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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 computationTue, 02 Dec 2014 14:46:43 +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/02/t141753161255agm928inosxjf.htm/, Retrieved Thu, 16 May 2024 12:52:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=262689, Retrieved Thu, 16 May 2024 12:52:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

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]
- RM D  [Multiple Regression] [] [2014-11-12 13:17:08] [c2c160edf30e228bd3a949bf24376c2c]
-    D      [Multiple Regression] [] [2014-12-02 14:46:43] [dab7ed139043e35d640785ec44e1a81a] [Current]
-   PD        [Multiple Regression] [] [2014-12-03 12:41:36] [c2c160edf30e228bd3a949bf24376c2c]
-    D          [Multiple Regression] [] [2014-12-03 14:30:39] [c2c160edf30e228bd3a949bf24376c2c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149	96	18	68	86
152	75	7	55	62
139	70	31	39	70
148	88	39	32	71
158	114	46	62	108
128	69	31	33	64
224	176	67	52	119
159	114	35	62	97
105	121	52	77	129
159	110	77	76	153
167	158	37	41	78
165	116	32	48	80
159	181	36	63	99
119	77	38	30	68
176	141	69	78	147
54	35	21	19	40
91	80	26	31	57
163	152	54	66	120
124	97	36	35	71
137	99	42	42	84
121	84	23	45	68
153	68	34	21	55
148	101	112	25	137
221	107	35	44	79
188	88	47	69	116
149	112	47	54	101
244	171	37	74	111
148	137	109	80	189
92	77	24	42	66
150	66	20	61	81
153	93	22	41	63
94	105	23	46	69
156	131	32	39	71
146	89	7	63	70
132	102	30	34	64
161	161	92	51	143
105	120	43	42	85
97	127	55	31	86
151	77	16	39	55
131	108	49	20	69
166	85	71	49	120
157	168	43	53	96
111	48	29	31	60
145	152	56	39	95
162	75	46	54	100
163	107	19	49	68
59	62	23	34	57
187	121	59	46	105
109	124	30	55	85
90	72	61	42	103
105	40	7	50	57
83	58	38	13	51
116	97	32	37	69
42	88	16	25	41
148	126	19	30	49
155	104	22	28	50
125	148	48	45	93
116	146	23	35	58
128	80	26	28	54
138	97	33	41	74
49	25	9	6	15
96	99	24	45	69
164	118	34	73	107
162	58	48	17	65
99	63	18	40	58
202	139	43	64	107
186	50	33	37	70
66	60	28	25	53
183	152	71	65	136
214	142	26	100	126
188	94	67	28	95
104	66	34	35	69
177	127	80	56	136
126	67	29	29	58
76	90	16	43	59
99	75	59	59	118
157	96	58	52	110
139	128	32	50	82
78	41	47	3	50
162	146	43	59	102
108	69	38	27	65
159	186	29	61	90
74	81	36	28	64
110	85	32	51	83
96	54	35	35	70
116	46	21	29	50
87	106	29	48	77
97	34	12	25	37
127	60	37	44	81
106	95	37	64	101
80	57	47	32	79
74	62	51	20	71
91	36	32	28	60
133	56	21	34	55
74	54	13	31	44
114	64	14	26	40
140	76	-2	58	56
95	98	20	23	43
98	88	24	21	45
121	35	11	21	32
126	102	23	33	56
98	61	24	16	40
95	80	14	20	34
110	49	52	37	89
70	78	15	35	50
102	90	23	33	56
86	45	19	27	46
130	55	35	41	76
96	96	24	40	64
102	43	39	35	74
100	52	29	28	57
94	60	13	32	45
52	54	8	22	30
98	51	18	44	62
118	51	24	27	51
99	38	19	17	36

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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
H [t] = -4.65514e-15 + 3.69314e-16LFM[t] 0B[t] + 1PRH[t] + 1CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
H




[t] =  -4.65514e-15 +  3.69314e-16LFM[t] 0B[t] +  1PRH[t] +  1CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]H




[t] =  -4.65514e-15 +  3.69314e-16LFM[t] 0B[t] +  1PRH[t] +  1CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&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
H [t] = -4.65514e-15 + 3.69314e-16LFM[t] 0B[t] + 1PRH[t] + 1CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-4.65514e-155.55056e-15-0.83870.4034510.201726
LFM3.69314e-165.48765e-176.737.76765e-103.88383e-10
B06.03748e-17010.5
PRH19.01982e-171.109e+1600
CH11.24306e-168.045e+1500

\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) & -4.65514e-15 & 5.55056e-15 & -0.8387 & 0.403451 & 0.201726 \tabularnewline
LFM & 3.69314e-16 & 5.48765e-17 & 6.73 & 7.76765e-10 & 3.88383e-10 \tabularnewline
B & 0 & 6.03748e-17 & 0 & 1 & 0.5 \tabularnewline
PRH & 1 & 9.01982e-17 & 1.109e+16 & 0 & 0 \tabularnewline
CH & 1 & 1.24306e-16 & 8.045e+15 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&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]-4.65514e-15[/C][C]5.55056e-15[/C][C]-0.8387[/C][C]0.403451[/C][C]0.201726[/C][/ROW]
[ROW][C]LFM[/C][C]3.69314e-16[/C][C]5.48765e-17[/C][C]6.73[/C][C]7.76765e-10[/C][C]3.88383e-10[/C][/ROW]
[ROW][C]B[/C][C]0[/C][C]6.03748e-17[/C][C]0[/C][C]1[/C][C]0.5[/C][/ROW]
[ROW][C]PRH[/C][C]1[/C][C]9.01982e-17[/C][C]1.109e+16[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CH[/C][C]1[/C][C]1.24306e-16[/C][C]8.045e+15[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&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)-4.65514e-155.55056e-15-0.83870.4034510.201726
LFM3.69314e-165.48765e-176.737.76765e-103.88383e-10
B06.03748e-17010.5
PRH19.01982e-171.109e+1600
CH11.24306e-168.045e+1500






Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)8.76883e+31
F-TEST (DF numerator)4
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71015e-14
Sum Squared Residuals3.24631e-26

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 8.76883e+31 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 111 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.71015e-14 \tabularnewline
Sum Squared Residuals & 3.24631e-26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.76883e+31[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]111[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.71015e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.24631e-26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)8.76883e+31
F-TEST (DF numerator)4
F-TEST (DF denominator)111
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71015e-14
Sum Squared Residuals3.24631e-26






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18686-6.71189e-14
262626.55326e-14
370705.72765e-14
47171-1.33371e-13
5108108-4.01178e-14
664645.68803e-16
71191197.99007e-15
897971.22804e-16
9129129-5.95647e-15
10153153-2.14463e-15
1178783.01932e-15
1280809.77464e-16
1399991.91929e-15
1468682.11341e-15
151471475.70615e-16
164040-8.25083e-16
1757572.15448e-15
181201203.97408e-15
1971713.49889e-15
2084841.40945e-15
2168681.34504e-15
2255553.82255e-15
23137137-4.5077e-15
2479791.05175e-15
251161161.88837e-15
261011011.86571e-15
271111112.28984e-15
281891899.37815e-15
2966666.34505e-16
308181-3.21448e-15
3163637.17779e-15
326969-1.40351e-15
3371714.14024e-15
347070-5.28856e-15
356464-2.93554e-16
361431431.82222e-16
3785855.75445e-16
3886861.17078e-15
3955555.45185e-17
406969-3.89917e-15
411201206.03651e-15
4296963.35301e-15
4360602.17131e-15
449595-2.55345e-15
451001003.24164e-15
466868-2.07709e-15
4757572.50955e-17
481051054.49138e-15
4985854.89278e-15
501031032.58043e-15
515757-1.85395e-15
5251515.48711e-15
5369691.52439e-15
544141-5.18608e-16
5549492.16056e-15
565050-3.80007e-15
5793932.53482e-15
5858581.69405e-15
5954543.12415e-15
6074743.43281e-15
611515-4.2217e-16
626969-2.01052e-16
631071071.66714e-15
6465651.57581e-15
655858-9.92572e-16
661071074.53442e-15
677070-4.14957e-16
6853532.13289e-15
691361361.78142e-15
701261266.05194e-15
7195953.87244e-15
7269691.95662e-15
731361363.33759e-15
7458582.26755e-15
755959-3.20528e-16
76118118-5.2244e-16
771101102.27471e-16
7882823.00025e-15
7950503.77098e-15
801021022.52775e-15
8165653.0114e-15
8290903.97166e-17
8364643.65197e-15
8483833.67301e-16
857070-1.11296e-16
8650501.66479e-15
8777775.57599e-16
883737-1.35414e-15
8981815.39974e-17
901011011.32723e-15
917979-1.52465e-15
9271714.26539e-15
9360601.82277e-16
945555-1.35758e-15
9544441.17005e-15
9640402.58437e-15
975656-3.00805e-15
9843439.59244e-16
994545-3.74008e-15
1003232-1.87535e-15
10156562.91759e-15
1024040-1.94057e-15
10334342.7428e-15
10489896.34126e-15
1055050-7.95569e-16
10656561.75505e-15
1074646-3.00786e-16
10876761.43402e-15
10964641.09281e-15
1107474-1.60167e-15
11157571.65074e-15
1124545-5.23299e-15
11330306.56334e-17
1146262-1.88918e-15
11551511.40232e-15
1163636-4.84724e-15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 86 & 86 & -6.71189e-14 \tabularnewline
2 & 62 & 62 & 6.55326e-14 \tabularnewline
3 & 70 & 70 & 5.72765e-14 \tabularnewline
4 & 71 & 71 & -1.33371e-13 \tabularnewline
5 & 108 & 108 & -4.01178e-14 \tabularnewline
6 & 64 & 64 & 5.68803e-16 \tabularnewline
7 & 119 & 119 & 7.99007e-15 \tabularnewline
8 & 97 & 97 & 1.22804e-16 \tabularnewline
9 & 129 & 129 & -5.95647e-15 \tabularnewline
10 & 153 & 153 & -2.14463e-15 \tabularnewline
11 & 78 & 78 & 3.01932e-15 \tabularnewline
12 & 80 & 80 & 9.77464e-16 \tabularnewline
13 & 99 & 99 & 1.91929e-15 \tabularnewline
14 & 68 & 68 & 2.11341e-15 \tabularnewline
15 & 147 & 147 & 5.70615e-16 \tabularnewline
16 & 40 & 40 & -8.25083e-16 \tabularnewline
17 & 57 & 57 & 2.15448e-15 \tabularnewline
18 & 120 & 120 & 3.97408e-15 \tabularnewline
19 & 71 & 71 & 3.49889e-15 \tabularnewline
20 & 84 & 84 & 1.40945e-15 \tabularnewline
21 & 68 & 68 & 1.34504e-15 \tabularnewline
22 & 55 & 55 & 3.82255e-15 \tabularnewline
23 & 137 & 137 & -4.5077e-15 \tabularnewline
24 & 79 & 79 & 1.05175e-15 \tabularnewline
25 & 116 & 116 & 1.88837e-15 \tabularnewline
26 & 101 & 101 & 1.86571e-15 \tabularnewline
27 & 111 & 111 & 2.28984e-15 \tabularnewline
28 & 189 & 189 & 9.37815e-15 \tabularnewline
29 & 66 & 66 & 6.34505e-16 \tabularnewline
30 & 81 & 81 & -3.21448e-15 \tabularnewline
31 & 63 & 63 & 7.17779e-15 \tabularnewline
32 & 69 & 69 & -1.40351e-15 \tabularnewline
33 & 71 & 71 & 4.14024e-15 \tabularnewline
34 & 70 & 70 & -5.28856e-15 \tabularnewline
35 & 64 & 64 & -2.93554e-16 \tabularnewline
36 & 143 & 143 & 1.82222e-16 \tabularnewline
37 & 85 & 85 & 5.75445e-16 \tabularnewline
38 & 86 & 86 & 1.17078e-15 \tabularnewline
39 & 55 & 55 & 5.45185e-17 \tabularnewline
40 & 69 & 69 & -3.89917e-15 \tabularnewline
41 & 120 & 120 & 6.03651e-15 \tabularnewline
42 & 96 & 96 & 3.35301e-15 \tabularnewline
43 & 60 & 60 & 2.17131e-15 \tabularnewline
44 & 95 & 95 & -2.55345e-15 \tabularnewline
45 & 100 & 100 & 3.24164e-15 \tabularnewline
46 & 68 & 68 & -2.07709e-15 \tabularnewline
47 & 57 & 57 & 2.50955e-17 \tabularnewline
48 & 105 & 105 & 4.49138e-15 \tabularnewline
49 & 85 & 85 & 4.89278e-15 \tabularnewline
50 & 103 & 103 & 2.58043e-15 \tabularnewline
51 & 57 & 57 & -1.85395e-15 \tabularnewline
52 & 51 & 51 & 5.48711e-15 \tabularnewline
53 & 69 & 69 & 1.52439e-15 \tabularnewline
54 & 41 & 41 & -5.18608e-16 \tabularnewline
55 & 49 & 49 & 2.16056e-15 \tabularnewline
56 & 50 & 50 & -3.80007e-15 \tabularnewline
57 & 93 & 93 & 2.53482e-15 \tabularnewline
58 & 58 & 58 & 1.69405e-15 \tabularnewline
59 & 54 & 54 & 3.12415e-15 \tabularnewline
60 & 74 & 74 & 3.43281e-15 \tabularnewline
61 & 15 & 15 & -4.2217e-16 \tabularnewline
62 & 69 & 69 & -2.01052e-16 \tabularnewline
63 & 107 & 107 & 1.66714e-15 \tabularnewline
64 & 65 & 65 & 1.57581e-15 \tabularnewline
65 & 58 & 58 & -9.92572e-16 \tabularnewline
66 & 107 & 107 & 4.53442e-15 \tabularnewline
67 & 70 & 70 & -4.14957e-16 \tabularnewline
68 & 53 & 53 & 2.13289e-15 \tabularnewline
69 & 136 & 136 & 1.78142e-15 \tabularnewline
70 & 126 & 126 & 6.05194e-15 \tabularnewline
71 & 95 & 95 & 3.87244e-15 \tabularnewline
72 & 69 & 69 & 1.95662e-15 \tabularnewline
73 & 136 & 136 & 3.33759e-15 \tabularnewline
74 & 58 & 58 & 2.26755e-15 \tabularnewline
75 & 59 & 59 & -3.20528e-16 \tabularnewline
76 & 118 & 118 & -5.2244e-16 \tabularnewline
77 & 110 & 110 & 2.27471e-16 \tabularnewline
78 & 82 & 82 & 3.00025e-15 \tabularnewline
79 & 50 & 50 & 3.77098e-15 \tabularnewline
80 & 102 & 102 & 2.52775e-15 \tabularnewline
81 & 65 & 65 & 3.0114e-15 \tabularnewline
82 & 90 & 90 & 3.97166e-17 \tabularnewline
83 & 64 & 64 & 3.65197e-15 \tabularnewline
84 & 83 & 83 & 3.67301e-16 \tabularnewline
85 & 70 & 70 & -1.11296e-16 \tabularnewline
86 & 50 & 50 & 1.66479e-15 \tabularnewline
87 & 77 & 77 & 5.57599e-16 \tabularnewline
88 & 37 & 37 & -1.35414e-15 \tabularnewline
89 & 81 & 81 & 5.39974e-17 \tabularnewline
90 & 101 & 101 & 1.32723e-15 \tabularnewline
91 & 79 & 79 & -1.52465e-15 \tabularnewline
92 & 71 & 71 & 4.26539e-15 \tabularnewline
93 & 60 & 60 & 1.82277e-16 \tabularnewline
94 & 55 & 55 & -1.35758e-15 \tabularnewline
95 & 44 & 44 & 1.17005e-15 \tabularnewline
96 & 40 & 40 & 2.58437e-15 \tabularnewline
97 & 56 & 56 & -3.00805e-15 \tabularnewline
98 & 43 & 43 & 9.59244e-16 \tabularnewline
99 & 45 & 45 & -3.74008e-15 \tabularnewline
100 & 32 & 32 & -1.87535e-15 \tabularnewline
101 & 56 & 56 & 2.91759e-15 \tabularnewline
102 & 40 & 40 & -1.94057e-15 \tabularnewline
103 & 34 & 34 & 2.7428e-15 \tabularnewline
104 & 89 & 89 & 6.34126e-15 \tabularnewline
105 & 50 & 50 & -7.95569e-16 \tabularnewline
106 & 56 & 56 & 1.75505e-15 \tabularnewline
107 & 46 & 46 & -3.00786e-16 \tabularnewline
108 & 76 & 76 & 1.43402e-15 \tabularnewline
109 & 64 & 64 & 1.09281e-15 \tabularnewline
110 & 74 & 74 & -1.60167e-15 \tabularnewline
111 & 57 & 57 & 1.65074e-15 \tabularnewline
112 & 45 & 45 & -5.23299e-15 \tabularnewline
113 & 30 & 30 & 6.56334e-17 \tabularnewline
114 & 62 & 62 & -1.88918e-15 \tabularnewline
115 & 51 & 51 & 1.40232e-15 \tabularnewline
116 & 36 & 36 & -4.84724e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&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]86[/C][C]86[/C][C]-6.71189e-14[/C][/ROW]
[ROW][C]2[/C][C]62[/C][C]62[/C][C]6.55326e-14[/C][/ROW]
[ROW][C]3[/C][C]70[/C][C]70[/C][C]5.72765e-14[/C][/ROW]
[ROW][C]4[/C][C]71[/C][C]71[/C][C]-1.33371e-13[/C][/ROW]
[ROW][C]5[/C][C]108[/C][C]108[/C][C]-4.01178e-14[/C][/ROW]
[ROW][C]6[/C][C]64[/C][C]64[/C][C]5.68803e-16[/C][/ROW]
[ROW][C]7[/C][C]119[/C][C]119[/C][C]7.99007e-15[/C][/ROW]
[ROW][C]8[/C][C]97[/C][C]97[/C][C]1.22804e-16[/C][/ROW]
[ROW][C]9[/C][C]129[/C][C]129[/C][C]-5.95647e-15[/C][/ROW]
[ROW][C]10[/C][C]153[/C][C]153[/C][C]-2.14463e-15[/C][/ROW]
[ROW][C]11[/C][C]78[/C][C]78[/C][C]3.01932e-15[/C][/ROW]
[ROW][C]12[/C][C]80[/C][C]80[/C][C]9.77464e-16[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]99[/C][C]1.91929e-15[/C][/ROW]
[ROW][C]14[/C][C]68[/C][C]68[/C][C]2.11341e-15[/C][/ROW]
[ROW][C]15[/C][C]147[/C][C]147[/C][C]5.70615e-16[/C][/ROW]
[ROW][C]16[/C][C]40[/C][C]40[/C][C]-8.25083e-16[/C][/ROW]
[ROW][C]17[/C][C]57[/C][C]57[/C][C]2.15448e-15[/C][/ROW]
[ROW][C]18[/C][C]120[/C][C]120[/C][C]3.97408e-15[/C][/ROW]
[ROW][C]19[/C][C]71[/C][C]71[/C][C]3.49889e-15[/C][/ROW]
[ROW][C]20[/C][C]84[/C][C]84[/C][C]1.40945e-15[/C][/ROW]
[ROW][C]21[/C][C]68[/C][C]68[/C][C]1.34504e-15[/C][/ROW]
[ROW][C]22[/C][C]55[/C][C]55[/C][C]3.82255e-15[/C][/ROW]
[ROW][C]23[/C][C]137[/C][C]137[/C][C]-4.5077e-15[/C][/ROW]
[ROW][C]24[/C][C]79[/C][C]79[/C][C]1.05175e-15[/C][/ROW]
[ROW][C]25[/C][C]116[/C][C]116[/C][C]1.88837e-15[/C][/ROW]
[ROW][C]26[/C][C]101[/C][C]101[/C][C]1.86571e-15[/C][/ROW]
[ROW][C]27[/C][C]111[/C][C]111[/C][C]2.28984e-15[/C][/ROW]
[ROW][C]28[/C][C]189[/C][C]189[/C][C]9.37815e-15[/C][/ROW]
[ROW][C]29[/C][C]66[/C][C]66[/C][C]6.34505e-16[/C][/ROW]
[ROW][C]30[/C][C]81[/C][C]81[/C][C]-3.21448e-15[/C][/ROW]
[ROW][C]31[/C][C]63[/C][C]63[/C][C]7.17779e-15[/C][/ROW]
[ROW][C]32[/C][C]69[/C][C]69[/C][C]-1.40351e-15[/C][/ROW]
[ROW][C]33[/C][C]71[/C][C]71[/C][C]4.14024e-15[/C][/ROW]
[ROW][C]34[/C][C]70[/C][C]70[/C][C]-5.28856e-15[/C][/ROW]
[ROW][C]35[/C][C]64[/C][C]64[/C][C]-2.93554e-16[/C][/ROW]
[ROW][C]36[/C][C]143[/C][C]143[/C][C]1.82222e-16[/C][/ROW]
[ROW][C]37[/C][C]85[/C][C]85[/C][C]5.75445e-16[/C][/ROW]
[ROW][C]38[/C][C]86[/C][C]86[/C][C]1.17078e-15[/C][/ROW]
[ROW][C]39[/C][C]55[/C][C]55[/C][C]5.45185e-17[/C][/ROW]
[ROW][C]40[/C][C]69[/C][C]69[/C][C]-3.89917e-15[/C][/ROW]
[ROW][C]41[/C][C]120[/C][C]120[/C][C]6.03651e-15[/C][/ROW]
[ROW][C]42[/C][C]96[/C][C]96[/C][C]3.35301e-15[/C][/ROW]
[ROW][C]43[/C][C]60[/C][C]60[/C][C]2.17131e-15[/C][/ROW]
[ROW][C]44[/C][C]95[/C][C]95[/C][C]-2.55345e-15[/C][/ROW]
[ROW][C]45[/C][C]100[/C][C]100[/C][C]3.24164e-15[/C][/ROW]
[ROW][C]46[/C][C]68[/C][C]68[/C][C]-2.07709e-15[/C][/ROW]
[ROW][C]47[/C][C]57[/C][C]57[/C][C]2.50955e-17[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]105[/C][C]4.49138e-15[/C][/ROW]
[ROW][C]49[/C][C]85[/C][C]85[/C][C]4.89278e-15[/C][/ROW]
[ROW][C]50[/C][C]103[/C][C]103[/C][C]2.58043e-15[/C][/ROW]
[ROW][C]51[/C][C]57[/C][C]57[/C][C]-1.85395e-15[/C][/ROW]
[ROW][C]52[/C][C]51[/C][C]51[/C][C]5.48711e-15[/C][/ROW]
[ROW][C]53[/C][C]69[/C][C]69[/C][C]1.52439e-15[/C][/ROW]
[ROW][C]54[/C][C]41[/C][C]41[/C][C]-5.18608e-16[/C][/ROW]
[ROW][C]55[/C][C]49[/C][C]49[/C][C]2.16056e-15[/C][/ROW]
[ROW][C]56[/C][C]50[/C][C]50[/C][C]-3.80007e-15[/C][/ROW]
[ROW][C]57[/C][C]93[/C][C]93[/C][C]2.53482e-15[/C][/ROW]
[ROW][C]58[/C][C]58[/C][C]58[/C][C]1.69405e-15[/C][/ROW]
[ROW][C]59[/C][C]54[/C][C]54[/C][C]3.12415e-15[/C][/ROW]
[ROW][C]60[/C][C]74[/C][C]74[/C][C]3.43281e-15[/C][/ROW]
[ROW][C]61[/C][C]15[/C][C]15[/C][C]-4.2217e-16[/C][/ROW]
[ROW][C]62[/C][C]69[/C][C]69[/C][C]-2.01052e-16[/C][/ROW]
[ROW][C]63[/C][C]107[/C][C]107[/C][C]1.66714e-15[/C][/ROW]
[ROW][C]64[/C][C]65[/C][C]65[/C][C]1.57581e-15[/C][/ROW]
[ROW][C]65[/C][C]58[/C][C]58[/C][C]-9.92572e-16[/C][/ROW]
[ROW][C]66[/C][C]107[/C][C]107[/C][C]4.53442e-15[/C][/ROW]
[ROW][C]67[/C][C]70[/C][C]70[/C][C]-4.14957e-16[/C][/ROW]
[ROW][C]68[/C][C]53[/C][C]53[/C][C]2.13289e-15[/C][/ROW]
[ROW][C]69[/C][C]136[/C][C]136[/C][C]1.78142e-15[/C][/ROW]
[ROW][C]70[/C][C]126[/C][C]126[/C][C]6.05194e-15[/C][/ROW]
[ROW][C]71[/C][C]95[/C][C]95[/C][C]3.87244e-15[/C][/ROW]
[ROW][C]72[/C][C]69[/C][C]69[/C][C]1.95662e-15[/C][/ROW]
[ROW][C]73[/C][C]136[/C][C]136[/C][C]3.33759e-15[/C][/ROW]
[ROW][C]74[/C][C]58[/C][C]58[/C][C]2.26755e-15[/C][/ROW]
[ROW][C]75[/C][C]59[/C][C]59[/C][C]-3.20528e-16[/C][/ROW]
[ROW][C]76[/C][C]118[/C][C]118[/C][C]-5.2244e-16[/C][/ROW]
[ROW][C]77[/C][C]110[/C][C]110[/C][C]2.27471e-16[/C][/ROW]
[ROW][C]78[/C][C]82[/C][C]82[/C][C]3.00025e-15[/C][/ROW]
[ROW][C]79[/C][C]50[/C][C]50[/C][C]3.77098e-15[/C][/ROW]
[ROW][C]80[/C][C]102[/C][C]102[/C][C]2.52775e-15[/C][/ROW]
[ROW][C]81[/C][C]65[/C][C]65[/C][C]3.0114e-15[/C][/ROW]
[ROW][C]82[/C][C]90[/C][C]90[/C][C]3.97166e-17[/C][/ROW]
[ROW][C]83[/C][C]64[/C][C]64[/C][C]3.65197e-15[/C][/ROW]
[ROW][C]84[/C][C]83[/C][C]83[/C][C]3.67301e-16[/C][/ROW]
[ROW][C]85[/C][C]70[/C][C]70[/C][C]-1.11296e-16[/C][/ROW]
[ROW][C]86[/C][C]50[/C][C]50[/C][C]1.66479e-15[/C][/ROW]
[ROW][C]87[/C][C]77[/C][C]77[/C][C]5.57599e-16[/C][/ROW]
[ROW][C]88[/C][C]37[/C][C]37[/C][C]-1.35414e-15[/C][/ROW]
[ROW][C]89[/C][C]81[/C][C]81[/C][C]5.39974e-17[/C][/ROW]
[ROW][C]90[/C][C]101[/C][C]101[/C][C]1.32723e-15[/C][/ROW]
[ROW][C]91[/C][C]79[/C][C]79[/C][C]-1.52465e-15[/C][/ROW]
[ROW][C]92[/C][C]71[/C][C]71[/C][C]4.26539e-15[/C][/ROW]
[ROW][C]93[/C][C]60[/C][C]60[/C][C]1.82277e-16[/C][/ROW]
[ROW][C]94[/C][C]55[/C][C]55[/C][C]-1.35758e-15[/C][/ROW]
[ROW][C]95[/C][C]44[/C][C]44[/C][C]1.17005e-15[/C][/ROW]
[ROW][C]96[/C][C]40[/C][C]40[/C][C]2.58437e-15[/C][/ROW]
[ROW][C]97[/C][C]56[/C][C]56[/C][C]-3.00805e-15[/C][/ROW]
[ROW][C]98[/C][C]43[/C][C]43[/C][C]9.59244e-16[/C][/ROW]
[ROW][C]99[/C][C]45[/C][C]45[/C][C]-3.74008e-15[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]32[/C][C]-1.87535e-15[/C][/ROW]
[ROW][C]101[/C][C]56[/C][C]56[/C][C]2.91759e-15[/C][/ROW]
[ROW][C]102[/C][C]40[/C][C]40[/C][C]-1.94057e-15[/C][/ROW]
[ROW][C]103[/C][C]34[/C][C]34[/C][C]2.7428e-15[/C][/ROW]
[ROW][C]104[/C][C]89[/C][C]89[/C][C]6.34126e-15[/C][/ROW]
[ROW][C]105[/C][C]50[/C][C]50[/C][C]-7.95569e-16[/C][/ROW]
[ROW][C]106[/C][C]56[/C][C]56[/C][C]1.75505e-15[/C][/ROW]
[ROW][C]107[/C][C]46[/C][C]46[/C][C]-3.00786e-16[/C][/ROW]
[ROW][C]108[/C][C]76[/C][C]76[/C][C]1.43402e-15[/C][/ROW]
[ROW][C]109[/C][C]64[/C][C]64[/C][C]1.09281e-15[/C][/ROW]
[ROW][C]110[/C][C]74[/C][C]74[/C][C]-1.60167e-15[/C][/ROW]
[ROW][C]111[/C][C]57[/C][C]57[/C][C]1.65074e-15[/C][/ROW]
[ROW][C]112[/C][C]45[/C][C]45[/C][C]-5.23299e-15[/C][/ROW]
[ROW][C]113[/C][C]30[/C][C]30[/C][C]6.56334e-17[/C][/ROW]
[ROW][C]114[/C][C]62[/C][C]62[/C][C]-1.88918e-15[/C][/ROW]
[ROW][C]115[/C][C]51[/C][C]51[/C][C]1.40232e-15[/C][/ROW]
[ROW][C]116[/C][C]36[/C][C]36[/C][C]-4.84724e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&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
18686-6.71189e-14
262626.55326e-14
370705.72765e-14
47171-1.33371e-13
5108108-4.01178e-14
664645.68803e-16
71191197.99007e-15
897971.22804e-16
9129129-5.95647e-15
10153153-2.14463e-15
1178783.01932e-15
1280809.77464e-16
1399991.91929e-15
1468682.11341e-15
151471475.70615e-16
164040-8.25083e-16
1757572.15448e-15
181201203.97408e-15
1971713.49889e-15
2084841.40945e-15
2168681.34504e-15
2255553.82255e-15
23137137-4.5077e-15
2479791.05175e-15
251161161.88837e-15
261011011.86571e-15
271111112.28984e-15
281891899.37815e-15
2966666.34505e-16
308181-3.21448e-15
3163637.17779e-15
326969-1.40351e-15
3371714.14024e-15
347070-5.28856e-15
356464-2.93554e-16
361431431.82222e-16
3785855.75445e-16
3886861.17078e-15
3955555.45185e-17
406969-3.89917e-15
411201206.03651e-15
4296963.35301e-15
4360602.17131e-15
449595-2.55345e-15
451001003.24164e-15
466868-2.07709e-15
4757572.50955e-17
481051054.49138e-15
4985854.89278e-15
501031032.58043e-15
515757-1.85395e-15
5251515.48711e-15
5369691.52439e-15
544141-5.18608e-16
5549492.16056e-15
565050-3.80007e-15
5793932.53482e-15
5858581.69405e-15
5954543.12415e-15
6074743.43281e-15
611515-4.2217e-16
626969-2.01052e-16
631071071.66714e-15
6465651.57581e-15
655858-9.92572e-16
661071074.53442e-15
677070-4.14957e-16
6853532.13289e-15
691361361.78142e-15
701261266.05194e-15
7195953.87244e-15
7269691.95662e-15
731361363.33759e-15
7458582.26755e-15
755959-3.20528e-16
76118118-5.2244e-16
771101102.27471e-16
7882823.00025e-15
7950503.77098e-15
801021022.52775e-15
8165653.0114e-15
8290903.97166e-17
8364643.65197e-15
8483833.67301e-16
857070-1.11296e-16
8650501.66479e-15
8777775.57599e-16
883737-1.35414e-15
8981815.39974e-17
901011011.32723e-15
917979-1.52465e-15
9271714.26539e-15
9360601.82277e-16
945555-1.35758e-15
9544441.17005e-15
9640402.58437e-15
975656-3.00805e-15
9843439.59244e-16
994545-3.74008e-15
1003232-1.87535e-15
10156562.91759e-15
1024040-1.94057e-15
10334342.7428e-15
10489896.34126e-15
1055050-7.95569e-16
10656561.75505e-15
1074646-3.00786e-16
10876761.43402e-15
10964641.09281e-15
1107474-1.60167e-15
11157571.65074e-15
1124545-5.23299e-15
11330306.56334e-17
1146262-1.88918e-15
11551511.40232e-15
1163636-4.84724e-15






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.01263920.02527850.987361
90.005915050.01183010.994085
100.07359740.1471950.926403
110.001250670.002501340.998749
128.498e-050.000169960.999915
130.01585560.03171120.984144
140.002900290.005800570.9971
150.7553690.4892620.244631
160.021030.04206010.97897
170.004905510.009811030.995094
180.7424550.515090.257545
190.8416620.3166750.158338
200.04215680.08431370.957843
210.7216240.5567510.278376
224.94424e-059.88849e-050.999951
238.18006e-071.63601e-060.999999
244.32966e-088.65931e-081
250.7382810.5234370.261719
260.8426340.3147330.157366
270.01055650.02111290.989444
280.6592730.6814550.340727
291.10493e-062.20985e-060.999999
300.9999991.52934e-067.64669e-07
3112.87801e-281.439e-28
320.9999050.0001890739.45364e-05
3319.47106e-124.73553e-12
347.63602e-050.000152720.999924
3511.06916e-215.34582e-22
361.64358e-063.28716e-060.999998
372.40098e-104.80196e-101
380.0007994580.001598920.999201
390.02881870.05763740.971181
402.68964e-075.37927e-071
412.80059e-095.60118e-091
4215.94747e-382.97374e-38
430.02502550.05005090.974975
440.0002878950.000575790.999712
450.006385060.01277010.993615
460.8511920.2976170.148808
470.9587910.08241750.0412088
484.27271e-118.54542e-111
490.5044250.9911490.495575
502.38779e-064.77558e-060.999998
510.940660.1186790.0593397
5215.72472e-092.86236e-09
530.9998930.0002134460.000106723
541.57238e-063.14475e-060.999998
551.26729e-052.53457e-050.999987
5612.37987e-141.18993e-14
5713.65405e-131.82703e-13
580.8762970.2474060.123703
590.9999921.54277e-057.71387e-06
600.5324030.9351950.467597
610.0161750.03235010.983825
620.1953270.3906540.804673
630.9999976.44901e-063.2245e-06
647.61155e-131.52231e-121
6511.02489e-105.12444e-11
6611.21007e-176.05037e-18
670.9999754.92545e-052.46273e-05
680.9563730.08725330.0436266
690.0007973830.001594770.999203
7013.56535e-141.78267e-14
710.3876480.7752960.612352
720.9988180.002363140.00118157
730.9726880.05462360.0273118
7418.59876e-084.29938e-08
7513.0228e-131.5114e-13
760.07729910.1545980.922701
7711.00041e-105.00206e-11
782.71432e-285.42863e-281
790.9999991.65275e-068.26373e-07
8015.05384e-072.52692e-07
810.999880.0002408550.000120428
820.9998560.0002888430.000144422
830.8216470.3567070.178353
840.0007649450.001529890.999235
850.9853420.02931650.0146582
8613.11242e-191.55621e-19
870.9328080.1343840.0671918
880.9998490.0003027410.000151371
8911.09009e-075.45046e-08
9016.20098e-153.10049e-15
910.9995750.0008505530.000425276
9211.3068e-086.534e-09
9312.31777e-221.15888e-22
9418.55474e-104.27737e-10
9511.88411e-099.42053e-10
9612.26334e-081.13167e-08
9711.7556e-128.778e-13
980.9999983.89998e-061.94999e-06
9914.14946e-072.07473e-07
1000.9999686.48867e-053.24434e-05
1010.9997170.0005650250.000282513
1020.9999892.21297e-051.10649e-05
1030.8960540.2078910.103946
1040.9999519.87594e-054.93797e-05
1050.9958520.008295740.00414787
1060.9839860.03202730.0160136
1070.9997040.0005915880.000295794
1080.9155730.1688530.0844267

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0126392 & 0.0252785 & 0.987361 \tabularnewline
9 & 0.00591505 & 0.0118301 & 0.994085 \tabularnewline
10 & 0.0735974 & 0.147195 & 0.926403 \tabularnewline
11 & 0.00125067 & 0.00250134 & 0.998749 \tabularnewline
12 & 8.498e-05 & 0.00016996 & 0.999915 \tabularnewline
13 & 0.0158556 & 0.0317112 & 0.984144 \tabularnewline
14 & 0.00290029 & 0.00580057 & 0.9971 \tabularnewline
15 & 0.755369 & 0.489262 & 0.244631 \tabularnewline
16 & 0.02103 & 0.0420601 & 0.97897 \tabularnewline
17 & 0.00490551 & 0.00981103 & 0.995094 \tabularnewline
18 & 0.742455 & 0.51509 & 0.257545 \tabularnewline
19 & 0.841662 & 0.316675 & 0.158338 \tabularnewline
20 & 0.0421568 & 0.0843137 & 0.957843 \tabularnewline
21 & 0.721624 & 0.556751 & 0.278376 \tabularnewline
22 & 4.94424e-05 & 9.88849e-05 & 0.999951 \tabularnewline
23 & 8.18006e-07 & 1.63601e-06 & 0.999999 \tabularnewline
24 & 4.32966e-08 & 8.65931e-08 & 1 \tabularnewline
25 & 0.738281 & 0.523437 & 0.261719 \tabularnewline
26 & 0.842634 & 0.314733 & 0.157366 \tabularnewline
27 & 0.0105565 & 0.0211129 & 0.989444 \tabularnewline
28 & 0.659273 & 0.681455 & 0.340727 \tabularnewline
29 & 1.10493e-06 & 2.20985e-06 & 0.999999 \tabularnewline
30 & 0.999999 & 1.52934e-06 & 7.64669e-07 \tabularnewline
31 & 1 & 2.87801e-28 & 1.439e-28 \tabularnewline
32 & 0.999905 & 0.000189073 & 9.45364e-05 \tabularnewline
33 & 1 & 9.47106e-12 & 4.73553e-12 \tabularnewline
34 & 7.63602e-05 & 0.00015272 & 0.999924 \tabularnewline
35 & 1 & 1.06916e-21 & 5.34582e-22 \tabularnewline
36 & 1.64358e-06 & 3.28716e-06 & 0.999998 \tabularnewline
37 & 2.40098e-10 & 4.80196e-10 & 1 \tabularnewline
38 & 0.000799458 & 0.00159892 & 0.999201 \tabularnewline
39 & 0.0288187 & 0.0576374 & 0.971181 \tabularnewline
40 & 2.68964e-07 & 5.37927e-07 & 1 \tabularnewline
41 & 2.80059e-09 & 5.60118e-09 & 1 \tabularnewline
42 & 1 & 5.94747e-38 & 2.97374e-38 \tabularnewline
43 & 0.0250255 & 0.0500509 & 0.974975 \tabularnewline
44 & 0.000287895 & 0.00057579 & 0.999712 \tabularnewline
45 & 0.00638506 & 0.0127701 & 0.993615 \tabularnewline
46 & 0.851192 & 0.297617 & 0.148808 \tabularnewline
47 & 0.958791 & 0.0824175 & 0.0412088 \tabularnewline
48 & 4.27271e-11 & 8.54542e-11 & 1 \tabularnewline
49 & 0.504425 & 0.991149 & 0.495575 \tabularnewline
50 & 2.38779e-06 & 4.77558e-06 & 0.999998 \tabularnewline
51 & 0.94066 & 0.118679 & 0.0593397 \tabularnewline
52 & 1 & 5.72472e-09 & 2.86236e-09 \tabularnewline
53 & 0.999893 & 0.000213446 & 0.000106723 \tabularnewline
54 & 1.57238e-06 & 3.14475e-06 & 0.999998 \tabularnewline
55 & 1.26729e-05 & 2.53457e-05 & 0.999987 \tabularnewline
56 & 1 & 2.37987e-14 & 1.18993e-14 \tabularnewline
57 & 1 & 3.65405e-13 & 1.82703e-13 \tabularnewline
58 & 0.876297 & 0.247406 & 0.123703 \tabularnewline
59 & 0.999992 & 1.54277e-05 & 7.71387e-06 \tabularnewline
60 & 0.532403 & 0.935195 & 0.467597 \tabularnewline
61 & 0.016175 & 0.0323501 & 0.983825 \tabularnewline
62 & 0.195327 & 0.390654 & 0.804673 \tabularnewline
63 & 0.999997 & 6.44901e-06 & 3.2245e-06 \tabularnewline
64 & 7.61155e-13 & 1.52231e-12 & 1 \tabularnewline
65 & 1 & 1.02489e-10 & 5.12444e-11 \tabularnewline
66 & 1 & 1.21007e-17 & 6.05037e-18 \tabularnewline
67 & 0.999975 & 4.92545e-05 & 2.46273e-05 \tabularnewline
68 & 0.956373 & 0.0872533 & 0.0436266 \tabularnewline
69 & 0.000797383 & 0.00159477 & 0.999203 \tabularnewline
70 & 1 & 3.56535e-14 & 1.78267e-14 \tabularnewline
71 & 0.387648 & 0.775296 & 0.612352 \tabularnewline
72 & 0.998818 & 0.00236314 & 0.00118157 \tabularnewline
73 & 0.972688 & 0.0546236 & 0.0273118 \tabularnewline
74 & 1 & 8.59876e-08 & 4.29938e-08 \tabularnewline
75 & 1 & 3.0228e-13 & 1.5114e-13 \tabularnewline
76 & 0.0772991 & 0.154598 & 0.922701 \tabularnewline
77 & 1 & 1.00041e-10 & 5.00206e-11 \tabularnewline
78 & 2.71432e-28 & 5.42863e-28 & 1 \tabularnewline
79 & 0.999999 & 1.65275e-06 & 8.26373e-07 \tabularnewline
80 & 1 & 5.05384e-07 & 2.52692e-07 \tabularnewline
81 & 0.99988 & 0.000240855 & 0.000120428 \tabularnewline
82 & 0.999856 & 0.000288843 & 0.000144422 \tabularnewline
83 & 0.821647 & 0.356707 & 0.178353 \tabularnewline
84 & 0.000764945 & 0.00152989 & 0.999235 \tabularnewline
85 & 0.985342 & 0.0293165 & 0.0146582 \tabularnewline
86 & 1 & 3.11242e-19 & 1.55621e-19 \tabularnewline
87 & 0.932808 & 0.134384 & 0.0671918 \tabularnewline
88 & 0.999849 & 0.000302741 & 0.000151371 \tabularnewline
89 & 1 & 1.09009e-07 & 5.45046e-08 \tabularnewline
90 & 1 & 6.20098e-15 & 3.10049e-15 \tabularnewline
91 & 0.999575 & 0.000850553 & 0.000425276 \tabularnewline
92 & 1 & 1.3068e-08 & 6.534e-09 \tabularnewline
93 & 1 & 2.31777e-22 & 1.15888e-22 \tabularnewline
94 & 1 & 8.55474e-10 & 4.27737e-10 \tabularnewline
95 & 1 & 1.88411e-09 & 9.42053e-10 \tabularnewline
96 & 1 & 2.26334e-08 & 1.13167e-08 \tabularnewline
97 & 1 & 1.7556e-12 & 8.778e-13 \tabularnewline
98 & 0.999998 & 3.89998e-06 & 1.94999e-06 \tabularnewline
99 & 1 & 4.14946e-07 & 2.07473e-07 \tabularnewline
100 & 0.999968 & 6.48867e-05 & 3.24434e-05 \tabularnewline
101 & 0.999717 & 0.000565025 & 0.000282513 \tabularnewline
102 & 0.999989 & 2.21297e-05 & 1.10649e-05 \tabularnewline
103 & 0.896054 & 0.207891 & 0.103946 \tabularnewline
104 & 0.999951 & 9.87594e-05 & 4.93797e-05 \tabularnewline
105 & 0.995852 & 0.00829574 & 0.00414787 \tabularnewline
106 & 0.983986 & 0.0320273 & 0.0160136 \tabularnewline
107 & 0.999704 & 0.000591588 & 0.000295794 \tabularnewline
108 & 0.915573 & 0.168853 & 0.0844267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&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]8[/C][C]0.0126392[/C][C]0.0252785[/C][C]0.987361[/C][/ROW]
[ROW][C]9[/C][C]0.00591505[/C][C]0.0118301[/C][C]0.994085[/C][/ROW]
[ROW][C]10[/C][C]0.0735974[/C][C]0.147195[/C][C]0.926403[/C][/ROW]
[ROW][C]11[/C][C]0.00125067[/C][C]0.00250134[/C][C]0.998749[/C][/ROW]
[ROW][C]12[/C][C]8.498e-05[/C][C]0.00016996[/C][C]0.999915[/C][/ROW]
[ROW][C]13[/C][C]0.0158556[/C][C]0.0317112[/C][C]0.984144[/C][/ROW]
[ROW][C]14[/C][C]0.00290029[/C][C]0.00580057[/C][C]0.9971[/C][/ROW]
[ROW][C]15[/C][C]0.755369[/C][C]0.489262[/C][C]0.244631[/C][/ROW]
[ROW][C]16[/C][C]0.02103[/C][C]0.0420601[/C][C]0.97897[/C][/ROW]
[ROW][C]17[/C][C]0.00490551[/C][C]0.00981103[/C][C]0.995094[/C][/ROW]
[ROW][C]18[/C][C]0.742455[/C][C]0.51509[/C][C]0.257545[/C][/ROW]
[ROW][C]19[/C][C]0.841662[/C][C]0.316675[/C][C]0.158338[/C][/ROW]
[ROW][C]20[/C][C]0.0421568[/C][C]0.0843137[/C][C]0.957843[/C][/ROW]
[ROW][C]21[/C][C]0.721624[/C][C]0.556751[/C][C]0.278376[/C][/ROW]
[ROW][C]22[/C][C]4.94424e-05[/C][C]9.88849e-05[/C][C]0.999951[/C][/ROW]
[ROW][C]23[/C][C]8.18006e-07[/C][C]1.63601e-06[/C][C]0.999999[/C][/ROW]
[ROW][C]24[/C][C]4.32966e-08[/C][C]8.65931e-08[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]0.738281[/C][C]0.523437[/C][C]0.261719[/C][/ROW]
[ROW][C]26[/C][C]0.842634[/C][C]0.314733[/C][C]0.157366[/C][/ROW]
[ROW][C]27[/C][C]0.0105565[/C][C]0.0211129[/C][C]0.989444[/C][/ROW]
[ROW][C]28[/C][C]0.659273[/C][C]0.681455[/C][C]0.340727[/C][/ROW]
[ROW][C]29[/C][C]1.10493e-06[/C][C]2.20985e-06[/C][C]0.999999[/C][/ROW]
[ROW][C]30[/C][C]0.999999[/C][C]1.52934e-06[/C][C]7.64669e-07[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]2.87801e-28[/C][C]1.439e-28[/C][/ROW]
[ROW][C]32[/C][C]0.999905[/C][C]0.000189073[/C][C]9.45364e-05[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]9.47106e-12[/C][C]4.73553e-12[/C][/ROW]
[ROW][C]34[/C][C]7.63602e-05[/C][C]0.00015272[/C][C]0.999924[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.06916e-21[/C][C]5.34582e-22[/C][/ROW]
[ROW][C]36[/C][C]1.64358e-06[/C][C]3.28716e-06[/C][C]0.999998[/C][/ROW]
[ROW][C]37[/C][C]2.40098e-10[/C][C]4.80196e-10[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0.000799458[/C][C]0.00159892[/C][C]0.999201[/C][/ROW]
[ROW][C]39[/C][C]0.0288187[/C][C]0.0576374[/C][C]0.971181[/C][/ROW]
[ROW][C]40[/C][C]2.68964e-07[/C][C]5.37927e-07[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.80059e-09[/C][C]5.60118e-09[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]5.94747e-38[/C][C]2.97374e-38[/C][/ROW]
[ROW][C]43[/C][C]0.0250255[/C][C]0.0500509[/C][C]0.974975[/C][/ROW]
[ROW][C]44[/C][C]0.000287895[/C][C]0.00057579[/C][C]0.999712[/C][/ROW]
[ROW][C]45[/C][C]0.00638506[/C][C]0.0127701[/C][C]0.993615[/C][/ROW]
[ROW][C]46[/C][C]0.851192[/C][C]0.297617[/C][C]0.148808[/C][/ROW]
[ROW][C]47[/C][C]0.958791[/C][C]0.0824175[/C][C]0.0412088[/C][/ROW]
[ROW][C]48[/C][C]4.27271e-11[/C][C]8.54542e-11[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0.504425[/C][C]0.991149[/C][C]0.495575[/C][/ROW]
[ROW][C]50[/C][C]2.38779e-06[/C][C]4.77558e-06[/C][C]0.999998[/C][/ROW]
[ROW][C]51[/C][C]0.94066[/C][C]0.118679[/C][C]0.0593397[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]5.72472e-09[/C][C]2.86236e-09[/C][/ROW]
[ROW][C]53[/C][C]0.999893[/C][C]0.000213446[/C][C]0.000106723[/C][/ROW]
[ROW][C]54[/C][C]1.57238e-06[/C][C]3.14475e-06[/C][C]0.999998[/C][/ROW]
[ROW][C]55[/C][C]1.26729e-05[/C][C]2.53457e-05[/C][C]0.999987[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]2.37987e-14[/C][C]1.18993e-14[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]3.65405e-13[/C][C]1.82703e-13[/C][/ROW]
[ROW][C]58[/C][C]0.876297[/C][C]0.247406[/C][C]0.123703[/C][/ROW]
[ROW][C]59[/C][C]0.999992[/C][C]1.54277e-05[/C][C]7.71387e-06[/C][/ROW]
[ROW][C]60[/C][C]0.532403[/C][C]0.935195[/C][C]0.467597[/C][/ROW]
[ROW][C]61[/C][C]0.016175[/C][C]0.0323501[/C][C]0.983825[/C][/ROW]
[ROW][C]62[/C][C]0.195327[/C][C]0.390654[/C][C]0.804673[/C][/ROW]
[ROW][C]63[/C][C]0.999997[/C][C]6.44901e-06[/C][C]3.2245e-06[/C][/ROW]
[ROW][C]64[/C][C]7.61155e-13[/C][C]1.52231e-12[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.02489e-10[/C][C]5.12444e-11[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.21007e-17[/C][C]6.05037e-18[/C][/ROW]
[ROW][C]67[/C][C]0.999975[/C][C]4.92545e-05[/C][C]2.46273e-05[/C][/ROW]
[ROW][C]68[/C][C]0.956373[/C][C]0.0872533[/C][C]0.0436266[/C][/ROW]
[ROW][C]69[/C][C]0.000797383[/C][C]0.00159477[/C][C]0.999203[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]3.56535e-14[/C][C]1.78267e-14[/C][/ROW]
[ROW][C]71[/C][C]0.387648[/C][C]0.775296[/C][C]0.612352[/C][/ROW]
[ROW][C]72[/C][C]0.998818[/C][C]0.00236314[/C][C]0.00118157[/C][/ROW]
[ROW][C]73[/C][C]0.972688[/C][C]0.0546236[/C][C]0.0273118[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]8.59876e-08[/C][C]4.29938e-08[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]3.0228e-13[/C][C]1.5114e-13[/C][/ROW]
[ROW][C]76[/C][C]0.0772991[/C][C]0.154598[/C][C]0.922701[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.00041e-10[/C][C]5.00206e-11[/C][/ROW]
[ROW][C]78[/C][C]2.71432e-28[/C][C]5.42863e-28[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0.999999[/C][C]1.65275e-06[/C][C]8.26373e-07[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]5.05384e-07[/C][C]2.52692e-07[/C][/ROW]
[ROW][C]81[/C][C]0.99988[/C][C]0.000240855[/C][C]0.000120428[/C][/ROW]
[ROW][C]82[/C][C]0.999856[/C][C]0.000288843[/C][C]0.000144422[/C][/ROW]
[ROW][C]83[/C][C]0.821647[/C][C]0.356707[/C][C]0.178353[/C][/ROW]
[ROW][C]84[/C][C]0.000764945[/C][C]0.00152989[/C][C]0.999235[/C][/ROW]
[ROW][C]85[/C][C]0.985342[/C][C]0.0293165[/C][C]0.0146582[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]3.11242e-19[/C][C]1.55621e-19[/C][/ROW]
[ROW][C]87[/C][C]0.932808[/C][C]0.134384[/C][C]0.0671918[/C][/ROW]
[ROW][C]88[/C][C]0.999849[/C][C]0.000302741[/C][C]0.000151371[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.09009e-07[/C][C]5.45046e-08[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]6.20098e-15[/C][C]3.10049e-15[/C][/ROW]
[ROW][C]91[/C][C]0.999575[/C][C]0.000850553[/C][C]0.000425276[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.3068e-08[/C][C]6.534e-09[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]2.31777e-22[/C][C]1.15888e-22[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]8.55474e-10[/C][C]4.27737e-10[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.88411e-09[/C][C]9.42053e-10[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]2.26334e-08[/C][C]1.13167e-08[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.7556e-12[/C][C]8.778e-13[/C][/ROW]
[ROW][C]98[/C][C]0.999998[/C][C]3.89998e-06[/C][C]1.94999e-06[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]4.14946e-07[/C][C]2.07473e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999968[/C][C]6.48867e-05[/C][C]3.24434e-05[/C][/ROW]
[ROW][C]101[/C][C]0.999717[/C][C]0.000565025[/C][C]0.000282513[/C][/ROW]
[ROW][C]102[/C][C]0.999989[/C][C]2.21297e-05[/C][C]1.10649e-05[/C][/ROW]
[ROW][C]103[/C][C]0.896054[/C][C]0.207891[/C][C]0.103946[/C][/ROW]
[ROW][C]104[/C][C]0.999951[/C][C]9.87594e-05[/C][C]4.93797e-05[/C][/ROW]
[ROW][C]105[/C][C]0.995852[/C][C]0.00829574[/C][C]0.00414787[/C][/ROW]
[ROW][C]106[/C][C]0.983986[/C][C]0.0320273[/C][C]0.0160136[/C][/ROW]
[ROW][C]107[/C][C]0.999704[/C][C]0.000591588[/C][C]0.000295794[/C][/ROW]
[ROW][C]108[/C][C]0.915573[/C][C]0.168853[/C][C]0.0844267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.01263920.02527850.987361
90.005915050.01183010.994085
100.07359740.1471950.926403
110.001250670.002501340.998749
128.498e-050.000169960.999915
130.01585560.03171120.984144
140.002900290.005800570.9971
150.7553690.4892620.244631
160.021030.04206010.97897
170.004905510.009811030.995094
180.7424550.515090.257545
190.8416620.3166750.158338
200.04215680.08431370.957843
210.7216240.5567510.278376
224.94424e-059.88849e-050.999951
238.18006e-071.63601e-060.999999
244.32966e-088.65931e-081
250.7382810.5234370.261719
260.8426340.3147330.157366
270.01055650.02111290.989444
280.6592730.6814550.340727
291.10493e-062.20985e-060.999999
300.9999991.52934e-067.64669e-07
3112.87801e-281.439e-28
320.9999050.0001890739.45364e-05
3319.47106e-124.73553e-12
347.63602e-050.000152720.999924
3511.06916e-215.34582e-22
361.64358e-063.28716e-060.999998
372.40098e-104.80196e-101
380.0007994580.001598920.999201
390.02881870.05763740.971181
402.68964e-075.37927e-071
412.80059e-095.60118e-091
4215.94747e-382.97374e-38
430.02502550.05005090.974975
440.0002878950.000575790.999712
450.006385060.01277010.993615
460.8511920.2976170.148808
470.9587910.08241750.0412088
484.27271e-118.54542e-111
490.5044250.9911490.495575
502.38779e-064.77558e-060.999998
510.940660.1186790.0593397
5215.72472e-092.86236e-09
530.9998930.0002134460.000106723
541.57238e-063.14475e-060.999998
551.26729e-052.53457e-050.999987
5612.37987e-141.18993e-14
5713.65405e-131.82703e-13
580.8762970.2474060.123703
590.9999921.54277e-057.71387e-06
600.5324030.9351950.467597
610.0161750.03235010.983825
620.1953270.3906540.804673
630.9999976.44901e-063.2245e-06
647.61155e-131.52231e-121
6511.02489e-105.12444e-11
6611.21007e-176.05037e-18
670.9999754.92545e-052.46273e-05
680.9563730.08725330.0436266
690.0007973830.001594770.999203
7013.56535e-141.78267e-14
710.3876480.7752960.612352
720.9988180.002363140.00118157
730.9726880.05462360.0273118
7418.59876e-084.29938e-08
7513.0228e-131.5114e-13
760.07729910.1545980.922701
7711.00041e-105.00206e-11
782.71432e-285.42863e-281
790.9999991.65275e-068.26373e-07
8015.05384e-072.52692e-07
810.999880.0002408550.000120428
820.9998560.0002888430.000144422
830.8216470.3567070.178353
840.0007649450.001529890.999235
850.9853420.02931650.0146582
8613.11242e-191.55621e-19
870.9328080.1343840.0671918
880.9998490.0003027410.000151371
8911.09009e-075.45046e-08
9016.20098e-153.10049e-15
910.9995750.0008505530.000425276
9211.3068e-086.534e-09
9312.31777e-221.15888e-22
9418.55474e-104.27737e-10
9511.88411e-099.42053e-10
9612.26334e-081.13167e-08
9711.7556e-128.778e-13
980.9999983.89998e-061.94999e-06
9914.14946e-072.07473e-07
1000.9999686.48867e-053.24434e-05
1010.9997170.0005650250.000282513
1020.9999892.21297e-051.10649e-05
1030.8960540.2078910.103946
1040.9999519.87594e-054.93797e-05
1050.9958520.008295740.00414787
1060.9839860.03202730.0160136
1070.9997040.0005915880.000295794
1080.9155730.1688530.0844267






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level660.653465NOK
5% type I error level750.742574NOK
10% type I error level810.80198NOK

\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 & 66 & 0.653465 & NOK \tabularnewline
5% type I error level & 75 & 0.742574 & NOK \tabularnewline
10% type I error level & 81 & 0.80198 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=262689&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]66[/C][C]0.653465[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]75[/C][C]0.742574[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]81[/C][C]0.80198[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=262689&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=262689&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level660.653465NOK
5% type I error level750.742574NOK
10% type I error level810.80198NOK


Figures (Output of Computation)

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Input Parameters & R Code

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