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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, 15 Dec 2010 10:03:46 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t1292407307lex8uac1av6qlpv.htm/, Retrieved Fri, 03 May 2024 07:45:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110320, Retrieved Fri, 03 May 2024 07:45:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
-   PD  [Kendall tau Correlation Matrix] [WS10, Pearson Cor...] [2010-12-10 12:56:18] [d946de7cca328fbcf207448a112523ab]
- RMPD      [Multiple Regression] [] [2010-12-15 10:03:46] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
998	1.2	613	-1906	-2.3	-0.6
499	2.3	998	-706	1.2	-1.1
59	1.3	499	326	2.3	-0.6
175	1.4	59	146	1.3	-2
-413	-1.5	175	625	1.4	0
-223	1.4	-413	104	-1.5	-1.1
110	-0.9	-223	65	1.4	3.4
13	-0.6	110	25	-0.9	0.8
74	1.8	13	3	-0.6	-3.2
643	-3.9	74	-393	1.8	3.1
44	2.4	643	-358	-3.9	-1.7
216	1.1	44	613	2.4	-2.3
-1189	-2.3	216	998	1.1	1.2
-47	-4.3	-1189	499	-2.3	2.3
279	1	-47	59	-4.3	1.3
374	0.8	279	175	1	1.4
13	0.3	374	-413	0.8	-1.5
152	2.2	13	-223	0.3	1.4
-27	1.7	152	110	2.2	-0.9
334	1.8	-27	13	1.7	-0.6
411	0.6	334	74	1.8	1.8
33	-2.6	411	643	0.6	-3.9
313	-0.3	33	44	-2.6	2.4
751	0.1	313	216	-0.3	1.1
446	0.9	751	-1189	0.1	-2.3
-329	2.2	446	-47	0.9	-4.3
-560	-2.2	-329	279	2.2	1
-783	0.4	-560	374	-2.2	0.8
-371	-1.1	-783	13	0.4	0.3
-308	-3	-371	152	-1.1	2.2
-264	-2.1	-308	-27	-3	1.7
-787	-1.5	-264	334	-2.1	1.8
-486	0.5	-787	411	-1.5	0.6
-243	3.8	-486	33	0.5	-2.6
-416	-1.9	-243	313	3.8	-0.3
-992	-1.6	-416	751	-1.9	0.1
-316	1.5	-992	446	-1.6	0.9
825	-2.6	-316	-329	1.5	2.2
1513	0.6	825	-560	-2.6	-2.2
138	-0.4	1513	-783	0.6	0.4
363	0.6	138	-371	-0.4	-1.1
180	2	363	-308	0.6	-3
-493	1	180	-264	2	-2.1
-325	-2.1	-493	-787	1	-1.5
-225	0.8	-325	-486	-2.1	0.5
-115	2.4	-225	-243	0.8	3.8
-145	-0.3	-115	-416	2.4	-1.9
-68	0.6	-145	-992	-0.3	-1.6
-335	-3	-68	-316	0.6	1.5
-832	-0.1	-335	825	-3	-2.6
-931	-2.7	-832	1513	-0.1	0.6
-149	-1.4	-931	138	-2.7	-0.4
-251	0.8	-149	363	-1.4	0.6
-43	-1	-251	180	0.8	2
1484	4.6	-43	-493	-1	1
195	-0.5	1484	-325	4.6	-2.1
170	1.8	195	-225	-0.5	0.8
-277	0.1	170	-115	1.8	2.4
-57	3	-277	-145	0.1	-0.3
-665	2.4	-57	-68	3	0.6
-220	5.5	-665	-335	2.4	-3
534	4.5	-220	-832	5.5	-0.1
-449	3.5	534	-931	4.5	-2.7
158	5	-449	-149	3.5	-1.4
-261	0.4	158	-251	5	0.8
-300	0.2	-261	-43	0.4	-1
-1276	-5.8	-300	1484	0.2	4.6
-108	0.9	-1276	195	-5.8	-0.5
-29	-4.3	-108	170	0.9	1.8
305	-3.8	-29	-277	-4.3	0.1
805	-2.3	305	-57	-3.8	3
-88	-1.8	805	-665	-2.3	2.4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
N12S[t] = -52.4100540892267 + 42.7705862096527N12T[t] + 0.256586786394052N12S1[t] -0.418733841178069N12S12[t] -41.6357254273286N12T1[t] + 45.5257486013084N12T12[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
N12S[t] =  -52.4100540892267 +  42.7705862096527N12T[t] +  0.256586786394052N12S1[t] -0.418733841178069N12S12[t] -41.6357254273286N12T1[t] +  45.5257486013084N12T12[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]N12S[t] =  -52.4100540892267 +  42.7705862096527N12T[t] +  0.256586786394052N12S1[t] -0.418733841178069N12S12[t] -41.6357254273286N12T1[t] +  45.5257486013084N12T12[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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
N12S[t] = -52.4100540892267 + 42.7705862096527N12T[t] + 0.256586786394052N12S1[t] -0.418733841178069N12S12[t] -41.6357254273286N12T1[t] + 45.5257486013084N12T12[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-52.410054089226749.770193-1.0530.2961620.148081
N12T42.770586209652726.4563691.61660.1107250.055362
N12S10.2565867863940520.1091762.35020.0217610.010881
N12S12-0.4187338411780690.108382-3.86350.0002570.000129
N12T1-41.635725427328622.549626-1.84640.0693180.034659
N12T1245.525748601308429.6872021.53350.1299290.064965

\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) & -52.4100540892267 & 49.770193 & -1.053 & 0.296162 & 0.148081 \tabularnewline
N12T & 42.7705862096527 & 26.456369 & 1.6166 & 0.110725 & 0.055362 \tabularnewline
N12S1 & 0.256586786394052 & 0.109176 & 2.3502 & 0.021761 & 0.010881 \tabularnewline
N12S12 & -0.418733841178069 & 0.108382 & -3.8635 & 0.000257 & 0.000129 \tabularnewline
N12T1 & -41.6357254273286 & 22.549626 & -1.8464 & 0.069318 & 0.034659 \tabularnewline
N12T12 & 45.5257486013084 & 29.687202 & 1.5335 & 0.129929 & 0.064965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&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]-52.4100540892267[/C][C]49.770193[/C][C]-1.053[/C][C]0.296162[/C][C]0.148081[/C][/ROW]
[ROW][C]N12T[/C][C]42.7705862096527[/C][C]26.456369[/C][C]1.6166[/C][C]0.110725[/C][C]0.055362[/C][/ROW]
[ROW][C]N12S1[/C][C]0.256586786394052[/C][C]0.109176[/C][C]2.3502[/C][C]0.021761[/C][C]0.010881[/C][/ROW]
[ROW][C]N12S12[/C][C]-0.418733841178069[/C][C]0.108382[/C][C]-3.8635[/C][C]0.000257[/C][C]0.000129[/C][/ROW]
[ROW][C]N12T1[/C][C]-41.6357254273286[/C][C]22.549626[/C][C]-1.8464[/C][C]0.069318[/C][C]0.034659[/C][/ROW]
[ROW][C]N12T12[/C][C]45.5257486013084[/C][C]29.687202[/C][C]1.5335[/C][C]0.129929[/C][C]0.064965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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)-52.410054089226749.770193-1.0530.2961620.148081
N12T42.770586209652726.4563691.61660.1107250.055362
N12S10.2565867863940520.1091762.35020.0217610.010881
N12S12-0.4187338411780690.108382-3.86350.0002570.000129
N12T1-41.635725427328622.549626-1.84640.0693180.034659
N12T1245.525748601308429.6872021.53350.1299290.064965






Multiple Linear Regression - Regression Statistics
Multiple R0.639027994821959
R-squared0.408356778166173
Adjusted R-squared0.363535321966641
F-TEST (value)9.11074321968223
F-TEST (DF numerator)5
F-TEST (DF denominator)66
p-value1.25979543208476e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation417.328344799402
Sum Squared Residuals11494754.5266186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.639027994821959 \tabularnewline
R-squared & 0.408356778166173 \tabularnewline
Adjusted R-squared & 0.363535321966641 \tabularnewline
F-TEST (value) & 9.11074321968223 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 1.25979543208476e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 417.328344799402 \tabularnewline
Sum Squared Residuals & 11494754.5266186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.639027994821959[/C][/ROW]
[ROW][C]R-squared[/C][C]0.408356778166173[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.363535321966641[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.11074321968223[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]1.25979543208476e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]417.328344799402[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11494754.5266186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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 R0.639027994821959
R-squared0.408356778166173
Adjusted R-squared0.363535321966641
F-TEST (value)9.11074321968223
F-TEST (DF numerator)5
F-TEST (DF denominator)66
p-value1.25979543208476e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation417.328344799402
Sum Squared Residuals11494754.5266186






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19981022.75577002938-24.755770029381
2499497.6208049117221.3791950882784
359-128.356335473738187.356335473738
4175-183.705694068606358.705694068606
5-413-391.6619121193-21.3380878807001
6-223-129.674630979422-93.3253690205783
7110-78.8426050741738188.842605074174
81313.5765464245182-0.576546424518219
974-94.044532480053168.044532480053
1064330.5199963641334612.480003635867
1144450.116928151422-406.116928151422
12216-455.391398108025671.391398108025
13-1189-504.424429654517-684.575570345483
14-47-549.882060295252502.882060295252
15279191.81274904961487.1872509503864
163742.21602869077702371.783971309223
1713127.724453047762-114.724453047762
18152189.643840791474-37.6438407914745
19-27-119.33135821180992.3313582118093
20334-85.8905644670497419.89056446705
2141134.9680217581648376.031978241835
2233-529.934123705366562.934123705366
23313142.316527741241170.683472258759
247514.30120006825166746.698799931748
25446567.781892916376-121.781892916376
26-329-57.429439031096-271.570560968904
27-560-393.821985501602-166.178014498398
28-783-207.577681765463-575.422318234537
29-371-308.805258192241-62.1947418077589
30-308-193.607109436505-114.392890563495
31-264-7.6502527228472-256.349747277153
32-787-154.780577085465-632.219422914535
33-486-315.289133298927-170.710866701073
34-243-167.586030515998-75.4139694840021
35-416-498.96193047429882.9619304742983
36-992-458.391756717271-533.608243282729
37-316-321.9532256181625.95322561816243
38825-69.228510205374894.228510205374
391513427.5235866595531085.47641334045
40138639.794981067667-501.794981067667
41363130.5874959455232.4125040545
4218093.683463813642686.3165361863574
43-493-31.7836351750391-461.216364824961
44-325-49.1063861439158-275.893613856084
45-225212.118253811015-437.118253811015
46-115233.948913624658-348.948913624658
47-145-106.980095825436-38.0199041745636
48-68291.080703924177-359.080703924177
49-335-22.542632735145-312.457367264855
50-832-481.558875205522-350.441124794478
51-931-983.4361231340452.4361231340408
52-149-314.750283784912165.750283784912
53-251-122.819935882812-128.180064117188
54-43-177.213098235085134.213098235085
551484426.8986683896561057.10133161034
56195155.93953316913339.0604668308667
57170226.065000294765-56.0650002947649
58-27777.958640828154-354.958640828154
59-5747.7222745562737-104.722274556274
60-665-33.5039199316133-631.496080068387
61-220-84.0291929230422-135.970807076958
62534198.445981997234335.554018002766
63-449313.865262069252-762.865262069252
64158-100.834334833842258.834334833842
65-261-61.4169414750066-199.583058524993
66-300-154.999571697726-145.000428302274
67-1276-797.76521185113-478.23478814887
68-108-204.25003179122196.2500317912209
69-29-290.745506123803261.745506123803
7030577.1961697126347227.803830287365
71805246.137398853682558.862601146318
72-88560.637223290022-648.637223290022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 998 & 1022.75577002938 & -24.755770029381 \tabularnewline
2 & 499 & 497.620804911722 & 1.3791950882784 \tabularnewline
3 & 59 & -128.356335473738 & 187.356335473738 \tabularnewline
4 & 175 & -183.705694068606 & 358.705694068606 \tabularnewline
5 & -413 & -391.6619121193 & -21.3380878807001 \tabularnewline
6 & -223 & -129.674630979422 & -93.3253690205783 \tabularnewline
7 & 110 & -78.8426050741738 & 188.842605074174 \tabularnewline
8 & 13 & 13.5765464245182 & -0.576546424518219 \tabularnewline
9 & 74 & -94.044532480053 & 168.044532480053 \tabularnewline
10 & 643 & 30.5199963641334 & 612.480003635867 \tabularnewline
11 & 44 & 450.116928151422 & -406.116928151422 \tabularnewline
12 & 216 & -455.391398108025 & 671.391398108025 \tabularnewline
13 & -1189 & -504.424429654517 & -684.575570345483 \tabularnewline
14 & -47 & -549.882060295252 & 502.882060295252 \tabularnewline
15 & 279 & 191.812749049614 & 87.1872509503864 \tabularnewline
16 & 374 & 2.21602869077702 & 371.783971309223 \tabularnewline
17 & 13 & 127.724453047762 & -114.724453047762 \tabularnewline
18 & 152 & 189.643840791474 & -37.6438407914745 \tabularnewline
19 & -27 & -119.331358211809 & 92.3313582118093 \tabularnewline
20 & 334 & -85.8905644670497 & 419.89056446705 \tabularnewline
21 & 411 & 34.9680217581648 & 376.031978241835 \tabularnewline
22 & 33 & -529.934123705366 & 562.934123705366 \tabularnewline
23 & 313 & 142.316527741241 & 170.683472258759 \tabularnewline
24 & 751 & 4.30120006825166 & 746.698799931748 \tabularnewline
25 & 446 & 567.781892916376 & -121.781892916376 \tabularnewline
26 & -329 & -57.429439031096 & -271.570560968904 \tabularnewline
27 & -560 & -393.821985501602 & -166.178014498398 \tabularnewline
28 & -783 & -207.577681765463 & -575.422318234537 \tabularnewline
29 & -371 & -308.805258192241 & -62.1947418077589 \tabularnewline
30 & -308 & -193.607109436505 & -114.392890563495 \tabularnewline
31 & -264 & -7.6502527228472 & -256.349747277153 \tabularnewline
32 & -787 & -154.780577085465 & -632.219422914535 \tabularnewline
33 & -486 & -315.289133298927 & -170.710866701073 \tabularnewline
34 & -243 & -167.586030515998 & -75.4139694840021 \tabularnewline
35 & -416 & -498.961930474298 & 82.9619304742983 \tabularnewline
36 & -992 & -458.391756717271 & -533.608243282729 \tabularnewline
37 & -316 & -321.953225618162 & 5.95322561816243 \tabularnewline
38 & 825 & -69.228510205374 & 894.228510205374 \tabularnewline
39 & 1513 & 427.523586659553 & 1085.47641334045 \tabularnewline
40 & 138 & 639.794981067667 & -501.794981067667 \tabularnewline
41 & 363 & 130.5874959455 & 232.4125040545 \tabularnewline
42 & 180 & 93.6834638136426 & 86.3165361863574 \tabularnewline
43 & -493 & -31.7836351750391 & -461.216364824961 \tabularnewline
44 & -325 & -49.1063861439158 & -275.893613856084 \tabularnewline
45 & -225 & 212.118253811015 & -437.118253811015 \tabularnewline
46 & -115 & 233.948913624658 & -348.948913624658 \tabularnewline
47 & -145 & -106.980095825436 & -38.0199041745636 \tabularnewline
48 & -68 & 291.080703924177 & -359.080703924177 \tabularnewline
49 & -335 & -22.542632735145 & -312.457367264855 \tabularnewline
50 & -832 & -481.558875205522 & -350.441124794478 \tabularnewline
51 & -931 & -983.43612313404 & 52.4361231340408 \tabularnewline
52 & -149 & -314.750283784912 & 165.750283784912 \tabularnewline
53 & -251 & -122.819935882812 & -128.180064117188 \tabularnewline
54 & -43 & -177.213098235085 & 134.213098235085 \tabularnewline
55 & 1484 & 426.898668389656 & 1057.10133161034 \tabularnewline
56 & 195 & 155.939533169133 & 39.0604668308667 \tabularnewline
57 & 170 & 226.065000294765 & -56.0650002947649 \tabularnewline
58 & -277 & 77.958640828154 & -354.958640828154 \tabularnewline
59 & -57 & 47.7222745562737 & -104.722274556274 \tabularnewline
60 & -665 & -33.5039199316133 & -631.496080068387 \tabularnewline
61 & -220 & -84.0291929230422 & -135.970807076958 \tabularnewline
62 & 534 & 198.445981997234 & 335.554018002766 \tabularnewline
63 & -449 & 313.865262069252 & -762.865262069252 \tabularnewline
64 & 158 & -100.834334833842 & 258.834334833842 \tabularnewline
65 & -261 & -61.4169414750066 & -199.583058524993 \tabularnewline
66 & -300 & -154.999571697726 & -145.000428302274 \tabularnewline
67 & -1276 & -797.76521185113 & -478.23478814887 \tabularnewline
68 & -108 & -204.250031791221 & 96.2500317912209 \tabularnewline
69 & -29 & -290.745506123803 & 261.745506123803 \tabularnewline
70 & 305 & 77.1961697126347 & 227.803830287365 \tabularnewline
71 & 805 & 246.137398853682 & 558.862601146318 \tabularnewline
72 & -88 & 560.637223290022 & -648.637223290022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&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]998[/C][C]1022.75577002938[/C][C]-24.755770029381[/C][/ROW]
[ROW][C]2[/C][C]499[/C][C]497.620804911722[/C][C]1.3791950882784[/C][/ROW]
[ROW][C]3[/C][C]59[/C][C]-128.356335473738[/C][C]187.356335473738[/C][/ROW]
[ROW][C]4[/C][C]175[/C][C]-183.705694068606[/C][C]358.705694068606[/C][/ROW]
[ROW][C]5[/C][C]-413[/C][C]-391.6619121193[/C][C]-21.3380878807001[/C][/ROW]
[ROW][C]6[/C][C]-223[/C][C]-129.674630979422[/C][C]-93.3253690205783[/C][/ROW]
[ROW][C]7[/C][C]110[/C][C]-78.8426050741738[/C][C]188.842605074174[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.5765464245182[/C][C]-0.576546424518219[/C][/ROW]
[ROW][C]9[/C][C]74[/C][C]-94.044532480053[/C][C]168.044532480053[/C][/ROW]
[ROW][C]10[/C][C]643[/C][C]30.5199963641334[/C][C]612.480003635867[/C][/ROW]
[ROW][C]11[/C][C]44[/C][C]450.116928151422[/C][C]-406.116928151422[/C][/ROW]
[ROW][C]12[/C][C]216[/C][C]-455.391398108025[/C][C]671.391398108025[/C][/ROW]
[ROW][C]13[/C][C]-1189[/C][C]-504.424429654517[/C][C]-684.575570345483[/C][/ROW]
[ROW][C]14[/C][C]-47[/C][C]-549.882060295252[/C][C]502.882060295252[/C][/ROW]
[ROW][C]15[/C][C]279[/C][C]191.812749049614[/C][C]87.1872509503864[/C][/ROW]
[ROW][C]16[/C][C]374[/C][C]2.21602869077702[/C][C]371.783971309223[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]127.724453047762[/C][C]-114.724453047762[/C][/ROW]
[ROW][C]18[/C][C]152[/C][C]189.643840791474[/C][C]-37.6438407914745[/C][/ROW]
[ROW][C]19[/C][C]-27[/C][C]-119.331358211809[/C][C]92.3313582118093[/C][/ROW]
[ROW][C]20[/C][C]334[/C][C]-85.8905644670497[/C][C]419.89056446705[/C][/ROW]
[ROW][C]21[/C][C]411[/C][C]34.9680217581648[/C][C]376.031978241835[/C][/ROW]
[ROW][C]22[/C][C]33[/C][C]-529.934123705366[/C][C]562.934123705366[/C][/ROW]
[ROW][C]23[/C][C]313[/C][C]142.316527741241[/C][C]170.683472258759[/C][/ROW]
[ROW][C]24[/C][C]751[/C][C]4.30120006825166[/C][C]746.698799931748[/C][/ROW]
[ROW][C]25[/C][C]446[/C][C]567.781892916376[/C][C]-121.781892916376[/C][/ROW]
[ROW][C]26[/C][C]-329[/C][C]-57.429439031096[/C][C]-271.570560968904[/C][/ROW]
[ROW][C]27[/C][C]-560[/C][C]-393.821985501602[/C][C]-166.178014498398[/C][/ROW]
[ROW][C]28[/C][C]-783[/C][C]-207.577681765463[/C][C]-575.422318234537[/C][/ROW]
[ROW][C]29[/C][C]-371[/C][C]-308.805258192241[/C][C]-62.1947418077589[/C][/ROW]
[ROW][C]30[/C][C]-308[/C][C]-193.607109436505[/C][C]-114.392890563495[/C][/ROW]
[ROW][C]31[/C][C]-264[/C][C]-7.6502527228472[/C][C]-256.349747277153[/C][/ROW]
[ROW][C]32[/C][C]-787[/C][C]-154.780577085465[/C][C]-632.219422914535[/C][/ROW]
[ROW][C]33[/C][C]-486[/C][C]-315.289133298927[/C][C]-170.710866701073[/C][/ROW]
[ROW][C]34[/C][C]-243[/C][C]-167.586030515998[/C][C]-75.4139694840021[/C][/ROW]
[ROW][C]35[/C][C]-416[/C][C]-498.961930474298[/C][C]82.9619304742983[/C][/ROW]
[ROW][C]36[/C][C]-992[/C][C]-458.391756717271[/C][C]-533.608243282729[/C][/ROW]
[ROW][C]37[/C][C]-316[/C][C]-321.953225618162[/C][C]5.95322561816243[/C][/ROW]
[ROW][C]38[/C][C]825[/C][C]-69.228510205374[/C][C]894.228510205374[/C][/ROW]
[ROW][C]39[/C][C]1513[/C][C]427.523586659553[/C][C]1085.47641334045[/C][/ROW]
[ROW][C]40[/C][C]138[/C][C]639.794981067667[/C][C]-501.794981067667[/C][/ROW]
[ROW][C]41[/C][C]363[/C][C]130.5874959455[/C][C]232.4125040545[/C][/ROW]
[ROW][C]42[/C][C]180[/C][C]93.6834638136426[/C][C]86.3165361863574[/C][/ROW]
[ROW][C]43[/C][C]-493[/C][C]-31.7836351750391[/C][C]-461.216364824961[/C][/ROW]
[ROW][C]44[/C][C]-325[/C][C]-49.1063861439158[/C][C]-275.893613856084[/C][/ROW]
[ROW][C]45[/C][C]-225[/C][C]212.118253811015[/C][C]-437.118253811015[/C][/ROW]
[ROW][C]46[/C][C]-115[/C][C]233.948913624658[/C][C]-348.948913624658[/C][/ROW]
[ROW][C]47[/C][C]-145[/C][C]-106.980095825436[/C][C]-38.0199041745636[/C][/ROW]
[ROW][C]48[/C][C]-68[/C][C]291.080703924177[/C][C]-359.080703924177[/C][/ROW]
[ROW][C]49[/C][C]-335[/C][C]-22.542632735145[/C][C]-312.457367264855[/C][/ROW]
[ROW][C]50[/C][C]-832[/C][C]-481.558875205522[/C][C]-350.441124794478[/C][/ROW]
[ROW][C]51[/C][C]-931[/C][C]-983.43612313404[/C][C]52.4361231340408[/C][/ROW]
[ROW][C]52[/C][C]-149[/C][C]-314.750283784912[/C][C]165.750283784912[/C][/ROW]
[ROW][C]53[/C][C]-251[/C][C]-122.819935882812[/C][C]-128.180064117188[/C][/ROW]
[ROW][C]54[/C][C]-43[/C][C]-177.213098235085[/C][C]134.213098235085[/C][/ROW]
[ROW][C]55[/C][C]1484[/C][C]426.898668389656[/C][C]1057.10133161034[/C][/ROW]
[ROW][C]56[/C][C]195[/C][C]155.939533169133[/C][C]39.0604668308667[/C][/ROW]
[ROW][C]57[/C][C]170[/C][C]226.065000294765[/C][C]-56.0650002947649[/C][/ROW]
[ROW][C]58[/C][C]-277[/C][C]77.958640828154[/C][C]-354.958640828154[/C][/ROW]
[ROW][C]59[/C][C]-57[/C][C]47.7222745562737[/C][C]-104.722274556274[/C][/ROW]
[ROW][C]60[/C][C]-665[/C][C]-33.5039199316133[/C][C]-631.496080068387[/C][/ROW]
[ROW][C]61[/C][C]-220[/C][C]-84.0291929230422[/C][C]-135.970807076958[/C][/ROW]
[ROW][C]62[/C][C]534[/C][C]198.445981997234[/C][C]335.554018002766[/C][/ROW]
[ROW][C]63[/C][C]-449[/C][C]313.865262069252[/C][C]-762.865262069252[/C][/ROW]
[ROW][C]64[/C][C]158[/C][C]-100.834334833842[/C][C]258.834334833842[/C][/ROW]
[ROW][C]65[/C][C]-261[/C][C]-61.4169414750066[/C][C]-199.583058524993[/C][/ROW]
[ROW][C]66[/C][C]-300[/C][C]-154.999571697726[/C][C]-145.000428302274[/C][/ROW]
[ROW][C]67[/C][C]-1276[/C][C]-797.76521185113[/C][C]-478.23478814887[/C][/ROW]
[ROW][C]68[/C][C]-108[/C][C]-204.250031791221[/C][C]96.2500317912209[/C][/ROW]
[ROW][C]69[/C][C]-29[/C][C]-290.745506123803[/C][C]261.745506123803[/C][/ROW]
[ROW][C]70[/C][C]305[/C][C]77.1961697126347[/C][C]227.803830287365[/C][/ROW]
[ROW][C]71[/C][C]805[/C][C]246.137398853682[/C][C]558.862601146318[/C][/ROW]
[ROW][C]72[/C][C]-88[/C][C]560.637223290022[/C][C]-648.637223290022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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
19981022.75577002938-24.755770029381
2499497.6208049117221.3791950882784
359-128.356335473738187.356335473738
4175-183.705694068606358.705694068606
5-413-391.6619121193-21.3380878807001
6-223-129.674630979422-93.3253690205783
7110-78.8426050741738188.842605074174
81313.5765464245182-0.576546424518219
974-94.044532480053168.044532480053
1064330.5199963641334612.480003635867
1144450.116928151422-406.116928151422
12216-455.391398108025671.391398108025
13-1189-504.424429654517-684.575570345483
14-47-549.882060295252502.882060295252
15279191.81274904961487.1872509503864
163742.21602869077702371.783971309223
1713127.724453047762-114.724453047762
18152189.643840791474-37.6438407914745
19-27-119.33135821180992.3313582118093
20334-85.8905644670497419.89056446705
2141134.9680217581648376.031978241835
2233-529.934123705366562.934123705366
23313142.316527741241170.683472258759
247514.30120006825166746.698799931748
25446567.781892916376-121.781892916376
26-329-57.429439031096-271.570560968904
27-560-393.821985501602-166.178014498398
28-783-207.577681765463-575.422318234537
29-371-308.805258192241-62.1947418077589
30-308-193.607109436505-114.392890563495
31-264-7.6502527228472-256.349747277153
32-787-154.780577085465-632.219422914535
33-486-315.289133298927-170.710866701073
34-243-167.586030515998-75.4139694840021
35-416-498.96193047429882.9619304742983
36-992-458.391756717271-533.608243282729
37-316-321.9532256181625.95322561816243
38825-69.228510205374894.228510205374
391513427.5235866595531085.47641334045
40138639.794981067667-501.794981067667
41363130.5874959455232.4125040545
4218093.683463813642686.3165361863574
43-493-31.7836351750391-461.216364824961
44-325-49.1063861439158-275.893613856084
45-225212.118253811015-437.118253811015
46-115233.948913624658-348.948913624658
47-145-106.980095825436-38.0199041745636
48-68291.080703924177-359.080703924177
49-335-22.542632735145-312.457367264855
50-832-481.558875205522-350.441124794478
51-931-983.4361231340452.4361231340408
52-149-314.750283784912165.750283784912
53-251-122.819935882812-128.180064117188
54-43-177.213098235085134.213098235085
551484426.8986683896561057.10133161034
56195155.93953316913339.0604668308667
57170226.065000294765-56.0650002947649
58-27777.958640828154-354.958640828154
59-5747.7222745562737-104.722274556274
60-665-33.5039199316133-631.496080068387
61-220-84.0291929230422-135.970807076958
62534198.445981997234335.554018002766
63-449313.865262069252-762.865262069252
64158-100.834334833842258.834334833842
65-261-61.4169414750066-199.583058524993
66-300-154.999571697726-145.000428302274
67-1276-797.76521185113-478.23478814887
68-108-204.25003179122196.2500317912209
69-29-290.745506123803261.745506123803
7030577.1961697126347227.803830287365
71805246.137398853682558.862601146318
72-88560.637223290022-648.637223290022






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.03680609130619750.0736121826123950.963193908693803
100.01662366614743570.03324733229487130.983376333852564
110.01203872991288780.02407745982577560.987961270087112
120.01709980792185760.03419961584371510.982900192078142
130.09251274761342460.1850254952268490.907487252386575
140.08289730004030480.165794600080610.917102699959695
150.1430761181542780.2861522363085560.856923881845722
160.1175196597853380.2350393195706760.882480340214662
170.09355603653790670.1871120730758130.906443963462093
180.09092416923986460.1818483384797290.909075830760135
190.06423243843177650.1284648768635530.935767561568223
200.04391290951194450.08782581902388910.956087090488055
210.03554838019233120.07109676038466240.964451619807669
220.06293644694816380.1258728938963280.937063553051836
230.05247516597773960.1049503319554790.94752483402226
240.1621455880740490.3242911761480990.83785441192595
250.1289946991975080.2579893983950170.871005300802492
260.1179921027886980.2359842055773950.882007897211302
270.1430689662486880.2861379324973750.856931033751312
280.2181573677658020.4363147355316040.781842632234198
290.1806151282211170.3612302564422340.819384871778883
300.1455991687827220.2911983375654450.854400831217278
310.1187561091656040.2375122183312080.881243890834396
320.1671634098451880.3343268196903770.832836590154812
330.1289342388425780.2578684776851570.871065761157422
340.09486348611418250.1897269722283650.905136513885818
350.07834044949487540.1566808989897510.921659550505125
360.08430264676648940.1686052935329790.91569735323351
370.06042630788704340.1208526157740870.939573692112957
380.1737984220534790.3475968441069580.82620157794652
390.5860980934589520.8278038130820960.413901906541048
400.6334184766996910.7331630466006170.366581523300309
410.5901155833746420.8197688332507160.409884416625358
420.5298209777150680.9403580445698630.470179022284932
430.5450950966597740.9098098066804520.454904903340226
440.5048773771889650.990245245622070.495122622811035
450.5130684427121720.9738631145756560.486931557287828
460.5199049699991240.9601900600017530.480095030000876
470.4588993693013410.9177987386026810.54110063069866
480.4239752889768440.8479505779536870.576024711023156
490.384591795841130.769183591682260.61540820415887
500.3336698194995470.6673396389990940.666330180500453
510.2766875872304660.5533751744609310.723312412769535
520.2211405672427470.4422811344854940.778859432757253
530.1665262484502810.3330524969005610.83347375154972
540.1236199596994120.2472399193988240.876380040300588
550.4284814862971020.8569629725942040.571518513702898
560.5963438179254230.8073123641491530.403656182074577
570.5343441718878120.9313116562243760.465655828112188
580.4758200629938670.9516401259877350.524179937006133
590.3695495657062840.7390991314125690.630450434293716
600.3819805874673310.7639611749346620.618019412532669
610.2678164105657170.5356328211314350.732183589434283
620.1692740768984370.3385481537968740.830725923101563
630.1483021915545750.2966043831091490.851697808445425

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0368060913061975 & 0.073612182612395 & 0.963193908693803 \tabularnewline
10 & 0.0166236661474357 & 0.0332473322948713 & 0.983376333852564 \tabularnewline
11 & 0.0120387299128878 & 0.0240774598257756 & 0.987961270087112 \tabularnewline
12 & 0.0170998079218576 & 0.0341996158437151 & 0.982900192078142 \tabularnewline
13 & 0.0925127476134246 & 0.185025495226849 & 0.907487252386575 \tabularnewline
14 & 0.0828973000403048 & 0.16579460008061 & 0.917102699959695 \tabularnewline
15 & 0.143076118154278 & 0.286152236308556 & 0.856923881845722 \tabularnewline
16 & 0.117519659785338 & 0.235039319570676 & 0.882480340214662 \tabularnewline
17 & 0.0935560365379067 & 0.187112073075813 & 0.906443963462093 \tabularnewline
18 & 0.0909241692398646 & 0.181848338479729 & 0.909075830760135 \tabularnewline
19 & 0.0642324384317765 & 0.128464876863553 & 0.935767561568223 \tabularnewline
20 & 0.0439129095119445 & 0.0878258190238891 & 0.956087090488055 \tabularnewline
21 & 0.0355483801923312 & 0.0710967603846624 & 0.964451619807669 \tabularnewline
22 & 0.0629364469481638 & 0.125872893896328 & 0.937063553051836 \tabularnewline
23 & 0.0524751659777396 & 0.104950331955479 & 0.94752483402226 \tabularnewline
24 & 0.162145588074049 & 0.324291176148099 & 0.83785441192595 \tabularnewline
25 & 0.128994699197508 & 0.257989398395017 & 0.871005300802492 \tabularnewline
26 & 0.117992102788698 & 0.235984205577395 & 0.882007897211302 \tabularnewline
27 & 0.143068966248688 & 0.286137932497375 & 0.856931033751312 \tabularnewline
28 & 0.218157367765802 & 0.436314735531604 & 0.781842632234198 \tabularnewline
29 & 0.180615128221117 & 0.361230256442234 & 0.819384871778883 \tabularnewline
30 & 0.145599168782722 & 0.291198337565445 & 0.854400831217278 \tabularnewline
31 & 0.118756109165604 & 0.237512218331208 & 0.881243890834396 \tabularnewline
32 & 0.167163409845188 & 0.334326819690377 & 0.832836590154812 \tabularnewline
33 & 0.128934238842578 & 0.257868477685157 & 0.871065761157422 \tabularnewline
34 & 0.0948634861141825 & 0.189726972228365 & 0.905136513885818 \tabularnewline
35 & 0.0783404494948754 & 0.156680898989751 & 0.921659550505125 \tabularnewline
36 & 0.0843026467664894 & 0.168605293532979 & 0.91569735323351 \tabularnewline
37 & 0.0604263078870434 & 0.120852615774087 & 0.939573692112957 \tabularnewline
38 & 0.173798422053479 & 0.347596844106958 & 0.82620157794652 \tabularnewline
39 & 0.586098093458952 & 0.827803813082096 & 0.413901906541048 \tabularnewline
40 & 0.633418476699691 & 0.733163046600617 & 0.366581523300309 \tabularnewline
41 & 0.590115583374642 & 0.819768833250716 & 0.409884416625358 \tabularnewline
42 & 0.529820977715068 & 0.940358044569863 & 0.470179022284932 \tabularnewline
43 & 0.545095096659774 & 0.909809806680452 & 0.454904903340226 \tabularnewline
44 & 0.504877377188965 & 0.99024524562207 & 0.495122622811035 \tabularnewline
45 & 0.513068442712172 & 0.973863114575656 & 0.486931557287828 \tabularnewline
46 & 0.519904969999124 & 0.960190060001753 & 0.480095030000876 \tabularnewline
47 & 0.458899369301341 & 0.917798738602681 & 0.54110063069866 \tabularnewline
48 & 0.423975288976844 & 0.847950577953687 & 0.576024711023156 \tabularnewline
49 & 0.38459179584113 & 0.76918359168226 & 0.61540820415887 \tabularnewline
50 & 0.333669819499547 & 0.667339638999094 & 0.666330180500453 \tabularnewline
51 & 0.276687587230466 & 0.553375174460931 & 0.723312412769535 \tabularnewline
52 & 0.221140567242747 & 0.442281134485494 & 0.778859432757253 \tabularnewline
53 & 0.166526248450281 & 0.333052496900561 & 0.83347375154972 \tabularnewline
54 & 0.123619959699412 & 0.247239919398824 & 0.876380040300588 \tabularnewline
55 & 0.428481486297102 & 0.856962972594204 & 0.571518513702898 \tabularnewline
56 & 0.596343817925423 & 0.807312364149153 & 0.403656182074577 \tabularnewline
57 & 0.534344171887812 & 0.931311656224376 & 0.465655828112188 \tabularnewline
58 & 0.475820062993867 & 0.951640125987735 & 0.524179937006133 \tabularnewline
59 & 0.369549565706284 & 0.739099131412569 & 0.630450434293716 \tabularnewline
60 & 0.381980587467331 & 0.763961174934662 & 0.618019412532669 \tabularnewline
61 & 0.267816410565717 & 0.535632821131435 & 0.732183589434283 \tabularnewline
62 & 0.169274076898437 & 0.338548153796874 & 0.830725923101563 \tabularnewline
63 & 0.148302191554575 & 0.296604383109149 & 0.851697808445425 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0368060913061975[/C][C]0.073612182612395[/C][C]0.963193908693803[/C][/ROW]
[ROW][C]10[/C][C]0.0166236661474357[/C][C]0.0332473322948713[/C][C]0.983376333852564[/C][/ROW]
[ROW][C]11[/C][C]0.0120387299128878[/C][C]0.0240774598257756[/C][C]0.987961270087112[/C][/ROW]
[ROW][C]12[/C][C]0.0170998079218576[/C][C]0.0341996158437151[/C][C]0.982900192078142[/C][/ROW]
[ROW][C]13[/C][C]0.0925127476134246[/C][C]0.185025495226849[/C][C]0.907487252386575[/C][/ROW]
[ROW][C]14[/C][C]0.0828973000403048[/C][C]0.16579460008061[/C][C]0.917102699959695[/C][/ROW]
[ROW][C]15[/C][C]0.143076118154278[/C][C]0.286152236308556[/C][C]0.856923881845722[/C][/ROW]
[ROW][C]16[/C][C]0.117519659785338[/C][C]0.235039319570676[/C][C]0.882480340214662[/C][/ROW]
[ROW][C]17[/C][C]0.0935560365379067[/C][C]0.187112073075813[/C][C]0.906443963462093[/C][/ROW]
[ROW][C]18[/C][C]0.0909241692398646[/C][C]0.181848338479729[/C][C]0.909075830760135[/C][/ROW]
[ROW][C]19[/C][C]0.0642324384317765[/C][C]0.128464876863553[/C][C]0.935767561568223[/C][/ROW]
[ROW][C]20[/C][C]0.0439129095119445[/C][C]0.0878258190238891[/C][C]0.956087090488055[/C][/ROW]
[ROW][C]21[/C][C]0.0355483801923312[/C][C]0.0710967603846624[/C][C]0.964451619807669[/C][/ROW]
[ROW][C]22[/C][C]0.0629364469481638[/C][C]0.125872893896328[/C][C]0.937063553051836[/C][/ROW]
[ROW][C]23[/C][C]0.0524751659777396[/C][C]0.104950331955479[/C][C]0.94752483402226[/C][/ROW]
[ROW][C]24[/C][C]0.162145588074049[/C][C]0.324291176148099[/C][C]0.83785441192595[/C][/ROW]
[ROW][C]25[/C][C]0.128994699197508[/C][C]0.257989398395017[/C][C]0.871005300802492[/C][/ROW]
[ROW][C]26[/C][C]0.117992102788698[/C][C]0.235984205577395[/C][C]0.882007897211302[/C][/ROW]
[ROW][C]27[/C][C]0.143068966248688[/C][C]0.286137932497375[/C][C]0.856931033751312[/C][/ROW]
[ROW][C]28[/C][C]0.218157367765802[/C][C]0.436314735531604[/C][C]0.781842632234198[/C][/ROW]
[ROW][C]29[/C][C]0.180615128221117[/C][C]0.361230256442234[/C][C]0.819384871778883[/C][/ROW]
[ROW][C]30[/C][C]0.145599168782722[/C][C]0.291198337565445[/C][C]0.854400831217278[/C][/ROW]
[ROW][C]31[/C][C]0.118756109165604[/C][C]0.237512218331208[/C][C]0.881243890834396[/C][/ROW]
[ROW][C]32[/C][C]0.167163409845188[/C][C]0.334326819690377[/C][C]0.832836590154812[/C][/ROW]
[ROW][C]33[/C][C]0.128934238842578[/C][C]0.257868477685157[/C][C]0.871065761157422[/C][/ROW]
[ROW][C]34[/C][C]0.0948634861141825[/C][C]0.189726972228365[/C][C]0.905136513885818[/C][/ROW]
[ROW][C]35[/C][C]0.0783404494948754[/C][C]0.156680898989751[/C][C]0.921659550505125[/C][/ROW]
[ROW][C]36[/C][C]0.0843026467664894[/C][C]0.168605293532979[/C][C]0.91569735323351[/C][/ROW]
[ROW][C]37[/C][C]0.0604263078870434[/C][C]0.120852615774087[/C][C]0.939573692112957[/C][/ROW]
[ROW][C]38[/C][C]0.173798422053479[/C][C]0.347596844106958[/C][C]0.82620157794652[/C][/ROW]
[ROW][C]39[/C][C]0.586098093458952[/C][C]0.827803813082096[/C][C]0.413901906541048[/C][/ROW]
[ROW][C]40[/C][C]0.633418476699691[/C][C]0.733163046600617[/C][C]0.366581523300309[/C][/ROW]
[ROW][C]41[/C][C]0.590115583374642[/C][C]0.819768833250716[/C][C]0.409884416625358[/C][/ROW]
[ROW][C]42[/C][C]0.529820977715068[/C][C]0.940358044569863[/C][C]0.470179022284932[/C][/ROW]
[ROW][C]43[/C][C]0.545095096659774[/C][C]0.909809806680452[/C][C]0.454904903340226[/C][/ROW]
[ROW][C]44[/C][C]0.504877377188965[/C][C]0.99024524562207[/C][C]0.495122622811035[/C][/ROW]
[ROW][C]45[/C][C]0.513068442712172[/C][C]0.973863114575656[/C][C]0.486931557287828[/C][/ROW]
[ROW][C]46[/C][C]0.519904969999124[/C][C]0.960190060001753[/C][C]0.480095030000876[/C][/ROW]
[ROW][C]47[/C][C]0.458899369301341[/C][C]0.917798738602681[/C][C]0.54110063069866[/C][/ROW]
[ROW][C]48[/C][C]0.423975288976844[/C][C]0.847950577953687[/C][C]0.576024711023156[/C][/ROW]
[ROW][C]49[/C][C]0.38459179584113[/C][C]0.76918359168226[/C][C]0.61540820415887[/C][/ROW]
[ROW][C]50[/C][C]0.333669819499547[/C][C]0.667339638999094[/C][C]0.666330180500453[/C][/ROW]
[ROW][C]51[/C][C]0.276687587230466[/C][C]0.553375174460931[/C][C]0.723312412769535[/C][/ROW]
[ROW][C]52[/C][C]0.221140567242747[/C][C]0.442281134485494[/C][C]0.778859432757253[/C][/ROW]
[ROW][C]53[/C][C]0.166526248450281[/C][C]0.333052496900561[/C][C]0.83347375154972[/C][/ROW]
[ROW][C]54[/C][C]0.123619959699412[/C][C]0.247239919398824[/C][C]0.876380040300588[/C][/ROW]
[ROW][C]55[/C][C]0.428481486297102[/C][C]0.856962972594204[/C][C]0.571518513702898[/C][/ROW]
[ROW][C]56[/C][C]0.596343817925423[/C][C]0.807312364149153[/C][C]0.403656182074577[/C][/ROW]
[ROW][C]57[/C][C]0.534344171887812[/C][C]0.931311656224376[/C][C]0.465655828112188[/C][/ROW]
[ROW][C]58[/C][C]0.475820062993867[/C][C]0.951640125987735[/C][C]0.524179937006133[/C][/ROW]
[ROW][C]59[/C][C]0.369549565706284[/C][C]0.739099131412569[/C][C]0.630450434293716[/C][/ROW]
[ROW][C]60[/C][C]0.381980587467331[/C][C]0.763961174934662[/C][C]0.618019412532669[/C][/ROW]
[ROW][C]61[/C][C]0.267816410565717[/C][C]0.535632821131435[/C][C]0.732183589434283[/C][/ROW]
[ROW][C]62[/C][C]0.169274076898437[/C][C]0.338548153796874[/C][C]0.830725923101563[/C][/ROW]
[ROW][C]63[/C][C]0.148302191554575[/C][C]0.296604383109149[/C][C]0.851697808445425[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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
90.03680609130619750.0736121826123950.963193908693803
100.01662366614743570.03324733229487130.983376333852564
110.01203872991288780.02407745982577560.987961270087112
120.01709980792185760.03419961584371510.982900192078142
130.09251274761342460.1850254952268490.907487252386575
140.08289730004030480.165794600080610.917102699959695
150.1430761181542780.2861522363085560.856923881845722
160.1175196597853380.2350393195706760.882480340214662
170.09355603653790670.1871120730758130.906443963462093
180.09092416923986460.1818483384797290.909075830760135
190.06423243843177650.1284648768635530.935767561568223
200.04391290951194450.08782581902388910.956087090488055
210.03554838019233120.07109676038466240.964451619807669
220.06293644694816380.1258728938963280.937063553051836
230.05247516597773960.1049503319554790.94752483402226
240.1621455880740490.3242911761480990.83785441192595
250.1289946991975080.2579893983950170.871005300802492
260.1179921027886980.2359842055773950.882007897211302
270.1430689662486880.2861379324973750.856931033751312
280.2181573677658020.4363147355316040.781842632234198
290.1806151282211170.3612302564422340.819384871778883
300.1455991687827220.2911983375654450.854400831217278
310.1187561091656040.2375122183312080.881243890834396
320.1671634098451880.3343268196903770.832836590154812
330.1289342388425780.2578684776851570.871065761157422
340.09486348611418250.1897269722283650.905136513885818
350.07834044949487540.1566808989897510.921659550505125
360.08430264676648940.1686052935329790.91569735323351
370.06042630788704340.1208526157740870.939573692112957
380.1737984220534790.3475968441069580.82620157794652
390.5860980934589520.8278038130820960.413901906541048
400.6334184766996910.7331630466006170.366581523300309
410.5901155833746420.8197688332507160.409884416625358
420.5298209777150680.9403580445698630.470179022284932
430.5450950966597740.9098098066804520.454904903340226
440.5048773771889650.990245245622070.495122622811035
450.5130684427121720.9738631145756560.486931557287828
460.5199049699991240.9601900600017530.480095030000876
470.4588993693013410.9177987386026810.54110063069866
480.4239752889768440.8479505779536870.576024711023156
490.384591795841130.769183591682260.61540820415887
500.3336698194995470.6673396389990940.666330180500453
510.2766875872304660.5533751744609310.723312412769535
520.2211405672427470.4422811344854940.778859432757253
530.1665262484502810.3330524969005610.83347375154972
540.1236199596994120.2472399193988240.876380040300588
550.4284814862971020.8569629725942040.571518513702898
560.5963438179254230.8073123641491530.403656182074577
570.5343441718878120.9313116562243760.465655828112188
580.4758200629938670.9516401259877350.524179937006133
590.3695495657062840.7390991314125690.630450434293716
600.3819805874673310.7639611749346620.618019412532669
610.2678164105657170.5356328211314350.732183589434283
620.1692740768984370.3385481537968740.830725923101563
630.1483021915545750.2966043831091490.851697808445425






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0545454545454545NOK
10% type I error level60.109090909090909NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0545454545454545 & NOK \tabularnewline
10% type I error level & 6 & 0.109090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110320&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0545454545454545[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.109090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110320&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110320&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 level30.0545454545454545NOK
10% type I error level60.109090909090909NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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