Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 12 Dec 2014 14:00:40 +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/2014/Dec/12/t1418392901tvgsd5qoeqcyran.htm/, Retrieved Thu, 31 Oct 2024 23:35:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266716, Retrieved Thu, 31 Oct 2024 23:35:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-12 14:00:40] [4897fbbb7461c8caec7645a3718e7cbe] [Current]
Feedback Forum

Post a new message
Dataseries X:
7.5 18 68
6.5 39 32
1.0 46 62
1.0 31 33
5.5 67 52
8.5 35 62
6.5 52 77
4.5 77 76
2.0 37 41
5.0 32 48
0.5 36 63
5.0 69 78
2.5 21 19
5.0 26 31
5.5 54 66
3.5 36 35
4.0 23 45
6.5 112 25
4.5 35 44
5.5 47 54
4.0 37 74
7.5 109 80
4.0 20 61
5.5 22 41
2.5 23 46
5.5 32 39
3.5 30 34
4.5 43 42
4.5 16 39
6.0 49 20
5.0 43 53
6.5 46 54
5.0 19 49
6.0 23 34
4.5 59 46
5.0 32 37
5.0 19 30
6.5 22 28
7.0 48 45
4.5 23 35
8.5 33 41
3.5 34 73
6.0 48 17
1.5 18 40
3.5 33 37
7.5 67 28
5.0 80 56
6.5 32 50
6.5 43 59
6.5 38 27
7.0 29 61
1.5 32 51
4.0 35 35
4.5 29 48
0.0 12 25
3.5 37 44
4.5 51 20
0.0 14 26
3.0 20 23
3.5 11 21
3.0 35 41
1.0 8 22




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 2.81236 + 0.035641PRH[t] + 0.00929074CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  2.81236 +  0.035641PRH[t] +  0.00929074CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  2.81236 +  0.035641PRH[t] +  0.00929074CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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
Ex[t] = + 2.81236 + 0.035641PRH[t] + 0.00929074CH[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.812360.7323323.840.0003025380.000151269
PRH0.0356410.01258822.8310.006330580.00316529
CH0.009290740.0156320.59430.5545560.277278

\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) & 2.81236 & 0.732332 & 3.84 & 0.000302538 & 0.000151269 \tabularnewline
PRH & 0.035641 & 0.0125882 & 2.831 & 0.00633058 & 0.00316529 \tabularnewline
CH & 0.00929074 & 0.015632 & 0.5943 & 0.554556 & 0.277278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]2.81236[/C][C]0.732332[/C][C]3.84[/C][C]0.000302538[/C][C]0.000151269[/C][/ROW]
[ROW][C]PRH[/C][C]0.035641[/C][C]0.0125882[/C][C]2.831[/C][C]0.00633058[/C][C]0.00316529[/C][/ROW]
[ROW][C]CH[/C][C]0.00929074[/C][C]0.015632[/C][C]0.5943[/C][C]0.554556[/C][C]0.277278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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)2.812360.7323323.840.0003025380.000151269
PRH0.0356410.01258822.8310.006330580.00316529
CH0.009290740.0156320.59430.5545560.277278







Multiple Linear Regression - Regression Statistics
Multiple R0.393727
R-squared0.155021
Adjusted R-squared0.126377
F-TEST (value)5.4121
F-TEST (DF numerator)2
F-TEST (DF denominator)59
p-value0.00694959
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.90699
Sum Squared Residuals214.56

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.393727 \tabularnewline
R-squared & 0.155021 \tabularnewline
Adjusted R-squared & 0.126377 \tabularnewline
F-TEST (value) & 5.4121 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.00694959 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.90699 \tabularnewline
Sum Squared Residuals & 214.56 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.393727[/C][/ROW]
[ROW][C]R-squared[/C][C]0.155021[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.126377[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.4121[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.00694959[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.90699[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]214.56[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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 R0.393727
R-squared0.155021
Adjusted R-squared0.126377
F-TEST (value)5.4121
F-TEST (DF numerator)2
F-TEST (DF denominator)59
p-value0.00694959
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.90699
Sum Squared Residuals214.56







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.54.085663.41434
26.54.499662.00034
315.02787-4.02787
414.22382-3.22382
55.55.68342-0.183422
68.54.635823.86418
76.55.381081.11892
84.56.26281-1.76281
924.51199-2.51199
1054.398820.601176
110.54.68075-4.18075
1255.99626-0.996263
132.53.73734-1.23734
1454.027030.972965
155.55.350160.149841
163.54.42061-0.920608
1744.05018-0.0501822
186.57.03642-0.536417
194.54.468580.0314164
205.54.989180.510817
2144.81859-0.818588
227.57.440480.0595153
2344.09191-0.091911
245.53.977381.52262
252.54.05947-1.55947
265.54.315211.18479
273.54.19747-0.697471
284.54.73513-0.23513
294.53.744950.755049
3064.744581.25542
3154.837330.162672
326.54.953541.54646
3353.944781.05522
3463.947982.05202
354.55.34255-0.842549
3654.296630.703375
3753.768261.23174
386.53.85662.6434
3974.941212.05879
404.53.957270.542725
418.54.369434.13057
423.54.70237-1.20237
4364.681071.31893
441.53.82552-2.32552
453.54.33227-0.832266
467.55.460442.03956
4756.18392-1.18392
486.54.41742.0826
496.54.893071.60693
506.54.417562.08244
5174.412682.58732
521.54.4267-2.9267
5344.38497-0.384967
544.54.29190.2081
5503.47232-3.47232
563.54.53987-1.03987
574.54.81586-0.315862
5803.55289-3.55289
5933.73886-0.738863
603.53.399510.100488
6134.44071-1.44071
6213.30188-2.30188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 4.08566 & 3.41434 \tabularnewline
2 & 6.5 & 4.49966 & 2.00034 \tabularnewline
3 & 1 & 5.02787 & -4.02787 \tabularnewline
4 & 1 & 4.22382 & -3.22382 \tabularnewline
5 & 5.5 & 5.68342 & -0.183422 \tabularnewline
6 & 8.5 & 4.63582 & 3.86418 \tabularnewline
7 & 6.5 & 5.38108 & 1.11892 \tabularnewline
8 & 4.5 & 6.26281 & -1.76281 \tabularnewline
9 & 2 & 4.51199 & -2.51199 \tabularnewline
10 & 5 & 4.39882 & 0.601176 \tabularnewline
11 & 0.5 & 4.68075 & -4.18075 \tabularnewline
12 & 5 & 5.99626 & -0.996263 \tabularnewline
13 & 2.5 & 3.73734 & -1.23734 \tabularnewline
14 & 5 & 4.02703 & 0.972965 \tabularnewline
15 & 5.5 & 5.35016 & 0.149841 \tabularnewline
16 & 3.5 & 4.42061 & -0.920608 \tabularnewline
17 & 4 & 4.05018 & -0.0501822 \tabularnewline
18 & 6.5 & 7.03642 & -0.536417 \tabularnewline
19 & 4.5 & 4.46858 & 0.0314164 \tabularnewline
20 & 5.5 & 4.98918 & 0.510817 \tabularnewline
21 & 4 & 4.81859 & -0.818588 \tabularnewline
22 & 7.5 & 7.44048 & 0.0595153 \tabularnewline
23 & 4 & 4.09191 & -0.091911 \tabularnewline
24 & 5.5 & 3.97738 & 1.52262 \tabularnewline
25 & 2.5 & 4.05947 & -1.55947 \tabularnewline
26 & 5.5 & 4.31521 & 1.18479 \tabularnewline
27 & 3.5 & 4.19747 & -0.697471 \tabularnewline
28 & 4.5 & 4.73513 & -0.23513 \tabularnewline
29 & 4.5 & 3.74495 & 0.755049 \tabularnewline
30 & 6 & 4.74458 & 1.25542 \tabularnewline
31 & 5 & 4.83733 & 0.162672 \tabularnewline
32 & 6.5 & 4.95354 & 1.54646 \tabularnewline
33 & 5 & 3.94478 & 1.05522 \tabularnewline
34 & 6 & 3.94798 & 2.05202 \tabularnewline
35 & 4.5 & 5.34255 & -0.842549 \tabularnewline
36 & 5 & 4.29663 & 0.703375 \tabularnewline
37 & 5 & 3.76826 & 1.23174 \tabularnewline
38 & 6.5 & 3.8566 & 2.6434 \tabularnewline
39 & 7 & 4.94121 & 2.05879 \tabularnewline
40 & 4.5 & 3.95727 & 0.542725 \tabularnewline
41 & 8.5 & 4.36943 & 4.13057 \tabularnewline
42 & 3.5 & 4.70237 & -1.20237 \tabularnewline
43 & 6 & 4.68107 & 1.31893 \tabularnewline
44 & 1.5 & 3.82552 & -2.32552 \tabularnewline
45 & 3.5 & 4.33227 & -0.832266 \tabularnewline
46 & 7.5 & 5.46044 & 2.03956 \tabularnewline
47 & 5 & 6.18392 & -1.18392 \tabularnewline
48 & 6.5 & 4.4174 & 2.0826 \tabularnewline
49 & 6.5 & 4.89307 & 1.60693 \tabularnewline
50 & 6.5 & 4.41756 & 2.08244 \tabularnewline
51 & 7 & 4.41268 & 2.58732 \tabularnewline
52 & 1.5 & 4.4267 & -2.9267 \tabularnewline
53 & 4 & 4.38497 & -0.384967 \tabularnewline
54 & 4.5 & 4.2919 & 0.2081 \tabularnewline
55 & 0 & 3.47232 & -3.47232 \tabularnewline
56 & 3.5 & 4.53987 & -1.03987 \tabularnewline
57 & 4.5 & 4.81586 & -0.315862 \tabularnewline
58 & 0 & 3.55289 & -3.55289 \tabularnewline
59 & 3 & 3.73886 & -0.738863 \tabularnewline
60 & 3.5 & 3.39951 & 0.100488 \tabularnewline
61 & 3 & 4.44071 & -1.44071 \tabularnewline
62 & 1 & 3.30188 & -2.30188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]7.5[/C][C]4.08566[/C][C]3.41434[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]4.49966[/C][C]2.00034[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]5.02787[/C][C]-4.02787[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.22382[/C][C]-3.22382[/C][/ROW]
[ROW][C]5[/C][C]5.5[/C][C]5.68342[/C][C]-0.183422[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]4.63582[/C][C]3.86418[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]5.38108[/C][C]1.11892[/C][/ROW]
[ROW][C]8[/C][C]4.5[/C][C]6.26281[/C][C]-1.76281[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]4.51199[/C][C]-2.51199[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.39882[/C][C]0.601176[/C][/ROW]
[ROW][C]11[/C][C]0.5[/C][C]4.68075[/C][C]-4.18075[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]5.99626[/C][C]-0.996263[/C][/ROW]
[ROW][C]13[/C][C]2.5[/C][C]3.73734[/C][C]-1.23734[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.02703[/C][C]0.972965[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.35016[/C][C]0.149841[/C][/ROW]
[ROW][C]16[/C][C]3.5[/C][C]4.42061[/C][C]-0.920608[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.05018[/C][C]-0.0501822[/C][/ROW]
[ROW][C]18[/C][C]6.5[/C][C]7.03642[/C][C]-0.536417[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.46858[/C][C]0.0314164[/C][/ROW]
[ROW][C]20[/C][C]5.5[/C][C]4.98918[/C][C]0.510817[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.81859[/C][C]-0.818588[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]7.44048[/C][C]0.0595153[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.09191[/C][C]-0.091911[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]3.97738[/C][C]1.52262[/C][/ROW]
[ROW][C]25[/C][C]2.5[/C][C]4.05947[/C][C]-1.55947[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]4.31521[/C][C]1.18479[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]4.19747[/C][C]-0.697471[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.73513[/C][C]-0.23513[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]3.74495[/C][C]0.755049[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.74458[/C][C]1.25542[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]4.83733[/C][C]0.162672[/C][/ROW]
[ROW][C]32[/C][C]6.5[/C][C]4.95354[/C][C]1.54646[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]3.94478[/C][C]1.05522[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]3.94798[/C][C]2.05202[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]5.34255[/C][C]-0.842549[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]4.29663[/C][C]0.703375[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]3.76826[/C][C]1.23174[/C][/ROW]
[ROW][C]38[/C][C]6.5[/C][C]3.8566[/C][C]2.6434[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]4.94121[/C][C]2.05879[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]3.95727[/C][C]0.542725[/C][/ROW]
[ROW][C]41[/C][C]8.5[/C][C]4.36943[/C][C]4.13057[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]4.70237[/C][C]-1.20237[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]4.68107[/C][C]1.31893[/C][/ROW]
[ROW][C]44[/C][C]1.5[/C][C]3.82552[/C][C]-2.32552[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]4.33227[/C][C]-0.832266[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]5.46044[/C][C]2.03956[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]6.18392[/C][C]-1.18392[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]4.4174[/C][C]2.0826[/C][/ROW]
[ROW][C]49[/C][C]6.5[/C][C]4.89307[/C][C]1.60693[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]4.41756[/C][C]2.08244[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]4.41268[/C][C]2.58732[/C][/ROW]
[ROW][C]52[/C][C]1.5[/C][C]4.4267[/C][C]-2.9267[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.38497[/C][C]-0.384967[/C][/ROW]
[ROW][C]54[/C][C]4.5[/C][C]4.2919[/C][C]0.2081[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]3.47232[/C][C]-3.47232[/C][/ROW]
[ROW][C]56[/C][C]3.5[/C][C]4.53987[/C][C]-1.03987[/C][/ROW]
[ROW][C]57[/C][C]4.5[/C][C]4.81586[/C][C]-0.315862[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]3.55289[/C][C]-3.55289[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]3.73886[/C][C]-0.738863[/C][/ROW]
[ROW][C]60[/C][C]3.5[/C][C]3.39951[/C][C]0.100488[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]4.44071[/C][C]-1.44071[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]3.30188[/C][C]-2.30188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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
17.54.085663.41434
26.54.499662.00034
315.02787-4.02787
414.22382-3.22382
55.55.68342-0.183422
68.54.635823.86418
76.55.381081.11892
84.56.26281-1.76281
924.51199-2.51199
1054.398820.601176
110.54.68075-4.18075
1255.99626-0.996263
132.53.73734-1.23734
1454.027030.972965
155.55.350160.149841
163.54.42061-0.920608
1744.05018-0.0501822
186.57.03642-0.536417
194.54.468580.0314164
205.54.989180.510817
2144.81859-0.818588
227.57.440480.0595153
2344.09191-0.091911
245.53.977381.52262
252.54.05947-1.55947
265.54.315211.18479
273.54.19747-0.697471
284.54.73513-0.23513
294.53.744950.755049
3064.744581.25542
3154.837330.162672
326.54.953541.54646
3353.944781.05522
3463.947982.05202
354.55.34255-0.842549
3654.296630.703375
3753.768261.23174
386.53.85662.6434
3974.941212.05879
404.53.957270.542725
418.54.369434.13057
423.54.70237-1.20237
4364.681071.31893
441.53.82552-2.32552
453.54.33227-0.832266
467.55.460442.03956
4756.18392-1.18392
486.54.41742.0826
496.54.893071.60693
506.54.417562.08244
5174.412682.58732
521.54.4267-2.9267
5344.38497-0.384967
544.54.29190.2081
5503.47232-3.47232
563.54.53987-1.03987
574.54.81586-0.315862
5803.55289-3.55289
5933.73886-0.738863
603.53.399510.100488
6134.44071-1.44071
6213.30188-2.30188







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9970460.005907310.00295366
70.9927040.0145920.00729601
80.9858330.02833390.014167
90.9857240.02855170.0142759
100.9731330.05373480.0268674
110.9972020.005596940.00279847
120.9948270.01034630.00517316
130.9909560.01808730.00904367
140.9870330.02593430.0129672
150.9784110.04317870.0215893
160.9661590.0676820.033841
170.9470620.1058770.0529383
180.9512930.09741380.0487069
190.9271150.145770.0728852
200.8986870.2026250.101313
210.8661230.2677530.133877
220.8375480.3249030.162452
230.7841130.4317740.215887
240.7673280.4653450.232672
250.7411550.517690.258845
260.704270.591460.29573
270.6417820.7164360.358218
280.5721450.8557090.427855
290.5121680.9756640.487832
300.4717810.9435620.528219
310.3985540.7971090.601446
320.3649250.7298490.635075
330.3175790.6351580.682421
340.3296910.6593820.670309
350.2928730.5857450.707127
360.2368220.4736440.763178
370.2102920.4205840.789708
380.2836090.5672170.716391
390.2755420.5510840.724458
400.2274810.4549630.772519
410.5328210.9343580.467179
420.4803220.9606450.519678
430.4444140.8888280.555586
440.4512760.9025520.548724
450.3776450.7552890.622355
460.3899030.7798070.610097
470.4731630.9463260.526837
480.4845830.9691670.515417
490.4165910.8331820.583409
500.5207880.9584240.479212
510.7816840.4366320.218316
520.8140490.3719020.185951
530.7224050.555190.277595
540.7138410.5723190.286159
550.731810.5363790.26819
560.5876620.8246770.412338

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.997046 & 0.00590731 & 0.00295366 \tabularnewline
7 & 0.992704 & 0.014592 & 0.00729601 \tabularnewline
8 & 0.985833 & 0.0283339 & 0.014167 \tabularnewline
9 & 0.985724 & 0.0285517 & 0.0142759 \tabularnewline
10 & 0.973133 & 0.0537348 & 0.0268674 \tabularnewline
11 & 0.997202 & 0.00559694 & 0.00279847 \tabularnewline
12 & 0.994827 & 0.0103463 & 0.00517316 \tabularnewline
13 & 0.990956 & 0.0180873 & 0.00904367 \tabularnewline
14 & 0.987033 & 0.0259343 & 0.0129672 \tabularnewline
15 & 0.978411 & 0.0431787 & 0.0215893 \tabularnewline
16 & 0.966159 & 0.067682 & 0.033841 \tabularnewline
17 & 0.947062 & 0.105877 & 0.0529383 \tabularnewline
18 & 0.951293 & 0.0974138 & 0.0487069 \tabularnewline
19 & 0.927115 & 0.14577 & 0.0728852 \tabularnewline
20 & 0.898687 & 0.202625 & 0.101313 \tabularnewline
21 & 0.866123 & 0.267753 & 0.133877 \tabularnewline
22 & 0.837548 & 0.324903 & 0.162452 \tabularnewline
23 & 0.784113 & 0.431774 & 0.215887 \tabularnewline
24 & 0.767328 & 0.465345 & 0.232672 \tabularnewline
25 & 0.741155 & 0.51769 & 0.258845 \tabularnewline
26 & 0.70427 & 0.59146 & 0.29573 \tabularnewline
27 & 0.641782 & 0.716436 & 0.358218 \tabularnewline
28 & 0.572145 & 0.855709 & 0.427855 \tabularnewline
29 & 0.512168 & 0.975664 & 0.487832 \tabularnewline
30 & 0.471781 & 0.943562 & 0.528219 \tabularnewline
31 & 0.398554 & 0.797109 & 0.601446 \tabularnewline
32 & 0.364925 & 0.729849 & 0.635075 \tabularnewline
33 & 0.317579 & 0.635158 & 0.682421 \tabularnewline
34 & 0.329691 & 0.659382 & 0.670309 \tabularnewline
35 & 0.292873 & 0.585745 & 0.707127 \tabularnewline
36 & 0.236822 & 0.473644 & 0.763178 \tabularnewline
37 & 0.210292 & 0.420584 & 0.789708 \tabularnewline
38 & 0.283609 & 0.567217 & 0.716391 \tabularnewline
39 & 0.275542 & 0.551084 & 0.724458 \tabularnewline
40 & 0.227481 & 0.454963 & 0.772519 \tabularnewline
41 & 0.532821 & 0.934358 & 0.467179 \tabularnewline
42 & 0.480322 & 0.960645 & 0.519678 \tabularnewline
43 & 0.444414 & 0.888828 & 0.555586 \tabularnewline
44 & 0.451276 & 0.902552 & 0.548724 \tabularnewline
45 & 0.377645 & 0.755289 & 0.622355 \tabularnewline
46 & 0.389903 & 0.779807 & 0.610097 \tabularnewline
47 & 0.473163 & 0.946326 & 0.526837 \tabularnewline
48 & 0.484583 & 0.969167 & 0.515417 \tabularnewline
49 & 0.416591 & 0.833182 & 0.583409 \tabularnewline
50 & 0.520788 & 0.958424 & 0.479212 \tabularnewline
51 & 0.781684 & 0.436632 & 0.218316 \tabularnewline
52 & 0.814049 & 0.371902 & 0.185951 \tabularnewline
53 & 0.722405 & 0.55519 & 0.277595 \tabularnewline
54 & 0.713841 & 0.572319 & 0.286159 \tabularnewline
55 & 0.73181 & 0.536379 & 0.26819 \tabularnewline
56 & 0.587662 & 0.824677 & 0.412338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]6[/C][C]0.997046[/C][C]0.00590731[/C][C]0.00295366[/C][/ROW]
[ROW][C]7[/C][C]0.992704[/C][C]0.014592[/C][C]0.00729601[/C][/ROW]
[ROW][C]8[/C][C]0.985833[/C][C]0.0283339[/C][C]0.014167[/C][/ROW]
[ROW][C]9[/C][C]0.985724[/C][C]0.0285517[/C][C]0.0142759[/C][/ROW]
[ROW][C]10[/C][C]0.973133[/C][C]0.0537348[/C][C]0.0268674[/C][/ROW]
[ROW][C]11[/C][C]0.997202[/C][C]0.00559694[/C][C]0.00279847[/C][/ROW]
[ROW][C]12[/C][C]0.994827[/C][C]0.0103463[/C][C]0.00517316[/C][/ROW]
[ROW][C]13[/C][C]0.990956[/C][C]0.0180873[/C][C]0.00904367[/C][/ROW]
[ROW][C]14[/C][C]0.987033[/C][C]0.0259343[/C][C]0.0129672[/C][/ROW]
[ROW][C]15[/C][C]0.978411[/C][C]0.0431787[/C][C]0.0215893[/C][/ROW]
[ROW][C]16[/C][C]0.966159[/C][C]0.067682[/C][C]0.033841[/C][/ROW]
[ROW][C]17[/C][C]0.947062[/C][C]0.105877[/C][C]0.0529383[/C][/ROW]
[ROW][C]18[/C][C]0.951293[/C][C]0.0974138[/C][C]0.0487069[/C][/ROW]
[ROW][C]19[/C][C]0.927115[/C][C]0.14577[/C][C]0.0728852[/C][/ROW]
[ROW][C]20[/C][C]0.898687[/C][C]0.202625[/C][C]0.101313[/C][/ROW]
[ROW][C]21[/C][C]0.866123[/C][C]0.267753[/C][C]0.133877[/C][/ROW]
[ROW][C]22[/C][C]0.837548[/C][C]0.324903[/C][C]0.162452[/C][/ROW]
[ROW][C]23[/C][C]0.784113[/C][C]0.431774[/C][C]0.215887[/C][/ROW]
[ROW][C]24[/C][C]0.767328[/C][C]0.465345[/C][C]0.232672[/C][/ROW]
[ROW][C]25[/C][C]0.741155[/C][C]0.51769[/C][C]0.258845[/C][/ROW]
[ROW][C]26[/C][C]0.70427[/C][C]0.59146[/C][C]0.29573[/C][/ROW]
[ROW][C]27[/C][C]0.641782[/C][C]0.716436[/C][C]0.358218[/C][/ROW]
[ROW][C]28[/C][C]0.572145[/C][C]0.855709[/C][C]0.427855[/C][/ROW]
[ROW][C]29[/C][C]0.512168[/C][C]0.975664[/C][C]0.487832[/C][/ROW]
[ROW][C]30[/C][C]0.471781[/C][C]0.943562[/C][C]0.528219[/C][/ROW]
[ROW][C]31[/C][C]0.398554[/C][C]0.797109[/C][C]0.601446[/C][/ROW]
[ROW][C]32[/C][C]0.364925[/C][C]0.729849[/C][C]0.635075[/C][/ROW]
[ROW][C]33[/C][C]0.317579[/C][C]0.635158[/C][C]0.682421[/C][/ROW]
[ROW][C]34[/C][C]0.329691[/C][C]0.659382[/C][C]0.670309[/C][/ROW]
[ROW][C]35[/C][C]0.292873[/C][C]0.585745[/C][C]0.707127[/C][/ROW]
[ROW][C]36[/C][C]0.236822[/C][C]0.473644[/C][C]0.763178[/C][/ROW]
[ROW][C]37[/C][C]0.210292[/C][C]0.420584[/C][C]0.789708[/C][/ROW]
[ROW][C]38[/C][C]0.283609[/C][C]0.567217[/C][C]0.716391[/C][/ROW]
[ROW][C]39[/C][C]0.275542[/C][C]0.551084[/C][C]0.724458[/C][/ROW]
[ROW][C]40[/C][C]0.227481[/C][C]0.454963[/C][C]0.772519[/C][/ROW]
[ROW][C]41[/C][C]0.532821[/C][C]0.934358[/C][C]0.467179[/C][/ROW]
[ROW][C]42[/C][C]0.480322[/C][C]0.960645[/C][C]0.519678[/C][/ROW]
[ROW][C]43[/C][C]0.444414[/C][C]0.888828[/C][C]0.555586[/C][/ROW]
[ROW][C]44[/C][C]0.451276[/C][C]0.902552[/C][C]0.548724[/C][/ROW]
[ROW][C]45[/C][C]0.377645[/C][C]0.755289[/C][C]0.622355[/C][/ROW]
[ROW][C]46[/C][C]0.389903[/C][C]0.779807[/C][C]0.610097[/C][/ROW]
[ROW][C]47[/C][C]0.473163[/C][C]0.946326[/C][C]0.526837[/C][/ROW]
[ROW][C]48[/C][C]0.484583[/C][C]0.969167[/C][C]0.515417[/C][/ROW]
[ROW][C]49[/C][C]0.416591[/C][C]0.833182[/C][C]0.583409[/C][/ROW]
[ROW][C]50[/C][C]0.520788[/C][C]0.958424[/C][C]0.479212[/C][/ROW]
[ROW][C]51[/C][C]0.781684[/C][C]0.436632[/C][C]0.218316[/C][/ROW]
[ROW][C]52[/C][C]0.814049[/C][C]0.371902[/C][C]0.185951[/C][/ROW]
[ROW][C]53[/C][C]0.722405[/C][C]0.55519[/C][C]0.277595[/C][/ROW]
[ROW][C]54[/C][C]0.713841[/C][C]0.572319[/C][C]0.286159[/C][/ROW]
[ROW][C]55[/C][C]0.73181[/C][C]0.536379[/C][C]0.26819[/C][/ROW]
[ROW][C]56[/C][C]0.587662[/C][C]0.824677[/C][C]0.412338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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
60.9970460.005907310.00295366
70.9927040.0145920.00729601
80.9858330.02833390.014167
90.9857240.02855170.0142759
100.9731330.05373480.0268674
110.9972020.005596940.00279847
120.9948270.01034630.00517316
130.9909560.01808730.00904367
140.9870330.02593430.0129672
150.9784110.04317870.0215893
160.9661590.0676820.033841
170.9470620.1058770.0529383
180.9512930.09741380.0487069
190.9271150.145770.0728852
200.8986870.2026250.101313
210.8661230.2677530.133877
220.8375480.3249030.162452
230.7841130.4317740.215887
240.7673280.4653450.232672
250.7411550.517690.258845
260.704270.591460.29573
270.6417820.7164360.358218
280.5721450.8557090.427855
290.5121680.9756640.487832
300.4717810.9435620.528219
310.3985540.7971090.601446
320.3649250.7298490.635075
330.3175790.6351580.682421
340.3296910.6593820.670309
350.2928730.5857450.707127
360.2368220.4736440.763178
370.2102920.4205840.789708
380.2836090.5672170.716391
390.2755420.5510840.724458
400.2274810.4549630.772519
410.5328210.9343580.467179
420.4803220.9606450.519678
430.4444140.8888280.555586
440.4512760.9025520.548724
450.3776450.7552890.622355
460.3899030.7798070.610097
470.4731630.9463260.526837
480.4845830.9691670.515417
490.4165910.8331820.583409
500.5207880.9584240.479212
510.7816840.4366320.218316
520.8140490.3719020.185951
530.7224050.555190.277595
540.7138410.5723190.286159
550.731810.5363790.26819
560.5876620.8246770.412338







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0392157NOK
5% type I error level90.176471NOK
10% type I error level120.235294NOK

\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 & 2 & 0.0392157 & NOK \tabularnewline
5% type I error level & 9 & 0.176471 & NOK \tabularnewline
10% type I error level & 12 & 0.235294 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266716&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]2[/C][C]0.0392157[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.176471[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.235294[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266716&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266716&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 level20.0392157NOK
5% type I error level90.176471NOK
10% type I error level120.235294NOK



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