FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 23 Nov 2010 20:06:26 +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/Nov/23/t1290542891wlut1f03yqmb0jh.htm/, Retrieved Fri, 26 Apr 2024 19:07:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99629, Retrieved Fri, 26 Apr 2024 19:07:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
F   PD    [Multiple Regression] [Happiness] [2010-11-23 20:06:26] [be034431ba35f7eb1ce695fc7ca4deb9] [Current]
Feedback Forum
2010-11-30 19:01:45 [Rik Goetschalckx] [reply] 
Bij het opstellen van de hypothesen is er een verbetering mogelijk. Leeftijd kan namelijk alleen positief zijn dus als alternatieve hypothese zou hier moeten staan Ha: β > 0

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	23	26	9	15	6	11	13	4
18	21	20	9	15	6	12	16	4
11	21	21	9	14	13	15	19	6
12	21	31	14	10	8	10	15	8
16	24	21	8	10	7	12	14	8
18	22	18	8	12	9	11	13	4
14	21	26	11	18	5	5	19	4
14	22	22	10	12	8	16	15	5
15	21	22	9	14	9	11	14	5
15	20	29	15	18	11	15	15	8
17	22	15	14	9	8	12	16	4
19	21	16	11	11	11	9	16	4
10	21	24	14	11	12	11	16	4
18	23	17	6	17	8	15	17	4
14	22	19	20	8	7	12	15	4
14	23	22	9	16	9	16	15	8
17	22	31	10	21	12	14	20	4
14	24	28	8	24	20	11	18	4
16	23	38	11	21	7	10	16	4
18	21	26	14	14	8	7	16	4
14	23	25	11	7	8	11	19	8
12	23	25	16	18	16	10	16	3
17	21	29	14	18	10	11	17	4
9	20	28	11	13	6	16	17	4
16	32	15	11	11	8	14	16	4
14	22	18	12	13	9	12	15	10
11	21	21	9	13	9	12	14	5
16	21	25	7	18	11	11	15	4
13	21	23	13	14	12	6	12	4
17	22	23	10	12	8	14	14	4
15	21	19	9	9	7	9	16	4
14	21	18	9	12	8	15	14	4
16	21	18	13	8	9	12	7	10
9	22	26	16	5	4	12	10	4
15	21	18	12	10	8	9	14	8
17	21	18	6	11	8	13	16	4
13	21	28	14	11	8	15	16	4
15	21	17	14	12	6	11	16	4
16	23	29	10	12	8	10	14	7
16	21	12	4	15	4	13	20	4
12	23	28	12	16	14	16	14	4
11	23	20	14	14	10	13	11	4
15	21	17	9	17	9	14	15	4
17	20	17	9	13	6	14	16	6
13	21	20	10	10	8	16	14	5
16	20	31	14	17	11	9	16	16
14	21	21	10	12	8	8	14	5
11	21	19	9	13	8	8	12	12
12	22	23	14	13	10	12	16	6
12	21	15	8	11	8	10	9	9
15	21	24	9	13	10	16	14	9
16	22	28	8	12	7	13	16	4
15	20	16	9	12	8	11	16	4
12	22	19	9	12	7	14	15	4
12	22	21	9	9	9	15	16	5
8	21	21	15	7	5	8	12	4
13	23	20	8	17	7	9	16	5
11	22	16	10	12	7	17	16	4
14	24	25	8	12	7	9	14	6
15	23	30	14	9	9	13	16	4
10	21	29	11	9	5	6	17	4
11	22	22	10	13	8	13	18	18
12	22	19	12	10	8	8	18	4
15	21	33	14	11	8	12	12	4
15	21	17	9	12	9	13	16	6
14	21	9	13	10	6	14	10	4
16	21	14	15	13	8	11	14	5
15	20	15	8	6	6	15	18	4
15	22	12	7	7	4	7	18	4
13	22	21	10	13	6	16	16	5
17	22	20	10	11	4	16	16	5
13	23	29	13	18	12	14	16	8
15	21	33	11	9	6	11	13	5
13	23	21	8	9	11	13	16	4
15	22	15	12	11	8	13	16	4
16	21	19	9	11	10	7	20	4
15	21	23	10	15	10	15	16	5
16	20	20	11	8	4	11	15	4
15	24	20	11	11	8	15	15	4
14	24	18	10	14	9	13	16	4
15	21	31	16	14	9	11	14	8
7	20	18	16	12	7	12	15	14
17	21	13	8	12	7	10	12	4
13	21	9	6	8	11	12	17	8
15	21	20	11	11	8	12	16	8
14	21	18	12	10	8	12	15	4
13	22	23	14	17	7	14	13	6
16	22	17	9	16	5	6	16	4
12	21	17	11	13	7	14	16	7
14	22	16	8	15	9	15	16	3
17	21	31	8	11	8	8	16	4
15	23	15	7	12	6	12	14	4
17	21	28	16	16	8	10	16	4
12	22	26	13	20	10	15	16	7
16	22	20	8	16	10	11	20	4
11	22	19	11	11	8	9	15	4
15	20	25	14	15	11	14	16	6
9	21	18	10	15	8	10	13	8
16	21	20	10	12	8	16	17	4
10	22	33	14	9	6	5	16	4
10	25	24	14	24	20	8	12	4
15	22	22	10	15	6	13	16	5
11	22	32	12	18	12	16	16	6
13	21	31	9	17	9	16	17	4
14	22	13	16	12	5	14	13	5
18	21	18	8	15	10	14	12	7
16	24	17	9	11	5	10	18	4
14	23	29	16	11	6	9	14	8
14	0	22	13	15	10	14	14	6
14	23	18	13	12	6	8	13	8
14	22	22	8	14	10	8	16	8
12	22	25	14	11	5	16	13	4
14	25	20	11	20	13	12	16	5
15	23	20	9	11	7	9	13	6
15	22	17	8	12	9	15	16	5
13	21	26	13	12	8	12	16	5
17	21	10	10	11	5	14	15	4
17	22	15	8	10	4	12	17	4
19	22	20	7	11	9	16	15	6
15	21	14	11	12	7	12	12	7
13	0	16	11	9	5	14	16	4
9	21	23	14	8	5	8	10	10
15	22	11	6	6	4	15	16	8
15	21	19	10	12	7	16	14	5
16	24	30	9	15	9	12	15	11
11	21	21	12	13	8	4	13	7
14	23	20	11	17	8	8	15	4
11	23	22	14	14	11	11	11	8
15	22	30	12	16	10	4	12	6
13	21	25	14	15	9	14	8	4
16	21	23	14	11	10	14	15	8
14	21	23	8	11	10	13	17	5
15	21	21	11	16	7	14	16	4
16	22	30	12	15	10	7	10	8
16	20	22	9	14	6	19	18	4
11	21	32	16	9	6	12	13	6
13	23	22	11	13	11	10	15	4
16	32	15	11	11	8	14	16	4
12	22	21	12	14	9	16	16	6
9	24	27	15	11	9	11	14	15
13	20	22	13	12	13	16	10	16
13	21	9	6	8	11	12	17	8
19	22	20	7	11	9	16	15	6
13	23	16	8	13	5	12	16	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 16.0606034156061 + 0.0191423359391710Age[t] -0.0115621244172054Concern_over_mistakes[t] -0.250443166783529Doubts_about_actions[t] + 0.0879285077749663Parental_expectations[t] -0.0909414328317329Parental_criticism[t] + 0.0351776347057642Popularity[t] + 0.0420782212752515Perceived_learning_competence[t] -0.143892749023934Amotivation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  16.0606034156061 +  0.0191423359391710Age[t] -0.0115621244172054Concern_over_mistakes[t] -0.250443166783529Doubts_about_actions[t] +  0.0879285077749663Parental_expectations[t] -0.0909414328317329Parental_criticism[t] +  0.0351776347057642Popularity[t] +  0.0420782212752515Perceived_learning_competence[t] -0.143892749023934Amotivation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  16.0606034156061 +  0.0191423359391710Age[t] -0.0115621244172054Concern_over_mistakes[t] -0.250443166783529Doubts_about_actions[t] +  0.0879285077749663Parental_expectations[t] -0.0909414328317329Parental_criticism[t] +  0.0351776347057642Popularity[t] +  0.0420782212752515Perceived_learning_competence[t] -0.143892749023934Amotivation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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
Happiness[t] = + 16.0606034156061 + 0.0191423359391710Age[t] -0.0115621244172054Concern_over_mistakes[t] -0.250443166783529Doubts_about_actions[t] + 0.0879285077749663Parental_expectations[t] -0.0909414328317329Parental_criticism[t] + 0.0351776347057642Popularity[t] + 0.0420782212752515Perceived_learning_competence[t] -0.143892749023934Amotivation[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.06060341560612.4754156.48800
Age0.01914233593917100.0621210.30810.7584470.379224
Concern_over_mistakes-0.01156212441720540.038417-0.3010.7639030.381952
Doubts_about_actions-0.2504431667835290.077967-3.21220.0016470.000824
Parental_expectations0.08792850777496630.0692611.26950.2064350.103218
Parental_criticism-0.09094143283173290.086108-1.05610.2927960.146398
Popularity0.03517763470576420.0635690.55340.5809190.29046
Perceived_learning_competence0.04207822127525150.0892990.47120.6382530.319126
Amotivation-0.1438927490239340.07338-1.96090.0519470.025974

\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) & 16.0606034156061 & 2.475415 & 6.488 & 0 & 0 \tabularnewline
Age & 0.0191423359391710 & 0.062121 & 0.3081 & 0.758447 & 0.379224 \tabularnewline
Concern_over_mistakes & -0.0115621244172054 & 0.038417 & -0.301 & 0.763903 & 0.381952 \tabularnewline
Doubts_about_actions & -0.250443166783529 & 0.077967 & -3.2122 & 0.001647 & 0.000824 \tabularnewline
Parental_expectations & 0.0879285077749663 & 0.069261 & 1.2695 & 0.206435 & 0.103218 \tabularnewline
Parental_criticism & -0.0909414328317329 & 0.086108 & -1.0561 & 0.292796 & 0.146398 \tabularnewline
Popularity & 0.0351776347057642 & 0.063569 & 0.5534 & 0.580919 & 0.29046 \tabularnewline
Perceived_learning_competence & 0.0420782212752515 & 0.089299 & 0.4712 & 0.638253 & 0.319126 \tabularnewline
Amotivation & -0.143892749023934 & 0.07338 & -1.9609 & 0.051947 & 0.025974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&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]16.0606034156061[/C][C]2.475415[/C][C]6.488[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Age[/C][C]0.0191423359391710[/C][C]0.062121[/C][C]0.3081[/C][C]0.758447[/C][C]0.379224[/C][/ROW]
[ROW][C]Concern_over_mistakes[/C][C]-0.0115621244172054[/C][C]0.038417[/C][C]-0.301[/C][C]0.763903[/C][C]0.381952[/C][/ROW]
[ROW][C]Doubts_about_actions[/C][C]-0.250443166783529[/C][C]0.077967[/C][C]-3.2122[/C][C]0.001647[/C][C]0.000824[/C][/ROW]
[ROW][C]Parental_expectations[/C][C]0.0879285077749663[/C][C]0.069261[/C][C]1.2695[/C][C]0.206435[/C][C]0.103218[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]-0.0909414328317329[/C][C]0.086108[/C][C]-1.0561[/C][C]0.292796[/C][C]0.146398[/C][/ROW]
[ROW][C]Popularity[/C][C]0.0351776347057642[/C][C]0.063569[/C][C]0.5534[/C][C]0.580919[/C][C]0.29046[/C][/ROW]
[ROW][C]Perceived_learning_competence[/C][C]0.0420782212752515[/C][C]0.089299[/C][C]0.4712[/C][C]0.638253[/C][C]0.319126[/C][/ROW]
[ROW][C]Amotivation[/C][C]-0.143892749023934[/C][C]0.07338[/C][C]-1.9609[/C][C]0.051947[/C][C]0.025974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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)16.06060341560612.4754156.48800
Age0.01914233593917100.0621210.30810.7584470.379224
Concern_over_mistakes-0.01156212441720540.038417-0.3010.7639030.381952
Doubts_about_actions-0.2504431667835290.077967-3.21220.0016470.000824
Parental_expectations0.08792850777496630.0692611.26950.2064350.103218
Parental_criticism-0.09094143283173290.086108-1.05610.2927960.146398
Popularity0.03517763470576420.0635690.55340.5809190.29046
Perceived_learning_competence0.04207822127525150.0892990.47120.6382530.319126
Amotivation-0.1438927490239340.07338-1.96090.0519470.025974






Multiple Linear Regression - Regression Statistics
Multiple R0.419576907453149
R-squared0.176044781267948
Adjusted R-squared0.127217805343086
F-TEST (value)3.60548196838682
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0.000793486487427386
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22706924744676
Sum Squared Residuals669.578053444616

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.419576907453149 \tabularnewline
R-squared & 0.176044781267948 \tabularnewline
Adjusted R-squared & 0.127217805343086 \tabularnewline
F-TEST (value) & 3.60548196838682 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0.000793486487427386 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22706924744676 \tabularnewline
Sum Squared Residuals & 669.578053444616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.419576907453149[/C][/ROW]
[ROW][C]R-squared[/C][C]0.176044781267948[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.127217805343086[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.60548196838682[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C]0.000793486487427386[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.22706924744676[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.578053444616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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 R0.419576907453149
R-squared0.176044781267948
Adjusted R-squared0.127217805343086
F-TEST (value)3.60548196838682
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0.000793486487427386
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22706924744676
Sum Squared Residuals669.578053444616






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11415.0779522881879-1.07795228818793
21815.27045266134442.72954733865561
31114.4783540692253-3.47835406922526
41212.5815235675167-0.581523567516666
51614.37644930117541.62355069882458
61814.86514029254993.13485970745012
71414.9349117583161-0.934911758316131
81414.5250987612011-0.525098761201125
91514.62334877995960.376651220040392
101512.94155425086172.05844574913827
111713.38573587313873.61426412686132
121913.90386072607035.09613927392967
131013.0394480669619-3.03944806696189
141816.23633848210391.76366151789606
151411.79776307855022.20223692144981
161414.6237786151202-0.623778615120154
171715.13255906608271.86744093391730
181415.0529811587830-1.05298115878296
191614.96600710455251.03399289544749
201813.50316453395634.49683546604375
211413.38021548273070.619784517269298
221212.9257332182720-0.925733218271973
231713.82109808623933.17890191376065
24914.6648587410489-5.66485874104889
251614.57470101784241.42529898215757
261413.20727371630340.792726283696607
271114.5821600313076-3.58216003130761
281615.44535087601060.554649123989363
291313.2210378228577-0.221037822857698
301714.54499589512112.45500410487893
311514.55796940216280.442030597837232
321414.8692849827572-0.869284982757222
331612.16141990450383.83858009549615
34912.5172485435579-3.51724854355791
351513.15546166252641.84453833747362
361715.54648714847181.45351285152818
371313.4976758394431-0.497675839443059
381513.75396004264771.24603995735231
391613.92237669866222.07762330133785
401617.0005388760699-1.00053887606991
411213.8818619789282-1.88186197892816
421113.4296137885327-2.42961378853269
431515.236448799787-0.236448799787013
441714.89270945447062.10729054552944
451314.3111454372712-1.31114543727118
461611.76081508188514.23918491811492
471414.1940192507578-0.194019250757793
481113.4641094884327-2.46410948843266
491213.1752845451896-1.17528454518956
501213.9607429899927-1.96074298999265
511514.02167176795160.978328232048416
521615.12799184727860.872008152721424
531514.81671279937990.183287200620092
541214.9747072136804-2.97470721368041
551214.4392776828147-2.43927768281471
56812.8347214829339-4.83472148293395
571315.3946704295832-2.39467042958323
581114.9065615455410-3.90656154554104
591414.6883104129871-0.688310412987106
601513.17568254469381.82431745530620
611014.0598900072451-4.05989000724508
621112.7631232913734-1.76312329137341
631213.8717481205393-1.87174812053932
641513.16601942813871.83398057186127
651514.51592134943380.484078650566199
661413.77410676568470.225893234315285
671613.21619969965282.78380030034716
681514.95789689096790.0421031090321294
691515.2697013986737-0.269701398673676
701314.848550480332-1.84855048033201
711714.86613845486282.13386154513725
721313.4158267459841-0.415826745984057
731513.78638261614841.21361738385159
741314.6005177994864-1.60051779948635
751514.09765685696140.902343143038571
761614.55895973490701.44104026509302
771514.58319754507570.416802454924332
781614.23955144709581.76044855290416
791514.35685112167360.643148878326449
801414.7749855796484-0.77498557964838
811512.33450924564832.66549075435175
82711.6855997390837-4.6855997390837
831715.00843558837921.99156441162081
841314.5452660368803-1.54526603688032
851513.66039843491831.33960156508174
861413.87864378401470.121356215985340
871313.7439434803704-0.7439434803704
881615.29208550290730.707914497092714
891214.176131274987-2.17613127498700
901415.5628880163819-1.56288801638194
911714.71940502395232.28059497604773
921515.5194923230004-0.519492323000411
931713.2605438712223.73945612877799
941213.9681810482396-1.96818104823960
951615.39733619091040.602663809089637
961114.1190627659778-3.11906276597783
971513.26914647660651.73085352339346
98914.0890899483988-5.08908994839878
991614.75712986567081.2428701343292
1001013.1132570563521-3.11325705635209
1011013.2577107599210-3.25771075992098
1021514.90731246734740.092687532652553
1031113.9705819710362-2.97058197103620
1041315.2290907699082-2.22909076990817
1051413.24481146678800.755188533212036
1061814.65061848287413.34938151712589
1071615.11559461728440.884405382715643
1081412.33460167211981.66539832788022
1091413.17821428813950.821785711860506
1101413.22378719285360.776212807146425
1111414.348938141212-0.348938141211985
1121213.7332718180091-1.73327181800905
1131414.5052954315628-0.505295431562779
1141514.34652880330020.653471196699786
1151514.99975487059200.000245129408036393
1161313.6097461096947-0.609746109694739
1171714.90313518860112.09686481139894
1181715.3821673342171.617832665783
1191914.97678982075554.02321017924451
1201513.88422098595111.11577901404888
1211314.0475514263170-1.04755142631702
122912.2024559719639-3.20245597196392
1231515.0654718210993-0.0654718210993186
1241514.58950601007000.41049398992995
1251613.89010666205192.10989333794814
1261113.3104871668195-2.31048716681952
1271414.6190363894438-0.619036389443828
1281112.6696218413593-1.66962184135928
1291513.40928756841401.59071243158604
1301313.4213314060550-0.42133140605503
1311612.72077674378893.27922325621113
1321414.7040927994066-0.704092799406585
1331514.82534654771490.174653452285116
1341613.05495002415792.94504997584208
1351615.47065745428670.529342545713309
1361112.4370137923298-1.43701379232980
1371314.0417290804259-1.04172908042588
1381614.57470101784241.42529898215757
1391214.0188756070208-2.01887560702079
140911.4175931514257-2.41759315142568
1411311.4875660791741.51243392082600
1421314.5452660368803-1.54526603688032
1431914.97678982075554.02321017924451
1441315.5205134149569-2.52051341495688

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 15.0779522881879 & -1.07795228818793 \tabularnewline
2 & 18 & 15.2704526613444 & 2.72954733865561 \tabularnewline
3 & 11 & 14.4783540692253 & -3.47835406922526 \tabularnewline
4 & 12 & 12.5815235675167 & -0.581523567516666 \tabularnewline
5 & 16 & 14.3764493011754 & 1.62355069882458 \tabularnewline
6 & 18 & 14.8651402925499 & 3.13485970745012 \tabularnewline
7 & 14 & 14.9349117583161 & -0.934911758316131 \tabularnewline
8 & 14 & 14.5250987612011 & -0.525098761201125 \tabularnewline
9 & 15 & 14.6233487799596 & 0.376651220040392 \tabularnewline
10 & 15 & 12.9415542508617 & 2.05844574913827 \tabularnewline
11 & 17 & 13.3857358731387 & 3.61426412686132 \tabularnewline
12 & 19 & 13.9038607260703 & 5.09613927392967 \tabularnewline
13 & 10 & 13.0394480669619 & -3.03944806696189 \tabularnewline
14 & 18 & 16.2363384821039 & 1.76366151789606 \tabularnewline
15 & 14 & 11.7977630785502 & 2.20223692144981 \tabularnewline
16 & 14 & 14.6237786151202 & -0.623778615120154 \tabularnewline
17 & 17 & 15.1325590660827 & 1.86744093391730 \tabularnewline
18 & 14 & 15.0529811587830 & -1.05298115878296 \tabularnewline
19 & 16 & 14.9660071045525 & 1.03399289544749 \tabularnewline
20 & 18 & 13.5031645339563 & 4.49683546604375 \tabularnewline
21 & 14 & 13.3802154827307 & 0.619784517269298 \tabularnewline
22 & 12 & 12.9257332182720 & -0.925733218271973 \tabularnewline
23 & 17 & 13.8210980862393 & 3.17890191376065 \tabularnewline
24 & 9 & 14.6648587410489 & -5.66485874104889 \tabularnewline
25 & 16 & 14.5747010178424 & 1.42529898215757 \tabularnewline
26 & 14 & 13.2072737163034 & 0.792726283696607 \tabularnewline
27 & 11 & 14.5821600313076 & -3.58216003130761 \tabularnewline
28 & 16 & 15.4453508760106 & 0.554649123989363 \tabularnewline
29 & 13 & 13.2210378228577 & -0.221037822857698 \tabularnewline
30 & 17 & 14.5449958951211 & 2.45500410487893 \tabularnewline
31 & 15 & 14.5579694021628 & 0.442030597837232 \tabularnewline
32 & 14 & 14.8692849827572 & -0.869284982757222 \tabularnewline
33 & 16 & 12.1614199045038 & 3.83858009549615 \tabularnewline
34 & 9 & 12.5172485435579 & -3.51724854355791 \tabularnewline
35 & 15 & 13.1554616625264 & 1.84453833747362 \tabularnewline
36 & 17 & 15.5464871484718 & 1.45351285152818 \tabularnewline
37 & 13 & 13.4976758394431 & -0.497675839443059 \tabularnewline
38 & 15 & 13.7539600426477 & 1.24603995735231 \tabularnewline
39 & 16 & 13.9223766986622 & 2.07762330133785 \tabularnewline
40 & 16 & 17.0005388760699 & -1.00053887606991 \tabularnewline
41 & 12 & 13.8818619789282 & -1.88186197892816 \tabularnewline
42 & 11 & 13.4296137885327 & -2.42961378853269 \tabularnewline
43 & 15 & 15.236448799787 & -0.236448799787013 \tabularnewline
44 & 17 & 14.8927094544706 & 2.10729054552944 \tabularnewline
45 & 13 & 14.3111454372712 & -1.31114543727118 \tabularnewline
46 & 16 & 11.7608150818851 & 4.23918491811492 \tabularnewline
47 & 14 & 14.1940192507578 & -0.194019250757793 \tabularnewline
48 & 11 & 13.4641094884327 & -2.46410948843266 \tabularnewline
49 & 12 & 13.1752845451896 & -1.17528454518956 \tabularnewline
50 & 12 & 13.9607429899927 & -1.96074298999265 \tabularnewline
51 & 15 & 14.0216717679516 & 0.978328232048416 \tabularnewline
52 & 16 & 15.1279918472786 & 0.872008152721424 \tabularnewline
53 & 15 & 14.8167127993799 & 0.183287200620092 \tabularnewline
54 & 12 & 14.9747072136804 & -2.97470721368041 \tabularnewline
55 & 12 & 14.4392776828147 & -2.43927768281471 \tabularnewline
56 & 8 & 12.8347214829339 & -4.83472148293395 \tabularnewline
57 & 13 & 15.3946704295832 & -2.39467042958323 \tabularnewline
58 & 11 & 14.9065615455410 & -3.90656154554104 \tabularnewline
59 & 14 & 14.6883104129871 & -0.688310412987106 \tabularnewline
60 & 15 & 13.1756825446938 & 1.82431745530620 \tabularnewline
61 & 10 & 14.0598900072451 & -4.05989000724508 \tabularnewline
62 & 11 & 12.7631232913734 & -1.76312329137341 \tabularnewline
63 & 12 & 13.8717481205393 & -1.87174812053932 \tabularnewline
64 & 15 & 13.1660194281387 & 1.83398057186127 \tabularnewline
65 & 15 & 14.5159213494338 & 0.484078650566199 \tabularnewline
66 & 14 & 13.7741067656847 & 0.225893234315285 \tabularnewline
67 & 16 & 13.2161996996528 & 2.78380030034716 \tabularnewline
68 & 15 & 14.9578968909679 & 0.0421031090321294 \tabularnewline
69 & 15 & 15.2697013986737 & -0.269701398673676 \tabularnewline
70 & 13 & 14.848550480332 & -1.84855048033201 \tabularnewline
71 & 17 & 14.8661384548628 & 2.13386154513725 \tabularnewline
72 & 13 & 13.4158267459841 & -0.415826745984057 \tabularnewline
73 & 15 & 13.7863826161484 & 1.21361738385159 \tabularnewline
74 & 13 & 14.6005177994864 & -1.60051779948635 \tabularnewline
75 & 15 & 14.0976568569614 & 0.902343143038571 \tabularnewline
76 & 16 & 14.5589597349070 & 1.44104026509302 \tabularnewline
77 & 15 & 14.5831975450757 & 0.416802454924332 \tabularnewline
78 & 16 & 14.2395514470958 & 1.76044855290416 \tabularnewline
79 & 15 & 14.3568511216736 & 0.643148878326449 \tabularnewline
80 & 14 & 14.7749855796484 & -0.77498557964838 \tabularnewline
81 & 15 & 12.3345092456483 & 2.66549075435175 \tabularnewline
82 & 7 & 11.6855997390837 & -4.6855997390837 \tabularnewline
83 & 17 & 15.0084355883792 & 1.99156441162081 \tabularnewline
84 & 13 & 14.5452660368803 & -1.54526603688032 \tabularnewline
85 & 15 & 13.6603984349183 & 1.33960156508174 \tabularnewline
86 & 14 & 13.8786437840147 & 0.121356215985340 \tabularnewline
87 & 13 & 13.7439434803704 & -0.7439434803704 \tabularnewline
88 & 16 & 15.2920855029073 & 0.707914497092714 \tabularnewline
89 & 12 & 14.176131274987 & -2.17613127498700 \tabularnewline
90 & 14 & 15.5628880163819 & -1.56288801638194 \tabularnewline
91 & 17 & 14.7194050239523 & 2.28059497604773 \tabularnewline
92 & 15 & 15.5194923230004 & -0.519492323000411 \tabularnewline
93 & 17 & 13.260543871222 & 3.73945612877799 \tabularnewline
94 & 12 & 13.9681810482396 & -1.96818104823960 \tabularnewline
95 & 16 & 15.3973361909104 & 0.602663809089637 \tabularnewline
96 & 11 & 14.1190627659778 & -3.11906276597783 \tabularnewline
97 & 15 & 13.2691464766065 & 1.73085352339346 \tabularnewline
98 & 9 & 14.0890899483988 & -5.08908994839878 \tabularnewline
99 & 16 & 14.7571298656708 & 1.2428701343292 \tabularnewline
100 & 10 & 13.1132570563521 & -3.11325705635209 \tabularnewline
101 & 10 & 13.2577107599210 & -3.25771075992098 \tabularnewline
102 & 15 & 14.9073124673474 & 0.092687532652553 \tabularnewline
103 & 11 & 13.9705819710362 & -2.97058197103620 \tabularnewline
104 & 13 & 15.2290907699082 & -2.22909076990817 \tabularnewline
105 & 14 & 13.2448114667880 & 0.755188533212036 \tabularnewline
106 & 18 & 14.6506184828741 & 3.34938151712589 \tabularnewline
107 & 16 & 15.1155946172844 & 0.884405382715643 \tabularnewline
108 & 14 & 12.3346016721198 & 1.66539832788022 \tabularnewline
109 & 14 & 13.1782142881395 & 0.821785711860506 \tabularnewline
110 & 14 & 13.2237871928536 & 0.776212807146425 \tabularnewline
111 & 14 & 14.348938141212 & -0.348938141211985 \tabularnewline
112 & 12 & 13.7332718180091 & -1.73327181800905 \tabularnewline
113 & 14 & 14.5052954315628 & -0.505295431562779 \tabularnewline
114 & 15 & 14.3465288033002 & 0.653471196699786 \tabularnewline
115 & 15 & 14.9997548705920 & 0.000245129408036393 \tabularnewline
116 & 13 & 13.6097461096947 & -0.609746109694739 \tabularnewline
117 & 17 & 14.9031351886011 & 2.09686481139894 \tabularnewline
118 & 17 & 15.382167334217 & 1.617832665783 \tabularnewline
119 & 19 & 14.9767898207555 & 4.02321017924451 \tabularnewline
120 & 15 & 13.8842209859511 & 1.11577901404888 \tabularnewline
121 & 13 & 14.0475514263170 & -1.04755142631702 \tabularnewline
122 & 9 & 12.2024559719639 & -3.20245597196392 \tabularnewline
123 & 15 & 15.0654718210993 & -0.0654718210993186 \tabularnewline
124 & 15 & 14.5895060100700 & 0.41049398992995 \tabularnewline
125 & 16 & 13.8901066620519 & 2.10989333794814 \tabularnewline
126 & 11 & 13.3104871668195 & -2.31048716681952 \tabularnewline
127 & 14 & 14.6190363894438 & -0.619036389443828 \tabularnewline
128 & 11 & 12.6696218413593 & -1.66962184135928 \tabularnewline
129 & 15 & 13.4092875684140 & 1.59071243158604 \tabularnewline
130 & 13 & 13.4213314060550 & -0.42133140605503 \tabularnewline
131 & 16 & 12.7207767437889 & 3.27922325621113 \tabularnewline
132 & 14 & 14.7040927994066 & -0.704092799406585 \tabularnewline
133 & 15 & 14.8253465477149 & 0.174653452285116 \tabularnewline
134 & 16 & 13.0549500241579 & 2.94504997584208 \tabularnewline
135 & 16 & 15.4706574542867 & 0.529342545713309 \tabularnewline
136 & 11 & 12.4370137923298 & -1.43701379232980 \tabularnewline
137 & 13 & 14.0417290804259 & -1.04172908042588 \tabularnewline
138 & 16 & 14.5747010178424 & 1.42529898215757 \tabularnewline
139 & 12 & 14.0188756070208 & -2.01887560702079 \tabularnewline
140 & 9 & 11.4175931514257 & -2.41759315142568 \tabularnewline
141 & 13 & 11.487566079174 & 1.51243392082600 \tabularnewline
142 & 13 & 14.5452660368803 & -1.54526603688032 \tabularnewline
143 & 19 & 14.9767898207555 & 4.02321017924451 \tabularnewline
144 & 13 & 15.5205134149569 & -2.52051341495688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&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]14[/C][C]15.0779522881879[/C][C]-1.07795228818793[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2704526613444[/C][C]2.72954733865561[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.4783540692253[/C][C]-3.47835406922526[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.5815235675167[/C][C]-0.581523567516666[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.3764493011754[/C][C]1.62355069882458[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.8651402925499[/C][C]3.13485970745012[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]14.9349117583161[/C][C]-0.934911758316131[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.5250987612011[/C][C]-0.525098761201125[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.6233487799596[/C][C]0.376651220040392[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.9415542508617[/C][C]2.05844574913827[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]13.3857358731387[/C][C]3.61426412686132[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]13.9038607260703[/C][C]5.09613927392967[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.0394480669619[/C][C]-3.03944806696189[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]16.2363384821039[/C][C]1.76366151789606[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]11.7977630785502[/C][C]2.20223692144981[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.6237786151202[/C][C]-0.623778615120154[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]15.1325590660827[/C][C]1.86744093391730[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]15.0529811587830[/C][C]-1.05298115878296[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.9660071045525[/C][C]1.03399289544749[/C][/ROW]
[ROW][C]20[/C][C]18[/C][C]13.5031645339563[/C][C]4.49683546604375[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.3802154827307[/C][C]0.619784517269298[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]12.9257332182720[/C][C]-0.925733218271973[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]13.8210980862393[/C][C]3.17890191376065[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]14.6648587410489[/C][C]-5.66485874104889[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]14.5747010178424[/C][C]1.42529898215757[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]13.2072737163034[/C][C]0.792726283696607[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]14.5821600313076[/C][C]-3.58216003130761[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]15.4453508760106[/C][C]0.554649123989363[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2210378228577[/C][C]-0.221037822857698[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]14.5449958951211[/C][C]2.45500410487893[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]14.5579694021628[/C][C]0.442030597837232[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.8692849827572[/C][C]-0.869284982757222[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]12.1614199045038[/C][C]3.83858009549615[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]12.5172485435579[/C][C]-3.51724854355791[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]13.1554616625264[/C][C]1.84453833747362[/C][/ROW]
[ROW][C]36[/C][C]17[/C][C]15.5464871484718[/C][C]1.45351285152818[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]13.4976758394431[/C][C]-0.497675839443059[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.7539600426477[/C][C]1.24603995735231[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]13.9223766986622[/C][C]2.07762330133785[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]17.0005388760699[/C][C]-1.00053887606991[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]13.8818619789282[/C][C]-1.88186197892816[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]13.4296137885327[/C][C]-2.42961378853269[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.236448799787[/C][C]-0.236448799787013[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]14.8927094544706[/C][C]2.10729054552944[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]14.3111454372712[/C][C]-1.31114543727118[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]11.7608150818851[/C][C]4.23918491811492[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.1940192507578[/C][C]-0.194019250757793[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]13.4641094884327[/C][C]-2.46410948843266[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]13.1752845451896[/C][C]-1.17528454518956[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]13.9607429899927[/C][C]-1.96074298999265[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]14.0216717679516[/C][C]0.978328232048416[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]15.1279918472786[/C][C]0.872008152721424[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]14.8167127993799[/C][C]0.183287200620092[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.9747072136804[/C][C]-2.97470721368041[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.4392776828147[/C][C]-2.43927768281471[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]12.8347214829339[/C][C]-4.83472148293395[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]15.3946704295832[/C][C]-2.39467042958323[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]14.9065615455410[/C][C]-3.90656154554104[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]14.6883104129871[/C][C]-0.688310412987106[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.1756825446938[/C][C]1.82431745530620[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]14.0598900072451[/C][C]-4.05989000724508[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]12.7631232913734[/C][C]-1.76312329137341[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]13.8717481205393[/C][C]-1.87174812053932[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]13.1660194281387[/C][C]1.83398057186127[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.5159213494338[/C][C]0.484078650566199[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]13.7741067656847[/C][C]0.225893234315285[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]13.2161996996528[/C][C]2.78380030034716[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]14.9578968909679[/C][C]0.0421031090321294[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]15.2697013986737[/C][C]-0.269701398673676[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.848550480332[/C][C]-1.84855048033201[/C][/ROW]
[ROW][C]71[/C][C]17[/C][C]14.8661384548628[/C][C]2.13386154513725[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.4158267459841[/C][C]-0.415826745984057[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.7863826161484[/C][C]1.21361738385159[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]14.6005177994864[/C][C]-1.60051779948635[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]14.0976568569614[/C][C]0.902343143038571[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.5589597349070[/C][C]1.44104026509302[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]14.5831975450757[/C][C]0.416802454924332[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.2395514470958[/C][C]1.76044855290416[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.3568511216736[/C][C]0.643148878326449[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]14.7749855796484[/C][C]-0.77498557964838[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]12.3345092456483[/C][C]2.66549075435175[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]11.6855997390837[/C][C]-4.6855997390837[/C][/ROW]
[ROW][C]83[/C][C]17[/C][C]15.0084355883792[/C][C]1.99156441162081[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]14.5452660368803[/C][C]-1.54526603688032[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]13.6603984349183[/C][C]1.33960156508174[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.8786437840147[/C][C]0.121356215985340[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.7439434803704[/C][C]-0.7439434803704[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2920855029073[/C][C]0.707914497092714[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.176131274987[/C][C]-2.17613127498700[/C][/ROW]
[ROW][C]90[/C][C]14[/C][C]15.5628880163819[/C][C]-1.56288801638194[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.7194050239523[/C][C]2.28059497604773[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]15.5194923230004[/C][C]-0.519492323000411[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]13.260543871222[/C][C]3.73945612877799[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]13.9681810482396[/C][C]-1.96818104823960[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.3973361909104[/C][C]0.602663809089637[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]14.1190627659778[/C][C]-3.11906276597783[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]13.2691464766065[/C][C]1.73085352339346[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]14.0890899483988[/C][C]-5.08908994839878[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]14.7571298656708[/C][C]1.2428701343292[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]13.1132570563521[/C][C]-3.11325705635209[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]13.2577107599210[/C][C]-3.25771075992098[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.9073124673474[/C][C]0.092687532652553[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]13.9705819710362[/C][C]-2.97058197103620[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]15.2290907699082[/C][C]-2.22909076990817[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]13.2448114667880[/C][C]0.755188533212036[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]14.6506184828741[/C][C]3.34938151712589[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.1155946172844[/C][C]0.884405382715643[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]12.3346016721198[/C][C]1.66539832788022[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]13.1782142881395[/C][C]0.821785711860506[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]13.2237871928536[/C][C]0.776212807146425[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]14.348938141212[/C][C]-0.348938141211985[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.7332718180091[/C][C]-1.73327181800905[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]14.5052954315628[/C][C]-0.505295431562779[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.3465288033002[/C][C]0.653471196699786[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.9997548705920[/C][C]0.000245129408036393[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]13.6097461096947[/C][C]-0.609746109694739[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]14.9031351886011[/C][C]2.09686481139894[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]15.382167334217[/C][C]1.617832665783[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]14.9767898207555[/C][C]4.02321017924451[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.8842209859511[/C][C]1.11577901404888[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.0475514263170[/C][C]-1.04755142631702[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]12.2024559719639[/C][C]-3.20245597196392[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.0654718210993[/C][C]-0.0654718210993186[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.5895060100700[/C][C]0.41049398992995[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.8901066620519[/C][C]2.10989333794814[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.3104871668195[/C][C]-2.31048716681952[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]14.6190363894438[/C][C]-0.619036389443828[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.6696218413593[/C][C]-1.66962184135928[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.4092875684140[/C][C]1.59071243158604[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.4213314060550[/C][C]-0.42133140605503[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]12.7207767437889[/C][C]3.27922325621113[/C][/ROW]
[ROW][C]132[/C][C]14[/C][C]14.7040927994066[/C][C]-0.704092799406585[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.8253465477149[/C][C]0.174653452285116[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]13.0549500241579[/C][C]2.94504997584208[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]15.4706574542867[/C][C]0.529342545713309[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]12.4370137923298[/C][C]-1.43701379232980[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]14.0417290804259[/C][C]-1.04172908042588[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]14.5747010178424[/C][C]1.42529898215757[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]14.0188756070208[/C][C]-2.01887560702079[/C][/ROW]
[ROW][C]140[/C][C]9[/C][C]11.4175931514257[/C][C]-2.41759315142568[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]11.487566079174[/C][C]1.51243392082600[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]14.5452660368803[/C][C]-1.54526603688032[/C][/ROW]
[ROW][C]143[/C][C]19[/C][C]14.9767898207555[/C][C]4.02321017924451[/C][/ROW]
[ROW][C]144[/C][C]13[/C][C]15.5205134149569[/C][C]-2.52051341495688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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
11415.0779522881879-1.07795228818793
21815.27045266134442.72954733865561
31114.4783540692253-3.47835406922526
41212.5815235675167-0.581523567516666
51614.37644930117541.62355069882458
61814.86514029254993.13485970745012
71414.9349117583161-0.934911758316131
81414.5250987612011-0.525098761201125
91514.62334877995960.376651220040392
101512.94155425086172.05844574913827
111713.38573587313873.61426412686132
121913.90386072607035.09613927392967
131013.0394480669619-3.03944806696189
141816.23633848210391.76366151789606
151411.79776307855022.20223692144981
161414.6237786151202-0.623778615120154
171715.13255906608271.86744093391730
181415.0529811587830-1.05298115878296
191614.96600710455251.03399289544749
201813.50316453395634.49683546604375
211413.38021548273070.619784517269298
221212.9257332182720-0.925733218271973
231713.82109808623933.17890191376065
24914.6648587410489-5.66485874104889
251614.57470101784241.42529898215757
261413.20727371630340.792726283696607
271114.5821600313076-3.58216003130761
281615.44535087601060.554649123989363
291313.2210378228577-0.221037822857698
301714.54499589512112.45500410487893
311514.55796940216280.442030597837232
321414.8692849827572-0.869284982757222
331612.16141990450383.83858009549615
34912.5172485435579-3.51724854355791
351513.15546166252641.84453833747362
361715.54648714847181.45351285152818
371313.4976758394431-0.497675839443059
381513.75396004264771.24603995735231
391613.92237669866222.07762330133785
401617.0005388760699-1.00053887606991
411213.8818619789282-1.88186197892816
421113.4296137885327-2.42961378853269
431515.236448799787-0.236448799787013
441714.89270945447062.10729054552944
451314.3111454372712-1.31114543727118
461611.76081508188514.23918491811492
471414.1940192507578-0.194019250757793
481113.4641094884327-2.46410948843266
491213.1752845451896-1.17528454518956
501213.9607429899927-1.96074298999265
511514.02167176795160.978328232048416
521615.12799184727860.872008152721424
531514.81671279937990.183287200620092
541214.9747072136804-2.97470721368041
551214.4392776828147-2.43927768281471
56812.8347214829339-4.83472148293395
571315.3946704295832-2.39467042958323
581114.9065615455410-3.90656154554104
591414.6883104129871-0.688310412987106
601513.17568254469381.82431745530620
611014.0598900072451-4.05989000724508
621112.7631232913734-1.76312329137341
631213.8717481205393-1.87174812053932
641513.16601942813871.83398057186127
651514.51592134943380.484078650566199
661413.77410676568470.225893234315285
671613.21619969965282.78380030034716
681514.95789689096790.0421031090321294
691515.2697013986737-0.269701398673676
701314.848550480332-1.84855048033201
711714.86613845486282.13386154513725
721313.4158267459841-0.415826745984057
731513.78638261614841.21361738385159
741314.6005177994864-1.60051779948635
751514.09765685696140.902343143038571
761614.55895973490701.44104026509302
771514.58319754507570.416802454924332
781614.23955144709581.76044855290416
791514.35685112167360.643148878326449
801414.7749855796484-0.77498557964838
811512.33450924564832.66549075435175
82711.6855997390837-4.6855997390837
831715.00843558837921.99156441162081
841314.5452660368803-1.54526603688032
851513.66039843491831.33960156508174
861413.87864378401470.121356215985340
871313.7439434803704-0.7439434803704
881615.29208550290730.707914497092714
891214.176131274987-2.17613127498700
901415.5628880163819-1.56288801638194
911714.71940502395232.28059497604773
921515.5194923230004-0.519492323000411
931713.2605438712223.73945612877799
941213.9681810482396-1.96818104823960
951615.39733619091040.602663809089637
961114.1190627659778-3.11906276597783
971513.26914647660651.73085352339346
98914.0890899483988-5.08908994839878
991614.75712986567081.2428701343292
1001013.1132570563521-3.11325705635209
1011013.2577107599210-3.25771075992098
1021514.90731246734740.092687532652553
1031113.9705819710362-2.97058197103620
1041315.2290907699082-2.22909076990817
1051413.24481146678800.755188533212036
1061814.65061848287413.34938151712589
1071615.11559461728440.884405382715643
1081412.33460167211981.66539832788022
1091413.17821428813950.821785711860506
1101413.22378719285360.776212807146425
1111414.348938141212-0.348938141211985
1121213.7332718180091-1.73327181800905
1131414.5052954315628-0.505295431562779
1141514.34652880330020.653471196699786
1151514.99975487059200.000245129408036393
1161313.6097461096947-0.609746109694739
1171714.90313518860112.09686481139894
1181715.3821673342171.617832665783
1191914.97678982075554.02321017924451
1201513.88422098595111.11577901404888
1211314.0475514263170-1.04755142631702
122912.2024559719639-3.20245597196392
1231515.0654718210993-0.0654718210993186
1241514.58950601007000.41049398992995
1251613.89010666205192.10989333794814
1261113.3104871668195-2.31048716681952
1271414.6190363894438-0.619036389443828
1281112.6696218413593-1.66962184135928
1291513.40928756841401.59071243158604
1301313.4213314060550-0.42133140605503
1311612.72077674378893.27922325621113
1321414.7040927994066-0.704092799406585
1331514.82534654771490.174653452285116
1341613.05495002415792.94504997584208
1351615.47065745428670.529342545713309
1361112.4370137923298-1.43701379232980
1371314.0417290804259-1.04172908042588
1381614.57470101784241.42529898215757
1391214.0188756070208-2.01887560702079
140911.4175931514257-2.41759315142568
1411311.4875660791741.51243392082600
1421314.5452660368803-1.54526603688032
1431914.97678982075554.02321017924451
1441315.5205134149569-2.52051341495688






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2907796304809040.5815592609618070.709220369519096
130.1878835847315160.3757671694630320.812116415268484
140.1783081385338290.3566162770676570.821691861466171
150.1379980003792500.2759960007585010.86200199962075
160.1726729983867770.3453459967735540.827327001613223
170.8339216650808170.3321566698383670.166078334919183
180.787209347469020.425581305061960.21279065253098
190.7677474308579050.4645051382841910.232252569142095
200.8123350506714680.3753298986570640.187664949328532
210.7936184050775710.4127631898448580.206381594922429
220.776022159903570.4479556801928600.223977840096430
230.7850598373153540.4298803253692930.214940162684646
240.8991596473100140.2016807053799720.100840352689986
250.8714424082765380.2571151834469230.128557591723462
260.8676718965533910.2646562068932180.132328103446609
270.9260445744588230.1479108510823530.0739554255411766
280.9008558465799120.1982883068401760.0991441534200878
290.8893848983927320.2212302032145350.110615101607268
300.9034646423119890.1930707153760220.096535357688011
310.873682188173570.2526356236528590.126317811826429
320.8441663165581190.3116673668837620.155833683441881
330.8538965758132620.2922068483734750.146103424186738
340.882430442312520.2351391153749600.117569557687480
350.861373844801670.2772523103966580.138626155198329
360.8463420965319410.3073158069361180.153657903468059
370.8182104197892730.3635791604214530.181789580210727
380.7932360793277340.4135278413445310.206763920672266
390.788880209811070.4222395803778580.211119790188929
400.7862335960716620.4275328078566760.213766403928338
410.7553805244190740.4892389511618530.244619475580926
420.795259949904220.4094801001915590.204740050095779
430.756641607953730.4867167840925390.243358392046270
440.7370282645689970.5259434708620060.262971735431003
450.697790751097850.6044184978042990.302209248902150
460.7346858609790640.5306282780418730.265314139020936
470.703926943100560.5921461137988790.296073056899439
480.8318325387768030.3363349224463940.168167461223197
490.8154405328738670.3691189342522660.184559467126133
500.8248269534403990.3503460931192020.175173046559601
510.8011025890580040.3977948218839920.198897410941996
520.7772980635390230.4454038729219530.222701936460977
530.7369692647620870.5260614704758260.263030735237913
540.7677248689347250.464550262130550.232275131065275
550.7680541591596220.4638916816807560.231945840840378
560.8944473699721840.2111052600556310.105552630027816
570.9075166756039970.1849666487920050.0924833243960027
580.943099613673950.1138007726521010.0569003863260505
590.9293616075545570.1412767848908870.0706383924454433
600.9257213796488220.1485572407023570.0742786203511783
610.9571112099592320.08577758008153690.0428887900407684
620.961572649930670.0768547001386580.038427350069329
630.9579808870839470.0840382258321060.042019112916053
640.9543501817434780.09129963651304410.0456498182565221
650.941795293602970.1164094127940600.0582047063970298
660.9256071451259940.1487857097480130.0743928548740063
670.9362313711994820.1275372576010370.0637686288005185
680.9221365762278110.1557268475443780.0778634237721892
690.902255781110510.1954884377789790.0977442188894894
700.897216616108380.2055667677832390.102783383891619
710.8946086664532120.2107826670935760.105391333546788
720.8721132170408250.255773565918350.127886782959175
730.8551080184887250.2897839630225490.144891981511275
740.8484124259811810.3031751480376390.151587574018819
750.8229791485854820.3540417028290360.177020851414518
760.8113577205327350.377284558934530.188642279467265
770.7762858410624970.4474283178750070.223714158937503
780.7584890637571230.4830218724857540.241510936242877
790.7198067009563390.5603865980873220.280193299043661
800.6805953307184340.6388093385631310.319404669281565
810.7094140114061270.5811719771877460.290585988593873
820.8147262490662580.3705475018674830.185273750933742
830.8015750368334260.3968499263331480.198424963166574
840.7813137585316840.4373724829366320.218686241468316
850.7575458457339530.4849083085320940.242454154266047
860.7153648569313840.5692702861372330.284635143068616
870.6748935699109240.6502128601781530.325106430089076
880.6411713402964070.7176573194071860.358828659703593
890.632519710398760.7349605792024790.367480289601240
900.6103654635168370.7792690729663250.389634536483163
910.6099564870432980.7800870259134050.390043512956702
920.5648827069821040.8702345860357910.435117293017896
930.7296258238924820.5407483522150360.270374176107518
940.7090792398490970.5818415203018050.290920760150902
950.6758013763985120.6483972472029770.324198623601488
960.7075268006815460.5849463986369090.292473199318454
970.7172316374191710.5655367251616580.282768362580829
980.9049240423539160.1901519152921680.0950759576460841
990.8902139097498040.2195721805003930.109786090250196
1000.8871172544029180.2257654911941640.112882745597082
1010.8970564044310830.2058871911378340.102943595568917
1020.8679700667201040.2640598665597920.132029933279896
1030.8917256142121950.2165487715756110.108274385787805
1040.9201785007834190.1596429984331610.0798214992165806
1050.9136967868782920.1726064262434160.0863032131217082
1060.9144645816534070.1710708366931860.0855354183465928
1070.8977321520603250.2045356958793510.102267847939675
1080.9239861321129310.1520277357741370.0760138678870685
1090.9058521744471050.1882956511057900.0941478255528952
1100.9077741694915220.1844516610169570.0922258305084784
1110.881030828354660.2379383432906810.118969171645341
1120.8685233114643610.2629533770712770.131476688535639
1130.8336666914294830.3326666171410350.166333308570517
1140.7872344138947140.4255311722105720.212765586105286
1150.7495093304263720.5009813391472560.250490669573628
1160.6891841591229170.6216316817541670.310815840877083
1170.742806991269870.5143860174602610.257193008730130
1180.7633510259805540.4732979480388920.236648974019446
1190.7699987298284860.4600025403430280.230001270171514
1200.776885921308330.446228157383340.22311407869167
1210.7910617307435560.4178765385128890.208938269256444
1220.7486246816433590.5027506367132830.251375318356641
1230.6938399731448760.6123200537102490.306160026855124
1240.6159224103681860.7681551792636270.384077589631814
1250.5434026958022920.9131946083954160.456597304197708
1260.4585017677743940.9170035355487870.541498232225606
1270.3742999508526890.7485999017053780.625700049147311
1280.316368023114010.632736046228020.68363197688599
1290.2480291790997550.4960583581995110.751970820900244
1300.2857234570890420.5714469141780840.714276542910958
1310.7598253649230380.4803492701539250.240174635076962
1320.6956168167533180.6087663664933640.304383183246682

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.290779630480904 & 0.581559260961807 & 0.709220369519096 \tabularnewline
13 & 0.187883584731516 & 0.375767169463032 & 0.812116415268484 \tabularnewline
14 & 0.178308138533829 & 0.356616277067657 & 0.821691861466171 \tabularnewline
15 & 0.137998000379250 & 0.275996000758501 & 0.86200199962075 \tabularnewline
16 & 0.172672998386777 & 0.345345996773554 & 0.827327001613223 \tabularnewline
17 & 0.833921665080817 & 0.332156669838367 & 0.166078334919183 \tabularnewline
18 & 0.78720934746902 & 0.42558130506196 & 0.21279065253098 \tabularnewline
19 & 0.767747430857905 & 0.464505138284191 & 0.232252569142095 \tabularnewline
20 & 0.812335050671468 & 0.375329898657064 & 0.187664949328532 \tabularnewline
21 & 0.793618405077571 & 0.412763189844858 & 0.206381594922429 \tabularnewline
22 & 0.77602215990357 & 0.447955680192860 & 0.223977840096430 \tabularnewline
23 & 0.785059837315354 & 0.429880325369293 & 0.214940162684646 \tabularnewline
24 & 0.899159647310014 & 0.201680705379972 & 0.100840352689986 \tabularnewline
25 & 0.871442408276538 & 0.257115183446923 & 0.128557591723462 \tabularnewline
26 & 0.867671896553391 & 0.264656206893218 & 0.132328103446609 \tabularnewline
27 & 0.926044574458823 & 0.147910851082353 & 0.0739554255411766 \tabularnewline
28 & 0.900855846579912 & 0.198288306840176 & 0.0991441534200878 \tabularnewline
29 & 0.889384898392732 & 0.221230203214535 & 0.110615101607268 \tabularnewline
30 & 0.903464642311989 & 0.193070715376022 & 0.096535357688011 \tabularnewline
31 & 0.87368218817357 & 0.252635623652859 & 0.126317811826429 \tabularnewline
32 & 0.844166316558119 & 0.311667366883762 & 0.155833683441881 \tabularnewline
33 & 0.853896575813262 & 0.292206848373475 & 0.146103424186738 \tabularnewline
34 & 0.88243044231252 & 0.235139115374960 & 0.117569557687480 \tabularnewline
35 & 0.86137384480167 & 0.277252310396658 & 0.138626155198329 \tabularnewline
36 & 0.846342096531941 & 0.307315806936118 & 0.153657903468059 \tabularnewline
37 & 0.818210419789273 & 0.363579160421453 & 0.181789580210727 \tabularnewline
38 & 0.793236079327734 & 0.413527841344531 & 0.206763920672266 \tabularnewline
39 & 0.78888020981107 & 0.422239580377858 & 0.211119790188929 \tabularnewline
40 & 0.786233596071662 & 0.427532807856676 & 0.213766403928338 \tabularnewline
41 & 0.755380524419074 & 0.489238951161853 & 0.244619475580926 \tabularnewline
42 & 0.79525994990422 & 0.409480100191559 & 0.204740050095779 \tabularnewline
43 & 0.75664160795373 & 0.486716784092539 & 0.243358392046270 \tabularnewline
44 & 0.737028264568997 & 0.525943470862006 & 0.262971735431003 \tabularnewline
45 & 0.69779075109785 & 0.604418497804299 & 0.302209248902150 \tabularnewline
46 & 0.734685860979064 & 0.530628278041873 & 0.265314139020936 \tabularnewline
47 & 0.70392694310056 & 0.592146113798879 & 0.296073056899439 \tabularnewline
48 & 0.831832538776803 & 0.336334922446394 & 0.168167461223197 \tabularnewline
49 & 0.815440532873867 & 0.369118934252266 & 0.184559467126133 \tabularnewline
50 & 0.824826953440399 & 0.350346093119202 & 0.175173046559601 \tabularnewline
51 & 0.801102589058004 & 0.397794821883992 & 0.198897410941996 \tabularnewline
52 & 0.777298063539023 & 0.445403872921953 & 0.222701936460977 \tabularnewline
53 & 0.736969264762087 & 0.526061470475826 & 0.263030735237913 \tabularnewline
54 & 0.767724868934725 & 0.46455026213055 & 0.232275131065275 \tabularnewline
55 & 0.768054159159622 & 0.463891681680756 & 0.231945840840378 \tabularnewline
56 & 0.894447369972184 & 0.211105260055631 & 0.105552630027816 \tabularnewline
57 & 0.907516675603997 & 0.184966648792005 & 0.0924833243960027 \tabularnewline
58 & 0.94309961367395 & 0.113800772652101 & 0.0569003863260505 \tabularnewline
59 & 0.929361607554557 & 0.141276784890887 & 0.0706383924454433 \tabularnewline
60 & 0.925721379648822 & 0.148557240702357 & 0.0742786203511783 \tabularnewline
61 & 0.957111209959232 & 0.0857775800815369 & 0.0428887900407684 \tabularnewline
62 & 0.96157264993067 & 0.076854700138658 & 0.038427350069329 \tabularnewline
63 & 0.957980887083947 & 0.084038225832106 & 0.042019112916053 \tabularnewline
64 & 0.954350181743478 & 0.0912996365130441 & 0.0456498182565221 \tabularnewline
65 & 0.94179529360297 & 0.116409412794060 & 0.0582047063970298 \tabularnewline
66 & 0.925607145125994 & 0.148785709748013 & 0.0743928548740063 \tabularnewline
67 & 0.936231371199482 & 0.127537257601037 & 0.0637686288005185 \tabularnewline
68 & 0.922136576227811 & 0.155726847544378 & 0.0778634237721892 \tabularnewline
69 & 0.90225578111051 & 0.195488437778979 & 0.0977442188894894 \tabularnewline
70 & 0.89721661610838 & 0.205566767783239 & 0.102783383891619 \tabularnewline
71 & 0.894608666453212 & 0.210782667093576 & 0.105391333546788 \tabularnewline
72 & 0.872113217040825 & 0.25577356591835 & 0.127886782959175 \tabularnewline
73 & 0.855108018488725 & 0.289783963022549 & 0.144891981511275 \tabularnewline
74 & 0.848412425981181 & 0.303175148037639 & 0.151587574018819 \tabularnewline
75 & 0.822979148585482 & 0.354041702829036 & 0.177020851414518 \tabularnewline
76 & 0.811357720532735 & 0.37728455893453 & 0.188642279467265 \tabularnewline
77 & 0.776285841062497 & 0.447428317875007 & 0.223714158937503 \tabularnewline
78 & 0.758489063757123 & 0.483021872485754 & 0.241510936242877 \tabularnewline
79 & 0.719806700956339 & 0.560386598087322 & 0.280193299043661 \tabularnewline
80 & 0.680595330718434 & 0.638809338563131 & 0.319404669281565 \tabularnewline
81 & 0.709414011406127 & 0.581171977187746 & 0.290585988593873 \tabularnewline
82 & 0.814726249066258 & 0.370547501867483 & 0.185273750933742 \tabularnewline
83 & 0.801575036833426 & 0.396849926333148 & 0.198424963166574 \tabularnewline
84 & 0.781313758531684 & 0.437372482936632 & 0.218686241468316 \tabularnewline
85 & 0.757545845733953 & 0.484908308532094 & 0.242454154266047 \tabularnewline
86 & 0.715364856931384 & 0.569270286137233 & 0.284635143068616 \tabularnewline
87 & 0.674893569910924 & 0.650212860178153 & 0.325106430089076 \tabularnewline
88 & 0.641171340296407 & 0.717657319407186 & 0.358828659703593 \tabularnewline
89 & 0.63251971039876 & 0.734960579202479 & 0.367480289601240 \tabularnewline
90 & 0.610365463516837 & 0.779269072966325 & 0.389634536483163 \tabularnewline
91 & 0.609956487043298 & 0.780087025913405 & 0.390043512956702 \tabularnewline
92 & 0.564882706982104 & 0.870234586035791 & 0.435117293017896 \tabularnewline
93 & 0.729625823892482 & 0.540748352215036 & 0.270374176107518 \tabularnewline
94 & 0.709079239849097 & 0.581841520301805 & 0.290920760150902 \tabularnewline
95 & 0.675801376398512 & 0.648397247202977 & 0.324198623601488 \tabularnewline
96 & 0.707526800681546 & 0.584946398636909 & 0.292473199318454 \tabularnewline
97 & 0.717231637419171 & 0.565536725161658 & 0.282768362580829 \tabularnewline
98 & 0.904924042353916 & 0.190151915292168 & 0.0950759576460841 \tabularnewline
99 & 0.890213909749804 & 0.219572180500393 & 0.109786090250196 \tabularnewline
100 & 0.887117254402918 & 0.225765491194164 & 0.112882745597082 \tabularnewline
101 & 0.897056404431083 & 0.205887191137834 & 0.102943595568917 \tabularnewline
102 & 0.867970066720104 & 0.264059866559792 & 0.132029933279896 \tabularnewline
103 & 0.891725614212195 & 0.216548771575611 & 0.108274385787805 \tabularnewline
104 & 0.920178500783419 & 0.159642998433161 & 0.0798214992165806 \tabularnewline
105 & 0.913696786878292 & 0.172606426243416 & 0.0863032131217082 \tabularnewline
106 & 0.914464581653407 & 0.171070836693186 & 0.0855354183465928 \tabularnewline
107 & 0.897732152060325 & 0.204535695879351 & 0.102267847939675 \tabularnewline
108 & 0.923986132112931 & 0.152027735774137 & 0.0760138678870685 \tabularnewline
109 & 0.905852174447105 & 0.188295651105790 & 0.0941478255528952 \tabularnewline
110 & 0.907774169491522 & 0.184451661016957 & 0.0922258305084784 \tabularnewline
111 & 0.88103082835466 & 0.237938343290681 & 0.118969171645341 \tabularnewline
112 & 0.868523311464361 & 0.262953377071277 & 0.131476688535639 \tabularnewline
113 & 0.833666691429483 & 0.332666617141035 & 0.166333308570517 \tabularnewline
114 & 0.787234413894714 & 0.425531172210572 & 0.212765586105286 \tabularnewline
115 & 0.749509330426372 & 0.500981339147256 & 0.250490669573628 \tabularnewline
116 & 0.689184159122917 & 0.621631681754167 & 0.310815840877083 \tabularnewline
117 & 0.74280699126987 & 0.514386017460261 & 0.257193008730130 \tabularnewline
118 & 0.763351025980554 & 0.473297948038892 & 0.236648974019446 \tabularnewline
119 & 0.769998729828486 & 0.460002540343028 & 0.230001270171514 \tabularnewline
120 & 0.77688592130833 & 0.44622815738334 & 0.22311407869167 \tabularnewline
121 & 0.791061730743556 & 0.417876538512889 & 0.208938269256444 \tabularnewline
122 & 0.748624681643359 & 0.502750636713283 & 0.251375318356641 \tabularnewline
123 & 0.693839973144876 & 0.612320053710249 & 0.306160026855124 \tabularnewline
124 & 0.615922410368186 & 0.768155179263627 & 0.384077589631814 \tabularnewline
125 & 0.543402695802292 & 0.913194608395416 & 0.456597304197708 \tabularnewline
126 & 0.458501767774394 & 0.917003535548787 & 0.541498232225606 \tabularnewline
127 & 0.374299950852689 & 0.748599901705378 & 0.625700049147311 \tabularnewline
128 & 0.31636802311401 & 0.63273604622802 & 0.68363197688599 \tabularnewline
129 & 0.248029179099755 & 0.496058358199511 & 0.751970820900244 \tabularnewline
130 & 0.285723457089042 & 0.571446914178084 & 0.714276542910958 \tabularnewline
131 & 0.759825364923038 & 0.480349270153925 & 0.240174635076962 \tabularnewline
132 & 0.695616816753318 & 0.608766366493364 & 0.304383183246682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&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]12[/C][C]0.290779630480904[/C][C]0.581559260961807[/C][C]0.709220369519096[/C][/ROW]
[ROW][C]13[/C][C]0.187883584731516[/C][C]0.375767169463032[/C][C]0.812116415268484[/C][/ROW]
[ROW][C]14[/C][C]0.178308138533829[/C][C]0.356616277067657[/C][C]0.821691861466171[/C][/ROW]
[ROW][C]15[/C][C]0.137998000379250[/C][C]0.275996000758501[/C][C]0.86200199962075[/C][/ROW]
[ROW][C]16[/C][C]0.172672998386777[/C][C]0.345345996773554[/C][C]0.827327001613223[/C][/ROW]
[ROW][C]17[/C][C]0.833921665080817[/C][C]0.332156669838367[/C][C]0.166078334919183[/C][/ROW]
[ROW][C]18[/C][C]0.78720934746902[/C][C]0.42558130506196[/C][C]0.21279065253098[/C][/ROW]
[ROW][C]19[/C][C]0.767747430857905[/C][C]0.464505138284191[/C][C]0.232252569142095[/C][/ROW]
[ROW][C]20[/C][C]0.812335050671468[/C][C]0.375329898657064[/C][C]0.187664949328532[/C][/ROW]
[ROW][C]21[/C][C]0.793618405077571[/C][C]0.412763189844858[/C][C]0.206381594922429[/C][/ROW]
[ROW][C]22[/C][C]0.77602215990357[/C][C]0.447955680192860[/C][C]0.223977840096430[/C][/ROW]
[ROW][C]23[/C][C]0.785059837315354[/C][C]0.429880325369293[/C][C]0.214940162684646[/C][/ROW]
[ROW][C]24[/C][C]0.899159647310014[/C][C]0.201680705379972[/C][C]0.100840352689986[/C][/ROW]
[ROW][C]25[/C][C]0.871442408276538[/C][C]0.257115183446923[/C][C]0.128557591723462[/C][/ROW]
[ROW][C]26[/C][C]0.867671896553391[/C][C]0.264656206893218[/C][C]0.132328103446609[/C][/ROW]
[ROW][C]27[/C][C]0.926044574458823[/C][C]0.147910851082353[/C][C]0.0739554255411766[/C][/ROW]
[ROW][C]28[/C][C]0.900855846579912[/C][C]0.198288306840176[/C][C]0.0991441534200878[/C][/ROW]
[ROW][C]29[/C][C]0.889384898392732[/C][C]0.221230203214535[/C][C]0.110615101607268[/C][/ROW]
[ROW][C]30[/C][C]0.903464642311989[/C][C]0.193070715376022[/C][C]0.096535357688011[/C][/ROW]
[ROW][C]31[/C][C]0.87368218817357[/C][C]0.252635623652859[/C][C]0.126317811826429[/C][/ROW]
[ROW][C]32[/C][C]0.844166316558119[/C][C]0.311667366883762[/C][C]0.155833683441881[/C][/ROW]
[ROW][C]33[/C][C]0.853896575813262[/C][C]0.292206848373475[/C][C]0.146103424186738[/C][/ROW]
[ROW][C]34[/C][C]0.88243044231252[/C][C]0.235139115374960[/C][C]0.117569557687480[/C][/ROW]
[ROW][C]35[/C][C]0.86137384480167[/C][C]0.277252310396658[/C][C]0.138626155198329[/C][/ROW]
[ROW][C]36[/C][C]0.846342096531941[/C][C]0.307315806936118[/C][C]0.153657903468059[/C][/ROW]
[ROW][C]37[/C][C]0.818210419789273[/C][C]0.363579160421453[/C][C]0.181789580210727[/C][/ROW]
[ROW][C]38[/C][C]0.793236079327734[/C][C]0.413527841344531[/C][C]0.206763920672266[/C][/ROW]
[ROW][C]39[/C][C]0.78888020981107[/C][C]0.422239580377858[/C][C]0.211119790188929[/C][/ROW]
[ROW][C]40[/C][C]0.786233596071662[/C][C]0.427532807856676[/C][C]0.213766403928338[/C][/ROW]
[ROW][C]41[/C][C]0.755380524419074[/C][C]0.489238951161853[/C][C]0.244619475580926[/C][/ROW]
[ROW][C]42[/C][C]0.79525994990422[/C][C]0.409480100191559[/C][C]0.204740050095779[/C][/ROW]
[ROW][C]43[/C][C]0.75664160795373[/C][C]0.486716784092539[/C][C]0.243358392046270[/C][/ROW]
[ROW][C]44[/C][C]0.737028264568997[/C][C]0.525943470862006[/C][C]0.262971735431003[/C][/ROW]
[ROW][C]45[/C][C]0.69779075109785[/C][C]0.604418497804299[/C][C]0.302209248902150[/C][/ROW]
[ROW][C]46[/C][C]0.734685860979064[/C][C]0.530628278041873[/C][C]0.265314139020936[/C][/ROW]
[ROW][C]47[/C][C]0.70392694310056[/C][C]0.592146113798879[/C][C]0.296073056899439[/C][/ROW]
[ROW][C]48[/C][C]0.831832538776803[/C][C]0.336334922446394[/C][C]0.168167461223197[/C][/ROW]
[ROW][C]49[/C][C]0.815440532873867[/C][C]0.369118934252266[/C][C]0.184559467126133[/C][/ROW]
[ROW][C]50[/C][C]0.824826953440399[/C][C]0.350346093119202[/C][C]0.175173046559601[/C][/ROW]
[ROW][C]51[/C][C]0.801102589058004[/C][C]0.397794821883992[/C][C]0.198897410941996[/C][/ROW]
[ROW][C]52[/C][C]0.777298063539023[/C][C]0.445403872921953[/C][C]0.222701936460977[/C][/ROW]
[ROW][C]53[/C][C]0.736969264762087[/C][C]0.526061470475826[/C][C]0.263030735237913[/C][/ROW]
[ROW][C]54[/C][C]0.767724868934725[/C][C]0.46455026213055[/C][C]0.232275131065275[/C][/ROW]
[ROW][C]55[/C][C]0.768054159159622[/C][C]0.463891681680756[/C][C]0.231945840840378[/C][/ROW]
[ROW][C]56[/C][C]0.894447369972184[/C][C]0.211105260055631[/C][C]0.105552630027816[/C][/ROW]
[ROW][C]57[/C][C]0.907516675603997[/C][C]0.184966648792005[/C][C]0.0924833243960027[/C][/ROW]
[ROW][C]58[/C][C]0.94309961367395[/C][C]0.113800772652101[/C][C]0.0569003863260505[/C][/ROW]
[ROW][C]59[/C][C]0.929361607554557[/C][C]0.141276784890887[/C][C]0.0706383924454433[/C][/ROW]
[ROW][C]60[/C][C]0.925721379648822[/C][C]0.148557240702357[/C][C]0.0742786203511783[/C][/ROW]
[ROW][C]61[/C][C]0.957111209959232[/C][C]0.0857775800815369[/C][C]0.0428887900407684[/C][/ROW]
[ROW][C]62[/C][C]0.96157264993067[/C][C]0.076854700138658[/C][C]0.038427350069329[/C][/ROW]
[ROW][C]63[/C][C]0.957980887083947[/C][C]0.084038225832106[/C][C]0.042019112916053[/C][/ROW]
[ROW][C]64[/C][C]0.954350181743478[/C][C]0.0912996365130441[/C][C]0.0456498182565221[/C][/ROW]
[ROW][C]65[/C][C]0.94179529360297[/C][C]0.116409412794060[/C][C]0.0582047063970298[/C][/ROW]
[ROW][C]66[/C][C]0.925607145125994[/C][C]0.148785709748013[/C][C]0.0743928548740063[/C][/ROW]
[ROW][C]67[/C][C]0.936231371199482[/C][C]0.127537257601037[/C][C]0.0637686288005185[/C][/ROW]
[ROW][C]68[/C][C]0.922136576227811[/C][C]0.155726847544378[/C][C]0.0778634237721892[/C][/ROW]
[ROW][C]69[/C][C]0.90225578111051[/C][C]0.195488437778979[/C][C]0.0977442188894894[/C][/ROW]
[ROW][C]70[/C][C]0.89721661610838[/C][C]0.205566767783239[/C][C]0.102783383891619[/C][/ROW]
[ROW][C]71[/C][C]0.894608666453212[/C][C]0.210782667093576[/C][C]0.105391333546788[/C][/ROW]
[ROW][C]72[/C][C]0.872113217040825[/C][C]0.25577356591835[/C][C]0.127886782959175[/C][/ROW]
[ROW][C]73[/C][C]0.855108018488725[/C][C]0.289783963022549[/C][C]0.144891981511275[/C][/ROW]
[ROW][C]74[/C][C]0.848412425981181[/C][C]0.303175148037639[/C][C]0.151587574018819[/C][/ROW]
[ROW][C]75[/C][C]0.822979148585482[/C][C]0.354041702829036[/C][C]0.177020851414518[/C][/ROW]
[ROW][C]76[/C][C]0.811357720532735[/C][C]0.37728455893453[/C][C]0.188642279467265[/C][/ROW]
[ROW][C]77[/C][C]0.776285841062497[/C][C]0.447428317875007[/C][C]0.223714158937503[/C][/ROW]
[ROW][C]78[/C][C]0.758489063757123[/C][C]0.483021872485754[/C][C]0.241510936242877[/C][/ROW]
[ROW][C]79[/C][C]0.719806700956339[/C][C]0.560386598087322[/C][C]0.280193299043661[/C][/ROW]
[ROW][C]80[/C][C]0.680595330718434[/C][C]0.638809338563131[/C][C]0.319404669281565[/C][/ROW]
[ROW][C]81[/C][C]0.709414011406127[/C][C]0.581171977187746[/C][C]0.290585988593873[/C][/ROW]
[ROW][C]82[/C][C]0.814726249066258[/C][C]0.370547501867483[/C][C]0.185273750933742[/C][/ROW]
[ROW][C]83[/C][C]0.801575036833426[/C][C]0.396849926333148[/C][C]0.198424963166574[/C][/ROW]
[ROW][C]84[/C][C]0.781313758531684[/C][C]0.437372482936632[/C][C]0.218686241468316[/C][/ROW]
[ROW][C]85[/C][C]0.757545845733953[/C][C]0.484908308532094[/C][C]0.242454154266047[/C][/ROW]
[ROW][C]86[/C][C]0.715364856931384[/C][C]0.569270286137233[/C][C]0.284635143068616[/C][/ROW]
[ROW][C]87[/C][C]0.674893569910924[/C][C]0.650212860178153[/C][C]0.325106430089076[/C][/ROW]
[ROW][C]88[/C][C]0.641171340296407[/C][C]0.717657319407186[/C][C]0.358828659703593[/C][/ROW]
[ROW][C]89[/C][C]0.63251971039876[/C][C]0.734960579202479[/C][C]0.367480289601240[/C][/ROW]
[ROW][C]90[/C][C]0.610365463516837[/C][C]0.779269072966325[/C][C]0.389634536483163[/C][/ROW]
[ROW][C]91[/C][C]0.609956487043298[/C][C]0.780087025913405[/C][C]0.390043512956702[/C][/ROW]
[ROW][C]92[/C][C]0.564882706982104[/C][C]0.870234586035791[/C][C]0.435117293017896[/C][/ROW]
[ROW][C]93[/C][C]0.729625823892482[/C][C]0.540748352215036[/C][C]0.270374176107518[/C][/ROW]
[ROW][C]94[/C][C]0.709079239849097[/C][C]0.581841520301805[/C][C]0.290920760150902[/C][/ROW]
[ROW][C]95[/C][C]0.675801376398512[/C][C]0.648397247202977[/C][C]0.324198623601488[/C][/ROW]
[ROW][C]96[/C][C]0.707526800681546[/C][C]0.584946398636909[/C][C]0.292473199318454[/C][/ROW]
[ROW][C]97[/C][C]0.717231637419171[/C][C]0.565536725161658[/C][C]0.282768362580829[/C][/ROW]
[ROW][C]98[/C][C]0.904924042353916[/C][C]0.190151915292168[/C][C]0.0950759576460841[/C][/ROW]
[ROW][C]99[/C][C]0.890213909749804[/C][C]0.219572180500393[/C][C]0.109786090250196[/C][/ROW]
[ROW][C]100[/C][C]0.887117254402918[/C][C]0.225765491194164[/C][C]0.112882745597082[/C][/ROW]
[ROW][C]101[/C][C]0.897056404431083[/C][C]0.205887191137834[/C][C]0.102943595568917[/C][/ROW]
[ROW][C]102[/C][C]0.867970066720104[/C][C]0.264059866559792[/C][C]0.132029933279896[/C][/ROW]
[ROW][C]103[/C][C]0.891725614212195[/C][C]0.216548771575611[/C][C]0.108274385787805[/C][/ROW]
[ROW][C]104[/C][C]0.920178500783419[/C][C]0.159642998433161[/C][C]0.0798214992165806[/C][/ROW]
[ROW][C]105[/C][C]0.913696786878292[/C][C]0.172606426243416[/C][C]0.0863032131217082[/C][/ROW]
[ROW][C]106[/C][C]0.914464581653407[/C][C]0.171070836693186[/C][C]0.0855354183465928[/C][/ROW]
[ROW][C]107[/C][C]0.897732152060325[/C][C]0.204535695879351[/C][C]0.102267847939675[/C][/ROW]
[ROW][C]108[/C][C]0.923986132112931[/C][C]0.152027735774137[/C][C]0.0760138678870685[/C][/ROW]
[ROW][C]109[/C][C]0.905852174447105[/C][C]0.188295651105790[/C][C]0.0941478255528952[/C][/ROW]
[ROW][C]110[/C][C]0.907774169491522[/C][C]0.184451661016957[/C][C]0.0922258305084784[/C][/ROW]
[ROW][C]111[/C][C]0.88103082835466[/C][C]0.237938343290681[/C][C]0.118969171645341[/C][/ROW]
[ROW][C]112[/C][C]0.868523311464361[/C][C]0.262953377071277[/C][C]0.131476688535639[/C][/ROW]
[ROW][C]113[/C][C]0.833666691429483[/C][C]0.332666617141035[/C][C]0.166333308570517[/C][/ROW]
[ROW][C]114[/C][C]0.787234413894714[/C][C]0.425531172210572[/C][C]0.212765586105286[/C][/ROW]
[ROW][C]115[/C][C]0.749509330426372[/C][C]0.500981339147256[/C][C]0.250490669573628[/C][/ROW]
[ROW][C]116[/C][C]0.689184159122917[/C][C]0.621631681754167[/C][C]0.310815840877083[/C][/ROW]
[ROW][C]117[/C][C]0.74280699126987[/C][C]0.514386017460261[/C][C]0.257193008730130[/C][/ROW]
[ROW][C]118[/C][C]0.763351025980554[/C][C]0.473297948038892[/C][C]0.236648974019446[/C][/ROW]
[ROW][C]119[/C][C]0.769998729828486[/C][C]0.460002540343028[/C][C]0.230001270171514[/C][/ROW]
[ROW][C]120[/C][C]0.77688592130833[/C][C]0.44622815738334[/C][C]0.22311407869167[/C][/ROW]
[ROW][C]121[/C][C]0.791061730743556[/C][C]0.417876538512889[/C][C]0.208938269256444[/C][/ROW]
[ROW][C]122[/C][C]0.748624681643359[/C][C]0.502750636713283[/C][C]0.251375318356641[/C][/ROW]
[ROW][C]123[/C][C]0.693839973144876[/C][C]0.612320053710249[/C][C]0.306160026855124[/C][/ROW]
[ROW][C]124[/C][C]0.615922410368186[/C][C]0.768155179263627[/C][C]0.384077589631814[/C][/ROW]
[ROW][C]125[/C][C]0.543402695802292[/C][C]0.913194608395416[/C][C]0.456597304197708[/C][/ROW]
[ROW][C]126[/C][C]0.458501767774394[/C][C]0.917003535548787[/C][C]0.541498232225606[/C][/ROW]
[ROW][C]127[/C][C]0.374299950852689[/C][C]0.748599901705378[/C][C]0.625700049147311[/C][/ROW]
[ROW][C]128[/C][C]0.31636802311401[/C][C]0.63273604622802[/C][C]0.68363197688599[/C][/ROW]
[ROW][C]129[/C][C]0.248029179099755[/C][C]0.496058358199511[/C][C]0.751970820900244[/C][/ROW]
[ROW][C]130[/C][C]0.285723457089042[/C][C]0.571446914178084[/C][C]0.714276542910958[/C][/ROW]
[ROW][C]131[/C][C]0.759825364923038[/C][C]0.480349270153925[/C][C]0.240174635076962[/C][/ROW]
[ROW][C]132[/C][C]0.695616816753318[/C][C]0.608766366493364[/C][C]0.304383183246682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&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
120.2907796304809040.5815592609618070.709220369519096
130.1878835847315160.3757671694630320.812116415268484
140.1783081385338290.3566162770676570.821691861466171
150.1379980003792500.2759960007585010.86200199962075
160.1726729983867770.3453459967735540.827327001613223
170.8339216650808170.3321566698383670.166078334919183
180.787209347469020.425581305061960.21279065253098
190.7677474308579050.4645051382841910.232252569142095
200.8123350506714680.3753298986570640.187664949328532
210.7936184050775710.4127631898448580.206381594922429
220.776022159903570.4479556801928600.223977840096430
230.7850598373153540.4298803253692930.214940162684646
240.8991596473100140.2016807053799720.100840352689986
250.8714424082765380.2571151834469230.128557591723462
260.8676718965533910.2646562068932180.132328103446609
270.9260445744588230.1479108510823530.0739554255411766
280.9008558465799120.1982883068401760.0991441534200878
290.8893848983927320.2212302032145350.110615101607268
300.9034646423119890.1930707153760220.096535357688011
310.873682188173570.2526356236528590.126317811826429
320.8441663165581190.3116673668837620.155833683441881
330.8538965758132620.2922068483734750.146103424186738
340.882430442312520.2351391153749600.117569557687480
350.861373844801670.2772523103966580.138626155198329
360.8463420965319410.3073158069361180.153657903468059
370.8182104197892730.3635791604214530.181789580210727
380.7932360793277340.4135278413445310.206763920672266
390.788880209811070.4222395803778580.211119790188929
400.7862335960716620.4275328078566760.213766403928338
410.7553805244190740.4892389511618530.244619475580926
420.795259949904220.4094801001915590.204740050095779
430.756641607953730.4867167840925390.243358392046270
440.7370282645689970.5259434708620060.262971735431003
450.697790751097850.6044184978042990.302209248902150
460.7346858609790640.5306282780418730.265314139020936
470.703926943100560.5921461137988790.296073056899439
480.8318325387768030.3363349224463940.168167461223197
490.8154405328738670.3691189342522660.184559467126133
500.8248269534403990.3503460931192020.175173046559601
510.8011025890580040.3977948218839920.198897410941996
520.7772980635390230.4454038729219530.222701936460977
530.7369692647620870.5260614704758260.263030735237913
540.7677248689347250.464550262130550.232275131065275
550.7680541591596220.4638916816807560.231945840840378
560.8944473699721840.2111052600556310.105552630027816
570.9075166756039970.1849666487920050.0924833243960027
580.943099613673950.1138007726521010.0569003863260505
590.9293616075545570.1412767848908870.0706383924454433
600.9257213796488220.1485572407023570.0742786203511783
610.9571112099592320.08577758008153690.0428887900407684
620.961572649930670.0768547001386580.038427350069329
630.9579808870839470.0840382258321060.042019112916053
640.9543501817434780.09129963651304410.0456498182565221
650.941795293602970.1164094127940600.0582047063970298
660.9256071451259940.1487857097480130.0743928548740063
670.9362313711994820.1275372576010370.0637686288005185
680.9221365762278110.1557268475443780.0778634237721892
690.902255781110510.1954884377789790.0977442188894894
700.897216616108380.2055667677832390.102783383891619
710.8946086664532120.2107826670935760.105391333546788
720.8721132170408250.255773565918350.127886782959175
730.8551080184887250.2897839630225490.144891981511275
740.8484124259811810.3031751480376390.151587574018819
750.8229791485854820.3540417028290360.177020851414518
760.8113577205327350.377284558934530.188642279467265
770.7762858410624970.4474283178750070.223714158937503
780.7584890637571230.4830218724857540.241510936242877
790.7198067009563390.5603865980873220.280193299043661
800.6805953307184340.6388093385631310.319404669281565
810.7094140114061270.5811719771877460.290585988593873
820.8147262490662580.3705475018674830.185273750933742
830.8015750368334260.3968499263331480.198424963166574
840.7813137585316840.4373724829366320.218686241468316
850.7575458457339530.4849083085320940.242454154266047
860.7153648569313840.5692702861372330.284635143068616
870.6748935699109240.6502128601781530.325106430089076
880.6411713402964070.7176573194071860.358828659703593
890.632519710398760.7349605792024790.367480289601240
900.6103654635168370.7792690729663250.389634536483163
910.6099564870432980.7800870259134050.390043512956702
920.5648827069821040.8702345860357910.435117293017896
930.7296258238924820.5407483522150360.270374176107518
940.7090792398490970.5818415203018050.290920760150902
950.6758013763985120.6483972472029770.324198623601488
960.7075268006815460.5849463986369090.292473199318454
970.7172316374191710.5655367251616580.282768362580829
980.9049240423539160.1901519152921680.0950759576460841
990.8902139097498040.2195721805003930.109786090250196
1000.8871172544029180.2257654911941640.112882745597082
1010.8970564044310830.2058871911378340.102943595568917
1020.8679700667201040.2640598665597920.132029933279896
1030.8917256142121950.2165487715756110.108274385787805
1040.9201785007834190.1596429984331610.0798214992165806
1050.9136967868782920.1726064262434160.0863032131217082
1060.9144645816534070.1710708366931860.0855354183465928
1070.8977321520603250.2045356958793510.102267847939675
1080.9239861321129310.1520277357741370.0760138678870685
1090.9058521744471050.1882956511057900.0941478255528952
1100.9077741694915220.1844516610169570.0922258305084784
1110.881030828354660.2379383432906810.118969171645341
1120.8685233114643610.2629533770712770.131476688535639
1130.8336666914294830.3326666171410350.166333308570517
1140.7872344138947140.4255311722105720.212765586105286
1150.7495093304263720.5009813391472560.250490669573628
1160.6891841591229170.6216316817541670.310815840877083
1170.742806991269870.5143860174602610.257193008730130
1180.7633510259805540.4732979480388920.236648974019446
1190.7699987298284860.4600025403430280.230001270171514
1200.776885921308330.446228157383340.22311407869167
1210.7910617307435560.4178765385128890.208938269256444
1220.7486246816433590.5027506367132830.251375318356641
1230.6938399731448760.6123200537102490.306160026855124
1240.6159224103681860.7681551792636270.384077589631814
1250.5434026958022920.9131946083954160.456597304197708
1260.4585017677743940.9170035355487870.541498232225606
1270.3742999508526890.7485999017053780.625700049147311
1280.316368023114010.632736046228020.68363197688599
1290.2480291790997550.4960583581995110.751970820900244
1300.2857234570890420.5714469141780840.714276542910958
1310.7598253649230380.4803492701539250.240174635076962
1320.6956168167533180.6087663664933640.304383183246682






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0330578512396694OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0330578512396694 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99629&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0330578512396694[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99629&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99629&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0330578512396694OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}