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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 computationSun, 14 Dec 2014 16:30:53 +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/14/t141857468475p25ujd4rylx0j.htm/, Retrieved Thu, 16 May 2024 11:36:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267741, Retrieved Thu, 16 May 2024 11:36:01 +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] [] [2014-12-14 16:30:53] [04df4205f362f56e0d1a9032a00a5d3d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149	96	18	68	7,5
152	75	7	55	2,5
139	70	31	39	6,0
148	88	39	32	6,5
158	114	46	62	1,0
128	69	31	33	1,0
224	176	67	52	5,5
159	114	35	62	8,5
105	121	52	77	6,5
159	110	77	76	4,5
167	158	37	41	2,0
165	116	32	48	5,0
159	181	36	63	0,5
119	77	38	30	5,0
176	141	69	78	5,0
54	35	21	19	2,5
163	152	54	66	5,5
124	97	36	35	3,5
121	84	23	45	4,0
153	68	34	21	0,5
148	101	112	25	6,5
221	107	35	44	4,5
188	88	47	69	7,5
149	112	47	54	5,5
244	171	37	74	4,0
150	66	20	61	4,0
153	93	22	41	5,5
94	105	23	46	2,5
156	131	32	39	5,5
146	89	7	63	0,5
132	102	30	34	3,5
161	161	92	51	2,5
105	120	43	42	4,5
97	127	55	31	4,5
151	77	16	39	4,5
166	85	71	49	2,5
157	168	43	53	5,0
111	48	29	31	0,0
145	152	56	39	5,0
162	75	46	54	6,5
163	107	19	49	5,0
187	121	59	46	4,5
109	124	30	55	5,5
105	40	7	50	7,5
148	126	19	30	5,0
125	148	48	45	7,0
116	146	23	35	4,5
138	97	33	41	8,5
164	118	34	73	3,5
162	58	48	17	6,0
99	63	18	40	1,5
202	139	43	64	9,0
186	50	33	37	3,5
183	152	71	65	4,0
214	142	26	100	6,5
188	94	67	28	7,5
177	127	80	56	5,0
126	67	29	29	5,5
157	96	58	52	1,0
139	128	32	50	6,5
78	41	47	3	NA
162	146	43	59	6,5
159	186	29	61	7,0
110	85	32	51	1,5
48	41	23	12	0,5
50	146	16	45	7,5
150	182	33	37	9
154	192	32	37	9,5
194	439	52	68	8
158	214	75	72	10
159	341	72	143	7
67	58	15	9	8,5
147	292	29	55	9
39	85	13	17	9,5
100	200	40	37	4
111	158	19	27	6
138	199	24	37	8
101	297	121	58	5,5
101	108	36	21	7,5
114	86	23	19	7
165	302	85	78	7,5
114	148	41	35	8
111	178	46	48	7
75	120	18	27	7
82	207	35	43	6
121	157	17	30	10
32	128	4	25	2,5
150	296	28	69	9
117	323	44	72	8
165	70	38	13	8,5
154	146	57	61	6
126	246	23	43	9
138	145	26	22	8
149	196	36	51	8
145	199	22	67	9
120	127	40	36	5,5
138	91	18	21	5
109	153	31	44	7
132	299	11	45	5,5
172	228	38	34	9
169	190	24	36	2
114	180	37	72	8,5
156	212	37	39	9
172	269	22	43	8,5
167	243	43	80	10
113	190	31	40	9
173	157	31	61	8
2	96	-4	23	10
165	222	21	29	7,5
165	222	21	29	7,5
118	165	32	54	6
158	249	26	43	10
49	122	32	20	3
155	274	33	61	10
151	268	30	57	5,5
220	255	67	54	10
141	132	22	36	6
122	92	33	16	5
44	171	24	40	4,5
152	117	28	27	7,5
107	219	41	61	5
154	279	31	69	8
103	148	33	34	5,5
154	130	41	21	7,5
175	181	21	34	9,5
143	234	52	34	8,5
110	85	29	13	6,5
131	66	11	12	6,5
167	236	26	51	10,5
137	135	7	19	8
121	218	13	81	10
149	199	20	42	9,5
168	112	52	22	9
140	278	28	85	10
168	113	39	25	4,5
94	84	9	22	4,5
51	86	19	19	0,5
145	222	60	45	4,5
66	167	19	45	5,5
109	207	14	51	6
128	85	24	24	8,5
164	237	-2	73	8,5
119	102	51	24	5,5
126	221	2	61	7
132	128	24	23	5
142	91	40	14	3,5
83	198	20	54	5
166	138	20	36	5
93	196	25	26	1,5
117	139	38	30	8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&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]5 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=267741&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 3.40625 + 0.00893793LFM[t] + 0.0182704B[t] -0.0184326PRH[t] -0.0162262CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  3.40625 +  0.00893793LFM[t] +  0.0182704B[t] -0.0184326PRH[t] -0.0162262CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  3.40625 +  0.00893793LFM[t] +  0.0182704B[t] -0.0184326PRH[t] -0.0162262CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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
Ex[t] = + 3.40625 + 0.00893793LFM[t] + 0.0182704B[t] -0.0184326PRH[t] -0.0162262CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.406250.72974.6686.89694e-063.44847e-06
LFM0.008937930.005369661.6650.0981820.049091
B0.01827040.003028926.0321.30107e-086.50533e-09
PRH-0.01843260.0101082-1.8240.07029620.0351481
CH-0.01622620.0114278-1.420.1578010.0789007

\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) & 3.40625 & 0.7297 & 4.668 & 6.89694e-06 & 3.44847e-06 \tabularnewline
LFM & 0.00893793 & 0.00536966 & 1.665 & 0.098182 & 0.049091 \tabularnewline
B & 0.0182704 & 0.00302892 & 6.032 & 1.30107e-08 & 6.50533e-09 \tabularnewline
PRH & -0.0184326 & 0.0101082 & -1.824 & 0.0702962 & 0.0351481 \tabularnewline
CH & -0.0162262 & 0.0114278 & -1.42 & 0.157801 & 0.0789007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&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]3.40625[/C][C]0.7297[/C][C]4.668[/C][C]6.89694e-06[/C][C]3.44847e-06[/C][/ROW]
[ROW][C]LFM[/C][C]0.00893793[/C][C]0.00536966[/C][C]1.665[/C][C]0.098182[/C][C]0.049091[/C][/ROW]
[ROW][C]B[/C][C]0.0182704[/C][C]0.00302892[/C][C]6.032[/C][C]1.30107e-08[/C][C]6.50533e-09[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0184326[/C][C]0.0101082[/C][C]-1.824[/C][C]0.0702962[/C][C]0.0351481[/C][/ROW]
[ROW][C]CH[/C][C]-0.0162262[/C][C]0.0114278[/C][C]-1.42[/C][C]0.157801[/C][C]0.0789007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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)3.406250.72974.6686.89694e-063.44847e-06
LFM0.008937930.005369661.6650.0981820.049091
B0.01827040.003028926.0321.30107e-086.50533e-09
PRH-0.01843260.0101082-1.8240.07029620.0351481
CH-0.01622620.0114278-1.420.1578010.0789007






Multiple Linear Regression - Regression Statistics
Multiple R0.485306
R-squared0.235522
Adjusted R-squared0.214287
F-TEST (value)11.091
F-TEST (DF numerator)4
F-TEST (DF denominator)144
p-value7.18664e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26397
Sum Squared Residuals738.083

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.485306 \tabularnewline
R-squared & 0.235522 \tabularnewline
Adjusted R-squared & 0.214287 \tabularnewline
F-TEST (value) & 11.091 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 7.18664e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.26397 \tabularnewline
Sum Squared Residuals & 738.083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.485306[/C][/ROW]
[ROW][C]R-squared[/C][C]0.235522[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.214287[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.091[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/C][/ROW]
[ROW][C]p-value[/C][C]7.18664e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.26397[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]738.083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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 R0.485306
R-squared0.235522
Adjusted R-squared0.214287
F-TEST (value)11.091
F-TEST (DF numerator)4
F-TEST (DF denominator)144
p-value7.18664e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26397
Sum Squared Residuals738.083






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.55.056792.44321
22.55.11362-2.61362
364.723311.27669
46.55.098741.40126
515.04734-4.04734
614.70408-3.70408
75.56.54518-1.04518
88.55.259033.24097
96.54.347532.15247
104.54.184620.315385
1126.43832-4.43832
1255.63167-0.631666
130.56.44849-5.94849
1454.689450.310547
1555.01795-0.0179496
162.53.83297-1.33297
175.55.57393-0.073934
183.55.05528-1.55528
1944.86832-0.868318
200.55.04868-4.54868
216.54.104262.39574
224.55.97736-1.47736
237.54.708432.79157
245.55.041730.458269
2546.82859-2.82859
2644.59433-0.594331
275.55.40210.0978977
282.54.99445-2.49445
295.55.97132-0.471316
300.55.18597-4.68597
313.55.34496-1.84496
322.55.26344-2.76344
334.55.06307-0.56307
344.55.07676-0.576755
354.55.23495-0.734949
362.54.33912-1.83912
3756.22633-1.22633
3804.23778-4.23778
3955.81429-0.814293
406.54.500351.99965
4155.67276-0.672755
424.55.45442-0.954424
435.55.200590.299413
447.54.135213.36479
4556.19412-1.19412
4675.612561.38744
474.56.11865-1.61865
488.55.138363.36164
493.55.21675-1.71675
5064.753261.24674
511.54.4613-2.9613
5295.920213.07979
533.54.77357-1.27357
5445.45556-1.45556
556.55.811490.688511
567.55.114672.38533
5754.925320.0746806
585.54.751430.748565
5914.6506-3.6506
606.55.586070.913928
61NANA0.728284
626.56.201320.298679
63710.025-3.02502
641.54.96569-3.46569
650.5-1.504482.00448
667.55.36352.1365
6796.600382.39962
689.512.599-3.09901
6984.177563.82244
701010.4101-0.410077
7173.142243.85776
728.58.128080.371919
7394.292344.70766
749.512.1164-2.61644
7544.49675-0.496747
7665.232730.76727
7789.06381-1.06381
785.53.277852.22215
797.55.764171.73583
8077.06624-0.0662374
817.55.305532.19447
8287.023720.976277
8375.499141.50086
8477.57825-0.578253
8562.556043.44396
861013.0515-3.05148
872.52.019240.480758
8899.37399-0.373989
8984.748553.25145
908.57.90970.590297
9164.905261.09474
9297.452661.54734
9386.827881.17212
9485.845382.15462
9598.977690.0223139
965.56.12975-0.629747
9753.890481.10952
98710.616-3.61595
995.54.357081.14292
100914.3616-5.3616
1012-0.6364572.63646
1028.56.859051.64095
10399.25505-0.255053
1048.55.747882.75212
105107.667142.33286
10697.259751.74025
10782.878615.12139
1081010.5794-0.57938
1097.58.07938-0.57938
1107.57.50947-0.00947375
11164.190781.80922
1121012.1588-2.15882
11331.199631.80037
1141012.6745-2.67446
1155.53.420332.07967
1161010.0885-0.0885219
11766.30965-0.309651
11856.33232-1.33232
1194.52.948221.55178
1207.59.11828-1.61828
12155.1891-0.1891
12288.3709-0.3709
1235.54.061351.43865
1247.55.338542.16146
1259.58.449451.05055
1268.57.196911.30309
1276.55.385491.11451
1286.53.90392.5961
12910.59.159921.34008
13084.916733.08327
131107.823652.17635
1329.56.138633.36137
13396.841382.15862
1341011.3478-1.34784
1354.55.25825-0.758253
1364.58.77481-4.27481
1370.52.92213-2.42213
1384.54.9669-0.466902
1395.56.57685-1.07685
14062.771473.22853
1418.58.05450.445504
1428.58.003940.496056
1435.56.04351-0.543513
14478.10907-1.10907
14556.87356-1.87356
1463.55.02076-1.52076
14756.45846-1.45846
148510.4358-5.43577
1491.5-0.6956612.19566
1508NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 5.05679 & 2.44321 \tabularnewline
2 & 2.5 & 5.11362 & -2.61362 \tabularnewline
3 & 6 & 4.72331 & 1.27669 \tabularnewline
4 & 6.5 & 5.09874 & 1.40126 \tabularnewline
5 & 1 & 5.04734 & -4.04734 \tabularnewline
6 & 1 & 4.70408 & -3.70408 \tabularnewline
7 & 5.5 & 6.54518 & -1.04518 \tabularnewline
8 & 8.5 & 5.25903 & 3.24097 \tabularnewline
9 & 6.5 & 4.34753 & 2.15247 \tabularnewline
10 & 4.5 & 4.18462 & 0.315385 \tabularnewline
11 & 2 & 6.43832 & -4.43832 \tabularnewline
12 & 5 & 5.63167 & -0.631666 \tabularnewline
13 & 0.5 & 6.44849 & -5.94849 \tabularnewline
14 & 5 & 4.68945 & 0.310547 \tabularnewline
15 & 5 & 5.01795 & -0.0179496 \tabularnewline
16 & 2.5 & 3.83297 & -1.33297 \tabularnewline
17 & 5.5 & 5.57393 & -0.073934 \tabularnewline
18 & 3.5 & 5.05528 & -1.55528 \tabularnewline
19 & 4 & 4.86832 & -0.868318 \tabularnewline
20 & 0.5 & 5.04868 & -4.54868 \tabularnewline
21 & 6.5 & 4.10426 & 2.39574 \tabularnewline
22 & 4.5 & 5.97736 & -1.47736 \tabularnewline
23 & 7.5 & 4.70843 & 2.79157 \tabularnewline
24 & 5.5 & 5.04173 & 0.458269 \tabularnewline
25 & 4 & 6.82859 & -2.82859 \tabularnewline
26 & 4 & 4.59433 & -0.594331 \tabularnewline
27 & 5.5 & 5.4021 & 0.0978977 \tabularnewline
28 & 2.5 & 4.99445 & -2.49445 \tabularnewline
29 & 5.5 & 5.97132 & -0.471316 \tabularnewline
30 & 0.5 & 5.18597 & -4.68597 \tabularnewline
31 & 3.5 & 5.34496 & -1.84496 \tabularnewline
32 & 2.5 & 5.26344 & -2.76344 \tabularnewline
33 & 4.5 & 5.06307 & -0.56307 \tabularnewline
34 & 4.5 & 5.07676 & -0.576755 \tabularnewline
35 & 4.5 & 5.23495 & -0.734949 \tabularnewline
36 & 2.5 & 4.33912 & -1.83912 \tabularnewline
37 & 5 & 6.22633 & -1.22633 \tabularnewline
38 & 0 & 4.23778 & -4.23778 \tabularnewline
39 & 5 & 5.81429 & -0.814293 \tabularnewline
40 & 6.5 & 4.50035 & 1.99965 \tabularnewline
41 & 5 & 5.67276 & -0.672755 \tabularnewline
42 & 4.5 & 5.45442 & -0.954424 \tabularnewline
43 & 5.5 & 5.20059 & 0.299413 \tabularnewline
44 & 7.5 & 4.13521 & 3.36479 \tabularnewline
45 & 5 & 6.19412 & -1.19412 \tabularnewline
46 & 7 & 5.61256 & 1.38744 \tabularnewline
47 & 4.5 & 6.11865 & -1.61865 \tabularnewline
48 & 8.5 & 5.13836 & 3.36164 \tabularnewline
49 & 3.5 & 5.21675 & -1.71675 \tabularnewline
50 & 6 & 4.75326 & 1.24674 \tabularnewline
51 & 1.5 & 4.4613 & -2.9613 \tabularnewline
52 & 9 & 5.92021 & 3.07979 \tabularnewline
53 & 3.5 & 4.77357 & -1.27357 \tabularnewline
54 & 4 & 5.45556 & -1.45556 \tabularnewline
55 & 6.5 & 5.81149 & 0.688511 \tabularnewline
56 & 7.5 & 5.11467 & 2.38533 \tabularnewline
57 & 5 & 4.92532 & 0.0746806 \tabularnewline
58 & 5.5 & 4.75143 & 0.748565 \tabularnewline
59 & 1 & 4.6506 & -3.6506 \tabularnewline
60 & 6.5 & 5.58607 & 0.913928 \tabularnewline
61 & NA & NA & 0.728284 \tabularnewline
62 & 6.5 & 6.20132 & 0.298679 \tabularnewline
63 & 7 & 10.025 & -3.02502 \tabularnewline
64 & 1.5 & 4.96569 & -3.46569 \tabularnewline
65 & 0.5 & -1.50448 & 2.00448 \tabularnewline
66 & 7.5 & 5.3635 & 2.1365 \tabularnewline
67 & 9 & 6.60038 & 2.39962 \tabularnewline
68 & 9.5 & 12.599 & -3.09901 \tabularnewline
69 & 8 & 4.17756 & 3.82244 \tabularnewline
70 & 10 & 10.4101 & -0.410077 \tabularnewline
71 & 7 & 3.14224 & 3.85776 \tabularnewline
72 & 8.5 & 8.12808 & 0.371919 \tabularnewline
73 & 9 & 4.29234 & 4.70766 \tabularnewline
74 & 9.5 & 12.1164 & -2.61644 \tabularnewline
75 & 4 & 4.49675 & -0.496747 \tabularnewline
76 & 6 & 5.23273 & 0.76727 \tabularnewline
77 & 8 & 9.06381 & -1.06381 \tabularnewline
78 & 5.5 & 3.27785 & 2.22215 \tabularnewline
79 & 7.5 & 5.76417 & 1.73583 \tabularnewline
80 & 7 & 7.06624 & -0.0662374 \tabularnewline
81 & 7.5 & 5.30553 & 2.19447 \tabularnewline
82 & 8 & 7.02372 & 0.976277 \tabularnewline
83 & 7 & 5.49914 & 1.50086 \tabularnewline
84 & 7 & 7.57825 & -0.578253 \tabularnewline
85 & 6 & 2.55604 & 3.44396 \tabularnewline
86 & 10 & 13.0515 & -3.05148 \tabularnewline
87 & 2.5 & 2.01924 & 0.480758 \tabularnewline
88 & 9 & 9.37399 & -0.373989 \tabularnewline
89 & 8 & 4.74855 & 3.25145 \tabularnewline
90 & 8.5 & 7.9097 & 0.590297 \tabularnewline
91 & 6 & 4.90526 & 1.09474 \tabularnewline
92 & 9 & 7.45266 & 1.54734 \tabularnewline
93 & 8 & 6.82788 & 1.17212 \tabularnewline
94 & 8 & 5.84538 & 2.15462 \tabularnewline
95 & 9 & 8.97769 & 0.0223139 \tabularnewline
96 & 5.5 & 6.12975 & -0.629747 \tabularnewline
97 & 5 & 3.89048 & 1.10952 \tabularnewline
98 & 7 & 10.616 & -3.61595 \tabularnewline
99 & 5.5 & 4.35708 & 1.14292 \tabularnewline
100 & 9 & 14.3616 & -5.3616 \tabularnewline
101 & 2 & -0.636457 & 2.63646 \tabularnewline
102 & 8.5 & 6.85905 & 1.64095 \tabularnewline
103 & 9 & 9.25505 & -0.255053 \tabularnewline
104 & 8.5 & 5.74788 & 2.75212 \tabularnewline
105 & 10 & 7.66714 & 2.33286 \tabularnewline
106 & 9 & 7.25975 & 1.74025 \tabularnewline
107 & 8 & 2.87861 & 5.12139 \tabularnewline
108 & 10 & 10.5794 & -0.57938 \tabularnewline
109 & 7.5 & 8.07938 & -0.57938 \tabularnewline
110 & 7.5 & 7.50947 & -0.00947375 \tabularnewline
111 & 6 & 4.19078 & 1.80922 \tabularnewline
112 & 10 & 12.1588 & -2.15882 \tabularnewline
113 & 3 & 1.19963 & 1.80037 \tabularnewline
114 & 10 & 12.6745 & -2.67446 \tabularnewline
115 & 5.5 & 3.42033 & 2.07967 \tabularnewline
116 & 10 & 10.0885 & -0.0885219 \tabularnewline
117 & 6 & 6.30965 & -0.309651 \tabularnewline
118 & 5 & 6.33232 & -1.33232 \tabularnewline
119 & 4.5 & 2.94822 & 1.55178 \tabularnewline
120 & 7.5 & 9.11828 & -1.61828 \tabularnewline
121 & 5 & 5.1891 & -0.1891 \tabularnewline
122 & 8 & 8.3709 & -0.3709 \tabularnewline
123 & 5.5 & 4.06135 & 1.43865 \tabularnewline
124 & 7.5 & 5.33854 & 2.16146 \tabularnewline
125 & 9.5 & 8.44945 & 1.05055 \tabularnewline
126 & 8.5 & 7.19691 & 1.30309 \tabularnewline
127 & 6.5 & 5.38549 & 1.11451 \tabularnewline
128 & 6.5 & 3.9039 & 2.5961 \tabularnewline
129 & 10.5 & 9.15992 & 1.34008 \tabularnewline
130 & 8 & 4.91673 & 3.08327 \tabularnewline
131 & 10 & 7.82365 & 2.17635 \tabularnewline
132 & 9.5 & 6.13863 & 3.36137 \tabularnewline
133 & 9 & 6.84138 & 2.15862 \tabularnewline
134 & 10 & 11.3478 & -1.34784 \tabularnewline
135 & 4.5 & 5.25825 & -0.758253 \tabularnewline
136 & 4.5 & 8.77481 & -4.27481 \tabularnewline
137 & 0.5 & 2.92213 & -2.42213 \tabularnewline
138 & 4.5 & 4.9669 & -0.466902 \tabularnewline
139 & 5.5 & 6.57685 & -1.07685 \tabularnewline
140 & 6 & 2.77147 & 3.22853 \tabularnewline
141 & 8.5 & 8.0545 & 0.445504 \tabularnewline
142 & 8.5 & 8.00394 & 0.496056 \tabularnewline
143 & 5.5 & 6.04351 & -0.543513 \tabularnewline
144 & 7 & 8.10907 & -1.10907 \tabularnewline
145 & 5 & 6.87356 & -1.87356 \tabularnewline
146 & 3.5 & 5.02076 & -1.52076 \tabularnewline
147 & 5 & 6.45846 & -1.45846 \tabularnewline
148 & 5 & 10.4358 & -5.43577 \tabularnewline
149 & 1.5 & -0.695661 & 2.19566 \tabularnewline
150 & 8 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&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]7.5[/C][C]5.05679[/C][C]2.44321[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]5.11362[/C][C]-2.61362[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]4.72331[/C][C]1.27669[/C][/ROW]
[ROW][C]4[/C][C]6.5[/C][C]5.09874[/C][C]1.40126[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]5.04734[/C][C]-4.04734[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]4.70408[/C][C]-3.70408[/C][/ROW]
[ROW][C]7[/C][C]5.5[/C][C]6.54518[/C][C]-1.04518[/C][/ROW]
[ROW][C]8[/C][C]8.5[/C][C]5.25903[/C][C]3.24097[/C][/ROW]
[ROW][C]9[/C][C]6.5[/C][C]4.34753[/C][C]2.15247[/C][/ROW]
[ROW][C]10[/C][C]4.5[/C][C]4.18462[/C][C]0.315385[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]6.43832[/C][C]-4.43832[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.63167[/C][C]-0.631666[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]6.44849[/C][C]-5.94849[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.68945[/C][C]0.310547[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]5.01795[/C][C]-0.0179496[/C][/ROW]
[ROW][C]16[/C][C]2.5[/C][C]3.83297[/C][C]-1.33297[/C][/ROW]
[ROW][C]17[/C][C]5.5[/C][C]5.57393[/C][C]-0.073934[/C][/ROW]
[ROW][C]18[/C][C]3.5[/C][C]5.05528[/C][C]-1.55528[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.86832[/C][C]-0.868318[/C][/ROW]
[ROW][C]20[/C][C]0.5[/C][C]5.04868[/C][C]-4.54868[/C][/ROW]
[ROW][C]21[/C][C]6.5[/C][C]4.10426[/C][C]2.39574[/C][/ROW]
[ROW][C]22[/C][C]4.5[/C][C]5.97736[/C][C]-1.47736[/C][/ROW]
[ROW][C]23[/C][C]7.5[/C][C]4.70843[/C][C]2.79157[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]5.04173[/C][C]0.458269[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]6.82859[/C][C]-2.82859[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.59433[/C][C]-0.594331[/C][/ROW]
[ROW][C]27[/C][C]5.5[/C][C]5.4021[/C][C]0.0978977[/C][/ROW]
[ROW][C]28[/C][C]2.5[/C][C]4.99445[/C][C]-2.49445[/C][/ROW]
[ROW][C]29[/C][C]5.5[/C][C]5.97132[/C][C]-0.471316[/C][/ROW]
[ROW][C]30[/C][C]0.5[/C][C]5.18597[/C][C]-4.68597[/C][/ROW]
[ROW][C]31[/C][C]3.5[/C][C]5.34496[/C][C]-1.84496[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]5.26344[/C][C]-2.76344[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]5.06307[/C][C]-0.56307[/C][/ROW]
[ROW][C]34[/C][C]4.5[/C][C]5.07676[/C][C]-0.576755[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]5.23495[/C][C]-0.734949[/C][/ROW]
[ROW][C]36[/C][C]2.5[/C][C]4.33912[/C][C]-1.83912[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]6.22633[/C][C]-1.22633[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]4.23778[/C][C]-4.23778[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]5.81429[/C][C]-0.814293[/C][/ROW]
[ROW][C]40[/C][C]6.5[/C][C]4.50035[/C][C]1.99965[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]5.67276[/C][C]-0.672755[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]5.45442[/C][C]-0.954424[/C][/ROW]
[ROW][C]43[/C][C]5.5[/C][C]5.20059[/C][C]0.299413[/C][/ROW]
[ROW][C]44[/C][C]7.5[/C][C]4.13521[/C][C]3.36479[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]6.19412[/C][C]-1.19412[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]5.61256[/C][C]1.38744[/C][/ROW]
[ROW][C]47[/C][C]4.5[/C][C]6.11865[/C][C]-1.61865[/C][/ROW]
[ROW][C]48[/C][C]8.5[/C][C]5.13836[/C][C]3.36164[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]5.21675[/C][C]-1.71675[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]4.75326[/C][C]1.24674[/C][/ROW]
[ROW][C]51[/C][C]1.5[/C][C]4.4613[/C][C]-2.9613[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]5.92021[/C][C]3.07979[/C][/ROW]
[ROW][C]53[/C][C]3.5[/C][C]4.77357[/C][C]-1.27357[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]5.45556[/C][C]-1.45556[/C][/ROW]
[ROW][C]55[/C][C]6.5[/C][C]5.81149[/C][C]0.688511[/C][/ROW]
[ROW][C]56[/C][C]7.5[/C][C]5.11467[/C][C]2.38533[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]4.92532[/C][C]0.0746806[/C][/ROW]
[ROW][C]58[/C][C]5.5[/C][C]4.75143[/C][C]0.748565[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]4.6506[/C][C]-3.6506[/C][/ROW]
[ROW][C]60[/C][C]6.5[/C][C]5.58607[/C][C]0.913928[/C][/ROW]
[ROW][C]61[/C][C]NA[/C][C]NA[/C][C]0.728284[/C][/ROW]
[ROW][C]62[/C][C]6.5[/C][C]6.20132[/C][C]0.298679[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]10.025[/C][C]-3.02502[/C][/ROW]
[ROW][C]64[/C][C]1.5[/C][C]4.96569[/C][C]-3.46569[/C][/ROW]
[ROW][C]65[/C][C]0.5[/C][C]-1.50448[/C][C]2.00448[/C][/ROW]
[ROW][C]66[/C][C]7.5[/C][C]5.3635[/C][C]2.1365[/C][/ROW]
[ROW][C]67[/C][C]9[/C][C]6.60038[/C][C]2.39962[/C][/ROW]
[ROW][C]68[/C][C]9.5[/C][C]12.599[/C][C]-3.09901[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]4.17756[/C][C]3.82244[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]10.4101[/C][C]-0.410077[/C][/ROW]
[ROW][C]71[/C][C]7[/C][C]3.14224[/C][C]3.85776[/C][/ROW]
[ROW][C]72[/C][C]8.5[/C][C]8.12808[/C][C]0.371919[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]4.29234[/C][C]4.70766[/C][/ROW]
[ROW][C]74[/C][C]9.5[/C][C]12.1164[/C][C]-2.61644[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]4.49675[/C][C]-0.496747[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]5.23273[/C][C]0.76727[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]9.06381[/C][C]-1.06381[/C][/ROW]
[ROW][C]78[/C][C]5.5[/C][C]3.27785[/C][C]2.22215[/C][/ROW]
[ROW][C]79[/C][C]7.5[/C][C]5.76417[/C][C]1.73583[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]7.06624[/C][C]-0.0662374[/C][/ROW]
[ROW][C]81[/C][C]7.5[/C][C]5.30553[/C][C]2.19447[/C][/ROW]
[ROW][C]82[/C][C]8[/C][C]7.02372[/C][C]0.976277[/C][/ROW]
[ROW][C]83[/C][C]7[/C][C]5.49914[/C][C]1.50086[/C][/ROW]
[ROW][C]84[/C][C]7[/C][C]7.57825[/C][C]-0.578253[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]2.55604[/C][C]3.44396[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]13.0515[/C][C]-3.05148[/C][/ROW]
[ROW][C]87[/C][C]2.5[/C][C]2.01924[/C][C]0.480758[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]9.37399[/C][C]-0.373989[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]4.74855[/C][C]3.25145[/C][/ROW]
[ROW][C]90[/C][C]8.5[/C][C]7.9097[/C][C]0.590297[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]4.90526[/C][C]1.09474[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]7.45266[/C][C]1.54734[/C][/ROW]
[ROW][C]93[/C][C]8[/C][C]6.82788[/C][C]1.17212[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]5.84538[/C][C]2.15462[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]8.97769[/C][C]0.0223139[/C][/ROW]
[ROW][C]96[/C][C]5.5[/C][C]6.12975[/C][C]-0.629747[/C][/ROW]
[ROW][C]97[/C][C]5[/C][C]3.89048[/C][C]1.10952[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]10.616[/C][C]-3.61595[/C][/ROW]
[ROW][C]99[/C][C]5.5[/C][C]4.35708[/C][C]1.14292[/C][/ROW]
[ROW][C]100[/C][C]9[/C][C]14.3616[/C][C]-5.3616[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]-0.636457[/C][C]2.63646[/C][/ROW]
[ROW][C]102[/C][C]8.5[/C][C]6.85905[/C][C]1.64095[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]9.25505[/C][C]-0.255053[/C][/ROW]
[ROW][C]104[/C][C]8.5[/C][C]5.74788[/C][C]2.75212[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]7.66714[/C][C]2.33286[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]7.25975[/C][C]1.74025[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]2.87861[/C][C]5.12139[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]10.5794[/C][C]-0.57938[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]8.07938[/C][C]-0.57938[/C][/ROW]
[ROW][C]110[/C][C]7.5[/C][C]7.50947[/C][C]-0.00947375[/C][/ROW]
[ROW][C]111[/C][C]6[/C][C]4.19078[/C][C]1.80922[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]12.1588[/C][C]-2.15882[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]1.19963[/C][C]1.80037[/C][/ROW]
[ROW][C]114[/C][C]10[/C][C]12.6745[/C][C]-2.67446[/C][/ROW]
[ROW][C]115[/C][C]5.5[/C][C]3.42033[/C][C]2.07967[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.0885[/C][C]-0.0885219[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]6.30965[/C][C]-0.309651[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]6.33232[/C][C]-1.33232[/C][/ROW]
[ROW][C]119[/C][C]4.5[/C][C]2.94822[/C][C]1.55178[/C][/ROW]
[ROW][C]120[/C][C]7.5[/C][C]9.11828[/C][C]-1.61828[/C][/ROW]
[ROW][C]121[/C][C]5[/C][C]5.1891[/C][C]-0.1891[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]8.3709[/C][C]-0.3709[/C][/ROW]
[ROW][C]123[/C][C]5.5[/C][C]4.06135[/C][C]1.43865[/C][/ROW]
[ROW][C]124[/C][C]7.5[/C][C]5.33854[/C][C]2.16146[/C][/ROW]
[ROW][C]125[/C][C]9.5[/C][C]8.44945[/C][C]1.05055[/C][/ROW]
[ROW][C]126[/C][C]8.5[/C][C]7.19691[/C][C]1.30309[/C][/ROW]
[ROW][C]127[/C][C]6.5[/C][C]5.38549[/C][C]1.11451[/C][/ROW]
[ROW][C]128[/C][C]6.5[/C][C]3.9039[/C][C]2.5961[/C][/ROW]
[ROW][C]129[/C][C]10.5[/C][C]9.15992[/C][C]1.34008[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]4.91673[/C][C]3.08327[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]7.82365[/C][C]2.17635[/C][/ROW]
[ROW][C]132[/C][C]9.5[/C][C]6.13863[/C][C]3.36137[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]6.84138[/C][C]2.15862[/C][/ROW]
[ROW][C]134[/C][C]10[/C][C]11.3478[/C][C]-1.34784[/C][/ROW]
[ROW][C]135[/C][C]4.5[/C][C]5.25825[/C][C]-0.758253[/C][/ROW]
[ROW][C]136[/C][C]4.5[/C][C]8.77481[/C][C]-4.27481[/C][/ROW]
[ROW][C]137[/C][C]0.5[/C][C]2.92213[/C][C]-2.42213[/C][/ROW]
[ROW][C]138[/C][C]4.5[/C][C]4.9669[/C][C]-0.466902[/C][/ROW]
[ROW][C]139[/C][C]5.5[/C][C]6.57685[/C][C]-1.07685[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]2.77147[/C][C]3.22853[/C][/ROW]
[ROW][C]141[/C][C]8.5[/C][C]8.0545[/C][C]0.445504[/C][/ROW]
[ROW][C]142[/C][C]8.5[/C][C]8.00394[/C][C]0.496056[/C][/ROW]
[ROW][C]143[/C][C]5.5[/C][C]6.04351[/C][C]-0.543513[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]8.10907[/C][C]-1.10907[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]6.87356[/C][C]-1.87356[/C][/ROW]
[ROW][C]146[/C][C]3.5[/C][C]5.02076[/C][C]-1.52076[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]6.45846[/C][C]-1.45846[/C][/ROW]
[ROW][C]148[/C][C]5[/C][C]10.4358[/C][C]-5.43577[/C][/ROW]
[ROW][C]149[/C][C]1.5[/C][C]-0.695661[/C][C]2.19566[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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
17.55.056792.44321
22.55.11362-2.61362
364.723311.27669
46.55.098741.40126
515.04734-4.04734
614.70408-3.70408
75.56.54518-1.04518
88.55.259033.24097
96.54.347532.15247
104.54.184620.315385
1126.43832-4.43832
1255.63167-0.631666
130.56.44849-5.94849
1454.689450.310547
1555.01795-0.0179496
162.53.83297-1.33297
175.55.57393-0.073934
183.55.05528-1.55528
1944.86832-0.868318
200.55.04868-4.54868
216.54.104262.39574
224.55.97736-1.47736
237.54.708432.79157
245.55.041730.458269
2546.82859-2.82859
2644.59433-0.594331
275.55.40210.0978977
282.54.99445-2.49445
295.55.97132-0.471316
300.55.18597-4.68597
313.55.34496-1.84496
322.55.26344-2.76344
334.55.06307-0.56307
344.55.07676-0.576755
354.55.23495-0.734949
362.54.33912-1.83912
3756.22633-1.22633
3804.23778-4.23778
3955.81429-0.814293
406.54.500351.99965
4155.67276-0.672755
424.55.45442-0.954424
435.55.200590.299413
447.54.135213.36479
4556.19412-1.19412
4675.612561.38744
474.56.11865-1.61865
488.55.138363.36164
493.55.21675-1.71675
5064.753261.24674
511.54.4613-2.9613
5295.920213.07979
533.54.77357-1.27357
5445.45556-1.45556
556.55.811490.688511
567.55.114672.38533
5754.925320.0746806
585.54.751430.748565
5914.6506-3.6506
606.55.586070.913928
61NANA0.728284
626.56.201320.298679
63710.025-3.02502
641.54.96569-3.46569
650.5-1.504482.00448
667.55.36352.1365
6796.600382.39962
689.512.599-3.09901
6984.177563.82244
701010.4101-0.410077
7173.142243.85776
728.58.128080.371919
7394.292344.70766
749.512.1164-2.61644
7544.49675-0.496747
7665.232730.76727
7789.06381-1.06381
785.53.277852.22215
797.55.764171.73583
8077.06624-0.0662374
817.55.305532.19447
8287.023720.976277
8375.499141.50086
8477.57825-0.578253
8562.556043.44396
861013.0515-3.05148
872.52.019240.480758
8899.37399-0.373989
8984.748553.25145
908.57.90970.590297
9164.905261.09474
9297.452661.54734
9386.827881.17212
9485.845382.15462
9598.977690.0223139
965.56.12975-0.629747
9753.890481.10952
98710.616-3.61595
995.54.357081.14292
100914.3616-5.3616
1012-0.6364572.63646
1028.56.859051.64095
10399.25505-0.255053
1048.55.747882.75212
105107.667142.33286
10697.259751.74025
10782.878615.12139
1081010.5794-0.57938
1097.58.07938-0.57938
1107.57.50947-0.00947375
11164.190781.80922
1121012.1588-2.15882
11331.199631.80037
1141012.6745-2.67446
1155.53.420332.07967
1161010.0885-0.0885219
11766.30965-0.309651
11856.33232-1.33232
1194.52.948221.55178
1207.59.11828-1.61828
12155.1891-0.1891
12288.3709-0.3709
1235.54.061351.43865
1247.55.338542.16146
1259.58.449451.05055
1268.57.196911.30309
1276.55.385491.11451
1286.53.90392.5961
12910.59.159921.34008
13084.916733.08327
131107.823652.17635
1329.56.138633.36137
13396.841382.15862
1341011.3478-1.34784
1354.55.25825-0.758253
1364.58.77481-4.27481
1370.52.92213-2.42213
1384.54.9669-0.466902
1395.56.57685-1.07685
14062.771473.22853
1418.58.05450.445504
1428.58.003940.496056
1435.56.04351-0.543513
14478.10907-1.10907
14556.87356-1.87356
1463.55.02076-1.52076
14756.45846-1.45846
148510.4358-5.43577
1491.5-0.6956612.19566
1508NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.979150.04169930.0208496
90.9582160.08356860.0417843
100.9385850.1228310.0614155
110.951210.09757980.0487899
120.9195120.1609760.0804881
130.9452950.1094110.0547054
140.9208460.1583080.0791541
150.8830190.2339610.116981
160.8400210.3199590.159979
170.7987180.4025640.201282
180.7393340.5213330.260666
190.6712260.6575490.328774
200.7529780.4940430.247022
210.7291370.5417260.270863
220.6715080.6569840.328492
230.6290430.7419150.370957
240.5694420.8611160.430558
250.5299970.9400060.470003
260.4827660.9655320.517234
270.4591570.9183150.540843
280.4112870.8225750.588713
290.4160810.8321620.583919
300.5578190.8843610.442181
310.5083520.9832960.491648
320.5359140.9281710.464086
330.49230.98460.5077
340.4489030.8978060.551097
350.4021010.8042030.597899
360.4508860.9017730.549114
370.4182220.8364430.581778
380.5618130.8763740.438187
390.5208120.9583760.479188
400.4975880.9951750.502412
410.4634120.9268230.536588
420.419220.838440.58078
430.3891720.7783440.610828
440.4530060.9060130.546994
450.4383410.8766810.561659
460.4575160.9150320.542484
470.4237950.8475890.576205
480.5391380.9217240.460862
490.5310820.9378360.468918
500.5056360.9887280.494364
510.5558770.8882470.444123
520.6275250.7449490.372475
530.6154290.7691430.384571
540.6007860.7984290.399214
550.5679990.8640020.432001
560.5730380.8539250.426962
570.5289990.9420020.471001
580.4934340.9868680.506566
590.6561070.6877870.343893
600.6370850.7258310.362915
610.610620.778760.38938
620.5932320.8135360.406768
630.6938320.6123350.306168
640.7541110.4917780.245889
650.784420.431160.21558
660.8110010.3779990.188999
670.8344790.3310410.165521
680.817520.3649590.18248
690.8539150.2921690.146085
700.846770.306460.15323
710.9082730.1834550.0917273
720.8942030.2115940.105797
730.9604060.07918870.0395944
740.9606450.0787110.0393555
750.9494310.1011380.0505689
760.9397980.1204030.0602017
770.9274110.1451790.0725893
780.9280540.1438920.0719462
790.9195270.1609460.0804732
800.9014620.1970750.0985377
810.8994720.2010550.100528
820.8795480.2409030.120452
830.8686860.2626280.131314
840.8420040.3159920.157996
850.8876230.2247550.112377
860.8943680.2112630.105632
870.8725860.2548280.127414
880.8452360.3095280.154764
890.860420.2791610.13958
900.8422980.3154030.157702
910.831110.3377790.16889
920.8180630.3638730.181937
930.7884160.4231680.211584
940.7683190.4633610.231681
950.7307210.5385580.269279
960.6991040.6017910.300896
970.6586580.6826830.341342
980.6732440.6535120.326756
990.6464080.7071840.353592
1000.8627210.2745580.137279
1010.8520790.2958430.147921
1020.839260.3214790.16074
1030.8046660.3906690.195334
1040.7950210.4099580.204979
1050.8090580.3818850.190942
1060.7788260.4423480.221174
1070.9832620.03347520.0167376
1080.9764650.04707060.0235353
1090.9675560.06488880.0324444
1100.9563710.08725770.0436288
1110.9571720.08565630.0428282
1120.9476460.1047080.052354
1130.9441830.1116350.0558174
1140.9517880.09642450.0482123
1150.9386430.1227130.0613567
1160.9233280.1533440.0766718
1170.8984410.2031180.101559
1180.8862660.2274690.113734
1190.8554840.2890320.144516
1200.834390.3312190.16561
1210.7988450.4023090.201155
1220.7487950.502410.251205
1230.700910.5981790.29909
1240.662530.674940.33747
1250.6803730.6392540.319627
1260.6710420.6579160.328958
1270.6143520.7712950.385648
1280.6427630.7144730.357237
1290.7302780.5394440.269722
1300.6712110.6575770.328789
1310.8215890.3568210.178411
1320.8780920.2438150.121908
1330.8281090.3437820.171891
1340.8086570.3826860.191343
1350.7307240.5385510.269276
1360.8951090.2097810.104891
1370.8365310.3269380.163469
1380.7755570.4488860.224443
1390.6628360.6743280.337164
1400.6457470.7085050.354253
1410.4748350.9496710.525165
1420.3118650.623730.688135

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.97915 & 0.0416993 & 0.0208496 \tabularnewline
9 & 0.958216 & 0.0835686 & 0.0417843 \tabularnewline
10 & 0.938585 & 0.122831 & 0.0614155 \tabularnewline
11 & 0.95121 & 0.0975798 & 0.0487899 \tabularnewline
12 & 0.919512 & 0.160976 & 0.0804881 \tabularnewline
13 & 0.945295 & 0.109411 & 0.0547054 \tabularnewline
14 & 0.920846 & 0.158308 & 0.0791541 \tabularnewline
15 & 0.883019 & 0.233961 & 0.116981 \tabularnewline
16 & 0.840021 & 0.319959 & 0.159979 \tabularnewline
17 & 0.798718 & 0.402564 & 0.201282 \tabularnewline
18 & 0.739334 & 0.521333 & 0.260666 \tabularnewline
19 & 0.671226 & 0.657549 & 0.328774 \tabularnewline
20 & 0.752978 & 0.494043 & 0.247022 \tabularnewline
21 & 0.729137 & 0.541726 & 0.270863 \tabularnewline
22 & 0.671508 & 0.656984 & 0.328492 \tabularnewline
23 & 0.629043 & 0.741915 & 0.370957 \tabularnewline
24 & 0.569442 & 0.861116 & 0.430558 \tabularnewline
25 & 0.529997 & 0.940006 & 0.470003 \tabularnewline
26 & 0.482766 & 0.965532 & 0.517234 \tabularnewline
27 & 0.459157 & 0.918315 & 0.540843 \tabularnewline
28 & 0.411287 & 0.822575 & 0.588713 \tabularnewline
29 & 0.416081 & 0.832162 & 0.583919 \tabularnewline
30 & 0.557819 & 0.884361 & 0.442181 \tabularnewline
31 & 0.508352 & 0.983296 & 0.491648 \tabularnewline
32 & 0.535914 & 0.928171 & 0.464086 \tabularnewline
33 & 0.4923 & 0.9846 & 0.5077 \tabularnewline
34 & 0.448903 & 0.897806 & 0.551097 \tabularnewline
35 & 0.402101 & 0.804203 & 0.597899 \tabularnewline
36 & 0.450886 & 0.901773 & 0.549114 \tabularnewline
37 & 0.418222 & 0.836443 & 0.581778 \tabularnewline
38 & 0.561813 & 0.876374 & 0.438187 \tabularnewline
39 & 0.520812 & 0.958376 & 0.479188 \tabularnewline
40 & 0.497588 & 0.995175 & 0.502412 \tabularnewline
41 & 0.463412 & 0.926823 & 0.536588 \tabularnewline
42 & 0.41922 & 0.83844 & 0.58078 \tabularnewline
43 & 0.389172 & 0.778344 & 0.610828 \tabularnewline
44 & 0.453006 & 0.906013 & 0.546994 \tabularnewline
45 & 0.438341 & 0.876681 & 0.561659 \tabularnewline
46 & 0.457516 & 0.915032 & 0.542484 \tabularnewline
47 & 0.423795 & 0.847589 & 0.576205 \tabularnewline
48 & 0.539138 & 0.921724 & 0.460862 \tabularnewline
49 & 0.531082 & 0.937836 & 0.468918 \tabularnewline
50 & 0.505636 & 0.988728 & 0.494364 \tabularnewline
51 & 0.555877 & 0.888247 & 0.444123 \tabularnewline
52 & 0.627525 & 0.744949 & 0.372475 \tabularnewline
53 & 0.615429 & 0.769143 & 0.384571 \tabularnewline
54 & 0.600786 & 0.798429 & 0.399214 \tabularnewline
55 & 0.567999 & 0.864002 & 0.432001 \tabularnewline
56 & 0.573038 & 0.853925 & 0.426962 \tabularnewline
57 & 0.528999 & 0.942002 & 0.471001 \tabularnewline
58 & 0.493434 & 0.986868 & 0.506566 \tabularnewline
59 & 0.656107 & 0.687787 & 0.343893 \tabularnewline
60 & 0.637085 & 0.725831 & 0.362915 \tabularnewline
61 & 0.61062 & 0.77876 & 0.38938 \tabularnewline
62 & 0.593232 & 0.813536 & 0.406768 \tabularnewline
63 & 0.693832 & 0.612335 & 0.306168 \tabularnewline
64 & 0.754111 & 0.491778 & 0.245889 \tabularnewline
65 & 0.78442 & 0.43116 & 0.21558 \tabularnewline
66 & 0.811001 & 0.377999 & 0.188999 \tabularnewline
67 & 0.834479 & 0.331041 & 0.165521 \tabularnewline
68 & 0.81752 & 0.364959 & 0.18248 \tabularnewline
69 & 0.853915 & 0.292169 & 0.146085 \tabularnewline
70 & 0.84677 & 0.30646 & 0.15323 \tabularnewline
71 & 0.908273 & 0.183455 & 0.0917273 \tabularnewline
72 & 0.894203 & 0.211594 & 0.105797 \tabularnewline
73 & 0.960406 & 0.0791887 & 0.0395944 \tabularnewline
74 & 0.960645 & 0.078711 & 0.0393555 \tabularnewline
75 & 0.949431 & 0.101138 & 0.0505689 \tabularnewline
76 & 0.939798 & 0.120403 & 0.0602017 \tabularnewline
77 & 0.927411 & 0.145179 & 0.0725893 \tabularnewline
78 & 0.928054 & 0.143892 & 0.0719462 \tabularnewline
79 & 0.919527 & 0.160946 & 0.0804732 \tabularnewline
80 & 0.901462 & 0.197075 & 0.0985377 \tabularnewline
81 & 0.899472 & 0.201055 & 0.100528 \tabularnewline
82 & 0.879548 & 0.240903 & 0.120452 \tabularnewline
83 & 0.868686 & 0.262628 & 0.131314 \tabularnewline
84 & 0.842004 & 0.315992 & 0.157996 \tabularnewline
85 & 0.887623 & 0.224755 & 0.112377 \tabularnewline
86 & 0.894368 & 0.211263 & 0.105632 \tabularnewline
87 & 0.872586 & 0.254828 & 0.127414 \tabularnewline
88 & 0.845236 & 0.309528 & 0.154764 \tabularnewline
89 & 0.86042 & 0.279161 & 0.13958 \tabularnewline
90 & 0.842298 & 0.315403 & 0.157702 \tabularnewline
91 & 0.83111 & 0.337779 & 0.16889 \tabularnewline
92 & 0.818063 & 0.363873 & 0.181937 \tabularnewline
93 & 0.788416 & 0.423168 & 0.211584 \tabularnewline
94 & 0.768319 & 0.463361 & 0.231681 \tabularnewline
95 & 0.730721 & 0.538558 & 0.269279 \tabularnewline
96 & 0.699104 & 0.601791 & 0.300896 \tabularnewline
97 & 0.658658 & 0.682683 & 0.341342 \tabularnewline
98 & 0.673244 & 0.653512 & 0.326756 \tabularnewline
99 & 0.646408 & 0.707184 & 0.353592 \tabularnewline
100 & 0.862721 & 0.274558 & 0.137279 \tabularnewline
101 & 0.852079 & 0.295843 & 0.147921 \tabularnewline
102 & 0.83926 & 0.321479 & 0.16074 \tabularnewline
103 & 0.804666 & 0.390669 & 0.195334 \tabularnewline
104 & 0.795021 & 0.409958 & 0.204979 \tabularnewline
105 & 0.809058 & 0.381885 & 0.190942 \tabularnewline
106 & 0.778826 & 0.442348 & 0.221174 \tabularnewline
107 & 0.983262 & 0.0334752 & 0.0167376 \tabularnewline
108 & 0.976465 & 0.0470706 & 0.0235353 \tabularnewline
109 & 0.967556 & 0.0648888 & 0.0324444 \tabularnewline
110 & 0.956371 & 0.0872577 & 0.0436288 \tabularnewline
111 & 0.957172 & 0.0856563 & 0.0428282 \tabularnewline
112 & 0.947646 & 0.104708 & 0.052354 \tabularnewline
113 & 0.944183 & 0.111635 & 0.0558174 \tabularnewline
114 & 0.951788 & 0.0964245 & 0.0482123 \tabularnewline
115 & 0.938643 & 0.122713 & 0.0613567 \tabularnewline
116 & 0.923328 & 0.153344 & 0.0766718 \tabularnewline
117 & 0.898441 & 0.203118 & 0.101559 \tabularnewline
118 & 0.886266 & 0.227469 & 0.113734 \tabularnewline
119 & 0.855484 & 0.289032 & 0.144516 \tabularnewline
120 & 0.83439 & 0.331219 & 0.16561 \tabularnewline
121 & 0.798845 & 0.402309 & 0.201155 \tabularnewline
122 & 0.748795 & 0.50241 & 0.251205 \tabularnewline
123 & 0.70091 & 0.598179 & 0.29909 \tabularnewline
124 & 0.66253 & 0.67494 & 0.33747 \tabularnewline
125 & 0.680373 & 0.639254 & 0.319627 \tabularnewline
126 & 0.671042 & 0.657916 & 0.328958 \tabularnewline
127 & 0.614352 & 0.771295 & 0.385648 \tabularnewline
128 & 0.642763 & 0.714473 & 0.357237 \tabularnewline
129 & 0.730278 & 0.539444 & 0.269722 \tabularnewline
130 & 0.671211 & 0.657577 & 0.328789 \tabularnewline
131 & 0.821589 & 0.356821 & 0.178411 \tabularnewline
132 & 0.878092 & 0.243815 & 0.121908 \tabularnewline
133 & 0.828109 & 0.343782 & 0.171891 \tabularnewline
134 & 0.808657 & 0.382686 & 0.191343 \tabularnewline
135 & 0.730724 & 0.538551 & 0.269276 \tabularnewline
136 & 0.895109 & 0.209781 & 0.104891 \tabularnewline
137 & 0.836531 & 0.326938 & 0.163469 \tabularnewline
138 & 0.775557 & 0.448886 & 0.224443 \tabularnewline
139 & 0.662836 & 0.674328 & 0.337164 \tabularnewline
140 & 0.645747 & 0.708505 & 0.354253 \tabularnewline
141 & 0.474835 & 0.949671 & 0.525165 \tabularnewline
142 & 0.311865 & 0.62373 & 0.688135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&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]0.97915[/C][C]0.0416993[/C][C]0.0208496[/C][/ROW]
[ROW][C]9[/C][C]0.958216[/C][C]0.0835686[/C][C]0.0417843[/C][/ROW]
[ROW][C]10[/C][C]0.938585[/C][C]0.122831[/C][C]0.0614155[/C][/ROW]
[ROW][C]11[/C][C]0.95121[/C][C]0.0975798[/C][C]0.0487899[/C][/ROW]
[ROW][C]12[/C][C]0.919512[/C][C]0.160976[/C][C]0.0804881[/C][/ROW]
[ROW][C]13[/C][C]0.945295[/C][C]0.109411[/C][C]0.0547054[/C][/ROW]
[ROW][C]14[/C][C]0.920846[/C][C]0.158308[/C][C]0.0791541[/C][/ROW]
[ROW][C]15[/C][C]0.883019[/C][C]0.233961[/C][C]0.116981[/C][/ROW]
[ROW][C]16[/C][C]0.840021[/C][C]0.319959[/C][C]0.159979[/C][/ROW]
[ROW][C]17[/C][C]0.798718[/C][C]0.402564[/C][C]0.201282[/C][/ROW]
[ROW][C]18[/C][C]0.739334[/C][C]0.521333[/C][C]0.260666[/C][/ROW]
[ROW][C]19[/C][C]0.671226[/C][C]0.657549[/C][C]0.328774[/C][/ROW]
[ROW][C]20[/C][C]0.752978[/C][C]0.494043[/C][C]0.247022[/C][/ROW]
[ROW][C]21[/C][C]0.729137[/C][C]0.541726[/C][C]0.270863[/C][/ROW]
[ROW][C]22[/C][C]0.671508[/C][C]0.656984[/C][C]0.328492[/C][/ROW]
[ROW][C]23[/C][C]0.629043[/C][C]0.741915[/C][C]0.370957[/C][/ROW]
[ROW][C]24[/C][C]0.569442[/C][C]0.861116[/C][C]0.430558[/C][/ROW]
[ROW][C]25[/C][C]0.529997[/C][C]0.940006[/C][C]0.470003[/C][/ROW]
[ROW][C]26[/C][C]0.482766[/C][C]0.965532[/C][C]0.517234[/C][/ROW]
[ROW][C]27[/C][C]0.459157[/C][C]0.918315[/C][C]0.540843[/C][/ROW]
[ROW][C]28[/C][C]0.411287[/C][C]0.822575[/C][C]0.588713[/C][/ROW]
[ROW][C]29[/C][C]0.416081[/C][C]0.832162[/C][C]0.583919[/C][/ROW]
[ROW][C]30[/C][C]0.557819[/C][C]0.884361[/C][C]0.442181[/C][/ROW]
[ROW][C]31[/C][C]0.508352[/C][C]0.983296[/C][C]0.491648[/C][/ROW]
[ROW][C]32[/C][C]0.535914[/C][C]0.928171[/C][C]0.464086[/C][/ROW]
[ROW][C]33[/C][C]0.4923[/C][C]0.9846[/C][C]0.5077[/C][/ROW]
[ROW][C]34[/C][C]0.448903[/C][C]0.897806[/C][C]0.551097[/C][/ROW]
[ROW][C]35[/C][C]0.402101[/C][C]0.804203[/C][C]0.597899[/C][/ROW]
[ROW][C]36[/C][C]0.450886[/C][C]0.901773[/C][C]0.549114[/C][/ROW]
[ROW][C]37[/C][C]0.418222[/C][C]0.836443[/C][C]0.581778[/C][/ROW]
[ROW][C]38[/C][C]0.561813[/C][C]0.876374[/C][C]0.438187[/C][/ROW]
[ROW][C]39[/C][C]0.520812[/C][C]0.958376[/C][C]0.479188[/C][/ROW]
[ROW][C]40[/C][C]0.497588[/C][C]0.995175[/C][C]0.502412[/C][/ROW]
[ROW][C]41[/C][C]0.463412[/C][C]0.926823[/C][C]0.536588[/C][/ROW]
[ROW][C]42[/C][C]0.41922[/C][C]0.83844[/C][C]0.58078[/C][/ROW]
[ROW][C]43[/C][C]0.389172[/C][C]0.778344[/C][C]0.610828[/C][/ROW]
[ROW][C]44[/C][C]0.453006[/C][C]0.906013[/C][C]0.546994[/C][/ROW]
[ROW][C]45[/C][C]0.438341[/C][C]0.876681[/C][C]0.561659[/C][/ROW]
[ROW][C]46[/C][C]0.457516[/C][C]0.915032[/C][C]0.542484[/C][/ROW]
[ROW][C]47[/C][C]0.423795[/C][C]0.847589[/C][C]0.576205[/C][/ROW]
[ROW][C]48[/C][C]0.539138[/C][C]0.921724[/C][C]0.460862[/C][/ROW]
[ROW][C]49[/C][C]0.531082[/C][C]0.937836[/C][C]0.468918[/C][/ROW]
[ROW][C]50[/C][C]0.505636[/C][C]0.988728[/C][C]0.494364[/C][/ROW]
[ROW][C]51[/C][C]0.555877[/C][C]0.888247[/C][C]0.444123[/C][/ROW]
[ROW][C]52[/C][C]0.627525[/C][C]0.744949[/C][C]0.372475[/C][/ROW]
[ROW][C]53[/C][C]0.615429[/C][C]0.769143[/C][C]0.384571[/C][/ROW]
[ROW][C]54[/C][C]0.600786[/C][C]0.798429[/C][C]0.399214[/C][/ROW]
[ROW][C]55[/C][C]0.567999[/C][C]0.864002[/C][C]0.432001[/C][/ROW]
[ROW][C]56[/C][C]0.573038[/C][C]0.853925[/C][C]0.426962[/C][/ROW]
[ROW][C]57[/C][C]0.528999[/C][C]0.942002[/C][C]0.471001[/C][/ROW]
[ROW][C]58[/C][C]0.493434[/C][C]0.986868[/C][C]0.506566[/C][/ROW]
[ROW][C]59[/C][C]0.656107[/C][C]0.687787[/C][C]0.343893[/C][/ROW]
[ROW][C]60[/C][C]0.637085[/C][C]0.725831[/C][C]0.362915[/C][/ROW]
[ROW][C]61[/C][C]0.61062[/C][C]0.77876[/C][C]0.38938[/C][/ROW]
[ROW][C]62[/C][C]0.593232[/C][C]0.813536[/C][C]0.406768[/C][/ROW]
[ROW][C]63[/C][C]0.693832[/C][C]0.612335[/C][C]0.306168[/C][/ROW]
[ROW][C]64[/C][C]0.754111[/C][C]0.491778[/C][C]0.245889[/C][/ROW]
[ROW][C]65[/C][C]0.78442[/C][C]0.43116[/C][C]0.21558[/C][/ROW]
[ROW][C]66[/C][C]0.811001[/C][C]0.377999[/C][C]0.188999[/C][/ROW]
[ROW][C]67[/C][C]0.834479[/C][C]0.331041[/C][C]0.165521[/C][/ROW]
[ROW][C]68[/C][C]0.81752[/C][C]0.364959[/C][C]0.18248[/C][/ROW]
[ROW][C]69[/C][C]0.853915[/C][C]0.292169[/C][C]0.146085[/C][/ROW]
[ROW][C]70[/C][C]0.84677[/C][C]0.30646[/C][C]0.15323[/C][/ROW]
[ROW][C]71[/C][C]0.908273[/C][C]0.183455[/C][C]0.0917273[/C][/ROW]
[ROW][C]72[/C][C]0.894203[/C][C]0.211594[/C][C]0.105797[/C][/ROW]
[ROW][C]73[/C][C]0.960406[/C][C]0.0791887[/C][C]0.0395944[/C][/ROW]
[ROW][C]74[/C][C]0.960645[/C][C]0.078711[/C][C]0.0393555[/C][/ROW]
[ROW][C]75[/C][C]0.949431[/C][C]0.101138[/C][C]0.0505689[/C][/ROW]
[ROW][C]76[/C][C]0.939798[/C][C]0.120403[/C][C]0.0602017[/C][/ROW]
[ROW][C]77[/C][C]0.927411[/C][C]0.145179[/C][C]0.0725893[/C][/ROW]
[ROW][C]78[/C][C]0.928054[/C][C]0.143892[/C][C]0.0719462[/C][/ROW]
[ROW][C]79[/C][C]0.919527[/C][C]0.160946[/C][C]0.0804732[/C][/ROW]
[ROW][C]80[/C][C]0.901462[/C][C]0.197075[/C][C]0.0985377[/C][/ROW]
[ROW][C]81[/C][C]0.899472[/C][C]0.201055[/C][C]0.100528[/C][/ROW]
[ROW][C]82[/C][C]0.879548[/C][C]0.240903[/C][C]0.120452[/C][/ROW]
[ROW][C]83[/C][C]0.868686[/C][C]0.262628[/C][C]0.131314[/C][/ROW]
[ROW][C]84[/C][C]0.842004[/C][C]0.315992[/C][C]0.157996[/C][/ROW]
[ROW][C]85[/C][C]0.887623[/C][C]0.224755[/C][C]0.112377[/C][/ROW]
[ROW][C]86[/C][C]0.894368[/C][C]0.211263[/C][C]0.105632[/C][/ROW]
[ROW][C]87[/C][C]0.872586[/C][C]0.254828[/C][C]0.127414[/C][/ROW]
[ROW][C]88[/C][C]0.845236[/C][C]0.309528[/C][C]0.154764[/C][/ROW]
[ROW][C]89[/C][C]0.86042[/C][C]0.279161[/C][C]0.13958[/C][/ROW]
[ROW][C]90[/C][C]0.842298[/C][C]0.315403[/C][C]0.157702[/C][/ROW]
[ROW][C]91[/C][C]0.83111[/C][C]0.337779[/C][C]0.16889[/C][/ROW]
[ROW][C]92[/C][C]0.818063[/C][C]0.363873[/C][C]0.181937[/C][/ROW]
[ROW][C]93[/C][C]0.788416[/C][C]0.423168[/C][C]0.211584[/C][/ROW]
[ROW][C]94[/C][C]0.768319[/C][C]0.463361[/C][C]0.231681[/C][/ROW]
[ROW][C]95[/C][C]0.730721[/C][C]0.538558[/C][C]0.269279[/C][/ROW]
[ROW][C]96[/C][C]0.699104[/C][C]0.601791[/C][C]0.300896[/C][/ROW]
[ROW][C]97[/C][C]0.658658[/C][C]0.682683[/C][C]0.341342[/C][/ROW]
[ROW][C]98[/C][C]0.673244[/C][C]0.653512[/C][C]0.326756[/C][/ROW]
[ROW][C]99[/C][C]0.646408[/C][C]0.707184[/C][C]0.353592[/C][/ROW]
[ROW][C]100[/C][C]0.862721[/C][C]0.274558[/C][C]0.137279[/C][/ROW]
[ROW][C]101[/C][C]0.852079[/C][C]0.295843[/C][C]0.147921[/C][/ROW]
[ROW][C]102[/C][C]0.83926[/C][C]0.321479[/C][C]0.16074[/C][/ROW]
[ROW][C]103[/C][C]0.804666[/C][C]0.390669[/C][C]0.195334[/C][/ROW]
[ROW][C]104[/C][C]0.795021[/C][C]0.409958[/C][C]0.204979[/C][/ROW]
[ROW][C]105[/C][C]0.809058[/C][C]0.381885[/C][C]0.190942[/C][/ROW]
[ROW][C]106[/C][C]0.778826[/C][C]0.442348[/C][C]0.221174[/C][/ROW]
[ROW][C]107[/C][C]0.983262[/C][C]0.0334752[/C][C]0.0167376[/C][/ROW]
[ROW][C]108[/C][C]0.976465[/C][C]0.0470706[/C][C]0.0235353[/C][/ROW]
[ROW][C]109[/C][C]0.967556[/C][C]0.0648888[/C][C]0.0324444[/C][/ROW]
[ROW][C]110[/C][C]0.956371[/C][C]0.0872577[/C][C]0.0436288[/C][/ROW]
[ROW][C]111[/C][C]0.957172[/C][C]0.0856563[/C][C]0.0428282[/C][/ROW]
[ROW][C]112[/C][C]0.947646[/C][C]0.104708[/C][C]0.052354[/C][/ROW]
[ROW][C]113[/C][C]0.944183[/C][C]0.111635[/C][C]0.0558174[/C][/ROW]
[ROW][C]114[/C][C]0.951788[/C][C]0.0964245[/C][C]0.0482123[/C][/ROW]
[ROW][C]115[/C][C]0.938643[/C][C]0.122713[/C][C]0.0613567[/C][/ROW]
[ROW][C]116[/C][C]0.923328[/C][C]0.153344[/C][C]0.0766718[/C][/ROW]
[ROW][C]117[/C][C]0.898441[/C][C]0.203118[/C][C]0.101559[/C][/ROW]
[ROW][C]118[/C][C]0.886266[/C][C]0.227469[/C][C]0.113734[/C][/ROW]
[ROW][C]119[/C][C]0.855484[/C][C]0.289032[/C][C]0.144516[/C][/ROW]
[ROW][C]120[/C][C]0.83439[/C][C]0.331219[/C][C]0.16561[/C][/ROW]
[ROW][C]121[/C][C]0.798845[/C][C]0.402309[/C][C]0.201155[/C][/ROW]
[ROW][C]122[/C][C]0.748795[/C][C]0.50241[/C][C]0.251205[/C][/ROW]
[ROW][C]123[/C][C]0.70091[/C][C]0.598179[/C][C]0.29909[/C][/ROW]
[ROW][C]124[/C][C]0.66253[/C][C]0.67494[/C][C]0.33747[/C][/ROW]
[ROW][C]125[/C][C]0.680373[/C][C]0.639254[/C][C]0.319627[/C][/ROW]
[ROW][C]126[/C][C]0.671042[/C][C]0.657916[/C][C]0.328958[/C][/ROW]
[ROW][C]127[/C][C]0.614352[/C][C]0.771295[/C][C]0.385648[/C][/ROW]
[ROW][C]128[/C][C]0.642763[/C][C]0.714473[/C][C]0.357237[/C][/ROW]
[ROW][C]129[/C][C]0.730278[/C][C]0.539444[/C][C]0.269722[/C][/ROW]
[ROW][C]130[/C][C]0.671211[/C][C]0.657577[/C][C]0.328789[/C][/ROW]
[ROW][C]131[/C][C]0.821589[/C][C]0.356821[/C][C]0.178411[/C][/ROW]
[ROW][C]132[/C][C]0.878092[/C][C]0.243815[/C][C]0.121908[/C][/ROW]
[ROW][C]133[/C][C]0.828109[/C][C]0.343782[/C][C]0.171891[/C][/ROW]
[ROW][C]134[/C][C]0.808657[/C][C]0.382686[/C][C]0.191343[/C][/ROW]
[ROW][C]135[/C][C]0.730724[/C][C]0.538551[/C][C]0.269276[/C][/ROW]
[ROW][C]136[/C][C]0.895109[/C][C]0.209781[/C][C]0.104891[/C][/ROW]
[ROW][C]137[/C][C]0.836531[/C][C]0.326938[/C][C]0.163469[/C][/ROW]
[ROW][C]138[/C][C]0.775557[/C][C]0.448886[/C][C]0.224443[/C][/ROW]
[ROW][C]139[/C][C]0.662836[/C][C]0.674328[/C][C]0.337164[/C][/ROW]
[ROW][C]140[/C][C]0.645747[/C][C]0.708505[/C][C]0.354253[/C][/ROW]
[ROW][C]141[/C][C]0.474835[/C][C]0.949671[/C][C]0.525165[/C][/ROW]
[ROW][C]142[/C][C]0.311865[/C][C]0.62373[/C][C]0.688135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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
80.979150.04169930.0208496
90.9582160.08356860.0417843
100.9385850.1228310.0614155
110.951210.09757980.0487899
120.9195120.1609760.0804881
130.9452950.1094110.0547054
140.9208460.1583080.0791541
150.8830190.2339610.116981
160.8400210.3199590.159979
170.7987180.4025640.201282
180.7393340.5213330.260666
190.6712260.6575490.328774
200.7529780.4940430.247022
210.7291370.5417260.270863
220.6715080.6569840.328492
230.6290430.7419150.370957
240.5694420.8611160.430558
250.5299970.9400060.470003
260.4827660.9655320.517234
270.4591570.9183150.540843
280.4112870.8225750.588713
290.4160810.8321620.583919
300.5578190.8843610.442181
310.5083520.9832960.491648
320.5359140.9281710.464086
330.49230.98460.5077
340.4489030.8978060.551097
350.4021010.8042030.597899
360.4508860.9017730.549114
370.4182220.8364430.581778
380.5618130.8763740.438187
390.5208120.9583760.479188
400.4975880.9951750.502412
410.4634120.9268230.536588
420.419220.838440.58078
430.3891720.7783440.610828
440.4530060.9060130.546994
450.4383410.8766810.561659
460.4575160.9150320.542484
470.4237950.8475890.576205
480.5391380.9217240.460862
490.5310820.9378360.468918
500.5056360.9887280.494364
510.5558770.8882470.444123
520.6275250.7449490.372475
530.6154290.7691430.384571
540.6007860.7984290.399214
550.5679990.8640020.432001
560.5730380.8539250.426962
570.5289990.9420020.471001
580.4934340.9868680.506566
590.6561070.6877870.343893
600.6370850.7258310.362915
610.610620.778760.38938
620.5932320.8135360.406768
630.6938320.6123350.306168
640.7541110.4917780.245889
650.784420.431160.21558
660.8110010.3779990.188999
670.8344790.3310410.165521
680.817520.3649590.18248
690.8539150.2921690.146085
700.846770.306460.15323
710.9082730.1834550.0917273
720.8942030.2115940.105797
730.9604060.07918870.0395944
740.9606450.0787110.0393555
750.9494310.1011380.0505689
760.9397980.1204030.0602017
770.9274110.1451790.0725893
780.9280540.1438920.0719462
790.9195270.1609460.0804732
800.9014620.1970750.0985377
810.8994720.2010550.100528
820.8795480.2409030.120452
830.8686860.2626280.131314
840.8420040.3159920.157996
850.8876230.2247550.112377
860.8943680.2112630.105632
870.8725860.2548280.127414
880.8452360.3095280.154764
890.860420.2791610.13958
900.8422980.3154030.157702
910.831110.3377790.16889
920.8180630.3638730.181937
930.7884160.4231680.211584
940.7683190.4633610.231681
950.7307210.5385580.269279
960.6991040.6017910.300896
970.6586580.6826830.341342
980.6732440.6535120.326756
990.6464080.7071840.353592
1000.8627210.2745580.137279
1010.8520790.2958430.147921
1020.839260.3214790.16074
1030.8046660.3906690.195334
1040.7950210.4099580.204979
1050.8090580.3818850.190942
1060.7788260.4423480.221174
1070.9832620.03347520.0167376
1080.9764650.04707060.0235353
1090.9675560.06488880.0324444
1100.9563710.08725770.0436288
1110.9571720.08565630.0428282
1120.9476460.1047080.052354
1130.9441830.1116350.0558174
1140.9517880.09642450.0482123
1150.9386430.1227130.0613567
1160.9233280.1533440.0766718
1170.8984410.2031180.101559
1180.8862660.2274690.113734
1190.8554840.2890320.144516
1200.834390.3312190.16561
1210.7988450.4023090.201155
1220.7487950.502410.251205
1230.700910.5981790.29909
1240.662530.674940.33747
1250.6803730.6392540.319627
1260.6710420.6579160.328958
1270.6143520.7712950.385648
1280.6427630.7144730.357237
1290.7302780.5394440.269722
1300.6712110.6575770.328789
1310.8215890.3568210.178411
1320.8780920.2438150.121908
1330.8281090.3437820.171891
1340.8086570.3826860.191343
1350.7307240.5385510.269276
1360.8951090.2097810.104891
1370.8365310.3269380.163469
1380.7755570.4488860.224443
1390.6628360.6743280.337164
1400.6457470.7085050.354253
1410.4748350.9496710.525165
1420.3118650.623730.688135






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0222222OK
10% type I error level110.0814815OK

\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 & 3 & 0.0222222 & OK \tabularnewline
10% type I error level & 11 & 0.0814815 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267741&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]3[/C][C]0.0222222[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0814815[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267741&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267741&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 level00OK
5% type I error level30.0222222OK
10% type I error level110.0814815OK


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')
}