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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 03:44:27 -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/t1258713903i6ptbxsajd9fk5a.htm/, Retrieved Fri, 29 Mar 2024 10:08:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58020, Retrieved Fri, 29 Mar 2024 10:08:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182
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] [W 7] [2009-11-18 20:45:25] [315ba876df544ad397193b5931d5f354]
-   PD        [Multiple Regression] [WS 7: Multiple Re...] [2009-11-20 10:44:27] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
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Dataseries X:
79.8	109.87
83.4	95.74
113.6	123.06
112.9	123.39
104	120.28
109.9	115.33
99	110.4
106.3	114.49
128.9	132.03
111.1	123.16
102.9	118.82
130	128.32
87	112.24
87.5	104.53
117.6	132.57
103.4	122.52
110.8	131.8
112.6	124.55
102.5	120.96
112.4	122.6
135.6	145.52
105.1	118.57
127.7	134.25
137	136.7
91	121.37
90.5	111.63
122.4	134.42
123.3	137.65
124.3	137.86
120	119.77
118.1	130.69
119	128.28
142.7	147.45
123.6	128.42
129.6	136.9
151.6	143.95
110.4	135.64
99.2	122.48
130.5	136.83
136.2	153.04
129.7	142.71
128	123.46
121.6	144.37
135.8	146.15
143.8	147.61
147.5	158.51
136.2	147.4
156.6	165.05
123.3	154.64
104.5	126.2
139.8	157.36
136.5	154.15
112.1	123.21
118.5	113.07
94.4	110.45
102.3	113.57
111.4	122.44
99.2	114.93
87.8	111.85
115.8	126.04




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=58020&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=58020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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
Investgoed[t] = -0.265915380217017 + 1.00324478774887Uitvoer[t] -27.2082055661959M1[t] -17.7491483570902M2[t] -10.7458319520821M3[t] -14.3164901701178M4[t] -13.5402815455929M5[t] + 0.110014736590909M6[t] -14.6658456995007M7[t] -8.21961363494651M8[t] -4.88144820951547M9[t] -9.68048635839078M10[t] -11.2145734937827M11[t] -0.0555664956132794t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Investgoed[t] =  -0.265915380217017 +  1.00324478774887Uitvoer[t] -27.2082055661959M1[t] -17.7491483570902M2[t] -10.7458319520821M3[t] -14.3164901701178M4[t] -13.5402815455929M5[t] +  0.110014736590909M6[t] -14.6658456995007M7[t] -8.21961363494651M8[t] -4.88144820951547M9[t] -9.68048635839078M10[t] -11.2145734937827M11[t] -0.0555664956132794t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Investgoed[t] =  -0.265915380217017 +  1.00324478774887Uitvoer[t] -27.2082055661959M1[t] -17.7491483570902M2[t] -10.7458319520821M3[t] -14.3164901701178M4[t] -13.5402815455929M5[t] +  0.110014736590909M6[t] -14.6658456995007M7[t] -8.21961363494651M8[t] -4.88144820951547M9[t] -9.68048635839078M10[t] -11.2145734937827M11[t] -0.0555664956132794t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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
Investgoed[t] = -0.265915380217017 + 1.00324478774887Uitvoer[t] -27.2082055661959M1[t] -17.7491483570902M2[t] -10.7458319520821M3[t] -14.3164901701178M4[t] -13.5402815455929M5[t] + 0.110014736590909M6[t] -14.6658456995007M7[t] -8.21961363494651M8[t] -4.88144820951547M9[t] -9.68048635839078M10[t] -11.2145734937827M11[t] -0.0555664956132794t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2659153802170177.714885-0.03450.9726530.486327
Uitvoer1.003244787748870.05643617.776600
M1-27.20820556619593.164989-8.596600
M2-17.74914835709023.411725-5.20244e-062e-06
M3-10.74583195208213.106695-3.45890.001180.00059
M4-14.31649017011783.103017-4.61373.2e-051.6e-05
M5-13.54028154559293.122402-4.33657.8e-053.9e-05
M60.1100147365909093.2757540.03360.9733540.486677
M7-14.66584569950073.209383-4.56973.7e-051.8e-05
M8-8.219613634946513.188231-2.57810.0132020.006601
M9-4.881448209515473.090582-1.57950.1210840.060542
M10-9.680486358390783.147703-3.07540.0035320.001766
M11-11.21457349378273.138524-3.57320.0008410.000421
t-0.05556649561327940.040998-1.35530.1819250.090962

\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) & -0.265915380217017 & 7.714885 & -0.0345 & 0.972653 & 0.486327 \tabularnewline
Uitvoer & 1.00324478774887 & 0.056436 & 17.7766 & 0 & 0 \tabularnewline
M1 & -27.2082055661959 & 3.164989 & -8.5966 & 0 & 0 \tabularnewline
M2 & -17.7491483570902 & 3.411725 & -5.2024 & 4e-06 & 2e-06 \tabularnewline
M3 & -10.7458319520821 & 3.106695 & -3.4589 & 0.00118 & 0.00059 \tabularnewline
M4 & -14.3164901701178 & 3.103017 & -4.6137 & 3.2e-05 & 1.6e-05 \tabularnewline
M5 & -13.5402815455929 & 3.122402 & -4.3365 & 7.8e-05 & 3.9e-05 \tabularnewline
M6 & 0.110014736590909 & 3.275754 & 0.0336 & 0.973354 & 0.486677 \tabularnewline
M7 & -14.6658456995007 & 3.209383 & -4.5697 & 3.7e-05 & 1.8e-05 \tabularnewline
M8 & -8.21961363494651 & 3.188231 & -2.5781 & 0.013202 & 0.006601 \tabularnewline
M9 & -4.88144820951547 & 3.090582 & -1.5795 & 0.121084 & 0.060542 \tabularnewline
M10 & -9.68048635839078 & 3.147703 & -3.0754 & 0.003532 & 0.001766 \tabularnewline
M11 & -11.2145734937827 & 3.138524 & -3.5732 & 0.000841 & 0.000421 \tabularnewline
t & -0.0555664956132794 & 0.040998 & -1.3553 & 0.181925 & 0.090962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&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]-0.265915380217017[/C][C]7.714885[/C][C]-0.0345[/C][C]0.972653[/C][C]0.486327[/C][/ROW]
[ROW][C]Uitvoer[/C][C]1.00324478774887[/C][C]0.056436[/C][C]17.7766[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-27.2082055661959[/C][C]3.164989[/C][C]-8.5966[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-17.7491483570902[/C][C]3.411725[/C][C]-5.2024[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M3[/C][C]-10.7458319520821[/C][C]3.106695[/C][C]-3.4589[/C][C]0.00118[/C][C]0.00059[/C][/ROW]
[ROW][C]M4[/C][C]-14.3164901701178[/C][C]3.103017[/C][C]-4.6137[/C][C]3.2e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]M5[/C][C]-13.5402815455929[/C][C]3.122402[/C][C]-4.3365[/C][C]7.8e-05[/C][C]3.9e-05[/C][/ROW]
[ROW][C]M6[/C][C]0.110014736590909[/C][C]3.275754[/C][C]0.0336[/C][C]0.973354[/C][C]0.486677[/C][/ROW]
[ROW][C]M7[/C][C]-14.6658456995007[/C][C]3.209383[/C][C]-4.5697[/C][C]3.7e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]M8[/C][C]-8.21961363494651[/C][C]3.188231[/C][C]-2.5781[/C][C]0.013202[/C][C]0.006601[/C][/ROW]
[ROW][C]M9[/C][C]-4.88144820951547[/C][C]3.090582[/C][C]-1.5795[/C][C]0.121084[/C][C]0.060542[/C][/ROW]
[ROW][C]M10[/C][C]-9.68048635839078[/C][C]3.147703[/C][C]-3.0754[/C][C]0.003532[/C][C]0.001766[/C][/ROW]
[ROW][C]M11[/C][C]-11.2145734937827[/C][C]3.138524[/C][C]-3.5732[/C][C]0.000841[/C][C]0.000421[/C][/ROW]
[ROW][C]t[/C][C]-0.0555664956132794[/C][C]0.040998[/C][C]-1.3553[/C][C]0.181925[/C][C]0.090962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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)-0.2659153802170177.714885-0.03450.9726530.486327
Uitvoer1.003244787748870.05643617.776600
M1-27.20820556619593.164989-8.596600
M2-17.74914835709023.411725-5.20244e-062e-06
M3-10.74583195208213.106695-3.45890.001180.00059
M4-14.31649017011783.103017-4.61373.2e-051.6e-05
M5-13.54028154559293.122402-4.33657.8e-053.9e-05
M60.1100147365909093.2757540.03360.9733540.486677
M7-14.66584569950073.209383-4.56973.7e-051.8e-05
M8-8.219613634946513.188231-2.57810.0132020.006601
M9-4.881448209515473.090582-1.57950.1210840.060542
M10-9.680486358390783.147703-3.07540.0035320.001766
M11-11.21457349378273.138524-3.57320.0008410.000421
t-0.05556649561327940.040998-1.35530.1819250.090962







Multiple Linear Regression - Regression Statistics
Multiple R0.970324156962185
R-squared0.941528969584375
Adjusted R-squared0.925004547945176
F-TEST (value)56.9780286500874
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.88345637341356
Sum Squared Residuals1097.01472294754

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.970324156962185 \tabularnewline
R-squared & 0.941528969584375 \tabularnewline
Adjusted R-squared & 0.925004547945176 \tabularnewline
F-TEST (value) & 56.9780286500874 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.88345637341356 \tabularnewline
Sum Squared Residuals & 1097.01472294754 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.970324156962185[/C][/ROW]
[ROW][C]R-squared[/C][C]0.941528969584375[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.925004547945176[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]56.9780286500874[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/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]4.88345637341356[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1097.01472294754[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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 R0.970324156962185
R-squared0.941528969584375
Adjusted R-squared0.925004547945176
F-TEST (value)56.9780286500874
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.88345637341356
Sum Squared Residuals1097.01472294754







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
179.882.6968173879417-2.89681738794174
283.477.92445925054325.47554074945685
3113.6112.2808567612371.31914323876273
4112.9108.9857028275453.91429717245464
5104106.586253666558-2.58625366655799
6109.9115.214921753772-5.3149217537716
79995.43749801846483.56250198153516
8106.3105.9314347692990.368565230701424
9128.9126.8109472762322.08905272376843
10111.1113.057561364410-1.95756136441048
11102.9107.113825354575-4.21382535457512
12130127.8036578363592.19634216364111
138784.40770958754782.59229041245216
1487.586.07618298749641.42381701250359
15117.6121.154916745370-3.55491674536967
16103.4107.446081914844-4.04608191484446
17110.8117.476835674066-6.67683567406565
18112.6123.798040749457-11.1980407494569
19102.5105.364965029734-2.86496502973356
20112.4113.400952050583-1.00095205058257
21135.6139.677921515605-4.07792151560453
22105.1107.785869841284-2.6858698412838
23127.7121.9270944821815.77290551781912
24137135.5440512103351.45594878966492
259192.9005365523357-1.90053655233570
2690.592.532423033154-2.03242303315405
27122.4122.3441216553460.0558783446542843
28123.3121.9583776061261.34162239387443
29124.3122.8897011404641.41029885953554
30120118.3357327166581.6642672833421
31118.1114.4597388671713.64026113282925
32119118.4325844976370.567415502363172
33142.7140.9473860086001.75261399139952
34123.6117.0010330532516.59896694674917
35129.6123.9188952223565.68110477764394
36151.6142.1507779741559.44922202584495
37110.4106.5500417261533.84995827384726
3899.2102.75083103287-3.55083103286996
39130.5124.0951436464616.40485635353882
40136.2136.731516942221-0.531516942221357
41129.7127.0886404136872.61135958631285
42128121.3709080360926.62909196390811
43121.6127.517329616216-5.91732961621599
44135.8135.6937709073500.106229092650180
45143.8140.4411072272813.35889277271905
46147.5146.5218707692550.978129230744951
47136.2133.7861675463602.41383245364013
48156.6162.652445048297-6.05244504829693
49123.3124.944894746022-1.64489474602197
50104.5105.816103695936-1.31610369593642
51139.8144.024961191586-4.22496119158616
52136.5137.178320709263-0.678320709263254
53112.1106.8585691052255.24143089477525
54118.5110.2803967440228.21960325597826
5594.492.82046846841491.57953153158514
56102.3102.341257775132-0.0412577751322
57111.4114.522637972282-3.12263797228246
5899.2102.133664971800-2.93366497179984
5987.897.454017394528-9.65401739452808
60115.8122.849067930854-7.04906793085405

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 79.8 & 82.6968173879417 & -2.89681738794174 \tabularnewline
2 & 83.4 & 77.9244592505432 & 5.47554074945685 \tabularnewline
3 & 113.6 & 112.280856761237 & 1.31914323876273 \tabularnewline
4 & 112.9 & 108.985702827545 & 3.91429717245464 \tabularnewline
5 & 104 & 106.586253666558 & -2.58625366655799 \tabularnewline
6 & 109.9 & 115.214921753772 & -5.3149217537716 \tabularnewline
7 & 99 & 95.4374980184648 & 3.56250198153516 \tabularnewline
8 & 106.3 & 105.931434769299 & 0.368565230701424 \tabularnewline
9 & 128.9 & 126.810947276232 & 2.08905272376843 \tabularnewline
10 & 111.1 & 113.057561364410 & -1.95756136441048 \tabularnewline
11 & 102.9 & 107.113825354575 & -4.21382535457512 \tabularnewline
12 & 130 & 127.803657836359 & 2.19634216364111 \tabularnewline
13 & 87 & 84.4077095875478 & 2.59229041245216 \tabularnewline
14 & 87.5 & 86.0761829874964 & 1.42381701250359 \tabularnewline
15 & 117.6 & 121.154916745370 & -3.55491674536967 \tabularnewline
16 & 103.4 & 107.446081914844 & -4.04608191484446 \tabularnewline
17 & 110.8 & 117.476835674066 & -6.67683567406565 \tabularnewline
18 & 112.6 & 123.798040749457 & -11.1980407494569 \tabularnewline
19 & 102.5 & 105.364965029734 & -2.86496502973356 \tabularnewline
20 & 112.4 & 113.400952050583 & -1.00095205058257 \tabularnewline
21 & 135.6 & 139.677921515605 & -4.07792151560453 \tabularnewline
22 & 105.1 & 107.785869841284 & -2.6858698412838 \tabularnewline
23 & 127.7 & 121.927094482181 & 5.77290551781912 \tabularnewline
24 & 137 & 135.544051210335 & 1.45594878966492 \tabularnewline
25 & 91 & 92.9005365523357 & -1.90053655233570 \tabularnewline
26 & 90.5 & 92.532423033154 & -2.03242303315405 \tabularnewline
27 & 122.4 & 122.344121655346 & 0.0558783446542843 \tabularnewline
28 & 123.3 & 121.958377606126 & 1.34162239387443 \tabularnewline
29 & 124.3 & 122.889701140464 & 1.41029885953554 \tabularnewline
30 & 120 & 118.335732716658 & 1.6642672833421 \tabularnewline
31 & 118.1 & 114.459738867171 & 3.64026113282925 \tabularnewline
32 & 119 & 118.432584497637 & 0.567415502363172 \tabularnewline
33 & 142.7 & 140.947386008600 & 1.75261399139952 \tabularnewline
34 & 123.6 & 117.001033053251 & 6.59896694674917 \tabularnewline
35 & 129.6 & 123.918895222356 & 5.68110477764394 \tabularnewline
36 & 151.6 & 142.150777974155 & 9.44922202584495 \tabularnewline
37 & 110.4 & 106.550041726153 & 3.84995827384726 \tabularnewline
38 & 99.2 & 102.75083103287 & -3.55083103286996 \tabularnewline
39 & 130.5 & 124.095143646461 & 6.40485635353882 \tabularnewline
40 & 136.2 & 136.731516942221 & -0.531516942221357 \tabularnewline
41 & 129.7 & 127.088640413687 & 2.61135958631285 \tabularnewline
42 & 128 & 121.370908036092 & 6.62909196390811 \tabularnewline
43 & 121.6 & 127.517329616216 & -5.91732961621599 \tabularnewline
44 & 135.8 & 135.693770907350 & 0.106229092650180 \tabularnewline
45 & 143.8 & 140.441107227281 & 3.35889277271905 \tabularnewline
46 & 147.5 & 146.521870769255 & 0.978129230744951 \tabularnewline
47 & 136.2 & 133.786167546360 & 2.41383245364013 \tabularnewline
48 & 156.6 & 162.652445048297 & -6.05244504829693 \tabularnewline
49 & 123.3 & 124.944894746022 & -1.64489474602197 \tabularnewline
50 & 104.5 & 105.816103695936 & -1.31610369593642 \tabularnewline
51 & 139.8 & 144.024961191586 & -4.22496119158616 \tabularnewline
52 & 136.5 & 137.178320709263 & -0.678320709263254 \tabularnewline
53 & 112.1 & 106.858569105225 & 5.24143089477525 \tabularnewline
54 & 118.5 & 110.280396744022 & 8.21960325597826 \tabularnewline
55 & 94.4 & 92.8204684684149 & 1.57953153158514 \tabularnewline
56 & 102.3 & 102.341257775132 & -0.0412577751322 \tabularnewline
57 & 111.4 & 114.522637972282 & -3.12263797228246 \tabularnewline
58 & 99.2 & 102.133664971800 & -2.93366497179984 \tabularnewline
59 & 87.8 & 97.454017394528 & -9.65401739452808 \tabularnewline
60 & 115.8 & 122.849067930854 & -7.04906793085405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&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]79.8[/C][C]82.6968173879417[/C][C]-2.89681738794174[/C][/ROW]
[ROW][C]2[/C][C]83.4[/C][C]77.9244592505432[/C][C]5.47554074945685[/C][/ROW]
[ROW][C]3[/C][C]113.6[/C][C]112.280856761237[/C][C]1.31914323876273[/C][/ROW]
[ROW][C]4[/C][C]112.9[/C][C]108.985702827545[/C][C]3.91429717245464[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]106.586253666558[/C][C]-2.58625366655799[/C][/ROW]
[ROW][C]6[/C][C]109.9[/C][C]115.214921753772[/C][C]-5.3149217537716[/C][/ROW]
[ROW][C]7[/C][C]99[/C][C]95.4374980184648[/C][C]3.56250198153516[/C][/ROW]
[ROW][C]8[/C][C]106.3[/C][C]105.931434769299[/C][C]0.368565230701424[/C][/ROW]
[ROW][C]9[/C][C]128.9[/C][C]126.810947276232[/C][C]2.08905272376843[/C][/ROW]
[ROW][C]10[/C][C]111.1[/C][C]113.057561364410[/C][C]-1.95756136441048[/C][/ROW]
[ROW][C]11[/C][C]102.9[/C][C]107.113825354575[/C][C]-4.21382535457512[/C][/ROW]
[ROW][C]12[/C][C]130[/C][C]127.803657836359[/C][C]2.19634216364111[/C][/ROW]
[ROW][C]13[/C][C]87[/C][C]84.4077095875478[/C][C]2.59229041245216[/C][/ROW]
[ROW][C]14[/C][C]87.5[/C][C]86.0761829874964[/C][C]1.42381701250359[/C][/ROW]
[ROW][C]15[/C][C]117.6[/C][C]121.154916745370[/C][C]-3.55491674536967[/C][/ROW]
[ROW][C]16[/C][C]103.4[/C][C]107.446081914844[/C][C]-4.04608191484446[/C][/ROW]
[ROW][C]17[/C][C]110.8[/C][C]117.476835674066[/C][C]-6.67683567406565[/C][/ROW]
[ROW][C]18[/C][C]112.6[/C][C]123.798040749457[/C][C]-11.1980407494569[/C][/ROW]
[ROW][C]19[/C][C]102.5[/C][C]105.364965029734[/C][C]-2.86496502973356[/C][/ROW]
[ROW][C]20[/C][C]112.4[/C][C]113.400952050583[/C][C]-1.00095205058257[/C][/ROW]
[ROW][C]21[/C][C]135.6[/C][C]139.677921515605[/C][C]-4.07792151560453[/C][/ROW]
[ROW][C]22[/C][C]105.1[/C][C]107.785869841284[/C][C]-2.6858698412838[/C][/ROW]
[ROW][C]23[/C][C]127.7[/C][C]121.927094482181[/C][C]5.77290551781912[/C][/ROW]
[ROW][C]24[/C][C]137[/C][C]135.544051210335[/C][C]1.45594878966492[/C][/ROW]
[ROW][C]25[/C][C]91[/C][C]92.9005365523357[/C][C]-1.90053655233570[/C][/ROW]
[ROW][C]26[/C][C]90.5[/C][C]92.532423033154[/C][C]-2.03242303315405[/C][/ROW]
[ROW][C]27[/C][C]122.4[/C][C]122.344121655346[/C][C]0.0558783446542843[/C][/ROW]
[ROW][C]28[/C][C]123.3[/C][C]121.958377606126[/C][C]1.34162239387443[/C][/ROW]
[ROW][C]29[/C][C]124.3[/C][C]122.889701140464[/C][C]1.41029885953554[/C][/ROW]
[ROW][C]30[/C][C]120[/C][C]118.335732716658[/C][C]1.6642672833421[/C][/ROW]
[ROW][C]31[/C][C]118.1[/C][C]114.459738867171[/C][C]3.64026113282925[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]118.432584497637[/C][C]0.567415502363172[/C][/ROW]
[ROW][C]33[/C][C]142.7[/C][C]140.947386008600[/C][C]1.75261399139952[/C][/ROW]
[ROW][C]34[/C][C]123.6[/C][C]117.001033053251[/C][C]6.59896694674917[/C][/ROW]
[ROW][C]35[/C][C]129.6[/C][C]123.918895222356[/C][C]5.68110477764394[/C][/ROW]
[ROW][C]36[/C][C]151.6[/C][C]142.150777974155[/C][C]9.44922202584495[/C][/ROW]
[ROW][C]37[/C][C]110.4[/C][C]106.550041726153[/C][C]3.84995827384726[/C][/ROW]
[ROW][C]38[/C][C]99.2[/C][C]102.75083103287[/C][C]-3.55083103286996[/C][/ROW]
[ROW][C]39[/C][C]130.5[/C][C]124.095143646461[/C][C]6.40485635353882[/C][/ROW]
[ROW][C]40[/C][C]136.2[/C][C]136.731516942221[/C][C]-0.531516942221357[/C][/ROW]
[ROW][C]41[/C][C]129.7[/C][C]127.088640413687[/C][C]2.61135958631285[/C][/ROW]
[ROW][C]42[/C][C]128[/C][C]121.370908036092[/C][C]6.62909196390811[/C][/ROW]
[ROW][C]43[/C][C]121.6[/C][C]127.517329616216[/C][C]-5.91732961621599[/C][/ROW]
[ROW][C]44[/C][C]135.8[/C][C]135.693770907350[/C][C]0.106229092650180[/C][/ROW]
[ROW][C]45[/C][C]143.8[/C][C]140.441107227281[/C][C]3.35889277271905[/C][/ROW]
[ROW][C]46[/C][C]147.5[/C][C]146.521870769255[/C][C]0.978129230744951[/C][/ROW]
[ROW][C]47[/C][C]136.2[/C][C]133.786167546360[/C][C]2.41383245364013[/C][/ROW]
[ROW][C]48[/C][C]156.6[/C][C]162.652445048297[/C][C]-6.05244504829693[/C][/ROW]
[ROW][C]49[/C][C]123.3[/C][C]124.944894746022[/C][C]-1.64489474602197[/C][/ROW]
[ROW][C]50[/C][C]104.5[/C][C]105.816103695936[/C][C]-1.31610369593642[/C][/ROW]
[ROW][C]51[/C][C]139.8[/C][C]144.024961191586[/C][C]-4.22496119158616[/C][/ROW]
[ROW][C]52[/C][C]136.5[/C][C]137.178320709263[/C][C]-0.678320709263254[/C][/ROW]
[ROW][C]53[/C][C]112.1[/C][C]106.858569105225[/C][C]5.24143089477525[/C][/ROW]
[ROW][C]54[/C][C]118.5[/C][C]110.280396744022[/C][C]8.21960325597826[/C][/ROW]
[ROW][C]55[/C][C]94.4[/C][C]92.8204684684149[/C][C]1.57953153158514[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]102.341257775132[/C][C]-0.0412577751322[/C][/ROW]
[ROW][C]57[/C][C]111.4[/C][C]114.522637972282[/C][C]-3.12263797228246[/C][/ROW]
[ROW][C]58[/C][C]99.2[/C][C]102.133664971800[/C][C]-2.93366497179984[/C][/ROW]
[ROW][C]59[/C][C]87.8[/C][C]97.454017394528[/C][C]-9.65401739452808[/C][/ROW]
[ROW][C]60[/C][C]115.8[/C][C]122.849067930854[/C][C]-7.04906793085405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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
179.882.6968173879417-2.89681738794174
283.477.92445925054325.47554074945685
3113.6112.2808567612371.31914323876273
4112.9108.9857028275453.91429717245464
5104106.586253666558-2.58625366655799
6109.9115.214921753772-5.3149217537716
79995.43749801846483.56250198153516
8106.3105.9314347692990.368565230701424
9128.9126.8109472762322.08905272376843
10111.1113.057561364410-1.95756136441048
11102.9107.113825354575-4.21382535457512
12130127.8036578363592.19634216364111
138784.40770958754782.59229041245216
1487.586.07618298749641.42381701250359
15117.6121.154916745370-3.55491674536967
16103.4107.446081914844-4.04608191484446
17110.8117.476835674066-6.67683567406565
18112.6123.798040749457-11.1980407494569
19102.5105.364965029734-2.86496502973356
20112.4113.400952050583-1.00095205058257
21135.6139.677921515605-4.07792151560453
22105.1107.785869841284-2.6858698412838
23127.7121.9270944821815.77290551781912
24137135.5440512103351.45594878966492
259192.9005365523357-1.90053655233570
2690.592.532423033154-2.03242303315405
27122.4122.3441216553460.0558783446542843
28123.3121.9583776061261.34162239387443
29124.3122.8897011404641.41029885953554
30120118.3357327166581.6642672833421
31118.1114.4597388671713.64026113282925
32119118.4325844976370.567415502363172
33142.7140.9473860086001.75261399139952
34123.6117.0010330532516.59896694674917
35129.6123.9188952223565.68110477764394
36151.6142.1507779741559.44922202584495
37110.4106.5500417261533.84995827384726
3899.2102.75083103287-3.55083103286996
39130.5124.0951436464616.40485635353882
40136.2136.731516942221-0.531516942221357
41129.7127.0886404136872.61135958631285
42128121.3709080360926.62909196390811
43121.6127.517329616216-5.91732961621599
44135.8135.6937709073500.106229092650180
45143.8140.4411072272813.35889277271905
46147.5146.5218707692550.978129230744951
47136.2133.7861675463602.41383245364013
48156.6162.652445048297-6.05244504829693
49123.3124.944894746022-1.64489474602197
50104.5105.816103695936-1.31610369593642
51139.8144.024961191586-4.22496119158616
52136.5137.178320709263-0.678320709263254
53112.1106.8585691052255.24143089477525
54118.5110.2803967440228.21960325597826
5594.492.82046846841491.57953153158514
56102.3102.341257775132-0.0412577751322
57111.4114.522637972282-3.12263797228246
5899.2102.133664971800-2.93366497179984
5987.897.454017394528-9.65401739452808
60115.8122.849067930854-7.04906793085405







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.3810490822886690.7620981645773380.618950917711331
180.3691238331778920.7382476663557830.630876166822108
190.2519062625710930.5038125251421870.748093737428907
200.1704279661142070.3408559322284140.829572033885793
210.1182096468334540.2364192936669090.881790353166546
220.08072137023408970.1614427404681790.91927862976591
230.4176352519936040.8352705039872070.582364748006396
240.3153780264805750.630756052961150.684621973519425
250.2597643575124910.5195287150249820.740235642487509
260.1975704569225920.3951409138451850.802429543077408
270.1860490816738880.3720981633477770.813950918326112
280.1518723232527130.3037446465054270.848127676747287
290.2275240397925880.4550480795851770.772475960207412
300.5008089621881260.9983820756237480.499191037811874
310.4311426375603370.8622852751206740.568857362439663
320.4063369701214930.8126739402429870.593663029878507
330.3592743367983570.7185486735967140.640725663201643
340.354187313255330.708374626510660.64581268674467
350.2875470165031210.5750940330062420.71245298349688
360.4561785119353510.9123570238707020.543821488064649
370.3726410869635040.7452821739270090.627358913036496
380.3752196732925450.750439346585090.624780326707455
390.459166677460830.918333354921660.54083332253917
400.3481975085184780.6963950170369570.651802491481522
410.2564704154586360.5129408309172710.743529584541365
420.1876382969635630.3752765939271260.812361703036437
430.4577234923967320.9154469847934630.542276507603268

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.381049082288669 & 0.762098164577338 & 0.618950917711331 \tabularnewline
18 & 0.369123833177892 & 0.738247666355783 & 0.630876166822108 \tabularnewline
19 & 0.251906262571093 & 0.503812525142187 & 0.748093737428907 \tabularnewline
20 & 0.170427966114207 & 0.340855932228414 & 0.829572033885793 \tabularnewline
21 & 0.118209646833454 & 0.236419293666909 & 0.881790353166546 \tabularnewline
22 & 0.0807213702340897 & 0.161442740468179 & 0.91927862976591 \tabularnewline
23 & 0.417635251993604 & 0.835270503987207 & 0.582364748006396 \tabularnewline
24 & 0.315378026480575 & 0.63075605296115 & 0.684621973519425 \tabularnewline
25 & 0.259764357512491 & 0.519528715024982 & 0.740235642487509 \tabularnewline
26 & 0.197570456922592 & 0.395140913845185 & 0.802429543077408 \tabularnewline
27 & 0.186049081673888 & 0.372098163347777 & 0.813950918326112 \tabularnewline
28 & 0.151872323252713 & 0.303744646505427 & 0.848127676747287 \tabularnewline
29 & 0.227524039792588 & 0.455048079585177 & 0.772475960207412 \tabularnewline
30 & 0.500808962188126 & 0.998382075623748 & 0.499191037811874 \tabularnewline
31 & 0.431142637560337 & 0.862285275120674 & 0.568857362439663 \tabularnewline
32 & 0.406336970121493 & 0.812673940242987 & 0.593663029878507 \tabularnewline
33 & 0.359274336798357 & 0.718548673596714 & 0.640725663201643 \tabularnewline
34 & 0.35418731325533 & 0.70837462651066 & 0.64581268674467 \tabularnewline
35 & 0.287547016503121 & 0.575094033006242 & 0.71245298349688 \tabularnewline
36 & 0.456178511935351 & 0.912357023870702 & 0.543821488064649 \tabularnewline
37 & 0.372641086963504 & 0.745282173927009 & 0.627358913036496 \tabularnewline
38 & 0.375219673292545 & 0.75043934658509 & 0.624780326707455 \tabularnewline
39 & 0.45916667746083 & 0.91833335492166 & 0.54083332253917 \tabularnewline
40 & 0.348197508518478 & 0.696395017036957 & 0.651802491481522 \tabularnewline
41 & 0.256470415458636 & 0.512940830917271 & 0.743529584541365 \tabularnewline
42 & 0.187638296963563 & 0.375276593927126 & 0.812361703036437 \tabularnewline
43 & 0.457723492396732 & 0.915446984793463 & 0.542276507603268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&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]17[/C][C]0.381049082288669[/C][C]0.762098164577338[/C][C]0.618950917711331[/C][/ROW]
[ROW][C]18[/C][C]0.369123833177892[/C][C]0.738247666355783[/C][C]0.630876166822108[/C][/ROW]
[ROW][C]19[/C][C]0.251906262571093[/C][C]0.503812525142187[/C][C]0.748093737428907[/C][/ROW]
[ROW][C]20[/C][C]0.170427966114207[/C][C]0.340855932228414[/C][C]0.829572033885793[/C][/ROW]
[ROW][C]21[/C][C]0.118209646833454[/C][C]0.236419293666909[/C][C]0.881790353166546[/C][/ROW]
[ROW][C]22[/C][C]0.0807213702340897[/C][C]0.161442740468179[/C][C]0.91927862976591[/C][/ROW]
[ROW][C]23[/C][C]0.417635251993604[/C][C]0.835270503987207[/C][C]0.582364748006396[/C][/ROW]
[ROW][C]24[/C][C]0.315378026480575[/C][C]0.63075605296115[/C][C]0.684621973519425[/C][/ROW]
[ROW][C]25[/C][C]0.259764357512491[/C][C]0.519528715024982[/C][C]0.740235642487509[/C][/ROW]
[ROW][C]26[/C][C]0.197570456922592[/C][C]0.395140913845185[/C][C]0.802429543077408[/C][/ROW]
[ROW][C]27[/C][C]0.186049081673888[/C][C]0.372098163347777[/C][C]0.813950918326112[/C][/ROW]
[ROW][C]28[/C][C]0.151872323252713[/C][C]0.303744646505427[/C][C]0.848127676747287[/C][/ROW]
[ROW][C]29[/C][C]0.227524039792588[/C][C]0.455048079585177[/C][C]0.772475960207412[/C][/ROW]
[ROW][C]30[/C][C]0.500808962188126[/C][C]0.998382075623748[/C][C]0.499191037811874[/C][/ROW]
[ROW][C]31[/C][C]0.431142637560337[/C][C]0.862285275120674[/C][C]0.568857362439663[/C][/ROW]
[ROW][C]32[/C][C]0.406336970121493[/C][C]0.812673940242987[/C][C]0.593663029878507[/C][/ROW]
[ROW][C]33[/C][C]0.359274336798357[/C][C]0.718548673596714[/C][C]0.640725663201643[/C][/ROW]
[ROW][C]34[/C][C]0.35418731325533[/C][C]0.70837462651066[/C][C]0.64581268674467[/C][/ROW]
[ROW][C]35[/C][C]0.287547016503121[/C][C]0.575094033006242[/C][C]0.71245298349688[/C][/ROW]
[ROW][C]36[/C][C]0.456178511935351[/C][C]0.912357023870702[/C][C]0.543821488064649[/C][/ROW]
[ROW][C]37[/C][C]0.372641086963504[/C][C]0.745282173927009[/C][C]0.627358913036496[/C][/ROW]
[ROW][C]38[/C][C]0.375219673292545[/C][C]0.75043934658509[/C][C]0.624780326707455[/C][/ROW]
[ROW][C]39[/C][C]0.45916667746083[/C][C]0.91833335492166[/C][C]0.54083332253917[/C][/ROW]
[ROW][C]40[/C][C]0.348197508518478[/C][C]0.696395017036957[/C][C]0.651802491481522[/C][/ROW]
[ROW][C]41[/C][C]0.256470415458636[/C][C]0.512940830917271[/C][C]0.743529584541365[/C][/ROW]
[ROW][C]42[/C][C]0.187638296963563[/C][C]0.375276593927126[/C][C]0.812361703036437[/C][/ROW]
[ROW][C]43[/C][C]0.457723492396732[/C][C]0.915446984793463[/C][C]0.542276507603268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&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
170.3810490822886690.7620981645773380.618950917711331
180.3691238331778920.7382476663557830.630876166822108
190.2519062625710930.5038125251421870.748093737428907
200.1704279661142070.3408559322284140.829572033885793
210.1182096468334540.2364192936669090.881790353166546
220.08072137023408970.1614427404681790.91927862976591
230.4176352519936040.8352705039872070.582364748006396
240.3153780264805750.630756052961150.684621973519425
250.2597643575124910.5195287150249820.740235642487509
260.1975704569225920.3951409138451850.802429543077408
270.1860490816738880.3720981633477770.813950918326112
280.1518723232527130.3037446465054270.848127676747287
290.2275240397925880.4550480795851770.772475960207412
300.5008089621881260.9983820756237480.499191037811874
310.4311426375603370.8622852751206740.568857362439663
320.4063369701214930.8126739402429870.593663029878507
330.3592743367983570.7185486735967140.640725663201643
340.354187313255330.708374626510660.64581268674467
350.2875470165031210.5750940330062420.71245298349688
360.4561785119353510.9123570238707020.543821488064649
370.3726410869635040.7452821739270090.627358913036496
380.3752196732925450.750439346585090.624780326707455
390.459166677460830.918333354921660.54083332253917
400.3481975085184780.6963950170369570.651802491481522
410.2564704154586360.5129408309172710.743529584541365
420.1876382969635630.3752765939271260.812361703036437
430.4577234923967320.9154469847934630.542276507603268







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58020&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58020&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58020&T=6

As an alternative you can also use a QR Code:  

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

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



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