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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 computationTue, 22 Nov 2011 12:46:03 -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/22/t1321983976kfsw0jtmrq75rgg.htm/, Retrieved Thu, 28 Mar 2024 21:17:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146339, Retrieved Thu, 28 Mar 2024 21:17:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
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]
- R PD  [Multiple Regression] [] [2011-11-21 14:40:07] [a1957df0bc37aec4aa3c994e6a08412c]
-    D    [Multiple Regression] [] [2011-11-21 16:00:17] [a1957df0bc37aec4aa3c994e6a08412c]
-    D      [Multiple Regression] [] [2011-11-22 15:35:53] [a1957df0bc37aec4aa3c994e6a08412c]
-    D          [Multiple Regression] [] [2011-11-22 17:46:03] [fdaf10f0fcbe7b8f79ecbd42ec74e6ad] [Current]
-    D            [Multiple Regression] [] [2011-11-22 18:09:11] [a1957df0bc37aec4aa3c994e6a08412c]
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Dataseries X:
-6	-4	38	2
-3	-2	37	2.3
-2	2	32	2.8
-5	-5	32	2.4
-11	-15	44	2.3
-11	-16	43	2.7
-11	-18	42	2.7
-10	-13	38	2.9
-14	-23	37	3
-8	-10	35	2.2
-9	-10	37	2.3
-5	-6	33	2.8
-1	-3	24	2.8
-2	-4	24	2.8
-5	-7	31	2.2
-4	-7	25	2.6
-6	-7	28	2.8
-2	-3	24	2.5
-2	0	25	2.4
-2	-5	16	2.3
-2	-3	17	1.9
2	3	11	1.7
1	2	12	2
-8	-7	39	2.1
-1	-1	19	1.7
1	0	14	1.8
-1	-3	15	1.8
2	4	7	1.8
2	2	12	1.3
1	3	12	1.3
-1	0	14	1.3
-2	-10	9	1.2
-2	-10	8	1.4
-1	-9	4	2.2
-8	-22	7	2.9
-4	-16	3	3.1
-6	-18	5	3.5
-3	-14	0	3.6
-3	-12	-2	4.4
-7	-17	6	4.1
-9	-23	11	5.1
-11	-28	9	5.8
-13	-31	17	5.9
-11	-21	21	5.4
-9	-19	21	5.5
-17	-22	41	4.8
-22	-22	57	3.2
-25	-25	65	2.7
-20	-16	68	2.1
-24	-22	73	1.9
-24	-21	71	0.6
-22	-10	71	0.7
-19	-7	70	-0.2
-18	-5	69	-1
-17	-4	65	-1.7
-11	7	57	-0.7
-11	6	57	-1
-12	3	57	-0.9
-10	10	55	0
-15	0	65	0.3
-15	-2	65	0.8
-15	-1	64	0.8
-13	2	60	1.9
-8	8	43	2.1
-13	-6	47	2.5
-9	-4	40	2.7
-7	4	31	2.4
-4	7	27	2.4
-4	3	24	2.9
-2	3	23	3.1
0	8	17	3
-2	3	16	3.4
-3	-3	15	3.7
1	4	8	3.5
-2	-5	5	3.5
-1	-1	6	3.3
1	5	5	3.1
-3	0	12	3.4
-4	-6	8	4
-9	-13	17	3.4
-9	-15	22	3.4
-7	-8	24	3.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Consumentenvertrouwen[t] = + 3.53365676380486 + 0.329541779733443Economische_situatie[t] -0.272106799242228Werkloosheid[t] -0.237889574124604HICP[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumentenvertrouwen[t] =  +  3.53365676380486 +  0.329541779733443Economische_situatie[t] -0.272106799242228Werkloosheid[t] -0.237889574124604HICP[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumentenvertrouwen[t] =  +  3.53365676380486 +  0.329541779733443Economische_situatie[t] -0.272106799242228Werkloosheid[t] -0.237889574124604HICP[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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
Consumentenvertrouwen[t] = + 3.53365676380486 + 0.329541779733443Economische_situatie[t] -0.272106799242228Werkloosheid[t] -0.237889574124604HICP[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.533656763804860.7726194.57361.8e-059e-06
Economische_situatie0.3295417797334430.02739212.030400
Werkloosheid-0.2721067992422280.013215-20.590100
HICP-0.2378895741246040.216039-1.10110.2742220.137111

\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) & 3.53365676380486 & 0.772619 & 4.5736 & 1.8e-05 & 9e-06 \tabularnewline
Economische_situatie & 0.329541779733443 & 0.027392 & 12.0304 & 0 & 0 \tabularnewline
Werkloosheid & -0.272106799242228 & 0.013215 & -20.5901 & 0 & 0 \tabularnewline
HICP & -0.237889574124604 & 0.216039 & -1.1011 & 0.274222 & 0.137111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&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]3.53365676380486[/C][C]0.772619[/C][C]4.5736[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]Economische_situatie[/C][C]0.329541779733443[/C][C]0.027392[/C][C]12.0304[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Werkloosheid[/C][C]-0.272106799242228[/C][C]0.013215[/C][C]-20.5901[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]HICP[/C][C]-0.237889574124604[/C][C]0.216039[/C][C]-1.1011[/C][C]0.274222[/C][C]0.137111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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)3.533656763804860.7726194.57361.8e-059e-06
Economische_situatie0.3295417797334430.02739212.030400
Werkloosheid-0.2721067992422280.013215-20.590100
HICP-0.2378895741246040.216039-1.10110.2742220.137111







Multiple Linear Regression - Regression Statistics
Multiple R0.964298115272344
R-squared0.929870855117796
Adjusted R-squared0.927173580314634
F-TEST (value)344.74457479372
F-TEST (DF numerator)3
F-TEST (DF denominator)78
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83563153615328
Sum Squared Residuals262.824364648596

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.964298115272344 \tabularnewline
R-squared & 0.929870855117796 \tabularnewline
Adjusted R-squared & 0.927173580314634 \tabularnewline
F-TEST (value) & 344.74457479372 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83563153615328 \tabularnewline
Sum Squared Residuals & 262.824364648596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.964298115272344[/C][/ROW]
[ROW][C]R-squared[/C][C]0.929870855117796[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.927173580314634[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]344.74457479372[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83563153615328[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]262.824364648596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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 R0.964298115272344
R-squared0.929870855117796
Adjusted R-squared0.927173580314634
F-TEST (value)344.74457479372
F-TEST (DF numerator)3
F-TEST (DF denominator)78
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83563153615328
Sum Squared Residuals262.824364648596







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-6-8.600347874582792.60034787458279
2-3-7.740524388111054.74052438811105
3-2-5.180768060028443.18076806002844
4-5-7.39240468851272.3924046885127
5-11-13.92931511934142.92931511934141
6-11-14.08190592948253.08190592948246
7-11-14.46888268970713.46888268970712
8-10-11.78032450889591.78032450889591
9-14-14.82742446440060.827424464400572
10-8-9.808856070081681.80885607008168
11-9-10.37685862597861.3768586259786
12-5-8.089209097138213.08920909713821
13-1-4.651622564757833.65162256475783
14-2-4.981164344491282.98116434449128
15-5-7.731803533912442.73180353391244
16-4-6.194318568108912.19431856810891
17-6-7.058216880660521.05821688066052
18-2-4.580255692520452.58025569252045
19-2-3.839948195149891.83994819514989
20-2-3.014906943224591.01490694322459
21-2-2.532774353350090.532774353350093
2221.124695035328850.875304964671146
2310.4516795841158020.548320415884198
24-8-9.88486897043781.8848689704378
25-1-2.370326477542741.37032647754274
261-0.704039659010621.70403965901062
27-1-1.964771797453180.964771797453176
2822.51887505461875-0.518875054618748
2920.6182022860030251.38179771399698
3010.9477440657364670.0522559342635332
31-1-0.585094871948318-0.414905128051682
32-2-2.496189715659140.496189715659143
33-2-2.271660831241840.271660831241836
34-1-1.044003513839160.0440035138391647
35-8-6.31088974998783-1.68911025001217
36-4-3.29278978944318-0.707210210556822
37-6-4.59124277704436-1.40875722295564
38-3-1.93633061931191-1.06366938068809
39-3-0.923345120660251-2.07665487933975
40-7-4.67654154102791-2.32345845897209
41-9-8.2522157897643-0.74778421023569
42-11-9.52223379183429-1.47776620816571
43-13-12.7115024823849-0.288497517615096
44-11-10.3855670949571-0.614432905042913
45-9-9.750272492902660.750272492902662
46-17-16.0145111150603-0.98548888493967
47-22-19.9875965843366-2.01240341566339
48-25-23.0341315304125-1.96586846958754
49-20-20.74184216606340.7418421660634
50-24-24.03204892585030.0320489258502785
51-24-22.8490371012704-1.15096289872961
52-22-19.247866481615-2.75213351838502
53-19-17.7730337264603-1.22696627353972
54-18-16.6515317084515-1.34846829154851
55-17-15.0670400298619-1.93295997013809
56-11-9.50311563298082-1.49688436701918
57-11-9.76129054047688-1.23870945952312
58-12-10.7737048370897-1.22629516291033
59-10-8.13679939718326-1.86320060281674
60-15-14.2246520591773-0.775347940822654
61-15-15.00268040570650.00268040570653505
62-15-14.4010318267309-0.598968173269137
63-13-12.5856578220987-0.414342177901312
64-8-6.03016947140508-1.96983052859492
65-13-11.827337414292-1.17266258570797
66-9-9.311084174954470.311084174954466
67-7-4.15442187166949-2.84557812833051
68-4-2.07736933550025-1.92263066449975
69-4-2.69816084376964-1.30183915623036
70-2-2.473631959352330.47363195935233
7100.830506692180712-0.830506692180712
72-2-0.640251236894115-1.35974876310589
73-3-2.41676198828992-0.583238011710076
7411.84235597936469-0.842355979364692
75-2-0.307199640509607-1.69280035949039
76-10.786438594006856-1.78643859400686
7713.08337398647466-2.08337398647466
78-3-0.540449379125531-2.45955062087447
79-4-1.57200660503204-2.42799339496796
80-9-6.18502651187142-2.81497348812858
81-9-8.20464406754945-0.795355932450549
82-7-6.4420652078998-0.557934792100192

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -6 & -8.60034787458279 & 2.60034787458279 \tabularnewline
2 & -3 & -7.74052438811105 & 4.74052438811105 \tabularnewline
3 & -2 & -5.18076806002844 & 3.18076806002844 \tabularnewline
4 & -5 & -7.3924046885127 & 2.3924046885127 \tabularnewline
5 & -11 & -13.9293151193414 & 2.92931511934141 \tabularnewline
6 & -11 & -14.0819059294825 & 3.08190592948246 \tabularnewline
7 & -11 & -14.4688826897071 & 3.46888268970712 \tabularnewline
8 & -10 & -11.7803245088959 & 1.78032450889591 \tabularnewline
9 & -14 & -14.8274244644006 & 0.827424464400572 \tabularnewline
10 & -8 & -9.80885607008168 & 1.80885607008168 \tabularnewline
11 & -9 & -10.3768586259786 & 1.3768586259786 \tabularnewline
12 & -5 & -8.08920909713821 & 3.08920909713821 \tabularnewline
13 & -1 & -4.65162256475783 & 3.65162256475783 \tabularnewline
14 & -2 & -4.98116434449128 & 2.98116434449128 \tabularnewline
15 & -5 & -7.73180353391244 & 2.73180353391244 \tabularnewline
16 & -4 & -6.19431856810891 & 2.19431856810891 \tabularnewline
17 & -6 & -7.05821688066052 & 1.05821688066052 \tabularnewline
18 & -2 & -4.58025569252045 & 2.58025569252045 \tabularnewline
19 & -2 & -3.83994819514989 & 1.83994819514989 \tabularnewline
20 & -2 & -3.01490694322459 & 1.01490694322459 \tabularnewline
21 & -2 & -2.53277435335009 & 0.532774353350093 \tabularnewline
22 & 2 & 1.12469503532885 & 0.875304964671146 \tabularnewline
23 & 1 & 0.451679584115802 & 0.548320415884198 \tabularnewline
24 & -8 & -9.8848689704378 & 1.8848689704378 \tabularnewline
25 & -1 & -2.37032647754274 & 1.37032647754274 \tabularnewline
26 & 1 & -0.70403965901062 & 1.70403965901062 \tabularnewline
27 & -1 & -1.96477179745318 & 0.964771797453176 \tabularnewline
28 & 2 & 2.51887505461875 & -0.518875054618748 \tabularnewline
29 & 2 & 0.618202286003025 & 1.38179771399698 \tabularnewline
30 & 1 & 0.947744065736467 & 0.0522559342635332 \tabularnewline
31 & -1 & -0.585094871948318 & -0.414905128051682 \tabularnewline
32 & -2 & -2.49618971565914 & 0.496189715659143 \tabularnewline
33 & -2 & -2.27166083124184 & 0.271660831241836 \tabularnewline
34 & -1 & -1.04400351383916 & 0.0440035138391647 \tabularnewline
35 & -8 & -6.31088974998783 & -1.68911025001217 \tabularnewline
36 & -4 & -3.29278978944318 & -0.707210210556822 \tabularnewline
37 & -6 & -4.59124277704436 & -1.40875722295564 \tabularnewline
38 & -3 & -1.93633061931191 & -1.06366938068809 \tabularnewline
39 & -3 & -0.923345120660251 & -2.07665487933975 \tabularnewline
40 & -7 & -4.67654154102791 & -2.32345845897209 \tabularnewline
41 & -9 & -8.2522157897643 & -0.74778421023569 \tabularnewline
42 & -11 & -9.52223379183429 & -1.47776620816571 \tabularnewline
43 & -13 & -12.7115024823849 & -0.288497517615096 \tabularnewline
44 & -11 & -10.3855670949571 & -0.614432905042913 \tabularnewline
45 & -9 & -9.75027249290266 & 0.750272492902662 \tabularnewline
46 & -17 & -16.0145111150603 & -0.98548888493967 \tabularnewline
47 & -22 & -19.9875965843366 & -2.01240341566339 \tabularnewline
48 & -25 & -23.0341315304125 & -1.96586846958754 \tabularnewline
49 & -20 & -20.7418421660634 & 0.7418421660634 \tabularnewline
50 & -24 & -24.0320489258503 & 0.0320489258502785 \tabularnewline
51 & -24 & -22.8490371012704 & -1.15096289872961 \tabularnewline
52 & -22 & -19.247866481615 & -2.75213351838502 \tabularnewline
53 & -19 & -17.7730337264603 & -1.22696627353972 \tabularnewline
54 & -18 & -16.6515317084515 & -1.34846829154851 \tabularnewline
55 & -17 & -15.0670400298619 & -1.93295997013809 \tabularnewline
56 & -11 & -9.50311563298082 & -1.49688436701918 \tabularnewline
57 & -11 & -9.76129054047688 & -1.23870945952312 \tabularnewline
58 & -12 & -10.7737048370897 & -1.22629516291033 \tabularnewline
59 & -10 & -8.13679939718326 & -1.86320060281674 \tabularnewline
60 & -15 & -14.2246520591773 & -0.775347940822654 \tabularnewline
61 & -15 & -15.0026804057065 & 0.00268040570653505 \tabularnewline
62 & -15 & -14.4010318267309 & -0.598968173269137 \tabularnewline
63 & -13 & -12.5856578220987 & -0.414342177901312 \tabularnewline
64 & -8 & -6.03016947140508 & -1.96983052859492 \tabularnewline
65 & -13 & -11.827337414292 & -1.17266258570797 \tabularnewline
66 & -9 & -9.31108417495447 & 0.311084174954466 \tabularnewline
67 & -7 & -4.15442187166949 & -2.84557812833051 \tabularnewline
68 & -4 & -2.07736933550025 & -1.92263066449975 \tabularnewline
69 & -4 & -2.69816084376964 & -1.30183915623036 \tabularnewline
70 & -2 & -2.47363195935233 & 0.47363195935233 \tabularnewline
71 & 0 & 0.830506692180712 & -0.830506692180712 \tabularnewline
72 & -2 & -0.640251236894115 & -1.35974876310589 \tabularnewline
73 & -3 & -2.41676198828992 & -0.583238011710076 \tabularnewline
74 & 1 & 1.84235597936469 & -0.842355979364692 \tabularnewline
75 & -2 & -0.307199640509607 & -1.69280035949039 \tabularnewline
76 & -1 & 0.786438594006856 & -1.78643859400686 \tabularnewline
77 & 1 & 3.08337398647466 & -2.08337398647466 \tabularnewline
78 & -3 & -0.540449379125531 & -2.45955062087447 \tabularnewline
79 & -4 & -1.57200660503204 & -2.42799339496796 \tabularnewline
80 & -9 & -6.18502651187142 & -2.81497348812858 \tabularnewline
81 & -9 & -8.20464406754945 & -0.795355932450549 \tabularnewline
82 & -7 & -6.4420652078998 & -0.557934792100192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&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]-6[/C][C]-8.60034787458279[/C][C]2.60034787458279[/C][/ROW]
[ROW][C]2[/C][C]-3[/C][C]-7.74052438811105[/C][C]4.74052438811105[/C][/ROW]
[ROW][C]3[/C][C]-2[/C][C]-5.18076806002844[/C][C]3.18076806002844[/C][/ROW]
[ROW][C]4[/C][C]-5[/C][C]-7.3924046885127[/C][C]2.3924046885127[/C][/ROW]
[ROW][C]5[/C][C]-11[/C][C]-13.9293151193414[/C][C]2.92931511934141[/C][/ROW]
[ROW][C]6[/C][C]-11[/C][C]-14.0819059294825[/C][C]3.08190592948246[/C][/ROW]
[ROW][C]7[/C][C]-11[/C][C]-14.4688826897071[/C][C]3.46888268970712[/C][/ROW]
[ROW][C]8[/C][C]-10[/C][C]-11.7803245088959[/C][C]1.78032450889591[/C][/ROW]
[ROW][C]9[/C][C]-14[/C][C]-14.8274244644006[/C][C]0.827424464400572[/C][/ROW]
[ROW][C]10[/C][C]-8[/C][C]-9.80885607008168[/C][C]1.80885607008168[/C][/ROW]
[ROW][C]11[/C][C]-9[/C][C]-10.3768586259786[/C][C]1.3768586259786[/C][/ROW]
[ROW][C]12[/C][C]-5[/C][C]-8.08920909713821[/C][C]3.08920909713821[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-4.65162256475783[/C][C]3.65162256475783[/C][/ROW]
[ROW][C]14[/C][C]-2[/C][C]-4.98116434449128[/C][C]2.98116434449128[/C][/ROW]
[ROW][C]15[/C][C]-5[/C][C]-7.73180353391244[/C][C]2.73180353391244[/C][/ROW]
[ROW][C]16[/C][C]-4[/C][C]-6.19431856810891[/C][C]2.19431856810891[/C][/ROW]
[ROW][C]17[/C][C]-6[/C][C]-7.05821688066052[/C][C]1.05821688066052[/C][/ROW]
[ROW][C]18[/C][C]-2[/C][C]-4.58025569252045[/C][C]2.58025569252045[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]-3.83994819514989[/C][C]1.83994819514989[/C][/ROW]
[ROW][C]20[/C][C]-2[/C][C]-3.01490694322459[/C][C]1.01490694322459[/C][/ROW]
[ROW][C]21[/C][C]-2[/C][C]-2.53277435335009[/C][C]0.532774353350093[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.12469503532885[/C][C]0.875304964671146[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.451679584115802[/C][C]0.548320415884198[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-9.8848689704378[/C][C]1.8848689704378[/C][/ROW]
[ROW][C]25[/C][C]-1[/C][C]-2.37032647754274[/C][C]1.37032647754274[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]-0.70403965901062[/C][C]1.70403965901062[/C][/ROW]
[ROW][C]27[/C][C]-1[/C][C]-1.96477179745318[/C][C]0.964771797453176[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]2.51887505461875[/C][C]-0.518875054618748[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]0.618202286003025[/C][C]1.38179771399698[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.947744065736467[/C][C]0.0522559342635332[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-0.585094871948318[/C][C]-0.414905128051682[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]-2.49618971565914[/C][C]0.496189715659143[/C][/ROW]
[ROW][C]33[/C][C]-2[/C][C]-2.27166083124184[/C][C]0.271660831241836[/C][/ROW]
[ROW][C]34[/C][C]-1[/C][C]-1.04400351383916[/C][C]0.0440035138391647[/C][/ROW]
[ROW][C]35[/C][C]-8[/C][C]-6.31088974998783[/C][C]-1.68911025001217[/C][/ROW]
[ROW][C]36[/C][C]-4[/C][C]-3.29278978944318[/C][C]-0.707210210556822[/C][/ROW]
[ROW][C]37[/C][C]-6[/C][C]-4.59124277704436[/C][C]-1.40875722295564[/C][/ROW]
[ROW][C]38[/C][C]-3[/C][C]-1.93633061931191[/C][C]-1.06366938068809[/C][/ROW]
[ROW][C]39[/C][C]-3[/C][C]-0.923345120660251[/C][C]-2.07665487933975[/C][/ROW]
[ROW][C]40[/C][C]-7[/C][C]-4.67654154102791[/C][C]-2.32345845897209[/C][/ROW]
[ROW][C]41[/C][C]-9[/C][C]-8.2522157897643[/C][C]-0.74778421023569[/C][/ROW]
[ROW][C]42[/C][C]-11[/C][C]-9.52223379183429[/C][C]-1.47776620816571[/C][/ROW]
[ROW][C]43[/C][C]-13[/C][C]-12.7115024823849[/C][C]-0.288497517615096[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-10.3855670949571[/C][C]-0.614432905042913[/C][/ROW]
[ROW][C]45[/C][C]-9[/C][C]-9.75027249290266[/C][C]0.750272492902662[/C][/ROW]
[ROW][C]46[/C][C]-17[/C][C]-16.0145111150603[/C][C]-0.98548888493967[/C][/ROW]
[ROW][C]47[/C][C]-22[/C][C]-19.9875965843366[/C][C]-2.01240341566339[/C][/ROW]
[ROW][C]48[/C][C]-25[/C][C]-23.0341315304125[/C][C]-1.96586846958754[/C][/ROW]
[ROW][C]49[/C][C]-20[/C][C]-20.7418421660634[/C][C]0.7418421660634[/C][/ROW]
[ROW][C]50[/C][C]-24[/C][C]-24.0320489258503[/C][C]0.0320489258502785[/C][/ROW]
[ROW][C]51[/C][C]-24[/C][C]-22.8490371012704[/C][C]-1.15096289872961[/C][/ROW]
[ROW][C]52[/C][C]-22[/C][C]-19.247866481615[/C][C]-2.75213351838502[/C][/ROW]
[ROW][C]53[/C][C]-19[/C][C]-17.7730337264603[/C][C]-1.22696627353972[/C][/ROW]
[ROW][C]54[/C][C]-18[/C][C]-16.6515317084515[/C][C]-1.34846829154851[/C][/ROW]
[ROW][C]55[/C][C]-17[/C][C]-15.0670400298619[/C][C]-1.93295997013809[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-9.50311563298082[/C][C]-1.49688436701918[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-9.76129054047688[/C][C]-1.23870945952312[/C][/ROW]
[ROW][C]58[/C][C]-12[/C][C]-10.7737048370897[/C][C]-1.22629516291033[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-8.13679939718326[/C][C]-1.86320060281674[/C][/ROW]
[ROW][C]60[/C][C]-15[/C][C]-14.2246520591773[/C][C]-0.775347940822654[/C][/ROW]
[ROW][C]61[/C][C]-15[/C][C]-15.0026804057065[/C][C]0.00268040570653505[/C][/ROW]
[ROW][C]62[/C][C]-15[/C][C]-14.4010318267309[/C][C]-0.598968173269137[/C][/ROW]
[ROW][C]63[/C][C]-13[/C][C]-12.5856578220987[/C][C]-0.414342177901312[/C][/ROW]
[ROW][C]64[/C][C]-8[/C][C]-6.03016947140508[/C][C]-1.96983052859492[/C][/ROW]
[ROW][C]65[/C][C]-13[/C][C]-11.827337414292[/C][C]-1.17266258570797[/C][/ROW]
[ROW][C]66[/C][C]-9[/C][C]-9.31108417495447[/C][C]0.311084174954466[/C][/ROW]
[ROW][C]67[/C][C]-7[/C][C]-4.15442187166949[/C][C]-2.84557812833051[/C][/ROW]
[ROW][C]68[/C][C]-4[/C][C]-2.07736933550025[/C][C]-1.92263066449975[/C][/ROW]
[ROW][C]69[/C][C]-4[/C][C]-2.69816084376964[/C][C]-1.30183915623036[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-2.47363195935233[/C][C]0.47363195935233[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.830506692180712[/C][C]-0.830506692180712[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-0.640251236894115[/C][C]-1.35974876310589[/C][/ROW]
[ROW][C]73[/C][C]-3[/C][C]-2.41676198828992[/C][C]-0.583238011710076[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.84235597936469[/C][C]-0.842355979364692[/C][/ROW]
[ROW][C]75[/C][C]-2[/C][C]-0.307199640509607[/C][C]-1.69280035949039[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]0.786438594006856[/C][C]-1.78643859400686[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]3.08337398647466[/C][C]-2.08337398647466[/C][/ROW]
[ROW][C]78[/C][C]-3[/C][C]-0.540449379125531[/C][C]-2.45955062087447[/C][/ROW]
[ROW][C]79[/C][C]-4[/C][C]-1.57200660503204[/C][C]-2.42799339496796[/C][/ROW]
[ROW][C]80[/C][C]-9[/C][C]-6.18502651187142[/C][C]-2.81497348812858[/C][/ROW]
[ROW][C]81[/C][C]-9[/C][C]-8.20464406754945[/C][C]-0.795355932450549[/C][/ROW]
[ROW][C]82[/C][C]-7[/C][C]-6.4420652078998[/C][C]-0.557934792100192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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
1-6-8.600347874582792.60034787458279
2-3-7.740524388111054.74052438811105
3-2-5.180768060028443.18076806002844
4-5-7.39240468851272.3924046885127
5-11-13.92931511934142.92931511934141
6-11-14.08190592948253.08190592948246
7-11-14.46888268970713.46888268970712
8-10-11.78032450889591.78032450889591
9-14-14.82742446440060.827424464400572
10-8-9.808856070081681.80885607008168
11-9-10.37685862597861.3768586259786
12-5-8.089209097138213.08920909713821
13-1-4.651622564757833.65162256475783
14-2-4.981164344491282.98116434449128
15-5-7.731803533912442.73180353391244
16-4-6.194318568108912.19431856810891
17-6-7.058216880660521.05821688066052
18-2-4.580255692520452.58025569252045
19-2-3.839948195149891.83994819514989
20-2-3.014906943224591.01490694322459
21-2-2.532774353350090.532774353350093
2221.124695035328850.875304964671146
2310.4516795841158020.548320415884198
24-8-9.88486897043781.8848689704378
25-1-2.370326477542741.37032647754274
261-0.704039659010621.70403965901062
27-1-1.964771797453180.964771797453176
2822.51887505461875-0.518875054618748
2920.6182022860030251.38179771399698
3010.9477440657364670.0522559342635332
31-1-0.585094871948318-0.414905128051682
32-2-2.496189715659140.496189715659143
33-2-2.271660831241840.271660831241836
34-1-1.044003513839160.0440035138391647
35-8-6.31088974998783-1.68911025001217
36-4-3.29278978944318-0.707210210556822
37-6-4.59124277704436-1.40875722295564
38-3-1.93633061931191-1.06366938068809
39-3-0.923345120660251-2.07665487933975
40-7-4.67654154102791-2.32345845897209
41-9-8.2522157897643-0.74778421023569
42-11-9.52223379183429-1.47776620816571
43-13-12.7115024823849-0.288497517615096
44-11-10.3855670949571-0.614432905042913
45-9-9.750272492902660.750272492902662
46-17-16.0145111150603-0.98548888493967
47-22-19.9875965843366-2.01240341566339
48-25-23.0341315304125-1.96586846958754
49-20-20.74184216606340.7418421660634
50-24-24.03204892585030.0320489258502785
51-24-22.8490371012704-1.15096289872961
52-22-19.247866481615-2.75213351838502
53-19-17.7730337264603-1.22696627353972
54-18-16.6515317084515-1.34846829154851
55-17-15.0670400298619-1.93295997013809
56-11-9.50311563298082-1.49688436701918
57-11-9.76129054047688-1.23870945952312
58-12-10.7737048370897-1.22629516291033
59-10-8.13679939718326-1.86320060281674
60-15-14.2246520591773-0.775347940822654
61-15-15.00268040570650.00268040570653505
62-15-14.4010318267309-0.598968173269137
63-13-12.5856578220987-0.414342177901312
64-8-6.03016947140508-1.96983052859492
65-13-11.827337414292-1.17266258570797
66-9-9.311084174954470.311084174954466
67-7-4.15442187166949-2.84557812833051
68-4-2.07736933550025-1.92263066449975
69-4-2.69816084376964-1.30183915623036
70-2-2.473631959352330.47363195935233
7100.830506692180712-0.830506692180712
72-2-0.640251236894115-1.35974876310589
73-3-2.41676198828992-0.583238011710076
7411.84235597936469-0.842355979364692
75-2-0.307199640509607-1.69280035949039
76-10.786438594006856-1.78643859400686
7713.08337398647466-2.08337398647466
78-3-0.540449379125531-2.45955062087447
79-4-1.57200660503204-2.42799339496796
80-9-6.18502651187142-2.81497348812858
81-9-8.20464406754945-0.795355932450549
82-7-6.4420652078998-0.557934792100192







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1898264519844980.3796529039689950.810173548015502
80.1545185070424620.3090370140849250.845481492957538
90.07543954583032840.1508790916606570.924560454169672
100.03607973181603450.07215946363206910.963920268183965
110.03077039034476310.06154078068952610.969229609655237
120.02303185057587090.04606370115174180.97696814942413
130.08752628276742590.1750525655348520.912473717232574
140.07773638386579340.1554727677315870.922263616134207
150.06893909752798360.1378781950559670.931060902472016
160.05394949383279170.1078989876655830.946050506167208
170.08181322572842130.1636264514568430.918186774271579
180.08204860991289420.1640972198257880.917951390087106
190.09463554100985020.18927108201970.90536445899015
200.07178624400771140.1435724880154230.928213755992289
210.05419254287192230.1083850857438450.945807457128078
220.0397334969559780.0794669939119560.960266503044022
230.03082294830749550.06164589661499090.969177051692505
240.0497114304559060.0994228609118120.950288569544094
250.04986386484323240.09972772968646470.950136135156768
260.0771109801713470.1542219603426940.922889019828653
270.07638526485993640.1527705297198730.923614735140064
280.09025793631809910.1805158726361980.9097420636819
290.1478747639584880.2957495279169750.852125236041512
300.1448199504734660.2896399009469330.855180049526534
310.1397384797465620.2794769594931240.860261520253438
320.2641723307348870.5283446614697750.735827669265112
330.3383178895244740.6766357790489480.661682110475526
340.3914696215369070.7829392430738140.608530378463093
350.3859434826580060.7718869653160120.614056517341994
360.3737602738155320.7475205476310640.626239726184468
370.3511984328706310.7023968657412620.648801567129369
380.3264724099312930.6529448198625870.673527590068707
390.3344245439120640.6688490878241280.665575456087936
400.3746802150610460.7493604301220930.625319784938954
410.3249736963836610.6499473927673220.675026303616339
420.2720801398161090.5441602796322190.727919860183891
430.2440463484328680.4880926968657350.755953651567132
440.2267055409414880.4534110818829760.773294459058512
450.2802238210012110.5604476420024230.719776178998789
460.5553616784216310.8892766431567380.444638321578369
470.9505077792928260.09898444141434730.0494922207071736
480.9923717184132560.01525656317348850.00762828158674424
490.993962885245790.01207422950842110.00603711475421057
500.9929478174044410.01410436519111730.00705218259555864
510.9913954345867920.01720913082641680.0086045654132084
520.9995836420338240.0008327159323517020.000416357966175851
530.9995214787076020.000957042584795920.00047852129239796
540.9992944875070680.001411024985863260.000705512492931632
550.9989150924845560.002169815030886980.00108490751544349
560.998946762472570.002106475054858150.00105323752742908
570.99860295945530.002794081089400760.00139704054470038
580.9982075721519940.003584855696012690.00179242784800635
590.9982826608319360.00343467833612810.00171733916806405
600.9970790051845770.00584198963084640.0029209948154232
610.9965450536305310.006909892738937180.00345494636946859
620.9970660087120530.005867982575894360.00293399128794718
630.9950940735010080.009811852997984480.00490592649899224
640.9960913520158490.007817295968302430.00390864798415122
650.9941233338585460.01175333228290750.00587666614145377
660.9921718537624020.01565629247519510.00782814623759757
670.9964770051739060.007045989652187880.00352299482609394
680.996818760839030.006362478321941270.00318123916097064
690.9961229640840830.007754071831834520.00387703591591726
700.9937130391981080.01257392160378350.00628696080189175
710.9847918074316480.03041638513670380.0152081925683519
720.9707734296154020.05845314076919690.0292265703845984
730.944842892432030.1103142151359410.0551571075679705
740.9182387314170650.163522537165870.0817612685829352
750.8670996954448270.2658006091103470.132900304555173

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.189826451984498 & 0.379652903968995 & 0.810173548015502 \tabularnewline
8 & 0.154518507042462 & 0.309037014084925 & 0.845481492957538 \tabularnewline
9 & 0.0754395458303284 & 0.150879091660657 & 0.924560454169672 \tabularnewline
10 & 0.0360797318160345 & 0.0721594636320691 & 0.963920268183965 \tabularnewline
11 & 0.0307703903447631 & 0.0615407806895261 & 0.969229609655237 \tabularnewline
12 & 0.0230318505758709 & 0.0460637011517418 & 0.97696814942413 \tabularnewline
13 & 0.0875262827674259 & 0.175052565534852 & 0.912473717232574 \tabularnewline
14 & 0.0777363838657934 & 0.155472767731587 & 0.922263616134207 \tabularnewline
15 & 0.0689390975279836 & 0.137878195055967 & 0.931060902472016 \tabularnewline
16 & 0.0539494938327917 & 0.107898987665583 & 0.946050506167208 \tabularnewline
17 & 0.0818132257284213 & 0.163626451456843 & 0.918186774271579 \tabularnewline
18 & 0.0820486099128942 & 0.164097219825788 & 0.917951390087106 \tabularnewline
19 & 0.0946355410098502 & 0.1892710820197 & 0.90536445899015 \tabularnewline
20 & 0.0717862440077114 & 0.143572488015423 & 0.928213755992289 \tabularnewline
21 & 0.0541925428719223 & 0.108385085743845 & 0.945807457128078 \tabularnewline
22 & 0.039733496955978 & 0.079466993911956 & 0.960266503044022 \tabularnewline
23 & 0.0308229483074955 & 0.0616458966149909 & 0.969177051692505 \tabularnewline
24 & 0.049711430455906 & 0.099422860911812 & 0.950288569544094 \tabularnewline
25 & 0.0498638648432324 & 0.0997277296864647 & 0.950136135156768 \tabularnewline
26 & 0.077110980171347 & 0.154221960342694 & 0.922889019828653 \tabularnewline
27 & 0.0763852648599364 & 0.152770529719873 & 0.923614735140064 \tabularnewline
28 & 0.0902579363180991 & 0.180515872636198 & 0.9097420636819 \tabularnewline
29 & 0.147874763958488 & 0.295749527916975 & 0.852125236041512 \tabularnewline
30 & 0.144819950473466 & 0.289639900946933 & 0.855180049526534 \tabularnewline
31 & 0.139738479746562 & 0.279476959493124 & 0.860261520253438 \tabularnewline
32 & 0.264172330734887 & 0.528344661469775 & 0.735827669265112 \tabularnewline
33 & 0.338317889524474 & 0.676635779048948 & 0.661682110475526 \tabularnewline
34 & 0.391469621536907 & 0.782939243073814 & 0.608530378463093 \tabularnewline
35 & 0.385943482658006 & 0.771886965316012 & 0.614056517341994 \tabularnewline
36 & 0.373760273815532 & 0.747520547631064 & 0.626239726184468 \tabularnewline
37 & 0.351198432870631 & 0.702396865741262 & 0.648801567129369 \tabularnewline
38 & 0.326472409931293 & 0.652944819862587 & 0.673527590068707 \tabularnewline
39 & 0.334424543912064 & 0.668849087824128 & 0.665575456087936 \tabularnewline
40 & 0.374680215061046 & 0.749360430122093 & 0.625319784938954 \tabularnewline
41 & 0.324973696383661 & 0.649947392767322 & 0.675026303616339 \tabularnewline
42 & 0.272080139816109 & 0.544160279632219 & 0.727919860183891 \tabularnewline
43 & 0.244046348432868 & 0.488092696865735 & 0.755953651567132 \tabularnewline
44 & 0.226705540941488 & 0.453411081882976 & 0.773294459058512 \tabularnewline
45 & 0.280223821001211 & 0.560447642002423 & 0.719776178998789 \tabularnewline
46 & 0.555361678421631 & 0.889276643156738 & 0.444638321578369 \tabularnewline
47 & 0.950507779292826 & 0.0989844414143473 & 0.0494922207071736 \tabularnewline
48 & 0.992371718413256 & 0.0152565631734885 & 0.00762828158674424 \tabularnewline
49 & 0.99396288524579 & 0.0120742295084211 & 0.00603711475421057 \tabularnewline
50 & 0.992947817404441 & 0.0141043651911173 & 0.00705218259555864 \tabularnewline
51 & 0.991395434586792 & 0.0172091308264168 & 0.0086045654132084 \tabularnewline
52 & 0.999583642033824 & 0.000832715932351702 & 0.000416357966175851 \tabularnewline
53 & 0.999521478707602 & 0.00095704258479592 & 0.00047852129239796 \tabularnewline
54 & 0.999294487507068 & 0.00141102498586326 & 0.000705512492931632 \tabularnewline
55 & 0.998915092484556 & 0.00216981503088698 & 0.00108490751544349 \tabularnewline
56 & 0.99894676247257 & 0.00210647505485815 & 0.00105323752742908 \tabularnewline
57 & 0.9986029594553 & 0.00279408108940076 & 0.00139704054470038 \tabularnewline
58 & 0.998207572151994 & 0.00358485569601269 & 0.00179242784800635 \tabularnewline
59 & 0.998282660831936 & 0.0034346783361281 & 0.00171733916806405 \tabularnewline
60 & 0.997079005184577 & 0.0058419896308464 & 0.0029209948154232 \tabularnewline
61 & 0.996545053630531 & 0.00690989273893718 & 0.00345494636946859 \tabularnewline
62 & 0.997066008712053 & 0.00586798257589436 & 0.00293399128794718 \tabularnewline
63 & 0.995094073501008 & 0.00981185299798448 & 0.00490592649899224 \tabularnewline
64 & 0.996091352015849 & 0.00781729596830243 & 0.00390864798415122 \tabularnewline
65 & 0.994123333858546 & 0.0117533322829075 & 0.00587666614145377 \tabularnewline
66 & 0.992171853762402 & 0.0156562924751951 & 0.00782814623759757 \tabularnewline
67 & 0.996477005173906 & 0.00704598965218788 & 0.00352299482609394 \tabularnewline
68 & 0.99681876083903 & 0.00636247832194127 & 0.00318123916097064 \tabularnewline
69 & 0.996122964084083 & 0.00775407183183452 & 0.00387703591591726 \tabularnewline
70 & 0.993713039198108 & 0.0125739216037835 & 0.00628696080189175 \tabularnewline
71 & 0.984791807431648 & 0.0304163851367038 & 0.0152081925683519 \tabularnewline
72 & 0.970773429615402 & 0.0584531407691969 & 0.0292265703845984 \tabularnewline
73 & 0.94484289243203 & 0.110314215135941 & 0.0551571075679705 \tabularnewline
74 & 0.918238731417065 & 0.16352253716587 & 0.0817612685829352 \tabularnewline
75 & 0.867099695444827 & 0.265800609110347 & 0.132900304555173 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&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]7[/C][C]0.189826451984498[/C][C]0.379652903968995[/C][C]0.810173548015502[/C][/ROW]
[ROW][C]8[/C][C]0.154518507042462[/C][C]0.309037014084925[/C][C]0.845481492957538[/C][/ROW]
[ROW][C]9[/C][C]0.0754395458303284[/C][C]0.150879091660657[/C][C]0.924560454169672[/C][/ROW]
[ROW][C]10[/C][C]0.0360797318160345[/C][C]0.0721594636320691[/C][C]0.963920268183965[/C][/ROW]
[ROW][C]11[/C][C]0.0307703903447631[/C][C]0.0615407806895261[/C][C]0.969229609655237[/C][/ROW]
[ROW][C]12[/C][C]0.0230318505758709[/C][C]0.0460637011517418[/C][C]0.97696814942413[/C][/ROW]
[ROW][C]13[/C][C]0.0875262827674259[/C][C]0.175052565534852[/C][C]0.912473717232574[/C][/ROW]
[ROW][C]14[/C][C]0.0777363838657934[/C][C]0.155472767731587[/C][C]0.922263616134207[/C][/ROW]
[ROW][C]15[/C][C]0.0689390975279836[/C][C]0.137878195055967[/C][C]0.931060902472016[/C][/ROW]
[ROW][C]16[/C][C]0.0539494938327917[/C][C]0.107898987665583[/C][C]0.946050506167208[/C][/ROW]
[ROW][C]17[/C][C]0.0818132257284213[/C][C]0.163626451456843[/C][C]0.918186774271579[/C][/ROW]
[ROW][C]18[/C][C]0.0820486099128942[/C][C]0.164097219825788[/C][C]0.917951390087106[/C][/ROW]
[ROW][C]19[/C][C]0.0946355410098502[/C][C]0.1892710820197[/C][C]0.90536445899015[/C][/ROW]
[ROW][C]20[/C][C]0.0717862440077114[/C][C]0.143572488015423[/C][C]0.928213755992289[/C][/ROW]
[ROW][C]21[/C][C]0.0541925428719223[/C][C]0.108385085743845[/C][C]0.945807457128078[/C][/ROW]
[ROW][C]22[/C][C]0.039733496955978[/C][C]0.079466993911956[/C][C]0.960266503044022[/C][/ROW]
[ROW][C]23[/C][C]0.0308229483074955[/C][C]0.0616458966149909[/C][C]0.969177051692505[/C][/ROW]
[ROW][C]24[/C][C]0.049711430455906[/C][C]0.099422860911812[/C][C]0.950288569544094[/C][/ROW]
[ROW][C]25[/C][C]0.0498638648432324[/C][C]0.0997277296864647[/C][C]0.950136135156768[/C][/ROW]
[ROW][C]26[/C][C]0.077110980171347[/C][C]0.154221960342694[/C][C]0.922889019828653[/C][/ROW]
[ROW][C]27[/C][C]0.0763852648599364[/C][C]0.152770529719873[/C][C]0.923614735140064[/C][/ROW]
[ROW][C]28[/C][C]0.0902579363180991[/C][C]0.180515872636198[/C][C]0.9097420636819[/C][/ROW]
[ROW][C]29[/C][C]0.147874763958488[/C][C]0.295749527916975[/C][C]0.852125236041512[/C][/ROW]
[ROW][C]30[/C][C]0.144819950473466[/C][C]0.289639900946933[/C][C]0.855180049526534[/C][/ROW]
[ROW][C]31[/C][C]0.139738479746562[/C][C]0.279476959493124[/C][C]0.860261520253438[/C][/ROW]
[ROW][C]32[/C][C]0.264172330734887[/C][C]0.528344661469775[/C][C]0.735827669265112[/C][/ROW]
[ROW][C]33[/C][C]0.338317889524474[/C][C]0.676635779048948[/C][C]0.661682110475526[/C][/ROW]
[ROW][C]34[/C][C]0.391469621536907[/C][C]0.782939243073814[/C][C]0.608530378463093[/C][/ROW]
[ROW][C]35[/C][C]0.385943482658006[/C][C]0.771886965316012[/C][C]0.614056517341994[/C][/ROW]
[ROW][C]36[/C][C]0.373760273815532[/C][C]0.747520547631064[/C][C]0.626239726184468[/C][/ROW]
[ROW][C]37[/C][C]0.351198432870631[/C][C]0.702396865741262[/C][C]0.648801567129369[/C][/ROW]
[ROW][C]38[/C][C]0.326472409931293[/C][C]0.652944819862587[/C][C]0.673527590068707[/C][/ROW]
[ROW][C]39[/C][C]0.334424543912064[/C][C]0.668849087824128[/C][C]0.665575456087936[/C][/ROW]
[ROW][C]40[/C][C]0.374680215061046[/C][C]0.749360430122093[/C][C]0.625319784938954[/C][/ROW]
[ROW][C]41[/C][C]0.324973696383661[/C][C]0.649947392767322[/C][C]0.675026303616339[/C][/ROW]
[ROW][C]42[/C][C]0.272080139816109[/C][C]0.544160279632219[/C][C]0.727919860183891[/C][/ROW]
[ROW][C]43[/C][C]0.244046348432868[/C][C]0.488092696865735[/C][C]0.755953651567132[/C][/ROW]
[ROW][C]44[/C][C]0.226705540941488[/C][C]0.453411081882976[/C][C]0.773294459058512[/C][/ROW]
[ROW][C]45[/C][C]0.280223821001211[/C][C]0.560447642002423[/C][C]0.719776178998789[/C][/ROW]
[ROW][C]46[/C][C]0.555361678421631[/C][C]0.889276643156738[/C][C]0.444638321578369[/C][/ROW]
[ROW][C]47[/C][C]0.950507779292826[/C][C]0.0989844414143473[/C][C]0.0494922207071736[/C][/ROW]
[ROW][C]48[/C][C]0.992371718413256[/C][C]0.0152565631734885[/C][C]0.00762828158674424[/C][/ROW]
[ROW][C]49[/C][C]0.99396288524579[/C][C]0.0120742295084211[/C][C]0.00603711475421057[/C][/ROW]
[ROW][C]50[/C][C]0.992947817404441[/C][C]0.0141043651911173[/C][C]0.00705218259555864[/C][/ROW]
[ROW][C]51[/C][C]0.991395434586792[/C][C]0.0172091308264168[/C][C]0.0086045654132084[/C][/ROW]
[ROW][C]52[/C][C]0.999583642033824[/C][C]0.000832715932351702[/C][C]0.000416357966175851[/C][/ROW]
[ROW][C]53[/C][C]0.999521478707602[/C][C]0.00095704258479592[/C][C]0.00047852129239796[/C][/ROW]
[ROW][C]54[/C][C]0.999294487507068[/C][C]0.00141102498586326[/C][C]0.000705512492931632[/C][/ROW]
[ROW][C]55[/C][C]0.998915092484556[/C][C]0.00216981503088698[/C][C]0.00108490751544349[/C][/ROW]
[ROW][C]56[/C][C]0.99894676247257[/C][C]0.00210647505485815[/C][C]0.00105323752742908[/C][/ROW]
[ROW][C]57[/C][C]0.9986029594553[/C][C]0.00279408108940076[/C][C]0.00139704054470038[/C][/ROW]
[ROW][C]58[/C][C]0.998207572151994[/C][C]0.00358485569601269[/C][C]0.00179242784800635[/C][/ROW]
[ROW][C]59[/C][C]0.998282660831936[/C][C]0.0034346783361281[/C][C]0.00171733916806405[/C][/ROW]
[ROW][C]60[/C][C]0.997079005184577[/C][C]0.0058419896308464[/C][C]0.0029209948154232[/C][/ROW]
[ROW][C]61[/C][C]0.996545053630531[/C][C]0.00690989273893718[/C][C]0.00345494636946859[/C][/ROW]
[ROW][C]62[/C][C]0.997066008712053[/C][C]0.00586798257589436[/C][C]0.00293399128794718[/C][/ROW]
[ROW][C]63[/C][C]0.995094073501008[/C][C]0.00981185299798448[/C][C]0.00490592649899224[/C][/ROW]
[ROW][C]64[/C][C]0.996091352015849[/C][C]0.00781729596830243[/C][C]0.00390864798415122[/C][/ROW]
[ROW][C]65[/C][C]0.994123333858546[/C][C]0.0117533322829075[/C][C]0.00587666614145377[/C][/ROW]
[ROW][C]66[/C][C]0.992171853762402[/C][C]0.0156562924751951[/C][C]0.00782814623759757[/C][/ROW]
[ROW][C]67[/C][C]0.996477005173906[/C][C]0.00704598965218788[/C][C]0.00352299482609394[/C][/ROW]
[ROW][C]68[/C][C]0.99681876083903[/C][C]0.00636247832194127[/C][C]0.00318123916097064[/C][/ROW]
[ROW][C]69[/C][C]0.996122964084083[/C][C]0.00775407183183452[/C][C]0.00387703591591726[/C][/ROW]
[ROW][C]70[/C][C]0.993713039198108[/C][C]0.0125739216037835[/C][C]0.00628696080189175[/C][/ROW]
[ROW][C]71[/C][C]0.984791807431648[/C][C]0.0304163851367038[/C][C]0.0152081925683519[/C][/ROW]
[ROW][C]72[/C][C]0.970773429615402[/C][C]0.0584531407691969[/C][C]0.0292265703845984[/C][/ROW]
[ROW][C]73[/C][C]0.94484289243203[/C][C]0.110314215135941[/C][C]0.0551571075679705[/C][/ROW]
[ROW][C]74[/C][C]0.918238731417065[/C][C]0.16352253716587[/C][C]0.0817612685829352[/C][/ROW]
[ROW][C]75[/C][C]0.867099695444827[/C][C]0.265800609110347[/C][C]0.132900304555173[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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
70.1898264519844980.3796529039689950.810173548015502
80.1545185070424620.3090370140849250.845481492957538
90.07543954583032840.1508790916606570.924560454169672
100.03607973181603450.07215946363206910.963920268183965
110.03077039034476310.06154078068952610.969229609655237
120.02303185057587090.04606370115174180.97696814942413
130.08752628276742590.1750525655348520.912473717232574
140.07773638386579340.1554727677315870.922263616134207
150.06893909752798360.1378781950559670.931060902472016
160.05394949383279170.1078989876655830.946050506167208
170.08181322572842130.1636264514568430.918186774271579
180.08204860991289420.1640972198257880.917951390087106
190.09463554100985020.18927108201970.90536445899015
200.07178624400771140.1435724880154230.928213755992289
210.05419254287192230.1083850857438450.945807457128078
220.0397334969559780.0794669939119560.960266503044022
230.03082294830749550.06164589661499090.969177051692505
240.0497114304559060.0994228609118120.950288569544094
250.04986386484323240.09972772968646470.950136135156768
260.0771109801713470.1542219603426940.922889019828653
270.07638526485993640.1527705297198730.923614735140064
280.09025793631809910.1805158726361980.9097420636819
290.1478747639584880.2957495279169750.852125236041512
300.1448199504734660.2896399009469330.855180049526534
310.1397384797465620.2794769594931240.860261520253438
320.2641723307348870.5283446614697750.735827669265112
330.3383178895244740.6766357790489480.661682110475526
340.3914696215369070.7829392430738140.608530378463093
350.3859434826580060.7718869653160120.614056517341994
360.3737602738155320.7475205476310640.626239726184468
370.3511984328706310.7023968657412620.648801567129369
380.3264724099312930.6529448198625870.673527590068707
390.3344245439120640.6688490878241280.665575456087936
400.3746802150610460.7493604301220930.625319784938954
410.3249736963836610.6499473927673220.675026303616339
420.2720801398161090.5441602796322190.727919860183891
430.2440463484328680.4880926968657350.755953651567132
440.2267055409414880.4534110818829760.773294459058512
450.2802238210012110.5604476420024230.719776178998789
460.5553616784216310.8892766431567380.444638321578369
470.9505077792928260.09898444141434730.0494922207071736
480.9923717184132560.01525656317348850.00762828158674424
490.993962885245790.01207422950842110.00603711475421057
500.9929478174044410.01410436519111730.00705218259555864
510.9913954345867920.01720913082641680.0086045654132084
520.9995836420338240.0008327159323517020.000416357966175851
530.9995214787076020.000957042584795920.00047852129239796
540.9992944875070680.001411024985863260.000705512492931632
550.9989150924845560.002169815030886980.00108490751544349
560.998946762472570.002106475054858150.00105323752742908
570.99860295945530.002794081089400760.00139704054470038
580.9982075721519940.003584855696012690.00179242784800635
590.9982826608319360.00343467833612810.00171733916806405
600.9970790051845770.00584198963084640.0029209948154232
610.9965450536305310.006909892738937180.00345494636946859
620.9970660087120530.005867982575894360.00293399128794718
630.9950940735010080.009811852997984480.00490592649899224
640.9960913520158490.007817295968302430.00390864798415122
650.9941233338585460.01175333228290750.00587666614145377
660.9921718537624020.01565629247519510.00782814623759757
670.9964770051739060.007045989652187880.00352299482609394
680.996818760839030.006362478321941270.00318123916097064
690.9961229640840830.007754071831834520.00387703591591726
700.9937130391981080.01257392160378350.00628696080189175
710.9847918074316480.03041638513670380.0152081925683519
720.9707734296154020.05845314076919690.0292265703845984
730.944842892432030.1103142151359410.0551571075679705
740.9182387314170650.163522537165870.0817612685829352
750.8670996954448270.2658006091103470.132900304555173







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.231884057971014NOK
5% type I error level250.36231884057971NOK
10% type I error level330.478260869565217NOK

\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 & 16 & 0.231884057971014 & NOK \tabularnewline
5% type I error level & 25 & 0.36231884057971 & NOK \tabularnewline
10% type I error level & 33 & 0.478260869565217 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146339&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]16[/C][C]0.231884057971014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.36231884057971[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.478260869565217[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146339&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146339&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 level160.231884057971014NOK
5% type I error level250.36231884057971NOK
10% type I error level330.478260869565217NOK



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')
}