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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 07:15:33 -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/2009/Nov/20/t1258726583pz9bevj7fnnfhqs.htm/, Retrieved Thu, 28 Mar 2024 20:21:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58187, Retrieved Thu, 28 Mar 2024 20:21:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
F R  D    [Multiple Regression] [Model 2] [2009-11-18 20:35:45] [1f74ef2f756548f1f3a7b6136ea56d7f]
-    D        [Multiple Regression] [model 2 ws 7] [2009-11-20 14:15:33] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
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Dataseries X:
100.01	0
103.84	0
104.48	0
95.43	0
104.80	0
108.64	0
105.65	0
108.42	0
115.35	0
113.64	0
115.24	0
100.33	0
101.29	0
104.48	0
99.26	0
100.11	0
103.52	0
101.18	0
96.39	0
97.56	0
96.39	0
85.10	0
79.77	0
79.13	0
80.84	0
82.75	0
92.55	0
96.60	0
96.92	0
95.32	0
98.52	0
100.22	0
104.91	0
103.10	0
97.13	0
103.42	0
111.72	0
118.11	0
111.62	0
100.22	0
102.03	0
105.76	0
107.68	0
110.77	0
105.44	0
112.26	0
114.07	0
117.90	0
124.72	0
126.42	0
134.73	0
135.79	0
143.36	0
140.37	0
144.74	0
151.98	0
150.92	0
163.38	0
154.43	0
146.66	0
157.95	0
162.10	0
180.42	0
179.57	0
171.58	0
185.43	0
190.64	0
203.00	0
202.36	0
193.41	0
186.17	0
192.24	0
209.60	0
206.41	0
209.82	0
230.37	0
235.80	0
232.07	0
244.64	0
242.19	0
217.48	0
209.39	0
211.73	0
221.00	0
203.11	0
214.71	0
224.19	0
238.04	0
238.36	0
246.24	0
259.87	0
249.97	0
266.48	0
282.98	0
306.31	0
301.73	1
314.62	1
332.62	1
355.51	1
370.32	1
408.13	1
433.58	1
440.51	1
386.29	1
342.84	1
254.97	1
203.42	1
170.09	1
174.03	1
167.85	1
177.01	1
188.19	1
211.20	1
240.91	1
230.26	1
251.25	1
241.66	1




Summary of computational 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 computational 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=58187&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]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=58187&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 129.735271512114 + 132.441278195489X[t] + 1.56547284878861M1[t] + 5.70547284878865M2[t] + 12.7354728487886M3[t] + 17.2404728487886M4[t] + 25.3464728487887M5[t] + 32.7264728487886M6[t] + 35.6664728487887M7[t] + 33.9414728487887M8[t] + 28.1594728487886M9[t] + 24.2412531328321M10[t] + 18.6901420217210M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  129.735271512114 +  132.441278195489X[t] +  1.56547284878861M1[t] +  5.70547284878865M2[t] +  12.7354728487886M3[t] +  17.2404728487886M4[t] +  25.3464728487887M5[t] +  32.7264728487886M6[t] +  35.6664728487887M7[t] +  33.9414728487887M8[t] +  28.1594728487886M9[t] +  24.2412531328321M10[t] +  18.6901420217210M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  129.735271512114 +  132.441278195489X[t] +  1.56547284878861M1[t] +  5.70547284878865M2[t] +  12.7354728487886M3[t] +  17.2404728487886M4[t] +  25.3464728487887M5[t] +  32.7264728487886M6[t] +  35.6664728487887M7[t] +  33.9414728487887M8[t] +  28.1594728487886M9[t] +  24.2412531328321M10[t] +  18.6901420217210M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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
Y[t] = + 129.735271512114 + 132.441278195489X[t] + 1.56547284878861M1[t] + 5.70547284878865M2[t] + 12.7354728487886M3[t] + 17.2404728487886M4[t] + 25.3464728487887M5[t] + 32.7264728487886M6[t] + 35.6664728487887M7[t] + 33.9414728487887M8[t] + 28.1594728487886M9[t] + 24.2412531328321M10[t] + 18.6901420217210M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)129.73527151211422.6523125.727200
X132.44127819548915.9390858.309200
M11.5654728487886130.8420170.05080.9596160.479808
M25.7054728487886530.8420170.1850.8535970.426798
M312.735472848788630.8420170.41290.6805110.340255
M417.240472848788630.8420170.5590.5773680.288684
M525.346472848788730.8420170.82180.4130620.206531
M632.726472848788630.8420171.06110.2911020.145551
M735.666472848788730.8420171.15640.2501570.125078
M833.941472848788730.8420171.10050.2736570.136829
M928.159472848788630.8420170.9130.3633420.181671
M1024.241253132832131.6906790.76490.4460440.223022
M1118.690142021721031.6906790.58980.5566250.278313

\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) & 129.735271512114 & 22.652312 & 5.7272 & 0 & 0 \tabularnewline
X & 132.441278195489 & 15.939085 & 8.3092 & 0 & 0 \tabularnewline
M1 & 1.56547284878861 & 30.842017 & 0.0508 & 0.959616 & 0.479808 \tabularnewline
M2 & 5.70547284878865 & 30.842017 & 0.185 & 0.853597 & 0.426798 \tabularnewline
M3 & 12.7354728487886 & 30.842017 & 0.4129 & 0.680511 & 0.340255 \tabularnewline
M4 & 17.2404728487886 & 30.842017 & 0.559 & 0.577368 & 0.288684 \tabularnewline
M5 & 25.3464728487887 & 30.842017 & 0.8218 & 0.413062 & 0.206531 \tabularnewline
M6 & 32.7264728487886 & 30.842017 & 1.0611 & 0.291102 & 0.145551 \tabularnewline
M7 & 35.6664728487887 & 30.842017 & 1.1564 & 0.250157 & 0.125078 \tabularnewline
M8 & 33.9414728487887 & 30.842017 & 1.1005 & 0.273657 & 0.136829 \tabularnewline
M9 & 28.1594728487886 & 30.842017 & 0.913 & 0.363342 & 0.181671 \tabularnewline
M10 & 24.2412531328321 & 31.690679 & 0.7649 & 0.446044 & 0.223022 \tabularnewline
M11 & 18.6901420217210 & 31.690679 & 0.5898 & 0.556625 & 0.278313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&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]129.735271512114[/C][C]22.652312[/C][C]5.7272[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]132.441278195489[/C][C]15.939085[/C][C]8.3092[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.56547284878861[/C][C]30.842017[/C][C]0.0508[/C][C]0.959616[/C][C]0.479808[/C][/ROW]
[ROW][C]M2[/C][C]5.70547284878865[/C][C]30.842017[/C][C]0.185[/C][C]0.853597[/C][C]0.426798[/C][/ROW]
[ROW][C]M3[/C][C]12.7354728487886[/C][C]30.842017[/C][C]0.4129[/C][C]0.680511[/C][C]0.340255[/C][/ROW]
[ROW][C]M4[/C][C]17.2404728487886[/C][C]30.842017[/C][C]0.559[/C][C]0.577368[/C][C]0.288684[/C][/ROW]
[ROW][C]M5[/C][C]25.3464728487887[/C][C]30.842017[/C][C]0.8218[/C][C]0.413062[/C][C]0.206531[/C][/ROW]
[ROW][C]M6[/C][C]32.7264728487886[/C][C]30.842017[/C][C]1.0611[/C][C]0.291102[/C][C]0.145551[/C][/ROW]
[ROW][C]M7[/C][C]35.6664728487887[/C][C]30.842017[/C][C]1.1564[/C][C]0.250157[/C][C]0.125078[/C][/ROW]
[ROW][C]M8[/C][C]33.9414728487887[/C][C]30.842017[/C][C]1.1005[/C][C]0.273657[/C][C]0.136829[/C][/ROW]
[ROW][C]M9[/C][C]28.1594728487886[/C][C]30.842017[/C][C]0.913[/C][C]0.363342[/C][C]0.181671[/C][/ROW]
[ROW][C]M10[/C][C]24.2412531328321[/C][C]31.690679[/C][C]0.7649[/C][C]0.446044[/C][C]0.223022[/C][/ROW]
[ROW][C]M11[/C][C]18.6901420217210[/C][C]31.690679[/C][C]0.5898[/C][C]0.556625[/C][C]0.278313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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)129.73527151211422.6523125.727200
X132.44127819548915.9390858.309200
M11.5654728487886130.8420170.05080.9596160.479808
M25.7054728487886530.8420170.1850.8535970.426798
M312.735472848788630.8420170.41290.6805110.340255
M417.240472848788630.8420170.5590.5773680.288684
M525.346472848788730.8420170.82180.4130620.206531
M632.726472848788630.8420171.06110.2911020.145551
M735.666472848788730.8420171.15640.2501570.125078
M833.941472848788730.8420171.10050.2736570.136829
M928.159472848788630.8420170.9130.3633420.181671
M1024.241253132832131.6906790.76490.4460440.223022
M1118.690142021721031.6906790.58980.5566250.278313







Multiple Linear Regression - Regression Statistics
Multiple R0.641911939068384
R-squared0.412050937518533
Adjusted R-squared0.344210661078363
F-TEST (value)6.07383930520882
F-TEST (DF numerator)12
F-TEST (DF denominator)104
p-value5.79472236861278e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation67.1210247974531
Sum Squared Residuals468544.124865472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.641911939068384 \tabularnewline
R-squared & 0.412050937518533 \tabularnewline
Adjusted R-squared & 0.344210661078363 \tabularnewline
F-TEST (value) & 6.07383930520882 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 5.79472236861278e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 67.1210247974531 \tabularnewline
Sum Squared Residuals & 468544.124865472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.641911939068384[/C][/ROW]
[ROW][C]R-squared[/C][C]0.412050937518533[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.344210661078363[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.07383930520882[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]104[/C][/ROW]
[ROW][C]p-value[/C][C]5.79472236861278e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]67.1210247974531[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]468544.124865472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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 R0.641911939068384
R-squared0.412050937518533
Adjusted R-squared0.344210661078363
F-TEST (value)6.07383930520882
F-TEST (DF numerator)12
F-TEST (DF denominator)104
p-value5.79472236861278e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation67.1210247974531
Sum Squared Residuals468544.124865472







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100.01131.300744360903-31.2907443609025
2103.84135.440744360902-31.600744360902
3104.48142.470744360902-37.9907443609022
495.43146.975744360902-51.5457443609022
5104.8155.081744360902-50.2817443609023
6108.64162.461744360902-53.8217443609023
7105.65165.401744360902-59.7517443609023
8108.42163.676744360902-55.2567443609021
9115.35157.894744360902-42.5447443609022
10113.64153.976524644946-40.3365246449457
11115.24148.425413533835-33.1854135338346
12100.33129.735271512114-29.4052715121136
13101.29131.300744360902-30.0107443609022
14104.48135.440744360902-30.9607443609023
1599.26142.470744360902-43.2107443609022
16100.11146.975744360902-46.8657443609023
17103.52155.081744360902-51.5617443609023
18101.18162.461744360902-61.2817443609023
1996.39165.401744360902-69.0117443609023
2097.56163.676744360902-66.1167443609023
2196.39157.894744360902-61.5047443609023
2285.1153.976524644946-68.8765246449457
2379.77148.425413533835-68.6554135338346
2479.13129.735271512114-50.6052715121136
2580.84131.300744360902-50.4607443609022
2682.75135.440744360902-52.6907443609023
2792.55142.470744360902-49.9207443609023
2896.6146.975744360902-50.3757443609023
2996.92155.081744360902-58.1617443609023
3095.32162.461744360902-67.1417443609023
3198.52165.401744360902-66.8817443609023
32100.22163.676744360902-63.4567443609023
33104.91157.894744360902-52.9847443609023
34103.1153.976524644946-50.8765246449457
3597.13148.425413533835-51.2954135338346
36103.42129.735271512114-26.3152715121136
37111.72131.300744360902-19.5807443609023
38118.11135.440744360902-17.3307443609023
39111.62142.470744360902-30.8507443609023
40100.22146.975744360902-46.7557443609023
41102.03155.081744360902-53.0517443609023
42105.76162.461744360902-56.7017443609023
43107.68165.401744360902-57.7217443609023
44110.77163.676744360902-52.9067443609023
45105.44157.894744360902-52.4547443609023
46112.26153.976524644946-41.7165246449457
47114.07148.425413533835-34.3554135338346
48117.9129.735271512114-11.8352715121136
49124.72131.300744360902-6.58074436090226
50126.42135.440744360902-9.02074436090227
51134.73142.470744360902-7.74074436090228
52135.79146.975744360902-11.1857443609023
53143.36155.081744360902-11.7217443609023
54140.37162.461744360902-22.0917443609023
55144.74165.401744360902-20.6617443609023
56151.98163.676744360902-11.6967443609023
57150.92157.894744360902-6.97474436090226
58163.38153.9765246449469.40347535505432
59154.43148.4254135338356.00458646616543
60146.66129.73527151211416.9247284878864
61157.95131.30074436090226.6492556390977
62162.1135.44074436090226.6592556390977
63180.42142.47074436090237.9492556390977
64179.57146.97574436090232.5942556390977
65171.58155.08174436090216.4982556390977
66185.43162.46174436090222.9682556390977
67190.64165.40174436090225.2382556390977
68203163.67674436090239.3232556390977
69202.36157.89474436090244.4652556390978
70193.41153.97652464494639.4334753550543
71186.17148.42541353383537.7445864661654
72192.24129.73527151211462.5047284878864
73209.6131.30074436090278.2992556390977
74206.41135.44074436090270.9692556390977
75209.82142.47074436090267.3492556390977
76230.37146.97574436090283.3942556390977
77235.8155.08174436090280.7182556390977
78232.07162.46174436090269.6082556390977
79244.64165.40174436090279.2382556390977
80242.19163.67674436090278.5132556390977
81217.48157.89474436090259.5852556390978
82209.39153.97652464494655.4134753550543
83211.73148.42541353383563.3045864661654
84221129.73527151211491.2647284878864
85203.11131.30074436090271.8092556390978
86214.71135.44074436090279.2692556390977
87224.19142.47074436090281.7192556390977
88238.04146.97574436090291.0642556390977
89238.36155.08174436090283.2782556390978
90246.24162.46174436090283.7782556390977
91259.87165.40174436090294.4682556390977
92249.97163.67674436090286.2932556390977
93266.48157.894744360902108.585255639098
94282.98153.976524644946129.003475355054
95306.31148.425413533835157.884586466165
96301.73262.17654970760239.5534502923977
97314.62263.74202255639150.8779774436091
98332.62267.88202255639164.737977443609
99355.51274.91202255639180.597977443609
100370.32279.41702255639190.902977443609
101408.13287.523022556391120.606977443609
102433.58294.903022556391138.676977443609
103440.51297.843022556391142.666977443609
104386.29296.11802255639190.171977443609
105342.84290.33602255639152.5039774436090
106254.97286.417802840434-31.4478028404344
107203.42280.866691729323-77.4466917293233
108170.09262.176549707602-92.0865497076023
109174.03263.742022556391-89.712022556391
110167.85267.882022556391-100.032022556391
111177.01274.912022556391-97.902022556391
112188.19279.417022556391-91.227022556391
113211.2287.523022556391-76.323022556391
114240.91294.903022556391-53.993022556391
115230.26297.843022556391-67.583022556391
116251.25296.118022556391-44.868022556391
117241.66290.336022556391-48.6760225563909

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.01 & 131.300744360903 & -31.2907443609025 \tabularnewline
2 & 103.84 & 135.440744360902 & -31.600744360902 \tabularnewline
3 & 104.48 & 142.470744360902 & -37.9907443609022 \tabularnewline
4 & 95.43 & 146.975744360902 & -51.5457443609022 \tabularnewline
5 & 104.8 & 155.081744360902 & -50.2817443609023 \tabularnewline
6 & 108.64 & 162.461744360902 & -53.8217443609023 \tabularnewline
7 & 105.65 & 165.401744360902 & -59.7517443609023 \tabularnewline
8 & 108.42 & 163.676744360902 & -55.2567443609021 \tabularnewline
9 & 115.35 & 157.894744360902 & -42.5447443609022 \tabularnewline
10 & 113.64 & 153.976524644946 & -40.3365246449457 \tabularnewline
11 & 115.24 & 148.425413533835 & -33.1854135338346 \tabularnewline
12 & 100.33 & 129.735271512114 & -29.4052715121136 \tabularnewline
13 & 101.29 & 131.300744360902 & -30.0107443609022 \tabularnewline
14 & 104.48 & 135.440744360902 & -30.9607443609023 \tabularnewline
15 & 99.26 & 142.470744360902 & -43.2107443609022 \tabularnewline
16 & 100.11 & 146.975744360902 & -46.8657443609023 \tabularnewline
17 & 103.52 & 155.081744360902 & -51.5617443609023 \tabularnewline
18 & 101.18 & 162.461744360902 & -61.2817443609023 \tabularnewline
19 & 96.39 & 165.401744360902 & -69.0117443609023 \tabularnewline
20 & 97.56 & 163.676744360902 & -66.1167443609023 \tabularnewline
21 & 96.39 & 157.894744360902 & -61.5047443609023 \tabularnewline
22 & 85.1 & 153.976524644946 & -68.8765246449457 \tabularnewline
23 & 79.77 & 148.425413533835 & -68.6554135338346 \tabularnewline
24 & 79.13 & 129.735271512114 & -50.6052715121136 \tabularnewline
25 & 80.84 & 131.300744360902 & -50.4607443609022 \tabularnewline
26 & 82.75 & 135.440744360902 & -52.6907443609023 \tabularnewline
27 & 92.55 & 142.470744360902 & -49.9207443609023 \tabularnewline
28 & 96.6 & 146.975744360902 & -50.3757443609023 \tabularnewline
29 & 96.92 & 155.081744360902 & -58.1617443609023 \tabularnewline
30 & 95.32 & 162.461744360902 & -67.1417443609023 \tabularnewline
31 & 98.52 & 165.401744360902 & -66.8817443609023 \tabularnewline
32 & 100.22 & 163.676744360902 & -63.4567443609023 \tabularnewline
33 & 104.91 & 157.894744360902 & -52.9847443609023 \tabularnewline
34 & 103.1 & 153.976524644946 & -50.8765246449457 \tabularnewline
35 & 97.13 & 148.425413533835 & -51.2954135338346 \tabularnewline
36 & 103.42 & 129.735271512114 & -26.3152715121136 \tabularnewline
37 & 111.72 & 131.300744360902 & -19.5807443609023 \tabularnewline
38 & 118.11 & 135.440744360902 & -17.3307443609023 \tabularnewline
39 & 111.62 & 142.470744360902 & -30.8507443609023 \tabularnewline
40 & 100.22 & 146.975744360902 & -46.7557443609023 \tabularnewline
41 & 102.03 & 155.081744360902 & -53.0517443609023 \tabularnewline
42 & 105.76 & 162.461744360902 & -56.7017443609023 \tabularnewline
43 & 107.68 & 165.401744360902 & -57.7217443609023 \tabularnewline
44 & 110.77 & 163.676744360902 & -52.9067443609023 \tabularnewline
45 & 105.44 & 157.894744360902 & -52.4547443609023 \tabularnewline
46 & 112.26 & 153.976524644946 & -41.7165246449457 \tabularnewline
47 & 114.07 & 148.425413533835 & -34.3554135338346 \tabularnewline
48 & 117.9 & 129.735271512114 & -11.8352715121136 \tabularnewline
49 & 124.72 & 131.300744360902 & -6.58074436090226 \tabularnewline
50 & 126.42 & 135.440744360902 & -9.02074436090227 \tabularnewline
51 & 134.73 & 142.470744360902 & -7.74074436090228 \tabularnewline
52 & 135.79 & 146.975744360902 & -11.1857443609023 \tabularnewline
53 & 143.36 & 155.081744360902 & -11.7217443609023 \tabularnewline
54 & 140.37 & 162.461744360902 & -22.0917443609023 \tabularnewline
55 & 144.74 & 165.401744360902 & -20.6617443609023 \tabularnewline
56 & 151.98 & 163.676744360902 & -11.6967443609023 \tabularnewline
57 & 150.92 & 157.894744360902 & -6.97474436090226 \tabularnewline
58 & 163.38 & 153.976524644946 & 9.40347535505432 \tabularnewline
59 & 154.43 & 148.425413533835 & 6.00458646616543 \tabularnewline
60 & 146.66 & 129.735271512114 & 16.9247284878864 \tabularnewline
61 & 157.95 & 131.300744360902 & 26.6492556390977 \tabularnewline
62 & 162.1 & 135.440744360902 & 26.6592556390977 \tabularnewline
63 & 180.42 & 142.470744360902 & 37.9492556390977 \tabularnewline
64 & 179.57 & 146.975744360902 & 32.5942556390977 \tabularnewline
65 & 171.58 & 155.081744360902 & 16.4982556390977 \tabularnewline
66 & 185.43 & 162.461744360902 & 22.9682556390977 \tabularnewline
67 & 190.64 & 165.401744360902 & 25.2382556390977 \tabularnewline
68 & 203 & 163.676744360902 & 39.3232556390977 \tabularnewline
69 & 202.36 & 157.894744360902 & 44.4652556390978 \tabularnewline
70 & 193.41 & 153.976524644946 & 39.4334753550543 \tabularnewline
71 & 186.17 & 148.425413533835 & 37.7445864661654 \tabularnewline
72 & 192.24 & 129.735271512114 & 62.5047284878864 \tabularnewline
73 & 209.6 & 131.300744360902 & 78.2992556390977 \tabularnewline
74 & 206.41 & 135.440744360902 & 70.9692556390977 \tabularnewline
75 & 209.82 & 142.470744360902 & 67.3492556390977 \tabularnewline
76 & 230.37 & 146.975744360902 & 83.3942556390977 \tabularnewline
77 & 235.8 & 155.081744360902 & 80.7182556390977 \tabularnewline
78 & 232.07 & 162.461744360902 & 69.6082556390977 \tabularnewline
79 & 244.64 & 165.401744360902 & 79.2382556390977 \tabularnewline
80 & 242.19 & 163.676744360902 & 78.5132556390977 \tabularnewline
81 & 217.48 & 157.894744360902 & 59.5852556390978 \tabularnewline
82 & 209.39 & 153.976524644946 & 55.4134753550543 \tabularnewline
83 & 211.73 & 148.425413533835 & 63.3045864661654 \tabularnewline
84 & 221 & 129.735271512114 & 91.2647284878864 \tabularnewline
85 & 203.11 & 131.300744360902 & 71.8092556390978 \tabularnewline
86 & 214.71 & 135.440744360902 & 79.2692556390977 \tabularnewline
87 & 224.19 & 142.470744360902 & 81.7192556390977 \tabularnewline
88 & 238.04 & 146.975744360902 & 91.0642556390977 \tabularnewline
89 & 238.36 & 155.081744360902 & 83.2782556390978 \tabularnewline
90 & 246.24 & 162.461744360902 & 83.7782556390977 \tabularnewline
91 & 259.87 & 165.401744360902 & 94.4682556390977 \tabularnewline
92 & 249.97 & 163.676744360902 & 86.2932556390977 \tabularnewline
93 & 266.48 & 157.894744360902 & 108.585255639098 \tabularnewline
94 & 282.98 & 153.976524644946 & 129.003475355054 \tabularnewline
95 & 306.31 & 148.425413533835 & 157.884586466165 \tabularnewline
96 & 301.73 & 262.176549707602 & 39.5534502923977 \tabularnewline
97 & 314.62 & 263.742022556391 & 50.8779774436091 \tabularnewline
98 & 332.62 & 267.882022556391 & 64.737977443609 \tabularnewline
99 & 355.51 & 274.912022556391 & 80.597977443609 \tabularnewline
100 & 370.32 & 279.417022556391 & 90.902977443609 \tabularnewline
101 & 408.13 & 287.523022556391 & 120.606977443609 \tabularnewline
102 & 433.58 & 294.903022556391 & 138.676977443609 \tabularnewline
103 & 440.51 & 297.843022556391 & 142.666977443609 \tabularnewline
104 & 386.29 & 296.118022556391 & 90.171977443609 \tabularnewline
105 & 342.84 & 290.336022556391 & 52.5039774436090 \tabularnewline
106 & 254.97 & 286.417802840434 & -31.4478028404344 \tabularnewline
107 & 203.42 & 280.866691729323 & -77.4466917293233 \tabularnewline
108 & 170.09 & 262.176549707602 & -92.0865497076023 \tabularnewline
109 & 174.03 & 263.742022556391 & -89.712022556391 \tabularnewline
110 & 167.85 & 267.882022556391 & -100.032022556391 \tabularnewline
111 & 177.01 & 274.912022556391 & -97.902022556391 \tabularnewline
112 & 188.19 & 279.417022556391 & -91.227022556391 \tabularnewline
113 & 211.2 & 287.523022556391 & -76.323022556391 \tabularnewline
114 & 240.91 & 294.903022556391 & -53.993022556391 \tabularnewline
115 & 230.26 & 297.843022556391 & -67.583022556391 \tabularnewline
116 & 251.25 & 296.118022556391 & -44.868022556391 \tabularnewline
117 & 241.66 & 290.336022556391 & -48.6760225563909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&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]100.01[/C][C]131.300744360903[/C][C]-31.2907443609025[/C][/ROW]
[ROW][C]2[/C][C]103.84[/C][C]135.440744360902[/C][C]-31.600744360902[/C][/ROW]
[ROW][C]3[/C][C]104.48[/C][C]142.470744360902[/C][C]-37.9907443609022[/C][/ROW]
[ROW][C]4[/C][C]95.43[/C][C]146.975744360902[/C][C]-51.5457443609022[/C][/ROW]
[ROW][C]5[/C][C]104.8[/C][C]155.081744360902[/C][C]-50.2817443609023[/C][/ROW]
[ROW][C]6[/C][C]108.64[/C][C]162.461744360902[/C][C]-53.8217443609023[/C][/ROW]
[ROW][C]7[/C][C]105.65[/C][C]165.401744360902[/C][C]-59.7517443609023[/C][/ROW]
[ROW][C]8[/C][C]108.42[/C][C]163.676744360902[/C][C]-55.2567443609021[/C][/ROW]
[ROW][C]9[/C][C]115.35[/C][C]157.894744360902[/C][C]-42.5447443609022[/C][/ROW]
[ROW][C]10[/C][C]113.64[/C][C]153.976524644946[/C][C]-40.3365246449457[/C][/ROW]
[ROW][C]11[/C][C]115.24[/C][C]148.425413533835[/C][C]-33.1854135338346[/C][/ROW]
[ROW][C]12[/C][C]100.33[/C][C]129.735271512114[/C][C]-29.4052715121136[/C][/ROW]
[ROW][C]13[/C][C]101.29[/C][C]131.300744360902[/C][C]-30.0107443609022[/C][/ROW]
[ROW][C]14[/C][C]104.48[/C][C]135.440744360902[/C][C]-30.9607443609023[/C][/ROW]
[ROW][C]15[/C][C]99.26[/C][C]142.470744360902[/C][C]-43.2107443609022[/C][/ROW]
[ROW][C]16[/C][C]100.11[/C][C]146.975744360902[/C][C]-46.8657443609023[/C][/ROW]
[ROW][C]17[/C][C]103.52[/C][C]155.081744360902[/C][C]-51.5617443609023[/C][/ROW]
[ROW][C]18[/C][C]101.18[/C][C]162.461744360902[/C][C]-61.2817443609023[/C][/ROW]
[ROW][C]19[/C][C]96.39[/C][C]165.401744360902[/C][C]-69.0117443609023[/C][/ROW]
[ROW][C]20[/C][C]97.56[/C][C]163.676744360902[/C][C]-66.1167443609023[/C][/ROW]
[ROW][C]21[/C][C]96.39[/C][C]157.894744360902[/C][C]-61.5047443609023[/C][/ROW]
[ROW][C]22[/C][C]85.1[/C][C]153.976524644946[/C][C]-68.8765246449457[/C][/ROW]
[ROW][C]23[/C][C]79.77[/C][C]148.425413533835[/C][C]-68.6554135338346[/C][/ROW]
[ROW][C]24[/C][C]79.13[/C][C]129.735271512114[/C][C]-50.6052715121136[/C][/ROW]
[ROW][C]25[/C][C]80.84[/C][C]131.300744360902[/C][C]-50.4607443609022[/C][/ROW]
[ROW][C]26[/C][C]82.75[/C][C]135.440744360902[/C][C]-52.6907443609023[/C][/ROW]
[ROW][C]27[/C][C]92.55[/C][C]142.470744360902[/C][C]-49.9207443609023[/C][/ROW]
[ROW][C]28[/C][C]96.6[/C][C]146.975744360902[/C][C]-50.3757443609023[/C][/ROW]
[ROW][C]29[/C][C]96.92[/C][C]155.081744360902[/C][C]-58.1617443609023[/C][/ROW]
[ROW][C]30[/C][C]95.32[/C][C]162.461744360902[/C][C]-67.1417443609023[/C][/ROW]
[ROW][C]31[/C][C]98.52[/C][C]165.401744360902[/C][C]-66.8817443609023[/C][/ROW]
[ROW][C]32[/C][C]100.22[/C][C]163.676744360902[/C][C]-63.4567443609023[/C][/ROW]
[ROW][C]33[/C][C]104.91[/C][C]157.894744360902[/C][C]-52.9847443609023[/C][/ROW]
[ROW][C]34[/C][C]103.1[/C][C]153.976524644946[/C][C]-50.8765246449457[/C][/ROW]
[ROW][C]35[/C][C]97.13[/C][C]148.425413533835[/C][C]-51.2954135338346[/C][/ROW]
[ROW][C]36[/C][C]103.42[/C][C]129.735271512114[/C][C]-26.3152715121136[/C][/ROW]
[ROW][C]37[/C][C]111.72[/C][C]131.300744360902[/C][C]-19.5807443609023[/C][/ROW]
[ROW][C]38[/C][C]118.11[/C][C]135.440744360902[/C][C]-17.3307443609023[/C][/ROW]
[ROW][C]39[/C][C]111.62[/C][C]142.470744360902[/C][C]-30.8507443609023[/C][/ROW]
[ROW][C]40[/C][C]100.22[/C][C]146.975744360902[/C][C]-46.7557443609023[/C][/ROW]
[ROW][C]41[/C][C]102.03[/C][C]155.081744360902[/C][C]-53.0517443609023[/C][/ROW]
[ROW][C]42[/C][C]105.76[/C][C]162.461744360902[/C][C]-56.7017443609023[/C][/ROW]
[ROW][C]43[/C][C]107.68[/C][C]165.401744360902[/C][C]-57.7217443609023[/C][/ROW]
[ROW][C]44[/C][C]110.77[/C][C]163.676744360902[/C][C]-52.9067443609023[/C][/ROW]
[ROW][C]45[/C][C]105.44[/C][C]157.894744360902[/C][C]-52.4547443609023[/C][/ROW]
[ROW][C]46[/C][C]112.26[/C][C]153.976524644946[/C][C]-41.7165246449457[/C][/ROW]
[ROW][C]47[/C][C]114.07[/C][C]148.425413533835[/C][C]-34.3554135338346[/C][/ROW]
[ROW][C]48[/C][C]117.9[/C][C]129.735271512114[/C][C]-11.8352715121136[/C][/ROW]
[ROW][C]49[/C][C]124.72[/C][C]131.300744360902[/C][C]-6.58074436090226[/C][/ROW]
[ROW][C]50[/C][C]126.42[/C][C]135.440744360902[/C][C]-9.02074436090227[/C][/ROW]
[ROW][C]51[/C][C]134.73[/C][C]142.470744360902[/C][C]-7.74074436090228[/C][/ROW]
[ROW][C]52[/C][C]135.79[/C][C]146.975744360902[/C][C]-11.1857443609023[/C][/ROW]
[ROW][C]53[/C][C]143.36[/C][C]155.081744360902[/C][C]-11.7217443609023[/C][/ROW]
[ROW][C]54[/C][C]140.37[/C][C]162.461744360902[/C][C]-22.0917443609023[/C][/ROW]
[ROW][C]55[/C][C]144.74[/C][C]165.401744360902[/C][C]-20.6617443609023[/C][/ROW]
[ROW][C]56[/C][C]151.98[/C][C]163.676744360902[/C][C]-11.6967443609023[/C][/ROW]
[ROW][C]57[/C][C]150.92[/C][C]157.894744360902[/C][C]-6.97474436090226[/C][/ROW]
[ROW][C]58[/C][C]163.38[/C][C]153.976524644946[/C][C]9.40347535505432[/C][/ROW]
[ROW][C]59[/C][C]154.43[/C][C]148.425413533835[/C][C]6.00458646616543[/C][/ROW]
[ROW][C]60[/C][C]146.66[/C][C]129.735271512114[/C][C]16.9247284878864[/C][/ROW]
[ROW][C]61[/C][C]157.95[/C][C]131.300744360902[/C][C]26.6492556390977[/C][/ROW]
[ROW][C]62[/C][C]162.1[/C][C]135.440744360902[/C][C]26.6592556390977[/C][/ROW]
[ROW][C]63[/C][C]180.42[/C][C]142.470744360902[/C][C]37.9492556390977[/C][/ROW]
[ROW][C]64[/C][C]179.57[/C][C]146.975744360902[/C][C]32.5942556390977[/C][/ROW]
[ROW][C]65[/C][C]171.58[/C][C]155.081744360902[/C][C]16.4982556390977[/C][/ROW]
[ROW][C]66[/C][C]185.43[/C][C]162.461744360902[/C][C]22.9682556390977[/C][/ROW]
[ROW][C]67[/C][C]190.64[/C][C]165.401744360902[/C][C]25.2382556390977[/C][/ROW]
[ROW][C]68[/C][C]203[/C][C]163.676744360902[/C][C]39.3232556390977[/C][/ROW]
[ROW][C]69[/C][C]202.36[/C][C]157.894744360902[/C][C]44.4652556390978[/C][/ROW]
[ROW][C]70[/C][C]193.41[/C][C]153.976524644946[/C][C]39.4334753550543[/C][/ROW]
[ROW][C]71[/C][C]186.17[/C][C]148.425413533835[/C][C]37.7445864661654[/C][/ROW]
[ROW][C]72[/C][C]192.24[/C][C]129.735271512114[/C][C]62.5047284878864[/C][/ROW]
[ROW][C]73[/C][C]209.6[/C][C]131.300744360902[/C][C]78.2992556390977[/C][/ROW]
[ROW][C]74[/C][C]206.41[/C][C]135.440744360902[/C][C]70.9692556390977[/C][/ROW]
[ROW][C]75[/C][C]209.82[/C][C]142.470744360902[/C][C]67.3492556390977[/C][/ROW]
[ROW][C]76[/C][C]230.37[/C][C]146.975744360902[/C][C]83.3942556390977[/C][/ROW]
[ROW][C]77[/C][C]235.8[/C][C]155.081744360902[/C][C]80.7182556390977[/C][/ROW]
[ROW][C]78[/C][C]232.07[/C][C]162.461744360902[/C][C]69.6082556390977[/C][/ROW]
[ROW][C]79[/C][C]244.64[/C][C]165.401744360902[/C][C]79.2382556390977[/C][/ROW]
[ROW][C]80[/C][C]242.19[/C][C]163.676744360902[/C][C]78.5132556390977[/C][/ROW]
[ROW][C]81[/C][C]217.48[/C][C]157.894744360902[/C][C]59.5852556390978[/C][/ROW]
[ROW][C]82[/C][C]209.39[/C][C]153.976524644946[/C][C]55.4134753550543[/C][/ROW]
[ROW][C]83[/C][C]211.73[/C][C]148.425413533835[/C][C]63.3045864661654[/C][/ROW]
[ROW][C]84[/C][C]221[/C][C]129.735271512114[/C][C]91.2647284878864[/C][/ROW]
[ROW][C]85[/C][C]203.11[/C][C]131.300744360902[/C][C]71.8092556390978[/C][/ROW]
[ROW][C]86[/C][C]214.71[/C][C]135.440744360902[/C][C]79.2692556390977[/C][/ROW]
[ROW][C]87[/C][C]224.19[/C][C]142.470744360902[/C][C]81.7192556390977[/C][/ROW]
[ROW][C]88[/C][C]238.04[/C][C]146.975744360902[/C][C]91.0642556390977[/C][/ROW]
[ROW][C]89[/C][C]238.36[/C][C]155.081744360902[/C][C]83.2782556390978[/C][/ROW]
[ROW][C]90[/C][C]246.24[/C][C]162.461744360902[/C][C]83.7782556390977[/C][/ROW]
[ROW][C]91[/C][C]259.87[/C][C]165.401744360902[/C][C]94.4682556390977[/C][/ROW]
[ROW][C]92[/C][C]249.97[/C][C]163.676744360902[/C][C]86.2932556390977[/C][/ROW]
[ROW][C]93[/C][C]266.48[/C][C]157.894744360902[/C][C]108.585255639098[/C][/ROW]
[ROW][C]94[/C][C]282.98[/C][C]153.976524644946[/C][C]129.003475355054[/C][/ROW]
[ROW][C]95[/C][C]306.31[/C][C]148.425413533835[/C][C]157.884586466165[/C][/ROW]
[ROW][C]96[/C][C]301.73[/C][C]262.176549707602[/C][C]39.5534502923977[/C][/ROW]
[ROW][C]97[/C][C]314.62[/C][C]263.742022556391[/C][C]50.8779774436091[/C][/ROW]
[ROW][C]98[/C][C]332.62[/C][C]267.882022556391[/C][C]64.737977443609[/C][/ROW]
[ROW][C]99[/C][C]355.51[/C][C]274.912022556391[/C][C]80.597977443609[/C][/ROW]
[ROW][C]100[/C][C]370.32[/C][C]279.417022556391[/C][C]90.902977443609[/C][/ROW]
[ROW][C]101[/C][C]408.13[/C][C]287.523022556391[/C][C]120.606977443609[/C][/ROW]
[ROW][C]102[/C][C]433.58[/C][C]294.903022556391[/C][C]138.676977443609[/C][/ROW]
[ROW][C]103[/C][C]440.51[/C][C]297.843022556391[/C][C]142.666977443609[/C][/ROW]
[ROW][C]104[/C][C]386.29[/C][C]296.118022556391[/C][C]90.171977443609[/C][/ROW]
[ROW][C]105[/C][C]342.84[/C][C]290.336022556391[/C][C]52.5039774436090[/C][/ROW]
[ROW][C]106[/C][C]254.97[/C][C]286.417802840434[/C][C]-31.4478028404344[/C][/ROW]
[ROW][C]107[/C][C]203.42[/C][C]280.866691729323[/C][C]-77.4466917293233[/C][/ROW]
[ROW][C]108[/C][C]170.09[/C][C]262.176549707602[/C][C]-92.0865497076023[/C][/ROW]
[ROW][C]109[/C][C]174.03[/C][C]263.742022556391[/C][C]-89.712022556391[/C][/ROW]
[ROW][C]110[/C][C]167.85[/C][C]267.882022556391[/C][C]-100.032022556391[/C][/ROW]
[ROW][C]111[/C][C]177.01[/C][C]274.912022556391[/C][C]-97.902022556391[/C][/ROW]
[ROW][C]112[/C][C]188.19[/C][C]279.417022556391[/C][C]-91.227022556391[/C][/ROW]
[ROW][C]113[/C][C]211.2[/C][C]287.523022556391[/C][C]-76.323022556391[/C][/ROW]
[ROW][C]114[/C][C]240.91[/C][C]294.903022556391[/C][C]-53.993022556391[/C][/ROW]
[ROW][C]115[/C][C]230.26[/C][C]297.843022556391[/C][C]-67.583022556391[/C][/ROW]
[ROW][C]116[/C][C]251.25[/C][C]296.118022556391[/C][C]-44.868022556391[/C][/ROW]
[ROW][C]117[/C][C]241.66[/C][C]290.336022556391[/C][C]-48.6760225563909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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
1100.01131.300744360903-31.2907443609025
2103.84135.440744360902-31.600744360902
3104.48142.470744360902-37.9907443609022
495.43146.975744360902-51.5457443609022
5104.8155.081744360902-50.2817443609023
6108.64162.461744360902-53.8217443609023
7105.65165.401744360902-59.7517443609023
8108.42163.676744360902-55.2567443609021
9115.35157.894744360902-42.5447443609022
10113.64153.976524644946-40.3365246449457
11115.24148.425413533835-33.1854135338346
12100.33129.735271512114-29.4052715121136
13101.29131.300744360902-30.0107443609022
14104.48135.440744360902-30.9607443609023
1599.26142.470744360902-43.2107443609022
16100.11146.975744360902-46.8657443609023
17103.52155.081744360902-51.5617443609023
18101.18162.461744360902-61.2817443609023
1996.39165.401744360902-69.0117443609023
2097.56163.676744360902-66.1167443609023
2196.39157.894744360902-61.5047443609023
2285.1153.976524644946-68.8765246449457
2379.77148.425413533835-68.6554135338346
2479.13129.735271512114-50.6052715121136
2580.84131.300744360902-50.4607443609022
2682.75135.440744360902-52.6907443609023
2792.55142.470744360902-49.9207443609023
2896.6146.975744360902-50.3757443609023
2996.92155.081744360902-58.1617443609023
3095.32162.461744360902-67.1417443609023
3198.52165.401744360902-66.8817443609023
32100.22163.676744360902-63.4567443609023
33104.91157.894744360902-52.9847443609023
34103.1153.976524644946-50.8765246449457
3597.13148.425413533835-51.2954135338346
36103.42129.735271512114-26.3152715121136
37111.72131.300744360902-19.5807443609023
38118.11135.440744360902-17.3307443609023
39111.62142.470744360902-30.8507443609023
40100.22146.975744360902-46.7557443609023
41102.03155.081744360902-53.0517443609023
42105.76162.461744360902-56.7017443609023
43107.68165.401744360902-57.7217443609023
44110.77163.676744360902-52.9067443609023
45105.44157.894744360902-52.4547443609023
46112.26153.976524644946-41.7165246449457
47114.07148.425413533835-34.3554135338346
48117.9129.735271512114-11.8352715121136
49124.72131.300744360902-6.58074436090226
50126.42135.440744360902-9.02074436090227
51134.73142.470744360902-7.74074436090228
52135.79146.975744360902-11.1857443609023
53143.36155.081744360902-11.7217443609023
54140.37162.461744360902-22.0917443609023
55144.74165.401744360902-20.6617443609023
56151.98163.676744360902-11.6967443609023
57150.92157.894744360902-6.97474436090226
58163.38153.9765246449469.40347535505432
59154.43148.4254135338356.00458646616543
60146.66129.73527151211416.9247284878864
61157.95131.30074436090226.6492556390977
62162.1135.44074436090226.6592556390977
63180.42142.47074436090237.9492556390977
64179.57146.97574436090232.5942556390977
65171.58155.08174436090216.4982556390977
66185.43162.46174436090222.9682556390977
67190.64165.40174436090225.2382556390977
68203163.67674436090239.3232556390977
69202.36157.89474436090244.4652556390978
70193.41153.97652464494639.4334753550543
71186.17148.42541353383537.7445864661654
72192.24129.73527151211462.5047284878864
73209.6131.30074436090278.2992556390977
74206.41135.44074436090270.9692556390977
75209.82142.47074436090267.3492556390977
76230.37146.97574436090283.3942556390977
77235.8155.08174436090280.7182556390977
78232.07162.46174436090269.6082556390977
79244.64165.40174436090279.2382556390977
80242.19163.67674436090278.5132556390977
81217.48157.89474436090259.5852556390978
82209.39153.97652464494655.4134753550543
83211.73148.42541353383563.3045864661654
84221129.73527151211491.2647284878864
85203.11131.30074436090271.8092556390978
86214.71135.44074436090279.2692556390977
87224.19142.47074436090281.7192556390977
88238.04146.97574436090291.0642556390977
89238.36155.08174436090283.2782556390978
90246.24162.46174436090283.7782556390977
91259.87165.40174436090294.4682556390977
92249.97163.67674436090286.2932556390977
93266.48157.894744360902108.585255639098
94282.98153.976524644946129.003475355054
95306.31148.425413533835157.884586466165
96301.73262.17654970760239.5534502923977
97314.62263.74202255639150.8779774436091
98332.62267.88202255639164.737977443609
99355.51274.91202255639180.597977443609
100370.32279.41702255639190.902977443609
101408.13287.523022556391120.606977443609
102433.58294.903022556391138.676977443609
103440.51297.843022556391142.666977443609
104386.29296.11802255639190.171977443609
105342.84290.33602255639152.5039774436090
106254.97286.417802840434-31.4478028404344
107203.42280.866691729323-77.4466917293233
108170.09262.176549707602-92.0865497076023
109174.03263.742022556391-89.712022556391
110167.85267.882022556391-100.032022556391
111177.01274.912022556391-97.902022556391
112188.19279.417022556391-91.227022556391
113211.2287.523022556391-76.323022556391
114240.91294.903022556391-53.993022556391
115230.26297.843022556391-67.583022556391
116251.25296.118022556391-44.868022556391
117241.66290.336022556391-48.6760225563909







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0001005686134090310.0002011372268180610.999899431386591
173.66648240437122e-067.33296480874243e-060.999996333517596
187.0463081447607e-071.40926162895214e-060.999999295369186
191.72434686863373e-073.44869373726745e-070.999999827565313
204.84653420864804e-089.69306841729608e-080.999999951534658
216.5388538386515e-081.3077707677303e-070.999999934611462
222.03430697890715e-074.0686139578143e-070.999999796569302
235.31374261310877e-071.06274852262175e-060.999999468625739
241.95922753030058e-073.91845506060116e-070.999999804077247
258.13379082974152e-081.62675816594830e-070.999999918662092
263.8205229898219e-087.6410459796438e-080.99999996179477
278.62674149687276e-091.72534829937455e-080.999999991373258
281.62736212930182e-093.25472425860364e-090.999999998372638
293.48234297539373e-106.96468595078746e-100.999999999651766
308.57328725031319e-111.71465745006264e-100.999999999914267
311.74971305352516e-113.49942610705032e-110.999999999982503
323.51033075336154e-127.02066150672307e-120.99999999999649
336.47507085385451e-131.29501417077090e-120.999999999999353
341.21930121353381e-132.43860242706762e-130.999999999999878
352.18429622580665e-144.36859245161331e-140.999999999999978
366.19592078100283e-151.23918415620057e-140.999999999999994
372.82749818775683e-155.65499637551365e-150.999999999999997
381.87349335107131e-153.74698670214263e-150.999999999999998
395.62562178111483e-161.12512435622297e-151
401.20216437593701e-162.40432875187402e-161
412.71558684721833e-175.43117369443665e-171
427.1077210350425e-181.4215442070085e-171
432.29551865196965e-184.59103730393931e-181
447.74584013088524e-191.54916802617705e-181
451.99562235729663e-193.99124471459325e-191
467.57149618309897e-201.51429923661979e-191
474.41448819736285e-208.8289763947257e-201
485.14806215383661e-201.02961243076732e-191
499.01734905629325e-201.80346981125865e-191
501.02804419678468e-192.05608839356935e-191
513.90091143625552e-197.80182287251104e-191
522.62587654223401e-185.25175308446802e-181
532.23785230506030e-174.47570461012061e-171
541.02415848378172e-162.04831696756345e-161
557.31188228357852e-161.46237645671570e-151
565.99212988801057e-151.19842597760211e-140.999999999999994
572.54164444812026e-145.08328889624051e-140.999999999999975
582.84008850603266e-135.68017701206532e-130.999999999999716
591.10050723185393e-122.20101446370787e-120.9999999999989
601.99053489249126e-123.98106978498252e-120.99999999999801
615.69877812457117e-121.13975562491423e-110.999999999994301
621.45940474976291e-112.91880949952581e-110.999999999985406
639.7387260189417e-111.94774520378834e-100.999999999902613
645.69296437894212e-101.13859287578842e-090.999999999430704
651.62136047855522e-093.24272095711043e-090.99999999837864
668.31149672393442e-091.66229934478688e-080.999999991688503
674.38166503089528e-088.76333006179057e-080.99999995618335
682.15220067810186e-074.30440135620372e-070.999999784779932
696.83275341266936e-071.36655068253387e-060.999999316724659
701.28098406921397e-062.56196813842793e-060.99999871901593
711.94765197416549e-063.89530394833097e-060.999998052348026
723.16064121013635e-066.32128242027271e-060.99999683935879
737.1932863886061e-061.43865727772122e-050.999992806713611
741.17015702942845e-052.34031405885690e-050.999988298429706
751.71338090827164e-053.42676181654329e-050.999982866190917
763.89588663396852e-057.79177326793705e-050.99996104113366
778.14895087762972e-050.0001629790175525940.999918510491224
780.0001439589304876260.0002879178609752520.999856041069512
790.0002689986962826180.0005379973925652350.999731001303717
800.0003827331550753960.0007654663101507920.999617266844925
810.0003870452001399660.0007740904002799320.99961295479986
820.000350914991686110.000701829983372220.999649085008314
830.0003143373685260720.0006286747370521450.999685662631474
840.0003031827547249410.0006063655094498810.999696817245275
850.0002269966451319870.0004539932902639730.999773003354868
860.0001797028581020730.0003594057162041450.999820297141898
870.0001455891736025920.0002911783472051830.999854410826397
880.0001302029751468800.0002604059502937610.999869797024853
890.0001173798653012250.000234759730602450.999882620134699
900.0001275397610288560.0002550795220577110.99987246023897
910.0001467660661979430.0002935321323958860.999853233933802
920.0001593048066885770.0003186096133771550.999840695193311
930.0001781617912168860.0003563235824337720.999821838208783
940.0001897366839478110.0003794733678956230.999810263316052
950.0002142268624575260.0004284537249150510.999785773137542
960.0001847583076215910.0003695166152431820.999815241692378
970.0001757075602963160.0003514151205926320.999824292439704
980.0002334927693483950.0004669855386967910.999766507230652
990.000407090039451350.00081418007890270.999592909960549
1000.0008131324444810210.001626264888962040.99918686755552
1010.002504271754941050.00500854350988210.997495728245059

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.000100568613409031 & 0.000201137226818061 & 0.999899431386591 \tabularnewline
17 & 3.66648240437122e-06 & 7.33296480874243e-06 & 0.999996333517596 \tabularnewline
18 & 7.0463081447607e-07 & 1.40926162895214e-06 & 0.999999295369186 \tabularnewline
19 & 1.72434686863373e-07 & 3.44869373726745e-07 & 0.999999827565313 \tabularnewline
20 & 4.84653420864804e-08 & 9.69306841729608e-08 & 0.999999951534658 \tabularnewline
21 & 6.5388538386515e-08 & 1.3077707677303e-07 & 0.999999934611462 \tabularnewline
22 & 2.03430697890715e-07 & 4.0686139578143e-07 & 0.999999796569302 \tabularnewline
23 & 5.31374261310877e-07 & 1.06274852262175e-06 & 0.999999468625739 \tabularnewline
24 & 1.95922753030058e-07 & 3.91845506060116e-07 & 0.999999804077247 \tabularnewline
25 & 8.13379082974152e-08 & 1.62675816594830e-07 & 0.999999918662092 \tabularnewline
26 & 3.8205229898219e-08 & 7.6410459796438e-08 & 0.99999996179477 \tabularnewline
27 & 8.62674149687276e-09 & 1.72534829937455e-08 & 0.999999991373258 \tabularnewline
28 & 1.62736212930182e-09 & 3.25472425860364e-09 & 0.999999998372638 \tabularnewline
29 & 3.48234297539373e-10 & 6.96468595078746e-10 & 0.999999999651766 \tabularnewline
30 & 8.57328725031319e-11 & 1.71465745006264e-10 & 0.999999999914267 \tabularnewline
31 & 1.74971305352516e-11 & 3.49942610705032e-11 & 0.999999999982503 \tabularnewline
32 & 3.51033075336154e-12 & 7.02066150672307e-12 & 0.99999999999649 \tabularnewline
33 & 6.47507085385451e-13 & 1.29501417077090e-12 & 0.999999999999353 \tabularnewline
34 & 1.21930121353381e-13 & 2.43860242706762e-13 & 0.999999999999878 \tabularnewline
35 & 2.18429622580665e-14 & 4.36859245161331e-14 & 0.999999999999978 \tabularnewline
36 & 6.19592078100283e-15 & 1.23918415620057e-14 & 0.999999999999994 \tabularnewline
37 & 2.82749818775683e-15 & 5.65499637551365e-15 & 0.999999999999997 \tabularnewline
38 & 1.87349335107131e-15 & 3.74698670214263e-15 & 0.999999999999998 \tabularnewline
39 & 5.62562178111483e-16 & 1.12512435622297e-15 & 1 \tabularnewline
40 & 1.20216437593701e-16 & 2.40432875187402e-16 & 1 \tabularnewline
41 & 2.71558684721833e-17 & 5.43117369443665e-17 & 1 \tabularnewline
42 & 7.1077210350425e-18 & 1.4215442070085e-17 & 1 \tabularnewline
43 & 2.29551865196965e-18 & 4.59103730393931e-18 & 1 \tabularnewline
44 & 7.74584013088524e-19 & 1.54916802617705e-18 & 1 \tabularnewline
45 & 1.99562235729663e-19 & 3.99124471459325e-19 & 1 \tabularnewline
46 & 7.57149618309897e-20 & 1.51429923661979e-19 & 1 \tabularnewline
47 & 4.41448819736285e-20 & 8.8289763947257e-20 & 1 \tabularnewline
48 & 5.14806215383661e-20 & 1.02961243076732e-19 & 1 \tabularnewline
49 & 9.01734905629325e-20 & 1.80346981125865e-19 & 1 \tabularnewline
50 & 1.02804419678468e-19 & 2.05608839356935e-19 & 1 \tabularnewline
51 & 3.90091143625552e-19 & 7.80182287251104e-19 & 1 \tabularnewline
52 & 2.62587654223401e-18 & 5.25175308446802e-18 & 1 \tabularnewline
53 & 2.23785230506030e-17 & 4.47570461012061e-17 & 1 \tabularnewline
54 & 1.02415848378172e-16 & 2.04831696756345e-16 & 1 \tabularnewline
55 & 7.31188228357852e-16 & 1.46237645671570e-15 & 1 \tabularnewline
56 & 5.99212988801057e-15 & 1.19842597760211e-14 & 0.999999999999994 \tabularnewline
57 & 2.54164444812026e-14 & 5.08328889624051e-14 & 0.999999999999975 \tabularnewline
58 & 2.84008850603266e-13 & 5.68017701206532e-13 & 0.999999999999716 \tabularnewline
59 & 1.10050723185393e-12 & 2.20101446370787e-12 & 0.9999999999989 \tabularnewline
60 & 1.99053489249126e-12 & 3.98106978498252e-12 & 0.99999999999801 \tabularnewline
61 & 5.69877812457117e-12 & 1.13975562491423e-11 & 0.999999999994301 \tabularnewline
62 & 1.45940474976291e-11 & 2.91880949952581e-11 & 0.999999999985406 \tabularnewline
63 & 9.7387260189417e-11 & 1.94774520378834e-10 & 0.999999999902613 \tabularnewline
64 & 5.69296437894212e-10 & 1.13859287578842e-09 & 0.999999999430704 \tabularnewline
65 & 1.62136047855522e-09 & 3.24272095711043e-09 & 0.99999999837864 \tabularnewline
66 & 8.31149672393442e-09 & 1.66229934478688e-08 & 0.999999991688503 \tabularnewline
67 & 4.38166503089528e-08 & 8.76333006179057e-08 & 0.99999995618335 \tabularnewline
68 & 2.15220067810186e-07 & 4.30440135620372e-07 & 0.999999784779932 \tabularnewline
69 & 6.83275341266936e-07 & 1.36655068253387e-06 & 0.999999316724659 \tabularnewline
70 & 1.28098406921397e-06 & 2.56196813842793e-06 & 0.99999871901593 \tabularnewline
71 & 1.94765197416549e-06 & 3.89530394833097e-06 & 0.999998052348026 \tabularnewline
72 & 3.16064121013635e-06 & 6.32128242027271e-06 & 0.99999683935879 \tabularnewline
73 & 7.1932863886061e-06 & 1.43865727772122e-05 & 0.999992806713611 \tabularnewline
74 & 1.17015702942845e-05 & 2.34031405885690e-05 & 0.999988298429706 \tabularnewline
75 & 1.71338090827164e-05 & 3.42676181654329e-05 & 0.999982866190917 \tabularnewline
76 & 3.89588663396852e-05 & 7.79177326793705e-05 & 0.99996104113366 \tabularnewline
77 & 8.14895087762972e-05 & 0.000162979017552594 & 0.999918510491224 \tabularnewline
78 & 0.000143958930487626 & 0.000287917860975252 & 0.999856041069512 \tabularnewline
79 & 0.000268998696282618 & 0.000537997392565235 & 0.999731001303717 \tabularnewline
80 & 0.000382733155075396 & 0.000765466310150792 & 0.999617266844925 \tabularnewline
81 & 0.000387045200139966 & 0.000774090400279932 & 0.99961295479986 \tabularnewline
82 & 0.00035091499168611 & 0.00070182998337222 & 0.999649085008314 \tabularnewline
83 & 0.000314337368526072 & 0.000628674737052145 & 0.999685662631474 \tabularnewline
84 & 0.000303182754724941 & 0.000606365509449881 & 0.999696817245275 \tabularnewline
85 & 0.000226996645131987 & 0.000453993290263973 & 0.999773003354868 \tabularnewline
86 & 0.000179702858102073 & 0.000359405716204145 & 0.999820297141898 \tabularnewline
87 & 0.000145589173602592 & 0.000291178347205183 & 0.999854410826397 \tabularnewline
88 & 0.000130202975146880 & 0.000260405950293761 & 0.999869797024853 \tabularnewline
89 & 0.000117379865301225 & 0.00023475973060245 & 0.999882620134699 \tabularnewline
90 & 0.000127539761028856 & 0.000255079522057711 & 0.99987246023897 \tabularnewline
91 & 0.000146766066197943 & 0.000293532132395886 & 0.999853233933802 \tabularnewline
92 & 0.000159304806688577 & 0.000318609613377155 & 0.999840695193311 \tabularnewline
93 & 0.000178161791216886 & 0.000356323582433772 & 0.999821838208783 \tabularnewline
94 & 0.000189736683947811 & 0.000379473367895623 & 0.999810263316052 \tabularnewline
95 & 0.000214226862457526 & 0.000428453724915051 & 0.999785773137542 \tabularnewline
96 & 0.000184758307621591 & 0.000369516615243182 & 0.999815241692378 \tabularnewline
97 & 0.000175707560296316 & 0.000351415120592632 & 0.999824292439704 \tabularnewline
98 & 0.000233492769348395 & 0.000466985538696791 & 0.999766507230652 \tabularnewline
99 & 0.00040709003945135 & 0.0008141800789027 & 0.999592909960549 \tabularnewline
100 & 0.000813132444481021 & 0.00162626488896204 & 0.99918686755552 \tabularnewline
101 & 0.00250427175494105 & 0.0050085435098821 & 0.997495728245059 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&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]16[/C][C]0.000100568613409031[/C][C]0.000201137226818061[/C][C]0.999899431386591[/C][/ROW]
[ROW][C]17[/C][C]3.66648240437122e-06[/C][C]7.33296480874243e-06[/C][C]0.999996333517596[/C][/ROW]
[ROW][C]18[/C][C]7.0463081447607e-07[/C][C]1.40926162895214e-06[/C][C]0.999999295369186[/C][/ROW]
[ROW][C]19[/C][C]1.72434686863373e-07[/C][C]3.44869373726745e-07[/C][C]0.999999827565313[/C][/ROW]
[ROW][C]20[/C][C]4.84653420864804e-08[/C][C]9.69306841729608e-08[/C][C]0.999999951534658[/C][/ROW]
[ROW][C]21[/C][C]6.5388538386515e-08[/C][C]1.3077707677303e-07[/C][C]0.999999934611462[/C][/ROW]
[ROW][C]22[/C][C]2.03430697890715e-07[/C][C]4.0686139578143e-07[/C][C]0.999999796569302[/C][/ROW]
[ROW][C]23[/C][C]5.31374261310877e-07[/C][C]1.06274852262175e-06[/C][C]0.999999468625739[/C][/ROW]
[ROW][C]24[/C][C]1.95922753030058e-07[/C][C]3.91845506060116e-07[/C][C]0.999999804077247[/C][/ROW]
[ROW][C]25[/C][C]8.13379082974152e-08[/C][C]1.62675816594830e-07[/C][C]0.999999918662092[/C][/ROW]
[ROW][C]26[/C][C]3.8205229898219e-08[/C][C]7.6410459796438e-08[/C][C]0.99999996179477[/C][/ROW]
[ROW][C]27[/C][C]8.62674149687276e-09[/C][C]1.72534829937455e-08[/C][C]0.999999991373258[/C][/ROW]
[ROW][C]28[/C][C]1.62736212930182e-09[/C][C]3.25472425860364e-09[/C][C]0.999999998372638[/C][/ROW]
[ROW][C]29[/C][C]3.48234297539373e-10[/C][C]6.96468595078746e-10[/C][C]0.999999999651766[/C][/ROW]
[ROW][C]30[/C][C]8.57328725031319e-11[/C][C]1.71465745006264e-10[/C][C]0.999999999914267[/C][/ROW]
[ROW][C]31[/C][C]1.74971305352516e-11[/C][C]3.49942610705032e-11[/C][C]0.999999999982503[/C][/ROW]
[ROW][C]32[/C][C]3.51033075336154e-12[/C][C]7.02066150672307e-12[/C][C]0.99999999999649[/C][/ROW]
[ROW][C]33[/C][C]6.47507085385451e-13[/C][C]1.29501417077090e-12[/C][C]0.999999999999353[/C][/ROW]
[ROW][C]34[/C][C]1.21930121353381e-13[/C][C]2.43860242706762e-13[/C][C]0.999999999999878[/C][/ROW]
[ROW][C]35[/C][C]2.18429622580665e-14[/C][C]4.36859245161331e-14[/C][C]0.999999999999978[/C][/ROW]
[ROW][C]36[/C][C]6.19592078100283e-15[/C][C]1.23918415620057e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]37[/C][C]2.82749818775683e-15[/C][C]5.65499637551365e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]38[/C][C]1.87349335107131e-15[/C][C]3.74698670214263e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]39[/C][C]5.62562178111483e-16[/C][C]1.12512435622297e-15[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]1.20216437593701e-16[/C][C]2.40432875187402e-16[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.71558684721833e-17[/C][C]5.43117369443665e-17[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]7.1077210350425e-18[/C][C]1.4215442070085e-17[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]2.29551865196965e-18[/C][C]4.59103730393931e-18[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]7.74584013088524e-19[/C][C]1.54916802617705e-18[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]1.99562235729663e-19[/C][C]3.99124471459325e-19[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]7.57149618309897e-20[/C][C]1.51429923661979e-19[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]4.41448819736285e-20[/C][C]8.8289763947257e-20[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]5.14806215383661e-20[/C][C]1.02961243076732e-19[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]9.01734905629325e-20[/C][C]1.80346981125865e-19[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]1.02804419678468e-19[/C][C]2.05608839356935e-19[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]3.90091143625552e-19[/C][C]7.80182287251104e-19[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]2.62587654223401e-18[/C][C]5.25175308446802e-18[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]2.23785230506030e-17[/C][C]4.47570461012061e-17[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]1.02415848378172e-16[/C][C]2.04831696756345e-16[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]7.31188228357852e-16[/C][C]1.46237645671570e-15[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]5.99212988801057e-15[/C][C]1.19842597760211e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]57[/C][C]2.54164444812026e-14[/C][C]5.08328889624051e-14[/C][C]0.999999999999975[/C][/ROW]
[ROW][C]58[/C][C]2.84008850603266e-13[/C][C]5.68017701206532e-13[/C][C]0.999999999999716[/C][/ROW]
[ROW][C]59[/C][C]1.10050723185393e-12[/C][C]2.20101446370787e-12[/C][C]0.9999999999989[/C][/ROW]
[ROW][C]60[/C][C]1.99053489249126e-12[/C][C]3.98106978498252e-12[/C][C]0.99999999999801[/C][/ROW]
[ROW][C]61[/C][C]5.69877812457117e-12[/C][C]1.13975562491423e-11[/C][C]0.999999999994301[/C][/ROW]
[ROW][C]62[/C][C]1.45940474976291e-11[/C][C]2.91880949952581e-11[/C][C]0.999999999985406[/C][/ROW]
[ROW][C]63[/C][C]9.7387260189417e-11[/C][C]1.94774520378834e-10[/C][C]0.999999999902613[/C][/ROW]
[ROW][C]64[/C][C]5.69296437894212e-10[/C][C]1.13859287578842e-09[/C][C]0.999999999430704[/C][/ROW]
[ROW][C]65[/C][C]1.62136047855522e-09[/C][C]3.24272095711043e-09[/C][C]0.99999999837864[/C][/ROW]
[ROW][C]66[/C][C]8.31149672393442e-09[/C][C]1.66229934478688e-08[/C][C]0.999999991688503[/C][/ROW]
[ROW][C]67[/C][C]4.38166503089528e-08[/C][C]8.76333006179057e-08[/C][C]0.99999995618335[/C][/ROW]
[ROW][C]68[/C][C]2.15220067810186e-07[/C][C]4.30440135620372e-07[/C][C]0.999999784779932[/C][/ROW]
[ROW][C]69[/C][C]6.83275341266936e-07[/C][C]1.36655068253387e-06[/C][C]0.999999316724659[/C][/ROW]
[ROW][C]70[/C][C]1.28098406921397e-06[/C][C]2.56196813842793e-06[/C][C]0.99999871901593[/C][/ROW]
[ROW][C]71[/C][C]1.94765197416549e-06[/C][C]3.89530394833097e-06[/C][C]0.999998052348026[/C][/ROW]
[ROW][C]72[/C][C]3.16064121013635e-06[/C][C]6.32128242027271e-06[/C][C]0.99999683935879[/C][/ROW]
[ROW][C]73[/C][C]7.1932863886061e-06[/C][C]1.43865727772122e-05[/C][C]0.999992806713611[/C][/ROW]
[ROW][C]74[/C][C]1.17015702942845e-05[/C][C]2.34031405885690e-05[/C][C]0.999988298429706[/C][/ROW]
[ROW][C]75[/C][C]1.71338090827164e-05[/C][C]3.42676181654329e-05[/C][C]0.999982866190917[/C][/ROW]
[ROW][C]76[/C][C]3.89588663396852e-05[/C][C]7.79177326793705e-05[/C][C]0.99996104113366[/C][/ROW]
[ROW][C]77[/C][C]8.14895087762972e-05[/C][C]0.000162979017552594[/C][C]0.999918510491224[/C][/ROW]
[ROW][C]78[/C][C]0.000143958930487626[/C][C]0.000287917860975252[/C][C]0.999856041069512[/C][/ROW]
[ROW][C]79[/C][C]0.000268998696282618[/C][C]0.000537997392565235[/C][C]0.999731001303717[/C][/ROW]
[ROW][C]80[/C][C]0.000382733155075396[/C][C]0.000765466310150792[/C][C]0.999617266844925[/C][/ROW]
[ROW][C]81[/C][C]0.000387045200139966[/C][C]0.000774090400279932[/C][C]0.99961295479986[/C][/ROW]
[ROW][C]82[/C][C]0.00035091499168611[/C][C]0.00070182998337222[/C][C]0.999649085008314[/C][/ROW]
[ROW][C]83[/C][C]0.000314337368526072[/C][C]0.000628674737052145[/C][C]0.999685662631474[/C][/ROW]
[ROW][C]84[/C][C]0.000303182754724941[/C][C]0.000606365509449881[/C][C]0.999696817245275[/C][/ROW]
[ROW][C]85[/C][C]0.000226996645131987[/C][C]0.000453993290263973[/C][C]0.999773003354868[/C][/ROW]
[ROW][C]86[/C][C]0.000179702858102073[/C][C]0.000359405716204145[/C][C]0.999820297141898[/C][/ROW]
[ROW][C]87[/C][C]0.000145589173602592[/C][C]0.000291178347205183[/C][C]0.999854410826397[/C][/ROW]
[ROW][C]88[/C][C]0.000130202975146880[/C][C]0.000260405950293761[/C][C]0.999869797024853[/C][/ROW]
[ROW][C]89[/C][C]0.000117379865301225[/C][C]0.00023475973060245[/C][C]0.999882620134699[/C][/ROW]
[ROW][C]90[/C][C]0.000127539761028856[/C][C]0.000255079522057711[/C][C]0.99987246023897[/C][/ROW]
[ROW][C]91[/C][C]0.000146766066197943[/C][C]0.000293532132395886[/C][C]0.999853233933802[/C][/ROW]
[ROW][C]92[/C][C]0.000159304806688577[/C][C]0.000318609613377155[/C][C]0.999840695193311[/C][/ROW]
[ROW][C]93[/C][C]0.000178161791216886[/C][C]0.000356323582433772[/C][C]0.999821838208783[/C][/ROW]
[ROW][C]94[/C][C]0.000189736683947811[/C][C]0.000379473367895623[/C][C]0.999810263316052[/C][/ROW]
[ROW][C]95[/C][C]0.000214226862457526[/C][C]0.000428453724915051[/C][C]0.999785773137542[/C][/ROW]
[ROW][C]96[/C][C]0.000184758307621591[/C][C]0.000369516615243182[/C][C]0.999815241692378[/C][/ROW]
[ROW][C]97[/C][C]0.000175707560296316[/C][C]0.000351415120592632[/C][C]0.999824292439704[/C][/ROW]
[ROW][C]98[/C][C]0.000233492769348395[/C][C]0.000466985538696791[/C][C]0.999766507230652[/C][/ROW]
[ROW][C]99[/C][C]0.00040709003945135[/C][C]0.0008141800789027[/C][C]0.999592909960549[/C][/ROW]
[ROW][C]100[/C][C]0.000813132444481021[/C][C]0.00162626488896204[/C][C]0.99918686755552[/C][/ROW]
[ROW][C]101[/C][C]0.00250427175494105[/C][C]0.0050085435098821[/C][C]0.997495728245059[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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
160.0001005686134090310.0002011372268180610.999899431386591
173.66648240437122e-067.33296480874243e-060.999996333517596
187.0463081447607e-071.40926162895214e-060.999999295369186
191.72434686863373e-073.44869373726745e-070.999999827565313
204.84653420864804e-089.69306841729608e-080.999999951534658
216.5388538386515e-081.3077707677303e-070.999999934611462
222.03430697890715e-074.0686139578143e-070.999999796569302
235.31374261310877e-071.06274852262175e-060.999999468625739
241.95922753030058e-073.91845506060116e-070.999999804077247
258.13379082974152e-081.62675816594830e-070.999999918662092
263.8205229898219e-087.6410459796438e-080.99999996179477
278.62674149687276e-091.72534829937455e-080.999999991373258
281.62736212930182e-093.25472425860364e-090.999999998372638
293.48234297539373e-106.96468595078746e-100.999999999651766
308.57328725031319e-111.71465745006264e-100.999999999914267
311.74971305352516e-113.49942610705032e-110.999999999982503
323.51033075336154e-127.02066150672307e-120.99999999999649
336.47507085385451e-131.29501417077090e-120.999999999999353
341.21930121353381e-132.43860242706762e-130.999999999999878
352.18429622580665e-144.36859245161331e-140.999999999999978
366.19592078100283e-151.23918415620057e-140.999999999999994
372.82749818775683e-155.65499637551365e-150.999999999999997
381.87349335107131e-153.74698670214263e-150.999999999999998
395.62562178111483e-161.12512435622297e-151
401.20216437593701e-162.40432875187402e-161
412.71558684721833e-175.43117369443665e-171
427.1077210350425e-181.4215442070085e-171
432.29551865196965e-184.59103730393931e-181
447.74584013088524e-191.54916802617705e-181
451.99562235729663e-193.99124471459325e-191
467.57149618309897e-201.51429923661979e-191
474.41448819736285e-208.8289763947257e-201
485.14806215383661e-201.02961243076732e-191
499.01734905629325e-201.80346981125865e-191
501.02804419678468e-192.05608839356935e-191
513.90091143625552e-197.80182287251104e-191
522.62587654223401e-185.25175308446802e-181
532.23785230506030e-174.47570461012061e-171
541.02415848378172e-162.04831696756345e-161
557.31188228357852e-161.46237645671570e-151
565.99212988801057e-151.19842597760211e-140.999999999999994
572.54164444812026e-145.08328889624051e-140.999999999999975
582.84008850603266e-135.68017701206532e-130.999999999999716
591.10050723185393e-122.20101446370787e-120.9999999999989
601.99053489249126e-123.98106978498252e-120.99999999999801
615.69877812457117e-121.13975562491423e-110.999999999994301
621.45940474976291e-112.91880949952581e-110.999999999985406
639.7387260189417e-111.94774520378834e-100.999999999902613
645.69296437894212e-101.13859287578842e-090.999999999430704
651.62136047855522e-093.24272095711043e-090.99999999837864
668.31149672393442e-091.66229934478688e-080.999999991688503
674.38166503089528e-088.76333006179057e-080.99999995618335
682.15220067810186e-074.30440135620372e-070.999999784779932
696.83275341266936e-071.36655068253387e-060.999999316724659
701.28098406921397e-062.56196813842793e-060.99999871901593
711.94765197416549e-063.89530394833097e-060.999998052348026
723.16064121013635e-066.32128242027271e-060.99999683935879
737.1932863886061e-061.43865727772122e-050.999992806713611
741.17015702942845e-052.34031405885690e-050.999988298429706
751.71338090827164e-053.42676181654329e-050.999982866190917
763.89588663396852e-057.79177326793705e-050.99996104113366
778.14895087762972e-050.0001629790175525940.999918510491224
780.0001439589304876260.0002879178609752520.999856041069512
790.0002689986962826180.0005379973925652350.999731001303717
800.0003827331550753960.0007654663101507920.999617266844925
810.0003870452001399660.0007740904002799320.99961295479986
820.000350914991686110.000701829983372220.999649085008314
830.0003143373685260720.0006286747370521450.999685662631474
840.0003031827547249410.0006063655094498810.999696817245275
850.0002269966451319870.0004539932902639730.999773003354868
860.0001797028581020730.0003594057162041450.999820297141898
870.0001455891736025920.0002911783472051830.999854410826397
880.0001302029751468800.0002604059502937610.999869797024853
890.0001173798653012250.000234759730602450.999882620134699
900.0001275397610288560.0002550795220577110.99987246023897
910.0001467660661979430.0002935321323958860.999853233933802
920.0001593048066885770.0003186096133771550.999840695193311
930.0001781617912168860.0003563235824337720.999821838208783
940.0001897366839478110.0003794733678956230.999810263316052
950.0002142268624575260.0004284537249150510.999785773137542
960.0001847583076215910.0003695166152431820.999815241692378
970.0001757075602963160.0003514151205926320.999824292439704
980.0002334927693483950.0004669855386967910.999766507230652
990.000407090039451350.00081418007890270.999592909960549
1000.0008131324444810210.001626264888962040.99918686755552
1010.002504271754941050.00500854350988210.997495728245059







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level861NOK
5% type I error level861NOK
10% type I error level861NOK

\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 & 86 & 1 & NOK \tabularnewline
5% type I error level & 86 & 1 & NOK \tabularnewline
10% type I error level & 86 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58187&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]86[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]86[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58187&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58187&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 level861NOK
5% type I error level861NOK
10% type I error level861NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}