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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 29 Nov 2011 09:41:02 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322577746sh3zapl6zqc4v55.htm/, Retrieved Thu, 28 Mar 2024 12:17:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148439, Retrieved Thu, 28 Mar 2024 12:17:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
3440
2678
2981
2260
2844
2546
2456
2295
2379
2479
2057
2280
2351
2276
2548
2311
2201
2725
2408
2139
1898
2539
2070
2063
2565
2442
2194
2798
2074
2628
2289
2154
2467
2137
1850
2075
1791
1755
2232
1952
1822
2522
2074
2366
2173
2094
1833
1858
2040
2133
2921
3252
3318
3554
2308
1621
1315
1501
1418
1657




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Bouwvergunningen[t] = + 2245.875 + 371.577083333334M1[t] + 198.179166666667M2[t] + 523.78125M3[t] + 470.383333333334M4[t] + 414.785416666667M5[t] + 765.1875M6[t] + 284.389583333334M7[t] + 99.591666666667M8[t] + 38.1937500000003M9[t] + 148.995833333334M10[t] -148.202083333333M11[t] -7.20208333333333t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bouwvergunningen[t] =  +  2245.875 +  371.577083333334M1[t] +  198.179166666667M2[t] +  523.78125M3[t] +  470.383333333334M4[t] +  414.785416666667M5[t] +  765.1875M6[t] +  284.389583333334M7[t] +  99.591666666667M8[t] +  38.1937500000003M9[t] +  148.995833333334M10[t] -148.202083333333M11[t] -7.20208333333333t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bouwvergunningen[t] =  +  2245.875 +  371.577083333334M1[t] +  198.179166666667M2[t] +  523.78125M3[t] +  470.383333333334M4[t] +  414.785416666667M5[t] +  765.1875M6[t] +  284.389583333334M7[t] +  99.591666666667M8[t] +  38.1937500000003M9[t] +  148.995833333334M10[t] -148.202083333333M11[t] -7.20208333333333t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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
Bouwvergunningen[t] = + 2245.875 + 371.577083333334M1[t] + 198.179166666667M2[t] + 523.78125M3[t] + 470.383333333334M4[t] + 414.785416666667M5[t] + 765.1875M6[t] + 284.389583333334M7[t] + 99.591666666667M8[t] + 38.1937500000003M9[t] + 148.995833333334M10[t] -148.202083333333M11[t] -7.20208333333333t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2245.875209.62901910.713600
M1371.577083333334255.0254331.4570.151760.07588
M2198.179166666667254.6444060.77830.4403170.220159
M3523.78125254.2991742.05970.0449890.022495
M4470.383333333334253.9898851.8520.0703160.035158
M5414.785416666667253.716671.63480.1087640.054382
M6765.1875253.4796453.01870.0040930.002046
M7284.389583333334253.2789131.12280.2672120.133606
M899.591666666667253.1145580.39350.6957560.347878
M938.1937500000003252.9866530.1510.8806440.440322
M10148.995833333334252.8952530.58920.5585750.279288
M11-148.202083333333252.840397-0.58610.560580.28029
t-7.202083333333333.040977-2.36830.0220320.011016

\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) & 2245.875 & 209.629019 & 10.7136 & 0 & 0 \tabularnewline
M1 & 371.577083333334 & 255.025433 & 1.457 & 0.15176 & 0.07588 \tabularnewline
M2 & 198.179166666667 & 254.644406 & 0.7783 & 0.440317 & 0.220159 \tabularnewline
M3 & 523.78125 & 254.299174 & 2.0597 & 0.044989 & 0.022495 \tabularnewline
M4 & 470.383333333334 & 253.989885 & 1.852 & 0.070316 & 0.035158 \tabularnewline
M5 & 414.785416666667 & 253.71667 & 1.6348 & 0.108764 & 0.054382 \tabularnewline
M6 & 765.1875 & 253.479645 & 3.0187 & 0.004093 & 0.002046 \tabularnewline
M7 & 284.389583333334 & 253.278913 & 1.1228 & 0.267212 & 0.133606 \tabularnewline
M8 & 99.591666666667 & 253.114558 & 0.3935 & 0.695756 & 0.347878 \tabularnewline
M9 & 38.1937500000003 & 252.986653 & 0.151 & 0.880644 & 0.440322 \tabularnewline
M10 & 148.995833333334 & 252.895253 & 0.5892 & 0.558575 & 0.279288 \tabularnewline
M11 & -148.202083333333 & 252.840397 & -0.5861 & 0.56058 & 0.28029 \tabularnewline
t & -7.20208333333333 & 3.040977 & -2.3683 & 0.022032 & 0.011016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&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]2245.875[/C][C]209.629019[/C][C]10.7136[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]371.577083333334[/C][C]255.025433[/C][C]1.457[/C][C]0.15176[/C][C]0.07588[/C][/ROW]
[ROW][C]M2[/C][C]198.179166666667[/C][C]254.644406[/C][C]0.7783[/C][C]0.440317[/C][C]0.220159[/C][/ROW]
[ROW][C]M3[/C][C]523.78125[/C][C]254.299174[/C][C]2.0597[/C][C]0.044989[/C][C]0.022495[/C][/ROW]
[ROW][C]M4[/C][C]470.383333333334[/C][C]253.989885[/C][C]1.852[/C][C]0.070316[/C][C]0.035158[/C][/ROW]
[ROW][C]M5[/C][C]414.785416666667[/C][C]253.71667[/C][C]1.6348[/C][C]0.108764[/C][C]0.054382[/C][/ROW]
[ROW][C]M6[/C][C]765.1875[/C][C]253.479645[/C][C]3.0187[/C][C]0.004093[/C][C]0.002046[/C][/ROW]
[ROW][C]M7[/C][C]284.389583333334[/C][C]253.278913[/C][C]1.1228[/C][C]0.267212[/C][C]0.133606[/C][/ROW]
[ROW][C]M8[/C][C]99.591666666667[/C][C]253.114558[/C][C]0.3935[/C][C]0.695756[/C][C]0.347878[/C][/ROW]
[ROW][C]M9[/C][C]38.1937500000003[/C][C]252.986653[/C][C]0.151[/C][C]0.880644[/C][C]0.440322[/C][/ROW]
[ROW][C]M10[/C][C]148.995833333334[/C][C]252.895253[/C][C]0.5892[/C][C]0.558575[/C][C]0.279288[/C][/ROW]
[ROW][C]M11[/C][C]-148.202083333333[/C][C]252.840397[/C][C]-0.5861[/C][C]0.56058[/C][C]0.28029[/C][/ROW]
[ROW][C]t[/C][C]-7.20208333333333[/C][C]3.040977[/C][C]-2.3683[/C][C]0.022032[/C][C]0.011016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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)2245.875209.62901910.713600
M1371.577083333334255.0254331.4570.151760.07588
M2198.179166666667254.6444060.77830.4403170.220159
M3523.78125254.2991742.05970.0449890.022495
M4470.383333333334253.9898851.8520.0703160.035158
M5414.785416666667253.716671.63480.1087640.054382
M6765.1875253.4796453.01870.0040930.002046
M7284.389583333334253.2789131.12280.2672120.133606
M899.591666666667253.1145580.39350.6957560.347878
M938.1937500000003252.9866530.1510.8806440.440322
M10148.995833333334252.8952530.58920.5585750.279288
M11-148.202083333333252.840397-0.58610.560580.28029
t-7.202083333333333.040977-2.36830.0220320.011016







Multiple Linear Regression - Regression Statistics
Multiple R0.634916141943439
R-squared0.403118507300341
Adjusted R-squared0.250723232568513
F-TEST (value)2.64521657912107
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.00863128557344173
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation399.746854071956
Sum Squared Residuals7510484.725

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.634916141943439 \tabularnewline
R-squared & 0.403118507300341 \tabularnewline
Adjusted R-squared & 0.250723232568513 \tabularnewline
F-TEST (value) & 2.64521657912107 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.00863128557344173 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 399.746854071956 \tabularnewline
Sum Squared Residuals & 7510484.725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.634916141943439[/C][/ROW]
[ROW][C]R-squared[/C][C]0.403118507300341[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.250723232568513[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.64521657912107[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.00863128557344173[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]399.746854071956[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7510484.725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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 R0.634916141943439
R-squared0.403118507300341
Adjusted R-squared0.250723232568513
F-TEST (value)2.64521657912107
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.00863128557344173
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation399.746854071956
Sum Squared Residuals7510484.725







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134402610.25829.750000000003
226782429.65248.35
329812748.05232.95
422602687.45-427.45
528442624.65219.35
625462967.85-421.85
724562479.85-23.85
822952287.857.15000000000001
923792219.25159.75
1024792322.85156.15
1120572018.4538.5499999999999
1222802159.45120.55
1323512523.825-172.825000000001
1422762343.225-67.225
1525482661.625-113.625
1623112601.025-290.025
1722012538.225-337.225
1827252881.425-156.425
1924082393.42514.575
2021392201.425-62.425
2118982132.825-234.825
2225392236.425302.575
2320701932.025137.975
2420632073.025-10.0249999999997
2525652437.4127.599999999999
2624422256.8185.2
2721942575.2-381.2
2827982514.6283.4
2920742451.8-377.8
3026282795-167
3122892307-18
322154211539
3324672046.4420.6
3421372150-13
3518501845.64.40000000000007
3620751986.688.4000000000003
3717912350.975-559.975000000001
3817552170.375-415.375
3922322488.775-256.775
4019522428.175-476.175
4118222365.375-543.375
4225222708.575-186.575
4320742220.575-146.575
4423662028.575337.425
4521731959.975213.025
4620942063.57530.425
4718331759.17573.8250000000001
4818581900.175-42.1749999999996
4920402264.55-224.550000000001
5021332083.9549.05
5129212402.35518.65
5232522341.75910.25
5333182278.951039.05
5435542622.15931.85
5523082134.15173.85
5616211942.15-321.15
5713151873.55-558.55
5815011977.15-476.15
5914181672.75-254.75
6016571813.75-156.75

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3440 & 2610.25 & 829.750000000003 \tabularnewline
2 & 2678 & 2429.65 & 248.35 \tabularnewline
3 & 2981 & 2748.05 & 232.95 \tabularnewline
4 & 2260 & 2687.45 & -427.45 \tabularnewline
5 & 2844 & 2624.65 & 219.35 \tabularnewline
6 & 2546 & 2967.85 & -421.85 \tabularnewline
7 & 2456 & 2479.85 & -23.85 \tabularnewline
8 & 2295 & 2287.85 & 7.15000000000001 \tabularnewline
9 & 2379 & 2219.25 & 159.75 \tabularnewline
10 & 2479 & 2322.85 & 156.15 \tabularnewline
11 & 2057 & 2018.45 & 38.5499999999999 \tabularnewline
12 & 2280 & 2159.45 & 120.55 \tabularnewline
13 & 2351 & 2523.825 & -172.825000000001 \tabularnewline
14 & 2276 & 2343.225 & -67.225 \tabularnewline
15 & 2548 & 2661.625 & -113.625 \tabularnewline
16 & 2311 & 2601.025 & -290.025 \tabularnewline
17 & 2201 & 2538.225 & -337.225 \tabularnewline
18 & 2725 & 2881.425 & -156.425 \tabularnewline
19 & 2408 & 2393.425 & 14.575 \tabularnewline
20 & 2139 & 2201.425 & -62.425 \tabularnewline
21 & 1898 & 2132.825 & -234.825 \tabularnewline
22 & 2539 & 2236.425 & 302.575 \tabularnewline
23 & 2070 & 1932.025 & 137.975 \tabularnewline
24 & 2063 & 2073.025 & -10.0249999999997 \tabularnewline
25 & 2565 & 2437.4 & 127.599999999999 \tabularnewline
26 & 2442 & 2256.8 & 185.2 \tabularnewline
27 & 2194 & 2575.2 & -381.2 \tabularnewline
28 & 2798 & 2514.6 & 283.4 \tabularnewline
29 & 2074 & 2451.8 & -377.8 \tabularnewline
30 & 2628 & 2795 & -167 \tabularnewline
31 & 2289 & 2307 & -18 \tabularnewline
32 & 2154 & 2115 & 39 \tabularnewline
33 & 2467 & 2046.4 & 420.6 \tabularnewline
34 & 2137 & 2150 & -13 \tabularnewline
35 & 1850 & 1845.6 & 4.40000000000007 \tabularnewline
36 & 2075 & 1986.6 & 88.4000000000003 \tabularnewline
37 & 1791 & 2350.975 & -559.975000000001 \tabularnewline
38 & 1755 & 2170.375 & -415.375 \tabularnewline
39 & 2232 & 2488.775 & -256.775 \tabularnewline
40 & 1952 & 2428.175 & -476.175 \tabularnewline
41 & 1822 & 2365.375 & -543.375 \tabularnewline
42 & 2522 & 2708.575 & -186.575 \tabularnewline
43 & 2074 & 2220.575 & -146.575 \tabularnewline
44 & 2366 & 2028.575 & 337.425 \tabularnewline
45 & 2173 & 1959.975 & 213.025 \tabularnewline
46 & 2094 & 2063.575 & 30.425 \tabularnewline
47 & 1833 & 1759.175 & 73.8250000000001 \tabularnewline
48 & 1858 & 1900.175 & -42.1749999999996 \tabularnewline
49 & 2040 & 2264.55 & -224.550000000001 \tabularnewline
50 & 2133 & 2083.95 & 49.05 \tabularnewline
51 & 2921 & 2402.35 & 518.65 \tabularnewline
52 & 3252 & 2341.75 & 910.25 \tabularnewline
53 & 3318 & 2278.95 & 1039.05 \tabularnewline
54 & 3554 & 2622.15 & 931.85 \tabularnewline
55 & 2308 & 2134.15 & 173.85 \tabularnewline
56 & 1621 & 1942.15 & -321.15 \tabularnewline
57 & 1315 & 1873.55 & -558.55 \tabularnewline
58 & 1501 & 1977.15 & -476.15 \tabularnewline
59 & 1418 & 1672.75 & -254.75 \tabularnewline
60 & 1657 & 1813.75 & -156.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&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]3440[/C][C]2610.25[/C][C]829.750000000003[/C][/ROW]
[ROW][C]2[/C][C]2678[/C][C]2429.65[/C][C]248.35[/C][/ROW]
[ROW][C]3[/C][C]2981[/C][C]2748.05[/C][C]232.95[/C][/ROW]
[ROW][C]4[/C][C]2260[/C][C]2687.45[/C][C]-427.45[/C][/ROW]
[ROW][C]5[/C][C]2844[/C][C]2624.65[/C][C]219.35[/C][/ROW]
[ROW][C]6[/C][C]2546[/C][C]2967.85[/C][C]-421.85[/C][/ROW]
[ROW][C]7[/C][C]2456[/C][C]2479.85[/C][C]-23.85[/C][/ROW]
[ROW][C]8[/C][C]2295[/C][C]2287.85[/C][C]7.15000000000001[/C][/ROW]
[ROW][C]9[/C][C]2379[/C][C]2219.25[/C][C]159.75[/C][/ROW]
[ROW][C]10[/C][C]2479[/C][C]2322.85[/C][C]156.15[/C][/ROW]
[ROW][C]11[/C][C]2057[/C][C]2018.45[/C][C]38.5499999999999[/C][/ROW]
[ROW][C]12[/C][C]2280[/C][C]2159.45[/C][C]120.55[/C][/ROW]
[ROW][C]13[/C][C]2351[/C][C]2523.825[/C][C]-172.825000000001[/C][/ROW]
[ROW][C]14[/C][C]2276[/C][C]2343.225[/C][C]-67.225[/C][/ROW]
[ROW][C]15[/C][C]2548[/C][C]2661.625[/C][C]-113.625[/C][/ROW]
[ROW][C]16[/C][C]2311[/C][C]2601.025[/C][C]-290.025[/C][/ROW]
[ROW][C]17[/C][C]2201[/C][C]2538.225[/C][C]-337.225[/C][/ROW]
[ROW][C]18[/C][C]2725[/C][C]2881.425[/C][C]-156.425[/C][/ROW]
[ROW][C]19[/C][C]2408[/C][C]2393.425[/C][C]14.575[/C][/ROW]
[ROW][C]20[/C][C]2139[/C][C]2201.425[/C][C]-62.425[/C][/ROW]
[ROW][C]21[/C][C]1898[/C][C]2132.825[/C][C]-234.825[/C][/ROW]
[ROW][C]22[/C][C]2539[/C][C]2236.425[/C][C]302.575[/C][/ROW]
[ROW][C]23[/C][C]2070[/C][C]1932.025[/C][C]137.975[/C][/ROW]
[ROW][C]24[/C][C]2063[/C][C]2073.025[/C][C]-10.0249999999997[/C][/ROW]
[ROW][C]25[/C][C]2565[/C][C]2437.4[/C][C]127.599999999999[/C][/ROW]
[ROW][C]26[/C][C]2442[/C][C]2256.8[/C][C]185.2[/C][/ROW]
[ROW][C]27[/C][C]2194[/C][C]2575.2[/C][C]-381.2[/C][/ROW]
[ROW][C]28[/C][C]2798[/C][C]2514.6[/C][C]283.4[/C][/ROW]
[ROW][C]29[/C][C]2074[/C][C]2451.8[/C][C]-377.8[/C][/ROW]
[ROW][C]30[/C][C]2628[/C][C]2795[/C][C]-167[/C][/ROW]
[ROW][C]31[/C][C]2289[/C][C]2307[/C][C]-18[/C][/ROW]
[ROW][C]32[/C][C]2154[/C][C]2115[/C][C]39[/C][/ROW]
[ROW][C]33[/C][C]2467[/C][C]2046.4[/C][C]420.6[/C][/ROW]
[ROW][C]34[/C][C]2137[/C][C]2150[/C][C]-13[/C][/ROW]
[ROW][C]35[/C][C]1850[/C][C]1845.6[/C][C]4.40000000000007[/C][/ROW]
[ROW][C]36[/C][C]2075[/C][C]1986.6[/C][C]88.4000000000003[/C][/ROW]
[ROW][C]37[/C][C]1791[/C][C]2350.975[/C][C]-559.975000000001[/C][/ROW]
[ROW][C]38[/C][C]1755[/C][C]2170.375[/C][C]-415.375[/C][/ROW]
[ROW][C]39[/C][C]2232[/C][C]2488.775[/C][C]-256.775[/C][/ROW]
[ROW][C]40[/C][C]1952[/C][C]2428.175[/C][C]-476.175[/C][/ROW]
[ROW][C]41[/C][C]1822[/C][C]2365.375[/C][C]-543.375[/C][/ROW]
[ROW][C]42[/C][C]2522[/C][C]2708.575[/C][C]-186.575[/C][/ROW]
[ROW][C]43[/C][C]2074[/C][C]2220.575[/C][C]-146.575[/C][/ROW]
[ROW][C]44[/C][C]2366[/C][C]2028.575[/C][C]337.425[/C][/ROW]
[ROW][C]45[/C][C]2173[/C][C]1959.975[/C][C]213.025[/C][/ROW]
[ROW][C]46[/C][C]2094[/C][C]2063.575[/C][C]30.425[/C][/ROW]
[ROW][C]47[/C][C]1833[/C][C]1759.175[/C][C]73.8250000000001[/C][/ROW]
[ROW][C]48[/C][C]1858[/C][C]1900.175[/C][C]-42.1749999999996[/C][/ROW]
[ROW][C]49[/C][C]2040[/C][C]2264.55[/C][C]-224.550000000001[/C][/ROW]
[ROW][C]50[/C][C]2133[/C][C]2083.95[/C][C]49.05[/C][/ROW]
[ROW][C]51[/C][C]2921[/C][C]2402.35[/C][C]518.65[/C][/ROW]
[ROW][C]52[/C][C]3252[/C][C]2341.75[/C][C]910.25[/C][/ROW]
[ROW][C]53[/C][C]3318[/C][C]2278.95[/C][C]1039.05[/C][/ROW]
[ROW][C]54[/C][C]3554[/C][C]2622.15[/C][C]931.85[/C][/ROW]
[ROW][C]55[/C][C]2308[/C][C]2134.15[/C][C]173.85[/C][/ROW]
[ROW][C]56[/C][C]1621[/C][C]1942.15[/C][C]-321.15[/C][/ROW]
[ROW][C]57[/C][C]1315[/C][C]1873.55[/C][C]-558.55[/C][/ROW]
[ROW][C]58[/C][C]1501[/C][C]1977.15[/C][C]-476.15[/C][/ROW]
[ROW][C]59[/C][C]1418[/C][C]1672.75[/C][C]-254.75[/C][/ROW]
[ROW][C]60[/C][C]1657[/C][C]1813.75[/C][C]-156.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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
134402610.25829.750000000003
226782429.65248.35
329812748.05232.95
422602687.45-427.45
528442624.65219.35
625462967.85-421.85
724562479.85-23.85
822952287.857.15000000000001
923792219.25159.75
1024792322.85156.15
1120572018.4538.5499999999999
1222802159.45120.55
1323512523.825-172.825000000001
1422762343.225-67.225
1525482661.625-113.625
1623112601.025-290.025
1722012538.225-337.225
1827252881.425-156.425
1924082393.42514.575
2021392201.425-62.425
2118982132.825-234.825
2225392236.425302.575
2320701932.025137.975
2420632073.025-10.0249999999997
2525652437.4127.599999999999
2624422256.8185.2
2721942575.2-381.2
2827982514.6283.4
2920742451.8-377.8
3026282795-167
3122892307-18
322154211539
3324672046.4420.6
3421372150-13
3518501845.64.40000000000007
3620751986.688.4000000000003
3717912350.975-559.975000000001
3817552170.375-415.375
3922322488.775-256.775
4019522428.175-476.175
4118222365.375-543.375
4225222708.575-186.575
4320742220.575-146.575
4423662028.575337.425
4521731959.975213.025
4620942063.57530.425
4718331759.17573.8250000000001
4818581900.175-42.1749999999996
4920402264.55-224.550000000001
5021332083.9549.05
5129212402.35518.65
5232522341.75910.25
5333182278.951039.05
5435542622.15931.85
5523082134.15173.85
5616211942.15-321.15
5713151873.55-558.55
5815011977.15-476.15
5914181672.75-254.75
6016571813.75-156.75







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.3852963559248810.7705927118497620.614703644075119
170.2380483528145360.4760967056290720.761951647185464
180.2762643023190650.5525286046381290.723735697680935
190.1969809643462420.3939619286924830.803019035653758
200.1211477952435110.2422955904870220.878852204756489
210.0712408091219870.1424816182439740.928759190878013
220.05746179205786690.1149235841157340.942538207942133
230.03825606816239280.07651213632478550.961743931837607
240.01967899730917120.03935799461834230.980321002690829
250.01188224641788950.02376449283577890.988117753582111
260.009688423909745890.01937684781949180.990311576090254
270.006029356799936650.01205871359987330.993970643200063
280.02212309078686990.04424618157373990.97787690921313
290.014893725794160.029787451588320.98510627420584
300.01035910962089310.02071821924178630.989640890379107
310.005263354991972580.01052670998394520.994736645008027
320.002702162335316670.005404324670633330.997297837664683
330.004382043322286450.008764086644572910.995617956677714
340.002696133172470980.005392266344941960.997303866827529
350.001420962120763940.002841924241527880.998579037879236
360.0009286032608492350.001857206521698470.999071396739151
370.001577587567919240.003155175135838490.998422412432081
380.0009425367205490360.001885073441098070.999057463279451
390.0004965421690109410.0009930843380218820.999503457830989
400.001165626672385220.002331253344770440.998834373327615
410.02306655402307550.0461331080461510.976933445976925
420.3866043749895490.7732087499790990.613395625010451
430.7495755617165440.5008488765669130.250424438283456
440.6765729213441430.6468541573117140.323427078655857

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.385296355924881 & 0.770592711849762 & 0.614703644075119 \tabularnewline
17 & 0.238048352814536 & 0.476096705629072 & 0.761951647185464 \tabularnewline
18 & 0.276264302319065 & 0.552528604638129 & 0.723735697680935 \tabularnewline
19 & 0.196980964346242 & 0.393961928692483 & 0.803019035653758 \tabularnewline
20 & 0.121147795243511 & 0.242295590487022 & 0.878852204756489 \tabularnewline
21 & 0.071240809121987 & 0.142481618243974 & 0.928759190878013 \tabularnewline
22 & 0.0574617920578669 & 0.114923584115734 & 0.942538207942133 \tabularnewline
23 & 0.0382560681623928 & 0.0765121363247855 & 0.961743931837607 \tabularnewline
24 & 0.0196789973091712 & 0.0393579946183423 & 0.980321002690829 \tabularnewline
25 & 0.0118822464178895 & 0.0237644928357789 & 0.988117753582111 \tabularnewline
26 & 0.00968842390974589 & 0.0193768478194918 & 0.990311576090254 \tabularnewline
27 & 0.00602935679993665 & 0.0120587135998733 & 0.993970643200063 \tabularnewline
28 & 0.0221230907868699 & 0.0442461815737399 & 0.97787690921313 \tabularnewline
29 & 0.01489372579416 & 0.02978745158832 & 0.98510627420584 \tabularnewline
30 & 0.0103591096208931 & 0.0207182192417863 & 0.989640890379107 \tabularnewline
31 & 0.00526335499197258 & 0.0105267099839452 & 0.994736645008027 \tabularnewline
32 & 0.00270216233531667 & 0.00540432467063333 & 0.997297837664683 \tabularnewline
33 & 0.00438204332228645 & 0.00876408664457291 & 0.995617956677714 \tabularnewline
34 & 0.00269613317247098 & 0.00539226634494196 & 0.997303866827529 \tabularnewline
35 & 0.00142096212076394 & 0.00284192424152788 & 0.998579037879236 \tabularnewline
36 & 0.000928603260849235 & 0.00185720652169847 & 0.999071396739151 \tabularnewline
37 & 0.00157758756791924 & 0.00315517513583849 & 0.998422412432081 \tabularnewline
38 & 0.000942536720549036 & 0.00188507344109807 & 0.999057463279451 \tabularnewline
39 & 0.000496542169010941 & 0.000993084338021882 & 0.999503457830989 \tabularnewline
40 & 0.00116562667238522 & 0.00233125334477044 & 0.998834373327615 \tabularnewline
41 & 0.0230665540230755 & 0.046133108046151 & 0.976933445976925 \tabularnewline
42 & 0.386604374989549 & 0.773208749979099 & 0.613395625010451 \tabularnewline
43 & 0.749575561716544 & 0.500848876566913 & 0.250424438283456 \tabularnewline
44 & 0.676572921344143 & 0.646854157311714 & 0.323427078655857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&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]0.385296355924881[/C][C]0.770592711849762[/C][C]0.614703644075119[/C][/ROW]
[ROW][C]17[/C][C]0.238048352814536[/C][C]0.476096705629072[/C][C]0.761951647185464[/C][/ROW]
[ROW][C]18[/C][C]0.276264302319065[/C][C]0.552528604638129[/C][C]0.723735697680935[/C][/ROW]
[ROW][C]19[/C][C]0.196980964346242[/C][C]0.393961928692483[/C][C]0.803019035653758[/C][/ROW]
[ROW][C]20[/C][C]0.121147795243511[/C][C]0.242295590487022[/C][C]0.878852204756489[/C][/ROW]
[ROW][C]21[/C][C]0.071240809121987[/C][C]0.142481618243974[/C][C]0.928759190878013[/C][/ROW]
[ROW][C]22[/C][C]0.0574617920578669[/C][C]0.114923584115734[/C][C]0.942538207942133[/C][/ROW]
[ROW][C]23[/C][C]0.0382560681623928[/C][C]0.0765121363247855[/C][C]0.961743931837607[/C][/ROW]
[ROW][C]24[/C][C]0.0196789973091712[/C][C]0.0393579946183423[/C][C]0.980321002690829[/C][/ROW]
[ROW][C]25[/C][C]0.0118822464178895[/C][C]0.0237644928357789[/C][C]0.988117753582111[/C][/ROW]
[ROW][C]26[/C][C]0.00968842390974589[/C][C]0.0193768478194918[/C][C]0.990311576090254[/C][/ROW]
[ROW][C]27[/C][C]0.00602935679993665[/C][C]0.0120587135998733[/C][C]0.993970643200063[/C][/ROW]
[ROW][C]28[/C][C]0.0221230907868699[/C][C]0.0442461815737399[/C][C]0.97787690921313[/C][/ROW]
[ROW][C]29[/C][C]0.01489372579416[/C][C]0.02978745158832[/C][C]0.98510627420584[/C][/ROW]
[ROW][C]30[/C][C]0.0103591096208931[/C][C]0.0207182192417863[/C][C]0.989640890379107[/C][/ROW]
[ROW][C]31[/C][C]0.00526335499197258[/C][C]0.0105267099839452[/C][C]0.994736645008027[/C][/ROW]
[ROW][C]32[/C][C]0.00270216233531667[/C][C]0.00540432467063333[/C][C]0.997297837664683[/C][/ROW]
[ROW][C]33[/C][C]0.00438204332228645[/C][C]0.00876408664457291[/C][C]0.995617956677714[/C][/ROW]
[ROW][C]34[/C][C]0.00269613317247098[/C][C]0.00539226634494196[/C][C]0.997303866827529[/C][/ROW]
[ROW][C]35[/C][C]0.00142096212076394[/C][C]0.00284192424152788[/C][C]0.998579037879236[/C][/ROW]
[ROW][C]36[/C][C]0.000928603260849235[/C][C]0.00185720652169847[/C][C]0.999071396739151[/C][/ROW]
[ROW][C]37[/C][C]0.00157758756791924[/C][C]0.00315517513583849[/C][C]0.998422412432081[/C][/ROW]
[ROW][C]38[/C][C]0.000942536720549036[/C][C]0.00188507344109807[/C][C]0.999057463279451[/C][/ROW]
[ROW][C]39[/C][C]0.000496542169010941[/C][C]0.000993084338021882[/C][C]0.999503457830989[/C][/ROW]
[ROW][C]40[/C][C]0.00116562667238522[/C][C]0.00233125334477044[/C][C]0.998834373327615[/C][/ROW]
[ROW][C]41[/C][C]0.0230665540230755[/C][C]0.046133108046151[/C][C]0.976933445976925[/C][/ROW]
[ROW][C]42[/C][C]0.386604374989549[/C][C]0.773208749979099[/C][C]0.613395625010451[/C][/ROW]
[ROW][C]43[/C][C]0.749575561716544[/C][C]0.500848876566913[/C][C]0.250424438283456[/C][/ROW]
[ROW][C]44[/C][C]0.676572921344143[/C][C]0.646854157311714[/C][C]0.323427078655857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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
160.3852963559248810.7705927118497620.614703644075119
170.2380483528145360.4760967056290720.761951647185464
180.2762643023190650.5525286046381290.723735697680935
190.1969809643462420.3939619286924830.803019035653758
200.1211477952435110.2422955904870220.878852204756489
210.0712408091219870.1424816182439740.928759190878013
220.05746179205786690.1149235841157340.942538207942133
230.03825606816239280.07651213632478550.961743931837607
240.01967899730917120.03935799461834230.980321002690829
250.01188224641788950.02376449283577890.988117753582111
260.009688423909745890.01937684781949180.990311576090254
270.006029356799936650.01205871359987330.993970643200063
280.02212309078686990.04424618157373990.97787690921313
290.014893725794160.029787451588320.98510627420584
300.01035910962089310.02071821924178630.989640890379107
310.005263354991972580.01052670998394520.994736645008027
320.002702162335316670.005404324670633330.997297837664683
330.004382043322286450.008764086644572910.995617956677714
340.002696133172470980.005392266344941960.997303866827529
350.001420962120763940.002841924241527880.998579037879236
360.0009286032608492350.001857206521698470.999071396739151
370.001577587567919240.003155175135838490.998422412432081
380.0009425367205490360.001885073441098070.999057463279451
390.0004965421690109410.0009930843380218820.999503457830989
400.001165626672385220.002331253344770440.998834373327615
410.02306655402307550.0461331080461510.976933445976925
420.3866043749895490.7732087499790990.613395625010451
430.7495755617165440.5008488765669130.250424438283456
440.6765729213441430.6468541573117140.323427078655857







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.310344827586207NOK
5% type I error level180.620689655172414NOK
10% type I error level190.655172413793103NOK

\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 & 9 & 0.310344827586207 & NOK \tabularnewline
5% type I error level & 18 & 0.620689655172414 & NOK \tabularnewline
10% type I error level & 19 & 0.655172413793103 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148439&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]9[/C][C]0.310344827586207[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.620689655172414[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.655172413793103[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148439&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148439&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 level90.310344827586207NOK
5% type I error level180.620689655172414NOK
10% type I error level190.655172413793103NOK



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')
}