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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 07:16:45 -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/t1258726675ubw0f0li6ot8f4c.htm/, Retrieved Thu, 28 Mar 2024 19:24:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58191, Retrieved Thu, 28 Mar 2024 19:24:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [workshop 7] [2009-11-20 13:56:36] [3d8acb8ffdb376c5fec19e610f8198c2]
-    D        [Multiple Regression] [workshop 7] [2009-11-20 14:16:45] [e81f30a5c3daacfe71a556c99a478849] [Current]
-    D          [Multiple Regression] [paper] [2009-12-13 10:16:40] [3d8acb8ffdb376c5fec19e610f8198c2]
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Dataseries X:
7.6	2.16
7.6	2.23
7.8	2.40
8.0	2.84
8.0	2.77
8.0	2.93
7.9	2.91
7.9	2.69
8.0	2.38
8.5	2.58
9.2	3.19
9.4	2.82
9.5	2.72
9.5	2.53
9.6	2.70
9.7	2.42
9.7	2.50
9.6	2.31
9.5	2.41
9.4	2.56
9.3	2.76
9.6	2.71
10.2	2.44
10.2	2.46
10.1	2.12
9.9	1.99
9.8	1.86
9.8	1.88
9.7	1.82
9.5	1.74
9.3	1.71
9.1	1.38
9.0	1.27
9.5	1.19
10.0	1.28
10.2	1.19
10.1	1.22
10.0	1.47
9.9	1.46
10.0	1.96
9.9	1.88
9.7	2.03
9.5	2.04
9.2	1.90
9.0	1.80
9.3	1.92
9.8	1.92
9.8	1.97
9.6	2.46
9.4	2.36
9.3	2.53
9.2	2.31
9.2	1.98
9.0	1.46
8.8	1.26
8.7	1.58
8.7	1.74
9.1	1.89
9.7	1.85
9.8	1.62
9.6	1.30
9.4	1.42
9.4	1.15
9.5	0.42
9.4	0.74
9.3	1.02
9.2	1.51
9.0	1.86
8.9	1.59
9.2	1.03
9.8	0.44
9.9	0.82
9.6	0.86
9.2	0.58
9.1	0.59
9.1	0.95
9.0	0.98
8.9	1.23
8.7	1.17
8.5	0.84
8.3	0.74
8.5	0.65
8.7	0.91
8.4	1.19
8.1	1.30
7.8	1.53
7.7	1.94
7.5	1.79
7.2	1.95
6.8	2.26
6.7	2.04
6.4	2.16
6.3	2.75
6.8	2.79
7.3	2.88
7.1	3.36
7.0	2.97
6.8	3.10
6.6	2.49
6.3	2.20
6.1	2.25
6.1	2.09
6.3	2.79
6.3	3.14
6.0	2.93
6.2	2.65
6.4	2.67
6.8	2.26
7.5	2.35
7.5	2.13
7.6	2.18
7.6	2.90
7.4	2.63
7.3	2.67
7.1	1.81
6.9	1.33
6.8	0.88
7.5	1.28
7.6	1.26
7.8	1.26
8.0	1.29
8.1	1.10
8.2	1.37
8.3	1.21
8.2	1.74
8.0	1.76
7.9	1.48
7.6	1.04
7.6	1.62
8.3	1.49
8.4	1.79
8.4	1.80
8.4	1.58
8.4	1.86
8.6	1.74
8.9	1.59
8.8	1.26
8.3	1.13
7.5	1.92
7.2	2.61
7.4	2.26
8.8	2.41
9.3	2.26
9.3	2.03
8.7	2.86
8.2	2.55
8.3	2.27
8.5	2.26
8.6	2.57
8.5	3.07
8.2	2.76
8.1	2.51
7.9	2.87
8.6	3.14
8.7	3.11
8.7	3.16
8.5	2.47
8.4	2.57
8.5	2.89
8.7	2.63
8.7	2.38
8.6	1.69
8.5	1.96
8.3	2.19
8.0	1.87
8.2	1.6
8.1	1.63
8.1	1.22




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&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]5 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=58191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 10.5537347085571 -0.432710919726205X[t] -0.204465428057700M1[t] -0.352280268157747M2[t] -0.323755304065414M3[t] -0.270642906480327M4[t] -0.343972417758249M5[t] -0.500509546033238M6[t] -0.657732902545404M7[t] -0.8260831112791M8[t] -0.926679664434522M9[t] -0.428237490165285M10[t] -0.0450763376473149M11[t] -0.0096030613809805t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  10.5537347085571 -0.432710919726205X[t] -0.204465428057700M1[t] -0.352280268157747M2[t] -0.323755304065414M3[t] -0.270642906480327M4[t] -0.343972417758249M5[t] -0.500509546033238M6[t] -0.657732902545404M7[t] -0.8260831112791M8[t] -0.926679664434522M9[t] -0.428237490165285M10[t] -0.0450763376473149M11[t] -0.0096030613809805t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  10.5537347085571 -0.432710919726205X[t] -0.204465428057700M1[t] -0.352280268157747M2[t] -0.323755304065414M3[t] -0.270642906480327M4[t] -0.343972417758249M5[t] -0.500509546033238M6[t] -0.657732902545404M7[t] -0.8260831112791M8[t] -0.926679664434522M9[t] -0.428237490165285M10[t] -0.0450763376473149M11[t] -0.0096030613809805t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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] = + 10.5537347085571 -0.432710919726205X[t] -0.204465428057700M1[t] -0.352280268157747M2[t] -0.323755304065414M3[t] -0.270642906480327M4[t] -0.343972417758249M5[t] -0.500509546033238M6[t] -0.657732902545404M7[t] -0.8260831112791M8[t] -0.926679664434522M9[t] -0.428237490165285M10[t] -0.0450763376473149M11[t] -0.0096030613809805t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.55373470855710.33049631.93300
X-0.4327109197262050.100677-4.2983e-051.5e-05
M1-0.2044654280577000.333939-0.61230.5412530.270626
M2-0.3522802681577470.333861-1.05520.2930.1465
M3-0.3237553040654140.333813-0.96990.3336320.166816
M4-0.2706429064803270.333752-0.81090.4186690.209334
M5-0.3439724177582490.333711-1.03070.3042740.152137
M6-0.5005095460332380.33367-1.50.1356580.067829
M7-0.6577329025454040.333662-1.97130.0504870.025244
M8-0.82608311127910.333637-2.4760.0143690.007185
M9-0.9266796644345220.333592-2.77790.0061520.003076
M10-0.4282374901652850.333572-1.28380.2011430.100572
M11-0.04507633764731490.333578-0.13510.8926860.446343
t-0.00960306138098050.001408-6.822100

\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) & 10.5537347085571 & 0.330496 & 31.933 & 0 & 0 \tabularnewline
X & -0.432710919726205 & 0.100677 & -4.298 & 3e-05 & 1.5e-05 \tabularnewline
M1 & -0.204465428057700 & 0.333939 & -0.6123 & 0.541253 & 0.270626 \tabularnewline
M2 & -0.352280268157747 & 0.333861 & -1.0552 & 0.293 & 0.1465 \tabularnewline
M3 & -0.323755304065414 & 0.333813 & -0.9699 & 0.333632 & 0.166816 \tabularnewline
M4 & -0.270642906480327 & 0.333752 & -0.8109 & 0.418669 & 0.209334 \tabularnewline
M5 & -0.343972417758249 & 0.333711 & -1.0307 & 0.304274 & 0.152137 \tabularnewline
M6 & -0.500509546033238 & 0.33367 & -1.5 & 0.135658 & 0.067829 \tabularnewline
M7 & -0.657732902545404 & 0.333662 & -1.9713 & 0.050487 & 0.025244 \tabularnewline
M8 & -0.8260831112791 & 0.333637 & -2.476 & 0.014369 & 0.007185 \tabularnewline
M9 & -0.926679664434522 & 0.333592 & -2.7779 & 0.006152 & 0.003076 \tabularnewline
M10 & -0.428237490165285 & 0.333572 & -1.2838 & 0.201143 & 0.100572 \tabularnewline
M11 & -0.0450763376473149 & 0.333578 & -0.1351 & 0.892686 & 0.446343 \tabularnewline
t & -0.0096030613809805 & 0.001408 & -6.8221 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&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]10.5537347085571[/C][C]0.330496[/C][C]31.933[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.432710919726205[/C][C]0.100677[/C][C]-4.298[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]M1[/C][C]-0.204465428057700[/C][C]0.333939[/C][C]-0.6123[/C][C]0.541253[/C][C]0.270626[/C][/ROW]
[ROW][C]M2[/C][C]-0.352280268157747[/C][C]0.333861[/C][C]-1.0552[/C][C]0.293[/C][C]0.1465[/C][/ROW]
[ROW][C]M3[/C][C]-0.323755304065414[/C][C]0.333813[/C][C]-0.9699[/C][C]0.333632[/C][C]0.166816[/C][/ROW]
[ROW][C]M4[/C][C]-0.270642906480327[/C][C]0.333752[/C][C]-0.8109[/C][C]0.418669[/C][C]0.209334[/C][/ROW]
[ROW][C]M5[/C][C]-0.343972417758249[/C][C]0.333711[/C][C]-1.0307[/C][C]0.304274[/C][C]0.152137[/C][/ROW]
[ROW][C]M6[/C][C]-0.500509546033238[/C][C]0.33367[/C][C]-1.5[/C][C]0.135658[/C][C]0.067829[/C][/ROW]
[ROW][C]M7[/C][C]-0.657732902545404[/C][C]0.333662[/C][C]-1.9713[/C][C]0.050487[/C][C]0.025244[/C][/ROW]
[ROW][C]M8[/C][C]-0.8260831112791[/C][C]0.333637[/C][C]-2.476[/C][C]0.014369[/C][C]0.007185[/C][/ROW]
[ROW][C]M9[/C][C]-0.926679664434522[/C][C]0.333592[/C][C]-2.7779[/C][C]0.006152[/C][C]0.003076[/C][/ROW]
[ROW][C]M10[/C][C]-0.428237490165285[/C][C]0.333572[/C][C]-1.2838[/C][C]0.201143[/C][C]0.100572[/C][/ROW]
[ROW][C]M11[/C][C]-0.0450763376473149[/C][C]0.333578[/C][C]-0.1351[/C][C]0.892686[/C][C]0.446343[/C][/ROW]
[ROW][C]t[/C][C]-0.0096030613809805[/C][C]0.001408[/C][C]-6.8221[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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)10.55373470855710.33049631.93300
X-0.4327109197262050.100677-4.2983e-051.5e-05
M1-0.2044654280577000.333939-0.61230.5412530.270626
M2-0.3522802681577470.333861-1.05520.2930.1465
M3-0.3237553040654140.333813-0.96990.3336320.166816
M4-0.2706429064803270.333752-0.81090.4186690.209334
M5-0.3439724177582490.333711-1.03070.3042740.152137
M6-0.5005095460332380.33367-1.50.1356580.067829
M7-0.6577329025454040.333662-1.97130.0504870.025244
M8-0.82608311127910.333637-2.4760.0143690.007185
M9-0.9266796644345220.333592-2.77790.0061520.003076
M10-0.4282374901652850.333572-1.28380.2011430.100572
M11-0.04507633764731490.333578-0.13510.8926860.446343
t-0.00960306138098050.001408-6.822100







Multiple Linear Regression - Regression Statistics
Multiple R0.590123889812548
R-squared0.348246205327493
Adjusted R-squared0.293228027855138
F-TEST (value)6.3296572392366
F-TEST (DF numerator)13
F-TEST (DF denominator)154
p-value1.73478809095684e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.882512122734266
Sum Squared Residuals119.939457603033

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.590123889812548 \tabularnewline
R-squared & 0.348246205327493 \tabularnewline
Adjusted R-squared & 0.293228027855138 \tabularnewline
F-TEST (value) & 6.3296572392366 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.73478809095684e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.882512122734266 \tabularnewline
Sum Squared Residuals & 119.939457603033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.590123889812548[/C][/ROW]
[ROW][C]R-squared[/C][C]0.348246205327493[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.293228027855138[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.3296572392366[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]1.73478809095684e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.882512122734266[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]119.939457603033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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 R0.590123889812548
R-squared0.348246205327493
Adjusted R-squared0.293228027855138
F-TEST (value)6.3296572392366
F-TEST (DF numerator)13
F-TEST (DF denominator)154
p-value1.73478809095684e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.882512122734266
Sum Squared Residuals119.939457603033







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.69.40501063250971-1.80501063250972
27.69.21730296664794-1.61730296664794
37.89.16266401300584-1.36266401300584
489.01578054453041-1.01578054453041
588.96313773625234-0.963137736252344
688.72776379944018-0.727763799440181
77.98.56959159994156-0.66959159994156
87.98.48683473216665-0.586834732166648
988.51077550274537-0.510775502745367
108.58.91307243168838-0.413072431688383
119.29.022676861792390.177323138207611
129.49.218253178357420.18174682164258
139.59.047455780891360.452544219108642
149.58.972252954158320.527747045841685
159.68.917614000516210.682385999483792
169.79.082282394243650.617717605756346
179.78.964732948006650.735267051993346
189.68.880807833098660.719192166901337
199.58.67071032323290.829289676767104
209.48.427850415159290.972149584840712
219.38.231108616677641.06889138332236
229.68.741583275552210.858416724447788
2310.29.231973315015280.968026684984722
2410.29.258792372887090.941207627112912
2510.19.191845596155320.908154403844684
269.99.09068011423870.809319885761306
279.89.165854436514450.634145563485547
289.89.200709554324040.599290445675963
299.79.14373963684870.556260363151293
309.59.012216320770830.487783679229167
319.38.858371230469470.441628769530527
329.18.823212563864440.276787436135555
3398.760611150497920.239388849502077
349.59.284067136964280.215932863035723
35109.61868124532590.381318754674092
3610.29.69309850436760.506901495632399
3710.19.466048687337130.633951312662866
38109.200453055924560.799546944075445
399.99.223702067833170.67629793216683
40109.050855944174170.949144055825825
419.99.002540245093370.897459754906632
429.78.771493417478470.928506582521531
439.58.600339890388060.89966010961194
449.28.482966149035050.717033850964947
4598.416037626471270.583962373528731
469.38.852951428992380.447048571007619
479.89.226509520129370.57349047987063
489.89.24034725040940.559652749590605
499.68.814250410304870.785749589695127
509.48.700103600796470.699896399203533
519.38.645464647154360.654535352845636
529.28.784170385698240.415829614301762
539.28.844032416548980.355967583451018
5498.902901905150640.0970980948493615
558.88.82261767120273-0.0226176712027328
568.78.506196906775670.193803093224328
578.78.326763545083080.373236454916924
589.18.75069602001240.349303979987598
599.79.141562547938440.55843745206156
609.89.27655933574180.5234406642582
619.69.20095834061550.399041659384495
629.48.991615128767330.408384871232667
639.49.127368979804760.272631020195239
649.59.4867572874090.0132427125910022
659.49.26535722043770.134642779562291
669.38.97805797325840.321942026741598
679.28.599203204699420.600796795300583
6898.269801112680570.730198887319433
698.98.276433446470240.623566553529761
709.29.007590674405170.192409325594828
719.89.636448208180620.163551791819379
729.99.5074913349510.392508665049002
739.69.276114408723270.323885591276731
749.29.23985556476558-0.03985556476558
759.19.25445035827967-0.15445035827967
769.19.14218376338234-0.0421837633823433
7799.04626986313165-0.0462698631316539
788.98.771951943544130.128048056455867
798.78.631088180834560.0689118191654395
808.58.59592951422953-0.0959295142295304
818.38.52900099166575-0.229000991665747
828.59.05678408732936-0.556784087329363
838.79.31783733933754-0.61783733933754
848.49.23215155808054-0.832151558080536
858.18.97048486747197-0.870484867471973
867.88.71354345445392-0.913543454453919
877.78.55505388007753-0.855053880077527
887.58.66346985424057-1.16346985424056
897.28.51130353442547-1.31130353442547
906.88.21102295965438-1.41102295965438
916.78.139392944101-1.43939294410099
926.47.90951436361917-1.50951436361917
936.37.54401530644431-1.24401530644431
946.88.01554598254352-1.21554598254352
957.38.35016009090515-1.05016009090515
967.18.1779321257029-1.07793212570291
9778.13262089495744-1.13262089495744
986.87.91895057391201-1.11895057391201
996.68.20182613765635-1.60182613765635
1006.38.37082164058105-2.07082164058105
1016.18.26625352193584-2.16625352193584
1026.18.16934707943606-2.06934707943607
1036.37.69962301773458-1.39962301773458
1046.37.37022092571573-1.07022092571573
10567.35089060432183-1.35089060432183
1066.27.96088877473342-1.76088877473342
1076.48.32579264747589-1.92579264747589
1086.88.53867740082996-1.73867740082997
1097.58.28566492861593-0.785664928615926
1107.58.22344342947466-0.723443429474663
1117.68.2207297861997-0.620729786199706
1127.67.95268726020095-0.352687260200946
1137.47.98658663586812-0.586586635868117
1147.37.8031380094231-0.5031380094231
1157.18.00844298249449-0.90844298249449
1166.98.03819095384839-1.13819095384839
1176.88.12271125318878-1.32271125318878
1187.58.43846599818655-0.938465998186555
1197.68.82067830771807-1.22067830771807
1207.88.8561515839844-1.05615158398440
12188.62910176695394-0.629101766953937
1228.18.55389894022089-0.453898940220889
1238.28.45598889460616-0.255988894606166
1248.38.56873197796646-0.268731977966465
1258.28.25646261785267-0.0564626178526745
12688.08166820980218-0.0816682098021803
1277.98.03600084943237-0.136000849432371
1287.68.04844038399722-0.448440383997226
1297.67.68726843601962-0.0872684360196234
1308.38.232359968472290.0676400315277145
1318.48.47610478369142-0.0761047836914144
1328.48.50725095076049-0.107250950760487
1338.48.388378863661570.0116211363384291
1348.48.10980190465720.290198095342794
1358.68.18064911773570.419350882264296
1368.98.289065091898740.610934908101259
1378.88.348927122749490.451072877250514
1388.38.239039352657920.0609606473420773
1397.57.73037130818108-0.230371308181076
1407.27.25384750345532-0.0538475034553171
1417.47.295096710823090.104903289176915
1428.87.719029185752411.08097081424759
1439.38.157493914848331.14250608515167
1449.38.29249070265171.00750929734831
1458.77.719272149840260.980727850159736
1468.27.695994633474360.50400536652564
1478.37.836075593709050.463924406290952
1488.57.883912039110420.616087960889582
1498.67.666839081336390.933160918663608
1508.57.284343431817321.21565656818268
1518.27.25165739903930.948342600960702
1528.17.181881858856170.918118141143828
1537.96.915906313218340.984093686781665
1548.67.287913477780521.31208652221948
1558.77.674452896509291.02554710349071
1568.77.688290626789321.01170937321068
1578.57.772792671961720.727207328038284
1588.47.572103678508070.827896321491931
1598.57.452558086907041.04744191309296
1608.77.608572262239961.09142773776004
1618.77.63381741951261.06618258048740
1628.67.766247764467720.833752235532283
1638.57.48258939824851.01741060175150
1648.37.205112616596791.09488738340321
16587.233380496372770.766619503627226
1668.27.83905155758710.360948442412894
1678.18.19962832113231-0.0996283211323095
1688.18.41251307448639-0.312513074486388

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.6 & 9.40501063250971 & -1.80501063250972 \tabularnewline
2 & 7.6 & 9.21730296664794 & -1.61730296664794 \tabularnewline
3 & 7.8 & 9.16266401300584 & -1.36266401300584 \tabularnewline
4 & 8 & 9.01578054453041 & -1.01578054453041 \tabularnewline
5 & 8 & 8.96313773625234 & -0.963137736252344 \tabularnewline
6 & 8 & 8.72776379944018 & -0.727763799440181 \tabularnewline
7 & 7.9 & 8.56959159994156 & -0.66959159994156 \tabularnewline
8 & 7.9 & 8.48683473216665 & -0.586834732166648 \tabularnewline
9 & 8 & 8.51077550274537 & -0.510775502745367 \tabularnewline
10 & 8.5 & 8.91307243168838 & -0.413072431688383 \tabularnewline
11 & 9.2 & 9.02267686179239 & 0.177323138207611 \tabularnewline
12 & 9.4 & 9.21825317835742 & 0.18174682164258 \tabularnewline
13 & 9.5 & 9.04745578089136 & 0.452544219108642 \tabularnewline
14 & 9.5 & 8.97225295415832 & 0.527747045841685 \tabularnewline
15 & 9.6 & 8.91761400051621 & 0.682385999483792 \tabularnewline
16 & 9.7 & 9.08228239424365 & 0.617717605756346 \tabularnewline
17 & 9.7 & 8.96473294800665 & 0.735267051993346 \tabularnewline
18 & 9.6 & 8.88080783309866 & 0.719192166901337 \tabularnewline
19 & 9.5 & 8.6707103232329 & 0.829289676767104 \tabularnewline
20 & 9.4 & 8.42785041515929 & 0.972149584840712 \tabularnewline
21 & 9.3 & 8.23110861667764 & 1.06889138332236 \tabularnewline
22 & 9.6 & 8.74158327555221 & 0.858416724447788 \tabularnewline
23 & 10.2 & 9.23197331501528 & 0.968026684984722 \tabularnewline
24 & 10.2 & 9.25879237288709 & 0.941207627112912 \tabularnewline
25 & 10.1 & 9.19184559615532 & 0.908154403844684 \tabularnewline
26 & 9.9 & 9.0906801142387 & 0.809319885761306 \tabularnewline
27 & 9.8 & 9.16585443651445 & 0.634145563485547 \tabularnewline
28 & 9.8 & 9.20070955432404 & 0.599290445675963 \tabularnewline
29 & 9.7 & 9.1437396368487 & 0.556260363151293 \tabularnewline
30 & 9.5 & 9.01221632077083 & 0.487783679229167 \tabularnewline
31 & 9.3 & 8.85837123046947 & 0.441628769530527 \tabularnewline
32 & 9.1 & 8.82321256386444 & 0.276787436135555 \tabularnewline
33 & 9 & 8.76061115049792 & 0.239388849502077 \tabularnewline
34 & 9.5 & 9.28406713696428 & 0.215932863035723 \tabularnewline
35 & 10 & 9.6186812453259 & 0.381318754674092 \tabularnewline
36 & 10.2 & 9.6930985043676 & 0.506901495632399 \tabularnewline
37 & 10.1 & 9.46604868733713 & 0.633951312662866 \tabularnewline
38 & 10 & 9.20045305592456 & 0.799546944075445 \tabularnewline
39 & 9.9 & 9.22370206783317 & 0.67629793216683 \tabularnewline
40 & 10 & 9.05085594417417 & 0.949144055825825 \tabularnewline
41 & 9.9 & 9.00254024509337 & 0.897459754906632 \tabularnewline
42 & 9.7 & 8.77149341747847 & 0.928506582521531 \tabularnewline
43 & 9.5 & 8.60033989038806 & 0.89966010961194 \tabularnewline
44 & 9.2 & 8.48296614903505 & 0.717033850964947 \tabularnewline
45 & 9 & 8.41603762647127 & 0.583962373528731 \tabularnewline
46 & 9.3 & 8.85295142899238 & 0.447048571007619 \tabularnewline
47 & 9.8 & 9.22650952012937 & 0.57349047987063 \tabularnewline
48 & 9.8 & 9.2403472504094 & 0.559652749590605 \tabularnewline
49 & 9.6 & 8.81425041030487 & 0.785749589695127 \tabularnewline
50 & 9.4 & 8.70010360079647 & 0.699896399203533 \tabularnewline
51 & 9.3 & 8.64546464715436 & 0.654535352845636 \tabularnewline
52 & 9.2 & 8.78417038569824 & 0.415829614301762 \tabularnewline
53 & 9.2 & 8.84403241654898 & 0.355967583451018 \tabularnewline
54 & 9 & 8.90290190515064 & 0.0970980948493615 \tabularnewline
55 & 8.8 & 8.82261767120273 & -0.0226176712027328 \tabularnewline
56 & 8.7 & 8.50619690677567 & 0.193803093224328 \tabularnewline
57 & 8.7 & 8.32676354508308 & 0.373236454916924 \tabularnewline
58 & 9.1 & 8.7506960200124 & 0.349303979987598 \tabularnewline
59 & 9.7 & 9.14156254793844 & 0.55843745206156 \tabularnewline
60 & 9.8 & 9.2765593357418 & 0.5234406642582 \tabularnewline
61 & 9.6 & 9.2009583406155 & 0.399041659384495 \tabularnewline
62 & 9.4 & 8.99161512876733 & 0.408384871232667 \tabularnewline
63 & 9.4 & 9.12736897980476 & 0.272631020195239 \tabularnewline
64 & 9.5 & 9.486757287409 & 0.0132427125910022 \tabularnewline
65 & 9.4 & 9.2653572204377 & 0.134642779562291 \tabularnewline
66 & 9.3 & 8.9780579732584 & 0.321942026741598 \tabularnewline
67 & 9.2 & 8.59920320469942 & 0.600796795300583 \tabularnewline
68 & 9 & 8.26980111268057 & 0.730198887319433 \tabularnewline
69 & 8.9 & 8.27643344647024 & 0.623566553529761 \tabularnewline
70 & 9.2 & 9.00759067440517 & 0.192409325594828 \tabularnewline
71 & 9.8 & 9.63644820818062 & 0.163551791819379 \tabularnewline
72 & 9.9 & 9.507491334951 & 0.392508665049002 \tabularnewline
73 & 9.6 & 9.27611440872327 & 0.323885591276731 \tabularnewline
74 & 9.2 & 9.23985556476558 & -0.03985556476558 \tabularnewline
75 & 9.1 & 9.25445035827967 & -0.15445035827967 \tabularnewline
76 & 9.1 & 9.14218376338234 & -0.0421837633823433 \tabularnewline
77 & 9 & 9.04626986313165 & -0.0462698631316539 \tabularnewline
78 & 8.9 & 8.77195194354413 & 0.128048056455867 \tabularnewline
79 & 8.7 & 8.63108818083456 & 0.0689118191654395 \tabularnewline
80 & 8.5 & 8.59592951422953 & -0.0959295142295304 \tabularnewline
81 & 8.3 & 8.52900099166575 & -0.229000991665747 \tabularnewline
82 & 8.5 & 9.05678408732936 & -0.556784087329363 \tabularnewline
83 & 8.7 & 9.31783733933754 & -0.61783733933754 \tabularnewline
84 & 8.4 & 9.23215155808054 & -0.832151558080536 \tabularnewline
85 & 8.1 & 8.97048486747197 & -0.870484867471973 \tabularnewline
86 & 7.8 & 8.71354345445392 & -0.913543454453919 \tabularnewline
87 & 7.7 & 8.55505388007753 & -0.855053880077527 \tabularnewline
88 & 7.5 & 8.66346985424057 & -1.16346985424056 \tabularnewline
89 & 7.2 & 8.51130353442547 & -1.31130353442547 \tabularnewline
90 & 6.8 & 8.21102295965438 & -1.41102295965438 \tabularnewline
91 & 6.7 & 8.139392944101 & -1.43939294410099 \tabularnewline
92 & 6.4 & 7.90951436361917 & -1.50951436361917 \tabularnewline
93 & 6.3 & 7.54401530644431 & -1.24401530644431 \tabularnewline
94 & 6.8 & 8.01554598254352 & -1.21554598254352 \tabularnewline
95 & 7.3 & 8.35016009090515 & -1.05016009090515 \tabularnewline
96 & 7.1 & 8.1779321257029 & -1.07793212570291 \tabularnewline
97 & 7 & 8.13262089495744 & -1.13262089495744 \tabularnewline
98 & 6.8 & 7.91895057391201 & -1.11895057391201 \tabularnewline
99 & 6.6 & 8.20182613765635 & -1.60182613765635 \tabularnewline
100 & 6.3 & 8.37082164058105 & -2.07082164058105 \tabularnewline
101 & 6.1 & 8.26625352193584 & -2.16625352193584 \tabularnewline
102 & 6.1 & 8.16934707943606 & -2.06934707943607 \tabularnewline
103 & 6.3 & 7.69962301773458 & -1.39962301773458 \tabularnewline
104 & 6.3 & 7.37022092571573 & -1.07022092571573 \tabularnewline
105 & 6 & 7.35089060432183 & -1.35089060432183 \tabularnewline
106 & 6.2 & 7.96088877473342 & -1.76088877473342 \tabularnewline
107 & 6.4 & 8.32579264747589 & -1.92579264747589 \tabularnewline
108 & 6.8 & 8.53867740082996 & -1.73867740082997 \tabularnewline
109 & 7.5 & 8.28566492861593 & -0.785664928615926 \tabularnewline
110 & 7.5 & 8.22344342947466 & -0.723443429474663 \tabularnewline
111 & 7.6 & 8.2207297861997 & -0.620729786199706 \tabularnewline
112 & 7.6 & 7.95268726020095 & -0.352687260200946 \tabularnewline
113 & 7.4 & 7.98658663586812 & -0.586586635868117 \tabularnewline
114 & 7.3 & 7.8031380094231 & -0.5031380094231 \tabularnewline
115 & 7.1 & 8.00844298249449 & -0.90844298249449 \tabularnewline
116 & 6.9 & 8.03819095384839 & -1.13819095384839 \tabularnewline
117 & 6.8 & 8.12271125318878 & -1.32271125318878 \tabularnewline
118 & 7.5 & 8.43846599818655 & -0.938465998186555 \tabularnewline
119 & 7.6 & 8.82067830771807 & -1.22067830771807 \tabularnewline
120 & 7.8 & 8.8561515839844 & -1.05615158398440 \tabularnewline
121 & 8 & 8.62910176695394 & -0.629101766953937 \tabularnewline
122 & 8.1 & 8.55389894022089 & -0.453898940220889 \tabularnewline
123 & 8.2 & 8.45598889460616 & -0.255988894606166 \tabularnewline
124 & 8.3 & 8.56873197796646 & -0.268731977966465 \tabularnewline
125 & 8.2 & 8.25646261785267 & -0.0564626178526745 \tabularnewline
126 & 8 & 8.08166820980218 & -0.0816682098021803 \tabularnewline
127 & 7.9 & 8.03600084943237 & -0.136000849432371 \tabularnewline
128 & 7.6 & 8.04844038399722 & -0.448440383997226 \tabularnewline
129 & 7.6 & 7.68726843601962 & -0.0872684360196234 \tabularnewline
130 & 8.3 & 8.23235996847229 & 0.0676400315277145 \tabularnewline
131 & 8.4 & 8.47610478369142 & -0.0761047836914144 \tabularnewline
132 & 8.4 & 8.50725095076049 & -0.107250950760487 \tabularnewline
133 & 8.4 & 8.38837886366157 & 0.0116211363384291 \tabularnewline
134 & 8.4 & 8.1098019046572 & 0.290198095342794 \tabularnewline
135 & 8.6 & 8.1806491177357 & 0.419350882264296 \tabularnewline
136 & 8.9 & 8.28906509189874 & 0.610934908101259 \tabularnewline
137 & 8.8 & 8.34892712274949 & 0.451072877250514 \tabularnewline
138 & 8.3 & 8.23903935265792 & 0.0609606473420773 \tabularnewline
139 & 7.5 & 7.73037130818108 & -0.230371308181076 \tabularnewline
140 & 7.2 & 7.25384750345532 & -0.0538475034553171 \tabularnewline
141 & 7.4 & 7.29509671082309 & 0.104903289176915 \tabularnewline
142 & 8.8 & 7.71902918575241 & 1.08097081424759 \tabularnewline
143 & 9.3 & 8.15749391484833 & 1.14250608515167 \tabularnewline
144 & 9.3 & 8.2924907026517 & 1.00750929734831 \tabularnewline
145 & 8.7 & 7.71927214984026 & 0.980727850159736 \tabularnewline
146 & 8.2 & 7.69599463347436 & 0.50400536652564 \tabularnewline
147 & 8.3 & 7.83607559370905 & 0.463924406290952 \tabularnewline
148 & 8.5 & 7.88391203911042 & 0.616087960889582 \tabularnewline
149 & 8.6 & 7.66683908133639 & 0.933160918663608 \tabularnewline
150 & 8.5 & 7.28434343181732 & 1.21565656818268 \tabularnewline
151 & 8.2 & 7.2516573990393 & 0.948342600960702 \tabularnewline
152 & 8.1 & 7.18188185885617 & 0.918118141143828 \tabularnewline
153 & 7.9 & 6.91590631321834 & 0.984093686781665 \tabularnewline
154 & 8.6 & 7.28791347778052 & 1.31208652221948 \tabularnewline
155 & 8.7 & 7.67445289650929 & 1.02554710349071 \tabularnewline
156 & 8.7 & 7.68829062678932 & 1.01170937321068 \tabularnewline
157 & 8.5 & 7.77279267196172 & 0.727207328038284 \tabularnewline
158 & 8.4 & 7.57210367850807 & 0.827896321491931 \tabularnewline
159 & 8.5 & 7.45255808690704 & 1.04744191309296 \tabularnewline
160 & 8.7 & 7.60857226223996 & 1.09142773776004 \tabularnewline
161 & 8.7 & 7.6338174195126 & 1.06618258048740 \tabularnewline
162 & 8.6 & 7.76624776446772 & 0.833752235532283 \tabularnewline
163 & 8.5 & 7.4825893982485 & 1.01741060175150 \tabularnewline
164 & 8.3 & 7.20511261659679 & 1.09488738340321 \tabularnewline
165 & 8 & 7.23338049637277 & 0.766619503627226 \tabularnewline
166 & 8.2 & 7.8390515575871 & 0.360948442412894 \tabularnewline
167 & 8.1 & 8.19962832113231 & -0.0996283211323095 \tabularnewline
168 & 8.1 & 8.41251307448639 & -0.312513074486388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&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]7.6[/C][C]9.40501063250971[/C][C]-1.80501063250972[/C][/ROW]
[ROW][C]2[/C][C]7.6[/C][C]9.21730296664794[/C][C]-1.61730296664794[/C][/ROW]
[ROW][C]3[/C][C]7.8[/C][C]9.16266401300584[/C][C]-1.36266401300584[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]9.01578054453041[/C][C]-1.01578054453041[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]8.96313773625234[/C][C]-0.963137736252344[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]8.72776379944018[/C][C]-0.727763799440181[/C][/ROW]
[ROW][C]7[/C][C]7.9[/C][C]8.56959159994156[/C][C]-0.66959159994156[/C][/ROW]
[ROW][C]8[/C][C]7.9[/C][C]8.48683473216665[/C][C]-0.586834732166648[/C][/ROW]
[ROW][C]9[/C][C]8[/C][C]8.51077550274537[/C][C]-0.510775502745367[/C][/ROW]
[ROW][C]10[/C][C]8.5[/C][C]8.91307243168838[/C][C]-0.413072431688383[/C][/ROW]
[ROW][C]11[/C][C]9.2[/C][C]9.02267686179239[/C][C]0.177323138207611[/C][/ROW]
[ROW][C]12[/C][C]9.4[/C][C]9.21825317835742[/C][C]0.18174682164258[/C][/ROW]
[ROW][C]13[/C][C]9.5[/C][C]9.04745578089136[/C][C]0.452544219108642[/C][/ROW]
[ROW][C]14[/C][C]9.5[/C][C]8.97225295415832[/C][C]0.527747045841685[/C][/ROW]
[ROW][C]15[/C][C]9.6[/C][C]8.91761400051621[/C][C]0.682385999483792[/C][/ROW]
[ROW][C]16[/C][C]9.7[/C][C]9.08228239424365[/C][C]0.617717605756346[/C][/ROW]
[ROW][C]17[/C][C]9.7[/C][C]8.96473294800665[/C][C]0.735267051993346[/C][/ROW]
[ROW][C]18[/C][C]9.6[/C][C]8.88080783309866[/C][C]0.719192166901337[/C][/ROW]
[ROW][C]19[/C][C]9.5[/C][C]8.6707103232329[/C][C]0.829289676767104[/C][/ROW]
[ROW][C]20[/C][C]9.4[/C][C]8.42785041515929[/C][C]0.972149584840712[/C][/ROW]
[ROW][C]21[/C][C]9.3[/C][C]8.23110861667764[/C][C]1.06889138332236[/C][/ROW]
[ROW][C]22[/C][C]9.6[/C][C]8.74158327555221[/C][C]0.858416724447788[/C][/ROW]
[ROW][C]23[/C][C]10.2[/C][C]9.23197331501528[/C][C]0.968026684984722[/C][/ROW]
[ROW][C]24[/C][C]10.2[/C][C]9.25879237288709[/C][C]0.941207627112912[/C][/ROW]
[ROW][C]25[/C][C]10.1[/C][C]9.19184559615532[/C][C]0.908154403844684[/C][/ROW]
[ROW][C]26[/C][C]9.9[/C][C]9.0906801142387[/C][C]0.809319885761306[/C][/ROW]
[ROW][C]27[/C][C]9.8[/C][C]9.16585443651445[/C][C]0.634145563485547[/C][/ROW]
[ROW][C]28[/C][C]9.8[/C][C]9.20070955432404[/C][C]0.599290445675963[/C][/ROW]
[ROW][C]29[/C][C]9.7[/C][C]9.1437396368487[/C][C]0.556260363151293[/C][/ROW]
[ROW][C]30[/C][C]9.5[/C][C]9.01221632077083[/C][C]0.487783679229167[/C][/ROW]
[ROW][C]31[/C][C]9.3[/C][C]8.85837123046947[/C][C]0.441628769530527[/C][/ROW]
[ROW][C]32[/C][C]9.1[/C][C]8.82321256386444[/C][C]0.276787436135555[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]8.76061115049792[/C][C]0.239388849502077[/C][/ROW]
[ROW][C]34[/C][C]9.5[/C][C]9.28406713696428[/C][C]0.215932863035723[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]9.6186812453259[/C][C]0.381318754674092[/C][/ROW]
[ROW][C]36[/C][C]10.2[/C][C]9.6930985043676[/C][C]0.506901495632399[/C][/ROW]
[ROW][C]37[/C][C]10.1[/C][C]9.46604868733713[/C][C]0.633951312662866[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.20045305592456[/C][C]0.799546944075445[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]9.22370206783317[/C][C]0.67629793216683[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]9.05085594417417[/C][C]0.949144055825825[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]9.00254024509337[/C][C]0.897459754906632[/C][/ROW]
[ROW][C]42[/C][C]9.7[/C][C]8.77149341747847[/C][C]0.928506582521531[/C][/ROW]
[ROW][C]43[/C][C]9.5[/C][C]8.60033989038806[/C][C]0.89966010961194[/C][/ROW]
[ROW][C]44[/C][C]9.2[/C][C]8.48296614903505[/C][C]0.717033850964947[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]8.41603762647127[/C][C]0.583962373528731[/C][/ROW]
[ROW][C]46[/C][C]9.3[/C][C]8.85295142899238[/C][C]0.447048571007619[/C][/ROW]
[ROW][C]47[/C][C]9.8[/C][C]9.22650952012937[/C][C]0.57349047987063[/C][/ROW]
[ROW][C]48[/C][C]9.8[/C][C]9.2403472504094[/C][C]0.559652749590605[/C][/ROW]
[ROW][C]49[/C][C]9.6[/C][C]8.81425041030487[/C][C]0.785749589695127[/C][/ROW]
[ROW][C]50[/C][C]9.4[/C][C]8.70010360079647[/C][C]0.699896399203533[/C][/ROW]
[ROW][C]51[/C][C]9.3[/C][C]8.64546464715436[/C][C]0.654535352845636[/C][/ROW]
[ROW][C]52[/C][C]9.2[/C][C]8.78417038569824[/C][C]0.415829614301762[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]8.84403241654898[/C][C]0.355967583451018[/C][/ROW]
[ROW][C]54[/C][C]9[/C][C]8.90290190515064[/C][C]0.0970980948493615[/C][/ROW]
[ROW][C]55[/C][C]8.8[/C][C]8.82261767120273[/C][C]-0.0226176712027328[/C][/ROW]
[ROW][C]56[/C][C]8.7[/C][C]8.50619690677567[/C][C]0.193803093224328[/C][/ROW]
[ROW][C]57[/C][C]8.7[/C][C]8.32676354508308[/C][C]0.373236454916924[/C][/ROW]
[ROW][C]58[/C][C]9.1[/C][C]8.7506960200124[/C][C]0.349303979987598[/C][/ROW]
[ROW][C]59[/C][C]9.7[/C][C]9.14156254793844[/C][C]0.55843745206156[/C][/ROW]
[ROW][C]60[/C][C]9.8[/C][C]9.2765593357418[/C][C]0.5234406642582[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]9.2009583406155[/C][C]0.399041659384495[/C][/ROW]
[ROW][C]62[/C][C]9.4[/C][C]8.99161512876733[/C][C]0.408384871232667[/C][/ROW]
[ROW][C]63[/C][C]9.4[/C][C]9.12736897980476[/C][C]0.272631020195239[/C][/ROW]
[ROW][C]64[/C][C]9.5[/C][C]9.486757287409[/C][C]0.0132427125910022[/C][/ROW]
[ROW][C]65[/C][C]9.4[/C][C]9.2653572204377[/C][C]0.134642779562291[/C][/ROW]
[ROW][C]66[/C][C]9.3[/C][C]8.9780579732584[/C][C]0.321942026741598[/C][/ROW]
[ROW][C]67[/C][C]9.2[/C][C]8.59920320469942[/C][C]0.600796795300583[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]8.26980111268057[/C][C]0.730198887319433[/C][/ROW]
[ROW][C]69[/C][C]8.9[/C][C]8.27643344647024[/C][C]0.623566553529761[/C][/ROW]
[ROW][C]70[/C][C]9.2[/C][C]9.00759067440517[/C][C]0.192409325594828[/C][/ROW]
[ROW][C]71[/C][C]9.8[/C][C]9.63644820818062[/C][C]0.163551791819379[/C][/ROW]
[ROW][C]72[/C][C]9.9[/C][C]9.507491334951[/C][C]0.392508665049002[/C][/ROW]
[ROW][C]73[/C][C]9.6[/C][C]9.27611440872327[/C][C]0.323885591276731[/C][/ROW]
[ROW][C]74[/C][C]9.2[/C][C]9.23985556476558[/C][C]-0.03985556476558[/C][/ROW]
[ROW][C]75[/C][C]9.1[/C][C]9.25445035827967[/C][C]-0.15445035827967[/C][/ROW]
[ROW][C]76[/C][C]9.1[/C][C]9.14218376338234[/C][C]-0.0421837633823433[/C][/ROW]
[ROW][C]77[/C][C]9[/C][C]9.04626986313165[/C][C]-0.0462698631316539[/C][/ROW]
[ROW][C]78[/C][C]8.9[/C][C]8.77195194354413[/C][C]0.128048056455867[/C][/ROW]
[ROW][C]79[/C][C]8.7[/C][C]8.63108818083456[/C][C]0.0689118191654395[/C][/ROW]
[ROW][C]80[/C][C]8.5[/C][C]8.59592951422953[/C][C]-0.0959295142295304[/C][/ROW]
[ROW][C]81[/C][C]8.3[/C][C]8.52900099166575[/C][C]-0.229000991665747[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]9.05678408732936[/C][C]-0.556784087329363[/C][/ROW]
[ROW][C]83[/C][C]8.7[/C][C]9.31783733933754[/C][C]-0.61783733933754[/C][/ROW]
[ROW][C]84[/C][C]8.4[/C][C]9.23215155808054[/C][C]-0.832151558080536[/C][/ROW]
[ROW][C]85[/C][C]8.1[/C][C]8.97048486747197[/C][C]-0.870484867471973[/C][/ROW]
[ROW][C]86[/C][C]7.8[/C][C]8.71354345445392[/C][C]-0.913543454453919[/C][/ROW]
[ROW][C]87[/C][C]7.7[/C][C]8.55505388007753[/C][C]-0.855053880077527[/C][/ROW]
[ROW][C]88[/C][C]7.5[/C][C]8.66346985424057[/C][C]-1.16346985424056[/C][/ROW]
[ROW][C]89[/C][C]7.2[/C][C]8.51130353442547[/C][C]-1.31130353442547[/C][/ROW]
[ROW][C]90[/C][C]6.8[/C][C]8.21102295965438[/C][C]-1.41102295965438[/C][/ROW]
[ROW][C]91[/C][C]6.7[/C][C]8.139392944101[/C][C]-1.43939294410099[/C][/ROW]
[ROW][C]92[/C][C]6.4[/C][C]7.90951436361917[/C][C]-1.50951436361917[/C][/ROW]
[ROW][C]93[/C][C]6.3[/C][C]7.54401530644431[/C][C]-1.24401530644431[/C][/ROW]
[ROW][C]94[/C][C]6.8[/C][C]8.01554598254352[/C][C]-1.21554598254352[/C][/ROW]
[ROW][C]95[/C][C]7.3[/C][C]8.35016009090515[/C][C]-1.05016009090515[/C][/ROW]
[ROW][C]96[/C][C]7.1[/C][C]8.1779321257029[/C][C]-1.07793212570291[/C][/ROW]
[ROW][C]97[/C][C]7[/C][C]8.13262089495744[/C][C]-1.13262089495744[/C][/ROW]
[ROW][C]98[/C][C]6.8[/C][C]7.91895057391201[/C][C]-1.11895057391201[/C][/ROW]
[ROW][C]99[/C][C]6.6[/C][C]8.20182613765635[/C][C]-1.60182613765635[/C][/ROW]
[ROW][C]100[/C][C]6.3[/C][C]8.37082164058105[/C][C]-2.07082164058105[/C][/ROW]
[ROW][C]101[/C][C]6.1[/C][C]8.26625352193584[/C][C]-2.16625352193584[/C][/ROW]
[ROW][C]102[/C][C]6.1[/C][C]8.16934707943606[/C][C]-2.06934707943607[/C][/ROW]
[ROW][C]103[/C][C]6.3[/C][C]7.69962301773458[/C][C]-1.39962301773458[/C][/ROW]
[ROW][C]104[/C][C]6.3[/C][C]7.37022092571573[/C][C]-1.07022092571573[/C][/ROW]
[ROW][C]105[/C][C]6[/C][C]7.35089060432183[/C][C]-1.35089060432183[/C][/ROW]
[ROW][C]106[/C][C]6.2[/C][C]7.96088877473342[/C][C]-1.76088877473342[/C][/ROW]
[ROW][C]107[/C][C]6.4[/C][C]8.32579264747589[/C][C]-1.92579264747589[/C][/ROW]
[ROW][C]108[/C][C]6.8[/C][C]8.53867740082996[/C][C]-1.73867740082997[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]8.28566492861593[/C][C]-0.785664928615926[/C][/ROW]
[ROW][C]110[/C][C]7.5[/C][C]8.22344342947466[/C][C]-0.723443429474663[/C][/ROW]
[ROW][C]111[/C][C]7.6[/C][C]8.2207297861997[/C][C]-0.620729786199706[/C][/ROW]
[ROW][C]112[/C][C]7.6[/C][C]7.95268726020095[/C][C]-0.352687260200946[/C][/ROW]
[ROW][C]113[/C][C]7.4[/C][C]7.98658663586812[/C][C]-0.586586635868117[/C][/ROW]
[ROW][C]114[/C][C]7.3[/C][C]7.8031380094231[/C][C]-0.5031380094231[/C][/ROW]
[ROW][C]115[/C][C]7.1[/C][C]8.00844298249449[/C][C]-0.90844298249449[/C][/ROW]
[ROW][C]116[/C][C]6.9[/C][C]8.03819095384839[/C][C]-1.13819095384839[/C][/ROW]
[ROW][C]117[/C][C]6.8[/C][C]8.12271125318878[/C][C]-1.32271125318878[/C][/ROW]
[ROW][C]118[/C][C]7.5[/C][C]8.43846599818655[/C][C]-0.938465998186555[/C][/ROW]
[ROW][C]119[/C][C]7.6[/C][C]8.82067830771807[/C][C]-1.22067830771807[/C][/ROW]
[ROW][C]120[/C][C]7.8[/C][C]8.8561515839844[/C][C]-1.05615158398440[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]8.62910176695394[/C][C]-0.629101766953937[/C][/ROW]
[ROW][C]122[/C][C]8.1[/C][C]8.55389894022089[/C][C]-0.453898940220889[/C][/ROW]
[ROW][C]123[/C][C]8.2[/C][C]8.45598889460616[/C][C]-0.255988894606166[/C][/ROW]
[ROW][C]124[/C][C]8.3[/C][C]8.56873197796646[/C][C]-0.268731977966465[/C][/ROW]
[ROW][C]125[/C][C]8.2[/C][C]8.25646261785267[/C][C]-0.0564626178526745[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]8.08166820980218[/C][C]-0.0816682098021803[/C][/ROW]
[ROW][C]127[/C][C]7.9[/C][C]8.03600084943237[/C][C]-0.136000849432371[/C][/ROW]
[ROW][C]128[/C][C]7.6[/C][C]8.04844038399722[/C][C]-0.448440383997226[/C][/ROW]
[ROW][C]129[/C][C]7.6[/C][C]7.68726843601962[/C][C]-0.0872684360196234[/C][/ROW]
[ROW][C]130[/C][C]8.3[/C][C]8.23235996847229[/C][C]0.0676400315277145[/C][/ROW]
[ROW][C]131[/C][C]8.4[/C][C]8.47610478369142[/C][C]-0.0761047836914144[/C][/ROW]
[ROW][C]132[/C][C]8.4[/C][C]8.50725095076049[/C][C]-0.107250950760487[/C][/ROW]
[ROW][C]133[/C][C]8.4[/C][C]8.38837886366157[/C][C]0.0116211363384291[/C][/ROW]
[ROW][C]134[/C][C]8.4[/C][C]8.1098019046572[/C][C]0.290198095342794[/C][/ROW]
[ROW][C]135[/C][C]8.6[/C][C]8.1806491177357[/C][C]0.419350882264296[/C][/ROW]
[ROW][C]136[/C][C]8.9[/C][C]8.28906509189874[/C][C]0.610934908101259[/C][/ROW]
[ROW][C]137[/C][C]8.8[/C][C]8.34892712274949[/C][C]0.451072877250514[/C][/ROW]
[ROW][C]138[/C][C]8.3[/C][C]8.23903935265792[/C][C]0.0609606473420773[/C][/ROW]
[ROW][C]139[/C][C]7.5[/C][C]7.73037130818108[/C][C]-0.230371308181076[/C][/ROW]
[ROW][C]140[/C][C]7.2[/C][C]7.25384750345532[/C][C]-0.0538475034553171[/C][/ROW]
[ROW][C]141[/C][C]7.4[/C][C]7.29509671082309[/C][C]0.104903289176915[/C][/ROW]
[ROW][C]142[/C][C]8.8[/C][C]7.71902918575241[/C][C]1.08097081424759[/C][/ROW]
[ROW][C]143[/C][C]9.3[/C][C]8.15749391484833[/C][C]1.14250608515167[/C][/ROW]
[ROW][C]144[/C][C]9.3[/C][C]8.2924907026517[/C][C]1.00750929734831[/C][/ROW]
[ROW][C]145[/C][C]8.7[/C][C]7.71927214984026[/C][C]0.980727850159736[/C][/ROW]
[ROW][C]146[/C][C]8.2[/C][C]7.69599463347436[/C][C]0.50400536652564[/C][/ROW]
[ROW][C]147[/C][C]8.3[/C][C]7.83607559370905[/C][C]0.463924406290952[/C][/ROW]
[ROW][C]148[/C][C]8.5[/C][C]7.88391203911042[/C][C]0.616087960889582[/C][/ROW]
[ROW][C]149[/C][C]8.6[/C][C]7.66683908133639[/C][C]0.933160918663608[/C][/ROW]
[ROW][C]150[/C][C]8.5[/C][C]7.28434343181732[/C][C]1.21565656818268[/C][/ROW]
[ROW][C]151[/C][C]8.2[/C][C]7.2516573990393[/C][C]0.948342600960702[/C][/ROW]
[ROW][C]152[/C][C]8.1[/C][C]7.18188185885617[/C][C]0.918118141143828[/C][/ROW]
[ROW][C]153[/C][C]7.9[/C][C]6.91590631321834[/C][C]0.984093686781665[/C][/ROW]
[ROW][C]154[/C][C]8.6[/C][C]7.28791347778052[/C][C]1.31208652221948[/C][/ROW]
[ROW][C]155[/C][C]8.7[/C][C]7.67445289650929[/C][C]1.02554710349071[/C][/ROW]
[ROW][C]156[/C][C]8.7[/C][C]7.68829062678932[/C][C]1.01170937321068[/C][/ROW]
[ROW][C]157[/C][C]8.5[/C][C]7.77279267196172[/C][C]0.727207328038284[/C][/ROW]
[ROW][C]158[/C][C]8.4[/C][C]7.57210367850807[/C][C]0.827896321491931[/C][/ROW]
[ROW][C]159[/C][C]8.5[/C][C]7.45255808690704[/C][C]1.04744191309296[/C][/ROW]
[ROW][C]160[/C][C]8.7[/C][C]7.60857226223996[/C][C]1.09142773776004[/C][/ROW]
[ROW][C]161[/C][C]8.7[/C][C]7.6338174195126[/C][C]1.06618258048740[/C][/ROW]
[ROW][C]162[/C][C]8.6[/C][C]7.76624776446772[/C][C]0.833752235532283[/C][/ROW]
[ROW][C]163[/C][C]8.5[/C][C]7.4825893982485[/C][C]1.01741060175150[/C][/ROW]
[ROW][C]164[/C][C]8.3[/C][C]7.20511261659679[/C][C]1.09488738340321[/C][/ROW]
[ROW][C]165[/C][C]8[/C][C]7.23338049637277[/C][C]0.766619503627226[/C][/ROW]
[ROW][C]166[/C][C]8.2[/C][C]7.8390515575871[/C][C]0.360948442412894[/C][/ROW]
[ROW][C]167[/C][C]8.1[/C][C]8.19962832113231[/C][C]-0.0996283211323095[/C][/ROW]
[ROW][C]168[/C][C]8.1[/C][C]8.41251307448639[/C][C]-0.312513074486388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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
17.69.40501063250971-1.80501063250972
27.69.21730296664794-1.61730296664794
37.89.16266401300584-1.36266401300584
489.01578054453041-1.01578054453041
588.96313773625234-0.963137736252344
688.72776379944018-0.727763799440181
77.98.56959159994156-0.66959159994156
87.98.48683473216665-0.586834732166648
988.51077550274537-0.510775502745367
108.58.91307243168838-0.413072431688383
119.29.022676861792390.177323138207611
129.49.218253178357420.18174682164258
139.59.047455780891360.452544219108642
149.58.972252954158320.527747045841685
159.68.917614000516210.682385999483792
169.79.082282394243650.617717605756346
179.78.964732948006650.735267051993346
189.68.880807833098660.719192166901337
199.58.67071032323290.829289676767104
209.48.427850415159290.972149584840712
219.38.231108616677641.06889138332236
229.68.741583275552210.858416724447788
2310.29.231973315015280.968026684984722
2410.29.258792372887090.941207627112912
2510.19.191845596155320.908154403844684
269.99.09068011423870.809319885761306
279.89.165854436514450.634145563485547
289.89.200709554324040.599290445675963
299.79.14373963684870.556260363151293
309.59.012216320770830.487783679229167
319.38.858371230469470.441628769530527
329.18.823212563864440.276787436135555
3398.760611150497920.239388849502077
349.59.284067136964280.215932863035723
35109.61868124532590.381318754674092
3610.29.69309850436760.506901495632399
3710.19.466048687337130.633951312662866
38109.200453055924560.799546944075445
399.99.223702067833170.67629793216683
40109.050855944174170.949144055825825
419.99.002540245093370.897459754906632
429.78.771493417478470.928506582521531
439.58.600339890388060.89966010961194
449.28.482966149035050.717033850964947
4598.416037626471270.583962373528731
469.38.852951428992380.447048571007619
479.89.226509520129370.57349047987063
489.89.24034725040940.559652749590605
499.68.814250410304870.785749589695127
509.48.700103600796470.699896399203533
519.38.645464647154360.654535352845636
529.28.784170385698240.415829614301762
539.28.844032416548980.355967583451018
5498.902901905150640.0970980948493615
558.88.82261767120273-0.0226176712027328
568.78.506196906775670.193803093224328
578.78.326763545083080.373236454916924
589.18.75069602001240.349303979987598
599.79.141562547938440.55843745206156
609.89.27655933574180.5234406642582
619.69.20095834061550.399041659384495
629.48.991615128767330.408384871232667
639.49.127368979804760.272631020195239
649.59.4867572874090.0132427125910022
659.49.26535722043770.134642779562291
669.38.97805797325840.321942026741598
679.28.599203204699420.600796795300583
6898.269801112680570.730198887319433
698.98.276433446470240.623566553529761
709.29.007590674405170.192409325594828
719.89.636448208180620.163551791819379
729.99.5074913349510.392508665049002
739.69.276114408723270.323885591276731
749.29.23985556476558-0.03985556476558
759.19.25445035827967-0.15445035827967
769.19.14218376338234-0.0421837633823433
7799.04626986313165-0.0462698631316539
788.98.771951943544130.128048056455867
798.78.631088180834560.0689118191654395
808.58.59592951422953-0.0959295142295304
818.38.52900099166575-0.229000991665747
828.59.05678408732936-0.556784087329363
838.79.31783733933754-0.61783733933754
848.49.23215155808054-0.832151558080536
858.18.97048486747197-0.870484867471973
867.88.71354345445392-0.913543454453919
877.78.55505388007753-0.855053880077527
887.58.66346985424057-1.16346985424056
897.28.51130353442547-1.31130353442547
906.88.21102295965438-1.41102295965438
916.78.139392944101-1.43939294410099
926.47.90951436361917-1.50951436361917
936.37.54401530644431-1.24401530644431
946.88.01554598254352-1.21554598254352
957.38.35016009090515-1.05016009090515
967.18.1779321257029-1.07793212570291
9778.13262089495744-1.13262089495744
986.87.91895057391201-1.11895057391201
996.68.20182613765635-1.60182613765635
1006.38.37082164058105-2.07082164058105
1016.18.26625352193584-2.16625352193584
1026.18.16934707943606-2.06934707943607
1036.37.69962301773458-1.39962301773458
1046.37.37022092571573-1.07022092571573
10567.35089060432183-1.35089060432183
1066.27.96088877473342-1.76088877473342
1076.48.32579264747589-1.92579264747589
1086.88.53867740082996-1.73867740082997
1097.58.28566492861593-0.785664928615926
1107.58.22344342947466-0.723443429474663
1117.68.2207297861997-0.620729786199706
1127.67.95268726020095-0.352687260200946
1137.47.98658663586812-0.586586635868117
1147.37.8031380094231-0.5031380094231
1157.18.00844298249449-0.90844298249449
1166.98.03819095384839-1.13819095384839
1176.88.12271125318878-1.32271125318878
1187.58.43846599818655-0.938465998186555
1197.68.82067830771807-1.22067830771807
1207.88.8561515839844-1.05615158398440
12188.62910176695394-0.629101766953937
1228.18.55389894022089-0.453898940220889
1238.28.45598889460616-0.255988894606166
1248.38.56873197796646-0.268731977966465
1258.28.25646261785267-0.0564626178526745
12688.08166820980218-0.0816682098021803
1277.98.03600084943237-0.136000849432371
1287.68.04844038399722-0.448440383997226
1297.67.68726843601962-0.0872684360196234
1308.38.232359968472290.0676400315277145
1318.48.47610478369142-0.0761047836914144
1328.48.50725095076049-0.107250950760487
1338.48.388378863661570.0116211363384291
1348.48.10980190465720.290198095342794
1358.68.18064911773570.419350882264296
1368.98.289065091898740.610934908101259
1378.88.348927122749490.451072877250514
1388.38.239039352657920.0609606473420773
1397.57.73037130818108-0.230371308181076
1407.27.25384750345532-0.0538475034553171
1417.47.295096710823090.104903289176915
1428.87.719029185752411.08097081424759
1439.38.157493914848331.14250608515167
1449.38.29249070265171.00750929734831
1458.77.719272149840260.980727850159736
1468.27.695994633474360.50400536652564
1478.37.836075593709050.463924406290952
1488.57.883912039110420.616087960889582
1498.67.666839081336390.933160918663608
1508.57.284343431817321.21565656818268
1518.27.25165739903930.948342600960702
1528.17.181881858856170.918118141143828
1537.96.915906313218340.984093686781665
1548.67.287913477780521.31208652221948
1558.77.674452896509291.02554710349071
1568.77.688290626789321.01170937321068
1578.57.772792671961720.727207328038284
1588.47.572103678508070.827896321491931
1598.57.452558086907041.04744191309296
1608.77.608572262239961.09142773776004
1618.77.63381741951261.06618258048740
1628.67.766247764467720.833752235532283
1638.57.48258939824851.01741060175150
1648.37.205112616596791.09488738340321
16587.233380496372770.766619503627226
1668.27.83905155758710.360948442412894
1678.18.19962832113231-0.0996283211323095
1688.18.41251307448639-0.312513074486388







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
175.97327540369492e-050.0001194655080738980.999940267245963
182.28885486892943e-064.57770973785886e-060.999997711145131
191.20385273000842e-072.40770546001684e-070.999999879614727
201.02874028820131e-062.05748057640261e-060.999998971259712
211.92943517935188e-053.85887035870376e-050.999980705648206
224.98642122541647e-059.97284245083295e-050.999950135787746
234.18174323688853e-058.36348647377705e-050.999958182567631
246.98652668805786e-050.0001397305337611570.99993013473312
258.66896870441793e-050.0001733793740883590.999913310312956
260.0001095221547820550.0002190443095641090.999890477845218
270.0001107550719235290.0002215101438470580.999889244928077
289.67550484680525e-050.0001935100969361050.999903244951532
296.90057555222698e-050.0001380115110445400.999930994244478
304.81495174362557e-059.62990348725113e-050.999951850482564
312.95030684367589e-055.90061368735178e-050.999970496931563
321.24531575205606e-052.49063150411213e-050.99998754684248
335.09779873208781e-061.01955974641756e-050.999994902201268
341.83098359420750e-063.66196718841499e-060.999998169016406
356.46267142568189e-071.29253428513638e-060.999999353732857
362.27272873136635e-074.5454574627327e-070.999999772727127
371.25594371439609e-072.51188742879217e-070.999999874405629
382.29575413595763e-074.59150827191527e-070.999999770424586
393.71550788404126e-077.43101576808253e-070.999999628449212
402.31382556731558e-064.62765113463115e-060.999997686174433
416.03988203339762e-061.20797640667952e-050.999993960117967
422.04191435861720e-054.08382871723439e-050.999979580856414
434.65190016578956e-059.30380033157911e-050.999953480998342
440.0001094488584318120.0002188977168636250.999890551141568
450.0002430437756265040.0004860875512530090.999756956224374
460.0005355406830334240.001071081366066850.999464459316967
470.001011178346364860.002022356692729710.998988821653635
480.002092800161616540.004185600323233080.997907199838383
490.003260784295754720.006521568591509430.996739215704245
500.00432993036356810.00865986072713620.995670069636432
510.005527457402327220.01105491480465440.994472542597673
520.007555233875849630.01511046775169930.99244476612415
530.009225595217597230.01845119043519450.990774404782403
540.01153823665890180.02307647331780350.988461763341098
550.01405363134752570.02810726269505130.985946368652474
560.01540862090047980.03081724180095970.98459137909952
570.01634755073263120.03269510146526250.983652449267369
580.01708955861940560.03417911723881130.982910441380594
590.02043163422568910.04086326845137810.97956836577431
600.02437521209816000.04875042419631990.97562478790184
610.02341086025228920.04682172050457840.97658913974771
620.02398391756078550.04796783512157110.976016082439214
630.02361857619676530.04723715239353050.976381423803235
640.02130787503234080.04261575006468160.97869212496766
650.01991707509548470.03983415019096940.980082924904515
660.01991702255729620.03983404511459250.980082977442704
670.02441976860489620.04883953720979240.975580231395104
680.03714946654559780.07429893309119560.962850533454402
690.05496995874408830.1099399174881770.945030041255912
700.06228980328745380.1245796065749080.937710196712546
710.07207341233518490.1441468246703700.927926587664815
720.1036944719463840.2073889438927680.896305528053616
730.1248414454798380.2496828909596750.875158554520162
740.1391560800854080.2783121601708160.860843919914592
750.1547940999817980.3095881999635950.845205900018202
760.1906854985617000.3813709971234010.8093145014383
770.2433637786639090.4867275573278180.756636221336091
780.3451920245967450.6903840491934890.654807975403255
790.4837806804871240.9675613609742480.516219319512876
800.6305620143044420.7388759713911170.369437985695558
810.7778416298790140.4443167402419720.222158370120986
820.8622813542108470.2754372915783070.137718645789153
830.9508683075839020.09826338483219560.0491316924160978
840.98857405607090.02285188785820010.0114259439291000
850.9956118234756650.008776353048670410.00438817652433520
860.9982803150228530.003439369954294480.00171968497714724
870.9992301194219640.001539761156071100.000769880578035552
880.9995919385681640.000816122863672820.00040806143183641
890.9997514540701730.0004970918596542410.000248545929827120
900.9998207225690670.0003585548618656530.000179277430932826
910.9998643500360640.0002712999278726070.000135649963936304
920.9998819123081010.0002361753837972630.000118087691898632
930.9998617213225120.0002765573549766520.000138278677488326
940.999803624059190.0003927518816188470.000196375940809424
950.999797853257320.0004042934853581540.000202146742679077
960.9997530360510210.0004939278979578950.000246963948978947
970.9996112616410760.0007774767178481480.000388738358924074
980.9993898350330770.001220329933845880.000610164966922942
990.9992800158991390.001439968201722950.000719984100861474
1000.999675156723270.000649686553461870.000324843276730935
1010.999910850969460.0001782980610802548.91490305401271e-05
1020.9999704206386325.91587227350711e-052.95793613675356e-05
1030.9999586140161038.27719677934485e-054.13859838967243e-05
1040.9999317185288140.0001365629423710596.82814711855295e-05
1050.9999065694585520.0001868610828957279.34305414478636e-05
1060.9999690441931526.19116136953796e-053.09558068476898e-05
1070.9999940582701141.18834597714386e-055.94172988571931e-06
1080.9999979001420414.19971591700326e-062.09985795850163e-06
1090.9999968130252666.37394946883827e-063.18697473441913e-06
1100.9999945862487361.08275025283937e-055.41375126419684e-06
1110.9999915908135051.68183729894116e-058.4091864947058e-06
1120.999993148469081.37030618392835e-056.85153091964173e-06
1130.999996167158537.66568293893442e-063.83284146946721e-06
1140.9999979910332054.017933590722e-062.008966795361e-06
1150.9999979235071654.15298566957474e-062.07649283478737e-06
1160.9999975747836084.85043278430980e-062.42521639215490e-06
1170.9999972762119975.44757600598732e-062.72378800299366e-06
1180.999997578130064.84373987897535e-062.42186993948767e-06
1190.999998535172892.92965421812767e-061.46482710906383e-06
1200.9999986657784122.66844317519157e-061.33422158759579e-06
1210.999997701743344.59651332141514e-062.29825666070757e-06
1220.999995130278249.73944352000669e-064.86972176000334e-06
1230.9999906073137861.8785372427205e-059.3926862136025e-06
1240.9999831478695473.37042609051697e-051.68521304525849e-05
1250.99997967584424.06483116010969e-052.03241558005484e-05
1260.9999747800781785.04398436439627e-052.52199218219814e-05
1270.9999520171542729.59656914564437e-054.79828457282219e-05
1280.9999029937992940.0001940124014111389.70062007055688e-05
1290.9998264259166460.0003471481667086710.000173574083354335
1300.9997127287113250.0005745425773499140.000287271288674957
1310.9995482980351570.0009034039296862060.000451701964843103
1320.9993120566405460.001375886718908000.000687943359453998
1330.9988328167926450.002334366414709820.00116718320735491
1340.9981994869558490.003601026088302720.00180051304415136
1350.9974631920768030.005073615846394570.00253680792319728
1360.9969514220846290.006097155830742850.00304857791537143
1370.9958789898193060.008242020361388740.00412101018069437
1380.9926366033629830.01472679327403330.00736339663701666
1390.993847832457510.01230433508497920.00615216754248962
1400.998857260919770.002285478160458530.00114273908022926
1410.9995073140757980.0009853718484042590.000492685924202130
1420.9991075677406670.001784864518666860.00089243225933343
1430.9995649403416350.0008701193167299970.000435059658364998
1440.9999951235774319.75284513716384e-064.87642256858192e-06
1450.9999886684400712.26631198579854e-051.13315599289927e-05
1460.999947490664410.0001050186711800635.25093355900315e-05
1470.999833654145930.0003326917081415170.000166345854070758
1480.9995563169463930.0008873661072142920.000443683053607146
1490.9988846995710040.002230600857991610.00111530042899581
1500.9982827211484120.003434557703176490.00171727885158824
1510.9952407186538880.009518562692223860.00475928134611193

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 5.97327540369492e-05 & 0.000119465508073898 & 0.999940267245963 \tabularnewline
18 & 2.28885486892943e-06 & 4.57770973785886e-06 & 0.999997711145131 \tabularnewline
19 & 1.20385273000842e-07 & 2.40770546001684e-07 & 0.999999879614727 \tabularnewline
20 & 1.02874028820131e-06 & 2.05748057640261e-06 & 0.999998971259712 \tabularnewline
21 & 1.92943517935188e-05 & 3.85887035870376e-05 & 0.999980705648206 \tabularnewline
22 & 4.98642122541647e-05 & 9.97284245083295e-05 & 0.999950135787746 \tabularnewline
23 & 4.18174323688853e-05 & 8.36348647377705e-05 & 0.999958182567631 \tabularnewline
24 & 6.98652668805786e-05 & 0.000139730533761157 & 0.99993013473312 \tabularnewline
25 & 8.66896870441793e-05 & 0.000173379374088359 & 0.999913310312956 \tabularnewline
26 & 0.000109522154782055 & 0.000219044309564109 & 0.999890477845218 \tabularnewline
27 & 0.000110755071923529 & 0.000221510143847058 & 0.999889244928077 \tabularnewline
28 & 9.67550484680525e-05 & 0.000193510096936105 & 0.999903244951532 \tabularnewline
29 & 6.90057555222698e-05 & 0.000138011511044540 & 0.999930994244478 \tabularnewline
30 & 4.81495174362557e-05 & 9.62990348725113e-05 & 0.999951850482564 \tabularnewline
31 & 2.95030684367589e-05 & 5.90061368735178e-05 & 0.999970496931563 \tabularnewline
32 & 1.24531575205606e-05 & 2.49063150411213e-05 & 0.99998754684248 \tabularnewline
33 & 5.09779873208781e-06 & 1.01955974641756e-05 & 0.999994902201268 \tabularnewline
34 & 1.83098359420750e-06 & 3.66196718841499e-06 & 0.999998169016406 \tabularnewline
35 & 6.46267142568189e-07 & 1.29253428513638e-06 & 0.999999353732857 \tabularnewline
36 & 2.27272873136635e-07 & 4.5454574627327e-07 & 0.999999772727127 \tabularnewline
37 & 1.25594371439609e-07 & 2.51188742879217e-07 & 0.999999874405629 \tabularnewline
38 & 2.29575413595763e-07 & 4.59150827191527e-07 & 0.999999770424586 \tabularnewline
39 & 3.71550788404126e-07 & 7.43101576808253e-07 & 0.999999628449212 \tabularnewline
40 & 2.31382556731558e-06 & 4.62765113463115e-06 & 0.999997686174433 \tabularnewline
41 & 6.03988203339762e-06 & 1.20797640667952e-05 & 0.999993960117967 \tabularnewline
42 & 2.04191435861720e-05 & 4.08382871723439e-05 & 0.999979580856414 \tabularnewline
43 & 4.65190016578956e-05 & 9.30380033157911e-05 & 0.999953480998342 \tabularnewline
44 & 0.000109448858431812 & 0.000218897716863625 & 0.999890551141568 \tabularnewline
45 & 0.000243043775626504 & 0.000486087551253009 & 0.999756956224374 \tabularnewline
46 & 0.000535540683033424 & 0.00107108136606685 & 0.999464459316967 \tabularnewline
47 & 0.00101117834636486 & 0.00202235669272971 & 0.998988821653635 \tabularnewline
48 & 0.00209280016161654 & 0.00418560032323308 & 0.997907199838383 \tabularnewline
49 & 0.00326078429575472 & 0.00652156859150943 & 0.996739215704245 \tabularnewline
50 & 0.0043299303635681 & 0.0086598607271362 & 0.995670069636432 \tabularnewline
51 & 0.00552745740232722 & 0.0110549148046544 & 0.994472542597673 \tabularnewline
52 & 0.00755523387584963 & 0.0151104677516993 & 0.99244476612415 \tabularnewline
53 & 0.00922559521759723 & 0.0184511904351945 & 0.990774404782403 \tabularnewline
54 & 0.0115382366589018 & 0.0230764733178035 & 0.988461763341098 \tabularnewline
55 & 0.0140536313475257 & 0.0281072626950513 & 0.985946368652474 \tabularnewline
56 & 0.0154086209004798 & 0.0308172418009597 & 0.98459137909952 \tabularnewline
57 & 0.0163475507326312 & 0.0326951014652625 & 0.983652449267369 \tabularnewline
58 & 0.0170895586194056 & 0.0341791172388113 & 0.982910441380594 \tabularnewline
59 & 0.0204316342256891 & 0.0408632684513781 & 0.97956836577431 \tabularnewline
60 & 0.0243752120981600 & 0.0487504241963199 & 0.97562478790184 \tabularnewline
61 & 0.0234108602522892 & 0.0468217205045784 & 0.97658913974771 \tabularnewline
62 & 0.0239839175607855 & 0.0479678351215711 & 0.976016082439214 \tabularnewline
63 & 0.0236185761967653 & 0.0472371523935305 & 0.976381423803235 \tabularnewline
64 & 0.0213078750323408 & 0.0426157500646816 & 0.97869212496766 \tabularnewline
65 & 0.0199170750954847 & 0.0398341501909694 & 0.980082924904515 \tabularnewline
66 & 0.0199170225572962 & 0.0398340451145925 & 0.980082977442704 \tabularnewline
67 & 0.0244197686048962 & 0.0488395372097924 & 0.975580231395104 \tabularnewline
68 & 0.0371494665455978 & 0.0742989330911956 & 0.962850533454402 \tabularnewline
69 & 0.0549699587440883 & 0.109939917488177 & 0.945030041255912 \tabularnewline
70 & 0.0622898032874538 & 0.124579606574908 & 0.937710196712546 \tabularnewline
71 & 0.0720734123351849 & 0.144146824670370 & 0.927926587664815 \tabularnewline
72 & 0.103694471946384 & 0.207388943892768 & 0.896305528053616 \tabularnewline
73 & 0.124841445479838 & 0.249682890959675 & 0.875158554520162 \tabularnewline
74 & 0.139156080085408 & 0.278312160170816 & 0.860843919914592 \tabularnewline
75 & 0.154794099981798 & 0.309588199963595 & 0.845205900018202 \tabularnewline
76 & 0.190685498561700 & 0.381370997123401 & 0.8093145014383 \tabularnewline
77 & 0.243363778663909 & 0.486727557327818 & 0.756636221336091 \tabularnewline
78 & 0.345192024596745 & 0.690384049193489 & 0.654807975403255 \tabularnewline
79 & 0.483780680487124 & 0.967561360974248 & 0.516219319512876 \tabularnewline
80 & 0.630562014304442 & 0.738875971391117 & 0.369437985695558 \tabularnewline
81 & 0.777841629879014 & 0.444316740241972 & 0.222158370120986 \tabularnewline
82 & 0.862281354210847 & 0.275437291578307 & 0.137718645789153 \tabularnewline
83 & 0.950868307583902 & 0.0982633848321956 & 0.0491316924160978 \tabularnewline
84 & 0.9885740560709 & 0.0228518878582001 & 0.0114259439291000 \tabularnewline
85 & 0.995611823475665 & 0.00877635304867041 & 0.00438817652433520 \tabularnewline
86 & 0.998280315022853 & 0.00343936995429448 & 0.00171968497714724 \tabularnewline
87 & 0.999230119421964 & 0.00153976115607110 & 0.000769880578035552 \tabularnewline
88 & 0.999591938568164 & 0.00081612286367282 & 0.00040806143183641 \tabularnewline
89 & 0.999751454070173 & 0.000497091859654241 & 0.000248545929827120 \tabularnewline
90 & 0.999820722569067 & 0.000358554861865653 & 0.000179277430932826 \tabularnewline
91 & 0.999864350036064 & 0.000271299927872607 & 0.000135649963936304 \tabularnewline
92 & 0.999881912308101 & 0.000236175383797263 & 0.000118087691898632 \tabularnewline
93 & 0.999861721322512 & 0.000276557354976652 & 0.000138278677488326 \tabularnewline
94 & 0.99980362405919 & 0.000392751881618847 & 0.000196375940809424 \tabularnewline
95 & 0.99979785325732 & 0.000404293485358154 & 0.000202146742679077 \tabularnewline
96 & 0.999753036051021 & 0.000493927897957895 & 0.000246963948978947 \tabularnewline
97 & 0.999611261641076 & 0.000777476717848148 & 0.000388738358924074 \tabularnewline
98 & 0.999389835033077 & 0.00122032993384588 & 0.000610164966922942 \tabularnewline
99 & 0.999280015899139 & 0.00143996820172295 & 0.000719984100861474 \tabularnewline
100 & 0.99967515672327 & 0.00064968655346187 & 0.000324843276730935 \tabularnewline
101 & 0.99991085096946 & 0.000178298061080254 & 8.91490305401271e-05 \tabularnewline
102 & 0.999970420638632 & 5.91587227350711e-05 & 2.95793613675356e-05 \tabularnewline
103 & 0.999958614016103 & 8.27719677934485e-05 & 4.13859838967243e-05 \tabularnewline
104 & 0.999931718528814 & 0.000136562942371059 & 6.82814711855295e-05 \tabularnewline
105 & 0.999906569458552 & 0.000186861082895727 & 9.34305414478636e-05 \tabularnewline
106 & 0.999969044193152 & 6.19116136953796e-05 & 3.09558068476898e-05 \tabularnewline
107 & 0.999994058270114 & 1.18834597714386e-05 & 5.94172988571931e-06 \tabularnewline
108 & 0.999997900142041 & 4.19971591700326e-06 & 2.09985795850163e-06 \tabularnewline
109 & 0.999996813025266 & 6.37394946883827e-06 & 3.18697473441913e-06 \tabularnewline
110 & 0.999994586248736 & 1.08275025283937e-05 & 5.41375126419684e-06 \tabularnewline
111 & 0.999991590813505 & 1.68183729894116e-05 & 8.4091864947058e-06 \tabularnewline
112 & 0.99999314846908 & 1.37030618392835e-05 & 6.85153091964173e-06 \tabularnewline
113 & 0.99999616715853 & 7.66568293893442e-06 & 3.83284146946721e-06 \tabularnewline
114 & 0.999997991033205 & 4.017933590722e-06 & 2.008966795361e-06 \tabularnewline
115 & 0.999997923507165 & 4.15298566957474e-06 & 2.07649283478737e-06 \tabularnewline
116 & 0.999997574783608 & 4.85043278430980e-06 & 2.42521639215490e-06 \tabularnewline
117 & 0.999997276211997 & 5.44757600598732e-06 & 2.72378800299366e-06 \tabularnewline
118 & 0.99999757813006 & 4.84373987897535e-06 & 2.42186993948767e-06 \tabularnewline
119 & 0.99999853517289 & 2.92965421812767e-06 & 1.46482710906383e-06 \tabularnewline
120 & 0.999998665778412 & 2.66844317519157e-06 & 1.33422158759579e-06 \tabularnewline
121 & 0.99999770174334 & 4.59651332141514e-06 & 2.29825666070757e-06 \tabularnewline
122 & 0.99999513027824 & 9.73944352000669e-06 & 4.86972176000334e-06 \tabularnewline
123 & 0.999990607313786 & 1.8785372427205e-05 & 9.3926862136025e-06 \tabularnewline
124 & 0.999983147869547 & 3.37042609051697e-05 & 1.68521304525849e-05 \tabularnewline
125 & 0.9999796758442 & 4.06483116010969e-05 & 2.03241558005484e-05 \tabularnewline
126 & 0.999974780078178 & 5.04398436439627e-05 & 2.52199218219814e-05 \tabularnewline
127 & 0.999952017154272 & 9.59656914564437e-05 & 4.79828457282219e-05 \tabularnewline
128 & 0.999902993799294 & 0.000194012401411138 & 9.70062007055688e-05 \tabularnewline
129 & 0.999826425916646 & 0.000347148166708671 & 0.000173574083354335 \tabularnewline
130 & 0.999712728711325 & 0.000574542577349914 & 0.000287271288674957 \tabularnewline
131 & 0.999548298035157 & 0.000903403929686206 & 0.000451701964843103 \tabularnewline
132 & 0.999312056640546 & 0.00137588671890800 & 0.000687943359453998 \tabularnewline
133 & 0.998832816792645 & 0.00233436641470982 & 0.00116718320735491 \tabularnewline
134 & 0.998199486955849 & 0.00360102608830272 & 0.00180051304415136 \tabularnewline
135 & 0.997463192076803 & 0.00507361584639457 & 0.00253680792319728 \tabularnewline
136 & 0.996951422084629 & 0.00609715583074285 & 0.00304857791537143 \tabularnewline
137 & 0.995878989819306 & 0.00824202036138874 & 0.00412101018069437 \tabularnewline
138 & 0.992636603362983 & 0.0147267932740333 & 0.00736339663701666 \tabularnewline
139 & 0.99384783245751 & 0.0123043350849792 & 0.00615216754248962 \tabularnewline
140 & 0.99885726091977 & 0.00228547816045853 & 0.00114273908022926 \tabularnewline
141 & 0.999507314075798 & 0.000985371848404259 & 0.000492685924202130 \tabularnewline
142 & 0.999107567740667 & 0.00178486451866686 & 0.00089243225933343 \tabularnewline
143 & 0.999564940341635 & 0.000870119316729997 & 0.000435059658364998 \tabularnewline
144 & 0.999995123577431 & 9.75284513716384e-06 & 4.87642256858192e-06 \tabularnewline
145 & 0.999988668440071 & 2.26631198579854e-05 & 1.13315599289927e-05 \tabularnewline
146 & 0.99994749066441 & 0.000105018671180063 & 5.25093355900315e-05 \tabularnewline
147 & 0.99983365414593 & 0.000332691708141517 & 0.000166345854070758 \tabularnewline
148 & 0.999556316946393 & 0.000887366107214292 & 0.000443683053607146 \tabularnewline
149 & 0.998884699571004 & 0.00223060085799161 & 0.00111530042899581 \tabularnewline
150 & 0.998282721148412 & 0.00343455770317649 & 0.00171727885158824 \tabularnewline
151 & 0.995240718653888 & 0.00951856269222386 & 0.00475928134611193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]5.97327540369492e-05[/C][C]0.000119465508073898[/C][C]0.999940267245963[/C][/ROW]
[ROW][C]18[/C][C]2.28885486892943e-06[/C][C]4.57770973785886e-06[/C][C]0.999997711145131[/C][/ROW]
[ROW][C]19[/C][C]1.20385273000842e-07[/C][C]2.40770546001684e-07[/C][C]0.999999879614727[/C][/ROW]
[ROW][C]20[/C][C]1.02874028820131e-06[/C][C]2.05748057640261e-06[/C][C]0.999998971259712[/C][/ROW]
[ROW][C]21[/C][C]1.92943517935188e-05[/C][C]3.85887035870376e-05[/C][C]0.999980705648206[/C][/ROW]
[ROW][C]22[/C][C]4.98642122541647e-05[/C][C]9.97284245083295e-05[/C][C]0.999950135787746[/C][/ROW]
[ROW][C]23[/C][C]4.18174323688853e-05[/C][C]8.36348647377705e-05[/C][C]0.999958182567631[/C][/ROW]
[ROW][C]24[/C][C]6.98652668805786e-05[/C][C]0.000139730533761157[/C][C]0.99993013473312[/C][/ROW]
[ROW][C]25[/C][C]8.66896870441793e-05[/C][C]0.000173379374088359[/C][C]0.999913310312956[/C][/ROW]
[ROW][C]26[/C][C]0.000109522154782055[/C][C]0.000219044309564109[/C][C]0.999890477845218[/C][/ROW]
[ROW][C]27[/C][C]0.000110755071923529[/C][C]0.000221510143847058[/C][C]0.999889244928077[/C][/ROW]
[ROW][C]28[/C][C]9.67550484680525e-05[/C][C]0.000193510096936105[/C][C]0.999903244951532[/C][/ROW]
[ROW][C]29[/C][C]6.90057555222698e-05[/C][C]0.000138011511044540[/C][C]0.999930994244478[/C][/ROW]
[ROW][C]30[/C][C]4.81495174362557e-05[/C][C]9.62990348725113e-05[/C][C]0.999951850482564[/C][/ROW]
[ROW][C]31[/C][C]2.95030684367589e-05[/C][C]5.90061368735178e-05[/C][C]0.999970496931563[/C][/ROW]
[ROW][C]32[/C][C]1.24531575205606e-05[/C][C]2.49063150411213e-05[/C][C]0.99998754684248[/C][/ROW]
[ROW][C]33[/C][C]5.09779873208781e-06[/C][C]1.01955974641756e-05[/C][C]0.999994902201268[/C][/ROW]
[ROW][C]34[/C][C]1.83098359420750e-06[/C][C]3.66196718841499e-06[/C][C]0.999998169016406[/C][/ROW]
[ROW][C]35[/C][C]6.46267142568189e-07[/C][C]1.29253428513638e-06[/C][C]0.999999353732857[/C][/ROW]
[ROW][C]36[/C][C]2.27272873136635e-07[/C][C]4.5454574627327e-07[/C][C]0.999999772727127[/C][/ROW]
[ROW][C]37[/C][C]1.25594371439609e-07[/C][C]2.51188742879217e-07[/C][C]0.999999874405629[/C][/ROW]
[ROW][C]38[/C][C]2.29575413595763e-07[/C][C]4.59150827191527e-07[/C][C]0.999999770424586[/C][/ROW]
[ROW][C]39[/C][C]3.71550788404126e-07[/C][C]7.43101576808253e-07[/C][C]0.999999628449212[/C][/ROW]
[ROW][C]40[/C][C]2.31382556731558e-06[/C][C]4.62765113463115e-06[/C][C]0.999997686174433[/C][/ROW]
[ROW][C]41[/C][C]6.03988203339762e-06[/C][C]1.20797640667952e-05[/C][C]0.999993960117967[/C][/ROW]
[ROW][C]42[/C][C]2.04191435861720e-05[/C][C]4.08382871723439e-05[/C][C]0.999979580856414[/C][/ROW]
[ROW][C]43[/C][C]4.65190016578956e-05[/C][C]9.30380033157911e-05[/C][C]0.999953480998342[/C][/ROW]
[ROW][C]44[/C][C]0.000109448858431812[/C][C]0.000218897716863625[/C][C]0.999890551141568[/C][/ROW]
[ROW][C]45[/C][C]0.000243043775626504[/C][C]0.000486087551253009[/C][C]0.999756956224374[/C][/ROW]
[ROW][C]46[/C][C]0.000535540683033424[/C][C]0.00107108136606685[/C][C]0.999464459316967[/C][/ROW]
[ROW][C]47[/C][C]0.00101117834636486[/C][C]0.00202235669272971[/C][C]0.998988821653635[/C][/ROW]
[ROW][C]48[/C][C]0.00209280016161654[/C][C]0.00418560032323308[/C][C]0.997907199838383[/C][/ROW]
[ROW][C]49[/C][C]0.00326078429575472[/C][C]0.00652156859150943[/C][C]0.996739215704245[/C][/ROW]
[ROW][C]50[/C][C]0.0043299303635681[/C][C]0.0086598607271362[/C][C]0.995670069636432[/C][/ROW]
[ROW][C]51[/C][C]0.00552745740232722[/C][C]0.0110549148046544[/C][C]0.994472542597673[/C][/ROW]
[ROW][C]52[/C][C]0.00755523387584963[/C][C]0.0151104677516993[/C][C]0.99244476612415[/C][/ROW]
[ROW][C]53[/C][C]0.00922559521759723[/C][C]0.0184511904351945[/C][C]0.990774404782403[/C][/ROW]
[ROW][C]54[/C][C]0.0115382366589018[/C][C]0.0230764733178035[/C][C]0.988461763341098[/C][/ROW]
[ROW][C]55[/C][C]0.0140536313475257[/C][C]0.0281072626950513[/C][C]0.985946368652474[/C][/ROW]
[ROW][C]56[/C][C]0.0154086209004798[/C][C]0.0308172418009597[/C][C]0.98459137909952[/C][/ROW]
[ROW][C]57[/C][C]0.0163475507326312[/C][C]0.0326951014652625[/C][C]0.983652449267369[/C][/ROW]
[ROW][C]58[/C][C]0.0170895586194056[/C][C]0.0341791172388113[/C][C]0.982910441380594[/C][/ROW]
[ROW][C]59[/C][C]0.0204316342256891[/C][C]0.0408632684513781[/C][C]0.97956836577431[/C][/ROW]
[ROW][C]60[/C][C]0.0243752120981600[/C][C]0.0487504241963199[/C][C]0.97562478790184[/C][/ROW]
[ROW][C]61[/C][C]0.0234108602522892[/C][C]0.0468217205045784[/C][C]0.97658913974771[/C][/ROW]
[ROW][C]62[/C][C]0.0239839175607855[/C][C]0.0479678351215711[/C][C]0.976016082439214[/C][/ROW]
[ROW][C]63[/C][C]0.0236185761967653[/C][C]0.0472371523935305[/C][C]0.976381423803235[/C][/ROW]
[ROW][C]64[/C][C]0.0213078750323408[/C][C]0.0426157500646816[/C][C]0.97869212496766[/C][/ROW]
[ROW][C]65[/C][C]0.0199170750954847[/C][C]0.0398341501909694[/C][C]0.980082924904515[/C][/ROW]
[ROW][C]66[/C][C]0.0199170225572962[/C][C]0.0398340451145925[/C][C]0.980082977442704[/C][/ROW]
[ROW][C]67[/C][C]0.0244197686048962[/C][C]0.0488395372097924[/C][C]0.975580231395104[/C][/ROW]
[ROW][C]68[/C][C]0.0371494665455978[/C][C]0.0742989330911956[/C][C]0.962850533454402[/C][/ROW]
[ROW][C]69[/C][C]0.0549699587440883[/C][C]0.109939917488177[/C][C]0.945030041255912[/C][/ROW]
[ROW][C]70[/C][C]0.0622898032874538[/C][C]0.124579606574908[/C][C]0.937710196712546[/C][/ROW]
[ROW][C]71[/C][C]0.0720734123351849[/C][C]0.144146824670370[/C][C]0.927926587664815[/C][/ROW]
[ROW][C]72[/C][C]0.103694471946384[/C][C]0.207388943892768[/C][C]0.896305528053616[/C][/ROW]
[ROW][C]73[/C][C]0.124841445479838[/C][C]0.249682890959675[/C][C]0.875158554520162[/C][/ROW]
[ROW][C]74[/C][C]0.139156080085408[/C][C]0.278312160170816[/C][C]0.860843919914592[/C][/ROW]
[ROW][C]75[/C][C]0.154794099981798[/C][C]0.309588199963595[/C][C]0.845205900018202[/C][/ROW]
[ROW][C]76[/C][C]0.190685498561700[/C][C]0.381370997123401[/C][C]0.8093145014383[/C][/ROW]
[ROW][C]77[/C][C]0.243363778663909[/C][C]0.486727557327818[/C][C]0.756636221336091[/C][/ROW]
[ROW][C]78[/C][C]0.345192024596745[/C][C]0.690384049193489[/C][C]0.654807975403255[/C][/ROW]
[ROW][C]79[/C][C]0.483780680487124[/C][C]0.967561360974248[/C][C]0.516219319512876[/C][/ROW]
[ROW][C]80[/C][C]0.630562014304442[/C][C]0.738875971391117[/C][C]0.369437985695558[/C][/ROW]
[ROW][C]81[/C][C]0.777841629879014[/C][C]0.444316740241972[/C][C]0.222158370120986[/C][/ROW]
[ROW][C]82[/C][C]0.862281354210847[/C][C]0.275437291578307[/C][C]0.137718645789153[/C][/ROW]
[ROW][C]83[/C][C]0.950868307583902[/C][C]0.0982633848321956[/C][C]0.0491316924160978[/C][/ROW]
[ROW][C]84[/C][C]0.9885740560709[/C][C]0.0228518878582001[/C][C]0.0114259439291000[/C][/ROW]
[ROW][C]85[/C][C]0.995611823475665[/C][C]0.00877635304867041[/C][C]0.00438817652433520[/C][/ROW]
[ROW][C]86[/C][C]0.998280315022853[/C][C]0.00343936995429448[/C][C]0.00171968497714724[/C][/ROW]
[ROW][C]87[/C][C]0.999230119421964[/C][C]0.00153976115607110[/C][C]0.000769880578035552[/C][/ROW]
[ROW][C]88[/C][C]0.999591938568164[/C][C]0.00081612286367282[/C][C]0.00040806143183641[/C][/ROW]
[ROW][C]89[/C][C]0.999751454070173[/C][C]0.000497091859654241[/C][C]0.000248545929827120[/C][/ROW]
[ROW][C]90[/C][C]0.999820722569067[/C][C]0.000358554861865653[/C][C]0.000179277430932826[/C][/ROW]
[ROW][C]91[/C][C]0.999864350036064[/C][C]0.000271299927872607[/C][C]0.000135649963936304[/C][/ROW]
[ROW][C]92[/C][C]0.999881912308101[/C][C]0.000236175383797263[/C][C]0.000118087691898632[/C][/ROW]
[ROW][C]93[/C][C]0.999861721322512[/C][C]0.000276557354976652[/C][C]0.000138278677488326[/C][/ROW]
[ROW][C]94[/C][C]0.99980362405919[/C][C]0.000392751881618847[/C][C]0.000196375940809424[/C][/ROW]
[ROW][C]95[/C][C]0.99979785325732[/C][C]0.000404293485358154[/C][C]0.000202146742679077[/C][/ROW]
[ROW][C]96[/C][C]0.999753036051021[/C][C]0.000493927897957895[/C][C]0.000246963948978947[/C][/ROW]
[ROW][C]97[/C][C]0.999611261641076[/C][C]0.000777476717848148[/C][C]0.000388738358924074[/C][/ROW]
[ROW][C]98[/C][C]0.999389835033077[/C][C]0.00122032993384588[/C][C]0.000610164966922942[/C][/ROW]
[ROW][C]99[/C][C]0.999280015899139[/C][C]0.00143996820172295[/C][C]0.000719984100861474[/C][/ROW]
[ROW][C]100[/C][C]0.99967515672327[/C][C]0.00064968655346187[/C][C]0.000324843276730935[/C][/ROW]
[ROW][C]101[/C][C]0.99991085096946[/C][C]0.000178298061080254[/C][C]8.91490305401271e-05[/C][/ROW]
[ROW][C]102[/C][C]0.999970420638632[/C][C]5.91587227350711e-05[/C][C]2.95793613675356e-05[/C][/ROW]
[ROW][C]103[/C][C]0.999958614016103[/C][C]8.27719677934485e-05[/C][C]4.13859838967243e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999931718528814[/C][C]0.000136562942371059[/C][C]6.82814711855295e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999906569458552[/C][C]0.000186861082895727[/C][C]9.34305414478636e-05[/C][/ROW]
[ROW][C]106[/C][C]0.999969044193152[/C][C]6.19116136953796e-05[/C][C]3.09558068476898e-05[/C][/ROW]
[ROW][C]107[/C][C]0.999994058270114[/C][C]1.18834597714386e-05[/C][C]5.94172988571931e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999997900142041[/C][C]4.19971591700326e-06[/C][C]2.09985795850163e-06[/C][/ROW]
[ROW][C]109[/C][C]0.999996813025266[/C][C]6.37394946883827e-06[/C][C]3.18697473441913e-06[/C][/ROW]
[ROW][C]110[/C][C]0.999994586248736[/C][C]1.08275025283937e-05[/C][C]5.41375126419684e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999991590813505[/C][C]1.68183729894116e-05[/C][C]8.4091864947058e-06[/C][/ROW]
[ROW][C]112[/C][C]0.99999314846908[/C][C]1.37030618392835e-05[/C][C]6.85153091964173e-06[/C][/ROW]
[ROW][C]113[/C][C]0.99999616715853[/C][C]7.66568293893442e-06[/C][C]3.83284146946721e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999997991033205[/C][C]4.017933590722e-06[/C][C]2.008966795361e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999997923507165[/C][C]4.15298566957474e-06[/C][C]2.07649283478737e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999997574783608[/C][C]4.85043278430980e-06[/C][C]2.42521639215490e-06[/C][/ROW]
[ROW][C]117[/C][C]0.999997276211997[/C][C]5.44757600598732e-06[/C][C]2.72378800299366e-06[/C][/ROW]
[ROW][C]118[/C][C]0.99999757813006[/C][C]4.84373987897535e-06[/C][C]2.42186993948767e-06[/C][/ROW]
[ROW][C]119[/C][C]0.99999853517289[/C][C]2.92965421812767e-06[/C][C]1.46482710906383e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999998665778412[/C][C]2.66844317519157e-06[/C][C]1.33422158759579e-06[/C][/ROW]
[ROW][C]121[/C][C]0.99999770174334[/C][C]4.59651332141514e-06[/C][C]2.29825666070757e-06[/C][/ROW]
[ROW][C]122[/C][C]0.99999513027824[/C][C]9.73944352000669e-06[/C][C]4.86972176000334e-06[/C][/ROW]
[ROW][C]123[/C][C]0.999990607313786[/C][C]1.8785372427205e-05[/C][C]9.3926862136025e-06[/C][/ROW]
[ROW][C]124[/C][C]0.999983147869547[/C][C]3.37042609051697e-05[/C][C]1.68521304525849e-05[/C][/ROW]
[ROW][C]125[/C][C]0.9999796758442[/C][C]4.06483116010969e-05[/C][C]2.03241558005484e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999974780078178[/C][C]5.04398436439627e-05[/C][C]2.52199218219814e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999952017154272[/C][C]9.59656914564437e-05[/C][C]4.79828457282219e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999902993799294[/C][C]0.000194012401411138[/C][C]9.70062007055688e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999826425916646[/C][C]0.000347148166708671[/C][C]0.000173574083354335[/C][/ROW]
[ROW][C]130[/C][C]0.999712728711325[/C][C]0.000574542577349914[/C][C]0.000287271288674957[/C][/ROW]
[ROW][C]131[/C][C]0.999548298035157[/C][C]0.000903403929686206[/C][C]0.000451701964843103[/C][/ROW]
[ROW][C]132[/C][C]0.999312056640546[/C][C]0.00137588671890800[/C][C]0.000687943359453998[/C][/ROW]
[ROW][C]133[/C][C]0.998832816792645[/C][C]0.00233436641470982[/C][C]0.00116718320735491[/C][/ROW]
[ROW][C]134[/C][C]0.998199486955849[/C][C]0.00360102608830272[/C][C]0.00180051304415136[/C][/ROW]
[ROW][C]135[/C][C]0.997463192076803[/C][C]0.00507361584639457[/C][C]0.00253680792319728[/C][/ROW]
[ROW][C]136[/C][C]0.996951422084629[/C][C]0.00609715583074285[/C][C]0.00304857791537143[/C][/ROW]
[ROW][C]137[/C][C]0.995878989819306[/C][C]0.00824202036138874[/C][C]0.00412101018069437[/C][/ROW]
[ROW][C]138[/C][C]0.992636603362983[/C][C]0.0147267932740333[/C][C]0.00736339663701666[/C][/ROW]
[ROW][C]139[/C][C]0.99384783245751[/C][C]0.0123043350849792[/C][C]0.00615216754248962[/C][/ROW]
[ROW][C]140[/C][C]0.99885726091977[/C][C]0.00228547816045853[/C][C]0.00114273908022926[/C][/ROW]
[ROW][C]141[/C][C]0.999507314075798[/C][C]0.000985371848404259[/C][C]0.000492685924202130[/C][/ROW]
[ROW][C]142[/C][C]0.999107567740667[/C][C]0.00178486451866686[/C][C]0.00089243225933343[/C][/ROW]
[ROW][C]143[/C][C]0.999564940341635[/C][C]0.000870119316729997[/C][C]0.000435059658364998[/C][/ROW]
[ROW][C]144[/C][C]0.999995123577431[/C][C]9.75284513716384e-06[/C][C]4.87642256858192e-06[/C][/ROW]
[ROW][C]145[/C][C]0.999988668440071[/C][C]2.26631198579854e-05[/C][C]1.13315599289927e-05[/C][/ROW]
[ROW][C]146[/C][C]0.99994749066441[/C][C]0.000105018671180063[/C][C]5.25093355900315e-05[/C][/ROW]
[ROW][C]147[/C][C]0.99983365414593[/C][C]0.000332691708141517[/C][C]0.000166345854070758[/C][/ROW]
[ROW][C]148[/C][C]0.999556316946393[/C][C]0.000887366107214292[/C][C]0.000443683053607146[/C][/ROW]
[ROW][C]149[/C][C]0.998884699571004[/C][C]0.00223060085799161[/C][C]0.00111530042899581[/C][/ROW]
[ROW][C]150[/C][C]0.998282721148412[/C][C]0.00343455770317649[/C][C]0.00171727885158824[/C][/ROW]
[ROW][C]151[/C][C]0.995240718653888[/C][C]0.00951856269222386[/C][C]0.00475928134611193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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
175.97327540369492e-050.0001194655080738980.999940267245963
182.28885486892943e-064.57770973785886e-060.999997711145131
191.20385273000842e-072.40770546001684e-070.999999879614727
201.02874028820131e-062.05748057640261e-060.999998971259712
211.92943517935188e-053.85887035870376e-050.999980705648206
224.98642122541647e-059.97284245083295e-050.999950135787746
234.18174323688853e-058.36348647377705e-050.999958182567631
246.98652668805786e-050.0001397305337611570.99993013473312
258.66896870441793e-050.0001733793740883590.999913310312956
260.0001095221547820550.0002190443095641090.999890477845218
270.0001107550719235290.0002215101438470580.999889244928077
289.67550484680525e-050.0001935100969361050.999903244951532
296.90057555222698e-050.0001380115110445400.999930994244478
304.81495174362557e-059.62990348725113e-050.999951850482564
312.95030684367589e-055.90061368735178e-050.999970496931563
321.24531575205606e-052.49063150411213e-050.99998754684248
335.09779873208781e-061.01955974641756e-050.999994902201268
341.83098359420750e-063.66196718841499e-060.999998169016406
356.46267142568189e-071.29253428513638e-060.999999353732857
362.27272873136635e-074.5454574627327e-070.999999772727127
371.25594371439609e-072.51188742879217e-070.999999874405629
382.29575413595763e-074.59150827191527e-070.999999770424586
393.71550788404126e-077.43101576808253e-070.999999628449212
402.31382556731558e-064.62765113463115e-060.999997686174433
416.03988203339762e-061.20797640667952e-050.999993960117967
422.04191435861720e-054.08382871723439e-050.999979580856414
434.65190016578956e-059.30380033157911e-050.999953480998342
440.0001094488584318120.0002188977168636250.999890551141568
450.0002430437756265040.0004860875512530090.999756956224374
460.0005355406830334240.001071081366066850.999464459316967
470.001011178346364860.002022356692729710.998988821653635
480.002092800161616540.004185600323233080.997907199838383
490.003260784295754720.006521568591509430.996739215704245
500.00432993036356810.00865986072713620.995670069636432
510.005527457402327220.01105491480465440.994472542597673
520.007555233875849630.01511046775169930.99244476612415
530.009225595217597230.01845119043519450.990774404782403
540.01153823665890180.02307647331780350.988461763341098
550.01405363134752570.02810726269505130.985946368652474
560.01540862090047980.03081724180095970.98459137909952
570.01634755073263120.03269510146526250.983652449267369
580.01708955861940560.03417911723881130.982910441380594
590.02043163422568910.04086326845137810.97956836577431
600.02437521209816000.04875042419631990.97562478790184
610.02341086025228920.04682172050457840.97658913974771
620.02398391756078550.04796783512157110.976016082439214
630.02361857619676530.04723715239353050.976381423803235
640.02130787503234080.04261575006468160.97869212496766
650.01991707509548470.03983415019096940.980082924904515
660.01991702255729620.03983404511459250.980082977442704
670.02441976860489620.04883953720979240.975580231395104
680.03714946654559780.07429893309119560.962850533454402
690.05496995874408830.1099399174881770.945030041255912
700.06228980328745380.1245796065749080.937710196712546
710.07207341233518490.1441468246703700.927926587664815
720.1036944719463840.2073889438927680.896305528053616
730.1248414454798380.2496828909596750.875158554520162
740.1391560800854080.2783121601708160.860843919914592
750.1547940999817980.3095881999635950.845205900018202
760.1906854985617000.3813709971234010.8093145014383
770.2433637786639090.4867275573278180.756636221336091
780.3451920245967450.6903840491934890.654807975403255
790.4837806804871240.9675613609742480.516219319512876
800.6305620143044420.7388759713911170.369437985695558
810.7778416298790140.4443167402419720.222158370120986
820.8622813542108470.2754372915783070.137718645789153
830.9508683075839020.09826338483219560.0491316924160978
840.98857405607090.02285188785820010.0114259439291000
850.9956118234756650.008776353048670410.00438817652433520
860.9982803150228530.003439369954294480.00171968497714724
870.9992301194219640.001539761156071100.000769880578035552
880.9995919385681640.000816122863672820.00040806143183641
890.9997514540701730.0004970918596542410.000248545929827120
900.9998207225690670.0003585548618656530.000179277430932826
910.9998643500360640.0002712999278726070.000135649963936304
920.9998819123081010.0002361753837972630.000118087691898632
930.9998617213225120.0002765573549766520.000138278677488326
940.999803624059190.0003927518816188470.000196375940809424
950.999797853257320.0004042934853581540.000202146742679077
960.9997530360510210.0004939278979578950.000246963948978947
970.9996112616410760.0007774767178481480.000388738358924074
980.9993898350330770.001220329933845880.000610164966922942
990.9992800158991390.001439968201722950.000719984100861474
1000.999675156723270.000649686553461870.000324843276730935
1010.999910850969460.0001782980610802548.91490305401271e-05
1020.9999704206386325.91587227350711e-052.95793613675356e-05
1030.9999586140161038.27719677934485e-054.13859838967243e-05
1040.9999317185288140.0001365629423710596.82814711855295e-05
1050.9999065694585520.0001868610828957279.34305414478636e-05
1060.9999690441931526.19116136953796e-053.09558068476898e-05
1070.9999940582701141.18834597714386e-055.94172988571931e-06
1080.9999979001420414.19971591700326e-062.09985795850163e-06
1090.9999968130252666.37394946883827e-063.18697473441913e-06
1100.9999945862487361.08275025283937e-055.41375126419684e-06
1110.9999915908135051.68183729894116e-058.4091864947058e-06
1120.999993148469081.37030618392835e-056.85153091964173e-06
1130.999996167158537.66568293893442e-063.83284146946721e-06
1140.9999979910332054.017933590722e-062.008966795361e-06
1150.9999979235071654.15298566957474e-062.07649283478737e-06
1160.9999975747836084.85043278430980e-062.42521639215490e-06
1170.9999972762119975.44757600598732e-062.72378800299366e-06
1180.999997578130064.84373987897535e-062.42186993948767e-06
1190.999998535172892.92965421812767e-061.46482710906383e-06
1200.9999986657784122.66844317519157e-061.33422158759579e-06
1210.999997701743344.59651332141514e-062.29825666070757e-06
1220.999995130278249.73944352000669e-064.86972176000334e-06
1230.9999906073137861.8785372427205e-059.3926862136025e-06
1240.9999831478695473.37042609051697e-051.68521304525849e-05
1250.99997967584424.06483116010969e-052.03241558005484e-05
1260.9999747800781785.04398436439627e-052.52199218219814e-05
1270.9999520171542729.59656914564437e-054.79828457282219e-05
1280.9999029937992940.0001940124014111389.70062007055688e-05
1290.9998264259166460.0003471481667086710.000173574083354335
1300.9997127287113250.0005745425773499140.000287271288674957
1310.9995482980351570.0009034039296862060.000451701964843103
1320.9993120566405460.001375886718908000.000687943359453998
1330.9988328167926450.002334366414709820.00116718320735491
1340.9981994869558490.003601026088302720.00180051304415136
1350.9974631920768030.005073615846394570.00253680792319728
1360.9969514220846290.006097155830742850.00304857791537143
1370.9958789898193060.008242020361388740.00412101018069437
1380.9926366033629830.01472679327403330.00736339663701666
1390.993847832457510.01230433508497920.00615216754248962
1400.998857260919770.002285478160458530.00114273908022926
1410.9995073140757980.0009853718484042590.000492685924202130
1420.9991075677406670.001784864518666860.00089243225933343
1430.9995649403416350.0008701193167299970.000435059658364998
1440.9999951235774319.75284513716384e-064.87642256858192e-06
1450.9999886684400712.26631198579854e-051.13315599289927e-05
1460.999947490664410.0001050186711800635.25093355900315e-05
1470.999833654145930.0003326917081415170.000166345854070758
1480.9995563169463930.0008873661072142920.000443683053607146
1490.9988846995710040.002230600857991610.00111530042899581
1500.9982827211484120.003434557703176490.00171727885158824
1510.9952407186538880.009518562692223860.00475928134611193







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level990.733333333333333NOK
5% type I error level1190.881481481481482NOK
10% type I error level1210.896296296296296NOK

\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 & 99 & 0.733333333333333 & NOK \tabularnewline
5% type I error level & 119 & 0.881481481481482 & NOK \tabularnewline
10% type I error level & 121 & 0.896296296296296 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58191&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]99[/C][C]0.733333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]119[/C][C]0.881481481481482[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]121[/C][C]0.896296296296296[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58191&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58191&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 level990.733333333333333NOK
5% type I error level1190.881481481481482NOK
10% type I error level1210.896296296296296NOK



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')
}