853476
200.1091310.2182620.890869
210.087510.175020.91249
220.05918570.1183710.940814
230.03595230.07190470.964048
240.02153650.04307310.978463
250.01237560.02475130.987624
260.01472880.02945770.985271
270.009226560.01845310.990773
280.01400460.02800930.985995
290.009015330.01803070.990985
300.01065160.02130310.989348
310.006316210.01263240.993684
320.004029090.008058170.995971
330.008818620.01763720.991181
340.005640330.01128070.99436
350.01634710.03269430.983653
360.01792010.03584020.98208
370.02733390.05466780.972666
380.03151680.06303370.968483
390.02855730.05711470.971443
400.02455090.04910180.975449
410.01893930.03787870.981061
420.02378760.04757520.976212
430.02240320.04480640.977597
440.02535680.05071360.974643
450.02057630.04115260.979424
460.02059370.04118750.979406
470.01922920.03845830.980771
480.01943690.03887390.980563
490.01546590.03093170.984534
500.01223120.02446240.987769
510.0278320.0556640.972168
520.0385440.07708790.961456
530.02996460.05992920.970035
540.04086650.08173290.959134
550.06863980.137280.93136
560.0760880.1521760.923912
570.06710650.1342130.932893
580.05778240.1155650.942218
590.06048610.1209720.939514
600.04948680.09897360.950513
610.1254010.2508020.874599
620.1039160.2078330.896084
630.1684010.3368030.831599
640.4472840.8945670.552716
650.8709880.2580240.129012
660.9398750.120250.0601251
670.9313650.1372710.0686355
680.929570.1408590.0704296
690.9608980.07820430.0391021
700.9519280.09614440.0480722
710.9668160.06636710.0331835
720.9568510.08629890.0431494
730.975980.04804070.0240204
740.9747350.05053020.0252651
750.9700240.05995250.0299762
760.9669550.06608930.0330447
770.9936030.01279440.0063972
780.9910340.01793290.00896645
790.9886150.02276960.0113848
800.9878380.02432370.0121619
810.989740.02052050.0102602
820.9919640.01607190.00803594
830.9922450.01550950.00775477
840.9908880.0182240.00911198
850.9894360.02112880.0105644
860.9890560.02188710.0109436
870.9845220.03095530.0154776
880.9802070.03958580.0197929
890.9818060.03638890.0181945
900.9757330.04853390.024267
910.9673070.06538610.032693
920.9695930.06081440.0304072
930.959780.08044050.0402202
940.9551660.0896680.044834
950.9581630.0836750.0418375
960.9888570.02228550.0111427
970.9916990.01660210.00830106
980.9874390.02512280.0125614
990.9822980.03540470.0177023
1000.9790960.04180870.0209043
1010.977850.04430.02215
1020.9688660.06226730.0311337
1030.95510.08980080.0449004
1040.9360650.127870.0639348
1050.913160.1736810.0868404
1060.8882820.2234360.111718
1070.8705040.2589930.129496
1080.8408410.3183180.159159
1090.8066920.3866160.193308
1100.7793790.4412430.220621
1110.7968930.4062140.203107
1120.7568280.4863440.243172
1130.7092210.5815590.290779
1140.635340.729320.36466
1150.601390.7972190.39861
1160.6013120.7973750.398688
1170.5396860.9206280.460314
1180.4835470.9670940.516453
1190.4395340.8790680.560466
1200.3571130.7142260.642887
1210.2935490.5870970.706451
1220.2091110.4182230.790889
1230.5142350.971530.485765
1240.3783930.7567870.621607






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00892857OK
5% type I error level440.392857NOK
10% type I error level680.607143NOK

\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 & 1 & 0.00892857 & OK \tabularnewline
5% type I error level & 44 & 0.392857 & NOK \tabularnewline
10% type I error level & 68 & 0.607143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268265&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]1[/C][C]0.00892857[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.392857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.607143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268265&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268265&T=6

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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 level10.00892857OK
5% type I error level440.392857NOK
10% type I error level680.607143NOK


Figures (Output of Computation)

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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')
}