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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 12:21:00 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258744890p909gg11vw6uqnv.htm/, Retrieved Fri, 29 Mar 2024 10:29:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58436, Retrieved Fri, 29 Mar 2024 10:29:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Ws 7 ] [2009-11-20 18:21:24] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-   P         [Multiple Regression] [Ws 7 (2)] [2009-11-20 19:21:00] [ba02bcb7e07025bbb7f8a074d38ad767] [Current]
-   P           [Multiple Regression] [Ws 7 (3)] [2009-11-20 19:24:54] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
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Dataseries X:
18.0	16.4
19.6	17.8
23.3	22.3
23.7	22.8
20.3	18.3
22.8	22.4
24.3	23.9
21.5	21.3
23.5	23.0
22.2	21.4
20.9	21.2
22.2	20.9
19.5	17.9
21.1	20.7
22.0	22.2
19.2	19.8
17.8	17.7
19.2	19.6
19.9	20.8
19.6	19.8
18.1	18.6
20.4	21.
18.1	18.6
18.6	18.9
17.6	17.3
19.4	20.0
19.3	19.9
18.6	19.5
16.9	16.2
16.4	17.6
19.0	19.8
18.7	19.4
17.1	17.2
21.5	21.1
17.8	17.8
18.1	17.5
19.0	18.0
18.9	19.1
16.8	17.7
18.1	19.2
15.7	15.1
15.1	16.3
18.3	18.6
16.5	17.2
16.9	17.8
18.4	19.1
16.4	16.6
15.7	16.0
16.9	16.7
16.6	17.4
16.7	17.9
16.6	17.8
14.4	13.9
14.5	15.9
17.5	17.9
14.3	15.4
15.4	16.4
17.2	17.9
14.6	15.3
14.2	14.6
14.9	14.9
14.1	15.0
15.6	16.7
14.6	16.3
11.9	11.7
13.5	15.1
14.2	15.5
13.7	15.0
14.4	15.4
15.3	16.0
14.3	14.7
14.5	14.8




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=58436&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=58436&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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] = -3.41688587976048 + 1.20546558206975X[t] + 0.734699728850762M1[t] -0.399983124851524M2[t] -1.07941969149607M3[t] -1.30156881538096M4[t] + 0.918927117380584M5[t] -1.14382590744882M6[t] -1.12257083876042M7[t] -0.918252357196105M8[t] -0.795192302966259M9[t] -0.822570838760416M10[t] -0.501366395517438M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -3.41688587976048 +  1.20546558206975X[t] +  0.734699728850762M1[t] -0.399983124851524M2[t] -1.07941969149607M3[t] -1.30156881538096M4[t] +  0.918927117380584M5[t] -1.14382590744882M6[t] -1.12257083876042M7[t] -0.918252357196105M8[t] -0.795192302966259M9[t] -0.822570838760416M10[t] -0.501366395517438M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -3.41688587976048 +  1.20546558206975X[t] +  0.734699728850762M1[t] -0.399983124851524M2[t] -1.07941969149607M3[t] -1.30156881538096M4[t] +  0.918927117380584M5[t] -1.14382590744882M6[t] -1.12257083876042M7[t] -0.918252357196105M8[t] -0.795192302966259M9[t] -0.822570838760416M10[t] -0.501366395517438M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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] = -3.41688587976048 + 1.20546558206975X[t] + 0.734699728850762M1[t] -0.399983124851524M2[t] -1.07941969149607M3[t] -1.30156881538096M4[t] + 0.918927117380584M5[t] -1.14382590744882M6[t] -1.12257083876042M7[t] -0.918252357196105M8[t] -0.795192302966259M9[t] -0.822570838760416M10[t] -0.501366395517438M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-3.416885879760480.530798-6.437300
X1.205465582069750.02839342.456700
M10.7346997288507620.3019442.43320.0180140.009007
M2-0.3999831248515240.303831-1.31650.1931090.096554
M3-1.079419691496070.309045-3.49280.0009130.000456
M4-1.301568815380960.307785-4.22888.3e-054.1e-05
M50.9189271173805840.3054023.00890.0038520.001926
M6-1.143825907448820.302514-3.78110.0003670.000183
M7-1.122570838760420.308844-3.63480.0005850.000293
M8-0.9182523571961050.30294-3.03110.0036150.001807
M9-0.7951923029662590.303063-2.62380.011050.005525
M10-0.8225708387604160.308844-2.66340.0099570.004978
M11-0.5013663955174380.301944-1.66050.1021280.051064

\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) & -3.41688587976048 & 0.530798 & -6.4373 & 0 & 0 \tabularnewline
X & 1.20546558206975 & 0.028393 & 42.4567 & 0 & 0 \tabularnewline
M1 & 0.734699728850762 & 0.301944 & 2.4332 & 0.018014 & 0.009007 \tabularnewline
M2 & -0.399983124851524 & 0.303831 & -1.3165 & 0.193109 & 0.096554 \tabularnewline
M3 & -1.07941969149607 & 0.309045 & -3.4928 & 0.000913 & 0.000456 \tabularnewline
M4 & -1.30156881538096 & 0.307785 & -4.2288 & 8.3e-05 & 4.1e-05 \tabularnewline
M5 & 0.918927117380584 & 0.305402 & 3.0089 & 0.003852 & 0.001926 \tabularnewline
M6 & -1.14382590744882 & 0.302514 & -3.7811 & 0.000367 & 0.000183 \tabularnewline
M7 & -1.12257083876042 & 0.308844 & -3.6348 & 0.000585 & 0.000293 \tabularnewline
M8 & -0.918252357196105 & 0.30294 & -3.0311 & 0.003615 & 0.001807 \tabularnewline
M9 & -0.795192302966259 & 0.303063 & -2.6238 & 0.01105 & 0.005525 \tabularnewline
M10 & -0.822570838760416 & 0.308844 & -2.6634 & 0.009957 & 0.004978 \tabularnewline
M11 & -0.501366395517438 & 0.301944 & -1.6605 & 0.102128 & 0.051064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&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]-3.41688587976048[/C][C]0.530798[/C][C]-6.4373[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1.20546558206975[/C][C]0.028393[/C][C]42.4567[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.734699728850762[/C][C]0.301944[/C][C]2.4332[/C][C]0.018014[/C][C]0.009007[/C][/ROW]
[ROW][C]M2[/C][C]-0.399983124851524[/C][C]0.303831[/C][C]-1.3165[/C][C]0.193109[/C][C]0.096554[/C][/ROW]
[ROW][C]M3[/C][C]-1.07941969149607[/C][C]0.309045[/C][C]-3.4928[/C][C]0.000913[/C][C]0.000456[/C][/ROW]
[ROW][C]M4[/C][C]-1.30156881538096[/C][C]0.307785[/C][C]-4.2288[/C][C]8.3e-05[/C][C]4.1e-05[/C][/ROW]
[ROW][C]M5[/C][C]0.918927117380584[/C][C]0.305402[/C][C]3.0089[/C][C]0.003852[/C][C]0.001926[/C][/ROW]
[ROW][C]M6[/C][C]-1.14382590744882[/C][C]0.302514[/C][C]-3.7811[/C][C]0.000367[/C][C]0.000183[/C][/ROW]
[ROW][C]M7[/C][C]-1.12257083876042[/C][C]0.308844[/C][C]-3.6348[/C][C]0.000585[/C][C]0.000293[/C][/ROW]
[ROW][C]M8[/C][C]-0.918252357196105[/C][C]0.30294[/C][C]-3.0311[/C][C]0.003615[/C][C]0.001807[/C][/ROW]
[ROW][C]M9[/C][C]-0.795192302966259[/C][C]0.303063[/C][C]-2.6238[/C][C]0.01105[/C][C]0.005525[/C][/ROW]
[ROW][C]M10[/C][C]-0.822570838760416[/C][C]0.308844[/C][C]-2.6634[/C][C]0.009957[/C][C]0.004978[/C][/ROW]
[ROW][C]M11[/C][C]-0.501366395517438[/C][C]0.301944[/C][C]-1.6605[/C][C]0.102128[/C][C]0.051064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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)-3.416885879760480.530798-6.437300
X1.205465582069750.02839342.456700
M10.7346997288507620.3019442.43320.0180140.009007
M2-0.3999831248515240.303831-1.31650.1931090.096554
M3-1.079419691496070.309045-3.49280.0009130.000456
M4-1.301568815380960.307785-4.22888.3e-054.1e-05
M50.9189271173805840.3054023.00890.0038520.001926
M6-1.143825907448820.302514-3.78110.0003670.000183
M7-1.122570838760420.308844-3.63480.0005850.000293
M8-0.9182523571961050.30294-3.03110.0036150.001807
M9-0.7951923029662590.303063-2.62380.011050.005525
M10-0.8225708387604160.308844-2.66340.0099570.004978
M11-0.5013663955174380.301944-1.66050.1021280.051064







Multiple Linear Regression - Regression Statistics
Multiple R0.985670635867928
R-squared0.971546602412285
Adjusted R-squared0.96575947069953
F-TEST (value)167.880506377776
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.522837820203883
Sum Squared Residuals16.1282037878973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985670635867928 \tabularnewline
R-squared & 0.971546602412285 \tabularnewline
Adjusted R-squared & 0.96575947069953 \tabularnewline
F-TEST (value) & 167.880506377776 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.522837820203883 \tabularnewline
Sum Squared Residuals & 16.1282037878973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985670635867928[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971546602412285[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.96575947069953[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]167.880506377776[/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[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.522837820203883[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.1282037878973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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 R0.985670635867928
R-squared0.971546602412285
Adjusted R-squared0.96575947069953
F-TEST (value)167.880506377776
F-TEST (DF numerator)12
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.522837820203883
Sum Squared Residuals16.1282037878973







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11817.08744939503420.912550604965844
219.617.64041835622951.95958164377053
323.322.38557690889880.914423091101224
423.722.76616057604880.93383942395124
520.319.56206138949640.73793861050355
622.822.4417172511530.358282748847004
724.324.2711706929460.0288293070539764
821.521.3412786611290.158721338871001
923.523.5136302048774-0.0136302048774122
1022.221.55750673777170.642493262228339
1120.921.6376180646007-0.737618064600691
1222.221.77734478549720.422655214502796
1319.518.89564776813870.604352231861272
1421.121.1362685442317-0.0362685442317284
152222.2650303506918-0.2650303506918
1619.219.14976382983950.0502361701604758
1717.818.8387820402546-1.03878204025460
1819.219.06641362135770.133586378642285
1919.920.5342273885298-0.634227388529817
2019.619.53308028802440.066919711975621
2118.118.2095816437705-0.109581643770531
2220.421.0753205049438-0.675320504943764
2318.118.5034075512194-0.403407551219352
2418.619.3664136213577-0.766413621357711
2517.618.1723684188969-0.572368418896882
2619.420.2924426367829-0.892442636782911
2719.319.4924595119314-0.192459511931384
2818.618.7881241552186-0.188124155218597
2916.917.03058366715-0.130583667149985
3016.416.6554824572182-0.255482457218225
311919.3287618064601-0.328761806460069
3218.719.0508940551965-0.35089405519648
3317.116.52192982887290.578070171127115
3421.521.19586706315070.304132936849261
3517.817.53903508556360.260964914436444
3618.117.67876180646010.421238193539931
371919.0161943263457-0.0161943263457042
3818.919.2075236129201-0.307523612920141
3916.816.8404352313779-0.0404352313779446
4018.118.4264844805977-0.326484480597673
4115.715.7045715268733-0.00457152687326499
4215.115.08837720052760.0116227994724468
4318.317.88220310797640.417796892023625
4416.516.39886977464300.101130225356960
4516.917.2452091781147-0.345209178114737
4618.418.7849358990112-0.384935899011249
4716.416.09247638707990.307523612920135
4815.715.8705634333555-0.170563433355452
4916.917.4490890696550-0.549089069655036
5016.617.1582321234016-0.558232123401568
5116.717.0815283477919-0.381528347791894
5216.616.7388326657000-0.138832665700031
5314.414.25801282838960.141987171610430
5414.514.6061909676997-0.106190967699654
5517.517.03837720052750.461622799472449
5614.314.22903172691750.0709682730825013
5715.415.5575573632171-0.157557363217089
5817.217.3383772005275-0.138377200527550
5914.614.52537113038920.074628869610807
6014.214.18291161845780.0170883815421924
6114.915.2792510219295-0.379251021929493
6214.114.2651147264342-0.165114726434182
6315.615.6349696493082-0.0349696493082004
6414.614.9306342925954-0.330634292595414
6511.911.60598854783610.294011452163872
6613.513.6418185020439-0.141818502043857
6714.214.14525980356020.0547401964398365
6813.713.7468454940896-0.0468454940896024
6914.414.35209178114730.0479082188526546
7015.315.04799259459500.252007405404966
7114.313.80209178114730.497908218852657
7214.514.42400473487180.0759952651282423

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 17.0874493950342 & 0.912550604965844 \tabularnewline
2 & 19.6 & 17.6404183562295 & 1.95958164377053 \tabularnewline
3 & 23.3 & 22.3855769088988 & 0.914423091101224 \tabularnewline
4 & 23.7 & 22.7661605760488 & 0.93383942395124 \tabularnewline
5 & 20.3 & 19.5620613894964 & 0.73793861050355 \tabularnewline
6 & 22.8 & 22.441717251153 & 0.358282748847004 \tabularnewline
7 & 24.3 & 24.271170692946 & 0.0288293070539764 \tabularnewline
8 & 21.5 & 21.341278661129 & 0.158721338871001 \tabularnewline
9 & 23.5 & 23.5136302048774 & -0.0136302048774122 \tabularnewline
10 & 22.2 & 21.5575067377717 & 0.642493262228339 \tabularnewline
11 & 20.9 & 21.6376180646007 & -0.737618064600691 \tabularnewline
12 & 22.2 & 21.7773447854972 & 0.422655214502796 \tabularnewline
13 & 19.5 & 18.8956477681387 & 0.604352231861272 \tabularnewline
14 & 21.1 & 21.1362685442317 & -0.0362685442317284 \tabularnewline
15 & 22 & 22.2650303506918 & -0.2650303506918 \tabularnewline
16 & 19.2 & 19.1497638298395 & 0.0502361701604758 \tabularnewline
17 & 17.8 & 18.8387820402546 & -1.03878204025460 \tabularnewline
18 & 19.2 & 19.0664136213577 & 0.133586378642285 \tabularnewline
19 & 19.9 & 20.5342273885298 & -0.634227388529817 \tabularnewline
20 & 19.6 & 19.5330802880244 & 0.066919711975621 \tabularnewline
21 & 18.1 & 18.2095816437705 & -0.109581643770531 \tabularnewline
22 & 20.4 & 21.0753205049438 & -0.675320504943764 \tabularnewline
23 & 18.1 & 18.5034075512194 & -0.403407551219352 \tabularnewline
24 & 18.6 & 19.3664136213577 & -0.766413621357711 \tabularnewline
25 & 17.6 & 18.1723684188969 & -0.572368418896882 \tabularnewline
26 & 19.4 & 20.2924426367829 & -0.892442636782911 \tabularnewline
27 & 19.3 & 19.4924595119314 & -0.192459511931384 \tabularnewline
28 & 18.6 & 18.7881241552186 & -0.188124155218597 \tabularnewline
29 & 16.9 & 17.03058366715 & -0.130583667149985 \tabularnewline
30 & 16.4 & 16.6554824572182 & -0.255482457218225 \tabularnewline
31 & 19 & 19.3287618064601 & -0.328761806460069 \tabularnewline
32 & 18.7 & 19.0508940551965 & -0.35089405519648 \tabularnewline
33 & 17.1 & 16.5219298288729 & 0.578070171127115 \tabularnewline
34 & 21.5 & 21.1958670631507 & 0.304132936849261 \tabularnewline
35 & 17.8 & 17.5390350855636 & 0.260964914436444 \tabularnewline
36 & 18.1 & 17.6787618064601 & 0.421238193539931 \tabularnewline
37 & 19 & 19.0161943263457 & -0.0161943263457042 \tabularnewline
38 & 18.9 & 19.2075236129201 & -0.307523612920141 \tabularnewline
39 & 16.8 & 16.8404352313779 & -0.0404352313779446 \tabularnewline
40 & 18.1 & 18.4264844805977 & -0.326484480597673 \tabularnewline
41 & 15.7 & 15.7045715268733 & -0.00457152687326499 \tabularnewline
42 & 15.1 & 15.0883772005276 & 0.0116227994724468 \tabularnewline
43 & 18.3 & 17.8822031079764 & 0.417796892023625 \tabularnewline
44 & 16.5 & 16.3988697746430 & 0.101130225356960 \tabularnewline
45 & 16.9 & 17.2452091781147 & -0.345209178114737 \tabularnewline
46 & 18.4 & 18.7849358990112 & -0.384935899011249 \tabularnewline
47 & 16.4 & 16.0924763870799 & 0.307523612920135 \tabularnewline
48 & 15.7 & 15.8705634333555 & -0.170563433355452 \tabularnewline
49 & 16.9 & 17.4490890696550 & -0.549089069655036 \tabularnewline
50 & 16.6 & 17.1582321234016 & -0.558232123401568 \tabularnewline
51 & 16.7 & 17.0815283477919 & -0.381528347791894 \tabularnewline
52 & 16.6 & 16.7388326657000 & -0.138832665700031 \tabularnewline
53 & 14.4 & 14.2580128283896 & 0.141987171610430 \tabularnewline
54 & 14.5 & 14.6061909676997 & -0.106190967699654 \tabularnewline
55 & 17.5 & 17.0383772005275 & 0.461622799472449 \tabularnewline
56 & 14.3 & 14.2290317269175 & 0.0709682730825013 \tabularnewline
57 & 15.4 & 15.5575573632171 & -0.157557363217089 \tabularnewline
58 & 17.2 & 17.3383772005275 & -0.138377200527550 \tabularnewline
59 & 14.6 & 14.5253711303892 & 0.074628869610807 \tabularnewline
60 & 14.2 & 14.1829116184578 & 0.0170883815421924 \tabularnewline
61 & 14.9 & 15.2792510219295 & -0.379251021929493 \tabularnewline
62 & 14.1 & 14.2651147264342 & -0.165114726434182 \tabularnewline
63 & 15.6 & 15.6349696493082 & -0.0349696493082004 \tabularnewline
64 & 14.6 & 14.9306342925954 & -0.330634292595414 \tabularnewline
65 & 11.9 & 11.6059885478361 & 0.294011452163872 \tabularnewline
66 & 13.5 & 13.6418185020439 & -0.141818502043857 \tabularnewline
67 & 14.2 & 14.1452598035602 & 0.0547401964398365 \tabularnewline
68 & 13.7 & 13.7468454940896 & -0.0468454940896024 \tabularnewline
69 & 14.4 & 14.3520917811473 & 0.0479082188526546 \tabularnewline
70 & 15.3 & 15.0479925945950 & 0.252007405404966 \tabularnewline
71 & 14.3 & 13.8020917811473 & 0.497908218852657 \tabularnewline
72 & 14.5 & 14.4240047348718 & 0.0759952651282423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&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]18[/C][C]17.0874493950342[/C][C]0.912550604965844[/C][/ROW]
[ROW][C]2[/C][C]19.6[/C][C]17.6404183562295[/C][C]1.95958164377053[/C][/ROW]
[ROW][C]3[/C][C]23.3[/C][C]22.3855769088988[/C][C]0.914423091101224[/C][/ROW]
[ROW][C]4[/C][C]23.7[/C][C]22.7661605760488[/C][C]0.93383942395124[/C][/ROW]
[ROW][C]5[/C][C]20.3[/C][C]19.5620613894964[/C][C]0.73793861050355[/C][/ROW]
[ROW][C]6[/C][C]22.8[/C][C]22.441717251153[/C][C]0.358282748847004[/C][/ROW]
[ROW][C]7[/C][C]24.3[/C][C]24.271170692946[/C][C]0.0288293070539764[/C][/ROW]
[ROW][C]8[/C][C]21.5[/C][C]21.341278661129[/C][C]0.158721338871001[/C][/ROW]
[ROW][C]9[/C][C]23.5[/C][C]23.5136302048774[/C][C]-0.0136302048774122[/C][/ROW]
[ROW][C]10[/C][C]22.2[/C][C]21.5575067377717[/C][C]0.642493262228339[/C][/ROW]
[ROW][C]11[/C][C]20.9[/C][C]21.6376180646007[/C][C]-0.737618064600691[/C][/ROW]
[ROW][C]12[/C][C]22.2[/C][C]21.7773447854972[/C][C]0.422655214502796[/C][/ROW]
[ROW][C]13[/C][C]19.5[/C][C]18.8956477681387[/C][C]0.604352231861272[/C][/ROW]
[ROW][C]14[/C][C]21.1[/C][C]21.1362685442317[/C][C]-0.0362685442317284[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]22.2650303506918[/C][C]-0.2650303506918[/C][/ROW]
[ROW][C]16[/C][C]19.2[/C][C]19.1497638298395[/C][C]0.0502361701604758[/C][/ROW]
[ROW][C]17[/C][C]17.8[/C][C]18.8387820402546[/C][C]-1.03878204025460[/C][/ROW]
[ROW][C]18[/C][C]19.2[/C][C]19.0664136213577[/C][C]0.133586378642285[/C][/ROW]
[ROW][C]19[/C][C]19.9[/C][C]20.5342273885298[/C][C]-0.634227388529817[/C][/ROW]
[ROW][C]20[/C][C]19.6[/C][C]19.5330802880244[/C][C]0.066919711975621[/C][/ROW]
[ROW][C]21[/C][C]18.1[/C][C]18.2095816437705[/C][C]-0.109581643770531[/C][/ROW]
[ROW][C]22[/C][C]20.4[/C][C]21.0753205049438[/C][C]-0.675320504943764[/C][/ROW]
[ROW][C]23[/C][C]18.1[/C][C]18.5034075512194[/C][C]-0.403407551219352[/C][/ROW]
[ROW][C]24[/C][C]18.6[/C][C]19.3664136213577[/C][C]-0.766413621357711[/C][/ROW]
[ROW][C]25[/C][C]17.6[/C][C]18.1723684188969[/C][C]-0.572368418896882[/C][/ROW]
[ROW][C]26[/C][C]19.4[/C][C]20.2924426367829[/C][C]-0.892442636782911[/C][/ROW]
[ROW][C]27[/C][C]19.3[/C][C]19.4924595119314[/C][C]-0.192459511931384[/C][/ROW]
[ROW][C]28[/C][C]18.6[/C][C]18.7881241552186[/C][C]-0.188124155218597[/C][/ROW]
[ROW][C]29[/C][C]16.9[/C][C]17.03058366715[/C][C]-0.130583667149985[/C][/ROW]
[ROW][C]30[/C][C]16.4[/C][C]16.6554824572182[/C][C]-0.255482457218225[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]19.3287618064601[/C][C]-0.328761806460069[/C][/ROW]
[ROW][C]32[/C][C]18.7[/C][C]19.0508940551965[/C][C]-0.35089405519648[/C][/ROW]
[ROW][C]33[/C][C]17.1[/C][C]16.5219298288729[/C][C]0.578070171127115[/C][/ROW]
[ROW][C]34[/C][C]21.5[/C][C]21.1958670631507[/C][C]0.304132936849261[/C][/ROW]
[ROW][C]35[/C][C]17.8[/C][C]17.5390350855636[/C][C]0.260964914436444[/C][/ROW]
[ROW][C]36[/C][C]18.1[/C][C]17.6787618064601[/C][C]0.421238193539931[/C][/ROW]
[ROW][C]37[/C][C]19[/C][C]19.0161943263457[/C][C]-0.0161943263457042[/C][/ROW]
[ROW][C]38[/C][C]18.9[/C][C]19.2075236129201[/C][C]-0.307523612920141[/C][/ROW]
[ROW][C]39[/C][C]16.8[/C][C]16.8404352313779[/C][C]-0.0404352313779446[/C][/ROW]
[ROW][C]40[/C][C]18.1[/C][C]18.4264844805977[/C][C]-0.326484480597673[/C][/ROW]
[ROW][C]41[/C][C]15.7[/C][C]15.7045715268733[/C][C]-0.00457152687326499[/C][/ROW]
[ROW][C]42[/C][C]15.1[/C][C]15.0883772005276[/C][C]0.0116227994724468[/C][/ROW]
[ROW][C]43[/C][C]18.3[/C][C]17.8822031079764[/C][C]0.417796892023625[/C][/ROW]
[ROW][C]44[/C][C]16.5[/C][C]16.3988697746430[/C][C]0.101130225356960[/C][/ROW]
[ROW][C]45[/C][C]16.9[/C][C]17.2452091781147[/C][C]-0.345209178114737[/C][/ROW]
[ROW][C]46[/C][C]18.4[/C][C]18.7849358990112[/C][C]-0.384935899011249[/C][/ROW]
[ROW][C]47[/C][C]16.4[/C][C]16.0924763870799[/C][C]0.307523612920135[/C][/ROW]
[ROW][C]48[/C][C]15.7[/C][C]15.8705634333555[/C][C]-0.170563433355452[/C][/ROW]
[ROW][C]49[/C][C]16.9[/C][C]17.4490890696550[/C][C]-0.549089069655036[/C][/ROW]
[ROW][C]50[/C][C]16.6[/C][C]17.1582321234016[/C][C]-0.558232123401568[/C][/ROW]
[ROW][C]51[/C][C]16.7[/C][C]17.0815283477919[/C][C]-0.381528347791894[/C][/ROW]
[ROW][C]52[/C][C]16.6[/C][C]16.7388326657000[/C][C]-0.138832665700031[/C][/ROW]
[ROW][C]53[/C][C]14.4[/C][C]14.2580128283896[/C][C]0.141987171610430[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]14.6061909676997[/C][C]-0.106190967699654[/C][/ROW]
[ROW][C]55[/C][C]17.5[/C][C]17.0383772005275[/C][C]0.461622799472449[/C][/ROW]
[ROW][C]56[/C][C]14.3[/C][C]14.2290317269175[/C][C]0.0709682730825013[/C][/ROW]
[ROW][C]57[/C][C]15.4[/C][C]15.5575573632171[/C][C]-0.157557363217089[/C][/ROW]
[ROW][C]58[/C][C]17.2[/C][C]17.3383772005275[/C][C]-0.138377200527550[/C][/ROW]
[ROW][C]59[/C][C]14.6[/C][C]14.5253711303892[/C][C]0.074628869610807[/C][/ROW]
[ROW][C]60[/C][C]14.2[/C][C]14.1829116184578[/C][C]0.0170883815421924[/C][/ROW]
[ROW][C]61[/C][C]14.9[/C][C]15.2792510219295[/C][C]-0.379251021929493[/C][/ROW]
[ROW][C]62[/C][C]14.1[/C][C]14.2651147264342[/C][C]-0.165114726434182[/C][/ROW]
[ROW][C]63[/C][C]15.6[/C][C]15.6349696493082[/C][C]-0.0349696493082004[/C][/ROW]
[ROW][C]64[/C][C]14.6[/C][C]14.9306342925954[/C][C]-0.330634292595414[/C][/ROW]
[ROW][C]65[/C][C]11.9[/C][C]11.6059885478361[/C][C]0.294011452163872[/C][/ROW]
[ROW][C]66[/C][C]13.5[/C][C]13.6418185020439[/C][C]-0.141818502043857[/C][/ROW]
[ROW][C]67[/C][C]14.2[/C][C]14.1452598035602[/C][C]0.0547401964398365[/C][/ROW]
[ROW][C]68[/C][C]13.7[/C][C]13.7468454940896[/C][C]-0.0468454940896024[/C][/ROW]
[ROW][C]69[/C][C]14.4[/C][C]14.3520917811473[/C][C]0.0479082188526546[/C][/ROW]
[ROW][C]70[/C][C]15.3[/C][C]15.0479925945950[/C][C]0.252007405404966[/C][/ROW]
[ROW][C]71[/C][C]14.3[/C][C]13.8020917811473[/C][C]0.497908218852657[/C][/ROW]
[ROW][C]72[/C][C]14.5[/C][C]14.4240047348718[/C][C]0.0759952651282423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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
11817.08744939503420.912550604965844
219.617.64041835622951.95958164377053
323.322.38557690889880.914423091101224
423.722.76616057604880.93383942395124
520.319.56206138949640.73793861050355
622.822.4417172511530.358282748847004
724.324.2711706929460.0288293070539764
821.521.3412786611290.158721338871001
923.523.5136302048774-0.0136302048774122
1022.221.55750673777170.642493262228339
1120.921.6376180646007-0.737618064600691
1222.221.77734478549720.422655214502796
1319.518.89564776813870.604352231861272
1421.121.1362685442317-0.0362685442317284
152222.2650303506918-0.2650303506918
1619.219.14976382983950.0502361701604758
1717.818.8387820402546-1.03878204025460
1819.219.06641362135770.133586378642285
1919.920.5342273885298-0.634227388529817
2019.619.53308028802440.066919711975621
2118.118.2095816437705-0.109581643770531
2220.421.0753205049438-0.675320504943764
2318.118.5034075512194-0.403407551219352
2418.619.3664136213577-0.766413621357711
2517.618.1723684188969-0.572368418896882
2619.420.2924426367829-0.892442636782911
2719.319.4924595119314-0.192459511931384
2818.618.7881241552186-0.188124155218597
2916.917.03058366715-0.130583667149985
3016.416.6554824572182-0.255482457218225
311919.3287618064601-0.328761806460069
3218.719.0508940551965-0.35089405519648
3317.116.52192982887290.578070171127115
3421.521.19586706315070.304132936849261
3517.817.53903508556360.260964914436444
3618.117.67876180646010.421238193539931
371919.0161943263457-0.0161943263457042
3818.919.2075236129201-0.307523612920141
3916.816.8404352313779-0.0404352313779446
4018.118.4264844805977-0.326484480597673
4115.715.7045715268733-0.00457152687326499
4215.115.08837720052760.0116227994724468
4318.317.88220310797640.417796892023625
4416.516.39886977464300.101130225356960
4516.917.2452091781147-0.345209178114737
4618.418.7849358990112-0.384935899011249
4716.416.09247638707990.307523612920135
4815.715.8705634333555-0.170563433355452
4916.917.4490890696550-0.549089069655036
5016.617.1582321234016-0.558232123401568
5116.717.0815283477919-0.381528347791894
5216.616.7388326657000-0.138832665700031
5314.414.25801282838960.141987171610430
5414.514.6061909676997-0.106190967699654
5517.517.03837720052750.461622799472449
5614.314.22903172691750.0709682730825013
5715.415.5575573632171-0.157557363217089
5817.217.3383772005275-0.138377200527550
5914.614.52537113038920.074628869610807
6014.214.18291161845780.0170883815421924
6114.915.2792510219295-0.379251021929493
6214.114.2651147264342-0.165114726434182
6315.615.6349696493082-0.0349696493082004
6414.614.9306342925954-0.330634292595414
6511.911.60598854783610.294011452163872
6613.513.6418185020439-0.141818502043857
6714.214.14525980356020.0547401964398365
6813.713.7468454940896-0.0468454940896024
6914.414.35209178114730.0479082188526546
7015.315.04799259459500.252007405404966
7114.313.80209178114730.497908218852657
7214.514.42400473487180.0759952651282423







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9999430811569980.0001138376860047425.69188430023711e-05
170.999999518653739.62692539743804e-074.81346269871902e-07
180.9999992235673871.55286522666963e-067.76432613334816e-07
190.9999994040131881.19197362386398e-065.95986811931988e-07
200.999998526665962.94666808025999e-061.47333404013000e-06
210.9999953169994549.36600109286098e-064.68300054643049e-06
220.9999988553927272.28921454524931e-061.14460727262465e-06
230.9999988075954692.3848090628943e-061.19240453144715e-06
240.9999998367622933.26475414824186e-071.63237707412093e-07
250.9999999424823991.15035201905097e-075.75176009525486e-08
260.9999999971623995.67520310122625e-092.83760155061313e-09
270.9999999913010391.73979217390166e-088.69896086950832e-09
280.9999999785615834.28768334043395e-082.14384167021698e-08
290.999999940459891.19080220746376e-075.9540110373188e-08
300.9999998338460733.32307854473301e-071.66153927236650e-07
310.9999999027797031.94440594027424e-079.72202970137118e-08
320.9999998750634282.49873143519910e-071.24936571759955e-07
330.9999999878653842.42692319722496e-081.21346159861248e-08
340.9999999871706232.56587533799543e-081.28293766899771e-08
350.9999999703207035.93585949494231e-082.96792974747115e-08
360.9999999874330612.51338776630236e-081.25669388315118e-08
370.9999999961052427.78951557615589e-093.89475778807795e-09
380.9999999918422761.63154479881632e-088.15772399408162e-09
390.999999976815984.6368038952554e-082.3184019476277e-08
400.9999999227646271.54470746348536e-077.7235373174268e-08
410.999999719006055.61987899112769e-072.80993949556384e-07
420.9999992109536661.57809266894906e-067.89046334474532e-07
430.9999988940407552.21191849070622e-061.10595924535311e-06
440.9999977180781464.56384370714652e-062.28192185357326e-06
450.99999324029511.35194098008716e-056.75970490043579e-06
460.9999887203401742.25593196512221e-051.12796598256111e-05
470.9999679401024446.41197951114319e-053.20598975557160e-05
480.9999020851885970.0001958296228056879.79148114028435e-05
490.999720888108020.0005582237839618120.000279111891980906
500.9994700318931280.001059936213744170.000529968106872087
510.9990454119112420.001909176177516020.00095458808875801
520.9975617181214140.004876563757171220.00243828187858561
530.9926343964208180.01473120715836340.00736560357918168
540.9780094447555880.04398111048882390.0219905552444119
550.9954989629676950.009002074064610860.00450103703230543
560.987239928878320.02552014224335870.0127600711216793

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.999943081156998 & 0.000113837686004742 & 5.69188430023711e-05 \tabularnewline
17 & 0.99999951865373 & 9.62692539743804e-07 & 4.81346269871902e-07 \tabularnewline
18 & 0.999999223567387 & 1.55286522666963e-06 & 7.76432613334816e-07 \tabularnewline
19 & 0.999999404013188 & 1.19197362386398e-06 & 5.95986811931988e-07 \tabularnewline
20 & 0.99999852666596 & 2.94666808025999e-06 & 1.47333404013000e-06 \tabularnewline
21 & 0.999995316999454 & 9.36600109286098e-06 & 4.68300054643049e-06 \tabularnewline
22 & 0.999998855392727 & 2.28921454524931e-06 & 1.14460727262465e-06 \tabularnewline
23 & 0.999998807595469 & 2.3848090628943e-06 & 1.19240453144715e-06 \tabularnewline
24 & 0.999999836762293 & 3.26475414824186e-07 & 1.63237707412093e-07 \tabularnewline
25 & 0.999999942482399 & 1.15035201905097e-07 & 5.75176009525486e-08 \tabularnewline
26 & 0.999999997162399 & 5.67520310122625e-09 & 2.83760155061313e-09 \tabularnewline
27 & 0.999999991301039 & 1.73979217390166e-08 & 8.69896086950832e-09 \tabularnewline
28 & 0.999999978561583 & 4.28768334043395e-08 & 2.14384167021698e-08 \tabularnewline
29 & 0.99999994045989 & 1.19080220746376e-07 & 5.9540110373188e-08 \tabularnewline
30 & 0.999999833846073 & 3.32307854473301e-07 & 1.66153927236650e-07 \tabularnewline
31 & 0.999999902779703 & 1.94440594027424e-07 & 9.72202970137118e-08 \tabularnewline
32 & 0.999999875063428 & 2.49873143519910e-07 & 1.24936571759955e-07 \tabularnewline
33 & 0.999999987865384 & 2.42692319722496e-08 & 1.21346159861248e-08 \tabularnewline
34 & 0.999999987170623 & 2.56587533799543e-08 & 1.28293766899771e-08 \tabularnewline
35 & 0.999999970320703 & 5.93585949494231e-08 & 2.96792974747115e-08 \tabularnewline
36 & 0.999999987433061 & 2.51338776630236e-08 & 1.25669388315118e-08 \tabularnewline
37 & 0.999999996105242 & 7.78951557615589e-09 & 3.89475778807795e-09 \tabularnewline
38 & 0.999999991842276 & 1.63154479881632e-08 & 8.15772399408162e-09 \tabularnewline
39 & 0.99999997681598 & 4.6368038952554e-08 & 2.3184019476277e-08 \tabularnewline
40 & 0.999999922764627 & 1.54470746348536e-07 & 7.7235373174268e-08 \tabularnewline
41 & 0.99999971900605 & 5.61987899112769e-07 & 2.80993949556384e-07 \tabularnewline
42 & 0.999999210953666 & 1.57809266894906e-06 & 7.89046334474532e-07 \tabularnewline
43 & 0.999998894040755 & 2.21191849070622e-06 & 1.10595924535311e-06 \tabularnewline
44 & 0.999997718078146 & 4.56384370714652e-06 & 2.28192185357326e-06 \tabularnewline
45 & 0.9999932402951 & 1.35194098008716e-05 & 6.75970490043579e-06 \tabularnewline
46 & 0.999988720340174 & 2.25593196512221e-05 & 1.12796598256111e-05 \tabularnewline
47 & 0.999967940102444 & 6.41197951114319e-05 & 3.20598975557160e-05 \tabularnewline
48 & 0.999902085188597 & 0.000195829622805687 & 9.79148114028435e-05 \tabularnewline
49 & 0.99972088810802 & 0.000558223783961812 & 0.000279111891980906 \tabularnewline
50 & 0.999470031893128 & 0.00105993621374417 & 0.000529968106872087 \tabularnewline
51 & 0.999045411911242 & 0.00190917617751602 & 0.00095458808875801 \tabularnewline
52 & 0.997561718121414 & 0.00487656375717122 & 0.00243828187858561 \tabularnewline
53 & 0.992634396420818 & 0.0147312071583634 & 0.00736560357918168 \tabularnewline
54 & 0.978009444755588 & 0.0439811104888239 & 0.0219905552444119 \tabularnewline
55 & 0.995498962967695 & 0.00900207406461086 & 0.00450103703230543 \tabularnewline
56 & 0.98723992887832 & 0.0255201422433587 & 0.0127600711216793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.999943081156998[/C][C]0.000113837686004742[/C][C]5.69188430023711e-05[/C][/ROW]
[ROW][C]17[/C][C]0.99999951865373[/C][C]9.62692539743804e-07[/C][C]4.81346269871902e-07[/C][/ROW]
[ROW][C]18[/C][C]0.999999223567387[/C][C]1.55286522666963e-06[/C][C]7.76432613334816e-07[/C][/ROW]
[ROW][C]19[/C][C]0.999999404013188[/C][C]1.19197362386398e-06[/C][C]5.95986811931988e-07[/C][/ROW]
[ROW][C]20[/C][C]0.99999852666596[/C][C]2.94666808025999e-06[/C][C]1.47333404013000e-06[/C][/ROW]
[ROW][C]21[/C][C]0.999995316999454[/C][C]9.36600109286098e-06[/C][C]4.68300054643049e-06[/C][/ROW]
[ROW][C]22[/C][C]0.999998855392727[/C][C]2.28921454524931e-06[/C][C]1.14460727262465e-06[/C][/ROW]
[ROW][C]23[/C][C]0.999998807595469[/C][C]2.3848090628943e-06[/C][C]1.19240453144715e-06[/C][/ROW]
[ROW][C]24[/C][C]0.999999836762293[/C][C]3.26475414824186e-07[/C][C]1.63237707412093e-07[/C][/ROW]
[ROW][C]25[/C][C]0.999999942482399[/C][C]1.15035201905097e-07[/C][C]5.75176009525486e-08[/C][/ROW]
[ROW][C]26[/C][C]0.999999997162399[/C][C]5.67520310122625e-09[/C][C]2.83760155061313e-09[/C][/ROW]
[ROW][C]27[/C][C]0.999999991301039[/C][C]1.73979217390166e-08[/C][C]8.69896086950832e-09[/C][/ROW]
[ROW][C]28[/C][C]0.999999978561583[/C][C]4.28768334043395e-08[/C][C]2.14384167021698e-08[/C][/ROW]
[ROW][C]29[/C][C]0.99999994045989[/C][C]1.19080220746376e-07[/C][C]5.9540110373188e-08[/C][/ROW]
[ROW][C]30[/C][C]0.999999833846073[/C][C]3.32307854473301e-07[/C][C]1.66153927236650e-07[/C][/ROW]
[ROW][C]31[/C][C]0.999999902779703[/C][C]1.94440594027424e-07[/C][C]9.72202970137118e-08[/C][/ROW]
[ROW][C]32[/C][C]0.999999875063428[/C][C]2.49873143519910e-07[/C][C]1.24936571759955e-07[/C][/ROW]
[ROW][C]33[/C][C]0.999999987865384[/C][C]2.42692319722496e-08[/C][C]1.21346159861248e-08[/C][/ROW]
[ROW][C]34[/C][C]0.999999987170623[/C][C]2.56587533799543e-08[/C][C]1.28293766899771e-08[/C][/ROW]
[ROW][C]35[/C][C]0.999999970320703[/C][C]5.93585949494231e-08[/C][C]2.96792974747115e-08[/C][/ROW]
[ROW][C]36[/C][C]0.999999987433061[/C][C]2.51338776630236e-08[/C][C]1.25669388315118e-08[/C][/ROW]
[ROW][C]37[/C][C]0.999999996105242[/C][C]7.78951557615589e-09[/C][C]3.89475778807795e-09[/C][/ROW]
[ROW][C]38[/C][C]0.999999991842276[/C][C]1.63154479881632e-08[/C][C]8.15772399408162e-09[/C][/ROW]
[ROW][C]39[/C][C]0.99999997681598[/C][C]4.6368038952554e-08[/C][C]2.3184019476277e-08[/C][/ROW]
[ROW][C]40[/C][C]0.999999922764627[/C][C]1.54470746348536e-07[/C][C]7.7235373174268e-08[/C][/ROW]
[ROW][C]41[/C][C]0.99999971900605[/C][C]5.61987899112769e-07[/C][C]2.80993949556384e-07[/C][/ROW]
[ROW][C]42[/C][C]0.999999210953666[/C][C]1.57809266894906e-06[/C][C]7.89046334474532e-07[/C][/ROW]
[ROW][C]43[/C][C]0.999998894040755[/C][C]2.21191849070622e-06[/C][C]1.10595924535311e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999997718078146[/C][C]4.56384370714652e-06[/C][C]2.28192185357326e-06[/C][/ROW]
[ROW][C]45[/C][C]0.9999932402951[/C][C]1.35194098008716e-05[/C][C]6.75970490043579e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999988720340174[/C][C]2.25593196512221e-05[/C][C]1.12796598256111e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999967940102444[/C][C]6.41197951114319e-05[/C][C]3.20598975557160e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999902085188597[/C][C]0.000195829622805687[/C][C]9.79148114028435e-05[/C][/ROW]
[ROW][C]49[/C][C]0.99972088810802[/C][C]0.000558223783961812[/C][C]0.000279111891980906[/C][/ROW]
[ROW][C]50[/C][C]0.999470031893128[/C][C]0.00105993621374417[/C][C]0.000529968106872087[/C][/ROW]
[ROW][C]51[/C][C]0.999045411911242[/C][C]0.00190917617751602[/C][C]0.00095458808875801[/C][/ROW]
[ROW][C]52[/C][C]0.997561718121414[/C][C]0.00487656375717122[/C][C]0.00243828187858561[/C][/ROW]
[ROW][C]53[/C][C]0.992634396420818[/C][C]0.0147312071583634[/C][C]0.00736560357918168[/C][/ROW]
[ROW][C]54[/C][C]0.978009444755588[/C][C]0.0439811104888239[/C][C]0.0219905552444119[/C][/ROW]
[ROW][C]55[/C][C]0.995498962967695[/C][C]0.00900207406461086[/C][C]0.00450103703230543[/C][/ROW]
[ROW][C]56[/C][C]0.98723992887832[/C][C]0.0255201422433587[/C][C]0.0127600711216793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9999430811569980.0001138376860047425.69188430023711e-05
170.999999518653739.62692539743804e-074.81346269871902e-07
180.9999992235673871.55286522666963e-067.76432613334816e-07
190.9999994040131881.19197362386398e-065.95986811931988e-07
200.999998526665962.94666808025999e-061.47333404013000e-06
210.9999953169994549.36600109286098e-064.68300054643049e-06
220.9999988553927272.28921454524931e-061.14460727262465e-06
230.9999988075954692.3848090628943e-061.19240453144715e-06
240.9999998367622933.26475414824186e-071.63237707412093e-07
250.9999999424823991.15035201905097e-075.75176009525486e-08
260.9999999971623995.67520310122625e-092.83760155061313e-09
270.9999999913010391.73979217390166e-088.69896086950832e-09
280.9999999785615834.28768334043395e-082.14384167021698e-08
290.999999940459891.19080220746376e-075.9540110373188e-08
300.9999998338460733.32307854473301e-071.66153927236650e-07
310.9999999027797031.94440594027424e-079.72202970137118e-08
320.9999998750634282.49873143519910e-071.24936571759955e-07
330.9999999878653842.42692319722496e-081.21346159861248e-08
340.9999999871706232.56587533799543e-081.28293766899771e-08
350.9999999703207035.93585949494231e-082.96792974747115e-08
360.9999999874330612.51338776630236e-081.25669388315118e-08
370.9999999961052427.78951557615589e-093.89475778807795e-09
380.9999999918422761.63154479881632e-088.15772399408162e-09
390.999999976815984.6368038952554e-082.3184019476277e-08
400.9999999227646271.54470746348536e-077.7235373174268e-08
410.999999719006055.61987899112769e-072.80993949556384e-07
420.9999992109536661.57809266894906e-067.89046334474532e-07
430.9999988940407552.21191849070622e-061.10595924535311e-06
440.9999977180781464.56384370714652e-062.28192185357326e-06
450.99999324029511.35194098008716e-056.75970490043579e-06
460.9999887203401742.25593196512221e-051.12796598256111e-05
470.9999679401024446.41197951114319e-053.20598975557160e-05
480.9999020851885970.0001958296228056879.79148114028435e-05
490.999720888108020.0005582237839618120.000279111891980906
500.9994700318931280.001059936213744170.000529968106872087
510.9990454119112420.001909176177516020.00095458808875801
520.9975617181214140.004876563757171220.00243828187858561
530.9926343964208180.01473120715836340.00736560357918168
540.9780094447555880.04398111048882390.0219905552444119
550.9954989629676950.009002074064610860.00450103703230543
560.987239928878320.02552014224335870.0127600711216793







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level380.926829268292683NOK
5% type I error level411NOK
10% type I error level411NOK

\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 & 38 & 0.926829268292683 & NOK \tabularnewline
5% type I error level & 41 & 1 & NOK \tabularnewline
10% type I error level & 41 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58436&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]38[/C][C]0.926829268292683[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58436&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58436&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 level380.926829268292683NOK
5% type I error level411NOK
10% type I error level411NOK



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')
}