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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 computationWed, 09 Dec 2009 07:20:25 -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/Dec/09/t12603685510ormh8cijwzqawr.htm/, Retrieved Thu, 31 Oct 2024 23:50:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64963, Retrieved Thu, 31 Oct 2024 23:50:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-12-09 14:20:25] [faa1ded5041cd5a0e2be04844f08502a] [Current]
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Dataseries X:
24	33
22	34
25	36
24	36
29	38
26	42
26	35
21	25
23	24
22	22
21	27
16	17
19	30
16	30
25	34
27	37
23	36
22	33
23	33
20	33
24	37
23	40
20	35
21	37
22	43
17	42
21	33
19	39
23	40
22	37
15	44
23	42
21	43
18	40
18	30
18	30
18	31
10	18
13	24
10	22
9	26
9	28
6	23
11	17
9	12
10	9
9	19
16	21
10	18
7	18
7	15
14	24
11	18
10	19
6	30
8	33
13	35
12	36
15	47
16	46
16	43




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=64963&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=64963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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
S.[t] = + 5.29372655439379 + 0.400869981642590E.S.[t] -0.35576928193259M1[t] -2.27843403304333M2[t] + 1.52156596695666M3[t] + 0.838782025700374M4[t] + 1.03878202570037M5[t] -0.241391970628143M6[t] -3.32243594859925M7[t] -0.71982600367148M8[t] + 0.6M9[t] -0.079304014685929M10[t] -1.36121797429962M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
S.[t] =  +  5.29372655439379 +  0.400869981642590E.S.[t] -0.35576928193259M1[t] -2.27843403304333M2[t] +  1.52156596695666M3[t] +  0.838782025700374M4[t] +  1.03878202570037M5[t] -0.241391970628143M6[t] -3.32243594859925M7[t] -0.71982600367148M8[t] +  0.6M9[t] -0.079304014685929M10[t] -1.36121797429962M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]S.[t] =  +  5.29372655439379 +  0.400869981642590E.S.[t] -0.35576928193259M1[t] -2.27843403304333M2[t] +  1.52156596695666M3[t] +  0.838782025700374M4[t] +  1.03878202570037M5[t] -0.241391970628143M6[t] -3.32243594859925M7[t] -0.71982600367148M8[t] +  0.6M9[t] -0.079304014685929M10[t] -1.36121797429962M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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
S.[t] = + 5.29372655439379 + 0.400869981642590E.S.[t] -0.35576928193259M1[t] -2.27843403304333M2[t] + 1.52156596695666M3[t] + 0.838782025700374M4[t] + 1.03878202570037M5[t] -0.241391970628143M6[t] -3.32243594859925M7[t] -0.71982600367148M8[t] + 0.6M9[t] -0.079304014685929M10[t] -1.36121797429962M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.293726554393793.3252941.5920.1179590.05898
E.S.0.4008699816425900.0760015.27453e-062e-06
M1-0.355769281932593.264875-0.1090.9136820.456841
M2-2.278434033043333.405549-0.6690.5066780.253339
M31.521565966956663.4055490.44680.6570360.328518
M40.8387820257003743.4044630.24640.8064410.40322
M51.038782025700373.4044630.30510.7615910.380796
M6-0.2413919706281433.404972-0.07090.9437770.471888
M7-3.322435948599253.409447-0.97450.3347040.167352
M8-0.719826003671483.402834-0.21150.8333640.416682
M90.63.40280.17630.860780.43039
M10-0.0793040146859293.403343-0.02330.9815060.490753
M11-1.361217974299623.404463-0.39980.6910530.345526

\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) & 5.29372655439379 & 3.325294 & 1.592 & 0.117959 & 0.05898 \tabularnewline
E.S. & 0.400869981642590 & 0.076001 & 5.2745 & 3e-06 & 2e-06 \tabularnewline
M1 & -0.35576928193259 & 3.264875 & -0.109 & 0.913682 & 0.456841 \tabularnewline
M2 & -2.27843403304333 & 3.405549 & -0.669 & 0.506678 & 0.253339 \tabularnewline
M3 & 1.52156596695666 & 3.405549 & 0.4468 & 0.657036 & 0.328518 \tabularnewline
M4 & 0.838782025700374 & 3.404463 & 0.2464 & 0.806441 & 0.40322 \tabularnewline
M5 & 1.03878202570037 & 3.404463 & 0.3051 & 0.761591 & 0.380796 \tabularnewline
M6 & -0.241391970628143 & 3.404972 & -0.0709 & 0.943777 & 0.471888 \tabularnewline
M7 & -3.32243594859925 & 3.409447 & -0.9745 & 0.334704 & 0.167352 \tabularnewline
M8 & -0.71982600367148 & 3.402834 & -0.2115 & 0.833364 & 0.416682 \tabularnewline
M9 & 0.6 & 3.4028 & 0.1763 & 0.86078 & 0.43039 \tabularnewline
M10 & -0.079304014685929 & 3.403343 & -0.0233 & 0.981506 & 0.490753 \tabularnewline
M11 & -1.36121797429962 & 3.404463 & -0.3998 & 0.691053 & 0.345526 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&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]5.29372655439379[/C][C]3.325294[/C][C]1.592[/C][C]0.117959[/C][C]0.05898[/C][/ROW]
[ROW][C]E.S.[/C][C]0.400869981642590[/C][C]0.076001[/C][C]5.2745[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M1[/C][C]-0.35576928193259[/C][C]3.264875[/C][C]-0.109[/C][C]0.913682[/C][C]0.456841[/C][/ROW]
[ROW][C]M2[/C][C]-2.27843403304333[/C][C]3.405549[/C][C]-0.669[/C][C]0.506678[/C][C]0.253339[/C][/ROW]
[ROW][C]M3[/C][C]1.52156596695666[/C][C]3.405549[/C][C]0.4468[/C][C]0.657036[/C][C]0.328518[/C][/ROW]
[ROW][C]M4[/C][C]0.838782025700374[/C][C]3.404463[/C][C]0.2464[/C][C]0.806441[/C][C]0.40322[/C][/ROW]
[ROW][C]M5[/C][C]1.03878202570037[/C][C]3.404463[/C][C]0.3051[/C][C]0.761591[/C][C]0.380796[/C][/ROW]
[ROW][C]M6[/C][C]-0.241391970628143[/C][C]3.404972[/C][C]-0.0709[/C][C]0.943777[/C][C]0.471888[/C][/ROW]
[ROW][C]M7[/C][C]-3.32243594859925[/C][C]3.409447[/C][C]-0.9745[/C][C]0.334704[/C][C]0.167352[/C][/ROW]
[ROW][C]M8[/C][C]-0.71982600367148[/C][C]3.402834[/C][C]-0.2115[/C][C]0.833364[/C][C]0.416682[/C][/ROW]
[ROW][C]M9[/C][C]0.6[/C][C]3.4028[/C][C]0.1763[/C][C]0.86078[/C][C]0.43039[/C][/ROW]
[ROW][C]M10[/C][C]-0.079304014685929[/C][C]3.403343[/C][C]-0.0233[/C][C]0.981506[/C][C]0.490753[/C][/ROW]
[ROW][C]M11[/C][C]-1.36121797429962[/C][C]3.404463[/C][C]-0.3998[/C][C]0.691053[/C][C]0.345526[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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)5.293726554393793.3252941.5920.1179590.05898
E.S.0.4008699816425900.0760015.27453e-062e-06
M1-0.355769281932593.264875-0.1090.9136820.456841
M2-2.278434033043333.405549-0.6690.5066780.253339
M31.521565966956663.4055490.44680.6570360.328518
M40.8387820257003743.4044630.24640.8064410.40322
M51.038782025700373.4044630.30510.7615910.380796
M6-0.2413919706281433.404972-0.07090.9437770.471888
M7-3.322435948599253.409447-0.97450.3347040.167352
M8-0.719826003671483.402834-0.21150.8333640.416682
M90.63.40280.17630.860780.43039
M10-0.0793040146859293.403343-0.02330.9815060.490753
M11-1.361217974299623.404463-0.39980.6910530.345526







Multiple Linear Regression - Regression Statistics
Multiple R0.62965823162227
R-squared0.396469488649684
Adjusted R-squared0.245586860812105
F-TEST (value)2.62766823677324
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.00881546071620987
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.38029882265957
Sum Squared Residuals1389.48554021337

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.62965823162227 \tabularnewline
R-squared & 0.396469488649684 \tabularnewline
Adjusted R-squared & 0.245586860812105 \tabularnewline
F-TEST (value) & 2.62766823677324 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.00881546071620987 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.38029882265957 \tabularnewline
Sum Squared Residuals & 1389.48554021337 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.62965823162227[/C][/ROW]
[ROW][C]R-squared[/C][C]0.396469488649684[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.245586860812105[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.62766823677324[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.00881546071620987[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.38029882265957[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1389.48554021337[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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 R0.62965823162227
R-squared0.396469488649684
Adjusted R-squared0.245586860812105
F-TEST (value)2.62766823677324
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.00881546071620987
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.38029882265957
Sum Squared Residuals1389.48554021337







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12418.16666666666675.83333333333329
22216.64487189719855.35512810280152
32521.24661186048373.75338813951632
42420.56382791922743.43617208077261
52921.56556788251267.43443211748743
62621.88887381275444.11112618724559
72616.00173996328529.99826003671482
82114.59565009178706.40434990821295
92315.51460611381597.48539388618406
102214.03356213584487.96643786415516
112114.75599808444416.24400191555591
121612.10851624231783.89148375768218
131916.96405672173892.03594327826111
141615.04139197062810.958608029371852
152520.44487189719854.5551281028015
162720.964697900876.03530209913002
172320.76382791922742.23617208077261
182218.28104397797113.71895602202889
192315.27.8
202017.80260994492782.19739005507223
212420.72591587516963.27408412483039
222321.24922180541141.75077819458855
232017.96295793758482.03704206241520
242120.12591587516960.874084124830393
252222.1753664830925-0.175366483092550
261719.8518317503392-2.85183175033922
272120.04400191555590.95599808444409
281921.7664378641552-2.76643786415516
292322.36730784579780.63269215420225
302219.88452390454152.11547609545854
311519.6095697980685-4.60956979806848
322321.41043977971111.58956022028893
332123.1311357650251-2.13113576502514
341821.2492218054114-3.24922180541145
351815.95860802937192.04139197062814
361817.31982600367150.680173996328518
371817.36492670338150.635073296618521
381010.2309521909171-0.230952190917076
391316.4361720807726-3.43617208077261
401014.9516481762311-4.95164817623114
41916.7551281028015-7.7551281028015
42916.2766940697582-7.27669406975816
43611.1913001835741-5.19130018357411
441111.3886902386463-0.388690238646338
45910.7041663341049-1.70416633410488
46108.822252374491181.17774762550882
47911.5490382313034-2.54903823130338
481613.71199616888822.28800383111182
491012.1536169420278-2.15361694202782
50710.2309521909171-3.23095219091708
51712.8283422459893-5.8283422459893
521415.7533881395163-1.75338813951632
531113.5481682496608-2.54816824966079
541012.6688642349749-2.66886423497486
55613.9973900550722-7.99739005507223
56817.8026099449278-9.80260994492777
571319.9241759118844-6.92417591188443
581219.6457418788411-7.64574187884109
591522.7733977172959-7.77339771729588
601623.7337457099529-7.73374570995291
611622.1753664830926-6.17536648309255

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 18.1666666666667 & 5.83333333333329 \tabularnewline
2 & 22 & 16.6448718971985 & 5.35512810280152 \tabularnewline
3 & 25 & 21.2466118604837 & 3.75338813951632 \tabularnewline
4 & 24 & 20.5638279192274 & 3.43617208077261 \tabularnewline
5 & 29 & 21.5655678825126 & 7.43443211748743 \tabularnewline
6 & 26 & 21.8888738127544 & 4.11112618724559 \tabularnewline
7 & 26 & 16.0017399632852 & 9.99826003671482 \tabularnewline
8 & 21 & 14.5956500917870 & 6.40434990821295 \tabularnewline
9 & 23 & 15.5146061138159 & 7.48539388618406 \tabularnewline
10 & 22 & 14.0335621358448 & 7.96643786415516 \tabularnewline
11 & 21 & 14.7559980844441 & 6.24400191555591 \tabularnewline
12 & 16 & 12.1085162423178 & 3.89148375768218 \tabularnewline
13 & 19 & 16.9640567217389 & 2.03594327826111 \tabularnewline
14 & 16 & 15.0413919706281 & 0.958608029371852 \tabularnewline
15 & 25 & 20.4448718971985 & 4.5551281028015 \tabularnewline
16 & 27 & 20.96469790087 & 6.03530209913002 \tabularnewline
17 & 23 & 20.7638279192274 & 2.23617208077261 \tabularnewline
18 & 22 & 18.2810439779711 & 3.71895602202889 \tabularnewline
19 & 23 & 15.2 & 7.8 \tabularnewline
20 & 20 & 17.8026099449278 & 2.19739005507223 \tabularnewline
21 & 24 & 20.7259158751696 & 3.27408412483039 \tabularnewline
22 & 23 & 21.2492218054114 & 1.75077819458855 \tabularnewline
23 & 20 & 17.9629579375848 & 2.03704206241520 \tabularnewline
24 & 21 & 20.1259158751696 & 0.874084124830393 \tabularnewline
25 & 22 & 22.1753664830925 & -0.175366483092550 \tabularnewline
26 & 17 & 19.8518317503392 & -2.85183175033922 \tabularnewline
27 & 21 & 20.0440019155559 & 0.95599808444409 \tabularnewline
28 & 19 & 21.7664378641552 & -2.76643786415516 \tabularnewline
29 & 23 & 22.3673078457978 & 0.63269215420225 \tabularnewline
30 & 22 & 19.8845239045415 & 2.11547609545854 \tabularnewline
31 & 15 & 19.6095697980685 & -4.60956979806848 \tabularnewline
32 & 23 & 21.4104397797111 & 1.58956022028893 \tabularnewline
33 & 21 & 23.1311357650251 & -2.13113576502514 \tabularnewline
34 & 18 & 21.2492218054114 & -3.24922180541145 \tabularnewline
35 & 18 & 15.9586080293719 & 2.04139197062814 \tabularnewline
36 & 18 & 17.3198260036715 & 0.680173996328518 \tabularnewline
37 & 18 & 17.3649267033815 & 0.635073296618521 \tabularnewline
38 & 10 & 10.2309521909171 & -0.230952190917076 \tabularnewline
39 & 13 & 16.4361720807726 & -3.43617208077261 \tabularnewline
40 & 10 & 14.9516481762311 & -4.95164817623114 \tabularnewline
41 & 9 & 16.7551281028015 & -7.7551281028015 \tabularnewline
42 & 9 & 16.2766940697582 & -7.27669406975816 \tabularnewline
43 & 6 & 11.1913001835741 & -5.19130018357411 \tabularnewline
44 & 11 & 11.3886902386463 & -0.388690238646338 \tabularnewline
45 & 9 & 10.7041663341049 & -1.70416633410488 \tabularnewline
46 & 10 & 8.82225237449118 & 1.17774762550882 \tabularnewline
47 & 9 & 11.5490382313034 & -2.54903823130338 \tabularnewline
48 & 16 & 13.7119961688882 & 2.28800383111182 \tabularnewline
49 & 10 & 12.1536169420278 & -2.15361694202782 \tabularnewline
50 & 7 & 10.2309521909171 & -3.23095219091708 \tabularnewline
51 & 7 & 12.8283422459893 & -5.8283422459893 \tabularnewline
52 & 14 & 15.7533881395163 & -1.75338813951632 \tabularnewline
53 & 11 & 13.5481682496608 & -2.54816824966079 \tabularnewline
54 & 10 & 12.6688642349749 & -2.66886423497486 \tabularnewline
55 & 6 & 13.9973900550722 & -7.99739005507223 \tabularnewline
56 & 8 & 17.8026099449278 & -9.80260994492777 \tabularnewline
57 & 13 & 19.9241759118844 & -6.92417591188443 \tabularnewline
58 & 12 & 19.6457418788411 & -7.64574187884109 \tabularnewline
59 & 15 & 22.7733977172959 & -7.77339771729588 \tabularnewline
60 & 16 & 23.7337457099529 & -7.73374570995291 \tabularnewline
61 & 16 & 22.1753664830926 & -6.17536648309255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&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]24[/C][C]18.1666666666667[/C][C]5.83333333333329[/C][/ROW]
[ROW][C]2[/C][C]22[/C][C]16.6448718971985[/C][C]5.35512810280152[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]21.2466118604837[/C][C]3.75338813951632[/C][/ROW]
[ROW][C]4[/C][C]24[/C][C]20.5638279192274[/C][C]3.43617208077261[/C][/ROW]
[ROW][C]5[/C][C]29[/C][C]21.5655678825126[/C][C]7.43443211748743[/C][/ROW]
[ROW][C]6[/C][C]26[/C][C]21.8888738127544[/C][C]4.11112618724559[/C][/ROW]
[ROW][C]7[/C][C]26[/C][C]16.0017399632852[/C][C]9.99826003671482[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]14.5956500917870[/C][C]6.40434990821295[/C][/ROW]
[ROW][C]9[/C][C]23[/C][C]15.5146061138159[/C][C]7.48539388618406[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]14.0335621358448[/C][C]7.96643786415516[/C][/ROW]
[ROW][C]11[/C][C]21[/C][C]14.7559980844441[/C][C]6.24400191555591[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]12.1085162423178[/C][C]3.89148375768218[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]16.9640567217389[/C][C]2.03594327826111[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.0413919706281[/C][C]0.958608029371852[/C][/ROW]
[ROW][C]15[/C][C]25[/C][C]20.4448718971985[/C][C]4.5551281028015[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]20.96469790087[/C][C]6.03530209913002[/C][/ROW]
[ROW][C]17[/C][C]23[/C][C]20.7638279192274[/C][C]2.23617208077261[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]18.2810439779711[/C][C]3.71895602202889[/C][/ROW]
[ROW][C]19[/C][C]23[/C][C]15.2[/C][C]7.8[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]17.8026099449278[/C][C]2.19739005507223[/C][/ROW]
[ROW][C]21[/C][C]24[/C][C]20.7259158751696[/C][C]3.27408412483039[/C][/ROW]
[ROW][C]22[/C][C]23[/C][C]21.2492218054114[/C][C]1.75077819458855[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]17.9629579375848[/C][C]2.03704206241520[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]20.1259158751696[/C][C]0.874084124830393[/C][/ROW]
[ROW][C]25[/C][C]22[/C][C]22.1753664830925[/C][C]-0.175366483092550[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]19.8518317503392[/C][C]-2.85183175033922[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]20.0440019155559[/C][C]0.95599808444409[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]21.7664378641552[/C][C]-2.76643786415516[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.3673078457978[/C][C]0.63269215420225[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]19.8845239045415[/C][C]2.11547609545854[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]19.6095697980685[/C][C]-4.60956979806848[/C][/ROW]
[ROW][C]32[/C][C]23[/C][C]21.4104397797111[/C][C]1.58956022028893[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]23.1311357650251[/C][C]-2.13113576502514[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]21.2492218054114[/C][C]-3.24922180541145[/C][/ROW]
[ROW][C]35[/C][C]18[/C][C]15.9586080293719[/C][C]2.04139197062814[/C][/ROW]
[ROW][C]36[/C][C]18[/C][C]17.3198260036715[/C][C]0.680173996328518[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]17.3649267033815[/C][C]0.635073296618521[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]10.2309521909171[/C][C]-0.230952190917076[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]16.4361720807726[/C][C]-3.43617208077261[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]14.9516481762311[/C][C]-4.95164817623114[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]16.7551281028015[/C][C]-7.7551281028015[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]16.2766940697582[/C][C]-7.27669406975816[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]11.1913001835741[/C][C]-5.19130018357411[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]11.3886902386463[/C][C]-0.388690238646338[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]10.7041663341049[/C][C]-1.70416633410488[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]8.82225237449118[/C][C]1.17774762550882[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]11.5490382313034[/C][C]-2.54903823130338[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]13.7119961688882[/C][C]2.28800383111182[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]12.1536169420278[/C][C]-2.15361694202782[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]10.2309521909171[/C][C]-3.23095219091708[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]12.8283422459893[/C][C]-5.8283422459893[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]15.7533881395163[/C][C]-1.75338813951632[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]13.5481682496608[/C][C]-2.54816824966079[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]12.6688642349749[/C][C]-2.66886423497486[/C][/ROW]
[ROW][C]55[/C][C]6[/C][C]13.9973900550722[/C][C]-7.99739005507223[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]17.8026099449278[/C][C]-9.80260994492777[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]19.9241759118844[/C][C]-6.92417591188443[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]19.6457418788411[/C][C]-7.64574187884109[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]22.7733977172959[/C][C]-7.77339771729588[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]23.7337457099529[/C][C]-7.73374570995291[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]22.1753664830926[/C][C]-6.17536648309255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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
12418.16666666666675.83333333333329
22216.64487189719855.35512810280152
32521.24661186048373.75338813951632
42420.56382791922743.43617208077261
52921.56556788251267.43443211748743
62621.88887381275444.11112618724559
72616.00173996328529.99826003671482
82114.59565009178706.40434990821295
92315.51460611381597.48539388618406
102214.03356213584487.96643786415516
112114.75599808444416.24400191555591
121612.10851624231783.89148375768218
131916.96405672173892.03594327826111
141615.04139197062810.958608029371852
152520.44487189719854.5551281028015
162720.964697900876.03530209913002
172320.76382791922742.23617208077261
182218.28104397797113.71895602202889
192315.27.8
202017.80260994492782.19739005507223
212420.72591587516963.27408412483039
222321.24922180541141.75077819458855
232017.96295793758482.03704206241520
242120.12591587516960.874084124830393
252222.1753664830925-0.175366483092550
261719.8518317503392-2.85183175033922
272120.04400191555590.95599808444409
281921.7664378641552-2.76643786415516
292322.36730784579780.63269215420225
302219.88452390454152.11547609545854
311519.6095697980685-4.60956979806848
322321.41043977971111.58956022028893
332123.1311357650251-2.13113576502514
341821.2492218054114-3.24922180541145
351815.95860802937192.04139197062814
361817.31982600367150.680173996328518
371817.36492670338150.635073296618521
381010.2309521909171-0.230952190917076
391316.4361720807726-3.43617208077261
401014.9516481762311-4.95164817623114
41916.7551281028015-7.7551281028015
42916.2766940697582-7.27669406975816
43611.1913001835741-5.19130018357411
441111.3886902386463-0.388690238646338
45910.7041663341049-1.70416633410488
46108.822252374491181.17774762550882
47911.5490382313034-2.54903823130338
481613.71199616888822.28800383111182
491012.1536169420278-2.15361694202782
50710.2309521909171-3.23095219091708
51712.8283422459893-5.8283422459893
521415.7533881395163-1.75338813951632
531113.5481682496608-2.54816824966079
541012.6688642349749-2.66886423497486
55613.9973900550722-7.99739005507223
56817.8026099449278-9.80260994492777
571319.9241759118844-6.92417591188443
581219.6457418788411-7.64574187884109
591522.7733977172959-7.77339771729588
601623.7337457099529-7.73374570995291
611622.1753664830926-6.17536648309255







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.03434451180194510.06868902360389010.965655488198055
170.02601928895786560.05203857791573110.973980711042134
180.06380902925598830.1276180585119770.936190970744012
190.05456972982888990.1091394596577800.94543027017111
200.1020104603589280.2040209207178560.897989539641072
210.1016054766984400.2032109533968800.89839452330156
220.08434114215101610.1686822843020320.915658857848984
230.06293880886070040.1258776177214010.9370611911393
240.03904100900776310.07808201801552620.960958990992237
250.02445450869432070.04890901738864140.97554549130568
260.02085731487295930.04171462974591870.97914268512704
270.02643451999604550.05286903999209110.973565480003954
280.05828302341726770.1165660468345350.941716976582732
290.07452090974531360.1490418194906270.925479090254686
300.1073973162011220.2147946324022440.892602683798878
310.3389427787462470.6778855574924930.661057221253753
320.5523266978459220.8953466043081560.447673302154078
330.6649910825293480.6700178349413040.335008917470652
340.7221917954810390.5556164090379220.277808204518961
350.8280179221518750.3439641556962510.171982077848125
360.7827011800130680.4345976399738650.217298819986932
370.852316206767470.2953675864650620.147683793232531
380.8833612890612620.2332774218774770.116638710938738
390.9519566534991430.09608669300171310.0480433465008566
400.9727649138076460.05447017238470890.0272350861923545
410.9803478724620710.03930425507585820.0196521275379291
420.9741907271304120.05161854573917620.0258092728695881
430.9515793822401860.09684123551962790.0484206177598139
440.9651219693595130.06975606128097350.0348780306404868
450.9038993326751340.1922013346497320.0961006673248658

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0343445118019451 & 0.0686890236038901 & 0.965655488198055 \tabularnewline
17 & 0.0260192889578656 & 0.0520385779157311 & 0.973980711042134 \tabularnewline
18 & 0.0638090292559883 & 0.127618058511977 & 0.936190970744012 \tabularnewline
19 & 0.0545697298288899 & 0.109139459657780 & 0.94543027017111 \tabularnewline
20 & 0.102010460358928 & 0.204020920717856 & 0.897989539641072 \tabularnewline
21 & 0.101605476698440 & 0.203210953396880 & 0.89839452330156 \tabularnewline
22 & 0.0843411421510161 & 0.168682284302032 & 0.915658857848984 \tabularnewline
23 & 0.0629388088607004 & 0.125877617721401 & 0.9370611911393 \tabularnewline
24 & 0.0390410090077631 & 0.0780820180155262 & 0.960958990992237 \tabularnewline
25 & 0.0244545086943207 & 0.0489090173886414 & 0.97554549130568 \tabularnewline
26 & 0.0208573148729593 & 0.0417146297459187 & 0.97914268512704 \tabularnewline
27 & 0.0264345199960455 & 0.0528690399920911 & 0.973565480003954 \tabularnewline
28 & 0.0582830234172677 & 0.116566046834535 & 0.941716976582732 \tabularnewline
29 & 0.0745209097453136 & 0.149041819490627 & 0.925479090254686 \tabularnewline
30 & 0.107397316201122 & 0.214794632402244 & 0.892602683798878 \tabularnewline
31 & 0.338942778746247 & 0.677885557492493 & 0.661057221253753 \tabularnewline
32 & 0.552326697845922 & 0.895346604308156 & 0.447673302154078 \tabularnewline
33 & 0.664991082529348 & 0.670017834941304 & 0.335008917470652 \tabularnewline
34 & 0.722191795481039 & 0.555616409037922 & 0.277808204518961 \tabularnewline
35 & 0.828017922151875 & 0.343964155696251 & 0.171982077848125 \tabularnewline
36 & 0.782701180013068 & 0.434597639973865 & 0.217298819986932 \tabularnewline
37 & 0.85231620676747 & 0.295367586465062 & 0.147683793232531 \tabularnewline
38 & 0.883361289061262 & 0.233277421877477 & 0.116638710938738 \tabularnewline
39 & 0.951956653499143 & 0.0960866930017131 & 0.0480433465008566 \tabularnewline
40 & 0.972764913807646 & 0.0544701723847089 & 0.0272350861923545 \tabularnewline
41 & 0.980347872462071 & 0.0393042550758582 & 0.0196521275379291 \tabularnewline
42 & 0.974190727130412 & 0.0516185457391762 & 0.0258092728695881 \tabularnewline
43 & 0.951579382240186 & 0.0968412355196279 & 0.0484206177598139 \tabularnewline
44 & 0.965121969359513 & 0.0697560612809735 & 0.0348780306404868 \tabularnewline
45 & 0.903899332675134 & 0.192201334649732 & 0.0961006673248658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0343445118019451[/C][C]0.0686890236038901[/C][C]0.965655488198055[/C][/ROW]
[ROW][C]17[/C][C]0.0260192889578656[/C][C]0.0520385779157311[/C][C]0.973980711042134[/C][/ROW]
[ROW][C]18[/C][C]0.0638090292559883[/C][C]0.127618058511977[/C][C]0.936190970744012[/C][/ROW]
[ROW][C]19[/C][C]0.0545697298288899[/C][C]0.109139459657780[/C][C]0.94543027017111[/C][/ROW]
[ROW][C]20[/C][C]0.102010460358928[/C][C]0.204020920717856[/C][C]0.897989539641072[/C][/ROW]
[ROW][C]21[/C][C]0.101605476698440[/C][C]0.203210953396880[/C][C]0.89839452330156[/C][/ROW]
[ROW][C]22[/C][C]0.0843411421510161[/C][C]0.168682284302032[/C][C]0.915658857848984[/C][/ROW]
[ROW][C]23[/C][C]0.0629388088607004[/C][C]0.125877617721401[/C][C]0.9370611911393[/C][/ROW]
[ROW][C]24[/C][C]0.0390410090077631[/C][C]0.0780820180155262[/C][C]0.960958990992237[/C][/ROW]
[ROW][C]25[/C][C]0.0244545086943207[/C][C]0.0489090173886414[/C][C]0.97554549130568[/C][/ROW]
[ROW][C]26[/C][C]0.0208573148729593[/C][C]0.0417146297459187[/C][C]0.97914268512704[/C][/ROW]
[ROW][C]27[/C][C]0.0264345199960455[/C][C]0.0528690399920911[/C][C]0.973565480003954[/C][/ROW]
[ROW][C]28[/C][C]0.0582830234172677[/C][C]0.116566046834535[/C][C]0.941716976582732[/C][/ROW]
[ROW][C]29[/C][C]0.0745209097453136[/C][C]0.149041819490627[/C][C]0.925479090254686[/C][/ROW]
[ROW][C]30[/C][C]0.107397316201122[/C][C]0.214794632402244[/C][C]0.892602683798878[/C][/ROW]
[ROW][C]31[/C][C]0.338942778746247[/C][C]0.677885557492493[/C][C]0.661057221253753[/C][/ROW]
[ROW][C]32[/C][C]0.552326697845922[/C][C]0.895346604308156[/C][C]0.447673302154078[/C][/ROW]
[ROW][C]33[/C][C]0.664991082529348[/C][C]0.670017834941304[/C][C]0.335008917470652[/C][/ROW]
[ROW][C]34[/C][C]0.722191795481039[/C][C]0.555616409037922[/C][C]0.277808204518961[/C][/ROW]
[ROW][C]35[/C][C]0.828017922151875[/C][C]0.343964155696251[/C][C]0.171982077848125[/C][/ROW]
[ROW][C]36[/C][C]0.782701180013068[/C][C]0.434597639973865[/C][C]0.217298819986932[/C][/ROW]
[ROW][C]37[/C][C]0.85231620676747[/C][C]0.295367586465062[/C][C]0.147683793232531[/C][/ROW]
[ROW][C]38[/C][C]0.883361289061262[/C][C]0.233277421877477[/C][C]0.116638710938738[/C][/ROW]
[ROW][C]39[/C][C]0.951956653499143[/C][C]0.0960866930017131[/C][C]0.0480433465008566[/C][/ROW]
[ROW][C]40[/C][C]0.972764913807646[/C][C]0.0544701723847089[/C][C]0.0272350861923545[/C][/ROW]
[ROW][C]41[/C][C]0.980347872462071[/C][C]0.0393042550758582[/C][C]0.0196521275379291[/C][/ROW]
[ROW][C]42[/C][C]0.974190727130412[/C][C]0.0516185457391762[/C][C]0.0258092728695881[/C][/ROW]
[ROW][C]43[/C][C]0.951579382240186[/C][C]0.0968412355196279[/C][C]0.0484206177598139[/C][/ROW]
[ROW][C]44[/C][C]0.965121969359513[/C][C]0.0697560612809735[/C][C]0.0348780306404868[/C][/ROW]
[ROW][C]45[/C][C]0.903899332675134[/C][C]0.192201334649732[/C][C]0.0961006673248658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.03434451180194510.06868902360389010.965655488198055
170.02601928895786560.05203857791573110.973980711042134
180.06380902925598830.1276180585119770.936190970744012
190.05456972982888990.1091394596577800.94543027017111
200.1020104603589280.2040209207178560.897989539641072
210.1016054766984400.2032109533968800.89839452330156
220.08434114215101610.1686822843020320.915658857848984
230.06293880886070040.1258776177214010.9370611911393
240.03904100900776310.07808201801552620.960958990992237
250.02445450869432070.04890901738864140.97554549130568
260.02085731487295930.04171462974591870.97914268512704
270.02643451999604550.05286903999209110.973565480003954
280.05828302341726770.1165660468345350.941716976582732
290.07452090974531360.1490418194906270.925479090254686
300.1073973162011220.2147946324022440.892602683798878
310.3389427787462470.6778855574924930.661057221253753
320.5523266978459220.8953466043081560.447673302154078
330.6649910825293480.6700178349413040.335008917470652
340.7221917954810390.5556164090379220.277808204518961
350.8280179221518750.3439641556962510.171982077848125
360.7827011800130680.4345976399738650.217298819986932
370.852316206767470.2953675864650620.147683793232531
380.8833612890612620.2332774218774770.116638710938738
390.9519566534991430.09608669300171310.0480433465008566
400.9727649138076460.05447017238470890.0272350861923545
410.9803478724620710.03930425507585820.0196521275379291
420.9741907271304120.05161854573917620.0258092728695881
430.9515793822401860.09684123551962790.0484206177598139
440.9651219693595130.06975606128097350.0348780306404868
450.9038993326751340.1922013346497320.0961006673248658







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.1NOK
10% type I error level120.4NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.1 & NOK \tabularnewline
10% type I error level & 12 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64963&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64963&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64963&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 level00OK
5% type I error level30.1NOK
10% type I error level120.4NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}