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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 03:05:24 -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/t12590572924dmvm2ysqnxhvzk.htm/, Retrieved Sat, 12 Oct 2024 13:57:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58975, Retrieved Sat, 12 Oct 2024 13:57:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact217
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-24 10:05:24] [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 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=58975&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=58975&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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] = + 103.708251028807 + 7.79209876543207X[t] -1.25631687242790M1[t] -0.0279835390946507M2[t] + 0.108683127572014M3[t] + 0.140349794238680M4[t] + 0.138683127572013M5[t] + 0.143683127572011M6[t] + 0.413683127572011M7[t] -0.314999999999997M8[t] -0.151666666666663M9[t] -0.0999999999999963M10[t] -0.0416666666666662M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  103.708251028807 +  7.79209876543207X[t] -1.25631687242790M1[t] -0.0279835390946507M2[t] +  0.108683127572014M3[t] +  0.140349794238680M4[t] +  0.138683127572013M5[t] +  0.143683127572011M6[t] +  0.413683127572011M7[t] -0.314999999999997M8[t] -0.151666666666663M9[t] -0.0999999999999963M10[t] -0.0416666666666662M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  103.708251028807 +  7.79209876543207X[t] -1.25631687242790M1[t] -0.0279835390946507M2[t] +  0.108683127572014M3[t] +  0.140349794238680M4[t] +  0.138683127572013M5[t] +  0.143683127572011M6[t] +  0.413683127572011M7[t] -0.314999999999997M8[t] -0.151666666666663M9[t] -0.0999999999999963M10[t] -0.0416666666666662M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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] = + 103.708251028807 + 7.79209876543207X[t] -1.25631687242790M1[t] -0.0279835390946507M2[t] + 0.108683127572014M3[t] + 0.140349794238680M4[t] + 0.138683127572013M5[t] + 0.143683127572011M6[t] + 0.413683127572011M7[t] -0.314999999999997M8[t] -0.151666666666663M9[t] -0.0999999999999963M10[t] -0.0416666666666662M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)103.7082510288072.17140447.760900
X7.792098765432071.2654326.157700
M1-1.256316872427902.692659-0.46660.6425250.321263
M2-0.02798353909465072.692659-0.01040.9917430.495872
M30.1086831275720142.6926590.04040.967940.48397
M40.1403497942386802.6926590.05210.9586070.479303
M50.1386831275720132.6926590.05150.9590980.479549
M60.1436831275720112.6926590.05340.9576250.478812
M70.4136831275720112.6926590.15360.8784230.439211
M8-0.3149999999999972.684387-0.11730.9069850.453493
M9-0.1516666666666632.684387-0.05650.9551350.477567
M10-0.09999999999999632.684387-0.03730.9704090.485205
M11-0.04166666666666622.684387-0.01550.9876680.493834

\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) & 103.708251028807 & 2.171404 & 47.7609 & 0 & 0 \tabularnewline
X & 7.79209876543207 & 1.265432 & 6.1577 & 0 & 0 \tabularnewline
M1 & -1.25631687242790 & 2.692659 & -0.4666 & 0.642525 & 0.321263 \tabularnewline
M2 & -0.0279835390946507 & 2.692659 & -0.0104 & 0.991743 & 0.495872 \tabularnewline
M3 & 0.108683127572014 & 2.692659 & 0.0404 & 0.96794 & 0.48397 \tabularnewline
M4 & 0.140349794238680 & 2.692659 & 0.0521 & 0.958607 & 0.479303 \tabularnewline
M5 & 0.138683127572013 & 2.692659 & 0.0515 & 0.959098 & 0.479549 \tabularnewline
M6 & 0.143683127572011 & 2.692659 & 0.0534 & 0.957625 & 0.478812 \tabularnewline
M7 & 0.413683127572011 & 2.692659 & 0.1536 & 0.878423 & 0.439211 \tabularnewline
M8 & -0.314999999999997 & 2.684387 & -0.1173 & 0.906985 & 0.453493 \tabularnewline
M9 & -0.151666666666663 & 2.684387 & -0.0565 & 0.955135 & 0.477567 \tabularnewline
M10 & -0.0999999999999963 & 2.684387 & -0.0373 & 0.970409 & 0.485205 \tabularnewline
M11 & -0.0416666666666662 & 2.684387 & -0.0155 & 0.987668 & 0.493834 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&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]103.708251028807[/C][C]2.171404[/C][C]47.7609[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]7.79209876543207[/C][C]1.265432[/C][C]6.1577[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.25631687242790[/C][C]2.692659[/C][C]-0.4666[/C][C]0.642525[/C][C]0.321263[/C][/ROW]
[ROW][C]M2[/C][C]-0.0279835390946507[/C][C]2.692659[/C][C]-0.0104[/C][C]0.991743[/C][C]0.495872[/C][/ROW]
[ROW][C]M3[/C][C]0.108683127572014[/C][C]2.692659[/C][C]0.0404[/C][C]0.96794[/C][C]0.48397[/C][/ROW]
[ROW][C]M4[/C][C]0.140349794238680[/C][C]2.692659[/C][C]0.0521[/C][C]0.958607[/C][C]0.479303[/C][/ROW]
[ROW][C]M5[/C][C]0.138683127572013[/C][C]2.692659[/C][C]0.0515[/C][C]0.959098[/C][C]0.479549[/C][/ROW]
[ROW][C]M6[/C][C]0.143683127572011[/C][C]2.692659[/C][C]0.0534[/C][C]0.957625[/C][C]0.478812[/C][/ROW]
[ROW][C]M7[/C][C]0.413683127572011[/C][C]2.692659[/C][C]0.1536[/C][C]0.878423[/C][C]0.439211[/C][/ROW]
[ROW][C]M8[/C][C]-0.314999999999997[/C][C]2.684387[/C][C]-0.1173[/C][C]0.906985[/C][C]0.453493[/C][/ROW]
[ROW][C]M9[/C][C]-0.151666666666663[/C][C]2.684387[/C][C]-0.0565[/C][C]0.955135[/C][C]0.477567[/C][/ROW]
[ROW][C]M10[/C][C]-0.0999999999999963[/C][C]2.684387[/C][C]-0.0373[/C][C]0.970409[/C][C]0.485205[/C][/ROW]
[ROW][C]M11[/C][C]-0.0416666666666662[/C][C]2.684387[/C][C]-0.0155[/C][C]0.987668[/C][C]0.493834[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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)103.7082510288072.17140447.760900
X7.792098765432071.2654326.157700
M1-1.256316872427902.692659-0.46660.6425250.321263
M2-0.02798353909465072.692659-0.01040.9917430.495872
M30.1086831275720142.6926590.04040.967940.48397
M40.1403497942386802.6926590.05210.9586070.479303
M50.1386831275720132.6926590.05150.9590980.479549
M60.1436831275720112.6926590.05340.9576250.478812
M70.4136831275720112.6926590.15360.8784230.439211
M8-0.3149999999999972.684387-0.11730.9069850.453493
M9-0.1516666666666632.684387-0.05650.9551350.477567
M10-0.09999999999999632.684387-0.03730.9704090.485205
M11-0.04166666666666622.684387-0.01550.9876680.493834







Multiple Linear Regression - Regression Statistics
Multiple R0.633977059741042
R-squared0.401926912277897
Adjusted R-squared0.280284928334419
F-TEST (value)3.30417919247892
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0.00106190316840671
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.64949479158511
Sum Squared Residuals1275.45030720165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.633977059741042 \tabularnewline
R-squared & 0.401926912277897 \tabularnewline
Adjusted R-squared & 0.280284928334419 \tabularnewline
F-TEST (value) & 3.30417919247892 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.00106190316840671 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.64949479158511 \tabularnewline
Sum Squared Residuals & 1275.45030720165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.633977059741042[/C][/ROW]
[ROW][C]R-squared[/C][C]0.401926912277897[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.280284928334419[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.30417919247892[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.00106190316840671[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.64949479158511[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1275.45030720165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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 R0.633977059741042
R-squared0.401926912277897
Adjusted R-squared0.280284928334419
F-TEST (value)3.30417919247892
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0.00106190316840671
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.64949479158511
Sum Squared Residuals1275.45030720165







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.52102.4519341563781.06806584362184
2103.5103.680267489712-0.180267489711939
3103.52103.816934156379-0.296934156378628
4103.53103.848600823045-0.318600823045288
5103.53103.846934156379-0.316934156378624
6103.53103.851934156379-0.321934156378616
7103.52104.121934156379-0.601934156378624
8103.54103.3932510288070.146748971193395
9103.59103.556584362140.0334156378600592
10103.59103.608251028807-0.0182510288066077
11103.59103.66658436214-0.0765843621399387
12103.59103.708251028807-0.118251028806605
13103.63102.4519341563791.17806584362129
14103.74103.6802674897120.0597325102880376
15103.7103.816934156379-0.116934156378618
16103.72103.848600823045-0.128600823045290
17103.81103.846934156379-0.0369341563786187
18103.8103.851934156379-0.0519341563786220
19104.22104.1219341563790.0980658436213797
20106.91111.185349794239-4.27534979423868
21107.06111.348683127572-4.28868312757201
22107.17111.400349794239-4.23034979423868
23107.25111.458683127572-4.20868312757201
24107.28111.500349794239-4.22034979423868
25107.24110.244032921811-3.00403292181079
26107.23111.472366255144-4.24236625514402
27107.34111.609032921811-4.26903292181069
28107.34111.640699588477-4.30069958847735
29107.3111.639032921811-4.33903292181069
30107.24111.644032921811-4.40403292181069
31107.3111.914032921811-4.61403292181069
32107.32111.185349794239-3.86534979423869
33107.28111.348683127572-4.06868312757201
34107.33111.400349794239-4.07034979423868
35107.33111.458683127572-4.12868312757201
36107.33111.500349794239-4.17034979423868
37107.28110.244032921811-2.96403292181078
38107.28111.472366255144-4.19236625514402
39107.29111.609032921811-4.31903292181069
40107.29111.640699588477-4.35069958847735
41107.23111.639032921811-4.40903292181069
42107.24111.644032921811-4.40403292181069
43107.24111.914032921811-4.67403292181069
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
49107.21110.244032921811-3.03403292181079
50113.89111.4723662551442.41763374485597
51114.05111.6090329218112.44096707818931
52114.05111.6406995884772.40930041152264
53114.05111.6390329218112.41096707818931
54114.05111.6440329218112.40596707818931
55115.12111.9140329218113.20596707818931
56115.68111.1853497942394.49465020576133
57116.05111.3486831275724.70131687242798
58116.18111.4003497942394.77965020576133
59116.35111.4586831275724.89131687242798
60116.44111.5003497942394.93965020576132
61117110.2440329218116.75596707818922
62117.61111.4723662551446.13763374485597
63118.17111.6090329218116.56096707818931
64118.33111.6406995884776.68930041152264
65118.33111.6390329218116.69096707818931
66118.42111.6440329218116.77596707818931
67118.5111.9140329218116.58596707818931
68118.67111.1853497942397.48465020576132
69119.09111.3486831275727.74131687242799
70119.14111.4003497942397.73965020576132
71119.23111.4586831275727.771316872428
72119.33111.5003497942397.82965020576132

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.52 & 102.451934156378 & 1.06806584362184 \tabularnewline
2 & 103.5 & 103.680267489712 & -0.180267489711939 \tabularnewline
3 & 103.52 & 103.816934156379 & -0.296934156378628 \tabularnewline
4 & 103.53 & 103.848600823045 & -0.318600823045288 \tabularnewline
5 & 103.53 & 103.846934156379 & -0.316934156378624 \tabularnewline
6 & 103.53 & 103.851934156379 & -0.321934156378616 \tabularnewline
7 & 103.52 & 104.121934156379 & -0.601934156378624 \tabularnewline
8 & 103.54 & 103.393251028807 & 0.146748971193395 \tabularnewline
9 & 103.59 & 103.55658436214 & 0.0334156378600592 \tabularnewline
10 & 103.59 & 103.608251028807 & -0.0182510288066077 \tabularnewline
11 & 103.59 & 103.66658436214 & -0.0765843621399387 \tabularnewline
12 & 103.59 & 103.708251028807 & -0.118251028806605 \tabularnewline
13 & 103.63 & 102.451934156379 & 1.17806584362129 \tabularnewline
14 & 103.74 & 103.680267489712 & 0.0597325102880376 \tabularnewline
15 & 103.7 & 103.816934156379 & -0.116934156378618 \tabularnewline
16 & 103.72 & 103.848600823045 & -0.128600823045290 \tabularnewline
17 & 103.81 & 103.846934156379 & -0.0369341563786187 \tabularnewline
18 & 103.8 & 103.851934156379 & -0.0519341563786220 \tabularnewline
19 & 104.22 & 104.121934156379 & 0.0980658436213797 \tabularnewline
20 & 106.91 & 111.185349794239 & -4.27534979423868 \tabularnewline
21 & 107.06 & 111.348683127572 & -4.28868312757201 \tabularnewline
22 & 107.17 & 111.400349794239 & -4.23034979423868 \tabularnewline
23 & 107.25 & 111.458683127572 & -4.20868312757201 \tabularnewline
24 & 107.28 & 111.500349794239 & -4.22034979423868 \tabularnewline
25 & 107.24 & 110.244032921811 & -3.00403292181079 \tabularnewline
26 & 107.23 & 111.472366255144 & -4.24236625514402 \tabularnewline
27 & 107.34 & 111.609032921811 & -4.26903292181069 \tabularnewline
28 & 107.34 & 111.640699588477 & -4.30069958847735 \tabularnewline
29 & 107.3 & 111.639032921811 & -4.33903292181069 \tabularnewline
30 & 107.24 & 111.644032921811 & -4.40403292181069 \tabularnewline
31 & 107.3 & 111.914032921811 & -4.61403292181069 \tabularnewline
32 & 107.32 & 111.185349794239 & -3.86534979423869 \tabularnewline
33 & 107.28 & 111.348683127572 & -4.06868312757201 \tabularnewline
34 & 107.33 & 111.400349794239 & -4.07034979423868 \tabularnewline
35 & 107.33 & 111.458683127572 & -4.12868312757201 \tabularnewline
36 & 107.33 & 111.500349794239 & -4.17034979423868 \tabularnewline
37 & 107.28 & 110.244032921811 & -2.96403292181078 \tabularnewline
38 & 107.28 & 111.472366255144 & -4.19236625514402 \tabularnewline
39 & 107.29 & 111.609032921811 & -4.31903292181069 \tabularnewline
40 & 107.29 & 111.640699588477 & -4.35069958847735 \tabularnewline
41 & 107.23 & 111.639032921811 & -4.40903292181069 \tabularnewline
42 & 107.24 & 111.644032921811 & -4.40403292181069 \tabularnewline
43 & 107.24 & 111.914032921811 & -4.67403292181069 \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 & 110.244032921811 & -3.03403292181079 \tabularnewline
50 & 113.89 & 111.472366255144 & 2.41763374485597 \tabularnewline
51 & 114.05 & 111.609032921811 & 2.44096707818931 \tabularnewline
52 & 114.05 & 111.640699588477 & 2.40930041152264 \tabularnewline
53 & 114.05 & 111.639032921811 & 2.41096707818931 \tabularnewline
54 & 114.05 & 111.644032921811 & 2.40596707818931 \tabularnewline
55 & 115.12 & 111.914032921811 & 3.20596707818931 \tabularnewline
56 & 115.68 & 111.185349794239 & 4.49465020576133 \tabularnewline
57 & 116.05 & 111.348683127572 & 4.70131687242798 \tabularnewline
58 & 116.18 & 111.400349794239 & 4.77965020576133 \tabularnewline
59 & 116.35 & 111.458683127572 & 4.89131687242798 \tabularnewline
60 & 116.44 & 111.500349794239 & 4.93965020576132 \tabularnewline
61 & 117 & 110.244032921811 & 6.75596707818922 \tabularnewline
62 & 117.61 & 111.472366255144 & 6.13763374485597 \tabularnewline
63 & 118.17 & 111.609032921811 & 6.56096707818931 \tabularnewline
64 & 118.33 & 111.640699588477 & 6.68930041152264 \tabularnewline
65 & 118.33 & 111.639032921811 & 6.69096707818931 \tabularnewline
66 & 118.42 & 111.644032921811 & 6.77596707818931 \tabularnewline
67 & 118.5 & 111.914032921811 & 6.58596707818931 \tabularnewline
68 & 118.67 & 111.185349794239 & 7.48465020576132 \tabularnewline
69 & 119.09 & 111.348683127572 & 7.74131687242799 \tabularnewline
70 & 119.14 & 111.400349794239 & 7.73965020576132 \tabularnewline
71 & 119.23 & 111.458683127572 & 7.771316872428 \tabularnewline
72 & 119.33 & 111.500349794239 & 7.82965020576132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&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]102.451934156378[/C][C]1.06806584362184[/C][/ROW]
[ROW][C]2[/C][C]103.5[/C][C]103.680267489712[/C][C]-0.180267489711939[/C][/ROW]
[ROW][C]3[/C][C]103.52[/C][C]103.816934156379[/C][C]-0.296934156378628[/C][/ROW]
[ROW][C]4[/C][C]103.53[/C][C]103.848600823045[/C][C]-0.318600823045288[/C][/ROW]
[ROW][C]5[/C][C]103.53[/C][C]103.846934156379[/C][C]-0.316934156378624[/C][/ROW]
[ROW][C]6[/C][C]103.53[/C][C]103.851934156379[/C][C]-0.321934156378616[/C][/ROW]
[ROW][C]7[/C][C]103.52[/C][C]104.121934156379[/C][C]-0.601934156378624[/C][/ROW]
[ROW][C]8[/C][C]103.54[/C][C]103.393251028807[/C][C]0.146748971193395[/C][/ROW]
[ROW][C]9[/C][C]103.59[/C][C]103.55658436214[/C][C]0.0334156378600592[/C][/ROW]
[ROW][C]10[/C][C]103.59[/C][C]103.608251028807[/C][C]-0.0182510288066077[/C][/ROW]
[ROW][C]11[/C][C]103.59[/C][C]103.66658436214[/C][C]-0.0765843621399387[/C][/ROW]
[ROW][C]12[/C][C]103.59[/C][C]103.708251028807[/C][C]-0.118251028806605[/C][/ROW]
[ROW][C]13[/C][C]103.63[/C][C]102.451934156379[/C][C]1.17806584362129[/C][/ROW]
[ROW][C]14[/C][C]103.74[/C][C]103.680267489712[/C][C]0.0597325102880376[/C][/ROW]
[ROW][C]15[/C][C]103.7[/C][C]103.816934156379[/C][C]-0.116934156378618[/C][/ROW]
[ROW][C]16[/C][C]103.72[/C][C]103.848600823045[/C][C]-0.128600823045290[/C][/ROW]
[ROW][C]17[/C][C]103.81[/C][C]103.846934156379[/C][C]-0.0369341563786187[/C][/ROW]
[ROW][C]18[/C][C]103.8[/C][C]103.851934156379[/C][C]-0.0519341563786220[/C][/ROW]
[ROW][C]19[/C][C]104.22[/C][C]104.121934156379[/C][C]0.0980658436213797[/C][/ROW]
[ROW][C]20[/C][C]106.91[/C][C]111.185349794239[/C][C]-4.27534979423868[/C][/ROW]
[ROW][C]21[/C][C]107.06[/C][C]111.348683127572[/C][C]-4.28868312757201[/C][/ROW]
[ROW][C]22[/C][C]107.17[/C][C]111.400349794239[/C][C]-4.23034979423868[/C][/ROW]
[ROW][C]23[/C][C]107.25[/C][C]111.458683127572[/C][C]-4.20868312757201[/C][/ROW]
[ROW][C]24[/C][C]107.28[/C][C]111.500349794239[/C][C]-4.22034979423868[/C][/ROW]
[ROW][C]25[/C][C]107.24[/C][C]110.244032921811[/C][C]-3.00403292181079[/C][/ROW]
[ROW][C]26[/C][C]107.23[/C][C]111.472366255144[/C][C]-4.24236625514402[/C][/ROW]
[ROW][C]27[/C][C]107.34[/C][C]111.609032921811[/C][C]-4.26903292181069[/C][/ROW]
[ROW][C]28[/C][C]107.34[/C][C]111.640699588477[/C][C]-4.30069958847735[/C][/ROW]
[ROW][C]29[/C][C]107.3[/C][C]111.639032921811[/C][C]-4.33903292181069[/C][/ROW]
[ROW][C]30[/C][C]107.24[/C][C]111.644032921811[/C][C]-4.40403292181069[/C][/ROW]
[ROW][C]31[/C][C]107.3[/C][C]111.914032921811[/C][C]-4.61403292181069[/C][/ROW]
[ROW][C]32[/C][C]107.32[/C][C]111.185349794239[/C][C]-3.86534979423869[/C][/ROW]
[ROW][C]33[/C][C]107.28[/C][C]111.348683127572[/C][C]-4.06868312757201[/C][/ROW]
[ROW][C]34[/C][C]107.33[/C][C]111.400349794239[/C][C]-4.07034979423868[/C][/ROW]
[ROW][C]35[/C][C]107.33[/C][C]111.458683127572[/C][C]-4.12868312757201[/C][/ROW]
[ROW][C]36[/C][C]107.33[/C][C]111.500349794239[/C][C]-4.17034979423868[/C][/ROW]
[ROW][C]37[/C][C]107.28[/C][C]110.244032921811[/C][C]-2.96403292181078[/C][/ROW]
[ROW][C]38[/C][C]107.28[/C][C]111.472366255144[/C][C]-4.19236625514402[/C][/ROW]
[ROW][C]39[/C][C]107.29[/C][C]111.609032921811[/C][C]-4.31903292181069[/C][/ROW]
[ROW][C]40[/C][C]107.29[/C][C]111.640699588477[/C][C]-4.35069958847735[/C][/ROW]
[ROW][C]41[/C][C]107.23[/C][C]111.639032921811[/C][C]-4.40903292181069[/C][/ROW]
[ROW][C]42[/C][C]107.24[/C][C]111.644032921811[/C][C]-4.40403292181069[/C][/ROW]
[ROW][C]43[/C][C]107.24[/C][C]111.914032921811[/C][C]-4.67403292181069[/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]110.244032921811[/C][C]-3.03403292181079[/C][/ROW]
[ROW][C]50[/C][C]113.89[/C][C]111.472366255144[/C][C]2.41763374485597[/C][/ROW]
[ROW][C]51[/C][C]114.05[/C][C]111.609032921811[/C][C]2.44096707818931[/C][/ROW]
[ROW][C]52[/C][C]114.05[/C][C]111.640699588477[/C][C]2.40930041152264[/C][/ROW]
[ROW][C]53[/C][C]114.05[/C][C]111.639032921811[/C][C]2.41096707818931[/C][/ROW]
[ROW][C]54[/C][C]114.05[/C][C]111.644032921811[/C][C]2.40596707818931[/C][/ROW]
[ROW][C]55[/C][C]115.12[/C][C]111.914032921811[/C][C]3.20596707818931[/C][/ROW]
[ROW][C]56[/C][C]115.68[/C][C]111.185349794239[/C][C]4.49465020576133[/C][/ROW]
[ROW][C]57[/C][C]116.05[/C][C]111.348683127572[/C][C]4.70131687242798[/C][/ROW]
[ROW][C]58[/C][C]116.18[/C][C]111.400349794239[/C][C]4.77965020576133[/C][/ROW]
[ROW][C]59[/C][C]116.35[/C][C]111.458683127572[/C][C]4.89131687242798[/C][/ROW]
[ROW][C]60[/C][C]116.44[/C][C]111.500349794239[/C][C]4.93965020576132[/C][/ROW]
[ROW][C]61[/C][C]117[/C][C]110.244032921811[/C][C]6.75596707818922[/C][/ROW]
[ROW][C]62[/C][C]117.61[/C][C]111.472366255144[/C][C]6.13763374485597[/C][/ROW]
[ROW][C]63[/C][C]118.17[/C][C]111.609032921811[/C][C]6.56096707818931[/C][/ROW]
[ROW][C]64[/C][C]118.33[/C][C]111.640699588477[/C][C]6.68930041152264[/C][/ROW]
[ROW][C]65[/C][C]118.33[/C][C]111.639032921811[/C][C]6.69096707818931[/C][/ROW]
[ROW][C]66[/C][C]118.42[/C][C]111.644032921811[/C][C]6.77596707818931[/C][/ROW]
[ROW][C]67[/C][C]118.5[/C][C]111.914032921811[/C][C]6.58596707818931[/C][/ROW]
[ROW][C]68[/C][C]118.67[/C][C]111.185349794239[/C][C]7.48465020576132[/C][/ROW]
[ROW][C]69[/C][C]119.09[/C][C]111.348683127572[/C][C]7.74131687242799[/C][/ROW]
[ROW][C]70[/C][C]119.14[/C][C]111.400349794239[/C][C]7.73965020576132[/C][/ROW]
[ROW][C]71[/C][C]119.23[/C][C]111.458683127572[/C][C]7.771316872428[/C][/ROW]
[ROW][C]72[/C][C]119.33[/C][C]111.500349794239[/C][C]7.82965020576132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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
1103.52102.4519341563781.06806584362184
2103.5103.680267489712-0.180267489711939
3103.52103.816934156379-0.296934156378628
4103.53103.848600823045-0.318600823045288
5103.53103.846934156379-0.316934156378624
6103.53103.851934156379-0.321934156378616
7103.52104.121934156379-0.601934156378624
8103.54103.3932510288070.146748971193395
9103.59103.556584362140.0334156378600592
10103.59103.608251028807-0.0182510288066077
11103.59103.66658436214-0.0765843621399387
12103.59103.708251028807-0.118251028806605
13103.63102.4519341563791.17806584362129
14103.74103.6802674897120.0597325102880376
15103.7103.816934156379-0.116934156378618
16103.72103.848600823045-0.128600823045290
17103.81103.846934156379-0.0369341563786187
18103.8103.851934156379-0.0519341563786220
19104.22104.1219341563790.0980658436213797
20106.91111.185349794239-4.27534979423868
21107.06111.348683127572-4.28868312757201
22107.17111.400349794239-4.23034979423868
23107.25111.458683127572-4.20868312757201
24107.28111.500349794239-4.22034979423868
25107.24110.244032921811-3.00403292181079
26107.23111.472366255144-4.24236625514402
27107.34111.609032921811-4.26903292181069
28107.34111.640699588477-4.30069958847735
29107.3111.639032921811-4.33903292181069
30107.24111.644032921811-4.40403292181069
31107.3111.914032921811-4.61403292181069
32107.32111.185349794239-3.86534979423869
33107.28111.348683127572-4.06868312757201
34107.33111.400349794239-4.07034979423868
35107.33111.458683127572-4.12868312757201
36107.33111.500349794239-4.17034979423868
37107.28110.244032921811-2.96403292181078
38107.28111.472366255144-4.19236625514402
39107.29111.609032921811-4.31903292181069
40107.29111.640699588477-4.35069958847735
41107.23111.639032921811-4.40903292181069
42107.24111.644032921811-4.40403292181069
43107.24111.914032921811-4.67403292181069
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
49107.21110.244032921811-3.03403292181079
50113.89111.4723662551442.41763374485597
51114.05111.6090329218112.44096707818931
52114.05111.6406995884772.40930041152264
53114.05111.6390329218112.41096707818931
54114.05111.6440329218112.40596707818931
55115.12111.9140329218113.20596707818931
56115.68111.1853497942394.49465020576133
57116.05111.3486831275724.70131687242798
58116.18111.4003497942394.77965020576133
59116.35111.4586831275724.89131687242798
60116.44111.5003497942394.93965020576132
61117110.2440329218116.75596707818922
62117.61111.4723662551446.13763374485597
63118.17111.6090329218116.56096707818931
64118.33111.6406995884776.68930041152264
65118.33111.6390329218116.69096707818931
66118.42111.6440329218116.77596707818931
67118.5111.9140329218116.58596707818931
68118.67111.1853497942397.48465020576132
69119.09111.3486831275727.74131687242799
70119.14111.4003497942397.73965020576132
71119.23111.4586831275727.771316872428
72119.33111.5003497942397.82965020576132







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
163.05761553116227e-056.11523106232454e-050.999969423844688
171.67616289768937e-063.35232579537874e-060.999998323837102
188.65071151287355e-081.73014230257471e-070.999999913492885
194.42190766381789e-088.84381532763578e-080.999999955780923
201.86561742461450e-093.73123484922901e-090.999999998134383
217.64247020955035e-111.52849404191007e-100.999999999923575
223.20560846422532e-126.41121692845065e-120.999999999996794
231.38580969181783e-132.77161938363566e-130.999999999999861
245.80915115406661e-151.16183023081332e-140.999999999999994
252.0791217265765e-164.158243453153e-161
267.24435287739447e-181.44887057547889e-171
272.98616000440021e-195.97232000880043e-191
281.13290598184980e-202.26581196369959e-201
293.78662500913844e-227.57325001827689e-221
301.28099039044866e-232.56198078089732e-231
316.44130023183776e-251.28826004636755e-241
326.58627463579632e-261.31725492715926e-251
333.28257033663368e-276.56514067326736e-271
341.63406811258701e-283.26813622517401e-281
357.45162305307745e-301.49032461061549e-291
363.39437538610767e-316.78875077221533e-311
371.12001750893996e-322.24003501787992e-321
385.89001857616625e-341.17800371523325e-331
393.52863098029060e-357.05726196058119e-351
402.37896549793899e-364.75793099587798e-361
412.0455254055737e-374.0910508111474e-371
421.94376099665675e-383.88752199331351e-381
435.48634687005665e-391.09726937401133e-381
441.44965644724045e-392.89931289448091e-391
457.67810623754994e-401.53562124750999e-391
461.06083314804767e-392.12166629609535e-391
477.66757483720664e-391.53351496744133e-381
481.27426931324897e-362.54853862649794e-361
494.81602224676094e-349.63204449352188e-341
504.11740315149802e-068.23480630299603e-060.999995882596848
510.002524442600480590.005048885200961180.99747555739952
520.03284371476007310.06568742952014630.967156285239927
530.1205977069739970.2411954139479950.879402293026003
540.2645492514563400.5290985029126810.73545074854366
550.3692430259134800.7384860518269610.63075697408652
560.4251770004335990.8503540008671980.574822999566401

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 3.05761553116227e-05 & 6.11523106232454e-05 & 0.999969423844688 \tabularnewline
17 & 1.67616289768937e-06 & 3.35232579537874e-06 & 0.999998323837102 \tabularnewline
18 & 8.65071151287355e-08 & 1.73014230257471e-07 & 0.999999913492885 \tabularnewline
19 & 4.42190766381789e-08 & 8.84381532763578e-08 & 0.999999955780923 \tabularnewline
20 & 1.86561742461450e-09 & 3.73123484922901e-09 & 0.999999998134383 \tabularnewline
21 & 7.64247020955035e-11 & 1.52849404191007e-10 & 0.999999999923575 \tabularnewline
22 & 3.20560846422532e-12 & 6.41121692845065e-12 & 0.999999999996794 \tabularnewline
23 & 1.38580969181783e-13 & 2.77161938363566e-13 & 0.999999999999861 \tabularnewline
24 & 5.80915115406661e-15 & 1.16183023081332e-14 & 0.999999999999994 \tabularnewline
25 & 2.0791217265765e-16 & 4.158243453153e-16 & 1 \tabularnewline
26 & 7.24435287739447e-18 & 1.44887057547889e-17 & 1 \tabularnewline
27 & 2.98616000440021e-19 & 5.97232000880043e-19 & 1 \tabularnewline
28 & 1.13290598184980e-20 & 2.26581196369959e-20 & 1 \tabularnewline
29 & 3.78662500913844e-22 & 7.57325001827689e-22 & 1 \tabularnewline
30 & 1.28099039044866e-23 & 2.56198078089732e-23 & 1 \tabularnewline
31 & 6.44130023183776e-25 & 1.28826004636755e-24 & 1 \tabularnewline
32 & 6.58627463579632e-26 & 1.31725492715926e-25 & 1 \tabularnewline
33 & 3.28257033663368e-27 & 6.56514067326736e-27 & 1 \tabularnewline
34 & 1.63406811258701e-28 & 3.26813622517401e-28 & 1 \tabularnewline
35 & 7.45162305307745e-30 & 1.49032461061549e-29 & 1 \tabularnewline
36 & 3.39437538610767e-31 & 6.78875077221533e-31 & 1 \tabularnewline
37 & 1.12001750893996e-32 & 2.24003501787992e-32 & 1 \tabularnewline
38 & 5.89001857616625e-34 & 1.17800371523325e-33 & 1 \tabularnewline
39 & 3.52863098029060e-35 & 7.05726196058119e-35 & 1 \tabularnewline
40 & 2.37896549793899e-36 & 4.75793099587798e-36 & 1 \tabularnewline
41 & 2.0455254055737e-37 & 4.0910508111474e-37 & 1 \tabularnewline
42 & 1.94376099665675e-38 & 3.88752199331351e-38 & 1 \tabularnewline
43 & 5.48634687005665e-39 & 1.09726937401133e-38 & 1 \tabularnewline
44 & 1.44965644724045e-39 & 2.89931289448091e-39 & 1 \tabularnewline
45 & 7.67810623754994e-40 & 1.53562124750999e-39 & 1 \tabularnewline
46 & 1.06083314804767e-39 & 2.12166629609535e-39 & 1 \tabularnewline
47 & 7.66757483720664e-39 & 1.53351496744133e-38 & 1 \tabularnewline
48 & 1.27426931324897e-36 & 2.54853862649794e-36 & 1 \tabularnewline
49 & 4.81602224676094e-34 & 9.63204449352188e-34 & 1 \tabularnewline
50 & 4.11740315149802e-06 & 8.23480630299603e-06 & 0.999995882596848 \tabularnewline
51 & 0.00252444260048059 & 0.00504888520096118 & 0.99747555739952 \tabularnewline
52 & 0.0328437147600731 & 0.0656874295201463 & 0.967156285239927 \tabularnewline
53 & 0.120597706973997 & 0.241195413947995 & 0.879402293026003 \tabularnewline
54 & 0.264549251456340 & 0.529098502912681 & 0.73545074854366 \tabularnewline
55 & 0.369243025913480 & 0.738486051826961 & 0.63075697408652 \tabularnewline
56 & 0.425177000433599 & 0.850354000867198 & 0.574822999566401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]3.05761553116227e-05[/C][C]6.11523106232454e-05[/C][C]0.999969423844688[/C][/ROW]
[ROW][C]17[/C][C]1.67616289768937e-06[/C][C]3.35232579537874e-06[/C][C]0.999998323837102[/C][/ROW]
[ROW][C]18[/C][C]8.65071151287355e-08[/C][C]1.73014230257471e-07[/C][C]0.999999913492885[/C][/ROW]
[ROW][C]19[/C][C]4.42190766381789e-08[/C][C]8.84381532763578e-08[/C][C]0.999999955780923[/C][/ROW]
[ROW][C]20[/C][C]1.86561742461450e-09[/C][C]3.73123484922901e-09[/C][C]0.999999998134383[/C][/ROW]
[ROW][C]21[/C][C]7.64247020955035e-11[/C][C]1.52849404191007e-10[/C][C]0.999999999923575[/C][/ROW]
[ROW][C]22[/C][C]3.20560846422532e-12[/C][C]6.41121692845065e-12[/C][C]0.999999999996794[/C][/ROW]
[ROW][C]23[/C][C]1.38580969181783e-13[/C][C]2.77161938363566e-13[/C][C]0.999999999999861[/C][/ROW]
[ROW][C]24[/C][C]5.80915115406661e-15[/C][C]1.16183023081332e-14[/C][C]0.999999999999994[/C][/ROW]
[ROW][C]25[/C][C]2.0791217265765e-16[/C][C]4.158243453153e-16[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]7.24435287739447e-18[/C][C]1.44887057547889e-17[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]2.98616000440021e-19[/C][C]5.97232000880043e-19[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.13290598184980e-20[/C][C]2.26581196369959e-20[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]3.78662500913844e-22[/C][C]7.57325001827689e-22[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.28099039044866e-23[/C][C]2.56198078089732e-23[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]6.44130023183776e-25[/C][C]1.28826004636755e-24[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]6.58627463579632e-26[/C][C]1.31725492715926e-25[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]3.28257033663368e-27[/C][C]6.56514067326736e-27[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.63406811258701e-28[/C][C]3.26813622517401e-28[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]7.45162305307745e-30[/C][C]1.49032461061549e-29[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]3.39437538610767e-31[/C][C]6.78875077221533e-31[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.12001750893996e-32[/C][C]2.24003501787992e-32[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]5.89001857616625e-34[/C][C]1.17800371523325e-33[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]3.52863098029060e-35[/C][C]7.05726196058119e-35[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]2.37896549793899e-36[/C][C]4.75793099587798e-36[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.0455254055737e-37[/C][C]4.0910508111474e-37[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1.94376099665675e-38[/C][C]3.88752199331351e-38[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]5.48634687005665e-39[/C][C]1.09726937401133e-38[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]1.44965644724045e-39[/C][C]2.89931289448091e-39[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]7.67810623754994e-40[/C][C]1.53562124750999e-39[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]1.06083314804767e-39[/C][C]2.12166629609535e-39[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]7.66757483720664e-39[/C][C]1.53351496744133e-38[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]1.27426931324897e-36[/C][C]2.54853862649794e-36[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]4.81602224676094e-34[/C][C]9.63204449352188e-34[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]4.11740315149802e-06[/C][C]8.23480630299603e-06[/C][C]0.999995882596848[/C][/ROW]
[ROW][C]51[/C][C]0.00252444260048059[/C][C]0.00504888520096118[/C][C]0.99747555739952[/C][/ROW]
[ROW][C]52[/C][C]0.0328437147600731[/C][C]0.0656874295201463[/C][C]0.967156285239927[/C][/ROW]
[ROW][C]53[/C][C]0.120597706973997[/C][C]0.241195413947995[/C][C]0.879402293026003[/C][/ROW]
[ROW][C]54[/C][C]0.264549251456340[/C][C]0.529098502912681[/C][C]0.73545074854366[/C][/ROW]
[ROW][C]55[/C][C]0.369243025913480[/C][C]0.738486051826961[/C][C]0.63075697408652[/C][/ROW]
[ROW][C]56[/C][C]0.425177000433599[/C][C]0.850354000867198[/C][C]0.574822999566401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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
163.05761553116227e-056.11523106232454e-050.999969423844688
171.67616289768937e-063.35232579537874e-060.999998323837102
188.65071151287355e-081.73014230257471e-070.999999913492885
194.42190766381789e-088.84381532763578e-080.999999955780923
201.86561742461450e-093.73123484922901e-090.999999998134383
217.64247020955035e-111.52849404191007e-100.999999999923575
223.20560846422532e-126.41121692845065e-120.999999999996794
231.38580969181783e-132.77161938363566e-130.999999999999861
245.80915115406661e-151.16183023081332e-140.999999999999994
252.0791217265765e-164.158243453153e-161
267.24435287739447e-181.44887057547889e-171
272.98616000440021e-195.97232000880043e-191
281.13290598184980e-202.26581196369959e-201
293.78662500913844e-227.57325001827689e-221
301.28099039044866e-232.56198078089732e-231
316.44130023183776e-251.28826004636755e-241
326.58627463579632e-261.31725492715926e-251
333.28257033663368e-276.56514067326736e-271
341.63406811258701e-283.26813622517401e-281
357.45162305307745e-301.49032461061549e-291
363.39437538610767e-316.78875077221533e-311
371.12001750893996e-322.24003501787992e-321
385.89001857616625e-341.17800371523325e-331
393.52863098029060e-357.05726196058119e-351
402.37896549793899e-364.75793099587798e-361
412.0455254055737e-374.0910508111474e-371
421.94376099665675e-383.88752199331351e-381
435.48634687005665e-391.09726937401133e-381
441.44965644724045e-392.89931289448091e-391
457.67810623754994e-401.53562124750999e-391
461.06083314804767e-392.12166629609535e-391
477.66757483720664e-391.53351496744133e-381
481.27426931324897e-362.54853862649794e-361
494.81602224676094e-349.63204449352188e-341
504.11740315149802e-068.23480630299603e-060.999995882596848
510.002524442600480590.005048885200961180.99747555739952
520.03284371476007310.06568742952014630.967156285239927
530.1205977069739970.2411954139479950.879402293026003
540.2645492514563400.5290985029126810.73545074854366
550.3692430259134800.7384860518269610.63075697408652
560.4251770004335990.8503540008671980.574822999566401







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level360.878048780487805NOK
5% type I error level360.878048780487805NOK
10% type I error level370.902439024390244NOK

\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 & 0.878048780487805 & NOK \tabularnewline
5% type I error level & 36 & 0.878048780487805 & NOK \tabularnewline
10% type I error level & 37 & 0.902439024390244 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58975&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]0.878048780487805[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.878048780487805[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.902439024390244[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58975&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58975&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 level360.878048780487805NOK
5% type I error level360.878048780487805NOK
10% type I error level370.902439024390244NOK



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