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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 21 Dec 2010 16:18:28 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292948345xpmkkbava3uvvng.htm/, Retrieved Sat, 18 May 2024 14:35:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113723, Retrieved Sat, 18 May 2024 14:35:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Cross Correlation Function] [Q7 - zonder trans...] [2008-12-01 20:04:13] [299afd6311e4c20059ea2f05c8dd029d]
F RM D    [Variance Reduction Matrix] [Q8] [2008-12-01 20:20:44] [299afd6311e4c20059ea2f05c8dd029d]
F    D      [Variance Reduction Matrix] [Q8 - 2] [2008-12-01 20:25:07] [299afd6311e4c20059ea2f05c8dd029d]
F RM D        [Standard Deviation-Mean Plot] [Deel 2: Step 1] [2008-12-08 20:09:35] [299afd6311e4c20059ea2f05c8dd029d]
F RM D          [ARIMA Backward Selection] [Deel 2: Step 5] [2008-12-08 20:35:27] [299afd6311e4c20059ea2f05c8dd029d]
-   P             [ARIMA Backward Selection] [Uitvoer vanuit Be...] [2008-12-14 15:42:25] [299afd6311e4c20059ea2f05c8dd029d]
- RMPD                [Multiple Regression] [] [2010-12-21 16:18:28] [40b262140b988d7b8204c4955f8b7651] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9,1	4,5	1,0	-1,0	3484,7
9,0	4,3	1,0	3,0	3411,1
9,0	4,3	1,3	2,0	3288,2
8,9	4,2	1,1	3,0	3280,4
8,8	4,0	0,8	5,0	3174,0
8,7	3,8	0,7	5,0	3165,3
8,5	4,1	0,7	3,0	3092,7
8,3	4,2	0,9	2,0	3053,1
8,1	4,0	1,3	1,0	3182,0
7,9	4,3	1,4	-4,0	2999,9
7,8	4,7	1,6	1,0	3249,6
7,6	5,0	2,1	1,0	3210,5
7,4	5,1	0,3	6,0	3030,3
7,2	5,4	2,1	3,0	2803,5
7,0	5,4	2,5	2,0	2767,6
7,0	5,4	2,3	2,0	2882,6
6,8	5,5	2,4	2,0	2863,4
6,8	5,8	3,0	-8,0	2897,1
6,7	5,7	1,7	0,0	3012,6
6,8	5,5	3,5	-2,0	3143,0
6,7	5,6	4,0	3,0	3032,9
6,7	5,6	3,7	5,0	3045,8
6,7	5,5	3,7	8,0	3110,5
6,5	5,5	3,0	8,0	3013,2
6,3	5,7	2,7	9,0	2987,1
6,3	5,6	2,5	11,0	2995,6
6,3	5,6	2,2	13,0	2833,2
6,5	5,4	2,9	12,0	2849,0
6,6	5,2	3,1	13,0	2794,8
6,5	5,1	3,0	15,0	2845,3
6,3	5,1	2,8	13,0	2915,0
6,3	5,0	2,5	16,0	2892,6
6,5	5,3	1,9	10,0	2604,4
7,0	5,4	1,9	14,0	2641,7
7,1	5,3	1,8	14,0	2659,8
7,3	5,1	2,0	15,0	2638,5
7,3	5,0	2,6	13,0	2720,3
7,4	5,0	2,5	8,0	2745,9
7,4	4,6	2,5	7,0	2735,7
7,3	4,8	1,6	3,0	2811,7
7,4	5,1	1,4	3,0	2799,4
7,5	5,1	0,8	4,0	2555,3
7,7	5,1	1,1	4,0	2305,0
7,7	5,4	1,3	0,0	2215,0
7,7	5,3	1,2	-4,0	2065,8
7,7	5,3	1,3	-14,0	1940,5
7,7	5,1	1,1	-18,0	2042,0
7,8	4,9	1,3	-8,0	1995,4
8,0	4,7	1,2	-1,0	1946,8
8,1	4,4	1,6	1,0	1765,9
8,1	4,6	1,7	2,0	1635,3
8,2	4,5	1,5	0,0	1833,4
8,2	4,2	0,9	1,0	1910,4
8,2	4,0	1,5	0,0	1959,7
8,1	3,9	1,4	-1,0	1969,6
8,1	4,1	1,6	-3,0	2061,4
8,2	4,1	1,7	-3,0	2093,5
8,3	3,7	1,4	-3,0	2120,9
8,3	3,8	1,8	-4,0	2174,6
8,4	4,1	1,7	-8,0	2196,7
8,5	4,1	1,4	-9,0	2350,4
8,5	4,0	1,2	-13,0	2440,3
8,4	4,3	1,0	-18,0	2408,6
8,0	4,4	1,7	-11,0	2472,8
7,9	4,2	2,4	-9,0	2407,6
8,1	4,2	2,0	-10,0	2454,6
8,5	4,0	2,1	-13,0	2448,1
8,8	4,0	2,0	-11,0	2497,8
8,8	4,3	1,8	-5,0	2645,6
8,6	4,4	2,7	-15,0	2756,8
8,3	4,4	2,3	-6,0	2849,3
8,3	4,3	1,9	-6,0	2921,4
8,3	4,1	2,0	-3,0	2981,9
8,4	4,1	2,3	-1,0	3080,6
8,4	3,9	2,8	-3,0	3106,2
8,5	3,8	2,4	-4,0	3119,3
8,6	3,7	2,3	-6,0	3061,3
8,6	3,5	2,7	0,0	3097,3
8,6	3,7	2,7	-4,0	3161,7
8,6	3,7	2,9	-2,0	3257,2
8,6	3,5	3,0	-2,0	3277,0
8,5	3,3	2,2	-6,0	3295,3
8,4	3,2	2,3	-7,0	3364,0
8,4	3,3	2,8	-6,0	3494,2
8,4	3,1	2,8	-6,0	3667,0
8,5	3,2	2,8	-3,0	3813,1
8,5	3,4	2,2	-2,0	3918,0
8,6	3,5	2,6	-5,0	3895,5
8,6	3,3	2,8	-11,0	3801,1
8,4	3,5	2,5	-11,0	3570,1
8,2	3,5	2,4	-11,0	3701,6
8,0	3,8	2,3	-10,0	3862,3
8,0	4,0	1,9	-14,0	3970,1
8,0	4,0	1,7	-8,0	4138,5
8,0	4,1	2,0	-9,0	4199,8
7,9	4,0	2,1	-5,0	4290,9
7,9	3,8	1,7	-1,0	4443,9
7,8	3,7	1,8	-2,0	4502,6
7,8	3,8	1,8	-5,0	4357,0
8,0	3,7	1,8	-4,0	4591,3
7,8	4,0	1,3	-6,0	4697,0
7,4	4,2	1,3	-2,0	4621,4
7,2	4,0	1,3	-2,0	4562,8
7,0	4,1	1,2	-2,0	4202,5
7,0	4,2	1,4	-2,0	4296,5
7,2	4,5	2,2	2,0	4435,2
7,2	4,6	2,9	1,0	4105,2
7,2	4,5	3,1	-8,0	4116,7
7,0	4,5	3,5	-1,0	3844,5
6,9	4,5	3,6	1,0	3721,0
6,8	4,4	4,4	-1,0	3674,4
6,8	4,3	4,1	2,0	3857,6
6,8	4,5	5,1	2,0	3801,1
6,9	4,1	5,8	1,0	3504,4
7,2	4,1	5,9	-1,0	3032,6
7,2	4,3	5,4	-2,0	3047,0
7,2	4,4	5,5	-2,0	2962,3
7,1	4,7	4,8	-1,0	2197,8
7,2	5,0	3,2	-8,0	2014,5
7,3	4,7	2,7	-4,0	1862,8
7,5	4,5	2,1	-6,0	1905,4
7,6	4,5	1,9	-3,0	1811,0
7,7	4,5	0,6	-3,0	1670,1
7,7	5,5	0,7	-7,0	1864,4
7,7	4,5	-0,2	-9,0	2052,0
7,8	4,4	-1,0	-11,0	2029,6
8,0	4,2	-1,7	-13,0	2070,8
8,1	3,9	-0,7	-11,0	2293,4
8,1	3,9	-1,0	-9,0	2443,3
8,0	4,2	-0,9	-17,0	2513,2
8,1	4,0	0,0	-22,0	2466,9
8,2	3,8	0,3	-25,0	2502,7
8,3	3,7	0,8	-20,0	2539,9
8,4	3,7	0,8	-24,0	2482,6
8,4	3,7	1,9	-24,0	2626,2
8,4	3,7	2,1	-22,0	2656,3
8,5	3,7	2,5	-19,0	2446,7
8,5	3,8	2,7	-18,0	2467,4
8,6	3,7	2,4	-17,0	2462,3
8,6	3,5	2,4	-11,0	2504,6
8,5	3,5	2,9	-11,0	2579,4
8,5	3,1	3,1	-12,0	2649,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 13.1121249136743 -0.931678442910017rente[t] -0.077702445244027hicp[t] -0.027613673258089consumer[t] -0.000195934660486037bel20[t] -0.00837512881878393t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  13.1121249136743 -0.931678442910017rente[t] -0.077702445244027hicp[t] -0.027613673258089consumer[t] -0.000195934660486037bel20[t] -0.00837512881878393t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  13.1121249136743 -0.931678442910017rente[t] -0.077702445244027hicp[t] -0.027613673258089consumer[t] -0.000195934660486037bel20[t] -0.00837512881878393t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 13.1121249136743 -0.931678442910017rente[t] -0.077702445244027hicp[t] -0.027613673258089consumer[t] -0.000195934660486037bel20[t] -0.00837512881878393t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.11212491367430.30890842.446800
rente-0.9316784429100170.051936-17.93900
hicp-0.0777024452440270.023244-3.3430.0010710.000535
consumer-0.0276136732580890.0045-6.136500
bel20-0.0001959346604860374e-05-4.94692e-061e-06
t-0.008375128818783930.000891-9.401400

\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) & 13.1121249136743 & 0.308908 & 42.4468 & 0 & 0 \tabularnewline
rente & -0.931678442910017 & 0.051936 & -17.939 & 0 & 0 \tabularnewline
hicp & -0.077702445244027 & 0.023244 & -3.343 & 0.001071 & 0.000535 \tabularnewline
consumer & -0.027613673258089 & 0.0045 & -6.1365 & 0 & 0 \tabularnewline
bel20 & -0.000195934660486037 & 4e-05 & -4.9469 & 2e-06 & 1e-06 \tabularnewline
t & -0.00837512881878393 & 0.000891 & -9.4014 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&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]13.1121249136743[/C][C]0.308908[/C][C]42.4468[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]rente[/C][C]-0.931678442910017[/C][C]0.051936[/C][C]-17.939[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]hicp[/C][C]-0.077702445244027[/C][C]0.023244[/C][C]-3.343[/C][C]0.001071[/C][C]0.000535[/C][/ROW]
[ROW][C]consumer[/C][C]-0.027613673258089[/C][C]0.0045[/C][C]-6.1365[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]bel20[/C][C]-0.000195934660486037[/C][C]4e-05[/C][C]-4.9469[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.00837512881878393[/C][C]0.000891[/C][C]-9.4014[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.11212491367430.30890842.446800
rente-0.9316784429100170.051936-17.93900
hicp-0.0777024452440270.023244-3.3430.0010710.000535
consumer-0.0276136732580890.0045-6.136500
bel20-0.0001959346604860374e-05-4.94692e-061e-06
t-0.008375128818783930.000891-9.401400






Multiple Linear Regression - Regression Statistics
Multiple R0.907212309133192
R-squared0.823034173842778
Adjusted R-squared0.81652807729288
F-TEST (value)126.50199202096
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.305771902075493
Sum Squared Residuals12.7155180294456

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.907212309133192 \tabularnewline
R-squared & 0.823034173842778 \tabularnewline
Adjusted R-squared & 0.81652807729288 \tabularnewline
F-TEST (value) & 126.50199202096 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.305771902075493 \tabularnewline
Sum Squared Residuals & 12.7155180294456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.907212309133192[/C][/ROW]
[ROW][C]R-squared[/C][C]0.823034173842778[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.81652807729288[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]126.50199202096[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.305771902075493[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12.7155180294456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.907212309133192
R-squared0.823034173842778
Adjusted R-squared0.81652807729288
F-TEST (value)126.50199202096
F-TEST (DF numerator)5
F-TEST (DF denominator)136
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.305771902075493
Sum Squared Residuals12.7155180294456






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.18.17833450837880.921665491621195
298.260261166121440.739738833878564
398.280269346761270.719730653238734
48.98.354517168375990.54548283162401
58.88.521408563071950.278591436928046
68.78.70884399890581-0.00884399890580678
78.58.490417540081480.00958245991851884
88.38.40870676373622-0.108706763736225
98.18.55794404092327-0.457944040923274
107.98.43604320267203-0.536043202672034
117.87.85246295662663-0.0524629566266297
127.67.533394117537830.0666058824621682
137.47.46895460539643-0.0689546053964328
147.27.168490543037890.0315094569621048
1577.16368216369104-0.163682163691038
1677.14831503796517-0.148315037965166
176.87.04276376581231-0.24276376581231
186.86.97779737149662-0.177797371496616
196.76.92006342643522-0.220063426435218
206.86.98783705154799-0.187837051547989
216.76.73094689564526-0.0309468956452573
226.76.688127596763230.0118724032367666
236.76.677402319927740.0225976800722627
246.56.74248334524506-0.242483345245064
256.36.54858348279808-0.248583482798081
266.36.5920238961888-0.292023896188795
276.36.58355194328997-0.283551943289974
286.56.73163869700478-0.231638697004783
296.66.87706475305945-0.277064753059452
306.56.90450566618535-0.404505666185349
316.36.95324172709567-0.653241727095673
326.36.98289309276172-0.682893092761718
336.56.96378630691695-0.463786306916955
3476.744480277938680.255519722061316
357.16.833496820580510.266503179419492
367.36.972476626305180.327523373694815
377.37.04984776591940.250152234080593
387.47.182295320607030.217704679392972
397.47.5762037757473-0.176203775747297
407.37.54698881790155-0.246988817901552
417.47.277060641582550.122939358417454
427.57.335520957276730.164479042723269
437.77.35287754040440.347122459595606
447.77.17754720213990.522452797860101
457.77.40979830651340.290201693486608
467.77.694340278710.00565972129000357
477.77.97840865251504-0.278408652515044
487.87.87382254582722-0.0738225458272172
4987.875780061807840.124219938192162
508.18.09604472133020.00395527866980601
518.17.89153905280640.208460947193608
528.28.008284947601310.191715052398691
538.28.28333417668643-0.0833341766864326
548.28.43262736379936-0.232627363799363
558.18.55086424391526-0.450864243915261
568.18.37785348214923-0.277853482149228
578.28.35541860620444-0.15541860620444
588.38.73765697842555-0.437656978425552
598.38.62212500920815-0.322125009208145
608.48.44814112907637-0.0481411290763736
618.58.460575249772180.0394247502278168
628.58.65374862134787-0.153748621347867
638.48.52568994373274-0.125689943732736
6488.1638805409423-0.163880540942305
657.98.24499698238222-0.344996982382217
668.18.28610757587629-0.18610757587629
678.58.54041248618253-0.0404124861825325
688.88.474842302745820.325157697254184
698.88.007862947734460.792137052265537
708.68.09073657223990.509263427760104
718.37.846795406100960.453204593899038
728.37.948542210649750.351457789350252
738.38.02403745915490.275962540845107
748.47.917785499256750.482214500743249
758.48.10710625560570.292893744394307
768.58.248026878381240.251973121618757
778.68.407181395202230.192818604797768
788.68.381325289541810.218674710458191
798.68.284450973038080.315549026961923
808.68.18659624857790.413403751422107
818.68.352907057539090.247092942460913
828.58.69989866224299-0.199898662242989
838.48.7910740952735-0.391074095273502
848.48.59755553348833-0.197555533488332
858.48.74165858391956-0.341658583919564
868.58.5286485371385-0.0286485371385016
878.58.332391967741060.167608032258944
888.68.287017566168860.312982433831137
898.68.63361590838169-0.0336159083816935
908.48.50747673112639-0.107476731126388
918.28.48110643897809-0.281106438978094
9288.14189764861251-0.141897648612512
9388.0676007459413-0.0676007459412967
9487.876088669796940.123911330203065
9587.766837841684240.233162158315763
967.97.715555972029420.184444027970583
977.97.784164813803530.115835186196472
987.87.8772995934389-0.0772995934389019
997.87.88712572667015-0.0871257266701506
10087.89839727793240.1016027220676
1017.87.683886891765430.116113108234571
1027.47.393534041665030.00646595833497051
1037.27.58297637253273-0.382976372532731
10477.55979890212047-0.559798902120468
10577.42429758187619-0.424297581876189
1067.26.936626133547410.263373866452591
1077.26.872963559985290.327036440014714
1087.27.188485597135910.0115144028640902
10977.00906719199719-0.00906719199719133
1106.96.96189240270785-0.0618924027078518
1116.87.04888106367968-0.248881063679675
1126.87.0382482631498-0.238248263149793
1136.86.776905308822440.0230946911775602
1146.97.17255733252114-0.272557332521139
1157.27.30408127851144-0.104081278511443
1167.27.173013897879760.0269861021202409
1177.27.080296345988740.119703654011262
1187.16.968987770651250.131012229348746
1197.27.034643557423620.165356442576378
1207.37.263891779063230.0361082209367677
1217.57.53535433595234-0.0353543359523406
1227.67.478174908357980.121825091642023
1237.77.598420152018910.10157984798109
1247.76.722980924265630.977019075734374
1257.77.73468644328548-0.0346864432854808
1267.87.94125739786398-0.141257397863985
12788.22076450780218-0.220764507802177
1288.18.315348064672-0.215348064672002
1298.18.24568571730339-0.145685717303391
13088.15725036438394-0.157250364383937
1318.18.41241886449848-0.312418864498481
1328.28.64289524961736-0.44289524961736
1338.38.54347960680704-0.243479606807037
1348.48.65678622706646-0.256786227066459
1358.48.53480219123345-0.134802191233451
1368.48.44976159356905-0.0497615935690538
1378.58.368532371716260.131467628283734
1388.58.219779388827530.280220611172475
1398.68.301268431383340.298731568616659
1408.68.305258915459470.294741084540533
1418.58.243376651414310.256623348585687
1428.58.6060698446669-0.106069844666894

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.1 & 8.1783345083788 & 0.921665491621195 \tabularnewline
2 & 9 & 8.26026116612144 & 0.739738833878564 \tabularnewline
3 & 9 & 8.28026934676127 & 0.719730653238734 \tabularnewline
4 & 8.9 & 8.35451716837599 & 0.54548283162401 \tabularnewline
5 & 8.8 & 8.52140856307195 & 0.278591436928046 \tabularnewline
6 & 8.7 & 8.70884399890581 & -0.00884399890580678 \tabularnewline
7 & 8.5 & 8.49041754008148 & 0.00958245991851884 \tabularnewline
8 & 8.3 & 8.40870676373622 & -0.108706763736225 \tabularnewline
9 & 8.1 & 8.55794404092327 & -0.457944040923274 \tabularnewline
10 & 7.9 & 8.43604320267203 & -0.536043202672034 \tabularnewline
11 & 7.8 & 7.85246295662663 & -0.0524629566266297 \tabularnewline
12 & 7.6 & 7.53339411753783 & 0.0666058824621682 \tabularnewline
13 & 7.4 & 7.46895460539643 & -0.0689546053964328 \tabularnewline
14 & 7.2 & 7.16849054303789 & 0.0315094569621048 \tabularnewline
15 & 7 & 7.16368216369104 & -0.163682163691038 \tabularnewline
16 & 7 & 7.14831503796517 & -0.148315037965166 \tabularnewline
17 & 6.8 & 7.04276376581231 & -0.24276376581231 \tabularnewline
18 & 6.8 & 6.97779737149662 & -0.177797371496616 \tabularnewline
19 & 6.7 & 6.92006342643522 & -0.220063426435218 \tabularnewline
20 & 6.8 & 6.98783705154799 & -0.187837051547989 \tabularnewline
21 & 6.7 & 6.73094689564526 & -0.0309468956452573 \tabularnewline
22 & 6.7 & 6.68812759676323 & 0.0118724032367666 \tabularnewline
23 & 6.7 & 6.67740231992774 & 0.0225976800722627 \tabularnewline
24 & 6.5 & 6.74248334524506 & -0.242483345245064 \tabularnewline
25 & 6.3 & 6.54858348279808 & -0.248583482798081 \tabularnewline
26 & 6.3 & 6.5920238961888 & -0.292023896188795 \tabularnewline
27 & 6.3 & 6.58355194328997 & -0.283551943289974 \tabularnewline
28 & 6.5 & 6.73163869700478 & -0.231638697004783 \tabularnewline
29 & 6.6 & 6.87706475305945 & -0.277064753059452 \tabularnewline
30 & 6.5 & 6.90450566618535 & -0.404505666185349 \tabularnewline
31 & 6.3 & 6.95324172709567 & -0.653241727095673 \tabularnewline
32 & 6.3 & 6.98289309276172 & -0.682893092761718 \tabularnewline
33 & 6.5 & 6.96378630691695 & -0.463786306916955 \tabularnewline
34 & 7 & 6.74448027793868 & 0.255519722061316 \tabularnewline
35 & 7.1 & 6.83349682058051 & 0.266503179419492 \tabularnewline
36 & 7.3 & 6.97247662630518 & 0.327523373694815 \tabularnewline
37 & 7.3 & 7.0498477659194 & 0.250152234080593 \tabularnewline
38 & 7.4 & 7.18229532060703 & 0.217704679392972 \tabularnewline
39 & 7.4 & 7.5762037757473 & -0.176203775747297 \tabularnewline
40 & 7.3 & 7.54698881790155 & -0.246988817901552 \tabularnewline
41 & 7.4 & 7.27706064158255 & 0.122939358417454 \tabularnewline
42 & 7.5 & 7.33552095727673 & 0.164479042723269 \tabularnewline
43 & 7.7 & 7.3528775404044 & 0.347122459595606 \tabularnewline
44 & 7.7 & 7.1775472021399 & 0.522452797860101 \tabularnewline
45 & 7.7 & 7.4097983065134 & 0.290201693486608 \tabularnewline
46 & 7.7 & 7.69434027871 & 0.00565972129000357 \tabularnewline
47 & 7.7 & 7.97840865251504 & -0.278408652515044 \tabularnewline
48 & 7.8 & 7.87382254582722 & -0.0738225458272172 \tabularnewline
49 & 8 & 7.87578006180784 & 0.124219938192162 \tabularnewline
50 & 8.1 & 8.0960447213302 & 0.00395527866980601 \tabularnewline
51 & 8.1 & 7.8915390528064 & 0.208460947193608 \tabularnewline
52 & 8.2 & 8.00828494760131 & 0.191715052398691 \tabularnewline
53 & 8.2 & 8.28333417668643 & -0.0833341766864326 \tabularnewline
54 & 8.2 & 8.43262736379936 & -0.232627363799363 \tabularnewline
55 & 8.1 & 8.55086424391526 & -0.450864243915261 \tabularnewline
56 & 8.1 & 8.37785348214923 & -0.277853482149228 \tabularnewline
57 & 8.2 & 8.35541860620444 & -0.15541860620444 \tabularnewline
58 & 8.3 & 8.73765697842555 & -0.437656978425552 \tabularnewline
59 & 8.3 & 8.62212500920815 & -0.322125009208145 \tabularnewline
60 & 8.4 & 8.44814112907637 & -0.0481411290763736 \tabularnewline
61 & 8.5 & 8.46057524977218 & 0.0394247502278168 \tabularnewline
62 & 8.5 & 8.65374862134787 & -0.153748621347867 \tabularnewline
63 & 8.4 & 8.52568994373274 & -0.125689943732736 \tabularnewline
64 & 8 & 8.1638805409423 & -0.163880540942305 \tabularnewline
65 & 7.9 & 8.24499698238222 & -0.344996982382217 \tabularnewline
66 & 8.1 & 8.28610757587629 & -0.18610757587629 \tabularnewline
67 & 8.5 & 8.54041248618253 & -0.0404124861825325 \tabularnewline
68 & 8.8 & 8.47484230274582 & 0.325157697254184 \tabularnewline
69 & 8.8 & 8.00786294773446 & 0.792137052265537 \tabularnewline
70 & 8.6 & 8.0907365722399 & 0.509263427760104 \tabularnewline
71 & 8.3 & 7.84679540610096 & 0.453204593899038 \tabularnewline
72 & 8.3 & 7.94854221064975 & 0.351457789350252 \tabularnewline
73 & 8.3 & 8.0240374591549 & 0.275962540845107 \tabularnewline
74 & 8.4 & 7.91778549925675 & 0.482214500743249 \tabularnewline
75 & 8.4 & 8.1071062556057 & 0.292893744394307 \tabularnewline
76 & 8.5 & 8.24802687838124 & 0.251973121618757 \tabularnewline
77 & 8.6 & 8.40718139520223 & 0.192818604797768 \tabularnewline
78 & 8.6 & 8.38132528954181 & 0.218674710458191 \tabularnewline
79 & 8.6 & 8.28445097303808 & 0.315549026961923 \tabularnewline
80 & 8.6 & 8.1865962485779 & 0.413403751422107 \tabularnewline
81 & 8.6 & 8.35290705753909 & 0.247092942460913 \tabularnewline
82 & 8.5 & 8.69989866224299 & -0.199898662242989 \tabularnewline
83 & 8.4 & 8.7910740952735 & -0.391074095273502 \tabularnewline
84 & 8.4 & 8.59755553348833 & -0.197555533488332 \tabularnewline
85 & 8.4 & 8.74165858391956 & -0.341658583919564 \tabularnewline
86 & 8.5 & 8.5286485371385 & -0.0286485371385016 \tabularnewline
87 & 8.5 & 8.33239196774106 & 0.167608032258944 \tabularnewline
88 & 8.6 & 8.28701756616886 & 0.312982433831137 \tabularnewline
89 & 8.6 & 8.63361590838169 & -0.0336159083816935 \tabularnewline
90 & 8.4 & 8.50747673112639 & -0.107476731126388 \tabularnewline
91 & 8.2 & 8.48110643897809 & -0.281106438978094 \tabularnewline
92 & 8 & 8.14189764861251 & -0.141897648612512 \tabularnewline
93 & 8 & 8.0676007459413 & -0.0676007459412967 \tabularnewline
94 & 8 & 7.87608866979694 & 0.123911330203065 \tabularnewline
95 & 8 & 7.76683784168424 & 0.233162158315763 \tabularnewline
96 & 7.9 & 7.71555597202942 & 0.184444027970583 \tabularnewline
97 & 7.9 & 7.78416481380353 & 0.115835186196472 \tabularnewline
98 & 7.8 & 7.8772995934389 & -0.0772995934389019 \tabularnewline
99 & 7.8 & 7.88712572667015 & -0.0871257266701506 \tabularnewline
100 & 8 & 7.8983972779324 & 0.1016027220676 \tabularnewline
101 & 7.8 & 7.68388689176543 & 0.116113108234571 \tabularnewline
102 & 7.4 & 7.39353404166503 & 0.00646595833497051 \tabularnewline
103 & 7.2 & 7.58297637253273 & -0.382976372532731 \tabularnewline
104 & 7 & 7.55979890212047 & -0.559798902120468 \tabularnewline
105 & 7 & 7.42429758187619 & -0.424297581876189 \tabularnewline
106 & 7.2 & 6.93662613354741 & 0.263373866452591 \tabularnewline
107 & 7.2 & 6.87296355998529 & 0.327036440014714 \tabularnewline
108 & 7.2 & 7.18848559713591 & 0.0115144028640902 \tabularnewline
109 & 7 & 7.00906719199719 & -0.00906719199719133 \tabularnewline
110 & 6.9 & 6.96189240270785 & -0.0618924027078518 \tabularnewline
111 & 6.8 & 7.04888106367968 & -0.248881063679675 \tabularnewline
112 & 6.8 & 7.0382482631498 & -0.238248263149793 \tabularnewline
113 & 6.8 & 6.77690530882244 & 0.0230946911775602 \tabularnewline
114 & 6.9 & 7.17255733252114 & -0.272557332521139 \tabularnewline
115 & 7.2 & 7.30408127851144 & -0.104081278511443 \tabularnewline
116 & 7.2 & 7.17301389787976 & 0.0269861021202409 \tabularnewline
117 & 7.2 & 7.08029634598874 & 0.119703654011262 \tabularnewline
118 & 7.1 & 6.96898777065125 & 0.131012229348746 \tabularnewline
119 & 7.2 & 7.03464355742362 & 0.165356442576378 \tabularnewline
120 & 7.3 & 7.26389177906323 & 0.0361082209367677 \tabularnewline
121 & 7.5 & 7.53535433595234 & -0.0353543359523406 \tabularnewline
122 & 7.6 & 7.47817490835798 & 0.121825091642023 \tabularnewline
123 & 7.7 & 7.59842015201891 & 0.10157984798109 \tabularnewline
124 & 7.7 & 6.72298092426563 & 0.977019075734374 \tabularnewline
125 & 7.7 & 7.73468644328548 & -0.0346864432854808 \tabularnewline
126 & 7.8 & 7.94125739786398 & -0.141257397863985 \tabularnewline
127 & 8 & 8.22076450780218 & -0.220764507802177 \tabularnewline
128 & 8.1 & 8.315348064672 & -0.215348064672002 \tabularnewline
129 & 8.1 & 8.24568571730339 & -0.145685717303391 \tabularnewline
130 & 8 & 8.15725036438394 & -0.157250364383937 \tabularnewline
131 & 8.1 & 8.41241886449848 & -0.312418864498481 \tabularnewline
132 & 8.2 & 8.64289524961736 & -0.44289524961736 \tabularnewline
133 & 8.3 & 8.54347960680704 & -0.243479606807037 \tabularnewline
134 & 8.4 & 8.65678622706646 & -0.256786227066459 \tabularnewline
135 & 8.4 & 8.53480219123345 & -0.134802191233451 \tabularnewline
136 & 8.4 & 8.44976159356905 & -0.0497615935690538 \tabularnewline
137 & 8.5 & 8.36853237171626 & 0.131467628283734 \tabularnewline
138 & 8.5 & 8.21977938882753 & 0.280220611172475 \tabularnewline
139 & 8.6 & 8.30126843138334 & 0.298731568616659 \tabularnewline
140 & 8.6 & 8.30525891545947 & 0.294741084540533 \tabularnewline
141 & 8.5 & 8.24337665141431 & 0.256623348585687 \tabularnewline
142 & 8.5 & 8.6060698446669 & -0.106069844666894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&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]9.1[/C][C]8.1783345083788[/C][C]0.921665491621195[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]8.26026116612144[/C][C]0.739738833878564[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]8.28026934676127[/C][C]0.719730653238734[/C][/ROW]
[ROW][C]4[/C][C]8.9[/C][C]8.35451716837599[/C][C]0.54548283162401[/C][/ROW]
[ROW][C]5[/C][C]8.8[/C][C]8.52140856307195[/C][C]0.278591436928046[/C][/ROW]
[ROW][C]6[/C][C]8.7[/C][C]8.70884399890581[/C][C]-0.00884399890580678[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]8.49041754008148[/C][C]0.00958245991851884[/C][/ROW]
[ROW][C]8[/C][C]8.3[/C][C]8.40870676373622[/C][C]-0.108706763736225[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.55794404092327[/C][C]-0.457944040923274[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]8.43604320267203[/C][C]-0.536043202672034[/C][/ROW]
[ROW][C]11[/C][C]7.8[/C][C]7.85246295662663[/C][C]-0.0524629566266297[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.53339411753783[/C][C]0.0666058824621682[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]7.46895460539643[/C][C]-0.0689546053964328[/C][/ROW]
[ROW][C]14[/C][C]7.2[/C][C]7.16849054303789[/C][C]0.0315094569621048[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.16368216369104[/C][C]-0.163682163691038[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]7.14831503796517[/C][C]-0.148315037965166[/C][/ROW]
[ROW][C]17[/C][C]6.8[/C][C]7.04276376581231[/C][C]-0.24276376581231[/C][/ROW]
[ROW][C]18[/C][C]6.8[/C][C]6.97779737149662[/C][C]-0.177797371496616[/C][/ROW]
[ROW][C]19[/C][C]6.7[/C][C]6.92006342643522[/C][C]-0.220063426435218[/C][/ROW]
[ROW][C]20[/C][C]6.8[/C][C]6.98783705154799[/C][C]-0.187837051547989[/C][/ROW]
[ROW][C]21[/C][C]6.7[/C][C]6.73094689564526[/C][C]-0.0309468956452573[/C][/ROW]
[ROW][C]22[/C][C]6.7[/C][C]6.68812759676323[/C][C]0.0118724032367666[/C][/ROW]
[ROW][C]23[/C][C]6.7[/C][C]6.67740231992774[/C][C]0.0225976800722627[/C][/ROW]
[ROW][C]24[/C][C]6.5[/C][C]6.74248334524506[/C][C]-0.242483345245064[/C][/ROW]
[ROW][C]25[/C][C]6.3[/C][C]6.54858348279808[/C][C]-0.248583482798081[/C][/ROW]
[ROW][C]26[/C][C]6.3[/C][C]6.5920238961888[/C][C]-0.292023896188795[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]6.58355194328997[/C][C]-0.283551943289974[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]6.73163869700478[/C][C]-0.231638697004783[/C][/ROW]
[ROW][C]29[/C][C]6.6[/C][C]6.87706475305945[/C][C]-0.277064753059452[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]6.90450566618535[/C][C]-0.404505666185349[/C][/ROW]
[ROW][C]31[/C][C]6.3[/C][C]6.95324172709567[/C][C]-0.653241727095673[/C][/ROW]
[ROW][C]32[/C][C]6.3[/C][C]6.98289309276172[/C][C]-0.682893092761718[/C][/ROW]
[ROW][C]33[/C][C]6.5[/C][C]6.96378630691695[/C][C]-0.463786306916955[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]6.74448027793868[/C][C]0.255519722061316[/C][/ROW]
[ROW][C]35[/C][C]7.1[/C][C]6.83349682058051[/C][C]0.266503179419492[/C][/ROW]
[ROW][C]36[/C][C]7.3[/C][C]6.97247662630518[/C][C]0.327523373694815[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]7.0498477659194[/C][C]0.250152234080593[/C][/ROW]
[ROW][C]38[/C][C]7.4[/C][C]7.18229532060703[/C][C]0.217704679392972[/C][/ROW]
[ROW][C]39[/C][C]7.4[/C][C]7.5762037757473[/C][C]-0.176203775747297[/C][/ROW]
[ROW][C]40[/C][C]7.3[/C][C]7.54698881790155[/C][C]-0.246988817901552[/C][/ROW]
[ROW][C]41[/C][C]7.4[/C][C]7.27706064158255[/C][C]0.122939358417454[/C][/ROW]
[ROW][C]42[/C][C]7.5[/C][C]7.33552095727673[/C][C]0.164479042723269[/C][/ROW]
[ROW][C]43[/C][C]7.7[/C][C]7.3528775404044[/C][C]0.347122459595606[/C][/ROW]
[ROW][C]44[/C][C]7.7[/C][C]7.1775472021399[/C][C]0.522452797860101[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]7.4097983065134[/C][C]0.290201693486608[/C][/ROW]
[ROW][C]46[/C][C]7.7[/C][C]7.69434027871[/C][C]0.00565972129000357[/C][/ROW]
[ROW][C]47[/C][C]7.7[/C][C]7.97840865251504[/C][C]-0.278408652515044[/C][/ROW]
[ROW][C]48[/C][C]7.8[/C][C]7.87382254582722[/C][C]-0.0738225458272172[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.87578006180784[/C][C]0.124219938192162[/C][/ROW]
[ROW][C]50[/C][C]8.1[/C][C]8.0960447213302[/C][C]0.00395527866980601[/C][/ROW]
[ROW][C]51[/C][C]8.1[/C][C]7.8915390528064[/C][C]0.208460947193608[/C][/ROW]
[ROW][C]52[/C][C]8.2[/C][C]8.00828494760131[/C][C]0.191715052398691[/C][/ROW]
[ROW][C]53[/C][C]8.2[/C][C]8.28333417668643[/C][C]-0.0833341766864326[/C][/ROW]
[ROW][C]54[/C][C]8.2[/C][C]8.43262736379936[/C][C]-0.232627363799363[/C][/ROW]
[ROW][C]55[/C][C]8.1[/C][C]8.55086424391526[/C][C]-0.450864243915261[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]8.37785348214923[/C][C]-0.277853482149228[/C][/ROW]
[ROW][C]57[/C][C]8.2[/C][C]8.35541860620444[/C][C]-0.15541860620444[/C][/ROW]
[ROW][C]58[/C][C]8.3[/C][C]8.73765697842555[/C][C]-0.437656978425552[/C][/ROW]
[ROW][C]59[/C][C]8.3[/C][C]8.62212500920815[/C][C]-0.322125009208145[/C][/ROW]
[ROW][C]60[/C][C]8.4[/C][C]8.44814112907637[/C][C]-0.0481411290763736[/C][/ROW]
[ROW][C]61[/C][C]8.5[/C][C]8.46057524977218[/C][C]0.0394247502278168[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]8.65374862134787[/C][C]-0.153748621347867[/C][/ROW]
[ROW][C]63[/C][C]8.4[/C][C]8.52568994373274[/C][C]-0.125689943732736[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]8.1638805409423[/C][C]-0.163880540942305[/C][/ROW]
[ROW][C]65[/C][C]7.9[/C][C]8.24499698238222[/C][C]-0.344996982382217[/C][/ROW]
[ROW][C]66[/C][C]8.1[/C][C]8.28610757587629[/C][C]-0.18610757587629[/C][/ROW]
[ROW][C]67[/C][C]8.5[/C][C]8.54041248618253[/C][C]-0.0404124861825325[/C][/ROW]
[ROW][C]68[/C][C]8.8[/C][C]8.47484230274582[/C][C]0.325157697254184[/C][/ROW]
[ROW][C]69[/C][C]8.8[/C][C]8.00786294773446[/C][C]0.792137052265537[/C][/ROW]
[ROW][C]70[/C][C]8.6[/C][C]8.0907365722399[/C][C]0.509263427760104[/C][/ROW]
[ROW][C]71[/C][C]8.3[/C][C]7.84679540610096[/C][C]0.453204593899038[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]7.94854221064975[/C][C]0.351457789350252[/C][/ROW]
[ROW][C]73[/C][C]8.3[/C][C]8.0240374591549[/C][C]0.275962540845107[/C][/ROW]
[ROW][C]74[/C][C]8.4[/C][C]7.91778549925675[/C][C]0.482214500743249[/C][/ROW]
[ROW][C]75[/C][C]8.4[/C][C]8.1071062556057[/C][C]0.292893744394307[/C][/ROW]
[ROW][C]76[/C][C]8.5[/C][C]8.24802687838124[/C][C]0.251973121618757[/C][/ROW]
[ROW][C]77[/C][C]8.6[/C][C]8.40718139520223[/C][C]0.192818604797768[/C][/ROW]
[ROW][C]78[/C][C]8.6[/C][C]8.38132528954181[/C][C]0.218674710458191[/C][/ROW]
[ROW][C]79[/C][C]8.6[/C][C]8.28445097303808[/C][C]0.315549026961923[/C][/ROW]
[ROW][C]80[/C][C]8.6[/C][C]8.1865962485779[/C][C]0.413403751422107[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.35290705753909[/C][C]0.247092942460913[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]8.69989866224299[/C][C]-0.199898662242989[/C][/ROW]
[ROW][C]83[/C][C]8.4[/C][C]8.7910740952735[/C][C]-0.391074095273502[/C][/ROW]
[ROW][C]84[/C][C]8.4[/C][C]8.59755553348833[/C][C]-0.197555533488332[/C][/ROW]
[ROW][C]85[/C][C]8.4[/C][C]8.74165858391956[/C][C]-0.341658583919564[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]8.5286485371385[/C][C]-0.0286485371385016[/C][/ROW]
[ROW][C]87[/C][C]8.5[/C][C]8.33239196774106[/C][C]0.167608032258944[/C][/ROW]
[ROW][C]88[/C][C]8.6[/C][C]8.28701756616886[/C][C]0.312982433831137[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]8.63361590838169[/C][C]-0.0336159083816935[/C][/ROW]
[ROW][C]90[/C][C]8.4[/C][C]8.50747673112639[/C][C]-0.107476731126388[/C][/ROW]
[ROW][C]91[/C][C]8.2[/C][C]8.48110643897809[/C][C]-0.281106438978094[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]8.14189764861251[/C][C]-0.141897648612512[/C][/ROW]
[ROW][C]93[/C][C]8[/C][C]8.0676007459413[/C][C]-0.0676007459412967[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]7.87608866979694[/C][C]0.123911330203065[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]7.76683784168424[/C][C]0.233162158315763[/C][/ROW]
[ROW][C]96[/C][C]7.9[/C][C]7.71555597202942[/C][C]0.184444027970583[/C][/ROW]
[ROW][C]97[/C][C]7.9[/C][C]7.78416481380353[/C][C]0.115835186196472[/C][/ROW]
[ROW][C]98[/C][C]7.8[/C][C]7.8772995934389[/C][C]-0.0772995934389019[/C][/ROW]
[ROW][C]99[/C][C]7.8[/C][C]7.88712572667015[/C][C]-0.0871257266701506[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]7.8983972779324[/C][C]0.1016027220676[/C][/ROW]
[ROW][C]101[/C][C]7.8[/C][C]7.68388689176543[/C][C]0.116113108234571[/C][/ROW]
[ROW][C]102[/C][C]7.4[/C][C]7.39353404166503[/C][C]0.00646595833497051[/C][/ROW]
[ROW][C]103[/C][C]7.2[/C][C]7.58297637253273[/C][C]-0.382976372532731[/C][/ROW]
[ROW][C]104[/C][C]7[/C][C]7.55979890212047[/C][C]-0.559798902120468[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]7.42429758187619[/C][C]-0.424297581876189[/C][/ROW]
[ROW][C]106[/C][C]7.2[/C][C]6.93662613354741[/C][C]0.263373866452591[/C][/ROW]
[ROW][C]107[/C][C]7.2[/C][C]6.87296355998529[/C][C]0.327036440014714[/C][/ROW]
[ROW][C]108[/C][C]7.2[/C][C]7.18848559713591[/C][C]0.0115144028640902[/C][/ROW]
[ROW][C]109[/C][C]7[/C][C]7.00906719199719[/C][C]-0.00906719199719133[/C][/ROW]
[ROW][C]110[/C][C]6.9[/C][C]6.96189240270785[/C][C]-0.0618924027078518[/C][/ROW]
[ROW][C]111[/C][C]6.8[/C][C]7.04888106367968[/C][C]-0.248881063679675[/C][/ROW]
[ROW][C]112[/C][C]6.8[/C][C]7.0382482631498[/C][C]-0.238248263149793[/C][/ROW]
[ROW][C]113[/C][C]6.8[/C][C]6.77690530882244[/C][C]0.0230946911775602[/C][/ROW]
[ROW][C]114[/C][C]6.9[/C][C]7.17255733252114[/C][C]-0.272557332521139[/C][/ROW]
[ROW][C]115[/C][C]7.2[/C][C]7.30408127851144[/C][C]-0.104081278511443[/C][/ROW]
[ROW][C]116[/C][C]7.2[/C][C]7.17301389787976[/C][C]0.0269861021202409[/C][/ROW]
[ROW][C]117[/C][C]7.2[/C][C]7.08029634598874[/C][C]0.119703654011262[/C][/ROW]
[ROW][C]118[/C][C]7.1[/C][C]6.96898777065125[/C][C]0.131012229348746[/C][/ROW]
[ROW][C]119[/C][C]7.2[/C][C]7.03464355742362[/C][C]0.165356442576378[/C][/ROW]
[ROW][C]120[/C][C]7.3[/C][C]7.26389177906323[/C][C]0.0361082209367677[/C][/ROW]
[ROW][C]121[/C][C]7.5[/C][C]7.53535433595234[/C][C]-0.0353543359523406[/C][/ROW]
[ROW][C]122[/C][C]7.6[/C][C]7.47817490835798[/C][C]0.121825091642023[/C][/ROW]
[ROW][C]123[/C][C]7.7[/C][C]7.59842015201891[/C][C]0.10157984798109[/C][/ROW]
[ROW][C]124[/C][C]7.7[/C][C]6.72298092426563[/C][C]0.977019075734374[/C][/ROW]
[ROW][C]125[/C][C]7.7[/C][C]7.73468644328548[/C][C]-0.0346864432854808[/C][/ROW]
[ROW][C]126[/C][C]7.8[/C][C]7.94125739786398[/C][C]-0.141257397863985[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]8.22076450780218[/C][C]-0.220764507802177[/C][/ROW]
[ROW][C]128[/C][C]8.1[/C][C]8.315348064672[/C][C]-0.215348064672002[/C][/ROW]
[ROW][C]129[/C][C]8.1[/C][C]8.24568571730339[/C][C]-0.145685717303391[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]8.15725036438394[/C][C]-0.157250364383937[/C][/ROW]
[ROW][C]131[/C][C]8.1[/C][C]8.41241886449848[/C][C]-0.312418864498481[/C][/ROW]
[ROW][C]132[/C][C]8.2[/C][C]8.64289524961736[/C][C]-0.44289524961736[/C][/ROW]
[ROW][C]133[/C][C]8.3[/C][C]8.54347960680704[/C][C]-0.243479606807037[/C][/ROW]
[ROW][C]134[/C][C]8.4[/C][C]8.65678622706646[/C][C]-0.256786227066459[/C][/ROW]
[ROW][C]135[/C][C]8.4[/C][C]8.53480219123345[/C][C]-0.134802191233451[/C][/ROW]
[ROW][C]136[/C][C]8.4[/C][C]8.44976159356905[/C][C]-0.0497615935690538[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]8.36853237171626[/C][C]0.131467628283734[/C][/ROW]
[ROW][C]138[/C][C]8.5[/C][C]8.21977938882753[/C][C]0.280220611172475[/C][/ROW]
[ROW][C]139[/C][C]8.6[/C][C]8.30126843138334[/C][C]0.298731568616659[/C][/ROW]
[ROW][C]140[/C][C]8.6[/C][C]8.30525891545947[/C][C]0.294741084540533[/C][/ROW]
[ROW][C]141[/C][C]8.5[/C][C]8.24337665141431[/C][C]0.256623348585687[/C][/ROW]
[ROW][C]142[/C][C]8.5[/C][C]8.6060698446669[/C][C]-0.106069844666894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.18.17833450837880.921665491621195
298.260261166121440.739738833878564
398.280269346761270.719730653238734
48.98.354517168375990.54548283162401
58.88.521408563071950.278591436928046
68.78.70884399890581-0.00884399890580678
78.58.490417540081480.00958245991851884
88.38.40870676373622-0.108706763736225
98.18.55794404092327-0.457944040923274
107.98.43604320267203-0.536043202672034
117.87.85246295662663-0.0524629566266297
127.67.533394117537830.0666058824621682
137.47.46895460539643-0.0689546053964328
147.27.168490543037890.0315094569621048
1577.16368216369104-0.163682163691038
1677.14831503796517-0.148315037965166
176.87.04276376581231-0.24276376581231
186.86.97779737149662-0.177797371496616
196.76.92006342643522-0.220063426435218
206.86.98783705154799-0.187837051547989
216.76.73094689564526-0.0309468956452573
226.76.688127596763230.0118724032367666
236.76.677402319927740.0225976800722627
246.56.74248334524506-0.242483345245064
256.36.54858348279808-0.248583482798081
266.36.5920238961888-0.292023896188795
276.36.58355194328997-0.283551943289974
286.56.73163869700478-0.231638697004783
296.66.87706475305945-0.277064753059452
306.56.90450566618535-0.404505666185349
316.36.95324172709567-0.653241727095673
326.36.98289309276172-0.682893092761718
336.56.96378630691695-0.463786306916955
3476.744480277938680.255519722061316
357.16.833496820580510.266503179419492
367.36.972476626305180.327523373694815
377.37.04984776591940.250152234080593
387.47.182295320607030.217704679392972
397.47.5762037757473-0.176203775747297
407.37.54698881790155-0.246988817901552
417.47.277060641582550.122939358417454
427.57.335520957276730.164479042723269
437.77.35287754040440.347122459595606
447.77.17754720213990.522452797860101
457.77.40979830651340.290201693486608
467.77.694340278710.00565972129000357
477.77.97840865251504-0.278408652515044
487.87.87382254582722-0.0738225458272172
4987.875780061807840.124219938192162
508.18.09604472133020.00395527866980601
518.17.89153905280640.208460947193608
528.28.008284947601310.191715052398691
538.28.28333417668643-0.0833341766864326
548.28.43262736379936-0.232627363799363
558.18.55086424391526-0.450864243915261
568.18.37785348214923-0.277853482149228
578.28.35541860620444-0.15541860620444
588.38.73765697842555-0.437656978425552
598.38.62212500920815-0.322125009208145
608.48.44814112907637-0.0481411290763736
618.58.460575249772180.0394247502278168
628.58.65374862134787-0.153748621347867
638.48.52568994373274-0.125689943732736
6488.1638805409423-0.163880540942305
657.98.24499698238222-0.344996982382217
668.18.28610757587629-0.18610757587629
678.58.54041248618253-0.0404124861825325
688.88.474842302745820.325157697254184
698.88.007862947734460.792137052265537
708.68.09073657223990.509263427760104
718.37.846795406100960.453204593899038
728.37.948542210649750.351457789350252
738.38.02403745915490.275962540845107
748.47.917785499256750.482214500743249
758.48.10710625560570.292893744394307
768.58.248026878381240.251973121618757
778.68.407181395202230.192818604797768
788.68.381325289541810.218674710458191
798.68.284450973038080.315549026961923
808.68.18659624857790.413403751422107
818.68.352907057539090.247092942460913
828.58.69989866224299-0.199898662242989
838.48.7910740952735-0.391074095273502
848.48.59755553348833-0.197555533488332
858.48.74165858391956-0.341658583919564
868.58.5286485371385-0.0286485371385016
878.58.332391967741060.167608032258944
888.68.287017566168860.312982433831137
898.68.63361590838169-0.0336159083816935
908.48.50747673112639-0.107476731126388
918.28.48110643897809-0.281106438978094
9288.14189764861251-0.141897648612512
9388.0676007459413-0.0676007459412967
9487.876088669796940.123911330203065
9587.766837841684240.233162158315763
967.97.715555972029420.184444027970583
977.97.784164813803530.115835186196472
987.87.8772995934389-0.0772995934389019
997.87.88712572667015-0.0871257266701506
10087.89839727793240.1016027220676
1017.87.683886891765430.116113108234571
1027.47.393534041665030.00646595833497051
1037.27.58297637253273-0.382976372532731
10477.55979890212047-0.559798902120468
10577.42429758187619-0.424297581876189
1067.26.936626133547410.263373866452591
1077.26.872963559985290.327036440014714
1087.27.188485597135910.0115144028640902
10977.00906719199719-0.00906719199719133
1106.96.96189240270785-0.0618924027078518
1116.87.04888106367968-0.248881063679675
1126.87.0382482631498-0.238248263149793
1136.86.776905308822440.0230946911775602
1146.97.17255733252114-0.272557332521139
1157.27.30408127851144-0.104081278511443
1167.27.173013897879760.0269861021202409
1177.27.080296345988740.119703654011262
1187.16.968987770651250.131012229348746
1197.27.034643557423620.165356442576378
1207.37.263891779063230.0361082209367677
1217.57.53535433595234-0.0353543359523406
1227.67.478174908357980.121825091642023
1237.77.598420152018910.10157984798109
1247.76.722980924265630.977019075734374
1257.77.73468644328548-0.0346864432854808
1267.87.94125739786398-0.141257397863985
12788.22076450780218-0.220764507802177
1288.18.315348064672-0.215348064672002
1298.18.24568571730339-0.145685717303391
13088.15725036438394-0.157250364383937
1318.18.41241886449848-0.312418864498481
1328.28.64289524961736-0.44289524961736
1338.38.54347960680704-0.243479606807037
1348.48.65678622706646-0.256786227066459
1358.48.53480219123345-0.134802191233451
1368.48.44976159356905-0.0497615935690538
1378.58.368532371716260.131467628283734
1388.58.219779388827530.280220611172475
1398.68.301268431383340.298731568616659
1408.68.305258915459470.294741084540533
1418.58.243376651414310.256623348585687
1428.58.6060698446669-0.106069844666894






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.004483010572678870.008966021145357750.99551698942732
100.001639076846019640.003278153692039270.99836092315398
110.0006294066720485340.001258813344097070.999370593327951
120.0001408610596616970.0002817221193233950.999859138940338
135.71577995495649e-050.000114315599099130.99994284220045
144.75046020049755e-059.5009204009951e-050.999952495397995
151.87396493091837e-053.74792986183673e-050.99998126035069
167.46176970286808e-061.49235394057362e-050.999992538230297
171.62235143885263e-063.24470287770526e-060.999998377648561
184.22508362207069e-058.45016724414138e-050.99995774916378
193.55527590601244e-057.11055181202488e-050.99996444724094
205.30666057012168e-050.0001061332114024340.999946933394299
213.15284574794616e-056.30569149589231e-050.99996847154252
222.64770848472695e-055.2954169694539e-050.999973522915153
231.32023171639995e-052.6404634327999e-050.999986797682836
246.43078329924307e-061.28615665984861e-050.9999935692167
252.68224619020621e-065.36449238041241e-060.99999731775381
261.31339766647128e-062.62679533294256e-060.999998686602333
273.16250954723244e-066.32501909446487e-060.999996837490453
283.38136104374955e-056.7627220874991e-050.999966186389562
290.0001810054142520530.0003620108285041060.999818994585748
300.0001532093400351810.0003064186800703620.999846790659965
310.0001427643933882610.0002855287867765230.999857235606612
320.0001608975232379010.0003217950464758030.999839102476762
330.02127827112388910.04255654224777820.97872172887611
340.4696451926336480.9392903852672950.530354807366352
350.8006992758593290.3986014482813430.199300724140671
360.9355164112142220.1289671775715560.0644835887857782
370.970387343454110.05922531309177840.0296126565458892
380.9789868622844330.04202627543113370.0210131377155669
390.9742403892931770.05151922141364650.0257596107068233
400.9719263344839670.05614733103206550.0280736655160327
410.9648747250942950.0702505498114110.0351252749057055
420.9584291774029820.0831416451940360.041570822597018
430.9691074900143850.06178501997123060.0308925099856153
440.9811275435303740.03774491293925140.0188724564696257
450.9768048442878820.04639031142423640.0231951557121182
460.9700073504552360.0599852990895290.0299926495447645
470.9755211768127620.04895764637447650.0244788231872382
480.969228546907960.06154290618408060.0307714530920403
490.9611879510705810.07762409785883770.0388120489294189
500.950754292783740.09849141443252130.0492457072162606
510.9451168304484950.109766339103010.0548831695515051
520.9339227387017330.1321545225965350.0660772612982673
530.92051217624950.1589756475009990.0794878237504994
540.9112139309709740.1775721380580510.0887860690290256
550.9294686799480530.1410626401038940.0705313200519472
560.925809057272070.148381885455860.0741909427279301
570.9141109773370050.171778045325990.0858890226629949
580.9299265209632040.1401469580735930.0700734790367963
590.9344744046468530.1310511907062940.065525595353147
600.9266864538717740.1466270922564520.0733135461282258
610.914254434942260.1714911301154780.0857455650577392
620.9042148848686760.1915702302626480.095785115131324
630.8944014212401770.2111971575196470.105598578759823
640.8983837437483330.2032325125033340.101616256251667
650.9353679595928050.129264080814390.0646320404071949
660.9520810981826480.09583780363470380.0479189018173519
670.9596710652791720.08065786944165530.0403289347208276
680.9663361975464550.06732760490708950.0336638024535447
690.9903065138381530.01938697232369310.00969348616184653
700.9921374800572320.01572503988553550.00786251994276774
710.991115197489850.01776960502030080.00888480251015039
720.9880025613544450.02399487729111060.0119974386455553
730.983624323757860.03275135248428050.0163756762421402
740.9832106147114380.03357877057712460.0167893852885623
750.978661434248090.04267713150382190.0213385657519109
760.9728519536200210.05429609275995710.0271480463799786
770.9653728411265320.06925431774693530.0346271588734677
780.9577763116213520.08444737675729630.0422236883786482
790.9545549817766760.09089003644664840.0454450182233242
800.962425269152980.07514946169403950.0375747308470197
810.9626045836611740.07479083267765170.0373954163388258
820.9666470347326180.06670593053476470.0333529652673823
830.9785434003680360.04291319926392820.0214565996319641
840.9766543566926670.04669128661466550.0233456433073328
850.9804838455863270.03903230882734580.0195161544136729
860.9749342222374430.05013155552511480.0250657777625574
870.9729886929560.05402261408800220.0270113070440011
880.9797481092782160.04050378144356760.0202518907217838
890.976194373455340.04761125308931870.0238056265446594
900.9713632963589150.05727340728217070.0286367036410853
910.9695091482969820.06098170340603580.0304908517030179
920.9640618118558720.07187637628825620.0359381881441281
930.9567946293404640.08641074131907270.0432053706595363
940.953031459534380.09393708093124080.0469685404656204
950.9604965416596230.07900691668075330.0395034583403766
960.970196312527740.05960737494452030.0298036874722601
970.9797896324527420.04042073509451640.0202103675472582
980.9836132547023120.0327734905953770.0163867452976885
990.9895603750961660.02087924980766720.0104396249038336
1000.9993595841937180.001280831612563240.000640415806281618
1010.9999917163179921.65673640165649e-058.28368200828243e-06
1020.9999990210993871.95780122568335e-069.78900612841675e-07
1030.9999994288658831.14226823325277e-065.71134116626386e-07
1040.999999262058491.47588301946389e-067.37941509731943e-07
1050.999998659030992.68193801836737e-061.34096900918368e-06
1060.9999994483973431.10320531306419e-065.51602656532093e-07
1070.9999999714650235.70699534934044e-082.85349767467022e-08
1080.9999999988569882.28602366416737e-091.14301183208369e-09
1090.9999999997310715.37857653288076e-102.68928826644038e-10
1100.999999999663686.72639309272512e-103.36319654636256e-10
1110.999999998821952.35609946338462e-091.17804973169231e-09
1120.999999996426477.1470585447841e-093.57352927239205e-09
1130.999999991848511.63029822046809e-088.15149110234045e-09
1140.9999999895594982.08810042232522e-081.04405021116261e-08
1150.9999999802260673.95478669220432e-081.97739334610216e-08
1160.9999999615732337.68535333439568e-083.84267666719784e-08
1170.9999999862198272.75603452379062e-081.37801726189531e-08
1180.9999999570579968.58840076801688e-084.29420038400844e-08
1190.9999998743166672.51366666761751e-071.25683333380876e-07
1200.9999997819505624.36098876495333e-072.18049438247666e-07
1210.9999992625024031.47499519382856e-067.37497596914282e-07
1220.9999972592626095.48147478277279e-062.7407373913864e-06
1230.999990077771451.98444571001379e-059.92222855006895e-06
1240.9999834186644223.31626711564182e-051.65813355782091e-05
1250.9999680786857886.38426284241642e-053.19213142120821e-05
1260.9999912793003921.74413992160579e-058.72069960802896e-06
1270.9999801989088643.96021822711256e-051.98010911355628e-05
1280.9999300554059230.000139889188154086.994459407704e-05
1290.9998410499029770.0003179001940464560.000158950097023228
1300.9992550307620840.001489938475831120.000744969237915558
1310.998853601740870.002292796518261610.0011463982591308
1320.9990022066118630.001995586776273850.000997793388136924
1330.9939096264435340.01218074711293210.00609037355646604

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00448301057267887 & 0.00896602114535775 & 0.99551698942732 \tabularnewline
10 & 0.00163907684601964 & 0.00327815369203927 & 0.99836092315398 \tabularnewline
11 & 0.000629406672048534 & 0.00125881334409707 & 0.999370593327951 \tabularnewline
12 & 0.000140861059661697 & 0.000281722119323395 & 0.999859138940338 \tabularnewline
13 & 5.71577995495649e-05 & 0.00011431559909913 & 0.99994284220045 \tabularnewline
14 & 4.75046020049755e-05 & 9.5009204009951e-05 & 0.999952495397995 \tabularnewline
15 & 1.87396493091837e-05 & 3.74792986183673e-05 & 0.99998126035069 \tabularnewline
16 & 7.46176970286808e-06 & 1.49235394057362e-05 & 0.999992538230297 \tabularnewline
17 & 1.62235143885263e-06 & 3.24470287770526e-06 & 0.999998377648561 \tabularnewline
18 & 4.22508362207069e-05 & 8.45016724414138e-05 & 0.99995774916378 \tabularnewline
19 & 3.55527590601244e-05 & 7.11055181202488e-05 & 0.99996444724094 \tabularnewline
20 & 5.30666057012168e-05 & 0.000106133211402434 & 0.999946933394299 \tabularnewline
21 & 3.15284574794616e-05 & 6.30569149589231e-05 & 0.99996847154252 \tabularnewline
22 & 2.64770848472695e-05 & 5.2954169694539e-05 & 0.999973522915153 \tabularnewline
23 & 1.32023171639995e-05 & 2.6404634327999e-05 & 0.999986797682836 \tabularnewline
24 & 6.43078329924307e-06 & 1.28615665984861e-05 & 0.9999935692167 \tabularnewline
25 & 2.68224619020621e-06 & 5.36449238041241e-06 & 0.99999731775381 \tabularnewline
26 & 1.31339766647128e-06 & 2.62679533294256e-06 & 0.999998686602333 \tabularnewline
27 & 3.16250954723244e-06 & 6.32501909446487e-06 & 0.999996837490453 \tabularnewline
28 & 3.38136104374955e-05 & 6.7627220874991e-05 & 0.999966186389562 \tabularnewline
29 & 0.000181005414252053 & 0.000362010828504106 & 0.999818994585748 \tabularnewline
30 & 0.000153209340035181 & 0.000306418680070362 & 0.999846790659965 \tabularnewline
31 & 0.000142764393388261 & 0.000285528786776523 & 0.999857235606612 \tabularnewline
32 & 0.000160897523237901 & 0.000321795046475803 & 0.999839102476762 \tabularnewline
33 & 0.0212782711238891 & 0.0425565422477782 & 0.97872172887611 \tabularnewline
34 & 0.469645192633648 & 0.939290385267295 & 0.530354807366352 \tabularnewline
35 & 0.800699275859329 & 0.398601448281343 & 0.199300724140671 \tabularnewline
36 & 0.935516411214222 & 0.128967177571556 & 0.0644835887857782 \tabularnewline
37 & 0.97038734345411 & 0.0592253130917784 & 0.0296126565458892 \tabularnewline
38 & 0.978986862284433 & 0.0420262754311337 & 0.0210131377155669 \tabularnewline
39 & 0.974240389293177 & 0.0515192214136465 & 0.0257596107068233 \tabularnewline
40 & 0.971926334483967 & 0.0561473310320655 & 0.0280736655160327 \tabularnewline
41 & 0.964874725094295 & 0.070250549811411 & 0.0351252749057055 \tabularnewline
42 & 0.958429177402982 & 0.083141645194036 & 0.041570822597018 \tabularnewline
43 & 0.969107490014385 & 0.0617850199712306 & 0.0308925099856153 \tabularnewline
44 & 0.981127543530374 & 0.0377449129392514 & 0.0188724564696257 \tabularnewline
45 & 0.976804844287882 & 0.0463903114242364 & 0.0231951557121182 \tabularnewline
46 & 0.970007350455236 & 0.059985299089529 & 0.0299926495447645 \tabularnewline
47 & 0.975521176812762 & 0.0489576463744765 & 0.0244788231872382 \tabularnewline
48 & 0.96922854690796 & 0.0615429061840806 & 0.0307714530920403 \tabularnewline
49 & 0.961187951070581 & 0.0776240978588377 & 0.0388120489294189 \tabularnewline
50 & 0.95075429278374 & 0.0984914144325213 & 0.0492457072162606 \tabularnewline
51 & 0.945116830448495 & 0.10976633910301 & 0.0548831695515051 \tabularnewline
52 & 0.933922738701733 & 0.132154522596535 & 0.0660772612982673 \tabularnewline
53 & 0.9205121762495 & 0.158975647500999 & 0.0794878237504994 \tabularnewline
54 & 0.911213930970974 & 0.177572138058051 & 0.0887860690290256 \tabularnewline
55 & 0.929468679948053 & 0.141062640103894 & 0.0705313200519472 \tabularnewline
56 & 0.92580905727207 & 0.14838188545586 & 0.0741909427279301 \tabularnewline
57 & 0.914110977337005 & 0.17177804532599 & 0.0858890226629949 \tabularnewline
58 & 0.929926520963204 & 0.140146958073593 & 0.0700734790367963 \tabularnewline
59 & 0.934474404646853 & 0.131051190706294 & 0.065525595353147 \tabularnewline
60 & 0.926686453871774 & 0.146627092256452 & 0.0733135461282258 \tabularnewline
61 & 0.91425443494226 & 0.171491130115478 & 0.0857455650577392 \tabularnewline
62 & 0.904214884868676 & 0.191570230262648 & 0.095785115131324 \tabularnewline
63 & 0.894401421240177 & 0.211197157519647 & 0.105598578759823 \tabularnewline
64 & 0.898383743748333 & 0.203232512503334 & 0.101616256251667 \tabularnewline
65 & 0.935367959592805 & 0.12926408081439 & 0.0646320404071949 \tabularnewline
66 & 0.952081098182648 & 0.0958378036347038 & 0.0479189018173519 \tabularnewline
67 & 0.959671065279172 & 0.0806578694416553 & 0.0403289347208276 \tabularnewline
68 & 0.966336197546455 & 0.0673276049070895 & 0.0336638024535447 \tabularnewline
69 & 0.990306513838153 & 0.0193869723236931 & 0.00969348616184653 \tabularnewline
70 & 0.992137480057232 & 0.0157250398855355 & 0.00786251994276774 \tabularnewline
71 & 0.99111519748985 & 0.0177696050203008 & 0.00888480251015039 \tabularnewline
72 & 0.988002561354445 & 0.0239948772911106 & 0.0119974386455553 \tabularnewline
73 & 0.98362432375786 & 0.0327513524842805 & 0.0163756762421402 \tabularnewline
74 & 0.983210614711438 & 0.0335787705771246 & 0.0167893852885623 \tabularnewline
75 & 0.97866143424809 & 0.0426771315038219 & 0.0213385657519109 \tabularnewline
76 & 0.972851953620021 & 0.0542960927599571 & 0.0271480463799786 \tabularnewline
77 & 0.965372841126532 & 0.0692543177469353 & 0.0346271588734677 \tabularnewline
78 & 0.957776311621352 & 0.0844473767572963 & 0.0422236883786482 \tabularnewline
79 & 0.954554981776676 & 0.0908900364466484 & 0.0454450182233242 \tabularnewline
80 & 0.96242526915298 & 0.0751494616940395 & 0.0375747308470197 \tabularnewline
81 & 0.962604583661174 & 0.0747908326776517 & 0.0373954163388258 \tabularnewline
82 & 0.966647034732618 & 0.0667059305347647 & 0.0333529652673823 \tabularnewline
83 & 0.978543400368036 & 0.0429131992639282 & 0.0214565996319641 \tabularnewline
84 & 0.976654356692667 & 0.0466912866146655 & 0.0233456433073328 \tabularnewline
85 & 0.980483845586327 & 0.0390323088273458 & 0.0195161544136729 \tabularnewline
86 & 0.974934222237443 & 0.0501315555251148 & 0.0250657777625574 \tabularnewline
87 & 0.972988692956 & 0.0540226140880022 & 0.0270113070440011 \tabularnewline
88 & 0.979748109278216 & 0.0405037814435676 & 0.0202518907217838 \tabularnewline
89 & 0.97619437345534 & 0.0476112530893187 & 0.0238056265446594 \tabularnewline
90 & 0.971363296358915 & 0.0572734072821707 & 0.0286367036410853 \tabularnewline
91 & 0.969509148296982 & 0.0609817034060358 & 0.0304908517030179 \tabularnewline
92 & 0.964061811855872 & 0.0718763762882562 & 0.0359381881441281 \tabularnewline
93 & 0.956794629340464 & 0.0864107413190727 & 0.0432053706595363 \tabularnewline
94 & 0.95303145953438 & 0.0939370809312408 & 0.0469685404656204 \tabularnewline
95 & 0.960496541659623 & 0.0790069166807533 & 0.0395034583403766 \tabularnewline
96 & 0.97019631252774 & 0.0596073749445203 & 0.0298036874722601 \tabularnewline
97 & 0.979789632452742 & 0.0404207350945164 & 0.0202103675472582 \tabularnewline
98 & 0.983613254702312 & 0.032773490595377 & 0.0163867452976885 \tabularnewline
99 & 0.989560375096166 & 0.0208792498076672 & 0.0104396249038336 \tabularnewline
100 & 0.999359584193718 & 0.00128083161256324 & 0.000640415806281618 \tabularnewline
101 & 0.999991716317992 & 1.65673640165649e-05 & 8.28368200828243e-06 \tabularnewline
102 & 0.999999021099387 & 1.95780122568335e-06 & 9.78900612841675e-07 \tabularnewline
103 & 0.999999428865883 & 1.14226823325277e-06 & 5.71134116626386e-07 \tabularnewline
104 & 0.99999926205849 & 1.47588301946389e-06 & 7.37941509731943e-07 \tabularnewline
105 & 0.99999865903099 & 2.68193801836737e-06 & 1.34096900918368e-06 \tabularnewline
106 & 0.999999448397343 & 1.10320531306419e-06 & 5.51602656532093e-07 \tabularnewline
107 & 0.999999971465023 & 5.70699534934044e-08 & 2.85349767467022e-08 \tabularnewline
108 & 0.999999998856988 & 2.28602366416737e-09 & 1.14301183208369e-09 \tabularnewline
109 & 0.999999999731071 & 5.37857653288076e-10 & 2.68928826644038e-10 \tabularnewline
110 & 0.99999999966368 & 6.72639309272512e-10 & 3.36319654636256e-10 \tabularnewline
111 & 0.99999999882195 & 2.35609946338462e-09 & 1.17804973169231e-09 \tabularnewline
112 & 0.99999999642647 & 7.1470585447841e-09 & 3.57352927239205e-09 \tabularnewline
113 & 0.99999999184851 & 1.63029822046809e-08 & 8.15149110234045e-09 \tabularnewline
114 & 0.999999989559498 & 2.08810042232522e-08 & 1.04405021116261e-08 \tabularnewline
115 & 0.999999980226067 & 3.95478669220432e-08 & 1.97739334610216e-08 \tabularnewline
116 & 0.999999961573233 & 7.68535333439568e-08 & 3.84267666719784e-08 \tabularnewline
117 & 0.999999986219827 & 2.75603452379062e-08 & 1.37801726189531e-08 \tabularnewline
118 & 0.999999957057996 & 8.58840076801688e-08 & 4.29420038400844e-08 \tabularnewline
119 & 0.999999874316667 & 2.51366666761751e-07 & 1.25683333380876e-07 \tabularnewline
120 & 0.999999781950562 & 4.36098876495333e-07 & 2.18049438247666e-07 \tabularnewline
121 & 0.999999262502403 & 1.47499519382856e-06 & 7.37497596914282e-07 \tabularnewline
122 & 0.999997259262609 & 5.48147478277279e-06 & 2.7407373913864e-06 \tabularnewline
123 & 0.99999007777145 & 1.98444571001379e-05 & 9.92222855006895e-06 \tabularnewline
124 & 0.999983418664422 & 3.31626711564182e-05 & 1.65813355782091e-05 \tabularnewline
125 & 0.999968078685788 & 6.38426284241642e-05 & 3.19213142120821e-05 \tabularnewline
126 & 0.999991279300392 & 1.74413992160579e-05 & 8.72069960802896e-06 \tabularnewline
127 & 0.999980198908864 & 3.96021822711256e-05 & 1.98010911355628e-05 \tabularnewline
128 & 0.999930055405923 & 0.00013988918815408 & 6.994459407704e-05 \tabularnewline
129 & 0.999841049902977 & 0.000317900194046456 & 0.000158950097023228 \tabularnewline
130 & 0.999255030762084 & 0.00148993847583112 & 0.000744969237915558 \tabularnewline
131 & 0.99885360174087 & 0.00229279651826161 & 0.0011463982591308 \tabularnewline
132 & 0.999002206611863 & 0.00199558677627385 & 0.000997793388136924 \tabularnewline
133 & 0.993909626443534 & 0.0121807471129321 & 0.00609037355646604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&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]9[/C][C]0.00448301057267887[/C][C]0.00896602114535775[/C][C]0.99551698942732[/C][/ROW]
[ROW][C]10[/C][C]0.00163907684601964[/C][C]0.00327815369203927[/C][C]0.99836092315398[/C][/ROW]
[ROW][C]11[/C][C]0.000629406672048534[/C][C]0.00125881334409707[/C][C]0.999370593327951[/C][/ROW]
[ROW][C]12[/C][C]0.000140861059661697[/C][C]0.000281722119323395[/C][C]0.999859138940338[/C][/ROW]
[ROW][C]13[/C][C]5.71577995495649e-05[/C][C]0.00011431559909913[/C][C]0.99994284220045[/C][/ROW]
[ROW][C]14[/C][C]4.75046020049755e-05[/C][C]9.5009204009951e-05[/C][C]0.999952495397995[/C][/ROW]
[ROW][C]15[/C][C]1.87396493091837e-05[/C][C]3.74792986183673e-05[/C][C]0.99998126035069[/C][/ROW]
[ROW][C]16[/C][C]7.46176970286808e-06[/C][C]1.49235394057362e-05[/C][C]0.999992538230297[/C][/ROW]
[ROW][C]17[/C][C]1.62235143885263e-06[/C][C]3.24470287770526e-06[/C][C]0.999998377648561[/C][/ROW]
[ROW][C]18[/C][C]4.22508362207069e-05[/C][C]8.45016724414138e-05[/C][C]0.99995774916378[/C][/ROW]
[ROW][C]19[/C][C]3.55527590601244e-05[/C][C]7.11055181202488e-05[/C][C]0.99996444724094[/C][/ROW]
[ROW][C]20[/C][C]5.30666057012168e-05[/C][C]0.000106133211402434[/C][C]0.999946933394299[/C][/ROW]
[ROW][C]21[/C][C]3.15284574794616e-05[/C][C]6.30569149589231e-05[/C][C]0.99996847154252[/C][/ROW]
[ROW][C]22[/C][C]2.64770848472695e-05[/C][C]5.2954169694539e-05[/C][C]0.999973522915153[/C][/ROW]
[ROW][C]23[/C][C]1.32023171639995e-05[/C][C]2.6404634327999e-05[/C][C]0.999986797682836[/C][/ROW]
[ROW][C]24[/C][C]6.43078329924307e-06[/C][C]1.28615665984861e-05[/C][C]0.9999935692167[/C][/ROW]
[ROW][C]25[/C][C]2.68224619020621e-06[/C][C]5.36449238041241e-06[/C][C]0.99999731775381[/C][/ROW]
[ROW][C]26[/C][C]1.31339766647128e-06[/C][C]2.62679533294256e-06[/C][C]0.999998686602333[/C][/ROW]
[ROW][C]27[/C][C]3.16250954723244e-06[/C][C]6.32501909446487e-06[/C][C]0.999996837490453[/C][/ROW]
[ROW][C]28[/C][C]3.38136104374955e-05[/C][C]6.7627220874991e-05[/C][C]0.999966186389562[/C][/ROW]
[ROW][C]29[/C][C]0.000181005414252053[/C][C]0.000362010828504106[/C][C]0.999818994585748[/C][/ROW]
[ROW][C]30[/C][C]0.000153209340035181[/C][C]0.000306418680070362[/C][C]0.999846790659965[/C][/ROW]
[ROW][C]31[/C][C]0.000142764393388261[/C][C]0.000285528786776523[/C][C]0.999857235606612[/C][/ROW]
[ROW][C]32[/C][C]0.000160897523237901[/C][C]0.000321795046475803[/C][C]0.999839102476762[/C][/ROW]
[ROW][C]33[/C][C]0.0212782711238891[/C][C]0.0425565422477782[/C][C]0.97872172887611[/C][/ROW]
[ROW][C]34[/C][C]0.469645192633648[/C][C]0.939290385267295[/C][C]0.530354807366352[/C][/ROW]
[ROW][C]35[/C][C]0.800699275859329[/C][C]0.398601448281343[/C][C]0.199300724140671[/C][/ROW]
[ROW][C]36[/C][C]0.935516411214222[/C][C]0.128967177571556[/C][C]0.0644835887857782[/C][/ROW]
[ROW][C]37[/C][C]0.97038734345411[/C][C]0.0592253130917784[/C][C]0.0296126565458892[/C][/ROW]
[ROW][C]38[/C][C]0.978986862284433[/C][C]0.0420262754311337[/C][C]0.0210131377155669[/C][/ROW]
[ROW][C]39[/C][C]0.974240389293177[/C][C]0.0515192214136465[/C][C]0.0257596107068233[/C][/ROW]
[ROW][C]40[/C][C]0.971926334483967[/C][C]0.0561473310320655[/C][C]0.0280736655160327[/C][/ROW]
[ROW][C]41[/C][C]0.964874725094295[/C][C]0.070250549811411[/C][C]0.0351252749057055[/C][/ROW]
[ROW][C]42[/C][C]0.958429177402982[/C][C]0.083141645194036[/C][C]0.041570822597018[/C][/ROW]
[ROW][C]43[/C][C]0.969107490014385[/C][C]0.0617850199712306[/C][C]0.0308925099856153[/C][/ROW]
[ROW][C]44[/C][C]0.981127543530374[/C][C]0.0377449129392514[/C][C]0.0188724564696257[/C][/ROW]
[ROW][C]45[/C][C]0.976804844287882[/C][C]0.0463903114242364[/C][C]0.0231951557121182[/C][/ROW]
[ROW][C]46[/C][C]0.970007350455236[/C][C]0.059985299089529[/C][C]0.0299926495447645[/C][/ROW]
[ROW][C]47[/C][C]0.975521176812762[/C][C]0.0489576463744765[/C][C]0.0244788231872382[/C][/ROW]
[ROW][C]48[/C][C]0.96922854690796[/C][C]0.0615429061840806[/C][C]0.0307714530920403[/C][/ROW]
[ROW][C]49[/C][C]0.961187951070581[/C][C]0.0776240978588377[/C][C]0.0388120489294189[/C][/ROW]
[ROW][C]50[/C][C]0.95075429278374[/C][C]0.0984914144325213[/C][C]0.0492457072162606[/C][/ROW]
[ROW][C]51[/C][C]0.945116830448495[/C][C]0.10976633910301[/C][C]0.0548831695515051[/C][/ROW]
[ROW][C]52[/C][C]0.933922738701733[/C][C]0.132154522596535[/C][C]0.0660772612982673[/C][/ROW]
[ROW][C]53[/C][C]0.9205121762495[/C][C]0.158975647500999[/C][C]0.0794878237504994[/C][/ROW]
[ROW][C]54[/C][C]0.911213930970974[/C][C]0.177572138058051[/C][C]0.0887860690290256[/C][/ROW]
[ROW][C]55[/C][C]0.929468679948053[/C][C]0.141062640103894[/C][C]0.0705313200519472[/C][/ROW]
[ROW][C]56[/C][C]0.92580905727207[/C][C]0.14838188545586[/C][C]0.0741909427279301[/C][/ROW]
[ROW][C]57[/C][C]0.914110977337005[/C][C]0.17177804532599[/C][C]0.0858890226629949[/C][/ROW]
[ROW][C]58[/C][C]0.929926520963204[/C][C]0.140146958073593[/C][C]0.0700734790367963[/C][/ROW]
[ROW][C]59[/C][C]0.934474404646853[/C][C]0.131051190706294[/C][C]0.065525595353147[/C][/ROW]
[ROW][C]60[/C][C]0.926686453871774[/C][C]0.146627092256452[/C][C]0.0733135461282258[/C][/ROW]
[ROW][C]61[/C][C]0.91425443494226[/C][C]0.171491130115478[/C][C]0.0857455650577392[/C][/ROW]
[ROW][C]62[/C][C]0.904214884868676[/C][C]0.191570230262648[/C][C]0.095785115131324[/C][/ROW]
[ROW][C]63[/C][C]0.894401421240177[/C][C]0.211197157519647[/C][C]0.105598578759823[/C][/ROW]
[ROW][C]64[/C][C]0.898383743748333[/C][C]0.203232512503334[/C][C]0.101616256251667[/C][/ROW]
[ROW][C]65[/C][C]0.935367959592805[/C][C]0.12926408081439[/C][C]0.0646320404071949[/C][/ROW]
[ROW][C]66[/C][C]0.952081098182648[/C][C]0.0958378036347038[/C][C]0.0479189018173519[/C][/ROW]
[ROW][C]67[/C][C]0.959671065279172[/C][C]0.0806578694416553[/C][C]0.0403289347208276[/C][/ROW]
[ROW][C]68[/C][C]0.966336197546455[/C][C]0.0673276049070895[/C][C]0.0336638024535447[/C][/ROW]
[ROW][C]69[/C][C]0.990306513838153[/C][C]0.0193869723236931[/C][C]0.00969348616184653[/C][/ROW]
[ROW][C]70[/C][C]0.992137480057232[/C][C]0.0157250398855355[/C][C]0.00786251994276774[/C][/ROW]
[ROW][C]71[/C][C]0.99111519748985[/C][C]0.0177696050203008[/C][C]0.00888480251015039[/C][/ROW]
[ROW][C]72[/C][C]0.988002561354445[/C][C]0.0239948772911106[/C][C]0.0119974386455553[/C][/ROW]
[ROW][C]73[/C][C]0.98362432375786[/C][C]0.0327513524842805[/C][C]0.0163756762421402[/C][/ROW]
[ROW][C]74[/C][C]0.983210614711438[/C][C]0.0335787705771246[/C][C]0.0167893852885623[/C][/ROW]
[ROW][C]75[/C][C]0.97866143424809[/C][C]0.0426771315038219[/C][C]0.0213385657519109[/C][/ROW]
[ROW][C]76[/C][C]0.972851953620021[/C][C]0.0542960927599571[/C][C]0.0271480463799786[/C][/ROW]
[ROW][C]77[/C][C]0.965372841126532[/C][C]0.0692543177469353[/C][C]0.0346271588734677[/C][/ROW]
[ROW][C]78[/C][C]0.957776311621352[/C][C]0.0844473767572963[/C][C]0.0422236883786482[/C][/ROW]
[ROW][C]79[/C][C]0.954554981776676[/C][C]0.0908900364466484[/C][C]0.0454450182233242[/C][/ROW]
[ROW][C]80[/C][C]0.96242526915298[/C][C]0.0751494616940395[/C][C]0.0375747308470197[/C][/ROW]
[ROW][C]81[/C][C]0.962604583661174[/C][C]0.0747908326776517[/C][C]0.0373954163388258[/C][/ROW]
[ROW][C]82[/C][C]0.966647034732618[/C][C]0.0667059305347647[/C][C]0.0333529652673823[/C][/ROW]
[ROW][C]83[/C][C]0.978543400368036[/C][C]0.0429131992639282[/C][C]0.0214565996319641[/C][/ROW]
[ROW][C]84[/C][C]0.976654356692667[/C][C]0.0466912866146655[/C][C]0.0233456433073328[/C][/ROW]
[ROW][C]85[/C][C]0.980483845586327[/C][C]0.0390323088273458[/C][C]0.0195161544136729[/C][/ROW]
[ROW][C]86[/C][C]0.974934222237443[/C][C]0.0501315555251148[/C][C]0.0250657777625574[/C][/ROW]
[ROW][C]87[/C][C]0.972988692956[/C][C]0.0540226140880022[/C][C]0.0270113070440011[/C][/ROW]
[ROW][C]88[/C][C]0.979748109278216[/C][C]0.0405037814435676[/C][C]0.0202518907217838[/C][/ROW]
[ROW][C]89[/C][C]0.97619437345534[/C][C]0.0476112530893187[/C][C]0.0238056265446594[/C][/ROW]
[ROW][C]90[/C][C]0.971363296358915[/C][C]0.0572734072821707[/C][C]0.0286367036410853[/C][/ROW]
[ROW][C]91[/C][C]0.969509148296982[/C][C]0.0609817034060358[/C][C]0.0304908517030179[/C][/ROW]
[ROW][C]92[/C][C]0.964061811855872[/C][C]0.0718763762882562[/C][C]0.0359381881441281[/C][/ROW]
[ROW][C]93[/C][C]0.956794629340464[/C][C]0.0864107413190727[/C][C]0.0432053706595363[/C][/ROW]
[ROW][C]94[/C][C]0.95303145953438[/C][C]0.0939370809312408[/C][C]0.0469685404656204[/C][/ROW]
[ROW][C]95[/C][C]0.960496541659623[/C][C]0.0790069166807533[/C][C]0.0395034583403766[/C][/ROW]
[ROW][C]96[/C][C]0.97019631252774[/C][C]0.0596073749445203[/C][C]0.0298036874722601[/C][/ROW]
[ROW][C]97[/C][C]0.979789632452742[/C][C]0.0404207350945164[/C][C]0.0202103675472582[/C][/ROW]
[ROW][C]98[/C][C]0.983613254702312[/C][C]0.032773490595377[/C][C]0.0163867452976885[/C][/ROW]
[ROW][C]99[/C][C]0.989560375096166[/C][C]0.0208792498076672[/C][C]0.0104396249038336[/C][/ROW]
[ROW][C]100[/C][C]0.999359584193718[/C][C]0.00128083161256324[/C][C]0.000640415806281618[/C][/ROW]
[ROW][C]101[/C][C]0.999991716317992[/C][C]1.65673640165649e-05[/C][C]8.28368200828243e-06[/C][/ROW]
[ROW][C]102[/C][C]0.999999021099387[/C][C]1.95780122568335e-06[/C][C]9.78900612841675e-07[/C][/ROW]
[ROW][C]103[/C][C]0.999999428865883[/C][C]1.14226823325277e-06[/C][C]5.71134116626386e-07[/C][/ROW]
[ROW][C]104[/C][C]0.99999926205849[/C][C]1.47588301946389e-06[/C][C]7.37941509731943e-07[/C][/ROW]
[ROW][C]105[/C][C]0.99999865903099[/C][C]2.68193801836737e-06[/C][C]1.34096900918368e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999999448397343[/C][C]1.10320531306419e-06[/C][C]5.51602656532093e-07[/C][/ROW]
[ROW][C]107[/C][C]0.999999971465023[/C][C]5.70699534934044e-08[/C][C]2.85349767467022e-08[/C][/ROW]
[ROW][C]108[/C][C]0.999999998856988[/C][C]2.28602366416737e-09[/C][C]1.14301183208369e-09[/C][/ROW]
[ROW][C]109[/C][C]0.999999999731071[/C][C]5.37857653288076e-10[/C][C]2.68928826644038e-10[/C][/ROW]
[ROW][C]110[/C][C]0.99999999966368[/C][C]6.72639309272512e-10[/C][C]3.36319654636256e-10[/C][/ROW]
[ROW][C]111[/C][C]0.99999999882195[/C][C]2.35609946338462e-09[/C][C]1.17804973169231e-09[/C][/ROW]
[ROW][C]112[/C][C]0.99999999642647[/C][C]7.1470585447841e-09[/C][C]3.57352927239205e-09[/C][/ROW]
[ROW][C]113[/C][C]0.99999999184851[/C][C]1.63029822046809e-08[/C][C]8.15149110234045e-09[/C][/ROW]
[ROW][C]114[/C][C]0.999999989559498[/C][C]2.08810042232522e-08[/C][C]1.04405021116261e-08[/C][/ROW]
[ROW][C]115[/C][C]0.999999980226067[/C][C]3.95478669220432e-08[/C][C]1.97739334610216e-08[/C][/ROW]
[ROW][C]116[/C][C]0.999999961573233[/C][C]7.68535333439568e-08[/C][C]3.84267666719784e-08[/C][/ROW]
[ROW][C]117[/C][C]0.999999986219827[/C][C]2.75603452379062e-08[/C][C]1.37801726189531e-08[/C][/ROW]
[ROW][C]118[/C][C]0.999999957057996[/C][C]8.58840076801688e-08[/C][C]4.29420038400844e-08[/C][/ROW]
[ROW][C]119[/C][C]0.999999874316667[/C][C]2.51366666761751e-07[/C][C]1.25683333380876e-07[/C][/ROW]
[ROW][C]120[/C][C]0.999999781950562[/C][C]4.36098876495333e-07[/C][C]2.18049438247666e-07[/C][/ROW]
[ROW][C]121[/C][C]0.999999262502403[/C][C]1.47499519382856e-06[/C][C]7.37497596914282e-07[/C][/ROW]
[ROW][C]122[/C][C]0.999997259262609[/C][C]5.48147478277279e-06[/C][C]2.7407373913864e-06[/C][/ROW]
[ROW][C]123[/C][C]0.99999007777145[/C][C]1.98444571001379e-05[/C][C]9.92222855006895e-06[/C][/ROW]
[ROW][C]124[/C][C]0.999983418664422[/C][C]3.31626711564182e-05[/C][C]1.65813355782091e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999968078685788[/C][C]6.38426284241642e-05[/C][C]3.19213142120821e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999991279300392[/C][C]1.74413992160579e-05[/C][C]8.72069960802896e-06[/C][/ROW]
[ROW][C]127[/C][C]0.999980198908864[/C][C]3.96021822711256e-05[/C][C]1.98010911355628e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999930055405923[/C][C]0.00013988918815408[/C][C]6.994459407704e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999841049902977[/C][C]0.000317900194046456[/C][C]0.000158950097023228[/C][/ROW]
[ROW][C]130[/C][C]0.999255030762084[/C][C]0.00148993847583112[/C][C]0.000744969237915558[/C][/ROW]
[ROW][C]131[/C][C]0.99885360174087[/C][C]0.00229279651826161[/C][C]0.0011463982591308[/C][/ROW]
[ROW][C]132[/C][C]0.999002206611863[/C][C]0.00199558677627385[/C][C]0.000997793388136924[/C][/ROW]
[ROW][C]133[/C][C]0.993909626443534[/C][C]0.0121807471129321[/C][C]0.00609037355646604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.004483010572678870.008966021145357750.99551698942732
100.001639076846019640.003278153692039270.99836092315398
110.0006294066720485340.001258813344097070.999370593327951
120.0001408610596616970.0002817221193233950.999859138940338
135.71577995495649e-050.000114315599099130.99994284220045
144.75046020049755e-059.5009204009951e-050.999952495397995
151.87396493091837e-053.74792986183673e-050.99998126035069
167.46176970286808e-061.49235394057362e-050.999992538230297
171.62235143885263e-063.24470287770526e-060.999998377648561
184.22508362207069e-058.45016724414138e-050.99995774916378
193.55527590601244e-057.11055181202488e-050.99996444724094
205.30666057012168e-050.0001061332114024340.999946933394299
213.15284574794616e-056.30569149589231e-050.99996847154252
222.64770848472695e-055.2954169694539e-050.999973522915153
231.32023171639995e-052.6404634327999e-050.999986797682836
246.43078329924307e-061.28615665984861e-050.9999935692167
252.68224619020621e-065.36449238041241e-060.99999731775381
261.31339766647128e-062.62679533294256e-060.999998686602333
273.16250954723244e-066.32501909446487e-060.999996837490453
283.38136104374955e-056.7627220874991e-050.999966186389562
290.0001810054142520530.0003620108285041060.999818994585748
300.0001532093400351810.0003064186800703620.999846790659965
310.0001427643933882610.0002855287867765230.999857235606612
320.0001608975232379010.0003217950464758030.999839102476762
330.02127827112388910.04255654224777820.97872172887611
340.4696451926336480.9392903852672950.530354807366352
350.8006992758593290.3986014482813430.199300724140671
360.9355164112142220.1289671775715560.0644835887857782
370.970387343454110.05922531309177840.0296126565458892
380.9789868622844330.04202627543113370.0210131377155669
390.9742403892931770.05151922141364650.0257596107068233
400.9719263344839670.05614733103206550.0280736655160327
410.9648747250942950.0702505498114110.0351252749057055
420.9584291774029820.0831416451940360.041570822597018
430.9691074900143850.06178501997123060.0308925099856153
440.9811275435303740.03774491293925140.0188724564696257
450.9768048442878820.04639031142423640.0231951557121182
460.9700073504552360.0599852990895290.0299926495447645
470.9755211768127620.04895764637447650.0244788231872382
480.969228546907960.06154290618408060.0307714530920403
490.9611879510705810.07762409785883770.0388120489294189
500.950754292783740.09849141443252130.0492457072162606
510.9451168304484950.109766339103010.0548831695515051
520.9339227387017330.1321545225965350.0660772612982673
530.92051217624950.1589756475009990.0794878237504994
540.9112139309709740.1775721380580510.0887860690290256
550.9294686799480530.1410626401038940.0705313200519472
560.925809057272070.148381885455860.0741909427279301
570.9141109773370050.171778045325990.0858890226629949
580.9299265209632040.1401469580735930.0700734790367963
590.9344744046468530.1310511907062940.065525595353147
600.9266864538717740.1466270922564520.0733135461282258
610.914254434942260.1714911301154780.0857455650577392
620.9042148848686760.1915702302626480.095785115131324
630.8944014212401770.2111971575196470.105598578759823
640.8983837437483330.2032325125033340.101616256251667
650.9353679595928050.129264080814390.0646320404071949
660.9520810981826480.09583780363470380.0479189018173519
670.9596710652791720.08065786944165530.0403289347208276
680.9663361975464550.06732760490708950.0336638024535447
690.9903065138381530.01938697232369310.00969348616184653
700.9921374800572320.01572503988553550.00786251994276774
710.991115197489850.01776960502030080.00888480251015039
720.9880025613544450.02399487729111060.0119974386455553
730.983624323757860.03275135248428050.0163756762421402
740.9832106147114380.03357877057712460.0167893852885623
750.978661434248090.04267713150382190.0213385657519109
760.9728519536200210.05429609275995710.0271480463799786
770.9653728411265320.06925431774693530.0346271588734677
780.9577763116213520.08444737675729630.0422236883786482
790.9545549817766760.09089003644664840.0454450182233242
800.962425269152980.07514946169403950.0375747308470197
810.9626045836611740.07479083267765170.0373954163388258
820.9666470347326180.06670593053476470.0333529652673823
830.9785434003680360.04291319926392820.0214565996319641
840.9766543566926670.04669128661466550.0233456433073328
850.9804838455863270.03903230882734580.0195161544136729
860.9749342222374430.05013155552511480.0250657777625574
870.9729886929560.05402261408800220.0270113070440011
880.9797481092782160.04050378144356760.0202518907217838
890.976194373455340.04761125308931870.0238056265446594
900.9713632963589150.05727340728217070.0286367036410853
910.9695091482969820.06098170340603580.0304908517030179
920.9640618118558720.07187637628825620.0359381881441281
930.9567946293404640.08641074131907270.0432053706595363
940.953031459534380.09393708093124080.0469685404656204
950.9604965416596230.07900691668075330.0395034583403766
960.970196312527740.05960737494452030.0298036874722601
970.9797896324527420.04042073509451640.0202103675472582
980.9836132547023120.0327734905953770.0163867452976885
990.9895603750961660.02087924980766720.0104396249038336
1000.9993595841937180.001280831612563240.000640415806281618
1010.9999917163179921.65673640165649e-058.28368200828243e-06
1020.9999990210993871.95780122568335e-069.78900612841675e-07
1030.9999994288658831.14226823325277e-065.71134116626386e-07
1040.999999262058491.47588301946389e-067.37941509731943e-07
1050.999998659030992.68193801836737e-061.34096900918368e-06
1060.9999994483973431.10320531306419e-065.51602656532093e-07
1070.9999999714650235.70699534934044e-082.85349767467022e-08
1080.9999999988569882.28602366416737e-091.14301183208369e-09
1090.9999999997310715.37857653288076e-102.68928826644038e-10
1100.999999999663686.72639309272512e-103.36319654636256e-10
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1120.999999996426477.1470585447841e-093.57352927239205e-09
1130.999999991848511.63029822046809e-088.15149110234045e-09
1140.9999999895594982.08810042232522e-081.04405021116261e-08
1150.9999999802260673.95478669220432e-081.97739334610216e-08
1160.9999999615732337.68535333439568e-083.84267666719784e-08
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1220.9999972592626095.48147478277279e-062.7407373913864e-06
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1270.9999801989088643.96021822711256e-051.98010911355628e-05
1280.9999300554059230.000139889188154086.994459407704e-05
1290.9998410499029770.0003179001940464560.000158950097023228
1300.9992550307620840.001489938475831120.000744969237915558
1310.998853601740870.002292796518261610.0011463982591308
1320.9990022066118630.001995586776273850.000997793388136924
1330.9939096264435340.01218074711293210.00609037355646604






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level570.456NOK
5% type I error level780.624NOK
10% type I error level1070.856NOK

\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 & 57 & 0.456 & NOK \tabularnewline
5% type I error level & 78 & 0.624 & NOK \tabularnewline
10% type I error level & 107 & 0.856 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113723&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]57[/C][C]0.456[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]78[/C][C]0.624[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]107[/C][C]0.856[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113723&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113723&T=6

As an alternative you can also use a QR Code:  
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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 level570.456NOK
5% type I error level780.624NOK
10% type I error level1070.856NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}