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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 computationWed, 18 Nov 2009 05:18:41 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258546792kjxr91v06m32f9k.htm/, Retrieved Wed, 08 Oct 2025 22:24:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57439, Retrieved Wed, 08 Oct 2025 22:24:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact452

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-18 12:18:41] [830aa0f7fb5acd5849dbc0c6ad889830] [Current]
-    D        [Multiple Regression] [] [2009-12-15 15:08:14] [6ba840d2473f9a55d7b3e13093db69b8]
-    D          [Multiple Regression] [] [2009-12-21 09:27:51] [6ba840d2473f9a55d7b3e13093db69b8]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
128332	0	128332	133639	142773	149657
120297	0	120297	128332	133639	142773
118632	0	118632	120297	128332	133639
155276	0	155276	118632	120297	128332
169316	0	169316	155276	118632	120297
167395	0	167395	169316	155276	118632
157939	0	157939	167395	169316	155276
149601	0	149601	157939	167395	169316
146310	0	146310	149601	157939	167395
141579	0	141579	146310	149601	157939
136473	0	136473	141579	146310	149601
129818	0	129818	136473	141579	146310
124226	0	124226	129818	136473	141579
116428	0	116428	124226	129818	136473
116440	0	116440	116428	124226	129818
147747	0	147747	116440	116428	124226
160069	0	160069	147747	116440	116428
163129	0	163129	160069	147747	116440
151108	0	151108	163129	160069	147747
141481	0	141481	151108	163129	160069
139174	0	139174	141481	151108	163129
134066	0	134066	139174	141481	151108
130104	0	130104	134066	139174	141481
123090	0	123090	130104	134066	139174
116598	0	116598	123090	130104	134066
109627	0	109627	116598	123090	130104
105428	0	105428	109627	116598	123090
137272	0	137272	105428	109627	116598
159836	0	159836	137272	105428	109627
155283	0	155283	159836	137272	105428
141514	0	141514	155283	159836	137272
131852	0	131852	141514	155283	159836
130691	0	130691	131852	141514	155283
128461	0	128461	130691	131852	141514
123066	0	123066	128461	130691	131852
117599	0	117599	123066	128461	130691
111599	0	111599	117599	123066	128461
105395	0	105395	111599	117599	123066
102334	0	102334	105395	111599	117599
131305	0	131305	102334	105395	111599
149033	0	149033	131305	102334	105395
144954	0	144954	149033	131305	102334
132404	0	132404	144954	149033	131305
122104	0	122104	132404	144954	149033
118755	0	118755	122104	132404	144954
116222	1	116222	118755	122104	132404
110924	1	110924	116222	118755	122104
103753	1	103753	110924	116222	118755
99983	1	99983	103753	110924	116222
93302	1	93302	99983	103753	110924
91496	1	91496	93302	99983	103753
119321	1	119321	91496	93302	99983
139261	1	139261	119321	91496	93302
133739	1	133739	139261	119321	91496
123913	1	123913	133739	139261	119321
113438	1	113438	123913	133739	139261
109416	1	109416	113438	123913	133739
109406	1	109406	109416	113438	123913
105645	1	105645	109406	109416	113438
101328	1	101328	105645	109406	109416
97686	1	97686	101328	105645	109406
93093	1	93093	97686	101328	105645
91382	1	91382	93093	97686	101328
122257	1	122257	91382	93093	97686
139183	1	139183	122257	91382	93093
139887	1	139887	139183	122257	91382
131822	1	131822	139887	139183	122257
116805	1	116805	131822	139887	139183
113706	1	113706	116805	131822	139887
113012	1	113012	113706	116805	131822
110452	1	110452	113012	113706	116805
107005	1	107005	110452	113012	113706
102841	1	102841	107005	110452	113012
98173	1	98173	102841	107005	110452
98181	1	98181	98173	102841	107005
137277	1	137277	98181	98173	102841
147579	1	147579	137277	98181	98173
146571	1	146571	147579	137277	98181
138920	1	138920	146571	147579	137277
130340	1	130340	138920	146571	147579
128140	1	128140	130340	138920	146571
127059	1	127059	128140	130340	138920
122860	1	122860	127059	128140	130340
117702	1	117702	122860	127059	128140
113537	1	113537	117702	122860	127059
108366	1	108366	113537	117702	122860
111078	1	111078	108366	113537	117702
150739	1	150739	111078	108366	113537
159129	1	159129	150739	111078	108366
157928	1	157928	159129	150739	111078
147768	1	147768	157928	159129	150739
137507	1	137507	147768	157928	159129
136919	1	136919	137507	147768	157928

Tables (Output of Computation)





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=57439&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=57439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 4.98452767156078e-11 -3.01949805919351e-12X[t] + 1Y1[t] -2.49776164561884e-16Y2[t] + 4.41694428091111e-16Y3[t] -9.48346587427379e-17Y4[t] -1.30159690262083e-12M1[t] + 1.40216929886628e-12M2[t] + 1.79548647757585e-11M3[t] + 2.06012856708727e-12M4[t] + 9.60641643989368e-12M5[t] -2.83394656130401e-13M6[t] -3.55891399098231e-12M7[t] -4.09295096627986e-12M8[t] -1.36597948230551e-12M9[t] + 9.01314836999521e-14M10[t] + 4.51695872953091e-13M11[t] -1.2363638791727e-13t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  4.98452767156078e-11 -3.01949805919351e-12X[t] +  1Y1[t] -2.49776164561884e-16Y2[t] +  4.41694428091111e-16Y3[t] -9.48346587427379e-17Y4[t] -1.30159690262083e-12M1[t] +  1.40216929886628e-12M2[t] +  1.79548647757585e-11M3[t] +  2.06012856708727e-12M4[t] +  9.60641643989368e-12M5[t] -2.83394656130401e-13M6[t] -3.55891399098231e-12M7[t] -4.09295096627986e-12M8[t] -1.36597948230551e-12M9[t] +  9.01314836999521e-14M10[t] +  4.51695872953091e-13M11[t] -1.2363638791727e-13t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  4.98452767156078e-11 -3.01949805919351e-12X[t] +  1Y1[t] -2.49776164561884e-16Y2[t] +  4.41694428091111e-16Y3[t] -9.48346587427379e-17Y4[t] -1.30159690262083e-12M1[t] +  1.40216929886628e-12M2[t] +  1.79548647757585e-11M3[t] +  2.06012856708727e-12M4[t] +  9.60641643989368e-12M5[t] -2.83394656130401e-13M6[t] -3.55891399098231e-12M7[t] -4.09295096627986e-12M8[t] -1.36597948230551e-12M9[t] +  9.01314836999521e-14M10[t] +  4.51695872953091e-13M11[t] -1.2363638791727e-13t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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
Y[t] = + 4.98452767156078e-11 -3.01949805919351e-12X[t] + 1Y1[t] -2.49776164561884e-16Y2[t] + 4.41694428091111e-16Y3[t] -9.48346587427379e-17Y4[t] -1.30159690262083e-12M1[t] + 1.40216929886628e-12M2[t] + 1.79548647757585e-11M3[t] + 2.06012856708727e-12M4[t] + 9.60641643989368e-12M5[t] -2.83394656130401e-13M6[t] -3.55891399098231e-12M7[t] -4.09295096627986e-12M8[t] -1.36597948230551e-12M9[t] + 9.01314836999521e-14M10[t] + 4.51695872953091e-13M11[t] -1.2363638791727e-13t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.98452767156078e-1101.82350.0722090.036105
X-3.01949805919351e-120-0.46050.6464710.323236
Y110155481178003689200
Y2-2.49776164561884e-160-0.3250.7460710.373035
Y34.41694428091111e-1600.57370.5678560.283928
Y4-9.48346587427379e-170-0.14520.8849190.442459
M1-1.30159690262083e-120-0.17710.8599320.429966
M21.40216929886628e-1200.18430.8542720.427136
M31.79548647757585e-1102.22260.0292550.014627
M42.06012856708727e-1200.07730.9386010.469301
M59.60641643989368e-1200.29440.7692740.384637
M6-2.83394656130401e-130-0.00920.9926850.496343
M7-3.55891399098231e-120-0.25980.7957660.397883
M8-4.09295096627986e-120-0.39650.692850.346425
M9-1.36597948230551e-120-0.12750.8989020.449451
M109.01314836999521e-1400.00990.9920940.496047
M114.51695872953091e-1300.060.9523350.476167
t-1.2363638791727e-130-1.12470.2643220.132161

\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) & 4.98452767156078e-11 & 0 & 1.8235 & 0.072209 & 0.036105 \tabularnewline
X & -3.01949805919351e-12 & 0 & -0.4605 & 0.646471 & 0.323236 \tabularnewline
Y1 & 1 & 0 & 1554811780036892 & 0 & 0 \tabularnewline
Y2 & -2.49776164561884e-16 & 0 & -0.325 & 0.746071 & 0.373035 \tabularnewline
Y3 & 4.41694428091111e-16 & 0 & 0.5737 & 0.567856 & 0.283928 \tabularnewline
Y4 & -9.48346587427379e-17 & 0 & -0.1452 & 0.884919 & 0.442459 \tabularnewline
M1 & -1.30159690262083e-12 & 0 & -0.1771 & 0.859932 & 0.429966 \tabularnewline
M2 & 1.40216929886628e-12 & 0 & 0.1843 & 0.854272 & 0.427136 \tabularnewline
M3 & 1.79548647757585e-11 & 0 & 2.2226 & 0.029255 & 0.014627 \tabularnewline
M4 & 2.06012856708727e-12 & 0 & 0.0773 & 0.938601 & 0.469301 \tabularnewline
M5 & 9.60641643989368e-12 & 0 & 0.2944 & 0.769274 & 0.384637 \tabularnewline
M6 & -2.83394656130401e-13 & 0 & -0.0092 & 0.992685 & 0.496343 \tabularnewline
M7 & -3.55891399098231e-12 & 0 & -0.2598 & 0.795766 & 0.397883 \tabularnewline
M8 & -4.09295096627986e-12 & 0 & -0.3965 & 0.69285 & 0.346425 \tabularnewline
M9 & -1.36597948230551e-12 & 0 & -0.1275 & 0.898902 & 0.449451 \tabularnewline
M10 & 9.01314836999521e-14 & 0 & 0.0099 & 0.992094 & 0.496047 \tabularnewline
M11 & 4.51695872953091e-13 & 0 & 0.06 & 0.952335 & 0.476167 \tabularnewline
t & -1.2363638791727e-13 & 0 & -1.1247 & 0.264322 & 0.132161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&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]4.98452767156078e-11[/C][C]0[/C][C]1.8235[/C][C]0.072209[/C][C]0.036105[/C][/ROW]
[ROW][C]X[/C][C]-3.01949805919351e-12[/C][C]0[/C][C]-0.4605[/C][C]0.646471[/C][C]0.323236[/C][/ROW]
[ROW][C]Y1[/C][C]1[/C][C]0[/C][C]1554811780036892[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-2.49776164561884e-16[/C][C]0[/C][C]-0.325[/C][C]0.746071[/C][C]0.373035[/C][/ROW]
[ROW][C]Y3[/C][C]4.41694428091111e-16[/C][C]0[/C][C]0.5737[/C][C]0.567856[/C][C]0.283928[/C][/ROW]
[ROW][C]Y4[/C][C]-9.48346587427379e-17[/C][C]0[/C][C]-0.1452[/C][C]0.884919[/C][C]0.442459[/C][/ROW]
[ROW][C]M1[/C][C]-1.30159690262083e-12[/C][C]0[/C][C]-0.1771[/C][C]0.859932[/C][C]0.429966[/C][/ROW]
[ROW][C]M2[/C][C]1.40216929886628e-12[/C][C]0[/C][C]0.1843[/C][C]0.854272[/C][C]0.427136[/C][/ROW]
[ROW][C]M3[/C][C]1.79548647757585e-11[/C][C]0[/C][C]2.2226[/C][C]0.029255[/C][C]0.014627[/C][/ROW]
[ROW][C]M4[/C][C]2.06012856708727e-12[/C][C]0[/C][C]0.0773[/C][C]0.938601[/C][C]0.469301[/C][/ROW]
[ROW][C]M5[/C][C]9.60641643989368e-12[/C][C]0[/C][C]0.2944[/C][C]0.769274[/C][C]0.384637[/C][/ROW]
[ROW][C]M6[/C][C]-2.83394656130401e-13[/C][C]0[/C][C]-0.0092[/C][C]0.992685[/C][C]0.496343[/C][/ROW]
[ROW][C]M7[/C][C]-3.55891399098231e-12[/C][C]0[/C][C]-0.2598[/C][C]0.795766[/C][C]0.397883[/C][/ROW]
[ROW][C]M8[/C][C]-4.09295096627986e-12[/C][C]0[/C][C]-0.3965[/C][C]0.69285[/C][C]0.346425[/C][/ROW]
[ROW][C]M9[/C][C]-1.36597948230551e-12[/C][C]0[/C][C]-0.1275[/C][C]0.898902[/C][C]0.449451[/C][/ROW]
[ROW][C]M10[/C][C]9.01314836999521e-14[/C][C]0[/C][C]0.0099[/C][C]0.992094[/C][C]0.496047[/C][/ROW]
[ROW][C]M11[/C][C]4.51695872953091e-13[/C][C]0[/C][C]0.06[/C][C]0.952335[/C][C]0.476167[/C][/ROW]
[ROW][C]t[/C][C]-1.2363638791727e-13[/C][C]0[/C][C]-1.1247[/C][C]0.264322[/C][C]0.132161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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)4.98452767156078e-1101.82350.0722090.036105
X-3.01949805919351e-120-0.46050.6464710.323236
Y110155481178003689200
Y2-2.49776164561884e-160-0.3250.7460710.373035
Y34.41694428091111e-1600.57370.5678560.283928
Y4-9.48346587427379e-170-0.14520.8849190.442459
M1-1.30159690262083e-120-0.17710.8599320.429966
M21.40216929886628e-1200.18430.8542720.427136
M31.79548647757585e-1102.22260.0292550.014627
M42.06012856708727e-1200.07730.9386010.469301
M59.60641643989368e-1200.29440.7692740.384637
M6-2.83394656130401e-130-0.00920.9926850.496343
M7-3.55891399098231e-120-0.25980.7957660.397883
M8-4.09295096627986e-120-0.39650.692850.346425
M9-1.36597948230551e-120-0.12750.8989020.449451
M109.01314836999521e-1400.00990.9920940.496047
M114.51695872953091e-1300.060.9523350.476167
t-1.2363638791727e-130-1.12470.2643220.132161






Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)1.07681114449627e+31
F-TEST (DF numerator)17
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.37158072232275e-11
Sum Squared Residuals1.41092525838555e-20

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 1.07681114449627e+31 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.37158072232275e-11 \tabularnewline
Sum Squared Residuals & 1.41092525838555e-20 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.07681114449627e+31[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.37158072232275e-11[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.41092525838555e-20[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)1.07681114449627e+31
F-TEST (DF numerator)17
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.37158072232275e-11
Sum Squared Residuals1.41092525838555e-20






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1128332128332-1.60859737146707e-11
2120297120297-1.37605820135475e-11
31186321186321.06887855028353e-10
4155276155276-6.39747438977671e-12
5169316169316-1.27614768315605e-12
6167395167395-2.57578003771998e-12
7157939157939-5.7977452444128e-12
8149601149601-5.98564426427101e-12
9146310146310-4.06726185639289e-12
10141579141579-3.32180587718908e-12
11136473136473-4.12204823905010e-12
12129818129818-3.22386245934479e-12
13124226124226-4.02860951884293e-13
14116428116428-3.01638790816659e-12
15116440116440-1.95638873161271e-11
16147747147747-1.80906855039255e-12
17160069160069-4.65257280451476e-12
18163129163129-4.17706985763493e-12
19151108151108-2.30097425246072e-13
20141481141481-2.39946084247326e-12
21139174139174-2.70654917890992e-12
22134066134066-8.62873757445712e-13
23130104130104-2.0845103637448e-12
24123090123090-3.56046308960268e-13
251165981165982.74225885243622e-13
261096271096272.50409084985783e-12
27105428105428-1.74098671074561e-11
281372721372722.35643336074059e-13
291598361598362.25638215636864e-12
30155283155283-2.23035155941677e-13
31141514141514-1.26429806042146e-12
321318521318523.6654787457858e-14
331306911306916.81105414234753e-13
341284611284612.42473161911677e-12
351230661230669.57247921663556e-13
361175991175991.75743665905940e-12
371115991115995.10881284363905e-12
381053951053954.21512391893714e-12
39102334102334-1.64803297891148e-11
401313051313052.52618919879434e-12
411490331490338.68047510823835e-13
421449541449543.63869397134802e-12
431324041324041.68001784400251e-12
441221041221043.69185243038706e-12
451187551187554.50912978260426e-12
46116222116222-1.65689345689226e-12
47110924110924-1.46156577083215e-12
48103753103753-2.99746702698748e-12
4999983999831.72623030444815e-12
5093302933022.52966442754244e-12
519149691496-1.67887848273893e-11
52119321119321-3.16582642679193e-15
53139261139261-4.35946407096588e-13
541337391337391.01876960971952e-12
55123913123913-1.70408845428109e-12
561134381134389.5087466848184e-13
571094161094169.18129226852446e-13
581094061094064.64899601869136e-13
591056451056452.17117712057014e-12
601013281013281.21563051873929e-12
6197686976862.47308571747363e-12
6293093930934.17578560034035e-12
639138291382-1.08443689608071e-11
641222571222571.04449843168007e-12
65139183139183-1.76455657695313e-12
661398871398871.75670946687663e-12
671318221318221.72243708894639e-12
681168051168051.74540425623926e-12
69113706113706-3.41175723763958e-13
701130121130122.15175139159425e-12
711104521104522.80582313861563e-12
721070051070051.84997185213298e-12
731028411028413.1433527415934e-12
7498173981738.6679028020584e-13
759818198181-1.15842688584235e-11
761372771372772.38352909197222e-12
771475791475791.56706447987388e-12
781465711465711.32333369233933e-12
791389201389202.41955977721843e-12
801303401303402.11666134347049e-12
81128140128140-3.29757741046734e-13
821270591270598.00190478946894e-13
831228601228601.73387619277773e-12
841177021177021.75433676536073e-12
851135371135373.76312717415715e-12
861083661083662.48551484483051e-12
87111078111078-1.42163481690352e-11
881507391507392.01984870807538e-12
891591291591293.43772932465418e-12
90157928157928-7.61621688986869e-13
911477681477683.17421447419409e-12
92137507137507-1.56342379292213e-13
931369191369191.33638007642201e-12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 128332 & 128332 & -1.60859737146707e-11 \tabularnewline
2 & 120297 & 120297 & -1.37605820135475e-11 \tabularnewline
3 & 118632 & 118632 & 1.06887855028353e-10 \tabularnewline
4 & 155276 & 155276 & -6.39747438977671e-12 \tabularnewline
5 & 169316 & 169316 & -1.27614768315605e-12 \tabularnewline
6 & 167395 & 167395 & -2.57578003771998e-12 \tabularnewline
7 & 157939 & 157939 & -5.7977452444128e-12 \tabularnewline
8 & 149601 & 149601 & -5.98564426427101e-12 \tabularnewline
9 & 146310 & 146310 & -4.06726185639289e-12 \tabularnewline
10 & 141579 & 141579 & -3.32180587718908e-12 \tabularnewline
11 & 136473 & 136473 & -4.12204823905010e-12 \tabularnewline
12 & 129818 & 129818 & -3.22386245934479e-12 \tabularnewline
13 & 124226 & 124226 & -4.02860951884293e-13 \tabularnewline
14 & 116428 & 116428 & -3.01638790816659e-12 \tabularnewline
15 & 116440 & 116440 & -1.95638873161271e-11 \tabularnewline
16 & 147747 & 147747 & -1.80906855039255e-12 \tabularnewline
17 & 160069 & 160069 & -4.65257280451476e-12 \tabularnewline
18 & 163129 & 163129 & -4.17706985763493e-12 \tabularnewline
19 & 151108 & 151108 & -2.30097425246072e-13 \tabularnewline
20 & 141481 & 141481 & -2.39946084247326e-12 \tabularnewline
21 & 139174 & 139174 & -2.70654917890992e-12 \tabularnewline
22 & 134066 & 134066 & -8.62873757445712e-13 \tabularnewline
23 & 130104 & 130104 & -2.0845103637448e-12 \tabularnewline
24 & 123090 & 123090 & -3.56046308960268e-13 \tabularnewline
25 & 116598 & 116598 & 2.74225885243622e-13 \tabularnewline
26 & 109627 & 109627 & 2.50409084985783e-12 \tabularnewline
27 & 105428 & 105428 & -1.74098671074561e-11 \tabularnewline
28 & 137272 & 137272 & 2.35643336074059e-13 \tabularnewline
29 & 159836 & 159836 & 2.25638215636864e-12 \tabularnewline
30 & 155283 & 155283 & -2.23035155941677e-13 \tabularnewline
31 & 141514 & 141514 & -1.26429806042146e-12 \tabularnewline
32 & 131852 & 131852 & 3.6654787457858e-14 \tabularnewline
33 & 130691 & 130691 & 6.81105414234753e-13 \tabularnewline
34 & 128461 & 128461 & 2.42473161911677e-12 \tabularnewline
35 & 123066 & 123066 & 9.57247921663556e-13 \tabularnewline
36 & 117599 & 117599 & 1.75743665905940e-12 \tabularnewline
37 & 111599 & 111599 & 5.10881284363905e-12 \tabularnewline
38 & 105395 & 105395 & 4.21512391893714e-12 \tabularnewline
39 & 102334 & 102334 & -1.64803297891148e-11 \tabularnewline
40 & 131305 & 131305 & 2.52618919879434e-12 \tabularnewline
41 & 149033 & 149033 & 8.68047510823835e-13 \tabularnewline
42 & 144954 & 144954 & 3.63869397134802e-12 \tabularnewline
43 & 132404 & 132404 & 1.68001784400251e-12 \tabularnewline
44 & 122104 & 122104 & 3.69185243038706e-12 \tabularnewline
45 & 118755 & 118755 & 4.50912978260426e-12 \tabularnewline
46 & 116222 & 116222 & -1.65689345689226e-12 \tabularnewline
47 & 110924 & 110924 & -1.46156577083215e-12 \tabularnewline
48 & 103753 & 103753 & -2.99746702698748e-12 \tabularnewline
49 & 99983 & 99983 & 1.72623030444815e-12 \tabularnewline
50 & 93302 & 93302 & 2.52966442754244e-12 \tabularnewline
51 & 91496 & 91496 & -1.67887848273893e-11 \tabularnewline
52 & 119321 & 119321 & -3.16582642679193e-15 \tabularnewline
53 & 139261 & 139261 & -4.35946407096588e-13 \tabularnewline
54 & 133739 & 133739 & 1.01876960971952e-12 \tabularnewline
55 & 123913 & 123913 & -1.70408845428109e-12 \tabularnewline
56 & 113438 & 113438 & 9.5087466848184e-13 \tabularnewline
57 & 109416 & 109416 & 9.18129226852446e-13 \tabularnewline
58 & 109406 & 109406 & 4.64899601869136e-13 \tabularnewline
59 & 105645 & 105645 & 2.17117712057014e-12 \tabularnewline
60 & 101328 & 101328 & 1.21563051873929e-12 \tabularnewline
61 & 97686 & 97686 & 2.47308571747363e-12 \tabularnewline
62 & 93093 & 93093 & 4.17578560034035e-12 \tabularnewline
63 & 91382 & 91382 & -1.08443689608071e-11 \tabularnewline
64 & 122257 & 122257 & 1.04449843168007e-12 \tabularnewline
65 & 139183 & 139183 & -1.76455657695313e-12 \tabularnewline
66 & 139887 & 139887 & 1.75670946687663e-12 \tabularnewline
67 & 131822 & 131822 & 1.72243708894639e-12 \tabularnewline
68 & 116805 & 116805 & 1.74540425623926e-12 \tabularnewline
69 & 113706 & 113706 & -3.41175723763958e-13 \tabularnewline
70 & 113012 & 113012 & 2.15175139159425e-12 \tabularnewline
71 & 110452 & 110452 & 2.80582313861563e-12 \tabularnewline
72 & 107005 & 107005 & 1.84997185213298e-12 \tabularnewline
73 & 102841 & 102841 & 3.1433527415934e-12 \tabularnewline
74 & 98173 & 98173 & 8.6679028020584e-13 \tabularnewline
75 & 98181 & 98181 & -1.15842688584235e-11 \tabularnewline
76 & 137277 & 137277 & 2.38352909197222e-12 \tabularnewline
77 & 147579 & 147579 & 1.56706447987388e-12 \tabularnewline
78 & 146571 & 146571 & 1.32333369233933e-12 \tabularnewline
79 & 138920 & 138920 & 2.41955977721843e-12 \tabularnewline
80 & 130340 & 130340 & 2.11666134347049e-12 \tabularnewline
81 & 128140 & 128140 & -3.29757741046734e-13 \tabularnewline
82 & 127059 & 127059 & 8.00190478946894e-13 \tabularnewline
83 & 122860 & 122860 & 1.73387619277773e-12 \tabularnewline
84 & 117702 & 117702 & 1.75433676536073e-12 \tabularnewline
85 & 113537 & 113537 & 3.76312717415715e-12 \tabularnewline
86 & 108366 & 108366 & 2.48551484483051e-12 \tabularnewline
87 & 111078 & 111078 & -1.42163481690352e-11 \tabularnewline
88 & 150739 & 150739 & 2.01984870807538e-12 \tabularnewline
89 & 159129 & 159129 & 3.43772932465418e-12 \tabularnewline
90 & 157928 & 157928 & -7.61621688986869e-13 \tabularnewline
91 & 147768 & 147768 & 3.17421447419409e-12 \tabularnewline
92 & 137507 & 137507 & -1.56342379292213e-13 \tabularnewline
93 & 136919 & 136919 & 1.33638007642201e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&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]128332[/C][C]128332[/C][C]-1.60859737146707e-11[/C][/ROW]
[ROW][C]2[/C][C]120297[/C][C]120297[/C][C]-1.37605820135475e-11[/C][/ROW]
[ROW][C]3[/C][C]118632[/C][C]118632[/C][C]1.06887855028353e-10[/C][/ROW]
[ROW][C]4[/C][C]155276[/C][C]155276[/C][C]-6.39747438977671e-12[/C][/ROW]
[ROW][C]5[/C][C]169316[/C][C]169316[/C][C]-1.27614768315605e-12[/C][/ROW]
[ROW][C]6[/C][C]167395[/C][C]167395[/C][C]-2.57578003771998e-12[/C][/ROW]
[ROW][C]7[/C][C]157939[/C][C]157939[/C][C]-5.7977452444128e-12[/C][/ROW]
[ROW][C]8[/C][C]149601[/C][C]149601[/C][C]-5.98564426427101e-12[/C][/ROW]
[ROW][C]9[/C][C]146310[/C][C]146310[/C][C]-4.06726185639289e-12[/C][/ROW]
[ROW][C]10[/C][C]141579[/C][C]141579[/C][C]-3.32180587718908e-12[/C][/ROW]
[ROW][C]11[/C][C]136473[/C][C]136473[/C][C]-4.12204823905010e-12[/C][/ROW]
[ROW][C]12[/C][C]129818[/C][C]129818[/C][C]-3.22386245934479e-12[/C][/ROW]
[ROW][C]13[/C][C]124226[/C][C]124226[/C][C]-4.02860951884293e-13[/C][/ROW]
[ROW][C]14[/C][C]116428[/C][C]116428[/C][C]-3.01638790816659e-12[/C][/ROW]
[ROW][C]15[/C][C]116440[/C][C]116440[/C][C]-1.95638873161271e-11[/C][/ROW]
[ROW][C]16[/C][C]147747[/C][C]147747[/C][C]-1.80906855039255e-12[/C][/ROW]
[ROW][C]17[/C][C]160069[/C][C]160069[/C][C]-4.65257280451476e-12[/C][/ROW]
[ROW][C]18[/C][C]163129[/C][C]163129[/C][C]-4.17706985763493e-12[/C][/ROW]
[ROW][C]19[/C][C]151108[/C][C]151108[/C][C]-2.30097425246072e-13[/C][/ROW]
[ROW][C]20[/C][C]141481[/C][C]141481[/C][C]-2.39946084247326e-12[/C][/ROW]
[ROW][C]21[/C][C]139174[/C][C]139174[/C][C]-2.70654917890992e-12[/C][/ROW]
[ROW][C]22[/C][C]134066[/C][C]134066[/C][C]-8.62873757445712e-13[/C][/ROW]
[ROW][C]23[/C][C]130104[/C][C]130104[/C][C]-2.0845103637448e-12[/C][/ROW]
[ROW][C]24[/C][C]123090[/C][C]123090[/C][C]-3.56046308960268e-13[/C][/ROW]
[ROW][C]25[/C][C]116598[/C][C]116598[/C][C]2.74225885243622e-13[/C][/ROW]
[ROW][C]26[/C][C]109627[/C][C]109627[/C][C]2.50409084985783e-12[/C][/ROW]
[ROW][C]27[/C][C]105428[/C][C]105428[/C][C]-1.74098671074561e-11[/C][/ROW]
[ROW][C]28[/C][C]137272[/C][C]137272[/C][C]2.35643336074059e-13[/C][/ROW]
[ROW][C]29[/C][C]159836[/C][C]159836[/C][C]2.25638215636864e-12[/C][/ROW]
[ROW][C]30[/C][C]155283[/C][C]155283[/C][C]-2.23035155941677e-13[/C][/ROW]
[ROW][C]31[/C][C]141514[/C][C]141514[/C][C]-1.26429806042146e-12[/C][/ROW]
[ROW][C]32[/C][C]131852[/C][C]131852[/C][C]3.6654787457858e-14[/C][/ROW]
[ROW][C]33[/C][C]130691[/C][C]130691[/C][C]6.81105414234753e-13[/C][/ROW]
[ROW][C]34[/C][C]128461[/C][C]128461[/C][C]2.42473161911677e-12[/C][/ROW]
[ROW][C]35[/C][C]123066[/C][C]123066[/C][C]9.57247921663556e-13[/C][/ROW]
[ROW][C]36[/C][C]117599[/C][C]117599[/C][C]1.75743665905940e-12[/C][/ROW]
[ROW][C]37[/C][C]111599[/C][C]111599[/C][C]5.10881284363905e-12[/C][/ROW]
[ROW][C]38[/C][C]105395[/C][C]105395[/C][C]4.21512391893714e-12[/C][/ROW]
[ROW][C]39[/C][C]102334[/C][C]102334[/C][C]-1.64803297891148e-11[/C][/ROW]
[ROW][C]40[/C][C]131305[/C][C]131305[/C][C]2.52618919879434e-12[/C][/ROW]
[ROW][C]41[/C][C]149033[/C][C]149033[/C][C]8.68047510823835e-13[/C][/ROW]
[ROW][C]42[/C][C]144954[/C][C]144954[/C][C]3.63869397134802e-12[/C][/ROW]
[ROW][C]43[/C][C]132404[/C][C]132404[/C][C]1.68001784400251e-12[/C][/ROW]
[ROW][C]44[/C][C]122104[/C][C]122104[/C][C]3.69185243038706e-12[/C][/ROW]
[ROW][C]45[/C][C]118755[/C][C]118755[/C][C]4.50912978260426e-12[/C][/ROW]
[ROW][C]46[/C][C]116222[/C][C]116222[/C][C]-1.65689345689226e-12[/C][/ROW]
[ROW][C]47[/C][C]110924[/C][C]110924[/C][C]-1.46156577083215e-12[/C][/ROW]
[ROW][C]48[/C][C]103753[/C][C]103753[/C][C]-2.99746702698748e-12[/C][/ROW]
[ROW][C]49[/C][C]99983[/C][C]99983[/C][C]1.72623030444815e-12[/C][/ROW]
[ROW][C]50[/C][C]93302[/C][C]93302[/C][C]2.52966442754244e-12[/C][/ROW]
[ROW][C]51[/C][C]91496[/C][C]91496[/C][C]-1.67887848273893e-11[/C][/ROW]
[ROW][C]52[/C][C]119321[/C][C]119321[/C][C]-3.16582642679193e-15[/C][/ROW]
[ROW][C]53[/C][C]139261[/C][C]139261[/C][C]-4.35946407096588e-13[/C][/ROW]
[ROW][C]54[/C][C]133739[/C][C]133739[/C][C]1.01876960971952e-12[/C][/ROW]
[ROW][C]55[/C][C]123913[/C][C]123913[/C][C]-1.70408845428109e-12[/C][/ROW]
[ROW][C]56[/C][C]113438[/C][C]113438[/C][C]9.5087466848184e-13[/C][/ROW]
[ROW][C]57[/C][C]109416[/C][C]109416[/C][C]9.18129226852446e-13[/C][/ROW]
[ROW][C]58[/C][C]109406[/C][C]109406[/C][C]4.64899601869136e-13[/C][/ROW]
[ROW][C]59[/C][C]105645[/C][C]105645[/C][C]2.17117712057014e-12[/C][/ROW]
[ROW][C]60[/C][C]101328[/C][C]101328[/C][C]1.21563051873929e-12[/C][/ROW]
[ROW][C]61[/C][C]97686[/C][C]97686[/C][C]2.47308571747363e-12[/C][/ROW]
[ROW][C]62[/C][C]93093[/C][C]93093[/C][C]4.17578560034035e-12[/C][/ROW]
[ROW][C]63[/C][C]91382[/C][C]91382[/C][C]-1.08443689608071e-11[/C][/ROW]
[ROW][C]64[/C][C]122257[/C][C]122257[/C][C]1.04449843168007e-12[/C][/ROW]
[ROW][C]65[/C][C]139183[/C][C]139183[/C][C]-1.76455657695313e-12[/C][/ROW]
[ROW][C]66[/C][C]139887[/C][C]139887[/C][C]1.75670946687663e-12[/C][/ROW]
[ROW][C]67[/C][C]131822[/C][C]131822[/C][C]1.72243708894639e-12[/C][/ROW]
[ROW][C]68[/C][C]116805[/C][C]116805[/C][C]1.74540425623926e-12[/C][/ROW]
[ROW][C]69[/C][C]113706[/C][C]113706[/C][C]-3.41175723763958e-13[/C][/ROW]
[ROW][C]70[/C][C]113012[/C][C]113012[/C][C]2.15175139159425e-12[/C][/ROW]
[ROW][C]71[/C][C]110452[/C][C]110452[/C][C]2.80582313861563e-12[/C][/ROW]
[ROW][C]72[/C][C]107005[/C][C]107005[/C][C]1.84997185213298e-12[/C][/ROW]
[ROW][C]73[/C][C]102841[/C][C]102841[/C][C]3.1433527415934e-12[/C][/ROW]
[ROW][C]74[/C][C]98173[/C][C]98173[/C][C]8.6679028020584e-13[/C][/ROW]
[ROW][C]75[/C][C]98181[/C][C]98181[/C][C]-1.15842688584235e-11[/C][/ROW]
[ROW][C]76[/C][C]137277[/C][C]137277[/C][C]2.38352909197222e-12[/C][/ROW]
[ROW][C]77[/C][C]147579[/C][C]147579[/C][C]1.56706447987388e-12[/C][/ROW]
[ROW][C]78[/C][C]146571[/C][C]146571[/C][C]1.32333369233933e-12[/C][/ROW]
[ROW][C]79[/C][C]138920[/C][C]138920[/C][C]2.41955977721843e-12[/C][/ROW]
[ROW][C]80[/C][C]130340[/C][C]130340[/C][C]2.11666134347049e-12[/C][/ROW]
[ROW][C]81[/C][C]128140[/C][C]128140[/C][C]-3.29757741046734e-13[/C][/ROW]
[ROW][C]82[/C][C]127059[/C][C]127059[/C][C]8.00190478946894e-13[/C][/ROW]
[ROW][C]83[/C][C]122860[/C][C]122860[/C][C]1.73387619277773e-12[/C][/ROW]
[ROW][C]84[/C][C]117702[/C][C]117702[/C][C]1.75433676536073e-12[/C][/ROW]
[ROW][C]85[/C][C]113537[/C][C]113537[/C][C]3.76312717415715e-12[/C][/ROW]
[ROW][C]86[/C][C]108366[/C][C]108366[/C][C]2.48551484483051e-12[/C][/ROW]
[ROW][C]87[/C][C]111078[/C][C]111078[/C][C]-1.42163481690352e-11[/C][/ROW]
[ROW][C]88[/C][C]150739[/C][C]150739[/C][C]2.01984870807538e-12[/C][/ROW]
[ROW][C]89[/C][C]159129[/C][C]159129[/C][C]3.43772932465418e-12[/C][/ROW]
[ROW][C]90[/C][C]157928[/C][C]157928[/C][C]-7.61621688986869e-13[/C][/ROW]
[ROW][C]91[/C][C]147768[/C][C]147768[/C][C]3.17421447419409e-12[/C][/ROW]
[ROW][C]92[/C][C]137507[/C][C]137507[/C][C]-1.56342379292213e-13[/C][/ROW]
[ROW][C]93[/C][C]136919[/C][C]136919[/C][C]1.33638007642201e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1128332128332-1.60859737146707e-11
2120297120297-1.37605820135475e-11
31186321186321.06887855028353e-10
4155276155276-6.39747438977671e-12
5169316169316-1.27614768315605e-12
6167395167395-2.57578003771998e-12
7157939157939-5.7977452444128e-12
8149601149601-5.98564426427101e-12
9146310146310-4.06726185639289e-12
10141579141579-3.32180587718908e-12
11136473136473-4.12204823905010e-12
12129818129818-3.22386245934479e-12
13124226124226-4.02860951884293e-13
14116428116428-3.01638790816659e-12
15116440116440-1.95638873161271e-11
16147747147747-1.80906855039255e-12
17160069160069-4.65257280451476e-12
18163129163129-4.17706985763493e-12
19151108151108-2.30097425246072e-13
20141481141481-2.39946084247326e-12
21139174139174-2.70654917890992e-12
22134066134066-8.62873757445712e-13
23130104130104-2.0845103637448e-12
24123090123090-3.56046308960268e-13
251165981165982.74225885243622e-13
261096271096272.50409084985783e-12
27105428105428-1.74098671074561e-11
281372721372722.35643336074059e-13
291598361598362.25638215636864e-12
30155283155283-2.23035155941677e-13
31141514141514-1.26429806042146e-12
321318521318523.6654787457858e-14
331306911306916.81105414234753e-13
341284611284612.42473161911677e-12
351230661230669.57247921663556e-13
361175991175991.75743665905940e-12
371115991115995.10881284363905e-12
381053951053954.21512391893714e-12
39102334102334-1.64803297891148e-11
401313051313052.52618919879434e-12
411490331490338.68047510823835e-13
421449541449543.63869397134802e-12
431324041324041.68001784400251e-12
441221041221043.69185243038706e-12
451187551187554.50912978260426e-12
46116222116222-1.65689345689226e-12
47110924110924-1.46156577083215e-12
48103753103753-2.99746702698748e-12
4999983999831.72623030444815e-12
5093302933022.52966442754244e-12
519149691496-1.67887848273893e-11
52119321119321-3.16582642679193e-15
53139261139261-4.35946407096588e-13
541337391337391.01876960971952e-12
55123913123913-1.70408845428109e-12
561134381134389.5087466848184e-13
571094161094169.18129226852446e-13
581094061094064.64899601869136e-13
591056451056452.17117712057014e-12
601013281013281.21563051873929e-12
6197686976862.47308571747363e-12
6293093930934.17578560034035e-12
639138291382-1.08443689608071e-11
641222571222571.04449843168007e-12
65139183139183-1.76455657695313e-12
661398871398871.75670946687663e-12
671318221318221.72243708894639e-12
681168051168051.74540425623926e-12
69113706113706-3.41175723763958e-13
701130121130122.15175139159425e-12
711104521104522.80582313861563e-12
721070051070051.84997185213298e-12
731028411028413.1433527415934e-12
7498173981738.6679028020584e-13
759818198181-1.15842688584235e-11
761372771372772.38352909197222e-12
771475791475791.56706447987388e-12
781465711465711.32333369233933e-12
791389201389202.41955977721843e-12
801303401303402.11666134347049e-12
81128140128140-3.29757741046734e-13
821270591270598.00190478946894e-13
831228601228601.73387619277773e-12
841177021177021.75433676536073e-12
851135371135373.76312717415715e-12
861083661083662.48551484483051e-12
87111078111078-1.42163481690352e-11
881507391507392.01984870807538e-12
891591291591293.43772932465418e-12
90157928157928-7.61621688986869e-13
911477681477683.17421447419409e-12
92137507137507-1.56342379292213e-13
931369191369191.33638007642201e-12






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1988130194646880.3976260389293770.801186980535312
220.5569831763835730.8860336472328530.443016823616427
2312.96667837642357e-281.48333918821178e-28
2419.09709128361718e-184.54854564180859e-18
251.9203811291999e-083.8407622583998e-080.999999980796189
260.9372757777911330.1254484444177340.062724222208867
270.8945498433580470.2109003132839060.105450156641953
282.01693023487021e-064.03386046974041e-060.999997983069765
290.2830618363956030.5661236727912060.716938163604397
300.9999999707010535.85978935082875e-082.92989467541437e-08
318.09111965235602e-050.0001618223930471200.999919088803476
328.77490971345086e-101.75498194269017e-090.99999999912251
330.9860908286192790.02781834276144220.0139091713807211
340.1699967183970850.3399934367941690.830003281602915
358.54309734355417e-081.70861946871083e-070.999999914569027
360.0001877045244384210.0003754090488768420.999812295475561
370.8946326638164370.2107346723671270.105367336183563
380.9999999997996034.00794642345337e-102.00397321172669e-10
391.10964321438754e-072.21928642877509e-070.999999889035678
400.9999998979684012.04063196955338e-071.02031598477669e-07
410.9999999999585898.28228475062736e-114.14114237531368e-11
420.0001393905836156850.000278781167231370.999860609416384
431.75923103581347e-133.51846207162694e-130.999999999999824
440.9999476643434190.0001046713131629685.23356565814842e-05
450.08975782211747060.1795156442349410.91024217788253
460.99979193975310.0004161204937992810.000208060246899640
470.6154995469678270.7690009060643460.384500453032173
483.63203519713349e-067.26407039426698e-060.999996367964803
490.8145305660022830.3709388679954330.185469433997716
500.4198511576140050.8397023152280110.580148842385995
510.9956249760536330.008750047892734780.00437502394636739
520.942956829230160.1140863415396820.057043170769841
530.04801434891424170.09602869782848340.951985651085758
540.1825628667138790.3651257334277580.817437133286121
550.7974155487328760.4051689025342470.202584451267124
560.9984288787643640.003142242471271970.00157112123563599
570.0979768731745330.1959537463490660.902023126825467
5813.23387340556884e-171.61693670278442e-17
590.08740843160031570.1748168632006310.912591568399684
600.9997123280898470.0005753438203062580.000287671910153129
610.2271193917110090.4542387834220180.772880608288991
620.9999930849540971.38300918056259e-056.91504590281293e-06
630.9999980858824133.82823517489459e-061.91411758744729e-06
640.9999990593094031.88138119498864e-069.40690597494319e-07
650.368355842323740.736711684647480.63164415767626
660.4128131941777010.8256263883554030.587186805822299
671.28562346791452e-072.57124693582904e-070.999999871437653
682.6852432913225e-095.370486582645e-090.999999997314757
690.996135173586950.007729652826099070.00386482641304954
705.10896870242281e-071.02179374048456e-060.99999948910313
710.4145027039974630.8290054079949260.585497296002537
720.9548500301060880.09029993978782370.0451499698939118

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.198813019464688 & 0.397626038929377 & 0.801186980535312 \tabularnewline
22 & 0.556983176383573 & 0.886033647232853 & 0.443016823616427 \tabularnewline
23 & 1 & 2.96667837642357e-28 & 1.48333918821178e-28 \tabularnewline
24 & 1 & 9.09709128361718e-18 & 4.54854564180859e-18 \tabularnewline
25 & 1.9203811291999e-08 & 3.8407622583998e-08 & 0.999999980796189 \tabularnewline
26 & 0.937275777791133 & 0.125448444417734 & 0.062724222208867 \tabularnewline
27 & 0.894549843358047 & 0.210900313283906 & 0.105450156641953 \tabularnewline
28 & 2.01693023487021e-06 & 4.03386046974041e-06 & 0.999997983069765 \tabularnewline
29 & 0.283061836395603 & 0.566123672791206 & 0.716938163604397 \tabularnewline
30 & 0.999999970701053 & 5.85978935082875e-08 & 2.92989467541437e-08 \tabularnewline
31 & 8.09111965235602e-05 & 0.000161822393047120 & 0.999919088803476 \tabularnewline
32 & 8.77490971345086e-10 & 1.75498194269017e-09 & 0.99999999912251 \tabularnewline
33 & 0.986090828619279 & 0.0278183427614422 & 0.0139091713807211 \tabularnewline
34 & 0.169996718397085 & 0.339993436794169 & 0.830003281602915 \tabularnewline
35 & 8.54309734355417e-08 & 1.70861946871083e-07 & 0.999999914569027 \tabularnewline
36 & 0.000187704524438421 & 0.000375409048876842 & 0.999812295475561 \tabularnewline
37 & 0.894632663816437 & 0.210734672367127 & 0.105367336183563 \tabularnewline
38 & 0.999999999799603 & 4.00794642345337e-10 & 2.00397321172669e-10 \tabularnewline
39 & 1.10964321438754e-07 & 2.21928642877509e-07 & 0.999999889035678 \tabularnewline
40 & 0.999999897968401 & 2.04063196955338e-07 & 1.02031598477669e-07 \tabularnewline
41 & 0.999999999958589 & 8.28228475062736e-11 & 4.14114237531368e-11 \tabularnewline
42 & 0.000139390583615685 & 0.00027878116723137 & 0.999860609416384 \tabularnewline
43 & 1.75923103581347e-13 & 3.51846207162694e-13 & 0.999999999999824 \tabularnewline
44 & 0.999947664343419 & 0.000104671313162968 & 5.23356565814842e-05 \tabularnewline
45 & 0.0897578221174706 & 0.179515644234941 & 0.91024217788253 \tabularnewline
46 & 0.9997919397531 & 0.000416120493799281 & 0.000208060246899640 \tabularnewline
47 & 0.615499546967827 & 0.769000906064346 & 0.384500453032173 \tabularnewline
48 & 3.63203519713349e-06 & 7.26407039426698e-06 & 0.999996367964803 \tabularnewline
49 & 0.814530566002283 & 0.370938867995433 & 0.185469433997716 \tabularnewline
50 & 0.419851157614005 & 0.839702315228011 & 0.580148842385995 \tabularnewline
51 & 0.995624976053633 & 0.00875004789273478 & 0.00437502394636739 \tabularnewline
52 & 0.94295682923016 & 0.114086341539682 & 0.057043170769841 \tabularnewline
53 & 0.0480143489142417 & 0.0960286978284834 & 0.951985651085758 \tabularnewline
54 & 0.182562866713879 & 0.365125733427758 & 0.817437133286121 \tabularnewline
55 & 0.797415548732876 & 0.405168902534247 & 0.202584451267124 \tabularnewline
56 & 0.998428878764364 & 0.00314224247127197 & 0.00157112123563599 \tabularnewline
57 & 0.097976873174533 & 0.195953746349066 & 0.902023126825467 \tabularnewline
58 & 1 & 3.23387340556884e-17 & 1.61693670278442e-17 \tabularnewline
59 & 0.0874084316003157 & 0.174816863200631 & 0.912591568399684 \tabularnewline
60 & 0.999712328089847 & 0.000575343820306258 & 0.000287671910153129 \tabularnewline
61 & 0.227119391711009 & 0.454238783422018 & 0.772880608288991 \tabularnewline
62 & 0.999993084954097 & 1.38300918056259e-05 & 6.91504590281293e-06 \tabularnewline
63 & 0.999998085882413 & 3.82823517489459e-06 & 1.91411758744729e-06 \tabularnewline
64 & 0.999999059309403 & 1.88138119498864e-06 & 9.40690597494319e-07 \tabularnewline
65 & 0.36835584232374 & 0.73671168464748 & 0.63164415767626 \tabularnewline
66 & 0.412813194177701 & 0.825626388355403 & 0.587186805822299 \tabularnewline
67 & 1.28562346791452e-07 & 2.57124693582904e-07 & 0.999999871437653 \tabularnewline
68 & 2.6852432913225e-09 & 5.370486582645e-09 & 0.999999997314757 \tabularnewline
69 & 0.99613517358695 & 0.00772965282609907 & 0.00386482641304954 \tabularnewline
70 & 5.10896870242281e-07 & 1.02179374048456e-06 & 0.99999948910313 \tabularnewline
71 & 0.414502703997463 & 0.829005407994926 & 0.585497296002537 \tabularnewline
72 & 0.954850030106088 & 0.0902999397878237 & 0.0451499698939118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&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]21[/C][C]0.198813019464688[/C][C]0.397626038929377[/C][C]0.801186980535312[/C][/ROW]
[ROW][C]22[/C][C]0.556983176383573[/C][C]0.886033647232853[/C][C]0.443016823616427[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.96667837642357e-28[/C][C]1.48333918821178e-28[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]9.09709128361718e-18[/C][C]4.54854564180859e-18[/C][/ROW]
[ROW][C]25[/C][C]1.9203811291999e-08[/C][C]3.8407622583998e-08[/C][C]0.999999980796189[/C][/ROW]
[ROW][C]26[/C][C]0.937275777791133[/C][C]0.125448444417734[/C][C]0.062724222208867[/C][/ROW]
[ROW][C]27[/C][C]0.894549843358047[/C][C]0.210900313283906[/C][C]0.105450156641953[/C][/ROW]
[ROW][C]28[/C][C]2.01693023487021e-06[/C][C]4.03386046974041e-06[/C][C]0.999997983069765[/C][/ROW]
[ROW][C]29[/C][C]0.283061836395603[/C][C]0.566123672791206[/C][C]0.716938163604397[/C][/ROW]
[ROW][C]30[/C][C]0.999999970701053[/C][C]5.85978935082875e-08[/C][C]2.92989467541437e-08[/C][/ROW]
[ROW][C]31[/C][C]8.09111965235602e-05[/C][C]0.000161822393047120[/C][C]0.999919088803476[/C][/ROW]
[ROW][C]32[/C][C]8.77490971345086e-10[/C][C]1.75498194269017e-09[/C][C]0.99999999912251[/C][/ROW]
[ROW][C]33[/C][C]0.986090828619279[/C][C]0.0278183427614422[/C][C]0.0139091713807211[/C][/ROW]
[ROW][C]34[/C][C]0.169996718397085[/C][C]0.339993436794169[/C][C]0.830003281602915[/C][/ROW]
[ROW][C]35[/C][C]8.54309734355417e-08[/C][C]1.70861946871083e-07[/C][C]0.999999914569027[/C][/ROW]
[ROW][C]36[/C][C]0.000187704524438421[/C][C]0.000375409048876842[/C][C]0.999812295475561[/C][/ROW]
[ROW][C]37[/C][C]0.894632663816437[/C][C]0.210734672367127[/C][C]0.105367336183563[/C][/ROW]
[ROW][C]38[/C][C]0.999999999799603[/C][C]4.00794642345337e-10[/C][C]2.00397321172669e-10[/C][/ROW]
[ROW][C]39[/C][C]1.10964321438754e-07[/C][C]2.21928642877509e-07[/C][C]0.999999889035678[/C][/ROW]
[ROW][C]40[/C][C]0.999999897968401[/C][C]2.04063196955338e-07[/C][C]1.02031598477669e-07[/C][/ROW]
[ROW][C]41[/C][C]0.999999999958589[/C][C]8.28228475062736e-11[/C][C]4.14114237531368e-11[/C][/ROW]
[ROW][C]42[/C][C]0.000139390583615685[/C][C]0.00027878116723137[/C][C]0.999860609416384[/C][/ROW]
[ROW][C]43[/C][C]1.75923103581347e-13[/C][C]3.51846207162694e-13[/C][C]0.999999999999824[/C][/ROW]
[ROW][C]44[/C][C]0.999947664343419[/C][C]0.000104671313162968[/C][C]5.23356565814842e-05[/C][/ROW]
[ROW][C]45[/C][C]0.0897578221174706[/C][C]0.179515644234941[/C][C]0.91024217788253[/C][/ROW]
[ROW][C]46[/C][C]0.9997919397531[/C][C]0.000416120493799281[/C][C]0.000208060246899640[/C][/ROW]
[ROW][C]47[/C][C]0.615499546967827[/C][C]0.769000906064346[/C][C]0.384500453032173[/C][/ROW]
[ROW][C]48[/C][C]3.63203519713349e-06[/C][C]7.26407039426698e-06[/C][C]0.999996367964803[/C][/ROW]
[ROW][C]49[/C][C]0.814530566002283[/C][C]0.370938867995433[/C][C]0.185469433997716[/C][/ROW]
[ROW][C]50[/C][C]0.419851157614005[/C][C]0.839702315228011[/C][C]0.580148842385995[/C][/ROW]
[ROW][C]51[/C][C]0.995624976053633[/C][C]0.00875004789273478[/C][C]0.00437502394636739[/C][/ROW]
[ROW][C]52[/C][C]0.94295682923016[/C][C]0.114086341539682[/C][C]0.057043170769841[/C][/ROW]
[ROW][C]53[/C][C]0.0480143489142417[/C][C]0.0960286978284834[/C][C]0.951985651085758[/C][/ROW]
[ROW][C]54[/C][C]0.182562866713879[/C][C]0.365125733427758[/C][C]0.817437133286121[/C][/ROW]
[ROW][C]55[/C][C]0.797415548732876[/C][C]0.405168902534247[/C][C]0.202584451267124[/C][/ROW]
[ROW][C]56[/C][C]0.998428878764364[/C][C]0.00314224247127197[/C][C]0.00157112123563599[/C][/ROW]
[ROW][C]57[/C][C]0.097976873174533[/C][C]0.195953746349066[/C][C]0.902023126825467[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]3.23387340556884e-17[/C][C]1.61693670278442e-17[/C][/ROW]
[ROW][C]59[/C][C]0.0874084316003157[/C][C]0.174816863200631[/C][C]0.912591568399684[/C][/ROW]
[ROW][C]60[/C][C]0.999712328089847[/C][C]0.000575343820306258[/C][C]0.000287671910153129[/C][/ROW]
[ROW][C]61[/C][C]0.227119391711009[/C][C]0.454238783422018[/C][C]0.772880608288991[/C][/ROW]
[ROW][C]62[/C][C]0.999993084954097[/C][C]1.38300918056259e-05[/C][C]6.91504590281293e-06[/C][/ROW]
[ROW][C]63[/C][C]0.999998085882413[/C][C]3.82823517489459e-06[/C][C]1.91411758744729e-06[/C][/ROW]
[ROW][C]64[/C][C]0.999999059309403[/C][C]1.88138119498864e-06[/C][C]9.40690597494319e-07[/C][/ROW]
[ROW][C]65[/C][C]0.36835584232374[/C][C]0.73671168464748[/C][C]0.63164415767626[/C][/ROW]
[ROW][C]66[/C][C]0.412813194177701[/C][C]0.825626388355403[/C][C]0.587186805822299[/C][/ROW]
[ROW][C]67[/C][C]1.28562346791452e-07[/C][C]2.57124693582904e-07[/C][C]0.999999871437653[/C][/ROW]
[ROW][C]68[/C][C]2.6852432913225e-09[/C][C]5.370486582645e-09[/C][C]0.999999997314757[/C][/ROW]
[ROW][C]69[/C][C]0.99613517358695[/C][C]0.00772965282609907[/C][C]0.00386482641304954[/C][/ROW]
[ROW][C]70[/C][C]5.10896870242281e-07[/C][C]1.02179374048456e-06[/C][C]0.99999948910313[/C][/ROW]
[ROW][C]71[/C][C]0.414502703997463[/C][C]0.829005407994926[/C][C]0.585497296002537[/C][/ROW]
[ROW][C]72[/C][C]0.954850030106088[/C][C]0.0902999397878237[/C][C]0.0451499698939118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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
210.1988130194646880.3976260389293770.801186980535312
220.5569831763835730.8860336472328530.443016823616427
2312.96667837642357e-281.48333918821178e-28
2419.09709128361718e-184.54854564180859e-18
251.9203811291999e-083.8407622583998e-080.999999980796189
260.9372757777911330.1254484444177340.062724222208867
270.8945498433580470.2109003132839060.105450156641953
282.01693023487021e-064.03386046974041e-060.999997983069765
290.2830618363956030.5661236727912060.716938163604397
300.9999999707010535.85978935082875e-082.92989467541437e-08
318.09111965235602e-050.0001618223930471200.999919088803476
328.77490971345086e-101.75498194269017e-090.99999999912251
330.9860908286192790.02781834276144220.0139091713807211
340.1699967183970850.3399934367941690.830003281602915
358.54309734355417e-081.70861946871083e-070.999999914569027
360.0001877045244384210.0003754090488768420.999812295475561
370.8946326638164370.2107346723671270.105367336183563
380.9999999997996034.00794642345337e-102.00397321172669e-10
391.10964321438754e-072.21928642877509e-070.999999889035678
400.9999998979684012.04063196955338e-071.02031598477669e-07
410.9999999999585898.28228475062736e-114.14114237531368e-11
420.0001393905836156850.000278781167231370.999860609416384
431.75923103581347e-133.51846207162694e-130.999999999999824
440.9999476643434190.0001046713131629685.23356565814842e-05
450.08975782211747060.1795156442349410.91024217788253
460.99979193975310.0004161204937992810.000208060246899640
470.6154995469678270.7690009060643460.384500453032173
483.63203519713349e-067.26407039426698e-060.999996367964803
490.8145305660022830.3709388679954330.185469433997716
500.4198511576140050.8397023152280110.580148842385995
510.9956249760536330.008750047892734780.00437502394636739
520.942956829230160.1140863415396820.057043170769841
530.04801434891424170.09602869782848340.951985651085758
540.1825628667138790.3651257334277580.817437133286121
550.7974155487328760.4051689025342470.202584451267124
560.9984288787643640.003142242471271970.00157112123563599
570.0979768731745330.1959537463490660.902023126825467
5813.23387340556884e-171.61693670278442e-17
590.08740843160031570.1748168632006310.912591568399684
600.9997123280898470.0005753438203062580.000287671910153129
610.2271193917110090.4542387834220180.772880608288991
620.9999930849540971.38300918056259e-056.91504590281293e-06
630.9999980858824133.82823517489459e-061.91411758744729e-06
640.9999990593094031.88138119498864e-069.40690597494319e-07
650.368355842323740.736711684647480.63164415767626
660.4128131941777010.8256263883554030.587186805822299
671.28562346791452e-072.57124693582904e-070.999999871437653
682.6852432913225e-095.370486582645e-090.999999997314757
690.996135173586950.007729652826099070.00386482641304954
705.10896870242281e-071.02179374048456e-060.99999948910313
710.4145027039974630.8290054079949260.585497296002537
720.9548500301060880.09029993978782370.0451499698939118






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level290.557692307692308NOK
5% type I error level300.576923076923077NOK
10% type I error level320.615384615384615NOK

\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 & 29 & 0.557692307692308 & NOK \tabularnewline
5% type I error level & 30 & 0.576923076923077 & NOK \tabularnewline
10% type I error level & 32 & 0.615384615384615 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57439&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]29[/C][C]0.557692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.576923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.615384615384615[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57439&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57439&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 level290.557692307692308NOK
5% type I error level300.576923076923077NOK
10% type I error level320.615384615384615NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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