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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 11:33:48 -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/t1258742440ppytnwgw8ezac04.htm/, Retrieved Fri, 29 Mar 2024 10:47:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58407, Retrieved Fri, 29 Mar 2024 10:47:39 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
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] [] [2009-11-20 18:33:48] [d1856923bab8a0db5ebd860815c7444f] [Current]
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Dataseries X:
0,2	1	0,6	0	1,9	3,2
0,9	1	0,2	0,6	0	1,9
2,4	1	0,9	0,2	0,6	0
4,7	1	2,4	0,9	0,2	0,6
9,4	1	4,7	2,4	0,9	0,2
12,5	1	9,4	4,7	2,4	0,9
15,8	1	12,5	9,4	4,7	2,4
18,2	1	15,8	12,5	9,4	4,7
16,8	0	18,2	15,8	12,5	9,4
17,3	0	16,8	18,2	15,8	12,5
19,3	0	17,3	16,8	18,2	15,8
17,9	0	19,3	17,3	16,8	18,2
20,2	0	17,9	19,3	17,3	16,8
18,7	0	20,2	17,9	19,3	17,3
20,1	0	18,7	20,2	17,9	19,3
18,2	0	20,1	18,7	20,2	17,9
18,4	0	18,2	20,1	18,7	20,2
18,2	0	18,4	18,2	20,1	18,7
18,9	0	18,2	18,4	18,2	20,1
19,9	0	18,9	18,2	18,4	18,2
21,3	0	19,9	18,9	18,2	18,4
20	0	21,3	19,9	18,9	18,2
19,5	0	20	21,3	19,9	18,9
19,6	0	19,5	20	21,3	19,9
20,9	0	19,6	19,5	20	21,3
21	0	20,9	19,6	19,5	20
19,9	0	21	20,9	19,6	19,5
19,6	0	19,9	21	20,9	19,6
20,9	0	19,6	19,9	21	20,9
21,7	0	20,9	19,6	19,9	21
22,9	0	21,7	20,9	19,6	19,9
21,5	0	22,9	21,7	20,9	19,6
21,3	0	21,5	22,9	21,7	20,9
23,5	0	21,3	21,5	22,9	21,7
21,6	0	23,5	21,3	21,5	22,9
24,5	0	21,6	23,5	21,3	21,5
22,2	0	24,5	21,6	23,5	21,3
23,5	0	22,2	24,5	21,6	23,5
20,9	0	23,5	22,2	24,5	21,6
20,7	0	20,9	23,5	22,2	24,5
18,1	0	20,7	20,9	23,5	22,2
17,1	0	18,1	20,7	20,9	23,5
14,8	0	17,1	18,1	20,7	20,9
13,8	0	14,8	17,1	18,1	20,7
15,2	0	13,8	14,8	17,1	18,1
16	0	15,2	13,8	14,8	17,1
17,6	0	16	15,2	13,8	14,8
15	0	17,6	16	15,2	13,8
15	0	15	17,6	16	15,2
16,3	0	15	15	17,6	16
19,4	0	16,3	15	15	17,6
21,3	0	19,4	16,3	15	15
20,5	0	21,3	19,4	16,3	15
21,1	0	20,5	21,3	19,4	16,3
21,6	0	21,1	20,5	21,3	19,4
22,6	0	21,6	21,1	20,5	21,3




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=58407&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=58407&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.519647105611932 + 1.28632287139419X[t] + 0.92813262756989Y1[t] + 0.336764561898729Y2[t] -0.439355918249917Y3[t] + 0.115147021620269Y4[t] + 0.182944690656144M1[t] + 0.332354786967214M2[t] + 0.351787973684101M3[t] + 0.236941355263420M4[t] + 0.445899792676542M5[t] + 0.6150272299015M6[t] + 0.59084113843511M7[t] + 0.333801824039458M8[t] + 0.49230003939364M9[t] + 0.912247008553877M10[t] + 0.618871180476981M11[t] + 0.00823090323162158t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.519647105611932 +  1.28632287139419X[t] +  0.92813262756989Y1[t] +  0.336764561898729Y2[t] -0.439355918249917Y3[t] +  0.115147021620269Y4[t] +  0.182944690656144M1[t] +  0.332354786967214M2[t] +  0.351787973684101M3[t] +  0.236941355263420M4[t] +  0.445899792676542M5[t] +  0.6150272299015M6[t] +  0.59084113843511M7[t] +  0.333801824039458M8[t] +  0.49230003939364M9[t] +  0.912247008553877M10[t] +  0.618871180476981M11[t] +  0.00823090323162158t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58407&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.519647105611932 +  1.28632287139419X[t] +  0.92813262756989Y1[t] +  0.336764561898729Y2[t] -0.439355918249917Y3[t] +  0.115147021620269Y4[t] +  0.182944690656144M1[t] +  0.332354786967214M2[t] +  0.351787973684101M3[t] +  0.236941355263420M4[t] +  0.445899792676542M5[t] +  0.6150272299015M6[t] +  0.59084113843511M7[t] +  0.333801824039458M8[t] +  0.49230003939364M9[t] +  0.912247008553877M10[t] +  0.618871180476981M11[t] +  0.00823090323162158t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58407&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&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
Y[t] = + 0.519647105611932 + 1.28632287139419X[t] + 0.92813262756989Y1[t] + 0.336764561898729Y2[t] -0.439355918249917Y3[t] + 0.115147021620269Y4[t] + 0.182944690656144M1[t] + 0.332354786967214M2[t] + 0.351787973684101M3[t] + 0.236941355263420M4[t] + 0.445899792676542M5[t] + 0.6150272299015M6[t] + 0.59084113843511M7[t] + 0.333801824039458M8[t] + 0.49230003939364M9[t] + 0.912247008553877M10[t] + 0.618871180476981M11[t] + 0.00823090323162158t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5196471056119322.0878430.24890.8047850.402392
X1.286322871394191.8234850.70540.4848510.242425
Y10.928132627569890.160895.76881e-061e-06
Y20.3367645618987290.2027261.66120.1049080.052454
Y3-0.4393559182499170.205414-2.13890.0389330.019466
Y40.1151470216202690.1524880.75510.4548310.227415
M10.1829446906561441.1423510.16010.8736130.436807
M20.3323547869672141.1390710.29180.7720440.386022
M30.3517879736841011.1349060.310.7582770.379138
M40.2369413552634201.1332380.20910.83550.41775
M50.4458997926765421.135870.39260.6968360.348418
M60.61502722990151.1403290.53930.5927970.296399
M70.590841138435111.1465630.51530.6093180.304659
M80.3338018240394581.1566650.28860.7744640.387232
M90.492300039393641.1918880.4130.6818970.340949
M100.9122470085538771.1874570.76820.4470950.223548
M110.6188711804769811.183090.52310.6039440.301972
t0.008230903231621580.0181770.45280.6532480.326624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.519647105611932 & 2.087843 & 0.2489 & 0.804785 & 0.402392 \tabularnewline
X & 1.28632287139419 & 1.823485 & 0.7054 & 0.484851 & 0.242425 \tabularnewline
Y1 & 0.92813262756989 & 0.16089 & 5.7688 & 1e-06 & 1e-06 \tabularnewline
Y2 & 0.336764561898729 & 0.202726 & 1.6612 & 0.104908 & 0.052454 \tabularnewline
Y3 & -0.439355918249917 & 0.205414 & -2.1389 & 0.038933 & 0.019466 \tabularnewline
Y4 & 0.115147021620269 & 0.152488 & 0.7551 & 0.454831 & 0.227415 \tabularnewline
M1 & 0.182944690656144 & 1.142351 & 0.1601 & 0.873613 & 0.436807 \tabularnewline
M2 & 0.332354786967214 & 1.139071 & 0.2918 & 0.772044 & 0.386022 \tabularnewline
M3 & 0.351787973684101 & 1.134906 & 0.31 & 0.758277 & 0.379138 \tabularnewline
M4 & 0.236941355263420 & 1.133238 & 0.2091 & 0.8355 & 0.41775 \tabularnewline
M5 & 0.445899792676542 & 1.13587 & 0.3926 & 0.696836 & 0.348418 \tabularnewline
M6 & 0.6150272299015 & 1.140329 & 0.5393 & 0.592797 & 0.296399 \tabularnewline
M7 & 0.59084113843511 & 1.146563 & 0.5153 & 0.609318 & 0.304659 \tabularnewline
M8 & 0.333801824039458 & 1.156665 & 0.2886 & 0.774464 & 0.387232 \tabularnewline
M9 & 0.49230003939364 & 1.191888 & 0.413 & 0.681897 & 0.340949 \tabularnewline
M10 & 0.912247008553877 & 1.187457 & 0.7682 & 0.447095 & 0.223548 \tabularnewline
M11 & 0.618871180476981 & 1.18309 & 0.5231 & 0.603944 & 0.301972 \tabularnewline
t & 0.00823090323162158 & 0.018177 & 0.4528 & 0.653248 & 0.326624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58407&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.519647105611932[/C][C]2.087843[/C][C]0.2489[/C][C]0.804785[/C][C]0.402392[/C][/ROW]
[ROW][C]X[/C][C]1.28632287139419[/C][C]1.823485[/C][C]0.7054[/C][C]0.484851[/C][C]0.242425[/C][/ROW]
[ROW][C]Y1[/C][C]0.92813262756989[/C][C]0.16089[/C][C]5.7688[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Y2[/C][C]0.336764561898729[/C][C]0.202726[/C][C]1.6612[/C][C]0.104908[/C][C]0.052454[/C][/ROW]
[ROW][C]Y3[/C][C]-0.439355918249917[/C][C]0.205414[/C][C]-2.1389[/C][C]0.038933[/C][C]0.019466[/C][/ROW]
[ROW][C]Y4[/C][C]0.115147021620269[/C][C]0.152488[/C][C]0.7551[/C][C]0.454831[/C][C]0.227415[/C][/ROW]
[ROW][C]M1[/C][C]0.182944690656144[/C][C]1.142351[/C][C]0.1601[/C][C]0.873613[/C][C]0.436807[/C][/ROW]
[ROW][C]M2[/C][C]0.332354786967214[/C][C]1.139071[/C][C]0.2918[/C][C]0.772044[/C][C]0.386022[/C][/ROW]
[ROW][C]M3[/C][C]0.351787973684101[/C][C]1.134906[/C][C]0.31[/C][C]0.758277[/C][C]0.379138[/C][/ROW]
[ROW][C]M4[/C][C]0.236941355263420[/C][C]1.133238[/C][C]0.2091[/C][C]0.8355[/C][C]0.41775[/C][/ROW]
[ROW][C]M5[/C][C]0.445899792676542[/C][C]1.13587[/C][C]0.3926[/C][C]0.696836[/C][C]0.348418[/C][/ROW]
[ROW][C]M6[/C][C]0.6150272299015[/C][C]1.140329[/C][C]0.5393[/C][C]0.592797[/C][C]0.296399[/C][/ROW]
[ROW][C]M7[/C][C]0.59084113843511[/C][C]1.146563[/C][C]0.5153[/C][C]0.609318[/C][C]0.304659[/C][/ROW]
[ROW][C]M8[/C][C]0.333801824039458[/C][C]1.156665[/C][C]0.2886[/C][C]0.774464[/C][C]0.387232[/C][/ROW]
[ROW][C]M9[/C][C]0.49230003939364[/C][C]1.191888[/C][C]0.413[/C][C]0.681897[/C][C]0.340949[/C][/ROW]
[ROW][C]M10[/C][C]0.912247008553877[/C][C]1.187457[/C][C]0.7682[/C][C]0.447095[/C][C]0.223548[/C][/ROW]
[ROW][C]M11[/C][C]0.618871180476981[/C][C]1.18309[/C][C]0.5231[/C][C]0.603944[/C][C]0.301972[/C][/ROW]
[ROW][C]t[/C][C]0.00823090323162158[/C][C]0.018177[/C][C]0.4528[/C][C]0.653248[/C][C]0.326624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58407&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5196471056119322.0878430.24890.8047850.402392
X1.286322871394191.8234850.70540.4848510.242425
Y10.928132627569890.160895.76881e-061e-06
Y20.3367645618987290.2027261.66120.1049080.052454
Y3-0.4393559182499170.205414-2.13890.0389330.019466
Y40.1151470216202690.1524880.75510.4548310.227415
M10.1829446906561441.1423510.16010.8736130.436807
M20.3323547869672141.1390710.29180.7720440.386022
M30.3517879736841011.1349060.310.7582770.379138
M40.2369413552634201.1332380.20910.83550.41775
M50.4458997926765421.135870.39260.6968360.348418
M60.61502722990151.1403290.53930.5927970.296399
M70.590841138435111.1465630.51530.6093180.304659
M80.3338018240394581.1566650.28860.7744640.387232
M90.492300039393641.1918880.4130.6818970.340949
M100.9122470085538771.1874570.76820.4470950.223548
M110.6188711804769811.183090.52310.6039440.301972
t0.008230903231621580.0181770.45280.6532480.326624







Multiple Linear Regression - Regression Statistics
Multiple R0.964983665084489
R-squared0.931193473879893
Adjusted R-squared0.900411606931424
F-TEST (value)30.2513643970582
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.67073379959745
Sum Squared Residuals106.071354306458

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.964983665084489 \tabularnewline
R-squared & 0.931193473879893 \tabularnewline
Adjusted R-squared & 0.900411606931424 \tabularnewline
F-TEST (value) & 30.2513643970582 \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 & 1.67073379959745 \tabularnewline
Sum Squared Residuals & 106.071354306458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58407&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.964983665084489[/C][/ROW]
[ROW][C]R-squared[/C][C]0.931193473879893[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.900411606931424[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.2513643970582[/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]1.67073379959745[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]106.071354306458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58407&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&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 R0.964983665084489
R-squared0.931193473879893
Adjusted R-squared0.900411606931424
F-TEST (value)30.2513643970582
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.67073379959745
Sum Squared Residuals106.071354306458







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.22.08771937194585-1.88771937194584
20.92.76125117416827-1.86125117416827
32.42.82150938662777-0.421509386627771
44.74.587658386394790.112341613605212
59.47.091091661875322.30890833812468
612.511.82680088203680.673199117963227
715.815.43305220064830.366947799351721
818.217.49091693630300.709083063697043
916.818.8893521989686-2.08935219896863
1017.317.7334605781177-0.433460578117702
1119.316.76644254794622.53355745205376
1217.919.0719009442286-1.17190094422856
1320.218.25633619392261.94366380607740
1418.719.2560735245281-0.556073524528114
1520.119.51148949427930.588510505720713
1618.219.0273881725968-0.827388172596795
1718.418.8764679346185-0.476467934618465
1818.217.81178131500110.388218684998854
1918.918.67353458857540.226465411424633
2019.918.7004155796021.19958442039798
2121.320.14191310706091.15808689293914
222021.8756626728503-1.87566267285030
2319.520.4966627157067-0.996662715706658
2419.618.48421093027841.11578906972161
2520.919.33218602996711.56781397003295
262120.80006273255910.199937267440922
2719.921.2568249130978-1.35682491309780
2819.619.6032917722089-0.00329177220886537
2920.919.27735584277541.6226441572246
3021.721.05506344274020.644936557259845
3122.922.22455333872230.675446661277677
3221.522.7532089299502-1.25320892995017
3321.321.8228762377230-0.522876237723018
3423.521.1588477133392.34115228666101
3521.623.6015163682619-2.00151636826194
3624.521.89497148819262.6050285118074
3722.223.1482666099516-0.94826660995159
3823.523.23591948782930.264080512170718
3920.922.2026859972483-1.3026859972483
4020.721.4651643555195-0.765164355519465
4118.119.7851384662620-1.68513846626202
4217.118.7740155782133-1.67401557821328
4314.816.7428288289092-1.94282882890921
4413.815.1418467955614-1.34184679556143
4515.213.74585845624751.45414154375251
461616.032029035693-0.0320290356930054
4717.617.13537836808520.46462163191484
481517.5489166373005-2.54891663730046
491515.6754917942129-0.675491794212917
5016.314.34669308091531.95330691908474
5119.416.90749020874682.49250979125315
5221.319.81649731328011.48350268671991
5320.522.2699460944688-1.76994609446879
5421.121.1323387820086-0.0323387820086480
5521.620.92603104314480.67396895685518
5622.621.91361175858340.68638824141658

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.2 & 2.08771937194585 & -1.88771937194584 \tabularnewline
2 & 0.9 & 2.76125117416827 & -1.86125117416827 \tabularnewline
3 & 2.4 & 2.82150938662777 & -0.421509386627771 \tabularnewline
4 & 4.7 & 4.58765838639479 & 0.112341613605212 \tabularnewline
5 & 9.4 & 7.09109166187532 & 2.30890833812468 \tabularnewline
6 & 12.5 & 11.8268008820368 & 0.673199117963227 \tabularnewline
7 & 15.8 & 15.4330522006483 & 0.366947799351721 \tabularnewline
8 & 18.2 & 17.4909169363030 & 0.709083063697043 \tabularnewline
9 & 16.8 & 18.8893521989686 & -2.08935219896863 \tabularnewline
10 & 17.3 & 17.7334605781177 & -0.433460578117702 \tabularnewline
11 & 19.3 & 16.7664425479462 & 2.53355745205376 \tabularnewline
12 & 17.9 & 19.0719009442286 & -1.17190094422856 \tabularnewline
13 & 20.2 & 18.2563361939226 & 1.94366380607740 \tabularnewline
14 & 18.7 & 19.2560735245281 & -0.556073524528114 \tabularnewline
15 & 20.1 & 19.5114894942793 & 0.588510505720713 \tabularnewline
16 & 18.2 & 19.0273881725968 & -0.827388172596795 \tabularnewline
17 & 18.4 & 18.8764679346185 & -0.476467934618465 \tabularnewline
18 & 18.2 & 17.8117813150011 & 0.388218684998854 \tabularnewline
19 & 18.9 & 18.6735345885754 & 0.226465411424633 \tabularnewline
20 & 19.9 & 18.700415579602 & 1.19958442039798 \tabularnewline
21 & 21.3 & 20.1419131070609 & 1.15808689293914 \tabularnewline
22 & 20 & 21.8756626728503 & -1.87566267285030 \tabularnewline
23 & 19.5 & 20.4966627157067 & -0.996662715706658 \tabularnewline
24 & 19.6 & 18.4842109302784 & 1.11578906972161 \tabularnewline
25 & 20.9 & 19.3321860299671 & 1.56781397003295 \tabularnewline
26 & 21 & 20.8000627325591 & 0.199937267440922 \tabularnewline
27 & 19.9 & 21.2568249130978 & -1.35682491309780 \tabularnewline
28 & 19.6 & 19.6032917722089 & -0.00329177220886537 \tabularnewline
29 & 20.9 & 19.2773558427754 & 1.6226441572246 \tabularnewline
30 & 21.7 & 21.0550634427402 & 0.644936557259845 \tabularnewline
31 & 22.9 & 22.2245533387223 & 0.675446661277677 \tabularnewline
32 & 21.5 & 22.7532089299502 & -1.25320892995017 \tabularnewline
33 & 21.3 & 21.8228762377230 & -0.522876237723018 \tabularnewline
34 & 23.5 & 21.158847713339 & 2.34115228666101 \tabularnewline
35 & 21.6 & 23.6015163682619 & -2.00151636826194 \tabularnewline
36 & 24.5 & 21.8949714881926 & 2.6050285118074 \tabularnewline
37 & 22.2 & 23.1482666099516 & -0.94826660995159 \tabularnewline
38 & 23.5 & 23.2359194878293 & 0.264080512170718 \tabularnewline
39 & 20.9 & 22.2026859972483 & -1.3026859972483 \tabularnewline
40 & 20.7 & 21.4651643555195 & -0.765164355519465 \tabularnewline
41 & 18.1 & 19.7851384662620 & -1.68513846626202 \tabularnewline
42 & 17.1 & 18.7740155782133 & -1.67401557821328 \tabularnewline
43 & 14.8 & 16.7428288289092 & -1.94282882890921 \tabularnewline
44 & 13.8 & 15.1418467955614 & -1.34184679556143 \tabularnewline
45 & 15.2 & 13.7458584562475 & 1.45414154375251 \tabularnewline
46 & 16 & 16.032029035693 & -0.0320290356930054 \tabularnewline
47 & 17.6 & 17.1353783680852 & 0.46462163191484 \tabularnewline
48 & 15 & 17.5489166373005 & -2.54891663730046 \tabularnewline
49 & 15 & 15.6754917942129 & -0.675491794212917 \tabularnewline
50 & 16.3 & 14.3466930809153 & 1.95330691908474 \tabularnewline
51 & 19.4 & 16.9074902087468 & 2.49250979125315 \tabularnewline
52 & 21.3 & 19.8164973132801 & 1.48350268671991 \tabularnewline
53 & 20.5 & 22.2699460944688 & -1.76994609446879 \tabularnewline
54 & 21.1 & 21.1323387820086 & -0.0323387820086480 \tabularnewline
55 & 21.6 & 20.9260310431448 & 0.67396895685518 \tabularnewline
56 & 22.6 & 21.9136117585834 & 0.68638824141658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58407&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]0.2[/C][C]2.08771937194585[/C][C]-1.88771937194584[/C][/ROW]
[ROW][C]2[/C][C]0.9[/C][C]2.76125117416827[/C][C]-1.86125117416827[/C][/ROW]
[ROW][C]3[/C][C]2.4[/C][C]2.82150938662777[/C][C]-0.421509386627771[/C][/ROW]
[ROW][C]4[/C][C]4.7[/C][C]4.58765838639479[/C][C]0.112341613605212[/C][/ROW]
[ROW][C]5[/C][C]9.4[/C][C]7.09109166187532[/C][C]2.30890833812468[/C][/ROW]
[ROW][C]6[/C][C]12.5[/C][C]11.8268008820368[/C][C]0.673199117963227[/C][/ROW]
[ROW][C]7[/C][C]15.8[/C][C]15.4330522006483[/C][C]0.366947799351721[/C][/ROW]
[ROW][C]8[/C][C]18.2[/C][C]17.4909169363030[/C][C]0.709083063697043[/C][/ROW]
[ROW][C]9[/C][C]16.8[/C][C]18.8893521989686[/C][C]-2.08935219896863[/C][/ROW]
[ROW][C]10[/C][C]17.3[/C][C]17.7334605781177[/C][C]-0.433460578117702[/C][/ROW]
[ROW][C]11[/C][C]19.3[/C][C]16.7664425479462[/C][C]2.53355745205376[/C][/ROW]
[ROW][C]12[/C][C]17.9[/C][C]19.0719009442286[/C][C]-1.17190094422856[/C][/ROW]
[ROW][C]13[/C][C]20.2[/C][C]18.2563361939226[/C][C]1.94366380607740[/C][/ROW]
[ROW][C]14[/C][C]18.7[/C][C]19.2560735245281[/C][C]-0.556073524528114[/C][/ROW]
[ROW][C]15[/C][C]20.1[/C][C]19.5114894942793[/C][C]0.588510505720713[/C][/ROW]
[ROW][C]16[/C][C]18.2[/C][C]19.0273881725968[/C][C]-0.827388172596795[/C][/ROW]
[ROW][C]17[/C][C]18.4[/C][C]18.8764679346185[/C][C]-0.476467934618465[/C][/ROW]
[ROW][C]18[/C][C]18.2[/C][C]17.8117813150011[/C][C]0.388218684998854[/C][/ROW]
[ROW][C]19[/C][C]18.9[/C][C]18.6735345885754[/C][C]0.226465411424633[/C][/ROW]
[ROW][C]20[/C][C]19.9[/C][C]18.700415579602[/C][C]1.19958442039798[/C][/ROW]
[ROW][C]21[/C][C]21.3[/C][C]20.1419131070609[/C][C]1.15808689293914[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]21.8756626728503[/C][C]-1.87566267285030[/C][/ROW]
[ROW][C]23[/C][C]19.5[/C][C]20.4966627157067[/C][C]-0.996662715706658[/C][/ROW]
[ROW][C]24[/C][C]19.6[/C][C]18.4842109302784[/C][C]1.11578906972161[/C][/ROW]
[ROW][C]25[/C][C]20.9[/C][C]19.3321860299671[/C][C]1.56781397003295[/C][/ROW]
[ROW][C]26[/C][C]21[/C][C]20.8000627325591[/C][C]0.199937267440922[/C][/ROW]
[ROW][C]27[/C][C]19.9[/C][C]21.2568249130978[/C][C]-1.35682491309780[/C][/ROW]
[ROW][C]28[/C][C]19.6[/C][C]19.6032917722089[/C][C]-0.00329177220886537[/C][/ROW]
[ROW][C]29[/C][C]20.9[/C][C]19.2773558427754[/C][C]1.6226441572246[/C][/ROW]
[ROW][C]30[/C][C]21.7[/C][C]21.0550634427402[/C][C]0.644936557259845[/C][/ROW]
[ROW][C]31[/C][C]22.9[/C][C]22.2245533387223[/C][C]0.675446661277677[/C][/ROW]
[ROW][C]32[/C][C]21.5[/C][C]22.7532089299502[/C][C]-1.25320892995017[/C][/ROW]
[ROW][C]33[/C][C]21.3[/C][C]21.8228762377230[/C][C]-0.522876237723018[/C][/ROW]
[ROW][C]34[/C][C]23.5[/C][C]21.158847713339[/C][C]2.34115228666101[/C][/ROW]
[ROW][C]35[/C][C]21.6[/C][C]23.6015163682619[/C][C]-2.00151636826194[/C][/ROW]
[ROW][C]36[/C][C]24.5[/C][C]21.8949714881926[/C][C]2.6050285118074[/C][/ROW]
[ROW][C]37[/C][C]22.2[/C][C]23.1482666099516[/C][C]-0.94826660995159[/C][/ROW]
[ROW][C]38[/C][C]23.5[/C][C]23.2359194878293[/C][C]0.264080512170718[/C][/ROW]
[ROW][C]39[/C][C]20.9[/C][C]22.2026859972483[/C][C]-1.3026859972483[/C][/ROW]
[ROW][C]40[/C][C]20.7[/C][C]21.4651643555195[/C][C]-0.765164355519465[/C][/ROW]
[ROW][C]41[/C][C]18.1[/C][C]19.7851384662620[/C][C]-1.68513846626202[/C][/ROW]
[ROW][C]42[/C][C]17.1[/C][C]18.7740155782133[/C][C]-1.67401557821328[/C][/ROW]
[ROW][C]43[/C][C]14.8[/C][C]16.7428288289092[/C][C]-1.94282882890921[/C][/ROW]
[ROW][C]44[/C][C]13.8[/C][C]15.1418467955614[/C][C]-1.34184679556143[/C][/ROW]
[ROW][C]45[/C][C]15.2[/C][C]13.7458584562475[/C][C]1.45414154375251[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]16.032029035693[/C][C]-0.0320290356930054[/C][/ROW]
[ROW][C]47[/C][C]17.6[/C][C]17.1353783680852[/C][C]0.46462163191484[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]17.5489166373005[/C][C]-2.54891663730046[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]15.6754917942129[/C][C]-0.675491794212917[/C][/ROW]
[ROW][C]50[/C][C]16.3[/C][C]14.3466930809153[/C][C]1.95330691908474[/C][/ROW]
[ROW][C]51[/C][C]19.4[/C][C]16.9074902087468[/C][C]2.49250979125315[/C][/ROW]
[ROW][C]52[/C][C]21.3[/C][C]19.8164973132801[/C][C]1.48350268671991[/C][/ROW]
[ROW][C]53[/C][C]20.5[/C][C]22.2699460944688[/C][C]-1.76994609446879[/C][/ROW]
[ROW][C]54[/C][C]21.1[/C][C]21.1323387820086[/C][C]-0.0323387820086480[/C][/ROW]
[ROW][C]55[/C][C]21.6[/C][C]20.9260310431448[/C][C]0.67396895685518[/C][/ROW]
[ROW][C]56[/C][C]22.6[/C][C]21.9136117585834[/C][C]0.68638824141658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58407&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&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
10.22.08771937194585-1.88771937194584
20.92.76125117416827-1.86125117416827
32.42.82150938662777-0.421509386627771
44.74.587658386394790.112341613605212
59.47.091091661875322.30890833812468
612.511.82680088203680.673199117963227
715.815.43305220064830.366947799351721
818.217.49091693630300.709083063697043
916.818.8893521989686-2.08935219896863
1017.317.7334605781177-0.433460578117702
1119.316.76644254794622.53355745205376
1217.919.0719009442286-1.17190094422856
1320.218.25633619392261.94366380607740
1418.719.2560735245281-0.556073524528114
1520.119.51148949427930.588510505720713
1618.219.0273881725968-0.827388172596795
1718.418.8764679346185-0.476467934618465
1818.217.81178131500110.388218684998854
1918.918.67353458857540.226465411424633
2019.918.7004155796021.19958442039798
2121.320.14191310706091.15808689293914
222021.8756626728503-1.87566267285030
2319.520.4966627157067-0.996662715706658
2419.618.48421093027841.11578906972161
2520.919.33218602996711.56781397003295
262120.80006273255910.199937267440922
2719.921.2568249130978-1.35682491309780
2819.619.6032917722089-0.00329177220886537
2920.919.27735584277541.6226441572246
3021.721.05506344274020.644936557259845
3122.922.22455333872230.675446661277677
3221.522.7532089299502-1.25320892995017
3321.321.8228762377230-0.522876237723018
3423.521.1588477133392.34115228666101
3521.623.6015163682619-2.00151636826194
3624.521.89497148819262.6050285118074
3722.223.1482666099516-0.94826660995159
3823.523.23591948782930.264080512170718
3920.922.2026859972483-1.3026859972483
4020.721.4651643555195-0.765164355519465
4118.119.7851384662620-1.68513846626202
4217.118.7740155782133-1.67401557821328
4314.816.7428288289092-1.94282882890921
4413.815.1418467955614-1.34184679556143
4515.213.74585845624751.45414154375251
461616.032029035693-0.0320290356930054
4717.617.13537836808520.46462163191484
481517.5489166373005-2.54891663730046
491515.6754917942129-0.675491794212917
5016.314.34669308091531.95330691908474
5119.416.90749020874682.49250979125315
5221.319.81649731328011.48350268671991
5320.522.2699460944688-1.76994609446879
5421.121.1323387820086-0.0323387820086480
5521.620.92603104314480.67396895685518
5622.621.91361175858340.68638824141658







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.193113669280710.386227338561420.80688633071929
220.4788712199718920.9577424399437830.521128780028108
230.6821375647804250.635724870439150.317862435219575
240.5885206738486010.8229586523027980.411479326151399
250.4951374376793580.9902748753587160.504862562320642
260.3683015973610170.7366031947220350.631698402638983
270.352927278495340.705854556990680.64707272150466
280.240590733759810.481181467519620.75940926624019
290.2302828445089110.4605656890178210.76971715549109
300.1727928396937630.3455856793875270.827207160306237
310.1368469173349940.2736938346699870.863153082665006
320.1194575204409730.2389150408819470.880542479559027
330.06599591045590460.1319918209118090.934004089544095
340.0849280850056920.1698561700113840.915071914994308
350.06041992134779780.1208398426955960.939580078652202

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.19311366928071 & 0.38622733856142 & 0.80688633071929 \tabularnewline
22 & 0.478871219971892 & 0.957742439943783 & 0.521128780028108 \tabularnewline
23 & 0.682137564780425 & 0.63572487043915 & 0.317862435219575 \tabularnewline
24 & 0.588520673848601 & 0.822958652302798 & 0.411479326151399 \tabularnewline
25 & 0.495137437679358 & 0.990274875358716 & 0.504862562320642 \tabularnewline
26 & 0.368301597361017 & 0.736603194722035 & 0.631698402638983 \tabularnewline
27 & 0.35292727849534 & 0.70585455699068 & 0.64707272150466 \tabularnewline
28 & 0.24059073375981 & 0.48118146751962 & 0.75940926624019 \tabularnewline
29 & 0.230282844508911 & 0.460565689017821 & 0.76971715549109 \tabularnewline
30 & 0.172792839693763 & 0.345585679387527 & 0.827207160306237 \tabularnewline
31 & 0.136846917334994 & 0.273693834669987 & 0.863153082665006 \tabularnewline
32 & 0.119457520440973 & 0.238915040881947 & 0.880542479559027 \tabularnewline
33 & 0.0659959104559046 & 0.131991820911809 & 0.934004089544095 \tabularnewline
34 & 0.084928085005692 & 0.169856170011384 & 0.915071914994308 \tabularnewline
35 & 0.0604199213477978 & 0.120839842695596 & 0.939580078652202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58407&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]0.19311366928071[/C][C]0.38622733856142[/C][C]0.80688633071929[/C][/ROW]
[ROW][C]22[/C][C]0.478871219971892[/C][C]0.957742439943783[/C][C]0.521128780028108[/C][/ROW]
[ROW][C]23[/C][C]0.682137564780425[/C][C]0.63572487043915[/C][C]0.317862435219575[/C][/ROW]
[ROW][C]24[/C][C]0.588520673848601[/C][C]0.822958652302798[/C][C]0.411479326151399[/C][/ROW]
[ROW][C]25[/C][C]0.495137437679358[/C][C]0.990274875358716[/C][C]0.504862562320642[/C][/ROW]
[ROW][C]26[/C][C]0.368301597361017[/C][C]0.736603194722035[/C][C]0.631698402638983[/C][/ROW]
[ROW][C]27[/C][C]0.35292727849534[/C][C]0.70585455699068[/C][C]0.64707272150466[/C][/ROW]
[ROW][C]28[/C][C]0.24059073375981[/C][C]0.48118146751962[/C][C]0.75940926624019[/C][/ROW]
[ROW][C]29[/C][C]0.230282844508911[/C][C]0.460565689017821[/C][C]0.76971715549109[/C][/ROW]
[ROW][C]30[/C][C]0.172792839693763[/C][C]0.345585679387527[/C][C]0.827207160306237[/C][/ROW]
[ROW][C]31[/C][C]0.136846917334994[/C][C]0.273693834669987[/C][C]0.863153082665006[/C][/ROW]
[ROW][C]32[/C][C]0.119457520440973[/C][C]0.238915040881947[/C][C]0.880542479559027[/C][/ROW]
[ROW][C]33[/C][C]0.0659959104559046[/C][C]0.131991820911809[/C][C]0.934004089544095[/C][/ROW]
[ROW][C]34[/C][C]0.084928085005692[/C][C]0.169856170011384[/C][C]0.915071914994308[/C][/ROW]
[ROW][C]35[/C][C]0.0604199213477978[/C][C]0.120839842695596[/C][C]0.939580078652202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58407&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&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
210.193113669280710.386227338561420.80688633071929
220.4788712199718920.9577424399437830.521128780028108
230.6821375647804250.635724870439150.317862435219575
240.5885206738486010.8229586523027980.411479326151399
250.4951374376793580.9902748753587160.504862562320642
260.3683015973610170.7366031947220350.631698402638983
270.352927278495340.705854556990680.64707272150466
280.240590733759810.481181467519620.75940926624019
290.2302828445089110.4605656890178210.76971715549109
300.1727928396937630.3455856793875270.827207160306237
310.1368469173349940.2736938346699870.863153082665006
320.1194575204409730.2389150408819470.880542479559027
330.06599591045590460.1319918209118090.934004089544095
340.0849280850056920.1698561700113840.915071914994308
350.06041992134779780.1208398426955960.939580078652202







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=58407&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=58407&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58407&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}