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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:12:38 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587232179px1963yn45jgsx.htm/, Retrieved Fri, 29 Mar 2024 07:28:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58132, Retrieved Fri, 29 Mar 2024 07:28:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsRob_WS7_1
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 13:12:38] [9002751dd674b8c934bf183fdf4510e9] [Current]
-   PD        [Multiple Regression] [] [2009-11-20 13:40:56] [4f76e114ed5e444b1133aad392380aad]
-   PD        [Multiple Regression] [] [2009-11-20 13:54:09] [4f76e114ed5e444b1133aad392380aad]
-   PD        [Multiple Regression] [] [2009-11-20 14:12:55] [4f76e114ed5e444b1133aad392380aad]
F   PD        [Multiple Regression] [] [2009-11-20 14:18:19] [4f76e114ed5e444b1133aad392380aad]
-    D          [Multiple Regression] [WS7 aanpassing] [2009-11-25 17:21:22] [626f1d98f4a7f05bcb9f17666b672c60]
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Dataseries X:
106370	100,3
109375	101,9
116476	102,1
123297	103,2
114813	103,7
117925	106,2
126466	107,7
131235	109,9
120546	111,7
123791	114,9
129813	116
133463	118,3
122987	120,4
125418	126
130199	128,1
133016	130,1
121454	130,8
122044	133,6
128313	134,2
131556	135,5
120027	136,2
123001	139,1
130111	139
132524	139,6
123742	138,7
124931	140,9
133646	141,3
136557	141,8
127509	142
128945	144,5
137191	144,6
139716	145,5
129083	146,8
131604	149,5
139413	149,9
143125	150,1
133948	150,9
137116	152,8
144864	153,1
149277	154
138796	154,9
143258	156,9
150034	158,4
154708	159,7
144888	160,2
148762	163,2
156500	163,7
161088	164,4
152772	163,7
158011	165,5
163318	165,6
169969	166,8
162269	167,5
165765	170,6
170600	170,9
174681	172
166364	171,8
170240	173,9
176150	174
182056	173,8
172218	173,9
177856	176
182253	176,6
188090	178,2
176863	179,2
183273	181,3
187969	181,8
194650	182,9
183036	183,8
189516	186,3
193805	187,4
200499	189,2
188142	189,7
193732	191,9
197126	192,6
205140	193,7
191751	194,2
196700	197,6
199784	199,3
207360	201,4
196101	203
200824	206,3
205743	207,1
212489	209,8
200810	211,1
203683	215,3
207286	217,4
210910	215,5
194915	210,9
217920	212,6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=0

As an alternative you can also use a 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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
HFCE[t] = + 7117.57957043553 + 942.117029576953RPI[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HFCE[t] =  +  7117.57957043553 +  942.117029576953RPI[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HFCE[t] =  +  7117.57957043553 +  942.117029576953RPI[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
HFCE[t] = + 7117.57957043553 + 942.117029576953RPI[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7117.579570435535207.5093791.36680.1751720.087586
RPI942.11702957695331.94257329.494100

\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) & 7117.57957043553 & 5207.509379 & 1.3668 & 0.175172 & 0.087586 \tabularnewline
RPI & 942.117029576953 & 31.942573 & 29.4941 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&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]7117.57957043553[/C][C]5207.509379[/C][C]1.3668[/C][C]0.175172[/C][C]0.087586[/C][/ROW]
[ROW][C]RPI[/C][C]942.117029576953[/C][C]31.942573[/C][C]29.4941[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=2

As an alternative you can also use a 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)7117.579570435535207.5093791.36680.1751720.087586
RPI942.11702957695331.94257329.494100







Multiple Linear Regression - Regression Statistics
Multiple R0.95295985390233
R-squared0.90813248314955
Adjusted R-squared0.907088534094432
F-TEST (value)869.901149578852
F-TEST (DF numerator)1
F-TEST (DF denominator)88
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9503.7112551085
Sum Squared Residuals7948206430.60187

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.95295985390233 \tabularnewline
R-squared & 0.90813248314955 \tabularnewline
Adjusted R-squared & 0.907088534094432 \tabularnewline
F-TEST (value) & 869.901149578852 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 88 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9503.7112551085 \tabularnewline
Sum Squared Residuals & 7948206430.60187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.95295985390233[/C][/ROW]
[ROW][C]R-squared[/C][C]0.90813248314955[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.907088534094432[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]869.901149578852[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]88[/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]9503.7112551085[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7948206430.60187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.95295985390233
R-squared0.90813248314955
Adjusted R-squared0.907088534094432
F-TEST (value)869.901149578852
F-TEST (DF numerator)1
F-TEST (DF denominator)88
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9503.7112551085
Sum Squared Residuals7948206430.60187







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106370101611.9176370044758.08236299578
2109375103119.3048843276255.69511567294
3116476103307.72829024213168.2717097576
4123297104344.05702277718952.9429772229
5114813104815.1155375669997.88446243442
6117925107170.40811150810754.5918884920
7126466108583.58365587317882.4163441266
8131235110656.24112094320578.7588790573
9120546112352.0517741818193.94822581879
10123791115366.8262688278424.17373117254
11129813116403.15500136213409.8449986379
12133463118570.02416938914892.9758306109
13122987120548.4699315012438.5300684993
14125418125824.325297132-406.32529713163
15130199127802.7710592432396.22894075677
16133016129687.0051183973328.99488160287
17121454130346.487039101-8892.48703910101
18122044132984.414721916-10940.4147219165
19128313133549.684939663-5236.68493966263
20131556134774.437078113-3218.43707811268
21120027135433.918998817-15406.9189988165
22123001138166.058384590-15165.0583845897
23130111138071.846681632-7960.84668163202
24132524138637.116899378-6113.11689937818
25123742137789.211572759-14047.2115727589
26124931139861.869037828-14930.8690378282
27133646140238.715849659-6592.71584965902
28136557140709.774364447-4152.7743644475
29127509140898.197770363-13389.1977703629
30128945143253.490344305-14308.4903443053
31137191143347.702047263-6156.70204726295
32139716144195.607373882-4479.60737388221
33129083145420.359512332-16337.3595123323
34131604147964.07549219-16360.0754921900
35139413148340.922304021-8927.92230402081
36143125148529.345709936-5404.34570993619
37133948149283.039333598-15335.0393335978
38137116151073.061689794-13957.0616897940
39144864151355.696798667-6491.69679866705
40149277152203.602125286-2926.60212528631
41138796153051.507451906-14255.5074519056
42143258154935.741511059-11677.7415110595
43150034156348.917055425-6314.91705542491
44154708157573.669193875-2865.66919387493
45144888158044.727708663-13156.7277086634
46148762160871.078797394-12109.0787973943
47156500161342.137312183-4842.13731218275
48161088162001.619232887-913.619232886629
49152772161342.137312183-8570.13731218275
50158011163037.947965421-5026.94796542127
51163318163132.159668379185.840331621038
52169969164262.7001038715706.29989612868
53162269164922.182024575-2653.18202457518
54165765167842.744816264-2077.74481626373
55170600168125.3799251372474.62007486318
56174681169161.7086576715519.29134232854
57166364168973.285251756-2609.28525175609
58170240170951.731013868-711.731013867682
59176150171045.9427168255104.05728317462
60182056170857.5193109111198.48068909
61172218170951.7310138681266.26898613232
62177856172930.1767759794925.82322402072
63182253173495.4469937258757.55300627456
64188090175002.83424104913087.1657589514
65176863175944.951270626918.048729374482
66183273177923.3970327375349.60296726286
67187969178394.4555475269574.54445247439
68194650179430.78428006015219.2157199397
69183036180278.6896066802757.31039332048
70189516182633.9821806226882.0178193781
71193805183670.31091315710134.6890868435
72200499185366.12156639515132.8784336050
73188142185837.1800811842304.81991881647
74193732187909.8375462535822.16245374717
75197126188569.3194669578556.68053304331
76205140189605.64819949115534.3518005087
77191751190076.7067142801674.29328572019
78196700193279.9046148413420.09538515854
79199784194881.5035651224902.49643487771
80207360196859.94932723410500.0506727661
81196101198367.336574557-2266.33657455701
82200824201476.322772161-652.322772160962
83205743202230.0163958233512.98360417749
84212489204773.7323756807715.2676243197
85200810205998.484514130-5188.48451413032
86203683209955.376038354-6272.37603835354
87207286211933.821800465-4647.82180046513
88210910210143.799444269766.200555731074
89194915205810.061108215-10895.0611082149
90217920207411.66005849610508.3399415042

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106370 & 101611.917637004 & 4758.08236299578 \tabularnewline
2 & 109375 & 103119.304884327 & 6255.69511567294 \tabularnewline
3 & 116476 & 103307.728290242 & 13168.2717097576 \tabularnewline
4 & 123297 & 104344.057022777 & 18952.9429772229 \tabularnewline
5 & 114813 & 104815.115537566 & 9997.88446243442 \tabularnewline
6 & 117925 & 107170.408111508 & 10754.5918884920 \tabularnewline
7 & 126466 & 108583.583655873 & 17882.4163441266 \tabularnewline
8 & 131235 & 110656.241120943 & 20578.7588790573 \tabularnewline
9 & 120546 & 112352.051774181 & 8193.94822581879 \tabularnewline
10 & 123791 & 115366.826268827 & 8424.17373117254 \tabularnewline
11 & 129813 & 116403.155001362 & 13409.8449986379 \tabularnewline
12 & 133463 & 118570.024169389 & 14892.9758306109 \tabularnewline
13 & 122987 & 120548.469931501 & 2438.5300684993 \tabularnewline
14 & 125418 & 125824.325297132 & -406.32529713163 \tabularnewline
15 & 130199 & 127802.771059243 & 2396.22894075677 \tabularnewline
16 & 133016 & 129687.005118397 & 3328.99488160287 \tabularnewline
17 & 121454 & 130346.487039101 & -8892.48703910101 \tabularnewline
18 & 122044 & 132984.414721916 & -10940.4147219165 \tabularnewline
19 & 128313 & 133549.684939663 & -5236.68493966263 \tabularnewline
20 & 131556 & 134774.437078113 & -3218.43707811268 \tabularnewline
21 & 120027 & 135433.918998817 & -15406.9189988165 \tabularnewline
22 & 123001 & 138166.058384590 & -15165.0583845897 \tabularnewline
23 & 130111 & 138071.846681632 & -7960.84668163202 \tabularnewline
24 & 132524 & 138637.116899378 & -6113.11689937818 \tabularnewline
25 & 123742 & 137789.211572759 & -14047.2115727589 \tabularnewline
26 & 124931 & 139861.869037828 & -14930.8690378282 \tabularnewline
27 & 133646 & 140238.715849659 & -6592.71584965902 \tabularnewline
28 & 136557 & 140709.774364447 & -4152.7743644475 \tabularnewline
29 & 127509 & 140898.197770363 & -13389.1977703629 \tabularnewline
30 & 128945 & 143253.490344305 & -14308.4903443053 \tabularnewline
31 & 137191 & 143347.702047263 & -6156.70204726295 \tabularnewline
32 & 139716 & 144195.607373882 & -4479.60737388221 \tabularnewline
33 & 129083 & 145420.359512332 & -16337.3595123323 \tabularnewline
34 & 131604 & 147964.07549219 & -16360.0754921900 \tabularnewline
35 & 139413 & 148340.922304021 & -8927.92230402081 \tabularnewline
36 & 143125 & 148529.345709936 & -5404.34570993619 \tabularnewline
37 & 133948 & 149283.039333598 & -15335.0393335978 \tabularnewline
38 & 137116 & 151073.061689794 & -13957.0616897940 \tabularnewline
39 & 144864 & 151355.696798667 & -6491.69679866705 \tabularnewline
40 & 149277 & 152203.602125286 & -2926.60212528631 \tabularnewline
41 & 138796 & 153051.507451906 & -14255.5074519056 \tabularnewline
42 & 143258 & 154935.741511059 & -11677.7415110595 \tabularnewline
43 & 150034 & 156348.917055425 & -6314.91705542491 \tabularnewline
44 & 154708 & 157573.669193875 & -2865.66919387493 \tabularnewline
45 & 144888 & 158044.727708663 & -13156.7277086634 \tabularnewline
46 & 148762 & 160871.078797394 & -12109.0787973943 \tabularnewline
47 & 156500 & 161342.137312183 & -4842.13731218275 \tabularnewline
48 & 161088 & 162001.619232887 & -913.619232886629 \tabularnewline
49 & 152772 & 161342.137312183 & -8570.13731218275 \tabularnewline
50 & 158011 & 163037.947965421 & -5026.94796542127 \tabularnewline
51 & 163318 & 163132.159668379 & 185.840331621038 \tabularnewline
52 & 169969 & 164262.700103871 & 5706.29989612868 \tabularnewline
53 & 162269 & 164922.182024575 & -2653.18202457518 \tabularnewline
54 & 165765 & 167842.744816264 & -2077.74481626373 \tabularnewline
55 & 170600 & 168125.379925137 & 2474.62007486318 \tabularnewline
56 & 174681 & 169161.708657671 & 5519.29134232854 \tabularnewline
57 & 166364 & 168973.285251756 & -2609.28525175609 \tabularnewline
58 & 170240 & 170951.731013868 & -711.731013867682 \tabularnewline
59 & 176150 & 171045.942716825 & 5104.05728317462 \tabularnewline
60 & 182056 & 170857.51931091 & 11198.48068909 \tabularnewline
61 & 172218 & 170951.731013868 & 1266.26898613232 \tabularnewline
62 & 177856 & 172930.176775979 & 4925.82322402072 \tabularnewline
63 & 182253 & 173495.446993725 & 8757.55300627456 \tabularnewline
64 & 188090 & 175002.834241049 & 13087.1657589514 \tabularnewline
65 & 176863 & 175944.951270626 & 918.048729374482 \tabularnewline
66 & 183273 & 177923.397032737 & 5349.60296726286 \tabularnewline
67 & 187969 & 178394.455547526 & 9574.54445247439 \tabularnewline
68 & 194650 & 179430.784280060 & 15219.2157199397 \tabularnewline
69 & 183036 & 180278.689606680 & 2757.31039332048 \tabularnewline
70 & 189516 & 182633.982180622 & 6882.0178193781 \tabularnewline
71 & 193805 & 183670.310913157 & 10134.6890868435 \tabularnewline
72 & 200499 & 185366.121566395 & 15132.8784336050 \tabularnewline
73 & 188142 & 185837.180081184 & 2304.81991881647 \tabularnewline
74 & 193732 & 187909.837546253 & 5822.16245374717 \tabularnewline
75 & 197126 & 188569.319466957 & 8556.68053304331 \tabularnewline
76 & 205140 & 189605.648199491 & 15534.3518005087 \tabularnewline
77 & 191751 & 190076.706714280 & 1674.29328572019 \tabularnewline
78 & 196700 & 193279.904614841 & 3420.09538515854 \tabularnewline
79 & 199784 & 194881.503565122 & 4902.49643487771 \tabularnewline
80 & 207360 & 196859.949327234 & 10500.0506727661 \tabularnewline
81 & 196101 & 198367.336574557 & -2266.33657455701 \tabularnewline
82 & 200824 & 201476.322772161 & -652.322772160962 \tabularnewline
83 & 205743 & 202230.016395823 & 3512.98360417749 \tabularnewline
84 & 212489 & 204773.732375680 & 7715.2676243197 \tabularnewline
85 & 200810 & 205998.484514130 & -5188.48451413032 \tabularnewline
86 & 203683 & 209955.376038354 & -6272.37603835354 \tabularnewline
87 & 207286 & 211933.821800465 & -4647.82180046513 \tabularnewline
88 & 210910 & 210143.799444269 & 766.200555731074 \tabularnewline
89 & 194915 & 205810.061108215 & -10895.0611082149 \tabularnewline
90 & 217920 & 207411.660058496 & 10508.3399415042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&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]106370[/C][C]101611.917637004[/C][C]4758.08236299578[/C][/ROW]
[ROW][C]2[/C][C]109375[/C][C]103119.304884327[/C][C]6255.69511567294[/C][/ROW]
[ROW][C]3[/C][C]116476[/C][C]103307.728290242[/C][C]13168.2717097576[/C][/ROW]
[ROW][C]4[/C][C]123297[/C][C]104344.057022777[/C][C]18952.9429772229[/C][/ROW]
[ROW][C]5[/C][C]114813[/C][C]104815.115537566[/C][C]9997.88446243442[/C][/ROW]
[ROW][C]6[/C][C]117925[/C][C]107170.408111508[/C][C]10754.5918884920[/C][/ROW]
[ROW][C]7[/C][C]126466[/C][C]108583.583655873[/C][C]17882.4163441266[/C][/ROW]
[ROW][C]8[/C][C]131235[/C][C]110656.241120943[/C][C]20578.7588790573[/C][/ROW]
[ROW][C]9[/C][C]120546[/C][C]112352.051774181[/C][C]8193.94822581879[/C][/ROW]
[ROW][C]10[/C][C]123791[/C][C]115366.826268827[/C][C]8424.17373117254[/C][/ROW]
[ROW][C]11[/C][C]129813[/C][C]116403.155001362[/C][C]13409.8449986379[/C][/ROW]
[ROW][C]12[/C][C]133463[/C][C]118570.024169389[/C][C]14892.9758306109[/C][/ROW]
[ROW][C]13[/C][C]122987[/C][C]120548.469931501[/C][C]2438.5300684993[/C][/ROW]
[ROW][C]14[/C][C]125418[/C][C]125824.325297132[/C][C]-406.32529713163[/C][/ROW]
[ROW][C]15[/C][C]130199[/C][C]127802.771059243[/C][C]2396.22894075677[/C][/ROW]
[ROW][C]16[/C][C]133016[/C][C]129687.005118397[/C][C]3328.99488160287[/C][/ROW]
[ROW][C]17[/C][C]121454[/C][C]130346.487039101[/C][C]-8892.48703910101[/C][/ROW]
[ROW][C]18[/C][C]122044[/C][C]132984.414721916[/C][C]-10940.4147219165[/C][/ROW]
[ROW][C]19[/C][C]128313[/C][C]133549.684939663[/C][C]-5236.68493966263[/C][/ROW]
[ROW][C]20[/C][C]131556[/C][C]134774.437078113[/C][C]-3218.43707811268[/C][/ROW]
[ROW][C]21[/C][C]120027[/C][C]135433.918998817[/C][C]-15406.9189988165[/C][/ROW]
[ROW][C]22[/C][C]123001[/C][C]138166.058384590[/C][C]-15165.0583845897[/C][/ROW]
[ROW][C]23[/C][C]130111[/C][C]138071.846681632[/C][C]-7960.84668163202[/C][/ROW]
[ROW][C]24[/C][C]132524[/C][C]138637.116899378[/C][C]-6113.11689937818[/C][/ROW]
[ROW][C]25[/C][C]123742[/C][C]137789.211572759[/C][C]-14047.2115727589[/C][/ROW]
[ROW][C]26[/C][C]124931[/C][C]139861.869037828[/C][C]-14930.8690378282[/C][/ROW]
[ROW][C]27[/C][C]133646[/C][C]140238.715849659[/C][C]-6592.71584965902[/C][/ROW]
[ROW][C]28[/C][C]136557[/C][C]140709.774364447[/C][C]-4152.7743644475[/C][/ROW]
[ROW][C]29[/C][C]127509[/C][C]140898.197770363[/C][C]-13389.1977703629[/C][/ROW]
[ROW][C]30[/C][C]128945[/C][C]143253.490344305[/C][C]-14308.4903443053[/C][/ROW]
[ROW][C]31[/C][C]137191[/C][C]143347.702047263[/C][C]-6156.70204726295[/C][/ROW]
[ROW][C]32[/C][C]139716[/C][C]144195.607373882[/C][C]-4479.60737388221[/C][/ROW]
[ROW][C]33[/C][C]129083[/C][C]145420.359512332[/C][C]-16337.3595123323[/C][/ROW]
[ROW][C]34[/C][C]131604[/C][C]147964.07549219[/C][C]-16360.0754921900[/C][/ROW]
[ROW][C]35[/C][C]139413[/C][C]148340.922304021[/C][C]-8927.92230402081[/C][/ROW]
[ROW][C]36[/C][C]143125[/C][C]148529.345709936[/C][C]-5404.34570993619[/C][/ROW]
[ROW][C]37[/C][C]133948[/C][C]149283.039333598[/C][C]-15335.0393335978[/C][/ROW]
[ROW][C]38[/C][C]137116[/C][C]151073.061689794[/C][C]-13957.0616897940[/C][/ROW]
[ROW][C]39[/C][C]144864[/C][C]151355.696798667[/C][C]-6491.69679866705[/C][/ROW]
[ROW][C]40[/C][C]149277[/C][C]152203.602125286[/C][C]-2926.60212528631[/C][/ROW]
[ROW][C]41[/C][C]138796[/C][C]153051.507451906[/C][C]-14255.5074519056[/C][/ROW]
[ROW][C]42[/C][C]143258[/C][C]154935.741511059[/C][C]-11677.7415110595[/C][/ROW]
[ROW][C]43[/C][C]150034[/C][C]156348.917055425[/C][C]-6314.91705542491[/C][/ROW]
[ROW][C]44[/C][C]154708[/C][C]157573.669193875[/C][C]-2865.66919387493[/C][/ROW]
[ROW][C]45[/C][C]144888[/C][C]158044.727708663[/C][C]-13156.7277086634[/C][/ROW]
[ROW][C]46[/C][C]148762[/C][C]160871.078797394[/C][C]-12109.0787973943[/C][/ROW]
[ROW][C]47[/C][C]156500[/C][C]161342.137312183[/C][C]-4842.13731218275[/C][/ROW]
[ROW][C]48[/C][C]161088[/C][C]162001.619232887[/C][C]-913.619232886629[/C][/ROW]
[ROW][C]49[/C][C]152772[/C][C]161342.137312183[/C][C]-8570.13731218275[/C][/ROW]
[ROW][C]50[/C][C]158011[/C][C]163037.947965421[/C][C]-5026.94796542127[/C][/ROW]
[ROW][C]51[/C][C]163318[/C][C]163132.159668379[/C][C]185.840331621038[/C][/ROW]
[ROW][C]52[/C][C]169969[/C][C]164262.700103871[/C][C]5706.29989612868[/C][/ROW]
[ROW][C]53[/C][C]162269[/C][C]164922.182024575[/C][C]-2653.18202457518[/C][/ROW]
[ROW][C]54[/C][C]165765[/C][C]167842.744816264[/C][C]-2077.74481626373[/C][/ROW]
[ROW][C]55[/C][C]170600[/C][C]168125.379925137[/C][C]2474.62007486318[/C][/ROW]
[ROW][C]56[/C][C]174681[/C][C]169161.708657671[/C][C]5519.29134232854[/C][/ROW]
[ROW][C]57[/C][C]166364[/C][C]168973.285251756[/C][C]-2609.28525175609[/C][/ROW]
[ROW][C]58[/C][C]170240[/C][C]170951.731013868[/C][C]-711.731013867682[/C][/ROW]
[ROW][C]59[/C][C]176150[/C][C]171045.942716825[/C][C]5104.05728317462[/C][/ROW]
[ROW][C]60[/C][C]182056[/C][C]170857.51931091[/C][C]11198.48068909[/C][/ROW]
[ROW][C]61[/C][C]172218[/C][C]170951.731013868[/C][C]1266.26898613232[/C][/ROW]
[ROW][C]62[/C][C]177856[/C][C]172930.176775979[/C][C]4925.82322402072[/C][/ROW]
[ROW][C]63[/C][C]182253[/C][C]173495.446993725[/C][C]8757.55300627456[/C][/ROW]
[ROW][C]64[/C][C]188090[/C][C]175002.834241049[/C][C]13087.1657589514[/C][/ROW]
[ROW][C]65[/C][C]176863[/C][C]175944.951270626[/C][C]918.048729374482[/C][/ROW]
[ROW][C]66[/C][C]183273[/C][C]177923.397032737[/C][C]5349.60296726286[/C][/ROW]
[ROW][C]67[/C][C]187969[/C][C]178394.455547526[/C][C]9574.54445247439[/C][/ROW]
[ROW][C]68[/C][C]194650[/C][C]179430.784280060[/C][C]15219.2157199397[/C][/ROW]
[ROW][C]69[/C][C]183036[/C][C]180278.689606680[/C][C]2757.31039332048[/C][/ROW]
[ROW][C]70[/C][C]189516[/C][C]182633.982180622[/C][C]6882.0178193781[/C][/ROW]
[ROW][C]71[/C][C]193805[/C][C]183670.310913157[/C][C]10134.6890868435[/C][/ROW]
[ROW][C]72[/C][C]200499[/C][C]185366.121566395[/C][C]15132.8784336050[/C][/ROW]
[ROW][C]73[/C][C]188142[/C][C]185837.180081184[/C][C]2304.81991881647[/C][/ROW]
[ROW][C]74[/C][C]193732[/C][C]187909.837546253[/C][C]5822.16245374717[/C][/ROW]
[ROW][C]75[/C][C]197126[/C][C]188569.319466957[/C][C]8556.68053304331[/C][/ROW]
[ROW][C]76[/C][C]205140[/C][C]189605.648199491[/C][C]15534.3518005087[/C][/ROW]
[ROW][C]77[/C][C]191751[/C][C]190076.706714280[/C][C]1674.29328572019[/C][/ROW]
[ROW][C]78[/C][C]196700[/C][C]193279.904614841[/C][C]3420.09538515854[/C][/ROW]
[ROW][C]79[/C][C]199784[/C][C]194881.503565122[/C][C]4902.49643487771[/C][/ROW]
[ROW][C]80[/C][C]207360[/C][C]196859.949327234[/C][C]10500.0506727661[/C][/ROW]
[ROW][C]81[/C][C]196101[/C][C]198367.336574557[/C][C]-2266.33657455701[/C][/ROW]
[ROW][C]82[/C][C]200824[/C][C]201476.322772161[/C][C]-652.322772160962[/C][/ROW]
[ROW][C]83[/C][C]205743[/C][C]202230.016395823[/C][C]3512.98360417749[/C][/ROW]
[ROW][C]84[/C][C]212489[/C][C]204773.732375680[/C][C]7715.2676243197[/C][/ROW]
[ROW][C]85[/C][C]200810[/C][C]205998.484514130[/C][C]-5188.48451413032[/C][/ROW]
[ROW][C]86[/C][C]203683[/C][C]209955.376038354[/C][C]-6272.37603835354[/C][/ROW]
[ROW][C]87[/C][C]207286[/C][C]211933.821800465[/C][C]-4647.82180046513[/C][/ROW]
[ROW][C]88[/C][C]210910[/C][C]210143.799444269[/C][C]766.200555731074[/C][/ROW]
[ROW][C]89[/C][C]194915[/C][C]205810.061108215[/C][C]-10895.0611082149[/C][/ROW]
[ROW][C]90[/C][C]217920[/C][C]207411.660058496[/C][C]10508.3399415042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106370101611.9176370044758.08236299578
2109375103119.3048843276255.69511567294
3116476103307.72829024213168.2717097576
4123297104344.05702277718952.9429772229
5114813104815.1155375669997.88446243442
6117925107170.40811150810754.5918884920
7126466108583.58365587317882.4163441266
8131235110656.24112094320578.7588790573
9120546112352.0517741818193.94822581879
10123791115366.8262688278424.17373117254
11129813116403.15500136213409.8449986379
12133463118570.02416938914892.9758306109
13122987120548.4699315012438.5300684993
14125418125824.325297132-406.32529713163
15130199127802.7710592432396.22894075677
16133016129687.0051183973328.99488160287
17121454130346.487039101-8892.48703910101
18122044132984.414721916-10940.4147219165
19128313133549.684939663-5236.68493966263
20131556134774.437078113-3218.43707811268
21120027135433.918998817-15406.9189988165
22123001138166.058384590-15165.0583845897
23130111138071.846681632-7960.84668163202
24132524138637.116899378-6113.11689937818
25123742137789.211572759-14047.2115727589
26124931139861.869037828-14930.8690378282
27133646140238.715849659-6592.71584965902
28136557140709.774364447-4152.7743644475
29127509140898.197770363-13389.1977703629
30128945143253.490344305-14308.4903443053
31137191143347.702047263-6156.70204726295
32139716144195.607373882-4479.60737388221
33129083145420.359512332-16337.3595123323
34131604147964.07549219-16360.0754921900
35139413148340.922304021-8927.92230402081
36143125148529.345709936-5404.34570993619
37133948149283.039333598-15335.0393335978
38137116151073.061689794-13957.0616897940
39144864151355.696798667-6491.69679866705
40149277152203.602125286-2926.60212528631
41138796153051.507451906-14255.5074519056
42143258154935.741511059-11677.7415110595
43150034156348.917055425-6314.91705542491
44154708157573.669193875-2865.66919387493
45144888158044.727708663-13156.7277086634
46148762160871.078797394-12109.0787973943
47156500161342.137312183-4842.13731218275
48161088162001.619232887-913.619232886629
49152772161342.137312183-8570.13731218275
50158011163037.947965421-5026.94796542127
51163318163132.159668379185.840331621038
52169969164262.7001038715706.29989612868
53162269164922.182024575-2653.18202457518
54165765167842.744816264-2077.74481626373
55170600168125.3799251372474.62007486318
56174681169161.7086576715519.29134232854
57166364168973.285251756-2609.28525175609
58170240170951.731013868-711.731013867682
59176150171045.9427168255104.05728317462
60182056170857.5193109111198.48068909
61172218170951.7310138681266.26898613232
62177856172930.1767759794925.82322402072
63182253173495.4469937258757.55300627456
64188090175002.83424104913087.1657589514
65176863175944.951270626918.048729374482
66183273177923.3970327375349.60296726286
67187969178394.4555475269574.54445247439
68194650179430.78428006015219.2157199397
69183036180278.6896066802757.31039332048
70189516182633.9821806226882.0178193781
71193805183670.31091315710134.6890868435
72200499185366.12156639515132.8784336050
73188142185837.1800811842304.81991881647
74193732187909.8375462535822.16245374717
75197126188569.3194669578556.68053304331
76205140189605.64819949115534.3518005087
77191751190076.7067142801674.29328572019
78196700193279.9046148413420.09538515854
79199784194881.5035651224902.49643487771
80207360196859.94932723410500.0506727661
81196101198367.336574557-2266.33657455701
82200824201476.322772161-652.322772160962
83205743202230.0163958233512.98360417749
84212489204773.7323756807715.2676243197
85200810205998.484514130-5188.48451413032
86203683209955.376038354-6272.37603835354
87207286211933.821800465-4647.82180046513
88210910210143.799444269766.200555731074
89194915205810.061108215-10895.0611082149
90217920207411.66005849610508.3399415042







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1685602743317520.3371205486635050.831439725668248
60.1413365741131610.2826731482263220.858663425886839
70.08060798345398580.1612159669079720.919392016546014
80.04885610508178730.09771221016357460.951143894918213
90.1438549650705600.2877099301411190.85614503492944
100.1549559380197460.3099118760394920.845044061980254
110.1262078399230690.2524156798461380.873792160076931
120.1193849474845360.2387698949690710.880615052515464
130.2217923700223800.4435847400447590.77820762997762
140.2761687844013220.5523375688026450.723831215598678
150.2429186008006590.4858372016013180.757081399199341
160.2113522443928470.4227044887856940.788647755607153
170.3167605053304730.6335210106609450.683239494669527
180.3623580190444910.7247160380889820.637641980955509
190.3004894623981630.6009789247963260.699510537601837
200.2473422670921780.4946845341843550.752657732907822
210.3109853230337060.6219706460674120.689014676966294
220.3063195058167180.6126390116334360.693680494183282
230.2457340640853290.4914681281706580.754265935914671
240.2014584723051190.4029169446102370.798541527694881
250.1837901576095290.3675803152190570.816209842390471
260.1629393087851250.3258786175702500.837060691214875
270.1359273268503380.2718546537006750.864072673149662
280.1294744418804950.2589488837609900.870525558119505
290.1052900557600700.2105801115201410.89470994423993
300.086091790559410.172183581118820.91390820944059
310.07779656397016150.1555931279403230.922203436029839
320.0796874984756190.1593749969512380.920312501524381
330.07448375119419680.1489675023883940.925516248805803
340.07019639149704640.1403927829940930.929803608502954
350.06431606720691420.1286321344138280.935683932793086
360.0714268427088060.1428536854176120.928573157291194
370.06934675115807260.1386935023161450.930653248841927
380.06801523928123370.1360304785624670.931984760718766
390.0769307434377820.1538614868755640.923069256562218
400.1090665848192480.2181331696384960.890933415180752
410.1194812019591190.2389624039182380.880518798040881
420.131928169373390.263856338746780.86807183062661
430.1605548137721910.3211096275443810.839445186227809
440.2188017052968220.4376034105936430.781198294703178
450.2783983287286960.5567966574573910.721601671271304
460.3723555389390910.7447110778781820.627644461060909
470.4584488205870210.9168976411740430.541551179412979
480.5617433721562190.8765132556875610.438256627843781
490.6586705044840250.682658991031950.341329495515975
500.739562470289420.520875059421160.26043752971058
510.8036391363936380.3927217272127240.196360863606362
520.8756367847939120.2487264304121770.124363215206088
530.9053287019302010.1893425961395990.0946712980697993
540.931585518069540.1368289638609190.0684144819304594
550.9465123957085250.1069752085829510.0534876042914754
560.9580273674544140.08394526509117260.0419726325455863
570.9734200172370960.05315996552580760.0265799827629038
580.9825053887627150.03498922247456930.0174946112372847
590.9850089778620290.0299820442759430.0149910221379715
600.9887125971244460.02257480575110740.0112874028755537
610.991708737980770.01658252403846010.00829126201923006
620.9921072326520450.01578553469590960.0078927673479548
630.9916083699506040.01678326009879110.00839163004939553
640.9924476991720630.01510460165587330.00755230082793667
650.9945579125433350.01088417491333060.00544208745666531
660.9939422135309460.01211557293810740.00605778646905371
670.9921708418016560.01565831639668810.00782915819834406
680.9928154571617790.01436908567644270.00718454283822133
690.9928628593187530.01427428136249410.00713714068124703
700.9898633971251880.02027320574962480.0101366028748124
710.9849710504121870.03005789917562620.0150289495878131
720.9858036906466250.02839261870675080.0141963093533754
730.9824879494254140.03502410114917250.0175120505745862
740.9725622855804660.05487542883906750.0274377144195337
750.9564096171942780.08718076561144460.0435903828057223
760.9655664972892990.06886700542140260.0344335027107013
770.9488453144325650.1023093711348700.0511546855674348
780.9181771551350350.1636456897299310.0818228448649654
790.8696032225209280.2607935549581440.130396777479072
800.8578759180600950.284248163879810.142124081939905
810.799115292737910.401769414524180.20088470726209
820.7107333881016870.5785332237966260.289266611898313
830.5847484386012670.8305031227974660.415251561398733
840.5682418958145610.8635162083708770.431758104185439
850.4106287688690480.8212575377380970.589371231130952

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.168560274331752 & 0.337120548663505 & 0.831439725668248 \tabularnewline
6 & 0.141336574113161 & 0.282673148226322 & 0.858663425886839 \tabularnewline
7 & 0.0806079834539858 & 0.161215966907972 & 0.919392016546014 \tabularnewline
8 & 0.0488561050817873 & 0.0977122101635746 & 0.951143894918213 \tabularnewline
9 & 0.143854965070560 & 0.287709930141119 & 0.85614503492944 \tabularnewline
10 & 0.154955938019746 & 0.309911876039492 & 0.845044061980254 \tabularnewline
11 & 0.126207839923069 & 0.252415679846138 & 0.873792160076931 \tabularnewline
12 & 0.119384947484536 & 0.238769894969071 & 0.880615052515464 \tabularnewline
13 & 0.221792370022380 & 0.443584740044759 & 0.77820762997762 \tabularnewline
14 & 0.276168784401322 & 0.552337568802645 & 0.723831215598678 \tabularnewline
15 & 0.242918600800659 & 0.485837201601318 & 0.757081399199341 \tabularnewline
16 & 0.211352244392847 & 0.422704488785694 & 0.788647755607153 \tabularnewline
17 & 0.316760505330473 & 0.633521010660945 & 0.683239494669527 \tabularnewline
18 & 0.362358019044491 & 0.724716038088982 & 0.637641980955509 \tabularnewline
19 & 0.300489462398163 & 0.600978924796326 & 0.699510537601837 \tabularnewline
20 & 0.247342267092178 & 0.494684534184355 & 0.752657732907822 \tabularnewline
21 & 0.310985323033706 & 0.621970646067412 & 0.689014676966294 \tabularnewline
22 & 0.306319505816718 & 0.612639011633436 & 0.693680494183282 \tabularnewline
23 & 0.245734064085329 & 0.491468128170658 & 0.754265935914671 \tabularnewline
24 & 0.201458472305119 & 0.402916944610237 & 0.798541527694881 \tabularnewline
25 & 0.183790157609529 & 0.367580315219057 & 0.816209842390471 \tabularnewline
26 & 0.162939308785125 & 0.325878617570250 & 0.837060691214875 \tabularnewline
27 & 0.135927326850338 & 0.271854653700675 & 0.864072673149662 \tabularnewline
28 & 0.129474441880495 & 0.258948883760990 & 0.870525558119505 \tabularnewline
29 & 0.105290055760070 & 0.210580111520141 & 0.89470994423993 \tabularnewline
30 & 0.08609179055941 & 0.17218358111882 & 0.91390820944059 \tabularnewline
31 & 0.0777965639701615 & 0.155593127940323 & 0.922203436029839 \tabularnewline
32 & 0.079687498475619 & 0.159374996951238 & 0.920312501524381 \tabularnewline
33 & 0.0744837511941968 & 0.148967502388394 & 0.925516248805803 \tabularnewline
34 & 0.0701963914970464 & 0.140392782994093 & 0.929803608502954 \tabularnewline
35 & 0.0643160672069142 & 0.128632134413828 & 0.935683932793086 \tabularnewline
36 & 0.071426842708806 & 0.142853685417612 & 0.928573157291194 \tabularnewline
37 & 0.0693467511580726 & 0.138693502316145 & 0.930653248841927 \tabularnewline
38 & 0.0680152392812337 & 0.136030478562467 & 0.931984760718766 \tabularnewline
39 & 0.076930743437782 & 0.153861486875564 & 0.923069256562218 \tabularnewline
40 & 0.109066584819248 & 0.218133169638496 & 0.890933415180752 \tabularnewline
41 & 0.119481201959119 & 0.238962403918238 & 0.880518798040881 \tabularnewline
42 & 0.13192816937339 & 0.26385633874678 & 0.86807183062661 \tabularnewline
43 & 0.160554813772191 & 0.321109627544381 & 0.839445186227809 \tabularnewline
44 & 0.218801705296822 & 0.437603410593643 & 0.781198294703178 \tabularnewline
45 & 0.278398328728696 & 0.556796657457391 & 0.721601671271304 \tabularnewline
46 & 0.372355538939091 & 0.744711077878182 & 0.627644461060909 \tabularnewline
47 & 0.458448820587021 & 0.916897641174043 & 0.541551179412979 \tabularnewline
48 & 0.561743372156219 & 0.876513255687561 & 0.438256627843781 \tabularnewline
49 & 0.658670504484025 & 0.68265899103195 & 0.341329495515975 \tabularnewline
50 & 0.73956247028942 & 0.52087505942116 & 0.26043752971058 \tabularnewline
51 & 0.803639136393638 & 0.392721727212724 & 0.196360863606362 \tabularnewline
52 & 0.875636784793912 & 0.248726430412177 & 0.124363215206088 \tabularnewline
53 & 0.905328701930201 & 0.189342596139599 & 0.0946712980697993 \tabularnewline
54 & 0.93158551806954 & 0.136828963860919 & 0.0684144819304594 \tabularnewline
55 & 0.946512395708525 & 0.106975208582951 & 0.0534876042914754 \tabularnewline
56 & 0.958027367454414 & 0.0839452650911726 & 0.0419726325455863 \tabularnewline
57 & 0.973420017237096 & 0.0531599655258076 & 0.0265799827629038 \tabularnewline
58 & 0.982505388762715 & 0.0349892224745693 & 0.0174946112372847 \tabularnewline
59 & 0.985008977862029 & 0.029982044275943 & 0.0149910221379715 \tabularnewline
60 & 0.988712597124446 & 0.0225748057511074 & 0.0112874028755537 \tabularnewline
61 & 0.99170873798077 & 0.0165825240384601 & 0.00829126201923006 \tabularnewline
62 & 0.992107232652045 & 0.0157855346959096 & 0.0078927673479548 \tabularnewline
63 & 0.991608369950604 & 0.0167832600987911 & 0.00839163004939553 \tabularnewline
64 & 0.992447699172063 & 0.0151046016558733 & 0.00755230082793667 \tabularnewline
65 & 0.994557912543335 & 0.0108841749133306 & 0.00544208745666531 \tabularnewline
66 & 0.993942213530946 & 0.0121155729381074 & 0.00605778646905371 \tabularnewline
67 & 0.992170841801656 & 0.0156583163966881 & 0.00782915819834406 \tabularnewline
68 & 0.992815457161779 & 0.0143690856764427 & 0.00718454283822133 \tabularnewline
69 & 0.992862859318753 & 0.0142742813624941 & 0.00713714068124703 \tabularnewline
70 & 0.989863397125188 & 0.0202732057496248 & 0.0101366028748124 \tabularnewline
71 & 0.984971050412187 & 0.0300578991756262 & 0.0150289495878131 \tabularnewline
72 & 0.985803690646625 & 0.0283926187067508 & 0.0141963093533754 \tabularnewline
73 & 0.982487949425414 & 0.0350241011491725 & 0.0175120505745862 \tabularnewline
74 & 0.972562285580466 & 0.0548754288390675 & 0.0274377144195337 \tabularnewline
75 & 0.956409617194278 & 0.0871807656114446 & 0.0435903828057223 \tabularnewline
76 & 0.965566497289299 & 0.0688670054214026 & 0.0344335027107013 \tabularnewline
77 & 0.948845314432565 & 0.102309371134870 & 0.0511546855674348 \tabularnewline
78 & 0.918177155135035 & 0.163645689729931 & 0.0818228448649654 \tabularnewline
79 & 0.869603222520928 & 0.260793554958144 & 0.130396777479072 \tabularnewline
80 & 0.857875918060095 & 0.28424816387981 & 0.142124081939905 \tabularnewline
81 & 0.79911529273791 & 0.40176941452418 & 0.20088470726209 \tabularnewline
82 & 0.710733388101687 & 0.578533223796626 & 0.289266611898313 \tabularnewline
83 & 0.584748438601267 & 0.830503122797466 & 0.415251561398733 \tabularnewline
84 & 0.568241895814561 & 0.863516208370877 & 0.431758104185439 \tabularnewline
85 & 0.410628768869048 & 0.821257537738097 & 0.589371231130952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&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]5[/C][C]0.168560274331752[/C][C]0.337120548663505[/C][C]0.831439725668248[/C][/ROW]
[ROW][C]6[/C][C]0.141336574113161[/C][C]0.282673148226322[/C][C]0.858663425886839[/C][/ROW]
[ROW][C]7[/C][C]0.0806079834539858[/C][C]0.161215966907972[/C][C]0.919392016546014[/C][/ROW]
[ROW][C]8[/C][C]0.0488561050817873[/C][C]0.0977122101635746[/C][C]0.951143894918213[/C][/ROW]
[ROW][C]9[/C][C]0.143854965070560[/C][C]0.287709930141119[/C][C]0.85614503492944[/C][/ROW]
[ROW][C]10[/C][C]0.154955938019746[/C][C]0.309911876039492[/C][C]0.845044061980254[/C][/ROW]
[ROW][C]11[/C][C]0.126207839923069[/C][C]0.252415679846138[/C][C]0.873792160076931[/C][/ROW]
[ROW][C]12[/C][C]0.119384947484536[/C][C]0.238769894969071[/C][C]0.880615052515464[/C][/ROW]
[ROW][C]13[/C][C]0.221792370022380[/C][C]0.443584740044759[/C][C]0.77820762997762[/C][/ROW]
[ROW][C]14[/C][C]0.276168784401322[/C][C]0.552337568802645[/C][C]0.723831215598678[/C][/ROW]
[ROW][C]15[/C][C]0.242918600800659[/C][C]0.485837201601318[/C][C]0.757081399199341[/C][/ROW]
[ROW][C]16[/C][C]0.211352244392847[/C][C]0.422704488785694[/C][C]0.788647755607153[/C][/ROW]
[ROW][C]17[/C][C]0.316760505330473[/C][C]0.633521010660945[/C][C]0.683239494669527[/C][/ROW]
[ROW][C]18[/C][C]0.362358019044491[/C][C]0.724716038088982[/C][C]0.637641980955509[/C][/ROW]
[ROW][C]19[/C][C]0.300489462398163[/C][C]0.600978924796326[/C][C]0.699510537601837[/C][/ROW]
[ROW][C]20[/C][C]0.247342267092178[/C][C]0.494684534184355[/C][C]0.752657732907822[/C][/ROW]
[ROW][C]21[/C][C]0.310985323033706[/C][C]0.621970646067412[/C][C]0.689014676966294[/C][/ROW]
[ROW][C]22[/C][C]0.306319505816718[/C][C]0.612639011633436[/C][C]0.693680494183282[/C][/ROW]
[ROW][C]23[/C][C]0.245734064085329[/C][C]0.491468128170658[/C][C]0.754265935914671[/C][/ROW]
[ROW][C]24[/C][C]0.201458472305119[/C][C]0.402916944610237[/C][C]0.798541527694881[/C][/ROW]
[ROW][C]25[/C][C]0.183790157609529[/C][C]0.367580315219057[/C][C]0.816209842390471[/C][/ROW]
[ROW][C]26[/C][C]0.162939308785125[/C][C]0.325878617570250[/C][C]0.837060691214875[/C][/ROW]
[ROW][C]27[/C][C]0.135927326850338[/C][C]0.271854653700675[/C][C]0.864072673149662[/C][/ROW]
[ROW][C]28[/C][C]0.129474441880495[/C][C]0.258948883760990[/C][C]0.870525558119505[/C][/ROW]
[ROW][C]29[/C][C]0.105290055760070[/C][C]0.210580111520141[/C][C]0.89470994423993[/C][/ROW]
[ROW][C]30[/C][C]0.08609179055941[/C][C]0.17218358111882[/C][C]0.91390820944059[/C][/ROW]
[ROW][C]31[/C][C]0.0777965639701615[/C][C]0.155593127940323[/C][C]0.922203436029839[/C][/ROW]
[ROW][C]32[/C][C]0.079687498475619[/C][C]0.159374996951238[/C][C]0.920312501524381[/C][/ROW]
[ROW][C]33[/C][C]0.0744837511941968[/C][C]0.148967502388394[/C][C]0.925516248805803[/C][/ROW]
[ROW][C]34[/C][C]0.0701963914970464[/C][C]0.140392782994093[/C][C]0.929803608502954[/C][/ROW]
[ROW][C]35[/C][C]0.0643160672069142[/C][C]0.128632134413828[/C][C]0.935683932793086[/C][/ROW]
[ROW][C]36[/C][C]0.071426842708806[/C][C]0.142853685417612[/C][C]0.928573157291194[/C][/ROW]
[ROW][C]37[/C][C]0.0693467511580726[/C][C]0.138693502316145[/C][C]0.930653248841927[/C][/ROW]
[ROW][C]38[/C][C]0.0680152392812337[/C][C]0.136030478562467[/C][C]0.931984760718766[/C][/ROW]
[ROW][C]39[/C][C]0.076930743437782[/C][C]0.153861486875564[/C][C]0.923069256562218[/C][/ROW]
[ROW][C]40[/C][C]0.109066584819248[/C][C]0.218133169638496[/C][C]0.890933415180752[/C][/ROW]
[ROW][C]41[/C][C]0.119481201959119[/C][C]0.238962403918238[/C][C]0.880518798040881[/C][/ROW]
[ROW][C]42[/C][C]0.13192816937339[/C][C]0.26385633874678[/C][C]0.86807183062661[/C][/ROW]
[ROW][C]43[/C][C]0.160554813772191[/C][C]0.321109627544381[/C][C]0.839445186227809[/C][/ROW]
[ROW][C]44[/C][C]0.218801705296822[/C][C]0.437603410593643[/C][C]0.781198294703178[/C][/ROW]
[ROW][C]45[/C][C]0.278398328728696[/C][C]0.556796657457391[/C][C]0.721601671271304[/C][/ROW]
[ROW][C]46[/C][C]0.372355538939091[/C][C]0.744711077878182[/C][C]0.627644461060909[/C][/ROW]
[ROW][C]47[/C][C]0.458448820587021[/C][C]0.916897641174043[/C][C]0.541551179412979[/C][/ROW]
[ROW][C]48[/C][C]0.561743372156219[/C][C]0.876513255687561[/C][C]0.438256627843781[/C][/ROW]
[ROW][C]49[/C][C]0.658670504484025[/C][C]0.68265899103195[/C][C]0.341329495515975[/C][/ROW]
[ROW][C]50[/C][C]0.73956247028942[/C][C]0.52087505942116[/C][C]0.26043752971058[/C][/ROW]
[ROW][C]51[/C][C]0.803639136393638[/C][C]0.392721727212724[/C][C]0.196360863606362[/C][/ROW]
[ROW][C]52[/C][C]0.875636784793912[/C][C]0.248726430412177[/C][C]0.124363215206088[/C][/ROW]
[ROW][C]53[/C][C]0.905328701930201[/C][C]0.189342596139599[/C][C]0.0946712980697993[/C][/ROW]
[ROW][C]54[/C][C]0.93158551806954[/C][C]0.136828963860919[/C][C]0.0684144819304594[/C][/ROW]
[ROW][C]55[/C][C]0.946512395708525[/C][C]0.106975208582951[/C][C]0.0534876042914754[/C][/ROW]
[ROW][C]56[/C][C]0.958027367454414[/C][C]0.0839452650911726[/C][C]0.0419726325455863[/C][/ROW]
[ROW][C]57[/C][C]0.973420017237096[/C][C]0.0531599655258076[/C][C]0.0265799827629038[/C][/ROW]
[ROW][C]58[/C][C]0.982505388762715[/C][C]0.0349892224745693[/C][C]0.0174946112372847[/C][/ROW]
[ROW][C]59[/C][C]0.985008977862029[/C][C]0.029982044275943[/C][C]0.0149910221379715[/C][/ROW]
[ROW][C]60[/C][C]0.988712597124446[/C][C]0.0225748057511074[/C][C]0.0112874028755537[/C][/ROW]
[ROW][C]61[/C][C]0.99170873798077[/C][C]0.0165825240384601[/C][C]0.00829126201923006[/C][/ROW]
[ROW][C]62[/C][C]0.992107232652045[/C][C]0.0157855346959096[/C][C]0.0078927673479548[/C][/ROW]
[ROW][C]63[/C][C]0.991608369950604[/C][C]0.0167832600987911[/C][C]0.00839163004939553[/C][/ROW]
[ROW][C]64[/C][C]0.992447699172063[/C][C]0.0151046016558733[/C][C]0.00755230082793667[/C][/ROW]
[ROW][C]65[/C][C]0.994557912543335[/C][C]0.0108841749133306[/C][C]0.00544208745666531[/C][/ROW]
[ROW][C]66[/C][C]0.993942213530946[/C][C]0.0121155729381074[/C][C]0.00605778646905371[/C][/ROW]
[ROW][C]67[/C][C]0.992170841801656[/C][C]0.0156583163966881[/C][C]0.00782915819834406[/C][/ROW]
[ROW][C]68[/C][C]0.992815457161779[/C][C]0.0143690856764427[/C][C]0.00718454283822133[/C][/ROW]
[ROW][C]69[/C][C]0.992862859318753[/C][C]0.0142742813624941[/C][C]0.00713714068124703[/C][/ROW]
[ROW][C]70[/C][C]0.989863397125188[/C][C]0.0202732057496248[/C][C]0.0101366028748124[/C][/ROW]
[ROW][C]71[/C][C]0.984971050412187[/C][C]0.0300578991756262[/C][C]0.0150289495878131[/C][/ROW]
[ROW][C]72[/C][C]0.985803690646625[/C][C]0.0283926187067508[/C][C]0.0141963093533754[/C][/ROW]
[ROW][C]73[/C][C]0.982487949425414[/C][C]0.0350241011491725[/C][C]0.0175120505745862[/C][/ROW]
[ROW][C]74[/C][C]0.972562285580466[/C][C]0.0548754288390675[/C][C]0.0274377144195337[/C][/ROW]
[ROW][C]75[/C][C]0.956409617194278[/C][C]0.0871807656114446[/C][C]0.0435903828057223[/C][/ROW]
[ROW][C]76[/C][C]0.965566497289299[/C][C]0.0688670054214026[/C][C]0.0344335027107013[/C][/ROW]
[ROW][C]77[/C][C]0.948845314432565[/C][C]0.102309371134870[/C][C]0.0511546855674348[/C][/ROW]
[ROW][C]78[/C][C]0.918177155135035[/C][C]0.163645689729931[/C][C]0.0818228448649654[/C][/ROW]
[ROW][C]79[/C][C]0.869603222520928[/C][C]0.260793554958144[/C][C]0.130396777479072[/C][/ROW]
[ROW][C]80[/C][C]0.857875918060095[/C][C]0.28424816387981[/C][C]0.142124081939905[/C][/ROW]
[ROW][C]81[/C][C]0.79911529273791[/C][C]0.40176941452418[/C][C]0.20088470726209[/C][/ROW]
[ROW][C]82[/C][C]0.710733388101687[/C][C]0.578533223796626[/C][C]0.289266611898313[/C][/ROW]
[ROW][C]83[/C][C]0.584748438601267[/C][C]0.830503122797466[/C][C]0.415251561398733[/C][/ROW]
[ROW][C]84[/C][C]0.568241895814561[/C][C]0.863516208370877[/C][C]0.431758104185439[/C][/ROW]
[ROW][C]85[/C][C]0.410628768869048[/C][C]0.821257537738097[/C][C]0.589371231130952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1685602743317520.3371205486635050.831439725668248
60.1413365741131610.2826731482263220.858663425886839
70.08060798345398580.1612159669079720.919392016546014
80.04885610508178730.09771221016357460.951143894918213
90.1438549650705600.2877099301411190.85614503492944
100.1549559380197460.3099118760394920.845044061980254
110.1262078399230690.2524156798461380.873792160076931
120.1193849474845360.2387698949690710.880615052515464
130.2217923700223800.4435847400447590.77820762997762
140.2761687844013220.5523375688026450.723831215598678
150.2429186008006590.4858372016013180.757081399199341
160.2113522443928470.4227044887856940.788647755607153
170.3167605053304730.6335210106609450.683239494669527
180.3623580190444910.7247160380889820.637641980955509
190.3004894623981630.6009789247963260.699510537601837
200.2473422670921780.4946845341843550.752657732907822
210.3109853230337060.6219706460674120.689014676966294
220.3063195058167180.6126390116334360.693680494183282
230.2457340640853290.4914681281706580.754265935914671
240.2014584723051190.4029169446102370.798541527694881
250.1837901576095290.3675803152190570.816209842390471
260.1629393087851250.3258786175702500.837060691214875
270.1359273268503380.2718546537006750.864072673149662
280.1294744418804950.2589488837609900.870525558119505
290.1052900557600700.2105801115201410.89470994423993
300.086091790559410.172183581118820.91390820944059
310.07779656397016150.1555931279403230.922203436029839
320.0796874984756190.1593749969512380.920312501524381
330.07448375119419680.1489675023883940.925516248805803
340.07019639149704640.1403927829940930.929803608502954
350.06431606720691420.1286321344138280.935683932793086
360.0714268427088060.1428536854176120.928573157291194
370.06934675115807260.1386935023161450.930653248841927
380.06801523928123370.1360304785624670.931984760718766
390.0769307434377820.1538614868755640.923069256562218
400.1090665848192480.2181331696384960.890933415180752
410.1194812019591190.2389624039182380.880518798040881
420.131928169373390.263856338746780.86807183062661
430.1605548137721910.3211096275443810.839445186227809
440.2188017052968220.4376034105936430.781198294703178
450.2783983287286960.5567966574573910.721601671271304
460.3723555389390910.7447110778781820.627644461060909
470.4584488205870210.9168976411740430.541551179412979
480.5617433721562190.8765132556875610.438256627843781
490.6586705044840250.682658991031950.341329495515975
500.739562470289420.520875059421160.26043752971058
510.8036391363936380.3927217272127240.196360863606362
520.8756367847939120.2487264304121770.124363215206088
530.9053287019302010.1893425961395990.0946712980697993
540.931585518069540.1368289638609190.0684144819304594
550.9465123957085250.1069752085829510.0534876042914754
560.9580273674544140.08394526509117260.0419726325455863
570.9734200172370960.05315996552580760.0265799827629038
580.9825053887627150.03498922247456930.0174946112372847
590.9850089778620290.0299820442759430.0149910221379715
600.9887125971244460.02257480575110740.0112874028755537
610.991708737980770.01658252403846010.00829126201923006
620.9921072326520450.01578553469590960.0078927673479548
630.9916083699506040.01678326009879110.00839163004939553
640.9924476991720630.01510460165587330.00755230082793667
650.9945579125433350.01088417491333060.00544208745666531
660.9939422135309460.01211557293810740.00605778646905371
670.9921708418016560.01565831639668810.00782915819834406
680.9928154571617790.01436908567644270.00718454283822133
690.9928628593187530.01427428136249410.00713714068124703
700.9898633971251880.02027320574962480.0101366028748124
710.9849710504121870.03005789917562620.0150289495878131
720.9858036906466250.02839261870675080.0141963093533754
730.9824879494254140.03502410114917250.0175120505745862
740.9725622855804660.05487542883906750.0274377144195337
750.9564096171942780.08718076561144460.0435903828057223
760.9655664972892990.06886700542140260.0344335027107013
770.9488453144325650.1023093711348700.0511546855674348
780.9181771551350350.1636456897299310.0818228448649654
790.8696032225209280.2607935549581440.130396777479072
800.8578759180600950.284248163879810.142124081939905
810.799115292737910.401769414524180.20088470726209
820.7107333881016870.5785332237966260.289266611898313
830.5847484386012670.8305031227974660.415251561398733
840.5682418958145610.8635162083708770.431758104185439
850.4106287688690480.8212575377380970.589371231130952







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.197530864197531NOK
10% type I error level220.271604938271605NOK

\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 & 16 & 0.197530864197531 & NOK \tabularnewline
10% type I error level & 22 & 0.271604938271605 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58132&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]16[/C][C]0.197530864197531[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.271604938271605[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58132&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58132&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.197530864197531NOK
10% type I error level220.271604938271605NOK



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