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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 03:34:40 -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/24/t125905895976xgn6sjj63oxi0.htm/, Retrieved Sat, 12 Oct 2024 13:19:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58984, Retrieved Sat, 12 Oct 2024 13:19:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact224
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-24 10:34:40] [4672b66a35a4d755714bdcf00037725e] [Current]
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Dataseries X:
103,52	0
103,5	0
103,52	0
103,53	0
103,53	0
103,53	0
103,52	0
103,54	0
103,59	0
103,59	0
103,59	0
103,59	0
103,63	0
103,74	0
103,7	0
103,72	0
103,81	0
103,8	0
104,22	0
106,91	1
107,06	1
107,17	1
107,25	1
107,28	1
107,24	1
107,23	1
107,34	1
107,34	1
107,3	1
107,24	1
107,3	1
107,32	1
107,28	1
107,33	1
107,33	1
107,33	1
107,28	1
107,28	1
107,29	1
107,29	1
107,23	1
107,24	1
107,24	1
107,2	1
107,23	1
107,2	1
107,21	1
107,24	1
107,21	1
113,89	1
114,05	1
114,05	1
114,05	1
114,05	1
115,12	1
115,68	1
116,05	1
116,18	1
116,35	1
116,44	1
117	1
117,61	1
118,17	1
118,33	1
118,33	1
118,42	1
118,5	1
118,67	1
119,09	1
119,14	1
119,23	1
119,33	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=58984&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=58984&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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] = + 100.335200181349 -2.32705377694079X[t] + 0.149120980679454M1[t] + 1.09636674339122M2[t] + 0.95194583943642M3[t] + 0.702524935481617M4[t] + 0.419770698193481M5[t] + 0.143683127572011M6[t] + 0.132595556950542M7[t] + 0.809350282485877M8[t] + 0.691596045197743M9[t] + 0.462175141242941M10[t] + 0.239420903954802M11[t] + 0.281087570621469t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  100.335200181349 -2.32705377694079X[t] +  0.149120980679454M1[t] +  1.09636674339122M2[t] +  0.95194583943642M3[t] +  0.702524935481617M4[t] +  0.419770698193481M5[t] +  0.143683127572011M6[t] +  0.132595556950542M7[t] +  0.809350282485877M8[t] +  0.691596045197743M9[t] +  0.462175141242941M10[t] +  0.239420903954802M11[t] +  0.281087570621469t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  100.335200181349 -2.32705377694079X[t] +  0.149120980679454M1[t] +  1.09636674339122M2[t] +  0.95194583943642M3[t] +  0.702524935481617M4[t] +  0.419770698193481M5[t] +  0.143683127572011M6[t] +  0.132595556950542M7[t] +  0.809350282485877M8[t] +  0.691596045197743M9[t] +  0.462175141242941M10[t] +  0.239420903954802M11[t] +  0.281087570621469t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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] = + 100.335200181349 -2.32705377694079X[t] + 0.149120980679454M1[t] + 1.09636674339122M2[t] + 0.95194583943642M3[t] + 0.702524935481617M4[t] + 0.419770698193481M5[t] + 0.143683127572011M6[t] + 0.132595556950542M7[t] + 0.809350282485877M8[t] + 0.691596045197743M9[t] + 0.462175141242941M10[t] + 0.239420903954802M11[t] + 0.281087570621469t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100.3352001813491.03061597.354700
X-2.327053776940790.902121-2.57950.012450.006225
M10.1491209806794541.2497860.11930.9054360.452718
M21.096366743391221.2484780.87820.3834790.191739
M30.951945839436421.2474590.76310.4484920.224246
M40.7025249354816171.246730.56350.5752710.287635
M50.4197706981934811.2462930.33680.7374720.368736
M60.1436831275720111.2461470.11530.9086040.454302
M70.1325955569505421.2462930.10640.9156390.457819
M80.8093502824858771.2446560.65030.5180920.259046
M90.6915960451977431.2436340.55610.5802760.290138
M100.4621751412429411.2429040.37190.7113590.355679
M110.2394209039548021.2424650.19270.8478680.423934
t0.2810875706214690.01906114.746900

\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) & 100.335200181349 & 1.030615 & 97.3547 & 0 & 0 \tabularnewline
X & -2.32705377694079 & 0.902121 & -2.5795 & 0.01245 & 0.006225 \tabularnewline
M1 & 0.149120980679454 & 1.249786 & 0.1193 & 0.905436 & 0.452718 \tabularnewline
M2 & 1.09636674339122 & 1.248478 & 0.8782 & 0.383479 & 0.191739 \tabularnewline
M3 & 0.95194583943642 & 1.247459 & 0.7631 & 0.448492 & 0.224246 \tabularnewline
M4 & 0.702524935481617 & 1.24673 & 0.5635 & 0.575271 & 0.287635 \tabularnewline
M5 & 0.419770698193481 & 1.246293 & 0.3368 & 0.737472 & 0.368736 \tabularnewline
M6 & 0.143683127572011 & 1.246147 & 0.1153 & 0.908604 & 0.454302 \tabularnewline
M7 & 0.132595556950542 & 1.246293 & 0.1064 & 0.915639 & 0.457819 \tabularnewline
M8 & 0.809350282485877 & 1.244656 & 0.6503 & 0.518092 & 0.259046 \tabularnewline
M9 & 0.691596045197743 & 1.243634 & 0.5561 & 0.580276 & 0.290138 \tabularnewline
M10 & 0.462175141242941 & 1.242904 & 0.3719 & 0.711359 & 0.355679 \tabularnewline
M11 & 0.239420903954802 & 1.242465 & 0.1927 & 0.847868 & 0.423934 \tabularnewline
t & 0.281087570621469 & 0.019061 & 14.7469 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&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]100.335200181349[/C][C]1.030615[/C][C]97.3547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-2.32705377694079[/C][C]0.902121[/C][C]-2.5795[/C][C]0.01245[/C][C]0.006225[/C][/ROW]
[ROW][C]M1[/C][C]0.149120980679454[/C][C]1.249786[/C][C]0.1193[/C][C]0.905436[/C][C]0.452718[/C][/ROW]
[ROW][C]M2[/C][C]1.09636674339122[/C][C]1.248478[/C][C]0.8782[/C][C]0.383479[/C][C]0.191739[/C][/ROW]
[ROW][C]M3[/C][C]0.95194583943642[/C][C]1.247459[/C][C]0.7631[/C][C]0.448492[/C][C]0.224246[/C][/ROW]
[ROW][C]M4[/C][C]0.702524935481617[/C][C]1.24673[/C][C]0.5635[/C][C]0.575271[/C][C]0.287635[/C][/ROW]
[ROW][C]M5[/C][C]0.419770698193481[/C][C]1.246293[/C][C]0.3368[/C][C]0.737472[/C][C]0.368736[/C][/ROW]
[ROW][C]M6[/C][C]0.143683127572011[/C][C]1.246147[/C][C]0.1153[/C][C]0.908604[/C][C]0.454302[/C][/ROW]
[ROW][C]M7[/C][C]0.132595556950542[/C][C]1.246293[/C][C]0.1064[/C][C]0.915639[/C][C]0.457819[/C][/ROW]
[ROW][C]M8[/C][C]0.809350282485877[/C][C]1.244656[/C][C]0.6503[/C][C]0.518092[/C][C]0.259046[/C][/ROW]
[ROW][C]M9[/C][C]0.691596045197743[/C][C]1.243634[/C][C]0.5561[/C][C]0.580276[/C][C]0.290138[/C][/ROW]
[ROW][C]M10[/C][C]0.462175141242941[/C][C]1.242904[/C][C]0.3719[/C][C]0.711359[/C][C]0.355679[/C][/ROW]
[ROW][C]M11[/C][C]0.239420903954802[/C][C]1.242465[/C][C]0.1927[/C][C]0.847868[/C][C]0.423934[/C][/ROW]
[ROW][C]t[/C][C]0.281087570621469[/C][C]0.019061[/C][C]14.7469[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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)100.3352001813491.03061597.354700
X-2.327053776940790.902121-2.57950.012450.006225
M10.1491209806794541.2497860.11930.9054360.452718
M21.096366743391221.2484780.87820.3834790.191739
M30.951945839436421.2474590.76310.4484920.224246
M40.7025249354816171.246730.56350.5752710.287635
M50.4197706981934811.2462930.33680.7374720.368736
M60.1436831275720111.2461470.11530.9086040.454302
M70.1325955569505421.2462930.10640.9156390.457819
M80.8093502824858771.2446560.65030.5180920.259046
M90.6915960451977431.2436340.55610.5802760.290138
M100.4621751412429411.2429040.37190.7113590.355679
M110.2394209039548021.2424650.19270.8478680.423934
t0.2810875706214690.01906114.746900







Multiple Linear Regression - Regression Statistics
Multiple R0.93492067185643
R-squared0.874076662664479
Adjusted R-squared0.845852466365138
F-TEST (value)30.9690541191741
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.15175963867601
Sum Squared Residuals268.544033472836

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.93492067185643 \tabularnewline
R-squared & 0.874076662664479 \tabularnewline
Adjusted R-squared & 0.845852466365138 \tabularnewline
F-TEST (value) & 30.9690541191741 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.15175963867601 \tabularnewline
Sum Squared Residuals & 268.544033472836 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.93492067185643[/C][/ROW]
[ROW][C]R-squared[/C][C]0.874076662664479[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.845852466365138[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.9690541191741[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.15175963867601[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]268.544033472836[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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 R0.93492067185643
R-squared0.874076662664479
Adjusted R-squared0.845852466365138
F-TEST (value)30.9690541191741
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.15175963867601
Sum Squared Residuals268.544033472836







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.52100.7654087326492.7545912673507
2103.5101.9937420659831.50625793401684
3103.52102.1304087326501.38959126735019
4103.53102.1620753993161.36792460068352
5103.53102.1604087326501.36959126735019
6103.53102.1654087326501.36459126735020
7103.52102.4354087326501.08459126735019
8103.54103.3932510288070.146748971193397
9103.59103.556584362140.0334156378600615
10103.59103.608251028807-0.0182510288066067
11103.59103.66658436214-0.0765843621399367
12103.59103.708251028807-0.118251028806602
13103.63104.138459580108-0.508459580107533
14103.74105.366792913441-1.62679291344077
15103.7105.503459580107-1.80345958010743
16103.72105.535126246774-1.8151262467741
17103.81105.533459580107-1.72345958010743
18103.8105.538459580107-1.73845958010743
19104.22105.808459580107-1.58845958010743
20106.91104.4392480993232.47075190067656
21107.06104.6025814326572.45741856734324
22107.17104.6542480993232.51575190067657
23107.25104.7125814326572.53741856734324
24107.28104.7542480993232.52575190067657
25107.24105.1844566506242.05554334937564
26107.23106.4127899839580.817210016042422
27107.34106.5494566506240.790543349375747
28107.34106.5811233172910.758876682709082
29107.3106.5794566506240.720543349375744
30107.24106.5844566506240.655543349375742
31107.3106.8544566506240.445543349375744
32107.32107.812298946781-0.492298946781062
33107.28107.975632280114-0.695632280114389
34107.33108.027298946781-0.697298946781059
35107.33108.085632280114-0.755632280114389
36107.33108.127298946781-0.797298946781055
37107.28108.557507498082-1.27750749808197
38107.28109.785840831415-2.50584083141520
39107.29109.922507498082-2.63250749808187
40107.29109.954174164749-2.66417416474854
41107.23109.952507498082-2.72250749808187
42107.24109.957507498082-2.71750749808188
43107.24110.227507498082-2.98750749808188
44107.2111.185349794239-3.98534979423868
45107.23111.348683127572-4.11868312757201
46107.2111.400349794239-4.20034979423868
47107.21111.458683127572-4.24868312757202
48107.24111.500349794239-4.26034979423868
49107.21111.930558345540-4.72055834553961
50113.89113.1588916788730.731108321127171
51114.05113.2955583455400.754441654460494
52114.05113.3272250122060.722774987793827
53114.05113.3255583455400.724441654460494
54114.05113.3305583455400.719441654460497
55115.12113.6005583455391.51944165446050
56115.68114.5584006416961.12159935830370
57116.05114.7217339750301.32826602497036
58116.18114.7734006416961.40659935830370
59116.35114.8317339750301.51826602497036
60116.44114.8734006416961.56659935830370
61117115.3036091929971.69639080700277
62117.61116.5319425263301.07805747366955
63118.17116.6686091929971.50139080700287
64118.33116.7002758596641.62972414033621
65118.33116.6986091929971.63139080700287
66118.42116.7036091929971.71639080700288
67118.5116.9736091929971.52639080700288
68118.67117.9314514891540.738548510846074
69119.09118.0947848224870.995215177512742
70119.14118.1464514891540.993548510846073
71119.23118.2047848224871.02521517751275
72119.33118.2464514891541.08354851084607

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.52 & 100.765408732649 & 2.7545912673507 \tabularnewline
2 & 103.5 & 101.993742065983 & 1.50625793401684 \tabularnewline
3 & 103.52 & 102.130408732650 & 1.38959126735019 \tabularnewline
4 & 103.53 & 102.162075399316 & 1.36792460068352 \tabularnewline
5 & 103.53 & 102.160408732650 & 1.36959126735019 \tabularnewline
6 & 103.53 & 102.165408732650 & 1.36459126735020 \tabularnewline
7 & 103.52 & 102.435408732650 & 1.08459126735019 \tabularnewline
8 & 103.54 & 103.393251028807 & 0.146748971193397 \tabularnewline
9 & 103.59 & 103.55658436214 & 0.0334156378600615 \tabularnewline
10 & 103.59 & 103.608251028807 & -0.0182510288066067 \tabularnewline
11 & 103.59 & 103.66658436214 & -0.0765843621399367 \tabularnewline
12 & 103.59 & 103.708251028807 & -0.118251028806602 \tabularnewline
13 & 103.63 & 104.138459580108 & -0.508459580107533 \tabularnewline
14 & 103.74 & 105.366792913441 & -1.62679291344077 \tabularnewline
15 & 103.7 & 105.503459580107 & -1.80345958010743 \tabularnewline
16 & 103.72 & 105.535126246774 & -1.8151262467741 \tabularnewline
17 & 103.81 & 105.533459580107 & -1.72345958010743 \tabularnewline
18 & 103.8 & 105.538459580107 & -1.73845958010743 \tabularnewline
19 & 104.22 & 105.808459580107 & -1.58845958010743 \tabularnewline
20 & 106.91 & 104.439248099323 & 2.47075190067656 \tabularnewline
21 & 107.06 & 104.602581432657 & 2.45741856734324 \tabularnewline
22 & 107.17 & 104.654248099323 & 2.51575190067657 \tabularnewline
23 & 107.25 & 104.712581432657 & 2.53741856734324 \tabularnewline
24 & 107.28 & 104.754248099323 & 2.52575190067657 \tabularnewline
25 & 107.24 & 105.184456650624 & 2.05554334937564 \tabularnewline
26 & 107.23 & 106.412789983958 & 0.817210016042422 \tabularnewline
27 & 107.34 & 106.549456650624 & 0.790543349375747 \tabularnewline
28 & 107.34 & 106.581123317291 & 0.758876682709082 \tabularnewline
29 & 107.3 & 106.579456650624 & 0.720543349375744 \tabularnewline
30 & 107.24 & 106.584456650624 & 0.655543349375742 \tabularnewline
31 & 107.3 & 106.854456650624 & 0.445543349375744 \tabularnewline
32 & 107.32 & 107.812298946781 & -0.492298946781062 \tabularnewline
33 & 107.28 & 107.975632280114 & -0.695632280114389 \tabularnewline
34 & 107.33 & 108.027298946781 & -0.697298946781059 \tabularnewline
35 & 107.33 & 108.085632280114 & -0.755632280114389 \tabularnewline
36 & 107.33 & 108.127298946781 & -0.797298946781055 \tabularnewline
37 & 107.28 & 108.557507498082 & -1.27750749808197 \tabularnewline
38 & 107.28 & 109.785840831415 & -2.50584083141520 \tabularnewline
39 & 107.29 & 109.922507498082 & -2.63250749808187 \tabularnewline
40 & 107.29 & 109.954174164749 & -2.66417416474854 \tabularnewline
41 & 107.23 & 109.952507498082 & -2.72250749808187 \tabularnewline
42 & 107.24 & 109.957507498082 & -2.71750749808188 \tabularnewline
43 & 107.24 & 110.227507498082 & -2.98750749808188 \tabularnewline
44 & 107.2 & 111.185349794239 & -3.98534979423868 \tabularnewline
45 & 107.23 & 111.348683127572 & -4.11868312757201 \tabularnewline
46 & 107.2 & 111.400349794239 & -4.20034979423868 \tabularnewline
47 & 107.21 & 111.458683127572 & -4.24868312757202 \tabularnewline
48 & 107.24 & 111.500349794239 & -4.26034979423868 \tabularnewline
49 & 107.21 & 111.930558345540 & -4.72055834553961 \tabularnewline
50 & 113.89 & 113.158891678873 & 0.731108321127171 \tabularnewline
51 & 114.05 & 113.295558345540 & 0.754441654460494 \tabularnewline
52 & 114.05 & 113.327225012206 & 0.722774987793827 \tabularnewline
53 & 114.05 & 113.325558345540 & 0.724441654460494 \tabularnewline
54 & 114.05 & 113.330558345540 & 0.719441654460497 \tabularnewline
55 & 115.12 & 113.600558345539 & 1.51944165446050 \tabularnewline
56 & 115.68 & 114.558400641696 & 1.12159935830370 \tabularnewline
57 & 116.05 & 114.721733975030 & 1.32826602497036 \tabularnewline
58 & 116.18 & 114.773400641696 & 1.40659935830370 \tabularnewline
59 & 116.35 & 114.831733975030 & 1.51826602497036 \tabularnewline
60 & 116.44 & 114.873400641696 & 1.56659935830370 \tabularnewline
61 & 117 & 115.303609192997 & 1.69639080700277 \tabularnewline
62 & 117.61 & 116.531942526330 & 1.07805747366955 \tabularnewline
63 & 118.17 & 116.668609192997 & 1.50139080700287 \tabularnewline
64 & 118.33 & 116.700275859664 & 1.62972414033621 \tabularnewline
65 & 118.33 & 116.698609192997 & 1.63139080700287 \tabularnewline
66 & 118.42 & 116.703609192997 & 1.71639080700288 \tabularnewline
67 & 118.5 & 116.973609192997 & 1.52639080700288 \tabularnewline
68 & 118.67 & 117.931451489154 & 0.738548510846074 \tabularnewline
69 & 119.09 & 118.094784822487 & 0.995215177512742 \tabularnewline
70 & 119.14 & 118.146451489154 & 0.993548510846073 \tabularnewline
71 & 119.23 & 118.204784822487 & 1.02521517751275 \tabularnewline
72 & 119.33 & 118.246451489154 & 1.08354851084607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&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]103.52[/C][C]100.765408732649[/C][C]2.7545912673507[/C][/ROW]
[ROW][C]2[/C][C]103.5[/C][C]101.993742065983[/C][C]1.50625793401684[/C][/ROW]
[ROW][C]3[/C][C]103.52[/C][C]102.130408732650[/C][C]1.38959126735019[/C][/ROW]
[ROW][C]4[/C][C]103.53[/C][C]102.162075399316[/C][C]1.36792460068352[/C][/ROW]
[ROW][C]5[/C][C]103.53[/C][C]102.160408732650[/C][C]1.36959126735019[/C][/ROW]
[ROW][C]6[/C][C]103.53[/C][C]102.165408732650[/C][C]1.36459126735020[/C][/ROW]
[ROW][C]7[/C][C]103.52[/C][C]102.435408732650[/C][C]1.08459126735019[/C][/ROW]
[ROW][C]8[/C][C]103.54[/C][C]103.393251028807[/C][C]0.146748971193397[/C][/ROW]
[ROW][C]9[/C][C]103.59[/C][C]103.55658436214[/C][C]0.0334156378600615[/C][/ROW]
[ROW][C]10[/C][C]103.59[/C][C]103.608251028807[/C][C]-0.0182510288066067[/C][/ROW]
[ROW][C]11[/C][C]103.59[/C][C]103.66658436214[/C][C]-0.0765843621399367[/C][/ROW]
[ROW][C]12[/C][C]103.59[/C][C]103.708251028807[/C][C]-0.118251028806602[/C][/ROW]
[ROW][C]13[/C][C]103.63[/C][C]104.138459580108[/C][C]-0.508459580107533[/C][/ROW]
[ROW][C]14[/C][C]103.74[/C][C]105.366792913441[/C][C]-1.62679291344077[/C][/ROW]
[ROW][C]15[/C][C]103.7[/C][C]105.503459580107[/C][C]-1.80345958010743[/C][/ROW]
[ROW][C]16[/C][C]103.72[/C][C]105.535126246774[/C][C]-1.8151262467741[/C][/ROW]
[ROW][C]17[/C][C]103.81[/C][C]105.533459580107[/C][C]-1.72345958010743[/C][/ROW]
[ROW][C]18[/C][C]103.8[/C][C]105.538459580107[/C][C]-1.73845958010743[/C][/ROW]
[ROW][C]19[/C][C]104.22[/C][C]105.808459580107[/C][C]-1.58845958010743[/C][/ROW]
[ROW][C]20[/C][C]106.91[/C][C]104.439248099323[/C][C]2.47075190067656[/C][/ROW]
[ROW][C]21[/C][C]107.06[/C][C]104.602581432657[/C][C]2.45741856734324[/C][/ROW]
[ROW][C]22[/C][C]107.17[/C][C]104.654248099323[/C][C]2.51575190067657[/C][/ROW]
[ROW][C]23[/C][C]107.25[/C][C]104.712581432657[/C][C]2.53741856734324[/C][/ROW]
[ROW][C]24[/C][C]107.28[/C][C]104.754248099323[/C][C]2.52575190067657[/C][/ROW]
[ROW][C]25[/C][C]107.24[/C][C]105.184456650624[/C][C]2.05554334937564[/C][/ROW]
[ROW][C]26[/C][C]107.23[/C][C]106.412789983958[/C][C]0.817210016042422[/C][/ROW]
[ROW][C]27[/C][C]107.34[/C][C]106.549456650624[/C][C]0.790543349375747[/C][/ROW]
[ROW][C]28[/C][C]107.34[/C][C]106.581123317291[/C][C]0.758876682709082[/C][/ROW]
[ROW][C]29[/C][C]107.3[/C][C]106.579456650624[/C][C]0.720543349375744[/C][/ROW]
[ROW][C]30[/C][C]107.24[/C][C]106.584456650624[/C][C]0.655543349375742[/C][/ROW]
[ROW][C]31[/C][C]107.3[/C][C]106.854456650624[/C][C]0.445543349375744[/C][/ROW]
[ROW][C]32[/C][C]107.32[/C][C]107.812298946781[/C][C]-0.492298946781062[/C][/ROW]
[ROW][C]33[/C][C]107.28[/C][C]107.975632280114[/C][C]-0.695632280114389[/C][/ROW]
[ROW][C]34[/C][C]107.33[/C][C]108.027298946781[/C][C]-0.697298946781059[/C][/ROW]
[ROW][C]35[/C][C]107.33[/C][C]108.085632280114[/C][C]-0.755632280114389[/C][/ROW]
[ROW][C]36[/C][C]107.33[/C][C]108.127298946781[/C][C]-0.797298946781055[/C][/ROW]
[ROW][C]37[/C][C]107.28[/C][C]108.557507498082[/C][C]-1.27750749808197[/C][/ROW]
[ROW][C]38[/C][C]107.28[/C][C]109.785840831415[/C][C]-2.50584083141520[/C][/ROW]
[ROW][C]39[/C][C]107.29[/C][C]109.922507498082[/C][C]-2.63250749808187[/C][/ROW]
[ROW][C]40[/C][C]107.29[/C][C]109.954174164749[/C][C]-2.66417416474854[/C][/ROW]
[ROW][C]41[/C][C]107.23[/C][C]109.952507498082[/C][C]-2.72250749808187[/C][/ROW]
[ROW][C]42[/C][C]107.24[/C][C]109.957507498082[/C][C]-2.71750749808188[/C][/ROW]
[ROW][C]43[/C][C]107.24[/C][C]110.227507498082[/C][C]-2.98750749808188[/C][/ROW]
[ROW][C]44[/C][C]107.2[/C][C]111.185349794239[/C][C]-3.98534979423868[/C][/ROW]
[ROW][C]45[/C][C]107.23[/C][C]111.348683127572[/C][C]-4.11868312757201[/C][/ROW]
[ROW][C]46[/C][C]107.2[/C][C]111.400349794239[/C][C]-4.20034979423868[/C][/ROW]
[ROW][C]47[/C][C]107.21[/C][C]111.458683127572[/C][C]-4.24868312757202[/C][/ROW]
[ROW][C]48[/C][C]107.24[/C][C]111.500349794239[/C][C]-4.26034979423868[/C][/ROW]
[ROW][C]49[/C][C]107.21[/C][C]111.930558345540[/C][C]-4.72055834553961[/C][/ROW]
[ROW][C]50[/C][C]113.89[/C][C]113.158891678873[/C][C]0.731108321127171[/C][/ROW]
[ROW][C]51[/C][C]114.05[/C][C]113.295558345540[/C][C]0.754441654460494[/C][/ROW]
[ROW][C]52[/C][C]114.05[/C][C]113.327225012206[/C][C]0.722774987793827[/C][/ROW]
[ROW][C]53[/C][C]114.05[/C][C]113.325558345540[/C][C]0.724441654460494[/C][/ROW]
[ROW][C]54[/C][C]114.05[/C][C]113.330558345540[/C][C]0.719441654460497[/C][/ROW]
[ROW][C]55[/C][C]115.12[/C][C]113.600558345539[/C][C]1.51944165446050[/C][/ROW]
[ROW][C]56[/C][C]115.68[/C][C]114.558400641696[/C][C]1.12159935830370[/C][/ROW]
[ROW][C]57[/C][C]116.05[/C][C]114.721733975030[/C][C]1.32826602497036[/C][/ROW]
[ROW][C]58[/C][C]116.18[/C][C]114.773400641696[/C][C]1.40659935830370[/C][/ROW]
[ROW][C]59[/C][C]116.35[/C][C]114.831733975030[/C][C]1.51826602497036[/C][/ROW]
[ROW][C]60[/C][C]116.44[/C][C]114.873400641696[/C][C]1.56659935830370[/C][/ROW]
[ROW][C]61[/C][C]117[/C][C]115.303609192997[/C][C]1.69639080700277[/C][/ROW]
[ROW][C]62[/C][C]117.61[/C][C]116.531942526330[/C][C]1.07805747366955[/C][/ROW]
[ROW][C]63[/C][C]118.17[/C][C]116.668609192997[/C][C]1.50139080700287[/C][/ROW]
[ROW][C]64[/C][C]118.33[/C][C]116.700275859664[/C][C]1.62972414033621[/C][/ROW]
[ROW][C]65[/C][C]118.33[/C][C]116.698609192997[/C][C]1.63139080700287[/C][/ROW]
[ROW][C]66[/C][C]118.42[/C][C]116.703609192997[/C][C]1.71639080700288[/C][/ROW]
[ROW][C]67[/C][C]118.5[/C][C]116.973609192997[/C][C]1.52639080700288[/C][/ROW]
[ROW][C]68[/C][C]118.67[/C][C]117.931451489154[/C][C]0.738548510846074[/C][/ROW]
[ROW][C]69[/C][C]119.09[/C][C]118.094784822487[/C][C]0.995215177512742[/C][/ROW]
[ROW][C]70[/C][C]119.14[/C][C]118.146451489154[/C][C]0.993548510846073[/C][/ROW]
[ROW][C]71[/C][C]119.23[/C][C]118.204784822487[/C][C]1.02521517751275[/C][/ROW]
[ROW][C]72[/C][C]119.33[/C][C]118.246451489154[/C][C]1.08354851084607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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
1103.52100.7654087326492.7545912673507
2103.5101.9937420659831.50625793401684
3103.52102.1304087326501.38959126735019
4103.53102.1620753993161.36792460068352
5103.53102.1604087326501.36959126735019
6103.53102.1654087326501.36459126735020
7103.52102.4354087326501.08459126735019
8103.54103.3932510288070.146748971193397
9103.59103.556584362140.0334156378600615
10103.59103.608251028807-0.0182510288066067
11103.59103.66658436214-0.0765843621399367
12103.59103.708251028807-0.118251028806602
13103.63104.138459580108-0.508459580107533
14103.74105.366792913441-1.62679291344077
15103.7105.503459580107-1.80345958010743
16103.72105.535126246774-1.8151262467741
17103.81105.533459580107-1.72345958010743
18103.8105.538459580107-1.73845958010743
19104.22105.808459580107-1.58845958010743
20106.91104.4392480993232.47075190067656
21107.06104.6025814326572.45741856734324
22107.17104.6542480993232.51575190067657
23107.25104.7125814326572.53741856734324
24107.28104.7542480993232.52575190067657
25107.24105.1844566506242.05554334937564
26107.23106.4127899839580.817210016042422
27107.34106.5494566506240.790543349375747
28107.34106.5811233172910.758876682709082
29107.3106.5794566506240.720543349375744
30107.24106.5844566506240.655543349375742
31107.3106.8544566506240.445543349375744
32107.32107.812298946781-0.492298946781062
33107.28107.975632280114-0.695632280114389
34107.33108.027298946781-0.697298946781059
35107.33108.085632280114-0.755632280114389
36107.33108.127298946781-0.797298946781055
37107.28108.557507498082-1.27750749808197
38107.28109.785840831415-2.50584083141520
39107.29109.922507498082-2.63250749808187
40107.29109.954174164749-2.66417416474854
41107.23109.952507498082-2.72250749808187
42107.24109.957507498082-2.71750749808188
43107.24110.227507498082-2.98750749808188
44107.2111.185349794239-3.98534979423868
45107.23111.348683127572-4.11868312757201
46107.2111.400349794239-4.20034979423868
47107.21111.458683127572-4.24868312757202
48107.24111.500349794239-4.26034979423868
49107.21111.930558345540-4.72055834553961
50113.89113.1588916788730.731108321127171
51114.05113.2955583455400.754441654460494
52114.05113.3272250122060.722774987793827
53114.05113.3255583455400.724441654460494
54114.05113.3305583455400.719441654460497
55115.12113.6005583455391.51944165446050
56115.68114.5584006416961.12159935830370
57116.05114.7217339750301.32826602497036
58116.18114.7734006416961.40659935830370
59116.35114.8317339750301.51826602497036
60116.44114.8734006416961.56659935830370
61117115.3036091929971.69639080700277
62117.61116.5319425263301.07805747366955
63118.17116.6686091929971.50139080700287
64118.33116.7002758596641.62972414033621
65118.33116.6986091929971.63139080700287
66118.42116.7036091929971.71639080700288
67118.5116.9736091929971.52639080700288
68118.67117.9314514891540.738548510846074
69119.09118.0947848224870.995215177512742
70119.14118.1464514891540.993548510846073
71119.23118.2047848224871.02521517751275
72119.33118.2464514891541.08354851084607







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
171.54955075409298e-053.09910150818596e-050.999984504492459
184.27673861299270e-078.55347722598539e-070.999999572326139
192.94340827849628e-065.88681655699257e-060.999997056591722
201.78954359541076e-073.57908719082152e-070.99999982104564
211.15789556670435e-082.31579113340871e-080.999999988421044
229.59869217606954e-101.91973843521391e-090.99999999904013
231.04205943435395e-102.08411886870791e-100.999999999895794
241.28638055784226e-112.57276111568452e-110.999999999987136
251.31242701928871e-122.62485403857742e-120.999999999998687
269.7874818907804e-141.95749637815608e-130.999999999999902
277.03301506835766e-151.40660301367153e-140.999999999999993
285.00242106693344e-161.00048421338669e-151
294.05395777031992e-178.10791554063984e-171
304.52501780906316e-189.05003561812632e-181
311.90904042905979e-183.81808085811959e-181
322.19960918995568e-194.39921837991137e-191
332.76567829207708e-205.53135658415417e-201
344.57596443666624e-219.15192887333249e-211
351.33891726382325e-212.6778345276465e-211
368.23991845632549e-221.64798369126510e-211
379.59646329539283e-221.91929265907857e-211
381.65864520190152e-223.31729040380303e-221
392.70303962986345e-235.4060792597269e-231
403.90707311348483e-247.81414622696966e-241
411.06546983322795e-242.13093966645590e-241
421.53972633725262e-253.07945267450524e-251
431.43311665075113e-252.86623330150225e-251
442.06171215882732e-264.12342431765465e-261
455.37940717330637e-271.07588143466127e-261
464.79289350038597e-279.58578700077195e-271
471.93496730183953e-263.86993460367906e-261
483.33130939550566e-246.66261879101131e-241
491.99361679206807e-093.98723358413615e-090.999999998006383
500.96861415088270.06277169823460150.0313858491173008
510.9915089205677880.01698215886442480.00849107943221239
520.9949996916125530.01000061677489370.00500030838744685
530.9965050829572630.006989834085474040.00349491704273702
540.9998838401363670.0002323197272650390.000116159863632519
550.999960439109977.91217800613651e-053.95608900306826e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 1.54955075409298e-05 & 3.09910150818596e-05 & 0.999984504492459 \tabularnewline
18 & 4.27673861299270e-07 & 8.55347722598539e-07 & 0.999999572326139 \tabularnewline
19 & 2.94340827849628e-06 & 5.88681655699257e-06 & 0.999997056591722 \tabularnewline
20 & 1.78954359541076e-07 & 3.57908719082152e-07 & 0.99999982104564 \tabularnewline
21 & 1.15789556670435e-08 & 2.31579113340871e-08 & 0.999999988421044 \tabularnewline
22 & 9.59869217606954e-10 & 1.91973843521391e-09 & 0.99999999904013 \tabularnewline
23 & 1.04205943435395e-10 & 2.08411886870791e-10 & 0.999999999895794 \tabularnewline
24 & 1.28638055784226e-11 & 2.57276111568452e-11 & 0.999999999987136 \tabularnewline
25 & 1.31242701928871e-12 & 2.62485403857742e-12 & 0.999999999998687 \tabularnewline
26 & 9.7874818907804e-14 & 1.95749637815608e-13 & 0.999999999999902 \tabularnewline
27 & 7.03301506835766e-15 & 1.40660301367153e-14 & 0.999999999999993 \tabularnewline
28 & 5.00242106693344e-16 & 1.00048421338669e-15 & 1 \tabularnewline
29 & 4.05395777031992e-17 & 8.10791554063984e-17 & 1 \tabularnewline
30 & 4.52501780906316e-18 & 9.05003561812632e-18 & 1 \tabularnewline
31 & 1.90904042905979e-18 & 3.81808085811959e-18 & 1 \tabularnewline
32 & 2.19960918995568e-19 & 4.39921837991137e-19 & 1 \tabularnewline
33 & 2.76567829207708e-20 & 5.53135658415417e-20 & 1 \tabularnewline
34 & 4.57596443666624e-21 & 9.15192887333249e-21 & 1 \tabularnewline
35 & 1.33891726382325e-21 & 2.6778345276465e-21 & 1 \tabularnewline
36 & 8.23991845632549e-22 & 1.64798369126510e-21 & 1 \tabularnewline
37 & 9.59646329539283e-22 & 1.91929265907857e-21 & 1 \tabularnewline
38 & 1.65864520190152e-22 & 3.31729040380303e-22 & 1 \tabularnewline
39 & 2.70303962986345e-23 & 5.4060792597269e-23 & 1 \tabularnewline
40 & 3.90707311348483e-24 & 7.81414622696966e-24 & 1 \tabularnewline
41 & 1.06546983322795e-24 & 2.13093966645590e-24 & 1 \tabularnewline
42 & 1.53972633725262e-25 & 3.07945267450524e-25 & 1 \tabularnewline
43 & 1.43311665075113e-25 & 2.86623330150225e-25 & 1 \tabularnewline
44 & 2.06171215882732e-26 & 4.12342431765465e-26 & 1 \tabularnewline
45 & 5.37940717330637e-27 & 1.07588143466127e-26 & 1 \tabularnewline
46 & 4.79289350038597e-27 & 9.58578700077195e-27 & 1 \tabularnewline
47 & 1.93496730183953e-26 & 3.86993460367906e-26 & 1 \tabularnewline
48 & 3.33130939550566e-24 & 6.66261879101131e-24 & 1 \tabularnewline
49 & 1.99361679206807e-09 & 3.98723358413615e-09 & 0.999999998006383 \tabularnewline
50 & 0.9686141508827 & 0.0627716982346015 & 0.0313858491173008 \tabularnewline
51 & 0.991508920567788 & 0.0169821588644248 & 0.00849107943221239 \tabularnewline
52 & 0.994999691612553 & 0.0100006167748937 & 0.00500030838744685 \tabularnewline
53 & 0.996505082957263 & 0.00698983408547404 & 0.00349491704273702 \tabularnewline
54 & 0.999883840136367 & 0.000232319727265039 & 0.000116159863632519 \tabularnewline
55 & 0.99996043910997 & 7.91217800613651e-05 & 3.95608900306826e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&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]17[/C][C]1.54955075409298e-05[/C][C]3.09910150818596e-05[/C][C]0.999984504492459[/C][/ROW]
[ROW][C]18[/C][C]4.27673861299270e-07[/C][C]8.55347722598539e-07[/C][C]0.999999572326139[/C][/ROW]
[ROW][C]19[/C][C]2.94340827849628e-06[/C][C]5.88681655699257e-06[/C][C]0.999997056591722[/C][/ROW]
[ROW][C]20[/C][C]1.78954359541076e-07[/C][C]3.57908719082152e-07[/C][C]0.99999982104564[/C][/ROW]
[ROW][C]21[/C][C]1.15789556670435e-08[/C][C]2.31579113340871e-08[/C][C]0.999999988421044[/C][/ROW]
[ROW][C]22[/C][C]9.59869217606954e-10[/C][C]1.91973843521391e-09[/C][C]0.99999999904013[/C][/ROW]
[ROW][C]23[/C][C]1.04205943435395e-10[/C][C]2.08411886870791e-10[/C][C]0.999999999895794[/C][/ROW]
[ROW][C]24[/C][C]1.28638055784226e-11[/C][C]2.57276111568452e-11[/C][C]0.999999999987136[/C][/ROW]
[ROW][C]25[/C][C]1.31242701928871e-12[/C][C]2.62485403857742e-12[/C][C]0.999999999998687[/C][/ROW]
[ROW][C]26[/C][C]9.7874818907804e-14[/C][C]1.95749637815608e-13[/C][C]0.999999999999902[/C][/ROW]
[ROW][C]27[/C][C]7.03301506835766e-15[/C][C]1.40660301367153e-14[/C][C]0.999999999999993[/C][/ROW]
[ROW][C]28[/C][C]5.00242106693344e-16[/C][C]1.00048421338669e-15[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]4.05395777031992e-17[/C][C]8.10791554063984e-17[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]4.52501780906316e-18[/C][C]9.05003561812632e-18[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.90904042905979e-18[/C][C]3.81808085811959e-18[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]2.19960918995568e-19[/C][C]4.39921837991137e-19[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]2.76567829207708e-20[/C][C]5.53135658415417e-20[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]4.57596443666624e-21[/C][C]9.15192887333249e-21[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.33891726382325e-21[/C][C]2.6778345276465e-21[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]8.23991845632549e-22[/C][C]1.64798369126510e-21[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]9.59646329539283e-22[/C][C]1.91929265907857e-21[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.65864520190152e-22[/C][C]3.31729040380303e-22[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]2.70303962986345e-23[/C][C]5.4060792597269e-23[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]3.90707311348483e-24[/C][C]7.81414622696966e-24[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]1.06546983322795e-24[/C][C]2.13093966645590e-24[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1.53972633725262e-25[/C][C]3.07945267450524e-25[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]1.43311665075113e-25[/C][C]2.86623330150225e-25[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]2.06171215882732e-26[/C][C]4.12342431765465e-26[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]5.37940717330637e-27[/C][C]1.07588143466127e-26[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]4.79289350038597e-27[/C][C]9.58578700077195e-27[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.93496730183953e-26[/C][C]3.86993460367906e-26[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]3.33130939550566e-24[/C][C]6.66261879101131e-24[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]1.99361679206807e-09[/C][C]3.98723358413615e-09[/C][C]0.999999998006383[/C][/ROW]
[ROW][C]50[/C][C]0.9686141508827[/C][C]0.0627716982346015[/C][C]0.0313858491173008[/C][/ROW]
[ROW][C]51[/C][C]0.991508920567788[/C][C]0.0169821588644248[/C][C]0.00849107943221239[/C][/ROW]
[ROW][C]52[/C][C]0.994999691612553[/C][C]0.0100006167748937[/C][C]0.00500030838744685[/C][/ROW]
[ROW][C]53[/C][C]0.996505082957263[/C][C]0.00698983408547404[/C][C]0.00349491704273702[/C][/ROW]
[ROW][C]54[/C][C]0.999883840136367[/C][C]0.000232319727265039[/C][C]0.000116159863632519[/C][/ROW]
[ROW][C]55[/C][C]0.99996043910997[/C][C]7.91217800613651e-05[/C][C]3.95608900306826e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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
171.54955075409298e-053.09910150818596e-050.999984504492459
184.27673861299270e-078.55347722598539e-070.999999572326139
192.94340827849628e-065.88681655699257e-060.999997056591722
201.78954359541076e-073.57908719082152e-070.99999982104564
211.15789556670435e-082.31579113340871e-080.999999988421044
229.59869217606954e-101.91973843521391e-090.99999999904013
231.04205943435395e-102.08411886870791e-100.999999999895794
241.28638055784226e-112.57276111568452e-110.999999999987136
251.31242701928871e-122.62485403857742e-120.999999999998687
269.7874818907804e-141.95749637815608e-130.999999999999902
277.03301506835766e-151.40660301367153e-140.999999999999993
285.00242106693344e-161.00048421338669e-151
294.05395777031992e-178.10791554063984e-171
304.52501780906316e-189.05003561812632e-181
311.90904042905979e-183.81808085811959e-181
322.19960918995568e-194.39921837991137e-191
332.76567829207708e-205.53135658415417e-201
344.57596443666624e-219.15192887333249e-211
351.33891726382325e-212.6778345276465e-211
368.23991845632549e-221.64798369126510e-211
379.59646329539283e-221.91929265907857e-211
381.65864520190152e-223.31729040380303e-221
392.70303962986345e-235.4060792597269e-231
403.90707311348483e-247.81414622696966e-241
411.06546983322795e-242.13093966645590e-241
421.53972633725262e-253.07945267450524e-251
431.43311665075113e-252.86623330150225e-251
442.06171215882732e-264.12342431765465e-261
455.37940717330637e-271.07588143466127e-261
464.79289350038597e-279.58578700077195e-271
471.93496730183953e-263.86993460367906e-261
483.33130939550566e-246.66261879101131e-241
491.99361679206807e-093.98723358413615e-090.999999998006383
500.96861415088270.06277169823460150.0313858491173008
510.9915089205677880.01698215886442480.00849107943221239
520.9949996916125530.01000061677489370.00500030838744685
530.9965050829572630.006989834085474040.00349491704273702
540.9998838401363670.0002323197272650390.000116159863632519
550.999960439109977.91217800613651e-053.95608900306826e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level360.923076923076923NOK
5% type I error level380.974358974358974NOK
10% type I error level391NOK

\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 & 36 & 0.923076923076923 & NOK \tabularnewline
5% type I error level & 38 & 0.974358974358974 & NOK \tabularnewline
10% type I error level & 39 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58984&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]36[/C][C]0.923076923076923[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]38[/C][C]0.974358974358974[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58984&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58984&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 level360.923076923076923NOK
5% type I error level380.974358974358974NOK
10% type I error level391NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}