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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 19 Dec 2010 13:42:28 +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/19/t12927660266iyusq0rm70m9tj.htm/, Retrieved Thu, 28 Mar 2024 17:42:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112376, Retrieved Thu, 28 Mar 2024 17:42:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
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] [c05c5ae4ce2db58f67fd725429d7f25c] [Current]
-               [Multiple Regression] [] [2010-12-20 22:10:46] [504b6ff240ec7a3fcbc007044ae7a0bb]
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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112376&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







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=112376&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=112376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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
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=112376&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=112376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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)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=112376&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=112376&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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 R0.995287469888219
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
8103.02102.8055454545450.214454545454550
9104.08104.135545454545-0.0555454545454519
10104.16104.339545454545-0.179545454545448
11104.24104.414659090909-0.174659090909094
12104.33104.602159090909-0.272159090909090
13104.73104.838772727273-0.108772727272686
14104.86105.220772727273-0.360772727272726
15105.03105.284772727273-0.254772727272721
16105.62105.800772727273-0.180772727272719
17105.63105.842772727273-0.212772727272730
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 & 0.214454545454550 \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 & -0.272159090909090 \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 & -0.212772727272730 \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=112376&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]0.214454545454550[/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]-0.272159090909090[/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]-0.212772727272730[/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=112376&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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
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
8103.02102.8055454545450.214454545454550
9104.08104.135545454545-0.0555454545454519
10104.16104.339545454545-0.179545454545448
11104.24104.414659090909-0.174659090909094
12104.33104.602159090909-0.272159090909090
13104.73104.838772727273-0.108772727272686
14104.86105.220772727273-0.360772727272726
15105.03105.284772727273-0.254772727272721
16105.62105.800772727273-0.180772727272719
17105.63105.842772727273-0.212772727272730
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
170.0197393027209290.0394786054418580.98026069727907
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
250.3642040979940800.7284081959881610.63579590200592
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
350.2932364281306200.5864728562612390.70676357186938
360.3470767144996570.6941534289993140.652923285500343
370.4150232060290500.8300464120580990.58497679397095
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 & 0.98026069727907 \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 & 0.364204097994080 & 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 & 0.293236428130620 & 0.586472856261239 & 0.70676357186938 \tabularnewline
36 & 0.347076714499657 & 0.694153428999314 & 0.652923285500343 \tabularnewline
37 & 0.415023206029050 & 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=112376&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]0.98026069727907[/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]0.364204097994080[/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]0.293236428130620[/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]0.415023206029050[/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=112376&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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
160.05770632094513370.1154126418902670.942293679054866
170.0197393027209290.0394786054418580.98026069727907
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
250.3642040979940800.7284081959881610.63579590200592
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
350.2932364281306200.5864728562612390.70676357186938
360.3470767144996570.6941534289993140.652923285500343
370.4150232060290500.8300464120580990.58497679397095
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=112376&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=112376&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112376&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 level10.037037037037037NOK
5% type I error level30.111111111111111NOK
10% type I error level30.111111111111111NOK



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')
}