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Description of Statistical Computation

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, 27 Nov 2009 04:45:11 -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/27/t1259322431cfq5u9gsdlhq9jn.htm/, Retrieved Mon, 29 Apr 2024 23:43:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60588, Retrieved Mon, 29 Apr 2024 23:43:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

Tree of Dependent Computations

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]
-   PD    [Multiple Regression] [4 lag ] [2009-11-19 20:53:48] [ba905ddf7cdf9ecb063c35348c4dab2e]
-    D        [Multiple Regression] [Review WS 7 2 maa...] [2009-11-27 11:45:11] [51d49d3536f6a59f2486a67bf50b2759] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	6.5	6.3	6.1
0	6.6	6.5	6.3
0	6.5	6.6	6.5
0	6.2	6.5	6.6
0	6.2	6.2	6.5
0	5.9	6.2	6.2
0	6.1	5.9	6.2
0	6.1	6.1	5.9
0	6.1	6.1	6.1
0	6.1	6.1	6.1
0	6.1	6.1	6.1
0	6.4	6.1	6.1
0	6.7	6.4	6.1
0	6.9	6.7	6.4
0	7	6.9	6.7
0	7	7	6.9
0	6.8	7	7
0	6.4	6.8	7
0	5.9	6.4	6.8
0	5.5	5.9	6.4
0	5.5	5.5	5.9
0	5.6	5.5	5.5
0	5.8	5.6	5.5
0	5.9	5.8	5.6
0	6.1	5.9	5.8
0	6.1	6.1	5.9
0	6	6.1	6.1
0	6	6	6.1
0	5.9	6	6
0	5.5	5.9	6
0	5.6	5.5	5.9
0	5.4	5.6	5.5
0	5.2	5.4	5.6
0	5.2	5.2	5.4
0	5.2	5.2	5.2
0	5.5	5.2	5.2
1	5.8	5.5	5.2
1	5.8	5.8	5.5
1	5.5	5.8	5.8
1	5.3	5.5	5.8
1	5.1	5.3	5.5
1	5.2	5.1	5.3
1	5.8	5.2	5.1
1	5.8	5.8	5.2
1	5.5	5.8	5.8
1	5	5.5	5.8
1	4.9	5	5.5
1	5.3	4.9	5
1	6.1	5.3	4.9
1	6.5	6.1	5.3
1	6.8	6.5	6.1
1	6.6	6.8	6.5
1	6.4	6.6	6.8
1	6.4	6.4	6.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 1.26367368122718 + 0.00440114533689808x[t] + 1.40345082424960y1[t] -0.576928969828826y2[t] + 0.006338650422903M1[t] -0.207233201114077M2[t] -0.214352974333265M3[t] -0.243844989034858M4[t] -0.197231539999112M5[t] -0.279849567342826M6[t] + 0.0186804379108398M7[t] -0.414227973933947M8[t] -0.269348540276246M9[t] -0.278787619681992M10[t] -0.183889745448256M11[t] -0.00166891303737878t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  1.26367368122718 +  0.00440114533689808x[t] +  1.40345082424960y1[t] -0.576928969828826y2[t] +  0.006338650422903M1[t] -0.207233201114077M2[t] -0.214352974333265M3[t] -0.243844989034858M4[t] -0.197231539999112M5[t] -0.279849567342826M6[t] +  0.0186804379108398M7[t] -0.414227973933947M8[t] -0.269348540276246M9[t] -0.278787619681992M10[t] -0.183889745448256M11[t] -0.00166891303737878t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  1.26367368122718 +  0.00440114533689808x[t] +  1.40345082424960y1[t] -0.576928969828826y2[t] +  0.006338650422903M1[t] -0.207233201114077M2[t] -0.214352974333265M3[t] -0.243844989034858M4[t] -0.197231539999112M5[t] -0.279849567342826M6[t] +  0.0186804379108398M7[t] -0.414227973933947M8[t] -0.269348540276246M9[t] -0.278787619681992M10[t] -0.183889745448256M11[t] -0.00166891303737878t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 1.26367368122718 + 0.00440114533689808x[t] + 1.40345082424960y1[t] -0.576928969828826y2[t] + 0.006338650422903M1[t] -0.207233201114077M2[t] -0.214352974333265M3[t] -0.243844989034858M4[t] -0.197231539999112M5[t] -0.279849567342826M6[t] + 0.0186804379108398M7[t] -0.414227973933947M8[t] -0.269348540276246M9[t] -0.278787619681992M10[t] -0.183889745448256M11[t] -0.00166891303737878t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.263673681227180.4600642.74670.009150.004575
x0.004401145336898080.0997830.04410.965050.482525
y11.403450824249600.13837610.142300
y2-0.5769289698288260.145249-3.9720.0003070.000153
M10.0063386504229030.1337770.04740.9624570.481228
M2-0.2072332011140770.141044-1.46930.1499870.074994
M3-0.2143529743332650.141761-1.51210.1387880.069394
M4-0.2438449890348580.14406-1.69270.0987060.049353
M5-0.1972315399991120.143918-1.37040.1785910.089296
M6-0.2798495673428260.139771-2.00220.0524370.026219
M70.01868043791083980.141620.13190.8957550.447878
M8-0.4142279739339470.135264-3.06240.0040210.002011
M9-0.2693485402762460.137015-1.96580.0566570.028328
M10-0.2787876196819920.13519-2.06220.0460760.023038
M11-0.1838897454482560.134215-1.37010.1786910.089346
t-0.001668913037378780.003155-0.5290.5999130.299957

\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) & 1.26367368122718 & 0.460064 & 2.7467 & 0.00915 & 0.004575 \tabularnewline
x & 0.00440114533689808 & 0.099783 & 0.0441 & 0.96505 & 0.482525 \tabularnewline
y1 & 1.40345082424960 & 0.138376 & 10.1423 & 0 & 0 \tabularnewline
y2 & -0.576928969828826 & 0.145249 & -3.972 & 0.000307 & 0.000153 \tabularnewline
M1 & 0.006338650422903 & 0.133777 & 0.0474 & 0.962457 & 0.481228 \tabularnewline
M2 & -0.207233201114077 & 0.141044 & -1.4693 & 0.149987 & 0.074994 \tabularnewline
M3 & -0.214352974333265 & 0.141761 & -1.5121 & 0.138788 & 0.069394 \tabularnewline
M4 & -0.243844989034858 & 0.14406 & -1.6927 & 0.098706 & 0.049353 \tabularnewline
M5 & -0.197231539999112 & 0.143918 & -1.3704 & 0.178591 & 0.089296 \tabularnewline
M6 & -0.279849567342826 & 0.139771 & -2.0022 & 0.052437 & 0.026219 \tabularnewline
M7 & 0.0186804379108398 & 0.14162 & 0.1319 & 0.895755 & 0.447878 \tabularnewline
M8 & -0.414227973933947 & 0.135264 & -3.0624 & 0.004021 & 0.002011 \tabularnewline
M9 & -0.269348540276246 & 0.137015 & -1.9658 & 0.056657 & 0.028328 \tabularnewline
M10 & -0.278787619681992 & 0.13519 & -2.0622 & 0.046076 & 0.023038 \tabularnewline
M11 & -0.183889745448256 & 0.134215 & -1.3701 & 0.178691 & 0.089346 \tabularnewline
t & -0.00166891303737878 & 0.003155 & -0.529 & 0.599913 & 0.299957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&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]1.26367368122718[/C][C]0.460064[/C][C]2.7467[/C][C]0.00915[/C][C]0.004575[/C][/ROW]
[ROW][C]x[/C][C]0.00440114533689808[/C][C]0.099783[/C][C]0.0441[/C][C]0.96505[/C][C]0.482525[/C][/ROW]
[ROW][C]y1[/C][C]1.40345082424960[/C][C]0.138376[/C][C]10.1423[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y2[/C][C]-0.576928969828826[/C][C]0.145249[/C][C]-3.972[/C][C]0.000307[/C][C]0.000153[/C][/ROW]
[ROW][C]M1[/C][C]0.006338650422903[/C][C]0.133777[/C][C]0.0474[/C][C]0.962457[/C][C]0.481228[/C][/ROW]
[ROW][C]M2[/C][C]-0.207233201114077[/C][C]0.141044[/C][C]-1.4693[/C][C]0.149987[/C][C]0.074994[/C][/ROW]
[ROW][C]M3[/C][C]-0.214352974333265[/C][C]0.141761[/C][C]-1.5121[/C][C]0.138788[/C][C]0.069394[/C][/ROW]
[ROW][C]M4[/C][C]-0.243844989034858[/C][C]0.14406[/C][C]-1.6927[/C][C]0.098706[/C][C]0.049353[/C][/ROW]
[ROW][C]M5[/C][C]-0.197231539999112[/C][C]0.143918[/C][C]-1.3704[/C][C]0.178591[/C][C]0.089296[/C][/ROW]
[ROW][C]M6[/C][C]-0.279849567342826[/C][C]0.139771[/C][C]-2.0022[/C][C]0.052437[/C][C]0.026219[/C][/ROW]
[ROW][C]M7[/C][C]0.0186804379108398[/C][C]0.14162[/C][C]0.1319[/C][C]0.895755[/C][C]0.447878[/C][/ROW]
[ROW][C]M8[/C][C]-0.414227973933947[/C][C]0.135264[/C][C]-3.0624[/C][C]0.004021[/C][C]0.002011[/C][/ROW]
[ROW][C]M9[/C][C]-0.269348540276246[/C][C]0.137015[/C][C]-1.9658[/C][C]0.056657[/C][C]0.028328[/C][/ROW]
[ROW][C]M10[/C][C]-0.278787619681992[/C][C]0.13519[/C][C]-2.0622[/C][C]0.046076[/C][C]0.023038[/C][/ROW]
[ROW][C]M11[/C][C]-0.183889745448256[/C][C]0.134215[/C][C]-1.3701[/C][C]0.178691[/C][C]0.089346[/C][/ROW]
[ROW][C]t[/C][C]-0.00166891303737878[/C][C]0.003155[/C][C]-0.529[/C][C]0.599913[/C][C]0.299957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=2

As an alternative you can also use a QR Code:  
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)1.263673681227180.4600642.74670.009150.004575
x0.004401145336898080.0997830.04410.965050.482525
y11.403450824249600.13837610.142300
y2-0.5769289698288260.145249-3.9720.0003070.000153
M10.0063386504229030.1337770.04740.9624570.481228
M2-0.2072332011140770.141044-1.46930.1499870.074994
M3-0.2143529743332650.141761-1.51210.1387880.069394
M4-0.2438449890348580.14406-1.69270.0987060.049353
M5-0.1972315399991120.143918-1.37040.1785910.089296
M6-0.2798495673428260.139771-2.00220.0524370.026219
M70.01868043791083980.141620.13190.8957550.447878
M8-0.4142279739339470.135264-3.06240.0040210.002011
M9-0.2693485402762460.137015-1.96580.0566570.028328
M10-0.2787876196819920.13519-2.06220.0460760.023038
M11-0.1838897454482560.134215-1.37010.1786910.089346
t-0.001668913037378780.003155-0.5290.5999130.299957






Multiple Linear Regression - Regression Statistics
Multiple R0.956170284263282
R-squared0.914261612508126
Adjusted R-squared0.880417512182387
F-TEST (value)27.0139139084398
F-TEST (DF numerator)15
F-TEST (DF denominator)38
p-value1.11022302462516e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.188181322950762
Sum Squared Residuals1.34566399168495

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.956170284263282 \tabularnewline
R-squared & 0.914261612508126 \tabularnewline
Adjusted R-squared & 0.880417512182387 \tabularnewline
F-TEST (value) & 27.0139139084398 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 1.11022302462516e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.188181322950762 \tabularnewline
Sum Squared Residuals & 1.34566399168495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.956170284263282[/C][/ROW]
[ROW][C]R-squared[/C][C]0.914261612508126[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.880417512182387[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.0139139084398[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.188181322950762[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.34566399168495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.956170284263282
R-squared0.914261612508126
Adjusted R-squared0.880417512182387
F-TEST (value)27.0139139084398
F-TEST (DF numerator)15
F-TEST (DF denominator)38
p-value1.11022302462516e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.188181322950762
Sum Squared Residuals1.34566399168495






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.56.59081689542933-0.0908168954293293
26.66.540880501739120.0591194982608785
36.56.55705110394175-0.0570511039417492
46.26.32785219679493-0.127852196794934
56.26.00945438250130.190545617498696
65.96.09824613306886-0.198246133068860
76.15.974071978010270.125928021989732
86.15.993263508926670.106736491073331
96.16.021088235581230.0789117644187742
106.16.00998024313810.0900197568618983
116.16.10320920433446-0.00320920433445819
126.46.285430036745330.114569963254665
136.76.71113502140574-0.0111350214057395
146.96.743850813157610.156149186842389
1576.842673600802320.157326399197683
1676.836471961522540.163528038477462
176.86.82372360053802-0.0237236005380234
186.46.45874649530701-0.0587464953070114
195.96.30961305178923-0.409613051789225
205.55.404081902713790.0959180972862084
215.55.274376578548690.225623421451313
225.65.494040174037090.105959825962906
235.85.727614217658410.0723857823415904
245.96.13283231793632-0.232832317936324
256.16.16246134378104-0.0624613437810437
266.16.17021784707372-0.0702178470737203
2766.04604336685139-0.0460433668513886
2865.874537356687460.125462643312543
295.95.97717478966871-0.0771747896687065
305.55.75254276686266-0.252542766862656
315.65.545716426361990.0542835736380142
325.45.48225577183631-0.0822557718363094
335.25.28708323062383-0.0870832306238308
345.25.110670867296550.0893291327034486
355.25.31928562245867-0.119285622458673
365.55.50150645486955-0.00150645486955109
375.85.93161258486685-0.131612584866852
385.85.96432837661872-0.164328376618725
395.55.78246099941351-0.28246099941351
405.35.33026482439966-0.0302648243996594
415.15.26759788649675-0.167597886496755
425.25.018006575231510.181993424768492
435.85.570598543838520.229401456161479
445.85.92039881652323-0.12039881652323
455.55.71745195524626-0.217451955246256
4655.28530871552825-0.285308715528253
474.94.849890955548460.0501090444515413
485.35.180231190448790.119768809551209
496.15.803974154517030.296025845482965
506.56.480722461410820.019277538589177
516.86.571770928991040.228229071008965
526.66.73087366059541-0.130873660595411
536.46.322049340795210.0779506592047891
546.46.072458029529970.327541970470034

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.5 & 6.59081689542933 & -0.0908168954293293 \tabularnewline
2 & 6.6 & 6.54088050173912 & 0.0591194982608785 \tabularnewline
3 & 6.5 & 6.55705110394175 & -0.0570511039417492 \tabularnewline
4 & 6.2 & 6.32785219679493 & -0.127852196794934 \tabularnewline
5 & 6.2 & 6.0094543825013 & 0.190545617498696 \tabularnewline
6 & 5.9 & 6.09824613306886 & -0.198246133068860 \tabularnewline
7 & 6.1 & 5.97407197801027 & 0.125928021989732 \tabularnewline
8 & 6.1 & 5.99326350892667 & 0.106736491073331 \tabularnewline
9 & 6.1 & 6.02108823558123 & 0.0789117644187742 \tabularnewline
10 & 6.1 & 6.0099802431381 & 0.0900197568618983 \tabularnewline
11 & 6.1 & 6.10320920433446 & -0.00320920433445819 \tabularnewline
12 & 6.4 & 6.28543003674533 & 0.114569963254665 \tabularnewline
13 & 6.7 & 6.71113502140574 & -0.0111350214057395 \tabularnewline
14 & 6.9 & 6.74385081315761 & 0.156149186842389 \tabularnewline
15 & 7 & 6.84267360080232 & 0.157326399197683 \tabularnewline
16 & 7 & 6.83647196152254 & 0.163528038477462 \tabularnewline
17 & 6.8 & 6.82372360053802 & -0.0237236005380234 \tabularnewline
18 & 6.4 & 6.45874649530701 & -0.0587464953070114 \tabularnewline
19 & 5.9 & 6.30961305178923 & -0.409613051789225 \tabularnewline
20 & 5.5 & 5.40408190271379 & 0.0959180972862084 \tabularnewline
21 & 5.5 & 5.27437657854869 & 0.225623421451313 \tabularnewline
22 & 5.6 & 5.49404017403709 & 0.105959825962906 \tabularnewline
23 & 5.8 & 5.72761421765841 & 0.0723857823415904 \tabularnewline
24 & 5.9 & 6.13283231793632 & -0.232832317936324 \tabularnewline
25 & 6.1 & 6.16246134378104 & -0.0624613437810437 \tabularnewline
26 & 6.1 & 6.17021784707372 & -0.0702178470737203 \tabularnewline
27 & 6 & 6.04604336685139 & -0.0460433668513886 \tabularnewline
28 & 6 & 5.87453735668746 & 0.125462643312543 \tabularnewline
29 & 5.9 & 5.97717478966871 & -0.0771747896687065 \tabularnewline
30 & 5.5 & 5.75254276686266 & -0.252542766862656 \tabularnewline
31 & 5.6 & 5.54571642636199 & 0.0542835736380142 \tabularnewline
32 & 5.4 & 5.48225577183631 & -0.0822557718363094 \tabularnewline
33 & 5.2 & 5.28708323062383 & -0.0870832306238308 \tabularnewline
34 & 5.2 & 5.11067086729655 & 0.0893291327034486 \tabularnewline
35 & 5.2 & 5.31928562245867 & -0.119285622458673 \tabularnewline
36 & 5.5 & 5.50150645486955 & -0.00150645486955109 \tabularnewline
37 & 5.8 & 5.93161258486685 & -0.131612584866852 \tabularnewline
38 & 5.8 & 5.96432837661872 & -0.164328376618725 \tabularnewline
39 & 5.5 & 5.78246099941351 & -0.28246099941351 \tabularnewline
40 & 5.3 & 5.33026482439966 & -0.0302648243996594 \tabularnewline
41 & 5.1 & 5.26759788649675 & -0.167597886496755 \tabularnewline
42 & 5.2 & 5.01800657523151 & 0.181993424768492 \tabularnewline
43 & 5.8 & 5.57059854383852 & 0.229401456161479 \tabularnewline
44 & 5.8 & 5.92039881652323 & -0.12039881652323 \tabularnewline
45 & 5.5 & 5.71745195524626 & -0.217451955246256 \tabularnewline
46 & 5 & 5.28530871552825 & -0.285308715528253 \tabularnewline
47 & 4.9 & 4.84989095554846 & 0.0501090444515413 \tabularnewline
48 & 5.3 & 5.18023119044879 & 0.119768809551209 \tabularnewline
49 & 6.1 & 5.80397415451703 & 0.296025845482965 \tabularnewline
50 & 6.5 & 6.48072246141082 & 0.019277538589177 \tabularnewline
51 & 6.8 & 6.57177092899104 & 0.228229071008965 \tabularnewline
52 & 6.6 & 6.73087366059541 & -0.130873660595411 \tabularnewline
53 & 6.4 & 6.32204934079521 & 0.0779506592047891 \tabularnewline
54 & 6.4 & 6.07245802952997 & 0.327541970470034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&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]6.5[/C][C]6.59081689542933[/C][C]-0.0908168954293293[/C][/ROW]
[ROW][C]2[/C][C]6.6[/C][C]6.54088050173912[/C][C]0.0591194982608785[/C][/ROW]
[ROW][C]3[/C][C]6.5[/C][C]6.55705110394175[/C][C]-0.0570511039417492[/C][/ROW]
[ROW][C]4[/C][C]6.2[/C][C]6.32785219679493[/C][C]-0.127852196794934[/C][/ROW]
[ROW][C]5[/C][C]6.2[/C][C]6.0094543825013[/C][C]0.190545617498696[/C][/ROW]
[ROW][C]6[/C][C]5.9[/C][C]6.09824613306886[/C][C]-0.198246133068860[/C][/ROW]
[ROW][C]7[/C][C]6.1[/C][C]5.97407197801027[/C][C]0.125928021989732[/C][/ROW]
[ROW][C]8[/C][C]6.1[/C][C]5.99326350892667[/C][C]0.106736491073331[/C][/ROW]
[ROW][C]9[/C][C]6.1[/C][C]6.02108823558123[/C][C]0.0789117644187742[/C][/ROW]
[ROW][C]10[/C][C]6.1[/C][C]6.0099802431381[/C][C]0.0900197568618983[/C][/ROW]
[ROW][C]11[/C][C]6.1[/C][C]6.10320920433446[/C][C]-0.00320920433445819[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]6.28543003674533[/C][C]0.114569963254665[/C][/ROW]
[ROW][C]13[/C][C]6.7[/C][C]6.71113502140574[/C][C]-0.0111350214057395[/C][/ROW]
[ROW][C]14[/C][C]6.9[/C][C]6.74385081315761[/C][C]0.156149186842389[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]6.84267360080232[/C][C]0.157326399197683[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]6.83647196152254[/C][C]0.163528038477462[/C][/ROW]
[ROW][C]17[/C][C]6.8[/C][C]6.82372360053802[/C][C]-0.0237236005380234[/C][/ROW]
[ROW][C]18[/C][C]6.4[/C][C]6.45874649530701[/C][C]-0.0587464953070114[/C][/ROW]
[ROW][C]19[/C][C]5.9[/C][C]6.30961305178923[/C][C]-0.409613051789225[/C][/ROW]
[ROW][C]20[/C][C]5.5[/C][C]5.40408190271379[/C][C]0.0959180972862084[/C][/ROW]
[ROW][C]21[/C][C]5.5[/C][C]5.27437657854869[/C][C]0.225623421451313[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]5.49404017403709[/C][C]0.105959825962906[/C][/ROW]
[ROW][C]23[/C][C]5.8[/C][C]5.72761421765841[/C][C]0.0723857823415904[/C][/ROW]
[ROW][C]24[/C][C]5.9[/C][C]6.13283231793632[/C][C]-0.232832317936324[/C][/ROW]
[ROW][C]25[/C][C]6.1[/C][C]6.16246134378104[/C][C]-0.0624613437810437[/C][/ROW]
[ROW][C]26[/C][C]6.1[/C][C]6.17021784707372[/C][C]-0.0702178470737203[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]6.04604336685139[/C][C]-0.0460433668513886[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]5.87453735668746[/C][C]0.125462643312543[/C][/ROW]
[ROW][C]29[/C][C]5.9[/C][C]5.97717478966871[/C][C]-0.0771747896687065[/C][/ROW]
[ROW][C]30[/C][C]5.5[/C][C]5.75254276686266[/C][C]-0.252542766862656[/C][/ROW]
[ROW][C]31[/C][C]5.6[/C][C]5.54571642636199[/C][C]0.0542835736380142[/C][/ROW]
[ROW][C]32[/C][C]5.4[/C][C]5.48225577183631[/C][C]-0.0822557718363094[/C][/ROW]
[ROW][C]33[/C][C]5.2[/C][C]5.28708323062383[/C][C]-0.0870832306238308[/C][/ROW]
[ROW][C]34[/C][C]5.2[/C][C]5.11067086729655[/C][C]0.0893291327034486[/C][/ROW]
[ROW][C]35[/C][C]5.2[/C][C]5.31928562245867[/C][C]-0.119285622458673[/C][/ROW]
[ROW][C]36[/C][C]5.5[/C][C]5.50150645486955[/C][C]-0.00150645486955109[/C][/ROW]
[ROW][C]37[/C][C]5.8[/C][C]5.93161258486685[/C][C]-0.131612584866852[/C][/ROW]
[ROW][C]38[/C][C]5.8[/C][C]5.96432837661872[/C][C]-0.164328376618725[/C][/ROW]
[ROW][C]39[/C][C]5.5[/C][C]5.78246099941351[/C][C]-0.28246099941351[/C][/ROW]
[ROW][C]40[/C][C]5.3[/C][C]5.33026482439966[/C][C]-0.0302648243996594[/C][/ROW]
[ROW][C]41[/C][C]5.1[/C][C]5.26759788649675[/C][C]-0.167597886496755[/C][/ROW]
[ROW][C]42[/C][C]5.2[/C][C]5.01800657523151[/C][C]0.181993424768492[/C][/ROW]
[ROW][C]43[/C][C]5.8[/C][C]5.57059854383852[/C][C]0.229401456161479[/C][/ROW]
[ROW][C]44[/C][C]5.8[/C][C]5.92039881652323[/C][C]-0.12039881652323[/C][/ROW]
[ROW][C]45[/C][C]5.5[/C][C]5.71745195524626[/C][C]-0.217451955246256[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]5.28530871552825[/C][C]-0.285308715528253[/C][/ROW]
[ROW][C]47[/C][C]4.9[/C][C]4.84989095554846[/C][C]0.0501090444515413[/C][/ROW]
[ROW][C]48[/C][C]5.3[/C][C]5.18023119044879[/C][C]0.119768809551209[/C][/ROW]
[ROW][C]49[/C][C]6.1[/C][C]5.80397415451703[/C][C]0.296025845482965[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]6.48072246141082[/C][C]0.019277538589177[/C][/ROW]
[ROW][C]51[/C][C]6.8[/C][C]6.57177092899104[/C][C]0.228229071008965[/C][/ROW]
[ROW][C]52[/C][C]6.6[/C][C]6.73087366059541[/C][C]-0.130873660595411[/C][/ROW]
[ROW][C]53[/C][C]6.4[/C][C]6.32204934079521[/C][C]0.0779506592047891[/C][/ROW]
[ROW][C]54[/C][C]6.4[/C][C]6.07245802952997[/C][C]0.327541970470034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.56.59081689542933-0.0908168954293293
26.66.540880501739120.0591194982608785
36.56.55705110394175-0.0570511039417492
46.26.32785219679493-0.127852196794934
56.26.00945438250130.190545617498696
65.96.09824613306886-0.198246133068860
76.15.974071978010270.125928021989732
86.15.993263508926670.106736491073331
96.16.021088235581230.0789117644187742
106.16.00998024313810.0900197568618983
116.16.10320920433446-0.00320920433445819
126.46.285430036745330.114569963254665
136.76.71113502140574-0.0111350214057395
146.96.743850813157610.156149186842389
1576.842673600802320.157326399197683
1676.836471961522540.163528038477462
176.86.82372360053802-0.0237236005380234
186.46.45874649530701-0.0587464953070114
195.96.30961305178923-0.409613051789225
205.55.404081902713790.0959180972862084
215.55.274376578548690.225623421451313
225.65.494040174037090.105959825962906
235.85.727614217658410.0723857823415904
245.96.13283231793632-0.232832317936324
256.16.16246134378104-0.0624613437810437
266.16.17021784707372-0.0702178470737203
2766.04604336685139-0.0460433668513886
2865.874537356687460.125462643312543
295.95.97717478966871-0.0771747896687065
305.55.75254276686266-0.252542766862656
315.65.545716426361990.0542835736380142
325.45.48225577183631-0.0822557718363094
335.25.28708323062383-0.0870832306238308
345.25.110670867296550.0893291327034486
355.25.31928562245867-0.119285622458673
365.55.50150645486955-0.00150645486955109
375.85.93161258486685-0.131612584866852
385.85.96432837661872-0.164328376618725
395.55.78246099941351-0.28246099941351
405.35.33026482439966-0.0302648243996594
415.15.26759788649675-0.167597886496755
425.25.018006575231510.181993424768492
435.85.570598543838520.229401456161479
445.85.92039881652323-0.12039881652323
455.55.71745195524626-0.217451955246256
4655.28530871552825-0.285308715528253
474.94.849890955548460.0501090444515413
485.35.180231190448790.119768809551209
496.15.803974154517030.296025845482965
506.56.480722461410820.019277538589177
516.86.571770928991040.228229071008965
526.66.73087366059541-0.130873660595411
536.46.322049340795210.0779506592047891
546.46.072458029529970.327541970470034






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7957838845802080.4084322308395850.204216115419792
200.6823485347365790.6353029305268430.317651465263421
210.6859410399155830.6281179201688350.314058960084417
220.7097046103577580.5805907792844850.290295389642242
230.7535954695926150.4928090608147690.246404530407384
240.830896364003240.3382072719935220.169103635996761
250.745429560071170.5091408798576590.254570439928830
260.6968985117924780.6062029764150450.303101488207523
270.6040226305470260.7919547389059480.395977369452974
280.7173949188210080.5652101623579830.282605081178992
290.7601235373241820.4797529253516360.239876462675818
300.7306422701468950.538715459706210.269357729853105
310.717149982597520.5657000348049580.282850017402479
320.6655879243234120.6688241513531760.334412075676588
330.6005383821651160.7989232356697680.399461617834884
340.6447556598176140.7104886803647710.355244340182386
350.4744736867783450.948947373556690.525526313221655

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.795783884580208 & 0.408432230839585 & 0.204216115419792 \tabularnewline
20 & 0.682348534736579 & 0.635302930526843 & 0.317651465263421 \tabularnewline
21 & 0.685941039915583 & 0.628117920168835 & 0.314058960084417 \tabularnewline
22 & 0.709704610357758 & 0.580590779284485 & 0.290295389642242 \tabularnewline
23 & 0.753595469592615 & 0.492809060814769 & 0.246404530407384 \tabularnewline
24 & 0.83089636400324 & 0.338207271993522 & 0.169103635996761 \tabularnewline
25 & 0.74542956007117 & 0.509140879857659 & 0.254570439928830 \tabularnewline
26 & 0.696898511792478 & 0.606202976415045 & 0.303101488207523 \tabularnewline
27 & 0.604022630547026 & 0.791954738905948 & 0.395977369452974 \tabularnewline
28 & 0.717394918821008 & 0.565210162357983 & 0.282605081178992 \tabularnewline
29 & 0.760123537324182 & 0.479752925351636 & 0.239876462675818 \tabularnewline
30 & 0.730642270146895 & 0.53871545970621 & 0.269357729853105 \tabularnewline
31 & 0.71714998259752 & 0.565700034804958 & 0.282850017402479 \tabularnewline
32 & 0.665587924323412 & 0.668824151353176 & 0.334412075676588 \tabularnewline
33 & 0.600538382165116 & 0.798923235669768 & 0.399461617834884 \tabularnewline
34 & 0.644755659817614 & 0.710488680364771 & 0.355244340182386 \tabularnewline
35 & 0.474473686778345 & 0.94894737355669 & 0.525526313221655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&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]19[/C][C]0.795783884580208[/C][C]0.408432230839585[/C][C]0.204216115419792[/C][/ROW]
[ROW][C]20[/C][C]0.682348534736579[/C][C]0.635302930526843[/C][C]0.317651465263421[/C][/ROW]
[ROW][C]21[/C][C]0.685941039915583[/C][C]0.628117920168835[/C][C]0.314058960084417[/C][/ROW]
[ROW][C]22[/C][C]0.709704610357758[/C][C]0.580590779284485[/C][C]0.290295389642242[/C][/ROW]
[ROW][C]23[/C][C]0.753595469592615[/C][C]0.492809060814769[/C][C]0.246404530407384[/C][/ROW]
[ROW][C]24[/C][C]0.83089636400324[/C][C]0.338207271993522[/C][C]0.169103635996761[/C][/ROW]
[ROW][C]25[/C][C]0.74542956007117[/C][C]0.509140879857659[/C][C]0.254570439928830[/C][/ROW]
[ROW][C]26[/C][C]0.696898511792478[/C][C]0.606202976415045[/C][C]0.303101488207523[/C][/ROW]
[ROW][C]27[/C][C]0.604022630547026[/C][C]0.791954738905948[/C][C]0.395977369452974[/C][/ROW]
[ROW][C]28[/C][C]0.717394918821008[/C][C]0.565210162357983[/C][C]0.282605081178992[/C][/ROW]
[ROW][C]29[/C][C]0.760123537324182[/C][C]0.479752925351636[/C][C]0.239876462675818[/C][/ROW]
[ROW][C]30[/C][C]0.730642270146895[/C][C]0.53871545970621[/C][C]0.269357729853105[/C][/ROW]
[ROW][C]31[/C][C]0.71714998259752[/C][C]0.565700034804958[/C][C]0.282850017402479[/C][/ROW]
[ROW][C]32[/C][C]0.665587924323412[/C][C]0.668824151353176[/C][C]0.334412075676588[/C][/ROW]
[ROW][C]33[/C][C]0.600538382165116[/C][C]0.798923235669768[/C][C]0.399461617834884[/C][/ROW]
[ROW][C]34[/C][C]0.644755659817614[/C][C]0.710488680364771[/C][C]0.355244340182386[/C][/ROW]
[ROW][C]35[/C][C]0.474473686778345[/C][C]0.94894737355669[/C][C]0.525526313221655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7957838845802080.4084322308395850.204216115419792
200.6823485347365790.6353029305268430.317651465263421
210.6859410399155830.6281179201688350.314058960084417
220.7097046103577580.5805907792844850.290295389642242
230.7535954695926150.4928090608147690.246404530407384
240.830896364003240.3382072719935220.169103635996761
250.745429560071170.5091408798576590.254570439928830
260.6968985117924780.6062029764150450.303101488207523
270.6040226305470260.7919547389059480.395977369452974
280.7173949188210080.5652101623579830.282605081178992
290.7601235373241820.4797529253516360.239876462675818
300.7306422701468950.538715459706210.269357729853105
310.717149982597520.5657000348049580.282850017402479
320.6655879243234120.6688241513531760.334412075676588
330.6005383821651160.7989232356697680.399461617834884
340.6447556598176140.7104886803647710.355244340182386
350.4744736867783450.948947373556690.525526313221655






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60588&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60588&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60588&T=6

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

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


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}