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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 07:23:24 -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/Nov/05/t1352118234ovtvnqovbmm1wsn.htm/, Retrieved Wed, 01 Feb 2023 16:06:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186009, Retrieved Wed, 01 Feb 2023 16:06:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 12:23:24] [44856cc66eb94b5de7ab259e2cb08a95] [Current]
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Dataseries X:
9	5	-1	6	24
11	5	-4	6	29
13	9	-6	8	29
12	10	-9	4	25
13	14	-13	8	16
15	19	-13	10	18
13	18	-10	9	13
16	16	-12	12	22
10	8	-9	9	15
14	10	-15	11	20
14	12	-14	11	19
15	13	-18	11	18
13	15	-13	11	13
8	3	-2	11	17
7	2	-1	9	17
3	-2	5	8	13
3	1	8	6	14
4	1	6	7	13
4	-1	7	8	17
0	-6	15	6	17
-4	-13	23	5	15
-14	-25	43	2	9
-18	-26	60	3	10
-8	-9	36	3	9
-1	1	28	7	14
1	3	23	8	18
2	6	23	7	18
0	2	22	7	12
1	5	22	6	16
0	5	24	6	12
-1	0	32	7	19
-3	-5	27	5	13
-3	-4	27	5	12
-3	-2	27	5	13
-4	-1	29	4	11
-8	-8	38	4	10
-9	-16	40	4	16
-13	-19	45	1	12
-18	-28	50	-1	6
-11	-11	43	3	8
-9	-4	44	4	6
-10	-9	44	3	8
-13	-12	49	2	8
-11	-10	42	1	9
-5	-2	36	4	13
-15	-13	57	3	8
-6	0	42	5	11
-6	0	39	6	8
-3	4	33	6	10
-1	7	32	6	15
-3	5	34	6	12
-4	2	37	6	13
-6	-2	38	5	12
0	6	28	6	15
-4	-3	31	5	13
-2	1	28	6	13
-2	0	30	5	16
-6	-7	39	7	14
-7	-6	38	4	12
-6	-4	39	5	15
-6	-4	38	6	14
-3	-2	37	6	19
-2	2	32	5	16
-5	-5	32	3	16
-11	-15	44	2	11
-11	-16	43	3	13
-11	-18	42	3	12
-10	-13	38	2	11
-14	-23	37	0	6
-8	-10	35	4	9
-9	-10	37	4	6
-5	-6	33	5	15
-1	-3	24	6	17
-2	-4	24	6	13
-5	-7	31	5	12
-4	-7	25	5	13
-6	-7	28	3	10
-2	-3	24	5	14
-2	0	25	5	13
-2	-5	16	5	10
-2	-3	17	3	11
2	3	11	6	12
1	2	12	6	7
-8	-7	39	4	11
-1	-1	19	6	9
1	0	14	5	13
-1	-3	15	4	12
2	4	7	5	5
2	2	12	5	13
1	3	12	4	11
-1	0	14	3	8
-2	-10	9	2	8
-2	-10	8	3	8
-1	-9	4	2	8
-8	-22	7	-1	0
-4	-16	3	0	3
-6	-18	5	-2	0
-3	-14	0	1	-1
-3	-12	-2	-2	-1
-7	-17	6	-2	-4
-9	-23	11	-2	1
-11	-28	9	-6	-1
-13	-31	17	-4	0
-11	-21	21	-2	-1
-9	-19	21	0	6
-17	-22	41	-5	0
-22	-22	57	-4	-3
-25	-25	65	-5	-3
-20	-16	68	-1	4
-24	-22	73	-2	1
-24	-21	71	-4	0
-22	-10	71	-1	-4
-19	-7	70	1	-2
-18	-5	69	1	3
-17	-4	65	-2	2
-11	7	57	1	5
-11	6	57	1	6
-12	3	57	3	6
-10	10	55	3	3
-15	0	65	1	4
-15	-2	65	1	7
-15	-1	64	0	5
-13	2	60	2	6
-8	8	43	2	1
-13	-6	47	-1	3
-9	-4	40	1	6
-7	4	31	0	0
-4	7	27	1	3
-4	3	24	1	4
-2	3	23	3	7
0	8	17	2	6
-2	3	16	0	6
-3	-3	15	0	6
1	4	8	3	6
-2	-5	5	-2	2
-1	-1	6	0	2
1	5	5	1	2
-3	0	12	-1	3
-4	-6	8	-2	-1
-9	-13	17	-1	-4
-9	-15	22	-1	4
-7	-8	24	1	5
-14	-20	36	-2	3
-12	-10	31	-5	-1
-16	-22	34	-5	-4
-20	-25	47	-6	0
-12	-10	33	-4	-1
-12	-8	35	-3	-1
-10	-9	31	-3	3
-10	-5	35	-1	2
-13	-7	39	-2	-4
-16	-11	46	-3	-3
-14	-11	40	-3	-1
-17	-16	50	-3	3





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186009&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186009&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
consumentenvert[t] = -0.0126014706029706 + 0.250036433976062Economie[t] -0.250696282667499Whl[t] + 0.275162218178276Financ[t] + 0.240262823809418`Spaarverm\r\r\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
consumentenvert[t] =  -0.0126014706029706 +  0.250036433976062Economie[t] -0.250696282667499Whl[t] +  0.275162218178276Financ[t] +  0.240262823809418`Spaarverm\r\r\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]consumentenvert[t] =  -0.0126014706029706 +  0.250036433976062Economie[t] -0.250696282667499Whl[t] +  0.275162218178276Financ[t] +  0.240262823809418`Spaarverm\r\r\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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
consumentenvert[t] = -0.0126014706029706 + 0.250036433976062Economie[t] -0.250696282667499Whl[t] + 0.275162218178276Financ[t] + 0.240262823809418`Spaarverm\r\r\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.01260147060297060.067967-0.18540.8531630.426581
Economie0.2500364339760620.00356570.144400
Whl-0.2506962826674990.001367-183.361900
Financ0.2751622181782760.01489518.473500
`Spaarverm\r\r\r`0.2402628238094180.00705434.062300

\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) & -0.0126014706029706 & 0.067967 & -0.1854 & 0.853163 & 0.426581 \tabularnewline
Economie & 0.250036433976062 & 0.003565 & 70.1444 & 0 & 0 \tabularnewline
Whl & -0.250696282667499 & 0.001367 & -183.3619 & 0 & 0 \tabularnewline
Financ & 0.275162218178276 & 0.014895 & 18.4735 & 0 & 0 \tabularnewline
`Spaarverm\r\r\r` & 0.240262823809418 & 0.007054 & 34.0623 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]-0.0126014706029706[/C][C]0.067967[/C][C]-0.1854[/C][C]0.853163[/C][C]0.426581[/C][/ROW]
[ROW][C]Economie[/C][C]0.250036433976062[/C][C]0.003565[/C][C]70.1444[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Whl[/C][C]-0.250696282667499[/C][C]0.001367[/C][C]-183.3619[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Financ[/C][C]0.275162218178276[/C][C]0.014895[/C][C]18.4735[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Spaarverm\r\r\r`[/C][C]0.240262823809418[/C][C]0.007054[/C][C]34.0623[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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)-0.01260147060297060.067967-0.18540.8531630.426581
Economie0.2500364339760620.00356570.144400
Whl-0.2506962826674990.001367-183.361900
Financ0.2751622181782760.01489518.473500
`Spaarverm\r\r\r`0.2402628238094180.00705434.062300







Multiple Linear Regression - Regression Statistics
Multiple R0.999346975958035
R-squared0.99869437835647
Adjusted R-squared0.998659328111006
F-TEST (value)28493.2206647462
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.313094003609779
Sum Squared Residuals14.6061504093637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999346975958035 \tabularnewline
R-squared & 0.99869437835647 \tabularnewline
Adjusted R-squared & 0.998659328111006 \tabularnewline
F-TEST (value) & 28493.2206647462 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.313094003609779 \tabularnewline
Sum Squared Residuals & 14.6061504093637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999346975958035[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99869437835647[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.998659328111006[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28493.2206647462[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/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]0.313094003609779[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.6061504093637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 R0.999346975958035
R-squared0.99869437835647
Adjusted R-squared0.998659328111006
F-TEST (value)28493.2206647462
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.313094003609779
Sum Squared Residuals14.6061504093637







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.905558062440520.0944419375594796
21110.85896102949010.141038970509895
31312.91082376708590.0891762329140841
41211.85124888111370.1487511188863
51312.79246320611630.20753679388372
61515.073495459972-0.073495459971976
71312.5948938407680.40510615923195
81615.58406560697050.415934393029485
91010.3243588659588-0.32435886595877
101414.0802479853195-0.0802479853195346
111414.0893617467947-0.08936174679474
121515.1019204876314-0.10192048763138
131313.1471978231989-0.147197823198915
1488.35015280138135-0.350152801381353
1577.29909564838124-0.29909564838124
1633.55855870305605-0.558558703056048
1733.2465175444346-0.246517544434602
1843.782809504138460.217190495861542
1944.26825386693478-0.268253866934783
2000.462176999357929-0.462176999357929
21-4-4.049336165611610.0493361656116098
22-14-14.33076262406570.330762624065675
23-18-18.32721082140150.327210821401532
24-8-8.300143483597920.300143483597917
25-1-1.492245890737110.492245890737113
2611.49752190396846-0.497521903968456
2721.972468987718360.0275310122816356
280-0.2185574083748910.218557408374891
2911.21744097061269-0.217440970612691
300-0.2450028899599810.245002889959981
31-1-1.543753336336080.543753336336081
32-3-3.532355472091950.532355472091952
33-3-3.522581861925310.522581861925308
34-3-2.78224617016377-0.217753829836233
35-4-3.78929016731982-0.210709832680183
36-8-8.036074572969160.0360745729691612
37-9-9.096181667256140.0961816672561438
38-13-12.8863103322943-0.113689667705675
39-18-18.38202103062940.382021030629437
40-11-10.795353154032-0.204646845968044
41-9-9.501157828307590.501157828307585
42-10-10.54597656874730.545976568747332
43-13-12.8247295021913-0.17527049780871
44-11-10.6046820499355-0.395317950064472
45-5-5.313674932349540.313674932349539
46-15-14.8051739793291-0.194826020670929
47-6-6.523143189842970.523143189842974
48-6-6.216680595090450.216680595090454
49-3-3.231831515562370.231831515562374
50-1-1.02971181191960.0297118119195983
51-3-2.75196571663497-0.248034283365025
52-4-4.013901042756240.0139010427562404
53-6-5.78016810331568-0.21983189668432
540-0.2769631152256630.276963115225663
55-4-4.035067734809830.0350677348098269
56-2-2.007670932724810.00767093272480671
57-2-2.313473678785890.313473678785888
58-6-6.25019647188810.250196471888099
59-7-7.05547605739820.0554760573982013
60-6-5.81014878250705-0.189851217492952
61-6-5.52455310547069-0.475446894529309
62-3-3.572469835803980.572469835803978
63-2-2.314793376168760.314793376168764
64-5-4.61537285035775-0.384627149642254
65-11-11.60056891935370.600568919353722
66-11-10.8442212048652-0.155778795134828
67-11-11.33386061395920.333860613959214
68-10-9.5963183553966-0.403681644603398
69-14-13.5976249678934-0.40237503210664
70-8-8.02432141672820.0243214167282036
71-9-9.246502453491460.246502453491457
72-5-4.80604395445417-0.193956045545826
73-1-1.043980242721380.0439802427213818
74-2-2.255067971935120.255067971935116
75-5-5.275476294523490.275476294523491
76-4-3.53103577470908-0.468964225290924
77-6-5.55423753049638-0.44576246950362
78-2-2.039930932327910.0399309323279119
79-2-1.78078073687664-0.219219263123355
80-2-1.49548483417771-0.504515165822289
81-2-1.55616986144022-0.443830138559779
8222.51397591676539-0.51397591676539
8310.8119290810747380.188070918925262
84-8-7.79647159785118-0.203528402148819
85-1-1.212528551907110.212528551907106
8610.9768783724658490.023121627534151
87-1-0.539352254117528-0.460647745882472
8821.809795496567250.190204503432753
8921.978343805752970.021656194247029
9011.47269237393192-0.472692373931921
91-1-0.774760182937792-0.225239817062208
92-2-2.296805327539190.296805327539186
93-2-1.77094682669341-0.229053173306589
94-1-0.793287480225627-0.206712519774373
95-8-7.5434392149271-0.4565607850729
96-4-4.04448479079420.0444847907942006
97-6-6.317063131866130.317063131866129
98-3-3.478212151898980.478212151898977
99-3-3.302233373146680.30223337314668
100-7-7.278774275795240.278774275795238
101-9-8.83116017394202-0.168839826057984
102-11-11.16112429881930.161124298819264
103-13-13.12621660192150.126216601921474
104-11-11.31857578028370.318575780283723
105-9-8.58633870930912-0.413661290690878
106-17-17.16776169833520.167761698335184
107-22-21.6245284742652-0.375471525734846
108-25-24.6553702557116-0.344629744288391
109-20-20.37464255855050.374642558550524
110-24-24.12429326535090.124293265350921
111-24-24.16345152620580.163451526205829
112-22-21.548615393172-0.451384606828001
113-19-19.51695972460090.51695972460093
114-18-17.5648764549342-0.435123545065785
115-17-17.37780436863240.3778043686324
116-11-11.07555820759260.0755582075926458
117-11-11.08533181775930.0853318177592905
118-12-11.2851166833309-0.714883316669077
119-10-9.75425755159175-0.245742448408252
120-15-15.07164633057450.0716463305744911
121-15-14.8509307270984-0.149069272901641
122-15-15.10588587625190.105885876251911
123-13-12.5624041834878-0.437595816512244
124-8-8.001662893330990.00166289333098973
125-13-12.8499191065818-0.150080893418161
126-9-9.323859352172410.323859352172414
127-7-6.78404049739121-0.215959502608789
128-4-4.03519537518650.0351953751864984
129-4-4.042989439278830.0429894392788264
130-2-2.521180248826520.521180248826522
1310-0.2822454249289110.282245424928911
132-2-1.83205574849827-0.16794425150173
133-3-3.081578069687140.0815780696871399
13411.24903760135261-0.249037601352614
135-2-2.586063842558490.586063842558492
136-1-1.286289952965190.286289952965193
13710.739787151736950.26021284826305
138-3-2.57533060936299-0.424669390637013
139-4-4.308977595965310.308977595965307
140-9-8.76112543105521-0.238874568944787
141-9-8.59257712186949-0.407422878130513
142-7-6.55312738920609-0.446872610793914
143-14-13.8679322910825-0.132067708917519
144-12-11.9006244877569-0.0993755122431316
145-16-16.37393901490040.37393901490036
146-20-19.6972109144466-0.302789085553358
147-12-12.12685483491360.126854834913592
148-12-11.8530123141182-0.146987685881808
149-10-10.13921232218660.139212322186583
150-10-9.8317901044052-0.168209895594798
151-13-13.05138726406210.051387264062108
152-16-15.8413063730077-0.158693626992292
153-14-13.8566030293839-0.143396970616126
154-17-16.6526967307015-0.347303269298495

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.90555806244052 & 0.0944419375594796 \tabularnewline
2 & 11 & 10.8589610294901 & 0.141038970509895 \tabularnewline
3 & 13 & 12.9108237670859 & 0.0891762329140841 \tabularnewline
4 & 12 & 11.8512488811137 & 0.1487511188863 \tabularnewline
5 & 13 & 12.7924632061163 & 0.20753679388372 \tabularnewline
6 & 15 & 15.073495459972 & -0.073495459971976 \tabularnewline
7 & 13 & 12.594893840768 & 0.40510615923195 \tabularnewline
8 & 16 & 15.5840656069705 & 0.415934393029485 \tabularnewline
9 & 10 & 10.3243588659588 & -0.32435886595877 \tabularnewline
10 & 14 & 14.0802479853195 & -0.0802479853195346 \tabularnewline
11 & 14 & 14.0893617467947 & -0.08936174679474 \tabularnewline
12 & 15 & 15.1019204876314 & -0.10192048763138 \tabularnewline
13 & 13 & 13.1471978231989 & -0.147197823198915 \tabularnewline
14 & 8 & 8.35015280138135 & -0.350152801381353 \tabularnewline
15 & 7 & 7.29909564838124 & -0.29909564838124 \tabularnewline
16 & 3 & 3.55855870305605 & -0.558558703056048 \tabularnewline
17 & 3 & 3.2465175444346 & -0.246517544434602 \tabularnewline
18 & 4 & 3.78280950413846 & 0.217190495861542 \tabularnewline
19 & 4 & 4.26825386693478 & -0.268253866934783 \tabularnewline
20 & 0 & 0.462176999357929 & -0.462176999357929 \tabularnewline
21 & -4 & -4.04933616561161 & 0.0493361656116098 \tabularnewline
22 & -14 & -14.3307626240657 & 0.330762624065675 \tabularnewline
23 & -18 & -18.3272108214015 & 0.327210821401532 \tabularnewline
24 & -8 & -8.30014348359792 & 0.300143483597917 \tabularnewline
25 & -1 & -1.49224589073711 & 0.492245890737113 \tabularnewline
26 & 1 & 1.49752190396846 & -0.497521903968456 \tabularnewline
27 & 2 & 1.97246898771836 & 0.0275310122816356 \tabularnewline
28 & 0 & -0.218557408374891 & 0.218557408374891 \tabularnewline
29 & 1 & 1.21744097061269 & -0.217440970612691 \tabularnewline
30 & 0 & -0.245002889959981 & 0.245002889959981 \tabularnewline
31 & -1 & -1.54375333633608 & 0.543753336336081 \tabularnewline
32 & -3 & -3.53235547209195 & 0.532355472091952 \tabularnewline
33 & -3 & -3.52258186192531 & 0.522581861925308 \tabularnewline
34 & -3 & -2.78224617016377 & -0.217753829836233 \tabularnewline
35 & -4 & -3.78929016731982 & -0.210709832680183 \tabularnewline
36 & -8 & -8.03607457296916 & 0.0360745729691612 \tabularnewline
37 & -9 & -9.09618166725614 & 0.0961816672561438 \tabularnewline
38 & -13 & -12.8863103322943 & -0.113689667705675 \tabularnewline
39 & -18 & -18.3820210306294 & 0.382021030629437 \tabularnewline
40 & -11 & -10.795353154032 & -0.204646845968044 \tabularnewline
41 & -9 & -9.50115782830759 & 0.501157828307585 \tabularnewline
42 & -10 & -10.5459765687473 & 0.545976568747332 \tabularnewline
43 & -13 & -12.8247295021913 & -0.17527049780871 \tabularnewline
44 & -11 & -10.6046820499355 & -0.395317950064472 \tabularnewline
45 & -5 & -5.31367493234954 & 0.313674932349539 \tabularnewline
46 & -15 & -14.8051739793291 & -0.194826020670929 \tabularnewline
47 & -6 & -6.52314318984297 & 0.523143189842974 \tabularnewline
48 & -6 & -6.21668059509045 & 0.216680595090454 \tabularnewline
49 & -3 & -3.23183151556237 & 0.231831515562374 \tabularnewline
50 & -1 & -1.0297118119196 & 0.0297118119195983 \tabularnewline
51 & -3 & -2.75196571663497 & -0.248034283365025 \tabularnewline
52 & -4 & -4.01390104275624 & 0.0139010427562404 \tabularnewline
53 & -6 & -5.78016810331568 & -0.21983189668432 \tabularnewline
54 & 0 & -0.276963115225663 & 0.276963115225663 \tabularnewline
55 & -4 & -4.03506773480983 & 0.0350677348098269 \tabularnewline
56 & -2 & -2.00767093272481 & 0.00767093272480671 \tabularnewline
57 & -2 & -2.31347367878589 & 0.313473678785888 \tabularnewline
58 & -6 & -6.2501964718881 & 0.250196471888099 \tabularnewline
59 & -7 & -7.0554760573982 & 0.0554760573982013 \tabularnewline
60 & -6 & -5.81014878250705 & -0.189851217492952 \tabularnewline
61 & -6 & -5.52455310547069 & -0.475446894529309 \tabularnewline
62 & -3 & -3.57246983580398 & 0.572469835803978 \tabularnewline
63 & -2 & -2.31479337616876 & 0.314793376168764 \tabularnewline
64 & -5 & -4.61537285035775 & -0.384627149642254 \tabularnewline
65 & -11 & -11.6005689193537 & 0.600568919353722 \tabularnewline
66 & -11 & -10.8442212048652 & -0.155778795134828 \tabularnewline
67 & -11 & -11.3338606139592 & 0.333860613959214 \tabularnewline
68 & -10 & -9.5963183553966 & -0.403681644603398 \tabularnewline
69 & -14 & -13.5976249678934 & -0.40237503210664 \tabularnewline
70 & -8 & -8.0243214167282 & 0.0243214167282036 \tabularnewline
71 & -9 & -9.24650245349146 & 0.246502453491457 \tabularnewline
72 & -5 & -4.80604395445417 & -0.193956045545826 \tabularnewline
73 & -1 & -1.04398024272138 & 0.0439802427213818 \tabularnewline
74 & -2 & -2.25506797193512 & 0.255067971935116 \tabularnewline
75 & -5 & -5.27547629452349 & 0.275476294523491 \tabularnewline
76 & -4 & -3.53103577470908 & -0.468964225290924 \tabularnewline
77 & -6 & -5.55423753049638 & -0.44576246950362 \tabularnewline
78 & -2 & -2.03993093232791 & 0.0399309323279119 \tabularnewline
79 & -2 & -1.78078073687664 & -0.219219263123355 \tabularnewline
80 & -2 & -1.49548483417771 & -0.504515165822289 \tabularnewline
81 & -2 & -1.55616986144022 & -0.443830138559779 \tabularnewline
82 & 2 & 2.51397591676539 & -0.51397591676539 \tabularnewline
83 & 1 & 0.811929081074738 & 0.188070918925262 \tabularnewline
84 & -8 & -7.79647159785118 & -0.203528402148819 \tabularnewline
85 & -1 & -1.21252855190711 & 0.212528551907106 \tabularnewline
86 & 1 & 0.976878372465849 & 0.023121627534151 \tabularnewline
87 & -1 & -0.539352254117528 & -0.460647745882472 \tabularnewline
88 & 2 & 1.80979549656725 & 0.190204503432753 \tabularnewline
89 & 2 & 1.97834380575297 & 0.021656194247029 \tabularnewline
90 & 1 & 1.47269237393192 & -0.472692373931921 \tabularnewline
91 & -1 & -0.774760182937792 & -0.225239817062208 \tabularnewline
92 & -2 & -2.29680532753919 & 0.296805327539186 \tabularnewline
93 & -2 & -1.77094682669341 & -0.229053173306589 \tabularnewline
94 & -1 & -0.793287480225627 & -0.206712519774373 \tabularnewline
95 & -8 & -7.5434392149271 & -0.4565607850729 \tabularnewline
96 & -4 & -4.0444847907942 & 0.0444847907942006 \tabularnewline
97 & -6 & -6.31706313186613 & 0.317063131866129 \tabularnewline
98 & -3 & -3.47821215189898 & 0.478212151898977 \tabularnewline
99 & -3 & -3.30223337314668 & 0.30223337314668 \tabularnewline
100 & -7 & -7.27877427579524 & 0.278774275795238 \tabularnewline
101 & -9 & -8.83116017394202 & -0.168839826057984 \tabularnewline
102 & -11 & -11.1611242988193 & 0.161124298819264 \tabularnewline
103 & -13 & -13.1262166019215 & 0.126216601921474 \tabularnewline
104 & -11 & -11.3185757802837 & 0.318575780283723 \tabularnewline
105 & -9 & -8.58633870930912 & -0.413661290690878 \tabularnewline
106 & -17 & -17.1677616983352 & 0.167761698335184 \tabularnewline
107 & -22 & -21.6245284742652 & -0.375471525734846 \tabularnewline
108 & -25 & -24.6553702557116 & -0.344629744288391 \tabularnewline
109 & -20 & -20.3746425585505 & 0.374642558550524 \tabularnewline
110 & -24 & -24.1242932653509 & 0.124293265350921 \tabularnewline
111 & -24 & -24.1634515262058 & 0.163451526205829 \tabularnewline
112 & -22 & -21.548615393172 & -0.451384606828001 \tabularnewline
113 & -19 & -19.5169597246009 & 0.51695972460093 \tabularnewline
114 & -18 & -17.5648764549342 & -0.435123545065785 \tabularnewline
115 & -17 & -17.3778043686324 & 0.3778043686324 \tabularnewline
116 & -11 & -11.0755582075926 & 0.0755582075926458 \tabularnewline
117 & -11 & -11.0853318177593 & 0.0853318177592905 \tabularnewline
118 & -12 & -11.2851166833309 & -0.714883316669077 \tabularnewline
119 & -10 & -9.75425755159175 & -0.245742448408252 \tabularnewline
120 & -15 & -15.0716463305745 & 0.0716463305744911 \tabularnewline
121 & -15 & -14.8509307270984 & -0.149069272901641 \tabularnewline
122 & -15 & -15.1058858762519 & 0.105885876251911 \tabularnewline
123 & -13 & -12.5624041834878 & -0.437595816512244 \tabularnewline
124 & -8 & -8.00166289333099 & 0.00166289333098973 \tabularnewline
125 & -13 & -12.8499191065818 & -0.150080893418161 \tabularnewline
126 & -9 & -9.32385935217241 & 0.323859352172414 \tabularnewline
127 & -7 & -6.78404049739121 & -0.215959502608789 \tabularnewline
128 & -4 & -4.0351953751865 & 0.0351953751864984 \tabularnewline
129 & -4 & -4.04298943927883 & 0.0429894392788264 \tabularnewline
130 & -2 & -2.52118024882652 & 0.521180248826522 \tabularnewline
131 & 0 & -0.282245424928911 & 0.282245424928911 \tabularnewline
132 & -2 & -1.83205574849827 & -0.16794425150173 \tabularnewline
133 & -3 & -3.08157806968714 & 0.0815780696871399 \tabularnewline
134 & 1 & 1.24903760135261 & -0.249037601352614 \tabularnewline
135 & -2 & -2.58606384255849 & 0.586063842558492 \tabularnewline
136 & -1 & -1.28628995296519 & 0.286289952965193 \tabularnewline
137 & 1 & 0.73978715173695 & 0.26021284826305 \tabularnewline
138 & -3 & -2.57533060936299 & -0.424669390637013 \tabularnewline
139 & -4 & -4.30897759596531 & 0.308977595965307 \tabularnewline
140 & -9 & -8.76112543105521 & -0.238874568944787 \tabularnewline
141 & -9 & -8.59257712186949 & -0.407422878130513 \tabularnewline
142 & -7 & -6.55312738920609 & -0.446872610793914 \tabularnewline
143 & -14 & -13.8679322910825 & -0.132067708917519 \tabularnewline
144 & -12 & -11.9006244877569 & -0.0993755122431316 \tabularnewline
145 & -16 & -16.3739390149004 & 0.37393901490036 \tabularnewline
146 & -20 & -19.6972109144466 & -0.302789085553358 \tabularnewline
147 & -12 & -12.1268548349136 & 0.126854834913592 \tabularnewline
148 & -12 & -11.8530123141182 & -0.146987685881808 \tabularnewline
149 & -10 & -10.1392123221866 & 0.139212322186583 \tabularnewline
150 & -10 & -9.8317901044052 & -0.168209895594798 \tabularnewline
151 & -13 & -13.0513872640621 & 0.051387264062108 \tabularnewline
152 & -16 & -15.8413063730077 & -0.158693626992292 \tabularnewline
153 & -14 & -13.8566030293839 & -0.143396970616126 \tabularnewline
154 & -17 & -16.6526967307015 & -0.347303269298495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]9[/C][C]8.90555806244052[/C][C]0.0944419375594796[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.8589610294901[/C][C]0.141038970509895[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]12.9108237670859[/C][C]0.0891762329140841[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.8512488811137[/C][C]0.1487511188863[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]12.7924632061163[/C][C]0.20753679388372[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]15.073495459972[/C][C]-0.073495459971976[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]12.594893840768[/C][C]0.40510615923195[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]15.5840656069705[/C][C]0.415934393029485[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.3243588659588[/C][C]-0.32435886595877[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0802479853195[/C][C]-0.0802479853195346[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0893617467947[/C][C]-0.08936174679474[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.1019204876314[/C][C]-0.10192048763138[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.1471978231989[/C][C]-0.147197823198915[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]8.35015280138135[/C][C]-0.350152801381353[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.29909564838124[/C][C]-0.29909564838124[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.55855870305605[/C][C]-0.558558703056048[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.2465175444346[/C][C]-0.246517544434602[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.78280950413846[/C][C]0.217190495861542[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.26825386693478[/C][C]-0.268253866934783[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.462176999357929[/C][C]-0.462176999357929[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]-4.04933616561161[/C][C]0.0493361656116098[/C][/ROW]
[ROW][C]22[/C][C]-14[/C][C]-14.3307626240657[/C][C]0.330762624065675[/C][/ROW]
[ROW][C]23[/C][C]-18[/C][C]-18.3272108214015[/C][C]0.327210821401532[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-8.30014348359792[/C][C]0.300143483597917[/C][/ROW]
[ROW][C]25[/C][C]-1[/C][C]-1.49224589073711[/C][C]0.492245890737113[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.49752190396846[/C][C]-0.497521903968456[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.97246898771836[/C][C]0.0275310122816356[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]-0.218557408374891[/C][C]0.218557408374891[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.21744097061269[/C][C]-0.217440970612691[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]-0.245002889959981[/C][C]0.245002889959981[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-1.54375333633608[/C][C]0.543753336336081[/C][/ROW]
[ROW][C]32[/C][C]-3[/C][C]-3.53235547209195[/C][C]0.532355472091952[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-3.52258186192531[/C][C]0.522581861925308[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]-2.78224617016377[/C][C]-0.217753829836233[/C][/ROW]
[ROW][C]35[/C][C]-4[/C][C]-3.78929016731982[/C][C]-0.210709832680183[/C][/ROW]
[ROW][C]36[/C][C]-8[/C][C]-8.03607457296916[/C][C]0.0360745729691612[/C][/ROW]
[ROW][C]37[/C][C]-9[/C][C]-9.09618166725614[/C][C]0.0961816672561438[/C][/ROW]
[ROW][C]38[/C][C]-13[/C][C]-12.8863103322943[/C][C]-0.113689667705675[/C][/ROW]
[ROW][C]39[/C][C]-18[/C][C]-18.3820210306294[/C][C]0.382021030629437[/C][/ROW]
[ROW][C]40[/C][C]-11[/C][C]-10.795353154032[/C][C]-0.204646845968044[/C][/ROW]
[ROW][C]41[/C][C]-9[/C][C]-9.50115782830759[/C][C]0.501157828307585[/C][/ROW]
[ROW][C]42[/C][C]-10[/C][C]-10.5459765687473[/C][C]0.545976568747332[/C][/ROW]
[ROW][C]43[/C][C]-13[/C][C]-12.8247295021913[/C][C]-0.17527049780871[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-10.6046820499355[/C][C]-0.395317950064472[/C][/ROW]
[ROW][C]45[/C][C]-5[/C][C]-5.31367493234954[/C][C]0.313674932349539[/C][/ROW]
[ROW][C]46[/C][C]-15[/C][C]-14.8051739793291[/C][C]-0.194826020670929[/C][/ROW]
[ROW][C]47[/C][C]-6[/C][C]-6.52314318984297[/C][C]0.523143189842974[/C][/ROW]
[ROW][C]48[/C][C]-6[/C][C]-6.21668059509045[/C][C]0.216680595090454[/C][/ROW]
[ROW][C]49[/C][C]-3[/C][C]-3.23183151556237[/C][C]0.231831515562374[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]-1.0297118119196[/C][C]0.0297118119195983[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]-2.75196571663497[/C][C]-0.248034283365025[/C][/ROW]
[ROW][C]52[/C][C]-4[/C][C]-4.01390104275624[/C][C]0.0139010427562404[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-5.78016810331568[/C][C]-0.21983189668432[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.276963115225663[/C][C]0.276963115225663[/C][/ROW]
[ROW][C]55[/C][C]-4[/C][C]-4.03506773480983[/C][C]0.0350677348098269[/C][/ROW]
[ROW][C]56[/C][C]-2[/C][C]-2.00767093272481[/C][C]0.00767093272480671[/C][/ROW]
[ROW][C]57[/C][C]-2[/C][C]-2.31347367878589[/C][C]0.313473678785888[/C][/ROW]
[ROW][C]58[/C][C]-6[/C][C]-6.2501964718881[/C][C]0.250196471888099[/C][/ROW]
[ROW][C]59[/C][C]-7[/C][C]-7.0554760573982[/C][C]0.0554760573982013[/C][/ROW]
[ROW][C]60[/C][C]-6[/C][C]-5.81014878250705[/C][C]-0.189851217492952[/C][/ROW]
[ROW][C]61[/C][C]-6[/C][C]-5.52455310547069[/C][C]-0.475446894529309[/C][/ROW]
[ROW][C]62[/C][C]-3[/C][C]-3.57246983580398[/C][C]0.572469835803978[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-2.31479337616876[/C][C]0.314793376168764[/C][/ROW]
[ROW][C]64[/C][C]-5[/C][C]-4.61537285035775[/C][C]-0.384627149642254[/C][/ROW]
[ROW][C]65[/C][C]-11[/C][C]-11.6005689193537[/C][C]0.600568919353722[/C][/ROW]
[ROW][C]66[/C][C]-11[/C][C]-10.8442212048652[/C][C]-0.155778795134828[/C][/ROW]
[ROW][C]67[/C][C]-11[/C][C]-11.3338606139592[/C][C]0.333860613959214[/C][/ROW]
[ROW][C]68[/C][C]-10[/C][C]-9.5963183553966[/C][C]-0.403681644603398[/C][/ROW]
[ROW][C]69[/C][C]-14[/C][C]-13.5976249678934[/C][C]-0.40237503210664[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-8.0243214167282[/C][C]0.0243214167282036[/C][/ROW]
[ROW][C]71[/C][C]-9[/C][C]-9.24650245349146[/C][C]0.246502453491457[/C][/ROW]
[ROW][C]72[/C][C]-5[/C][C]-4.80604395445417[/C][C]-0.193956045545826[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-1.04398024272138[/C][C]0.0439802427213818[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-2.25506797193512[/C][C]0.255067971935116[/C][/ROW]
[ROW][C]75[/C][C]-5[/C][C]-5.27547629452349[/C][C]0.275476294523491[/C][/ROW]
[ROW][C]76[/C][C]-4[/C][C]-3.53103577470908[/C][C]-0.468964225290924[/C][/ROW]
[ROW][C]77[/C][C]-6[/C][C]-5.55423753049638[/C][C]-0.44576246950362[/C][/ROW]
[ROW][C]78[/C][C]-2[/C][C]-2.03993093232791[/C][C]0.0399309323279119[/C][/ROW]
[ROW][C]79[/C][C]-2[/C][C]-1.78078073687664[/C][C]-0.219219263123355[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-1.49548483417771[/C][C]-0.504515165822289[/C][/ROW]
[ROW][C]81[/C][C]-2[/C][C]-1.55616986144022[/C][C]-0.443830138559779[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]2.51397591676539[/C][C]-0.51397591676539[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.811929081074738[/C][C]0.188070918925262[/C][/ROW]
[ROW][C]84[/C][C]-8[/C][C]-7.79647159785118[/C][C]-0.203528402148819[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.21252855190711[/C][C]0.212528551907106[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.976878372465849[/C][C]0.023121627534151[/C][/ROW]
[ROW][C]87[/C][C]-1[/C][C]-0.539352254117528[/C][C]-0.460647745882472[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.80979549656725[/C][C]0.190204503432753[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.97834380575297[/C][C]0.021656194247029[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.47269237393192[/C][C]-0.472692373931921[/C][/ROW]
[ROW][C]91[/C][C]-1[/C][C]-0.774760182937792[/C][C]-0.225239817062208[/C][/ROW]
[ROW][C]92[/C][C]-2[/C][C]-2.29680532753919[/C][C]0.296805327539186[/C][/ROW]
[ROW][C]93[/C][C]-2[/C][C]-1.77094682669341[/C][C]-0.229053173306589[/C][/ROW]
[ROW][C]94[/C][C]-1[/C][C]-0.793287480225627[/C][C]-0.206712519774373[/C][/ROW]
[ROW][C]95[/C][C]-8[/C][C]-7.5434392149271[/C][C]-0.4565607850729[/C][/ROW]
[ROW][C]96[/C][C]-4[/C][C]-4.0444847907942[/C][C]0.0444847907942006[/C][/ROW]
[ROW][C]97[/C][C]-6[/C][C]-6.31706313186613[/C][C]0.317063131866129[/C][/ROW]
[ROW][C]98[/C][C]-3[/C][C]-3.47821215189898[/C][C]0.478212151898977[/C][/ROW]
[ROW][C]99[/C][C]-3[/C][C]-3.30223337314668[/C][C]0.30223337314668[/C][/ROW]
[ROW][C]100[/C][C]-7[/C][C]-7.27877427579524[/C][C]0.278774275795238[/C][/ROW]
[ROW][C]101[/C][C]-9[/C][C]-8.83116017394202[/C][C]-0.168839826057984[/C][/ROW]
[ROW][C]102[/C][C]-11[/C][C]-11.1611242988193[/C][C]0.161124298819264[/C][/ROW]
[ROW][C]103[/C][C]-13[/C][C]-13.1262166019215[/C][C]0.126216601921474[/C][/ROW]
[ROW][C]104[/C][C]-11[/C][C]-11.3185757802837[/C][C]0.318575780283723[/C][/ROW]
[ROW][C]105[/C][C]-9[/C][C]-8.58633870930912[/C][C]-0.413661290690878[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-17.1677616983352[/C][C]0.167761698335184[/C][/ROW]
[ROW][C]107[/C][C]-22[/C][C]-21.6245284742652[/C][C]-0.375471525734846[/C][/ROW]
[ROW][C]108[/C][C]-25[/C][C]-24.6553702557116[/C][C]-0.344629744288391[/C][/ROW]
[ROW][C]109[/C][C]-20[/C][C]-20.3746425585505[/C][C]0.374642558550524[/C][/ROW]
[ROW][C]110[/C][C]-24[/C][C]-24.1242932653509[/C][C]0.124293265350921[/C][/ROW]
[ROW][C]111[/C][C]-24[/C][C]-24.1634515262058[/C][C]0.163451526205829[/C][/ROW]
[ROW][C]112[/C][C]-22[/C][C]-21.548615393172[/C][C]-0.451384606828001[/C][/ROW]
[ROW][C]113[/C][C]-19[/C][C]-19.5169597246009[/C][C]0.51695972460093[/C][/ROW]
[ROW][C]114[/C][C]-18[/C][C]-17.5648764549342[/C][C]-0.435123545065785[/C][/ROW]
[ROW][C]115[/C][C]-17[/C][C]-17.3778043686324[/C][C]0.3778043686324[/C][/ROW]
[ROW][C]116[/C][C]-11[/C][C]-11.0755582075926[/C][C]0.0755582075926458[/C][/ROW]
[ROW][C]117[/C][C]-11[/C][C]-11.0853318177593[/C][C]0.0853318177592905[/C][/ROW]
[ROW][C]118[/C][C]-12[/C][C]-11.2851166833309[/C][C]-0.714883316669077[/C][/ROW]
[ROW][C]119[/C][C]-10[/C][C]-9.75425755159175[/C][C]-0.245742448408252[/C][/ROW]
[ROW][C]120[/C][C]-15[/C][C]-15.0716463305745[/C][C]0.0716463305744911[/C][/ROW]
[ROW][C]121[/C][C]-15[/C][C]-14.8509307270984[/C][C]-0.149069272901641[/C][/ROW]
[ROW][C]122[/C][C]-15[/C][C]-15.1058858762519[/C][C]0.105885876251911[/C][/ROW]
[ROW][C]123[/C][C]-13[/C][C]-12.5624041834878[/C][C]-0.437595816512244[/C][/ROW]
[ROW][C]124[/C][C]-8[/C][C]-8.00166289333099[/C][C]0.00166289333098973[/C][/ROW]
[ROW][C]125[/C][C]-13[/C][C]-12.8499191065818[/C][C]-0.150080893418161[/C][/ROW]
[ROW][C]126[/C][C]-9[/C][C]-9.32385935217241[/C][C]0.323859352172414[/C][/ROW]
[ROW][C]127[/C][C]-7[/C][C]-6.78404049739121[/C][C]-0.215959502608789[/C][/ROW]
[ROW][C]128[/C][C]-4[/C][C]-4.0351953751865[/C][C]0.0351953751864984[/C][/ROW]
[ROW][C]129[/C][C]-4[/C][C]-4.04298943927883[/C][C]0.0429894392788264[/C][/ROW]
[ROW][C]130[/C][C]-2[/C][C]-2.52118024882652[/C][C]0.521180248826522[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.282245424928911[/C][C]0.282245424928911[/C][/ROW]
[ROW][C]132[/C][C]-2[/C][C]-1.83205574849827[/C][C]-0.16794425150173[/C][/ROW]
[ROW][C]133[/C][C]-3[/C][C]-3.08157806968714[/C][C]0.0815780696871399[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.24903760135261[/C][C]-0.249037601352614[/C][/ROW]
[ROW][C]135[/C][C]-2[/C][C]-2.58606384255849[/C][C]0.586063842558492[/C][/ROW]
[ROW][C]136[/C][C]-1[/C][C]-1.28628995296519[/C][C]0.286289952965193[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.73978715173695[/C][C]0.26021284826305[/C][/ROW]
[ROW][C]138[/C][C]-3[/C][C]-2.57533060936299[/C][C]-0.424669390637013[/C][/ROW]
[ROW][C]139[/C][C]-4[/C][C]-4.30897759596531[/C][C]0.308977595965307[/C][/ROW]
[ROW][C]140[/C][C]-9[/C][C]-8.76112543105521[/C][C]-0.238874568944787[/C][/ROW]
[ROW][C]141[/C][C]-9[/C][C]-8.59257712186949[/C][C]-0.407422878130513[/C][/ROW]
[ROW][C]142[/C][C]-7[/C][C]-6.55312738920609[/C][C]-0.446872610793914[/C][/ROW]
[ROW][C]143[/C][C]-14[/C][C]-13.8679322910825[/C][C]-0.132067708917519[/C][/ROW]
[ROW][C]144[/C][C]-12[/C][C]-11.9006244877569[/C][C]-0.0993755122431316[/C][/ROW]
[ROW][C]145[/C][C]-16[/C][C]-16.3739390149004[/C][C]0.37393901490036[/C][/ROW]
[ROW][C]146[/C][C]-20[/C][C]-19.6972109144466[/C][C]-0.302789085553358[/C][/ROW]
[ROW][C]147[/C][C]-12[/C][C]-12.1268548349136[/C][C]0.126854834913592[/C][/ROW]
[ROW][C]148[/C][C]-12[/C][C]-11.8530123141182[/C][C]-0.146987685881808[/C][/ROW]
[ROW][C]149[/C][C]-10[/C][C]-10.1392123221866[/C][C]0.139212322186583[/C][/ROW]
[ROW][C]150[/C][C]-10[/C][C]-9.8317901044052[/C][C]-0.168209895594798[/C][/ROW]
[ROW][C]151[/C][C]-13[/C][C]-13.0513872640621[/C][C]0.051387264062108[/C][/ROW]
[ROW][C]152[/C][C]-16[/C][C]-15.8413063730077[/C][C]-0.158693626992292[/C][/ROW]
[ROW][C]153[/C][C]-14[/C][C]-13.8566030293839[/C][C]-0.143396970616126[/C][/ROW]
[ROW][C]154[/C][C]-17[/C][C]-16.6526967307015[/C][C]-0.347303269298495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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
198.905558062440520.0944419375594796
21110.85896102949010.141038970509895
31312.91082376708590.0891762329140841
41211.85124888111370.1487511188863
51312.79246320611630.20753679388372
61515.073495459972-0.073495459971976
71312.5948938407680.40510615923195
81615.58406560697050.415934393029485
91010.3243588659588-0.32435886595877
101414.0802479853195-0.0802479853195346
111414.0893617467947-0.08936174679474
121515.1019204876314-0.10192048763138
131313.1471978231989-0.147197823198915
1488.35015280138135-0.350152801381353
1577.29909564838124-0.29909564838124
1633.55855870305605-0.558558703056048
1733.2465175444346-0.246517544434602
1843.782809504138460.217190495861542
1944.26825386693478-0.268253866934783
2000.462176999357929-0.462176999357929
21-4-4.049336165611610.0493361656116098
22-14-14.33076262406570.330762624065675
23-18-18.32721082140150.327210821401532
24-8-8.300143483597920.300143483597917
25-1-1.492245890737110.492245890737113
2611.49752190396846-0.497521903968456
2721.972468987718360.0275310122816356
280-0.2185574083748910.218557408374891
2911.21744097061269-0.217440970612691
300-0.2450028899599810.245002889959981
31-1-1.543753336336080.543753336336081
32-3-3.532355472091950.532355472091952
33-3-3.522581861925310.522581861925308
34-3-2.78224617016377-0.217753829836233
35-4-3.78929016731982-0.210709832680183
36-8-8.036074572969160.0360745729691612
37-9-9.096181667256140.0961816672561438
38-13-12.8863103322943-0.113689667705675
39-18-18.38202103062940.382021030629437
40-11-10.795353154032-0.204646845968044
41-9-9.501157828307590.501157828307585
42-10-10.54597656874730.545976568747332
43-13-12.8247295021913-0.17527049780871
44-11-10.6046820499355-0.395317950064472
45-5-5.313674932349540.313674932349539
46-15-14.8051739793291-0.194826020670929
47-6-6.523143189842970.523143189842974
48-6-6.216680595090450.216680595090454
49-3-3.231831515562370.231831515562374
50-1-1.02971181191960.0297118119195983
51-3-2.75196571663497-0.248034283365025
52-4-4.013901042756240.0139010427562404
53-6-5.78016810331568-0.21983189668432
540-0.2769631152256630.276963115225663
55-4-4.035067734809830.0350677348098269
56-2-2.007670932724810.00767093272480671
57-2-2.313473678785890.313473678785888
58-6-6.25019647188810.250196471888099
59-7-7.05547605739820.0554760573982013
60-6-5.81014878250705-0.189851217492952
61-6-5.52455310547069-0.475446894529309
62-3-3.572469835803980.572469835803978
63-2-2.314793376168760.314793376168764
64-5-4.61537285035775-0.384627149642254
65-11-11.60056891935370.600568919353722
66-11-10.8442212048652-0.155778795134828
67-11-11.33386061395920.333860613959214
68-10-9.5963183553966-0.403681644603398
69-14-13.5976249678934-0.40237503210664
70-8-8.02432141672820.0243214167282036
71-9-9.246502453491460.246502453491457
72-5-4.80604395445417-0.193956045545826
73-1-1.043980242721380.0439802427213818
74-2-2.255067971935120.255067971935116
75-5-5.275476294523490.275476294523491
76-4-3.53103577470908-0.468964225290924
77-6-5.55423753049638-0.44576246950362
78-2-2.039930932327910.0399309323279119
79-2-1.78078073687664-0.219219263123355
80-2-1.49548483417771-0.504515165822289
81-2-1.55616986144022-0.443830138559779
8222.51397591676539-0.51397591676539
8310.8119290810747380.188070918925262
84-8-7.79647159785118-0.203528402148819
85-1-1.212528551907110.212528551907106
8610.9768783724658490.023121627534151
87-1-0.539352254117528-0.460647745882472
8821.809795496567250.190204503432753
8921.978343805752970.021656194247029
9011.47269237393192-0.472692373931921
91-1-0.774760182937792-0.225239817062208
92-2-2.296805327539190.296805327539186
93-2-1.77094682669341-0.229053173306589
94-1-0.793287480225627-0.206712519774373
95-8-7.5434392149271-0.4565607850729
96-4-4.04448479079420.0444847907942006
97-6-6.317063131866130.317063131866129
98-3-3.478212151898980.478212151898977
99-3-3.302233373146680.30223337314668
100-7-7.278774275795240.278774275795238
101-9-8.83116017394202-0.168839826057984
102-11-11.16112429881930.161124298819264
103-13-13.12621660192150.126216601921474
104-11-11.31857578028370.318575780283723
105-9-8.58633870930912-0.413661290690878
106-17-17.16776169833520.167761698335184
107-22-21.6245284742652-0.375471525734846
108-25-24.6553702557116-0.344629744288391
109-20-20.37464255855050.374642558550524
110-24-24.12429326535090.124293265350921
111-24-24.16345152620580.163451526205829
112-22-21.548615393172-0.451384606828001
113-19-19.51695972460090.51695972460093
114-18-17.5648764549342-0.435123545065785
115-17-17.37780436863240.3778043686324
116-11-11.07555820759260.0755582075926458
117-11-11.08533181775930.0853318177592905
118-12-11.2851166833309-0.714883316669077
119-10-9.75425755159175-0.245742448408252
120-15-15.07164633057450.0716463305744911
121-15-14.8509307270984-0.149069272901641
122-15-15.10588587625190.105885876251911
123-13-12.5624041834878-0.437595816512244
124-8-8.001662893330990.00166289333098973
125-13-12.8499191065818-0.150080893418161
126-9-9.323859352172410.323859352172414
127-7-6.78404049739121-0.215959502608789
128-4-4.03519537518650.0351953751864984
129-4-4.042989439278830.0429894392788264
130-2-2.521180248826520.521180248826522
1310-0.2822454249289110.282245424928911
132-2-1.83205574849827-0.16794425150173
133-3-3.081578069687140.0815780696871399
13411.24903760135261-0.249037601352614
135-2-2.586063842558490.586063842558492
136-1-1.286289952965190.286289952965193
13710.739787151736950.26021284826305
138-3-2.57533060936299-0.424669390637013
139-4-4.308977595965310.308977595965307
140-9-8.76112543105521-0.238874568944787
141-9-8.59257712186949-0.407422878130513
142-7-6.55312738920609-0.446872610793914
143-14-13.8679322910825-0.132067708917519
144-12-11.9006244877569-0.0993755122431316
145-16-16.37393901490040.37393901490036
146-20-19.6972109144466-0.302789085553358
147-12-12.12685483491360.126854834913592
148-12-11.8530123141182-0.146987685881808
149-10-10.13921232218660.139212322186583
150-10-9.8317901044052-0.168209895594798
151-13-13.05138726406210.051387264062108
152-16-15.8413063730077-0.158693626992292
153-14-13.8566030293839-0.143396970616126
154-17-16.6526967307015-0.347303269298495







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3125028048881740.6250056097763470.687497195111826
90.3793136596139260.7586273192278520.620686340386074
100.2500537195951390.5001074391902780.749946280404861
110.1534431297072640.3068862594145280.846556870292736
120.08720367060177570.1744073412035510.912796329398224
130.05821924198098760.1164384839619750.941780758019012
140.03296597664485370.06593195328970740.967034023355146
150.01717585898235430.03435171796470860.982824141017646
160.0103877273654120.0207754547308240.989612272634588
170.005084313494892260.01016862698978450.994915686505108
180.03083133335199130.06166266670398270.969168666648009
190.01872345261864920.03744690523729850.981276547381351
200.01550369825738190.03100739651476390.984496301742618
210.04278709899478050.08557419798956110.957212901005219
220.1184822682742940.2369645365485880.881517731725706
230.08738909040860930.1747781808172190.912610909591391
240.06641416836174990.13282833672350.93358583163825
250.04994804092565970.09989608185131930.95005195907434
260.3217334992786010.6434669985572010.678266500721399
270.2940156278330330.5880312556660660.705984372166967
280.2431426888896170.4862853777792330.756857311110383
290.2956561354839380.5913122709678760.704343864516062
300.2462493570402260.4924987140804510.753750642959774
310.2951846263528060.5903692527056120.704815373647194
320.3421249102434290.6842498204868580.657875089756571
330.3699528536236580.7399057072473150.630047146376342
340.4186502334360950.8373004668721890.581349766563905
350.4734135565031430.9468271130062860.526586443496857
360.4259983721890080.8519967443780170.574001627810992
370.3728189809972730.7456379619945460.627181019002727
380.3516457175859750.703291435171950.648354282414025
390.3546646841429470.7093293682858950.645335315857053
400.3704726104987580.7409452209975160.629527389501242
410.3774194449034770.7548388898069540.622580555096523
420.4089756791022740.8179513582045480.591024320897726
430.4391864500184870.8783729000369740.560813549981513
440.5531349972579370.8937300054841260.446865002742063
450.5245129252847110.9509741494305780.475487074715289
460.5362639777337720.9274720445324560.463736022266228
470.5652281569441120.8695436861117770.434771843055888
480.5226713780299930.9546572439400130.477328621970007
490.4824226676157590.9648453352315180.517577332384241
500.4518377213142380.9036754426284770.548162278685762
510.4806296863543990.9612593727087980.519370313645601
520.4389828749186420.8779657498372840.561017125081358
530.4376015452032110.8752030904064220.562398454796789
540.4127923282856460.8255846565712920.587207671714354
550.3673435130831580.7346870261663160.632656486916842
560.3242075723789030.6484151447578060.675792427621097
570.3159919669542050.631983933908410.684008033045795
580.3014606847055170.6029213694110340.698539315294483
590.2645711906709280.5291423813418550.735428809329072
600.2547271483574930.5094542967149870.745272851642507
610.3233626318083080.6467252636166170.676637368191692
620.4384995552686160.8769991105372320.561500444731384
630.4449385055256590.8898770110513170.555061494474341
640.4839107742815110.9678215485630220.516089225718489
650.6333772245640360.7332455508719270.366622775435964
660.60326526526250.7934694694750.3967347347375
670.6453349835483070.7093300329033850.354665016451693
680.6708454809273830.6583090381452330.329154519072617
690.675149089095270.6497018218094610.32485091090473
700.6359228594801990.7281542810396030.364077140519801
710.6290654950659640.7418690098680710.370934504934036
720.6026501774325170.7946996451349670.397349822567483
730.577350126882950.84529974623410.42264987311705
740.5979030542786620.8041938914426770.402096945721338
750.6357601641736690.7284796716526620.364239835826331
760.6554860418062690.6890279163874620.344513958193731
770.6749446671829560.6501106656340880.325055332817044
780.6537593822023120.6924812355953750.346240617797687
790.6263336990228480.7473326019543050.373666300977152
800.6516153729633670.6967692540732670.348384627036633
810.6576662963493730.6846674073012540.342333703650627
820.6968066717076830.6063866565846350.303193328292317
830.6848941697450980.6302116605098030.315105830254902
840.6563368336823770.6873263326352460.343663166317623
850.6556394515230520.6887210969538950.344360548476948
860.6238298127610550.752340374477890.376170187238945
870.6295150307837860.7409699384324280.370484969216214
880.6182894912293580.7634210175412840.381710508770642
890.5832666305596040.8334667388807920.416733369440396
900.6079900715232430.7840198569535130.392009928476757
910.5726119592497630.8547760815004730.427388040750237
920.6245773776864130.7508452446271750.375422622313587
930.5826328182878820.8347343634242360.417367181712118
940.5430159257414530.9139681485170940.456984074258547
950.5805889813883920.8388220372232150.419411018611608
960.5577550696384440.8844898607231120.442244930361556
970.5851312815168190.8297374369663610.41486871848318
980.6593256235866630.6813487528266740.340674376413337
990.6481071275376770.7037857449246460.351892872462323
1000.6325575482744120.7348849034511760.367442451725588
1010.5935917746552830.8128164506894340.406408225344717
1020.5528404425387960.8943191149224080.447159557461204
1030.5170058827203520.9659882345592970.482994117279648
1040.5382832263871170.9234335472257670.461716773612883
1050.5412285168237850.9175429663524290.458771483176214
1060.5047060171288970.9905879657422070.495293982871104
1070.526514967745510.9469700645089790.47348503225449
1080.5356584428083010.9286831143833970.464341557191699
1090.5974129920076950.8051740159846090.402587007992305
1100.5943467157114970.8113065685770050.405653284288503
1110.591783898936590.8164322021268190.40821610106341
1120.6311372726231670.7377254547536670.368862727376833
1130.820669070314270.3586618593714590.17933092968573
1140.8143081693567450.371383661286510.185691830643255
1150.8504884090340450.2990231819319110.149511590965955
1160.8212167293278280.3575665413443440.178783270672172
1170.7937381954593620.4125236090812760.206261804540638
1180.8840394066455450.231921186708910.115960593354455
1190.8671701828295910.2656596343408180.132829817170409
1200.8512299572650610.2975400854698790.148770042734939
1210.8171311162305920.3657377675388160.182868883769408
1220.8191465672247710.3617068655504580.180853432775229
1230.8052477769970190.3895044460059620.194752223002981
1240.7585303988285950.4829392023428090.241469601171405
1250.7070642629624570.5858714740750860.292935737037543
1260.7949775952479120.4100448095041770.205022404752088
1270.7796502542302210.4406994915395580.220349745769779
1280.7258955120695410.5482089758609170.274104487930459
1290.6655726675946060.6688546648107870.334427332405394
1300.9050640805873280.1898718388253450.0949359194126724
1310.9414765961964250.117046807607150.058523403803575
1320.9193742955379530.1612514089240950.0806257044620473
1330.9021835420789960.1956329158420080.0978164579210038
1340.8640611281490520.2718777437018960.135938871850948
1350.9081829753660050.183634049267990.0918170246339949
1360.9082822867796890.1834354264406210.0917177132203107
1370.9323519558084680.1352960883830630.0676480441915316
1380.9577609613198050.08447807736038970.0422390386801949
1390.9478654893571780.1042690212856440.0521345106428222
1400.9541879336187890.09162413276242170.0458120663812109
1410.9575350512289360.08492989754212860.0424649487710643
1420.9860985538425730.02780289231485460.0139014461574273
1430.9882142315963060.02357153680738860.0117857684036943
1440.9763528969483740.04729420610325170.0236471030516259
1450.9570756989104280.08584860217914430.0429243010895721
1460.9298690191725080.1402619616549850.0701309808274924

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.312502804888174 & 0.625005609776347 & 0.687497195111826 \tabularnewline
9 & 0.379313659613926 & 0.758627319227852 & 0.620686340386074 \tabularnewline
10 & 0.250053719595139 & 0.500107439190278 & 0.749946280404861 \tabularnewline
11 & 0.153443129707264 & 0.306886259414528 & 0.846556870292736 \tabularnewline
12 & 0.0872036706017757 & 0.174407341203551 & 0.912796329398224 \tabularnewline
13 & 0.0582192419809876 & 0.116438483961975 & 0.941780758019012 \tabularnewline
14 & 0.0329659766448537 & 0.0659319532897074 & 0.967034023355146 \tabularnewline
15 & 0.0171758589823543 & 0.0343517179647086 & 0.982824141017646 \tabularnewline
16 & 0.010387727365412 & 0.020775454730824 & 0.989612272634588 \tabularnewline
17 & 0.00508431349489226 & 0.0101686269897845 & 0.994915686505108 \tabularnewline
18 & 0.0308313333519913 & 0.0616626667039827 & 0.969168666648009 \tabularnewline
19 & 0.0187234526186492 & 0.0374469052372985 & 0.981276547381351 \tabularnewline
20 & 0.0155036982573819 & 0.0310073965147639 & 0.984496301742618 \tabularnewline
21 & 0.0427870989947805 & 0.0855741979895611 & 0.957212901005219 \tabularnewline
22 & 0.118482268274294 & 0.236964536548588 & 0.881517731725706 \tabularnewline
23 & 0.0873890904086093 & 0.174778180817219 & 0.912610909591391 \tabularnewline
24 & 0.0664141683617499 & 0.1328283367235 & 0.93358583163825 \tabularnewline
25 & 0.0499480409256597 & 0.0998960818513193 & 0.95005195907434 \tabularnewline
26 & 0.321733499278601 & 0.643466998557201 & 0.678266500721399 \tabularnewline
27 & 0.294015627833033 & 0.588031255666066 & 0.705984372166967 \tabularnewline
28 & 0.243142688889617 & 0.486285377779233 & 0.756857311110383 \tabularnewline
29 & 0.295656135483938 & 0.591312270967876 & 0.704343864516062 \tabularnewline
30 & 0.246249357040226 & 0.492498714080451 & 0.753750642959774 \tabularnewline
31 & 0.295184626352806 & 0.590369252705612 & 0.704815373647194 \tabularnewline
32 & 0.342124910243429 & 0.684249820486858 & 0.657875089756571 \tabularnewline
33 & 0.369952853623658 & 0.739905707247315 & 0.630047146376342 \tabularnewline
34 & 0.418650233436095 & 0.837300466872189 & 0.581349766563905 \tabularnewline
35 & 0.473413556503143 & 0.946827113006286 & 0.526586443496857 \tabularnewline
36 & 0.425998372189008 & 0.851996744378017 & 0.574001627810992 \tabularnewline
37 & 0.372818980997273 & 0.745637961994546 & 0.627181019002727 \tabularnewline
38 & 0.351645717585975 & 0.70329143517195 & 0.648354282414025 \tabularnewline
39 & 0.354664684142947 & 0.709329368285895 & 0.645335315857053 \tabularnewline
40 & 0.370472610498758 & 0.740945220997516 & 0.629527389501242 \tabularnewline
41 & 0.377419444903477 & 0.754838889806954 & 0.622580555096523 \tabularnewline
42 & 0.408975679102274 & 0.817951358204548 & 0.591024320897726 \tabularnewline
43 & 0.439186450018487 & 0.878372900036974 & 0.560813549981513 \tabularnewline
44 & 0.553134997257937 & 0.893730005484126 & 0.446865002742063 \tabularnewline
45 & 0.524512925284711 & 0.950974149430578 & 0.475487074715289 \tabularnewline
46 & 0.536263977733772 & 0.927472044532456 & 0.463736022266228 \tabularnewline
47 & 0.565228156944112 & 0.869543686111777 & 0.434771843055888 \tabularnewline
48 & 0.522671378029993 & 0.954657243940013 & 0.477328621970007 \tabularnewline
49 & 0.482422667615759 & 0.964845335231518 & 0.517577332384241 \tabularnewline
50 & 0.451837721314238 & 0.903675442628477 & 0.548162278685762 \tabularnewline
51 & 0.480629686354399 & 0.961259372708798 & 0.519370313645601 \tabularnewline
52 & 0.438982874918642 & 0.877965749837284 & 0.561017125081358 \tabularnewline
53 & 0.437601545203211 & 0.875203090406422 & 0.562398454796789 \tabularnewline
54 & 0.412792328285646 & 0.825584656571292 & 0.587207671714354 \tabularnewline
55 & 0.367343513083158 & 0.734687026166316 & 0.632656486916842 \tabularnewline
56 & 0.324207572378903 & 0.648415144757806 & 0.675792427621097 \tabularnewline
57 & 0.315991966954205 & 0.63198393390841 & 0.684008033045795 \tabularnewline
58 & 0.301460684705517 & 0.602921369411034 & 0.698539315294483 \tabularnewline
59 & 0.264571190670928 & 0.529142381341855 & 0.735428809329072 \tabularnewline
60 & 0.254727148357493 & 0.509454296714987 & 0.745272851642507 \tabularnewline
61 & 0.323362631808308 & 0.646725263616617 & 0.676637368191692 \tabularnewline
62 & 0.438499555268616 & 0.876999110537232 & 0.561500444731384 \tabularnewline
63 & 0.444938505525659 & 0.889877011051317 & 0.555061494474341 \tabularnewline
64 & 0.483910774281511 & 0.967821548563022 & 0.516089225718489 \tabularnewline
65 & 0.633377224564036 & 0.733245550871927 & 0.366622775435964 \tabularnewline
66 & 0.6032652652625 & 0.793469469475 & 0.3967347347375 \tabularnewline
67 & 0.645334983548307 & 0.709330032903385 & 0.354665016451693 \tabularnewline
68 & 0.670845480927383 & 0.658309038145233 & 0.329154519072617 \tabularnewline
69 & 0.67514908909527 & 0.649701821809461 & 0.32485091090473 \tabularnewline
70 & 0.635922859480199 & 0.728154281039603 & 0.364077140519801 \tabularnewline
71 & 0.629065495065964 & 0.741869009868071 & 0.370934504934036 \tabularnewline
72 & 0.602650177432517 & 0.794699645134967 & 0.397349822567483 \tabularnewline
73 & 0.57735012688295 & 0.8452997462341 & 0.42264987311705 \tabularnewline
74 & 0.597903054278662 & 0.804193891442677 & 0.402096945721338 \tabularnewline
75 & 0.635760164173669 & 0.728479671652662 & 0.364239835826331 \tabularnewline
76 & 0.655486041806269 & 0.689027916387462 & 0.344513958193731 \tabularnewline
77 & 0.674944667182956 & 0.650110665634088 & 0.325055332817044 \tabularnewline
78 & 0.653759382202312 & 0.692481235595375 & 0.346240617797687 \tabularnewline
79 & 0.626333699022848 & 0.747332601954305 & 0.373666300977152 \tabularnewline
80 & 0.651615372963367 & 0.696769254073267 & 0.348384627036633 \tabularnewline
81 & 0.657666296349373 & 0.684667407301254 & 0.342333703650627 \tabularnewline
82 & 0.696806671707683 & 0.606386656584635 & 0.303193328292317 \tabularnewline
83 & 0.684894169745098 & 0.630211660509803 & 0.315105830254902 \tabularnewline
84 & 0.656336833682377 & 0.687326332635246 & 0.343663166317623 \tabularnewline
85 & 0.655639451523052 & 0.688721096953895 & 0.344360548476948 \tabularnewline
86 & 0.623829812761055 & 0.75234037447789 & 0.376170187238945 \tabularnewline
87 & 0.629515030783786 & 0.740969938432428 & 0.370484969216214 \tabularnewline
88 & 0.618289491229358 & 0.763421017541284 & 0.381710508770642 \tabularnewline
89 & 0.583266630559604 & 0.833466738880792 & 0.416733369440396 \tabularnewline
90 & 0.607990071523243 & 0.784019856953513 & 0.392009928476757 \tabularnewline
91 & 0.572611959249763 & 0.854776081500473 & 0.427388040750237 \tabularnewline
92 & 0.624577377686413 & 0.750845244627175 & 0.375422622313587 \tabularnewline
93 & 0.582632818287882 & 0.834734363424236 & 0.417367181712118 \tabularnewline
94 & 0.543015925741453 & 0.913968148517094 & 0.456984074258547 \tabularnewline
95 & 0.580588981388392 & 0.838822037223215 & 0.419411018611608 \tabularnewline
96 & 0.557755069638444 & 0.884489860723112 & 0.442244930361556 \tabularnewline
97 & 0.585131281516819 & 0.829737436966361 & 0.41486871848318 \tabularnewline
98 & 0.659325623586663 & 0.681348752826674 & 0.340674376413337 \tabularnewline
99 & 0.648107127537677 & 0.703785744924646 & 0.351892872462323 \tabularnewline
100 & 0.632557548274412 & 0.734884903451176 & 0.367442451725588 \tabularnewline
101 & 0.593591774655283 & 0.812816450689434 & 0.406408225344717 \tabularnewline
102 & 0.552840442538796 & 0.894319114922408 & 0.447159557461204 \tabularnewline
103 & 0.517005882720352 & 0.965988234559297 & 0.482994117279648 \tabularnewline
104 & 0.538283226387117 & 0.923433547225767 & 0.461716773612883 \tabularnewline
105 & 0.541228516823785 & 0.917542966352429 & 0.458771483176214 \tabularnewline
106 & 0.504706017128897 & 0.990587965742207 & 0.495293982871104 \tabularnewline
107 & 0.52651496774551 & 0.946970064508979 & 0.47348503225449 \tabularnewline
108 & 0.535658442808301 & 0.928683114383397 & 0.464341557191699 \tabularnewline
109 & 0.597412992007695 & 0.805174015984609 & 0.402587007992305 \tabularnewline
110 & 0.594346715711497 & 0.811306568577005 & 0.405653284288503 \tabularnewline
111 & 0.59178389893659 & 0.816432202126819 & 0.40821610106341 \tabularnewline
112 & 0.631137272623167 & 0.737725454753667 & 0.368862727376833 \tabularnewline
113 & 0.82066907031427 & 0.358661859371459 & 0.17933092968573 \tabularnewline
114 & 0.814308169356745 & 0.37138366128651 & 0.185691830643255 \tabularnewline
115 & 0.850488409034045 & 0.299023181931911 & 0.149511590965955 \tabularnewline
116 & 0.821216729327828 & 0.357566541344344 & 0.178783270672172 \tabularnewline
117 & 0.793738195459362 & 0.412523609081276 & 0.206261804540638 \tabularnewline
118 & 0.884039406645545 & 0.23192118670891 & 0.115960593354455 \tabularnewline
119 & 0.867170182829591 & 0.265659634340818 & 0.132829817170409 \tabularnewline
120 & 0.851229957265061 & 0.297540085469879 & 0.148770042734939 \tabularnewline
121 & 0.817131116230592 & 0.365737767538816 & 0.182868883769408 \tabularnewline
122 & 0.819146567224771 & 0.361706865550458 & 0.180853432775229 \tabularnewline
123 & 0.805247776997019 & 0.389504446005962 & 0.194752223002981 \tabularnewline
124 & 0.758530398828595 & 0.482939202342809 & 0.241469601171405 \tabularnewline
125 & 0.707064262962457 & 0.585871474075086 & 0.292935737037543 \tabularnewline
126 & 0.794977595247912 & 0.410044809504177 & 0.205022404752088 \tabularnewline
127 & 0.779650254230221 & 0.440699491539558 & 0.220349745769779 \tabularnewline
128 & 0.725895512069541 & 0.548208975860917 & 0.274104487930459 \tabularnewline
129 & 0.665572667594606 & 0.668854664810787 & 0.334427332405394 \tabularnewline
130 & 0.905064080587328 & 0.189871838825345 & 0.0949359194126724 \tabularnewline
131 & 0.941476596196425 & 0.11704680760715 & 0.058523403803575 \tabularnewline
132 & 0.919374295537953 & 0.161251408924095 & 0.0806257044620473 \tabularnewline
133 & 0.902183542078996 & 0.195632915842008 & 0.0978164579210038 \tabularnewline
134 & 0.864061128149052 & 0.271877743701896 & 0.135938871850948 \tabularnewline
135 & 0.908182975366005 & 0.18363404926799 & 0.0918170246339949 \tabularnewline
136 & 0.908282286779689 & 0.183435426440621 & 0.0917177132203107 \tabularnewline
137 & 0.932351955808468 & 0.135296088383063 & 0.0676480441915316 \tabularnewline
138 & 0.957760961319805 & 0.0844780773603897 & 0.0422390386801949 \tabularnewline
139 & 0.947865489357178 & 0.104269021285644 & 0.0521345106428222 \tabularnewline
140 & 0.954187933618789 & 0.0916241327624217 & 0.0458120663812109 \tabularnewline
141 & 0.957535051228936 & 0.0849298975421286 & 0.0424649487710643 \tabularnewline
142 & 0.986098553842573 & 0.0278028923148546 & 0.0139014461574273 \tabularnewline
143 & 0.988214231596306 & 0.0235715368073886 & 0.0117857684036943 \tabularnewline
144 & 0.976352896948374 & 0.0472942061032517 & 0.0236471030516259 \tabularnewline
145 & 0.957075698910428 & 0.0858486021791443 & 0.0429243010895721 \tabularnewline
146 & 0.929869019172508 & 0.140261961654985 & 0.0701309808274924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]8[/C][C]0.312502804888174[/C][C]0.625005609776347[/C][C]0.687497195111826[/C][/ROW]
[ROW][C]9[/C][C]0.379313659613926[/C][C]0.758627319227852[/C][C]0.620686340386074[/C][/ROW]
[ROW][C]10[/C][C]0.250053719595139[/C][C]0.500107439190278[/C][C]0.749946280404861[/C][/ROW]
[ROW][C]11[/C][C]0.153443129707264[/C][C]0.306886259414528[/C][C]0.846556870292736[/C][/ROW]
[ROW][C]12[/C][C]0.0872036706017757[/C][C]0.174407341203551[/C][C]0.912796329398224[/C][/ROW]
[ROW][C]13[/C][C]0.0582192419809876[/C][C]0.116438483961975[/C][C]0.941780758019012[/C][/ROW]
[ROW][C]14[/C][C]0.0329659766448537[/C][C]0.0659319532897074[/C][C]0.967034023355146[/C][/ROW]
[ROW][C]15[/C][C]0.0171758589823543[/C][C]0.0343517179647086[/C][C]0.982824141017646[/C][/ROW]
[ROW][C]16[/C][C]0.010387727365412[/C][C]0.020775454730824[/C][C]0.989612272634588[/C][/ROW]
[ROW][C]17[/C][C]0.00508431349489226[/C][C]0.0101686269897845[/C][C]0.994915686505108[/C][/ROW]
[ROW][C]18[/C][C]0.0308313333519913[/C][C]0.0616626667039827[/C][C]0.969168666648009[/C][/ROW]
[ROW][C]19[/C][C]0.0187234526186492[/C][C]0.0374469052372985[/C][C]0.981276547381351[/C][/ROW]
[ROW][C]20[/C][C]0.0155036982573819[/C][C]0.0310073965147639[/C][C]0.984496301742618[/C][/ROW]
[ROW][C]21[/C][C]0.0427870989947805[/C][C]0.0855741979895611[/C][C]0.957212901005219[/C][/ROW]
[ROW][C]22[/C][C]0.118482268274294[/C][C]0.236964536548588[/C][C]0.881517731725706[/C][/ROW]
[ROW][C]23[/C][C]0.0873890904086093[/C][C]0.174778180817219[/C][C]0.912610909591391[/C][/ROW]
[ROW][C]24[/C][C]0.0664141683617499[/C][C]0.1328283367235[/C][C]0.93358583163825[/C][/ROW]
[ROW][C]25[/C][C]0.0499480409256597[/C][C]0.0998960818513193[/C][C]0.95005195907434[/C][/ROW]
[ROW][C]26[/C][C]0.321733499278601[/C][C]0.643466998557201[/C][C]0.678266500721399[/C][/ROW]
[ROW][C]27[/C][C]0.294015627833033[/C][C]0.588031255666066[/C][C]0.705984372166967[/C][/ROW]
[ROW][C]28[/C][C]0.243142688889617[/C][C]0.486285377779233[/C][C]0.756857311110383[/C][/ROW]
[ROW][C]29[/C][C]0.295656135483938[/C][C]0.591312270967876[/C][C]0.704343864516062[/C][/ROW]
[ROW][C]30[/C][C]0.246249357040226[/C][C]0.492498714080451[/C][C]0.753750642959774[/C][/ROW]
[ROW][C]31[/C][C]0.295184626352806[/C][C]0.590369252705612[/C][C]0.704815373647194[/C][/ROW]
[ROW][C]32[/C][C]0.342124910243429[/C][C]0.684249820486858[/C][C]0.657875089756571[/C][/ROW]
[ROW][C]33[/C][C]0.369952853623658[/C][C]0.739905707247315[/C][C]0.630047146376342[/C][/ROW]
[ROW][C]34[/C][C]0.418650233436095[/C][C]0.837300466872189[/C][C]0.581349766563905[/C][/ROW]
[ROW][C]35[/C][C]0.473413556503143[/C][C]0.946827113006286[/C][C]0.526586443496857[/C][/ROW]
[ROW][C]36[/C][C]0.425998372189008[/C][C]0.851996744378017[/C][C]0.574001627810992[/C][/ROW]
[ROW][C]37[/C][C]0.372818980997273[/C][C]0.745637961994546[/C][C]0.627181019002727[/C][/ROW]
[ROW][C]38[/C][C]0.351645717585975[/C][C]0.70329143517195[/C][C]0.648354282414025[/C][/ROW]
[ROW][C]39[/C][C]0.354664684142947[/C][C]0.709329368285895[/C][C]0.645335315857053[/C][/ROW]
[ROW][C]40[/C][C]0.370472610498758[/C][C]0.740945220997516[/C][C]0.629527389501242[/C][/ROW]
[ROW][C]41[/C][C]0.377419444903477[/C][C]0.754838889806954[/C][C]0.622580555096523[/C][/ROW]
[ROW][C]42[/C][C]0.408975679102274[/C][C]0.817951358204548[/C][C]0.591024320897726[/C][/ROW]
[ROW][C]43[/C][C]0.439186450018487[/C][C]0.878372900036974[/C][C]0.560813549981513[/C][/ROW]
[ROW][C]44[/C][C]0.553134997257937[/C][C]0.893730005484126[/C][C]0.446865002742063[/C][/ROW]
[ROW][C]45[/C][C]0.524512925284711[/C][C]0.950974149430578[/C][C]0.475487074715289[/C][/ROW]
[ROW][C]46[/C][C]0.536263977733772[/C][C]0.927472044532456[/C][C]0.463736022266228[/C][/ROW]
[ROW][C]47[/C][C]0.565228156944112[/C][C]0.869543686111777[/C][C]0.434771843055888[/C][/ROW]
[ROW][C]48[/C][C]0.522671378029993[/C][C]0.954657243940013[/C][C]0.477328621970007[/C][/ROW]
[ROW][C]49[/C][C]0.482422667615759[/C][C]0.964845335231518[/C][C]0.517577332384241[/C][/ROW]
[ROW][C]50[/C][C]0.451837721314238[/C][C]0.903675442628477[/C][C]0.548162278685762[/C][/ROW]
[ROW][C]51[/C][C]0.480629686354399[/C][C]0.961259372708798[/C][C]0.519370313645601[/C][/ROW]
[ROW][C]52[/C][C]0.438982874918642[/C][C]0.877965749837284[/C][C]0.561017125081358[/C][/ROW]
[ROW][C]53[/C][C]0.437601545203211[/C][C]0.875203090406422[/C][C]0.562398454796789[/C][/ROW]
[ROW][C]54[/C][C]0.412792328285646[/C][C]0.825584656571292[/C][C]0.587207671714354[/C][/ROW]
[ROW][C]55[/C][C]0.367343513083158[/C][C]0.734687026166316[/C][C]0.632656486916842[/C][/ROW]
[ROW][C]56[/C][C]0.324207572378903[/C][C]0.648415144757806[/C][C]0.675792427621097[/C][/ROW]
[ROW][C]57[/C][C]0.315991966954205[/C][C]0.63198393390841[/C][C]0.684008033045795[/C][/ROW]
[ROW][C]58[/C][C]0.301460684705517[/C][C]0.602921369411034[/C][C]0.698539315294483[/C][/ROW]
[ROW][C]59[/C][C]0.264571190670928[/C][C]0.529142381341855[/C][C]0.735428809329072[/C][/ROW]
[ROW][C]60[/C][C]0.254727148357493[/C][C]0.509454296714987[/C][C]0.745272851642507[/C][/ROW]
[ROW][C]61[/C][C]0.323362631808308[/C][C]0.646725263616617[/C][C]0.676637368191692[/C][/ROW]
[ROW][C]62[/C][C]0.438499555268616[/C][C]0.876999110537232[/C][C]0.561500444731384[/C][/ROW]
[ROW][C]63[/C][C]0.444938505525659[/C][C]0.889877011051317[/C][C]0.555061494474341[/C][/ROW]
[ROW][C]64[/C][C]0.483910774281511[/C][C]0.967821548563022[/C][C]0.516089225718489[/C][/ROW]
[ROW][C]65[/C][C]0.633377224564036[/C][C]0.733245550871927[/C][C]0.366622775435964[/C][/ROW]
[ROW][C]66[/C][C]0.6032652652625[/C][C]0.793469469475[/C][C]0.3967347347375[/C][/ROW]
[ROW][C]67[/C][C]0.645334983548307[/C][C]0.709330032903385[/C][C]0.354665016451693[/C][/ROW]
[ROW][C]68[/C][C]0.670845480927383[/C][C]0.658309038145233[/C][C]0.329154519072617[/C][/ROW]
[ROW][C]69[/C][C]0.67514908909527[/C][C]0.649701821809461[/C][C]0.32485091090473[/C][/ROW]
[ROW][C]70[/C][C]0.635922859480199[/C][C]0.728154281039603[/C][C]0.364077140519801[/C][/ROW]
[ROW][C]71[/C][C]0.629065495065964[/C][C]0.741869009868071[/C][C]0.370934504934036[/C][/ROW]
[ROW][C]72[/C][C]0.602650177432517[/C][C]0.794699645134967[/C][C]0.397349822567483[/C][/ROW]
[ROW][C]73[/C][C]0.57735012688295[/C][C]0.8452997462341[/C][C]0.42264987311705[/C][/ROW]
[ROW][C]74[/C][C]0.597903054278662[/C][C]0.804193891442677[/C][C]0.402096945721338[/C][/ROW]
[ROW][C]75[/C][C]0.635760164173669[/C][C]0.728479671652662[/C][C]0.364239835826331[/C][/ROW]
[ROW][C]76[/C][C]0.655486041806269[/C][C]0.689027916387462[/C][C]0.344513958193731[/C][/ROW]
[ROW][C]77[/C][C]0.674944667182956[/C][C]0.650110665634088[/C][C]0.325055332817044[/C][/ROW]
[ROW][C]78[/C][C]0.653759382202312[/C][C]0.692481235595375[/C][C]0.346240617797687[/C][/ROW]
[ROW][C]79[/C][C]0.626333699022848[/C][C]0.747332601954305[/C][C]0.373666300977152[/C][/ROW]
[ROW][C]80[/C][C]0.651615372963367[/C][C]0.696769254073267[/C][C]0.348384627036633[/C][/ROW]
[ROW][C]81[/C][C]0.657666296349373[/C][C]0.684667407301254[/C][C]0.342333703650627[/C][/ROW]
[ROW][C]82[/C][C]0.696806671707683[/C][C]0.606386656584635[/C][C]0.303193328292317[/C][/ROW]
[ROW][C]83[/C][C]0.684894169745098[/C][C]0.630211660509803[/C][C]0.315105830254902[/C][/ROW]
[ROW][C]84[/C][C]0.656336833682377[/C][C]0.687326332635246[/C][C]0.343663166317623[/C][/ROW]
[ROW][C]85[/C][C]0.655639451523052[/C][C]0.688721096953895[/C][C]0.344360548476948[/C][/ROW]
[ROW][C]86[/C][C]0.623829812761055[/C][C]0.75234037447789[/C][C]0.376170187238945[/C][/ROW]
[ROW][C]87[/C][C]0.629515030783786[/C][C]0.740969938432428[/C][C]0.370484969216214[/C][/ROW]
[ROW][C]88[/C][C]0.618289491229358[/C][C]0.763421017541284[/C][C]0.381710508770642[/C][/ROW]
[ROW][C]89[/C][C]0.583266630559604[/C][C]0.833466738880792[/C][C]0.416733369440396[/C][/ROW]
[ROW][C]90[/C][C]0.607990071523243[/C][C]0.784019856953513[/C][C]0.392009928476757[/C][/ROW]
[ROW][C]91[/C][C]0.572611959249763[/C][C]0.854776081500473[/C][C]0.427388040750237[/C][/ROW]
[ROW][C]92[/C][C]0.624577377686413[/C][C]0.750845244627175[/C][C]0.375422622313587[/C][/ROW]
[ROW][C]93[/C][C]0.582632818287882[/C][C]0.834734363424236[/C][C]0.417367181712118[/C][/ROW]
[ROW][C]94[/C][C]0.543015925741453[/C][C]0.913968148517094[/C][C]0.456984074258547[/C][/ROW]
[ROW][C]95[/C][C]0.580588981388392[/C][C]0.838822037223215[/C][C]0.419411018611608[/C][/ROW]
[ROW][C]96[/C][C]0.557755069638444[/C][C]0.884489860723112[/C][C]0.442244930361556[/C][/ROW]
[ROW][C]97[/C][C]0.585131281516819[/C][C]0.829737436966361[/C][C]0.41486871848318[/C][/ROW]
[ROW][C]98[/C][C]0.659325623586663[/C][C]0.681348752826674[/C][C]0.340674376413337[/C][/ROW]
[ROW][C]99[/C][C]0.648107127537677[/C][C]0.703785744924646[/C][C]0.351892872462323[/C][/ROW]
[ROW][C]100[/C][C]0.632557548274412[/C][C]0.734884903451176[/C][C]0.367442451725588[/C][/ROW]
[ROW][C]101[/C][C]0.593591774655283[/C][C]0.812816450689434[/C][C]0.406408225344717[/C][/ROW]
[ROW][C]102[/C][C]0.552840442538796[/C][C]0.894319114922408[/C][C]0.447159557461204[/C][/ROW]
[ROW][C]103[/C][C]0.517005882720352[/C][C]0.965988234559297[/C][C]0.482994117279648[/C][/ROW]
[ROW][C]104[/C][C]0.538283226387117[/C][C]0.923433547225767[/C][C]0.461716773612883[/C][/ROW]
[ROW][C]105[/C][C]0.541228516823785[/C][C]0.917542966352429[/C][C]0.458771483176214[/C][/ROW]
[ROW][C]106[/C][C]0.504706017128897[/C][C]0.990587965742207[/C][C]0.495293982871104[/C][/ROW]
[ROW][C]107[/C][C]0.52651496774551[/C][C]0.946970064508979[/C][C]0.47348503225449[/C][/ROW]
[ROW][C]108[/C][C]0.535658442808301[/C][C]0.928683114383397[/C][C]0.464341557191699[/C][/ROW]
[ROW][C]109[/C][C]0.597412992007695[/C][C]0.805174015984609[/C][C]0.402587007992305[/C][/ROW]
[ROW][C]110[/C][C]0.594346715711497[/C][C]0.811306568577005[/C][C]0.405653284288503[/C][/ROW]
[ROW][C]111[/C][C]0.59178389893659[/C][C]0.816432202126819[/C][C]0.40821610106341[/C][/ROW]
[ROW][C]112[/C][C]0.631137272623167[/C][C]0.737725454753667[/C][C]0.368862727376833[/C][/ROW]
[ROW][C]113[/C][C]0.82066907031427[/C][C]0.358661859371459[/C][C]0.17933092968573[/C][/ROW]
[ROW][C]114[/C][C]0.814308169356745[/C][C]0.37138366128651[/C][C]0.185691830643255[/C][/ROW]
[ROW][C]115[/C][C]0.850488409034045[/C][C]0.299023181931911[/C][C]0.149511590965955[/C][/ROW]
[ROW][C]116[/C][C]0.821216729327828[/C][C]0.357566541344344[/C][C]0.178783270672172[/C][/ROW]
[ROW][C]117[/C][C]0.793738195459362[/C][C]0.412523609081276[/C][C]0.206261804540638[/C][/ROW]
[ROW][C]118[/C][C]0.884039406645545[/C][C]0.23192118670891[/C][C]0.115960593354455[/C][/ROW]
[ROW][C]119[/C][C]0.867170182829591[/C][C]0.265659634340818[/C][C]0.132829817170409[/C][/ROW]
[ROW][C]120[/C][C]0.851229957265061[/C][C]0.297540085469879[/C][C]0.148770042734939[/C][/ROW]
[ROW][C]121[/C][C]0.817131116230592[/C][C]0.365737767538816[/C][C]0.182868883769408[/C][/ROW]
[ROW][C]122[/C][C]0.819146567224771[/C][C]0.361706865550458[/C][C]0.180853432775229[/C][/ROW]
[ROW][C]123[/C][C]0.805247776997019[/C][C]0.389504446005962[/C][C]0.194752223002981[/C][/ROW]
[ROW][C]124[/C][C]0.758530398828595[/C][C]0.482939202342809[/C][C]0.241469601171405[/C][/ROW]
[ROW][C]125[/C][C]0.707064262962457[/C][C]0.585871474075086[/C][C]0.292935737037543[/C][/ROW]
[ROW][C]126[/C][C]0.794977595247912[/C][C]0.410044809504177[/C][C]0.205022404752088[/C][/ROW]
[ROW][C]127[/C][C]0.779650254230221[/C][C]0.440699491539558[/C][C]0.220349745769779[/C][/ROW]
[ROW][C]128[/C][C]0.725895512069541[/C][C]0.548208975860917[/C][C]0.274104487930459[/C][/ROW]
[ROW][C]129[/C][C]0.665572667594606[/C][C]0.668854664810787[/C][C]0.334427332405394[/C][/ROW]
[ROW][C]130[/C][C]0.905064080587328[/C][C]0.189871838825345[/C][C]0.0949359194126724[/C][/ROW]
[ROW][C]131[/C][C]0.941476596196425[/C][C]0.11704680760715[/C][C]0.058523403803575[/C][/ROW]
[ROW][C]132[/C][C]0.919374295537953[/C][C]0.161251408924095[/C][C]0.0806257044620473[/C][/ROW]
[ROW][C]133[/C][C]0.902183542078996[/C][C]0.195632915842008[/C][C]0.0978164579210038[/C][/ROW]
[ROW][C]134[/C][C]0.864061128149052[/C][C]0.271877743701896[/C][C]0.135938871850948[/C][/ROW]
[ROW][C]135[/C][C]0.908182975366005[/C][C]0.18363404926799[/C][C]0.0918170246339949[/C][/ROW]
[ROW][C]136[/C][C]0.908282286779689[/C][C]0.183435426440621[/C][C]0.0917177132203107[/C][/ROW]
[ROW][C]137[/C][C]0.932351955808468[/C][C]0.135296088383063[/C][C]0.0676480441915316[/C][/ROW]
[ROW][C]138[/C][C]0.957760961319805[/C][C]0.0844780773603897[/C][C]0.0422390386801949[/C][/ROW]
[ROW][C]139[/C][C]0.947865489357178[/C][C]0.104269021285644[/C][C]0.0521345106428222[/C][/ROW]
[ROW][C]140[/C][C]0.954187933618789[/C][C]0.0916241327624217[/C][C]0.0458120663812109[/C][/ROW]
[ROW][C]141[/C][C]0.957535051228936[/C][C]0.0849298975421286[/C][C]0.0424649487710643[/C][/ROW]
[ROW][C]142[/C][C]0.986098553842573[/C][C]0.0278028923148546[/C][C]0.0139014461574273[/C][/ROW]
[ROW][C]143[/C][C]0.988214231596306[/C][C]0.0235715368073886[/C][C]0.0117857684036943[/C][/ROW]
[ROW][C]144[/C][C]0.976352896948374[/C][C]0.0472942061032517[/C][C]0.0236471030516259[/C][/ROW]
[ROW][C]145[/C][C]0.957075698910428[/C][C]0.0858486021791443[/C][C]0.0429243010895721[/C][/ROW]
[ROW][C]146[/C][C]0.929869019172508[/C][C]0.140261961654985[/C][C]0.0701309808274924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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
80.3125028048881740.6250056097763470.687497195111826
90.3793136596139260.7586273192278520.620686340386074
100.2500537195951390.5001074391902780.749946280404861
110.1534431297072640.3068862594145280.846556870292736
120.08720367060177570.1744073412035510.912796329398224
130.05821924198098760.1164384839619750.941780758019012
140.03296597664485370.06593195328970740.967034023355146
150.01717585898235430.03435171796470860.982824141017646
160.0103877273654120.0207754547308240.989612272634588
170.005084313494892260.01016862698978450.994915686505108
180.03083133335199130.06166266670398270.969168666648009
190.01872345261864920.03744690523729850.981276547381351
200.01550369825738190.03100739651476390.984496301742618
210.04278709899478050.08557419798956110.957212901005219
220.1184822682742940.2369645365485880.881517731725706
230.08738909040860930.1747781808172190.912610909591391
240.06641416836174990.13282833672350.93358583163825
250.04994804092565970.09989608185131930.95005195907434
260.3217334992786010.6434669985572010.678266500721399
270.2940156278330330.5880312556660660.705984372166967
280.2431426888896170.4862853777792330.756857311110383
290.2956561354839380.5913122709678760.704343864516062
300.2462493570402260.4924987140804510.753750642959774
310.2951846263528060.5903692527056120.704815373647194
320.3421249102434290.6842498204868580.657875089756571
330.3699528536236580.7399057072473150.630047146376342
340.4186502334360950.8373004668721890.581349766563905
350.4734135565031430.9468271130062860.526586443496857
360.4259983721890080.8519967443780170.574001627810992
370.3728189809972730.7456379619945460.627181019002727
380.3516457175859750.703291435171950.648354282414025
390.3546646841429470.7093293682858950.645335315857053
400.3704726104987580.7409452209975160.629527389501242
410.3774194449034770.7548388898069540.622580555096523
420.4089756791022740.8179513582045480.591024320897726
430.4391864500184870.8783729000369740.560813549981513
440.5531349972579370.8937300054841260.446865002742063
450.5245129252847110.9509741494305780.475487074715289
460.5362639777337720.9274720445324560.463736022266228
470.5652281569441120.8695436861117770.434771843055888
480.5226713780299930.9546572439400130.477328621970007
490.4824226676157590.9648453352315180.517577332384241
500.4518377213142380.9036754426284770.548162278685762
510.4806296863543990.9612593727087980.519370313645601
520.4389828749186420.8779657498372840.561017125081358
530.4376015452032110.8752030904064220.562398454796789
540.4127923282856460.8255846565712920.587207671714354
550.3673435130831580.7346870261663160.632656486916842
560.3242075723789030.6484151447578060.675792427621097
570.3159919669542050.631983933908410.684008033045795
580.3014606847055170.6029213694110340.698539315294483
590.2645711906709280.5291423813418550.735428809329072
600.2547271483574930.5094542967149870.745272851642507
610.3233626318083080.6467252636166170.676637368191692
620.4384995552686160.8769991105372320.561500444731384
630.4449385055256590.8898770110513170.555061494474341
640.4839107742815110.9678215485630220.516089225718489
650.6333772245640360.7332455508719270.366622775435964
660.60326526526250.7934694694750.3967347347375
670.6453349835483070.7093300329033850.354665016451693
680.6708454809273830.6583090381452330.329154519072617
690.675149089095270.6497018218094610.32485091090473
700.6359228594801990.7281542810396030.364077140519801
710.6290654950659640.7418690098680710.370934504934036
720.6026501774325170.7946996451349670.397349822567483
730.577350126882950.84529974623410.42264987311705
740.5979030542786620.8041938914426770.402096945721338
750.6357601641736690.7284796716526620.364239835826331
760.6554860418062690.6890279163874620.344513958193731
770.6749446671829560.6501106656340880.325055332817044
780.6537593822023120.6924812355953750.346240617797687
790.6263336990228480.7473326019543050.373666300977152
800.6516153729633670.6967692540732670.348384627036633
810.6576662963493730.6846674073012540.342333703650627
820.6968066717076830.6063866565846350.303193328292317
830.6848941697450980.6302116605098030.315105830254902
840.6563368336823770.6873263326352460.343663166317623
850.6556394515230520.6887210969538950.344360548476948
860.6238298127610550.752340374477890.376170187238945
870.6295150307837860.7409699384324280.370484969216214
880.6182894912293580.7634210175412840.381710508770642
890.5832666305596040.8334667388807920.416733369440396
900.6079900715232430.7840198569535130.392009928476757
910.5726119592497630.8547760815004730.427388040750237
920.6245773776864130.7508452446271750.375422622313587
930.5826328182878820.8347343634242360.417367181712118
940.5430159257414530.9139681485170940.456984074258547
950.5805889813883920.8388220372232150.419411018611608
960.5577550696384440.8844898607231120.442244930361556
970.5851312815168190.8297374369663610.41486871848318
980.6593256235866630.6813487528266740.340674376413337
990.6481071275376770.7037857449246460.351892872462323
1000.6325575482744120.7348849034511760.367442451725588
1010.5935917746552830.8128164506894340.406408225344717
1020.5528404425387960.8943191149224080.447159557461204
1030.5170058827203520.9659882345592970.482994117279648
1040.5382832263871170.9234335472257670.461716773612883
1050.5412285168237850.9175429663524290.458771483176214
1060.5047060171288970.9905879657422070.495293982871104
1070.526514967745510.9469700645089790.47348503225449
1080.5356584428083010.9286831143833970.464341557191699
1090.5974129920076950.8051740159846090.402587007992305
1100.5943467157114970.8113065685770050.405653284288503
1110.591783898936590.8164322021268190.40821610106341
1120.6311372726231670.7377254547536670.368862727376833
1130.820669070314270.3586618593714590.17933092968573
1140.8143081693567450.371383661286510.185691830643255
1150.8504884090340450.2990231819319110.149511590965955
1160.8212167293278280.3575665413443440.178783270672172
1170.7937381954593620.4125236090812760.206261804540638
1180.8840394066455450.231921186708910.115960593354455
1190.8671701828295910.2656596343408180.132829817170409
1200.8512299572650610.2975400854698790.148770042734939
1210.8171311162305920.3657377675388160.182868883769408
1220.8191465672247710.3617068655504580.180853432775229
1230.8052477769970190.3895044460059620.194752223002981
1240.7585303988285950.4829392023428090.241469601171405
1250.7070642629624570.5858714740750860.292935737037543
1260.7949775952479120.4100448095041770.205022404752088
1270.7796502542302210.4406994915395580.220349745769779
1280.7258955120695410.5482089758609170.274104487930459
1290.6655726675946060.6688546648107870.334427332405394
1300.9050640805873280.1898718388253450.0949359194126724
1310.9414765961964250.117046807607150.058523403803575
1320.9193742955379530.1612514089240950.0806257044620473
1330.9021835420789960.1956329158420080.0978164579210038
1340.8640611281490520.2718777437018960.135938871850948
1350.9081829753660050.183634049267990.0918170246339949
1360.9082822867796890.1834354264406210.0917177132203107
1370.9323519558084680.1352960883830630.0676480441915316
1380.9577609613198050.08447807736038970.0422390386801949
1390.9478654893571780.1042690212856440.0521345106428222
1400.9541879336187890.09162413276242170.0458120663812109
1410.9575350512289360.08492989754212860.0424649487710643
1420.9860985538425730.02780289231485460.0139014461574273
1430.9882142315963060.02357153680738860.0117857684036943
1440.9763528969483740.04729420610325170.0236471030516259
1450.9570756989104280.08584860217914430.0429243010895721
1460.9298690191725080.1402619616549850.0701309808274924







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0575539568345324NOK
10% type I error level160.115107913669065NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.0575539568345324 & NOK \tabularnewline
10% type I error level & 16 & 0.115107913669065 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0575539568345324[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.115107913669065[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0575539568345324NOK
10% type I error level160.115107913669065NOK



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')
}