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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 17 Dec 2011 10:24:02 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/17/t1324135483lyxaskc9uxel110.htm/, Retrieved Fri, 19 Apr 2024 07:05:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156388, Retrieved Fri, 19 Apr 2024 07:05:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MR 2 gewogen som] [2011-12-17 15:24:02] [10a6f28c51bb1cb94db47cee32729d66] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
-1	0	1	-1	1	1	1	0	112,8380813
-1	1	1	1	1	1	1	-1	113,1303269
-1	1	-1	0	0	1	1	-1	97,63171117
-1	0	-1	-1	1	1	0	1	142,5151687
-1	-1	1	-1	-1	1	-1	-1	164,8611618
1	-1	1	-1	1	1	1	1	112,485371
-1	1	1	1	1	1	1	-1	150,1989732
-1	1	1	-1	1	0	0	-1	176,719424
-1	0	-1	0	1	0	1	-1	113,0698623
0	0	0	0	0	0	0	0	116,528822
-1	1	-1	-1	1	0	1	1	112,8481587
-1	1	-1	1	1	1	-1	-1	12,81250709
0	1	1	1	-1	1	-1	1	116,4885123
-1	-1	0	1	1	1	-1	-1	12,42327815
-1	1	-1	1	1	1	1	1	124,2529363
0	1	-1	1	1	1	1	-1	135,1538421
0	1	1	-1	1	1	-1	-1	53,00018956
-1	0	-1	1	0	-1	1	1	64,2235733
-1	1	-1	-1	1	1	-1	-1	120,4311889
-1	-1	-1	-1	1	0	1	-1	113,1101721
-1	-1	-1	1	0	-1	0	-1	131,1607783
-1	1	-1	1	1	1	1	-1	105,2147414
-1	-1	-1	1	1	1	1	1	127,6514314
-1	-1	1	-1	-1	-1	1	-1	146,357071
1	1	-1	1	1	0	-1	1	146,5989294
-1	1	-1	1	1	1	1	-1	109,0969534
-1	1	1	0	1	-1	1	0	165,0123233
-1	-1	-1	1	1	1	1	-1	79,59118241
1	1	1	-1	1	0	1	1	105,5472968
-1	-1	-1	-1	1	1	1	-1	105,3759803
-1	1	1	1	1	-1	-1	-1	143,0190405
-1	-1	1	-1	-1	1	1	-1	120,0280915
-1	1	-1	-1	-1	0	1	-1	108,7442431
-1	1	1	-1	1	1	1	-1	101,5844652
1	-1	1	-1	-1	1	1	1	149,9268825
1	-1	1	1	-1	1	1	-1	149,8160307
-1	1	-1	1	0	-1	0	1	105,3155157
0	1	1	0	1	0	1	0	12,79436771
-1	1	1	1	1	1	1	-1	124,3436333
-1	1	0	1	1	0	-1	1	123,7994517
-1	-1	1	1	1	1	1	-1	131,6041855
-1	-1	1	-1	1	-1	-1	-1	109,0062565
-1	-1	1	1	0	1	1	1	75,39656986
-1	1	1	0	1	1	1	-1	139,378687
-1	1	1	0	-1	1	1	0	124,4242527
-1	1	-1	1	1	-1	0	-1	108,9155595
-1	1	-1	-1	-1	0	1	-1	105,3054383
-1	1	-1	1	0	0	0	0	79,01676853
-1	-1	-1	1	1	0	1	1	153,799017
-1	-1	-1	-1	0	1	1	-1	75,49734422
-1	1	1	0	0	1	1	0	112,878391
-1	-1	-1	1	1	1	1	-1	82,94936774
-1	-1	-1	1	0	0	1	1	157,9130101
0	0	0	0	0	0	0	0	120,5319633
-1	1	-1	0	1	1	1	1	13,50228354
-1	-1	-1	1	-1	0	1	-1	116,921842
-1	-1	-1	1	1	1	1	-1	124,1622394
-1	-1	-1	0	-1	0	1	-1	149,7958758
-1	1	-1	1	1	1	1	-1	142,0516066
-1	0	0	0	0	0	0	0	90,02852611
-1	1	0	0	0	1	1	1	160,7471687
1	-1	-1	1	1	1	1	-1	116,3877379
1	1	1	-1	1	0	1	1	150,1082763
-1	-1	1	1	1	1	1	-1	139,1569834
-1	-1	1	0	1	0	-1	-1	116,8714549
-1	1	-1	0	1	0	1	-1	146,6493166
-1	1	0	-1	1	1	1	1	120,340492
0	-1	1	-1	-1	0	1	-1	67,76315248
-1	-1	-1	1	0	0	1	1	146,7601684
-1	1	1	1	1	1	1	1	112,5156033
-1	-1	-1	1	-1	0	1	-1	139,1166736
0	1	0	1	0	1	1	-1	119,8366202
-1	-1	0	-1	0	1	1	1	63,95148252
-1	1	-1	-1	0	0	1	-1	45,79002452
-1	1	0	1	0	-1	1	0	49,11797754
0	0	0	0	0	0	0	0	64,02202457
0	0	-1	1	1	-1	1	1	64,17318612
-1	1	0	1	-1	1	1	0	75,22525344
-1	1	1	-1	-1	0	-1	-1	64,35457997
-1	-1	1	-1	0	-1	1	0	63,85070815
-1	0	1	-1	0	0	1	1	63,80032097
-1	-1	0	-1	-1	-1	1	0	82,62688977
0	1	0	0	0	0	0	-1	56,75139491
0	1	-1	1	0	1	-1	0	45,53808861
-1	-1	-1	1	1	1	1	-1	63,97163739

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 16 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
IF[t] = + 104.050814851764 + 1.2766372277298t1[t] -2.70702734440879t2[t] + 5.83120838946325t3[t] + 3.17459431388783t4[t] + 1.2728852200125t5[t] -1.14031184634879t6[t] + 6.89281730701038t7[t] -0.416328408204501t8[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IF[t] =  +  104.050814851764 +  1.2766372277298t1[t] -2.70702734440879t2[t] +  5.83120838946325t3[t] +  3.17459431388783t4[t] +  1.2728852200125t5[t] -1.14031184634879t6[t] +  6.89281730701038t7[t] -0.416328408204501t8[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IF[t] =  +  104.050814851764 +  1.2766372277298t1[t] -2.70702734440879t2[t] +  5.83120838946325t3[t] +  3.17459431388783t4[t] +  1.2728852200125t5[t] -1.14031184634879t6[t] +  6.89281730701038t7[t] -0.416328408204501t8[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
IF[t] = + 104.050814851764 + 1.2766372277298t1[t] -2.70702734440879t2[t] + 5.83120838946325t3[t] + 3.17459431388783t4[t] + 1.2728852200125t5[t] -1.14031184634879t6[t] + 6.89281730701038t7[t] -0.416328408204501t8[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)104.0508148517647.62047313.654100
t11.27663722772987.270490.17560.8610820.430541
t2-2.707027344408794.740261-0.57110.5696360.284818
t35.831208389463255.0400121.1570.2509030.125451
t43.174594313887835.1437480.61720.5389640.269482
t51.27288522001255.7518370.22130.8254520.412726
t6-1.140311846348796.151907-0.18540.8534410.426721
t76.892817307010385.9453061.15940.2499340.124967
t8-0.4163284082045015.096044-0.08170.9351030.467551

\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) & 104.050814851764 & 7.620473 & 13.6541 & 0 & 0 \tabularnewline
t1 & 1.2766372277298 & 7.27049 & 0.1756 & 0.861082 & 0.430541 \tabularnewline
t2 & -2.70702734440879 & 4.740261 & -0.5711 & 0.569636 & 0.284818 \tabularnewline
t3 & 5.83120838946325 & 5.040012 & 1.157 & 0.250903 & 0.125451 \tabularnewline
t4 & 3.17459431388783 & 5.143748 & 0.6172 & 0.538964 & 0.269482 \tabularnewline
t5 & 1.2728852200125 & 5.751837 & 0.2213 & 0.825452 & 0.412726 \tabularnewline
t6 & -1.14031184634879 & 6.151907 & -0.1854 & 0.853441 & 0.426721 \tabularnewline
t7 & 6.89281730701038 & 5.945306 & 1.1594 & 0.249934 & 0.124967 \tabularnewline
t8 & -0.416328408204501 & 5.096044 & -0.0817 & 0.935103 & 0.467551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&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]104.050814851764[/C][C]7.620473[/C][C]13.6541[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t1[/C][C]1.2766372277298[/C][C]7.27049[/C][C]0.1756[/C][C]0.861082[/C][C]0.430541[/C][/ROW]
[ROW][C]t2[/C][C]-2.70702734440879[/C][C]4.740261[/C][C]-0.5711[/C][C]0.569636[/C][C]0.284818[/C][/ROW]
[ROW][C]t3[/C][C]5.83120838946325[/C][C]5.040012[/C][C]1.157[/C][C]0.250903[/C][C]0.125451[/C][/ROW]
[ROW][C]t4[/C][C]3.17459431388783[/C][C]5.143748[/C][C]0.6172[/C][C]0.538964[/C][C]0.269482[/C][/ROW]
[ROW][C]t5[/C][C]1.2728852200125[/C][C]5.751837[/C][C]0.2213[/C][C]0.825452[/C][C]0.412726[/C][/ROW]
[ROW][C]t6[/C][C]-1.14031184634879[/C][C]6.151907[/C][C]-0.1854[/C][C]0.853441[/C][C]0.426721[/C][/ROW]
[ROW][C]t7[/C][C]6.89281730701038[/C][C]5.945306[/C][C]1.1594[/C][C]0.249934[/C][C]0.124967[/C][/ROW]
[ROW][C]t8[/C][C]-0.416328408204501[/C][C]5.096044[/C][C]-0.0817[/C][C]0.935103[/C][C]0.467551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)104.0508148517647.62047313.654100
t11.27663722772987.270490.17560.8610820.430541
t2-2.707027344408794.740261-0.57110.5696360.284818
t35.831208389463255.0400121.1570.2509030.125451
t43.174594313887835.1437480.61720.5389640.269482
t51.27288522001255.7518370.22130.8254520.412726
t6-1.140311846348796.151907-0.18540.8534410.426721
t76.892817307010385.9453061.15940.2499340.124967
t8-0.4163284082045015.096044-0.08170.9351030.467551






Multiple Linear Regression - Regression Statistics
Multiple R0.198043535424771
R-squared0.0392212419235427
Adjusted R-squared-0.0619133641897687
F-TEST (value)0.387812277427562
F-TEST (DF numerator)8
F-TEST (DF denominator)76
p-value0.923972268591978
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.165703364111
Sum Squared Residuals116580.376320421

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.198043535424771 \tabularnewline
R-squared & 0.0392212419235427 \tabularnewline
Adjusted R-squared & -0.0619133641897687 \tabularnewline
F-TEST (value) & 0.387812277427562 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value & 0.923972268591978 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39.165703364111 \tabularnewline
Sum Squared Residuals & 116580.376320421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.198043535424771[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0392212419235427[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0619133641897687[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.387812277427562[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/C][/ROW]
[ROW][C]p-value[/C][C]0.923972268591978[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39.165703364111[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]116580.376320421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.198043535424771
R-squared0.0392212419235427
Adjusted R-squared-0.0619133641897687
F-TEST (value)0.387812277427562
F-TEST (DF numerator)8
F-TEST (DF denominator)76
p-value0.923972268591978
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.165703364111
Sum Squared Residuals116580.376320421






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.8380813112.4561823802840.381898919716274
2113.1303269116.514672071855-3.384345171855
397.63171117100.404775759028-2.77306458902817
4142.515168793.484619886142249.0305488138578
5164.861161899.248133078851165.6130287211489
6112.485371117.300155771948-4.81478477194752
7150.1989732116.51467207185533.684301128145
8176.719424104.41297798341872.3064460165822
9113.0698623105.5250001697987.54486213020175
10116.528822104.05081485176412.4780071482361
11112.848158798.810721695092614.0374370049074
1212.8125070991.0666206789077-78.2541135889077
13116.4885123100.6272474291315.86126487087
1412.42327815102.311883757189-89.8886056071886
15124.2529363104.01959847651920.2333378234805
16135.1538421106.12889252065829.0249495793417
1753.0001895697.6564860577884-44.6562964977884
1864.2235733107.734364293613-43.5107909936134
19120.431188984.71743205113235.7137568488679
20113.1101721105.0574332003198.05273889968078
21131.1607783104.38123114742126.7795471525792
22105.2147414104.8522552929280.362486107071501
23127.6514314109.43365316533718.2177782346629
24146.357071115.31439138556931.0426796144305
25146.598929493.927550164307152.6713792356929
26109.0969534104.8522552929284.24469810707151
27165.0123233115.2043730424649.8079502575398
2879.59118241110.266309981746-30.6751275717461
29105.5472968113.026412929479-7.47911612947872
30105.3759803103.917121353971.45885894602957
31143.0190405105.00966115053238.0093793494682
32120.0280915113.0337676928726.99432380712808
33108.744243197.097608071476611.6466350285234
34101.5844652110.165483444079-8.58101824407934
35149.9268825114.75438533192335.1724971680775
36149.8160307121.93623077610727.8797999238928
37105.315515798.13451964219427.18099605780582
3812.79436771115.340698423841-102.546330713841
39124.3436333116.5146720718557.828961228145
40123.799451797.205484098310826.5939676016892
41131.6041855121.9287267606739.67545873932743
42109.0062565104.0745272115744.93172928842627
4375.39656986119.823184724251-44.4266148642511
44139.378687113.34007775796726.0386092420329
45124.4242527110.37797890973814.0462737902623
46108.9155595100.2400616786168.6754978213843
47105.305438397.09760807147668.20783022852337
4879.0167685397.41053620405-18.3937676740499
49153.799017110.57396501168643.2250519883141
5075.49734422102.644236133958-27.1468919139579
51112.878391111.650864129751.22752687024983
5282.94936774110.266309981746-27.3169422417461
53157.9130101109.30107979167348.6119303083266
54120.5319633104.05081485176416.4811484482361
5513.50228354100.845004162632-87.3427206226317
56116.921842108.860851388078.06099061193012
57124.1622394110.26630998174613.8959294182539
58149.7958758105.68625707418244.109618725818
59142.0516066104.85225529292937.1993513070715
6090.02852611102.774177624034-12.7456515140341
61160.7471687105.40332733208255.3438413679176
62116.3877379112.8195844372063.56815346279433
63150.1082763113.02641292947937.0818633705213
64139.1569834121.92872676067317.2282566393274
65116.8714549106.10880967911310.7626452208872
66146.6493166102.81797282538943.8313437746105
67120.340492103.50161823820716.8388737617929
6867.76315248115.450716766951-47.6875642869505
69146.7601684109.30107979167337.4590886083266
70112.5156033115.682015255446-3.16641195544599
71139.1166736108.8608513880730.2558222119301
72119.8366202110.6872156901099.14940450989095
7363.95148252107.642787707012-43.6913051870122
7445.7900245298.370493291489-52.5804687714891
7549.11797754111.274873746872-62.1568962068723
7664.02202457104.050814851764-40.0287902817639
7764.17318612110.283886741356-46.1107006213557
7875.22525344107.721364834162-32.4961113941622
7964.3545799794.9743902363824-30.6198102663824
8063.85070815116.170948197377-52.3202400473775
8163.80032097111.907280598415-48.1069596284154
8282.62688977109.066854587902-26.4399648179018
8356.75139491101.76011591556-45.0087210055596
8445.5380886190.6540442784205-45.1159556684205
8563.97163739110.266309981746-46.2946725917461

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.8380813 & 112.456182380284 & 0.381898919716274 \tabularnewline
2 & 113.1303269 & 116.514672071855 & -3.384345171855 \tabularnewline
3 & 97.63171117 & 100.404775759028 & -2.77306458902817 \tabularnewline
4 & 142.5151687 & 93.4846198861422 & 49.0305488138578 \tabularnewline
5 & 164.8611618 & 99.2481330788511 & 65.6130287211489 \tabularnewline
6 & 112.485371 & 117.300155771948 & -4.81478477194752 \tabularnewline
7 & 150.1989732 & 116.514672071855 & 33.684301128145 \tabularnewline
8 & 176.719424 & 104.412977983418 & 72.3064460165822 \tabularnewline
9 & 113.0698623 & 105.525000169798 & 7.54486213020175 \tabularnewline
10 & 116.528822 & 104.050814851764 & 12.4780071482361 \tabularnewline
11 & 112.8481587 & 98.8107216950926 & 14.0374370049074 \tabularnewline
12 & 12.81250709 & 91.0666206789077 & -78.2541135889077 \tabularnewline
13 & 116.4885123 & 100.62724742913 & 15.86126487087 \tabularnewline
14 & 12.42327815 & 102.311883757189 & -89.8886056071886 \tabularnewline
15 & 124.2529363 & 104.019598476519 & 20.2333378234805 \tabularnewline
16 & 135.1538421 & 106.128892520658 & 29.0249495793417 \tabularnewline
17 & 53.00018956 & 97.6564860577884 & -44.6562964977884 \tabularnewline
18 & 64.2235733 & 107.734364293613 & -43.5107909936134 \tabularnewline
19 & 120.4311889 & 84.717432051132 & 35.7137568488679 \tabularnewline
20 & 113.1101721 & 105.057433200319 & 8.05273889968078 \tabularnewline
21 & 131.1607783 & 104.381231147421 & 26.7795471525792 \tabularnewline
22 & 105.2147414 & 104.852255292928 & 0.362486107071501 \tabularnewline
23 & 127.6514314 & 109.433653165337 & 18.2177782346629 \tabularnewline
24 & 146.357071 & 115.314391385569 & 31.0426796144305 \tabularnewline
25 & 146.5989294 & 93.9275501643071 & 52.6713792356929 \tabularnewline
26 & 109.0969534 & 104.852255292928 & 4.24469810707151 \tabularnewline
27 & 165.0123233 & 115.20437304246 & 49.8079502575398 \tabularnewline
28 & 79.59118241 & 110.266309981746 & -30.6751275717461 \tabularnewline
29 & 105.5472968 & 113.026412929479 & -7.47911612947872 \tabularnewline
30 & 105.3759803 & 103.91712135397 & 1.45885894602957 \tabularnewline
31 & 143.0190405 & 105.009661150532 & 38.0093793494682 \tabularnewline
32 & 120.0280915 & 113.033767692872 & 6.99432380712808 \tabularnewline
33 & 108.7442431 & 97.0976080714766 & 11.6466350285234 \tabularnewline
34 & 101.5844652 & 110.165483444079 & -8.58101824407934 \tabularnewline
35 & 149.9268825 & 114.754385331923 & 35.1724971680775 \tabularnewline
36 & 149.8160307 & 121.936230776107 & 27.8797999238928 \tabularnewline
37 & 105.3155157 & 98.1345196421942 & 7.18099605780582 \tabularnewline
38 & 12.79436771 & 115.340698423841 & -102.546330713841 \tabularnewline
39 & 124.3436333 & 116.514672071855 & 7.828961228145 \tabularnewline
40 & 123.7994517 & 97.2054840983108 & 26.5939676016892 \tabularnewline
41 & 131.6041855 & 121.928726760673 & 9.67545873932743 \tabularnewline
42 & 109.0062565 & 104.074527211574 & 4.93172928842627 \tabularnewline
43 & 75.39656986 & 119.823184724251 & -44.4266148642511 \tabularnewline
44 & 139.378687 & 113.340077757967 & 26.0386092420329 \tabularnewline
45 & 124.4242527 & 110.377978909738 & 14.0462737902623 \tabularnewline
46 & 108.9155595 & 100.240061678616 & 8.6754978213843 \tabularnewline
47 & 105.3054383 & 97.0976080714766 & 8.20783022852337 \tabularnewline
48 & 79.01676853 & 97.41053620405 & -18.3937676740499 \tabularnewline
49 & 153.799017 & 110.573965011686 & 43.2250519883141 \tabularnewline
50 & 75.49734422 & 102.644236133958 & -27.1468919139579 \tabularnewline
51 & 112.878391 & 111.65086412975 & 1.22752687024983 \tabularnewline
52 & 82.94936774 & 110.266309981746 & -27.3169422417461 \tabularnewline
53 & 157.9130101 & 109.301079791673 & 48.6119303083266 \tabularnewline
54 & 120.5319633 & 104.050814851764 & 16.4811484482361 \tabularnewline
55 & 13.50228354 & 100.845004162632 & -87.3427206226317 \tabularnewline
56 & 116.921842 & 108.86085138807 & 8.06099061193012 \tabularnewline
57 & 124.1622394 & 110.266309981746 & 13.8959294182539 \tabularnewline
58 & 149.7958758 & 105.686257074182 & 44.109618725818 \tabularnewline
59 & 142.0516066 & 104.852255292929 & 37.1993513070715 \tabularnewline
60 & 90.02852611 & 102.774177624034 & -12.7456515140341 \tabularnewline
61 & 160.7471687 & 105.403327332082 & 55.3438413679176 \tabularnewline
62 & 116.3877379 & 112.819584437206 & 3.56815346279433 \tabularnewline
63 & 150.1082763 & 113.026412929479 & 37.0818633705213 \tabularnewline
64 & 139.1569834 & 121.928726760673 & 17.2282566393274 \tabularnewline
65 & 116.8714549 & 106.108809679113 & 10.7626452208872 \tabularnewline
66 & 146.6493166 & 102.817972825389 & 43.8313437746105 \tabularnewline
67 & 120.340492 & 103.501618238207 & 16.8388737617929 \tabularnewline
68 & 67.76315248 & 115.450716766951 & -47.6875642869505 \tabularnewline
69 & 146.7601684 & 109.301079791673 & 37.4590886083266 \tabularnewline
70 & 112.5156033 & 115.682015255446 & -3.16641195544599 \tabularnewline
71 & 139.1166736 & 108.86085138807 & 30.2558222119301 \tabularnewline
72 & 119.8366202 & 110.687215690109 & 9.14940450989095 \tabularnewline
73 & 63.95148252 & 107.642787707012 & -43.6913051870122 \tabularnewline
74 & 45.79002452 & 98.370493291489 & -52.5804687714891 \tabularnewline
75 & 49.11797754 & 111.274873746872 & -62.1568962068723 \tabularnewline
76 & 64.02202457 & 104.050814851764 & -40.0287902817639 \tabularnewline
77 & 64.17318612 & 110.283886741356 & -46.1107006213557 \tabularnewline
78 & 75.22525344 & 107.721364834162 & -32.4961113941622 \tabularnewline
79 & 64.35457997 & 94.9743902363824 & -30.6198102663824 \tabularnewline
80 & 63.85070815 & 116.170948197377 & -52.3202400473775 \tabularnewline
81 & 63.80032097 & 111.907280598415 & -48.1069596284154 \tabularnewline
82 & 82.62688977 & 109.066854587902 & -26.4399648179018 \tabularnewline
83 & 56.75139491 & 101.76011591556 & -45.0087210055596 \tabularnewline
84 & 45.53808861 & 90.6540442784205 & -45.1159556684205 \tabularnewline
85 & 63.97163739 & 110.266309981746 & -46.2946725917461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&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]112.8380813[/C][C]112.456182380284[/C][C]0.381898919716274[/C][/ROW]
[ROW][C]2[/C][C]113.1303269[/C][C]116.514672071855[/C][C]-3.384345171855[/C][/ROW]
[ROW][C]3[/C][C]97.63171117[/C][C]100.404775759028[/C][C]-2.77306458902817[/C][/ROW]
[ROW][C]4[/C][C]142.5151687[/C][C]93.4846198861422[/C][C]49.0305488138578[/C][/ROW]
[ROW][C]5[/C][C]164.8611618[/C][C]99.2481330788511[/C][C]65.6130287211489[/C][/ROW]
[ROW][C]6[/C][C]112.485371[/C][C]117.300155771948[/C][C]-4.81478477194752[/C][/ROW]
[ROW][C]7[/C][C]150.1989732[/C][C]116.514672071855[/C][C]33.684301128145[/C][/ROW]
[ROW][C]8[/C][C]176.719424[/C][C]104.412977983418[/C][C]72.3064460165822[/C][/ROW]
[ROW][C]9[/C][C]113.0698623[/C][C]105.525000169798[/C][C]7.54486213020175[/C][/ROW]
[ROW][C]10[/C][C]116.528822[/C][C]104.050814851764[/C][C]12.4780071482361[/C][/ROW]
[ROW][C]11[/C][C]112.8481587[/C][C]98.8107216950926[/C][C]14.0374370049074[/C][/ROW]
[ROW][C]12[/C][C]12.81250709[/C][C]91.0666206789077[/C][C]-78.2541135889077[/C][/ROW]
[ROW][C]13[/C][C]116.4885123[/C][C]100.62724742913[/C][C]15.86126487087[/C][/ROW]
[ROW][C]14[/C][C]12.42327815[/C][C]102.311883757189[/C][C]-89.8886056071886[/C][/ROW]
[ROW][C]15[/C][C]124.2529363[/C][C]104.019598476519[/C][C]20.2333378234805[/C][/ROW]
[ROW][C]16[/C][C]135.1538421[/C][C]106.128892520658[/C][C]29.0249495793417[/C][/ROW]
[ROW][C]17[/C][C]53.00018956[/C][C]97.6564860577884[/C][C]-44.6562964977884[/C][/ROW]
[ROW][C]18[/C][C]64.2235733[/C][C]107.734364293613[/C][C]-43.5107909936134[/C][/ROW]
[ROW][C]19[/C][C]120.4311889[/C][C]84.717432051132[/C][C]35.7137568488679[/C][/ROW]
[ROW][C]20[/C][C]113.1101721[/C][C]105.057433200319[/C][C]8.05273889968078[/C][/ROW]
[ROW][C]21[/C][C]131.1607783[/C][C]104.381231147421[/C][C]26.7795471525792[/C][/ROW]
[ROW][C]22[/C][C]105.2147414[/C][C]104.852255292928[/C][C]0.362486107071501[/C][/ROW]
[ROW][C]23[/C][C]127.6514314[/C][C]109.433653165337[/C][C]18.2177782346629[/C][/ROW]
[ROW][C]24[/C][C]146.357071[/C][C]115.314391385569[/C][C]31.0426796144305[/C][/ROW]
[ROW][C]25[/C][C]146.5989294[/C][C]93.9275501643071[/C][C]52.6713792356929[/C][/ROW]
[ROW][C]26[/C][C]109.0969534[/C][C]104.852255292928[/C][C]4.24469810707151[/C][/ROW]
[ROW][C]27[/C][C]165.0123233[/C][C]115.20437304246[/C][C]49.8079502575398[/C][/ROW]
[ROW][C]28[/C][C]79.59118241[/C][C]110.266309981746[/C][C]-30.6751275717461[/C][/ROW]
[ROW][C]29[/C][C]105.5472968[/C][C]113.026412929479[/C][C]-7.47911612947872[/C][/ROW]
[ROW][C]30[/C][C]105.3759803[/C][C]103.91712135397[/C][C]1.45885894602957[/C][/ROW]
[ROW][C]31[/C][C]143.0190405[/C][C]105.009661150532[/C][C]38.0093793494682[/C][/ROW]
[ROW][C]32[/C][C]120.0280915[/C][C]113.033767692872[/C][C]6.99432380712808[/C][/ROW]
[ROW][C]33[/C][C]108.7442431[/C][C]97.0976080714766[/C][C]11.6466350285234[/C][/ROW]
[ROW][C]34[/C][C]101.5844652[/C][C]110.165483444079[/C][C]-8.58101824407934[/C][/ROW]
[ROW][C]35[/C][C]149.9268825[/C][C]114.754385331923[/C][C]35.1724971680775[/C][/ROW]
[ROW][C]36[/C][C]149.8160307[/C][C]121.936230776107[/C][C]27.8797999238928[/C][/ROW]
[ROW][C]37[/C][C]105.3155157[/C][C]98.1345196421942[/C][C]7.18099605780582[/C][/ROW]
[ROW][C]38[/C][C]12.79436771[/C][C]115.340698423841[/C][C]-102.546330713841[/C][/ROW]
[ROW][C]39[/C][C]124.3436333[/C][C]116.514672071855[/C][C]7.828961228145[/C][/ROW]
[ROW][C]40[/C][C]123.7994517[/C][C]97.2054840983108[/C][C]26.5939676016892[/C][/ROW]
[ROW][C]41[/C][C]131.6041855[/C][C]121.928726760673[/C][C]9.67545873932743[/C][/ROW]
[ROW][C]42[/C][C]109.0062565[/C][C]104.074527211574[/C][C]4.93172928842627[/C][/ROW]
[ROW][C]43[/C][C]75.39656986[/C][C]119.823184724251[/C][C]-44.4266148642511[/C][/ROW]
[ROW][C]44[/C][C]139.378687[/C][C]113.340077757967[/C][C]26.0386092420329[/C][/ROW]
[ROW][C]45[/C][C]124.4242527[/C][C]110.377978909738[/C][C]14.0462737902623[/C][/ROW]
[ROW][C]46[/C][C]108.9155595[/C][C]100.240061678616[/C][C]8.6754978213843[/C][/ROW]
[ROW][C]47[/C][C]105.3054383[/C][C]97.0976080714766[/C][C]8.20783022852337[/C][/ROW]
[ROW][C]48[/C][C]79.01676853[/C][C]97.41053620405[/C][C]-18.3937676740499[/C][/ROW]
[ROW][C]49[/C][C]153.799017[/C][C]110.573965011686[/C][C]43.2250519883141[/C][/ROW]
[ROW][C]50[/C][C]75.49734422[/C][C]102.644236133958[/C][C]-27.1468919139579[/C][/ROW]
[ROW][C]51[/C][C]112.878391[/C][C]111.65086412975[/C][C]1.22752687024983[/C][/ROW]
[ROW][C]52[/C][C]82.94936774[/C][C]110.266309981746[/C][C]-27.3169422417461[/C][/ROW]
[ROW][C]53[/C][C]157.9130101[/C][C]109.301079791673[/C][C]48.6119303083266[/C][/ROW]
[ROW][C]54[/C][C]120.5319633[/C][C]104.050814851764[/C][C]16.4811484482361[/C][/ROW]
[ROW][C]55[/C][C]13.50228354[/C][C]100.845004162632[/C][C]-87.3427206226317[/C][/ROW]
[ROW][C]56[/C][C]116.921842[/C][C]108.86085138807[/C][C]8.06099061193012[/C][/ROW]
[ROW][C]57[/C][C]124.1622394[/C][C]110.266309981746[/C][C]13.8959294182539[/C][/ROW]
[ROW][C]58[/C][C]149.7958758[/C][C]105.686257074182[/C][C]44.109618725818[/C][/ROW]
[ROW][C]59[/C][C]142.0516066[/C][C]104.852255292929[/C][C]37.1993513070715[/C][/ROW]
[ROW][C]60[/C][C]90.02852611[/C][C]102.774177624034[/C][C]-12.7456515140341[/C][/ROW]
[ROW][C]61[/C][C]160.7471687[/C][C]105.403327332082[/C][C]55.3438413679176[/C][/ROW]
[ROW][C]62[/C][C]116.3877379[/C][C]112.819584437206[/C][C]3.56815346279433[/C][/ROW]
[ROW][C]63[/C][C]150.1082763[/C][C]113.026412929479[/C][C]37.0818633705213[/C][/ROW]
[ROW][C]64[/C][C]139.1569834[/C][C]121.928726760673[/C][C]17.2282566393274[/C][/ROW]
[ROW][C]65[/C][C]116.8714549[/C][C]106.108809679113[/C][C]10.7626452208872[/C][/ROW]
[ROW][C]66[/C][C]146.6493166[/C][C]102.817972825389[/C][C]43.8313437746105[/C][/ROW]
[ROW][C]67[/C][C]120.340492[/C][C]103.501618238207[/C][C]16.8388737617929[/C][/ROW]
[ROW][C]68[/C][C]67.76315248[/C][C]115.450716766951[/C][C]-47.6875642869505[/C][/ROW]
[ROW][C]69[/C][C]146.7601684[/C][C]109.301079791673[/C][C]37.4590886083266[/C][/ROW]
[ROW][C]70[/C][C]112.5156033[/C][C]115.682015255446[/C][C]-3.16641195544599[/C][/ROW]
[ROW][C]71[/C][C]139.1166736[/C][C]108.86085138807[/C][C]30.2558222119301[/C][/ROW]
[ROW][C]72[/C][C]119.8366202[/C][C]110.687215690109[/C][C]9.14940450989095[/C][/ROW]
[ROW][C]73[/C][C]63.95148252[/C][C]107.642787707012[/C][C]-43.6913051870122[/C][/ROW]
[ROW][C]74[/C][C]45.79002452[/C][C]98.370493291489[/C][C]-52.5804687714891[/C][/ROW]
[ROW][C]75[/C][C]49.11797754[/C][C]111.274873746872[/C][C]-62.1568962068723[/C][/ROW]
[ROW][C]76[/C][C]64.02202457[/C][C]104.050814851764[/C][C]-40.0287902817639[/C][/ROW]
[ROW][C]77[/C][C]64.17318612[/C][C]110.283886741356[/C][C]-46.1107006213557[/C][/ROW]
[ROW][C]78[/C][C]75.22525344[/C][C]107.721364834162[/C][C]-32.4961113941622[/C][/ROW]
[ROW][C]79[/C][C]64.35457997[/C][C]94.9743902363824[/C][C]-30.6198102663824[/C][/ROW]
[ROW][C]80[/C][C]63.85070815[/C][C]116.170948197377[/C][C]-52.3202400473775[/C][/ROW]
[ROW][C]81[/C][C]63.80032097[/C][C]111.907280598415[/C][C]-48.1069596284154[/C][/ROW]
[ROW][C]82[/C][C]82.62688977[/C][C]109.066854587902[/C][C]-26.4399648179018[/C][/ROW]
[ROW][C]83[/C][C]56.75139491[/C][C]101.76011591556[/C][C]-45.0087210055596[/C][/ROW]
[ROW][C]84[/C][C]45.53808861[/C][C]90.6540442784205[/C][C]-45.1159556684205[/C][/ROW]
[ROW][C]85[/C][C]63.97163739[/C][C]110.266309981746[/C][C]-46.2946725917461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=4

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.8380813112.4561823802840.381898919716274
2113.1303269116.514672071855-3.384345171855
397.63171117100.404775759028-2.77306458902817
4142.515168793.484619886142249.0305488138578
5164.861161899.248133078851165.6130287211489
6112.485371117.300155771948-4.81478477194752
7150.1989732116.51467207185533.684301128145
8176.719424104.41297798341872.3064460165822
9113.0698623105.5250001697987.54486213020175
10116.528822104.05081485176412.4780071482361
11112.848158798.810721695092614.0374370049074
1212.8125070991.0666206789077-78.2541135889077
13116.4885123100.6272474291315.86126487087
1412.42327815102.311883757189-89.8886056071886
15124.2529363104.01959847651920.2333378234805
16135.1538421106.12889252065829.0249495793417
1753.0001895697.6564860577884-44.6562964977884
1864.2235733107.734364293613-43.5107909936134
19120.431188984.71743205113235.7137568488679
20113.1101721105.0574332003198.05273889968078
21131.1607783104.38123114742126.7795471525792
22105.2147414104.8522552929280.362486107071501
23127.6514314109.43365316533718.2177782346629
24146.357071115.31439138556931.0426796144305
25146.598929493.927550164307152.6713792356929
26109.0969534104.8522552929284.24469810707151
27165.0123233115.2043730424649.8079502575398
2879.59118241110.266309981746-30.6751275717461
29105.5472968113.026412929479-7.47911612947872
30105.3759803103.917121353971.45885894602957
31143.0190405105.00966115053238.0093793494682
32120.0280915113.0337676928726.99432380712808
33108.744243197.097608071476611.6466350285234
34101.5844652110.165483444079-8.58101824407934
35149.9268825114.75438533192335.1724971680775
36149.8160307121.93623077610727.8797999238928
37105.315515798.13451964219427.18099605780582
3812.79436771115.340698423841-102.546330713841
39124.3436333116.5146720718557.828961228145
40123.799451797.205484098310826.5939676016892
41131.6041855121.9287267606739.67545873932743
42109.0062565104.0745272115744.93172928842627
4375.39656986119.823184724251-44.4266148642511
44139.378687113.34007775796726.0386092420329
45124.4242527110.37797890973814.0462737902623
46108.9155595100.2400616786168.6754978213843
47105.305438397.09760807147668.20783022852337
4879.0167685397.41053620405-18.3937676740499
49153.799017110.57396501168643.2250519883141
5075.49734422102.644236133958-27.1468919139579
51112.878391111.650864129751.22752687024983
5282.94936774110.266309981746-27.3169422417461
53157.9130101109.30107979167348.6119303083266
54120.5319633104.05081485176416.4811484482361
5513.50228354100.845004162632-87.3427206226317
56116.921842108.860851388078.06099061193012
57124.1622394110.26630998174613.8959294182539
58149.7958758105.68625707418244.109618725818
59142.0516066104.85225529292937.1993513070715
6090.02852611102.774177624034-12.7456515140341
61160.7471687105.40332733208255.3438413679176
62116.3877379112.8195844372063.56815346279433
63150.1082763113.02641292947937.0818633705213
64139.1569834121.92872676067317.2282566393274
65116.8714549106.10880967911310.7626452208872
66146.6493166102.81797282538943.8313437746105
67120.340492103.50161823820716.8388737617929
6867.76315248115.450716766951-47.6875642869505
69146.7601684109.30107979167337.4590886083266
70112.5156033115.682015255446-3.16641195544599
71139.1166736108.8608513880730.2558222119301
72119.8366202110.6872156901099.14940450989095
7363.95148252107.642787707012-43.6913051870122
7445.7900245298.370493291489-52.5804687714891
7549.11797754111.274873746872-62.1568962068723
7664.02202457104.050814851764-40.0287902817639
7764.17318612110.283886741356-46.1107006213557
7875.22525344107.721364834162-32.4961113941622
7964.3545799794.9743902363824-30.6198102663824
8063.85070815116.170948197377-52.3202400473775
8163.80032097111.907280598415-48.1069596284154
8282.62688977109.066854587902-26.4399648179018
8356.75139491101.76011591556-45.0087210055596
8445.5380886190.6540442784205-45.1159556684205
8563.97163739110.266309981746-46.2946725917461






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.761350764109840.4772984717803210.238649235890161
130.6209131835223860.7581736329552280.379086816477614
140.6458053394249220.7083893211501550.354194660575078
150.6293863381906720.7412273236186560.370613661809328
160.7506455801626110.4987088396747780.249354419837389
170.7394716751926750.521056649614650.260528324807325
180.7946176297831050.410764740433790.205382370216895
190.7693933187463760.4612133625072480.230606681253624
200.6938548528490480.6122902943019030.306145147150952
210.7402954872296330.5194090255407340.259704512770367
220.6636605511068140.6726788977863720.336339448893186
230.6494408211699820.7011183576600350.350559178830018
240.6215137068703840.7569725862592320.378486293129616
250.7512733829562150.4974532340875690.248726617043784
260.6843051924411320.6313896151177360.315694807558868
270.6767857762615470.6464284474769050.323214223738453
280.6241882682218730.7516234635562540.375811731778127
290.622507750465460.7549844990690810.37749224953454
300.5497877939594860.9004244120810290.450212206040514
310.5389516066728160.9220967866543680.461048393327184
320.4719155663334270.9438311326668540.528084433666573
330.4351827612416740.8703655224833480.564817238758326
340.3862768167405250.772553633481050.613723183259475
350.3684616837496490.7369233674992990.63153831625035
360.3474482439799810.6948964879599630.652551756020019
370.2947930823123050.589586164624610.705206917687695
380.704250832599220.5914983348015610.29574916740078
390.6459992072381810.7080015855236380.354000792761819
400.6108398315066230.7783203369867540.389160168493377
410.5516875540208470.8966248919583050.448312445979153
420.5007926485490020.9984147029019970.499207351450998
430.5294484557512780.9411030884974450.470551544248722
440.4894746547082730.9789493094165470.510525345291727
450.4326608721827380.8653217443654750.567339127817262
460.3744790094059160.7489580188118330.625520990594084
470.3448276499892290.6896552999784580.655172350010771
480.2971498871751720.5942997743503440.702850112824828
490.3044633113679030.6089266227358050.695536688632097
500.2685658133310840.5371316266621670.731434186668916
510.2153977566162660.4307955132325320.784602243383734
520.1940181503288730.3880363006577450.805981849671128
530.2180185922900660.4360371845801320.781981407709934
540.195919960781750.39183992156350.80408003921825
550.4462928033651640.8925856067303280.553707196634836
560.3824060872753620.7648121745507240.617593912724638
570.3202762000498670.6405524000997330.679723799950133
580.3903358498553470.7806716997106950.609664150144653
590.3638464372675230.7276928745350460.636153562732477
600.3026607159237340.6053214318474680.697339284076266
610.4071570185595080.8143140371190170.592842981440492
620.3302891913366230.6605783826732470.669710808663377
630.4949189708726780.9898379417453560.505081029127322
640.412683846090420.8253676921808410.58731615390958
650.3781819828794680.7563639657589350.621818017120533
660.5564136906342520.8871726187314960.443586309365748
670.72013439056850.5597312188629990.2798656094315
680.7029865956404330.5940268087191340.297013404359567
690.8519178660964880.2961642678070250.148082133903512
700.9593267372086580.08134652558268450.0406732627913422
710.9717156620642670.05656867587146530.0282843379357326
720.9928451758379870.01430964832402650.00715482416201324
730.9738869130447350.05222617391053050.0261130869552653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.76135076410984 & 0.477298471780321 & 0.238649235890161 \tabularnewline
13 & 0.620913183522386 & 0.758173632955228 & 0.379086816477614 \tabularnewline
14 & 0.645805339424922 & 0.708389321150155 & 0.354194660575078 \tabularnewline
15 & 0.629386338190672 & 0.741227323618656 & 0.370613661809328 \tabularnewline
16 & 0.750645580162611 & 0.498708839674778 & 0.249354419837389 \tabularnewline
17 & 0.739471675192675 & 0.52105664961465 & 0.260528324807325 \tabularnewline
18 & 0.794617629783105 & 0.41076474043379 & 0.205382370216895 \tabularnewline
19 & 0.769393318746376 & 0.461213362507248 & 0.230606681253624 \tabularnewline
20 & 0.693854852849048 & 0.612290294301903 & 0.306145147150952 \tabularnewline
21 & 0.740295487229633 & 0.519409025540734 & 0.259704512770367 \tabularnewline
22 & 0.663660551106814 & 0.672678897786372 & 0.336339448893186 \tabularnewline
23 & 0.649440821169982 & 0.701118357660035 & 0.350559178830018 \tabularnewline
24 & 0.621513706870384 & 0.756972586259232 & 0.378486293129616 \tabularnewline
25 & 0.751273382956215 & 0.497453234087569 & 0.248726617043784 \tabularnewline
26 & 0.684305192441132 & 0.631389615117736 & 0.315694807558868 \tabularnewline
27 & 0.676785776261547 & 0.646428447476905 & 0.323214223738453 \tabularnewline
28 & 0.624188268221873 & 0.751623463556254 & 0.375811731778127 \tabularnewline
29 & 0.62250775046546 & 0.754984499069081 & 0.37749224953454 \tabularnewline
30 & 0.549787793959486 & 0.900424412081029 & 0.450212206040514 \tabularnewline
31 & 0.538951606672816 & 0.922096786654368 & 0.461048393327184 \tabularnewline
32 & 0.471915566333427 & 0.943831132666854 & 0.528084433666573 \tabularnewline
33 & 0.435182761241674 & 0.870365522483348 & 0.564817238758326 \tabularnewline
34 & 0.386276816740525 & 0.77255363348105 & 0.613723183259475 \tabularnewline
35 & 0.368461683749649 & 0.736923367499299 & 0.63153831625035 \tabularnewline
36 & 0.347448243979981 & 0.694896487959963 & 0.652551756020019 \tabularnewline
37 & 0.294793082312305 & 0.58958616462461 & 0.705206917687695 \tabularnewline
38 & 0.70425083259922 & 0.591498334801561 & 0.29574916740078 \tabularnewline
39 & 0.645999207238181 & 0.708001585523638 & 0.354000792761819 \tabularnewline
40 & 0.610839831506623 & 0.778320336986754 & 0.389160168493377 \tabularnewline
41 & 0.551687554020847 & 0.896624891958305 & 0.448312445979153 \tabularnewline
42 & 0.500792648549002 & 0.998414702901997 & 0.499207351450998 \tabularnewline
43 & 0.529448455751278 & 0.941103088497445 & 0.470551544248722 \tabularnewline
44 & 0.489474654708273 & 0.978949309416547 & 0.510525345291727 \tabularnewline
45 & 0.432660872182738 & 0.865321744365475 & 0.567339127817262 \tabularnewline
46 & 0.374479009405916 & 0.748958018811833 & 0.625520990594084 \tabularnewline
47 & 0.344827649989229 & 0.689655299978458 & 0.655172350010771 \tabularnewline
48 & 0.297149887175172 & 0.594299774350344 & 0.702850112824828 \tabularnewline
49 & 0.304463311367903 & 0.608926622735805 & 0.695536688632097 \tabularnewline
50 & 0.268565813331084 & 0.537131626662167 & 0.731434186668916 \tabularnewline
51 & 0.215397756616266 & 0.430795513232532 & 0.784602243383734 \tabularnewline
52 & 0.194018150328873 & 0.388036300657745 & 0.805981849671128 \tabularnewline
53 & 0.218018592290066 & 0.436037184580132 & 0.781981407709934 \tabularnewline
54 & 0.19591996078175 & 0.3918399215635 & 0.80408003921825 \tabularnewline
55 & 0.446292803365164 & 0.892585606730328 & 0.553707196634836 \tabularnewline
56 & 0.382406087275362 & 0.764812174550724 & 0.617593912724638 \tabularnewline
57 & 0.320276200049867 & 0.640552400099733 & 0.679723799950133 \tabularnewline
58 & 0.390335849855347 & 0.780671699710695 & 0.609664150144653 \tabularnewline
59 & 0.363846437267523 & 0.727692874535046 & 0.636153562732477 \tabularnewline
60 & 0.302660715923734 & 0.605321431847468 & 0.697339284076266 \tabularnewline
61 & 0.407157018559508 & 0.814314037119017 & 0.592842981440492 \tabularnewline
62 & 0.330289191336623 & 0.660578382673247 & 0.669710808663377 \tabularnewline
63 & 0.494918970872678 & 0.989837941745356 & 0.505081029127322 \tabularnewline
64 & 0.41268384609042 & 0.825367692180841 & 0.58731615390958 \tabularnewline
65 & 0.378181982879468 & 0.756363965758935 & 0.621818017120533 \tabularnewline
66 & 0.556413690634252 & 0.887172618731496 & 0.443586309365748 \tabularnewline
67 & 0.7201343905685 & 0.559731218862999 & 0.2798656094315 \tabularnewline
68 & 0.702986595640433 & 0.594026808719134 & 0.297013404359567 \tabularnewline
69 & 0.851917866096488 & 0.296164267807025 & 0.148082133903512 \tabularnewline
70 & 0.959326737208658 & 0.0813465255826845 & 0.0406732627913422 \tabularnewline
71 & 0.971715662064267 & 0.0565686758714653 & 0.0282843379357326 \tabularnewline
72 & 0.992845175837987 & 0.0143096483240265 & 0.00715482416201324 \tabularnewline
73 & 0.973886913044735 & 0.0522261739105305 & 0.0261130869552653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&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]12[/C][C]0.76135076410984[/C][C]0.477298471780321[/C][C]0.238649235890161[/C][/ROW]
[ROW][C]13[/C][C]0.620913183522386[/C][C]0.758173632955228[/C][C]0.379086816477614[/C][/ROW]
[ROW][C]14[/C][C]0.645805339424922[/C][C]0.708389321150155[/C][C]0.354194660575078[/C][/ROW]
[ROW][C]15[/C][C]0.629386338190672[/C][C]0.741227323618656[/C][C]0.370613661809328[/C][/ROW]
[ROW][C]16[/C][C]0.750645580162611[/C][C]0.498708839674778[/C][C]0.249354419837389[/C][/ROW]
[ROW][C]17[/C][C]0.739471675192675[/C][C]0.52105664961465[/C][C]0.260528324807325[/C][/ROW]
[ROW][C]18[/C][C]0.794617629783105[/C][C]0.41076474043379[/C][C]0.205382370216895[/C][/ROW]
[ROW][C]19[/C][C]0.769393318746376[/C][C]0.461213362507248[/C][C]0.230606681253624[/C][/ROW]
[ROW][C]20[/C][C]0.693854852849048[/C][C]0.612290294301903[/C][C]0.306145147150952[/C][/ROW]
[ROW][C]21[/C][C]0.740295487229633[/C][C]0.519409025540734[/C][C]0.259704512770367[/C][/ROW]
[ROW][C]22[/C][C]0.663660551106814[/C][C]0.672678897786372[/C][C]0.336339448893186[/C][/ROW]
[ROW][C]23[/C][C]0.649440821169982[/C][C]0.701118357660035[/C][C]0.350559178830018[/C][/ROW]
[ROW][C]24[/C][C]0.621513706870384[/C][C]0.756972586259232[/C][C]0.378486293129616[/C][/ROW]
[ROW][C]25[/C][C]0.751273382956215[/C][C]0.497453234087569[/C][C]0.248726617043784[/C][/ROW]
[ROW][C]26[/C][C]0.684305192441132[/C][C]0.631389615117736[/C][C]0.315694807558868[/C][/ROW]
[ROW][C]27[/C][C]0.676785776261547[/C][C]0.646428447476905[/C][C]0.323214223738453[/C][/ROW]
[ROW][C]28[/C][C]0.624188268221873[/C][C]0.751623463556254[/C][C]0.375811731778127[/C][/ROW]
[ROW][C]29[/C][C]0.62250775046546[/C][C]0.754984499069081[/C][C]0.37749224953454[/C][/ROW]
[ROW][C]30[/C][C]0.549787793959486[/C][C]0.900424412081029[/C][C]0.450212206040514[/C][/ROW]
[ROW][C]31[/C][C]0.538951606672816[/C][C]0.922096786654368[/C][C]0.461048393327184[/C][/ROW]
[ROW][C]32[/C][C]0.471915566333427[/C][C]0.943831132666854[/C][C]0.528084433666573[/C][/ROW]
[ROW][C]33[/C][C]0.435182761241674[/C][C]0.870365522483348[/C][C]0.564817238758326[/C][/ROW]
[ROW][C]34[/C][C]0.386276816740525[/C][C]0.77255363348105[/C][C]0.613723183259475[/C][/ROW]
[ROW][C]35[/C][C]0.368461683749649[/C][C]0.736923367499299[/C][C]0.63153831625035[/C][/ROW]
[ROW][C]36[/C][C]0.347448243979981[/C][C]0.694896487959963[/C][C]0.652551756020019[/C][/ROW]
[ROW][C]37[/C][C]0.294793082312305[/C][C]0.58958616462461[/C][C]0.705206917687695[/C][/ROW]
[ROW][C]38[/C][C]0.70425083259922[/C][C]0.591498334801561[/C][C]0.29574916740078[/C][/ROW]
[ROW][C]39[/C][C]0.645999207238181[/C][C]0.708001585523638[/C][C]0.354000792761819[/C][/ROW]
[ROW][C]40[/C][C]0.610839831506623[/C][C]0.778320336986754[/C][C]0.389160168493377[/C][/ROW]
[ROW][C]41[/C][C]0.551687554020847[/C][C]0.896624891958305[/C][C]0.448312445979153[/C][/ROW]
[ROW][C]42[/C][C]0.500792648549002[/C][C]0.998414702901997[/C][C]0.499207351450998[/C][/ROW]
[ROW][C]43[/C][C]0.529448455751278[/C][C]0.941103088497445[/C][C]0.470551544248722[/C][/ROW]
[ROW][C]44[/C][C]0.489474654708273[/C][C]0.978949309416547[/C][C]0.510525345291727[/C][/ROW]
[ROW][C]45[/C][C]0.432660872182738[/C][C]0.865321744365475[/C][C]0.567339127817262[/C][/ROW]
[ROW][C]46[/C][C]0.374479009405916[/C][C]0.748958018811833[/C][C]0.625520990594084[/C][/ROW]
[ROW][C]47[/C][C]0.344827649989229[/C][C]0.689655299978458[/C][C]0.655172350010771[/C][/ROW]
[ROW][C]48[/C][C]0.297149887175172[/C][C]0.594299774350344[/C][C]0.702850112824828[/C][/ROW]
[ROW][C]49[/C][C]0.304463311367903[/C][C]0.608926622735805[/C][C]0.695536688632097[/C][/ROW]
[ROW][C]50[/C][C]0.268565813331084[/C][C]0.537131626662167[/C][C]0.731434186668916[/C][/ROW]
[ROW][C]51[/C][C]0.215397756616266[/C][C]0.430795513232532[/C][C]0.784602243383734[/C][/ROW]
[ROW][C]52[/C][C]0.194018150328873[/C][C]0.388036300657745[/C][C]0.805981849671128[/C][/ROW]
[ROW][C]53[/C][C]0.218018592290066[/C][C]0.436037184580132[/C][C]0.781981407709934[/C][/ROW]
[ROW][C]54[/C][C]0.19591996078175[/C][C]0.3918399215635[/C][C]0.80408003921825[/C][/ROW]
[ROW][C]55[/C][C]0.446292803365164[/C][C]0.892585606730328[/C][C]0.553707196634836[/C][/ROW]
[ROW][C]56[/C][C]0.382406087275362[/C][C]0.764812174550724[/C][C]0.617593912724638[/C][/ROW]
[ROW][C]57[/C][C]0.320276200049867[/C][C]0.640552400099733[/C][C]0.679723799950133[/C][/ROW]
[ROW][C]58[/C][C]0.390335849855347[/C][C]0.780671699710695[/C][C]0.609664150144653[/C][/ROW]
[ROW][C]59[/C][C]0.363846437267523[/C][C]0.727692874535046[/C][C]0.636153562732477[/C][/ROW]
[ROW][C]60[/C][C]0.302660715923734[/C][C]0.605321431847468[/C][C]0.697339284076266[/C][/ROW]
[ROW][C]61[/C][C]0.407157018559508[/C][C]0.814314037119017[/C][C]0.592842981440492[/C][/ROW]
[ROW][C]62[/C][C]0.330289191336623[/C][C]0.660578382673247[/C][C]0.669710808663377[/C][/ROW]
[ROW][C]63[/C][C]0.494918970872678[/C][C]0.989837941745356[/C][C]0.505081029127322[/C][/ROW]
[ROW][C]64[/C][C]0.41268384609042[/C][C]0.825367692180841[/C][C]0.58731615390958[/C][/ROW]
[ROW][C]65[/C][C]0.378181982879468[/C][C]0.756363965758935[/C][C]0.621818017120533[/C][/ROW]
[ROW][C]66[/C][C]0.556413690634252[/C][C]0.887172618731496[/C][C]0.443586309365748[/C][/ROW]
[ROW][C]67[/C][C]0.7201343905685[/C][C]0.559731218862999[/C][C]0.2798656094315[/C][/ROW]
[ROW][C]68[/C][C]0.702986595640433[/C][C]0.594026808719134[/C][C]0.297013404359567[/C][/ROW]
[ROW][C]69[/C][C]0.851917866096488[/C][C]0.296164267807025[/C][C]0.148082133903512[/C][/ROW]
[ROW][C]70[/C][C]0.959326737208658[/C][C]0.0813465255826845[/C][C]0.0406732627913422[/C][/ROW]
[ROW][C]71[/C][C]0.971715662064267[/C][C]0.0565686758714653[/C][C]0.0282843379357326[/C][/ROW]
[ROW][C]72[/C][C]0.992845175837987[/C][C]0.0143096483240265[/C][C]0.00715482416201324[/C][/ROW]
[ROW][C]73[/C][C]0.973886913044735[/C][C]0.0522261739105305[/C][C]0.0261130869552653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.761350764109840.4772984717803210.238649235890161
130.6209131835223860.7581736329552280.379086816477614
140.6458053394249220.7083893211501550.354194660575078
150.6293863381906720.7412273236186560.370613661809328
160.7506455801626110.4987088396747780.249354419837389
170.7394716751926750.521056649614650.260528324807325
180.7946176297831050.410764740433790.205382370216895
190.7693933187463760.4612133625072480.230606681253624
200.6938548528490480.6122902943019030.306145147150952
210.7402954872296330.5194090255407340.259704512770367
220.6636605511068140.6726788977863720.336339448893186
230.6494408211699820.7011183576600350.350559178830018
240.6215137068703840.7569725862592320.378486293129616
250.7512733829562150.4974532340875690.248726617043784
260.6843051924411320.6313896151177360.315694807558868
270.6767857762615470.6464284474769050.323214223738453
280.6241882682218730.7516234635562540.375811731778127
290.622507750465460.7549844990690810.37749224953454
300.5497877939594860.9004244120810290.450212206040514
310.5389516066728160.9220967866543680.461048393327184
320.4719155663334270.9438311326668540.528084433666573
330.4351827612416740.8703655224833480.564817238758326
340.3862768167405250.772553633481050.613723183259475
350.3684616837496490.7369233674992990.63153831625035
360.3474482439799810.6948964879599630.652551756020019
370.2947930823123050.589586164624610.705206917687695
380.704250832599220.5914983348015610.29574916740078
390.6459992072381810.7080015855236380.354000792761819
400.6108398315066230.7783203369867540.389160168493377
410.5516875540208470.8966248919583050.448312445979153
420.5007926485490020.9984147029019970.499207351450998
430.5294484557512780.9411030884974450.470551544248722
440.4894746547082730.9789493094165470.510525345291727
450.4326608721827380.8653217443654750.567339127817262
460.3744790094059160.7489580188118330.625520990594084
470.3448276499892290.6896552999784580.655172350010771
480.2971498871751720.5942997743503440.702850112824828
490.3044633113679030.6089266227358050.695536688632097
500.2685658133310840.5371316266621670.731434186668916
510.2153977566162660.4307955132325320.784602243383734
520.1940181503288730.3880363006577450.805981849671128
530.2180185922900660.4360371845801320.781981407709934
540.195919960781750.39183992156350.80408003921825
550.4462928033651640.8925856067303280.553707196634836
560.3824060872753620.7648121745507240.617593912724638
570.3202762000498670.6405524000997330.679723799950133
580.3903358498553470.7806716997106950.609664150144653
590.3638464372675230.7276928745350460.636153562732477
600.3026607159237340.6053214318474680.697339284076266
610.4071570185595080.8143140371190170.592842981440492
620.3302891913366230.6605783826732470.669710808663377
630.4949189708726780.9898379417453560.505081029127322
640.412683846090420.8253676921808410.58731615390958
650.3781819828794680.7563639657589350.621818017120533
660.5564136906342520.8871726187314960.443586309365748
670.72013439056850.5597312188629990.2798656094315
680.7029865956404330.5940268087191340.297013404359567
690.8519178660964880.2961642678070250.148082133903512
700.9593267372086580.08134652558268450.0406732627913422
710.9717156620642670.05656867587146530.0282843379357326
720.9928451758379870.01430964832402650.00715482416201324
730.9738869130447350.05222617391053050.0261130869552653






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0161290322580645OK
10% type I error level40.064516129032258OK

\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 & 1 & 0.0161290322580645 & OK \tabularnewline
10% type I error level & 4 & 0.064516129032258 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156388&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]1[/C][C]0.0161290322580645[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.064516129032258[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156388&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156388&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0161290322580645OK
10% type I error level40.064516129032258OK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}