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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 07:45:44 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587284405fzyceng6on73ft.htm/, Retrieved Thu, 28 Mar 2024 11:35:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58227, Retrieved Thu, 28 Mar 2024 11:35:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
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 14:10:54] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-20 14:45:44] [4057bfb3a128b4e91b455d276991f7f0] [Current]
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Dataseries X:
20	0	21	20	22	22
21	0	20	21	20	22
21	0	21	20	21	20
21	0	21	21	20	21
19	0	21	21	21	20
21	0	19	21	21	21
21	0	21	19	21	21
22	0	21	21	19	21
19	0	22	21	21	19
24	0	19	22	21	21
22	0	24	19	22	21
22	0	22	24	19	22
22	0	22	22	24	19
24	0	22	22	22	24
22	0	24	22	22	22
23	0	22	24	22	22
24	0	23	22	24	22
21	0	24	23	22	24
20	0	21	24	23	22
22	0	20	21	24	23
23	0	22	20	21	24
23	0	23	22	20	21
22	0	23	23	22	20
20	0	22	23	23	22
21	1	20	22	23	23
21	1	21	20	22	23
20	1	21	21	20	22
20	1	20	21	21	20
17	1	20	20	21	21
18	1	17	20	20	21
19	1	18	17	20	20
19	1	19	18	17	20
20	1	19	19	18	17
21	1	20	19	19	18
20	1	21	20	19	19
21	1	20	21	20	19
19	1	21	20	21	20
22	1	19	21	20	21
20	1	22	19	21	20
18	1	20	22	19	21
16	1	18	20	22	19
17	1	16	18	20	22
18	1	17	16	18	20
19	1	18	17	16	18
18	1	19	18	17	16
20	1	18	19	18	17
21	1	20	18	19	18
18	1	21	20	18	19
19	1	18	21	20	18
19	1	19	18	21	20
19	1	19	19	18	21
21	1	19	19	19	18
19	1	21	19	19	19
19	1	19	21	19	19
17	1	19	19	21	19
16	1	17	19	19	21
16	1	16	17	19	19
17	1	16	16	17	19
16	1	17	16	16	17
15	1	16	17	16	16
16	1	15	16	17	16
16	1	16	15	16	17
16	1	16	16	15	16
18	1	16	16	16	15
19	1	18	16	16	16
16	1	19	18	16	16
16	1	16	19	18	16
16	1	16	16	19	18




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&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]6 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=58227&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 8.0159995930798 -0.396793923176626X[t] + 0.429040951968163Y1[t] + 0.117021660229381Y2[t] + 0.0321909561234374Y3[t] + 0.0338671313400513Y4[t] + 0.522084696539833M1[t] + 1.60881904762785M2[t] + 0.429102182903779M3[t] + 1.21952238869631M4[t] -0.0690080080649838M5[t] + 0.0599839230344053M6[t] + 0.21231296050147M7[t] + 0.870779207535017M8[t] + 0.429799214978465M9[t] + 2.35819701640652M10[t] + 0.84752047137127M11[t] -0.027329651821742t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  8.0159995930798 -0.396793923176626X[t] +  0.429040951968163Y1[t] +  0.117021660229381Y2[t] +  0.0321909561234374Y3[t] +  0.0338671313400513Y4[t] +  0.522084696539833M1[t] +  1.60881904762785M2[t] +  0.429102182903779M3[t] +  1.21952238869631M4[t] -0.0690080080649838M5[t] +  0.0599839230344053M6[t] +  0.21231296050147M7[t] +  0.870779207535017M8[t] +  0.429799214978465M9[t] +  2.35819701640652M10[t] +  0.84752047137127M11[t] -0.027329651821742t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  8.0159995930798 -0.396793923176626X[t] +  0.429040951968163Y1[t] +  0.117021660229381Y2[t] +  0.0321909561234374Y3[t] +  0.0338671313400513Y4[t] +  0.522084696539833M1[t] +  1.60881904762785M2[t] +  0.429102182903779M3[t] +  1.21952238869631M4[t] -0.0690080080649838M5[t] +  0.0599839230344053M6[t] +  0.21231296050147M7[t] +  0.870779207535017M8[t] +  0.429799214978465M9[t] +  2.35819701640652M10[t] +  0.84752047137127M11[t] -0.027329651821742t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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] = + 8.0159995930798 -0.396793923176626X[t] + 0.429040951968163Y1[t] + 0.117021660229381Y2[t] + 0.0321909561234374Y3[t] + 0.0338671313400513Y4[t] + 0.522084696539833M1[t] + 1.60881904762785M2[t] + 0.429102182903779M3[t] + 1.21952238869631M4[t] -0.0690080080649838M5[t] + 0.0599839230344053M6[t] + 0.21231296050147M7[t] + 0.870779207535017M8[t] + 0.429799214978465M9[t] + 2.35819701640652M10[t] + 0.84752047137127M11[t] -0.027329651821742t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.01599959307983.5193162.27770.0270530.013527
X-0.3967939231766260.640905-0.61910.5386510.269326
Y10.4290409519681630.1424213.01250.0040580.002029
Y20.1170216602293810.1507430.77630.441230.220615
Y30.03219095612343740.1525930.2110.8337760.416888
Y40.03386713134005130.1430650.23670.8138370.406918
M10.5220846965398330.9071110.57550.5675030.283752
M21.608819047627850.8919561.80370.0773050.038652
M30.4291021829037790.8657430.49560.6223160.311158
M41.219522388696310.8292261.47070.1476440.073822
M5-0.06900800806498380.884278-0.0780.9381090.469054
M60.05998392303440530.8509470.07050.9440840.472042
M70.212312960501470.8998260.23590.8144360.407218
M80.8707792075350170.8868250.98190.3308740.165437
M90.4297992149784650.9051790.47480.6369810.31849
M102.358197016406520.8778212.68640.0097780.004889
M110.847520471371270.9302270.91110.366620.18331
t-0.0273296518217420.018785-1.45490.1519480.075974

\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) & 8.0159995930798 & 3.519316 & 2.2777 & 0.027053 & 0.013527 \tabularnewline
X & -0.396793923176626 & 0.640905 & -0.6191 & 0.538651 & 0.269326 \tabularnewline
Y1 & 0.429040951968163 & 0.142421 & 3.0125 & 0.004058 & 0.002029 \tabularnewline
Y2 & 0.117021660229381 & 0.150743 & 0.7763 & 0.44123 & 0.220615 \tabularnewline
Y3 & 0.0321909561234374 & 0.152593 & 0.211 & 0.833776 & 0.416888 \tabularnewline
Y4 & 0.0338671313400513 & 0.143065 & 0.2367 & 0.813837 & 0.406918 \tabularnewline
M1 & 0.522084696539833 & 0.907111 & 0.5755 & 0.567503 & 0.283752 \tabularnewline
M2 & 1.60881904762785 & 0.891956 & 1.8037 & 0.077305 & 0.038652 \tabularnewline
M3 & 0.429102182903779 & 0.865743 & 0.4956 & 0.622316 & 0.311158 \tabularnewline
M4 & 1.21952238869631 & 0.829226 & 1.4707 & 0.147644 & 0.073822 \tabularnewline
M5 & -0.0690080080649838 & 0.884278 & -0.078 & 0.938109 & 0.469054 \tabularnewline
M6 & 0.0599839230344053 & 0.850947 & 0.0705 & 0.944084 & 0.472042 \tabularnewline
M7 & 0.21231296050147 & 0.899826 & 0.2359 & 0.814436 & 0.407218 \tabularnewline
M8 & 0.870779207535017 & 0.886825 & 0.9819 & 0.330874 & 0.165437 \tabularnewline
M9 & 0.429799214978465 & 0.905179 & 0.4748 & 0.636981 & 0.31849 \tabularnewline
M10 & 2.35819701640652 & 0.877821 & 2.6864 & 0.009778 & 0.004889 \tabularnewline
M11 & 0.84752047137127 & 0.930227 & 0.9111 & 0.36662 & 0.18331 \tabularnewline
t & -0.027329651821742 & 0.018785 & -1.4549 & 0.151948 & 0.075974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&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]8.0159995930798[/C][C]3.519316[/C][C]2.2777[/C][C]0.027053[/C][C]0.013527[/C][/ROW]
[ROW][C]X[/C][C]-0.396793923176626[/C][C]0.640905[/C][C]-0.6191[/C][C]0.538651[/C][C]0.269326[/C][/ROW]
[ROW][C]Y1[/C][C]0.429040951968163[/C][C]0.142421[/C][C]3.0125[/C][C]0.004058[/C][C]0.002029[/C][/ROW]
[ROW][C]Y2[/C][C]0.117021660229381[/C][C]0.150743[/C][C]0.7763[/C][C]0.44123[/C][C]0.220615[/C][/ROW]
[ROW][C]Y3[/C][C]0.0321909561234374[/C][C]0.152593[/C][C]0.211[/C][C]0.833776[/C][C]0.416888[/C][/ROW]
[ROW][C]Y4[/C][C]0.0338671313400513[/C][C]0.143065[/C][C]0.2367[/C][C]0.813837[/C][C]0.406918[/C][/ROW]
[ROW][C]M1[/C][C]0.522084696539833[/C][C]0.907111[/C][C]0.5755[/C][C]0.567503[/C][C]0.283752[/C][/ROW]
[ROW][C]M2[/C][C]1.60881904762785[/C][C]0.891956[/C][C]1.8037[/C][C]0.077305[/C][C]0.038652[/C][/ROW]
[ROW][C]M3[/C][C]0.429102182903779[/C][C]0.865743[/C][C]0.4956[/C][C]0.622316[/C][C]0.311158[/C][/ROW]
[ROW][C]M4[/C][C]1.21952238869631[/C][C]0.829226[/C][C]1.4707[/C][C]0.147644[/C][C]0.073822[/C][/ROW]
[ROW][C]M5[/C][C]-0.0690080080649838[/C][C]0.884278[/C][C]-0.078[/C][C]0.938109[/C][C]0.469054[/C][/ROW]
[ROW][C]M6[/C][C]0.0599839230344053[/C][C]0.850947[/C][C]0.0705[/C][C]0.944084[/C][C]0.472042[/C][/ROW]
[ROW][C]M7[/C][C]0.21231296050147[/C][C]0.899826[/C][C]0.2359[/C][C]0.814436[/C][C]0.407218[/C][/ROW]
[ROW][C]M8[/C][C]0.870779207535017[/C][C]0.886825[/C][C]0.9819[/C][C]0.330874[/C][C]0.165437[/C][/ROW]
[ROW][C]M9[/C][C]0.429799214978465[/C][C]0.905179[/C][C]0.4748[/C][C]0.636981[/C][C]0.31849[/C][/ROW]
[ROW][C]M10[/C][C]2.35819701640652[/C][C]0.877821[/C][C]2.6864[/C][C]0.009778[/C][C]0.004889[/C][/ROW]
[ROW][C]M11[/C][C]0.84752047137127[/C][C]0.930227[/C][C]0.9111[/C][C]0.36662[/C][C]0.18331[/C][/ROW]
[ROW][C]t[/C][C]-0.027329651821742[/C][C]0.018785[/C][C]-1.4549[/C][C]0.151948[/C][C]0.075974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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)8.01599959307983.5193162.27770.0270530.013527
X-0.3967939231766260.640905-0.61910.5386510.269326
Y10.4290409519681630.1424213.01250.0040580.002029
Y20.1170216602293810.1507430.77630.441230.220615
Y30.03219095612343740.1525930.2110.8337760.416888
Y40.03386713134005130.1430650.23670.8138370.406918
M10.5220846965398330.9071110.57550.5675030.283752
M21.608819047627850.8919561.80370.0773050.038652
M30.4291021829037790.8657430.49560.6223160.311158
M41.219522388696310.8292261.47070.1476440.073822
M5-0.06900800806498380.884278-0.0780.9381090.469054
M60.05998392303440530.8509470.07050.9440840.472042
M70.212312960501470.8998260.23590.8144360.407218
M80.8707792075350170.8868250.98190.3308740.165437
M90.4297992149784650.9051790.47480.6369810.31849
M102.358197016406520.8778212.68640.0097780.004889
M110.847520471371270.9302270.91110.366620.18331
t-0.0273296518217420.018785-1.45490.1519480.075974







Multiple Linear Regression - Regression Statistics
Multiple R0.86441380380356
R-squared0.747211224206141
Adjusted R-squared0.661263040436229
F-TEST (value)8.69374071017563
F-TEST (DF numerator)17
F-TEST (DF denominator)50
p-value1.00810626513237e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.35086548652037
Sum Squared Residuals91.2418781335953

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.86441380380356 \tabularnewline
R-squared & 0.747211224206141 \tabularnewline
Adjusted R-squared & 0.661263040436229 \tabularnewline
F-TEST (value) & 8.69374071017563 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 1.00810626513237e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.35086548652037 \tabularnewline
Sum Squared Residuals & 91.2418781335953 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.86441380380356[/C][/ROW]
[ROW][C]R-squared[/C][C]0.747211224206141[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.661263040436229[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.69374071017563[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/C][/ROW]
[ROW][C]p-value[/C][C]1.00810626513237e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.35086548652037[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]91.2418781335953[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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 R0.86441380380356
R-squared0.747211224206141
Adjusted R-squared0.661263040436229
F-TEST (value)8.69374071017563
F-TEST (DF numerator)17
F-TEST (DF denominator)50
p-value1.00810626513237e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.35086548652037
Sum Squared Residuals91.2418781335953







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12021.3143257579137-1.31432575791368
22121.9973292531943-0.997329253194314
32121.0667587218306-0.0667587218305936
42121.9485471112474-0.948547111247382
51920.6310108874477-1.63101088744773
62119.90845839412911.09154160587090
72120.6574963632520.342503636748009
82221.45829436667570.541705633324316
91921.4156733238323-2.41567332383232
102422.21437454044361.78562545955636
112222.5026990788628-0.502699078862749
122221.29216961585010.707830384149943
132221.61223472670640.387765273293582
142422.77659317042611.22340682957392
152222.3598942951365-0.359894295136486
162322.49894626562970.501053734370291
172421.44246576080302.55753423919705
182122.0935430027114-1.09354300271137
192021.0128978861249-1.01289788612492
202220.92998663614391.07001336385609
212321.14003149844231.85996850155770
222323.5703915703319-0.570391570331944
232222.1799218146112-0.179921814611156
242020.9759559582535-0.97595595825352
252120.13268064696930.867319353030671
262121.3548920216216-0.35489202162157
272020.1666181217182-0.166618121718209
282020.4651244171642-0.465124417164170
291719.0661098396918-2.06610983969180
301817.84845830694150.151541693058476
311918.01756653252680.982433467473184
321919.0981928715659-0.098192871565853
332018.67749444952021.32250555047978
342121.0736616385582-0.0736616385581894
352020.1155851852388-0.115585185238791
362118.96090672643042.03909327356957
371919.8337391503508-0.833739150350797
382220.15375978112671.84624021887326
392019.99811662481000.00188337518995838
401820.2236754746258-2.22367547462582
411617.8445288073379-1.84452880733791
421716.89128534399370.108714656006254
431817.07916618622150.920833813778507
441918.12424921870390.875750781296134
451818.1664588799664-0.166458879966450
462019.82156582529750.17843417470253
472119.09067795961091.90932204038909
481818.8805882840614-0.880588284061438
491918.23575691401120.764243085988755
501919.4730628033611-0.473062803361084
511918.32033221001440.679667789985612
522119.01401232608851.98598767391154
531918.59010131278180.409898687218198
541918.06772500858190.932274991418116
551718.0230629860153-1.02306298601532
561617.7994700277240-1.79947002772403
571616.6003418482387-0.600341848238704
581718.3200064253688-1.32000642536876
591617.1111159616764-1.11111596167639
601515.8903794154045-0.890379415404548
611615.87126280404850.128737195951467
621617.2443629702702-1.24436297027021
631616.0882800264903-0.0882800264902834
641816.84969440524451.15030559475554
651916.42578339193782.5742166080622
661617.1905299436424-1.19052994364237
671616.2098100458595-0.209810045859461
681616.5898068791867-0.589806879186663

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20 & 21.3143257579137 & -1.31432575791368 \tabularnewline
2 & 21 & 21.9973292531943 & -0.997329253194314 \tabularnewline
3 & 21 & 21.0667587218306 & -0.0667587218305936 \tabularnewline
4 & 21 & 21.9485471112474 & -0.948547111247382 \tabularnewline
5 & 19 & 20.6310108874477 & -1.63101088744773 \tabularnewline
6 & 21 & 19.9084583941291 & 1.09154160587090 \tabularnewline
7 & 21 & 20.657496363252 & 0.342503636748009 \tabularnewline
8 & 22 & 21.4582943666757 & 0.541705633324316 \tabularnewline
9 & 19 & 21.4156733238323 & -2.41567332383232 \tabularnewline
10 & 24 & 22.2143745404436 & 1.78562545955636 \tabularnewline
11 & 22 & 22.5026990788628 & -0.502699078862749 \tabularnewline
12 & 22 & 21.2921696158501 & 0.707830384149943 \tabularnewline
13 & 22 & 21.6122347267064 & 0.387765273293582 \tabularnewline
14 & 24 & 22.7765931704261 & 1.22340682957392 \tabularnewline
15 & 22 & 22.3598942951365 & -0.359894295136486 \tabularnewline
16 & 23 & 22.4989462656297 & 0.501053734370291 \tabularnewline
17 & 24 & 21.4424657608030 & 2.55753423919705 \tabularnewline
18 & 21 & 22.0935430027114 & -1.09354300271137 \tabularnewline
19 & 20 & 21.0128978861249 & -1.01289788612492 \tabularnewline
20 & 22 & 20.9299866361439 & 1.07001336385609 \tabularnewline
21 & 23 & 21.1400314984423 & 1.85996850155770 \tabularnewline
22 & 23 & 23.5703915703319 & -0.570391570331944 \tabularnewline
23 & 22 & 22.1799218146112 & -0.179921814611156 \tabularnewline
24 & 20 & 20.9759559582535 & -0.97595595825352 \tabularnewline
25 & 21 & 20.1326806469693 & 0.867319353030671 \tabularnewline
26 & 21 & 21.3548920216216 & -0.35489202162157 \tabularnewline
27 & 20 & 20.1666181217182 & -0.166618121718209 \tabularnewline
28 & 20 & 20.4651244171642 & -0.465124417164170 \tabularnewline
29 & 17 & 19.0661098396918 & -2.06610983969180 \tabularnewline
30 & 18 & 17.8484583069415 & 0.151541693058476 \tabularnewline
31 & 19 & 18.0175665325268 & 0.982433467473184 \tabularnewline
32 & 19 & 19.0981928715659 & -0.098192871565853 \tabularnewline
33 & 20 & 18.6774944495202 & 1.32250555047978 \tabularnewline
34 & 21 & 21.0736616385582 & -0.0736616385581894 \tabularnewline
35 & 20 & 20.1155851852388 & -0.115585185238791 \tabularnewline
36 & 21 & 18.9609067264304 & 2.03909327356957 \tabularnewline
37 & 19 & 19.8337391503508 & -0.833739150350797 \tabularnewline
38 & 22 & 20.1537597811267 & 1.84624021887326 \tabularnewline
39 & 20 & 19.9981166248100 & 0.00188337518995838 \tabularnewline
40 & 18 & 20.2236754746258 & -2.22367547462582 \tabularnewline
41 & 16 & 17.8445288073379 & -1.84452880733791 \tabularnewline
42 & 17 & 16.8912853439937 & 0.108714656006254 \tabularnewline
43 & 18 & 17.0791661862215 & 0.920833813778507 \tabularnewline
44 & 19 & 18.1242492187039 & 0.875750781296134 \tabularnewline
45 & 18 & 18.1664588799664 & -0.166458879966450 \tabularnewline
46 & 20 & 19.8215658252975 & 0.17843417470253 \tabularnewline
47 & 21 & 19.0906779596109 & 1.90932204038909 \tabularnewline
48 & 18 & 18.8805882840614 & -0.880588284061438 \tabularnewline
49 & 19 & 18.2357569140112 & 0.764243085988755 \tabularnewline
50 & 19 & 19.4730628033611 & -0.473062803361084 \tabularnewline
51 & 19 & 18.3203322100144 & 0.679667789985612 \tabularnewline
52 & 21 & 19.0140123260885 & 1.98598767391154 \tabularnewline
53 & 19 & 18.5901013127818 & 0.409898687218198 \tabularnewline
54 & 19 & 18.0677250085819 & 0.932274991418116 \tabularnewline
55 & 17 & 18.0230629860153 & -1.02306298601532 \tabularnewline
56 & 16 & 17.7994700277240 & -1.79947002772403 \tabularnewline
57 & 16 & 16.6003418482387 & -0.600341848238704 \tabularnewline
58 & 17 & 18.3200064253688 & -1.32000642536876 \tabularnewline
59 & 16 & 17.1111159616764 & -1.11111596167639 \tabularnewline
60 & 15 & 15.8903794154045 & -0.890379415404548 \tabularnewline
61 & 16 & 15.8712628040485 & 0.128737195951467 \tabularnewline
62 & 16 & 17.2443629702702 & -1.24436297027021 \tabularnewline
63 & 16 & 16.0882800264903 & -0.0882800264902834 \tabularnewline
64 & 18 & 16.8496944052445 & 1.15030559475554 \tabularnewline
65 & 19 & 16.4257833919378 & 2.5742166080622 \tabularnewline
66 & 16 & 17.1905299436424 & -1.19052994364237 \tabularnewline
67 & 16 & 16.2098100458595 & -0.209810045859461 \tabularnewline
68 & 16 & 16.5898068791867 & -0.589806879186663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&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]20[/C][C]21.3143257579137[/C][C]-1.31432575791368[/C][/ROW]
[ROW][C]2[/C][C]21[/C][C]21.9973292531943[/C][C]-0.997329253194314[/C][/ROW]
[ROW][C]3[/C][C]21[/C][C]21.0667587218306[/C][C]-0.0667587218305936[/C][/ROW]
[ROW][C]4[/C][C]21[/C][C]21.9485471112474[/C][C]-0.948547111247382[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]20.6310108874477[/C][C]-1.63101088744773[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]19.9084583941291[/C][C]1.09154160587090[/C][/ROW]
[ROW][C]7[/C][C]21[/C][C]20.657496363252[/C][C]0.342503636748009[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]21.4582943666757[/C][C]0.541705633324316[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.4156733238323[/C][C]-2.41567332383232[/C][/ROW]
[ROW][C]10[/C][C]24[/C][C]22.2143745404436[/C][C]1.78562545955636[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]22.5026990788628[/C][C]-0.502699078862749[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]21.2921696158501[/C][C]0.707830384149943[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]21.6122347267064[/C][C]0.387765273293582[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.7765931704261[/C][C]1.22340682957392[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]22.3598942951365[/C][C]-0.359894295136486[/C][/ROW]
[ROW][C]16[/C][C]23[/C][C]22.4989462656297[/C][C]0.501053734370291[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]21.4424657608030[/C][C]2.55753423919705[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]22.0935430027114[/C][C]-1.09354300271137[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]21.0128978861249[/C][C]-1.01289788612492[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]20.9299866361439[/C][C]1.07001336385609[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.1400314984423[/C][C]1.85996850155770[/C][/ROW]
[ROW][C]22[/C][C]23[/C][C]23.5703915703319[/C][C]-0.570391570331944[/C][/ROW]
[ROW][C]23[/C][C]22[/C][C]22.1799218146112[/C][C]-0.179921814611156[/C][/ROW]
[ROW][C]24[/C][C]20[/C][C]20.9759559582535[/C][C]-0.97595595825352[/C][/ROW]
[ROW][C]25[/C][C]21[/C][C]20.1326806469693[/C][C]0.867319353030671[/C][/ROW]
[ROW][C]26[/C][C]21[/C][C]21.3548920216216[/C][C]-0.35489202162157[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]20.1666181217182[/C][C]-0.166618121718209[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]20.4651244171642[/C][C]-0.465124417164170[/C][/ROW]
[ROW][C]29[/C][C]17[/C][C]19.0661098396918[/C][C]-2.06610983969180[/C][/ROW]
[ROW][C]30[/C][C]18[/C][C]17.8484583069415[/C][C]0.151541693058476[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]18.0175665325268[/C][C]0.982433467473184[/C][/ROW]
[ROW][C]32[/C][C]19[/C][C]19.0981928715659[/C][C]-0.098192871565853[/C][/ROW]
[ROW][C]33[/C][C]20[/C][C]18.6774944495202[/C][C]1.32250555047978[/C][/ROW]
[ROW][C]34[/C][C]21[/C][C]21.0736616385582[/C][C]-0.0736616385581894[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]20.1155851852388[/C][C]-0.115585185238791[/C][/ROW]
[ROW][C]36[/C][C]21[/C][C]18.9609067264304[/C][C]2.03909327356957[/C][/ROW]
[ROW][C]37[/C][C]19[/C][C]19.8337391503508[/C][C]-0.833739150350797[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]20.1537597811267[/C][C]1.84624021887326[/C][/ROW]
[ROW][C]39[/C][C]20[/C][C]19.9981166248100[/C][C]0.00188337518995838[/C][/ROW]
[ROW][C]40[/C][C]18[/C][C]20.2236754746258[/C][C]-2.22367547462582[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]17.8445288073379[/C][C]-1.84452880733791[/C][/ROW]
[ROW][C]42[/C][C]17[/C][C]16.8912853439937[/C][C]0.108714656006254[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.0791661862215[/C][C]0.920833813778507[/C][/ROW]
[ROW][C]44[/C][C]19[/C][C]18.1242492187039[/C][C]0.875750781296134[/C][/ROW]
[ROW][C]45[/C][C]18[/C][C]18.1664588799664[/C][C]-0.166458879966450[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]19.8215658252975[/C][C]0.17843417470253[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]19.0906779596109[/C][C]1.90932204038909[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]18.8805882840614[/C][C]-0.880588284061438[/C][/ROW]
[ROW][C]49[/C][C]19[/C][C]18.2357569140112[/C][C]0.764243085988755[/C][/ROW]
[ROW][C]50[/C][C]19[/C][C]19.4730628033611[/C][C]-0.473062803361084[/C][/ROW]
[ROW][C]51[/C][C]19[/C][C]18.3203322100144[/C][C]0.679667789985612[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]19.0140123260885[/C][C]1.98598767391154[/C][/ROW]
[ROW][C]53[/C][C]19[/C][C]18.5901013127818[/C][C]0.409898687218198[/C][/ROW]
[ROW][C]54[/C][C]19[/C][C]18.0677250085819[/C][C]0.932274991418116[/C][/ROW]
[ROW][C]55[/C][C]17[/C][C]18.0230629860153[/C][C]-1.02306298601532[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]17.7994700277240[/C][C]-1.79947002772403[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.6003418482387[/C][C]-0.600341848238704[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]18.3200064253688[/C][C]-1.32000642536876[/C][/ROW]
[ROW][C]59[/C][C]16[/C][C]17.1111159616764[/C][C]-1.11111596167639[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.8903794154045[/C][C]-0.890379415404548[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]15.8712628040485[/C][C]0.128737195951467[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]17.2443629702702[/C][C]-1.24436297027021[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.0882800264903[/C][C]-0.0882800264902834[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]16.8496944052445[/C][C]1.15030559475554[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]16.4257833919378[/C][C]2.5742166080622[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]17.1905299436424[/C][C]-1.19052994364237[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]16.2098100458595[/C][C]-0.209810045859461[/C][/ROW]
[ROW][C]68[/C][C]16[/C][C]16.5898068791867[/C][C]-0.589806879186663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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
12021.3143257579137-1.31432575791368
22121.9973292531943-0.997329253194314
32121.0667587218306-0.0667587218305936
42121.9485471112474-0.948547111247382
51920.6310108874477-1.63101088744773
62119.90845839412911.09154160587090
72120.6574963632520.342503636748009
82221.45829436667570.541705633324316
91921.4156733238323-2.41567332383232
102422.21437454044361.78562545955636
112222.5026990788628-0.502699078862749
122221.29216961585010.707830384149943
132221.61223472670640.387765273293582
142422.77659317042611.22340682957392
152222.3598942951365-0.359894295136486
162322.49894626562970.501053734370291
172421.44246576080302.55753423919705
182122.0935430027114-1.09354300271137
192021.0128978861249-1.01289788612492
202220.92998663614391.07001336385609
212321.14003149844231.85996850155770
222323.5703915703319-0.570391570331944
232222.1799218146112-0.179921814611156
242020.9759559582535-0.97595595825352
252120.13268064696930.867319353030671
262121.3548920216216-0.35489202162157
272020.1666181217182-0.166618121718209
282020.4651244171642-0.465124417164170
291719.0661098396918-2.06610983969180
301817.84845830694150.151541693058476
311918.01756653252680.982433467473184
321919.0981928715659-0.098192871565853
332018.67749444952021.32250555047978
342121.0736616385582-0.0736616385581894
352020.1155851852388-0.115585185238791
362118.96090672643042.03909327356957
371919.8337391503508-0.833739150350797
382220.15375978112671.84624021887326
392019.99811662481000.00188337518995838
401820.2236754746258-2.22367547462582
411617.8445288073379-1.84452880733791
421716.89128534399370.108714656006254
431817.07916618622150.920833813778507
441918.12424921870390.875750781296134
451818.1664588799664-0.166458879966450
462019.82156582529750.17843417470253
472119.09067795961091.90932204038909
481818.8805882840614-0.880588284061438
491918.23575691401120.764243085988755
501919.4730628033611-0.473062803361084
511918.32033221001440.679667789985612
522119.01401232608851.98598767391154
531918.59010131278180.409898687218198
541918.06772500858190.932274991418116
551718.0230629860153-1.02306298601532
561617.7994700277240-1.79947002772403
571616.6003418482387-0.600341848238704
581718.3200064253688-1.32000642536876
591617.1111159616764-1.11111596167639
601515.8903794154045-0.890379415404548
611615.87126280404850.128737195951467
621617.2443629702702-1.24436297027021
631616.0882800264903-0.0882800264902834
641816.84969440524451.15030559475554
651916.42578339193782.5742166080622
661617.1905299436424-1.19052994364237
671616.2098100458595-0.209810045859461
681616.5898068791867-0.589806879186663







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.836013165368370.3279736692632610.163986834631631
220.9018511866828810.1962976266342370.0981488133171186
230.8262189099143640.3475621801712720.173781090085636
240.8806994036101140.2386011927797720.119300596389886
250.8219181003021040.3561637993957910.178081899697896
260.7363750714062290.5272498571875420.263624928593771
270.6439590490794580.7120819018410830.356040950920542
280.5472784527900110.9054430944199780.452721547209989
290.6556197668427330.6887604663145340.344380233157267
300.5559325867492740.8881348265014520.444067413250726
310.4888564184109220.9777128368218430.511143581589078
320.3921391686918860.7842783373837730.607860831308114
330.3942775993068610.7885551986137220.605722400693139
340.3012689863276370.6025379726552740.698731013672363
350.2280811441842660.4561622883685320.771918855815734
360.2905000540011880.5810001080023760.709499945998812
370.2575957147906490.5151914295812980.742404285209351
380.3350717637846890.6701435275693770.664928236215311
390.2910725156420830.5821450312841650.708927484357917
400.4764222354010310.9528444708020630.523577764598969
410.8264605811516750.3470788376966500.173539418848325
420.8253328869515020.3493342260969950.174667113048498
430.7325282466361920.5349435067276150.267471753363808
440.6695705817784120.6608588364431750.330429418221588
450.5334836268952730.9330327462094530.466516373104727
460.3877856549165640.7755713098331270.612214345083437
470.5084061676163430.9831876647673140.491593832383657

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.83601316536837 & 0.327973669263261 & 0.163986834631631 \tabularnewline
22 & 0.901851186682881 & 0.196297626634237 & 0.0981488133171186 \tabularnewline
23 & 0.826218909914364 & 0.347562180171272 & 0.173781090085636 \tabularnewline
24 & 0.880699403610114 & 0.238601192779772 & 0.119300596389886 \tabularnewline
25 & 0.821918100302104 & 0.356163799395791 & 0.178081899697896 \tabularnewline
26 & 0.736375071406229 & 0.527249857187542 & 0.263624928593771 \tabularnewline
27 & 0.643959049079458 & 0.712081901841083 & 0.356040950920542 \tabularnewline
28 & 0.547278452790011 & 0.905443094419978 & 0.452721547209989 \tabularnewline
29 & 0.655619766842733 & 0.688760466314534 & 0.344380233157267 \tabularnewline
30 & 0.555932586749274 & 0.888134826501452 & 0.444067413250726 \tabularnewline
31 & 0.488856418410922 & 0.977712836821843 & 0.511143581589078 \tabularnewline
32 & 0.392139168691886 & 0.784278337383773 & 0.607860831308114 \tabularnewline
33 & 0.394277599306861 & 0.788555198613722 & 0.605722400693139 \tabularnewline
34 & 0.301268986327637 & 0.602537972655274 & 0.698731013672363 \tabularnewline
35 & 0.228081144184266 & 0.456162288368532 & 0.771918855815734 \tabularnewline
36 & 0.290500054001188 & 0.581000108002376 & 0.709499945998812 \tabularnewline
37 & 0.257595714790649 & 0.515191429581298 & 0.742404285209351 \tabularnewline
38 & 0.335071763784689 & 0.670143527569377 & 0.664928236215311 \tabularnewline
39 & 0.291072515642083 & 0.582145031284165 & 0.708927484357917 \tabularnewline
40 & 0.476422235401031 & 0.952844470802063 & 0.523577764598969 \tabularnewline
41 & 0.826460581151675 & 0.347078837696650 & 0.173539418848325 \tabularnewline
42 & 0.825332886951502 & 0.349334226096995 & 0.174667113048498 \tabularnewline
43 & 0.732528246636192 & 0.534943506727615 & 0.267471753363808 \tabularnewline
44 & 0.669570581778412 & 0.660858836443175 & 0.330429418221588 \tabularnewline
45 & 0.533483626895273 & 0.933032746209453 & 0.466516373104727 \tabularnewline
46 & 0.387785654916564 & 0.775571309833127 & 0.612214345083437 \tabularnewline
47 & 0.508406167616343 & 0.983187664767314 & 0.491593832383657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&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]21[/C][C]0.83601316536837[/C][C]0.327973669263261[/C][C]0.163986834631631[/C][/ROW]
[ROW][C]22[/C][C]0.901851186682881[/C][C]0.196297626634237[/C][C]0.0981488133171186[/C][/ROW]
[ROW][C]23[/C][C]0.826218909914364[/C][C]0.347562180171272[/C][C]0.173781090085636[/C][/ROW]
[ROW][C]24[/C][C]0.880699403610114[/C][C]0.238601192779772[/C][C]0.119300596389886[/C][/ROW]
[ROW][C]25[/C][C]0.821918100302104[/C][C]0.356163799395791[/C][C]0.178081899697896[/C][/ROW]
[ROW][C]26[/C][C]0.736375071406229[/C][C]0.527249857187542[/C][C]0.263624928593771[/C][/ROW]
[ROW][C]27[/C][C]0.643959049079458[/C][C]0.712081901841083[/C][C]0.356040950920542[/C][/ROW]
[ROW][C]28[/C][C]0.547278452790011[/C][C]0.905443094419978[/C][C]0.452721547209989[/C][/ROW]
[ROW][C]29[/C][C]0.655619766842733[/C][C]0.688760466314534[/C][C]0.344380233157267[/C][/ROW]
[ROW][C]30[/C][C]0.555932586749274[/C][C]0.888134826501452[/C][C]0.444067413250726[/C][/ROW]
[ROW][C]31[/C][C]0.488856418410922[/C][C]0.977712836821843[/C][C]0.511143581589078[/C][/ROW]
[ROW][C]32[/C][C]0.392139168691886[/C][C]0.784278337383773[/C][C]0.607860831308114[/C][/ROW]
[ROW][C]33[/C][C]0.394277599306861[/C][C]0.788555198613722[/C][C]0.605722400693139[/C][/ROW]
[ROW][C]34[/C][C]0.301268986327637[/C][C]0.602537972655274[/C][C]0.698731013672363[/C][/ROW]
[ROW][C]35[/C][C]0.228081144184266[/C][C]0.456162288368532[/C][C]0.771918855815734[/C][/ROW]
[ROW][C]36[/C][C]0.290500054001188[/C][C]0.581000108002376[/C][C]0.709499945998812[/C][/ROW]
[ROW][C]37[/C][C]0.257595714790649[/C][C]0.515191429581298[/C][C]0.742404285209351[/C][/ROW]
[ROW][C]38[/C][C]0.335071763784689[/C][C]0.670143527569377[/C][C]0.664928236215311[/C][/ROW]
[ROW][C]39[/C][C]0.291072515642083[/C][C]0.582145031284165[/C][C]0.708927484357917[/C][/ROW]
[ROW][C]40[/C][C]0.476422235401031[/C][C]0.952844470802063[/C][C]0.523577764598969[/C][/ROW]
[ROW][C]41[/C][C]0.826460581151675[/C][C]0.347078837696650[/C][C]0.173539418848325[/C][/ROW]
[ROW][C]42[/C][C]0.825332886951502[/C][C]0.349334226096995[/C][C]0.174667113048498[/C][/ROW]
[ROW][C]43[/C][C]0.732528246636192[/C][C]0.534943506727615[/C][C]0.267471753363808[/C][/ROW]
[ROW][C]44[/C][C]0.669570581778412[/C][C]0.660858836443175[/C][C]0.330429418221588[/C][/ROW]
[ROW][C]45[/C][C]0.533483626895273[/C][C]0.933032746209453[/C][C]0.466516373104727[/C][/ROW]
[ROW][C]46[/C][C]0.387785654916564[/C][C]0.775571309833127[/C][C]0.612214345083437[/C][/ROW]
[ROW][C]47[/C][C]0.508406167616343[/C][C]0.983187664767314[/C][C]0.491593832383657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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
210.836013165368370.3279736692632610.163986834631631
220.9018511866828810.1962976266342370.0981488133171186
230.8262189099143640.3475621801712720.173781090085636
240.8806994036101140.2386011927797720.119300596389886
250.8219181003021040.3561637993957910.178081899697896
260.7363750714062290.5272498571875420.263624928593771
270.6439590490794580.7120819018410830.356040950920542
280.5472784527900110.9054430944199780.452721547209989
290.6556197668427330.6887604663145340.344380233157267
300.5559325867492740.8881348265014520.444067413250726
310.4888564184109220.9777128368218430.511143581589078
320.3921391686918860.7842783373837730.607860831308114
330.3942775993068610.7885551986137220.605722400693139
340.3012689863276370.6025379726552740.698731013672363
350.2280811441842660.4561622883685320.771918855815734
360.2905000540011880.5810001080023760.709499945998812
370.2575957147906490.5151914295812980.742404285209351
380.3350717637846890.6701435275693770.664928236215311
390.2910725156420830.5821450312841650.708927484357917
400.4764222354010310.9528444708020630.523577764598969
410.8264605811516750.3470788376966500.173539418848325
420.8253328869515020.3493342260969950.174667113048498
430.7325282466361920.5349435067276150.267471753363808
440.6695705817784120.6608588364431750.330429418221588
450.5334836268952730.9330327462094530.466516373104727
460.3877856549165640.7755713098331270.612214345083437
470.5084061676163430.9831876647673140.491593832383657







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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58227&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58227&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58227&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 level00OK
10% type I error level00OK



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')
}