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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 19 Nov 2011 12:00:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/19/t1321722370lrsiox1znoamav1.htm/, Retrieved Thu, 28 Mar 2024 10:27:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145530, Retrieved Thu, 28 Mar 2024 10:27:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7] [2010-11-21 17:50:24] [65eb19f81eab2b6e672eafaed2a27190]
-   PD    [Multiple Regression] [workshop 7] [2010-11-21 18:15:20] [65eb19f81eab2b6e672eafaed2a27190]
F   P       [Multiple Regression] [WS7 Celebrity] [2010-11-21 18:23:11] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 17:46:53] [1429a1a14191a86916b95357f6de790b]
-    D          [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 18:43:58] [1429a1a14191a86916b95357f6de790b]
-    D              [Multiple Regression] [] [2011-11-19 17:00:50] [539ae27d3016cec7ecb6ecd6e9a1efc7] [Current]
- RM                  [Multiple Regression] [] [2011-11-22 22:35:20] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=145530&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=145530&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
D[t] = + 7.47561072472881 + 0.248860445943252CM[t] -0.105949734415458PE[t] + 0.147529539189438PC[t] -0.192213547252973PS[t] + 0.109733980302452`O `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
D[t] =  +  7.47561072472881 +  0.248860445943252CM[t] -0.105949734415458PE[t] +  0.147529539189438PC[t] -0.192213547252973PS[t] +  0.109733980302452`O
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]D[t] =  +  7.47561072472881 +  0.248860445943252CM[t] -0.105949734415458PE[t] +  0.147529539189438PC[t] -0.192213547252973PS[t] +  0.109733980302452`O
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
D[t] = + 7.47561072472881 + 0.248860445943252CM[t] -0.105949734415458PE[t] + 0.147529539189438PC[t] -0.192213547252973PS[t] + 0.109733980302452`O `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.475610724728811.5829114.72275e-063e-06
CM0.2488604459432520.0400376.215700
PE-0.1059497344154580.073943-1.43290.1539410.07697
PC0.1475295391894380.0928761.58850.1142460.057123
PS-0.1922135472529730.056762-3.38639e-040.00045
`O `0.1097339803024520.0566451.93720.054560.02728

\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) & 7.47561072472881 & 1.582911 & 4.7227 & 5e-06 & 3e-06 \tabularnewline
CM & 0.248860445943252 & 0.040037 & 6.2157 & 0 & 0 \tabularnewline
PE & -0.105949734415458 & 0.073943 & -1.4329 & 0.153941 & 0.07697 \tabularnewline
PC & 0.147529539189438 & 0.092876 & 1.5885 & 0.114246 & 0.057123 \tabularnewline
PS & -0.192213547252973 & 0.056762 & -3.3863 & 9e-04 & 0.00045 \tabularnewline
`O
` & 0.109733980302452 & 0.056645 & 1.9372 & 0.05456 & 0.02728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&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]7.47561072472881[/C][C]1.582911[/C][C]4.7227[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]CM[/C][C]0.248860445943252[/C][C]0.040037[/C][C]6.2157[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PE[/C][C]-0.105949734415458[/C][C]0.073943[/C][C]-1.4329[/C][C]0.153941[/C][C]0.07697[/C][/ROW]
[ROW][C]PC[/C][C]0.147529539189438[/C][C]0.092876[/C][C]1.5885[/C][C]0.114246[/C][C]0.057123[/C][/ROW]
[ROW][C]PS[/C][C]-0.192213547252973[/C][C]0.056762[/C][C]-3.3863[/C][C]9e-04[/C][C]0.00045[/C][/ROW]
[ROW][C]`O
`[/C][C]0.109733980302452[/C][C]0.056645[/C][C]1.9372[/C][C]0.05456[/C][C]0.02728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.475610724728811.5829114.72275e-063e-06
CM0.2488604459432520.0400376.215700
PE-0.1059497344154580.073943-1.43290.1539410.07697
PC0.1475295391894380.0928761.58850.1142460.057123
PS-0.1922135472529730.056762-3.38639e-040.00045
`O `0.1097339803024520.0566451.93720.054560.02728







Multiple Linear Regression - Regression Statistics
Multiple R0.489435601598432
R-squared0.239547208112019
Adjusted R-squared0.214695809684307
F-TEST (value)9.63918424183728
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.12764863902504e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48174999937267
Sum Squared Residuals942.339708086093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.489435601598432 \tabularnewline
R-squared & 0.239547208112019 \tabularnewline
Adjusted R-squared & 0.214695809684307 \tabularnewline
F-TEST (value) & 9.63918424183728 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.12764863902504e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.48174999937267 \tabularnewline
Sum Squared Residuals & 942.339708086093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.489435601598432[/C][/ROW]
[ROW][C]R-squared[/C][C]0.239547208112019[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.214695809684307[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.63918424183728[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]5.12764863902504e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.48174999937267[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]942.339708086093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.489435601598432
R-squared0.239547208112019
Adjusted R-squared0.214695809684307
F-TEST (value)9.63918424183728
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value5.12764863902504e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48174999937267
Sum Squared Residuals942.339708086093







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.29312717286251.7068728271375
21111.8542529115495-0.854252911549478
368.06227222418893-2.06227222418893
41210.94766187021821.05233812978182
589.86771537760798-1.86771537760798
61010.1722752103671-0.172275210367121
7109.815501548018540.184498451981456
8119.798257711867741.20174228813226
91610.86292597640135.13707402359868
101110.06846354793810.931536452061921
111312.46801729933280.531982700667187
121213.0446779919111-1.04467799191107
13813.0401936526073-5.0401936526073
141210.64643066955751.35356933044252
15119.216542628658621.78345737134138
1647.34559388921211-3.34559388921211
17911.0418798989866-2.04187989898656
1889.84912475091086-1.84912475091086
19810.0892984205506-2.08929842055058
201412.78573201422131.21426798577869
211512.24066353886462.75933646113537
221613.81079071214162.18920928785844
23911.7886240057284-2.78862400572839
241414.3750612147334-0.375061214733427
251111.4470289360053-0.447028936005265
2689.40860163673439-1.40860163673439
2799.57702770843314-0.577027708433137
28911.6336575242935-2.63365752429345
29910.493867050423-1.49386705042302
3099.50925250089076-0.509252500890763
311011.7568378002112-1.75683780021115
321611.78656257954444.21343742045558
33119.177553570830231.82244642916977
34810.0509478301168-2.05094783011685
3598.731933578433420.268066421566578
361612.64966523783793.35033476216208
371112.6518033098243-1.65180330982433
38169.323564668306196.67643533169381
391212.3345724892786-0.334572489278586
401210.40812051127091.59187948872914
411412.55523100999551.44476899000448
42910.513552972001-1.51355297200096
431010.720577052591-0.720577052591046
4498.467233116696030.532766883303966
451010.0998879313339-0.0998879313338744
461210.12238521132491.8776147886751
471411.61859043037722.38140956962285
481412.10241708141731.89758291858266
491012.3199460232452-2.3199460232452
50149.930037175714064.06996282428594
511612.21905574437433.78094425562571
52910.4610692871838-1.46106928718377
531011.5899396475331-1.58993964753306
5469.04003585641423-3.04003585641423
55811.2797735574175-3.27977355741748
561312.42398211448310.576017885516871
571010.8246452755993-0.824645275599272
5888.90914169166691-0.909141691666908
5979.15425950080064-2.15425950080064
60159.82730075734655.1726992426535
6199.89283171897349-0.892831718973492
621010.2300011704603-0.230001170460277
631210.21279600455761.78720399544244
641310.45575712227612.54424287772388
65108.40823995068841.5917600493116
661111.8100631875226-0.810063187522585
67813.6099912716085-5.60999127160853
6899.12149677428637-0.121496774286372
69138.632326738954954.36767326104505
701110.40561970477630.594380295223706
71812.7748897940749-4.77488979407492
72910.7705154168815-1.77051541688151
73912.3432616515329-3.34326165153292
741512.51126332882672.48873667117329
75911.1799243643951-2.1799243643951
761011.5683831232456-1.56838312324565
77148.925492224975635.07450777502436
781210.95106714811911.04893285188092
791211.08607200896430.913927991035744
801111.5603427842052-0.560342784205161
811411.45482861275892.54517138724109
82611.555341171216-5.55534117121603
831211.26666002449910.733339975500888
84810.0611961307754-2.06119613077543
851412.40021087240691.59978912759315
861110.82853569541520.171464304584766
87109.909944005595810.0900559944041917
881410.19657995960673.80342004039329
891212.0383027999683-0.0383027999683423
901011.0020524420421-1.00205244204212
911413.00890757491080.991092425089236
9258.98335267342666-3.98335267342666
931110.49772918968480.50227081031523
941010.2124533208688-0.21245332086875
95911.4401404868287-2.44014048682871
961011.4379445722869-1.43794457228688
971613.7416191351072.25838086489298
981312.82375200191720.17624799808281
99910.8098388947891-1.80983889478915
1001011.378532076788-1.37853207678798
1011010.9689523566192-0.968952356619231
10279.47465402457373-2.47465402457373
10399.8265428213743-0.826542821374303
104810.2188112017114-2.21881120171137
1051412.84726377845961.1527362215404
1061411.64770189192352.3522981080765
107811.0805233342968-3.08052333429676
108911.5401625785906-2.54016257859058
1091411.80641633494812.19358366505186
1101410.74122910547213.25877089452787
11189.86606244753663-1.86606244753663
112813.6926336074711-5.69263360747113
113810.9525118828595-2.95251188285948
11478.55085509620331-1.55085509620331
11567.44027047059842-1.44027047059841
11689.4148100433135-1.4148100433135
11768.31398706901332-2.31398706901332
118119.878542611848731.12145738815127
1191411.84055919747662.15944080252337
1201111.1231055546712-0.123105554671166
1211111.9576290110093-0.95762901100931
122119.20908031081361.7909196891864
1231410.369676129173.63032387082998
124810.3570748050233-2.35707480502331
1252011.48036020640788.51963979359219
1261110.31930752669050.680692473309463
12789.14405571417421-1.14405571417421
1281110.70756723233170.29243276766828
1291010.6161673181472-0.616167318147201
1301413.44859195478560.551408045214407
1311110.5581054305360.441894569463998
132910.5952141906548-1.59521419065478
13399.71061082418663-0.710610824186625
134810.1579861032424-2.15798610324236
1351012.0589843809759-2.05898438097594
1361310.71887836091522.28112163908477
1371310.1115013821012.88849861789897
138129.295575308630782.70442469136922
139810.35973838043-2.35973838042996
1401311.01433939692891.98566060307108
1411412.43100648340811.56899351659187
1421211.66742817500660.332571824993393
1431410.91691389589673.08308610410329
1441511.29481067952253.7051893204775
1451310.51659551539372.4834044846063
1461611.85614356507834.14385643492171
147911.996511861034-2.99651186103404
148910.4782744530865-1.47827445308649
149911.0181531709424-2.01815317094244
150811.2929483065479-3.2929483065479
151710.0317337558054-3.0317337558054
1521611.94923416719554.05076583280446
1531113.3481625806087-2.34816258060873
154910.0171718546235-1.01717185462351
155119.833263436725081.16673656327492
15699.86642518204478-0.866425182044778
1571412.47898283927281.52101716072717
1581311.02203943823141.97796056176859
1591614.46476152485541.53523847514457

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 12.2931271728625 & 1.7068728271375 \tabularnewline
2 & 11 & 11.8542529115495 & -0.854252911549478 \tabularnewline
3 & 6 & 8.06227222418893 & -2.06227222418893 \tabularnewline
4 & 12 & 10.9476618702182 & 1.05233812978182 \tabularnewline
5 & 8 & 9.86771537760798 & -1.86771537760798 \tabularnewline
6 & 10 & 10.1722752103671 & -0.172275210367121 \tabularnewline
7 & 10 & 9.81550154801854 & 0.184498451981456 \tabularnewline
8 & 11 & 9.79825771186774 & 1.20174228813226 \tabularnewline
9 & 16 & 10.8629259764013 & 5.13707402359868 \tabularnewline
10 & 11 & 10.0684635479381 & 0.931536452061921 \tabularnewline
11 & 13 & 12.4680172993328 & 0.531982700667187 \tabularnewline
12 & 12 & 13.0446779919111 & -1.04467799191107 \tabularnewline
13 & 8 & 13.0401936526073 & -5.0401936526073 \tabularnewline
14 & 12 & 10.6464306695575 & 1.35356933044252 \tabularnewline
15 & 11 & 9.21654262865862 & 1.78345737134138 \tabularnewline
16 & 4 & 7.34559388921211 & -3.34559388921211 \tabularnewline
17 & 9 & 11.0418798989866 & -2.04187989898656 \tabularnewline
18 & 8 & 9.84912475091086 & -1.84912475091086 \tabularnewline
19 & 8 & 10.0892984205506 & -2.08929842055058 \tabularnewline
20 & 14 & 12.7857320142213 & 1.21426798577869 \tabularnewline
21 & 15 & 12.2406635388646 & 2.75933646113537 \tabularnewline
22 & 16 & 13.8107907121416 & 2.18920928785844 \tabularnewline
23 & 9 & 11.7886240057284 & -2.78862400572839 \tabularnewline
24 & 14 & 14.3750612147334 & -0.375061214733427 \tabularnewline
25 & 11 & 11.4470289360053 & -0.447028936005265 \tabularnewline
26 & 8 & 9.40860163673439 & -1.40860163673439 \tabularnewline
27 & 9 & 9.57702770843314 & -0.577027708433137 \tabularnewline
28 & 9 & 11.6336575242935 & -2.63365752429345 \tabularnewline
29 & 9 & 10.493867050423 & -1.49386705042302 \tabularnewline
30 & 9 & 9.50925250089076 & -0.509252500890763 \tabularnewline
31 & 10 & 11.7568378002112 & -1.75683780021115 \tabularnewline
32 & 16 & 11.7865625795444 & 4.21343742045558 \tabularnewline
33 & 11 & 9.17755357083023 & 1.82244642916977 \tabularnewline
34 & 8 & 10.0509478301168 & -2.05094783011685 \tabularnewline
35 & 9 & 8.73193357843342 & 0.268066421566578 \tabularnewline
36 & 16 & 12.6496652378379 & 3.35033476216208 \tabularnewline
37 & 11 & 12.6518033098243 & -1.65180330982433 \tabularnewline
38 & 16 & 9.32356466830619 & 6.67643533169381 \tabularnewline
39 & 12 & 12.3345724892786 & -0.334572489278586 \tabularnewline
40 & 12 & 10.4081205112709 & 1.59187948872914 \tabularnewline
41 & 14 & 12.5552310099955 & 1.44476899000448 \tabularnewline
42 & 9 & 10.513552972001 & -1.51355297200096 \tabularnewline
43 & 10 & 10.720577052591 & -0.720577052591046 \tabularnewline
44 & 9 & 8.46723311669603 & 0.532766883303966 \tabularnewline
45 & 10 & 10.0998879313339 & -0.0998879313338744 \tabularnewline
46 & 12 & 10.1223852113249 & 1.8776147886751 \tabularnewline
47 & 14 & 11.6185904303772 & 2.38140956962285 \tabularnewline
48 & 14 & 12.1024170814173 & 1.89758291858266 \tabularnewline
49 & 10 & 12.3199460232452 & -2.3199460232452 \tabularnewline
50 & 14 & 9.93003717571406 & 4.06996282428594 \tabularnewline
51 & 16 & 12.2190557443743 & 3.78094425562571 \tabularnewline
52 & 9 & 10.4610692871838 & -1.46106928718377 \tabularnewline
53 & 10 & 11.5899396475331 & -1.58993964753306 \tabularnewline
54 & 6 & 9.04003585641423 & -3.04003585641423 \tabularnewline
55 & 8 & 11.2797735574175 & -3.27977355741748 \tabularnewline
56 & 13 & 12.4239821144831 & 0.576017885516871 \tabularnewline
57 & 10 & 10.8246452755993 & -0.824645275599272 \tabularnewline
58 & 8 & 8.90914169166691 & -0.909141691666908 \tabularnewline
59 & 7 & 9.15425950080064 & -2.15425950080064 \tabularnewline
60 & 15 & 9.8273007573465 & 5.1726992426535 \tabularnewline
61 & 9 & 9.89283171897349 & -0.892831718973492 \tabularnewline
62 & 10 & 10.2300011704603 & -0.230001170460277 \tabularnewline
63 & 12 & 10.2127960045576 & 1.78720399544244 \tabularnewline
64 & 13 & 10.4557571222761 & 2.54424287772388 \tabularnewline
65 & 10 & 8.4082399506884 & 1.5917600493116 \tabularnewline
66 & 11 & 11.8100631875226 & -0.810063187522585 \tabularnewline
67 & 8 & 13.6099912716085 & -5.60999127160853 \tabularnewline
68 & 9 & 9.12149677428637 & -0.121496774286372 \tabularnewline
69 & 13 & 8.63232673895495 & 4.36767326104505 \tabularnewline
70 & 11 & 10.4056197047763 & 0.594380295223706 \tabularnewline
71 & 8 & 12.7748897940749 & -4.77488979407492 \tabularnewline
72 & 9 & 10.7705154168815 & -1.77051541688151 \tabularnewline
73 & 9 & 12.3432616515329 & -3.34326165153292 \tabularnewline
74 & 15 & 12.5112633288267 & 2.48873667117329 \tabularnewline
75 & 9 & 11.1799243643951 & -2.1799243643951 \tabularnewline
76 & 10 & 11.5683831232456 & -1.56838312324565 \tabularnewline
77 & 14 & 8.92549222497563 & 5.07450777502436 \tabularnewline
78 & 12 & 10.9510671481191 & 1.04893285188092 \tabularnewline
79 & 12 & 11.0860720089643 & 0.913927991035744 \tabularnewline
80 & 11 & 11.5603427842052 & -0.560342784205161 \tabularnewline
81 & 14 & 11.4548286127589 & 2.54517138724109 \tabularnewline
82 & 6 & 11.555341171216 & -5.55534117121603 \tabularnewline
83 & 12 & 11.2666600244991 & 0.733339975500888 \tabularnewline
84 & 8 & 10.0611961307754 & -2.06119613077543 \tabularnewline
85 & 14 & 12.4002108724069 & 1.59978912759315 \tabularnewline
86 & 11 & 10.8285356954152 & 0.171464304584766 \tabularnewline
87 & 10 & 9.90994400559581 & 0.0900559944041917 \tabularnewline
88 & 14 & 10.1965799596067 & 3.80342004039329 \tabularnewline
89 & 12 & 12.0383027999683 & -0.0383027999683423 \tabularnewline
90 & 10 & 11.0020524420421 & -1.00205244204212 \tabularnewline
91 & 14 & 13.0089075749108 & 0.991092425089236 \tabularnewline
92 & 5 & 8.98335267342666 & -3.98335267342666 \tabularnewline
93 & 11 & 10.4977291896848 & 0.50227081031523 \tabularnewline
94 & 10 & 10.2124533208688 & -0.21245332086875 \tabularnewline
95 & 9 & 11.4401404868287 & -2.44014048682871 \tabularnewline
96 & 10 & 11.4379445722869 & -1.43794457228688 \tabularnewline
97 & 16 & 13.741619135107 & 2.25838086489298 \tabularnewline
98 & 13 & 12.8237520019172 & 0.17624799808281 \tabularnewline
99 & 9 & 10.8098388947891 & -1.80983889478915 \tabularnewline
100 & 10 & 11.378532076788 & -1.37853207678798 \tabularnewline
101 & 10 & 10.9689523566192 & -0.968952356619231 \tabularnewline
102 & 7 & 9.47465402457373 & -2.47465402457373 \tabularnewline
103 & 9 & 9.8265428213743 & -0.826542821374303 \tabularnewline
104 & 8 & 10.2188112017114 & -2.21881120171137 \tabularnewline
105 & 14 & 12.8472637784596 & 1.1527362215404 \tabularnewline
106 & 14 & 11.6477018919235 & 2.3522981080765 \tabularnewline
107 & 8 & 11.0805233342968 & -3.08052333429676 \tabularnewline
108 & 9 & 11.5401625785906 & -2.54016257859058 \tabularnewline
109 & 14 & 11.8064163349481 & 2.19358366505186 \tabularnewline
110 & 14 & 10.7412291054721 & 3.25877089452787 \tabularnewline
111 & 8 & 9.86606244753663 & -1.86606244753663 \tabularnewline
112 & 8 & 13.6926336074711 & -5.69263360747113 \tabularnewline
113 & 8 & 10.9525118828595 & -2.95251188285948 \tabularnewline
114 & 7 & 8.55085509620331 & -1.55085509620331 \tabularnewline
115 & 6 & 7.44027047059842 & -1.44027047059841 \tabularnewline
116 & 8 & 9.4148100433135 & -1.4148100433135 \tabularnewline
117 & 6 & 8.31398706901332 & -2.31398706901332 \tabularnewline
118 & 11 & 9.87854261184873 & 1.12145738815127 \tabularnewline
119 & 14 & 11.8405591974766 & 2.15944080252337 \tabularnewline
120 & 11 & 11.1231055546712 & -0.123105554671166 \tabularnewline
121 & 11 & 11.9576290110093 & -0.95762901100931 \tabularnewline
122 & 11 & 9.2090803108136 & 1.7909196891864 \tabularnewline
123 & 14 & 10.36967612917 & 3.63032387082998 \tabularnewline
124 & 8 & 10.3570748050233 & -2.35707480502331 \tabularnewline
125 & 20 & 11.4803602064078 & 8.51963979359219 \tabularnewline
126 & 11 & 10.3193075266905 & 0.680692473309463 \tabularnewline
127 & 8 & 9.14405571417421 & -1.14405571417421 \tabularnewline
128 & 11 & 10.7075672323317 & 0.29243276766828 \tabularnewline
129 & 10 & 10.6161673181472 & -0.616167318147201 \tabularnewline
130 & 14 & 13.4485919547856 & 0.551408045214407 \tabularnewline
131 & 11 & 10.558105430536 & 0.441894569463998 \tabularnewline
132 & 9 & 10.5952141906548 & -1.59521419065478 \tabularnewline
133 & 9 & 9.71061082418663 & -0.710610824186625 \tabularnewline
134 & 8 & 10.1579861032424 & -2.15798610324236 \tabularnewline
135 & 10 & 12.0589843809759 & -2.05898438097594 \tabularnewline
136 & 13 & 10.7188783609152 & 2.28112163908477 \tabularnewline
137 & 13 & 10.111501382101 & 2.88849861789897 \tabularnewline
138 & 12 & 9.29557530863078 & 2.70442469136922 \tabularnewline
139 & 8 & 10.35973838043 & -2.35973838042996 \tabularnewline
140 & 13 & 11.0143393969289 & 1.98566060307108 \tabularnewline
141 & 14 & 12.4310064834081 & 1.56899351659187 \tabularnewline
142 & 12 & 11.6674281750066 & 0.332571824993393 \tabularnewline
143 & 14 & 10.9169138958967 & 3.08308610410329 \tabularnewline
144 & 15 & 11.2948106795225 & 3.7051893204775 \tabularnewline
145 & 13 & 10.5165955153937 & 2.4834044846063 \tabularnewline
146 & 16 & 11.8561435650783 & 4.14385643492171 \tabularnewline
147 & 9 & 11.996511861034 & -2.99651186103404 \tabularnewline
148 & 9 & 10.4782744530865 & -1.47827445308649 \tabularnewline
149 & 9 & 11.0181531709424 & -2.01815317094244 \tabularnewline
150 & 8 & 11.2929483065479 & -3.2929483065479 \tabularnewline
151 & 7 & 10.0317337558054 & -3.0317337558054 \tabularnewline
152 & 16 & 11.9492341671955 & 4.05076583280446 \tabularnewline
153 & 11 & 13.3481625806087 & -2.34816258060873 \tabularnewline
154 & 9 & 10.0171718546235 & -1.01717185462351 \tabularnewline
155 & 11 & 9.83326343672508 & 1.16673656327492 \tabularnewline
156 & 9 & 9.86642518204478 & -0.866425182044778 \tabularnewline
157 & 14 & 12.4789828392728 & 1.52101716072717 \tabularnewline
158 & 13 & 11.0220394382314 & 1.97796056176859 \tabularnewline
159 & 16 & 14.4647615248554 & 1.53523847514457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&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]14[/C][C]12.2931271728625[/C][C]1.7068728271375[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.8542529115495[/C][C]-0.854252911549478[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]8.06227222418893[/C][C]-2.06227222418893[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.9476618702182[/C][C]1.05233812978182[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]9.86771537760798[/C][C]-1.86771537760798[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.1722752103671[/C][C]-0.172275210367121[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]9.81550154801854[/C][C]0.184498451981456[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.79825771186774[/C][C]1.20174228813226[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]10.8629259764013[/C][C]5.13707402359868[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]10.0684635479381[/C][C]0.931536452061921[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]12.4680172993328[/C][C]0.531982700667187[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.0446779919111[/C][C]-1.04467799191107[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]13.0401936526073[/C][C]-5.0401936526073[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.6464306695575[/C][C]1.35356933044252[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]9.21654262865862[/C][C]1.78345737134138[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.34559388921211[/C][C]-3.34559388921211[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]11.0418798989866[/C][C]-2.04187989898656[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]9.84912475091086[/C][C]-1.84912475091086[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]10.0892984205506[/C][C]-2.08929842055058[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]12.7857320142213[/C][C]1.21426798577869[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]12.2406635388646[/C][C]2.75933646113537[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]13.8107907121416[/C][C]2.18920928785844[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]11.7886240057284[/C][C]-2.78862400572839[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.3750612147334[/C][C]-0.375061214733427[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.4470289360053[/C][C]-0.447028936005265[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]9.40860163673439[/C][C]-1.40860163673439[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.57702770843314[/C][C]-0.577027708433137[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]11.6336575242935[/C][C]-2.63365752429345[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.493867050423[/C][C]-1.49386705042302[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.50925250089076[/C][C]-0.509252500890763[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]11.7568378002112[/C][C]-1.75683780021115[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]11.7865625795444[/C][C]4.21343742045558[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]9.17755357083023[/C][C]1.82244642916977[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.0509478301168[/C][C]-2.05094783011685[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.73193357843342[/C][C]0.268066421566578[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]12.6496652378379[/C][C]3.35033476216208[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.6518033098243[/C][C]-1.65180330982433[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]9.32356466830619[/C][C]6.67643533169381[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]12.3345724892786[/C][C]-0.334572489278586[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]10.4081205112709[/C][C]1.59187948872914[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.5552310099955[/C][C]1.44476899000448[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.513552972001[/C][C]-1.51355297200096[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]10.720577052591[/C][C]-0.720577052591046[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]8.46723311669603[/C][C]0.532766883303966[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.0998879313339[/C][C]-0.0998879313338744[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.1223852113249[/C][C]1.8776147886751[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]11.6185904303772[/C][C]2.38140956962285[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]12.1024170814173[/C][C]1.89758291858266[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]12.3199460232452[/C][C]-2.3199460232452[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]9.93003717571406[/C][C]4.06996282428594[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]12.2190557443743[/C][C]3.78094425562571[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.4610692871838[/C][C]-1.46106928718377[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.5899396475331[/C][C]-1.58993964753306[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]9.04003585641423[/C][C]-3.04003585641423[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]11.2797735574175[/C][C]-3.27977355741748[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.4239821144831[/C][C]0.576017885516871[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.8246452755993[/C][C]-0.824645275599272[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.90914169166691[/C][C]-0.909141691666908[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.15425950080064[/C][C]-2.15425950080064[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]9.8273007573465[/C][C]5.1726992426535[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.89283171897349[/C][C]-0.892831718973492[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.2300011704603[/C][C]-0.230001170460277[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.2127960045576[/C][C]1.78720399544244[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]10.4557571222761[/C][C]2.54424287772388[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]8.4082399506884[/C][C]1.5917600493116[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.8100631875226[/C][C]-0.810063187522585[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]13.6099912716085[/C][C]-5.60999127160853[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]9.12149677428637[/C][C]-0.121496774286372[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.63232673895495[/C][C]4.36767326104505[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.4056197047763[/C][C]0.594380295223706[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]12.7748897940749[/C][C]-4.77488979407492[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.7705154168815[/C][C]-1.77051541688151[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]12.3432616515329[/C][C]-3.34326165153292[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.5112633288267[/C][C]2.48873667117329[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]11.1799243643951[/C][C]-2.1799243643951[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]11.5683831232456[/C][C]-1.56838312324565[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]8.92549222497563[/C][C]5.07450777502436[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.9510671481191[/C][C]1.04893285188092[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.0860720089643[/C][C]0.913927991035744[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.5603427842052[/C][C]-0.560342784205161[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]11.4548286127589[/C][C]2.54517138724109[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]11.555341171216[/C][C]-5.55534117121603[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.2666600244991[/C][C]0.733339975500888[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.0611961307754[/C][C]-2.06119613077543[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]12.4002108724069[/C][C]1.59978912759315[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.8285356954152[/C][C]0.171464304584766[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.90994400559581[/C][C]0.0900559944041917[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]10.1965799596067[/C][C]3.80342004039329[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.0383027999683[/C][C]-0.0383027999683423[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]11.0020524420421[/C][C]-1.00205244204212[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.0089075749108[/C][C]0.991092425089236[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]8.98335267342666[/C][C]-3.98335267342666[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]10.4977291896848[/C][C]0.50227081031523[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.2124533208688[/C][C]-0.21245332086875[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]11.4401404868287[/C][C]-2.44014048682871[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4379445722869[/C][C]-1.43794457228688[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.741619135107[/C][C]2.25838086489298[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.8237520019172[/C][C]0.17624799808281[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.8098388947891[/C][C]-1.80983889478915[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.378532076788[/C][C]-1.37853207678798[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.9689523566192[/C][C]-0.968952356619231[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]9.47465402457373[/C][C]-2.47465402457373[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]9.8265428213743[/C][C]-0.826542821374303[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]10.2188112017114[/C][C]-2.21881120171137[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.8472637784596[/C][C]1.1527362215404[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]11.6477018919235[/C][C]2.3522981080765[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]11.0805233342968[/C][C]-3.08052333429676[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]11.5401625785906[/C][C]-2.54016257859058[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.8064163349481[/C][C]2.19358366505186[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]10.7412291054721[/C][C]3.25877089452787[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]9.86606244753663[/C][C]-1.86606244753663[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]13.6926336074711[/C][C]-5.69263360747113[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.9525118828595[/C][C]-2.95251188285948[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]8.55085509620331[/C][C]-1.55085509620331[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]7.44027047059842[/C][C]-1.44027047059841[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]9.4148100433135[/C][C]-1.4148100433135[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]8.31398706901332[/C][C]-2.31398706901332[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]9.87854261184873[/C][C]1.12145738815127[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]11.8405591974766[/C][C]2.15944080252337[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]11.1231055546712[/C][C]-0.123105554671166[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]11.9576290110093[/C][C]-0.95762901100931[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]9.2090803108136[/C][C]1.7909196891864[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]10.36967612917[/C][C]3.63032387082998[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.3570748050233[/C][C]-2.35707480502331[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]11.4803602064078[/C][C]8.51963979359219[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.3193075266905[/C][C]0.680692473309463[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.14405571417421[/C][C]-1.14405571417421[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.7075672323317[/C][C]0.29243276766828[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.6161673181472[/C][C]-0.616167318147201[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.4485919547856[/C][C]0.551408045214407[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.558105430536[/C][C]0.441894569463998[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.5952141906548[/C][C]-1.59521419065478[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]9.71061082418663[/C][C]-0.710610824186625[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.1579861032424[/C][C]-2.15798610324236[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]12.0589843809759[/C][C]-2.05898438097594[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.7188783609152[/C][C]2.28112163908477[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]10.111501382101[/C][C]2.88849861789897[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]9.29557530863078[/C][C]2.70442469136922[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]10.35973838043[/C][C]-2.35973838042996[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]11.0143393969289[/C][C]1.98566060307108[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]12.4310064834081[/C][C]1.56899351659187[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.6674281750066[/C][C]0.332571824993393[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.9169138958967[/C][C]3.08308610410329[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]11.2948106795225[/C][C]3.7051893204775[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]10.5165955153937[/C][C]2.4834044846063[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]11.8561435650783[/C][C]4.14385643492171[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]11.996511861034[/C][C]-2.99651186103404[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.4782744530865[/C][C]-1.47827445308649[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.0181531709424[/C][C]-2.01815317094244[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]11.2929483065479[/C][C]-3.2929483065479[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]10.0317337558054[/C][C]-3.0317337558054[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]11.9492341671955[/C][C]4.05076583280446[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]13.3481625806087[/C][C]-2.34816258060873[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]10.0171718546235[/C][C]-1.01717185462351[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]9.83326343672508[/C][C]1.16673656327492[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.86642518204478[/C][C]-0.866425182044778[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.4789828392728[/C][C]1.52101716072717[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.0220394382314[/C][C]1.97796056176859[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.4647615248554[/C][C]1.53523847514457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.29312717286251.7068728271375
21111.8542529115495-0.854252911549478
368.06227222418893-2.06227222418893
41210.94766187021821.05233812978182
589.86771537760798-1.86771537760798
61010.1722752103671-0.172275210367121
7109.815501548018540.184498451981456
8119.798257711867741.20174228813226
91610.86292597640135.13707402359868
101110.06846354793810.931536452061921
111312.46801729933280.531982700667187
121213.0446779919111-1.04467799191107
13813.0401936526073-5.0401936526073
141210.64643066955751.35356933044252
15119.216542628658621.78345737134138
1647.34559388921211-3.34559388921211
17911.0418798989866-2.04187989898656
1889.84912475091086-1.84912475091086
19810.0892984205506-2.08929842055058
201412.78573201422131.21426798577869
211512.24066353886462.75933646113537
221613.81079071214162.18920928785844
23911.7886240057284-2.78862400572839
241414.3750612147334-0.375061214733427
251111.4470289360053-0.447028936005265
2689.40860163673439-1.40860163673439
2799.57702770843314-0.577027708433137
28911.6336575242935-2.63365752429345
29910.493867050423-1.49386705042302
3099.50925250089076-0.509252500890763
311011.7568378002112-1.75683780021115
321611.78656257954444.21343742045558
33119.177553570830231.82244642916977
34810.0509478301168-2.05094783011685
3598.731933578433420.268066421566578
361612.64966523783793.35033476216208
371112.6518033098243-1.65180330982433
38169.323564668306196.67643533169381
391212.3345724892786-0.334572489278586
401210.40812051127091.59187948872914
411412.55523100999551.44476899000448
42910.513552972001-1.51355297200096
431010.720577052591-0.720577052591046
4498.467233116696030.532766883303966
451010.0998879313339-0.0998879313338744
461210.12238521132491.8776147886751
471411.61859043037722.38140956962285
481412.10241708141731.89758291858266
491012.3199460232452-2.3199460232452
50149.930037175714064.06996282428594
511612.21905574437433.78094425562571
52910.4610692871838-1.46106928718377
531011.5899396475331-1.58993964753306
5469.04003585641423-3.04003585641423
55811.2797735574175-3.27977355741748
561312.42398211448310.576017885516871
571010.8246452755993-0.824645275599272
5888.90914169166691-0.909141691666908
5979.15425950080064-2.15425950080064
60159.82730075734655.1726992426535
6199.89283171897349-0.892831718973492
621010.2300011704603-0.230001170460277
631210.21279600455761.78720399544244
641310.45575712227612.54424287772388
65108.40823995068841.5917600493116
661111.8100631875226-0.810063187522585
67813.6099912716085-5.60999127160853
6899.12149677428637-0.121496774286372
69138.632326738954954.36767326104505
701110.40561970477630.594380295223706
71812.7748897940749-4.77488979407492
72910.7705154168815-1.77051541688151
73912.3432616515329-3.34326165153292
741512.51126332882672.48873667117329
75911.1799243643951-2.1799243643951
761011.5683831232456-1.56838312324565
77148.925492224975635.07450777502436
781210.95106714811911.04893285188092
791211.08607200896430.913927991035744
801111.5603427842052-0.560342784205161
811411.45482861275892.54517138724109
82611.555341171216-5.55534117121603
831211.26666002449910.733339975500888
84810.0611961307754-2.06119613077543
851412.40021087240691.59978912759315
861110.82853569541520.171464304584766
87109.909944005595810.0900559944041917
881410.19657995960673.80342004039329
891212.0383027999683-0.0383027999683423
901011.0020524420421-1.00205244204212
911413.00890757491080.991092425089236
9258.98335267342666-3.98335267342666
931110.49772918968480.50227081031523
941010.2124533208688-0.21245332086875
95911.4401404868287-2.44014048682871
961011.4379445722869-1.43794457228688
971613.7416191351072.25838086489298
981312.82375200191720.17624799808281
99910.8098388947891-1.80983889478915
1001011.378532076788-1.37853207678798
1011010.9689523566192-0.968952356619231
10279.47465402457373-2.47465402457373
10399.8265428213743-0.826542821374303
104810.2188112017114-2.21881120171137
1051412.84726377845961.1527362215404
1061411.64770189192352.3522981080765
107811.0805233342968-3.08052333429676
108911.5401625785906-2.54016257859058
1091411.80641633494812.19358366505186
1101410.74122910547213.25877089452787
11189.86606244753663-1.86606244753663
112813.6926336074711-5.69263360747113
113810.9525118828595-2.95251188285948
11478.55085509620331-1.55085509620331
11567.44027047059842-1.44027047059841
11689.4148100433135-1.4148100433135
11768.31398706901332-2.31398706901332
118119.878542611848731.12145738815127
1191411.84055919747662.15944080252337
1201111.1231055546712-0.123105554671166
1211111.9576290110093-0.95762901100931
122119.20908031081361.7909196891864
1231410.369676129173.63032387082998
124810.3570748050233-2.35707480502331
1252011.48036020640788.51963979359219
1261110.31930752669050.680692473309463
12789.14405571417421-1.14405571417421
1281110.70756723233170.29243276766828
1291010.6161673181472-0.616167318147201
1301413.44859195478560.551408045214407
1311110.5581054305360.441894569463998
132910.5952141906548-1.59521419065478
13399.71061082418663-0.710610824186625
134810.1579861032424-2.15798610324236
1351012.0589843809759-2.05898438097594
1361310.71887836091522.28112163908477
1371310.1115013821012.88849861789897
138129.295575308630782.70442469136922
139810.35973838043-2.35973838042996
1401311.01433939692891.98566060307108
1411412.43100648340811.56899351659187
1421211.66742817500660.332571824993393
1431410.91691389589673.08308610410329
1441511.29481067952253.7051893204775
1451310.51659551539372.4834044846063
1461611.85614356507834.14385643492171
147911.996511861034-2.99651186103404
148910.4782744530865-1.47827445308649
149911.0181531709424-2.01815317094244
150811.2929483065479-3.2929483065479
151710.0317337558054-3.0317337558054
1521611.94923416719554.05076583280446
1531113.3481625806087-2.34816258060873
154910.0171718546235-1.01717185462351
155119.833263436725081.16673656327492
15699.86642518204478-0.866425182044778
1571412.47898283927281.52101716072717
1581311.02203943823141.97796056176859
1591614.46476152485541.53523847514457







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.4760910550835970.9521821101671930.523908944916403
100.3201550270949730.6403100541899450.679844972905027
110.3248035770065440.6496071540130890.675196422993456
120.2332662135478370.4665324270956730.766733786452163
130.7935649342439490.4128701315121030.206435065756051
140.7182915930166480.5634168139667040.281708406983352
150.6411679409241780.7176641181516450.358832059075822
160.6876981015960450.6246037968079090.312301898403955
170.6671424506926950.665715098614610.332857549307305
180.6450498443546710.7099003112906590.354950155645329
190.576319759185510.847360481628980.42368024081449
200.5750993130201880.8498013739596240.424900686979812
210.5854050607462210.8291898785075570.414594939253779
220.5551267227454310.8897465545091380.444873277254569
230.5580177092238270.8839645815523460.441982290776173
240.5100359875155980.9799280249688050.489964012484402
250.439543343723470.879086687446940.56045665627653
260.3772782191390640.7545564382781270.622721780860936
270.3140968973300810.6281937946601620.685903102669919
280.3066929128411290.6133858256822590.693307087158871
290.2602298920831540.5204597841663070.739770107916846
300.210038175247730.4200763504954590.78996182475227
310.1928814458251910.3857628916503820.807118554174809
320.2628840279305760.5257680558611520.737115972069424
330.2353967668760130.4707935337520260.764603233123987
340.2350070384630680.4700140769261360.764992961536932
350.1914058803216810.3828117606433620.808594119678319
360.2123026424518890.4246052849037780.787697357548111
370.209648116699030.419296233398060.79035188330097
380.6065409214176010.7869181571647980.393459078582399
390.5541357040779060.8917285918441880.445864295922094
400.5209909649787220.9580180700425560.479009035021278
410.4812581833942130.9625163667884260.518741816605787
420.4465859091935920.8931718183871840.553414090806408
430.3991132253500860.7982264507001720.600886774649914
440.3504186908920970.7008373817841930.649581309107903
450.301831574820830.6036631496416590.69816842517917
460.2853008566236090.5706017132472180.714699143376391
470.2768806420432340.5537612840864690.723119357956766
480.2559988860107930.5119977720215860.744001113989207
490.2566997297984680.5133994595969370.743300270201532
500.3202450943021860.6404901886043720.679754905697814
510.3708248884582020.7416497769164050.629175111541798
520.3408376593679320.6816753187358640.659162340632068
530.3149336797837010.6298673595674030.685066320216299
540.3317609942292090.6635219884584180.668239005770791
550.3516627269695550.703325453939110.648337273030445
560.3078419809111960.6156839618223930.692158019088804
570.2702389864801310.5404779729602620.729761013519869
580.235665698972970.471331397945940.76433430102703
590.2232941948092930.4465883896185860.776705805190707
600.3579269921742770.7158539843485540.642073007825723
610.3174180963853770.6348361927707540.682581903614623
620.2763943664137770.5527887328275550.723605633586223
630.2565355137038470.5130710274076940.743464486296153
640.267828266525850.5356565330517010.73217173347415
650.244010307289830.488020614579660.75598969271017
660.2123243296541280.4246486593082570.787675670345872
670.3900405194804410.7800810389608820.609959480519559
680.3452248333952020.6904496667904050.654775166604798
690.4348223328023870.8696446656047740.565177667197613
700.3914104293847130.7828208587694260.608589570615287
710.5189491596785250.9621016806429510.481050840321475
720.4964641348828480.9929282697656970.503535865117152
730.5316413619160290.9367172761679430.468358638083971
740.531792542649690.936414914700620.46820745735031
750.5199119996504830.9601760006990340.480088000349517
760.4924269756511760.9848539513023520.507573024348824
770.6444577932175830.7110844135648330.355542206782417
780.6084456071006010.7831087857987970.391554392899399
790.5690317708749190.8619364582501630.430968229125081
800.5262010242553730.9475979514892540.473798975744627
810.5276742274230920.9446515451538160.472325772576908
820.699493157708580.6010136845828410.30050684229142
830.6612887997968870.6774224004062250.338711200203113
840.6442793134014950.711441373197010.355720686598505
850.6158431184276750.768313763144650.384156881572325
860.571432471276690.857135057446620.42856752872331
870.5251999182376910.9496001635246180.474800081762309
880.5947034542766070.8105930914467870.405296545723393
890.5501320021921650.899735995615670.449867997807835
900.5095759725822570.9808480548354870.490424027417743
910.4695217894787720.9390435789575440.530478210521228
920.5280562790193020.9438874419613970.471943720980698
930.48426725764520.9685345152903990.5157327423548
940.4372002147788650.874400429557730.562799785221135
950.433931396934590.867862793869180.56606860306541
960.404100875860120.808201751720240.59589912413988
970.3881945294911050.7763890589822090.611805470508895
980.343594980740840.6871899614816810.65640501925916
990.3201680745847970.6403361491695950.679831925415203
1000.2903850907658410.5807701815316810.709614909234159
1010.2563523177064830.5127046354129650.743647682293518
1020.2485215058385210.4970430116770430.751478494161479
1030.2138548464631030.4277096929262060.786145153536897
1040.2040883099245420.4081766198490850.795911690075458
1050.1751053196910620.3502106393821240.824894680308938
1060.1705582360070880.3411164720141770.829441763992912
1070.1845784174631280.3691568349262560.815421582536872
1080.1892484344544850.378496868908970.810751565545515
1090.1767211157757240.3534422315514480.823278884224276
1100.2020290859471660.4040581718943310.797970914052834
1110.1813187487825350.362637497565070.818681251217465
1120.4064985654256340.8129971308512680.593501434574366
1130.4654861537006630.9309723074013270.534513846299337
1140.4296490704198640.8592981408397280.570350929580136
1150.4221359960815160.8442719921630310.577864003918484
1160.3797399711744270.7594799423488550.620260028825573
1170.3654728599611790.7309457199223570.634527140038821
1180.3234207292343270.6468414584686530.676579270765673
1190.3023314100445040.6046628200890080.697668589955496
1200.2575413582707130.5150827165414250.742458641729287
1210.2292171408644740.4584342817289480.770782859135526
1220.2159628943072030.4319257886144070.784037105692797
1230.227881162521210.455762325042420.77211883747879
1240.2414529895261930.4829059790523870.758547010473807
1250.7909565384385380.4180869231229250.209043461561462
1260.7545370211290960.4909259577418090.245462978870904
1270.707232254855740.585535490288520.29276774514426
1280.6506107075469160.6987785849061680.349389292453084
1290.5912115626811270.8175768746377460.408788437318873
1300.5297755104851690.9404489790296630.470224489514831
1310.4735939314960030.9471878629920050.526406068503997
1320.4183883637591680.8367767275183350.581611636240832
1330.3562589083787210.7125178167574430.643741091621279
1340.3167361894820380.6334723789640760.683263810517962
1350.2853675651239480.5707351302478950.714632434876052
1360.2626906262701640.5253812525403290.737309373729836
1370.2989576152246370.5979152304492750.701042384775363
1380.3079749035670510.6159498071341020.692025096432949
1390.3283313735041980.6566627470083960.671668626495802
1400.2653169781260990.5306339562521980.734683021873901
1410.2082087039308980.4164174078617960.791791296069102
1420.1580485502325650.3160971004651290.841951449767435
1430.1844470314786920.3688940629573830.815552968521309
1440.1735916470909870.3471832941819750.826408352909013
1450.1562550312980210.3125100625960410.843744968701979
1460.2732007710779950.546401542155990.726799228922005
1470.2397919065037310.4795838130074610.760208093496269
1480.1592210510123050.3184421020246110.840778948987695
1490.112589417860190.2251788357203790.88741058213981
1500.160999460301460.321998920602920.83900053969854

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.476091055083597 & 0.952182110167193 & 0.523908944916403 \tabularnewline
10 & 0.320155027094973 & 0.640310054189945 & 0.679844972905027 \tabularnewline
11 & 0.324803577006544 & 0.649607154013089 & 0.675196422993456 \tabularnewline
12 & 0.233266213547837 & 0.466532427095673 & 0.766733786452163 \tabularnewline
13 & 0.793564934243949 & 0.412870131512103 & 0.206435065756051 \tabularnewline
14 & 0.718291593016648 & 0.563416813966704 & 0.281708406983352 \tabularnewline
15 & 0.641167940924178 & 0.717664118151645 & 0.358832059075822 \tabularnewline
16 & 0.687698101596045 & 0.624603796807909 & 0.312301898403955 \tabularnewline
17 & 0.667142450692695 & 0.66571509861461 & 0.332857549307305 \tabularnewline
18 & 0.645049844354671 & 0.709900311290659 & 0.354950155645329 \tabularnewline
19 & 0.57631975918551 & 0.84736048162898 & 0.42368024081449 \tabularnewline
20 & 0.575099313020188 & 0.849801373959624 & 0.424900686979812 \tabularnewline
21 & 0.585405060746221 & 0.829189878507557 & 0.414594939253779 \tabularnewline
22 & 0.555126722745431 & 0.889746554509138 & 0.444873277254569 \tabularnewline
23 & 0.558017709223827 & 0.883964581552346 & 0.441982290776173 \tabularnewline
24 & 0.510035987515598 & 0.979928024968805 & 0.489964012484402 \tabularnewline
25 & 0.43954334372347 & 0.87908668744694 & 0.56045665627653 \tabularnewline
26 & 0.377278219139064 & 0.754556438278127 & 0.622721780860936 \tabularnewline
27 & 0.314096897330081 & 0.628193794660162 & 0.685903102669919 \tabularnewline
28 & 0.306692912841129 & 0.613385825682259 & 0.693307087158871 \tabularnewline
29 & 0.260229892083154 & 0.520459784166307 & 0.739770107916846 \tabularnewline
30 & 0.21003817524773 & 0.420076350495459 & 0.78996182475227 \tabularnewline
31 & 0.192881445825191 & 0.385762891650382 & 0.807118554174809 \tabularnewline
32 & 0.262884027930576 & 0.525768055861152 & 0.737115972069424 \tabularnewline
33 & 0.235396766876013 & 0.470793533752026 & 0.764603233123987 \tabularnewline
34 & 0.235007038463068 & 0.470014076926136 & 0.764992961536932 \tabularnewline
35 & 0.191405880321681 & 0.382811760643362 & 0.808594119678319 \tabularnewline
36 & 0.212302642451889 & 0.424605284903778 & 0.787697357548111 \tabularnewline
37 & 0.20964811669903 & 0.41929623339806 & 0.79035188330097 \tabularnewline
38 & 0.606540921417601 & 0.786918157164798 & 0.393459078582399 \tabularnewline
39 & 0.554135704077906 & 0.891728591844188 & 0.445864295922094 \tabularnewline
40 & 0.520990964978722 & 0.958018070042556 & 0.479009035021278 \tabularnewline
41 & 0.481258183394213 & 0.962516366788426 & 0.518741816605787 \tabularnewline
42 & 0.446585909193592 & 0.893171818387184 & 0.553414090806408 \tabularnewline
43 & 0.399113225350086 & 0.798226450700172 & 0.600886774649914 \tabularnewline
44 & 0.350418690892097 & 0.700837381784193 & 0.649581309107903 \tabularnewline
45 & 0.30183157482083 & 0.603663149641659 & 0.69816842517917 \tabularnewline
46 & 0.285300856623609 & 0.570601713247218 & 0.714699143376391 \tabularnewline
47 & 0.276880642043234 & 0.553761284086469 & 0.723119357956766 \tabularnewline
48 & 0.255998886010793 & 0.511997772021586 & 0.744001113989207 \tabularnewline
49 & 0.256699729798468 & 0.513399459596937 & 0.743300270201532 \tabularnewline
50 & 0.320245094302186 & 0.640490188604372 & 0.679754905697814 \tabularnewline
51 & 0.370824888458202 & 0.741649776916405 & 0.629175111541798 \tabularnewline
52 & 0.340837659367932 & 0.681675318735864 & 0.659162340632068 \tabularnewline
53 & 0.314933679783701 & 0.629867359567403 & 0.685066320216299 \tabularnewline
54 & 0.331760994229209 & 0.663521988458418 & 0.668239005770791 \tabularnewline
55 & 0.351662726969555 & 0.70332545393911 & 0.648337273030445 \tabularnewline
56 & 0.307841980911196 & 0.615683961822393 & 0.692158019088804 \tabularnewline
57 & 0.270238986480131 & 0.540477972960262 & 0.729761013519869 \tabularnewline
58 & 0.23566569897297 & 0.47133139794594 & 0.76433430102703 \tabularnewline
59 & 0.223294194809293 & 0.446588389618586 & 0.776705805190707 \tabularnewline
60 & 0.357926992174277 & 0.715853984348554 & 0.642073007825723 \tabularnewline
61 & 0.317418096385377 & 0.634836192770754 & 0.682581903614623 \tabularnewline
62 & 0.276394366413777 & 0.552788732827555 & 0.723605633586223 \tabularnewline
63 & 0.256535513703847 & 0.513071027407694 & 0.743464486296153 \tabularnewline
64 & 0.26782826652585 & 0.535656533051701 & 0.73217173347415 \tabularnewline
65 & 0.24401030728983 & 0.48802061457966 & 0.75598969271017 \tabularnewline
66 & 0.212324329654128 & 0.424648659308257 & 0.787675670345872 \tabularnewline
67 & 0.390040519480441 & 0.780081038960882 & 0.609959480519559 \tabularnewline
68 & 0.345224833395202 & 0.690449666790405 & 0.654775166604798 \tabularnewline
69 & 0.434822332802387 & 0.869644665604774 & 0.565177667197613 \tabularnewline
70 & 0.391410429384713 & 0.782820858769426 & 0.608589570615287 \tabularnewline
71 & 0.518949159678525 & 0.962101680642951 & 0.481050840321475 \tabularnewline
72 & 0.496464134882848 & 0.992928269765697 & 0.503535865117152 \tabularnewline
73 & 0.531641361916029 & 0.936717276167943 & 0.468358638083971 \tabularnewline
74 & 0.53179254264969 & 0.93641491470062 & 0.46820745735031 \tabularnewline
75 & 0.519911999650483 & 0.960176000699034 & 0.480088000349517 \tabularnewline
76 & 0.492426975651176 & 0.984853951302352 & 0.507573024348824 \tabularnewline
77 & 0.644457793217583 & 0.711084413564833 & 0.355542206782417 \tabularnewline
78 & 0.608445607100601 & 0.783108785798797 & 0.391554392899399 \tabularnewline
79 & 0.569031770874919 & 0.861936458250163 & 0.430968229125081 \tabularnewline
80 & 0.526201024255373 & 0.947597951489254 & 0.473798975744627 \tabularnewline
81 & 0.527674227423092 & 0.944651545153816 & 0.472325772576908 \tabularnewline
82 & 0.69949315770858 & 0.601013684582841 & 0.30050684229142 \tabularnewline
83 & 0.661288799796887 & 0.677422400406225 & 0.338711200203113 \tabularnewline
84 & 0.644279313401495 & 0.71144137319701 & 0.355720686598505 \tabularnewline
85 & 0.615843118427675 & 0.76831376314465 & 0.384156881572325 \tabularnewline
86 & 0.57143247127669 & 0.85713505744662 & 0.42856752872331 \tabularnewline
87 & 0.525199918237691 & 0.949600163524618 & 0.474800081762309 \tabularnewline
88 & 0.594703454276607 & 0.810593091446787 & 0.405296545723393 \tabularnewline
89 & 0.550132002192165 & 0.89973599561567 & 0.449867997807835 \tabularnewline
90 & 0.509575972582257 & 0.980848054835487 & 0.490424027417743 \tabularnewline
91 & 0.469521789478772 & 0.939043578957544 & 0.530478210521228 \tabularnewline
92 & 0.528056279019302 & 0.943887441961397 & 0.471943720980698 \tabularnewline
93 & 0.4842672576452 & 0.968534515290399 & 0.5157327423548 \tabularnewline
94 & 0.437200214778865 & 0.87440042955773 & 0.562799785221135 \tabularnewline
95 & 0.43393139693459 & 0.86786279386918 & 0.56606860306541 \tabularnewline
96 & 0.40410087586012 & 0.80820175172024 & 0.59589912413988 \tabularnewline
97 & 0.388194529491105 & 0.776389058982209 & 0.611805470508895 \tabularnewline
98 & 0.34359498074084 & 0.687189961481681 & 0.65640501925916 \tabularnewline
99 & 0.320168074584797 & 0.640336149169595 & 0.679831925415203 \tabularnewline
100 & 0.290385090765841 & 0.580770181531681 & 0.709614909234159 \tabularnewline
101 & 0.256352317706483 & 0.512704635412965 & 0.743647682293518 \tabularnewline
102 & 0.248521505838521 & 0.497043011677043 & 0.751478494161479 \tabularnewline
103 & 0.213854846463103 & 0.427709692926206 & 0.786145153536897 \tabularnewline
104 & 0.204088309924542 & 0.408176619849085 & 0.795911690075458 \tabularnewline
105 & 0.175105319691062 & 0.350210639382124 & 0.824894680308938 \tabularnewline
106 & 0.170558236007088 & 0.341116472014177 & 0.829441763992912 \tabularnewline
107 & 0.184578417463128 & 0.369156834926256 & 0.815421582536872 \tabularnewline
108 & 0.189248434454485 & 0.37849686890897 & 0.810751565545515 \tabularnewline
109 & 0.176721115775724 & 0.353442231551448 & 0.823278884224276 \tabularnewline
110 & 0.202029085947166 & 0.404058171894331 & 0.797970914052834 \tabularnewline
111 & 0.181318748782535 & 0.36263749756507 & 0.818681251217465 \tabularnewline
112 & 0.406498565425634 & 0.812997130851268 & 0.593501434574366 \tabularnewline
113 & 0.465486153700663 & 0.930972307401327 & 0.534513846299337 \tabularnewline
114 & 0.429649070419864 & 0.859298140839728 & 0.570350929580136 \tabularnewline
115 & 0.422135996081516 & 0.844271992163031 & 0.577864003918484 \tabularnewline
116 & 0.379739971174427 & 0.759479942348855 & 0.620260028825573 \tabularnewline
117 & 0.365472859961179 & 0.730945719922357 & 0.634527140038821 \tabularnewline
118 & 0.323420729234327 & 0.646841458468653 & 0.676579270765673 \tabularnewline
119 & 0.302331410044504 & 0.604662820089008 & 0.697668589955496 \tabularnewline
120 & 0.257541358270713 & 0.515082716541425 & 0.742458641729287 \tabularnewline
121 & 0.229217140864474 & 0.458434281728948 & 0.770782859135526 \tabularnewline
122 & 0.215962894307203 & 0.431925788614407 & 0.784037105692797 \tabularnewline
123 & 0.22788116252121 & 0.45576232504242 & 0.77211883747879 \tabularnewline
124 & 0.241452989526193 & 0.482905979052387 & 0.758547010473807 \tabularnewline
125 & 0.790956538438538 & 0.418086923122925 & 0.209043461561462 \tabularnewline
126 & 0.754537021129096 & 0.490925957741809 & 0.245462978870904 \tabularnewline
127 & 0.70723225485574 & 0.58553549028852 & 0.29276774514426 \tabularnewline
128 & 0.650610707546916 & 0.698778584906168 & 0.349389292453084 \tabularnewline
129 & 0.591211562681127 & 0.817576874637746 & 0.408788437318873 \tabularnewline
130 & 0.529775510485169 & 0.940448979029663 & 0.470224489514831 \tabularnewline
131 & 0.473593931496003 & 0.947187862992005 & 0.526406068503997 \tabularnewline
132 & 0.418388363759168 & 0.836776727518335 & 0.581611636240832 \tabularnewline
133 & 0.356258908378721 & 0.712517816757443 & 0.643741091621279 \tabularnewline
134 & 0.316736189482038 & 0.633472378964076 & 0.683263810517962 \tabularnewline
135 & 0.285367565123948 & 0.570735130247895 & 0.714632434876052 \tabularnewline
136 & 0.262690626270164 & 0.525381252540329 & 0.737309373729836 \tabularnewline
137 & 0.298957615224637 & 0.597915230449275 & 0.701042384775363 \tabularnewline
138 & 0.307974903567051 & 0.615949807134102 & 0.692025096432949 \tabularnewline
139 & 0.328331373504198 & 0.656662747008396 & 0.671668626495802 \tabularnewline
140 & 0.265316978126099 & 0.530633956252198 & 0.734683021873901 \tabularnewline
141 & 0.208208703930898 & 0.416417407861796 & 0.791791296069102 \tabularnewline
142 & 0.158048550232565 & 0.316097100465129 & 0.841951449767435 \tabularnewline
143 & 0.184447031478692 & 0.368894062957383 & 0.815552968521309 \tabularnewline
144 & 0.173591647090987 & 0.347183294181975 & 0.826408352909013 \tabularnewline
145 & 0.156255031298021 & 0.312510062596041 & 0.843744968701979 \tabularnewline
146 & 0.273200771077995 & 0.54640154215599 & 0.726799228922005 \tabularnewline
147 & 0.239791906503731 & 0.479583813007461 & 0.760208093496269 \tabularnewline
148 & 0.159221051012305 & 0.318442102024611 & 0.840778948987695 \tabularnewline
149 & 0.11258941786019 & 0.225178835720379 & 0.88741058213981 \tabularnewline
150 & 0.16099946030146 & 0.32199892060292 & 0.83900053969854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&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]0.476091055083597[/C][C]0.952182110167193[/C][C]0.523908944916403[/C][/ROW]
[ROW][C]10[/C][C]0.320155027094973[/C][C]0.640310054189945[/C][C]0.679844972905027[/C][/ROW]
[ROW][C]11[/C][C]0.324803577006544[/C][C]0.649607154013089[/C][C]0.675196422993456[/C][/ROW]
[ROW][C]12[/C][C]0.233266213547837[/C][C]0.466532427095673[/C][C]0.766733786452163[/C][/ROW]
[ROW][C]13[/C][C]0.793564934243949[/C][C]0.412870131512103[/C][C]0.206435065756051[/C][/ROW]
[ROW][C]14[/C][C]0.718291593016648[/C][C]0.563416813966704[/C][C]0.281708406983352[/C][/ROW]
[ROW][C]15[/C][C]0.641167940924178[/C][C]0.717664118151645[/C][C]0.358832059075822[/C][/ROW]
[ROW][C]16[/C][C]0.687698101596045[/C][C]0.624603796807909[/C][C]0.312301898403955[/C][/ROW]
[ROW][C]17[/C][C]0.667142450692695[/C][C]0.66571509861461[/C][C]0.332857549307305[/C][/ROW]
[ROW][C]18[/C][C]0.645049844354671[/C][C]0.709900311290659[/C][C]0.354950155645329[/C][/ROW]
[ROW][C]19[/C][C]0.57631975918551[/C][C]0.84736048162898[/C][C]0.42368024081449[/C][/ROW]
[ROW][C]20[/C][C]0.575099313020188[/C][C]0.849801373959624[/C][C]0.424900686979812[/C][/ROW]
[ROW][C]21[/C][C]0.585405060746221[/C][C]0.829189878507557[/C][C]0.414594939253779[/C][/ROW]
[ROW][C]22[/C][C]0.555126722745431[/C][C]0.889746554509138[/C][C]0.444873277254569[/C][/ROW]
[ROW][C]23[/C][C]0.558017709223827[/C][C]0.883964581552346[/C][C]0.441982290776173[/C][/ROW]
[ROW][C]24[/C][C]0.510035987515598[/C][C]0.979928024968805[/C][C]0.489964012484402[/C][/ROW]
[ROW][C]25[/C][C]0.43954334372347[/C][C]0.87908668744694[/C][C]0.56045665627653[/C][/ROW]
[ROW][C]26[/C][C]0.377278219139064[/C][C]0.754556438278127[/C][C]0.622721780860936[/C][/ROW]
[ROW][C]27[/C][C]0.314096897330081[/C][C]0.628193794660162[/C][C]0.685903102669919[/C][/ROW]
[ROW][C]28[/C][C]0.306692912841129[/C][C]0.613385825682259[/C][C]0.693307087158871[/C][/ROW]
[ROW][C]29[/C][C]0.260229892083154[/C][C]0.520459784166307[/C][C]0.739770107916846[/C][/ROW]
[ROW][C]30[/C][C]0.21003817524773[/C][C]0.420076350495459[/C][C]0.78996182475227[/C][/ROW]
[ROW][C]31[/C][C]0.192881445825191[/C][C]0.385762891650382[/C][C]0.807118554174809[/C][/ROW]
[ROW][C]32[/C][C]0.262884027930576[/C][C]0.525768055861152[/C][C]0.737115972069424[/C][/ROW]
[ROW][C]33[/C][C]0.235396766876013[/C][C]0.470793533752026[/C][C]0.764603233123987[/C][/ROW]
[ROW][C]34[/C][C]0.235007038463068[/C][C]0.470014076926136[/C][C]0.764992961536932[/C][/ROW]
[ROW][C]35[/C][C]0.191405880321681[/C][C]0.382811760643362[/C][C]0.808594119678319[/C][/ROW]
[ROW][C]36[/C][C]0.212302642451889[/C][C]0.424605284903778[/C][C]0.787697357548111[/C][/ROW]
[ROW][C]37[/C][C]0.20964811669903[/C][C]0.41929623339806[/C][C]0.79035188330097[/C][/ROW]
[ROW][C]38[/C][C]0.606540921417601[/C][C]0.786918157164798[/C][C]0.393459078582399[/C][/ROW]
[ROW][C]39[/C][C]0.554135704077906[/C][C]0.891728591844188[/C][C]0.445864295922094[/C][/ROW]
[ROW][C]40[/C][C]0.520990964978722[/C][C]0.958018070042556[/C][C]0.479009035021278[/C][/ROW]
[ROW][C]41[/C][C]0.481258183394213[/C][C]0.962516366788426[/C][C]0.518741816605787[/C][/ROW]
[ROW][C]42[/C][C]0.446585909193592[/C][C]0.893171818387184[/C][C]0.553414090806408[/C][/ROW]
[ROW][C]43[/C][C]0.399113225350086[/C][C]0.798226450700172[/C][C]0.600886774649914[/C][/ROW]
[ROW][C]44[/C][C]0.350418690892097[/C][C]0.700837381784193[/C][C]0.649581309107903[/C][/ROW]
[ROW][C]45[/C][C]0.30183157482083[/C][C]0.603663149641659[/C][C]0.69816842517917[/C][/ROW]
[ROW][C]46[/C][C]0.285300856623609[/C][C]0.570601713247218[/C][C]0.714699143376391[/C][/ROW]
[ROW][C]47[/C][C]0.276880642043234[/C][C]0.553761284086469[/C][C]0.723119357956766[/C][/ROW]
[ROW][C]48[/C][C]0.255998886010793[/C][C]0.511997772021586[/C][C]0.744001113989207[/C][/ROW]
[ROW][C]49[/C][C]0.256699729798468[/C][C]0.513399459596937[/C][C]0.743300270201532[/C][/ROW]
[ROW][C]50[/C][C]0.320245094302186[/C][C]0.640490188604372[/C][C]0.679754905697814[/C][/ROW]
[ROW][C]51[/C][C]0.370824888458202[/C][C]0.741649776916405[/C][C]0.629175111541798[/C][/ROW]
[ROW][C]52[/C][C]0.340837659367932[/C][C]0.681675318735864[/C][C]0.659162340632068[/C][/ROW]
[ROW][C]53[/C][C]0.314933679783701[/C][C]0.629867359567403[/C][C]0.685066320216299[/C][/ROW]
[ROW][C]54[/C][C]0.331760994229209[/C][C]0.663521988458418[/C][C]0.668239005770791[/C][/ROW]
[ROW][C]55[/C][C]0.351662726969555[/C][C]0.70332545393911[/C][C]0.648337273030445[/C][/ROW]
[ROW][C]56[/C][C]0.307841980911196[/C][C]0.615683961822393[/C][C]0.692158019088804[/C][/ROW]
[ROW][C]57[/C][C]0.270238986480131[/C][C]0.540477972960262[/C][C]0.729761013519869[/C][/ROW]
[ROW][C]58[/C][C]0.23566569897297[/C][C]0.47133139794594[/C][C]0.76433430102703[/C][/ROW]
[ROW][C]59[/C][C]0.223294194809293[/C][C]0.446588389618586[/C][C]0.776705805190707[/C][/ROW]
[ROW][C]60[/C][C]0.357926992174277[/C][C]0.715853984348554[/C][C]0.642073007825723[/C][/ROW]
[ROW][C]61[/C][C]0.317418096385377[/C][C]0.634836192770754[/C][C]0.682581903614623[/C][/ROW]
[ROW][C]62[/C][C]0.276394366413777[/C][C]0.552788732827555[/C][C]0.723605633586223[/C][/ROW]
[ROW][C]63[/C][C]0.256535513703847[/C][C]0.513071027407694[/C][C]0.743464486296153[/C][/ROW]
[ROW][C]64[/C][C]0.26782826652585[/C][C]0.535656533051701[/C][C]0.73217173347415[/C][/ROW]
[ROW][C]65[/C][C]0.24401030728983[/C][C]0.48802061457966[/C][C]0.75598969271017[/C][/ROW]
[ROW][C]66[/C][C]0.212324329654128[/C][C]0.424648659308257[/C][C]0.787675670345872[/C][/ROW]
[ROW][C]67[/C][C]0.390040519480441[/C][C]0.780081038960882[/C][C]0.609959480519559[/C][/ROW]
[ROW][C]68[/C][C]0.345224833395202[/C][C]0.690449666790405[/C][C]0.654775166604798[/C][/ROW]
[ROW][C]69[/C][C]0.434822332802387[/C][C]0.869644665604774[/C][C]0.565177667197613[/C][/ROW]
[ROW][C]70[/C][C]0.391410429384713[/C][C]0.782820858769426[/C][C]0.608589570615287[/C][/ROW]
[ROW][C]71[/C][C]0.518949159678525[/C][C]0.962101680642951[/C][C]0.481050840321475[/C][/ROW]
[ROW][C]72[/C][C]0.496464134882848[/C][C]0.992928269765697[/C][C]0.503535865117152[/C][/ROW]
[ROW][C]73[/C][C]0.531641361916029[/C][C]0.936717276167943[/C][C]0.468358638083971[/C][/ROW]
[ROW][C]74[/C][C]0.53179254264969[/C][C]0.93641491470062[/C][C]0.46820745735031[/C][/ROW]
[ROW][C]75[/C][C]0.519911999650483[/C][C]0.960176000699034[/C][C]0.480088000349517[/C][/ROW]
[ROW][C]76[/C][C]0.492426975651176[/C][C]0.984853951302352[/C][C]0.507573024348824[/C][/ROW]
[ROW][C]77[/C][C]0.644457793217583[/C][C]0.711084413564833[/C][C]0.355542206782417[/C][/ROW]
[ROW][C]78[/C][C]0.608445607100601[/C][C]0.783108785798797[/C][C]0.391554392899399[/C][/ROW]
[ROW][C]79[/C][C]0.569031770874919[/C][C]0.861936458250163[/C][C]0.430968229125081[/C][/ROW]
[ROW][C]80[/C][C]0.526201024255373[/C][C]0.947597951489254[/C][C]0.473798975744627[/C][/ROW]
[ROW][C]81[/C][C]0.527674227423092[/C][C]0.944651545153816[/C][C]0.472325772576908[/C][/ROW]
[ROW][C]82[/C][C]0.69949315770858[/C][C]0.601013684582841[/C][C]0.30050684229142[/C][/ROW]
[ROW][C]83[/C][C]0.661288799796887[/C][C]0.677422400406225[/C][C]0.338711200203113[/C][/ROW]
[ROW][C]84[/C][C]0.644279313401495[/C][C]0.71144137319701[/C][C]0.355720686598505[/C][/ROW]
[ROW][C]85[/C][C]0.615843118427675[/C][C]0.76831376314465[/C][C]0.384156881572325[/C][/ROW]
[ROW][C]86[/C][C]0.57143247127669[/C][C]0.85713505744662[/C][C]0.42856752872331[/C][/ROW]
[ROW][C]87[/C][C]0.525199918237691[/C][C]0.949600163524618[/C][C]0.474800081762309[/C][/ROW]
[ROW][C]88[/C][C]0.594703454276607[/C][C]0.810593091446787[/C][C]0.405296545723393[/C][/ROW]
[ROW][C]89[/C][C]0.550132002192165[/C][C]0.89973599561567[/C][C]0.449867997807835[/C][/ROW]
[ROW][C]90[/C][C]0.509575972582257[/C][C]0.980848054835487[/C][C]0.490424027417743[/C][/ROW]
[ROW][C]91[/C][C]0.469521789478772[/C][C]0.939043578957544[/C][C]0.530478210521228[/C][/ROW]
[ROW][C]92[/C][C]0.528056279019302[/C][C]0.943887441961397[/C][C]0.471943720980698[/C][/ROW]
[ROW][C]93[/C][C]0.4842672576452[/C][C]0.968534515290399[/C][C]0.5157327423548[/C][/ROW]
[ROW][C]94[/C][C]0.437200214778865[/C][C]0.87440042955773[/C][C]0.562799785221135[/C][/ROW]
[ROW][C]95[/C][C]0.43393139693459[/C][C]0.86786279386918[/C][C]0.56606860306541[/C][/ROW]
[ROW][C]96[/C][C]0.40410087586012[/C][C]0.80820175172024[/C][C]0.59589912413988[/C][/ROW]
[ROW][C]97[/C][C]0.388194529491105[/C][C]0.776389058982209[/C][C]0.611805470508895[/C][/ROW]
[ROW][C]98[/C][C]0.34359498074084[/C][C]0.687189961481681[/C][C]0.65640501925916[/C][/ROW]
[ROW][C]99[/C][C]0.320168074584797[/C][C]0.640336149169595[/C][C]0.679831925415203[/C][/ROW]
[ROW][C]100[/C][C]0.290385090765841[/C][C]0.580770181531681[/C][C]0.709614909234159[/C][/ROW]
[ROW][C]101[/C][C]0.256352317706483[/C][C]0.512704635412965[/C][C]0.743647682293518[/C][/ROW]
[ROW][C]102[/C][C]0.248521505838521[/C][C]0.497043011677043[/C][C]0.751478494161479[/C][/ROW]
[ROW][C]103[/C][C]0.213854846463103[/C][C]0.427709692926206[/C][C]0.786145153536897[/C][/ROW]
[ROW][C]104[/C][C]0.204088309924542[/C][C]0.408176619849085[/C][C]0.795911690075458[/C][/ROW]
[ROW][C]105[/C][C]0.175105319691062[/C][C]0.350210639382124[/C][C]0.824894680308938[/C][/ROW]
[ROW][C]106[/C][C]0.170558236007088[/C][C]0.341116472014177[/C][C]0.829441763992912[/C][/ROW]
[ROW][C]107[/C][C]0.184578417463128[/C][C]0.369156834926256[/C][C]0.815421582536872[/C][/ROW]
[ROW][C]108[/C][C]0.189248434454485[/C][C]0.37849686890897[/C][C]0.810751565545515[/C][/ROW]
[ROW][C]109[/C][C]0.176721115775724[/C][C]0.353442231551448[/C][C]0.823278884224276[/C][/ROW]
[ROW][C]110[/C][C]0.202029085947166[/C][C]0.404058171894331[/C][C]0.797970914052834[/C][/ROW]
[ROW][C]111[/C][C]0.181318748782535[/C][C]0.36263749756507[/C][C]0.818681251217465[/C][/ROW]
[ROW][C]112[/C][C]0.406498565425634[/C][C]0.812997130851268[/C][C]0.593501434574366[/C][/ROW]
[ROW][C]113[/C][C]0.465486153700663[/C][C]0.930972307401327[/C][C]0.534513846299337[/C][/ROW]
[ROW][C]114[/C][C]0.429649070419864[/C][C]0.859298140839728[/C][C]0.570350929580136[/C][/ROW]
[ROW][C]115[/C][C]0.422135996081516[/C][C]0.844271992163031[/C][C]0.577864003918484[/C][/ROW]
[ROW][C]116[/C][C]0.379739971174427[/C][C]0.759479942348855[/C][C]0.620260028825573[/C][/ROW]
[ROW][C]117[/C][C]0.365472859961179[/C][C]0.730945719922357[/C][C]0.634527140038821[/C][/ROW]
[ROW][C]118[/C][C]0.323420729234327[/C][C]0.646841458468653[/C][C]0.676579270765673[/C][/ROW]
[ROW][C]119[/C][C]0.302331410044504[/C][C]0.604662820089008[/C][C]0.697668589955496[/C][/ROW]
[ROW][C]120[/C][C]0.257541358270713[/C][C]0.515082716541425[/C][C]0.742458641729287[/C][/ROW]
[ROW][C]121[/C][C]0.229217140864474[/C][C]0.458434281728948[/C][C]0.770782859135526[/C][/ROW]
[ROW][C]122[/C][C]0.215962894307203[/C][C]0.431925788614407[/C][C]0.784037105692797[/C][/ROW]
[ROW][C]123[/C][C]0.22788116252121[/C][C]0.45576232504242[/C][C]0.77211883747879[/C][/ROW]
[ROW][C]124[/C][C]0.241452989526193[/C][C]0.482905979052387[/C][C]0.758547010473807[/C][/ROW]
[ROW][C]125[/C][C]0.790956538438538[/C][C]0.418086923122925[/C][C]0.209043461561462[/C][/ROW]
[ROW][C]126[/C][C]0.754537021129096[/C][C]0.490925957741809[/C][C]0.245462978870904[/C][/ROW]
[ROW][C]127[/C][C]0.70723225485574[/C][C]0.58553549028852[/C][C]0.29276774514426[/C][/ROW]
[ROW][C]128[/C][C]0.650610707546916[/C][C]0.698778584906168[/C][C]0.349389292453084[/C][/ROW]
[ROW][C]129[/C][C]0.591211562681127[/C][C]0.817576874637746[/C][C]0.408788437318873[/C][/ROW]
[ROW][C]130[/C][C]0.529775510485169[/C][C]0.940448979029663[/C][C]0.470224489514831[/C][/ROW]
[ROW][C]131[/C][C]0.473593931496003[/C][C]0.947187862992005[/C][C]0.526406068503997[/C][/ROW]
[ROW][C]132[/C][C]0.418388363759168[/C][C]0.836776727518335[/C][C]0.581611636240832[/C][/ROW]
[ROW][C]133[/C][C]0.356258908378721[/C][C]0.712517816757443[/C][C]0.643741091621279[/C][/ROW]
[ROW][C]134[/C][C]0.316736189482038[/C][C]0.633472378964076[/C][C]0.683263810517962[/C][/ROW]
[ROW][C]135[/C][C]0.285367565123948[/C][C]0.570735130247895[/C][C]0.714632434876052[/C][/ROW]
[ROW][C]136[/C][C]0.262690626270164[/C][C]0.525381252540329[/C][C]0.737309373729836[/C][/ROW]
[ROW][C]137[/C][C]0.298957615224637[/C][C]0.597915230449275[/C][C]0.701042384775363[/C][/ROW]
[ROW][C]138[/C][C]0.307974903567051[/C][C]0.615949807134102[/C][C]0.692025096432949[/C][/ROW]
[ROW][C]139[/C][C]0.328331373504198[/C][C]0.656662747008396[/C][C]0.671668626495802[/C][/ROW]
[ROW][C]140[/C][C]0.265316978126099[/C][C]0.530633956252198[/C][C]0.734683021873901[/C][/ROW]
[ROW][C]141[/C][C]0.208208703930898[/C][C]0.416417407861796[/C][C]0.791791296069102[/C][/ROW]
[ROW][C]142[/C][C]0.158048550232565[/C][C]0.316097100465129[/C][C]0.841951449767435[/C][/ROW]
[ROW][C]143[/C][C]0.184447031478692[/C][C]0.368894062957383[/C][C]0.815552968521309[/C][/ROW]
[ROW][C]144[/C][C]0.173591647090987[/C][C]0.347183294181975[/C][C]0.826408352909013[/C][/ROW]
[ROW][C]145[/C][C]0.156255031298021[/C][C]0.312510062596041[/C][C]0.843744968701979[/C][/ROW]
[ROW][C]146[/C][C]0.273200771077995[/C][C]0.54640154215599[/C][C]0.726799228922005[/C][/ROW]
[ROW][C]147[/C][C]0.239791906503731[/C][C]0.479583813007461[/C][C]0.760208093496269[/C][/ROW]
[ROW][C]148[/C][C]0.159221051012305[/C][C]0.318442102024611[/C][C]0.840778948987695[/C][/ROW]
[ROW][C]149[/C][C]0.11258941786019[/C][C]0.225178835720379[/C][C]0.88741058213981[/C][/ROW]
[ROW][C]150[/C][C]0.16099946030146[/C][C]0.32199892060292[/C][C]0.83900053969854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.4760910550835970.9521821101671930.523908944916403
100.3201550270949730.6403100541899450.679844972905027
110.3248035770065440.6496071540130890.675196422993456
120.2332662135478370.4665324270956730.766733786452163
130.7935649342439490.4128701315121030.206435065756051
140.7182915930166480.5634168139667040.281708406983352
150.6411679409241780.7176641181516450.358832059075822
160.6876981015960450.6246037968079090.312301898403955
170.6671424506926950.665715098614610.332857549307305
180.6450498443546710.7099003112906590.354950155645329
190.576319759185510.847360481628980.42368024081449
200.5750993130201880.8498013739596240.424900686979812
210.5854050607462210.8291898785075570.414594939253779
220.5551267227454310.8897465545091380.444873277254569
230.5580177092238270.8839645815523460.441982290776173
240.5100359875155980.9799280249688050.489964012484402
250.439543343723470.879086687446940.56045665627653
260.3772782191390640.7545564382781270.622721780860936
270.3140968973300810.6281937946601620.685903102669919
280.3066929128411290.6133858256822590.693307087158871
290.2602298920831540.5204597841663070.739770107916846
300.210038175247730.4200763504954590.78996182475227
310.1928814458251910.3857628916503820.807118554174809
320.2628840279305760.5257680558611520.737115972069424
330.2353967668760130.4707935337520260.764603233123987
340.2350070384630680.4700140769261360.764992961536932
350.1914058803216810.3828117606433620.808594119678319
360.2123026424518890.4246052849037780.787697357548111
370.209648116699030.419296233398060.79035188330097
380.6065409214176010.7869181571647980.393459078582399
390.5541357040779060.8917285918441880.445864295922094
400.5209909649787220.9580180700425560.479009035021278
410.4812581833942130.9625163667884260.518741816605787
420.4465859091935920.8931718183871840.553414090806408
430.3991132253500860.7982264507001720.600886774649914
440.3504186908920970.7008373817841930.649581309107903
450.301831574820830.6036631496416590.69816842517917
460.2853008566236090.5706017132472180.714699143376391
470.2768806420432340.5537612840864690.723119357956766
480.2559988860107930.5119977720215860.744001113989207
490.2566997297984680.5133994595969370.743300270201532
500.3202450943021860.6404901886043720.679754905697814
510.3708248884582020.7416497769164050.629175111541798
520.3408376593679320.6816753187358640.659162340632068
530.3149336797837010.6298673595674030.685066320216299
540.3317609942292090.6635219884584180.668239005770791
550.3516627269695550.703325453939110.648337273030445
560.3078419809111960.6156839618223930.692158019088804
570.2702389864801310.5404779729602620.729761013519869
580.235665698972970.471331397945940.76433430102703
590.2232941948092930.4465883896185860.776705805190707
600.3579269921742770.7158539843485540.642073007825723
610.3174180963853770.6348361927707540.682581903614623
620.2763943664137770.5527887328275550.723605633586223
630.2565355137038470.5130710274076940.743464486296153
640.267828266525850.5356565330517010.73217173347415
650.244010307289830.488020614579660.75598969271017
660.2123243296541280.4246486593082570.787675670345872
670.3900405194804410.7800810389608820.609959480519559
680.3452248333952020.6904496667904050.654775166604798
690.4348223328023870.8696446656047740.565177667197613
700.3914104293847130.7828208587694260.608589570615287
710.5189491596785250.9621016806429510.481050840321475
720.4964641348828480.9929282697656970.503535865117152
730.5316413619160290.9367172761679430.468358638083971
740.531792542649690.936414914700620.46820745735031
750.5199119996504830.9601760006990340.480088000349517
760.4924269756511760.9848539513023520.507573024348824
770.6444577932175830.7110844135648330.355542206782417
780.6084456071006010.7831087857987970.391554392899399
790.5690317708749190.8619364582501630.430968229125081
800.5262010242553730.9475979514892540.473798975744627
810.5276742274230920.9446515451538160.472325772576908
820.699493157708580.6010136845828410.30050684229142
830.6612887997968870.6774224004062250.338711200203113
840.6442793134014950.711441373197010.355720686598505
850.6158431184276750.768313763144650.384156881572325
860.571432471276690.857135057446620.42856752872331
870.5251999182376910.9496001635246180.474800081762309
880.5947034542766070.8105930914467870.405296545723393
890.5501320021921650.899735995615670.449867997807835
900.5095759725822570.9808480548354870.490424027417743
910.4695217894787720.9390435789575440.530478210521228
920.5280562790193020.9438874419613970.471943720980698
930.48426725764520.9685345152903990.5157327423548
940.4372002147788650.874400429557730.562799785221135
950.433931396934590.867862793869180.56606860306541
960.404100875860120.808201751720240.59589912413988
970.3881945294911050.7763890589822090.611805470508895
980.343594980740840.6871899614816810.65640501925916
990.3201680745847970.6403361491695950.679831925415203
1000.2903850907658410.5807701815316810.709614909234159
1010.2563523177064830.5127046354129650.743647682293518
1020.2485215058385210.4970430116770430.751478494161479
1030.2138548464631030.4277096929262060.786145153536897
1040.2040883099245420.4081766198490850.795911690075458
1050.1751053196910620.3502106393821240.824894680308938
1060.1705582360070880.3411164720141770.829441763992912
1070.1845784174631280.3691568349262560.815421582536872
1080.1892484344544850.378496868908970.810751565545515
1090.1767211157757240.3534422315514480.823278884224276
1100.2020290859471660.4040581718943310.797970914052834
1110.1813187487825350.362637497565070.818681251217465
1120.4064985654256340.8129971308512680.593501434574366
1130.4654861537006630.9309723074013270.534513846299337
1140.4296490704198640.8592981408397280.570350929580136
1150.4221359960815160.8442719921630310.577864003918484
1160.3797399711744270.7594799423488550.620260028825573
1170.3654728599611790.7309457199223570.634527140038821
1180.3234207292343270.6468414584686530.676579270765673
1190.3023314100445040.6046628200890080.697668589955496
1200.2575413582707130.5150827165414250.742458641729287
1210.2292171408644740.4584342817289480.770782859135526
1220.2159628943072030.4319257886144070.784037105692797
1230.227881162521210.455762325042420.77211883747879
1240.2414529895261930.4829059790523870.758547010473807
1250.7909565384385380.4180869231229250.209043461561462
1260.7545370211290960.4909259577418090.245462978870904
1270.707232254855740.585535490288520.29276774514426
1280.6506107075469160.6987785849061680.349389292453084
1290.5912115626811270.8175768746377460.408788437318873
1300.5297755104851690.9404489790296630.470224489514831
1310.4735939314960030.9471878629920050.526406068503997
1320.4183883637591680.8367767275183350.581611636240832
1330.3562589083787210.7125178167574430.643741091621279
1340.3167361894820380.6334723789640760.683263810517962
1350.2853675651239480.5707351302478950.714632434876052
1360.2626906262701640.5253812525403290.737309373729836
1370.2989576152246370.5979152304492750.701042384775363
1380.3079749035670510.6159498071341020.692025096432949
1390.3283313735041980.6566627470083960.671668626495802
1400.2653169781260990.5306339562521980.734683021873901
1410.2082087039308980.4164174078617960.791791296069102
1420.1580485502325650.3160971004651290.841951449767435
1430.1844470314786920.3688940629573830.815552968521309
1440.1735916470909870.3471832941819750.826408352909013
1450.1562550312980210.3125100625960410.843744968701979
1460.2732007710779950.546401542155990.726799228922005
1470.2397919065037310.4795838130074610.760208093496269
1480.1592210510123050.3184421020246110.840778948987695
1490.112589417860190.2251788357203790.88741058213981
1500.160999460301460.321998920602920.83900053969854







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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145530&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145530&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145530&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}