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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 06:54:09 -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/t1258725465rmtllii0syw32ii.htm/, Retrieved Thu, 28 Mar 2024 23:37:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58165, Retrieved Thu, 28 Mar 2024 23:37:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsRob_WS7_3
Estimated Impact118
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] [4f76e114ed5e444b1133aad392380aad]
-   PD        [Multiple Regression] [] [2009-11-20 13:54:09] [9002751dd674b8c934bf183fdf4510e9] [Current]
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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 time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&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]4 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=58165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
HFCE[t] = + 207810.550167275 -916.412977675751RPI[t] -12387.1281946683Q1[t] -7662.91380445677Q2[t] -3693.15077859989Q3[t] + 2256.38373280118t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HFCE[t] =  +  207810.550167275 -916.412977675751RPI[t] -12387.1281946683Q1[t] -7662.91380445677Q2[t] -3693.15077859989Q3[t] +  2256.38373280118t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HFCE[t] =  +  207810.550167275 -916.412977675751RPI[t] -12387.1281946683Q1[t] -7662.91380445677Q2[t] -3693.15077859989Q3[t] +  2256.38373280118t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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] = + 207810.550167275 -916.412977675751RPI[t] -12387.1281946683Q1[t] -7662.91380445677Q2[t] -3693.15077859989Q3[t] + 2256.38373280118t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)207810.55016727515140.95500413.725100
RPI-916.412977675751142.774914-6.418600
Q1-12387.12819466831503.800975-8.237200
Q2-7662.913804456771498.259582-5.11452e-061e-06
Q3-3693.150778599891513.506414-2.44010.0167850.008392
t2256.38373280118172.29359213.096200

\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) & 207810.550167275 & 15140.955004 & 13.7251 & 0 & 0 \tabularnewline
RPI & -916.412977675751 & 142.774914 & -6.4186 & 0 & 0 \tabularnewline
Q1 & -12387.1281946683 & 1503.800975 & -8.2372 & 0 & 0 \tabularnewline
Q2 & -7662.91380445677 & 1498.259582 & -5.1145 & 2e-06 & 1e-06 \tabularnewline
Q3 & -3693.15077859989 & 1513.506414 & -2.4401 & 0.016785 & 0.008392 \tabularnewline
t & 2256.38373280118 & 172.293592 & 13.0962 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&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]207810.550167275[/C][C]15140.955004[/C][C]13.7251[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]RPI[/C][C]-916.412977675751[/C][C]142.774914[/C][C]-6.4186[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Q1[/C][C]-12387.1281946683[/C][C]1503.800975[/C][C]-8.2372[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Q2[/C][C]-7662.91380445677[/C][C]1498.259582[/C][C]-5.1145[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Q3[/C][C]-3693.15077859989[/C][C]1513.506414[/C][C]-2.4401[/C][C]0.016785[/C][C]0.008392[/C][/ROW]
[ROW][C]t[/C][C]2256.38373280118[/C][C]172.293592[/C][C]13.0962[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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)207810.55016727515140.95500413.725100
RPI-916.412977675751142.774914-6.418600
Q1-12387.12819466831503.800975-8.237200
Q2-7662.913804456771498.259582-5.11452e-061e-06
Q3-3693.150778599891513.506414-2.44010.0167850.008392
t2256.38373280118172.29359213.096200







Multiple Linear Regression - Regression Statistics
Multiple R0.98769504249566
R-squared0.975541496970502
Adjusted R-squared0.974085633694937
F-TEST (value)670.07768747493
F-TEST (DF numerator)5
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5019.13495884401
Sum Squared Residuals2116104121.74756

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98769504249566 \tabularnewline
R-squared & 0.975541496970502 \tabularnewline
Adjusted R-squared & 0.974085633694937 \tabularnewline
F-TEST (value) & 670.07768747493 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5019.13495884401 \tabularnewline
Sum Squared Residuals & 2116104121.74756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98769504249566[/C][/ROW]
[ROW][C]R-squared[/C][C]0.975541496970502[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.974085633694937[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]670.07768747493[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/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]5019.13495884401[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2116104121.74756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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 R0.98769504249566
R-squared0.975541496970502
Adjusted R-squared0.974085633694937
F-TEST (value)670.07768747493
F-TEST (DF numerator)5
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5019.13495884401
Sum Squared Residuals2116104121.74756







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106370105763.584044531606.415955469496
2109375111277.921403262-1902.92140326173
3116476117320.785566385-844.785566384756
4123297122262.2658023431034.73419765748
5114813111673.3148516383139.68514836246
6117925116362.8805304611562.11946953911
7126466121214.4078226055251.59217739472
8131235125147.8337831206087.16621688032
9120546113367.5459614367178.45403856375
10123791117415.6225558876375.37744411349
11129813122633.7150391017179.28496089875
12133463126475.4997018486987.5002981519
13122987114420.2879868628566.71201313807
14125418116268.9734348909149.02656510961
15130199120570.6529404299628.34705957062
16133016124687.3614964798328.63850352105
17121454113915.1279502397538.87204976118
18122044118329.7697357593714.23026424060
19128313124006.0687078124306.93129218798
20131556128764.2663482352791.7336517654
21120027117992.0328019952034.96719800550
22123001122315.033289747685.966710252511
23130111128632.8213461731478.17865382688
24132524134032.508070969-1508.50807096875
25123742124726.535289010-984.535289009851
26124931129691.024861136-4760.02486113585
27133646135550.606428724-1904.60642872361
28136557141041.934451287-4484.93445128681
29127509130727.907393885-3218.90739388459
30128945135417.473072708-6472.47307270788
31137191141551.978533598-4360.97853359837
32139716146676.741365091-6960.74136509126
33129083135354.660032246-6271.6600322457
34131604139860.943115534-8256.94311553384
35139413145720.524683122-6307.5246831216
36143125151486.776598988-8361.77659898753
37133948140622.901754980-6674.90175497984
38137116145862.315220409-8746.31522040857
39144864151813.538085764-6949.53808576393
40149277156938.300917257-7661.30091725682
41138796145982.784775482-7186.78477548156
42143258151130.556943143-7872.55694314272
43150034155982.084235287-5948.08423528716
44154708160740.281875710-6032.28187570976
45144888150151.330925005-5263.33092500481
46148762154382.690114990-5620.69011499023
47156500160150.630384810-3650.63038481041
48161088165458.675811838-4370.67581183844
49152772155969.420434344-3197.4204343444
50158011161300.475197541-3289.47519754071
51163318167434.980658431-4116.9806584312
52169969172284.819596621-2315.81959662135
53162269161512.586050381756.413949618739
54165765165652.303942599112.696057400897
55170600171603.526807954-1003.52680795443
56174681176545.007043912-1864.00704391218
57166364166597.545177580-233.545177580242
58170240171653.676047474-1413.67604747384
59176150177788.181508364-1638.18150836433
60182056183920.998615301-1864.99861530054
61172218173698.612855666-1480.61285566590
62177856178754.743725559-898.74372555949
63182253184431.042697612-2178.04269761211
64188090188914.316444732-824.316444731976
65176863177867.159005189-1004.15900518914
66183273182923.289875083349.710124917284
67187969188691.230144903-722.230144902905
68194650193632.7103808611017.28961913935
69183036182677.194239085358.805760914604
70189516187366.7599179092149.24008209132
71193805192584.8524011231220.14759887658
72200499196884.8435527083614.15644729185
73188142186295.8926020031846.10739799680
74193732191260.3821741292471.6178258708
75197126196845.039848414280.960151585752
76205140201786.5200843723353.47991562801
77191751191197.569133667553.430866332951
78196700195062.3631325821637.63686741785
79199784199730.60782919153.3921708085906
80207360203755.6750874733604.32491252657
81196101192158.6698613253942.33013867486
82200824196115.1051580084708.89484199219
83205743201608.1215345254134.87846547471
84212489205083.3410062027405.6589937982
85200810193761.2596733567048.74032664374
86203683196892.9232901316790.07670986924
87207286201194.6027956706091.39720433025
88210910208885.3219646552024.67803534524
89194915202970.077200096-8055.07720009614
90217920208392.773261069527.22673893995

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106370 & 105763.584044531 & 606.415955469496 \tabularnewline
2 & 109375 & 111277.921403262 & -1902.92140326173 \tabularnewline
3 & 116476 & 117320.785566385 & -844.785566384756 \tabularnewline
4 & 123297 & 122262.265802343 & 1034.73419765748 \tabularnewline
5 & 114813 & 111673.314851638 & 3139.68514836246 \tabularnewline
6 & 117925 & 116362.880530461 & 1562.11946953911 \tabularnewline
7 & 126466 & 121214.407822605 & 5251.59217739472 \tabularnewline
8 & 131235 & 125147.833783120 & 6087.16621688032 \tabularnewline
9 & 120546 & 113367.545961436 & 7178.45403856375 \tabularnewline
10 & 123791 & 117415.622555887 & 6375.37744411349 \tabularnewline
11 & 129813 & 122633.715039101 & 7179.28496089875 \tabularnewline
12 & 133463 & 126475.499701848 & 6987.5002981519 \tabularnewline
13 & 122987 & 114420.287986862 & 8566.71201313807 \tabularnewline
14 & 125418 & 116268.973434890 & 9149.02656510961 \tabularnewline
15 & 130199 & 120570.652940429 & 9628.34705957062 \tabularnewline
16 & 133016 & 124687.361496479 & 8328.63850352105 \tabularnewline
17 & 121454 & 113915.127950239 & 7538.87204976118 \tabularnewline
18 & 122044 & 118329.769735759 & 3714.23026424060 \tabularnewline
19 & 128313 & 124006.068707812 & 4306.93129218798 \tabularnewline
20 & 131556 & 128764.266348235 & 2791.7336517654 \tabularnewline
21 & 120027 & 117992.032801995 & 2034.96719800550 \tabularnewline
22 & 123001 & 122315.033289747 & 685.966710252511 \tabularnewline
23 & 130111 & 128632.821346173 & 1478.17865382688 \tabularnewline
24 & 132524 & 134032.508070969 & -1508.50807096875 \tabularnewline
25 & 123742 & 124726.535289010 & -984.535289009851 \tabularnewline
26 & 124931 & 129691.024861136 & -4760.02486113585 \tabularnewline
27 & 133646 & 135550.606428724 & -1904.60642872361 \tabularnewline
28 & 136557 & 141041.934451287 & -4484.93445128681 \tabularnewline
29 & 127509 & 130727.907393885 & -3218.90739388459 \tabularnewline
30 & 128945 & 135417.473072708 & -6472.47307270788 \tabularnewline
31 & 137191 & 141551.978533598 & -4360.97853359837 \tabularnewline
32 & 139716 & 146676.741365091 & -6960.74136509126 \tabularnewline
33 & 129083 & 135354.660032246 & -6271.6600322457 \tabularnewline
34 & 131604 & 139860.943115534 & -8256.94311553384 \tabularnewline
35 & 139413 & 145720.524683122 & -6307.5246831216 \tabularnewline
36 & 143125 & 151486.776598988 & -8361.77659898753 \tabularnewline
37 & 133948 & 140622.901754980 & -6674.90175497984 \tabularnewline
38 & 137116 & 145862.315220409 & -8746.31522040857 \tabularnewline
39 & 144864 & 151813.538085764 & -6949.53808576393 \tabularnewline
40 & 149277 & 156938.300917257 & -7661.30091725682 \tabularnewline
41 & 138796 & 145982.784775482 & -7186.78477548156 \tabularnewline
42 & 143258 & 151130.556943143 & -7872.55694314272 \tabularnewline
43 & 150034 & 155982.084235287 & -5948.08423528716 \tabularnewline
44 & 154708 & 160740.281875710 & -6032.28187570976 \tabularnewline
45 & 144888 & 150151.330925005 & -5263.33092500481 \tabularnewline
46 & 148762 & 154382.690114990 & -5620.69011499023 \tabularnewline
47 & 156500 & 160150.630384810 & -3650.63038481041 \tabularnewline
48 & 161088 & 165458.675811838 & -4370.67581183844 \tabularnewline
49 & 152772 & 155969.420434344 & -3197.4204343444 \tabularnewline
50 & 158011 & 161300.475197541 & -3289.47519754071 \tabularnewline
51 & 163318 & 167434.980658431 & -4116.9806584312 \tabularnewline
52 & 169969 & 172284.819596621 & -2315.81959662135 \tabularnewline
53 & 162269 & 161512.586050381 & 756.413949618739 \tabularnewline
54 & 165765 & 165652.303942599 & 112.696057400897 \tabularnewline
55 & 170600 & 171603.526807954 & -1003.52680795443 \tabularnewline
56 & 174681 & 176545.007043912 & -1864.00704391218 \tabularnewline
57 & 166364 & 166597.545177580 & -233.545177580242 \tabularnewline
58 & 170240 & 171653.676047474 & -1413.67604747384 \tabularnewline
59 & 176150 & 177788.181508364 & -1638.18150836433 \tabularnewline
60 & 182056 & 183920.998615301 & -1864.99861530054 \tabularnewline
61 & 172218 & 173698.612855666 & -1480.61285566590 \tabularnewline
62 & 177856 & 178754.743725559 & -898.74372555949 \tabularnewline
63 & 182253 & 184431.042697612 & -2178.04269761211 \tabularnewline
64 & 188090 & 188914.316444732 & -824.316444731976 \tabularnewline
65 & 176863 & 177867.159005189 & -1004.15900518914 \tabularnewline
66 & 183273 & 182923.289875083 & 349.710124917284 \tabularnewline
67 & 187969 & 188691.230144903 & -722.230144902905 \tabularnewline
68 & 194650 & 193632.710380861 & 1017.28961913935 \tabularnewline
69 & 183036 & 182677.194239085 & 358.805760914604 \tabularnewline
70 & 189516 & 187366.759917909 & 2149.24008209132 \tabularnewline
71 & 193805 & 192584.852401123 & 1220.14759887658 \tabularnewline
72 & 200499 & 196884.843552708 & 3614.15644729185 \tabularnewline
73 & 188142 & 186295.892602003 & 1846.10739799680 \tabularnewline
74 & 193732 & 191260.382174129 & 2471.6178258708 \tabularnewline
75 & 197126 & 196845.039848414 & 280.960151585752 \tabularnewline
76 & 205140 & 201786.520084372 & 3353.47991562801 \tabularnewline
77 & 191751 & 191197.569133667 & 553.430866332951 \tabularnewline
78 & 196700 & 195062.363132582 & 1637.63686741785 \tabularnewline
79 & 199784 & 199730.607829191 & 53.3921708085906 \tabularnewline
80 & 207360 & 203755.675087473 & 3604.32491252657 \tabularnewline
81 & 196101 & 192158.669861325 & 3942.33013867486 \tabularnewline
82 & 200824 & 196115.105158008 & 4708.89484199219 \tabularnewline
83 & 205743 & 201608.121534525 & 4134.87846547471 \tabularnewline
84 & 212489 & 205083.341006202 & 7405.6589937982 \tabularnewline
85 & 200810 & 193761.259673356 & 7048.74032664374 \tabularnewline
86 & 203683 & 196892.923290131 & 6790.07670986924 \tabularnewline
87 & 207286 & 201194.602795670 & 6091.39720433025 \tabularnewline
88 & 210910 & 208885.321964655 & 2024.67803534524 \tabularnewline
89 & 194915 & 202970.077200096 & -8055.07720009614 \tabularnewline
90 & 217920 & 208392.77326106 & 9527.22673893995 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&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]105763.584044531[/C][C]606.415955469496[/C][/ROW]
[ROW][C]2[/C][C]109375[/C][C]111277.921403262[/C][C]-1902.92140326173[/C][/ROW]
[ROW][C]3[/C][C]116476[/C][C]117320.785566385[/C][C]-844.785566384756[/C][/ROW]
[ROW][C]4[/C][C]123297[/C][C]122262.265802343[/C][C]1034.73419765748[/C][/ROW]
[ROW][C]5[/C][C]114813[/C][C]111673.314851638[/C][C]3139.68514836246[/C][/ROW]
[ROW][C]6[/C][C]117925[/C][C]116362.880530461[/C][C]1562.11946953911[/C][/ROW]
[ROW][C]7[/C][C]126466[/C][C]121214.407822605[/C][C]5251.59217739472[/C][/ROW]
[ROW][C]8[/C][C]131235[/C][C]125147.833783120[/C][C]6087.16621688032[/C][/ROW]
[ROW][C]9[/C][C]120546[/C][C]113367.545961436[/C][C]7178.45403856375[/C][/ROW]
[ROW][C]10[/C][C]123791[/C][C]117415.622555887[/C][C]6375.37744411349[/C][/ROW]
[ROW][C]11[/C][C]129813[/C][C]122633.715039101[/C][C]7179.28496089875[/C][/ROW]
[ROW][C]12[/C][C]133463[/C][C]126475.499701848[/C][C]6987.5002981519[/C][/ROW]
[ROW][C]13[/C][C]122987[/C][C]114420.287986862[/C][C]8566.71201313807[/C][/ROW]
[ROW][C]14[/C][C]125418[/C][C]116268.973434890[/C][C]9149.02656510961[/C][/ROW]
[ROW][C]15[/C][C]130199[/C][C]120570.652940429[/C][C]9628.34705957062[/C][/ROW]
[ROW][C]16[/C][C]133016[/C][C]124687.361496479[/C][C]8328.63850352105[/C][/ROW]
[ROW][C]17[/C][C]121454[/C][C]113915.127950239[/C][C]7538.87204976118[/C][/ROW]
[ROW][C]18[/C][C]122044[/C][C]118329.769735759[/C][C]3714.23026424060[/C][/ROW]
[ROW][C]19[/C][C]128313[/C][C]124006.068707812[/C][C]4306.93129218798[/C][/ROW]
[ROW][C]20[/C][C]131556[/C][C]128764.266348235[/C][C]2791.7336517654[/C][/ROW]
[ROW][C]21[/C][C]120027[/C][C]117992.032801995[/C][C]2034.96719800550[/C][/ROW]
[ROW][C]22[/C][C]123001[/C][C]122315.033289747[/C][C]685.966710252511[/C][/ROW]
[ROW][C]23[/C][C]130111[/C][C]128632.821346173[/C][C]1478.17865382688[/C][/ROW]
[ROW][C]24[/C][C]132524[/C][C]134032.508070969[/C][C]-1508.50807096875[/C][/ROW]
[ROW][C]25[/C][C]123742[/C][C]124726.535289010[/C][C]-984.535289009851[/C][/ROW]
[ROW][C]26[/C][C]124931[/C][C]129691.024861136[/C][C]-4760.02486113585[/C][/ROW]
[ROW][C]27[/C][C]133646[/C][C]135550.606428724[/C][C]-1904.60642872361[/C][/ROW]
[ROW][C]28[/C][C]136557[/C][C]141041.934451287[/C][C]-4484.93445128681[/C][/ROW]
[ROW][C]29[/C][C]127509[/C][C]130727.907393885[/C][C]-3218.90739388459[/C][/ROW]
[ROW][C]30[/C][C]128945[/C][C]135417.473072708[/C][C]-6472.47307270788[/C][/ROW]
[ROW][C]31[/C][C]137191[/C][C]141551.978533598[/C][C]-4360.97853359837[/C][/ROW]
[ROW][C]32[/C][C]139716[/C][C]146676.741365091[/C][C]-6960.74136509126[/C][/ROW]
[ROW][C]33[/C][C]129083[/C][C]135354.660032246[/C][C]-6271.6600322457[/C][/ROW]
[ROW][C]34[/C][C]131604[/C][C]139860.943115534[/C][C]-8256.94311553384[/C][/ROW]
[ROW][C]35[/C][C]139413[/C][C]145720.524683122[/C][C]-6307.5246831216[/C][/ROW]
[ROW][C]36[/C][C]143125[/C][C]151486.776598988[/C][C]-8361.77659898753[/C][/ROW]
[ROW][C]37[/C][C]133948[/C][C]140622.901754980[/C][C]-6674.90175497984[/C][/ROW]
[ROW][C]38[/C][C]137116[/C][C]145862.315220409[/C][C]-8746.31522040857[/C][/ROW]
[ROW][C]39[/C][C]144864[/C][C]151813.538085764[/C][C]-6949.53808576393[/C][/ROW]
[ROW][C]40[/C][C]149277[/C][C]156938.300917257[/C][C]-7661.30091725682[/C][/ROW]
[ROW][C]41[/C][C]138796[/C][C]145982.784775482[/C][C]-7186.78477548156[/C][/ROW]
[ROW][C]42[/C][C]143258[/C][C]151130.556943143[/C][C]-7872.55694314272[/C][/ROW]
[ROW][C]43[/C][C]150034[/C][C]155982.084235287[/C][C]-5948.08423528716[/C][/ROW]
[ROW][C]44[/C][C]154708[/C][C]160740.281875710[/C][C]-6032.28187570976[/C][/ROW]
[ROW][C]45[/C][C]144888[/C][C]150151.330925005[/C][C]-5263.33092500481[/C][/ROW]
[ROW][C]46[/C][C]148762[/C][C]154382.690114990[/C][C]-5620.69011499023[/C][/ROW]
[ROW][C]47[/C][C]156500[/C][C]160150.630384810[/C][C]-3650.63038481041[/C][/ROW]
[ROW][C]48[/C][C]161088[/C][C]165458.675811838[/C][C]-4370.67581183844[/C][/ROW]
[ROW][C]49[/C][C]152772[/C][C]155969.420434344[/C][C]-3197.4204343444[/C][/ROW]
[ROW][C]50[/C][C]158011[/C][C]161300.475197541[/C][C]-3289.47519754071[/C][/ROW]
[ROW][C]51[/C][C]163318[/C][C]167434.980658431[/C][C]-4116.9806584312[/C][/ROW]
[ROW][C]52[/C][C]169969[/C][C]172284.819596621[/C][C]-2315.81959662135[/C][/ROW]
[ROW][C]53[/C][C]162269[/C][C]161512.586050381[/C][C]756.413949618739[/C][/ROW]
[ROW][C]54[/C][C]165765[/C][C]165652.303942599[/C][C]112.696057400897[/C][/ROW]
[ROW][C]55[/C][C]170600[/C][C]171603.526807954[/C][C]-1003.52680795443[/C][/ROW]
[ROW][C]56[/C][C]174681[/C][C]176545.007043912[/C][C]-1864.00704391218[/C][/ROW]
[ROW][C]57[/C][C]166364[/C][C]166597.545177580[/C][C]-233.545177580242[/C][/ROW]
[ROW][C]58[/C][C]170240[/C][C]171653.676047474[/C][C]-1413.67604747384[/C][/ROW]
[ROW][C]59[/C][C]176150[/C][C]177788.181508364[/C][C]-1638.18150836433[/C][/ROW]
[ROW][C]60[/C][C]182056[/C][C]183920.998615301[/C][C]-1864.99861530054[/C][/ROW]
[ROW][C]61[/C][C]172218[/C][C]173698.612855666[/C][C]-1480.61285566590[/C][/ROW]
[ROW][C]62[/C][C]177856[/C][C]178754.743725559[/C][C]-898.74372555949[/C][/ROW]
[ROW][C]63[/C][C]182253[/C][C]184431.042697612[/C][C]-2178.04269761211[/C][/ROW]
[ROW][C]64[/C][C]188090[/C][C]188914.316444732[/C][C]-824.316444731976[/C][/ROW]
[ROW][C]65[/C][C]176863[/C][C]177867.159005189[/C][C]-1004.15900518914[/C][/ROW]
[ROW][C]66[/C][C]183273[/C][C]182923.289875083[/C][C]349.710124917284[/C][/ROW]
[ROW][C]67[/C][C]187969[/C][C]188691.230144903[/C][C]-722.230144902905[/C][/ROW]
[ROW][C]68[/C][C]194650[/C][C]193632.710380861[/C][C]1017.28961913935[/C][/ROW]
[ROW][C]69[/C][C]183036[/C][C]182677.194239085[/C][C]358.805760914604[/C][/ROW]
[ROW][C]70[/C][C]189516[/C][C]187366.759917909[/C][C]2149.24008209132[/C][/ROW]
[ROW][C]71[/C][C]193805[/C][C]192584.852401123[/C][C]1220.14759887658[/C][/ROW]
[ROW][C]72[/C][C]200499[/C][C]196884.843552708[/C][C]3614.15644729185[/C][/ROW]
[ROW][C]73[/C][C]188142[/C][C]186295.892602003[/C][C]1846.10739799680[/C][/ROW]
[ROW][C]74[/C][C]193732[/C][C]191260.382174129[/C][C]2471.6178258708[/C][/ROW]
[ROW][C]75[/C][C]197126[/C][C]196845.039848414[/C][C]280.960151585752[/C][/ROW]
[ROW][C]76[/C][C]205140[/C][C]201786.520084372[/C][C]3353.47991562801[/C][/ROW]
[ROW][C]77[/C][C]191751[/C][C]191197.569133667[/C][C]553.430866332951[/C][/ROW]
[ROW][C]78[/C][C]196700[/C][C]195062.363132582[/C][C]1637.63686741785[/C][/ROW]
[ROW][C]79[/C][C]199784[/C][C]199730.607829191[/C][C]53.3921708085906[/C][/ROW]
[ROW][C]80[/C][C]207360[/C][C]203755.675087473[/C][C]3604.32491252657[/C][/ROW]
[ROW][C]81[/C][C]196101[/C][C]192158.669861325[/C][C]3942.33013867486[/C][/ROW]
[ROW][C]82[/C][C]200824[/C][C]196115.105158008[/C][C]4708.89484199219[/C][/ROW]
[ROW][C]83[/C][C]205743[/C][C]201608.121534525[/C][C]4134.87846547471[/C][/ROW]
[ROW][C]84[/C][C]212489[/C][C]205083.341006202[/C][C]7405.6589937982[/C][/ROW]
[ROW][C]85[/C][C]200810[/C][C]193761.259673356[/C][C]7048.74032664374[/C][/ROW]
[ROW][C]86[/C][C]203683[/C][C]196892.923290131[/C][C]6790.07670986924[/C][/ROW]
[ROW][C]87[/C][C]207286[/C][C]201194.602795670[/C][C]6091.39720433025[/C][/ROW]
[ROW][C]88[/C][C]210910[/C][C]208885.321964655[/C][C]2024.67803534524[/C][/ROW]
[ROW][C]89[/C][C]194915[/C][C]202970.077200096[/C][C]-8055.07720009614[/C][/ROW]
[ROW][C]90[/C][C]217920[/C][C]208392.77326106[/C][C]9527.22673893995[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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
1106370105763.584044531606.415955469496
2109375111277.921403262-1902.92140326173
3116476117320.785566385-844.785566384756
4123297122262.2658023431034.73419765748
5114813111673.3148516383139.68514836246
6117925116362.8805304611562.11946953911
7126466121214.4078226055251.59217739472
8131235125147.8337831206087.16621688032
9120546113367.5459614367178.45403856375
10123791117415.6225558876375.37744411349
11129813122633.7150391017179.28496089875
12133463126475.4997018486987.5002981519
13122987114420.2879868628566.71201313807
14125418116268.9734348909149.02656510961
15130199120570.6529404299628.34705957062
16133016124687.3614964798328.63850352105
17121454113915.1279502397538.87204976118
18122044118329.7697357593714.23026424060
19128313124006.0687078124306.93129218798
20131556128764.2663482352791.7336517654
21120027117992.0328019952034.96719800550
22123001122315.033289747685.966710252511
23130111128632.8213461731478.17865382688
24132524134032.508070969-1508.50807096875
25123742124726.535289010-984.535289009851
26124931129691.024861136-4760.02486113585
27133646135550.606428724-1904.60642872361
28136557141041.934451287-4484.93445128681
29127509130727.907393885-3218.90739388459
30128945135417.473072708-6472.47307270788
31137191141551.978533598-4360.97853359837
32139716146676.741365091-6960.74136509126
33129083135354.660032246-6271.6600322457
34131604139860.943115534-8256.94311553384
35139413145720.524683122-6307.5246831216
36143125151486.776598988-8361.77659898753
37133948140622.901754980-6674.90175497984
38137116145862.315220409-8746.31522040857
39144864151813.538085764-6949.53808576393
40149277156938.300917257-7661.30091725682
41138796145982.784775482-7186.78477548156
42143258151130.556943143-7872.55694314272
43150034155982.084235287-5948.08423528716
44154708160740.281875710-6032.28187570976
45144888150151.330925005-5263.33092500481
46148762154382.690114990-5620.69011499023
47156500160150.630384810-3650.63038481041
48161088165458.675811838-4370.67581183844
49152772155969.420434344-3197.4204343444
50158011161300.475197541-3289.47519754071
51163318167434.980658431-4116.9806584312
52169969172284.819596621-2315.81959662135
53162269161512.586050381756.413949618739
54165765165652.303942599112.696057400897
55170600171603.526807954-1003.52680795443
56174681176545.007043912-1864.00704391218
57166364166597.545177580-233.545177580242
58170240171653.676047474-1413.67604747384
59176150177788.181508364-1638.18150836433
60182056183920.998615301-1864.99861530054
61172218173698.612855666-1480.61285566590
62177856178754.743725559-898.74372555949
63182253184431.042697612-2178.04269761211
64188090188914.316444732-824.316444731976
65176863177867.159005189-1004.15900518914
66183273182923.289875083349.710124917284
67187969188691.230144903-722.230144902905
68194650193632.7103808611017.28961913935
69183036182677.194239085358.805760914604
70189516187366.7599179092149.24008209132
71193805192584.8524011231220.14759887658
72200499196884.8435527083614.15644729185
73188142186295.8926020031846.10739799680
74193732191260.3821741292471.6178258708
75197126196845.039848414280.960151585752
76205140201786.5200843723353.47991562801
77191751191197.569133667553.430866332951
78196700195062.3631325821637.63686741785
79199784199730.60782919153.3921708085906
80207360203755.6750874733604.32491252657
81196101192158.6698613253942.33013867486
82200824196115.1051580084708.89484199219
83205743201608.1215345254134.87846547471
84212489205083.3410062027405.6589937982
85200810193761.2596733567048.74032664374
86203683196892.9232901316790.07670986924
87207286201194.6027956706091.39720433025
88210910208885.3219646552024.67803534524
89194915202970.077200096-8055.07720009614
90217920208392.773261069527.22673893995







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.01336042205505580.02672084411011160.986639577944944
100.002584890055301170.005169780110602330.997415109944699
110.001077408554936540.002154817109873080.998922591445063
120.001075904946608910.002151809893217830.998924095053391
130.0007535473096564780.001507094619312960.999246452690344
140.0004069354686939780.0008138709373879560.999593064531306
150.0002007400050162540.0004014800100325080.999799259994984
160.0002828884395684160.0005657768791368320.999717111560432
170.003769155847630460.007538311695260920.99623084415237
180.04687239658915040.09374479317830070.95312760341085
190.1887962428680560.3775924857361120.811203757131944
200.4869445261601880.9738890523203760.513055473839812
210.7823894266431550.4352211467136890.217610573356845
220.8773258635035580.2453482729928850.122674136496442
230.9440848908387070.1118302183225860.0559151091612931
240.9758492672943880.04830146541122340.0241507327056117
250.9851139451569260.02977210968614720.0148860548430736
260.9837812816767980.03243743664640430.0162187183232021
270.987077457586390.02584508482721850.0129225424136092
280.984578877504540.03084224499091890.0154211224954594
290.9867557186478630.02648856270427380.0132442813521369
300.9801699199754760.03966016004904750.0198300800245237
310.9785723324617950.04285533507640930.0214276675382047
320.9687580235004760.06248395299904720.0312419764995236
330.957866898542040.08426620291592060.0421331014579603
340.9432829810507680.1134340378984640.0567170189492318
350.9245360427738840.1509279144522320.0754639572261158
360.90419959916230.1916008016754010.0958004008377004
370.8837851647513930.2324296704972140.116214835248607
380.8854720918373420.2290558163253160.114527908162658
390.868488578220090.263022843559820.13151142177991
400.8656585681463490.2686828637073020.134341431853651
410.8523286128748870.2953427742502260.147671387125113
420.8907703363459880.2184593273080250.109229663654012
430.886182444247510.2276351115049810.113817555752490
440.8987232488405860.2025535023188280.101276751159414
450.9028014315391640.1943971369216720.0971985684608359
460.9392059423792390.1215881152415220.0607940576207611
470.9456326655262990.1087346689474020.0543673344737011
480.9597038444545630.08059231109087380.0402961555454369
490.967668420159860.06466315968028070.0323315798401403
500.9831109489509650.03377810209807080.0168890510490354
510.983973552753160.03205289449367810.0160264472468391
520.9878614265909540.02427714681809150.0121385734090458
530.9935748523762970.01285029524740590.00642514762370294
540.9955516579132430.00889668417351440.0044483420867572
550.994834116374960.01033176725008150.00516588362504075
560.9946205993824280.01075880123514370.00537940061757184
570.993851770496730.01229645900653920.00614822950326962
580.9942138701179660.01157225976406840.00578612988203419
590.991982753702990.01603449259402120.0080172462970106
600.990681379595890.01863724080821880.00931862040410941
610.9861916453851020.02761670922979540.0138083546148977
620.9848109364327480.03037812713450390.0151890635672520
630.9784663081601330.04306738367973430.0215336918398671
640.9742013675516950.05159726489661010.0257986324483050
650.9627290395794070.07454192084118540.0372709604205927
660.9606348876716080.07873022465678420.0393651123283921
670.94475437772050.1104912445589990.0552456222794994
680.9284640438082750.1430719123834510.0715359561917254
690.8991121375802360.2017757248395280.100887862419764
700.8800620893396640.2398758213206720.119937910660336
710.8373408676271170.3253182647457660.162659132372883
720.7987524309842340.4024951380315320.201247569015766
730.7441095260342570.5117809479314860.255890473965743
740.6901728536962650.619654292607470.309827146303735
750.5998476996734990.8003046006530010.400152300326501
760.5207585625224240.9584828749551520.479241437477576
770.4224703663056360.8449407326112720.577529633694364
780.3699464444254910.7398928888509820.630053555574509
790.2908649932240980.5817299864481970.709135006775902
800.2000780679642170.4001561359284330.799921932035784
810.1312748641271970.2625497282543950.868725135872803

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0133604220550558 & 0.0267208441101116 & 0.986639577944944 \tabularnewline
10 & 0.00258489005530117 & 0.00516978011060233 & 0.997415109944699 \tabularnewline
11 & 0.00107740855493654 & 0.00215481710987308 & 0.998922591445063 \tabularnewline
12 & 0.00107590494660891 & 0.00215180989321783 & 0.998924095053391 \tabularnewline
13 & 0.000753547309656478 & 0.00150709461931296 & 0.999246452690344 \tabularnewline
14 & 0.000406935468693978 & 0.000813870937387956 & 0.999593064531306 \tabularnewline
15 & 0.000200740005016254 & 0.000401480010032508 & 0.999799259994984 \tabularnewline
16 & 0.000282888439568416 & 0.000565776879136832 & 0.999717111560432 \tabularnewline
17 & 0.00376915584763046 & 0.00753831169526092 & 0.99623084415237 \tabularnewline
18 & 0.0468723965891504 & 0.0937447931783007 & 0.95312760341085 \tabularnewline
19 & 0.188796242868056 & 0.377592485736112 & 0.811203757131944 \tabularnewline
20 & 0.486944526160188 & 0.973889052320376 & 0.513055473839812 \tabularnewline
21 & 0.782389426643155 & 0.435221146713689 & 0.217610573356845 \tabularnewline
22 & 0.877325863503558 & 0.245348272992885 & 0.122674136496442 \tabularnewline
23 & 0.944084890838707 & 0.111830218322586 & 0.0559151091612931 \tabularnewline
24 & 0.975849267294388 & 0.0483014654112234 & 0.0241507327056117 \tabularnewline
25 & 0.985113945156926 & 0.0297721096861472 & 0.0148860548430736 \tabularnewline
26 & 0.983781281676798 & 0.0324374366464043 & 0.0162187183232021 \tabularnewline
27 & 0.98707745758639 & 0.0258450848272185 & 0.0129225424136092 \tabularnewline
28 & 0.98457887750454 & 0.0308422449909189 & 0.0154211224954594 \tabularnewline
29 & 0.986755718647863 & 0.0264885627042738 & 0.0132442813521369 \tabularnewline
30 & 0.980169919975476 & 0.0396601600490475 & 0.0198300800245237 \tabularnewline
31 & 0.978572332461795 & 0.0428553350764093 & 0.0214276675382047 \tabularnewline
32 & 0.968758023500476 & 0.0624839529990472 & 0.0312419764995236 \tabularnewline
33 & 0.95786689854204 & 0.0842662029159206 & 0.0421331014579603 \tabularnewline
34 & 0.943282981050768 & 0.113434037898464 & 0.0567170189492318 \tabularnewline
35 & 0.924536042773884 & 0.150927914452232 & 0.0754639572261158 \tabularnewline
36 & 0.9041995991623 & 0.191600801675401 & 0.0958004008377004 \tabularnewline
37 & 0.883785164751393 & 0.232429670497214 & 0.116214835248607 \tabularnewline
38 & 0.885472091837342 & 0.229055816325316 & 0.114527908162658 \tabularnewline
39 & 0.86848857822009 & 0.26302284355982 & 0.13151142177991 \tabularnewline
40 & 0.865658568146349 & 0.268682863707302 & 0.134341431853651 \tabularnewline
41 & 0.852328612874887 & 0.295342774250226 & 0.147671387125113 \tabularnewline
42 & 0.890770336345988 & 0.218459327308025 & 0.109229663654012 \tabularnewline
43 & 0.88618244424751 & 0.227635111504981 & 0.113817555752490 \tabularnewline
44 & 0.898723248840586 & 0.202553502318828 & 0.101276751159414 \tabularnewline
45 & 0.902801431539164 & 0.194397136921672 & 0.0971985684608359 \tabularnewline
46 & 0.939205942379239 & 0.121588115241522 & 0.0607940576207611 \tabularnewline
47 & 0.945632665526299 & 0.108734668947402 & 0.0543673344737011 \tabularnewline
48 & 0.959703844454563 & 0.0805923110908738 & 0.0402961555454369 \tabularnewline
49 & 0.96766842015986 & 0.0646631596802807 & 0.0323315798401403 \tabularnewline
50 & 0.983110948950965 & 0.0337781020980708 & 0.0168890510490354 \tabularnewline
51 & 0.98397355275316 & 0.0320528944936781 & 0.0160264472468391 \tabularnewline
52 & 0.987861426590954 & 0.0242771468180915 & 0.0121385734090458 \tabularnewline
53 & 0.993574852376297 & 0.0128502952474059 & 0.00642514762370294 \tabularnewline
54 & 0.995551657913243 & 0.0088966841735144 & 0.0044483420867572 \tabularnewline
55 & 0.99483411637496 & 0.0103317672500815 & 0.00516588362504075 \tabularnewline
56 & 0.994620599382428 & 0.0107588012351437 & 0.00537940061757184 \tabularnewline
57 & 0.99385177049673 & 0.0122964590065392 & 0.00614822950326962 \tabularnewline
58 & 0.994213870117966 & 0.0115722597640684 & 0.00578612988203419 \tabularnewline
59 & 0.99198275370299 & 0.0160344925940212 & 0.0080172462970106 \tabularnewline
60 & 0.99068137959589 & 0.0186372408082188 & 0.00931862040410941 \tabularnewline
61 & 0.986191645385102 & 0.0276167092297954 & 0.0138083546148977 \tabularnewline
62 & 0.984810936432748 & 0.0303781271345039 & 0.0151890635672520 \tabularnewline
63 & 0.978466308160133 & 0.0430673836797343 & 0.0215336918398671 \tabularnewline
64 & 0.974201367551695 & 0.0515972648966101 & 0.0257986324483050 \tabularnewline
65 & 0.962729039579407 & 0.0745419208411854 & 0.0372709604205927 \tabularnewline
66 & 0.960634887671608 & 0.0787302246567842 & 0.0393651123283921 \tabularnewline
67 & 0.9447543777205 & 0.110491244558999 & 0.0552456222794994 \tabularnewline
68 & 0.928464043808275 & 0.143071912383451 & 0.0715359561917254 \tabularnewline
69 & 0.899112137580236 & 0.201775724839528 & 0.100887862419764 \tabularnewline
70 & 0.880062089339664 & 0.239875821320672 & 0.119937910660336 \tabularnewline
71 & 0.837340867627117 & 0.325318264745766 & 0.162659132372883 \tabularnewline
72 & 0.798752430984234 & 0.402495138031532 & 0.201247569015766 \tabularnewline
73 & 0.744109526034257 & 0.511780947931486 & 0.255890473965743 \tabularnewline
74 & 0.690172853696265 & 0.61965429260747 & 0.309827146303735 \tabularnewline
75 & 0.599847699673499 & 0.800304600653001 & 0.400152300326501 \tabularnewline
76 & 0.520758562522424 & 0.958482874955152 & 0.479241437477576 \tabularnewline
77 & 0.422470366305636 & 0.844940732611272 & 0.577529633694364 \tabularnewline
78 & 0.369946444425491 & 0.739892888850982 & 0.630053555574509 \tabularnewline
79 & 0.290864993224098 & 0.581729986448197 & 0.709135006775902 \tabularnewline
80 & 0.200078067964217 & 0.400156135928433 & 0.799921932035784 \tabularnewline
81 & 0.131274864127197 & 0.262549728254395 & 0.868725135872803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0133604220550558[/C][C]0.0267208441101116[/C][C]0.986639577944944[/C][/ROW]
[ROW][C]10[/C][C]0.00258489005530117[/C][C]0.00516978011060233[/C][C]0.997415109944699[/C][/ROW]
[ROW][C]11[/C][C]0.00107740855493654[/C][C]0.00215481710987308[/C][C]0.998922591445063[/C][/ROW]
[ROW][C]12[/C][C]0.00107590494660891[/C][C]0.00215180989321783[/C][C]0.998924095053391[/C][/ROW]
[ROW][C]13[/C][C]0.000753547309656478[/C][C]0.00150709461931296[/C][C]0.999246452690344[/C][/ROW]
[ROW][C]14[/C][C]0.000406935468693978[/C][C]0.000813870937387956[/C][C]0.999593064531306[/C][/ROW]
[ROW][C]15[/C][C]0.000200740005016254[/C][C]0.000401480010032508[/C][C]0.999799259994984[/C][/ROW]
[ROW][C]16[/C][C]0.000282888439568416[/C][C]0.000565776879136832[/C][C]0.999717111560432[/C][/ROW]
[ROW][C]17[/C][C]0.00376915584763046[/C][C]0.00753831169526092[/C][C]0.99623084415237[/C][/ROW]
[ROW][C]18[/C][C]0.0468723965891504[/C][C]0.0937447931783007[/C][C]0.95312760341085[/C][/ROW]
[ROW][C]19[/C][C]0.188796242868056[/C][C]0.377592485736112[/C][C]0.811203757131944[/C][/ROW]
[ROW][C]20[/C][C]0.486944526160188[/C][C]0.973889052320376[/C][C]0.513055473839812[/C][/ROW]
[ROW][C]21[/C][C]0.782389426643155[/C][C]0.435221146713689[/C][C]0.217610573356845[/C][/ROW]
[ROW][C]22[/C][C]0.877325863503558[/C][C]0.245348272992885[/C][C]0.122674136496442[/C][/ROW]
[ROW][C]23[/C][C]0.944084890838707[/C][C]0.111830218322586[/C][C]0.0559151091612931[/C][/ROW]
[ROW][C]24[/C][C]0.975849267294388[/C][C]0.0483014654112234[/C][C]0.0241507327056117[/C][/ROW]
[ROW][C]25[/C][C]0.985113945156926[/C][C]0.0297721096861472[/C][C]0.0148860548430736[/C][/ROW]
[ROW][C]26[/C][C]0.983781281676798[/C][C]0.0324374366464043[/C][C]0.0162187183232021[/C][/ROW]
[ROW][C]27[/C][C]0.98707745758639[/C][C]0.0258450848272185[/C][C]0.0129225424136092[/C][/ROW]
[ROW][C]28[/C][C]0.98457887750454[/C][C]0.0308422449909189[/C][C]0.0154211224954594[/C][/ROW]
[ROW][C]29[/C][C]0.986755718647863[/C][C]0.0264885627042738[/C][C]0.0132442813521369[/C][/ROW]
[ROW][C]30[/C][C]0.980169919975476[/C][C]0.0396601600490475[/C][C]0.0198300800245237[/C][/ROW]
[ROW][C]31[/C][C]0.978572332461795[/C][C]0.0428553350764093[/C][C]0.0214276675382047[/C][/ROW]
[ROW][C]32[/C][C]0.968758023500476[/C][C]0.0624839529990472[/C][C]0.0312419764995236[/C][/ROW]
[ROW][C]33[/C][C]0.95786689854204[/C][C]0.0842662029159206[/C][C]0.0421331014579603[/C][/ROW]
[ROW][C]34[/C][C]0.943282981050768[/C][C]0.113434037898464[/C][C]0.0567170189492318[/C][/ROW]
[ROW][C]35[/C][C]0.924536042773884[/C][C]0.150927914452232[/C][C]0.0754639572261158[/C][/ROW]
[ROW][C]36[/C][C]0.9041995991623[/C][C]0.191600801675401[/C][C]0.0958004008377004[/C][/ROW]
[ROW][C]37[/C][C]0.883785164751393[/C][C]0.232429670497214[/C][C]0.116214835248607[/C][/ROW]
[ROW][C]38[/C][C]0.885472091837342[/C][C]0.229055816325316[/C][C]0.114527908162658[/C][/ROW]
[ROW][C]39[/C][C]0.86848857822009[/C][C]0.26302284355982[/C][C]0.13151142177991[/C][/ROW]
[ROW][C]40[/C][C]0.865658568146349[/C][C]0.268682863707302[/C][C]0.134341431853651[/C][/ROW]
[ROW][C]41[/C][C]0.852328612874887[/C][C]0.295342774250226[/C][C]0.147671387125113[/C][/ROW]
[ROW][C]42[/C][C]0.890770336345988[/C][C]0.218459327308025[/C][C]0.109229663654012[/C][/ROW]
[ROW][C]43[/C][C]0.88618244424751[/C][C]0.227635111504981[/C][C]0.113817555752490[/C][/ROW]
[ROW][C]44[/C][C]0.898723248840586[/C][C]0.202553502318828[/C][C]0.101276751159414[/C][/ROW]
[ROW][C]45[/C][C]0.902801431539164[/C][C]0.194397136921672[/C][C]0.0971985684608359[/C][/ROW]
[ROW][C]46[/C][C]0.939205942379239[/C][C]0.121588115241522[/C][C]0.0607940576207611[/C][/ROW]
[ROW][C]47[/C][C]0.945632665526299[/C][C]0.108734668947402[/C][C]0.0543673344737011[/C][/ROW]
[ROW][C]48[/C][C]0.959703844454563[/C][C]0.0805923110908738[/C][C]0.0402961555454369[/C][/ROW]
[ROW][C]49[/C][C]0.96766842015986[/C][C]0.0646631596802807[/C][C]0.0323315798401403[/C][/ROW]
[ROW][C]50[/C][C]0.983110948950965[/C][C]0.0337781020980708[/C][C]0.0168890510490354[/C][/ROW]
[ROW][C]51[/C][C]0.98397355275316[/C][C]0.0320528944936781[/C][C]0.0160264472468391[/C][/ROW]
[ROW][C]52[/C][C]0.987861426590954[/C][C]0.0242771468180915[/C][C]0.0121385734090458[/C][/ROW]
[ROW][C]53[/C][C]0.993574852376297[/C][C]0.0128502952474059[/C][C]0.00642514762370294[/C][/ROW]
[ROW][C]54[/C][C]0.995551657913243[/C][C]0.0088966841735144[/C][C]0.0044483420867572[/C][/ROW]
[ROW][C]55[/C][C]0.99483411637496[/C][C]0.0103317672500815[/C][C]0.00516588362504075[/C][/ROW]
[ROW][C]56[/C][C]0.994620599382428[/C][C]0.0107588012351437[/C][C]0.00537940061757184[/C][/ROW]
[ROW][C]57[/C][C]0.99385177049673[/C][C]0.0122964590065392[/C][C]0.00614822950326962[/C][/ROW]
[ROW][C]58[/C][C]0.994213870117966[/C][C]0.0115722597640684[/C][C]0.00578612988203419[/C][/ROW]
[ROW][C]59[/C][C]0.99198275370299[/C][C]0.0160344925940212[/C][C]0.0080172462970106[/C][/ROW]
[ROW][C]60[/C][C]0.99068137959589[/C][C]0.0186372408082188[/C][C]0.00931862040410941[/C][/ROW]
[ROW][C]61[/C][C]0.986191645385102[/C][C]0.0276167092297954[/C][C]0.0138083546148977[/C][/ROW]
[ROW][C]62[/C][C]0.984810936432748[/C][C]0.0303781271345039[/C][C]0.0151890635672520[/C][/ROW]
[ROW][C]63[/C][C]0.978466308160133[/C][C]0.0430673836797343[/C][C]0.0215336918398671[/C][/ROW]
[ROW][C]64[/C][C]0.974201367551695[/C][C]0.0515972648966101[/C][C]0.0257986324483050[/C][/ROW]
[ROW][C]65[/C][C]0.962729039579407[/C][C]0.0745419208411854[/C][C]0.0372709604205927[/C][/ROW]
[ROW][C]66[/C][C]0.960634887671608[/C][C]0.0787302246567842[/C][C]0.0393651123283921[/C][/ROW]
[ROW][C]67[/C][C]0.9447543777205[/C][C]0.110491244558999[/C][C]0.0552456222794994[/C][/ROW]
[ROW][C]68[/C][C]0.928464043808275[/C][C]0.143071912383451[/C][C]0.0715359561917254[/C][/ROW]
[ROW][C]69[/C][C]0.899112137580236[/C][C]0.201775724839528[/C][C]0.100887862419764[/C][/ROW]
[ROW][C]70[/C][C]0.880062089339664[/C][C]0.239875821320672[/C][C]0.119937910660336[/C][/ROW]
[ROW][C]71[/C][C]0.837340867627117[/C][C]0.325318264745766[/C][C]0.162659132372883[/C][/ROW]
[ROW][C]72[/C][C]0.798752430984234[/C][C]0.402495138031532[/C][C]0.201247569015766[/C][/ROW]
[ROW][C]73[/C][C]0.744109526034257[/C][C]0.511780947931486[/C][C]0.255890473965743[/C][/ROW]
[ROW][C]74[/C][C]0.690172853696265[/C][C]0.61965429260747[/C][C]0.309827146303735[/C][/ROW]
[ROW][C]75[/C][C]0.599847699673499[/C][C]0.800304600653001[/C][C]0.400152300326501[/C][/ROW]
[ROW][C]76[/C][C]0.520758562522424[/C][C]0.958482874955152[/C][C]0.479241437477576[/C][/ROW]
[ROW][C]77[/C][C]0.422470366305636[/C][C]0.844940732611272[/C][C]0.577529633694364[/C][/ROW]
[ROW][C]78[/C][C]0.369946444425491[/C][C]0.739892888850982[/C][C]0.630053555574509[/C][/ROW]
[ROW][C]79[/C][C]0.290864993224098[/C][C]0.581729986448197[/C][C]0.709135006775902[/C][/ROW]
[ROW][C]80[/C][C]0.200078067964217[/C][C]0.400156135928433[/C][C]0.799921932035784[/C][/ROW]
[ROW][C]81[/C][C]0.131274864127197[/C][C]0.262549728254395[/C][C]0.868725135872803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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
90.01336042205505580.02672084411011160.986639577944944
100.002584890055301170.005169780110602330.997415109944699
110.001077408554936540.002154817109873080.998922591445063
120.001075904946608910.002151809893217830.998924095053391
130.0007535473096564780.001507094619312960.999246452690344
140.0004069354686939780.0008138709373879560.999593064531306
150.0002007400050162540.0004014800100325080.999799259994984
160.0002828884395684160.0005657768791368320.999717111560432
170.003769155847630460.007538311695260920.99623084415237
180.04687239658915040.09374479317830070.95312760341085
190.1887962428680560.3775924857361120.811203757131944
200.4869445261601880.9738890523203760.513055473839812
210.7823894266431550.4352211467136890.217610573356845
220.8773258635035580.2453482729928850.122674136496442
230.9440848908387070.1118302183225860.0559151091612931
240.9758492672943880.04830146541122340.0241507327056117
250.9851139451569260.02977210968614720.0148860548430736
260.9837812816767980.03243743664640430.0162187183232021
270.987077457586390.02584508482721850.0129225424136092
280.984578877504540.03084224499091890.0154211224954594
290.9867557186478630.02648856270427380.0132442813521369
300.9801699199754760.03966016004904750.0198300800245237
310.9785723324617950.04285533507640930.0214276675382047
320.9687580235004760.06248395299904720.0312419764995236
330.957866898542040.08426620291592060.0421331014579603
340.9432829810507680.1134340378984640.0567170189492318
350.9245360427738840.1509279144522320.0754639572261158
360.90419959916230.1916008016754010.0958004008377004
370.8837851647513930.2324296704972140.116214835248607
380.8854720918373420.2290558163253160.114527908162658
390.868488578220090.263022843559820.13151142177991
400.8656585681463490.2686828637073020.134341431853651
410.8523286128748870.2953427742502260.147671387125113
420.8907703363459880.2184593273080250.109229663654012
430.886182444247510.2276351115049810.113817555752490
440.8987232488405860.2025535023188280.101276751159414
450.9028014315391640.1943971369216720.0971985684608359
460.9392059423792390.1215881152415220.0607940576207611
470.9456326655262990.1087346689474020.0543673344737011
480.9597038444545630.08059231109087380.0402961555454369
490.967668420159860.06466315968028070.0323315798401403
500.9831109489509650.03377810209807080.0168890510490354
510.983973552753160.03205289449367810.0160264472468391
520.9878614265909540.02427714681809150.0121385734090458
530.9935748523762970.01285029524740590.00642514762370294
540.9955516579132430.00889668417351440.0044483420867572
550.994834116374960.01033176725008150.00516588362504075
560.9946205993824280.01075880123514370.00537940061757184
570.993851770496730.01229645900653920.00614822950326962
580.9942138701179660.01157225976406840.00578612988203419
590.991982753702990.01603449259402120.0080172462970106
600.990681379595890.01863724080821880.00931862040410941
610.9861916453851020.02761670922979540.0138083546148977
620.9848109364327480.03037812713450390.0151890635672520
630.9784663081601330.04306738367973430.0215336918398671
640.9742013675516950.05159726489661010.0257986324483050
650.9627290395794070.07454192084118540.0372709604205927
660.9606348876716080.07873022465678420.0393651123283921
670.94475437772050.1104912445589990.0552456222794994
680.9284640438082750.1430719123834510.0715359561917254
690.8991121375802360.2017757248395280.100887862419764
700.8800620893396640.2398758213206720.119937910660336
710.8373408676271170.3253182647457660.162659132372883
720.7987524309842340.4024951380315320.201247569015766
730.7441095260342570.5117809479314860.255890473965743
740.6901728536962650.619654292607470.309827146303735
750.5998476996734990.8003046006530010.400152300326501
760.5207585625224240.9584828749551520.479241437477576
770.4224703663056360.8449407326112720.577529633694364
780.3699464444254910.7398928888509820.630053555574509
790.2908649932240980.5817299864481970.709135006775902
800.2000780679642170.4001561359284330.799921932035784
810.1312748641271970.2625497282543950.868725135872803







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.123287671232877NOK
5% type I error level310.424657534246575NOK
10% type I error level390.534246575342466NOK

\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 & 9 & 0.123287671232877 & NOK \tabularnewline
5% type I error level & 31 & 0.424657534246575 & NOK \tabularnewline
10% type I error level & 39 & 0.534246575342466 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58165&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]9[/C][C]0.123287671232877[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.424657534246575[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.534246575342466[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58165&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58165&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 level90.123287671232877NOK
5% type I error level310.424657534246575NOK
10% type I error level390.534246575342466NOK



Parameters (Session):
par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = 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')
}