Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 09:42:15 -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/19/t12586491378iybqaft9mcpgav.htm/, Retrieved Thu, 28 Mar 2024 10:38:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57817, Retrieved Thu, 28 Mar 2024 10:38:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws7l2
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 16:42:15] [42ed2e0ab6f351a3dce7cf3f388e378d] [Current]
Feedback Forum

Post a new message
Dataseries X:
98,8	6,3
100,5	6,1
110,4	6,1
96,4	6,3
101,9	6,3
106,2	6
81	6,2
94,7	6,4
101	6,8
109,4	7,5
102,3	7,5
90,7	7,6
96,2	7,6
96,1	7,4
106	7,3
103,1	7,1
102	6,9
104,7	6,8
86	7,5
92,1	7,6
106,9	7,8
112,6	8
101,7	8,1
92	8,2
97,4	8,3
97	8,2
105,4	8
102,7	7,9
98,1	7,6
104,5	7,6
87,4	8,3
89,9	8,4
109,8	8,4
111,7	8,4
98,6	8,4
96,9	8,6
95,1	8,9
97	8,8
112,7	8,3
102,9	7,5
97,4	7,2
111,4	7,4
87,4	8,8
96,8	9,3
114,1	9,3
110,3	8,7
103,9	8,2
101,6	8,3
94,6	8,5
95,9	8,6
104,7	8,5
102,8	8,2
98,1	8,1
113,9	7,9
80,9	8,6
95,7	8,7
113,2	8,7
105,9	8,5
108,8	8,4
102,3	8,5
99	8,7
100,7	8,7
115,5	8,6
100,7	8,5
109,9	8,3
114,6	8
85,4	8,2
100,5	8,1
114,8	8,1
116,5	8
112,9	7,9
102	7,9
106	8
105,3	8
118,8	7,9
106,1	8
109,3	7,7
117,2	7,2
92,5	7,5
104,2	7,3
112,5	7
122,4	7
113,3	7
100	7,2
110,7	7,3
112,8	7,1
109,8	6,8
117,3	6,4
109,1	6,1
115,9	6,5
96	7,7
99,8	7,9
116,8	7,5
115,7	6,9
99,4	6,6
94,3	6,9
91	7,7




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=57817&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=57817&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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
Y[t] = + 100.808693644493 -0.421986537277549X[t] + 1.28993303416181M1[t] + 3.17167550485208M2[t] + 12.8478278608285M3[t] + 6.35093055337301M4[t] + 5.48625841420152M5[t] + 13.2690597604738M6[t] -10.4210993268639M7[t] -0.736125841420165M8[t] + 13.6835993268639M9[t] + 15.5769503365680M10[t] + 7.57947685112434M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  100.808693644493 -0.421986537277549X[t] +  1.28993303416181M1[t] +  3.17167550485208M2[t] +  12.8478278608285M3[t] +  6.35093055337301M4[t] +  5.48625841420152M5[t] +  13.2690597604738M6[t] -10.4210993268639M7[t] -0.736125841420165M8[t] +  13.6835993268639M9[t] +  15.5769503365680M10[t] +  7.57947685112434M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  100.808693644493 -0.421986537277549X[t] +  1.28993303416181M1[t] +  3.17167550485208M2[t] +  12.8478278608285M3[t] +  6.35093055337301M4[t] +  5.48625841420152M5[t] +  13.2690597604738M6[t] -10.4210993268639M7[t] -0.736125841420165M8[t] +  13.6835993268639M9[t] +  15.5769503365680M10[t] +  7.57947685112434M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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
Y[t] = + 100.808693644493 -0.421986537277549X[t] + 1.28993303416181M1[t] + 3.17167550485208M2[t] + 12.8478278608285M3[t] + 6.35093055337301M4[t] + 5.48625841420152M5[t] + 13.2690597604738M6[t] -10.4210993268639M7[t] -0.736125841420165M8[t] + 13.6835993268639M9[t] + 15.5769503365680M10[t] + 7.57947685112434M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100.8086936444936.1134916.489500
X-0.4219865372775490.735183-0.5740.5675090.283755
M11.289933034161812.6230950.49180.6241720.312086
M23.171675504852082.6992311.1750.2433030.121652
M312.84782786082852.7036074.75218e-064e-06
M46.350930553373012.7160732.33830.021750.010875
M55.486258414201522.7379222.00380.0483140.024157
M613.26905976047382.7512154.8236e-063e-06
M7-10.42109932686392.69934-3.86060.0002210.000111
M8-0.7361258414201652.699481-0.27270.785760.39288
M913.68359932686392.699345.06922e-061e-06
M1015.57695033656802.6991535.771100
M117.579476851124342.7009822.80620.0062290.003115

\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) & 100.808693644493 & 6.11349 & 16.4895 & 0 & 0 \tabularnewline
X & -0.421986537277549 & 0.735183 & -0.574 & 0.567509 & 0.283755 \tabularnewline
M1 & 1.28993303416181 & 2.623095 & 0.4918 & 0.624172 & 0.312086 \tabularnewline
M2 & 3.17167550485208 & 2.699231 & 1.175 & 0.243303 & 0.121652 \tabularnewline
M3 & 12.8478278608285 & 2.703607 & 4.7521 & 8e-06 & 4e-06 \tabularnewline
M4 & 6.35093055337301 & 2.716073 & 2.3383 & 0.02175 & 0.010875 \tabularnewline
M5 & 5.48625841420152 & 2.737922 & 2.0038 & 0.048314 & 0.024157 \tabularnewline
M6 & 13.2690597604738 & 2.751215 & 4.823 & 6e-06 & 3e-06 \tabularnewline
M7 & -10.4210993268639 & 2.69934 & -3.8606 & 0.000221 & 0.000111 \tabularnewline
M8 & -0.736125841420165 & 2.699481 & -0.2727 & 0.78576 & 0.39288 \tabularnewline
M9 & 13.6835993268639 & 2.69934 & 5.0692 & 2e-06 & 1e-06 \tabularnewline
M10 & 15.5769503365680 & 2.699153 & 5.7711 & 0 & 0 \tabularnewline
M11 & 7.57947685112434 & 2.700982 & 2.8062 & 0.006229 & 0.003115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&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]100.808693644493[/C][C]6.11349[/C][C]16.4895[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.421986537277549[/C][C]0.735183[/C][C]-0.574[/C][C]0.567509[/C][C]0.283755[/C][/ROW]
[ROW][C]M1[/C][C]1.28993303416181[/C][C]2.623095[/C][C]0.4918[/C][C]0.624172[/C][C]0.312086[/C][/ROW]
[ROW][C]M2[/C][C]3.17167550485208[/C][C]2.699231[/C][C]1.175[/C][C]0.243303[/C][C]0.121652[/C][/ROW]
[ROW][C]M3[/C][C]12.8478278608285[/C][C]2.703607[/C][C]4.7521[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]M4[/C][C]6.35093055337301[/C][C]2.716073[/C][C]2.3383[/C][C]0.02175[/C][C]0.010875[/C][/ROW]
[ROW][C]M5[/C][C]5.48625841420152[/C][C]2.737922[/C][C]2.0038[/C][C]0.048314[/C][C]0.024157[/C][/ROW]
[ROW][C]M6[/C][C]13.2690597604738[/C][C]2.751215[/C][C]4.823[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M7[/C][C]-10.4210993268639[/C][C]2.69934[/C][C]-3.8606[/C][C]0.000221[/C][C]0.000111[/C][/ROW]
[ROW][C]M8[/C][C]-0.736125841420165[/C][C]2.699481[/C][C]-0.2727[/C][C]0.78576[/C][C]0.39288[/C][/ROW]
[ROW][C]M9[/C][C]13.6835993268639[/C][C]2.69934[/C][C]5.0692[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M10[/C][C]15.5769503365680[/C][C]2.699153[/C][C]5.7711[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]7.57947685112434[/C][C]2.700982[/C][C]2.8062[/C][C]0.006229[/C][C]0.003115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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)100.8086936444936.1134916.489500
X-0.4219865372775490.735183-0.5740.5675090.283755
M11.289933034161812.6230950.49180.6241720.312086
M23.171675504852082.6992311.1750.2433030.121652
M312.84782786082852.7036074.75218e-064e-06
M46.350930553373012.7160732.33830.021750.010875
M55.486258414201522.7379222.00380.0483140.024157
M613.26905976047382.7512154.8236e-063e-06
M7-10.42109932686392.69934-3.86060.0002210.000111
M8-0.7361258414201652.699481-0.27270.785760.39288
M913.68359932686392.699345.06922e-061e-06
M1015.57695033656802.6991535.771100
M117.579476851124342.7009822.80620.0062290.003115







Multiple Linear Regression - Regression Statistics
Multiple R0.823138113126992
R-squared0.677556353282265
Adjusted R-squared0.631492975179731
F-TEST (value)14.7092198009029
F-TEST (DF numerator)12
F-TEST (DF denominator)84
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.3981798736166
Sum Squared Residuals2447.78905962523

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.823138113126992 \tabularnewline
R-squared & 0.677556353282265 \tabularnewline
Adjusted R-squared & 0.631492975179731 \tabularnewline
F-TEST (value) & 14.7092198009029 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 4.44089209850063e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.3981798736166 \tabularnewline
Sum Squared Residuals & 2447.78905962523 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.823138113126992[/C][/ROW]
[ROW][C]R-squared[/C][C]0.677556353282265[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.631492975179731[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.7092198009029[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/C][/ROW]
[ROW][C]p-value[/C][C]4.44089209850063e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.3981798736166[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2447.78905962523[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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 R0.823138113126992
R-squared0.677556353282265
Adjusted R-squared0.631492975179731
F-TEST (value)14.7092198009029
F-TEST (DF numerator)12
F-TEST (DF denominator)84
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.3981798736166
Sum Squared Residuals2447.78905962523







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.899.440111493805-0.64011149380493
2100.5101.406251271952-0.906251271951684
3110.4111.082403627928-0.682403627928092
496.4104.501109013017-8.1011090130171
5101.9103.636436873846-1.73643687384559
6106.2111.545834181301-5.34583418130114
78187.771277786508-6.77127778650797
894.797.3718539644962-2.67185396449618
9101111.622784517869-10.6227845178692
10109.4113.220744951479-3.82074495147908
11102.3105.223271466035-2.92327146603534
1290.797.6015959611832-6.90159596118324
1396.298.891528995345-2.6915289953451
1496.1100.857668773491-4.75766877349087
15106110.576019783195-4.57601978319505
16103.1104.163519783195-1.06351978319505
17102103.383244951479-1.38324495147908
18104.7111.208244951479-6.50824495147908
198687.2226952880471-1.22269528804714
2092.196.8654701197631-4.76547011976311
21106.9111.200797980592-4.30079798059163
22112.6113.009751682840-0.409751682840315
23101.7104.970079543669-3.27007954366883
249297.3484040388167-5.34840403881674
2597.498.5961384192508-1.19613841925080
2697100.520079543669-3.52007954366882
27105.4110.280629207101-4.88062920710076
28102.7103.825930553373-1.12593055337301
2998.1103.087854375385-4.9878543753848
30104.5110.870655721657-6.37065572165704
3187.486.88510605822510.514893941774906
3289.996.527880889941-6.62788088994106
33109.8110.947606058225-1.14760605822511
34111.7112.840957067929-1.14095706792928
3598.6104.843483582486-6.24348358248557
3696.997.1796094239057-0.279609423905713
3795.198.3429464968843-3.24294649688429
3897100.266887621302-3.26688762130230
39112.7110.1540332459182.5459667540825
40102.9103.994725168284-1.09472516828402
4197.4103.256648990296-5.85664899029581
42111.4110.9550530291130.444946970887453
4387.486.67411278958630.725887210413681
4496.896.14809300639130.651906993608724
45114.1110.5678181746753.53218182532468
46110.3112.714361106746-2.41436110674603
47103.9104.927880889941-1.02788088994107
48101.697.3062053850894.29379461491101
4994.698.5117411117953-3.9117411117953
5095.9100.351284928758-4.45128492875780
51104.7110.069635938462-5.36963593846199
52102.8103.699334592190-0.899334592189746
5398.1102.876861106746-4.77686110674603
54113.9110.7440597604743.15594023952623
5580.986.7585100970418-5.85851009704183
5695.796.4012849287578-0.701284928757804
57113.2110.8210100970422.37898990295816
58105.9112.798758414202-6.89875841420153
59108.8104.8434835824863.95651641751443
60102.397.22180807763355.07819192236652
619998.42734380433980.572656195660208
62100.7100.309086275030.390913724969953
63115.5110.0274372847345.47256271526576
64100.7103.572738631006-2.87273863100648
65109.9102.7924637992917.10753620070949
66114.6110.7018611067463.89813889325398
6785.486.9273047119529-1.52730471195285
68100.596.65447685112433.84552314887567
69114.8111.0742020194083.72579798059163
70116.5113.0097516828403.49024831715969
71112.9105.0544768511247.84552314887566
7210297.4754.52499999999999
7310698.7227343804347.27726561956593
74105.3100.6044768511244.69552314887566
75118.8110.3228278608298.47717213917147
76106.1103.7837318996452.31626810035474
77109.3103.0456557216576.25434427834296
78117.2111.0394503365686.16054966343194
7992.587.22269528804715.27730471195286
80104.296.99206608094647.20793391905363
81112.5111.5383872104140.961612789586324
82122.4113.4317382201188.96826177988215
83113.3105.4342647346747.86573526532586
8410097.77039057609432.22960942390571
85110.799.018124956528411.6818750434716
86112.8100.98426473467411.8157352653259
87109.8110.787013051834-0.987013051833833
88117.3104.45891035928912.8410896407107
89109.1103.7208341813015.37916581869887
90115.9111.3348409126624.56515908733766
919687.13829798059168.86170201940837
9299.896.73887415857983.06112584142015
93116.8111.3273939417755.4726060582251
94115.7113.4739368738462.22606312615439
9599.4105.603059349585-6.20305934958515
9694.397.8969865372776-3.59698653727756
979198.8493303416173-7.84933034161734

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.8 & 99.440111493805 & -0.64011149380493 \tabularnewline
2 & 100.5 & 101.406251271952 & -0.906251271951684 \tabularnewline
3 & 110.4 & 111.082403627928 & -0.682403627928092 \tabularnewline
4 & 96.4 & 104.501109013017 & -8.1011090130171 \tabularnewline
5 & 101.9 & 103.636436873846 & -1.73643687384559 \tabularnewline
6 & 106.2 & 111.545834181301 & -5.34583418130114 \tabularnewline
7 & 81 & 87.771277786508 & -6.77127778650797 \tabularnewline
8 & 94.7 & 97.3718539644962 & -2.67185396449618 \tabularnewline
9 & 101 & 111.622784517869 & -10.6227845178692 \tabularnewline
10 & 109.4 & 113.220744951479 & -3.82074495147908 \tabularnewline
11 & 102.3 & 105.223271466035 & -2.92327146603534 \tabularnewline
12 & 90.7 & 97.6015959611832 & -6.90159596118324 \tabularnewline
13 & 96.2 & 98.891528995345 & -2.6915289953451 \tabularnewline
14 & 96.1 & 100.857668773491 & -4.75766877349087 \tabularnewline
15 & 106 & 110.576019783195 & -4.57601978319505 \tabularnewline
16 & 103.1 & 104.163519783195 & -1.06351978319505 \tabularnewline
17 & 102 & 103.383244951479 & -1.38324495147908 \tabularnewline
18 & 104.7 & 111.208244951479 & -6.50824495147908 \tabularnewline
19 & 86 & 87.2226952880471 & -1.22269528804714 \tabularnewline
20 & 92.1 & 96.8654701197631 & -4.76547011976311 \tabularnewline
21 & 106.9 & 111.200797980592 & -4.30079798059163 \tabularnewline
22 & 112.6 & 113.009751682840 & -0.409751682840315 \tabularnewline
23 & 101.7 & 104.970079543669 & -3.27007954366883 \tabularnewline
24 & 92 & 97.3484040388167 & -5.34840403881674 \tabularnewline
25 & 97.4 & 98.5961384192508 & -1.19613841925080 \tabularnewline
26 & 97 & 100.520079543669 & -3.52007954366882 \tabularnewline
27 & 105.4 & 110.280629207101 & -4.88062920710076 \tabularnewline
28 & 102.7 & 103.825930553373 & -1.12593055337301 \tabularnewline
29 & 98.1 & 103.087854375385 & -4.9878543753848 \tabularnewline
30 & 104.5 & 110.870655721657 & -6.37065572165704 \tabularnewline
31 & 87.4 & 86.8851060582251 & 0.514893941774906 \tabularnewline
32 & 89.9 & 96.527880889941 & -6.62788088994106 \tabularnewline
33 & 109.8 & 110.947606058225 & -1.14760605822511 \tabularnewline
34 & 111.7 & 112.840957067929 & -1.14095706792928 \tabularnewline
35 & 98.6 & 104.843483582486 & -6.24348358248557 \tabularnewline
36 & 96.9 & 97.1796094239057 & -0.279609423905713 \tabularnewline
37 & 95.1 & 98.3429464968843 & -3.24294649688429 \tabularnewline
38 & 97 & 100.266887621302 & -3.26688762130230 \tabularnewline
39 & 112.7 & 110.154033245918 & 2.5459667540825 \tabularnewline
40 & 102.9 & 103.994725168284 & -1.09472516828402 \tabularnewline
41 & 97.4 & 103.256648990296 & -5.85664899029581 \tabularnewline
42 & 111.4 & 110.955053029113 & 0.444946970887453 \tabularnewline
43 & 87.4 & 86.6741127895863 & 0.725887210413681 \tabularnewline
44 & 96.8 & 96.1480930063913 & 0.651906993608724 \tabularnewline
45 & 114.1 & 110.567818174675 & 3.53218182532468 \tabularnewline
46 & 110.3 & 112.714361106746 & -2.41436110674603 \tabularnewline
47 & 103.9 & 104.927880889941 & -1.02788088994107 \tabularnewline
48 & 101.6 & 97.306205385089 & 4.29379461491101 \tabularnewline
49 & 94.6 & 98.5117411117953 & -3.9117411117953 \tabularnewline
50 & 95.9 & 100.351284928758 & -4.45128492875780 \tabularnewline
51 & 104.7 & 110.069635938462 & -5.36963593846199 \tabularnewline
52 & 102.8 & 103.699334592190 & -0.899334592189746 \tabularnewline
53 & 98.1 & 102.876861106746 & -4.77686110674603 \tabularnewline
54 & 113.9 & 110.744059760474 & 3.15594023952623 \tabularnewline
55 & 80.9 & 86.7585100970418 & -5.85851009704183 \tabularnewline
56 & 95.7 & 96.4012849287578 & -0.701284928757804 \tabularnewline
57 & 113.2 & 110.821010097042 & 2.37898990295816 \tabularnewline
58 & 105.9 & 112.798758414202 & -6.89875841420153 \tabularnewline
59 & 108.8 & 104.843483582486 & 3.95651641751443 \tabularnewline
60 & 102.3 & 97.2218080776335 & 5.07819192236652 \tabularnewline
61 & 99 & 98.4273438043398 & 0.572656195660208 \tabularnewline
62 & 100.7 & 100.30908627503 & 0.390913724969953 \tabularnewline
63 & 115.5 & 110.027437284734 & 5.47256271526576 \tabularnewline
64 & 100.7 & 103.572738631006 & -2.87273863100648 \tabularnewline
65 & 109.9 & 102.792463799291 & 7.10753620070949 \tabularnewline
66 & 114.6 & 110.701861106746 & 3.89813889325398 \tabularnewline
67 & 85.4 & 86.9273047119529 & -1.52730471195285 \tabularnewline
68 & 100.5 & 96.6544768511243 & 3.84552314887567 \tabularnewline
69 & 114.8 & 111.074202019408 & 3.72579798059163 \tabularnewline
70 & 116.5 & 113.009751682840 & 3.49024831715969 \tabularnewline
71 & 112.9 & 105.054476851124 & 7.84552314887566 \tabularnewline
72 & 102 & 97.475 & 4.52499999999999 \tabularnewline
73 & 106 & 98.722734380434 & 7.27726561956593 \tabularnewline
74 & 105.3 & 100.604476851124 & 4.69552314887566 \tabularnewline
75 & 118.8 & 110.322827860829 & 8.47717213917147 \tabularnewline
76 & 106.1 & 103.783731899645 & 2.31626810035474 \tabularnewline
77 & 109.3 & 103.045655721657 & 6.25434427834296 \tabularnewline
78 & 117.2 & 111.039450336568 & 6.16054966343194 \tabularnewline
79 & 92.5 & 87.2226952880471 & 5.27730471195286 \tabularnewline
80 & 104.2 & 96.9920660809464 & 7.20793391905363 \tabularnewline
81 & 112.5 & 111.538387210414 & 0.961612789586324 \tabularnewline
82 & 122.4 & 113.431738220118 & 8.96826177988215 \tabularnewline
83 & 113.3 & 105.434264734674 & 7.86573526532586 \tabularnewline
84 & 100 & 97.7703905760943 & 2.22960942390571 \tabularnewline
85 & 110.7 & 99.0181249565284 & 11.6818750434716 \tabularnewline
86 & 112.8 & 100.984264734674 & 11.8157352653259 \tabularnewline
87 & 109.8 & 110.787013051834 & -0.987013051833833 \tabularnewline
88 & 117.3 & 104.458910359289 & 12.8410896407107 \tabularnewline
89 & 109.1 & 103.720834181301 & 5.37916581869887 \tabularnewline
90 & 115.9 & 111.334840912662 & 4.56515908733766 \tabularnewline
91 & 96 & 87.1382979805916 & 8.86170201940837 \tabularnewline
92 & 99.8 & 96.7388741585798 & 3.06112584142015 \tabularnewline
93 & 116.8 & 111.327393941775 & 5.4726060582251 \tabularnewline
94 & 115.7 & 113.473936873846 & 2.22606312615439 \tabularnewline
95 & 99.4 & 105.603059349585 & -6.20305934958515 \tabularnewline
96 & 94.3 & 97.8969865372776 & -3.59698653727756 \tabularnewline
97 & 91 & 98.8493303416173 & -7.84933034161734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&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]98.8[/C][C]99.440111493805[/C][C]-0.64011149380493[/C][/ROW]
[ROW][C]2[/C][C]100.5[/C][C]101.406251271952[/C][C]-0.906251271951684[/C][/ROW]
[ROW][C]3[/C][C]110.4[/C][C]111.082403627928[/C][C]-0.682403627928092[/C][/ROW]
[ROW][C]4[/C][C]96.4[/C][C]104.501109013017[/C][C]-8.1011090130171[/C][/ROW]
[ROW][C]5[/C][C]101.9[/C][C]103.636436873846[/C][C]-1.73643687384559[/C][/ROW]
[ROW][C]6[/C][C]106.2[/C][C]111.545834181301[/C][C]-5.34583418130114[/C][/ROW]
[ROW][C]7[/C][C]81[/C][C]87.771277786508[/C][C]-6.77127778650797[/C][/ROW]
[ROW][C]8[/C][C]94.7[/C][C]97.3718539644962[/C][C]-2.67185396449618[/C][/ROW]
[ROW][C]9[/C][C]101[/C][C]111.622784517869[/C][C]-10.6227845178692[/C][/ROW]
[ROW][C]10[/C][C]109.4[/C][C]113.220744951479[/C][C]-3.82074495147908[/C][/ROW]
[ROW][C]11[/C][C]102.3[/C][C]105.223271466035[/C][C]-2.92327146603534[/C][/ROW]
[ROW][C]12[/C][C]90.7[/C][C]97.6015959611832[/C][C]-6.90159596118324[/C][/ROW]
[ROW][C]13[/C][C]96.2[/C][C]98.891528995345[/C][C]-2.6915289953451[/C][/ROW]
[ROW][C]14[/C][C]96.1[/C][C]100.857668773491[/C][C]-4.75766877349087[/C][/ROW]
[ROW][C]15[/C][C]106[/C][C]110.576019783195[/C][C]-4.57601978319505[/C][/ROW]
[ROW][C]16[/C][C]103.1[/C][C]104.163519783195[/C][C]-1.06351978319505[/C][/ROW]
[ROW][C]17[/C][C]102[/C][C]103.383244951479[/C][C]-1.38324495147908[/C][/ROW]
[ROW][C]18[/C][C]104.7[/C][C]111.208244951479[/C][C]-6.50824495147908[/C][/ROW]
[ROW][C]19[/C][C]86[/C][C]87.2226952880471[/C][C]-1.22269528804714[/C][/ROW]
[ROW][C]20[/C][C]92.1[/C][C]96.8654701197631[/C][C]-4.76547011976311[/C][/ROW]
[ROW][C]21[/C][C]106.9[/C][C]111.200797980592[/C][C]-4.30079798059163[/C][/ROW]
[ROW][C]22[/C][C]112.6[/C][C]113.009751682840[/C][C]-0.409751682840315[/C][/ROW]
[ROW][C]23[/C][C]101.7[/C][C]104.970079543669[/C][C]-3.27007954366883[/C][/ROW]
[ROW][C]24[/C][C]92[/C][C]97.3484040388167[/C][C]-5.34840403881674[/C][/ROW]
[ROW][C]25[/C][C]97.4[/C][C]98.5961384192508[/C][C]-1.19613841925080[/C][/ROW]
[ROW][C]26[/C][C]97[/C][C]100.520079543669[/C][C]-3.52007954366882[/C][/ROW]
[ROW][C]27[/C][C]105.4[/C][C]110.280629207101[/C][C]-4.88062920710076[/C][/ROW]
[ROW][C]28[/C][C]102.7[/C][C]103.825930553373[/C][C]-1.12593055337301[/C][/ROW]
[ROW][C]29[/C][C]98.1[/C][C]103.087854375385[/C][C]-4.9878543753848[/C][/ROW]
[ROW][C]30[/C][C]104.5[/C][C]110.870655721657[/C][C]-6.37065572165704[/C][/ROW]
[ROW][C]31[/C][C]87.4[/C][C]86.8851060582251[/C][C]0.514893941774906[/C][/ROW]
[ROW][C]32[/C][C]89.9[/C][C]96.527880889941[/C][C]-6.62788088994106[/C][/ROW]
[ROW][C]33[/C][C]109.8[/C][C]110.947606058225[/C][C]-1.14760605822511[/C][/ROW]
[ROW][C]34[/C][C]111.7[/C][C]112.840957067929[/C][C]-1.14095706792928[/C][/ROW]
[ROW][C]35[/C][C]98.6[/C][C]104.843483582486[/C][C]-6.24348358248557[/C][/ROW]
[ROW][C]36[/C][C]96.9[/C][C]97.1796094239057[/C][C]-0.279609423905713[/C][/ROW]
[ROW][C]37[/C][C]95.1[/C][C]98.3429464968843[/C][C]-3.24294649688429[/C][/ROW]
[ROW][C]38[/C][C]97[/C][C]100.266887621302[/C][C]-3.26688762130230[/C][/ROW]
[ROW][C]39[/C][C]112.7[/C][C]110.154033245918[/C][C]2.5459667540825[/C][/ROW]
[ROW][C]40[/C][C]102.9[/C][C]103.994725168284[/C][C]-1.09472516828402[/C][/ROW]
[ROW][C]41[/C][C]97.4[/C][C]103.256648990296[/C][C]-5.85664899029581[/C][/ROW]
[ROW][C]42[/C][C]111.4[/C][C]110.955053029113[/C][C]0.444946970887453[/C][/ROW]
[ROW][C]43[/C][C]87.4[/C][C]86.6741127895863[/C][C]0.725887210413681[/C][/ROW]
[ROW][C]44[/C][C]96.8[/C][C]96.1480930063913[/C][C]0.651906993608724[/C][/ROW]
[ROW][C]45[/C][C]114.1[/C][C]110.567818174675[/C][C]3.53218182532468[/C][/ROW]
[ROW][C]46[/C][C]110.3[/C][C]112.714361106746[/C][C]-2.41436110674603[/C][/ROW]
[ROW][C]47[/C][C]103.9[/C][C]104.927880889941[/C][C]-1.02788088994107[/C][/ROW]
[ROW][C]48[/C][C]101.6[/C][C]97.306205385089[/C][C]4.29379461491101[/C][/ROW]
[ROW][C]49[/C][C]94.6[/C][C]98.5117411117953[/C][C]-3.9117411117953[/C][/ROW]
[ROW][C]50[/C][C]95.9[/C][C]100.351284928758[/C][C]-4.45128492875780[/C][/ROW]
[ROW][C]51[/C][C]104.7[/C][C]110.069635938462[/C][C]-5.36963593846199[/C][/ROW]
[ROW][C]52[/C][C]102.8[/C][C]103.699334592190[/C][C]-0.899334592189746[/C][/ROW]
[ROW][C]53[/C][C]98.1[/C][C]102.876861106746[/C][C]-4.77686110674603[/C][/ROW]
[ROW][C]54[/C][C]113.9[/C][C]110.744059760474[/C][C]3.15594023952623[/C][/ROW]
[ROW][C]55[/C][C]80.9[/C][C]86.7585100970418[/C][C]-5.85851009704183[/C][/ROW]
[ROW][C]56[/C][C]95.7[/C][C]96.4012849287578[/C][C]-0.701284928757804[/C][/ROW]
[ROW][C]57[/C][C]113.2[/C][C]110.821010097042[/C][C]2.37898990295816[/C][/ROW]
[ROW][C]58[/C][C]105.9[/C][C]112.798758414202[/C][C]-6.89875841420153[/C][/ROW]
[ROW][C]59[/C][C]108.8[/C][C]104.843483582486[/C][C]3.95651641751443[/C][/ROW]
[ROW][C]60[/C][C]102.3[/C][C]97.2218080776335[/C][C]5.07819192236652[/C][/ROW]
[ROW][C]61[/C][C]99[/C][C]98.4273438043398[/C][C]0.572656195660208[/C][/ROW]
[ROW][C]62[/C][C]100.7[/C][C]100.30908627503[/C][C]0.390913724969953[/C][/ROW]
[ROW][C]63[/C][C]115.5[/C][C]110.027437284734[/C][C]5.47256271526576[/C][/ROW]
[ROW][C]64[/C][C]100.7[/C][C]103.572738631006[/C][C]-2.87273863100648[/C][/ROW]
[ROW][C]65[/C][C]109.9[/C][C]102.792463799291[/C][C]7.10753620070949[/C][/ROW]
[ROW][C]66[/C][C]114.6[/C][C]110.701861106746[/C][C]3.89813889325398[/C][/ROW]
[ROW][C]67[/C][C]85.4[/C][C]86.9273047119529[/C][C]-1.52730471195285[/C][/ROW]
[ROW][C]68[/C][C]100.5[/C][C]96.6544768511243[/C][C]3.84552314887567[/C][/ROW]
[ROW][C]69[/C][C]114.8[/C][C]111.074202019408[/C][C]3.72579798059163[/C][/ROW]
[ROW][C]70[/C][C]116.5[/C][C]113.009751682840[/C][C]3.49024831715969[/C][/ROW]
[ROW][C]71[/C][C]112.9[/C][C]105.054476851124[/C][C]7.84552314887566[/C][/ROW]
[ROW][C]72[/C][C]102[/C][C]97.475[/C][C]4.52499999999999[/C][/ROW]
[ROW][C]73[/C][C]106[/C][C]98.722734380434[/C][C]7.27726561956593[/C][/ROW]
[ROW][C]74[/C][C]105.3[/C][C]100.604476851124[/C][C]4.69552314887566[/C][/ROW]
[ROW][C]75[/C][C]118.8[/C][C]110.322827860829[/C][C]8.47717213917147[/C][/ROW]
[ROW][C]76[/C][C]106.1[/C][C]103.783731899645[/C][C]2.31626810035474[/C][/ROW]
[ROW][C]77[/C][C]109.3[/C][C]103.045655721657[/C][C]6.25434427834296[/C][/ROW]
[ROW][C]78[/C][C]117.2[/C][C]111.039450336568[/C][C]6.16054966343194[/C][/ROW]
[ROW][C]79[/C][C]92.5[/C][C]87.2226952880471[/C][C]5.27730471195286[/C][/ROW]
[ROW][C]80[/C][C]104.2[/C][C]96.9920660809464[/C][C]7.20793391905363[/C][/ROW]
[ROW][C]81[/C][C]112.5[/C][C]111.538387210414[/C][C]0.961612789586324[/C][/ROW]
[ROW][C]82[/C][C]122.4[/C][C]113.431738220118[/C][C]8.96826177988215[/C][/ROW]
[ROW][C]83[/C][C]113.3[/C][C]105.434264734674[/C][C]7.86573526532586[/C][/ROW]
[ROW][C]84[/C][C]100[/C][C]97.7703905760943[/C][C]2.22960942390571[/C][/ROW]
[ROW][C]85[/C][C]110.7[/C][C]99.0181249565284[/C][C]11.6818750434716[/C][/ROW]
[ROW][C]86[/C][C]112.8[/C][C]100.984264734674[/C][C]11.8157352653259[/C][/ROW]
[ROW][C]87[/C][C]109.8[/C][C]110.787013051834[/C][C]-0.987013051833833[/C][/ROW]
[ROW][C]88[/C][C]117.3[/C][C]104.458910359289[/C][C]12.8410896407107[/C][/ROW]
[ROW][C]89[/C][C]109.1[/C][C]103.720834181301[/C][C]5.37916581869887[/C][/ROW]
[ROW][C]90[/C][C]115.9[/C][C]111.334840912662[/C][C]4.56515908733766[/C][/ROW]
[ROW][C]91[/C][C]96[/C][C]87.1382979805916[/C][C]8.86170201940837[/C][/ROW]
[ROW][C]92[/C][C]99.8[/C][C]96.7388741585798[/C][C]3.06112584142015[/C][/ROW]
[ROW][C]93[/C][C]116.8[/C][C]111.327393941775[/C][C]5.4726060582251[/C][/ROW]
[ROW][C]94[/C][C]115.7[/C][C]113.473936873846[/C][C]2.22606312615439[/C][/ROW]
[ROW][C]95[/C][C]99.4[/C][C]105.603059349585[/C][C]-6.20305934958515[/C][/ROW]
[ROW][C]96[/C][C]94.3[/C][C]97.8969865372776[/C][C]-3.59698653727756[/C][/ROW]
[ROW][C]97[/C][C]91[/C][C]98.8493303416173[/C][C]-7.84933034161734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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
198.899.440111493805-0.64011149380493
2100.5101.406251271952-0.906251271951684
3110.4111.082403627928-0.682403627928092
496.4104.501109013017-8.1011090130171
5101.9103.636436873846-1.73643687384559
6106.2111.545834181301-5.34583418130114
78187.771277786508-6.77127778650797
894.797.3718539644962-2.67185396449618
9101111.622784517869-10.6227845178692
10109.4113.220744951479-3.82074495147908
11102.3105.223271466035-2.92327146603534
1290.797.6015959611832-6.90159596118324
1396.298.891528995345-2.6915289953451
1496.1100.857668773491-4.75766877349087
15106110.576019783195-4.57601978319505
16103.1104.163519783195-1.06351978319505
17102103.383244951479-1.38324495147908
18104.7111.208244951479-6.50824495147908
198687.2226952880471-1.22269528804714
2092.196.8654701197631-4.76547011976311
21106.9111.200797980592-4.30079798059163
22112.6113.009751682840-0.409751682840315
23101.7104.970079543669-3.27007954366883
249297.3484040388167-5.34840403881674
2597.498.5961384192508-1.19613841925080
2697100.520079543669-3.52007954366882
27105.4110.280629207101-4.88062920710076
28102.7103.825930553373-1.12593055337301
2998.1103.087854375385-4.9878543753848
30104.5110.870655721657-6.37065572165704
3187.486.88510605822510.514893941774906
3289.996.527880889941-6.62788088994106
33109.8110.947606058225-1.14760605822511
34111.7112.840957067929-1.14095706792928
3598.6104.843483582486-6.24348358248557
3696.997.1796094239057-0.279609423905713
3795.198.3429464968843-3.24294649688429
3897100.266887621302-3.26688762130230
39112.7110.1540332459182.5459667540825
40102.9103.994725168284-1.09472516828402
4197.4103.256648990296-5.85664899029581
42111.4110.9550530291130.444946970887453
4387.486.67411278958630.725887210413681
4496.896.14809300639130.651906993608724
45114.1110.5678181746753.53218182532468
46110.3112.714361106746-2.41436110674603
47103.9104.927880889941-1.02788088994107
48101.697.3062053850894.29379461491101
4994.698.5117411117953-3.9117411117953
5095.9100.351284928758-4.45128492875780
51104.7110.069635938462-5.36963593846199
52102.8103.699334592190-0.899334592189746
5398.1102.876861106746-4.77686110674603
54113.9110.7440597604743.15594023952623
5580.986.7585100970418-5.85851009704183
5695.796.4012849287578-0.701284928757804
57113.2110.8210100970422.37898990295816
58105.9112.798758414202-6.89875841420153
59108.8104.8434835824863.95651641751443
60102.397.22180807763355.07819192236652
619998.42734380433980.572656195660208
62100.7100.309086275030.390913724969953
63115.5110.0274372847345.47256271526576
64100.7103.572738631006-2.87273863100648
65109.9102.7924637992917.10753620070949
66114.6110.7018611067463.89813889325398
6785.486.9273047119529-1.52730471195285
68100.596.65447685112433.84552314887567
69114.8111.0742020194083.72579798059163
70116.5113.0097516828403.49024831715969
71112.9105.0544768511247.84552314887566
7210297.4754.52499999999999
7310698.7227343804347.27726561956593
74105.3100.6044768511244.69552314887566
75118.8110.3228278608298.47717213917147
76106.1103.7837318996452.31626810035474
77109.3103.0456557216576.25434427834296
78117.2111.0394503365686.16054966343194
7992.587.22269528804715.27730471195286
80104.296.99206608094647.20793391905363
81112.5111.5383872104140.961612789586324
82122.4113.4317382201188.96826177988215
83113.3105.4342647346747.86573526532586
8410097.77039057609432.22960942390571
85110.799.018124956528411.6818750434716
86112.8100.98426473467411.8157352653259
87109.8110.787013051834-0.987013051833833
88117.3104.45891035928912.8410896407107
89109.1103.7208341813015.37916581869887
90115.9111.3348409126624.56515908733766
919687.13829798059168.86170201940837
9299.896.73887415857983.06112584142015
93116.8111.3273939417755.4726060582251
94115.7113.4739368738462.22606312615439
9599.4105.603059349585-6.20305934958515
9694.397.8969865372776-3.59698653727756
979198.8493303416173-7.84933034161734







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.2937304445079710.5874608890159420.706269555492029
170.1582499673393310.3164999346786620.841750032660669
180.08393580068370080.1678716013674020.916064199316299
190.1017454779945500.2034909559891000.89825452200545
200.06153883421854570.1230776684370910.938461165781454
210.07696749787794920.1539349957558980.92303250212205
220.05109505491352130.1021901098270430.948904945086479
230.02798428887534460.05596857775068920.972015711124655
240.01622219383170370.03244438766340730.983777806168296
250.00806792496144460.01613584992288920.991932075038555
260.004391781421473150.00878356284294630.995608218578527
270.002995156638864910.005990313277729830.997004843361135
280.002113884861999710.004227769723999410.997886115138
290.001862743832352010.003725487664704020.998137256167648
300.001221032737228990.002442065474457980.998778967262771
310.001113641435688970.002227282871377930.99888635856431
320.001112554222524780.002225108445049560.998887445777475
330.001885790549567290.003771581099134580.998114209450433
340.0009651816322625130.001930363264525030.999034818367738
350.0008657965112211190.001731593022442240.99913420348878
360.001094464100954470.002188928201908950.998905535899046
370.0007030532610314950.001406106522062990.999296946738969
380.0004044311148243050.000808862229648610.999595568885176
390.0005287020320058320.001057404064011660.999471297967994
400.0003446190509863960.0006892381019727910.999655380949014
410.0004569547415426520.0009139094830853040.999543045258457
420.0007762261343155930.001552452268631190.999223773865684
430.0004649760444776270.0009299520889552540.999535023955522
440.0003504551476750010.0007009102953500010.999649544852325
450.000710843511050.00142168702210.99928915648895
460.0004087469316390500.0008174938632781010.999591253068361
470.0002732768379682170.0005465536759364340.999726723162032
480.000742924306343120.001485848612686240.999257075693657
490.0005966886307459760.001193377261491950.999403311369254
500.0006175428047682370.001235085609536470.999382457195232
510.0008683825696834740.001736765139366950.999131617430317
520.0005423918561415810.001084783712283160.999457608143858
530.00071455800350430.00142911600700860.999285441996496
540.0009133667237597690.001826733447519540.99908663327624
550.001592573236569350.003185146473138690.99840742676343
560.001110996979775350.00222199395955070.998889003020225
570.0009293384778440180.001858676955688040.999070661522156
580.002340157853242820.004680315706485640.997659842146757
590.002805093029443860.005610186058887730.997194906970556
600.003862022671035950.007724045342071890.996137977328964
610.002474895669496740.004949791338993490.997525104330503
620.002379944715724150.00475988943144830.997620055284276
630.002605901311822050.005211802623644110.997394098688178
640.003928251255220850.00785650251044170.99607174874478
650.005889591226098230.01177918245219650.994110408773902
660.004704734886817660.009409469773635320.995295265113182
670.005688551342451270.01137710268490250.994311448657549
680.005111686190902990.01022337238180600.994888313809097
690.004012056615888960.008024113231777930.995987943384111
700.003648506835255390.007297013670510780.996351493164745
710.006864810052359640.01372962010471930.99313518994764
720.006504941742122360.01300988348424470.993495058257878
730.008765394512577720.01753078902515540.991234605487422
740.009074205494805980.01814841098961200.990925794505194
750.01894440082396030.03788880164792070.98105559917604
760.02191448174526740.04382896349053480.978085518254733
770.01517772855150920.03035545710301850.98482227144849
780.01010269454273560.02020538908547110.989897305457264
790.006922909573707390.01384581914741480.993077090426293
800.005455546727401670.01091109345480330.994544453272598
810.002364116988803780.004728233977607550.997635883011196

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.293730444507971 & 0.587460889015942 & 0.706269555492029 \tabularnewline
17 & 0.158249967339331 & 0.316499934678662 & 0.841750032660669 \tabularnewline
18 & 0.0839358006837008 & 0.167871601367402 & 0.916064199316299 \tabularnewline
19 & 0.101745477994550 & 0.203490955989100 & 0.89825452200545 \tabularnewline
20 & 0.0615388342185457 & 0.123077668437091 & 0.938461165781454 \tabularnewline
21 & 0.0769674978779492 & 0.153934995755898 & 0.92303250212205 \tabularnewline
22 & 0.0510950549135213 & 0.102190109827043 & 0.948904945086479 \tabularnewline
23 & 0.0279842888753446 & 0.0559685777506892 & 0.972015711124655 \tabularnewline
24 & 0.0162221938317037 & 0.0324443876634073 & 0.983777806168296 \tabularnewline
25 & 0.0080679249614446 & 0.0161358499228892 & 0.991932075038555 \tabularnewline
26 & 0.00439178142147315 & 0.0087835628429463 & 0.995608218578527 \tabularnewline
27 & 0.00299515663886491 & 0.00599031327772983 & 0.997004843361135 \tabularnewline
28 & 0.00211388486199971 & 0.00422776972399941 & 0.997886115138 \tabularnewline
29 & 0.00186274383235201 & 0.00372548766470402 & 0.998137256167648 \tabularnewline
30 & 0.00122103273722899 & 0.00244206547445798 & 0.998778967262771 \tabularnewline
31 & 0.00111364143568897 & 0.00222728287137793 & 0.99888635856431 \tabularnewline
32 & 0.00111255422252478 & 0.00222510844504956 & 0.998887445777475 \tabularnewline
33 & 0.00188579054956729 & 0.00377158109913458 & 0.998114209450433 \tabularnewline
34 & 0.000965181632262513 & 0.00193036326452503 & 0.999034818367738 \tabularnewline
35 & 0.000865796511221119 & 0.00173159302244224 & 0.99913420348878 \tabularnewline
36 & 0.00109446410095447 & 0.00218892820190895 & 0.998905535899046 \tabularnewline
37 & 0.000703053261031495 & 0.00140610652206299 & 0.999296946738969 \tabularnewline
38 & 0.000404431114824305 & 0.00080886222964861 & 0.999595568885176 \tabularnewline
39 & 0.000528702032005832 & 0.00105740406401166 & 0.999471297967994 \tabularnewline
40 & 0.000344619050986396 & 0.000689238101972791 & 0.999655380949014 \tabularnewline
41 & 0.000456954741542652 & 0.000913909483085304 & 0.999543045258457 \tabularnewline
42 & 0.000776226134315593 & 0.00155245226863119 & 0.999223773865684 \tabularnewline
43 & 0.000464976044477627 & 0.000929952088955254 & 0.999535023955522 \tabularnewline
44 & 0.000350455147675001 & 0.000700910295350001 & 0.999649544852325 \tabularnewline
45 & 0.00071084351105 & 0.0014216870221 & 0.99928915648895 \tabularnewline
46 & 0.000408746931639050 & 0.000817493863278101 & 0.999591253068361 \tabularnewline
47 & 0.000273276837968217 & 0.000546553675936434 & 0.999726723162032 \tabularnewline
48 & 0.00074292430634312 & 0.00148584861268624 & 0.999257075693657 \tabularnewline
49 & 0.000596688630745976 & 0.00119337726149195 & 0.999403311369254 \tabularnewline
50 & 0.000617542804768237 & 0.00123508560953647 & 0.999382457195232 \tabularnewline
51 & 0.000868382569683474 & 0.00173676513936695 & 0.999131617430317 \tabularnewline
52 & 0.000542391856141581 & 0.00108478371228316 & 0.999457608143858 \tabularnewline
53 & 0.0007145580035043 & 0.0014291160070086 & 0.999285441996496 \tabularnewline
54 & 0.000913366723759769 & 0.00182673344751954 & 0.99908663327624 \tabularnewline
55 & 0.00159257323656935 & 0.00318514647313869 & 0.99840742676343 \tabularnewline
56 & 0.00111099697977535 & 0.0022219939595507 & 0.998889003020225 \tabularnewline
57 & 0.000929338477844018 & 0.00185867695568804 & 0.999070661522156 \tabularnewline
58 & 0.00234015785324282 & 0.00468031570648564 & 0.997659842146757 \tabularnewline
59 & 0.00280509302944386 & 0.00561018605888773 & 0.997194906970556 \tabularnewline
60 & 0.00386202267103595 & 0.00772404534207189 & 0.996137977328964 \tabularnewline
61 & 0.00247489566949674 & 0.00494979133899349 & 0.997525104330503 \tabularnewline
62 & 0.00237994471572415 & 0.0047598894314483 & 0.997620055284276 \tabularnewline
63 & 0.00260590131182205 & 0.00521180262364411 & 0.997394098688178 \tabularnewline
64 & 0.00392825125522085 & 0.0078565025104417 & 0.99607174874478 \tabularnewline
65 & 0.00588959122609823 & 0.0117791824521965 & 0.994110408773902 \tabularnewline
66 & 0.00470473488681766 & 0.00940946977363532 & 0.995295265113182 \tabularnewline
67 & 0.00568855134245127 & 0.0113771026849025 & 0.994311448657549 \tabularnewline
68 & 0.00511168619090299 & 0.0102233723818060 & 0.994888313809097 \tabularnewline
69 & 0.00401205661588896 & 0.00802411323177793 & 0.995987943384111 \tabularnewline
70 & 0.00364850683525539 & 0.00729701367051078 & 0.996351493164745 \tabularnewline
71 & 0.00686481005235964 & 0.0137296201047193 & 0.99313518994764 \tabularnewline
72 & 0.00650494174212236 & 0.0130098834842447 & 0.993495058257878 \tabularnewline
73 & 0.00876539451257772 & 0.0175307890251554 & 0.991234605487422 \tabularnewline
74 & 0.00907420549480598 & 0.0181484109896120 & 0.990925794505194 \tabularnewline
75 & 0.0189444008239603 & 0.0378888016479207 & 0.98105559917604 \tabularnewline
76 & 0.0219144817452674 & 0.0438289634905348 & 0.978085518254733 \tabularnewline
77 & 0.0151777285515092 & 0.0303554571030185 & 0.98482227144849 \tabularnewline
78 & 0.0101026945427356 & 0.0202053890854711 & 0.989897305457264 \tabularnewline
79 & 0.00692290957370739 & 0.0138458191474148 & 0.993077090426293 \tabularnewline
80 & 0.00545554672740167 & 0.0109110934548033 & 0.994544453272598 \tabularnewline
81 & 0.00236411698880378 & 0.00472823397760755 & 0.997635883011196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&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]16[/C][C]0.293730444507971[/C][C]0.587460889015942[/C][C]0.706269555492029[/C][/ROW]
[ROW][C]17[/C][C]0.158249967339331[/C][C]0.316499934678662[/C][C]0.841750032660669[/C][/ROW]
[ROW][C]18[/C][C]0.0839358006837008[/C][C]0.167871601367402[/C][C]0.916064199316299[/C][/ROW]
[ROW][C]19[/C][C]0.101745477994550[/C][C]0.203490955989100[/C][C]0.89825452200545[/C][/ROW]
[ROW][C]20[/C][C]0.0615388342185457[/C][C]0.123077668437091[/C][C]0.938461165781454[/C][/ROW]
[ROW][C]21[/C][C]0.0769674978779492[/C][C]0.153934995755898[/C][C]0.92303250212205[/C][/ROW]
[ROW][C]22[/C][C]0.0510950549135213[/C][C]0.102190109827043[/C][C]0.948904945086479[/C][/ROW]
[ROW][C]23[/C][C]0.0279842888753446[/C][C]0.0559685777506892[/C][C]0.972015711124655[/C][/ROW]
[ROW][C]24[/C][C]0.0162221938317037[/C][C]0.0324443876634073[/C][C]0.983777806168296[/C][/ROW]
[ROW][C]25[/C][C]0.0080679249614446[/C][C]0.0161358499228892[/C][C]0.991932075038555[/C][/ROW]
[ROW][C]26[/C][C]0.00439178142147315[/C][C]0.0087835628429463[/C][C]0.995608218578527[/C][/ROW]
[ROW][C]27[/C][C]0.00299515663886491[/C][C]0.00599031327772983[/C][C]0.997004843361135[/C][/ROW]
[ROW][C]28[/C][C]0.00211388486199971[/C][C]0.00422776972399941[/C][C]0.997886115138[/C][/ROW]
[ROW][C]29[/C][C]0.00186274383235201[/C][C]0.00372548766470402[/C][C]0.998137256167648[/C][/ROW]
[ROW][C]30[/C][C]0.00122103273722899[/C][C]0.00244206547445798[/C][C]0.998778967262771[/C][/ROW]
[ROW][C]31[/C][C]0.00111364143568897[/C][C]0.00222728287137793[/C][C]0.99888635856431[/C][/ROW]
[ROW][C]32[/C][C]0.00111255422252478[/C][C]0.00222510844504956[/C][C]0.998887445777475[/C][/ROW]
[ROW][C]33[/C][C]0.00188579054956729[/C][C]0.00377158109913458[/C][C]0.998114209450433[/C][/ROW]
[ROW][C]34[/C][C]0.000965181632262513[/C][C]0.00193036326452503[/C][C]0.999034818367738[/C][/ROW]
[ROW][C]35[/C][C]0.000865796511221119[/C][C]0.00173159302244224[/C][C]0.99913420348878[/C][/ROW]
[ROW][C]36[/C][C]0.00109446410095447[/C][C]0.00218892820190895[/C][C]0.998905535899046[/C][/ROW]
[ROW][C]37[/C][C]0.000703053261031495[/C][C]0.00140610652206299[/C][C]0.999296946738969[/C][/ROW]
[ROW][C]38[/C][C]0.000404431114824305[/C][C]0.00080886222964861[/C][C]0.999595568885176[/C][/ROW]
[ROW][C]39[/C][C]0.000528702032005832[/C][C]0.00105740406401166[/C][C]0.999471297967994[/C][/ROW]
[ROW][C]40[/C][C]0.000344619050986396[/C][C]0.000689238101972791[/C][C]0.999655380949014[/C][/ROW]
[ROW][C]41[/C][C]0.000456954741542652[/C][C]0.000913909483085304[/C][C]0.999543045258457[/C][/ROW]
[ROW][C]42[/C][C]0.000776226134315593[/C][C]0.00155245226863119[/C][C]0.999223773865684[/C][/ROW]
[ROW][C]43[/C][C]0.000464976044477627[/C][C]0.000929952088955254[/C][C]0.999535023955522[/C][/ROW]
[ROW][C]44[/C][C]0.000350455147675001[/C][C]0.000700910295350001[/C][C]0.999649544852325[/C][/ROW]
[ROW][C]45[/C][C]0.00071084351105[/C][C]0.0014216870221[/C][C]0.99928915648895[/C][/ROW]
[ROW][C]46[/C][C]0.000408746931639050[/C][C]0.000817493863278101[/C][C]0.999591253068361[/C][/ROW]
[ROW][C]47[/C][C]0.000273276837968217[/C][C]0.000546553675936434[/C][C]0.999726723162032[/C][/ROW]
[ROW][C]48[/C][C]0.00074292430634312[/C][C]0.00148584861268624[/C][C]0.999257075693657[/C][/ROW]
[ROW][C]49[/C][C]0.000596688630745976[/C][C]0.00119337726149195[/C][C]0.999403311369254[/C][/ROW]
[ROW][C]50[/C][C]0.000617542804768237[/C][C]0.00123508560953647[/C][C]0.999382457195232[/C][/ROW]
[ROW][C]51[/C][C]0.000868382569683474[/C][C]0.00173676513936695[/C][C]0.999131617430317[/C][/ROW]
[ROW][C]52[/C][C]0.000542391856141581[/C][C]0.00108478371228316[/C][C]0.999457608143858[/C][/ROW]
[ROW][C]53[/C][C]0.0007145580035043[/C][C]0.0014291160070086[/C][C]0.999285441996496[/C][/ROW]
[ROW][C]54[/C][C]0.000913366723759769[/C][C]0.00182673344751954[/C][C]0.99908663327624[/C][/ROW]
[ROW][C]55[/C][C]0.00159257323656935[/C][C]0.00318514647313869[/C][C]0.99840742676343[/C][/ROW]
[ROW][C]56[/C][C]0.00111099697977535[/C][C]0.0022219939595507[/C][C]0.998889003020225[/C][/ROW]
[ROW][C]57[/C][C]0.000929338477844018[/C][C]0.00185867695568804[/C][C]0.999070661522156[/C][/ROW]
[ROW][C]58[/C][C]0.00234015785324282[/C][C]0.00468031570648564[/C][C]0.997659842146757[/C][/ROW]
[ROW][C]59[/C][C]0.00280509302944386[/C][C]0.00561018605888773[/C][C]0.997194906970556[/C][/ROW]
[ROW][C]60[/C][C]0.00386202267103595[/C][C]0.00772404534207189[/C][C]0.996137977328964[/C][/ROW]
[ROW][C]61[/C][C]0.00247489566949674[/C][C]0.00494979133899349[/C][C]0.997525104330503[/C][/ROW]
[ROW][C]62[/C][C]0.00237994471572415[/C][C]0.0047598894314483[/C][C]0.997620055284276[/C][/ROW]
[ROW][C]63[/C][C]0.00260590131182205[/C][C]0.00521180262364411[/C][C]0.997394098688178[/C][/ROW]
[ROW][C]64[/C][C]0.00392825125522085[/C][C]0.0078565025104417[/C][C]0.99607174874478[/C][/ROW]
[ROW][C]65[/C][C]0.00588959122609823[/C][C]0.0117791824521965[/C][C]0.994110408773902[/C][/ROW]
[ROW][C]66[/C][C]0.00470473488681766[/C][C]0.00940946977363532[/C][C]0.995295265113182[/C][/ROW]
[ROW][C]67[/C][C]0.00568855134245127[/C][C]0.0113771026849025[/C][C]0.994311448657549[/C][/ROW]
[ROW][C]68[/C][C]0.00511168619090299[/C][C]0.0102233723818060[/C][C]0.994888313809097[/C][/ROW]
[ROW][C]69[/C][C]0.00401205661588896[/C][C]0.00802411323177793[/C][C]0.995987943384111[/C][/ROW]
[ROW][C]70[/C][C]0.00364850683525539[/C][C]0.00729701367051078[/C][C]0.996351493164745[/C][/ROW]
[ROW][C]71[/C][C]0.00686481005235964[/C][C]0.0137296201047193[/C][C]0.99313518994764[/C][/ROW]
[ROW][C]72[/C][C]0.00650494174212236[/C][C]0.0130098834842447[/C][C]0.993495058257878[/C][/ROW]
[ROW][C]73[/C][C]0.00876539451257772[/C][C]0.0175307890251554[/C][C]0.991234605487422[/C][/ROW]
[ROW][C]74[/C][C]0.00907420549480598[/C][C]0.0181484109896120[/C][C]0.990925794505194[/C][/ROW]
[ROW][C]75[/C][C]0.0189444008239603[/C][C]0.0378888016479207[/C][C]0.98105559917604[/C][/ROW]
[ROW][C]76[/C][C]0.0219144817452674[/C][C]0.0438289634905348[/C][C]0.978085518254733[/C][/ROW]
[ROW][C]77[/C][C]0.0151777285515092[/C][C]0.0303554571030185[/C][C]0.98482227144849[/C][/ROW]
[ROW][C]78[/C][C]0.0101026945427356[/C][C]0.0202053890854711[/C][C]0.989897305457264[/C][/ROW]
[ROW][C]79[/C][C]0.00692290957370739[/C][C]0.0138458191474148[/C][C]0.993077090426293[/C][/ROW]
[ROW][C]80[/C][C]0.00545554672740167[/C][C]0.0109110934548033[/C][C]0.994544453272598[/C][/ROW]
[ROW][C]81[/C][C]0.00236411698880378[/C][C]0.00472823397760755[/C][C]0.997635883011196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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
160.2937304445079710.5874608890159420.706269555492029
170.1582499673393310.3164999346786620.841750032660669
180.08393580068370080.1678716013674020.916064199316299
190.1017454779945500.2034909559891000.89825452200545
200.06153883421854570.1230776684370910.938461165781454
210.07696749787794920.1539349957558980.92303250212205
220.05109505491352130.1021901098270430.948904945086479
230.02798428887534460.05596857775068920.972015711124655
240.01622219383170370.03244438766340730.983777806168296
250.00806792496144460.01613584992288920.991932075038555
260.004391781421473150.00878356284294630.995608218578527
270.002995156638864910.005990313277729830.997004843361135
280.002113884861999710.004227769723999410.997886115138
290.001862743832352010.003725487664704020.998137256167648
300.001221032737228990.002442065474457980.998778967262771
310.001113641435688970.002227282871377930.99888635856431
320.001112554222524780.002225108445049560.998887445777475
330.001885790549567290.003771581099134580.998114209450433
340.0009651816322625130.001930363264525030.999034818367738
350.0008657965112211190.001731593022442240.99913420348878
360.001094464100954470.002188928201908950.998905535899046
370.0007030532610314950.001406106522062990.999296946738969
380.0004044311148243050.000808862229648610.999595568885176
390.0005287020320058320.001057404064011660.999471297967994
400.0003446190509863960.0006892381019727910.999655380949014
410.0004569547415426520.0009139094830853040.999543045258457
420.0007762261343155930.001552452268631190.999223773865684
430.0004649760444776270.0009299520889552540.999535023955522
440.0003504551476750010.0007009102953500010.999649544852325
450.000710843511050.00142168702210.99928915648895
460.0004087469316390500.0008174938632781010.999591253068361
470.0002732768379682170.0005465536759364340.999726723162032
480.000742924306343120.001485848612686240.999257075693657
490.0005966886307459760.001193377261491950.999403311369254
500.0006175428047682370.001235085609536470.999382457195232
510.0008683825696834740.001736765139366950.999131617430317
520.0005423918561415810.001084783712283160.999457608143858
530.00071455800350430.00142911600700860.999285441996496
540.0009133667237597690.001826733447519540.99908663327624
550.001592573236569350.003185146473138690.99840742676343
560.001110996979775350.00222199395955070.998889003020225
570.0009293384778440180.001858676955688040.999070661522156
580.002340157853242820.004680315706485640.997659842146757
590.002805093029443860.005610186058887730.997194906970556
600.003862022671035950.007724045342071890.996137977328964
610.002474895669496740.004949791338993490.997525104330503
620.002379944715724150.00475988943144830.997620055284276
630.002605901311822050.005211802623644110.997394098688178
640.003928251255220850.00785650251044170.99607174874478
650.005889591226098230.01177918245219650.994110408773902
660.004704734886817660.009409469773635320.995295265113182
670.005688551342451270.01137710268490250.994311448657549
680.005111686190902990.01022337238180600.994888313809097
690.004012056615888960.008024113231777930.995987943384111
700.003648506835255390.007297013670510780.996351493164745
710.006864810052359640.01372962010471930.99313518994764
720.006504941742122360.01300988348424470.993495058257878
730.008765394512577720.01753078902515540.991234605487422
740.009074205494805980.01814841098961200.990925794505194
750.01894440082396030.03788880164792070.98105559917604
760.02191448174526740.04382896349053480.978085518254733
770.01517772855150920.03035545710301850.98482227144849
780.01010269454273560.02020538908547110.989897305457264
790.006922909573707390.01384581914741480.993077090426293
800.005455546727401670.01091109345480330.994544453272598
810.002364116988803780.004728233977607550.997635883011196







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level430.651515151515151NOK
5% type I error level580.878787878787879NOK
10% type I error level590.893939393939394NOK

\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 & 43 & 0.651515151515151 & NOK \tabularnewline
5% type I error level & 58 & 0.878787878787879 & NOK \tabularnewline
10% type I error level & 59 & 0.893939393939394 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57817&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]43[/C][C]0.651515151515151[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]58[/C][C]0.878787878787879[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.893939393939394[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57817&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57817&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 level430.651515151515151NOK
5% type I error level580.878787878787879NOK
10% type I error level590.893939393939394NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}