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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Jan 2008 06:56:54 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Jan/20/t1200837348nrvfakk5mq2iczb.htm/, Retrieved Wed, 15 May 2024 21:42:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=8034, Retrieved Wed, 15 May 2024 21:42:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact350

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Goldfeld-Quandt] [2008-01-20 13:56:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106	87	1	65.3	170
2.2	70	1	65.73	165
62.3	75	1	69.44	168
14.7	79	1	73.74	170
5	64.5	1	74.31	157
74.4	75	0	70.53	146
66.1	70	0	69.42	149
22	67	1	69.77	159
3.4	52	0	65.47	151
0.3	67.2	1	66.2	174
53.2	47	0	70.46	156
0	46.4	0	74.44	151.5
57.2	76	0	69.28	146
9.2	71.6	1	67.67	157
15.9	63.8	1	67.22	171.5
17.6	48.2	1	64.85	150
21	64.5	1	71.35	170
7.6	75.9	1	72.28	164.5
71.6	80	1	71.87	163
12.9	56	1	67.34	162.5
10.5	75.5	0	73.5	161
25.7	77	1	64.91	166.5
26.8	88	0	68.13	160
7.3	48	0	72.5	147
17.1	73	1	72.36	162.5
27.3	72	1	70.59	161
16.5	64	1	74.76	163.5
5.4	76	0	65.63	161
5.6	67.4	1	67.04	172.5
36.5	73.7	1	66.72	169.5
1.1	59.2	0	65.8	158
3.9	53	0	72.44	153.5
34.2	41.9	1	71.83	165.5
40.3	65.5	1	72.67	153.5
15.6	63	1	69.56	157.5
15.5	54	0	67	145.5
52.9	77.7	0	68.86	156
1.6	47.6	0	71.25	163
14.2	53.1	1	69.88	159
7.5	55.5	1	67.18	167
2	64	1	67.47	157.5
71.4	75.6	1	73.2	156
3.2	57	0	69.6	156.5
20	63	0	71.24	148.5
2.8	59.5	1	73.83	162.5
15.3	84.5	1	66.07	164
8	59.9	0	70.68	152
36.6	60	1	74.01	157.5
3.8	64	0	68.53	148
25.5	54	0	66.72	145.5
3.2	53.8	0	72.69	154.5
33.1	84	1	67.46	166.5
42	63.2	0	73.81	157
16.2	54.3	1	72.96	150
0	60	0	71.65	152
22.7	68	1	72.79	171
36.4	74	1	73.83	165.5
69	74	1	66.74	165
11.2	68.5	1	65.62	168.5
12.5	76	0	66.18	154
51.7	83	0	67.78	156.5
3.6	62.5	0	68.84	152
22.2	57	1	65.27	164.5
39.2	85	1	72.84	161
27.9	50	1	75.36	162
58.8	53	1	76.88	169
1	57	0	76.51	150
4.7	46	1	80.63	146
25.6	65.4	1	75.27	165
5.3	71.4	1	81.19	165.5
38.7	41	1	81.3	164
31.6	66	1	77.77	163
19.3	69.5	1	75.51	167.5
26.5	59	1	78.64	166
12.8	80	1	80.68	167.5
18.3	72	1	77.4	162
13.2	73	0	80.71	165
36	66.4	0	83.16	145
34.1	37	0	87.99	139
71.5	70	1	72.21	164
43.3	75	1	70.24	167
47.7	54	1	66.06	163
74.9	76.2	1	68.67	162.5
0.9	74.9	1	68.77	159.5
35.9	98	1	68.07	169
45.8	86.5	0	67.33	152.5
54.2	72.8	1	69.47	165
34	65	1	70.81	166
7.9	50	1	73.17	163
54.5	81	1	71.28	167.5
8.2	52	0	69.47	157.5
49.3	68	1	65.31	160
46.9	58.5	1	70.23	162
16.8	65.5	1	73.23	164.5
2.8	62.5	0	68.67	150
60.9	64	1	72.66	167
5.6	55.7	0	74.79	155
6.6	84	1	73.04	173.5
22.9	63.7	1	69.95	173
51.1	65	0	67.51	156
23.3	87.5	0	67.5	149.5
11.5	79	1	71.32	167
79.1	58.5	0	71.23	146
53.6	75	1	67.49	166
1.5	52.5	0	68.62	151.5
40.4	57.5	1	72.53	164
25.4	70	1	66.67	160
6.7	72	1	66.19	152.5
76	88	1	78.4	160
0.6	58	1	75.67	163
43.4	73	1	76.07	168
13	56	1	82.88	165.5
27.8	49	0	77.14	147
6.5	54.7	0	77.31	158
7.1	67	1	76.58	168
6	47	0	82.86	154.5
6.5	47	0	76.64	147

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&T=0

[TABLE]
[ROW][C]Summary of compuational 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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=0

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


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

Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 61.5198389564683 + 0.716989169203628weight[t] + 10.0572486368982sex[t] + 0.119109062022990age[t] -0.608987531164764`height `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  61.5198389564683 +  0.716989169203628weight[t] +  10.0572486368982sex[t] +  0.119109062022990age[t] -0.608987531164764`height
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  61.5198389564683 +  0.716989169203628weight[t] +  10.0572486368982sex[t] +  0.119109062022990age[t] -0.608987531164764`height
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 61.5198389564683 + 0.716989169203628weight[t] + 10.0572486368982sex[t] + 0.119109062022990age[t] -0.608987531164764`height `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)61.519838956468366.9729040.91860.360290.180145
weight0.7169891692036280.1869783.83460.0002080.000104
sex10.05724863689825.8828871.70960.0901130.045057
age0.1191090620229900.4453980.26740.7896370.394818
`height `-0.6089875311647640.380946-1.59860.1127230.056362

\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) & 61.5198389564683 & 66.972904 & 0.9186 & 0.36029 & 0.180145 \tabularnewline
weight & 0.716989169203628 & 0.186978 & 3.8346 & 0.000208 & 0.000104 \tabularnewline
sex & 10.0572486368982 & 5.882887 & 1.7096 & 0.090113 & 0.045057 \tabularnewline
age & 0.119109062022990 & 0.445398 & 0.2674 & 0.789637 & 0.394818 \tabularnewline
`height
` & -0.608987531164764 & 0.380946 & -1.5986 & 0.112723 & 0.056362 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&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]61.5198389564683[/C][C]66.972904[/C][C]0.9186[/C][C]0.36029[/C][C]0.180145[/C][/ROW]
[ROW][C]weight[/C][C]0.716989169203628[/C][C]0.186978[/C][C]3.8346[/C][C]0.000208[/C][C]0.000104[/C][/ROW]
[ROW][C]sex[/C][C]10.0572486368982[/C][C]5.882887[/C][C]1.7096[/C][C]0.090113[/C][C]0.045057[/C][/ROW]
[ROW][C]age[/C][C]0.119109062022990[/C][C]0.445398[/C][C]0.2674[/C][C]0.789637[/C][C]0.394818[/C][/ROW]
[ROW][C]`height
`[/C][C]-0.608987531164764[/C][C]0.380946[/C][C]-1.5986[/C][C]0.112723[/C][C]0.056362[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)61.519838956468366.9729040.91860.360290.180145
weight0.7169891692036280.1869783.83460.0002080.000104
sex10.05724863689825.8828871.70960.0901130.045057
age0.1191090620229900.4453980.26740.7896370.394818
`height `-0.6089875311647640.380946-1.59860.1127230.056362






Multiple Linear Regression - Regression Statistics
Multiple R0.378964915955848
R-squared0.143614407525423
Adjusted R-squared0.113029207794188
F-TEST (value)4.69555238440237
F-TEST (DF numerator)4
F-TEST (DF denominator)112
p-value0.00153379659481434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.6102713221872
Sum Squared Residuals52304.4285812773

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.378964915955848 \tabularnewline
R-squared & 0.143614407525423 \tabularnewline
Adjusted R-squared & 0.113029207794188 \tabularnewline
F-TEST (value) & 4.69555238440237 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 112 \tabularnewline
p-value & 0.00153379659481434 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 21.6102713221872 \tabularnewline
Sum Squared Residuals & 52304.4285812773 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.378964915955848[/C][/ROW]
[ROW][C]R-squared[/C][C]0.143614407525423[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.113029207794188[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.69555238440237[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]112[/C][/ROW]
[ROW][C]p-value[/C][C]0.00153379659481434[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]21.6102713221872[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]52304.4285812773[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.378964915955848
R-squared0.143614407525423
Adjusted R-squared0.113029207794188
F-TEST (value)4.69555238440237
F-TEST (DF numerator)4
F-TEST (DF denominator)112
p-value0.00153379659481434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21.6102713221872
Sum Squared Residuals52304.4285812773






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110638.205086766173567.7949132338265
22.229.1124254422055-26.9124254422055
362.331.312303314834630.9876966851654
414.733.4744538960185-18.7744538960185
5531.0628410130609-26.0628410130609
674.434.782609241166339.6173907588337
766.129.238489742808336.8615102571917
82231.0965837321561-9.09658373215608
93.414.6442288398227-11.2442288398227
100.321.6799492471033-21.3799492471033
1153.28.6086995574754444.5913004425246
12011.3930040130462-11.3930040130462
1357.235.350712082841221.8492879171588
149.235.362579942574-26.162579942574
1515.920.8861461429863-4.98614614298629
1617.622.5120585464576-4.91205854645763
172122.7934402843309-1.79344028433093
187.634.4273196623399-26.8273196623399
1971.638.231621837392533.3683781626075
2012.920.7888114911236-7.88881149112362
2110.526.3600447725049-15.8600447725049
2225.733.1201988990249-7.4201988990249
2326.835.2917812556516-8.49178125565158
247.315.0495589936888-7.74955899368884
2517.133.5755548589407-16.4755548589407
2627.333.5612239467035-6.26122394670354
2716.526.7995265537985-10.2995265537985
285.425.7811510389858-20.3811510389858
295.622.8368799897905-17.2368799897905
3036.529.14275944942027.35724055057975
311.115.5829441304030-14.4829441304030
323.914.6689393434146-10.7689393434146
3334.29.387101300341424.8128986996586
3440.333.71594767962356.58405232037649
3515.629.1170954490639-13.5170954490639
3615.519.6098754645313-4.10987546453131
3752.930.429692552790022.4703074472100
381.64.87007649984242-3.27007649984242
3914.221.1435362770482-6.94353627704817
407.517.6708155663567-10.1708155663567
41229.5851466786395-27.5851466786395
4271.439.498197263540431.9018027364596
433.215.3716636905896-12.1716636905896
442024.7408378168471-4.74083781684715
452.824.0712913958655-21.2712913958655
4615.340.1582530079107-24.8582530079107
47820.3200139585064-12.3200139585064
4836.627.49616326745539.1038367325447
493.825.4395351935509-21.6395351935509
5025.519.57652492716495.92347507283513
513.214.6633204131185-11.4633204131185
5233.138.4428511916089-5.34285119160892
534220.013951925186521.9860480748135
5416.227.8516669716062-11.6516669716062
55020.5072486655890-20.5072486655890
5622.724.8654318946920-2.16543189469197
5736.432.64067175582393.75932824417615
586932.100682271663236.8993177283368
5911.225.8923833325009-14.6923833325009
6012.530.1095737412518-17.6095737412518
6151.733.796603597002117.9033964029979
623.621.9650251243135-18.3650251243135
6322.220.04126983961012.15873016038987
6439.243.1500785359024-3.95007853590244
6527.917.746624918908610.1533750810914
6658.815.815725482641142.9842745173589
67120.1531262617394-19.1531262617394
684.725.2501734975915-20.5501734975915
6925.626.9505757155681-1.35057571556814
705.331.6531426123836-26.3531426123836
7138.710.78325516216327.916744837837
7231.628.89651693447732.70348306552269
7319.328.3963486562766-9.09634865627662
7426.522.15425504051764.34574495948237
7512.836.5405287835736-23.7405287835736
7618.333.7633691279153-15.4633691279153
7713.222.9903980620225-9.79039806202254
783630.72983737053025.27016262946979
7934.113.879577752503220.2204222474968
8071.530.493239695279241.0067603047208
8143.332.016578095617811.2834219043822
8247.718.897879787744628.8021202122554
8374.935.430407761527539.4695922384725
840.936.3371953412594-35.4371953412594
8535.947.0308872603818-11.1308872603818
8645.838.68841673596357.11158326403653
8754.231.565463007941622.6345369920584
883425.52356610009948.47643389990062
897.916.8767885419135-8.97678854191351
9054.536.137892769761118.3621072302389
918.211.1622461353437-2.96224613534367
9249.330.673358953572418.6266410464276
9346.923.230003368961523.6699966310385
9416.827.0837859115440-10.2837859115440
952.823.1627516460991-20.3627516460991
9660.924.417941164473536.4820588355265
975.615.9712350992713-10.3712350992713
986.634.8445670395439-28.2445670395439
9922.920.22613366864152.67386633135845
10051.121.163132870172929.9368671298271
10123.341.2526170392053-17.9526170392053
10211.535.0131725594171-23.5131725594171
10379.123.035664292722556.0643357072775
10453.632.298015706219321.3019842937807
1051.515.0734232042145-13.5734232042145
10640.421.568989980081218.8310100199188
10725.432.2693256163309-6.86932561633093
1086.738.2135380887029-31.5135380887029
1097646.572279959525929.4277200404741
1100.622.9104745506-22.3104745506
11143.430.668018057639812.7319819423602
1121320.8128037214666-7.8128037214666
11327.816.319214210679111.4807857893209
1146.513.7274381728713-7.22743817287133
1157.126.4268286640498-19.3268286640498
116610.9991332233077-4.99913322330766
1176.514.8256813412604-8.32568134126039

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106 & 38.2050867661735 & 67.7949132338265 \tabularnewline
2 & 2.2 & 29.1124254422055 & -26.9124254422055 \tabularnewline
3 & 62.3 & 31.3123033148346 & 30.9876966851654 \tabularnewline
4 & 14.7 & 33.4744538960185 & -18.7744538960185 \tabularnewline
5 & 5 & 31.0628410130609 & -26.0628410130609 \tabularnewline
6 & 74.4 & 34.7826092411663 & 39.6173907588337 \tabularnewline
7 & 66.1 & 29.2384897428083 & 36.8615102571917 \tabularnewline
8 & 22 & 31.0965837321561 & -9.09658373215608 \tabularnewline
9 & 3.4 & 14.6442288398227 & -11.2442288398227 \tabularnewline
10 & 0.3 & 21.6799492471033 & -21.3799492471033 \tabularnewline
11 & 53.2 & 8.60869955747544 & 44.5913004425246 \tabularnewline
12 & 0 & 11.3930040130462 & -11.3930040130462 \tabularnewline
13 & 57.2 & 35.3507120828412 & 21.8492879171588 \tabularnewline
14 & 9.2 & 35.362579942574 & -26.162579942574 \tabularnewline
15 & 15.9 & 20.8861461429863 & -4.98614614298629 \tabularnewline
16 & 17.6 & 22.5120585464576 & -4.91205854645763 \tabularnewline
17 & 21 & 22.7934402843309 & -1.79344028433093 \tabularnewline
18 & 7.6 & 34.4273196623399 & -26.8273196623399 \tabularnewline
19 & 71.6 & 38.2316218373925 & 33.3683781626075 \tabularnewline
20 & 12.9 & 20.7888114911236 & -7.88881149112362 \tabularnewline
21 & 10.5 & 26.3600447725049 & -15.8600447725049 \tabularnewline
22 & 25.7 & 33.1201988990249 & -7.4201988990249 \tabularnewline
23 & 26.8 & 35.2917812556516 & -8.49178125565158 \tabularnewline
24 & 7.3 & 15.0495589936888 & -7.74955899368884 \tabularnewline
25 & 17.1 & 33.5755548589407 & -16.4755548589407 \tabularnewline
26 & 27.3 & 33.5612239467035 & -6.26122394670354 \tabularnewline
27 & 16.5 & 26.7995265537985 & -10.2995265537985 \tabularnewline
28 & 5.4 & 25.7811510389858 & -20.3811510389858 \tabularnewline
29 & 5.6 & 22.8368799897905 & -17.2368799897905 \tabularnewline
30 & 36.5 & 29.1427594494202 & 7.35724055057975 \tabularnewline
31 & 1.1 & 15.5829441304030 & -14.4829441304030 \tabularnewline
32 & 3.9 & 14.6689393434146 & -10.7689393434146 \tabularnewline
33 & 34.2 & 9.3871013003414 & 24.8128986996586 \tabularnewline
34 & 40.3 & 33.7159476796235 & 6.58405232037649 \tabularnewline
35 & 15.6 & 29.1170954490639 & -13.5170954490639 \tabularnewline
36 & 15.5 & 19.6098754645313 & -4.10987546453131 \tabularnewline
37 & 52.9 & 30.4296925527900 & 22.4703074472100 \tabularnewline
38 & 1.6 & 4.87007649984242 & -3.27007649984242 \tabularnewline
39 & 14.2 & 21.1435362770482 & -6.94353627704817 \tabularnewline
40 & 7.5 & 17.6708155663567 & -10.1708155663567 \tabularnewline
41 & 2 & 29.5851466786395 & -27.5851466786395 \tabularnewline
42 & 71.4 & 39.4981972635404 & 31.9018027364596 \tabularnewline
43 & 3.2 & 15.3716636905896 & -12.1716636905896 \tabularnewline
44 & 20 & 24.7408378168471 & -4.74083781684715 \tabularnewline
45 & 2.8 & 24.0712913958655 & -21.2712913958655 \tabularnewline
46 & 15.3 & 40.1582530079107 & -24.8582530079107 \tabularnewline
47 & 8 & 20.3200139585064 & -12.3200139585064 \tabularnewline
48 & 36.6 & 27.4961632674553 & 9.1038367325447 \tabularnewline
49 & 3.8 & 25.4395351935509 & -21.6395351935509 \tabularnewline
50 & 25.5 & 19.5765249271649 & 5.92347507283513 \tabularnewline
51 & 3.2 & 14.6633204131185 & -11.4633204131185 \tabularnewline
52 & 33.1 & 38.4428511916089 & -5.34285119160892 \tabularnewline
53 & 42 & 20.0139519251865 & 21.9860480748135 \tabularnewline
54 & 16.2 & 27.8516669716062 & -11.6516669716062 \tabularnewline
55 & 0 & 20.5072486655890 & -20.5072486655890 \tabularnewline
56 & 22.7 & 24.8654318946920 & -2.16543189469197 \tabularnewline
57 & 36.4 & 32.6406717558239 & 3.75932824417615 \tabularnewline
58 & 69 & 32.1006822716632 & 36.8993177283368 \tabularnewline
59 & 11.2 & 25.8923833325009 & -14.6923833325009 \tabularnewline
60 & 12.5 & 30.1095737412518 & -17.6095737412518 \tabularnewline
61 & 51.7 & 33.7966035970021 & 17.9033964029979 \tabularnewline
62 & 3.6 & 21.9650251243135 & -18.3650251243135 \tabularnewline
63 & 22.2 & 20.0412698396101 & 2.15873016038987 \tabularnewline
64 & 39.2 & 43.1500785359024 & -3.95007853590244 \tabularnewline
65 & 27.9 & 17.7466249189086 & 10.1533750810914 \tabularnewline
66 & 58.8 & 15.8157254826411 & 42.9842745173589 \tabularnewline
67 & 1 & 20.1531262617394 & -19.1531262617394 \tabularnewline
68 & 4.7 & 25.2501734975915 & -20.5501734975915 \tabularnewline
69 & 25.6 & 26.9505757155681 & -1.35057571556814 \tabularnewline
70 & 5.3 & 31.6531426123836 & -26.3531426123836 \tabularnewline
71 & 38.7 & 10.783255162163 & 27.916744837837 \tabularnewline
72 & 31.6 & 28.8965169344773 & 2.70348306552269 \tabularnewline
73 & 19.3 & 28.3963486562766 & -9.09634865627662 \tabularnewline
74 & 26.5 & 22.1542550405176 & 4.34574495948237 \tabularnewline
75 & 12.8 & 36.5405287835736 & -23.7405287835736 \tabularnewline
76 & 18.3 & 33.7633691279153 & -15.4633691279153 \tabularnewline
77 & 13.2 & 22.9903980620225 & -9.79039806202254 \tabularnewline
78 & 36 & 30.7298373705302 & 5.27016262946979 \tabularnewline
79 & 34.1 & 13.8795777525032 & 20.2204222474968 \tabularnewline
80 & 71.5 & 30.4932396952792 & 41.0067603047208 \tabularnewline
81 & 43.3 & 32.0165780956178 & 11.2834219043822 \tabularnewline
82 & 47.7 & 18.8978797877446 & 28.8021202122554 \tabularnewline
83 & 74.9 & 35.4304077615275 & 39.4695922384725 \tabularnewline
84 & 0.9 & 36.3371953412594 & -35.4371953412594 \tabularnewline
85 & 35.9 & 47.0308872603818 & -11.1308872603818 \tabularnewline
86 & 45.8 & 38.6884167359635 & 7.11158326403653 \tabularnewline
87 & 54.2 & 31.5654630079416 & 22.6345369920584 \tabularnewline
88 & 34 & 25.5235661000994 & 8.47643389990062 \tabularnewline
89 & 7.9 & 16.8767885419135 & -8.97678854191351 \tabularnewline
90 & 54.5 & 36.1378927697611 & 18.3621072302389 \tabularnewline
91 & 8.2 & 11.1622461353437 & -2.96224613534367 \tabularnewline
92 & 49.3 & 30.6733589535724 & 18.6266410464276 \tabularnewline
93 & 46.9 & 23.2300033689615 & 23.6699966310385 \tabularnewline
94 & 16.8 & 27.0837859115440 & -10.2837859115440 \tabularnewline
95 & 2.8 & 23.1627516460991 & -20.3627516460991 \tabularnewline
96 & 60.9 & 24.4179411644735 & 36.4820588355265 \tabularnewline
97 & 5.6 & 15.9712350992713 & -10.3712350992713 \tabularnewline
98 & 6.6 & 34.8445670395439 & -28.2445670395439 \tabularnewline
99 & 22.9 & 20.2261336686415 & 2.67386633135845 \tabularnewline
100 & 51.1 & 21.1631328701729 & 29.9368671298271 \tabularnewline
101 & 23.3 & 41.2526170392053 & -17.9526170392053 \tabularnewline
102 & 11.5 & 35.0131725594171 & -23.5131725594171 \tabularnewline
103 & 79.1 & 23.0356642927225 & 56.0643357072775 \tabularnewline
104 & 53.6 & 32.2980157062193 & 21.3019842937807 \tabularnewline
105 & 1.5 & 15.0734232042145 & -13.5734232042145 \tabularnewline
106 & 40.4 & 21.5689899800812 & 18.8310100199188 \tabularnewline
107 & 25.4 & 32.2693256163309 & -6.86932561633093 \tabularnewline
108 & 6.7 & 38.2135380887029 & -31.5135380887029 \tabularnewline
109 & 76 & 46.5722799595259 & 29.4277200404741 \tabularnewline
110 & 0.6 & 22.9104745506 & -22.3104745506 \tabularnewline
111 & 43.4 & 30.6680180576398 & 12.7319819423602 \tabularnewline
112 & 13 & 20.8128037214666 & -7.8128037214666 \tabularnewline
113 & 27.8 & 16.3192142106791 & 11.4807857893209 \tabularnewline
114 & 6.5 & 13.7274381728713 & -7.22743817287133 \tabularnewline
115 & 7.1 & 26.4268286640498 & -19.3268286640498 \tabularnewline
116 & 6 & 10.9991332233077 & -4.99913322330766 \tabularnewline
117 & 6.5 & 14.8256813412604 & -8.32568134126039 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&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]106[/C][C]38.2050867661735[/C][C]67.7949132338265[/C][/ROW]
[ROW][C]2[/C][C]2.2[/C][C]29.1124254422055[/C][C]-26.9124254422055[/C][/ROW]
[ROW][C]3[/C][C]62.3[/C][C]31.3123033148346[/C][C]30.9876966851654[/C][/ROW]
[ROW][C]4[/C][C]14.7[/C][C]33.4744538960185[/C][C]-18.7744538960185[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]31.0628410130609[/C][C]-26.0628410130609[/C][/ROW]
[ROW][C]6[/C][C]74.4[/C][C]34.7826092411663[/C][C]39.6173907588337[/C][/ROW]
[ROW][C]7[/C][C]66.1[/C][C]29.2384897428083[/C][C]36.8615102571917[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]31.0965837321561[/C][C]-9.09658373215608[/C][/ROW]
[ROW][C]9[/C][C]3.4[/C][C]14.6442288398227[/C][C]-11.2442288398227[/C][/ROW]
[ROW][C]10[/C][C]0.3[/C][C]21.6799492471033[/C][C]-21.3799492471033[/C][/ROW]
[ROW][C]11[/C][C]53.2[/C][C]8.60869955747544[/C][C]44.5913004425246[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]11.3930040130462[/C][C]-11.3930040130462[/C][/ROW]
[ROW][C]13[/C][C]57.2[/C][C]35.3507120828412[/C][C]21.8492879171588[/C][/ROW]
[ROW][C]14[/C][C]9.2[/C][C]35.362579942574[/C][C]-26.162579942574[/C][/ROW]
[ROW][C]15[/C][C]15.9[/C][C]20.8861461429863[/C][C]-4.98614614298629[/C][/ROW]
[ROW][C]16[/C][C]17.6[/C][C]22.5120585464576[/C][C]-4.91205854645763[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]22.7934402843309[/C][C]-1.79344028433093[/C][/ROW]
[ROW][C]18[/C][C]7.6[/C][C]34.4273196623399[/C][C]-26.8273196623399[/C][/ROW]
[ROW][C]19[/C][C]71.6[/C][C]38.2316218373925[/C][C]33.3683781626075[/C][/ROW]
[ROW][C]20[/C][C]12.9[/C][C]20.7888114911236[/C][C]-7.88881149112362[/C][/ROW]
[ROW][C]21[/C][C]10.5[/C][C]26.3600447725049[/C][C]-15.8600447725049[/C][/ROW]
[ROW][C]22[/C][C]25.7[/C][C]33.1201988990249[/C][C]-7.4201988990249[/C][/ROW]
[ROW][C]23[/C][C]26.8[/C][C]35.2917812556516[/C][C]-8.49178125565158[/C][/ROW]
[ROW][C]24[/C][C]7.3[/C][C]15.0495589936888[/C][C]-7.74955899368884[/C][/ROW]
[ROW][C]25[/C][C]17.1[/C][C]33.5755548589407[/C][C]-16.4755548589407[/C][/ROW]
[ROW][C]26[/C][C]27.3[/C][C]33.5612239467035[/C][C]-6.26122394670354[/C][/ROW]
[ROW][C]27[/C][C]16.5[/C][C]26.7995265537985[/C][C]-10.2995265537985[/C][/ROW]
[ROW][C]28[/C][C]5.4[/C][C]25.7811510389858[/C][C]-20.3811510389858[/C][/ROW]
[ROW][C]29[/C][C]5.6[/C][C]22.8368799897905[/C][C]-17.2368799897905[/C][/ROW]
[ROW][C]30[/C][C]36.5[/C][C]29.1427594494202[/C][C]7.35724055057975[/C][/ROW]
[ROW][C]31[/C][C]1.1[/C][C]15.5829441304030[/C][C]-14.4829441304030[/C][/ROW]
[ROW][C]32[/C][C]3.9[/C][C]14.6689393434146[/C][C]-10.7689393434146[/C][/ROW]
[ROW][C]33[/C][C]34.2[/C][C]9.3871013003414[/C][C]24.8128986996586[/C][/ROW]
[ROW][C]34[/C][C]40.3[/C][C]33.7159476796235[/C][C]6.58405232037649[/C][/ROW]
[ROW][C]35[/C][C]15.6[/C][C]29.1170954490639[/C][C]-13.5170954490639[/C][/ROW]
[ROW][C]36[/C][C]15.5[/C][C]19.6098754645313[/C][C]-4.10987546453131[/C][/ROW]
[ROW][C]37[/C][C]52.9[/C][C]30.4296925527900[/C][C]22.4703074472100[/C][/ROW]
[ROW][C]38[/C][C]1.6[/C][C]4.87007649984242[/C][C]-3.27007649984242[/C][/ROW]
[ROW][C]39[/C][C]14.2[/C][C]21.1435362770482[/C][C]-6.94353627704817[/C][/ROW]
[ROW][C]40[/C][C]7.5[/C][C]17.6708155663567[/C][C]-10.1708155663567[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]29.5851466786395[/C][C]-27.5851466786395[/C][/ROW]
[ROW][C]42[/C][C]71.4[/C][C]39.4981972635404[/C][C]31.9018027364596[/C][/ROW]
[ROW][C]43[/C][C]3.2[/C][C]15.3716636905896[/C][C]-12.1716636905896[/C][/ROW]
[ROW][C]44[/C][C]20[/C][C]24.7408378168471[/C][C]-4.74083781684715[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]24.0712913958655[/C][C]-21.2712913958655[/C][/ROW]
[ROW][C]46[/C][C]15.3[/C][C]40.1582530079107[/C][C]-24.8582530079107[/C][/ROW]
[ROW][C]47[/C][C]8[/C][C]20.3200139585064[/C][C]-12.3200139585064[/C][/ROW]
[ROW][C]48[/C][C]36.6[/C][C]27.4961632674553[/C][C]9.1038367325447[/C][/ROW]
[ROW][C]49[/C][C]3.8[/C][C]25.4395351935509[/C][C]-21.6395351935509[/C][/ROW]
[ROW][C]50[/C][C]25.5[/C][C]19.5765249271649[/C][C]5.92347507283513[/C][/ROW]
[ROW][C]51[/C][C]3.2[/C][C]14.6633204131185[/C][C]-11.4633204131185[/C][/ROW]
[ROW][C]52[/C][C]33.1[/C][C]38.4428511916089[/C][C]-5.34285119160892[/C][/ROW]
[ROW][C]53[/C][C]42[/C][C]20.0139519251865[/C][C]21.9860480748135[/C][/ROW]
[ROW][C]54[/C][C]16.2[/C][C]27.8516669716062[/C][C]-11.6516669716062[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]20.5072486655890[/C][C]-20.5072486655890[/C][/ROW]
[ROW][C]56[/C][C]22.7[/C][C]24.8654318946920[/C][C]-2.16543189469197[/C][/ROW]
[ROW][C]57[/C][C]36.4[/C][C]32.6406717558239[/C][C]3.75932824417615[/C][/ROW]
[ROW][C]58[/C][C]69[/C][C]32.1006822716632[/C][C]36.8993177283368[/C][/ROW]
[ROW][C]59[/C][C]11.2[/C][C]25.8923833325009[/C][C]-14.6923833325009[/C][/ROW]
[ROW][C]60[/C][C]12.5[/C][C]30.1095737412518[/C][C]-17.6095737412518[/C][/ROW]
[ROW][C]61[/C][C]51.7[/C][C]33.7966035970021[/C][C]17.9033964029979[/C][/ROW]
[ROW][C]62[/C][C]3.6[/C][C]21.9650251243135[/C][C]-18.3650251243135[/C][/ROW]
[ROW][C]63[/C][C]22.2[/C][C]20.0412698396101[/C][C]2.15873016038987[/C][/ROW]
[ROW][C]64[/C][C]39.2[/C][C]43.1500785359024[/C][C]-3.95007853590244[/C][/ROW]
[ROW][C]65[/C][C]27.9[/C][C]17.7466249189086[/C][C]10.1533750810914[/C][/ROW]
[ROW][C]66[/C][C]58.8[/C][C]15.8157254826411[/C][C]42.9842745173589[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]20.1531262617394[/C][C]-19.1531262617394[/C][/ROW]
[ROW][C]68[/C][C]4.7[/C][C]25.2501734975915[/C][C]-20.5501734975915[/C][/ROW]
[ROW][C]69[/C][C]25.6[/C][C]26.9505757155681[/C][C]-1.35057571556814[/C][/ROW]
[ROW][C]70[/C][C]5.3[/C][C]31.6531426123836[/C][C]-26.3531426123836[/C][/ROW]
[ROW][C]71[/C][C]38.7[/C][C]10.783255162163[/C][C]27.916744837837[/C][/ROW]
[ROW][C]72[/C][C]31.6[/C][C]28.8965169344773[/C][C]2.70348306552269[/C][/ROW]
[ROW][C]73[/C][C]19.3[/C][C]28.3963486562766[/C][C]-9.09634865627662[/C][/ROW]
[ROW][C]74[/C][C]26.5[/C][C]22.1542550405176[/C][C]4.34574495948237[/C][/ROW]
[ROW][C]75[/C][C]12.8[/C][C]36.5405287835736[/C][C]-23.7405287835736[/C][/ROW]
[ROW][C]76[/C][C]18.3[/C][C]33.7633691279153[/C][C]-15.4633691279153[/C][/ROW]
[ROW][C]77[/C][C]13.2[/C][C]22.9903980620225[/C][C]-9.79039806202254[/C][/ROW]
[ROW][C]78[/C][C]36[/C][C]30.7298373705302[/C][C]5.27016262946979[/C][/ROW]
[ROW][C]79[/C][C]34.1[/C][C]13.8795777525032[/C][C]20.2204222474968[/C][/ROW]
[ROW][C]80[/C][C]71.5[/C][C]30.4932396952792[/C][C]41.0067603047208[/C][/ROW]
[ROW][C]81[/C][C]43.3[/C][C]32.0165780956178[/C][C]11.2834219043822[/C][/ROW]
[ROW][C]82[/C][C]47.7[/C][C]18.8978797877446[/C][C]28.8021202122554[/C][/ROW]
[ROW][C]83[/C][C]74.9[/C][C]35.4304077615275[/C][C]39.4695922384725[/C][/ROW]
[ROW][C]84[/C][C]0.9[/C][C]36.3371953412594[/C][C]-35.4371953412594[/C][/ROW]
[ROW][C]85[/C][C]35.9[/C][C]47.0308872603818[/C][C]-11.1308872603818[/C][/ROW]
[ROW][C]86[/C][C]45.8[/C][C]38.6884167359635[/C][C]7.11158326403653[/C][/ROW]
[ROW][C]87[/C][C]54.2[/C][C]31.5654630079416[/C][C]22.6345369920584[/C][/ROW]
[ROW][C]88[/C][C]34[/C][C]25.5235661000994[/C][C]8.47643389990062[/C][/ROW]
[ROW][C]89[/C][C]7.9[/C][C]16.8767885419135[/C][C]-8.97678854191351[/C][/ROW]
[ROW][C]90[/C][C]54.5[/C][C]36.1378927697611[/C][C]18.3621072302389[/C][/ROW]
[ROW][C]91[/C][C]8.2[/C][C]11.1622461353437[/C][C]-2.96224613534367[/C][/ROW]
[ROW][C]92[/C][C]49.3[/C][C]30.6733589535724[/C][C]18.6266410464276[/C][/ROW]
[ROW][C]93[/C][C]46.9[/C][C]23.2300033689615[/C][C]23.6699966310385[/C][/ROW]
[ROW][C]94[/C][C]16.8[/C][C]27.0837859115440[/C][C]-10.2837859115440[/C][/ROW]
[ROW][C]95[/C][C]2.8[/C][C]23.1627516460991[/C][C]-20.3627516460991[/C][/ROW]
[ROW][C]96[/C][C]60.9[/C][C]24.4179411644735[/C][C]36.4820588355265[/C][/ROW]
[ROW][C]97[/C][C]5.6[/C][C]15.9712350992713[/C][C]-10.3712350992713[/C][/ROW]
[ROW][C]98[/C][C]6.6[/C][C]34.8445670395439[/C][C]-28.2445670395439[/C][/ROW]
[ROW][C]99[/C][C]22.9[/C][C]20.2261336686415[/C][C]2.67386633135845[/C][/ROW]
[ROW][C]100[/C][C]51.1[/C][C]21.1631328701729[/C][C]29.9368671298271[/C][/ROW]
[ROW][C]101[/C][C]23.3[/C][C]41.2526170392053[/C][C]-17.9526170392053[/C][/ROW]
[ROW][C]102[/C][C]11.5[/C][C]35.0131725594171[/C][C]-23.5131725594171[/C][/ROW]
[ROW][C]103[/C][C]79.1[/C][C]23.0356642927225[/C][C]56.0643357072775[/C][/ROW]
[ROW][C]104[/C][C]53.6[/C][C]32.2980157062193[/C][C]21.3019842937807[/C][/ROW]
[ROW][C]105[/C][C]1.5[/C][C]15.0734232042145[/C][C]-13.5734232042145[/C][/ROW]
[ROW][C]106[/C][C]40.4[/C][C]21.5689899800812[/C][C]18.8310100199188[/C][/ROW]
[ROW][C]107[/C][C]25.4[/C][C]32.2693256163309[/C][C]-6.86932561633093[/C][/ROW]
[ROW][C]108[/C][C]6.7[/C][C]38.2135380887029[/C][C]-31.5135380887029[/C][/ROW]
[ROW][C]109[/C][C]76[/C][C]46.5722799595259[/C][C]29.4277200404741[/C][/ROW]
[ROW][C]110[/C][C]0.6[/C][C]22.9104745506[/C][C]-22.3104745506[/C][/ROW]
[ROW][C]111[/C][C]43.4[/C][C]30.6680180576398[/C][C]12.7319819423602[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]20.8128037214666[/C][C]-7.8128037214666[/C][/ROW]
[ROW][C]113[/C][C]27.8[/C][C]16.3192142106791[/C][C]11.4807857893209[/C][/ROW]
[ROW][C]114[/C][C]6.5[/C][C]13.7274381728713[/C][C]-7.22743817287133[/C][/ROW]
[ROW][C]115[/C][C]7.1[/C][C]26.4268286640498[/C][C]-19.3268286640498[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]10.9991332233077[/C][C]-4.99913322330766[/C][/ROW]
[ROW][C]117[/C][C]6.5[/C][C]14.8256813412604[/C][C]-8.32568134126039[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110638.205086766173567.7949132338265
22.229.1124254422055-26.9124254422055
362.331.312303314834630.9876966851654
414.733.4744538960185-18.7744538960185
5531.0628410130609-26.0628410130609
674.434.782609241166339.6173907588337
766.129.238489742808336.8615102571917
82231.0965837321561-9.09658373215608
93.414.6442288398227-11.2442288398227
100.321.6799492471033-21.3799492471033
1153.28.6086995574754444.5913004425246
12011.3930040130462-11.3930040130462
1357.235.350712082841221.8492879171588
149.235.362579942574-26.162579942574
1515.920.8861461429863-4.98614614298629
1617.622.5120585464576-4.91205854645763
172122.7934402843309-1.79344028433093
187.634.4273196623399-26.8273196623399
1971.638.231621837392533.3683781626075
2012.920.7888114911236-7.88881149112362
2110.526.3600447725049-15.8600447725049
2225.733.1201988990249-7.4201988990249
2326.835.2917812556516-8.49178125565158
247.315.0495589936888-7.74955899368884
2517.133.5755548589407-16.4755548589407
2627.333.5612239467035-6.26122394670354
2716.526.7995265537985-10.2995265537985
285.425.7811510389858-20.3811510389858
295.622.8368799897905-17.2368799897905
3036.529.14275944942027.35724055057975
311.115.5829441304030-14.4829441304030
323.914.6689393434146-10.7689393434146
3334.29.387101300341424.8128986996586
3440.333.71594767962356.58405232037649
3515.629.1170954490639-13.5170954490639
3615.519.6098754645313-4.10987546453131
3752.930.429692552790022.4703074472100
381.64.87007649984242-3.27007649984242
3914.221.1435362770482-6.94353627704817
407.517.6708155663567-10.1708155663567
41229.5851466786395-27.5851466786395
4271.439.498197263540431.9018027364596
433.215.3716636905896-12.1716636905896
442024.7408378168471-4.74083781684715
452.824.0712913958655-21.2712913958655
4615.340.1582530079107-24.8582530079107
47820.3200139585064-12.3200139585064
4836.627.49616326745539.1038367325447
493.825.4395351935509-21.6395351935509
5025.519.57652492716495.92347507283513
513.214.6633204131185-11.4633204131185
5233.138.4428511916089-5.34285119160892
534220.013951925186521.9860480748135
5416.227.8516669716062-11.6516669716062
55020.5072486655890-20.5072486655890
5622.724.8654318946920-2.16543189469197
5736.432.64067175582393.75932824417615
586932.100682271663236.8993177283368
5911.225.8923833325009-14.6923833325009
6012.530.1095737412518-17.6095737412518
6151.733.796603597002117.9033964029979
623.621.9650251243135-18.3650251243135
6322.220.04126983961012.15873016038987
6439.243.1500785359024-3.95007853590244
6527.917.746624918908610.1533750810914
6658.815.815725482641142.9842745173589
67120.1531262617394-19.1531262617394
684.725.2501734975915-20.5501734975915
6925.626.9505757155681-1.35057571556814
705.331.6531426123836-26.3531426123836
7138.710.78325516216327.916744837837
7231.628.89651693447732.70348306552269
7319.328.3963486562766-9.09634865627662
7426.522.15425504051764.34574495948237
7512.836.5405287835736-23.7405287835736
7618.333.7633691279153-15.4633691279153
7713.222.9903980620225-9.79039806202254
783630.72983737053025.27016262946979
7934.113.879577752503220.2204222474968
8071.530.493239695279241.0067603047208
8143.332.016578095617811.2834219043822
8247.718.897879787744628.8021202122554
8374.935.430407761527539.4695922384725
840.936.3371953412594-35.4371953412594
8535.947.0308872603818-11.1308872603818
8645.838.68841673596357.11158326403653
8754.231.565463007941622.6345369920584
883425.52356610009948.47643389990062
897.916.8767885419135-8.97678854191351
9054.536.137892769761118.3621072302389
918.211.1622461353437-2.96224613534367
9249.330.673358953572418.6266410464276
9346.923.230003368961523.6699966310385
9416.827.0837859115440-10.2837859115440
952.823.1627516460991-20.3627516460991
9660.924.417941164473536.4820588355265
975.615.9712350992713-10.3712350992713
986.634.8445670395439-28.2445670395439
9922.920.22613366864152.67386633135845
10051.121.163132870172929.9368671298271
10123.341.2526170392053-17.9526170392053
10211.535.0131725594171-23.5131725594171
10379.123.035664292722556.0643357072775
10453.632.298015706219321.3019842937807
1051.515.0734232042145-13.5734232042145
10640.421.568989980081218.8310100199188
10725.432.2693256163309-6.86932561633093
1086.738.2135380887029-31.5135380887029
1097646.572279959525929.4277200404741
1100.622.9104745506-22.3104745506
11143.430.668018057639812.7319819423602
1121320.8128037214666-7.8128037214666
11327.816.319214210679111.4807857893209
1146.513.7274381728713-7.22743817287133
1157.126.4268286640498-19.3268286640498
116610.9991332233077-4.99913322330766
1176.514.8256813412604-8.32568134126039






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8622349919992030.2755300160015940.137765008000797
90.7873390110136640.4253219779726730.212660988986336
100.6817775426588840.6364449146822320.318222457341116
110.978632314691060.04273537061788140.0213676853089407
120.9640280322815730.07194393543685480.0359719677184274
130.9615333751342360.07693324973152890.0384666248657645
140.9446293300403980.1107413399192050.0553706699596024
150.9146362582948150.1707274834103710.0853637417051854
160.9545313580883720.0909372838232570.0454686419116285
170.9343328529178230.1313342941643530.0656671470821766
180.9343949656642840.1312100686714330.0656050343357163
190.9578525118714460.08429497625710830.0421474881285542
200.9414408336275180.1171183327449640.0585591663724820
210.9702323273670220.05953534526595690.0297676726329784
220.9648547056503230.07029058869935470.0351452943496773
230.979106900447650.04178619910469920.0208930995523496
240.969770572297730.0604588554045390.0302294277022695
250.9602273303525870.07954533929482690.0397726696474134
260.9441196584500460.1117606830999070.0558803415499537
270.9260818434490170.1478363131019660.0739181565509829
280.9462340761685420.1075318476629150.0537659238314577
290.9337204833667190.1325590332665620.0662795166332812
300.9149595709685710.1700808580628580.0850404290314289
310.9026190721558240.1947618556883510.0973809278441757
320.8769336562317530.2461326875364930.123066343768247
330.9210283839656440.1579432320687130.0789716160343564
340.8987374724686650.2025250550626710.101262527531335
350.8793441977051430.2413116045897140.120655802294857
360.849818247184610.3003635056307780.150181752815389
370.8413530990673470.3172938018653070.158646900932653
380.8042282185279010.3915435629441980.195771781472099
390.765110241687630.4697795166247410.234889758312371
400.7278809042233980.5442381915532040.272119095776602
410.752167370001990.4956652599960210.247832629998011
420.7973610677495870.4052778645008250.202638932250412
430.7697836611456040.4604326777087920.230216338854396
440.7301682443275860.5396635113448290.269831755672414
450.7219708896950190.5560582206099620.278029110304981
460.743155584306750.51368883138650.25684441569325
470.7140284637885510.5719430724228970.285971536211448
480.6770036040210660.6459927919578680.322996395978934
490.6831586486153710.6336827027692570.316841351384629
500.6356069912365870.7287860175268260.364393008763413
510.5988578969098140.8022842061803720.401142103090186
520.5484342085502990.9031315828994020.451565791449701
530.545927871483970.908144257032060.45407212851603
540.5095266902882650.980946619423470.490473309711735
550.5090735690888140.9818528618223730.490926430911187
560.4567116016149610.9134232032299220.543288398385039
570.4042020996565030.8084041993130060.595797900343497
580.5029373059424020.9941253881151970.497062694057598
590.4819014126201350.963802825240270.518098587379865
600.4722317229706520.9444634459413040.527768277029348
610.4525838693614590.9051677387229180.547416130638541
620.4441167291109720.8882334582219440.555883270889028
630.4058343745643160.8116687491286330.594165625435684
640.3572420739630870.7144841479261750.642757926036913
650.3214013230502070.6428026461004140.678598676949793
660.4532365374251050.906473074850210.546763462574895
670.4495309872058680.8990619744117360.550469012794132
680.4671233779575820.9342467559151640.532876622042418
690.4129605554733280.8259211109466550.587039444526672
700.433812858194880.867625716389760.56618714180512
710.4550531139497960.9101062278995930.544946886050204
720.3997222314978420.7994444629956840.600277768502158
730.3558103179986030.7116206359972060.644189682001397
740.3051734627514100.6103469255028190.69482653724859
750.3058760529062130.6117521058124270.694123947093787
760.2855313004612590.5710626009225180.714468699538741
770.2425512212084850.4851024424169710.757448778791515
780.201772071374450.40354414274890.79822792862555
790.1895305745132000.3790611490263990.8104694254868
800.2843346298850410.5686692597700820.715665370114959
810.2450802220366360.4901604440732720.754919777963364
820.2540175961880380.5080351923760770.745982403811962
830.3553865518170940.7107731036341890.644613448182906
840.4611448912979710.9222897825959420.538855108702029
850.4161674151946460.8323348303892920.583832584805354
860.3594706862844520.7189413725689040.640529313715548
870.3508260602021940.7016521204043880.649173939797806
880.2984440143248570.5968880286497140.701555985675143
890.2580746610533810.5161493221067610.74192533894662
900.2434090439762590.4868180879525180.756590956023741
910.1952699191650920.3905398383301840.804730080834908
920.1712074195841050.3424148391682100.828792580415895
930.1690359756560480.3380719513120960.830964024343952
940.1343044171550740.2686088343101490.865695582844926
950.1307790327906590.2615580655813180.86922096720934
960.2140137049962880.4280274099925760.785986295003712
970.1779072736996780.3558145473993570.822092726300322
980.1918221971373840.3836443942747680.808177802862616
990.1443547299406130.2887094598812270.855645270059387
1000.1687678961452010.3375357922904020.831232103854799
1010.2306206924403670.4612413848807340.769379307559633
1020.2828329059374960.5656658118749920.717167094062504
1030.7019220772633870.5961558454732250.298077922736613
1040.7114726470152940.5770547059694130.288527352984706
1050.6086752738429690.7826494523140620.391324726157031
1060.894161389820040.2116772203599190.105838610179960
1070.9024162264764630.1951675470470740.0975837735235371
1080.8976952515945040.2046094968109910.102304748405496
1090.9248997178752190.1502005642495620.0751002821247811

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.862234991999203 & 0.275530016001594 & 0.137765008000797 \tabularnewline
9 & 0.787339011013664 & 0.425321977972673 & 0.212660988986336 \tabularnewline
10 & 0.681777542658884 & 0.636444914682232 & 0.318222457341116 \tabularnewline
11 & 0.97863231469106 & 0.0427353706178814 & 0.0213676853089407 \tabularnewline
12 & 0.964028032281573 & 0.0719439354368548 & 0.0359719677184274 \tabularnewline
13 & 0.961533375134236 & 0.0769332497315289 & 0.0384666248657645 \tabularnewline
14 & 0.944629330040398 & 0.110741339919205 & 0.0553706699596024 \tabularnewline
15 & 0.914636258294815 & 0.170727483410371 & 0.0853637417051854 \tabularnewline
16 & 0.954531358088372 & 0.090937283823257 & 0.0454686419116285 \tabularnewline
17 & 0.934332852917823 & 0.131334294164353 & 0.0656671470821766 \tabularnewline
18 & 0.934394965664284 & 0.131210068671433 & 0.0656050343357163 \tabularnewline
19 & 0.957852511871446 & 0.0842949762571083 & 0.0421474881285542 \tabularnewline
20 & 0.941440833627518 & 0.117118332744964 & 0.0585591663724820 \tabularnewline
21 & 0.970232327367022 & 0.0595353452659569 & 0.0297676726329784 \tabularnewline
22 & 0.964854705650323 & 0.0702905886993547 & 0.0351452943496773 \tabularnewline
23 & 0.97910690044765 & 0.0417861991046992 & 0.0208930995523496 \tabularnewline
24 & 0.96977057229773 & 0.060458855404539 & 0.0302294277022695 \tabularnewline
25 & 0.960227330352587 & 0.0795453392948269 & 0.0397726696474134 \tabularnewline
26 & 0.944119658450046 & 0.111760683099907 & 0.0558803415499537 \tabularnewline
27 & 0.926081843449017 & 0.147836313101966 & 0.0739181565509829 \tabularnewline
28 & 0.946234076168542 & 0.107531847662915 & 0.0537659238314577 \tabularnewline
29 & 0.933720483366719 & 0.132559033266562 & 0.0662795166332812 \tabularnewline
30 & 0.914959570968571 & 0.170080858062858 & 0.0850404290314289 \tabularnewline
31 & 0.902619072155824 & 0.194761855688351 & 0.0973809278441757 \tabularnewline
32 & 0.876933656231753 & 0.246132687536493 & 0.123066343768247 \tabularnewline
33 & 0.921028383965644 & 0.157943232068713 & 0.0789716160343564 \tabularnewline
34 & 0.898737472468665 & 0.202525055062671 & 0.101262527531335 \tabularnewline
35 & 0.879344197705143 & 0.241311604589714 & 0.120655802294857 \tabularnewline
36 & 0.84981824718461 & 0.300363505630778 & 0.150181752815389 \tabularnewline
37 & 0.841353099067347 & 0.317293801865307 & 0.158646900932653 \tabularnewline
38 & 0.804228218527901 & 0.391543562944198 & 0.195771781472099 \tabularnewline
39 & 0.76511024168763 & 0.469779516624741 & 0.234889758312371 \tabularnewline
40 & 0.727880904223398 & 0.544238191553204 & 0.272119095776602 \tabularnewline
41 & 0.75216737000199 & 0.495665259996021 & 0.247832629998011 \tabularnewline
42 & 0.797361067749587 & 0.405277864500825 & 0.202638932250412 \tabularnewline
43 & 0.769783661145604 & 0.460432677708792 & 0.230216338854396 \tabularnewline
44 & 0.730168244327586 & 0.539663511344829 & 0.269831755672414 \tabularnewline
45 & 0.721970889695019 & 0.556058220609962 & 0.278029110304981 \tabularnewline
46 & 0.74315558430675 & 0.5136888313865 & 0.25684441569325 \tabularnewline
47 & 0.714028463788551 & 0.571943072422897 & 0.285971536211448 \tabularnewline
48 & 0.677003604021066 & 0.645992791957868 & 0.322996395978934 \tabularnewline
49 & 0.683158648615371 & 0.633682702769257 & 0.316841351384629 \tabularnewline
50 & 0.635606991236587 & 0.728786017526826 & 0.364393008763413 \tabularnewline
51 & 0.598857896909814 & 0.802284206180372 & 0.401142103090186 \tabularnewline
52 & 0.548434208550299 & 0.903131582899402 & 0.451565791449701 \tabularnewline
53 & 0.54592787148397 & 0.90814425703206 & 0.45407212851603 \tabularnewline
54 & 0.509526690288265 & 0.98094661942347 & 0.490473309711735 \tabularnewline
55 & 0.509073569088814 & 0.981852861822373 & 0.490926430911187 \tabularnewline
56 & 0.456711601614961 & 0.913423203229922 & 0.543288398385039 \tabularnewline
57 & 0.404202099656503 & 0.808404199313006 & 0.595797900343497 \tabularnewline
58 & 0.502937305942402 & 0.994125388115197 & 0.497062694057598 \tabularnewline
59 & 0.481901412620135 & 0.96380282524027 & 0.518098587379865 \tabularnewline
60 & 0.472231722970652 & 0.944463445941304 & 0.527768277029348 \tabularnewline
61 & 0.452583869361459 & 0.905167738722918 & 0.547416130638541 \tabularnewline
62 & 0.444116729110972 & 0.888233458221944 & 0.555883270889028 \tabularnewline
63 & 0.405834374564316 & 0.811668749128633 & 0.594165625435684 \tabularnewline
64 & 0.357242073963087 & 0.714484147926175 & 0.642757926036913 \tabularnewline
65 & 0.321401323050207 & 0.642802646100414 & 0.678598676949793 \tabularnewline
66 & 0.453236537425105 & 0.90647307485021 & 0.546763462574895 \tabularnewline
67 & 0.449530987205868 & 0.899061974411736 & 0.550469012794132 \tabularnewline
68 & 0.467123377957582 & 0.934246755915164 & 0.532876622042418 \tabularnewline
69 & 0.412960555473328 & 0.825921110946655 & 0.587039444526672 \tabularnewline
70 & 0.43381285819488 & 0.86762571638976 & 0.56618714180512 \tabularnewline
71 & 0.455053113949796 & 0.910106227899593 & 0.544946886050204 \tabularnewline
72 & 0.399722231497842 & 0.799444462995684 & 0.600277768502158 \tabularnewline
73 & 0.355810317998603 & 0.711620635997206 & 0.644189682001397 \tabularnewline
74 & 0.305173462751410 & 0.610346925502819 & 0.69482653724859 \tabularnewline
75 & 0.305876052906213 & 0.611752105812427 & 0.694123947093787 \tabularnewline
76 & 0.285531300461259 & 0.571062600922518 & 0.714468699538741 \tabularnewline
77 & 0.242551221208485 & 0.485102442416971 & 0.757448778791515 \tabularnewline
78 & 0.20177207137445 & 0.4035441427489 & 0.79822792862555 \tabularnewline
79 & 0.189530574513200 & 0.379061149026399 & 0.8104694254868 \tabularnewline
80 & 0.284334629885041 & 0.568669259770082 & 0.715665370114959 \tabularnewline
81 & 0.245080222036636 & 0.490160444073272 & 0.754919777963364 \tabularnewline
82 & 0.254017596188038 & 0.508035192376077 & 0.745982403811962 \tabularnewline
83 & 0.355386551817094 & 0.710773103634189 & 0.644613448182906 \tabularnewline
84 & 0.461144891297971 & 0.922289782595942 & 0.538855108702029 \tabularnewline
85 & 0.416167415194646 & 0.832334830389292 & 0.583832584805354 \tabularnewline
86 & 0.359470686284452 & 0.718941372568904 & 0.640529313715548 \tabularnewline
87 & 0.350826060202194 & 0.701652120404388 & 0.649173939797806 \tabularnewline
88 & 0.298444014324857 & 0.596888028649714 & 0.701555985675143 \tabularnewline
89 & 0.258074661053381 & 0.516149322106761 & 0.74192533894662 \tabularnewline
90 & 0.243409043976259 & 0.486818087952518 & 0.756590956023741 \tabularnewline
91 & 0.195269919165092 & 0.390539838330184 & 0.804730080834908 \tabularnewline
92 & 0.171207419584105 & 0.342414839168210 & 0.828792580415895 \tabularnewline
93 & 0.169035975656048 & 0.338071951312096 & 0.830964024343952 \tabularnewline
94 & 0.134304417155074 & 0.268608834310149 & 0.865695582844926 \tabularnewline
95 & 0.130779032790659 & 0.261558065581318 & 0.86922096720934 \tabularnewline
96 & 0.214013704996288 & 0.428027409992576 & 0.785986295003712 \tabularnewline
97 & 0.177907273699678 & 0.355814547399357 & 0.822092726300322 \tabularnewline
98 & 0.191822197137384 & 0.383644394274768 & 0.808177802862616 \tabularnewline
99 & 0.144354729940613 & 0.288709459881227 & 0.855645270059387 \tabularnewline
100 & 0.168767896145201 & 0.337535792290402 & 0.831232103854799 \tabularnewline
101 & 0.230620692440367 & 0.461241384880734 & 0.769379307559633 \tabularnewline
102 & 0.282832905937496 & 0.565665811874992 & 0.717167094062504 \tabularnewline
103 & 0.701922077263387 & 0.596155845473225 & 0.298077922736613 \tabularnewline
104 & 0.711472647015294 & 0.577054705969413 & 0.288527352984706 \tabularnewline
105 & 0.608675273842969 & 0.782649452314062 & 0.391324726157031 \tabularnewline
106 & 0.89416138982004 & 0.211677220359919 & 0.105838610179960 \tabularnewline
107 & 0.902416226476463 & 0.195167547047074 & 0.0975837735235371 \tabularnewline
108 & 0.897695251594504 & 0.204609496810991 & 0.102304748405496 \tabularnewline
109 & 0.924899717875219 & 0.150200564249562 & 0.0751002821247811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&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]0.862234991999203[/C][C]0.275530016001594[/C][C]0.137765008000797[/C][/ROW]
[ROW][C]9[/C][C]0.787339011013664[/C][C]0.425321977972673[/C][C]0.212660988986336[/C][/ROW]
[ROW][C]10[/C][C]0.681777542658884[/C][C]0.636444914682232[/C][C]0.318222457341116[/C][/ROW]
[ROW][C]11[/C][C]0.97863231469106[/C][C]0.0427353706178814[/C][C]0.0213676853089407[/C][/ROW]
[ROW][C]12[/C][C]0.964028032281573[/C][C]0.0719439354368548[/C][C]0.0359719677184274[/C][/ROW]
[ROW][C]13[/C][C]0.961533375134236[/C][C]0.0769332497315289[/C][C]0.0384666248657645[/C][/ROW]
[ROW][C]14[/C][C]0.944629330040398[/C][C]0.110741339919205[/C][C]0.0553706699596024[/C][/ROW]
[ROW][C]15[/C][C]0.914636258294815[/C][C]0.170727483410371[/C][C]0.0853637417051854[/C][/ROW]
[ROW][C]16[/C][C]0.954531358088372[/C][C]0.090937283823257[/C][C]0.0454686419116285[/C][/ROW]
[ROW][C]17[/C][C]0.934332852917823[/C][C]0.131334294164353[/C][C]0.0656671470821766[/C][/ROW]
[ROW][C]18[/C][C]0.934394965664284[/C][C]0.131210068671433[/C][C]0.0656050343357163[/C][/ROW]
[ROW][C]19[/C][C]0.957852511871446[/C][C]0.0842949762571083[/C][C]0.0421474881285542[/C][/ROW]
[ROW][C]20[/C][C]0.941440833627518[/C][C]0.117118332744964[/C][C]0.0585591663724820[/C][/ROW]
[ROW][C]21[/C][C]0.970232327367022[/C][C]0.0595353452659569[/C][C]0.0297676726329784[/C][/ROW]
[ROW][C]22[/C][C]0.964854705650323[/C][C]0.0702905886993547[/C][C]0.0351452943496773[/C][/ROW]
[ROW][C]23[/C][C]0.97910690044765[/C][C]0.0417861991046992[/C][C]0.0208930995523496[/C][/ROW]
[ROW][C]24[/C][C]0.96977057229773[/C][C]0.060458855404539[/C][C]0.0302294277022695[/C][/ROW]
[ROW][C]25[/C][C]0.960227330352587[/C][C]0.0795453392948269[/C][C]0.0397726696474134[/C][/ROW]
[ROW][C]26[/C][C]0.944119658450046[/C][C]0.111760683099907[/C][C]0.0558803415499537[/C][/ROW]
[ROW][C]27[/C][C]0.926081843449017[/C][C]0.147836313101966[/C][C]0.0739181565509829[/C][/ROW]
[ROW][C]28[/C][C]0.946234076168542[/C][C]0.107531847662915[/C][C]0.0537659238314577[/C][/ROW]
[ROW][C]29[/C][C]0.933720483366719[/C][C]0.132559033266562[/C][C]0.0662795166332812[/C][/ROW]
[ROW][C]30[/C][C]0.914959570968571[/C][C]0.170080858062858[/C][C]0.0850404290314289[/C][/ROW]
[ROW][C]31[/C][C]0.902619072155824[/C][C]0.194761855688351[/C][C]0.0973809278441757[/C][/ROW]
[ROW][C]32[/C][C]0.876933656231753[/C][C]0.246132687536493[/C][C]0.123066343768247[/C][/ROW]
[ROW][C]33[/C][C]0.921028383965644[/C][C]0.157943232068713[/C][C]0.0789716160343564[/C][/ROW]
[ROW][C]34[/C][C]0.898737472468665[/C][C]0.202525055062671[/C][C]0.101262527531335[/C][/ROW]
[ROW][C]35[/C][C]0.879344197705143[/C][C]0.241311604589714[/C][C]0.120655802294857[/C][/ROW]
[ROW][C]36[/C][C]0.84981824718461[/C][C]0.300363505630778[/C][C]0.150181752815389[/C][/ROW]
[ROW][C]37[/C][C]0.841353099067347[/C][C]0.317293801865307[/C][C]0.158646900932653[/C][/ROW]
[ROW][C]38[/C][C]0.804228218527901[/C][C]0.391543562944198[/C][C]0.195771781472099[/C][/ROW]
[ROW][C]39[/C][C]0.76511024168763[/C][C]0.469779516624741[/C][C]0.234889758312371[/C][/ROW]
[ROW][C]40[/C][C]0.727880904223398[/C][C]0.544238191553204[/C][C]0.272119095776602[/C][/ROW]
[ROW][C]41[/C][C]0.75216737000199[/C][C]0.495665259996021[/C][C]0.247832629998011[/C][/ROW]
[ROW][C]42[/C][C]0.797361067749587[/C][C]0.405277864500825[/C][C]0.202638932250412[/C][/ROW]
[ROW][C]43[/C][C]0.769783661145604[/C][C]0.460432677708792[/C][C]0.230216338854396[/C][/ROW]
[ROW][C]44[/C][C]0.730168244327586[/C][C]0.539663511344829[/C][C]0.269831755672414[/C][/ROW]
[ROW][C]45[/C][C]0.721970889695019[/C][C]0.556058220609962[/C][C]0.278029110304981[/C][/ROW]
[ROW][C]46[/C][C]0.74315558430675[/C][C]0.5136888313865[/C][C]0.25684441569325[/C][/ROW]
[ROW][C]47[/C][C]0.714028463788551[/C][C]0.571943072422897[/C][C]0.285971536211448[/C][/ROW]
[ROW][C]48[/C][C]0.677003604021066[/C][C]0.645992791957868[/C][C]0.322996395978934[/C][/ROW]
[ROW][C]49[/C][C]0.683158648615371[/C][C]0.633682702769257[/C][C]0.316841351384629[/C][/ROW]
[ROW][C]50[/C][C]0.635606991236587[/C][C]0.728786017526826[/C][C]0.364393008763413[/C][/ROW]
[ROW][C]51[/C][C]0.598857896909814[/C][C]0.802284206180372[/C][C]0.401142103090186[/C][/ROW]
[ROW][C]52[/C][C]0.548434208550299[/C][C]0.903131582899402[/C][C]0.451565791449701[/C][/ROW]
[ROW][C]53[/C][C]0.54592787148397[/C][C]0.90814425703206[/C][C]0.45407212851603[/C][/ROW]
[ROW][C]54[/C][C]0.509526690288265[/C][C]0.98094661942347[/C][C]0.490473309711735[/C][/ROW]
[ROW][C]55[/C][C]0.509073569088814[/C][C]0.981852861822373[/C][C]0.490926430911187[/C][/ROW]
[ROW][C]56[/C][C]0.456711601614961[/C][C]0.913423203229922[/C][C]0.543288398385039[/C][/ROW]
[ROW][C]57[/C][C]0.404202099656503[/C][C]0.808404199313006[/C][C]0.595797900343497[/C][/ROW]
[ROW][C]58[/C][C]0.502937305942402[/C][C]0.994125388115197[/C][C]0.497062694057598[/C][/ROW]
[ROW][C]59[/C][C]0.481901412620135[/C][C]0.96380282524027[/C][C]0.518098587379865[/C][/ROW]
[ROW][C]60[/C][C]0.472231722970652[/C][C]0.944463445941304[/C][C]0.527768277029348[/C][/ROW]
[ROW][C]61[/C][C]0.452583869361459[/C][C]0.905167738722918[/C][C]0.547416130638541[/C][/ROW]
[ROW][C]62[/C][C]0.444116729110972[/C][C]0.888233458221944[/C][C]0.555883270889028[/C][/ROW]
[ROW][C]63[/C][C]0.405834374564316[/C][C]0.811668749128633[/C][C]0.594165625435684[/C][/ROW]
[ROW][C]64[/C][C]0.357242073963087[/C][C]0.714484147926175[/C][C]0.642757926036913[/C][/ROW]
[ROW][C]65[/C][C]0.321401323050207[/C][C]0.642802646100414[/C][C]0.678598676949793[/C][/ROW]
[ROW][C]66[/C][C]0.453236537425105[/C][C]0.90647307485021[/C][C]0.546763462574895[/C][/ROW]
[ROW][C]67[/C][C]0.449530987205868[/C][C]0.899061974411736[/C][C]0.550469012794132[/C][/ROW]
[ROW][C]68[/C][C]0.467123377957582[/C][C]0.934246755915164[/C][C]0.532876622042418[/C][/ROW]
[ROW][C]69[/C][C]0.412960555473328[/C][C]0.825921110946655[/C][C]0.587039444526672[/C][/ROW]
[ROW][C]70[/C][C]0.43381285819488[/C][C]0.86762571638976[/C][C]0.56618714180512[/C][/ROW]
[ROW][C]71[/C][C]0.455053113949796[/C][C]0.910106227899593[/C][C]0.544946886050204[/C][/ROW]
[ROW][C]72[/C][C]0.399722231497842[/C][C]0.799444462995684[/C][C]0.600277768502158[/C][/ROW]
[ROW][C]73[/C][C]0.355810317998603[/C][C]0.711620635997206[/C][C]0.644189682001397[/C][/ROW]
[ROW][C]74[/C][C]0.305173462751410[/C][C]0.610346925502819[/C][C]0.69482653724859[/C][/ROW]
[ROW][C]75[/C][C]0.305876052906213[/C][C]0.611752105812427[/C][C]0.694123947093787[/C][/ROW]
[ROW][C]76[/C][C]0.285531300461259[/C][C]0.571062600922518[/C][C]0.714468699538741[/C][/ROW]
[ROW][C]77[/C][C]0.242551221208485[/C][C]0.485102442416971[/C][C]0.757448778791515[/C][/ROW]
[ROW][C]78[/C][C]0.20177207137445[/C][C]0.4035441427489[/C][C]0.79822792862555[/C][/ROW]
[ROW][C]79[/C][C]0.189530574513200[/C][C]0.379061149026399[/C][C]0.8104694254868[/C][/ROW]
[ROW][C]80[/C][C]0.284334629885041[/C][C]0.568669259770082[/C][C]0.715665370114959[/C][/ROW]
[ROW][C]81[/C][C]0.245080222036636[/C][C]0.490160444073272[/C][C]0.754919777963364[/C][/ROW]
[ROW][C]82[/C][C]0.254017596188038[/C][C]0.508035192376077[/C][C]0.745982403811962[/C][/ROW]
[ROW][C]83[/C][C]0.355386551817094[/C][C]0.710773103634189[/C][C]0.644613448182906[/C][/ROW]
[ROW][C]84[/C][C]0.461144891297971[/C][C]0.922289782595942[/C][C]0.538855108702029[/C][/ROW]
[ROW][C]85[/C][C]0.416167415194646[/C][C]0.832334830389292[/C][C]0.583832584805354[/C][/ROW]
[ROW][C]86[/C][C]0.359470686284452[/C][C]0.718941372568904[/C][C]0.640529313715548[/C][/ROW]
[ROW][C]87[/C][C]0.350826060202194[/C][C]0.701652120404388[/C][C]0.649173939797806[/C][/ROW]
[ROW][C]88[/C][C]0.298444014324857[/C][C]0.596888028649714[/C][C]0.701555985675143[/C][/ROW]
[ROW][C]89[/C][C]0.258074661053381[/C][C]0.516149322106761[/C][C]0.74192533894662[/C][/ROW]
[ROW][C]90[/C][C]0.243409043976259[/C][C]0.486818087952518[/C][C]0.756590956023741[/C][/ROW]
[ROW][C]91[/C][C]0.195269919165092[/C][C]0.390539838330184[/C][C]0.804730080834908[/C][/ROW]
[ROW][C]92[/C][C]0.171207419584105[/C][C]0.342414839168210[/C][C]0.828792580415895[/C][/ROW]
[ROW][C]93[/C][C]0.169035975656048[/C][C]0.338071951312096[/C][C]0.830964024343952[/C][/ROW]
[ROW][C]94[/C][C]0.134304417155074[/C][C]0.268608834310149[/C][C]0.865695582844926[/C][/ROW]
[ROW][C]95[/C][C]0.130779032790659[/C][C]0.261558065581318[/C][C]0.86922096720934[/C][/ROW]
[ROW][C]96[/C][C]0.214013704996288[/C][C]0.428027409992576[/C][C]0.785986295003712[/C][/ROW]
[ROW][C]97[/C][C]0.177907273699678[/C][C]0.355814547399357[/C][C]0.822092726300322[/C][/ROW]
[ROW][C]98[/C][C]0.191822197137384[/C][C]0.383644394274768[/C][C]0.808177802862616[/C][/ROW]
[ROW][C]99[/C][C]0.144354729940613[/C][C]0.288709459881227[/C][C]0.855645270059387[/C][/ROW]
[ROW][C]100[/C][C]0.168767896145201[/C][C]0.337535792290402[/C][C]0.831232103854799[/C][/ROW]
[ROW][C]101[/C][C]0.230620692440367[/C][C]0.461241384880734[/C][C]0.769379307559633[/C][/ROW]
[ROW][C]102[/C][C]0.282832905937496[/C][C]0.565665811874992[/C][C]0.717167094062504[/C][/ROW]
[ROW][C]103[/C][C]0.701922077263387[/C][C]0.596155845473225[/C][C]0.298077922736613[/C][/ROW]
[ROW][C]104[/C][C]0.711472647015294[/C][C]0.577054705969413[/C][C]0.288527352984706[/C][/ROW]
[ROW][C]105[/C][C]0.608675273842969[/C][C]0.782649452314062[/C][C]0.391324726157031[/C][/ROW]
[ROW][C]106[/C][C]0.89416138982004[/C][C]0.211677220359919[/C][C]0.105838610179960[/C][/ROW]
[ROW][C]107[/C][C]0.902416226476463[/C][C]0.195167547047074[/C][C]0.0975837735235371[/C][/ROW]
[ROW][C]108[/C][C]0.897695251594504[/C][C]0.204609496810991[/C][C]0.102304748405496[/C][/ROW]
[ROW][C]109[/C][C]0.924899717875219[/C][C]0.150200564249562[/C][C]0.0751002821247811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8622349919992030.2755300160015940.137765008000797
90.7873390110136640.4253219779726730.212660988986336
100.6817775426588840.6364449146822320.318222457341116
110.978632314691060.04273537061788140.0213676853089407
120.9640280322815730.07194393543685480.0359719677184274
130.9615333751342360.07693324973152890.0384666248657645
140.9446293300403980.1107413399192050.0553706699596024
150.9146362582948150.1707274834103710.0853637417051854
160.9545313580883720.0909372838232570.0454686419116285
170.9343328529178230.1313342941643530.0656671470821766
180.9343949656642840.1312100686714330.0656050343357163
190.9578525118714460.08429497625710830.0421474881285542
200.9414408336275180.1171183327449640.0585591663724820
210.9702323273670220.05953534526595690.0297676726329784
220.9648547056503230.07029058869935470.0351452943496773
230.979106900447650.04178619910469920.0208930995523496
240.969770572297730.0604588554045390.0302294277022695
250.9602273303525870.07954533929482690.0397726696474134
260.9441196584500460.1117606830999070.0558803415499537
270.9260818434490170.1478363131019660.0739181565509829
280.9462340761685420.1075318476629150.0537659238314577
290.9337204833667190.1325590332665620.0662795166332812
300.9149595709685710.1700808580628580.0850404290314289
310.9026190721558240.1947618556883510.0973809278441757
320.8769336562317530.2461326875364930.123066343768247
330.9210283839656440.1579432320687130.0789716160343564
340.8987374724686650.2025250550626710.101262527531335
350.8793441977051430.2413116045897140.120655802294857
360.849818247184610.3003635056307780.150181752815389
370.8413530990673470.3172938018653070.158646900932653
380.8042282185279010.3915435629441980.195771781472099
390.765110241687630.4697795166247410.234889758312371
400.7278809042233980.5442381915532040.272119095776602
410.752167370001990.4956652599960210.247832629998011
420.7973610677495870.4052778645008250.202638932250412
430.7697836611456040.4604326777087920.230216338854396
440.7301682443275860.5396635113448290.269831755672414
450.7219708896950190.5560582206099620.278029110304981
460.743155584306750.51368883138650.25684441569325
470.7140284637885510.5719430724228970.285971536211448
480.6770036040210660.6459927919578680.322996395978934
490.6831586486153710.6336827027692570.316841351384629
500.6356069912365870.7287860175268260.364393008763413
510.5988578969098140.8022842061803720.401142103090186
520.5484342085502990.9031315828994020.451565791449701
530.545927871483970.908144257032060.45407212851603
540.5095266902882650.980946619423470.490473309711735
550.5090735690888140.9818528618223730.490926430911187
560.4567116016149610.9134232032299220.543288398385039
570.4042020996565030.8084041993130060.595797900343497
580.5029373059424020.9941253881151970.497062694057598
590.4819014126201350.963802825240270.518098587379865
600.4722317229706520.9444634459413040.527768277029348
610.4525838693614590.9051677387229180.547416130638541
620.4441167291109720.8882334582219440.555883270889028
630.4058343745643160.8116687491286330.594165625435684
640.3572420739630870.7144841479261750.642757926036913
650.3214013230502070.6428026461004140.678598676949793
660.4532365374251050.906473074850210.546763462574895
670.4495309872058680.8990619744117360.550469012794132
680.4671233779575820.9342467559151640.532876622042418
690.4129605554733280.8259211109466550.587039444526672
700.433812858194880.867625716389760.56618714180512
710.4550531139497960.9101062278995930.544946886050204
720.3997222314978420.7994444629956840.600277768502158
730.3558103179986030.7116206359972060.644189682001397
740.3051734627514100.6103469255028190.69482653724859
750.3058760529062130.6117521058124270.694123947093787
760.2855313004612590.5710626009225180.714468699538741
770.2425512212084850.4851024424169710.757448778791515
780.201772071374450.40354414274890.79822792862555
790.1895305745132000.3790611490263990.8104694254868
800.2843346298850410.5686692597700820.715665370114959
810.2450802220366360.4901604440732720.754919777963364
820.2540175961880380.5080351923760770.745982403811962
830.3553865518170940.7107731036341890.644613448182906
840.4611448912979710.9222897825959420.538855108702029
850.4161674151946460.8323348303892920.583832584805354
860.3594706862844520.7189413725689040.640529313715548
870.3508260602021940.7016521204043880.649173939797806
880.2984440143248570.5968880286497140.701555985675143
890.2580746610533810.5161493221067610.74192533894662
900.2434090439762590.4868180879525180.756590956023741
910.1952699191650920.3905398383301840.804730080834908
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940.1343044171550740.2686088343101490.865695582844926
950.1307790327906590.2615580655813180.86922096720934
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970.1779072736996780.3558145473993570.822092726300322
980.1918221971373840.3836443942747680.808177802862616
990.1443547299406130.2887094598812270.855645270059387
1000.1687678961452010.3375357922904020.831232103854799
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1040.7114726470152940.5770547059694130.288527352984706
1050.6086752738429690.7826494523140620.391324726157031
1060.894161389820040.2116772203599190.105838610179960
1070.9024162264764630.1951675470470740.0975837735235371
1080.8976952515945040.2046094968109910.102304748405496
1090.9248997178752190.1502005642495620.0751002821247811






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0196078431372549OK
10% type I error level100.0980392156862745OK

\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 & 2 & 0.0196078431372549 & OK \tabularnewline
10% type I error level & 10 & 0.0980392156862745 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=8034&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]2[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0980392156862745[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=8034&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=8034&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0196078431372549OK
10% type I error level100.0980392156862745OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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