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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 13:25:41 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t13560279848b2pv0vy2z6x13l.htm/, Retrieved Fri, 29 Mar 2024 10:31:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202992, Retrieved Fri, 29 Mar 2024 10:31:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple Regressi...] [2009-12-15 19:28:15] [1eab65e90adf64584b8e6f0da23ff414]
- R           [Multiple Regression] [multiple regressi...] [2012-12-20 18:25:41] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
105.67	96.90	96.33
123.61	95.10	96.33
113.08	97.00	95.05
106.46	112.70	96.84
123.38	102.90	96.92
109.87	97.40	97.44
95.74	111.40	97.78
123.06	87.40	97.69
123.39	96.80	96.67
120.28	114.10	98.29
115.33	110.30	98.20
110.40	103.90	98.71
114.49	101.60	98.54
132.03	94.60	98.20
123.16	95.90	100.80
118.82	104.70	101.33
128.32	102.80	101.88
112.24	98.10	101.85
104.53	113.90	102.04
132.57	80.90	102.22
122.52	95.70	102.63
131.80	113.20	102.65
124.55	105.90	102.54
120.96	108.80	102.37
122.60	102.30	102.68
145.52	99.00	102.76
118.57	100.70	102.82
134.25	115.50	103.31
136.70	100.70	103.23
121.37	109.90	103.60
111.63	114.60	103.95
134.42	85.40	103.93
137.65	100.50	104.25
137.86	114.80	104.38
119.77	116.50	104.36
130.69	112.90	104.32
128.28	102.00	104.58
147.45	106.00	104.68
128.42	105.30	104.92
136.90	118.80	105.46
143.95	106.10	105.23
135.64	109.30	105.58
122.48	117.20	105.34
136.83	92.50	105.28
153.04	104.20	105.70
142.71	112.50	105.67
123.46	122.40	105.71
144.37	113.30	106.19
146.15	100.00	106.93
147.61	110.70	107.44
158.51	112.80	107.85
147.40	109.80	108.71
165.05	117.30	109.32
154.64	109.10	109.49
126.20	115.90	110.20
157.36	96.00	110.62
154.15	99.80	111.22
123.21	116.80	110.88
113.07	115.70	111.15
110.45	99.40	111.29
113.57	94.30	111.09
122.44	91.00	111.24
114.93	93.20	111.45
111.85	103.10	111.75
126.04	94.10	111.07
121.34	91.80	111.17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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 time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Uitvoer[t] = -41.763558353025 + 1.39214975483713TIP[t] + 0.0611793827340806index[t] + 11.0612384622746M1[t] + 25.6224237457094M2[t] + 13.0495707174011M3[t] -1.25943975198516M4[t] + 19.2286263713111M5[t] + 9.50320871169308M6[t] -19.6451676889559M7[t] + 41.2540745646M8[t] + 27.0430471556376M9[t] -0.913405329572837M10[t] -12.92958249395M11[t] + 0.246133906068339t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Uitvoer[t] =  -41.763558353025 +  1.39214975483713TIP[t] +  0.0611793827340806index[t] +  11.0612384622746M1[t] +  25.6224237457094M2[t] +  13.0495707174011M3[t] -1.25943975198516M4[t] +  19.2286263713111M5[t] +  9.50320871169308M6[t] -19.6451676889559M7[t] +  41.2540745646M8[t] +  27.0430471556376M9[t] -0.913405329572837M10[t] -12.92958249395M11[t] +  0.246133906068339t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Uitvoer[t] =  -41.763558353025 +  1.39214975483713TIP[t] +  0.0611793827340806index[t] +  11.0612384622746M1[t] +  25.6224237457094M2[t] +  13.0495707174011M3[t] -1.25943975198516M4[t] +  19.2286263713111M5[t] +  9.50320871169308M6[t] -19.6451676889559M7[t] +  41.2540745646M8[t] +  27.0430471556376M9[t] -0.913405329572837M10[t] -12.92958249395M11[t] +  0.246133906068339t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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
Uitvoer[t] = -41.763558353025 + 1.39214975483713TIP[t] + 0.0611793827340806index[t] + 11.0612384622746M1[t] + 25.6224237457094M2[t] + 13.0495707174011M3[t] -1.25943975198516M4[t] + 19.2286263713111M5[t] + 9.50320871169308M6[t] -19.6451676889559M7[t] + 41.2540745646M8[t] + 27.0430471556376M9[t] -0.913405329572837M10[t] -12.92958249395M11[t] + 0.246133906068339t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-41.763558353025140.650485-0.29690.7677250.383862
TIP1.392149754837130.2214496.286600
index0.06117938273408061.3935560.04390.9651540.482577
M111.06123846227465.8933951.87690.066260.03313
M225.62242374570945.9207314.32767e-053.5e-05
M313.04957071740115.8168382.24340.0292420.014621
M4-1.259439751985165.719368-0.22020.826590.413295
M519.22862637131115.6770393.38710.0013680.000684
M69.503208711693085.7260451.65960.1031230.051562
M7-19.64516768895596.162256-3.1880.0024480.001224
M841.25407456467.1847155.74191e-060
M927.04304715563766.1278124.41325.3e-052.6e-05
M10-0.9134053295728376.087566-0.150.8813210.440661
M11-12.929582493956.049551-2.13730.0373920.018696
t0.2461339060683390.341750.72020.474680.23734

\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) & -41.763558353025 & 140.650485 & -0.2969 & 0.767725 & 0.383862 \tabularnewline
TIP & 1.39214975483713 & 0.221449 & 6.2866 & 0 & 0 \tabularnewline
index & 0.0611793827340806 & 1.393556 & 0.0439 & 0.965154 & 0.482577 \tabularnewline
M1 & 11.0612384622746 & 5.893395 & 1.8769 & 0.06626 & 0.03313 \tabularnewline
M2 & 25.6224237457094 & 5.920731 & 4.3276 & 7e-05 & 3.5e-05 \tabularnewline
M3 & 13.0495707174011 & 5.816838 & 2.2434 & 0.029242 & 0.014621 \tabularnewline
M4 & -1.25943975198516 & 5.719368 & -0.2202 & 0.82659 & 0.413295 \tabularnewline
M5 & 19.2286263713111 & 5.677039 & 3.3871 & 0.001368 & 0.000684 \tabularnewline
M6 & 9.50320871169308 & 5.726045 & 1.6596 & 0.103123 & 0.051562 \tabularnewline
M7 & -19.6451676889559 & 6.162256 & -3.188 & 0.002448 & 0.001224 \tabularnewline
M8 & 41.2540745646 & 7.184715 & 5.7419 & 1e-06 & 0 \tabularnewline
M9 & 27.0430471556376 & 6.127812 & 4.4132 & 5.3e-05 & 2.6e-05 \tabularnewline
M10 & -0.913405329572837 & 6.087566 & -0.15 & 0.881321 & 0.440661 \tabularnewline
M11 & -12.92958249395 & 6.049551 & -2.1373 & 0.037392 & 0.018696 \tabularnewline
t & 0.246133906068339 & 0.34175 & 0.7202 & 0.47468 & 0.23734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&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]-41.763558353025[/C][C]140.650485[/C][C]-0.2969[/C][C]0.767725[/C][C]0.383862[/C][/ROW]
[ROW][C]TIP[/C][C]1.39214975483713[/C][C]0.221449[/C][C]6.2866[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]index[/C][C]0.0611793827340806[/C][C]1.393556[/C][C]0.0439[/C][C]0.965154[/C][C]0.482577[/C][/ROW]
[ROW][C]M1[/C][C]11.0612384622746[/C][C]5.893395[/C][C]1.8769[/C][C]0.06626[/C][C]0.03313[/C][/ROW]
[ROW][C]M2[/C][C]25.6224237457094[/C][C]5.920731[/C][C]4.3276[/C][C]7e-05[/C][C]3.5e-05[/C][/ROW]
[ROW][C]M3[/C][C]13.0495707174011[/C][C]5.816838[/C][C]2.2434[/C][C]0.029242[/C][C]0.014621[/C][/ROW]
[ROW][C]M4[/C][C]-1.25943975198516[/C][C]5.719368[/C][C]-0.2202[/C][C]0.82659[/C][C]0.413295[/C][/ROW]
[ROW][C]M5[/C][C]19.2286263713111[/C][C]5.677039[/C][C]3.3871[/C][C]0.001368[/C][C]0.000684[/C][/ROW]
[ROW][C]M6[/C][C]9.50320871169308[/C][C]5.726045[/C][C]1.6596[/C][C]0.103123[/C][C]0.051562[/C][/ROW]
[ROW][C]M7[/C][C]-19.6451676889559[/C][C]6.162256[/C][C]-3.188[/C][C]0.002448[/C][C]0.001224[/C][/ROW]
[ROW][C]M8[/C][C]41.2540745646[/C][C]7.184715[/C][C]5.7419[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]27.0430471556376[/C][C]6.127812[/C][C]4.4132[/C][C]5.3e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]M10[/C][C]-0.913405329572837[/C][C]6.087566[/C][C]-0.15[/C][C]0.881321[/C][C]0.440661[/C][/ROW]
[ROW][C]M11[/C][C]-12.92958249395[/C][C]6.049551[/C][C]-2.1373[/C][C]0.037392[/C][C]0.018696[/C][/ROW]
[ROW][C]t[/C][C]0.246133906068339[/C][C]0.34175[/C][C]0.7202[/C][C]0.47468[/C][C]0.23734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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)-41.763558353025140.650485-0.29690.7677250.383862
TIP1.392149754837130.2214496.286600
index0.06117938273408061.3935560.04390.9651540.482577
M111.06123846227465.8933951.87690.066260.03313
M225.62242374570945.9207314.32767e-053.5e-05
M313.04957071740115.8168382.24340.0292420.014621
M4-1.259439751985165.719368-0.22020.826590.413295
M519.22862637131115.6770393.38710.0013680.000684
M69.503208711693085.7260451.65960.1031230.051562
M7-19.64516768895596.162256-3.1880.0024480.001224
M841.25407456467.1847155.74191e-060
M927.04304715563766.1278124.41325.3e-052.6e-05
M10-0.9134053295728376.087566-0.150.8813210.440661
M11-12.929582493956.049551-2.13730.0373920.018696
t0.2461339060683390.341750.72020.474680.23734







Multiple Linear Regression - Regression Statistics
Multiple R0.834961366215876
R-squared0.697160483073082
Adjusted R-squared0.614028066661771
F-TEST (value)8.38614481772993
F-TEST (DF numerator)14
F-TEST (DF denominator)51
p-value6.12966200019827e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.2784167987223
Sum Squared Residuals4390.53993283142

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.834961366215876 \tabularnewline
R-squared & 0.697160483073082 \tabularnewline
Adjusted R-squared & 0.614028066661771 \tabularnewline
F-TEST (value) & 8.38614481772993 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 6.12966200019827e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.2784167987223 \tabularnewline
Sum Squared Residuals & 4390.53993283142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.834961366215876[/C][/ROW]
[ROW][C]R-squared[/C][C]0.697160483073082[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.614028066661771[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.38614481772993[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]6.12966200019827e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.2784167987223[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4390.53993283142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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 R0.834961366215876
R-squared0.697160483073082
Adjusted R-squared0.614028066661771
F-TEST (value)8.38614481772993
F-TEST (DF numerator)14
F-TEST (DF denominator)51
p-value6.12966200019827e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.2784167987223
Sum Squared Residuals4390.53993283142







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1105.67110.33653519781-4.66653519781005
2123.61122.6379848286060.972015171393728
3113.08112.8780406306570.201959369342775
4106.46120.781426313376-14.3214263133763
5123.38127.877453095956-4.49745309595575
6109.87110.773158969824-0.903158969823526
795.74101.381814033092-5.64181403309237
8123.06129.110089932179-6.05008993217937
9123.39128.169001154365-4.77900115436549
10120.28124.641983933935-4.36198393393502
11115.33107.5762654627997.75373453720091
12110.4111.873424917054-1.47342491705412
13114.49119.968452354207-5.47845235420686
14132.03125.0099222697217.02007773027949
15123.16114.6520642238778.50793577612259
16118.82112.8725305759755.94746942402467
17128.32130.995294731653-2.67529473165314
18112.24114.971071748887-2.73107174888689
19104.53108.076419463452-3.54641946345245
20132.57123.2918660023439.27813399765652
21122.52129.95587241796-7.43587241795985
22131.8126.6093981361225.19060186387773
23124.55104.66993193540219.8800680645983
24120.96121.872482129383-0.912482129382874
25122.6124.149846699932-1.54984669993201
26145.52134.36796604909111.1520339509086
27118.57124.411572273039-5.84157227303856
28134.25130.982489978853.26751002115011
29136.7131.1079792860065.59202071399374
30121.37134.45910964857-13.0891096485698
31111.63112.121383785681-0.491383785680592
32134.42132.6147635164061.80523648359408
33137.65139.690908714027-2.04090871402736
34137.86131.8962849488125.96371505118829
35119.77122.491672686071-2.7216726860714
36130.69130.6532027933670.0367972066333477
37128.28126.8020494734961.4779505265043
38147.45147.1840856206210.265914379379175
39128.42133.897544721851-5.47754472185101
40136.9138.661726715511-1.76172671551076
41143.95141.7015536004152.24844639958501
42135.64136.698561846301-1.05856184630103
43122.48118.7796193630783.70038063692242
44136.83145.535225815261-8.7052258152606
45153.04147.8841797847095.15582021529075
46142.71131.72686878923310.9831312107667
47123.46133.741555279122-10.2815552791216
48144.37134.27807501383410.0919249861657
49146.15127.11512838606719.0348716139334
50147.61156.849651437521-9.23965143752146
51158.51147.4715303473611.0384696526396
52147.4129.28481878868218.1151812113176
53165.05160.4974614027934.5525385972067
54154.64139.61295015464415.0270498453561
55126.2120.2207633546975.97923664530298
56157.36153.6880547338113.67194526618937
57154.15145.0500379289389.09996207106195
58123.21140.985464191898-17.7754641918977
59113.07127.700574636606-14.6305746366062
60110.45118.192815146362-7.74281514636205
61113.57122.387987888489-8.8179878884888
62122.44132.61038979444-10.1703897944396
63114.93123.359247803215-8.42924780321539
64111.85123.097007627605-11.2470076276053
65126.04131.260257883177-5.22025788317656
66121.34118.5851476317752.75485236822513

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.67 & 110.33653519781 & -4.66653519781005 \tabularnewline
2 & 123.61 & 122.637984828606 & 0.972015171393728 \tabularnewline
3 & 113.08 & 112.878040630657 & 0.201959369342775 \tabularnewline
4 & 106.46 & 120.781426313376 & -14.3214263133763 \tabularnewline
5 & 123.38 & 127.877453095956 & -4.49745309595575 \tabularnewline
6 & 109.87 & 110.773158969824 & -0.903158969823526 \tabularnewline
7 & 95.74 & 101.381814033092 & -5.64181403309237 \tabularnewline
8 & 123.06 & 129.110089932179 & -6.05008993217937 \tabularnewline
9 & 123.39 & 128.169001154365 & -4.77900115436549 \tabularnewline
10 & 120.28 & 124.641983933935 & -4.36198393393502 \tabularnewline
11 & 115.33 & 107.576265462799 & 7.75373453720091 \tabularnewline
12 & 110.4 & 111.873424917054 & -1.47342491705412 \tabularnewline
13 & 114.49 & 119.968452354207 & -5.47845235420686 \tabularnewline
14 & 132.03 & 125.009922269721 & 7.02007773027949 \tabularnewline
15 & 123.16 & 114.652064223877 & 8.50793577612259 \tabularnewline
16 & 118.82 & 112.872530575975 & 5.94746942402467 \tabularnewline
17 & 128.32 & 130.995294731653 & -2.67529473165314 \tabularnewline
18 & 112.24 & 114.971071748887 & -2.73107174888689 \tabularnewline
19 & 104.53 & 108.076419463452 & -3.54641946345245 \tabularnewline
20 & 132.57 & 123.291866002343 & 9.27813399765652 \tabularnewline
21 & 122.52 & 129.95587241796 & -7.43587241795985 \tabularnewline
22 & 131.8 & 126.609398136122 & 5.19060186387773 \tabularnewline
23 & 124.55 & 104.669931935402 & 19.8800680645983 \tabularnewline
24 & 120.96 & 121.872482129383 & -0.912482129382874 \tabularnewline
25 & 122.6 & 124.149846699932 & -1.54984669993201 \tabularnewline
26 & 145.52 & 134.367966049091 & 11.1520339509086 \tabularnewline
27 & 118.57 & 124.411572273039 & -5.84157227303856 \tabularnewline
28 & 134.25 & 130.98248997885 & 3.26751002115011 \tabularnewline
29 & 136.7 & 131.107979286006 & 5.59202071399374 \tabularnewline
30 & 121.37 & 134.45910964857 & -13.0891096485698 \tabularnewline
31 & 111.63 & 112.121383785681 & -0.491383785680592 \tabularnewline
32 & 134.42 & 132.614763516406 & 1.80523648359408 \tabularnewline
33 & 137.65 & 139.690908714027 & -2.04090871402736 \tabularnewline
34 & 137.86 & 131.896284948812 & 5.96371505118829 \tabularnewline
35 & 119.77 & 122.491672686071 & -2.7216726860714 \tabularnewline
36 & 130.69 & 130.653202793367 & 0.0367972066333477 \tabularnewline
37 & 128.28 & 126.802049473496 & 1.4779505265043 \tabularnewline
38 & 147.45 & 147.184085620621 & 0.265914379379175 \tabularnewline
39 & 128.42 & 133.897544721851 & -5.47754472185101 \tabularnewline
40 & 136.9 & 138.661726715511 & -1.76172671551076 \tabularnewline
41 & 143.95 & 141.701553600415 & 2.24844639958501 \tabularnewline
42 & 135.64 & 136.698561846301 & -1.05856184630103 \tabularnewline
43 & 122.48 & 118.779619363078 & 3.70038063692242 \tabularnewline
44 & 136.83 & 145.535225815261 & -8.7052258152606 \tabularnewline
45 & 153.04 & 147.884179784709 & 5.15582021529075 \tabularnewline
46 & 142.71 & 131.726868789233 & 10.9831312107667 \tabularnewline
47 & 123.46 & 133.741555279122 & -10.2815552791216 \tabularnewline
48 & 144.37 & 134.278075013834 & 10.0919249861657 \tabularnewline
49 & 146.15 & 127.115128386067 & 19.0348716139334 \tabularnewline
50 & 147.61 & 156.849651437521 & -9.23965143752146 \tabularnewline
51 & 158.51 & 147.47153034736 & 11.0384696526396 \tabularnewline
52 & 147.4 & 129.284818788682 & 18.1151812113176 \tabularnewline
53 & 165.05 & 160.497461402793 & 4.5525385972067 \tabularnewline
54 & 154.64 & 139.612950154644 & 15.0270498453561 \tabularnewline
55 & 126.2 & 120.220763354697 & 5.97923664530298 \tabularnewline
56 & 157.36 & 153.688054733811 & 3.67194526618937 \tabularnewline
57 & 154.15 & 145.050037928938 & 9.09996207106195 \tabularnewline
58 & 123.21 & 140.985464191898 & -17.7754641918977 \tabularnewline
59 & 113.07 & 127.700574636606 & -14.6305746366062 \tabularnewline
60 & 110.45 & 118.192815146362 & -7.74281514636205 \tabularnewline
61 & 113.57 & 122.387987888489 & -8.8179878884888 \tabularnewline
62 & 122.44 & 132.61038979444 & -10.1703897944396 \tabularnewline
63 & 114.93 & 123.359247803215 & -8.42924780321539 \tabularnewline
64 & 111.85 & 123.097007627605 & -11.2470076276053 \tabularnewline
65 & 126.04 & 131.260257883177 & -5.22025788317656 \tabularnewline
66 & 121.34 & 118.585147631775 & 2.75485236822513 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&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]105.67[/C][C]110.33653519781[/C][C]-4.66653519781005[/C][/ROW]
[ROW][C]2[/C][C]123.61[/C][C]122.637984828606[/C][C]0.972015171393728[/C][/ROW]
[ROW][C]3[/C][C]113.08[/C][C]112.878040630657[/C][C]0.201959369342775[/C][/ROW]
[ROW][C]4[/C][C]106.46[/C][C]120.781426313376[/C][C]-14.3214263133763[/C][/ROW]
[ROW][C]5[/C][C]123.38[/C][C]127.877453095956[/C][C]-4.49745309595575[/C][/ROW]
[ROW][C]6[/C][C]109.87[/C][C]110.773158969824[/C][C]-0.903158969823526[/C][/ROW]
[ROW][C]7[/C][C]95.74[/C][C]101.381814033092[/C][C]-5.64181403309237[/C][/ROW]
[ROW][C]8[/C][C]123.06[/C][C]129.110089932179[/C][C]-6.05008993217937[/C][/ROW]
[ROW][C]9[/C][C]123.39[/C][C]128.169001154365[/C][C]-4.77900115436549[/C][/ROW]
[ROW][C]10[/C][C]120.28[/C][C]124.641983933935[/C][C]-4.36198393393502[/C][/ROW]
[ROW][C]11[/C][C]115.33[/C][C]107.576265462799[/C][C]7.75373453720091[/C][/ROW]
[ROW][C]12[/C][C]110.4[/C][C]111.873424917054[/C][C]-1.47342491705412[/C][/ROW]
[ROW][C]13[/C][C]114.49[/C][C]119.968452354207[/C][C]-5.47845235420686[/C][/ROW]
[ROW][C]14[/C][C]132.03[/C][C]125.009922269721[/C][C]7.02007773027949[/C][/ROW]
[ROW][C]15[/C][C]123.16[/C][C]114.652064223877[/C][C]8.50793577612259[/C][/ROW]
[ROW][C]16[/C][C]118.82[/C][C]112.872530575975[/C][C]5.94746942402467[/C][/ROW]
[ROW][C]17[/C][C]128.32[/C][C]130.995294731653[/C][C]-2.67529473165314[/C][/ROW]
[ROW][C]18[/C][C]112.24[/C][C]114.971071748887[/C][C]-2.73107174888689[/C][/ROW]
[ROW][C]19[/C][C]104.53[/C][C]108.076419463452[/C][C]-3.54641946345245[/C][/ROW]
[ROW][C]20[/C][C]132.57[/C][C]123.291866002343[/C][C]9.27813399765652[/C][/ROW]
[ROW][C]21[/C][C]122.52[/C][C]129.95587241796[/C][C]-7.43587241795985[/C][/ROW]
[ROW][C]22[/C][C]131.8[/C][C]126.609398136122[/C][C]5.19060186387773[/C][/ROW]
[ROW][C]23[/C][C]124.55[/C][C]104.669931935402[/C][C]19.8800680645983[/C][/ROW]
[ROW][C]24[/C][C]120.96[/C][C]121.872482129383[/C][C]-0.912482129382874[/C][/ROW]
[ROW][C]25[/C][C]122.6[/C][C]124.149846699932[/C][C]-1.54984669993201[/C][/ROW]
[ROW][C]26[/C][C]145.52[/C][C]134.367966049091[/C][C]11.1520339509086[/C][/ROW]
[ROW][C]27[/C][C]118.57[/C][C]124.411572273039[/C][C]-5.84157227303856[/C][/ROW]
[ROW][C]28[/C][C]134.25[/C][C]130.98248997885[/C][C]3.26751002115011[/C][/ROW]
[ROW][C]29[/C][C]136.7[/C][C]131.107979286006[/C][C]5.59202071399374[/C][/ROW]
[ROW][C]30[/C][C]121.37[/C][C]134.45910964857[/C][C]-13.0891096485698[/C][/ROW]
[ROW][C]31[/C][C]111.63[/C][C]112.121383785681[/C][C]-0.491383785680592[/C][/ROW]
[ROW][C]32[/C][C]134.42[/C][C]132.614763516406[/C][C]1.80523648359408[/C][/ROW]
[ROW][C]33[/C][C]137.65[/C][C]139.690908714027[/C][C]-2.04090871402736[/C][/ROW]
[ROW][C]34[/C][C]137.86[/C][C]131.896284948812[/C][C]5.96371505118829[/C][/ROW]
[ROW][C]35[/C][C]119.77[/C][C]122.491672686071[/C][C]-2.7216726860714[/C][/ROW]
[ROW][C]36[/C][C]130.69[/C][C]130.653202793367[/C][C]0.0367972066333477[/C][/ROW]
[ROW][C]37[/C][C]128.28[/C][C]126.802049473496[/C][C]1.4779505265043[/C][/ROW]
[ROW][C]38[/C][C]147.45[/C][C]147.184085620621[/C][C]0.265914379379175[/C][/ROW]
[ROW][C]39[/C][C]128.42[/C][C]133.897544721851[/C][C]-5.47754472185101[/C][/ROW]
[ROW][C]40[/C][C]136.9[/C][C]138.661726715511[/C][C]-1.76172671551076[/C][/ROW]
[ROW][C]41[/C][C]143.95[/C][C]141.701553600415[/C][C]2.24844639958501[/C][/ROW]
[ROW][C]42[/C][C]135.64[/C][C]136.698561846301[/C][C]-1.05856184630103[/C][/ROW]
[ROW][C]43[/C][C]122.48[/C][C]118.779619363078[/C][C]3.70038063692242[/C][/ROW]
[ROW][C]44[/C][C]136.83[/C][C]145.535225815261[/C][C]-8.7052258152606[/C][/ROW]
[ROW][C]45[/C][C]153.04[/C][C]147.884179784709[/C][C]5.15582021529075[/C][/ROW]
[ROW][C]46[/C][C]142.71[/C][C]131.726868789233[/C][C]10.9831312107667[/C][/ROW]
[ROW][C]47[/C][C]123.46[/C][C]133.741555279122[/C][C]-10.2815552791216[/C][/ROW]
[ROW][C]48[/C][C]144.37[/C][C]134.278075013834[/C][C]10.0919249861657[/C][/ROW]
[ROW][C]49[/C][C]146.15[/C][C]127.115128386067[/C][C]19.0348716139334[/C][/ROW]
[ROW][C]50[/C][C]147.61[/C][C]156.849651437521[/C][C]-9.23965143752146[/C][/ROW]
[ROW][C]51[/C][C]158.51[/C][C]147.47153034736[/C][C]11.0384696526396[/C][/ROW]
[ROW][C]52[/C][C]147.4[/C][C]129.284818788682[/C][C]18.1151812113176[/C][/ROW]
[ROW][C]53[/C][C]165.05[/C][C]160.497461402793[/C][C]4.5525385972067[/C][/ROW]
[ROW][C]54[/C][C]154.64[/C][C]139.612950154644[/C][C]15.0270498453561[/C][/ROW]
[ROW][C]55[/C][C]126.2[/C][C]120.220763354697[/C][C]5.97923664530298[/C][/ROW]
[ROW][C]56[/C][C]157.36[/C][C]153.688054733811[/C][C]3.67194526618937[/C][/ROW]
[ROW][C]57[/C][C]154.15[/C][C]145.050037928938[/C][C]9.09996207106195[/C][/ROW]
[ROW][C]58[/C][C]123.21[/C][C]140.985464191898[/C][C]-17.7754641918977[/C][/ROW]
[ROW][C]59[/C][C]113.07[/C][C]127.700574636606[/C][C]-14.6305746366062[/C][/ROW]
[ROW][C]60[/C][C]110.45[/C][C]118.192815146362[/C][C]-7.74281514636205[/C][/ROW]
[ROW][C]61[/C][C]113.57[/C][C]122.387987888489[/C][C]-8.8179878884888[/C][/ROW]
[ROW][C]62[/C][C]122.44[/C][C]132.61038979444[/C][C]-10.1703897944396[/C][/ROW]
[ROW][C]63[/C][C]114.93[/C][C]123.359247803215[/C][C]-8.42924780321539[/C][/ROW]
[ROW][C]64[/C][C]111.85[/C][C]123.097007627605[/C][C]-11.2470076276053[/C][/ROW]
[ROW][C]65[/C][C]126.04[/C][C]131.260257883177[/C][C]-5.22025788317656[/C][/ROW]
[ROW][C]66[/C][C]121.34[/C][C]118.585147631775[/C][C]2.75485236822513[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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
1105.67110.33653519781-4.66653519781005
2123.61122.6379848286060.972015171393728
3113.08112.8780406306570.201959369342775
4106.46120.781426313376-14.3214263133763
5123.38127.877453095956-4.49745309595575
6109.87110.773158969824-0.903158969823526
795.74101.381814033092-5.64181403309237
8123.06129.110089932179-6.05008993217937
9123.39128.169001154365-4.77900115436549
10120.28124.641983933935-4.36198393393502
11115.33107.5762654627997.75373453720091
12110.4111.873424917054-1.47342491705412
13114.49119.968452354207-5.47845235420686
14132.03125.0099222697217.02007773027949
15123.16114.6520642238778.50793577612259
16118.82112.8725305759755.94746942402467
17128.32130.995294731653-2.67529473165314
18112.24114.971071748887-2.73107174888689
19104.53108.076419463452-3.54641946345245
20132.57123.2918660023439.27813399765652
21122.52129.95587241796-7.43587241795985
22131.8126.6093981361225.19060186387773
23124.55104.66993193540219.8800680645983
24120.96121.872482129383-0.912482129382874
25122.6124.149846699932-1.54984669993201
26145.52134.36796604909111.1520339509086
27118.57124.411572273039-5.84157227303856
28134.25130.982489978853.26751002115011
29136.7131.1079792860065.59202071399374
30121.37134.45910964857-13.0891096485698
31111.63112.121383785681-0.491383785680592
32134.42132.6147635164061.80523648359408
33137.65139.690908714027-2.04090871402736
34137.86131.8962849488125.96371505118829
35119.77122.491672686071-2.7216726860714
36130.69130.6532027933670.0367972066333477
37128.28126.8020494734961.4779505265043
38147.45147.1840856206210.265914379379175
39128.42133.897544721851-5.47754472185101
40136.9138.661726715511-1.76172671551076
41143.95141.7015536004152.24844639958501
42135.64136.698561846301-1.05856184630103
43122.48118.7796193630783.70038063692242
44136.83145.535225815261-8.7052258152606
45153.04147.8841797847095.15582021529075
46142.71131.72686878923310.9831312107667
47123.46133.741555279122-10.2815552791216
48144.37134.27807501383410.0919249861657
49146.15127.11512838606719.0348716139334
50147.61156.849651437521-9.23965143752146
51158.51147.4715303473611.0384696526396
52147.4129.28481878868218.1151812113176
53165.05160.4974614027934.5525385972067
54154.64139.61295015464415.0270498453561
55126.2120.2207633546975.97923664530298
56157.36153.6880547338113.67194526618937
57154.15145.0500379289389.09996207106195
58123.21140.985464191898-17.7754641918977
59113.07127.700574636606-14.6305746366062
60110.45118.192815146362-7.74281514636205
61113.57122.387987888489-8.8179878884888
62122.44132.61038979444-10.1703897944396
63114.93123.359247803215-8.42924780321539
64111.85123.097007627605-11.2470076276053
65126.04131.260257883177-5.22025788317656
66121.34118.5851476317752.75485236822513







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.02310352010704750.0462070402140950.976896479892952
190.006497080279164150.01299416055832830.993502919720836
200.001378113476917940.002756226953835870.998621886523082
210.001722067610446330.003444135220892650.998277932389554
220.0007394619044244770.001478923808848950.999260538095575
230.0004013060107548850.0008026120215097690.999598693989245
240.0001886331069300870.0003772662138601740.99981136689307
254.96477799116407e-059.92955598232814e-050.999950352220088
267.41820691158862e-050.0001483641382317720.999925817930884
270.0009731326550903830.001946265310180770.99902686734491
280.001515784784851070.003031569569702130.998484215215149
290.0008425074524606730.001685014904921350.999157492547539
300.000822945100525790.001645890201051580.999177054899474
310.0003582657500312990.0007165315000625980.999641734249969
320.0002620157364961740.0005240314729923480.999737984263504
330.000143703739675480.000287407479350960.999856296260325
346.23364720439404e-050.0001246729440878810.999937663527956
350.0002275756684500980.0004551513369001960.99977242433155
360.0001131961193254590.0002263922386509180.999886803880675
374.74522687517062e-059.49045375034125e-050.999952547731248
382.06111633731371e-054.12223267462742e-050.999979388836627
391.14866714176029e-052.29733428352058e-050.999988513328582
408.78317949079064e-061.75663589815813e-050.999991216820509
412.88829158308803e-065.77658316617607e-060.999997111708417
425.17837094290668e-050.0001035674188581340.999948216290571
434.46360310082084e-058.92720620164168e-050.999955363968992
440.00545766706019420.01091533412038840.994542332939806
450.0860460976032310.1720921952064620.913953902396769
460.05060001565427530.1012000313085510.949399984345725
470.3630517844660960.7261035689321920.636948215533904
480.2568323176810140.5136646353620270.743167682318986

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.0231035201070475 & 0.046207040214095 & 0.976896479892952 \tabularnewline
19 & 0.00649708027916415 & 0.0129941605583283 & 0.993502919720836 \tabularnewline
20 & 0.00137811347691794 & 0.00275622695383587 & 0.998621886523082 \tabularnewline
21 & 0.00172206761044633 & 0.00344413522089265 & 0.998277932389554 \tabularnewline
22 & 0.000739461904424477 & 0.00147892380884895 & 0.999260538095575 \tabularnewline
23 & 0.000401306010754885 & 0.000802612021509769 & 0.999598693989245 \tabularnewline
24 & 0.000188633106930087 & 0.000377266213860174 & 0.99981136689307 \tabularnewline
25 & 4.96477799116407e-05 & 9.92955598232814e-05 & 0.999950352220088 \tabularnewline
26 & 7.41820691158862e-05 & 0.000148364138231772 & 0.999925817930884 \tabularnewline
27 & 0.000973132655090383 & 0.00194626531018077 & 0.99902686734491 \tabularnewline
28 & 0.00151578478485107 & 0.00303156956970213 & 0.998484215215149 \tabularnewline
29 & 0.000842507452460673 & 0.00168501490492135 & 0.999157492547539 \tabularnewline
30 & 0.00082294510052579 & 0.00164589020105158 & 0.999177054899474 \tabularnewline
31 & 0.000358265750031299 & 0.000716531500062598 & 0.999641734249969 \tabularnewline
32 & 0.000262015736496174 & 0.000524031472992348 & 0.999737984263504 \tabularnewline
33 & 0.00014370373967548 & 0.00028740747935096 & 0.999856296260325 \tabularnewline
34 & 6.23364720439404e-05 & 0.000124672944087881 & 0.999937663527956 \tabularnewline
35 & 0.000227575668450098 & 0.000455151336900196 & 0.99977242433155 \tabularnewline
36 & 0.000113196119325459 & 0.000226392238650918 & 0.999886803880675 \tabularnewline
37 & 4.74522687517062e-05 & 9.49045375034125e-05 & 0.999952547731248 \tabularnewline
38 & 2.06111633731371e-05 & 4.12223267462742e-05 & 0.999979388836627 \tabularnewline
39 & 1.14866714176029e-05 & 2.29733428352058e-05 & 0.999988513328582 \tabularnewline
40 & 8.78317949079064e-06 & 1.75663589815813e-05 & 0.999991216820509 \tabularnewline
41 & 2.88829158308803e-06 & 5.77658316617607e-06 & 0.999997111708417 \tabularnewline
42 & 5.17837094290668e-05 & 0.000103567418858134 & 0.999948216290571 \tabularnewline
43 & 4.46360310082084e-05 & 8.92720620164168e-05 & 0.999955363968992 \tabularnewline
44 & 0.0054576670601942 & 0.0109153341203884 & 0.994542332939806 \tabularnewline
45 & 0.086046097603231 & 0.172092195206462 & 0.913953902396769 \tabularnewline
46 & 0.0506000156542753 & 0.101200031308551 & 0.949399984345725 \tabularnewline
47 & 0.363051784466096 & 0.726103568932192 & 0.636948215533904 \tabularnewline
48 & 0.256832317681014 & 0.513664635362027 & 0.743167682318986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&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]18[/C][C]0.0231035201070475[/C][C]0.046207040214095[/C][C]0.976896479892952[/C][/ROW]
[ROW][C]19[/C][C]0.00649708027916415[/C][C]0.0129941605583283[/C][C]0.993502919720836[/C][/ROW]
[ROW][C]20[/C][C]0.00137811347691794[/C][C]0.00275622695383587[/C][C]0.998621886523082[/C][/ROW]
[ROW][C]21[/C][C]0.00172206761044633[/C][C]0.00344413522089265[/C][C]0.998277932389554[/C][/ROW]
[ROW][C]22[/C][C]0.000739461904424477[/C][C]0.00147892380884895[/C][C]0.999260538095575[/C][/ROW]
[ROW][C]23[/C][C]0.000401306010754885[/C][C]0.000802612021509769[/C][C]0.999598693989245[/C][/ROW]
[ROW][C]24[/C][C]0.000188633106930087[/C][C]0.000377266213860174[/C][C]0.99981136689307[/C][/ROW]
[ROW][C]25[/C][C]4.96477799116407e-05[/C][C]9.92955598232814e-05[/C][C]0.999950352220088[/C][/ROW]
[ROW][C]26[/C][C]7.41820691158862e-05[/C][C]0.000148364138231772[/C][C]0.999925817930884[/C][/ROW]
[ROW][C]27[/C][C]0.000973132655090383[/C][C]0.00194626531018077[/C][C]0.99902686734491[/C][/ROW]
[ROW][C]28[/C][C]0.00151578478485107[/C][C]0.00303156956970213[/C][C]0.998484215215149[/C][/ROW]
[ROW][C]29[/C][C]0.000842507452460673[/C][C]0.00168501490492135[/C][C]0.999157492547539[/C][/ROW]
[ROW][C]30[/C][C]0.00082294510052579[/C][C]0.00164589020105158[/C][C]0.999177054899474[/C][/ROW]
[ROW][C]31[/C][C]0.000358265750031299[/C][C]0.000716531500062598[/C][C]0.999641734249969[/C][/ROW]
[ROW][C]32[/C][C]0.000262015736496174[/C][C]0.000524031472992348[/C][C]0.999737984263504[/C][/ROW]
[ROW][C]33[/C][C]0.00014370373967548[/C][C]0.00028740747935096[/C][C]0.999856296260325[/C][/ROW]
[ROW][C]34[/C][C]6.23364720439404e-05[/C][C]0.000124672944087881[/C][C]0.999937663527956[/C][/ROW]
[ROW][C]35[/C][C]0.000227575668450098[/C][C]0.000455151336900196[/C][C]0.99977242433155[/C][/ROW]
[ROW][C]36[/C][C]0.000113196119325459[/C][C]0.000226392238650918[/C][C]0.999886803880675[/C][/ROW]
[ROW][C]37[/C][C]4.74522687517062e-05[/C][C]9.49045375034125e-05[/C][C]0.999952547731248[/C][/ROW]
[ROW][C]38[/C][C]2.06111633731371e-05[/C][C]4.12223267462742e-05[/C][C]0.999979388836627[/C][/ROW]
[ROW][C]39[/C][C]1.14866714176029e-05[/C][C]2.29733428352058e-05[/C][C]0.999988513328582[/C][/ROW]
[ROW][C]40[/C][C]8.78317949079064e-06[/C][C]1.75663589815813e-05[/C][C]0.999991216820509[/C][/ROW]
[ROW][C]41[/C][C]2.88829158308803e-06[/C][C]5.77658316617607e-06[/C][C]0.999997111708417[/C][/ROW]
[ROW][C]42[/C][C]5.17837094290668e-05[/C][C]0.000103567418858134[/C][C]0.999948216290571[/C][/ROW]
[ROW][C]43[/C][C]4.46360310082084e-05[/C][C]8.92720620164168e-05[/C][C]0.999955363968992[/C][/ROW]
[ROW][C]44[/C][C]0.0054576670601942[/C][C]0.0109153341203884[/C][C]0.994542332939806[/C][/ROW]
[ROW][C]45[/C][C]0.086046097603231[/C][C]0.172092195206462[/C][C]0.913953902396769[/C][/ROW]
[ROW][C]46[/C][C]0.0506000156542753[/C][C]0.101200031308551[/C][C]0.949399984345725[/C][/ROW]
[ROW][C]47[/C][C]0.363051784466096[/C][C]0.726103568932192[/C][C]0.636948215533904[/C][/ROW]
[ROW][C]48[/C][C]0.256832317681014[/C][C]0.513664635362027[/C][C]0.743167682318986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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
180.02310352010704750.0462070402140950.976896479892952
190.006497080279164150.01299416055832830.993502919720836
200.001378113476917940.002756226953835870.998621886523082
210.001722067610446330.003444135220892650.998277932389554
220.0007394619044244770.001478923808848950.999260538095575
230.0004013060107548850.0008026120215097690.999598693989245
240.0001886331069300870.0003772662138601740.99981136689307
254.96477799116407e-059.92955598232814e-050.999950352220088
267.41820691158862e-050.0001483641382317720.999925817930884
270.0009731326550903830.001946265310180770.99902686734491
280.001515784784851070.003031569569702130.998484215215149
290.0008425074524606730.001685014904921350.999157492547539
300.000822945100525790.001645890201051580.999177054899474
310.0003582657500312990.0007165315000625980.999641734249969
320.0002620157364961740.0005240314729923480.999737984263504
330.000143703739675480.000287407479350960.999856296260325
346.23364720439404e-050.0001246729440878810.999937663527956
350.0002275756684500980.0004551513369001960.99977242433155
360.0001131961193254590.0002263922386509180.999886803880675
374.74522687517062e-059.49045375034125e-050.999952547731248
382.06111633731371e-054.12223267462742e-050.999979388836627
391.14866714176029e-052.29733428352058e-050.999988513328582
408.78317949079064e-061.75663589815813e-050.999991216820509
412.88829158308803e-065.77658316617607e-060.999997111708417
425.17837094290668e-050.0001035674188581340.999948216290571
434.46360310082084e-058.92720620164168e-050.999955363968992
440.00545766706019420.01091533412038840.994542332939806
450.0860460976032310.1720921952064620.913953902396769
460.05060001565427530.1012000313085510.949399984345725
470.3630517844660960.7261035689321920.636948215533904
480.2568323176810140.5136646353620270.743167682318986







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level240.774193548387097NOK
5% type I error level270.870967741935484NOK
10% type I error level270.870967741935484NOK

\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 & 24 & 0.774193548387097 & NOK \tabularnewline
5% type I error level & 27 & 0.870967741935484 & NOK \tabularnewline
10% type I error level & 27 & 0.870967741935484 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202992&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]24[/C][C]0.774193548387097[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.870967741935484[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.870967741935484[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202992&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202992&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 level240.774193548387097NOK
5% type I error level270.870967741935484NOK
10% type I error level270.870967741935484NOK



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')
}