FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:20:42 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258720207b00zj70fvnfzvwx.htm/, Retrieved Fri, 19 Apr 2024 01:52:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58082, Retrieved Fri, 19 Apr 2024 01:52:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS7,MR3
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-20 12:20:42] [30f5b608e5a1bbbae86b1702c0071566] [Current]
-   P     [Multiple Regression] [Ws7 correctie] [2009-11-27 19:25:57] [e0fc65a5811681d807296d590d5b45de]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.3	2
1.2	2.1
1.1	2.1
1.4	2.5
1.2	2.2
1.5	2.3
1.1	2.3
1.3	2.2
1.5	2.2
1.1	1.6
1.4	1.8
1.3	1.7
1.5	1.9
1.6	1.8
1.7	1.9
1.1	1.5
1.6	1
1.3	0.8
1.7	1.1
1.6	1.5
1.7	1.7
1.9	2.3
1.8	2.4
1.9	3
1.6	3
1.5	3.2
1.6	3.2
1.6	3.2
1.7	3.5
2	4
2	4.3
1.9	4.1
1.7	4
1.8	4.1
1.9	4.2
1.7	4.5
2	5.6
2.1	6.5
2.4	7.6
2.5	8.5
2.5	8.7
2.6	8.3
2.2	8.3
2.5	8.5
2.8	8.7
2.8	8.7
2.9	8.5
3	7.9
3.1	7
2.9	5.8
2.7	4.5
2.2	3.7
2.5	3.1
2.3	2.7
2.6	2.3
2.3	1.8
2.2	1.5
1.8	1.2
1.8	1

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=0

As an alternative you can also use a QR Code:  
QR Code


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

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






Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 0.959584165715367 + 0.100154534940552inflatie_levensmiddelen[t] + 0.0190340617400944t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  0.959584165715367 +  0.100154534940552inflatie_levensmiddelen[t] +  0.0190340617400944t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  0.959584165715367 +  0.100154534940552inflatie_levensmiddelen[t] +  0.0190340617400944t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 0.959584165715367 + 0.100154534940552inflatie_levensmiddelen[t] + 0.0190340617400944t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9595841657153670.06271115.301700
inflatie_levensmiddelen0.1001545349405520.0135087.414200
t0.01903406174009440.0019529.7500

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.959584165715367 & 0.062711 & 15.3017 & 0 & 0 \tabularnewline
inflatie_levensmiddelen & 0.100154534940552 & 0.013508 & 7.4142 & 0 & 0 \tabularnewline
t & 0.0190340617400944 & 0.001952 & 9.75 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.959584165715367[/C][C]0.062711[/C][C]15.3017[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inflatie_levensmiddelen[/C][C]0.100154534940552[/C][C]0.013508[/C][C]7.4142[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.0190340617400944[/C][C]0.001952[/C][C]9.75[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9595841657153670.06271115.301700
inflatie_levensmiddelen0.1001545349405520.0135087.414200
t0.01903406174009440.0019529.7500






Multiple Linear Regression - Regression Statistics
Multiple R0.915662322330004
R-squared0.838437488534777
Adjusted R-squared0.83266739883959
F-TEST (value)145.307531221605
F-TEST (DF numerator)2
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.222614569674175
Sum Squared Residuals2.77520581134822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.915662322330004 \tabularnewline
R-squared & 0.838437488534777 \tabularnewline
Adjusted R-squared & 0.83266739883959 \tabularnewline
F-TEST (value) & 145.307531221605 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.222614569674175 \tabularnewline
Sum Squared Residuals & 2.77520581134822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.915662322330004[/C][/ROW]
[ROW][C]R-squared[/C][C]0.838437488534777[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.83266739883959[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]145.307531221605[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/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]0.222614569674175[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.77520581134822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.915662322330004
R-squared0.838437488534777
Adjusted R-squared0.83266739883959
F-TEST (value)145.307531221605
F-TEST (DF numerator)2
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.222614569674175
Sum Squared Residuals2.77520581134822






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.31.178927297336560.121072702663439
21.21.20797681257071-0.00797681257071426
31.11.22701087431081-0.127010874310808
41.41.286106750027120.113893249972877
51.21.27509445128505-0.0750944512850522
61.51.304143966519200.195856033480798
71.11.32317802825930-0.223178028259296
81.31.33219663650534-0.0321966365053355
91.51.351230698245430.14876930175457
101.11.31017203902119-0.210172039021193
111.41.349237007749400.0507629922506017
121.31.35825561599544-0.0582556159954374
131.51.397320584723640.102679415276358
141.61.406339192969680.193660807030319
151.71.435388708203830.264611291796169
161.11.41436095596770-0.314360955967705
171.61.383317750237520.216682249762477
181.31.38232090498951-0.0823209049895077
191.71.431401327211770.268598672788232
201.61.490497202928080.109502797071918
211.71.529562171656290.170437828343713
221.91.608688954360710.291311045639287
231.81.637738469594860.162261530405138
241.91.716865252299290.183134747700712
251.61.73589931403938-0.135899314039382
261.51.77496428276759-0.274964282767587
271.61.79399834450768-0.193998344507681
281.61.81303240624778-0.213032406247776
291.71.86211282847004-0.162112828470036
3021.931224157680410.0687758423195941
3121.980304579902670.0196954200973343
321.91.97930773465465-0.07930773465465
331.71.98832634290069-0.288326342900689
341.82.01737585813484-0.217375858134839
351.92.04642537336899-0.146425373368988
361.72.09550579559125-0.395505795591248
3722.22470984576595-0.224709845765949
382.12.33388298895254-0.23388298895254
392.42.46308703912724-0.0630870391272415
402.52.57226018231383-0.0722601823138322
412.52.61132515104204-0.111325151042037
422.62.590297398805910.0097026011940892
432.22.60933146054601-0.409331460546005
442.52.64839642927421-0.14839642927421
452.82.687461398002410.112538601997585
462.82.706495459742510.0935045402574906
472.92.705498614494490.194501385505507
4832.664439955270260.335560044729743
493.12.593334935563850.506665064436145
502.92.492183555375290.407816444624713
512.72.381016721692660.318983278307335
522.22.31992715548032-0.119927155480318
532.52.278868496256080.221131503743919
542.32.257840744019960.0421592559800446
552.62.236812991783830.363187008216171
562.32.205769786053650.0942302139463522
572.22.194757487311580.00524251268842362
581.82.18374518856951-0.383745188569506
591.82.18274834332149-0.38274834332149

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.3 & 1.17892729733656 & 0.121072702663439 \tabularnewline
2 & 1.2 & 1.20797681257071 & -0.00797681257071426 \tabularnewline
3 & 1.1 & 1.22701087431081 & -0.127010874310808 \tabularnewline
4 & 1.4 & 1.28610675002712 & 0.113893249972877 \tabularnewline
5 & 1.2 & 1.27509445128505 & -0.0750944512850522 \tabularnewline
6 & 1.5 & 1.30414396651920 & 0.195856033480798 \tabularnewline
7 & 1.1 & 1.32317802825930 & -0.223178028259296 \tabularnewline
8 & 1.3 & 1.33219663650534 & -0.0321966365053355 \tabularnewline
9 & 1.5 & 1.35123069824543 & 0.14876930175457 \tabularnewline
10 & 1.1 & 1.31017203902119 & -0.210172039021193 \tabularnewline
11 & 1.4 & 1.34923700774940 & 0.0507629922506017 \tabularnewline
12 & 1.3 & 1.35825561599544 & -0.0582556159954374 \tabularnewline
13 & 1.5 & 1.39732058472364 & 0.102679415276358 \tabularnewline
14 & 1.6 & 1.40633919296968 & 0.193660807030319 \tabularnewline
15 & 1.7 & 1.43538870820383 & 0.264611291796169 \tabularnewline
16 & 1.1 & 1.41436095596770 & -0.314360955967705 \tabularnewline
17 & 1.6 & 1.38331775023752 & 0.216682249762477 \tabularnewline
18 & 1.3 & 1.38232090498951 & -0.0823209049895077 \tabularnewline
19 & 1.7 & 1.43140132721177 & 0.268598672788232 \tabularnewline
20 & 1.6 & 1.49049720292808 & 0.109502797071918 \tabularnewline
21 & 1.7 & 1.52956217165629 & 0.170437828343713 \tabularnewline
22 & 1.9 & 1.60868895436071 & 0.291311045639287 \tabularnewline
23 & 1.8 & 1.63773846959486 & 0.162261530405138 \tabularnewline
24 & 1.9 & 1.71686525229929 & 0.183134747700712 \tabularnewline
25 & 1.6 & 1.73589931403938 & -0.135899314039382 \tabularnewline
26 & 1.5 & 1.77496428276759 & -0.274964282767587 \tabularnewline
27 & 1.6 & 1.79399834450768 & -0.193998344507681 \tabularnewline
28 & 1.6 & 1.81303240624778 & -0.213032406247776 \tabularnewline
29 & 1.7 & 1.86211282847004 & -0.162112828470036 \tabularnewline
30 & 2 & 1.93122415768041 & 0.0687758423195941 \tabularnewline
31 & 2 & 1.98030457990267 & 0.0196954200973343 \tabularnewline
32 & 1.9 & 1.97930773465465 & -0.07930773465465 \tabularnewline
33 & 1.7 & 1.98832634290069 & -0.288326342900689 \tabularnewline
34 & 1.8 & 2.01737585813484 & -0.217375858134839 \tabularnewline
35 & 1.9 & 2.04642537336899 & -0.146425373368988 \tabularnewline
36 & 1.7 & 2.09550579559125 & -0.395505795591248 \tabularnewline
37 & 2 & 2.22470984576595 & -0.224709845765949 \tabularnewline
38 & 2.1 & 2.33388298895254 & -0.23388298895254 \tabularnewline
39 & 2.4 & 2.46308703912724 & -0.0630870391272415 \tabularnewline
40 & 2.5 & 2.57226018231383 & -0.0722601823138322 \tabularnewline
41 & 2.5 & 2.61132515104204 & -0.111325151042037 \tabularnewline
42 & 2.6 & 2.59029739880591 & 0.0097026011940892 \tabularnewline
43 & 2.2 & 2.60933146054601 & -0.409331460546005 \tabularnewline
44 & 2.5 & 2.64839642927421 & -0.14839642927421 \tabularnewline
45 & 2.8 & 2.68746139800241 & 0.112538601997585 \tabularnewline
46 & 2.8 & 2.70649545974251 & 0.0935045402574906 \tabularnewline
47 & 2.9 & 2.70549861449449 & 0.194501385505507 \tabularnewline
48 & 3 & 2.66443995527026 & 0.335560044729743 \tabularnewline
49 & 3.1 & 2.59333493556385 & 0.506665064436145 \tabularnewline
50 & 2.9 & 2.49218355537529 & 0.407816444624713 \tabularnewline
51 & 2.7 & 2.38101672169266 & 0.318983278307335 \tabularnewline
52 & 2.2 & 2.31992715548032 & -0.119927155480318 \tabularnewline
53 & 2.5 & 2.27886849625608 & 0.221131503743919 \tabularnewline
54 & 2.3 & 2.25784074401996 & 0.0421592559800446 \tabularnewline
55 & 2.6 & 2.23681299178383 & 0.363187008216171 \tabularnewline
56 & 2.3 & 2.20576978605365 & 0.0942302139463522 \tabularnewline
57 & 2.2 & 2.19475748731158 & 0.00524251268842362 \tabularnewline
58 & 1.8 & 2.18374518856951 & -0.383745188569506 \tabularnewline
59 & 1.8 & 2.18274834332149 & -0.38274834332149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&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]1.3[/C][C]1.17892729733656[/C][C]0.121072702663439[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.20797681257071[/C][C]-0.00797681257071426[/C][/ROW]
[ROW][C]3[/C][C]1.1[/C][C]1.22701087431081[/C][C]-0.127010874310808[/C][/ROW]
[ROW][C]4[/C][C]1.4[/C][C]1.28610675002712[/C][C]0.113893249972877[/C][/ROW]
[ROW][C]5[/C][C]1.2[/C][C]1.27509445128505[/C][C]-0.0750944512850522[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.30414396651920[/C][C]0.195856033480798[/C][/ROW]
[ROW][C]7[/C][C]1.1[/C][C]1.32317802825930[/C][C]-0.223178028259296[/C][/ROW]
[ROW][C]8[/C][C]1.3[/C][C]1.33219663650534[/C][C]-0.0321966365053355[/C][/ROW]
[ROW][C]9[/C][C]1.5[/C][C]1.35123069824543[/C][C]0.14876930175457[/C][/ROW]
[ROW][C]10[/C][C]1.1[/C][C]1.31017203902119[/C][C]-0.210172039021193[/C][/ROW]
[ROW][C]11[/C][C]1.4[/C][C]1.34923700774940[/C][C]0.0507629922506017[/C][/ROW]
[ROW][C]12[/C][C]1.3[/C][C]1.35825561599544[/C][C]-0.0582556159954374[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]1.39732058472364[/C][C]0.102679415276358[/C][/ROW]
[ROW][C]14[/C][C]1.6[/C][C]1.40633919296968[/C][C]0.193660807030319[/C][/ROW]
[ROW][C]15[/C][C]1.7[/C][C]1.43538870820383[/C][C]0.264611291796169[/C][/ROW]
[ROW][C]16[/C][C]1.1[/C][C]1.41436095596770[/C][C]-0.314360955967705[/C][/ROW]
[ROW][C]17[/C][C]1.6[/C][C]1.38331775023752[/C][C]0.216682249762477[/C][/ROW]
[ROW][C]18[/C][C]1.3[/C][C]1.38232090498951[/C][C]-0.0823209049895077[/C][/ROW]
[ROW][C]19[/C][C]1.7[/C][C]1.43140132721177[/C][C]0.268598672788232[/C][/ROW]
[ROW][C]20[/C][C]1.6[/C][C]1.49049720292808[/C][C]0.109502797071918[/C][/ROW]
[ROW][C]21[/C][C]1.7[/C][C]1.52956217165629[/C][C]0.170437828343713[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.60868895436071[/C][C]0.291311045639287[/C][/ROW]
[ROW][C]23[/C][C]1.8[/C][C]1.63773846959486[/C][C]0.162261530405138[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]1.71686525229929[/C][C]0.183134747700712[/C][/ROW]
[ROW][C]25[/C][C]1.6[/C][C]1.73589931403938[/C][C]-0.135899314039382[/C][/ROW]
[ROW][C]26[/C][C]1.5[/C][C]1.77496428276759[/C][C]-0.274964282767587[/C][/ROW]
[ROW][C]27[/C][C]1.6[/C][C]1.79399834450768[/C][C]-0.193998344507681[/C][/ROW]
[ROW][C]28[/C][C]1.6[/C][C]1.81303240624778[/C][C]-0.213032406247776[/C][/ROW]
[ROW][C]29[/C][C]1.7[/C][C]1.86211282847004[/C][C]-0.162112828470036[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]1.93122415768041[/C][C]0.0687758423195941[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.98030457990267[/C][C]0.0196954200973343[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.97930773465465[/C][C]-0.07930773465465[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]1.98832634290069[/C][C]-0.288326342900689[/C][/ROW]
[ROW][C]34[/C][C]1.8[/C][C]2.01737585813484[/C][C]-0.217375858134839[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]2.04642537336899[/C][C]-0.146425373368988[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]2.09550579559125[/C][C]-0.395505795591248[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]2.22470984576595[/C][C]-0.224709845765949[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.33388298895254[/C][C]-0.23388298895254[/C][/ROW]
[ROW][C]39[/C][C]2.4[/C][C]2.46308703912724[/C][C]-0.0630870391272415[/C][/ROW]
[ROW][C]40[/C][C]2.5[/C][C]2.57226018231383[/C][C]-0.0722601823138322[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]2.61132515104204[/C][C]-0.111325151042037[/C][/ROW]
[ROW][C]42[/C][C]2.6[/C][C]2.59029739880591[/C][C]0.0097026011940892[/C][/ROW]
[ROW][C]43[/C][C]2.2[/C][C]2.60933146054601[/C][C]-0.409331460546005[/C][/ROW]
[ROW][C]44[/C][C]2.5[/C][C]2.64839642927421[/C][C]-0.14839642927421[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.68746139800241[/C][C]0.112538601997585[/C][/ROW]
[ROW][C]46[/C][C]2.8[/C][C]2.70649545974251[/C][C]0.0935045402574906[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.70549861449449[/C][C]0.194501385505507[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.66443995527026[/C][C]0.335560044729743[/C][/ROW]
[ROW][C]49[/C][C]3.1[/C][C]2.59333493556385[/C][C]0.506665064436145[/C][/ROW]
[ROW][C]50[/C][C]2.9[/C][C]2.49218355537529[/C][C]0.407816444624713[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.38101672169266[/C][C]0.318983278307335[/C][/ROW]
[ROW][C]52[/C][C]2.2[/C][C]2.31992715548032[/C][C]-0.119927155480318[/C][/ROW]
[ROW][C]53[/C][C]2.5[/C][C]2.27886849625608[/C][C]0.221131503743919[/C][/ROW]
[ROW][C]54[/C][C]2.3[/C][C]2.25784074401996[/C][C]0.0421592559800446[/C][/ROW]
[ROW][C]55[/C][C]2.6[/C][C]2.23681299178383[/C][C]0.363187008216171[/C][/ROW]
[ROW][C]56[/C][C]2.3[/C][C]2.20576978605365[/C][C]0.0942302139463522[/C][/ROW]
[ROW][C]57[/C][C]2.2[/C][C]2.19475748731158[/C][C]0.00524251268842362[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]2.18374518856951[/C][C]-0.383745188569506[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]2.18274834332149[/C][C]-0.38274834332149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.31.178927297336560.121072702663439
21.21.20797681257071-0.00797681257071426
31.11.22701087431081-0.127010874310808
41.41.286106750027120.113893249972877
51.21.27509445128505-0.0750944512850522
61.51.304143966519200.195856033480798
71.11.32317802825930-0.223178028259296
81.31.33219663650534-0.0321966365053355
91.51.351230698245430.14876930175457
101.11.31017203902119-0.210172039021193
111.41.349237007749400.0507629922506017
121.31.35825561599544-0.0582556159954374
131.51.397320584723640.102679415276358
141.61.406339192969680.193660807030319
151.71.435388708203830.264611291796169
161.11.41436095596770-0.314360955967705
171.61.383317750237520.216682249762477
181.31.38232090498951-0.0823209049895077
191.71.431401327211770.268598672788232
201.61.490497202928080.109502797071918
211.71.529562171656290.170437828343713
221.91.608688954360710.291311045639287
231.81.637738469594860.162261530405138
241.91.716865252299290.183134747700712
251.61.73589931403938-0.135899314039382
261.51.77496428276759-0.274964282767587
271.61.79399834450768-0.193998344507681
281.61.81303240624778-0.213032406247776
291.71.86211282847004-0.162112828470036
3021.931224157680410.0687758423195941
3121.980304579902670.0196954200973343
321.91.97930773465465-0.07930773465465
331.71.98832634290069-0.288326342900689
341.82.01737585813484-0.217375858134839
351.92.04642537336899-0.146425373368988
361.72.09550579559125-0.395505795591248
3722.22470984576595-0.224709845765949
382.12.33388298895254-0.23388298895254
392.42.46308703912724-0.0630870391272415
402.52.57226018231383-0.0722601823138322
412.52.61132515104204-0.111325151042037
422.62.590297398805910.0097026011940892
432.22.60933146054601-0.409331460546005
442.52.64839642927421-0.14839642927421
452.82.687461398002410.112538601997585
462.82.706495459742510.0935045402574906
472.92.705498614494490.194501385505507
4832.664439955270260.335560044729743
493.12.593334935563850.506665064436145
502.92.492183555375290.407816444624713
512.72.381016721692660.318983278307335
522.22.31992715548032-0.119927155480318
532.52.278868496256080.221131503743919
542.32.257840744019960.0421592559800446
552.62.236812991783830.363187008216171
562.32.205769786053650.0942302139463522
572.22.194757487311580.00524251268842362
581.82.18374518856951-0.383745188569506
591.82.18274834332149-0.38274834332149






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.259293064175490.518586128350980.74070693582451
70.2423224668747880.4846449337495770.757677533125212
80.1602580270606740.3205160541213470.839741972939326
90.1715414308029310.3430828616058630.828458569197069
100.1057808602713680.2115617205427350.894219139728633
110.08547930448679620.1709586089735920.914520695513204
120.04774144623250290.09548289246500580.952258553767497
130.03369298735748730.06738597471497460.966307012642513
140.03331571125301740.06663142250603480.966684288746983
150.03287058065651730.06574116131303460.967129419343483
160.06747674738126280.1349534947625260.932523252618737
170.1116855389385590.2233710778771180.88831446106144
180.07507795154372560.1501559030874510.924922048456274
190.0908075537659760.1816151075319520.909192446234024
200.06398791415520760.1279758283104150.936012085844792
210.04855831059336670.09711662118673340.951441689406633
220.04955984773798880.09911969547597760.950440152262011
230.04907240724994920.09814481449989840.95092759275005
240.05713496472020540.1142699294404110.942865035279794
250.09400451699838840.1880090339967770.905995483001612
260.1384174057865650.276834811573130.861582594213435
270.1211911323099160.2423822646198310.878808867690084
280.0999999973979110.1999999947958220.900000002602089
290.07224280448124490.1444856089624900.927757195518755
300.08300968180706540.1660193636141310.916990318192935
310.08411656432569580.1682331286513920.915883435674304
320.07303640146952990.1460728029390600.92696359853047
330.06392375419462960.1278475083892590.936076245805370
340.04949444157313850.0989888831462770.950505558426862
350.04351851441079590.08703702882159180.956481485589204
360.03806339420467600.07612678840935190.961936605795324
370.02639128302878780.05278256605757560.973608716971212
380.01743929047351850.0348785809470370.982560709526481
390.01865796098336720.03731592196673440.981342039016633
400.01466861281883810.02933722563767610.985331387181162
410.009359609899572560.01871921979914510.990640390100427
420.006929990751544570.01385998150308910.993070009248455
430.02547711364513310.05095422729026630.974522886354867
440.0874633542159260.1749267084318520.912536645784074
450.1550329897806420.3100659795612840.844967010219358
460.2573633840141980.5147267680283970.742636615985802
470.3258350121380870.6516700242761750.674164987861913
480.3506519363584330.7013038727168650.649348063641567
490.3781358792349830.7562717584699660.621864120765017
500.4010946605294230.8021893210588460.598905339470577
510.658972579581410.6820548408371810.341027420418591
520.6076169583782560.7847660832434890.392383041621744
530.4492287820283470.8984575640566930.550771217971653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.25929306417549 & 0.51858612835098 & 0.74070693582451 \tabularnewline
7 & 0.242322466874788 & 0.484644933749577 & 0.757677533125212 \tabularnewline
8 & 0.160258027060674 & 0.320516054121347 & 0.839741972939326 \tabularnewline
9 & 0.171541430802931 & 0.343082861605863 & 0.828458569197069 \tabularnewline
10 & 0.105780860271368 & 0.211561720542735 & 0.894219139728633 \tabularnewline
11 & 0.0854793044867962 & 0.170958608973592 & 0.914520695513204 \tabularnewline
12 & 0.0477414462325029 & 0.0954828924650058 & 0.952258553767497 \tabularnewline
13 & 0.0336929873574873 & 0.0673859747149746 & 0.966307012642513 \tabularnewline
14 & 0.0333157112530174 & 0.0666314225060348 & 0.966684288746983 \tabularnewline
15 & 0.0328705806565173 & 0.0657411613130346 & 0.967129419343483 \tabularnewline
16 & 0.0674767473812628 & 0.134953494762526 & 0.932523252618737 \tabularnewline
17 & 0.111685538938559 & 0.223371077877118 & 0.88831446106144 \tabularnewline
18 & 0.0750779515437256 & 0.150155903087451 & 0.924922048456274 \tabularnewline
19 & 0.090807553765976 & 0.181615107531952 & 0.909192446234024 \tabularnewline
20 & 0.0639879141552076 & 0.127975828310415 & 0.936012085844792 \tabularnewline
21 & 0.0485583105933667 & 0.0971166211867334 & 0.951441689406633 \tabularnewline
22 & 0.0495598477379888 & 0.0991196954759776 & 0.950440152262011 \tabularnewline
23 & 0.0490724072499492 & 0.0981448144998984 & 0.95092759275005 \tabularnewline
24 & 0.0571349647202054 & 0.114269929440411 & 0.942865035279794 \tabularnewline
25 & 0.0940045169983884 & 0.188009033996777 & 0.905995483001612 \tabularnewline
26 & 0.138417405786565 & 0.27683481157313 & 0.861582594213435 \tabularnewline
27 & 0.121191132309916 & 0.242382264619831 & 0.878808867690084 \tabularnewline
28 & 0.099999997397911 & 0.199999994795822 & 0.900000002602089 \tabularnewline
29 & 0.0722428044812449 & 0.144485608962490 & 0.927757195518755 \tabularnewline
30 & 0.0830096818070654 & 0.166019363614131 & 0.916990318192935 \tabularnewline
31 & 0.0841165643256958 & 0.168233128651392 & 0.915883435674304 \tabularnewline
32 & 0.0730364014695299 & 0.146072802939060 & 0.92696359853047 \tabularnewline
33 & 0.0639237541946296 & 0.127847508389259 & 0.936076245805370 \tabularnewline
34 & 0.0494944415731385 & 0.098988883146277 & 0.950505558426862 \tabularnewline
35 & 0.0435185144107959 & 0.0870370288215918 & 0.956481485589204 \tabularnewline
36 & 0.0380633942046760 & 0.0761267884093519 & 0.961936605795324 \tabularnewline
37 & 0.0263912830287878 & 0.0527825660575756 & 0.973608716971212 \tabularnewline
38 & 0.0174392904735185 & 0.034878580947037 & 0.982560709526481 \tabularnewline
39 & 0.0186579609833672 & 0.0373159219667344 & 0.981342039016633 \tabularnewline
40 & 0.0146686128188381 & 0.0293372256376761 & 0.985331387181162 \tabularnewline
41 & 0.00935960989957256 & 0.0187192197991451 & 0.990640390100427 \tabularnewline
42 & 0.00692999075154457 & 0.0138599815030891 & 0.993070009248455 \tabularnewline
43 & 0.0254771136451331 & 0.0509542272902663 & 0.974522886354867 \tabularnewline
44 & 0.087463354215926 & 0.174926708431852 & 0.912536645784074 \tabularnewline
45 & 0.155032989780642 & 0.310065979561284 & 0.844967010219358 \tabularnewline
46 & 0.257363384014198 & 0.514726768028397 & 0.742636615985802 \tabularnewline
47 & 0.325835012138087 & 0.651670024276175 & 0.674164987861913 \tabularnewline
48 & 0.350651936358433 & 0.701303872716865 & 0.649348063641567 \tabularnewline
49 & 0.378135879234983 & 0.756271758469966 & 0.621864120765017 \tabularnewline
50 & 0.401094660529423 & 0.802189321058846 & 0.598905339470577 \tabularnewline
51 & 0.65897257958141 & 0.682054840837181 & 0.341027420418591 \tabularnewline
52 & 0.607616958378256 & 0.784766083243489 & 0.392383041621744 \tabularnewline
53 & 0.449228782028347 & 0.898457564056693 & 0.550771217971653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&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]0.25929306417549[/C][C]0.51858612835098[/C][C]0.74070693582451[/C][/ROW]
[ROW][C]7[/C][C]0.242322466874788[/C][C]0.484644933749577[/C][C]0.757677533125212[/C][/ROW]
[ROW][C]8[/C][C]0.160258027060674[/C][C]0.320516054121347[/C][C]0.839741972939326[/C][/ROW]
[ROW][C]9[/C][C]0.171541430802931[/C][C]0.343082861605863[/C][C]0.828458569197069[/C][/ROW]
[ROW][C]10[/C][C]0.105780860271368[/C][C]0.211561720542735[/C][C]0.894219139728633[/C][/ROW]
[ROW][C]11[/C][C]0.0854793044867962[/C][C]0.170958608973592[/C][C]0.914520695513204[/C][/ROW]
[ROW][C]12[/C][C]0.0477414462325029[/C][C]0.0954828924650058[/C][C]0.952258553767497[/C][/ROW]
[ROW][C]13[/C][C]0.0336929873574873[/C][C]0.0673859747149746[/C][C]0.966307012642513[/C][/ROW]
[ROW][C]14[/C][C]0.0333157112530174[/C][C]0.0666314225060348[/C][C]0.966684288746983[/C][/ROW]
[ROW][C]15[/C][C]0.0328705806565173[/C][C]0.0657411613130346[/C][C]0.967129419343483[/C][/ROW]
[ROW][C]16[/C][C]0.0674767473812628[/C][C]0.134953494762526[/C][C]0.932523252618737[/C][/ROW]
[ROW][C]17[/C][C]0.111685538938559[/C][C]0.223371077877118[/C][C]0.88831446106144[/C][/ROW]
[ROW][C]18[/C][C]0.0750779515437256[/C][C]0.150155903087451[/C][C]0.924922048456274[/C][/ROW]
[ROW][C]19[/C][C]0.090807553765976[/C][C]0.181615107531952[/C][C]0.909192446234024[/C][/ROW]
[ROW][C]20[/C][C]0.0639879141552076[/C][C]0.127975828310415[/C][C]0.936012085844792[/C][/ROW]
[ROW][C]21[/C][C]0.0485583105933667[/C][C]0.0971166211867334[/C][C]0.951441689406633[/C][/ROW]
[ROW][C]22[/C][C]0.0495598477379888[/C][C]0.0991196954759776[/C][C]0.950440152262011[/C][/ROW]
[ROW][C]23[/C][C]0.0490724072499492[/C][C]0.0981448144998984[/C][C]0.95092759275005[/C][/ROW]
[ROW][C]24[/C][C]0.0571349647202054[/C][C]0.114269929440411[/C][C]0.942865035279794[/C][/ROW]
[ROW][C]25[/C][C]0.0940045169983884[/C][C]0.188009033996777[/C][C]0.905995483001612[/C][/ROW]
[ROW][C]26[/C][C]0.138417405786565[/C][C]0.27683481157313[/C][C]0.861582594213435[/C][/ROW]
[ROW][C]27[/C][C]0.121191132309916[/C][C]0.242382264619831[/C][C]0.878808867690084[/C][/ROW]
[ROW][C]28[/C][C]0.099999997397911[/C][C]0.199999994795822[/C][C]0.900000002602089[/C][/ROW]
[ROW][C]29[/C][C]0.0722428044812449[/C][C]0.144485608962490[/C][C]0.927757195518755[/C][/ROW]
[ROW][C]30[/C][C]0.0830096818070654[/C][C]0.166019363614131[/C][C]0.916990318192935[/C][/ROW]
[ROW][C]31[/C][C]0.0841165643256958[/C][C]0.168233128651392[/C][C]0.915883435674304[/C][/ROW]
[ROW][C]32[/C][C]0.0730364014695299[/C][C]0.146072802939060[/C][C]0.92696359853047[/C][/ROW]
[ROW][C]33[/C][C]0.0639237541946296[/C][C]0.127847508389259[/C][C]0.936076245805370[/C][/ROW]
[ROW][C]34[/C][C]0.0494944415731385[/C][C]0.098988883146277[/C][C]0.950505558426862[/C][/ROW]
[ROW][C]35[/C][C]0.0435185144107959[/C][C]0.0870370288215918[/C][C]0.956481485589204[/C][/ROW]
[ROW][C]36[/C][C]0.0380633942046760[/C][C]0.0761267884093519[/C][C]0.961936605795324[/C][/ROW]
[ROW][C]37[/C][C]0.0263912830287878[/C][C]0.0527825660575756[/C][C]0.973608716971212[/C][/ROW]
[ROW][C]38[/C][C]0.0174392904735185[/C][C]0.034878580947037[/C][C]0.982560709526481[/C][/ROW]
[ROW][C]39[/C][C]0.0186579609833672[/C][C]0.0373159219667344[/C][C]0.981342039016633[/C][/ROW]
[ROW][C]40[/C][C]0.0146686128188381[/C][C]0.0293372256376761[/C][C]0.985331387181162[/C][/ROW]
[ROW][C]41[/C][C]0.00935960989957256[/C][C]0.0187192197991451[/C][C]0.990640390100427[/C][/ROW]
[ROW][C]42[/C][C]0.00692999075154457[/C][C]0.0138599815030891[/C][C]0.993070009248455[/C][/ROW]
[ROW][C]43[/C][C]0.0254771136451331[/C][C]0.0509542272902663[/C][C]0.974522886354867[/C][/ROW]
[ROW][C]44[/C][C]0.087463354215926[/C][C]0.174926708431852[/C][C]0.912536645784074[/C][/ROW]
[ROW][C]45[/C][C]0.155032989780642[/C][C]0.310065979561284[/C][C]0.844967010219358[/C][/ROW]
[ROW][C]46[/C][C]0.257363384014198[/C][C]0.514726768028397[/C][C]0.742636615985802[/C][/ROW]
[ROW][C]47[/C][C]0.325835012138087[/C][C]0.651670024276175[/C][C]0.674164987861913[/C][/ROW]
[ROW][C]48[/C][C]0.350651936358433[/C][C]0.701303872716865[/C][C]0.649348063641567[/C][/ROW]
[ROW][C]49[/C][C]0.378135879234983[/C][C]0.756271758469966[/C][C]0.621864120765017[/C][/ROW]
[ROW][C]50[/C][C]0.401094660529423[/C][C]0.802189321058846[/C][C]0.598905339470577[/C][/ROW]
[ROW][C]51[/C][C]0.65897257958141[/C][C]0.682054840837181[/C][C]0.341027420418591[/C][/ROW]
[ROW][C]52[/C][C]0.607616958378256[/C][C]0.784766083243489[/C][C]0.392383041621744[/C][/ROW]
[ROW][C]53[/C][C]0.449228782028347[/C][C]0.898457564056693[/C][C]0.550771217971653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.259293064175490.518586128350980.74070693582451
70.2423224668747880.4846449337495770.757677533125212
80.1602580270606740.3205160541213470.839741972939326
90.1715414308029310.3430828616058630.828458569197069
100.1057808602713680.2115617205427350.894219139728633
110.08547930448679620.1709586089735920.914520695513204
120.04774144623250290.09548289246500580.952258553767497
130.03369298735748730.06738597471497460.966307012642513
140.03331571125301740.06663142250603480.966684288746983
150.03287058065651730.06574116131303460.967129419343483
160.06747674738126280.1349534947625260.932523252618737
170.1116855389385590.2233710778771180.88831446106144
180.07507795154372560.1501559030874510.924922048456274
190.0908075537659760.1816151075319520.909192446234024
200.06398791415520760.1279758283104150.936012085844792
210.04855831059336670.09711662118673340.951441689406633
220.04955984773798880.09911969547597760.950440152262011
230.04907240724994920.09814481449989840.95092759275005
240.05713496472020540.1142699294404110.942865035279794
250.09400451699838840.1880090339967770.905995483001612
260.1384174057865650.276834811573130.861582594213435
270.1211911323099160.2423822646198310.878808867690084
280.0999999973979110.1999999947958220.900000002602089
290.07224280448124490.1444856089624900.927757195518755
300.08300968180706540.1660193636141310.916990318192935
310.08411656432569580.1682331286513920.915883435674304
320.07303640146952990.1460728029390600.92696359853047
330.06392375419462960.1278475083892590.936076245805370
340.04949444157313850.0989888831462770.950505558426862
350.04351851441079590.08703702882159180.956481485589204
360.03806339420467600.07612678840935190.961936605795324
370.02639128302878780.05278256605757560.973608716971212
380.01743929047351850.0348785809470370.982560709526481
390.01865796098336720.03731592196673440.981342039016633
400.01466861281883810.02933722563767610.985331387181162
410.009359609899572560.01871921979914510.990640390100427
420.006929990751544570.01385998150308910.993070009248455
430.02547711364513310.05095422729026630.974522886354867
440.0874633542159260.1749267084318520.912536645784074
450.1550329897806420.3100659795612840.844967010219358
460.2573633840141980.5147267680283970.742636615985802
470.3258350121380870.6516700242761750.674164987861913
480.3506519363584330.7013038727168650.649348063641567
490.3781358792349830.7562717584699660.621864120765017
500.4010946605294230.8021893210588460.598905339470577
510.658972579581410.6820548408371810.341027420418591
520.6076169583782560.7847660832434890.392383041621744
530.4492287820283470.8984575640566930.550771217971653






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.104166666666667NOK
10% type I error level170.354166666666667NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 5 & 0.104166666666667 & NOK \tabularnewline
10% type I error level & 17 & 0.354166666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58082&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.104166666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.354166666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58082&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58082&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.104166666666667NOK
10% type I error level170.354166666666667NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}