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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Dec 2009 05:20:57 -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/Dec/20/t1261311725mx2kh2cs8od3co1.htm/, Retrieved Sat, 27 Apr 2024 10:56:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69854, Retrieved Sat, 27 Apr 2024 10:56:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

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] [Multiple regressi...] [2009-11-14 12:18:51] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-20 12:20:57] [8cd69d0f4298074aa572ca2f9b39b6ae] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-1.2	23.6
-2.4	25.7
0.8	32.5
-0.1	33.5
-1.5	34.5
-4.4	27.9
-4.2	45.3
3.5	40.8
10	58.5
8.6	32.5
9.5	35.5
9.9	46.7
10.4	53.2
16	36.1
12.7	54
10.2	58.1
8.9	41.8
12.6	43.1
13.6	76
14.8	42.8
9.5	41
13.7	61.4
17	34.2
14.7	53.8
17.4	80.7
9	79.5
9.1	96.5
12.2	108.3
15.9	100.1
12.9	108.5
10.9	127.4
10.6	86.5
13.2	71.4
9.6	88.2
6.4	135.6
5.8	70.5
-1	87.5
-0.2	73.3
2.7	92.2
3.6	61.1
-0.9	45.7
0.3	30.5
-1.1	34.8
-2.5	29.2
-3.4	56.7
-3.5	67.1
-3.9	41.8
-4.6	46.8
-0.1	50.1
4.3	81.9
10.2	115.8
8.7	102.5
13.3	106.6
15	101.4
20.7	136.1
20.7	143.4
26.4	127.5
31.2	113.8
31.4	75.3
26.6	98.5
26.6	113.7
19.2	103.7
6.5	73.9
3.1	52.5
-0.2	63.9
-4	44.9
-12.6	31.3
-13	24.9
-17.6	22.8
-21.7	24.8
-23.2	22.8
-16.8	20.9
-19.8	21.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Energiedragers[t] = -7.36746638506505 + 0.236669034135203Invoer[t] -2.56663152756032M1[t] -0.76835819175296M2[t] -3.97043927651090M3[t] -2.75825331497566M4[t] -2.20191074851504M5[t] -1.28672975866373M6[t] -5.86821153019543M7[t] -1.44912310628503M8[t] -1.18873828155045M9[t] -1.61257552120687M10[t] -0.0488920455135976M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Energiedragers[t] =  -7.36746638506505 +  0.236669034135203Invoer[t] -2.56663152756032M1[t] -0.76835819175296M2[t] -3.97043927651090M3[t] -2.75825331497566M4[t] -2.20191074851504M5[t] -1.28672975866373M6[t] -5.86821153019543M7[t] -1.44912310628503M8[t] -1.18873828155045M9[t] -1.61257552120687M10[t] -0.0488920455135976M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Energiedragers[t] =  -7.36746638506505 +  0.236669034135203Invoer[t] -2.56663152756032M1[t] -0.76835819175296M2[t] -3.97043927651090M3[t] -2.75825331497566M4[t] -2.20191074851504M5[t] -1.28672975866373M6[t] -5.86821153019543M7[t] -1.44912310628503M8[t] -1.18873828155045M9[t] -1.61257552120687M10[t] -0.0488920455135976M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Energiedragers[t] = -7.36746638506505 + 0.236669034135203Invoer[t] -2.56663152756032M1[t] -0.76835819175296M2[t] -3.97043927651090M3[t] -2.75825331497566M4[t] -2.20191074851504M5[t] -1.28672975866373M6[t] -5.86821153019543M7[t] -1.44912310628503M8[t] -1.18873828155045M9[t] -1.61257552120687M10[t] -0.0488920455135976M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-7.367466385065054.346891-1.69490.0952830.047641
Invoer0.2366690341352030.0342986.900300
M1-2.566631527560325.312628-0.48310.630770.315385
M2-0.768358191752965.521738-0.13920.8897960.444898
M3-3.970439276510905.558126-0.71430.4777820.238891
M4-2.758253314975665.528363-0.49890.6196550.309827
M5-2.201910748515045.519074-0.3990.6913360.345668
M6-1.286729758663735.511063-0.23350.8161820.408091
M7-5.868211530195435.548183-1.05770.294440.14722
M8-1.449123106285035.512721-0.26290.7935520.396776
M9-1.188738281550455.514891-0.21560.8300690.415035
M10-1.612575521206875.517568-0.29230.7710950.385547
M11-0.04889204551359765.510171-0.00890.992950.496475

\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) & -7.36746638506505 & 4.346891 & -1.6949 & 0.095283 & 0.047641 \tabularnewline
Invoer & 0.236669034135203 & 0.034298 & 6.9003 & 0 & 0 \tabularnewline
M1 & -2.56663152756032 & 5.312628 & -0.4831 & 0.63077 & 0.315385 \tabularnewline
M2 & -0.76835819175296 & 5.521738 & -0.1392 & 0.889796 & 0.444898 \tabularnewline
M3 & -3.97043927651090 & 5.558126 & -0.7143 & 0.477782 & 0.238891 \tabularnewline
M4 & -2.75825331497566 & 5.528363 & -0.4989 & 0.619655 & 0.309827 \tabularnewline
M5 & -2.20191074851504 & 5.519074 & -0.399 & 0.691336 & 0.345668 \tabularnewline
M6 & -1.28672975866373 & 5.511063 & -0.2335 & 0.816182 & 0.408091 \tabularnewline
M7 & -5.86821153019543 & 5.548183 & -1.0577 & 0.29444 & 0.14722 \tabularnewline
M8 & -1.44912310628503 & 5.512721 & -0.2629 & 0.793552 & 0.396776 \tabularnewline
M9 & -1.18873828155045 & 5.514891 & -0.2156 & 0.830069 & 0.415035 \tabularnewline
M10 & -1.61257552120687 & 5.517568 & -0.2923 & 0.771095 & 0.385547 \tabularnewline
M11 & -0.0488920455135976 & 5.510171 & -0.0089 & 0.99295 & 0.496475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&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]-7.36746638506505[/C][C]4.346891[/C][C]-1.6949[/C][C]0.095283[/C][C]0.047641[/C][/ROW]
[ROW][C]Invoer[/C][C]0.236669034135203[/C][C]0.034298[/C][C]6.9003[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-2.56663152756032[/C][C]5.312628[/C][C]-0.4831[/C][C]0.63077[/C][C]0.315385[/C][/ROW]
[ROW][C]M2[/C][C]-0.76835819175296[/C][C]5.521738[/C][C]-0.1392[/C][C]0.889796[/C][C]0.444898[/C][/ROW]
[ROW][C]M3[/C][C]-3.97043927651090[/C][C]5.558126[/C][C]-0.7143[/C][C]0.477782[/C][C]0.238891[/C][/ROW]
[ROW][C]M4[/C][C]-2.75825331497566[/C][C]5.528363[/C][C]-0.4989[/C][C]0.619655[/C][C]0.309827[/C][/ROW]
[ROW][C]M5[/C][C]-2.20191074851504[/C][C]5.519074[/C][C]-0.399[/C][C]0.691336[/C][C]0.345668[/C][/ROW]
[ROW][C]M6[/C][C]-1.28672975866373[/C][C]5.511063[/C][C]-0.2335[/C][C]0.816182[/C][C]0.408091[/C][/ROW]
[ROW][C]M7[/C][C]-5.86821153019543[/C][C]5.548183[/C][C]-1.0577[/C][C]0.29444[/C][C]0.14722[/C][/ROW]
[ROW][C]M8[/C][C]-1.44912310628503[/C][C]5.512721[/C][C]-0.2629[/C][C]0.793552[/C][C]0.396776[/C][/ROW]
[ROW][C]M9[/C][C]-1.18873828155045[/C][C]5.514891[/C][C]-0.2156[/C][C]0.830069[/C][C]0.415035[/C][/ROW]
[ROW][C]M10[/C][C]-1.61257552120687[/C][C]5.517568[/C][C]-0.2923[/C][C]0.771095[/C][C]0.385547[/C][/ROW]
[ROW][C]M11[/C][C]-0.0488920455135976[/C][C]5.510171[/C][C]-0.0089[/C][C]0.99295[/C][C]0.496475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=2

As an alternative you can also use a QR Code:  
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)-7.367466385065054.346891-1.69490.0952830.047641
Invoer0.2366690341352030.0342986.900300
M1-2.566631527560325.312628-0.48310.630770.315385
M2-0.768358191752965.521738-0.13920.8897960.444898
M3-3.970439276510905.558126-0.71430.4777820.238891
M4-2.758253314975665.528363-0.49890.6196550.309827
M5-2.201910748515045.519074-0.3990.6913360.345668
M6-1.286729758663735.511063-0.23350.8161820.408091
M7-5.868211530195435.548183-1.05770.294440.14722
M8-1.449123106285035.512721-0.26290.7935520.396776
M9-1.188738281550455.514891-0.21560.8300690.415035
M10-1.612575521206875.517568-0.29230.7710950.385547
M11-0.04889204551359765.510171-0.00890.992950.496475






Multiple Linear Regression - Regression Statistics
Multiple R0.667430694342178
R-squared0.445463731750082
Adjusted R-squared0.334556478100099
F-TEST (value)4.0165428057207
F-TEST (DF numerator)12
F-TEST (DF denominator)60
p-value0.000146322219272688
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.54356708174346
Sum Squared Residuals5464.78035862425

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.667430694342178 \tabularnewline
R-squared & 0.445463731750082 \tabularnewline
Adjusted R-squared & 0.334556478100099 \tabularnewline
F-TEST (value) & 4.0165428057207 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 60 \tabularnewline
p-value & 0.000146322219272688 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.54356708174346 \tabularnewline
Sum Squared Residuals & 5464.78035862425 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.667430694342178[/C][/ROW]
[ROW][C]R-squared[/C][C]0.445463731750082[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.334556478100099[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.0165428057207[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]60[/C][/ROW]
[ROW][C]p-value[/C][C]0.000146322219272688[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.54356708174346[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5464.78035862425[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.667430694342178
R-squared0.445463731750082
Adjusted R-squared0.334556478100099
F-TEST (value)4.0165428057207
F-TEST (DF numerator)12
F-TEST (DF denominator)60
p-value0.000146322219272688
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.54356708174346
Sum Squared Residuals5464.78035862425






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.2-4.348708707034633.14870870703463
2-2.4-2.05343039954331-0.34656960045669
30.8-3.646162052181874.44616205218187
4-0.1-2.197307056511432.09730705651143
5-1.5-1.40429545591560-0.0957045440843955
6-4.4-2.05113009135663-2.34886990864337
7-4.2-2.51457066893580-1.68542933106420
83.50.8395071013661842.66049289863382
9105.288933830293844.71106616970616
108.6-1.288298296877859.88829829687785
119.50.985392281221038.51460771877897
129.93.684977509048916.21502249095109
1310.42.656694703367407.7433052966326
14160.40792755546279715.5920724445372
1512.71.4422221817249911.257777818275
1610.23.624751183214566.57524881678544
178.90.3233884932713778.57661150672862
1812.61.5462392274984511.0537607725015
1913.64.751168679014928.84883132098508
2014.81.3128451696365913.4871548303634
219.51.147225732927808.3527742670722
2213.75.551436789629518.14856321037048
23170.6777225368452716.3222774631547
2414.75.365327651408849.33467234859116
2517.49.165093142085478.23490685791453
26910.6793636369306-1.67936363693060
279.111.5006561324711-2.40065613247111
2812.215.5055366968017-3.30553669680173
2915.914.12119318335371.77880681664631
3012.917.0243940599407-4.1243940599407
3110.916.9159570335643-6.01595703356434
3210.611.6552819613449-1.05528196134495
3313.28.341964370637964.85803562936204
349.611.8941669044529-2.29416690445295
356.424.6759625981548-18.2759625981548
365.89.31770052146673-3.51770052146673
37-110.7744425742049-11.7744425742049
38-0.29.21201562529234-9.41201562529234
392.710.4829792856897-7.78297928568973
403.64.33475828562016-0.734758285620164
41-0.91.24639772639867-2.14639772639867
420.3-1.435790602605111.73579060260511
43-1.1-4.999595527355433.89959552735543
44-2.5-1.90585369460217-0.594146305397834
45-3.44.86292956885048-8.26292956885048
46-3.56.90045028420017-10.4004502842002
47-3.92.47640719627281-6.37640719627281
48-4.63.70864441246243-8.30864441246243
49-0.11.92302069754827-2.02302069754827
504.311.2473693188551-6.94736931885508
5110.216.0683684912805-5.86836849128052
528.714.1328562988176-5.43285629881756
5313.315.6595419052325-2.35954190523251
541515.3440439175808-0.344043917580766
5520.718.97497763054061.72502236945940
5620.725.1217500036380-4.42175000363798
5726.421.61909718562284.78090281437717
5831.217.952894178314113.2471058216859
5931.410.404819839802120.9951801601979
6026.615.944433477252410.6555665227476
6126.616.97517126854729.62482873145284
6219.216.40675426300252.7932457369975
636.56.151935961015530.348064038984474
643.12.299404592057420.80059540794258
65-0.25.55377414765936-5.75377414765936
66-41.97224348894181-5.97224348894181
67-12.6-5.82793714682864-6.77206285317136
68-13-2.92353054138354-10.0764694586165
69-17.6-3.16015068833289-14.4398493116671
70-21.7-3.11064985971891-18.5893501402811
71-23.2-2.02030445229605-21.1796955477040
72-16.8-2.42108357163932-14.3789164283607
73-19.8-4.84571367871853-14.9542863212815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.2 & -4.34870870703463 & 3.14870870703463 \tabularnewline
2 & -2.4 & -2.05343039954331 & -0.34656960045669 \tabularnewline
3 & 0.8 & -3.64616205218187 & 4.44616205218187 \tabularnewline
4 & -0.1 & -2.19730705651143 & 2.09730705651143 \tabularnewline
5 & -1.5 & -1.40429545591560 & -0.0957045440843955 \tabularnewline
6 & -4.4 & -2.05113009135663 & -2.34886990864337 \tabularnewline
7 & -4.2 & -2.51457066893580 & -1.68542933106420 \tabularnewline
8 & 3.5 & 0.839507101366184 & 2.66049289863382 \tabularnewline
9 & 10 & 5.28893383029384 & 4.71106616970616 \tabularnewline
10 & 8.6 & -1.28829829687785 & 9.88829829687785 \tabularnewline
11 & 9.5 & 0.98539228122103 & 8.51460771877897 \tabularnewline
12 & 9.9 & 3.68497750904891 & 6.21502249095109 \tabularnewline
13 & 10.4 & 2.65669470336740 & 7.7433052966326 \tabularnewline
14 & 16 & 0.407927555462797 & 15.5920724445372 \tabularnewline
15 & 12.7 & 1.44222218172499 & 11.257777818275 \tabularnewline
16 & 10.2 & 3.62475118321456 & 6.57524881678544 \tabularnewline
17 & 8.9 & 0.323388493271377 & 8.57661150672862 \tabularnewline
18 & 12.6 & 1.54623922749845 & 11.0537607725015 \tabularnewline
19 & 13.6 & 4.75116867901492 & 8.84883132098508 \tabularnewline
20 & 14.8 & 1.31284516963659 & 13.4871548303634 \tabularnewline
21 & 9.5 & 1.14722573292780 & 8.3527742670722 \tabularnewline
22 & 13.7 & 5.55143678962951 & 8.14856321037048 \tabularnewline
23 & 17 & 0.67772253684527 & 16.3222774631547 \tabularnewline
24 & 14.7 & 5.36532765140884 & 9.33467234859116 \tabularnewline
25 & 17.4 & 9.16509314208547 & 8.23490685791453 \tabularnewline
26 & 9 & 10.6793636369306 & -1.67936363693060 \tabularnewline
27 & 9.1 & 11.5006561324711 & -2.40065613247111 \tabularnewline
28 & 12.2 & 15.5055366968017 & -3.30553669680173 \tabularnewline
29 & 15.9 & 14.1211931833537 & 1.77880681664631 \tabularnewline
30 & 12.9 & 17.0243940599407 & -4.1243940599407 \tabularnewline
31 & 10.9 & 16.9159570335643 & -6.01595703356434 \tabularnewline
32 & 10.6 & 11.6552819613449 & -1.05528196134495 \tabularnewline
33 & 13.2 & 8.34196437063796 & 4.85803562936204 \tabularnewline
34 & 9.6 & 11.8941669044529 & -2.29416690445295 \tabularnewline
35 & 6.4 & 24.6759625981548 & -18.2759625981548 \tabularnewline
36 & 5.8 & 9.31770052146673 & -3.51770052146673 \tabularnewline
37 & -1 & 10.7744425742049 & -11.7744425742049 \tabularnewline
38 & -0.2 & 9.21201562529234 & -9.41201562529234 \tabularnewline
39 & 2.7 & 10.4829792856897 & -7.78297928568973 \tabularnewline
40 & 3.6 & 4.33475828562016 & -0.734758285620164 \tabularnewline
41 & -0.9 & 1.24639772639867 & -2.14639772639867 \tabularnewline
42 & 0.3 & -1.43579060260511 & 1.73579060260511 \tabularnewline
43 & -1.1 & -4.99959552735543 & 3.89959552735543 \tabularnewline
44 & -2.5 & -1.90585369460217 & -0.594146305397834 \tabularnewline
45 & -3.4 & 4.86292956885048 & -8.26292956885048 \tabularnewline
46 & -3.5 & 6.90045028420017 & -10.4004502842002 \tabularnewline
47 & -3.9 & 2.47640719627281 & -6.37640719627281 \tabularnewline
48 & -4.6 & 3.70864441246243 & -8.30864441246243 \tabularnewline
49 & -0.1 & 1.92302069754827 & -2.02302069754827 \tabularnewline
50 & 4.3 & 11.2473693188551 & -6.94736931885508 \tabularnewline
51 & 10.2 & 16.0683684912805 & -5.86836849128052 \tabularnewline
52 & 8.7 & 14.1328562988176 & -5.43285629881756 \tabularnewline
53 & 13.3 & 15.6595419052325 & -2.35954190523251 \tabularnewline
54 & 15 & 15.3440439175808 & -0.344043917580766 \tabularnewline
55 & 20.7 & 18.9749776305406 & 1.72502236945940 \tabularnewline
56 & 20.7 & 25.1217500036380 & -4.42175000363798 \tabularnewline
57 & 26.4 & 21.6190971856228 & 4.78090281437717 \tabularnewline
58 & 31.2 & 17.9528941783141 & 13.2471058216859 \tabularnewline
59 & 31.4 & 10.4048198398021 & 20.9951801601979 \tabularnewline
60 & 26.6 & 15.9444334772524 & 10.6555665227476 \tabularnewline
61 & 26.6 & 16.9751712685472 & 9.62482873145284 \tabularnewline
62 & 19.2 & 16.4067542630025 & 2.7932457369975 \tabularnewline
63 & 6.5 & 6.15193596101553 & 0.348064038984474 \tabularnewline
64 & 3.1 & 2.29940459205742 & 0.80059540794258 \tabularnewline
65 & -0.2 & 5.55377414765936 & -5.75377414765936 \tabularnewline
66 & -4 & 1.97224348894181 & -5.97224348894181 \tabularnewline
67 & -12.6 & -5.82793714682864 & -6.77206285317136 \tabularnewline
68 & -13 & -2.92353054138354 & -10.0764694586165 \tabularnewline
69 & -17.6 & -3.16015068833289 & -14.4398493116671 \tabularnewline
70 & -21.7 & -3.11064985971891 & -18.5893501402811 \tabularnewline
71 & -23.2 & -2.02030445229605 & -21.1796955477040 \tabularnewline
72 & -16.8 & -2.42108357163932 & -14.3789164283607 \tabularnewline
73 & -19.8 & -4.84571367871853 & -14.9542863212815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&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]-1.2[/C][C]-4.34870870703463[/C][C]3.14870870703463[/C][/ROW]
[ROW][C]2[/C][C]-2.4[/C][C]-2.05343039954331[/C][C]-0.34656960045669[/C][/ROW]
[ROW][C]3[/C][C]0.8[/C][C]-3.64616205218187[/C][C]4.44616205218187[/C][/ROW]
[ROW][C]4[/C][C]-0.1[/C][C]-2.19730705651143[/C][C]2.09730705651143[/C][/ROW]
[ROW][C]5[/C][C]-1.5[/C][C]-1.40429545591560[/C][C]-0.0957045440843955[/C][/ROW]
[ROW][C]6[/C][C]-4.4[/C][C]-2.05113009135663[/C][C]-2.34886990864337[/C][/ROW]
[ROW][C]7[/C][C]-4.2[/C][C]-2.51457066893580[/C][C]-1.68542933106420[/C][/ROW]
[ROW][C]8[/C][C]3.5[/C][C]0.839507101366184[/C][C]2.66049289863382[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]5.28893383029384[/C][C]4.71106616970616[/C][/ROW]
[ROW][C]10[/C][C]8.6[/C][C]-1.28829829687785[/C][C]9.88829829687785[/C][/ROW]
[ROW][C]11[/C][C]9.5[/C][C]0.98539228122103[/C][C]8.51460771877897[/C][/ROW]
[ROW][C]12[/C][C]9.9[/C][C]3.68497750904891[/C][C]6.21502249095109[/C][/ROW]
[ROW][C]13[/C][C]10.4[/C][C]2.65669470336740[/C][C]7.7433052966326[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]0.407927555462797[/C][C]15.5920724445372[/C][/ROW]
[ROW][C]15[/C][C]12.7[/C][C]1.44222218172499[/C][C]11.257777818275[/C][/ROW]
[ROW][C]16[/C][C]10.2[/C][C]3.62475118321456[/C][C]6.57524881678544[/C][/ROW]
[ROW][C]17[/C][C]8.9[/C][C]0.323388493271377[/C][C]8.57661150672862[/C][/ROW]
[ROW][C]18[/C][C]12.6[/C][C]1.54623922749845[/C][C]11.0537607725015[/C][/ROW]
[ROW][C]19[/C][C]13.6[/C][C]4.75116867901492[/C][C]8.84883132098508[/C][/ROW]
[ROW][C]20[/C][C]14.8[/C][C]1.31284516963659[/C][C]13.4871548303634[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]1.14722573292780[/C][C]8.3527742670722[/C][/ROW]
[ROW][C]22[/C][C]13.7[/C][C]5.55143678962951[/C][C]8.14856321037048[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]0.67772253684527[/C][C]16.3222774631547[/C][/ROW]
[ROW][C]24[/C][C]14.7[/C][C]5.36532765140884[/C][C]9.33467234859116[/C][/ROW]
[ROW][C]25[/C][C]17.4[/C][C]9.16509314208547[/C][C]8.23490685791453[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]10.6793636369306[/C][C]-1.67936363693060[/C][/ROW]
[ROW][C]27[/C][C]9.1[/C][C]11.5006561324711[/C][C]-2.40065613247111[/C][/ROW]
[ROW][C]28[/C][C]12.2[/C][C]15.5055366968017[/C][C]-3.30553669680173[/C][/ROW]
[ROW][C]29[/C][C]15.9[/C][C]14.1211931833537[/C][C]1.77880681664631[/C][/ROW]
[ROW][C]30[/C][C]12.9[/C][C]17.0243940599407[/C][C]-4.1243940599407[/C][/ROW]
[ROW][C]31[/C][C]10.9[/C][C]16.9159570335643[/C][C]-6.01595703356434[/C][/ROW]
[ROW][C]32[/C][C]10.6[/C][C]11.6552819613449[/C][C]-1.05528196134495[/C][/ROW]
[ROW][C]33[/C][C]13.2[/C][C]8.34196437063796[/C][C]4.85803562936204[/C][/ROW]
[ROW][C]34[/C][C]9.6[/C][C]11.8941669044529[/C][C]-2.29416690445295[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]24.6759625981548[/C][C]-18.2759625981548[/C][/ROW]
[ROW][C]36[/C][C]5.8[/C][C]9.31770052146673[/C][C]-3.51770052146673[/C][/ROW]
[ROW][C]37[/C][C]-1[/C][C]10.7744425742049[/C][C]-11.7744425742049[/C][/ROW]
[ROW][C]38[/C][C]-0.2[/C][C]9.21201562529234[/C][C]-9.41201562529234[/C][/ROW]
[ROW][C]39[/C][C]2.7[/C][C]10.4829792856897[/C][C]-7.78297928568973[/C][/ROW]
[ROW][C]40[/C][C]3.6[/C][C]4.33475828562016[/C][C]-0.734758285620164[/C][/ROW]
[ROW][C]41[/C][C]-0.9[/C][C]1.24639772639867[/C][C]-2.14639772639867[/C][/ROW]
[ROW][C]42[/C][C]0.3[/C][C]-1.43579060260511[/C][C]1.73579060260511[/C][/ROW]
[ROW][C]43[/C][C]-1.1[/C][C]-4.99959552735543[/C][C]3.89959552735543[/C][/ROW]
[ROW][C]44[/C][C]-2.5[/C][C]-1.90585369460217[/C][C]-0.594146305397834[/C][/ROW]
[ROW][C]45[/C][C]-3.4[/C][C]4.86292956885048[/C][C]-8.26292956885048[/C][/ROW]
[ROW][C]46[/C][C]-3.5[/C][C]6.90045028420017[/C][C]-10.4004502842002[/C][/ROW]
[ROW][C]47[/C][C]-3.9[/C][C]2.47640719627281[/C][C]-6.37640719627281[/C][/ROW]
[ROW][C]48[/C][C]-4.6[/C][C]3.70864441246243[/C][C]-8.30864441246243[/C][/ROW]
[ROW][C]49[/C][C]-0.1[/C][C]1.92302069754827[/C][C]-2.02302069754827[/C][/ROW]
[ROW][C]50[/C][C]4.3[/C][C]11.2473693188551[/C][C]-6.94736931885508[/C][/ROW]
[ROW][C]51[/C][C]10.2[/C][C]16.0683684912805[/C][C]-5.86836849128052[/C][/ROW]
[ROW][C]52[/C][C]8.7[/C][C]14.1328562988176[/C][C]-5.43285629881756[/C][/ROW]
[ROW][C]53[/C][C]13.3[/C][C]15.6595419052325[/C][C]-2.35954190523251[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]15.3440439175808[/C][C]-0.344043917580766[/C][/ROW]
[ROW][C]55[/C][C]20.7[/C][C]18.9749776305406[/C][C]1.72502236945940[/C][/ROW]
[ROW][C]56[/C][C]20.7[/C][C]25.1217500036380[/C][C]-4.42175000363798[/C][/ROW]
[ROW][C]57[/C][C]26.4[/C][C]21.6190971856228[/C][C]4.78090281437717[/C][/ROW]
[ROW][C]58[/C][C]31.2[/C][C]17.9528941783141[/C][C]13.2471058216859[/C][/ROW]
[ROW][C]59[/C][C]31.4[/C][C]10.4048198398021[/C][C]20.9951801601979[/C][/ROW]
[ROW][C]60[/C][C]26.6[/C][C]15.9444334772524[/C][C]10.6555665227476[/C][/ROW]
[ROW][C]61[/C][C]26.6[/C][C]16.9751712685472[/C][C]9.62482873145284[/C][/ROW]
[ROW][C]62[/C][C]19.2[/C][C]16.4067542630025[/C][C]2.7932457369975[/C][/ROW]
[ROW][C]63[/C][C]6.5[/C][C]6.15193596101553[/C][C]0.348064038984474[/C][/ROW]
[ROW][C]64[/C][C]3.1[/C][C]2.29940459205742[/C][C]0.80059540794258[/C][/ROW]
[ROW][C]65[/C][C]-0.2[/C][C]5.55377414765936[/C][C]-5.75377414765936[/C][/ROW]
[ROW][C]66[/C][C]-4[/C][C]1.97224348894181[/C][C]-5.97224348894181[/C][/ROW]
[ROW][C]67[/C][C]-12.6[/C][C]-5.82793714682864[/C][C]-6.77206285317136[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-2.92353054138354[/C][C]-10.0764694586165[/C][/ROW]
[ROW][C]69[/C][C]-17.6[/C][C]-3.16015068833289[/C][C]-14.4398493116671[/C][/ROW]
[ROW][C]70[/C][C]-21.7[/C][C]-3.11064985971891[/C][C]-18.5893501402811[/C][/ROW]
[ROW][C]71[/C][C]-23.2[/C][C]-2.02030445229605[/C][C]-21.1796955477040[/C][/ROW]
[ROW][C]72[/C][C]-16.8[/C][C]-2.42108357163932[/C][C]-14.3789164283607[/C][/ROW]
[ROW][C]73[/C][C]-19.8[/C][C]-4.84571367871853[/C][C]-14.9542863212815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.2-4.348708707034633.14870870703463
2-2.4-2.05343039954331-0.34656960045669
30.8-3.646162052181874.44616205218187
4-0.1-2.197307056511432.09730705651143
5-1.5-1.40429545591560-0.0957045440843955
6-4.4-2.05113009135663-2.34886990864337
7-4.2-2.51457066893580-1.68542933106420
83.50.8395071013661842.66049289863382
9105.288933830293844.71106616970616
108.6-1.288298296877859.88829829687785
119.50.985392281221038.51460771877897
129.93.684977509048916.21502249095109
1310.42.656694703367407.7433052966326
14160.40792755546279715.5920724445372
1512.71.4422221817249911.257777818275
1610.23.624751183214566.57524881678544
178.90.3233884932713778.57661150672862
1812.61.5462392274984511.0537607725015
1913.64.751168679014928.84883132098508
2014.81.3128451696365913.4871548303634
219.51.147225732927808.3527742670722
2213.75.551436789629518.14856321037048
23170.6777225368452716.3222774631547
2414.75.365327651408849.33467234859116
2517.49.165093142085478.23490685791453
26910.6793636369306-1.67936363693060
279.111.5006561324711-2.40065613247111
2812.215.5055366968017-3.30553669680173
2915.914.12119318335371.77880681664631
3012.917.0243940599407-4.1243940599407
3110.916.9159570335643-6.01595703356434
3210.611.6552819613449-1.05528196134495
3313.28.341964370637964.85803562936204
349.611.8941669044529-2.29416690445295
356.424.6759625981548-18.2759625981548
365.89.31770052146673-3.51770052146673
37-110.7744425742049-11.7744425742049
38-0.29.21201562529234-9.41201562529234
392.710.4829792856897-7.78297928568973
403.64.33475828562016-0.734758285620164
41-0.91.24639772639867-2.14639772639867
420.3-1.435790602605111.73579060260511
43-1.1-4.999595527355433.89959552735543
44-2.5-1.90585369460217-0.594146305397834
45-3.44.86292956885048-8.26292956885048
46-3.56.90045028420017-10.4004502842002
47-3.92.47640719627281-6.37640719627281
48-4.63.70864441246243-8.30864441246243
49-0.11.92302069754827-2.02302069754827
504.311.2473693188551-6.94736931885508
5110.216.0683684912805-5.86836849128052
528.714.1328562988176-5.43285629881756
5313.315.6595419052325-2.35954190523251
541515.3440439175808-0.344043917580766
5520.718.97497763054061.72502236945940
5620.725.1217500036380-4.42175000363798
5726.421.61909718562284.78090281437717
5831.217.952894178314113.2471058216859
5931.410.404819839802120.9951801601979
6026.615.944433477252410.6555665227476
6126.616.97517126854729.62482873145284
6219.216.40675426300252.7932457369975
636.56.151935961015530.348064038984474
643.12.299404592057420.80059540794258
65-0.25.55377414765936-5.75377414765936
66-41.97224348894181-5.97224348894181
67-12.6-5.82793714682864-6.77206285317136
68-13-2.92353054138354-10.0764694586165
69-17.6-3.16015068833289-14.4398493116671
70-21.7-3.11064985971891-18.5893501402811
71-23.2-2.02030445229605-21.1796955477040
72-16.8-2.42108357163932-14.3789164283607
73-19.8-4.84571367871853-14.9542863212815






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1871050355329190.3742100710658380.812894964467081
170.1198394196856640.2396788393713290.880160580314336
180.0976980443713740.1953960887427480.902301955628626
190.04816594486535510.09633188973071020.951834055134645
200.0570439817301090.1140879634602180.942956018269891
210.05570783399661030.1114156679932210.94429216600339
220.05540590224046160.1108118044809230.944594097759538
230.0713233171918750.142646634383750.928676682808125
240.04931977377635540.09863954755271080.950680226223645
250.04179964225497580.08359928450995160.958200357745024
260.08994446234593290.1798889246918660.910055537654067
270.1026217071154150.205243414230830.897378292884585
280.07902933701815340.1580586740363070.920970662981847
290.05024889184883050.1004977836976610.94975110815117
300.03654117367225190.07308234734450380.963458826327748
310.03008471841763470.06016943683526940.969915281582365
320.02112890410442410.04225780820884820.978871095895576
330.01466693272548220.02933386545096440.985333067274518
340.01158714071645190.02317428143290370.988412859283548
350.1015057394525810.2030114789051620.898494260547419
360.08526583954420710.1705316790884140.914734160455793
370.1271189277529870.2542378555059740.872881072247013
380.1179880484295780.2359760968591560.882011951570422
390.09640684719184470.1928136943836890.903593152808155
400.06927578964543820.1385515792908760.930724210354562
410.06044575737220320.1208915147444060.939554242627797
420.05414669370556230.1082933874111250.945853306294438
430.0573686524099130.1147373048198260.942631347590087
440.0886074615444990.1772149230889980.911392538455501
450.09333165195169020.1866633039033800.90666834804831
460.1072284009871460.2144568019742920.892771599012854
470.1002238085724510.2004476171449020.899776191427549
480.09394080020388810.1878816004077760.906059199796112
490.0660195400521870.1320390801043740.933980459947813
500.04453194418688190.08906388837376370.955468055813118
510.03964999258441380.07929998516882750.960350007415586
520.04127078057893180.08254156115786370.958729219421068
530.02479511611408730.04959023222817470.975204883885913
540.0146603610439850.029320722087970.985339638956015
550.01581867678414000.03163735356828000.98418132321586
560.07890448241276950.1578089648255390.921095517587231
570.115727496299130.231454992598260.88427250370087

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.187105035532919 & 0.374210071065838 & 0.812894964467081 \tabularnewline
17 & 0.119839419685664 & 0.239678839371329 & 0.880160580314336 \tabularnewline
18 & 0.097698044371374 & 0.195396088742748 & 0.902301955628626 \tabularnewline
19 & 0.0481659448653551 & 0.0963318897307102 & 0.951834055134645 \tabularnewline
20 & 0.057043981730109 & 0.114087963460218 & 0.942956018269891 \tabularnewline
21 & 0.0557078339966103 & 0.111415667993221 & 0.94429216600339 \tabularnewline
22 & 0.0554059022404616 & 0.110811804480923 & 0.944594097759538 \tabularnewline
23 & 0.071323317191875 & 0.14264663438375 & 0.928676682808125 \tabularnewline
24 & 0.0493197737763554 & 0.0986395475527108 & 0.950680226223645 \tabularnewline
25 & 0.0417996422549758 & 0.0835992845099516 & 0.958200357745024 \tabularnewline
26 & 0.0899444623459329 & 0.179888924691866 & 0.910055537654067 \tabularnewline
27 & 0.102621707115415 & 0.20524341423083 & 0.897378292884585 \tabularnewline
28 & 0.0790293370181534 & 0.158058674036307 & 0.920970662981847 \tabularnewline
29 & 0.0502488918488305 & 0.100497783697661 & 0.94975110815117 \tabularnewline
30 & 0.0365411736722519 & 0.0730823473445038 & 0.963458826327748 \tabularnewline
31 & 0.0300847184176347 & 0.0601694368352694 & 0.969915281582365 \tabularnewline
32 & 0.0211289041044241 & 0.0422578082088482 & 0.978871095895576 \tabularnewline
33 & 0.0146669327254822 & 0.0293338654509644 & 0.985333067274518 \tabularnewline
34 & 0.0115871407164519 & 0.0231742814329037 & 0.988412859283548 \tabularnewline
35 & 0.101505739452581 & 0.203011478905162 & 0.898494260547419 \tabularnewline
36 & 0.0852658395442071 & 0.170531679088414 & 0.914734160455793 \tabularnewline
37 & 0.127118927752987 & 0.254237855505974 & 0.872881072247013 \tabularnewline
38 & 0.117988048429578 & 0.235976096859156 & 0.882011951570422 \tabularnewline
39 & 0.0964068471918447 & 0.192813694383689 & 0.903593152808155 \tabularnewline
40 & 0.0692757896454382 & 0.138551579290876 & 0.930724210354562 \tabularnewline
41 & 0.0604457573722032 & 0.120891514744406 & 0.939554242627797 \tabularnewline
42 & 0.0541466937055623 & 0.108293387411125 & 0.945853306294438 \tabularnewline
43 & 0.057368652409913 & 0.114737304819826 & 0.942631347590087 \tabularnewline
44 & 0.088607461544499 & 0.177214923088998 & 0.911392538455501 \tabularnewline
45 & 0.0933316519516902 & 0.186663303903380 & 0.90666834804831 \tabularnewline
46 & 0.107228400987146 & 0.214456801974292 & 0.892771599012854 \tabularnewline
47 & 0.100223808572451 & 0.200447617144902 & 0.899776191427549 \tabularnewline
48 & 0.0939408002038881 & 0.187881600407776 & 0.906059199796112 \tabularnewline
49 & 0.066019540052187 & 0.132039080104374 & 0.933980459947813 \tabularnewline
50 & 0.0445319441868819 & 0.0890638883737637 & 0.955468055813118 \tabularnewline
51 & 0.0396499925844138 & 0.0792999851688275 & 0.960350007415586 \tabularnewline
52 & 0.0412707805789318 & 0.0825415611578637 & 0.958729219421068 \tabularnewline
53 & 0.0247951161140873 & 0.0495902322281747 & 0.975204883885913 \tabularnewline
54 & 0.014660361043985 & 0.02932072208797 & 0.985339638956015 \tabularnewline
55 & 0.0158186767841400 & 0.0316373535682800 & 0.98418132321586 \tabularnewline
56 & 0.0789044824127695 & 0.157808964825539 & 0.921095517587231 \tabularnewline
57 & 0.11572749629913 & 0.23145499259826 & 0.88427250370087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&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]0.187105035532919[/C][C]0.374210071065838[/C][C]0.812894964467081[/C][/ROW]
[ROW][C]17[/C][C]0.119839419685664[/C][C]0.239678839371329[/C][C]0.880160580314336[/C][/ROW]
[ROW][C]18[/C][C]0.097698044371374[/C][C]0.195396088742748[/C][C]0.902301955628626[/C][/ROW]
[ROW][C]19[/C][C]0.0481659448653551[/C][C]0.0963318897307102[/C][C]0.951834055134645[/C][/ROW]
[ROW][C]20[/C][C]0.057043981730109[/C][C]0.114087963460218[/C][C]0.942956018269891[/C][/ROW]
[ROW][C]21[/C][C]0.0557078339966103[/C][C]0.111415667993221[/C][C]0.94429216600339[/C][/ROW]
[ROW][C]22[/C][C]0.0554059022404616[/C][C]0.110811804480923[/C][C]0.944594097759538[/C][/ROW]
[ROW][C]23[/C][C]0.071323317191875[/C][C]0.14264663438375[/C][C]0.928676682808125[/C][/ROW]
[ROW][C]24[/C][C]0.0493197737763554[/C][C]0.0986395475527108[/C][C]0.950680226223645[/C][/ROW]
[ROW][C]25[/C][C]0.0417996422549758[/C][C]0.0835992845099516[/C][C]0.958200357745024[/C][/ROW]
[ROW][C]26[/C][C]0.0899444623459329[/C][C]0.179888924691866[/C][C]0.910055537654067[/C][/ROW]
[ROW][C]27[/C][C]0.102621707115415[/C][C]0.20524341423083[/C][C]0.897378292884585[/C][/ROW]
[ROW][C]28[/C][C]0.0790293370181534[/C][C]0.158058674036307[/C][C]0.920970662981847[/C][/ROW]
[ROW][C]29[/C][C]0.0502488918488305[/C][C]0.100497783697661[/C][C]0.94975110815117[/C][/ROW]
[ROW][C]30[/C][C]0.0365411736722519[/C][C]0.0730823473445038[/C][C]0.963458826327748[/C][/ROW]
[ROW][C]31[/C][C]0.0300847184176347[/C][C]0.0601694368352694[/C][C]0.969915281582365[/C][/ROW]
[ROW][C]32[/C][C]0.0211289041044241[/C][C]0.0422578082088482[/C][C]0.978871095895576[/C][/ROW]
[ROW][C]33[/C][C]0.0146669327254822[/C][C]0.0293338654509644[/C][C]0.985333067274518[/C][/ROW]
[ROW][C]34[/C][C]0.0115871407164519[/C][C]0.0231742814329037[/C][C]0.988412859283548[/C][/ROW]
[ROW][C]35[/C][C]0.101505739452581[/C][C]0.203011478905162[/C][C]0.898494260547419[/C][/ROW]
[ROW][C]36[/C][C]0.0852658395442071[/C][C]0.170531679088414[/C][C]0.914734160455793[/C][/ROW]
[ROW][C]37[/C][C]0.127118927752987[/C][C]0.254237855505974[/C][C]0.872881072247013[/C][/ROW]
[ROW][C]38[/C][C]0.117988048429578[/C][C]0.235976096859156[/C][C]0.882011951570422[/C][/ROW]
[ROW][C]39[/C][C]0.0964068471918447[/C][C]0.192813694383689[/C][C]0.903593152808155[/C][/ROW]
[ROW][C]40[/C][C]0.0692757896454382[/C][C]0.138551579290876[/C][C]0.930724210354562[/C][/ROW]
[ROW][C]41[/C][C]0.0604457573722032[/C][C]0.120891514744406[/C][C]0.939554242627797[/C][/ROW]
[ROW][C]42[/C][C]0.0541466937055623[/C][C]0.108293387411125[/C][C]0.945853306294438[/C][/ROW]
[ROW][C]43[/C][C]0.057368652409913[/C][C]0.114737304819826[/C][C]0.942631347590087[/C][/ROW]
[ROW][C]44[/C][C]0.088607461544499[/C][C]0.177214923088998[/C][C]0.911392538455501[/C][/ROW]
[ROW][C]45[/C][C]0.0933316519516902[/C][C]0.186663303903380[/C][C]0.90666834804831[/C][/ROW]
[ROW][C]46[/C][C]0.107228400987146[/C][C]0.214456801974292[/C][C]0.892771599012854[/C][/ROW]
[ROW][C]47[/C][C]0.100223808572451[/C][C]0.200447617144902[/C][C]0.899776191427549[/C][/ROW]
[ROW][C]48[/C][C]0.0939408002038881[/C][C]0.187881600407776[/C][C]0.906059199796112[/C][/ROW]
[ROW][C]49[/C][C]0.066019540052187[/C][C]0.132039080104374[/C][C]0.933980459947813[/C][/ROW]
[ROW][C]50[/C][C]0.0445319441868819[/C][C]0.0890638883737637[/C][C]0.955468055813118[/C][/ROW]
[ROW][C]51[/C][C]0.0396499925844138[/C][C]0.0792999851688275[/C][C]0.960350007415586[/C][/ROW]
[ROW][C]52[/C][C]0.0412707805789318[/C][C]0.0825415611578637[/C][C]0.958729219421068[/C][/ROW]
[ROW][C]53[/C][C]0.0247951161140873[/C][C]0.0495902322281747[/C][C]0.975204883885913[/C][/ROW]
[ROW][C]54[/C][C]0.014660361043985[/C][C]0.02932072208797[/C][C]0.985339638956015[/C][/ROW]
[ROW][C]55[/C][C]0.0158186767841400[/C][C]0.0316373535682800[/C][C]0.98418132321586[/C][/ROW]
[ROW][C]56[/C][C]0.0789044824127695[/C][C]0.157808964825539[/C][C]0.921095517587231[/C][/ROW]
[ROW][C]57[/C][C]0.11572749629913[/C][C]0.23145499259826[/C][C]0.88427250370087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1871050355329190.3742100710658380.812894964467081
170.1198394196856640.2396788393713290.880160580314336
180.0976980443713740.1953960887427480.902301955628626
190.04816594486535510.09633188973071020.951834055134645
200.0570439817301090.1140879634602180.942956018269891
210.05570783399661030.1114156679932210.94429216600339
220.05540590224046160.1108118044809230.944594097759538
230.0713233171918750.142646634383750.928676682808125
240.04931977377635540.09863954755271080.950680226223645
250.04179964225497580.08359928450995160.958200357745024
260.08994446234593290.1798889246918660.910055537654067
270.1026217071154150.205243414230830.897378292884585
280.07902933701815340.1580586740363070.920970662981847
290.05024889184883050.1004977836976610.94975110815117
300.03654117367225190.07308234734450380.963458826327748
310.03008471841763470.06016943683526940.969915281582365
320.02112890410442410.04225780820884820.978871095895576
330.01466693272548220.02933386545096440.985333067274518
340.01158714071645190.02317428143290370.988412859283548
350.1015057394525810.2030114789051620.898494260547419
360.08526583954420710.1705316790884140.914734160455793
370.1271189277529870.2542378555059740.872881072247013
380.1179880484295780.2359760968591560.882011951570422
390.09640684719184470.1928136943836890.903593152808155
400.06927578964543820.1385515792908760.930724210354562
410.06044575737220320.1208915147444060.939554242627797
420.05414669370556230.1082933874111250.945853306294438
430.0573686524099130.1147373048198260.942631347590087
440.0886074615444990.1772149230889980.911392538455501
450.09333165195169020.1866633039033800.90666834804831
460.1072284009871460.2144568019742920.892771599012854
470.1002238085724510.2004476171449020.899776191427549
480.09394080020388810.1878816004077760.906059199796112
490.0660195400521870.1320390801043740.933980459947813
500.04453194418688190.08906388837376370.955468055813118
510.03964999258441380.07929998516882750.960350007415586
520.04127078057893180.08254156115786370.958729219421068
530.02479511611408730.04959023222817470.975204883885913
540.0146603610439850.029320722087970.985339638956015
550.01581867678414000.03163735356828000.98418132321586
560.07890448241276950.1578089648255390.921095517587231
570.115727496299130.231454992598260.88427250370087






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.142857142857143NOK
10% type I error level140.333333333333333NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 6 & 0.142857142857143 & NOK \tabularnewline
10% type I error level & 14 & 0.333333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69854&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69854&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69854&T=6

As an alternative you can also use a QR Code:  
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 level00OK
5% type I error level60.142857142857143NOK
10% type I error level140.333333333333333NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}