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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 26 Nov 2011 13:25:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/26/t1322331927elygv8m38fnx55q.htm/, Retrieved Mon, 30 Jan 2023 00:48:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147438, Retrieved Mon, 30 Jan 2023 00:48:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multivariate regr...] [2009-11-19 09:22:31] [21324e9cdf3569788a3d630236984d87]
-    D      [Multiple Regression] [] [2010-12-07 12:41:22] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:25:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P             [Multiple Regression] [] [2011-11-27 16:54:17] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
112.3	0
117.3	0
111.1	1
102.2	1
104.3	1
122.9	1
107.6	1
121.3	1
131.5	1
89	1
104.4	1
128.9	1
135.9	1
133.3	1
121.3	1
120.5	0
120.4	0
137.9	0
126.1	0
133.2	0
151.1	0
105	0
119	0
140.4	0
156.6	0
137.1	0
122.7	0
125.8	0
139.3	0
134.9	0
149.2	0
132.3	0
149	0
117.2	0
119.6	0
152	0
149.4	0
127.3	0
114.1	0
102.1	0
107.7	0
104.4	0
102.1	0
96	1
109.3	0
90	1
83.9	1
112	1
114.3	1
103.6	1
91.7	1
80.8	1
87.2	1
109.2	1
102.7	1
95.1	1
117.5	1
85.1	1
92.1	1
113.5	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 time5 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Promet[t] = + 152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Promet[t] =  +  152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Promet[t] =  +  152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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
Promet[t] = + 152.062865531415 -18.0165590135056Dummy[t] -2.89726267371302M1[t] -12.5469035036211M2[t] -20.153232530828M3[t] -29.3261851634371M4[t] -23.4958259933451M5[t] -13.0854668232531M6[t] -17.0751076531611M7[t] -15.101436680368M8[t] -2.27438931297713M9[t] -32.760718340184M10[t] -25.890359170092M11[t] -0.330359170091995t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)152.0628655314156.5177623.330500
Dummy-18.01655901350563.207827-5.61641e-061e-06
M1-2.897262673713027.726951-0.3750.7094170.354708
M2-12.54690350362117.716216-1.6260.1107730.055386
M3-20.1532325308287.688197-2.62130.0118330.005917
M4-29.32618516343717.698043-3.80960.0004110.000206
M5-23.49582599334517.690613-3.05510.0037360.001868
M6-13.08546682325317.684292-1.70290.0953380.047669
M7-17.07510765316117.679083-2.22360.0311280.015564
M8-15.1014366803687.651862-1.97360.0544560.027228
M9-2.274389312977137.672007-0.29650.7682190.384109
M10-32.7607183401847.645135-4.28529.2e-054.6e-05
M11-25.8903591700927.643452-3.38730.0014550.000727
t-0.3303591700919950.092602-3.56750.0008550.000428

\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) & 152.062865531415 & 6.51776 & 23.3305 & 0 & 0 \tabularnewline
Dummy & -18.0165590135056 & 3.207827 & -5.6164 & 1e-06 & 1e-06 \tabularnewline
M1 & -2.89726267371302 & 7.726951 & -0.375 & 0.709417 & 0.354708 \tabularnewline
M2 & -12.5469035036211 & 7.716216 & -1.626 & 0.110773 & 0.055386 \tabularnewline
M3 & -20.153232530828 & 7.688197 & -2.6213 & 0.011833 & 0.005917 \tabularnewline
M4 & -29.3261851634371 & 7.698043 & -3.8096 & 0.000411 & 0.000206 \tabularnewline
M5 & -23.4958259933451 & 7.690613 & -3.0551 & 0.003736 & 0.001868 \tabularnewline
M6 & -13.0854668232531 & 7.684292 & -1.7029 & 0.095338 & 0.047669 \tabularnewline
M7 & -17.0751076531611 & 7.679083 & -2.2236 & 0.031128 & 0.015564 \tabularnewline
M8 & -15.101436680368 & 7.651862 & -1.9736 & 0.054456 & 0.027228 \tabularnewline
M9 & -2.27438931297713 & 7.672007 & -0.2965 & 0.768219 & 0.384109 \tabularnewline
M10 & -32.760718340184 & 7.645135 & -4.2852 & 9.2e-05 & 4.6e-05 \tabularnewline
M11 & -25.890359170092 & 7.643452 & -3.3873 & 0.001455 & 0.000727 \tabularnewline
t & -0.330359170091995 & 0.092602 & -3.5675 & 0.000855 & 0.000428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]152.062865531415[/C][C]6.51776[/C][C]23.3305[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-18.0165590135056[/C][C]3.207827[/C][C]-5.6164[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]-2.89726267371302[/C][C]7.726951[/C][C]-0.375[/C][C]0.709417[/C][C]0.354708[/C][/ROW]
[ROW][C]M2[/C][C]-12.5469035036211[/C][C]7.716216[/C][C]-1.626[/C][C]0.110773[/C][C]0.055386[/C][/ROW]
[ROW][C]M3[/C][C]-20.153232530828[/C][C]7.688197[/C][C]-2.6213[/C][C]0.011833[/C][C]0.005917[/C][/ROW]
[ROW][C]M4[/C][C]-29.3261851634371[/C][C]7.698043[/C][C]-3.8096[/C][C]0.000411[/C][C]0.000206[/C][/ROW]
[ROW][C]M5[/C][C]-23.4958259933451[/C][C]7.690613[/C][C]-3.0551[/C][C]0.003736[/C][C]0.001868[/C][/ROW]
[ROW][C]M6[/C][C]-13.0854668232531[/C][C]7.684292[/C][C]-1.7029[/C][C]0.095338[/C][C]0.047669[/C][/ROW]
[ROW][C]M7[/C][C]-17.0751076531611[/C][C]7.679083[/C][C]-2.2236[/C][C]0.031128[/C][C]0.015564[/C][/ROW]
[ROW][C]M8[/C][C]-15.101436680368[/C][C]7.651862[/C][C]-1.9736[/C][C]0.054456[/C][C]0.027228[/C][/ROW]
[ROW][C]M9[/C][C]-2.27438931297713[/C][C]7.672007[/C][C]-0.2965[/C][C]0.768219[/C][C]0.384109[/C][/ROW]
[ROW][C]M10[/C][C]-32.760718340184[/C][C]7.645135[/C][C]-4.2852[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[ROW][C]M11[/C][C]-25.890359170092[/C][C]7.643452[/C][C]-3.3873[/C][C]0.001455[/C][C]0.000727[/C][/ROW]
[ROW][C]t[/C][C]-0.330359170091995[/C][C]0.092602[/C][C]-3.5675[/C][C]0.000855[/C][C]0.000428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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)152.0628655314156.5177623.330500
Dummy-18.01655901350563.207827-5.61641e-061e-06
M1-2.897262673713027.726951-0.3750.7094170.354708
M2-12.54690350362117.716216-1.6260.1107730.055386
M3-20.1532325308287.688197-2.62130.0118330.005917
M4-29.32618516343717.698043-3.80960.0004110.000206
M5-23.49582599334517.690613-3.05510.0037360.001868
M6-13.08546682325317.684292-1.70290.0953380.047669
M7-17.07510765316117.679083-2.22360.0311280.015564
M8-15.1014366803687.651862-1.97360.0544560.027228
M9-2.274389312977137.672007-0.29650.7682190.384109
M10-32.7607183401847.645135-4.28529.2e-054.6e-05
M11-25.8903591700927.643452-3.38730.0014550.000727
t-0.3303591700919950.092602-3.56750.0008550.000428







Multiple Linear Regression - Regression Statistics
Multiple R0.826784265662317
R-squared0.683572221946777
Adjusted R-squared0.59414698032304
F-TEST (value)7.64406346054904
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value9.7017964995061e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.0844718082049
Sum Squared Residuals6717.5851086318

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.826784265662317 \tabularnewline
R-squared & 0.683572221946777 \tabularnewline
Adjusted R-squared & 0.59414698032304 \tabularnewline
F-TEST (value) & 7.64406346054904 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 9.7017964995061e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 12.0844718082049 \tabularnewline
Sum Squared Residuals & 6717.5851086318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.826784265662317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.683572221946777[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.59414698032304[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.64406346054904[/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]9.7017964995061e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]12.0844718082049[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6717.5851086318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 R0.826784265662317
R-squared0.683572221946777
Adjusted R-squared0.59414698032304
F-TEST (value)7.64406346054904
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value9.7017964995061e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.0844718082049
Sum Squared Residuals6717.5851086318







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.3148.83524368761-36.5352436876098
2117.3138.85524368761-21.5552436876101
3111.1112.901996476806-1.80199647680566
4102.2103.398684674105-1.19868467410453
5104.3108.898684674105-4.59868467410454
6122.9118.9786846741053.92131532589548
7107.6114.658684674105-7.05868467410454
8121.3116.3019964768064.99800352319436
9131.5128.7986846741052.70131532589547
108997.9819964768056-8.98199647680565
11104.4104.521996476806-0.121996476805641
12128.9130.081996476806-1.18199647680566
13135.9126.8543746330019.04562536699935
14133.3116.87437463300116.4256253669994
15121.3108.93768643570212.3623135642983
16120.5117.4509336465063.04906635349383
17120.4122.950933646506-2.55093364650617
18137.9133.0309336465064.86906635349382
19126.1128.710933646506-2.61093364650618
20133.2130.3542454492072.84575455079269
21151.1142.8509336465068.24906635349382
22105112.034245449207-7.03424544920729
23119118.5742454492070.42575455079271
24140.4144.134245449207-3.73424544920731
25156.6140.90662360540215.6933763945977
26137.1130.9266236054026.17337639459777
27122.7122.989935408103-0.289935408103347
28125.8113.48662360540212.3133763945978
29139.3118.98662360540220.3133763945978
30134.9129.0666236054025.83337639459776
31149.2124.74662360540224.4533763945978
32132.3126.3899354081035.91006459189666
33149138.88662360540210.1133763945978
34117.2108.0699354081039.13006459189666
35119.6114.6099354081034.99006459189665
36152140.16993540810311.8300645918966
37149.4136.94231356429812.4576864357016
38127.3126.9623135642980.337686435701711
39114.1119.025625366999-4.92562536699941
40102.1109.522313564298-7.4223135642983
41107.7115.022313564298-7.3223135642983
42104.4125.102313564298-20.7023135642983
43102.1120.782313564298-18.6823135642983
4496104.409066353494-8.40906635349383
45109.3134.922313564298-25.6223135642983
469086.08906635349383.91093364650617
4783.992.6290663534938-8.72906635349382
48112118.189066353494-6.18906635349383
49114.3114.961444509689-0.66144450968883
50103.6104.981444509689-1.38144450968876
5191.797.0447563123899-5.34475631238987
5280.887.5414445096888-6.74144450968877
5387.293.0414445096888-5.84144450968877
54109.2103.1214445096896.07855549031123
55102.798.80144450968883.89855549031124
5695.1100.44475631239-5.34475631238989
57117.5112.9414445096894.55855549031124
5885.182.12475631238992.97524368761011
5992.188.66475631238993.43524368761011
60113.5114.22475631239-0.724756312389895

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 148.83524368761 & -36.5352436876098 \tabularnewline
2 & 117.3 & 138.85524368761 & -21.5552436876101 \tabularnewline
3 & 111.1 & 112.901996476806 & -1.80199647680566 \tabularnewline
4 & 102.2 & 103.398684674105 & -1.19868467410453 \tabularnewline
5 & 104.3 & 108.898684674105 & -4.59868467410454 \tabularnewline
6 & 122.9 & 118.978684674105 & 3.92131532589548 \tabularnewline
7 & 107.6 & 114.658684674105 & -7.05868467410454 \tabularnewline
8 & 121.3 & 116.301996476806 & 4.99800352319436 \tabularnewline
9 & 131.5 & 128.798684674105 & 2.70131532589547 \tabularnewline
10 & 89 & 97.9819964768056 & -8.98199647680565 \tabularnewline
11 & 104.4 & 104.521996476806 & -0.121996476805641 \tabularnewline
12 & 128.9 & 130.081996476806 & -1.18199647680566 \tabularnewline
13 & 135.9 & 126.854374633001 & 9.04562536699935 \tabularnewline
14 & 133.3 & 116.874374633001 & 16.4256253669994 \tabularnewline
15 & 121.3 & 108.937686435702 & 12.3623135642983 \tabularnewline
16 & 120.5 & 117.450933646506 & 3.04906635349383 \tabularnewline
17 & 120.4 & 122.950933646506 & -2.55093364650617 \tabularnewline
18 & 137.9 & 133.030933646506 & 4.86906635349382 \tabularnewline
19 & 126.1 & 128.710933646506 & -2.61093364650618 \tabularnewline
20 & 133.2 & 130.354245449207 & 2.84575455079269 \tabularnewline
21 & 151.1 & 142.850933646506 & 8.24906635349382 \tabularnewline
22 & 105 & 112.034245449207 & -7.03424544920729 \tabularnewline
23 & 119 & 118.574245449207 & 0.42575455079271 \tabularnewline
24 & 140.4 & 144.134245449207 & -3.73424544920731 \tabularnewline
25 & 156.6 & 140.906623605402 & 15.6933763945977 \tabularnewline
26 & 137.1 & 130.926623605402 & 6.17337639459777 \tabularnewline
27 & 122.7 & 122.989935408103 & -0.289935408103347 \tabularnewline
28 & 125.8 & 113.486623605402 & 12.3133763945978 \tabularnewline
29 & 139.3 & 118.986623605402 & 20.3133763945978 \tabularnewline
30 & 134.9 & 129.066623605402 & 5.83337639459776 \tabularnewline
31 & 149.2 & 124.746623605402 & 24.4533763945978 \tabularnewline
32 & 132.3 & 126.389935408103 & 5.91006459189666 \tabularnewline
33 & 149 & 138.886623605402 & 10.1133763945978 \tabularnewline
34 & 117.2 & 108.069935408103 & 9.13006459189666 \tabularnewline
35 & 119.6 & 114.609935408103 & 4.99006459189665 \tabularnewline
36 & 152 & 140.169935408103 & 11.8300645918966 \tabularnewline
37 & 149.4 & 136.942313564298 & 12.4576864357016 \tabularnewline
38 & 127.3 & 126.962313564298 & 0.337686435701711 \tabularnewline
39 & 114.1 & 119.025625366999 & -4.92562536699941 \tabularnewline
40 & 102.1 & 109.522313564298 & -7.4223135642983 \tabularnewline
41 & 107.7 & 115.022313564298 & -7.3223135642983 \tabularnewline
42 & 104.4 & 125.102313564298 & -20.7023135642983 \tabularnewline
43 & 102.1 & 120.782313564298 & -18.6823135642983 \tabularnewline
44 & 96 & 104.409066353494 & -8.40906635349383 \tabularnewline
45 & 109.3 & 134.922313564298 & -25.6223135642983 \tabularnewline
46 & 90 & 86.0890663534938 & 3.91093364650617 \tabularnewline
47 & 83.9 & 92.6290663534938 & -8.72906635349382 \tabularnewline
48 & 112 & 118.189066353494 & -6.18906635349383 \tabularnewline
49 & 114.3 & 114.961444509689 & -0.66144450968883 \tabularnewline
50 & 103.6 & 104.981444509689 & -1.38144450968876 \tabularnewline
51 & 91.7 & 97.0447563123899 & -5.34475631238987 \tabularnewline
52 & 80.8 & 87.5414445096888 & -6.74144450968877 \tabularnewline
53 & 87.2 & 93.0414445096888 & -5.84144450968877 \tabularnewline
54 & 109.2 & 103.121444509689 & 6.07855549031123 \tabularnewline
55 & 102.7 & 98.8014445096888 & 3.89855549031124 \tabularnewline
56 & 95.1 & 100.44475631239 & -5.34475631238989 \tabularnewline
57 & 117.5 & 112.941444509689 & 4.55855549031124 \tabularnewline
58 & 85.1 & 82.1247563123899 & 2.97524368761011 \tabularnewline
59 & 92.1 & 88.6647563123899 & 3.43524368761011 \tabularnewline
60 & 113.5 & 114.22475631239 & -0.724756312389895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]112.3[/C][C]148.83524368761[/C][C]-36.5352436876098[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]138.85524368761[/C][C]-21.5552436876101[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]112.901996476806[/C][C]-1.80199647680566[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]103.398684674105[/C][C]-1.19868467410453[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]108.898684674105[/C][C]-4.59868467410454[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]118.978684674105[/C][C]3.92131532589548[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]114.658684674105[/C][C]-7.05868467410454[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]116.301996476806[/C][C]4.99800352319436[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]128.798684674105[/C][C]2.70131532589547[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]97.9819964768056[/C][C]-8.98199647680565[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]104.521996476806[/C][C]-0.121996476805641[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]130.081996476806[/C][C]-1.18199647680566[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]126.854374633001[/C][C]9.04562536699935[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]116.874374633001[/C][C]16.4256253669994[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]108.937686435702[/C][C]12.3623135642983[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]117.450933646506[/C][C]3.04906635349383[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]122.950933646506[/C][C]-2.55093364650617[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]133.030933646506[/C][C]4.86906635349382[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]128.710933646506[/C][C]-2.61093364650618[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]130.354245449207[/C][C]2.84575455079269[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]142.850933646506[/C][C]8.24906635349382[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]112.034245449207[/C][C]-7.03424544920729[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]118.574245449207[/C][C]0.42575455079271[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]144.134245449207[/C][C]-3.73424544920731[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]140.906623605402[/C][C]15.6933763945977[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]130.926623605402[/C][C]6.17337639459777[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]122.989935408103[/C][C]-0.289935408103347[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]113.486623605402[/C][C]12.3133763945978[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]118.986623605402[/C][C]20.3133763945978[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]129.066623605402[/C][C]5.83337639459776[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]124.746623605402[/C][C]24.4533763945978[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]126.389935408103[/C][C]5.91006459189666[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]138.886623605402[/C][C]10.1133763945978[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]108.069935408103[/C][C]9.13006459189666[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]114.609935408103[/C][C]4.99006459189665[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]140.169935408103[/C][C]11.8300645918966[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]136.942313564298[/C][C]12.4576864357016[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]126.962313564298[/C][C]0.337686435701711[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]119.025625366999[/C][C]-4.92562536699941[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]109.522313564298[/C][C]-7.4223135642983[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]115.022313564298[/C][C]-7.3223135642983[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]125.102313564298[/C][C]-20.7023135642983[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]120.782313564298[/C][C]-18.6823135642983[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]104.409066353494[/C][C]-8.40906635349383[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]134.922313564298[/C][C]-25.6223135642983[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]86.0890663534938[/C][C]3.91093364650617[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]92.6290663534938[/C][C]-8.72906635349382[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]118.189066353494[/C][C]-6.18906635349383[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]114.961444509689[/C][C]-0.66144450968883[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]104.981444509689[/C][C]-1.38144450968876[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]97.0447563123899[/C][C]-5.34475631238987[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]87.5414445096888[/C][C]-6.74144450968877[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]93.0414445096888[/C][C]-5.84144450968877[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]103.121444509689[/C][C]6.07855549031123[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]98.8014445096888[/C][C]3.89855549031124[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]100.44475631239[/C][C]-5.34475631238989[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]112.941444509689[/C][C]4.55855549031124[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]82.1247563123899[/C][C]2.97524368761011[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]88.6647563123899[/C][C]3.43524368761011[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]114.22475631239[/C][C]-0.724756312389895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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
1112.3148.83524368761-36.5352436876098
2117.3138.85524368761-21.5552436876101
3111.1112.901996476806-1.80199647680566
4102.2103.398684674105-1.19868467410453
5104.3108.898684674105-4.59868467410454
6122.9118.9786846741053.92131532589548
7107.6114.658684674105-7.05868467410454
8121.3116.3019964768064.99800352319436
9131.5128.7986846741052.70131532589547
108997.9819964768056-8.98199647680565
11104.4104.521996476806-0.121996476805641
12128.9130.081996476806-1.18199647680566
13135.9126.8543746330019.04562536699935
14133.3116.87437463300116.4256253669994
15121.3108.93768643570212.3623135642983
16120.5117.4509336465063.04906635349383
17120.4122.950933646506-2.55093364650617
18137.9133.0309336465064.86906635349382
19126.1128.710933646506-2.61093364650618
20133.2130.3542454492072.84575455079269
21151.1142.8509336465068.24906635349382
22105112.034245449207-7.03424544920729
23119118.5742454492070.42575455079271
24140.4144.134245449207-3.73424544920731
25156.6140.90662360540215.6933763945977
26137.1130.9266236054026.17337639459777
27122.7122.989935408103-0.289935408103347
28125.8113.48662360540212.3133763945978
29139.3118.98662360540220.3133763945978
30134.9129.0666236054025.83337639459776
31149.2124.74662360540224.4533763945978
32132.3126.3899354081035.91006459189666
33149138.88662360540210.1133763945978
34117.2108.0699354081039.13006459189666
35119.6114.6099354081034.99006459189665
36152140.16993540810311.8300645918966
37149.4136.94231356429812.4576864357016
38127.3126.9623135642980.337686435701711
39114.1119.025625366999-4.92562536699941
40102.1109.522313564298-7.4223135642983
41107.7115.022313564298-7.3223135642983
42104.4125.102313564298-20.7023135642983
43102.1120.782313564298-18.6823135642983
4496104.409066353494-8.40906635349383
45109.3134.922313564298-25.6223135642983
469086.08906635349383.91093364650617
4783.992.6290663534938-8.72906635349382
48112118.189066353494-6.18906635349383
49114.3114.961444509689-0.66144450968883
50103.6104.981444509689-1.38144450968876
5191.797.0447563123899-5.34475631238987
5280.887.5414445096888-6.74144450968877
5387.293.0414445096888-5.84144450968877
54109.2103.1214445096896.07855549031123
55102.798.80144450968883.89855549031124
5695.1100.44475631239-5.34475631238989
57117.5112.9414445096894.55855549031124
5885.182.12475631238992.97524368761011
5992.188.66475631238993.43524368761011
60113.5114.22475631239-0.724756312389895







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.05555877896412960.1111175579282590.94444122103587
180.01543920873242950.0308784174648590.98456079126757
190.005283867510604070.01056773502120810.994716132489396
200.001897092427876120.003794184855752250.998102907572124
210.0006909050749159740.001381810149831950.999309094925084
220.0002532100110329530.0005064200220659060.999746789988967
237.06649167315714e-050.0001413298334631430.999929335083268
244.57711078581477e-059.15422157162954e-050.999954228892142
257.4563589341809e-050.0001491271786836180.999925436410658
260.000889920901442960.001779841802885920.999110079098557
270.004967729448393980.009935458896787960.995032270551606
280.003274110818629020.006548221637258030.99672588918137
290.002776066926898230.005552133853796460.997223933073102
300.007016933880321850.01403386776064370.992983066119678
310.01957598054841970.03915196109683940.98042401945158
320.03234036807983420.06468073615966840.967659631920166
330.05141607105333450.1028321421066690.948583928946665
340.03429050820401690.06858101640803390.965709491795983
350.03607854661955660.07215709323911310.963921453380443
360.07779658970317250.1555931794063450.922203410296828
370.1702635166705270.3405270333410550.829736483329473
380.3540612583990930.7081225167981860.645938741600907
390.5448585747968580.9102828504062850.455141425203142
400.7932890800937250.4134218398125510.206710919906275
410.9896963901771860.02060721964562820.0103036098228141
420.9801715165856230.03965696682875480.0198284834143774
430.9641245939243580.07175081215128420.0358754060756421

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0555587789641296 & 0.111117557928259 & 0.94444122103587 \tabularnewline
18 & 0.0154392087324295 & 0.030878417464859 & 0.98456079126757 \tabularnewline
19 & 0.00528386751060407 & 0.0105677350212081 & 0.994716132489396 \tabularnewline
20 & 0.00189709242787612 & 0.00379418485575225 & 0.998102907572124 \tabularnewline
21 & 0.000690905074915974 & 0.00138181014983195 & 0.999309094925084 \tabularnewline
22 & 0.000253210011032953 & 0.000506420022065906 & 0.999746789988967 \tabularnewline
23 & 7.06649167315714e-05 & 0.000141329833463143 & 0.999929335083268 \tabularnewline
24 & 4.57711078581477e-05 & 9.15422157162954e-05 & 0.999954228892142 \tabularnewline
25 & 7.4563589341809e-05 & 0.000149127178683618 & 0.999925436410658 \tabularnewline
26 & 0.00088992090144296 & 0.00177984180288592 & 0.999110079098557 \tabularnewline
27 & 0.00496772944839398 & 0.00993545889678796 & 0.995032270551606 \tabularnewline
28 & 0.00327411081862902 & 0.00654822163725803 & 0.99672588918137 \tabularnewline
29 & 0.00277606692689823 & 0.00555213385379646 & 0.997223933073102 \tabularnewline
30 & 0.00701693388032185 & 0.0140338677606437 & 0.992983066119678 \tabularnewline
31 & 0.0195759805484197 & 0.0391519610968394 & 0.98042401945158 \tabularnewline
32 & 0.0323403680798342 & 0.0646807361596684 & 0.967659631920166 \tabularnewline
33 & 0.0514160710533345 & 0.102832142106669 & 0.948583928946665 \tabularnewline
34 & 0.0342905082040169 & 0.0685810164080339 & 0.965709491795983 \tabularnewline
35 & 0.0360785466195566 & 0.0721570932391131 & 0.963921453380443 \tabularnewline
36 & 0.0777965897031725 & 0.155593179406345 & 0.922203410296828 \tabularnewline
37 & 0.170263516670527 & 0.340527033341055 & 0.829736483329473 \tabularnewline
38 & 0.354061258399093 & 0.708122516798186 & 0.645938741600907 \tabularnewline
39 & 0.544858574796858 & 0.910282850406285 & 0.455141425203142 \tabularnewline
40 & 0.793289080093725 & 0.413421839812551 & 0.206710919906275 \tabularnewline
41 & 0.989696390177186 & 0.0206072196456282 & 0.0103036098228141 \tabularnewline
42 & 0.980171516585623 & 0.0396569668287548 & 0.0198284834143774 \tabularnewline
43 & 0.964124593924358 & 0.0717508121512842 & 0.0358754060756421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]0.0555587789641296[/C][C]0.111117557928259[/C][C]0.94444122103587[/C][/ROW]
[ROW][C]18[/C][C]0.0154392087324295[/C][C]0.030878417464859[/C][C]0.98456079126757[/C][/ROW]
[ROW][C]19[/C][C]0.00528386751060407[/C][C]0.0105677350212081[/C][C]0.994716132489396[/C][/ROW]
[ROW][C]20[/C][C]0.00189709242787612[/C][C]0.00379418485575225[/C][C]0.998102907572124[/C][/ROW]
[ROW][C]21[/C][C]0.000690905074915974[/C][C]0.00138181014983195[/C][C]0.999309094925084[/C][/ROW]
[ROW][C]22[/C][C]0.000253210011032953[/C][C]0.000506420022065906[/C][C]0.999746789988967[/C][/ROW]
[ROW][C]23[/C][C]7.06649167315714e-05[/C][C]0.000141329833463143[/C][C]0.999929335083268[/C][/ROW]
[ROW][C]24[/C][C]4.57711078581477e-05[/C][C]9.15422157162954e-05[/C][C]0.999954228892142[/C][/ROW]
[ROW][C]25[/C][C]7.4563589341809e-05[/C][C]0.000149127178683618[/C][C]0.999925436410658[/C][/ROW]
[ROW][C]26[/C][C]0.00088992090144296[/C][C]0.00177984180288592[/C][C]0.999110079098557[/C][/ROW]
[ROW][C]27[/C][C]0.00496772944839398[/C][C]0.00993545889678796[/C][C]0.995032270551606[/C][/ROW]
[ROW][C]28[/C][C]0.00327411081862902[/C][C]0.00654822163725803[/C][C]0.99672588918137[/C][/ROW]
[ROW][C]29[/C][C]0.00277606692689823[/C][C]0.00555213385379646[/C][C]0.997223933073102[/C][/ROW]
[ROW][C]30[/C][C]0.00701693388032185[/C][C]0.0140338677606437[/C][C]0.992983066119678[/C][/ROW]
[ROW][C]31[/C][C]0.0195759805484197[/C][C]0.0391519610968394[/C][C]0.98042401945158[/C][/ROW]
[ROW][C]32[/C][C]0.0323403680798342[/C][C]0.0646807361596684[/C][C]0.967659631920166[/C][/ROW]
[ROW][C]33[/C][C]0.0514160710533345[/C][C]0.102832142106669[/C][C]0.948583928946665[/C][/ROW]
[ROW][C]34[/C][C]0.0342905082040169[/C][C]0.0685810164080339[/C][C]0.965709491795983[/C][/ROW]
[ROW][C]35[/C][C]0.0360785466195566[/C][C]0.0721570932391131[/C][C]0.963921453380443[/C][/ROW]
[ROW][C]36[/C][C]0.0777965897031725[/C][C]0.155593179406345[/C][C]0.922203410296828[/C][/ROW]
[ROW][C]37[/C][C]0.170263516670527[/C][C]0.340527033341055[/C][C]0.829736483329473[/C][/ROW]
[ROW][C]38[/C][C]0.354061258399093[/C][C]0.708122516798186[/C][C]0.645938741600907[/C][/ROW]
[ROW][C]39[/C][C]0.544858574796858[/C][C]0.910282850406285[/C][C]0.455141425203142[/C][/ROW]
[ROW][C]40[/C][C]0.793289080093725[/C][C]0.413421839812551[/C][C]0.206710919906275[/C][/ROW]
[ROW][C]41[/C][C]0.989696390177186[/C][C]0.0206072196456282[/C][C]0.0103036098228141[/C][/ROW]
[ROW][C]42[/C][C]0.980171516585623[/C][C]0.0396569668287548[/C][C]0.0198284834143774[/C][/ROW]
[ROW][C]43[/C][C]0.964124593924358[/C][C]0.0717508121512842[/C][C]0.0358754060756421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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
170.05555877896412960.1111175579282590.94444122103587
180.01543920873242950.0308784174648590.98456079126757
190.005283867510604070.01056773502120810.994716132489396
200.001897092427876120.003794184855752250.998102907572124
210.0006909050749159740.001381810149831950.999309094925084
220.0002532100110329530.0005064200220659060.999746789988967
237.06649167315714e-050.0001413298334631430.999929335083268
244.57711078581477e-059.15422157162954e-050.999954228892142
257.4563589341809e-050.0001491271786836180.999925436410658
260.000889920901442960.001779841802885920.999110079098557
270.004967729448393980.009935458896787960.995032270551606
280.003274110818629020.006548221637258030.99672588918137
290.002776066926898230.005552133853796460.997223933073102
300.007016933880321850.01403386776064370.992983066119678
310.01957598054841970.03915196109683940.98042401945158
320.03234036807983420.06468073615966840.967659631920166
330.05141607105333450.1028321421066690.948583928946665
340.03429050820401690.06858101640803390.965709491795983
350.03607854661955660.07215709323911310.963921453380443
360.07779658970317250.1555931794063450.922203410296828
370.1702635166705270.3405270333410550.829736483329473
380.3540612583990930.7081225167981860.645938741600907
390.5448585747968580.9102828504062850.455141425203142
400.7932890800937250.4134218398125510.206710919906275
410.9896963901771860.02060721964562820.0103036098228141
420.9801715165856230.03965696682875480.0198284834143774
430.9641245939243580.07175081215128420.0358754060756421







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.37037037037037NOK
5% type I error level160.592592592592593NOK
10% type I error level200.740740740740741NOK

\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 & 10 & 0.37037037037037 & NOK \tabularnewline
5% type I error level & 16 & 0.592592592592593 & NOK \tabularnewline
10% type I error level & 20 & 0.740740740740741 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147438&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]10[/C][C]0.37037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.740740740740741[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147438&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 level100.37037037037037NOK
5% type I error level160.592592592592593NOK
10% type I error level200.740740740740741NOK



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')
}