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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, 20 Dec 2010 22:10:46 +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/20/t1292883170vpts4iwwx3jmghw.htm/, Retrieved Fri, 28 Nov 2025 19:42:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113155, Retrieved Fri, 28 Nov 2025 19:42:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact478

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [] [2010-11-26 09:51:05] [7789b9488494790f41ddb7f073cada1b]
- RMPD    [Multiple Regression] [] [2010-12-17 11:54:43] [7789b9488494790f41ddb7f073cada1b]
-   PD      [Multiple Regression] [] [2010-12-19 13:42:28] [7789b9488494790f41ddb7f073cada1b]
-               [Multiple Regression] [] [2010-12-20 22:10:46] [0bf4568947c4284a0258563e64d5d827] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.76
102.37
102.38
102.86
102.87
102.92
102.95
103.02
104.08
104.16
104.24
104.33
104.73
104.86
105.03
105.62
105.63
105.63
105.94
106.61
107.69
107.78
107.93
108.48
108.14
108.48
108.48
108.89
108.93
109.21
109.47
109.80
111.73
111.85
112.12
112.15
112.17
112.67
112.80
113.44
113.53
114.53
114.51
115.05
116.67
117.07
116.92
117.00
117.02
117.35
117.36
117.82
117.88
118.24
118.50
118.80
119.76
120.09

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
cultuurbestedingen[t] = + 100.676931818182 -0.0904886363636757M1[t] -0.0355909090909093M2[t] -0.298693181818184M3[t] -0.109795454545457M4[t] -0.394897727272728M5[t] -0.384000000000005M6[t] -0.543102272727275M7[t] -0.488204545454551M8[t] + 0.514693181818181M9[t] + 0.391590909090904M10[t] + 0.139602272727273M11[t] + 0.327102272727273t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
cultuurbestedingen[t] =  +  100.676931818182 -0.0904886363636757M1[t] -0.0355909090909093M2[t] -0.298693181818184M3[t] -0.109795454545457M4[t] -0.394897727272728M5[t] -0.384000000000005M6[t] -0.543102272727275M7[t] -0.488204545454551M8[t] +  0.514693181818181M9[t] +  0.391590909090904M10[t] +  0.139602272727273M11[t] +  0.327102272727273t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]cultuurbestedingen[t] =  +  100.676931818182 -0.0904886363636757M1[t] -0.0355909090909093M2[t] -0.298693181818184M3[t] -0.109795454545457M4[t] -0.394897727272728M5[t] -0.384000000000005M6[t] -0.543102272727275M7[t] -0.488204545454551M8[t] +  0.514693181818181M9[t] +  0.391590909090904M10[t] +  0.139602272727273M11[t] +  0.327102272727273t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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
cultuurbestedingen[t] = + 100.676931818182 -0.0904886363636757M1[t] -0.0355909090909093M2[t] -0.298693181818184M3[t] -0.109795454545457M4[t] -0.394897727272728M5[t] -0.384000000000005M6[t] -0.543102272727275M7[t] -0.488204545454551M8[t] + 0.514693181818181M9[t] + 0.391590909090904M10[t] + 0.139602272727273M11[t] + 0.327102272727273t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100.6769318181820.337006298.739200
M1-0.09048863636367570.40885-0.22130.8258410.41292
M2-0.03559090909090930.408592-0.08710.9309740.465487
M3-0.2986931818181840.408392-0.73140.4683340.234167
M4-0.1097954545454570.408249-0.26890.7892030.394602
M5-0.3948977272727280.408163-0.96750.3384660.169233
M6-0.3840000000000050.408135-0.94090.3517970.175899
M7-0.5431022727272750.408163-1.33060.1900240.095012
M8-0.4882045454545510.408249-1.19580.2380190.119009
M90.5146931818181810.4083921.26030.2140610.10703
M100.3915909090909040.4085920.95840.3429870.171494
M110.1396022727272730.4302390.32450.7470810.373541
t0.3271022727272730.00483467.66500

\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) & 100.676931818182 & 0.337006 & 298.7392 & 0 & 0 \tabularnewline
M1 & -0.0904886363636757 & 0.40885 & -0.2213 & 0.825841 & 0.41292 \tabularnewline
M2 & -0.0355909090909093 & 0.408592 & -0.0871 & 0.930974 & 0.465487 \tabularnewline
M3 & -0.298693181818184 & 0.408392 & -0.7314 & 0.468334 & 0.234167 \tabularnewline
M4 & -0.109795454545457 & 0.408249 & -0.2689 & 0.789203 & 0.394602 \tabularnewline
M5 & -0.394897727272728 & 0.408163 & -0.9675 & 0.338466 & 0.169233 \tabularnewline
M6 & -0.384000000000005 & 0.408135 & -0.9409 & 0.351797 & 0.175899 \tabularnewline
M7 & -0.543102272727275 & 0.408163 & -1.3306 & 0.190024 & 0.095012 \tabularnewline
M8 & -0.488204545454551 & 0.408249 & -1.1958 & 0.238019 & 0.119009 \tabularnewline
M9 & 0.514693181818181 & 0.408392 & 1.2603 & 0.214061 & 0.10703 \tabularnewline
M10 & 0.391590909090904 & 0.408592 & 0.9584 & 0.342987 & 0.171494 \tabularnewline
M11 & 0.139602272727273 & 0.430239 & 0.3245 & 0.747081 & 0.373541 \tabularnewline
t & 0.327102272727273 & 0.004834 & 67.665 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&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]100.676931818182[/C][C]0.337006[/C][C]298.7392[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0904886363636757[/C][C]0.40885[/C][C]-0.2213[/C][C]0.825841[/C][C]0.41292[/C][/ROW]
[ROW][C]M2[/C][C]-0.0355909090909093[/C][C]0.408592[/C][C]-0.0871[/C][C]0.930974[/C][C]0.465487[/C][/ROW]
[ROW][C]M3[/C][C]-0.298693181818184[/C][C]0.408392[/C][C]-0.7314[/C][C]0.468334[/C][C]0.234167[/C][/ROW]
[ROW][C]M4[/C][C]-0.109795454545457[/C][C]0.408249[/C][C]-0.2689[/C][C]0.789203[/C][C]0.394602[/C][/ROW]
[ROW][C]M5[/C][C]-0.394897727272728[/C][C]0.408163[/C][C]-0.9675[/C][C]0.338466[/C][C]0.169233[/C][/ROW]
[ROW][C]M6[/C][C]-0.384000000000005[/C][C]0.408135[/C][C]-0.9409[/C][C]0.351797[/C][C]0.175899[/C][/ROW]
[ROW][C]M7[/C][C]-0.543102272727275[/C][C]0.408163[/C][C]-1.3306[/C][C]0.190024[/C][C]0.095012[/C][/ROW]
[ROW][C]M8[/C][C]-0.488204545454551[/C][C]0.408249[/C][C]-1.1958[/C][C]0.238019[/C][C]0.119009[/C][/ROW]
[ROW][C]M9[/C][C]0.514693181818181[/C][C]0.408392[/C][C]1.2603[/C][C]0.214061[/C][C]0.10703[/C][/ROW]
[ROW][C]M10[/C][C]0.391590909090904[/C][C]0.408592[/C][C]0.9584[/C][C]0.342987[/C][C]0.171494[/C][/ROW]
[ROW][C]M11[/C][C]0.139602272727273[/C][C]0.430239[/C][C]0.3245[/C][C]0.747081[/C][C]0.373541[/C][/ROW]
[ROW][C]t[/C][C]0.327102272727273[/C][C]0.004834[/C][C]67.665[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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)100.6769318181820.337006298.739200
M1-0.09048863636367570.40885-0.22130.8258410.41292
M2-0.03559090909090930.408592-0.08710.9309740.465487
M3-0.2986931818181840.408392-0.73140.4683340.234167
M4-0.1097954545454570.408249-0.26890.7892030.394602
M5-0.3948977272727280.408163-0.96750.3384660.169233
M6-0.3840000000000050.408135-0.94090.3517970.175899
M7-0.5431022727272750.408163-1.33060.1900240.095012
M8-0.4882045454545510.408249-1.19580.2380190.119009
M90.5146931818181810.4083921.26030.2140610.10703
M100.3915909090909040.4085920.95840.3429870.171494
M110.1396022727272730.4302390.32450.7470810.373541
t0.3271022727272730.00483467.66500






Multiple Linear Regression - Regression Statistics
Multiple R0.995287469888219
R-squared0.990597147716492
Adjusted R-squared0.98808972044089
F-TEST (value)395.065155968917
F-TEST (DF numerator)12
F-TEST (DF denominator)45
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.608411215437963
Sum Squared Residuals16.6573893181815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.995287469888219 \tabularnewline
R-squared & 0.990597147716492 \tabularnewline
Adjusted R-squared & 0.98808972044089 \tabularnewline
F-TEST (value) & 395.065155968917 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.608411215437963 \tabularnewline
Sum Squared Residuals & 16.6573893181815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.995287469888219[/C][/ROW]
[ROW][C]R-squared[/C][C]0.990597147716492[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.98808972044089[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]395.065155968917[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/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]0.608411215437963[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.6573893181815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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 R0.995287469888219
R-squared0.990597147716492
Adjusted R-squared0.98808972044089
F-TEST (value)395.065155968917
F-TEST (DF numerator)12
F-TEST (DF denominator)45
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.608411215437963
Sum Squared Residuals16.6573893181815






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.76100.9135454545460.8464545454544
2102.37101.2955454545451.07445454545456
3102.38101.3595454545451.02045454545455
4102.86101.8755454545450.984454545454554
5102.87101.9175454545450.952454545454556
6102.92102.2555454545450.664454545454556
7102.95102.4235454545450.526454545454552
8103.02102.8055454545450.21445454545455
9104.08104.135545454545-0.0555454545454519
10104.16104.339545454545-0.179545454545448
11104.24104.414659090909-0.174659090909094
12104.33104.602159090909-0.27215909090909
13104.73104.838772727273-0.108772727272686
14104.86105.220772727273-0.360772727272726
15105.03105.284772727273-0.254772727272721
16105.62105.800772727273-0.180772727272719
17105.63105.842772727273-0.21277272727273
18105.63106.180772727273-0.550772727272726
19105.94106.348772727273-0.408772727272727
20106.61106.730772727273-0.120772727272722
21107.69108.060772727273-0.370772727272728
22107.78108.264772727273-0.484772727272721
23107.93108.339886363636-0.409886363636357
24108.48108.527386363636-0.0473863636363603
25108.14108.764-0.623999999999961
26108.48109.146-0.665999999999997
27108.48109.21-0.729999999999996
28108.89109.726-0.836
29108.93109.768-0.837999999999994
30109.21110.106-0.896000000000003
31109.47110.274-0.804000000000001
32109.8110.656-0.856
33111.73111.986-0.255999999999998
34111.85112.19-0.340000000000003
35112.12112.265113636364-0.145113636363636
36112.15112.452613636364-0.302613636363634
37112.17112.689227272727-0.519227272727236
38112.67113.071227272727-0.401227272727275
39112.8113.135227272727-0.335227272727279
40113.44113.651227272727-0.211227272727276
41113.53113.693227272727-0.163227272727276
42114.53114.0312272727270.498772727272728
43114.51114.1992272727270.31077272727273
44115.05114.5812272727270.468772727272723
45116.67115.9112272727270.758772727272726
46117.07116.1152272727270.954772727272719
47116.92116.1903409090910.729659090909087
48117116.3778409090910.622159090909084
49117.02116.6144545454550.405545454545484
50117.35116.9964545454550.353545454545441
51117.36117.0604545454550.299545454545449
52117.82117.5764545454550.243545454545441
53117.88117.6184545454550.261545454545443
54118.24117.9564545454550.283545454545445
55118.5118.1244545454550.375545454545446
56118.8118.5064545454550.293545454545448
57119.76119.836454545455-0.0764545454545479
58120.09120.0404545454550.0495454545454534

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 100.913545454546 & 0.8464545454544 \tabularnewline
2 & 102.37 & 101.295545454545 & 1.07445454545456 \tabularnewline
3 & 102.38 & 101.359545454545 & 1.02045454545455 \tabularnewline
4 & 102.86 & 101.875545454545 & 0.984454545454554 \tabularnewline
5 & 102.87 & 101.917545454545 & 0.952454545454556 \tabularnewline
6 & 102.92 & 102.255545454545 & 0.664454545454556 \tabularnewline
7 & 102.95 & 102.423545454545 & 0.526454545454552 \tabularnewline
8 & 103.02 & 102.805545454545 & 0.21445454545455 \tabularnewline
9 & 104.08 & 104.135545454545 & -0.0555454545454519 \tabularnewline
10 & 104.16 & 104.339545454545 & -0.179545454545448 \tabularnewline
11 & 104.24 & 104.414659090909 & -0.174659090909094 \tabularnewline
12 & 104.33 & 104.602159090909 & -0.27215909090909 \tabularnewline
13 & 104.73 & 104.838772727273 & -0.108772727272686 \tabularnewline
14 & 104.86 & 105.220772727273 & -0.360772727272726 \tabularnewline
15 & 105.03 & 105.284772727273 & -0.254772727272721 \tabularnewline
16 & 105.62 & 105.800772727273 & -0.180772727272719 \tabularnewline
17 & 105.63 & 105.842772727273 & -0.21277272727273 \tabularnewline
18 & 105.63 & 106.180772727273 & -0.550772727272726 \tabularnewline
19 & 105.94 & 106.348772727273 & -0.408772727272727 \tabularnewline
20 & 106.61 & 106.730772727273 & -0.120772727272722 \tabularnewline
21 & 107.69 & 108.060772727273 & -0.370772727272728 \tabularnewline
22 & 107.78 & 108.264772727273 & -0.484772727272721 \tabularnewline
23 & 107.93 & 108.339886363636 & -0.409886363636357 \tabularnewline
24 & 108.48 & 108.527386363636 & -0.0473863636363603 \tabularnewline
25 & 108.14 & 108.764 & -0.623999999999961 \tabularnewline
26 & 108.48 & 109.146 & -0.665999999999997 \tabularnewline
27 & 108.48 & 109.21 & -0.729999999999996 \tabularnewline
28 & 108.89 & 109.726 & -0.836 \tabularnewline
29 & 108.93 & 109.768 & -0.837999999999994 \tabularnewline
30 & 109.21 & 110.106 & -0.896000000000003 \tabularnewline
31 & 109.47 & 110.274 & -0.804000000000001 \tabularnewline
32 & 109.8 & 110.656 & -0.856 \tabularnewline
33 & 111.73 & 111.986 & -0.255999999999998 \tabularnewline
34 & 111.85 & 112.19 & -0.340000000000003 \tabularnewline
35 & 112.12 & 112.265113636364 & -0.145113636363636 \tabularnewline
36 & 112.15 & 112.452613636364 & -0.302613636363634 \tabularnewline
37 & 112.17 & 112.689227272727 & -0.519227272727236 \tabularnewline
38 & 112.67 & 113.071227272727 & -0.401227272727275 \tabularnewline
39 & 112.8 & 113.135227272727 & -0.335227272727279 \tabularnewline
40 & 113.44 & 113.651227272727 & -0.211227272727276 \tabularnewline
41 & 113.53 & 113.693227272727 & -0.163227272727276 \tabularnewline
42 & 114.53 & 114.031227272727 & 0.498772727272728 \tabularnewline
43 & 114.51 & 114.199227272727 & 0.31077272727273 \tabularnewline
44 & 115.05 & 114.581227272727 & 0.468772727272723 \tabularnewline
45 & 116.67 & 115.911227272727 & 0.758772727272726 \tabularnewline
46 & 117.07 & 116.115227272727 & 0.954772727272719 \tabularnewline
47 & 116.92 & 116.190340909091 & 0.729659090909087 \tabularnewline
48 & 117 & 116.377840909091 & 0.622159090909084 \tabularnewline
49 & 117.02 & 116.614454545455 & 0.405545454545484 \tabularnewline
50 & 117.35 & 116.996454545455 & 0.353545454545441 \tabularnewline
51 & 117.36 & 117.060454545455 & 0.299545454545449 \tabularnewline
52 & 117.82 & 117.576454545455 & 0.243545454545441 \tabularnewline
53 & 117.88 & 117.618454545455 & 0.261545454545443 \tabularnewline
54 & 118.24 & 117.956454545455 & 0.283545454545445 \tabularnewline
55 & 118.5 & 118.124454545455 & 0.375545454545446 \tabularnewline
56 & 118.8 & 118.506454545455 & 0.293545454545448 \tabularnewline
57 & 119.76 & 119.836454545455 & -0.0764545454545479 \tabularnewline
58 & 120.09 & 120.040454545455 & 0.0495454545454534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&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]101.76[/C][C]100.913545454546[/C][C]0.8464545454544[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]101.295545454545[/C][C]1.07445454545456[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]101.359545454545[/C][C]1.02045454545455[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]101.875545454545[/C][C]0.984454545454554[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]101.917545454545[/C][C]0.952454545454556[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.255545454545[/C][C]0.664454545454556[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.423545454545[/C][C]0.526454545454552[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]102.805545454545[/C][C]0.21445454545455[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.135545454545[/C][C]-0.0555454545454519[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.339545454545[/C][C]-0.179545454545448[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.414659090909[/C][C]-0.174659090909094[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.602159090909[/C][C]-0.27215909090909[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.838772727273[/C][C]-0.108772727272686[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.220772727273[/C][C]-0.360772727272726[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.284772727273[/C][C]-0.254772727272721[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.800772727273[/C][C]-0.180772727272719[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.842772727273[/C][C]-0.21277272727273[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.180772727273[/C][C]-0.550772727272726[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.348772727273[/C][C]-0.408772727272727[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.730772727273[/C][C]-0.120772727272722[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]108.060772727273[/C][C]-0.370772727272728[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.264772727273[/C][C]-0.484772727272721[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.339886363636[/C][C]-0.409886363636357[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.527386363636[/C][C]-0.0473863636363603[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]108.764[/C][C]-0.623999999999961[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]109.146[/C][C]-0.665999999999997[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]109.21[/C][C]-0.729999999999996[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]109.726[/C][C]-0.836[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.768[/C][C]-0.837999999999994[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]110.106[/C][C]-0.896000000000003[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]110.274[/C][C]-0.804000000000001[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]110.656[/C][C]-0.856[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.986[/C][C]-0.255999999999998[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]112.19[/C][C]-0.340000000000003[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]112.265113636364[/C][C]-0.145113636363636[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.452613636364[/C][C]-0.302613636363634[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.689227272727[/C][C]-0.519227272727236[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]113.071227272727[/C][C]-0.401227272727275[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]113.135227272727[/C][C]-0.335227272727279[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.651227272727[/C][C]-0.211227272727276[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.693227272727[/C][C]-0.163227272727276[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.031227272727[/C][C]0.498772727272728[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.199227272727[/C][C]0.31077272727273[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.581227272727[/C][C]0.468772727272723[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]115.911227272727[/C][C]0.758772727272726[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.115227272727[/C][C]0.954772727272719[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.190340909091[/C][C]0.729659090909087[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.377840909091[/C][C]0.622159090909084[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]116.614454545455[/C][C]0.405545454545484[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]116.996454545455[/C][C]0.353545454545441[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.060454545455[/C][C]0.299545454545449[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]117.576454545455[/C][C]0.243545454545441[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]117.618454545455[/C][C]0.261545454545443[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]117.956454545455[/C][C]0.283545454545445[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.124454545455[/C][C]0.375545454545446[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.506454545455[/C][C]0.293545454545448[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.836454545455[/C][C]-0.0764545454545479[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]120.040454545455[/C][C]0.0495454545454534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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
1101.76100.9135454545460.8464545454544
2102.37101.2955454545451.07445454545456
3102.38101.3595454545451.02045454545455
4102.86101.8755454545450.984454545454554
5102.87101.9175454545450.952454545454556
6102.92102.2555454545450.664454545454556
7102.95102.4235454545450.526454545454552
8103.02102.8055454545450.21445454545455
9104.08104.135545454545-0.0555454545454519
10104.16104.339545454545-0.179545454545448
11104.24104.414659090909-0.174659090909094
12104.33104.602159090909-0.27215909090909
13104.73104.838772727273-0.108772727272686
14104.86105.220772727273-0.360772727272726
15105.03105.284772727273-0.254772727272721
16105.62105.800772727273-0.180772727272719
17105.63105.842772727273-0.21277272727273
18105.63106.180772727273-0.550772727272726
19105.94106.348772727273-0.408772727272727
20106.61106.730772727273-0.120772727272722
21107.69108.060772727273-0.370772727272728
22107.78108.264772727273-0.484772727272721
23107.93108.339886363636-0.409886363636357
24108.48108.527386363636-0.0473863636363603
25108.14108.764-0.623999999999961
26108.48109.146-0.665999999999997
27108.48109.21-0.729999999999996
28108.89109.726-0.836
29108.93109.768-0.837999999999994
30109.21110.106-0.896000000000003
31109.47110.274-0.804000000000001
32109.8110.656-0.856
33111.73111.986-0.255999999999998
34111.85112.19-0.340000000000003
35112.12112.265113636364-0.145113636363636
36112.15112.452613636364-0.302613636363634
37112.17112.689227272727-0.519227272727236
38112.67113.071227272727-0.401227272727275
39112.8113.135227272727-0.335227272727279
40113.44113.651227272727-0.211227272727276
41113.53113.693227272727-0.163227272727276
42114.53114.0312272727270.498772727272728
43114.51114.1992272727270.31077272727273
44115.05114.5812272727270.468772727272723
45116.67115.9112272727270.758772727272726
46117.07116.1152272727270.954772727272719
47116.92116.1903409090910.729659090909087
48117116.3778409090910.622159090909084
49117.02116.6144545454550.405545454545484
50117.35116.9964545454550.353545454545441
51117.36117.0604545454550.299545454545449
52117.82117.5764545454550.243545454545441
53117.88117.6184545454550.261545454545443
54118.24117.9564545454550.283545454545445
55118.5118.1244545454550.375545454545446
56118.8118.5064545454550.293545454545448
57119.76119.836454545455-0.0764545454545479
58120.09120.0404545454550.0495454545454534






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.05770632094513370.1154126418902670.942293679054866
170.0197393027209290.0394786054418580.980260697279071
180.005350631464762950.01070126292952590.994649368535237
190.004268996061025860.008537992122051720.995731003938974
200.1013772482251390.2027544964502780.898622751774861
210.1934964689507040.3869929379014090.806503531049296
220.235644735312610.471289470625220.76435526468739
230.2600273828887290.5200547657774590.73997261711127
240.4596184475720130.9192368951440270.540381552427987
250.364204097994080.7284081959881610.63579590200592
260.2727598284990190.5455196569980370.727240171500981
270.1929158901286450.385831780257290.807084109871355
280.1332821016137120.2665642032274240.866717898386288
290.0875443865868330.1750887731736660.912455613413167
300.07358455869272030.1471691173854410.92641544130728
310.06127173599399660.1225434719879930.938728264006003
320.06184627745478460.1236925549095690.938153722545215
330.1248868540578390.2497737081156770.875113145942161
340.1916970332066580.3833940664133150.808302966793342
350.293236428130620.5864728562612390.70676357186938
360.3470767144996570.6941534289993140.652923285500343
370.415023206029050.8300464120580990.58497679397095
380.4817163292919940.9634326585839870.518283670708006
390.5544444437309490.8911111125381020.445555556269051
400.6125931549972510.7748136900054970.387406845002749
410.7433545096738950.513290980652210.256645490326105
420.6981786964600790.6036426070798420.301821303539921

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0577063209451337 & 0.115412641890267 & 0.942293679054866 \tabularnewline
17 & 0.019739302720929 & 0.039478605441858 & 0.980260697279071 \tabularnewline
18 & 0.00535063146476295 & 0.0107012629295259 & 0.994649368535237 \tabularnewline
19 & 0.00426899606102586 & 0.00853799212205172 & 0.995731003938974 \tabularnewline
20 & 0.101377248225139 & 0.202754496450278 & 0.898622751774861 \tabularnewline
21 & 0.193496468950704 & 0.386992937901409 & 0.806503531049296 \tabularnewline
22 & 0.23564473531261 & 0.47128947062522 & 0.76435526468739 \tabularnewline
23 & 0.260027382888729 & 0.520054765777459 & 0.73997261711127 \tabularnewline
24 & 0.459618447572013 & 0.919236895144027 & 0.540381552427987 \tabularnewline
25 & 0.36420409799408 & 0.728408195988161 & 0.63579590200592 \tabularnewline
26 & 0.272759828499019 & 0.545519656998037 & 0.727240171500981 \tabularnewline
27 & 0.192915890128645 & 0.38583178025729 & 0.807084109871355 \tabularnewline
28 & 0.133282101613712 & 0.266564203227424 & 0.866717898386288 \tabularnewline
29 & 0.087544386586833 & 0.175088773173666 & 0.912455613413167 \tabularnewline
30 & 0.0735845586927203 & 0.147169117385441 & 0.92641544130728 \tabularnewline
31 & 0.0612717359939966 & 0.122543471987993 & 0.938728264006003 \tabularnewline
32 & 0.0618462774547846 & 0.123692554909569 & 0.938153722545215 \tabularnewline
33 & 0.124886854057839 & 0.249773708115677 & 0.875113145942161 \tabularnewline
34 & 0.191697033206658 & 0.383394066413315 & 0.808302966793342 \tabularnewline
35 & 0.29323642813062 & 0.586472856261239 & 0.70676357186938 \tabularnewline
36 & 0.347076714499657 & 0.694153428999314 & 0.652923285500343 \tabularnewline
37 & 0.41502320602905 & 0.830046412058099 & 0.58497679397095 \tabularnewline
38 & 0.481716329291994 & 0.963432658583987 & 0.518283670708006 \tabularnewline
39 & 0.554444443730949 & 0.891111112538102 & 0.445555556269051 \tabularnewline
40 & 0.612593154997251 & 0.774813690005497 & 0.387406845002749 \tabularnewline
41 & 0.743354509673895 & 0.51329098065221 & 0.256645490326105 \tabularnewline
42 & 0.698178696460079 & 0.603642607079842 & 0.301821303539921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&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]16[/C][C]0.0577063209451337[/C][C]0.115412641890267[/C][C]0.942293679054866[/C][/ROW]
[ROW][C]17[/C][C]0.019739302720929[/C][C]0.039478605441858[/C][C]0.980260697279071[/C][/ROW]
[ROW][C]18[/C][C]0.00535063146476295[/C][C]0.0107012629295259[/C][C]0.994649368535237[/C][/ROW]
[ROW][C]19[/C][C]0.00426899606102586[/C][C]0.00853799212205172[/C][C]0.995731003938974[/C][/ROW]
[ROW][C]20[/C][C]0.101377248225139[/C][C]0.202754496450278[/C][C]0.898622751774861[/C][/ROW]
[ROW][C]21[/C][C]0.193496468950704[/C][C]0.386992937901409[/C][C]0.806503531049296[/C][/ROW]
[ROW][C]22[/C][C]0.23564473531261[/C][C]0.47128947062522[/C][C]0.76435526468739[/C][/ROW]
[ROW][C]23[/C][C]0.260027382888729[/C][C]0.520054765777459[/C][C]0.73997261711127[/C][/ROW]
[ROW][C]24[/C][C]0.459618447572013[/C][C]0.919236895144027[/C][C]0.540381552427987[/C][/ROW]
[ROW][C]25[/C][C]0.36420409799408[/C][C]0.728408195988161[/C][C]0.63579590200592[/C][/ROW]
[ROW][C]26[/C][C]0.272759828499019[/C][C]0.545519656998037[/C][C]0.727240171500981[/C][/ROW]
[ROW][C]27[/C][C]0.192915890128645[/C][C]0.38583178025729[/C][C]0.807084109871355[/C][/ROW]
[ROW][C]28[/C][C]0.133282101613712[/C][C]0.266564203227424[/C][C]0.866717898386288[/C][/ROW]
[ROW][C]29[/C][C]0.087544386586833[/C][C]0.175088773173666[/C][C]0.912455613413167[/C][/ROW]
[ROW][C]30[/C][C]0.0735845586927203[/C][C]0.147169117385441[/C][C]0.92641544130728[/C][/ROW]
[ROW][C]31[/C][C]0.0612717359939966[/C][C]0.122543471987993[/C][C]0.938728264006003[/C][/ROW]
[ROW][C]32[/C][C]0.0618462774547846[/C][C]0.123692554909569[/C][C]0.938153722545215[/C][/ROW]
[ROW][C]33[/C][C]0.124886854057839[/C][C]0.249773708115677[/C][C]0.875113145942161[/C][/ROW]
[ROW][C]34[/C][C]0.191697033206658[/C][C]0.383394066413315[/C][C]0.808302966793342[/C][/ROW]
[ROW][C]35[/C][C]0.29323642813062[/C][C]0.586472856261239[/C][C]0.70676357186938[/C][/ROW]
[ROW][C]36[/C][C]0.347076714499657[/C][C]0.694153428999314[/C][C]0.652923285500343[/C][/ROW]
[ROW][C]37[/C][C]0.41502320602905[/C][C]0.830046412058099[/C][C]0.58497679397095[/C][/ROW]
[ROW][C]38[/C][C]0.481716329291994[/C][C]0.963432658583987[/C][C]0.518283670708006[/C][/ROW]
[ROW][C]39[/C][C]0.554444443730949[/C][C]0.891111112538102[/C][C]0.445555556269051[/C][/ROW]
[ROW][C]40[/C][C]0.612593154997251[/C][C]0.774813690005497[/C][C]0.387406845002749[/C][/ROW]
[ROW][C]41[/C][C]0.743354509673895[/C][C]0.51329098065221[/C][C]0.256645490326105[/C][/ROW]
[ROW][C]42[/C][C]0.698178696460079[/C][C]0.603642607079842[/C][C]0.301821303539921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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
160.05770632094513370.1154126418902670.942293679054866
170.0197393027209290.0394786054418580.980260697279071
180.005350631464762950.01070126292952590.994649368535237
190.004268996061025860.008537992122051720.995731003938974
200.1013772482251390.2027544964502780.898622751774861
210.1934964689507040.3869929379014090.806503531049296
220.235644735312610.471289470625220.76435526468739
230.2600273828887290.5200547657774590.73997261711127
240.4596184475720130.9192368951440270.540381552427987
250.364204097994080.7284081959881610.63579590200592
260.2727598284990190.5455196569980370.727240171500981
270.1929158901286450.385831780257290.807084109871355
280.1332821016137120.2665642032274240.866717898386288
290.0875443865868330.1750887731736660.912455613413167
300.07358455869272030.1471691173854410.92641544130728
310.06127173599399660.1225434719879930.938728264006003
320.06184627745478460.1236925549095690.938153722545215
330.1248868540578390.2497737081156770.875113145942161
340.1916970332066580.3833940664133150.808302966793342
350.293236428130620.5864728562612390.70676357186938
360.3470767144996570.6941534289993140.652923285500343
370.415023206029050.8300464120580990.58497679397095
380.4817163292919940.9634326585839870.518283670708006
390.5544444437309490.8911111125381020.445555556269051
400.6125931549972510.7748136900054970.387406845002749
410.7433545096738950.513290980652210.256645490326105
420.6981786964600790.6036426070798420.301821303539921






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.037037037037037NOK
5% type I error level30.111111111111111NOK
10% type I error level30.111111111111111NOK

\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 & 1 & 0.037037037037037 & NOK \tabularnewline
5% type I error level & 3 & 0.111111111111111 & NOK \tabularnewline
10% type I error level & 3 & 0.111111111111111 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113155&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]1[/C][C]0.037037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.111111111111111[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.111111111111111[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113155&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113155&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 level10.037037037037037NOK
5% type I error level30.111111111111111NOK
10% type I error level30.111111111111111NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

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')
}