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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 15 Dec 2012 05:55:28 -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/2012/Dec/15/t1355569456sq7y6fejqbvhltc.htm/, Retrieved Tue, 30 Apr 2024 09:34:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199826, Retrieved Tue, 30 Apr 2024 09:34:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [regressiemodel co...] [2012-12-15 10:55:28] [b4b733de199089e913cc2b6ea19b06b9] [Current]
- R P     [Multiple Regression] [seasonal dummies] [2012-12-19 12:35:33] [2c4ddb4bf62114b8025bb962e2c7a2b5]
-    D      [Multiple Regression] [seasonal dummies] [2012-12-19 13:28:37] [2c4ddb4bf62114b8025bb962e2c7a2b5]
-    D        [Multiple Regression] [regressie met sea...] [2012-12-19 14:40:27] [2c4ddb4bf62114b8025bb962e2c7a2b5]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-3	-19	53	24	-2	-29
-4	-20	50	24	-4	-29
-7	-21	50	31	-5	-27
-7	-19	51	25	-2	-29
-7	-17	53	28	-4	-24
-3	-16	49	24	-4	-29
0	-10	54	25	-5	-21
-5	-16	57	16	-7	-20
-3	-10	58	17	-5	-26
3	-8	56	11	-6	-19
2	-7	60	12	-4	-22
-7	-15	55	39	-2	-22
-1	-7	54	19	-3	-15
0	-6	52	14	0	-16
-3	-6	55	15	-4	-22
4	2	56	7	-3	-21
2	-4	54	12	-3	-11
3	-4	53	12	-3	-10
0	-8	59	14	-4	-6
-10	-10	62	9	-5	-8
-10	-16	63	8	-5	-15
-9	-14	64	4	-6	-16
-22	-30	75	7	-10	-24
-16	-33	77	3	-11	-27
-18	-40	79	5	-13	-33
-14	-38	77	0	-12	-29
-12	-39	82	-2	-13	-34
-17	-46	83	6	-12	-37
-23	-50	81	11	-15	-31
-28	-55	78	9	-14	-33
-31	-66	79	17	-16	-25
-21	-63	79	21	-16	-27
-19	-56	73	21	-12	-21
-22	-66	72	41	-16	-32
-22	-63	67	57	-15	-31
-25	-69	67	65	-17	-32
-16	-69	50	68	-15	-30
-22	-72	45	73	-14	-34
-21	-69	39	71	-15	-35
-10	-67	39	71	-14	-37
-7	-64	37	70	-16	-32
-5	-61	30	69	-11	-28
-4	-58	24	65	-14	-26
7	-47	27	57	-12	-24
6	-44	19	57	-11	-27
3	-42	19	57	-13	-26
10	-34	25	55	-12	-27
0	-38	16	65	-12	-27
-2	-41	20	65	-10	-24
-1	-38	25	64	-12	-28
2	-37	34	60	-11	-23
8	-22	39	43	-10	-23
-6	-37	40	47	-12	-29
-4	-36	38	40	-12	-25
4	-25	42	31	-11	-24
7	-15	46	27	-12	-20
3	-17	48	24	-9	-22
3	-19	51	23	-6	-24
8	-12	55	17	-7	-27
3	-17	52	16	-7	-25
-3	-21	55	15	-10	-26
4	-10	58	8	-8	-24
-5	-19	72	5	-11	-26
-1	-14	70	6	-12	-22
5	-8	70	5	-11	-20
0	-16	63	12	-11	-26
-6	-14	66	8	-9	-22
-13	-30	65	17	-9	-29
-15	-33	55	22	-12	-30
-8	-37	57	24	-10	-26
-20	-47	60	36	-10	-30
-10	-48	63	31	-13	-33
-22	-50	65	34	-13	-33
-25	-56	61	47	-12	-31
-10	-47	65	33	-14	-36
-8	-37	63	35	-9	-43
-9	-35	59	31	-12	-40
-5	-29	56	35	-10	-38
-7	-28	54	39	-13	-41
-11	-29	56	46	-11	-38
-11	-33	54	40	-11	-40
-16	-41	58	50	-11	-41

Tables (Output of Computation)





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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 25.8050372634699 + 0.428060309492314X_1t[t] -0.450777521641689X_2t[t] -0.0776137468286691X_3t[t] -0.816444705642473X_4t[t] + 0.00518584246664621X_5t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  25.8050372634699 +  0.428060309492314X_1t[t] -0.450777521641689X_2t[t] -0.0776137468286691X_3t[t] -0.816444705642473X_4t[t] +  0.00518584246664621X_5t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  25.8050372634699 +  0.428060309492314X_1t[t] -0.450777521641689X_2t[t] -0.0776137468286691X_3t[t] -0.816444705642473X_4t[t] +  0.00518584246664621X_5t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 25.8050372634699 + 0.428060309492314X_1t[t] -0.450777521641689X_2t[t] -0.0776137468286691X_3t[t] -0.816444705642473X_4t[t] + 0.00518584246664621X_5t[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.80503726346994.0645816.348800
X_1t0.4280603094923140.0596897.171500
X_2t-0.4507775216416890.064245-7.016600
X_3t-0.07761374682866910.062086-1.25010.21510.10755
X_4t-0.8164447056424730.204637-3.98970.0001517.6e-05
X_5t0.005185842466646210.0809310.06410.9490770.474539

\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) & 25.8050372634699 & 4.064581 & 6.3488 & 0 & 0 \tabularnewline
X_1t & 0.428060309492314 & 0.059689 & 7.1715 & 0 & 0 \tabularnewline
X_2t & -0.450777521641689 & 0.064245 & -7.0166 & 0 & 0 \tabularnewline
X_3t & -0.0776137468286691 & 0.062086 & -1.2501 & 0.2151 & 0.10755 \tabularnewline
X_4t & -0.816444705642473 & 0.204637 & -3.9897 & 0.000151 & 7.6e-05 \tabularnewline
X_5t & 0.00518584246664621 & 0.080931 & 0.0641 & 0.949077 & 0.474539 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&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]25.8050372634699[/C][C]4.064581[/C][C]6.3488[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_1t[/C][C]0.428060309492314[/C][C]0.059689[/C][C]7.1715[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_2t[/C][C]-0.450777521641689[/C][C]0.064245[/C][C]-7.0166[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.0776137468286691[/C][C]0.062086[/C][C]-1.2501[/C][C]0.2151[/C][C]0.10755[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.816444705642473[/C][C]0.204637[/C][C]-3.9897[/C][C]0.000151[/C][C]7.6e-05[/C][/ROW]
[ROW][C]X_5t[/C][C]0.00518584246664621[/C][C]0.080931[/C][C]0.0641[/C][C]0.949077[/C][C]0.474539[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.80503726346994.0645816.348800
X_1t0.4280603094923140.0596897.171500
X_2t-0.4507775216416890.064245-7.016600
X_3t-0.07761374682866910.062086-1.25010.21510.10755
X_4t-0.8164447056424730.204637-3.98970.0001517.6e-05
X_5t0.005185842466646210.0809310.06410.9490770.474539






Multiple Linear Regression - Regression Statistics
Multiple R0.913826048354758
R-squared0.835078046651673
Adjusted R-squared0.824227918141914
F-TEST (value)76.9648069975043
F-TEST (DF numerator)5
F-TEST (DF denominator)76
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.06840558070266
Sum Squared Residuals1257.94622165103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.913826048354758 \tabularnewline
R-squared & 0.835078046651673 \tabularnewline
Adjusted R-squared & 0.824227918141914 \tabularnewline
F-TEST (value) & 76.9648069975043 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.06840558070266 \tabularnewline
Sum Squared Residuals & 1257.94622165103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.913826048354758[/C][/ROW]
[ROW][C]R-squared[/C][C]0.835078046651673[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.824227918141914[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]76.9648069975043[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.06840558070266[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1257.94622165103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.913826048354758
R-squared0.835078046651673
Adjusted R-squared0.824227918141914
F-TEST (value)76.9648069975043
F-TEST (DF numerator)5
F-TEST (DF denominator)76
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.06840558070266
Sum Squared Residuals1257.94622165103






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-3-6.599547208029443.59954720802944
2-4-4.042385541311720.042385541311725
3-7-4.18692568802895-2.81307431197105
4-7-5.77560591157472-1.22439408842528
5-7-4.3950629527413-2.6049370472587
6-3-1.87936678170078-1.12063321829922
70-0.7845748344083680.784574834408368
8-5-2.36867028107771-2.63132971892229
9-3-1.992704158679-1.007295841321
1031.083399587470011.91660041252999
112-2.017710875117984.01771087511798
12-7-6.91676631850706-0.0832336814929382
13-1-0.63648578144448-0.36351421855552
140-1.373321653919511.37332165391951
15-30.431395802096771-3.43139580209677
1643.214751867847120.785248132152878
1721.211734744699730.788265255300269
1831.667698108808071.33230189119193
190-2.06724767715962.0672476771596
20-10-3.08155910621677-6.91844089378323
21-10-6.0593856352502-3.9406143647498
22-9-4.53232868741676-4.46767131258324
23-22-13.3483955350016-8.65160446499836
24-16-14.4227893412048-1.57721065879525
25-18-16.8742196881066-1.1257803118934
26-14-15.52417662747111.52417662747114
27-12-17.26038155820535.26038155820532
28-17-22.1604934539655.16049345396497
29-23-20.8787992110669-2.1212007889331
30-28-22.3383570905218-5.66164290947817
31-31-26.4443318401902-4.55566815980979
32-21-25.48097758396124.48097758396124
33-19-23.01455405543494.01455405543492
34-22-25.1879200098533.18792000985297
35-22-23.70293028560211.70293028560211
36-25-25.26449854836710.264498548367052
37-16-19.4566396472963.456639647296
38-22-19.7121897772169-2.2878102227831
39-21-14.7568573620567-6.24314263794334
40-10-14.72755313364784.7275531336478
41-7-10.80538479144063.80538479144063
42-5-10.3496276229895.34962762298898
43-4-3.59062077548652-0.409379224513484
447-1.235897687718438.23589768771843
4562.822501180849613.17749881915039
4635.31669705358583-2.31669705358583
47105.370111345222434.62988865477757
4806.93873033374168-6.93873033374168
49-22.23410743481298-4.23410743481298
50-12.95416054332851-3.95416054332851
512-1.154837347948943.15483734794894
5283.515168676672224.48483132332778
53-6-2.06519411818378-3.93480588181622
54-4-0.171539167740823-3.82846083225918
5542.621279008390071.37872099160993
5676.246415079570190.753584920429807
5732.261874855927480.738125144072518
583-2.328670383014265.32867038301426
5980.1312113560797387.86878864392026
603-0.5687721946948073.56877219469481
61-3-1.11158397629969-1.88841602370031
6241.165525364639732.83447463536027
63-5-6.326099051294611.3260990512946
64-1-2.524668131869271.52466813186927
655-0.6847655487958955.6847655487959
660-1.528216655843151.52821665584315
67-6-3.32611965588727-2.67388034411273
68-13-10.4591317048472-2.54086829515285
69-15-5.17945787658978-9.82054212341022
70-8-9.560627692918111.56062769291811
71-20-16.1456716845769-3.85432831542307
72-10-15.10421923532355.10421923532349
73-22-17.0947361380775-4.9052638619225
74-25-19.6750396379465-5.32496036205349
75-10-14.93205428452944.93205428452936
76-8-14.02364806545916.02364806545906
77-9-8.58907072826565-0.410929271734354
78-5-6.601349020053031.60134902005303
79-7-3.14841206506453-3.85158793493547
80-11-6.63865552952591-4.36134447047409
81-11-6.99403092817307-4.00596907182693
82-16-13.0029468014317-2.99705319856833

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -3 & -6.59954720802944 & 3.59954720802944 \tabularnewline
2 & -4 & -4.04238554131172 & 0.042385541311725 \tabularnewline
3 & -7 & -4.18692568802895 & -2.81307431197105 \tabularnewline
4 & -7 & -5.77560591157472 & -1.22439408842528 \tabularnewline
5 & -7 & -4.3950629527413 & -2.6049370472587 \tabularnewline
6 & -3 & -1.87936678170078 & -1.12063321829922 \tabularnewline
7 & 0 & -0.784574834408368 & 0.784574834408368 \tabularnewline
8 & -5 & -2.36867028107771 & -2.63132971892229 \tabularnewline
9 & -3 & -1.992704158679 & -1.007295841321 \tabularnewline
10 & 3 & 1.08339958747001 & 1.91660041252999 \tabularnewline
11 & 2 & -2.01771087511798 & 4.01771087511798 \tabularnewline
12 & -7 & -6.91676631850706 & -0.0832336814929382 \tabularnewline
13 & -1 & -0.63648578144448 & -0.36351421855552 \tabularnewline
14 & 0 & -1.37332165391951 & 1.37332165391951 \tabularnewline
15 & -3 & 0.431395802096771 & -3.43139580209677 \tabularnewline
16 & 4 & 3.21475186784712 & 0.785248132152878 \tabularnewline
17 & 2 & 1.21173474469973 & 0.788265255300269 \tabularnewline
18 & 3 & 1.66769810880807 & 1.33230189119193 \tabularnewline
19 & 0 & -2.0672476771596 & 2.0672476771596 \tabularnewline
20 & -10 & -3.08155910621677 & -6.91844089378323 \tabularnewline
21 & -10 & -6.0593856352502 & -3.9406143647498 \tabularnewline
22 & -9 & -4.53232868741676 & -4.46767131258324 \tabularnewline
23 & -22 & -13.3483955350016 & -8.65160446499836 \tabularnewline
24 & -16 & -14.4227893412048 & -1.57721065879525 \tabularnewline
25 & -18 & -16.8742196881066 & -1.1257803118934 \tabularnewline
26 & -14 & -15.5241766274711 & 1.52417662747114 \tabularnewline
27 & -12 & -17.2603815582053 & 5.26038155820532 \tabularnewline
28 & -17 & -22.160493453965 & 5.16049345396497 \tabularnewline
29 & -23 & -20.8787992110669 & -2.1212007889331 \tabularnewline
30 & -28 & -22.3383570905218 & -5.66164290947817 \tabularnewline
31 & -31 & -26.4443318401902 & -4.55566815980979 \tabularnewline
32 & -21 & -25.4809775839612 & 4.48097758396124 \tabularnewline
33 & -19 & -23.0145540554349 & 4.01455405543492 \tabularnewline
34 & -22 & -25.187920009853 & 3.18792000985297 \tabularnewline
35 & -22 & -23.7029302856021 & 1.70293028560211 \tabularnewline
36 & -25 & -25.2644985483671 & 0.264498548367052 \tabularnewline
37 & -16 & -19.456639647296 & 3.456639647296 \tabularnewline
38 & -22 & -19.7121897772169 & -2.2878102227831 \tabularnewline
39 & -21 & -14.7568573620567 & -6.24314263794334 \tabularnewline
40 & -10 & -14.7275531336478 & 4.7275531336478 \tabularnewline
41 & -7 & -10.8053847914406 & 3.80538479144063 \tabularnewline
42 & -5 & -10.349627622989 & 5.34962762298898 \tabularnewline
43 & -4 & -3.59062077548652 & -0.409379224513484 \tabularnewline
44 & 7 & -1.23589768771843 & 8.23589768771843 \tabularnewline
45 & 6 & 2.82250118084961 & 3.17749881915039 \tabularnewline
46 & 3 & 5.31669705358583 & -2.31669705358583 \tabularnewline
47 & 10 & 5.37011134522243 & 4.62988865477757 \tabularnewline
48 & 0 & 6.93873033374168 & -6.93873033374168 \tabularnewline
49 & -2 & 2.23410743481298 & -4.23410743481298 \tabularnewline
50 & -1 & 2.95416054332851 & -3.95416054332851 \tabularnewline
51 & 2 & -1.15483734794894 & 3.15483734794894 \tabularnewline
52 & 8 & 3.51516867667222 & 4.48483132332778 \tabularnewline
53 & -6 & -2.06519411818378 & -3.93480588181622 \tabularnewline
54 & -4 & -0.171539167740823 & -3.82846083225918 \tabularnewline
55 & 4 & 2.62127900839007 & 1.37872099160993 \tabularnewline
56 & 7 & 6.24641507957019 & 0.753584920429807 \tabularnewline
57 & 3 & 2.26187485592748 & 0.738125144072518 \tabularnewline
58 & 3 & -2.32867038301426 & 5.32867038301426 \tabularnewline
59 & 8 & 0.131211356079738 & 7.86878864392026 \tabularnewline
60 & 3 & -0.568772194694807 & 3.56877219469481 \tabularnewline
61 & -3 & -1.11158397629969 & -1.88841602370031 \tabularnewline
62 & 4 & 1.16552536463973 & 2.83447463536027 \tabularnewline
63 & -5 & -6.32609905129461 & 1.3260990512946 \tabularnewline
64 & -1 & -2.52466813186927 & 1.52466813186927 \tabularnewline
65 & 5 & -0.684765548795895 & 5.6847655487959 \tabularnewline
66 & 0 & -1.52821665584315 & 1.52821665584315 \tabularnewline
67 & -6 & -3.32611965588727 & -2.67388034411273 \tabularnewline
68 & -13 & -10.4591317048472 & -2.54086829515285 \tabularnewline
69 & -15 & -5.17945787658978 & -9.82054212341022 \tabularnewline
70 & -8 & -9.56062769291811 & 1.56062769291811 \tabularnewline
71 & -20 & -16.1456716845769 & -3.85432831542307 \tabularnewline
72 & -10 & -15.1042192353235 & 5.10421923532349 \tabularnewline
73 & -22 & -17.0947361380775 & -4.9052638619225 \tabularnewline
74 & -25 & -19.6750396379465 & -5.32496036205349 \tabularnewline
75 & -10 & -14.9320542845294 & 4.93205428452936 \tabularnewline
76 & -8 & -14.0236480654591 & 6.02364806545906 \tabularnewline
77 & -9 & -8.58907072826565 & -0.410929271734354 \tabularnewline
78 & -5 & -6.60134902005303 & 1.60134902005303 \tabularnewline
79 & -7 & -3.14841206506453 & -3.85158793493547 \tabularnewline
80 & -11 & -6.63865552952591 & -4.36134447047409 \tabularnewline
81 & -11 & -6.99403092817307 & -4.00596907182693 \tabularnewline
82 & -16 & -13.0029468014317 & -2.99705319856833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&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]-3[/C][C]-6.59954720802944[/C][C]3.59954720802944[/C][/ROW]
[ROW][C]2[/C][C]-4[/C][C]-4.04238554131172[/C][C]0.042385541311725[/C][/ROW]
[ROW][C]3[/C][C]-7[/C][C]-4.18692568802895[/C][C]-2.81307431197105[/C][/ROW]
[ROW][C]4[/C][C]-7[/C][C]-5.77560591157472[/C][C]-1.22439408842528[/C][/ROW]
[ROW][C]5[/C][C]-7[/C][C]-4.3950629527413[/C][C]-2.6049370472587[/C][/ROW]
[ROW][C]6[/C][C]-3[/C][C]-1.87936678170078[/C][C]-1.12063321829922[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.784574834408368[/C][C]0.784574834408368[/C][/ROW]
[ROW][C]8[/C][C]-5[/C][C]-2.36867028107771[/C][C]-2.63132971892229[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]-1.992704158679[/C][C]-1.007295841321[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]1.08339958747001[/C][C]1.91660041252999[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]-2.01771087511798[/C][C]4.01771087511798[/C][/ROW]
[ROW][C]12[/C][C]-7[/C][C]-6.91676631850706[/C][C]-0.0832336814929382[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-0.63648578144448[/C][C]-0.36351421855552[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]-1.37332165391951[/C][C]1.37332165391951[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]0.431395802096771[/C][C]-3.43139580209677[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]3.21475186784712[/C][C]0.785248132152878[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]1.21173474469973[/C][C]0.788265255300269[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.66769810880807[/C][C]1.33230189119193[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-2.0672476771596[/C][C]2.0672476771596[/C][/ROW]
[ROW][C]20[/C][C]-10[/C][C]-3.08155910621677[/C][C]-6.91844089378323[/C][/ROW]
[ROW][C]21[/C][C]-10[/C][C]-6.0593856352502[/C][C]-3.9406143647498[/C][/ROW]
[ROW][C]22[/C][C]-9[/C][C]-4.53232868741676[/C][C]-4.46767131258324[/C][/ROW]
[ROW][C]23[/C][C]-22[/C][C]-13.3483955350016[/C][C]-8.65160446499836[/C][/ROW]
[ROW][C]24[/C][C]-16[/C][C]-14.4227893412048[/C][C]-1.57721065879525[/C][/ROW]
[ROW][C]25[/C][C]-18[/C][C]-16.8742196881066[/C][C]-1.1257803118934[/C][/ROW]
[ROW][C]26[/C][C]-14[/C][C]-15.5241766274711[/C][C]1.52417662747114[/C][/ROW]
[ROW][C]27[/C][C]-12[/C][C]-17.2603815582053[/C][C]5.26038155820532[/C][/ROW]
[ROW][C]28[/C][C]-17[/C][C]-22.160493453965[/C][C]5.16049345396497[/C][/ROW]
[ROW][C]29[/C][C]-23[/C][C]-20.8787992110669[/C][C]-2.1212007889331[/C][/ROW]
[ROW][C]30[/C][C]-28[/C][C]-22.3383570905218[/C][C]-5.66164290947817[/C][/ROW]
[ROW][C]31[/C][C]-31[/C][C]-26.4443318401902[/C][C]-4.55566815980979[/C][/ROW]
[ROW][C]32[/C][C]-21[/C][C]-25.4809775839612[/C][C]4.48097758396124[/C][/ROW]
[ROW][C]33[/C][C]-19[/C][C]-23.0145540554349[/C][C]4.01455405543492[/C][/ROW]
[ROW][C]34[/C][C]-22[/C][C]-25.187920009853[/C][C]3.18792000985297[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-23.7029302856021[/C][C]1.70293028560211[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-25.2644985483671[/C][C]0.264498548367052[/C][/ROW]
[ROW][C]37[/C][C]-16[/C][C]-19.456639647296[/C][C]3.456639647296[/C][/ROW]
[ROW][C]38[/C][C]-22[/C][C]-19.7121897772169[/C][C]-2.2878102227831[/C][/ROW]
[ROW][C]39[/C][C]-21[/C][C]-14.7568573620567[/C][C]-6.24314263794334[/C][/ROW]
[ROW][C]40[/C][C]-10[/C][C]-14.7275531336478[/C][C]4.7275531336478[/C][/ROW]
[ROW][C]41[/C][C]-7[/C][C]-10.8053847914406[/C][C]3.80538479144063[/C][/ROW]
[ROW][C]42[/C][C]-5[/C][C]-10.349627622989[/C][C]5.34962762298898[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-3.59062077548652[/C][C]-0.409379224513484[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]-1.23589768771843[/C][C]8.23589768771843[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]2.82250118084961[/C][C]3.17749881915039[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]5.31669705358583[/C][C]-2.31669705358583[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]5.37011134522243[/C][C]4.62988865477757[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]6.93873033374168[/C][C]-6.93873033374168[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]2.23410743481298[/C][C]-4.23410743481298[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]2.95416054332851[/C][C]-3.95416054332851[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]-1.15483734794894[/C][C]3.15483734794894[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]3.51516867667222[/C][C]4.48483132332778[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-2.06519411818378[/C][C]-3.93480588181622[/C][/ROW]
[ROW][C]54[/C][C]-4[/C][C]-0.171539167740823[/C][C]-3.82846083225918[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]2.62127900839007[/C][C]1.37872099160993[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]6.24641507957019[/C][C]0.753584920429807[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.26187485592748[/C][C]0.738125144072518[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]-2.32867038301426[/C][C]5.32867038301426[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]0.131211356079738[/C][C]7.86878864392026[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]-0.568772194694807[/C][C]3.56877219469481[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-1.11158397629969[/C][C]-1.88841602370031[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]1.16552536463973[/C][C]2.83447463536027[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-6.32609905129461[/C][C]1.3260990512946[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-2.52466813186927[/C][C]1.52466813186927[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]-0.684765548795895[/C][C]5.6847655487959[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-1.52821665584315[/C][C]1.52821665584315[/C][/ROW]
[ROW][C]67[/C][C]-6[/C][C]-3.32611965588727[/C][C]-2.67388034411273[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-10.4591317048472[/C][C]-2.54086829515285[/C][/ROW]
[ROW][C]69[/C][C]-15[/C][C]-5.17945787658978[/C][C]-9.82054212341022[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-9.56062769291811[/C][C]1.56062769291811[/C][/ROW]
[ROW][C]71[/C][C]-20[/C][C]-16.1456716845769[/C][C]-3.85432831542307[/C][/ROW]
[ROW][C]72[/C][C]-10[/C][C]-15.1042192353235[/C][C]5.10421923532349[/C][/ROW]
[ROW][C]73[/C][C]-22[/C][C]-17.0947361380775[/C][C]-4.9052638619225[/C][/ROW]
[ROW][C]74[/C][C]-25[/C][C]-19.6750396379465[/C][C]-5.32496036205349[/C][/ROW]
[ROW][C]75[/C][C]-10[/C][C]-14.9320542845294[/C][C]4.93205428452936[/C][/ROW]
[ROW][C]76[/C][C]-8[/C][C]-14.0236480654591[/C][C]6.02364806545906[/C][/ROW]
[ROW][C]77[/C][C]-9[/C][C]-8.58907072826565[/C][C]-0.410929271734354[/C][/ROW]
[ROW][C]78[/C][C]-5[/C][C]-6.60134902005303[/C][C]1.60134902005303[/C][/ROW]
[ROW][C]79[/C][C]-7[/C][C]-3.14841206506453[/C][C]-3.85158793493547[/C][/ROW]
[ROW][C]80[/C][C]-11[/C][C]-6.63865552952591[/C][C]-4.36134447047409[/C][/ROW]
[ROW][C]81[/C][C]-11[/C][C]-6.99403092817307[/C][C]-4.00596907182693[/C][/ROW]
[ROW][C]82[/C][C]-16[/C][C]-13.0029468014317[/C][C]-2.99705319856833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-3-6.599547208029443.59954720802944
2-4-4.042385541311720.042385541311725
3-7-4.18692568802895-2.81307431197105
4-7-5.77560591157472-1.22439408842528
5-7-4.3950629527413-2.6049370472587
6-3-1.87936678170078-1.12063321829922
70-0.7845748344083680.784574834408368
8-5-2.36867028107771-2.63132971892229
9-3-1.992704158679-1.007295841321
1031.083399587470011.91660041252999
112-2.017710875117984.01771087511798
12-7-6.91676631850706-0.0832336814929382
13-1-0.63648578144448-0.36351421855552
140-1.373321653919511.37332165391951
15-30.431395802096771-3.43139580209677
1643.214751867847120.785248132152878
1721.211734744699730.788265255300269
1831.667698108808071.33230189119193
190-2.06724767715962.0672476771596
20-10-3.08155910621677-6.91844089378323
21-10-6.0593856352502-3.9406143647498
22-9-4.53232868741676-4.46767131258324
23-22-13.3483955350016-8.65160446499836
24-16-14.4227893412048-1.57721065879525
25-18-16.8742196881066-1.1257803118934
26-14-15.52417662747111.52417662747114
27-12-17.26038155820535.26038155820532
28-17-22.1604934539655.16049345396497
29-23-20.8787992110669-2.1212007889331
30-28-22.3383570905218-5.66164290947817
31-31-26.4443318401902-4.55566815980979
32-21-25.48097758396124.48097758396124
33-19-23.01455405543494.01455405543492
34-22-25.1879200098533.18792000985297
35-22-23.70293028560211.70293028560211
36-25-25.26449854836710.264498548367052
37-16-19.4566396472963.456639647296
38-22-19.7121897772169-2.2878102227831
39-21-14.7568573620567-6.24314263794334
40-10-14.72755313364784.7275531336478
41-7-10.80538479144063.80538479144063
42-5-10.3496276229895.34962762298898
43-4-3.59062077548652-0.409379224513484
447-1.235897687718438.23589768771843
4562.822501180849613.17749881915039
4635.31669705358583-2.31669705358583
47105.370111345222434.62988865477757
4806.93873033374168-6.93873033374168
49-22.23410743481298-4.23410743481298
50-12.95416054332851-3.95416054332851
512-1.154837347948943.15483734794894
5283.515168676672224.48483132332778
53-6-2.06519411818378-3.93480588181622
54-4-0.171539167740823-3.82846083225918
5542.621279008390071.37872099160993
5676.246415079570190.753584920429807
5732.261874855927480.738125144072518
583-2.328670383014265.32867038301426
5980.1312113560797387.86878864392026
603-0.5687721946948073.56877219469481
61-3-1.11158397629969-1.88841602370031
6241.165525364639732.83447463536027
63-5-6.326099051294611.3260990512946
64-1-2.524668131869271.52466813186927
655-0.6847655487958955.6847655487959
660-1.528216655843151.52821665584315
67-6-3.32611965588727-2.67388034411273
68-13-10.4591317048472-2.54086829515285
69-15-5.17945787658978-9.82054212341022
70-8-9.560627692918111.56062769291811
71-20-16.1456716845769-3.85432831542307
72-10-15.10421923532355.10421923532349
73-22-17.0947361380775-4.9052638619225
74-25-19.6750396379465-5.32496036205349
75-10-14.93205428452944.93205428452936
76-8-14.02364806545916.02364806545906
77-9-8.58907072826565-0.410929271734354
78-5-6.601349020053031.60134902005303
79-7-3.14841206506453-3.85158793493547
80-11-6.63865552952591-4.36134447047409
81-11-6.99403092817307-4.00596907182693
82-16-13.0029468014317-2.99705319856833






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2028754612663840.4057509225327690.797124538733616
100.09717027781360470.1943405556272090.902829722186395
110.04608492002809740.09216984005619470.953915079971903
120.01819306563624080.03638613127248170.981806934363759
130.01774716824188440.03549433648376870.982252831758116
140.008908199001472950.01781639800294590.991091800998527
150.02011777509385740.04023555018771480.979882224906143
160.009478354528368630.01895670905673730.990521645471631
170.004370182015541070.008740364031082130.995629817984459
180.002117632753827050.004235265507654090.997882367246173
190.0009061939040116830.001812387808023370.999093806095988
200.0179954122119960.03599082442399210.982004587788004
210.01231563019321440.02463126038642870.987684369806786
220.008934557162968620.01786911432593720.991065442837031
230.008891602261929110.01778320452385820.991108397738071
240.0250404994770240.0500809989540480.974959500522976
250.03104389399615060.06208778799230110.968956106003849
260.04319380512377050.08638761024754090.95680619487623
270.08990974184170710.1798194836834140.910090258158293
280.1126542170731440.2253084341462890.887345782926856
290.08088564329185780.1617712865837160.919114356708142
300.0752523490025860.1505046980051720.924747650997414
310.07381254711130360.1476250942226070.926187452888696
320.171426434124230.342852868248460.82857356587577
330.1902386330039910.3804772660079810.809761366996009
340.1754022099946590.3508044199893180.824597790005341
350.1345219266337260.2690438532674520.865478073366274
360.1015809881886620.2031619763773230.898419011811338
370.09101750653139680.1820350130627940.908982493468603
380.07816106282172950.1563221256434590.92183893717827
390.08547540500926680.1709508100185340.914524594990733
400.1246008274584630.2492016549169260.875399172541537
410.1567450708457340.3134901416914670.843254929154266
420.1837849874299250.3675699748598490.816215012570075
430.1464800989603810.2929601979207610.853519901039619
440.332924365995090.6658487319901790.66707563400491
450.3363936109792710.6727872219585430.663606389020729
460.3125626091111120.6251252182222240.687437390888888
470.4161607731704250.832321546340850.583839226829575
480.4844765710374210.9689531420748410.515523428962579
490.4666879071844940.9333758143689880.533312092815506
500.4247395151129360.8494790302258720.575260484887064
510.424085810711470.8481716214229390.57591418928853
520.4762805897818190.9525611795636380.523719410218181
530.4281785055680470.8563570111360930.571821494431953
540.3733082032386380.7466164064772760.626691796761362
550.3434236234455460.6868472468910920.656576376554454
560.3354401418617220.6708802837234440.664559858138278
570.2930190484208690.5860380968417390.706980951579131
580.313540885755990.627081771511980.68645911424401
590.4557719700197670.9115439400395330.544228029980233
600.503407433301050.9931851333978990.49659256669895
610.4292284895451520.8584569790903040.570771510454848
620.4386498116490920.8772996232981840.561350188350908
630.4178419343591650.8356838687183310.582158065640835
640.3448296584599650.689659316919930.655170341540035
650.3583071016091350.716614203218270.641692898390865
660.3407087142648410.6814174285296820.659291285735159
670.2658559436484650.5317118872969290.734144056351535
680.3447453447391160.6894906894782320.655254655260884
690.6576315077738550.6847369844522890.342368492226145
700.5755620893680950.8488758212638090.424437910631904
710.4752256323381870.9504512646763740.524774367661813
720.5589858702480480.8820282595039040.441014129751952
730.8534444259152250.293111148169550.146555574084775

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.202875461266384 & 0.405750922532769 & 0.797124538733616 \tabularnewline
10 & 0.0971702778136047 & 0.194340555627209 & 0.902829722186395 \tabularnewline
11 & 0.0460849200280974 & 0.0921698400561947 & 0.953915079971903 \tabularnewline
12 & 0.0181930656362408 & 0.0363861312724817 & 0.981806934363759 \tabularnewline
13 & 0.0177471682418844 & 0.0354943364837687 & 0.982252831758116 \tabularnewline
14 & 0.00890819900147295 & 0.0178163980029459 & 0.991091800998527 \tabularnewline
15 & 0.0201177750938574 & 0.0402355501877148 & 0.979882224906143 \tabularnewline
16 & 0.00947835452836863 & 0.0189567090567373 & 0.990521645471631 \tabularnewline
17 & 0.00437018201554107 & 0.00874036403108213 & 0.995629817984459 \tabularnewline
18 & 0.00211763275382705 & 0.00423526550765409 & 0.997882367246173 \tabularnewline
19 & 0.000906193904011683 & 0.00181238780802337 & 0.999093806095988 \tabularnewline
20 & 0.017995412211996 & 0.0359908244239921 & 0.982004587788004 \tabularnewline
21 & 0.0123156301932144 & 0.0246312603864287 & 0.987684369806786 \tabularnewline
22 & 0.00893455716296862 & 0.0178691143259372 & 0.991065442837031 \tabularnewline
23 & 0.00889160226192911 & 0.0177832045238582 & 0.991108397738071 \tabularnewline
24 & 0.025040499477024 & 0.050080998954048 & 0.974959500522976 \tabularnewline
25 & 0.0310438939961506 & 0.0620877879923011 & 0.968956106003849 \tabularnewline
26 & 0.0431938051237705 & 0.0863876102475409 & 0.95680619487623 \tabularnewline
27 & 0.0899097418417071 & 0.179819483683414 & 0.910090258158293 \tabularnewline
28 & 0.112654217073144 & 0.225308434146289 & 0.887345782926856 \tabularnewline
29 & 0.0808856432918578 & 0.161771286583716 & 0.919114356708142 \tabularnewline
30 & 0.075252349002586 & 0.150504698005172 & 0.924747650997414 \tabularnewline
31 & 0.0738125471113036 & 0.147625094222607 & 0.926187452888696 \tabularnewline
32 & 0.17142643412423 & 0.34285286824846 & 0.82857356587577 \tabularnewline
33 & 0.190238633003991 & 0.380477266007981 & 0.809761366996009 \tabularnewline
34 & 0.175402209994659 & 0.350804419989318 & 0.824597790005341 \tabularnewline
35 & 0.134521926633726 & 0.269043853267452 & 0.865478073366274 \tabularnewline
36 & 0.101580988188662 & 0.203161976377323 & 0.898419011811338 \tabularnewline
37 & 0.0910175065313968 & 0.182035013062794 & 0.908982493468603 \tabularnewline
38 & 0.0781610628217295 & 0.156322125643459 & 0.92183893717827 \tabularnewline
39 & 0.0854754050092668 & 0.170950810018534 & 0.914524594990733 \tabularnewline
40 & 0.124600827458463 & 0.249201654916926 & 0.875399172541537 \tabularnewline
41 & 0.156745070845734 & 0.313490141691467 & 0.843254929154266 \tabularnewline
42 & 0.183784987429925 & 0.367569974859849 & 0.816215012570075 \tabularnewline
43 & 0.146480098960381 & 0.292960197920761 & 0.853519901039619 \tabularnewline
44 & 0.33292436599509 & 0.665848731990179 & 0.66707563400491 \tabularnewline
45 & 0.336393610979271 & 0.672787221958543 & 0.663606389020729 \tabularnewline
46 & 0.312562609111112 & 0.625125218222224 & 0.687437390888888 \tabularnewline
47 & 0.416160773170425 & 0.83232154634085 & 0.583839226829575 \tabularnewline
48 & 0.484476571037421 & 0.968953142074841 & 0.515523428962579 \tabularnewline
49 & 0.466687907184494 & 0.933375814368988 & 0.533312092815506 \tabularnewline
50 & 0.424739515112936 & 0.849479030225872 & 0.575260484887064 \tabularnewline
51 & 0.42408581071147 & 0.848171621422939 & 0.57591418928853 \tabularnewline
52 & 0.476280589781819 & 0.952561179563638 & 0.523719410218181 \tabularnewline
53 & 0.428178505568047 & 0.856357011136093 & 0.571821494431953 \tabularnewline
54 & 0.373308203238638 & 0.746616406477276 & 0.626691796761362 \tabularnewline
55 & 0.343423623445546 & 0.686847246891092 & 0.656576376554454 \tabularnewline
56 & 0.335440141861722 & 0.670880283723444 & 0.664559858138278 \tabularnewline
57 & 0.293019048420869 & 0.586038096841739 & 0.706980951579131 \tabularnewline
58 & 0.31354088575599 & 0.62708177151198 & 0.68645911424401 \tabularnewline
59 & 0.455771970019767 & 0.911543940039533 & 0.544228029980233 \tabularnewline
60 & 0.50340743330105 & 0.993185133397899 & 0.49659256669895 \tabularnewline
61 & 0.429228489545152 & 0.858456979090304 & 0.570771510454848 \tabularnewline
62 & 0.438649811649092 & 0.877299623298184 & 0.561350188350908 \tabularnewline
63 & 0.417841934359165 & 0.835683868718331 & 0.582158065640835 \tabularnewline
64 & 0.344829658459965 & 0.68965931691993 & 0.655170341540035 \tabularnewline
65 & 0.358307101609135 & 0.71661420321827 & 0.641692898390865 \tabularnewline
66 & 0.340708714264841 & 0.681417428529682 & 0.659291285735159 \tabularnewline
67 & 0.265855943648465 & 0.531711887296929 & 0.734144056351535 \tabularnewline
68 & 0.344745344739116 & 0.689490689478232 & 0.655254655260884 \tabularnewline
69 & 0.657631507773855 & 0.684736984452289 & 0.342368492226145 \tabularnewline
70 & 0.575562089368095 & 0.848875821263809 & 0.424437910631904 \tabularnewline
71 & 0.475225632338187 & 0.950451264676374 & 0.524774367661813 \tabularnewline
72 & 0.558985870248048 & 0.882028259503904 & 0.441014129751952 \tabularnewline
73 & 0.853444425915225 & 0.29311114816955 & 0.146555574084775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&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]0.202875461266384[/C][C]0.405750922532769[/C][C]0.797124538733616[/C][/ROW]
[ROW][C]10[/C][C]0.0971702778136047[/C][C]0.194340555627209[/C][C]0.902829722186395[/C][/ROW]
[ROW][C]11[/C][C]0.0460849200280974[/C][C]0.0921698400561947[/C][C]0.953915079971903[/C][/ROW]
[ROW][C]12[/C][C]0.0181930656362408[/C][C]0.0363861312724817[/C][C]0.981806934363759[/C][/ROW]
[ROW][C]13[/C][C]0.0177471682418844[/C][C]0.0354943364837687[/C][C]0.982252831758116[/C][/ROW]
[ROW][C]14[/C][C]0.00890819900147295[/C][C]0.0178163980029459[/C][C]0.991091800998527[/C][/ROW]
[ROW][C]15[/C][C]0.0201177750938574[/C][C]0.0402355501877148[/C][C]0.979882224906143[/C][/ROW]
[ROW][C]16[/C][C]0.00947835452836863[/C][C]0.0189567090567373[/C][C]0.990521645471631[/C][/ROW]
[ROW][C]17[/C][C]0.00437018201554107[/C][C]0.00874036403108213[/C][C]0.995629817984459[/C][/ROW]
[ROW][C]18[/C][C]0.00211763275382705[/C][C]0.00423526550765409[/C][C]0.997882367246173[/C][/ROW]
[ROW][C]19[/C][C]0.000906193904011683[/C][C]0.00181238780802337[/C][C]0.999093806095988[/C][/ROW]
[ROW][C]20[/C][C]0.017995412211996[/C][C]0.0359908244239921[/C][C]0.982004587788004[/C][/ROW]
[ROW][C]21[/C][C]0.0123156301932144[/C][C]0.0246312603864287[/C][C]0.987684369806786[/C][/ROW]
[ROW][C]22[/C][C]0.00893455716296862[/C][C]0.0178691143259372[/C][C]0.991065442837031[/C][/ROW]
[ROW][C]23[/C][C]0.00889160226192911[/C][C]0.0177832045238582[/C][C]0.991108397738071[/C][/ROW]
[ROW][C]24[/C][C]0.025040499477024[/C][C]0.050080998954048[/C][C]0.974959500522976[/C][/ROW]
[ROW][C]25[/C][C]0.0310438939961506[/C][C]0.0620877879923011[/C][C]0.968956106003849[/C][/ROW]
[ROW][C]26[/C][C]0.0431938051237705[/C][C]0.0863876102475409[/C][C]0.95680619487623[/C][/ROW]
[ROW][C]27[/C][C]0.0899097418417071[/C][C]0.179819483683414[/C][C]0.910090258158293[/C][/ROW]
[ROW][C]28[/C][C]0.112654217073144[/C][C]0.225308434146289[/C][C]0.887345782926856[/C][/ROW]
[ROW][C]29[/C][C]0.0808856432918578[/C][C]0.161771286583716[/C][C]0.919114356708142[/C][/ROW]
[ROW][C]30[/C][C]0.075252349002586[/C][C]0.150504698005172[/C][C]0.924747650997414[/C][/ROW]
[ROW][C]31[/C][C]0.0738125471113036[/C][C]0.147625094222607[/C][C]0.926187452888696[/C][/ROW]
[ROW][C]32[/C][C]0.17142643412423[/C][C]0.34285286824846[/C][C]0.82857356587577[/C][/ROW]
[ROW][C]33[/C][C]0.190238633003991[/C][C]0.380477266007981[/C][C]0.809761366996009[/C][/ROW]
[ROW][C]34[/C][C]0.175402209994659[/C][C]0.350804419989318[/C][C]0.824597790005341[/C][/ROW]
[ROW][C]35[/C][C]0.134521926633726[/C][C]0.269043853267452[/C][C]0.865478073366274[/C][/ROW]
[ROW][C]36[/C][C]0.101580988188662[/C][C]0.203161976377323[/C][C]0.898419011811338[/C][/ROW]
[ROW][C]37[/C][C]0.0910175065313968[/C][C]0.182035013062794[/C][C]0.908982493468603[/C][/ROW]
[ROW][C]38[/C][C]0.0781610628217295[/C][C]0.156322125643459[/C][C]0.92183893717827[/C][/ROW]
[ROW][C]39[/C][C]0.0854754050092668[/C][C]0.170950810018534[/C][C]0.914524594990733[/C][/ROW]
[ROW][C]40[/C][C]0.124600827458463[/C][C]0.249201654916926[/C][C]0.875399172541537[/C][/ROW]
[ROW][C]41[/C][C]0.156745070845734[/C][C]0.313490141691467[/C][C]0.843254929154266[/C][/ROW]
[ROW][C]42[/C][C]0.183784987429925[/C][C]0.367569974859849[/C][C]0.816215012570075[/C][/ROW]
[ROW][C]43[/C][C]0.146480098960381[/C][C]0.292960197920761[/C][C]0.853519901039619[/C][/ROW]
[ROW][C]44[/C][C]0.33292436599509[/C][C]0.665848731990179[/C][C]0.66707563400491[/C][/ROW]
[ROW][C]45[/C][C]0.336393610979271[/C][C]0.672787221958543[/C][C]0.663606389020729[/C][/ROW]
[ROW][C]46[/C][C]0.312562609111112[/C][C]0.625125218222224[/C][C]0.687437390888888[/C][/ROW]
[ROW][C]47[/C][C]0.416160773170425[/C][C]0.83232154634085[/C][C]0.583839226829575[/C][/ROW]
[ROW][C]48[/C][C]0.484476571037421[/C][C]0.968953142074841[/C][C]0.515523428962579[/C][/ROW]
[ROW][C]49[/C][C]0.466687907184494[/C][C]0.933375814368988[/C][C]0.533312092815506[/C][/ROW]
[ROW][C]50[/C][C]0.424739515112936[/C][C]0.849479030225872[/C][C]0.575260484887064[/C][/ROW]
[ROW][C]51[/C][C]0.42408581071147[/C][C]0.848171621422939[/C][C]0.57591418928853[/C][/ROW]
[ROW][C]52[/C][C]0.476280589781819[/C][C]0.952561179563638[/C][C]0.523719410218181[/C][/ROW]
[ROW][C]53[/C][C]0.428178505568047[/C][C]0.856357011136093[/C][C]0.571821494431953[/C][/ROW]
[ROW][C]54[/C][C]0.373308203238638[/C][C]0.746616406477276[/C][C]0.626691796761362[/C][/ROW]
[ROW][C]55[/C][C]0.343423623445546[/C][C]0.686847246891092[/C][C]0.656576376554454[/C][/ROW]
[ROW][C]56[/C][C]0.335440141861722[/C][C]0.670880283723444[/C][C]0.664559858138278[/C][/ROW]
[ROW][C]57[/C][C]0.293019048420869[/C][C]0.586038096841739[/C][C]0.706980951579131[/C][/ROW]
[ROW][C]58[/C][C]0.31354088575599[/C][C]0.62708177151198[/C][C]0.68645911424401[/C][/ROW]
[ROW][C]59[/C][C]0.455771970019767[/C][C]0.911543940039533[/C][C]0.544228029980233[/C][/ROW]
[ROW][C]60[/C][C]0.50340743330105[/C][C]0.993185133397899[/C][C]0.49659256669895[/C][/ROW]
[ROW][C]61[/C][C]0.429228489545152[/C][C]0.858456979090304[/C][C]0.570771510454848[/C][/ROW]
[ROW][C]62[/C][C]0.438649811649092[/C][C]0.877299623298184[/C][C]0.561350188350908[/C][/ROW]
[ROW][C]63[/C][C]0.417841934359165[/C][C]0.835683868718331[/C][C]0.582158065640835[/C][/ROW]
[ROW][C]64[/C][C]0.344829658459965[/C][C]0.68965931691993[/C][C]0.655170341540035[/C][/ROW]
[ROW][C]65[/C][C]0.358307101609135[/C][C]0.71661420321827[/C][C]0.641692898390865[/C][/ROW]
[ROW][C]66[/C][C]0.340708714264841[/C][C]0.681417428529682[/C][C]0.659291285735159[/C][/ROW]
[ROW][C]67[/C][C]0.265855943648465[/C][C]0.531711887296929[/C][C]0.734144056351535[/C][/ROW]
[ROW][C]68[/C][C]0.344745344739116[/C][C]0.689490689478232[/C][C]0.655254655260884[/C][/ROW]
[ROW][C]69[/C][C]0.657631507773855[/C][C]0.684736984452289[/C][C]0.342368492226145[/C][/ROW]
[ROW][C]70[/C][C]0.575562089368095[/C][C]0.848875821263809[/C][C]0.424437910631904[/C][/ROW]
[ROW][C]71[/C][C]0.475225632338187[/C][C]0.950451264676374[/C][C]0.524774367661813[/C][/ROW]
[ROW][C]72[/C][C]0.558985870248048[/C][C]0.882028259503904[/C][C]0.441014129751952[/C][/ROW]
[ROW][C]73[/C][C]0.853444425915225[/C][C]0.29311114816955[/C][C]0.146555574084775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2028754612663840.4057509225327690.797124538733616
100.09717027781360470.1943405556272090.902829722186395
110.04608492002809740.09216984005619470.953915079971903
120.01819306563624080.03638613127248170.981806934363759
130.01774716824188440.03549433648376870.982252831758116
140.008908199001472950.01781639800294590.991091800998527
150.02011777509385740.04023555018771480.979882224906143
160.009478354528368630.01895670905673730.990521645471631
170.004370182015541070.008740364031082130.995629817984459
180.002117632753827050.004235265507654090.997882367246173
190.0009061939040116830.001812387808023370.999093806095988
200.0179954122119960.03599082442399210.982004587788004
210.01231563019321440.02463126038642870.987684369806786
220.008934557162968620.01786911432593720.991065442837031
230.008891602261929110.01778320452385820.991108397738071
240.0250404994770240.0500809989540480.974959500522976
250.03104389399615060.06208778799230110.968956106003849
260.04319380512377050.08638761024754090.95680619487623
270.08990974184170710.1798194836834140.910090258158293
280.1126542170731440.2253084341462890.887345782926856
290.08088564329185780.1617712865837160.919114356708142
300.0752523490025860.1505046980051720.924747650997414
310.07381254711130360.1476250942226070.926187452888696
320.171426434124230.342852868248460.82857356587577
330.1902386330039910.3804772660079810.809761366996009
340.1754022099946590.3508044199893180.824597790005341
350.1345219266337260.2690438532674520.865478073366274
360.1015809881886620.2031619763773230.898419011811338
370.09101750653139680.1820350130627940.908982493468603
380.07816106282172950.1563221256434590.92183893717827
390.08547540500926680.1709508100185340.914524594990733
400.1246008274584630.2492016549169260.875399172541537
410.1567450708457340.3134901416914670.843254929154266
420.1837849874299250.3675699748598490.816215012570075
430.1464800989603810.2929601979207610.853519901039619
440.332924365995090.6658487319901790.66707563400491
450.3363936109792710.6727872219585430.663606389020729
460.3125626091111120.6251252182222240.687437390888888
470.4161607731704250.832321546340850.583839226829575
480.4844765710374210.9689531420748410.515523428962579
490.4666879071844940.9333758143689880.533312092815506
500.4247395151129360.8494790302258720.575260484887064
510.424085810711470.8481716214229390.57591418928853
520.4762805897818190.9525611795636380.523719410218181
530.4281785055680470.8563570111360930.571821494431953
540.3733082032386380.7466164064772760.626691796761362
550.3434236234455460.6868472468910920.656576376554454
560.3354401418617220.6708802837234440.664559858138278
570.2930190484208690.5860380968417390.706980951579131
580.313540885755990.627081771511980.68645911424401
590.4557719700197670.9115439400395330.544228029980233
600.503407433301050.9931851333978990.49659256669895
610.4292284895451520.8584569790903040.570771510454848
620.4386498116490920.8772996232981840.561350188350908
630.4178419343591650.8356838687183310.582158065640835
640.3448296584599650.689659316919930.655170341540035
650.3583071016091350.716614203218270.641692898390865
660.3407087142648410.6814174285296820.659291285735159
670.2658559436484650.5317118872969290.734144056351535
680.3447453447391160.6894906894782320.655254655260884
690.6576315077738550.6847369844522890.342368492226145
700.5755620893680950.8488758212638090.424437910631904
710.4752256323381870.9504512646763740.524774367661813
720.5589858702480480.8820282595039040.441014129751952
730.8534444259152250.293111148169550.146555574084775






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0461538461538462NOK
5% type I error level120.184615384615385NOK
10% type I error level160.246153846153846NOK

\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 & 3 & 0.0461538461538462 & NOK \tabularnewline
5% type I error level & 12 & 0.184615384615385 & NOK \tabularnewline
10% type I error level & 16 & 0.246153846153846 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199826&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]3[/C][C]0.0461538461538462[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.184615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.246153846153846[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199826&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199826&T=6

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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 level30.0461538461538462NOK
5% type I error level120.184615384615385NOK
10% type I error level160.246153846153846NOK


Figures (Output of Computation)

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Input Parameters & R Code

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