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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 14:32:43 -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/t125875335387jwjzq7ccxtskr.htm/, Retrieved Fri, 29 Mar 2024 12:43:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58475, Retrieved Fri, 29 Mar 2024 12:43:30 +0000
QR Codes:

Original text written by user:Uitleg in Word documet
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
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 14:10:54] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [Regressiemodel 4 ...] [2009-11-20 21:32:43] [8eb8270f5a1cfdf0409dcfcbf10be18b] [Current]
-    D        [Multiple Regression] [Regressiemodel d=...] [2009-12-16 13:47:46] [54d83950395cfb8ca1091bdb7440f70a]
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Dataseries X:
96.38	108.3	98.30	95.62	93.11	96.96
100.82	113.2	96.38	98.30	95.62	93.11
99.06	105	100.82	96.38	98.30	95.62
94.03	104	99.06	100.82	96.38	98.30
102.07	109.8	94.03	99.06	100.82	96.38
99.31	98.6	102.07	94.03	99.06	100.82
98.64	93.5	99.31	102.07	94.03	99.06
101.82	98.2	98.64	99.31	102.07	94.03
99.14	88	101.82	98.64	99.31	102.07
97.63	85.3	99.14	101.82	98.64	99.31
100.06	96.8	97.63	99.14	101.82	98.64
101.32	98.8	100.06	97.63	99.14	101.82
101.49	110.3	101.32	100.06	97.63	99.14
105.43	111.6	101.49	101.32	100.06	97.63
105.09	111.2	105.43	101.49	101.32	100.06
99.48	106.9	105.09	105.43	101.49	101.32
108.53	117.6	99.48	105.09	105.43	101.49
104.34	97	108.53	99.48	105.09	105.43
106.10	97.3	104.34	108.53	99.48	105.09
107.35	98.4	106.10	104.34	108.53	99.48
103.00	87.6	107.35	106.10	104.34	108.53
104.50	87.4	103.00	107.35	106.10	104.34
105.17	94.7	104.50	103.00	107.35	106.10
104.84	101.5	105.17	104.50	103.00	107.35
106.18	110.4	104.84	105.17	104.50	103.00
108.86	108.4	106.18	104.84	105.17	104.50
107.77	109.7	108.86	106.18	104.84	105.17
102.74	105.2	107.77	108.86	106.18	104.84
112.63	111.1	102.74	107.77	108.86	106.18
106.26	96.2	112.63	102.74	107.77	108.86
108.86	97.3	106.26	112.63	102.74	107.77
111.38	98.9	108.86	106.26	112.63	102.74
106.85	91.7	111.38	108.86	106.26	112.63
107.86	90.9	106.85	111.38	108.86	106.26
107.94	98.8	107.86	106.85	111.38	108.86
111.38	111.5	107.94	107.86	106.85	111.38
111.29	119	111.38	107.94	107.86	106.85
113.72	115.3	111.29	111.38	107.94	107.86
111.88	116.3	113.72	111.29	111.38	107.94
109.87	113.6	111.88	113.72	111.29	111.38
113.72	115.1	109.87	111.88	113.72	111.29
111.71	109.7	113.72	109.87	111.88	113.72
114.81	97.6	111.71	113.72	109.87	111.88
112.05	100.8	114.81	111.71	113.72	109.87
111.54	94	112.05	114.81	111.71	113.72
110.87	87.2	111.54	112.05	114.81	111.71
110.87	102.9	110.87	111.54	112.05	114.81
115.48	111.3	110.87	110.87	111.54	112.05
111.63	106.6	115.48	110.87	110.87	111.54
116.24	108.9	111.63	115.48	110.87	110.87
113.56	108.3	116.24	111.63	115.48	110.87
106.01	100.5	113.56	116.24	111.63	115.48
110.45	104	106.01	113.56	116.24	111.63
107.77	89.9	110.45	106.01	113.56	116.24
108.61	86.8	107.77	110.45	106.01	113.56
108.19	91.2	108.61	107.77	110.45	106.01




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
BESTC[t] = -6.92605201264424 + 0.259801879535493INDUSTR[t] + 0.155336451654076Y1[t] + 0.0649152601439141Y2[t] + 0.580833848588579Y3[t] + 0.0285751652133791Y4[t] -2.62125269928172M1[t] + 0.187869465600903M2[t] -2.89674579770143M3[t] -6.45300354254062M4[t] -2.02482601983913M5[t] -2.17130347109709M6[t] + 3.40503068660951M7[t] -0.58488239993721M8[t] + 0.271970079284188M9[t] + 0.552450778154068M10[t] -1.88858950952297M11[t] + 0.00827710547737372t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BESTC[t] =  -6.92605201264424 +  0.259801879535493INDUSTR[t] +  0.155336451654076Y1[t] +  0.0649152601439141Y2[t] +  0.580833848588579Y3[t] +  0.0285751652133791Y4[t] -2.62125269928172M1[t] +  0.187869465600903M2[t] -2.89674579770143M3[t] -6.45300354254062M4[t] -2.02482601983913M5[t] -2.17130347109709M6[t] +  3.40503068660951M7[t] -0.58488239993721M8[t] +  0.271970079284188M9[t] +  0.552450778154068M10[t] -1.88858950952297M11[t] +  0.00827710547737372t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BESTC[t] =  -6.92605201264424 +  0.259801879535493INDUSTR[t] +  0.155336451654076Y1[t] +  0.0649152601439141Y2[t] +  0.580833848588579Y3[t] +  0.0285751652133791Y4[t] -2.62125269928172M1[t] +  0.187869465600903M2[t] -2.89674579770143M3[t] -6.45300354254062M4[t] -2.02482601983913M5[t] -2.17130347109709M6[t] +  3.40503068660951M7[t] -0.58488239993721M8[t] +  0.271970079284188M9[t] +  0.552450778154068M10[t] -1.88858950952297M11[t] +  0.00827710547737372t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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
BESTC[t] = -6.92605201264424 + 0.259801879535493INDUSTR[t] + 0.155336451654076Y1[t] + 0.0649152601439141Y2[t] + 0.580833848588579Y3[t] + 0.0285751652133791Y4[t] -2.62125269928172M1[t] + 0.187869465600903M2[t] -2.89674579770143M3[t] -6.45300354254062M4[t] -2.02482601983913M5[t] -2.17130347109709M6[t] + 3.40503068660951M7[t] -0.58488239993721M8[t] + 0.271970079284188M9[t] + 0.552450778154068M10[t] -1.88858950952297M11[t] + 0.00827710547737372t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6.9260520126442412.419377-0.55770.5803330.290166
INDUSTR0.2598018795354930.0504565.1498e-064e-06
Y10.1553364516540760.1277931.21550.231660.11583
Y20.06491526014391410.1342930.48340.6315960.315798
Y30.5808338485885790.1219494.76292.8e-051.4e-05
Y40.02857516521337910.1574210.18150.8569240.428462
M1-2.621252699281720.959351-2.73230.009490.004745
M20.1878694656009031.0284260.18270.8560230.428011
M3-2.896745797701431.065543-2.71860.0098250.004913
M4-6.453003542540621.00255-6.436600
M5-2.024826019839131.086113-1.86430.070020.03501
M6-2.171303471097090.954236-2.27540.0286050.014303
M73.405030686609511.3568392.50950.016470.008235
M8-0.584882399937211.429526-0.40910.6847310.342365
M90.2719700792841881.2737980.21350.832070.416035
M100.5524507781540681.559290.35430.7250750.362538
M11-1.888589509522970.950698-1.98650.0542210.02711
t0.008277105477373720.046890.17650.860820.43041

\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) & -6.92605201264424 & 12.419377 & -0.5577 & 0.580333 & 0.290166 \tabularnewline
INDUSTR & 0.259801879535493 & 0.050456 & 5.149 & 8e-06 & 4e-06 \tabularnewline
Y1 & 0.155336451654076 & 0.127793 & 1.2155 & 0.23166 & 0.11583 \tabularnewline
Y2 & 0.0649152601439141 & 0.134293 & 0.4834 & 0.631596 & 0.315798 \tabularnewline
Y3 & 0.580833848588579 & 0.121949 & 4.7629 & 2.8e-05 & 1.4e-05 \tabularnewline
Y4 & 0.0285751652133791 & 0.157421 & 0.1815 & 0.856924 & 0.428462 \tabularnewline
M1 & -2.62125269928172 & 0.959351 & -2.7323 & 0.00949 & 0.004745 \tabularnewline
M2 & 0.187869465600903 & 1.028426 & 0.1827 & 0.856023 & 0.428011 \tabularnewline
M3 & -2.89674579770143 & 1.065543 & -2.7186 & 0.009825 & 0.004913 \tabularnewline
M4 & -6.45300354254062 & 1.00255 & -6.4366 & 0 & 0 \tabularnewline
M5 & -2.02482601983913 & 1.086113 & -1.8643 & 0.07002 & 0.03501 \tabularnewline
M6 & -2.17130347109709 & 0.954236 & -2.2754 & 0.028605 & 0.014303 \tabularnewline
M7 & 3.40503068660951 & 1.356839 & 2.5095 & 0.01647 & 0.008235 \tabularnewline
M8 & -0.58488239993721 & 1.429526 & -0.4091 & 0.684731 & 0.342365 \tabularnewline
M9 & 0.271970079284188 & 1.273798 & 0.2135 & 0.83207 & 0.416035 \tabularnewline
M10 & 0.552450778154068 & 1.55929 & 0.3543 & 0.725075 & 0.362538 \tabularnewline
M11 & -1.88858950952297 & 0.950698 & -1.9865 & 0.054221 & 0.02711 \tabularnewline
t & 0.00827710547737372 & 0.04689 & 0.1765 & 0.86082 & 0.43041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&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]-6.92605201264424[/C][C]12.419377[/C][C]-0.5577[/C][C]0.580333[/C][C]0.290166[/C][/ROW]
[ROW][C]INDUSTR[/C][C]0.259801879535493[/C][C]0.050456[/C][C]5.149[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Y1[/C][C]0.155336451654076[/C][C]0.127793[/C][C]1.2155[/C][C]0.23166[/C][C]0.11583[/C][/ROW]
[ROW][C]Y2[/C][C]0.0649152601439141[/C][C]0.134293[/C][C]0.4834[/C][C]0.631596[/C][C]0.315798[/C][/ROW]
[ROW][C]Y3[/C][C]0.580833848588579[/C][C]0.121949[/C][C]4.7629[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Y4[/C][C]0.0285751652133791[/C][C]0.157421[/C][C]0.1815[/C][C]0.856924[/C][C]0.428462[/C][/ROW]
[ROW][C]M1[/C][C]-2.62125269928172[/C][C]0.959351[/C][C]-2.7323[/C][C]0.00949[/C][C]0.004745[/C][/ROW]
[ROW][C]M2[/C][C]0.187869465600903[/C][C]1.028426[/C][C]0.1827[/C][C]0.856023[/C][C]0.428011[/C][/ROW]
[ROW][C]M3[/C][C]-2.89674579770143[/C][C]1.065543[/C][C]-2.7186[/C][C]0.009825[/C][C]0.004913[/C][/ROW]
[ROW][C]M4[/C][C]-6.45300354254062[/C][C]1.00255[/C][C]-6.4366[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-2.02482601983913[/C][C]1.086113[/C][C]-1.8643[/C][C]0.07002[/C][C]0.03501[/C][/ROW]
[ROW][C]M6[/C][C]-2.17130347109709[/C][C]0.954236[/C][C]-2.2754[/C][C]0.028605[/C][C]0.014303[/C][/ROW]
[ROW][C]M7[/C][C]3.40503068660951[/C][C]1.356839[/C][C]2.5095[/C][C]0.01647[/C][C]0.008235[/C][/ROW]
[ROW][C]M8[/C][C]-0.58488239993721[/C][C]1.429526[/C][C]-0.4091[/C][C]0.684731[/C][C]0.342365[/C][/ROW]
[ROW][C]M9[/C][C]0.271970079284188[/C][C]1.273798[/C][C]0.2135[/C][C]0.83207[/C][C]0.416035[/C][/ROW]
[ROW][C]M10[/C][C]0.552450778154068[/C][C]1.55929[/C][C]0.3543[/C][C]0.725075[/C][C]0.362538[/C][/ROW]
[ROW][C]M11[/C][C]-1.88858950952297[/C][C]0.950698[/C][C]-1.9865[/C][C]0.054221[/C][C]0.02711[/C][/ROW]
[ROW][C]t[/C][C]0.00827710547737372[/C][C]0.04689[/C][C]0.1765[/C][C]0.86082[/C][C]0.43041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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)-6.9260520126442412.419377-0.55770.5803330.290166
INDUSTR0.2598018795354930.0504565.1498e-064e-06
Y10.1553364516540760.1277931.21550.231660.11583
Y20.06491526014391410.1342930.48340.6315960.315798
Y30.5808338485885790.1219494.76292.8e-051.4e-05
Y40.02857516521337910.1574210.18150.8569240.428462
M1-2.621252699281720.959351-2.73230.009490.004745
M20.1878694656009031.0284260.18270.8560230.428011
M3-2.896745797701431.065543-2.71860.0098250.004913
M4-6.453003542540621.00255-6.436600
M5-2.024826019839131.086113-1.86430.070020.03501
M6-2.171303471097090.954236-2.27540.0286050.014303
M73.405030686609511.3568392.50950.016470.008235
M8-0.584882399937211.429526-0.40910.6847310.342365
M90.2719700792841881.2737980.21350.832070.416035
M100.5524507781540681.559290.35430.7250750.362538
M11-1.888589509522970.950698-1.98650.0542210.02711
t0.008277105477373720.046890.17650.860820.43041







Multiple Linear Regression - Regression Statistics
Multiple R0.985914410283178
R-squared0.972027224404027
Adjusted R-squared0.959513087953198
F-TEST (value)77.6743347991521
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07109106142283
Sum Squared Residuals43.5949703506755

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985914410283178 \tabularnewline
R-squared & 0.972027224404027 \tabularnewline
Adjusted R-squared & 0.959513087953198 \tabularnewline
F-TEST (value) & 77.6743347991521 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.07109106142283 \tabularnewline
Sum Squared Residuals & 43.5949703506755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985914410283178[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972027224404027[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.959513087953198[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]77.6743347991521[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]1.07109106142283[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]43.5949703506755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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 R0.985914410283178
R-squared0.972027224404027
Adjusted R-squared0.959513087953198
F-TEST (value)77.6743347991521
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07109106142283
Sum Squared Residuals43.5949703506755







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
196.3896.926373980974-0.546373980973961
2100.82102.240407944953-1.42040794495348
399.0699.2271092997082-0.167109299708232
494.0394.3955388344205-0.365538834420504
5102.07101.9672891247560.102710875244458
699.3198.94629520098380.363704799016176
798.64100.327220414353-1.68722041435251
8101.82101.8095827881230.0104172118765759
999.1499.10185279973480.0381472002652051
1097.6398.0112482316177-0.381248231617671
11100.0699.98558200271150.0744179972884663
12101.32101.2157322226460.104267777353873
13101.49100.9903057005930.49969429940682
14105.43105.621925591510-0.191925591509872
15105.09103.8660161963021.22398380369764
1699.4899.5385856687715-0.0585856687714791
17108.53108.1547548672770.375245132722899
18104.34103.6213587235480.718641276452006
19106.1105.952340475710.147659524290009
20107.35107.354123429917-0.00412342991744168
21103103.546725557649-0.546725557648704
22104.5104.0914911278450.40850887215464
23105.17104.2822395636210.887760436378853
24104.84105.656298987443-0.816298987443351
25106.18106.0947411209600.0852588790398619
26108.86109.011286867993-0.151286867992541
27107.77106.6034494832481.16655051675178
28102.74102.6599041034480.0800958965522788
29112.63109.3720152711123.25798472888756
30106.26106.0159932163980.244006783601768
31108.86108.5861640843750.273835915625037
32111.38110.6112893592640.768710640735976
33106.85106.7487697143010.101230285699228
34107.86107.6177425485100.242257451489595
35107.94108.638234630358-0.698234630357818
36111.38111.3534085265680.0265914734319444
37111.29111.685694332439-0.395694332439290
38113.72113.826482487517-0.106482487516764
39111.88113.381925865696-1.50192586569553
40109.87109.0504286846550.819571315344856
41113.72114.853770272849-1.13377027284880
42111.71112.780908813621-1.07090881362101
43114.81113.9395604784990.870439521500768
44112.05113.319128074169-1.26912807416897
45111.54111.1326519283160.40734807168427
46110.87111.139518092027-0.269518092026565
47110.87111.133943803310-0.263943803309501
48115.48114.7945602633420.685439736657536
49111.63111.2728848650330.357115134966568
50116.24114.3698971080271.87010289197265
51113.56114.281499155046-0.72149915504565
52106.01106.485542708705-0.475542708705153
53110.45113.052170464006-2.60217046400612
54107.77108.025444045449-0.255444045448937
55108.61108.2147145470630.395285452936694
56108.19107.6958763485260.494123651473859

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96.38 & 96.926373980974 & -0.546373980973961 \tabularnewline
2 & 100.82 & 102.240407944953 & -1.42040794495348 \tabularnewline
3 & 99.06 & 99.2271092997082 & -0.167109299708232 \tabularnewline
4 & 94.03 & 94.3955388344205 & -0.365538834420504 \tabularnewline
5 & 102.07 & 101.967289124756 & 0.102710875244458 \tabularnewline
6 & 99.31 & 98.9462952009838 & 0.363704799016176 \tabularnewline
7 & 98.64 & 100.327220414353 & -1.68722041435251 \tabularnewline
8 & 101.82 & 101.809582788123 & 0.0104172118765759 \tabularnewline
9 & 99.14 & 99.1018527997348 & 0.0381472002652051 \tabularnewline
10 & 97.63 & 98.0112482316177 & -0.381248231617671 \tabularnewline
11 & 100.06 & 99.9855820027115 & 0.0744179972884663 \tabularnewline
12 & 101.32 & 101.215732222646 & 0.104267777353873 \tabularnewline
13 & 101.49 & 100.990305700593 & 0.49969429940682 \tabularnewline
14 & 105.43 & 105.621925591510 & -0.191925591509872 \tabularnewline
15 & 105.09 & 103.866016196302 & 1.22398380369764 \tabularnewline
16 & 99.48 & 99.5385856687715 & -0.0585856687714791 \tabularnewline
17 & 108.53 & 108.154754867277 & 0.375245132722899 \tabularnewline
18 & 104.34 & 103.621358723548 & 0.718641276452006 \tabularnewline
19 & 106.1 & 105.95234047571 & 0.147659524290009 \tabularnewline
20 & 107.35 & 107.354123429917 & -0.00412342991744168 \tabularnewline
21 & 103 & 103.546725557649 & -0.546725557648704 \tabularnewline
22 & 104.5 & 104.091491127845 & 0.40850887215464 \tabularnewline
23 & 105.17 & 104.282239563621 & 0.887760436378853 \tabularnewline
24 & 104.84 & 105.656298987443 & -0.816298987443351 \tabularnewline
25 & 106.18 & 106.094741120960 & 0.0852588790398619 \tabularnewline
26 & 108.86 & 109.011286867993 & -0.151286867992541 \tabularnewline
27 & 107.77 & 106.603449483248 & 1.16655051675178 \tabularnewline
28 & 102.74 & 102.659904103448 & 0.0800958965522788 \tabularnewline
29 & 112.63 & 109.372015271112 & 3.25798472888756 \tabularnewline
30 & 106.26 & 106.015993216398 & 0.244006783601768 \tabularnewline
31 & 108.86 & 108.586164084375 & 0.273835915625037 \tabularnewline
32 & 111.38 & 110.611289359264 & 0.768710640735976 \tabularnewline
33 & 106.85 & 106.748769714301 & 0.101230285699228 \tabularnewline
34 & 107.86 & 107.617742548510 & 0.242257451489595 \tabularnewline
35 & 107.94 & 108.638234630358 & -0.698234630357818 \tabularnewline
36 & 111.38 & 111.353408526568 & 0.0265914734319444 \tabularnewline
37 & 111.29 & 111.685694332439 & -0.395694332439290 \tabularnewline
38 & 113.72 & 113.826482487517 & -0.106482487516764 \tabularnewline
39 & 111.88 & 113.381925865696 & -1.50192586569553 \tabularnewline
40 & 109.87 & 109.050428684655 & 0.819571315344856 \tabularnewline
41 & 113.72 & 114.853770272849 & -1.13377027284880 \tabularnewline
42 & 111.71 & 112.780908813621 & -1.07090881362101 \tabularnewline
43 & 114.81 & 113.939560478499 & 0.870439521500768 \tabularnewline
44 & 112.05 & 113.319128074169 & -1.26912807416897 \tabularnewline
45 & 111.54 & 111.132651928316 & 0.40734807168427 \tabularnewline
46 & 110.87 & 111.139518092027 & -0.269518092026565 \tabularnewline
47 & 110.87 & 111.133943803310 & -0.263943803309501 \tabularnewline
48 & 115.48 & 114.794560263342 & 0.685439736657536 \tabularnewline
49 & 111.63 & 111.272884865033 & 0.357115134966568 \tabularnewline
50 & 116.24 & 114.369897108027 & 1.87010289197265 \tabularnewline
51 & 113.56 & 114.281499155046 & -0.72149915504565 \tabularnewline
52 & 106.01 & 106.485542708705 & -0.475542708705153 \tabularnewline
53 & 110.45 & 113.052170464006 & -2.60217046400612 \tabularnewline
54 & 107.77 & 108.025444045449 & -0.255444045448937 \tabularnewline
55 & 108.61 & 108.214714547063 & 0.395285452936694 \tabularnewline
56 & 108.19 & 107.695876348526 & 0.494123651473859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&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]96.38[/C][C]96.926373980974[/C][C]-0.546373980973961[/C][/ROW]
[ROW][C]2[/C][C]100.82[/C][C]102.240407944953[/C][C]-1.42040794495348[/C][/ROW]
[ROW][C]3[/C][C]99.06[/C][C]99.2271092997082[/C][C]-0.167109299708232[/C][/ROW]
[ROW][C]4[/C][C]94.03[/C][C]94.3955388344205[/C][C]-0.365538834420504[/C][/ROW]
[ROW][C]5[/C][C]102.07[/C][C]101.967289124756[/C][C]0.102710875244458[/C][/ROW]
[ROW][C]6[/C][C]99.31[/C][C]98.9462952009838[/C][C]0.363704799016176[/C][/ROW]
[ROW][C]7[/C][C]98.64[/C][C]100.327220414353[/C][C]-1.68722041435251[/C][/ROW]
[ROW][C]8[/C][C]101.82[/C][C]101.809582788123[/C][C]0.0104172118765759[/C][/ROW]
[ROW][C]9[/C][C]99.14[/C][C]99.1018527997348[/C][C]0.0381472002652051[/C][/ROW]
[ROW][C]10[/C][C]97.63[/C][C]98.0112482316177[/C][C]-0.381248231617671[/C][/ROW]
[ROW][C]11[/C][C]100.06[/C][C]99.9855820027115[/C][C]0.0744179972884663[/C][/ROW]
[ROW][C]12[/C][C]101.32[/C][C]101.215732222646[/C][C]0.104267777353873[/C][/ROW]
[ROW][C]13[/C][C]101.49[/C][C]100.990305700593[/C][C]0.49969429940682[/C][/ROW]
[ROW][C]14[/C][C]105.43[/C][C]105.621925591510[/C][C]-0.191925591509872[/C][/ROW]
[ROW][C]15[/C][C]105.09[/C][C]103.866016196302[/C][C]1.22398380369764[/C][/ROW]
[ROW][C]16[/C][C]99.48[/C][C]99.5385856687715[/C][C]-0.0585856687714791[/C][/ROW]
[ROW][C]17[/C][C]108.53[/C][C]108.154754867277[/C][C]0.375245132722899[/C][/ROW]
[ROW][C]18[/C][C]104.34[/C][C]103.621358723548[/C][C]0.718641276452006[/C][/ROW]
[ROW][C]19[/C][C]106.1[/C][C]105.95234047571[/C][C]0.147659524290009[/C][/ROW]
[ROW][C]20[/C][C]107.35[/C][C]107.354123429917[/C][C]-0.00412342991744168[/C][/ROW]
[ROW][C]21[/C][C]103[/C][C]103.546725557649[/C][C]-0.546725557648704[/C][/ROW]
[ROW][C]22[/C][C]104.5[/C][C]104.091491127845[/C][C]0.40850887215464[/C][/ROW]
[ROW][C]23[/C][C]105.17[/C][C]104.282239563621[/C][C]0.887760436378853[/C][/ROW]
[ROW][C]24[/C][C]104.84[/C][C]105.656298987443[/C][C]-0.816298987443351[/C][/ROW]
[ROW][C]25[/C][C]106.18[/C][C]106.094741120960[/C][C]0.0852588790398619[/C][/ROW]
[ROW][C]26[/C][C]108.86[/C][C]109.011286867993[/C][C]-0.151286867992541[/C][/ROW]
[ROW][C]27[/C][C]107.77[/C][C]106.603449483248[/C][C]1.16655051675178[/C][/ROW]
[ROW][C]28[/C][C]102.74[/C][C]102.659904103448[/C][C]0.0800958965522788[/C][/ROW]
[ROW][C]29[/C][C]112.63[/C][C]109.372015271112[/C][C]3.25798472888756[/C][/ROW]
[ROW][C]30[/C][C]106.26[/C][C]106.015993216398[/C][C]0.244006783601768[/C][/ROW]
[ROW][C]31[/C][C]108.86[/C][C]108.586164084375[/C][C]0.273835915625037[/C][/ROW]
[ROW][C]32[/C][C]111.38[/C][C]110.611289359264[/C][C]0.768710640735976[/C][/ROW]
[ROW][C]33[/C][C]106.85[/C][C]106.748769714301[/C][C]0.101230285699228[/C][/ROW]
[ROW][C]34[/C][C]107.86[/C][C]107.617742548510[/C][C]0.242257451489595[/C][/ROW]
[ROW][C]35[/C][C]107.94[/C][C]108.638234630358[/C][C]-0.698234630357818[/C][/ROW]
[ROW][C]36[/C][C]111.38[/C][C]111.353408526568[/C][C]0.0265914734319444[/C][/ROW]
[ROW][C]37[/C][C]111.29[/C][C]111.685694332439[/C][C]-0.395694332439290[/C][/ROW]
[ROW][C]38[/C][C]113.72[/C][C]113.826482487517[/C][C]-0.106482487516764[/C][/ROW]
[ROW][C]39[/C][C]111.88[/C][C]113.381925865696[/C][C]-1.50192586569553[/C][/ROW]
[ROW][C]40[/C][C]109.87[/C][C]109.050428684655[/C][C]0.819571315344856[/C][/ROW]
[ROW][C]41[/C][C]113.72[/C][C]114.853770272849[/C][C]-1.13377027284880[/C][/ROW]
[ROW][C]42[/C][C]111.71[/C][C]112.780908813621[/C][C]-1.07090881362101[/C][/ROW]
[ROW][C]43[/C][C]114.81[/C][C]113.939560478499[/C][C]0.870439521500768[/C][/ROW]
[ROW][C]44[/C][C]112.05[/C][C]113.319128074169[/C][C]-1.26912807416897[/C][/ROW]
[ROW][C]45[/C][C]111.54[/C][C]111.132651928316[/C][C]0.40734807168427[/C][/ROW]
[ROW][C]46[/C][C]110.87[/C][C]111.139518092027[/C][C]-0.269518092026565[/C][/ROW]
[ROW][C]47[/C][C]110.87[/C][C]111.133943803310[/C][C]-0.263943803309501[/C][/ROW]
[ROW][C]48[/C][C]115.48[/C][C]114.794560263342[/C][C]0.685439736657536[/C][/ROW]
[ROW][C]49[/C][C]111.63[/C][C]111.272884865033[/C][C]0.357115134966568[/C][/ROW]
[ROW][C]50[/C][C]116.24[/C][C]114.369897108027[/C][C]1.87010289197265[/C][/ROW]
[ROW][C]51[/C][C]113.56[/C][C]114.281499155046[/C][C]-0.72149915504565[/C][/ROW]
[ROW][C]52[/C][C]106.01[/C][C]106.485542708705[/C][C]-0.475542708705153[/C][/ROW]
[ROW][C]53[/C][C]110.45[/C][C]113.052170464006[/C][C]-2.60217046400612[/C][/ROW]
[ROW][C]54[/C][C]107.77[/C][C]108.025444045449[/C][C]-0.255444045448937[/C][/ROW]
[ROW][C]55[/C][C]108.61[/C][C]108.214714547063[/C][C]0.395285452936694[/C][/ROW]
[ROW][C]56[/C][C]108.19[/C][C]107.695876348526[/C][C]0.494123651473859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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
196.3896.926373980974-0.546373980973961
2100.82102.240407944953-1.42040794495348
399.0699.2271092997082-0.167109299708232
494.0394.3955388344205-0.365538834420504
5102.07101.9672891247560.102710875244458
699.3198.94629520098380.363704799016176
798.64100.327220414353-1.68722041435251
8101.82101.8095827881230.0104172118765759
999.1499.10185279973480.0381472002652051
1097.6398.0112482316177-0.381248231617671
11100.0699.98558200271150.0744179972884663
12101.32101.2157322226460.104267777353873
13101.49100.9903057005930.49969429940682
14105.43105.621925591510-0.191925591509872
15105.09103.8660161963021.22398380369764
1699.4899.5385856687715-0.0585856687714791
17108.53108.1547548672770.375245132722899
18104.34103.6213587235480.718641276452006
19106.1105.952340475710.147659524290009
20107.35107.354123429917-0.00412342991744168
21103103.546725557649-0.546725557648704
22104.5104.0914911278450.40850887215464
23105.17104.2822395636210.887760436378853
24104.84105.656298987443-0.816298987443351
25106.18106.0947411209600.0852588790398619
26108.86109.011286867993-0.151286867992541
27107.77106.6034494832481.16655051675178
28102.74102.6599041034480.0800958965522788
29112.63109.3720152711123.25798472888756
30106.26106.0159932163980.244006783601768
31108.86108.5861640843750.273835915625037
32111.38110.6112893592640.768710640735976
33106.85106.7487697143010.101230285699228
34107.86107.6177425485100.242257451489595
35107.94108.638234630358-0.698234630357818
36111.38111.3534085265680.0265914734319444
37111.29111.685694332439-0.395694332439290
38113.72113.826482487517-0.106482487516764
39111.88113.381925865696-1.50192586569553
40109.87109.0504286846550.819571315344856
41113.72114.853770272849-1.13377027284880
42111.71112.780908813621-1.07090881362101
43114.81113.9395604784990.870439521500768
44112.05113.319128074169-1.26912807416897
45111.54111.1326519283160.40734807168427
46110.87111.139518092027-0.269518092026565
47110.87111.133943803310-0.263943803309501
48115.48114.7945602633420.685439736657536
49111.63111.2728848650330.357115134966568
50116.24114.3698971080271.87010289197265
51113.56114.281499155046-0.72149915504565
52106.01106.485542708705-0.475542708705153
53110.45113.052170464006-2.60217046400612
54107.77108.025444045449-0.255444045448937
55108.61108.2147145470630.395285452936694
56108.19107.6958763485260.494123651473859







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.08656576647860960.1731315329572190.91343423352139
220.04061854673787010.08123709347574030.95938145326213
230.01394936269667910.02789872539335810.98605063730332
240.01319206698206900.02638413396413810.98680793301793
250.01002379512124820.02004759024249630.989976204878752
260.007723423065590930.01544684613118190.99227657693441
270.002898299780920580.005796599561841170.99710170021908
280.001431683150098310.002863366300196620.998568316849902
290.2552517549312510.5105035098625020.744748245068749
300.2062684144456460.4125368288912930.793731585554354
310.1248438043122080.2496876086244160.875156195687792
320.1229958226525910.2459916453051820.87700417734741
330.07939645802145790.1587929160429160.920603541978542
340.06126789770766570.1225357954153310.938732102292334
350.06829487477886990.1365897495577400.93170512522113

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0865657664786096 & 0.173131532957219 & 0.91343423352139 \tabularnewline
22 & 0.0406185467378701 & 0.0812370934757403 & 0.95938145326213 \tabularnewline
23 & 0.0139493626966791 & 0.0278987253933581 & 0.98605063730332 \tabularnewline
24 & 0.0131920669820690 & 0.0263841339641381 & 0.98680793301793 \tabularnewline
25 & 0.0100237951212482 & 0.0200475902424963 & 0.989976204878752 \tabularnewline
26 & 0.00772342306559093 & 0.0154468461311819 & 0.99227657693441 \tabularnewline
27 & 0.00289829978092058 & 0.00579659956184117 & 0.99710170021908 \tabularnewline
28 & 0.00143168315009831 & 0.00286336630019662 & 0.998568316849902 \tabularnewline
29 & 0.255251754931251 & 0.510503509862502 & 0.744748245068749 \tabularnewline
30 & 0.206268414445646 & 0.412536828891293 & 0.793731585554354 \tabularnewline
31 & 0.124843804312208 & 0.249687608624416 & 0.875156195687792 \tabularnewline
32 & 0.122995822652591 & 0.245991645305182 & 0.87700417734741 \tabularnewline
33 & 0.0793964580214579 & 0.158792916042916 & 0.920603541978542 \tabularnewline
34 & 0.0612678977076657 & 0.122535795415331 & 0.938732102292334 \tabularnewline
35 & 0.0682948747788699 & 0.136589749557740 & 0.93170512522113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&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]21[/C][C]0.0865657664786096[/C][C]0.173131532957219[/C][C]0.91343423352139[/C][/ROW]
[ROW][C]22[/C][C]0.0406185467378701[/C][C]0.0812370934757403[/C][C]0.95938145326213[/C][/ROW]
[ROW][C]23[/C][C]0.0139493626966791[/C][C]0.0278987253933581[/C][C]0.98605063730332[/C][/ROW]
[ROW][C]24[/C][C]0.0131920669820690[/C][C]0.0263841339641381[/C][C]0.98680793301793[/C][/ROW]
[ROW][C]25[/C][C]0.0100237951212482[/C][C]0.0200475902424963[/C][C]0.989976204878752[/C][/ROW]
[ROW][C]26[/C][C]0.00772342306559093[/C][C]0.0154468461311819[/C][C]0.99227657693441[/C][/ROW]
[ROW][C]27[/C][C]0.00289829978092058[/C][C]0.00579659956184117[/C][C]0.99710170021908[/C][/ROW]
[ROW][C]28[/C][C]0.00143168315009831[/C][C]0.00286336630019662[/C][C]0.998568316849902[/C][/ROW]
[ROW][C]29[/C][C]0.255251754931251[/C][C]0.510503509862502[/C][C]0.744748245068749[/C][/ROW]
[ROW][C]30[/C][C]0.206268414445646[/C][C]0.412536828891293[/C][C]0.793731585554354[/C][/ROW]
[ROW][C]31[/C][C]0.124843804312208[/C][C]0.249687608624416[/C][C]0.875156195687792[/C][/ROW]
[ROW][C]32[/C][C]0.122995822652591[/C][C]0.245991645305182[/C][C]0.87700417734741[/C][/ROW]
[ROW][C]33[/C][C]0.0793964580214579[/C][C]0.158792916042916[/C][C]0.920603541978542[/C][/ROW]
[ROW][C]34[/C][C]0.0612678977076657[/C][C]0.122535795415331[/C][C]0.938732102292334[/C][/ROW]
[ROW][C]35[/C][C]0.0682948747788699[/C][C]0.136589749557740[/C][C]0.93170512522113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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
210.08656576647860960.1731315329572190.91343423352139
220.04061854673787010.08123709347574030.95938145326213
230.01394936269667910.02789872539335810.98605063730332
240.01319206698206900.02638413396413810.98680793301793
250.01002379512124820.02004759024249630.989976204878752
260.007723423065590930.01544684613118190.99227657693441
270.002898299780920580.005796599561841170.99710170021908
280.001431683150098310.002863366300196620.998568316849902
290.2552517549312510.5105035098625020.744748245068749
300.2062684144456460.4125368288912930.793731585554354
310.1248438043122080.2496876086244160.875156195687792
320.1229958226525910.2459916453051820.87700417734741
330.07939645802145790.1587929160429160.920603541978542
340.06126789770766570.1225357954153310.938732102292334
350.06829487477886990.1365897495577400.93170512522113







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.133333333333333NOK
5% type I error level60.4NOK
10% type I error level70.466666666666667NOK

\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 & 2 & 0.133333333333333 & NOK \tabularnewline
5% type I error level & 6 & 0.4 & NOK \tabularnewline
10% type I error level & 7 & 0.466666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58475&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]2[/C][C]0.133333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.4[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.466666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58475&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58475&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 level20.133333333333333NOK
5% type I error level60.4NOK
10% type I error level70.466666666666667NOK



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')
}