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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 13:45:36 -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/24/t1259095580oo7ab03s8tdojwv.htm/, Retrieved Wed, 06 Dec 2023 06:56:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59273, Retrieved Wed, 06 Dec 2023 06:56:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-20 13:13:49] [badc6a9acdc45286bea7f74742e15a21]
-   PD      [Multiple Regression] [] [2009-11-20 13:28:34] [2c5be225250d91402426bbbf07a5e2b3]
-   PD          [Multiple Regression] [4e multiple ] [2009-11-24 20:45:36] [244731fa3e7e6c85774b8c0902c58f85] [Current]
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Dataseries X:
2,40	2,00	1,70	1,00	1,20	1,40
2,00	2,00	2,40	1,70	1,00	1,20
2,10	2,00	2,00	2,40	1,70	1,00
2,00	2,00	2,10	2,00	2,40	1,70
1,80	2,00	2,00	2,10	2,00	2,40
2,70	2,00	1,80	2,00	2,10	2,00
2,30	2,00	2,70	1,80	2,00	2,10
1,90	2,00	2,30	2,70	1,80	2,00
2,00	2,00	1,90	2,30	2,70	1,80
2,30	2,00	2,00	1,90	2,30	2,70
2,80	2,00	2,30	2,00	1,90	2,30
2,40	2,00	2,80	2,30	2,00	1,90
2,30	2,00	2,40	2,80	2,30	2,00
2,70	2,00	2,30	2,40	2,80	2,30
2,70	2,00	2,70	2,30	2,40	2,80
2,90	2,00	2,70	2,70	2,30	2,40
3,00	2,00	2,90	2,70	2,70	2,30
2,20	2,00	3,00	2,90	2,70	2,70
2,30	2,00	2,20	3,00	2,90	2,70
2,80	2,21	2,30	2,20	3,00	2,90
2,80	2,25	2,80	2,30	2,20	3,00
2,80	2,25	2,80	2,80	2,30	2,20
2,20	2,45	2,80	2,80	2,80	2,30
2,60	2,50	2,20	2,80	2,80	2,80
2,80	2,50	2,60	2,20	2,80	2,80
2,50	2,64	2,80	2,60	2,20	2,80
2,40	2,75	2,50	2,80	2,60	2,20
2,30	2,93	2,40	2,50	2,80	2,60
1,90	3,00	2,30	2,40	2,50	2,80
1,70	3,17	1,90	2,30	2,40	2,50
2,00	3,25	1,70	1,90	2,30	2,40
2,10	3,39	2,00	1,70	1,90	2,30
1,70	3,50	2,10	2,00	1,70	1,90
1,80	3,50	1,70	2,10	2,00	1,70
1,80	3,65	1,80	1,70	2,10	2,00
1,80	3,75	1,80	1,80	1,70	2,10
1,30	3,75	1,80	1,80	1,80	1,70
1,30	3,90	1,30	1,80	1,80	1,80
1,30	4,00	1,30	1,30	1,80	1,80
1,20	4,00	1,30	1,30	1,30	1,80
1,40	4,00	1,20	1,30	1,30	1,30
2,20	4,00	1,40	1,20	1,30	1,30
2,90	4,00	2,20	1,40	1,20	1,30
3,10	4,00	2,90	2,20	1,40	1,20
3,50	4,00	3,10	2,90	2,20	1,40
3,60	4,00	3,50	3,10	2,90	2,20
4,40	4,00	3,60	3,50	3,10	2,90
4,10	4,00	4,40	3,60	3,50	3,10
5,10	4,00	4,10	4,40	3,60	3,50
5,80	4,00	5,10	4,10	4,40	3,60
5,90	4,18	5,80	5,10	4,10	4,40
5,40	4,25	5,90	5,80	5,10	4,10
5,50	4,25	5,40	5,90	5,80	5,10
4,80	3,97	5,50	5,40	5,90	5,80
3,20	3,42	4,80	5,50	5,40	5,90
2,70	2,75	3,20	4,80	5,50	5,40




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
X1[t] = + 0.185036980311549 + 0.073579736810377X2[t] + 1.11584285808511X3[t] -0.256942291868568X4[t] + 0.278431112422916X5[t] -0.293078735356719X6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  +  0.185036980311549 +  0.073579736810377X2[t] +  1.11584285808511X3[t] -0.256942291868568X4[t] +  0.278431112422916X5[t] -0.293078735356719X6[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  +  0.185036980311549 +  0.073579736810377X2[t] +  1.11584285808511X3[t] -0.256942291868568X4[t] +  0.278431112422916X5[t] -0.293078735356719X6[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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
X1[t] = + 0.185036980311549 + 0.073579736810377X2[t] + 1.11584285808511X3[t] -0.256942291868568X4[t] + 0.278431112422916X5[t] -0.293078735356719X6[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1850369803115490.2316570.79880.4282110.214106
X20.0735797368103770.072531.01450.3152430.157621
X31.115842858085110.135428.239900
X4-0.2569422918685680.207051-1.2410.2204110.110206
X50.2784311124229160.2111571.31860.1933120.096656
X6-0.2930787353567190.156309-1.8750.066640.03332

\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) & 0.185036980311549 & 0.231657 & 0.7988 & 0.428211 & 0.214106 \tabularnewline
X2 & 0.073579736810377 & 0.07253 & 1.0145 & 0.315243 & 0.157621 \tabularnewline
X3 & 1.11584285808511 & 0.13542 & 8.2399 & 0 & 0 \tabularnewline
X4 & -0.256942291868568 & 0.207051 & -1.241 & 0.220411 & 0.110206 \tabularnewline
X5 & 0.278431112422916 & 0.211157 & 1.3186 & 0.193312 & 0.096656 \tabularnewline
X6 & -0.293078735356719 & 0.156309 & -1.875 & 0.06664 & 0.03332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]0.185036980311549[/C][C]0.231657[/C][C]0.7988[/C][C]0.428211[/C][C]0.214106[/C][/ROW]
[ROW][C]X2[/C][C]0.073579736810377[/C][C]0.07253[/C][C]1.0145[/C][C]0.315243[/C][C]0.157621[/C][/ROW]
[ROW][C]X3[/C][C]1.11584285808511[/C][C]0.13542[/C][C]8.2399[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X4[/C][C]-0.256942291868568[/C][C]0.207051[/C][C]-1.241[/C][C]0.220411[/C][C]0.110206[/C][/ROW]
[ROW][C]X5[/C][C]0.278431112422916[/C][C]0.211157[/C][C]1.3186[/C][C]0.193312[/C][C]0.096656[/C][/ROW]
[ROW][C]X6[/C][C]-0.293078735356719[/C][C]0.156309[/C][C]-1.875[/C][C]0.06664[/C][C]0.03332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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)0.1850369803115490.2316570.79880.4282110.214106
X20.0735797368103770.072531.01450.3152430.157621
X31.115842858085110.135428.239900
X4-0.2569422918685680.207051-1.2410.2204110.110206
X50.2784311124229160.2111571.31860.1933120.096656
X6-0.2930787353567190.156309-1.8750.066640.03332







Multiple Linear Regression - Regression Statistics
Multiple R0.930382067608297
R-squared0.86561079172709
Adjusted R-squared0.8521718708998
F-TEST (value)64.4107367586582
F-TEST (DF numerator)5
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.442858093411831
Sum Squared Residuals9.8061645450181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.930382067608297 \tabularnewline
R-squared & 0.86561079172709 \tabularnewline
Adjusted R-squared & 0.8521718708998 \tabularnewline
F-TEST (value) & 64.4107367586582 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.442858093411831 \tabularnewline
Sum Squared Residuals & 9.8061645450181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.930382067608297[/C][/ROW]
[ROW][C]R-squared[/C][C]0.86561079172709[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.8521718708998[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]64.4107367586582[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]0.442858093411831[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.8061645450181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 R0.930382067608297
R-squared0.86561079172709
Adjusted R-squared0.8521718708998
F-TEST (value)64.4107367586582
F-TEST (DF numerator)5
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.442858093411831
Sum Squared Residuals9.8061645450181







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.41.895994126216500.504005873783495
222.50015404715485-0.500154047154846
32.12.12747482538019-0.0274748253801892
422.33158269188247-0.331582691882465
51.81.87777661716823-0.077776617168228
62.71.825376880123040.874623119876957
72.32.82387292599539-0.523872925995388
81.92.11990937113072-0.219909371130724
922.08555289289608-0.0855528928960763
102.31.92477078866180.375229211338199
112.82.239688466074000.560311533926003
122.42.86560181294096-0.465601812940959
132.32.34501498396384-0.0450149839638354
142.72.387499550507190.312500449492806
152.72.601619110280570.0983808897194326
162.92.588230576433540.311769423566464
1732.952079466555400.0479205334446046
182.22.89504379984750-0.695043799847505
192.32.032361506677150.267638493322853
202.82.334178734881640.465821265118362
212.82.617296360735740.182703639264255
222.82.751131314329130.0488686856708722
232.22.87575494436699-0.675754944366989
242.62.063388848678090.536611151321915
252.82.663891367033270.136108632966732
262.52.62752551760257-0.127525517602565
272.42.53669765903566-0.136697659035659
282.32.45389514175548-0.153895141755482
291.92.23101058591234-0.331010585912336
301.71.88295673648764-0.182956736487639
3121.769916222856260.230083777143744
322.12.084294130375460.0157058696245409
331.72.18843477133064-0.488434771330645
341.81.85854847970796-0.0585484797079644
351.82.02386613342073-0.223866133420735
361.81.86484955941008-0.0648495594100774
371.32.00992416479506-0.709924164795057
381.31.43373182273839-0.133731822738388
391.31.56956094235371-0.26956094235371
401.21.43034538614225-0.230345386142252
411.41.4653004680121-0.0653004680121008
422.21.714163268815980.485836731184022
432.92.527605985668060.372394014331942
443.13.18813624885303-0.0881362488530329
453.53.395574359029050.104425640970954
463.63.75096183430004-0.15096183430004
474.43.6103003110960.789699688903997
484.14.53003706627505-0.430037066275055
495.13.900341992454271.19965800754573
505.85.286704554502610.513295445497391
515.95.506104293907230.393895706092768
525.45.8093342900144-0.409334290014404
535.55.127541675124320.372458324875683
544.85.16968277705279-0.369682777052794
553.24.15390626221353-0.953906262213525
562.72.673501348843050.0264986511569475

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.4 & 1.89599412621650 & 0.504005873783495 \tabularnewline
2 & 2 & 2.50015404715485 & -0.500154047154846 \tabularnewline
3 & 2.1 & 2.12747482538019 & -0.0274748253801892 \tabularnewline
4 & 2 & 2.33158269188247 & -0.331582691882465 \tabularnewline
5 & 1.8 & 1.87777661716823 & -0.077776617168228 \tabularnewline
6 & 2.7 & 1.82537688012304 & 0.874623119876957 \tabularnewline
7 & 2.3 & 2.82387292599539 & -0.523872925995388 \tabularnewline
8 & 1.9 & 2.11990937113072 & -0.219909371130724 \tabularnewline
9 & 2 & 2.08555289289608 & -0.0855528928960763 \tabularnewline
10 & 2.3 & 1.9247707886618 & 0.375229211338199 \tabularnewline
11 & 2.8 & 2.23968846607400 & 0.560311533926003 \tabularnewline
12 & 2.4 & 2.86560181294096 & -0.465601812940959 \tabularnewline
13 & 2.3 & 2.34501498396384 & -0.0450149839638354 \tabularnewline
14 & 2.7 & 2.38749955050719 & 0.312500449492806 \tabularnewline
15 & 2.7 & 2.60161911028057 & 0.0983808897194326 \tabularnewline
16 & 2.9 & 2.58823057643354 & 0.311769423566464 \tabularnewline
17 & 3 & 2.95207946655540 & 0.0479205334446046 \tabularnewline
18 & 2.2 & 2.89504379984750 & -0.695043799847505 \tabularnewline
19 & 2.3 & 2.03236150667715 & 0.267638493322853 \tabularnewline
20 & 2.8 & 2.33417873488164 & 0.465821265118362 \tabularnewline
21 & 2.8 & 2.61729636073574 & 0.182703639264255 \tabularnewline
22 & 2.8 & 2.75113131432913 & 0.0488686856708722 \tabularnewline
23 & 2.2 & 2.87575494436699 & -0.675754944366989 \tabularnewline
24 & 2.6 & 2.06338884867809 & 0.536611151321915 \tabularnewline
25 & 2.8 & 2.66389136703327 & 0.136108632966732 \tabularnewline
26 & 2.5 & 2.62752551760257 & -0.127525517602565 \tabularnewline
27 & 2.4 & 2.53669765903566 & -0.136697659035659 \tabularnewline
28 & 2.3 & 2.45389514175548 & -0.153895141755482 \tabularnewline
29 & 1.9 & 2.23101058591234 & -0.331010585912336 \tabularnewline
30 & 1.7 & 1.88295673648764 & -0.182956736487639 \tabularnewline
31 & 2 & 1.76991622285626 & 0.230083777143744 \tabularnewline
32 & 2.1 & 2.08429413037546 & 0.0157058696245409 \tabularnewline
33 & 1.7 & 2.18843477133064 & -0.488434771330645 \tabularnewline
34 & 1.8 & 1.85854847970796 & -0.0585484797079644 \tabularnewline
35 & 1.8 & 2.02386613342073 & -0.223866133420735 \tabularnewline
36 & 1.8 & 1.86484955941008 & -0.0648495594100774 \tabularnewline
37 & 1.3 & 2.00992416479506 & -0.709924164795057 \tabularnewline
38 & 1.3 & 1.43373182273839 & -0.133731822738388 \tabularnewline
39 & 1.3 & 1.56956094235371 & -0.26956094235371 \tabularnewline
40 & 1.2 & 1.43034538614225 & -0.230345386142252 \tabularnewline
41 & 1.4 & 1.4653004680121 & -0.0653004680121008 \tabularnewline
42 & 2.2 & 1.71416326881598 & 0.485836731184022 \tabularnewline
43 & 2.9 & 2.52760598566806 & 0.372394014331942 \tabularnewline
44 & 3.1 & 3.18813624885303 & -0.0881362488530329 \tabularnewline
45 & 3.5 & 3.39557435902905 & 0.104425640970954 \tabularnewline
46 & 3.6 & 3.75096183430004 & -0.15096183430004 \tabularnewline
47 & 4.4 & 3.610300311096 & 0.789699688903997 \tabularnewline
48 & 4.1 & 4.53003706627505 & -0.430037066275055 \tabularnewline
49 & 5.1 & 3.90034199245427 & 1.19965800754573 \tabularnewline
50 & 5.8 & 5.28670455450261 & 0.513295445497391 \tabularnewline
51 & 5.9 & 5.50610429390723 & 0.393895706092768 \tabularnewline
52 & 5.4 & 5.8093342900144 & -0.409334290014404 \tabularnewline
53 & 5.5 & 5.12754167512432 & 0.372458324875683 \tabularnewline
54 & 4.8 & 5.16968277705279 & -0.369682777052794 \tabularnewline
55 & 3.2 & 4.15390626221353 & -0.953906262213525 \tabularnewline
56 & 2.7 & 2.67350134884305 & 0.0264986511569475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]2.4[/C][C]1.89599412621650[/C][C]0.504005873783495[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2.50015404715485[/C][C]-0.500154047154846[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.12747482538019[/C][C]-0.0274748253801892[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]2.33158269188247[/C][C]-0.331582691882465[/C][/ROW]
[ROW][C]5[/C][C]1.8[/C][C]1.87777661716823[/C][C]-0.077776617168228[/C][/ROW]
[ROW][C]6[/C][C]2.7[/C][C]1.82537688012304[/C][C]0.874623119876957[/C][/ROW]
[ROW][C]7[/C][C]2.3[/C][C]2.82387292599539[/C][C]-0.523872925995388[/C][/ROW]
[ROW][C]8[/C][C]1.9[/C][C]2.11990937113072[/C][C]-0.219909371130724[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]2.08555289289608[/C][C]-0.0855528928960763[/C][/ROW]
[ROW][C]10[/C][C]2.3[/C][C]1.9247707886618[/C][C]0.375229211338199[/C][/ROW]
[ROW][C]11[/C][C]2.8[/C][C]2.23968846607400[/C][C]0.560311533926003[/C][/ROW]
[ROW][C]12[/C][C]2.4[/C][C]2.86560181294096[/C][C]-0.465601812940959[/C][/ROW]
[ROW][C]13[/C][C]2.3[/C][C]2.34501498396384[/C][C]-0.0450149839638354[/C][/ROW]
[ROW][C]14[/C][C]2.7[/C][C]2.38749955050719[/C][C]0.312500449492806[/C][/ROW]
[ROW][C]15[/C][C]2.7[/C][C]2.60161911028057[/C][C]0.0983808897194326[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]2.58823057643354[/C][C]0.311769423566464[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]2.95207946655540[/C][C]0.0479205334446046[/C][/ROW]
[ROW][C]18[/C][C]2.2[/C][C]2.89504379984750[/C][C]-0.695043799847505[/C][/ROW]
[ROW][C]19[/C][C]2.3[/C][C]2.03236150667715[/C][C]0.267638493322853[/C][/ROW]
[ROW][C]20[/C][C]2.8[/C][C]2.33417873488164[/C][C]0.465821265118362[/C][/ROW]
[ROW][C]21[/C][C]2.8[/C][C]2.61729636073574[/C][C]0.182703639264255[/C][/ROW]
[ROW][C]22[/C][C]2.8[/C][C]2.75113131432913[/C][C]0.0488686856708722[/C][/ROW]
[ROW][C]23[/C][C]2.2[/C][C]2.87575494436699[/C][C]-0.675754944366989[/C][/ROW]
[ROW][C]24[/C][C]2.6[/C][C]2.06338884867809[/C][C]0.536611151321915[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.66389136703327[/C][C]0.136108632966732[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]2.62752551760257[/C][C]-0.127525517602565[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.53669765903566[/C][C]-0.136697659035659[/C][/ROW]
[ROW][C]28[/C][C]2.3[/C][C]2.45389514175548[/C][C]-0.153895141755482[/C][/ROW]
[ROW][C]29[/C][C]1.9[/C][C]2.23101058591234[/C][C]-0.331010585912336[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.88295673648764[/C][C]-0.182956736487639[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.76991622285626[/C][C]0.230083777143744[/C][/ROW]
[ROW][C]32[/C][C]2.1[/C][C]2.08429413037546[/C][C]0.0157058696245409[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]2.18843477133064[/C][C]-0.488434771330645[/C][/ROW]
[ROW][C]34[/C][C]1.8[/C][C]1.85854847970796[/C][C]-0.0585484797079644[/C][/ROW]
[ROW][C]35[/C][C]1.8[/C][C]2.02386613342073[/C][C]-0.223866133420735[/C][/ROW]
[ROW][C]36[/C][C]1.8[/C][C]1.86484955941008[/C][C]-0.0648495594100774[/C][/ROW]
[ROW][C]37[/C][C]1.3[/C][C]2.00992416479506[/C][C]-0.709924164795057[/C][/ROW]
[ROW][C]38[/C][C]1.3[/C][C]1.43373182273839[/C][C]-0.133731822738388[/C][/ROW]
[ROW][C]39[/C][C]1.3[/C][C]1.56956094235371[/C][C]-0.26956094235371[/C][/ROW]
[ROW][C]40[/C][C]1.2[/C][C]1.43034538614225[/C][C]-0.230345386142252[/C][/ROW]
[ROW][C]41[/C][C]1.4[/C][C]1.4653004680121[/C][C]-0.0653004680121008[/C][/ROW]
[ROW][C]42[/C][C]2.2[/C][C]1.71416326881598[/C][C]0.485836731184022[/C][/ROW]
[ROW][C]43[/C][C]2.9[/C][C]2.52760598566806[/C][C]0.372394014331942[/C][/ROW]
[ROW][C]44[/C][C]3.1[/C][C]3.18813624885303[/C][C]-0.0881362488530329[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]3.39557435902905[/C][C]0.104425640970954[/C][/ROW]
[ROW][C]46[/C][C]3.6[/C][C]3.75096183430004[/C][C]-0.15096183430004[/C][/ROW]
[ROW][C]47[/C][C]4.4[/C][C]3.610300311096[/C][C]0.789699688903997[/C][/ROW]
[ROW][C]48[/C][C]4.1[/C][C]4.53003706627505[/C][C]-0.430037066275055[/C][/ROW]
[ROW][C]49[/C][C]5.1[/C][C]3.90034199245427[/C][C]1.19965800754573[/C][/ROW]
[ROW][C]50[/C][C]5.8[/C][C]5.28670455450261[/C][C]0.513295445497391[/C][/ROW]
[ROW][C]51[/C][C]5.9[/C][C]5.50610429390723[/C][C]0.393895706092768[/C][/ROW]
[ROW][C]52[/C][C]5.4[/C][C]5.8093342900144[/C][C]-0.409334290014404[/C][/ROW]
[ROW][C]53[/C][C]5.5[/C][C]5.12754167512432[/C][C]0.372458324875683[/C][/ROW]
[ROW][C]54[/C][C]4.8[/C][C]5.16968277705279[/C][C]-0.369682777052794[/C][/ROW]
[ROW][C]55[/C][C]3.2[/C][C]4.15390626221353[/C][C]-0.953906262213525[/C][/ROW]
[ROW][C]56[/C][C]2.7[/C][C]2.67350134884305[/C][C]0.0264986511569475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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
12.41.895994126216500.504005873783495
222.50015404715485-0.500154047154846
32.12.12747482538019-0.0274748253801892
422.33158269188247-0.331582691882465
51.81.87777661716823-0.077776617168228
62.71.825376880123040.874623119876957
72.32.82387292599539-0.523872925995388
81.92.11990937113072-0.219909371130724
922.08555289289608-0.0855528928960763
102.31.92477078866180.375229211338199
112.82.239688466074000.560311533926003
122.42.86560181294096-0.465601812940959
132.32.34501498396384-0.0450149839638354
142.72.387499550507190.312500449492806
152.72.601619110280570.0983808897194326
162.92.588230576433540.311769423566464
1732.952079466555400.0479205334446046
182.22.89504379984750-0.695043799847505
192.32.032361506677150.267638493322853
202.82.334178734881640.465821265118362
212.82.617296360735740.182703639264255
222.82.751131314329130.0488686856708722
232.22.87575494436699-0.675754944366989
242.62.063388848678090.536611151321915
252.82.663891367033270.136108632966732
262.52.62752551760257-0.127525517602565
272.42.53669765903566-0.136697659035659
282.32.45389514175548-0.153895141755482
291.92.23101058591234-0.331010585912336
301.71.88295673648764-0.182956736487639
3121.769916222856260.230083777143744
322.12.084294130375460.0157058696245409
331.72.18843477133064-0.488434771330645
341.81.85854847970796-0.0585484797079644
351.82.02386613342073-0.223866133420735
361.81.86484955941008-0.0648495594100774
371.32.00992416479506-0.709924164795057
381.31.43373182273839-0.133731822738388
391.31.56956094235371-0.26956094235371
401.21.43034538614225-0.230345386142252
411.41.4653004680121-0.0653004680121008
422.21.714163268815980.485836731184022
432.92.527605985668060.372394014331942
443.13.18813624885303-0.0881362488530329
453.53.395574359029050.104425640970954
463.63.75096183430004-0.15096183430004
474.43.6103003110960.789699688903997
484.14.53003706627505-0.430037066275055
495.13.900341992454271.19965800754573
505.85.286704554502610.513295445497391
515.95.506104293907230.393895706092768
525.45.8093342900144-0.409334290014404
535.55.127541675124320.372458324875683
544.85.16968277705279-0.369682777052794
553.24.15390626221353-0.953906262213525
562.72.673501348843050.0264986511569475







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.4969747686794110.9939495373588230.503025231320589
100.335258125042570.670516250085140.66474187495743
110.4833349297409010.9666698594818020.516665070259099
120.4050261404586230.8100522809172460.594973859541377
130.3130670856387130.6261341712774250.686932914361287
140.2917029573793060.5834059147586120.708297042620694
150.2164662962041520.4329325924083050.783533703795848
160.2385704096342580.4771408192685160.761429590365742
170.2100696132225790.4201392264451590.78993038677742
180.2582764156720420.5165528313440840.741723584327958
190.191079720131870.382159440263740.80892027986813
200.1594327534291310.3188655068582630.840567246570869
210.1137352533242800.2274705066485600.88626474667572
220.07828063260400340.1565612652080070.921719367395997
230.1151780837006880.2303561674013760.884821916299312
240.1049468137950480.2098936275900960.895053186204952
250.0799646057514120.1599292115028240.920035394248588
260.05472371466145710.1094474293229140.945276285338543
270.03418510636912040.06837021273824080.96581489363088
280.02336310304115950.0467262060823190.97663689695884
290.02331751881092670.04663503762185340.976682481189073
300.01619361025375990.03238722050751980.98380638974624
310.01258924333200880.02517848666401760.987410756667991
320.008484213946794350.01696842789358870.991515786053206
330.005651321185058160.01130264237011630.994348678814942
340.003188677718801370.006377355437602750.996811322281199
350.001602845289687890.003205690579375790.998397154710312
360.000782291577711320.001564583155422640.999217708422289
370.001298733834412360.002597467668824730.998701266165588
380.0006935433372693810.001387086674538760.99930645666273
390.000402407605562380.000804815211124760.999597592394438
400.0002947663275607810.0005895326551215610.99970523367244
410.0003223672489922520.0006447344979845040.999677632751008
420.0008310851799478030.001662170359895610.999168914820052
430.002352469098759110.004704938197518220.997647530901241
440.003177372310685290.006354744621370590.996822627689315
450.003953030777001170.007906061554002330.996046969222999
460.008983238921819250.01796647784363850.991016761078181
470.008224163356190520.01644832671238100.99177583664381

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.496974768679411 & 0.993949537358823 & 0.503025231320589 \tabularnewline
10 & 0.33525812504257 & 0.67051625008514 & 0.66474187495743 \tabularnewline
11 & 0.483334929740901 & 0.966669859481802 & 0.516665070259099 \tabularnewline
12 & 0.405026140458623 & 0.810052280917246 & 0.594973859541377 \tabularnewline
13 & 0.313067085638713 & 0.626134171277425 & 0.686932914361287 \tabularnewline
14 & 0.291702957379306 & 0.583405914758612 & 0.708297042620694 \tabularnewline
15 & 0.216466296204152 & 0.432932592408305 & 0.783533703795848 \tabularnewline
16 & 0.238570409634258 & 0.477140819268516 & 0.761429590365742 \tabularnewline
17 & 0.210069613222579 & 0.420139226445159 & 0.78993038677742 \tabularnewline
18 & 0.258276415672042 & 0.516552831344084 & 0.741723584327958 \tabularnewline
19 & 0.19107972013187 & 0.38215944026374 & 0.80892027986813 \tabularnewline
20 & 0.159432753429131 & 0.318865506858263 & 0.840567246570869 \tabularnewline
21 & 0.113735253324280 & 0.227470506648560 & 0.88626474667572 \tabularnewline
22 & 0.0782806326040034 & 0.156561265208007 & 0.921719367395997 \tabularnewline
23 & 0.115178083700688 & 0.230356167401376 & 0.884821916299312 \tabularnewline
24 & 0.104946813795048 & 0.209893627590096 & 0.895053186204952 \tabularnewline
25 & 0.079964605751412 & 0.159929211502824 & 0.920035394248588 \tabularnewline
26 & 0.0547237146614571 & 0.109447429322914 & 0.945276285338543 \tabularnewline
27 & 0.0341851063691204 & 0.0683702127382408 & 0.96581489363088 \tabularnewline
28 & 0.0233631030411595 & 0.046726206082319 & 0.97663689695884 \tabularnewline
29 & 0.0233175188109267 & 0.0466350376218534 & 0.976682481189073 \tabularnewline
30 & 0.0161936102537599 & 0.0323872205075198 & 0.98380638974624 \tabularnewline
31 & 0.0125892433320088 & 0.0251784866640176 & 0.987410756667991 \tabularnewline
32 & 0.00848421394679435 & 0.0169684278935887 & 0.991515786053206 \tabularnewline
33 & 0.00565132118505816 & 0.0113026423701163 & 0.994348678814942 \tabularnewline
34 & 0.00318867771880137 & 0.00637735543760275 & 0.996811322281199 \tabularnewline
35 & 0.00160284528968789 & 0.00320569057937579 & 0.998397154710312 \tabularnewline
36 & 0.00078229157771132 & 0.00156458315542264 & 0.999217708422289 \tabularnewline
37 & 0.00129873383441236 & 0.00259746766882473 & 0.998701266165588 \tabularnewline
38 & 0.000693543337269381 & 0.00138708667453876 & 0.99930645666273 \tabularnewline
39 & 0.00040240760556238 & 0.00080481521112476 & 0.999597592394438 \tabularnewline
40 & 0.000294766327560781 & 0.000589532655121561 & 0.99970523367244 \tabularnewline
41 & 0.000322367248992252 & 0.000644734497984504 & 0.999677632751008 \tabularnewline
42 & 0.000831085179947803 & 0.00166217035989561 & 0.999168914820052 \tabularnewline
43 & 0.00235246909875911 & 0.00470493819751822 & 0.997647530901241 \tabularnewline
44 & 0.00317737231068529 & 0.00635474462137059 & 0.996822627689315 \tabularnewline
45 & 0.00395303077700117 & 0.00790606155400233 & 0.996046969222999 \tabularnewline
46 & 0.00898323892181925 & 0.0179664778436385 & 0.991016761078181 \tabularnewline
47 & 0.00822416335619052 & 0.0164483267123810 & 0.99177583664381 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]9[/C][C]0.496974768679411[/C][C]0.993949537358823[/C][C]0.503025231320589[/C][/ROW]
[ROW][C]10[/C][C]0.33525812504257[/C][C]0.67051625008514[/C][C]0.66474187495743[/C][/ROW]
[ROW][C]11[/C][C]0.483334929740901[/C][C]0.966669859481802[/C][C]0.516665070259099[/C][/ROW]
[ROW][C]12[/C][C]0.405026140458623[/C][C]0.810052280917246[/C][C]0.594973859541377[/C][/ROW]
[ROW][C]13[/C][C]0.313067085638713[/C][C]0.626134171277425[/C][C]0.686932914361287[/C][/ROW]
[ROW][C]14[/C][C]0.291702957379306[/C][C]0.583405914758612[/C][C]0.708297042620694[/C][/ROW]
[ROW][C]15[/C][C]0.216466296204152[/C][C]0.432932592408305[/C][C]0.783533703795848[/C][/ROW]
[ROW][C]16[/C][C]0.238570409634258[/C][C]0.477140819268516[/C][C]0.761429590365742[/C][/ROW]
[ROW][C]17[/C][C]0.210069613222579[/C][C]0.420139226445159[/C][C]0.78993038677742[/C][/ROW]
[ROW][C]18[/C][C]0.258276415672042[/C][C]0.516552831344084[/C][C]0.741723584327958[/C][/ROW]
[ROW][C]19[/C][C]0.19107972013187[/C][C]0.38215944026374[/C][C]0.80892027986813[/C][/ROW]
[ROW][C]20[/C][C]0.159432753429131[/C][C]0.318865506858263[/C][C]0.840567246570869[/C][/ROW]
[ROW][C]21[/C][C]0.113735253324280[/C][C]0.227470506648560[/C][C]0.88626474667572[/C][/ROW]
[ROW][C]22[/C][C]0.0782806326040034[/C][C]0.156561265208007[/C][C]0.921719367395997[/C][/ROW]
[ROW][C]23[/C][C]0.115178083700688[/C][C]0.230356167401376[/C][C]0.884821916299312[/C][/ROW]
[ROW][C]24[/C][C]0.104946813795048[/C][C]0.209893627590096[/C][C]0.895053186204952[/C][/ROW]
[ROW][C]25[/C][C]0.079964605751412[/C][C]0.159929211502824[/C][C]0.920035394248588[/C][/ROW]
[ROW][C]26[/C][C]0.0547237146614571[/C][C]0.109447429322914[/C][C]0.945276285338543[/C][/ROW]
[ROW][C]27[/C][C]0.0341851063691204[/C][C]0.0683702127382408[/C][C]0.96581489363088[/C][/ROW]
[ROW][C]28[/C][C]0.0233631030411595[/C][C]0.046726206082319[/C][C]0.97663689695884[/C][/ROW]
[ROW][C]29[/C][C]0.0233175188109267[/C][C]0.0466350376218534[/C][C]0.976682481189073[/C][/ROW]
[ROW][C]30[/C][C]0.0161936102537599[/C][C]0.0323872205075198[/C][C]0.98380638974624[/C][/ROW]
[ROW][C]31[/C][C]0.0125892433320088[/C][C]0.0251784866640176[/C][C]0.987410756667991[/C][/ROW]
[ROW][C]32[/C][C]0.00848421394679435[/C][C]0.0169684278935887[/C][C]0.991515786053206[/C][/ROW]
[ROW][C]33[/C][C]0.00565132118505816[/C][C]0.0113026423701163[/C][C]0.994348678814942[/C][/ROW]
[ROW][C]34[/C][C]0.00318867771880137[/C][C]0.00637735543760275[/C][C]0.996811322281199[/C][/ROW]
[ROW][C]35[/C][C]0.00160284528968789[/C][C]0.00320569057937579[/C][C]0.998397154710312[/C][/ROW]
[ROW][C]36[/C][C]0.00078229157771132[/C][C]0.00156458315542264[/C][C]0.999217708422289[/C][/ROW]
[ROW][C]37[/C][C]0.00129873383441236[/C][C]0.00259746766882473[/C][C]0.998701266165588[/C][/ROW]
[ROW][C]38[/C][C]0.000693543337269381[/C][C]0.00138708667453876[/C][C]0.99930645666273[/C][/ROW]
[ROW][C]39[/C][C]0.00040240760556238[/C][C]0.00080481521112476[/C][C]0.999597592394438[/C][/ROW]
[ROW][C]40[/C][C]0.000294766327560781[/C][C]0.000589532655121561[/C][C]0.99970523367244[/C][/ROW]
[ROW][C]41[/C][C]0.000322367248992252[/C][C]0.000644734497984504[/C][C]0.999677632751008[/C][/ROW]
[ROW][C]42[/C][C]0.000831085179947803[/C][C]0.00166217035989561[/C][C]0.999168914820052[/C][/ROW]
[ROW][C]43[/C][C]0.00235246909875911[/C][C]0.00470493819751822[/C][C]0.997647530901241[/C][/ROW]
[ROW][C]44[/C][C]0.00317737231068529[/C][C]0.00635474462137059[/C][C]0.996822627689315[/C][/ROW]
[ROW][C]45[/C][C]0.00395303077700117[/C][C]0.00790606155400233[/C][C]0.996046969222999[/C][/ROW]
[ROW][C]46[/C][C]0.00898323892181925[/C][C]0.0179664778436385[/C][C]0.991016761078181[/C][/ROW]
[ROW][C]47[/C][C]0.00822416335619052[/C][C]0.0164483267123810[/C][C]0.99177583664381[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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
90.4969747686794110.9939495373588230.503025231320589
100.335258125042570.670516250085140.66474187495743
110.4833349297409010.9666698594818020.516665070259099
120.4050261404586230.8100522809172460.594973859541377
130.3130670856387130.6261341712774250.686932914361287
140.2917029573793060.5834059147586120.708297042620694
150.2164662962041520.4329325924083050.783533703795848
160.2385704096342580.4771408192685160.761429590365742
170.2100696132225790.4201392264451590.78993038677742
180.2582764156720420.5165528313440840.741723584327958
190.191079720131870.382159440263740.80892027986813
200.1594327534291310.3188655068582630.840567246570869
210.1137352533242800.2274705066485600.88626474667572
220.07828063260400340.1565612652080070.921719367395997
230.1151780837006880.2303561674013760.884821916299312
240.1049468137950480.2098936275900960.895053186204952
250.0799646057514120.1599292115028240.920035394248588
260.05472371466145710.1094474293229140.945276285338543
270.03418510636912040.06837021273824080.96581489363088
280.02336310304115950.0467262060823190.97663689695884
290.02331751881092670.04663503762185340.976682481189073
300.01619361025375990.03238722050751980.98380638974624
310.01258924333200880.02517848666401760.987410756667991
320.008484213946794350.01696842789358870.991515786053206
330.005651321185058160.01130264237011630.994348678814942
340.003188677718801370.006377355437602750.996811322281199
350.001602845289687890.003205690579375790.998397154710312
360.000782291577711320.001564583155422640.999217708422289
370.001298733834412360.002597467668824730.998701266165588
380.0006935433372693810.001387086674538760.99930645666273
390.000402407605562380.000804815211124760.999597592394438
400.0002947663275607810.0005895326551215610.99970523367244
410.0003223672489922520.0006447344979845040.999677632751008
420.0008310851799478030.001662170359895610.999168914820052
430.002352469098759110.004704938197518220.997647530901241
440.003177372310685290.006354744621370590.996822627689315
450.003953030777001170.007906061554002330.996046969222999
460.008983238921819250.01796647784363850.991016761078181
470.008224163356190520.01644832671238100.99177583664381







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.307692307692308NOK
5% type I error level200.512820512820513NOK
10% type I error level210.538461538461538NOK

\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 & 12 & 0.307692307692308 & NOK \tabularnewline
5% type I error level & 20 & 0.512820512820513 & NOK \tabularnewline
10% type I error level & 21 & 0.538461538461538 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59273&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]12[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.512820512820513[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59273&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59273&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 level120.307692307692308NOK
5% type I error level200.512820512820513NOK
10% type I error level210.538461538461538NOK



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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}