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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 computationThu, 19 Nov 2009 14:42:35 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258667031eefblv66m4rzch6.htm/, Retrieved Thu, 28 Mar 2024 16:02:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57967, Retrieved Thu, 28 Mar 2024 16:02:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Model 3 - WZM & W...] [2009-11-19 21:42:35] [acc980be4047884b6edd254cd7beb9fa] [Current]
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Dataseries X:
8.2	20.3
8	20.3
7.5	20.3
6.8	15.8
6.5	15.8
6.6	15.8
7.6	23.2
8	23.2
8.1	23.2
7.7	20.9
7.5	20.9
7.6	20.9
7.8	19.8
7.8	19.8
7.8	19.8
7.5	20.6
7.5	20.6
7.1	20.6
7.5	21.1
7.5	21.1
7.6	21.1
7.7	22.4
7.7	22.4
7.9	22.4
8.1	20.5
8.2	20.5
8.2	20.5
8.2	18.4
7.9	18.4
7.3	18.4
6.9	17.6
6.6	17.6
6.7	17.6
6.9	18.5
7	18.5
7.1	18.5
7.2	17.3
7.1	17.3
6.9	17.3
7	16.2
6.8	16.2
6.4	16.2
6.7	18.5
6.6	18.5
6.4	18.5
6.3	16.3
6.2	16.3
6.5	16.3
6.8	16.8
6.8	16.8
6.4	16.8
6.1	14.8
5.8	14.8
6.1	14.8
7.2	21.4
7.3	21.4
6.9	21.4
6.1	16.1
5.8	16.1
6.2	16.1
7.1	19.6
7.7	19.6
7.9	19.6
7.7	18.9
7.4	18.9
7.5	18.9
8	21.9
8.1	21.9
8	21.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&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]7 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=57967&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.04749099291583 + 0.268190282588306X[t] + 0.411430607756518M1[t] + 0.479213827669841M2[t] + 0.330330380916495M3[t] + 0.527218052971105M4[t] + 0.295001272884426M5[t] + 0.146117826131079M6[t] -0.218701515485238M7[t] -0.184251628905251M8[t] -0.249801742325264M9[t] -0.122233106493307M10[t] -0.221116553246654M11[t] -0.00111655324665354t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2.04749099291583 +  0.268190282588306X[t] +  0.411430607756518M1[t] +  0.479213827669841M2[t] +  0.330330380916495M3[t] +  0.527218052971105M4[t] +  0.295001272884426M5[t] +  0.146117826131079M6[t] -0.218701515485238M7[t] -0.184251628905251M8[t] -0.249801742325264M9[t] -0.122233106493307M10[t] -0.221116553246654M11[t] -0.00111655324665354t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2.04749099291583 +  0.268190282588306X[t] +  0.411430607756518M1[t] +  0.479213827669841M2[t] +  0.330330380916495M3[t] +  0.527218052971105M4[t] +  0.295001272884426M5[t] +  0.146117826131079M6[t] -0.218701515485238M7[t] -0.184251628905251M8[t] -0.249801742325264M9[t] -0.122233106493307M10[t] -0.221116553246654M11[t] -0.00111655324665354t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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] = + 2.04749099291583 + 0.268190282588306X[t] + 0.411430607756518M1[t] + 0.479213827669841M2[t] + 0.330330380916495M3[t] + 0.527218052971105M4[t] + 0.295001272884426M5[t] + 0.146117826131079M6[t] -0.218701515485238M7[t] -0.184251628905251M8[t] -0.249801742325264M9[t] -0.122233106493307M10[t] -0.221116553246654M11[t] -0.00111655324665354t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.047490992915830.4371714.68351.9e-059e-06
X0.2681902825883060.02026413.234800
M10.4114306077565180.1880972.18730.0329880.016494
M20.4792138276698410.1880152.54880.013630.006815
M30.3303303809164950.1879561.75750.0843990.042199
M40.5272180529711050.1902272.77150.0075980.003799
M50.2950012728844260.1900821.5520.1264060.063203
M60.1461178261310790.189960.76920.4450630.222531
M7-0.2187015154852380.191431-1.14250.2582140.129107
M8-0.1842516289052510.191606-0.96160.340450.170225
M9-0.2498017423252640.191801-1.30240.1982080.099104
M10-0.1222331064933070.196255-0.62280.5359720.267986
M11-0.2211165532466540.196224-1.12690.2646940.132347
t-0.001116553246653540.002033-0.54910.585170.292585

\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) & 2.04749099291583 & 0.437171 & 4.6835 & 1.9e-05 & 9e-06 \tabularnewline
X & 0.268190282588306 & 0.020264 & 13.2348 & 0 & 0 \tabularnewline
M1 & 0.411430607756518 & 0.188097 & 2.1873 & 0.032988 & 0.016494 \tabularnewline
M2 & 0.479213827669841 & 0.188015 & 2.5488 & 0.01363 & 0.006815 \tabularnewline
M3 & 0.330330380916495 & 0.187956 & 1.7575 & 0.084399 & 0.042199 \tabularnewline
M4 & 0.527218052971105 & 0.190227 & 2.7715 & 0.007598 & 0.003799 \tabularnewline
M5 & 0.295001272884426 & 0.190082 & 1.552 & 0.126406 & 0.063203 \tabularnewline
M6 & 0.146117826131079 & 0.18996 & 0.7692 & 0.445063 & 0.222531 \tabularnewline
M7 & -0.218701515485238 & 0.191431 & -1.1425 & 0.258214 & 0.129107 \tabularnewline
M8 & -0.184251628905251 & 0.191606 & -0.9616 & 0.34045 & 0.170225 \tabularnewline
M9 & -0.249801742325264 & 0.191801 & -1.3024 & 0.198208 & 0.099104 \tabularnewline
M10 & -0.122233106493307 & 0.196255 & -0.6228 & 0.535972 & 0.267986 \tabularnewline
M11 & -0.221116553246654 & 0.196224 & -1.1269 & 0.264694 & 0.132347 \tabularnewline
t & -0.00111655324665354 & 0.002033 & -0.5491 & 0.58517 & 0.292585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&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]2.04749099291583[/C][C]0.437171[/C][C]4.6835[/C][C]1.9e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]X[/C][C]0.268190282588306[/C][C]0.020264[/C][C]13.2348[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.411430607756518[/C][C]0.188097[/C][C]2.1873[/C][C]0.032988[/C][C]0.016494[/C][/ROW]
[ROW][C]M2[/C][C]0.479213827669841[/C][C]0.188015[/C][C]2.5488[/C][C]0.01363[/C][C]0.006815[/C][/ROW]
[ROW][C]M3[/C][C]0.330330380916495[/C][C]0.187956[/C][C]1.7575[/C][C]0.084399[/C][C]0.042199[/C][/ROW]
[ROW][C]M4[/C][C]0.527218052971105[/C][C]0.190227[/C][C]2.7715[/C][C]0.007598[/C][C]0.003799[/C][/ROW]
[ROW][C]M5[/C][C]0.295001272884426[/C][C]0.190082[/C][C]1.552[/C][C]0.126406[/C][C]0.063203[/C][/ROW]
[ROW][C]M6[/C][C]0.146117826131079[/C][C]0.18996[/C][C]0.7692[/C][C]0.445063[/C][C]0.222531[/C][/ROW]
[ROW][C]M7[/C][C]-0.218701515485238[/C][C]0.191431[/C][C]-1.1425[/C][C]0.258214[/C][C]0.129107[/C][/ROW]
[ROW][C]M8[/C][C]-0.184251628905251[/C][C]0.191606[/C][C]-0.9616[/C][C]0.34045[/C][C]0.170225[/C][/ROW]
[ROW][C]M9[/C][C]-0.249801742325264[/C][C]0.191801[/C][C]-1.3024[/C][C]0.198208[/C][C]0.099104[/C][/ROW]
[ROW][C]M10[/C][C]-0.122233106493307[/C][C]0.196255[/C][C]-0.6228[/C][C]0.535972[/C][C]0.267986[/C][/ROW]
[ROW][C]M11[/C][C]-0.221116553246654[/C][C]0.196224[/C][C]-1.1269[/C][C]0.264694[/C][C]0.132347[/C][/ROW]
[ROW][C]t[/C][C]-0.00111655324665354[/C][C]0.002033[/C][C]-0.5491[/C][C]0.58517[/C][C]0.292585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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)2.047490992915830.4371714.68351.9e-059e-06
X0.2681902825883060.02026413.234800
M10.4114306077565180.1880972.18730.0329880.016494
M20.4792138276698410.1880152.54880.013630.006815
M30.3303303809164950.1879561.75750.0843990.042199
M40.5272180529711050.1902272.77150.0075980.003799
M50.2950012728844260.1900821.5520.1264060.063203
M60.1461178261310790.189960.76920.4450630.222531
M7-0.2187015154852380.191431-1.14250.2582140.129107
M8-0.1842516289052510.191606-0.96160.340450.170225
M9-0.2498017423252640.191801-1.30240.1982080.099104
M10-0.1222331064933070.196255-0.62280.5359720.267986
M11-0.2211165532466540.196224-1.12690.2646940.132347
t-0.001116553246653540.002033-0.54910.585170.292585







Multiple Linear Regression - Regression Statistics
Multiple R0.907202855997636
R-squared0.823017021930267
Adjusted R-squared0.78118468165924
F-TEST (value)19.6741807079885
F-TEST (DF numerator)13
F-TEST (DF denominator)55
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.310240057275317
Sum Squared Residuals5.29368912260055

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.907202855997636 \tabularnewline
R-squared & 0.823017021930267 \tabularnewline
Adjusted R-squared & 0.78118468165924 \tabularnewline
F-TEST (value) & 19.6741807079885 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 4.44089209850063e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.310240057275317 \tabularnewline
Sum Squared Residuals & 5.29368912260055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.907202855997636[/C][/ROW]
[ROW][C]R-squared[/C][C]0.823017021930267[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.78118468165924[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.6741807079885[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]4.44089209850063e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.310240057275317[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.29368912260055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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 R0.907202855997636
R-squared0.823017021930267
Adjusted R-squared0.78118468165924
F-TEST (value)19.6741807079885
F-TEST (DF numerator)13
F-TEST (DF denominator)55
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.310240057275317
Sum Squared Residuals5.29368912260055







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.27.902067783968330.297932216031667
287.968734450634990.0312655493650105
37.57.81873445063499-0.318734450634988
46.86.80764929779557-0.0076492977955676
56.56.57431596446223-0.074315964462234
66.66.424315964462230.175684035537766
77.68.04298816075273-0.442988160752732
888.07632149408606-0.0763214940860637
98.18.00965482741940.090345172580602
107.77.51926926005160.180730739948404
117.57.41926926005160.080730739948404
127.67.6392692600516-0.0392692600515964
137.87.754574003714320.0454259962856753
147.87.821240670381-0.0212406703809934
157.87.6712406703810.128759329619006
167.58.0815640152596-0.581564015259596
177.57.84823068192626-0.348230681926263
187.17.69823068192626-0.598230681926263
197.57.466389928357450.0336100716425545
207.57.499723261690780.000276738309221314
217.67.433056595024110.166943404975887
227.77.90815604497421-0.208156044974213
237.77.80815604497421-0.108156044974213
247.98.02815604497421-0.128156044974213
258.17.92890856256630.171091437433703
268.27.995575229232960.204424770767034
278.27.845575229232970.354424770767033
288.27.478146754605480.721853245394521
297.97.244813421272140.655186578727855
307.37.094813421272150.205186578727855
316.96.514325300338530.38567469966147
326.66.547658633671860.0523413663281359
336.76.48099196700520.219008032994803
346.96.848815303919980.0511846960800246
3576.748815303919980.251184696080024
367.16.968815303919980.131184696080024
377.27.057301019323870.142698980676127
387.17.12396768599054-0.0239676859905426
396.96.97396768599054-0.0739676859905425
4076.874729493951360.125270506048638
416.86.641396160618030.158603839381971
426.46.49139616061803-0.0913961606180284
436.76.74229791570816-0.0422979157081632
446.66.7756312490415-0.175631249041497
456.46.70896458237483-0.30896458237483
466.36.245398043265860.0546019567341405
476.26.145398043265860.0546019567341408
486.56.365398043265860.134601956734141
496.86.90980723906988-0.109807239069878
506.86.97647390573655-0.176473905736547
516.46.82647390573655-0.426473905736547
526.16.48586445936789-0.385864459367891
535.86.25253112603456-0.452531126034558
546.16.10253112603456-0.00253112603455805
557.27.50665109625441-0.306651096254409
567.37.53998442958774-0.239984429587743
576.97.47331776292108-0.573317762921076
586.16.17836134778836-0.078361347788356
595.86.07836134778836-0.278361347788356
606.26.29836134778836-0.0983613477883555
617.17.6473413913573-0.547341391357294
627.77.71400805802396-0.0140080580239620
637.97.564008058023960.335991941976038
647.77.57204597902010.127954020979896
657.47.338712645686770.0612873543132294
667.57.188712645686770.311287354313229
6787.627347598588720.37265240141128
688.17.660680931922050.439319068077946
6987.594014265255390.405985734744613

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.2 & 7.90206778396833 & 0.297932216031667 \tabularnewline
2 & 8 & 7.96873445063499 & 0.0312655493650105 \tabularnewline
3 & 7.5 & 7.81873445063499 & -0.318734450634988 \tabularnewline
4 & 6.8 & 6.80764929779557 & -0.0076492977955676 \tabularnewline
5 & 6.5 & 6.57431596446223 & -0.074315964462234 \tabularnewline
6 & 6.6 & 6.42431596446223 & 0.175684035537766 \tabularnewline
7 & 7.6 & 8.04298816075273 & -0.442988160752732 \tabularnewline
8 & 8 & 8.07632149408606 & -0.0763214940860637 \tabularnewline
9 & 8.1 & 8.0096548274194 & 0.090345172580602 \tabularnewline
10 & 7.7 & 7.5192692600516 & 0.180730739948404 \tabularnewline
11 & 7.5 & 7.4192692600516 & 0.080730739948404 \tabularnewline
12 & 7.6 & 7.6392692600516 & -0.0392692600515964 \tabularnewline
13 & 7.8 & 7.75457400371432 & 0.0454259962856753 \tabularnewline
14 & 7.8 & 7.821240670381 & -0.0212406703809934 \tabularnewline
15 & 7.8 & 7.671240670381 & 0.128759329619006 \tabularnewline
16 & 7.5 & 8.0815640152596 & -0.581564015259596 \tabularnewline
17 & 7.5 & 7.84823068192626 & -0.348230681926263 \tabularnewline
18 & 7.1 & 7.69823068192626 & -0.598230681926263 \tabularnewline
19 & 7.5 & 7.46638992835745 & 0.0336100716425545 \tabularnewline
20 & 7.5 & 7.49972326169078 & 0.000276738309221314 \tabularnewline
21 & 7.6 & 7.43305659502411 & 0.166943404975887 \tabularnewline
22 & 7.7 & 7.90815604497421 & -0.208156044974213 \tabularnewline
23 & 7.7 & 7.80815604497421 & -0.108156044974213 \tabularnewline
24 & 7.9 & 8.02815604497421 & -0.128156044974213 \tabularnewline
25 & 8.1 & 7.9289085625663 & 0.171091437433703 \tabularnewline
26 & 8.2 & 7.99557522923296 & 0.204424770767034 \tabularnewline
27 & 8.2 & 7.84557522923297 & 0.354424770767033 \tabularnewline
28 & 8.2 & 7.47814675460548 & 0.721853245394521 \tabularnewline
29 & 7.9 & 7.24481342127214 & 0.655186578727855 \tabularnewline
30 & 7.3 & 7.09481342127215 & 0.205186578727855 \tabularnewline
31 & 6.9 & 6.51432530033853 & 0.38567469966147 \tabularnewline
32 & 6.6 & 6.54765863367186 & 0.0523413663281359 \tabularnewline
33 & 6.7 & 6.4809919670052 & 0.219008032994803 \tabularnewline
34 & 6.9 & 6.84881530391998 & 0.0511846960800246 \tabularnewline
35 & 7 & 6.74881530391998 & 0.251184696080024 \tabularnewline
36 & 7.1 & 6.96881530391998 & 0.131184696080024 \tabularnewline
37 & 7.2 & 7.05730101932387 & 0.142698980676127 \tabularnewline
38 & 7.1 & 7.12396768599054 & -0.0239676859905426 \tabularnewline
39 & 6.9 & 6.97396768599054 & -0.0739676859905425 \tabularnewline
40 & 7 & 6.87472949395136 & 0.125270506048638 \tabularnewline
41 & 6.8 & 6.64139616061803 & 0.158603839381971 \tabularnewline
42 & 6.4 & 6.49139616061803 & -0.0913961606180284 \tabularnewline
43 & 6.7 & 6.74229791570816 & -0.0422979157081632 \tabularnewline
44 & 6.6 & 6.7756312490415 & -0.175631249041497 \tabularnewline
45 & 6.4 & 6.70896458237483 & -0.30896458237483 \tabularnewline
46 & 6.3 & 6.24539804326586 & 0.0546019567341405 \tabularnewline
47 & 6.2 & 6.14539804326586 & 0.0546019567341408 \tabularnewline
48 & 6.5 & 6.36539804326586 & 0.134601956734141 \tabularnewline
49 & 6.8 & 6.90980723906988 & -0.109807239069878 \tabularnewline
50 & 6.8 & 6.97647390573655 & -0.176473905736547 \tabularnewline
51 & 6.4 & 6.82647390573655 & -0.426473905736547 \tabularnewline
52 & 6.1 & 6.48586445936789 & -0.385864459367891 \tabularnewline
53 & 5.8 & 6.25253112603456 & -0.452531126034558 \tabularnewline
54 & 6.1 & 6.10253112603456 & -0.00253112603455805 \tabularnewline
55 & 7.2 & 7.50665109625441 & -0.306651096254409 \tabularnewline
56 & 7.3 & 7.53998442958774 & -0.239984429587743 \tabularnewline
57 & 6.9 & 7.47331776292108 & -0.573317762921076 \tabularnewline
58 & 6.1 & 6.17836134778836 & -0.078361347788356 \tabularnewline
59 & 5.8 & 6.07836134778836 & -0.278361347788356 \tabularnewline
60 & 6.2 & 6.29836134778836 & -0.0983613477883555 \tabularnewline
61 & 7.1 & 7.6473413913573 & -0.547341391357294 \tabularnewline
62 & 7.7 & 7.71400805802396 & -0.0140080580239620 \tabularnewline
63 & 7.9 & 7.56400805802396 & 0.335991941976038 \tabularnewline
64 & 7.7 & 7.5720459790201 & 0.127954020979896 \tabularnewline
65 & 7.4 & 7.33871264568677 & 0.0612873543132294 \tabularnewline
66 & 7.5 & 7.18871264568677 & 0.311287354313229 \tabularnewline
67 & 8 & 7.62734759858872 & 0.37265240141128 \tabularnewline
68 & 8.1 & 7.66068093192205 & 0.439319068077946 \tabularnewline
69 & 8 & 7.59401426525539 & 0.405985734744613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&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]8.2[/C][C]7.90206778396833[/C][C]0.297932216031667[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]7.96873445063499[/C][C]0.0312655493650105[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]7.81873445063499[/C][C]-0.318734450634988[/C][/ROW]
[ROW][C]4[/C][C]6.8[/C][C]6.80764929779557[/C][C]-0.0076492977955676[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]6.57431596446223[/C][C]-0.074315964462234[/C][/ROW]
[ROW][C]6[/C][C]6.6[/C][C]6.42431596446223[/C][C]0.175684035537766[/C][/ROW]
[ROW][C]7[/C][C]7.6[/C][C]8.04298816075273[/C][C]-0.442988160752732[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]8.07632149408606[/C][C]-0.0763214940860637[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.0096548274194[/C][C]0.090345172580602[/C][/ROW]
[ROW][C]10[/C][C]7.7[/C][C]7.5192692600516[/C][C]0.180730739948404[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.4192692600516[/C][C]0.080730739948404[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.6392692600516[/C][C]-0.0392692600515964[/C][/ROW]
[ROW][C]13[/C][C]7.8[/C][C]7.75457400371432[/C][C]0.0454259962856753[/C][/ROW]
[ROW][C]14[/C][C]7.8[/C][C]7.821240670381[/C][C]-0.0212406703809934[/C][/ROW]
[ROW][C]15[/C][C]7.8[/C][C]7.671240670381[/C][C]0.128759329619006[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]8.0815640152596[/C][C]-0.581564015259596[/C][/ROW]
[ROW][C]17[/C][C]7.5[/C][C]7.84823068192626[/C][C]-0.348230681926263[/C][/ROW]
[ROW][C]18[/C][C]7.1[/C][C]7.69823068192626[/C][C]-0.598230681926263[/C][/ROW]
[ROW][C]19[/C][C]7.5[/C][C]7.46638992835745[/C][C]0.0336100716425545[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]7.49972326169078[/C][C]0.000276738309221314[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]7.43305659502411[/C][C]0.166943404975887[/C][/ROW]
[ROW][C]22[/C][C]7.7[/C][C]7.90815604497421[/C][C]-0.208156044974213[/C][/ROW]
[ROW][C]23[/C][C]7.7[/C][C]7.80815604497421[/C][C]-0.108156044974213[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]8.02815604497421[/C][C]-0.128156044974213[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]7.9289085625663[/C][C]0.171091437433703[/C][/ROW]
[ROW][C]26[/C][C]8.2[/C][C]7.99557522923296[/C][C]0.204424770767034[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]7.84557522923297[/C][C]0.354424770767033[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]7.47814675460548[/C][C]0.721853245394521[/C][/ROW]
[ROW][C]29[/C][C]7.9[/C][C]7.24481342127214[/C][C]0.655186578727855[/C][/ROW]
[ROW][C]30[/C][C]7.3[/C][C]7.09481342127215[/C][C]0.205186578727855[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]6.51432530033853[/C][C]0.38567469966147[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]6.54765863367186[/C][C]0.0523413663281359[/C][/ROW]
[ROW][C]33[/C][C]6.7[/C][C]6.4809919670052[/C][C]0.219008032994803[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]6.84881530391998[/C][C]0.0511846960800246[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]6.74881530391998[/C][C]0.251184696080024[/C][/ROW]
[ROW][C]36[/C][C]7.1[/C][C]6.96881530391998[/C][C]0.131184696080024[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.05730101932387[/C][C]0.142698980676127[/C][/ROW]
[ROW][C]38[/C][C]7.1[/C][C]7.12396768599054[/C][C]-0.0239676859905426[/C][/ROW]
[ROW][C]39[/C][C]6.9[/C][C]6.97396768599054[/C][C]-0.0739676859905425[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.87472949395136[/C][C]0.125270506048638[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]6.64139616061803[/C][C]0.158603839381971[/C][/ROW]
[ROW][C]42[/C][C]6.4[/C][C]6.49139616061803[/C][C]-0.0913961606180284[/C][/ROW]
[ROW][C]43[/C][C]6.7[/C][C]6.74229791570816[/C][C]-0.0422979157081632[/C][/ROW]
[ROW][C]44[/C][C]6.6[/C][C]6.7756312490415[/C][C]-0.175631249041497[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]6.70896458237483[/C][C]-0.30896458237483[/C][/ROW]
[ROW][C]46[/C][C]6.3[/C][C]6.24539804326586[/C][C]0.0546019567341405[/C][/ROW]
[ROW][C]47[/C][C]6.2[/C][C]6.14539804326586[/C][C]0.0546019567341408[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]6.36539804326586[/C][C]0.134601956734141[/C][/ROW]
[ROW][C]49[/C][C]6.8[/C][C]6.90980723906988[/C][C]-0.109807239069878[/C][/ROW]
[ROW][C]50[/C][C]6.8[/C][C]6.97647390573655[/C][C]-0.176473905736547[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.82647390573655[/C][C]-0.426473905736547[/C][/ROW]
[ROW][C]52[/C][C]6.1[/C][C]6.48586445936789[/C][C]-0.385864459367891[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]6.25253112603456[/C][C]-0.452531126034558[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]6.10253112603456[/C][C]-0.00253112603455805[/C][/ROW]
[ROW][C]55[/C][C]7.2[/C][C]7.50665109625441[/C][C]-0.306651096254409[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]7.53998442958774[/C][C]-0.239984429587743[/C][/ROW]
[ROW][C]57[/C][C]6.9[/C][C]7.47331776292108[/C][C]-0.573317762921076[/C][/ROW]
[ROW][C]58[/C][C]6.1[/C][C]6.17836134778836[/C][C]-0.078361347788356[/C][/ROW]
[ROW][C]59[/C][C]5.8[/C][C]6.07836134778836[/C][C]-0.278361347788356[/C][/ROW]
[ROW][C]60[/C][C]6.2[/C][C]6.29836134778836[/C][C]-0.0983613477883555[/C][/ROW]
[ROW][C]61[/C][C]7.1[/C][C]7.6473413913573[/C][C]-0.547341391357294[/C][/ROW]
[ROW][C]62[/C][C]7.7[/C][C]7.71400805802396[/C][C]-0.0140080580239620[/C][/ROW]
[ROW][C]63[/C][C]7.9[/C][C]7.56400805802396[/C][C]0.335991941976038[/C][/ROW]
[ROW][C]64[/C][C]7.7[/C][C]7.5720459790201[/C][C]0.127954020979896[/C][/ROW]
[ROW][C]65[/C][C]7.4[/C][C]7.33871264568677[/C][C]0.0612873543132294[/C][/ROW]
[ROW][C]66[/C][C]7.5[/C][C]7.18871264568677[/C][C]0.311287354313229[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]7.62734759858872[/C][C]0.37265240141128[/C][/ROW]
[ROW][C]68[/C][C]8.1[/C][C]7.66068093192205[/C][C]0.439319068077946[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]7.59401426525539[/C][C]0.405985734744613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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
18.27.902067783968330.297932216031667
287.968734450634990.0312655493650105
37.57.81873445063499-0.318734450634988
46.86.80764929779557-0.0076492977955676
56.56.57431596446223-0.074315964462234
66.66.424315964462230.175684035537766
77.68.04298816075273-0.442988160752732
888.07632149408606-0.0763214940860637
98.18.00965482741940.090345172580602
107.77.51926926005160.180730739948404
117.57.41926926005160.080730739948404
127.67.6392692600516-0.0392692600515964
137.87.754574003714320.0454259962856753
147.87.821240670381-0.0212406703809934
157.87.6712406703810.128759329619006
167.58.0815640152596-0.581564015259596
177.57.84823068192626-0.348230681926263
187.17.69823068192626-0.598230681926263
197.57.466389928357450.0336100716425545
207.57.499723261690780.000276738309221314
217.67.433056595024110.166943404975887
227.77.90815604497421-0.208156044974213
237.77.80815604497421-0.108156044974213
247.98.02815604497421-0.128156044974213
258.17.92890856256630.171091437433703
268.27.995575229232960.204424770767034
278.27.845575229232970.354424770767033
288.27.478146754605480.721853245394521
297.97.244813421272140.655186578727855
307.37.094813421272150.205186578727855
316.96.514325300338530.38567469966147
326.66.547658633671860.0523413663281359
336.76.48099196700520.219008032994803
346.96.848815303919980.0511846960800246
3576.748815303919980.251184696080024
367.16.968815303919980.131184696080024
377.27.057301019323870.142698980676127
387.17.12396768599054-0.0239676859905426
396.96.97396768599054-0.0739676859905425
4076.874729493951360.125270506048638
416.86.641396160618030.158603839381971
426.46.49139616061803-0.0913961606180284
436.76.74229791570816-0.0422979157081632
446.66.7756312490415-0.175631249041497
456.46.70896458237483-0.30896458237483
466.36.245398043265860.0546019567341405
476.26.145398043265860.0546019567341408
486.56.365398043265860.134601956734141
496.86.90980723906988-0.109807239069878
506.86.97647390573655-0.176473905736547
516.46.82647390573655-0.426473905736547
526.16.48586445936789-0.385864459367891
535.86.25253112603456-0.452531126034558
546.16.10253112603456-0.00253112603455805
557.27.50665109625441-0.306651096254409
567.37.53998442958774-0.239984429587743
576.97.47331776292108-0.573317762921076
586.16.17836134778836-0.078361347788356
595.86.07836134778836-0.278361347788356
606.26.29836134778836-0.0983613477883555
617.17.6473413913573-0.547341391357294
627.77.71400805802396-0.0140080580239620
637.97.564008058023960.335991941976038
647.77.57204597902010.127954020979896
657.47.338712645686770.0612873543132294
667.57.188712645686770.311287354313229
6787.627347598588720.37265240141128
688.17.660680931922050.439319068077946
6987.594014265255390.405985734744613







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.3056519093596630.6113038187193250.694348090640337
180.2854086144745450.570817228949090.714591385525455
190.1966634686670160.3933269373340320.803336531332984
200.1348722203425600.2697444406851210.86512777965744
210.08237131535557910.1647426307111580.917628684644421
220.06327431767388660.1265486353477730.936725682326113
230.04048949334261850.0809789866852370.959510506657381
240.03688078623460540.07376157246921080.963119213765395
250.02009080203625540.04018160407251080.979909197963745
260.01780545384919120.03561090769838240.982194546150809
270.03688346263098700.07376692526197390.963116537369013
280.3234751867326410.6469503734652820.676524813267359
290.4805154893309130.9610309786618260.519484510669087
300.4006506051082230.8013012102164470.599349394891777
310.4103227990719480.8206455981438950.589677200928052
320.4900175957921980.9800351915843970.509982404207802
330.587065477599510.825869044800980.41293452240049
340.5492042533335580.9015914933328840.450795746666442
350.4655200677032940.9310401354065880.534479932296706
360.395444174624780.790888349249560.60455582537522
370.4489931153497590.8979862306995180.551006884650241
380.4211935633492320.8423871266984650.578806436650768
390.3811121740796180.7622243481592350.618887825920382
400.3398851554929240.6797703109858470.660114844507076
410.3631905608657360.7263811217314720.636809439134264
420.2880607192222510.5761214384445030.711939280777749
430.2487790958654780.4975581917309570.751220904134522
440.2011078187808940.4022156375617870.798892181219106
450.2058922118320770.4117844236641540.794107788167923
460.1678276126962370.3356552253924730.832172387303763
470.2203550451268630.4407100902537250.779644954873137
480.4139415727167280.8278831454334570.586058427283272
490.929960309660230.1400793806795410.0700396903397707
500.9772147432733460.0455705134533080.022785256726654
510.9681717314430270.06365653711394570.0318282685569728
520.921342074674320.1573158506513590.0786579253256795

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.305651909359663 & 0.611303818719325 & 0.694348090640337 \tabularnewline
18 & 0.285408614474545 & 0.57081722894909 & 0.714591385525455 \tabularnewline
19 & 0.196663468667016 & 0.393326937334032 & 0.803336531332984 \tabularnewline
20 & 0.134872220342560 & 0.269744440685121 & 0.86512777965744 \tabularnewline
21 & 0.0823713153555791 & 0.164742630711158 & 0.917628684644421 \tabularnewline
22 & 0.0632743176738866 & 0.126548635347773 & 0.936725682326113 \tabularnewline
23 & 0.0404894933426185 & 0.080978986685237 & 0.959510506657381 \tabularnewline
24 & 0.0368807862346054 & 0.0737615724692108 & 0.963119213765395 \tabularnewline
25 & 0.0200908020362554 & 0.0401816040725108 & 0.979909197963745 \tabularnewline
26 & 0.0178054538491912 & 0.0356109076983824 & 0.982194546150809 \tabularnewline
27 & 0.0368834626309870 & 0.0737669252619739 & 0.963116537369013 \tabularnewline
28 & 0.323475186732641 & 0.646950373465282 & 0.676524813267359 \tabularnewline
29 & 0.480515489330913 & 0.961030978661826 & 0.519484510669087 \tabularnewline
30 & 0.400650605108223 & 0.801301210216447 & 0.599349394891777 \tabularnewline
31 & 0.410322799071948 & 0.820645598143895 & 0.589677200928052 \tabularnewline
32 & 0.490017595792198 & 0.980035191584397 & 0.509982404207802 \tabularnewline
33 & 0.58706547759951 & 0.82586904480098 & 0.41293452240049 \tabularnewline
34 & 0.549204253333558 & 0.901591493332884 & 0.450795746666442 \tabularnewline
35 & 0.465520067703294 & 0.931040135406588 & 0.534479932296706 \tabularnewline
36 & 0.39544417462478 & 0.79088834924956 & 0.60455582537522 \tabularnewline
37 & 0.448993115349759 & 0.897986230699518 & 0.551006884650241 \tabularnewline
38 & 0.421193563349232 & 0.842387126698465 & 0.578806436650768 \tabularnewline
39 & 0.381112174079618 & 0.762224348159235 & 0.618887825920382 \tabularnewline
40 & 0.339885155492924 & 0.679770310985847 & 0.660114844507076 \tabularnewline
41 & 0.363190560865736 & 0.726381121731472 & 0.636809439134264 \tabularnewline
42 & 0.288060719222251 & 0.576121438444503 & 0.711939280777749 \tabularnewline
43 & 0.248779095865478 & 0.497558191730957 & 0.751220904134522 \tabularnewline
44 & 0.201107818780894 & 0.402215637561787 & 0.798892181219106 \tabularnewline
45 & 0.205892211832077 & 0.411784423664154 & 0.794107788167923 \tabularnewline
46 & 0.167827612696237 & 0.335655225392473 & 0.832172387303763 \tabularnewline
47 & 0.220355045126863 & 0.440710090253725 & 0.779644954873137 \tabularnewline
48 & 0.413941572716728 & 0.827883145433457 & 0.586058427283272 \tabularnewline
49 & 0.92996030966023 & 0.140079380679541 & 0.0700396903397707 \tabularnewline
50 & 0.977214743273346 & 0.045570513453308 & 0.022785256726654 \tabularnewline
51 & 0.968171731443027 & 0.0636565371139457 & 0.0318282685569728 \tabularnewline
52 & 0.92134207467432 & 0.157315850651359 & 0.0786579253256795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.305651909359663[/C][C]0.611303818719325[/C][C]0.694348090640337[/C][/ROW]
[ROW][C]18[/C][C]0.285408614474545[/C][C]0.57081722894909[/C][C]0.714591385525455[/C][/ROW]
[ROW][C]19[/C][C]0.196663468667016[/C][C]0.393326937334032[/C][C]0.803336531332984[/C][/ROW]
[ROW][C]20[/C][C]0.134872220342560[/C][C]0.269744440685121[/C][C]0.86512777965744[/C][/ROW]
[ROW][C]21[/C][C]0.0823713153555791[/C][C]0.164742630711158[/C][C]0.917628684644421[/C][/ROW]
[ROW][C]22[/C][C]0.0632743176738866[/C][C]0.126548635347773[/C][C]0.936725682326113[/C][/ROW]
[ROW][C]23[/C][C]0.0404894933426185[/C][C]0.080978986685237[/C][C]0.959510506657381[/C][/ROW]
[ROW][C]24[/C][C]0.0368807862346054[/C][C]0.0737615724692108[/C][C]0.963119213765395[/C][/ROW]
[ROW][C]25[/C][C]0.0200908020362554[/C][C]0.0401816040725108[/C][C]0.979909197963745[/C][/ROW]
[ROW][C]26[/C][C]0.0178054538491912[/C][C]0.0356109076983824[/C][C]0.982194546150809[/C][/ROW]
[ROW][C]27[/C][C]0.0368834626309870[/C][C]0.0737669252619739[/C][C]0.963116537369013[/C][/ROW]
[ROW][C]28[/C][C]0.323475186732641[/C][C]0.646950373465282[/C][C]0.676524813267359[/C][/ROW]
[ROW][C]29[/C][C]0.480515489330913[/C][C]0.961030978661826[/C][C]0.519484510669087[/C][/ROW]
[ROW][C]30[/C][C]0.400650605108223[/C][C]0.801301210216447[/C][C]0.599349394891777[/C][/ROW]
[ROW][C]31[/C][C]0.410322799071948[/C][C]0.820645598143895[/C][C]0.589677200928052[/C][/ROW]
[ROW][C]32[/C][C]0.490017595792198[/C][C]0.980035191584397[/C][C]0.509982404207802[/C][/ROW]
[ROW][C]33[/C][C]0.58706547759951[/C][C]0.82586904480098[/C][C]0.41293452240049[/C][/ROW]
[ROW][C]34[/C][C]0.549204253333558[/C][C]0.901591493332884[/C][C]0.450795746666442[/C][/ROW]
[ROW][C]35[/C][C]0.465520067703294[/C][C]0.931040135406588[/C][C]0.534479932296706[/C][/ROW]
[ROW][C]36[/C][C]0.39544417462478[/C][C]0.79088834924956[/C][C]0.60455582537522[/C][/ROW]
[ROW][C]37[/C][C]0.448993115349759[/C][C]0.897986230699518[/C][C]0.551006884650241[/C][/ROW]
[ROW][C]38[/C][C]0.421193563349232[/C][C]0.842387126698465[/C][C]0.578806436650768[/C][/ROW]
[ROW][C]39[/C][C]0.381112174079618[/C][C]0.762224348159235[/C][C]0.618887825920382[/C][/ROW]
[ROW][C]40[/C][C]0.339885155492924[/C][C]0.679770310985847[/C][C]0.660114844507076[/C][/ROW]
[ROW][C]41[/C][C]0.363190560865736[/C][C]0.726381121731472[/C][C]0.636809439134264[/C][/ROW]
[ROW][C]42[/C][C]0.288060719222251[/C][C]0.576121438444503[/C][C]0.711939280777749[/C][/ROW]
[ROW][C]43[/C][C]0.248779095865478[/C][C]0.497558191730957[/C][C]0.751220904134522[/C][/ROW]
[ROW][C]44[/C][C]0.201107818780894[/C][C]0.402215637561787[/C][C]0.798892181219106[/C][/ROW]
[ROW][C]45[/C][C]0.205892211832077[/C][C]0.411784423664154[/C][C]0.794107788167923[/C][/ROW]
[ROW][C]46[/C][C]0.167827612696237[/C][C]0.335655225392473[/C][C]0.832172387303763[/C][/ROW]
[ROW][C]47[/C][C]0.220355045126863[/C][C]0.440710090253725[/C][C]0.779644954873137[/C][/ROW]
[ROW][C]48[/C][C]0.413941572716728[/C][C]0.827883145433457[/C][C]0.586058427283272[/C][/ROW]
[ROW][C]49[/C][C]0.92996030966023[/C][C]0.140079380679541[/C][C]0.0700396903397707[/C][/ROW]
[ROW][C]50[/C][C]0.977214743273346[/C][C]0.045570513453308[/C][C]0.022785256726654[/C][/ROW]
[ROW][C]51[/C][C]0.968171731443027[/C][C]0.0636565371139457[/C][C]0.0318282685569728[/C][/ROW]
[ROW][C]52[/C][C]0.92134207467432[/C][C]0.157315850651359[/C][C]0.0786579253256795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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
170.3056519093596630.6113038187193250.694348090640337
180.2854086144745450.570817228949090.714591385525455
190.1966634686670160.3933269373340320.803336531332984
200.1348722203425600.2697444406851210.86512777965744
210.08237131535557910.1647426307111580.917628684644421
220.06327431767388660.1265486353477730.936725682326113
230.04048949334261850.0809789866852370.959510506657381
240.03688078623460540.07376157246921080.963119213765395
250.02009080203625540.04018160407251080.979909197963745
260.01780545384919120.03561090769838240.982194546150809
270.03688346263098700.07376692526197390.963116537369013
280.3234751867326410.6469503734652820.676524813267359
290.4805154893309130.9610309786618260.519484510669087
300.4006506051082230.8013012102164470.599349394891777
310.4103227990719480.8206455981438950.589677200928052
320.4900175957921980.9800351915843970.509982404207802
330.587065477599510.825869044800980.41293452240049
340.5492042533335580.9015914933328840.450795746666442
350.4655200677032940.9310401354065880.534479932296706
360.395444174624780.790888349249560.60455582537522
370.4489931153497590.8979862306995180.551006884650241
380.4211935633492320.8423871266984650.578806436650768
390.3811121740796180.7622243481592350.618887825920382
400.3398851554929240.6797703109858470.660114844507076
410.3631905608657360.7263811217314720.636809439134264
420.2880607192222510.5761214384445030.711939280777749
430.2487790958654780.4975581917309570.751220904134522
440.2011078187808940.4022156375617870.798892181219106
450.2058922118320770.4117844236641540.794107788167923
460.1678276126962370.3356552253924730.832172387303763
470.2203550451268630.4407100902537250.779644954873137
480.4139415727167280.8278831454334570.586058427283272
490.929960309660230.1400793806795410.0700396903397707
500.9772147432733460.0455705134533080.022785256726654
510.9681717314430270.06365653711394570.0318282685569728
520.921342074674320.1573158506513590.0786579253256795







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0833333333333333NOK
10% type I error level70.194444444444444NOK

\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 & 3 & 0.0833333333333333 & NOK \tabularnewline
10% type I error level & 7 & 0.194444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57967&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]3[/C][C]0.0833333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.194444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57967&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57967&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 level00OK
5% type I error level30.0833333333333333NOK
10% type I error level70.194444444444444NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}