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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 computationFri, 20 Nov 2009 06:25:42 -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/t1258723702z7b81op08gp9uxk.htm/, Retrieved Thu, 28 Mar 2024 15:07:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58139, Retrieved Thu, 28 Mar 2024 15:07:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
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 14:14:11] [b98453cac15ba1066b407e146608df68]
F    D      [Multiple Regression] [model 5] [2009-11-20 13:25:42] [986e3c28a4248c495afaef9fd432264f] [Current]
Feedback Forum
2009-11-21 09:45:02 [] [reply
denk ook dat model 5 het beste is maar er zit nog altijd een structuur in de autocorrelatie en de trend is ook niet helemaal verdwenen; ook zijn de residuen niet normaal verdeeld, er zit nog steeds afwijking in de staarten

Post a new message
Dataseries X:
98.2	137.7	98.54	98.71
96.92	148.3	98.2	98.54
99.06	152.2	96.92	98.2
99.65	169.4	99.06	96.92
99.82	168.6	99.65	99.06
99.99	161.1	99.82	99.65
100.33	174.1	99.99	99.82
99.31	179	100.33	99.99
101.1	190.6	99.31	100.33
101.1	190	101.1	99.31
100.93	181.6	101.1	101.1
100.85	174.8	100.93	101.1
100.93	180.5	100.85	100.93
99.6	196.8	100.93	100.85
101.88	193.8	99.6	100.93
101.81	197	101.88	99.6
102.38	216.3	101.81	101.88
102.74	221.4	102.38	101.81
102.82	217.9	102.74	102.38
101.72	229.7	102.82	102.74
103.47	227.4	101.72	102.82
102.98	204.2	103.47	101.72
102.68	196.6	102.98	103.47
102.9	198.8	102.68	102.98
103.03	207.5	102.9	102.68
101.29	190.7	103.03	102.9
103.69	201.6	101.29	103.03
103.68	210.5	103.69	101.29
104.2	223.5	103.68	103.69
104.08	223.8	104.2	103.68
104.16	231.2	104.08	104.2
103.05	244	104.16	104.08
104.66	234.7	103.05	104.16
104.46	250.2	104.66	103.05
104.95	265.7	104.46	104.66
105.85	287.6	104.95	104.46
106.23	283.3	105.85	104.95
104.86	295.4	106.23	105.85
107.44	312.3	104.86	106.23
108.23	333.8	107.44	104.86
108.45	347.7	108.23	107.44
109.39	383.2	108.45	108.23
110.15	407.1	109.39	108.45
109.13	413.6	110.15	109.39
110.28	362.7	109.13	110.15
110.17	321.9	110.28	109.13
109.99	239.4	110.17	110.28
109.26	191	109.99	110.17
109.11	159.7	109.26	109.99
107.06	163.4	109.11	109.26
109.53	157.6	107.06	109.11
108.92	166.2	109.53	107.06
109.24	176.7	108.92	109.53
109.12	198.3	109.24	108.92
109	226.2	109.12	109.24
107.23	216.2	109	109.12
109.49	235.9	107.23	109
109.04	226.9	109.49	107.23




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=58139&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=58139&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 16.11068832504 + 0.00550259812927356X[t] + 0.563481661967873Y1[t] + 0.264939208226435Y2[t] -0.0089156783423854M1[t] -1.63469029339611M2[t] + 1.55987630003095M3[t] + 0.681932272652926M4[t] + 0.249103850212106M5[t] + 0.170577036072006M6[t] + 0.0643935082957647M7[t] -1.38645209180325M8[t] + 0.9533266763827M9[t] + 0.0969898423860808M10[t] -0.171226582904688M11[t] + 0.0245832256744107t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  16.11068832504 +  0.00550259812927356X[t] +  0.563481661967873Y1[t] +  0.264939208226435Y2[t] -0.0089156783423854M1[t] -1.63469029339611M2[t] +  1.55987630003095M3[t] +  0.681932272652926M4[t] +  0.249103850212106M5[t] +  0.170577036072006M6[t] +  0.0643935082957647M7[t] -1.38645209180325M8[t] +  0.9533266763827M9[t] +  0.0969898423860808M10[t] -0.171226582904688M11[t] +  0.0245832256744107t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  16.11068832504 +  0.00550259812927356X[t] +  0.563481661967873Y1[t] +  0.264939208226435Y2[t] -0.0089156783423854M1[t] -1.63469029339611M2[t] +  1.55987630003095M3[t] +  0.681932272652926M4[t] +  0.249103850212106M5[t] +  0.170577036072006M6[t] +  0.0643935082957647M7[t] -1.38645209180325M8[t] +  0.9533266763827M9[t] +  0.0969898423860808M10[t] -0.171226582904688M11[t] +  0.0245832256744107t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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
Y[t] = + 16.11068832504 + 0.00550259812927356X[t] + 0.563481661967873Y1[t] + 0.264939208226435Y2[t] -0.0089156783423854M1[t] -1.63469029339611M2[t] + 1.55987630003095M3[t] + 0.681932272652926M4[t] + 0.249103850212106M5[t] + 0.170577036072006M6[t] + 0.0643935082957647M7[t] -1.38645209180325M8[t] + 0.9533266763827M9[t] + 0.0969898423860808M10[t] -0.171226582904688M11[t] + 0.0245832256744107t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.110688325044.3494663.70410.0006130.000307
X0.005502598129273560.0008696.334500
Y10.5634816619678730.139384.04280.0002210.00011
Y20.2649392082264350.1304382.03110.0486010.0243
M1-0.00891567834238540.184012-0.04850.9615860.480793
M2-1.634690293396110.183923-8.887900
M31.559876300030950.2853585.46642e-061e-06
M40.6819322726529260.3618671.88450.0664330.033216
M50.2491038502121060.1841431.35280.1833650.091683
M60.1705770360720060.1884060.90540.3704360.185218
M70.06439350829576470.1859280.34630.730820.36541
M8-1.386452091803250.185907-7.457800
M90.95332667638270.2575043.70220.0006170.000308
M100.09698984238608080.287760.33710.7377570.368878
M11-0.1712265829046880.19489-0.87860.3846280.192314
t0.02458322567441070.0093082.64120.0115530.005776

\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) & 16.11068832504 & 4.349466 & 3.7041 & 0.000613 & 0.000307 \tabularnewline
X & 0.00550259812927356 & 0.000869 & 6.3345 & 0 & 0 \tabularnewline
Y1 & 0.563481661967873 & 0.13938 & 4.0428 & 0.000221 & 0.00011 \tabularnewline
Y2 & 0.264939208226435 & 0.130438 & 2.0311 & 0.048601 & 0.0243 \tabularnewline
M1 & -0.0089156783423854 & 0.184012 & -0.0485 & 0.961586 & 0.480793 \tabularnewline
M2 & -1.63469029339611 & 0.183923 & -8.8879 & 0 & 0 \tabularnewline
M3 & 1.55987630003095 & 0.285358 & 5.4664 & 2e-06 & 1e-06 \tabularnewline
M4 & 0.681932272652926 & 0.361867 & 1.8845 & 0.066433 & 0.033216 \tabularnewline
M5 & 0.249103850212106 & 0.184143 & 1.3528 & 0.183365 & 0.091683 \tabularnewline
M6 & 0.170577036072006 & 0.188406 & 0.9054 & 0.370436 & 0.185218 \tabularnewline
M7 & 0.0643935082957647 & 0.185928 & 0.3463 & 0.73082 & 0.36541 \tabularnewline
M8 & -1.38645209180325 & 0.185907 & -7.4578 & 0 & 0 \tabularnewline
M9 & 0.9533266763827 & 0.257504 & 3.7022 & 0.000617 & 0.000308 \tabularnewline
M10 & 0.0969898423860808 & 0.28776 & 0.3371 & 0.737757 & 0.368878 \tabularnewline
M11 & -0.171226582904688 & 0.19489 & -0.8786 & 0.384628 & 0.192314 \tabularnewline
t & 0.0245832256744107 & 0.009308 & 2.6412 & 0.011553 & 0.005776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&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]16.11068832504[/C][C]4.349466[/C][C]3.7041[/C][C]0.000613[/C][C]0.000307[/C][/ROW]
[ROW][C]X[/C][C]0.00550259812927356[/C][C]0.000869[/C][C]6.3345[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y1[/C][C]0.563481661967873[/C][C]0.13938[/C][C]4.0428[/C][C]0.000221[/C][C]0.00011[/C][/ROW]
[ROW][C]Y2[/C][C]0.264939208226435[/C][C]0.130438[/C][C]2.0311[/C][C]0.048601[/C][C]0.0243[/C][/ROW]
[ROW][C]M1[/C][C]-0.0089156783423854[/C][C]0.184012[/C][C]-0.0485[/C][C]0.961586[/C][C]0.480793[/C][/ROW]
[ROW][C]M2[/C][C]-1.63469029339611[/C][C]0.183923[/C][C]-8.8879[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]1.55987630003095[/C][C]0.285358[/C][C]5.4664[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M4[/C][C]0.681932272652926[/C][C]0.361867[/C][C]1.8845[/C][C]0.066433[/C][C]0.033216[/C][/ROW]
[ROW][C]M5[/C][C]0.249103850212106[/C][C]0.184143[/C][C]1.3528[/C][C]0.183365[/C][C]0.091683[/C][/ROW]
[ROW][C]M6[/C][C]0.170577036072006[/C][C]0.188406[/C][C]0.9054[/C][C]0.370436[/C][C]0.185218[/C][/ROW]
[ROW][C]M7[/C][C]0.0643935082957647[/C][C]0.185928[/C][C]0.3463[/C][C]0.73082[/C][C]0.36541[/C][/ROW]
[ROW][C]M8[/C][C]-1.38645209180325[/C][C]0.185907[/C][C]-7.4578[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]0.9533266763827[/C][C]0.257504[/C][C]3.7022[/C][C]0.000617[/C][C]0.000308[/C][/ROW]
[ROW][C]M10[/C][C]0.0969898423860808[/C][C]0.28776[/C][C]0.3371[/C][C]0.737757[/C][C]0.368878[/C][/ROW]
[ROW][C]M11[/C][C]-0.171226582904688[/C][C]0.19489[/C][C]-0.8786[/C][C]0.384628[/C][C]0.192314[/C][/ROW]
[ROW][C]t[/C][C]0.0245832256744107[/C][C]0.009308[/C][C]2.6412[/C][C]0.011553[/C][C]0.005776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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)16.110688325044.3494663.70410.0006130.000307
X0.005502598129273560.0008696.334500
Y10.5634816619678730.139384.04280.0002210.00011
Y20.2649392082264350.1304382.03110.0486010.0243
M1-0.00891567834238540.184012-0.04850.9615860.480793
M2-1.634690293396110.183923-8.887900
M31.559876300030950.2853585.46642e-061e-06
M40.6819322726529260.3618671.88450.0664330.033216
M50.2491038502121060.1841431.35280.1833650.091683
M60.1705770360720060.1884060.90540.3704360.185218
M70.06439350829576470.1859280.34630.730820.36541
M8-1.386452091803250.185907-7.457800
M90.95332667638270.2575043.70220.0006170.000308
M100.09698984238608080.287760.33710.7377570.368878
M11-0.1712265829046880.19489-0.87860.3846280.192314
t0.02458322567441070.0093082.64120.0115530.005776







Multiple Linear Regression - Regression Statistics
Multiple R0.99806437715787
R-squared0.996132500951525
Adjusted R-squared0.994751251291355
F-TEST (value)721.182078574541
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.272965625676694
Sum Squared Residuals3.12942977764490

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99806437715787 \tabularnewline
R-squared & 0.996132500951525 \tabularnewline
Adjusted R-squared & 0.994751251291355 \tabularnewline
F-TEST (value) & 721.182078574541 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.272965625676694 \tabularnewline
Sum Squared Residuals & 3.12942977764490 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99806437715787[/C][/ROW]
[ROW][C]R-squared[/C][C]0.996132500951525[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.994751251291355[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]721.182078574541[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/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]0.272965625676694[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.12942977764490[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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 R0.99806437715787
R-squared0.996132500951525
Adjusted R-squared0.994751251291355
F-TEST (value)721.182078574541
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.272965625676694
Sum Squared Residuals3.12942977764490







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.298.5616958491187-0.361695849118701
296.9296.7822085694420.137791430558001
399.0699.2114826631318-0.151482663131774
499.6599.31949511933310.330504880666927
599.8299.80627193022890.0137280697711273
699.9999.96316487118170.0268351288182420
7100.33100.0939298926940.236070107306488
899.3198.931253679570.378746320430079
9101.1100.8747738473200.225226152680380
10101.1100.7781128626510.321887137348628
11100.93100.962499021474-0.0324990214744211
12100.85101.02509928024-0.175099280239939
13100.93100.982013438553-0.0520134385528849
1499.699.494397794980.105602205019939
15101.88101.968804345935-0.0888043459345502
16101.81102.065420900590-0.255420900590195
17102.38102.3279935261370.0520064738627035
18102.74102.6047519908770.135248009123261
19102.82102.85776134232-0.037761342319952
20101.72101.6368862737400.0831137262602805
21103.47103.3899576003960.0800423996037959
22102.98103.125203494870-0.145203494869545
23102.68103.027288149503-0.347288149502712
24102.9102.936338963345-0.0363389633449008
25103.03103.044363317567-0.0143633175666123
26101.29101.482267521481-0.192267521481133
27103.69103.815379665437-0.125379665437035
28103.68103.902353753493-0.222353753492841
29104.2104.1958616155310.00413838446924012
30104.08104.433929878645-0.353929878644888
31104.16104.463199391541-0.303199391541281
32103.05103.120656101142-0.070656101141637
33104.66104.829574424274-0.169574424273526
34104.46104.696234041592-0.236234041591993
35104.95104.8517469058300.0982530941696496
36105.85105.3911817861600.458818213840477
37106.23106.0201418693380.209858130662301
38104.86104.938100236274-0.0781002362741863
39107.44107.578950985990-0.138950985990441
40108.23107.9347120166730.295287983326902
41108.45108.731646604082-0.281646604082416
42109.39109.2063131893380.183686810662242
43110.15109.8441843705850.305815629414825
44109.13109.130977802829-0.000977802829298137
45110.28110.841860054954-0.56186005495448
46110.17110.163366361830.00663363816999925
47109.99109.7084659231930.281534076807483
48109.26109.507379970256-0.247379970255636
49109.11108.8917855254240.218214474575898
50107.06107.0330258778230.0269741221773794
51109.53109.0253823395060.5046176604938
52108.92109.068018209911-0.148018209910793
53109.24109.0282263240210.211773675979345
54109.12109.1118400699590.00815993004114356
55109109.20092500286-0.20092500286008
56107.23107.620226142719-0.390226142719424
57109.49109.0638340730560.426165926943829
58109.04108.9870832390570.0529167609429108

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.2 & 98.5616958491187 & -0.361695849118701 \tabularnewline
2 & 96.92 & 96.782208569442 & 0.137791430558001 \tabularnewline
3 & 99.06 & 99.2114826631318 & -0.151482663131774 \tabularnewline
4 & 99.65 & 99.3194951193331 & 0.330504880666927 \tabularnewline
5 & 99.82 & 99.8062719302289 & 0.0137280697711273 \tabularnewline
6 & 99.99 & 99.9631648711817 & 0.0268351288182420 \tabularnewline
7 & 100.33 & 100.093929892694 & 0.236070107306488 \tabularnewline
8 & 99.31 & 98.93125367957 & 0.378746320430079 \tabularnewline
9 & 101.1 & 100.874773847320 & 0.225226152680380 \tabularnewline
10 & 101.1 & 100.778112862651 & 0.321887137348628 \tabularnewline
11 & 100.93 & 100.962499021474 & -0.0324990214744211 \tabularnewline
12 & 100.85 & 101.02509928024 & -0.175099280239939 \tabularnewline
13 & 100.93 & 100.982013438553 & -0.0520134385528849 \tabularnewline
14 & 99.6 & 99.49439779498 & 0.105602205019939 \tabularnewline
15 & 101.88 & 101.968804345935 & -0.0888043459345502 \tabularnewline
16 & 101.81 & 102.065420900590 & -0.255420900590195 \tabularnewline
17 & 102.38 & 102.327993526137 & 0.0520064738627035 \tabularnewline
18 & 102.74 & 102.604751990877 & 0.135248009123261 \tabularnewline
19 & 102.82 & 102.85776134232 & -0.037761342319952 \tabularnewline
20 & 101.72 & 101.636886273740 & 0.0831137262602805 \tabularnewline
21 & 103.47 & 103.389957600396 & 0.0800423996037959 \tabularnewline
22 & 102.98 & 103.125203494870 & -0.145203494869545 \tabularnewline
23 & 102.68 & 103.027288149503 & -0.347288149502712 \tabularnewline
24 & 102.9 & 102.936338963345 & -0.0363389633449008 \tabularnewline
25 & 103.03 & 103.044363317567 & -0.0143633175666123 \tabularnewline
26 & 101.29 & 101.482267521481 & -0.192267521481133 \tabularnewline
27 & 103.69 & 103.815379665437 & -0.125379665437035 \tabularnewline
28 & 103.68 & 103.902353753493 & -0.222353753492841 \tabularnewline
29 & 104.2 & 104.195861615531 & 0.00413838446924012 \tabularnewline
30 & 104.08 & 104.433929878645 & -0.353929878644888 \tabularnewline
31 & 104.16 & 104.463199391541 & -0.303199391541281 \tabularnewline
32 & 103.05 & 103.120656101142 & -0.070656101141637 \tabularnewline
33 & 104.66 & 104.829574424274 & -0.169574424273526 \tabularnewline
34 & 104.46 & 104.696234041592 & -0.236234041591993 \tabularnewline
35 & 104.95 & 104.851746905830 & 0.0982530941696496 \tabularnewline
36 & 105.85 & 105.391181786160 & 0.458818213840477 \tabularnewline
37 & 106.23 & 106.020141869338 & 0.209858130662301 \tabularnewline
38 & 104.86 & 104.938100236274 & -0.0781002362741863 \tabularnewline
39 & 107.44 & 107.578950985990 & -0.138950985990441 \tabularnewline
40 & 108.23 & 107.934712016673 & 0.295287983326902 \tabularnewline
41 & 108.45 & 108.731646604082 & -0.281646604082416 \tabularnewline
42 & 109.39 & 109.206313189338 & 0.183686810662242 \tabularnewline
43 & 110.15 & 109.844184370585 & 0.305815629414825 \tabularnewline
44 & 109.13 & 109.130977802829 & -0.000977802829298137 \tabularnewline
45 & 110.28 & 110.841860054954 & -0.56186005495448 \tabularnewline
46 & 110.17 & 110.16336636183 & 0.00663363816999925 \tabularnewline
47 & 109.99 & 109.708465923193 & 0.281534076807483 \tabularnewline
48 & 109.26 & 109.507379970256 & -0.247379970255636 \tabularnewline
49 & 109.11 & 108.891785525424 & 0.218214474575898 \tabularnewline
50 & 107.06 & 107.033025877823 & 0.0269741221773794 \tabularnewline
51 & 109.53 & 109.025382339506 & 0.5046176604938 \tabularnewline
52 & 108.92 & 109.068018209911 & -0.148018209910793 \tabularnewline
53 & 109.24 & 109.028226324021 & 0.211773675979345 \tabularnewline
54 & 109.12 & 109.111840069959 & 0.00815993004114356 \tabularnewline
55 & 109 & 109.20092500286 & -0.20092500286008 \tabularnewline
56 & 107.23 & 107.620226142719 & -0.390226142719424 \tabularnewline
57 & 109.49 & 109.063834073056 & 0.426165926943829 \tabularnewline
58 & 109.04 & 108.987083239057 & 0.0529167609429108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&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]98.2[/C][C]98.5616958491187[/C][C]-0.361695849118701[/C][/ROW]
[ROW][C]2[/C][C]96.92[/C][C]96.782208569442[/C][C]0.137791430558001[/C][/ROW]
[ROW][C]3[/C][C]99.06[/C][C]99.2114826631318[/C][C]-0.151482663131774[/C][/ROW]
[ROW][C]4[/C][C]99.65[/C][C]99.3194951193331[/C][C]0.330504880666927[/C][/ROW]
[ROW][C]5[/C][C]99.82[/C][C]99.8062719302289[/C][C]0.0137280697711273[/C][/ROW]
[ROW][C]6[/C][C]99.99[/C][C]99.9631648711817[/C][C]0.0268351288182420[/C][/ROW]
[ROW][C]7[/C][C]100.33[/C][C]100.093929892694[/C][C]0.236070107306488[/C][/ROW]
[ROW][C]8[/C][C]99.31[/C][C]98.93125367957[/C][C]0.378746320430079[/C][/ROW]
[ROW][C]9[/C][C]101.1[/C][C]100.874773847320[/C][C]0.225226152680380[/C][/ROW]
[ROW][C]10[/C][C]101.1[/C][C]100.778112862651[/C][C]0.321887137348628[/C][/ROW]
[ROW][C]11[/C][C]100.93[/C][C]100.962499021474[/C][C]-0.0324990214744211[/C][/ROW]
[ROW][C]12[/C][C]100.85[/C][C]101.02509928024[/C][C]-0.175099280239939[/C][/ROW]
[ROW][C]13[/C][C]100.93[/C][C]100.982013438553[/C][C]-0.0520134385528849[/C][/ROW]
[ROW][C]14[/C][C]99.6[/C][C]99.49439779498[/C][C]0.105602205019939[/C][/ROW]
[ROW][C]15[/C][C]101.88[/C][C]101.968804345935[/C][C]-0.0888043459345502[/C][/ROW]
[ROW][C]16[/C][C]101.81[/C][C]102.065420900590[/C][C]-0.255420900590195[/C][/ROW]
[ROW][C]17[/C][C]102.38[/C][C]102.327993526137[/C][C]0.0520064738627035[/C][/ROW]
[ROW][C]18[/C][C]102.74[/C][C]102.604751990877[/C][C]0.135248009123261[/C][/ROW]
[ROW][C]19[/C][C]102.82[/C][C]102.85776134232[/C][C]-0.037761342319952[/C][/ROW]
[ROW][C]20[/C][C]101.72[/C][C]101.636886273740[/C][C]0.0831137262602805[/C][/ROW]
[ROW][C]21[/C][C]103.47[/C][C]103.389957600396[/C][C]0.0800423996037959[/C][/ROW]
[ROW][C]22[/C][C]102.98[/C][C]103.125203494870[/C][C]-0.145203494869545[/C][/ROW]
[ROW][C]23[/C][C]102.68[/C][C]103.027288149503[/C][C]-0.347288149502712[/C][/ROW]
[ROW][C]24[/C][C]102.9[/C][C]102.936338963345[/C][C]-0.0363389633449008[/C][/ROW]
[ROW][C]25[/C][C]103.03[/C][C]103.044363317567[/C][C]-0.0143633175666123[/C][/ROW]
[ROW][C]26[/C][C]101.29[/C][C]101.482267521481[/C][C]-0.192267521481133[/C][/ROW]
[ROW][C]27[/C][C]103.69[/C][C]103.815379665437[/C][C]-0.125379665437035[/C][/ROW]
[ROW][C]28[/C][C]103.68[/C][C]103.902353753493[/C][C]-0.222353753492841[/C][/ROW]
[ROW][C]29[/C][C]104.2[/C][C]104.195861615531[/C][C]0.00413838446924012[/C][/ROW]
[ROW][C]30[/C][C]104.08[/C][C]104.433929878645[/C][C]-0.353929878644888[/C][/ROW]
[ROW][C]31[/C][C]104.16[/C][C]104.463199391541[/C][C]-0.303199391541281[/C][/ROW]
[ROW][C]32[/C][C]103.05[/C][C]103.120656101142[/C][C]-0.070656101141637[/C][/ROW]
[ROW][C]33[/C][C]104.66[/C][C]104.829574424274[/C][C]-0.169574424273526[/C][/ROW]
[ROW][C]34[/C][C]104.46[/C][C]104.696234041592[/C][C]-0.236234041591993[/C][/ROW]
[ROW][C]35[/C][C]104.95[/C][C]104.851746905830[/C][C]0.0982530941696496[/C][/ROW]
[ROW][C]36[/C][C]105.85[/C][C]105.391181786160[/C][C]0.458818213840477[/C][/ROW]
[ROW][C]37[/C][C]106.23[/C][C]106.020141869338[/C][C]0.209858130662301[/C][/ROW]
[ROW][C]38[/C][C]104.86[/C][C]104.938100236274[/C][C]-0.0781002362741863[/C][/ROW]
[ROW][C]39[/C][C]107.44[/C][C]107.578950985990[/C][C]-0.138950985990441[/C][/ROW]
[ROW][C]40[/C][C]108.23[/C][C]107.934712016673[/C][C]0.295287983326902[/C][/ROW]
[ROW][C]41[/C][C]108.45[/C][C]108.731646604082[/C][C]-0.281646604082416[/C][/ROW]
[ROW][C]42[/C][C]109.39[/C][C]109.206313189338[/C][C]0.183686810662242[/C][/ROW]
[ROW][C]43[/C][C]110.15[/C][C]109.844184370585[/C][C]0.305815629414825[/C][/ROW]
[ROW][C]44[/C][C]109.13[/C][C]109.130977802829[/C][C]-0.000977802829298137[/C][/ROW]
[ROW][C]45[/C][C]110.28[/C][C]110.841860054954[/C][C]-0.56186005495448[/C][/ROW]
[ROW][C]46[/C][C]110.17[/C][C]110.16336636183[/C][C]0.00663363816999925[/C][/ROW]
[ROW][C]47[/C][C]109.99[/C][C]109.708465923193[/C][C]0.281534076807483[/C][/ROW]
[ROW][C]48[/C][C]109.26[/C][C]109.507379970256[/C][C]-0.247379970255636[/C][/ROW]
[ROW][C]49[/C][C]109.11[/C][C]108.891785525424[/C][C]0.218214474575898[/C][/ROW]
[ROW][C]50[/C][C]107.06[/C][C]107.033025877823[/C][C]0.0269741221773794[/C][/ROW]
[ROW][C]51[/C][C]109.53[/C][C]109.025382339506[/C][C]0.5046176604938[/C][/ROW]
[ROW][C]52[/C][C]108.92[/C][C]109.068018209911[/C][C]-0.148018209910793[/C][/ROW]
[ROW][C]53[/C][C]109.24[/C][C]109.028226324021[/C][C]0.211773675979345[/C][/ROW]
[ROW][C]54[/C][C]109.12[/C][C]109.111840069959[/C][C]0.00815993004114356[/C][/ROW]
[ROW][C]55[/C][C]109[/C][C]109.20092500286[/C][C]-0.20092500286008[/C][/ROW]
[ROW][C]56[/C][C]107.23[/C][C]107.620226142719[/C][C]-0.390226142719424[/C][/ROW]
[ROW][C]57[/C][C]109.49[/C][C]109.063834073056[/C][C]0.426165926943829[/C][/ROW]
[ROW][C]58[/C][C]109.04[/C][C]108.987083239057[/C][C]0.0529167609429108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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
198.298.5616958491187-0.361695849118701
296.9296.7822085694420.137791430558001
399.0699.2114826631318-0.151482663131774
499.6599.31949511933310.330504880666927
599.8299.80627193022890.0137280697711273
699.9999.96316487118170.0268351288182420
7100.33100.0939298926940.236070107306488
899.3198.931253679570.378746320430079
9101.1100.8747738473200.225226152680380
10101.1100.7781128626510.321887137348628
11100.93100.962499021474-0.0324990214744211
12100.85101.02509928024-0.175099280239939
13100.93100.982013438553-0.0520134385528849
1499.699.494397794980.105602205019939
15101.88101.968804345935-0.0888043459345502
16101.81102.065420900590-0.255420900590195
17102.38102.3279935261370.0520064738627035
18102.74102.6047519908770.135248009123261
19102.82102.85776134232-0.037761342319952
20101.72101.6368862737400.0831137262602805
21103.47103.3899576003960.0800423996037959
22102.98103.125203494870-0.145203494869545
23102.68103.027288149503-0.347288149502712
24102.9102.936338963345-0.0363389633449008
25103.03103.044363317567-0.0143633175666123
26101.29101.482267521481-0.192267521481133
27103.69103.815379665437-0.125379665437035
28103.68103.902353753493-0.222353753492841
29104.2104.1958616155310.00413838446924012
30104.08104.433929878645-0.353929878644888
31104.16104.463199391541-0.303199391541281
32103.05103.120656101142-0.070656101141637
33104.66104.829574424274-0.169574424273526
34104.46104.696234041592-0.236234041591993
35104.95104.8517469058300.0982530941696496
36105.85105.3911817861600.458818213840477
37106.23106.0201418693380.209858130662301
38104.86104.938100236274-0.0781002362741863
39107.44107.578950985990-0.138950985990441
40108.23107.9347120166730.295287983326902
41108.45108.731646604082-0.281646604082416
42109.39109.2063131893380.183686810662242
43110.15109.8441843705850.305815629414825
44109.13109.130977802829-0.000977802829298137
45110.28110.841860054954-0.56186005495448
46110.17110.163366361830.00663363816999925
47109.99109.7084659231930.281534076807483
48109.26109.507379970256-0.247379970255636
49109.11108.8917855254240.218214474575898
50107.06107.0330258778230.0269741221773794
51109.53109.0253823395060.5046176604938
52108.92109.068018209911-0.148018209910793
53109.24109.0282263240210.211773675979345
54109.12109.1118400699590.00815993004114356
55109109.20092500286-0.20092500286008
56107.23107.620226142719-0.390226142719424
57109.49109.0638340730560.426165926943829
58109.04108.9870832390570.0529167609429108







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.1772499204398210.3544998408796420.82275007956018
200.1308189226089430.2616378452178860.869181077391057
210.08912919544359350.1782583908871870.910870804556406
220.0676857564844730.1353715129689460.932314243515527
230.0407010407712560.0814020815425120.959298959228744
240.01785222235413260.03570444470826510.982147777645867
250.007424392563677920.01484878512735580.992575607436322
260.003807830797741770.007615661595483540.996192169202258
270.002690387169929460.005380774339858930.99730961283007
280.001464880566900360.002929761133800720.9985351194331
290.0005881537797589060.001176307559517810.99941184622024
300.002263562796437950.004527125592875890.997736437203562
310.005103802514300230.01020760502860050.9948961974857
320.006751019369270380.01350203873854080.99324898063073
330.005610575737596870.01122115147519370.994389424262403
340.007405742692298770.01481148538459750.9925942573077
350.003745814526026750.00749162905205350.996254185473973
360.008352981413394840.01670596282678970.991647018586605
370.01947532296344170.03895064592688330.980524677036558
380.02689242058421830.05378484116843660.973107579415782
390.01981259211731120.03962518423462250.980187407882689

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.177249920439821 & 0.354499840879642 & 0.82275007956018 \tabularnewline
20 & 0.130818922608943 & 0.261637845217886 & 0.869181077391057 \tabularnewline
21 & 0.0891291954435935 & 0.178258390887187 & 0.910870804556406 \tabularnewline
22 & 0.067685756484473 & 0.135371512968946 & 0.932314243515527 \tabularnewline
23 & 0.040701040771256 & 0.081402081542512 & 0.959298959228744 \tabularnewline
24 & 0.0178522223541326 & 0.0357044447082651 & 0.982147777645867 \tabularnewline
25 & 0.00742439256367792 & 0.0148487851273558 & 0.992575607436322 \tabularnewline
26 & 0.00380783079774177 & 0.00761566159548354 & 0.996192169202258 \tabularnewline
27 & 0.00269038716992946 & 0.00538077433985893 & 0.99730961283007 \tabularnewline
28 & 0.00146488056690036 & 0.00292976113380072 & 0.9985351194331 \tabularnewline
29 & 0.000588153779758906 & 0.00117630755951781 & 0.99941184622024 \tabularnewline
30 & 0.00226356279643795 & 0.00452712559287589 & 0.997736437203562 \tabularnewline
31 & 0.00510380251430023 & 0.0102076050286005 & 0.9948961974857 \tabularnewline
32 & 0.00675101936927038 & 0.0135020387385408 & 0.99324898063073 \tabularnewline
33 & 0.00561057573759687 & 0.0112211514751937 & 0.994389424262403 \tabularnewline
34 & 0.00740574269229877 & 0.0148114853845975 & 0.9925942573077 \tabularnewline
35 & 0.00374581452602675 & 0.0074916290520535 & 0.996254185473973 \tabularnewline
36 & 0.00835298141339484 & 0.0167059628267897 & 0.991647018586605 \tabularnewline
37 & 0.0194753229634417 & 0.0389506459268833 & 0.980524677036558 \tabularnewline
38 & 0.0268924205842183 & 0.0537848411684366 & 0.973107579415782 \tabularnewline
39 & 0.0198125921173112 & 0.0396251842346225 & 0.980187407882689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&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]19[/C][C]0.177249920439821[/C][C]0.354499840879642[/C][C]0.82275007956018[/C][/ROW]
[ROW][C]20[/C][C]0.130818922608943[/C][C]0.261637845217886[/C][C]0.869181077391057[/C][/ROW]
[ROW][C]21[/C][C]0.0891291954435935[/C][C]0.178258390887187[/C][C]0.910870804556406[/C][/ROW]
[ROW][C]22[/C][C]0.067685756484473[/C][C]0.135371512968946[/C][C]0.932314243515527[/C][/ROW]
[ROW][C]23[/C][C]0.040701040771256[/C][C]0.081402081542512[/C][C]0.959298959228744[/C][/ROW]
[ROW][C]24[/C][C]0.0178522223541326[/C][C]0.0357044447082651[/C][C]0.982147777645867[/C][/ROW]
[ROW][C]25[/C][C]0.00742439256367792[/C][C]0.0148487851273558[/C][C]0.992575607436322[/C][/ROW]
[ROW][C]26[/C][C]0.00380783079774177[/C][C]0.00761566159548354[/C][C]0.996192169202258[/C][/ROW]
[ROW][C]27[/C][C]0.00269038716992946[/C][C]0.00538077433985893[/C][C]0.99730961283007[/C][/ROW]
[ROW][C]28[/C][C]0.00146488056690036[/C][C]0.00292976113380072[/C][C]0.9985351194331[/C][/ROW]
[ROW][C]29[/C][C]0.000588153779758906[/C][C]0.00117630755951781[/C][C]0.99941184622024[/C][/ROW]
[ROW][C]30[/C][C]0.00226356279643795[/C][C]0.00452712559287589[/C][C]0.997736437203562[/C][/ROW]
[ROW][C]31[/C][C]0.00510380251430023[/C][C]0.0102076050286005[/C][C]0.9948961974857[/C][/ROW]
[ROW][C]32[/C][C]0.00675101936927038[/C][C]0.0135020387385408[/C][C]0.99324898063073[/C][/ROW]
[ROW][C]33[/C][C]0.00561057573759687[/C][C]0.0112211514751937[/C][C]0.994389424262403[/C][/ROW]
[ROW][C]34[/C][C]0.00740574269229877[/C][C]0.0148114853845975[/C][C]0.9925942573077[/C][/ROW]
[ROW][C]35[/C][C]0.00374581452602675[/C][C]0.0074916290520535[/C][C]0.996254185473973[/C][/ROW]
[ROW][C]36[/C][C]0.00835298141339484[/C][C]0.0167059628267897[/C][C]0.991647018586605[/C][/ROW]
[ROW][C]37[/C][C]0.0194753229634417[/C][C]0.0389506459268833[/C][C]0.980524677036558[/C][/ROW]
[ROW][C]38[/C][C]0.0268924205842183[/C][C]0.0537848411684366[/C][C]0.973107579415782[/C][/ROW]
[ROW][C]39[/C][C]0.0198125921173112[/C][C]0.0396251842346225[/C][C]0.980187407882689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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
190.1772499204398210.3544998408796420.82275007956018
200.1308189226089430.2616378452178860.869181077391057
210.08912919544359350.1782583908871870.910870804556406
220.0676857564844730.1353715129689460.932314243515527
230.0407010407712560.0814020815425120.959298959228744
240.01785222235413260.03570444470826510.982147777645867
250.007424392563677920.01484878512735580.992575607436322
260.003807830797741770.007615661595483540.996192169202258
270.002690387169929460.005380774339858930.99730961283007
280.001464880566900360.002929761133800720.9985351194331
290.0005881537797589060.001176307559517810.99941184622024
300.002263562796437950.004527125592875890.997736437203562
310.005103802514300230.01020760502860050.9948961974857
320.006751019369270380.01350203873854080.99324898063073
330.005610575737596870.01122115147519370.994389424262403
340.007405742692298770.01481148538459750.9925942573077
350.003745814526026750.00749162905205350.996254185473973
360.008352981413394840.01670596282678970.991647018586605
370.01947532296344170.03895064592688330.980524677036558
380.02689242058421830.05378484116843660.973107579415782
390.01981259211731120.03962518423462250.980187407882689







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.285714285714286NOK
5% type I error level150.714285714285714NOK
10% type I error level170.80952380952381NOK

\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 & 6 & 0.285714285714286 & NOK \tabularnewline
5% type I error level & 15 & 0.714285714285714 & NOK \tabularnewline
10% type I error level & 17 & 0.80952380952381 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58139&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]6[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.80952380952381[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58139&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58139&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 level60.285714285714286NOK
5% type I error level150.714285714285714NOK
10% type I error level170.80952380952381NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}