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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 11:15:41 +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/t1418642182l1e1a047mrcc4re.htm/, Retrieved Thu, 16 May 2024 19:04:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268114, Retrieved Thu, 16 May 2024 19:04:11 +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] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD  [Multiple Regression] [] [2014-11-13 18:59:36] [95c11abf048d3a1e472aeccb09199113]
-    D    [Multiple Regression] [] [2014-11-13 19:46:46] [95c11abf048d3a1e472aeccb09199113]
-    D      [Multiple Regression] [] [2014-12-15 10:41:33] [2fea329c6e322b1612c5dc504f90c0ef]
- R PD          [Multiple Regression] [] [2014-12-15 11:15:41] [4bf1efda48b6e8e35beb7b429a900cbb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9	149	96	18	68	86
7.4	152	75	7	55	62
12.2	139	70	31	39	70
12.8	148	88	39	32	71
7.4	158	114	46	62	108
6.7	128	69	31	33	64
12.6	224	176	67	52	119
14.8	159	114	35	62	97
13.3	105	121	52	77	129
11.1	159	110	77	76	153
8.2	167	158	37	41	78
11.4	165	116	32	48	80
6.4	159	181	36	63	99
10.6	119	77	38	30	68
12	176	141	69	78	147
6.3	54	35	21	19	40
11.3	91	80	26	31	57
11.9	163	152	54	66	120
9.3	124	97	36	35	71
9.6	137	99	42	42	84
10	121	84	23	45	68
6.4	153	68	34	21	55
13.8	148	101	112	25	137
10.8	221	107	35	44	79
13.8	188	88	47	69	116
11.7	149	112	47	54	101
10.9	244	171	37	74	111
16.1	148	137	109	80	189
13.4	92	77	24	42	66
9.9	150	66	20	61	81
11.5	153	93	22	41	63
8.3	94	105	23	46	69
11.7	156	131	32	39	71
6.1	146	89	7	63	70
9	132	102	30	34	64
9.7	161	161	92	51	143
10.8	105	120	43	42	85
10.3	97	127	55	31	86
10.4	151	77	16	39	55
12.7	131	108	49	20	69
9.3	166	85	71	49	120
11.8	157	168	43	53	96
5.9	111	48	29	31	60
11.4	145	152	56	39	95
13	162	75	46	54	100
10.8	163	107	19	49	68
12.3	59	62	23	34	57
11.3	187	121	59	46	105
11.8	109	124	30	55	85
7.9	90	72	61	42	103
12.7	105	40	7	50	57
12.3	83	58	38	13	51
11.6	116	97	32	37	69
6.7	42	88	16	25	41
10.9	148	126	19	30	49
12.1	155	104	22	28	50
13.3	125	148	48	45	93
10.1	116	146	23	35	58
5.7	128	80	26	28	54
14.3	138	97	33	41	74
8	49	25	9	6	15
13.3	96	99	24	45	69
9.3	164	118	34	73	107
12.5	162	58	48	17	65
7.6	99	63	18	40	58
15.9	202	139	43	64	107
9.2	186	50	33	37	70
9.1	66	60	28	25	53
11.1	183	152	71	65	136
13	214	142	26	100	126
14.5	188	94	67	28	95
12.2	104	66	34	35	69
12.3	177	127	80	56	136
11.4	126	67	29	29	58
8.8	76	90	16	43	59
14.6	99	75	59	59	118
7.3	157	96	58	52	110
12.6	139	128	32	50	82
13	162	146	43	59	102
12.6	108	69	38	27	65
13.2	159	186	29	61	90
9.9	74	81	36	28	64
7.7	110	85	32	51	83
10.5	96	54	35	35	70
13.4	116	46	21	29	50
10.9	87	106	29	48	77
4.3	97	34	12	25	37
10.3	127	60	37	44	81
11.8	106	95	37	64	101
11.2	80	57	47	32	79
11.4	74	62	51	20	71
8.6	91	36	32	28	60
13.2	133	56	21	34	55
12.6	74	54	13	31	44
5.6	114	64	14	26	40
9.9	140	76	-2	58	56
8.8	95	98	20	23	43
7.7	98	88	24	21	45
9	121	35	11	21	32
7.3	126	102	23	33	56
11.4	98	61	24	16	40
13.6	95	80	14	20	34
7.9	110	49	52	37	89
10.7	70	78	15	35	50
10.3	102	90	23	33	56
8.3	86	45	19	27	46
9.6	130	55	35	41	76
14.2	96	96	24	40	64
8.5	102	43	39	35	74
13.5	100	52	29	28	57
4.9	94	60	13	32	45
6.4	52	54	8	22	30
9.6	98	51	18	44	62
11.6	118	51	24	27	51
11.1	99	38	19	17	36
4.35	48	41	23	12	34

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=268114&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=268114&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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
TOT[t] = + 7.83388 + 0.00355067LFM[t] + 0.0048505B[t] -4.76617PRH[t] -4.77965CH[t] + 4.79778H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  7.83388 +  0.00355067LFM[t] +  0.0048505B[t] -4.76617PRH[t] -4.77965CH[t] +  4.79778H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  7.83388 +  0.00355067LFM[t] +  0.0048505B[t] -4.76617PRH[t] -4.77965CH[t] +  4.79778H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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] = + 7.83388 + 0.00355067LFM[t] + 0.0048505B[t] -4.76617PRH[t] -4.77965CH[t] + 4.79778H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.833880.77295410.131.91269e-179.56346e-18
LFM0.003550670.00755120.47020.6391340.319567
B0.00485050.008312910.58350.5607580.280379
PRH-4.766172.41043-1.9770.05050860.0252543
CH-4.779652.4119-1.9820.05000820.0250041
H4.797782.410751.990.04905360.0245268

\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) & 7.83388 & 0.772954 & 10.13 & 1.91269e-17 & 9.56346e-18 \tabularnewline
LFM & 0.00355067 & 0.0075512 & 0.4702 & 0.639134 & 0.319567 \tabularnewline
B & 0.0048505 & 0.00831291 & 0.5835 & 0.560758 & 0.280379 \tabularnewline
PRH & -4.76617 & 2.41043 & -1.977 & 0.0505086 & 0.0252543 \tabularnewline
CH & -4.77965 & 2.4119 & -1.982 & 0.0500082 & 0.0250041 \tabularnewline
H & 4.79778 & 2.41075 & 1.99 & 0.0490536 & 0.0245268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&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]7.83388[/C][C]0.772954[/C][C]10.13[/C][C]1.91269e-17[/C][C]9.56346e-18[/C][/ROW]
[ROW][C]LFM[/C][C]0.00355067[/C][C]0.0075512[/C][C]0.4702[/C][C]0.639134[/C][C]0.319567[/C][/ROW]
[ROW][C]B[/C][C]0.0048505[/C][C]0.00831291[/C][C]0.5835[/C][C]0.560758[/C][C]0.280379[/C][/ROW]
[ROW][C]PRH[/C][C]-4.76617[/C][C]2.41043[/C][C]-1.977[/C][C]0.0505086[/C][C]0.0252543[/C][/ROW]
[ROW][C]CH[/C][C]-4.77965[/C][C]2.4119[/C][C]-1.982[/C][C]0.0500082[/C][C]0.0250041[/C][/ROW]
[ROW][C]H[/C][C]4.79778[/C][C]2.41075[/C][C]1.99[/C][C]0.0490536[/C][C]0.0245268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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)7.833880.77295410.131.91269e-179.56346e-18
LFM0.003550670.00755120.47020.6391340.319567
B0.00485050.008312910.58350.5607580.280379
PRH-4.766172.41043-1.9770.05050860.0252543
CH-4.779652.4119-1.9820.05000820.0250041
H4.797782.410751.990.04905360.0245268






Multiple Linear Regression - Regression Statistics
Multiple R0.44429
R-squared0.197394
Adjusted R-squared0.160912
F-TEST (value)5.4107
F-TEST (DF numerator)5
F-TEST (DF denominator)110
p-value0.000173198
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35296
Sum Squared Residuals609.007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.44429 \tabularnewline
R-squared & 0.197394 \tabularnewline
Adjusted R-squared & 0.160912 \tabularnewline
F-TEST (value) & 5.4107 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 110 \tabularnewline
p-value & 0.000173198 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.35296 \tabularnewline
Sum Squared Residuals & 609.007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.44429[/C][/ROW]
[ROW][C]R-squared[/C][C]0.197394[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.160912[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.4107[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]110[/C][/ROW]
[ROW][C]p-value[/C][C]0.000173198[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.35296[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]609.007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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 R0.44429
R-squared0.197394
Adjusted R-squared0.160912
F-TEST (value)5.4107
F-TEST (DF numerator)5
F-TEST (DF denominator)110
p-value0.000173198
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35296
Sum Squared Residuals609.007






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.910.63032.26973
27.49.95567-2.55567
312.210.35391.8461
412.810.59922.20084
57.411.5259-4.1259
66.710.2012-3.50122
712.612.54350.0564582
814.811.18173.61828
913.311.83321.46675
1011.112.7438-1.6438
118.211.1061-2.90609
1211.410.86410.535901
136.411.5564-5.15644
1410.610.3750.225028
151212.7379-0.737884
166.39.20365-2.90365
1711.39.928891.37111
1811.912.0534-0.153372
199.310.5171-1.21715
209.610.8896-1.28957
211010.2138-0.213764
226.410.1624-3.76244
2313.812.8430.95701
2410.811.0416-0.241606
2513.811.66482.13519
2611.711.37080.329171
2710.912.0408-1.14076
2816.113.91982.18021
2913.410.05413.34593
309.910.4246-0.524631
3111.510.26691.23308
328.310.2379-1.93788
3311.710.74180.958249
346.110.1473-4.04729
35910.362-1.36201
369.713.0193-3.31926
3710.810.9094-0.109425
3810.311.0949-0.794911
3910.49.956280.443718
4012.710.73441.9656
419.311.9683-2.66827
4211.811.52630.273713
435.99.93952-4.03952
4411.411.5632-0.163238
451311.20591.79409
4610.810.42050.379483
4712.39.687512.61249
4811.311.7837-0.483729
4911.810.76771.03226
507.911.1924-3.29235
5112.79.528383.17162
5212.39.846822.45318
5311.610.39861.20145
546.79.36883-2.66883
5510.910.1150.785009
5612.110.09172.00828
5713.311.32871.97131
5810.110.3155-0.215465
595.710.0059-4.30588
6014.310.58083.71921
6188.52239-0.522394
6213.310.22943.07063
639.311.3867-2.08666
6412.510.5161.98405
657.69.78509-2.18509
6615.911.74484.15519
679.210.4507-1.25074
689.19.69757-0.597572
6911.112.6437-1.54366
701311.91721.08284
7114.511.58292.91709
7212.210.23251.96745
7312.312.6225-0.32245
7411.410.04871.35131
758.89.82555-1.02555
7614.611.48383.11619
777.311.6331-4.3331
7812.610.86621.73376
791311.54611.4539
8012.610.24272.35727
8113.211.32321.87685
829.910.1351-0.235115
837.710.5728-2.87283
8410.510.17750.322452
8513.49.658433.74157
8610.910.44380.456194
874.39.17574-4.87574
8810.310.5431-0.243094
8911.811.00090.79915
9011.210.46020.739752
9111.410.37211.02789
928.69.85076-1.25076
9313.29.857933.34207
9412.69.331463.26854
955.69.46297-3.86297
969.99.687790.212214
978.89.69571-0.89571
987.79.74805-2.04805
9999.16168-0.161685
1007.310.1013-2.80129
10111.49.526451.87355
10213.69.364354.23565
1037.910.7767-2.87666
10410.79.569411.13059
10510.39.957870.342127
1068.39.44758-1.14758
1079.610.4119-0.811887
10814.210.12424.07582
1098.510.2719-1.77194
11013.59.865493.63451
1114.99.44971-4.54971
1126.48.93214-2.53214
1139.69.79585-0.195846
11411.69.748361.85164
11511.19.278511.82149
1164.354.354.23939e-13

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.6303 & 2.26973 \tabularnewline
2 & 7.4 & 9.95567 & -2.55567 \tabularnewline
3 & 12.2 & 10.3539 & 1.8461 \tabularnewline
4 & 12.8 & 10.5992 & 2.20084 \tabularnewline
5 & 7.4 & 11.5259 & -4.1259 \tabularnewline
6 & 6.7 & 10.2012 & -3.50122 \tabularnewline
7 & 12.6 & 12.5435 & 0.0564582 \tabularnewline
8 & 14.8 & 11.1817 & 3.61828 \tabularnewline
9 & 13.3 & 11.8332 & 1.46675 \tabularnewline
10 & 11.1 & 12.7438 & -1.6438 \tabularnewline
11 & 8.2 & 11.1061 & -2.90609 \tabularnewline
12 & 11.4 & 10.8641 & 0.535901 \tabularnewline
13 & 6.4 & 11.5564 & -5.15644 \tabularnewline
14 & 10.6 & 10.375 & 0.225028 \tabularnewline
15 & 12 & 12.7379 & -0.737884 \tabularnewline
16 & 6.3 & 9.20365 & -2.90365 \tabularnewline
17 & 11.3 & 9.92889 & 1.37111 \tabularnewline
18 & 11.9 & 12.0534 & -0.153372 \tabularnewline
19 & 9.3 & 10.5171 & -1.21715 \tabularnewline
20 & 9.6 & 10.8896 & -1.28957 \tabularnewline
21 & 10 & 10.2138 & -0.213764 \tabularnewline
22 & 6.4 & 10.1624 & -3.76244 \tabularnewline
23 & 13.8 & 12.843 & 0.95701 \tabularnewline
24 & 10.8 & 11.0416 & -0.241606 \tabularnewline
25 & 13.8 & 11.6648 & 2.13519 \tabularnewline
26 & 11.7 & 11.3708 & 0.329171 \tabularnewline
27 & 10.9 & 12.0408 & -1.14076 \tabularnewline
28 & 16.1 & 13.9198 & 2.18021 \tabularnewline
29 & 13.4 & 10.0541 & 3.34593 \tabularnewline
30 & 9.9 & 10.4246 & -0.524631 \tabularnewline
31 & 11.5 & 10.2669 & 1.23308 \tabularnewline
32 & 8.3 & 10.2379 & -1.93788 \tabularnewline
33 & 11.7 & 10.7418 & 0.958249 \tabularnewline
34 & 6.1 & 10.1473 & -4.04729 \tabularnewline
35 & 9 & 10.362 & -1.36201 \tabularnewline
36 & 9.7 & 13.0193 & -3.31926 \tabularnewline
37 & 10.8 & 10.9094 & -0.109425 \tabularnewline
38 & 10.3 & 11.0949 & -0.794911 \tabularnewline
39 & 10.4 & 9.95628 & 0.443718 \tabularnewline
40 & 12.7 & 10.7344 & 1.9656 \tabularnewline
41 & 9.3 & 11.9683 & -2.66827 \tabularnewline
42 & 11.8 & 11.5263 & 0.273713 \tabularnewline
43 & 5.9 & 9.93952 & -4.03952 \tabularnewline
44 & 11.4 & 11.5632 & -0.163238 \tabularnewline
45 & 13 & 11.2059 & 1.79409 \tabularnewline
46 & 10.8 & 10.4205 & 0.379483 \tabularnewline
47 & 12.3 & 9.68751 & 2.61249 \tabularnewline
48 & 11.3 & 11.7837 & -0.483729 \tabularnewline
49 & 11.8 & 10.7677 & 1.03226 \tabularnewline
50 & 7.9 & 11.1924 & -3.29235 \tabularnewline
51 & 12.7 & 9.52838 & 3.17162 \tabularnewline
52 & 12.3 & 9.84682 & 2.45318 \tabularnewline
53 & 11.6 & 10.3986 & 1.20145 \tabularnewline
54 & 6.7 & 9.36883 & -2.66883 \tabularnewline
55 & 10.9 & 10.115 & 0.785009 \tabularnewline
56 & 12.1 & 10.0917 & 2.00828 \tabularnewline
57 & 13.3 & 11.3287 & 1.97131 \tabularnewline
58 & 10.1 & 10.3155 & -0.215465 \tabularnewline
59 & 5.7 & 10.0059 & -4.30588 \tabularnewline
60 & 14.3 & 10.5808 & 3.71921 \tabularnewline
61 & 8 & 8.52239 & -0.522394 \tabularnewline
62 & 13.3 & 10.2294 & 3.07063 \tabularnewline
63 & 9.3 & 11.3867 & -2.08666 \tabularnewline
64 & 12.5 & 10.516 & 1.98405 \tabularnewline
65 & 7.6 & 9.78509 & -2.18509 \tabularnewline
66 & 15.9 & 11.7448 & 4.15519 \tabularnewline
67 & 9.2 & 10.4507 & -1.25074 \tabularnewline
68 & 9.1 & 9.69757 & -0.597572 \tabularnewline
69 & 11.1 & 12.6437 & -1.54366 \tabularnewline
70 & 13 & 11.9172 & 1.08284 \tabularnewline
71 & 14.5 & 11.5829 & 2.91709 \tabularnewline
72 & 12.2 & 10.2325 & 1.96745 \tabularnewline
73 & 12.3 & 12.6225 & -0.32245 \tabularnewline
74 & 11.4 & 10.0487 & 1.35131 \tabularnewline
75 & 8.8 & 9.82555 & -1.02555 \tabularnewline
76 & 14.6 & 11.4838 & 3.11619 \tabularnewline
77 & 7.3 & 11.6331 & -4.3331 \tabularnewline
78 & 12.6 & 10.8662 & 1.73376 \tabularnewline
79 & 13 & 11.5461 & 1.4539 \tabularnewline
80 & 12.6 & 10.2427 & 2.35727 \tabularnewline
81 & 13.2 & 11.3232 & 1.87685 \tabularnewline
82 & 9.9 & 10.1351 & -0.235115 \tabularnewline
83 & 7.7 & 10.5728 & -2.87283 \tabularnewline
84 & 10.5 & 10.1775 & 0.322452 \tabularnewline
85 & 13.4 & 9.65843 & 3.74157 \tabularnewline
86 & 10.9 & 10.4438 & 0.456194 \tabularnewline
87 & 4.3 & 9.17574 & -4.87574 \tabularnewline
88 & 10.3 & 10.5431 & -0.243094 \tabularnewline
89 & 11.8 & 11.0009 & 0.79915 \tabularnewline
90 & 11.2 & 10.4602 & 0.739752 \tabularnewline
91 & 11.4 & 10.3721 & 1.02789 \tabularnewline
92 & 8.6 & 9.85076 & -1.25076 \tabularnewline
93 & 13.2 & 9.85793 & 3.34207 \tabularnewline
94 & 12.6 & 9.33146 & 3.26854 \tabularnewline
95 & 5.6 & 9.46297 & -3.86297 \tabularnewline
96 & 9.9 & 9.68779 & 0.212214 \tabularnewline
97 & 8.8 & 9.69571 & -0.89571 \tabularnewline
98 & 7.7 & 9.74805 & -2.04805 \tabularnewline
99 & 9 & 9.16168 & -0.161685 \tabularnewline
100 & 7.3 & 10.1013 & -2.80129 \tabularnewline
101 & 11.4 & 9.52645 & 1.87355 \tabularnewline
102 & 13.6 & 9.36435 & 4.23565 \tabularnewline
103 & 7.9 & 10.7767 & -2.87666 \tabularnewline
104 & 10.7 & 9.56941 & 1.13059 \tabularnewline
105 & 10.3 & 9.95787 & 0.342127 \tabularnewline
106 & 8.3 & 9.44758 & -1.14758 \tabularnewline
107 & 9.6 & 10.4119 & -0.811887 \tabularnewline
108 & 14.2 & 10.1242 & 4.07582 \tabularnewline
109 & 8.5 & 10.2719 & -1.77194 \tabularnewline
110 & 13.5 & 9.86549 & 3.63451 \tabularnewline
111 & 4.9 & 9.44971 & -4.54971 \tabularnewline
112 & 6.4 & 8.93214 & -2.53214 \tabularnewline
113 & 9.6 & 9.79585 & -0.195846 \tabularnewline
114 & 11.6 & 9.74836 & 1.85164 \tabularnewline
115 & 11.1 & 9.27851 & 1.82149 \tabularnewline
116 & 4.35 & 4.35 & 4.23939e-13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&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]10.6303[/C][C]2.26973[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]9.95567[/C][C]-2.55567[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]10.3539[/C][C]1.8461[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]10.5992[/C][C]2.20084[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]11.5259[/C][C]-4.1259[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]10.2012[/C][C]-3.50122[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]12.5435[/C][C]0.0564582[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]11.1817[/C][C]3.61828[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]11.8332[/C][C]1.46675[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]12.7438[/C][C]-1.6438[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]11.1061[/C][C]-2.90609[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]10.8641[/C][C]0.535901[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]11.5564[/C][C]-5.15644[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]10.375[/C][C]0.225028[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]12.7379[/C][C]-0.737884[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]9.20365[/C][C]-2.90365[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]9.92889[/C][C]1.37111[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]12.0534[/C][C]-0.153372[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]10.5171[/C][C]-1.21715[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]10.8896[/C][C]-1.28957[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]10.2138[/C][C]-0.213764[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]10.1624[/C][C]-3.76244[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]12.843[/C][C]0.95701[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]11.0416[/C][C]-0.241606[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]11.6648[/C][C]2.13519[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.3708[/C][C]0.329171[/C][/ROW]
[ROW][C]27[/C][C]10.9[/C][C]12.0408[/C][C]-1.14076[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]13.9198[/C][C]2.18021[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]10.0541[/C][C]3.34593[/C][/ROW]
[ROW][C]30[/C][C]9.9[/C][C]10.4246[/C][C]-0.524631[/C][/ROW]
[ROW][C]31[/C][C]11.5[/C][C]10.2669[/C][C]1.23308[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]10.2379[/C][C]-1.93788[/C][/ROW]
[ROW][C]33[/C][C]11.7[/C][C]10.7418[/C][C]0.958249[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]10.1473[/C][C]-4.04729[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.362[/C][C]-1.36201[/C][/ROW]
[ROW][C]36[/C][C]9.7[/C][C]13.0193[/C][C]-3.31926[/C][/ROW]
[ROW][C]37[/C][C]10.8[/C][C]10.9094[/C][C]-0.109425[/C][/ROW]
[ROW][C]38[/C][C]10.3[/C][C]11.0949[/C][C]-0.794911[/C][/ROW]
[ROW][C]39[/C][C]10.4[/C][C]9.95628[/C][C]0.443718[/C][/ROW]
[ROW][C]40[/C][C]12.7[/C][C]10.7344[/C][C]1.9656[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]11.9683[/C][C]-2.66827[/C][/ROW]
[ROW][C]42[/C][C]11.8[/C][C]11.5263[/C][C]0.273713[/C][/ROW]
[ROW][C]43[/C][C]5.9[/C][C]9.93952[/C][C]-4.03952[/C][/ROW]
[ROW][C]44[/C][C]11.4[/C][C]11.5632[/C][C]-0.163238[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]11.2059[/C][C]1.79409[/C][/ROW]
[ROW][C]46[/C][C]10.8[/C][C]10.4205[/C][C]0.379483[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]9.68751[/C][C]2.61249[/C][/ROW]
[ROW][C]48[/C][C]11.3[/C][C]11.7837[/C][C]-0.483729[/C][/ROW]
[ROW][C]49[/C][C]11.8[/C][C]10.7677[/C][C]1.03226[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]11.1924[/C][C]-3.29235[/C][/ROW]
[ROW][C]51[/C][C]12.7[/C][C]9.52838[/C][C]3.17162[/C][/ROW]
[ROW][C]52[/C][C]12.3[/C][C]9.84682[/C][C]2.45318[/C][/ROW]
[ROW][C]53[/C][C]11.6[/C][C]10.3986[/C][C]1.20145[/C][/ROW]
[ROW][C]54[/C][C]6.7[/C][C]9.36883[/C][C]-2.66883[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]10.115[/C][C]0.785009[/C][/ROW]
[ROW][C]56[/C][C]12.1[/C][C]10.0917[/C][C]2.00828[/C][/ROW]
[ROW][C]57[/C][C]13.3[/C][C]11.3287[/C][C]1.97131[/C][/ROW]
[ROW][C]58[/C][C]10.1[/C][C]10.3155[/C][C]-0.215465[/C][/ROW]
[ROW][C]59[/C][C]5.7[/C][C]10.0059[/C][C]-4.30588[/C][/ROW]
[ROW][C]60[/C][C]14.3[/C][C]10.5808[/C][C]3.71921[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.52239[/C][C]-0.522394[/C][/ROW]
[ROW][C]62[/C][C]13.3[/C][C]10.2294[/C][C]3.07063[/C][/ROW]
[ROW][C]63[/C][C]9.3[/C][C]11.3867[/C][C]-2.08666[/C][/ROW]
[ROW][C]64[/C][C]12.5[/C][C]10.516[/C][C]1.98405[/C][/ROW]
[ROW][C]65[/C][C]7.6[/C][C]9.78509[/C][C]-2.18509[/C][/ROW]
[ROW][C]66[/C][C]15.9[/C][C]11.7448[/C][C]4.15519[/C][/ROW]
[ROW][C]67[/C][C]9.2[/C][C]10.4507[/C][C]-1.25074[/C][/ROW]
[ROW][C]68[/C][C]9.1[/C][C]9.69757[/C][C]-0.597572[/C][/ROW]
[ROW][C]69[/C][C]11.1[/C][C]12.6437[/C][C]-1.54366[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]11.9172[/C][C]1.08284[/C][/ROW]
[ROW][C]71[/C][C]14.5[/C][C]11.5829[/C][C]2.91709[/C][/ROW]
[ROW][C]72[/C][C]12.2[/C][C]10.2325[/C][C]1.96745[/C][/ROW]
[ROW][C]73[/C][C]12.3[/C][C]12.6225[/C][C]-0.32245[/C][/ROW]
[ROW][C]74[/C][C]11.4[/C][C]10.0487[/C][C]1.35131[/C][/ROW]
[ROW][C]75[/C][C]8.8[/C][C]9.82555[/C][C]-1.02555[/C][/ROW]
[ROW][C]76[/C][C]14.6[/C][C]11.4838[/C][C]3.11619[/C][/ROW]
[ROW][C]77[/C][C]7.3[/C][C]11.6331[/C][C]-4.3331[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.8662[/C][C]1.73376[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.5461[/C][C]1.4539[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]10.2427[/C][C]2.35727[/C][/ROW]
[ROW][C]81[/C][C]13.2[/C][C]11.3232[/C][C]1.87685[/C][/ROW]
[ROW][C]82[/C][C]9.9[/C][C]10.1351[/C][C]-0.235115[/C][/ROW]
[ROW][C]83[/C][C]7.7[/C][C]10.5728[/C][C]-2.87283[/C][/ROW]
[ROW][C]84[/C][C]10.5[/C][C]10.1775[/C][C]0.322452[/C][/ROW]
[ROW][C]85[/C][C]13.4[/C][C]9.65843[/C][C]3.74157[/C][/ROW]
[ROW][C]86[/C][C]10.9[/C][C]10.4438[/C][C]0.456194[/C][/ROW]
[ROW][C]87[/C][C]4.3[/C][C]9.17574[/C][C]-4.87574[/C][/ROW]
[ROW][C]88[/C][C]10.3[/C][C]10.5431[/C][C]-0.243094[/C][/ROW]
[ROW][C]89[/C][C]11.8[/C][C]11.0009[/C][C]0.79915[/C][/ROW]
[ROW][C]90[/C][C]11.2[/C][C]10.4602[/C][C]0.739752[/C][/ROW]
[ROW][C]91[/C][C]11.4[/C][C]10.3721[/C][C]1.02789[/C][/ROW]
[ROW][C]92[/C][C]8.6[/C][C]9.85076[/C][C]-1.25076[/C][/ROW]
[ROW][C]93[/C][C]13.2[/C][C]9.85793[/C][C]3.34207[/C][/ROW]
[ROW][C]94[/C][C]12.6[/C][C]9.33146[/C][C]3.26854[/C][/ROW]
[ROW][C]95[/C][C]5.6[/C][C]9.46297[/C][C]-3.86297[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]9.68779[/C][C]0.212214[/C][/ROW]
[ROW][C]97[/C][C]8.8[/C][C]9.69571[/C][C]-0.89571[/C][/ROW]
[ROW][C]98[/C][C]7.7[/C][C]9.74805[/C][C]-2.04805[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]9.16168[/C][C]-0.161685[/C][/ROW]
[ROW][C]100[/C][C]7.3[/C][C]10.1013[/C][C]-2.80129[/C][/ROW]
[ROW][C]101[/C][C]11.4[/C][C]9.52645[/C][C]1.87355[/C][/ROW]
[ROW][C]102[/C][C]13.6[/C][C]9.36435[/C][C]4.23565[/C][/ROW]
[ROW][C]103[/C][C]7.9[/C][C]10.7767[/C][C]-2.87666[/C][/ROW]
[ROW][C]104[/C][C]10.7[/C][C]9.56941[/C][C]1.13059[/C][/ROW]
[ROW][C]105[/C][C]10.3[/C][C]9.95787[/C][C]0.342127[/C][/ROW]
[ROW][C]106[/C][C]8.3[/C][C]9.44758[/C][C]-1.14758[/C][/ROW]
[ROW][C]107[/C][C]9.6[/C][C]10.4119[/C][C]-0.811887[/C][/ROW]
[ROW][C]108[/C][C]14.2[/C][C]10.1242[/C][C]4.07582[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]10.2719[/C][C]-1.77194[/C][/ROW]
[ROW][C]110[/C][C]13.5[/C][C]9.86549[/C][C]3.63451[/C][/ROW]
[ROW][C]111[/C][C]4.9[/C][C]9.44971[/C][C]-4.54971[/C][/ROW]
[ROW][C]112[/C][C]6.4[/C][C]8.93214[/C][C]-2.53214[/C][/ROW]
[ROW][C]113[/C][C]9.6[/C][C]9.79585[/C][C]-0.195846[/C][/ROW]
[ROW][C]114[/C][C]11.6[/C][C]9.74836[/C][C]1.85164[/C][/ROW]
[ROW][C]115[/C][C]11.1[/C][C]9.27851[/C][C]1.82149[/C][/ROW]
[ROW][C]116[/C][C]4.35[/C][C]4.35[/C][C]4.23939e-13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.910.63032.26973
27.49.95567-2.55567
312.210.35391.8461
412.810.59922.20084
57.411.5259-4.1259
66.710.2012-3.50122
712.612.54350.0564582
814.811.18173.61828
913.311.83321.46675
1011.112.7438-1.6438
118.211.1061-2.90609
1211.410.86410.535901
136.411.5564-5.15644
1410.610.3750.225028
151212.7379-0.737884
166.39.20365-2.90365
1711.39.928891.37111
1811.912.0534-0.153372
199.310.5171-1.21715
209.610.8896-1.28957
211010.2138-0.213764
226.410.1624-3.76244
2313.812.8430.95701
2410.811.0416-0.241606
2513.811.66482.13519
2611.711.37080.329171
2710.912.0408-1.14076
2816.113.91982.18021
2913.410.05413.34593
309.910.4246-0.524631
3111.510.26691.23308
328.310.2379-1.93788
3311.710.74180.958249
346.110.1473-4.04729
35910.362-1.36201
369.713.0193-3.31926
3710.810.9094-0.109425
3810.311.0949-0.794911
3910.49.956280.443718
4012.710.73441.9656
419.311.9683-2.66827
4211.811.52630.273713
435.99.93952-4.03952
4411.411.5632-0.163238
451311.20591.79409
4610.810.42050.379483
4712.39.687512.61249
4811.311.7837-0.483729
4911.810.76771.03226
507.911.1924-3.29235
5112.79.528383.17162
5212.39.846822.45318
5311.610.39861.20145
546.79.36883-2.66883
5510.910.1150.785009
5612.110.09172.00828
5713.311.32871.97131
5810.110.3155-0.215465
595.710.0059-4.30588
6014.310.58083.71921
6188.52239-0.522394
6213.310.22943.07063
639.311.3867-2.08666
6412.510.5161.98405
657.69.78509-2.18509
6615.911.74484.15519
679.210.4507-1.25074
689.19.69757-0.597572
6911.112.6437-1.54366
701311.91721.08284
7114.511.58292.91709
7212.210.23251.96745
7312.312.6225-0.32245
7411.410.04871.35131
758.89.82555-1.02555
7614.611.48383.11619
777.311.6331-4.3331
7812.610.86621.73376
791311.54611.4539
8012.610.24272.35727
8113.211.32321.87685
829.910.1351-0.235115
837.710.5728-2.87283
8410.510.17750.322452
8513.49.658433.74157
8610.910.44380.456194
874.39.17574-4.87574
8810.310.5431-0.243094
8911.811.00090.79915
9011.210.46020.739752
9111.410.37211.02789
928.69.85076-1.25076
9313.29.857933.34207
9412.69.331463.26854
955.69.46297-3.86297
969.99.687790.212214
978.89.69571-0.89571
987.79.74805-2.04805
9999.16168-0.161685
1007.310.1013-2.80129
10111.49.526451.87355
10213.69.364354.23565
1037.910.7767-2.87666
10410.79.569411.13059
10510.39.957870.342127
1068.39.44758-1.14758
1079.610.4119-0.811887
10814.210.12424.07582
1098.510.2719-1.77194
11013.59.865493.63451
1114.99.44971-4.54971
1126.48.93214-2.53214
1139.69.79585-0.195846
11411.69.748361.85164
11511.19.278511.82149
1164.354.354.23939e-13






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9826590.03468110.0173405
100.967540.06492060.0324603
110.974740.05052020.0252601
120.9549850.09002980.0450149
130.9722510.05549790.027749
140.9533340.09333250.0466663
150.9261650.147670.0738349
160.9109950.1780090.0890046
170.9124110.1751780.0875888
180.8787150.2425710.121285
190.8346970.3306070.165303
200.7860050.4279890.213995
210.7248440.5503110.275156
220.77250.4549990.2275
230.7323710.5352580.267629
240.6699960.6600080.330004
250.62530.74940.3747
260.5613820.8772360.438618
270.5010010.9979970.498999
280.4586650.9173310.541335
290.5668790.8662410.433121
300.5128590.9742820.487141
310.484630.9692590.51537
320.4400340.8800680.559966
330.4261380.8522770.573862
340.5304010.9391990.469599
350.4794670.9589350.520533
360.5203750.959250.479625
370.4669380.9338760.533062
380.4121730.8243460.587827
390.3614780.7229570.638522
400.369730.7394590.63027
410.4064970.8129940.593503
420.366420.732840.63358
430.4635670.9271340.536433
440.4125210.8250420.587479
450.3889980.7779950.611002
460.3427750.6855510.657225
470.3681840.7363680.631816
480.3228880.6457760.677112
490.2852270.5704530.714773
500.3334930.6669860.666507
510.3872310.7744620.612769
520.393740.7874790.60626
530.3546510.7093020.645349
540.3637410.7274820.636259
550.3266930.6533850.673307
560.3121770.6243540.687823
570.2960160.5920330.703984
580.2572420.5144830.742758
590.382180.764360.61782
600.4483940.8967880.551606
610.3955220.7910450.604478
620.4255870.8511730.574413
630.4154470.8308950.584553
640.3898490.7796980.610151
650.3786130.7572260.621387
660.4625290.9250590.537471
670.4226270.8452540.577373
680.3712910.7425820.628709
690.3579780.7159560.642022
700.3147590.6295190.685241
710.318420.6368410.68158
720.2998370.5996740.700163
730.2553340.5106680.744666
740.2229950.4459890.777005
750.1921790.3843580.807821
760.2250410.4500830.774959
770.3463550.6927110.653645
780.307280.614560.69272
790.2638470.5276950.736153
800.2509650.501930.749035
810.2170030.4340050.782997
820.1755040.3510090.824496
830.1920030.3840050.807997
840.1526330.3052660.847367
850.213190.4263790.78681
860.1703050.340610.829695
870.2892290.5784580.710771
880.2349450.4698890.765055
890.1914350.382870.808565
900.1530550.306110.846945
910.124580.249160.87542
920.09686080.1937220.903139
930.1254090.2508190.874591
940.1481760.2963530.851824
950.2237150.4474310.776285
960.1706290.3412580.829371
970.1407020.2814040.859298
980.1736940.3473880.826306
990.1248040.2496080.875196
1000.3817520.7635040.618248
1010.3040050.6080110.695995
1020.2526320.5052650.747368
1030.2770170.5540330.722983
1040.2176260.4352520.782374
1050.208790.4175810.79121
1060.1266310.2532620.873369
1070.09836390.1967280.901636

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.982659 & 0.0346811 & 0.0173405 \tabularnewline
10 & 0.96754 & 0.0649206 & 0.0324603 \tabularnewline
11 & 0.97474 & 0.0505202 & 0.0252601 \tabularnewline
12 & 0.954985 & 0.0900298 & 0.0450149 \tabularnewline
13 & 0.972251 & 0.0554979 & 0.027749 \tabularnewline
14 & 0.953334 & 0.0933325 & 0.0466663 \tabularnewline
15 & 0.926165 & 0.14767 & 0.0738349 \tabularnewline
16 & 0.910995 & 0.178009 & 0.0890046 \tabularnewline
17 & 0.912411 & 0.175178 & 0.0875888 \tabularnewline
18 & 0.878715 & 0.242571 & 0.121285 \tabularnewline
19 & 0.834697 & 0.330607 & 0.165303 \tabularnewline
20 & 0.786005 & 0.427989 & 0.213995 \tabularnewline
21 & 0.724844 & 0.550311 & 0.275156 \tabularnewline
22 & 0.7725 & 0.454999 & 0.2275 \tabularnewline
23 & 0.732371 & 0.535258 & 0.267629 \tabularnewline
24 & 0.669996 & 0.660008 & 0.330004 \tabularnewline
25 & 0.6253 & 0.7494 & 0.3747 \tabularnewline
26 & 0.561382 & 0.877236 & 0.438618 \tabularnewline
27 & 0.501001 & 0.997997 & 0.498999 \tabularnewline
28 & 0.458665 & 0.917331 & 0.541335 \tabularnewline
29 & 0.566879 & 0.866241 & 0.433121 \tabularnewline
30 & 0.512859 & 0.974282 & 0.487141 \tabularnewline
31 & 0.48463 & 0.969259 & 0.51537 \tabularnewline
32 & 0.440034 & 0.880068 & 0.559966 \tabularnewline
33 & 0.426138 & 0.852277 & 0.573862 \tabularnewline
34 & 0.530401 & 0.939199 & 0.469599 \tabularnewline
35 & 0.479467 & 0.958935 & 0.520533 \tabularnewline
36 & 0.520375 & 0.95925 & 0.479625 \tabularnewline
37 & 0.466938 & 0.933876 & 0.533062 \tabularnewline
38 & 0.412173 & 0.824346 & 0.587827 \tabularnewline
39 & 0.361478 & 0.722957 & 0.638522 \tabularnewline
40 & 0.36973 & 0.739459 & 0.63027 \tabularnewline
41 & 0.406497 & 0.812994 & 0.593503 \tabularnewline
42 & 0.36642 & 0.73284 & 0.63358 \tabularnewline
43 & 0.463567 & 0.927134 & 0.536433 \tabularnewline
44 & 0.412521 & 0.825042 & 0.587479 \tabularnewline
45 & 0.388998 & 0.777995 & 0.611002 \tabularnewline
46 & 0.342775 & 0.685551 & 0.657225 \tabularnewline
47 & 0.368184 & 0.736368 & 0.631816 \tabularnewline
48 & 0.322888 & 0.645776 & 0.677112 \tabularnewline
49 & 0.285227 & 0.570453 & 0.714773 \tabularnewline
50 & 0.333493 & 0.666986 & 0.666507 \tabularnewline
51 & 0.387231 & 0.774462 & 0.612769 \tabularnewline
52 & 0.39374 & 0.787479 & 0.60626 \tabularnewline
53 & 0.354651 & 0.709302 & 0.645349 \tabularnewline
54 & 0.363741 & 0.727482 & 0.636259 \tabularnewline
55 & 0.326693 & 0.653385 & 0.673307 \tabularnewline
56 & 0.312177 & 0.624354 & 0.687823 \tabularnewline
57 & 0.296016 & 0.592033 & 0.703984 \tabularnewline
58 & 0.257242 & 0.514483 & 0.742758 \tabularnewline
59 & 0.38218 & 0.76436 & 0.61782 \tabularnewline
60 & 0.448394 & 0.896788 & 0.551606 \tabularnewline
61 & 0.395522 & 0.791045 & 0.604478 \tabularnewline
62 & 0.425587 & 0.851173 & 0.574413 \tabularnewline
63 & 0.415447 & 0.830895 & 0.584553 \tabularnewline
64 & 0.389849 & 0.779698 & 0.610151 \tabularnewline
65 & 0.378613 & 0.757226 & 0.621387 \tabularnewline
66 & 0.462529 & 0.925059 & 0.537471 \tabularnewline
67 & 0.422627 & 0.845254 & 0.577373 \tabularnewline
68 & 0.371291 & 0.742582 & 0.628709 \tabularnewline
69 & 0.357978 & 0.715956 & 0.642022 \tabularnewline
70 & 0.314759 & 0.629519 & 0.685241 \tabularnewline
71 & 0.31842 & 0.636841 & 0.68158 \tabularnewline
72 & 0.299837 & 0.599674 & 0.700163 \tabularnewline
73 & 0.255334 & 0.510668 & 0.744666 \tabularnewline
74 & 0.222995 & 0.445989 & 0.777005 \tabularnewline
75 & 0.192179 & 0.384358 & 0.807821 \tabularnewline
76 & 0.225041 & 0.450083 & 0.774959 \tabularnewline
77 & 0.346355 & 0.692711 & 0.653645 \tabularnewline
78 & 0.30728 & 0.61456 & 0.69272 \tabularnewline
79 & 0.263847 & 0.527695 & 0.736153 \tabularnewline
80 & 0.250965 & 0.50193 & 0.749035 \tabularnewline
81 & 0.217003 & 0.434005 & 0.782997 \tabularnewline
82 & 0.175504 & 0.351009 & 0.824496 \tabularnewline
83 & 0.192003 & 0.384005 & 0.807997 \tabularnewline
84 & 0.152633 & 0.305266 & 0.847367 \tabularnewline
85 & 0.21319 & 0.426379 & 0.78681 \tabularnewline
86 & 0.170305 & 0.34061 & 0.829695 \tabularnewline
87 & 0.289229 & 0.578458 & 0.710771 \tabularnewline
88 & 0.234945 & 0.469889 & 0.765055 \tabularnewline
89 & 0.191435 & 0.38287 & 0.808565 \tabularnewline
90 & 0.153055 & 0.30611 & 0.846945 \tabularnewline
91 & 0.12458 & 0.24916 & 0.87542 \tabularnewline
92 & 0.0968608 & 0.193722 & 0.903139 \tabularnewline
93 & 0.125409 & 0.250819 & 0.874591 \tabularnewline
94 & 0.148176 & 0.296353 & 0.851824 \tabularnewline
95 & 0.223715 & 0.447431 & 0.776285 \tabularnewline
96 & 0.170629 & 0.341258 & 0.829371 \tabularnewline
97 & 0.140702 & 0.281404 & 0.859298 \tabularnewline
98 & 0.173694 & 0.347388 & 0.826306 \tabularnewline
99 & 0.124804 & 0.249608 & 0.875196 \tabularnewline
100 & 0.381752 & 0.763504 & 0.618248 \tabularnewline
101 & 0.304005 & 0.608011 & 0.695995 \tabularnewline
102 & 0.252632 & 0.505265 & 0.747368 \tabularnewline
103 & 0.277017 & 0.554033 & 0.722983 \tabularnewline
104 & 0.217626 & 0.435252 & 0.782374 \tabularnewline
105 & 0.20879 & 0.417581 & 0.79121 \tabularnewline
106 & 0.126631 & 0.253262 & 0.873369 \tabularnewline
107 & 0.0983639 & 0.196728 & 0.901636 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&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]9[/C][C]0.982659[/C][C]0.0346811[/C][C]0.0173405[/C][/ROW]
[ROW][C]10[/C][C]0.96754[/C][C]0.0649206[/C][C]0.0324603[/C][/ROW]
[ROW][C]11[/C][C]0.97474[/C][C]0.0505202[/C][C]0.0252601[/C][/ROW]
[ROW][C]12[/C][C]0.954985[/C][C]0.0900298[/C][C]0.0450149[/C][/ROW]
[ROW][C]13[/C][C]0.972251[/C][C]0.0554979[/C][C]0.027749[/C][/ROW]
[ROW][C]14[/C][C]0.953334[/C][C]0.0933325[/C][C]0.0466663[/C][/ROW]
[ROW][C]15[/C][C]0.926165[/C][C]0.14767[/C][C]0.0738349[/C][/ROW]
[ROW][C]16[/C][C]0.910995[/C][C]0.178009[/C][C]0.0890046[/C][/ROW]
[ROW][C]17[/C][C]0.912411[/C][C]0.175178[/C][C]0.0875888[/C][/ROW]
[ROW][C]18[/C][C]0.878715[/C][C]0.242571[/C][C]0.121285[/C][/ROW]
[ROW][C]19[/C][C]0.834697[/C][C]0.330607[/C][C]0.165303[/C][/ROW]
[ROW][C]20[/C][C]0.786005[/C][C]0.427989[/C][C]0.213995[/C][/ROW]
[ROW][C]21[/C][C]0.724844[/C][C]0.550311[/C][C]0.275156[/C][/ROW]
[ROW][C]22[/C][C]0.7725[/C][C]0.454999[/C][C]0.2275[/C][/ROW]
[ROW][C]23[/C][C]0.732371[/C][C]0.535258[/C][C]0.267629[/C][/ROW]
[ROW][C]24[/C][C]0.669996[/C][C]0.660008[/C][C]0.330004[/C][/ROW]
[ROW][C]25[/C][C]0.6253[/C][C]0.7494[/C][C]0.3747[/C][/ROW]
[ROW][C]26[/C][C]0.561382[/C][C]0.877236[/C][C]0.438618[/C][/ROW]
[ROW][C]27[/C][C]0.501001[/C][C]0.997997[/C][C]0.498999[/C][/ROW]
[ROW][C]28[/C][C]0.458665[/C][C]0.917331[/C][C]0.541335[/C][/ROW]
[ROW][C]29[/C][C]0.566879[/C][C]0.866241[/C][C]0.433121[/C][/ROW]
[ROW][C]30[/C][C]0.512859[/C][C]0.974282[/C][C]0.487141[/C][/ROW]
[ROW][C]31[/C][C]0.48463[/C][C]0.969259[/C][C]0.51537[/C][/ROW]
[ROW][C]32[/C][C]0.440034[/C][C]0.880068[/C][C]0.559966[/C][/ROW]
[ROW][C]33[/C][C]0.426138[/C][C]0.852277[/C][C]0.573862[/C][/ROW]
[ROW][C]34[/C][C]0.530401[/C][C]0.939199[/C][C]0.469599[/C][/ROW]
[ROW][C]35[/C][C]0.479467[/C][C]0.958935[/C][C]0.520533[/C][/ROW]
[ROW][C]36[/C][C]0.520375[/C][C]0.95925[/C][C]0.479625[/C][/ROW]
[ROW][C]37[/C][C]0.466938[/C][C]0.933876[/C][C]0.533062[/C][/ROW]
[ROW][C]38[/C][C]0.412173[/C][C]0.824346[/C][C]0.587827[/C][/ROW]
[ROW][C]39[/C][C]0.361478[/C][C]0.722957[/C][C]0.638522[/C][/ROW]
[ROW][C]40[/C][C]0.36973[/C][C]0.739459[/C][C]0.63027[/C][/ROW]
[ROW][C]41[/C][C]0.406497[/C][C]0.812994[/C][C]0.593503[/C][/ROW]
[ROW][C]42[/C][C]0.36642[/C][C]0.73284[/C][C]0.63358[/C][/ROW]
[ROW][C]43[/C][C]0.463567[/C][C]0.927134[/C][C]0.536433[/C][/ROW]
[ROW][C]44[/C][C]0.412521[/C][C]0.825042[/C][C]0.587479[/C][/ROW]
[ROW][C]45[/C][C]0.388998[/C][C]0.777995[/C][C]0.611002[/C][/ROW]
[ROW][C]46[/C][C]0.342775[/C][C]0.685551[/C][C]0.657225[/C][/ROW]
[ROW][C]47[/C][C]0.368184[/C][C]0.736368[/C][C]0.631816[/C][/ROW]
[ROW][C]48[/C][C]0.322888[/C][C]0.645776[/C][C]0.677112[/C][/ROW]
[ROW][C]49[/C][C]0.285227[/C][C]0.570453[/C][C]0.714773[/C][/ROW]
[ROW][C]50[/C][C]0.333493[/C][C]0.666986[/C][C]0.666507[/C][/ROW]
[ROW][C]51[/C][C]0.387231[/C][C]0.774462[/C][C]0.612769[/C][/ROW]
[ROW][C]52[/C][C]0.39374[/C][C]0.787479[/C][C]0.60626[/C][/ROW]
[ROW][C]53[/C][C]0.354651[/C][C]0.709302[/C][C]0.645349[/C][/ROW]
[ROW][C]54[/C][C]0.363741[/C][C]0.727482[/C][C]0.636259[/C][/ROW]
[ROW][C]55[/C][C]0.326693[/C][C]0.653385[/C][C]0.673307[/C][/ROW]
[ROW][C]56[/C][C]0.312177[/C][C]0.624354[/C][C]0.687823[/C][/ROW]
[ROW][C]57[/C][C]0.296016[/C][C]0.592033[/C][C]0.703984[/C][/ROW]
[ROW][C]58[/C][C]0.257242[/C][C]0.514483[/C][C]0.742758[/C][/ROW]
[ROW][C]59[/C][C]0.38218[/C][C]0.76436[/C][C]0.61782[/C][/ROW]
[ROW][C]60[/C][C]0.448394[/C][C]0.896788[/C][C]0.551606[/C][/ROW]
[ROW][C]61[/C][C]0.395522[/C][C]0.791045[/C][C]0.604478[/C][/ROW]
[ROW][C]62[/C][C]0.425587[/C][C]0.851173[/C][C]0.574413[/C][/ROW]
[ROW][C]63[/C][C]0.415447[/C][C]0.830895[/C][C]0.584553[/C][/ROW]
[ROW][C]64[/C][C]0.389849[/C][C]0.779698[/C][C]0.610151[/C][/ROW]
[ROW][C]65[/C][C]0.378613[/C][C]0.757226[/C][C]0.621387[/C][/ROW]
[ROW][C]66[/C][C]0.462529[/C][C]0.925059[/C][C]0.537471[/C][/ROW]
[ROW][C]67[/C][C]0.422627[/C][C]0.845254[/C][C]0.577373[/C][/ROW]
[ROW][C]68[/C][C]0.371291[/C][C]0.742582[/C][C]0.628709[/C][/ROW]
[ROW][C]69[/C][C]0.357978[/C][C]0.715956[/C][C]0.642022[/C][/ROW]
[ROW][C]70[/C][C]0.314759[/C][C]0.629519[/C][C]0.685241[/C][/ROW]
[ROW][C]71[/C][C]0.31842[/C][C]0.636841[/C][C]0.68158[/C][/ROW]
[ROW][C]72[/C][C]0.299837[/C][C]0.599674[/C][C]0.700163[/C][/ROW]
[ROW][C]73[/C][C]0.255334[/C][C]0.510668[/C][C]0.744666[/C][/ROW]
[ROW][C]74[/C][C]0.222995[/C][C]0.445989[/C][C]0.777005[/C][/ROW]
[ROW][C]75[/C][C]0.192179[/C][C]0.384358[/C][C]0.807821[/C][/ROW]
[ROW][C]76[/C][C]0.225041[/C][C]0.450083[/C][C]0.774959[/C][/ROW]
[ROW][C]77[/C][C]0.346355[/C][C]0.692711[/C][C]0.653645[/C][/ROW]
[ROW][C]78[/C][C]0.30728[/C][C]0.61456[/C][C]0.69272[/C][/ROW]
[ROW][C]79[/C][C]0.263847[/C][C]0.527695[/C][C]0.736153[/C][/ROW]
[ROW][C]80[/C][C]0.250965[/C][C]0.50193[/C][C]0.749035[/C][/ROW]
[ROW][C]81[/C][C]0.217003[/C][C]0.434005[/C][C]0.782997[/C][/ROW]
[ROW][C]82[/C][C]0.175504[/C][C]0.351009[/C][C]0.824496[/C][/ROW]
[ROW][C]83[/C][C]0.192003[/C][C]0.384005[/C][C]0.807997[/C][/ROW]
[ROW][C]84[/C][C]0.152633[/C][C]0.305266[/C][C]0.847367[/C][/ROW]
[ROW][C]85[/C][C]0.21319[/C][C]0.426379[/C][C]0.78681[/C][/ROW]
[ROW][C]86[/C][C]0.170305[/C][C]0.34061[/C][C]0.829695[/C][/ROW]
[ROW][C]87[/C][C]0.289229[/C][C]0.578458[/C][C]0.710771[/C][/ROW]
[ROW][C]88[/C][C]0.234945[/C][C]0.469889[/C][C]0.765055[/C][/ROW]
[ROW][C]89[/C][C]0.191435[/C][C]0.38287[/C][C]0.808565[/C][/ROW]
[ROW][C]90[/C][C]0.153055[/C][C]0.30611[/C][C]0.846945[/C][/ROW]
[ROW][C]91[/C][C]0.12458[/C][C]0.24916[/C][C]0.87542[/C][/ROW]
[ROW][C]92[/C][C]0.0968608[/C][C]0.193722[/C][C]0.903139[/C][/ROW]
[ROW][C]93[/C][C]0.125409[/C][C]0.250819[/C][C]0.874591[/C][/ROW]
[ROW][C]94[/C][C]0.148176[/C][C]0.296353[/C][C]0.851824[/C][/ROW]
[ROW][C]95[/C][C]0.223715[/C][C]0.447431[/C][C]0.776285[/C][/ROW]
[ROW][C]96[/C][C]0.170629[/C][C]0.341258[/C][C]0.829371[/C][/ROW]
[ROW][C]97[/C][C]0.140702[/C][C]0.281404[/C][C]0.859298[/C][/ROW]
[ROW][C]98[/C][C]0.173694[/C][C]0.347388[/C][C]0.826306[/C][/ROW]
[ROW][C]99[/C][C]0.124804[/C][C]0.249608[/C][C]0.875196[/C][/ROW]
[ROW][C]100[/C][C]0.381752[/C][C]0.763504[/C][C]0.618248[/C][/ROW]
[ROW][C]101[/C][C]0.304005[/C][C]0.608011[/C][C]0.695995[/C][/ROW]
[ROW][C]102[/C][C]0.252632[/C][C]0.505265[/C][C]0.747368[/C][/ROW]
[ROW][C]103[/C][C]0.277017[/C][C]0.554033[/C][C]0.722983[/C][/ROW]
[ROW][C]104[/C][C]0.217626[/C][C]0.435252[/C][C]0.782374[/C][/ROW]
[ROW][C]105[/C][C]0.20879[/C][C]0.417581[/C][C]0.79121[/C][/ROW]
[ROW][C]106[/C][C]0.126631[/C][C]0.253262[/C][C]0.873369[/C][/ROW]
[ROW][C]107[/C][C]0.0983639[/C][C]0.196728[/C][C]0.901636[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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
90.9826590.03468110.0173405
100.967540.06492060.0324603
110.974740.05052020.0252601
120.9549850.09002980.0450149
130.9722510.05549790.027749
140.9533340.09333250.0466663
150.9261650.147670.0738349
160.9109950.1780090.0890046
170.9124110.1751780.0875888
180.8787150.2425710.121285
190.8346970.3306070.165303
200.7860050.4279890.213995
210.7248440.5503110.275156
220.77250.4549990.2275
230.7323710.5352580.267629
240.6699960.6600080.330004
250.62530.74940.3747
260.5613820.8772360.438618
270.5010010.9979970.498999
280.4586650.9173310.541335
290.5668790.8662410.433121
300.5128590.9742820.487141
310.484630.9692590.51537
320.4400340.8800680.559966
330.4261380.8522770.573862
340.5304010.9391990.469599
350.4794670.9589350.520533
360.5203750.959250.479625
370.4669380.9338760.533062
380.4121730.8243460.587827
390.3614780.7229570.638522
400.369730.7394590.63027
410.4064970.8129940.593503
420.366420.732840.63358
430.4635670.9271340.536433
440.4125210.8250420.587479
450.3889980.7779950.611002
460.3427750.6855510.657225
470.3681840.7363680.631816
480.3228880.6457760.677112
490.2852270.5704530.714773
500.3334930.6669860.666507
510.3872310.7744620.612769
520.393740.7874790.60626
530.3546510.7093020.645349
540.3637410.7274820.636259
550.3266930.6533850.673307
560.3121770.6243540.687823
570.2960160.5920330.703984
580.2572420.5144830.742758
590.382180.764360.61782
600.4483940.8967880.551606
610.3955220.7910450.604478
620.4255870.8511730.574413
630.4154470.8308950.584553
640.3898490.7796980.610151
650.3786130.7572260.621387
660.4625290.9250590.537471
670.4226270.8452540.577373
680.3712910.7425820.628709
690.3579780.7159560.642022
700.3147590.6295190.685241
710.318420.6368410.68158
720.2998370.5996740.700163
730.2553340.5106680.744666
740.2229950.4459890.777005
750.1921790.3843580.807821
760.2250410.4500830.774959
770.3463550.6927110.653645
780.307280.614560.69272
790.2638470.5276950.736153
800.2509650.501930.749035
810.2170030.4340050.782997
820.1755040.3510090.824496
830.1920030.3840050.807997
840.1526330.3052660.847367
850.213190.4263790.78681
860.1703050.340610.829695
870.2892290.5784580.710771
880.2349450.4698890.765055
890.1914350.382870.808565
900.1530550.306110.846945
910.124580.249160.87542
920.09686080.1937220.903139
930.1254090.2508190.874591
940.1481760.2963530.851824
950.2237150.4474310.776285
960.1706290.3412580.829371
970.1407020.2814040.859298
980.1736940.3473880.826306
990.1248040.2496080.875196
1000.3817520.7635040.618248
1010.3040050.6080110.695995
1020.2526320.5052650.747368
1030.2770170.5540330.722983
1040.2176260.4352520.782374
1050.208790.4175810.79121
1060.1266310.2532620.873369
1070.09836390.1967280.901636






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.010101OK
10% type I error level60.0606061OK

\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 & 1 & 0.010101 & OK \tabularnewline
10% type I error level & 6 & 0.0606061 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268114&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]1[/C][C]0.010101[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0606061[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268114&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268114&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 level10.010101OK
10% type I error level60.0606061OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}