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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 04 Dec 2014 16:10:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/04/t141771042502nhj0m3ju61cba.htm/, Retrieved Thu, 16 May 2024 06:42:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263344, Retrieved Thu, 16 May 2024 06:42:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper statistiek ...] [2014-12-04 16:10:19] [0a6fc2c777821367d2239c664b701a36] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
0	0	26	50	13	12	21	149	96	86	12,9
0	1	57	62	8	8	22	139	70	70	12,2
0	0	37	54	14	11	22	148	88	71	12,8
0	1	67	71	16	13	18	158	114	108	7,4
0	1	43	54	14	11	23	128	69	64	6,7
0	1	52	65	13	10	12	224	176	119	12,6
0	0	52	73	15	7	20	159	114	97	14,8
0	1	43	52	13	10	22	105	121	129	13,3
0	1	84	84	20	15	21	159	110	153	11,1
0	1	67	42	17	12	19	167	158	78	8,2
0	1	49	66	15	12	22	165	116	80	11,4
0	1	70	65	16	10	15	159	181	99	6,4
0	1	52	78	12	10	20	119	77	68	10,6
0	0	58	73	17	14	19	176	141	147	12,0
0	0	68	75	11	6	18	54	35	40	6,3
1	0	62	72	16	12	15	91	80	57	11,3
0	1	43	66	16	14	20	163	152	120	11,9
0	0	56	70	15	11	21	124	97	71	9,3
1	1	56	61	13	8	21	137	99	84	9,6
0	0	74	81	14	12	15	121	84	68	10,0
0	1	65	71	19	15	16	153	68	55	6,4
0	1	63	69	16	13	23	148	101	137	13,8
0	0	58	71	17	11	21	221	107	79	10,8
0	1	57	72	10	12	18	188	88	116	13,8
0	1	63	68	15	7	25	149	112	101	11,7
0	1	53	70	14	11	9	244	171	111	10,9
1	1	57	68	14	7	30	148	137	189	16,1
1	0	51	61	16	12	20	92	77	66	13,4
0	1	64	67	15	12	23	150	66	81	9,9
0	0	53	76	17	13	16	153	93	63	11,5
0	0	29	70	14	9	16	94	105	69	8,3
0	0	54	60	16	11	19	156	131	71	11,7
0	1	58	72	15	12	25	132	102	64	9,0
0	1	43	69	16	15	18	161	161	143	9,7
0	1	51	71	16	12	23	105	120	85	10,8
0	1	53	62	10	6	21	97	127	86	10,3
0	0	54	70	8	5	10	151	77	55	10,4
1	1	56	64	17	13	14	131	108	69	12,7
0	1	61	58	14	11	22	166	85	120	9,3
0	0	47	76	10	6	26	157	168	96	11,8
0	1	39	52	14	12	23	111	48	60	5,9
0	1	48	59	12	10	23	145	152	95	11,4
0	1	50	68	16	6	24	162	75	100	13,0
0	1	35	76	16	12	24	163	107	68	10,8
1	1	30	65	16	11	18	59	62	57	12,3
0	0	68	67	8	6	23	187	121	105	11,3
0	1	49	59	16	12	15	109	124	85	11,8
1	1	61	69	15	12	19	90	72	103	7,9
0	0	67	76	8	8	16	105	40	57	12,7
1	1	47	63	13	10	25	83	58	51	12,3
1	1	56	75	14	11	23	116	97	69	11,6
1	1	50	63	13	7	17	42	88	41	6,7
0	1	43	60	16	12	19	148	126	49	10,9
1	1	67	73	19	13	21	155	104	50	12,1
0	1	62	63	19	14	18	125	148	93	13,3
0	1	57	70	14	12	27	116	146	58	10,1
1	0	41	75	15	6	21	128	80	54	5,7
0	1	54	66	13	14	13	138	97	74	14,3
1	0	45	63	10	10	8	49	25	15	8,0
1	1	48	63	16	12	29	96	99	69	13,3
0	1	61	64	15	11	28	164	118	107	9,3
0	0	56	70	11	10	23	162	58	65	12,5
0	0	41	75	9	7	21	99	63	58	7,6
0	1	43	61	16	12	19	202	139	107	15,9
0	0	53	60	12	7	19	186	50	70	9,2
1	1	44	62	12	12	20	66	60	53	9,1
0	0	66	73	14	12	18	183	152	136	11,1
0	1	58	61	14	10	19	214	142	126	13,0
0	1	46	66	13	10	17	188	94	95	14,5
1	0	37	64	15	12	19	104	66	69	12,2
0	0	51	59	17	12	25	177	127	136	12,3
0	0	51	64	14	12	19	126	67	58	11,4
1	0	56	60	11	8	22	76	90	59	8,8
1	1	66	56	9	10	23	99	75	118	14,6
0	0	37	78	7	5	14	139	128	82	12,6
0	0	42	67	15	10	16	162	146	102	13,0
1	1	38	59	12	12	24	108	69	65	12,6
0	0	66	66	15	11	20	159	186	90	13,2
1	0	34	68	14	9	12	74	81	64	9,9
0	1	53	71	16	12	24	110	85	83	7,7
1	0	49	66	14	11	22	96	54	70	10,5
1	0	55	73	13	10	12	116	46	50	13,4
1	0	49	72	16	12	22	87	106	77	10,9
1	1	59	71	13	10	20	97	34	37	4,3
1	0	40	59	16	9	10	127	60	81	10,3
1	1	58	64	16	11	23	106	95	101	11,8
1	1	60	66	16	12	17	80	57	79	11,2
1	0	63	78	10	7	22	74	62	71	11,4
1	0	56	68	12	11	24	91	36	60	8,6
1	0	54	73	12	12	18	133	56	55	13,2
1	1	52	62	12	6	21	74	54	44	12,6
1	1	34	65	12	9	20	114	64	40	5,6
1	1	69	68	19	15	20	140	76	56	9,9
1	0	32	65	14	10	22	95	98	43	8,8
1	1	48	60	13	11	19	98	88	45	7,7
1	0	67	71	16	12	20	121	35	32	9,0
1	1	58	65	15	12	26	126	102	56	7,3
1	1	57	68	12	12	23	98	61	40	11,4
1	1	42	64	8	11	24	95	80	34	13,6
1	1	64	74	10	9	21	110	49	89	7,9
1	1	58	69	16	11	21	70	78	50	10,7
1	0	66	76	16	12	19	102	90	56	10,3
1	1	26	68	10	12	8	86	45	46	8,3
1	1	61	72	18	14	17	130	55	76	9,6
1	1	52	67	12	8	20	96	96	64	14,2
1	0	51	63	16	10	11	102	43	74	8,5
1	0	55	59	10	9	8	100	52	57	13,5
1	0	50	73	14	10	15	94	60	45	4,9
1	0	60	66	12	9	18	52	54	30	6,4
1	0	56	62	11	10	18	98	51	62	9,6
1	0	63	69	15	12	19	118	51	51	11,6
1	1	61	66	7	11	19	99	38	36	11,1

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 Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&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 Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
[t] = + 8.21364 + 1.09396group[t] -0.516147gender[t] -0.0200329AMS.I[t] -0.0104859AMS.E[t] -0.233009CONFSTATTOT[t] + 0.171819CONFSOFTTOT[t] + 0.0359297NUMERACYTOT[t] + 0.0129624LFM[t] + 0.0082412B[t] + 0.0304547H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  +  8.21364 +  1.09396group[t] -0.516147gender[t] -0.0200329AMS.I[t] -0.0104859AMS.E[t] -0.233009CONFSTATTOT[t] +  0.171819CONFSOFTTOT[t] +  0.0359297NUMERACYTOT[t] +  0.0129624LFM[t] +  0.0082412B[t] +  0.0304547H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  +  8.21364 +  1.09396group[t] -0.516147gender[t] -0.0200329AMS.I[t] -0.0104859AMS.E[t] -0.233009CONFSTATTOT[t] +  0.171819CONFSOFTTOT[t] +  0.0359297NUMERACYTOT[t] +  0.0129624LFM[t] +  0.0082412B[t] +  0.0304547H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
[t] = + 8.21364 + 1.09396group[t] -0.516147gender[t] -0.0200329AMS.I[t] -0.0104859AMS.E[t] -0.233009CONFSTATTOT[t] + 0.171819CONFSOFTTOT[t] + 0.0359297NUMERACYTOT[t] + 0.0129624LFM[t] + 0.0082412B[t] + 0.0304547H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.213642.804062.9290.004201140.00210057
group1.093960.5855591.8680.06462830.0323142
gender-0.5161470.48393-1.0670.2887080.144354
AMS.I-0.02003290.0219449-0.91290.3634840.181742
AMS.E-0.01048590.0341966-0.30660.7597510.379875
CONFSTATTOT-0.2330090.104162-2.2370.02748450.0137423
CONFSOFTTOT0.1718190.1238971.3870.1685610.0842804
NUMERACYTOT0.03592970.05198290.69120.4910370.245519
LFM0.01296240.008240781.5730.1188570.0594283
B0.00824120.008463650.97370.3325230.166262
H0.03045470.0100923.0180.00322360.0016118

\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) & 8.21364 & 2.80406 & 2.929 & 0.00420114 & 0.00210057 \tabularnewline
group & 1.09396 & 0.585559 & 1.868 & 0.0646283 & 0.0323142 \tabularnewline
gender & -0.516147 & 0.48393 & -1.067 & 0.288708 & 0.144354 \tabularnewline
AMS.I & -0.0200329 & 0.0219449 & -0.9129 & 0.363484 & 0.181742 \tabularnewline
AMS.E & -0.0104859 & 0.0341966 & -0.3066 & 0.759751 & 0.379875 \tabularnewline
CONFSTATTOT & -0.233009 & 0.104162 & -2.237 & 0.0274845 & 0.0137423 \tabularnewline
CONFSOFTTOT & 0.171819 & 0.123897 & 1.387 & 0.168561 & 0.0842804 \tabularnewline
NUMERACYTOT & 0.0359297 & 0.0519829 & 0.6912 & 0.491037 & 0.245519 \tabularnewline
LFM & 0.0129624 & 0.00824078 & 1.573 & 0.118857 & 0.0594283 \tabularnewline
B & 0.0082412 & 0.00846365 & 0.9737 & 0.332523 & 0.166262 \tabularnewline
H & 0.0304547 & 0.010092 & 3.018 & 0.0032236 & 0.0016118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&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]8.21364[/C][C]2.80406[/C][C]2.929[/C][C]0.00420114[/C][C]0.00210057[/C][/ROW]
[ROW][C]group[/C][C]1.09396[/C][C]0.585559[/C][C]1.868[/C][C]0.0646283[/C][C]0.0323142[/C][/ROW]
[ROW][C]gender[/C][C]-0.516147[/C][C]0.48393[/C][C]-1.067[/C][C]0.288708[/C][C]0.144354[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0200329[/C][C]0.0219449[/C][C]-0.9129[/C][C]0.363484[/C][C]0.181742[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0104859[/C][C]0.0341966[/C][C]-0.3066[/C][C]0.759751[/C][C]0.379875[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.233009[/C][C]0.104162[/C][C]-2.237[/C][C]0.0274845[/C][C]0.0137423[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.171819[/C][C]0.123897[/C][C]1.387[/C][C]0.168561[/C][C]0.0842804[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0359297[/C][C]0.0519829[/C][C]0.6912[/C][C]0.491037[/C][C]0.245519[/C][/ROW]
[ROW][C]LFM[/C][C]0.0129624[/C][C]0.00824078[/C][C]1.573[/C][C]0.118857[/C][C]0.0594283[/C][/ROW]
[ROW][C]B[/C][C]0.0082412[/C][C]0.00846365[/C][C]0.9737[/C][C]0.332523[/C][C]0.166262[/C][/ROW]
[ROW][C]H[/C][C]0.0304547[/C][C]0.010092[/C][C]3.018[/C][C]0.0032236[/C][C]0.0016118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&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)8.213642.804062.9290.004201140.00210057
group1.093960.5855591.8680.06462830.0323142
gender-0.5161470.48393-1.0670.2887080.144354
AMS.I-0.02003290.0219449-0.91290.3634840.181742
AMS.E-0.01048590.0341966-0.30660.7597510.379875
CONFSTATTOT-0.2330090.104162-2.2370.02748450.0137423
CONFSOFTTOT0.1718190.1238971.3870.1685610.0842804
NUMERACYTOT0.03592970.05198290.69120.4910370.245519
LFM0.01296240.008240781.5730.1188570.0594283
B0.00824120.008463650.97370.3325230.166262
H0.03045470.0100923.0180.00322360.0016118






Multiple Linear Regression - Regression Statistics
Multiple R0.492098
R-squared0.24216
Adjusted R-squared0.167127
F-TEST (value)3.22735
F-TEST (DF numerator)10
F-TEST (DF denominator)101
p-value0.00121007
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25539
Sum Squared Residuals513.766

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.492098 \tabularnewline
R-squared & 0.24216 \tabularnewline
Adjusted R-squared & 0.167127 \tabularnewline
F-TEST (value) & 3.22735 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 101 \tabularnewline
p-value & 0.00121007 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.25539 \tabularnewline
Sum Squared Residuals & 513.766 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.492098[/C][/ROW]
[ROW][C]R-squared[/C][C]0.24216[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.167127[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.22735[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]101[/C][/ROW]
[ROW][C]p-value[/C][C]0.00121007[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.25539[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]513.766[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&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 R0.492098
R-squared0.24216
Adjusted R-squared0.167127
F-TEST (value)3.22735
F-TEST (DF numerator)10
F-TEST (DF denominator)101
p-value0.00121007
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25539
Sum Squared Residuals513.766






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.29740.602625
212.210.71691.48309
312.811.13051.66954
47.411.0397-3.63969
56.79.90103-3.20103
612.613.0726-0.472562
714.810.78734.01273
813.312.05721.24277
911.111.4327-0.332661
108.210.5405-2.34051
1111.410.91210.487897
126.410.7103-4.31029
1310.69.726570.87343
141213.3335-1.33347
156.37.3861-1.0861
1611.39.757991.54201
1711.912.56-0.660017
189.310.0762-0.776194
199.611.2798-1.67985
20109.552120.44788
216.48.59449-2.19449
2213.811.96691.83313
2310.811.143-0.343025
2413.812.8740.925997
2511.710.25861.44145
2610.912.8055-1.90552
2716.114.75851.34155
2813.410.53572.86432
299.910.061-0.161012
3011.59.870651.62935
318.39.94295-1.64295
3211.710.61121.08875
3399.74627-0.746269
349.713.3772-3.67722
3510.810.030.769977
3610.310.3641-0.0640576
3710.410.01910.380931
3812.710.46352.23649
399.311.7925-2.49246
4011.812.4534-0.653427
415.99.65871-3.75871
4211.411.8911-0.491113
43139.911353.08865
4410.810.4610.338999
4512.39.329132.97087
4611.312.761-1.46097
4711.89.99331.8067
487.910.9921-3.09209
4912.79.586483.11352
5012.39.883662.41634
5111.610.74180.858163
526.78.4319-1.7319
5310.99.672371.22763
5412.19.633762.46624
5513.310.09223.20785
5610.110.06460.0353717
575.79.95314-4.25314
5814.310.60893.69107
5988.71905-0.719047
6013.310.70652.59346
619.311.5622-2.26224
6212.510.89671.60329
637.610.0349-2.43486
6415.912.23543.66463
659.210.5669-1.36692
669.110.2083-1.10828
6711.113.3391-2.23905
681312.81620.183835
6914.511.48863.01142
7012.211.1381.06198
7112.313.055-0.755024
7211.49.955021.44498
738.810.6822-1.88218
7414.612.82461.77542
7512.611.73950.860501
761311.87711.12287
7712.611.48771.1123
7813.211.64771.55233
799.910.2047-0.304692
807.79.74135-2.04135
8110.510.8735-0.373493
8213.49.966013.43399
8310.911.0414-0.141443
844.38.93705-4.63705
8510.310.6727-0.372662
8611.811.17950.620456
8711.29.754561.44544
8811.410.52320.876825
898.610.7325-2.13247
9013.211.23331.96669
9112.68.833193.76681
925.610.1209-4.52094
939.99.711370.188628
948.810.5801-1.78009
957.710.1103-2.41027
9699.10461-0.10461
977.310.6281-3.32815
9811.410.01981.38015
9913.611.09342.5066
1007.911.2443-3.34434
10110.78.895321.80468
10210.39.974180.325822
1038.310.4633-2.16326
1049.610.0895-0.489499
10514.210.32893.87113
1068.59.94076-1.44076
10713.510.55152.94846
1084.99.61889-4.71889
1096.48.84326-2.44326
1109.610.9163-1.31626
11111.610.07441.5256
11211.110.51180.588207

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.2974 & 0.602625 \tabularnewline
2 & 12.2 & 10.7169 & 1.48309 \tabularnewline
3 & 12.8 & 11.1305 & 1.66954 \tabularnewline
4 & 7.4 & 11.0397 & -3.63969 \tabularnewline
5 & 6.7 & 9.90103 & -3.20103 \tabularnewline
6 & 12.6 & 13.0726 & -0.472562 \tabularnewline
7 & 14.8 & 10.7873 & 4.01273 \tabularnewline
8 & 13.3 & 12.0572 & 1.24277 \tabularnewline
9 & 11.1 & 11.4327 & -0.332661 \tabularnewline
10 & 8.2 & 10.5405 & -2.34051 \tabularnewline
11 & 11.4 & 10.9121 & 0.487897 \tabularnewline
12 & 6.4 & 10.7103 & -4.31029 \tabularnewline
13 & 10.6 & 9.72657 & 0.87343 \tabularnewline
14 & 12 & 13.3335 & -1.33347 \tabularnewline
15 & 6.3 & 7.3861 & -1.0861 \tabularnewline
16 & 11.3 & 9.75799 & 1.54201 \tabularnewline
17 & 11.9 & 12.56 & -0.660017 \tabularnewline
18 & 9.3 & 10.0762 & -0.776194 \tabularnewline
19 & 9.6 & 11.2798 & -1.67985 \tabularnewline
20 & 10 & 9.55212 & 0.44788 \tabularnewline
21 & 6.4 & 8.59449 & -2.19449 \tabularnewline
22 & 13.8 & 11.9669 & 1.83313 \tabularnewline
23 & 10.8 & 11.143 & -0.343025 \tabularnewline
24 & 13.8 & 12.874 & 0.925997 \tabularnewline
25 & 11.7 & 10.2586 & 1.44145 \tabularnewline
26 & 10.9 & 12.8055 & -1.90552 \tabularnewline
27 & 16.1 & 14.7585 & 1.34155 \tabularnewline
28 & 13.4 & 10.5357 & 2.86432 \tabularnewline
29 & 9.9 & 10.061 & -0.161012 \tabularnewline
30 & 11.5 & 9.87065 & 1.62935 \tabularnewline
31 & 8.3 & 9.94295 & -1.64295 \tabularnewline
32 & 11.7 & 10.6112 & 1.08875 \tabularnewline
33 & 9 & 9.74627 & -0.746269 \tabularnewline
34 & 9.7 & 13.3772 & -3.67722 \tabularnewline
35 & 10.8 & 10.03 & 0.769977 \tabularnewline
36 & 10.3 & 10.3641 & -0.0640576 \tabularnewline
37 & 10.4 & 10.0191 & 0.380931 \tabularnewline
38 & 12.7 & 10.4635 & 2.23649 \tabularnewline
39 & 9.3 & 11.7925 & -2.49246 \tabularnewline
40 & 11.8 & 12.4534 & -0.653427 \tabularnewline
41 & 5.9 & 9.65871 & -3.75871 \tabularnewline
42 & 11.4 & 11.8911 & -0.491113 \tabularnewline
43 & 13 & 9.91135 & 3.08865 \tabularnewline
44 & 10.8 & 10.461 & 0.338999 \tabularnewline
45 & 12.3 & 9.32913 & 2.97087 \tabularnewline
46 & 11.3 & 12.761 & -1.46097 \tabularnewline
47 & 11.8 & 9.9933 & 1.8067 \tabularnewline
48 & 7.9 & 10.9921 & -3.09209 \tabularnewline
49 & 12.7 & 9.58648 & 3.11352 \tabularnewline
50 & 12.3 & 9.88366 & 2.41634 \tabularnewline
51 & 11.6 & 10.7418 & 0.858163 \tabularnewline
52 & 6.7 & 8.4319 & -1.7319 \tabularnewline
53 & 10.9 & 9.67237 & 1.22763 \tabularnewline
54 & 12.1 & 9.63376 & 2.46624 \tabularnewline
55 & 13.3 & 10.0922 & 3.20785 \tabularnewline
56 & 10.1 & 10.0646 & 0.0353717 \tabularnewline
57 & 5.7 & 9.95314 & -4.25314 \tabularnewline
58 & 14.3 & 10.6089 & 3.69107 \tabularnewline
59 & 8 & 8.71905 & -0.719047 \tabularnewline
60 & 13.3 & 10.7065 & 2.59346 \tabularnewline
61 & 9.3 & 11.5622 & -2.26224 \tabularnewline
62 & 12.5 & 10.8967 & 1.60329 \tabularnewline
63 & 7.6 & 10.0349 & -2.43486 \tabularnewline
64 & 15.9 & 12.2354 & 3.66463 \tabularnewline
65 & 9.2 & 10.5669 & -1.36692 \tabularnewline
66 & 9.1 & 10.2083 & -1.10828 \tabularnewline
67 & 11.1 & 13.3391 & -2.23905 \tabularnewline
68 & 13 & 12.8162 & 0.183835 \tabularnewline
69 & 14.5 & 11.4886 & 3.01142 \tabularnewline
70 & 12.2 & 11.138 & 1.06198 \tabularnewline
71 & 12.3 & 13.055 & -0.755024 \tabularnewline
72 & 11.4 & 9.95502 & 1.44498 \tabularnewline
73 & 8.8 & 10.6822 & -1.88218 \tabularnewline
74 & 14.6 & 12.8246 & 1.77542 \tabularnewline
75 & 12.6 & 11.7395 & 0.860501 \tabularnewline
76 & 13 & 11.8771 & 1.12287 \tabularnewline
77 & 12.6 & 11.4877 & 1.1123 \tabularnewline
78 & 13.2 & 11.6477 & 1.55233 \tabularnewline
79 & 9.9 & 10.2047 & -0.304692 \tabularnewline
80 & 7.7 & 9.74135 & -2.04135 \tabularnewline
81 & 10.5 & 10.8735 & -0.373493 \tabularnewline
82 & 13.4 & 9.96601 & 3.43399 \tabularnewline
83 & 10.9 & 11.0414 & -0.141443 \tabularnewline
84 & 4.3 & 8.93705 & -4.63705 \tabularnewline
85 & 10.3 & 10.6727 & -0.372662 \tabularnewline
86 & 11.8 & 11.1795 & 0.620456 \tabularnewline
87 & 11.2 & 9.75456 & 1.44544 \tabularnewline
88 & 11.4 & 10.5232 & 0.876825 \tabularnewline
89 & 8.6 & 10.7325 & -2.13247 \tabularnewline
90 & 13.2 & 11.2333 & 1.96669 \tabularnewline
91 & 12.6 & 8.83319 & 3.76681 \tabularnewline
92 & 5.6 & 10.1209 & -4.52094 \tabularnewline
93 & 9.9 & 9.71137 & 0.188628 \tabularnewline
94 & 8.8 & 10.5801 & -1.78009 \tabularnewline
95 & 7.7 & 10.1103 & -2.41027 \tabularnewline
96 & 9 & 9.10461 & -0.10461 \tabularnewline
97 & 7.3 & 10.6281 & -3.32815 \tabularnewline
98 & 11.4 & 10.0198 & 1.38015 \tabularnewline
99 & 13.6 & 11.0934 & 2.5066 \tabularnewline
100 & 7.9 & 11.2443 & -3.34434 \tabularnewline
101 & 10.7 & 8.89532 & 1.80468 \tabularnewline
102 & 10.3 & 9.97418 & 0.325822 \tabularnewline
103 & 8.3 & 10.4633 & -2.16326 \tabularnewline
104 & 9.6 & 10.0895 & -0.489499 \tabularnewline
105 & 14.2 & 10.3289 & 3.87113 \tabularnewline
106 & 8.5 & 9.94076 & -1.44076 \tabularnewline
107 & 13.5 & 10.5515 & 2.94846 \tabularnewline
108 & 4.9 & 9.61889 & -4.71889 \tabularnewline
109 & 6.4 & 8.84326 & -2.44326 \tabularnewline
110 & 9.6 & 10.9163 & -1.31626 \tabularnewline
111 & 11.6 & 10.0744 & 1.5256 \tabularnewline
112 & 11.1 & 10.5118 & 0.588207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12.9[/C][C]12.2974[/C][C]0.602625[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.7169[/C][C]1.48309[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.1305[/C][C]1.66954[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.0397[/C][C]-3.63969[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]9.90103[/C][C]-3.20103[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]13.0726[/C][C]-0.472562[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.7873[/C][C]4.01273[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.0572[/C][C]1.24277[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.4327[/C][C]-0.332661[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.5405[/C][C]-2.34051[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.9121[/C][C]0.487897[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.7103[/C][C]-4.31029[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.72657[/C][C]0.87343[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3335[/C][C]-1.33347[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]7.3861[/C][C]-1.0861[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]9.75799[/C][C]1.54201[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]12.56[/C][C]-0.660017[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.0762[/C][C]-0.776194[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.2798[/C][C]-1.67985[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.55212[/C][C]0.44788[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]8.59449[/C][C]-2.19449[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.9669[/C][C]1.83313[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]11.143[/C][C]-0.343025[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]12.874[/C][C]0.925997[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]10.2586[/C][C]1.44145[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]12.8055[/C][C]-1.90552[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]14.7585[/C][C]1.34155[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]10.5357[/C][C]2.86432[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.061[/C][C]-0.161012[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]9.87065[/C][C]1.62935[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]9.94295[/C][C]-1.64295[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.6112[/C][C]1.08875[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]9.74627[/C][C]-0.746269[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]13.3772[/C][C]-3.67722[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.03[/C][C]0.769977[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.3641[/C][C]-0.0640576[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.0191[/C][C]0.380931[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.4635[/C][C]2.23649[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]11.7925[/C][C]-2.49246[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]12.4534[/C][C]-0.653427[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]9.65871[/C][C]-3.75871[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]11.8911[/C][C]-0.491113[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]9.91135[/C][C]3.08865[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]10.461[/C][C]0.338999[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]9.32913[/C][C]2.97087[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]12.761[/C][C]-1.46097[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]9.9933[/C][C]1.8067[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.9921[/C][C]-3.09209[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]9.58648[/C][C]3.11352[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]9.88366[/C][C]2.41634[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.7418[/C][C]0.858163[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]8.4319[/C][C]-1.7319[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.67237[/C][C]1.22763[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]9.63376[/C][C]2.46624[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.0922[/C][C]3.20785[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.0646[/C][C]0.0353717[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]9.95314[/C][C]-4.25314[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.6089[/C][C]3.69107[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.71905[/C][C]-0.719047[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.7065[/C][C]2.59346[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]11.5622[/C][C]-2.26224[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.8967[/C][C]1.60329[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.0349[/C][C]-2.43486[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]12.2354[/C][C]3.66463[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.5669[/C][C]-1.36692[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.2083[/C][C]-1.10828[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]13.3391[/C][C]-2.23905[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.8162[/C][C]0.183835[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]11.4886[/C][C]3.01142[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]11.138[/C][C]1.06198[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]13.055[/C][C]-0.755024[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]9.95502[/C][C]1.44498[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.6822[/C][C]-1.88218[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]12.8246[/C][C]1.77542[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]11.7395[/C][C]0.860501[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.8771[/C][C]1.12287[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]11.4877[/C][C]1.1123[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]11.6477[/C][C]1.55233[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]10.2047[/C][C]-0.304692[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]9.74135[/C][C]-2.04135[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.8735[/C][C]-0.373493[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]9.96601[/C][C]3.43399[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]11.0414[/C][C]-0.141443[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]8.93705[/C][C]-4.63705[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]10.6727[/C][C]-0.372662[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]11.1795[/C][C]0.620456[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]9.75456[/C][C]1.44544[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]10.5232[/C][C]0.876825[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.7325[/C][C]-2.13247[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]11.2333[/C][C]1.96669[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]8.83319[/C][C]3.76681[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]10.1209[/C][C]-4.52094[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]9.71137[/C][C]0.188628[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]10.5801[/C][C]-1.78009[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]10.1103[/C][C]-2.41027[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]9.10461[/C][C]-0.10461[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]10.6281[/C][C]-3.32815[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]10.0198[/C][C]1.38015[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]11.0934[/C][C]2.5066[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]11.2443[/C][C]-3.34434[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]8.89532[/C][C]1.80468[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]9.97418[/C][C]0.325822[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]10.4633[/C][C]-2.16326[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]10.0895[/C][C]-0.489499[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]10.3289[/C][C]3.87113[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]9.94076[/C][C]-1.44076[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.5515[/C][C]2.94846[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]9.61889[/C][C]-4.71889[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]8.84326[/C][C]-2.44326[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]10.9163[/C][C]-1.31626[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]10.0744[/C][C]1.5256[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]10.5118[/C][C]0.588207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&T=4

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.29740.602625
212.210.71691.48309
312.811.13051.66954
47.411.0397-3.63969
56.79.90103-3.20103
612.613.0726-0.472562
714.810.78734.01273
813.312.05721.24277
911.111.4327-0.332661
108.210.5405-2.34051
1111.410.91210.487897
126.410.7103-4.31029
1310.69.726570.87343
141213.3335-1.33347
156.37.3861-1.0861
1611.39.757991.54201
1711.912.56-0.660017
189.310.0762-0.776194
199.611.2798-1.67985
20109.552120.44788
216.48.59449-2.19449
2213.811.96691.83313
2310.811.143-0.343025
2413.812.8740.925997
2511.710.25861.44145
2610.912.8055-1.90552
2716.114.75851.34155
2813.410.53572.86432
299.910.061-0.161012
3011.59.870651.62935
318.39.94295-1.64295
3211.710.61121.08875
3399.74627-0.746269
349.713.3772-3.67722
3510.810.030.769977
3610.310.3641-0.0640576
3710.410.01910.380931
3812.710.46352.23649
399.311.7925-2.49246
4011.812.4534-0.653427
415.99.65871-3.75871
4211.411.8911-0.491113
43139.911353.08865
4410.810.4610.338999
4512.39.329132.97087
4611.312.761-1.46097
4711.89.99331.8067
487.910.9921-3.09209
4912.79.586483.11352
5012.39.883662.41634
5111.610.74180.858163
526.78.4319-1.7319
5310.99.672371.22763
5412.19.633762.46624
5513.310.09223.20785
5610.110.06460.0353717
575.79.95314-4.25314
5814.310.60893.69107
5988.71905-0.719047
6013.310.70652.59346
619.311.5622-2.26224
6212.510.89671.60329
637.610.0349-2.43486
6415.912.23543.66463
659.210.5669-1.36692
669.110.2083-1.10828
6711.113.3391-2.23905
681312.81620.183835
6914.511.48863.01142
7012.211.1381.06198
7112.313.055-0.755024
7211.49.955021.44498
738.810.6822-1.88218
7414.612.82461.77542
7512.611.73950.860501
761311.87711.12287
7712.611.48771.1123
7813.211.64771.55233
799.910.2047-0.304692
807.79.74135-2.04135
8110.510.8735-0.373493
8213.49.966013.43399
8310.911.0414-0.141443
844.38.93705-4.63705
8510.310.6727-0.372662
8611.811.17950.620456
8711.29.754561.44544
8811.410.52320.876825
898.610.7325-2.13247
9013.211.23331.96669
9112.68.833193.76681
925.610.1209-4.52094
939.99.711370.188628
948.810.5801-1.78009
957.710.1103-2.41027
9699.10461-0.10461
977.310.6281-3.32815
9811.410.01981.38015
9913.611.09342.5066
1007.911.2443-3.34434
10110.78.895321.80468
10210.39.974180.325822
1038.310.4633-2.16326
1049.610.0895-0.489499
10514.210.32893.87113
1068.59.94076-1.44076
10713.510.55152.94846
1084.99.61889-4.71889
1096.48.84326-2.44326
1109.610.9163-1.31626
11111.610.07441.5256
11211.110.51180.588207






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.6999790.6000420.300021
150.555790.888420.44421
160.4093010.8186020.590699
170.2869890.5739780.713011
180.1933250.3866490.806675
190.594410.811180.40559
200.5170250.965950.482975
210.4413940.8827880.558606
220.4249610.8499220.575039
230.3558760.7117520.644124
240.2774670.5549340.722533
250.2243260.4486510.775674
260.18320.3664010.8168
270.1676720.3353430.832328
280.1934940.3869880.806506
290.1446780.2893550.855322
300.1204250.2408490.879575
310.130390.2607790.86961
320.1057850.2115690.894215
330.07789070.1557810.922109
340.0999980.1999960.900002
350.0923560.1847120.907644
360.06750950.1350190.93249
370.04772150.09544310.952278
380.06797780.1359560.932022
390.0723910.1447820.927609
400.06678140.1335630.933219
410.1190770.2381550.880923
420.09548590.1909720.904514
430.1105890.2211770.889411
440.08352090.1670420.916479
450.08820190.1764040.911798
460.08123790.1624760.918762
470.09340170.1868030.906598
480.1524120.3048250.847588
490.1883750.3767510.811625
500.18940.37880.8106
510.1544350.3088710.845565
520.1585070.3170130.841493
530.1435630.2871270.856437
540.1372540.2745090.862746
550.2021790.4043590.797821
560.1644920.3289840.835508
570.3396290.6792580.660371
580.4029530.8059060.597047
590.3604320.7208640.639568
600.3783180.7566360.621682
610.3755280.7510550.624472
620.3524430.7048860.647557
630.3459540.6919090.654046
640.414160.828320.58584
650.3782670.7565340.621733
660.3372990.6745990.662701
670.3674350.7348690.632565
680.3347620.6695250.665238
690.3503470.7006940.649653
700.3273930.6547860.672607
710.2787510.5575010.721249
720.2634250.5268510.736575
730.2695190.5390390.730481
740.2371520.4743030.762848
750.1947230.3894460.805277
760.1645620.3291240.835438
770.1507240.3014490.849276
780.1353080.2706150.864692
790.1033170.2066350.896683
800.08319420.1663880.916806
810.06643440.1328690.933566
820.09565190.1913040.904348
830.06945440.1389090.930546
840.1265310.2530620.873469
850.09623590.1924720.903764
860.06900480.138010.930995
870.05659410.1131880.943406
880.03951230.07902470.960488
890.02790840.05581680.972092
900.03944610.07889220.960554
910.06623830.1324770.933762
920.07031330.1406270.929687
930.04444010.08888020.95556
940.02859960.05719930.9714
950.05043910.1008780.949561
960.03147730.06295460.968523
970.7402860.5194290.259714
980.577910.8441810.42209

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.699979 & 0.600042 & 0.300021 \tabularnewline
15 & 0.55579 & 0.88842 & 0.44421 \tabularnewline
16 & 0.409301 & 0.818602 & 0.590699 \tabularnewline
17 & 0.286989 & 0.573978 & 0.713011 \tabularnewline
18 & 0.193325 & 0.386649 & 0.806675 \tabularnewline
19 & 0.59441 & 0.81118 & 0.40559 \tabularnewline
20 & 0.517025 & 0.96595 & 0.482975 \tabularnewline
21 & 0.441394 & 0.882788 & 0.558606 \tabularnewline
22 & 0.424961 & 0.849922 & 0.575039 \tabularnewline
23 & 0.355876 & 0.711752 & 0.644124 \tabularnewline
24 & 0.277467 & 0.554934 & 0.722533 \tabularnewline
25 & 0.224326 & 0.448651 & 0.775674 \tabularnewline
26 & 0.1832 & 0.366401 & 0.8168 \tabularnewline
27 & 0.167672 & 0.335343 & 0.832328 \tabularnewline
28 & 0.193494 & 0.386988 & 0.806506 \tabularnewline
29 & 0.144678 & 0.289355 & 0.855322 \tabularnewline
30 & 0.120425 & 0.240849 & 0.879575 \tabularnewline
31 & 0.13039 & 0.260779 & 0.86961 \tabularnewline
32 & 0.105785 & 0.211569 & 0.894215 \tabularnewline
33 & 0.0778907 & 0.155781 & 0.922109 \tabularnewline
34 & 0.099998 & 0.199996 & 0.900002 \tabularnewline
35 & 0.092356 & 0.184712 & 0.907644 \tabularnewline
36 & 0.0675095 & 0.135019 & 0.93249 \tabularnewline
37 & 0.0477215 & 0.0954431 & 0.952278 \tabularnewline
38 & 0.0679778 & 0.135956 & 0.932022 \tabularnewline
39 & 0.072391 & 0.144782 & 0.927609 \tabularnewline
40 & 0.0667814 & 0.133563 & 0.933219 \tabularnewline
41 & 0.119077 & 0.238155 & 0.880923 \tabularnewline
42 & 0.0954859 & 0.190972 & 0.904514 \tabularnewline
43 & 0.110589 & 0.221177 & 0.889411 \tabularnewline
44 & 0.0835209 & 0.167042 & 0.916479 \tabularnewline
45 & 0.0882019 & 0.176404 & 0.911798 \tabularnewline
46 & 0.0812379 & 0.162476 & 0.918762 \tabularnewline
47 & 0.0934017 & 0.186803 & 0.906598 \tabularnewline
48 & 0.152412 & 0.304825 & 0.847588 \tabularnewline
49 & 0.188375 & 0.376751 & 0.811625 \tabularnewline
50 & 0.1894 & 0.3788 & 0.8106 \tabularnewline
51 & 0.154435 & 0.308871 & 0.845565 \tabularnewline
52 & 0.158507 & 0.317013 & 0.841493 \tabularnewline
53 & 0.143563 & 0.287127 & 0.856437 \tabularnewline
54 & 0.137254 & 0.274509 & 0.862746 \tabularnewline
55 & 0.202179 & 0.404359 & 0.797821 \tabularnewline
56 & 0.164492 & 0.328984 & 0.835508 \tabularnewline
57 & 0.339629 & 0.679258 & 0.660371 \tabularnewline
58 & 0.402953 & 0.805906 & 0.597047 \tabularnewline
59 & 0.360432 & 0.720864 & 0.639568 \tabularnewline
60 & 0.378318 & 0.756636 & 0.621682 \tabularnewline
61 & 0.375528 & 0.751055 & 0.624472 \tabularnewline
62 & 0.352443 & 0.704886 & 0.647557 \tabularnewline
63 & 0.345954 & 0.691909 & 0.654046 \tabularnewline
64 & 0.41416 & 0.82832 & 0.58584 \tabularnewline
65 & 0.378267 & 0.756534 & 0.621733 \tabularnewline
66 & 0.337299 & 0.674599 & 0.662701 \tabularnewline
67 & 0.367435 & 0.734869 & 0.632565 \tabularnewline
68 & 0.334762 & 0.669525 & 0.665238 \tabularnewline
69 & 0.350347 & 0.700694 & 0.649653 \tabularnewline
70 & 0.327393 & 0.654786 & 0.672607 \tabularnewline
71 & 0.278751 & 0.557501 & 0.721249 \tabularnewline
72 & 0.263425 & 0.526851 & 0.736575 \tabularnewline
73 & 0.269519 & 0.539039 & 0.730481 \tabularnewline
74 & 0.237152 & 0.474303 & 0.762848 \tabularnewline
75 & 0.194723 & 0.389446 & 0.805277 \tabularnewline
76 & 0.164562 & 0.329124 & 0.835438 \tabularnewline
77 & 0.150724 & 0.301449 & 0.849276 \tabularnewline
78 & 0.135308 & 0.270615 & 0.864692 \tabularnewline
79 & 0.103317 & 0.206635 & 0.896683 \tabularnewline
80 & 0.0831942 & 0.166388 & 0.916806 \tabularnewline
81 & 0.0664344 & 0.132869 & 0.933566 \tabularnewline
82 & 0.0956519 & 0.191304 & 0.904348 \tabularnewline
83 & 0.0694544 & 0.138909 & 0.930546 \tabularnewline
84 & 0.126531 & 0.253062 & 0.873469 \tabularnewline
85 & 0.0962359 & 0.192472 & 0.903764 \tabularnewline
86 & 0.0690048 & 0.13801 & 0.930995 \tabularnewline
87 & 0.0565941 & 0.113188 & 0.943406 \tabularnewline
88 & 0.0395123 & 0.0790247 & 0.960488 \tabularnewline
89 & 0.0279084 & 0.0558168 & 0.972092 \tabularnewline
90 & 0.0394461 & 0.0788922 & 0.960554 \tabularnewline
91 & 0.0662383 & 0.132477 & 0.933762 \tabularnewline
92 & 0.0703133 & 0.140627 & 0.929687 \tabularnewline
93 & 0.0444401 & 0.0888802 & 0.95556 \tabularnewline
94 & 0.0285996 & 0.0571993 & 0.9714 \tabularnewline
95 & 0.0504391 & 0.100878 & 0.949561 \tabularnewline
96 & 0.0314773 & 0.0629546 & 0.968523 \tabularnewline
97 & 0.740286 & 0.519429 & 0.259714 \tabularnewline
98 & 0.57791 & 0.844181 & 0.42209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]14[/C][C]0.699979[/C][C]0.600042[/C][C]0.300021[/C][/ROW]
[ROW][C]15[/C][C]0.55579[/C][C]0.88842[/C][C]0.44421[/C][/ROW]
[ROW][C]16[/C][C]0.409301[/C][C]0.818602[/C][C]0.590699[/C][/ROW]
[ROW][C]17[/C][C]0.286989[/C][C]0.573978[/C][C]0.713011[/C][/ROW]
[ROW][C]18[/C][C]0.193325[/C][C]0.386649[/C][C]0.806675[/C][/ROW]
[ROW][C]19[/C][C]0.59441[/C][C]0.81118[/C][C]0.40559[/C][/ROW]
[ROW][C]20[/C][C]0.517025[/C][C]0.96595[/C][C]0.482975[/C][/ROW]
[ROW][C]21[/C][C]0.441394[/C][C]0.882788[/C][C]0.558606[/C][/ROW]
[ROW][C]22[/C][C]0.424961[/C][C]0.849922[/C][C]0.575039[/C][/ROW]
[ROW][C]23[/C][C]0.355876[/C][C]0.711752[/C][C]0.644124[/C][/ROW]
[ROW][C]24[/C][C]0.277467[/C][C]0.554934[/C][C]0.722533[/C][/ROW]
[ROW][C]25[/C][C]0.224326[/C][C]0.448651[/C][C]0.775674[/C][/ROW]
[ROW][C]26[/C][C]0.1832[/C][C]0.366401[/C][C]0.8168[/C][/ROW]
[ROW][C]27[/C][C]0.167672[/C][C]0.335343[/C][C]0.832328[/C][/ROW]
[ROW][C]28[/C][C]0.193494[/C][C]0.386988[/C][C]0.806506[/C][/ROW]
[ROW][C]29[/C][C]0.144678[/C][C]0.289355[/C][C]0.855322[/C][/ROW]
[ROW][C]30[/C][C]0.120425[/C][C]0.240849[/C][C]0.879575[/C][/ROW]
[ROW][C]31[/C][C]0.13039[/C][C]0.260779[/C][C]0.86961[/C][/ROW]
[ROW][C]32[/C][C]0.105785[/C][C]0.211569[/C][C]0.894215[/C][/ROW]
[ROW][C]33[/C][C]0.0778907[/C][C]0.155781[/C][C]0.922109[/C][/ROW]
[ROW][C]34[/C][C]0.099998[/C][C]0.199996[/C][C]0.900002[/C][/ROW]
[ROW][C]35[/C][C]0.092356[/C][C]0.184712[/C][C]0.907644[/C][/ROW]
[ROW][C]36[/C][C]0.0675095[/C][C]0.135019[/C][C]0.93249[/C][/ROW]
[ROW][C]37[/C][C]0.0477215[/C][C]0.0954431[/C][C]0.952278[/C][/ROW]
[ROW][C]38[/C][C]0.0679778[/C][C]0.135956[/C][C]0.932022[/C][/ROW]
[ROW][C]39[/C][C]0.072391[/C][C]0.144782[/C][C]0.927609[/C][/ROW]
[ROW][C]40[/C][C]0.0667814[/C][C]0.133563[/C][C]0.933219[/C][/ROW]
[ROW][C]41[/C][C]0.119077[/C][C]0.238155[/C][C]0.880923[/C][/ROW]
[ROW][C]42[/C][C]0.0954859[/C][C]0.190972[/C][C]0.904514[/C][/ROW]
[ROW][C]43[/C][C]0.110589[/C][C]0.221177[/C][C]0.889411[/C][/ROW]
[ROW][C]44[/C][C]0.0835209[/C][C]0.167042[/C][C]0.916479[/C][/ROW]
[ROW][C]45[/C][C]0.0882019[/C][C]0.176404[/C][C]0.911798[/C][/ROW]
[ROW][C]46[/C][C]0.0812379[/C][C]0.162476[/C][C]0.918762[/C][/ROW]
[ROW][C]47[/C][C]0.0934017[/C][C]0.186803[/C][C]0.906598[/C][/ROW]
[ROW][C]48[/C][C]0.152412[/C][C]0.304825[/C][C]0.847588[/C][/ROW]
[ROW][C]49[/C][C]0.188375[/C][C]0.376751[/C][C]0.811625[/C][/ROW]
[ROW][C]50[/C][C]0.1894[/C][C]0.3788[/C][C]0.8106[/C][/ROW]
[ROW][C]51[/C][C]0.154435[/C][C]0.308871[/C][C]0.845565[/C][/ROW]
[ROW][C]52[/C][C]0.158507[/C][C]0.317013[/C][C]0.841493[/C][/ROW]
[ROW][C]53[/C][C]0.143563[/C][C]0.287127[/C][C]0.856437[/C][/ROW]
[ROW][C]54[/C][C]0.137254[/C][C]0.274509[/C][C]0.862746[/C][/ROW]
[ROW][C]55[/C][C]0.202179[/C][C]0.404359[/C][C]0.797821[/C][/ROW]
[ROW][C]56[/C][C]0.164492[/C][C]0.328984[/C][C]0.835508[/C][/ROW]
[ROW][C]57[/C][C]0.339629[/C][C]0.679258[/C][C]0.660371[/C][/ROW]
[ROW][C]58[/C][C]0.402953[/C][C]0.805906[/C][C]0.597047[/C][/ROW]
[ROW][C]59[/C][C]0.360432[/C][C]0.720864[/C][C]0.639568[/C][/ROW]
[ROW][C]60[/C][C]0.378318[/C][C]0.756636[/C][C]0.621682[/C][/ROW]
[ROW][C]61[/C][C]0.375528[/C][C]0.751055[/C][C]0.624472[/C][/ROW]
[ROW][C]62[/C][C]0.352443[/C][C]0.704886[/C][C]0.647557[/C][/ROW]
[ROW][C]63[/C][C]0.345954[/C][C]0.691909[/C][C]0.654046[/C][/ROW]
[ROW][C]64[/C][C]0.41416[/C][C]0.82832[/C][C]0.58584[/C][/ROW]
[ROW][C]65[/C][C]0.378267[/C][C]0.756534[/C][C]0.621733[/C][/ROW]
[ROW][C]66[/C][C]0.337299[/C][C]0.674599[/C][C]0.662701[/C][/ROW]
[ROW][C]67[/C][C]0.367435[/C][C]0.734869[/C][C]0.632565[/C][/ROW]
[ROW][C]68[/C][C]0.334762[/C][C]0.669525[/C][C]0.665238[/C][/ROW]
[ROW][C]69[/C][C]0.350347[/C][C]0.700694[/C][C]0.649653[/C][/ROW]
[ROW][C]70[/C][C]0.327393[/C][C]0.654786[/C][C]0.672607[/C][/ROW]
[ROW][C]71[/C][C]0.278751[/C][C]0.557501[/C][C]0.721249[/C][/ROW]
[ROW][C]72[/C][C]0.263425[/C][C]0.526851[/C][C]0.736575[/C][/ROW]
[ROW][C]73[/C][C]0.269519[/C][C]0.539039[/C][C]0.730481[/C][/ROW]
[ROW][C]74[/C][C]0.237152[/C][C]0.474303[/C][C]0.762848[/C][/ROW]
[ROW][C]75[/C][C]0.194723[/C][C]0.389446[/C][C]0.805277[/C][/ROW]
[ROW][C]76[/C][C]0.164562[/C][C]0.329124[/C][C]0.835438[/C][/ROW]
[ROW][C]77[/C][C]0.150724[/C][C]0.301449[/C][C]0.849276[/C][/ROW]
[ROW][C]78[/C][C]0.135308[/C][C]0.270615[/C][C]0.864692[/C][/ROW]
[ROW][C]79[/C][C]0.103317[/C][C]0.206635[/C][C]0.896683[/C][/ROW]
[ROW][C]80[/C][C]0.0831942[/C][C]0.166388[/C][C]0.916806[/C][/ROW]
[ROW][C]81[/C][C]0.0664344[/C][C]0.132869[/C][C]0.933566[/C][/ROW]
[ROW][C]82[/C][C]0.0956519[/C][C]0.191304[/C][C]0.904348[/C][/ROW]
[ROW][C]83[/C][C]0.0694544[/C][C]0.138909[/C][C]0.930546[/C][/ROW]
[ROW][C]84[/C][C]0.126531[/C][C]0.253062[/C][C]0.873469[/C][/ROW]
[ROW][C]85[/C][C]0.0962359[/C][C]0.192472[/C][C]0.903764[/C][/ROW]
[ROW][C]86[/C][C]0.0690048[/C][C]0.13801[/C][C]0.930995[/C][/ROW]
[ROW][C]87[/C][C]0.0565941[/C][C]0.113188[/C][C]0.943406[/C][/ROW]
[ROW][C]88[/C][C]0.0395123[/C][C]0.0790247[/C][C]0.960488[/C][/ROW]
[ROW][C]89[/C][C]0.0279084[/C][C]0.0558168[/C][C]0.972092[/C][/ROW]
[ROW][C]90[/C][C]0.0394461[/C][C]0.0788922[/C][C]0.960554[/C][/ROW]
[ROW][C]91[/C][C]0.0662383[/C][C]0.132477[/C][C]0.933762[/C][/ROW]
[ROW][C]92[/C][C]0.0703133[/C][C]0.140627[/C][C]0.929687[/C][/ROW]
[ROW][C]93[/C][C]0.0444401[/C][C]0.0888802[/C][C]0.95556[/C][/ROW]
[ROW][C]94[/C][C]0.0285996[/C][C]0.0571993[/C][C]0.9714[/C][/ROW]
[ROW][C]95[/C][C]0.0504391[/C][C]0.100878[/C][C]0.949561[/C][/ROW]
[ROW][C]96[/C][C]0.0314773[/C][C]0.0629546[/C][C]0.968523[/C][/ROW]
[ROW][C]97[/C][C]0.740286[/C][C]0.519429[/C][C]0.259714[/C][/ROW]
[ROW][C]98[/C][C]0.57791[/C][C]0.844181[/C][C]0.42209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.6999790.6000420.300021
150.555790.888420.44421
160.4093010.8186020.590699
170.2869890.5739780.713011
180.1933250.3866490.806675
190.594410.811180.40559
200.5170250.965950.482975
210.4413940.8827880.558606
220.4249610.8499220.575039
230.3558760.7117520.644124
240.2774670.5549340.722533
250.2243260.4486510.775674
260.18320.3664010.8168
270.1676720.3353430.832328
280.1934940.3869880.806506
290.1446780.2893550.855322
300.1204250.2408490.879575
310.130390.2607790.86961
320.1057850.2115690.894215
330.07789070.1557810.922109
340.0999980.1999960.900002
350.0923560.1847120.907644
360.06750950.1350190.93249
370.04772150.09544310.952278
380.06797780.1359560.932022
390.0723910.1447820.927609
400.06678140.1335630.933219
410.1190770.2381550.880923
420.09548590.1909720.904514
430.1105890.2211770.889411
440.08352090.1670420.916479
450.08820190.1764040.911798
460.08123790.1624760.918762
470.09340170.1868030.906598
480.1524120.3048250.847588
490.1883750.3767510.811625
500.18940.37880.8106
510.1544350.3088710.845565
520.1585070.3170130.841493
530.1435630.2871270.856437
540.1372540.2745090.862746
550.2021790.4043590.797821
560.1644920.3289840.835508
570.3396290.6792580.660371
580.4029530.8059060.597047
590.3604320.7208640.639568
600.3783180.7566360.621682
610.3755280.7510550.624472
620.3524430.7048860.647557
630.3459540.6919090.654046
640.414160.828320.58584
650.3782670.7565340.621733
660.3372990.6745990.662701
670.3674350.7348690.632565
680.3347620.6695250.665238
690.3503470.7006940.649653
700.3273930.6547860.672607
710.2787510.5575010.721249
720.2634250.5268510.736575
730.2695190.5390390.730481
740.2371520.4743030.762848
750.1947230.3894460.805277
760.1645620.3291240.835438
770.1507240.3014490.849276
780.1353080.2706150.864692
790.1033170.2066350.896683
800.08319420.1663880.916806
810.06643440.1328690.933566
820.09565190.1913040.904348
830.06945440.1389090.930546
840.1265310.2530620.873469
850.09623590.1924720.903764
860.06900480.138010.930995
870.05659410.1131880.943406
880.03951230.07902470.960488
890.02790840.05581680.972092
900.03944610.07889220.960554
910.06623830.1324770.933762
920.07031330.1406270.929687
930.04444010.08888020.95556
940.02859960.05719930.9714
950.05043910.1008780.949561
960.03147730.06295460.968523
970.7402860.5194290.259714
980.577910.8441810.42209






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0823529OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0823529 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263344&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0823529[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263344&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263344&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 level00OK
10% type I error level70.0823529OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '11'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}