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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 computationMon, 13 Dec 2010 19:52:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292269844xlhq0cvl7tx67kh.htm/, Retrieved Mon, 06 May 2024 11:08:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109128, Retrieved Mon, 06 May 2024 11:08:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Pearsons Corr Mam...] [2010-12-09 13:00:40] [6ca0fc48dd5333d51a15728999009c83]
- RMPD  [Multiple Regression] [Bonus: MR SWS] [2010-12-11 15:01:53] [6ca0fc48dd5333d51a15728999009c83]
-    D    [Multiple Regression] [Bonus: MR SWS 2 var] [2010-12-11 15:29:26] [6ca0fc48dd5333d51a15728999009c83]
-    D      [Multiple Regression] [Bonus: MR SWS cor...] [2010-12-13 19:20:15] [6ca0fc48dd5333d51a15728999009c83]
-    D        [Multiple Regression] [Bonus: MR PS correct] [2010-12-13 19:32:39] [6ca0fc48dd5333d51a15728999009c83]
-    D            [Multiple Regression] [Bonus: MR totaalm...] [2010-12-13 19:52:07] [b4ba846736d082ffaee409a197f454c7] [Current]
-    D              [Multiple Regression] [Bonus: MR SWS tot...] [2010-12-13 19:58:24] [6ca0fc48dd5333d51a15728999009c83]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6,30000000	0,30103000	0,65321251	0,00000000	0,81954394	1,62324929	3	1	3
2,10000000	0,25527251	1,83884909	3,40602894	3,66304097	2,79518459	3	5	4
9,10000000	-0,15490196	1,43136376	1,02325246	2,25406445	2,25527251	4	4	4
15,80000000	0,59106461	1,27875360	-1,63827216	-0,52287875	1,54406804	1	1	1
5,20000000	0,00000000	1,48287358	2,20411998	2,22788670	2,59328607	4	5	4
10,90000000	0,55630250	1,44715803	0,51851394	1,40823997	1,79934055	1	2	1
8,30000000	0,14612804	1,69897000	1,71733758	2,64345268	2,36172784	1	1	1
11,00000000	0,17609126	0,84509804	-0,37161107	0,80617997	2,04921802	5	4	4
3,20000000	-0,15490196	1,47712125	2,66745295	2,62634037	2,44870632	5	5	5
6,30000000	0,32221929	0,54406804	-1,12493874	0,07918125	1,62324929	1	1	1
6,60000000	0,61278386	0,77815125	-0,10513034	0,54406804	1,62324929	2	2	2
9,50000000	0,07918125	1,01703334	-0,69897000	0,69897000	2,07918125	2	2	2
3,30000000	-0,30103000	1,30103000	1,44185218	2,06069784	2,17026172	5	5	5
11,00000000	0,53147892	0,59106461	-0,92081875	0,00000000	1,20411998	3	1	2
4,70000000	0,17609126	1,61278386	1,92941893	2,51188336	2,49136169	1	3	1
10,40000000	0,53147892	0,95424251	-0,99567863	0,60205999	1,44715803	5	1	3
7,40000000	-0,09691001	0,88081359	0,01703334	0,74036269	1,83250891	5	3	4
2,10000000	-0,09691001	1,66275783	2,71683772	2,81624130	2,52633928	5	5	5
17,90000000	0,30103000	1,38021124	-2,00000000	-0,60205999	1,69897000	1	1	1
6,10000000	0,27875360	2,00000000	1,79239169	3,12057393	2,42651126	1	1	1
11,90000000	0,11394335	0,50514998	-1,63827216	-0,39794001	1,27875360	4	1	3
13,80000000	0,74818803	0,69897000	0,23044892	0,79934055	1,07918125	2	1	1
14,30000000	0,49136169	0,81291336	0,54406804	1,03342376	2,07918125	2	1	1
15,20000000	0,25527251	1,07918125	-0,31875876	1,19033170	2,14612804	2	2	2
10,00000000	-0,04575749	1,30535137	1,00000000	2,06069784	2,23044892	4	4	4
11,90000000	0,25527251	1,11394335	0,20951501	1,05690485	1,23044892	2	1	2
6,50000000	0,27875360	1,43136376	2,28330123	2,25527251	2,06069784	4	4	4
7,50000000	-0,04575749	1,25527251	0,39794001	1,08278537	1,49136169	5	5	5
10,60000000	0,41497335	0,67209786	-0,55284197	0,27875360	1,32221929	3	1	3
7,40000000	0,38021124	0,99122608	0,62685341	1,70243054	1,71600334	1	1	1
8,40000000	0,07918125	1,46239800	0,83250891	2,25285303	2,21484385	2	3	2
5,70000000	-0,04575749	0,84509804	-0,12493874	1,08990511	2,35218252	2	2	2
4,90000000	-0,30103000	0,77815125	0,55630250	1,32221929	2,35218252	3	2	3
3,20000000	-0,22184875	1,30103000	1,74429298	2,24303805	2,17897695	5	5	5
11,00000000	0,36172784	0,65321251	-0,04575749	0,41497335	1,77815125	2	1	2
4,90000000	-0,30103000	0,87506126	0,30103000	1,08990511	2,30103000	3	1	3
13,20000000	0,41497335	0,36172784	-0,98296666	0,39794001	1,66275783	3	2	2
9,70000000	-0,22184875	1,38021124	0,62221402	1,76342799	2,32221929	4	3	4
12,80000000	0,81954394	0,47712125	0,54406804	0,59106461	1,14612804	2	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 7.1951928995231 + 3.36679479806534logPS[t] + 3.43730613389774LogL[t] -1.65097896957639LogWb[t] -0.880440616713212LogWbr[t] -0.315657161277412LogTg[t] + 1.33733450509432P[t] + 0.31497942209904S[t] -1.88373963984934D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  7.1951928995231 +  3.36679479806534logPS[t] +  3.43730613389774LogL[t] -1.65097896957639LogWb[t] -0.880440616713212LogWbr[t] -0.315657161277412LogTg[t] +  1.33733450509432P[t] +  0.31497942209904S[t] -1.88373963984934D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  7.1951928995231 +  3.36679479806534logPS[t] +  3.43730613389774LogL[t] -1.65097896957639LogWb[t] -0.880440616713212LogWbr[t] -0.315657161277412LogTg[t] +  1.33733450509432P[t] +  0.31497942209904S[t] -1.88373963984934D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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
SWS[t] = + 7.1951928995231 + 3.36679479806534logPS[t] + 3.43730613389774LogL[t] -1.65097896957639LogWb[t] -0.880440616713212LogWbr[t] -0.315657161277412LogTg[t] + 1.33733450509432P[t] + 0.31497942209904S[t] -1.88373963984934D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.19519289952314.5167291.5930.1216410.060821
logPS3.366794798065342.7451371.22650.2295660.114783
LogL3.437306133897741.7820171.92890.0632540.031627
LogWb-1.650978969576391.157343-1.42650.1640420.082021
LogWbr-0.8804406167132121.620254-0.54340.5908720.295436
LogTg-0.3156571612774121.911325-0.16520.8699330.434967
P1.337334505094320.9937341.34580.1884610.09423
S0.314979422099040.6260660.50310.6185620.309281
D-1.883739639849341.344971-1.40060.1715990.085799

\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) & 7.1951928995231 & 4.516729 & 1.593 & 0.121641 & 0.060821 \tabularnewline
logPS & 3.36679479806534 & 2.745137 & 1.2265 & 0.229566 & 0.114783 \tabularnewline
LogL & 3.43730613389774 & 1.782017 & 1.9289 & 0.063254 & 0.031627 \tabularnewline
LogWb & -1.65097896957639 & 1.157343 & -1.4265 & 0.164042 & 0.082021 \tabularnewline
LogWbr & -0.880440616713212 & 1.620254 & -0.5434 & 0.590872 & 0.295436 \tabularnewline
LogTg & -0.315657161277412 & 1.911325 & -0.1652 & 0.869933 & 0.434967 \tabularnewline
P & 1.33733450509432 & 0.993734 & 1.3458 & 0.188461 & 0.09423 \tabularnewline
S & 0.31497942209904 & 0.626066 & 0.5031 & 0.618562 & 0.309281 \tabularnewline
D & -1.88373963984934 & 1.344971 & -1.4006 & 0.171599 & 0.085799 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&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]7.1951928995231[/C][C]4.516729[/C][C]1.593[/C][C]0.121641[/C][C]0.060821[/C][/ROW]
[ROW][C]logPS[/C][C]3.36679479806534[/C][C]2.745137[/C][C]1.2265[/C][C]0.229566[/C][C]0.114783[/C][/ROW]
[ROW][C]LogL[/C][C]3.43730613389774[/C][C]1.782017[/C][C]1.9289[/C][C]0.063254[/C][C]0.031627[/C][/ROW]
[ROW][C]LogWb[/C][C]-1.65097896957639[/C][C]1.157343[/C][C]-1.4265[/C][C]0.164042[/C][C]0.082021[/C][/ROW]
[ROW][C]LogWbr[/C][C]-0.880440616713212[/C][C]1.620254[/C][C]-0.5434[/C][C]0.590872[/C][C]0.295436[/C][/ROW]
[ROW][C]LogTg[/C][C]-0.315657161277412[/C][C]1.911325[/C][C]-0.1652[/C][C]0.869933[/C][C]0.434967[/C][/ROW]
[ROW][C]P[/C][C]1.33733450509432[/C][C]0.993734[/C][C]1.3458[/C][C]0.188461[/C][C]0.09423[/C][/ROW]
[ROW][C]S[/C][C]0.31497942209904[/C][C]0.626066[/C][C]0.5031[/C][C]0.618562[/C][C]0.309281[/C][/ROW]
[ROW][C]D[/C][C]-1.88373963984934[/C][C]1.344971[/C][C]-1.4006[/C][C]0.171599[/C][C]0.085799[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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)7.19519289952314.5167291.5930.1216410.060821
logPS3.366794798065342.7451371.22650.2295660.114783
LogL3.437306133897741.7820171.92890.0632540.031627
LogWb-1.650978969576391.157343-1.42650.1640420.082021
LogWbr-0.8804406167132121.620254-0.54340.5908720.295436
LogTg-0.3156571612774121.911325-0.16520.8699330.434967
P1.337334505094320.9937341.34580.1884610.09423
S0.314979422099040.6260660.50310.6185620.309281
D-1.883739639849341.344971-1.40060.1715990.085799






Multiple Linear Regression - Regression Statistics
Multiple R0.827535230476664
R-squared0.684814557680065
Adjusted R-squared0.600765106394749
F-TEST (value)8.14775762610727
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value8.60521084933286e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50737645171981
Sum Squared Residuals188.608100119170

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.827535230476664 \tabularnewline
R-squared & 0.684814557680065 \tabularnewline
Adjusted R-squared & 0.600765106394749 \tabularnewline
F-TEST (value) & 8.14775762610727 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 8.60521084933286e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.50737645171981 \tabularnewline
Sum Squared Residuals & 188.608100119170 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.827535230476664[/C][/ROW]
[ROW][C]R-squared[/C][C]0.684814557680065[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.600765106394749[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.14775762610727[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]8.60521084933286e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.50737645171981[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]188.608100119170[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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 R0.827535230476664
R-squared0.684814557680065
Adjusted R-squared0.600765106394749
F-TEST (value)8.14775762610727
F-TEST (DF numerator)8
F-TEST (DF denominator)30
p-value8.60521084933286e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50737645171981
Sum Squared Residuals188.608100119170






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.37.8958044878963-1.5958044878963
22.12.69658014772338-0.596580147723377
39.16.282171262937772.81782873706223
415.816.0269484715526-0.226948471552595
55.25.26249293057238-0.0624929305723761
610.911.4621261371318-0.56212613713175
78.37.387442672340010.912557327659991
81110.36142649721470.638573502785344
93.23.104662012621410.0953379873785867
106.311.1938873777178-4.89388737771781
116.610.6523611679286-4.05236116792863
129.510.3770581741205-0.877058174120512
133.34.61674814027528-1.31674814027528
141112.7159103261441-1.71591032614409
154.77.54681065354392-2.84681065354392
1610.414.2719906375552-3.87199063755515
177.48.73478334003063-1.33478334003063
182.13.66476365493473-1.56476365493473
1917.916.01722594700661.88277405299342
206.18.30425894808346-2.20425894808346
2111.911.9797381243703-0.0797381243703365
2213.811.79778163058442.00221836941558
2314.310.28522615668214.01477384331789
2415.210.10207491142345.09792508857664
25106.432968019440433.56703198055957
2611.99.440929674192952.45907032580705
276.55.722241789484550.777758210515445
287.58.11768635117774-0.617686351177743
2910.69.82822961703690.771770382963105
307.48.5755283622153-1.17552836221530
318.48.283528196658160.116471803341846
325.77.98733731949211-2.28733731949211
334.95.02211161370968-0.122111613709682
343.24.22072100883204-1.02072100883204
35119.029415915101431.97058408489857
364.95.68237659159673-0.782376591596732
3713.211.45780873778071.74219126221931
389.76.638918933083883.06108106691612
3912.810.91992405980611.88007594019387

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 7.8958044878963 & -1.5958044878963 \tabularnewline
2 & 2.1 & 2.69658014772338 & -0.596580147723377 \tabularnewline
3 & 9.1 & 6.28217126293777 & 2.81782873706223 \tabularnewline
4 & 15.8 & 16.0269484715526 & -0.226948471552595 \tabularnewline
5 & 5.2 & 5.26249293057238 & -0.0624929305723761 \tabularnewline
6 & 10.9 & 11.4621261371318 & -0.56212613713175 \tabularnewline
7 & 8.3 & 7.38744267234001 & 0.912557327659991 \tabularnewline
8 & 11 & 10.3614264972147 & 0.638573502785344 \tabularnewline
9 & 3.2 & 3.10466201262141 & 0.0953379873785867 \tabularnewline
10 & 6.3 & 11.1938873777178 & -4.89388737771781 \tabularnewline
11 & 6.6 & 10.6523611679286 & -4.05236116792863 \tabularnewline
12 & 9.5 & 10.3770581741205 & -0.877058174120512 \tabularnewline
13 & 3.3 & 4.61674814027528 & -1.31674814027528 \tabularnewline
14 & 11 & 12.7159103261441 & -1.71591032614409 \tabularnewline
15 & 4.7 & 7.54681065354392 & -2.84681065354392 \tabularnewline
16 & 10.4 & 14.2719906375552 & -3.87199063755515 \tabularnewline
17 & 7.4 & 8.73478334003063 & -1.33478334003063 \tabularnewline
18 & 2.1 & 3.66476365493473 & -1.56476365493473 \tabularnewline
19 & 17.9 & 16.0172259470066 & 1.88277405299342 \tabularnewline
20 & 6.1 & 8.30425894808346 & -2.20425894808346 \tabularnewline
21 & 11.9 & 11.9797381243703 & -0.0797381243703365 \tabularnewline
22 & 13.8 & 11.7977816305844 & 2.00221836941558 \tabularnewline
23 & 14.3 & 10.2852261566821 & 4.01477384331789 \tabularnewline
24 & 15.2 & 10.1020749114234 & 5.09792508857664 \tabularnewline
25 & 10 & 6.43296801944043 & 3.56703198055957 \tabularnewline
26 & 11.9 & 9.44092967419295 & 2.45907032580705 \tabularnewline
27 & 6.5 & 5.72224178948455 & 0.777758210515445 \tabularnewline
28 & 7.5 & 8.11768635117774 & -0.617686351177743 \tabularnewline
29 & 10.6 & 9.8282296170369 & 0.771770382963105 \tabularnewline
30 & 7.4 & 8.5755283622153 & -1.17552836221530 \tabularnewline
31 & 8.4 & 8.28352819665816 & 0.116471803341846 \tabularnewline
32 & 5.7 & 7.98733731949211 & -2.28733731949211 \tabularnewline
33 & 4.9 & 5.02211161370968 & -0.122111613709682 \tabularnewline
34 & 3.2 & 4.22072100883204 & -1.02072100883204 \tabularnewline
35 & 11 & 9.02941591510143 & 1.97058408489857 \tabularnewline
36 & 4.9 & 5.68237659159673 & -0.782376591596732 \tabularnewline
37 & 13.2 & 11.4578087377807 & 1.74219126221931 \tabularnewline
38 & 9.7 & 6.63891893308388 & 3.06108106691612 \tabularnewline
39 & 12.8 & 10.9199240598061 & 1.88007594019387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&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]6.3[/C][C]7.8958044878963[/C][C]-1.5958044878963[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]2.69658014772338[/C][C]-0.596580147723377[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.28217126293777[/C][C]2.81782873706223[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]16.0269484715526[/C][C]-0.226948471552595[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]5.26249293057238[/C][C]-0.0624929305723761[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.4621261371318[/C][C]-0.56212613713175[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.38744267234001[/C][C]0.912557327659991[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.3614264972147[/C][C]0.638573502785344[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.10466201262141[/C][C]0.0953379873785867[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.1938873777178[/C][C]-4.89388737771781[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]10.6523611679286[/C][C]-4.05236116792863[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]10.3770581741205[/C][C]-0.877058174120512[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]4.61674814027528[/C][C]-1.31674814027528[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]12.7159103261441[/C][C]-1.71591032614409[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]7.54681065354392[/C][C]-2.84681065354392[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]14.2719906375552[/C][C]-3.87199063755515[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]8.73478334003063[/C][C]-1.33478334003063[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]3.66476365493473[/C][C]-1.56476365493473[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]16.0172259470066[/C][C]1.88277405299342[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]8.30425894808346[/C][C]-2.20425894808346[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]11.9797381243703[/C][C]-0.0797381243703365[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.7977816305844[/C][C]2.00221836941558[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]10.2852261566821[/C][C]4.01477384331789[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]10.1020749114234[/C][C]5.09792508857664[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.43296801944043[/C][C]3.56703198055957[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]9.44092967419295[/C][C]2.45907032580705[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]5.72224178948455[/C][C]0.777758210515445[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]8.11768635117774[/C][C]-0.617686351177743[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]9.8282296170369[/C][C]0.771770382963105[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]8.5755283622153[/C][C]-1.17552836221530[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.28352819665816[/C][C]0.116471803341846[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]7.98733731949211[/C][C]-2.28733731949211[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]5.02211161370968[/C][C]-0.122111613709682[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]4.22072100883204[/C][C]-1.02072100883204[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]9.02941591510143[/C][C]1.97058408489857[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]5.68237659159673[/C][C]-0.782376591596732[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.4578087377807[/C][C]1.74219126221931[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]6.63891893308388[/C][C]3.06108106691612[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]10.9199240598061[/C][C]1.88007594019387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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
16.37.8958044878963-1.5958044878963
22.12.69658014772338-0.596580147723377
39.16.282171262937772.81782873706223
415.816.0269484715526-0.226948471552595
55.25.26249293057238-0.0624929305723761
610.911.4621261371318-0.56212613713175
78.37.387442672340010.912557327659991
81110.36142649721470.638573502785344
93.23.104662012621410.0953379873785867
106.311.1938873777178-4.89388737771781
116.610.6523611679286-4.05236116792863
129.510.3770581741205-0.877058174120512
133.34.61674814027528-1.31674814027528
141112.7159103261441-1.71591032614409
154.77.54681065354392-2.84681065354392
1610.414.2719906375552-3.87199063755515
177.48.73478334003063-1.33478334003063
182.13.66476365493473-1.56476365493473
1917.916.01722594700661.88277405299342
206.18.30425894808346-2.20425894808346
2111.911.9797381243703-0.0797381243703365
2213.811.79778163058442.00221836941558
2314.310.28522615668214.01477384331789
2415.210.10207491142345.09792508857664
25106.432968019440433.56703198055957
2611.99.440929674192952.45907032580705
276.55.722241789484550.777758210515445
287.58.11768635117774-0.617686351177743
2910.69.82822961703690.771770382963105
307.48.5755283622153-1.17552836221530
318.48.283528196658160.116471803341846
325.77.98733731949211-2.28733731949211
334.95.02211161370968-0.122111613709682
343.24.22072100883204-1.02072100883204
35119.029415915101431.97058408489857
364.95.68237659159673-0.782376591596732
3713.211.45780873778071.74219126221931
389.76.638918933083883.06108106691612
3912.810.91992405980611.88007594019387






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.02971583101868750.0594316620373750.970284168981313
130.06056893804769240.1211378760953850.939431061952308
140.02477397931107530.04954795862215050.975226020688925
150.01517949431954810.03035898863909620.984820505680452
160.450376719546940.900753439093880.54962328045306
170.3420661351949970.6841322703899950.657933864805003
180.3294196138706770.6588392277413530.670580386129323
190.2292751686065900.4585503372131790.77072483139341
200.46363192857550.9272638571510.5363680714245
210.4895874825929150.979174965185830.510412517407085
220.6222292705109060.7555414589781870.377770729489094
230.7270304646897390.5459390706205210.272969535310261
240.8444701626445190.3110596747109620.155529837355481
250.9162012656488760.1675974687022480.083798734351124
260.8841106977207850.2317786045584310.115889302279215
270.806265654321640.3874686913567190.193734345678359

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0297158310186875 & 0.059431662037375 & 0.970284168981313 \tabularnewline
13 & 0.0605689380476924 & 0.121137876095385 & 0.939431061952308 \tabularnewline
14 & 0.0247739793110753 & 0.0495479586221505 & 0.975226020688925 \tabularnewline
15 & 0.0151794943195481 & 0.0303589886390962 & 0.984820505680452 \tabularnewline
16 & 0.45037671954694 & 0.90075343909388 & 0.54962328045306 \tabularnewline
17 & 0.342066135194997 & 0.684132270389995 & 0.657933864805003 \tabularnewline
18 & 0.329419613870677 & 0.658839227741353 & 0.670580386129323 \tabularnewline
19 & 0.229275168606590 & 0.458550337213179 & 0.77072483139341 \tabularnewline
20 & 0.4636319285755 & 0.927263857151 & 0.5363680714245 \tabularnewline
21 & 0.489587482592915 & 0.97917496518583 & 0.510412517407085 \tabularnewline
22 & 0.622229270510906 & 0.755541458978187 & 0.377770729489094 \tabularnewline
23 & 0.727030464689739 & 0.545939070620521 & 0.272969535310261 \tabularnewline
24 & 0.844470162644519 & 0.311059674710962 & 0.155529837355481 \tabularnewline
25 & 0.916201265648876 & 0.167597468702248 & 0.083798734351124 \tabularnewline
26 & 0.884110697720785 & 0.231778604558431 & 0.115889302279215 \tabularnewline
27 & 0.80626565432164 & 0.387468691356719 & 0.193734345678359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&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]12[/C][C]0.0297158310186875[/C][C]0.059431662037375[/C][C]0.970284168981313[/C][/ROW]
[ROW][C]13[/C][C]0.0605689380476924[/C][C]0.121137876095385[/C][C]0.939431061952308[/C][/ROW]
[ROW][C]14[/C][C]0.0247739793110753[/C][C]0.0495479586221505[/C][C]0.975226020688925[/C][/ROW]
[ROW][C]15[/C][C]0.0151794943195481[/C][C]0.0303589886390962[/C][C]0.984820505680452[/C][/ROW]
[ROW][C]16[/C][C]0.45037671954694[/C][C]0.90075343909388[/C][C]0.54962328045306[/C][/ROW]
[ROW][C]17[/C][C]0.342066135194997[/C][C]0.684132270389995[/C][C]0.657933864805003[/C][/ROW]
[ROW][C]18[/C][C]0.329419613870677[/C][C]0.658839227741353[/C][C]0.670580386129323[/C][/ROW]
[ROW][C]19[/C][C]0.229275168606590[/C][C]0.458550337213179[/C][C]0.77072483139341[/C][/ROW]
[ROW][C]20[/C][C]0.4636319285755[/C][C]0.927263857151[/C][C]0.5363680714245[/C][/ROW]
[ROW][C]21[/C][C]0.489587482592915[/C][C]0.97917496518583[/C][C]0.510412517407085[/C][/ROW]
[ROW][C]22[/C][C]0.622229270510906[/C][C]0.755541458978187[/C][C]0.377770729489094[/C][/ROW]
[ROW][C]23[/C][C]0.727030464689739[/C][C]0.545939070620521[/C][C]0.272969535310261[/C][/ROW]
[ROW][C]24[/C][C]0.844470162644519[/C][C]0.311059674710962[/C][C]0.155529837355481[/C][/ROW]
[ROW][C]25[/C][C]0.916201265648876[/C][C]0.167597468702248[/C][C]0.083798734351124[/C][/ROW]
[ROW][C]26[/C][C]0.884110697720785[/C][C]0.231778604558431[/C][C]0.115889302279215[/C][/ROW]
[ROW][C]27[/C][C]0.80626565432164[/C][C]0.387468691356719[/C][C]0.193734345678359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&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
120.02971583101868750.0594316620373750.970284168981313
130.06056893804769240.1211378760953850.939431061952308
140.02477397931107530.04954795862215050.975226020688925
150.01517949431954810.03035898863909620.984820505680452
160.450376719546940.900753439093880.54962328045306
170.3420661351949970.6841322703899950.657933864805003
180.3294196138706770.6588392277413530.670580386129323
190.2292751686065900.4585503372131790.77072483139341
200.46363192857550.9272638571510.5363680714245
210.4895874825929150.979174965185830.510412517407085
220.6222292705109060.7555414589781870.377770729489094
230.7270304646897390.5459390706205210.272969535310261
240.8444701626445190.3110596747109620.155529837355481
250.9162012656488760.1675974687022480.083798734351124
260.8841106977207850.2317786045584310.115889302279215
270.806265654321640.3874686913567190.193734345678359






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.125NOK
10% type I error level30.1875NOK

\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 & 2 & 0.125 & NOK \tabularnewline
10% type I error level & 3 & 0.1875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109128&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]2[/C][C]0.125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.1875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109128&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109128&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.125NOK
10% type I error level30.1875NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}