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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 04:15:35 -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/t12587159997dbw7i1r2xsug0k.htm/, Retrieved Thu, 28 Mar 2024 23:36:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58038, Retrieved Thu, 28 Mar 2024 23:36:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
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 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Model 1] [2009-11-20 11:15:35] [986e3c28a4248c495afaef9fd432264f] [Current]
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Dataseries X:
98.71	153.4
98.54	145
98.2	137.7
96.92	148.3
99.06	152.2
99.65	169.4
99.82	168.6
99.99	161.1
100.33	174.1
99.31	179
101.1	190.6
101.1	190
100.93	181.6
100.85	174.8
100.93	180.5
99.6	196.8
101.88	193.8
101.81	197
102.38	216.3
102.74	221.4
102.82	217.9
101.72	229.7
103.47	227.4
102.98	204.2
102.68	196.6
102.9	198.8
103.03	207.5
101.29	190.7
103.69	201.6
103.68	210.5
104.2	223.5
104.08	223.8
104.16	231.2
103.05	244
104.66	234.7
104.46	250.2
104.95	265.7
105.85	287.6
106.23	283.3
104.86	295.4
107.44	312.3
108.23	333.8
108.45	347.7
109.39	383.2
110.15	407.1
109.13	413.6
110.28	362.7
110.17	321.9
109.99	239.4
109.26	191
109.11	159.7
107.06	163.4
109.53	157.6
108.92	166.2
109.24	176.7
109.12	198.3
109	226.2
107.23	216.2
109.49	235.9
109.04	226.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&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]3 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=58038&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 96.4375783171408 + 0.0352788806088186X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  96.4375783171408 +  0.0352788806088186X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  96.4375783171408 +  0.0352788806088186X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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] = + 96.4375783171408 + 0.0352788806088186X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)96.43757831714081.43695367.112600
X0.03527888060881860.006185.708700

\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) & 96.4375783171408 & 1.436953 & 67.1126 & 0 & 0 \tabularnewline
X & 0.0352788806088186 & 0.00618 & 5.7087 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&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]96.4375783171408[/C][C]1.436953[/C][C]67.1126[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.0352788806088186[/C][C]0.00618[/C][C]5.7087[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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)96.43757831714081.43695367.112600
X0.03527888060881860.006185.708700







Multiple Linear Regression - Regression Statistics
Multiple R0.599788787480733
R-squared0.359746589587608
Adjusted R-squared0.348707737683946
F-TEST (value)32.5891308921600
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value4.09724077887752e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10991109710113
Sum Squared Residuals560.949727848618

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.599788787480733 \tabularnewline
R-squared & 0.359746589587608 \tabularnewline
Adjusted R-squared & 0.348707737683946 \tabularnewline
F-TEST (value) & 32.5891308921600 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 4.09724077887752e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.10991109710113 \tabularnewline
Sum Squared Residuals & 560.949727848618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.599788787480733[/C][/ROW]
[ROW][C]R-squared[/C][C]0.359746589587608[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.348707737683946[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.5891308921600[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]4.09724077887752e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.10991109710113[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]560.949727848618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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 R0.599788787480733
R-squared0.359746589587608
Adjusted R-squared0.348707737683946
F-TEST (value)32.5891308921600
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value4.09724077887752e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10991109710113
Sum Squared Residuals560.949727848618







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.71101.849358602534-3.13935860253352
298.54101.553016005420-3.0130160054195
398.2101.295480176975-3.09548017697513
496.92101.669436311429-4.74943631142861
599.06101.807023945803-2.747023945803
699.65102.413820692275-2.76382069227468
799.82102.385597587788-2.56559758778763
899.99102.121005983221-2.13100598322149
9100.33102.579631431136-2.24963143113613
1099.31102.752497946119-3.44249794611934
11101.1103.161732961182-2.06173296118164
12101.1103.140565632816-2.04056563281635
13100.93102.844223035702-1.91422303570226
14100.85102.604326647562-1.75432664756231
15100.93102.805416267033-1.87541626703256
1699.6103.380462020956-3.78046202095632
17101.88103.274625379130-1.39462537912986
18101.81103.387517797078-1.57751779707807
19102.38104.068400192828-1.68840019282828
20102.74104.248322483933-1.50832248393326
21102.82104.124846401802-1.30484640180239
22101.72104.541137192986-2.82113719298645
23103.47104.459995767586-0.989995767586163
24102.98103.641525737462-0.661525737461566
25102.68103.373406244835-0.693406244834542
26102.9103.451019782174-0.551019782173944
27103.03103.757946043471-0.72794604347067
28101.29103.165260849243-1.87526084924251
29103.69103.5498006478790.140199352121356
30103.68103.863782685297-0.183782685297121
31104.2104.322408133212-0.122408133211766
32104.08104.332991797394-0.252991797394417
33104.16104.594055513900-0.434055513899676
34103.05105.045625185693-1.99562518569255
35104.66104.717531596031-0.0575315960305406
36104.46105.264354245467-0.804354245467232
37104.95105.811176894904-0.861176894903911
38105.85106.583784380237-0.733784380237048
39106.23106.432085193619-0.202085193619118
40104.86106.858959648986-1.99895964898583
41107.44107.455172731275-0.0151727312748642
42108.23108.2136686643640.0163313356355422
43108.45108.704045104827-0.254045104827037
44109.39109.956445366440-0.5664453664401
45110.15110.799610612991-0.649610612990861
46109.13111.028923336948-1.89892333694819
47110.28109.2332283139591.04677168604068
48110.17107.7938499851202.37615001488048
49109.99104.8833423348925.10665766510801
50109.26103.1758445134256.08415548657484
51109.11102.0716155503697.03838444963086
52107.06102.2021474086224.85785259137823
53109.53101.9975299010917.53247009890938
54108.92102.3009282743266.61907172567354
55109.24102.6713565207196.56864347928094
56109.12103.4333803418705.68661965813046
57109104.4176611108564.58233888914442
58107.23104.0648723047673.16512769523261
59109.49104.7598662527614.73013374723887
60109.04104.4423563272824.59764367271825

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.71 & 101.849358602534 & -3.13935860253352 \tabularnewline
2 & 98.54 & 101.553016005420 & -3.0130160054195 \tabularnewline
3 & 98.2 & 101.295480176975 & -3.09548017697513 \tabularnewline
4 & 96.92 & 101.669436311429 & -4.74943631142861 \tabularnewline
5 & 99.06 & 101.807023945803 & -2.747023945803 \tabularnewline
6 & 99.65 & 102.413820692275 & -2.76382069227468 \tabularnewline
7 & 99.82 & 102.385597587788 & -2.56559758778763 \tabularnewline
8 & 99.99 & 102.121005983221 & -2.13100598322149 \tabularnewline
9 & 100.33 & 102.579631431136 & -2.24963143113613 \tabularnewline
10 & 99.31 & 102.752497946119 & -3.44249794611934 \tabularnewline
11 & 101.1 & 103.161732961182 & -2.06173296118164 \tabularnewline
12 & 101.1 & 103.140565632816 & -2.04056563281635 \tabularnewline
13 & 100.93 & 102.844223035702 & -1.91422303570226 \tabularnewline
14 & 100.85 & 102.604326647562 & -1.75432664756231 \tabularnewline
15 & 100.93 & 102.805416267033 & -1.87541626703256 \tabularnewline
16 & 99.6 & 103.380462020956 & -3.78046202095632 \tabularnewline
17 & 101.88 & 103.274625379130 & -1.39462537912986 \tabularnewline
18 & 101.81 & 103.387517797078 & -1.57751779707807 \tabularnewline
19 & 102.38 & 104.068400192828 & -1.68840019282828 \tabularnewline
20 & 102.74 & 104.248322483933 & -1.50832248393326 \tabularnewline
21 & 102.82 & 104.124846401802 & -1.30484640180239 \tabularnewline
22 & 101.72 & 104.541137192986 & -2.82113719298645 \tabularnewline
23 & 103.47 & 104.459995767586 & -0.989995767586163 \tabularnewline
24 & 102.98 & 103.641525737462 & -0.661525737461566 \tabularnewline
25 & 102.68 & 103.373406244835 & -0.693406244834542 \tabularnewline
26 & 102.9 & 103.451019782174 & -0.551019782173944 \tabularnewline
27 & 103.03 & 103.757946043471 & -0.72794604347067 \tabularnewline
28 & 101.29 & 103.165260849243 & -1.87526084924251 \tabularnewline
29 & 103.69 & 103.549800647879 & 0.140199352121356 \tabularnewline
30 & 103.68 & 103.863782685297 & -0.183782685297121 \tabularnewline
31 & 104.2 & 104.322408133212 & -0.122408133211766 \tabularnewline
32 & 104.08 & 104.332991797394 & -0.252991797394417 \tabularnewline
33 & 104.16 & 104.594055513900 & -0.434055513899676 \tabularnewline
34 & 103.05 & 105.045625185693 & -1.99562518569255 \tabularnewline
35 & 104.66 & 104.717531596031 & -0.0575315960305406 \tabularnewline
36 & 104.46 & 105.264354245467 & -0.804354245467232 \tabularnewline
37 & 104.95 & 105.811176894904 & -0.861176894903911 \tabularnewline
38 & 105.85 & 106.583784380237 & -0.733784380237048 \tabularnewline
39 & 106.23 & 106.432085193619 & -0.202085193619118 \tabularnewline
40 & 104.86 & 106.858959648986 & -1.99895964898583 \tabularnewline
41 & 107.44 & 107.455172731275 & -0.0151727312748642 \tabularnewline
42 & 108.23 & 108.213668664364 & 0.0163313356355422 \tabularnewline
43 & 108.45 & 108.704045104827 & -0.254045104827037 \tabularnewline
44 & 109.39 & 109.956445366440 & -0.5664453664401 \tabularnewline
45 & 110.15 & 110.799610612991 & -0.649610612990861 \tabularnewline
46 & 109.13 & 111.028923336948 & -1.89892333694819 \tabularnewline
47 & 110.28 & 109.233228313959 & 1.04677168604068 \tabularnewline
48 & 110.17 & 107.793849985120 & 2.37615001488048 \tabularnewline
49 & 109.99 & 104.883342334892 & 5.10665766510801 \tabularnewline
50 & 109.26 & 103.175844513425 & 6.08415548657484 \tabularnewline
51 & 109.11 & 102.071615550369 & 7.03838444963086 \tabularnewline
52 & 107.06 & 102.202147408622 & 4.85785259137823 \tabularnewline
53 & 109.53 & 101.997529901091 & 7.53247009890938 \tabularnewline
54 & 108.92 & 102.300928274326 & 6.61907172567354 \tabularnewline
55 & 109.24 & 102.671356520719 & 6.56864347928094 \tabularnewline
56 & 109.12 & 103.433380341870 & 5.68661965813046 \tabularnewline
57 & 109 & 104.417661110856 & 4.58233888914442 \tabularnewline
58 & 107.23 & 104.064872304767 & 3.16512769523261 \tabularnewline
59 & 109.49 & 104.759866252761 & 4.73013374723887 \tabularnewline
60 & 109.04 & 104.442356327282 & 4.59764367271825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&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]98.71[/C][C]101.849358602534[/C][C]-3.13935860253352[/C][/ROW]
[ROW][C]2[/C][C]98.54[/C][C]101.553016005420[/C][C]-3.0130160054195[/C][/ROW]
[ROW][C]3[/C][C]98.2[/C][C]101.295480176975[/C][C]-3.09548017697513[/C][/ROW]
[ROW][C]4[/C][C]96.92[/C][C]101.669436311429[/C][C]-4.74943631142861[/C][/ROW]
[ROW][C]5[/C][C]99.06[/C][C]101.807023945803[/C][C]-2.747023945803[/C][/ROW]
[ROW][C]6[/C][C]99.65[/C][C]102.413820692275[/C][C]-2.76382069227468[/C][/ROW]
[ROW][C]7[/C][C]99.82[/C][C]102.385597587788[/C][C]-2.56559758778763[/C][/ROW]
[ROW][C]8[/C][C]99.99[/C][C]102.121005983221[/C][C]-2.13100598322149[/C][/ROW]
[ROW][C]9[/C][C]100.33[/C][C]102.579631431136[/C][C]-2.24963143113613[/C][/ROW]
[ROW][C]10[/C][C]99.31[/C][C]102.752497946119[/C][C]-3.44249794611934[/C][/ROW]
[ROW][C]11[/C][C]101.1[/C][C]103.161732961182[/C][C]-2.06173296118164[/C][/ROW]
[ROW][C]12[/C][C]101.1[/C][C]103.140565632816[/C][C]-2.04056563281635[/C][/ROW]
[ROW][C]13[/C][C]100.93[/C][C]102.844223035702[/C][C]-1.91422303570226[/C][/ROW]
[ROW][C]14[/C][C]100.85[/C][C]102.604326647562[/C][C]-1.75432664756231[/C][/ROW]
[ROW][C]15[/C][C]100.93[/C][C]102.805416267033[/C][C]-1.87541626703256[/C][/ROW]
[ROW][C]16[/C][C]99.6[/C][C]103.380462020956[/C][C]-3.78046202095632[/C][/ROW]
[ROW][C]17[/C][C]101.88[/C][C]103.274625379130[/C][C]-1.39462537912986[/C][/ROW]
[ROW][C]18[/C][C]101.81[/C][C]103.387517797078[/C][C]-1.57751779707807[/C][/ROW]
[ROW][C]19[/C][C]102.38[/C][C]104.068400192828[/C][C]-1.68840019282828[/C][/ROW]
[ROW][C]20[/C][C]102.74[/C][C]104.248322483933[/C][C]-1.50832248393326[/C][/ROW]
[ROW][C]21[/C][C]102.82[/C][C]104.124846401802[/C][C]-1.30484640180239[/C][/ROW]
[ROW][C]22[/C][C]101.72[/C][C]104.541137192986[/C][C]-2.82113719298645[/C][/ROW]
[ROW][C]23[/C][C]103.47[/C][C]104.459995767586[/C][C]-0.989995767586163[/C][/ROW]
[ROW][C]24[/C][C]102.98[/C][C]103.641525737462[/C][C]-0.661525737461566[/C][/ROW]
[ROW][C]25[/C][C]102.68[/C][C]103.373406244835[/C][C]-0.693406244834542[/C][/ROW]
[ROW][C]26[/C][C]102.9[/C][C]103.451019782174[/C][C]-0.551019782173944[/C][/ROW]
[ROW][C]27[/C][C]103.03[/C][C]103.757946043471[/C][C]-0.72794604347067[/C][/ROW]
[ROW][C]28[/C][C]101.29[/C][C]103.165260849243[/C][C]-1.87526084924251[/C][/ROW]
[ROW][C]29[/C][C]103.69[/C][C]103.549800647879[/C][C]0.140199352121356[/C][/ROW]
[ROW][C]30[/C][C]103.68[/C][C]103.863782685297[/C][C]-0.183782685297121[/C][/ROW]
[ROW][C]31[/C][C]104.2[/C][C]104.322408133212[/C][C]-0.122408133211766[/C][/ROW]
[ROW][C]32[/C][C]104.08[/C][C]104.332991797394[/C][C]-0.252991797394417[/C][/ROW]
[ROW][C]33[/C][C]104.16[/C][C]104.594055513900[/C][C]-0.434055513899676[/C][/ROW]
[ROW][C]34[/C][C]103.05[/C][C]105.045625185693[/C][C]-1.99562518569255[/C][/ROW]
[ROW][C]35[/C][C]104.66[/C][C]104.717531596031[/C][C]-0.0575315960305406[/C][/ROW]
[ROW][C]36[/C][C]104.46[/C][C]105.264354245467[/C][C]-0.804354245467232[/C][/ROW]
[ROW][C]37[/C][C]104.95[/C][C]105.811176894904[/C][C]-0.861176894903911[/C][/ROW]
[ROW][C]38[/C][C]105.85[/C][C]106.583784380237[/C][C]-0.733784380237048[/C][/ROW]
[ROW][C]39[/C][C]106.23[/C][C]106.432085193619[/C][C]-0.202085193619118[/C][/ROW]
[ROW][C]40[/C][C]104.86[/C][C]106.858959648986[/C][C]-1.99895964898583[/C][/ROW]
[ROW][C]41[/C][C]107.44[/C][C]107.455172731275[/C][C]-0.0151727312748642[/C][/ROW]
[ROW][C]42[/C][C]108.23[/C][C]108.213668664364[/C][C]0.0163313356355422[/C][/ROW]
[ROW][C]43[/C][C]108.45[/C][C]108.704045104827[/C][C]-0.254045104827037[/C][/ROW]
[ROW][C]44[/C][C]109.39[/C][C]109.956445366440[/C][C]-0.5664453664401[/C][/ROW]
[ROW][C]45[/C][C]110.15[/C][C]110.799610612991[/C][C]-0.649610612990861[/C][/ROW]
[ROW][C]46[/C][C]109.13[/C][C]111.028923336948[/C][C]-1.89892333694819[/C][/ROW]
[ROW][C]47[/C][C]110.28[/C][C]109.233228313959[/C][C]1.04677168604068[/C][/ROW]
[ROW][C]48[/C][C]110.17[/C][C]107.793849985120[/C][C]2.37615001488048[/C][/ROW]
[ROW][C]49[/C][C]109.99[/C][C]104.883342334892[/C][C]5.10665766510801[/C][/ROW]
[ROW][C]50[/C][C]109.26[/C][C]103.175844513425[/C][C]6.08415548657484[/C][/ROW]
[ROW][C]51[/C][C]109.11[/C][C]102.071615550369[/C][C]7.03838444963086[/C][/ROW]
[ROW][C]52[/C][C]107.06[/C][C]102.202147408622[/C][C]4.85785259137823[/C][/ROW]
[ROW][C]53[/C][C]109.53[/C][C]101.997529901091[/C][C]7.53247009890938[/C][/ROW]
[ROW][C]54[/C][C]108.92[/C][C]102.300928274326[/C][C]6.61907172567354[/C][/ROW]
[ROW][C]55[/C][C]109.24[/C][C]102.671356520719[/C][C]6.56864347928094[/C][/ROW]
[ROW][C]56[/C][C]109.12[/C][C]103.433380341870[/C][C]5.68661965813046[/C][/ROW]
[ROW][C]57[/C][C]109[/C][C]104.417661110856[/C][C]4.58233888914442[/C][/ROW]
[ROW][C]58[/C][C]107.23[/C][C]104.064872304767[/C][C]3.16512769523261[/C][/ROW]
[ROW][C]59[/C][C]109.49[/C][C]104.759866252761[/C][C]4.73013374723887[/C][/ROW]
[ROW][C]60[/C][C]109.04[/C][C]104.442356327282[/C][C]4.59764367271825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.71101.849358602534-3.13935860253352
298.54101.553016005420-3.0130160054195
398.2101.295480176975-3.09548017697513
496.92101.669436311429-4.74943631142861
599.06101.807023945803-2.747023945803
699.65102.413820692275-2.76382069227468
799.82102.385597587788-2.56559758778763
899.99102.121005983221-2.13100598322149
9100.33102.579631431136-2.24963143113613
1099.31102.752497946119-3.44249794611934
11101.1103.161732961182-2.06173296118164
12101.1103.140565632816-2.04056563281635
13100.93102.844223035702-1.91422303570226
14100.85102.604326647562-1.75432664756231
15100.93102.805416267033-1.87541626703256
1699.6103.380462020956-3.78046202095632
17101.88103.274625379130-1.39462537912986
18101.81103.387517797078-1.57751779707807
19102.38104.068400192828-1.68840019282828
20102.74104.248322483933-1.50832248393326
21102.82104.124846401802-1.30484640180239
22101.72104.541137192986-2.82113719298645
23103.47104.459995767586-0.989995767586163
24102.98103.641525737462-0.661525737461566
25102.68103.373406244835-0.693406244834542
26102.9103.451019782174-0.551019782173944
27103.03103.757946043471-0.72794604347067
28101.29103.165260849243-1.87526084924251
29103.69103.5498006478790.140199352121356
30103.68103.863782685297-0.183782685297121
31104.2104.322408133212-0.122408133211766
32104.08104.332991797394-0.252991797394417
33104.16104.594055513900-0.434055513899676
34103.05105.045625185693-1.99562518569255
35104.66104.717531596031-0.0575315960305406
36104.46105.264354245467-0.804354245467232
37104.95105.811176894904-0.861176894903911
38105.85106.583784380237-0.733784380237048
39106.23106.432085193619-0.202085193619118
40104.86106.858959648986-1.99895964898583
41107.44107.455172731275-0.0151727312748642
42108.23108.2136686643640.0163313356355422
43108.45108.704045104827-0.254045104827037
44109.39109.956445366440-0.5664453664401
45110.15110.799610612991-0.649610612990861
46109.13111.028923336948-1.89892333694819
47110.28109.2332283139591.04677168604068
48110.17107.7938499851202.37615001488048
49109.99104.8833423348925.10665766510801
50109.26103.1758445134256.08415548657484
51109.11102.0716155503697.03838444963086
52107.06102.2021474086224.85785259137823
53109.53101.9975299010917.53247009890938
54108.92102.3009282743266.61907172567354
55109.24102.6713565207196.56864347928094
56109.12103.4333803418705.68661965813046
57109104.4176611108564.58233888914442
58107.23104.0648723047673.16512769523261
59109.49104.7598662527614.73013374723887
60109.04104.4423563272824.59764367271825







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.03585541999039610.07171083998079230.964144580009604
60.009729858466491860.01945971693298370.990270141533508
70.002472730841408000.004945461682816010.997527269158592
80.0009556632194890670.001911326438978130.99904433678051
90.0002270487900899610.0004540975801799220.99977295120991
100.0001566290963893020.0003132581927786030.99984337090361
114.15943811837635e-058.3188762367527e-050.999958405618816
121.05934263619861e-052.11868527239722e-050.999989406573638
133.25549715380238e-066.51099430760477e-060.999996744502846
141.46578474621620e-062.93156949243240e-060.999998534215254
154.54946237088145e-079.0989247417629e-070.999999545053763
163.42981794052023e-066.85963588104046e-060.99999657018206
171.83136944449096e-063.66273888898191e-060.999998168630555
187.4450303426787e-071.48900606853574e-060.999999255496966
192.61414513116035e-075.2282902623207e-070.999999738585487
208.82399348133479e-081.76479869626696e-070.999999911760065
213.10695095238061e-086.21390190476123e-080.99999996893049
228.63965045551294e-081.72793009110259e-070.999999913603495
234.08004903738326e-088.16009807476652e-080.99999995919951
245.52666573714547e-081.10533314742909e-070.999999944733343
258.79496479926245e-081.75899295985249e-070.999999912050352
261.42799719221884e-072.85599438443768e-070.99999985720028
271.3714584700427e-072.7429169400854e-070.999999862854153
282.35718134081456e-074.71436268162913e-070.999999764281866
291.02297625952874e-062.04595251905749e-060.99999897702374
301.77203587476277e-063.54407174952554e-060.999998227964125
312.00869973245732e-064.01739946491464e-060.999997991300268
322.54377576228906e-065.08755152457811e-060.999997456224238
333.38905168206004e-066.77810336412008e-060.999996610948318
346.29058400230478e-050.0001258116800460960.999937094159977
350.0001467337465506890.0002934674931013780.99985326625345
360.0006438437450284010.001287687490056800.999356156254972
370.003244586245985460.006489172491970920.996755413754015
380.01008032838960610.02016065677921230.989919671610394
390.02110327730110860.04220655460221720.978896722698891
400.512336364128830.975327271742340.48766363587117
410.6420201274651420.7159597450697170.357979872534858
420.6673216419390350.665356716121930.332678358060965
430.7013047875824160.5973904248351690.298695212417584
440.6815820378411020.6368359243177960.318417962158898
450.6433124421368080.7133751157263840.356687557863192
460.7599356226822470.4801287546355060.240064377317753
470.7006473015083850.5987053969832310.299352698491616
480.7256341002962430.5487317994075140.274365899703757
490.9284032511703040.1431934976593910.0715967488296957
500.9713599408158860.05728011836822820.0286400591841141
510.9819863335611580.03602733287768310.0180136664388415
520.9948515732441660.01029685351166870.00514842675583434
530.9917997318045170.01640053639096580.0082002681954829
540.978802504091990.04239499181602040.0211974959080102
550.9500177653407810.09996446931843710.0499822346592186

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0358554199903961 & 0.0717108399807923 & 0.964144580009604 \tabularnewline
6 & 0.00972985846649186 & 0.0194597169329837 & 0.990270141533508 \tabularnewline
7 & 0.00247273084140800 & 0.00494546168281601 & 0.997527269158592 \tabularnewline
8 & 0.000955663219489067 & 0.00191132643897813 & 0.99904433678051 \tabularnewline
9 & 0.000227048790089961 & 0.000454097580179922 & 0.99977295120991 \tabularnewline
10 & 0.000156629096389302 & 0.000313258192778603 & 0.99984337090361 \tabularnewline
11 & 4.15943811837635e-05 & 8.3188762367527e-05 & 0.999958405618816 \tabularnewline
12 & 1.05934263619861e-05 & 2.11868527239722e-05 & 0.999989406573638 \tabularnewline
13 & 3.25549715380238e-06 & 6.51099430760477e-06 & 0.999996744502846 \tabularnewline
14 & 1.46578474621620e-06 & 2.93156949243240e-06 & 0.999998534215254 \tabularnewline
15 & 4.54946237088145e-07 & 9.0989247417629e-07 & 0.999999545053763 \tabularnewline
16 & 3.42981794052023e-06 & 6.85963588104046e-06 & 0.99999657018206 \tabularnewline
17 & 1.83136944449096e-06 & 3.66273888898191e-06 & 0.999998168630555 \tabularnewline
18 & 7.4450303426787e-07 & 1.48900606853574e-06 & 0.999999255496966 \tabularnewline
19 & 2.61414513116035e-07 & 5.2282902623207e-07 & 0.999999738585487 \tabularnewline
20 & 8.82399348133479e-08 & 1.76479869626696e-07 & 0.999999911760065 \tabularnewline
21 & 3.10695095238061e-08 & 6.21390190476123e-08 & 0.99999996893049 \tabularnewline
22 & 8.63965045551294e-08 & 1.72793009110259e-07 & 0.999999913603495 \tabularnewline
23 & 4.08004903738326e-08 & 8.16009807476652e-08 & 0.99999995919951 \tabularnewline
24 & 5.52666573714547e-08 & 1.10533314742909e-07 & 0.999999944733343 \tabularnewline
25 & 8.79496479926245e-08 & 1.75899295985249e-07 & 0.999999912050352 \tabularnewline
26 & 1.42799719221884e-07 & 2.85599438443768e-07 & 0.99999985720028 \tabularnewline
27 & 1.3714584700427e-07 & 2.7429169400854e-07 & 0.999999862854153 \tabularnewline
28 & 2.35718134081456e-07 & 4.71436268162913e-07 & 0.999999764281866 \tabularnewline
29 & 1.02297625952874e-06 & 2.04595251905749e-06 & 0.99999897702374 \tabularnewline
30 & 1.77203587476277e-06 & 3.54407174952554e-06 & 0.999998227964125 \tabularnewline
31 & 2.00869973245732e-06 & 4.01739946491464e-06 & 0.999997991300268 \tabularnewline
32 & 2.54377576228906e-06 & 5.08755152457811e-06 & 0.999997456224238 \tabularnewline
33 & 3.38905168206004e-06 & 6.77810336412008e-06 & 0.999996610948318 \tabularnewline
34 & 6.29058400230478e-05 & 0.000125811680046096 & 0.999937094159977 \tabularnewline
35 & 0.000146733746550689 & 0.000293467493101378 & 0.99985326625345 \tabularnewline
36 & 0.000643843745028401 & 0.00128768749005680 & 0.999356156254972 \tabularnewline
37 & 0.00324458624598546 & 0.00648917249197092 & 0.996755413754015 \tabularnewline
38 & 0.0100803283896061 & 0.0201606567792123 & 0.989919671610394 \tabularnewline
39 & 0.0211032773011086 & 0.0422065546022172 & 0.978896722698891 \tabularnewline
40 & 0.51233636412883 & 0.97532727174234 & 0.48766363587117 \tabularnewline
41 & 0.642020127465142 & 0.715959745069717 & 0.357979872534858 \tabularnewline
42 & 0.667321641939035 & 0.66535671612193 & 0.332678358060965 \tabularnewline
43 & 0.701304787582416 & 0.597390424835169 & 0.298695212417584 \tabularnewline
44 & 0.681582037841102 & 0.636835924317796 & 0.318417962158898 \tabularnewline
45 & 0.643312442136808 & 0.713375115726384 & 0.356687557863192 \tabularnewline
46 & 0.759935622682247 & 0.480128754635506 & 0.240064377317753 \tabularnewline
47 & 0.700647301508385 & 0.598705396983231 & 0.299352698491616 \tabularnewline
48 & 0.725634100296243 & 0.548731799407514 & 0.274365899703757 \tabularnewline
49 & 0.928403251170304 & 0.143193497659391 & 0.0715967488296957 \tabularnewline
50 & 0.971359940815886 & 0.0572801183682282 & 0.0286400591841141 \tabularnewline
51 & 0.981986333561158 & 0.0360273328776831 & 0.0180136664388415 \tabularnewline
52 & 0.994851573244166 & 0.0102968535116687 & 0.00514842675583434 \tabularnewline
53 & 0.991799731804517 & 0.0164005363909658 & 0.0082002681954829 \tabularnewline
54 & 0.97880250409199 & 0.0423949918160204 & 0.0211974959080102 \tabularnewline
55 & 0.950017765340781 & 0.0999644693184371 & 0.0499822346592186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&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]5[/C][C]0.0358554199903961[/C][C]0.0717108399807923[/C][C]0.964144580009604[/C][/ROW]
[ROW][C]6[/C][C]0.00972985846649186[/C][C]0.0194597169329837[/C][C]0.990270141533508[/C][/ROW]
[ROW][C]7[/C][C]0.00247273084140800[/C][C]0.00494546168281601[/C][C]0.997527269158592[/C][/ROW]
[ROW][C]8[/C][C]0.000955663219489067[/C][C]0.00191132643897813[/C][C]0.99904433678051[/C][/ROW]
[ROW][C]9[/C][C]0.000227048790089961[/C][C]0.000454097580179922[/C][C]0.99977295120991[/C][/ROW]
[ROW][C]10[/C][C]0.000156629096389302[/C][C]0.000313258192778603[/C][C]0.99984337090361[/C][/ROW]
[ROW][C]11[/C][C]4.15943811837635e-05[/C][C]8.3188762367527e-05[/C][C]0.999958405618816[/C][/ROW]
[ROW][C]12[/C][C]1.05934263619861e-05[/C][C]2.11868527239722e-05[/C][C]0.999989406573638[/C][/ROW]
[ROW][C]13[/C][C]3.25549715380238e-06[/C][C]6.51099430760477e-06[/C][C]0.999996744502846[/C][/ROW]
[ROW][C]14[/C][C]1.46578474621620e-06[/C][C]2.93156949243240e-06[/C][C]0.999998534215254[/C][/ROW]
[ROW][C]15[/C][C]4.54946237088145e-07[/C][C]9.0989247417629e-07[/C][C]0.999999545053763[/C][/ROW]
[ROW][C]16[/C][C]3.42981794052023e-06[/C][C]6.85963588104046e-06[/C][C]0.99999657018206[/C][/ROW]
[ROW][C]17[/C][C]1.83136944449096e-06[/C][C]3.66273888898191e-06[/C][C]0.999998168630555[/C][/ROW]
[ROW][C]18[/C][C]7.4450303426787e-07[/C][C]1.48900606853574e-06[/C][C]0.999999255496966[/C][/ROW]
[ROW][C]19[/C][C]2.61414513116035e-07[/C][C]5.2282902623207e-07[/C][C]0.999999738585487[/C][/ROW]
[ROW][C]20[/C][C]8.82399348133479e-08[/C][C]1.76479869626696e-07[/C][C]0.999999911760065[/C][/ROW]
[ROW][C]21[/C][C]3.10695095238061e-08[/C][C]6.21390190476123e-08[/C][C]0.99999996893049[/C][/ROW]
[ROW][C]22[/C][C]8.63965045551294e-08[/C][C]1.72793009110259e-07[/C][C]0.999999913603495[/C][/ROW]
[ROW][C]23[/C][C]4.08004903738326e-08[/C][C]8.16009807476652e-08[/C][C]0.99999995919951[/C][/ROW]
[ROW][C]24[/C][C]5.52666573714547e-08[/C][C]1.10533314742909e-07[/C][C]0.999999944733343[/C][/ROW]
[ROW][C]25[/C][C]8.79496479926245e-08[/C][C]1.75899295985249e-07[/C][C]0.999999912050352[/C][/ROW]
[ROW][C]26[/C][C]1.42799719221884e-07[/C][C]2.85599438443768e-07[/C][C]0.99999985720028[/C][/ROW]
[ROW][C]27[/C][C]1.3714584700427e-07[/C][C]2.7429169400854e-07[/C][C]0.999999862854153[/C][/ROW]
[ROW][C]28[/C][C]2.35718134081456e-07[/C][C]4.71436268162913e-07[/C][C]0.999999764281866[/C][/ROW]
[ROW][C]29[/C][C]1.02297625952874e-06[/C][C]2.04595251905749e-06[/C][C]0.99999897702374[/C][/ROW]
[ROW][C]30[/C][C]1.77203587476277e-06[/C][C]3.54407174952554e-06[/C][C]0.999998227964125[/C][/ROW]
[ROW][C]31[/C][C]2.00869973245732e-06[/C][C]4.01739946491464e-06[/C][C]0.999997991300268[/C][/ROW]
[ROW][C]32[/C][C]2.54377576228906e-06[/C][C]5.08755152457811e-06[/C][C]0.999997456224238[/C][/ROW]
[ROW][C]33[/C][C]3.38905168206004e-06[/C][C]6.77810336412008e-06[/C][C]0.999996610948318[/C][/ROW]
[ROW][C]34[/C][C]6.29058400230478e-05[/C][C]0.000125811680046096[/C][C]0.999937094159977[/C][/ROW]
[ROW][C]35[/C][C]0.000146733746550689[/C][C]0.000293467493101378[/C][C]0.99985326625345[/C][/ROW]
[ROW][C]36[/C][C]0.000643843745028401[/C][C]0.00128768749005680[/C][C]0.999356156254972[/C][/ROW]
[ROW][C]37[/C][C]0.00324458624598546[/C][C]0.00648917249197092[/C][C]0.996755413754015[/C][/ROW]
[ROW][C]38[/C][C]0.0100803283896061[/C][C]0.0201606567792123[/C][C]0.989919671610394[/C][/ROW]
[ROW][C]39[/C][C]0.0211032773011086[/C][C]0.0422065546022172[/C][C]0.978896722698891[/C][/ROW]
[ROW][C]40[/C][C]0.51233636412883[/C][C]0.97532727174234[/C][C]0.48766363587117[/C][/ROW]
[ROW][C]41[/C][C]0.642020127465142[/C][C]0.715959745069717[/C][C]0.357979872534858[/C][/ROW]
[ROW][C]42[/C][C]0.667321641939035[/C][C]0.66535671612193[/C][C]0.332678358060965[/C][/ROW]
[ROW][C]43[/C][C]0.701304787582416[/C][C]0.597390424835169[/C][C]0.298695212417584[/C][/ROW]
[ROW][C]44[/C][C]0.681582037841102[/C][C]0.636835924317796[/C][C]0.318417962158898[/C][/ROW]
[ROW][C]45[/C][C]0.643312442136808[/C][C]0.713375115726384[/C][C]0.356687557863192[/C][/ROW]
[ROW][C]46[/C][C]0.759935622682247[/C][C]0.480128754635506[/C][C]0.240064377317753[/C][/ROW]
[ROW][C]47[/C][C]0.700647301508385[/C][C]0.598705396983231[/C][C]0.299352698491616[/C][/ROW]
[ROW][C]48[/C][C]0.725634100296243[/C][C]0.548731799407514[/C][C]0.274365899703757[/C][/ROW]
[ROW][C]49[/C][C]0.928403251170304[/C][C]0.143193497659391[/C][C]0.0715967488296957[/C][/ROW]
[ROW][C]50[/C][C]0.971359940815886[/C][C]0.0572801183682282[/C][C]0.0286400591841141[/C][/ROW]
[ROW][C]51[/C][C]0.981986333561158[/C][C]0.0360273328776831[/C][C]0.0180136664388415[/C][/ROW]
[ROW][C]52[/C][C]0.994851573244166[/C][C]0.0102968535116687[/C][C]0.00514842675583434[/C][/ROW]
[ROW][C]53[/C][C]0.991799731804517[/C][C]0.0164005363909658[/C][C]0.0082002681954829[/C][/ROW]
[ROW][C]54[/C][C]0.97880250409199[/C][C]0.0423949918160204[/C][C]0.0211974959080102[/C][/ROW]
[ROW][C]55[/C][C]0.950017765340781[/C][C]0.0999644693184371[/C][C]0.0499822346592186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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
50.03585541999039610.07171083998079230.964144580009604
60.009729858466491860.01945971693298370.990270141533508
70.002472730841408000.004945461682816010.997527269158592
80.0009556632194890670.001911326438978130.99904433678051
90.0002270487900899610.0004540975801799220.99977295120991
100.0001566290963893020.0003132581927786030.99984337090361
114.15943811837635e-058.3188762367527e-050.999958405618816
121.05934263619861e-052.11868527239722e-050.999989406573638
133.25549715380238e-066.51099430760477e-060.999996744502846
141.46578474621620e-062.93156949243240e-060.999998534215254
154.54946237088145e-079.0989247417629e-070.999999545053763
163.42981794052023e-066.85963588104046e-060.99999657018206
171.83136944449096e-063.66273888898191e-060.999998168630555
187.4450303426787e-071.48900606853574e-060.999999255496966
192.61414513116035e-075.2282902623207e-070.999999738585487
208.82399348133479e-081.76479869626696e-070.999999911760065
213.10695095238061e-086.21390190476123e-080.99999996893049
228.63965045551294e-081.72793009110259e-070.999999913603495
234.08004903738326e-088.16009807476652e-080.99999995919951
245.52666573714547e-081.10533314742909e-070.999999944733343
258.79496479926245e-081.75899295985249e-070.999999912050352
261.42799719221884e-072.85599438443768e-070.99999985720028
271.3714584700427e-072.7429169400854e-070.999999862854153
282.35718134081456e-074.71436268162913e-070.999999764281866
291.02297625952874e-062.04595251905749e-060.99999897702374
301.77203587476277e-063.54407174952554e-060.999998227964125
312.00869973245732e-064.01739946491464e-060.999997991300268
322.54377576228906e-065.08755152457811e-060.999997456224238
333.38905168206004e-066.77810336412008e-060.999996610948318
346.29058400230478e-050.0001258116800460960.999937094159977
350.0001467337465506890.0002934674931013780.99985326625345
360.0006438437450284010.001287687490056800.999356156254972
370.003244586245985460.006489172491970920.996755413754015
380.01008032838960610.02016065677921230.989919671610394
390.02110327730110860.04220655460221720.978896722698891
400.512336364128830.975327271742340.48766363587117
410.6420201274651420.7159597450697170.357979872534858
420.6673216419390350.665356716121930.332678358060965
430.7013047875824160.5973904248351690.298695212417584
440.6815820378411020.6368359243177960.318417962158898
450.6433124421368080.7133751157263840.356687557863192
460.7599356226822470.4801287546355060.240064377317753
470.7006473015083850.5987053969832310.299352698491616
480.7256341002962430.5487317994075140.274365899703757
490.9284032511703040.1431934976593910.0715967488296957
500.9713599408158860.05728011836822820.0286400591841141
510.9819863335611580.03602733287768310.0180136664388415
520.9948515732441660.01029685351166870.00514842675583434
530.9917997318045170.01640053639096580.0082002681954829
540.978802504091990.04239499181602040.0211974959080102
550.9500177653407810.09996446931843710.0499822346592186







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.607843137254902NOK
5% type I error level380.745098039215686NOK
10% type I error level410.80392156862745NOK

\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 & 31 & 0.607843137254902 & NOK \tabularnewline
5% type I error level & 38 & 0.745098039215686 & NOK \tabularnewline
10% type I error level & 41 & 0.80392156862745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58038&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]31[/C][C]0.607843137254902[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]38[/C][C]0.745098039215686[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.80392156862745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58038&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58038&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 level310.607843137254902NOK
5% type I error level380.745098039215686NOK
10% type I error level410.80392156862745NOK



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')
}